diff --git a/Paper/AllSkyMC/repeat.sh b/Paper/AllSkyMC/AllSkyMC_repeat.sh
similarity index 53%
rename from Paper/AllSkyMC/repeat.sh
rename to Paper/AllSkyMC/AllSkyMC_repeat.sh
index 6ec0f9b3a8d02a1922d130fc178fa365e8a6721a..0f98c59e2193363a0f61d499da7226571ab60008 100755
--- a/Paper/AllSkyMC/repeat.sh
+++ b/Paper/AllSkyMC/AllSkyMC_repeat.sh
@@ -4,9 +4,8 @@
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<10;n++))
+for ((n=0;n<1;n++))
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
-cp /local/user/gregory.ashton/MCResults*txt /home/gregory.ashton/PyFstat/Paper/AllSkyMC/CollectedOutput
+cp /local/user/gregory.ashton/MCResults_"$1".txt $(pwd)/CollectedOutput
diff --git a/Paper/AllSkyMC/MCResults.txt b/Paper/AllSkyMC/MCResults.txt
deleted file mode 100644
index 561dd86e2aa932b7be43b56de39460c3e184c58d..0000000000000000000000000000000000000000
--- a/Paper/AllSkyMC/MCResults.txt
+++ /dev/null
@@ -1,693 +0,0 @@
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-150.0 1.33333333e-25 1.83382411e-08 1.45118793e-13 1.56700000e+02 1.55075867e+02
-200.0 1.00000000e-25 -4.46838232e-07 5.15552744e-13 2.81000000e+01 2.82982674e+01
-250.0 8.00000000e-26 -3.24153664e-07 -2.27148947e-13 5.61000000e+01 6.23434258e+01
-300.0 6.66666667e-26 8.49900164e-07 1.58048759e-13 1.82000000e+01 2.79501820e+01
-350.0 5.71428571e-26 -6.12452922e-07 -2.22944595e-13 1.25000000e+01 2.41541119e+01
-400.0 5.00000000e-26 -6.10321536e-07 3.35439731e-14 9.70000000e+00 2.87102795e+01
-100.0 2.00000000e-25 2.00245008e-07 -3.17138514e-13 1.76800000e+02 1.45316666e+02
-150.0 1.33333333e-25 3.41318035e-07 -3.58211984e-14 6.31000000e+01 5.30364037e+01
-200.0 1.00000000e-25 9.29674506e-08 6.61589964e-14 1.65900000e+02 1.36813187e+02
-250.0 8.00000000e-26 -1.62339050e-06 1.91978149e-14 1.90000000e+01 3.15320702e+01
-300.0 6.66666667e-26 -8.52393157e-07 -5.61271254e-13 5.13000000e+01 4.08834343e+01
-350.0 5.71428571e-26 -1.84072467e-06 8.21849334e-14 2.72000000e+01 4.54150162e+01
-400.0 5.00000000e-26 -2.29038309e-06 -8.96822846e-14 3.33000000e+01 2.67008438e+01
-100.0 2.00000000e-25 -2.16292797e-08 6.41483133e-14 4.78800000e+02 5.42441467e+02
-150.0 1.33333333e-25 -1.46889434e-07 -7.03552647e-14 8.83000000e+01 1.05605057e+02
-200.0 1.00000000e-25 -1.10819039e-06 3.69210530e-13 4.17000000e+01 2.92813225e+01
-250.0 8.00000000e-26 -1.26783071e-06 1.33697838e-13 1.96000000e+01 2.70255642e+01
-300.0 6.66666667e-26 2.16740611e-06 -5.73873545e-13 3.90000000e+01 2.75142155e+01
-350.0 5.71428571e-26 -1.32431677e-06 -1.39336326e-13 1.18000000e+01 3.10304279e+01
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-100.0 2.00000000e-25 -1.87679568e-07 -7.59351822e-15 1.56200000e+02 1.28389099e+02
-150.0 1.33333333e-25 1.72509523e-07 3.93244779e-13 1.63500000e+02 1.57067200e+02
-200.0 1.00000000e-25 1.31377081e-06 -7.92547271e-15 5.06000000e+01 2.63263302e+01
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-400.0 5.00000000e-26 -1.28974448e-06 -4.99177002e-13 3.59000000e+01 3.07177486e+01
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-150.0 1.33333333e-25 1.16422044e-07 8.81781953e-14 2.47100000e+02 2.60390778e+02
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-150.0 1.33333333e-25 -1.66300321e-07 -1.32515282e-13 2.23600000e+02 2.62754211e+02
