mrm8488/an-inquiry-into-matplotlib-figures.ipynb

## an-inquiry-into-matplotlib-figures.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False\n",
    "# Speed up tab autocompletion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib has many styles available, we can see the available options using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-ticks',\n",
       " 'fivethirtyeight',\n",
       " 'seaborn-whitegrid',\n",
       " 'classic',\n",
       " '_classic_test',\n",
       " 'fast',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-pastel',\n",
       " 'grayscale',\n",
       " 'seaborn-notebook',\n",
       " 'ggplot',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-muted',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'seaborn-paper',\n",
       " 'bmh',\n",
       " 'tableau-colorblind10',\n",
       " 'seaborn-white',\n",
       " 'dark_background',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-deep']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall use `seaborn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration\n",
    "Going into `plt.subplots` documentation (hit `Shift+Tab+Tab`) reveals some of the other matplotlib internals that it uses in order to give us what we asked for. \n",
    "\n",
    "These include :<br>\n",
    "1. `plt.subplot`\n",
    "3. `plt.figure`\n",
    "3. `mpl.figure.Figure`\n",
    "4. `mpl.figure.Figure.add_subplot`\n",
    "5. `mpl.gridspec.GridSpec`\n",
    "6. `mpl.axes.Axes`\n",
    "\n",
    "Let's try and figure out what these functions / classes do."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recreating `plt.subplots` with basic matplotlib functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '2x2 Grid')"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is what we need to recreate. We'll try and get a bit more creative though\n",
    "fig, ax = plt.subplots(2,2)\n",
    "fig.suptitle(\"2x2 Grid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deviating..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I didn't know this was possible:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = mpl.figure.Figure();\n",
    "\n",
    "fig.suptitle(\"Exploring a Figure\")\n",
    "\n",
    "ax1 = mpl.axes.Axes(fig=fig, rect=[0,0,0.8,0.8], fc='red')\n",
    "ax2 = mpl.axes.Axes(fig=fig, rect=[0.2, 0.2, 0.4, 0.4], fc='blue')\n",
    "# rect : [left, bottom, width, height] : specifies position and size of an Axes inside of a Figure\n",
    "\n",
    "fig.add_axes(ax1)\n",
    "fig.add_axes(ax2)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*That means you can put random axes at random places inside of a Figure. Hmm. Interesting.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recreating our subplots literally from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An `Axes` in matplotlib is simply a place where you can draw plots. Think of it as a page in a notebook (where the figure is the notebook) except the fact that this page can be of various sizes and can be placed at various positions.\n",
    "\n",
    "A `Figure` simply holds a given number of axes at a time\n",
    "\n",
    "You can create a Figure (Notebook hard-cover) and Axes (pages) separately and then decide where to put each `Axes` in the `Figure`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 4 Axes>"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = mpl.figure.Figure(); fig\n",
    "\n",
    "fig.suptitle(\"Recreating plt.subplots(2, 2)\")\n",
    "\n",
    "ax1 = mpl.axes.Axes(fig=fig, rect=[0,0,0.42,0.42])\n",
    "ax2 = mpl.axes.Axes(fig=fig, rect=[0, 0.5, 0.42, 0.42])\n",
    "ax3 = mpl.axes.Axes(fig=fig, rect=[0.5,0,0.42,0.42])\n",
    "ax4 = mpl.axes.Axes(fig=fig, rect=[0.5, 0.5, 0.42, 0.42])\n",
    "# rect : [left, bottom, width, height] : specifies position and size of an Axes inside of a Figure\n",
    "\n",
    "fig.add_axes(ax1)\n",
    "fig.add_axes(ax2)\n",
    "fig.add_axes(ax3)\n",
    "fig.add_axes(ax4)\n",
    "\n",
    "fig\n",
    "\n",
    "# That means you can put random axes at random places inside of a Figure. Hmm. Interesting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using gridspec.GridSpec\n",
    "Docs : https://matplotlib.org/api/_as_gen/matplotlib.gridspec.GridSpec.html#matplotlib.gridspec.GridSpec\n",
    "\n",
    "GridSpec objects allow us fine-grained control over how our plot is exactly divided into subplots.<br>\n",
    "You can essentially decide how your plot will be divided into subplots and what their relative sizes will be using a GridSpec.\n",
    "\n",
    "Using our previous analogy, this helps us restrict (or pre-decide) where and how our pages will be fit inside of any notebook they go into. Or look at it another way, we can have each page say how it wants to be placed no matter what notebook you put them into by using `GridSpec`\n",
    "\n",
    "Lets see how you can use this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(576x396)\n",
      "Figure(576x396)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# But first, let's get this over with\n",
    "fig1 = plt.figure();\n",
    "fig2 = mpl.figure.Figure(); \n",
    "\n",
    "print(fig1)\n",
    "print(fig2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It seems as if both fig1 and fig2 give back identical objects**\n",
    "\n",
    "So, we'll switch to using just `plt.figure()` instead of `mpl.figure.Figure()` to save keystrokes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "def annotate_axes(fig):\n",
    "    \"\"\"Taken from https://matplotlib.org/gallery/userdemo/demo_gridspec03.html#sphx-glr-gallery-userdemo-demo-gridspec03-py\n",
    "       takes a figure and puts an 'axN' label in the center of each Axes\n",
    "    \"\"\"\n",
    "    for i, ax in enumerate(fig.axes):\n",
    "        ax.text(0.5, 0.5, \"ax%d\" % (i+1), va=\"center\", ha=\"center\")\n",
    "        ax.tick_params(labelbottom=False, labelleft=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x396 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "# We will try and vary axis sizes here just to see what happens\n",
    "# Look at the resulting figure and try and figure out what's going on with each parameter\n",
