outlier correction mp

sgoldenlab · Nov 9, 2024 · 0901665 · 0901665
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diff --git a/docs/simba.outlier_tools.rst b/docs/simba.outlier_tools.rst
@@ -1,17 +1,33 @@
-Location outlier methods
+Location outlier removed
 --------------------------------------------------------
 
 .. automodule:: simba.outlier_tools.outlier_corrector_location.OutlierCorrecterLocation
    :members:
    :show-inheritance:
 
-Movement outlier methods
+Movement outlier remover
 --------------------------------------------------------
 
 .. automodule:: simba.outlier_tools.outlier_corrector_movement.OutlierCorrecterMovement
    :members:
    :show-inheritance:
 
+
+Movement outlier remover: multi-core
+--------------------------------------------------------
+
+.. automodule:: simba.outlier_tools.outlier_corrector_movement_mp.OutlierCorrecterMovementMultiProcess
+   :members:
+   :show-inheritance:
+
+
+Location outlier remover: multi-core
+--------------------------------------------------------
+
+.. automodule:: simba.outlier_tools.outlier_corrector_location_mp.OutlierCorrecterLocationMultiprocess
+   :members:
+   :show-inheritance:
+
 Advanced movement outlier correction
 --------------------------------------------------------
 

diff --git a/simba/SimBA.py b/simba/SimBA.py
@@ -324,7 +324,7 @@ def __init__(self, config_path: str):
         label_outliercorrection = CreateLabelFrameWithIcon(parent=tab4, header="OUTLIER CORRECTION", icon_name=Keys.DOCUMENTATION.value, icon_link=Links.OUTLIERS_DOC.value)
         button_settings_outlier = SimbaButton(parent=label_outliercorrection, txt="SETTINGS", txt_clr='blue', img='settings', font=Formats.FONT_REGULAR.value, cmd=OutlierSettingsPopUp, cmd_kwargs={'config_path': lambda:self.config_path})
 
-        button_outliercorrection = SimbaButton(parent=label_outliercorrection, txt="RUN OUTLIER CORRECTION", txt_clr='green', img='rocket', font=Formats.FONT_REGULAR.value, cmd=self.correct_outlier, thread=True)
+        button_outliercorrection = SimbaButton(parent=label_outliercorrection, txt="RUN OUTLIER CORRECTION", txt_clr='green', img='rocket', font=Formats.FONT_REGULAR.value, cmd=self.correct_outlier, thread=False)
         button_skipOC = SimbaButton(parent=label_outliercorrection, txt="SKIP OUTLIER CORRECTION (CAUTION)", txt_clr='red', img='skip_2', font=Formats.FONT_REGULAR.value, cmd=self.initiate_skip_outlier_correction, thread=True)
 
         label_extractfeatures = CreateLabelFrameWithIcon(parent=tab5, header="EXTRACT FEATURES", icon_name=Keys.DOCUMENTATION.value, icon_link=Links.EXTRACT_FEATURES.value)

diff --git a/simba/mixins/statistics_mixin.py b/simba/mixins/statistics_mixin.py
@@ -4192,6 +4192,13 @@ def symmetry_index(x: np.ndarray, y: np.ndarray, agg_type: Literal['mean', 'medi
 
         Zero indicates perfect symmetry. Positive values pepresent increasing asymmetry between the two measurements.
 
+        The Symmetry Index (SI) is calculated as:
+
+        .. math::
+           SI = \frac{|x_i - y_i|}{0.5 \times (x_i + y_i)} \times 100
+
+        where :math:`x_i` and :math:`y_i` are the values of the two measurements at each time point.
+
         :param np.ndarray x: A 1-dimensional array of measurements from one side (e.g., left side), representing a time series or sequence of measurements.
         :param np.ndarray y: A 1-dimensional array of measurements from the other side (e.g., right side), of the same length as `x`.
         :param Literal['mean', 'median'] agg_type: The aggregation method used to summarize the Symmetry Index across all time points.
@@ -4201,13 +4208,11 @@ def symmetry_index(x: np.ndarray, y: np.ndarray, agg_type: Literal['mean', 'medi
         :example:
         >>> x = np.random.randint(0, 155, (100,))
         >>>y = np.random.randint(0, 155, (100,))
-        >>> symmetry_index(x=x, y=y)
+        >>> Statistics.symmetry_index(x=x, y=y)
         """
 
-        check_valid_array(data=x, source=f'{Statistics.symmetry_index.__name__} x', accepted_ndims=(1,), min_axis_0=1,
-                          accepted_dtypes=Formats.NUMERIC_DTYPES.value)
-        check_valid_array(data=x, source=f'{Statistics.symmetry_index.__name__} y', accepted_ndims=(1,), min_axis_0=1,
-                          accepted_axis_0_shape=[x.shape[0]], accepted_dtypes=Formats.NUMERIC_DTYPES.value)
+        check_valid_array(data=x, source=f'{Statistics.symmetry_index.__name__} x', accepted_ndims=(1,), min_axis_0=1, accepted_dtypes=Formats.NUMERIC_DTYPES.value)
+        check_valid_array(data=x, source=f'{Statistics.symmetry_index.__name__} y', accepted_ndims=(1,), min_axis_0=1, accepted_axis_0_shape=[x.shape[0]], accepted_dtypes=Formats.NUMERIC_DTYPES.value)
         check_str(name=f'{Statistics.symmetry_index.__name__} agg_type', value=agg_type, options=('mean', 'median'))
         si_values = np.abs(x - y) / (0.5 * (x + y)) * 100
         if agg_type == 'mean':