s2spy.preprocess
================

.. py:module:: s2spy.preprocess

.. autoapi-nested-parse::

   Preprocessor for s2spy workflow.



Classes
-------

.. autoapisummary::

   s2spy.preprocess.Preprocessor


Functions
---------

.. autoapisummary::

   s2spy.preprocess._linregress
   s2spy.preprocess._trend_linear
   s2spy.preprocess._subtract_linear_trend
   s2spy.preprocess._get_trend
   s2spy.preprocess._subtract_trend
   s2spy.preprocess._get_climatology
   s2spy.preprocess._subtract_climatology
   s2spy.preprocess._check_input_data
   s2spy.preprocess._check_temporal_resolution
   s2spy.preprocess._check_data_resolution_match


Module Contents
---------------

.. py:function:: _linregress(x: numpy.ndarray, y: numpy.ndarray) -> tuple[float, float]

   Calculate the slope and intercept between two arrays using scipy's linregress.

   Used to make linregress more ufunc-friendly.

   :param x: First array.
   :param y: Second array.

   :returns: slope, intercept


.. py:function:: _trend_linear(data: Union[xarray.DataArray, xarray.Dataset]) -> dict

   Calculate the linear trend over time.

   :param data: The input data of which you want to know the trend.

   :returns: Dictionary containing the linear trend information (slope and intercept)


.. py:function:: _subtract_linear_trend(data: Union[xarray.DataArray, xarray.Dataset], trend: dict)

   Subtract a previously calclulated linear trend from (new) data.


.. py:function:: _get_trend(data: Union[xarray.DataArray, xarray.Dataset], method: str)

   Calculate the trend, with a certain method. Only linear is implemented.


.. py:function:: _subtract_trend(data: Union[xarray.DataArray, xarray.Dataset], method: str, trend: dict)

   Subtract the previously calculated trend from (new) data. Only linear is implemented.


.. py:function:: _get_climatology(data: Union[xarray.Dataset, xarray.DataArray], timescale: Literal['monthly', 'weekly', 'daily'])

   Calculate the climatology of timeseries data.


.. py:function:: _subtract_climatology(data: Union[xarray.Dataset, xarray.DataArray], timescale: Literal['monthly', 'weekly', 'daily'], climatology: Union[xarray.Dataset, xarray.DataArray])

.. py:function:: _check_input_data(data: Union[xarray.DataArray, xarray.Dataset])

   Check the input data for compatiblity with the preprocessor.

   :param data: Data to validate.

   :raises ValueError: If the input data is of the wrong type.
   :raises ValueError: If the input data does not have a 'time' dimension.


.. py:function:: _check_temporal_resolution(timescale: Literal['monthly', 'weekly', 'daily']) -> Literal['monthly', 'weekly', 'daily']

.. py:function:: _check_data_resolution_match(data: Union[xarray.DataArray, xarray.Dataset], timescale: Literal['monthly', 'weekly', 'daily'])

   Check if the temporal resolution of input is the same as given timescale.


.. py:class:: Preprocessor(rolling_window_size: Union[int, None], timescale: Literal['monthly', 'weekly', 'daily'], rolling_min_periods: int = 1, subtract_climatology: bool = True, detrend: Union[str, None] = 'linear')

   Preprocessor for s2s data.


   .. py:attribute:: _window_size


   .. py:attribute:: _min_periods


   .. py:attribute:: _detrend


   .. py:attribute:: _subtract_climatology


   .. py:attribute:: _climatology
      :type:  Union[xarray.DataArray, xarray.Dataset]


   .. py:attribute:: _trend
      :type:  dict


   .. py:attribute:: _is_fit
      :value: False



   .. py:method:: fit(data: Union[xarray.DataArray, xarray.Dataset]) -> None

      Fit this Preprocessor to input data.

      :param data: Input data for fitting.



   .. py:method:: transform(data: Union[xarray.DataArray, xarray.Dataset]) -> Union[xarray.DataArray, xarray.Dataset]

      Apply the preprocessing steps to the input data.

      :param data: Input data to perform preprocessing.

      :returns: Preprocessed data.



   .. py:method:: fit_transform(data: Union[xarray.DataArray, xarray.Dataset]) -> Union[xarray.DataArray, xarray.Dataset]

      Fit this Preprocessor to input data, and then apply the steps to the data.

      :param data: Input data for fit and transform.

      :returns: Preprocessed data.



   .. py:property:: trend
      :type: dict

      Return the stored trend (dictionary).


   .. py:property:: climatology
      :type: Union[xarray.DataArray, xarray.Dataset]

      Return the stored climatology data.