Lec5_Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"500\"\n",
       "            src=\"https://matplotlib.org/gallery.html#lines_bars_and_markers\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x21ebf9c5470>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "\n",
    "IFrame(\n",
    "    \"https://matplotlib.org/gallery.html#lines_bars_and_markers\",\n",
    "    width=\"100%\",\n",
    "    height=500,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1.2\n",
      "module://ipykernel.pylab.backend_inline\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "print(matplotlib.__version__)\n",
    "print(matplotlib.get_backend())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact\n",
    "import ipywidgets as widgets\n",
    "import sys\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Axes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAASEElEQVR4nO3de7CtdV3H8ffHc/AGmJinMg5ClopICrRDzELzNqgkZZpgUmrTmRzNS46kUTnN5BijmeU1RsEUvAVekFDBEh0nRQ8ICR4tIAIE45AhJpai3/5YD8M6i9/eZx3Ye/2ew3m/ZtawnvX81l6f9WPO/uznsp6VqkKSpFl36R1AkjROFoQkqcmCkCQ1WRCSpCYLQpLUZEFIkposCKmzJM9J8tneOaRZFoTu9JJckeS7Sf5n6vam3rmksVvfO4C0IL9SVZ/sHULambgFoV1WkrcmOW1q+YQk/5iJvZKcmWRrkv8e7m+cGntukj9P8s/DFslHk/xoklOT3Jjki0n2mxpfSV6U5PIk1yd5bZLmv78k+yc5J8k3k3wtyW+s5TxIy7EgtCt7GfCw4RjALwG/A/x2Ta4/cxfgZGBf4P7Ad4HZ3VJHA8cCewM/DXxueM59gC3Aq2bG/xqwBBwCHAU8bzZQkt2Bc4D3AD8GHAO8JclD7+iblXaUBaFdxYeT3DB1+92qugl4NvB64BTg96vqaoCq+q+qOr2qbqqqbwOvBh498zNPrqrLqupbwMeAy6rqk1V1M/D3wMEz40+oqm9W1ZXAG5j88p91JHBFVZ1cVTdX1QXA6cDTV2capPl5DEK7il9tHYOoqi8kuZzJX+sfuOXxJPcE/go4AthreHjPJOuq6gfD8n9O/ajvNpb3mHm5q6bu/wfwk42c+wKPSHLD1GPrgXcv98akteIWhHZpSV4A3A24BjhuatXLgAcDj6iqewGH3/KUO/By+0zdv//wmrOuAj5dVfeeuu1RVc+/A68r3S4WhHZZSR4E/DmT3UzHAsclOWhYvSeTrYAbktyH2x5PuD1ePhz83gd4MfD+xpgzgQclOTbJbsPt55M8ZBVeX9ohFoR2FR+d+RzEh5gcdzihqi6qqn8D/gh4d5K7MTlGcA/geuDzwMdXIcNHgPOBC4F/AN4xO2A43vFEJgfArwG+AZzAZCtHWqj4hUHS2ktSwAOr6tLeWaR5uQUhSWrqWhBJXprkkiQXJ3lvkrv3zCNJulW3gkiyN/AiYKmqDgTWMdnvKt3pVFXcvaSdTe9dTOuBeyRZD9yT9ml/kqQOun1Qrqq+nuR1wJVMTic8u6rOnh2XZBOwCWD33Xf/uf3333+xQSVpJ3f++edfX1UbdvR53c5iSrIXk0sIPBO4gcmlCU6rqlOWe87S0lJt3rx5QQkl6c4hyflVtbSjz+u5i+nxwL9X1daq+j7wQeAXOuaRJE3pWRBXAocluWeSAI9jcgVMSdIIdCuIqjoPOA24APjykOXEXnkkSdvqejXXqnoVq3ONG0nSKut9mqskaaQsCElSkwUhSWqyICRJTRaEJKnJgpAkNVkQkqQmC0KS1GRBSJKaLAhJUpMFIUlqsiAkSU0WhCSpyYKQJDVZEJKkJgtCktRkQUiSmiwISVKTBSFJarIgJElNFoQkqcmCkCQ1WRCSpCYLQpLUZEFIkposCElSkwUhSWqyICRJTRaEJKnJgpAkNVkQkqQmC0KS1GRBSJKaLAhJUpMFIUlq6loQSe6d5LQkX02yJckje+aRJN1qfefX/2vg41X19CR3Be7ZOY8kadCtIJLcCzgceA5AVX0P+F6vPJKkbfXcxfQAYCtwcpIvJXl7kt1nByXZlGRzks1bt25dfEpJ2kX1LIj1wCHAW6vqYOA7wCtmB1XViVW1VFVLGzZsWHRGSdpl9SyIq4Grq+q8Yfk0JoUhSRqBbgVRVd8Arkry4OGhxwFf6ZVHkrSt3mcx/T5w6nAG0+XAczvnkSQNuhZEVV0ILPXMIElq85PUkqQmC0KS1GRBSJKaLAhJUpMFIUlqsiAkSU0WhCSpyYKQJDVZEJKkJgtCktRkQUiSmiwISVKTBSFJarIgJElNFoQkqcmCkCQ1WRCSpCYLQpLUZEFIkposCElSkwUhSWqyICRJTRaEJKnJgpAkNVkQkqQmC0KS1GRBSJKaLAhJUpMFIUlqsiAkSU0WhCSpyYKQJDVZEJKkJgtCktTUvSCSrEvypSRn9s4iSbpV94IAXgxs6R1CkrStrgWRZCPwFODtPXNIkm6r9xbEG4DjgB8uNyDJpiSbk2zeunXr4pJJ0i6uW0EkORK4rqrOX2lcVZ1YVUtVtbRhw4YFpZMk9dyCeBTw1CRXAO8DHpvklI55JElTuhVEVb2yqjZW1X7A0cA/VdWze+WRJG2r9zEISdJIre8dAKCqzgXO7RxDkjTFLQhJUpMFIUlqsiAkSU3bLYgkr0lyryTrk3wiyX8medYiwkmS+plnC+JJVXUjcCRwHfBQ4A/XNJUkqbt5CuKWM52eDLy3qq4Hau0iSZLGYJ7TXD+W5GLgB8ALktwX+L+1jSVJ6m27WxBV9XLgscDPVdX3gf8FnrbWwSRJfS27BZHk0VX16SRPnXpsesjX1zKYJKmvlXYxPQH4NPCMxroCzliTRJKkUVi2IKrqj4f/Hru4OJKksZjncxAnJ9lzanljkrPXNpYkqbd5TnPdDHwhyROTPBf4FPDWtY0lSeptu6e5VtWbk1zEpBiuBw6pqmvXPJkkqat5djEdA5wEPA84BfhokgPXOpgkqa95Pij3m8DhVfUNgCSPBE4FHr6WwSRJfc2zi+nImeXPJXn+2kWSJI3B3N8ol+RBTL47+llMPk190FqFkiT1t2JBJNnIpBSOAdYB+wCPqKpLF5BNktTRsgepk3wG+CSwJ/DsqjoIuNFykKRdw0pnMX0buAfwI0xKArzMtyTtMpYtiKp6CnAw8BXghCSXAXslOWRR4SRJ/ax4DKKqvgmcCJyY5CeZHI94W5Ifr6p9FxFQktTHPJfaAKCqrgHeVVWHAr+8dpEkSWMwd0EMzgaoqsvXIIskaUR2tCCy/SGSpDuDlU5zPSvJfjMPn7SmaSRJo7HSFsQ7gbOTHJ9kN4CqeuNCUkmSulvpG+U+kOQfgD8FNid5N/DDqfWvX0A+SVIn27sW0/eB7wB3Y/JhuR+uPFySdGexbEEkOQJ4PXAGky8JumlhqSRJ3a20BXE88IyqumRRYSRJ47HSMYhfWmQQSdK47OjnICRJuwgLQpLU1K0gkuyT5FNJtiS5JMmLe2WRJN3W3F85ugZuBl5WVRck2RM4P8k5VfWVjpkkSYNuWxBVdW1VXTDc/zawBdi7Vx5J0rZGcQxiuObTwcB5jXWbkmxOsnnr1q2LjiZJu6zuBZFkD+B04CVVdePs+qo6saqWqmppw4YNiw8oSbuorgUxXATwdODUqvpgzyySpG31PIspwDuALV74T5LGp+cWxKOAY4HHJrlwuD25Yx5J0pRup7lW1WfxG+okabS6H6SWJI2TBSFJarIgJElNFoQkqcmCkCQ1WRCSpCYLQpLUZEFIkposCElSkwUhSWqyICRJTRaEJKnJgpAkNVkQkqQmC0KS1GRBSJKaLAhJUpMFIUlqsiAkSU0WhCSpyYKQJDVZEJKkJgtCktRkQUiSmiwISVKTBSFJarIgJElNFoQkqcmCkCQ1WRCSpCYLQpLUZEFIkposCElSkwUhSWrqWhBJjkjytSSXJnlFzyySpG11K4gk64A3A08CDgCOSXJArzySpG313II4FLi0qi6vqu8B7wOO6phHkjSlZ0HsDVw1tXz18Ng2kmxKsjnJ5q1bty4snCTt6noWRBqP1W0eqDqxqpaqamnDhg0LiCVJgr4FcTWwz9TyRuCaTlkkSTN6FsQXgQcm+akkdwWOBs7omEeSNGV9rxeuqpuTvBD4BLAOOKmqLumVR5K0rW4FAVBVZwFn9cwgSWrzk9SSpCYLQpLUZEFIkposCElSkwUhSWqyICRJTRaEJKnJgpAkNVkQkqQmC0KS1GRBSJKaLAhJUpMFIUlqsiAkSU0WhCSpyYKQJDVZEJKkJgtCktRkQUiSmiwISVKTBSFJarIgJElNFoQkqcmCkCQ1WRCSpCYLQpLUZEFIkposCElSkwUhSWqyICRJTRaEJKnJgpAkNVkQkqQmC0KS1GRBSJKauhREktcm+WqSf0nyoST37pFDkrS8XlsQ5wAHVtXDgH8FXtkphyRpGV0KoqrOrqqbh8XPAxt75JAkLW997wDA84D3L7cyySZg07D4f0kuXkiqO+a+wPW9Q8zBnKtnZ8gI5lxtO0vOB9+eJ6WqVjvI5AcnnwR+orHq+Kr6yDDmeGAJeFrNESTJ5qpaWt2kq8+cq2tnyLkzZARzrrY7e84124KoqsevtD7JbwNHAo+bpxwkSYvVZRdTkiOAPwQeXVU39cggSVpZr7OY3gTsCZyT5MIkb5vzeSeuYabVZM7VtTPk3BkygjlX250655odg5Ak7dz8JLUkqcmCkCQ1jbIgkhyR5GtJLk3yisb6xyT51nD84sIkf9oh40lJrlvucxmZ+JvhPfxLkkMWnXHIsb2cY5jLfZJ8KsmWJJckeXFjTPf5nDPnGObz7km+kOSiIeefNcaMYT7nydl9Pocc65J8KcmZjXXd53Iqy0o5d3wuq2pUN2AdcBnwAOCuwEXAATNjHgOc2Tnn4cAhwMXLrH8y8DEgwGHAeSPNOYa5vB9wyHB/TyaXX5n9f959PufMOYb5DLDHcH834DzgsBHO5zw5u8/nkOMPgPe0soxhLufMucNzOcYtiEOBS6vq8qr6HvA+4KjOmW6jqj4DfHOFIUcB76qJzwP3TnK/xaS71Rw5u6uqa6vqguH+t4EtwN4zw7rP55w5uxvm6H+Gxd2G2+zZKGOYz3lydpdkI/AU4O3LDOk+lzBXzh02xoLYG7hqavlq2v8IHzlsmn4syUMXE22HzPs+xmA0c5lkP+BgJn9NThvVfK6QE0Ywn8OuhguB64BzqmqU8zlHTug/n28AjgN+uMz6Ucwl288JOziXYyyINB6b/aviAmDfqno48Ebgw2ueasfN8z7GYDRzmWQP4HTgJVV14+zqxlO6zOd2co5iPqvqB1V1EJMLYR6a5MCZIaOYzzlydp3PJEcC11XV+SsNazy20LmcM+cOz+UYC+JqYJ+p5Y3ANdMDqurGWzZNq+osYLck911cxLls932MwVjmMsluTH7pnlpVH2wMGcV8bi/nWOZzKs8NwLnAETOrRjGft1gu5wjm81HAU5NcwWR392OTnDIzZgxzud2ct2cux1gQXwQemOSnktwVOBo4Y3pAkp9IkuH+oUzex38tPOnKzgB+azjD4TDgW1V1be9Qs8Ywl8PrvwPYUlWvX2ZY9/mcJ+dI5nNDhi/hSnIP4PHAV2eGjWE+t5uz93xW1SuramNV7cfkd9E/VdWzZ4Z1n8t5ct6euRzD5b63UVU3J3kh8AkmZzSdVFWXJPm9Yf3bgKcDz09yM/Bd4OgaDtMvSpL3Mjkr4L5JrgZexeQg2y0Zz2JydsOlwE3AcxeZbwdydp9LJn/9HAt8edgfDfBHwP2nco5hPufJOYb5vB/wd0nWMfkl8IGqOnPm39AY5nOenGOYz9sY4Vw23dG59FIbkqSmMe5ikiSNgAUhSWqyICRJTRaEJKnJgpAkNVkQ0iCTq7X+e5L7DMt7Dcv7LjP+ouE04nl+9tuTHLCaeaW15mmu0pQkxwE/U1WbkvwtcEVVvaYx7iHAB4D7AA+qqu8sOKq05tyCkLb1V8BhSV4C/CLwl8uMexbwbuBs4KkASdYn+WKSxwzLr0ny6uH+uUmWhovTvTPJxUm+nOSla/2GpNtrdJ+klnqqqu8neTnwceCJwyXnW54JPAF4MPBC4L3DVQCeA5yW5EVMriv0iJnnHQTsXVUHAtxyqQlpjNyCkG7rScC1wOyVRQFI8vPA1qr6D+AfgUOS7AVQVZcw2bL4KPC8RsFcDjwgyRuTHAHMXg1WGg0LQpqS5CAmWwaHAS9Ncr8kr87wNY3DsGOA/YcrZ14G3Av49akf87PADcCPz/78qvpv4OFMrlz6Albxy12k1WZBSIPhSpdvZfI9D1cCrwVeV1XHV9VBVXVQkrsAzwAeVlX7DVfPPIpJaZDkacCPMvmq17+Z3YU0XF75LlV1OvAnTL4OVholC0K61e8CV1bVOcPyW5hsKTx6aszhwNer6utTj30GOCDJPsBfAL9TVf8KvAn465nX2Bs4d9gaeSfwytV/G9Lq8DRXSVKTWxCSpCYLQpLUZEFIkposCElSkwUhSWqyICRJTRaEJKnp/wGSOYAi+/tkPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlim([0.5, 4.5])\n",
    "ax.set_ylim([-2 , 8])\n",
    "ax.set_title(\"Example\")\n",
    "ax.set_xlabel(\"X-Axis\")\n",
    "ax.set_ylabel(\"Y-Axis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set(\n",
    "    xlim=[0.5, 4.5],\n",
    "    ylim=[-2, 8],\n",
    "    title=\"Example 2\",\n",
    "    xlabel=\"X-Axis\",\n",
    "    ylabel=\"Y-Axis\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Plotting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot([1, 2, 3, 4], [10, 20, 25, 35])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## simple approach "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot([1, 2, 3, 4], [10, 20, 25, 35])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "axes[0, 0].set(title=\"Upper Left\")\n",
    "axes[0, 1].set_title(\"Upper Right\")\n",
    "\n",
    "#tight_layout makes sure titles and tick labels don't overlap\n",
    "fig.tight_layout()\n",
    "fig.suptitle(\"Four subplots\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input Data: 1D Series "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(2)\n",
    "x = np.arange(5)\n",
    "y = np.random.random(5) * 2\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8719898 , 0.05185246, 1.09932496, 0.87064479, 0.8407356 ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding errorbars..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(2)\n",
    "x = np.arange(5)\n",
    "y = np.random.random(5)  * 2\n",
    "error = y * 0.1\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(x, y, yerr=error)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we have negative values, we can use axhline to draw an axis \"spine\" to mark the zero line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "x = np.arange(5)\n",
    "y = np.random.randn(5)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(x, y)\n",
    "ax.axhline(y=0, color='black', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "vert_bars = ax.bar(x, y) #store the output of .bar call\n",
    "\n",
    "for bar, height in zip(vert_bars, y):\n",
    "    if height < 0:\n",
    "        bar.set(color='red')\n",
    "        \n",
    "ax.axhline(y=0, color='black', linewidth=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rectangle(xy=(-0.4, 0), width=0.8, height=1.62435, angle=0)\n",
