Enforce linting of docstrings

examples/compute_kinematics.py

@@ -1,7 +1,6 @@
 # ruff: noqa: E402
-"""
-Compute and visualise kinematics
-=================================
+"""Compute and visualise kinematics.
+====================================
 
 Compute displacement, velocity and acceleration data on an example dataset and
 visualise the results.

examples/filter_and_interpolate.py

@@ -1,5 +1,4 @@
-"""
-Filtering and interpolation
+"""Filtering and interpolation
 ============================
 
 Filter out points with low confidence scores and interpolate over

examples/load_and_explore_poses.py

@@ -1,5 +1,4 @@
-"""
-Load and explore pose tracks
+"""Load and explore pose tracks
 ============================
 
 Load and explore an example dataset of pose tracks.

movement/analysis/kinematics.py

@@ -1,12 +1,18 @@
+"""Functions for computing kinematic variables."""
+
 import numpy as np
 import xarray as xr
 
 from movement.logging import log_error
 
 
 def compute_displacement(data: xr.DataArray) -> xr.DataArray:
-    """Compute the displacement between consecutive positions
-    of each keypoint for each individual across time.
+    """Compute displacement between consecutive positions.
+
+    This is the difference between consecutive positions of each keypoint for
+    each individual across time. At each time point ``t``, it's defined as a
+    vector in cartesian ``(x,y)`` coordinates, pointing from the previous
+    ``(t-1)`` to the current ``(t)`` position.
 
     Parameters
     ----------
@@ -17,6 +23,7 @@ def compute_displacement(data: xr.DataArray) -> xr.DataArray:
     -------
     xarray.DataArray
         An xarray DataArray containing the computed displacement.
+
     """
     _validate_time_dimension(data)
     result = data.diff(dim="time")
@@ -25,8 +32,11 @@ def compute_displacement(data: xr.DataArray) -> xr.DataArray:
 
 
 def compute_velocity(data: xr.DataArray) -> xr.DataArray:
-    """Compute the velocity between consecutive positions
-    of each keypoint for each individual across time.
+    """Compute the velocity in cartesian ``(x,y)`` coordinates.
+
+    Velocity is the first derivative of position for each keypoint
+    and individual across time. It's computed using numerical differentiation
+    and assumes equidistant time spacing.
 
     Parameters
     ----------
@@ -38,17 +48,16 @@ def compute_velocity(data: xr.DataArray) -> xr.DataArray:
     xarray.DataArray
         An xarray DataArray containing the computed velocity.
 
-    Notes
-    -----
-    This function computes velocity using numerical differentiation
-    and assumes equidistant time spacing.
     """
     return _compute_approximate_derivative(data, order=1)
 
 
 def compute_acceleration(data: xr.DataArray) -> xr.DataArray:
-    """Compute the acceleration between consecutive positions
-    of each keypoint for each individual across time.
+    """Compute acceleration in cartesian ``(x,y)`` coordinates.
+
+    Acceleration represents the second derivative of position for each keypoint
+    and individual across time. It's computed using numerical differentiation
+    and assumes equidistant time spacing.
 
     Parameters
     ----------
@@ -60,19 +69,16 @@ def compute_acceleration(data: xr.DataArray) -> xr.DataArray:
     xarray.DataArray
         An xarray DataArray containing the computed acceleration.
 
-    Notes
-    -----
-    This function computes acceleration using numerical differentiation
-    and assumes equidistant time spacing.
     """
     return _compute_approximate_derivative(data, order=2)
 
 
 def _compute_approximate_derivative(
     data: xr.DataArray, order: int
 ) -> xr.DataArray:
-    """Compute velocity or acceleration using numerical differentiation,
-    assuming equidistant time spacing.
+    """Compute the derivative using numerical differentiation.
+
+    This assumes equidistant time spacing.
 
     Parameters
     ----------
@@ -86,6 +92,7 @@ def _compute_approximate_derivative(
     -------
     xarray.DataArray
         An xarray DataArray containing the derived variable.
+
     """
     if not isinstance(order, int):
         raise log_error(
@@ -119,6 +126,7 @@ def _validate_time_dimension(data: xr.DataArray) -> None:
     ------
     ValueError
         If the input data does not contain a ``time`` dimension.
+
     """
     if "time" not in data.dims:
         raise log_error(

movement/filtering.py

@@ -1,3 +1,5 @@
+"""Functions for filtering and interpolating pose tracks in xarray datasets."""
+
 import logging
 from datetime import datetime
 from functools import wraps
@@ -7,9 +9,9 @@
 
 
 def log_to_attrs(func):
-    """
-    Decorator that logs the operation performed by the wrapped function
-    and appends the log entry to the xarray.Dataset's "log" attribute.
+    """Log the operation performed by the wrapped function.
+
+    This decorator appends log entries to the xarray.Dataset's "log" attribute.
     For the decorator to work, the wrapped function must accept an
     xarray.Dataset as its first argument and return an xarray.Dataset.
     """
@@ -37,18 +39,18 @@ def wrapper(*args, **kwargs):
 
 
 def report_nan_values(ds: xr.Dataset, ds_label: str = "dataset"):
-    """
-    Report the number and percentage of points that are NaN for each individual
-    and each keypoint in the provided dataset.
+    """Report the number and percentage of points that are NaN.
+
+    Numbers are reported for each individual and keypoint in the dataset.
 
     Parameters
     ----------
     ds : xarray.Dataset
         Dataset containing pose tracks, confidence scores, and metadata.
     ds_label : str
         Label to identify the dataset in the report. Default is "dataset".
-    """
 
+    """
     # Compile the report
     nan_report = f"\nMissing points (marked as NaN) in {ds_label}:"
     for ind in ds.individuals.values:
@@ -77,8 +79,7 @@ def interpolate_over_time(
     max_gap: Union[int, None] = None,
     print_report: bool = True,
 ) -> Union[xr.Dataset, None]:
-    """
-    Fill in NaN values by interpolating over the time dimension.
+    """Fill in NaN values by interpolating over the time dimension.
 
     Parameters
     ----------
@@ -100,6 +101,7 @@ def interpolate_over_time(
     ds_interpolated : xr.Dataset
         The provided dataset (ds), where NaN values have been
         interpolated over using the parameters provided.
+
     """
     ds_interpolated = ds.copy()
     position_interpolated = ds.position.interpolate_na(
@@ -118,9 +120,10 @@ def filter_by_confidence(
     threshold: float = 0.6,
     print_report: bool = True,
 ) -> Union[xr.Dataset, None]:
-    """
-    Drop all points where the associated confidence value falls below a
-    user-defined threshold.
+    """Drop all points below a certain confidence threshold.
+
+    Position points with an associated confidence value below the threshold are
+    converted to NaN.
 
     Parameters
     ----------
@@ -150,6 +153,7 @@ def filter_by_confidence(
     datasets and does not have the same meaning across pose estimation
     frameworks. We advise users to inspect the confidence values
     in their dataset and adjust the threshold accordingly.
+
     """
     ds_thresholded = ds.copy()
     ds_thresholded.update(