tutorials/python-vis_all/passing_axis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Passing Axis to Functions\n",
    "\n",
    "This notebook highlights the power of passing individual axis to functions.  We show two functions (`plot_mass_frac` and `plot_tau`) that accept an optional parameter `passed_ax`.  If a `matplotlib` axis is specified here, the plotting will be done on this axis and returned.  This is super useful if you want to make a single Figure with multiple combined panels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's adjust some of the basic plotting parameters (ala the `Dos-n-Donts_py` notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fontsize=18\n",
    "plt.rc('text',usetex=True)\n",
    "plt.rc('xtick',labelsize=fontsize)\n",
    "plt.rc('ytick',labelsize=fontsize)\n",
    "plt.rc('axes',labelsize=fontsize)\n",
    "plt.rc('font',**{'family': 'serif','serif': ['Times']})\n",
    "axis_width = 2\n",
    "tickwidth = 2\n",
    "major_ticklength = 6\n",
    "minor_ticklength = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I couldn't do a notebook about plotting and not inject some of my own functions that I use to make my plots look B-E-A-utiful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def adjust_axis(ax, axis_width, tickwidth, major_ticklength,\n",
    "                minor_ticklength):\n",
    "    \"\"\"\n",
    "    Adjusts the tick properties of the axis.  Sets the tick lengths, widths and\n",
    "    also ensures that the ticks are pointing inwards.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    ax : ``matplotlib`` axis. \n",
    "        The axis we're adjusting.\n",
    "\n",
    "    axis_width, tickwidth : Floats.\n",
    "        The desired width (or thickness) of the axis and ticks.\n",
    "\n",
    "    major_ticklength, minor_ticklength : Floats.\n",
    "        The desired length of the major and minor ticks.\n",
    "\n",
    "    Returns\n",
    "    ---------\n",
    "\n",
    "    ax : ``matplotlib`` axis. \n",
    "        The axis with the properties adjusted.\n",
    "    \"\"\"\n",
    "\n",
    "    for axis in ['top','bottom','left','right']:\n",
    "        ax.spines[axis].set_linewidth(axis_width)\n",
    "\n",
    "    ax.tick_params(which = 'both', direction='in',\n",
    "                   width = tickwidth)\n",
    "    ax.tick_params(which = 'major',\n",
    "                   length = major_ticklength)\n",
    "    ax.tick_params(which = 'minor',\n",
    "                   length = minor_ticklength)\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to generate some data that we will plot.  Normally this would be some actual, useful data but for now let's just generate some junk stuff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_data(num_models, z):\n",
    "    \"\"\"\n",
    "    Generates some data for plotting a number of models.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    num_models : Integer.\n",
    "        The number of models we will be plotting.\n",
    "\n",
    "    z : List of floats. Length is ``num_models``.\n",
    "        The redshift range we're generating data for.\n",
    "\n",
    "    Returns\n",
    "    ---------\n",
    "\n",
    "    mass_frac_allmodels, tau_allmodels : 2D nested list of floats. Outer length is ``num_models``,\n",
    "                                         inner length is ``len(z)``.\n",
    "        The generated data for the two variables we are plotting. Currently just junk data.  \n",
    "    \"\"\"\n",
    "    \n",
    "    mass_frac_allmodels = []\n",
    "    tau_allmodels = []\n",
    "\n",
    "    for model_number in range(num_models):\n",
    "        mass_frac_allmodels.append([])\n",
    "        tau_allmodels.append([])\n",
    "        for snapnum, redshift in enumerate(z):\n",
    "            mass_frac_allmodels[model_number].append(snapnum + model_number*2)\n",
    "            tau_allmodels[model_number].append(snapnum*(model_number+1)**2)\n",
    "\n",
    "    return mass_frac_allmodels, tau_allmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_models = 2\n",
