class ComplexCoherenceSpectrumData(arrays.MappedArray):
"""
Result of a NodeComplexCoherence Analysis.
"""
cross_spectrum = arrays.ComplexArray(
label="The cross spectrum",
file_storage=core.FILE_STORAGE_EXPAND,
doc=""" A complex ndarray that contains the nodes x nodes cross
spectrum for every frequency frequency and for every segment.""")
array_data = arrays.ComplexArray(
label="Complex Coherence",
file_storage=core.FILE_STORAGE_EXPAND,
doc="""The complex coherence coefficients calculated from the cross
spectrum. The imaginary values of this complex ndarray represent the
imaginary coherence.""")
source = time_series.TimeSeries(
label="Source time-series",
doc="""Links to the time-series on which the node_coherence is
applied.""")
epoch_length = basic.Float(
label="Epoch length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
segment_length = basic.Float(
label="Segment length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
# frequency = arrays.FloatArray(
# label = "Frequency",
# doc = """DOC ME""")
windowing_function = basic.String(
label="Windowing function",
doc="""The windowing function applied to each time segment prior to
application of the FFT.""")
# number_of_epochs = basic.Integer(
# label = "Number of epochs",
# doc = """DOC ME""")
#
# number_of_segments = basic.Integer(
# label = "Number of segments",
# doc = """DOC ME""")
__generate_table__ = True
class Covariance(arrays.MappedArray):
"""Covariance datatype."""
array_data = arrays.ComplexArray(file_storage=core.FILE_STORAGE_EXPAND)
source = time_series.TimeSeries(
label="Source time-series",
doc="Links to the time-series on which NodeCovariance is applied.")
__generate_table__ = True
def configure(self):
"""After populating few fields, compute the rest of the fields"""
# Do not call super, because that accesses data not-chunked
self.nr_dimensions = len(self.read_data_shape())
for i in range(self.nr_dimensions):
setattr(self, 'length_%dd' % (i + 1), int(self.read_data_shape()[i]))
def write_data_slice(self, partial_result):
"""
Append chunk.
"""
self.store_data_chunk('array_data', partial_result, grow_dimension=2, close_file=False)
def _find_summary_info(self):
summary = {"Graph type": self.__class__.__name__,
"Source": self.source.title}
summary.update(self.get_info_about_array('array_data'))
return summary
def test_bool_array(self):
"""
Create a boolean array, check that shape is correct.
"""
data = numpy.array([[False for _ in range(12)] for _ in range(10)])
array_dt = arrays.ComplexArray()
array_dt.data = data
self.assertEqual(array_dt.shape, (10, 12))
def test_complex_array(self):
"""
Create a complex array, check that shape is correct.
"""
data = numpy.array([numpy.complex(100, 2) for _ in range(100)])
array_dt = arrays.ComplexArray()
array_dt.data = data
self.assertEqual(array_dt.shape, (100,))
class CovarianceData(arrays.MappedArray):
"""
Result of a Covariance Analysis.
"""
#Overwrite attribute from superclass
array_data = arrays.ComplexArray(file_storage=core.FILE_STORAGE_EXPAND)
source = time_series.TimeSeries(
label="Source time-series",
doc="Links to the time-series on which NodeCovariance is applied.")
__generate_table__ = True
class FourierSpectrumData(arrays.MappedArray):
"""
Result of a Fourier Analysis.
"""
#Overwrite attribute from superclass
array_data = arrays.ComplexArray(file_storage=core.FILE_STORAGE_EXPAND)
source = time_series.TimeSeries(
label="Source time-series",
doc="Links to the time-series on which the FFT is applied.")
segment_length = basic.Float(
label="Segment length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
windowing_function = basic.String(
label="Windowing function",
doc="""The windowing function applied to each time segment prior to
application of the FFT.""")