      "Rectangle(xy=(0.6, 0), width=0.8, height=-0.611756, angle=0)\n",
      "Rectangle(xy=(1.6, 0), width=0.8, height=-0.528172, angle=0)\n",
      "Rectangle(xy=(2.6, 0), width=0.8, height=-1.07297, angle=0)\n",
      "Rectangle(xy=(3.6, 0), width=0.8, height=0.865408, angle=0)\n"
     ]
    }
   ],
   "source": [
    "for bar in vert_bars:\n",
    "    print(bar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filled Regions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "y = np.random.randn(100).cumsum()\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.fill_between(x, y, color='lightblue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 200)\n",
    "y1 = 2 * x + 1\n",
    "y2 = 3 * x + 1.2\n",
    "y_mean = 0.5 * x * np.cos(2*x) + 2.5 * x + 1.1\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#plot the envelope with 'fill_between'\n",
    "ax.fill_between(x, y1, y2, color='yellow')\n",
    "\n",
    "#plot the centerline with plot\n",
    "ax.plot(x, y_mean, color='black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### data keyword argument "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using specialized data structures such as Pandas DataFrame and XArray, the input data to be plotted are accessed like dictionary elements. This can get very repetitive and tedious as one types out a plotting command accessing those elements. So, the data keyword argument was added to many of the plotting functions in v1.5. With this feature, one can pass in a single dictionary-like object as data, and use the string key names in the place of the usual input data arguments.\n",
    "Let's revisit the above example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 100)\n",
    "data_obj = {\n",
    "    \"x\": x,\n",
    "    \"y1\": 2 * x +1,\n",
    "    \"y2\": 3 * x + 1.2,\n",
    "    \"mean\": 0.5 * x * np.cos(2 * x) + 2.5 * x +1.1\n",
    "}\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#plot the envelope with 'fill_between'\n",
    "ax.fill_between(\"x\", \"y1\", \"y2\", color='yellow', data=data_obj)\n",
    "\n",
    "#plot the centerline with plot\n",
    "ax.plot(\"x\", \"mean\", color='black', data=data_obj)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Input Data: 2D Arrays or Images "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d = np.arange(9).reshape((3, 3))\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.imshow(arr_2d)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "imshow is used most of the times to display images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For visualizing matrices, matshow provides better defaults, e.g. axis labelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.matshow(arr_2d)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colorbars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(arr_2d)\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VvlnXbEnnsiTNYlNLUnMGj6TmDB5JzRk8kpozeCQ1Z/BIas7gkdTc/wPBm95J7/a90QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "im = ax.matshow(arr_2d)\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "cax = fig.add_axes([0.27, 0.8, 0.5, 0.05])\n",
    "\n",
    "im = ax.imshow(arr_2d)\n",
    "fig.colorbar(im, cax=cax, orientation='horizontal')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shared parameters for imshow, pcolormesh, contour, scatter, etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we mentioned earlier, any plotting method that creates a ScalarMappable will have some common kwargs. The ones you'll use the most frequently are:\n",
    "\n",
    "* cmap : The colormap (or name of the colormap) used to display the input. (We'll go over the different colormaps in the next section.)\n",
    "* vmin : The minimum data value that will correspond to the \"bottom\" of the colormap (defaults to the minimum of your input data).\n",
    "* vmax : The maximum data value that will correspond to the \"top\" of the colormap (defaults to the maximum of your input data).\n",
    "* norm : A Normalize instance to control how the data values are mapped to the colormap. By default, this will be a linear scaling between vmin and vmax, but other norms are available (e.g. LogNorm, PowerNorm, etc).\n",
    "\n",
    "vmin and vmax are particularly useful. Quite often, you'll want the colors to be mapped to a set range of data values, which aren't the min/max of your input data. For example, you might want a symmetric ranges of values around 0.\n",
    "\n",
    "As an example of that, let's use a divergent colormap on some example data. We'll also use interpolation=\"nearest\" to \"turn off\" interpolation of the cells in the input dataset. Note how the colormap is not centered at zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.cbook import get_sample_data\n",
    "data = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(data, cmap='seismic')\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(data, cmap='seismic',\n",
    "               vmin=-2, vmax=2)\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter for n dimensional data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 50\n",
    "x1 = np.random.random(n)\n",
    "x2 = np.random.random(n) * 50\n",
    "x3 = np.random.random(n)\n",
    "\n",
    "y = x1 + x2 + x3\n",
    "fig, ax = plt.subplots()\n",
    "sc = ax.scatter(x=x1, y=y, s=x2, c=x3, marker='o')\n",
    "fig.colorbar(sc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing statistical distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw samples from a normal distrbution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "μ = 0\n",
    "σ = 1\n",
    "num_samples = 1000\n",
    "dist = np.random.normal(μ, σ, num_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Side note on unicode characters as variables. Since Python 3 we can use any unicode character such as μ and σ as variables. This can make sense in scientific programming if strong naming conventions exist. A good example is mean and standard deviation of a normal distribution. To easily obtain common characters you can use latex style and type e.g. \\mu followed by tab to obtain μ. However, do not overuse this. Clearly named characters are often easier to read."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histrograms "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histograms a great way to visualize univariate distributions. The data is divided into equally sized bins. Then we count how many data points fall into each bin. Finally, we draw a bar with the width of the corresponding bin and the height of the count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(dist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can control the number of bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(dist, bins=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or let it be automatically determined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(dist, bins='auto')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using density=True will create a normalized histogram that can be interpreted as a probability density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(dist, bins='auto', density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boxplots are another standard way to summarize univariate distributions. The give a compact visual description of important summary statistics. A box is drawn at the 25% and 75% quantile, that is where most of the data is. Additionally, the median is marked by a line inside the box. The whiskers extend 1.5 times the inter quartile range beyond the quartiles. Every point beyond that is drawn individually as a flier or outlier. See also https://flowingdata.com/2008/02/15/how-to-read-and-use-a-box-and-whisker-plot/ for a nice illustration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(dist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(dist, notch=True, showfliers=False,\n",
    "           medianprops={'color': 'blue'}, widths=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[array([ 1.051...23276943e-01]), array([ 2.996...56217073e+00]), array([ 5.861...  3.13430491]), array([1.0326..., 3.01359952]), array([-4.157...83776857e-01])]'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import reprlib # This is for obtaining printable versions of large data sets.\n",
    "means = [0, -1, 2.5, 4.3, -3.6]\n",
    "sigmas = [1.2, 5, 3, 1.5, 2]\n",
    "# Each distribution has a different number of samples.\n",
    "nums = [150, 1000, 100, 200, 500]\n",
    "\n",
    "dists = [np.random.normal(*args) for args in zip(means, sigmas, nums)]\n",
    "reprlib.repr(dists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(dists)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violinplots "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Violinplots are a third common way to visualize distributions. For violinplots a kernel density estimate is computed for the whole range of data. This gives a smooth estimate of the probabiliy density function underlying the data. The violinplotfunction behaves similar to boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.violinplot(dist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.violinplot(dists)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie charts "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pie charts a well known way to visualize categorical distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [15, 30, 45, 20]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotating plots "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Especially for scientific figures, you often want to hightlight the part of a plot that supports your hypothesis. With maplotlib you have the full flexibility to do this using annotate. By default it will just add text at certain x, y coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-2, 2)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "\n",
