6_data_vis_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.4, 4.8]\n",
      "[4.5, 3.0]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams.update({\"axes.grid\": False})\n",
    "print(plt.rcParams['figure.figsize'])\n",
    "plt.rcParams['figure.figsize'] = (4.5, 3)\n",
    "print(plt.rcParams['figure.figsize'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# ICS 434: DATA SCIENCE FUNDAMENTALS\n",
    "\n",
    "## Plotting and Visualization Using Matplotlib\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting and Visualization Using `Matplotlib`\n",
    "\n",
    "- Making plots is one of the most critical tasks in data science\n",
    "- `Matplotlib` is a desktop package for creating primarily 2D publication-ready plots\n",
    " - Inspired by MATLAB and commands almost equivalent across MATLAB and Matplotlib\n",
    "- `Matplotlib` has spawned many add-on toolkits for data visualization that build on top of `Matplotlib`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Some Popular Plotting Tools\n",
    "\n",
    "* Seaborn: https://seaborn.pydata.org/\n",
    "  * Desktop publishing with best practices to that is based on research on how people best perceive information\n",
    "  * Excellent default parameters\n",
    "\n",
    "* Plotly (https://plot.ly/python/)\n",
    "  * Has an API for Python (as well as other languages)\n",
    "  * The app itself is built on Python and DJANGO\n",
    "  * The same company that created Dash\n",
    "\n",
    "  \n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Some Popular Plotting Tools -- Cont'd\n",
    "\n",
    "\n",
    "\n",
    "* Dash by plotly (https://dash.plot.ly/gallery)  \n",
    "  * Intuitive to use, elegant design\n",
    "  * Support interactive visualization\n",
    "\n",
    "* Bokeh (https://bokeh.pydata.org/en/latest/docs/gallery.html)\n",
    "  * Python interactive visualization for web browsers \n",
    "  \n",
    "* plotnice (aka. ggplot2) (https://plotnine.readthedocs.io/en/stable/index.html)\n",
    "  * `plotnine` is an implementation of a grammar of graphics in Python\n",
    "  * based on ggplot2, the most popular libry used in with the R language\n",
    "  \n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Matplotlib: a Brief Intro\n",
    "\n",
    "* `Matplotlib` can be overwhelming, a good place to start is the gallery (https://matplotlib.org/stable/gallery/index.html) \n",
    "* A multitude of plots to use for different scenarios\n",
    "* `Matplotlib`'s main module is `pyplot`\n",
    "  * Imported using the `plt` alias\n",
    "\n",
    "```python\n",
    "import matplotlib.pyplot as plt\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Matplotlib: Plotting with X and Y axes\n",
    "\n",
    "```python \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [1,2,3,5,6,7,8,9,10]\n",
    "y = [1,2,3,5,6,7,8,9,10]\n",
    "\n",
    "plt.plot(x, y)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11e40e940>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,2,3,5,6,7,8,9,10]\n",
    "y = [1,2,3,5,6,7,8,9,10]\n",
    "\n",
    "plt.scatter(x, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21618cc3c70>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.arange(1, 11)\n",
    "y = [2**i for i in x]\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plot Figure and Axes\n",
    "\n",
    "*  Plots in `Matplotlib` reside within a Figure object.\n",
    "  * A figure contains the overall window where plotting happens.\n",
    "    * Contains the title, axes, labels, ticks, etc.\n",
    "\n",
    "```python\n",
    "    plt.figure()\n",
    "```\n",
    "\n",
    "* Figures are empty by default. Therefore, you need to add an axes (plot) to visualize your data.\n",
