From d04e8535cdd429b72d1fc606ca72c9cd1755bbd1 Mon Sep 17 00:00:00 2001 From: Kai Bruegge Date: Wed, 6 Feb 2019 11:48:04 +0100 Subject: [PATCH] rename average_size to average_intensity --- ctapipe/io/containers.py | 2 +- ctapipe/reco/HillasReconstructor.py | 2 +- ctapipe/reco/hillas_intersection.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ctapipe/io/containers.py b/ctapipe/io/containers.py index d41b7542660..35c36da43b4 100644 --- a/ctapipe/io/containers.py +++ b/ctapipe/io/containers.py @@ -347,7 +347,7 @@ class ReconstructedShowerContainer(Container): 'list of the telescope ids used in the' ' reconstruction of the shower' )) - average_size = Field(0.0, 'average size of used') + average_intensity = Field(0.0, 'average intensity of the intensities used for reconstruction') goodness_of_fit = Field(0.0, 'measure of algorithm success (if fit)') diff --git a/ctapipe/reco/HillasReconstructor.py b/ctapipe/reco/HillasReconstructor.py index 2503659a114..70ed8a4b753 100644 --- a/ctapipe/reco/HillasReconstructor.py +++ b/ctapipe/reco/HillasReconstructor.py @@ -160,7 +160,7 @@ class are set to np.nan result.core_uncert = np.nan result.tel_ids = [h for h in hillas_dict.keys()] - result.average_size = np.mean([h.intensity for h in hillas_dict.values()]) + result.average_intensity = np.mean([h.intensity for h in hillas_dict.values()]) result.is_valid = True result.alt_uncert = err_est_dir diff --git a/ctapipe/reco/hillas_intersection.py b/ctapipe/reco/hillas_intersection.py index 2890e8a17b9..7eb5f3a84d8 100644 --- a/ctapipe/reco/hillas_intersection.py +++ b/ctapipe/reco/hillas_intersection.py @@ -109,7 +109,7 @@ def predict(self, hillas_parameters, tel_x, tel_y, array_direction): result.core_uncert = np.sqrt(core_err_x**2 + core_err_y**2) * u.m result.tel_ids = [h for h in hillas_parameters.keys()] - result.average_size = np.mean([h.intensity for h in hillas_parameters.values()]) + result.average_intensity = np.mean([h.intensity for h in hillas_parameters.values()]) result.is_valid = True src_error = np.sqrt(err_x**2 + err_y**2)