Why is bioconda recipe more constrained than setup.py?

[corneliusroemer]

I noticed that the bioconda pins are much more restrictive than the pins in setup.py

What's the reason for this? Can I relax the bioconda pins so that pathogen-embed doesn't constrain all the environments it's installed in, most notably nextstrain conda-base?

See setup.py constraints:

pathogen-embed/setup.py

Lines 26 to 37 in 617a561

	install_requires=['numpy',
	'pandas',
	"biopython",
	'scikit-learn >=1.3,<1.5',
	'umap-learn ==0.5.*',
	# Pin Numba at maximum supported version for the pinned umap-learn version.
	# For more details see:
	# https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit
	'numba <0.59.0',
	'matplotlib',
	'hdbscan'
	],

vs. bioconda recipe constraints:

    - python >=3.8
    - numpy >=1.24.4,<2
    - pandas >=1.2.0,<2
    - biopython >=1.83,<2
    - scikit-learn >=1.3,<1.5
    - umap-learn >=0.5.0,<0.6.0
    - numba <0.59.0
    - matplotlib-base >=3,<4
    - hdbscan >=0.8.36,<0.9.0

https://github.com/bioconda/bioconda-recipes/blob/127cf1a35701ee0a808af57380d515d4865a5ef5/recipes/pathogen-embed/meta.yaml#L27-L35

[huddlej]

@corneliusroemer It's an oversight on my part that setup.py isn't as constrained as the Bioconda recipe. I'd plan to eventually follow Augur's lead in that respect.

Are there specific packages in the Bioconda recipe that are causing issues in the Nextstrain conda recipe?