{
 "metadata": {
  "name": "",
  "signature": "sha256:94563df1df5a42207776fccfcf713b77bb61a90f210b0c7d28dc4eda5f54382e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Colour - Tutorial"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Colour](https://github.com/KelSolaar/Colour/) spreads over various domains of the Colour Science from colour models to optical phenomenons, this tutorial will not give you a complete overview of the API but will still be good introduction.\n",
      "\n",
      "> Note: A directory full of examples is available at this path in your [Colour](https://github.com/KelSolaar/Colour/) installation: *colour/examples*. You can also explore it directly on Github: https://github.com/colour-science/colour/tree/master/colour/examples"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from colour.plotting import *\n",
      "\n",
      "visible_spectrum_plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AdwIJAAAAoDuBBAAAANCdQAIAAADoTiABAAAAdCeQAAAAALoTSAAA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\nJEmSJEnqnAkJSZIkSZLUuf8HRdGM+NFG1n0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f622c0836d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Overview"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Colour](https://github.com/KelSolaar/Colour/) is organised around various sub-packages:\n",
      "\n",
      "*   **adaptation**: Chromatic adaptation models and transformations.\n",
      "*   **algebra**: Algebra utilities.\n",
      "*   **appearance**: Colour appearance models.\n",
      "*   **characterisation**: Colour fitting and camera characterisation.\n",
      "*   **colorimetry**: Core objects for colour computations.\n",
      "*   **constants**: *CIE* and *CODATA* constants.\n",
      "*   **corresponding**: Corresponding colour chromaticities computations.\n",
      "*   **difference**: Colour difference computations.\n",
      "*   **examples**: Examples for the sub-packages.\n",
      "*   **io**: Input / output objects.\n",
      "*   **models**: Colour models.\n",
      "*   **notation**: Colour notation systems.\n",
      "*   **phenomenons**: Computation of various optical phenomenons.\n",
      "*   **plotting**: Diagrams, figures, etc...\n",
      "*   **quality**: Colour quality computation.\n",
      "*   **recovery**: Reflectance recovery.\n",
      "*   **temperature**: Colour temperature and correlated colour temperature computation.\n",
      "*   **utilities**: Various utilities and data structures.\n",
      "*   **volume**: Colourspace volumes computation and optimal colour stimuli.\n",
      "\n",
      "Most of the public API is available from the root colour namespace:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import colour\n",
      "\n",
      "print(colour.__all__[:5] + ['...'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['CHROMATIC_ADAPTATION_TRANSFORMS', 'XYZ_SCALING_CAT', 'VON_KRIES_CAT', 'BRADFORD_CAT', 'SHARP_CAT', '...']\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The various sub-packages also expose their public API:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pprint import pprint\n",
      "\n",
      "import colour.plotting\n",
      "\n",
      "for sub_package in ('adaptation', 'algebra', 'appearance', 'characterisation',\n",
      "                    'colorimetry', 'constants', 'corresponding', 'difference', \n",
      "                    'io', 'models', 'notation', 'phenomenons', 'plotting', \n",
      "                    'quality', 'recovery', 'temperature', 'utilities',\n",
      "                    'volume'):\n",
      "    print(sub_package.title())\n",
      "    pprint(getattr(colour, sub_package).__all__)\n",
      "    print('\\n')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Adaptation\n",
        "['CHROMATIC_ADAPTATION_TRANSFORMS',\n",
        " 'XYZ_SCALING_CAT',\n",
        " 'VON_KRIES_CAT',\n",
        " 'BRADFORD_CAT',\n",
        " 'SHARP_CAT',\n",
        " 'FAIRCHILD_CAT',\n",
        " 'CMCCAT97_CAT',\n",
        " 'CMCCAT2000_CAT',\n",
        " 'CAT02_CAT',\n",
        " 'CAT02_BRILL_CAT',\n",
        " 'BS_CAT',\n",
        " 'BS_PC_CAT',\n",
        " 'chromatic_adaptation_matrix_VonKries',\n",
        " 'chromatic_adaptation_VonKries',\n",
        " 'chromatic_adaptation_Fairchild1990',\n",
        " 'CMCCAT2000_InductionFactors',\n",
        " 'CMCCAT2000_VIEWING_CONDITIONS',\n",
        " 'CMCCAT2000_forward',\n",
        " 'CMCCAT2000_reverse',\n",
        " 'chromatic_adaptation_CMCCAT2000',\n",
        " 'chromatic_adaptation_CIE1994']\n",
        "\n",
        "\n",
        "Algebra\n",
        "['cartesian_to_spherical',\n",
        " 'spherical_to_cartesian',\n",
        " 'cartesian_to_cylindrical',\n",
        " 'cylindrical_to_cartesian',\n",
        " 'Extrapolator1d',\n",
        " 'LinearInterpolator',\n",
        " 'SpragueInterpolator',\n",
        " 'CubicSplineInterpolator',\n",
        " 'PchipInterpolator',\n",
        " 'is_identity',\n",
        " 'random_triplet_generator']\n",
        "\n",
        "\n",
        "Appearance\n",
        "['Hunt_InductionFactors',\n",
        " 'HUNT_VIEWING_CONDITIONS',\n",
        " 'Hunt_Specification',\n",
        " 'XYZ_to_Hunt',\n",
        " 'ATD95_Specification',\n",
        " 'XYZ_to_ATD95',\n",
        " 'CIECAM02_InductionFactors',\n",
        " 'CIECAM02_VIEWING_CONDITIONS',\n",
        " 'CIECAM02_Specification',\n",
        " 'XYZ_to_CIECAM02',\n",
        " 'CIECAM02_to_XYZ',\n",
        " 'LLAB_VIEWING_CONDITIONS',\n",
        " 'LLAB_Specification',\n",
        " 'XYZ_to_LLAB',\n",
        " 'Nayatani95_Specification',\n",
        " 'XYZ_to_Nayatani95',\n",
        " 'RLAB_VIEWING_CONDITIONS',\n",
        " 'RLAB_D_FACTOR',\n",
        " 'RLAB_Specification',\n",
        " 'XYZ_to_RLAB']\n",
        "\n",
        "\n",
        "Characterisation\n",
        "['COLOURCHECKERS',\n",
        " 'COLOURCHECKER_INDEXES_TO_NAMES_MAPPING',\n",
        " 'COLOURCHECKERS_SPDS',\n",
        " 'first_order_colour_fit']\n",
        "\n",
        "\n",
        "Colorimetry\n",
        "['DEFAULT_WAVELENGTH_DECIMALS',\n",
        " 'SpectralMapping',\n",
        " 'SpectralShape',\n",
        " 'SpectralPowerDistribution',\n",
        " 'TriSpectralPowerDistribution',\n",
        " 'DEFAULT_SPECTRAL_SHAPE',\n",
        " 'constant_spd',\n",
        " 'zeros_spd',\n",
        " 'ones_spd',\n",
        " 'blackbody_spd',\n",
        " 'blackbody_spectral_radiance',\n",
        " 'planck_law',\n",
        " 'LMS_ConeFundamentals',\n",
        " 'RGB_ColourMatchingFunctions',\n",
        " 'XYZ_ColourMatchingFunctions',\n",
        " 'CMFS',\n",
        " 'LMS_CMFS',\n",
        " 'RGB_CMFS',\n",
        " 'STANDARD_OBSERVERS_CMFS',\n",
        " 'ILLUMINANTS',\n",
        " 'D_ILLUMINANTS_S_SPDS',\n",
