def empirical_interpolation_for_time_series(waveforms):
"""Calculate empirical nodes and corresponding empirical interpolating functions.
Parameters
----------
waveforms : HDF5TimeSeriesSet
Returns
-------
empirical_node_indices : List of ints
The indices of the empirical nodes in the TimeSeries.
B_j : List of TimeSeries
The empirical interpolating functions
that are 1 at the node T_j and
0 at the other nodes T_i (for i!=j).
"""
Nwave = len(waveforms.get_parameters())
# Convert the list of TimeSeries to a list of numpy arrays
#wave_np = [waveforms[i].numpy() for i in range(Nwave)]
wave_np = [waveforms.get_waveform_data(i).numpy() for i in range(Nwave)]
# Determine the empirical nodes
empirical_node_indices = eim.generate_empirical_nodes(wave_np)
# Determine the empirical interpolating functions B_j(t)
B_j_np = eim.generate_interpolant_list(wave_np, empirical_node_indices)
# Convert the arrays to TimeSeries.
delta_t = waveforms.get_waveform_data(0).delta_t
epoch = waveforms.get_waveform_data(0).start_time
B_j = [pycbc.types.TimeSeries(B_j_np[j], delta_t=delta_t, epoch=epoch) for j in range(Nwave)]
return empirical_node_indices, B_j
def empirical_interpolation_uniform_spacing(waveforms, xs, datatype):
"""Calculate empirical nodes and corresponding empirical interpolating functions
from a set of reduced basis waveforms.
Parameters
----------
waveforms : List like set of Waveform objects
Could be HDF5WaveformSet
datatype : string {'amp', 'phase'}
Returns
-------
empirical_node_indices : List of ints
The indices of the empirical nodes in the Waveform objects.
B_j : List of Waveform objects
The empirical interpolating functions
that are 1 at the node T_j and
0 at the other nodes T_i (for i!=j).
"""
nwave = len(waveforms)
if len(xs) != nwave:
raise Exception, "Number of waveforms and xs must be the same."
# Convert the list of Waveform objects to a list of complex numpy arrays
if datatype == 'amp':
wave_np = [waveforms[i].amp for i in range(nwave)]
elif datatype == 'phase':
wave_np = [waveforms[i].phase for i in range(nwave)]
else:
raise Exception, "datatype must be one of {'amp', 'phase'}."
# Determine the empirical node indices
empirical_node_indices = np.array(
[arg_nearest(waveforms[0].x, xi) for xi in xs])
# Determine the empirical interpolating functions B_j(t)
B_j_np = eim.generate_interpolant_list(wave_np, empirical_node_indices)
# Convert the arrays to Waveform objects.
xarr = waveforms[0].x
B_j = []
for j in range(nwave):
if datatype == 'amp':
amp = B_j_np[j]
B_j.append(
wave.Waveform.from_amp_phase(xarr, amp, np.zeros(len(xarr))))
elif datatype == 'phase':
phase = B_j_np[j]
B_j.append(
wave.Waveform.from_amp_phase(xarr, np.zeros(len(xarr)), phase))
else:
raise Exception, "datatype must be one of {'amp', 'phase'}."
return empirical_node_indices, B_j
def empirical_interpolation_for_time_domain_waveform(waveforms, datatype):
"""Calculate empirical nodes and corresponding empirical interpolating functions
from a set of reduced basis waveforms.
Parameters
----------
waveforms : HDF5TimeDomainWaveformSet
datatype : string {'complex', 'amp', 'phase'}
Returns
-------
empirical_node_indices : List of ints
The indices of the empirical nodes in the TimeDomainWaveforms.
B_j : List of TimeDomainWaveform
The empirical interpolating functions
that are 1 at the node T_j and
0 at the other nodes T_i (for i!=j).
"""
Nwave = waveforms.get_len()
# Convert the list of TimeDomainWaveform to a list of complex numpy arrays
if datatype == 'complex':
wave_np = [waveforms.get_waveform_data(i).get_complex() for i in range(Nwave)]
elif datatype == 'amp':
wave_np = [waveforms.get_waveform_data(i).amp for i in range(Nwave)]
elif datatype == 'phase':
wave_np = [waveforms.get_waveform_data(i).phase for i in range(Nwave)]
else:
raise Exception, "datatype must be one of {'complex', 'amp', 'phase'}."
# Determine the empirical nodes
empirical_node_indices = eim.generate_empirical_nodes(wave_np)
# Determine the empirical interpolating functions B_j(t)
B_j_np = eim.generate_interpolant_list(wave_np, empirical_node_indices)
# Convert the arrays to TimeDomainWaveforms.
time = waveforms.get_waveform_data(0).time
B_j = []
for j in range(Nwave):
if datatype == 'complex':
comp = B_j_np[j]
B_j.append(complex_to_time_domain_waveform(time, comp))
elif datatype == 'amp':
amp = B_j_np[j]
B_j.append(tdwave.TimeDomainWaveform(time, amp, np.zeros(len(time))))
elif datatype == 'phase':
phase = B_j_np[j]
B_j.append(tdwave.TimeDomainWaveform(time, np.zeros(len(time)), phase))
else:
raise Exception, "datatype must be one of {'complex', 'amp', 'phase'}."
return empirical_node_indices, B_j