colour.plotting.plot_constant_hue_loci¶
- colour.plotting.plot_constant_hue_loci(data, model, scatter_kwargs=None, **kwargs)[source]¶
Plots given constant hue loci colour matches data such as that from [] or [] that are easily loaded with Colour - Datasets.
- Parameters
data (array_like) –
Constant hue loci colour matches data expected to be an array_like as follows:
[ ('name', XYZ_r, XYZ_cr, (XYZ_ct, XYZ_ct, XYZ_ct, ...), {metadata}), ('name', XYZ_r, XYZ_cr, (XYZ_ct, XYZ_ct, XYZ_ct, ...), {metadata}), ('name', XYZ_r, XYZ_cr, (XYZ_ct, XYZ_ct, XYZ_ct, ...), {metadata}), ... ]
where
name
is the hue angle or name,XYZ_r
the CIE XYZ tristimulus values of the reference illuminant,XYZ_cr
the CIE XYZ tristimulus values of the reference colour under the reference illuminant,XYZ_ct
the CIE XYZ tristimulus values of the colour matches under the reference illuminant andmetadata
the dataset metadata.model (unicode, optional) – {‘CIE XYZ’, ‘CIE xyY’, ‘CIE xy’, ‘CIE Lab’, ‘CIE LCHab’, ‘CIE Luv’, ‘CIE Luv uv’, ‘CIE LCHuv’, ‘CIE UCS’, ‘CIE UCS uv’, ‘CIE UVW’, ‘DIN 99’, ‘Hunter Lab’, ‘Hunter Rdab’, ‘IPT’, ‘JzAzBz’, ‘OSA UCS’, ‘hdr-CIELAB’, ‘hdr-IPT’}, Colourspace model.
scatter_kwargs (dict, optional) –
Keyword arguments for the
plt.scatter()
definition. The following special keyword arguments can also be used:c : unicode or array_like, if
c
is set to RGB, the scatter will use the colours as given by theRGB
argument.
**kwargs (dict, optional) – {
colour.plotting.artist()
,colour.plotting.plot_multi_functions()
,colour.plotting.render()
}, Please refer to the documentation of the previously listed definitions. Also handles keywords arguments for deprecation management.
- Returns
Current figure and axes.
- Return type
References
[], [], []
Examples
>>> 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, ... ], ... ]) >>> plot_constant_hue_loci(data, 'IPT') (<Figure size ... with 1 Axes>, <...AxesSubplot...>)