        " 'ILLUMINANTS_RELATIVE_SPDS',\n",
        " 'LIGHT_SOURCES',\n",
        " 'LIGHT_SOURCES_RELATIVE_SPDS',\n",
        " 'LEFS',\n",
        " 'PHOTOPIC_LEFS',\n",
        " 'SCOTOPIC_LEFS',\n",
        " 'BANDPASS_CORRECTION_METHODS',\n",
        " 'bandpass_correction',\n",
        " 'bandpass_correction_Stearns1988',\n",
        " 'D_illuminant_relative_spd',\n",
        " 'mesopic_luminous_efficiency_function',\n",
        " 'mesopic_weighting_function',\n",
        " 'LIGHTNESS_METHODS',\n",
        " 'lightness',\n",
        " 'lightness_Glasser1958',\n",
        " 'lightness_Wyszecki1963',\n",
        " 'lightness_1976',\n",
        " 'LUMINANCE_METHODS',\n",
        " 'luminance',\n",
        " 'luminance_Newhall1943',\n",
        " 'luminance_ASTMD153508',\n",
        " 'luminance_1976',\n",
        " 'luminous_flux',\n",
        " 'luminous_efficacy',\n",
        " 'RGB_10_degree_cmfs_to_LMS_10_degree_cmfs',\n",
        " 'RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs',\n",
        " 'RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs',\n",
        " 'LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs',\n",
        " 'LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs',\n",
        " 'spectral_to_XYZ',\n",
        " 'wavelength_to_XYZ',\n",
        " 'WHITENESS_METHODS',\n",
        " 'whiteness',\n",
        " 'whiteness_Berger1959',\n",
        " 'whiteness_Taube1960',\n",
        " 'whiteness_Stensby1968',\n",
        " 'whiteness_ASTM313',\n",
        " 'whiteness_Ganz1979',\n",
        " 'whiteness_CIE2004']\n",
        "\n",
        "\n",
        "Constants\n",
        "['CIE_E',\n",
        " 'CIE_K',\n",
        " 'K_M',\n",
        " 'KP_M',\n",
        " 'AVOGADRO_CONSTANT',\n",
        " 'BOLTZMANN_CONSTANT',\n",
        " 'LIGHT_SPEED',\n",
        " 'PLANCK_CONSTANT',\n",
        " 'FLOATING_POINT_NUMBER_PATTERN',\n",
        " 'INTEGER_THRESHOLD',\n",
        " 'EPSILON']\n",
        "\n",
        "\n",
        "Corresponding\n",
        "['BRENEMAN_EXPERIMENTS',\n",
        " 'BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES',\n",
        " 'corresponding_chromaticities_prediction_CIE1994',\n",
        " 'corresponding_chromaticities_prediction_CMCCAT2000',\n",
        " 'corresponding_chromaticities_prediction_Fairchild1990',\n",
        " 'corresponding_chromaticities_prediction_VonKries',\n",
        " 'CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS']\n",
        "\n",
        "\n",
        "Difference\n",
        "['DELTA_E_METHODS',\n",
        " 'delta_E',\n",
        " 'delta_E_CIE1976',\n",
        " 'delta_E_CIE1994',\n",
        " 'delta_E_CIE2000',\n",
        " 'delta_E_CMC']\n",
        "\n",
        "\n",
        "Io\n",
        "['IES_TM2714_Spd',\n",
        " 'read_image',\n",
        " 'write_image',\n",
        " 'read_spectral_data_from_csv_file',\n",
        " 'read_spds_from_csv_file',\n",
        " 'write_spds_to_csv_file',\n",
        " 'read_spds_from_xrite_file']\n",
        "\n",
        "\n",
        "Models\n",
        "['XYZ_to_xyY',\n",
        " 'xyY_to_XYZ',\n",
        " 'xy_to_xyY',\n",
        " 'xyY_to_xy',\n",
        " 'xy_to_XYZ',\n",
        " 'XYZ_to_xy',\n",
        " 'RGB_Colourspace',\n",
        " 'normalised_primary_matrix',\n",
        " 'primaries_whitepoint',\n",
        " 'RGB_luminance_equation',\n",
        " 'RGB_luminance',\n",
        " 'ACES_RICD',\n",
        " 'RGB_COLOURSPACES',\n",
        " 'ACES_2065_1_COLOURSPACE',\n",
        " 'ACES_CC_COLOURSPACE',\n",
        " 'ACES_PROXY_COLOURSPACE',\n",
        " 'ACES_CG_COLOURSPACE',\n",
        " 'ADOBE_RGB_1998_COLOURSPACE',\n",
        " 'ADOBE_WIDE_GAMUT_RGB_COLOURSPACE',\n",
        " 'ALEXA_WIDE_GAMUT_RGB_COLOURSPACE',\n",
        " 'APPLE_RGB_COLOURSPACE',\n",
        " 'BEST_RGB_COLOURSPACE',\n",
        " 'BETA_RGB_COLOURSPACE',\n",
        " 'CIE_RGB_COLOURSPACE',\n",
        " 'CINEMA_GAMUT_COLOURSPACE',\n",
        " 'COLOR_MATCH_RGB_COLOURSPACE',\n",
        " 'DCI_P3_COLOURSPACE',\n",
        " 'DCI_P3_P_COLOURSPACE',\n",
        " 'DON_RGB_4_COLOURSPACE',\n",
        " 'ECI_RGB_V2_COLOURSPACE',\n",
        " 'EKTA_SPACE_PS_5_COLOURSPACE',\n",
        " 'MAX_RGB_COLOURSPACE',\n",
        " 'NTSC_RGB_COLOURSPACE',\n",
        " 'PAL_SECAM_RGB_COLOURSPACE',\n",
        " 'PROPHOTO_RGB_COLOURSPACE',\n",
        " 'REC_709_COLOURSPACE',\n",
        " 'REC_2020_COLOURSPACE',\n",
        " 'RED_COLOR_COLOURSPACE',\n",
        " 'RED_COLOR_2_COLOURSPACE',\n",
        " 'RED_COLOR_3_COLOURSPACE',\n",
        " 'RED_COLOR_4_COLOURSPACE',\n",
        " 'DRAGON_COLOR_COLOURSPACE',\n",
        " 'DRAGON_COLOR_2_COLOURSPACE',\n",
        " 'RUSSELL_RGB_COLOURSPACE',\n",
        " 'SMPTE_C_RGB_COLOURSPACE',\n",
        " 'S_GAMUT_COLOURSPACE',\n",
        " 'S_GAMUT3_COLOURSPACE',\n",
        " 'S_GAMUT3_CINE_COLOURSPACE',\n",
        " 'sRGB_COLOURSPACE',\n",
        " 'V_GAMUT_COLOURSPACE',\n",
        " 'XTREME_RGB_COLOURSPACE',\n",
        " 'POINTER_GAMUT_ILLUMINANT',\n",
        " 'POINTER_GAMUT_DATA',\n",
        " 'POINTER_GAMUT_BOUNDARIES',\n",
        " 'XYZ_to_Lab',\n",
        " 'Lab_to_XYZ',\n",
        " 'Lab_to_LCHab',\n",
        " 'LCHab_to_Lab',\n",
        " 'XYZ_to_Luv',\n",
        " 'Luv_to_XYZ',\n",
        " 'Luv_to_uv',\n",
        " 'Luv_uv_to_xy',\n",
        " 'Luv_to_LCHuv',\n",
        " 'LCHuv_to_Luv',\n",
        " 'XYZ_to_UCS',\n",
        " 'UCS_to_XYZ',\n",
        " 'UCS_to_uv',\n",
        " 'UCS_uv_to_xy',\n",
        " 'XYZ_to_UVW',\n",
        " 'XYZ_to_IPT',\n",
        " 'IPT_to_XYZ',\n",
        " 'IPT_hue_angle',\n",
        " 'LINEAR_TO_LOG_METHODS',\n",
        " 'LOG_TO_LINEAR_METHODS',\n",
        " 'linear_to_log',\n",
        " 'log_to_linear',\n",
        " 'linear_to_cineon',\n",
        " 'cineon_to_linear',\n",
        " 'linear_to_panalog',\n",
        " 'panalog_to_linear',\n",
        " 'linear_to_red_log_film',\n",
        " 'red_log_film_to_linear',\n",
        " 'linear_to_viper_log',\n",
        " 'viper_log_to_linear',\n",
        " 'linear_to_pivoted_log',\n",
        " 'pivoted_log_to_linear',\n",
        " 'linear_to_c_log',\n",
        " 'c_log_to_linear',\n",
        " 'linear_to_aces_cc',\n",
        " 'aces_cc_to_linear',\n",
        " 'linear_to_alexa_log_c',\n",
        " 'alexa_log_c_to_linear',\n",
        " 'linear_to_s_log',\n",
        " 'linear_to_dci_p3_log',\n",
        " 'dci_p3_log_to_linear',\n",
        " 's_log_to_linear',\n",
        " 'linear_to_s_log2',\n",
        " 's_log2_to_linear',\n",
        " 'linear_to_s_log3',\n",
        " 's_log3_to_linear',\n",
        " 'linear_to_v_log',\n",
        " 'v_log_to_linear',\n",
        " 'XYZ_to_RGB',\n",
        " 'RGB_to_XYZ',\n",
        " 'RGB_to_RGB',\n",
        " 'XYZ_to_sRGB',\n",
        " 'sRGB_to_XYZ',\n",