    "z = np.arange(6, 15, 0.5)\n",
    "model_tags = [\"Data1\", \"Data2\"]\n",
    "\n",
    "mass_frac_allmodels, tau_allmodels = get_data(num_models, z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have our data let's do some plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_mass_frac(z, mass_frac_allmodels, model_tags, passed_ax=None):\n",
    "    \"\"\"\n",
    "    Plots the evolution of the mass fraction.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    z : List of floats. Length is the number of models.\n",
    "        The redshift range we're plotting over.\n",
    "        \n",
    "    mass_frac_allmodels : 2D nested list of floats. Outer length is the number of models,\n",
    "                          inner length is ``len(z)``.\n",
    "        The mass fraction at each redshift for each model.\n",
    "\n",
    "    model_tags : List of strings.  Length is the number of models.\n",
    "        The model entry that will be placed in the legend.\n",
    "        \n",
    "    passed_ax : ``matplotlib`` axes, optional\n",
    "        If defined, the history will be plotted onto the passed axis and\n",
    "        returned.  The figure will not be shown in the Notebook. \n",
    "\n",
    "    Returns\n",
    "    ---------\n",
    "    \n",
    "    If ``passed_ax = None``, None. The figure is displayed in the Notebook.\n",
    "    \n",
    "    If ``passed_ax`` is passed, the axis is returned with the ionization\n",
    "    history plotted.\n",
    "    \"\"\"\n",
    "    \n",
    "    # If we haven't passed an axis, set up a new Figure and axis object.\n",
    "    if passed_ax:\n",
    "        ax1 = passed_ax\n",
    "    else:\n",
    "        fig1 = plt.figure(figsize = (8,8))\n",
    "        ax1 = fig1.add_subplot(111)\n",
    "\n",
    "    # Go through each model and plot the data.\n",
    "    for model_number in range(len(mass_frac_allmodels)):\n",
    "        ax1.plot(z, mass_frac_allmodels[model_number], label=model_tags[model_number])\n",
    "    \n",
    "    # Make things look prettier.\n",
    "    ax1.set_xlabel(r\"$z$\")\n",
    "    ax1.set_ylabel(r\"$\\langle \\chi_{HI}\\rangle$\")\n",
    "    ax1 = adjust_axis(ax1, axis_width, tickwidth, major_ticklength, minor_ticklength)\n",
    "    \n",
    "    # If we're passing an axis, we will deal with putting the legends on later.\n",
    "    if not passed_ax:\n",
    "        leg = ax1.legend(loc='lower right', numpoints=1, labelspacing=0.1)\n",
    "        leg.draw_frame(False)  # Don't want a box frame\n",
    "        for t in leg.get_texts():  # Reduce the size of the text\n",
    "            t.set_fontsize(fontsize)\n",
    "\n",
    "    # If we passed an axis, return it. Otherwise, show the figure.\n",
    "    if passed_ax:\n",
    "        return ax1\n",
    "    else:\n",
    "        fig1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_tau(z, tau_allmodels, model_tags, passed_ax=None):\n",
    "    \"\"\"\n",
    "    Plots the evolution of tau.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    z : List of floats. Length is the number of models.\n",
    "        The redshift range we're plotting over.\n",
    "        \n",
    "    tau_frac_allmodels : 2D nested list of floats. Outer length is the number of models,\n",
    "                         inner length is ``len(z)``.\n",
    "        The tau value at each redshift for each model.\n",
    "\n",
    "    model_tags : List of strings.  Length is the number of models.\n",
    "        The model entry that will be placed in the legend.\n",
    "        \n",
    "    passed_ax : ``matplotlib`` axes, optional\n",
    "        If defined, the history will be plotted onto the passed axis and\n",
    "        returned.  The figure will not be shown in the Notebook. \n",
    "\n",
    "    Returns\n",
    "    ---------\n",
    "    \n",
    "    If ``passed_ax = None``, None. The figure is displayed in the Notebook.\n",
    "    \n",
    "    If ``passed_ax`` is passed, the axis is returned with the ionization\n",
