def compute_nice_minmax(mp):
mn = np.amin(mp)
mx = np.amax(mp)
emn = np.floor(np.log10(np.fabs(mn)))
emx = np.ceil(np.log10(np.fabs(mx)))
imn = int(10.**(-emn + 1) * mn)
imx = int(10.**(-emx + 2) * mx)
mno = imn * 10.**(emn - 1)
mxo = imx * 10.**(emx - 2)
return mno, mxo
# Initialize the map calculators
mc_HL = snl.MapCalculator([dets['Hanford'], dets['Livingstone']],
f_pivot=f_ref)
mc_HV = snl.MapCalculator([dets['Hanford'], dets['VIRGO']], f_pivot=f_ref)
npix = hp.nside2npix(nside)
theta, phi = hp.pix2ang(nside, np.arange(npix))
plt.figure(figsize=(7, 9))
for i, lf in enumerate([1, 2, 3]):
f = 10.**lf
resp_HL = mc_HL.get_antenna(0, 1, 0., f, theta, phi)
resp_HV = mc_HV.get_antenna(0, 1, 0., f, theta, phi)
mn, mx = compute_nice_minmax(np.real(resp_HL))
hp.mollview(
np.real(resp_HL),
sub=420 + 2 * i + 1,
notext=True,
cbar=True,
min=mn,
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
rc('text', usetex=True)
t_obs = 1
f_ref = 63.
nside = 16
obs_time = t_obs * 365 * 24 * 3600.
freqs = np.geomspace(10., 1010., 1010)
dets = [
snl.GroundDetector('Hanford', 46.4, -119.4, 171.8, 'data/aLIGO.txt'),
snl.GroundDetector('Livingstone', 30.7, -90.8, 243.0, 'data/aLIGO.txt'),
snl.GroundDetector('Virgo', 43.6, 10.5, 116.5, 'data/Virgo.txt'),
snl.GroundDetector('KAGRA', 36.3, 137.2, 225.0, 'data/KAGRA.txt')
]
mc = snl.MapCalculator(dets[:2], f_pivot=f_ref)
pi2_HL = mc.get_pi_curve(obs_time, freqs, nside, no_autos=True, nsigma=2)
mc = snl.MapCalculator(dets[:3], f_pivot=f_ref)
pi2_HLV = mc.get_pi_curve(obs_time, freqs, nside, no_autos=True, nsigma=2)
mc = snl.MapCalculator(dets[:4], f_pivot=f_ref)
pi2_HLVK = mc.get_pi_curve(obs_time, freqs, nside, no_autos=True, nsigma=2)
pi2_HLVKa = mc.get_pi_curve(obs_time, freqs, nside, no_autos=False, nsigma=2)
# Plotting
plt.plot(freqs, pi2_HL, 'k--', label='LIGO')
plt.plot(freqs, pi2_HLV, 'k:', label='+ Virgo')
plt.plot(freqs, pi2_HLVK, 'k-', label='+ KAGRA')
plt.plot(freqs, pi2_HLVKa, 'k-.', label='+ auto correlations')
plt.loglog()
plt.xlim([10, 1000])
plt.ylim([1E-10, 1E-5])
import os
import numpy as np
import matplotlib.pyplot as plt
import healpy as hp
import schnell as snl
from matplotlib import rc
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica'], 'size': 15})
rc('text', usetex=True)
dets = [snl.LISADetector(i) for i in range(3)]
# Correlation between detectors
rho = snl.NoiseCorrelationLISA(dets[0])
mc = snl.MapCalculator(dets, f_pivot=1E-2, corr_matrix=rho)
nside = 16
lfreqs = np.linspace(-4, 0, 101)
freqs = 10.**lfreqs
# One year
obs_time = 365 * 24 * 3600.
# 1-day intervals
nframes = 24 * 365
t_frames = np.linspace(0, obs_time, nframes + 1)[:-1]
inoi_tot = mc.get_Ninv_t(t_frames, freqs, nside, is_fspacing_log=True)
def plot_inoise_map(inoi, lims=[None, None], which='', figname=None):
hp.mollview(
1E-9 * np.sqrt(inoi),
max=lims[1],
min=lims[0],
import numpy as np
import schnell as snl
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
rc('text', usetex=True)
t_obs = 4
f_ref = 1E-2
nside = 16
dets = [snl.LISADetector(i) for i in range(3)]
rho = snl.NoiseCorrelationLISA(dets[0])
mc = snl.MapCalculator(dets, f_pivot=f_ref, corr_matrix=rho)
freqs = np.geomspace(1E-4, 0.2, 1001)
obs_time = t_obs * 365 * 24 * 3600
pi_LISA = mc.get_pi_curve(obs_time,
freqs,
nside,
is_fspacing_log=True,
nsigma=5,
proj={
'vectors': np.ones(3),
'deproject': True
})
# Plotting
plt.plot(freqs, pi_LISA, 'k--', label='LISA')
plt.loglog()
plt.xlabel(r'$f\,[{\rm Hz}]$', fontsize=16)
fname_psd='data/ET.txt', detector_id=i)
for i in range(3)]
dets = {'Hanford': snl.GroundDetector('Hanford', 46.4, -119.4, 171.8,
'data/aLIGO.txt'),
'Livingstone': snl.GroundDetector('Livingstone', 30.7, -90.8, 243.0,
'data/aLIGO.txt'),
'VIRGO': snl.GroundDetector('Virgo', 43.6, 10.5, 116.5,
'data/Virgo.txt'),
'Kagra': snl.GroundDetector('KAGRA', 36.3, 137.2, 225.0,
'data/KAGRA.txt')}
print("HLVK")
detectors = [dets['Hanford'], dets['Livingstone'],
dets['VIRGO'], dets['Kagra']]
no_autos = [True, True, True, True]
mc_HLVK = snl.MapCalculator(detectors, f_pivot=f_ref)
nl_HLVK = mc_HLVK.get_N_ell(obs_time, freqs, nside,
no_autos=no_autos)
detectors = [dets['Hanford'], dets['Livingstone'],
dets['VIRGO'], dets['Kagra']] + et
corr = np.array([[1., 0., 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 0.],
[0., 0., 0., 1., 0., 0., 0.],
[0., 1., 0., 0., 1., rE, rE],
[0., 1., 0., 0., rE, 1., rE],
[0., 1., 0., 0., rE, rE, 1.]])