amplitude = arrays.FloatArray(label="Amplitude",
file_storage=core.FILE_STORAGE_EXPAND)
phase = arrays.FloatArray(label="Phase",
file_storage=core.FILE_STORAGE_EXPAND)
power = arrays.FloatArray(label="Power",
file_storage=core.FILE_STORAGE_EXPAND)
average_power = arrays.FloatArray(label="Average Power",
file_storage=core.FILE_STORAGE_EXPAND)
normalised_average_power = arrays.FloatArray(
label="Normalised Power", file_storage=core.FILE_STORAGE_EXPAND)
__generate_table__ = True
class WaveletCoefficientsData(arrays.MappedArray):
"""
This class bundles all the elements of a Wavelet Analysis into a single
object, including the input TimeSeries datatype and the output results as
arrays (FloatArray)
"""
#Overwrite attribute from superclass
array_data = arrays.ComplexArray()
source = time_series.TimeSeries(label="Source time-series")
mother = basic.String(
label="Mother wavelet",
default="morlet",
doc="""A string specifying the type of mother wavelet to use,
default is 'morlet'.""") # default to 'morlet'
sample_period = basic.Float(label="Sample period")
#sample_rate = basic.Integer(label = "") inversely related
frequencies = arrays.FloatArray(
label="Frequencies", doc="A vector that maps scales to frequencies.")
normalisation = basic.String(label="Normalisation type")
# 'unit energy' | 'gabor'
q_ratio = basic.Float(label="Q-ratio", default=5.0)
amplitude = arrays.FloatArray(label="Amplitude",
file_storage=core.FILE_STORAGE_EXPAND)
phase = arrays.FloatArray(label="Phase",
file_storage=core.FILE_STORAGE_EXPAND)
power = arrays.FloatArray(label="Power",
file_storage=core.FILE_STORAGE_EXPAND)
__generate_table__ = True
class FourierSpectrum(arrays.MappedArray):
"""
Result of a Fourier Analysis.
"""
#Overwrite attribute from superclass
array_data = arrays.ComplexArray(file_storage=core.FILE_STORAGE_EXPAND)
source = time_series.TimeSeries(
label="Source time-series",
doc="Links to the time-series on which the FFT is applied.")
segment_length = basic.Float(
label="Segment length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
windowing_function = basic.String(
label="Windowing function",
doc="""The windowing function applied to each time segment prior to
application of the FFT.""")
amplitude = arrays.FloatArray(label="Amplitude",
file_storage=core.FILE_STORAGE_EXPAND)
phase = arrays.FloatArray(label="Phase",
file_storage=core.FILE_STORAGE_EXPAND)
power = arrays.FloatArray(label="Power",
file_storage=core.FILE_STORAGE_EXPAND)
average_power = arrays.FloatArray(label="Average Power",
file_storage=core.FILE_STORAGE_EXPAND)
normalised_average_power = arrays.FloatArray(
label="Normalised Power", file_storage=core.FILE_STORAGE_EXPAND)
_frequency = None
_freq_step = None
_max_freq = None
__generate_table__ = True
def configure(self):
"""After populating few fields, compute the rest of the fields"""
# Do not call super, because that accesses data not-chunked
self.nr_dimensions = len(self.read_data_shape())
for i in range(self.nr_dimensions):
setattr(self, 'length_%dd' % (i + 1),
int(self.read_data_shape()[i]))
if self.trait.use_storage is False and sum(
self.get_data_shape('array_data')) != 0:
if self.amplitude.size == 0:
self.compute_amplitude()
if self.phase.size == 0:
self.compute_phase()
if self.power.size == 0:
self.compute_power()
if self.average_power.size == 0:
self.compute_average_power()
if self.normalised_average_power.size == 0:
self.compute_normalised_average_power()
def write_data_slice(self, partial_result):
"""
Append chunk.
"""
# self.store_data_chunk('array_data', partial_result, grow_dimension=2, close_file=False)
self.store_data_chunk('array_data',
partial_result.array_data,
grow_dimension=2,
close_file=False)
partial_result.compute_amplitude()
self.store_data_chunk('amplitude',
partial_result.amplitude,
grow_dimension=2,
close_file=False)
partial_result.compute_phase()
self.store_data_chunk('phase',
partial_result.phase,
grow_dimension=2,
close_file=False)
partial_result.compute_power()
self.store_data_chunk('power',
partial_result.power,
grow_dimension=2,
close_file=False)
partial_result.compute_average_power()
self.store_data_chunk('average_power',
partial_result.average_power,
grow_dimension=2,
close_file=False)
partial_result.compute_normalised_average_power()
self.store_data_chunk('normalised_average_power',
partial_result.normalised_average_power,
grow_dimension=2,
close_file=False)
def _find_summary_info(self):
"""
Gather scientifically interesting summary information from an instance of this datatype.
