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[postprocessor] use nanmean for radial averages

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Noe Brucy
2021-02-11 11:35:05 +01:00
parent 19badde3d9
commit e6635aa8b3
1 changed files with 4 additions and 3 deletions
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postprocessor.py
+4 -3
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@@ -1005,12 +1005,13 @@ class PostProcessor(HDF5Container):
# mean of all the cells in the bin # mean of all the cells in the bin
mean_bin = np.zeros(len(radial_bins) - 1) mean_bin = np.zeros(len(radial_bins) - 1)
for j in range(len(radial_bins) - 1): for j in range(len(radial_bins) - 1):
mask_bin = bins_on_map == j
if mass_weighted: if mass_weighted:
weight = coldens[bins_on_map == j] weight = coldens[mask_bin]
mean_bin[j] = np.mean(dmap[bins_on_map == j] * weight) mean_bin[j] = np.nanmean(dmap[mask_bin] * weight)
mean_bin[j] = mean_bin[j] / np.mean(weight) mean_bin[j] = mean_bin[j] / np.mean(weight)
else: else:
mean_bin[j] = np.mean(dmap[bins_on_map == j]) mean_bin[j] = np.nanmean(dmap[mask_bin])
return mean_bin return mean_bin
def _rad_avg_map(self, name, ax_los="z", mass_weighted=False): def _rad_avg_map(self, name, ax_los="z", mass_weighted=False):