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Need a way to inherit properties' docstrings so that fsigma, cutmask have docstrings for each fit.
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So we don't end up with phantom directories representing areas without available data.
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Two bugs detected in fit_with_bxa currently under fjcarrera_test:
- Using the option --use_tbabs_table=1 produces an error:
Error: setPars() got an unexpected keyword argument 'tbabs'
- T…
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Hi GPTFF team, thank you very much for your innovative work. I have some questions:
1. During neural network fitting, is the ref_energy being fitted? When predicting, is this value restored to the …
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Thank you for providing a powerful Bayesian-torch library! I am currently learning about this and now I have a problem. I want to fit a distribution using a variational Bayesian network, but I don't k…
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Error in `dplyr::mutate()`:
ℹ In argument: `fit = stats::predict(curve)`.
ℹ In group 1: `Well = G7`.
Caused by error:
! `fit` must be size 373 or 1, not 2238.
Run `rlang::last_trace()` to see whe…
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Thank you for your excellent open-source work.
Some point clouds include normals, while others do not. I noticed that in CloudCompare, it is possible to generate normals using quadric fitting with…
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I was talking to a user today and he said that he would like to see sequential fitting. So the next run has the start values of the previous run, as this is what they would do in real life.
I thin…
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Hi,
Thanks for providing this great software. We are wondering how to to deal with transmission data fitting with your code given transmission has different components for its psi and delta (raw d…
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Hi, thanks for your implementation, but not sure why I got stuck on the line 87, train.py: loss, preds = model.fit(x, y).
The tqdm seems not change:
![image](https://github.com/user-attachment…