From 250087be42d8022f9fd27c82cdbb40a61ca4beca Mon Sep 17 00:00:00 2001 From: Praful <51020328+praful-potphode@users.noreply.github.com> Date: Wed, 7 Feb 2024 09:50:20 +0530 Subject: [PATCH] Add files via upload --- Matplotlib/Untitled.ipynb | 784 ++++++++++++++++++++++++++++++ Matplotlib/bar.ipynb | 390 +++++++++++++++ Matplotlib/bardiagram_python.png | Bin 0 -> 1378 bytes Matplotlib/demo_dash.py | 24 + Matplotlib/fill_between.ipynb | 84 ++++ Matplotlib/linediagram_python.png | Bin 0 -> 521 bytes Matplotlib/oneaboveanother.png | Bin 0 -> 1419 bytes Matplotlib/plot.ipynb | 396 +++++++++++++++ Matplotlib/scatter.png | Bin 0 -> 3639 bytes Matplotlib/scatter_plot.ipynb | 85 ++++ Matplotlib/stackplot.ipynb | 83 ++++ Matplotlib/stem.ipynb | 81 +++ Matplotlib/step.ipynb | 81 +++ Matplotlib/test.png | Bin 0 -> 771 bytes 14 files changed, 2008 insertions(+) create mode 100644 Matplotlib/Untitled.ipynb create mode 100644 Matplotlib/bar.ipynb create mode 100644 Matplotlib/bardiagram_python.png create mode 100644 Matplotlib/demo_dash.py create mode 100644 Matplotlib/fill_between.ipynb create mode 100644 Matplotlib/linediagram_python.png create mode 100644 Matplotlib/oneaboveanother.png create mode 100644 Matplotlib/plot.ipynb create mode 100644 Matplotlib/scatter.png create mode 100644 Matplotlib/scatter_plot.ipynb create mode 100644 Matplotlib/stackplot.ipynb create mode 100644 Matplotlib/stem.ipynb create mode 100644 Matplotlib/step.ipynb create mode 100644 Matplotlib/test.png diff --git a/Matplotlib/Untitled.ipynb b/Matplotlib/Untitled.ipynb new file mode 100644 index 0000000..0d8d7b2 --- /dev/null +++ b/Matplotlib/Untitled.ipynb @@ -0,0 +1,784 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "03a9cb9e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(\n", + " [0,1,2,3,4],[0,3,5,7,9]\n", + ")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "430e94df", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcsAAAHPCAYAAAA8k/FRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAARiElEQVR4nO3db4hl913H8c93s4l2rFgxA2qS3YkggorauoTUQpHWQlFJHtgHgWk1giwoav0DpXXBorDPRPyLZUgrVa9WiUViqX8KbREfGLtJozaJSqzdbWol04qpOmKN/fng3jTJZDffO+3cuWd2Xy8Y7twzJ7PfX87uee+592RSY4wAAFd2Yt0DAMDUiSUANMQSABpiCQANsQSAhlgCQOPkKr7pjTfeOLa2tlbxrQFgZR544IFPjTE2929fSSy3trZy4cKFVXxrAFiZqrp4ue1ehgWAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBKAY2c2S7a2khMn5o+z2Wp/vZX8/ywBYFVms+Ts2WRvb/784sX58yTZ3l7Nr+nKEoBj5dy5Z0L5tL29+fZVEUsAjpVLlw62/TCIJQDHyqlTB9t+GMQSgGPl/PlkY+O52zY25ttXRSwBOFa2t5OdneT06aRq/rizs7qbexJ3wwJwDG1vrzaO+7myBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaCxVCyr6ier6uGq+khV/V5VfemqBwOAqWhjWVU3JfnxJGfGGN+c5Lokd616MACYimVfhj2Z5EVVdTLJRpJ/Wd1IADAtbSzHGJ9I8gtJLiX5ZJInxxh/vurBAGAqlnkZ9iuT3Jnk1iRfm+TLqur1l9nvbFVdqKoLu7u7hz8pAKzJMi/DfleSfx5j7I4x/jfJu5N8x/6dxhg7Y4wzY4wzm5ubhz0nAKzNMrG8lOT2qtqoqkry6iSPrnYsAJiOZd6zvD/JvUkeTPJ3i39mZ8VzAcBknFxmpzHGW5O8dcWzAMAk+Qk+ANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYglwALNZsrWVnDgxf5zN1j0RR+HkugcAOC5ms+Ts2WRvb/784sX58yTZ3l7fXKyeK0uAJZ0790won7a3N9/O1U0sAZZ06dLBtnP1EEuAJZ06dbDtXD3EEmBJ588nGxvP3baxMd/O1U0sAZa0vZ3s7CSnTydV88edHTf3XAvcDQtwANvb4ngtcmUJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQGOpWFbVS6rq3qr6+6p6tKpevurBAGAqTi653y8n+dMxxuuq6oYkGyucCQAmpY1lVX1FklcmuTtJxhifTfLZ1Y4FANOxzMuwtybZTfKbVfXhqrqnqr5s/05VdbaqLlTVhd3d3UMfFADWZZlYnkzysiS/McZ4aZL/SvLm/TuNMXbGGGfGGGc2NzcPeUwAWJ9lYvl4ksfHGPcvnt+beTwB4JrQxnKM8a9JPl5V37DY9Ookj6x0KgCYkGXvhv2xJLPFnbAfTfKDqxsJAKZlqViOMR5Kcma1owDANPkJPgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBKOidks2dpKTpyYP85m654Irh0n1z0A0JvNkrNnk729+fOLF+fPk2R7e31zwbXClSUcA+fOPRPKp+3tzbcDqyeWcAxcunSw7cDhEks4Bk6dOth24HCJJRwD588nGxvP3baxMd8OrJ5YwjGwvZ3s7CSnTydV88edHTf3wFFxNywcE9vb4gjr4soSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgMbSsayq66rqw1X1nlUOBABTc5AryzcmeXRVgwDAVC0Vy6q6Ocn3JLlnteMAwPQse2X5S0nelORzqxsFAKapjWVVfW+SJ8YYDzT7na2qC1V1YXd399AGBIB1W+bK8hVJ7qiqjyV5V5JXVdXv7N9pjLEzxjgzxjizubl5yGMCwPq0sRxjvGWMcfMYYyvJXUneP8Z4/conA4CJ8N9ZAkDj5EF2HmN8MMkHVzIJAEyUK0sAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmLJC5rNkq2t5MSJ+eNstu6JAI7eyXUPwHTNZsnZs8ne3vz5xYvz50myvb2+uQCOmitLrujcuWdC+bS9vfl2gGuJWHJFly4dbDvA1UosuaJTpw62HeBqJZZc0fnzycbGc7dtbMy3A1xLxJIr2t5OdnaS06eTqvnjzo6be4Brj7theUHb2+II4MoSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgEYby6q6pao+UFWPVNXDVfXGoxgMAKbi5BL7PJXkp8cYD1bVlyd5oKreN8Z4ZMWzAcAktFeWY4xPjjEeXHz+H0keTXLTqgcDgKk40HuWVbWV5KVJ7l/JNAAwQUvHsqpenOQPk/zEGOMzl/n62aq6UFUXdnd3D3NGAFirpWJZVddnHsrZGOPdl9tnjLEzxjgzxjizubl5mDMCwFotczdsJXl7kkfHGL+4+pEAYFqWubJ8RZI3JHlVVT20+PjuFc8FAJPR/qcjY4y/TFJHMAsATJKf4AMADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0xBIAGmIJAA2xBICGWAJAQywBoCGWANAQSwBoiCUANMQSABpiCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0loplVb22qv6hqh6rqjeveqgkmc2Sra3kxIn542x2FL8qADzfyW6Hqrouya8neU2Sx5N8qKruG2M8sqqhZrPk7Nlkb2/+/OLF+fMk2d5e1a8KAJe3zJXlbUkeG2N8dIzx2STvSnLnKoc6d+6ZUD5tb2++HQCO2jKxvCnJx5/1/PHFtueoqrNVdaGqLuzu7n5RQ126dLDtALBKh3aDzxhjZ4xxZoxxZnNz84v6XqdOHWw7AKzSMrH8RJJbnvX85sW2lTl/PtnYeO62jY35dgA4asvE8kNJvr6qbq2qG5LcleS+VQ61vZ3s7CSnTydV88edHTf3ALAe7d2wY4ynqupHk/xZkuuSvGOM8fCqB9veFkcApqGNZZKMMd6b5L0rngUAJslP8AGAhlgCQEMsAaAhlgDQEEsAaIglADTEEgAaYgkADbEEgIZYAkBDLAGgIZYA0BBLAGiIJQA0aoxx+N+0ajfJxUP6djcm+dQhfa91sYZpuBrWkFwd67CGabCG5zs9xtjcv3ElsTxMVXVhjHFm3XN8MaxhGq6GNSRXxzqsYRqsYXlehgWAhlgCQOM4xHJn3QMcAmuYhqthDcnVsQ5rmAZrWNLk37MEgHU7DleWALBWk4llVb22qv6hqh6rqjdf5utfUlW/v/j6/VW1tYYxX9ASa7i7qnar6qHFxw+tY84XUlXvqKonquojV/h6VdWvLNb4t1X1sqOesbPEGr6zqp581nH42aOe8YVU1S1V9YGqeqSqHq6qN15mn0kfhyXXMOnjkCRV9aVV9ddV9TeLdfzcZfaZ9LlpyTVM/tyUJFV1XVV9uKrec5mvrfY4jDHW/pHkuiT/lOTrktyQ5G+SfOO+fX4kydsWn9+V5PfXPfcXsIa7k/zaumdt1vHKJC9L8pErfP27k/xJkkpye5L71z3zF7CG70zynnXP+QLzf02Sly0+//Ik/3iZ30uTPg5LrmHSx2ExYyV58eLz65Pcn+T2fftM/dy0zBomf25azPlTSX73cr9vVn0cpnJleVuSx8YYHx1jfDbJu5LcuW+fO5O8c/H5vUleXVV1hDN2llnD5I0x/iLJv73ALncm+a0x91dJXlJVX3M00y1niTVM2hjjk2OMBxef/0eSR5PctG+3SR+HJdcweYt/v/+5eHr94mP/jR6TPjctuYbJq6qbk3xPknuusMtKj8NUYnlTko8/6/njef4frM/vM8Z4KsmTSb7qSKZbzjJrSJLvW7xsdm9V3XI0ox2qZdc5dS9fvCz1J1X1Tese5koWLyW9NPOrgWc7NsfhBdaQHIPjsHjp76EkTyR53xjjisdiouemZdaQTP/c9EtJ3pTkc1f4+kqPw1Riea344yRbY4xvSfK+PPO3II7Wg5n/SKtvTfKrSf5oveNcXlW9OMkfJvmJMcZn1j3PF6JZw7E4DmOM/xtjfFuSm5PcVlXfvOaRDmyJNUz63FRV35vkiTHGA+uaYSqx/ESSZ/9N5ubFtsvuU1Unk3xFkk8fyXTLadcwxvj0GON/Fk/vSfLtRzTbYVrmWE3aGOMzT78sNcZ4b5Lrq+rGNY/1HFV1feaRmY0x3n2ZXSZ/HLo1HIfj8GxjjH9P8oEkr933pamfmz7vSms4BuemVyS5o6o+lvlbXK+qqt/Zt89Kj8NUYvmhJF9fVbdW1Q2Zvzl737597kvyA4vPX5fk/WPxTu5EtGvY957SHZm/j3Pc3Jfk+xd3Y96e5MkxxifXPdRBVNVXP/1eRlXdlvmfg8mc3BazvT3Jo2OMX7zCbpM+DsusYerHIUmqarOqXrL4/EVJXpPk7/ftNulz0zJrmPq5aYzxljHGzWOMrczPre8fY7x+324rPQ4nD+sbfTHGGE9V1Y8m+bPM7yp9xxjj4ar6+SQXxhj3Zf4H77er6rHMb964a30TP9+Sa/jxqrojyVOZr+HutQ18BVX1e5nfpXhjVT2e5K2Z3xCQMcbbkrw38zsxH0uyl+QH1zPplS2xhtcl+eGqeirJfye5a0ont8z/Fv2GJH+3eJ8pSX4myank2ByHZdYw9eOQzO/qfWdVXZd5zP9gjPGe43RuynJrmPy56XKO8jj4CT4A0JjKy7AAMFliCQANsQSAhlgCQEMsAaAhlgDQEEsAaIglADT+HxLZmbA2Eu2/AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig=plt.figure(figsize=(3,3))\n", + "ax=fig.add_axes((2,2,2,2))\n", + "ax.plot([0,1,2,3,4],[0,3,5,7,9],'bo')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f053e6c5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig=plt.figure(figsize=(3,3))\n", + "ax=fig.add_axes((2,2,2,2))\n", + "ax.plot([0,1,2,3,4],[0,3,5,7,9],color='green',linestyle='dashed')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d036ee77", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig=plt.figure(figsize=(3,3))\n", + "ax1=fig.add_axes((2,2,2,2))\n", + "ax2=fig.add_axes((1,1,1,1))\n", + "ax1.plot([0,1,2,3,4],[0,3,5,7,9],color='green',linestyle='dashed')\n", + "ax2.plot([0,1,2,3,4],[0,4,6,8,10],color='red',linestyle='solid')\n", + "# fig.savefig('test.