def plot_cvd_simulation_Machado2009(RGB, deficiency='Protanomaly', severity=0.5, M_a=None, **kwargs): """ Performs colour vision deficiency simulation on given *RGB* colourspace array using *Machado et al. (2009)* model. Parameters ---------- RGB : array_like *RGB* colourspace array. deficiency : unicode, optional {'Protanomaly', 'Deuteranomaly', 'Tritanomaly'} Colour blindness / vision deficiency type. severity : numeric, optional Severity of the colour vision deficiency in domain [0, 1]. M_a : array_like, optional Anomalous trichromacy matrix to use instead of Machado (2010) pre-computed matrix. Other Parameters ---------------- \\**kwargs : dict, optional {:func:`colour.plotting.artist`, :func:`colour.plotting.plot_image`, :func:`colour.plotting.render`}, Please refer to the documentation of the previously listed definitions. Notes ----- - Input *RGB* array is expected to be linearly encoded. Returns ------- tuple Current figure and axes. Examples -------- >>> import numpy as np >>> RGB = np.random.rand(32, 32, 3) >>> plot_cvd_simulation_Machado2009(RGB) # doctest: +SKIP .. image:: ../_static/Plotting_Plot_CVD_Simulation_Machado2009.png :align: center :alt: plot_cvd_simulation_Machado2009 """ if M_a is None: M_a = cvd_matrix_Machado2009(deficiency, severity) text = 'Deficiency: {0} - Severity: {1}'.format(deficiency, severity) settings = {'text_parameters': {'text': None if M_a is None else text}} settings.update(kwargs) return plot_image( COLOUR_STYLE_CONSTANTS.colour.colourspace.encoding_cctf( dot_vector(M_a, RGB)), **settings)
def plot_cvd_simulation_Machado2009(RGB, deficiency='Protanomaly', severity=0.5, M_a=None, **kwargs): """ Performs colour vision deficiency simulation on given *RGB* colourspace array using *Machado et al. (2009)* model. Parameters ---------- RGB : array_like *RGB* colourspace array. deficiency : unicode, optional {'Protanomaly', 'Deuteranomaly', 'Tritanomaly'} Colour blindness / vision deficiency type. severity : numeric, optional Severity of the colour vision deficiency in domain [0, 1]. M_a : array_like, optional Anomalous trichromacy matrix to use instead of Machado (2010) pre-computed matrix. Other Parameters ---------------- \\**kwargs : dict, optional {:func:`colour.plotting.artist`, :func:`colour.plotting.plot_image`, :func:`colour.plotting.render`}, Please refer to the documentation of the previously listed definitions. Notes ----- - Input *RGB* array is expected to be linearly encoded. Returns ------- tuple Current figure and axes. Examples -------- >>> import numpy as np >>> RGB = np.random.rand(32, 32, 3) >>> plot_cvd_simulation_Machado2009(RGB) # doctest: +SKIP .. image:: ../_static/Plotting_Plot_CVD_Simulation_Machado2009.png :align: center :alt: plot_cvd_simulation_Machado2009 """ if M_a is None: M_a = cvd_matrix_Machado2009(deficiency, severity) text = 'Deficiency: {0} - Severity: {1}'.format(deficiency, severity) settings = {'text_parameters': {'text': None if M_a is None else text}} settings.update(kwargs) return plot_image( COLOUR_STYLE_CONSTANTS.colour.colourspace.encoding_cctf( dot_vector(M_a, RGB)), **settings)
def test_plot_image(self): """Test :func:`colour.plotting.common.plot_image` definition.""" path = os.path.join(colour.__path__[0], "..", "docs", "_static", "Logo_Medium_001.png") # Distribution does not ship the documentation thus we are skipping # this unit test if the image does not exist. if not os.path.exists(path): # pragma: no cover return figure, axes = plot_image(read_image(path)) self.assertIsInstance(figure, Figure) self.assertIsInstance(axes, Axes)
from colour.utilities.verbose import message_box RESOURCES_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'resources') colour_style() ISHIHARA_CBT_3_IMAGE = colour.oetf_reverse(colour.read_image( os.path.join(RESOURCES_DIRECTORY, 'Ishihara_Colour_Blindness_Test_Plate_3.png')), function='sRGB') message_box('Colour Blindness Plots') message_box('Displaying "Ishihara Colour Blindness Test - Plate 3".') plot_image(ISHIHARA_CBT_3_IMAGE, 'Normal Trichromat', label_colour='black') print('\n') message_box('Simulating average "Protanomaly" on ' '"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) ' 'model and pre-computed matrix.') plot_cvd_simulation_Machado2009(ISHIHARA_CBT_3_IMAGE, 'Protanomaly', 0.5, label_colour='black') print('\n') M_a = colour.anomalous_trichromacy_matrix_Machado2009( colour.LMS_CMFS.get('Stockman & Sharpe 2 Degree Cone Fundamentals'),
def generate_documentation_plots(output_directory): """ Generates documentation plots. Parameters ---------- output_directory : unicode Output directory. """ filter_warnings() colour_style() np.random.seed(0) # ************************************************************************* # "README.rst" # ************************************************************************* filename = os.path.join(output_directory, 'Examples_Colour_Automatic_Conversion_Graph.png') plot_automatic_colour_conversion_graph(filename) arguments = { 'tight_layout': True, 'transparent_background': True, 'filename': os.path.join(output_directory, 'Examples_Plotting_Visible_Spectrum.png') } plt.close( plot_visible_spectrum('CIE 1931 2 Degree Standard Observer', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Examples_Plotting_Illuminant_F1_SD.png') plt.close(plot_single_illuminant_sd('FL1', **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Examples_Plotting_Blackbodies.png') blackbody_sds = [ sd_blackbody(i, SpectralShape(0, 10000, 10)) for i in range(1000, 15000, 1000) ] plt.close( plot_multi_sds(blackbody_sds, y_label='W / (sr m$^2$) / m', use_sds_colours=True, normalise_sds_colours=True, legend_location='upper right', bounding_box=(0, 1250, 0, 2.5e15), **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Examples_Plotting_Cone_Fundamentals.png') plt.close( plot_single_cmfs('Stockman & Sharpe 2 Degree Cone Fundamentals', y_label='Sensitivity', bounding_box=(390, 870, 0, 1.1), **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Examples_Plotting_Luminous_Efficiency.png') plt.close( plot_multi_sds((sd_mesopic_luminous_efficiency_function(0.2), PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'], SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']), y_label='Luminous Efficiency', legend_location='upper right', y_tighten=True, margins=(0, 0, 0, .1), **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Examples_Plotting_BabelColor_Average.png') plt.close( plot_multi_sds(COLOURCHECKERS_SDS['BabelColor Average'].values(), use_sds_colours=True, title=('BabelColor Average - ' 'Spectral Distributions'), **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Examples_Plotting_ColorChecker_2005.png') plt.close( plot_single_colour_checker('ColorChecker 2005', text_parameters={'visible': False}, **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Examples_Plotting_Chromaticities_Prediction.png') plt.close( plot_corresponding_chromaticities_prediction(2, 