shnitsel.analyze.pls
====================

.. py:module:: shnitsel.analyze.pls


Functions
---------

.. autoapisummary::

   shnitsel.analyze.pls.pls
   shnitsel.analyze.pls.pls_ds


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

.. py:function:: pls(xdata_array, ydata_array, n_components = 2, common_dim = None)

   Performs the partial least squares analysis on the two data arrays provided as arguments and returns the
   requested number of resulting main components.

   :param xdata_array: First set of data. Shape should be (n_samples, n_features) with n_samples being the length of the common dimension with `ydata_array`.
   :type xdata_array: xr.DataArray
   :param ydata_array: Second set of data. Shape should be (n_samples, n_targets) with n_samples being the length of the common dimension with `xdata_array`.
   :type ydata_array: xr.DataArray
   :param n_components: Number of most relevant main components that should be returned. Defaults to 2.
   :type n_components: int, optional
   :param common_dim: The common dimension which should not be reduced in the course of the analysis. Defaults to None and will attempt to find a single common dimension.
   :type common_dim: str | None, optional

   :raises ValueError: If either `xdata_array` or `ydata_array` do not have exactly 2 dimensions.
   :raises ValueError: If no common_dim was set and x and y data did not have exactly 1 dimension in common to allow for automatic identification of the common dimension.

   :returns: The dataset holding the results of the PLS analysis. Results will either be in variables with the same name as `xdata_array` or `ydata_array` or in variables `x` and `y` if the names on the respective array are not set.
   :rtype: xr.Dataset


.. py:function:: pls_ds(dataset, xname, yname, n_components = 2, common_dim = None)

   Wrapper function  to perform partial least square analysis on two variables of a dataset

   :param dataset: The dataset holding the variables to apply PLS to
   :type dataset: xr.Dataset
   :param xname: The name of the variable to use as the x data for the PLS.
   :type xname: str
   :param yname: The name of the variable to use as the y data for the PLS.
   :type yname: str
   :param n_components: Number of most relevant main components that should be returned. Defaults to 2.
   :type n_components: int, optional
   :param common_dim: The common dimension which should not be reduced in the course of the analysis. Defaults to None and will attempt to find a single common dimension.
   :type common_dim: str | None, optional

   :returns: The result of the call to `pls()`. Has the results of the PLS as variables in either the same names as `xname` and `yname` or in `x` and `y`.s
   :rtype: xr.Dataset