mc_HLVKE = snl.MapCalculator(detectors, f_pivot=f_ref,
corr_matrix=corr)
print("HLVKEx")
no_autos = [[True, False, False, False, False, False, False],
rc('text', usetex=True)
t_obs = 1
f_ref = 63
nside = 64
obs_time = t_obs * 365 * 24 * 3600.
freqs = np.linspace(10., 1010., 101)
dets = [
snl.GroundDetector('Hanford', 46.4, -119.4, 171.8, 'data/aLIGO.txt'),
snl.GroundDetector('Livingstone', 30.7, -90.8, 243.0, 'data/aLIGO.txt'),
snl.GroundDetector('Virgo', 43.6, 10.5, 116.5, 'data/Virgo.txt'),
snl.GroundDetector('KAGRA', 36.3, 137.2, 225.0, 'data/KAGRA.txt')
]
print("0")
mc = snl.MapCalculator(dets, f_pivot=f_ref, spectral_index=0.)
nl_a0 = mc.get_N_ell(obs_time, freqs, nside, no_autos=True)
print("2/3")
mc = snl.MapCalculator(dets, f_pivot=f_ref, spectral_index=2. / 3.)
nl_a2o3 = mc.get_N_ell(obs_time, freqs, nside, no_autos=True)
print("3")
mc = snl.MapCalculator(dets, f_pivot=f_ref, spectral_index=3.)
nl_a3 = mc.get_N_ell(obs_time, freqs, nside, no_autos=True)
ls = np.arange(3 * nside)
plt.figure()
plt.plot(ls, (ls + 0.5) * nl_a3, 'k--', label=r'$\alpha=3$')
plt.plot(ls, (ls + 0.5) * nl_a2o3, 'k-', label=r'$\alpha=2/3$')
plt.plot(ls, (ls + 0.5) * nl_a0, 'k:', label=r'$\alpha=0$')
plt.loglog()
plt.xlabel(r'$\ell$', fontsize=16)
f_ref = 63.
nside = 16
dets = {
'Hanford':
snl.GroundDetector('Hanford', 46.4, -119.4, 171.8, 'data/aLIGO.txt'),
'Livingstone':
snl.GroundDetector('Livingstone', 30.7, -90.8, 243.0, 'data/aLIGO.txt'),
'VIRGO':
snl.GroundDetector('Virgo', 43.6, 10.5, 116.5, 'data/Virgo.txt'),
'Kagra':
snl.GroundDetector('KAGRA', 36.3, 137.2, 225.0, 'data/KAGRA.txt')
}
# Initialize the map calculator
mcal_hl = snl.MapCalculator([dets['Hanford'], dets['Livingstone']],
f_pivot=f_ref)
mcal_all = snl.MapCalculator([d for _, d in dets.items()], f_pivot=f_ref)
freqs = np.linspace(10., 1010., 101)
# One day
obs_time = 24 * 3600.
# 10-min intervals
nframes = 24 * 6
t_frames = np.linspace(0, obs_time, nframes + 1)[:-1]
inoi_hl = mcal_hl.get_Ninv_t(t_frames, freqs, nside, no_autos=True)
def plot_inoise_map(inoi, lims=[None, None], which='', figname=None):
hp.mollview(
1E-5 * np.sqrt(inoi),
import numpy as np
import schnell as snl
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']})
rc('text', usetex=True)
t_obs = 4
f_ref = 1E-2
nside = 32
dets = [snl.LISADetector(i) for i in range(3)]
rho = snl.NoiseCorrelationLISA(dets[0])
mc_auto = snl.MapCalculator([dets[0]], f_pivot=f_ref)
mca = snl.MapCalculator(dets, f_pivot=f_ref, corr_matrix=rho)
freqs = np.geomspace(1E-4, 1, 201)
obs_time = t_obs * 365 * 24 * 3600
nl_auto = mc_auto.get_N_ell(obs_time, freqs, nside, is_fspacing_log=True)
nl_AE = mca.get_N_ell(obs_time,
freqs,
nside,
is_fspacing_log=True,
proj={
'vectors': np.ones(3),
'deproject': True
})
nl_all = mca.get_N_ell(obs_time, freqs, nside, is_fspacing_log=True)
ls = np.arange(3 * nside)
plt.figure()
