Clear all CI checks on all platforms

.github/workflows/build_env_run_tests.yml

@@ -65,7 +65,7 @@ jobs:
       # Only run codebase checks and tests for ubuntu.
       - name: ruff
         if: matrix.os == 'ubuntu-latest'
-        run: python -m ruff check --config ./pyproject.toml .
+        run: python -m ruff check .
       - name: pyright
         if: matrix.os == 'ubuntu-latest'
         run: python -m pyright --level error --project ./pyproject.toml . 

.pre-commit-config.yaml

@@ -1,16 +1,12 @@
 # For info on running pre-commit manually, see `pre-commit run --help`
-
-default_language_version:
-  python: python3.11
-
 files: "^(test|aeon)\/.*$"
 repos:
   - repo: meta
     hooks:
       - id: identity
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.6.0
     hooks:
       - id: check-json
       - id: check-yaml
@@ -25,20 +21,17 @@ repos:
       - id: trailing-whitespace
         args: [--markdown-linebreak-ext=md]
 
-  - repo: https://github.com/psf/black
-    rev: 23.7.0
-    hooks:
-      - id: black
-        args: [--check, --config, ./pyproject.toml]
-
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.286
+    rev: v0.6.4
     hooks:
+      # Run the linter with the `--fix` flag.
       - id: ruff
-        args: [--config, ./pyproject.toml]
+        args: [ --fix ]
+      # Run the formatter.
+      - id: ruff-format
 
   - repo: https://github.com/RobertCraigie/pyright-python
-    rev: v1.1.324
+    rev: v1.1.380
     hooks:
       - id: pyright
         args: [--level, error, --project, ./pyproject.toml]

aeon/README.md

@@ -1 +1 @@
-#
+#

aeon/__init__.py

@@ -9,5 +9,5 @@
 finally:
     del version, PackageNotFoundError
 
-# Set functions avaialable directly under the 'aeon' top-level namespace
-from aeon.io.api import load
+# Set functions available directly under the 'aeon' top-level namespace
+from aeon.io.api import load as load  # noqa: PLC0414

aeon/analysis/__init__.py

@@ -1 +0,0 @@
-#

aeon/analysis/block_plotting.py

@@ -1,17 +1,7 @@
-import os
-import pathlib
 from colorsys import hls_to_rgb, rgb_to_hls
-from contextlib import contextmanager
-from pathlib import Path
 
-import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
 import plotly
-import plotly.express as px
-import plotly.graph_objs as go
-import seaborn as sns
-from numpy.lib.stride_tricks import as_strided
 
 """Standardize subject colors, patch colors, and markers."""
 
@@ -35,27 +25,21 @@
     "star",
 ]
 patch_markers_symbols = ["●", "⧓", "■", "⧗", "♦", "✖", "×", "▲", "★"]
-patch_markers_dict = {
-    marker: symbol for marker, symbol in zip(patch_markers, patch_markers_symbols)
-}
+patch_markers_dict = dict(zip(patch_markers, patch_markers_symbols, strict=False))
 patch_markers_linestyles = ["solid", "dash", "dot", "dashdot", "longdashdot"]
 
 
 def gen_hex_grad(hex_col, vals, min_l=0.3):
     """Generates an array of hex color values based on a gradient defined by unit-normalized values."""
     # Convert hex to rgb to hls
-    h, l, s = rgb_to_hls(
-        *[int(hex_col.lstrip("#")[i: i + 2], 16) / 255 for i in (0, 2, 4)]
-    )
+    h, l, s = rgb_to_hls(*[int(hex_col.lstrip("#")[i : i + 2], 16) / 255 for i in (0, 2, 4)])  # noqa: E741
     grad = np.empty(shape=(len(vals),), dtype="<U10")  # init grad
     for i, val in enumerate(vals):
-        cur_l = (l * val) + (
-            min_l * (1 - val)
-        )  # get cur lightness relative to `hex_col`
+        cur_l = (l * val) + (min_l * (1 - val))  # get cur lightness relative to `hex_col`
         cur_l = max(min(cur_l, l), min_l)  # set min, max bounds
         cur_rgb_col = hls_to_rgb(h, cur_l, s)  # convert to rgb
-        cur_hex_col = "#%02x%02x%02x" % tuple(
-            int(c * 255) for c in cur_rgb_col
+        cur_hex_col = "#{:02x}{:02x}{:02x}".format(
+            *tuple(int(c * 255) for c in cur_rgb_col)
         )  # convert to hex
         grad[i] = cur_hex_col
 

aeon/analysis/movies.py

@@ -8,8 +8,7 @@
 
 
 def gridframes(frames, width, height, shape=None):
-    """Arranges a set of frames into a grid layout with the specified
-    pixel dimensions and shape.
+    """Arranges a set of frames into a grid layout with the specified pixel dimensions and shape.
 
