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Source code for tvb.adapters.analyzers.wavelet_adapter

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#   Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
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"""
Adapter that uses the traits module to generate interfaces for
ContinuousWaveletTransform Analyzer.

.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>

"""

import uuid

import numpy
from tvb.adapters.datatypes.db.spectral import WaveletCoefficientsIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.adapters.datatypes.h5.spectral_h5 import WaveletCoefficientsH5
from tvb.adapters.datatypes.h5.time_series_h5 import TimeSeriesH5
from tvb.analyzers.wavelet import compute_continuous_wavelet_transform
from tvb.basic.neotraits.api import Attr, Range, 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 FormField, Form, TraitDataTypeSelectField, StrField, FloatField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.time_series import TimeSeries


[docs]class WaveletAdapterModel(ViewModel): time_series = DataTypeGidAttr( linked_datatype=TimeSeries, label="Time Series", required=True, doc="""The timeseries to which the wavelet is to be applied.""") mother = Attr( field_type=str, label="Wavelet function", default="morlet", doc="""The mother wavelet function used in the transform. Default is 'morlet', possibilities are: 'morlet'...""") sample_period = Float( label="Sample period of result (ms)", default=7.8125, # 7.8125 => 128 Hz doc="""The sampling period of the computed wavelet spectrum. NOTE: This should be an integral multiple of the of the sampling period of the source time series, otherwise the actual resulting sample period will be the first correct value below that requested.""") frequencies = Attr( field_type=Range, label="Frequency range of result (kHz).", default=Range(lo=0.008, hi=0.060, step=0.002), doc="""The frequency resolution and range returned. Requested frequencies are converted internally into appropriate scales.""") normalisation = Attr( field_type=str, label="Normalisation", default="energy", doc="""The type of normalisation for the resulting wavet spectrum. Default is 'energy', options are: 'energy'; 'gabor'.""") q_ratio = Float( label="Q-ratio", default=5.0, doc="""NFC. Must be greater than 5. Ratios of the center frequencies to bandwidths.""")
[docs]class RangeForm(Form): def __init__(self): super(RangeForm, self).__init__() self.lo = FloatField( Float(label='Lo', default=WaveletAdapterModel.frequencies.default.lo, doc='start of range'), name='Lo') self.hi = FloatField( Float(label='Hi', default=WaveletAdapterModel.frequencies.default.hi, doc='end of range'), name='Hi') self.step = FloatField( Float(label='Step', default=WaveletAdapterModel.frequencies.default.step, doc='step of range'), name='Step')
[docs]class ContinuousWaveletTransformAdapterForm(ABCAdapterForm): def __init__(self): super(ContinuousWaveletTransformAdapterForm, self).__init__() self.time_series = TraitDataTypeSelectField(WaveletAdapterModel.time_series, name=self.get_input_name(), conditions=self.get_filters(), has_all_option=True) self.mother = StrField(WaveletAdapterModel.mother) self.sample_period = FloatField(WaveletAdapterModel.sample_period) self.normalisation = StrField(WaveletAdapterModel.normalisation) self.q_ratio = FloatField(WaveletAdapterModel.q_ratio) self.frequencies = FormField(RangeForm, name='frequencies', label=WaveletAdapterModel.frequencies.label, doc=WaveletAdapterModel.frequencies.doc) @staticmethod
[docs] def get_view_model(): return WaveletAdapterModel
@staticmethod
[docs] def get_required_datatype(): return TimeSeriesIndex
[docs] def fill_trait(self, datatype): super(ContinuousWaveletTransformAdapterForm, self).fill_trait(datatype) datatype.frequencies.lo = self.frequencies.form.lo.value datatype.frequencies.step = self.frequencies.form.step.value datatype.frequencies.hi = self.frequencies.form.hi.value
@staticmethod
[docs] def get_input_name(): return 'time_series'
@staticmethod
[docs] def get_filters(): return FilterChain(fields=[FilterChain.datatype + '.data_ndim'], operations=["=="], values=[4])
