colour.read_spectral_data_from_csv_file#

colour.read_spectral_data_from_csv_file(path: str | PathLike, **kwargs: Any) → Dict[str, NDArrayFloat][source]#

Read spectral data from the specified CSV file in the following form:

390, 4.15003e-04, 3.68349e-04, 9.54729e-03
395, 1.05192e-03, 9.58658e-04, 2.38250e-02
400, 2.40836e-03, 2.26991e-03, 5.66498e-02
...
830, 9.74306e-07, 9.53411e-08, 0.00000

and convert it to a dict as follows:

{
    'wavelength': ndarray,
    'field 1': ndarray,
    'field 2': ndarray,
    ...,
    'field n': ndarray
}

Parameters:

path (str | PathLike) – CSV file path.
kwargs (Any) – Keywords arguments passed to numpy.recfromcsv() definition.

Returns:

CSV file content.

Return type:

dict

Raises:

IOError – If the file cannot be read.

Notes

A CSV spectral data file should at least define two fields: one for the wavelengths and one for the associated values of one spectral distribution.

Examples

>>> import os
>>> from pprint import pprint
>>> csv_file = os.path.join(
...     os.path.dirname(__file__),
...     "tests",
...     "resources",
...     "colorchecker_n_ohta.csv",
... )
>>> sds_data = read_spectral_data_from_csv_file(csv_file)
>>> pprint(list(sds_data.keys()))
['wavelength',
 '1',
 '2',
 '3',
 '4',
 '5',
 '6',
 '7',
 '8',
 '9',
 '10',
 '11',
 '12',
 '13',
 '14',
 '15',
 '16',
 '17',
 '18',
 '19',
 '20',
 '21',
 '22',
 '23',
 '24']

colour.read_spectral_data_from_csv_file#

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