colour.read_spectral_data_from_csv_file

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

Reads the spectral data from given 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 returns it as an OrderedDict of dict as follows:

OrderedDict([ (‘field’, {‘wavelength’: ‘value’, …, ‘wavelength’: ‘value’}), …, (‘field’, {‘wavelength’: ‘value’, …, ‘wavelength’: ‘value’})])

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 as spectral data fields names.

  • default (numeric, optional) – Default value for fields row with missing value.

Returns

CSV file content.

Return type

OrderedDict

Raises

RuntimeError – If the CSV spectral data file doesn’t define the appropriate fields.

Notes

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

  • If no value is provided for the fields names, the first line of the file will be used as spectral data fields names.

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()))
['1',
 '2',
 '3',
 '4',
 '5',
 '6',
 '7',
 '8',
 '9',
 '10',
 '11',
 '12',
 '13',
 '14',
 '15',
 '16',
 '17',
 '18',
 '19',
 '20',
 '21',
 '22',
 '23',
 '24']