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Fix clustering benchmarks for missing ID.value

Merged Wouter Deconinck requested to merge fix-ecal-benchmarks-for-missing-ID-value into master
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8
@@ -158,10 +158,14 @@ if __name__ == '__main__':
print('{} do not have valid entries, skip it'.format(coll))
continue
df.rename(columns={c: c.replace(coll + '.', '') for c in df.columns}, inplace=True)
# print(df[['eta', 'polar.theta', 'position.x', 'position.y', 'position.z']])
df['r'] = np.sqrt(df['position.x'].values**2 + df['position.y'].values**2 + df['position.z'].values**2)
df['phi'] = np.arctan2(df['position.y'].values, df['position.x'].values)
df['theta'] = np.arccos(df['position.z'].values/df['r'].values)
df['eta'] = -np.log(np.tan(df['theta'].values/2.))
# print(df[['eta', 'theta', 'position.x', 'position.y', 'position.z']])
fig, axs = plt.subplots(2, 2, figsize=(12, 8), dpi=160)
ncl = df.groupby('event')['ID.value'].nunique().values
axs[0][0].hist(ncl, weights=np.repeat(1./float(ncl.shape[0]), ncl.shape[0]),
ncl = df.groupby('event')['event'].count()
axs[0][0].hist(ncl, weights=np.repeat(1./float(ncl.count()), ncl.count()),
bins=np.arange(0, 10), align='mid', ec='k')
axs[0][0].set_xlabel('Number of Clusters', fontsize=16)
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