Some housekeeping

rdstar/studies/low_level_cut_study/low_level_cut_defintions.py

+"""
+This file generates usefull dictionaries for the low level cut study.
 
+Patrick Ecker 2020
+"""
 
 from rdstar.studies.low_level_cut_study.final_state_particles_cut_study import fsp_collection as fsp_collection_loose
 from rdstar.studies.low_level_cut_study.final_state_particles_cut_study import fsp_cut_sets as fsp_cut_sets_loose
@@ -11,8 +15,7 @@ from rdstar.online_analysis.decay_channels import D_decay_label_to_cut_info_list
     D_decay_label_to_decay_id, Dstar_decay_label_to_decay_id, Dstar_decay_channels, D_decay_channels
 
 
-
-ntuple_labels_dict = {
+fsp_ntuple_labels_dict = {
     "e_ntuple": "e",
     "mu_ntuple": "mu",
     "piCharged_ntuple": "pi+",
@@ -23,13 +26,13 @@ ntuple_labels_dict = {
 }
 
 fsp_cut_dict = {}
-for n_tuple in ntuple_labels_dict.keys():
-    fsps = [fsp for fsp in fsp_collection.all_fsps if fsp.label == ntuple_labels_dict[n_tuple]]
+for n_tuple in fsp_ntuple_labels_dict.keys():
+    fsps = [fsp for fsp in fsp_collection.all_fsps if fsp.label == fsp_ntuple_labels_dict[n_tuple]]
     assert len(fsps)==1
     fsp = fsps[0]
     cut_infos = fsp_cut_sets[fsp.label]
 
-    fsps_loose = [fsp for fsp in fsp_collection_loose.all_fsps if fsp.label == ntuple_labels_dict[n_tuple]]
+    fsps_loose = [fsp for fsp in fsp_collection_loose.all_fsps if fsp.label == fsp_ntuple_labels_dict[n_tuple]]
     assert len(fsps_loose)==1
     fsp = fsps_loose[0]
     cut_infos_loose = fsp_cut_sets_loose[fsp.label]

rdstar/studies/low_level_cut_study/plotting.py

-import matplotlib.pyplot as plt
+"""
+This file contains the plotting tools for the low level cut study.
+
+Patrick Ecker 2020
+"""
 
-from typing import Any, Union, Optional, List, Tuple, Dict, Generator
+import matplotlib.pyplot as plt
+from typing import List, Tuple, Dict
 import pandas as pd
 import copy
 import os
 
-from rdstar.offline_analysis.selection.selection_info_containers import CutInfo
-
 from templatefitter.plotter.plot_style import set_matplotlibrc_params, KITColors
 
+from rdstar.offline_analysis.selection.selection_info_containers import CutInfo
 from rdstar.studies.low_level_cut_study.low_level_cut_defintions import fsp_cut_dict, D_cut_dict, \
     combined_decay_mode_id_dict, combined_channel_dict
 
+__all__ = [
+    "LowLevelCutPlotter"
+]
+
 
 class LowLevelCutPlotter:
 
@@ -26,6 +34,7 @@ class LowLevelCutPlotter:
         self.df = df
 
     def apply_cuts(self, pivot: CutInfo, cut_infos: List[CutInfo]) -> pd.DataFrame:
+        assert isinstance(self.df, pd.DataFrame), "You need to get a Dataframe first!"
         df_temp = copy.copy(self.df)
         cut_infos = [cut_info.make_root_compatible() for cut_info in cut_infos if cut_info != pivot]
         cut_strings = [cut_info.cut_str for cut_info in cut_infos]
@@ -47,15 +56,17 @@ class LowLevelCutPlotter:
     def plot_on(self, save: bool = True) -> Dict[str, os.fspath]:
         output = {}
         relevant_keys = [key for key in self.cut_dicts if key.startswith(self.n_tuple)]
+
         for key in relevant_keys:
             channel = self.cut_dicts[key]["channel"]
             channel_cut = f"extraInfo__bodecayModeID__bc == {combined_decay_mode_id_dict[channel]}" if channel else None
+
             for cut_info in self.cut_dicts[key]["cuts"]:
                 set_matplotlibrc_params()
                 temp_cut_infos = copy.copy(self.cut_dicts[key]["cuts"])
-
                 new_cut_info = cut_info.make_root_compatible()
                 cut_var = new_cut_info.cut_var
+
                 df = self.apply_cuts(pivot=cut_info, cut_infos=temp_cut_infos)
                 if channel_cut:
                     df.query(channel_cut, inplace=True)
@@ -71,9 +82,14 @@ class LowLevelCutPlotter:
                     continue
 
