flake8 code quality changes.

Author: Jorge Prado Gonzalez

examples/07_km3net/01_convert_km3net.py

@@ -29,9 +29,9 @@ def main(backend: str, triggered: str, HNL: str, OUTPUT_DIR: str) -> None:
     else:
         outdir = f"{EXAMPLE_OUTPUT_DIR}/{backend}"
     os.makedirs(outdir, exist_ok=True)
-    print(60*'*')
+    print(60 * "*")
     print(f"Saving to {outdir}")
-    print(60*'*')
+    print(60 * "*")
     if backend == "parquet":
         save_method = ParquetWriter(truth_table="truth")
     elif backend == "sqlite":

examples/07_km3net/README.md

@@ -38,8 +38,8 @@ The generated SQLite or Parquet file contains:
 
 The output files can be read using Python.
 
-- **If you chose to create a Parquet output**:  
-  You will find several `.parquet` files in the output folder, each corresponding to a different extracted table (e.g., a table with the true event information, a table with pulse information, etc.).  
+- **If you chose to create a Parquet output**:
+  You will find several `.parquet` files in the output folder, each corresponding to a different extracted table (e.g., a table with the true event information, a table with pulse information, etc.).
   To read one of these tables:
 
   ```python
@@ -48,8 +48,8 @@ The output files can be read using Python.
   df = pd.read_parquet("FILE_NAME.parquet")
   print(df.head())
 
-- **If you chose to create an SQLite output**:  
-  In this case, you will find a single `.db` file per converted input, which contains all the tables inside.  
+- **If you chose to create an SQLite output**:
+  In this case, you will find a single `.db` file per converted input, which contains all the tables inside.
   To list the table names and preview their contents:
 
   ```python
@@ -85,4 +85,3 @@ or
 ```bash
 python 01_convert_km3net.py --help
 ```
-

src/graphnet/data/extractors/km3net/km3netpulseextractor.py

@@ -65,7 +65,7 @@ def _extract_pulse_map(self, file: Any) -> pd.DataFrame:
         hits_array = hits.arrays(keys_to_extract, library="ak")
         hits_array["entry"] = ak.local_index(hits_array, axis=0)
         df = ak.to_dataframe(hits_array).reset_index(drop=True)
-        
+
         # Add unique event ID
         unique_extended = [
             int(unique_id[index]) for index in df["entry"].values
@@ -77,7 +77,11 @@ def _extract_pulse_map(self, file: Any) -> pd.DataFrame:
             df = df[df["trig"] != 0]
 
         # Final processing
-        df = df.drop(["entry", "subentry"], axis=1) if "subentry" in df.columns else df.drop(["entry"], axis=1)
+        df = (
+            df.drop(["entry", "subentry"], axis=1)
+            if "subentry" in df.columns
+            else df.drop(["entry"], axis=1)
+        )
         df = creating_time_zero(df)
         df = df.reset_index(drop=True)
 
@@ -89,41 +93,45 @@ def _determine_unique_id(self, file: Any) -> np.ndarray:
             primaries = file.mc_trks[:, 0]
             nus_flavor = [12, 14, 16]
 
-            if (abs(np.array(primaries.pdgid)[0]) not in nus_flavor) and (abs(np.array(primaries.pdgid)[0]) > 0.1):  # Muon
+            if (abs(np.array(primaries.pdgid)[0]) not in nus_flavor) and (
+                abs(np.array(primaries.pdgid)[0]) > 0.1
+            ):  # Muon
                 return create_unique_id_run_by_run(
                     file_type="muon",
                     run_id=np.array(file.run_id),
                     evt_id=np.array(file.id),
                     hnl_model="none",
                 )
-            elif (5914 in file.mc_trks.pdgid[0]) or (file.mc_trks.pdgid[0][0] == 0):  # HNL
+            elif (5914 in file.mc_trks.pdgid[0]) or (
+                file.mc_trks.pdgid[0][0] == 0
+            ):  # HNL
                 try:
                     model_hnl = file.header.model.interaction
                 except Exception:
                     model_hnl = "none"
                 return create_unique_id_run_by_run(
-                        file_type="hnl",
-                        run_id=np.array(file.run_id),
-                        evt_id=np.array(file.id),
-                        hnl_model=model_hnl,
-                    )
+                    file_type="hnl",
+                    run_id=np.array(file.run_id),
+                    evt_id=np.array(file.id),
+                    hnl_model=model_hnl,
+                )
             elif (abs(np.array(primaries.pdgid)[0]) in nus_flavor) and (
                 5914 not in file.mc_trks.pdgid[0]
             ):  # Neutrino
                 return create_unique_id_run_by_run(
-                        file_type="neutrino",
-                        run_id=np.array(file.run_id),
-                        evt_id=np.array(file.id),
-                        hnl_model="none",
-                    )
+                    file_type="neutrino",
+                    run_id=np.array(file.run_id),
+                    evt_id=np.array(file.id),
+                    hnl_model="none",
+                )
         elif len(file.mc_trks.E[0]) == 0:  # Events or Noise
             if file.header["calibration"] == "dynamical":  # Data
                 return create_unique_id_run_by_run(
-                        file_type="data",
-                        run_id=np.array(file.run_id),
-                        evt_id=np.array(file.id),
-                        hnl_model="none",
-                    )
+                    file_type="data",
+                    run_id=np.array(file.run_id),
+                    evt_id=np.array(file.id),
+                    hnl_model="none",
+                )
             elif file.header["calibration"] == "statical":  # Noise
                 return create_unique_id_run_by_run(
                     file_type="noise",