        " 'spectral_to_aces_relative_exposure_values']\n",
        "\n",
        "\n",
        "Notation\n",
        "['MUNSELL_COLOURS_ALL',\n",
        " 'MUNSELL_COLOURS_1929',\n",
        " 'MUNSELL_COLOURS_REAL',\n",
        " 'MUNSELL_COLOURS',\n",
        " 'munsell_value',\n",
        " 'MUNSELL_VALUE_METHODS',\n",
        " 'munsell_value_Priest1920',\n",
        " 'munsell_value_Munsell1933',\n",
        " 'munsell_value_Moon1943',\n",
        " 'munsell_value_Saunderson1944',\n",
        " 'munsell_value_Ladd1955',\n",
        " 'munsell_value_McCamy1987',\n",
        " 'munsell_value_ASTMD153508',\n",
        " 'munsell_colour_to_xyY',\n",
        " 'xyY_to_munsell_colour']\n",
        "\n",
        "\n",
        "Phenomenons\n",
        "['scattering_cross_section',\n",
        " 'rayleigh_optical_depth',\n",
        " 'rayleigh_scattering',\n",
        " 'rayleigh_scattering_spd']\n",
        "\n",
        "\n",
        "Plotting\n",
        "['ASTM_G_173_ETR',\n",
        " 'PLOTTING_RESOURCES_DIRECTORY',\n",
        " 'DEFAULT_FIGURE_ASPECT_RATIO',\n",
        " 'DEFAULT_FIGURE_WIDTH',\n",
        " 'DEFAULT_FIGURE_HEIGHT',\n",
        " 'DEFAULT_FIGURE_SIZE',\n",
        " 'DEFAULT_FONT_SIZE',\n",
        " 'DEFAULT_COLOUR_CYCLE',\n",
        " 'DEFAULT_HATCH_PATTERNS',\n",
        " 'DEFAULT_PARAMETERS',\n",
        " 'DEFAULT_PLOTTING_ILLUMINANT',\n",
        " 'DEFAULT_PLOTTING_OECF',\n",
        " 'ColourParameter',\n",
        " 'colour_cycle',\n",
        " 'canvas',\n",
        " 'camera',\n",
        " 'decorate',\n",
        " 'boundaries',\n",
        " 'display',\n",
        " 'label_rectangles',\n",
        " 'equal_axes3d',\n",
        " 'get_RGB_colourspace',\n",
        " 'get_cmfs',\n",
        " 'get_illuminant',\n",
        " 'colour_parameter',\n",
        " 'colour_parameters_plot',\n",
        " 'single_colour_plot',\n",
        " 'multi_colour_plot',\n",
        " 'image_plot',\n",
        " 'single_spd_plot',\n",
        " 'multi_spd_plot',\n",
        " 'single_cmfs_plot',\n",
        " 'multi_cmfs_plot',\n",
        " 'single_illuminant_relative_spd_plot',\n",
        " 'multi_illuminants_relative_spd_plot',\n",
        " 'visible_spectrum_plot',\n",
        " 'single_lightness_function_plot',\n",
        " 'multi_lightness_function_plot',\n",
        " 'blackbody_spectral_radiance_plot',\n",
        " 'blackbody_colours_plot',\n",
        " 'colour_checker_plot',\n",
        " 'CIE_1931_chromaticity_diagram_plot',\n",
        " 'CIE_1960_UCS_chromaticity_diagram_plot',\n",
        " 'CIE_1976_UCS_chromaticity_diagram_plot',\n",
        " 'spds_CIE_1931_chromaticity_diagram_plot',\n",
        " 'spds_CIE_1960_UCS_chromaticity_diagram_plot',\n",
        " 'spds_CIE_1976_UCS_chromaticity_diagram_plot',\n",
        " 'corresponding_chromaticities_prediction_plot',\n",
        " 'quad',\n",
        " 'grid',\n",
        " 'cube',\n",
        " 'RGB_colourspaces_CIE_1931_chromaticity_diagram_plot',\n",
        " 'RGB_colourspaces_CIE_1960_UCS_chromaticity_diagram_plot',\n",
        " 'RGB_colourspaces_CIE_1976_UCS_chromaticity_diagram_plot',\n",
        " 'RGB_chromaticity_coordinates_CIE_1931_chromaticity_diagram_plot',\n",
        " 'RGB_chromaticity_coordinates_CIE_1960_UCS_chromaticity_diagram_plot',\n",
        " 'RGB_chromaticity_coordinates_CIE_1976_UCS_chromaticity_diagram_plot',\n",
        " 'single_transfer_function_plot',\n",
        " 'multi_transfer_function_plot',\n",
        " 'single_munsell_value_function_plot',\n",
        " 'multi_munsell_value_function_plot',\n",
        " 'single_rayleigh_scattering_spd_plot',\n",
        " 'the_blue_sky_plot',\n",
        " 'single_spd_colour_rendering_index_bars_plot',\n",
        " 'multi_spd_colour_rendering_index_bars_plot',\n",
        " 'single_spd_colour_quality_scale_bars_plot',\n",
        " 'multi_spd_colour_quality_scale_bars_plot',\n",
        " 'planckian_locus_CIE_1931_chromaticity_diagram_plot',\n",
        " 'planckian_locus_CIE_1960_UCS_chromaticity_diagram_plot',\n",
        " 'RGB_colourspaces_gamuts_plot',\n",
        " 'RGB_scatter_plot']\n",
        "\n",
        "\n",
        "Quality\n",
        "['TCS_SPDS',\n",
        " 'VS_SPDS',\n",
        " 'CRI_Specification',\n",
        " 'colour_rendering_index',\n",
        " 'CQS_Specification',\n",
        " 'colour_quality_scale']\n",
        "\n",
        "\n",
        "Recovery\n",
        "['SMITS_1999_SPDS', 'RGB_to_spectral_Smits1999']\n",
        "\n",
        "\n",
        "Temperature\n",
        "['CCT_TO_UV_METHODS',\n",
        " 'UV_TO_CCT_METHODS',\n",
        " 'CCT_to_uv',\n",
        " 'CCT_to_uv_Ohno2013',\n",
        " 'CCT_to_uv_Robertson1968',\n",
        " 'uv_to_CCT',\n",
        " 'uv_to_CCT_Ohno2013',\n",
        " 'uv_to_CCT_Robertson1968',\n",
        " 'CCT_TO_XY_METHODS',\n",
        " 'XY_TO_CCT_METHODS',\n",
        " 'CCT_to_xy',\n",
        " 'CCT_to_xy_Kang2002',\n",
        " 'CCT_to_xy_CIE_D',\n",
        " 'xy_to_CCT',\n",
        " 'xy_to_CCT_McCamy1992',\n",
        " 'xy_to_CCT_Hernandez1999']\n",
        "\n",
        "\n",
        "Utilities\n",
        "['handle_numpy_errors',\n",
        " 'ignore_numpy_errors',\n",
        " 'raise_numpy_errors',\n",
        " 'print_numpy_errors',\n",
        " 'warn_numpy_errors',\n",
        " 'ignore_python_warnings',\n",
        " 'batch',\n",
        " 'is_openimageio_installed',\n",
        " 'is_scipy_installed',\n",
        " 'is_iterable',\n",
        " 'is_string',\n",
        " 'is_numeric',\n",
        " 'is_integer',\n",
        " 'as_numeric',\n",
        " 'closest',\n",
        " 'normalise',\n",
        " 'steps',\n",
        " 'is_uniform',\n",
        " 'in_array',\n",
        " 'tstack',\n",
        " 'tsplit',\n",
        " 'row_as_diagonal',\n",
        " 'dot_vector',\n",
        " 'dot_matrix',\n",
        " 'ArbitraryPrecisionMapping',\n",
        " 'Lookup',\n",
        " 'Structure',\n",
        " 'CaseInsensitiveMapping',\n",
        " 'message_box',\n",
        " 'warning']\n",
        "\n",
        "\n",
        "Volume\n",
        "['ILLUMINANTS_OPTIMAL_COLOUR_STIMULI',\n",
        " 'is_within_macadam_limits',\n",
        " 'is_within_mesh_volume',\n",
        " 'is_within_pointer_gamut',\n",
        " 'is_within_visible_spectrum',\n",
        " 'RGB_colourspace_limits',\n",
        " 'RGB_colourspace_volume_MonteCarlo',\n",
        " 'RGB_colourspace_volume_coverage_MonteCarlo',\n",
        " 'RGB_colourspace_pointer_gamut_coverage_MonteCarlo',\n",
        " 'RGB_colourspace_visible_spectrum_coverage_MonteCarlo']\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The code is well documented and almost every docstrings have usage examples:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(colour.CCT_to_uv_Ohno2013.__doc__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "    Returns the *CIE UCS* colourspace *uv* chromaticity coordinates from given\n",
