colour.SpectralDistribution_UPRTek

class colour.SpectralDistribution_UPRTek(path: str, **kwargs: Any)[source]

Bases: colour.io.tm2714.SpectralDistribution_IESTM2714

Implement support to read and write IES TM-27-14 spectral data XML file from a UPRTek Pseudo-XLS file.

Parameters
  • path (str) – Path for UPRTek Pseudo-XLS file.

  • kwargs (Any) –

Attributes

Methods

Examples

>>> from os.path import dirname, join
>>> from colour import SpectralShape
>>> directory = join(dirname(__file__), 'tests', 'resources')
>>> sd = SpectralDistribution_UPRTek(
...     join(directory, 'ESPD2021_0104_231446.xls'))
>>> print(sd.read().align(SpectralShape(380, 780, 10)))
[[  3.80000000e+02   3.02670000e-02]
 [  3.90000000e+02   3.52230000e-02]
 [  4.00000000e+02   1.93250000e-02]
 [  4.10000000e+02   2.94260000e-02]
 [  4.20000000e+02   8.76780000e-02]
 [  4.30000000e+02   6.32578000e-01]
 [  4.40000000e+02   3.62565900e+00]
 [  4.50000000e+02   1.42069180e+01]
 [  4.60000000e+02   1.70112970e+01]
 [  4.70000000e+02   1.19673130e+01]
 [  4.80000000e+02   8.42736200e+00]
 [  4.90000000e+02   7.97729800e+00]
 [  5.00000000e+02   8.71903600e+00]
 [  5.10000000e+02   9.55321500e+00]
 [  5.20000000e+02   9.90610500e+00]
 [  5.30000000e+02   9.91394400e+00]
 [  5.40000000e+02   9.74738000e+00]
 [  5.50000000e+02   9.53404900e+00]
 [  5.60000000e+02   9.27392200e+00]
 [  5.70000000e+02   9.02323400e+00]
 [  5.80000000e+02   8.91788800e+00]
 [  5.90000000e+02   9.11454600e+00]
 [  6.00000000e+02   9.55787100e+00]
 [  6.10000000e+02   1.00600760e+01]
 [  6.20000000e+02   1.04846200e+01]
 [  6.30000000e+02   1.05679540e+01]
 [  6.40000000e+02   1.04359870e+01]
 [  6.50000000e+02   9.82122300e+00]
 [  6.60000000e+02   8.77578300e+00]
 [  6.70000000e+02   7.56471800e+00]
 [  6.80000000e+02   6.29808600e+00]
 [  6.90000000e+02   5.15623400e+00]
 [  7.00000000e+02   4.05390600e+00]
 [  7.10000000e+02   3.06638600e+00]
 [  7.20000000e+02   2.19250000e+00]
 [  7.30000000e+02   1.53922800e+00]
 [  7.40000000e+02   1.14938200e+00]
 [  7.50000000e+02   9.05095000e-01]
 [  7.60000000e+02   6.90947000e-01]
 [  7.70000000e+02   5.08426000e-01]
 [  7.80000000e+02   4.11766000e-01]]
>>> sd.header.comments
'{"Model Name": "CV600", "Serial Number": "19J00789", "Time": "2021/01/04_23:14:46", "Memo": [], "LUX": 695.154907, "fc": 64.605476, "CCT": 5198.0, "Duv": -0.00062, "I-Time": 12000.0, "X": 682.470886, "Y": 695.154907, "Z": 631.635071, "x": 0.339663, "y": 0.345975, "u\'": 0.209915, "v\'": 0.481087, "LambdaP": 456.0, "LambdaPValue": 18.404581, "CRI": 92.956993, "R1": 91.651062, "R2": 93.014732, "R3": 97.032013, "R4": 93.513229, "R5": 92.48259, "R6": 91.48687, "R7": 93.016129, "R8": 91.459312, "R9": 77.613075, "R10": 86.981613, "R11": 94.841324, "R12": 74.139542, "R13": 91.073837, "R14": 97.064323, "R15": 88.615669, "TLCI": 97.495056, "TLMF-A": 1.270032, "SSI-A": 44.881924, "Rf": 87.234917, "Rg": 98.510712, "IRR": 2.607891}'
>>> sd.metadata.keys()
dict_keys(['Model Name', 'Serial Number', 'Time', 'Memo', 'LUX', 'fc', 'CCT', 'Duv', 'I-Time', 'X', 'Y', 'Z', 'x', 'y', "u'", "v'", 'LambdaP', 'LambdaPValue', 'CRI', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8', 'R9', 'R10', 'R11', 'R12', 'R13', 'R14', 'R15', 'TLCI', 'TLMF-A', 'SSI-A', 'Rf', 'Rg', 'IRR'])
>>> sd.write(join(directory, 'ESPD2021_0104_231446.spdx'))
... 
__init__(path: str, **kwargs: Any)[source]
Parameters
  • path (str) –

