Use the impact distance calculation from ctapipe. #209
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As a cross-check, find attached the (real data) stereo files from the tests created with the current version from the master and the version from this branch. The calculated impact distances are consistent: In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df_orig = pd.read_hdf("dl1_stereo_MAGIC_LST-1.Run15337.0002_original.h5", key="events/parameters")
In [4]: df_new = pd.read_hdf("dl1_stereo_MAGIC_LST-1.Run15337.0002.h5", key="events/parameters")
In [5]: impact_orig = np.array(df_orig["impact"])
In [6]: impact_new = np.array(df_new["impact"])
In [7]: np.allclose(impact_new, impact_orig, equal_nan=True)
Out[7]: True |
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@jsitarek can you quickly review this PR? It is here since a long time |
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## master #209 +/- ##
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Coverage 77.23% 77.23%
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Files 21 21
Lines 2614 2614
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Hits 2019 2019
Misses 595 595 ☔ View full report in Codecov by Sentry. |
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Until now, we were using a function internal to MCP to calculate the impact distance. Since ctapipe provides it and it is calculated when calling the
HillasReconstructor, it makes sense to use that.