Source code for colour.plotting.corresponding

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Corresponding Chromaticities Prediction Plotting
================================================

Defines corresponding chromaticities prediction plotting objects:

-   :func:`corresponding_chromaticities_prediction_plot`
"""

from __future__ import division
import pylab

from colour.corresponding import CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS
from colour.plotting import (
    CIE_1976_UCS_chromaticity_diagram_plot,
    DEFAULT_FIGURE_WIDTH,
    canvas,
    display)

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = '[email protected]'
__status__ = 'Production'

__all__ = ['get_corresponding_chromaticities_prediction_model',
           'corresponding_chromaticities_prediction_plot']


[docs]def get_corresponding_chromaticities_prediction_model(model): """ Returns the corresponding chromaticities prediction model with given name. Parameters ---------- model : unicode Corresponding chromaticities prediction models name. Returns ------- object Corresponding chromaticities prediction models. Raises ------ KeyError If the given corresponding chromaticities prediction model is not found in the factory corresponding chromaticities prediction models. """ model, name = ( CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS.get(model), model) if model is None: models = ', '.join( sorted(CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS.keys())) raise KeyError( ('"{0}" not found in factory corresponding chromaticities ' 'prediction models: "{1}".').format(name, models)) return model
[docs]def corresponding_chromaticities_prediction_plot( experiment=1, model='Von Kries', transform='CAT02', **kwargs): """ Plots given chromatic adaptation model corresponding chromaticities prediction. Parameters ---------- model : unicode, optional Corresponding chromaticities prediction models name. model : unicode, optional Corresponding chromaticities prediction models name. transform : unicode, optional Transformation to use with Von Kries chromatic adaptation model. \*\*kwargs : \*\* Keywords arguments. Returns ------- bool Definition success. Examples -------- >>> corresponding_chromaticities_prediction_plot() # doctest: +SKIP True """ settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)} settings.update(kwargs) canvas(**settings) model, name = ( get_corresponding_chromaticities_prediction_model(model), model) settings.update({ 'title': (('Corresponding Chromaticities Prediction\n{0} ({1}) - ' 'Experiment {2}\nCIE 1960 UCS Chromaticity Diagram').format( name, transform, experiment) if name.lower() in ('von kries', 'vonkries') else ('Corresponding Chromaticities Prediction\n{0} - ' 'Experiment {1}\nCIE 1960 UCS Chromaticity Diagram').format( name, experiment)), 'standalone': False}) settings.update(kwargs) if not CIE_1976_UCS_chromaticity_diagram_plot(**settings): return results = model(experiment, transform=transform) for result in results: name, uvp_t, uvp_m, uvp_p = result pylab.arrow(uvp_t[0], uvp_t[1], uvp_p[0] - uvp_t[0] - 0.1 * (uvp_p[0] - uvp_t[0]), uvp_p[1] - uvp_t[1] - 0.1 * (uvp_p[1] - uvp_t[1]), head_width=0.005, head_length=0.005, linewidth=0.5, color='black') pylab.plot(uvp_t[0], uvp_t[1], 'o', color='white') pylab.plot(uvp_m[0], uvp_m[1], '^', color='white') pylab.plot(uvp_p[0], uvp_p[1], '^', color='black') settings.update({'standalone': True}) return display(**settings)