The Virtual Brain Project

Source code for tvb.adapters.visualizers.fourier_spectrum

# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Framework Package. This package holds all Data Management, and
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# TheVirtualBrain-Scientific Package (for simulators). See content of the
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# (c) 2012-2017, Baycrest Centre for Geriatric Care ("Baycrest") and others
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#   CITATION:
# When using The Virtual Brain for scientific publications, please cite it as follows:
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#   Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
#   Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
#       The Virtual Brain: a simulator of primate brain network dynamics.
#   Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)
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"""
.. moduleauthor:: Dan Pop <dan.pop@codemart.ro>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
.. moduleauthor:: Stuart A. Knock <stuart.knock@gmail.com>

"""
import json
import numpy
from tvb.core.adapters.abcdisplayer import ABCDisplayer
from tvb.datatypes.spectral import FourierSpectrum


[docs]class FourierSpectrumDisplay(ABCDisplayer): """ This viewer takes as inputs a result form FFT analysis, and returns required parameters for a MatplotLib representation. """ _ui_name = "Fourier Visualizer" _ui_subsection = "fourier"
[docs] def get_input_tree(self): """ Accept as input result from FFT Analysis. """ return [{'name': 'input_data', 'label': 'Fourier Result', 'type': FourierSpectrum, 'required': True, 'description': 'Fourier Analysis to display'}]
[docs] def get_required_memory_size(self, **kwargs): """ Return the required memory to run this algorithm. """ return numpy.prod(kwargs['input_data'].read_data_shape()) * 8
[docs] def generate_preview(self, **kwargs): return self.launch(**kwargs)
[docs] def launch(self, **kwargs): self.log.debug("Plot started...") input_data = kwargs['input_data'] shape = list(input_data.read_data_shape()) state_list = input_data.source.labels_dimensions.get(input_data.source.labels_ordering[1], []) mode_list = range(shape[3]) available_scales = ["Linear", "Logarithmic"] params = dict(matrix_shape=json.dumps([shape[0], shape[2]]), plotName=input_data.source.type, url_base=ABCDisplayer.paths2url(input_data, "get_fourier_data", parameter=""), xAxisName="Frequency [kHz]", yAxisName="Power", available_scales=available_scales, state_list=state_list, mode_list=mode_list, normalize_list=["no", "yes"], normalize="no", state_variable=state_list[0], mode=mode_list[0], xscale=available_scales[0], yscale=available_scales[0], x_values=json.dumps(input_data.frequency[slice(shape[0])].tolist()), xmin=input_data.freq_step, xmax=input_data.max_freq) return self.build_display_result("fourier_spectrum/view", params)