Commit 04eb2bc3 authored by Gregory Ashton's avatar Gregory Ashton
Browse files

Adds histograms of directed and all-sky searches in noise

parent 30fd2378
import pyfstat
import numpy as np
import os
import sys
import time
ID = sys.argv[1]
outdir = sys.argv[2]
label = 'run_{}'.format(ID)
data_label = '{}_data'.format(label)
results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data
sqrtSX = 2e-23
tstart = 1000000000
Tspan = 100*86400
tend = tstart + Tspan
# Fixed properties of the signal
F0_center = 30
F1_center = 1e-10
F2 = 0
tref = .5*(tstart+tend)
VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
nsteps = 50
run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 11, False),
((nsteps, 0), 6, False),
((nsteps, 0), 3, False),
((nsteps, nsteps), 1, False)]
DeltaAlpha = 0.05
DeltaDelta = 0.05
h0 = 0
F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0
F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1
Alpha = np.random.uniform(0, 2*np.pi)
Delta = np.arccos(2*np.random.uniform(0, 1)-1)-np.pi/2
fAlpha = np.random.uniform(0, 1)
Alpha_min = Alpha - DeltaAlpha*(1-fAlpha)
Alpha_max = Alpha + DeltaAlpha*fAlpha
fDelta = np.random.uniform(0, 1)
Delta_min = Delta - DeltaDelta*(1-fDelta)
Delta_max = Delta + DeltaDelta*fDelta
psi = np.random.uniform(-np.pi/4, np.pi/4)
phi = np.random.uniform(0, 2*np.pi)
cosi = np.random.uniform(-1, 1)
data = pyfstat.Writer(
label=data_label, outdir=outdir, tref=tref,
tstart=tstart, F0=F0, F1=F1, F2=F2, duration=Tspan, Alpha=Alpha,
Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
detector='H1,L1')
data.make_data()
predicted_twoF = data.predict_fstat()
startTime = time.time()
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
'upper': F0+DeltaF0/2.},
'F1': {'type': 'unif',
'lower': F1-DeltaF1/2.,
'upper': F1+DeltaF1/2.},
'F2': F2,
'Alpha': {'type': 'unif',
'lower': Alpha_min,
'upper': Alpha_max},
'Delta': {'type': 'unif',
'lower': Delta_min,
'upper': Delta_max},
}
ntemps = 1
log10temperature_min = -1
nwalkers = 100
mcmc = pyfstat.MCMCFollowUpSearch(
label=label, outdir=outdir,
sftfilepath='{}/*{}*sft'.format(outdir, data_label),
theta_prior=theta_prior,
tref=tref, minStartTime=tstart, maxStartTime=tend,
nwalkers=nwalkers, ntemps=ntemps,
log10temperature_min=log10temperature_min)
mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
gen_tex_table=False)
d, maxtwoF = mcmc.get_max_twoF()
dF0 = F0 - d['F0']
dF1 = F1 - d['F1']
runTime = time.time() - startTime
with open(results_file_name, 'a') as f:
f.write('{:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n'
.format(dF0, dF1, predicted_twoF, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label))
import pyfstat
import numpy as np
outdir = 'data'
label = 'allsky_setup'
data_label = '{}_data'.format(label)
# Properties of the GW data
sqrtSX = 2e-23
tstart = 1000000000
Tspan = 100*86400
tend = tstart + Tspan
# Fixed properties of the signal
F0_center = 30
F1_center = 1e-10
F2 = 0
tref = .5*(tstart+tend)
VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
DeltaAlpha = 0.05
DeltaDelta = 0.05
depth = 100
nsteps = 50
run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 11, False),
((nsteps, 0), 6, False),
((nsteps, 0), 3, False),
((nsteps, nsteps), 1, False)]
h0 = sqrtSX / float(depth)
r = np.random.uniform(0, 1)
theta = np.random.uniform(0, 2*np.pi)
F0 = F0_center + 3*np.sqrt(r)*np.cos(theta)/(np.pi**2 * Tspan**2)
F1 = F1_center + 45*np.sqrt(r)*np.sin(theta)/(4*np.pi**2 * Tspan**4)
Alpha = 0
Delta = 0
psi = np.random.uniform(-np.pi/4, np.pi/4)
phi = np.random.uniform(0, 2*np.pi)
cosi = np.random.uniform(-1, 1)
data = pyfstat.Writer(
label=data_label, outdir=outdir, tref=tref,
tstart=tstart, F0=F0, F1=F1, F2=F2, duration=Tspan, Alpha=Alpha,
Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
detector='H1,L1')
data.make_data()
predicted_twoF = data.predict_fstat()
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
'upper': F0+DeltaF0/2.},
'F1': {'type': 'unif',
'lower': F1-DeltaF1/2.,
'upper': F1+DeltaF1/2.},
'F2': F2,
'Alpha': {'type': 'unif',
'lower': Alpha-DeltaAlpha/2.,
'upper': Alpha+DeltaAlpha/2.},
'Delta': {'type': 'unif',
'lower': Delta-DeltaDelta/2.,
'upper': Delta+DeltaDelta/2.},
}
ntemps = 1
log10temperature_min = -1
nwalkers = 100
mcmc = pyfstat.MCMCFollowUpSearch(
label=label, outdir=outdir,
sftfilepath='{}/*{}*sft'.format(outdir, data_label),
theta_prior=theta_prior,
tref=tref, minStartTime=tstart, maxStartTime=tend,
nwalkers=nwalkers, ntemps=ntemps,
log10temperature_min=log10temperature_min)
mcmc.run(run_setup)