"""
summary = {
"Spectral type": self.__class__.__name__,
"Source": self.source.title,
"Segment length": self.segment_length,
"Windowing function": self.windowing_function,
"Frequency step": self.freq_step,
"Maximum frequency": self.max_freq
}
return summary
@property
def freq_step(self):
""" Frequency step size of the complex Fourier spectrum."""
if self._freq_step is None:
self._freq_step = 1.0 / self.segment_length
msg = "%s: Frequency step size is %s"
LOG.debug(msg % (str(self), str(self._freq_step)))
return self._freq_step
@property
def max_freq(self):
""" Amplitude of the complex Fourier spectrum."""
if self._max_freq is None:
self._max_freq = 0.5 / self.source.sample_period
msg = "%s: Max frequency is %s"
LOG.debug(msg % (str(self), str(self._max_freq)))
return self._max_freq
@property
def frequency(self):
""" Frequencies represented the complex Fourier spectrum."""
if self._frequency is None:
self._frequency = numpy.arange(self.freq_step,
self.max_freq + self.freq_step,
self.freq_step)
util.log_debug_array(LOG, self._frequency, "frequency")
return self._frequency
def compute_amplitude(self):
""" Amplitude of the complex Fourier spectrum."""
self.amplitude = numpy.abs(self.array_data)
self.trait["amplitude"].log_debug(owner=self.__class__.__name__)
def compute_phase(self):
""" Phase of the Fourier spectrum."""
self.phase = numpy.angle(self.array_data)
self.trait["phase"].log_debug(owner=self.__class__.__name__)
def compute_power(self):
""" Power of the complex Fourier spectrum."""
self.power = numpy.abs(self.array_data)**2
self.trait["power"].log_debug(owner=self.__class__.__name__)
def compute_average_power(self):
""" Average-power of the complex Fourier spectrum."""
self.average_power = numpy.mean(numpy.abs(self.array_data)**2, axis=-1)
self.trait["average_power"].log_debug(owner=self.__class__.__name__)
def compute_normalised_average_power(self):
""" Normalised-average-power of the complex Fourier spectrum."""
self.normalised_average_power = (self.average_power /
numpy.sum(self.average_power, axis=0))
self.trait["normalised_average_power"].log_debug(
owner=self.__class__.__name__)
class ComplexCoherenceSpectrum(arrays.MappedArray):
"""
Result of a NodeComplexCoherence Analysis.
"""
cross_spectrum = arrays.ComplexArray(
label="The cross spectrum",
file_storage=core.FILE_STORAGE_EXPAND,
doc=""" A complex ndarray that contains the nodes x nodes cross
spectrum for every frequency frequency and for every segment."""
)
array_data = arrays.ComplexArray(
label="Complex Coherence",
file_storage=core.FILE_STORAGE_EXPAND,
doc="""The complex coherence coefficients calculated from the cross
spectrum. The imaginary values of this complex ndarray represent the
imaginary coherence.""")
source = time_series.TimeSeries(
label="Source time-series",
doc="""Links to the time-series on which the node_coherence is
applied.""")
epoch_length = basic.Float(
label="Epoch length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
segment_length = basic.Float(
label="Segment length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
windowing_function = basic.String(
label="Windowing function",
doc="""The windowing function applied to each time segment prior to
application of the FFT.""")
__generate_table__ = True
_frequency = None
_freq_step = None
_max_freq = None
def configure(self):
"""After populating few fields, compute the rest of the fields"""
# Do not call super, because that accesses data not-chunked
self.configure_chunk_safe()
def write_data_slice(self, partial_result):
"""
Append chunk.
"""
self.store_data_chunk('cross_spectrum',
partial_result.cross_spectrum,
grow_dimension=2,
close_file=False)
self.store_data_chunk('array_data',
partial_result.array_data,
grow_dimension=2,
close_file=False)
def _find_summary_info(self):
"""
Gather scientifically interesting summary information from an instance of this datatype.
