{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting with `matplotlib`\n",
    "\n",
    "- [Download the lecture notes](https://philchodrow.github.io/PIC16A/content/np_plt/plt_1.ipynb). \n",
    "\n",
    "In this lecture, we'll introduce the `matplotlib` package for data visualization. `matplotlib` is second core library for computational and data science in Python. It works very well with `numpy`. \n",
    "\n",
    "In this course, we'll only make use of tools in the `matplotlib.pyplot` module. The standard way to import these tools is: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've seen a few examples of using `matplotlib` functions before, e.g. when visualizing random walks. In these cases, we called `plt.plot()` and it \"just worked.\" In order to obtain finer-grained control over our visualizations, we are going to start with the \"object-oriented interface\" to `matplotlib`. Here are the main ingredients: \n",
    "\n",
    "1. A *figure* is the overall space in which visualizations may be created. \n",
    "2. An *axis* is a single visualization (i.e. \"a plot\"). Figures can contain one or more axes. \n",
    "\n",
    "<figure class=\"image\" style=\"width:80%\">\n",
    "  <img src=\"https://2.bp.blogspot.com/-F__I5bCe3uI/XRS1Y3gdP9I/AAAAAAAAHxk/plElt8VRYRMp42BuHTe9wkD29hZaPvo-ACLcBGAs/s1600/figure_axes.PNG\" alt=\"Four blank axes (plots) with gridlines, arranged in a 2x2 grid. The four axes form a single figure\">\n",
    "  <figcaption><i>Image from the <a href = \"https://www.listendata.com/2019/06/matplotlib-tutorial-learn-plot-python.html\"> ListenData</a> matplotlib tutorial.  </i></figcaption>\n",
    "</figure>\n",
    "\n",
    "The basic workflow for `matplotlib` figures is:\n",
    "\n",
    "1. Create a figure. \n",
    "2. Add one or more axes. \n",
    "3. Add data markers (i.e. \"the plot\") to the axes. \n",
    "4. Add annotation (axis labels, titles, ticks, legends)\n",
    "5. Save the figure. \n",
    "\n",
    "Let's work through these stages. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Create a Figure and 2. Add Axes\n",
    "\n",
    "Unless you need something very customized, it's easiest to do these two steps simultaneously, using `plt.subplots()`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "# create a single figure with a single axis (1 row x 1 column)\n",
    "fig, ax = plt.subplots(1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# two plots side by side, using figsize to stretch \n",
    "# them out to a better aspect ratio\n",
    "fig, ax = plt.subplots(1, 2, figsize = (5, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "# 2x2 grid\n",
    "fig, ax = plt.subplots(2, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a moment to notice what kind of object `ax` is: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a `numpy` array! It's shape is: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means, for example, that if we want the axis in the first row and second column, we can access it like this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e2f97f1f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax[0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Data Markers\n",
    "\n",
    "To add data markings, we need: \n",
    "\n",
    "1. Data.\n",
    "2. A choice of how to visualize that data. \n",
    "3. An axis on which to put the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize = (7, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-efc66d13071f>:5: RuntimeWarning: divide by zero encountered in log\n",
      "  z = np.log(x)\n"
     ]
    }
   ],
   "source": [
    "# something to plot\n",
    "\n",
    "x = np.linspace(0, 2*np.pi, 21)\n",
    "y = np.sin(x)\n",
    "z = np.log(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax[0].scatter(x, y)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax[1].plot(x,z, label = \"#bestLogarithm\")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add as many layers as you want: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax[1].scatter(x, z)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annotations\n",
    "\n",
    "Don't forget to label your axes!! We can also add axis titles and an overall figure title. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax[0].set(xlabel = \"x\",\n",
    "          ylabel = \"y\",\n",
    "          title = \"plot 1\")\n",
    "ax[1].set(xlabel = \"x\",\n",
    "          title = \"plot 2\")\n",
    "fig.suptitle(\"My awesome plots\")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When layering similar kinds of data markings, `matplotlib` will usually change the color each time. If you `label` your markings, then you'll be able to tell them apart with a `legend()`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax[0].scatter(x, np.cos(x))\n",
    "ax[1].scatter(x, np.exp(x/3), label = \"#bestExponential\")\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax[1].legend()\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the Figure\n",
    "\n",
    "Once you're happy with how the figure looks, you can save it in one of several file formats using the `savefig()` method. PNG is usually a good choice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"my_awesome_plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the basic components in place to create figures with multiple axes, populate them with data markings, annotate them, and save the results. In the next lecture, we'll look at plotting 2d data, including mathematical functions and images. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}