Apply ruff linting

pyproject.toml

@@ -43,7 +43,7 @@ build-backend = "poetry.core.masonry.api"
 disable = [
     "missing-module-docstring",
 ]
-good-names = ["i", "j", "k", "x", "y", "z", "ds", "dt", "ax"]
+good-names = ["i", "j", "k", "x", "y", "z", "u", "v", "w", "ds", "dt", "ax"]
 
 [tool.pylint.design]
 max-args = 10 # maximum number of arguments for function / method
@@ -90,7 +90,16 @@ max-args = 10
 "tests/*.py" = [
     "D",      # pydocstyle
     "PT009",  # pytest-unittest-assertion
+    "PT027",  # pytest-unittest-raises-assertion
     "INP001", # implicit-namespace-package
+    "ANN201", # missing-return-type-undocumented-public-function
+    "S311",   # suspicious-non-cryptographic-random-usage
+]
+
+[tool.coverage.report]
+exclude_lines = [
+    "pragma: no cover",
+    "if TYPE_CHECKING:",
 ]
 
 [tool.tox]

src/arcade_collection/input/generate_setup_file.py

@@ -99,7 +99,7 @@ def make_setup_file(
         Contents of ARCADE setup file.
     """
 
-    root = ET.fromstring("<set></set>")
+    root = ET.fromstring("<set></set>")  # noqa: S314
     series = ET.SubElement(
         root,
         "series",

src/arcade_collection/output/convert_model_units.py

@@ -1,17 +1,20 @@
+from __future__ import annotations
+
 import re
-from typing import Optional, Union
+from typing import TYPE_CHECKING
 
-import pandas as pd
+if TYPE_CHECKING:
+    import pandas as pd
 
 
 def convert_model_units(
     data: pd.DataFrame,
-    ds: Optional[float],
-    dt: Optional[float],
-    regions: Optional[Union[list[str], str]] = None,
+    ds: float | None,
+    dt: float | None,
+    regions: list[str] | str | None = None,
 ) -> None:
     """
-    Converts data from simulation units to true units.
+    Convert data from simulation units to true units.
 
     Simulations use spatial unit of voxels and temporal unit of ticks. Spatial
     resolution (microns/voxel) and temporal resolution (hours/tick) are used to
@@ -87,7 +90,7 @@ def convert_model_units(
 
 def convert_temporal_units(data: pd.DataFrame, dt: float) -> None:
     """
-    Converts temporal data from simulation units to true units.
+    Convert temporal data from simulation units to true units.
 
     Simulations use temporal unit of ticks. Temporal resolution (hours/tick) is
     used to convert data to true units.
@@ -112,9 +115,9 @@ def convert_temporal_units(data: pd.DataFrame, dt: float) -> None:
         data["time"] = round(dt * data["TICK"], 2)
 
 
-def convert_spatial_units(data: pd.DataFrame, ds: float, region: Optional[str] = None) -> None:
+def convert_spatial_units(data: pd.DataFrame, ds: float, region: str | None = None) -> None:
     """
-    Converts spatial data from simulation units to true units.
+    Convert spatial data from simulation units to true units.
 
     Simulations use spatial unit of voxels. Spatial resolution (microns/voxel)
     is used to convert data to true units.
@@ -183,7 +186,7 @@ def convert_spatial_units(data: pd.DataFrame, ds: float, region: Optional[str] =
 
 def estimate_temporal_resolution(key: str) -> float:
     """
-    Estimates temporal resolution based on condition key.
+    Estimate temporal resolution based on condition key.
 
     If the key contains ``DT##``, where ``##`` denotes the temporal resolution
     in minutes/tick, temporal resolution is estimated from ``##``. Otherwise,
@@ -206,7 +209,7 @@ def estimate_temporal_resolution(key: str) -> float:
 
 def estimate_spatial_resolution(key: str) -> float:
     """
-    Estimates spatial resolution based on condition key.
+    Estimate spatial resolution based on condition key.
 
     If the key contains ``DS##``, where ``##`` denotes the spatial resolution
     in micron/voxel, spatial resolution is estimated from ``##``. Otherwise,

src/arcade_collection/output/extract_tick_json.py

@@ -1,16 +1,20 @@
+from __future__ import annotations
+
 import json
-import tarfile
-from typing import Optional, Union
+from typing import TYPE_CHECKING
 
 import numpy as np
 
+if TYPE_CHECKING:
+    import tarfile
+
 
 def extract_tick_json(
     tar: tarfile.TarFile,
     key: str,
-    tick: Union[int, float],
-    extension: Optional[str] = None,
-    field: Optional[str] = None,
+    tick: float,
+    extension: str | None = None,
+    field: str | None = None,
 ) -> list:
     """
     Extract json for specified tick from tar archive.
@@ -60,7 +64,10 @@ def extract_tick_json(
     else:
         member = tar.extractfile(f"{key}{formatted_tick}.{extension}.json")
 
-    assert member is not None
+    if member is None:
+        message = "File does not exist in archive."
+        raise RuntimeError(message)
+
     tick_json = json.loads(member.read().decode("utf-8"))
 
     if isinstance(tick, float):

src/arcade_collection/output/get_location_voxels.py

@@ -1,7 +1,7 @@
-from typing import Optional
+from __future__ import annotations
 
 
-def get_location_voxels(location: dict, region: Optional[str] = None) -> list[tuple[int, int, int]]:
+def get_location_voxels(location: dict, region: str | None = None) -> list[tuple[int, int, int]]:
     """
     Get list of voxels from location for specified region.
 