'Von Kries', 'Bianco', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png') plt.close( plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS( ['A', 'B', 'C'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png') RGB = np.random.random((32, 32, 3)) plt.close( plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931( RGB, 'ITU-R BT.709', colourspaces=['ACEScg', 'S-Gamut'], show_pointer_gamut=True, **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Examples_Plotting_CRI.png') plt.close( plot_single_sd_colour_rendering_index_bars(ILLUMINANTS_SDS['FL2'], **arguments)[0]) # ************************************************************************* # Documentation # ************************************************************************* arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_CVD_Simulation_Machado2009.png') plt.close(plot_cvd_simulation_Machado2009(RGB, **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Single_Colour_Checker.png') plt.close(plot_single_colour_checker('ColorChecker 2005', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Multi_Colour_Checkers.png') plt.close( plot_multi_colour_checkers(['ColorChecker 1976', 'ColorChecker 2005'], **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Single_SD.png') data = { 500: 0.0651, 520: 0.0705, 540: 0.0772, 560: 0.0870, 580: 0.1128, 600: 0.1360 } sd = SpectralDistribution(data, name='Custom') plt.close(plot_single_sd(sd, **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Multi_SDS.png') data_1 = { 500: 0.004900, 510: 0.009300, 520: 0.063270, 530: 0.165500, 540: 0.290400, 550: 0.433450, 560: 0.594500 } data_2 = { 500: 0.323000, 510: 0.503000, 520: 0.710000, 530: 0.862000, 540: 0.954000, 550: 0.994950, 560: 0.995000 } spd1 = SpectralDistribution(data_1, name='Custom 1') spd2 = SpectralDistribution(data_2, name='Custom 2') plt.close(plot_multi_sds([spd1, spd2], **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Single_CMFS.png') plt.close( plot_single_cmfs('CIE 1931 2 Degree Standard Observer', **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Multi_CMFS.png') cmfs = ('CIE 1931 2 Degree Standard Observer', 'CIE 1964 10 Degree Standard Observer') plt.close(plot_multi_cmfs(cmfs, **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Single_Illuminant_SD.png') plt.close(plot_single_illuminant_sd('A', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Multi_Illuminant_SDS.png') plt.close(plot_multi_illuminant_sds(['A', 'B', 'C'], **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Visible_Spectrum.png') plt.close(plot_visible_spectrum(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Single_Lightness_Function.png') plt.close(plot_single_lightness_function('CIE 1976', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Multi_Lightness_Functions.png') plt.close( plot_multi_lightness_functions(['CIE 1976', 'Wyszecki 1963'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Single_Luminance_Function.png') plt.close(plot_single_luminance_function('CIE 1976', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Multi_Luminance_Functions.png') plt.close( plot_multi_luminance_functions(['CIE 1976', 'Newhall 1943'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Blackbody_Spectral_Radiance.png') plt.close( plot_blackbody_spectral_radiance(3500, blackbody='VY Canis Major', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Blackbody_Colours.png') plt.close( plot_blackbody_colours(SpectralShape(150, 12500, 50), **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Single_Colour_Swatch.png') RGB = ColourSwatch(RGB=(0.45620519, 0.03081071, 0.04091952)) plt.close(plot_single_colour_swatch(RGB, **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Multi_Colour_Swatches.png') RGB_1 = ColourSwatch(RGB=(0.45293517, 0.31732158, 0.26414773)) RGB_2 = ColourSwatch(RGB=(0.77875824, 0.57726450, 0.50453169)) plt.close(plot_multi_colour_swatches([RGB_1, RGB_2], **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Single_Function.png') plt.close(plot_single_function(lambda x: x**(1 / 2.2), **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Multi_Functions.png') functions = { 'Gamma 2.2': lambda x: x**(1 / 2.2), 'Gamma 2.4': lambda x: x**(1 / 2.4), 'Gamma 2.6': lambda x: x**(1 / 2.6), } plt.close(plot_multi_functions(functions, **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Image.png') path = os.path.join(output_directory, 'Logo_Medium_001.png') plt.close(plot_image(read_image(str(path)), **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Corresponding_Chromaticities_Prediction.png') plt.close( plot_corresponding_chromaticities_prediction(1, 'Von Kries', 'CAT02', **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Spectral_Locus.png') plt.close( plot_spectral_locus(spectral_locus_colours='RGB', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Chromaticity_Diagram_Colours.png') plt.close(plot_chromaticity_diagram_colours(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Chromaticity_Diagram.png') plt.close(plot_chromaticity_diagram(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1931.png') plt.close(plot_chromaticity_diagram_CIE1931(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1960UCS.png') plt.close(plot_chromaticity_diagram_CIE1960UCS(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1976UCS.png') plt.close(plot_chromaticity_diagram_CIE1976UCS(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_SDS_In_Chromaticity_Diagram.png') A = ILLUMINANTS_SDS['A'] D65 = ILLUMINANTS_SDS['D65'] plt.close(plot_sds_in_chromaticity_diagram([A, D65], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1931.png') plt.close( plot_sds_in_chromaticity_diagram_CIE1931([A, D65], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1960UCS.png') plt.close( plot_sds_in_chromaticity_diagram_CIE1960UCS([A, D65], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1976UCS.png') plt.close( plot_sds_in_chromaticity_diagram_CIE1976UCS([A, D65], **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Pointer_Gamut.png') plt.close(plot_pointer_gamut(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png') plt.close( plot_RGB_colourspaces_in_chromaticity_diagram( ['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram_CIE1931.png') plt.close( plot_RGB_colourspaces_in_chromaticity_diagram_CIE1931( ['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Colourspaces_In_' 'Chromaticity_Diagram_CIE1960UCS.png') plt.close( plot_RGB_colourspaces_in_chromaticity_diagram_CIE1960UCS( ['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Colourspaces_In_' 'Chromaticity_Diagram_CIE1976UCS.png') plt.close( plot_RGB_colourspaces_in_chromaticity_diagram_CIE1976UCS( ['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Chromaticities_In_' 'Chromaticity_Diagram.png') RGB = np.random.random((128, 128, 3)) plt.close( plot_RGB_chromaticities_in_chromaticity_diagram( RGB, 'ITU-R BT.709', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Chromaticities_In_' 