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-150.0 1.33333333e-25 -3.88231360e-07 5.25761519e-14 7.38000000e+01 9.97120819e+01
-200.0 1.00000000e-25 -4.81573146e-08 -3.06539173e-13 1.06300000e+02 1.08868156e+02
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-400.0 5.00000000e-26 4.49693932e-07 3.56316425e-14 3.34000000e+01 2.53006420e+01
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-200.0 1.00000000e-25 -1.11748408e-06 1.45975297e-13 2.64000000e+01 2.57817078e+01
-250.0 8.00000000e-26 1.13985524e-06 -6.06474749e-14 3.69000000e+01 2.61923218e+01
-300.0 6.66666667e-26 1.34719464e-07 1.64228534e-13 2.79000000e+01 6.82637405e+01
-350.0 5.71428571e-26 6.74422118e-07 -6.25120830e-14 2.24000000e+01 2.50688667e+01
-400.0 5.00000000e-26 1.07954257e-07 -3.19077877e-13 1.24000000e+01 3.35822105e+01
-100.0 2.00000000e-25 9.54673460e-08 4.27184425e-13 2.48600000e+02 2.28204147e+02
-150.0 1.33333333e-25 -5.15130218e-07 -5.51502123e-13 4.47000000e+01 2.76664085e+01
-200.0 1.00000000e-25 6.94589311e-08 7.22180649e-14 1.27300000e+02 1.46018692e+02
-250.0 8.00000000e-26 -1.04288504e-06 2.25935752e-13 3.97000000e+01 2.85804749e+01
-300.0 6.66666667e-26 -1.77325310e-06 -4.24266286e-13 1.43000000e+01 2.40833530e+01
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-250.0 8.00000000e-26 3.16656106e-08 1.61705144e-13 8.14000000e+01 7.44811554e+01
-300.0 6.66666667e-26 -1.23690263e-06 1.97950273e-13 1.66000000e+01 3.36092529e+01
-350.0 5.71428571e-26 3.40620222e-07 2.76097273e-13 3.21000000e+01 4.20734787e+01
-400.0 5.00000000e-26 -1.64977097e-06 -2.51174146e-13 1.05000000e+01 2.88038750e+01
diff --git a/Paper/AllSkyMC/generate_data.py b/Paper/AllSkyMC/generate_data.py
index 81bc4a3878a9ffefe09ada9a5fb4923e606993d4..23c1589cf181030dd486d21642a4c37a7eff0b26 100644
--- a/Paper/AllSkyMC/generate_data.py
+++ b/Paper/AllSkyMC/generate_data.py
@@ -12,14 +12,14 @@ data_label = '{}_data'.format(label)
results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data
-sqrtSX = 2e-23
+sqrtSX = 1e-23
tstart = 1000000000
Tspan = 100*86400
tend = tstart + Tspan
# Fixed properties of the signal
F0_center = 30
-F1_center = 1e-10
+F1_center = -1e-10
F2 = 0
tref = .5*(tstart+tend)
@@ -31,7 +31,7 @@ DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
DeltaAlpha = 0.02
DeltaDelta = 0.02
-depths = np.linspace(100, 400, 7)
+depths = np.linspace(100, 400, 9)
nsteps = 50
run_setup = [((nsteps, 0), 20, False),
@@ -45,7 +45,7 @@ for depth in depths:
h0 = sqrtSX / float(depth)
F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0
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
Alpha = Alpha_center + np.random.uniform(-0.5, 0.5)*DeltaAlpha
Delta = Delta_center + np.random.uniform(-0.5, 0.5)*DeltaDelta
@@ -59,7 +59,6 @@ for depth in depths:
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',
@@ -95,6 +94,6 @@ for depth in depths:
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} {:1.8e} {}\n'
- .format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF, runTime))
+ f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n'
+ .format(depth, h0, dF0, dF1, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label))
