Review documentation

src/biopsykit/example_data.py

@@ -251,7 +251,7 @@ def get_time_log_example() -> pd.DataFrame:
     -------
     data : :class:`~pandas.DataFrame`
         dataframe with example time log information. The time log match the data from the two ECG data example
-        functions :func:`~biosykit.utils.get_ecg_example` and :func:`~biosykit.utils.get_ecg_example_02`
+        functions :func:`~biopsykit.example_data.get_ecg_example` and :func:`~biopsykit.example_data.get_ecg_example_02`
 
     """
     return load_time_log(_EXAMPLE_DATA_PATH.joinpath("ecg_time_log.xlsx"))

src/biopsykit/questionnaires/utils.py

@@ -483,7 +483,7 @@ def bin_scale(
     ----------
     data : :class:`~pandas.DataFrame` or :class:`~pandas.Series`
         data with scales to be binned
-    bins : The criteria to bin by. ``bins``can have one of the following types:
+    bins : The criteria to bin by ``bins`` can have one of the following types:
 
         * ``int`` : Defines the number of equal-width bins in the range of ``data``. The range of ``x`` is extended by
           0.1% on each side to include the minimum and maximum values of `x`.
@@ -608,7 +608,7 @@ def compute_scores(
         dictionary with questionnaire names to be computed (keys) and columns of the questionnaires (values)
     quest_kwargs : dict
         dictionary with optional arguments to be passed to questionnaire functions. The dictionary is expected
-        consist of questionnaire names (keys) and ``**kwargs`` dictionaries (values) with arguments per questionnaire
+        consist of questionnaire names (keys) and \*\*kwargs dictionaries (values) with arguments per questionnaire
 
     Returns
     -------

src/biopsykit/signals/ecg/ecg.py

@@ -1116,6 +1116,7 @@ def _correct_outlier_correlation(rpeaks: pd.DataFrame, bool_mask: np.array, corr
     corr_thres : float
         threshold for cross-correlation coefficient. Beats below that threshold will be marked as outlier
     **kwargs : additional parameters required for this outlier function, such as:
+
         * ecg_signal :class:`~pandas.DataFrame`
           dataframe with processed ECG signal. Output from :meth:`biopsykit.signals.ecg.EcgProcessor.ecg_process()`
         * sampling_rate : float

src/biopsykit/signals/ecg/plotting.py

@@ -579,7 +579,7 @@ def rr_distribution_plot(
         dataframe with R peaks. Output of :meth:`~biopsykit.signals.ecg.ecg.EcgProcessor.ecg_process()`.
     sampling_rate : float, optional
         Sampling rate of recorded data in Hz. Default: 256
-    *+kwargs
+    **kwargs
         Additional parameters to configure the plot. Parameters include:
 
         * ``figsize``: Figure size

src/biopsykit/sleep/plotting.py

@@ -40,10 +40,10 @@ def sleep_imu_plot(
 
     Parameters
     ----------
-    data : :class:`~pandas.DataFrame´
+    data : :class:`~pandas.DataFrame`
         data to plot. Data must either be acceleration data (:obj:`~biopsykit.utils.datatype_helper.AccDataFrame`),
         gyroscope data (:obj:`~biopsykit.utils.datatype_helper.GyrDataFrame`), or IMU data
-        (:obj:`~biopsykit.utils.datatype_helper.ImuDataFrame`)
+        (:obj:`~biopsykit.utils.datatype_helper.ImuDataFrame`).
     datastreams : str or list of str, optional
         list of datastreams indicating which type of data should be plotted or ``None`` to only plot acceleration data.
         If more than one type of datastream is specified each datastream is plotted row-wise in its own subplot.
@@ -53,37 +53,40 @@ def sleep_imu_plot(
     downsample_factor : int, optional
         downsample factor to apply to raw input data before plotting or ``None`` to not downsample data before
         plotting (downsample factor 1). Default: ``None``
-    kwargs
+    **kwargs
         optional arguments for plot configuration.
-
         To configure which type of sleep endpoint annotations to plot:
-            * ``plot_sleep_onset``: whether to plot sleep onset annotations or not: Default: ``True``
-            * ``plot_wake_onset``: whether to plot wake onset annotations or not: Default: ``True``
-            * ``plot_bed_start``: whether to plot bed interval start annotations or not: Default: ``True``
-            * ``plot_bed_end``: whether to plot bed interval end annotations or not: Default: ``True``
-            * ``plot_sleep_wake``: whether to plot vspans of detected sleep/wake phases or not: Default: ``True``
+
+        * ``plot_sleep_onset``: whether to plot sleep onset annotations or not: Default: ``True``
+        * ``plot_wake_onset``: whether to plot wake onset annotations or not: Default: ``True``
+        * ``plot_bed_start``: whether to plot bed interval start annotations or not: Default: ``True``
+        * ``plot_bed_end``: whether to plot bed interval end annotations or not: Default: ``True``
+        * ``plot_sleep_wake``: whether to plot vspans of detected sleep/wake phases or not: Default: ``True``
 
         To style general plot appearance:
-            * ``axs``: pre-existing axes for the plot. Otherwise, a new figure and axes objects are created and
-              returned.
-            * ``colormap``: colormap to plot different axes from input data
-            * ``figsize``: tuple specifying figure dimensions
+
+        * ``axs``: pre-existing axes for the plot. Otherwise, a new figure and axes objects are created and
+          returned.
+        * ``colormap``: colormap to plot different axes from input data
+        * ``figsize``: tuple specifying figure dimensions
 
         To style axes:
-            * ``xlabel``: label of x axis. Default: "Time"
-            * ``ylabel``: label of y axis. Default: "Acceleration [$m/s^2$]" for acceleration data and
-              "Angular Velocity [$°/s$]" for gyroscope data
+
+        * ``xlabel``: label of x axis. Default: "Time"
+        * ``ylabel``: label of y axis. Default: "Acceleration [$m/s^2$]" for acceleration data and
+          "Angular Velocity [$°/s$]" for gyroscope data.
 
         To style legend:
-            * ``legend_loc``: location of legend. Default: "lower left"
-            * ``legend_fontsize``: font size of legend labels. Default: "smaller"
+
+        * ``legend_loc``: location of legend. Default: "lower left"
+        * ``legend_fontsize``: font size of legend labels. Default: "smaller"
 
 
     Returns
     -------
-    fig : :class:`matplotlib.figure.Figure`
+    fig : :class:`~matplotlib.figure.Figure`
         figure object
-    axs : list of :class:`matplotlib.axes.Axes`
+    axs : list of :class:`~matplotlib.axes.Axes`
         list of subplot axes objects
 
     """

src/biopsykit/sleep/sleep_endpoints/sleep_endpoints.py

@@ -14,23 +14,24 @@ def compute_sleep_endpoints(
     """Compute a set of sleep endpoints based on sleep/wake information and time spent in bed.
 
     This functions computes the following sleep endpoints:
-        * ``date``: date of recording if input data is time-aware, ``0`` otherwise. **NOTE**: If the participant went
-          to bed between 12 am and 6 am (i.e, the beginning of ``bed_interval`` between 12 am and 6 am) ``date`` will
-          be set to the day before (because this night is assumed to "belong" to the day before)
-        * ``sleep_onset``: Sleep Onset, i.e., time of falling asleep, in absolute time
-        * ``wake_onset``: Wake Onset, i.e., time of awakening, in absolute time
-        * ``net_sleep_duration``: Net duration spent sleeping, in minutes
-        * ``bed_interval_start``: Bed Interval Start, i.e, time when participant went to bed, in absolute time
-        * ``bed_interval_end``: Bed Interval End, i.e, time when participant left bed, in absolute time
-        * ``sleep_efficiency``: Sleep Efficiency, defined as the ratio between net sleep duration and sleep duration
-          in percent
-        * ``sleep_onset_latency``: Sleep Onset Latency, i.e., time in bed needed to fall asleep, in minutes
-        * ``getup_latency``: Get Up Latency, i.e., time in bed after awakening until getting up, in minutes
-        * ``wake_after_sleep_onset``: Wake After Sleep Onset (WASO), i.e., total time awake after falling asleep, in
-          minutes
-        * ``sleep_bouts``: List with start and end times of sleep bouts
-        * ``wake_bouts``: List with start and end times of wake bouts
-        * ``number_wake_bouts``: Total number of wake bouts
+
+    * ``date``: date of recording if input data is time-aware, ``0`` otherwise. **NOTE**: If the participant went
+      to bed between 12 am and 6 am (i.e, the beginning of ``bed_interval`` between 12 am and 6 am) ``date`` will
+      be set to the day before (because this night is assumed to "belong" to the day before).
+    * ``sleep_onset``: Sleep Onset, i.e., time of falling asleep, in absolute time
+    * ``wake_onset``: Wake Onset, i.e., time of awakening, in absolute time
+    * ``net_sleep_duration``: Net duration spent sleeping, in minutes
+    * ``bed_interval_start``: Bed Interval Start, i.e, time when participant went to bed, in absolute time
+    * ``bed_interval_end``: Bed Interval End, i.e, time when participant left bed, in absolute time
+    * ``sleep_efficiency``: Sleep Efficiency, defined as the ratio between net sleep duration and sleep duration
+      in percent
+    * ``sleep_onset_latency``: Sleep Onset Latency, i.e., time in bed needed to fall asleep, in minutes
+    * ``getup_latency``: Get Up Latency, i.e., time in bed after awakening until getting up, in minutes
+    * ``wake_after_sleep_onset``: Wake After Sleep Onset (WASO), i.e., total time awake after falling asleep, in
+      minutes
+    * ``sleep_bouts``: List with start and end times of sleep bouts
+    * ``wake_bouts``: List with start and end times of wake bouts
+    * ``number_wake_bouts``: Total number of wake bouts
 
