{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The right* way to plot: colorbars\n",
    "\\* It really is ... or is it ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PyCoffee @ Vitacura - 2 June 2016 - fvogt@eso.org\n",
    "\n",
    "**premise:** \n",
    "- colorbars in matplotlib are easy to use and at first it all seems pretty dumb\n",
    "- but as with everything, there is much more to them than meets the eye: \n",
    "\n",
    "   <span style=\"color:red;\">**Colorbars form a magical & untamed Wonderland, where all your wildest dreams can come true !** </span>\n",
    "   \n",
    "   http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "   \n",
    "   http://matplotlib.org/users/colormaps.html\n",
    "\n",
    "**this notebook:** \n",
    "- tips and tricks to tame your colorbars.\n",
    "\n",
    "**utility**: \n",
    "- let your Science rule the colorbar, and not your colorbar rule your Science !\n",
    "\n",
    "**cost**: \n",
    "- From 2 characters to a few lines of code !\n",
    "\n",
    "**gain**:\n",
    "- total control over your colorbars\n",
    "- accuracy\n",
    "- elegance\n",
    "- infinite powers\n",
    "- ...\n",
    "\n",
    "** take-home message **: \n",
    "- colorbars are fun ... and powerful !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125f8a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Qlpf9wYqlalq1XJJWF7NUl6eaulSqp1I+6UcMyw761yve97dvt8u3Bi0OeaVjplSOz1xq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\npIcCxwAPjIidks4C9mD0jsUeU4q4heEdnHK6ncl2JPs9Rse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MzDqkc3c6JB0OPB44BHg0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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125f8350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Have colormaps separated into categories:\n",
    "# http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "\n",
    "cmaps = [('The ONE choice ?',\n",
    "                            ['viridis', 'inferno', 'plasma', 'magma',\n",
    "         'Blues', 'BuGn', 'BuPu',\n",
    "                             'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',\n",
    "                             'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',\n",
    "                             'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd',\n",
    "         'afmhot', 'autumn', 'bone', 'cool',\n",
    "                             'copper', 'gist_heat', 'gray', 'hot',\n",
    "                             'pink', 'spring', 'summer', 'winter',\n",
    "         'BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',\n",
    "                             'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',\n",
    "                             'seismic',\n",
    "         'Accent', 'Dark2', 'Paired', 'Pastel1',\n",
    "                             'Pastel2', 'Set1', 'Set2', 'Set3',\n",
    "         'gist_earth', 'terrain', 'ocean', 'gist_stern',\n",
    "                             'brg', 'CMRmap', 'cubehelix',\n",
    "                             'gnuplot', 'gnuplot2', 'gist_ncar',\n",
    "                             'nipy_spectral', 'jet', 'rainbow',\n",
    "                             'gist_rainbow', 'hsv', 'flag', 'prism'])]\n",
    "\n",
    "nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)\n",
    "gradient = np.linspace(0, 1, 256)\n",
    "gradient = np.vstack((gradient, gradient))\n",
    "\n",
    "def plot_color_gradients(cmap_category, cmap_list):\n",
    "    fig, axes = plt.subplots(nrows=nrows, figsize=(8,20))\n",
    "    fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)\n",
    "    axes[0].set_title(cmap_category, fontsize=14)\n",
    "\n",
    "    for ax, name in zip(axes, cmap_list):\n",
    "        ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))\n",
    "        pos = list(ax.get_position().bounds)\n",
    "        x_text = pos[0] - 0.01\n",
    "        y_text = pos[1] + pos[3]/2.\n",
    "        fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)\n",
    "\n",
    "    # Turn off *all* ticks & spines, not just the ones with colormaps.\n",
    "    for ax in axes:\n",
    "        ax.set_axis_off()\n",
    "\n",
    "fig0 = plt.figure(1, figsize=(20,20))\n",
    "           \n",
    "for cmap_category, cmap_list in cmaps:\n",
    "    plot_color_gradients(cmap_category, cmap_list)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A) Set the scene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np # For creating some fake data\n",
    "import random\n",