diff --git a/Paper/AllSkyMC/plot_data.py b/Paper/AllSkyMC/plot_data.py
index 37811a586c174cfdd4ab586678a2b7f0055b985c..415e2acf056561f63b985d8dc435b8c2da58d1fb 100644
--- a/Paper/AllSkyMC/plot_data.py
+++ b/Paper/AllSkyMC/plot_data.py
@@ -31,8 +31,7 @@ def binomialConfidenceInterval(N, K, confidence=0.95):
df_list = []
for fn in filenames:
df = pd.read_csv(
- fn, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', 'twoF_predicted',
- 'twoF', 'runTime'])
+ fn, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', 'twoF', 'runTime'])
df['CLUSTER_ID'] = fn.split('_')[1]
df_list.append(df)
df = pd.concat(df_list)
@@ -52,9 +51,9 @@ for d in depths:
yerr = np.abs(recovery_fraction - np.array(recovery_fraction_CI).T)
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,
- label='Monte-Carlo result')
+ label='Monte-Carlo result', zorder=10)
fname = 'analytic_data.txt'
if os.path.isfile(fname):
diff --git a/Paper/AllSkyMC/runTimeHist.png b/Paper/AllSkyMC/runTimeHist.png
index ee6df4216d4f111e51301ca2dc30b61a8fb99e33..1335a76b9c0a12c3b73208e637c1d8b7dff68645 100644
Binary files a/Paper/AllSkyMC/runTimeHist.png and b/Paper/AllSkyMC/runTimeHist.png differ
diff --git a/Paper/AllSkyMC/submitfile b/Paper/AllSkyMC/submitfile
index 191a502764a6e221a87d0ccc89d49be609c8587c..2354ed631ad6ac25fcaa1b7fd28994dc2e2cfffd 100644
--- a/Paper/AllSkyMC/submitfile
+++ b/Paper/AllSkyMC/submitfile
@@ -1,12 +1,12 @@
-Executable= repeat.sh
-Arguments= $(Cluster)_$(Process)
+Executable=AllSkyMC_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.$(Process)
+Output=CollectedOutput/out.$(Cluster).$(Process)
+Error=CollectedOutput/err.$(Cluster).$(Process)
+Log=CollectedOutput/log.$(Cluster).$(Process)
request_cpus = 1
request_memory = 16 GB
-Queue 100
+Queue 70
diff --git a/Paper/AllSkyMCNoiseOnly/allsky_noise_twoF_histogram.png b/Paper/AllSkyMCNoiseOnly/allsky_noise_twoF_histogram.png
deleted file mode 100644
index 2e4a80ec22349299deb6933f833912fb7db325d9..0000000000000000000000000000000000000000
Binary files a/Paper/AllSkyMCNoiseOnly/allsky_noise_twoF_histogram.png and /dev/null differ
diff --git a/Paper/AllSkyMCNoiseOnly/generate_data.py b/Paper/AllSkyMCNoiseOnly/generate_data.py
index cc26b72bdc649338d211dd25eb64825c116f8979..ec082f540b7d15a0d07787a1c07a811197392878 100644
--- a/Paper/AllSkyMCNoiseOnly/generate_data.py
+++ b/Paper/AllSkyMCNoiseOnly/generate_data.py
@@ -9,17 +9,17 @@ outdir = sys.argv[2]
label = 'run_{}'.format(ID)
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
-sqrtSX = 2e-23
+sqrtSX = 1e-23
tstart = 1000000000
Tspan = 100*86400
tend = tstart + Tspan
# Fixed properties of the signal
F0_center = 30
-F1_center = 1e-10
+F1_center = -1e-10
F2 = 0
tref = .5*(tstart+tend)
@@ -28,6 +28,9 @@ VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
+DeltaAlpha = 0.02
+DeltaDelta = 0.02
+
nsteps = 50
run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 11, False),
@@ -35,21 +38,14 @@ run_setup = [((nsteps, 0), 20, 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
-
+Alpha_center = np.random.uniform(DeltaAlpha, 2*np.pi-DeltaAlpha)
+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
+Delta = Delta_center + np.random.uniform(-0.5, 0.5)*DeltaDelta
