colour.read_sds_from_csv_file

colour.read_sds_from_csv_file(path, **kwargs)[source]

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

Parameters

path (unicode) – CSV file path.

Other Parameters

**kwargs (dict, optional) – Keywords arguments passed to np.recfromcsv() definition.

Returns

colour.SpectralDistribution 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')
>>> sds = read_sds_from_csv_file(csv_file)
>>> print(tuple(sds.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):
...     sds['1']  
SpectralDistribution([[ 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_kwargs={},
                     extrapolator=Extrapolator,
                     extrapolator_kwargs={...})