766: 1.6180000e-001, 768: 1.6770000e-001, 770: 1.5340000e-001, 772: 1.1740000e-001, 774: 9.2280000e-002, 776: 9.0480000e-002, 778: 9.0020000e-002, 780: 8.8190000e-002 } street_light_spd = colour.SpectralPowerDistribution(street_light_spd_data, name='Street Light') bandpass_corrected_street_light_spd = street_light_spd.copy() bandpass_corrected_street_light_spd.name = 'Street Light (Bandpass Corrected)' bandpass_corrected_street_light_spd = colour.bandpass_correction( bandpass_corrected_street_light_spd, method='Stearns 1988') multi_spd_plot((street_light_spd, bandpass_corrected_street_light_spd), title='Stearns Bandpass Correction') print('\n') message_box('Plotting a single "cone fundamentals" colour matching functions.') single_cmfs_plot('Stockman & Sharpe 2 Degree Cone Fundamentals', y_label='Sensitivity', bounding_box=(390, 870, 0, 1.1)) print('\n') message_box('Plotting multiple "cone fundamentals" colour matching functions.') multi_cmfs_plot([
766: 1.6180000e-001, 768: 1.6770000e-001, 770: 1.5340000e-001, 772: 1.1740000e-001, 774: 9.2280000e-002, 776: 9.0480000e-002, 778: 9.0020000e-002, 780: 8.8190000e-002, } street_light_spd = colour.SpectralPowerDistribution("Street Light", street_light_spd_data) bandpass_corrected_street_light_spd = street_light_spd.clone() bandpass_corrected_street_light_spd.name = "Street Light (Bandpass Corrected)" bandpass_corrected_street_light_spd = colour.bandpass_correction( bandpass_corrected_street_light_spd, method="Stearns 1988" ) multi_spd_plot([street_light_spd, bandpass_corrected_street_light_spd], title="Stearns Bandpass Correction") print("\n") message_box('Plotting a single "cone fundamentals" colour matching functions.') single_cmfs_plot("Stockman & Sharpe 2 Degree Cone Fundamentals", y_label="Sensitivity", bounding_box=[390, 870, 0, 1.1]) print("\n") message_box('Plotting multiple "cone fundamentals" colour matching functions.') multi_cmfs_plot( ["Stockman & Sharpe 2 Degree Cone Fundamentals", "Stockman & Sharpe 10 Degree Cone Fundamentals"], y_label="Sensitivity",
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 } spd = colour.SpectralPowerDistribution(sample_spd_data, name='Sample') uncorrected_values = spd.values print(np.dstack((uncorrected_values, colour.bandpass_correction(spd).values)))
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} spd = colour.SpectralPowerDistribution('Sample', sample_spd_data) uncorrected_values = spd.values print(np.dstack((uncorrected_values, colour.bandpass_correction(spd).values)))
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 = colour.SpectralDistribution(sample_sd_data, name='Sample') uncorrected_values = sd.values print(np.dstack([uncorrected_values, colour.bandpass_correction(sd).values]))
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_sample = colour.SpectralDistribution(data_sample, name="Sample") uncorrected_values = sd_sample.values print( np.dstack( [uncorrected_values, colour.bandpass_correction(sd_sample).values] ) )
766: 1.6180000e-001, 768: 1.6770000e-001, 770: 1.5340000e-001, 772: 1.1740000e-001, 774: 9.2280000e-002, 776: 9.0480000e-002, 778: 9.0020000e-002, 780: 8.8190000e-002 } sd_street_light = colour.SpectralDistribution(data_street_light, name='Street Light') sd_bandpass_corrected_street_light = sd_street_light.copy() sd_bandpass_corrected_street_light.name = 'Street Light (Bandpass Corrected)' sd_bandpass_corrected_street_light = colour.bandpass_correction( sd_bandpass_corrected_street_light, method='Stearns 1988') plot_multi_sds((sd_street_light, sd_bandpass_corrected_street_light), title='Stearns Bandpass Correction') print('\n') message_box('Plotting a single "cone fundamentals" colour matching functions.') plot_single_cmfs('Stockman & Sharpe 2 Degree Cone Fundamentals', y_label='Sensitivity', bounding_box=(390, 870, 0, 1.1)) print('\n') message_box('Plotting multiple "cone fundamentals" colour matching functions.') plot_multi_cmfs([
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_sample = colour.SpectralDistribution(data_sample, name='Sample') uncorrected_values = sd_sample.values print( np.dstack( [uncorrected_values, colour.bandpass_correction(sd_sample).values]))
766: 1.6180000e-001, 768: 1.6770000e-001, 770: 1.5340000e-001, 772: 1.1740000e-001, 774: 9.2280000e-002, 776: 9.0480000e-002, 778: 9.0020000e-002, 780: 8.8190000e-002 } street_light_sd = colour.SpectralDistribution( street_light_sd_data, name='Street Light') bandpass_corrected_street_light_sd = street_light_sd.copy() bandpass_corrected_street_light_sd.name = 'Street Light (Bandpass Corrected)' bandpass_corrected_street_light_sd = colour.bandpass_correction( bandpass_corrected_street_light_sd, method='Stearns 1988') plot_multi_sds( (street_light_sd, bandpass_corrected_street_light_sd), title='Stearns Bandpass Correction') print('\n') message_box('Plotting a single "cone fundamentals" colour matching functions.') plot_single_cmfs( 'Stockman & Sharpe 2 Degree Cone Fundamentals', y_label='Sensitivity', bounding_box=(390, 870, 0, 1.1)) print('\n')