'Chromaticity_Diagram_CIE1931.png') plt.close( plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931( RGB, 'ITU-R BT.709', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Chromaticities_In_' 'Chromaticity_Diagram_CIE1960UCS.png') plt.close( plot_RGB_chromaticities_in_chromaticity_diagram_CIE1960UCS( RGB, 'ITU-R BT.709', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Chromaticities_In_' 'Chromaticity_Diagram_CIE1976UCS.png') plt.close( plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS( RGB, 'ITU-R BT.709', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_Chromaticity_Diagram.png') plt.close( plot_ellipses_MacAdam1942_in_chromaticity_diagram(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_' 'Chromaticity_Diagram_CIE1931.png') plt.close( plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1931( **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_' 'Chromaticity_Diagram_CIE1960UCS.png') plt.close( plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS( **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_' 'Chromaticity_Diagram_CIE1976UCS.png') plt.close( plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1976UCS( **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Single_CCTF.png') plt.close(plot_single_cctf('ITU-R BT.709', **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Multi_CCTFs.png') plt.close(plot_multi_cctfs(['ITU-R BT.709', 'sRGB'], **arguments)[0]) data = np.array([ [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.40920000, 0.28120000, 0.30600000]), np.array([ [0.02495100, 0.01908600, 0.02032900], [0.10944300, 0.06235900, 0.06788100], [0.27186500, 0.18418700, 0.19565300], [0.48898900, 0.40749400, 0.44854600], ]), None, ], [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.30760000, 0.48280000, 0.42770000]), np.array([ [0.02108000, 0.02989100, 0.02790400], [0.06194700, 0.11251000, 0.09334400], [0.15255800, 0.28123300, 0.23234900], [0.34157700, 0.56681300, 0.47035300], ]), None, ], [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.39530000, 0.28120000, 0.18450000]), np.array([ [0.02436400, 0.01908600, 0.01468800], [0.10331200, 0.06235900, 0.02854600], [0.26311900, 0.18418700, 0.12109700], [0.43158700, 0.40749400, 0.39008600], ]), None, ], [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.20510000, 0.18420000, 0.57130000]), np.array([ [0.03039800, 0.02989100, 0.06123300], [0.08870000, 0.08498400, 0.21843500], [0.18405800, 0.18418700, 0.40111400], [0.32550100, 0.34047200, 0.50296900], [0.53826100, 0.56681300, 0.80010400], ]), None, ], [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.35770000, 0.28120000, 0.11250000]), np.array([ [0.03678100, 0.02989100, 0.01481100], [0.17127700, 0.11251000, 0.01229900], [0.30080900, 0.28123300, 0.21229800], [0.52976000, 0.40749400, 0.11720000], ]), None, ], ]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Constant_Hue_Loci.png') plt.close(plot_constant_hue_loci(data, 'IPT', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Single_Munsell_Value_Function.png') plt.close(plot_single_munsell_value_function('ASTM D1535', **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Multi_Munsell_Value_Functions.png') plt.close( plot_multi_munsell_value_functions(['ASTM D1535', 'McCamy 1987'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Single_SD_Rayleigh_Scattering.png') plt.close(plot_single_sd_rayleigh_scattering(**arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_The_Blue_Sky.png') plt.close(plot_the_blue_sky(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Colour_Quality_Bars.png') illuminant = ILLUMINANTS_SDS['FL2'] light_source = LIGHT_SOURCES_SDS['Kinoton 75P'] light_source = light_source.copy().align(SpectralShape(360, 830, 1)) cqs_i = colour_quality_scale(illuminant, additional_data=True) cqs_l = colour_quality_scale(light_source, additional_data=True) plt.close(plot_colour_quality_bars([cqs_i, cqs_l], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Single_SD_Colour_Rendering_Index_Bars.png') illuminant = ILLUMINANTS_SDS['FL2'] plt.close( plot_single_sd_colour_rendering_index_bars(illuminant, **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Multi_SDS_Colour_Rendering_Indexes_Bars.png') light_source = LIGHT_SOURCES_SDS['Kinoton 75P'] plt.close( plot_multi_sds_colour_rendering_indexes_bars( [illuminant, light_source], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Single_SD_Colour_Quality_Scale_Bars.png') illuminant = ILLUMINANTS_SDS['FL2'] plt.close( plot_single_sd_colour_quality_scale_bars(illuminant, **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Multi_SDS_Colour_Quality_Scales_Bars.png') light_source = LIGHT_SOURCES_SDS['Kinoton 75P'] plt.close( plot_multi_sds_colour_quality_scales_bars([illuminant, light_source], **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_Planckian_Locus.png') plt.close(plot_planckian_locus(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Planckian_Locus_CIE1931.png') plt.close(plot_planckian_locus_CIE1931(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Planckian_Locus_CIE1960UCS.png') plt.close(plot_planckian_locus_CIE1960UCS(**arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram.png') plt.close( plot_planckian_locus_in_chromaticity_diagram(['A', 'B', 'C'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1931.png') plt.close( plot_planckian_locus_in_chromaticity_diagram_CIE1931(['A', 'B', 'C'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1960UCS.png') plt.close( plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS( ['A', 'B', 'C'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Colourspaces_Gamuts.png') plt.close( plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Plotting_Plot_RGB_Colourspaces_Gamuts.png') plt.close( plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Plotting_Plot_RGB_Scatter.png') plt.close(plot_RGB_scatter(RGB, 'ITU-R BT.709', **arguments)[0]) filename = os.path.join( output_directory, 'Plotting_Plot_Colour_Automatic_Conversion_Graph.png') plot_automatic_colour_conversion_graph(filename) # ************************************************************************* # "tutorial.rst" # ************************************************************************* arguments['filename'] = os.path.join(output_directory, 'Tutorial_Visible_Spectrum.png') plt.close(plot_visible_spectrum(**arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Tutorial_Sample_SD.png') sample_sd_data = { 380: 0.048, 385: 0.051, 390: 0.055, 395: 0.060, 400: 0.065, 405: 0.068, 410: 0.068, 415: 0.067, 420: 0.064, 425: 0.062, 430: 0.059, 435: 0.057, 440: 0.055, 445: 0.054, 450: 0.053, 455: 0.053, 460: 0.052, 465: 0.052, 470: 0.052, 475: 0.053, 480: 0.054, 485: 0.055, 490: 0.057, 495: 0.059, 500: 0.061, 505: 0.062, 510: 0.065, 515: 0.067, 520: 0.070, 525: 0.072, 530: 0.074, 535: 0.075, 540: 0.076, 545: 0.078, 550: 0.079, 555: 0.082, 560: 0.087, 565: 0.092, 570: 0.100, 