        "    correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}` and\n",
        "    colour matching functions using Ohno (2013) method.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    CCT : numeric\n",
        "        Correlated colour temperature :math:`T_{cp}`.\n",
        "    D_uv : numeric, optional\n",
        "        :math:`\\Delta_{uv}`.\n",
        "    cmfs : XYZ_ColourMatchingFunctions, optional\n",
        "        Standard observer colour matching functions.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    ndarray\n",
        "        *CIE UCS* colourspace *uv* chromaticity coordinates.\n",
        "\n",
        "    References\n",
        "    ----------\n",
        "    .. [4]  Ohno, Y. (2014). Practical Use and Calculation of CCT and Duv.\n",
        "            LEUKOS, 10(1), 47\u201355. doi:10.1080/15502724.2014.839020\n",
        "\n",
        "    Examples\n",
        "    --------\n",
        "    >>> from colour import STANDARD_OBSERVERS_CMFS\n",
        "    >>> cmfs = 'CIE 1931 2 Degree Standard Observer'\n",
        "    >>> cmfs = STANDARD_OBSERVERS_CMFS.get(cmfs)\n",
        "    >>> CCT = 6507.4342201047066\n",
        "    >>> D_uv = 0.003223690901512735\n",
        "    >>> CCT_to_uv_Ohno2013(CCT, D_uv, cmfs)  # doctest: +ELLIPSIS\n",
        "    array([ 0.1978003...,  0.3122005...])\n",
        "    \n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At the core of [Colour](https://github.com/KelSolaar/Colour/) is the *colour.colorimetry* sub-package, it defines the objects needed for spectral related computations and many others:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pprint import pprint\n",
      "import colour.colorimetry as colorimetry\n",
      "\n",
      "pprint(colorimetry.__all__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['DEFAULT_WAVELENGTH_DECIMALS',\n",
        " 'SpectralMapping',\n",
        " 'SpectralShape',\n",
        " 'SpectralPowerDistribution',\n",
        " 'TriSpectralPowerDistribution',\n",
        " 'DEFAULT_SPECTRAL_SHAPE',\n",
        " 'constant_spd',\n",
        " 'zeros_spd',\n",
        " 'ones_spd',\n",
        " 'blackbody_spd',\n",
        " 'blackbody_spectral_radiance',\n",
        " 'planck_law',\n",
        " 'LMS_ConeFundamentals',\n",
        " 'RGB_ColourMatchingFunctions',\n",
        " 'XYZ_ColourMatchingFunctions',\n",
        " 'CMFS',\n",
        " 'LMS_CMFS',\n",
        " 'RGB_CMFS',\n",
        " 'STANDARD_OBSERVERS_CMFS',\n",
        " 'ILLUMINANTS',\n",
        " 'D_ILLUMINANTS_S_SPDS',\n",
        " 'ILLUMINANTS_RELATIVE_SPDS',\n",
        " 'LIGHT_SOURCES',\n",
        " 'LIGHT_SOURCES_RELATIVE_SPDS',\n",
        " 'LEFS',\n",
        " 'PHOTOPIC_LEFS',\n",
        " 'SCOTOPIC_LEFS',\n",
        " 'BANDPASS_CORRECTION_METHODS',\n",
        " 'bandpass_correction',\n",
        " 'bandpass_correction_Stearns1988',\n",
        " 'D_illuminant_relative_spd',\n",
        " 'mesopic_luminous_efficiency_function',\n",
        " 'mesopic_weighting_function',\n",
        " 'LIGHTNESS_METHODS',\n",
        " 'lightness',\n",
        " 'lightness_Glasser1958',\n",
        " 'lightness_Wyszecki1963',\n",
        " 'lightness_1976',\n",
        " 'LUMINANCE_METHODS',\n",
        " 'luminance',\n",
        " 'luminance_Newhall1943',\n",
        " 'luminance_ASTMD153508',\n",
        " 'luminance_1976',\n",
        " 'luminous_flux',\n",
        " 'luminous_efficacy',\n",
        " 'RGB_10_degree_cmfs_to_LMS_10_degree_cmfs',\n",
        " 'RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs',\n",
        " 'RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs',\n",
        " 'LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs',\n",
        " 'LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs',\n",
        " 'spectral_to_XYZ',\n",
        " 'wavelength_to_XYZ',\n",
        " 'WHITENESS_METHODS',\n",
        " 'whiteness',\n",
        " 'whiteness_Berger1959',\n",
        " 'whiteness_Taube1960',\n",
        " 'whiteness_Stensby1968',\n",
        " 'whiteness_ASTM313',\n",
        " 'whiteness_Ganz1979',\n",
        " 'whiteness_CIE2004']\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Colour](https://github.com/KelSolaar/Colour/) computations are based on a comprehensive dataset available in pretty much each sub-packages, for example *colour.colorimetry.dataset* defines the following data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import colour.colorimetry.dataset as dataset\n",
      "\n",
      "pprint(dataset.__all__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['CMFS',\n",
        " 'LMS_CMFS',\n",
        " 'RGB_CMFS',\n",
        " 'STANDARD_OBSERVERS_CMFS',\n",
        " 'ILLUMINANTS',\n",
        " 'D_ILLUMINANTS_S_SPDS',\n",
        " 'ILLUMINANTS_RELATIVE_SPDS',\n",
        " 'LIGHT_SOURCES',\n",
        " 'LIGHT_SOURCES_RELATIVE_SPDS',\n",
        " 'LEFS',\n",
        " 'PHOTOPIC_LEFS',\n",
        " 'SCOTOPIC_LEFS']\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Spectral Computations"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "From Spectral Power Distribution"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Whether it be a sample spectral power distribution, colour matching functions or illuminants, spectral data is manipulated using an object built with the *colour.SpectralPowerDistribution* class or based on it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Defining a sample spectral power distribution data.\n",
      "sample_spd_data = {\n",
      "    380: 0.048,\n",
      "    385: 0.051,\n",
      "    390: 0.055,\n",
      "    395: 0.060,\n",
      "    400: 0.065,\n",
      "    405: 0.068,\n",
      "    410: 0.068,\n",
      "    415: 0.067,\n",
      "    420: 0.064,\n",
      "    425: 0.062,\n",
      "    430: 0.059,\n",
      "    435: 0.057,\n",
      "    440: 0.055,\n",
      "    445: 0.054,\n",
      "    450: 0.053,\n",
      "    455: 0.053,\n",
      "    460: 0.052,\n",
      "    465: 0.052,\n",
      "    470: 0.052,\n",
      "    475: 0.053,\n",
      "    480: 0.054,\n",
      "    485: 0.055,\n",
      "    490: 0.057,\n",
      "    495: 0.059,\n",
      "    500: 0.061,\n",
      "    505: 0.062,\n",
      "    510: 0.065,\n",
      "    515: 0.067,\n",
      "    520: 0.070,\n",
      "    525: 0.072,\n",
      "    530: 0.074,\n",
      "    535: 0.075,\n",
      "    540: 0.076,\n",
      "    545: 0.078,\n",
      "    550: 0.079,\n",
      "    555: 0.082,\n",
      "    560: 0.087,\n",
      "    565: 0.092,\n",
      "    570: 0.100,\n",
      "    575: 0.107,\n",
      "    580: 0.115,\n",
      "    585: 0.122,\n",
      "    590: 0.129,\n",
      "    595: 0.134,\n",
      "    600: 0.138,\n",
