Console scripts

README.md

@@ -23,8 +23,8 @@ The following command will set up a conda virtual environment, add the necessary
 
 *magic-cta-pipe* scripts to perform the analysis of MAGIC only and MAGIC+LST1 data. Within the *scripts* folder we have the subdirectories:
 
-* *magic*: scripts for the analysis of MAGIC data (currently under heavy restructuring in order to be used with ctapipe v0.12)
-* *lst1_magic_real*: scripts for the MAGIC+LST1 analysis (updated to be used with ctapipe v0.12)
+* *magic*: scripts for the analysis of MAGIC data (**currently under heavy restructuring in order to be used with ctapipe v0.12**)
+* *lst1_magic*: scripts for the MAGIC+LST1 analysis (updated to be used with ctapipe v0.12)
 
 <!--
 A brief description:

magicctapipe/__init__.py

@@ -2,6 +2,7 @@
 from . import irfs
 from . import train
 from . import utils
+from . import scripts
 
 from .version import __version__
 
@@ -10,5 +11,6 @@
     "irfs",
     "train",
     "utils",
+    "scripts",
     "__version__",
 ]

magicctapipe/scripts/__init__.py

@@ -0,0 +1,21 @@
+from .lst1_magic import (
+    dl1_to_dl2,
+    event_coincidence,
+    mc_dl0_to_dl1,
+    stereo_reco,
+    train_rf_regressor,
+    train_rf_classifier,
+    magic_cal_to_dl1,
+    merge_hdf_files,
+    )
+
+__all__ = [
+    "dl1_to_dl2",
+    "event_coincidence",
+    "mc_dl0_to_dl1",
+    "stereo_reco",
+    "train_rf_regressor",
+    "train_rf_classifier",
+    "magic_cal_to_dl1",
+    "merge_hdf_files",
+]

magicctapipe/scripts/lst1_magic/__init__.py

@@ -0,0 +1,21 @@
+from .lst1_magic_dl1_to_dl2 import dl1_to_dl2
+from .lst1_magic_event_coincidence import event_coincidence
+from .lst1_magic_mc_dl0_to_dl1 import mc_dl0_to_dl1
+from .lst1_magic_stereo_reco import stereo_reco
+from .lst1_magic_train_rfs import (
+    train_rf_regressor,
+    train_rf_classifier,
+)
+from .magic_data_cal_to_dl1 import magic_cal_to_dl1
+from .merge_hdf_files import merge_hdf_files
+
+__all__ = [
+    "dl1_to_dl2",
+    "event_coincidence",
+    "mc_dl0_to_dl1",
+    "stereo_reco",
+    "train_rf_regressor",
+    "train_rf_classifier",
+    "magic_cal_to_dl1",
+    "merge_hdf_files",
+]

magicctapipe/scripts/lst1_magic_real/config.yaml → magicctapipe/scripts/lst1_magic/config.yaml

@@ -6,7 +6,7 @@ mc_allowed_tels:
 
 LST:
 
-    # The following settings are derived from lstchain v0.8.4: 
+    # The following settings are derived from lstchain v0.8.4:
     # - lstchain_standard_config.json
     # - lstchain/data/lstchain_dl1ab_tune_MC_to_Crab_config.json
 
@@ -21,7 +21,7 @@ LST:
         extra_bias_in_dim_pixels: 0.5
         transition_charge: 8
         extra_noise_in_bright_pixels: 1.2
-    
+
     increase_psf:
         use: true
         smeared_light_fraction: 0.125
@@ -65,14 +65,14 @@ MAGIC:
         pedestalType: 'FromExtractorRndm'
 
 
-event_coincidence: 
-    type_lst_time: 'dragon_time' 
+event_coincidence:
+    type_lst_time: 'dragon_time'
     window_width: 6.0e-7      # unit: [sec]
-    offset_start: -5.0e-6     # unit: [sec] 
+    offset_start: -5.0e-6     # unit: [sec]
     offset_stop: 0.0          # unit: [sec]
-
 
-stereo_reco: 
+
+stereo_reco:
     quality_cuts: '(intensity > 50) & (width > 0)'
 
 
@@ -97,7 +97,7 @@ energy_regressor:
         max_samples: null
 
     features: [
-        'intensity', 
+        'intensity',
         'length',
         'width',
         'skewness',
@@ -128,7 +128,7 @@ direction_regressor:
         warm_start: false
         ccp_alpha: 0.0
         max_samples: null
-        
+
     features: [
         'intensity',
         'length',
@@ -141,7 +141,7 @@ direction_regressor:
         'impact'
     ]
 
-        
+
 event_classifier:
     settings:
         n_estimators: 100
@@ -174,7 +174,3 @@ event_classifier:
         'h_max',
         'impact'
     ]
-
-
-
-

magicctapipe/scripts/lst1_magic_real/lst1_magic_dl1_to_dl2.py → magicctapipe/scripts/lst1_magic/lst1_magic_dl1_to_dl2.py

@@ -2,14 +2,14 @@
 # coding: utf-8
 
 """
-Author: Yoshiki Ohtani (ICRR, ohtani@icrr.u-tokyo.ac.jp) 
+Author: Yoshiki Ohtani (ICRR, ohtani@icrr.u-tokyo.ac.jp)
 
 Reconstruct the DL2 parameters (i.e., energy, direction and gammaness) with trained RFs.
 The RFs will be applied per telescope combination and per telescope type.
-If real data is input, the parameters in the Alt/Az coordinate will be transformed to the RA/Dec coordinate. 
+If real data is input, the parameters in the Alt/Az coordinate will be transformed to the RA/Dec coordinate.
 