    "\n",
    "# Ok, let us start by creating some fake data\n",
    "x,y = np.mgrid[-4:4:0.4,-4:4:0.4]\n",
    "z = np.sqrt(x**2+y**2)\n",
    "\n",
    "for i in range(30):\n",
    "    \n",
    "    a = random.choice(range(len(z)))\n",
    "    b = random.choice(range(len(z)))\n",
    "    \n",
    "    z[a,b] = np.nan\n",
    "\n",
    "# `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n",
    "# with random floats sampled from a univariate \"normal\" (Gaussian)\n",
    "# distribution of mean 0 and variance 1.\n",
    "\n",
    "# Import the other useful modules\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt # Gives access to basic plotting functions\n",
    "import matplotlib.gridspec as gridspec # GRIDSPEC !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### B) A basic example of how most people do things (but shouldn't ?!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cuKTrzOw2M3vnRIuROYqN3ENcZI5io+8BrcqcOXqpu280s+eqPki6191vHHrQhRdcqkWL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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1117709d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, create the figure\n",
    "plt.close(1)\n",
    "fig = plt.figure(1, figsize=(12,8))\n",
    "\n",
    "# The plot itself\n",
    "plt1 = plt.imshow(z, cmap = 'magma', vmin =0 , vmax = 4, interpolation='nearest')\n",
    "\n",
    "# Define the limits, labels, ticks as required\n",
    "plt.grid(True)\n",
    "plt.ylabel(r'My y label')\n",
    "plt.xlabel(r'My x label')\n",
    "\n",
    "plt.colorbar(plt1, label='Colorbar !')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### B) The same plot the \"proper\" way: gridspec !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3dOHTj2gPJSN3L5xBNosMVwRkuOIgwxUDGS7sD3dfL2l97+snzOxuSUdLWj1t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\nABJjwgUAAJAYEy4AAIDEmHABAAAk9v8DVl95nT7ZjpEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f4b0490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, create the figure\n",
    "plt.close(1)\n",
    "fig = plt.figure(1, figsize=(8,8))\n",
    "\n",
    "# Now, create the gridspec structure, as required\n",
    "gs = gridspec.GridSpec(1,2, height_ratios=[1], width_ratios=[1,0.05])\n",
    "gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.02, hspace=0.03)\n",
    "\n",
    "ax1 = plt.subplot(gs[0,0])\n",
    "\n",
    "# The plot itself\n",
    "plt1 = plt.imshow(z, cmap = 'magma_r', vmin =0 , vmax = 4, interpolation='nearest')\n",
    "\n",
    "# Define the limits, labels, ticks as required\n",
    "ax1.grid(True)\n",
    "ax1.set_ylabel(r'My y label')\n",
    "ax1.set_xlabel(r'My x label')\n",
    "\n",
    "# And now the colorbar\n",
    "# --------------------------------------------------------\n",
    "cbax = plt.subplot(gs[0,1]) # Place it where it should be.\n",
    "\n",
    "cb = plt.colorbar(cax = cbax, mappable = plt1, orientation = 'vertical', ticklocation = 'right')\n",
    "cb.set_label(r'Colorbar !', labelpad=10)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TIP #1:** you can invert a named-colorbar by adding ``_r`` to its name, e.g.: \n",
    "\n",
    "``magma`` $\\rightarrow$ ``magma_r``\n",
    "\n",
    "**TIP #2:** you can place a colorbar horizontaly or vertically.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VsaB00pLTdNKS06a373n2e7M1Ky0fSKXyRMnMXirpjZLWm9k9aiy9vFfSqZLc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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110773b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, create the figure\n",
    "plt.close(1)\n",
    "fig = plt.figure(1, figsize=(7.6,8))\n",
    "\n",
    "# Now, create the gridspec structure, as required\n",
    "gs = gridspec.GridSpec(3,3, height_ratios=[0.05,1,0.05], width_ratios=[0.05,1,0.05])\n",
    "gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.02, hspace=0.03)\n",
    "\n",
    "ax1 = plt.subplot(gs[1,1])\n",
    "\n",
    "# The plot itself\n",
    "plt1 = plt.imshow(z, cmap = 'magma_r', vmin =0 , vmax = 4, interpolation='nearest')\n",
    "\n",
    "# Define the limits, labels, ticks as required\n",
    "ax1.grid(True)\n",
    "ax1.set_ylabel(r'')\n",
    "ax1.set_xlabel(r'')\n",
    "ax1.set_xticklabels([])\n",
    "ax1.set_yticklabels([])\n",
    "\n",
    "# And now the colorbar\n",
    "# --------------------------------------------------------\n",
    "# Right\n",
    "cbax = plt.subplot(gs[1,2]) # Place it where it should be.\n",
    "cb = plt.colorbar(cax = cbax, mappable = plt1, orientation = 'vertical', ticklocation = 'right')\n",
