Merge branch 'dev-aberti-fix-mono-generator' into 'master'

Fixes for mono generator and use of relative paths

See merge request !17

README.md

@@ -94,4 +94,5 @@ Some general information about the simulated data, useful for IRF calculation, a
 - v0.2.0: Unification of data and MC reading
 - v0.2.1: Monitoring data (Dead pixel and pedestal information)
 - v0.2.2: Added MC Header info
-- v0.2.3: Solve issue when interpolating information from drive reports, causing crashes when using pointing information in astropy SkyCoord objects. Make the reader faster when searching for ids of mono and stereo events
\ No newline at end of file
+- v0.2.3: Solve issue when interpolating information from drive reports, causing crashes when using pointing information in astropy SkyCoord objects. Make the reader faster when searching for ids of mono and stereo events
+- v0.2.4: fixes in mono_event_generator; fix to allow the use of relative paths as `input_url`
\ No newline at end of file

ctapipe_io_magic/__init__.py

@@ -8,6 +8,7 @@ import logging
 import glob
 import re
 import os.path
+from pathlib import Path
 
 import numpy as np
 
@@ -109,7 +110,8 @@ class MAGICEventSource(EventSource):
             the 'input_url' parameter.
         """
 
-        self.file_list = glob.glob(kwargs['input_url'])
+        file_path = Path(kwargs['input_url'])
+        self.file_list = glob.glob(str(file_path.absolute()))
         if not self.file_list:
             raise ValueError("Unreadable or wrong wildcard file path given.")
         self.file_list.sort()
@@ -980,6 +982,11 @@ class MarsRun:
 
         event_data['file_edges'] = [0]
 
+        # if no file in the list (e.g. when reading mono information), then simply
+        # return empty dicts/array
+        if len(file_list) == 0:
+            return event_data, monitoring_data, mcheader_data
+
         drive_data = dict()
         drive_data['mjd'] = np.array([])
         drive_data['zd']  = np.array([])
@@ -1442,33 +1449,43 @@ class MarsRun:
         mono_ids['M1'] = []
         mono_ids['M2'] = []
 
-        if not self.is_mc:
-            m1_data = self.event_data['M1']['stereo_event_number'][np.where(self.event_data['M1']['trigger_pattern'] == DATA_TRIGGER_PATTERN)]
-            m2_data = self.event_data['M2']['stereo_event_number'][np.where(self.event_data['M2']['trigger_pattern'] == DATA_TRIGGER_PATTERN)]
-
-            m1_ids_data = np.where(self.event_data['M1']['trigger_pattern'] == DATA_TRIGGER_PATTERN)[0]
-            m2_ids_data = np.where(self.event_data['M2']['trigger_pattern'] == DATA_TRIGGER_PATTERN)[0]
-
-            stereo_numbers = np.intersect1d(m1_data, m2_data)
-
-            m1_ids_stereo = np.searchsorted(self.event_data['M1']['stereo_event_number'], stereo_numbers)
-            m2_ids_stereo = np.searchsorted(self.event_data['M2']['stereo_event_number'], stereo_numbers)
-
-            # remove ids that have stereo trigger from the array of ids of data events
-            # see: https://stackoverflow.com/questions/52417929/remove-elements-from-one-array-if-present-in-another-array-keep-duplicates-nu
-
-            sidx1 = m1_ids_stereo.argsort()
-            idx1 = np.searchsorted(m1_ids_stereo,m1_ids_data,sorter=sidx1)
-            idx1[idx1==len(m1_ids_stereo)] = 0
-            m1_ids_mono = m1_ids_data[m1_ids_stereo[sidx1[idx1]] != m1_ids_data]
-
-            sidx2 = m2_ids_stereo.argsort()
-            idx2 = np.searchsorted(m2_ids_stereo,m2_ids_data,sorter=sidx2)
-            idx2[idx2==len(m2_ids_stereo)] = 0
-            m2_ids_mono = m2_ids_data[m2_ids_stereo[sidx2[idx2]] != m2_ids_data]
+        n_m1_events = len(self.event_data['M1']['stereo_event_number'])
+        n_m2_events = len(self.event_data['M2']['stereo_event_number'])
 
