Blackened and ruffed

.git-blame-ignore-revs

@@ -1,2 +1,3 @@
 # Normalize line endings
-89ea7df05f77475bcfd874f3bccab878d653af6a
+89ea7df05f77475bcfd874f3bccab878d653af6a
+947eb6c1f701050a03d319feee168260f2a485a0

.pre-commit-config.yaml

@@ -3,7 +3,7 @@
 default_language_version:
   python: python3.11
 
-files: "^(docker|aeon\/dj_pipeline)\/.*$"
+files: "^(test|aeon(?!\/dj_pipeline\/).*)$"
 repos:
   - repo: meta
     hooks:
@@ -30,7 +30,7 @@ repos:
     hooks:
       - id: black
         args: [--check, --config, ./pyproject.toml]
-  
+
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.0.286
     hooks:

aeon/analysis/movies.py

@@ -1,12 +1,14 @@
-import cv2
 import math
+
+import cv2
 import numpy as np
 import pandas as pd
-import aeon.io.video as video
+
+from aeon.io import video
+
 
 def gridframes(frames, width, height, shape=None):
-    '''
-    Arranges a set of frames into a grid layout with the specified
+    """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.
@@ -16,15 +18,15 @@ def gridframes(frames, width, height, shape=None):
     Either the number of frames to include, or the number of rows and columns
     in the output grid image layout.
     :return: A new image containing the arrangement of the frames in a grid.
-    '''
+    """
     if shape is None:
         shape = len(frames)
-    if type(shape) not in [list,tuple]:
+    if type(shape) not in [list, tuple]:
         shape = math.ceil(math.sqrt(shape))
         shape = (shape, shape)
 
     dsize = (height, width, 3)
-    cellsize = (height // shape[0], width // shape[1],3)
+    cellsize = (height // shape[0], width // shape[1], 3)
     grid = np.zeros(dsize, dtype=np.uint8)
     for i in range(shape[0]):
         for j in range(shape[1]):
@@ -39,19 +41,20 @@ def gridframes(frames, width, height, shape=None):
             grid[i0:i1, j0:j1] = cv2.resize(frame, (cellsize[1], cellsize[0]))
     return grid
 
+
 def averageframes(frames):
     """Returns the average of the specified collection of frames."""
     return cv2.convertScaleAbs(sum(np.multiply(1 / len(frames), frames)))
 
+
 def groupframes(frames, n, fun):
-    '''
-    Applies the specified function to each group of n-frames.
+    """Applies the specified function to each group of n-frames.
 
     :param iterable frames: A sequence of frames to process.
     :param int n: The number of frames in each group.
     :param callable fun: The function used to process each group of frames.
     :return: An iterable returning the results of applying the function to each group.
-    '''
+    """
     i = 0
     group = []
     for frame in frames:
@@ -61,9 +64,9 @@ def groupframes(frames, n, fun):
             group.clear()
             i = i + 1
 
+
 def triggerclip(data, events, before=pd.Timedelta(0), after=pd.Timedelta(0)):
-    '''
-    Split video data around the specified sequence of event timestamps.
+    """Split video data around the specified sequence of event timestamps.
 
     :param DataFrame data:
     A pandas DataFrame where each row specifies video acquisition path and frame number.
@@ -72,7 +75,7 @@ def triggerclip(data, events, before=pd.Timedelta(0), after=pd.Timedelta(0)):
     :param Timedelta after: The right offset from each timestamp used to clip the data.
     :return:
     A pandas DataFrame containing the frames, clip and sequence numbers for each event timestamp.
-    '''
+    """
     if before is not pd.Timedelta:
         before = pd.Timedelta(before)
     if after is not pd.Timedelta:
@@ -81,30 +84,30 @@ def triggerclip(data, events, before=pd.Timedelta(0), after=pd.Timedelta(0)):
         events = events.index
 
     clips = []
-    for i,index in enumerate(events):
-        clip = data.loc[(index-before):(index+after)].copy()
-        clip['frame_sequence'] = list(range(len(clip)))
-        clip['clip_sequence'] = i
+    for i, index in enumerate(events):
+        clip = data.loc[(index - before) : (index + after)].copy()
+        clip["frame_sequence"] = list(range(len(clip)))
+        clip["clip_sequence"] = i
         clips.append(clip)
     return pd.concat(clips)
 
+
 def collatemovie(clipdata, fun):
-    '''
-    Collates a set of video clips into a single movie using the specified aggregation function.
+    """Collates a set of video clips into a single movie using the specified aggregation function.
 
