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Commit 3c12c166 authored by Leo Tsukada's avatar Leo Tsukada
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dtdphitest.py : edited to add pp-plot drawing and automatically parse 

characteristic SNRs and psd file nmae.
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gstlal-inspiral/tests/dtdphitest.py
+ 98
− 31
View file @ 3c12c166
import sys
import numpy
import numpy.random
import scipy
from scipy import interpolate as interp
from scipy import stats
import lal
from gstlal.stats import inspiral_extrinsics
import h5py
import pdb
import matplotlib
matplotlib.rcParams['text.usetex'] = True
......@@ -15,10 +21,11 @@ matplotlib.rcParams['legend.fontsize'] = 10
matplotlib.use('Agg')
from matplotlib import pyplot
TPDPDF = inspiral_extrinsics.InspiralExtrinsics()
R = {"H":lal.CachedDetectors[lal.LHO_4K_DETECTOR].response,"L":lal.CachedDetectors[lal.LLO_4K_DETECTOR].response,"V":lal.CachedDetectors[lal.VIRGO_DETECTOR].response}
X = {"H":lal.CachedDetectors[lal.LHO_4K_DETECTOR].location,"L":lal.CachedDetectors[lal.LLO_4K_DETECTOR].location,"V":lal.CachedDetectors[lal.VIRGO_DETECTOR].location}
TPDPDF = inspiral_extrinsics.InspiralExtrinsics()
R = {"H1":lal.CachedDetectors[lal.LHO_4K_DETECTOR].response,"L1":lal.CachedDetectors[lal.LLO_4K_DETECTOR].response,"V1":lal.CachedDetectors[lal.VIRGO_DETECTOR].response,"K1":lal.CachedDetectors[lal.KAGRA_DETECTOR].response}
X = {"H1":lal.CachedDetectors[lal.LHO_4K_DETECTOR].location,"L1":lal.CachedDetectors[lal.LLO_4K_DETECTOR].location,"V1":lal.CachedDetectors[lal.VIRGO_DETECTOR].location,"K1":lal.CachedDetectors[lal.KAGRA_DETECTOR].location}
refhorizon = {"H1": 110., "L1": 140., "V1": 45., "K1": 45.}
def random_source(R = R, X = X, epoch = lal.LIGOTimeGPS(0), gmst = lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(0))):
......@@ -42,31 +49,59 @@ def random_source(R = R, X = X, epoch = lal.LIGOTimeGPS(0), gmst = lal.Greenwich
return T, Deff, phi
ndraws = 100000 #100000 maybe take 10min to iterate
delta_phi_HV = []
delta_t_HV = []
def dtdphi2prob(dt, dphi, ifo1, ifo2, refsnr, refhorizon=refhorizon):
"""function that takes dt,dphi samples and return the probability (density) values derived from the new pdf
:dt: the time delay between detector pair
:dphi: the difference of the coalescence phase between detector pair
:returns: dtdpipdf(dt, dphi) * snr^4 (assuming snr = {"H1": 5., "V1": 2.25} and horizon = {"H1": 110., "V1": 45.})
"""
# ifo1 += "1"
# ifo2 += "1"
t = {ifo1: 0, ifo2: dt}
phi = {ifo1: 0, ifo2: dphi}
snr = {ifo1: refsnr[ifo1], ifo2: refsnr[ifo2]} # our choice of characteristic SNR
horizon = {ifo1: refhorizon[ifo1], ifo2: refhorizon[ifo2]} # 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) * (sum(s**2 for s in snr.values())**.5)**4
return float(p)
pdf_fname = sys.argv[1]
ifo1, ifo2 = sorted(sys.argv[2].split(","))
combo = ifo1 + "," + ifo2
ndraws = 100000#100000 maybe take 10min to iterate
delta_phi_list = []
delta_t_list = []
prob_simulation = []
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
# 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 4.0 --K-snr 4.0`
data = h5py.File(pdf_fname)
# pdb.set_trace()
refsnr = dict((ifo, data["SNR"][ifo].value) for ifo in data["SNR"])
transmat_dtdphi = numpy.array([[data["transtt"][combo].value, data["transtp"][combo].value], [data["transpt"][combo].value, data["transpp"][combo].value]])
covmat_dtdphi = numpy.linalg.inv(numpy.dot(transmat_dtdphi.T, transmat_dtdphi))
sigmadd = 1. / (data["transdd"][combo].value)**2
rd_slice = (refhorizon[ifo1] / refsnr[ifo1]) / (refhorizon[ifo2] / refsnr[ifo2])
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.
