run flake8 --all-files

examples/01_icetray/03_i3_deployer_example.py

@@ -50,7 +50,7 @@ def main() -> None:
     model_config = f"{base_path}/{model_name}/{model_name}_config.yml"
     state_dict = f"{base_path}/{model_name}/{model_name}_state_dict.pth"
     output_folder = f"{EXAMPLE_OUTPUT_DIR}/i3_deployment/upgrade_03_04"
-    gcd_file = f"{TEST_DATA_DIR}/i3/upgrade_genie_step4_140028_000998/GeoCalibDetectorStatus_ICUpgrade.v58.mixed.V0.i3.bz2"
+    gcd_file = f"{TEST_DATA_DIR}/i3/upgrade_genie_step4_140028_000998/GeoCalibDetectorStatus_ICUpgrade.v58.mixed.V0.i3.bz2"  # noqa: E501
     input_files = []
     for folder in input_folders:
         input_files.extend(glob(join(folder, "*.i3.gz")))

examples/01_icetray/04_i3_module_in_native_icetray_example.py

@@ -86,7 +86,7 @@ def main() -> None:
     model_config = f"{base_path}/{model_name}/{model_name}_config.yml"
     state_dict = f"{base_path}/{model_name}/{model_name}_state_dict.pth"
     output_folder = f"{EXAMPLE_OUTPUT_DIR}/i3_deployment/upgrade"
-    gcd_file = f"{TEST_DATA_DIR}/i3/upgrade_genie_step4_140028_000998/GeoCalibDetectorStatus_ICUpgrade.v58.mixed.V0.i3.bz2"
+    gcd_file = f"{TEST_DATA_DIR}/i3/upgrade_genie_step4_140028_000998/GeoCalibDetectorStatus_ICUpgrade.v58.mixed.V0.i3.bz2"  # noqa:E501
     features = FEATURES.UPGRADE
     input_files = []
     for folder in input_folders:

examples/04_training/07_train_normalizing_flow.py

@@ -4,7 +4,6 @@
 from typing import Any, Dict, List, Optional
 
 from pytorch_lightning.loggers import WandbLogger
-import torch
 from torch.optim.adam import Adam
 
 from graphnet.constants import EXAMPLE_DATA_DIR, EXAMPLE_OUTPUT_DIR

setup.cfg

@@ -18,7 +18,9 @@ omit =
 exclude =
     versioneer.py
 # Ignore unused imports in __init__ files
-per-file-ignores=__init__.py:F401
+per-file-ignores=
+    __init__.py:F401
+    src/graphnet/utilities/imports.py:F401
 ignore=E203,W503
 
 [docformatter]

src/graphnet/__init__.py

@@ -32,4 +32,6 @@
 
 from . import _version
 
-__version__ = _version.get_versions()["version"]  # type: ignore[no-untyped-call]
+__version__ = _version.get_versions()[  # type: ignore[no-untyped-call]
+    "version"
+]

src/graphnet/data/curated_datamodule.py

@@ -31,8 +31,8 @@ def __init__(
         features: Optional[List[str]] = None,
         backend: str = "parquet",
         train_dataloader_kwargs: Optional[Dict[str, Any]] = None,
-        validation_dataloader_kwargs: Dict[str, Any] = None,
-        test_dataloader_kwargs: Dict[str, Any] = None,
+        validation_dataloader_kwargs: Optional[Dict[str, Any]] = None,
+        test_dataloader_kwargs: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Construct CuratedDataset.
 

src/graphnet/data/dataset/samplers.py

@@ -34,7 +34,7 @@
 )
 
 from collections import defaultdict
-from multiprocessing import Pool, cpu_count, get_context
+from multiprocessing import get_context
 
 import numpy as np
 import torch

src/graphnet/data/extractors/icecube/i3extractor.py

@@ -24,9 +24,9 @@ def __init__(self, extractor_name: str):
         """Construct I3Extractor.
 
         Args:
-            extractor_name: Name of the `I3Extractor` instance. Used to keep track of the
-                provenance of different data, and to name tables to which this
-                data is saved.
+            extractor_name: Name of the `I3Extractor` instance. Used to keep
+                track of the provenance of different data, and to name tables
+                to which this data is saved.
         """
         # Member variable(s)
         self._i3_file: str = ""

src/graphnet/data/pre_configured/dataconverters.py

@@ -25,10 +25,12 @@ def __init__(
         """Convert I3 files to Parquet.
 