psi = np.random.uniform(-np.pi/4, np.pi/4)
phi = np.random.uniform(0, 2*np.pi)
cosi = np.random.uniform(-1, 1)
@@ -60,25 +56,24 @@ data = pyfstat.Writer(
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.},
+ 'lower': F0_center-DeltaF0,
+ 'upper': F0_center+DeltaF0},
'F1': {'type': 'unif',
- 'lower': F1-DeltaF1/2.,
- 'upper': F1+DeltaF1/2.},
+ 'lower': F1_center-DeltaF1,
+ 'upper': F1_center+DeltaF1},
'F2': F2,
'Alpha': {'type': 'unif',
- 'lower': Alpha_min,
- 'upper': Alpha_max},
+ 'lower': Alpha_center-DeltaAlpha,
+ 'upper': Alpha_center+DeltaAlpha},
'Delta': {'type': 'unif',
- 'lower': Delta_min,
- 'upper': Delta_max},
+ 'lower': Delta_center-DeltaDelta,
+ 'upper': Delta_center+DeltaDelta},
}
-ntemps = 1
+ntemps = 2
log10temperature_min = -1
nwalkers = 100
@@ -96,6 +91,6 @@ 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))
+ f.write('{:1.8e} {:1.8e} {:1.8e} {}\n'
+ .format(dF0, dF1, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label))
diff --git a/Paper/AllSkyMCNoiseOnly/plot_data.py b/Paper/AllSkyMCNoiseOnly/plot_data.py
index 8165be5dd1ad2355967e278aed4a78887b9cf062..ed0bd07eca40600ba6bc66e112d5550468535ff7 100644
--- a/Paper/AllSkyMCNoiseOnly/plot_data.py
+++ b/Paper/AllSkyMCNoiseOnly/plot_data.py
@@ -24,11 +24,11 @@ def maxtwoFinNoise(twoF, Ntrials):
df_list = []
for fn in filenames:
df = pd.read_csv(
- fn, sep=' ', names=['dF0', 'dF1', 'twoF_predicted',
- 'twoF', 'runTime'])
+ fn, sep=' ', names=['dF0', 'dF1', 'twoF', 'runTime'])
df['CLUSTER_ID'] = fn.split('_')[1]
df_list.append(df)
df = pd.concat(df_list)
+print 'Number of samples = ', len(df)
fig, ax = plt.subplots()
ax.hist(df.twoF, bins=50, histtype='step', color='k', normed=True, linewidth=1)
diff --git a/Paper/AllSkyMCNoiseOnly/submitfile b/Paper/AllSkyMCNoiseOnly/submitfile
index 191a502764a6e221a87d0ccc89d49be609c8587c..6add374c6853fd6816ae8ed6dc7690d1d48ef04d 100644
--- a/Paper/AllSkyMCNoiseOnly/submitfile
+++ b/Paper/AllSkyMCNoiseOnly/submitfile
@@ -1,11 +1,11 @@
-Executable= repeat.sh
+Executable= AllSkyMCNoiseOnly_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.$(Process)
+Output=CollectedOutput/out.$(Cluster).$(Process)
+Error=CollectedOutput/err.$(Cluster).$(Process)
+Log=CollectedOutput/log.$(Cluster).$(Process)
request_cpus = 1
request_memory = 16 GB
diff --git a/Paper/DirectedMC/repeat.sh b/Paper/DirectedMC/DirectedMC_repeat.sh
similarity index 53%
rename from Paper/DirectedMC/repeat.sh
rename to Paper/DirectedMC/DirectedMC_repeat.sh
index ee17fcaa8b5cf02e2436a18319b959162c9c7afa..0f98c59e2193363a0f61d499da7226571ab60008 100755
--- a/Paper/DirectedMC/repeat.sh
+++ b/Paper/DirectedMC/DirectedMC_repeat.sh
@@ -4,9 +4,8 @@
export PATH="/home/gregory.ashton/anaconda2/bin:$PATH"
export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib
-rm /local/user/gregory.ashton/MCResults*txt
-for ((n=0;n<10;n++))
+for ((n=0;n<1;n++))
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
-cp /local/user/gregory.ashton/MCResults*txt /home/gregory.ashton/PyFstat/Paper/DirectedMC/CollectedOutput
+cp /local/user/gregory.ashton/MCResults_"$1".txt $(pwd)/CollectedOutput
diff --git a/Paper/DirectedMC/generate_data.py b/Paper/DirectedMC/generate_data.py
