durationH = endtime - starttime
oldstarttime = starttime
oldendtime = endtime
Xspacing = 2.44140625E-4
pathtoinput = "/home/spxha/"
strainH = TimeSeries.read(pathtoinput+'S6framesH1.lcf',channel='H1:LDAS-STRAIN', start=starttime, end=endtime)
strainL = TimeSeries.read(pathtoinput+'S6framesL1.lcf',channel='L1:LDAS-STRAIN', start=starttime, end=endtime)
num_points = int(durationH/Xspacing)
h0_min = 0.0000001
h0_vals_num = 30
#----------------------------
# Applying a bandpass filter
#----------------------------
coefsL = get_filter_coefs('L1')
coefsH = get_filter_coefs('H1')
strainL = filter_data(strainL,coefsL)
strainH = filter_data(strainH,coefsH)
timeH = np.arange(starttime, endtime, Xspacing)
timeL = timeH
detMap = {'H1': lal.LALDetectorIndexLHODIFF, 'L1':
lal.LALDetectorIndexLLODIFF}
detH1 = lal.CachedDetectors[detMap['H1']]
detL1 = lal.CachedDetectors[detMap['L1']]
tgps = lal.LIGOTimeGPS(starttime, 0)
#---------- Get right ascension and declination of source in radians ----------#
numseg30 = int((endtime-starttime)/30.)
seg30 = starttime + 30*np.linspace(1,numseg30,numseg30) # 30 second update rate
tdelay = [[0] for _ in range(numseg30)]
for i in range(numseg30-1):
for j in range(int(len(timeL)*Xspacing)):
def validity_test(starttime, endtime, h0_max=0.001):
#------- Packages ---------#
print 'Importing packages'
import numpy as np
import astropy, gwpy, h5py, lal, sys, os
from astropy.coordinates import get_sun
import astropy.time as Time
from scipy.signal import butter, filtfilt
from gwpy.timeseries import TimeSeries
from antres import antenna_response as ant_res
from scipy.misc import logsumexp
from notchfilt import get_filter_coefs, filter_data
from optparse import OptionParser
#-------- Importing, filtering and timeshifting data ----------#
print 'Reading data'
durationH = endtime - starttime # same for durationL, but not used anyway
oldstarttime = starttime # for use later
oldendtime = endtime # for use later
Xspacing = 2.44140625E-4
gpsTime = np.linspace(starttime,endtime,int(1/Xspacing))
pathtoinput = "/home/spxha/"
strainH = TimeSeries.read(pathtoinput+'S6framesH1.lcf',channel='H1:LDAS-STRAIN', start=starttime, end=endtime)
strainL = TimeSeries.read(pathtoinput+'S6framesL1.lcf',channel='L1:LDAS-STRAIN', start=starttime, end=endtime)
num_points = int(durationH/Xspacing)
h0_min=0.00005
h0_vals_num=30
# Adding in a fake signal
# frequency in the middle of the range we're interested in 125 Hz
omega = 250.0*np.pi
amplitude = 0.5e-24 # (hopefully) middle of h0 values
# the signal to introduce is of the form amplitude*np.sin(omega*t)
# first get timeL and timeH in synch with the sun's position
print 'Getting the detectors in sync'
timeH = np.arange(starttime, endtime, Xspacing)
timeL = timeH
detMap = {'H1': lal.LALDetectorIndexLHODIFF, 'L1':
lal.LALDetectorIndexLLODIFF}
detH1 = lal.CachedDetectors[detMap['H1']]
detL1 = lal.CachedDetectors[detMap['L1']]
tgps = lal.LIGOTimeGPS(starttime, 0)
#---------- Get right ascension and declination of source in radians ----------#
numseg30 = int((endtime-starttime)/30.)
seg30 = starttime + 30*np.linspace(1,numseg30,numseg30) # 30 second update rate
tdelay = [[0] for _ in range(numseg30)]
tdelay = [[0] for _ in range(numseg30)]
for i in range(numseg30-1):
for j in range(int(len(timeL)*Xspacing)):
if ((timeL[j/Xspacing]>seg30[i])&(timeL[j/Xspacing]<seg30[i+1])):
coordstime=seg30[i]
coords = get_sun(Time.Time(coordstime,format='gps'))
tdelay[i] = lal.ArrivalTimeDiff(detH1.location, detL1.location, coords.ra.hour*np.pi/12, coords.dec.hour*np.pi/12, tgps)
coordstime = seg30[-1]
coords = get_sun(Time.Time(coordstime,format='gps'))
tdelay[-1] = lal.ArrivalTimeDiff(detH1.location, detL1.location, coords.ra.hour*np.pi/12, coords.dec.hour*np.pi/12, tgps)
tdelay = np.array(tdelay)
tdelay = np.repeat(tdelay,int(30/Xspacing))