         Args:
-            gcd_rescue: gcd_rescue: Path to a GCD file that will be used if no GCD file is
-                        found in subfolder. `I3Reader` will recursively search
-                        the input directory for I3-GCD file pairs. By IceCube
-                        convention, a folder containing i3 files will have an
+            gcd_rescue: gcd_rescue: Path to a GCD file that will be used if
+                        no GCD file is found in subfolder. `I3Reader` will
+                        recursively search the input directory for I3-GCD file
+                        pairs.
+                        By IceCube convention,
+                        a folder containing i3 files will have an
                         accompanying GCD file. However, in some cases, this
                         convention is broken. In cases where a folder contains
                         i3 files but no GCD file, the `gcd_rescue` is used
@@ -70,10 +72,11 @@ def __init__(
         """Convert I3 files to SQLite.
 
         Args:
-            gcd_rescue: gcd_rescue: Path to a GCD file that will be used if no GCD file is
-                        found in subfolder. `I3Reader` will recursively search
-                        the input directory for I3-GCD file pairs. By IceCube
-                        convention, a folder containing i3 files will have an
+            gcd_rescue: gcd_rescue: Path to a GCD file that will be used if
+                        no GCD file is found in subfolder. `I3Reader` will
+                        recursively search the input directory for I3-GCD file
+                        pairs. By IceCube convention,
+                        a folder containing i3 files will have an
                         accompanying GCD file. However, in some cases, this
                         convention is broken. In cases where a folder contains
                         i3 files but no GCD file, the `gcd_rescue` is used

src/graphnet/data/readers/graphnet_file_reader.py

@@ -125,7 +125,9 @@ def _validate_extractors(
     ) -> None:
         for extractor in extractors:
             try:
-                assert isinstance(extractor, tuple(self.accepted_extractors))  # type: ignore
+                assert isinstance(
+                    extractor, tuple(self.accepted_extractors)  # type: ignore
+                )
             except AssertionError as e:
                 self.error(
                     f"{extractor.__class__.__name__}"
@@ -164,5 +166,7 @@ def _validate_file(self, file: str) -> None:
             assert file.lower().endswith(tuple(self.accepted_file_extensions))
         except AssertionError:
             self.error(
-                f'{self.__class__.__name__} accepts {self.accepted_file_extensions} but {file.split("/")[-1]} has extension {os.path.splitext(file)[1]}.'
+                f"{self.__class__.__name__} accepts "
+                f'{self.accepted_file_extensions} but {file.split("/")[-1]} '
+                f"has extension {os.path.splitext(file)[1]}."
             )

src/graphnet/data/readers/i3reader.py

@@ -1,6 +1,6 @@
 """Module containing different I3Reader."""
 
-from typing import List, Union, OrderedDict
+from typing import List, Union, OrderedDict, Optional
 
 from graphnet.utilities.imports import has_icecube_package
 from graphnet.data.extractors.icecube.utilities.i3_filters import (
@@ -27,7 +27,7 @@ class I3Reader(GraphNeTFileReader):
     def __init__(
         self,
         gcd_rescue: str,
-        i3_filters: Union[I3Filter, List[I3Filter]] = None,
+        i3_filters: Optional[Union[I3Filter, List[I3Filter]]] = None,
         icetray_verbose: int = 0,
     ):
         """Initialize `I3Reader`.
@@ -65,7 +65,9 @@ def __init__(
         # Base class constructor
         super().__init__(name=__name__, class_name=self.__class__.__name__)
 
-    def __call__(self, file_path: I3FileSet) -> List[OrderedDict]:  # type: ignore
+    def __call__(
+        self, file_path: I3FileSet
+    ) -> List[OrderedDict]:  # noqa: E501  # type: ignore
         """Extract data from single I3 file.
 
         Args:

src/graphnet/data/sqlite/deprecated_methods.py

@@ -3,13 +3,10 @@
 This code will be removed in GraphNeT 2.0.
 """
 
-from typing import List, Union, Type
+from typing import List, Union
 
 from graphnet.data.extractors.icecube import I3Extractor
-from graphnet.data.extractors.icecube.utilities.i3_filters import (
-    I3Filter,
-    NullSplitI3Filter,
-)
+from graphnet.data.extractors.icecube.utilities.i3_filters import I3Filter
 from graphnet.data import I3ToSQLiteConverter
 
 
@@ -28,10 +25,11 @@ def __init__(
         """Convert I3 files to Parquet.
 
