def build_output_arrays(self, wavelet_pow_array, wavelet_phase_array,
time_axis):
wavelet_pow_array_xray = None
wavelet_phase_array_xray = None
if isinstance(self.time_series, xr.DataArray):
dims = list(self.time_series.dims[:-1] + (
'frequency',
'time',
))
transposed_dims = []
# NOTE all computaitons up till this point assume that frequency position is -2 whereas
# the default setting for this filter sets frequency axis index to 0. To avoid unnecessary transpositions
# we need to adjust position of the frequency axis in the internal computations
# getting frequency dim position as positive integer
self.frequency_dim_pos = (len(dims) +
self.frequency_dim_pos) % len(dims)
orig_frequency_idx = dims.index('frequency')
if self.frequency_dim_pos != orig_frequency_idx:
transposed_dims = dims[:orig_frequency_idx] + dims[
orig_frequency_idx + 1:]
transposed_dims.insert(self.frequency_dim_pos, 'frequency')
coords = {
dim_name: self.time_series.coords[dim_name]
for dim_name in self.time_series.dims[:-1]
}
coords['frequency'] = self.freqs
coords['time'] = time_axis
if 'samplerate' not in coords:
coords['samplerate'] = self.time_series.coords['samplerate']
if 'offsets' in list(self.time_series.coords.keys()):
coords['offsets'] = ('time', self.time_series['offsets'])
if wavelet_pow_array is not None:
wavelet_pow_array_xray = TimeSeriesX(wavelet_pow_array,
coords=coords,
dims=dims)
if len(transposed_dims):
wavelet_pow_array_xray = wavelet_pow_array_xray.transpose(
*transposed_dims)
wavelet_pow_array_xray.attrs = self.time_series.attrs.copy()
if wavelet_phase_array is not None:
wavelet_phase_array_xray = TimeSeriesX(wavelet_phase_array,
coords=coords,
dims=dims)
if len(transposed_dims):
wavelet_phase_array_xray = wavelet_phase_array_xray.transpose(
*transposed_dims)
wavelet_phase_array_xray.attrs = self.time_series.attrs.copy()
return wavelet_pow_array_xray, wavelet_phase_array_xray
def build_output_arrays(self, wavelet_pow_array, wavelet_phase_array,
time_axis):
wavelet_pow_array_xray = None
wavelet_phase_array_xray = None
if isinstance(self.time_series, xr.DataArray):
dims = list(self.time_series.dims[:-1] + (
'frequency',
'time',
))
transposed_dims = []
# getting frequency dim position as positive integer
self.frequency_dim_pos = (len(dims) +
self.frequency_dim_pos) % len(dims)
orig_frequency_idx = dims.index('frequency')
if self.frequency_dim_pos != orig_frequency_idx:
transposed_dims = dims[:orig_frequency_idx] + dims[
orig_frequency_idx + 1:]
transposed_dims.insert(self.frequency_dim_pos, 'frequency')
coords = {
dim_name: self.time_series.coords[dim_name]
for dim_name in self.time_series.dims[:-1]
}
coords['frequency'] = self.freqs
coords['time'] = time_axis
if wavelet_pow_array is not None:
wavelet_pow_array_xray = self.construct_output_array(
wavelet_pow_array, dims=dims, coords=coords)
if wavelet_phase_array is not None:
wavelet_phase_array_xray = self.construct_output_array(
wavelet_phase_array, dims=dims, coords=coords)
if wavelet_pow_array_xray is not None:
wavelet_pow_array_xray = TimeSeriesX(wavelet_pow_array_xray)
if len(transposed_dims):
wavelet_pow_array_xray = wavelet_pow_array_xray.transpose(
*transposed_dims)
wavelet_pow_array_xray.attrs = self.time_series.attrs.copy()
if wavelet_phase_array_xray is not None:
wavelet_phase_array_xray = TimeSeriesX(
wavelet_phase_array_xray)
if len(transposed_dims):
wavelet_phase_array_xray = wavelet_phase_array_xray.transpose(
*transposed_dims)
wavelet_phase_array_xray.attrs = self.time_series.attrs.copy()
return wavelet_pow_array_xray, wavelet_phase_array_xray
def build_output_arrays(self, wavelet_pow_array, wavelet_phase_array, time_axis):
wavelet_pow_array_xray = None
wavelet_phase_array_xray = None
if isinstance(self.time_series, xr.DataArray):
