phobson/ShiftedColorMap.ipynb

## ShiftedColorMap.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "%config InlineBackend.figure_format = 'png'\n",
      "%config InlineBackend.dpi = 300\n",
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "from mpl_toolkits.axes_grid1 import AxesGrid\n",
      "\n",
      "def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'):\n",
      "    '''\n",
      "    Function to offset the \"center\" of a colormap. Useful for\n",
      "    data with a negative min and positive max and you want the\n",
      "    middle of the colormap's dynamic range to be at zero\n",
      "    \n",
      "    Input\n",
      "    -----\n",
      "      cmap : The matplotlib colormap to be altered\n",
      "      start : Offset from lowest point in the colormap's range.\n",
      "          Defaults to 0.0 (no lower ofset). Should be between\n",
      "          0.0 and 1.0.\n",
      "      midpoint : The new center of the colormap. Defaults to \n",
      "          0.5 (no shift). Should be between 0.0 and 1.0. In\n",
      "          general, this should be  1 - vmax/(vmax + abs(vmin))\n",
      "          For example if your data range from -15.0 to +5.0 and\n",
      "          you want the center of the colormap at 0.0, `midpoint`\n",
      "          should be set to  1 - 5/(5 + 15)) or 0.75\n",
      "      stop : Offset from highets point in the colormap's range.\n",
      "          Defaults to 1.0 (no upper ofset). Should be between\n",
      "          0.0 and 1.0.\n",
      "    '''\n",
      "    cdict = {\n",
      "        'red': [],\n",
      "        'green': [],\n",
      "        'blue': [],\n",
      "        'alpha': []\n",
      "    }\n",
      "      \n",
      "    # regular index to compute the colors\n",
      "    reg_index = np.linspace(start, stop, 257)\n",
      "\n",
      "    # shifted index to match the data\n",
      "    shift_index = np.hstack([\n",
      "        np.linspace(0.0, midpoint, 128, endpoint=False), \n",
      "        np.linspace(midpoint, 1.0, 129, endpoint=True)\n",
      "    ])\n",
      "    \n",
      "    for ri, si in zip(reg_index, shift_index):\n",
      "        r, g, b, a = cmap(ri)\n",
      "\n",
      "        cdict['red'].append((si, r, r))\n",
      "        cdict['green'].append((si, g, g))\n",
      "        cdict['blue'].append((si, b, b))\n",
      "        cdict['alpha'].append((si, a, a))\n",
      "        \n",
      "    newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)\n",
      "    plt.register_cmap(cmap=newcmap)\n",
      "\n",
      "    return newcmap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 10\n",
      "fs = 11\n",
      "biased_data = np.random.random_integers(low=-15, high=5, size=(N,N))\n",
      "\n",
      "orig_cmap = matplotlib.cm.coolwarm\n",
      "shifted_cmap = shiftedColorMap(orig_cmap, midpoint=0.75, name='shifted')\n",
      "shrunk_cmap = shiftedColorMap(orig_cmap, start=0.15, midpoint=0.75, stop=0.85, name='shrunk')\n",
      "\n",
      "fig = plt.figure(figsize=(10,10))\n",
      "grid = AxesGrid(fig, 111, nrows_ncols=(2, 2), axes_pad=0.5,\n",
      "                label_mode=\"1\", share_all=True,\n",
      "                cbar_location=\"right\", cbar_mode=\"each\",\n",
      "                cbar_size=\"7%\", cbar_pad=\"2%\")\n",
      "\n",
      "# normal cmap\n",
      "im0 = grid[0].pcolor(biased_data, cmap=orig_cmap)\n",
      "grid.cbar_axes[0].colorbar(im0)\n",
      "grid[0].set_title('Default behavior (hard to see bias)', fontsize=fs)\n",
      "\n",
      "im1 = grid[1].pcolor(biased_data, cmap=orig_cmap, vmax=15, vmin=-15)\n",
      "grid.cbar_axes[1].colorbar(im1)\n",
      "grid[1].set_title('Centered zero manually,\\nbut lost upper end of dynamic range', fontsize=fs)\n",
      "\n",
      "im2 = grid[2].pcolor(biased_data, cmap=shifted_cmap)\n",
      "grid.cbar_axes[2].colorbar(im2)\n",
      "grid[2].set_title('Recentered cmap with function', fontsize=fs)\n",
      "\n",
      "im3 = grid[3].pcolor(biased_data, cmap=shrunk_cmap)\n",
      "grid.cbar_axes[3].colorbar(im3)\n",
      "grid[3].set_title('Recentered cmap with function\\nand shrunk range', fontsize=fs)\n",
      "\n",
      "for ax in grid:\n",
      "    ax.set_xlim(left=0, right=N)\n",
      "    ax.set_ylim(bottom=0, top=N)\n",
      "    ax.set_yticks([])\n",
      "    ax.set_xticks([])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WNkUFYI+lHEVxBXgilJ0hdwD2WMpRFFeAN+zsOA/AIjs5iuIK8ISlIXcA9ljK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\n/uDjTuP/YOJqp/F/9CsnncZ//5pn7CS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       "text": [
        "<matplotlib.figure.Figure at 0xbd38eb8>"
       ]
      }
     ],
     "prompt_number": 47
    }
   ],
   "metadata": {}
  }
 ]
}
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