    "* To add a plot, you need to determine the geometry and indicate where you want the plot to go.\n",
    "  * This returns the axes of a new subplot. It's an object of type `Axes` or  `AxesSubplot`.\n",
    "\n",
    "```python\n",
    "fig.add_axes(rect)\n",
    "# or \n",
    "fig.add_subplot()\n",
    "```\n",
    "\n",
    "* Therefore, Figure and plot/subplot (axes) are two different things."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](images/fig_axes.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 450x300 with 0 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 450x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ax is of type <class 'matplotlib.axes._axes.Axes'>\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "# add_axes takes a rectangle [x0, y0, width, height]\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "print(f\"ax is of type {type(ax)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x216194d7e80>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "# add_axes takes a rectangle [x0, y0, width, height]\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "ax.plot([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ax is of type <class 'matplotlib.axes._subplots.AxesSubplot'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "print(f\"ax is of type {type(ax)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ax is of type <class 'matplotlib.axes._subplots.AxesSubplot'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "ax.plot([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])\n",
    "print(f\"ax is of type {type(ax)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting Steps\n",
    "\n",
    "* When you issue the command plt.plot(x, y), the following happens\n",
    "  * Matplotlib uses the figure created in a cell or creates one if none exists\n",
    "  * Uses the last axes used or creates one if necessary\n",
    "```python  \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "ax.plot([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])\n",
    "```\n",
    "* Is equivalent to\n",
    "\n",
    "```python\n",
    "plt.plot([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dPYCf3NYHfz+L2fGkHVEZiJik9OwF5q7OY3tx4xq4Mb4TizPS6BcdZnIyaY9UBiJtzOUy+NuXR1n03n5q6pwEB/gx+56BPPL9BK0BMY3KQKQNHT1zgcxVOewoOQvA4N6dWZyRRkKXUJOTSXunMhBpAy6XwVtfHObFjYVcrHcSEujHnHsTmZbeGz+tAfEAKgMRNzt8uobMVbl8eahxDQxJiGRxho1eUVoD4jlUBiJu4nQZvPH5YbI/OEBtvYuOQf48MTqRHw3ppTUgHkdlIOIGJaeqyVyZy64j5wBI7xPF4gwbcZEdTU4m8u1UBiKtyOky+Ov2QyzZVIijwUVokD9z7xvED2+J1xoQj6YyEGklxRXVzF6Zw1dHzwNwW78uLJqSSmxnrQHxfCoDkRZqcLpY9ukhfv/hQeoaXIQFB/DkmEHcPzgOi0VrQLyDykCkBQ6WVzF7RQ45xyoBuGNAV7ImpxLTqYPJyUSaRmUg0gwNThevbCvhpQ+LqHO6CA8J4Ldjk5h6U6zWgHilJr0v7tKlS7HZbERERBAREUF6ejrvv/++u7KJeKQDJ+1M+vPnZH9QSJ3TxYjEaDb/5g5+cLNOC4n3atIyiI2NZdGiRfTr1w+AN998kwkTJvDVV1+RnJzsloAinqLe6WLplq/548dF1DsNIkICeGZcMpNv7KkSEK9nMQzDaMkXiIyMJDs7m5/85CfXdX+73Y7VaqWyspKICP0cV/EOBScqmb0il31ldgBGDurGC5NSiI4IMTmZyNU15fm22dcMnE4nK1asoKamhvT09Kvez+Fw4HA4rggn4i3qGlz86ZNi/vRJMQ0ug04dA1kwPpnxaTFaA+JTmlwGeXl5pKenU1tbS1hYGGvWrCEpKemq98/KymLBggUtCilihvzjlcxakcOBk1UA3JPcjecmphAdrjUgvqfJp4nq6uo4evQo58+fZ9WqVbz22mts3br1qoXwbcsgLi5Op4nEYzkanPzxo2KWbv0ap8sgMjSIBeOTGWvroTUgXqUpp4lafM1g5MiR9O3bl1deeaXVw4m0tdxj55m1IoeD5dUAjEntwYIJyXQJCzY5mUjTtck1g28YhnHFv/xFvFFtvZOXPiri1W0lOF0GUaFBPDcxhftSe5gdTaRNNKkM5s2bx+jRo4mLi6Oqqorly5ezZcsWNm7c6K58Im731dFzzF6ZS3FF4xoYlxbDgvHJRIYGmZxMpO00qQzKy8t56KGHKCsrw2q1YrPZ2LhxI6NGjXJXPhG3qa138vvNB1n2aQkuA7qEBbNwYgr3pnQ3O5pIm2tSGfzlL39xVw6RNrX7yFlmr8il5HQNAJO+15OnxybRWWtA2im9N5G0KxfrnCzZVMhfPzuEYUB0eDDPT0plVFI3s6OJmEplIO3Gl4fOkrkyh8NnLgAw5cZYnh6bhLVjoMnJRMynMhCfd6GugcUbC3nzi8MYBnSPCOGFySmMSNQaEPmGykB82o6SM2SuzOXo2cY18IObY3lyTBLWDloDIpdTGYhPqnE08OLGA7z1xREAelhDyJqcyvCB0SYnE/FMKgPxOZ8XnyZzVS7Hzl0E4IFb4pl3XyLhIVoDIlejMhCfUVVbT9b7B3j770cB6NmpA4umpHJ7/64mJxPxfCoD8QmfFp3iiVV5HD/fuAZ+dGs8T4weRFiw/oiLXA/9TRGvZq+t54V397N8ZykAcZEdeHGyjaH9upicTMS7qAzEa20prGDu6jzKKmsBmJbei8x7EwnVGhBpMv2tEa9TebGehe/sY8XuYwD0iurIi1Ns3NonyuRkIt5LZSBe5eMD5cxdnUe53YHFAo8MTWDWPQPoGKQ/yiItob9B4hUqL9Sz4J0CVu85DkBCl1CyM2zc3DvS5GQivkFlIB5v875y5q3J41RV4xr46W0JzBw1kA5B/mZHE/EZKgPxWOdq6liwoYC1e08A0KdrKNkZadzUq7PJyUR8j8pAPNLG/DKeWlvA6WoHfhb42bA+/GbkAEICtQZE3EFlIB7lTLWDZ9YX8E5uGQD9o8PInprGDXGdzA0m4uNUBuIx3s0t4+l1+ZypqcPfz8Iv7+jDr+/qT3CA1oCIu6kMxHSnqx08vS6f9/JOAjCwWzhLpqaRGms1OZlI+6EyENMYhsGG3DKeWZfPuQv1BPhZ+NXwvkwf0U9rQKSNqQzEFBVVtfx2bT4fFJQDMKhHBNkZNlJ6ag2ImEFlIG3KMAzW7T3B/A0FnP/fNfDoiH78ang/ggL8zI4n0m6pDKTNlNtreXJNHh/urwAgOSaC7Iw0kmIiTE4mIioDcTvDMFi15zjPbijAXttAoL+FX4/ozy+H9yXQX2tAxBOoDMStTlbWMnd1Lp8UngIgtaeVJVPTGNg93ORkInI5lYG4hWEYrNh1jOfe3UdVbQNB/n48Pqo/P7+9DwFaAyIeR2Ugre7E+Ys8sTqPbQcb10BaXCeWZNjo301rQMRTqQyk1RiGwfKdpTz/7n6qHQ0EBfjxr6MG8JPbErQGRDycykBaxbFzF3hiVR7bi08DcGN8JxZnpNEvOszkZCJyPVQG0iIul8HfvjzKovf2U1PnJDjAj9n3DOSR7yfg72cxO56IXCeVgTRb6dkLZK7M5YuSMwAM7t2ZxRlpJHQJNTmZiDSVykCazOUy+I8dR3hx4wEu1DkJCfRjzr2JTEvvjZ/WgIhXUhlIkxw5U8Pslbl8eegsAEMSIlmcYaNXlNaAiDdTGch1cbkM3vj8MIs/OEBtvYuOQf48MTqRHw3ppTUg4gNUBvKdSk5Vk7kyl11HzgGQ3ieKxRk24iI7mpxMRFqLykCuyuky+Ov2QyzZVIijwUVokD9z7xvED2+J1xoQ8TEqA/lWxRXVZK7MYc/R8wDc1q8Li6akEttZa0DEF6kM5AoNThevbT/E7zYfpK7BRVhwAE+NGcQ/DY7DYtEaEPFVKgO5pKi8ilkrc8kpPQ/AHQO6kjU5lZhOHcwNJiJupzIQGpwuXtlWwksfFlHndBEeEsBvxyYx9aZYrQGRdkJl0M4dOGln9opc8o5XAjAiMZoXJqXS3RpicjIRaUsqg3aq3uli6Zav+ePHRdQ7DSJCAnhmXDKTb+ypNSDSDjXpfYWzsrIYPHgw4eHhREdHM3HiRAoLC92VTdxk3wk7E//0Gb/bfJB6p8HIQd34cOYdTNFpIZF2q0llsHXrVqZPn86OHTvYvHkzDQ0N3H333dTU1Lgrn7SiugYXv998kPEvb6fghJ1OHQN56f4bWPbjm4iO0GkhkfbMYhiG0dxffOrUKaKjo9m6dSvDhg371vs4HA4cDsel23a7nbi4OCorK4mIiGjuoaWJ8o9XMmtFDgdOVgFwT3I3npuYQnS4SkDEV9ntdqxW63U937bomkFlZeNFx8jIyKveJysriwULFrTkMNICjgYnL39czJ+3fI3TZRAZGsSC8cmMtfXQKSERuaTZy8AwDCZMmMC5c+f49NNPr3o/LQPz5B47z+wVuRSWN66BMak9WDAhmS5hwSYnE5G20CbL4NFHHyU3N5ft27df837BwcEEB+vJpy3V1jv5w0dFvLKtBKfLICo0iOcmpnBfag+zo4mIh2pWGcyYMYP169ezbds2YmNjWzuTtMBXR88xe2UuxRXVAIxLi2HB+GQiQ4NMTiYinqxJZWAYBjNmzGDNmjVs2bKFhIQEd+WSJqqtd/L7zQdZ9mkJLgO6hAWzcGIK96Z0NzuaiHiBJpXB9OnTefvtt1m3bh3h4eGcPHkSAKvVSocOev8as+w+co7ZK3MoOdX4Lb6TvteTp8cm0VlrQESuU5MuIF/tu09ef/11Hn744ev6Gk25oCHXdrHOyb9tKuQvnx3CMCA6PJjnJ6UyKqmb2dFExAO47QJyC16SIK1s5+GzZK7M5dDpxjUw5cZYnh6bhLVjoMnJRMQb6b2JvMyFugayPyjkjc8PYxjQPSKEFyanMCJRa0BEmk9l4EV2lJwhc2UuR89eAGDqTbE8NTYJawetARFpGZWBF6hxNPDixgO89cURAHpYQ8ianMrwgdEmJxMRX6Ey8HCfF58mc1Uux85dBOCBW+KZd18i4SFaAyLSelQGHqra0UDWe/v529+PAtCzUwcWTUnl9v5dTU4mIr5IZeCBthedZs6qXI6fb1wDP7o1nidGDyIsWL9dIuIeenbxIPbaerLe289/fVkKQFxkB16cbGNovy4mJxMRX6cy8BBbCiuYuzqPsspaAKal9yLz3kRCtQZEpA3omcZklRfrWfjOPlbsPgZAr6iOvDjFxq19okxOJiLticrARB8fKGfu6jzK7Q4sFnh4aG9m3zOQjkH6bRGRtqVnHRNUXqhnwTsFrN5zHICELqEszrAxuPfVf2KciIg7qQza2OZ95Ty5Jo+KqsY18NPbEpg5aiAdgvzNjiYi7ZjKoI2cq6ljwYYC1u49AUCfrqFkZ6RxU6/OJicTEVEZtImN+Sd5am0+p6sd+FngZ8P68JuRAwgJ1BoQEc+gMnCjM9UOnllfwDu5ZQD0jw5jcYaN78VrDYiIZ1EZuMl7eWX8dm0+Z2rq8Pez8Ithffj1Xf21BkTEI6kMWtnpagdPr8vnvbzGHwk6sFs42VNt2GI7mRtMROQaVAatxDAM3skt4+l1+Zy7UI+/n4Xpw/syfUQ/ggO0BkTEs6kMWkFFVS2/XZvPBwXlAAzqEUF2ho2UnlaTk4mIXB+VQQsYhsG6vSeYv6GA8xfqCfCz8OiIfvxqeD+CAvzMjicict1UBs1UYa9l3pp8PtzfuAaSYyLIzkgjKSbC5GQiIk2nMmgiwzBYvec4CzYUYK9tINDfwq9H9OeXw/sS6K81ICLeSWXQBCcra5m7OpdPCk8BkNrTSvZUG4ndtQZExLupDK6DYRis2H2M597ZR1VtA0H+fjw2sj+/GNaHAK0BEfEBKoPvcOL8RZ5Ynce2g41rIC2uE0sybPTvFm5yMhGR1qMyuArDMFi+s5Tn391PtaOBoAA/Zo4awE9vS9AaEBGfozL4FsfOXWDu6jw+LToNwPfiO5GdkUa/6DCTk4mIuIfK4DIul8HbXx4l67391NQ5CQ7wY/Y9A3nk+wn4+1nMjici4jYqg/9VevYCmStz+aLkDAA39+rM4gwbfbpqDYiI72v3ZeByGfzn34+w6P0DXKhzEhLoR+Y9iUwb2ltrQETajXZdBkfO1JC5Mpe/HzoLwC0JkSyeYqN3l1CTk4mItK12WQYul8Ebnx9m8QcHqK130THInzn3JvLQrb3w0xoQkXao3ZXBodM1ZK7MYefhcwCk94nixSk24qM6mpxMRMQ87aYMnC6D1z87RPYHhTgaXIQG+TP3vkH88JZ4rQERaffaRRkUV1STuTKHPUfPA3Bbvy4smpJKbGetARER8PEycLoMXvu0hH/bfJC6BhdhwQE8OWYQ9w+Ow2LRGhAR+YbPlkFReRWzVuaSU3oegGEDupI1OZWenTqYG0xExAP5XBk0OF28sq2Elz4sos7pIjwkgN+OSWLqzbFaAyIiV+FTZVB4sorZK3PIPVYJwJ0Du/LC5FR6WLUGRESuxSfKoN7p4v9t+Zo/fFxEvdMgIiSAZ8YlM/nGnloDIiLXwevLYN8JO7NX5lBwwg7AyEHRPD8plW4RISYnExHxHk1+Y/5t27Yxbtw4YmJisFgsrF271g2xvltdg4t///Ag41/eTsEJO506BvLv/3QDy358s4pARKSJmrwMampqSEtL45FHHmHKlCnuyPSd8o9XMmtFDgdOVgFwd1I3Fk5KITpcJSAi0hxNLoPRo0czevRod2T5To4GJy9/XMyft3yN02XQuWMgCyakMM7WQ9cGRERawO3XDBwOBw6H49Jtu93e7K/1z2/s5LPixp83cF9qdxaMT6FreHCLM4qItHdu/2G+WVlZWK3WSx9xcXHN/lo/GtKLqNAg/vTDG/nzgzepCEREWonFMAyj2b/YYmHNmjVMnDjxqvf5tmUQFxdHZWUlERERTT5mVW094SGBzYkrItKu2O12rFbrdT3fuv00UXBwMMHBrfcveBWBiEjrc/tpIhER8XxNXgbV1dUUFxdfun3o0CH27t1LZGQk8fHxrRpORETaRpPLYNeuXdx5552Xbs+cOROAadOm8cYbb7RaMBERaTtNLoPhw4fTgmvOIiLigXTNQEREVAYiImLCu5Z+c4qpJa9EFhGR7/bN8+z1nNpv8zKoqmp8c7mWvBJZRESuX1VVFVar9Zr3adErkJvD5XJx4sQJwsPDm/zmct+8erm0tLRZr15u7/T4NZ8eu+bTY9d8LX3sDMOgqqqKmJgY/PyufVWgzZeBn58fsbGxLfoaERER+kPVAnr8mk+PXfPpsWu+ljx237UIvqELyCIiojIQEREvK4Pg4GCeeeaZVn3ju/ZEj1/z6bFrPj12zdeWj12bX0AWERHP41XLQERE3ENlICIiKgMREVEZiIgIKgMREcGLymDbtm2MGzeOmJgYLBYLa9euNTuSV8jKymLw4MGEh4cTHR3NxIkTKSwsNDuW11i6dCk2m+3SK0DT09N5//33zY7ldbKysrBYLDz++ONmR/EK8+fPx2KxXPHRvXt3tx7Ta8qgpqaGtLQ0Xn75ZbOjeJWtW7cyffp0duzYwebNm2loaODuu++mpqbG7GheITY2lkWLFrFr1y527drFiBEjmDBhAgUFBWZH8xo7d+7k1VdfxWazmR3FqyQnJ1NWVnbpIy8vz63Ha/P3Jmqu0aNHM3r0aLNjeJ2NGzdecfv1118nOjqa3bt3M2zYMJNSeY9x48Zdcfv5559n6dKl7Nixg+TkZJNSeY/q6moefPBBli1bxsKFC82O41UCAgLcvgYu5zXLQFpHZWUlAJGRkSYn8T5Op5Ply5dTU1NDenq62XG8wvTp0xkzZgwjR440O4rXKSoqIiYmhoSEBO6//35KSkrcejyvWQbScoZhMHPmTG677TZSUlLMjuM18vLySE9Pp7a2lrCwMNasWUNSUpLZsTze8uXL2bNnDzt37jQ7itcZMmQIb731FgMGDKC8vJyFCxcydOhQCgoKiIqKcssxVQbtyKOPPkpubi7bt283O4pXGThwIHv37uX8+fOsWrWKadOmsXXrVhXCNZSWlvLYY4+xadMmQkJCzI7jdS4/JZ6amkp6ejp9+/blzTffZObMmW45psqgnZgxYwbr169n27ZtLf55Eu1NUFAQ/fr1A+Dmm29m586dvPTSS7zyyismJ/Ncu3fvpqKigptuuunS55xOJ9u2bePll1/G4XDg7+9vYkLvEhoaSmpqKkVFRW47hsrAxxmGwYwZM1izZg1btmwhISHB7EhezzAMHA6H2TE82l133fUP3/3yyCOPkJiYyJw5c1QETeRwONi/fz+33367247hNWVQXV1NcXHxpduHDh1i7969REZGEh8fb2IyzzZ9+nTefvtt1q1bR3h4OCdPngQaf/pRhw4dTE7n+ebNm8fo0aOJi4ujqqqK5cuXs2XLln/4Li25Unh4+D9clwoNDSUqKkrXq67DrFmzGDduHPHx8VRUVLBw4ULsdjvTpk1z2zG9pgx27drFnXfeeen2N+fNpk2bxhtvvGFSKs+3dOlSAIYPH37F519//XUefvjhtg/kZcrLy3nooYcoKyvDarVis9nYuHEjo0aNMjua+LBjx47xwAMPcPr0abp27cqtt97Kjh076NWrl9uOqZ9nICIiep2BiIioDEREBJWBiIigMhAREVQGIiKCykBERFAZiIgIKgMREUFlICIiqAxERASVgYiIAP8DIyEV6GvA68QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.plot([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# automatically plot on any cerated figure and axis \n",
    "plt.plot([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])\n",
    "_ = plt.plot([1, 2, 3, 4, 5], [1, 4, 9, 16, 25])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Creating Multiple Subplots\n",
    "\n",
    "* It is possible to draw multiple plots (subplots) in one figure using the `subplots()` or `add_subplot()` functions\n",
    "  * Each subplot has its own set of axes\n",
    "\n",
    "    \n",
    "* Subplot layout is organized in rows and columns\n",
    " * A subplot needs to know where in the row/column layout it belongs\n",
    "\n",
    "```python\n",
    "add_subplot(num_rows, num_cols, position_of_subplot)   \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([1, 2, 3, 4, 5],\n",
       " [1, 2, 3, 4, 5],\n",
       " [1, 4, 9, 16, 25],\n",
       " [1, 8, 27, 64, 125],\n",
       " [1, 16, 81, 256, 625])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample data\n",
    "x = [1, 2, 3, 4, 5]\n",
    "y_1 = [1, 2, 3, 4, 5]\n",