index 2b270df8fc05cba4424ffaa7216edccf60d3a8f8..2e4c7ef17d364cff68a3a424f1e3b1675521c4ae 100644
--- a/Paper/DirectedMC/generate_data.py
+++ b/Paper/DirectedMC/generate_data.py
@@ -12,17 +12,17 @@ data_label = '{}_data'.format(label)
results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data
-sqrtSX = 2e-23
+sqrtSX = 1e-23
tstart = 1000000000
Tspan = 100*86400
tend = tstart + Tspan
# Fixed properties of the signal
F0_center = 30
-F1_center = 1e-10
+F1_center = -1e-10
F2 = 0
-Alpha = 5e-3
-Delta = 6e-2
+Alpha = np.radians(83.6292)
+Delta = np.radians(22.0144)
tref = .5*(tstart+tend)
@@ -30,9 +30,9 @@ VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
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),
((nsteps, 0), 7, False),
((nsteps, 0), 2, False),
@@ -53,7 +53,6 @@ for depth in depths:
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',
@@ -80,11 +79,12 @@ for depth in depths:
log10temperature_min=log10temperature_min)
mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
gen_tex_table=False)
+ mcmc.print_summary()
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} {:1.8e} {}\n'
- .format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF, runTime))
+ f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n'
+ .format(depth, h0, dF0, dF1, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label))
diff --git a/Paper/DirectedMC/plot_data.py b/Paper/DirectedMC/plot_data.py
index fe9a2e39efe3dccfd963ba6a5a0841565ecd8a4d..6e22b9bde654c971d213e753b1a890ebfec42955 100644
--- a/Paper/DirectedMC/plot_data.py
+++ b/Paper/DirectedMC/plot_data.py
@@ -31,10 +31,12 @@ def binomialConfidenceInterval(N, K, confidence=0.95):
df_list = []
for fn in filenames:
df = pd.read_csv(
- fn, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', 'twoF_predicted',
- 'twoF', 'runTime'])
+ fn, sep=' ', names=['depth', 'h0', 'dF0', 'dF1', 'twoF', 'runTime'])
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)
twoFstar = 60
@@ -52,9 +54,9 @@ for d in depths:
yerr = np.abs(recovery_fraction - np.array(recovery_fraction_CI).T)
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,
- label='Monte-Carlo result')
+ label='Monte-Carlo result', zorder=10)
fname = 'analytic_data.txt'
if os.path.isfile(fname):
diff --git a/Paper/DirectedMC/runTimeHist.png b/Paper/DirectedMC/runTimeHist.png
index 13cb78f5b50209ae275f7dda07dfe150d0f42403..38b0640185d0a10a7ee1148f0e85ccedcae5c62b 100644
Binary files a/Paper/DirectedMC/runTimeHist.png and b/Paper/DirectedMC/runTimeHist.png differ
diff --git a/Paper/DirectedMC/submitfile b/Paper/DirectedMC/submitfile
index 7f057b75593f21a6a3a04b6c577f164c038340e0..e468d6d7b30eff9fdb725c4ef9719404bf2d3c71 100644
--- a/Paper/DirectedMC/submitfile
+++ b/Paper/DirectedMC/submitfile
@@ -1,12 +1,12 @@
-Executable= repeat.sh
-Arguments= $(Cluster)_$(Process)
+Executable=DirectedMC_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.$(Process)
+Output=CollectedOutput/out.$(Cluster).$(Process)
+Error=CollectedOutput/err.$(Cluster).$(Process)
+Log=CollectedOutput/log.$(Cluster).$(Process)
request_cpus = 1
-request_memory = 8 GB
+request_memory = 16 GB
-Queue 100
+Queue 6
diff --git a/Paper/AllSkyMCNoiseOnly/repeat.sh b/Paper/DirectedMCNoiseOnly/DirectedMCNoiseOnly_repeat.sh
similarity index 57%
