lmcinnes / glasbey Goto Github PK

Hi Leland,
When I install it with "pip install glasbey", I met the following ERROR message:

ERROR: Package 'glasbey' requires a different Python: 3.9.13 not in '>=3.10'

Do you have any idea how to solve it?

Thanks for the nice library. I've come across a bug that occurrs when I want to extend a matplotlib colormap, e.g., tab10, but request fewer colors than the initial colormap contains.

For instance

glasbey.extend_palette("tab10", 10)
glasbey.extend_palette("tab10", 15)

both work fine, but if I request something <10, e.g.,

glasbey.extend_palette("tab10", 5)

I get the following error

ValueError                                Traceback (most recent call last)
Input In [24], in <cell line: 2>()
      1 import glasbey
----> 2 glasbey.extend_palette("tab10", 5)

File ~/miniconda3/envs/ml/lib/python3.10/site-packages/glasbey/_glasbey.py:338, in extend_palette(palette, palette_size, grid_size, as_hex, grid_space, lightness_bounds, chroma_bounds, hue_bounds, red_bounds, green_bounds, blue_bounds, colorblind_safe, cvd_type, cvd_severity)
    334 if not colorblind_safe:
    335     palette = cspace_convert(palette, "sRGB1", "CAM02-UCS").astype(
    336         np.float32, order="C"
    337     )
--> 338     palette = generate_palette_cam02ucs(colors, palette, np.uint32(palette_size))
    339 else:
    340     cvd_space = {
    341         "name": "sRGB1+CVD",
    342         "cvd_type": cvd_type,
    343         "severity": cvd_severity,
    344     }

ValueError: cannot assign slice from input of different size

I understand that it makes no sense to actually call this function when the colormap contains enough colors, but I'm trying to integrate this into my plotting utils where the number of colors I need can be highly variable, so this would be a "nice to have". If nothing else, a less crpytic error would be preferrable.

Hi Leland, cool work! I wonder if you considered the following scenario: there is a 2D embedding (e.g. UMAP) of some data that contain K classes. We want to make a scatter plot and assign a distinct colour to each class. We want to do this assignment such that neighbouring classes (i.e. classes that partially overlap in the embedding, or are simply adjacent to each other) have as distinct colours as possible. I.e. if there are two shades of red, then they should be given to classes that are far away in the embedding.

I guess this could be be approached by first generating a colormap of K distinct suitable colours, and then assigning them to classes to maximize colour dissimilarity between "neighbouring" classes (could be measured by kNN overlap or some other method). This can be either solved greedily or with annealing.

Does this sound like something that could potentially be within the scope of glasbey?

Hi and thanks for the snappy glasbey implementation here.

I work on a number of visualization libs (napari, and related projects), and am making a new lightweight cmap library for those who need, but don't want to bring on big dependencies like matplotlib. I'd like to include this library as the functional backing for glasbey, but the dependency on matplotlib is a dealbreaker. (ideally, numba would also be optional, but I can see how that's the a lot of the crux of what this library has to offer over the previous implementation).

I wonder if you'd consider vendoring just the few conversion functions you need from matplotlib, and making any export to matplotlib colormap instances lazily import matplotlib? (if someone is exporting to matplotlib colormap, it's likely they have it installed in their environment, but those who want a lean glasbey implementation don't necessarily)

I'd happily make a PR to this effect, but want to gauge your openness first.

thanks!