snake_case EventAccumulator methods

.github/workflows/paper.yml

@@ -13,7 +13,7 @@ jobs:
 
     steps:
       - name: Checkout
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Build draft PDF
         uses: openjournals/openjournals-draft-action@master

.pre-commit-config.yaml

@@ -7,14 +7,14 @@ default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.2
+    rev: v0.1.9
     hooks:
       - id: ruff
         args: [--fix]
       - id: ruff-format
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.6.1
+    rev: v1.8.0
     hooks:
       - id: mypy
 
@@ -36,6 +36,7 @@ repos:
       - id: codespell
         stages: [commit, commit-msg]
         exclude_types: [jupyter, bib]
+        args: [--check-filenames]
 
   - repo: https://github.com/kynan/nbstripout
     rev: 0.6.1

examples/wandb_integration.ipynb

@@ -262,7 +262,7 @@
     "class ConvNet(nn.Module):\n",
     "    \"\"\"Just your average CNN.\"\"\"\n",
     "\n",
-    "    def __init__(self, kernels, classes=10):\n",
+    "    def __init__(self, kernels: list, classes: int = 10) -> None:\n",
     "        super().__init__()\n",
     "\n",
     "        self.layer1 = nn.Sequential(\n",
@@ -277,7 +277,7 @@
     "        )\n",
     "        self.fc = nn.Linear(7 * 7 * kernels[-1], classes)\n",
     "\n",
-    "    def forward(self, x):\n",
+    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
     "        out = self.layer1(x)\n",
     "        out = self.layer2(out)\n",
     "        out = out.reshape(out.size(0), -1)\n",
@@ -343,7 +343,7 @@
     "    optimizer: torch.optim.Optimizer,\n",
     "    epochs: int,\n",
     "    log_freq: int = 10,\n",
-    "):\n",
+    ") -> None:\n",
     "    # Run training and track with wandb\n",
     "    sample_count = batch_count = 0  # number of examples seen\n",
     "\n",

pyproject.toml

@@ -65,45 +65,29 @@ warn_unused_ignores = true
 [tool.ruff]
 target-version = "py38"
 include = ["**/pyproject.toml", "*.ipynb", "*.py", "*.pyi"]
-select = [
-  "B",    # flake8-bugbear
-  "C4",   # flake8-comprehensions
-  "D",    # pydocstyle
-  "E",    # pycodestyle error
-  "EXE",  # flake8-executable
-  "F",    # pyflakes
-  "FA",   # flake8-future-annotations
-  "FLY",  # flynt
-  "I",    # isort
-  "ICN",  # flake8-import-conventions
-  "ISC",  # flake8-implicit-str-concat
-  "PD",   # pandas-vet
-  "PERF", # perflint
-  "PIE",  # flake8-pie
-  "PL",   # pylint
-  "PT",   # flake8-pytest-style
-  "PYI",  # flakes8-pyi
-  "Q",    # flake8-quotes
-  "RET",  # flake8-return
-  "RSE",  # flake8-raise
-  "RUF",  # Ruff-specific rules
-  "SIM",  # flake8-simplify
-  "SLOT", # flake8-slots
-  "TCH",  # flake8-type-checking
-  "TID",  # tidy imports
-  "UP",   # pyupgrade
-  "W",    # pycodestyle warning
-  "YTT",  # flake8-2020
-]
+select = ["ALL"]
 ignore = [
-  "D100",  # Missing docstring in public module
-  "D205",  # 1 blank line required between summary line and description
-  "PLR",   # pylint refactor
-  "PT006", # pytest-parametrize-names-wrong-type
+  "ANN101",
+  "ANN401",
+  "ARG001",
+  "C901",
+  "COM812",
+  "D100",   # Missing docstring in public module
+  "D205",   # 1 blank line required between summary line and description
+  "DTZ005",
+  "EM101",
+  "EM102",
+  "FBT001",
+  "FBT002",
+  "PLR",    # pylint refactor
+  "PT006",  # pytest-parametrize-names-wrong-type
+  "PTH",
+  "T201",
+  "TRY003",
 ]
 pydocstyle.convention = "google"
 
 [tool.ruff.per-file-ignores]
-"tests/*" = ["D103", "D104"]
+"tests/*" = ["D103", "D104", "INP001", "S101"]
 "__init__.py" = ["F401"]
 "examples/*" = ["D102", "D103", "D107", "E402", "FA102"]

tensorboard_reducer/event_loader.py

@@ -58,15 +58,15 @@ def __init__(self, path: str) -> None:
             path (str): The path to the event file.
         """
         self._first_event_timestamp = None
-        self.scalars = reservoir.Reservoir(size=10000)
+        self._scalars = reservoir.Reservoir(size=10000)
 
         self._generator_mutex = threading.Lock()
         self.path = path
-        self._generator = _GeneratorFromPath(path)
+        self._generator = _generator_from_path(path)
 
         self.file_version: float | None = None
 
-    def Reload(self) -> EventAccumulator:
+    def reload(self) -> EventAccumulator:
         """Synchronously load all events added since last calling Reload. If Reload was
         never called, loads all events in the file.
 
