Remove implicit scipy dependency. #1305
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| lambda_ *= 3 # increase damping | ||
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| if np.linalg.norm(delta_params) < tol: | ||
| break |
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I assume you copied this algorithm from somewhere. Should give the source.
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It's ChatGPT + wikipedia. I can add a comment to that effect.
tests/test_vonkarman.py
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| lams = [300.0, 500.0, 1100.0] | ||
| r0_500s = [0.05, 0.15, 0.3] | ||
| L0s = [1e10, 25.0, 10.0] | ||
| do_deltas = [False, True] |
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How slow were the slow tests? I'm assuming you checked this and found the "slow" ones were still fast enough to run all the time.
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On my laptop this this is 2.07 sec for all tests vs 0.07 sec for just the fast tests. I can wrap this in a if __name__ == '__main__' block if you prefer.
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Sure. I usually try to keep each test under 1 sec if possible for they pytest run. There are some exceptions, but this seems like one where we don't need the slow tests to spot regressions.
galsim/utilities.py
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| params = np.array(x0) | ||
| lambda_ = lambda_init | ||
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| for _ in range(max_iter): |
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The code coverage miss is this loop never ending. Should either add a test with a low max_iter, (e.g. 1 probably), or add #pragma: no branch to this line.
rmjarvis
added
build
Addresses #1253 .
Adds a basic Levenberg-Marquardt optimizer that can fill in for
scipy.linalg.least_squaresin simple cases (such as used inFittedSIPWCS). It's a small bit slower on average, so we might want to consider making it a fallback option when scipy is unavailable though I didn't bother for now. (The fitted sip unit tests takes 3.0 s now, vs. 2.25 s using scipy).Along the way I also refactored our
if __name__ == __main__test running code so you can use-k testname(similar to pytest) when running directly withpython. There are command line options for running the profiler too.