# -*- 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 to download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (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
# 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/>.
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#
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as explained here:
# https://www.thevirtualbrain.org/tvb/zwei/neuroscience-publications
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"""
Plot the power of a WaveletCoefficients object
.. moduleauthor:: Dan Pop <dan.pop@codemart.ro>
.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>
"""
import json
from tvb.adapters.datatypes.db.spectral import WaveletCoefficientsIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import ABCDisplayer
from tvb.core.neocom import h5
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.spectral import WaveletCoefficients
[docs]
class WaveletSpectrogramVisualizerModel(ViewModel):
input_data = DataTypeGidAttr(
linked_datatype=WaveletCoefficients,
label='Wavelet transform Result',
doc='Wavelet spectrogram to display'
)
[docs]
class WaveletSpectrogramVisualizer(ABCDisplayer):
"""
Plot the power of a WaveletCoefficients object using SVG an D3.
"""
_ui_name = "Spectrogram of Wavelet Power"
_ui_subsection = "wavelet"
[docs]
def get_required_memory_size(self, view_model):
# type: (WaveletSpectrogramVisualizerModel) -> int
"""
Return the required memory to run this algorithm.
"""
wavelet_idx = self.load_entity_by_gid(view_model.input_data)
shape = wavelet_idx.shape
return shape[0] * shape[1] * 8
[docs]
def launch(self, view_model):
# type: (WaveletSpectrogramVisualizerModel) -> dict
with h5.h5_file_for_gid(view_model.input_data) as input_h5:
shape = input_h5.array_data.shape
input_sample_period = input_h5.sample_period.load()
input_frequencies = input_h5.frequencies.load()
ts_index = self.load_entity_by_gid(input_h5.source.load())
slices = (slice(shape[0]),
slice(shape[1]),
slice(0, 1, None),
slice(0, shape[3], None),
slice(0, 1, None))
data_matrix = input_h5.power[slices]
data_matrix = data_matrix.sum(axis=3)
assert isinstance(ts_index, TimeSeriesIndex)
wavelet_sample_period = ts_index.sample_period * max((1, int(input_sample_period / ts_index.sample_period)))
end_time = ts_index.start_time + (wavelet_sample_period * shape[1])
if len(input_frequencies):
freq_lo = input_frequencies[0]
freq_hi = input_frequencies[-1]
else:
freq_lo = 0
freq_hi = 1
scale_range_start = max(1, int(0.25 * shape[1]))
scale_range_end = max(1, int(0.75 * shape[1]))
scale_min = data_matrix[:, scale_range_start:scale_range_end, :].min()
scale_max = data_matrix[:, scale_range_start:scale_range_end, :].max()
matrix_data = ABCDisplayer.dump_with_precision(data_matrix.flat)
matrix_shape = json.dumps(data_matrix.squeeze().shape)
params = dict(canvasName="Wavelet Spectrogram for: " + ts_index.title,
xAxisName="Time (%s)" % str(ts_index.sample_period_unit),
yAxisName="Frequency (%s)" % str("kHz"),
title=self._ui_name,
matrix_data=matrix_data,
matrix_shape=matrix_shape,
start_time=ts_index.start_time,
end_time=end_time,
freq_lo=freq_lo,
freq_hi=freq_hi,
vmin=scale_min,
vmax=scale_max)
return self.build_display_result("wavelet/wavelet_view", params,
pages={"controlPage": "wavelet/controls"})