diff --git a/examples/glitch_examples/fully_coherent_search_using_MCMC_on_glitching_data.py b/examples/glitch_examples/fully_coherent_search_using_MCMC_on_glitching_data.py
deleted file mode 100644
index 011cd5d0b4f417f6ed2bcff92c7ed57f5118012c..0000000000000000000000000000000000000000
--- a/examples/glitch_examples/fully_coherent_search_using_MCMC_on_glitching_data.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numpy as np
-from pyfstat import MCMCSearch
-
-F0 = 30.0
-F1 = -1e-10
-F2 = 0
-Alpha = np.radians(83.6292)
-Delta = np.radians(22.0144)
-
-tref = 362750407.0
-
-tstart = 1000000000
-duration = 100*86400
-tend = tstart + duration
-
-theta_prior = {'F0': {'type': 'unif', 'lower': F0-1e-4, 'upper': F0+1e-4},
- 'F1': {'type': 'unif', 'lower': F1*(1+1e-3), 'upper': F1*(1-1e-3)},
- 'F2': F2,
- 'Alpha': Alpha,
- 'Delta': Delta
- }
-
-ntemps = 2
-log10beta_min = -0.01
-nwalkers = 100
-nsteps = [500, 500]
-
-mcmc = MCMCSearch('fully_coherent_search_using_MCMC_on_glitching_data', 'data',
- sftfilepattern='data/*_glitch*.sft',
- theta_prior=theta_prior, tref=tref, minStartTime=tstart, maxStartTime=tend,
- nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
- log10beta_min=log10beta_min)
-mcmc.run()
-mcmc.plot_corner(add_prior=True)
-mcmc.print_summary()
diff --git a/examples/glitch_examples/glitch_robust_search.py b/examples/glitch_examples/glitch_robust_search.py
deleted file mode 100644
index 5964b6f9f11b9fe6bf39d86c2e3f62d0e9e7e307..0000000000000000000000000000000000000000
--- a/examples/glitch_examples/glitch_robust_search.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import numpy as np
-import pyfstat
-
-outdir = 'data'
-label = 'glitch_robust_search'
-
-# Properties of the GW data
-tstart = 1000000000
-Tspan = 60 * 86400
-
-# Fixed properties of the signal
-F0s = 30
-F1s = -1e-8
-F2s = 0
-Alpha = np.radians(83.6292)
-Delta = np.radians(22.0144)
-
-tref = tstart + .5 * Tspan
-
-sftfilepattern = 'data/*glitching_signal*sft'
-
-F0_width = np.sqrt(3)/(np.pi*Tspan)
-F1_width = np.sqrt(45/4.)/(np.pi*Tspan**2)
-DeltaF0 = 50 * F0_width
-DeltaF1 = 50 * F1_width
-
-theta_prior = {'F0': {'type': 'unif',
- 'lower': F0s-DeltaF0,
- 'upper': F0s+DeltaF0},
- 'F1': {'type': 'unif',
- 'lower': F1s-DeltaF1,
- 'upper': F1s+DeltaF1},
- 'F2': F2s,
- 'delta_F0': {'type': 'unif',
- 'lower': 0,
- 'upper': 1e-5},
- 'delta_F1': {'type': 'unif',
- 'lower': -1e-11,
- 'upper': 1e-11},
- 'tglitch': {'type': 'unif',
- 'lower': tstart+0.1*Tspan,
- 'upper': tstart+0.9*Tspan},
- 'Alpha': Alpha,
- 'Delta': Delta,
- }
-
-search = pyfstat.MCMCGlitchSearch(
- label=label, outdir=outdir, sftfilepattern=sftfilepattern,
- theta_prior=theta_prior, nglitch=1, tref=tref, nsteps=[500, 500],
- ntemps=3, log10beta_min=-0.5, minStartTime=tstart,
- maxStartTime=tstart+Tspan)
-search.run()
-search.plot_corner(label_offset=0.8, add_prior=True)
-search.print_summary()
diff --git a/examples/glitch_examples/glitch_robust_search_make_simulated_data.py b/examples/glitch_examples/glitch_robust_search_make_simulated_data.py
deleted file mode 100644
index 106eddaea2821d34c433490f53f69597261c64e6..0000000000000000000000000000000000000000