dims = list(self.time_series.dims[:-1] + ('frequency', 'time',))
transposed_dims = []
# NOTE all computaitons up till this point assume that frequency position is -2 whereas
# the default setting for this filter sets frequency axis index to 0. To avoid unnecessary transpositions
# we need to adjust position of the frequency axis in the internal computations
# getting frequency dim position as positive integer
self.frequency_dim_pos = (len(dims) + self.frequency_dim_pos) % len(dims)
orig_frequency_idx = dims.index('frequency')
if self.frequency_dim_pos != orig_frequency_idx:
transposed_dims = dims[:orig_frequency_idx] + dims[orig_frequency_idx + 1:]
transposed_dims.insert(self.frequency_dim_pos, 'frequency')
coords = {dim_name: self.time_series.coords[dim_name] for dim_name in self.time_series.dims[:-1]}
coords['frequency'] = self.freqs
coords['time'] = time_axis
if 'offsets' in self.time_series.coords.keys():
coords['offsets'] = ('time', self.time_series['offsets'])
if wavelet_pow_array is not None:
wavelet_pow_array_xray = self.construct_output_array(wavelet_pow_array, dims=dims, coords=coords)
if wavelet_phase_array is not None:
wavelet_phase_array_xray = self.construct_output_array(wavelet_phase_array, dims=dims, coords=coords)
if wavelet_pow_array_xray is not None:
wavelet_pow_array_xray = TimeSeriesX(wavelet_pow_array_xray)
if len(transposed_dims):
wavelet_pow_array_xray = wavelet_pow_array_xray.transpose(*transposed_dims)
wavelet_pow_array_xray.attrs = self.time_series.attrs.copy()
if wavelet_phase_array_xray is not None:
wavelet_phase_array_xray = TimeSeriesX(wavelet_phase_array_xray)
if len(transposed_dims):
wavelet_phase_array_xray = wavelet_phase_array_xray.transpose(*transposed_dims)
wavelet_phase_array_xray.attrs = self.time_series.attrs.copy()
return wavelet_pow_array_xray, wavelet_phase_array_xray
def build_output_arrays(self, wavelet_pow_array, wavelet_phase_array, time_axis):
wavelet_pow_array_xray = None
wavelet_phase_array_xray = None
if isinstance(self.time_series, xray.DataArray):
dims = list(self.time_series.dims[:-1] + ("frequency", "time"))
transposed_dims = []
# getting frequency dim position as positive integer
self.frequency_dim_pos = (len(dims) + self.frequency_dim_pos) % len(dims)
orig_frequency_idx = dims.index("frequency")
if self.frequency_dim_pos != orig_frequency_idx:
transposed_dims = dims[:orig_frequency_idx] + dims[orig_frequency_idx + 1 :]
transposed_dims.insert(self.frequency_dim_pos, "frequency")
coords = {dim_name: self.time_series.coords[dim_name] for dim_name in self.time_series.dims[:-1]}
coords["frequency"] = self.freqs
coords["time"] = time_axis
if wavelet_pow_array is not None:
wavelet_pow_array_xray = self.construct_output_array(wavelet_pow_array, dims=dims, coords=coords)
if wavelet_phase_array is not None:
wavelet_phase_array_xray = self.construct_output_array(wavelet_phase_array, dims=dims, coords=coords)
if wavelet_pow_array_xray is not None:
wavelet_pow_array_xray = TimeSeriesX(wavelet_pow_array_xray)
if len(transposed_dims):
wavelet_pow_array_xray = wavelet_pow_array_xray.transpose(*transposed_dims)
wavelet_pow_array_xray.attrs = self.time_series.attrs.copy()
if wavelet_phase_array_xray is not None:
wavelet_phase_array_xray = TimeSeriesX(wavelet_phase_array_xray)
if len(transposed_dims):
wavelet_phase_array_xray = wavelet_phase_array_xray.transpose(*transposed_dims)
wavelet_phase_array_xray.attrs = self.time_series.attrs.copy()
return wavelet_pow_array_xray, wavelet_phase_array_xray
def filter(self):
time_axis = self.time_series['time']
time_axis_size = time_axis.shape[0]
samplerate = float(self.time_series['samplerate'])
wavelet_dims = self.time_series.shape[:-1] + (self.freqs.shape[0],)
print wavelet_dims
powers_reshaped = np.array([[]], dtype=np.float)
