diff --git a/examples/fully_coherent_search_using_MCMC.py b/examples/fully_coherent_search_using_MCMC.py
index 82fd4dd37425573a127c03e7580e7a2a2d956d12..72874c74e5b0fdc9270607f7a1a8d1b28f004aed 100644
--- a/examples/fully_coherent_search_using_MCMC.py
+++ b/examples/fully_coherent_search_using_MCMC.py
@@ -47,8 +47,8 @@ theta_prior = {'F0': {'type': 'unif',
'Delta': Delta
}
-ntemps = 1
-log10beta_min = -1
+ntemps = 2
+log10beta_min = -0.5
nwalkers = 100
nsteps = [300, 300]
@@ -57,6 +57,9 @@ mcmc = pyfstat.MCMCSearch(
sftfilepattern='{}/*{}*sft'.format(outdir, label), theta_prior=theta_prior,
tref=tref, minStartTime=tstart, maxStartTime=tend, nsteps=nsteps,
nwalkers=nwalkers, ntemps=ntemps, log10beta_min=log10beta_min)
-mcmc.run(subtractions=[F0, F1])
+mcmc.transform_dictionary = dict(
+ F0=dict(subtractor=F0, symbol='$f-f^\mathrm{s}$'),
+ F1=dict(subtractor=F1, symbol='$\dot{f}-\dot{f}^\mathrm{s}$'))
+mcmc.run()
mcmc.plot_corner(add_prior=True)
mcmc.print_summary()
diff --git a/examples/grid_examples/grid_F0F1F2.py b/examples/grid_examples/grid_F0F1F2.py
index 7456e535a19bf9de2faea5ae348b898f72036cfe..d06775ffd6ec84b54f5a6941b5eae487b4aabd23 100644
--- a/examples/grid_examples/grid_F0F1F2.py
+++ b/examples/grid_examples/grid_F0F1F2.py
@@ -1,9 +1,13 @@
import pyfstat
import numpy as np
import matplotlib.pyplot as plt
-from projection_matrix import projection_matrix
-plt.style.use('paper')
+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")
F0 = 30.0
F1 = 1e-10
@@ -55,6 +59,6 @@ twoF = search.data[:, -1].reshape((len(F0_vals), len(F1_vals), len(F2_vals)))
xyz = [F0_vals, F1_vals, F2_vals]
labels = ['$f - f_0$', '$\dot{f} - \dot{f}_0$', '$\ddot{f} - \ddot{f}_0$',
'$\widetilde{2\mathcal{F}}$']
-fig, axes = projection_matrix(twoF, xyz, projection='log_mean', labels=labels,
- whspace=0.1, factor=1.8)
+fig, axes = gridcorner.gridcorner(
+ twoF, xyz, projection='log_mean', labels=labels, whspace=0.1, factor=1.8)
fig.savefig('{}/{}_projection_matrix.png'.format(outdir, label))
diff --git a/examples/grid_examples/grided_frequency_search.py b/examples/grid_examples/grided_frequency_search.py
index 5e4a423ce8a080081de364bf058287d184df9b01..e8f5d55f1a362c79bb631d2ea2b5dd428d3550f7 100644
--- a/examples/grid_examples/grided_frequency_search.py
+++ b/examples/grid_examples/grided_frequency_search.py
@@ -2,8 +2,6 @@ import pyfstat
import numpy as np
import matplotlib.pyplot as plt
-plt.style.use('paper')
-
F0 = 30.0
F1 = 0
F2 = 0
diff --git a/examples/other_examples/twoF_cumulative.py b/examples/other_examples/twoF_cumulative.py
index cd7f0426b7fc92a8244b17958877c3e8a1672d47..760fd23e90068ad589918e6a5694e568b0a31cdf 100644
--- a/examples/other_examples/twoF_cumulative.py
+++ b/examples/other_examples/twoF_cumulative.py
@@ -56,7 +56,7 @@ mcmc = pyfstat.MCMCSearch(
sftfilepattern='data/*'+data_label+'*sft', theta_prior=theta_prior, tref=tref,
minStartTime=tstart, maxStartTime=tend, nsteps=nsteps, nwalkers=nwalkers,
ntemps=ntemps, log10beta_min=log10beta_min)
-mcmc.run(context='paper', subtractions=[30, -1e-10])
+mcmc.run()
mcmc.plot_corner(add_prior=True)
mcmc.print_summary()