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a0029007", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#subplots https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subplots_demo.html\n", + "import matplotlib.pyplot as plt\n", + "fig=plt.figure(figsize=(3,3))\n", + "ax1=fig.add_axes((0,1,1,1))# 0 is x 1 is y\n", + "ax2=fig.add_axes((0,2,1,1))# 0 is x 2 is y it starts from southwest\n", + "\n", + "ax3=fig.add_axes((1,1,1,1))# 0 is x 1 is y\n", + "ax4=fig.add_axes((1,2,1,1))# 0 is x 2 is y it starts from southwest\n", + "ax1.plot([0,1,2,3,4],[0,3,5,7,9],color='green',linestyle='dashed')\n", + "ax2.plot([0,1,2,3,4],[0,4,6,8,10],color='red',linestyle='solid')\n", + "ax3.plot([0,1,2,3,4],[0,4,6,8,10],color='green',linestyle='solid',marker='o')\n", + "ax4.plot([0,1,2,3,4],[0,6,8,10,12],color='blue',linestyle='solid')\n", + "plt.savefig('oneaboveanother.png')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "043eb584", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x=[0,1,2,3,4]\n", + "y=[0,3,5,7,9]\n", + "z=[0,4,6,8,10]\n", + "fig, axs = plt.subplots(2)\n", + "fig.suptitle('Vertically stacked subplots')\n", + "axs[0].plot(x, y)\n", + "axs[1].plot(x, z)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "728b37e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEVCAYAAADJrK/3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAofElEQVR4nO3dd5xU9fX/8deRpVeRXmSRZkGRIqLYxW5iiQ0Fv6KJgA0TS9T4+yb5JqbaY6LyjaIBxIIYY+zd2GFpgvTeWdrSFrad3x8z+80El9nZ3Zm5d2bfz8djH+yUvffMzIczdz73zPmYuyMiIuF1QNABiIhIfErUIiIhp0QtIhJyStQiIiGnRC0iEnJK1CIiIadEnWXM7Akz+39BxxHLzJab2ZDo778wswlBx7Q/ZuZm1j0J2/nIzH6YjJhitlnt587MTjGz1cmMR9JHiTpAsQks5rprzOzT6m7T3Ue5+69qHl3FzCw3msxyUrWPqkpFUqzNzOwZM/t10HHIvylRZxEzqxN0DCKSfErUIWdmh0WPGLeZ2Vwz+37Mbc+Y2eNm9oaZ7QJOjT0aMrPXzGxnzE+ZmV0Tve14M5tqZgXRf4+P2e5HZvYrM/vMzHaY2Ttm1ip68yfRf7dFt3mcmXUzsw/MbLOZbTKziWbWIoHH9rqZ3bzPdbPN7KIK7tvAzCZE97EtGnNbM7sPOBF4LBrPY9H7P2Jmq8xsu5nlmdmJMduqY2b3mNmS6OPLM7POFezzhOg2TolevtbM5pnZVjN728y6xNz3DDObH30+HwMszuMeaGbTorFtMLMHo9d/Z3qigk9dDczshWjc082szz73vdvMvo3GOM7MGuwnhgrHlZldD1wF3Bl9Pl+LXv9TM1sT3e8CMzt9f49PUsDd9RPQD7AcGLLPddcAn0Z/rwssBu4B6gGnATuAXtHbnwEKgMFE3nQbRK/7dQX7OgdYC3QGWgJbgeFADjA0evmg6H0/ApYAPYGG0cu/i96WCziQE7Pt7sAZQH2gNZFk/nBFjxP4BTAh+vtlwFcx9+sDbAbqVRD/SOA1oBFQB+gPNIuJ94f73H8YcFD08d0GrAcaRG+7A/gG6EUkofaJeewefTxnA6uAgdHrL4i+FodFt3kv8Hn0tlbR1+WS6Gv2Y6Bk35hiYvsCGB79vQkwKPr7KcDq/Y2R6HNXHLOf24FlQN2Y+86JeY0/Kx8LsdsmsXH165gYekWfiw4xY6Bb0P9/atOPjqiD9/foUc02M9sG/CXmtkFE/iP/zt2L3P0D4J9EEmu5V939M3cvc/c9Fe3AzHoCzwKXufsq4DxgkbuPd/cSd58EzAe+F/Nn49x9obsXAi8CR+/vAbj7Ynd/1933uns+8CBwcgKP/R9ATzPrEb08HHjB3YsquG8xkcTb3d1L3T3P3bfHiWmCu2+OPr4HiLyJ9Ire/EPgXndf4BGz3H1zzJ9fCjwJnOPuX0evGwX81t3nuXsJ8Bvg6OhR9bnAXHef7O7FwMNE3hj2pxjobmat3H2nu38Z5777yovZz4NE3pwHxdz+mLuvcvctwH3851gpl8i4ilVK5Pk73Mzquvtyd19ShZilhpSog3ehu7co/wFuiLmtA7DK3ctirlsBdIy5vCrexs2sOfAqkcRUfpKyQ3Q7sfbdbmyi2U3kP/b+9tHWzJ6PfjTeDkwgcpQZV/SN5QVgmJkdQCRRjN/P3ccDbwPPm9laM/uDmdWNE9Pt0WmKgugbYPOYmDoT+cSwP7cCL7r7nJjrugCPxLyhbiFyNN6R6OsU87ic+K/LdUQ+rcyPTuGcH+e++4rdTxmwOrr/79xO5DWNva1cIuPq/7j7YiLPyS+AjdHXuqLtSoooUYfbWqBzNImVOxhYE3N5v+0Po3/3HPChu4/dZ7td9rn7vtvdn4r295vo9Ue6ezMi0w77naPdx7NE5kRPB3a7+xcV7tS92N1/6e6HA8cD5wNXVxRTdD76TiJTKwdG3wALYmJaBXSLE9OlwIVmNibmulXAyNg3VXdv6O6fA+uIJP/y/Vvs5QoeyyJ3Hwq0AX4PTDazxsAuIlM75dupQ2QqKVbsfg4AOhF5Pb9zO5HXNPa2cpWNq++8xu7+nLufQGTceDRuSRMl6nD7isjR7J1mVjd6Uut7wPMJ/v19QGNgzD7Xv0FkyuFKM8sxs8uBw4l8/K1MPlAGHBJzXVNgJ1BgZh2JzAEnJJqYy4AH2P/RNGZ2qpkdGU1e24lMH5QfEW6oIJ6SaKw5ZvbfQLOY2/8K/MrMeljEUWZ2UMzta4m8cYwxs9HR654A7jazI6LxNDezS6O3vQ4cYWYXW6Rs8RagXZzHMszMWkePaLdFry4DFhI5WXhe9NPCvUSmHGL1j9nPrcBeIHbq5EYz62RmLYGfEfnEsq/KxtV/PJ9m1svMTjOz+sAeoJB/P/eSBkrUIRadq/0ekROBm4jMX1/t7vMT3MRQIvORW+3flR9XRedjzydykm0zkaPP8919UwIx7SbyBvBZdBpgEPBLoB+Ro9bXgSlVeZzA34AjiUyZ7E87YDKRJD0P+Jh/J/ZHgEuilQ6PEpkieYtI4ltBJLnETgk8SGTe/Z3o9p4ictI09nGuJJKs7zKzH7r7K0SOIp+PTu/MIfK6EH3eLgV+R+T57EHkRN7+nA3MNbOd0divcPdCdy8gMvX1VyJHt7uITG3EehW4nH+fDL44Ol9d7rno41pKZHrnO/XQCYyrp4jMR28zs78TebP4XfS+64l8Erg7zuOTJLPIdJpIcMzsauD66EdrqSYzW06k0uS9oGOR5NIRtQTKzBoROYocW9l9RWorJWoJjJmdRWQeeQORj+wiUgFNfYiIhJyOqEVEQk6JWkQk5JSoRURCTolaRCTklKhFREJOiVpEJOSUqEVEQk6JWkQk5JSoRURCTolaRCTklKhFREJOiVpEJOSUqEVEQk6JWkQk5HJSsdFWrVp5bm5uKjYtQl5e3iZ333fR15TTuJZUijeuU5Koc3NzmTZtWio2LYKZrQhivxrXkkrxxrWmPkREQk6JWkQk5JSoRURCTolaRCTklKhFREJOiVqynpk9bWYbzWxOzHUtzexdM1sU/ffAIGMUiUeJWmqDZ4Cz97nuLuB9d+8BvB+9LBJKStQSOk9+vITpK7cmbXvu/gmwZZ+rLwCejf7+LHBh0nYoUoGCwmJ+8Y+57NpbUuW/VaKWUHnzm3X89s35vDJ9Tap31dbd10V/Xw+0rehOZna9mU0zs2n5+fmpjkmy1N6SUkaOn8bEr1Ywf/2OKv+9ErWExuKNO7j9pVkc3bkF955/WNr26+4O+H5uG+vuA9x9QOvWaf/WumQBd+enk2fz5dIt/PGSPvTvUvXTIUrUEgo79hRz/fg8Gtarw+PD+lE/p06qd7nBzNoDRP/dmOodSu30wDsL+fvMtdxxVi8u7NuxWttQopbAuTu3vzSLFZt389iV/WjfvGE6dvsP4L+iv/8X8Go6diq1y6SvV/LYh4sZOrAzN5zSrdrbUaKWwD3+8RLenruBu885lEGHHJT07ZvZJOALoJeZrTaz64DfAWeY2SJgSPSySNJ8uGAj9/59Dif3bM2vLuiNmVV7WynpnieSqH8tyuf+txdw/lHtue6ErinZh7sP3c9Np6dkh1LrzVlTwI0Tp3Nou6b8+ap+5NSp2TGxjqglMKu37uaWSTPo0aYpf7jkqBodcYiExeqtuxnxzFQObFSPp685hib1a348rCNqCcSe4lJGTcijpNR5Ynh/GtXTUJTMV1BYzIhxU9lTXMrEHx5L22YNkrJd/e+QtHN37v37HOas2c5frx5A11aNgw5JpMbKa6WXb97Fs9cOpGfbpknbthK1pN3Er1YyOW81t5zWnSGHV/g9E5GMElsr/fDlR3N8t1ZJ3b7mqCWtpq/cyi9fm8spvVozZkjPoMMRSYpk1ErHo0QtaZO/Yy+jJ+TRvnlDHr78aOocoJOHkvnKa6WvOKZmtdLxaOpD0qK4tIybnptOQWExU0YPpEWjekGHJFJj/1ErfWHNaqXjUaKWtPjdm/P5atkWHrq8D4d3aBZ0OCI1Vl4r3attpFa6bg1rpePR1Iek3D9mreWpT5dxzfG5XNS3U9DhiNRYea10i4Z1GTciObXS8ShRS0rNX7+dn06ezTG5B3LPuenriCeSKrG10s9cOzBptdLxKFFLyhQUFjNyfB5NGuTw5yv7US9Hw00yW2yt9JPD+ie1VjoezVFLSpSVOT95YSZrthby/PWDaJOGow6RVIqtlX7o8j4c3z25tdLx6BBHUuJPHyzm/fkb+X/nH86A3JZBhyNSY+W10ref2TPt51oSStRm9mMzm2tmc8xskpnp8Ej268P5G3n4/YVc3LcjVx/XJehwRGostlb6xlO7p33/lSZqM+sI3AIMcPfeQB3gilQHJplpxeZdjHl+Boe2a8Z9Fx2pjniS8T5KU610PIlOfeQADc0sB2gErE1dSJKpCotKGTk+DzPjyWH9aVgv5ctpiaRUOmul46l0r+6+BrgfWAmsAwrc/Z1976fVmms3d+euKbNZsGEHj1xxNAcf1CjokERqZM22Qq59ZirN01QrHU8iUx8HAhcAXYEOQGMzG7bv/bRac+32zOfLeXXmWn4ypCen9GoTdDgiNRKplf6awjTWSseTyHH8EGCZu+e7ezEwBTg+tWFJJvl62Rbue30eQw5rG8iJFpFkKiopY9T4PJZtSm+tdDyJJOqVwCAza2SRWfTTgXmpDUsyxYbte7hh4nQ6t2zEg5f34QB1xJMM5u789OXZfLF0M3+45Ki01krHk8gc9VfAZGA68E30b8amOC7JAEUlZdwwcTq7i0p4Ylh/mjWoG3RIIjXy4LsLeWXGmkBqpeNJaHbc3X8O/DzFsUiG+fXr35K3YiuPXdmXXu2C/3goUhPPf72SP30QXK10PPpmolTLy3mr+dsXK/jRiV05/6gOQYcjUiMfLdjIzwKulY5HiVqqbM6aAu555RsGHdKSn559aNDhiNRIWGql4wlfRBJqW3cVMWpCHgc2qsdjV/YjJ4SDWiRRYaqVjiecUUkolZY5Y16Yycbte3lh5CBaNakfdEgi1RZbK/3y6OMDr5WOR4laEvbQuwv5ZGE+v7noSPoefGDQ4YhUW2yt9LMjBoaiVjoeJWpJyDtz1/PYh4u5fEBnhg7sHHQ4ItUWWyud7r7S1aUJRqnU0vyd3PbiLI7q1JxfXnBE6M6Ii1RFea30bWeEq1Y6HiVqiWvX3hJGjs+jbs4BPD6sPw3qqiOeZK7yWunLB3TmptPCVSsdjxK17Je7c+fk2SzJ38mfhvalY4uGQYeUdFoUo/Yor5U+qWdrfn1R+Gql41Gilv36338t5fVv1nHn2YcyOAPm8apKi2LUHrG10n8Jaa10PJkVraTN54s38bs353NO73aMPOmQoMNJJS2KkeUypVY6HiVq+Y612wq5adIMurZqzB8v7ZNRHxGrItFFMSRz/V+tdFEp40YE31e6upSo5T/sKS5l9IQ8ikrKeHL4gIw8+khUIotiaOWizBVbK/3E8P4Z3ThMiVr+wy9fm8us1QXcf2kfurdpEnQ4qVbpohhauSgzuTt3RWulf/+DozL+HIsStfyf579eyaSvV3HDKd04u3e7oMNJBy2KkaUeenchU6K10hf3y4xa6XiUqAWAWau28d+vzuXEHq247cxeQYeTFloUIzu9MHUlj2ZgrXQ82TsBKQnbvHMvoyfk0bppfR65oi91atFyWloUI7t8vDCfe17JzFrpeJSoa7mS0jJunjSDTbuKmDL6eFo2rhd0SCLVMndtATdMyMvYWul4sueRSLX88e0FfL5kM/dd2JveHZsHHY5ItazZVsiIcZldKx1Pdj0aqZLXZ6/jyU+WMmzQwVw6QB3xJDPF1kpPDnlf6epSoq6lFm3YwR2TZ9H34Bb89/lHBB2OSLUUlZQxesK/+0pncq10PErUtdD2PcWMHJ9Ho3p1ePyq/tTL0QyYZJ7yWunPl2zmwcsyo690dSlR1zJlZc5tL85ixZbdTPzhsbRrnn0fE6V2yLZa6Xh0KFXLPP7xEt79dgP3nHsYgw45KOhwRKolG2ul41GirkU+WZjP/e8s4Ht9OnDt4NygwxGplmytlY5HibqWWLVlN7c8P4OebZry+x8cWSsGt2SfbK6Vjqd2PMpabk9xKaMm5FFa5jw5vD+N6unUhGSebK+Vjqf2PNJayt352StzmLt2O0/91wByWzUOOiSRKqsNtdLxKFFnuQlfreTl6asZc3oPTj+sbdDhiFRZbamVjkeJOovlrdjK/7w2l1N7tWbM6T2CDkekympTrXQ8mqPOUht37OGGiXm0b96Qhy/vywG1qCOeZI/aVCsdj46os1BxaRk3TZxBQWExr9wwkOaN6gYdkkiV1bZa6XiUqLPQb96Yx9fLt/DIFUdzWPtmQYcjUmW1sVY6Hk19ZJlXZ65h3GfLGTE4lwuO7hh0OCJVVltrpePRM5BF5q3bzk9fns3A3Jbcc+5hQYcjUmW1uVY6noQStZm1MLPJZjbfzOaZ2XGpDkyqpmB3MaMm5NGsQV0eu6qvjkIk42zfU8y146ZSWFTKuBEDa12tdDyJvl09Arzl7peYWT2gUQpjkioqK3NufWEGa7cV8vz1x9GmqQa4ZJbyWumlm3bW2lrpeCpN1GbWHDgJuAbA3YuAotSGJVXx6AeL+HBBPr+64Aj6dzkw6HBEqsTduWvKbD5bXLtrpeNJ5PNxVyAfGGdmM8zsr2b2ne8hm9n1ZjbNzKbl5+cnPVCp2PvzNvDwe4v4Qb9ODBvUJehwRKrsofcWMWW6aqXjSSRR5wD9gMfdvS+wC7hr3zu5+1h3H+DuA1q3bp3kMKUiyzft4tYXZnJEh2bcpxImyUAvTl3Fo+8vUq10JRJJ1KuB1e7+VfTyZCKJWwK0u6iEURPyqHOA8cSw/jSoWyfokESq5OOF+dz9yjeqlU5ApYna3dcDq8ysV/Sq04FvUxqVxBXpf/ANCzbs4NEr+tK5pc7tSmYpr5XuqVrphCRa9XEzMDFa8bEUGJG6kKQy4z5bzj9mreWOs3pxUk9NM0lmWbutkGufmUqzhnUZd41qpROR0DPk7jOBAakNRRLx1dLN3PfGPM48vC2jT+4WdDgiVbJ9TzEjxk1l995SXhp9nBZXTpDeyjLI+oI93PjcDLq0bMQDl/VRRzzJKOW10kvyd/LstQM5tJ360CRKiTpD7C0pZfTEPHYXlTDpR8fStIE64knmiK2VfuDSPgxWrXSVKFFniF/981tmrNzGn6/sR4+2+taWZJbyWumfnNGTH/RXrXRV6VRrBnhp2iomfLmSkScdwnlHtQ86HJEqKa+VvmxAJ25WrXS1KFGH3Jw1Bfzs73M4vttB3HFWr8r/QKpEDcdSq7xW+sQerbjvoiNVK11NmvoIsa27ihg5Po9Wjevxp6F9yVGtaSqo4ViKqFY6eZSoQ6q0zLnl+Rnk79jLS6OO46Am9YMOKeuo4Vjq7FsrrZPfNaO3uJB68N0F/GvRJv7ngiPo07lF0OFkq0objqnZWNXF1kqPG3GMaqWTQIk6hN6as54/f7iEoQM7c8XAg4MOJ5tV2nBMzcaqJrZW+onh/VUrnSRK1CGzeONObn9pFn06t+AX3z8i6HCynRqOJVFsrfTvf3CUaqWTSIk6RHbujXTEq59zAI9f1Y/6OeqIl0pqOJZcqpVOHZ1MDAl3546XZrE0fycTfngsHVo0DDqk2kINx5JAtdKppUQdEmM/Wcqbc9Zzz7mHcnw3fWRMFzUcqznVSqeepj5C4LPFm/j9W/M578j2/OjEQ4IORyRhqpVODz2rAVuzrZCbJ82gW+sm/OGSo3Q0IhlDtdLpo0QdoD3FpYyekEdxSRlPDu9PYzVQlwyhWun0UmYI0M9fncvs1QWMHd6fQ1o3CTockYSor3T6KVEHZNLXK3lh2ipuOrU7Zx7RLuhwRBLi7tw95Rv1lU4zTX0EYOaqbfz81bmc1LM1Pz6jZ9DhiCTs4fcW8fL01aqVTjMl6jTbtHMvoyfk0aZZfR694mjqaDktyRAvTlvFI6qVDoSmPtKopLSMm56bzpZdRbw8+nhaNKoXdEgiCflkYT73TFGtdFCUqNPoD28v4MulW3jwsj707tg86HBEEvLt2u3cMHE63ds0Ua10QPSMp8k/Z69l7CdLufq4LlzcT3N7khnWFURqpZs2yOGZEQNVKx0QJeo0WLhhB3dOnk3/Lgdy73mHBx2OSELKa6V37S1RrXTANPWRYtv3FDNyfB6N6+fwl6v6US9H740SfkUlZdwwYTqLN+7kmRGqlQ6aEnUKlZU5P3lhFqu27Oa5Hw2ibTMdkUj4lddKf7p4E/df2ocTeqhWOmg6vEuhv3y0mPfmbeBn5x3GwK4tgw5HJCHltdI/HtKTS1QrHQpK1Cny0YKNPPDuQi48ugPXHJ8bdDgiCSmvlb60fyduOV210mGhRJ0Cq7bsZszzM+nVtim/vVgd8SQzxNZK/+Zi1UqHiRJ1khUWlTJyfB7uzpPD+9OwnpbTkvBTrXS46WRiErk7P3vlG+at387T1xxDl4MaBx2SSKVUKx1+ettMovFfrmDKjDXcenpPTu3VJuhwRCqlWunMoCPqJJm2fAv/89q3nH5oGzWskYygWunMoUSdBBu37+GGidPpdGBDHrz8aA5QRzwJOdVKZxYl6hoqLi3jxuems2NPCX+7biDNG2p+T8KvvFb61iE9VCudARKeozazOmY2w8z+mcqAMs19r89j6vKt/P6So/TRUTJCea30Jf07Meb0HkGHIwmoysnEMcC8VAWSiV6ZsZpnPl/OdSd05ft9OgQdjkilYmulf6ta6YyRUKI2s07AecBfUxtO5vh27XbunvINx3ZtyV3nHBp0OCKVUq105kr0lXoYuBMo298dzOx6M5tmZtPy8/OTEVtobdtdxMgJ02jRsB6PXakBL+FXXivdpH4O40Yco1rpDFNphjGz84GN7p4X737uPtbdB7j7gNatWyctwLApK3NufWEm6wv28Jdh/WjdtH7QIYnEVV4rvTNaK92+ecOgQ5IqSuRQcDDwfTNbDjwPnGZmE1IaVYg9/P4iPlqQz8+/dwT9Dj4w6HBE4oqtlX58WD8Oa68T3pmo0kTt7ne7eyd3zwWuAD5w92EpjyyE3vt2A49GO4tddezBQYcjEldsrfRvLz6SE3tk7yfdbKfJ1QQt27SLH784k94dm/GrC3vrbLmEXmyt9KUDOgcdjtRAlRK1u3/k7uenKpiw2rW3hFHj88g5wHhiWH8a1FVHvGyRrd8PUK10dtERdSXcnZ++PJtFG3fw6NC+dDqwUdAhSXJl3fcDVCudfZSoK/HUp8v45+x13H5WL83xZZls/H6AaqWzk17FOL5Yspnfvjmfs45oy+iTuwUdjiTfw1Ty/YBMsruohOueVa10NlKi3o91BYXcPGk6uQc14v5L++jjY5ZJ9PsBmfRFrinT17CuYA8PX3G0aqWzjBJ1BfaWlDJ6wnQKi0p5cnh/HZlkp4S+H5ApX+QqK3PGfbaMozo151iteJ91lKgr8D+vfcvMVdu4/9I+dG/TNOhwJAWy7fsBnyzKZ0n+Lq4d3FWf/rKQEvU+Xpy6iolfrWTUyd0458j2QYcjkpCnP1tOm6b1OVdjNispUceYvXob9746hxO6t+L2M3sGHY6kSaZ/P2DRhh18sjCfq4/rQr0c/ZfORnpVo7bsKmL0hOm0blKfR4f2JUdlTZIhxn2+nPo5BzB0oNoaZCstxQWUljm3TJpB/s69TB51HC0b1ws6JJGEbN1VxJTpq7mob0cOaqJOjtlKh43A/e8s4NPFm/j1Bb05qlOLoMMRSdikqSvZU1zGiMFdgw5FUqjWJ+q35qzj8Y+WcOWxB3PZMWpcI5mjuLSMv32+ghO6t6JXO1UnZbNanagXb9zJbS/O4ujOLfj59w4POhyRKnlzznrWb9/DtSfkBh2KpFitTdQ79hQzcvw0GtStw+PD+lE/Rx3xJLM8/ekyurZqzCk92wQdiqRYrUzU7s4dL81m+ebdPHZlP33dVjLO9JVbmblqGyMG53LAAfqCS7arlYn6qU+X8dbc9dx9zqEc1+2goMMRqbKnP11G0wY5/KBfp6BDkTSodYl6y64iHn5vEacf2obrTtCZcsk8a7cV8uac9QwdeDCN66vCtjaodYn68Y8Ws7uohLvOOVQ9ESQj/e2LFbg7Vx/XJehQJE1qVaJeV1DIs1+s4KK+nejRVuVMknl2F5Uw6euVnN27nVYbqkVqVaJ+9P3FuDu3DtEacpKZpkxfQ0FhMdfqCy61Sq1J1Ms37eLFaau4cuDBdG6pIxHJPLE9p/t3OTDocCSNak2ifui9hdStY9x4WvegQxGpFvWcrr1qRaKet247/5i1lhGDu9KmaYOgwxGpFvWcrr1qRaJ+4J0FNKmfw6iTtECtZCb1nK7dsv4Vz1uxlffmbWTUyd1o3khrH0pmUs/p2i2rE7W788e359OqST1GDM4NOhyRalHPacnqRP3p4k18uXQLN53anUb19A0uyUzqOS1Zm6gjR9ML6NiiIUOP1cdFyUzqOS2QxYn67bnrmb26gFuH9FALU8lY6jktkKWJurTMuf+dhXRr3ZiL+nYMOhyRalPPaYEsTdSvzFgTWb3lzF5aTVwylnpOS7msy2J7S0p56N2FHNmxOef0bhd0OCLVpp7TUi7rEvULU1exZlsht5/VS1+zlYylntMSK6sS9e6iEh59fzHHdm3JST1aBR2OSLWp57TEyqpE/czny9m0cy936GhaMph6Tsu+Kk3UZtbZzD40s2/NbK6ZjUlHYFVVUFjMEx8t4bRD2zAgt2XQ4YhUm3pOy74SmfwqAW5z9+lm1hTIM7N33f3bFMdWJWM/WcL2PSXcfmavoEMRqTb1nJaKVHpE7e7r3H169PcdwDwgVMXJ+Tv28vSny/lenw4c3qFZ0OGIVJt6TktFqjRHbWa5QF/gqwpuu97MppnZtPz8/CSFl5g/f7iYotIyfnJGz7TuVyTZ1HNaKpJwojazJsDLwK3uvn3f2919rLsPcPcBrVu3TmaMca3eupuJX63gsgGd6Nqqcdr2K5kvbOdf1HNa9iehAk0zq0skSU909ympDalqHnlvEWbGzadpwVqpslCdf1HPadmfRKo+DHgKmOfuD6Y+pMQt3riDl6evZvigLnRo0TDocCTDhOn8i3pOSzyJfL4aDAwHTjOzmdGfc1McV0IefHchDevW4YZTtMSW1Mz+zr+k69yLek5LPJVOfbj7p0DoTj9/s7qAN75Zzy2n99ARiNRIvPMv7j4WGAswYMAAT8X+1XNaKpOxZyz++M4CWjSqy49O1BGIVF8Yzr+o57RUJiMT9ZdLN/PJwnxuOKUbTRtowVqpnrCcf1HPaalMxiXq8iW22jarz9XH5QYdjmS2wM+/qOe0JCLj+id+uGAjeSu2ct9FvWlQV0tsSfWF4fyLek5LIjLqiLqszPnj2ws5uGUjLhvQOehwRGpEPaclURmVqP/5zTrmrdvOT87oSV0tsSUZTj2nJVEZk+2KS8t48J0FHNquKd/v0yHocERqRD2npSoyJlG/nLea5Zt3c9uZvXTSRTKeek5LVWREot5TXMoj7y+i78EtGHKYSpgks6nntFRVRiTqCV+uYF3BHi2xJVlBPaelqkKfqHfuLeEvHy3hhO6tOL6bFqyVzKee01JVoU/UT/1rGVt2FXHHWVpiSzKfek5LdYR6pGzdVcT//mspZx3Rlj6dWwQdjkiNqee0VEeoE/XjHy9hV1EJt2nBWskC6jkt1RXaRL2+YA/Pfr6ci/p2pGdbtX6UzKee01JdoU3Uf/pgEWXu/HiIFqyVzKee01IToUzUKzbv4oWpq7jimIPp3FLf2pLMp57TUhOhTNQPvbuQnDrGzad1DzoUkaRQz2mpidAl6vnrt/PqrLVcc3xX2jRrEHQ4IjWmntNSU6FL1Pe/vZAm9XMYdfIhQYcikhTqOS01FapEPX3lVt6bt4GRJx1Ci0b1gg5HpMbUc1qSITSJ2t3541sLaNWknsqXJGuo57QkQ2gS9WeLN/PF0s3ceGp3HXlIVlDPaUmWUCTqyIK18+nQvAFXHquv1kp2UM9pSZZQJOq3525g1uoCbh3Sk/o5WrBWMp96TksyBZ6oS8ucB95ZwCGtG3Nxv45BhyOSFOo5LckUeKJ+deYaFm3cyW1n9CJHC9ZKllDPaUmmQDNjUUkZD723kN4dm3FO73ZBhiKSNIs3que0JFego+iFqStZtaWQ27VgrWSRcZ+p57QkV2CJendRCY9+sJiBuS05uWfroMIQSaptu4t4WT2nJckCS9TPfr6C/B17ueNsLVgr2WPS16vYU1zGNYNzgw5FskggibqgsJgnPl7Cqb1ac0xuyyBCEEm64tIy/vbFcgZ3P4hD2zULOhzJIoEk6v/9ZCkFhcVaYkuyyltz1rOuYI++4CJJl/ZEnb9jL09/tozzj2pP747N0717kZR5+rNl5B7UiFN7qee0JFfaE/VfPlrM3pIyfnKGltiS7DF95VZmrNzGiMFdVcEkSZdQojazs81sgZktNrO7qruz1Vt3M/HLlVzSrxOHtG5S3c2IJE2yxva4z5bTtEEOl/RXz2lJvkoTtZnVAf4MnAMcDgw1s8Ors7NH318EwJghParz5yJJlayxva6gkDe+WccVx3RW50dJiUSOqAcCi919qbsXAc8DF1R1R4s37mRy3mqGDepChxYNq/rnIqmQlLH9757TucmOTwRILFF3BFbFXF4dve4/mNn1ZjbNzKbl5+d/ZyPFpWUM7t6KG0/tVu1gRZKs0rFd2bgGaN+8AcMHdaFzS/WcltRI2uc0dx8LjAUYMGCA73v7Ye2bMf66Y5O1O5G0qGxcAzqSlpRL5Ih6DdA55nKn6HUimU5jWzJCIol6KtDDzLqaWT3gCuAfqQ1LJC00tiUjVDr14e4lZnYT8DZQB3ja3eemPDKRFNPYlkyR0By1u78BvJHiWETSTmNbMoG6mouIhJwStYhIyClRi4iEnBK1iEjImXuFNfw126hZPrCigptaAZuSvsPqUSzfFZY4IH4sXdw97eu3xRnXkDnPXTqFJQ4ITyzVGtcpSdT7Y2bT3H1A2nYYh2IJbxwQrlgSEaZ4wxJLWOKA8MRS3Tg09SEiEnJK1CIiIZfuRD02zfuLR7F8V1jigHDFkogwxRuWWMISB4QnlmrFkdY5ahERqTpNfYiIhFzaEnWy1qZLQhxPm9lGM5sTVAzRODqb2Ydm9q2ZzTWzMQHG0sDMvjazWdFYfhlULNF46pjZDDP7Z5BxJEpj+ztxhGJsh21cR2Oq1thOS6JO5rqLSfAMcHZA+45VAtzm7ocDg4AbA3xO9gKnuXsf4GjgbDMbFFAsAGOAeQHuP2Ea2xUKy9gO27iGao7tdB1RJ2VtumRw90+ALUHse5841rn79OjvO4i8eN9Z4ixNsbi774xerBv9CeTkhZl1As4D/hrE/qtBY/u7cYRibIdpXEPNxna6EnVC6y7WVmaWC/QFvgowhjpmNhPYCLzr7kHF8jBwJ1AW0P6rSmM7jqDHdojGNdRgbOtkYsDMrAnwMnCru28PKg53L3X3o4ksRzXQzHqnOwYzOx/Y6O556d63JF8YxnYYxjXUfGynK1FrbboKmFldIgN5ortPCToeAHffBnxIMHOdg4Hvm9lyIlMIp5nZhADiqAqN7QqEbWwHPK6hhmM7XYlaa9Ptw8wMeAqY5+4PBhxLazNrEf29IXAGMD/dcbj73e7eyd1ziYyRD9x9WLrjqCKN7X2EZWyHZVxDzcd2WhK1u5cA5WvTzQNeDGptOjObBHwB9DKz1WZ2XRBxEHmHHU7knXVm9OfcgGJpD3xoZrOJJJ533T0jSuOCprFdobCM7awZ1/pmoohIyOlkoohIyClRi4iEnBK1iEjIKVGLiIScErWISMgpUYuIhJwStYhIyClRi4iE3P8Hkp1ID9iWh7UAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x=[0,1,2,3,4]\n", + "y=[0,3,5,7,9]\n", + "z=[0,4,6,8,10]\n", + "fig, (ax1, ax2) = plt.subplots(1, 2)\n", + "fig.suptitle('Horizontally stacked subplots')\n", + "ax1.plot(x, y)\n", + "ax2.plot(x, z)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "13cf6e6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x=[0,1,2,3,4]\n", + "y=[0,3,5,7,9]\n", + "z=[0,4,6,8,10]\n", + "fig, axs = plt.subplots(2, 2)\n", + "axs[0, 0].plot(x, y)\n", + "\n", + "axs[0, 1].plot(x, y, 'tab:orange')\n", + "\n", + "axs[1, 0].plot(x, z, 'tab:green')\n", + "\n", + "axs[1, 1].plot(x, z, 'tab:red')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "67297dce", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "x=['s1','s2','s3','s4']\n", + "y=[100,120,90,80]\n", + "fig=plt.figure()\n", + "plt.bar(x,y,width=0.5,color='tab:green',edgecolor='black')\n", + "plt.xlabel('Student Names')\n", + "plt.ylabel('Total Marks')\n", + "plt.title('Total Marks for each