rename from Paper/AllSkyMCNoiseOnly/repeat.sh
rename to Paper/DirectedMCNoiseOnly/DirectedMCNoiseOnly_repeat.sh
index 5f43c35213b7e51665a94cdbbc5d67fc9b7b1e3c..7b4d26d400c71a2f0d5c9a86e624a66fdbf3ca52 100755
--- a/Paper/AllSkyMCNoiseOnly/repeat.sh
+++ b/Paper/DirectedMCNoiseOnly/DirectedMCNoiseOnly_repeat.sh
@@ -4,9 +4,8 @@
export PATH="/home/gregory.ashton/anaconda2/bin:$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
+/home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --no-template-counting --no-interactive
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
diff --git a/Paper/DirectedMCNoiseOnly/generate_data.py b/Paper/DirectedMCNoiseOnly/generate_data.py
index 952ccd4cfac6276b1c3251d66bbdeddfbb64209d..5e388df7d554e62fe381c994e0dda3864aea3fd8 100644
--- a/Paper/DirectedMCNoiseOnly/generate_data.py
+++ b/Paper/DirectedMCNoiseOnly/generate_data.py
@@ -10,27 +10,27 @@ outdir = sys.argv[2]
label = 'run_{}'.format(ID)
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
-sqrtSX = 2e-23
+sqrtSX = 1e-23
tstart = 1000000000
Tspan = 100*86400
tend = tstart + Tspan
# Fixed properties of the signal
F0_center = 30
-F1_center = 1e-10
+F1_center = -1e-10
F2 = 0
-Alpha = 5e-3
-Delta = 6e-2
+Alpha = np.radians(83.6292)
+Delta = np.radians(22.0144)
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
+nsteps = 25
run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 7, False),
((nsteps, 0), 2, False),
@@ -50,21 +50,20 @@ data = pyfstat.Writer(
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.},
+ 'lower': F0_center-DeltaF0,
+ 'upper': F0_center+DeltaF0},
'F1': {'type': 'unif',
- 'lower': F1-DeltaF1/2.,
- 'upper': F1+DeltaF1/2.},
+ 'lower': F1_center-DeltaF1,
+ 'upper': F1_center+DeltaF1},
'F2': F2,
'Alpha': Alpha,
'Delta': Delta
}
-ntemps = 1
+ntemps = 2
log10temperature_min = -1
nwalkers = 100
@@ -77,11 +76,12 @@ mcmc = pyfstat.MCMCFollowUpSearch(
log10temperature_min=log10temperature_min)
mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
gen_tex_table=False)
+mcmc.print_summary()
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))
+ f.write('{:1.8e} {:1.8e} {:1.8e} {}\n'
+ .format(dF0, dF1, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label))
diff --git a/Paper/DirectedMCNoiseOnly/repeat.sh b/Paper/DirectedMCNoiseOnly/repeat.sh
deleted file mode 100755
index 0142257850dd0ab734558a72caa5ffae365cedfa..0000000000000000000000000000000000000000
--- a/Paper/DirectedMCNoiseOnly/repeat.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/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
diff --git a/Paper/DirectedMCNoiseOnly/submitfile b/Paper/DirectedMCNoiseOnly/submitfile
index 7f057b75593f21a6a3a04b6c577f164c038340e0..77d69ee4d0b86673b02ec07c08b2ad850c054343 100644
--- a/Paper/DirectedMCNoiseOnly/submitfile
+++ b/Paper/DirectedMCNoiseOnly/submitfile
@@ -1,12 +1,12 @@
-Executable= repeat.sh
+Executable= DirectedMCNoiseOnly_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.$(Process)
+Output=CollectedOutput/out.$(Cluster).$(Process)
+Error=CollectedOutput/err.$(Cluster).$(Process)
+Log=CollectedOutput/log.$(Cluster).$(Process)
request_cpus = 1
-request_memory = 8 GB
+request_memory = 16 GB
-Queue 100
+Queue 1