diff --git a/examples/semi_coherent_directed_follow_up.py b/examples/semi_coherent_directed_follow_up.py
index 1ae81449b1e4c8f171559f73118366f07b485b63..e84976e34b6e28e68e9dcfe04d8067735843ed6e 100644
--- a/examples/semi_coherent_directed_follow_up.py
+++ b/examples/semi_coherent_directed_follow_up.py
@@ -2,8 +2,6 @@ import pyfstat
import numpy as np
import matplotlib.pyplot as plt
-plt.style.use('./paper-style.mplstyle')
-
F0 = 30.0
F1 = -1e-10
F2 = 0
@@ -63,8 +61,8 @@ NstarMax = 1000
Nsegs0 = 100
fig, axes = plt.subplots(nrows=2, figsize=(3.4, 3.5))
fig, axes = mcmc.run(
- NstarMax=NstarMax, Nsegs0=Nsegs0, subtractions=[F0, F1], labelpad=0.01,
- plot_det_stat=False, return_fig=True, context='paper', fig=fig,
+ NstarMax=NstarMax, Nsegs0=Nsegs0, labelpad=0.01,
+ plot_det_stat=False, return_fig=True, fig=fig,
axes=axes)
for ax in axes:
ax.grid()
diff --git a/examples/semi_coherent_search_using_MCMC.py b/examples/semi_coherent_search_using_MCMC.py
index cd8d95acb64775b25a4140760bd3dceb07b3119d..1422fd344ac0191291832f2cd225d6e6eb06eb84 100644
--- a/examples/semi_coherent_search_using_MCMC.py
+++ b/examples/semi_coherent_search_using_MCMC.py
@@ -58,6 +58,9 @@ mcmc = pyfstat.MCMCSemiCoherentSearch(
theta_prior=theta_prior, tref=tref, minStartTime=tstart, maxStartTime=tend,
nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
log10beta_min=log10beta_min)
+mcmc.transform_dictionary = dict(
+ F0=dict(subtractor=F0, symbol='$f-f^\mathrm{s}$'),
+ F1=dict(subtractor=F1, symbol='$\dot{f}-\dot{f}^\mathrm{s}$'))
mcmc.run()
mcmc.plot_corner(add_prior=True)
mcmc.print_summary()
diff --git a/examples/other_examples/transient_search_using_MCMC.py b/examples/transient_examples/long_transient_search_MCMC.py
similarity index 78%
rename from examples/other_examples/transient_search_using_MCMC.py
rename to examples/transient_examples/long_transient_search_MCMC.py
index b70dc3d85c1d17d5ab5906887dfabc4184922f5c..a9255fa63b7b008f70468e86edff0364e11de60a 100644
--- a/examples/other_examples/transient_search_using_MCMC.py
+++ b/examples/transient_examples/long_transient_search_MCMC.py
@@ -25,9 +25,7 @@ theta_prior = {'F0': {'type': 'unif',
'F2': F2,
'Alpha': Alpha,
'Delta': Delta,
- 'transient_tstart': {'type': 'unif',
- 'lower': minStartTime,
- 'upper': maxStartTime},
+ 'transient_tstart': minStartTime,
'transient_duration': {'type': 'halfnorm',
'loc': 0.001*Tspan,
'scale': 0.5*Tspan}
@@ -39,11 +37,12 @@ nwalkers = 100
nsteps = [100, 100]
mcmc = pyfstat.MCMCTransientSearch(
- label='transient_search', outdir='data',
- sftfilepattern='data/*simulated_transient_signal*sft',
+ label='transient_search', outdir='data_l',
+ sftfilepattern='data_l/*simulated_transient_signal*sft',
theta_prior=theta_prior, tref=tref, minStartTime=minStartTime,
maxStartTime=maxStartTime, nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
- log10beta_min=log10beta_min)
+ log10beta_min=log10beta_min,
+ transientWindowType='rect')
mcmc.run()
mcmc.plot_corner(label_offset=0.7)
mcmc.print_summary()
diff --git a/examples/other_examples/transient_search_using_MCMC_make_simulated_data.py b/examples/transient_examples/long_transient_search_make_simulated_data.py
similarity index 79%