575: 0.107, 580: 0.115, 585: 0.122, 590: 0.129, 595: 0.134, 600: 0.138, 605: 0.142, 610: 0.146, 615: 0.150, 620: 0.154, 625: 0.158, 630: 0.163, 635: 0.167, 640: 0.173, 645: 0.180, 650: 0.188, 655: 0.196, 660: 0.204, 665: 0.213, 670: 0.222, 675: 0.231, 680: 0.242, 685: 0.251, 690: 0.261, 695: 0.271, 700: 0.282, 705: 0.294, 710: 0.305, 715: 0.318, 720: 0.334, 725: 0.354, 730: 0.372, 735: 0.392, 740: 0.409, 745: 0.420, 750: 0.436, 755: 0.450, 760: 0.462, 765: 0.465, 770: 0.448, 775: 0.432, 780: 0.421 } sd = SpectralDistribution(sample_sd_data, name='Sample') plt.close(plot_single_sd(sd, **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Tutorial_SD_Interpolation.png') sd_copy = sd.copy() sd_copy.interpolate(SpectralShape(400, 770, 1)) plt.close( plot_multi_sds([sd, sd_copy], bounding_box=[730, 780, 0.25, 0.5], **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Tutorial_Sample_Swatch.png') sd = SpectralDistribution(sample_sd_data) cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'] illuminant = ILLUMINANTS_SDS['D65'] with domain_range_scale('1'): XYZ = sd_to_XYZ(sd, cmfs, illuminant) RGB = XYZ_to_sRGB(XYZ) plt.close( plot_single_colour_swatch(ColourSwatch('Sample', RGB), text_parameters={'size': 'x-large'}, **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Tutorial_Neutral5.png') patch_name = 'neutral 5 (.70 D)' patch_sd = COLOURCHECKERS_SDS['ColorChecker N Ohta'][patch_name] with domain_range_scale('1'): XYZ = sd_to_XYZ(patch_sd, cmfs, illuminant) RGB = XYZ_to_sRGB(XYZ) plt.close( plot_single_colour_swatch(ColourSwatch(patch_name.title(), RGB), text_parameters={'size': 'x-large'}, **arguments)[0]) arguments['filename'] = os.path.join(output_directory, 'Tutorial_Colour_Checker.png') plt.close( plot_single_colour_checker(colour_checker='ColorChecker 2005', text_parameters={'visible': False}, **arguments)[0]) arguments['filename'] = os.path.join( output_directory, 'Tutorial_CIE_1931_Chromaticity_Diagram.png') xy = XYZ_to_xy(XYZ) plot_chromaticity_diagram_CIE1931(standalone=False) x, y = xy plt.plot(x, y, 'o-', color='white') # Annotating the plot. plt.annotate(patch_sd.name.title(), xy=xy, xytext=(-50, 30), textcoords='offset points', arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=-0.2')) plt.close( render(standalone=True, limits=(-0.1, 0.9, -0.1, 0.9), x_tighten=True, y_tighten=True, **arguments)[0]) # ************************************************************************* # "basics.rst" # ************************************************************************* arguments['filename'] = os.path.join(output_directory, 'Basics_Logo_Small_001_CIE_XYZ.png') RGB = read_image(os.path.join(output_directory, 'Logo_Small_001.png'))[..., 0:3] XYZ = sRGB_to_XYZ(RGB) plt.close( plot_image(XYZ, text_parameters={'text': 'sRGB to XYZ'}, **arguments)[0])
colour_style() ISHIHARA_CBT_3_IMAGE = colour.cctf_decoding( colour.read_image( os.path.join(RESOURCES_DIRECTORY, "Ishihara_Colour_Blindness_Test_Plate_3.png")), function="sRGB", ) message_box("Colour Blindness Plots") message_box('Displaying "Ishihara Colour Blindness Test - Plate 3".') plot_image( colour.cctf_encoding(ISHIHARA_CBT_3_IMAGE), text_kwargs={ "text": "Normal Trichromat", "color": "black" }, ) print("\n") message_box( 'Simulating average "Protanomaly" on ' '"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) model and ' "pre-computed matrix.") plot_cvd_simulation_Machado2009( ISHIHARA_CBT_3_IMAGE, "Protanomaly", 0.5, text_kwargs={
RESOURCES_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'resources') colour_style() ISHIHARA_CBT_3_IMAGE = colour.cctf_decoding(colour.read_image( os.path.join(RESOURCES_DIRECTORY, 'Ishihara_Colour_Blindness_Test_Plate_3.png')), function='sRGB') message_box('Colour Blindness Plots') message_box('Displaying "Ishihara Colour Blindness Test - Plate 3".') plot_image(colour.cctf_encoding(ISHIHARA_CBT_3_IMAGE), text_parameters={ 'text': 'Normal Trichromat', 'color': 'black' }) print('\n') message_box('Simulating average "Protanomaly" on ' '"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) ' 'model and pre-computed matrix.') plot_cvd_simulation_Machado2009(ISHIHARA_CBT_3_IMAGE, 'Protanomaly', 0.5, text_parameters={ 'text': 'Protanomaly - 50%', 'color': 'black' })
def plot_colour_vector_graphic( specification: ColourQuality_Specification_ANSIIESTM3018, **kwargs: Any) -> Tuple[plt.Figure, plt.Axes]: """ Plot *Color Vector Graphic* according to *ANSI/IES TM-30-18 Colour Rendition Report*. Parameters ---------- specification *ANSI/IES TM-30-18 Colour Rendition Report* specification. Other Parameters ---------------- kwargs {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, See the documentation of the previously listed definitions. Returns ------- :class:`tuple` Current figure and axes Examples -------- >>> from colour import SDS_ILLUMINANTS >>> from colour.quality import colour_fidelity_index_ANSIIESTM3018 >>> sd = SDS_ILLUMINANTS['FL2'] >>> specification = colour_fidelity_index_ANSIIESTM3018(sd, True) >>> plot_colour_vector_graphic(specification) ... # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) """ settings: Dict[str, Any] = dict(kwargs) settings["standalone"] = False # Background background_image = read_image( os.path.join(RESOURCES_DIRECTORY_ANSIIESTM3018, "CVG_Background.jpg")) _figure, axes = plot_image( background_image, imshow_kwargs={"extent": [-1.5, 1.5, -1.5, 1.5]}, **settings, ) # Lines dividing the hues in 16 equal parts along with bin numbers. axes.plot(0, 0, "+", color="#A6A6A6") for i in range(16): angle = 2 * np.pi * i / 16 dx = np.cos(angle) dy = np.sin(angle) axes.plot( (0.15 * dx, 1.5 * dx), (0.15 * dy, 1.5 * dy), "--", color="#A6A6A6", lw=0.75, zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line, ) angle = 2 * np.pi * (i + 0.5) / 16 axes.annotate( str(i + 1), color="#A6A6A6", ha="center", va="center", xy=(1.41 * np.cos(angle), 1.41 * np.sin(angle)), weight="bold", size=9, zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_annotation, ) # Circles. circle = plt.Circle( (0, 0), 1, color="black", lw=1.25, fill=False, zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_polygon, ) axes.add_artist(circle) for radius in [0.8, 0.9, 1.1, 1.2]: circle = plt.Circle( (0, 0), radius, color="white", lw=0.75, fill=False, zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_polygon, ) axes.add_artist(circle) # -/+20% marks near the white circles. props = dict(ha="right", color="white", size=7) axes.annotate( "-20%", xy=(0, -0.8), va="bottom", zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_annotation, **props, ) axes.annotate( "+20%", xy=(0, -1.2), va="top", zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_annotation, **props, ) # Average "CAM02" h correlate for each bin, in radians. average_hues = np.radians([ np.mean( as_float_array([ cast( Floating, specification.colorimetry_data[1][i].CAM.h, ) for i in specification.bins[j] ])) for j in range(16) ]) xy_reference = np.transpose( np.vstack([np.cos(average_hues), np.sin(average_hues)])) # Arrow offsets as defined by the standard. offsets = (specification.averages_test - specification.averages_reference ) / specification.average_norms[:, np.newaxis] xy_test = xy_reference + offsets # Arrows. for i in range(16): axes.arrow( xy_reference[i, 0], xy_reference[i, 1], offsets[i, 0], offsets[i, 1], length_includes_head=True, width=0.005, head_width=0.04, linewidth=None, color=_COLOURS_BIN_ARROW[i], zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_annotation, ) # Red (test) gamut shape. loop = np.append(xy_test, xy_test[0, np.newaxis], axis=0) axes.plot( loop[:, 0], loop[:, 1], "-", color="#F05046", lw=2, zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line, ) def corner_label_and_text(label: str, text: str, ha: str, va: str): """Draw a label and text in given corner.""" x = -1.45 if ha == "left" else 1.45 y = 1.45 if va == "top" else -1.45 y_text = -15 if va == "top" else 15 axes.annotate( text, xy=(x, y), color="black", ha=ha, va=va, weight="bold", size="larger", zorder=CONSTANTS_COLOUR_STYLE.zorder.foreground_label, ) axes.annotate( label, xy=(x, y), color="black", xytext=(0, y_text), textcoords="offset points", ha=ha, va=va, size="small", zorder=CONSTANTS_COLOUR_STYLE.zorder.foreground_label, ) corner_label_and_text("$R_f$", f"{specification.R_f:.0f}", "left", "top") corner_label_and_text("$R_g$", f"{specification.R_g:.0f}", "right", "top") corner_label_and_text("CCT", f"{specification.CCT:.0f} K", "left", "bottom") corner_label_and_text("$D_{uv}$", f"{specification.D_uv:.4f}", "right", "bottom") settings = {"standalone": True} settings.update(kwargs) return render(**settings)
def plot_colour_vector_graphic(specification, **kwargs): """ Plots *Color Vector Graphic* according to *ANSI/IES TM-30-18 Colour Rendition Report*. Parameters ---------- specification : ColourQuality_Specification_ANSIIESTM3018 *ANSI/IES TM-30-18 Colour Rendition Report* specification. Other Parameters ---------------- \\**kwargs : dict, optional {:func:`colour.plotting.artist`, :func:`colour.plotting.render`}, Please refer to the documentation of the previously listed definitions. Returns ------- tuple Current figure and axes Examples -------- >>> from colour import SDS_ILLUMINANTS >>> from colour.quality import colour_fidelity_index_ANSIIESTM3018 >>> sd = SDS_ILLUMINANTS['FL2'] >>> specification = colour_fidelity_index_ANSIIESTM3018(sd, True) >>> plot_colour_vector_graphic(specification) ... # doctest: +ELLIPSIS (<Figure size ... with 1 Axes>, <...AxesSubplot...>) """ settings = kwargs.copy() settings['standalone'] = False # Background background_image = read_image( os.path.join(RESOURCES_DIRECTORY_ANSIIESTM3018, 'CVG_Background.jpg')) _figure, axes = plot_image( background_image, imshow_kwargs={'extent': [-1.5, 1.5, -1.5, 1.5]}, **settings) # Lines dividing the hues in 16 equal parts along with bin numbers. axes.plot(0, 0, '+', color='#A6A6A6') for i in range(16): angle = 2 * np.pi * i / 16 dx = np.cos(angle) dy = np.sin(angle) axes.plot((0.15 * dx, 1.5 * dx), (0.15 * dy, 1.5 * dy), '--', color='#A6A6A6', lw=0.75) angle = 2 * np.pi * (i + 0.5) / 16 axes.annotate(str(i + 1), color='#A6A6A6', ha='center', va='center', xy=(1.41 * np.cos(angle), 1.41 * np.sin(angle)), weight='bold', size=9) # Circles. circle = plt.Circle((0, 0), 1, color='black', lw=1.25, fill=False) axes.add_artist(circle) for radius in [0.8, 0.9, 1.1, 1.2]: circle = plt.Circle((0, 0), radius, color='white', lw=0.75, fill=False) axes.add_artist(circle) # -/+20% marks near the white circles. props = dict(ha='right', color='white', size=7) axes.annotate('-20%', xy=(0, -0.8), va='bottom', **props) axes.annotate('+20%', xy=(0, -1.2), va='top', **props) # Average "CAM02" h correlate for each bin, in radians. average_hues = as_float_array([ np.mean([ specification.colorimetry_data[1][i].CAM.h for i in specification.bins[j] ]) for j in range(16) ]) / 180 * np.pi xy_reference = np.transpose( np.vstack([np.cos(average_hues), np.sin(average_hues)])) # Arrow offsets as defined by the standard. offsets = ( (specification.averages_test - specification.averages_reference) / specification.average_norms[:, np.newaxis]) xy_test = xy_reference + offsets # Arrows. for i in range(16): axes.arrow(xy_reference[i, 0], xy_reference[i, 1], offsets[i, 0], offsets[i, 1], length_includes_head=True, width=0.005, head_width=0.04, linewidth=None, color=_BIN_ARROW_COLOURS[i]) # Red (test) gamut shape. loop = np.append(xy_test, xy_test[0, np.newaxis], axis=0) axes.plot(loop[:, 0], loop[:, 1], '-', color='#F05046', lw=2) def corner_label_and_text(label, text, ha, va): """ Draws a label and text in given corner. """ x = -1.45 if ha == 'left' else 1.45 y = 1.45 if va == 'top' else -1.45 y_text = -15 if va == 'top' else 15 axes.annotate(text, xy=(x, y), color='black', ha=ha, va=va, weight='bold', size='larger') axes.annotate(label, xy=(x, y), color='black', xytext=(0, y_text), textcoords='offset points', ha=ha, va=va, size='small') corner_label_and_text('$R_f$', '{0:.0f}'.format(specification.R_f), 'left', 'top') corner_label_and_text('$R_g$', '{0:.0f}'.format(specification.R_g), 'right', 'top') corner_label_and_text('CCT', '{0:.0f} K'.format(specification.CCT), 'left', 'bottom') corner_label_and_text('$D_{uv}$', '{0:.4f}'.format(specification.D_uv), 'right', 'bottom') settings = {'standalone': True} settings.update(kwargs) return render(**settings)
def generate_documentation_plots(output_directory: str): """ Generate documentation plots. Parameters ---------- output_directory Output directory. """ filter_warnings() colour_style() np.random.seed(0) # ************************************************************************* # "README.rst" # ************************************************************************* filename = os.path.join( output_directory, "Examples_Colour_Automatic_Conversion_Graph.png" ) plot_automatic_colour_conversion_graph(filename) arguments = { "tight_layout": True, "transparent_background": True, "filename": os.path.join( output_directory, "Examples_Plotting_Visible_Spectrum.png" ), } plt.close( plot_visible_spectrum( "CIE 1931 2 Degree Standard Observer", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_Illuminant_F1_SD.png" ) plt.close(plot_single_illuminant_sd("FL1", **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_Blackbodies.png" ) blackbody_sds = [ sd_blackbody(i, SpectralShape(0, 10000, 10)) for i in range(1000, 15000, 1000) ] plt.close( plot_multi_sds( blackbody_sds, y_label="W / (sr m$^2$) / m", plot_kwargs={"use_sd_colours": True, "normalise_sd_colours": True}, legend_location="upper right", bounding_box=(0, 1250, 0, 2.5e6), **arguments, )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_Cone_Fundamentals.png" ) plt.close( plot_single_cmfs( "Stockman & Sharpe 2 Degree Cone Fundamentals", y_label="Sensitivity", bounding_box=(390, 870, 0, 