    "# Plot a line and add some simple annotations\n",
    "line, = ax.plot(t, s)\n",
    "ax.annotate('local maximum', xy=(3, 1))\n",
    "ax.set(ylim=(-2, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-2, 2)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "\n",
    "line, = ax.plot(t, s)\n",
    "ax.annotate(\n",
    "     'local maximum',\n",
    "     xy=(3, 1),\n",
    "     xytext=(3.5, 1.5)\n",
    ")\n",
    "\n",
    "ax.set(ylim=(-2, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we have to, we can go very fancy on the arrow styles. See https://matplotlib.org/tutorials/text/annotations.html for a detailed overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-2, 2)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "\n",
    "# Plot a line and add some simple annotations\n",
    "line, = ax.plot(t, s)\n",
    "ax.annotate(\n",
    "    'local maximum', \n",
    "    xy=(3, 1), \n",
    "    xytext=(3.5, 1.5),\n",
    "    arrowprops=dict(arrowstyle='wedge', connectionstyle=\"angle3\")\n",
    ")\n",
    "\n",
    "ax.set(ylim=(-2, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-2, 2)]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "t = np.arange(0.0, 5.0, 0.01)\n",
    "s = np.cos(2*np.pi*t)\n",
    "\n",
    "# Plot a line and add some simple annotations\n",
    "line, = ax.plot(t, s)\n",
    "ax.annotate(\n",
    "    'local maximum', \n",
    "    xy=(3, 1), \n",
    "    xytext=(3.5, 1.5),\n",
    "    arrowprops=dict()\n",
    ")\n",
    "\n",
    "ax.set(ylim=(-2, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to speak \"MPL\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 5.0, 0.2)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, t, linewidth=5)\n",
    "ax.plot(t, t**2, linewidth=5)\n",
    "ax.plot(t, t**3, linewidth=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Colornames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, colors can be given as strings. For very basic colors, you can even get away with just a single letter:\n",
    "\n",
    "* b: blue\n",
    "* g: green\n",
    "* r: red\n",
    "* c: cyan\n",
    "* m: magenta\n",
    "* y: yellow\n",
    "* k: black\n",
    "* w: white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 5.0, 0.2)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, t, linewidth=5, color='blue')\n",
    "ax.plot(t, t**2, linewidth=5, color='g')\n",
    "ax.plot(t, t**3, linewidth=5, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hex values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colors can also be specified by supplying a HTML/CSS hex string, such as '#0000FF' for blue. Support for an optional alpha channel was added for v2.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tuBxqP6enT7OMwl1Exrf3J8Etj4nMvwTqrlOwZ6Gkw93Misxso5n9Jva+2szuMrMtsfXc5MsUkYzY93PY8R7A47fNuwCWfUvBnqVSceV+GbB5xPsrgXXuvhpYF3svIrmm885gWIFEQ/fOPTc296n++M9WSf2fMbM64PXADSOazwZujL2+EXhjMucQkQw48HvY9lZgKH7bnDfBip+AFYVelkxesr92rwc+wtG/2he6eytAbL0g0Q+a2UVmtt7M1nd0dCRZhoikzP7b4amzwfvjt816Lay4Bawk/LpkSqYd7mZ2JtDu7gmmNZ+Yu69x9wZ3b6ipqZluGSKSSh3fD67YEwX7zFfCqtshUhZ+XTJlyQywfDJwlpm9DigHZpnZTUCbmS1291YzWwy0p6JQEUkjd2j7AjRflXh71UuDSa0jFeHWJdM27St3d7/K3evcvR44F/iju58HrAXOj+12PnBn0lWKSPp4FJo+PHawV74kNiZ7Zbh1SVLSMTXKtcCtZnYhsAs4Jw3nEJFU8EHY+V7Ye2Pi7bNeC6tug8iMcOuSpKUk3N39XuDe2Ou9wGmpOK6IpFG0B7adCwfWJt5e/U6o/5G+PM1RmtRQpBANHYCtZ8Ph+xJvr3k/LL1e97HnMIW7SKEZaIMtZ0DPpsTbaz8TDAKmJ09zmsJdpJD07YAtr4K+rQk2WjCcQM1/hF2VpIHCXaRQ9DwKW14DAy3x26wE6n8K1W8Lvy5JC4W7SCE4/ABsfT0M7Y/fFpkBK++A2a8Jvy5JG4W7SL7r/DVsPxei3fHbiubCMb+DqheFX5eklb4KF8lX7tD638E4MYmCvaQWjv2Tgj1P6cpdJB8NdcHO98D+XyTeXnYMrL4LyupDLUvCo3AXyTd9O+CpN0LPw4m3V5wIq/8HShaGWpaES90yIvnk0H3wxAvGDvZZr4Nj71GwFwCFu0g+cIf2b8M/T4fBPYn3WXQlHLMWimaHW5tkhLplRHJdtB8a3wd7vp94u1VA/Q+g+u3h1iUZpXAXyWUDbfDUm6HrL4m3lyyFY34FM54Xbl2ScQp3kVzVtR6eehMMNCXeXvVSWHk7lCSc6VLynPrcRXLR3pvhyVPGDvb5F8PqdQr2AqYrd5Fc4gPQfDW0fXmMHYph2Teg5pJQy5Lso3AXyRW9W2H7O6H7wcTbi2tg5W0w89Rw65KspHAXyXbusPfH0Ph+iHYl3qfiBFj1KyhbHmppkr0U7iLZbHAf7LwYOm8be5+5b4XlP9QE1nIUhbtItjp0D2x/99hfmlIEtZ8NHk7SrEkyisJdJNtE+6HlGmj7IuCJ9yldAStuhqoXh1qa5A6Fu0g26X0Str8Duh8ae5/qdwd3xBTNCq8uSZ8uYBvw7NQeVuEukg3cg+EDGi8H70m8T9FsWPZdqD433NoktQaB9cDdseUBYB7QDKSwd03hLpJpg3tg579D56/G3qfqVFjxUyhdFl5dkhoOPMmRML8HODhqn1ZgM/Cs1J1W4S6SSQf+B3ZeCAOtY+xQDLWfgUUfASsKtTRJQguwjiDM1xFclU/kbhTuIjmvvxmarhh7piSAstXBl6aVLwivLpmeTuB+jgT649M4xt3AB1JXksJdJEw+CB3fhuaPQ/TQ2PvNuxCWXg9FVeHVJpN3CPgTQRfLPcBGIJrE8RYDS1NQ1wjTDnczWwr8BFhE8LHWuPvXzKwa+DlQD+wA3uru+5MvVSTHdT0IOy+Bno1j71M0F5Z/H+a+Oby6ZGJdwF84EubrgaEkjjcTeAVwOnAa8ExS+mUqJHflPgh8yN0fMrOZwAYzuwu4AFjn7tea2ZXAlcBHky9VJEcNdkLLx6DjO4x53zrAzNOg/sdQWhdWZTKWHoK7WIbD/EFgIInjlQAvJgjz04EXkPZ+k2kf3t1bCb7jxd0PmdlmYAlwNvDy2G43AveicJdC5A77fwaNV8Bg29j7FddA3Veg+jw9aZopB4H/R9Bvfj/JhznAczkS5qcAIY8OkZLfHWZWD5wI/A1YGAt+3L3VzBIOKG1mFwEXASxbptu7JM/0boFd/wmH7h5/v/kXwZLPQ3F1OHVJYA/wZ46EebJ95gCrCLpaTgNeCWR4KP2kw93MqoDbgcvd/aBN8srD3dcAawAaGhrG+VtVJIdEe2H3tbD78+D9Y+9X8ZzggSQNHxCOFo4E+f3AYyk4Zj1BmL+CoK8ixV+IJiupcDezEoJgv9nd74g1t5nZ4thV+2KgPdkiRbKeO3TeAc1XQt/WsfeLVAb3rS/4AJhuVkuLKEF4/2XEsj0Fx63jSJi/giDcs1gyd8sY8ANgs7tfN2LTWuB84NrY+s6kKhTJdgfvhuaroHv9+PvN+dfg9sbSLLvEy3XdBH3kfyHoankAOJCC49YCL+NImK8i5Xe0pFMylw4nA+8CHjGzTbG2qwlC/VYzuxDYBZyTXIkiWarrwSDUD/1x/P1Kl8PSb8KcM8OpK9+1Enz5+WeCQN9IcO9eslYCp45YVpJTYT5aMnfL/JmxP/pp0z2uSNbreRxaPg6dv5xgx2JY9GFY9HFNpDFdfQTh/dcRy84UHftZHAnyUwi6XfKIOv1EJqtvJ7R+Cvb+hAlvrah6OSz7JlT8SwiF5QkHGgkC/IHY+iFgnO+lJy1CcGviqQRdLS8FalJw3CymcBeZyEA77P4cdHx3/DtgACpODG5tnPVq3bM+kcPABoL+8uEwH2v8tKmqAl5E0Hl8cuz1zBQdO0co3EXGMnQA2r4CbdeNPTH1sLLVUPtfMPctYJFw6ssl/cAjBEH+99h6M8nfWz6sjiDEXxpbP5uCT7cC//giCfQ3QvvXoWMNREcPvD1KyRJY/EmYfwFYSSjlZb0o8E+ODvJNpKZ7BaCIILxPHrHoOcg4CneRYV0boP062HcrE95+UVQNi66CBZdCpCKU8rJSFHiKoHvlIYIBtTYQPxlFMhYSjMvyotjSQOiP8ucihbsUNo/Cgd8G3S+H75t4/0glLPwgLPxQMO1dIRkiuCJ/iCNhvpHUBnkJwUAmI8N8OTl9S2KmKNylMEW7g7te2r4Kff+ceH8rgZr/gEVXQ8nC9NeXaQPAEwQBPhzmmwiGvk2lFQQjJL6QINBPBMpTfI4CpXCXwjKwG9q/FQy/O7R34v2tHOa9O+iCKatPe3kZ0Qk8HFs2xZbHSF0f+bAFBEF+Umz9AmB+is8hT1O4S/5zh+6/Q8f3YN9NE9/OCMEwvDXvC67WS/LkhmgnmD5nZIg/HGtLtSqCvvHhID+JYGAtda+ERuEu+au/OQjzvT+G3icm9zPlzwz61KvPg0gO9w/sBx4luP1weP0IqRlzZbQqgu6U5wPPi62PJbirRTJG4S75JdoDnXcGgX7wLiZ9I/XM04NQn/Wa3LpPvZfgfvGRAf4I0Jym880mCPDhEH8esJrgCVDJKgp3yX3u0PXXIND3/zx4+GgyrASq3wELroAZz01riUnrA7YAj49YHom1JTOX53jmAydwJMifT/AFqII8JyjcJXf1N8Lenwah3rdl8j9XNBdqLgn61Etr01betHQDT3IkwDfH1ltJX4gb8AyCsVdOiC3PBRajPvIcpnCX3NK3Aw6shc5fwaF7GXfC6dGqToV558Pct2V2lEYnmObtyRHLcIhvZ0ofacoqgedwdJAfjx4KykMKd8luHoXuDdC5Fg7cCT2PTO3nS+uDQJ/3bihbmZYSx9RLcMU9MsT/GVvvT/O5IwRX488mCO/h9SrUrVIgFO6SfaK9cGhdLNB/DQNTHCowUglzz4F5F0DVKen9gnSA4FbCrSOW4QDfSeoGxhpPHUcCfDjEn4keBipwCnfJDgMdwTAAB9bCwT8ET5BO1cxXBoE+519T2+3SR9BdsjXBsoP09YWPVkswwcTI5Xhgbkjnl5yicJfMGNwHh/8Mh+4LxnTp3si0LnPLVgWBXv0uKFs+vVocaAO2EYT4yGUbwWSR6ewHH2058SF+HDAnxBok5yncJRwD7XD4/liY3x/rO59mYs54Psw+C+acDRXPmXhSDCfo495JcKU9OsR3AD3TK2XaygjuDz92xDIc4lUh1yKhGCT4I7A3tu4b9X4VqR2NQeEu6dHfPCLM75v8E6KJWGnQ5TLnbJh9JpSOmuxygOChnV0EAb4rwetUD3g1WUs4OsCHl2XoCc40c4LhcUaHaaKAHa99rDCe6vuJ/i69CXhnsh96BIW7JG+gFbo3BV0r3Ruh+yHo35bcMYuqgyCvOgsOvxpaZgajEzbHlqbYshNoIdxuk9EWAseMWFYRBPgzKLir8CFSE4hTCdvRbcOvUz3uWbr1pfh4CneZPI9C3zbo2TgiyDfCYFtqjt+1Gp46CzacBX99CTQWQ3tqDp20JRwd4CODPINzcw5fnSYbkpMJ28kcO6zvlvNRb4qPp3CXeB6FgWbo2wq9W6Dn0VigPwzRQ6k7z/bjYOOpsOFlwbq9buKfSZcqYCXB4/Ujl5VAPUc95DPZq9Mw/vzPtatTGZuu3CU1PAoDLcFj+71bg/XTr7eCp/o6AtjybHjoZfDQqUGY7wtv0ouBcuhcDvuXwZ4V0LYCdq+ElhXQuAL2zIM+m1y46upUpiNC8D368FI+6n2qB5ZWuOcj92Bi5/6WIMAHmqG7BbpaoLcRBrYCT0EkjbeIDEXgnyfEwvxlsOmlcGBe2k7XtgB2LYOdy4P18DL8fs98NE5KASrh6AAdHaqjAzbZ9+O1hR22Cvcs4QQ3fcRdMUahvwsGDw5hhzqJHN5HpGs/Rb37KOnfR+ngbsqiLZRZC6UlLZRWNFNS2UKkdJyHgFL9wOZACTx1PDx5YhDoT5wIW54L3anpjN69EJqXQFNdsB75etcyaFwKvQU8R3U2MaYWeqkI3PGOXcgjLRR0uA/3nU7r23mHwT4YGl56IdoXLN4L3geR7iHKerqo6O2ivK+Lsv5uyge6qBjoomywm4qhLsqjXZR7N2V0McMOUVG8j/LS/cwq30fxjH0UVe6H2ftgZmwYWyPoH87UXRiHZwYB/uSJR8J827NgsHTKhzowC1oXw+5FwTI6uJvqgu0DUz90QSkmtVeYE7WN974Y/YGULdIW7mZ2BvA1grt5b3D3a1N17CFg902XE6EfB9w9uBPOefo9BL0TsVe4c+R2OXeMKBGPLbHXFUSp9CgRG3q6zYgSsShFDBKJ9BOJDBAp6idSNAAl/VA8EFtir0tGrKtz+OuuQ7Nh12poOiZYb31OcEXesgJ87Ouh/hJoXxAsw8E91rpnRoifJ4WGr05TFYjTuSIdua2Qr05lbGkJdzMrAr4FvIrgbuS/m9lad388JccHaleswcrDfqwwzxyeFQR342poPCZY74q97gw6qQeKYe+8IKw7joX2U2Kva46E+PDrjho4MJu0XLolujoN84p0dN+prk4l26Xryv0kYKu7bwMws58BZxOMWJ20CJl9ZiUn9JVBRy10LIGOWob21tLdtYSunsXs7VtB6+AxtJbXsK/a2DsP9h4Pe18G+6qDMN87D7rmwUAVlNn4AbiAYO7jqVx5TjVsdXUqMjXpCvclQOOI903AC0fuYGYXARcBLFu2bOpn8MK7dop2z2Koq5rBvrkM9FXTP1BN39B8uqO1dNkSukpq6S6tpatyCT2z5+KrDXsBMAeKK46EdDlBl/3xjB20JejqVCSXpSvcE+XCURfb7r4GWAPQ0NCQlxfiHjV8oBIfnIEPVuJDleAzwCtxr8RsBhapxCKVRIpmYMVVUDYXyquhohqq5sLM6qCtaA4RKyZCELy6OURExpOucG8i+Et9WB3BCCAp82TbV7HoIBEjthgRIBIJvsGNmBGx2OsIFBF7H1siRUUURSJYJAJFsWXk66eXouAHiouhtBRKS46sI6XBJMvDazt6bVaGTTRioYhIGqQr3P8OrDazFQTDPJ0LvCOVJzjuzRel8nAiInklLeHu7oNm9j7gDwQXzz9098fScS4REYmXtvvc3f13wO/SdXwRERmb7jATEclDCncRkTykcBcRyUMKdxGRPGTDg2xltAizDoLZMKdrPrAnReXkGn32wlXIn7+QPzsc+fzL3T3hPB9ZEe7JMrP17t6Q6ToyQZ+9MD87FPbnL+TPDpP7/OqWERHJQwp3EZE8lC/hvibTBauR+CsAAAKdSURBVGSQPnvhKuTPX8ifHSbx+fOiz11ERI6WL1fuIiIygsJdRCQP5XS4m9kZZvakmW01syszXU+YzOyHZtZuZo9mupawmdlSM7vHzDab2WNmdlmmawqLmZWb2YNm9nDss3860zWFzcyKzGyjmf0m07WEzcx2mNkjZrbJzNaPu2+u9rnHJuH+JyMm4QbenqpJuLOdmZ0KHAZ+4u7HZ7qeMJnZYmCxuz9kZjOBDcAbC+H/vQWzv1S6+2EzKwH+DFzm7n/NcGmhMbMPAg3ALHc/M9P1hMnMdgAN7j7hA1y5fOX+9CTc7t4PDE/CXRDc/X5gX6bryAR3b3X3h2KvDwGbCebtzXseOBx7WxJbcvMKbRrMrA54PXBDpmvJdrkc7okm4S6If+ByhJnVAycCf8tsJeGJdUtsAtqBu9y9YD47cD3wESCa6UIyxIH/NbMNZjbudHS5HO4TTsIt+c3MqoDbgcvd/WCm6wmLuw+5+wkEcxOfZGYF0S1nZmcC7e6+IdO1ZNDJ7v484LXApbHu2YRyOdzTPgm3ZK9Yf/PtwM3ufkem68kEd+8E7gXOyHApYTkZOCvW7/wz4JVmdlNmSwqXu7fE1u3ALwm6pxPK5XB/ehJuMyslmIR7bYZrkhDEvlT8AbDZ3a/LdD1hMrMaM5sTe10BnA48kdmqwuHuV7l7nbvXE/x7/6O7n5fhskJjZpWxGwgws0rg1cCYd8vlbLi7+yAwPAn3ZuDWQpqE28xuAR4AjjWzJjO7MNM1hehk4F0EV26bYsvrMl1USBYD95jZPwgucO5y94K7JbBALQT+bGYPAw8Cv3X334+1c87eCikiImPL2St3EREZm8JdRCQPKdxFRPKQwl1EJA8p3EVE8pDCXUQkDyncRUTy0P8HOVg+5w77mO8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 5.0, 0.2)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, t, linewidth=5, color='#00ffff')\n",
