Comments (4)
newville
commented on June 15, 2024
@cassberk Thanks. I would have sworn that we had a test for this(!) but it looks like we have a few different tests for saving/loading of non-builtin models but not this case.
FWIW, saving/loading python functions is fragile.
I don't disagree with anything you say here, but I think I want to look at this issue a bit more closely, like is there something we deliberately changed about this?
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cassberk
commented on June 15, 2024
Understandable, Let me know what you decide / uncover. Unless I'm missing something, it is preventing me from updating to lmfit 1.2.
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newville
commented on June 15, 2024
@cassberk Thanks for your patience (and code). I think this is now resolved reasonably well in the master branch.
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cassberk
commented on June 15, 2024
@newville Thanks a lot! Any timeline as to when I can expect these changes in a new release?
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Related Issues (20)
- Test suite: Skipped tests cause test suite failures HOT 6
- Test suite failures: Pytest fails with multiprocessing, ConstantModel has not independent_vars HOT 1
- Test suite failures: Pytest fails with multiprocessing, ConstantModel has not independent_vars HOT 1
- Test suite failures: Pytest fails with multiprocessing, ConstantModel has not independent_vars HOT 1
- Test suite failures: Pytest fails with multiprocessing, ConstantModel has not independent_vars HOT 1
- Cannot select method of scipy least_squares function HOT 6
- Model.make_params() sometimes ignores set a parameter with a prefix
- an aborted fit with `method='least_squares'` does not report correct fit results and statistics HOT 3
- ModelResult.eval_uncertainty does not work for complex models HOT 2
- loaded ModelResult bug (eval_uncertainty() not working) HOT 1
- `result.params.uvars` not correctly created with Minimize HOT 1
- jacobian matrix HOT 1
- Inconsistencies with non-parameter model function arguments HOT 1
- Retain default values for keyword arguments when manually choosing param_names
- Model.fit() gives the wrong 'rsquared' when 'weights' is given HOT 3
- OS systems where multithreading.Pool is not allowed HOT 1
- lmfit-py is affected by the uncertainities Python3.12 incompatibility HOT 9
- Defining `self.min_rel_change` in confidence.py as variable
- Default `method="leastsq"` in `Model().fit()` when `Parameters` have bounds HOT 2
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