rename from examples/other_examples/transient_search_using_MCMC_make_simulated_data.py
rename to examples/transient_examples/long_transient_search_make_simulated_data.py
index 4fa3dd1d1157446b5de54dfb9b7f3b132889fdc1..de68fccdb40e16063bd870c0b69829b667dc49c3 100644
--- a/examples/other_examples/transient_search_using_MCMC_make_simulated_data.py
+++ b/examples/transient_examples/long_transient_search_make_simulated_data.py
@@ -19,8 +19,8 @@ h0 = 1e-23
sqrtSX = 1e-22
transient = pyfstat.Writer(
- label='simulated_transient_signal', outdir='data', tref=tref,
+ label='simulated_transient_signal', outdir='data_l', tref=tref,
tstart=transient_tstart, F0=F0, F1=F1, F2=F2, duration=transient_duration,
Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX, minStartTime=minStartTime,
- maxStartTime=maxStartTime)
+ maxStartTime=maxStartTime, transientWindowType='rect')
transient.make_data()
diff --git a/examples/transient_examples/short_transient_search_MCMC.py b/examples/transient_examples/short_transient_search_MCMC.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b8f504a074f20372c9142d6d220f0650b0e498b
--- /dev/null
+++ b/examples/transient_examples/short_transient_search_MCMC.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+
+import pyfstat
+
+F0 = 30.0
+F1 = -1e-10
+F2 = 0
+Alpha = 0.5
+Delta = 1
+
+minStartTime = 1000000000
+maxStartTime = minStartTime + 2*86400
+Tspan = maxStartTime - minStartTime
+tref = minStartTime
+
+Tsft = 1800
+
+DeltaF0 = 1e-2
+DeltaF1 = 1e-9
+
+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,
+ 'transient_tstart': {'type': 'unif',
+ 'lower': minStartTime,
+ 'upper': maxStartTime-2*Tsft},
+ 'transient_duration': {'type': 'unif',
+ 'lower': 2*Tsft,
+ 'upper': Tspan-2*Tsft}
+ }
+
+ntemps = 2
+log10beta_min = -1
+nwalkers = 100
+nsteps = [100, 100]
+
+mcmc = pyfstat.MCMCTransientSearch(
+ label='transient_search', outdir='data_s',
+ sftfilepattern='data_s/*simulated_transient_signal*sft',
+ theta_prior=theta_prior, tref=tref, minStartTime=minStartTime,
+ maxStartTime=maxStartTime, nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
+ log10beta_min=log10beta_min,
+ transientWindowType='rect')
+mcmc.run()
+mcmc.plot_corner(label_offset=0.7)
+mcmc.print_summary()
diff --git a/examples/transient_examples/short_transient_search_gridded.py b/examples/transient_examples/short_transient_search_gridded.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd9b83b2f85d9e076231042efbe1c0a8b9b495a5
--- /dev/null
+++ b/examples/transient_examples/short_transient_search_gridded.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+
+import pyfstat
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+datadir = 'data_s'
+
+F0 = 30.0
+F1 = -1e-10
+F2 = 0
+Alpha = 0.5
+Delta = 1
+
+minStartTime = 1000000000
+maxStartTime = minStartTime + 2*86400
+Tspan = maxStartTime - minStartTime
+tref = minStartTime
+
+Tsft = 1800
+
+m = 0.001
+dF0 = np.sqrt(12*m)/(np.pi*Tspan)
+DeltaF0 = 100*dF0
+F0s = [F0-DeltaF0/2., F0+DeltaF0/2., dF0]
+F1s = [F1]
+F2s = [F2]
+Alphas = [Alpha]
+Deltas = [Delta]
+
+print('Standard CW search:')
+search1 = pyfstat.GridSearch(
+ label='CW', outdir=datadir,