--- a/examples/glitch_examples/glitch_robust_search_make_simulated_data.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import numpy as np
-import pyfstat
-
-outdir = 'data'
-label = 'simulated_glitching_signal'
-
-# Properties of the GW data
-tstart = 1000000000
-Tspan = 60 * 86400
-
-tref = tstart + .5 * Tspan
-
-# Fixed properties of the signal
-F0s = 30
-F1s = -1e-8
-F2s = 0
-Alpha = np.radians(83.6292)
-Delta = np.radians(22.0144)
-h0 = 1e-25
-sqrtSX = 1e-24
-psi = -0.1
-phi = 0
-cosi = 0.5
-
-# Glitch properties
-dtglitch = 0.45 * Tspan # time (in secs) after minStartTime
-dF0 = 5e-6
-dF1 = 1e-12
-
-
-detectors = 'H1'
-
-glitch_data = pyfstat.Writer(
- label=label, outdir=outdir, tref=tref, tstart=tstart,
- F0=F0s, F1=F1s, F2=F2s, duration=Tspan, Alpha=Alpha,
- Delta=Delta, sqrtSX=sqrtSX, dtglitch=dtglitch,
- h0=h0, cosi=cosi, phi=phi, psi=psi,
- delta_F0=dF0, delta_F1=dF1, add_noise=True)
-
-glitch_data.make_data()
diff --git a/examples/glitch_examples/make_fake_data.py b/examples/glitch_examples/make_simulated_data.py
similarity index 81%
rename from examples/glitch_examples/make_fake_data.py
rename to examples/glitch_examples/make_simulated_data.py
index a2e7fc8fef25e511a90ba7d1b09f166d54e149ba..ffa9e3c8378a609a284397f63e14e50ab85f9e1e 100644
--- a/examples/glitch_examples/make_fake_data.py
+++ b/examples/glitch_examples/make_simulated_data.py
@@ -1,6 +1,7 @@
from pyfstat import Writer, GlitchWriter
import numpy as np
+outdir = 'data'
# First, we generate data with a reasonably strong smooth signal
# Define parameters of the Crab pulsar as an example
@@ -9,19 +10,19 @@ F1 = -1e-10
F2 = 0
Alpha = np.radians(83.6292)
Delta = np.radians(22.0144)
-tref = 362750407.0
# Signal strength
-h0 = 1e-23
+h0 = 5e-24
# Properties of the GW data
sqrtSX = 1e-22
tstart = 1000000000
duration = 100*86400
tend = tstart+duration
+tref = tstart + 0.5*duration
data = Writer(
- label='basic', outdir='data', tref=tref, tstart=tstart, F0=F0, F1=F1,
+ label='0_glitch', outdir=outdir, tref=tref, tstart=tstart, F0=F0, F1=F1,
F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX)
data.make_data()
@@ -31,11 +32,11 @@ print 'Predicted twoF value: {}\n'.format(twoF)
# Next, taking the same signal parameters, we include a glitch half way through
dtglitch = duration/2.0
-delta_F0 = 4e-5
+delta_F0 = 5e-6
delta_F1 = 0
glitch_data = GlitchWriter(
- label='glitch', outdir='data', tref=tref, tstart=tstart, F0=F0, F1=F1,
+ label='1_glitch', outdir=outdir, tref=tref, tstart=tstart, F0=F0, F1=F1,
F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX,
dtglitch=dtglitch, delta_F0=delta_F0, delta_F1=delta_F1)
glitch_data.make_data()
@@ -49,7 +50,7 @@ delta_F1 = [0, 0]
delta_F2 = [0, 0]
two_glitch_data = GlitchWriter(
- label='twoglitch', outdir='data', tref=tref, tstart=tstart, F0=F0, F1=F1,
+ label='2_glitch', outdir=outdir, tref=tref, tstart=tstart, F0=F0, F1=F1,
F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX,
dtglitch=dtglitch, delta_phi=delta_phi, delta_F0=delta_F0,
delta_F1=delta_F1, delta_F2=delta_F2)
diff --git a/examples/glitch_examples/semi_coherent_glitch_search_using_MCMC.py b/examples/glitch_examples/semi_coherent_glitch_search_using_MCMC.py
deleted file mode 100644
