Matplotlib-Multiple-Subplots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Subplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Matplotlib supports *subplots* (groups of smaller axes that can exist together within a single figure.) Subplots might be insets, grids of plots, or other more complicated layouts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-white')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plt.axes: Subplots by Hand\n",
    "\n",
    "- The most basic method of creating an axes is to use the ``plt.axes`` function. This creates a standard axes object that fills the entire figure.\n",
    "- ``plt.axes`` also takes an optional argument that is a list of four numbers representing ``[left, bottom, width, height]`` in the figure coordinate system, which ranges from 0 (bottom left) to 1 (top right) of the figure.\n",
    "- For example, we might create an inset axes at the top-right corner of another axes by setting the *x* and *y* position to 0.65 (that is, starting at 65% of the width and 65% of the height of the figure) and the *x* and *y* extents to 0.2 (that is, the size of the axes is 20% of the width and 20% of the height of the figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1AoGAXC6X2traFAqFlJeXp02bNukvf/mLhoaG9NFHH0mSnnnmGfn9/rQPDgCYLaVz6rt3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\nOhgIF6mizgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f632c60b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.axes()  # standard axes\n",
    "ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The object-oriented equivalent of this command is ``fig.add_axes()``. Let's use this to create two vertically stacked axes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MpREBeDtb8vzWs2SquK9zU/v8SCa7z1zj5X7eDApwlTqOoMNE8RfUjplRQzu4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\ndJ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63081a1b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4], xticklabels=[], ylim=(-1.2, 1.2))\n",
    "ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4],                 ylim=(-1.2, 1.2))\n",
    "\n",
    "x = np.linspace(0, 10)\n",
    "ax1.plot(np.sin(x))\n",
    "ax2.plot(np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We now have two axes (the top with no tick labels) that are just touching: the bottom of the upper panel (at position 0.5) matches the top of the lower panel (at position 0.1 + 0.4)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plt.subplot: Simple Grids of Subplots\n",
    "\n",
    "- Matplotlib has several convenience routines to create aligned columns or rows of subplots.\n",
    "- The simplest is ``plt.subplot()``, which creates a single subplot within a grid. It takes three integer arguments - #rows, #columns, & the index of the plot to be created in this scheme, which runs from the upper left to the bottom right:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xu7N371785Cc/ccsNGk9mBIC5OpnrsmXLsGzZMpv92bdvH6ZOnYpBgwbZbGsv5mrJVZ/X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6314048e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1, 7):\n",
    "    plt.subplot(2, 3, i)\n",
    "    plt.text(0.5, 0.5, str((2, 3, i)), fontsize=16, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``plt.subplots_adjust`` can adjust the spacing between these plots. The following code uses the equivalent object-oriented command, ``fig.add_subplot()``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LX/xCHDlyxGL+n/70J3H9+nWLmp2Vt0svcax2n3zyCS5duoTly5cr83bu3Ik/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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6307f25fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "for i in range(1, 7):\n",
    "    ax = fig.add_subplot(2, 3, i)\n",
    "    ax.text(0.5, 0.5, str((2, 3, i)), fontsize=16, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We used the ``hspace`` and ``wspace`` arguments of ``plt.subplots_adjust``, which specify the spacing along the height and width of the figure, in units of the subplot size (in this case, the space is 40% of the subplot width and height)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plt.subplots: The Whole Grid in One Go\n",
    "\n",
    "- ``plt.subplots()`` creates a full grid of subplots in a single line, returning them in a NumPy array. The arguments are the #rows and #columns, along with optional keywords ``sharex`` and ``sharey``, which allow you to specify the relationships between different axes.\n",
    "- Here we'll create a $2 \\times 3$ grid of subplots, where all axes in the same row share their y-axis scale, and all axes in the same column share their x-axis scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1lPLy8iRJe/bsUXV1tfbs2aMbN27o/fff15dffpnGsgEAq+Ea9l6vV7FYzBlPTEzI4/Es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\n7AHAAIQ9ABiAsAcAAxD2AGCA/wPA6/UurTTCywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6307f41080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- By specifying ``sharex`` and ``sharey`` we've automatically removed inner labels on the grid to make the plot cleaner.\n",
    "- The resulting grid of axes instances is returned within a NumPy array, allowing for convenient specification of the desired axes using standard array indexing notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZMwdvvfUWVq9ebfNjcr6+vti/f79D36nk5+dj3LhxWLNmzYjPQUNjrvenschV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\n015eSK2sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6307f41080>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axes are in a two-dimensional array, indexed by [row, col]\n",
    "for i in range(2):\n",
    "    for j in range(3):\n",
    "        ax[i, j].text(0.5, 0.5, str((i, j)),\n",
    "                      fontsize=16, ha='center')\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``plt.subplots()`` is more consistent with Python's conventional 0-based indexing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plt.GridSpec: More Complicated Arrangements\n",
    "\n",
    "- To go beyond a regular grid to subplots that span multiple rows and columns, ``plt.GridSpec()`` is the best tool.\n",
    "- For example, a gridspec for a grid of two rows and three columns with some specified width and height space looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From this we can specify subplot locations and extents using Python slicing syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VCwDbt2/Hu+++i7q6OjidTjQ3N091iSGb6td3uqy12hoDwM2bN7Fp0ybs3r17RDhMtWC1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\nMOyJiATAsCciEsD/AK6vrlKyKR9BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6307caf588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(grid[0, 0])\n",
    "plt.subplot(grid[0, 1:])\n",
    "plt.subplot(grid[1, :2])\n",
    "plt.subplot(grid[1, 2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is often used to create multi-axes histogram plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nh0wmg0KhEMFOOp1uVengWk9bOmdjy3g+nycajXL9+nWKxSJ9fX243W4++OAD6vU6drsd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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6307d9d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create some normally distributed data\n",
    "mean = [0, 0]\n",
    "cov = [[1, 1], [1, 2]]\n",
    "x, y = np.random.multivariate_normal(mean, cov, 3000).T\n",
    "\n",
    "# Set up the axes with gridspec\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "grid = plt.GridSpec(4, 4, hspace=0.2, wspace=0.2)\n",
    "main_ax = fig.add_subplot(grid[:-1, 1:])\n",
    "y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\n",
    "x_hist = fig.add_subplot(grid[-1, 1:], yticklabels=[], sharex=main_ax)\n",
    "\n",
    "# scatter points on the main axes\n",
    "main_ax.plot(x, y, 'ok', markersize=3, alpha=0.2)\n",
    "\n",
    "# histogram on the attached axes\n",
    "x_hist.hist(x, 40, histtype='stepfilled',\n",
    "            orientation='vertical', color='gray')\n",
    "x_hist.invert_yaxis()\n",
    "\n",
    "y_hist.hist(y, 40, histtype='stepfilled',\n",
    "            orientation='horizontal', color='gray')\n",
    "y_hist.invert_xaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This type of distribution plotted alongside its margins is common enough that it has its own plotting API in the Seaborn package; see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb) for more details."
   ]
  }
 ],
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