Commit 300ad290 authored by Gregory Ashton's avatar Gregory Ashton
Browse files

Complete run of MC results

Use N=1000 for the recoveries and N=10000 for the test in noise
parent 435e631e
...@@ -4,9 +4,8 @@ ...@@ -4,9 +4,8 @@
export PATH="/home/gregory.ashton/anaconda2/bin:$PATH" export PATH="/home/gregory.ashton/anaconda2/bin:$PATH"
export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib
rm /local/user/gregory.ashton/MCResults*txt for ((n=0;n<1;n++))
for ((n=0;n<10;n++))
do do
/home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --quite --no-template-counting /home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --no-template-counting --no-interactive
done done
cp /local/user/gregory.ashton/MCResults*txt /home/gregory.ashton/PyFstat/Paper/AllSkyMC/CollectedOutput cp /local/user/gregory.ashton/MCResults_"$1".txt $(pwd)/CollectedOutput
This diff is collapsed.
...@@ -12,14 +12,14 @@ data_label = '{}_data'.format(label) ...@@ -12,14 +12,14 @@ data_label = '{}_data'.format(label)
results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID) results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data # Properties of the GW data
sqrtSX = 2e-23 sqrtSX = 1e-23
tstart = 1000000000 tstart = 1000000000
Tspan = 100*86400 Tspan = 100*86400
tend = tstart + Tspan tend = tstart + Tspan
# Fixed properties of the signal # Fixed properties of the signal
F0_center = 30 F0_center = 30
F1_center = 1e-10 F1_center = -1e-10
F2 = 0 F2 = 0
tref = .5*(tstart+tend) tref = .5*(tstart+tend)
...@@ -31,7 +31,7 @@ DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2) ...@@ -31,7 +31,7 @@ DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
DeltaAlpha = 0.02 DeltaAlpha = 0.02
DeltaDelta = 0.02 DeltaDelta = 0.02
depths = np.linspace(100, 400, 7) depths = np.linspace(100, 400, 9)
nsteps = 50 nsteps = 50
run_setup = [((nsteps, 0), 20, False), run_setup = [((nsteps, 0), 20, False),
...@@ -45,7 +45,7 @@ for depth in depths: ...@@ -45,7 +45,7 @@ for depth in depths:
h0 = sqrtSX / float(depth) h0 = sqrtSX / float(depth)
F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0 F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0
F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1 F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1
Alpha_center = np.random.uniform(0, 2*np.pi) Alpha_center = np.random.uniform(DeltaAlpha, 2*np.pi-DeltaAlpha)
Delta_center = np.arccos(2*np.random.uniform(0, 1)-1)-np.pi/2 Delta_center = np.arccos(2*np.random.uniform(0, 1)-1)-np.pi/2
Alpha = Alpha_center + np.random.uniform(-0.5, 0.5)*DeltaAlpha Alpha = Alpha_center + np.random.uniform(-0.5, 0.5)*DeltaAlpha
Delta = Delta_center + np.random.uniform(-0.5, 0.5)*DeltaDelta Delta = Delta_center + np.random.uniform(-0.5, 0.5)*DeltaDelta
...@@ -59,7 +59,6 @@ for depth in depths: ...@@ -59,7 +59,6 @@ for depth in depths:
Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi, Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
detector='H1,L1') detector='H1,L1')
data.make_data() data.make_data()
predicted_twoF = data.predict_fstat()
startTime = time.time() startTime = time.time()
theta_prior = {'F0': {'type': 'unif', theta_prior = {'F0': {'type': 'unif',
...@@ -95,6 +94,6 @@ for depth in depths: ...@@ -95,6 +94,6 @@ for depth in depths:
dF1 = F1 - d['F1'] dF1 = F1 - d['F1']
runTime = time.time() - startTime runTime = time.time() - startTime
with open(results_file_name, 'a') as f: with open(results_file_name, 'a') as f:
f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n' f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n'
.format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF, runTime)) .format(depth, h0, dF0, dF1, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label)) os.system('rm {}/*{}*'.format(outdir, label))
...@@ -31,8 +31,7 @@ def binomialConfidenceInterval(N, K, confidence=0.95): ...@@ -31,8 +31,7 @@ def binomialConfidenceInterval(N, K, confidence=0.95):
df_list = [] df_list = []
for fn in filenames: for fn in filenames:
df = pd.read_csv( df = pd.read_csv(
fn, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', 'twoF_predicted', fn, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', 'twoF', 'runTime'])
'twoF', 'runTime'])
df['CLUSTER_ID'] = fn.split('_')[1] df['CLUSTER_ID'] = fn.split('_')[1]
df_list.append(df) df_list.append(df)
df = pd.concat(df_list) df = pd.concat(df_list)
...@@ -52,9 +51,9 @@ for d in depths: ...@@ -52,9 +51,9 @@ for d in depths:
yerr = np.abs(recovery_fraction - np.array(recovery_fraction_CI).T) yerr = np.abs(recovery_fraction - np.array(recovery_fraction_CI).T)
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.errorbar(depths, recovery_fraction, yerr=yerr, fmt='sk', marker='s', ms=2, ax.errorbar(depths, recovery_fraction, yerr=yerr, fmt='sr', marker='s', ms=2,
capsize=1, capthick=0.5, elinewidth=0.5, capsize=1, capthick=0.5, elinewidth=0.5,
label='Monte-Carlo result') label='Monte-Carlo result', zorder=10)
fname = 'analytic_data.txt' fname = 'analytic_data.txt'
if os.path.isfile(fname): if os.path.isfile(fname):
......
Paper/AllSkyMC/runTimeHist.png

