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Fix missing dependencies in notebooks #43
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speth
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Cantera:main
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Naikless:fix-missing-dependencies-in-notebooks
Dec 4, 2023
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,8 +1,12 @@ | ||
name: cantera-latest | ||
name: cantera-latest-release | ||
channels: | ||
- cantera | ||
- cantera/label/dev | ||
- conda-forge | ||
dependencies: | ||
- cantera | ||
- matplotlib | ||
- pandas | ||
- scipy | ||
- seaborn | ||
- coolprop | ||
- python-graphviz | ||
- ipywidgets |
16,733 changes: 8,258 additions & 8,475 deletions
16,733
flames/flame_speed_with_convergence_analysis.ipynb
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2,164 changes: 1,081 additions & 1,083 deletions
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flames/flame_speed_with_sensitivity_analysis.ipynb
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2,290 changes: 1,117 additions & 1,173 deletions
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flames/twin_premixed_flame_axisymmetric.ipynb
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I appreciate the intention of this change. However, adding
conda-forge
will (I believe) result in some packages like NumPy and SciPy coming from that channel rather than thedefaults
. In that case, there will be an incompatibility in the compiled clib code between Cantera/Python/NumPy. There is a Cantera package onconda-forge
, but we don't publish pre-releases there so I don't think that'll work for this purpose. I'm honestly not sure how to continue from here 😦There was a problem hiding this comment.
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I don't think any of the Jupyter examples require the development version of Cantera at this point, so we could just install everything from
conda-forge
, dropping thecantera
andcantera/label/dev
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That seems reasonable to me, since the Jupyter examples probably should only contain stuff that is available in the stable release without requiring the user to install the dev version if they want to reproduce the example on their own machine. I'll change
environment.yaml
accordingly.As a side note/question: how serious are these incompatibilities in practice? Because I would assume mixing channels by installing cantera from the cantera channel and everything else from conda-forge is a relatively common thing to do for people that don't pay much attention to this issue (which, until today, included myself).
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In the worst case, I believe they can lead to segmentation faults in the memory which cause an immediate crash of Python. More likely would be that some symbols are named differently or are placed in a different location in the binary, which would also cause errors. Here are some docs from conda-forge about it: https://conda-forge.org/docs/user/tipsandtricks.html#using-multiple-channels