student')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "190e77aa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEVCAYAAAAPRfkLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAlPElEQVR4nO3de3zcdZ3v8denTa9p06RNJrS0aXrNFBAUwr2FNgUFRPCsAosrIgrdVVldRJHjniNnPbqrx7N71tteuisKR3RFjq6suspu03KpXNrKnSZtKS29kUmaXpK2aW6f88fvl2EIk3TaZuY3ybyfj0cenfn9fpnfJ79J553f9/v7fb/m7oiIiACMiroAERHJHwoFERFJUiiIiEiSQkFERJIUCiIikqRQEBGRJIWCZJ2Z/ZGZPTIEr+NmNj98/AMz+8rJVzf0zKw6rLVoCF5rm5ldNhR1pbzmCR87M/uomT0xlPVIflEoFDgz+42ZfTnN8mvN7I3j/WBL94Ho7g+4+7uHot5sysYHcCEzszVmdmvUdcjxUSjIfcCHzcz6Lb8JeMDduzN9oaH4y1hEoqVQkH8FpgFL+haYWRlwNXC/mY0ys7vN7FUz22tmD5rZ1HC7vrOCj5vZ60A98Fj4MvvNrN3MLuzf5GBmp5vZf5hZq5k1mdkXw+XnmdmTZrbfzPaY2XfMbOyxfgAze8nM3pfyfIyZtZjZu9JsW25mvwz30Wpmj4c/4/8FqoB/C+u+K9z+p+EZ0wEze8zMTk95rQlm9tdmtj1c/4SZTUizzw+EZyFnDHY8w21vCl9vr5n9+TF+7qvM7BUzazOzXWb2uXD525p4UpveQuXhe9BmZo+a2ex+237azLaGx/EbZpb2s8LMLjKzdeHPv87MLgqXf5Xgd+o74fH8jgX+j5klzOygmb1oZmcM9jNKBNxdXwX+BfwT8M8pz/8YeC58/BngKWAmMA74R+DH4bpqwIH7gWJgQsqyopTX+yjwRPh4MrAHuBMYHz4/P1x3DnABUBS+zkbgz1Jex4H54eMfAF8JH98F/CRlu2uBFwf4Wf8K+AdgTPi1BLBw3Tbgsn7bfyyscRzwt33HJVz3XWANcCowGrgo3C55DIBbgC0pdQ92PE8D2oFLwnV/A3T3ryll/3uAJeHjMuDs/sd7kGPXlrKfb6ZuH267GphKEJSbgFvTvJdTgX0EZ5VFwI3h82nh+jV93xc+fw+wASgFDFgETI/6919f/X6voi5AX9F/AYuB/cD48Pla4I7w8UZgecq204Eu3vzgdmBuyvrkB2LKstQPkhuBZzOs68+An6c8HygUZoQfciXh84eAuwZ4zS8Dv+h7nX7rtg30ARyuLw1rmEJwln0EOCvNdn3H4HPAK8DMlHWDHc8vAf+Ssq4Y6ByoJuB1ggAv6bc8ebwHOXap+5kE9ACzUra9ImX9J4FVad7Lm4Bn+u3nSeCj4eM1vDUU6ggC5gJgVNS/9/pK/6XmI8HdnwBagPeb2TzgPOBH4erZwM/D5pb9BB9qPUBlykvsOI7dzQJeTbfCzBaGTTtvmNlB4C+B8gzq300QZB8ws1LgSuCBATb/BsFf7o+EzSN3D/S6ZjbazL4WNvUcJAgNwprKCc500v4soc8D33X3nSnLBjueM0g5lu5+CNg7yOt/ALgK2B42AV04yLb9pe6nHWgN9/+29cD2fuv6zAjX0W/bU9Pt0N3rge8QnGElzGylmZUcR82SAwoF6XM/8BHgw8Bv3b0pXL4DuNLdS1O+xrv7rpTv9QEep7MDmDvAur8HGoAF7l4CfJGgmSET94W1Xwc82a++N4tzb3P3O919LnAN8FkzWz5A7R8iaIq6jODsoDpcbgQh2gHMG6SmdwP/zcw+kLJssOO5hyA0g52YTSTo70nL3de5+7VAjKBv6MFw1SFgYsrrnJLm21P3M4mgKWh3uvUETUip6/rsJgg5+m3bd+zf9rvg7t9y93MImsoWEgSn5BGFgvS5n+DD7zaCD9g+/wB8ta8j0swqzOzaQV6nGehl4A/+XwLTzezPzGycmU02s/PDdZOBg0C7mcWBTxxH/f8KnE3QZn//QBuZ2dVmNt/MDDhA8Fd6b7i6qV/dk4GjBH+tTyQ4cwHA3XuBe4G/MbMZ4VnFhWY2LuX7XwauAL5rZteEywY7ng8BV5vZYgs62L/MAP9HzWysBfd/THH3LoLj1vdzPA+cbmbvNLPxwP9I8xJXpeznfwJPuXvq2cHnzazMzGYRHNOfpHmNXwMLzexDZlZkZjcQfNj/Mlz/luNpZuea2flmNoYguDpSapY8oVAQANx9G/A7gnbsh1NWfTN8/oiZtRF0kp7/thd483UOA18F1oZNJBf0W98GXA68D3gD2AwsC1d/juCv8zaCzu90H0QD7fcI8P+AOcDPBtl0AfCfBB26TwJ/5+6rw3V/RfCX/f7wSp77CZpDdhH0DTzV77U+B7wIrCNofvk6/f5PufvzBFdy/ZOZXckgx9PdXwY+RdB0t4eg0za16am/m4BtYdPWnwB/FL7OJoJA+U+C45vuZrMfAfeEdZ9DcJaV6hcEncLPAb8Cvtf/Bdx9b/iz3UkQnHcBV7t7S7jJN4EPmtk+M/sWUELwvu4jOK57CZrzJI/0XXUhMuyZ2ZeAhe7e/wNOjoOZOUET3paoa5Hc081GMiKE1/p/nOCvZxE5QWo+kmHPzG4j6MD9d3d/7Fjbi8jA1HwkIiJJOlMQEZEkhYKIiCQpFEREJEmhICIiSQoFERFJUiiIiEiSQkFERJIUCiIikqRQEBGRJIWCiIgkKRRERCRJoSAiIkkKBRERSVIoiIhI0rCeZKe8vNyrq6ujLkNEZFjZsGFDi7tXpFs3rEOhurqa9evXR12GiMiwYmbbB1qn5iMREUlSKIiISJJCQUREkoZ1n4JE6NEC78u5tDbqCkSyQmcKIiKSpFAQEZEkhYKIiCQpFEREJEmhICIiSQoFERFJUiiIiEiSQkFERJIUCiIikqRQEBGRJIWCiIgkZS0UzOxeM0uY2Uspy6aa2X+Y2ebw37JwuZnZt8xsi5m9YGZnZ6suEREZWDbPFH4AXNFv2d3AKndfAKwKnwNcCSwIv1YAf5/FukREZABZCwV3fwxo7bf4WuC+8PF9wPtTlt/vgaeAUjObnq3aREQkvVz3KVS6+57w8RtAZfj4VGBHynY7w2UiIpJDkc2n4O5uZn6832dmKwiamKiqqjrxAjQfQNQViEgeyvWZQlNfs1D4byJcvguYlbLdzHDZ27j7SnevdffaioqKrBYrIlJojisUzKzMzM48if09DNwcPr4Z+EXK8o+EVyFdABxIaWYSEZEcOWbzkZmtAa4Jt90AJMxsrbt/9hjf92NgKVBuZjuBe4CvAQ+a2ceB7cD14ea/Bq4CtgCHgVtO5IcREZGTk0mfwhR3P2hmtxJcIXSPmb1wrG9y9xsHWLU8zbYOfCqDWkREJIsyaT4qCtv/rwd+meV6REQkQpmEwpeB3wJb3H2dmc0FNme3LBERiUImzUer3P2nfU/cfauZfS6LNYmISEQyCYV/M7Mr3f0ggJmdBjwInJHVykRGskK/TwZ0r0yeyqT56C8JgmGSmZ0D/BT4cHbLEhGRKBzzTMHdf2VmY4BHgMnAf3H3TVmvTEREcm7AUDCzbwOpw1BMAV4Fbjcz3P3T2S5ORERya7Azhf6NnhuyWYiIiERvwFBw9/vMbDTBDWt/lMOaREQkIoN2NLt7DzDbzMbmqB4REYlQJpekbgXWmtnDwKG+he7+N1mrSkTkWAr9st4sXdKbSSi8Gn6NIrj6SERERqhMLkn9i1wUIiIi0ctk6OwK4C7gdGB833J3r8tiXSIiEoFM7mh+AGgA5gB/AWwD1mWxJhERiUgmoTDN3b8HdLn7o+7+MUBnCSIiI1AmHc1d4b97zOy9wG5gavZKEhGRqGQSCl8xsynAncC3gRLgjqxWJSIikcjk6qO+2dYOAMuyW46IiEQpk6uP5gB/ClSnbu/u12SvLBERiUImzUf/CnwP+DegN6vViIhIpDIJhQ53/1bWKxERkchlEgrfNLN7CCbZOdq30N1/n7WqREQkEpmEwjuAmwjuTehrPnJ0r4KIyIiTSShcB8x1985sFyMiItHK5I7ml4DSLNchIiJ5IJMzhVKgwczW8dY+BV2SKiIywmQSCvdkvQoREckLmdzR/GguChERkehl0qcgIiIFQqEgIiJJmfQpDDkz2wa0AT1At7vXmtlU4CcEYyxtA653931R1CciUqgGDAUze5HgJrW3rQLc3c88yX0vc/eWlOd3A6vc/Wtmdnf4/AsnuQ8RETkOg50pXJ2zKgLXAkvDx/cBa1AoiIjk1ICh4O7bs7hfBx4xMwf+0d1XApXuvidc/wZQme4bzWwFsAKgqqoqiyWKiBSeY3Y0m9kFZrbOzNrNrNPMeszs4Enud7G7nw1cCXzKzC5JXenuTvqmK9x9pbvXunttRUXFSZYhIiKpMrn66DvAjcBmYAJwK/Ddk9mpu+8K/00APwfOA5rMbDpA+G/iZPYhIiLHL6NLUt19CzDa3Xvc/fvAFSe6QzMrNrPJfY+BdxOMr/QwcHO42c3AL050HyIicmIyuST1sJmNBZ4zs/8F7OHk7m+oBH5uZn37/5G7/yYcW+lBM/s4sB24/iT2ISIiJyCTULiJIARuB+4AZgF/cKI7dPetwFlplu8Flp/o64qIyMnL5C/+97t7h7sfdPe/cPfPkvvLVUVEJAcyCYWb0yz76BDXISIieWCwO5pvBD4EzDGzh1NWlQCt2S5MRERyb7A+hd8RdCqXA3+dsrwNeCGbRYmISDSOdUfzduBCM6sEzg1XbXT37lwUJyIiuZXJHc3XAc8A1xFcJvq0mX0w24WJiEjuZXJJ6n8Dzg3vPsbMKoD/BB7KZmEiIpJ7mVx9NKovEEJ7M/w+EREZZjI5U/iNmf0W+HH4/Abg37NXkoiIROWYoeDunzezPwAWh4tWuvvPs1uWiIhE4ZihYGZfd/cvAD9Ls0xEREaQTPoGLk+z7MqhLkRERKI32B3NnwA+Ccw1s9Sb1SYDa7NdmIiI5N5gzUc/IuhQ/ivg7pTlbe6uYS5EREagwe5oPgAcIJh1TURECoDuNxARkSSFgoiIJCkUREQkSaEgIiJJCgUREUlSKIiISJJCQUREkhQKIiKSpFAQEZEkhYKIiCQpFEREJEmhICIiSQoFERFJUiiIiEiSQkFERJLyKhTM7AozazSzLWZ297G/Q0REhlLehIKZjQa+SzD/82nAjWZ2WrRViYgUlrwJBeA8YIu7b3X3TuBfgGsjrklEpKAMNkdzrp0K7Eh5vhM4v/9GZrYCWBE+bTezxhzUlg3lQEvURQxjOn4nT8fw5Azn4zd7oBX5FAoZcfeVwMqo6zhZZrbe3WujrmO40vE7eTqGJ2ekHr98aj7aBcxKeT4zXCYiIjmST6GwDlhgZnPMbCzwh8DDEdckIlJQ8qb5yN27zex24LfAaOBed3854rKyadg3gUVMx+/k6RienBF5/Mzdo65BRETyRD41H4mISMQUCiIikqRQiIiZ3R4O5+FmVh51PcONmT0QDonykpnda2Zjoq5pODGz75nZ82b2gpk9ZGaToq5pODKzb5lZe9R1DCWFQnTWApcB26MuZJh6AIgD7wAmALdGW86wc4e7n+XuZwKvA7dHXdBwY2a1QFnUdQw1hUIOmFmxmf0q/MvsJTO7wd2fdfdtUdc2HAxw/H7tIeAZgvtaJI0Bjt/BcJ0RhKquOBlAuuMXjtX2DeCuqOsbanlzSeoIdwWw293fC2BmUyKuZ7gZ8PiFzUY3AZ+JqLbhIO3xM7PvA1cBrwB3Rlde3kt3/G4HHnb3PUGujhw6U8iNF4HLzezrZrbE3Q9EXdAwM9jx+zvgMXd/PKLahoO0x8/dbwFmABuBG6IsMM+95fgBxcB1wLejLSs7FAo54O6bgLMJfrm+YmZfirikYWWg42dm9wAVwGcjLC/vDfb75+49BCMSfyCi8vJe/+MH3AbMB7aY2TZgopltia7CoaXmoxwwsxlAq7v/0Mz2o07R45Lu+JnZrcB7gOXu3htpgXkuzfG7zczmu/uWsE/hGqAh0iLzWLrfP3c/JWV9u7vPj6zAIaZQyI13AN8ws16gC/iEmX2aoJPqFOAFM/u1uyss0nvb8QOeIrhy68mwTfdn7v7l6ErMa/2P36eA+8ysBDDgeYJjKuml+/0bsTTMhYiIJKlPQUREkhQKIiKSpFAQEZGkYd3RXF5e7tXV1VGXISIyrGzYsKHF3SvSrYskFMzsXuBqIOHuZ4TLpgI/AaqBbcD17r5vsNeprq5m/fr12S1WRGSEMbMBx1yLqvnoBwS3jqe6G1jl7guAVeFzERHJoUhCwd0fA1r7Lb4WuC98fB/w/lzWJCIyHHR09bC6McGeA0ey8vr51KdQ6e57wsdvAJXpNjKzFcAKgKqqqhyVJiISnTcOdFDfkKC+oYm1W/ZypKuHL14VZ8Ul84Z8X/kUCknu7maW9q46d19JOGF2bW2t7rwTkRGnp9d5fud+VjckWLUxwSt7DgJwaukEPnjOTOoWxbhw7rSs7DufQqHJzKaHQ9FOBxJRFyQikisHO7p4fFML9Q0J1jQm2Huok1EG58wu4wtXxKmLx1hYOYlsD9WdT6HwMHAz8LXw319EW46ISHZtbW6nPjwbWLetle5eZ8qEMSytqaAuHuPShRWUThyb05qiuiT1x8BSoNzMdgL3EITBg2b2cYKBzq6PojYRkWzp7O7lmddak/0D2/YeBmBh5SRuXTKXuniMs6tKKRod3X3FkYSCu984wKrlOS1ERCTLmtuOsroxQf3GBE9saaH9aDdji0Zx0bxpfGzxHJbVxJg1dWLUZSblU/ORiMiw19vrvLz7YPJs4PmdwUSBlSXjeN9ZM6iLx7h4/jQmjs3Pj9/8rEpEZBg5dLSbJ7a0UL8xwerGBIm2o5jBO2eVcuflC6lbFOO06SVZ7yQeCgoFEZET8Prew9Q3NLGqIcHTW1vp7Oll8rgiLlkYdhLXVFA+aVzUZR43hYKISAa6enrZsH1f2CyUYEuiHYC5FcV85MLZ1C2KcW71VMZE2Ek8FBQKIiIDaD3UyaObgktGH9vUzMGObsaMNs6fM40bz6uiLh5jTnlx1GUOKYWCiEjI3Wl4oy15NvDs6/vodSifNI73nH4KyxfFWLyggknjRu5H58j9yUREMnCks4cnt7awamOC1Q0Jdh/oAOAdp07hT+sWUBeP8Y5TpzBqVP53Eg8FhYKIFJxd+49Q3xCEwNotLRzt7mXi2NEsnl/OZy5bwLKaGLGS8VGXGQmFgoiMeD29znM79rFqY9As1PBGGwBVUycm+wbOnzuVcUWjI640egoFERmRDhzu4tHNzawOB5jbd7iL0aOM2tllfPGqOHXxSuZVFA+LewdySaEgIiOCu/Nqc3vybGD99n309DplE8ewrCbGsniMSxZWMGXCmKhLzWsKBREZtjq6enj6tdZg3oGGJna0BrORxU+ZzJ9cGgww985ZZYwukE7ioaBQEJFhpelgRxgCQSfx4c4exhWNYvH8cv74knnUxWPMKJ0QdZnDlkJBRPJab6/zwq4DyQHmXtoVzEI2Y8p4/uDsU1ker+TCedMYP0adxENBoSAieaeto4snNgezkK1ubKal/SijDM6uKuPz76lh+aIYNZWT1UmcBQoFEckL21oOsSo8G3jmtVa6epyS8UVcWhNjeTgLWVlxbmchK0QKBRGJRGd3L+u3tSaHlNjacgiABbFJfOziOdTFY5wzuyzSWcgKkUJBRHKmpf0oaxqbqW9o4vFNLbQd7Wbs6FFcMG8aN19UTV08v2YhK0QKBRHJGvdgFrK+q4We37kfd4hNHsd7z5wezkJWTvEIHmBuuNE7ISJD6nBnN2u37KW+oYnVDc28cbADMzhzZil3XLaQuniM02cMj1nICpFCQURO2o7Ww6xuDOYdeHLrXjq7e5k0roglC8qpi8dYWhOjYvLwm4WsEOVVKJjZHcCtgAMvAre4e0e0VYlIf909vfz+9f2samhidUOCTU3BLGRzyou56YLZ1MWDWcjGFqmTeLjJm1Aws1OBTwOnufsRM3sQ+EPgB5EWJiIA7D/cyaObmlm1McGjm5o5cKSLolHGeXOmcn3tLOriMeZWTIq6TDlJeRMKoSJggpl1AROB3RHXI1Kw3J1NTe3JO4k3bA9mIZtWPJbLT6ukLh5j8YJySsZrgLmRJG9Cwd13mdn/Bl4HjgCPuPsj/bczsxXACoCqqqrcFikywnV09fDk1r3UhyON7tofDDB3+owSbl82n7pFlZxZQLOQFaK8CQUzKwOuBeYA+4GfmtmH3f2Hqdu5+0pgJUBtba3nuk6RkWbPgTdnIXtiSwsdXb1MGDOaxQvKub1uPstqYpwypTBnIStEeRMKwGXAa+7eDGBmPwMuAn446HeJyHHp6XWe37k/eTbwyp5ggLmZZRO4oXYWdYsqOX/OVA0wV6DyKRReBy4ws4kEzUfLgfXRliQyMhzs6OLxTS2samhiTWMzrYc6GT3KOGd2GXdfGWd5PMb82CTdOyD5Ewru/rSZPQT8HugGniVsJhKR4+PubG05lDwbWLetle5ep3TiGJYurGBZOMBc6UQNMCdvlTehAODu9wD3RF2HyHB0tLuHZ157c4C57XsPA8EsZLddMpfl8RjvnFWqAeZkUHkVCiJyfBJtHaxpaKa+IcHjm5s5FM5CdtG8ady6ZC7LaiqYWaYB5iRzCgWRYaS3NxhgblVDE/UNCV7YeQCA6VPGc+27TmV5PMZF88qZMFadxHJiFAoiea79aDdPbG5hdUOC+sYEzW1HMYN3zSrlc+9eSF28kkXTNQuZDA2Fgkge2r73ULJv4OmtrXT29DJ5fBGXLKxIzkI2bZIGmJOhp1AQyQNdPb2s37YvHGm0iVebg1nI5lUUc/NFs6mLV1JbXcYYdRJLlikURCLSeqiTNY3B5DOPbWqmraObMaONC+ZO48PhSKOzpxVHXaYUGIWCSI64Oxv3tCXPBp7dEcxCVjF5HFeecQp18UoWLyhnkmYhkwjpt08ki4509vC7V1tYFY4ttOdAMD3ImTOn8Om6BSxfFOOMGRpgTvKHQkFkiO3aHwwwV7+xid+9upej3b1MHDuaJQvKueOyhSytqSBWogHmJD8pFEROUk+v8+zr+5JnAw1vtAFQNXUiN55XxfJFMc6bM5VxRbp3QPKfQkHkBBw43MWjm5up39jEmk3N7D8czEJWW13Gn1+1iGXxGPMqinXvgAw7CgWRDLg7WxLtrArvHdiwfR89vc7U4rHU1cSoWxRjyYIKpkzQLGQyvCkURAbQ0dXD06+1Ur+xifrGBDtag1nIFk0v4ROXzmNZOMDcaHUSywiiUBBJ0XSwI3kn8RObWzjS1cP4MaO4eF45f3LpPJbVxJhROiHqMkWyRqEgBa2313lh1wHqNzaxqiHBy7uDWchOLZ3AB8+ZSV08xoXzpmkWMikYCgUpOG0dXTy+uYX6hgRrGhO0tHcyyuDsqjLuuqKGuniMmkoNMCeFSaEgBeG1lkOs2tjE6sYEz7zWSlePUzK+iEtrYskB5sqKNQuZiEJBRqTO7l7WbXtzFrLXWoIB5hbEJvGxxXOoq4lxzuwyzUIm0o9CQUaM5rajrGlMhLOQtdB+tJuxo0dxwbxpfPSiauriMWZN1SxkIoNRKMiw5R7MQlbfEIw0+sLOYIC52ORxvO+s6SyriXHx/HKKNcCcSMb0v0WGlcOdwSxk9Q0JVjcmaDoYzEJ25sxS7rhsIXXxGKfPKFEnscgJyqtQMLNS4J+BMwAHPubuT0ZalERuR+vh5NnAU1v30tndy6RxRSxZUE5dPMbSmhgVkzULmchQyKtQAL4J/MbdP2hmYwE1ABeg7p5eNmzfR31jgvqNCTYn2gGYU17MTeHkM+dWT2VskTqJRYZa3oSCmU0BLgE+CuDunUBnlDVJ7uw71Mmjm5pZ1ZDg0cYEBzu6KRplnD93KjecO4u6eIy5FZOiLlNkxMubUADmAM3A983sLGAD8Bl3P5S6kZmtAFYAVFVV5bxIGRruTmNTG6s2BsNN//71ffQ6TCsey7tPP4W6eIzFC8opGa8B5kRyydw96hoAMLNa4CngYnd/2sy+CRx09/8+0PfU1tb6+vXrc1ajnJyOrh6efHUvqxqaWN3QzK79wQBzp88oYXk8xrJ4jLNmlmoWMpEsM7MN7l6bbl0+nSnsBHa6+9Ph84eAuyOsR4bAngN9s5AlWPtqCx1dvUwYM5rFC8q5vW4+y2pinDJFs5CJ5Iu8CQV3f8PMdphZjbs3AsuBV6KuS45PT6/z3I791Dc0Ud/QzMY9wQBzM8smcEPtLOoWVXL+nKkaYE4kT+VNKIT+FHggvPJoK3BLxPVIBg4c6eLxzc3Ub0ywZlMzrYc6GT3KOGd2GXdfGWd5PMb82CTdOyAyDORVKLj7c0Dadi7JH+7Oq82HwrOBBOu2BbOQlU4cw9KFFdQtquTSBRVMmahOYpHhJq9CQfLX0e4ennmtNbhaqDHB9r2HAYifMpkVl8xleTzGu6rKNAuZyDCnUJABJQ52sLrxzVnIDnX2MK5oFBfNm8atS+ayrKaCmWW6v1BkJFEoSFJvr/PS7gOs2hgEwYu7DgAwfcp43v+uU6mLx7hoXjkTxqqTWGSkUigUuPaj3TyxuTmcd6CZlvZggLl3zSrl8++pYVlNjEXTNQuZSKFQKBSg7XsPJfsGntq6l64eZ/L4Ii5ZWJGchWzaJA0wJ1KIFAoFoKunl/Xb9lHfEExOv7U5GDlkXkUxt1w8h2U1MWqryxijWchECp5CYYTa236UNY3N1DcmeGxTM20dwSxk58+dmhxpdPa04qjLFJE8o1AYIdydjXvakmcDz+0IZiGrmDyOq86YzrJwgLlJmoVMRAahT4hh7EhnD2u3tFDfGIw0uudABwBnzZzCZ5YvYHm8ktNnlGiAORHJmEJhmNm57zCrw1nInnx1L0e7eykeO5olCyq44/IYS2sqiE3WAHMicmIUCnmuu6eXZ3fsT4402tjUBsDsaRP50PlVLI9Xcu6cMsYV6d4BETl5CoU8tP9wMAtZfUOCRzc1s/9wF0WjjHOrp/LnVy2iblGMueXFundARIacQiEPuDubE+3Js4ENrwcDzE0tHktdPMbyeCVLFmoWMhHJPoVCRDq6enhq695k/8DOfcEsZKdNL+GTS+clZyHTAHMikksKhRx640AwwNyqjQnWbmnhSFcP48eMYvH8cj65dD7L4hVMnzIh6jJFpIApFLKot9d5fuf+5NnAy7uDWchOLZ3AB8+ZSd2iGBfOnaZZyEQkbygUhlhbRxePb25h1cYEj25K0NLeySiDc2aX8YUr4tTFYyys1CxkIpKfFApDYGtz2EnckOCZ11rp7nWmTBjD0poK6sIB5konjo26TBGRY1IonIDO7l7WbXtzFrLXWoIB5hZWTuLWJXOpi8c4u6qUIg0wJyLDjEIhQ81tR1kTzkL2+OYW2o92MzacheyWi6tZVhNj1lTNQiYiw5tCYQDuzsu7DwazkDUmeH7HfgAqS8bxvrNmUBePcfH8aUwcq0MoIiNH3n2imdloYD2wy92vzuW+Dx3tDgaYawiahZoOBrOQvXNWKXdevpC6RTFOm16iTmIRGbHyLhSAzwAbgZJc7GxH62Hqw0tGn3p1L509vUweF8xCVhePcWlNBeWahUxECkRehYKZzQTeC3wV+Gy29vPK7oP84rld1Dck2JxoB2BuRTEfuXA2dYtinFs9VbOQiUhByqtQAP4WuAuYPNAGZrYCWAFQVVV1Qjt5aute7l37GufPmcaN51VRF49RXa5ZyERE8iYUzOxqIOHuG8xs6UDbuftKYCVAbW2tn8i+rqudyfXnztIsZCIi/eTTp+LFwDVmdhUwHigxsx+6+4eHekeTNdqoiEhaedNw7u7/1d1nuns18IdAfTYCQUREBpY3oSAiItEz9xNqls8LZtYMbD/Bby8HWoawHBkael/yj96T/HQy78tsd69It2JYh8LJMLP17l4bdR3yVnpf8o/ek/yUrfdFzUciIpKkUBARkaRCDoWVURcgael9yT96T/JTVt6Xgu1TEBGRtyvkMwUREemnYEPBzG43sy1m5mZWHnU9EjCzB8ys0cxeMrN7zUy3n0fMzL5nZs+b2Qtm9pCZTYq6JnmTmX3LzNqH6vUKNhSAtcBlnPh9DpIdDwBx4B3ABODWaMsR4A53P8vdzwReB26PuiAJmFktUDaUr1kQoWBmxWb2q/CvnZfM7AZ3f9bdt0VdWyEb4H35tYeAZ4CZUddZSAZ4Tw6G64wgqNURmWPp3pdwQrJvEIwsPWTyaUC8bLoC2O3u7wUwsykR1yOBAd+XsNnoJoJJlyR30r4nZvZ94CrgFeDO6MorWOnel9uBh919z1DOBlkQZwrAi8DlZvZ1M1vi7geiLkiAwd+XvwMec/fHI6qtUKV9T9z9FmAGwayIN0RZYIF6y/sCFAPXAd8e6h0VRCi4+ybgbIID+xUz+1LEJQkDvy9mdg9QQRZn35P0Bvu/4u49wL8AH4iovILV/30BbgPmA1vMbBsw0cy2DMW+CqL5yMxmAK3u/kMz2486L/NCuvfFzG4F3gMsd/feSAssQGnek9vMbL67bwn7FK4BGiItsgCl+7/i7qekrG939/lDsa+CCAWCK1m+YWa9QBfwCTP7NEEHzSnAC2b2a3dXWOTW294X4CmCK8KeDNtJf+buX46uxILT/z35FHCfmZUABjxP8D5JbqX7v5IVuqNZRESSCqJPQUREMqNQEBGRJIWCiIgkKRRERCRJoSAiIkkKBRERSVIoiIhIkkJBRESS/j98EhX4P4W66wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x=['s1','s2','s3','s4']\n", + "y=[100,120,90,80]\n", + "z=[4,6,8,10]\n", + "fig, axs = plt.subplots(2)\n", + "fig.suptitle('Vertically stacked subplots')\n", + "axs[0].bar(x, y,color='pink')\n", + "axs[0].set_ylabel('total marks')\n", + "axs[1].plot(x, z)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c6bb8c20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dict1={'S1':200,'S2':230,'S3':140,'S4':100,'S5':90}\n", + "x=list(dict1.keys())\n", + "y=list(dict1.values())\n", + "z=list(dict1.values())\n", + "fig, axs = plt.subplots(2)\n", + "fig.suptitle('Vertically stacked subplots')\n", + "axs[0].bar(x, y,color='pink')\n", + "axs[0].set_ylabel('total marks')\n", + "axs[1].plot(x, z)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "2ad3fb05", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dict1={'S1':200,'S2':230,'S3':140,'S4':100,'S5':90}\n", + "\n", + "x=list(dict1.keys())\n", + "y=list(dict1.values())\n", + "plt.style.use('Solarize_Light2')\n", + "fig, ax = plt.subplots()\n", + "fig.suptitle('Marks of each students')\n", + "ax.barh(x, y,color='pink')\n", + "ax.set_ylabel('total marks')\n", + "ax.set_xlabel('Student Names')\n", + "ax.set_xticks([0,50,100,125,150,175,200,225,250])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "d9cbe730", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Solarize_Light2',\n", + " '_classic_test_patch',\n", + " '_mpl-gallery',\n", + " '_mpl-gallery-nogrid',\n", + " 'bmh',\n", + " 'classic',\n", + " 'dark_background',\n", + " 'fast',\n", + " 'fivethirtyeight',\n", + " 'ggplot',\n", + " 'grayscale',\n", + " 'seaborn',\n", + " 'seaborn-bright',\n", + " 'seaborn-colorblind',\n", + " 'seaborn-dark',\n", + " 'seaborn-dark-palette',\n", + " 'seaborn-darkgrid',\n", + " 'seaborn-deep',\n", + " 'seaborn-muted',\n", + " 'seaborn-notebook',\n", + " 'seaborn-paper',\n", + " 'seaborn-pastel',\n", + " 'seaborn-poster',\n", + " 'seaborn-talk',\n", + " 'seaborn-ticks',\n", + " 'seaborn-white',\n", + " 'seaborn-whitegrid',\n", + " 'tableau-colorblind10']" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.style.available" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "7268be30", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "s1=[50,70,60,90]\n", + "s2=[60,50,80,85]\n", + "subjects=['M1','M2','M3','M4']\n", + "fig=plt.subplots()\n", + "plt.bar([1,2,3,4],s1,width=0.2)\n", + "plt.bar([1.2,2.2,3.2,4.2],s2,width=0.2)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7834262e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + "
subjectsstudent1student2
0M15060
1M27050
2M36080
3M49085
\n", + "