21.1 KB | W: | H:

Paper/AllSkyMC/runTimeHist.png

23.1 KB | W: | H:

Paper/AllSkyMC/runTimeHist.png
Paper/AllSkyMC/runTimeHist.png
Paper/AllSkyMC/runTimeHist.png
Paper/AllSkyMC/runTimeHist.png
  • 2-up
  • Swipe
  • Onion skin
Executable= repeat.sh Executable=AllSkyMC_repeat.sh
Arguments= $(Cluster)_$(Process) Arguments=$(Cluster)_$(Process)
Universe=vanilla Universe=vanilla
Input=/dev/null Input=/dev/null
accounting_group = ligo.dev.o2.cw.explore.test accounting_group = ligo.dev.o2.cw.explore.test
Output=CollectedOutput/out.$(Process) Output=CollectedOutput/out.$(Cluster).$(Process)
Error=CollectedOutput/err.$(Process) Error=CollectedOutput/err.$(Cluster).$(Process)
Log=CollectedOutput/log.$(Process) Log=CollectedOutput/log.$(Cluster).$(Process)
request_cpus = 1 request_cpus = 1
request_memory = 16 GB request_memory = 16 GB
Queue 100 Queue 70
...@@ -9,17 +9,17 @@ outdir = sys.argv[2] ...@@ -9,17 +9,17 @@ outdir = sys.argv[2]
label = 'run_{}'.format(ID) label = 'run_{}'.format(ID)
data_label = '{}_data'.format(label) data_label = '{}_data'.format(label)
results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID) results_file_name = '{}/NoiseOnlyMCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data # Properties of the GW data
sqrtSX = 2e-23 sqrtSX = 1e-23
tstart = 1000000000 tstart = 1000000000
Tspan = 100*86400 Tspan = 100*86400
tend = tstart + Tspan tend = tstart + Tspan
# Fixed properties of the signal # Fixed properties of the signal
F0_center = 30 F0_center = 30
F1_center = 1e-10 F1_center = -1e-10
F2 = 0 F2 = 0
tref = .5*(tstart+tend) tref = .5*(tstart+tend)
...@@ -28,6 +28,9 @@ VF0 = VF1 = 100 ...@@ -28,6 +28,9 @@ VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan) DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2) DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
DeltaAlpha = 0.02
DeltaDelta = 0.02
nsteps = 50 nsteps = 50
run_setup = [((nsteps, 0), 20, False), run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 11, False), ((nsteps, 0), 11, False),
...@@ -35,21 +38,14 @@ run_setup = [((nsteps, 0), 20, False), ...@@ -35,21 +38,14 @@ run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 3, False), ((nsteps, 0), 3, False),
((nsteps, nsteps), 1, False)] ((nsteps, nsteps), 1, False)]
DeltaAlpha = 0.05
DeltaDelta = 0.05
h0 = 0 h0 = 0
F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0 F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0
F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1 F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1
Alpha = np.random.uniform(0, 2*np.pi) Alpha_center = np.random.uniform(DeltaAlpha, 2*np.pi-DeltaAlpha)
Delta = np.arccos(2*np.random.uniform(0, 1)-1)-np.pi/2 Delta_center = np.arccos(2*np.random.uniform(0, 1)-1)-np.pi/2
fAlpha = np.random.uniform(0, 1) Alpha = Alpha_center + np.random.uniform(-0.5, 0.5)*DeltaAlpha
Alpha_min = Alpha - DeltaAlpha*(1-fAlpha) Delta = Delta_center + np.random.uniform(-0.5, 0.5)*DeltaDelta
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) psi = np.random.uniform(-np.pi/4, np.pi/4)
phi = np.random.uniform(0, 2*np.pi) phi = np.random.uniform(0, 2*np.pi)
cosi = np.random.uniform(-1, 1) cosi = np.random.uniform(-1, 1)
...@@ -60,25 +56,24 @@ data = pyfstat.Writer( ...@@ -60,25 +56,24 @@ data = pyfstat.Writer(
Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi, Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
detector='H1,L1') detector='H1,L1')
data.make_data() data.make_data()
predicted_twoF = data.predict_fstat()
startTime = time.time() startTime = time.time()
theta_prior = {'F0': {'type': 'unif', theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2., 'lower': F0_center-DeltaF0,