phases_reshaped = np.array([[]], dtype=np.float)
wavelets_complex_reshaped = np.array([[]], dtype=np.complex)
if self.output == 'power':
powers_reshaped = np.empty(shape=(np.prod(wavelet_dims), self.time_series.shape[-1]), dtype=np.float)
if self.output == 'phase':
phases_reshaped = np.empty(shape=(np.prod(wavelet_dims), self.time_series.shape[-1]), dtype=np.float)
if self.output == 'both':
powers_reshaped = np.empty(shape=(np.prod(wavelet_dims), self.time_series.shape[-1]), dtype=np.float)
phases_reshaped = np.empty(shape=(np.prod(wavelet_dims), self.time_series.shape[-1]), dtype=np.float)
if self.output == 'complex':
wavelets_complex_reshaped = np.empty(shape=(np.prod(wavelet_dims), self.time_series.shape[-1]),
dtype=np.complex)
# mt = morlet.MorletWaveletTransformMP(self.cpus)
mt = MorletWaveletTransformMP(self.cpus)
time_series_reshaped = self.time_series.data.reshape(np.prod(self.time_series.shape[:-1]),
self.time_series.shape[-1])
if self.output == 'power':
mt.set_output_type(morlet.POWER)
if self.output == 'phase':
mt.set_output_type(morlet.PHASE)
if self.output == 'both':
mt.set_output_type(morlet.BOTH)
if self.output == 'complex':
mt.set_output_type(morlet.COMPLEX)
mt.set_signal_array(time_series_reshaped)
mt.set_wavelet_pow_array(powers_reshaped)
mt.set_wavelet_phase_array(phases_reshaped)
mt.set_wavelet_complex_array(wavelets_complex_reshaped)
# mt.initialize_arrays(time_series_reshaped, wavelets_reshaped)
mt.initialize_signal_props(float(self.time_series['samplerate']))
mt.initialize_wavelet_props(self.width, self.freqs)
mt.prepare_run()
s = time.time()
mt.compute_wavelets_threads()
powers_final = None
phases_final = None
wavelet_complex_final = None
if self.output == 'power':
powers_final = powers_reshaped.reshape(wavelet_dims + (self.time_series.shape[-1],))
if self.output == 'phase':
phases_final = phases_reshaped.reshape(wavelet_dims + (self.time_series.shape[-1],))
if self.output == 'both':
powers_final = powers_reshaped.reshape(wavelet_dims + (self.time_series.shape[-1],))
phases_final = phases_reshaped.reshape(wavelet_dims + (self.time_series.shape[-1],))
if self.output == 'complex':
wavelet_complex_final = wavelets_complex_reshaped.reshape(wavelet_dims + (self.time_series.shape[-1],))
# wavelets_final = powers_reshaped.reshape( wavelet_dims+(self.time_series.shape[-1],) )
coords = {k: v for k, v in self.time_series.coords.items()}
coords['frequency'] = self.freqs
powers_ts = None
phases_ts = None
wavelet_complex_ts = None
if powers_final is not None:
powers_ts = TimeSeriesX(powers_final,
dims=self.time_series.dims[:-1] + ('frequency', self.time_series.dims[-1],),
coords=coords
)
final_dims = (powers_ts.dims[-2],) + powers_ts.dims[:-2] + (powers_ts.dims[-1],)
powers_ts = powers_ts.transpose(*final_dims)
if phases_final is not None:
phases_ts = TimeSeriesX(phases_final,
dims=self.time_series.dims[:-1] + ('frequency', self.time_series.dims[-1],),
coords=coords
)
final_dims = (phases_ts.dims[-2],) + phases_ts.dims[:-2] + (phases_ts.dims[-1],)
phases_ts = phases_ts.transpose(*final_dims)
if wavelet_complex_final is not None:
wavelet_complex_ts = TimeSeriesX(wavelet_complex_final,
dims=self.time_series.dims[:-1] + (
'frequency', self.time_series.dims[-1],),
coords=coords
)
final_dims = (wavelet_complex_ts.dims[-2],) + wavelet_complex_ts.dims[:-2] + (wavelet_complex_ts.dims[-1],)
wavelet_complex_ts = wavelet_complex_ts.transpose(*final_dims)
if wavelet_complex_ts is not None:
return wavelet_complex_ts, None
else:
return powers_ts, phases_ts
def filter(self):
time_axis = self.time_series['time']
time_axis_size = time_axis.shape[0]
samplerate = float(self.time_series['samplerate'])
wavelet_dims = self.time_series.shape[:-1] + (self.freqs.shape[0], )
print(wavelet_dims)
