From cb7c547ea2981a93cc8f45d726710606fd921b01 Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Fri, 24 Mar 2017 15:22:48 +0100
Subject: [PATCH] Generalises noise only glitch MCMC to Nglitches
---
.../SingleGlitchMCNoiseOnly.sh | 2 +-
.../generate_data.py | 15 ++++++++-------
.../plot_data.py | 8 ++++----
.../submitfile | 0
4 files changed, 13 insertions(+), 12 deletions(-)
rename Paper/{SingleGlitchMCNoiseOnly => GlitchMCNoiseOnly}/SingleGlitchMCNoiseOnly.sh (84%)
rename Paper/{SingleGlitchMCNoiseOnly => GlitchMCNoiseOnly}/generate_data.py (90%)
rename Paper/{SingleGlitchMCNoiseOnly => GlitchMCNoiseOnly}/plot_data.py (77%)
rename Paper/{SingleGlitchMCNoiseOnly => GlitchMCNoiseOnly}/submitfile (100%)
diff --git a/Paper/SingleGlitchMCNoiseOnly/SingleGlitchMCNoiseOnly.sh b/Paper/GlitchMCNoiseOnly/SingleGlitchMCNoiseOnly.sh
similarity index 84%
rename from Paper/SingleGlitchMCNoiseOnly/SingleGlitchMCNoiseOnly.sh
rename to Paper/GlitchMCNoiseOnly/SingleGlitchMCNoiseOnly.sh
index ab5264c..e532a79 100755
--- a/Paper/SingleGlitchMCNoiseOnly/SingleGlitchMCNoiseOnly.sh
+++ b/Paper/GlitchMCNoiseOnly/SingleGlitchMCNoiseOnly.sh
@@ -6,6 +6,6 @@ export MPLCONFIGDIR=/home/gregory.ashton/.config/matplotlib
for ((n=0;n<90;n++))
do
-/home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --no-template-counting --no-interactive
+/home/gregory.ashton/anaconda2/bin/python generate_data.py "$1" /local/user/gregory.ashton --no-interactive
done
cp /local/user/gregory.ashton/NoiseOnlyMCResults_"$1".txt $(pwd)/CollectedOutput
diff --git a/Paper/SingleGlitchMCNoiseOnly/generate_data.py b/Paper/GlitchMCNoiseOnly/generate_data.py
similarity index 90%
rename from Paper/SingleGlitchMCNoiseOnly/generate_data.py
rename to Paper/GlitchMCNoiseOnly/generate_data.py
index cdedbdc..24e2eb5 100644
--- a/Paper/SingleGlitchMCNoiseOnly/generate_data.py
+++ b/Paper/GlitchMCNoiseOnly/generate_data.py
@@ -4,6 +4,7 @@ import os
import sys
import time
+nglitch = 2
ID = sys.argv[1]
outdir = sys.argv[2]
@@ -80,7 +81,7 @@ glitch_mcmc = pyfstat.MCMCGlitchSearch(
sftfilepath='{}/*{}*sft'.format(outdir, data_label),
theta_prior=theta_prior,
tref=tref, minStartTime=tstart, maxStartTime=tend, nsteps=nsteps,
- nwalkers=nwalkers, ntemps=ntemps,
+ nwalkers=nwalkers, ntemps=ntemps, nglitch=nglitch,
log10temperature_min=log10temperature_min)
glitch_mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
gen_tex_table=False)
@@ -88,12 +89,12 @@ glitch_mcmc.print_summary()
d, maxtwoF = glitch_mcmc.get_max_twoF()
dF0 = F0 - d['F0']
dF1 = F1 - d['F1']
-tglitch = d['tglitch']
-R = (tglitch - tstart) / Tspan
-delta_F0 = d['delta_F0']
-delta_F1 = d['delta_F1']
+#tglitch = d['tglitch']
+#R = (tglitch - tstart) / Tspan
+#delta_F0 = d['delta_F0']
+#delta_F1 = d['delta_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(dF0, dF1, R, delta_F0, delta_F1, maxtwoF, runTime))
+ f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.1f}\n'
+ .format(nglitch, dF0, dF1, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label))
diff --git a/Paper/SingleGlitchMCNoiseOnly/plot_data.py b/Paper/GlitchMCNoiseOnly/plot_data.py
similarity index 77%
rename from Paper/SingleGlitchMCNoiseOnly/plot_data.py
rename to Paper/GlitchMCNoiseOnly/plot_data.py
index 679e74e..3e3378d 100644
--- a/Paper/SingleGlitchMCNoiseOnly/plot_data.py
+++ b/Paper/GlitchMCNoiseOnly/plot_data.py
@@ -17,8 +17,7 @@ Tspan = 100 * 86400
df_list = []
for fn in filenames:
df = pd.read_csv(
- fn, sep=' ', names=['dF0', 'dF1', 'R', 'delta_F0', 'delta_F1',
- 'twoF', 'runTime'])
+ fn, sep=' ', names=['nglitches', 'dF0', 'dF1', 'twoF', 'runTime'])
df['CLUSTER_ID'] = fn.split('_')[1]
df_list.append(df)
df = pd.concat(df_list)
@@ -26,8 +25,9 @@ print 'Number of samples = ', len(df)
print 'Max twoF', df.twoF.max()
fig, ax = plt.subplots()
-ax.hist(df.twoF, bins=50, histtype='step', color='k', normed=True, linewidth=1,
- label='Monte-Carlo histogram')
+for ng in np.unique(df.nglitches.values):
+ ax.hist(df[df.nglitches==ng].twoF, bins=20, histtype='step', normed=True,
+ linewidth=1, label='$N_\mathrm{{glitches}}={}$'.format(ng))
ax.set_xlabel('$\widehat{2\mathcal{F}}$ for 1 glitch')
ax.set_xlim(0, 90)
diff --git a/Paper/SingleGlitchMCNoiseOnly/submitfile b/Paper/GlitchMCNoiseOnly/submitfile
similarity index 100%
rename from Paper/SingleGlitchMCNoiseOnly/submitfile
rename to Paper/GlitchMCNoiseOnly/submitfile
--
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