'upper': F0+DeltaF0/2.}, 'upper': F0_center+DeltaF0},
'F1': {'type': 'unif', 'F1': {'type': 'unif',
'lower': F1-DeltaF1/2., 'lower': F1_center-DeltaF1,
'upper': F1+DeltaF1/2.}, 'upper': F1_center+DeltaF1},
'F2': F2, 'F2': F2,
'Alpha': {'type': 'unif', 'Alpha': {'type': 'unif',
'lower': Alpha_min, 'lower': Alpha_center-DeltaAlpha,
'upper': Alpha_max}, 'upper': Alpha_center+DeltaAlpha},
'Delta': {'type': 'unif', 'Delta': {'type': 'unif',
'lower': Delta_min, 'lower': Delta_center-DeltaDelta,
'upper': Delta_max}, 'upper': Delta_center+DeltaDelta},
} }
ntemps = 1 ntemps = 2
log10temperature_min = -1 log10temperature_min = -1
nwalkers = 100 nwalkers = 100
...@@ -96,6 +91,6 @@ dF0 = F0 - d['F0'] ...@@ -96,6 +91,6 @@ dF0 = F0 - d['F0']
dF1 = F1 - d['F1'] dF1 = F1 - d['F1']
runTime = time.time() - startTime runTime = time.time() - startTime
with open(results_file_name, 'a') as f: with open(results_file_name, 'a') as f:
f.write('{:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n' f.write('{:1.8e} {:1.8e} {:1.8e} {}\n'
.format(dF0, dF1, predicted_twoF, maxtwoF, runTime)) .format(dF0, dF1, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label)) os.system('rm {}/*{}*'.format(outdir, label))
...@@ -24,11 +24,11 @@ def maxtwoFinNoise(twoF, Ntrials): ...@@ -24,11 +24,11 @@ def maxtwoFinNoise(twoF, Ntrials):
df_list = [] df_list = []
for fn in filenames: for fn in filenames:
df = pd.read_csv( df = pd.read_csv(
fn, sep=' ', names=['dF0', 'dF1', 'twoF_predicted', fn, sep=' ', names=['dF0', 'dF1', 'twoF', 'runTime'])
'twoF', 'runTime'])
df['CLUSTER_ID'] = fn.split('_')[1] df['CLUSTER_ID'] = fn.split('_')[1]
df_list.append(df) df_list.append(df)
df = pd.concat(df_list) df = pd.concat(df_list)
print 'Number of samples = ', len(df)
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.hist(df.twoF, bins=50, histtype='step', color='k', normed=True, linewidth=1) ax.hist(df.twoF, bins=50, histtype='step', color='k', normed=True, linewidth=1)
......
Executable= repeat.sh Executable= AllSkyMCNoiseOnly_repeat.sh
Arguments= $(Cluster)_$(Process) Arguments= $(Cluster)_$(Process)
Universe=vanilla Universe=vanilla
Input=/dev/null Input=/dev/null
accounting_group = ligo.dev.o2.cw.explore.test accounting_group = ligo.dev.o2.cw.explore.test
Output=CollectedOutput/out.$(Process) Output=CollectedOutput/out.$(Cluster).$(Process)
Error=CollectedOutput/err.$(Process) Error=CollectedOutput/err.$(Cluster).$(Process)
Log=CollectedOutput/log.$(Process) Log=CollectedOutput/log.$(Cluster).$(Process)
request_cpus = 1 request_cpus = 1
request_memory = 16 GB request_memory = 16 GB
......
...@@ -4,9 +4,8 @@ ...@@ -4,9 +4,8 @@
export PATH="/home/gregory.ashton/anaconda2/bin:$PATH" export PATH="/home/gregory.ashton/anaconda2/bin:$PATH"
export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib
rm /local/user/gregory.ashton/MCResults*txt for ((n=0;n<1;n++))
for ((n=0;n<10;n++))
do do
/home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --quite --no-template-counting /home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --no-template-counting --no-interactive
done done
cp /local/user/gregory.ashton/MCResults*txt /home/gregory.ashton/PyFstat/Paper/DirectedMC/CollectedOutput cp /local/user/gregory.ashton/MCResults_"$1".txt $(pwd)/CollectedOutput
...@@ -12,17 +12,17 @@ data_label = '{}_data'.format(label) ...@@ -12,17 +12,17 @@ data_label = '{}_data'.format(label)
results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID) results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data # Properties of the GW data
sqrtSX = 2e-23 sqrtSX = 1e-23
tstart = 1000000000 tstart = 1000000000