index 06caa43aefb397dd574c3685d0cab9709092b36b..0000000000000000000000000000000000000000
--- a/examples/glitch_examples/semi_coherent_glitch_search_using_MCMC.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import pyfstat
-
-F0 = 30.0
-F1 = -1e-10
-F2 = 0
-Alpha = 5e-3
-Delta = 6e-2
-tref = 362750407.0
-
-tstart = 1000000000
-duration = 100*86400
-tend = tstart + duration
-
-theta_prior = {'F0': {'type': 'norm', 'loc': F0, 'scale': abs(1e-6*F0)},
- 'F1': {'type': 'norm', 'loc': F1, 'scale': abs(1e-6*F1)},
- 'F2': F2,
- 'Alpha': Alpha,
- 'Delta': Delta,
- 'delta_F0': {'type': 'halfnorm', 'loc': 0,
- 'scale': 1e-5*F0},
- 'delta_F1': 0,
- 'tglitch': {'type': 'unif',
- 'lower': tstart+0.1*duration,
- 'upper': tstart+0.9*duration},
- }
-
-ntemps = 4
-log10beta_min = -1
-nwalkers = 100
-nsteps = [5000, 1000, 1000]
-
-mcmc = pyfstat.MCMCGlitchSearch(
- 'semi_coherent_glitch_search_using_MCMC', 'data',
- sftfilepattern='data/*_glitch*sft', theta_prior=theta_prior, tref=tref,
- tstart=tstart, tend=tend, nsteps=nsteps, nwalkers=nwalkers,
- scatter_val=1e-10, nglitch=1, ntemps=ntemps,
- log10beta_min=log10beta_min)
-
-mcmc.run()
-mcmc.plot_corner(add_prior=True)
-mcmc.print_summary()
diff --git a/examples/glitch_examples/semi_coherent_twoglitch_search_using_MCMC.py b/examples/glitch_examples/semi_coherent_twoglitch_search_using_MCMC.py
deleted file mode 100644
index b467557320391cc5fc6bb8569823efa55314878d..0000000000000000000000000000000000000000
--- a/examples/glitch_examples/semi_coherent_twoglitch_search_using_MCMC.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import pyfstat
-
-F0 = 30.0
-F1 = -1e-10
-F2 = 0
-Alpha = 5e-3
-Delta = 6e-2
-tref = 362750407.0
-
-tstart = 1000000000
-duration = 100*86400
-tend = tstart + duration
-
-theta_prior = {'F0': {'type': 'norm', 'loc': F0, 'scale': abs(1e-6*F0)},
- 'F1': {'type': 'norm', 'loc': F1, 'scale': abs(1e-6*F1)},
- 'F2': F2,
- 'Alpha': Alpha,
- 'Delta': Delta,
- 'delta_F0_0': {'type': 'halfnorm', 'loc': 0,
- 'scale': 1e-7*F0},
- 'delta_F0_1': {'type': 'halfnorm', 'loc': 0,
- 'scale': 1e-7*F0},
- 'delta_F1_0': 0,
- 'delta_F1_1': 0,
- 'tglitch_0': {'type': 'unif',
- 'lower': tstart+0.01*duration,
- 'upper': tstart+0.5*duration},
- 'tglitch_1': {'type': 'unif',
- 'lower': tstart+0.5*duration,
- 'upper': tstart+0.99*duration},
- }
-
-nwalkers = 100
-nsteps = [1000, 1000, 5000]
-
-mcmc = pyfstat.MCMCGlitchSearch(
- 'semi_coherent_twoglitch_search', 'data', sftfilepattern='data/*twoglitch*sft',
- theta_prior=theta_prior, tref=tref, tstart=tstart,
- tend=tend, nsteps=nsteps, nwalkers=nwalkers, scatter_val=1e-10, nglitch=2)
-
-mcmc.run()
-mcmc.plot_corner(add_prior=True, tglitch_ratio=True, figsize=(10, 10))
-mcmc.print_summary()
diff --git a/examples/glitch_examples/semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch.py b/examples/glitch_examples/semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch.py
new file mode 100644
index 0000000000000000000000000000000000000000..9da04a410059f8b472a11211a1c41c6703ace4ec
--- /dev/null
+++ b/examples/glitch_examples/semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch.py
@@ -0,0 +1,51 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pyfstat