                 fig, ax = plt.subplots()
-                cuts = list(new_cut_info.cut_limits) if isinstance(new_cut_info.cut_limits, Tuple) else [new_cut_info.cut_limits]
+
+                if isinstance(new_cut_info.cut_limits, Tuple):
+                    cuts = list(new_cut_info.cut_limits)
+                else:
+                    cuts = [new_cut_info.cut_limits]
                 for cut in cuts:
                     ax.axvline(cut, color=KITColors.kit_red)
+
                 df_bkg = df.query("isSignal == 0")
                 bkg_data = df_bkg[cut_var].abs() if new_cut_info.cut_abs else df_bkg[cut_var]
                 df_sig = df.query("isSignal == 1")
@@ -94,6 +110,7 @@ class LowLevelCutPlotter:
                 plt.ylabel("Entries")
                 plt.title(combined_channel_dict[channel], loc="right")
                 plt.legend(loc="best")
+
                 save_path = os.path.join(self.target_path, f"{key}_{new_cut_info.cut_name}_evaluation.pdf")
                 output.update({f"{key}_{new_cut_info.cut_name}_evaluation": save_path})
                 if save:
@@ -104,10 +121,8 @@ class LowLevelCutPlotter:
         return output
 
 if __name__ == '__main__':
-    #df = pd.read_hdf("/ceph/pecker/rdstar/low_level_cut_study/prod_4thAug/CombinedNTuples/e_ntuple_total.h5")
     df = pd.read_hdf("/ceph/pecker/rdstar/low_level_cut_study/CombinedNTuples/dcharged_ntuple_total.h5")
     llcp = LowLevelCutPlotter(n_tuple="dcharged_ntuple", target_path="")
     print(llcp.generate_output_dict())
     llcp.get_df(df=df)
-    #llcp.apply_cuts(pivot=fsp_cut_dict["e_ntuple"]["cuts"][0], cut_infos=fsp_cut_dict["e_ntuple"]["cuts"])
     llcp.plot_on(save=False)
\ No newline at end of file

rdstar/studies/low_level_cut_study/reconstruction_fsp_low_level_cut_study.py

+"""
+Steering file for the n-tuple production for the fsp low level cut study.
+
+Patrick Ecker 2020
+"""
+
 import basf2
 import b2biiConversion
 
@@ -6,7 +12,7 @@ from modularAnalysis import *
 from analysistools.processing import path_utilities
 
 from rdstar.online_analysis.decay_utilities import Reconstructor
-from rdstar.online_analysis import observables, decay_utilities, decay_channels
+from rdstar.online_analysis import decay_channels
 from rdstar.studies.low_level_cut_study.final_state_particles_cut_study import fsp_collection
 
 input_files, input_sequences, output_dict, config = path_utilities.get_commandline_args()

rdstar/studies/low_level_cut_study/reconstruction_low_level_cut_study.py

+"""
+Steering file for the n-tuple production for the D/D* low level cut study.
+
+Patrick Ecker 2020
+"""
 
 import basf2
 import b2biiConversion
@@ -5,10 +10,9 @@ import b2biiConversion
 from modularAnalysis import *
 
 from analysistools.processing import path_utilities
-from analysistools.processing import reco_utilities
 from analysistools.processing import skim_cuts_utils
 
-from rdstar.online_analysis import observables, decay_utilities, skim_cuts
+from rdstar.online_analysis import decay_utilities, skim_cuts
 from rdstar.studies.low_level_cut_study import decay_channels_cut_study
 from rdstar.online_analysis.decay_utilities import Reconstructor
 
@@ -30,7 +34,6 @@ b2biiConversion.convertBelleMdstToBelleIIMdst(
 
 setAnalysisConfigParams({'mcMatchingVersion': 'Belle'}, main_path)
 
-
 # Get Final State Particles
 main_path.add_module('CurlerCloneMarker')
 decay_utilities.fsp.fsp_collection.get_final_state_particles(path=main_path, use_mc_info=config['use_mc_info'])
@@ -38,12 +41,10 @@ skim_cuts_utils.apply_skim_from_dict(main_path, skim_cuts.pre_skim_cuts)
 
 reconstructor = Reconstructor(path=main_path, config=config, decay_channels_module=decay_channels_cut_study)
 
-
 # Build intermediate D mesons
 reconstructor.reconstruct_D()
 reconstructor.reconstruct_Dstar()
 
-
 ###########
 # D meson #
 ###########