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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# (c) 2012-2024, Baycrest Centre for Geriatric Care ("Baycrest") and others
#
# This program is free software: you can redistribute it and/or modify it under the
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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.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 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_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 launch(self, view_model):
# type: (FourierSpectrumModel) -> dict
self.log.debug("Plot started...")
# these partial loads are dangerous for TS and FS instances, but efficient
fourier_spectrum = FourierSpectrum()
with h5.h5_file_for_gid(view_model.input_data) 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(input_h5.source.load())
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)