+from make_simulated_data import tstart, duration, tref, F0, F1, F2, Alpha, Delta, outdir
+
+plt.style.use('paper')
+
+label = 'semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch'
+
+Nstar = 100
+F0_width = np.sqrt(Nstar)*np.sqrt(12)/(np.pi*duration)
+F1_width = np.sqrt(Nstar)*np.sqrt(180)/(np.pi*duration**2)
+
+theta_prior = {
+ 'F0': {'type': 'unif',
+ 'lower': F0-F0_width/2.,
+ 'upper': F0+F0_width/2.},
+ 'F1': {'type': 'unif',
+ 'lower': F1-F1_width/2.,
+ 'upper': F1+F1_width/2.},
+ 'F2': F2,
+ 'delta_F0': {'type': 'unif',
+ 'lower': 0,
+ 'upper': 1e-5},
+ 'delta_F1': 0,
+ 'tglitch': {'type': 'unif',
+ 'lower': tstart+0.1*duration,
+ 'upper': tstart+0.9*duration},
+ 'Alpha': Alpha,
+ 'Delta': Delta,
+ }
+
+ntemps = 2
+log10beta_min = -0.5
+nwalkers = 100
+nsteps = [500, 500]
+
+mcmc = pyfstat.MCMCGlitchSearch(
+ label=label, sftfilepattern='data/*1_glitch*sft', theta_prior=theta_prior,
+ tref=tref, minStartTime=tstart, maxStartTime=tstart+duration,
+ nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
+ log10beta_min=log10beta_min, nglitch=1)
+
+mcmc.transform_dictionary['F0'] = dict(
+ subtractor=F0, symbol='$f-f^\mathrm{s}$')
+mcmc.transform_dictionary['F1'] = dict(
+ subtractor=F1, symbol='$\dot{f}-\dot{f}^\mathrm{s}$')
+
+mcmc.run()
+mcmc.plot_corner()
+mcmc.print_summary()
diff --git a/examples/glitch_examples/semicoherent_glitch_robust_directed_grid_search_on_1_glitch.py b/examples/glitch_examples/semicoherent_glitch_robust_directed_grid_search_on_1_glitch.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c7cafe30169a8ff84c1eadf6c4855be637bf318
--- /dev/null
+++ b/examples/glitch_examples/semicoherent_glitch_robust_directed_grid_search_on_1_glitch.py
@@ -0,0 +1,53 @@
+import pyfstat
+import numpy as np
+import matplotlib.pyplot as plt
+from make_simulated_data import tstart, duration, tref, F0, F1, F2, Alpha, Delta, outdir
+
+try:
+ from gridcorner import gridcorner
+except ImportError:
+ raise ImportError(
+ "Python module 'gridcorner' not found, please install from "
+ "https://gitlab.aei.uni-hannover.de/GregAshton/gridcorner")
+
+label = 'semicoherent_glitch_robust_directed_grid_search_on_1_glitch'
+
+plt.style.use('paper')
+
+Nstar = 100
+F0_width = np.sqrt(Nstar)*np.sqrt(12)/(np.pi*duration)
+F1_width = np.sqrt(Nstar)*np.sqrt(180)/(np.pi*duration**2)
+N = 41
+F0s = [F0-F0_width/2., F0+F0_width/2., F0_width/N]
+F1s = [F1-F1_width/2., F1+F1_width/2., F1_width/N]
+F2s = [F2]
+Alphas = [Alpha]
+Deltas = [Delta]
+
+max_delta_F0 = 1e-5
+tglitchs = [tstart+0.1*duration, tstart+0.9*duration, float(duration)/N]
+delta_F0s = [0, max_delta_F0, max_delta_F0/N]
+delta_F1s = [0]
+
+search = pyfstat.GridGlitchSearch(
+ label, outdir, 'data/*1_glitch*sft', F0s=F0s, F1s=F1s, F2s=F2s,
+ Alphas=Alphas, Deltas=Deltas, tref=tref, minStartTime=tstart,
+ maxStartTime=tstart+duration, tglitchs=tglitchs, delta_F0s=delta_F0s,
+ delta_F1s=delta_F1s)
+search.run()
+
+F0_vals = np.unique(search.data[:, 0]) - F0
+F1_vals = np.unique(search.data[:, 1]) - F1
+delta_F0s_vals = np.unique(search.data[:, 5])
+tglitch_vals = np.unique(search.data[:, 7])
+tglitch_vals_days = (tglitch_vals-tstart) / 86400.