powers_reshaped = np.array([[]], dtype=np.float)
phases_reshaped = np.array([[]], dtype=np.float)
wavelets_complex_reshaped = np.array([[]], dtype=np.complex)
if self.output == 'power':
powers_reshaped = np.empty(shape=(np.prod(wavelet_dims),
self.time_series.shape[-1]),
dtype=np.float)
if self.output == 'phase':
phases_reshaped = np.empty(shape=(np.prod(wavelet_dims),
self.time_series.shape[-1]),
dtype=np.float)
if self.output == 'both':
powers_reshaped = np.empty(shape=(np.prod(wavelet_dims),
self.time_series.shape[-1]),
dtype=np.float)
phases_reshaped = np.empty(shape=(np.prod(wavelet_dims),
self.time_series.shape[-1]),
dtype=np.float)
if self.output == 'complex':
wavelets_complex_reshaped = np.empty(
shape=(np.prod(wavelet_dims), self.time_series.shape[-1]),
dtype=np.complex)
# mt = morlet.MorletWaveletTransformMP(self.cpus)
# mt = MorletWaveletTransformMP(self.cpus)
mt = MorletWaveletTransformMP(self.cpus)
time_series_reshaped = np.ascontiguousarray(
self.time_series.data.reshape(np.prod(self.time_series.shape[:-1]),
self.time_series.shape[-1]),
self.time_series.data.dtype)
if self.output == 'power':
mt.set_output_type(morlet.POWER)
if self.output == 'phase':
mt.set_output_type(morlet.PHASE)
if self.output == 'both':
mt.set_output_type(morlet.BOTH)
if self.output == 'complex':
mt.set_output_type(morlet.COMPLEX)
mt.set_signal_array(time_series_reshaped)
mt.set_wavelet_pow_array(powers_reshaped)
mt.set_wavelet_phase_array(phases_reshaped)
mt.set_wavelet_complex_array(wavelets_complex_reshaped)
# mt.initialize_arrays(time_series_reshaped, wavelets_reshaped)
mt.initialize_signal_props(float(self.time_series['samplerate']))
mt.initialize_wavelet_props(self.width, self.freqs)
mt.prepare_run()
s = time.time()
mt.compute_wavelets_threads()
powers_final = None
phases_final = None
wavelet_complex_final = None
if self.output == 'power':
powers_final = powers_reshaped.reshape(
wavelet_dims + (self.time_series.shape[-1], ))
if self.output == 'phase':
phases_final = phases_reshaped.reshape(
wavelet_dims + (self.time_series.shape[-1], ))
if self.output == 'both':
powers_final = powers_reshaped.reshape(
wavelet_dims + (self.time_series.shape[-1], ))
phases_final = phases_reshaped.reshape(
wavelet_dims + (self.time_series.shape[-1], ))
if self.output == 'complex':
wavelet_complex_final = wavelets_complex_reshaped.reshape(
wavelet_dims + (self.time_series.shape[-1], ))
# wavelets_final = powers_reshaped.reshape( wavelet_dims+(self.time_series.shape[-1],) )
coords = {k: v for k, v in list(self.time_series.coords.items())}
coords['frequency'] = self.freqs
powers_ts = None
phases_ts = None
wavelet_complex_ts = None
if powers_final is not None:
powers_ts = TimeSeriesX(powers_final,
dims=self.time_series.dims[:-1] + (
'frequency',
self.time_series.dims[-1],
),
coords=coords)
final_dims = (powers_ts.dims[-2], ) + powers_ts.dims[:-2] + (
powers_ts.dims[-1], )
powers_ts = powers_ts.transpose(*final_dims)
if phases_final is not None:
phases_ts = TimeSeriesX(phases_final,
dims=self.time_series.dims[:-1] + (
'frequency',
self.time_series.dims[-1],
),
coords=coords)
final_dims = (phases_ts.dims[-2], ) + phases_ts.dims[:-2] + (
phases_ts.dims[-1], )
phases_ts = phases_ts.transpose(*final_dims)
if wavelet_complex_final is not None:
wavelet_complex_ts = TimeSeriesX(wavelet_complex_final,
dims=self.time_series.dims[:-1] +
(
'frequency',
self.time_series.dims[-1],
),
coords=coords)
final_dims = (wavelet_complex_ts.dims[-2],
) + wavelet_complex_ts.dims[:-2] + (
wavelet_complex_ts.dims[-1], )
wavelet_complex_ts = wavelet_complex_ts.transpose(*final_dims)
if self.verbose:
print('CPP total time wavelet loop: ', time.time() - s)
if wavelet_complex_ts is not None:
return wavelet_complex_ts, None
else:
return powers_ts, phases_ts