    "ax.plot(t, t**2, linewidth=5, color='#ff00ff')\n",
    "ax.plot(t, t**3, linewidth=5, color='#ffcc00')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 256 Shades of Gray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A gray level can be given instead of a color by passing a string representation of a number between 0 and 1, inclusive. '0.0' is black, while '1.0' is white. '0.75' would be a light shade of gray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 5.0, 0.2)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, t, linewidth=5, color='1.0')\n",
    "ax.plot(t, t**2, linewidth=5, color='0.5')\n",
    "ax.plot(t, t**3, linewidth=5, color='0.0')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RGB tuples "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may come upon instances where the previous ways of specifying colors do not work. This can sometimes happen in some of the deeper, stranger levels of the library. When all else fails, the universal language of colors for matplotlib is the RGB[A] tuple. This is the \"Red\", \"Green\", \"Blue\", and sometimes \"Alpha\" tuple of floats in the range of [0, 1]. One means full saturation of that channel, so a red RGBA tuple would be (1.0, 0.0, 0.0, 1.0), whereas a partly transparent green RGBA tuple would be (0.0, 1.0, 0.0, 0.75). The documentation will usually specify whether it accepts RGB or RGBA tuples. Sometimes, a list of tuples would be required for multiple colors, and you can even supply a Nx3 or Nx4 numpy array in such cases.\n",
    "\n",
    "In functions such as plot() and scatter(), while it may appear that they can take a color specification, what they really need is a \"format specification\", which includes color as part of the format. Unfortunately, such specifications are string only and so RGB[A] tuples are not supported for such arguments (but you can still pass an RGB[A] tuple for a \"color\" argument).\n",
    "\n",
    "Oftentimes there is a separate argument for \"alpha\" where-ever you can specify a color. The value for \"alpha\" will usually take precedence over the alpha value in the RGBA tuple. There is no easy way around this inconsistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 5.0, 0.2)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, t, linewidth=5, color=(0, 0, 1))\n",
    "ax.plot(t, t**2, linewidth=5, color=(0, 0.5, 0.5))\n",
    "ax.plot(t, t**3, linewidth=5, color=(0, 0, 1, 0.3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cycle references"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the advent of fancier color cycles coming from the many available styles, users needed a way to reference those colors in the style without explicitly knowing what they are. So, in v2.0, the ability to reference the first 10 iterations of the color cycle was added. Whereever one could specify a color, you can supply a 2 character string of 'C#'. So, 'C0' would be the first color, 'C1' would be the second, and so on and so forth up to 'C9'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 5.0, 0.2)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, t, linewidth=5, color='C2')\n",
    "ax.plot(t, t**2, linewidth=5, color='C3')\n",
    "ax.plot(t, t**3, linewidth=5, color='C5')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markers "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Markers](https://matplotlib.org/api/markers_api.html) are commonly used in plot() and scatter() plots, but also show up elsewhere. There is a wide set of markers available, and custom markers can even be specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KvNCRzqv4YLd93iEo6G5F0uqCn3GfY2aPmdl0Sq/SMmFmn+Orm51SXjvhbSMrU4Efpc5sty8WuA7ojE+Qir3bvRJ+bvv9MuksKpFGSzMV6BjsEfJsR+mxckd8tX6BmT1lZq8AGzZTWfJn78DXuz7dqaydTWPZdlXP7SxC9ZV31ld65pTyDK8GXQEMkXSMpM6Stgvnm1m5BNhK0rWStpS0maRjJaW3vvL5DQOGSeobrFN7SOon6fhlUl4B6X/7mC+Bw4H5FUadDxwe4i9F2CL/K3CRpL2C1e31FB/o38InWadI+n4wJmyu8+RLgDPlby1sGs5fdwaGV5DG1bgR0+WhvfUG+jVT+arBa7hR3ldF/L8CngReL5NOA7Cd3GJ/7aT19XLgEWA6cGPo0zvgW+DLtMcEL+MK/8jQzk7E236TCJOA0bgh7M6SugM3AZ8Dt5SMvDRXAEdLOk7SJpLOxo3gInVMtZX3TSy7bZTmC+D/MqR1NnARbiAyDbgbWD9rQczsOdxYpQvwOH7OdViJ8g3EDXPOBP6LWwwfAryRNc/WTjA+q0SB5xV3UaM1vL7H4xbB44EXgccKBTSzD4GjgZ/iK5VBNJ+V7R9xBX5xKMNBwCGhHWUinKsejG/lP4/bdgxopvItd9YfurPhFs+LigRZBPQN4UoxLISdCnzIsmfHLUYw8DoIHxufAG7ELbWLvtMdDBIvAS7Hz+T3wt9UaA6OwSc894W/7YB9zCzzpNjMbsfHqsHAs/jK/dJmKl+kSmg5/J5BUYK1+QsUtjbPMw8oaW0eqW0ynn1nUdyROmDGgInDcOPNZL+eD1y1/tCdS1qbRyKRbFR15R0Ucm9cQadXuF8E995Rcdc3GVbgUXG3LgaxrKznBfdIJNIMVHvbnJOv3v0B/JeYrsXPchaHv9fiK+4Hqli8SDNRQoFHxd3KWH/oznPxs/v8Gx1zgROCeyQSaQaqum0eWfFIbaFHxd1KCa+D/Qs3jHoc2CnDWXckEslIVN6R5U5CgUfF3YoJP9gyGdh+/aE7v1QufCQSyU5U3pGqMPzQXisXeh0s0rqYMWDiSusP3bnYq2ORSKSRROUdiUQikUidUXWDtUgkEolEIpURlXckEolEInVGVN6RSCQSidQZUXlHIpFIJFJnROUdiUQikUidEZV3JBKJRCJ1RlTekUgkEonUGVF5A5JGShpT7XJEIpHlg6Q+kuZUuxyRwkT5lCcq70ikiUhatdpliEQihZG0SrXL0BLUhPJ+dFznto+O63zZo+M635G4Lnt0XOe2WdOQtJqkyyW9L2mBpMcl7ZTw7yLpPkmzJM2RNFlSd0k54Ghgf0kWrp4hzlBJL0maL6lB0sWS2qbyPTvkOUfSjZIGSWpI+LeRNFDS25IWSpoi6cCEf6eQ5yGSHpY0T9JUSXs1ukJrjHXGP/f5OuOfswzX5+XSkrRLkO2cIMsnJHULfr+Q9GaowzGSTpZkibg5SS+m0ltqhi+ps6TRkmZKmivpGUm9UnEaQlrXS/oMuDm4d5R0m6RPwzVW0iZNrL66pNOAseo0YOzxnQaM/SD8Vda4kiZIulrSFYm6vERSm+C/qqSLJM0IMnpK0t6J+D1Dn9ojtI95kp6WtHXeH7gBWCPR53PB76iQ3mxJH0i6U1LHVPn2D+PCAkmPSTospNEpEebg0NcXhr5/riQl/BsknSfpGkmfh2c5qzF1XXVyHTqQ6/Bfch06VBq1SH8+hUbKJyH7/SQ9KWkRsHfh3OubqivvoKAfxD8h+LPE1Q94sAIFfjFwKNAX2AqYAjwoaV1J6wGTAAP2ArYGRgArAcOAO4BHgHXD9e+Q5tyQXlfgJOAw4Nx8hpIOw79RfG5IcxpwRqpcpwJnAb8FugP3AvdI6pEKNxj4I7Al8BRwm6T2GZ+91vmf5ggnaWVgNC7LLfEvVl0BfCXpR8BI/FOyPYD7gd83oqztgQfwdrIlcDcury6pcGcA04FtgXMktQPGAwuAXYEdgPeAR4LfCkOnAWPXwuv/UuDb4e/9wT0rR+Lj0w7ACcDxwGnB7wa8jo/A+9Qo4H5JW6bSGAIMwPvmx8DNQYH+O6Q1jyV9fliIsyrep7cEegFr4x/RAUDS94B7gLEhzB/xsYdEmG2AO0O47qEMZwOnpMp3Oj5ObQ1cBFwsaYds1VNT9AI2B/avJFKJ/jyRRsonwUXAeUAX4ImKnqZOqOpvmycU93b4JyLTzAeeBPbZY/fXFhRLR9IawKfAsWZ2Y3BbCXgZF6yAo4BNzGxRgfgjgbXNrFfaLxWuH3CmmW0c7icDz5tZv0SYfwCbmlmncP8OcI2Z/T4RZgIww8yOCrP1N4B+ZnZN8O8IzAB2NrNJpcpUD6wz/rnMjWzmbj2KrtAkfRMfhHua2T9TfrcA3zazvRJu1wG/NDOF+xzQ28y6JcL0Aa40s6ITJUmPA2PM7A/hvgGYYmYHJML0xQfoTS10qtAGPwBONLM7MlVAndNpwNgd8QnqmsBqCa+FwOfATxuG7v/vQnHzhP6xHrBZoi7Pwyf0uwKvAJ3M7K1EnL8B75rZSWFlPR7Yx8weCv474kpiAzObkUXuIV4XfFKejzcE+CmweaJs5+CT743MrEHSzcC6ZrZ7Ip0cPj6tH+4bgMlmdngizCvAqHw7qxtyHf6BT3YfIjdrn6zRyvTnPjROPj1x2fc2s7sreo46Y+Uq5z8En20VW12vHvyH4LPUYnQGVsG/HwyAmX0VlOvmIf1JhRR3KST1xmeAG+MrspXClacL8JdUtCeATUP8NfFB6F+pMJOA/VJuLyT+fzf8/U4l5W3tmNknYaL1kKRHgUeBO83sbXx35P5UlMnALyvJI0wEB+Gz+nXxdtWWpeUD8HTqfhtgI2B2YncUoB3ePls9nQaMPQlfIRWaiK+Gr8If6TRgbP+Gofv/uUxyj+eVY2AycAGwEz4Zn5qq59WAcak0ivWpGcUyDVvrg/Ddm2+GvAC+F+J1AZ5KlS29suuKr8yTTAIGSVrTzPLHQ+k29S710OdzHQ4CeiZcdgl/e5LrcEXCfQK5WfcWS6ZMfy5IBvnkSffPVke1lXdHiivuPG1DuFLkBVhohWcJ/8xI2h64DfgdPnH4DPgJS7ZvkumXo1i5knzxtYeZhYGp6scatYaZHSPpcmAfXB6DJf2UbDJeXCBc2phlWEj7THyFNw+4Ed+uSzI3dd8GeA4/WknzSYaytQa2p3x/Xg3fCi+nvEthwA9J9JnA/NR90j/f34r2qTBxewg/Qvs5vmuyNr6Nm5e/KN/nS4VJuqfLb6XKV0Osih8jpvXHasCvw/9fsOyiZRlK9OdlyCifPOn+2eqoh4aShVeBRfisHPh6y3IHYCrwDLCTilsFL2LpFTXAjsA7ZnaBmT1lZq8AG6bCTMe3/JN8fR9m2O8myxXYKZQr0gjM7Hkzu8jMegITcIPDqbjySJK+/xD4rpZesqVtD3YCbjSzu83sBXw2n2Xl/Ay+Q/ORmb2aulYU5X0LMLtMmDkEA78y/Cglp+3xvjQZV47rFKjndyooa6E+3wVXBueY2WNmNp1lV8LT8IlDkvQYMJXCfX6GmZWrn9onN+t2/Mz5dXxym2Qe8BrQg9ysTEdFRfpzY+WzwtAqlLeZzcVn8kODlWHXcP9d4KpwtQfukPRDSRtLOjxhNNYAdJO0maS15a8WvAx0lHSkpO9LOhE4PJX1FUAfSX0lbSLpN/g2f3J2fQlwZshvU0m/B3YGhrdEXbRmJG0kfwPgfyVtKGk34Af4YPlHYE+59f8mko4DDkolMQHfajtHblX+S6B3KszLwEGStpbUHbiJ8qtJcIX0PjBa0q6hrLtIGq4Vx+J8HOV381bGzyTLsR5weeiTvXGjz8vM7GW8rkdK6h365raSzpR0cAVlbQDaStor9Pl2wFv42fwpId398a36JFcDnSUNC2U7GDeogyX9fjiwq/yNhE0lHQn0J2XYVtfkZk3Fj4pWS/msBmwT/EtSpj830Dj5rDBUW3m/g1vnlmJBCFeO3+JW4zfg25c/wA1W3gsz8l3wrZXxwLPAr4AvQ9y/4DPqp/HV2Y5mdj+ueC/Hz6b2As5PZmhmt+GNZ2hIsxveuZPP9MeQzsXAi7hCOcTMnsvwTJGlmYfbE9yJK9lR+EB+kZk9jp9vn4jL62Agl4xsZtOC//EskemFqTzOwLfjJuJW54+H/0tiZvPwNvZ6KN/0UL61cGPKVk/D0P0XAX+n9Jbx2BCuHDfjK68n8P75V+Cy4HcM3s8vxut5DF73b2Ytq5n9G++rt+J9/jdm9iG+6vsprkAGkXp7xMzeBA7Bt3ifx4/Ufhe8F4Qwz+BvzByC9/mh4boya/nqhJ3xPvkF8BU+ns4L7lko1Z8bJZ8ViVZhbV5LSLoXWDlpibyi01zW5pUSVmx35q3NIy1PpwFjD8RtBNYs4P058POGofvfVyqNYG3+opmlX62qSSSdir+WuJaZLa52eZYbuQ534hOUp/HX4K7EX528k9ysQ6tZtBWBqq68g0LeB1fQaeW8gBpX3JLaSeovaYuwhXYOcCBwfbXLFolUiX/gK7AvClxfBv+6Rv7jP9uFbd/DgYHAyBVKcTub4LsOO5Cb9SRuY/Q7wts2kZalqivvPGEFPoSlrcrfAc6uVcUNIGl1/PWkrfCdg1eAi80si0HOCkP45bQsP9Qye+ZuPQqt2BpFXHlXh04Dxq7Ksta/AIuybJnX+spb0mXA/wO+hRs03gb8vtJXUSORplATyjsSiUQikUh2qm2wFolEIpFIpEKi8o5EIpFIpM6IyjsSiUQikTojKu9IJBKJROqMqLwjkUgkEqkzovKORCKRSKTOiMo7EolEIpE6IyrvSCQSiUTqjKi8I5FIJBKpM6LybgThU38vNmN6PSWZpLVLhGmQdGZz5VmPSJogqUW+zNTcMm1JJI2UNKaEf9n2VMu0pJwjtUOUc9OIyrsEkjqFQXDblNcwYNdqlCnSYix3mcYJWaQY4WNHd0l6PYxBuWqXKdL8SDpO0kRJn0j6TNJ4STtliRuVdxEkFfqwAgBmNsfMPl6e5WkJJLWRtFK1y1FN8nXQWmTakpTqE5Fmpx3QAJwHvFHdokRakJ7A7cAewI+Al4CHJG1SLmJNKe/uo7pP6D6q+4RK4kg6QdL7klZOud8iaXTi/gBJ/5G0QNIbkgYnB6OwCspJul7SZ/hH4fOd5qkw+50Qwi6zxSrpaElTJC0M5RmZ8DtD0guS5kp6R9J1kr5RyXMG2ku6SdIcSTPTq7Zy+UjqE+LuF8q/COjaiHJUkzaSLpT0kaQPJA2T9HU7lrSWpFGSPpU0X9IjkrZI+Besg6RMEzsu6ashkc4ukp4I7el9SZel2tMESVcVK2toSxsCl+TTD+7fknSrpBmh/P+VdEwj62p7Sc+FMv5H0jaJ8pXNJzzDn0O5PwT+lTXjEf3GrTGi37jBI/qN+zT8XaPCspeT86qSLgrlnyvpKUl7J/wHhj7ynYTbrZKeycspS7+U1FfSW5LmSbpf0kl5WSXCnCDpVUmLwt/jUv4m6XhJd4a8Xpd0VKmHN7OnzOxMM7sFmFdh3S0Xhh/aa53hh/b6WYFrnQqSWdHlfKSZXWlmz5rZS8CJwGz8U9mlK65cgDrgDuAbwJ55B0lr4N/Vvinc740r4yuBLYC+QG/gwlRaZwDT8Q/KnwNsF9z3AdYFDi5UAEknANcANwA/APYD/psIshg4LeR9REj3T4141jOAacDWwCDgQknJMmXJpy0+mz8B2Bx4sxHlqCZH4t+F/l/gFPx5D034j8RnsAfizz8PeFD++dY85ergbVze+WvTEGYCgKSOwAPAs/jnYH8JHI5/1jZrWQ/GPyf5+0Q++bI9A/TC5XgFcI2kPcpVTAGGAb/F2/PrwFhJ7SrM5yhAwM7AL8plOKLfOI3oN+4IvL5Ow/vmacCbI/qNO3xEv3FZP89aTs434MccRwDdgVHA/ZK2DP4X4p/ovR5A0i/wNnFE4tOdJfuLpB2A64ARQA/gPvx71STCHISPK5cD3fB6vErSAannOR8YDWyJr7Sul7RhxrqoOYYf2kvAg7gcrktcNwAPBP8sRDkvzap43/y0XMCa+iRoftU95egpPSuJJ+leYI6Z/TzcHy//OOEAACAASURBVIUL4rtmtkDSY8DDZnZBIs5PceX+P2aWX1VNMbMDEmE64avvH5rZ0wn3HNDbzLqF+xnATWY2IGN598EFvLqZLZbUExgPfNvMPioSpwF4xcz2SrhdB3Qxs4JnJAXy6YN3hm3N7D9ZylpLyFerq5nZDgm3h4E3zexY+VbTy8CuZvZY8O8AvAX0N7PritVBWqYJ9zZ4Z14b6Bna02B8gNnUzBaHcH3wCdxaZjavXFnDfQNwpZkNK/Pct+HtOx9vJLC2mfUqEr4n3p6Oyn9bXlJ7fLJwppldlzGfCcA3zewHpcqXZ0S/cevgA/rGQKGV9lzgVWCfk6/efWaxdDLIuTM+YHcys7cSYf4GvGtmJ4X7DYHngWuBfsBvzezPJfJN95dbcXnukwhzLXBc/hvxkv4FvGRmfRNhRgIb5/tlWMENNbOzw/3KwOfA8WZ2U7HyJNJ7EbjLzHLlwi4vhh/a63DgLxSX87H9bx9zW6k0opwLlu0SfCGwuZl9Xipsa1h5gyvhnyZWFUfijX1BuN8GOFe+XTpH0hzgFrzhJbd4nqZCwnZNR+DREmF2l/Rw2PqZDdyDz7Aq2V4CmFzgfvMK8/kSeK7CfGuJF1L37wL5LbOu+Cz763oys1nAFBL1RGV1cBG+m/LTRHvqCkzOK+7AJLyuN85Y1oJIWknSuWGb7+PQVg8GvpexvEmS9TCHRD1UkE8lk7ydKa64Ce4bh3DlKFV3W+O7AVNTfXp/oHM+gpm9CZwKnAU8lh7QM/SXLsCTqXI8kbrvyrLHCZNYur0t9Txm9iXwIWXaQq0y/NBea+Cr0FJyvnL4ob3aFfFPEuW8pJyn4ruBB5dT3AArlwtQJ4zBB+QDJT2Kb6H/OOHfBt8GubNA3A8T/89tRN4lt4fCrHAsPks9H/gYb5S34g2oWaggn4Vm9lVz5ZuFaV26TsgSruv0aT0zBPsidW8smYSWkkVyiylTHUg6Gp/J72RmyZWiUukVy6dUWYtxJtAfH4ymAHPwrcHmHuiz5lNpnyhXr1nbXqm6axPuf1gg3PzU/S4hz+9JWs3MFkLm/lJKzumylXNrTFuoVc7Bt3ZLsXoId16ZcFHOfK24/wDsa2bpiURB6rXxLEUQ1F34ivtQYCbwz0SQZ/Dt5VcLXF+WSDp/ZlLUItvM3gfewa0FC7Et3khON7PJZvYysF6mB1uW7QvcT2uBfOqVqXibTm7DrYmflU2tJCFJ/wv8Gd96fr5APjsoYVgD7IS3l9cqyGYRy7atnYD7zez/zOy5kN6mlZQ9wdftRW4H0o0l7aU581nePIsPuOsU6M/v5AMFe5Ajgd2BNVnaJiFLf5nGEruXPOn7aXhdJtmJCttbnXESbg1finbAyU3MZ4WQs6QzgMHA/mY2KWu81rLyBt86fwTYCLgltaX5e2CMpDdxA7cv8YFsOzP7TYk0P8BneHuH88kFYRs2zWDgMknv47O8dsAeZjYcP7NpA5wm6R58QD2tkc+4vaSz8YlKT9yA6Mjg15z5NCsZV9RNxsxekb9hcI2k44HPcNl8jh+TZELSOsC9wFXAE+Ee4Csz+zC4n4YbrFwBfB8Yip9fV2IZ3ADsLOkmfDfgI/zM/lD5u54fAb/C2/SzFaSb5zy5lfi7+KpjEUvqoTnzWa6Y2cuSbgZGSuqPT86/ifeJ183sHknr4autc8zsMbkdzHhJD5jZw2TrL38EJkk6C/gbvro7KBXmEuBOSf8B/oEbtx5JEePWrMgtpfNbsm2BdST1wG0SXm1K2s1Avv2XUuDzcLujRrOCyPksfIw6Cng5MdbML6JrvqZVrLwDj+Er4M0JVuZ5zOwh/JxkN/xs40lgAG7IVJSwKv81cCw+AI4uEu7P+CzzOOBF3Ghni+D3Ar41eQY+SzsW37JsDJfi56/P4lss55vZXS2QTz1zDC7f+8LfdsA+ZpbeZitFF3z7uD/wXuJ6CiDM+vfFLc2fwy1db8W3CSvhfGADfNWbP775Qyj3A3ibnou/KdEYBgDD8UFvE6CXmeW3wZsznyTlfjeguX5X4Bjc8PBi/A2RMfig+6Yk4VbJzwKXAYQVzVBcEXwrS38xs8l4n/41fpb5U9wGYkEizN/wic/pIZ1TgZPM7P4mPt96ofzP4ue7J4T/CxobLmcuJFEHRZjPsm/zNIbWLueTgVVwy/TkWHNFuYitwto8EolUn4zW5q8A+5ayNq9lJF0G7Glm3atdlmoSrM2vBdoX8M5kbV7L1IOcW9PKOxKJVJGgkLfCVzEfs+THReaF+2OBretJcUs6S1IPSRtL6ocbMF5f7XLVALfhO0Zz8WOp/JWfoN1evaJVTj3KuaZW3pFIpHUQflHtbHxbcARw4clX716TvxRWCkm342esHfDffLgGuMLiwEn4JbVCr/1N7H/7mLqZoEF9yjkq70gkEolE6oy4bR6JRCKRSJ0RlXckEolEInVGVN6RSCQSidQZUXlHIpFIJFJnROUdiUQikUidEZV3JBKJRCJ1RlTekUgkEonUGVF5RyKRSCRSZ0TlHYlEIpFIndFqlbekTpJM0rYZw4+UNKbCPBokVfTlLkkHSnpF0peSRlYSN1L7hDbXu8I4x0t6S9JiSbkWKlqrRFIfSXOqlHfFsi6QRhxDMpBFzitaXbYK5S1pgqQrU85vA+vin2ysJa4D7gY2BE6t5uATyUalE8EK014L/+3vS4COwDBJOUkvNnde9UKR/lyM2/Hvqa9ItIoxpEbkXLd12SqUdyHM7Cszmxm+yV0TSPoGsDbwkJm9U+5j6ysquVxuleWRj6SVwzeBq8mGwMrAGDN7z8zqYuCoBSStYmbzzeyDapdlebEijiEtJed6r8u6V95hq2NX4OSwOrKwUlpmtSSpi6T7JM2SNEfSZEkFv9cqaUtJ70kaXEFZOki6VtIHkmZL+mc+f0k9gU9D0HGhbD3xD82vkSh7rjH10BrI5XLK5XL9gDdyudx30v5y+odtroWSZkgakvAfKuklSfPDFtrFktom/HOSXgyz69eAhXjdS9JvJL0W4k6RdFQi6zfC36eCjCZkfSZJHSXdJunTcI2VtEnw6wM8G4K+HtLuAwwCtki0iT5Z86tlHh3Xed1Hx3W+6tFxnWcUC1OiP/cM/+8n6UlJi4C90yslSZ0ljZY0U9JcSc9I6pXKo0HSeZKukfR5aEdnpcJsGvrvgtCm9gtjRp8SZS8q66ysKGNIU+WcMY9WXZcrV7sAzcCpwKbAdOCc4PYhsEEykKT1gEnAv4C9gM+A7YCV0glK2hkYDVxgZpdlKYQkAWOBWUAv4BPgaLxhbAb8G9gC+C9wSLj/BDgNuBDoHJJaIVdeuVyuE/793N0AAwqtvi8ETgTOAB4Dvo1/PzrPXKAv8A6wOXA1rqAHJsJsBBwB/AxYBCwA/gD0xj9f+RKwA/AXSZ+a2Vi8nTwJ7AM8H+KVRVI7YDwu611DvDOBRyR1xbcC3wMeDHm8DcwGuuFtqGdIqq5WBGkeHdd5XVwGxwACVisRvFh/7hT+vwjoD7yK19X+qfjtgQeA84D5wKHAPZJ+YGbTE+FOxydJlwD7An+UNMnMJktqA9wLzAS2B1YHLi9V7nKyNrOyn0Ot9zFknfHPrQ7cCFw4c7cez5YJ3lQ5l6Te6zILda+8zWxWmJ3NM7OvvyGrZXdDT8YH95+ZWX7wfTkdKMzSbwFOMbMbKyjKbkAP4NtmNj+4DZR0APBzM7tYUn7b55N8WSXN8sewuvr+bXORy+UEnAAMB/KrZOVyuXeS4SS1xwfc08zs+uD8KjA5H8bMLkhEaZB0IT6AJpX3qrg83g/proFPBn5sZhNDmDckbYe3mbH4oALwcYVyOgxXVsfkvwss6QTgA6CXmd0h6eMQ9sNEm5gDfFnvbaJCpQ1k6s85M/tHAfd8/OfxCVaewaEP9sYnaXn+YWb589Y/Sfo1sAfenvYCNsPbxDshn9PxiX8xSsoauKPcs1P/Y8g5eD1fXS5gU+WcgXqvy7LUvfKugK2ASQnFXYht8Bn3EWZ2Z4XpbwO0Az5MNbS2LJnFRRIUWG2X6qGb44P/o8UCyC1/TwM2xldgK7HszsqMvOJOpNsWeFBS8uP2qwANWZ6jBNvgK/3ZqTbRjlbcJhqjtCvg6VKeYTI2CFeY6+JybAu8kAqavn8XyB/VdAHezSvuwFPA4hJZN4es63YMWWf8c5sDvwX+b+ZuPYr20QooKecM1G1dZmW5Ke8ZAyZOyBJu/aE792yhImSZur2Bz5T7SrrPzBZWkH4b4H1g5wJ+n1eQzgpBLpc7AbiUxGq7TJSS/pK2B24Dfoev0D8DfgIMSwWdm7rP230cALyV8vuiTJnK0QZ/2+GwAn6fNDHtmqOFlXaetPzSDMOPN84EXgHm4Vu5q6bCpWVrLGkLCveV0ByyrosxZJ3xz3ViyfY2eLlH4dvb/Zspm3JyLkdd1GVTaC0r70UUOLtO8QxwlKRVS6y+P8EH/EeBeyUdVIECfwb4LrDYzF7PGAeylb1VkcvlNiTD1lqKqfj59R74oJxmR+Cd5Na5pA0rSHdDMxtXJEy+vVQqp2eAw4GPzOyzCuLVa5uYhA/qTTWEbcrz7wTcaGZ3A8gNFjtT4IisBNOAjpLWM7N3g9u2lH6uxso6nUY9jCF98N2NNONm7tbjwwLuxWjJctdLXTaa5aa8W3BFDb69uZ2kTriBQaGZ7lVAP+AOuQX5p8APgWlm9vW74Gb2kaQ9gHG4ocvBGRX4I/iZ2GhJv8ENMdbBVwGPJM5TC5W9raS9cMvjeVmMW+qZXC73ZrAqz6+8yw72ZjZb0hXAEEkLcYO1bwHbmNmf8cG5o6Qj8XPLvfHBNEu6w/D3qxXSbY8bKi02s2vx3Zj5uNVrA7Ag42slN+MrwNGSzsdX9hsABwJXm1mhSQh4m9hQ0tYhzuwKd4GqxY4sWXm3ofEr7wbK9+divAwcJGk0vroexJLdnaw8jBsujpL/6MfqeFv9kuIr8sbKOkm9jCEjgQmJ+zbBbat1xj/3nZm79cj6SlcDjZdzOeqlLhtN3b8qFhiGz5im4sZF30sHCOdXu+DbZ+NxwfwK75DpsB8Bu+Od725JWQxtDNgPV/p/wTv/Hbjhy7sl4v0bX4XeGsr+m3J5tQZyudw1uLXnhOCUZZvybNwKdSC+OrobWB/AzO7HLYcvx88z9wLOz1icgUAOH3z/iw/ehxBeEQu/FfBr4FhclqOzJBo6/S7A68Cd+AAyCliLJa+pFOJu4O/4DtCHZJiE1AJ77P7azD12f+1k/Mc0/opPeBoz6Sjbn0twBj7ZmohbnT8e/s+MmS0GDsInH0/iMhuMt9EFReI0VtbJNOpiDJm5W4+Gmbv1mJC4xuFKsT3LHlOVoilyLkm91GVTUDCMjESqQrA2P57UKjy4R+qYR8d1XociK/E9dn+truQraUv8THtbM/tPtctTi6wz/rkL8Ff09mwmo7VICaLyjtQEwfL8r/iOhwHfy+VyRX/MI1I/FFLita68JR2EG029gp/jX4obsm1lcdAsSIXveUeaSFTekZohsQo/D9gml8utMD97uSKQUOIH7rH7a+tXuzylkPQLvB1ugG97TwBOT71mGIlUjai8IzVHLpdbJZfLNfU1rUgkEmm1ROUdiUQikUid0VqszSORSCQSWWGIyjsSiUQikTojKu9IJBKJROqMqLwjkUgkEqkzovKORCKRSKTOiMo7EolEIpE6IyrvSCQSiUTqjKi8I5FIJBKpM1qt8pbUSZJJ2jZj+JGSxizvfEOcPpLmNDXvSG0iqWdoE2tXECcn6cWWLFdroJp9J8i0dwukG8eQFI19vtZcl61CeUuaIOnKlPPbwLr4l4CWJy2SbxzMq0djBoAU/8bbxMfNWKxmm3DWGkX6czFuxz9B2ppYIcaQ5STnVluXrUJ5F8LMvjKzmeFbzMsFSatWI9+aJddhlWoXoRSSVpbUol+3krSKmS0KbSL+FnEzEup2vpm1mg/YxDFkWRor51Zfl2ZW1xcwEv+EZPLqFC7Dv7+bD9sFuA+YBcwBJgPdE+mMSYTdEngPGFwibwNOBu7BPx84rEi+++Mfg18APAYcli9n8O8TyrMH8GJIazywUcI//Yx9ql33Ja9Ba37HBq05wwat2c8GralmkrWA/vhnGhcCM4AhCf+hoZ7nAw3AxUDbhH8u1G8f4DXgK6B9SPc3wW0+MAU4KiXn5DWhSPnysj8cGBfSOgXoGdzXToTtC7wFzAPuB07y7rhMWQ8L5ZoN/C2fRvBPl6tnNWXebWS3Gd1Gdruq28hu6zZBxiMLPFenRB3uBzwJLAJ65ftOIn5nYDQwM/SjZ4BeqTwa8C+GXQN8HtrRWakwmwL/xPvsSyHfOcl+F8rTO3HfEbgN/wrZp8BYYJMyz7tCjiFNlXOsS2sVyrsDvi15PbBOuFZKCw1YD/gI79jbhc55FNAj0ZjGhP93Bj7BPwFYrrF8AByLb+lsVCDf7+GK5lJgM6A3PminG8sXwCOhbD8AngUeCv6rh4Y4PfGMq1e77kteg9bsaIPWXGyD1jQbtOY4G7Rmp2aQ9RDgM1zxbQzsAJyU8B8I7BhksF+o5wsS/rnQEf8BbA10A1YGBofOvE+Q4REh3P4h3g+DvPYOdf/NIuXLy74hyHkjYH1SyjuUezHw29AOjwM+ZFnlPQe4N7SHHYA3gWuCf3t8K/HhRJtYtZoy7zaym3Ub2W1Bt5Hd5jdWiVO8P+frcArw49Dfvs2yyntLoB/QPbSRc3EF0CURpgE/wjglhPlVSHuH4N8G+C/wKNAj1P0ToY/2SfX/3uH/dsDL+DjyA3yhcF2QWbsSz1u3Y8jUzbpsNXWzLndO3axL20rjNlXOra0uG3NVraM360P4t3avTLmlhTY4dKSCA1zodGPwWd7nwC8y5GvAn8rkOwSYRviCW3A7p0BjMWCzRJgj8UGnTbjPAS9Wu64rulxxmw1a8ysbtObcpqzCcWW1AOhXQZx+wKuJ+1zolN9NuK2Br5B3TsW9HPh7IZmWyC8frn/KPT8g5ZX3rcCDqTDXsqzyXgB0SLidm3qekSR2i6p9BeVtTVXiRfpzvg4PSbn3ofyg/jhwXuK+Abg1FeaVfBh8kvYl0DHh/7+kVlgsrbz7hjSS/XwlfJLw/0qUrW7HkKmbddlz6mZdbOpmXX7fmPgtIOe6rcvGXK32zLsAWwGTzGxRiTDb4CudX5rZjRnTfbqMfxfgKQsSDzxRINxCM3spcf8usArwjYzlqGXa4LPVPwOPkuvQqRFpbA6shq+GCiKpt6RJkmYGa9HL8Jl2khlm9n4q3bbAg5Lm5C/gRHwLtjFkaRNPptwKtYk3zWxW4v5d4DuNLNPyZjW8XvsCr3cf1f2q7qO6r9sM6ZasW0lrSLpY0lRJnwZZbsuy7eCF1H2ybrsA75rZOwn/p/DdkmJsg6/0Zifa0CxgLcq3o7ocQ7pOn/YIcBMwYFqXrl2bOflyddLYeDVZl41h5WoXIE2nAWMnZAnXMHT/nhUmncUw6Q1826WvpPvMbGGGOHMz5GtlwoDP9JPk47SWCVa+/ncF/kuuwxnkZl3TiPiFPaXt8fPG3wGn49vrP8G3t5Kk5ZWv3wPw7bMkX1RQvlJ5pMnaJtL5G/XXHlYLf/sCx3Qf1f0G4IIpR095r5HplavbYfjxx5n4SngecCOwaipcqbrNKp8kbXCL5sMK+H1SJm49jyH98TPkh6Z16dqHpSc4DV2nT2toZLrl6qSx8Wq5LiuiZgrSRBbhW1SleAbYSVK6Eyf5BDdSWA+4V9JqJcJmZRp+Zppku0akk+UZ64E2+Png1eQ6bFhBvKn4WdUeRfx3BN4xswvM7CkzewXIkn4+3Q3N7NXU9WYIk9+taa76n8aybaC1t4n8SvwEYFKZsE15rp2AG83sbjN7ATdGq3QHZRrQUdJ6CbdtKT1ePoOfn39UoB2VU95ZylOTY0jX6dM+wCctG+C7YuMTV5/lXZ4M1GxdVkrNrbwbsaIGP8