      "    605: 0.142,\n",
      "    610: 0.146,\n",
      "    615: 0.150,\n",
      "    620: 0.154,\n",
      "    625: 0.158,\n",
      "    630: 0.163,\n",
      "    635: 0.167,\n",
      "    640: 0.173,\n",
      "    645: 0.180,\n",
      "    650: 0.188,\n",
      "    655: 0.196,\n",
      "    660: 0.204,\n",
      "    665: 0.213,\n",
      "    670: 0.222,\n",
      "    675: 0.231,\n",
      "    680: 0.242,\n",
      "    685: 0.251,\n",
      "    690: 0.261,\n",
      "    695: 0.271,\n",
      "    700: 0.282,\n",
      "    705: 0.294,\n",
      "    710: 0.305,\n",
      "    715: 0.318,\n",
      "    720: 0.334,\n",
      "    725: 0.354,\n",
      "    730: 0.372,\n",
      "    735: 0.392,\n",
      "    740: 0.409,\n",
      "    745: 0.420,\n",
      "    750: 0.436,\n",
      "    755: 0.450,\n",
      "    760: 0.462,\n",
      "    765: 0.465,\n",
      "    770: 0.448,\n",
      "    775: 0.432,\n",
      "    780: 0.421}\n",
      "\n",
      "spd = colour.SpectralPowerDistribution('Sample', sample_spd_data)\n",
      "print(spd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x7f62051ff5d0>\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The sample spectral power distribution can be easily plotted against the visible spectrum:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plotting the sample spectral power distribution.\n",
      "single_spd_plot(spd)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/AOoi2HBSgHoi+4BIkiRJkmrW/fffz3PPPQc4VaO3MSEhSZIkSapJq1ev5qyz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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f620529f8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With the sample spectral power distribution defined, we can retrieve its shape:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Displaying the sample spectral power distribution shape.\n",
      "print(spd.shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(380.0, 780.0, 5.0)\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The shape returned is an instance of *colour.SpectralShape* class:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "repr(spd.shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "'SpectralShape(380.0, 780.0, 5.0)'"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*colour.SpectralShape* is used throughout [Colour](https://github.com/KelSolaar/Colour/) to define spectral dimensions and is instantiated as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Using *colour.SpectralShape* with iteration.\n",
      "shape = colour.SpectralShape(start=0, end=10, steps=1)\n",
      "for wavelength in shape:\n",
      "    print(wavelength)\n",
      "\n",
      "# *colour.SpectralShape.range* method is providing the complete range of values. \n",
      "shape = colour.SpectralShape(0, 10, 0.5)\n",
      "shape.range()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0\n",
        "1.0\n",
        "2.0\n",
        "3.0\n",
        "4.0\n",
        "5.0\n",
        "6.0\n",
        "7.0\n",
        "8.0\n",
        "9.0\n",
        "10.0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([  0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,\n",
        "         4.5,   5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,\n",
        "         9. ,   9.5,  10. ])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Colour](https://github.com/KelSolaar/Colour/) defines three convenient objects to create constant spectral power distributions:\n",
      "\n",
      "*  *colour.constant_spd*\n",
      "*  *colour.zeros_spd*\n",
      "*  *colour.ones_spd*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Defining a constant spectral power distribution.\n",
      "constant_spd = colour.constant_spd(100)\n",
      "print('\"Constant Spectral Power Distribution\"')\n",
      "print(constant_spd.shape)\n",
      "print(constant_spd[400])\n",
      "\n",
      "# Defining a zeros filled spectral power distribution.\n",
      "print('\\n\"Zeros Filled Spectral Power Distribution\"')\n",
      "zeros_spd = colour.zeros_spd()\n",
      "print(zeros_spd.shape)\n",
      "print(zeros_spd[400])\n",
      "\n",
      "# Defining a ones filled spectral power distribution.\n",
      "print('\\n\"Ones Filled Spectral Power Distribution\"')\n",
      "ones_spd = colour.ones_spd()\n",
      "print(ones_spd.shape)\n",
      "print(ones_spd[400])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\"Constant Spectral Power Distribution\"\n",
        "(360.0, 830.0, 1.0)\n",
        "100.0\n",
        "\n",
        "\"Zeros Filled Spectral Power Distribution\"\n",
        "(360.0, 830.0, 1.0)\n",
        "0.0\n",
        "\n",
        "\"Ones Filled Spectral Power Distribution\"\n",
        "(360.0, 830.0, 1.0)\n",
        "1.0\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By default the shape used by *colour.constant_spd*, *colour.zeros_spd* and *colour.ones_spd* is the one defined by *colour.DEFAULT_SPECTRAL_SHAPE* attribute using the *CIE 1931 2\u00b0 Standard Observer* shape."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(repr(colour.DEFAULT_SPECTRAL_SHAPE))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SpectralShape(360.0, 830.0, 1.0)\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A custom shape can be passed to construct a constant spectral power distribution with tailored dimensions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colour.ones_spd(colour.SpectralShape(400, 700, 5))[450]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "array(1.0)"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The *colour.SpectralPowerDistribution* class supports the following arithmetical operations:\n",
      "\n",
      "* *addition*\n",
      "* *subtraction*\n",
      "* *multiplication*\n",
      "* *division*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "spd1 = colour.ones_spd()\n",
      "print('\"Ones Filled Spectral Power Distribution\"')\n",
      "print(spd1[400])\n",
      "\n",
      "print('\\n\"x2 Constant Multiplied\"')\n",
      "print((spd1 * 2)[400])\n",
      "\n",
      "print('\\n\"+ Spectral Power Distribution\"')\n",
      "print((spd1 + colour.ones_spd())[400])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\"Ones Filled Spectral Power Distribution\"\n",
        "1.0\n",
        "\n",
        "\"x2 Constant Multiplied\"\n",
        "2.0\n",
        "\n",
        "\"+ Spectral Power Distribution\"\n",
        "3.0\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Often interpolation of the spectral power distribution is needed, this is achieved with the *colour.SpectralPowerDistribution.interpolate* method. Depending on the wavelengths uniformity, the default interpolation method will differ. Following *CIE 167:2005* recommendation: The method developed by *Sprague* (1880) should be used for interpolating functions having a uniformly spaced independent variable and a *Cubic Spline* method for non-uniformly spaced independent variable. <a name=\"back_reference_1\"><a href=\"#reference_1\">[1]</a></a>\n",