     :param list frames: A list of frames to include in the grid layout.
     :param int width: The width of the output grid image, in pixels.
@@ -65,7 +64,7 @@ def groupframes(frames, n, fun):
             i = i + 1
 
 
-def triggerclip(data, events, before=pd.Timedelta(0), after=pd.Timedelta(0)):
+def triggerclip(data, events, before=None, after=None):
     """Split video data around the specified sequence of event timestamps.
 
     :param DataFrame data:
@@ -76,10 +75,16 @@ def triggerclip(data, events, before=pd.Timedelta(0), after=pd.Timedelta(0)):
     :return:
     A pandas DataFrame containing the frames, clip and sequence numbers for each event timestamp.
     """
-    if before is not pd.Timedelta:
+    if before is None:
+        before = pd.Timedelta(0)
+    elif before is not pd.Timedelta:
         before = pd.Timedelta(before)
-    if after is not pd.Timedelta:
+
+    if after is None:
+        after = pd.Timedelta(0)
+    elif after is not pd.Timedelta:
         after = pd.Timedelta(after)
+
     if events is not pd.Index:
         events = events.index
 
@@ -107,8 +112,7 @@ def collatemovie(clipdata, fun):
 
 
 def gridmovie(clipdata, width, height, shape=None):
-    """Collates a set of video clips into a grid movie with the specified pixel dimensions
-    and grid layout.
+    """Collates a set of video clips into a grid movie with the specified pixel dimensions and grid layout.
 
     :param DataFrame clipdata:
     A pandas DataFrame where each row specifies video path, frame number, clip and sequence number.

aeon/analysis/plotting.py

@@ -6,7 +6,7 @@
 from matplotlib import colors
 from matplotlib.collections import LineCollection
 
-from aeon.analysis.utils import *
+from aeon.analysis.utils import rate, sessiontime
 
 
 def heatmap(position, frequency, ax=None, **kwargs):
@@ -60,16 +60,17 @@ def rateplot(
     ax=None,
     **kwargs,
 ):
-    """Plot the continuous event rate and raster of a discrete event sequence, given the specified
-    window size and sampling frequency.
+    """Plot the continuous event rate and raster of a discrete event sequence.
+
+    The window size and sampling frequency can be specified.
 
     :param Series events: The discrete sequence of events.
     :param offset window: The time period of each window used to compute the rate.
     :param DateOffset, Timedelta or str frequency: The sampling frequency for the continuous rate.
     :param number, optional weight: A weight used to scale the continuous rate of each window.
     :param datetime, optional start: The left bound of the time range for the continuous rate.
     :param datetime, optional end: The right bound of the time range for the continuous rate.
-    :param datetime, optional smooth: The size of the smoothing kernel applied to the continuous rate output.
+    :param datetime, optional smooth: The size of the smoothing kernel applied to the rate output.
     :param DateOffset, Timedelta or str, optional smooth:
     The size of the smoothing kernel applied to the continuous rate output.
     :param bool, optional center: Specifies whether to center the convolution kernels.
@@ -108,8 +109,8 @@ def colorline(
     x,
     y,
     z=None,
-    cmap=plt.get_cmap("copper"),
-    norm=plt.Normalize(0.0, 1.0),
+    cmap=None,
+    norm=None,
     ax=None,
     **kwargs,
 ):
@@ -128,6 +129,10 @@ def colorline(
         ax = plt.gca()
     if z is None:
         z = np.linspace(0.0, 1.0, len(x))
+    if cmap is None:
+        cmap = plt.get_cmap("copper")
+    if norm is None:
+        norm = plt.Normalize(0.0, 1.0)
     z = np.asarray(z)
     points = np.array([x, y]).T.reshape(-1, 1, 2)
     segments = np.concatenate([points[:-1], points[1:]], axis=1)

aeon/analysis/readme.md

@@ -1 +1 @@
-#
+#

aeon/analysis/utils.py

@@ -3,8 +3,9 @@
 
 
 def distancetravelled(angle, radius=4.0):
-    """Calculates the total distance travelled on the wheel, by taking into account
-    its radius and the total number of turns in both directions across time.
+    """Calculates the total distance travelled on the wheel.
+
+    Takes into account the wheel radius and the total number of turns in both directions across time.
 
     :param Series angle: A series of magnetic encoder measurements.
     :param float radius: The radius of the wheel, in metric units.
@@ -22,10 +23,11 @@ def distancetravelled(angle, radius=4.0):
 
 
 def visits(data, onset="Enter", offset="Exit"):
-    """Computes duration, onset and offset times from paired events. Allows for missing data
-    by trying to match event onset times with subsequent offset times. If the match fails,
-    event offset metadata is filled with NaN. Any additional metadata columns in the data
-    frame will be paired and included in the output.
+    """Computes duration, onset and offset times from paired events.
+
+    Allows for missing data by trying to match event onset times with subsequent offset times.
+    If the match fails, event offset metadata is filled with NaN. Any additional metadata columns
+    in the data frame will be paired and included in the output.
 