PaTlIn3MinUGe5Cj8DvUPSYNwa9IfANDP7+h1AM/tI0h64tfA9kg7OuAIvxtXAGZKGAX8BtsAHMKhsdt8AbChpa3yFOLuJ5aoWi/Gz3DPIzXqzXOA8ZjZb0hXAEEkLcSvRbwHbmNmfcWOhjpKOxN8i2Bu3+s6S7jBgWHht7DH8fH17YLGZXYvvxswH9pbUACxIbWdXyh+BSZLOwi3IdwEOakQ6DcC+kjbDz1ZnmVljdwtamoW47G8ALigTtoHy/bkYLwMHSRqNr64H4ZOGSngYN2AcJelM/MjnUnw1VqzP3oyv9kdLOh/voxsABwJXh8lkY6nZMWRal67fwY363mLJWXEyv3LlaaycG0vN1mWltJaV9zB8JjQVt9pNn28Rzq92wbfPxuPWg79i2e0RzOwjYHe8893dlBV4WL0dgm/hPo9v6f4ueC+oIKm7gb/js9sPyaCYaox8x5gAbFHhlnmes4GLcKvyaXidrA9gZvcDl+CGZi8AewHnZ0x3IG6AciZuZfwwLrM3QtpfAr/GrVjfxd9YaDRmNhm3MP91KOtPw3NV0h7AB59p+Dnfh/juQ62xEJ/4/BX4/pSjp5w85egpM8vEKdufS3AGPtmaCDyAG6tNrKTAZrYYn0ythtsmjMINXo0iMjKzefj48jpwJ26JPAo/8/60kvwLpF3LY8hwfLK7T9fp08Z3nT5tQuJqKBO3KXJuFDVelxWhpc/tI8sDSacCvwfWCgNF6yTXId+4lqy24Vpys2KjSyHpMmBPM+te7bI0lu6juiflutRKO4PCrmkkbYlvD29rZv+pgfJUfQyZ1qXrnvhE94Ku06dlnSjXHLVQl42h5rbNWyOSTsatVT/Et2MHAiPrqaFUTK7D+vhKRfhq+5fkZjVUs0i1RNgyfxjfLtwTP9I5p6qFah5ahdKWdBBu/PQK/rrRpfhK7ZkqlacWx5CPgbuAC6tYhoqp0bqsmKi8lw8b4wPzt3ADmqvxmV5rZhG+xXwBcbVdiG3xbfoO+Pb82cAVVS1R03kHP1KoW6Wd4H/wo4wN8G3vCfiPNlWrHdfcGNJ1+rRngZ9VswyNpObqsjHEbfNIy5HrsAq5WbVqQBWJRCJ1S1TekUgkEonUGa3F2jwSiUQikRWGqLwjkUgkEqkzovKORCKRSKTOiMo7EolEIpE6IyrvSCQSiUTqjKi8I5FIJBKpM2pKeY/oN27CiH7jJlS7HJFIJBKJ1DI1pbwjkUgkEomUp9Uqb0mdJJmkbTOGHylpTEuXKxKJNB+S+kiaU6W8TVLvauS9olFNOdcqrUJ5S5og6cqU89vAuviXgCKRRlPpRDDSNIr052LcDny/JcsTaRminJtGq/0wiZl9BdT7xxEiLYiklYGvqvixiUgTkLSKmc3HvxceaaVEORem7lfekkYCuwInh9WRhZXSMqslSV0k3SdplqQ5kiZLKvj9ZElbSnpP0uASeS+zbSapQdKZqTCnSBoraZ6kNyUd1eQHXwGR01/SK5IWSpohaUjCf6iklyTND3K4WFLbhH9O0othC+41/POVa4R0fyPptRB3SkpGb4S/TwV5TihSvoIr9GQ7SYQ5QtIkSQskTZf042aqppphxoCJ684YMPG1GQMmrpM1Ton+3DP8v5+kJyUtAvZOb6dK6ixptKSZkuZKekZSr1QeDZLOk3SNpM9DOzorFWZTSf8M8nkp5DtHUp8SZe8o6TZJn4ZrrKRNyjxv3Y8huVyuU87plDVOU+VcJM26r8tKqHvlDZwKTMa/H7xuuN5OB5K0HjAJ/8b0XsDWwAhgpQJhdwbGAxeb2bnNUMbfAfcBPYBrgRvTA3wkExfi394dAmyBf44wKeu5QF+gK3AScBiQlt9GwBEh7pbAAuAPwC+Bk4HNQ/rXSNo/xNku/N0Hb18HN8OzXAz8EW8TDwOjJXVshnRriYH4t7AHVhCnXH++CDgP6AI8USB+e+ABvI9vCdwN3COpSyrc6cAUfBy4CLhY0g4AktoA9wJf4t977gMMAlYrVmhJ7fAxYwGulHYA3gMeCX5NpZbHkE54/XSqIE5T5dwUarkuM1P32+ZmNivMzuaZ2dfb5JLSQU/GB/efmdmi4PZyOlCYpd8CnGJmNzZTMe8xs2vC/4Ml7QacBtTljK8aSGqPD7inmdn1wflVfAAAwMwuSERpkHQh/s3spPJYFfi5mb0f0l0DOAP4sZlNDGHekLQd3mbGAh8G94+TbayJ/NnM7ghlOBXYGzgRH7DqnhkDJq4LHIMvEI6ZMWDiBesP3bls3WXozzkz+0cB93z854HnE06DJR0A9MYnaXn+YWb589Y/Sfo1sAfenvYCNsPbxDshn9OBf5Uo+mGAgGPyxzCSTgA+AHoBd5R79jK0qjGkqXJuIq2iLuteeVfAVsCkhOIuxDb4jPsIM7uzGfOeXOB+/0IBI0XZHF/5PFosQNgyOw3YGF+BrcSyOysz8oo7kW5b4EFJybPvVYCGphe7KMlJx2JJT4SytBYG4soMXIEPxCdDTeXpUp5hMjYIV5jr4nJsC7yQCpq+fxf4Tvi/C/BuXnEHngIWl8h6G3xXZ3ZK0bQDOpcqc0ZWtDGkpJybSKuoy9awbZ6VLFO3N4CpQF9JRbfIEliBdFeptGArIsMP7bVhhVFKyk/S9sBtwEPAAfhk7TyWlcfc1H2+DxyAb6Plry2ASs+h84P712WVtMK1h8SqO9+HVsNX35nPvkuQll+aYfiRyEB8+7oH8CS+45Lki9S9saQtKNxXQhv8zZYeqWtT4JoS8VrDGNIm9bc5KCfnQrSGusxMTSnvk6/evefJV+/esxFRF1Hg7DrFM8BOktKdOMkn+NbZesC9GRT4h/jsHgBJ303eJ9i+wP20Mmm3WoYf2usEoCH8zcpU3MBsjyL+OwLvmNkFZvaUmb0CZJkg5NPd0MxeTV1vhjD53ZpybSy/vZ5sAz2KhP26QPGyzQAAE1pJREFUTciXatvRetpEctWdJ7/6zkKW/lyMnYAbzexuM3sBmEHlK99pQMdgJ5NnW0qPl8/gOz4fFWhHn5SIV9djSC6X2xwYGW5HhvusNEXOhajruqyUmlLeTaAB2C5YK64dDE7SXIVvpd4h6YeSNpZ0uKSlBlcz+whXEOvjhi6lFPg43FpyW0lb4Y14QYFwB0s6TtImks4O6V9e6UO2BoYf2qsTcGm4vTTcl8XMZgNXAEMkHROsireTdGII8jI+4B4p6fvB/fCM6Q4DhknqG9pFD0n9JB0fgn2Av6ayt6TvSupQJK35wOPAbyVtIel/Q9qFOFFSb0mb4W1hQ+DPWeqilimw6s5Tyeq7gfL9uRgvAwdJ2lr+JslN+LZ5JTwMvASMkr91sj3eZr+k+Ir8ZuB93PBwV0kbSdpF0vAyFud1OYbkcrnVc7ncH/Ddhvb4c7QHnsvlchfkcrnVMyTTQOPlXIi6rMvG0lqU9zB8FjcVn319Lx0gnF/tgm+fjQeeBX6Fd8h02I+A3YENgLtLKPD+wOvABOAu4Dp8oE+TAw7Bz9lOxI1ansr6cK2F4Yf2EnA9SwbTtsBfg3sWzsatUAfiM+W78UkWZnY/cAneCV/AjY7Oz5juQFxGZwL/xQfvQwiviJnZl8CvgWPxs9HRJdLqG/4+hW+XFjNAG4Abyj2PW7EfZGYzMpa3lim06s6TdfVdtj+X4Ay8D07Erc4fD/9nxswWAwfhE44ngVHAYFxxF1IGmNk8fHx5HbgTmB7irQV8WiK7eh1DbsTf5LgN6JLL5fbAbQVux9t8FmPfpsi5EPVal41C8fcpWpZgBPUzM7ur2mWpNsMP7dWPwqvLfv1vH1PqXLDVIKkTPin4oZm1pFHOciesul+n9Ep3PvD9LJbntYSkLfFV5rZm9p/lnHfNjSG5XG4r4Ju5XG4ZA9JcLrcn8HEul3t2+ZesNLVYl43l/7d39kFaVfcd/3wxkIi8pDMMoKBgQNnVWhZCiQjIEmKJAZshmhircbZY32rbjNL6EiFsSyhoEZoMEslMI6JUqRrHZK22KruOjgSNQkmyiwGSTSOKiTElrLysL6d/nPOEu5fndd+evff5fWbuPPeee96e8zv3/M75nXPvMeXdw6SpsnSFYB7/KXAiHUdmDt+gn71oc0Nr7+esd0m58l6Htzzkm2o6Cvzb6JUzu2PleY8haQF+0dRu/PvLq/H1dlJvf5HP2pDuI01lmRazudGHiZnL4yZVUbr53Oib/Dn5FTfh/ud7IS9dZTCwFm/S3YSfpplrn9I1+gqV9J53WXDOmUKCa4HZee73w68xuIb8r9UkHudcK8W9tpg4Rq+cObrceeguwgeauusjTV3C2pDuI01laWZzo0e569L5/fFm4lPIr7QcfjHY6Ys2N8TfwTUMwzAimPI2epy7Lp0/HOi/aHPDvjx+RgPtizY3ZFsdahiGYUQw5W0YhmEYCcMWrBmGYRhGwjDlbRiGYRgJw5S3YRiGYSQMU96GYRiGkTBMeRuGYRhGwjDlbRiGYRgJw5S30eOMbNwxfGTjjlEF/Iwe2bhjeG/lyTAMI8mk9j3vUjeAkLQBGOacm9/FdNuAv3HObehKPGlhZOOOkr+wtn92TWq+sCZpGH67w9nOuaYyZyd1SKoD1jrnBpUh7R7Z5MLakOPprJzTXJapGHlLapK0Nub8K+Bk/DZ+RpkIivgbFP6et4BlfVFxSxoryUmaUu68VAI5nudcbAY+0ZP5MXoGk3PXSO3GJM65D4BE7RmcYtYDXwJmkb3D+CHQBHynF/OEpI8AH9hOUd3D2FufeB3IOz0S2Ne6cl6XNzGR1N85dxi/payRUkzO2Un8yDuYu2cBN4TRkQsjpeNGS5KqJH1f0gFJbZK2SjonR7wTJb0paXmetMeH3uMRSa9JOs7kLukcSc9IOizpHUkbJA0N96pDHkeG64GS2iU9GQl/taTd4Tzzny6W9LSkQ5KaJV3QyeLrFfbPrnH4fZ6P4M3jUVxwXxj85USeRZJ2Szoq6XVJKyL3VwY5HJbUKulOSR+L3K+X9BNJdZL24veWPinEe7OkvSHsjyVdEUn6F+H35VD+TXny+KeSXgl1YjvwqSx+zpe0Lfh5S9IaSQPCvQslHQwdCySdEdL8diT8cklPh/PacH9OiPOQpB9JmpyvLHuI7+PLNB9Hgcdz3czzPGf+5+ckvSSpHZgbZNkWCT9O0uOS9kt6V9Kr8ecy1I3FktZL+n2oR/8Q83OmpOciz/bnQptRlyfvoyQ9JOl34XhC0hn5CqNS25CuyjlHnBVVlolX3sBXga3AvXgz+cl4k3kHJJ0CvIBXFhcAk4G7gROy+J0JNAJ3Ouduz5aopH7AY/gynIZXTvVE9jOWNBB4CmgDpgILgPPwe1vjnGsB3gJqQ5DpwAFgRqbxDveaYskvB74FTAReBh6S1OtzfqWwf3ZNK7CI7Pt537R/ds0vi4jmn4ElwArgbOCLdJT1u3g5VAN/DXwZiMvvdOAvQtiJ+I7DN4CrgBuAs0L86yXNC2Gmht/P4uvXF7JlTtJJwBPAz4EpwK3AqpifUcCTwHZgUkj3spAmwPP4/c0znc5a4G06bqlay/F1YkVIbzLwW2CTpN7e/nAZx3fO4nwY/OWi0PN8B7AYqAK2ZQk/CF++F+Dl+yjwPUlVMX83Aj/Gl9cdwJ2SpkGHZ/t94FygDlhKnr3Kw7PeiK9Ps/BtwpvAM+FetjDJbUPqh06ifuhnctz7DPVDJxWIoaty7kCiy7KzOOcSf4TCXBtzG4tvSKaE6+XAL4EBOeLYADQA84HfA1cWSPPPgA+A0yJuM0KadeH6arzwB0f81AY/48P1ZmB9JI/fBlqBacHtdeDy2H+6NhLfqOA2o9xyKHSM2LJdI7Zs3zJiy/YPRmzZ7sLvsyO2bFcRMh6EbxivK6FeXAfsiVzXA+8BIyJuJ+HNcTNjYf8V+M9sdSlPetcA/wcMirhdEcLWRmS8B+gX8VOHH5EODNfbgNvC+Sa84jiMb+AGAu3A9Fh9mhuJb3pwG93bMh5zS8O6Mbc0HBlzS4PLchwZc0vD3UXILdvznPmfF8fc64C2AvH9EFgcuW4FHoz52Z3xA8zFK+5RkfvnRZ/t4OaAS8L5whCHIvdPwHekvpQjX8ltQ5YOedgtHeLc0iH3u6VDhge34W7pkAeC+8O9KedEl2UnjzSMvItlEvCCc649j59P4ntvVznnNhaIrxrY55z734jbNvzIIupnp3PuYMTtxeDnrHDdxLGeXi2+9/4cUBtMbqM4vqe3M3L+Rvjt869ZxcznUKS5PHAWvhf9bC4Pki6R9EIwmbYBa4DTYt5ed869FYv3Y8BTwSzaFsJeD4wr6o8dIyPvqHlvaxY/W51z0XryAjAAGB+umzhWJ2bhR5IvBbfp+A7IS7F4+0qdyDf6LjTqLoa8b45IOkl+uqQ5mK7b8FaMeD3YGbt+g2PlVQW84ZyLbmH7Mh2f7TifxFt1Dkbq0AHgj8hdj5LchlyJt1hdCuyifugWYBd+bcsy4CslxJWNgm8IxUhyWXaKPqW8n90yrunZLeOaeij6YkyIvwCagYWScprISohP5G7IMu5NwJmhYkwJ1014M2ktfuQY3wf7DyuyXeju0cdkmYtgPr8pXN5YpLkcCpS3pHOBh4D/Ai7Cd9YWA/1jXt+NXWfK7SKgJnKcje/Nl0J31onpks4CBgOv0LFOvOici6/Kj16XrU60rpz3Jt4UGp/7Pgrc27pyXlcXkcblF2cVfkpkCb7jU4Pv6AyI+YuXn+NYeeWTUS764d9sqYkdZ+IXbGYjuW1I/YHD1B9YgjcVt4W02oCJ1B/4OvUHjuQNX5hCco6T3LLsJIlo8IugnSxz1zFexc9dxB/iKO8Ac/DvJD9WQIE3A6MknRpxm0rHMm0GJkoaHHE7L/hpgQ7zLLfjK8av8b296fh5u6YC/ytx7J9dsx4Yu392TSmry5vxCmBOjvvT8T3vZc65l51zu4ExJcQ7xjm3J3ZkOhYZa02hOtYMnBPmvjOcm8XPtDBHl2FGSGNvuH4eb2W4GW8t+oCODUhTEf+rnGQbfZcy6i7mec7FDGCjc+5R59xOvJmzVAtKC/7ZPiXiNoX87eWreMvJ21nq0Ts5wiS/Dak/0II3aQPUheti6Yqc4yS/LEskLcq7FZgaVv8NizWMGdbh503/Q35F8HhJl0mqiXpyzr2NVxCj8QtdcinwZ/Bmoo2SasJilzX4ubIMm/A9yI1hleP5+F7495xzeyL+nsPPjTaGPLTiP+zxBfpQZelOShhxAxBMXd8EVkj6S/lVxVMlXR+8/Az/8F4u6RPB/bIi410FrJK0MNSLGknXSbomePs1fs55rqQRmdWpWfh3vPy/K+nssOo0vmBuHb5zuC6sbp0HrMTP/R0KeWrDK4M/1Am8+f1U/Or1pkL/q5xkGX2XOupupfDznIufAQskTZZ/k+QB/LRIKTwNvAbcJ//WybnAarxsc43cNuEb/cclzZJ0uvxbBXcp94rztLQhH8Z+i6WVzss5TlrKsmjSorxX4XtxzfhCjs9vEUwd5+PNZ4341b5/S0fhZvy+DXwa31g+mk2BhznLBfgy3AZsxM8BHY34OYRf/DIEb7p7HN8IL4xF14jvgTZF3JqyuFU6t+FXoS7B95QfxXeycM79APgX/EKznfhe8teLjHcJfjHb3wM/xTfeFxNeEXPOvQ/8HfBX+DmtrK86BaU7HzgDr3xXAbfE/OwDLsSb9XfgV7o+CHwtFl2HOuGcO4JfeHWU4+e7+yLR0Xepc90Fn+c83ITvbD2PXyvww3BeNJFn+6P4sr4Pv3jJcWy9RjzMIXz78nPgYbwiuQ8/5/27AulUahvSFTl3oBLLsk99HjUz3z3n03try5sTwzC6ythbn1gHXAvc07py3g3lzk9XkDQR39ma4px7pdz56VPUD63FK7zZ1B9oKm9mKofUfmHNMIyysww/0unqCvNeR9ICvIl1N/6VoNXA/+AtKkZHWoF/DL9GL2Ejb8MwjBiSrsS/rXAq3uzdBNwYe83QMMqGjbwNwzBihO88FPrWg2GUjbQsWDMMwzCMisGUt2EYhmEkjD41520YhmEYRmFs5G0YhmEYCcOUt2EYhmEkDFPehmEYhpEwTHkbhmEYRsIw5W0YhmEYCcOUt2EYhmEkDFPeRSCpVpKTNKwrfoy+jck5/ZiMK4NKkHNqlLekDUEQi2PuJQlIUpOktZ3IwovAycBvOxHWKBKTc/oxGVcGJueukbZvmx8Bbpa03jn3m95M2DnXDuzvzTSTRktV9Tj8fstXAIOBg8ADwOrqXS17S4jK5Jx+TMaVgcm5k6Rm5B1oxG9LtySXB0nnS9om6YiktyStkTQg3NsAzAJuCD0/J2lsJPjEEPaQpB9JmhyJt0NvUVKdpDZJcyT9RNK7kholnR7Lz20hH22SNkpaKqm1e4qj79BSVX0hsBO4GhgCKPxeDewM94vF5Jx+TMaVgcm5k6RNeX8I3ApcJ2lc/KakUcCTwHZgEnAVcBmwInj5KrAVuBdvTjkZ+FUkihUh/sl4U8smScqTn48CtwELgWnAx4F7Ivn5MrAUuD3E2YIfmaaKMOJ+BBgI9I/d7h/cHwn+isHknH5MxpWBybmzOOdScQAbgIZw3gg8FM5rAQcMA5YDe4B+kXB1wFFgYLhuAtbG4s7EMTfiNj24jY6nE4nXARMiYS4H2jPp4yvdPbG0/htoLXd5dufRPKHq7uYJVe3NE6pcnqO9eULV2kJxmZzTf5iMK+MwOXftSNvIO8PNwBclTYm5VwNbnXMfRtxeAAYA44uId2fk/I3wOzyP/6POuddiYfrje3MAVcBLsTDbishH0riC40fccfoDXykxXpNz+jEZVwYm5xJJpfJ2zr0MPArcEbslfM8qa7Aion4vi/98Zfh+jjT6ZXFLM4OL9DeolEhNzunHZFwZmJxLJ5XKO/A1YCbw2YhbMzBNUvR/z8CbRTKrnduBE3olh7ALmBpzi1+ngYNF+mvrRNwm5/RjMq4MTM4lkFrl7ZzbA3wHv6AhwzrgFGCdpGpJ84CV+PmSQ8FPKzBV0lhJw2KVprv5JlAnaaGkMyTdDHyKPtKz60YeoGMPOBvvAfeXGrHJOf2YjCsDk3NppFZ5B/6JiBnEObcPuBC/anEH8F3gQXyPL8MqfE+uGfgNcFpPZc459xCwDF8ZtwN/jF/ZeKSn0iwTqylOea/pZPwm5/RjMq4MTM5ForBazugjSHoM+Ihz7qJy56U7Ce9xP4Jf/BFdvPZeOC6p3tXyZDnyVg7SKmfjGCbjyqBcck7bF9YShaSBwPXAU/je5sXA58Nvqqje1fJkS1X1nwA34leVD8LPcd8PrCnxC2uJopLkXKmYjCuDviRnG3mXEUknAj/Am4ROBHYDdzrnNpU1Y0a3YnJOPybjyqAvydmUt2EYhmEkjLQvWDMMwzCM1GHK2zAMwzAShilvwzAMw0gYprwNwzAMI2GY8jYMwzCMhGHK2zAMwzAShilvwzAMw0gYprwNwzAMI2GY8jYMwzCMhGHK2zAMwzAShilvwzAMw0gYprwNwzAMI2GY8jYMwzCMhGHK2zAMwzASxv8DXzMDcJ1ajIIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 540x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs, ys = np.mgrid[:4, 9:0:-1]\n",