src/graphnet/data/extractors/km3net/km3nettruthextractor.py

@@ -12,9 +12,11 @@
     assert_no_uint_values,
     filter_None_NaN,
 )
+
 if has_km3net_package() or TYPE_CHECKING:
     import km3io as ki
 
+
 class KM3NeTTruthExtractor(KM3NeTExtractor):
     """Class for extracting the truth information from a file."""
 
@@ -150,11 +152,11 @@ def _extract_event_attributes(
             raise ValueError("File type not recognized")
 
         unique_id = create_unique_id_run_by_run(
-                    file_type=file_type,
-                    run_id=np.array(file.run_id),
-                    evt_id=np.array(file.id),
-                    hnl_model=model_hnl,
-                )
+            file_type=file_type,
+            run_id=np.array(file.run_id),
+            evt_id=np.array(file.id),
+            hnl_model=model_hnl,
+        )
 
         return {
             "run_id": run_id,
@@ -359,7 +361,9 @@ def _extract_truth_dataframe(self, file: Any) -> Any:
             ##############################################################
             # MUON-FILE####################################################
             ##############################################################
-            if (abs(np.array(primaries.pdgid)[0]) not in nus_flavor) and (abs(np.array(primaries.pdgid)[0]) > 0.1):
+            if (abs(np.array(primaries.pdgid)[0]) not in nus_flavor) and (
+                abs(np.array(primaries.pdgid)[0]) > 0.1
+            ):
                 primaries_jshower = ki.tools.best_jshower(file.trks)
                 primaries_jmuon = ki.tools.best_jmuon(file.trks)
                 dict_truth = self._construct_truth_dictionary(
@@ -374,7 +378,9 @@ def _extract_truth_dataframe(self, file: Any) -> Any:
             ###############################################################
             # HNL-FILE######################################################
             ###############################################################
-            elif (5914 in file.mc_trks.pdgid[0]) or (file.mc_trks.pdgid[0][0] == 0):
+            elif (5914 in file.mc_trks.pdgid[0]) or (
+                file.mc_trks.pdgid[0][0] == 0
+            ):
                 primaries_jshower = ki.tools.best_jshower(file.trks)
                 primaries_jmuon = ki.tools.best_jmuon(file.trks)
                 dict_truth = self._construct_truth_dictionary(

src/graphnet/data/extractors/km3net/utilities/km3net_utilities.py

@@ -6,14 +6,12 @@
 import pandas as pd
 
 
-
-
 def create_unique_id_run_by_run(
     file_type: str,
     run_id: List[int],
     evt_id: List[int],
     hnl_model: str,
-)->List[int]:
+) -> List[int]:
     """Create a unique ID for each event based on its parameters.
 
     Args:
@@ -33,11 +31,18 @@ def create_unique_id_run_by_run(
     }
 
     hnl_type_dict = {
-        'none': 0, #possibility of adding hnl models to break run-filetype degeneracy
+        "none": 0,  # possibility of adding hnl models to break run-filetype degeneracy
     }
     unique_id = []
     for i in range(len(run_id)):
-        unique_id.append(int(str(run_id[i]) + str(evt_id[i]) + str(file_type_dict[file_type]) + str(hnl_type_dict[hnl_model])))
+        unique_id.append(
+            int(
+                str(run_id[i])
+                + str(evt_id[i])
+                + str(file_type_dict[file_type])
+                + str(hnl_type_dict[hnl_model])
+            )
+        )
 
     return unique_id
 

src/graphnet/data/readers/km3netreader.py

@@ -17,7 +17,7 @@
 
 # km3net specific imports
 if has_km3net_package() or TYPE_CHECKING:
-    import km3io as ki # pyright: reportMissingImports=false
+    import km3io as ki  # pyright: reportMissingImports=false
 
 
 class KM3NeTReader(GraphNeTFileReader):

src/graphnet/utilities/imports.py

@@ -46,6 +46,7 @@ def has_jammy_flows_package() -> bool:
         )
         return False
 
+
 def has_km3net_package() -> bool:
     """Check whether the `km3net` packages are available."""
     try:
@@ -58,6 +59,7 @@ def has_km3net_package() -> bool:
         )
         return False
 
+
 def requires_icecube(test_function: Callable) -> Callable:
     """Decorate `test_function` for use only if `icecube` module is present."""