    "y_2 = [i**2 for i in x]\n",
    "y_3 = [i**3 for i in x]\n",
    "y_4 = [i**4 for i in x]\n",
    "\n",
    "x, y_1, y_2, y_3, y_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 450x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating the subplots\n",
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_subplot(2, 2, 1) # equivalent to ax1 = fig.add_subplot(221)\n",
    "ax2 = fig.add_subplot(2, 2, 2) # equivalent to ax2 = fig.add_subplot(222)\n",
    "ax3 = fig.add_subplot(2, 2, 3) # equivalent to ax3 = fig.add_subplot(223)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2161973d970>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using add_subplot\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_subplot(2, 2, 1) # equivalent to ax1 = fig.add_subplot(221)\n",
    "ax2 = fig.add_subplot(2, 2, 2) # equivalent to ax2 = fig.add_subplot(222)\n",
    "ax3 = fig.add_subplot(2, 2, 3) # equivalent to ax3 = fig.add_subplot(223)\n",
    "\n",
    "ax1.plot(x, y_1)\n",
    "ax2.plot(x, y_2)\n",
    "ax3.plot(x, y_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x216197f7df0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax1 = fig.add_subplot(2, 2, 1) # equivalent to ax1 = fig.add_subplot(221)\n",
    "ax2 = fig.add_subplot(2, 2, 4) # equivalent to ax2 = fig.add_subplot(224)\n",
    "\n",
    "ax1.plot(x, y_1) \n",
    "ax2.plot(x, y_3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot uses the last plot used.\n",
    "\n",
    "fig = plt.figure(figsize=(11,2))\n",
    "ax1 = fig.add_subplot(1, 2, 1) # equivalent to ax1 = fig.add_subplot(121)\n",
    "ax2 = fig.add_subplot(1, 2, 2) # equivalent to ax2 = fig.add_subplot(122)\n",
    "_ = plt.plot(np.random.randn(50).cumsum(), 'k--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2161a9c66d0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using subplot()\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(x, y_1)\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(x, y_2)\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(x, y_3)\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.plot(x, y_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Creating an Array of Subplots\n",
    "\n",
    "* Creating an array of subplots is a common task\n",
    "* `matplotlib` includes a function that generates the figure and all the plots using a single function\n",
    "* The plots can share the same x-axis or y-axis\n",
    "* Many other options to control the spacing, margins, figure size, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[<AxesSubplot: > <AxesSubplot: > <AxesSubplot: >]\n",
      " [<AxesSubplot: > <AxesSubplot: > <AxesSubplot: >]]\n",
      "---------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2161ab7ffa0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 3, figsize=(14,6))\n",
    "print(axes)\n",
    "\n",
    "print(\"---------------------\")\n",
    "\n",
    "axes[0, 0].plot(x, y_1)\n",
    "axes[1, 2].plot(x, y_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[<AxesSubplot: > <AxesSubplot: > <AxesSubplot: >]\n",
      " [<AxesSubplot: > <AxesSubplot: > <AxesSubplot: >]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 3, figsize=(14,6))\n",
    "print(axes)\n",
    "\n",
    "axes[0, 0].plot(x, y_1)\n",
    "axes[1, 2].plot(x, y_4);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Basic Plotting Parameters\n",
    "\n",
    "* Plots have various functions to customize them\n",
    "* Options are either:\n",
    "  * plot specific\n",
    "    * E.g., plot size, plot title, x-axis lebel, whitespace, legend, etc. \n",
    "  * line color, line type (e.g.: dashed, width=2), labels, ...\n",
    "\n",