         Args:
-            gcd_rescue: gcd_rescue: Path to a GCD file that will be used if no GCD file is
-                        found in subfolder. `I3Reader` will recursively search
-                        the input directory for I3-GCD file pairs. By IceCube
-                        convention, a folder containing i3 files will have an
+            gcd_rescue: gcd_rescue: Path to a GCD file that will be used if no
+                        GCD file is found in subfolder. `I3Reader` will
+                        recursively search the input directory for I3-GCD file
+                        pairs. By IceCube convention,
+                        a folder containing i3 files will have an
                         accompanying GCD file. However, in some cases, this
                         convention is broken. In cases where a folder contains
                         i3 files but no GCD file, the `gcd_rescue` is used

src/graphnet/data/utilities/parquet_to_sqlite.py

@@ -1,3 +1,3 @@
 """Utilities for converting files from Parquet to SQLite."""
 
-from graphnet.data.pre_configured import ParquetToSQLiteConverter
+from graphnet.data.pre_configured import ParquetToSQLiteConverter  # noqa: F401

src/graphnet/data/utilities/random.py

@@ -9,7 +9,8 @@ def pairwise_shuffle(
 ) -> Tuple[List[str], List[str]]:
     """Shuffle the I3 file list and the correponding gcd file list.
 
-    This is handy because it ensures a more even extraction load for each worker.
+    This is handy because it ensures a more even extraction load for each
+    worker.
 
     Args:
         files_list: List of I3 file paths.

src/graphnet/data/utilities/sqlite_utilities.py

@@ -1,7 +1,7 @@
 """SQLite-specific utility functions for use in `graphnet.data`."""
 
 import os.path
-from typing import List, Dict, Tuple
+from typing import List, Dict, Tuple, Union
 
 import pandas as pd
 import sqlalchemy
@@ -30,7 +30,9 @@ def query_database(database: str, query: str) -> pd.DataFrame:
         return pd.read_sql(query, conn)
 
 
-def get_primary_keys(database: str) -> Tuple[Dict[str, str], str]:
+def get_primary_keys(
+    database: str,
+) -> Tuple[Dict[str, Union[str, None]], Union[str, None]]:
     """Get name of primary key column for each table in database.
 
     Args:
@@ -50,7 +52,7 @@ def get_primary_keys(database: str) -> Tuple[Dict[str, str], str]:
 
         integer_primary_key = {}
         for table in table_names:
-            query = f"SELECT l.name FROM pragma_table_info('{table}') as l WHERE l.pk = 1;"
+            query = f"SELECT l.name FROM pragma_table_info('{table}') as l WHERE l.pk = 1;"  # noqa: E501
             first_primary_key = [
                 key[0] for key in conn.execute(query).fetchall()
             ]
@@ -78,7 +80,7 @@ def database_table_exists(database_path: str, table_name: str) -> bool:
     """Check whether `table_name` exists in database at `database_path`."""
     if not database_exists(database_path):
         return False
-    query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{table_name}';"
+    query = f"SELECT name FROM sqlite_master WHERE type='table' AND name='{table_name}';"  # noqa: E501
     with sqlite3.connect(database_path) as conn:
         result = pd.read_sql(query, conn)
     return len(result) == 1

src/graphnet/deployment/i3modules/__init__.py

@@ -5,5 +5,5 @@
 detector configurations.
 """
 
-from .deprecated_methods import *
+from .deprecated_methods import *  # noqa: F403
 from graphnet.deployment.icecube import I3InferenceModule, I3PulseCleanerModule

src/graphnet/models/coarsening.py

@@ -8,7 +8,6 @@
 from torch_geometric.data import Data, Batch
 from sklearn.cluster import DBSCAN
 
-# from torch_geometric.utils import unbatch_edge_index
 from graphnet.models.components.pool import (
     group_by,
     avg_pool,
@@ -28,7 +27,7 @@
 
 # NOTE: From [https://github.com/pyg-team/pytorch_geometric/pull/4903]
 # TODO:  Remove once bumping to torch_geometric>=2.1.0
-#       See [https://github.com/pyg-team/pytorch_geometric/blob/master/CHANGELOG.md]
+#       See [https://github.com/pyg-team/pytorch_geometric/blob/master/CHANGELOG.md] # noqa: E501
 
 
 def unbatch_edge_index(edge_index: Tensor, batch: Tensor) -> List[Tensor]:
@@ -170,15 +169,18 @@ def _reconstruct_batch(self, original: Data, pooled: Data) -> Data:
         return pooled
 
     def _add_slice_dict(self, original: Data, pooled: Data) -> Data:
-        # Copy original slice_dict and count nodes in each graph in pooled batch
+        # Copy original slice_dict and count nodes in each
+        # graph in pooled batch
         slice_dict = deepcopy(original._slice_dict)
         _, counts = torch.unique_consecutive(pooled.batch, return_counts=True)
-        # Reconstruct the entry in slice_dict for pulsemaps - only these are affected by pooling
+        # Reconstruct the entry in slice_dict for pulsemaps -
+        # only these are affected by pooling
         pulsemap_slice = [0]
         for i in range(len(counts)):
             pulsemap_slice.append(pulsemap_slice[i] + counts[i].item())
 