# make sure tdelay and timeL are of same length in case integer-ing caused slight inconsistency.
b = np.ones(len(timeL)-len(tdelay))*tdelay[-1]
tdelay = np.append(tdelay,b)
timeL = timeL - tdelay
#------------ Down-sampling ------------#
print 'Down-sampling'
Xspacing = Xspacing*8
num_points = int(durationH/Xspacing)
newtimeL, newtimeH, newstrainH, newstrainL, newtdelay = [[0 for _ in range(num_points)] for _ in range(5)]
for i in range(num_points):
j = 8*i + 4
newstrainH[i] = np.mean(strainH[j-4:j+4])
newstrainL[i] = np.mean(strainL[j-4:j+4])
newtimeH[i] = timeH[j]
newtimeL[i] = timeL[j]
newtdelay[i] = tdelay[j]
newstrainH = newstrainH[76800:len(newstrainH)]
newstrainL = newstrainL[76800:len(newstrainL)]
newtimeH = newtimeH[76800:len(newtimeH)]
newtimeL = newtimeL[76800:len(newtimeL)]
newtdelay = newtdelay[76800:len(newtdelay)]
starttime = starttime + 150
durationH = int(newtimeH[-1]) - int(newtimeH[0])
num_points = int(durationH/Xspacing)
del timeL, timeH, tdelay, strainH, strainL
#----------- add in fake signal ------------#
newtimeH = newtimeH[0:num_points]
newtimeL = newtimeL[0:num_points]
newtdelay = newtdelay[0:num_points]
print 'Insert fake signal'
numseg = int((durationH)/600)
segs = np.linspace(0,numseg,numseg+1)*600
segs = segs + newtimeL[0]
psi1=np.pi/4
ra,dec,fp,fc = [[0 for _ in range(numseg+1)] for _ in range(4)]
for i in range(numseg+1):
coordstime = segs[i]
coords = get_sun(Time.Time(coordstime,format='gps'))
ra[i] = coords.ra.hour*np.pi/12
dec[i] = coords.dec.hour*np.pi/12
FcX0, FpX0, FcY0, FpY0 = [[0 for _ in range(num_points)] for _ in range(4)]
for i in range(num_points):
FpX0[i], FcX0[i] = ant_res(gpsTime[int(i*Xspacing/600.)], ra[int(i*Xspacing/600.)], dec[int(i*Xspacing/600.)], 0, 'H1')
FpY0[i], FcY0[i] = ant_res(gpsTime[int(i*Xspacing/600.)], ra[int(i*Xspacing/600.)], dec[int(i*Xspacing/600.)], 0, 'L1')
cos2pi1 = np.cos(2*psi1)
sin2pi1 = np.sin(2*psi1)
fakeH, fakeL, FpX, FcX, FpY, FcY = [[0 for _ in range(num_points)] for _ in range(6)]
for i in range(num_points):
FpX[i] = FpX0[i]*cos2pi1 + FcX0[i]*sin2pi1
FcX[i] = FcX0[i]*cos2pi1 - FpX0[i]*sin2pi1
FpY[i] = FpY0[i]*cos2pi1 + FcY0[i]*sin2pi1
FcY[i] = FcY0[i]*cos2pi1 - FpY0[i]*sin2pi1
for i in range(len(newtimeH)):
fakeH[i] = amplitude*(FpX[i]*np.sin(omega*newtimeH[i]) + FcX[i]*np.cos(omega*newtimeH[i]))
fakeL[i] = amplitude*(FpY[i]*np.sin(omega*newtimeL[i]) + FcY[i]*np.cos(omega*newtimeL[i]))
tdelayidx = [0 for _ in range(len(newtdelay))]
for i in range(len(newtdelay)):
tdelayidx[i] = int(newtdelay[i]*Xspacing)
idxmax= np.max(tdelayidx)
print len(newtdelay), len(newstrainL)
newstrainL0,newstrainH0 = [[0 for _ in range(len(newtdelay)-idxmax)] for _ in range(2)]
for i in range(idxmax,len(newtdelay)):
newstrainL0[i-idxmax]=newstrainL[i-tdelayidx[i]]
newstrainH0=newstrainH[0:len(newstrainL0)]