"""
summary = {
"Spectral type": self.__class__.__name__,
"Source": self.source.title,
"Frequency step": self.freq_step,
"Maximum frequency": self.max_freq
}
# summary["FFT length (time-points)"] = self.fft_points
# summary["Number of epochs"] = self.number_of_epochs
return summary
@property
def freq_step(self):
""" Frequency step size of the Complex Coherence Spectrum."""
if self._freq_step is None:
self._freq_step = 1.0 / self.segment_length
msg = "%s: Frequency step size is %s"
LOG.debug(msg % (str(self), str(self._freq_step)))
return self._freq_step
@property
def max_freq(self):
""" Maximum frequency represented in the Complex Coherence Spectrum."""
if self._max_freq is None:
self._max_freq = 0.5 / self.source.sample_period
msg = "%s: Max frequency is %s"
LOG.debug(msg % (str(self), str(self._max_freq)))
return self._max_freq
@property
def frequency(self):
""" Frequencies represented in the Complex Coherence Spectrum."""
if self._frequency is None:
self._frequency = numpy.arange(self.freq_step,
self.max_freq + self.freq_step,
self.freq_step)
util.log_debug_array(LOG, self._frequency, "frequency")
return self._frequency
class WaveletCoefficients(arrays.MappedArray):
"""
This class bundles all the elements of a Wavelet Analysis into a single
object, including the input TimeSeries datatype and the output results as
arrays (FloatArray)
"""
#Overwrite attribute from superclass
array_data = arrays.ComplexArray()
source = time_series.TimeSeries(label="Source time-series")
mother = basic.String(
label="Mother wavelet",
default="morlet",
doc="""A string specifying the type of mother wavelet to use,
default is 'morlet'.""") # default to 'morlet'
sample_period = basic.Float(label="Sample period")
#sample_rate = basic.Integer(label = "") inversely related
frequencies = arrays.FloatArray(
label="Frequencies", doc="A vector that maps scales to frequencies.")
normalisation = basic.String(label="Normalisation type")
# 'unit energy' | 'gabor'
q_ratio = basic.Float(label="Q-ratio", default=5.0)
amplitude = arrays.FloatArray(label="Amplitude",
file_storage=core.FILE_STORAGE_EXPAND)
phase = arrays.FloatArray(label="Phase",
file_storage=core.FILE_STORAGE_EXPAND)
power = arrays.FloatArray(label="Power",
file_storage=core.FILE_STORAGE_EXPAND)
_frequency = None
_time = None
__generate_table__ = True
def configure(self):
"""After populating few fields, compute the rest of the fields"""
# Do not call super, because that accesses data not-chunked
self.nr_dimensions = len(self.read_data_shape())
for i in range(self.nr_dimensions):
setattr(self, 'length_%dd' % (i + 1),
int(self.read_data_shape()[i]))
if self.trait.use_storage is False and sum(
self.get_data_shape('array_data')) != 0:
if self.amplitude.size == 0:
self.compute_amplitude()
if self.phase.size == 0:
self.compute_phase()
if self.power.size == 0:
self.compute_power()
def _find_summary_info(self):
"""
Gather scientifically interesting summary information from an instance of this datatype.
"""
summary = {
"Spectral type": self.__class__.__name__,
"Source": self.source.title,
"Wavelet type": self.mother,
"Normalisation": self.normalisation,
"Q-ratio": self.q_ratio,
"Sample period": self.sample_period,
"Number of scales": self.frequencies.shape[0],
"Minimum frequency": self.frequencies[0],
"Maximum frequency": self.frequencies[-1]
}
return summary
@property
def frequency(self):
""" Frequencies represented by the wavelet spectrogram."""
if self._frequency is None:
self._frequency = numpy.arange(self.frequencies.lo,
self.frequencies.hi,
self.frequencies.step)
util.log_debug_array(LOG, self._frequency, "frequency")
return self._frequency
def compute_amplitude(self):
""" Amplitude of the complex Wavelet coefficients."""
self.amplitude = numpy.abs(self.array_data)
def compute_phase(self):
""" Phase of the Wavelet coefficients."""
self.phase = numpy.angle(self.array_data)
def compute_power(self):
""" Power of the complex Wavelet coefficients."""
self.power = numpy.abs(self.array_data)**2
def write_data_slice(self, partial_result):
"""
Append chunk.
"""
self.store_data_chunk('array_data',
partial_result.array_data,
grow_dimension=2,
close_file=False)
partial_result.compute_amplitude()
self.store_data_chunk('amplitude',
partial_result.amplitude,
grow_dimension=2,
close_file=False)
partial_result.compute_phase()
self.store_data_chunk('phase',
partial_result.phase,
grow_dimension=2,
close_file=False)
partial_result.compute_power()
self.store_data_chunk('power',
partial_result.power,
grow_dimension=2,
close_file=False)
class ComplexCoherenceSpectrum(arrays.MappedArray):
"""
Result of a NodeComplexCoherence Analysis.