    "cb.set_label(r'Colorbar !', labelpad=10)\n",
    "# Left\n",
    "cbax = plt.subplot(gs[1,0]) # Place it where it should be.\n",
    "cb = plt.colorbar(cax = cbax, mappable = plt1, orientation = 'vertical', ticklocation = 'left')\n",
    "cb.set_label(r'Colorbar !', labelpad=10)\n",
    "# Top\n",
    "cbax = plt.subplot(gs[0,1]) # Place it where it should be.\n",
    "cb = plt.colorbar(cax = cbax, mappable = plt1, orientation = 'horizontal', ticklocation = 'top')\n",
    "cb.set_label(r'Colorbar !', labelpad=10)\n",
    "# Top\n",
    "cbax = plt.subplot(gs[2,1]) # Place it where it should be.\n",
    "cb = plt.colorbar(cax = cbax, mappable = plt1, orientation = 'horizontal', ticklocation = 'bottom')\n",
    "cb.set_label(r'Colorbar !', labelpad=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### C) Discretize colorbars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# To break a colorbar in pieces ... lovely tool !\n",
    "def cmap_discretize(cmap, N):\n",
    "    \"\"\"Return a discrete colormap from the continuous colormap cmap.    \n",
    "    cmap: colormap instance, eg. cm.jet.  \n",
    "    N: number of colors.    \n",
    "    Example\n",
    "    x = resize(arange(100), (5,100))\n",
    "    djet = cmap_discretize(cm.jet, 5)\n",
    "    imshow(x, cmap=djet)\n",
    "    \"\"\"\n",
    "    if type(cmap) == str:\n",
    "        cmap = mpl.cm.get_cmap(cmap)\n",
    "    #colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))\n",
    "    # Fred's update ... dont' start with the colormap edges !\n",
    "    colors_i = np.concatenate((np.linspace(1./N*0.5, 1-(1./N*0.5), N), (0., 0., 0.)))\n",
    "    colors_rgba = cmap(colors_i)\n",
    "    indices = np.linspace(0, 1., N+1)\n",
    "    cdict = {}\n",
    "    for ki,key in enumerate(('red','green','blue')):\n",
    "        cdict[key] = [ (indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1) ]\n",
    "    # Return colormap object.\n",
    "    return mpl.colors.LinearSegmentedColormap(cmap.name + \"_%d\"%N, cdict, 1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FsD1HrdtODX9EymclXWV7jaTtJb2rtyMrLHA1scM1Ze5plR5/88BFlR6/F2acv7Cr/Y/Y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\nREUFLnoAOTDPedDhyoE1jQy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110454490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Discretize the colorbar into 5 bins\n",
    "magma_5 = cmap_discretize('magma',5)\n",
    "\n",
    "# First, create the figure\n",
    "plt.close(1)\n",
    "fig = plt.figure(1, figsize=(8,8))\n",
    "\n",
    "# Now, create the gridspec structure, as required\n",
    "gs = gridspec.GridSpec(1,2, height_ratios=[1], width_ratios=[1,0.05])\n",
    "gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.02, hspace=0.03)\n",
    "\n",
    "ax1 = plt.subplot(gs[0,0])\n",
    "\n",
    "# The plot itself\n",
    "plt1 = plt.imshow(z, cmap = magma_5, vmin =0 , vmax = 4, interpolation='nearest')\n",
    "\n",
    "# Define the limits, labels, ticks as required\n",
    "ax1.grid(True)\n",
    "ax1.set_ylabel(r'My y label')\n",
    "ax1.set_xlabel(r'My x label')\n",
    "\n",
    "# And now the colorbar\n",
    "# --------------------------------------------------------\n",
    "cbax = plt.subplot(gs[0,1]) # Place it where it should be.\n",
    "\n",
    "cb = plt.colorbar(cax = cbax, mappable = plt1, orientation = 'vertical', ticklocation = 'right')\n",
    "cb.set_label(r'Colorbar !', labelpad=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### D) Improving the colorbar\n",
    "\n",
    "**TIP #3**: you can set the colors for NaNs in a colorbar !\n",
    "\n",
    "**TIP #4:** Define specific colors for upper and lower limits - to clearly show which point land outside the range !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DUjU4a0Mv5+H6ezObJ+mDkj4k6auS/nIb6gyuSl0AoiDDVZAqdQGIgQwXBoGZ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\nVeoCEEWVugBEQ4arDFXqAtAuMly1slp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f41da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Discretize the colorbar into 5 bins\n",
    "my_Blues = mpl.cm.get_cmap('Blues_r')\n",
    "my_Blues.set_bad(color=(0.5,0.5,0.5), alpha=1) \n",
    "my_Blues.set_over(color=(1,0,0), alpha=1)\n",
    "my_Blues.set_under(color=(0,1,0),alpha=1)\n",
    "\n",
    "# First, create the figure\n",
    "plt.close(1)\n",
    "fig = plt.figure(1, figsize=(8,8))\n",
    "\n",
    "# Now, create the gridspec structure, as required\n",
    "gs = gridspec.GridSpec(1,2, height_ratios=[1], width_ratios=[1,0.05])\n",