" + ], + "text/plain": [ + " subjects student1 student2\n", + "0 M1 50 60\n", + "1 M2 70 50\n", + "2 M3 60 80\n", + "3 M4 90 85" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "s1=[50,70,60,90]\n", + "s2=[60,50,80,85]\n", + "subjects=['M1','M2','M3','M4']\n", + "dict1={'subjects':subjects,'student1':s1,'student2':s2}\n", + "df=pd.DataFrame(dict1)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "99a9305b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot.bar(rot=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "51e8ea76", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot.bar(stacked=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3e3a83e1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# data from https://allisonhorst.github.io/palmerpenguins/\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "species = (\"Adelie\", \"Chinstrap\", \"Gentoo\")\n", + "penguin_means = {\n", + " 'Bill Depth': (18.35, 18.43, 14.98),\n", + " 'Bill Length': (38.79, 48.83, 47.50),\n", + " 'Flipper Length': (189.95, 195.82, 217.19),\n", + "}\n", + "\n", + "x = np.arange(len(species)) # the label locations\n", + "width = 0.25 # the width of the bars\n", + "multiplier = 0\n", + "\n", + "fig, ax = plt.subplots(layout='constrained')\n", + "\n", + "for attribute, measurement in penguin_means.items():\n", + " offset = width * multiplier\n", + " rects = ax.bar(x + offset, measurement, width, label=attribute)\n", + " ax.bar_label(rects, padding=3)\n", + " multiplier += 1\n", + "\n", + "# Add some text for labels, title and custom x-axis tick labels, etc.\n", + "ax.set_ylabel('Length (mm)')\n", + "ax.set_title('Penguin attributes by species')\n", + "ax.set_xticks(x + width, species)\n", + "ax.legend(loc='upper left')\n", + "ax.set_ylim(0, 250)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a6f1af05", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "s1=[50,70,60,90]\n", + "s2=[60,50,80,85]\n", + "subjects=['M1','M2','M3','M4']\n", + "fig=plt.subplots()\n", + "plt1=plt.bar(subjects,s1)\n", + "plt2=plt.bar(subjects,s2,bottom=s1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "17d594df", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "data=[10,20,30,40]\n", + "fig,ax=plt.subplots()\n", + "ax.pie(data,explode=[0.1,0.1,0.1,0.1])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb0e499f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Matplotlib/bar.ipynb b/Matplotlib/bar.ipynb new file mode 100644 index 0000000..d4543ee --- /dev/null +++ b/Matplotlib/bar.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# bar(x, height)\n", + "\n", + "See `~matplotlib.axes.Axes.bar`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data:\n", + "x = 0.5 + np.arange(8)\n", + "y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.bar(x, y, width=1, edgecolor=\"orange\", linewidth=0.7,color='green')\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n", + " ylim=(0, 8), yticks=np.arange(1, 8))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data:\n", + "dict1={'S1':100,'S2':110,'S3':120,'S4':90}\n", + "\n", + "# plot\n", + "fig = plt.figure()\n", + "\n", + "plt.bar(dict1.keys(),dict1.values())\n", + "\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data:\n", + "x = ['1990','1991','1992','1993']\n", + "y = [80,90,100,110]\n", + "z=[70,100,110,120]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "ax.barh(x, y,label='CY',color='green')\n", + "\n", + "plt.legend(loc='lower right')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data:\n", + "x = ['1990','1991','1992','1993']\n", + "y = [80,90,100,110]\n", + "z=[70,100,110,120]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "ax.bar(x, y,label='CY',color='green')\n", + "ax.bar(x, z,label='PY',bottom=y,color='blue')\n", + "plt.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data:\n", + "x = np.arange(4)\n", + "y = [80,90,100,110]\n", + "z=[70,100,110,120]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "ax.bar(x, y,label='CY',color='green',width=0.3)\n", + "ax.bar(x+0.3, z,label='PY',color='blue',width=0.3)\n", + "plt.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data:\n", + "x = ['1990','1991','1992','1993']\n", + "y = [80,90,100,110]\n", + "z=[70,100,110,120]\n", + "a=[20,30,40,50]\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "ax.bar(x, y,label='CY',color='green')\n", + "ax.bar(x, z,label='PY',bottom=y,color='blue')\n", + "ax.bar(x, a,label='LY',bottom=z,color='orange')\n", + "plt.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAALkAAADHCAYAAABFhfLtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPJUlEQVR4nO3de4xc5XnH8e8vNhdfKAZspw6mcSCq62Ap5iIXRLmHiwklghYVV6Q3QlqRAE4vUdIooqlUhJqUBoJwi8wtATsqGKSIBoolvLhBQLDBMQbTFogBO4a1qai9BoOxf/3jnEXDMrO7M+P3zMy7z0caeeYcP+d913p89p1znvO+sk0IOftYpzsQQmqR5CF7keQhe5HkIXuR5CF7keQhe5HkIXuR5BWRdJek24dsO1XSm5JmJGrzdyW9LunQmm1fkLRZ0sEp2uxGiptB1ZB0GPAc8EXbKyQdCKwDrrV9R8J2lwK7bf+xpCnA88Bf2P5Jqja7TZzJK2L7TeBK4BZJk4BrgJfqJbik3y7PwONqtl0oaV35fr6k1ZK2S3pD0vXDNH0VsEDSOcA/A4+OpQSHOJNXTtJyYD/gJGCe7dca/L2XKM64K8rP9wBrbF8n6XHgZts/kjQZmGv7iWHaXAj8ADDwGdtb9+1P1d3iTF69K4AzgL9vlOClZcBCAEkHAeeV2wB2A5+WNNX2wHAJXnoCOBh4eKwlOESSV872G8A2ivH5cJYCF0k6ALgIeNr2K+W+y4DfBF6Q9JSk80c41i3AD4HzJJ3Yeu970/hOdyDUZ/t5Sa8AC4A/pEj6wX3/AyyU9DGK/wD3SjrM9s6hx5F0GXAEcD6wGlgi6Rjb71Xxc3SDOJN3t6XA1cApwD2DGyVdKmma7b3AW+XmvUODJX0C+C5wue13gX8B3gS+lbjfXSWSvLstA04FHrG9rWb7ucBzkgaAG4BLbL9TJ/5m4Me2/xPAxVWGy4FFko5O2/XuEVdXQvbiTB6yF0keshdJHrIXSR6yF0kesteVN4OmTp3qWbNmtRy/c+dOJk2a1JH4Trbd6fh2227XmjVrttme9pEdtrvuddxxx7kdK1eu7Fh8J9vudHy7bbcLWO06+RTDlZC9SPKQvUjykL1I8pC9SPKQvUjykL1I8pC9pEku6WpJ6yU9J2lRue3i8vNeScenbD8ESJjkkuZSFOjPBz4LnC/p08B6ike2VqVqO4RaKc/kc4Anbb9t+33gUeAi2xts/1fCdkP4kJRJvh44WdJhkiZSTKlwRML2Qqgr6eNv5ZPiVwA7KaZgeNf2onJfH/DXtlcPjZszZ44XL17ccrsDAwNMnjy5I/GdbLvT8e223a7TTz99je2Pfs+rV9CS4gVcC1xR87kPOL7e340Crd6M79YCraSltpKm2+6X9BsUXzZPSNleCPWkridfXs7muhv4iu23JF1IMS/fNODfJa21fU7ifoQxLGmS2z65zrb7gftTthtCrbjjGbIXSR6yF0keshdJHrIXSR6yl7oK8WtlxeF6ScskHSjpq5JelGRJU1O2HwKkrUI8nGJRpuNtzwXGAZcAjwGfA14ZJjyEfSb1zaDxwARJu4GJwK9sPwMgKXHTIRSSncltbwa+B7wKbAH+z/bDqdoLoZFkVYiSDgGWA39AseTHPcC9tu8q92+kGMpsGxobVYi9GT/mqhCBi4Fbaz7/EcXak4OfNwJT68VGFWJvxndrFWLKqyuvAidImqhiAH4msCFheyHUlXJM/iRwL/A08GzZ1i2SrpK0CZgJrJO0JFUfQoD0VYjXUKwhX+vG8hVCJeKOZ8heJHnIXiR5yF4kecheJHnIXsoCrdmS1ta8tktaFHMhhqolu4ToYiq4eQCSxgGbKR5gnkgxPcW/pmo7hFpVLXF4JvCS7Q/Ka6MKMVSlqjH5JcCyitoK4UOSzoUIIGl/4FfA0bbfqNneR8yFmFX8mKtCHHwBXwAerrO9j5gLMav4sViFOGghMVQJHZT6QeZJwFnAfTXbLiyrEE+kmAvxP1L2IYTUVYg7gcOGbIu5EEOl4o5nyF4kecheJHnIXiR5yF4keche6jWDpgBLgLmAgT+jeID57yjW+ZzvOnc8Q57e37aNPTsGWo4fd9Bkxk9tfvrM1AVaNwAP2f798vb+RIqJhqIKcQzas2OAlxcsaDn+yAcf7K4kl3QwcArwJwC23wPeo0jyqEIMlUk5Jv8UsBW4XdIzkpaUd0BDqFTKuRCPB54ATrL9pKQbgO22v13u7yOqELOKHyl23vTpbLno91rtGjPuW87a/v6G+xtVIaYck28CNrmYSQuK2bS+MZrASZMmcdppp7XccF9fX8fiO9l2p+NHin33lxtbOu6gCRMmttS3lNPEvQ68Jml2uelM4PlU7YXQSOrr5FcCd0taR/G857VRhRiqlroKcS0wdIwUVYihUnHHM2QvkjxkL5I8ZC+SPGQvkjxkL/WDzBslPVvOhbi63BZzIYZKVTFN3On+8DKG64kqxFChquZC/IDtDRBViKE6DYcrkn4qaVabxzfwsKQ1kr7c5rFCaEnDKkRJFwP/ANwJ/KPt3U0fXDrc9mZJ04EVwJW2V5X7+ogqxKzie64K0fY9kh4Evg2slvQjYG/N/utH6pTtzeWf/ZLuB+YDq0aKiyrE3ozv1SrE94CdwAHAQUNew5I0SdJBg++Bsym+dIZQqYZncknnAtcDPwGOtf12k8f+OHB/+QVzPLDU9kOSLgR+AEyjqEJca/uclnofqjXQD7u2N9w9/6hDYduLjeP3Nt6V0nBXV74FXGz7uVYObPtl4LN1tkcVYq/atR1uOq7h7okjxV/61D7tzmgNNyY/ucqOhJBK3NYP2YskD9mLJA/ZiyQP2Uue5JLGlZMLPVB+/qqkFyVZUvNzfoXQpCrO5FcDG2o+PwZ8Dnil/l8PYd9KXU8+E/g8xcy2ANh+xvbGlO2GUCv1mfz7wNfp2L2uENLOans+0G97jaTTmonduXMnfX19Lbc9MDDQsfhOtp06fv5Rh458V3MY7c67+c47b/N4Cz9byocmTgIukHQecCDwa5Lusn3pSIFRhdil8cPVpYxCuw/KdONciN+0PdP2LOAS4JHRJHgI+1rl18klXVXOhTgTWCdpyUgxIbSjkmc8bfcBfeX7G4Ebq2g3BIg7nmEMiCQP2at8SorQWSMtMzhv+vSGz2LuP3EPvTiRSCT5GNPOMoOz++7rySSP4UrIXiR5yF6yJJd0oKSfS/pFOcHnd8rtUWobKpVyTP4ucIbtAUn7AT8rJyt6DHiA8rp5CKklS3IX1TiDX+P3K1+2/QzEhJ+hOqnrycdJWgv0AytqFq4NoTKplzjcA8yTNIViNq25tkecKi5KbdPFz5s+veVje297jwXkWGr7AdtvSVoJnMso5kOMUtt08e1MuqmPtfeLP7tSW0nTyjM4kiYAZwEvpGovhEZSjslnACvLJcefohiTPxCltqFqKa+urAOOqbM9Sm1DpeKOZ8heJHnIXiR5yF6U2vaYrTveZceuxmuUTTvyaF7e2rhe/NfbvNbdiyLJe8yOXbs5458ebTl+/Zfm7MPe9IaU18mPkLRS0vNlFeLV5fZYdjxUKuWZ/H3gr2w/Xa4Ct0bSCmLZ8VCxlNfJtwBbyvc7JG0ADre9AqIKMVSnkqsr5fLlxwBRhRgql/yLp6TJwHJgke3Gi0DWiCrExvHTjjy65WMD2K1fXYkqxDrKJ4KWA3fbvm+0cVGF2Dh+uMuDoyG1/ss7qhCHUPET3QpssH19qnZCGEnqqZu/CDxbPh0E8LfAAcSy46FCKa+u/AwazkUTy46HykTtSsheJHnIXtSuVKydCTcBPOGQBL3KWyR5xdqZcBNg/0ce34e9GRtiuBKyl/I6+W2S+iWtr9kWFYihcinP5HdQzLNSa7ACcVXCdkP4kJTXyVeVhVm12zZAVCCGasWYPGSvK6+u5FyF2M5chNBeFWG78VGFuA/lXIXYzlyE0F4VYbvxUYUYQpdKdiaXtAw4DZhazn14DfC/5FCBONAPu+o//zH/qENh24uNY8fejBAdl/LqysIGu3q/AnHXdrjpuLq7Jo4Ue+lT+7w7YXgxXAnZiyQP2YskD9mLJA/ZiyQP2evIzSBJtwHnA/2251bdfrsPLuw/cU/Dh1dD9+nUHc87gJuAH3ai8XYfXJjdd18keQ/pyHDF9iqKG0MhJBdj8pC9rizQGqkK8RNH/ha7Gddw/2Gz5vDcq1sb7v/k+++30722qvHarcSLKsS+puPUbsOtKh+oeKDeF09JW4FXKu9U6HWftD1t6MauPJPX62gIrerImLysUHwcmC1pk6TLOtGPMDZ0bLgSQlWyurpSbxqMJmLrLuTVRPyBkn4u6Rdl/Hda6MM4Sc9IeqCF2I2SnpW0VtLqFuKnSLpX0guSNkg6sYnY2WW7g6/tkhY124dkbGfzAk4BjgXWtxA7Azi2fH8Q8N/AZ5qIFzC5fL8fxdIxJzTZh78EllJ8IW+2/xuBqW38290JfKl8vz8wpcXjjANep/gS2PGcsJ3Xmdxt3GSyvcX20+X7HcAG4PAm4m17sFZgv/I16rGgpJnA54Elo+70PiLpYIoTxK0Att+z/VaLhzsTeMl211wdyyrJ95VWF/IqhxtrgX5ghe1m4r8PfJ3WH5Az8LCkNZK+3GTsp4CtwO3lcGmJpEkt9uMSYFmLsUlEkg/RykJeg2zvsT0PmAnMlzSq4jNJg8Vqa5rtb43fsX0ssAD4iqRTmogdTzHMW2z7GGAn8I1mOyBpf+AC4J5mY1OKJK/R6kJeQ5W/6lfy0WnyGjkJuEDSRuDHwBmS7mqyzc3ln/0Uz9HObyJ8E7Cp5jfPvRRJ36wFwNO232ghNplI8lK7C3lJmiZpSvl+AnAW8MJoYm1/0/ZM27Moft0/YvvSJtqeVK56TTnMOJti3slRsf068Jqk2eWmM4HnRxtfYyFdNlSBLr3j2ap602DYvnWU4XUX8rL901HGzwDulDSO4uTxb7abvhTYoo8D95eT94wHltp+qMljXAncXQ45Xgb+tJng8j/XWcCfN9lucnEzKGQvhishe5HkIXuR5CF7keQhe5HkIXuR5D2grJD8paRDy8+HlJ9ndbhrPSGSvAfYfg1YDFxXbroOuMX2xo51qofEdfIeUZYcrAFuAy4H5tne3dle9Yas7njmzPZuSX8DPAScHQk+ejFc6S0LgC1A5VPr9bJI8h4haR5FbcgJwNckzehsj3pHJHkPKCskF1PUuL8KfBf4Xmd71TsiyXvD5cCrtleUn28G5kg6tYN96hlxdSVkL87kIXuR5CF7keQhe5HkIXuR5CF7keQhe5HkIXuR5CF7/w96xL6vGt236wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data:\n", + "x = [0,1,2,3,4,5,6,7,8]\n", + "y = [0,1,4,9,16,25,36,49,64]\n", + "color_bars= ['tab:red', 'tab:blue', 'tab:red', 'tab:orange']\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.bar(x, y, width=1, edgecolor=\"white\", linewidth=0.7,color=color_bars)\n", + "\n", + "ax.set(xlim=(0, 9), xticks=np.arange(1, 8),\n", + " ylim=(0, 100), yticks=np.arange(1, 100,10))\n", + "ax.set_ylabel('Y')\n", + "ax.set_xlabel('X')\n", + "ax.set_title('Y vs X')\n", + "plt.savefig('bardiagram_python.png')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.style.use('_mpl-gallery-nogrid')\n", + "\n", + "# make data:\n", + "x = [0,1,2,3,4,5,6,7,8,1]\n", + "y = [0,1,4,9,16,25,36,49,64,11]\n", + "\n", + "z=[11,13,21,22,23,12,31,32,33,38]\n", + "# plot\n", + "fig, ax = plt.subplots(figsize=(5,5))\n", + "\n", + "ax.scatter(x, y,marker='^',label='y')\n", + "ax.scatter(x, z,marker='o',color='red',label='z')\n", + "ax.set(xlim=(0, 9), xticks=np.arange(1, 8),\n", + " ylim=(0, 100), yticks=np.arange(1, 80,10))\n", + "\n", + "ax.set_title('Y vs X')\n", + "plt.legend()\n", + "plt.savefig('scatter.png')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data:\n", + "\n", + "#y = [0,1,4,9,16,25,36,49,64,0,1,0,1,4,9,16,25,36,49,64,25,36,49,64,36,49,64,0,1,4,9,16,25,36,49,64,0,1,4,9,16,25,36,49,64]\n", + "rng = np.random.default_rng(19680801)\n", + "N_points=100000\n", + "dist1 = rng.standard_normal(N_points)\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.hist(dist1,bins=20)\n", + "#ax.hist(y,bins=5)\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Matplotlib/bardiagram_python.png b/Matplotlib/bardiagram_python.png new file mode 100644 index 0000000000000000000000000000000000000000..6180e2e32551567199696b54d34f8c42f7b71573 GIT binary patch literal 1378 zcmeAS@N?(olHy`uVBq!ia0vp^6F``Q4M;wBd$a>cS(dm)lmzFem6RtIr84*?mK5aV zm*iw7DU_ua6=&w>8S9zq8R;lwl#~<{Tj}fP!WHP{7p3cKnaR5X4dE>Ch%9Dc;1&j9 zMuu5)Bp4W2&U?BzhE&XXd-q^=OsL55kH#-Eywsz_H)^doxo8{9-PFa>XBRw>SN!5% zpw2ouRs4|=M`Zch2g}YbYxCT#!8LPg$iy94TxD$?X|qz|Jk^i4p1aa}VdsAB?{~lN zlh>GXpt4+S|DDS3bB!mr?Uoi;m6>K{Z~wl#yZfNHrIppDu+>wS*gd_q^7YlNp?CAj z@87vQZTj@si!VziU0ohyUAlMIwo_-y(k2}hpX$({py)6`K*57UK!t@vh>3-hv8jc@ z(FrVU0@OEcT1|4Y@(-o<_I5LK^XYJ7-&*+`X<(cA24yMNUYW>{m*u&dd&#%&S#SI%eg5Xu?di?e!wt?&%&dK%@M3zHdF{hjy!-d;db#&tKo#%8p29wx zqcUCLf(yvtQsKA1jYun3m>t~cH__#I*&8!LE{POjOS8bDZ@^xEaV$N?-`vir_T1f)t@HA1Hn~F+kfZ1^ z!w?pafYw4DQAVzismN5oQZNshdMv}Z%A{{$5_?liUd^$uC6AS_Jip@`{`%!H#tt!FL#c*0u1{bAK)rXzt8LFWH{?LA)h{GhP(&YLsaZnCd8Z~oU-nGyc; z{G%)DYU^{d-_PAVJ?(Iv_={uh+1qd5T{bgv_0?Iw>=xK-ym?nHD}BGa@6MZd`OCi@ zom#$ppE%cE{>b0o+<))gk#BvNyX@`b@@4D7ujx70O1`__bpKG0d~4-zo%RO|OYOKm ztL&6%>i?+Of9U^Lm7kaS_0v9UEY#XmGhxQBx5ECZk_)cn&#%z>dA!PLUJ6jH&7}7Z zHYd+TUt%Z}IUf%u=gTa(aImCp*DA$G^LdLaOwY`i`S;U@zzg~Niuex)?vavum$!NS z-ly}|)S8y6+m@Qgtv$PUAJ5&2;_~W$SG(&fOD|V9KQyk_+f{Dx#e4PaeFa~SJ-NT- zy2RtnA2Y9o&AjP%`0m>?J3rs6`Iu_2%lCc0%$Ile-oIu&kFP9#_U6tWQ>osQ{CAh{ eV%xO+gZRvVy(" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data\n", + "np.random.seed(1)\n", + "x = np.linspace(0, 8, 16)\n", + "y1 = 3 + 4*x/8 + np.random.uniform(0.0, 0.5, len(x))\n", + "y2 = 1 + 2*x/8 + np.random.uniform(0.0, 0.5, len(x))\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.fill_between(x, y1, y2, alpha=.5, linewidth=0)\n", + "ax.plot(x, (y1 + y2)/2, linewidth=2)\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n", + " ylim=(0, 8), yticks=np.arange(1, 8))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Matplotlib/linediagram_python.png b/Matplotlib/linediagram_python.png new file mode 100644 index 0000000000000000000000000000000000000000..b3979228c33195ca6c8e1e0bf69be786b9b7b685 GIT binary patch literal 521 zcmeAS@N?(olHy`uVBq!ia0vp^6F``Q4M;wBd$a>cS(dm)lmzFem6RtIr84*?mK5aV zm*iw7DU_ua6=&w>8S9zq8R;lwl#~<{Tj}fP!WHP{7p3cKnaR5X4dE>Ch%9Dc;1&j9 zMuu5)Bp4VN%RF5iLn`LHy>yWGfP%n*4VV6w+m$Ksx1{(_aKCn@rr_0n*#ozET#^)o sm?uhf9C1)#Q!*6jZ1flvq9i14el2B@eX`3U3m7d7p00i_>zopr05O1*NdN!< literal 0 HcmV?d00001 diff --git a/Matplotlib/oneaboveanother.png b/Matplotlib/oneaboveanother.png new file mode 100644 index 0000000000000000000000000000000000000000..5b0175a3bc9ae8df30163d7aad1cd0b90ed753a3 GIT binary patch literal 1419 zcmeAS@N?(olHy`uVBq!ia0vp^H$a$!4M=t>=)MI~mL;wcCBgY=CFO}lsSLh}B?US8 zB{`W%3T3H9#hLke#(JiDMmh=^B_##LR{Hw6a0Pn#Md|ulX7a8;LpTdOB8wRqxP?KO zkzv*x2?hq1AD%9bAr*7p-rC=tS}u9~s;7mp_`^Vs@1+rGH`&UJ3R zJH|EVKh#+6d4H#PhUcg4D)pyM-kb8|Xz-fdvu_^0`*7aP!*AZLth;^YPfq#$(u*tk zPRhOG`+i9-u2}N$fxTV{0VUH8>ndh?7;>?ZhXU>e5egCky zecnr(v}gNf{(O3-=KYeb+j(=+-g$pIs`B2m$o6D=`mYnU%<}Y+yuk!QC zyEy+(5tI7k4`zQ{uj2pao%JM}Nl%WQu_?PT@!ZpsD(B~>J@l+UDChg>&%1w#<^ShC zS*aqw=Rv{ryz=@PPnJ5LFBIJU`}*C#M?e0qfAWIKzT(Epb7}p5KL7ae@A37iRU8J! z*C*}KKRMlh^S>nr%VhduAjd~%#!?&+Dn{c*OFpM*_XUjBX6 z+lSnqc5|QXUmp2J@wlk5alXf=w<`1R+}$r" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "#plt.style.use('_mpl-gallery')\n", + "plt.style.use('ggplot')\n", + "\n", + "# make data\n", + "x = np.linspace(0, 10, 100)\n", + "y = 4 + 2 * np.sin(2 * x)\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(x, y, linewidth=2.0)\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(1,8),\n", + " ylim=(0, 8), yticks=np.arange(1, 8))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data\n", + "x = [0,1,2,3,4,5]\n", + "y = [0,1,4,9,16,25]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(x, y, linewidth=2.0)\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(0,8),\n", + " ylim=(0, 26), yticks=np.arange(0, 26,2))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data\n", + "x = [0,1,2,3,4,5]\n", + "y = [0,1,4,9,16,25]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(x, y, linewidth=2.0)\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(0,8),\n", + " ylim=(0, 26), yticks=np.arange(0, 26,2))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data\n", + "x = [0,1,2,3,4,5]\n", + "y = [0,1,4,9,16,25]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(x, y, linewidth=2.0)\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(0,8),\n", + " ylim=(0, 26), yticks=np.arange(0, 26,2))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "# make data\n", + "x = [0,1,2,3,4,5]\n", + "y = [0,1,4,9,16,25]\n", + "\n", + "# plot\n", + "fig= plt.figure()\n", + "ax1=fig.add_axes((1,1,1,1))\n", + "ax1.plot(x, y, linewidth=2.0)\n", + "ax1.set(xlim=(0, 8), xticks=np.arange(0,8),\n", + " ylim=(0, 26), yticks=np.arange(0, 26,2))\n", + "ax2=fig.add_axes((2,2,2,2))\n", + "ax2.plot(x, [0,1,8,27,64,125], linewidth=2.0)\n", + "\n", + "ax2.set(xlim=(0, 8), xticks=np.arange(0,8),\n", + " ylim=(0, 