+ sftfilepattern=os.path.join(datadir,'*simulated_transient_signal*sft'),
+ F0s=F0s, F1s=F1s, F2s=F2s, Alphas=Alphas, Deltas=Deltas, tref=tref,
+ minStartTime=minStartTime, maxStartTime=maxStartTime,
+ BSGL=False)
+search1.run()
+search1.print_max_twoF()
+
+search1.plot_1D(xkey='F0',
+ xlabel='freq [Hz]', ylabel='$2\mathcal{F}$')
+
+print('with t0,tau bands:')
+search2 = pyfstat.GridSearch(
+ label='tCW', outdir=datadir,
+ sftfilepattern=os.path.join(datadir,'*simulated_transient_signal*sft'),
+ F0s=F0s, F1s=F1s, F2s=F2s, Alphas=Alphas, Deltas=Deltas, tref=tref,
+ minStartTime=minStartTime, maxStartTime=maxStartTime,
+ transientWindowType='rect', t0Band=Tspan-2*Tsft, tauBand=Tspan,
+ BSGL=False)
+search2.run()
+search2.print_max_twoF()
+
+search2.plot_1D(xkey='F0',
+ xlabel='freq [Hz]', ylabel='$2\mathcal{F}$')
diff --git a/examples/transient_examples/short_transient_search_make_simulated_data.py b/examples/transient_examples/short_transient_search_make_simulated_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..320b4e4eb07dd1fce589156f0e1cfa96ea0b78a6
--- /dev/null
+++ b/examples/transient_examples/short_transient_search_make_simulated_data.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python
+
+import pyfstat
+
+F0 = 30.0
+F1 = -1e-10
+F2 = 0
+Alpha = 0.5
+Delta = 1
+
+minStartTime = 1000000000
+maxStartTime = minStartTime + 2*86400
+
+transient_tstart = minStartTime + 0.5*86400
+transient_duration = 1*86400
+tref = minStartTime
+
+h0 = 1e-23
+sqrtSX = 1e-22
+detectors = 'H1,L1'
+
+Tsft = 1800
+
+transient = pyfstat.Writer(
+ label='simulated_transient_signal', outdir='data_s',
+ tref=tref, tstart=transient_tstart, duration=transient_duration,
+ F0=F0, F1=F1, F2=F2, Alpha=Alpha, Delta=Delta, h0=h0,
+ detectors=detectors,sqrtSX=sqrtSX,
+ minStartTime=minStartTime, maxStartTime=maxStartTime,
+ transientWindowType='rect', Tsft=Tsft)
+transient.make_data()
diff --git a/examples/using_initialisation.py b/examples/using_initialisation.py
index 5e7ea5e019f0d8b3abf155c41d861d6b7a0ad825..efe43364f0ea6ada232d6fbb972b39ad9550aef3 100644
--- a/examples/using_initialisation.py
+++ b/examples/using_initialisation.py
@@ -59,6 +59,6 @@ mcmc = pyfstat.MCMCSearch(
nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
log10beta_min=log10beta_min)
mcmc.setup_initialisation(100, scatter_val=1e-10)
-mcmc.run(subtractions=[F0, F1])
+mcmc.run()
mcmc.plot_corner(add_prior=True)
mcmc.print_summary()
diff --git a/pyfstat/core.py b/pyfstat/core.py
index a5087659a588916ea08773d86ed5485e93f532b6..5d06781e156ea0c700fe4561deee3f1da2a122b6 100755
--- a/pyfstat/core.py
+++ b/pyfstat/core.py
@@ -330,7 +330,8 @@ class ComputeFstat(BaseSearchClass):
@helper_functions.initializer
def __init__(self, tref, sftfilepattern=None, minStartTime=None,
- maxStartTime=None, binary=False, transient=True, BSGL=False,
+ maxStartTime=None, binary=False, BSGL=False,
+ transientWindowType=None, t0Band=None, tauBand=None,
detectors=None, minCoverFreq=None, maxCoverFreq=None,
injectSources=None, injectSqrtSX=None, assumeSqrtSX=None,
SSBprec=None):
@@ -347,10 +348,18 @@ class ComputeFstat(BaseSearchClass):
this epoch
binary : bool
If true, search of binary parameters.