Tspan = 100*86400 Tspan = 100*86400
tend = tstart + Tspan tend = tstart + Tspan
# Fixed properties of the signal # Fixed properties of the signal
F0_center = 30 F0_center = 30
F1_center = 1e-10 F1_center = -1e-10
F2 = 0 F2 = 0
Alpha = 5e-3 Alpha = np.radians(83.6292)
Delta = 6e-2 Delta = np.radians(22.0144)
tref = .5*(tstart+tend) tref = .5*(tstart+tend)
...@@ -30,9 +30,9 @@ VF0 = VF1 = 100 ...@@ -30,9 +30,9 @@ VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan) DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2) DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
depths = np.linspace(100, 400, 7) depths = np.linspace(100, 400, 9)
nsteps = 50 nsteps = 25
run_setup = [((nsteps, 0), 20, False), run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 7, False), ((nsteps, 0), 7, False),
((nsteps, 0), 2, False), ((nsteps, 0), 2, False),
...@@ -53,7 +53,6 @@ for depth in depths: ...@@ -53,7 +53,6 @@ for depth in depths:
Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi, Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
detector='H1,L1') detector='H1,L1')
data.make_data() data.make_data()
predicted_twoF = data.predict_fstat()
startTime = time.time() startTime = time.time()
theta_prior = {'F0': {'type': 'unif', theta_prior = {'F0': {'type': 'unif',
...@@ -80,11 +79,12 @@ for depth in depths: ...@@ -80,11 +79,12 @@ for depth in depths:
log10temperature_min=log10temperature_min) log10temperature_min=log10temperature_min)
mcmc.run(run_setup=run_setup, create_plots=False, log_table=False, mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
gen_tex_table=False) gen_tex_table=False)
mcmc.print_summary()
d, maxtwoF = mcmc.get_max_twoF() d, maxtwoF = mcmc.get_max_twoF()
dF0 = F0 - d['F0'] dF0 = F0 - d['F0']
dF1 = F1 - d['F1'] dF1 = F1 - d['F1']
runTime = time.time() - startTime runTime = time.time() - startTime
with open(results_file_name, 'a') as f: with open(results_file_name, 'a') as f:
f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n' f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n'
.format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF, runTime)) .format(depth, h0, dF0, dF1, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label)) os.system('rm {}/*{}*'.format(outdir, label))
...@@ -31,10 +31,12 @@ def binomialConfidenceInterval(N, K, confidence=0.95): ...@@ -31,10 +31,12 @@ def binomialConfidenceInterval(N, K, confidence=0.95):
df_list = [] df_list = []
for fn in filenames: for fn in filenames:
df = pd.read_csv( df = pd.read_csv(
fn, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', 'twoF_predicted', fn, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', 'twoF', 'runTime'])
'twoF', 'runTime'])
df['CLUSTER_ID'] = fn.split('_')[1] df['CLUSTER_ID'] = fn.split('_')[1]
df_list.append(df) if len(df) != 9:
print len(df), fn
else:
df_list.append(df)
df = pd.concat(df_list) df = pd.concat(df_list)
twoFstar = 60 twoFstar = 60
...@@ -52,9 +54,9 @@ for d in depths: ...@@ -52,9 +54,9 @@ for d in depths:
yerr = np.abs(recovery_fraction - np.array(recovery_fraction_CI).T) yerr = np.abs(recovery_fraction - np.array(recovery_fraction_CI).T)
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.errorbar(depths, recovery_fraction, yerr=yerr, fmt='sk', marker='s', ms=2, ax.errorbar(depths, recovery_fraction, yerr=yerr, fmt='sr', marker='s', ms=2,
capsize=1, capthick=0.5, elinewidth=0.5, capsize=1, capthick=0.5, elinewidth=0.5,
label='Monte-Carlo result') label='Monte-Carlo result', zorder=10)
fname = 'analytic_data.txt' fname = 'analytic_data.txt'
if os.path.isfile(fname): if os.path.isfile(fname):
......
Paper/DirectedMC/runTimeHist.png

