lkluft/centered_colormap.ipynb

## centered_colormap.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true,
        "collapsed": false,
        "ExecuteTime": {
          "start_time": "2016-04-27T09:56:48.446315",
          "end_time": "2016-04-27T09:56:48.777059"
        }
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef abs_range(v):\n    abs_max = np.abs(v).max()\n    return -abs_max, abs_max\n\nx = np.linspace(-5, 5, 100)\ny = np.linspace(-5, 5, 100)[:, np.newaxis]\nz = x + y + 2.3\n\nvmin = np.min((0, z.min()))\nvmax = np.max((0, z.max()))\n\nfig, ax = plt.subplots()\npc = ax.pcolormesh(x, y, z, cmap='RdBu_r', vmin=vmin, vmax=vmax)\nax.set_xlim(x.min(), x.max())\nax.set_ylim(y.min(), y.max())\ncb = fig.colorbar(pc)\ncb.set_clim(abs_range(z))",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x7fdb82f39048>",
            "image/png": 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dXm3mmN5tVElcXf8MkrmYHFpubIXSxDmSTwdwpZldgCz0cQ1JQ3bWfs7MdtRN\nLE9ZCDFYJrOI41CVODcOV1wwfv0AgC2zzp2ep+ySumQO8E80SejmbHfbfbo5G/BPNBlqbeZFuzk7\nlKqYcspIEieEEAnRqqd88PAKjtww5+dAE15zj9QZ2Rb8Ek3aLAPa6s3ZgH+iyVDLgLaozog9b2l/\nhdcci+UeXj2imLIQYrD0MXyhQ1kIMViaetDXJK0/6Dt4eP37xFyhjLmTR0psAyVzQJqJJm3WZq6T\nzOVsAyVzma1fokmwZM4Z1yfJXHHesr00kWgS2g/kQxmxaFAS1xh60CeEEAnRqSSuca+5acmcY9u4\nZK7CtuvazHWSuUrbOSRzma1foklKkjmfNWJJ5rL29Gozz5KoEhPFlIUQIiF6GL1IJ3lk1WvulWQO\niFebuUXJnLvdPt2cnbftj2TO3UPjkjmg97WZYyJPWQghEmLmLOIEaPVQPuzxCwqOMwNzesIltgO9\nOdvdbtPqDHeNlMqA1qkzJmzK6KIM6ILdnL2IyFMWQgyWQ/KUhRAiHRRTrmHW+E76kjnXpmHJHOAf\nymixNnOdZC4b55do0mZt5jrJHJBmbeY6yVzO1p2g49rMnUnieugpK3lECCESImlP2SVJyVzOtmHJ\nHJBkbeY6yVw2zi/RpM3azLWSOWeTKdVmrpfMrc8bKplzx7UpmYuJdMpCCJEQiinXsGyWdxTmICnJ\nXKVt2pK5bAt+iSbBkjmnI6nazC3enO2Oa1wyByRZm7krSVwfq8QppiyEGCyHls3rZ1ZI/nuSD5O8\nffxzboXduSTvIXkvybf7zN1+TDn3iR42X/fqDB/bWOqMwhxrczWbaJLSzdk524bVGYDKgBbnLdtL\nLHXGxH76wYfN7MNVnSRHAD4G4GUAvg9gJ8lrzeyeaZMqpiyEGCwNx5TrPke2ArjPzPYCAMmrAVwE\nYOqhrPCFEGKwLJt5/czJJSR3k/w0yWNK+jcBeMh5//C4bSrdSuKSD2UkJJkD/BNNWqzN7FWRzjPR\npM3azHWSOSDN2sw+X/F9E03qJHNZe5zazLNI5mISIsMleT2AjW4Tsi1fBuATAN5rZkbyTwF8GMC/\nDtjqGgpfCCEGS9WhfP8dt+B7d9w6dayZvcJzmSsBfKmkfR+A45z3m8dtU0kneWTN9QpfJ8lEkwWV\nzOVsAyVz7nYbl8wBSdZmblUyB0SrzVwnmasa1xQnnXIaTjrltLX3O/768pnGk3yamT0yfvu7AL5T\nYrYTwElV9BXnAAAHoUlEQVQkjwewH8CrAFxcN7c8ZSHEYHHDmpH5EMktyD45HwTwJgAg+XQAV5rZ\nBWa2TPISADuQfQJ+xsz21E0c5VAm+TYAfw7gH5nZT6rslldWUPtsMfk4M9BMbebpkjkgzdrMbd6c\n7a7Rp5uzJ2zKSFAyl5u3xUSTmDRV5N7MXlPRvh/ABc77rwB4zixzBx/KJDcDeAWAvaFzCSFETBb1\n5pGPALgUwHV1htkvqMq3KqFPXnOfbs4G/L3mhG7Ozsb5JZqkdHM2ELMMaHs3Z+ds3Qkavjl70Qk6\nlEleCOAhM7uTbOLLhxBCzM8gPeUpWr13A3gXstCF21fJnddcgdXDe+NzX4RNz/+tWfcrhBggN9z2\nbXz9tm9Hn3eQh3KVVo/kCwCcAODvmZ20mwHsIrnVzH5QNuZ5F70BS873w+WKr0ylLLpkDvBPNGmx\nNrNX8sjaVGGSuWycX6JJqGQubxsomXM22bRkzt1DuGRufd6mJXNnbz0FZ7/4hWvv//TTV5fueVYG\neShXYWbfAfC01fckHwBwqpn9NMbGhBAilF82J4lrjJg6ZUPNh/PhqZ9aZekIJaT08K+4nzWakMwB\nKSaa1Enm3O2mVJu5TjJXbTu7ZC5rL2wQTUnm1o1bTTSJKJlbdKIdymb2rFhzCSFEDBYqfDEPfr8g\nSeZ6VZu5xZuzgXi1mdu8ORuIWJvZy9NOrzZzV5I4HcpCCJEQfbwOSoeyEGKwyFOuYfZfULehjKQk\ncznbQMkc4J/9FyiZy0z8sv/arM1cJ5kD/LP/VJs5XDLnu59Z0aEshBAJoUO5hrBfkKdkzjXp+uGf\nu5ccc0jmKm3TlsxlW/BLNGmzNnOdZM5dQ7WZ25XMxSSfoNYP5CkLIQaLPOUa4vyCFlUy52PrJ5kD\n0kw0abM2c51kLmcbKJnLbP0STYIlc864PknmivMuMvKUhRCDRZ5yDfF/QUPymltUZzi2fbo528s2\nkjqj0nYOdUZm65dokpI6w2eNWOqMrP0QYjO9tEOayFMWQgyWBu/oawwdykKIwdJU+ILk1QBOHr/9\nNQA/NbNTS+weBPAzZF9LDpnZ1rq52w1fHF4B5pWW1aLazJ3UZm5RMudut2nJnLvdpGoztyiZc/fQ\nuGQOyIUyUsfMXrX6muR/APBohekKgHNmKWksT1kIMVhaetD3LwD804o+wiu5Yp1WD2Uzy7zltdWb\n8Zof2bMTT3vui9D/h3/Aqi/x3V3fwvNefLqXbZ9qM9/2rZux7Ywzx8tPl8y5221aMueuMatk7uab\nbsSZZ21vpDZznWRuwqaMOWsz3/DNW3D2S05rRDI3uYc4NH0okzwLwCNm9r0KEwNwPcllAFeY2ZV1\nczYVS+iUA3t2db2F6Nx7+y1db6ERdn3r5q63EJ1v3HRT11tohBu+eWvXW5gZWzGvnzJIXk/y287P\nneP//o5jdjGAv5myhTPGsebzAfwRyTPr9tyup7xiuY+BprxmMxt/Qg5HMmcAllf/7iRVm7lGMgdM\n9ZoNhuWVSbumazPXSeaycX6JJsXYsSH7aaI2c51kDmiyNjMBjhqRzLm2MVmpOHB/ev8dePT+3VPH\nVt1PugrJJQC/C2DiAZ8zx/7xf39I8hoAWwFM9UQUUxZCDBarqKf8qyduwa+euGXt/d6/++t5pn8F\ngD1m9v2yTpJHARiZ2WMkjwbwSgDvqZuUVZuODcl2FhJCDAIzCwozj+Vox3ua7zWzE2ac/68AfMvM\nrnDang7gSjO7gOQzAVyD7EvABgCfM7MP1M7b1qEshBCinkE+6BNCiL6iQ1kIIRJi0IcyybeRXCH5\nlK73EgOSHyK5h+Rukv+D5JO73tO8kDyX5D0k7yX59q73EwOSm0l+jeRdY/nUm7veUyxIjkjeTvK6\nrvcydAZ7KJPcjOzp6N6u9xKRHQCeb2ZbANwH4J0d72cuSI4AfAzAPwPwfAAXk/zNbncVhcMA3mpm\nzwdwOjJd6hD+XADwFgB3d72JRWCwhzKAjwC4tOtNxMTMvmq2pii9BcDmLvcTwFYA95nZXjM7BOBq\nABd1vKdgzOwRM9s9fv0YgD0ANnW7q3DGDs75AD7d9V4WgUEeyiQvBPCQmd3Z9V4a5PUAvtz1JuZk\nE4CHnPcPYwCHlwvJEwBsAdC/NLhJVh0cSbVaoLfJIySvB7DRbUL2l+bdAN6FLHTh9vWCKX+uy8zs\nS2Oby5CVAfx8B1sUNZB8IoAvAHjL2GPuLSR/G8ABM9tN8hz06N9SX+ntoVyVAknyBQBOAPD3JIns\nK/4uklvN7ActbnEuPFI7X4vsq+RLW9lQM+wDcJzzfvO4rfeQ3IDsQP6smV3b9X4icAaAC0meD+AJ\nAJ5E8ioze03H+xosg08eIfkAgFNnqWeaKiTPBfAfAWw3sx93vZ95GdcM+C6AlwHYD+B/A7jYzPZ0\nurEIkLwKwI/M7K1d7yU2JM8G8DYzu7DrvQyZQcaUCxiG85XrcgBPRFYK8HaSn+h6Q/NgZssALkGm\nJrkLwNUDOZDPAPBqAC8lecf4/9G5Xe9L9IvBe8pCCNEnFsFTFkKI3qBDWQghEkKHshBCJIQOZSGE\nSAgdykIIkRA6lIUQIiF0KAshRELoUBZCiIT4/5nyo4fmkcGfAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/8add95eaf498898507e995105b38b38c"