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
mean = [t[ifo1] - t[ifo2], p[ifo1] - p[ifo2]]
dt, dp = numpy.random.multivariate_normal(mean, covmat_dtdphi)
rd = numpy.log(d[ifo1] / d[ifo2]) + numpy.random.randn() * numpy.sqrt(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 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
if numpy.log(rd_slice + 0.05) >= rd >= numpy.log(rd_slice - 0.05):
delta_phi_list.append(dp)
delta_t_list.append(dt)
prob_simulation.append(dtdphi2prob(dt, dp, ifo1, ifo2, refsnr, refhorizon))
i += 1
prob_simulation.sort()
num1 = 100
num2 = 101
......@@ -78,28 +113,60 @@ 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": 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)
p = dtdphi2prob(dt, dp, ifo1, ifo2, refsnr, refhorizon)
Prob[j,i] = p
DPProb[i] += p
DTProb[j] += p
pyplot.figure(figsize=(7.5,7.5))
pyplot.subplot(221)
pyplot.hist(delta_phi_HV, dphivec, normed = True, label = "Simulation")
pyplot.hist(delta_phi_list, 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.ylabel(r"""$P(\Delta\phi | s, \{D_{%s}, D_{%s}\}, \{\rho_{%s}, \rho_{%s}\})$""" % (ifo1, ifo2, ifo1, ifo2))
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.xlabel(r"""$\phi_{%s} - \phi_{%s}$""" % (ifo1, ifo2))
pyplot.ylabel(r"""$t_{%s} - t_{%s}$""" % (ifo1, ifo2))
pyplot.subplot(224)
pyplot.hist(delta_t_HV, dtvec, normed = True, orientation = "horizontal")
pyplot.hist(delta_t_list, 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")
pyplot.xlabel(r"""$P(\Delta t | s, \{D_{%s}, D_{%s}\}, \{\rho_{%s}, \rho_{%s}\})$""" % (ifo1, ifo2, ifo1, ifo2))
pyplot.savefig("%s%sPDF.pdf" % (ifo1, ifo2))
if False:
prob_grid = numpy.sort(Prob.flatten())
percentiles_grid = numpy.cumsum(prob_grid)
percentiles_grid /= percentiles_grid[-1]
p_interp = interp.interp1d(prob_grid, percentiles_grid)
# plot of the interpolate function of percentile to see if it is a good approximation
fig = pyplot.figure()
ax1 = fig.add_subplot(111)
ax1.loglog(prob_grid, percentiles_grid, linestyle="None", color="r", marker=".")
ax1.loglog(prob_grid, p_interp(prob_grid))
pyplot.savefig("./percentile_interp_check.png")
# make pp-plot from the interpolate function of percentiles
try:
percentiles_exp = sorted(p_interp(prob_simulation))
except ValueError:
prob_simulation = numpy.array(prob_simulation)
percentiles_exp = numpy.empty(len(prob_simulation))
mask_ok = [(prob_simulation < max(prob_grid)) & (prob_simulation > min(prob_grid))]
mask_above = [prob_simulation > max(prob_grid)]
mask_below = [prob_simulation < min(prob_grid)]
percentiles_exp[mask_ok] = sorted(p_interp(prob_simulation[mask_ok]))
percentiles_exp[mask_above] = 1.
percentiles_exp[mask_below] = 0
percentiles_inj = numpy.cumsum(numpy.ones(len(percentiles_exp))) / len(percentiles_exp)
fig = pyplot.figure()
ax2 = fig.add_subplot(111)
ax2.plot([0,1], [0, 1], linestyle="--")
ax2.plot(percentiles_exp, percentiles_inj, linestyle="-", color="r", label="simulation")
CI = 0.9
quant = stats.norm.ppf(CI * 0.5 + 0.5)
err = quant * numpy.sqrt(percentiles_inj * (1 - percentiles_inj) / len(percentiles_inj))
ax2.fill_between(percentiles_exp, percentiles_inj - err, percentiles_inj + err, facecolor='r', label="90$\%$ measurement uncertainty", alpha=.3)
pyplot.legend(loc = "lower right")
ax2.set_xlabel("Percentile computed from the pdf")
ax2.set_ylabel("Percentile in the injection set")
pyplot.savefig("./%s%s_pp_plot.pdf" % (ifo1, ifo2))
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