colour.read_spds_from_csv_file

colour.read_spds_from_csv_file(path, delimiter=u', ', fields=None, default=0)[source]

Reads the spectral data from given CSV file and return its content as an OrderedDict of colour.SpectralPowerDistribution classes.

Parameters:
  • path (unicode) – Absolute CSV file path.
  • delimiter (unicode, optional) – CSV file content delimiter.
  • fields (array_like, optional) – CSV file spectral data fields names. If no value is provided the first line of the file will be used for as spectral data fields names.
  • default (numeric) – Default value for fields row with missing value.
Returns:

colour.SpectralPowerDistribution classes of given CSV file.

Return type:

OrderedDict

Examples

>>> from colour.utilities import numpy_print_options
>>> import os
>>> csv_file = os.path.join(os.path.dirname(__file__), 'tests',
...                         'resources', 'colorchecker_n_ohta.csv')
>>> spds = read_spds_from_csv_file(csv_file)
>>> print(tuple(spds.keys()))
('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24')
>>> with numpy_print_options(suppress=True):
...     spds['1']  
SpectralPowerDistribution([[ 380.   ,    0.048],
                           [ 385.   ,    0.051],
                           [ 390.   ,    0.055],
                           [ 395.   ,    0.06 ],
                           [ 400.   ,    0.065],
                           [ 405.   ,    0.068],
                           [ 410.   ,    0.068],
                           [ 415.   ,    0.067],
                           [ 420.   ,    0.064],
                           [ 425.   ,    0.062],
                           [ 430.   ,    0.059],
                           [ 435.   ,    0.057],
                           [ 440.   ,    0.055],
                           [ 445.   ,    0.054],
                           [ 450.   ,    0.053],
                           [ 455.   ,    0.053],
                           [ 460.   ,    0.052],
                           [ 465.   ,    0.052],
                           [ 470.   ,    0.052],
                           [ 475.   ,    0.053],
                           [ 480.   ,    0.054],
                           [ 485.   ,    0.055],
                           [ 490.   ,    0.057],
                           [ 495.   ,    0.059],
                           [ 500.   ,    0.061],
                           [ 505.   ,    0.062],
                           [ 510.   ,    0.065],
                           [ 515.   ,    0.067],
                           [ 520.   ,    0.07 ],
                           [ 525.   ,    0.072],
                           [ 530.   ,    0.074],
                           [ 535.   ,    0.075],
                           [ 540.   ,    0.076],
                           [ 545.   ,    0.078],
                           [ 550.   ,    0.079],
                           [ 555.   ,    0.082],
                           [ 560.   ,    0.087],
                           [ 565.   ,    0.092],
                           [ 570.   ,    0.1  ],
                           [ 575.   ,    0.107],
                           [ 580.   ,    0.115],
                           [ 585.   ,    0.122],
                           [ 590.   ,    0.129],
                           [ 595.   ,    0.134],
                           [ 600.   ,    0.138],
                           [ 605.   ,    0.142],
                           [ 610.   ,    0.146],
                           [ 615.   ,    0.15 ],
                           [ 620.   ,    0.154],
                           [ 625.   ,    0.158],
                           [ 630.   ,    0.163],
                           [ 635.   ,    0.167],
                           [ 640.   ,    0.173],
                           [ 645.   ,    0.18 ],
                           [ 650.   ,    0.188],
                           [ 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.   ,    0.42 ],
                           [ 750.   ,    0.436],
                           [ 755.   ,    0.45 ],
                           [ 760.   ,    0.462],
                           [ 765.   ,    0.465],
                           [ 770.   ,    0.448],
                           [ 775.   ,    0.432],
                           [ 780.   ,    0.421]],
                          interpolator=SpragueInterpolator,
                          interpolator_args={},
                          extrapolator=Extrapolator,
                          extrapolator_args={...})