def test_sensitivity_w_rednoise():
'''Test making sensitivity curves with complex RN.'''
psrs2 = hsim.sim_pta(timespan=timespan,
cad=23,
sigma=1e-7,
phi=phi,
theta=theta,
A_rn=6e-16,
alpha=-2 / 3.,
freqs=freqs)
psrs3 = hsim.sim_pta(timespan=timespan,
cad=23,
sigma=1e-7,
phi=phi,
theta=theta,
A_rn=A_rn,
alpha=alphas,
freqs=freqs)
spectra2 = []
for p in psrs2:
sp = hsen.Spectrum(p, freqs=freqs)
_ = sp.NcalInv
spectra2.append(sp)
spectra3 = []
for p in psrs3:
sp = hsen.Spectrum(p, freqs=freqs)
_ = sp.NcalInv
spectra3.append(sp)
spec = spectra3[0]
spec.Tf
spec.S_I
spec.S_R
spec.h_c
spec.Omega_gw
sigma = spec.toaerrs.mean()
dt = hsen.get_dt(spec.toas).to('s').value
spec.add_white_noise_power(sigma, dt)
spec.add_red_noise_power(A=6.8e-16, gamma=13 / 3.)
spec.psd_prefit
spec.psd_postfit
spec.add_noise_power(spec.psd_prefit)
hsen.corr_from_psd(freqs, spec.psd_prefit, spec.toas)
sc2a = hsen.GWBSensitivityCurve(spectra2)
sc2b = hsen.DeterSensitivityCurve(spectra2)
sc3a = hsen.GWBSensitivityCurve(spectra3)
sc3b = hsen.DeterSensitivityCurve(spectra3)
sc3a.h_c
sc3a.Omega_gw
sc3a.S_effIJ
sc3b.h_c
sc3b.Omega_gw
def sc_simple():
'''Test and keep a simple sensitivity curve'''
psrs = hsim.sim_pta(timespan=timespan,
cad=23,
sigma=1e-7,
phi=phi,
theta=theta)
psr = psrs[0]
hsen.R_matrix(psr.designmatrix, psr.N)
hsen.G_matrix(psr.designmatrix)
hsen.quantize_fast(psr.toas, psr.toaerrs)
hsen.get_Tf(psr.designmatrix,
psr.toas,
psr.N,
from_G=True,
exact_astro_freqs=True)
spectra = []
for p in psrs:
sp = hsen.Spectrum(p, freqs=freqs)
_ = sp.NcalInv
spectra.append(sp)
sc1a = hsen.GWBSensitivityCurve(spectra)
sc1b = hsen.DeterSensitivityCurve(spectra)
return sc1a, sc1b
def test_simple_pta(pta_simple):
"""Sensitivity Tests"""
spectra = []
for p in pta_simple:
sp = hsen.Spectrum(p, freqs=freqs)
sp.Tf
spectra.append(sp)
hsen.GWBSensitivityCurve(spectra)
hsen.DeterSensitivityCurve(spectra)
hsky.SkySensitivity(spectra,theta_gw,phi_gw)
def test_nonGR():
psrs = hsim.sim_pta(timespan=timespan, cad=23, sigma=1e-7,
phi=phi,theta=theta)
spectra = []
for p in psrs:
sp = hsen.Spectrum(p, freqs=freqs)
_ = sp.NcalInv
spectra.append(sp)
hsen.GWBSensitivityCurve(spectra, orf='st')
hsen.GWBSensitivityCurve(spectra, orf='dipole')
hsen.GWBSensitivityCurve(spectra, orf='monopole')
def Init_PTA(self):
"""Initializes a PTA in hasasia
Notes
-----
See Hazboun, Romano, Smith (2019) <https://arxiv.org/abs/1907.04341> for details
"""
#Random Sky Locations of Pulsars
phi = np.random.uniform(0, 2 * np.pi, size=self.N_p)
cos_theta = np.random.uniform(-1, 1, size=self.N_p)
theta = np.arccos(cos_theta)
if hasattr(self, 'A_GWB'):
if not hasattr(self, 'alpha_GWB'):
self.alpha_GWB = -2 / 3.