    "gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.02, hspace=0.03)\n",
    "\n",
    "ax1 = plt.subplot(gs[0,0])\n",
    "\n",
    "# The plot itself\n",
    "plt1 = plt.imshow(z, cmap = my_Blues, vmin =1 , vmax = 4, interpolation='nearest')\n",
    "\n",
    "# Define the limits, labels, ticks as required\n",
    "ax1.grid(True)\n",
    "ax1.set_ylabel(r'My y label')\n",
    "ax1.set_xlabel(r'My x label')\n",
    "\n",
    "# And now the colorbar\n",
    "# --------------------------------------------------------\n",
    "cbax = plt.subplot(gs[0,1]) # Place it where it should be.\n",
    "\n",
    "cb = plt.colorbar(cax = cbax, mappable = plt1, orientation = 'vertical', ticklocation = 'right')\n",
    "cb.set_label(r'Colorbar !', labelpad=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### E) Designing your own colorbar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define the inverse-cmap as well\n",
    "def reverse_colourmap(cdict):\n",
    "    new_cdict = {}\n",
    "    for channel in cdict:\n",
    "        data = []\n",
    "        for t in cdict[channel]:\n",
    "            data.append((1. - t[0], t[1], t[2]))\n",
    "        \n",
    "        new_cdict[channel] = data[::-1]\n",
    "    return new_cdict\n",
    "\n",
    "# Define the colorbar\n",
    "cdict_alligator = {\n",
    "'red'  :  ( (0.00, 0./255, 0./255),\n",
    "            (0.00, 0./255, 0./255),      (0.2, 20./255., 20./255.),\n",
    "            (0.40, 46./255., 46./255.),  (0.6, 108./255., 108./255.),\n",
    "            (0.8, 207./255., 207./255.), (1.00, 255./255.,255./255.),\n",
    "            (1.00, 255./255, 255./255),),\n",
    "\n",
    "'green' : ( (0.00, 0./255, 0./255),\n",
    "            (0.00, 25./255., 25./255.),  (0.2, 85./255., 85./255.),\n",
    "            (0.4, 139./255., 139./255.), (0.6, 177./255., 177./255.),  \n",
    "            (0.8, 234./255, 234./255),   (1.00, 248./255.,248./255.),\n",
    "            (1.00, 255./255, 255./255),),\n",
    "            \n",
    "'blue':   ( (0.00, 0./255, 0./255),\n",
    "            (0.00, 25./255., 25./255.),  (0.2, 81./255., 81./255.),\n",
    "            (0.4, 87./255., 87./255.),   (0.6, 86./255., 86./255.),\n",
    "            (0.8, 45./255, 45./255),     (1.00, 215./255.,215./255.),\n",
    "            (1.00, 255./255, 255./255),),   \n",
    "}\n",
    "\n",
    "alligator = plt.matplotlib.colors.LinearSegmentedColormap('alligator', cdict_alligator, 1024)\n",
    "alligator.set_bad(color=(0.5,0.5,0.5), alpha=1) \n",
    "\n",
    "# And the reverse one\n",
    "cdict_alligator_r = reverse_colourmap(cdict_alligator)\n",
    "alligator_r = plt.matplotlib.colors.LinearSegmentedColormap('alligator_r',cdict_alligator_r, 1024) \n",
    "alligator_r.set_bad(color=(0.5,0.5,0.5), alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r438OCfTLdPIeyFO3GS7kiTWNgxERN0ka7GC/yw7yUF9uP6alk4LrREkvlXSx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\nAAAgsf8fpRgUEUwf4yMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11140c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, create the figure\n",
    "plt.close(1)\n",
    "fig = plt.figure(1, figsize=(8,8))\n",
    "\n",
    "# Now, create the gridspec structure, as required\n",
    "gs = gridspec.GridSpec(1,2, height_ratios=[1], width_ratios=[1,0.05])\n",
    "gs.update(left=0.05, right=0.95, bottom=0.08, top=0.93, wspace=0.02, hspace=0.03)\n",
    "\n",
    "ax1 = plt.subplot(gs[0,0])\n",
    "\n",
    "# The plot itself\n",
    "plt1 = plt.imshow(z, cmap = alligator_r, vmin =0 , vmax = 4, interpolation='nearest')\n",
    "\n",
    "# Define the limits, labels, ticks as required\n",
    "ax1.grid(True)\n",
    "ax1.set_ylabel(r'My y label')\n",
    "ax1.set_xlabel(r'My x label')\n",
    "\n",
    "# And now the colorbar\n",
    "# --------------------------------------------------------\n",
    "cbax = plt.subplot(gs[0,1]) # Place it where it should be.\n",
    "\n",
    "cb = plt.colorbar(cax = cbax, mappable = plt1, orientation = 'vertical', ticklocation = 'right')\n",
    "cb.set_label(r'Colorbar !', labelpad=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Alligator in action: http://fpavogt.github.io/brutus/\n",
    "\n",
    "![title](./09_HCG91c_eline-a-6562.81_F.png) \n",
    "![title](./17_HCG91c_pyqz_<Tot[O]+12{KDE}>.png) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example of a custom colorbar: http://fpavogt.github.io/pyqz/\n",
    "\n",
    "![title](./pyqz_demo.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}