    "    history plotted.\n",
    "    \"\"\"\n",
    "    \n",
    "    # If we haven't passed an axis, set up a new Figure and axis object.\n",
    "    if passed_ax:\n",
    "        ax1 = passed_ax\n",
    "    else:\n",
    "        fig1 = plt.figure(figsize = (8,8))\n",
    "        ax1 = fig1.add_subplot(111)\n",
    "        \n",
    "    # Go through each model and plot the data.\n",
    "    for model_number in range(len(mass_frac_allmodels)):\n",
    "        ax1.plot(z, tau_allmodels[model_number], label=model_tags[model_number])\n",
    "\n",
    "    # Make things look pretty.\n",
    "    ax1.set_xlabel(r\"$z$\")\n",
    "    ax1.set_ylabel(r\"$\\tau$\")\n",
    "    ax1 = adjust_axis(ax1, axis_width, tickwidth, major_ticklength, minor_ticklength)\n",
    "\n",
    "    # If we're passing an axis, we will deal with putting the legends on later.\n",
    "    if not passed_ax:\n",
    "        leg = ax1.legend(loc='lower right', numpoints=1, labelspacing=0.1)\n",
    "        leg.draw_frame(False)  # Don't want a box frame\n",
    "        for t in leg.get_texts():  # Reduce the size of the text\n",
    "            t.set_fontsize(fontsize)\n",
    "\n",
    "    # If we passed an axis, return it. Otherwise, show the figure.\n",
    "    if passed_ax:\n",
    "        return ax1\n",
    "    else:\n",
    "        fig1.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay now let's call each of these functions individually and see the Figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/100921091/miniconda/envs/py3.6/lib/python3.6/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e13198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e13828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mass_frac(z, mass_frac_allmodels, model_tags)\n",
    "plot_tau(z, tau_allmodels, model_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome!  We now have two individual Figures that could be presented separately. Now let's combine them into a single Figure..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_combined(z, mass_frac_allmodels, tau_allmodels, model_tags):\n",
    "    \"\"\"\n",
    "    Plots the evolution of mass fraction and tau on a single Figure.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "\n",
    "    z : List of floats. Length is the number of models.\n",
    "        The redshift range we're plotting over.\n",
    "        \n",
    "    mass_frac_allmodels, tau_frac_allmodels : 2D nested list of floats. Outer length is the number of models,\n",
    "                                              inner length is ``len(z)``.\n",
    "        The mass fraction and tau value at each redshift for each model.\n",
    "\n",
    "    model_tags : List of strings.  Length is the number of models.\n",
    "        The model entry that will be placed in the legend.\n",
    "\n",
    "    Returns\n",
    "    ---------\n",
    "    \n",
    "    None. The figure is displayed in the Notebook.\n",
    "    \"\"\"\n",
    "\n",
    "    # Generate a 1 row, 2 column plot.\n",
    "    fig, ax = plt.subplots(nrows = 1, ncols = 2,\n",
    "                           sharex=False, sharey=False, figsize=(16, 8))\n",
    "\n",
    "    # Then pass each axis to its function.\n",
    "    ax[0] = plot_mass_frac(z, mass_frac_allmodels, model_tags, ax[0])\n",
    "    ax[1] = plot_tau(z, tau_allmodels, model_tags, ax[1])\n",
    "\n",
    "    # We'll keep the legend on the mass fraction plot.\n",
    "    leg = ax[0].legend(loc='lower right', numpoints=1, labelspacing=0.1)\n",
    "    leg.draw_frame(False)\n",
    "    for t in leg.get_texts():\n",
    "        t.set_fontsize(fontsize)\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/100921091/miniconda/envs/py3.6/lib/python3.6/site-packages/matplotlib/figure.py:403: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e13400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_combined(z, mass_frac_allmodels, tau_allmodels, model_tags)"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}