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:py:mod:`wildboar.datasets.preprocess`
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.. py:module:: wildboar.datasets.preprocess

.. autoapi-nested-parse::

   Utilities for preprocessing time series.

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


Functions
---------

.. autoapisummary::

   wildboar.datasets.preprocess.maxabs_scale
   wildboar.datasets.preprocess.minmax_scale
   wildboar.datasets.preprocess.named_preprocess
   wildboar.datasets.preprocess.standardize
   wildboar.datasets.preprocess.truncate



.. py:function:: maxabs_scale(x)

   
   Scale each time series by its maximum absolute value.


   :Parameters:

       **x** : ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The samples.

   :Returns:

       ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The transformed samples.













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.. py:function:: minmax_scale(x, min=0, max=1)

   
   Scale x along the time dimension.

   Each time series is scaled such that each value is between min and max.

   :Parameters:

       **x** : ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The samples.

       **min** : float, optional
           The minimum value.

       **max** : float, optional
           The maximum value.

   :Returns:

       ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The transformed samples.













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.. py:function:: named_preprocess(name)

   
   Get a named preprocessor.


   :Parameters:

       **name** : str
           The name of the preprocessor.

   :Returns:

       callable
           The preprocessor function.













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.. py:function:: standardize(x)

   
   Scale x along the time dimension.

   The resulting array will have zero mean and unit standard deviation.

   :Parameters:

       **x** : ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The samples.

   :Returns:

       ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The standardized samples.













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.. py:function:: truncate(x, n_shortest=None)

   
   Truncate x to the shortest sequence.


   :Parameters:

       **x** : ndarray of shape (n_samples, n_timestep) or (n_samples, n_dims, n_timestep)
           The samples.

       **n_shortest** : int, optional
           The maximum size.

   :Returns:

       ndarray of shape (n_samples, n_shortest) or (n_samples, n_dims, n_shortest)
           The truncated samples.













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