Python bandpass_correction Examples

Example #1

File: first_test.py Project: ajun73/Work_Code

    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([

Example #2

File: examples_colorimetry_plots.py Project: canavandl/colour

    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",

Example #3

File: examples_correction.py Project: ajun73/Work_Code

    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)))

Example #4

File: examples_correction.py Project: Nick-Shaw/colour

    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)))

Example #5

    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]))

Example #6

File: examples_correction.py Project: colour-science/colour

    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]
    )
)

Example #7

File: examples_colorimetry_plots.py Project: wenh06/colour

    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([

Example #8

    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]))

Example #9

File: examples_colorimetry_plots.py Project: colour-science/colour

    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')