  • kwargs (Any) –

property metadata: Dict

Getter property for the metadata.

Returns

Metadata.

Return type

dict

read() colour.io.uprtek_sekonic.SpectralDistribution_UPRTek[source]

Read and parses the spectral data from a given UPRTek CSV file.

Returns

UPRTek spectral distribution.

Return type

colour.SpectralDistribution_UPRTek

Examples

>>> from os.path import dirname, join
>>> from colour import SpectralShape
>>> directory = join(dirname(__file__), 'tests', 'resources')
>>> sd = SpectralDistribution_UPRTek(
...     join(directory, 'ESPD2021_0104_231446.xls'))
>>> print(sd.read().align(SpectralShape(380, 780, 10)))
[[  3.80000000e+02   3.02670000e-02]
 [  3.90000000e+02   3.52230000e-02]
 [  4.00000000e+02   1.93250000e-02]
 [  4.10000000e+02   2.94260000e-02]
 [  4.20000000e+02   8.76780000e-02]
 [  4.30000000e+02   6.32578000e-01]
 [  4.40000000e+02   3.62565900e+00]
 [  4.50000000e+02   1.42069180e+01]
 [  4.60000000e+02   1.70112970e+01]
 [  4.70000000e+02   1.19673130e+01]
 [  4.80000000e+02   8.42736200e+00]
 [  4.90000000e+02   7.97729800e+00]
 [  5.00000000e+02   8.71903600e+00]
 [  5.10000000e+02   9.55321500e+00]
 [  5.20000000e+02   9.90610500e+00]
 [  5.30000000e+02   9.91394400e+00]
 [  5.40000000e+02   9.74738000e+00]
 [  5.50000000e+02   9.53404900e+00]
 [  5.60000000e+02   9.27392200e+00]
 [  5.70000000e+02   9.02323400e+00]
 [  5.80000000e+02   8.91788800e+00]
 [  5.90000000e+02   9.11454600e+00]
 [  6.00000000e+02   9.55787100e+00]
 [  6.10000000e+02   1.00600760e+01]
 [  6.20000000e+02   1.04846200e+01]
 [  6.30000000e+02   1.05679540e+01]
 [  6.40000000e+02   1.04359870e+01]
 [  6.50000000e+02   9.82122300e+00]
 [  6.60000000e+02   8.77578300e+00]
 [  6.70000000e+02   7.56471800e+00]
 [  6.80000000e+02   6.29808600e+00]
 [  6.90000000e+02   5.15623400e+00]
 [  7.00000000e+02   4.05390600e+00]
 [  7.10000000e+02   3.06638600e+00]
 [  7.20000000e+02   2.19250000e+00]
 [  7.30000000e+02   1.53922800e+00]
 [  7.40000000e+02   1.14938200e+00]
 [  7.50000000e+02   9.05095000e-01]
 [  7.60000000e+02   6.90947000e-01]
 [  7.70000000e+02   5.08426000e-01]
 [  7.80000000e+02   4.11766000e-01]]