Gaussian Fitting Tool #116
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… distribution and fits to three different guassian distributions then returns the fit params in a dictionary
| offset = initial_guess.pop("offset", np.mean(y[-10:])) | ||
| amplitude = initial_guess.pop("amplitude", y.max() - offset) | ||
| mu = initial_guess.pop("mu", np.argmax(y)) | ||
| sigma = initial_guess.pop("sigma", y.shape[0] / 5) |
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Maybe reconsider dividing by 5 there
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At the moment I don't have a more mathematical approach to determine sigma but it is something I have been thinking about.
| @staticmethod | ||
| def gaussian_with_linear_background(x, amp, mu, sig, offset, slope): | ||
| return ( | ||
| amp * np.exp(-np.power(x - mu, 2.0) / (2 * np.power(sig, 2.0))) |
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In the pydantic model version, I was going to make a model the gaussian functions. It would simply this. I can also just edit this code for the initial PR
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ok so this will get updated in the next PR?
| ) + offset | ||
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| @staticmethod | ||
| def two_dim_gaussian(x, y, A, x0, y0, sigma_x, sigma_y): |
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add comment here about possible use with images
tests/unit_tests/lcls_tools/common/data_analysis/fitting/test_fit_gauss.py
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tests/unit_tests/lcls_tools/common/data_analysis/fitting/test_fit_gauss.py
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@nneveu and I discussed today and I think we should change this to use scipy.minimize instead of scipy.curve_fit before this PR is accepted. @phys-cgarnier let me know if you need help w making this change |
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Hi all,
Sorry I was on vacation but was unable to update my status or sign into my email until just now. I will be at the meeting today if that works for everyone.
Best,
Chris.
From: Ryan Roussel ***@***.***>
Date: Monday, November 27, 2023 at 1:57 PM
To: slaclab/lcls-tools ***@***.***>
Cc: Garnier, Chris ***@***.***>, Mention ***@***.***>
Subject: Re: [slaclab/lcls-tools] Gaussian Fitting Tool (PR #116)
@nneveu<https://github.com/nneveu> and I discussed today and I think we should change this to use scipy.minimize instead of scipy.curve_fit before this PR is accepted. @phys-cgarnier<https://github.com/phys-cgarnier> let me know if you need help w making this change
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@phys-cgarnier I think the load issue might be with the test. If you run the test locally does it load sklearn ok? You might need to import it in the test as well. |
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I wonder if we can use scipy.stats in some places here.
https://docs.scipy.org/doc/scipy/tutorial/stats.html
General comments for next PR, let's try to have fewer inputs without names/dicts and a few more comments.
Thanks for working on this @phys-cgarnier !
| param_names = method.__code__.co_varnames | ||
| param_names = list(param_names) | ||
| for i in range(len(param_names)): | ||
| if param_names[i] == "x": |
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Why the 'x' here in these two functions?
* initial commit of fitting tool * initial submission of gaussian fit tool, this tool takes a normalized distribution and fits to three different guassian distributions then returns the fit params in a dictionary * reformatted scripts with black * did more reformatting then used black for final check * updated for pr * fixed flake8 errors * fixed flake8 * attempt to fix flake8 errors * hopefully last flake8 fix.... * added sklearn to requirements.txt * changed sklearn to scikit-learn * black on fitting_tool.py --------- Co-authored-by: Nicole <nneveu@stanford.edu>
Initial pull request of gaussian fit tool, this tool receives an array of normalized data and attempts to fit it to three different methods: standard gaussian, super gaussian, and double gaussian. Then returns the fit parameters in a dictionary