-        # Identifies pulsemap entries in slice_dict and set them to pulsemap_slice
+        # Identifies pulsemap entries in slice_dict and
+        # set them to pulsemap_slice
         for field in slice_dict.keys():
             if (original._num_graphs) == slice_dict[field][-1]:
                 pass  # not pulsemap, so skip

src/graphnet/models/components/layers.py

@@ -9,7 +9,6 @@
 from torch_geometric.typing import Adj, PairTensor
 from torch_geometric.nn.conv import MessagePassing
 from torch_geometric.nn.inits import reset
-from torch_geometric.data import Data
 import torch.nn as nn
 from torch.nn.functional import linear
 from torch.nn.modules import TransformerEncoder, TransformerEncoderLayer

src/graphnet/models/components/pool.py

@@ -9,11 +9,11 @@
 from torch_geometric.nn.pool.pool import pool_edge, pool_batch, pool_pos
 from torch_scatter import scatter, scatter_std
 
-from torch_geometric.nn.pool import (
-    avg_pool,
+from torch_geometric.nn.pool import (  # noqa:F401
     max_pool,
-    avg_pool_x,
     max_pool_x,
+    avg_pool_x,
+    avg_pool,
 )
 
 
@@ -90,8 +90,8 @@ def group_by(data: Union[Data, Batch], keys: List[str]) -> LongTensor:
     This grouping is done with in each event in case of batching. This allows
     for, e.g., assigning the same index to all pulses on the same PMT or DOM in
     the same event. This can be used for coarsening graphs, e.g., from pulse-
-    level to DOM-level by aggregating feature across each group returned by this
-    method.
+    level to DOM-level by aggregating feature across each group returned by
+    this method.
 
     Example:
       Given:
@@ -140,7 +140,7 @@ def sum_pool_x(
     batch: LongTensor,
     size: Optional[int] = None,
 ) -> Tensor:
-    r"""Sum-pool node features according to the clustering defined in `cluster`.
+    r"""Sum-pool node features according to the cluster defined in `cluster`.
 
     Args:
         cluster: Cluster vector :math:`\mathbf{c} \in \{ 0,
@@ -172,7 +172,7 @@ def std_pool_x(
     batch: LongTensor,
     size: Optional[int] = None,
 ) -> Tensor:
-    r"""Std-pool node features according to the clustering defined in `cluster`.
+    r"""Std-pool node features according to the cluster defined in `cluster`.
 
     Args:
         cluster: Cluster vector :math:`\mathbf{c} \in \{ 0,
@@ -201,7 +201,7 @@ def std_pool_x(
 def sum_pool(
     cluster: LongTensor, data: Data, transform: Optional[Callable] = None
 ) -> Data:
-    r"""Pool and coarsen graph according to the clustering defined in `cluster`.
+    r"""Pool and coarsen graph according to the cluster defined in `cluster`.
 
     All nodes within the same cluster will be represented as one node.
     Final node features are defined by the *sum* of features of all nodes
@@ -235,7 +235,7 @@ def sum_pool(
 def std_pool(
     cluster: LongTensor, data: Data, transform: Optional[Callable] = None
 ) -> Data:
-    r"""Pool and coarsen graph according to the clustering defined in `cluster`.
+    r"""Pool and coarsen graph according to the cluster defined in `cluster`.
 
     All nodes within the same cluster will be represented as one node.
     Final node features are defined by the *std* of features of all nodes

src/graphnet/models/detector/detector.py

@@ -66,7 +66,9 @@ def _standardize(
     ) -> Data:
         for idx, feature in enumerate(input_feature_names):
             try:
-                input_features[:, idx] = self.feature_map()[feature](  # type: ignore
+                input_features[:, idx] = self.feature_map()[
+                    feature
+                ](  # noqa: E501 # type: ignore
                     input_features[:, idx]
                 )
             except KeyError as e: