feat: Add gaze.transforms.smooth()

src/pymovements/gaze/gaze_dataframe.py

@@ -405,6 +405,52 @@ def pos2vel(
         """
         self.transform('pos2vel', method=method, **kwargs)
 
+    def smooth(
+            self,
+            method: str = 'savitzky_golay',
+            window_length: int = 7,
+            degree: int = 2,
+            column: str = 'position',
+            padding: str | float | int | None = 'nearest',
+            **kwargs: int | float | str,
+    ) -> None:
+        """Smooth data in a column.
+
+        Parameters
+        ----------
+        method:
+            The method to use for smoothing. Choose from ``savitzky_golay``, ``moving_average``,
+            ``exponential_moving_average``. See :func:`~transforms.smooth()` for details.
+        window_length:
+            For ``moving_average`` this is the window size to calculate the mean of the subsequent
+            samples. For ``savitzky_golay`` this is the window size to use for the polynomial fit.
+            For ``exponential_moving_average`` this is the span parameter.
+        degree:
+            The degree of the polynomial to use. This has only an effect if using
+            ``savitzky_golay`` as smoothing method. `degree` must be less than `window_length`.
+        column:
+            The input column name to which the smoothing is applied.
+        padding:
+            Must be either ``None``, a scalar or one of the strings
+            ``mirror``, ``nearest`` or ``wrap``.
+            This determines the type of extension to use for the padded signal to
+            which the filter is applied.
+            When passing ``None``, no extension padding is used.
+            When passing a scalar value, data will be padded using the passed value.
+            See :func:`~transforms.smooth()` for details on the padding methods.
+        **kwargs:
+            Additional keyword arguments to be passed to the :func:`~transforms.smooth()` method.
+        """
+        self.transform(
+            'smooth',
+            column=column,
+            method=method,
+            degree=degree,
+            window_length=window_length,
+            padding=padding,
+            **kwargs,
+        )
+
     def detect(
             self,
             method: Callable[..., pm.EventDataFrame] | str,

src/pymovements/gaze/transforms.py

@@ -31,7 +31,6 @@
 
 from pymovements.utils import checks
 
-
 TransformMethod = TypeVar('TransformMethod', bound=Callable[..., pl.Expr])
 
 
@@ -558,6 +557,165 @@ def savitzky_golay(
     ).alias(output_column)
 
 
+@register_transform
+def smooth(
+        *,
+        method: str,
+        window_length: int,
+        n_components: int,
+        degree: int | None = None,
+        column: str = 'position',
+        padding: str | float | int | None = 'nearest',
+) -> pl.Expr:
+    """
+    Smooth data in a column.
+
+    Parameters
+    ----------
+    method:
+        The method to use for smoothing. See Notes for more details.
+    window_length
+        For ``moving_average`` this is the window size to calculate the mean of the subsequent
+        samples. For ``savitzky_golay`` this is the window size to use for the polynomial fit.
+        For ``exponential_moving_average`` this is the span parameter.
+    n_components:
+        Number of components in input column.
+    degree:
+        The degree of the polynomial to use. This has only an effect if using ``savitzky_golay`` as
+        smoothing method. `degree` must be less than `window_length`.
+    column:
+        The input column name to which the smoothing is applied.
+    padding:
+        Must be either ``None``, a scalar or one of the strings ``mirror``, ``nearest`` or ``wrap``.
+        This determines the type of extension to use for the padded signal to
+        which the filter is applied.
+        When passing ``None``, no extension padding is used.
+        When passing a scalar value, data will be padded using the passed value.
+        See the Notes for more details on the padding methods.
+
+    Returns
+    -------
+    polars.Expr
+        The respective polars expression.
+
+    Notes
+    -----
+    There following methods are available for smoothing:
+
+    * ``savitzky_golay``: Smooth data by applying a Savitzky-Golay filter.
+    See :py:func:`~pymovements.gaze.transforms.savitzky_golay` for further details.
+    * ``moving_average``: Smooth data by calculating the mean of the subsequent samples.
+    Each smoothed sample is calculated by the mean of the samples in the window around the sample.
+    * ``exponential_moving_average``: Smooth data by exponentially weighted moving average.
+
+    Details on the `padding` options:
+
+    * ``None``: No padding extension is used.
+    * scalar value (int or float): The padding extension contains the specified scalar value.
+    * ``mirror``: Repeats the values at the edges in reverse order. The value closest to the edge is
+      not included.
+    * ``nearest``: The padding extension contains the nearest input value.
+    * ``wrap``: The padding extension contains the values from the other end of the array.
+
+    Given the input is ``[1, 2, 3, 4, 5, 6, 7, 8]``, and
+    `window_length` is 7, the following table shows the padded data for
+    the various ``padding`` options:
+
+    +-------------+-------------+----------------------------+-------------+
+    | mode        |   padding   |           input            |   padding   |
+    +=============+=============+============================+=============+
+    | ``None``    | ``-  -  -`` | ``1  2  3  4  5  6  7  8`` | ``-  -  -`` |
+    +-------------+-------------+----------------------------+-------------+
+    | ``0``       | ``0  0  0`` | ``1  2  3  4  5  6  7  8`` | ``0  0  0`` |
+    +-------------+-------------+----------------------------+-------------+
+    | ``1``       | ``1  1  1`` | ``1  2  3  4  5  6  7  8`` | ``1  1  1`` |
+    +-------------+-------------+----------------------------+-------------+
+    | ``nearest`` | ``1  1  1`` | ``1  2  3  4  5  6  7  8`` | ``8  8  8`` |
+    +-------------+-------------+----------------------------+-------------+
+    | ``mirror``  | ``4  3  2`` | ``1  2  3  4  5  6  7  8`` | ``7  6  5`` |
+    +-------------+-------------+----------------------------+-------------+
+    | ``wrap``    | ``6  7  8`` | ``1  2  3  4  5  6  7  8`` | ``1  2  3`` |
+    +-------------+-------------+----------------------------+-------------+
+
+    """
+    _check_window_length(window_length=window_length)
+    _check_padding(padding=padding)
+
+    if method in {'moving_average', 'exponential_moving_average'}:
+        pad_kwargs: dict[str, Any] = {'pad_width': 0}
+        pad_func = _identity
+
+        if isinstance(padding, (int, float)):
+            pad_kwargs['constant_values'] = padding
+            padding = 'constant'
+        elif padding == 'nearest':
+            # option 'nearest' is called 'edge' for np.pad
+            padding = 'edge'
+        elif padding == 'mirror':
+            # option 'mirror' is called 'reflect' for np.pad
+            padding = 'reflect'
+
+        if padding is not None:
+            pad_kwargs['mode'] = padding
+            pad_kwargs['pad_width'] = np.ceil(window_length / 2).astype(int)
+
+            pad_func = partial(
+                np.pad,
+                **pad_kwargs,
+            )
+
+        if method == 'moving_average':
+
+            return pl.concat_list(
+                [
+                    pl.col(column).list.get(component).map(pad_func).list.explode()
+                    .rolling_mean(window_size=window_length, center=True)
+                    .shift(periods=pad_kwargs['pad_width'])
+                    .slice(pad_kwargs['pad_width'] * 2)
+                    for component in range(n_components)
+                ],
+            ).alias(column)
+
+        return pl.concat_list(
+            [
+                pl.col(column).list.get(component).map(pad_func).list.explode()
+                .ewm_mean(
+                    span=window_length,
+                    adjust=False,
+                    min_periods=window_length,
+                ).shift(periods=pad_kwargs['pad_width'])
+                .slice(pad_kwargs['pad_width'] * 2)
+                for component in range(n_components)
+            ],
+        ).alias(column)
+
+    if method == 'savitzky_golay':
+        if degree is None:
+            raise TypeError("'degree' must not be none for method 'savitzky_golay'")
+
+        return savitzky_golay(
+            window_length=window_length,
+            degree=degree,
+            sampling_rate=1,
+            padding=padding,
+            derivative=0,
+            n_components=n_components,
+            input_column=column,
+            output_column=None,
+        )
+
+    supported_methods = ['moving_average', 'exponential_moving_average', 'savitzky_golay']
+
+    raise ValueError(
+        f"Unknown method '{method}'. Supported methods are: {supported_methods}",
+    )
+
+
+def _identity(x: Any) -> Any:
+    """Identity function as placeholder for None as padding."""
+    return x
+
+
 def _check_window_length(window_length: Any) -> None:
     """Check that window length is an integer and greater than zero."""
     checks.check_is_not_none(window_length=window_length)

tests/gaze/gaze_transform_test.py

@@ -439,6 +439,50 @@ def fixture_experiment():
             ),
             id='pos2vel_preceding_trialize_single_column_str',
         ),
+        pytest.param(
+            {
+                'data': pl.from_dict(
+                    {
+                        'x_dva': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+                        'y_dva': [2.0, 2.0, 2.0, 2.0, 2.0, 2.0],
+                    },
+                ),
+                'position_columns': ['x_dva', 'y_dva'],
+            },
+            'smooth', {'method': 'moving_average', 'window_length': 3},
+            pm.GazeDataFrame(
+                data=pl.from_dict(
+                    {
+                        'x_dva': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+                        'y_dva': [2.0, 2.0, 2.0, 2.0, 2.0, 2.0],
+                    },
+                ),
+                position_columns=['x_dva', 'y_dva'],
+            ),
+            id='smooth',
+        ),
+        pytest.param(
+            {
+                'data': pl.from_dict(
+                    {
+                        'x_dva': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+                        'y_dva': [2.0, 2.0, 2.0, 2.0, 2.0, 2.0],
+                    },
+                ),
+                'position_columns': ['x_dva', 'y_dva'],
+            },
+            pm.gaze.transforms.smooth, {'method': 'moving_average', 'window_length': 3},
+            pm.GazeDataFrame(
+                data=pl.from_dict(
+                    {
+                        'x_dva': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+                        'y_dva': [2.0, 2.0, 2.0, 2.0, 2.0, 2.0],
+                    },
+                ),
+                position_columns=['x_dva', 'y_dva'],
+            ),
+            id='smooth_method_pass',
+        ),
 
     ],
 )
@@ -750,3 +794,75 @@ def test_gaze_dataframe_pos2vel_exceptions(init_kwargs, exception, expected_msg)
 
     msg, = excinfo.value.args
     assert msg == expected_msg
+
+
+@pytest.mark.parametrize(
+    ('gaze_init_kwargs', 'kwargs', 'expected'),
+    [
+        pytest.param(
+            {
+                'data': pl.from_dict(
+                    {
+                        'x_pix': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                        'y_pix': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                        'x_dva': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                        'y_dva': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                    },
+                ),
+                'pixel_columns': ['x_pix', 'y_pix'],
+                'position_columns': ['x_dva', 'y_dva'],
+            },
+            {'method': 'moving_average', 'column': 'pixel', 'window_length': 3},
+            pm.GazeDataFrame(
+                data=pl.from_dict(
+                    {
+
+                        'x_pix': [1 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 3],
+                        'y_pix': [1 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 3],
+                        'x_dva': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                        'y_dva': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                    },
+                ),
+                pixel_columns=['x_pix', 'y_pix'],
+                position_columns=['x_dva', 'y_dva'],
+            ),
+            id='pixel',
+        ),
+        pytest.param(
+            {
+                'data': pl.from_dict(
+                    {
+                        'x_pix': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                        'y_pix': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                        'x_dva': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                        'y_dva': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                    },
+                ),
+                'pixel_columns': ['x_pix', 'y_pix'],
+                'position_columns': ['x_dva', 'y_dva'],
+            },
+            {'method': 'moving_average', 'column': 'position', 'window_length': 3},
+            pm.GazeDataFrame(
+                data=pl.from_dict(
+                    {
+
+                        'x_pix': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                        'y_pix': [0.0, 1.0, 0.0, 0.0, 1.0, 0.0],
+                        'x_dva': [1 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 3],
+                        'y_dva': [1 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 3],
+                    },
+                ),
+                pixel_columns=['x_pix', 'y_pix'],
+                position_columns=['x_dva', 'y_dva'],
+            ),
+            id='position',
+        ),
+    ],
+)
+def test_gaze_dataframe_smooth_expected_column(
+        gaze_init_kwargs, kwargs, expected,
+):
+    gaze = pm.GazeDataFrame(**gaze_init_kwargs)
+    gaze.smooth(**kwargs)
+
+    assert_frame_equal(gaze.frame, expected.frame)