Evaluating beam pattern functions is >10 times slower than LAL

Evaluating beam pattern functions is >10 times slower than LAL, which is one of the bottlenecks for low-latency analysis.

Script:

import numpy as np
import time
import bilby
import lal
import lalsimulation

ifos = bilby.gw.detector.InterferometerList(["L1"])
ifo = ifos[0]
gpstime = 1305303144
trial = 100

ts_bilby, ts_lal = [], []
for i in range(trial):
    ra, dec, psi = 2. * np.pi * np.random.uniform(), np.pi * np.random.uniform() - np.pi / 2., np.pi * np.random.uniform()
    # measure bilby's run time
    s = time.time()
    fplus, fcross = ifo.antenna_response(ra, dec, gpstime, psi, "plus"), ifo.antenna_response(ra, dec, gpstime, psi, "cross")
    e = time.time()
    ts_bilby.append(e - s)
    # measure lal's run time
    s = time.time()
    gmst = lal.GreenwichMeanSiderealTime(gpstime)
    fplus, fcross = lal.ComputeDetAMResponse(
        lalsimulation.DetectorPrefixToLALDetector("L1").response, ra, dec, psi, gmst)
    e = time.time()
    ts_lal.append(e - s)


print("Bilby's method takes {} seconds.".format(np.mean(ts_bilby)))
print("LAL's method takes {} seconds.".format(np.mean(ts_lal)))

Output:

Bilby's method takes 6.371736526489258e-05 seconds.
LAL's method takes 4.072189331054687e-06 seconds.