Commit 6ae80036 authored by Leo Tsukada's avatar Leo Tsukada

dtdphitest.py: updated after implementing the correct covariacne matrix

parent dfddc5e7
Pipeline #69006 passed with stages
in 26 minutes and 33 seconds
......@@ -5,6 +5,13 @@ import lal
from gstlal.stats import inspiral_extrinsics
import matplotlib
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['lines.markersize'] = 2
matplotlib.rcParams['lines.linewidth'] = 1
matplotlib.rcParams['font.size'] = 10
matplotlib.rcParams['savefig.dpi'] = 300
matplotlib.rcParams['text.latex.preamble'].append(r'\usepackage{amsmath}')
matplotlib.rcParams['legend.fontsize'] = 10
matplotlib.use('Agg')
from matplotlib import pyplot
......@@ -36,52 +43,63 @@ def random_source(R = R, X = X, epoch = lal.LIGOTimeGPS(0), gmst = lal.Greenwich
return T, Deff, phi
ndraws = 1000000
ndraws = 100000 #100000 maybe take 10min to iterate
delta_phi_HV = []
delta_t_HV = []
i = 0
# the following cov matrix elements were derived from running `gstlal_inspiral_compute_dtdphideff_cov_matrix --psd-xml share/O3/2019-05-09-H1L1V1psd_new.xml.gz --H-snr 5 --L-snr 7.0 --V-snr 2.25`
covmat = [[0.000001, 0.000610], [0.000610, 0.648023]]
sigmadd = 0.487371
while i < ndraws:
t,d,p = random_source()
# Use EXACTLY the same covariance matrix assumptions as the gstlal
# code. It could be a bad assumption, but we are testing the method
# not the assumptions by doing this.
dpHV = p["H"] - p["V"] + numpy.random.randn() * inspiral_extrinsics.TimePhaseSNR.sigma["phase"]
dtHV = t["H"] - t["V"] + numpy.random.randn() * inspiral_extrinsics.TimePhaseSNR.sigma["time"]
rdHV = d["H"] / d["V"] - 1.0 + numpy.random.randn() * inspiral_extrinsics.TimePhaseSNR.sigma["deff"]
mean = [t["H"] - t["V"], p["H"] - p["V"]]
dtHV, dpHV = numpy.random.multivariate_normal(mean, covmat)
rdHV = numpy.log(d["H"] / d["V"]) + numpy.random.randn() * sigmadd
# only choose things with almost the same effective distance ratio for
# this test to agree with setting the same horizon and snr below
if 0.1 >= rdHV >= -0.1:
if numpy.log(1.1 + 0.1) >= rdHV >= numpy.log(1.1 - 0.1): # 1.1 here comes from Deff_V1 / Deff_H1 = (110/5) / (45/2.25)
delta_phi_HV.append(dpHV)
delta_t_HV.append(dtHV)
print i
i += 1
num = 100
dphivec = numpy.linspace(-2 * numpy.pi, 2 * numpy.pi, num)
dtvec = numpy.linspace(-0.028, 0.028, num)
DPProb = numpy.zeros(num)
DTProb = numpy.zeros(num)
num1 = 100
num2 = 101
dtvec = numpy.linspace(-0.028, 0.028, num1)
dphivec = numpy.linspace(-2 * numpy.pi, 2 * numpy.pi, num2)
DTProb = numpy.zeros(num1)
DPProb = numpy.zeros(num2)
Prob = numpy.zeros((num1,num2))
for j, dt in enumerate(dtvec):
for i, dp in enumerate(dphivec):
t = {"H1": 0, "V1": dt}
phi = {"H1": 0, "V1": dp}
snr = {"H1": 1., "V1": 1.}
horizon = {"H1": 1., "V1": 1.}
snr = {"H1": 5., "V1": 2.25} # our choice of characteristic SNR
horizon = {"H1": 110., "V1": 45.} # typical horizon distance taken from the summary page on 2019 May 09.
# signature is (time, phase, snr, horizon)
p = TPDPDF.time_phase_snr(t, phi, snr, horizon)
Prob[j,i] = p
DPProb[i] += p
DTProb[j] += p
pyplot.figure(figsize=(10,4))
pyplot.subplot(121)
pyplot.hist(delta_phi_HV, dphivec, normed = True)
pyplot.plot(dphivec, DPProb / numpy.sum(DPProb) / (dphivec[1] - dphivec[0]))
pyplot.xlabel("H phase - V phase")
pyplot.subplot(122)
pyplot.hist(delta_t_HV, dtvec, normed = True)
pyplot.plot(dtvec, DTProb / numpy.sum(DTProb) / (dtvec[1] - dtvec[0]))
pyplot.xlabel("H time - V time")
pyplot.savefig("HVtest.png")
pyplot.figure(figsize=(7.5,7.5))
pyplot.subplot(221)
pyplot.hist(delta_phi_HV, dphivec, normed = True, label = "Simulation")
pyplot.plot(dphivec, DPProb / numpy.sum(DPProb) / (dphivec[1] - dphivec[0]), label ="Direct Calculation")
pyplot.ylabel(r"""$P(\Delta\phi | s, D_H = D_V, \rho_H = \rho_V)$""")
pyplot.legend(bbox_to_anchor=(1, 1), loc='upper left', ncol=1)
pyplot.subplot(223)
pyplot.pcolor(dphivec, dtvec, Prob)
pyplot.xlabel(r"""$\phi_H - \phi_V$""")
pyplot.ylabel(r"""$t_H - t_V$""")
pyplot.subplot(224)
pyplot.hist(delta_t_HV, dtvec, normed = True, orientation = "horizontal")
pyplot.plot(DTProb / numpy.sum(DTProb) / (dtvec[1] - dtvec[0]), dtvec)
pyplot.xlabel(r"""$P(\Delta t | s, D_H = D_V, \rho_H = \rho_V)$""")
pyplot.savefig("HVPDF.pdf")
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