Source code for tvb.adapters.visualizers.wavelet_spectrogram

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Plot the power of a WaveletCoefficients object

.. moduleauthor:: Dan Pop <>
.. 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 WaveletSpectrogramVisualizerForm(ABCAdapterForm): # TODO: add all fields here def __init__(self): super(WaveletSpectrogramVisualizerForm, self).__init__() self.input_data = TraitDataTypeSelectField(WaveletSpectrogramVisualizerModel.input_data, name='input_data', conditions=self.get_filters())
[docs] @staticmethod def get_view_model(): return WaveletSpectrogramVisualizerModel
[docs] @staticmethod def get_required_datatype(): return WaveletCoefficientsIndex
[docs] @staticmethod def get_filters(): return None
[docs] @staticmethod def get_input_name(): return 'input_data'
[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_form_class(self): return WaveletSpectrogramVisualizerForm
[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"})