Source code for tvb.adapters.analyzers.node_complex_coherence_adapter

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Adapter that uses the traits module to generate interfaces for FFT Analyzer.

.. moduleauthor:: Lia Domide <>
.. moduleauthor:: Paula Sanz Leon <paula@tvb.invalid>


import numpy
from tvb.adapters.datatypes.db.spectral import ComplexCoherenceSpectrumIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.adapters.datatypes.h5.spectral_h5 import ComplexCoherenceSpectrumH5
from tvb.analyzers.node_complex_coherence import calculate_complex_cross_coherence, complex_coherence_result_shape
from tvb.basic.neotraits.api import Attr, Int, Float
from tvb.core.adapters.abcadapter import ABCAdapterForm, ABCAdapter
from tvb.core.entities.filters.chain import FilterChain
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.time_series import TimeSeries

[docs]class NodeComplexCoherenceModel(ViewModel): time_series = DataTypeGidAttr( linked_datatype=TimeSeries, label="Time Series", required=True, doc="""The timeseries for which the CrossCoherence and ComplexCoherence is to be computed.""" ) epoch_length = Float( label="Epoch length [ms]", default=1000.0, required=False, doc="""In general for lengthy EEG recordings (~30 min), the timeseries are divided into equally sized segments (~ 20-40s). These contain the event that is to be characterized by means of the cross coherence. Additionally each epoch block will be further divided into segments to which the FFT will be applied.""") segment_length = Float( label="Segment length [ms]", default=500.0, required=False, doc="""The timeseries can be segmented into equally sized blocks (overlapping if necessary). The segment length determines the frequency resolution of the resulting power spectra -- longer windows produce finer frequency resolution. """) segment_shift = Float( label="Segment shift [ms]", default=250.0, required=False, doc="""Time length by which neighboring segments are shifted. e.g. `segment shift` = `segment_length` / 2 means 50% overlapping segments.""") window_function = Attr( field_type=str, label="Windowing function", default='hanning', required=False, doc="""Windowing functions can be applied before the FFT is performed. Default is `hanning`, possibilities are: 'hamming'; 'bartlett'; 'blackman'; and 'hanning'. See, numpy.<function_name>.""") average_segments = Attr( field_type=bool, label="Average across segments", default=True, required=False, doc="""Flag. If `True`, compute the mean Cross Spectrum across segments.""") subtract_epoch_average = Attr( field_type=bool, label="Subtract average across epochs", default=True, required=False, doc="""Flag. If `True` and if the number of epochs is > 1, you can optionally subtract the mean across epochs before computing the complex coherence.""") zeropad = Int( label="Zeropadding", default=0, required=False, doc="""Adds `n` zeros at the end of each segment and at the end of window_function. It is not yet functional.""") detrend_ts = Attr( field_type=bool, label="Detrend time series", default=False, required=False, doc="""Flag. If `True` removes linear trend along the time dimension before applying FFT.""") max_freq = Float( label="Maximum frequency", default=1024.0, required=False, doc="""Maximum frequency points (e.g. 32., 64., 128.) represented in the output. Default is segment_length / 2 + 1.""") npat = Float( label="dummy variable", default=1.0, required=False, doc="""This attribute appears to be related to an input projection matrix... Which is not yet implemented""")
[docs]class NodeComplexCoherenceForm(ABCAdapterForm): def __init__(self): super(NodeComplexCoherenceForm, self).__init__() self.time_series = TraitDataTypeSelectField(NodeComplexCoherenceModel.time_series, name=self.get_input_name(), conditions=self.get_filters()) @staticmethod
[docs] def get_view_model(): return NodeComplexCoherenceModel
[docs] def get_required_datatype(): return TimeSeriesIndex
[docs] def get_filters(): return FilterChain(fields=[FilterChain.datatype + '.data_ndim'], operations=["=="], values=[4])
[docs] def get_input_name(): return "time_series"
[docs]class NodeComplexCoherenceAdapter(ABCAdapter): """ TVB adapter for calling the NodeComplexCoherence algorithm. """ _ui_name = "Complex Coherence of Nodes" _ui_description = "Compute the node complex (imaginary) coherence for a TimeSeries input DataType." _ui_subsection = "complexcoherence"
[docs] def get_form_class(self): return NodeComplexCoherenceForm
[docs] def get_output(self): return [ComplexCoherenceSpectrumIndex]
[docs] def get_required_memory_size(self, view_model): # type: (NodeComplexCoherenceModel) -> int """ Return the required memory to run this algorithm. """ input_size = * 8.0 output_size = self.result_size(self.input_shape, view_model.max_freq, view_model.epoch_length, view_model.segment_length, view_model.segment_shift, self.input_time_series_index.sample_period, view_model.zeropad, view_model.average_segments) return input_size + output_size
[docs] def get_required_disk_size(self, view_model): # type: (NodeComplexCoherenceModel) -> int """ Returns the required disk size to be able to run the adapter (in kB). """ result = self.result_size(self.input_shape, view_model.max_freq, view_model.epoch_length, view_model.segment_length, view_model.segment_shift, self.input_time_series_index.sample_period, view_model.zeropad, view_model.average_segments) return self.array_size2kb(result)
[docs] def configure(self, view_model): # type: (NodeComplexCoherenceModel) -> None """ Do any configuration needed before launching """ self.input_time_series_index = self.load_entity_by_gid(view_model.time_series) self.input_shape = (self.input_time_series_index.data_length_1d, self.input_time_series_index.data_length_2d, self.input_time_series_index.data_length_3d, self.input_time_series_index.data_length_4d) self.log.debug("Time series shape is %s" % (str(self.input_shape)))
[docs] def launch(self, view_model): # type: (NodeComplexCoherenceModel) -> [ComplexCoherenceSpectrumIndex] """ Launch algorithm and build results. :param view_model: the ViewModel keeping the algorithm inputs :return: the complex coherence for the specified time series """ # TODO ---------- Iterate over slices and compose final result ------------## time_series = h5.load_from_index(self.input_time_series_index) ht_result = calculate_complex_cross_coherence(time_series, view_model.epoch_length, view_model.segment_length, view_model.segment_shift, view_model.window_function, view_model.average_segments, view_model.subtract_epoch_average, view_model.zeropad, view_model.detrend_ts, view_model.max_freq, view_model.npat) self.log.debug("got ComplexCoherenceSpectrum result") self.log.debug("ComplexCoherenceSpectrum segment_length is %s" % (str(ht_result.segment_length))) self.log.debug("ComplexCoherenceSpectrum epoch_length is %s" % (str(ht_result.epoch_length))) self.log.debug("ComplexCoherenceSpectrum windowing_function is %s" % (str(ht_result.windowing_function))) complex_coherence_index = h5.store_complete(ht_result, self.storage_path) result_path = h5.path_for(self.storage_path, ComplexCoherenceSpectrumH5, complex_coherence_index.gid) ica_h5 = ComplexCoherenceSpectrumH5(path=result_path) self.fill_index_from_h5(complex_coherence_index, ica_h5) ica_h5.close() return complex_coherence_index
[docs] def result_size(input_shape, max_freq, epoch_length, segment_length, segment_shift, sample_period, zeropad, average_segments): """ Returns the storage size in Bytes of the main result (complex array) of the ComplexCoherence """ result_size =, max_freq, epoch_length, segment_length, segment_shift, sample_period, zeropad, average_segments)[0]) * 2.0 * 8.0 return result_size