 Usage:
-$ python lst1_magic_dl1_to_dl2.py 
+$ python lst1_magic_dl1_to_dl2.py
 --input-file "./data/dl1_stereo/dl1_stereo_lst1_magic_run03265.0040.h5"
 --output-file "./data/dl2/dl2_lst1_magic_run03265.0040.h5"
 --energy-regressors "./data/rfs/energy_regressors_*.joblib"
@@ -19,7 +19,6 @@
 
 import glob
 import time
-import yaml
 import tables
 import logging
 import argparse
@@ -59,7 +58,7 @@ def apply_rfs(data, rfs_mask, estimator, tel_descriptions=None):
         estimator.load(path)
 
         tel_ids = list(estimator.telescope_rfs.keys())
-        
+
         df = data.query(f'(tel_id == {tel_ids}) & (multiplicity == {len(tel_ids)})')
         df.dropna(subset=estimator.feature_names, inplace=True)
         df['multiplicity'] = df.groupby(['obs_id', 'event_id']).size()
@@ -70,14 +69,14 @@ def apply_rfs(data, rfs_mask, estimator, tel_descriptions=None):
         if n_events == 0:
             logger.info('--> No corresponding events are found. Skipping.')
             continue
-        
+
         logger.info(f'--> {n_events} events are found. Applying...')
 
-        if tel_descriptions != None:
+        if tel_descriptions is not None:
             df_reco = estimator.predict(df, tel_descriptions)
         else:
             df_reco = estimator.predict(df)
-            
+
         reco_params = reco_params.append(df_reco)
 
     reco_params.sort_index(inplace=True)
@@ -86,7 +85,7 @@ def apply_rfs(data, rfs_mask, estimator, tel_descriptions=None):
 
 
 def dl1_to_dl2(
-        input_file, output_file, 
+        input_file, output_file,
         energy_regressors=None, direction_regressors=None, event_classifiers=None
     ):
 
@@ -100,16 +99,16 @@ def dl1_to_dl2(
     subarray = SubarrayDescription.from_hdf(input_file)
 
     # --- reconstruct energy ---
-    if energy_regressors != None:
+    if energy_regressors is not None:
 
-        estimator = EnergyRegressor() 
+        estimator = EnergyRegressor()
         logger.info('\nReconstucting the energy...')
-        
+
         reco_params = apply_rfs(data_joint, energy_regressors, estimator)
         data_joint = data_joint.join(reco_params)
-        
+
     # --- reconstruct direction ---
-    if direction_regressors != None:
+    if direction_regressors is not None:
 
         estimator = DirectionRegressor()
         logger.info('\nReconstructing the direction...')
@@ -136,34 +135,34 @@ def dl1_to_dl2(
                 az=u.Quantity(data_joint['az_tel_mean'].values, u.rad),
                 timestamp=timestamps
             )
-            
+
             reco_ra, reco_dec = transform_to_radec(
-                alt=u.Quantity(data_joint['reco_alt'].values, u.deg), 
+                alt=u.Quantity(data_joint['reco_alt'].values, u.deg),
                 az=u.Quantity(data_joint['reco_az'].values, u.deg),
                 timestamp=timestamps
-            ) 
+            )
 
             reco_ra_mean, reco_dec_mean = transform_to_radec(
-                alt=u.Quantity(data_joint['reco_alt_mean'].values, u.deg), 
+                alt=u.Quantity(data_joint['reco_alt_mean'].values, u.deg),
                 az=u.Quantity(data_joint['reco_az_mean'].values, u.deg),
                 timestamp=timestamps
-            ) 
+            )
 
             data_joint['ra_tel'] = ra_tel.to(u.deg).value
             data_joint['dec_tel'] = dec_tel.to(u.deg).value
             data_joint['ra_tel_mean'] = ra_tel_mean.to(u.deg).value
             data_joint['dec_tel_mean'] = dec_tel_mean.to(u.deg).value
             data_joint['reco_ra'] = reco_ra.to(u.deg).value
-            data_joint['reco_dec'] = reco_dec.to(u.deg).value  
+            data_joint['reco_dec'] = reco_dec.to(u.deg).value
             data_joint['reco_ra_mean'] = reco_ra_mean.to(u.deg).value
             data_joint['reco_dec_mean'] = reco_dec_mean.to(u.deg).value
 
     # --- classify event type ---
-    if event_classifiers != None:
+    if event_classifiers is not None:
 
         estimator = EventClassifier()
         logger.info('\nClassifying the event type...')
-        
+
         reco_params = apply_rfs(data_joint, event_classifiers, estimator)
         data_joint = data_joint.join(reco_params)
 
@@ -195,7 +194,7 @@ def main():
     parser = argparse.ArgumentParser()
 
     parser.add_argument(
-        '--input-file', '-i', dest='input_file', type=str, 
+        '--input-file', '-i', dest='input_file', type=str,
         help='Path to an input DL1-stereo data file.'
     )
 
@@ -222,7 +221,7 @@ def main():
     args = parser.parse_args()
 
     dl1_to_dl2(
-        args.input_file, args.output_file, 
+        args.input_file, args.output_file,
         args.energy_regressors, args.direction_regressors, args.event_classifiers
     )