newstrainH0 = newstrainH0 + fakeH
newstrainL0 = newstrainL0 + fakeL
del newstrainL, newstrainH, fakeL, fakeH
# H1 and L1 are now in sync and filtered between 100 and 150 Hz.
del FcX,FcX0,FpX,FpX0,FcY,FcY0,FpY,FpY0
#----- Applying a bandpass filter -----#
print 'Filtering data'
coefsL = get_filter_coefs('L1')
coefsH = get_filter_coefs('H1')
newstrainL0 = np.array(newstrainL0)
newstrainH0 = np.array(newstrainH0)
newstrainL = filter_data(newstrainL0,coefsL)
newstrainH = filter_data(newstrainH0,coefsH)
del coefsL, coefsH
############################################################
#------------ Finding probability distribution ------------#
# ------------ Defining some stuff for p ------------- #
print 'Finding likelihood Part 1/2'
psi_array = np.linspace(0,np.pi,10)
dpsi = psi_array[1]-psi_array[0]
sigmaA = 10.0
h0min = h0_min*np.std(newstrainH)
h0max = h0_max*np.std(newstrainH)
h0_array = np.linspace(h0min,h0max,h0_vals_num)
invSigma0 = np.array([[(1./sigmaA**2), 0.], [0., (1./sigmaA**2)]])
detSigma0 = sigmaA**4
dX = newstrainH
dY = newstrainL
del newstrainH, newstrainL, newstrainH0, newstrainL0, newtimeL, newtimeH
FcX0, FpX0, FcY0, FpY0 = [[0 for _ in range(num_points)] for _ in range(4)]
for i in range(num_points):
FpX0[i], FcX0[i] = ant_res(gpsTime[int(i*Xspacing/600.)], ra[int(i*Xspacing/600.)], dec[int(i*Xspacing/600.)], 0, 'H1')
FpY0[i], FcY0[i] = ant_res(gpsTime[int(i*Xspacing/600.)], ra[int(i*Xspacing/600.)], dec[int(i*Xspacing/600.)], 0, 'L1')
p = [0 for _ in range(len(h0_array))]
ppsi = [0 for _ in range(len(psi_array))]
logdpsi_2 = np.log(0.5*dpsi)
cos2pi, sin2pi = [[0 for _ in range(len(psi_array))] for _ in range(2)]
FpX, FcX, FpY, FcY = [[[0 for _ in range(num_points)] for _ in range(len(psi_array))] for _ in range(4)]
for k in range(len(psi_array)):
cos2pi[k] = np.cos(2*psi_array[k])
sin2pi[k] = np.sin(2*psi_array[k])
for i in range(num_points):
FpX[k][i] = FpX0[i]*cos2pi[k] + FcX0[i]*sin2pi[k]
FcX[k][i] = FcX0[i]*cos2pi[k] - FpX0[i]*sin2pi[k]
FpY[k][i] = FpY0[i]*cos2pi[k] + FcY0[i]*sin2pi[k]
FcY[k][i] = FcY0[i]*cos2pi[k] - FpY0[i]*sin2pi[k]
print 'Finding likelihoot Part 2/2. This will take a while... '
for i in range(num_points):
d = np.array([dX[i], dY[i]])
d.shape = (2,1)
if (i + int(60/Xspacing)<num_points):
int1 = i + int(60/Xspacing)
else:
int1 = i
if (i - int(60/Xspacing)>0):
int0 = i - int(60/Xspacing)
else:
int0 = 0
sigmaX = np.std(dX[int0:int1])
sigmaY = np.std(dY[int0:int1])
C = np.array([[sigmaX**2, 0.], [0., sigmaY**2]])
invC = np.array([[(1./sigmaX**2), 0.], [0., (1/sigmaY**2)]])
detC = sigmaX**2 * sigmaY**2
for j in range(len(h0_array)):
for k in range(len(psi_array)):
M = h0_array[j]*np.array([[FpX[k][i], FpY[k][i]], [FcX[k][i], FcY[k][i]]])
M = np.array([[M[0][0][0],M[0][1][0]],[M[1][0][0], M[1][1][0]]])
invSigma = np.dot(M.T, np.dot(invC, M)) + invSigma0
Sigma = np.linalg.inv(invSigma)
detSigma = np.linalg.det(Sigma)
chi = np.dot(Sigma, np.dot(M.T, np.dot(invC, d)))
ppsi[k] = 0.5*np.log(detSigma) - 0.5*np.log(16.*np.pi**4*detSigma0*detC) - 0.5*(np.vdot(d.T, np.dot(invC, d)) + np.vdot(chi.T, np.dot(invSigma, chi)))
p[j] += logdpsi_2 + logsumexp([logsumexp(ppsi[:-1]), logsumexp(ppsi[1:])])
# Write into a file.
wm = 'all/siginj'
if os.path.exists(wm)==False:
os.mkdir(wm)
else:
pass
np.savetxt(wm+'/p'+str(oldstarttime)+'.txt',p)
np.savetxt(wm+'/h0'+str(oldstarttime)+'.txt',h0_array)
strainL0 = strainL
print 'Filtering data'
ord = 4
Wn = [100.0/2918.0,150.0/2918.0]
type = 'bandpass'
bL,aL = butter(ord,Wn, btype=type)
bH,aH = butter(ord,Wn, btype=type)
strainL1 = filtfilt(bL,aL,strainL)
strainH1 = filtfilt(bH,aH,strainH)
print 'Standard deviations before adding the notch-filters', np.std(strainL1), np.std(strainH1)
coefsL = get_filter_coefs('L1')
coefsH = get_filter_coefs('H1')
strainL2 = filter_data(strainL,coefsL)
strainH2 = filter_data(strainH,coefsH)
print 'Standard deviations after adding the notch-filters', np.std(strainL2), np.std(strainH2)
print 'Plotting'
plt.figure(1)
plt.plot(gpsTime,strainL0,'-')
plt.title('Raw data of L1 detector for '+str(durationH/60)+' minutes')
plt.xlabel('TimeSeries')
plt.ylabel('Strain')
plt.savefig('rawdata.png')
plt.show(1)
#
# plt.figure(2)
# plt.plot(gpsTime,strainH2,'-')