@@ -75,24 +75,24 @@ def Reload(self) -> EventAccumulator:
         """
         with self._generator_mutex:
             for event in self._generator.Load():
-                self._ProcessEvent(event)
+                self._process_event(event)
         return self
 
-    def _ProcessEvent(self, event: Event) -> None:
+    def _process_event(self, event: Event) -> None:
         """Called whenever an event is loaded."""
         if self._first_event_timestamp is None:
             self._first_event_timestamp = event.wall_time
 
         if event.HasField("file_version"):
-            new_file_version = _ParseFileVersion(event.file_version)
+            new_file_version = _parse_file_version(event.file_version)
             self.file_version = new_file_version
 
         if event.HasField("summary"):
             for value in event.summary.value:
                 if value.HasField("simple_value"):
                     datum = value.simple_value
                     tag = value.tag
-                    self._ProcessScalar(tag, event.wall_time, event.step, datum)
+                    self._process_scalar(tag, event.wall_time, event.step, datum)
 
     @property
     def scalar_tags(self) -> list[str]:
@@ -101,9 +101,9 @@ def scalar_tags(self) -> list[str]:
         Returns:
             list[str]: All scalar tags
         """
-        return self.scalars.Keys()
+        return self._scalars.Keys()
 
-    def Scalars(self, tag: str) -> tuple[ScalarEvent, ...]:
+    def scalars(self, tag: str) -> tuple[ScalarEvent, ...]:
         """Given a summary tag, return all associated ScalarEvents.
 
         Args:
@@ -115,17 +115,17 @@ def Scalars(self, tag: str) -> tuple[ScalarEvent, ...]:
         Returns:
             tuple[ScalarEvent, ...]: An array of ScalarEvents.
         """
-        return self.scalars.Items(tag)
+        return self._scalars.Items(tag)
 
-    def _ProcessScalar(
+    def _process_scalar(
         self, tag: str, wall_time: float, step: int, scalar: float
     ) -> None:
         """Process a simple value by adding it to accumulated state."""
         sv = ScalarEvent(wall_time=wall_time, step=step, value=scalar)
-        self.scalars.AddItem(tag, sv)
+        self._scalars.AddItem(tag, sv)
 
 
-def _GeneratorFromPath(path: str) -> directory_watcher.DirectoryWatcher:
+def _generator_from_path(path: str) -> directory_watcher.DirectoryWatcher:
     """Create an event generator for file or directory at given path string."""
     return directory_watcher.DirectoryWatcher(
         path,
@@ -134,7 +134,7 @@ def _GeneratorFromPath(path: str) -> directory_watcher.DirectoryWatcher:
     )
 
 
-def _ParseFileVersion(file_version: str) -> float:
+def _parse_file_version(file_version: str) -> float:
     """Convert the string file_version in event.proto into a float.
 
     Args:

tensorboard_reducer/load.py

@@ -63,7 +63,7 @@ def load_tb_events(
     # EventAccumulator that only loads scalars and ignores histograms, images and other
     # time-consuming data.
     accumulators = [
-        EventAccumulator(dirname).Reload()
+        EventAccumulator(dirname).reload()
         for dirname in tqdm(input_dirs, disable=not verbose, desc="Loading runs")
     ]
 
@@ -102,7 +102,7 @@ def load_tb_events(
 
         for tag in accumulator.scalar_tags:
             # accumulator.Scalars() returns columns 'step', 'wall_time', 'value'
-            df_scalar = pd.DataFrame(accumulator.Scalars(tag)).set_index("step")
+            df_scalar = pd.DataFrame(accumulator.scalars(tag)).set_index("step")
             df_scalar = df_scalar.drop(columns="wall_time")
 
             if handle_dup_steps is None and not df_scalar.index.is_unique:
@@ -143,10 +143,11 @@ def load_tb_events(
                     "shortest run (same behavior as zip())."
                 )
 
-    assert len(load_dict) > 0, (
-        f"Got {len(input_dirs)} input directories but no TensorBoard event files "
-        "found inside them."
-    )
+    if len(load_dict) == 0:
+        raise FileNotFoundError(
+            f"Got {len(input_dirs)} input directories but no TensorBoard event files "
+            "found inside them."
+        )
 
     out_dict: dict[str, pd.DataFrame] = {}
 

tensorboard_reducer/write.py

@@ -31,7 +31,7 @@ def _rm_rf_or_raise(path: str, overwrite: bool) -> None:
         is_data_file = any(ext in path.lower() for ext in _known_extensions)
 
         if overwrite and (is_data_file or is_tb_dir):
-            os.system(f"rm -rf {path}")
+            os.system(f"rm -rf {path}")  # noqa: S605
         elif overwrite:
             ValueError(
                 f"Received the overwrite flag but the content of '{path}' does not "

tests/conftest.py

@@ -22,6 +22,6 @@ def events_dict() -> dict[str, pd.DataFrame]:
 
 @pytest.fixture(scope="session")
 def reduced_events(
-    events_dict: dict[str, pd.DataFrame]
+    events_dict: dict[str, pd.DataFrame],
 ) -> dict[str, dict[str, pd.DataFrame]]:
     return tbr.reduce_events(events_dict, REDUCE_OPS)

tests/test_write.py

@@ -99,7 +99,7 @@ def test_write_data_file(
 
 
 def test_write_data_file_with_bad_ext(
-    reduced_events: dict[str, dict[str, pd.DataFrame]]
+    reduced_events: dict[str, dict[str, pd.DataFrame]],
 ) -> None:
     with pytest.raises(ValueError, match="has unknown extension, should be one of"):
         tbr.write_data_file(reduced_events, "foo.bad_ext")