      "\n",
      "We can check the uniformity of the sample spectral power distribution:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Checking the sample spectral power distribution uniformity.\n",
      "print(spd.is_uniform())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since the sample spectral power distribution is uniform the interpolation will be using the *colour.SpragueInterpolator* interpolator.\n",
      "\n",
      "> Note: Interpolation happens in place and may alter your original data, use the *colour.SpectralPowerDistribution.clone* method to produce a copy of your spectral power distribution before interpolation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Cloning the sample spectral power distribution.\n",
      "clone_spd = spd.clone()\n",
      "\n",
      "# Interpolating the cloned sample spectral power distribution.\n",
      "clone_spd.interpolate(colour.SpectralShape(400, 770, 1))\n",
      "clone_spd[401]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array(0.06580960000000001)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Comparing the interpolated spectral power distribution with the original one.\n",
      "multi_spd_plot([spd, clone_spd], bounding_box=[730,780, 0.1, 0.5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nCcOQi3cusvfibvYe/SX5RTvZV3GGuzHNu/IHIaRfg3ml8WT1msC88YvIyFpJ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\nDJZgAwAAABgswQYAAAAwWP8/IdAE6gtJEgAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f620584b950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Extrapolation although dangerous can be used to help aligning two spectral power distributions together. *CIE publication CIE 15:2004 \"Colorimetry\"* recommends that unmeasured values may be set equal to the nearest measured value of the appropriate quantity in truncation: <a name=\"back_reference_2\"><a href=\"#reference_2\">[2]</a></a>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extrapolating the cloned sample spectral power distribution.\n",
      "clone_spd.extrapolate(colour.SpectralShape(340, 830))\n",
      "clone_spd[340], clone_spd[830]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(array(0.065), array(0.448))"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The extrapolation can be forced to use *Linear* method instead of the *Constant* default method or even use arbitrary constant *left* and *right* values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extrapolating the cloned sample spectral power distribution with *Linear* method.\n",
      "# We first trim the spectral power distribution using the interpolation method,\n",
      "# the steps being the same, no interpolation will actually occur.\n",
      "clone_spd.interpolate(colour.SpectralShape(400, 700))  \n",
      "clone_spd.extrapolate(colour.SpectralShape(340, 830), method='Linear', right=0)\n",
      "clone_spd[340], clone_spd[830]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(array(0.01642399999999955), array(0.0))"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Aligning a spectral power distribution is a convenient way to first interpolatesthe current data within its original bounds then if needed extrapolates any missing values to match the requested shape:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Aligning the cloned sample spectral power distribution.\n",
      "# We first trim the spectral power distribution as above.\n",
      "clone_spd.interpolate(colour.SpectralShape(400, 700))  \n",
      "clone_spd.align(colour.SpectralShape(340, 830, 5))\n",
      "clone_spd[340], clone_spd[830]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(array(0.065), array(0.282))"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The *colour.SpectralPowerDistribution* class also supports various arithmetic operations like *addition*, *subtraction*, *multiplication* or *division* with *numeric* and *array_like* variables or other *colour.SpectralPowerDistribution* class instances:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "spd = colour.SpectralPowerDistribution('Sample', {410: 0.50, 420: 0.75, 430: 1., 440: 0.75, 450: 0.50})\n",
      "print((spd.clone() + 1).values)\n",
      "print((spd.clone() * 2).values)\n",
      "print((spd * [0.35, 1.55, 0.75, 2.55, 0.95]).values)\n",
      "print((spd * colour.constant_spd(2, spd.shape) * colour.constant_spd(3, spd.shape)).values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 1.5   1.75  2.    1.75  1.5 ]\n",
        "[ 1.   1.5  2.   1.5  1. ]\n",
        "[ 0.175   1.1625  0.75    1.9125  0.475 ]\n",
        "[  1.05    6.975   4.5    11.475   2.85 ]\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The spectral power distribution can be normalised with an arbitrary factor:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(spd.normalise().values)\n",
      "print(spd.normalise(100).values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.09150327  0.60784314  0.39215686  1.          0.24836601]\n",
        "[   9.1503268    60.78431373   39.21568627  100.           24.83660131]\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "To Tristimulus Values"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From a given spectral power distribution, *CIE XYZ* tristimulus values can be calculated:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "spd = colour.SpectralPowerDistribution('Sample', sample_spd_data)\n",
      "cmfs = colour.STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']\n",
      "illuminant = colour.ILLUMINANTS_RELATIVE_SPDS['D65']\n",
      "\n",
      "# Calculating the sample spectral power distribution *CIE XYZ* tristimulus values.\n",
      "XYZ = colour.spectral_to_XYZ(spd, cmfs, illuminant)\n",
      "print(XYZ)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 10.9706809    9.7027807    6.05480757]\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> Note: Output *CIE XYZ* colourspace matrix is in domain [0, 100]."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "From *CIE XYZ* Colourspace"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*CIE XYZ* is the central colourspace for Colour Science from which many computations are available, cascading to even more computations:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Displaying objects interacting directly with the *CIE XYZ* colourspace.\n",
      "pprint([name for name in colour.__all__ if name.startswith('XYZ_to')])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['XYZ_to_Hunt',\n",
        " 'XYZ_to_ATD95',\n",
        " 'XYZ_to_CIECAM02',\n",
        " 'XYZ_to_LLAB',\n",
        " 'XYZ_to_Nayatani95',\n",
        " 'XYZ_to_RLAB',\n",
        " 'XYZ_to_xyY',\n",
        " 'XYZ_to_xy',\n",
        " 'XYZ_to_Lab',\n",
        " 'XYZ_to_Luv',\n",
        " 'XYZ_to_UCS',\n",
        " 'XYZ_to_UVW',\n",
        " 'XYZ_to_IPT',\n",
        " 'XYZ_to_RGB',\n",
        " 'XYZ_to_sRGB']\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "To Screen Colours"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can for instance converts the *CIE XYZ* tristimulus values into *sRGB* colourspace *RGB* values in order to display them on screen:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The output domain of *colour.spectral_to_XYZ* is [0, 100] and the input domain of *colour.XYZ_to_sRGB* is [0, 1].\n",
      "# We need to take it in account and rescale the input *CIE XYZ* colourspace matrix.\n",
      "RGB = colour.XYZ_to_sRGB(XYZ / 100)\n",
      "print(RGB)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.45678515  0.30983401  0.24860046]\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plotting the *sRGB* colourspace colour of the *Sample* spectral power distribution.\n",
      "single_colour_plot(colour_parameter('Sample', RGB), text_size=32)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HqnNFe31j55ab7vfSxEJsTc7M1pa+3tq5sa+6VqyowbGJOnz6fM3NN+vi6ET9r18frO9/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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6205fb4990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "To Colour Rendition Charts"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the same way, we can compute values from a colour rendition chart sample.\n",
      "\n",
      "> Note: This is useful for render time checks in the VFX industry, where you can use a synthetic colour chart into your render and ensure the colour management is acting as expected.\n",
      "\n",
      "The *colour.characterisation* sub-package contains the dataset for various colour rendition charts:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Colour rendition charts chromaticity coordinates.\n",
      "print(sorted(colour.characterisation.COLOURCHECKERS.keys()))\n",
      "\n",
      "# Colour rendition charts spectral power distributions.\n",
      "print(sorted(colour.characterisation.COLOURCHECKERS_SPDS.keys()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'BabelColor Average', u'ColorChecker 1976', u'ColorChecker 2005', u'babel_average', u'cc2005']\n",
        "[u'BabelColor Average', u'ColorChecker N Ohta', u'babel_average', u'cc_ohta']\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> Note: The above *cc2005*, *babel_average* and *cc_ohta* keys are convenient aliases for respectively *ColorChecker 2005*, *BabelColor Average* and *ColorChecker N Ohta* keys."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plotting the *sRGB* colourspace colour of *neutral 5 (.70 D)* patch.\n",
      "patch_name = 'neutral 5 (.70 D)'\n",
      "patch_spd = colour.COLOURCHECKERS_SPDS['ColorChecker N Ohta'][patch_name]\n",
      "XYZ = colour.spectral_to_XYZ(patch_spd, cmfs, illuminant)\n",
      "RGB = colour.XYZ_to_sRGB(XYZ / 100)\n",
      "\n",
      "single_colour_plot(colour_parameter(patch_name.title(), RGB), text_size=32)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fHy0tLbFkyZLYt29/6R6ovZkzZ268//570dXVFd///vdjwYIFMXLkyFJ0jR8/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\nAzPNwL8k8XxjAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f62060465d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[Colour](https://github.com/KelSolaar/Colour/) defines a convenient plotting object to draw synthetic colour rendition charts figures:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colour_checker_plot(colour_checker='ColorChecker 2005', text_display=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/colour-science/colour/colour/models/dataset/srgb.py:111: RuntimeWarning: invalid value encountered in power\n",
        "  1.055 * (value ** (1 / 2.4)) - 0.055)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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j+++/f1544YW8+93vbu931113ted66KGH5otf/GKP7X+tr3zlK9lrr71y/PHH\np6OjIzvttFOmTJmSbbfd9iU93pdixRVX7PP2Rx55JMOGDcvw4cNzwgkn5Ktf/Wre+c539vPs+k9/\n/ix3f85LKS/78QFeDAEHvCJe97rX5fOf/3yOO+64TJkyJc8++2wWLlyYm2++OaeddlqS1qrSUUcd\nlZtvvjk333xzvvzlL2ellVbq83jrrbdeJkyYkM7OzkyePDnbb799e9uJJ56YY489Nuecc05mzpyZ\njo6OfPGLX8zEiRMzfvz4/PnPf06S3HvvvTn88MN7HHf06NG59dZb82//9m/ZY4892revtNJKOfLI\nIzNlypTccsstOfbYY7Pyyiu3t48cOTITJ07M9OnTc/3112frrbdebM5rr712Jk2alH322SdJsskm\nm+Syyy5LZ2dnJk6cmC222KK974QJE/JP//RP+f73v59f/OIXectb3tLjWPPnz8/YsWPbK2o333xz\n/vu//zvvec97kiQ77rhjfv3rX+eGG27IggULctZZZ2WDDTbIuuuumyQZNWpUxo8fnzlz5mTOnDm5\n8MILezze5OX7hXXgwIEZN25cOjs7c8cdd+SSSy7pc7/HHnsst912W4/4OOKII3LVVVfl/PPPzxNP\nPJEkefjhh3PWWWdl+vTpfc71vvvuy29/+9ust956f9N811lnnXz605/O4YcfnjvvvDPPP/98nn32\n2Vx99dU5//zzk7RezyeddFJuu+22TJkyZamrTZtttlmuvPLK3HnnnfnBD36QTTfdtL1typQp2XLL\nLdtfH3LIITn55JOTtFZ+7r333uy5556ZMmVKLrzwwmXO/ZZbbsncuXPzjne8o33b/vvvn+uvvz4/\n//nPc/rpp+f1r399j+PvvvvumTJlSm6//fYej+M1r3lNTjzxxNxxxx35yU9+kg033LDHWIvmvs02\n2+SAAw7IzjvvnJkzZ+ZHP/pRksVPHx4zZkymTJmSW2+9NSeddFJWW221FzWP3l7qz/K2226bH/3o\nR5k+fXqmTp2agw8+uL2t93N+0UUXZcKECUmS6dOnZ+bMmdl4442X+X0AeDkJOOAVsemmm2bllVfO\njTfeuMR9xowZk4033ri9KrTxxhtnzJgxi+03YMCAnH322bnllluy1VZb5YQTTsgpp5zSjpMk2XXX\nXXP22Wdn2LBhmTVrVjbZZJNcf/31S53jG9/4xrzuda/LBz7wgXzlK1/JMccck9e97nVJWqcbvu1t\nb8uoUaM4HI6rAAANkUlEQVTy4Q9/OIMHD27/4rfRRhvlpJNOysknn5wtttgie++9d2bPnt3j2EOH\nDs2ECRNyySWX5MILL8ygQYMybty4jB07Nh0dHfnmN7+ZM888MwMHDmzf56Mf/WiOPvrobL755j1O\nfezLWmutlXXXXTe/+c1vkiTvfOc7c99997W3z58/Pw8++GA7jt7xjnf02P6rX/1qsVWbU045Jbfd\ndlvOP//8vOtd71rq+Euz77775qGHHsr73ve+bLXVVj1O1Uz+J74GDx6c97///bn77ruTJKussko2\n2WST3HDDDX/VeBtttFHWXXfd3HPPPX/TfLfccss8/PDDS73/0UcfndVWWy0f+tCHsvfee2fUqFHZ\nc889F9vvDW94Q8aNG5eLL744HR0dueiiizJu3Lh2RPU+xbavU/S22GKL7LLLLvnHf/zHpc67lJLt\nt98+q6++eu69994krdNAR44cmb333jvvf//78+STT+arX/1qj/ttvvnm2WmnnTJ69OgcdNBBefvb\n354kOfjgg/OWt7wlO+ywQ/bbb7+MGjVqsbk2TZNbb70155xzTq699toMGzas/Tx0f2x77rlnRo0a\nlc985jPZYYcdsuqqq+aYY455UfPo7rWvfe1L/lmeN29ejjzyyGyxxRYZM2ZM/u7v/m6xswAWPef7\n7rtv9t577yTJ8OHDM2zYsPbrE6C/CDjgFTFw4MA88cQTS30PyW677ZaxY8dm7ty5mTt3bsaOHZvd\nd999sf022WSTrLLKKjnvvPPywgsvpLOzMz/72c+y6667tveZMmVKZs2alSR5/etfnxVWWCF/+tOf\nljrH559/PmPHjs3ChQtzyy23ZN68ee1fIj/xiU/kpJNOylNPPZV58+bl3HPPzS677JIk+fjHP56r\nrroqd9xxR5Jkzpw5eeCBB9rHXX/99XPxxRfnzDPPzA9/+MMkrTi7+eab2+9Z+/nPf5577rmnfYpe\n0zSZOHFifve736VpmrzwwgtLnPeAAQNyyimnZOLEifn973+fpBU/3d9HliRPP/10e9Vj1VVXzdNP\nP91j26qrrtr++ogjjsjIkSMzcuTIdHZ25vzzz2//AvzXWrBgQdZee+0MHTo0Cxcu7PF+x1JKxo4d\nmxkzZmTatGl58MEHc/bZZyf5n+/bo48+2mNenZ2dmTlzZo9Vmk022SSdnZ2ZMWNGrrjiikyaNCkP\nPvjg3zTfgQMH9hiztxVWWCG77LJLTj/99DzzzDOZPXt2Lrzwwj5fq9tuu20eeOCBXH311WmaJtde\ne20eeOCBHivG3fW16nnWWWfl2WefzYIFC/q8z6BBg9LZ2ZlZs2blrLPOypFHHpn/+q//StI6VfaM\nM87InDlz2q/vHXfcscc4Y8eOzYIFC3L//ffnvvvuywYbbJAk2WmnnTJu3Lg89dRTeeSRR3LJJZcs\ncVW2lLLUFduPfOQjufDCC/PHP/4xzzzzTE4//fTssssuS51HX6fAvhw/y9OnT2//Q8f999+fa6+9\nNiNGjOhx/+7PuVMngVeagANeEXPnzs0aa6yx1F+GBg0a1GPlavbs2Rk0aFCf+z388MM9buu+b9M0\nPVasnnzyySxcuDBrr732MufYPTDnz5+fVVddNWuuuWZWWWWVXHXVVens7ExnZ2fOPffcrLHGGkla\nK0dLioVSSnbbbbc8/PDDPVaShgwZkp122ql9vM7Ozmy++eZ54xvf2N5nWatui45/8skn57nnnss3\nvvGN9u3z5s1rx9oiq6++ejvq5s2b1yPIVl999cybN6/99axZs7JgwYI8++yzOe+88/LUU08t8YqD\nM2fOzMyZMzNjxowMHjx4se0XXHBBHnzwwVxwwQW54YYbst9++7W3NU2Tgw46KMOHD89nPvOZbLnl\nlu1T9fr6vp166qnp6OjITTfd1OP9Sf/+7/+ejo6ODB8+PNtss03WX3/9HHbYYX3O99xzz23PuXv0\nLzJ37tylvlbWWGONDBgwYLHXal+PfdCgQYt9H5e075Is63UwZ86c9mO/5JJLeoTt0KFDc9ZZZ7Vf\nY1dffXVeeOGFHq+z7jG06DXf19xfzOtxSdZee+3Fnq8BAwYsdR6rrLLKYsd5qT/LSbLxxhvn4osv\nzm233ZY777wzn/rUp3qsfCcv7bECvNwEHPCKmDVrVp577rnssMMOS9xnzpw5GTp0aPvrIUOGZM6c\nOX3u96Y3vanHbUOGDMkjjzzS53Hnz5+fWbNm5cMf/vDfNPcnnngi8+fPz2677ZaOjo50dHRkxIgR\n7aB5+OGHs8466/R536ZpctZZZ2Xu3Lk59dRT2wH70EMPZfLkye3jdXR0ZNiwYbngggt63HdZTjjh\nhKy55pr5/Oc/375iZJL85je/6bGCscoqq+Stb31re+Wh9/YNNthgqVdXXNpcFl08ZPjw4X1+D+bN\nm5dvfvOb2XHHHXPQQQdl9OjR6ejoWGy/GTNm5NJLL80RRxyRJHnmmWdy9913Z8cdd1xs36Wt+Dz+\n+OO58cYbl7jKdcABB7TnfM011yy2/Y477sjgwYPz3ve+t8/7P/HEE3n++ecXe632/keFpHWBkUUX\nlum+76Lnad68eT1WPrsHzSIv9sqHCxYsyKmnnprVV189o0aNStJ6ne2///49XmebbbbZMlewklZQ\ndZ/7m9/85iXuu6w59vWz/fzzzy91pbMvL/VnOWn9I8BNN92U7bbbLiNGjMjll1++2GtpWae1AvQn\nAQe8Ip5++ul85zvfyTHHHJORI0fmta99bQYMGJD3v//97YsPXHPNNTnwwAMzcODADBw4MAcddFAm\nTZq02LHuvvvuzJ8/P/vtt18GDBiQESNGZLvttsu1116bpO/T0E499dTsscce2Xfffdv/2r7BBhu0\nL6CyNE3T5Morr8xRRx3VXnUbNGhQ+0IlP/zhD7PHHnuko6MjpZQMGjSox/t3FixYkC984QtZZZVV\n2heomDx5crbbbrtsvfXWWWGFFbLyyitnxIgRPVYcl3Xq1te+9rWst956OeiggxY7ve7GG2/M+uuv\nnx122CErr7xyDj744Nx3333tUywnTZqU0aNHZ9CgQRk0aFBGjx7dvgT7m970pmy22WZZaaWVsvLK\nK7efs7vuumuZz1Vftt1227ztbW9L0nodLFy4sEdsdnfxxRdno402al8o4pRTTsmee+6Z/fbbL2uu\nuWaS1orn0KFDl/iL9cCBA/OhD33ob77c/x/+8IdcdtllOe2007LFFlu0n4dddtkl++23XxYuXJjr\nrrsuX/jCF7LqqqtmyJAh+exnP5vJkycvdqxbbrkl6667bnbdddesuOKK2XnnnfP2t789P/vZz5K0\nLriyyy67ZMUVV8yGG26YHXfc8SUFw/PPP58LL7yw/X65yy+/PIcddlg7vtZYY40lhm1v1113XQ44\n4ICsvvrqGTx4cPbaa68l7vvoo4/2CLTerrnmmowePTpDhw7NqquumsMOOyzXXnvtUh/rkl7/L+Vn\nOWmdPvzkk09mwYIF2WijjbLbbrstdR6PP/54Fi5c2H4NA/Q3AQe8Yi666KKcfPLJOfDAA3Pbbbdl\n6tSp+fu///vcdNNNSZKzzz4799xzTyZPnpzJkyfnnnvuybhx49r3X/RL1oIFC3LggQfmAx/4QG6/\n/fYcffTROfLII9tx0tdnr82aNSuf/exn09HRkRtuuCF33HFHvv71r7d/ke5+/L6ceuqpefDBB3PF\nFVdk+vTpGT9+fPuiKffcc0/+5V/+JUcddVSmT5+eCRMmLLZa8fzzz+fzn/981lprrZxwwgl55JFH\ncvDBB+dzn/tcbr/99kybNi377LNPj19alzafIUOG5JOf/GQ22GCD3HrrrYudEjh37twceuih+cIX\nvpDOzs5suOGG+ed//uf2/a+44opMmzat/VxPmzat/dlhq622Wo499tjccccdufnmm7P11lvngAMO\nyJNPPrnE+SzNOuusk/Hjx2fmzJm57LLL8r3vfa/HFSS7mzt3bn784x+3T7P8xS9+kdGjR2f48OG5\n7rrr0tnZmfPOOy+dnZ3tD8tumiabbrpp+zm4+uqr89hjj/U4pfSvdcIJJ+R73/tejjnmmHR2duaG\nG27IBz/4wUydOjVJcvzxx+eZZ57JjTfemEsvvTQ/+clP2ldf7P76mzt3bsaMGZN99tknP//5z7Pv\nvvvmwAMPbF898dvf/nbe+ta35s4778whhxySn/zkJz3m8WJirvc+V111VdZaa61sv/32mTBhQqZO\nnZoLLrggM2bMyOWXX97jKopLO/7YsWMze/bs3HTTTTnvvPMyadKkJe7/05/+NElr9XLR+zx7z2nS\npEm55JJLcuONN+aZZ57J8ccfv9R5LGmsl/qzfNxxx+XQQw/NjBkzctBBB+W6665b6rjz58/POeec\nk+9///vp7OzMRhtttMRjAywPZRn/2tX0PtUDAACA5Wf27NlpmqbPUw+swAEAAFRCwAEAAFRCwAEA\nAFRCwAEAAFRCwAEAAFRCwAEAAFRCwAEAAFRCwAEAAFRCwAEAAFRCwAEAAFRCwAEAAFRCwAEAAFRC\nwAEAAFSiNE2z5I2lLHkjAAAAy0XTNKWv25cacAAAALx6OIUSAACgEgIOAACgEgIOAACgEgIOAACg\nEgIOAACgEv8fdkpknlONAeEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f62060a18d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "To Chromaticity Coordinates"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Given a spectral power distribution, chromaticity coordinates *xy* can be computed using the *colour.XYZ_to_xy* definition:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Computing *xy* chromaticity coordinates for the *neutral 5 (.70 D)* patch.\n",
      "xy =  colour.XYZ_to_xy(XYZ)\n",
      "print(xy)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.31260313  0.32871262]\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Chromaticity coordinates *xy* can be plotted into the *CIE 1931 Chromaticity Diagram*:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab\n",
      "\n",
      "# Plotting the *CIE 1931 Chromaticity Diagram*.\n",
      "# The argument *standalone=False* is passed so that the plot doesn't get displayed\n",
      "# and can be used as a basis for other plots.\n",
      "CIE_1931_chromaticity_diagram_plot(standalone=False)\n",
      "\n",
      "# Plotting the *xy* chromaticity coordinates.\n",
      "x, y = xy\n",
      "pylab.plot(x, y, 'o-', color='white')\n",
      "\n",
      "# Annotating the plot.\n",
      "pylab.annotate(patch_spd.name.title(),\n",
      "               xy=xy,\n",
      "               xytext=(-50, 30),\n",
      "               textcoords='offset points',\n",
      "               arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=-0.2'))\n",
      "\n",
      "# Displaying the plot.\n",
      "display(standalone=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8BHiLos7Gn2OM9e7u0elxY4xvhxB+QVGI8KpY3AGgEdXvveUo5oAfRTHkfdfav8r28HWA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\nCSOBny0BbRauGHQ+BNwHXA2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ccstx7bXX2lkhSZIkDVF+S63y5JNPMmXKFFhxOGy/fHlt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bJtrmivrujPkhd9E0mG4iqL/qyaRTE5MbnSol3Lop2wYKIoZ16Mc8D6qvu8Q2j0bTKd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WAagAUATgcwc906TJgwoSG2KTzyyCO49dZbcdFFF+HJJ58skhUFFFBAU0A1NSyKhEUdoaur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VBsmixAjJ4sQwaZ6KYQwIRNspBBGHRZnxsnxd8hW1mXK2dW0OC1dk5cubi82eF2+eu64z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1Sc1ctUhQo/Yktl4SVK+HnXQxziHV7hOSawnjUA1DuiHoOHDObuu+/mmGOOScerCoIgCIIgCMJh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\nCxZmZmZmZmZmVjj/B1Th912mSDNZAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f62061086d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "To More"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We hope that this small introduction has been useful and gave you the envy to see more, if you want to explore the API a good place to start is the [IPython Notebooks](http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/colour.ipynb) page."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "References"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1. <a href=\"#back_reference_1\">^<a> <a name=\"reference_1\"></a>CIE TC 1-38. (2005). 9. INTERPOLATION. In CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations (pp. 14\u201319). ISBN:978-3-901-90641-1\n",
      "2. <a href=\"#back_reference_2\">^<a> <a name=\"reference_2\"></a>CIE TC 1-48. (2004). CIE 015:2004 Colorimetry, 3rd Edition. CIE 015:2004 Colorimetry, 3rd Edition (pp. 1\u201382). ISBN:978-3-901-90633-6"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}