- transient : bool
- If true, allow for the Fstat to be computed over a transient range.
BSGL : bool
If true, compute the BSGL rather than the twoF value.
+ transientWindowType: str
+ If 'rect' or 'exp',
+ allow for the Fstat to be computed over a transient range.
+ ('none' instead of None explicitly calls the transient-window
+ function, but with the full range, for debugging)
+ t0Band, tauBand: int
+ if >0, search t0 in (minStartTime,minStartTime+t0Band)
+ and tau in (2*Tsft,2*Tsft+tauBand).
+ if =0, only compute CW Fstat with t0=minStartTime,
+ tau=maxStartTime-minStartTime.
detectors : str
Two character reference to the data to use, specify None for no
contraint. If multiple-separate by comma.
@@ -477,7 +486,7 @@ class ComputeFstat(BaseSearchClass):
logging.info('Initialising FstatInput')
dFreq = 0
- if self.transient:
+ if self.transientWindowType:
self.whatToCompute = lalpulsar.FSTATQ_ATOMS_PER_DET
else:
self.whatToCompute = lalpulsar.FSTATQ_2F
@@ -593,14 +602,41 @@ class ComputeFstat(BaseSearchClass):
self.whatToCompute = (self.whatToCompute +
lalpulsar.FSTATQ_2F_PER_DET)
- if self.transient:
+ if self.transientWindowType:
logging.info('Initialising transient parameters')
self.windowRange = lalpulsar.transientWindowRange_t()
- self.windowRange.type = lalpulsar.TRANSIENT_RECTANGULAR
- self.windowRange.t0Band = 0
- self.windowRange.dt0 = 1
- self.windowRange.tauBand = 0
- self.windowRange.dtau = 1
+ transientWindowTypes = {'none': lalpulsar.TRANSIENT_NONE,
+ 'rect': lalpulsar.TRANSIENT_RECTANGULAR,
+ 'exp': lalpulsar.TRANSIENT_EXPONENTIAL}
+ if self.transientWindowType in transientWindowTypes:
+ self.windowRange.type = transientWindowTypes[self.transientWindowType]
+ else:
+ raise ValueError(
+ 'Unknown window-type ({}) passed as input, [{}] allows.'
+ .format(self.transientWindowType,
+ ', '.join(transientWindowTypes)))
+
+ self.Tsft = int(1.0/SFTCatalog.data[0].header.deltaF)
+ if self.t0Band is None:
+ self.windowRange.t0Band = 0
+ self.windowRange.dt0 = 1
+ else:
+ if not isinstance(self.t0Band, int):
+ logging.warn('Casting non-integer t0Band={} to int...'
+ .format(self.t0Band))
+ self.t0Band = int(self.t0Band)
+ self.windowRange.t0Band = self.t0Band
+ self.windowRange.dt0 = self.Tsft
+ if self.tauBand is None:
+ self.windowRange.tauBand = 0
+ self.windowRange.dtau = 1
+ else:
+ if not isinstance(self.tauBand, int):
+ logging.warn('Casting non-integer tauBand={} to int...'
