Cannot compute qtransform for BBHx waveform

[mj-will]

Trying to call qtransform for a TimeSeries containing a LISA-like waveform from BBHx raises an error.

Error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[14], line 30
     28 wf_fd = get_fd_det_waveform(**params)["LISA_A"]
     29 wf_td = wf_fd.to_timeseries()
---> 30 times, freqs, power = wf_td.qtransform(
     31     delta_t=wf_td.delta_t,
     32     delta_f=wf_td.delta_f,
     33     frange=(1e-4, 1e-1),
     34 )

File ~/git_repos/pycbc_worktrees/lisa_ew/pycbc/types/timeseries.py:754, in TimeSeries.qtransform(self, delta_t, delta_f, logfsteps, frange, qrange, mismatch, return_complex)
    751     frange = (30, int(self.sample_rate / 2 * 8))
    753 q_base = qtiling(self, qrange, frange, mismatch)
--> 754 _, times, freqs, q_plane = qplane(q_base, self.to_frequencyseries(),
    755                                   return_complex=return_complex)
    756 if logfsteps and delta_f:
    757     raise ValueError("Provide only one (or none) of delta_f and logfsteps")

File ~/git_repos/pycbc_worktrees/lisa_ew/pycbc/filter/qtransform.py:69, in qplane(qplane_tile_dict, fseries, return_complex)
     67 energies = []
     68 for f0 in qplane_tile_dict[q]:
---> 69     energy = qseries(fseries, q, f0, return_complex=return_complex)
     70     menergy = abs(energy).max()
     71     energies.append(energy)

File ~/git_repos/pycbc_worktrees/lisa_ew/pycbc/filter/qtransform.py:213, in qseries(fseries, Q, f0, return_complex)
    210 end = int(start + window_size)
    211 center = (start + end) // 2
--> 213 windowed = fseries[start:end] * (1 - xfrequencies ** 2) ** 2 * norm
    215 tlen = (len(fseries)-1) * 2
    216 windowed.resize(tlen)

File ~/git_repos/pycbc_worktrees/lisa_ew/pycbc/types/array.py:217, in Array.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
    215 def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
    216     inputs = [i.numpy() if isinstance(i, Array) else i for i in inputs]
--> 217     ret = getattr(ufunc, method)(*inputs, **kwargs)
    218     if hasattr(ret, 'shape') and ret.shape == self.shape:
    219         ret = self._return(ret)

ValueError: operands could not be broadcast together with shapes (2315721,) (2374927,) 

Code to reproduce

Note: this requires having BBHx and the PyCBC BBHx plugin installed.

import numpy as np
from pycbc.waveform import get_fd_det_waveform

params = {}
params["ifos"] = "LISA_A"
params["ref_frame"] = "LISA"
params["approximant"] = "BBHX_PhenomD"
params["coa_phase"] = 0.0
params["tdi"] = 1.5
params["mass1"] = 1e6
params["mass2"] = 8e5
params["spin1z"] = 0.0
params["spin2z"] = 0.0
params["distance"] = 410
params["inclination"] = np.pi / 2
params["t_obs_start"] = 31536000
params["delta_f"] = 1.0 / params["t_obs_start"]
params["f_lower"] = 1e-4
params["f_ref"] = 8e-4
params["f_final"] = 0.1
params["delta_t"] = 1 / 0.2
params["t_offset"] = 9206958.120016199
params["tc"] = 4799624.274911478
params["eclipticlongitude"] = 0.5
params["eclipticlatitude"] = 0.23
params["polarization"] = 0.1

wf_fd = get_fd_det_waveform(**params)["LISA_A"]
wf_td = wf_fd.to_timeseries()
times, freqs, power = wf_td.qtransform(
    delta_t=wf_td.delta_t,
    delta_f=wf_td.delta_f,
    frange=(1e-4, 1e-1),
)