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Change actions for mamba / pip install methods. #2207

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merged 4 commits into from
Jan 13, 2023

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nbiederbeck
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for mamba: use mamba-org/provision-with-micromamba
for pip: use actions/setup-python

@nbiederbeck nbiederbeck force-pushed the provision-with-micromamba branch 7 times, most recently from 7f02e7e to 1a9438e Compare January 11, 2023 10:52
@StFroese
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StFroese commented Jan 11, 2023

Should we also update the installation instruction in the README and use mamba instead of conda?
I mean it states using mamba if solving environment takes to long but is there anything against telling the user to use mamba directly?

@nbiederbeck nbiederbeck force-pushed the provision-with-micromamba branch 7 times, most recently from 3fe4ae4 to 401542a Compare January 11, 2023 11:39
for mamba: use mamba-org/provision-with-micromamba
for pip: use actions/setup-python
@kosack
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kosack commented Jan 12, 2023

Eventually, mamba won't be needed explicitly as the mamba solver will be included in a future version of conda (I think it's experimentally there already), but for now I think it's fine to just recommend mamba, as we know that installing packages from conda-forge with conda can be a slow nightmare due.

@maxnoe
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maxnoe commented Jan 12, 2023

I think we should always prefer recommending the open-source builds by conda-forge / mamba over the closed source (binary) blob packages by Anaconda Inc.

maxnoe
maxnoe previously approved these changes Jan 12, 2023
@nbiederbeck
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I also update codecov here, instead of #2206. I think it is reasonable to have this in a single PR.

maxnoe
maxnoe previously approved these changes Jan 12, 2023
@LukasNickel
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There has been a new matplotlib release yesterday (3.6.3), that the pip install uses, but mamba does not.
It is not immediately obvious to me however what change in matplotlib would cause the tests to fail now.
https://matplotlib.org/stable/users/github_stats.html

@kosack
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kosack commented Jan 12, 2023

seems they have change the behavior of how vmin/vmax are calculated such that it breaks if there are NaN or Inf values? The test is failing when setting a CameraDisplay to log-scale, which will cause NaNs for any negative value (but that was not an issue in the past). Could also be a side effect from something in numpy

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6 participants