 
     Parameters

src/biopsykit/sleep/sleep_processing_pipeline/sleep_processing_pipeline.py

@@ -16,13 +16,14 @@ def predict_pipeline_acceleration(
     """Apply sleep processing pipeline on raw acceleration data.
 
     This function processes raw acceleration data collected during sleep. The pipeline consists of the following steps:
-        * *Activity Count Conversion*: Convert raw acceleration data into activity counts. Most sleep/wake detection
-          algorithms use activity counts (as typically provided by Actigraphs) as input data
-        * *Wear Detection*: Detect wear and non-wear periods. Cut data to longest continuous wear block
-        * *Rest Periods*: Detect rest periods, i.e., periods with large physical inactivity. The longest continuous
-          rest period (*Major Rest Period*) is used to determine the *Bed Interval*, i.e., the period spent in bed
-        * *Sleep/Wake Detection*: Apply sleep/wake detection algorithm to classify phases of sleep and wake.
-        * *Sleep Endpoint Computation*: Compute Sleep Endpoints from sleep/wake detection results and bed interval
+
+    * *Activity Count Conversion*: Convert raw acceleration data into activity counts. Most sleep/wake detection
+      algorithms use activity counts (as typically provided by Actigraphs) as input data.
+    * *Wear Detection*: Detect wear and non-wear periods. Cut data to longest continuous wear block.
+    * *Rest Periods*: Detect rest periods, i.e., periods with large physical inactivity. The longest continuous
+      rest period (*Major Rest Period*) is used to determine the *Bed Interval*, i.e., the period spent in bed.
+    * *Sleep/Wake Detection*: Apply sleep/wake detection algorithm to classify phases of sleep and wake.
+    * *Sleep Endpoint Computation*: Compute Sleep Endpoints from sleep/wake detection results and bed interval.
 
     Parameters
     ----------
@@ -34,10 +35,10 @@ def predict_pipeline_acceleration(
         ``True`` if input data is provided in :math:`m/s^2` and should be converted in :math:`g`, ``False`` if input
         data is already in :math:`g` and does not need to be converted.
         Default: ``True``
-    kwargs :
+    **kwargs :
         additional parameters to configure sleep/wake detection. The possible parameters depend on the selected
         sleep/wake detection algorithm and are passed to
-        :class:`~biopsykit.sleep.sleep_wake_detection.SleepWakeDetection`
+        :class:`~biopsykit.sleep.sleep_wake_detection.SleepWakeDetection`.
 
 
     Returns

src/biopsykit/stats/stats.py

@@ -294,7 +294,7 @@ def display_results(self, sig_only: Optional[Union[str, bool, Sequence[str], Dic
         **kwargs
             additional arguments to be passed to the function, such as:
 
-            * category names: ``True`` to display results of this category, ``False`` to skip displaying results
+            * ``category`` names: ``True`` to display results of this category, ``False`` to skip displaying results
               of this category. Default: show results from all categories
             * ``grouped``: ``True`` to group results by the variable "groupby" specified in the parameter
               dictionary when initializing the ``StatsPipeline`` instance.
@@ -441,7 +441,7 @@ def sig_brackets(
 
             * ``str``: only one feature is plotted in the boxplot
               (returns significance brackets of only one feature)
-            * ``list``: multiple features are combined into *one* :class:`matplotlib.axes.Axes` object
+            * ``list``: multiple features are combined into *one* :class:`~matplotlib.axes.Axes` object
               (returns significance brackets of multiple features)
             * ``dict``: dictionary with feature (or list of features) per subplot if boxplots are structured in
               subplots (``subplots`` is ``True``) (returns dictionary with significance brackets per subplot)

src/biopsykit/utils/array_handling.py

@@ -9,7 +9,7 @@
 
 
 def sanitize_input_1d(data: arr_t) -> np.ndarray:
-    """Convert 1-d array-like data (numpy array, pandas dataframe/series) to a numpy array.
+    """Convert 1-d array-like data (:class:`~numpy.ndarray`, :class:`~pandas.DataFrame`/:class:`~pandas.Series`) to a numpy array.
 
     Parameters
     ----------
@@ -19,8 +19,8 @@ def sanitize_input_1d(data: arr_t) -> np.ndarray:
 
     Returns
     -------
-    array_like
-        data as 1-d numpy array
+    :class:`~numpy.ndarray`
+        data as 1-d :class:`~numpy.ndarray`
 
     """
     if isinstance(data, (pd.Series, pd.DataFrame)):
@@ -36,7 +36,7 @@ def sanitize_input_nd(
     data: arr_t,
     ncols: Optional[Union[int, Tuple[int, ...]]] = None,
 ) -> np.ndarray:
-    """Convert n-d array-like data (numpy array, pandas dataframe/series) to a numpy array.
+    """Convert n-d array-like data (:class:`~numpy.ndarray`, :class:`~pandas.DataFrame`/:class:`~pandas.Series`) to a numpy array.
 
     Parameters
     ----------
@@ -49,7 +49,7 @@ def sanitize_input_nd(
 
     Returns
     -------
-    array_like
+    :class:`~numpy.ndarray`
         data as n-d numpy array
 
     """
@@ -86,10 +86,12 @@ def find_extrema_in_radius(
         array with indices for which to search for extrema values around
     radius: int or tuple of int
         radius around ``indices`` to search for extrema:
-            * if ``radius`` is an ``int`` then search for extrema equally in both directions in the interval
-              [index - radius, index + radius].
-            * if ``radius`` is a ``tuple`` then search for extrema in the interval
-              [ index - radius[0], index + radius[1] ]
+
+        * if ``radius`` is an ``int`` then search for extrema equally in both directions in the interval
+          [index - radius, index + radius].
+        * if ``radius`` is a ``tuple`` then search for extrema in the interval
+          [ index - radius[0], index + radius[1] ]
+
     extrema_type : {'min', 'max'}, optional
         extrema type to be searched for. Default: 'min'
 
@@ -168,9 +170,9 @@ def remove_outlier_and_interpolate(
     x_old: Optional[np.ndarray] = None,
     desired_length: Optional[int] = None,
 ) -> np.ndarray:
-    """Remove outlier, impute missing values and optionally interpolate data to desired length.
+    """Remove outliers, impute missing values and optionally interpolate data to desired length.
 
-    Detected outlier are removed from array and imputed by linear interpolation.
+    Detected outliers are removed from array and imputed by linear interpolation.
     Optionally, the output array can be linearly interpolated to a new length.
 
 
@@ -190,8 +192,8 @@ def remove_outlier_and_interpolate(
 
     Returns
     -------
-    array_like
-        data with removed and imputed outlier, optionally interpolated to desired length
+    :class:`~numpy.ndarray`
+        data with removed and imputed outliers, optionally interpolated to desired length
 
 
     Raises
@@ -260,13 +262,13 @@ def sliding_window(
 
     Returns
     -------
-    array_like
+    :class:`~numpy.ndarray`
         sliding windows from input array.
 
 
     See Also
     --------
-    `sliding_window_view`
+    :func:`~biopsykit.utils.array_handling.sliding_window_view`
         create sliding window of input array. low-level function with less input parameter configuration possibilities
 
     """
@@ -358,7 +360,7 @@ def sliding_window_view(array: np.ndarray, window_length: int, overlap: int, nan
 
     .. warning::
        This function will return by default a view onto your input array, modifying values in your result will directly
-       affect your input data which might lead to unexpected behaviour! If padding is disabled (default) last window
+       affect your input data which might lead to unexpected behaviour! If padding is disabled (default), last window
        fraction of input may not be returned! However, if `nan_padding` is enabled, this will always return a copy
        instead of a view of your input data, independent if padding was actually performed or not!
 
@@ -378,7 +380,7 @@ def sliding_window_view(array: np.ndarray, window_length: int, overlap: int, nan
 
     Returns
     -------
-    array_like
+    :class:`~numpy.ndarray`
         windowed view (or copy if ``nan_padding`` is ``True``) of input array as specified,
         last window might be nan-padded if necessary to match window size