The Virtual Brain Project

Source code for tvb.adapters.analyzers.fourier_adapter

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Adapter that uses the traits module to generate interfaces for FFT Analyzer.

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

import psutil
import numpy
import math
import tvb.analyzers.fft as fft
import tvb.core.adapters.abcadapter as abcadapter
import tvb.basic.filters.chain as entities_filter
import tvb.datatypes.time_series as datatypes_time_series
import tvb.datatypes.spectral as spectral
from tvb.basic.logger.builder import get_logger

LOG = get_logger(__name__)

[docs]class FourierAdapter(abcadapter.ABCAsynchronous): """ TVB adapter for calling the FFT algorithm. """ _ui_name = "Fourier Spectral Analysis" _ui_description = "Calculate the FFT of a TimeSeries entity." _ui_subsection = "fourier"
[docs] def get_input_tree(self): """ Return a list of lists describing the interface to the analyzer. This is used by the GUI to generate the menus and fields necessary for defining a simulation. """ algorithm = fft.FFT() algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY tree = algorithm.interface[self.INTERFACE_ATTRIBUTES] for node in tree: if node['name'] == 'time_series': node['conditions'] = entities_filter.FilterChain( fields=[entities_filter.FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[4]) return tree
[docs] def get_output(self): return [spectral.FourierSpectrum]
def __init__(self): super(FourierAdapter, self).__init__() self.algorithm = fft.FFT() self.memory_factor = 1
[docs] def configure(self, time_series, segment_length=None, window_function=None, detrend=None): """ Do any configuration needed before launching. :param time_series: the input time series to which the fft is to be applied :param segment_length: the block size which determines the frequency resolution \ of the resulting power spectra :param window_function: windowing functions can be applied before the FFT is performed :type window_function: None; ‘hamming’; ‘bartlett’; ‘blackman’; ‘hanning’ :param detrend: None; specify if detrend is performed on the time series """ shape = time_series.read_data_shape() LOG.debug("time_series shape is %s" % (str(shape))) LOG.debug("Provided segment_length is %s" % (str(segment_length))) LOG.debug("Provided window_function is %s" % (str(window_function))) LOG.debug("Detrend is %s" % (str(detrend))) ##-------------------- Fill Algorithm for Analysis -------------------## #The enumerate set function isn't working well. A get around strategy is to create a new algorithm algorithm = fft.FFT() if segment_length is not None: algorithm.segment_length = segment_length algorithm.window_function = window_function algorithm.time_series = time_series algorithm.detrend = detrend self.algorithm = algorithm LOG.debug("Using segment_length is %s" % (str(self.algorithm.segment_length))) LOG.debug("Using window_function is %s" % (str(self.algorithm.window_function))) LOG.debug("Using detrend is %s" % (str(self.algorithm.detrend)))
[docs] def get_required_memory_size(self, **kwargs): """ Returns the required memory to be able to run the adapter. """ input_shape = self.algorithm.time_series.read_data_shape() input_size = * 8.0 output_size = self.algorithm.result_size(input_shape, self.algorithm.segment_length, self.algorithm.time_series.sample_period) total_free_memory = psutil.virtual_memory().free + psutil.swap_memory().free total_required_memory = input_size + output_size while total_required_memory / self.memory_factor / total_free_memory > 0.8: self.memory_factor += 1 return total_required_memory / self.memory_factor
[docs] def get_required_disk_size(self, **kwargs): """ Returns the required disk size to be able to run the adapter (in kB). """ input_shape = self.algorithm.time_series.read_data_shape() output_size = self.algorithm.result_size(input_shape, self.algorithm.segment_length, self.algorithm.time_series.sample_period) return self.array_size2kb(output_size)
[docs] def launch(self, time_series, segment_length=None, window_function=None, detrend=None): """ Launch algorithm and build results. :param time_series: the input time series to which the fft is to be applied :param segment_length: the block size which determines the frequency resolution \ of the resulting power spectra :param window_function: windowing functions can be applied before the FFT is performed :type window_function: None; ‘hamming’; ‘bartlett’; ‘blackman’; ‘hanning’ :returns: the fourier spectrum for the specified time series :rtype: `FourierSpectrum` """ shape = time_series.read_data_shape() block_size = int(math.floor(time_series.read_data_shape()[2] / self.memory_factor)) blocks = int(math.ceil(time_series.read_data_shape()[2] / block_size)) ##----------- Prepare a FourierSpectrum object for result ------------## spectra = spectral.FourierSpectrum(source=time_series, segment_length=self.algorithm.segment_length, windowing_function=str(window_function), storage_path=self.storage_path) ##------------- NOTE: Assumes 4D, Simulator timeSeries. --------------## node_slice = [slice(shape[0]), slice(shape[1]), None, slice(shape[3])] ##---------- Iterate over slices and compose final result ------------## small_ts = datatypes_time_series.TimeSeries(use_storage=False) small_ts.sample_period = time_series.sample_period for block in range(blocks): node_slice[2] = slice(block * block_size, min([(block+1) * block_size, shape[2]]), 1) = time_series.read_data_slice(tuple(node_slice)) self.algorithm.time_series = small_ts partial_result = self.algorithm.evaluate() if blocks <= 1 and len(partial_result.array_data) == 0: self.add_operation_additional_info( "Fourier produced empty result (most probably due to a very short input TimeSeries).") return None spectra.write_data_slice(partial_result) LOG.debug("partial segment_length is %s" % (str(partial_result.segment_length))) spectra.segment_length = partial_result.segment_length spectra.close_file() return spectra