"""
cross_spectrum = arrays.ComplexArray(
label="The cross spectrum",
file_storage=core.FILE_STORAGE_EXPAND,
doc=""" A complex ndarray that contains the nodes x nodes cross
spectrum for every frequency frequency and for every segment.""")
array_data = arrays.ComplexArray(
label="Complex Coherence",
file_storage=core.FILE_STORAGE_EXPAND,
doc="""The complex coherence coefficients calculated from the cross
spectrum. The imaginary values of this complex ndarray represent the
imaginary coherence.""")
source = time_series.TimeSeries(
label="Source time-series",
doc="""Links to the time-series on which the node_coherence is
applied.""")
epoch_length = basic.Float(
label="Epoch length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
segment_length = basic.Float(
label="Segment length",
doc="""The timeseries was segmented into equally sized blocks
(overlapping if necessary), prior to the application of the FFT.
The segement length determines the frequency resolution of the
resulting spectra.""")
windowing_function = basic.String(
label="Windowing function",
doc="""The windowing function applied to each time segment prior to
application of the FFT.""")
__generate_table__ = True
_frequency = None
_freq_step = None
_max_freq = None
spectrum_types = ["Imaginary", "Real", "Absolute"]
def configure(self):
"""After populating few fields, compute the rest of the fields"""
# Do not call super, because that accesses data not-chunked
self.configure_chunk_safe()
def write_data_slice(self, partial_result):
"""
Append chunk.
"""
self.store_data_chunk('cross_spectrum', partial_result.cross_spectrum, grow_dimension=2, close_file=False)
self.store_data_chunk('array_data', partial_result.array_data, grow_dimension=2, close_file=False)
def _find_summary_info(self):
"""
Gather scientifically interesting summary information from an instance of this datatype.
"""
summary = {"Spectral type": self.__class__.__name__,
"Source": self.source.title,
"Frequency step": self.freq_step,
"Maximum frequency": self.max_freq,
"Epoch length": self.epoch_length,
"Segment length": self.segment_length,
"Windowing function": self.windowing_function
}
return summary
@property
def freq_step(self):
""" Frequency step size of the Complex Coherence Spectrum."""
if self._freq_step is None:
self._freq_step = 1.0 / self.segment_length
msg = "%s: Frequency step size is %s"
LOG.debug(msg % (str(self), str(self._freq_step)))
return self._freq_step
@property
def max_freq(self):
""" Maximum frequency represented in the Complex Coherence Spectrum."""
if self._max_freq is None:
self._max_freq = 0.5 / self.source.sample_period
msg = "%s: Max frequency is %s"
LOG.debug(msg % (str(self), str(self._max_freq)))
return self._max_freq
@property
def frequency(self):
""" Frequencies represented in the Complex Coherence Spectrum."""
if self._frequency is None:
self._frequency = numpy.arange(self.freq_step,
self.max_freq + self.freq_step,
self.freq_step)
util.log_debug_array(LOG, self._frequency, "frequency")
return self._frequency
def get_spectrum_data(self, selected_spectrum):
shape = list(self.read_data_shape())
slices = (slice(shape[0]), slice(shape[1]), slice(shape[2]),)
if selected_spectrum == self.spectrum_types[0]:
data_matrix = self.get_data('array_data', slices).imag
indices = numpy.triu_indices(shape[0], 1)
data_matrix = data_matrix[indices]
elif selected_spectrum == self.spectrum_types[1]:
data_matrix = self.get_data('array_data', slices).real
data_matrix = data_matrix.reshape(shape[0] * shape[0], shape[2])
else:
data_matrix = self.get_data('array_data', slices)
data_matrix = numpy.absolute(data_matrix)
data_matrix = data_matrix.reshape(shape[0] * shape[0], shape[2])
coh_spec_sd = numpy.std(data_matrix, axis=0)
coh_spec_av = numpy.mean(data_matrix, axis=0)
ymin = numpy.amin(coh_spec_av - coh_spec_sd)
ymax = numpy.amax(coh_spec_av + coh_spec_sd)
coh_spec_sd = json.dumps(coh_spec_sd.tolist())
coh_spec_av = json.dumps(coh_spec_av.tolist())
return dict(coh_spec_sd=coh_spec_sd,
coh_spec_av=coh_spec_av,
ymin=ymin,
ymax=ymax)