    "gs = mpl.gridspec.GridSpec(nrows=2, ncols=2, width_ratios=[1, 2], height_ratios=[4, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can pass `GridSpec` objects to a `Figure` to create subplots in your desired sizes and proportions like so :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 4 Axes>"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.clear()\n",
    "ax1, ax2, ax3, ax4 = [fig.add_subplot(gs[0]),\n",
    "                     fig.add_subplot(gs[1]),\n",
    "                     fig.add_subplot(gs[2]),\n",
    "                     fig.add_subplot(gs[3])]\n",
    "\n",
    "annotate_axes(fig)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Doing the same thing in a simpler way\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_gs_to_fig(fig, gs): \n",
    "    for g in gs: fig.add_subplot(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 4 Axes>"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.clear()\n",
    "add_gs_to_fig(fig, gs)\n",
    "annotate_axes(fig)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A very weird observation: (which gives us yet more clarity)\n",
    "Notice how after each print operation, different addresses get printed for each `gs` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.gridspec.SubplotSpec at 0x1a2d3fd750>,\n",
       " <matplotlib.gridspec.SubplotSpec at 0x1a2d3fde90>,\n",
       " <matplotlib.gridspec.SubplotSpec at 0x1a2c056450>,\n",
       " <matplotlib.gridspec.SubplotSpec at 0x1a2c056750>)"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs[0], gs[1], gs[2], gs[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.gridspec.SubplotSpec at 0x1a2d3e7550>,\n",
       " <matplotlib.gridspec.SubplotSpec at 0x1a2d3e7750>,\n",
       " <matplotlib.gridspec.SubplotSpec at 0x1a2d3e7dd0>,\n",
       " <matplotlib.gridspec.SubplotSpec at 0x1a2d3e7310>)"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs[0], gs[1], gs[2], gs[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<matplotlib.gridspec.SubplotSpec object at 0x1a2d3ded10> <matplotlib.gridspec.SubplotSpec object at 0x1a2c063650> <matplotlib.gridspec.SubplotSpec object at 0x1a2c0632d0> <matplotlib.gridspec.SubplotSpec object at 0x1a2c063f90>\n"
     ]
    }
   ],
   "source": [
    "print(gs[0,0], gs[0,1], gs[1, 0], gs[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<matplotlib.gridspec.SubplotSpec object at 0x1a2d3b94d0> <matplotlib.gridspec.SubplotSpec object at 0x1a2d3b9c10> <matplotlib.gridspec.SubplotSpec object at 0x1a2d3b9fd0> <matplotlib.gridspec.SubplotSpec object at 0x1a2d3b9d90>\n"
     ]
    }
   ],
   "source": [
    "print(gs[0,0], gs[0,1], gs[1, 0], gs[1, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lets understand why this happens:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Notice how a group of `gs` objects indexed into at the same time also produces just one object instead of multiple objects*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.gridspec.SubplotSpec at 0x1a2d707210>,\n",
       " <matplotlib.gridspec.SubplotSpec at 0x1a2c063c10>)"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs[:,:], gs[:, 0]\n",
    "# both output just one object each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets try another `gs` object, this time a little more crowded\n",
    "# I chose the ratios randomly\n",
    "gs = mpl.gridspec.GridSpec(nrows=3, ncols=3, width_ratios=[1, 2, 1], height_ratios=[4, 1, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now notice how we can create different kinds of plots **while using the same `GridSpec` object** using this knowledge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(gs[:2, 0])\n",
    "ax2 = fig.add_subplot(gs[2, 0])\n",
    "ax3 = fig.add_subplot(gs[:, 1:])\n",
    "annotate_axes(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 9 Axes>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.clear()\n",
    "add_gs_to_fig(fig, gs)\n",
    "annotate_axes(fig)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This ability to create different grid variations that `GridSpec` provides is probably the reason for that anomaly we just saw.\n",
    "\n",
    "It creates new objects everytime you index into it because it will be very troublesome to store all permutations of grid-spec objects into one group in memory (try and count permutations for a `GridSpec` of 10x10 and you'll know why)\n",
    "\n",
    "---\n",
    "## Now let's finally create `plt.subplots(2,2)` once again using GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yayy\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "gs = mpl.gridspec.GridSpec(nrows=2, ncols=2)\n",
    "add_gs_to_fig(fig, gs)\n",
    "annotate_axes(fig)\n",
    "fig.suptitle(\"We're done!\")\n",
    "print(\"yayy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A few unrelated (maybe) words ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I read a tweet by someone cursing matplotlib (for its API) a while ago and decided to finally understand what the hell is going on with these figures and axes and subplots and what not inside of this library. So I sat by myself and wrote the only thing I ever write when using matplotlib : `fig, ax = plt.subplots(a, b)` and made a commitment to get to the depth of what's going on under the hood here.\n",
    "So I opened up the documentation for this function, made a list of all the other important seeming parts of the library that were mentioned here and went into the official docs for each, while also still tinkering with some other parts and functionalities that were not explicitly mentioned in the official docs.\n",
    "The result of this exercise is: \n",
    "1. This notebook\n",
    "2. Me (and hopefully, anyone reading this) having a much more fundamental understanding of the most important components of a very important python visualization library\n",
    "\n",
    "This makes me reflect on how sometimes it is very important to take a few hours off from your day and just dive deeply into something with an intention to completely take apart as many layers and abstractions as you can. In the end you come out feeling much more powerful and capable than what you were when you decided to dive deep.\n",
    "\n",
    "Anyways, I hope you learned something from this notebook.\n",
    "\n",
    "To best absorb the contents of this notebook, I would recommend that you try and replicate each part of the notebook without looking at it again (barring a few sneak-peeks maybe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}