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- Added multi_shadow_find - Added normalize_product function to normalize the product of location_likelihoods. - Added plot_multi_shadows function to plot the output of multi_shadow_find. - Added unit test for multi_shadow_find in test_shadowfinder.py. - Ensured that the unit test checks the type, shape, and values of the normalized output. - Code untested. tests may not pass.
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@GalenReich this PR is a bit stale, but I could revive it if there's still interest in this requirement. |
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I have added functionality to allow users to input the details of multiple shadows taken at the same location across timestamps. This should allow users to narrow down the possible locations. I implement multiprocessing across cores for performance. This requires adding multiprocessing modules to the dependencies.
This is accomplished through two new static functions in shadowfinder.py
multi_shadow_findand
plot_multi_shadowsI refactored the
ShadowFinder().plot_shadowsfunction to separate out some of the plotting functionality into ShadowFinder()._plot_shadows. This enables code re-use inplot_shadows.unit tests and example
The example script is used to visualize that the results make logical sense. I show that this can be applied to three shadows, although in principle, it should scale to any number.
Example: three shadows across different times with local time formats.