diff --git a/Paper/allsky_noise_twoF_histogram.png b/Paper/allsky_noise_twoF_histogram.png
new file mode 100644
index 0000000000000000000000000000000000000000..9623605b5845b4664f726cfdd5a397ff18b21218
Binary files /dev/null and b/Paper/allsky_noise_twoF_histogram.png differ
diff --git a/Paper/allsky_recovery.png b/Paper/allsky_recovery.png
index aef4bf549a6be72b4719f42ca2b2db77048eea65..2eff0db48b4af9e4d5600640ac2e96d8c235ddda 100644
Binary files a/Paper/allsky_recovery.png and b/Paper/allsky_recovery.png differ
diff --git a/Paper/directed_noise_twoF_histogram.png b/Paper/directed_noise_twoF_histogram.png
index e8b23a5f85c29004d657dc6b578ae161fcdb22f2..9dfd21c9f0af3f517363d7f6333470d7effab6d3 100644
Binary files a/Paper/directed_noise_twoF_histogram.png and b/Paper/directed_noise_twoF_histogram.png differ
diff --git a/Paper/directed_recovery.png b/Paper/directed_recovery.png
index d6c0f2be4c7a40db63e50397a34a428f77d26c9a..6799cffed4b184a9851ce36d916929ab9d362687 100644
Binary files a/Paper/directed_recovery.png and b/Paper/directed_recovery.png differ
diff --git a/pyfstat.py b/pyfstat.py
index f49e6ba39b3c53dfe39c05f68d4b46150d23e533..0c664345acdb550ae953d66188cbc6e6956b98bc 100755
--- a/pyfstat.py
+++ b/pyfstat.py
@@ -52,6 +52,8 @@ else:
parser = argparse.ArgumentParser()
parser.add_argument("-q", "--quite", help="Decrease output verbosity",
action="store_true")
+parser.add_argument("--no-interactive", help="Don't use interactive output",
+ action="store_true")
parser.add_argument("-c", "--clean", help="Don't use cached data",
action="store_true")
parser.add_argument("-u", "--use-old-data", action="store_true")
@@ -61,7 +63,7 @@ parser.add_argument('unittest_args', nargs='*')
args, unknown = parser.parse_known_args()
sys.argv[1:] = args.unittest_args
-if args.quite:
+if args.quite or args.no_interactive:
def tqdm(x, *args, **kwargs):
return x
@@ -431,7 +433,7 @@ class ComputeFstat(object):
detector_names = list(set([d.header.name for d in SFTCatalog.data]))
self.detector_names = detector_names
SFT_timestamps = [d.header.epoch for d in SFTCatalog.data]
- if args.quite is False:
+ if args.quite is False and args.no_interactive is False:
try:
from bashplotlib.histogram import plot_hist
print('Data timestamps histogram:')
@@ -1749,17 +1751,18 @@ class MCMCSearch(BaseSearchClass):
def print_summary(self):
max_twoFd, max_twoF = self.get_max_twoF()
median_std_d = self.get_median_stds()
- print('\nSummary:')
+ logging.info('Summary:')
if hasattr(self, 'theta0_idx'):
- print('theta0 index: {}'.format(self.theta0_idx))
- print('Max twoF: {} with parameters:'.format(max_twoF))
+ logging.info('theta0 index: {}'.format(self.theta0_idx))
+ logging.info('Max twoF: {} with parameters:'.format(max_twoF))
for k in np.sort(max_twoFd.keys()):
print(' {:10s} = {:1.9e}'.format(k, max_twoFd[k]))
- print('\nMedian +/- std for production values')
+ logging.info('Median +/- std for production values')
for k in np.sort(median_std_d.keys()):
if 'std' not in k:
- print(' {:10s} = {:1.9e} +/- {:1.9e}'.format(
+ logging.info(' {:10s} = {:1.9e} +/- {:1.9e}'.format(
k, median_std_d[k], median_std_d[k+'_std']))
+ logging.info('\n')
def CF_twoFmax(self, theta, twoFmax, ntrials):
Fmax = twoFmax/2.0