#Make a set of psrs with the same parameters with a GWB as red noise
psrs = hassim.sim_pta(timespan=self.T_obs.value,cad=self.cadence.value,sigma=self.sigma.value,\
phi=phi, theta=theta, Npsrs=self.N_p,A_rn=self.A_GWB,alpha=self.alpha_GWB,freqs=self.fT.value)
elif hasattr(self, 'A_rn_min') or hasattr(self, 'alpha_rn_min'):
if not hasattr(self, 'A_rn_min'):
A_rn = np.random.uniform(1e-16, 1e-12, size=self.N_p)
else:
A_rn = np.random.uniform(self.A_rn_min,
self.A_rn_max,
size=self.N_p)
if not hasattr(self, 'alpha_rn_min'):
alphas = np.random.uniform(-3 / 4, 1, size=self.N_p)
else:
alphas = np.random.uniform(self.alpha_rn_min,
self.alpha_rn_max,
size=self.N_p)
#Make a set of psrs with uniformly sampled red noise
psrs = hassim.sim_pta(timespan=self.T_obs.value,cad=self.cadence.value,sigma=self.sigma.value,\
phi=phi, theta=theta, Npsrs=self.N_p,A_rn=A_rn,alpha=alphas,freqs=self.fT.value)
else:
#Make a set of psrs with the same parameters
psrs = hassim.sim_pta(timespan=self.T_obs.value,cad=self.cadence.value,sigma=self.sigma.value,\
phi=phi, theta=theta, Npsrs=self.N_p,freqs=self.fT.value)
#Get Spectra of pulsars
spectra = []
for p in psrs:
sp = hassens.Spectrum(p, freqs=self.fT.value)
spectra.append(sp)
self._sensitivitycurve = hassens.DeterSensitivityCurve(spectra)
def sm_simple():
'''Test and keep a simple sensitivity skymap'''
#Make a set of random sky positions
phi = np.random.uniform(0, 2 * np.pi, size=33)
cos_theta = np.random.uniform(-1, 1, size=33)
theta = np.arccos(cos_theta)
#Adding one well-placed sky position for plots.
phi = np.append(np.array(np.deg2rad(60)), phi)
theta = np.append(np.array(np.deg2rad(50)), theta)
#Define the timsespans and TOA errors for the pulsars
timespans = np.random.uniform(3.0, 11.4, size=34)
Tspan = timespans.max() * 365.25 * 24 * 3600
sigma = 1e-7 # 100 ns
#Simulate a set of identical pulsars, with different sky positions.
psrs = hsim.sim_pta(timespan=11.4,
cad=23,
sigma=sigma,
phi=phi,
theta=theta)
freqs = np.logspace(np.log10(1 / (5 * Tspan)), np.log10(2e-7), 500)
spectra = []
for p in psrs:
sp = hsen.Spectrum(p, freqs=freqs)
sp.NcalInv
spectra.append(sp)
#Normally use the healpy functions to get the sky coordinates
#Here just pull random coordinates using numpy to avoid needing healpy
phi_gw = np.random.uniform(0, 2 * np.pi, size=1000)
cos_theta_gw = np.random.uniform(-1, 1, size=1000)
theta_gw = np.arccos(cos_theta_gw)
SM = hsky.SkySensitivity(spectra, theta_gw, phi_gw)
return SM
if args.A_gwb != 0.0:
gwb = hsen.red_noise_powerlaw(A=args.A_gwb, gamma=13 / 3., freqs=freqs)
corr += hsen.corr_from_psd(freqs=freqs, psd=gwb, toas=ePsr.toas[::thin])
if args.rednoise:
Amp, gam = rn[ePsr.name]
plaw = hsen.red_noise_powerlaw(A=Amp, gamma=gam, freqs=freqs)
corr += hsen.corr_from_psd(freqs=freqs, psd=plaw, toas=ePsr.toas[::thin])
psr = hsen.Pulsar(toas=ePsr.toas[::thin],
toaerrs=ePsr.toaerrs[::thin],
phi=ePsr.phi,
theta=ePsr.theta,
N=corr,
designmatrix=ePsr.Mmat[::thin, :])
psr.name = ePsr.name
_ = psr.G
if args.savepsr:
with open(args.outdir + f'/{args.label}_psr_{ePsr.name}.has',
'wb') as fout:
pickle.dump(psr, fout)
sp = hsen.Spectrum(psr, freqs=freqs)
sp.name = psr.name
_ = sp.NcalInv
_ = sp.P_n
with open(args.outdir + f'/{args.label}_spec_{ePsr.name}.has', 'wb') as fout:
pickle.dump(sp, fout)
