# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Framework Package. This package holds all Data Management, and
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# CITATION:
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"""
Adapter that uses the traits module to generate interfaces for FFT 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 CoherenceSpectrumIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.adapters.datatypes.h5.spectral_h5 import CoherenceSpectrumH5
from tvb.analyzers.node_coherence import calculate_cross_coherence
from tvb.basic.neotraits.api import Int
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, IntField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.time_series import TimeSeries
[docs]class NodeCoherenceModel(ViewModel):
time_series = DataTypeGidAttr(
linked_datatype=TimeSeries,
label="Time Series",
required=True,
doc="""The timeseries to which the Cross Coherence is to be applied."""
)
nfft = Int(
label="Data-points per block",
default=256,
doc="""Should be a power of 2...""")
[docs]class NodeCoherenceAdapter(ABCAdapter):
""" TVB adapter for calling the NodeCoherence algorithm. """
_ui_name = "Cross coherence of nodes"
_ui_description = "Compute Node Coherence for a TimeSeries input DataType."
_ui_subsection = "coherence"
[docs] def get_output(self):
return [CoherenceSpectrumIndex]
[docs] def get_required_memory_size(self, view_model):
# type: (NodeCoherenceModel) -> int
"""
Return the required memory to run this algorithm.
"""
used_shape = (self.input_shape[0],
1,
self.input_shape[2],
self.input_shape[3])
input_size = numpy.prod(used_shape) * 8.0
output_size = self.result_size(used_shape, view_model.nfft)
return input_size + output_size
[docs] def get_required_disk_size(self, view_model):
# type: (NodeCoherenceModel) -> int
"""
Returns the required disk size to be able to run the adapter (in kB).
"""
used_shape = (self.input_shape[0],
1,
self.input_shape[2],
self.input_shape[3])
return self.array_size2kb(self.result_size(used_shape, view_model.nfft))
[docs] def launch(self, view_model):
# type: (NodeCoherenceModel) -> [CoherenceSpectrumIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the node coherence for the specified time series
"""
# -------------------- Prepare result entities -----------------------##
coherence_spectrum_index = CoherenceSpectrumIndex()
dest_path = self.path_for(CoherenceSpectrumH5, coherence_spectrum_index.gid)
coherence_h5 = CoherenceSpectrumH5(dest_path)
# ------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
input_shape = time_series_h5.data.shape
node_slice = [slice(input_shape[0]), None, slice(input_shape[2]), slice(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()
partial_coh = None
for var in range(input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
partial_coh = calculate_cross_coherence(small_ts, view_model.nfft)
coherence_h5.write_data_slice(partial_coh)
time_series_h5.close()
if partial_coh is None:
self.log.warn(f"Empty TS {input_shape[1]} ?")
return None
partial_coh.source.gid = view_model.time_series
partial_coh.gid = uuid.UUID(coherence_spectrum_index.gid)
coherence_spectrum_index.fill_from_has_traits(partial_coh)
self.fill_index_from_h5(coherence_spectrum_index, coherence_h5)
coherence_h5.store(partial_coh, scalars_only=True)
coherence_h5.frequency.store(partial_coh.frequency)
coherence_h5.close()
return coherence_spectrum_index
[docs] def result_size(self, input_shape, nfft):
"""
Returns the storage size in Bytes of the main result of NodeCoherence.
"""
# TODO This depends on input array dtype!
result_size = numpy.sum(list(map(numpy.prod, self.result_shape(input_shape, nfft)))) * 8.0 # Bytes
return result_size
[docs] @staticmethod
def result_shape(input_shape, nfft):
"""Returns the shape of the main result of NodeCoherence."""
freq_len = nfft / 2 + 1
freq_shape = (freq_len,)
result_shape = (freq_len, input_shape[2], input_shape[2], input_shape[1], input_shape[3])
return [result_shape, freq_shape]
