PySEOBNR introduction
The notebook shows how to get started with pyseobnr
.
[1]:
import warnings
# silence warnings coming from LAL
warnings.filterwarnings("ignore", "Wswiglal-redir-stdio")
import numpy as np
from matplotlib import pyplot as plt
# import the library
from pyseobnr.generate_waveform import GenerateWaveform
[2]:
# Start with the usual parameter definitions
# Masses in solar masses
m1 = 50.0
m2 = 30.0
s1x, s1y, s1z = 0.0, 0.0, 0.5
s2x, s2y, s2z = 0.0, 0.0, 0.8
deltaT = 1.0 / 2048.0
f_min = 20.0
f_max = 1024.0
distance = 1000.0 # Mpc
inclination = np.pi / 3.0
phiRef = 0.0
approximant = "SEOBNRv5HM"
params_dict = {
"mass1": m1,
"mass2": m2,
"spin1x": s1x,
"spin1y": s1y,
"spin1z": s1z,
"spin2x": s2x,
"spin2y": s2y,
"spin2z": s2z,
"deltaT": deltaT,
"f22_start": f_min,
"phi_ref": phiRef,
"distance": distance,
"inclination": inclination,
"f_max": f_max,
"approximant": approximant,
}
[3]:
# We call the generator with the parameters
wfm_gen = GenerateWaveform(params_dict)
# Generate mode dictionary
times, hlm = wfm_gen.generate_td_modes()
Plot some modes
[4]:
plt.figure()
plt.plot(times, hlm[(2, 2)].real)
plt.xlabel("Time (seconds)")
plt.ylabel(r"$\Re[h_{22}]$")
plt.grid(True)
plt.show()

[5]:
plt.figure()
plt.plot(times, hlm[(3, 3)].imag)
plt.xlabel("Time (seconds)")
plt.ylabel(r"$\Im[h_{33}]$")
plt.grid(True)
plt.show()

Expert mode
[6]:
from pyseobnr.generate_waveform import generate_modes_opt
q = 5.3
chi_1 = 0.9
chi_2 = 0.3
omega0 = 0.0137 # This is the orbital frequency in geometric units with M=1
_, _, model = generate_modes_opt(q, chi_1, chi_2, omega0, debug=True)
model
[6]:
<pyseobnr.models.SEOBNRv5HM.SEOBNRv5HM_opt at 0x7f62dd164af0>
[7]:
t, r, phi, pr, pphi, H, Omega, _ = model.dynamics.T
[8]:
import pandas as pd
frame_dynamics = pd.DataFrame(
data=model.dynamics,
columns="t, r, phi, pr, pphi, H, Omega, Omega_circular".replace(" ", "").split(","),
)
frame_dynamics
[8]:
t | r | phi | pr | pphi | H | Omega | Omega_circular | |
---|---|---|---|---|---|---|---|---|
0 | 0.000000 | 17.371216 | 0.000000 | -0.000325 | 4.436354 | 7.460756 | 0.013700 | 0.013700 |
1 | 91.725333 | 17.343818 | 1.258109 | -0.000325 | 4.433223 | 7.460713 | 0.013732 | 0.013732 |
2 | 183.450666 | 17.316357 | 2.519172 | -0.000327 | 4.430076 | 7.460670 | 0.013764 | 0.013764 |
3 | 275.175999 | 17.288693 | 3.783213 | -0.000330 | 4.426911 | 7.460626 | 0.013797 | 0.013797 |
4 | 366.901332 | 17.260762 | 5.050276 | -0.000333 | 4.423729 | 7.460582 | 0.013830 | 0.013830 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
2838 | 14876.611713 | 2.078110 | 326.974132 | -0.235719 | 2.169854 | 7.334338 | 0.207813 | 0.215060 |
2839 | 14876.711713 | 2.067839 | 326.994952 | -0.239651 | 2.168466 | 7.333996 | 0.208553 | 0.216080 |
2840 | 14876.811713 | 2.057290 | 327.015846 | -0.243713 | 2.167039 | 7.333640 | 0.209319 | 0.217148 |
2841 | 14876.911713 | 2.046448 | 327.036816 | -0.247912 | 2.165571 | 7.333271 | 0.210114 | 0.218265 |
2842 | 14877.011713 | 2.035300 | 327.057864 | -0.252252 | 2.164060 | 7.332888 | 0.210941 | 0.219438 |
2843 rows × 8 columns
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