    "```python\n",
    "plt.plot(x, y, color='red', linestyle='--')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2161ae12100>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w2GPA0KFipzzvGmINeAwMIvIORiMwfTpw+TJw8aIIjkcfBaqrla6MfsbAICLlSZJYVPDUKcvjW7aI3kZdnTJ1kQUGBhEpb+1aMQnPlmnTgOBgeeshmxgYRKSsr79uef/tUaOAF1+Utx5qEQODiJRz+rToQdi65NS1q7gk1cHti2qTixgYRKSM998HYmKAo0dtt2dmir0uyGswMIhIXrW1wLx5wNSpQFWV7XOSk4FJk+Sti1rFvh4RyaeoCJg5E/j225bPGTQIWL1atpLIcexhEJHnNTaKEIiNtR8WUVHABx+0vFkSKYqBQUSe9Z//iH0sFi8Wk/JaMn262II1Kkq+2sgpDAwi8pwdO8TA9qeftnxOSAiwfr24I+qGG+SqjFzAMQwicr/aWiAlBXjrLfvnDR8ObN4M9O4tT13UJuxhEJF7HTwIDBliPyxUKuC3vwU+/5xh4UPYwyAi92hsBF59FVi61P5YhcEgljC/+275aiO3YGAQUduVlwMJCUB2tv3zpk0D3nhDzOImn8NLUkTUNjt3AgMG2A+LLl3EAoNZWQwLH8YeBhG55uJFYOFCICPD/nnDhomB7ehoeeoij2EPg4icd+iQ2KfCXlioVGI845//ZFj4CfYwiMhxjY3Aa68BS5YAly61fF5EBLBxIzBmjGylkecxMIjIMRUVQGIisGeP/fOmTgXefBPo1k2Wskg+vCRFRI559VX7YdGlC/Duu8B77zEs/BQDg4gck5oK9O1ru23oUDFh77HHxNgF+SUGBhE5JjgY2LoVUKuvHVOpxHgGB7YDAgODiBwXEwO88or48003iUUF09KAoCBl6yJZcNCbiJzzzDNiDsYTTwDduytdDcmIPQwiEnbtAkpLWz/v6vwKhkXAYWAQBbr6etFreOABYNYsoKFB6YrISzEwiALZkSNi6Y6//lU8P3AAWLFC2ZrIazEwiALVvn0iLL76yvL48uXirieiZhgYRIFqxAixN0VzjY3i0lRNjfw1kVdjYBAFqpAQsY92h2Y3SwYHA8uWiZnbRNdxOjDy8vIQFxeH8PBwqFQq7Nixw6JdkiSkpqYiPDwcwcHBGDNmDI4ePequeonInYYNA37/+2vPBw0CiovFLbOcsU3NOB0YtbW1GDhwINLT0222r169GmvWrEF6ejoKCgqg1+sxbtw4VFdXt7lYIvKARYuAe+4BnnsOyM8Hbr9d6YrIS6kkSZJcfrFKhe3btyM+Ph6A6F2Eh4djwYIFWLx4MQDAbDajZ8+eeOWVV/Dkk09avYfZbIbZbG56bjKZYDAYYDQaERoa6mppRCRJYhxCo2n93MuXgY4dPV8TeR2TyQStVuvQb65bxzBKS0tRUVGB8ePHNx1Tq9UYPXo0vvzyS5uvSUtLg1arbXoYbA3CEZFzKiuBhx4C4uPFIHZrGBbkALcGRkVFBQCgZ8+eFsd79uzZ1Nbc0qVLYTQamx5lZWXuLIko8OzZI9Z8+r//A/bvB/74R6UrIj/hkbukVM0GyyRJsjp2lVqtRmhoqMWDiFxQXw/85jfAhAnA2bPXjv/2t0BhoXJ1kd9wa2Do9XoAsOpNVFZWWvU6iMiNvv4aGD4c+POfrdsaGoCZMzmvgtrMrYERFRUFvV6P7OzspmOXLl1Cbm4uRo4c6c6PIiJADGxnZABDhgCHD7d8nskEfPedfHWRX3J6efOamhqcPHmy6XlpaSlKSkrQrVs39OrVCwsWLMDKlSsRHR2N6OhorFy5Ep07d8bMmTPdWjhRwDt3Dnj8cWDnTvvnPfggsHYtcOON8tRFfsvpwCgsLMTYsWObnqekpAAAEhISsG7dOixatAh1dXV4+umnUVVVheHDh2Pv3r3QOHJrHxE5JjsbmDMHaOFmEgBAp05iwPvppzkJj9yiTfMwPMGZe4KJAo7ZLJbtWLPG/nkDBojtVPv1k6cu8lnO/OZyxz0iX3HsmBi8Limxf96zzwKrVokeBpEbMTCIvJ0kAW+9JW6Zratr+TydDli/XtxWS+QBDAwib/bTT2Jg+8MP7Z/3y1+KgW3evk4exOXNibzVvn1ixra9sFCrgb/8RczqZliQhzEwiLzNpUvA888D48YB5eUtn9e/P1BQACQn8y4okgUvSRF5k+PHgRkzgIMH7Z+XnAy88orY7IhIJgwMIm8gScA77wALFgAXL7Z8nk4HZGaKMQsimTEwiJR2/jwwbx6wfbv98yZMANat41gFKYZjGERK2r8fGDjQflgEBYlFBT/+mGFBimIPg0gJly4BL7wA/OEP4nJUS/r2FTO2Y2Lkq42oBQwMIrl9+62YsV1UZP+8pCQRKBzYJi/BwCCSiySJyXXPPGN/YLtHDzGw/eCD8tVG5ACOYRDJ5S9/EbO27YXF/fcDR44wLMgrMTCI5DJnDmAw2G4LCgJefRXYtQv4eedKIm/DwCCSS9euwMaN1rOy77gD+Pe/xRyMdvy/JHkv/uskktPo0cDSpdeeP/UUUFgobq0l8nIc9CaSW2oqcOgQ8MQTwEMPKV0NkcMYGETuUlUFXLki7nKyp2NHsboskY/hJSmitpIkYM8ecVlp7lz7E/GIfBgDg8hVtbXA8uVAdLRY56msTPQcXn9d6cqIPIKBQeSqoCAgPR347jvL4wsXAl99pUxNRB7EwCByVceOwKOPWh+vrxd7WtTXy18TkQcxMIha0tDQ+jmJibaP63RATY1byyFSGgOD6Hp1dWJ12PvvB6ZMaf38AQOAIUOuPe/eHVizBsjObv1uKSIfw9tqiSRJzLTOzASysgCjURxv107sqR0WZv/1jz8uzpk7F3jgAUCt9nzNRApQSZJ33QNoMpmg1WphNBoRGhqqdDnkz86cEUt1rFsHfPON7XNWrwaef17Wsojk5MxvLi9JUWCprwf+/nexJ7bBACxZ0nJYAKLX4V3/TUWkGF6SIv8nSWKzosxMMT5RVeX4a48dAwoKgP/5H8/VR+QjGBjkvyoqgM2bxSUnV+ZF3H67GJe4+WZ3V0bkkxgY5F8uXRKzrdetE3tLXLni3Ou1WjGHIjFR9CqaL0VOFMAYGOQfDh4UIbF5M3D+vHOvVamAceNEb2LSJO6hTdQCBgb5rnPnrl1yOnTI+ddHR4uexJw5QESEu6sj8jsMDPItly+LS03r1olLT47Mxr6eRgM88ojoTcTG8pITkRMYGOQbjhwRdzlt2iR6Fs5QqYB77hG9iSlTgM6dPVIikb9jYJD3On8e2LJF9CaKi51//S23XLvkFBnp7uqIAo7bJ+6lpqZCpVJZPPR6vbs/hvzdiy+K5Taeeca5sOjSRVxuys0FTp4EXniBYUHkJh7pYfTr1w/79u1ret6+fXtPfAz5s4gIMV7hqDFjRG/iV78CQkI8VRVRQPNIYHTo0MHhXoXZbIbZbG56bjKZPFES+Zpp04BnnxWrx7YkMvLaJadbbpGtNKJA5ZG1pE6cOIHw8HBERUVh+vTpOHXqVIvnpqWlQavVNj0MBoMnSiJvceUKsG9f6+szabW2lxcPDgZmzwb27wdOnQJSUxkWRDJx+2q1n3