| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# datasets.miscfx_sp

## Module: `datasets.miscfx_sp`

Misc function performing operations on datasets which are based on scipy

`mvpa.datasets.miscfx_sp.``detrend`(dataset, perchunk=False, model='linear', polyord=None, opt_reg=None)

  Given a dataset, detrend the data inplace either entirely or per each chunk

  Parameters:

  - dataset (Dataset) – dataset to operate on
  - perchunk (bool) – either to operate on whole dataset at once or on each chunk separately
  - model – Type of detrending model to run. If ‘linear’ or ‘constant’, scipy.signal.detrend is used to perform a linear or demeaning detrend. Polynomial detrending is activated when ‘regress’ is used or when polyord or opt_reg are specified.
  - polyord (int or list) – Order of the Legendre polynomial to remove from the data. This will remove every polynomial up to and including the provided value. For example, 3 will remove 0th, 1st, 2nd, and 3rd order polynomials from the data. N.B.: The 0th polynomial is the baseline shift, the 1st is the linear trend. If you specify a single int and perchunk is True, then this value is used for each chunk. You can also specify a different polyord value for each chunk by providing a list or ndarray of polyord values the length of the number of chunks.
  - opt_reg (ndarray) – Optional ndarray of additional information to regress out from the dataset. One example would be to regress out motion parameters. As with the data, time is on the first axis.

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