{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matteo colourmaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matteo Niccoli has made some awesome colourmaps, and has published a routine to convert them to Matplotlib colourmaps. This is very cool! \n",
    "\n",
    "He explains all in his 25 April 2014 blog post [Convert colour palettes to Python Matplotlib colour palettes](http://mycarta.wordpress.com/2014/04/25/convert-color-palettes-to-python-matplotlib-colormaps/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a bit at the start where you are downloading data, unzipping, reformatting, etc. I thought I'd try doing all that in Python too..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can read the data straight from the web. We'll need some of the standard libraries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import urllib2, zipfile, StringIO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can read the zip file and examine its contents. We'll keep it in memory using a <code>StringIO</code> object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cube1_0-1.csv',\n",
       " '__MACOSX/',\n",
       " '__MACOSX/._cube1_0-1.csv',\n",
       " 'cubeYF_0-1.csv',\n",
       " '__MACOSX/._cubeYF_0-1.csv',\n",
       " 'Linear_L_0-1.csv',\n",
       " '__MACOSX/._Linear_L_0-1.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remote_file = 'http://mycarta.files.wordpress.com/2013/03/0-1.odt'\n",
    "\n",
    "# Read the data from the web, as normal.\n",
    "web_data = urllib2.urlopen(remote_file)\n",
    " \n",
    "# Convert the bytes into a file-like object in memory\n",
    "zip_in_memory = StringIO.StringIO(web_data.read())\n",
    " \n",
    "# Instantiate a ZipFile object from the file-like in memory.\n",
    "z = zipfile.ZipFile(zip_in_memory)\n",
    " \n",
    "z.namelist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need file number 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = z.open(z.namelist()[5])\n",
    "raw_data = f.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the first 100 characters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.0143,0.0143,0.0143\\r0.0404,0.0125,0.0325\\r0.0516,0.0154,0.0443\\r0.0616,0.0184,0.0530\\r0.0699,0.0215,0.'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just need to split this to get a list of strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0.0143,0.0143,0.0143',\n",
       " '0.0404,0.0125,0.0325',\n",
       " '0.0516,0.0154,0.0443',\n",
       " '0.0616,0.0184,0.0530',\n",
       " '0.0699,0.0215,0.0615',\n",
       " '0.0814,0.0229,0.0687',\n",
       " '0.0857,0.0273,0.0763',\n",
       " '0.0928,0.0305,0.0805',\n",
       " '0.1008,0.0330,0.0846',\n",
       " '0.1064,0.0356,0.0939']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_o_strings = raw_data.split()\n",
    "list_o_strings[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use a nested list comprehension to step over the list of strings, splitting each string on its commas, and converting each element (which will still be a string) to a floating point number..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0143, 0.0143, 0.0143],\n",
       " [0.0404, 0.0125, 0.0325],\n",
       " [0.0516, 0.0154, 0.0443],\n",
       " [0.0616, 0.0184, 0.053],\n",
       " [0.0699, 0.0215, 0.0615],\n",
       " [0.0814, 0.0229, 0.0687],\n",
       " [0.0857, 0.0273, 0.0763],\n",
       " [0.0928, 0.0305, 0.0805],\n",
       " [0.1008, 0.033, 0.0846],\n",
       " [0.1064, 0.0356, 0.0939]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_o_lists = [[float(num) for num in string.split(',')] for string in list_o_strings]\n",
    "list_o_lists[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.0143,  0.0143,  0.0143],\n",
       "       [ 0.0404,  0.0125,  0.0325],\n",
       "       [ 0.0516,  0.0154,  0.0443]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LinL = np.array(list_o_lists)\n",
    "LinL[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Win!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continue with Matteo's workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll just run Matteo's code..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b3 = LinL[:,2] # value of blue at sample n\n",
    "b2 = LinL[:,2] # value of blue at sample n\n",
    "b1 = linspace(0, 1, len(b2)) # position of sample n - ranges from 0 to 1\n",
    "\n",
    "# Setting up columns for tuples\n",
    "g3 = LinL[:,1]\n",
    "g2 = LinL[:,1]\n",
    "g1 = linspace(0,1,len(g2))\n",
    "\n",
    "r3 = LinL[:,0]\n",
    "r2 = LinL[:,0]\n",
    "r1 = linspace(0,1,len(r2))\n",
    "\n",
    "# Creating tuples\n",
    "R = zip(r1,r2,r3)\n",
    "G = zip(g1,g2,g3)\n",
    "B = zip(b1,b2,b3)\n",
    "\n",
    "# Transposing\n",
    "RGB = zip(R,G,B)\n",
    "rgb = zip(*RGB)\n",
    "\n",
    "# Creating dictionary\n",
    "k = ['red', 'green', 'blue']\n",
