Colour Temperature Ramblings

temperature_ramblings.ipynb

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  "name": "",
  "signature": "sha256:06ae9bb8a6c1a7728bcb24a584025e014ea1fe4f0388f1908f3d7105959056ef"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Colour Temperature Ramblings"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "User Defined Illuminant"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import colour  # Importing the API namespace.\n",
      "from colour.plotting import *  # Importing all the plotting objects.\n",
      "\n",
      "# Defining some convenient shortcuts variables.\n",
      "sRGB_w = colour.sRGB_COLOURSPACE.whitepoint  # *CIE Standard Illuminant D Series* *D65*.\n",
      "sRGB_XYZ_to_RGB = colour.sRGB_COLOURSPACE.XYZ_to_RGB_matrix\n",
      "sRGB_RGB_to_XYZ = colour.sRGB_COLOURSPACE.RGB_to_XYZ_matrix\n",
      "sRGB_oecf = colour.sRGB_COLOURSPACE.transfer_function  # Opto-electronic conversion function from *CIE XYZ* to *RGB*.\n",
      "sRGB_inverse_oecf = colour.sRGB_COLOURSPACE.inverse_transfer_function  # Opto-electronic conversion function from *CIE XYZ* to *RGB*.\n",
      "\n",
      "# Retrieving *CIE Standard Illuminant E* chromaticity coordinates.\n",
      "# E = colour.ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['E']\n",
      "E = colour.ILLUMINANTS['cie_2_1931'][\n",
      "    'E']  # Accessing the data using a convenient alias.\n",
      "\n",
      "# Defining a 50% gray *sRGB* colour.\n",
      "RGB = (0.5, 0.5, 0.5)\n",
      "\n",
      "# Converting the 50% gray colour to *CIE XYZ*, under the *CIE Standard Illuminant E*.\n",
      "XYZ = colour.RGB_to_XYZ(RGB,\n",
      "                        sRGB_w,\n",
      "                        E,\n",
      "                        sRGB_RGB_to_XYZ,\n",
      "                        'CAT02',\n",
      "                        sRGB_inverse_oecf)\n",
      "\n",
      "# Defining a convenient function to perform *temperature* change.\n",
      "def temperature(XYZ, illuminant):\n",
      "    return colour.XYZ_to_RGB(XYZ,\n",
      "                             E,\n",
      "                             illuminant,\n",
      "                             sRGB_XYZ_to_RGB,\n",
      "                             'CAT02',\n",
      "                             sRGB_oecf)\n",
      "\n",
      "# Plotting various colour patches to illustrate the *temperature* change using\n",
      "# a given illuminant.\n",
      "multi_colour_plot(\n",
      "    (colour_parameter('Gray \"Original\"', RGB),\n",
      "     colour_parameter('Gray \"A\"',\n",
      "                      temperature(XYZ,\n",
      "                                  colour.ILLUMINANTS['cie_2_1931']['A'])),\n",
      "     colour_parameter('Gray \"B\"',\n",
      "                      temperature(XYZ,\n",
      "                                  colour.ILLUMINANTS['cie_2_1931']['B'])),\n",
      "     colour_parameter('Gray \"C\"',\n",
      "                      temperature(XYZ,\n",
      "                                  colour.ILLUMINANTS['cie_2_1931']['C'])),     \n",
      "     colour_parameter('Gray \"D50\"',\n",
      "                      temperature(XYZ,\n",
      "                                  colour.ILLUMINANTS['cie_2_1931']['D50'])),\n",
      "     colour_parameter('Gray \"D55\"',\n",
      "                      temperature(XYZ,\n",
      "                                  colour.ILLUMINANTS['cie_2_1931']['D55'])),\n",
      "     colour_parameter('Gray \"D60\"',\n",
      "                      temperature(XYZ,\n",
      "                                  colour.ILLUMINANTS['cie_2_1931']['D60'])),\n",
      "     colour_parameter('Gray \"D65\"',\n",
      "                      temperature(XYZ,\n",
      "                                  colour.ILLUMINANTS['cie_2_1931']['D65'])),\n",
      "     colour_parameter('Gray \"D75\"',\n",
      "                      temperature(XYZ,\n",
      "                                  colour.ILLUMINANTS['cie_2_1931']['D75']))),\n",
      "    text_size=16)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/home/vagrant/anaconda/envs/python2.7/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to Bitstream Vera Sans\n",
        "  (prop.get_family(), self.defaultFamily[fontext]))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Aqk+exV8+PGByeQkA/vjO36DV6vD9Jx+au8HX+X2tSYhBRfUp5JdUmoTKFObf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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f721c44ced0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Using Correlated Colour Temperature"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def CCT_to_xy(CCT, tint=0):\n",
      "    return colour.UCS_uv_to_xy(colour.CCT_to_uv_ohno2013(CCT, tint / 6000))\n",
      "\n",
      "# Plotting various colour patches to illustrate the *temperature* change using\n",
      "# a given correlated colour temperature and tint.\n",
      "multi_colour_plot(\n",
      "    (colour_parameter('Gray \"Original\"', RGB),\n",
      "     colour_parameter('Gray \"3K\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(3000))),\n",
      "     colour_parameter('Gray \"4K\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(4000))),\n",
      "     colour_parameter('Gray \"5K\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(5000))),     \n",
      "     colour_parameter('Gray \"6K\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(6000))),\n",
      "     colour_parameter('Gray \"7K\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(7000))),\n",
      "     colour_parameter('Gray \"8K\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(8000))),\n",
      "     colour_parameter('Gray \"9K\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(9000))),\n",
      "     colour_parameter('Gray \"10K\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(10000))),\n",
      "     colour_parameter('Gray \"6K, -150T\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(6000, -150.))),\n",
      "     colour_parameter('Gray \"6K, 0T\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(6000, 0))),\n",
      "     colour_parameter('Gray \"6K, 150T\"',\n",
      "                      temperature(XYZ,\n",
      "                                  CCT_to_xy(6000, 150.)))),\n",
      "    text_size=8)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0pmfRzS+2jh08wa6Ne7lSc5Wls1bz7KTx7N5SwYnDp7BtGFzSH0cn\nB7n52ayav4457y8kp0cW4RFh7cZ5WN1t1+zf/sdf3bfBF2xYysD8vmSmBPczghsWb1zO+CFjAl9I\nmOKy+/J92/aqtZ9Q2Ls3aanB/YTghjXr1lMyrNjIs69qV/V92/bGVVvpUdiN5LR7cxa0ec12BpcM\nCHwhYYpf/fNv4Q7dCvp3Zt/U0k0ruVJ75Z6FDPzfLspX98mnG7haW3vPQgb+byTl69nyyU7qrtbf\ns5ABD803jN+mBxazGz9alQfnxu+75MEaOubOP2uRr05/ziQiRlDMRMQIipmIGEExExEj3O2nGRuA\nkrusIyLybSkFRjzonRARERERERERERERERER+er+f9uw9LCRT1lNAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f721c336f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 2
    }
   ],
   "metadata": {}
  }
 ]
}