zyCS5evIg+ffrg7NmzWLFiBb755hscPXoU3bt3tzrfVg/DYDBwtVp/8803wPr1wIYNYpXYvDzgrrvsv2bfPjGhDgBGjRJjE1OnAvx3QeQ2zqxW6/HlzWtra9G7d28sWrQIKSkprZ7P5c39zPffi6U28vMtjz/2GPDuu/Zf29gIvPIK8PDDwK23eqxEokDmVcubd+nSBQMGDMCJEyc8/VHkjcLDAVv/27/3HlBba/+17doBS5cyLIi8hMcDw2w249ixYwhrbdcy8k9BQcDMmdbHa2qA99+Xvx4icpnbA2PhwoXIzc1FaWkp/vWvf2Hq1KkwmUxISEhw90eR0qqrgR9+aP28xETbxz/6yK3lEJFnuT0wTp8+jRkzZuC2227DlClTEBQUhPz8fERy8pR/aGwUdyjNmQPo9eLW19bceScQEyP+rFaLtZx27xb7ZxORz+Ce3uSYU6fEXU7r11v2Kjp0AP7zH0Cns//6994TS31Mnw507erZWonIYc785nItKWpZTQ3wj3+ItZxyc22f09Aglhj/zW/sv9e0aW4vj4jk5fFBb/IxjY0iHObOFZecrq7LZE9mZusT8YjI57GHEegaG8Vtr0VF4rFjh7j85IwjR4Cvvwb69fNIiUTkHRgYgeTKFeD4cbH669WAOHhQXHpyhU4HPPookJDAsCAKAAwMf9XQIJbjKCq6FhAlJa1PlmtNhw5AXJy4VXbiRKBjR3dUS0Q+gIHhDy5fBo4du9ZrKC4W4WBvpVdnDRokQmLmTODGG933vkTkMxgYvurQIeDNN0VAHD4M1Ne7/zN69ABmzRJBMWiQ+9+fiHwKA8NXVVQAGRnuf98ePcQqsrNnAw88IJb2ICICA8N71NeLu42KisQKr6tW2T9/yJC2f6ZOJ97n+kdEBKBStf29icjvMDCUtmEDsGYNcPSoGKi+avFi+zOie/QAevUCfvzRsc8JCxOBMHjwtXAID2c4EJHDGBhKu3hRjEc0V1wM3Huv/dcOHmw7MG666VooXA0IrhZMRG3EwHCnmhoxr+HqbazBwWJg2p6WLi05EhhDhojPuf6S0uDBQM+ertVPRGQHFx90lckkwuH6eQ7Hj1sukdG1q1hwz95ln/p6QKOxvBwFiBVdW1vNtbFRbDJEROQiLj7obkaj5ezo4mLg229bf11VlRjAjopq+ZxOncQs6eaXpX76qfX3Z1gQkYwYGM1VVVmGQ1ER8N13rr9fUZH9wADEzOnbb792WenOO7kEOBF5ncAOjPPnrcOhtNS9n1FUBEydav+c5cvd+5lERB4QOIFx7pzlJaWiIse2F3WVSiV6Dd26ee4ziIhk5J+BcfasZa+huBgoK/Pc57VrB/TtaznHYeBAICTEc59JRCQz/wuMH34Abr7Zc+/fvr0YpG4eDp07e+4ziYi8gP8FRq9eYsC4qqrt79WhA9C/v+UEuJgYMb+CiCjA+F9gqFTix/3TT517XceOwIABlhPgBgwQt70SEZEfBgYgfvDtBUZQkOgpXD9Dun9/rsxKRGSHfwbG4MHX/typkxhjuP6yUr9+3CmOiMhJ/hkYo0cDa9eKcLjjDoYDEZEb+Gdg6PXA3LlKV0FE5Fe4GBERETmEgUFERA5hYBARkUMYGERE5BAGBhEROcTr7pK6ugGgyWRSuBIiIv939bfWkc1XvS4wqqurAQAGg0HhSoiIAkd1dTW0Wq3dc7xuT+/GxkacOXMGGo0GKnt7YbfAZDLBYDCgrKzMu/cE91L8/lzH7851/O5c19bvTpIkVFdXIzw8HO1a2fbZ63oY7dq1Q0RERJvfJzQ0lP/w2oDfn+v43bmO353r2vLdtdazuIqD3kRE5BAGBhEROcTvAkOtVuPFF1+EWq1WuhSfxO/PdfzuXMfvznVyfndeN+hNRETeye96GERE5BkMDCIicggDg4iIHMLAICIihzAwiIjIIX4TGHl5eYiLi0N4eDhUKhV27NihdEk+Iy0tDcOGDYNGo4FOp0N8fDyOHz+udFk+ISMjAzExMU2zbGNjY/HJJ58oXZZPSktLg0qlwoIFC5QuxSekpqZCpVJZPPR6vUc/028Co7a2FgMHDkR6errSpfic3NxcJCUlIT8/H9nZ2WhoaMD48eNRW1urdGleLyIiAqtWrUJhYSEKCwtxzz33YNKkSTh69KjSpfmUgoICvPXWW4iJiVG6FJ/Sr18/lJeXNz2OHDni0c/zurWkXDVx4kRMnDhR6TJ80u7duy2eZ2ZmQqfToaioCHfffbdCVfmGuLg4i+cvv/wyMjIykJ+fj379+ilUlW+pqanBrFmz8Pbbb2PFihVKl+NTOnTo4PFexfX8podB7mM0GgEA3bp1U7gS33LlyhVkZWWhtrYWsbGxSpfjM5KSkvDAAw/gvvvuU7oUn3PixAmEh4cjKioK06dPx6lTpzz6eX7TwyD3kCQJKSkpGDVqFPr37690OT7hyJEjiI2NRX19PUJCQrB9+3b07dtX6bJ8QlZWFoqLi1FQUKB0KT5n+PDh2LBhA/r06YOzZ89ixYoVGDlyJI4ePYru3bt75DMZGGRh/vz5OHz4ML744gulS/EZt912G0pKSnDhwgW8//77SEhIQG5uLkOjFWVlZXj22Wexd+9edOrUSelyfM71l+AHDBiA2NhY9O7dG+vXr0dKSopHPpOBQU2Sk5Oxc+dO5OXluWVPkkARFBSEW2+9FQAwdOhQFBQU4LXXXsObb76pcGXeraioCJWVlRgyZEjTsStXriAvLw/p6ekwm81o3769ghX6li5dumDAgAE4ceKExz6DgUGQJAnJycnYvn07cnJyEBUVpXRJPk2SJJjNZqXL8Hr33nuv1V09c+fOxe23347FixczLJxkNptx7Ngx3HXXXR77DL8JjJqaGpw8ebLpeWlpKUpKStCtWzf06tVLwcq8X1JSErZs2YIPP/wQGo0GFRUVAMQuXMHBwQpX592WLVuGiRMnwmAwoLq6GllZWcjJybG684ysaTQaq3GyLl26oHv37hw/c8DChQsRFxeHXr16obKyEitWrIDJZEJCQoLHPtNvAqOwsBBjx45ten71Gl5CQgLWrVunUFW+ISMjAwAwZswYi+OZmZlITEyUvyAfcvbsWcyePRvl5eXQarWIiYnB7t27MW7cOKVLIz93+vRpzJgxAz/99BNuvPFGjBgxAvn5+YiMjPTYZ3I/DCIicgjnYRARkUMYGERE5BAGBhEROYSBQUREDmFgEBGRQxgYRETkEAYGERE5hIFBREQOYWAQEZFDGBhEROQQBgYRETnk/wGWbfzI6f10zwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [1.9**i for i in x]\n",
    "plt.plot(x, y, color='red', linestyle='-.', linewidth=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Basic Plotting Parameters -- Cont'd\n",
    "\n",
    "- In addition to the data, `plot` accepts a string abbreviating color and line style\n",
    "  - This is a convenience\n",
    "```python\n",
    "    plt.plot(x, y, 'rx')\n",