+
+twoF = search.data[:, -1].reshape((len(F0_vals), len(F1_vals),
+ len(delta_F0s_vals), len(tglitch_vals)))
+xyz = [F0_vals, F1_vals, delta_F0s_vals, tglitch_vals_days]
+labels = ['$f - f_0$\n[Hz]', '$\dot{f} - \dot{f}_0$\n[Hz/s]',
+ '$\delta f$\n[Hz]', '$t^g_0$\n[days]', '$\widehat{2\mathcal{F}}$']
+fig, axes = gridcorner(
+ twoF, xyz, projection='log_mean', whspace=0.1, factor=1.2, labels=labels)
+fig.savefig('{}/{}_projection_matrix.png'.format(outdir, label),
+ bbox_inches='tight')
diff --git a/examples/glitch_examples/standard_directed_MCMC_search_on_1_glitch.py b/examples/glitch_examples/standard_directed_MCMC_search_on_1_glitch.py
new file mode 100644
index 0000000000000000000000000000000000000000..7856a59557bd9b30acbbe4af58558e59c0e5f9f6
--- /dev/null
+++ b/examples/glitch_examples/standard_directed_MCMC_search_on_1_glitch.py
@@ -0,0 +1,44 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pyfstat
+from make_simulated_data import tstart, duration, tref, F0, F1, F2, Alpha, Delta, outdir
+
+plt.style.use('paper')
+
+label = 'standard_directed_MCMC_search_on_1_glitch'
+
+Nstar = 10000
+F0_width = np.sqrt(Nstar)*np.sqrt(12)/(np.pi*duration)
+F1_width = np.sqrt(Nstar)*np.sqrt(180)/(np.pi*duration**2)
+
+theta_prior = {
+ 'F0': {'type': 'unif',
+ 'lower': F0-F0_width/2.,
+ 'upper': F0+F0_width/2.},
+ 'F1': {'type': 'unif',
+ 'lower': F1-F1_width/2.,
+ 'upper': F1+F1_width/2.},
+ 'F2': F2,
+ 'Alpha': Alpha,
+ 'Delta': Delta,
+ }
+
+ntemps = 2
+log10beta_min = -0.5
+nwalkers = 100
+nsteps = [500, 2000]
+
+mcmc = pyfstat.MCMCSearch(
+ label=label, sftfilepattern='data/*1_glitch*sft', theta_prior=theta_prior,
+ tref=tref, minStartTime=tstart, maxStartTime=tstart+duration,
+ nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
+ log10beta_min=log10beta_min)
+
+mcmc.transform_dictionary['F0'] = dict(
+ subtractor=F0, symbol='$f-f^\mathrm{s}$')
+mcmc.transform_dictionary['F1'] = dict(
+ subtractor=F1, symbol='$\dot{f}-\dot{f}^\mathrm{s}$')
+
+mcmc.run()
+mcmc.plot_corner()
+mcmc.print_summary()