colour.plotting.plot_single_sd#
- colour.plotting.plot_single_sd(sd: SpectralDistribution, cmfs: MultiSpectralDistributions | str | Sequence[MultiSpectralDistributions | str] = 'CIE 1931 2 Degree Standard Observer', out_of_gamut_clipping: bool = True, modulate_colours_with_sd_amplitude: bool = False, equalize_sd_amplitude: bool = False, **kwargs: Any) Tuple[Figure, Axes] [source]#
Plot given spectral distribution.
- Parameters:
sd (SpectralDistribution) – Spectral distribution to plot.
cmfs (MultiSpectralDistributions | str | Sequence[MultiSpectralDistributions | str]) – Standard observer colour matching functions used for computing the spectrum domain and colours.
cmfs
can be of any type or form supported by thecolour.plotting.common.filter_cmfs()
definition.out_of_gamut_clipping (bool) – Whether to clip out of gamut colours otherwise, the colours will be offset by the absolute minimal colour leading to a rendering on gray background, less saturated and smoother.
modulate_colours_with_sd_amplitude (bool) – Whether to modulate the colours with the spectral distribution amplitude.
equalize_sd_amplitude (bool) – Whether to equalize the spectral distribution amplitude. Equalization occurs after the colours modulation thus setting both arguments to True will generate a spectrum strip where each wavelength colour is modulated by the spectral distribution amplitude. The usual 5% margin above the spectral distribution is also omitted.
kwargs (Any) – {
colour.plotting.artist()
,colour.plotting.render()
}, See the documentation of the previously listed definitions.
- Returns:
Current figure and axes.
- Return type:
References
[Spi15]
Examples
>>> from colour import SpectralDistribution >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360, ... } >>> sd = SpectralDistribution(data, name="Custom") >>> plot_single_sd(sd) (<Figure size ... with 1 Axes>, <...Axes...>)