     :param DataFrame clipdata:
     A pandas DataFrame where each row specifies video path, frame number, clip and sequence number.
     This DataFrame can be obtained from the output of the triggerclip function.
     :param callable fun: The aggregation function used to process the frames in each clip.
     :return: The sequence of processed frames representing the collated movie.
-    '''
-    clipcount = len(clipdata.groupby('clip_sequence').frame_sequence.count())
-    allframes = video.frames(clipdata.sort_values(by=['frame_sequence', 'clip_sequence']))
+    """
+    clipcount = len(clipdata.groupby("clip_sequence").frame_sequence.count())
+    allframes = video.frames(clipdata.sort_values(by=["frame_sequence", "clip_sequence"]))
     return groupframes(allframes, clipcount, fun)
 
+
 def gridmovie(clipdata, width, height, shape=None):
-    '''
-    Collates a set of video clips into a grid movie with the specified pixel dimensions
+    """Collates a set of video clips into a grid movie with the specified pixel dimensions
     and grid layout.
 
     :param DataFrame clipdata:
@@ -116,5 +119,5 @@ def gridmovie(clipdata, width, height, shape=None):
     Either the number of frames to include, or the number of rows and columns
     in the output grid movie layout.
     :return: The sequence of processed frames representing the collated grid movie.
-    '''
-    return collatemovie(clipdata, lambda g:gridframes(g, width, height, shape))
+    """
+    return collatemovie(clipdata, lambda g: gridframes(g, width, height, shape))

aeon/analysis/plotting.py

@@ -1,17 +1,16 @@
 import math
 
-import matplotlib.colors as colors
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from matplotlib import colors
 from matplotlib.collections import LineCollection
 
 from aeon.analysis.utils import *
 
 
 def heatmap(position, frequency, ax=None, **kwargs):
-    """
-    Draw a heatmap of time spent in each location from specified position data and sampling frequency.
+    """Draw a heatmap of time spent in each location from specified position data and sampling frequency.
 
     :param Series position: A series of position data containing x and y coordinates.
     :param number frequency: The sampling frequency for the position data.
@@ -33,8 +32,7 @@ def heatmap(position, frequency, ax=None, **kwargs):
 
 
 def circle(x, y, radius, fmt=None, ax=None, **kwargs):
-    """
-    Plot a circle centered at the given x, y position with the specified radius.
+    """Plot a circle centered at the given x, y position with the specified radius.
 
     :param number x: The x-component of the circle center.
     :param number y: The y-component of the circle center.
@@ -62,8 +60,7 @@ def rateplot(
     ax=None,
     **kwargs,
 ):
-    """
-    Plot the continuous event rate and raster of a discrete event sequence, given the specified
+    """Plot the continuous event rate and raster of a discrete event sequence, given the specified
     window size and sampling frequency.
 
     :param Series events: The discrete sequence of events.
@@ -79,9 +76,7 @@ def rateplot(
     :param Axes, optional ax: The Axes on which to draw the rate plot and raster.
     """
     label = kwargs.pop("label", None)
-    eventrate = rate(
-        events, window, frequency, weight, start, end, smooth=smooth, center=center
-    )
+    eventrate = rate(events, window, frequency, weight, start, end, smooth=smooth, center=center)
     if ax is None:
         ax = plt.gca()
     ax.plot(
@@ -90,14 +85,11 @@ def rateplot(
         label=label,
         **kwargs,
     )
-    ax.vlines(
-        sessiontime(events.index, eventrate.index[0]), -0.2, -0.1, linewidth=1, **kwargs
-    )
+    ax.vlines(sessiontime(events.index, eventrate.index[0]), -0.2, -0.1, linewidth=1, **kwargs)
 
 
 def set_ymargin(ax, bottom, top):
-    """
-    Set the vertical margins of the specified Axes.
+    """Set the vertical margins of the specified Axes.
 
     :param Axes ax: The Axes for which to specify the vertical margin.
     :param number bottom: The size of the bottom margin.
@@ -121,8 +113,7 @@ def colorline(
     ax=None,
     **kwargs,
 ):
-    """
-    Plot a dynamically colored line on the specified Axes.
+    """Plot a dynamically colored line on the specified Axes.
 
     :param array-like x, y: The horizontal / vertical coordinates of the data points.
     :param array-like, optional z: