Vishal - Plotting Spds

figure_1.png

figure_2.png

vishal.py

import numpy as np
import pylab

import colour
from colour.plotting import *

fields = ('wl', 'a', 'b', 'c', 'd', 'e')
spds = colour.read_spds_from_csv_file(
  '/Users/kelsolaar/Documents/Development/colour-science/colour-ramblings/ramblings_modules/vishal/data_set.txt',
  delimiter='	',
  fields=fields)

def spds_CIE_1931_chromaticity_diagram_plot(
        spds,
        cmfs='CIE 1931 2 Degree Standard Observer',
        annotate=True,
        **kwargs):
    """
    Plots given spectral power distribution chromaticity coordinates into the
    *CIE 1931 Chromaticity Diagram*.

    Parameters
    ----------
    spds : array_like, optional
        Spectral power distributions to plot.
    cmfs : unicode, optional
        Standard observer colour matching functions used for diagram bounds.
    annotate : bool
        Should resulting chromaticity coordinates annotated with their
        respective spectral power distribution names.
    \**kwargs : dict, optional
        Keywords arguments.

    Returns
    -------
    Figure
        Current figure or None.

    Examples
    --------
    >>> from colour import ILLUMINANTS_RELATIVE_SPDS
    >>> A = ILLUMINANTS_RELATIVE_SPDS['A']
    >>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
    >>> spds_CIE_1931_chromaticity_diagram_plot([A, D65])  # doctest: +SKIP
    """

    settings = {}
    settings.update(kwargs)
    settings.update({'standalone': False})

    CIE_1931_chromaticity_diagram_plot(cmfs=cmfs, **settings)

    xy = []
    for spd in spds:
        XYZ = colour.spectral_to_XYZ(spd) / 100
        xy.append(colour.XYZ_to_xy(XYZ))
    xy = np.array(xy)

    pylab.plot(xy[..., 0], xy[..., 1], 'o--', color='black')

    settings.update({
        'x_tighten': True,
        'y_tighten': True,
        'limits': (-0.1, 0.9, -0.1, 0.9),
        'standalone': True})
    settings.update(kwargs)

    boundaries(**settings)
    decorate(**settings)

    return display(**settings)

spds_CIE_1931_chromaticity_diagram_plot(spds.values())

spd_names = spds.keys()
spds = spds.values()
XYZ = [colour.spectral_to_XYZ(spd) / 100 for spd in spds]
RGB = colour.XYZ_to_sRGB(XYZ)
colour_parameters = [ColourParameter(spd_names[i], np.clip(RGB[i] , 0, 1))
    for i in range(len(spd_names))]

multi_colour_plot(colour_parameters, across=len(spd_names), spacing=0.1)