22.8 KB | W: | H:

Paper/DirectedMC/runTimeHist.png

21.1 KB | W: | H:

Paper/DirectedMC/runTimeHist.png
Paper/DirectedMC/runTimeHist.png
Paper/DirectedMC/runTimeHist.png
Paper/DirectedMC/runTimeHist.png
  • 2-up
  • Swipe
  • Onion skin
Executable= repeat.sh Executable=DirectedMC_repeat.sh
Arguments= $(Cluster)_$(Process) Arguments=$(Cluster)_$(Process)
Universe=vanilla Universe=vanilla
Input=/dev/null Input=/dev/null
accounting_group = ligo.dev.o2.cw.explore.test accounting_group = ligo.dev.o2.cw.explore.test
Output=CollectedOutput/out.$(Process) Output=CollectedOutput/out.$(Cluster).$(Process)
Error=CollectedOutput/err.$(Process) Error=CollectedOutput/err.$(Cluster).$(Process)
Log=CollectedOutput/log.$(Process) Log=CollectedOutput/log.$(Cluster).$(Process)
request_cpus = 1 request_cpus = 1
request_memory = 8 GB request_memory = 16 GB
Queue 100 Queue 6
...@@ -4,9 +4,8 @@ ...@@ -4,9 +4,8 @@
export PATH="/home/gregory.ashton/anaconda2/bin:$PATH" export PATH="/home/gregory.ashton/anaconda2/bin:$PATH"
export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib
rm /local/user/gregory.ashton/MCResults*txt
for ((n=0;n<100;n++)) for ((n=0;n<100;n++))
do do
/home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --quite --no-template-counting /home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --no-template-counting --no-interactive
done done
cp /local/user/gregory.ashton/MCResults*txt /home/gregory.ashton/PyFstat/Paper/AllSkyMCNoiseOnly/CollectedOutput cp /local/user/gregory.ashton/NoiseOnlyMCResults_"$1".txt $(pwd)/CollectedOutput
...@@ -10,27 +10,27 @@ outdir = sys.argv[2] ...@@ -10,27 +10,27 @@ outdir = sys.argv[2]
label = 'run_{}'.format(ID) label = 'run_{}'.format(ID)
data_label = '{}_data'.format(label) data_label = '{}_data'.format(label)
results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID) results_file_name = '{}/NoiseOnlyMCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data # Properties of the GW data
sqrtSX = 2e-23 sqrtSX = 1e-23
tstart = 1000000000 tstart = 1000000000
Tspan = 100*86400 Tspan = 100*86400
tend = tstart + Tspan tend = tstart + Tspan
# Fixed properties of the signal # Fixed properties of the signal
F0_center = 30 F0_center = 30
F1_center = 1e-10 F1_center = -1e-10
F2 = 0 F2 = 0
Alpha = 5e-3 Alpha = np.radians(83.6292)
Delta = 6e-2 Delta = np.radians(22.0144)
tref = .5*(tstart+tend) tref = .5*(tstart+tend)
VF0 = VF1 = 100 VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan) DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2) DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
nsteps = 20 nsteps = 25
run_setup = [((nsteps, 0), 20, False), run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 7, False), ((nsteps, 0), 7, False),
((nsteps, 0), 2, False), ((nsteps, 0), 2, False),
...@@ -50,21 +50,20 @@ data = pyfstat.Writer( ...@@ -50,21 +50,20 @@ data = pyfstat.Writer(
Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi, Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
detector='H1,L1') detector='H1,L1')
data.make_data() data.make_data()
predicted_twoF = data.predict_fstat()
startTime = time.time() startTime = time.time()
theta_prior = {'F0': {'type': 'unif', theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2., 'lower': F0_center-DeltaF0,
'upper': F0+DeltaF0/2.}, 'upper': F0_center+DeltaF0},