126), yticks=np.arange(0, 126,2))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data\n", + "x = [0,1,2,3,4,5]\n", + "y = [0,1,4,9,16,25]\n", + "\n", + "# plot 1\n", + "fig, ax = plt.subplots(1,1)\n", + "plt.plot(x, y, linewidth=2.0)\n", + "\n", + "# plot 2\n", + "fig, ax = plt.subplots(1,1)\n", + "plt.plot(x, [0,1,8,27,64,125], linewidth=2.0)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# make data\n", + "x = np.linspace(0, 10, 100)\n", + "y = 4 + 2 * np.sin(2 * x)\n", + "z=4 + 2 * np.cos(2 * x)\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots(figsize=(5,5))\n", + "\n", + "ax.plot(x, y, linewidth=2.0,label='sin',color='green',linestyle='dashed')\n", + "ax.plot(x, z, linewidth=2.0,label='cosine',color='blue')\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(1,8),\n", + " ylim=(0, 8), yticks=np.arange(1, 8))\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# make data\n", + "x = [0,1,2,3,4,5,6,7,8,9,10]\n", + "y = [0,1,4,9,16,25,36,49,64,81,100]\n", + "z=[0,1,8,27,64,125,216,343,512,729,1000]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots(figsize=(5,5))\n", + "\n", + "ax.plot(x, y, linewidth=2.0,label='sin',color='green',linestyle='dashed')\n", + "ax.plot(x, z, linewidth=2.0,label='cosine',color='blue',marker='s')\n", + "\n", + "\n", + "ax.set(xlim=(0, 11), xticks=np.arange(1,11),\n", + " ylim=(0, 1100), yticks=np.arange(0, 1100,100))\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# make data\n", + "x = [0,1,2,3,4,5,6,7,8,9,10]\n", + "y = [0,1,4,9,16,25,36,49,64,81,100]\n", + "z=[0,1,8,27,64,125,216,343,512,729,1000]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots(figsize=(5,5))\n", + "\n", + "ax.plot(x, y, linewidth=2.0,label='square',color='green',linestyle='dashed')\n", + "ax1=ax.twinx()\n", + "ax1.plot(x, z, linewidth=2.0,label='cube',color='blue',marker='s')\n", + "\n", + "\n", + "ax.set(xlim=(0, 10), xticks=np.arange(1,10),\n", + " ylim=(0, 110), yticks=np.arange(0, 100,10))\n", + "ax1.set(xlim=(0, 11), xticks=np.arange(1,11),\n", + " ylim=(0, 1100), yticks=np.arange(0, 1100,100))\n", + "\n", + "\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Matplotlib/scatter.png b/Matplotlib/scatter.png new file mode 100644 index 0000000000000000000000000000000000000000..adab5e1b43dc053624e156e03d5f5a5a5a8bc87e GIT binary patch literal 3639 zcmeHKX;4#H8jV1+BL=Xcks=7nqBfue1t9@7VNn(l1QcYIMX?hwkw_$B8kZ13R8SBM z1h9>W5Ce_uAu5Y3LTJRqC8A*ySt5%B5+L)yrQ4dWnwhHkGgb5B)qD5dbMO7${mwn# zc}aWS990yx6+s}7in9}99|*J}a_N$n1>WEXf_s3QH5TcM^+bnbalyyKK(4{q=woQ? zu_GaRv0=wCN6;t>Q%ln=oAe^F*l5glGqYd50Zh@yBg{5zg&=`R6r!CDU_c-x^`&bC zDC?#cu;MLe#15~MH^+GOQU1a#);nCfmHduarS%nRrT({^8CI9W*CZycb={|R9h%m< z+0gHYeFrah+|SfViUvR9%E(I1KvwCWy{NIRNlgLwkg0jYf5b7^y(H8-py7U5L0RMI zEbS^c>nDn^-PYZr{UJQQNY#uUKKP8BFAti$VJrrLMqQ~ZuBt_zxfO6)^eBp6=iUDF z>9uH$GZPQ0t3|2##sPx51Vc6kgJG1G9=}hM(mPuIQq(6XwgN4WlF`;HjEH4}_H$4b zp;1vA4;FjBaD36--+#Ehy}coC*M-xv<}?(`fDgA6GkJsRpx z2}6J}mvpQrYb4Lf(puKV-WzYzXx)w0`uW~PjnmW97+0Z`O7gAbnDMwbWeQOHeNrly z)9s}Z2u#iD=8nLW!Nf(oxrrAMxs95E@e>~x=I7(%=LRy&;vqy?66xGPIk&c6U8$`? z#N-ru()c6v`b#7dkH?!G%trGctJ=lC6U#%*XR=-uSJqaqMuKC)!pa0DcofPMh@-C= zl8`K5icR0(M~X9Za!!8+|9G7fB9!Es_Lz`~E?qSD+)H~hOdA_7YW6)V^b}WLu$K-k z-Uh3%;n`Q%G}_;OpJwFVRMC5S4Tm0>it8od0)?w0gYLoLFrjh?`SidwAO7q_-^4>X zNW&hpy5H%ifPkJ~N4+6f!GyotAUE+UyEz0L4%c@9a+;J17e)38KlsIR{s~T)Y%!YXun{zf1@-lGi6Hwd z>*Oo$NybR$@E^bJVKAAxnx_6-A#7=lvNB$IQvgSJtXXM>ypK3*l$ax5}irgB`V#48ro);|fyd3$~u&Nt@2 zsXMjV+IsGSwmmPes15e#yOKS^461Y!++NCVC^v1A#!$j9t12 zYLP*D_-;&1w+gn#GjkbXZwts0@Myqh*>B?2ilg!4M{s1{>KNKB_5HnVVGxj(3a2B- zSS)YQEc5TB_rG>B_NUly(*|QOV0CpK$Dv?UeT6a)i0G}Vr8YRC12H;^?WNOky^e@y zBLftrpeK{=?5U|#{_C=`?z)xck0#~tp?!@d(#fQGVh)AC7y%jRCr~>n7c;Z)lP{ln z3AeN4+aT}pu#lL|{EzQQ2aCnkmG4#0vYr5MNvLFW?Lx<=NQ#*=A z3(OHc^3acG|134FaK&Bg*n2lW9rN}N;F0r5m8GRe$hyH&Dyz!?R@WfGU@#@$9GPlP zJg+$H+BxR7jXmZT{24JhJN-J8ta}Immtv>J9}id2cqSpkO_Zx{4dw$9#=5t8Q4#Lx z1eukW6}|~MP>F+Vy*{?MEIP?2Gs*B9Eix+0cV{ZG%csl80#H1w1H#(Xge__I(urNK zrb_6J8jS@o^m!+r=@u7&EDnP(G%~jxh}-f2NB#wXjR&y1t{o0A@WOUx-}w7SwA#*V z?H#ck?Nf(3sR5e(E4J*|bw} zRTF|;6riJdKqA_T{5&+kKHghhyV7@3gH@HQ@A;k>jG13dVNSV2*TrMe-l46B3eVVwJ zPJwyMfc5lcvbI3K=i03#=;5HohYz!J6Euc$fUHrno@im$|_1a&H1Gh#11sdczNbgotX{b?X` z+^L-dbB4io;Sn`aDRm_gd+ADByfPK4Axn0lJ)tmt+!3IXY|pNGjq_N}KnVwnOX1AlW(~gYk>~Q;X*i%w8YgQ%(6+1}HNyBW!A)LIxI>6s9<>g>e z5>#%}_X44uSg!5duZpeHWmIa#%tj-MpA!!GPs{B*-+VA-U5?81hoel2U!CsL`gzMs z3(d!&k|{dBJ^Db=m4-L!*hiFX8ag`YUXK&M!bc}57L~(%NG$a>Nb2p~Vf3OZP9!T= z5|CO+unECch&ObkzZFVTf`$f%*RU!@WA>jB<|Q*LvH43Af%@)x+gehX3p1}8TU&QK zHwUu;7du-)U)1qgTnOxQr@LJz=)A=}J6hh8Z~SJwu2X_sE#mhj?L8y~dlYXAzUxYL z%uF@fS*|cmnw&t~ffIE-;5qU=Mr?!DX7xPLq+Qx&Le+9{1c?s+`gacUdz#2mKkK4s zt$q+y1`nDyhMOx(@r>-qs>&Dbg)OOk>BstP!pDMHmbn5JvefM&#gkj=PJ@gJ5+RLp z=K86sL-752vQ%H>QOtKemw=}<$qcGh&ZtJ9JA4rpJ>arj%=60a& zfnB-Al`PO})~u(DK515-l(7;< zsN;_P{_1JH;M1& literal 0 HcmV?d00001 diff --git a/Matplotlib/scatter_plot.ipynb b/Matplotlib/scatter_plot.ipynb new file mode 100644 index 0000000..9588e15 --- /dev/null +++ b/Matplotlib/scatter_plot.ipynb @@ -0,0 +1,85 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# scatter(x, y)\n", + "\n", + "See `~matplotlib.axes.Axes.scatter`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make the data\n", + "np.random.seed(3)\n", + "x = 4 + np.random.normal(0, 2, 24)\n", + "y = 4 + np.random.normal(0, 2, len(x))\n", + "# size and color:\n", + "sizes = np.random.uniform(15, 80, len(x))\n", + "colors = np.random.uniform(15, 80, len(x))\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(x, y, s=sizes, c=colors, vmin=0, vmax=100)\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n", + " ylim=(0, 8), yticks=np.arange(1, 8))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Matplotlib/stackplot.ipynb b/Matplotlib/stackplot.ipynb new file mode 100644 index 0000000..47e8494 --- /dev/null +++ b/Matplotlib/stackplot.ipynb @@ -0,0 +1,83 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# stackplot(x, y)\n", + "See `~matplotlib.axes.Axes.stackplot`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data\n", + "x = np.arange(0, 10, 2)\n", + "ay = [1, 1.25, 2, 2.75, 3]\n", + "by = [1, 1, 1, 1, 1]\n", + "cy = [2, 1, 2, 1, 2]\n", + "y = np.vstack([ay, by, cy])\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.stackplot(x, y)\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n", + " ylim=(0, 8), yticks=np.arange(1, 8))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Matplotlib/stem.ipynb b/Matplotlib/stem.ipynb new file mode 100644 index 0000000..935623b --- /dev/null +++ b/Matplotlib/stem.ipynb @@ -0,0 +1,81 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# stem(x, y)\n", + "\n", + "See `~matplotlib.axes.Axes.stem`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data\n", + "x = 0.5 + np.arange(8)\n", + "y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.stem(x, y)\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n", + " ylim=(0, 8), yticks=np.arange(1, 8))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Matplotlib/step.ipynb b/Matplotlib/step.ipynb new file mode 100644 index 0000000..1a6d7f0 --- /dev/null +++ b/Matplotlib/step.ipynb @@ -0,0 +1,81 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# step(x, y)\n", + "\n", + "See `~matplotlib.axes.Axes.step`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.style.use('_mpl-gallery')\n", + "\n", + "# make data\n", + "x = 0.5 + np.arange(8)\n", + "y = [4.8, 5.5, 3.5, 4.6, 6.5, 6.6, 2.6, 3.0]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.step(x, y, linewidth=2.5)\n", + "\n", + "ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n", + " ylim=(0, 8), yticks=np.arange(1, 8))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Matplotlib/test.png b/Matplotlib/test.png new file mode 100644 index 0000000000000000000000000000000000000000..ff98c10a5c47ad054c295831a4f97f4c1a6543c9 GIT binary patch literal 771 zcmeAS@N?(olHy`uVBq!ia0vp^H$a$!4M=t>=)MI~mL;wcCBgY=CFO}lsSLh}B?US8 zB{`W%3T3H9#hLke#(JiDMmh=^B_##LR{Hw6a0Pn#Md|ulX7a8;LpTdOB8wRqxP?KO zkzv*x2?hqH0#6smkcv5PFFW!wC