     :param DataFrame data: A pandas data frame containing visit onset and offset events.
     :param str, optional onset: The label used to identify event onsets.
@@ -69,16 +71,17 @@ def visits(data, onset="Enter", offset="Exit"):
 
 
 def rate(events, window, frequency, weight=1, start=None, end=None, smooth=None, center=False):
-    """Computes the continuous event rate from a discrete event sequence, given the specified
-    window size and sampling frequency.
+    """Computes the continuous event rate from a discrete event sequence.
+
+    The window size and sampling frequency can be specified.
 
     :param Series events: The discrete sequence of events.
     :param offset window: The time period of each window used to compute the rate.
     :param DateOffset, Timedelta or str frequency: The sampling frequency for the continuous rate.
     :param number, optional weight: A weight used to scale the continuous rate of each window.
     :param datetime, optional start: The left bound of the time range for the continuous rate.
     :param datetime, optional end: The right bound of the time range for the continuous rate.
-    :param datetime, optional smooth: The size of the smoothing kernel applied to the continuous rate output.
+    :param datetime, optional smooth: The size of the smoothing kernel applied to the rate output.
     :param DateOffset, Timedelta or str, optional smooth:
     The size of the smoothing kernel applied to the continuous rate output.
     :param bool, optional center: Specifies whether to center the convolution kernels.
@@ -98,6 +101,7 @@ def rate(events, window, frequency, weight=1, start=None, end=None, smooth=None,
 def get_events_rates(
     events, window_len_sec, frequency, unit_len_sec=60, start=None, end=None, smooth=None, center=False
 ):
+    """Computes the event rate from a sequence of events over a specified window."""
     # events is an array with the time (in seconds) of event occurence
     # window_len_sec is the size of the window over which the event rate is estimated
     # unit_len_sec is the length of one sample point

aeon/dj_pipeline/__init__.py

@@ -31,9 +31,10 @@ def dict_to_uuid(key) -> uuid.UUID:
 
 
 def fetch_stream(query, drop_pk=True):
-    """
+    """Fetches data from a Stream table based on a query and returns it as a DataFrame.
+
     Provided a query containing data from a Stream table,
-     fetch and aggregate the data into one DataFrame indexed by "time"
+    fetch and aggregate the data into one DataFrame indexed by "time"
     """
     df = (query & "sample_count > 0").fetch(format="frame").reset_index()
     cols2explode = [

aeon/dj_pipeline/acquisition.py

@@ -1,18 +1,18 @@
 import datetime
+import json
 import pathlib
 import re
+
 import datajoint as dj
 import numpy as np
 import pandas as pd
-import json
 
-from aeon.io import api as io_api
-from aeon.schema import schemas as aeon_schemas
-from aeon.io import reader as io_reader
 from aeon.analysis import utils as analysis_utils
-
 from aeon.dj_pipeline import get_schema_name, lab, subject
 from aeon.dj_pipeline.utils import paths
+from aeon.io import api as io_api
+from aeon.io import reader as io_reader
+from aeon.schema import schemas as aeon_schemas
 
 logger = dj.logger
 schema = dj.schema(get_schema_name("acquisition"))
@@ -181,7 +181,7 @@ class Epoch(dj.Manual):
 
     @classmethod
     def ingest_epochs(cls, experiment_name):
-        """Ingest epochs for the specified "experiment_name" """
+        """Ingest epochs for the specified ``experiment_name``."""
         device_name = _ref_device_mapping.get(experiment_name, "CameraTop")
 
         all_chunks, raw_data_dirs = _get_all_chunks(experiment_name, device_name)
@@ -475,7 +475,7 @@ class MessageLog(dj.Part):
         -> master
         ---
         sample_count: int      # number of data points acquired from this stream for a given chunk
-        timestamps: longblob   # (datetime) 
+        timestamps: longblob   # (datetime)
         priority: longblob
         type: longblob
         message: longblob
@@ -604,9 +604,7 @@ def _match_experiment_directory(experiment_name, path, directories):
 
 
 def create_chunk_restriction(experiment_name, start_time, end_time):
-    """
-    Create a time restriction string for the chunks between the specified "start" and "end" times
-    """
+    """Create a time restriction string for the chunks between the specified "start" and "end" times."""
     start_restriction = f'"{start_time}" BETWEEN chunk_start AND chunk_end'
     end_restriction = f'"{end_time}" BETWEEN chunk_start AND chunk_end'
     start_query = Chunk & {"experiment_name": experiment_name} & start_restriction