baseline.py

"""Util function to baseline correct data
"""

# Authors: Alexandre Gramfort <gramfort@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)

import numpy as np

from . import verbose
from .utils import logger


@verbose
def rescale(data, times, baseline, mode, verbose=None, copy=True):
    """Rescale aka baseline correct data

    Parameters
    ----------
    data : array
        It can be of any shape. The only constraint is that the last
        dimension should be time.
    times : 1D array
        Time instants is seconds.
    baseline : tuple or list of length 2, or None
        The time interval to apply rescaling / baseline correction.
        If None do not apply it. If baseline is (a, b)
        the interval is between "a (s)" and "b (s)".
        If a is None the beginning of the data is used
        and if b is None then b is set to the end of the interval.
        If baseline is equal ot (None, None) all the time
        interval is used. If None, no correction is applied.
    mode : 'logratio' | 'ratio' | 'zscore' | 'mean' | 'percent'
        Do baseline correction with ratio (power is divided by mean
        power during baseline) or zscore (power is divided by standard
        deviation of power during baseline after subtracting the mean,
        power = [power - mean(power_baseline)] / std(power_baseline)).
    verbose : bool, str, int, or None
        If not None, override default verbose level (see mne.verbose).
    copy : bool
        Operate on a copy of the data, or in place.

    Returns
    -------
    data_scaled: array
        Array of same shape as data after rescaling.
    """
    if copy:
        data = data.copy()

    valid_modes = ['logratio', 'ratio', 'zscore', 'mean', 'percent']
    if mode not in valid_modes:
        raise Exception('mode should be any of : %s' % valid_modes)

    if baseline is not None:
        logger.info("Applying baseline correction ... (mode: %s)" % mode)
        bmin, bmax = baseline
        if bmin is None:
            imin = 0
        else:
            imin = int(np.where(times >= bmin)[0][0])
        if bmax is None:
            imax = len(times)
        else:
            imax = int(np.where(times <= bmax)[0][-1]) + 1

        mean = np.mean(data[..., imin:imax], axis=-1)[..., None]
        if mode == 'mean':
            data -= mean
        if mode == 'logratio':
            data /= mean
            data = np.log10(data)  # a value of 1 means 10 times bigger
        if mode == 'ratio':
            data /= mean
        elif mode == 'zscore':
            std = np.std(data[..., imin:imax], axis=-1)[..., None]
            data -= mean
            data /= std
        elif mode == 'percent':
            data -= mean
            data /= mean

    else:
        logger.info("No baseline correction applied...")

    return data