    "markers = [\".\", \"+\", \",\", \"x\", \"o\", \"D\", \"d\", \"\", \"8\", \"s\", \"p\", \"*\", \"|\", \"_\", \"h\", \"H\", 0, 4, \"<\", \"3\",\n",
    "           1, 5, \">\", \"4\", 2, 6, \"^\", \"2\", 3, 7, \"v\", \"1\", \"None\", None, \" \", \"\"]\n",
    "descripts = [\"point\", \"plus\", \"pixel\", \"cross\", \"circle\", \"diamond\", \"thin diamond\", \"\",\n",
    "             \"octagon\", \"square\", \"pentagon\", \"star\", \"vertical bar\", \"horizontal bar\", \"hexagon 1\", \"hexagon 2\",\n",
    "             \"tick left\", \"caret left\", \"triangle left\", \"tri left\", \"tick right\", \"caret right\", \"triangle right\", \"tri right\",\n",
    "             \"tick up\", \"caret up\", \"triangle up\", \"tri up\", \"tick down\", \"caret down\", \"triangle down\", \"tri down\",\n",
    "             \"Nothing\", \"Nothing\", \"Nothing\", \"Nothing\"]\n",
    "fig, ax = plt.subplots(1, 1, figsize=(7.5, 4))\n",
    "for x, y, m, d in zip(xs.T.flat, ys.T.flat, markers, descripts):\n",
    "    ax.scatter(x, y, marker=m, s=100)\n",
    "    ax.text(x + 0.1, y - 0.1, d, size=14)\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 5.0, 0.2)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, t, '.', linewidth=5)\n",
    "ax.plot(t, t**2, 'o', linewidth=5)\n",
    "ax.plot(t, t**3, marker='+', linewidth=1)\n",
    "ax.plot(t, -t, ls='', marker='v', linewidth=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linestyles "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Line styles are about as commonly used as colors. There are a few predefined linestyles available to use. Note that there are some advanced techniques to specify some custom line styles. Here is an example of a custom dash pattern.\n",
    "\n",
    "| Linestyle  | description |\n",
    "| ---------- | ------------|\n",
    "| '-'        | solid       |\n",
    "| '--'       | dashed      |\n",
    "| '-.'       | dashdot     |\n",
    "| ':'        | dotted      |\n",
    "| 'None'     | draw nothing|\n",
    "| ''         | draw nothing|\n",
    "| \"          | draw nothing|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, don't mix up \".-\" (line with dot markers) and \"-.\" (dash-dot line) when using the plot function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 5.0, 0.2)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, t, '-', linewidth=5)\n",
    "ax.plot(t, t**2, '--', linewidth=5)\n",
    "ax.plot(t, t**3, '-.', linewidth=5)\n",
    "ax.plot(t, -t, linestyle=':', linewidth=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many arguments have a short and a long name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.bar([1, 2, 3, 4], [10, 20, 15, 13], ls='--', ec='r', lw=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.bar([1, 2, 3, 4], [10, 20, 15, 13], linestyle='--', edgecolor='r', linewidth=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything combined in the fmt string. Color, marker and linestyle can be specified in a single fmt string argument in that order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 5.0, 0.2)\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, t, 'k.-')\n",
    "ax.plot(t, t**2, 'bo--')\n",
    "ax.plot(t, t**3, 'g+-.')\n",
    "ax.plot(t, -t, 'r^:')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colormaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another very important property of many figures is the colormap. The job of a colormap is to relate a scalar value to a color. In addition to the regular portion of the colormap, an \"over\", \"under\" and \"bad\" color can be optionally defined as well. NaNs will trigger the \"bad\" part of the colormap.\n",
    "\n",
    "As we all know, we create figures in order to convey information visually to our readers. There is much care and consideration that have gone into the design of these colormaps. Your choice in which colormap to use depends on what you are displaying. In mpl, the \"jet\" colormap has historically been used by default, but it will often not be the colormap you would want to use. Much discussion has taken place on the mailing lists with regards to what colormap should be default. The v2.0 release of Matplotlib adopted a new default colormap, 'viridis', along with some other stylistic changes to the defaults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cmap(name, value_range=(0, 1)):\n",
    "    gradient = np.linspace(*value_range, 256)\n",
    "    gradient = np.vstack((gradient, gradient))\n",
    "    fig, ax = plt.subplots(figsize=plt.figaspect(0.1))\n",
    "    ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name), vmin=0, vmax=1)\n",
    "    pos = list(ax.get_position().bounds)\n",
    "    x_text = pos[0] - 0.01\n",
    "    y_text = pos[1] + pos[3]/2.\n",
    "    ax.set_title(name, fontsize=20)\n",
    "    ax.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cmap(\"jet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cmap(\"jet\", value_range=(0.1, 0.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cmap(\"jet\", value_range=(0.6, 0.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cmap(\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cmap(\"viridis\", value_range=(0.1, 0.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cmap(\"viridis\", value_range=(0.6, 0.9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the full gallery of all the pre-defined colormaps, organized by the types of data they are usually used for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3xJ2vsX2BNZIOAt4v6YPAjcDtOI2/mdlUTTXARERnTP3pwOmL6ZvbxqbxNzOz6ZiZW2RmZnbP4gBjZmatcLp+MzNrha9gzMysFQ4wZmbWiml/THnZrXryVlEp3RYc3u9lTrka116Mr+YZP2K/mTntFcV+L6ByfxflPVty/9QGVbP/TJXreutNYzVQ7vdXNThfr381Zk+WofKcPV6qFXP6V8Nj5+z3UuwXM+Jc5DJakY575Q5UK9KfkOqbuoHy0Piy/3zj89hmfUGV6vL+LNEcN+NV9evy+GCw3OvT/PBHzUdn/PyI3pYfEURA5D07IvKeMLm9KTf9o877w8S4/kHUg+11Ob45X2+LkEhzNufPx8166lhiex1pffO11+UeKpH3Rcnt3bpXN9DebcaPaO+OmSuPG3euyGN7c3drolvM1w3qbhAD5dSnLDfjIx/PN18MnC/PV+wPE1FT1938d5eOI8r2bvF+urlPnft3e302ZL6Imgvjy7O5H0yRhn+1pM9LmvdLkkreJOlKSVfk1PyPWq71mpnZ4kw9wNDPMbY7cDfw8gX6vxLYF3h0RPxPUtr+L+W0/QPU2w7RzMyW2ywEmNKZwCMkrZTUS7cv6UhJq3Lx9cBfR8SvASLia8B3SGn7kXSHpLdKOht40rKu3szMemYmwEhaATwLuGiePtsA946Iq4eaziWl7Qe4N7A6Ip4QEd9uZbFmZragWQgwTY6xc4HrgE9uwBwCmi/0dIF/n9DazMxsA83Cp8h6WyE3JK1jMPhtCRARv5J0p6SdIuKaov0xwBn5+DfRfBzCzMymZhauYEa5EXiQpPtL2gI4qGh7L3C0pK0AJB0IPJmUtt/MzGbELFzBzBERayW9FTgb+CFwWdH8YeC+wEWSusDPgEMiYs3yr9TMzMaZeoCJiK3H1B8NHD2iPoC35Nei5zMzs+U1q7fIzMxsI+cAY2ZmrXC6fjMza4WvYMzMrBUOMGZm1oqpf4psub1cK6OJqh1BR6Kjpqxe3Ya2V7l+Mf2rSlSdCnWadPqD5aoj1El1TftAuaPUpyrLc8c37alcUTXna/r3xlfpVZRHtvfWk9t7KfmLtk5FNVReeO4i3X5VDabvHyqrqqBTljt574Mx5aZvc75qdLl5Lyl1ftXfa6E5LtPvV1U/PX/Tpv7PZnB8Z7D/8HxNWzkf6qXTTynSo0iv36S3rwfKZXszZin9h89fji/7L3d7Sp9fL7m8oWO73e685aZuvnLTvxm7vvOX4yOiN39z3PzsFtu+lJ/H9ddfP1vp+ofS9J8sadsx/Y6T9Lx8vELSO3Oq/h/k11HLu3IzM1uMad4iK9P030pKw7+QtwMPBvbI6WWeAmzW4hrNzGwDzcotsrOAPSFtKEb6tv4BpG/xK9ffC3gJsDIifgMQEbcDq3L724CbI+JDufwO4Mb8hU0zM1tmU3/InzcFewbwpVx1KPBIYA9SQNk31z8CuC4HlVE+Cbwwz1kBhwEntLRsMzNbwDQDTJOm/xbgfsDXc/1TgX+JiG5E3AB8c9RgSS/Kz2B+LOmhEXEtcIukvYHfAc6PiFvafxtmZjbK1J/BADsCmzP4DGbUtz+vAnaQdB+AiPhUHv9LoNka+RPAEcCLgGNbWreZmS3C1G+RRcQvgVcBR0raDPgWcJikjqTtgafnfr8m3QY7RtKW0Lu9tnkx3ReBZwKPB766fO/CzMyGzcRD/og4X9IFpOcmnyU94L8IuIL+RmIARwFvA1ZLuh1YAxwP3JDnuVvSacAvvOmYmdl0TS3ADKfVj4iDi+JfjRmzFnhDfs2RH+4/EfjDCS3TzMw20NRvkU2KpN1Iz2lOjYgrp70eM7NN3UzcIpuEiLgE2Gna6zAzs8Tp+s3MrBX3mFtkZmY2WxxgzMysFfeYZzCL5XT9Ttffbx+frj+kwfT5qoihcqpTv1yk4+/1na9/kc5/VP8A6nwHuznupa8HIqBu0uv32lP/OmJwfEBNFO05JT5Fe0Dk7zg3czU30Lt1UEfQ7aXPh25E7/zdgLou2iN6dak9qINi/FA5Ip+Don/QrSnmS32a9TQvgHVD5XRcj+3frYN187TNW46groPI5ahzOYbKTXuvP/32WI/xNaP7N1sb1N3eqynXQ+Woa6LbHdu/GdMrd+e2jy/X3H3+sfeIdP2nS7o8j7tU0kuXd9VmZrZYU08Vs57p+gEOzyli9gPeI2nzhQaYmdnym5VnMGcBD4GUrl/SMZIukXQK8KAxY7YG7gS6edwdTYOk50k6rt0lm5nZfKb+DKZI1//JXFWm698OuITBxJUnSLoL2Bl4jVPCmJnNpo0xXf/hEbEnsAMpQeaOy7ZiMzNbtKk/g2Hx6foHRMRNwPeBJ4wYs+WkFmlmZhtm6s9gFpuuf1jeQnlv4OpcdaOkXXPCy0OXYelmZjaPqT+DgfVK1w/pGcwaYAvguIg4L9e/Afgy8GNgNelDAGZmNiUbW7r+/eeZ70TgxIkszszMlmzqt8jMzOyeyQHGzMxa4QBjZmat8H4wZmbWCl/BmJlZK2biY8rL6XN6ZC9jelX1X2PLWqB9Cf1VqfeCnD6/UpEOX70U/b1y7lOW++OrgfmqzojxQ+WqkxcN0MzV208gr2Vce7MVwED/oePmzS5m7II/vMX8cBf6y52/Xb2/nLnp+st0/HPam7ayvRoaP6e/5rZXRZmKyP8HrCMdR+TtBKiIqKhze9Ahot+/aRvbPzq9uqa9znXN+CjO380p45t0+s1xXaTbHyw3dUU6/yWMb1L7N+n/65yyvi7S/3cHyv0+vfmLlPvDY8tytykPje2Xoe7Wc9fSlLuRU+rXg+3dfv8o+9c1dXfue4tivrqee77o9tP306376fzz+Zt26nqg3Dtu0v2P6t+NOfM1eyc0fZt26uD7Z79ittL1m5nZPVsrAWZ993qR9E5J7ynqd5R0jaRt8x4wj8v110q6KM99kaRD2li/mZktXVtXMOu718vbgEMk7ZrLHwL+LiJ+MaLv03MOs+cBR09sxWZmNlHLcYtswb1eImIN8Drgo5KeBdwnIk5YYN5tgNvyvCslrW4aJB0padXk34qZmS1Wqw/512evl4j4iqS/AD4NPHmeaU+TJGAn4I9aWrqZmS1RWwGm2etlJXAeI/Z6AW6QNLzXy0eArSLi8nnmfnpE3Czp4cCpkk6f7NLNzGwSWn0Gw/rv9VLn14Ii4mrgRmA3YB2D78X7wZiZTVmrz2A2dK+XxZD0IOBhwI9IgeZBku4vaQvgoKWv3szMlqL1L1qu514vi3GapC6wGfCGiLgRQNJbgbOBHwKXTWLtZma24VoJMBuy10vudzpw+lDd/sXxynnGHo0/tmxmNjP8TX4zM2uFA4yZmbXC6frNzKwVvoIxM7NWOF2/0/XPcLr++coa+uFOptxP1z+cfn84Xf+o9PtL7D8nXT/U+WthETV11EQu11GnurKdVNdrJ40Z2Z6Pe+2573B7eb7B9PkjysRQ+v16bpmyHL3zzykzt71bpPtP6f9TCn9ImeebLQEG2ynGMzi+Hu5bbg1Q9o2huVJK/26R3r+b65r+TV2/