Fixed tests (now they don't fail, locally)

magicctapipe/io/tests/test_io.py

@@ -16,6 +16,7 @@
 import pandas as pd
 
 
+
 def test_format_object():
     """
     Simple check on a string
@@ -94,7 +95,7 @@ def test_load_train_data_files_g(gamma_stereo):
     """
 
     events = load_train_data_files(str(gamma_stereo[0]))
-    assert list(events.keys()) == ["LST1_M1", "LST1_M2", "LST1_M1_M2"]
+    assert list(events.keys()) == ["M1_M2", "LST1_M1", "LST1_M2", "LST1_M1_M2"]
     data = events["LST1_M1"]
     assert np.all(data["combo_type"]) == 1
     assert "off_axis" in data.columns
@@ -221,7 +222,7 @@ def test_load_irf_files(IRF):
     """
     Check on IRF dictionaries
     """
-
+    
     irf, header = load_irf_files(str(IRF))
     assert set(list(irf.keys())).issubset(
         set(

magicctapipe/io/tests/test_io_monly.py

@@ -54,7 +54,7 @@ def test_get_stereo_events_mc(gamma_stereo_monly, p_stereo_monly, config_gen):
         event_data.sort_index(inplace=True)
         data = get_stereo_events(event_data, config_gen)
         assert np.all(data["multiplicity"] == 2)
-        assert np.all(data["combo_type"] == 0)
+        assert np.all(data["combo_type"] == 3)
 
 
 def test_get_stereo_events_mc_cut(gamma_stereo_monly, p_stereo_monly, config_gen):
@@ -334,8 +334,8 @@ def test_get_stereo_events_data(stereo_monly, config_gen):
         event_data.set_index(["obs_id", "event_id", "tel_id"], inplace=True)
         event_data.sort_index(inplace=True)
         data = get_stereo_events(event_data, config_gen)
-        assert np.all(data["multiplicity"] == 2)
-        assert np.all(data["combo_type"] == 0)
+        assert np.all(data["multiplicity"] == 2)    
+        assert np.all(data["combo_type"] == 3)
 
 
 def test_get_stereo_events_data_cut(stereo_monly, config_gen):

magicctapipe/resources/config.yaml

@@ -1,9 +1,11 @@
 mc_tel_ids:
     LST-1: 1
+    LST-2: 0
+    LST-3: 0
+    LST-4: 0
     MAGIC-I: 2
     MAGIC-II: 3
 
-
 LST:
     image_extractor:
         type: "LocalPeakWindowSum"
@@ -240,6 +242,8 @@ create_irf:
 
 dl2_to_dl3:
     interpolation_method: "nearest"  # select "nearest", "linear" or "cubic"
+    interpolation_scheme: "cosZdAz" # select "cosZdAz" or "cosZd"
+    max_distance: "45. deg"  # angle type Quantity, or comment out to remove the cut
     source_name: "Crab"
     source_ra: null  # used when the source name cannot be resolved
     source_dec: null  # used when the source name cannot be resolved

magicctapipe/resources/config_monly.yaml

@@ -1,5 +1,8 @@
 mc_tel_ids:
     LST-1: 1
+    LST-2: 0
+    LST-3: 0
+    LST-4: 0
     MAGIC-I: 2
     MAGIC-II: 3
 
@@ -240,6 +243,8 @@ create_irf:
 
 dl2_to_dl3:
     interpolation_method: "nearest"  # select "nearest", "linear" or "cubic"
+    interpolation_scheme: "cosZdAz" # select "cosZdAz" or "cosZd"
+    max_distance: "45. deg"  # angle type Quantity, or comment out to remove the cut
     source_name: "Crab"
     source_ra: null  # used when the source name cannot be resolved
     source_dec: null  # used when the source name cannot be resolved

magicctapipe/scripts/lst1_magic/lst1_magic_dl1_stereo_to_dl2.py

@@ -29,7 +29,7 @@
 from astropy.coordinates import AltAz, SkyCoord, angular_separation
 from ctapipe.coordinates import TelescopeFrame
 from ctapipe.instrument import SubarrayDescription
-from magicctapipe.io import get_stereo_events, save_pandas_data_in_table
+from magicctapipe.io import get_stereo_events_old, save_pandas_data_in_table
 from magicctapipe.reco import DispRegressor, EnergyRegressor, EventClassifier
 
 __all__ = ["apply_rfs", "reconstruct_arrival_direction", "dl1_stereo_to_dl2"]
@@ -266,7 +266,7 @@ def dl1_stereo_to_dl2(input_file_dl1, input_dir_rfs, output_dir):
     is_simulation = "true_energy" in event_data.columns
     logger.info(f"\nIs simulation: {is_simulation}")
 
-    event_data = get_stereo_events(event_data)
+    event_data = get_stereo_events_old(event_data)
 
     subarray = SubarrayDescription.from_hdf(input_file_dl1)
     tel_descriptions = subarray.tel

magicctapipe/scripts/lst1_magic/lst1_magic_event_coincidence.py

@@ -191,7 +191,7 @@ def event_coincidence(input_file_lst, input_dir_magic, output_dir, config):
 
     if pre_offset_search:
         logger.info("\nPre offset search will be performed.")
-        n_pre_offset_search_events = config_coinc["n_pre_offset_search_events"]
+        n_pre_offset_search_events = config_coinc["n_pre_offset_search_events"] #n_pre_offset_search_events is the number of events used to estimate the time offset. Around 100 events may be enough
     else:
         logger.info("\noffset scan range defined in the config file will be used.")
         offset_start = u.Quantity(config_coinc["time_offset"]["start"])
@@ -245,7 +245,7 @@ def event_coincidence(input_file_lst, input_dir_magic, output_dir, config):
             )
 
             logger.info(
-                f"\nExtracting the {tel_name} events taken when LST-1 observed for pre offset search..."
+                f"\nExtracting the {tel_name} events taken when LST observed for pre offset search..."
             )
 
             time_lolim = timestamps_lst[0] - window_half_width
@@ -349,8 +349,8 @@ def event_coincidence(input_file_lst, input_dir_magic, output_dir, config):
 
         time_offsets = u.Quantity(time_offsets.round(), unit="ns", dtype=int)
 
-        # Extract the MAGIC events taken when LST-1 observed
-        logger.info(f"\nExtracting the {tel_name} events taken when LST-1 observed...")
+        # Extract the MAGIC events taken when LST observed
+        logger.info(f"\nExtracting the {tel_name} events taken when LST observed...")
 
         time_lolim = timestamps_lst[0] + time_offsets[0] - window_half_width
         time_uplim = timestamps_lst[-1] + time_offsets[-1] + window_half_width

magicctapipe/scripts/lst1_magic/lst1_magic_mc_dl0_to_dl1.py

@@ -287,7 +287,7 @@ def mc_dl0_to_dl1(input_file, output_dir, config, focal_length):
         LSTs_in_use = ''.join(str(k) for k in LSTs_in_use)
     elif len(LSTs_in_use) > 0:
         LSTs_in_use = 'LST'+'_LST'.join(str(k) for k in LSTs_in_use)
-        print('lst',LSTs_in_use)
+
     MAGICs_IDs = np.asarray(list(assigned_tel_ids.values())[4:6])
     MAGICs_in_use = np.where(MAGICs_IDs > 0)[0] + 1    #Here we select which MAGICs are/is in use