    },
    "language_info": {
      "file_extension": ".py",
      "nbconvert_exporter": "python",
      "mimetype": "text/x-python",
      "version": "3.5.1",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "name": "python",
      "pygments_lexer": "ipython3"
    },
    "toc": {
      "toc_window_display": false,
      "toc_cell": false,
      "toc_number_sections": true,
      "toc_threshold": 6
    },
    "gist": {
      "id": "8add95eaf498898507e995105b38b38c",
      "data": {
        "description": "Center a colormap around zero.",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
	{
	"cells": [
	{
	"metadata": {
	"trusted": true,
	"collapsed": false,
	"ExecuteTime": {
	"start_time": "2016-04-27T09:56:48.446315",
	"end_time": "2016-04-27T09:56:48.777059"
	}
	},
	"cell_type": "code",
	"source": "%matplotlib inline\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef abs_range(v):\n abs_max = np.abs(v).max()\n return -abs_max, abs_max\n\nx = np.linspace(-5, 5, 100)\ny = np.linspace(-5, 5, 100)[:, np.newaxis]\nz = x + y + 2.3\n\nvmin = np.min((0, z.min()))\nvmax = np.max((0, z.max()))\n\nfig, ax = plt.subplots()\npc = ax.pcolormesh(x, y, z, cmap='RdBu_r', vmin=vmin, vmax=vmax)\nax.set_xlim(x.min(), x.max())\nax.set_ylim(y.min(), y.max())\ncb = fig.colorbar(pc)\ncb.set_clim(abs_range(z))",
	"execution_count": 1,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"text/plain": "<matplotlib.figure.Figure at 0x7fdb82f39048>",
	"image/png": 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	"kernelspec": {
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	"display_name": "Python 3",
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	"_draft": {
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	"toc": {
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	"toc_cell": false,
	"toc_number_sections": true,
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	"gist": {
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	"data": {
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	"nbformat_minor": 0
	}