vezf79Pvz9B80WTH743tFusttyaInNq/l15/VDkWaF9K/4D42Hedrt/MzJbPxANMi6n6t5b0MUlXSzpf0nmSXjLp9ZuZ2WS0cQXTVqr+T5CyJ+8cEXsDzwTuN8F1m5nZBLV9i2wiqfpzYst9gDdFvkkcETdFxHty+/75audESZdJOiFnXDYzsylpLcAUqfq/lKvKVP0vAfZt+kbEV0hXO58GXjFiukcBFzTBZYy9gdeQkl/uBOy3xLdgZmZL0EaAaVL130K6hTUnVX9E3ACMStV/zgKp+gGQdFR+znNDUf29iPhJDkLNVgFmZjYlrT2DYbKp+i8BHi2lz25GxDvyObYp+txVHHfZBD+CbWY2S1q7RTbJVP0RcRVwLvD2fOsNSVsCfs5iZjajWv1f/oRT9b8YeC9wlaRbgTXA6ye4XDMzm6CJB5gWU/X/CnjZYsZGxNjzmJnZ8vA3+c3MrBUOMGZm1gqn6zczs1b4CsbMzFrhAGNmZq3Y5L6M+KnqGdHfYkRUHXr7q1RV2kOl07R3lLcMafZXScedTjE+jynH97ZAGZ6/0x/T9E9bgKTblGlLkOhvEaIY2lIken164xUD7VXRrqH2amh8b/sR7wczo/vBqLcfSxB5f5ZmD4503GuPdBxR9KemuQXeHK/vfM13o5v9UepQUVaxZ4rWv51+e9pvpSyrN0fZPrAny/AeLXP2bOmXh/eHGd4bZsG5Bsqi24W6zmurRd3VYDnXzVtu+neHyjVz5xsxf3TTeqo6ei8AdYfKTXveYKaqo1cHUOX+asp1UHWHyrluYL6i/fiTnt/OfjA5bcvFki7M6VuesNQ5i7n/dgPH9dL8m5nZdCwpwEh6EnAQ8JiI2BM4EPjxJBaWjQwwOTOzb++Zmc2wpf6S3h64OSLuAoiImyPiBknXSnqPpO/l1yMAJD1Q0r9LOie/9sv1W0v6lKSL8pXQcyW9m5w4M6ffXynpUkkfBb4PPDRvQHZuvoJ6yxLfi5mZTdBSA8zXSL/or5D0UUlPK9p+FRH7AMcAH8x1HwI+EBGPB55L2kQM4O+AX0bEHvlK6JsR8Qb6m5cdnvs9Evh0ROwdET8CjoqIxwF7Ak+TtOcS34+ZmU3Ikh7yR8Qdkh4LPIWUvPLfJL0hN/9L8ecH8vGBwG7FXmDbSLpPrj+smPe2Maf8UUR8tyj/kaSX5vexPWkvmAuX8p7MzGwylvwpsojokvKAnS7pIuCFTVPZLf9ZAU/Ku1j25N0nF/ONzzuLMQ8DjgQeHxG3SToO2HJD3oOZmU3eUh/yP1LSzkXVXsCP8vEfF3+elY+/RpHwUtJeY+rvmw/X5lT/o2xDCji/lLQd8KwNfR9mZjZ5S30GszVwvKRLJF1IukW1KrdtIels4NXAa3Pdq4DH5Qf5lwAvz/VvB+4raXVO79/sFfNPwIWSThg+cURcAJwPXAwcC/zXEt+LmZlN0FKfwZwH7Dtcn5+xfCQi3jLU/2b6VzZl/R30b62V9a9ncM+X3Yfajxizrv0XXLyZmbXK3yUxM7NWtJIqJiJWtjGvmZltPJyu38zMWuFbZGZm1goHGDMza8Uml65/v4PPiCaTgKqKqlIvRbuUjlUV7SraK/X6AL2x/flENVRWJarifM0cvfFScT7l85Xnp1+WemP65yvLDI6XRoxn6Pz99krNz4BcTu29DPLjyjkxQ3P+1MaItlTXtM9Xbo775Rgq560IxpUZbs/j8/d50/lioCxF739cym1SDM7X69+0N+eLwfYx5aosU8+db7g9inIU/fOxcnr+dNwv0/SP0f1p+vbmrwfLzdhm/ua4aKc835xy6t+kfE/t/fFEnfLg9+YfLDfjKcZHPX58cxx1Mb7ub1dAHttrL8ZGPbdv5PFNe3nuqOvemHSq3F4PtkdTzmuNofl67Uss19257amuWE83qMvx3Zo6p9+P3Lcs13VNrM3zrwtibVCvy+35uNe+Njho3eXtpOtfDEmrJB25HOcyM7PZsFHeIpO0yV15mZltbBYVYCS9IH/7/gJJn5G0o6RTc92pknbI/UbWF/M8SNJ5+fjRkqIYe7Wke0k6WNLZks6X9I2cBqa5CvonSV8DPi3pCEknSTpZ0g8l/ZWk1+Vx35V0v4n+pMzMbL0sGGAkPQo4CjggIh5NSv1yDClt/p7ACcDRufu4egAi4ufAlpK2IWVgPhd4iqQdgZ9HxK+BbwNPjIi9gX8F/qaY4rHAIRHxp7m8O/CnwD7AO4Bf53FnAS9Yr5+EmZlN1GJuNR0AnJjTvBARt+adLP8gt38G+Id8PK6+9B1gP+CpwDuBZwICzsztv01K+789sDnww2Lsl4YyMZ8WEbcDt0v6JXByrr+ItEeMmZlNyWJukS0mlf649lH1Z5KuXnYE/gN4NPBk4Fu5/cPAMRGxB/AyBlPw38mgu4rjuijXbIKfkDMzmyWLCTCnkjb2uj9AfrbxHfobhB1Ouq3FPPWlbwF/BlwZETVwK/Bs+tmQfwu4Ph/PSYBpZmYbhwX/lx8RF0t6B3CGpC4pRf6rgGMl/R/gJuBFufu4+nK+a/P3MJorlkRb8kQAAAZ4SURBVG8Dv13sYrkK+Lyk64HvAg/bwPdmZmZTtMnlIvMXLf1Fy/75/EVLf9HSX7Tc6L9oaWZmmx4HGDMza8Umd4vMzMyWh69gzMysFQ4wZmbWik3uy4gS0XwKqqr6r1ktS9DpDLY3n1zqdAb7N32nNX6htsW8t3H9R61tVP970vlF/5NK1HX/1Va52+1/Smuh/k3fhebvdlO56TtL5x/XNmruUWvz+P74iI3rU2SSVkpaPe11mJnZhpnZADMJTutvZjY9sx5gVkg6Pqf/P1HSUyV9AUDSIZLWSNpc0paSrsn1p0t6p6QzSJmfzcxsCmb9f/iPBP4iIv5L0rGktPx757anAKuBx5Pex9nFuG0j4mnLulIzMxsw6wHmxxHRJMH8LCnX2VWSdiUFm/eT0v536Kf7B/i3ZV2lmZnNMeu3yIa/BRqkQPIsYC3wDVKq/zLdP8xN629mZsts1gPMDnlzM4A/IWVe/hbwGuCsiLgJuD+wC3DxdJZoZmajzPotskuBF0r6R+BK4GOkq5jt6F+xXEjabtk5b8zMZsjMBpiIuBbYbUzzFkW/lw6N27+9VZmZ2WLN+i0yMzPbSDnAmJlZKxxgzMysFd4PxszMWuErGDMza8XMfoqsLdLBkb74D+nPisFypyhXQ+VOMWZU/01zPimVV6zop5/fbLNUXpH/hTXHZbnTSf3GtZflpu98893Tzl9Rw7p1qWLduv5rUuW1a/sp15u2tWtTudsd7N/0XWj8pNdbrqc8X3O8nu05AT11fpXHLm94edXGlq7fzMw2blMLMJLeKunACczzFUnbTmJNZmY2OVO7RRYRfz+heZ49iXnMzGyyJnoFI+nekk6RdIGk1ZL+WNJjJZ0h6TxJX5W0fe57nKTn5eN3S7ok7/vyvqL9Y5JOk3SNpKdJOlbSpZKOK855raQH5OMX5DkukPSZSb43MzNbP5O+gnkmcENE/B6ApN8C/j9wSETcJOmPgXcAf94MkHQ/4FBgl4iIodtd9wUOAJ4DnAzsB7wYOEfSXhHxg2KeRwFHAftFxM15XjMzm5JJB5iLgPdJeg/wZeA2YHfg65Igfezop0NjfgX8BviEpFPyuMbJOehcBNwYERcBSLoYWAn8oOh7AHBiRNwMEBG3Tvi9mZnZephogImIKyQ9Fng28C7g68DFEfGkecask7QP8AzgMOCvSMEC4K78Z10cN+XhtYu5+8eYmdmUTPoZzIOBX0fEZ4H3AU8AHtjs6SJps3wrqxyzNfBbEfEV0j4ve23g6U8F/kjS/fO8vkVmZjZFk75FtgfwXkk1acfJvwTWAUfn5zErgA8yuDnYfYD/kLQl6SrktRty4oi4WNI7gDMkdYHzgSM29I2YmdnSTPoW2VeBr45oeuqIvkcUxX3ma897w+w+pm1lcXw8cPz6rNnMzNrhb/KbmVkrHGDMzKwVTtdvZmat8BWMmZm1wgHGzMxa4QBjZmatcIAxM7NWOMCYmVkrHGDMzKwVDjBmZtYKBxgzM2uFA4yZmbXCAcbMzFrhAGNmZq1wgDEzs1Y4wJiZWSscYMzMrBUOMGZm1goHGDMza4UDjJmZtcIBxszMWuEAY2ZmrXCAMTOzVjjAmJlZKxxgzMysFQ4wZmbWCgcYMzNrhQOMmZm1wgHGzMxa4QBjZmatcIAxM7NWOMCYmVkrHGDMzKwVDjBmZtYKBxgzM2uFA4yZmbXCAcbMzFrhAGNmZq1wgDEzs1Y4wJiZWSscYMzMrBUOMGZm1goHGDMza4UDjJmZtcIBxszMWuEAY2ZmrXCAMTOzVjjAmJlZKxxgzMysFQ4wZmbWCgcYMzNrhQOMmZm1wgHGzMxa4QBjZmatcIAxM7NWOMCYmVkrHGDMzKwVDjBmZtYKBxgzM2uFA4yZmbXCAcbMzFrhAGNmZq1wgDEzs1Y4wJiZWSscYMzMrBUOMGZm1goHGDMza4UDjJmZtcIBxszMWuEAY2ZmrXCAMTOzVjjAmJlZKxxgzMysFQ4wZmbWCgcYMzNrhQOMmZm1wgHGzMxa4QBjZmatcIAxM7NWOMCYmVkrHGDMzKwVDjBmZtYKBxgzM2uFA4yZmbXCAcbMzFrhAGNmZq1wgDEzs1Y4wJiZWSscYMzMrBUOMGZm1goHGDMza4UDjJmZtcIBxszMWuEAY2ZmrXCAMTOzVjjAmJlZKxxgzMysFQ4wZmbWCgcYMzNrhQOMmZm14r8B25z45Q1ZwXYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load http://matplotlib.org/mpl_examples/color/colormaps_reference.py\n",
    "\"\"\"\n",
    "==================\n",
    "Colormap reference\n",
    "==================\n",
    "\n",
    "Reference for colormaps included with Matplotlib.\n",
    "\n",
    "This reference example shows all colormaps included with Matplotlib. Note that\n",
    "any colormap listed here can be reversed by appending \"_r\" (e.g., \"pink_r\").\n",
    "These colormaps are divided into the following categories:\n",
    "\n",
    "Sequential:\n",
    "    These colormaps are approximately monochromatic colormaps varying smoothly\n",
    "    between two color tones---usually from low saturation (e.g. white) to high\n",
    "    saturation (e.g. a bright blue). Sequential colormaps are ideal for\n",
    "    representing most scientific data since they show a clear progression from\n",
    "    low-to-high values.\n",
    "\n",
    "Diverging:\n",
    "    These colormaps have a median value (usually light in color) and vary\n",
    "    smoothly to two different color tones at high and low values. Diverging\n",
    "    colormaps are ideal when your data has a median value that is significant\n",
    "    (e.g.  0, such that positive and negative values are represented by\n",
    "    different colors of the colormap).\n",
    "\n",
    "Qualitative:\n",
    "    These colormaps vary rapidly in color. Qualitative colormaps are useful for\n",
    "    choosing a set of discrete colors. For example::\n",
    "\n",
    "        color_list = plt.cm.Set3(np.linspace(0, 1, 12))\n",
    "\n",
    "    gives a list of RGB colors that are good for plotting a series of lines on\n",
    "    a dark background.\n",
    "\n",
    "Miscellaneous:\n",
    "    Colormaps that don't fit into the categories above.\n",
    "\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Have colormaps separated into categories:\n",
    "# http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "cmaps = [('Perceptually Uniform Sequential', [\n",
    "            'viridis', 'plasma', 'inferno', 'magma']),\n",
    "         ('Sequential', [\n",
    "            'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',\n",
    "            'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',\n",
    "            'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']),\n",
    "         ('Sequential (2)', [\n",
    "            'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',\n",
    "            'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',\n",
    "            'hot', 'afmhot', 'gist_heat', 'copper']),\n",
    "         ('Diverging', [\n",
    "            'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',\n",
    "            'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']),\n",
    "         ('Qualitative', [\n",
    "            'Pastel1', 'Pastel2', 'Paired', 'Accent',\n",
    "            'Dark2', 'Set1', 'Set2', 'Set3',\n",
    "            'tab10', 'tab20', 'tab20b', 'tab20c']),\n",
    "         ('Miscellaneous', [\n",
    "            'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',\n",
    "            'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',\n",
    "            'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]\n",
    "\n",
    "\n",
    "nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)\n",
    "gradient = np.linspace(0, 1, 256)\n",
    "gradient = np.vstack((gradient, gradient))\n",
    "\n",
    "\n",
    "def plot_color_gradients(cmap_category, cmap_list, nrows):\n",
    "    fig, axes = plt.subplots(nrows=nrows)\n",
    "    fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)\n",
    "    axes[0].set_title(cmap_category + ' colormaps', fontsize=14)\n",
    "\n",
    "    for ax, name in zip(axes, cmap_list):\n",
    "        ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))\n",
    "        pos = list(ax.get_position().bounds)\n",
    "        x_text = pos[0] - 0.01\n",
    "        y_text = pos[1] + pos[3]/2.\n",
    "        fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)\n",
    "\n",
    "    # Turn off *all* ticks & spines, not just the ones with colormaps.\n",
    "    for ax in axes:\n",
    "        ax.set_axis_off()\n",
    "\n",
    "\n",
    "for cmap_category, cmap_list in cmaps:\n",
    "    plot_color_gradients(cmap_category, cmap_list, nrows)\n",
    "\n",
    "plt.show()"
   ]
  },
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   "outputs": [],
   "source": []
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