datasets: TCS: Adding TCS15

[cmuellner]

Summary

TCS15 was added to the CIE 1995 test color samples to get a better representation of the Japanese skin complexion.

This commit adds this color sample and adjust the CRI test file accordingly.

Note, that the TCS15 dataset is published from 380-780 nm in 5 nm increments. This differs from all other TCS data, which is published from 360-830 nm (also in 5 nm increments).

Source of data is the CIE's open access dataset page: https://www.cie.co.at/datatable/spectral-radiance-factors-test-colour-sample-15-japanese-skin-complexion-5nm-wavelength

Data source reference:
CIE 2024, Spectral radiance factors of test-colour sample #15 of the Japanese skin complexion, 5nm wavelength steps, International Commission on Illumination (CIE), Vienna, AT, DOI: 10.25039/CIE.DS.7chm7z5h

Preflight

Code Style and Quality

• Unit tests have been implemented and passed.
• Pyright static checking has been run and passed.
• Pre-commit hooks have been run and passed.
• New transformations have been added to the Automatic Colour Conversion Graph.
• New transformations have been exported to the relevant namespaces, e.g. colour, colour.models.

I observed the following fails on the develop branch (06edf82f054 with no changes on top) on my Fedora 41 (x86-64) machine:

FAILED colour/io/image.py::colour.io.image.read_image
FAILED colour/io/image.py::colour.io.image.read_image_Imageio
FAILED colour/io/tests/test_image.py::TestReadImageImageio::test_read_image_Imageio
FAILED colour/io/tests/test_image.py::TestWriteImageImageio::test_write_image_Imageio
FAILED colour/io/tests/test_image.py::TestWriteImage::test_write_image - Valu...
FAILED colour/io/tests/test_image.py::TestReadImage::test_read_image - ValueE...

This commit seems to trigger the following additional fails, but I cannot find an obvious reason for that:

FAILED colour/colorimetry/tests/test_uniformity.py::TestSpectralUniformity::test_spectral_uniformity
FAILED colour/colorimetry/uniformity.py::colour.colorimetry.uniformity.spectral_uniformity

The pre-commit hook triggers the error RuntimeError: failed to find interpreter for Builtin discover of python_spec='python3.10'.

Documentation

• New features are documented along with examples if relevant.
• The documentation is Sphinx and numpydoc compliant.

[cmuellner]

Force-pushed the following changes:

• White-space issues identified by pre-commit.ci

[KelSolaar]

Hi @cmuellner,

Thank you! The image io related tests failure is related to the fact that you probably don't have the Freeimage backend installed: uv run python -c "import imageio;imageio.plugins.freeimage.download()"

The spectral uniformity tests are on the other hand a side effect to the fact you added a new TCS sample which leads me to the following point: We should not modify the existing samples because this will change CRI and everything that depends on it. What we should probably do instead is:

• Deprecate the colour.quality.datasets.tcs.SDS_TCS attribute and rename it to colour.quality.datasets.tcs.SDS_TCS_CIE_1995
• Create a new colour.quality.datasets.tcs.SDS_TCS_CIE_2024 attribute with the Japanese skin sample
• Add a new method argument to the colour.colour_rendering_index definition that takes two values, e.g., ["CIE 1995", "CIE 2024"] allowing to switch to between the two datasets. The colour.colour_quality_scale definition can be used as an example on how to proceed
• Add unit tests for the new method

Let me know if that makes sense!

Cheers,

Thomas

[cmuellner]

The image io related tests failure is related to the fact that you probably don't have the Freeimage backend installed: uv run python -c "import imageio;imageio.plugins.freeimage.download()"

Yes, this fixed the failing tests.

The spectral uniformity tests are on the other hand a side effect to the fact you added a new TCS sample which leads me to the following point: We should not modify the existing samples because this will change CRI and everything that depends on it. What we should probably do instead is:

• Deprecate the colour.quality.datasets.tcs.SDS_TCS attribute and rename it to colour.quality.datasets.tcs.SDS_TCS_CIE_1995
• Create a new colour.quality.datasets.tcs.SDS_TCS_CIE_2024 attribute with the Japanese skin sample
• Add a new method argument to the colour.colour_rendering_index definition that takes two values, e.g., ["CIE 1995", "CIE 2024"] allowing to switch to between the two datasets. The colour.colour_quality_scale definition can be used as an example on how to proceed
• Add unit tests for the new method

Thanks for this advice!
I've done so except for deprecating SDS_TCS (I haven't figured out how this works).

Please have a look at the changes (especially: is the naming of SDS_TCS_SETS ok for you?).

PR is rebased and all tests pass.