-            mono_ids['M1'] = m1_ids_mono.tolist()
-            mono_ids['M2'] = m2_ids_mono.tolist()
+        if not self.is_mc:
+            if (n_m1_events != 0) and (n_m2_events != 0):
+                m1_data = self.event_data['M1']['stereo_event_number'][np.where(self.event_data['M1']['trigger_pattern'] == DATA_TRIGGER_PATTERN)]
+                m2_data = self.event_data['M2']['stereo_event_number'][np.where(self.event_data['M2']['trigger_pattern'] == DATA_TRIGGER_PATTERN)]
+
+                m1_ids_data = np.where(self.event_data['M1']['trigger_pattern'] == DATA_TRIGGER_PATTERN)[0]
+                m2_ids_data = np.where(self.event_data['M2']['trigger_pattern'] == DATA_TRIGGER_PATTERN)[0]
+
+                stereo_numbers = np.intersect1d(m1_data, m2_data)
+
+                m1_ids_stereo = np.searchsorted(self.event_data['M1']['stereo_event_number'], stereo_numbers)
+                m2_ids_stereo = np.searchsorted(self.event_data['M2']['stereo_event_number'], stereo_numbers)
+
+                # remove ids that have stereo trigger from the array of ids of data events
+                # see: https://stackoverflow.com/questions/52417929/remove-elements-from-one-array-if-present-in-another-array-keep-duplicates-nu
+
+                sidx1 = m1_ids_stereo.argsort()
+                idx1 = np.searchsorted(m1_ids_stereo,m1_ids_data,sorter=sidx1)
+                idx1[idx1==len(m1_ids_stereo)] = 0
+                m1_ids_mono = m1_ids_data[m1_ids_stereo[sidx1[idx1]] != m1_ids_data]
+
+                sidx2 = m2_ids_stereo.argsort()
+                idx2 = np.searchsorted(m2_ids_stereo,m2_ids_data,sorter=sidx2)
+                idx2[idx2==len(m2_ids_stereo)] = 0
+                m2_ids_mono = m2_ids_data[m2_ids_stereo[sidx2[idx2]] != m2_ids_data]
+
+                mono_ids['M1'] = m1_ids_mono.tolist()
+                mono_ids['M2'] = m2_ids_mono.tolist()
+            elif (n_m1_events != 0) and (n_m2_events == 0):
+                m1_ids_data = np.where(self.event_data['M1']['trigger_pattern'] == DATA_TRIGGER_PATTERN)[0]
+                mono_ids['M1'] = m1_ids_data.tolist()
+            elif (n_m1_events == 0) and (n_m2_events != 0):
+                m2_ids_data = np.where(self.event_data['M2']['trigger_pattern'] == DATA_TRIGGER_PATTERN)[0]
+                mono_ids['M2'] = m2_ids_data.tolist()
         else:
             # just find ids where event stereo number is 0 (which is given to mono events) and pattern is MC trigger
             m1_mono_mask = np.logical_and(self.event_data['M1']['trigger_pattern'] == MC_TRIGGER_PATTERN, self.event_data['M1']['stereo_event_number'] == 0)

setup.py

@@ -10,7 +10,7 @@ with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
 setup(
     name='ctapipe_io_magic',
     packages=find_packages(),
-    version='0.2.3',
+    version='0.2.4',
     description='ctapipe plugin for reading of the calibrated MAGIC files',
     long_description=long_description,
     long_description_content_type='text/markdown',

[Moritz Huetten]

Hi @g-alessioberti, Thanks a lot! Just had a look and it looks all fine. Cheers Moritz