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
from tqdm import tqdm
from oct2py import octave
import glob
filenames = glob.glob("CollectedOutput/*.txt")
plt.style.use('paper')
Tspan = 100 * 86400
def maxtwoFinNoise(twoF, Ntrials):
F = twoF/2.0
alpha = (1 + F)*np.exp(-F)
a = Ntrials/2.0*F*np.exp(-F)
b = (1 - alpha)**(Ntrials-1)
return a*b
df_list = []
for fn in filenames:
df = pd.read_csv(
fn, sep=' ', names=['dF0', 'dF1', 'twoF_predicted',
'twoF', 'runTime'])
df['CLUSTER_ID'] = fn.split('_')[1]
df_list.append(df)
df = pd.concat(df_list)
fig, ax = plt.subplots()
ax.hist(df.twoF, bins=50, histtype='step', color='k', normed=True, linewidth=1)
twoFsmooth = np.linspace(0, df.twoF.max(), 100)
# ax.plot(twoFsmooth, maxtwoFinNoise(twoFsmooth, 8e5), '-r')
ax.set_xlabel('$\widetilde{2\mathcal{F}}$')
ax.set_xlim(0, 60)
fig.tight_layout()
fig.savefig('allsky_noise_twoF_histogram.png')
#!/bin/bash
. /home/gregory.ashton/lalsuite-install/etc/lalapps-user-env.sh
export PATH="/home/gregory.ashton/anaconda2/bin:$PATH"
export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib
rm /local/user/gregory.ashton/MCResults*txt
for ((n=0;n<100;n++))
do
/home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --quite --no-template-counting
done
cp /local/user/gregory.ashton/MCResults*txt /home/gregory.ashton/PyFstat/Paper/AllSkyMCNoiseOnly/CollectedOutput
Executable= repeat.sh
Arguments= $(Cluster)_$(Process)
Universe=vanilla
Input=/dev/null
accounting_group = ligo.dev.o2.cw.explore.test
Output=CollectedOutput/out.$(Process)
Error=CollectedOutput/err.$(Process)
Log=CollectedOutput/log.$(Process)
request_cpus = 1
request_memory = 16 GB
Queue 100
import pyfstat
import numpy as np
import os
import sys
import time
ID = sys.argv[1]
outdir = sys.argv[2]
label = 'run_{}'.format(ID)
data_label = '{}_data'.format(label)
results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data
sqrtSX = 2e-23
tstart = 1000000000
Tspan = 100*86400
tend = tstart + Tspan
# Fixed properties of the signal
F0_center = 30
F1_center = 1e-10
F2 = 0
Alpha = 5e-3
Delta = 6e-2
tref = .5*(tstart+tend)
VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
nsteps = 20
run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 7, False),
((nsteps, 0), 2, False),
((nsteps, nsteps), 1, False)]
h0 = 0
F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0
F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1
psi = np.random.uniform(-np.pi/4, np.pi/4)
phi = np.random.uniform(0, 2*np.pi)
cosi = np.random.uniform(-1, 1)
data = pyfstat.Writer(
label=data_label, outdir=outdir, tref=tref,
tstart=tstart, F0=F0, F1=F1, F2=F2, duration=Tspan, Alpha=Alpha,
Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
detector='H1,L1')
data.make_data()
predicted_twoF = data.predict_fstat()
startTime = time.time()
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
'upper': F0+DeltaF0/2.},
'F1': {'type': 'unif',
'lower': F1-DeltaF1/2.,
'upper': F1+DeltaF1/2.},
'F2': F2,
'Alpha': Alpha,
'Delta': Delta
}
ntemps = 1
log10temperature_min = -1
nwalkers = 100
mcmc = pyfstat.MCMCFollowUpSearch(
label=label, outdir=outdir,
sftfilepath='{}/*{}*sft'.format(outdir, data_label),
theta_prior=theta_prior,
tref=tref, minStartTime=tstart, maxStartTime=tend,
nwalkers=nwalkers, ntemps=ntemps,
log10temperature_min=log10temperature_min)
mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
gen_tex_table=False)
d, maxtwoF = mcmc.get_max_twoF()
dF0 = F0 - d['F0']
dF1 = F1 - d['F1']
runTime = time.time() - startTime
with open(results_file_name, 'a') as f:
f.write('{:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n'
.format(dF0, dF1, predicted_twoF, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label))
#!/bin/bash
. /home/gregory.ashton/lalsuite-install/etc/lalapps-user-env.sh
export PATH="/home/gregory.ashton/anaconda2/bin:$PATH"
export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib
rm /local/user/gregory.ashton/MCResults*txt
for ((n=0;n<100;n++))
do
/home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --quite --no-template-counting
done
cp /local/user/gregory.ashton/MCResults*txt /home/gregory.ashton/PyFstat/Paper/DirectedMCNoiseOnly/CollectedOutput
Executable= repeat.sh
Arguments= $(Cluster)_$(Process)
Universe=vanilla
Input=/dev/null
accounting_group = ligo.dev.o2.cw.explore.test
Output=CollectedOutput/out.$(Process)
Error=CollectedOutput/err.$(Process)
Log=CollectedOutput/log.$(Process)
request_cpus = 1
request_memory = 8 GB
Queue 100
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