Closed freemansw1 closed 10 months ago
All modified and coverable lines are covered by tests :white_check_mark:
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I fixed the Windows bugs through keeping data types constant. Happy to wait for a re-review to merge if you want another look at it @JuliaKukulies @w-k-jones
I'm happy to merge straight away, I don't think the fix requires a re-review as it looks like a pretty straightforward (and sensible) change
Actually, as a workflow change, should this be merged into RC_v1.5.x
or main
?
Edit: it has some changes from RC_v1.5.x
included, so I think it's best to keep the merge target as is
Looks like #293 broke windows compatibility... will need to look into this before merging.
Looks like #293 broke windows compatibility... will need to look into this before merging.
Could that also be related to the change of dtypes in the feature detection/segmentation dataframes? What was the actual problem before you set these to constant in utils.general.combine_feature_dataframes
? Is that a thing we should consider doing in all functions that somehow modify the pandas dataframes?
Looks like #293 broke windows compatibility... will need to look into this before merging.
Could that also be related to the change of dtypes in the feature detection/segmentation dataframes? What was the actual problem before you set these to constant in
utils.general.combine_feature_dataframes
? Is that a thing we should consider doing in all functions that somehow modify the pandas dataframes?
At first glance on my windows machine, it's not a datatype issue... rather the dataframe comes up empty (now, is this itself a result of a data type issue? Perhaps). I will work to debug here.
Looks like #293 broke windows compatibility... will need to look into this before merging.
Could that also be related to the change of dtypes in the feature detection/segmentation dataframes? What was the actual problem before you set these to constant in
utils.general.combine_feature_dataframes
? Is that a thing we should consider doing in all functions that somehow modify the pandas dataframes?At first glance on my windows machine, it's not a datatype issue... rather the dataframe comes up empty (now, is this itself a result of a data type issue? Perhaps). I will work to debug here.
On second thought, I'm going to strongly bet that this is a time issue. Ugh. Times are going to be the death of me.
Okay, this was actually an amazing issue to fix. On Windows, the lines in the test dataset making that are: https://github.com/tobac-project/tobac/blob/5f096e3e66da07cfd48a6a4588b993fc1263a0c2/tobac/testing.py#L435
result in a float output (with a real <1 value even!). When they are converted to xarray, xarray preserves the milliseconds contained in these values, but our Iris feature detection path does not use them, resulting in incompatible times. This was a neat issue to find actually! Anyway it's resolved now!
Great job @freemansw1! Do you want to go ahead and merge this?
This PR adds matrix testing (a matrix of Linux, MacOS, Windows for Pythons 3.8, 3.9, 3.10, 3.11). Right now, this is configured to run on the primary tobac repo (i.e., this one) only, and not on fork repos to save computation time.
Note that this has indicated a bug/issue in testing with
int32
vsint64
datatypes on Windows. I have a windows computer at home; I will have to debug there.