    "LinearL = dict(zip(k,rgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And test it with a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w2qpU9W2anMAEJalekxFUBlnKTr2TUOC3F3gRODotqAlKUk2p1Mqr08pOnVVumZmgJNXk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d94f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_cmap = matplotlib.colors.LinearSegmentedColormap('matteo', LinearL)\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.pcolor(rand(10,10),cmap=my_cmap)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reverse it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make a reversed version, I think we have to reverse <code>rgb</code> and repeat the process. It's a three-element list, where each element is a tuple of tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def reverse_colourmap(cmap):\n",
    "    reverse = []\n",
    "    for channel in cmap:\n",
    "        data = []\n",
    "        for t in channel:\n",
    "            data.append((1 - t[0], t[1], t[2]))\n",
    "        reverse.append(sorted(data))\n",
    "        \n",
    "    return reverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rgb_r = reverse_colourmap(rgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i9LFucgJHUJKqNHiEoJk0Zaf+lVDgdyvwDHBys6AmKEllLZQ+h+Zlpy4otdRMUJLKYksI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\nJEmSJEmSJEmSJEmSJEmSVM//Ax8TfWUm1FIwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a8d0e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LinearL_r = dict(zip(k,rgb_r))\n",
    "my_cmap_r = matplotlib.colors.LinearSegmentedColormap('matteo_r', LinearL_r)\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.pcolor(rand(10,10),cmap=my_cmap_r)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <code>sorted()</code> is important. As well as having the right <code>x</code> values, the coloumap channels must be in order.\n",
    "\n",
    "Instead of reversing the finished colourmap, let's do it from the start, with the original code..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def gen_cmap(name, array, start, end):\n",
    "    b3 = array[:,2] # value of blue at sample n\n",
    "    b2 = array[:,2] # value of blue at sample n\n",
    "    b1 = linspace(start, end, len(b2)) # position of sample n - ranges from 0 to 1\n",
    "    \n",
    "    # Setting up columns for tuples\n",
    "    g3 = array[:,1]\n",
    "    g2 = array[:,1]\n",
    "    g1 = linspace(start, end, len(g2))\n",
    "    \n",
    "    r3 = array[:,0]\n",
    "    r2 = array[:,0]\n",
    "    r1 = linspace(start, end, len(r2))\n",
    "    \n",
    "    # Creating tuples\n",
    "    R = sorted(zip(r1,r2,r3))\n",
    "    G = sorted(zip(g1,g2,g3))\n",
    "    B = sorted(zip(b1,b2,b3))\n",
    "    \n",
    "    # Transposing\n",
    "    RGB = zip(R,G,B)\n",
    "    rgb = zip(*RGB)\n",
    "    \n",
    "    # Creating dictionary\n",
    "    k = ['red', 'green', 'blue']\n",
    "    LinearL = dict(zip(k,rgb))\n",
    "    \n",
    "    return matplotlib.colors.LinearSegmentedColormap(name, LinearL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CmvfFW7IQ7l/U8qNp2k7Vu4lQf/4JWN40n5bfUtJ6pVlB2Gl2OIZcfcFap6RpOzUDWEoY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d94c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_cmap_r = gen_cmap('matteo_r', LinL, 1, 0)\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.pcolor(rand(10,10),cmap=my_cmap_r)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And just check this works with the original..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fXYHn6vanYW8lvbwkSbY2VHXZqe0JZacGl5SaRqVY58z0a93kBI6gJFUr5T4QKkvZqb8n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\nSZIkSZIkSZIkSZIkSVI9/w/g4A232zSrfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a7cc190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_cmap = gen_cmap('matteo', LinL, 0, 1)\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.pcolor(rand(10,10),cmap=my_cmap)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ha, I just read about <code>numpy.flipud(a)</code> and realized you could just flip the array we started with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mhjbod5dscUqaunj1fk3IP9sBTzY7ybt4kqompGwN1NfFm0Coize85t1satWEB9Nd0+QE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\nktTM/wO0QZ8gjuMEKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab24150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_cmap_r = gen_cmap('matteo_r', np.flipud(LinL), 0, 1)\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.pcolor(rand(10,10),cmap=my_cmap_r)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy is awesome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For the impatient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the built-in cubehelix from matplotlib to produce a similar colourmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['f']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a775b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa6a990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%pylab inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib._cm, matplotlib.cm\n",
    "name    = \"matteohelix\"\n",
    "specs   = matplotlib._cm.cubehelix(gamma=1.4,s=0.4,r=-0.8,h=2.0)\n",
    "specs_r = matplotlib.cm._reverse_cmap_spec(specs)\n",
    "matplotlib.cm.register_cmap(name=name     , data=specs)\n",
    "matplotlib.cm.register_cmap(name=name+\"_r\", data=specs_r)\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.pcolor(rand(10,10),cmap=matplotlib.cm.get_cmap(name))\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.pcolor(rand(10,10),cmap=matplotlib.cm.get_cmap(name+'_r'))\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}