@@ -22,11 +22,9 @@ def get_location_voxels(location: dict, region: Optional[str] = None) -> list[tu
         List of x, y, z voxels.
     """
 
-    voxels = [
+    return [
         (x, y, z)
         for loc in location["location"]
         for x, y, z in loc["voxels"]
         if not region or loc["region"] == region
     ]
-
-    return voxels

src/arcade_collection/output/parse_growth_file.py

@@ -61,9 +61,7 @@ def parse_growth_file(tar: tarfile.TarFile) -> pd.DataFrame:
             ]
         )
 
-    timepoints_df = pd.DataFrame(all_timepoints, columns=GROWTH_COLUMNS)
-
-    return timepoints_df
+    return pd.DataFrame(all_timepoints, columns=GROWTH_COLUMNS)
 
 
 def parse_growth_timepoint(data: dict, seed: int) -> list:
@@ -110,8 +108,10 @@ def parse_growth_timepoint(data: dict, seed: int) -> list:
 
     Parameters
     ----------
-    timepoint
+    data
         Original simulation data.
+    seed
+        Random seed.
 
     Returns
     -------
@@ -123,23 +123,14 @@ def parse_growth_timepoint(data: dict, seed: int) -> list:
     time = data["time"]
 
     for location, cells in data["cells"]:
-        u, v, w, z = location
-
         for cell in cells:
             _, population, state, position, volume, cycles = cell
-
-            if len(cycles) == 0:
-                cycle = None
-            else:
-                cycle = np.mean(cycles)
+            cycle = None if len(cycles) == 0 else np.mean(cycles)
 
             data_list = [
                 time,
                 seed,
-                u,
-                v,
-                w,
-                z,
+                *location,
                 position,
                 population,
                 CELL_STATES[state],

src/arcade_collection/output/parse_locations_file.py

@@ -60,7 +60,7 @@ def parse_locations_file(tar: tarfile.TarFile, regions: list[str]) -> pd.DataFra
     return pd.DataFrame(all_locations, columns=columns)
 
 
-def parse_location_tick(tick: int, locations: dict, regions: list[str]) -> list:
+def parse_location_tick(tick: int, location: dict, regions: list[str]) -> list:
     """
     Parse location data for a single simulation tick.
 
@@ -105,7 +105,7 @@ def parse_location_tick(tick: int, locations: dict, regions: list[str]) -> list:
     ----------
     tick
         Simulation tick.
-    loc
+    location
         Original location data.
     regions
         List of regions.
@@ -116,31 +116,31 @@ def parse_location_tick(tick: int, locations: dict, regions: list[str]) -> list:
         Parsed location data.
     """
 
-    if "center" in locations:
-        voxels = np.array([voxel for region in locations["location"] for voxel in region["voxels"]])
+    if "center" in location:
+        voxels = np.array([voxel for region in location["location"] for voxel in region["voxels"]])
         mins = np.min(voxels, axis=0)
         maxs = np.max(voxels, axis=0)
-        parsed = [locations["id"], tick, *locations["center"], *mins, *maxs]
+        parsed = [location["id"], tick, *location["center"], *mins, *maxs]
     else:
-        parsed = [locations["id"], tick, -1, -1, -1, -1, -1, -1, -1, -1, -1]
+        parsed = [location["id"], tick, -1, -1, -1, -1, -1, -1, -1, -1, -1]
 
     for reg in regions:
         region_voxels = np.array(
             [
                 voxel
-                for region in locations["location"]
+                for region in location["location"]
                 for voxel in region["voxels"]
                 if region["region"] == reg
             ]
         )
 
         if len(region_voxels) == 0:
-            parsed = parsed + [-1, -1, -1, -1, -1, -1, -1, -1, -1]
+            parsed = [*parsed, -1, -1, -1, -1, -1, -1, -1, -1, -1]
             continue
 
         center = [int(value + 0.5) for value in region_voxels.mean(axis=0)]
         mins = np.min(region_voxels, axis=0)
         maxs = np.max(region_voxels, axis=0)
-        parsed = parsed + [*center, *mins, *maxs]
+        parsed = [*parsed, *center, *mins, *maxs]
 
     return parsed

tests/arcade_collection/input/test_convert_to_cells_file.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import unittest
 
 import numpy as np
@@ -16,7 +18,7 @@
 DEFAULT_REGION_NAME = "DEFAULT"
 
 
-def make_samples(cell_id, volume, height, region):
+def make_samples(cell_id: int, volume: int, height: int, region: str | None):
     return pd.DataFrame(
         {
             "id": [cell_id] * volume,