[docs]class ContinuousWaveletTransformAdapter(ABCAdapter): """ TVB adapter for calling the ContinuousWaveletTransform algorithm. """ _ui_name = "Continuous Wavelet Transform" _ui_description = "Compute Wavelet Tranformation for a TimeSeries input DataType." _ui_subsection = "wavelet"
[docs] def get_form_class(self): return ContinuousWaveletTransformAdapterForm
[docs] def get_output(self): return [WaveletCoefficientsIndex]
[docs] def configure(self, view_model): """ Store the input shape to be later used to estimate memory usage """ self.input_time_series_index = self.load_entity_by_gid(view_model.time_series) input_shape = [] for length in [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]: if length is not None: input_shape.append(length) self.input_shape = tuple(input_shape) self.log.debug("Time series shape is %s" % str(self.input_shape))
[docs] def get_required_memory_size(self, view_model): """ Return the required memory to run this algorithm. """ used_shape = (self.input_shape[0], self.input_shape[1], 1, self.input_shape[3]) input_size = numpy.prod(used_shape) * 8.0 output_size = self.result_size(view_model.frequencies, view_model.sample_period, used_shape, self.input_time_series_index.sample_period) return input_size + output_size
[docs] def get_required_disk_size(self, view_model): """ Returns the required disk size to be able to run the adapter.(in kB) """ used_shape = (self.input_shape[0], self.input_shape[1], 1, self.input_shape[3]) return self.array_size2kb(self.result_size(view_model.frequencies, view_model.sample_period, used_shape, self.input_time_series_index.sample_period))
[docs] def launch(self, view_model): # type: (WaveletAdapterModel) -> (WaveletCoefficientsIndex) """ Launch algorithm and build results. :param view_model: the ViewModel keeping the algorithm inputs :return: the wavelet coefficients for the specified time series """ frequencies_array = numpy.array([]) if view_model.frequencies is not None: frequencies_array = view_model.frequencies.to_array() time_series_h5 = h5.h5_file_for_index(self.input_time_series_index) assert isinstance(time_series_h5, TimeSeriesH5) # --------------------- Prepare result entities ----------------------## wavelet_index = WaveletCoefficientsIndex() dest_path = h5.path_for(self.storage_path, WaveletCoefficientsH5, wavelet_index.gid) wavelet_h5 = WaveletCoefficientsH5(path=dest_path) # ------------- NOTE: Assumes 4D, Simulator timeSeries. --------------## node_slice = [slice(self.input_shape[0]), slice(self.input_shape[1]), None, slice(self.input_shape[3])] # ---------- Iterate over slices and compose final result ------------## small_ts = TimeSeries() small_ts.sample_period = time_series_h5.sample_period.load() small_ts.sample_period_unit = time_series_h5.sample_period_unit.load() for node in range(self.input_shape[2]): node_slice[2] = slice(node, node + 1) small_ts.data = time_series_h5.read_data_slice(tuple(node_slice)) partial_wavelet = compute_continuous_wavelet_transform(small_ts, view_model.frequencies, view_model.sample_period, view_model.q_ratio, view_model.normalisation, view_model.mother) wavelet_h5.write_data_slice(partial_wavelet) time_series_h5.close() partial_wavelet.source.gid = view_model.time_series partial_wavelet.gid = uuid.UUID(wavelet_index.gid) wavelet_index.fill_from_has_traits(partial_wavelet) self.fill_index_from_h5(wavelet_index, wavelet_h5) wavelet_h5.store(partial_wavelet, scalars_only=True) wavelet_h5.frequencies.store(frequencies_array) wavelet_h5.close() return wavelet_index
@staticmethod
[docs] def result_shape(frequencies, sample_period, input_shape, input_sample_period): """ Returns the shape of the main result (complex array) of the continuous wavelet transform. """ freq_len = int((frequencies.hi - frequencies.lo) / frequencies.step) temporal_step = max((1, sample_period / input_sample_period)) nt = int(round(input_shape[0] / temporal_step)) result_shape = (freq_len, nt,) + input_shape[1:] return result_shape
[docs] def result_size(self, frequencies, sample_period, input_shape, input_sample_period): """ Returns the storage size in Bytes of the main result (complex array) of the continuous wavelet transform. """ result_size = numpy.prod( self.result_shape(frequencies, sample_period, input_shape, input_sample_period)) * 2.0 * 8.0 # complex*Bytes return result_size