1.1), **arguments, )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_Luminous_Efficiency.png" ) plt.close( plot_multi_sds( ( sd_mesopic_luminous_efficiency_function(0.2), SDS_LEFS_PHOTOPIC["CIE 1924 Photopic Standard Observer"], SDS_LEFS_SCOTOPIC["CIE 1951 Scotopic Standard Observer"], ), y_label="Luminous Efficiency", legend_location="upper right", y_tighten=True, margins=(0, 0, 0, 0.1), **arguments, )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_BabelColor_Average.png" ) plt.close( plot_multi_sds( SDS_COLOURCHECKERS["BabelColor Average"].values(), plot_kwargs={"use_sd_colours": True}, title=("BabelColor Average - " "Spectral Distributions"), **arguments, )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_ColorChecker_2005.png" ) plt.close( plot_single_colour_checker( "ColorChecker 2005", text_kwargs={"visible": False}, **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_Chromaticities_Prediction.png" ) plt.close( plot_corresponding_chromaticities_prediction( 2, "Von Kries", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png", ) RGB = np.random.random((32, 32, 3)) plt.close( plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931( RGB, "ITU-R BT.709", colourspaces=["ACEScg", "S-Gamut"], show_pointer_gamut=True, **arguments, )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_CRI.png" ) plt.close( plot_single_sd_colour_rendering_index_bars( SDS_ILLUMINANTS["FL2"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_Colour_Rendition_Report.png" ) plt.close( plot_single_sd_colour_rendition_report( SDS_ILLUMINANTS["FL2"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_Plot_Visible_Spectrum_Section.png" ) plt.close( plot_visible_spectrum_section( section_colours="RGB", section_opacity=0.15, **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_Plot_RGB_Colourspace_Section.png" ) plt.close( plot_RGB_colourspace_section( "sRGB", section_colours="RGB", section_opacity=0.15, **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png", ) plt.close( plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS( ["A", "B", "C"], **arguments )[0] ) # ************************************************************************* # Documentation # ************************************************************************* arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_CVD_Simulation_Machado2009.png" ) plt.close(plot_cvd_simulation_Machado2009(RGB, **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_Colour_Checker.png" ) plt.close(plot_single_colour_checker("ColorChecker 2005", **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_Colour_Checkers.png" ) plt.close( plot_multi_colour_checkers( ["ColorChecker 1976", "ColorChecker 2005"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_SD.png" ) data = { 500: 0.0651, 520: 0.0705, 540: 0.0772, 560: 0.0870, 580: 0.1128, 600: 0.1360, } sd = SpectralDistribution(data, name="Custom") plt.close(plot_single_sd(sd, **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_SDS.png" ) data_1 = { 500: 0.004900, 510: 0.009300, 520: 0.063270, 530: 0.165500, 540: 0.290400, 550: 0.433450, 560: 0.594500, } data_2 = { 500: 0.323000, 510: 0.503000, 520: 0.710000, 530: 0.862000, 540: 0.954000, 550: 0.994950, 560: 0.995000, } spd1 = SpectralDistribution(data_1, name="Custom 1") spd2 = SpectralDistribution(data_2, name="Custom 2") plt.close(plot_multi_sds([spd1, spd2], **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_CMFS.png" ) plt.close( plot_single_cmfs("CIE 1931 2 Degree Standard Observer", **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_CMFS.png" ) cmfs = ( "CIE 1931 2 Degree Standard Observer", "CIE 1964 10 Degree Standard Observer", ) plt.close(plot_multi_cmfs(cmfs, **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_Illuminant_SD.png" ) plt.close(plot_single_illuminant_sd("A", **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_Illuminant_SDS.png" ) plt.close(plot_multi_illuminant_sds(["A", "B", "C"], **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Visible_Spectrum.png" ) plt.close(plot_visible_spectrum(**arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_Lightness_Function.png" ) plt.close(plot_single_lightness_function("CIE 1976", **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_Lightness_Functions.png" ) plt.close( plot_multi_lightness_functions( ["CIE 1976", "Wyszecki 1963"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_Luminance_Function.png" ) plt.close(plot_single_luminance_function("CIE 1976", **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_Luminance_Functions.png" ) plt.close( plot_multi_luminance_functions( ["CIE 1976", "Newhall 1943"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Blackbody_Spectral_Radiance.png" ) plt.close( plot_blackbody_spectral_radiance( 3500, blackbody="VY Canis Major", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Blackbody_Colours.png" ) plt.close( plot_blackbody_colours(SpectralShape(150, 12500, 50), **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_Colour_Swatch.png" ) RGB = ColourSwatch((0.45620519, 0.03081071, 0.04091952)) plt.close(plot_single_colour_swatch(RGB, **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_Colour_Swatches.png" ) RGB_1 = ColourSwatch((0.45293517, 0.31732158, 0.26414773)) RGB_2 = ColourSwatch((0.77875824, 0.57726450, 0.50453169)) plt.close(plot_multi_colour_swatches([RGB_1, RGB_2], **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_Function.png" ) plt.close(plot_single_function(lambda x: x ** (1 / 2.2), **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_Functions.png" ) functions = { "Gamma 2.2": lambda x: x ** (1 / 2.2), "Gamma 2.4": lambda x: x ** (1 / 2.4), "Gamma 2.6": lambda x: x ** (1 / 2.6), } plt.close(plot_multi_functions(functions, **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Image.png" ) path = os.path.join( colour.__path__[0], "examples", "plotting", "resources", "Ishihara_Colour_Blindness_Test_Plate_3.png", ) plt.close(plot_image(read_image(str(path)), **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Corresponding_Chromaticities_Prediction.png", ) plt.close( plot_corresponding_chromaticities_prediction( 1, "Von Kries", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Spectral_Locus.png" ) plt.close( plot_spectral_locus(spectral_locus_colours="RGB", **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Chromaticity_Diagram_Colours.png" ) plt.close( plot_chromaticity_diagram_colours(diagram_colours="RGB", **arguments)[ 0 ] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Chromaticity_Diagram.png" ) plt.close(plot_chromaticity_diagram(**arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1931.png" ) plt.close(plot_chromaticity_diagram_CIE1931(**arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1960UCS.png" ) plt.close(plot_chromaticity_diagram_CIE1960UCS(**arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1976UCS.png" ) plt.close(plot_chromaticity_diagram_CIE1976UCS(**arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_SDS_In_Chromaticity_Diagram.png" ) A = SDS_ILLUMINANTS["A"] D65 = SDS_ILLUMINANTS["D65"] plt.close(plot_sds_in_chromaticity_diagram([A, D65], **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1931.png", ) plt.close( plot_sds_in_chromaticity_diagram_CIE1931([A, D65], **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1960UCS.png", ) plt.close( plot_sds_in_chromaticity_diagram_CIE1960UCS([A, D65], **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1976UCS.png", ) plt.close( plot_sds_in_chromaticity_diagram_CIE1976UCS([A, D65], **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Pointer_Gamut.png" ) plt.close(plot_pointer_gamut(**arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png", ) plt.close( plot_RGB_colourspaces_in_chromaticity_diagram( ["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram_CIE1931.png", ) plt.close( plot_RGB_colourspaces_in_chromaticity_diagram_CIE1931( ["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Colourspaces_In_" "Chromaticity_Diagram_CIE1960UCS.png", ) plt.close( plot_RGB_colourspaces_in_chromaticity_diagram_CIE1960UCS( ["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Colourspaces_In_" "Chromaticity_Diagram_CIE1976UCS.png", ) plt.close( plot_RGB_colourspaces_in_chromaticity_diagram_CIE1976UCS( ["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Chromaticities_In_" "Chromaticity_Diagram.png", ) RGB = np.random.random((128, 128, 3)) plt.close( plot_RGB_chromaticities_in_chromaticity_diagram( RGB, "ITU-R BT.709", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Chromaticities_In_" "Chromaticity_Diagram_CIE1931.png", ) plt.close( plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931( RGB, "ITU-R BT.709", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Chromaticities_In_" "Chromaticity_Diagram_CIE1960UCS.png", ) plt.close( plot_RGB_chromaticities_in_chromaticity_diagram_CIE1960UCS( RGB, "ITU-R BT.709", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Chromaticities_In_" "Chromaticity_Diagram_CIE1976UCS.png", ) plt.close( plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS( RGB, "ITU-R BT.709", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Ellipses_MacAdam1942_In_Chromaticity_Diagram.png", ) plt.close( plot_ellipses_MacAdam1942_in_chromaticity_diagram(**arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Ellipses_MacAdam1942_In_" "Chromaticity_Diagram_CIE1931.png", ) plt.close( plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1931(**arguments)[ 0 ] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Ellipses_MacAdam1942_In_" "Chromaticity_Diagram_CIE1960UCS.png", ) plt.close( plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS( **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Ellipses_MacAdam1942_In_" "Chromaticity_Diagram_CIE1976UCS.png", ) plt.close( plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1976UCS( **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_CCTF.png" ) plt.close(plot_single_cctf("ITU-R BT.709", **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_CCTFs.png" ) plt.close(plot_multi_cctfs(["ITU-R BT.709", "sRGB"], **arguments)[0]) data = np.array( [ [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.40920000, 0.28120000, 0.30600000]), np.array( [ [0.02495100, 0.01908600, 0.02032900], [0.10944300, 0.06235900, 0.06788100], [0.27186500, 0.18418700, 0.19565300], [0.48898900, 0.40749400, 0.44854600], ] ), None, ], [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.30760000, 0.48280000, 0.42770000]), np.array( [ [0.02108000, 0.02989100, 0.02790400], [0.06194700, 0.11251000, 0.09334400], [0.15255800, 0.28123300, 0.23234900], [0.34157700, 0.56681300, 0.47035300], ] ), None, ], [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.39530000, 0.28120000, 0.18450000]), np.array( [ [0.02436400, 0.01908600, 0.01468800], [0.10331200, 0.06235900, 0.02854600], [0.26311900, 0.18418700, 0.12109700], [0.43158700, 0.40749400, 0.39008600], ] ), None, ], [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.20510000, 0.18420000, 0.57130000]), np.array( [ [0.03039800, 0.02989100, 0.06123300], [0.08870000, 0.08498400, 0.21843500], [0.18405800, 0.18418700, 0.40111400], [0.32550100, 0.34047200, 0.50296900], [0.53826100, 0.56681300, 0.80010400], ] ), None, ], [ None, np.array([0.95010000, 1.00000000, 1.08810000]), np.array([0.35770000, 0.28120000, 0.11250000]), np.array( [ [0.03678100, 0.02989100, 0.01481100], [0.17127700, 0.11251000, 0.01229900], [0.30080900, 0.28123300, 0.21229800], [0.52976000, 0.40749400, 0.11720000], ] ), None, ], ] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Constant_Hue_Loci.png" ) plt.close(plot_constant_hue_loci(data, "IPT", **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_Munsell_Value_Function.png" ) plt.close(plot_single_munsell_value_function("ASTM D1535", **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_Munsell_Value_Functions.png" ) plt.close( plot_multi_munsell_value_functions( ["ASTM D1535", "McCamy 1987"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_SD_Rayleigh_Scattering.png" ) plt.close(plot_single_sd_rayleigh_scattering(**arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_The_Blue_Sky.png" ) plt.close(plot_the_blue_sky(**arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Colour_Quality_Bars.png" ) illuminant = SDS_ILLUMINANTS["FL2"] light_source = SDS_LIGHT_SOURCES["Kinoton 75P"] light_source = light_source.copy().align(SpectralShape(360, 830, 1)) cqs_i = colour_quality_scale(illuminant, additional_data=True) cqs_l = colour_quality_scale(light_source, additional_data=True) plt.close(plot_colour_quality_bars([cqs_i, cqs_l], **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_SD_Colour_Rendering_Index_Bars.png", ) illuminant = SDS_ILLUMINANTS["FL2"] plt.close( plot_single_sd_colour_rendering_index_bars(illuminant, **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_SDS_Colour_Rendering_Indexes_Bars.png", ) light_source = SDS_LIGHT_SOURCES["Kinoton 75P"] plt.close( plot_multi_sds_colour_rendering_indexes_bars( [illuminant, light_source], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_SD_Colour_Quality_Scale_Bars.png", ) illuminant = SDS_ILLUMINANTS["FL2"] plt.close( plot_single_sd_colour_quality_scale_bars(illuminant, **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Multi_SDS_Colour_Quality_Scales_Bars.png", ) light_source = SDS_LIGHT_SOURCES["Kinoton 75P"] plt.close( plot_multi_sds_colour_quality_scales_bars( [illuminant, light_source], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Hull_Section_Colours.png" ) vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64) XYZ_vertices = RGB_to_XYZ( vertices["position"] + 0.5, RGB_COLOURSPACE_sRGB.whitepoint, RGB_COLOURSPACE_sRGB.whitepoint, RGB_COLOURSPACE_sRGB.matrix_RGB_to_XYZ, ) hull = trimesh.Trimesh(XYZ_vertices, faces, process=False) plt.close( plot_hull_section_colours(hull, section_colours="RGB", **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Hull_Section_Contour.png" ) plt.close( plot_hull_section_contour(hull, section_colours="RGB", **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Visible_Spectrum_Section.png" ) plt.close( plot_visible_spectrum_section( section_colours="RGB", section_opacity=0.15, **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Colourspace_Section.png" ) plt.close( plot_RGB_colourspace_section( "sRGB", section_colours="RGB", section_opacity=0.15, **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Planckian_Locus.png" ) plt.close( plot_planckian_locus(planckian_locus_colours="RGB", **arguments)[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram.png", ) plt.close( plot_planckian_locus_in_chromaticity_diagram( ["A", "B", "C"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1931.png", ) plt.close( plot_planckian_locus_in_chromaticity_diagram_CIE1931( ["A", "B", "C"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1960UCS.png", ) plt.close( plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS( ["A", "B", "C"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_SD_Colour_Rendition_Report_Full.png", ) plt.close( plot_single_sd_colour_rendition_report( SDS_ILLUMINANTS["FL2"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_SD_Colour_Rendition_Report_Intermediate.png", ) plt.close( plot_single_sd_colour_rendition_report( SDS_ILLUMINANTS["FL2"], "Intermediate", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_Single_SD_Colour_Rendition_Report_Simple.png", ) plt.close( plot_single_sd_colour_rendition_report( SDS_ILLUMINANTS["FL2"], "Simple", **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Colourspaces_Gamuts.png" ) plt.close( plot_RGB_colourspaces_gamuts( ["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Colourspaces_Gamuts.png" ) plt.close( plot_RGB_colourspaces_gamuts( ["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Plotting_Plot_RGB_Scatter.png" ) plt.close(plot_RGB_scatter(RGB, "ITU-R BT.709", **arguments)[0]) filename = os.path.join( output_directory, "Plotting_Plot_Colour_Automatic_Conversion_Graph.png" ) plot_automatic_colour_conversion_graph(filename) # ************************************************************************* # "tutorial.rst" # ************************************************************************* arguments["filename"] = os.path.join( output_directory, "Tutorial_Visible_Spectrum.png" ) plt.close(plot_visible_spectrum(**arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Tutorial_Sample_SD.png" ) sample_sd_data = { 380: 0.048, 385: 0.051, 390: 0.055, 395: 0.060, 400: 0.065, 405: 0.068, 410: 0.068, 415: 0.067, 420: 0.064, 425: 0.062, 430: 0.059, 435: 0.057, 440: 0.055, 445: 0.054, 450: 0.053, 455: 0.053, 460: 0.052, 465: 0.052, 470: 0.052, 475: 0.053, 480: 0.054, 485: 0.055, 490: 0.057, 495: 0.059, 500: 0.061, 505: 0.062, 510: 0.065, 515: 0.067, 520: 0.070, 525: 0.072, 530: 0.074, 535: 0.075, 540: 0.076, 545: 0.078, 550: 0.079, 555: 0.082, 560: 0.087, 565: 0.092, 570: 0.100, 575: 0.107, 580: 0.115, 585: 0.122, 590: 0.129, 595: 0.134, 600: 0.138, 605: 0.142, 610: 0.146, 615: 0.150, 620: 0.154, 625: 0.158, 630: 0.163, 635: 0.167, 640: 0.173, 645: 0.180, 650: 0.188, 655: 0.196, 660: 0.204, 665: 0.213, 670: 0.222, 675: 0.231, 680: 0.242, 685: 0.251, 690: 0.261, 695: 0.271, 700: 0.282, 705: 0.294, 710: 0.305, 715: 0.318, 720: 0.334, 725: 0.354, 730: 0.372, 735: 0.392, 740: 0.409, 745: 0.420, 750: 0.436, 755: 0.450, 760: 0.462, 765: 0.465, 770: 0.448, 775: 0.432, 780: 0.421, } sd = SpectralDistribution(sample_sd_data, name="Sample") plt.close(plot_single_sd(sd, **arguments)[0]) arguments["filename"] = os.path.join( output_directory, "Tutorial_SD_Interpolation.png" ) sd_copy = sd.copy() sd_copy.interpolate(SpectralShape(400, 770, 1)) plt.close( plot_multi_sds( [sd, sd_copy], bounding_box=[730, 780, 0.25, 0.5], **arguments )[0] ) arguments["filename"] = os.path.join( output_directory, "Tutorial_Sample_Swatch.png" ) sd = SpectralDistribution(sample_sd_data) cmfs = MSDS_CMFS_STANDARD_OBSERVER["CIE 1931 2 Degree Standard Observer"] illuminant = SDS_ILLUMINANTS["D65"] with domain_range_scale("1"): XYZ = sd_to_XYZ(sd, cmfs, illuminant) RGB = XYZ_to_sRGB(XYZ) plt.close( plot_single_colour_swatch( ColourSwatch(RGB, "Sample"), text_kwargs={"size": "x-large"}, **arguments, )[0] ) arguments["filename"] = os.path.join( output_directory, "Tutorial_Neutral5.png" ) patch_name = "neutral 5 (.70 D)" patch_sd = SDS_COLOURCHECKERS["ColorChecker N Ohta"][patch_name] with domain_range_scale("1"): XYZ = sd_to_XYZ(patch_sd, cmfs, illuminant) RGB = XYZ_to_sRGB(XYZ) plt.close( plot_single_colour_swatch( ColourSwatch(RGB, patch_name.title()), text_kwargs={"size": "x-large"}, **arguments, )[0] ) arguments["filename"] = os.path.join( output_directory, "Tutorial_Colour_Checker.png" ) plt.close( plot_single_colour_checker( colour_checker="ColorChecker 2005", text_kwargs={"visible": False}, **arguments, )[0] ) arguments["filename"] = os.path.join( output_directory, "Tutorial_CIE_1931_Chromaticity_Diagram.png" ) xy = XYZ_to_xy(XYZ) plot_chromaticity_diagram_CIE1931(standalone=False) x, y = xy plt.plot(x, y, "o-", color="white") # Annotating the plot. plt.annotate( patch_sd.name.title(), xy=xy, xytext=(-50, 30), textcoords="offset points", arrowprops=dict(arrowstyle="->", connectionstyle="arc3, rad=-0.2"), ) plt.close( render( standalone=True, limits=(-0.1, 0.9, -0.1, 0.9), x_tighten=True, y_tighten=True, **arguments, )[0] ) # ************************************************************************* # "basics.rst" # ************************************************************************* arguments["filename"] = os.path.join( output_directory, "Basics_Logo_Small_001_CIE_XYZ.png" ) RGB = read_image(os.path.join(output_directory, "Logo_Small_001.png"))[ ..., 0:3 ] XYZ = sRGB_to_XYZ(RGB) plt.close( plot_image(XYZ, text_kwargs={"text": "sRGB to XYZ"}, **arguments)[0] )
RESOURCES_DIRECTORY = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'resources') colour_style() ISHIHARA_CBT_3_IMAGE = colour.oetf_reverse( colour.read_image( os.path.join(RESOURCES_DIRECTORY, 'Ishihara_Colour_Blindness_Test_Plate_3.png')), function='sRGB') message_box('Colour Blindness Plots') message_box('Displaying "Ishihara Colour Blindness Test - Plate 3".') plot_image(colour.oetf(ISHIHARA_CBT_3_IMAGE), text_parameters={'text': 'Normal Trichromat', 'color': 'black'}) print('\n') message_box('Simulating average "Protanomaly" on ' '"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) ' 'model and pre-computed matrix.') plot_cvd_simulation_Machado2009( ISHIHARA_CBT_3_IMAGE, 'Protanomaly', 0.5, text_parameters={'text': 'Protanomaly - 50%', 'color': 'black'}) print('\n') M_a = colour.anomalous_trichromacy_matrix_Machado2009( colour.LMS_CMFS.get('Stockman & Sharpe 2 Degree Cone Fundamentals'), colour.DISPLAYS_RGB_PRIMARIES['Typical CRT Brainard 1997'],