TheVirtualBrain:

TheDocumentationwebsite.

Source code for tvb.adapters.visualizers.fourier_spectrum

# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Framework Package. This package holds all Data Management, and
# Web-UI helpful to run brain-simulations. To use it, you also need do download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (c) 2012-2020, Baycrest Centre for Geriatric Care ("Baycrest") and others
#
# This program is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE.  See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program.  If not, see <http://www.gnu.org/licenses/>.
#
#
#   CITATION:
# When using The Virtual Brain for scientific publications, please cite it as follows:
#
#   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)
#
#

"""
.. 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.adapters.datatypes.db.spectral import FourierSpectrumIndex
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import ABCDisplayer, URLGenerator
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neocom import h5
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.spectral import FourierSpectrum
from tvb.datatypes.time_series import TimeSeries


[docs]class FourierSpectrumModel(ViewModel): input_data = DataTypeGidAttr( linked_datatype=FourierSpectrum, label='Fourier Result', doc='Fourier Analysis to display' )
[docs]class FourierSpectrumForm(ABCAdapterForm): def __init__(self, prefix='', project_id=None): super(FourierSpectrumForm, self).__init__(prefix, project_id) self.input_data = TraitDataTypeSelectField(FourierSpectrumModel.input_data, self, name='input_data', conditions=self.get_filters()) @staticmethod
[docs] def get_view_model(): return FourierSpectrumModel
@staticmethod
[docs] def get_input_name(): return "input_data"
@staticmethod
[docs] def get_required_datatype(): return FourierSpectrumIndex
@staticmethod
[docs] def get_filters(): return None
[docs] def get_traited_datatype(self): return None
[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_form_class(self): return FourierSpectrumForm
[docs] def get_required_memory_size(self, view_model): # type: (FourierSpectrumModel) -> dict """ Return the required memory to run this algorithm. """ fs_input_index = self.load_entity_by_gid(view_model.input_data) return numpy.prod(fs_input_index.get_data_shape()) * 8
[docs] def generate_preview(self, view_model, figure_size=None): # type: (FourierSpectrumModel, (int,int)) -> dict return self.launch(view_model)
[docs] def launch(self, view_model): # type: (FourierSpectrumModel) -> dict self.log.debug("Plot started...") # these partial loads are dangerous for TS and FS instances, but efficient fs_input_index = self.load_entity_by_gid(view_model.input_data) fourier_spectrum = FourierSpectrum() with h5.h5_file_for_index(fs_input_index) as input_h5: shape = list(input_h5.array_data.shape) fourier_spectrum.segment_length = input_h5.segment_length.load() fourier_spectrum.windowing_function = input_h5.windowing_function.load() ts_index = self.load_entity_by_gid(fs_input_index.fk_source_gid) state_list = ts_index.get_labels_for_dimension(1) if len(state_list) == 0: state_list = list(range(shape[1])) fourier_spectrum.source = TimeSeries(sample_period=ts_index.sample_period) mode_list = list(range(shape[3])) available_scales = ["Linear", "Logarithmic"] params = dict(matrix_shape=json.dumps([shape[0], shape[2]]), plotName=ts_index.title, url_base=URLGenerator.build_h5_url(view_model.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(fourier_spectrum.frequency[slice(shape[0])].tolist()), xmin=fourier_spectrum.freq_step, xmax=fourier_spectrum.max_freq) return self.build_display_result("fourier_spectrum/view", params)