    "    # or\n",
    "    plt.plot(x, y, 'k--')\n",
    "```\n",
    "\n",
    "* `plt.plot(x, y, 'k--')` can be expressed as `plt.plot(x, y, color='k', linestyle='--')`\n",
    "\n",
    "* You can also specify color using hex code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "\n",
    "y = 1.9**x \n",
    "plt.plot(x, y, 'k--')\n",
    "\n",
    "y = 30*x + 4\n",
    "plt.plot(x, y, 'rx')\n",
    "\n",
    "y = 40*x + 4\n",
    "plt.plot(x, y, color='#feb24c');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plot Legend\n",
    "\n",
    "* Adding legends is critical for identifying plot elements\n",
    "* The easiest way to add a legend is to pass a label when adding each  element and calling the `legend()` function on the plot\n",
    "  * The label can be a `latex` equation\n",
    "* The legend is placed on the plot in a way that interferes the least with the plot\n",
    "  * You can change the location of the legend manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "x = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "y = [1.9**i for i in x]\n",
    "plt.plot(x, y, 'k--', label=\"$1.9^x$\")\n",
    "\n",
    "y = [30*i + 4 for i in x]\n",
    "plt.plot(x, y, 'rx', label=\"_no_legend_\")\n",
    "\n",
    "y = [40*i + 4 for i in x]\n",
    "plt.plot(x, y, color='#feb24c', label=\"$40 \\cdot x + 4$\")\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plot Labels\n",
    "\n",
    "* The minimum required labels to describe a plot are meaningful `x` and `y` label, and a plot title\n",
    "* Axes can have a title, and so does the figure\n",
    "* Title and labels can be customized\n",
    "  * Change font type and size, color, etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,4))\n",
    "ax = fig.add_subplot()\n",
    "_ = ax.plot([1, 2, 3, 4, 5], [1, 4, 9, 16, 25])\n",
    "\n",
    "fig.suptitle('This a generic figure title')\n",
    "ax.set_title(\"This is some plot title\")\n",
    "ax.set_xlabel(\"A range of values\")\n",
    "\n",
    "# can you guess why I am using _ = here?\n",
    "_ = ax.set_ylabel(\"Some other range of values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Changing Style Via Presets\n",
    "\n",
    "* Matplotlib provides various style presets to improve the look and feel of the plots\n",
    "  * The presets control the fonts, the colors, borders and line widths, etc.\n",
    "  * See https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\n",
    "!~[](https://www.dropbox.com/s/h8fcd13v06bkhyf/styles.png?dl=1)\n",
    "\n",
    "```python\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Changing Style Via Presets\n",
    "\n",
    "* Matplotlib provides various style presets to improve the look and feel of the plots\n",
    "  * The presets control the fonts, the colors, borders and line widths, etc.\n",
    "  * See https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\n",
    "\n",
    "```python\n",
    "plt.style.use('seaborn-v0_8-white')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-v0_8-white')\n",
    "fig, axes = plt.subplots(2, 3, sharex=True, sharey=True, figsize=(8,3))\n",
    "\n",
    "axes[0, 0].plot(x, y_3)\n",
    "axes[1, 2].plot(x, y_4)\n",
    "\n",
    "# Do not put any whitespace between the subplots\n",
    "# useful when sharing x and/or y axis\n",
    "# used when it makes sense\n",
    "plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Anatomy of a Matplotlib Figure\n",
    "* The features of a matplotlib figure  (https://matplotlib.org/stable/gallery/showcase/anatomy.html#)\n",
    "<img src=\"https://matplotlib.org/stable/_images/sphx_glr_anatomy_001.png\" width=\"550\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}