Source code for tvb.analyzers.fft

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Calculate an FFT on a TimeSeries DataType and return a FourierSpectrum DataType.

.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>


import numpy
import scipy.signal

from tvb.basic.logger.builder import get_logger
from import narray_describe
from tvb.datatypes.spectral import FourierSpectrum

log = get_logger(__name__)

    'hamming': numpy.hamming,
    'bartlett': numpy.bartlett,
    'blackman': numpy.blackman,
    'hanning': numpy.hanning

A module for calculating the FFT of a TimeSeries object of TVB and returning
a FourierSpectrum object. A segment length and windowing function can be
optionally specified. By default the time series is segmented into 1 second
blocks and no windowing function is applied.

[docs]def compute_fast_fourier_transform(time_series, segment_length, window_function, detrend): """ # type: (TimeSeries, float, function, bool) -> FourierSpectrum Calculate the FFT of time_series broken into segments of length segment_length and filtered by window_function. Parameters __________ time_series : TimeSeries The TimeSeries to which the FFT is to be applied. segment_length : float The segment length determines the frequency resolution of the resulting power spectra -- longer windows produce finer frequency resolution window_function : str Windowing functions can be applied before the FFT is performed. Default is None, possibilities are: 'hamming'; 'bartlett';'blackman'; and 'hanning'. See, numpy.<function_name>. detrend : bool Default is True, False means no detrending is performed on the time series. """ tpts =[0] time_series_length = tpts * time_series.sample_period # Segment time-series, overlapping if necessary nseg = int(numpy.ceil(time_series_length / segment_length)) if nseg > 1: seg_tpts = numpy.ceil(segment_length / time_series.sample_period) overlap = (seg_tpts * nseg - tpts) / (nseg - 1.0) starts = [max(seg * (seg_tpts - overlap), 0) for seg in range(nseg)] segments = [[int(start):int(start) + int(seg_tpts)] for start in starts] segments = [segment[:, :, :, :, numpy.newaxis] for segment in segments] ts = numpy.concatenate(segments, axis=4) else: segment_length = time_series_length ts =[:, :, :, :, numpy.newaxis] seg_tpts = ts.shape[0] log.debug("Segment length being used is: %s" % segment_length) # Base-line correct the segmented time-series if detrend: ts = scipy.signal.detrend(ts, axis=0) log.debug("time_series " + narray_describe(ts)) # Apply windowing function if window_function is not None: wf = SUPPORTED_WINDOWING_FUNCTIONS[window_function.value] window_mask = numpy.reshape(wf(int(seg_tpts)), (int(seg_tpts), 1, 1, 1, 1)) ts = ts * window_mask # Calculate the FFT result = numpy.fft.fft(ts, axis=0) nfreq = result.shape[0] // 2 result = result[1:nfreq + 1, :] log.debug("result " + narray_describe(result)) spectra = FourierSpectrum( source=time_series, segment_length=segment_length, array_data=result, windowing_function=window_function ) spectra.configure() return spectra