Format and upgrade Python

.pre-commit-config.yaml

@@ -20,6 +20,7 @@ repos:
     rev: v3.19.1
     hooks:
       - id: pyupgrade
+        args: ["--py310-plus"]
   - repo: https://github.com/shellcheck-py/shellcheck-py
     rev: v0.10.0.1
     hooks:

machine_learning_hep/__main__.py

@@ -13,6 +13,7 @@
 #############################################################################
 
 import sys
+
 from machine_learning_hep.steer_analysis import main
 
 sys.exit(main())

machine_learning_hep/analysis/analyzer.py

@@ -12,13 +12,15 @@
 ##   along with this program. if not, see <https://www.gnu.org/licenses/>. ##
 #############################################################################
 
-from os.path import exists, join
-from os import makedirs
 import os
+from os import makedirs
+from os.path import exists, join
+
+from machine_learning_hep.io import dump_yaml_from_dict
 
 # HF specific imports
 from machine_learning_hep.workflow.workflow_base import WorkflowBase
-from machine_learning_hep.io import dump_yaml_from_dict
+
 
 class Analyzer(WorkflowBase):
     def __init__(self, datap, case, typean, period):
@@ -28,15 +30,16 @@ def __init__(self, datap, case, typean, period):
         for mcordata in ("mc", "data"):
             dp = datap["analysis"][typean][mcordata]
             prefix_dir_res = dp.get("prefix_dir_res", "")
-            results_dir = prefix_dir_res + os.path.expandvars(dp["results"][period]) \
-                    if period is not None \
-                    else prefix_dir_res + os.path.expandvars(dp["resultsallp"])
+            results_dir = (
+                prefix_dir_res + os.path.expandvars(dp["results"][period])
+                if period is not None
+                else prefix_dir_res + os.path.expandvars(dp["resultsallp"])
+            )
             if not exists(results_dir):
                 # create otput directories in case they do not exist
                 makedirs(results_dir)
             if mcordata == "data":
-                dump_yaml_from_dict({case: datap},
-                                    join(results_dir, f"database_{case}_{typean}.yml"))
+                dump_yaml_from_dict({case: datap}, join(results_dir, f"database_{case}_{typean}.yml"))
 
 
 class AnalyzerAfterBurner(WorkflowBase):

machine_learning_hep/analysis/analyzer_manager.py

@@ -12,14 +12,14 @@
 
 from machine_learning_hep.logger import get_logger
 
+
 # pylint: disable=too-many-instance-attributes
 class AnalyzerManager:
     """
     Manager class handling analysis and systematic objects
     """
 
     def __init__(self, ana_class, database, case, typean, doperiodbyperiod, *args):
-
         self.ana_class = ana_class
         self.database = database
         self.case = case
@@ -36,7 +36,6 @@ def __init__(self, ana_class, database, case, typean, doperiodbyperiod, *args):
 
         self.is_initialized = False
 
-
     def get_analyzers(self, none_for_unused_period=True):
         self.initialize()
         if not none_for_unused_period:
@@ -50,7 +49,6 @@ def get_analyzers(self, none_for_unused_period=True):
         analyzers[-1] = self.analyzers[-1]
         return analyzers
 
-
     def initialize(self):
         """
         Collect all analyzer objects required in a list and initialises the after_burner if present
@@ -65,10 +63,8 @@ def initialize(self):
 
         for ip, period in enumerate(useperiod):
             if self.doperiodbyperiod and period:
-                self.analyzers.append(self.ana_class(self.database, self.case, self.typean, ip,
-                                                     *self.add_args))
-        self.analyzers.append(self.ana_class(self.database, self.case, self.typean, None,
-                                             *self.add_args))
+                self.analyzers.append(self.ana_class(self.database, self.case, self.typean, ip, *self.add_args))
+        self.analyzers.append(self.ana_class(self.database, self.case, self.typean, None, *self.add_args))
 
         if self.doperiodbyperiod:
             # get after-burner, if any
@@ -79,7 +75,6 @@ def initialize(self):
 
         self.is_initialized = True
 
-
     def analyze(self, ana_steps):
         """
         Gives a list of analyzers and analysis steps do each step for each analyzer
@@ -88,14 +83,16 @@ def analyze(self, ana_steps):
         """
 
         if not ana_steps:
-            self.logger.info("No analysis steps to be done for Analyzer class %s. Return...",
-                             self.ana_class.__name__)
+            self.logger.info("No analysis steps to be done for Analyzer class %s. Return...", self.ana_class.__name__)
             return
 
         self.initialize()
 
-        self.logger.info("Run all registered analyzers of type %s for following analysis steps: %s",
-                         self.ana_class.__name__, ana_steps)
+        self.logger.info(
+            "Run all registered analyzers of type %s for following analysis steps: %s",
+            self.ana_class.__name__,
+            ana_steps,
+        )
 
         # Collect potentially failed systematic steps
         failed_steps = []