+ .format(self.tauBand))
+ self.tauBand = int(self.tauBand)
+ self.windowRange.tauBand = self.tauBand
+ self.windowRange.dtau = self.Tsft
def get_fullycoherent_twoF(self, tstart, tend, F0, F1, F2, Alpha, Delta,
asini=None, period=None, ecc=None, tp=None,
@@ -624,7 +660,7 @@ class ComputeFstat(BaseSearchClass):
self.whatToCompute
)
- if self.transient is False:
+ if not self.transientWindowType:
if self.BSGL is False:
return self.FstatResults.twoF[0]
@@ -636,13 +672,20 @@ class ComputeFstat(BaseSearchClass):
return log10_BSGL/np.log10(np.exp(1))
self.windowRange.t0 = int(tstart) # TYPE UINT4
- self.windowRange.tau = int(tend - tstart) # TYPE UINT4
+ if self.windowRange.tauBand == 0:
+ # true single-template search also in transient params:
+ # actual (t0,tau) window was set with tstart, tend before
+ self.windowRange.tau = int(tend - tstart) # TYPE UINT4
+ else:
+ # grid search: start at minimum tau required for nondegenerate
+ # F-stat computation
+ self.windowRange.tau = int(2*self.Tsft)
FS = lalpulsar.ComputeTransientFstatMap(
self.FstatResults.multiFatoms[0], self.windowRange, False)
+ twoF = 2*np.max(FS.F_mn.data)
if self.BSGL is False:
- twoF = 2*FS.F_mn.data[0][0]
if np.isnan(twoF):
return 0
else:
@@ -657,10 +700,17 @@ class ComputeFstat(BaseSearchClass):
FS1 = lalpulsar.ComputeTransientFstatMap(
FstatResults_single.multiFatoms[0], self.windowRange, False)
- self.twoFX[0] = 2*FS0.F_mn.data[0][0]
- self.twoFX[1] = 2*FS1.F_mn.data[0][0]
+ # for now, use the Doppler parameter with
+ # multi-detector F maximised over t0,tau
+ # to return BSGL
+ # FIXME: should we instead compute BSGL over the whole F_mn
+ # and return the maximum of that?
+ idx_maxTwoF = np.argmax(FS.F_mn.data)
+
+ self.twoFX[0] = 2*FS0.F_mn.data[idx_maxTwoF]
+ self.twoFX[1] = 2*FS1.F_mn.data[idx_maxTwoF]
log10_BSGL = lalpulsar.ComputeBSGL(
- 2*FS.F_mn.data[0][0], self.twoFX, self.BSGLSetup)
+ twoF, self.twoFX, self.BSGLSetup)
return log10_BSGL/np.log10(np.exp(1))
@@ -696,8 +746,9 @@ class ComputeFstat(BaseSearchClass):
max_tau = SFTmaxStartTime - tstart
taus = np.linspace(min_tau, max_tau, npoints)
twoFs = []
- if self.transient is False:
- self.transient = True
+ if not self.transientWindowType:
+ # still call the transient-Fstat-map function, but using the full range
+ self.transientWindowType = 'none'
self.init_computefstatistic_single_point()
for tau in taus:
twoFs.append(self.get_fullycoherent_twoF(
@@ -868,7 +919,9 @@ class SemiCoherentSearch(ComputeFstat):
self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)
self.set_ephemeris_files()
- self.transient = True
+ self.transientWindowType = 'rect'
+ self.t0Band = None
+ self.tauBand = None
self.init_computefstatistic_single_point()
self.init_semicoherent_parameters()
@@ -876,7 +929,7 @@ class SemiCoherentSearch(ComputeFstat):
logging.info(('Initialising semicoherent parameters from {} to {} in'
' {} segments').format(
self.minStartTime, self.maxStartTime, self.nsegs))
- self.transient = True
+ self.transientWindowType = 'rect'
self.whatToCompute = lalpulsar.FSTATQ_2F+lalpulsar.FSTATQ_ATOMS_PER_DET
self.tboundaries = np.linspace(self.minStartTime, self.maxStartTime,
self.nsegs+1)
@@ -915,7 +968,7 @@ class SemiCoherentSearch(ComputeFstat):
self.whatToCompute
)
- #if self.transient is False:
+ #if not self.transientWindowType:
# if self.BSGL is False:
# return self.FstatResults.twoF[0]
# twoF = np.float(self.FstatResults.twoF[0])
@@ -1005,8 +1058,10 @@ class SemiCoherentGlitchSearch(ComputeFstat):
self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)
self.set_ephemeris_files()
- self.transient = True
- self.binary = False
+ self.transientWindowType = 'rect'
+ self.t0Band = None
+ self.tauBand = None
+ self.binary = False
self.init_computefstatistic_single_point()
def get_semicoherent_nglitch_twoF(self, F0, F1, F2, Alpha, Delta, *args):
diff --git a/pyfstat/grid_based_searches.py b/pyfstat/grid_based_searches.py
index dadb855066df6fd72799d5ac0e3def4f10eb8053..3557bee0b7ff204d801a182033b546c8a04b48aa 100644
--- a/pyfstat/grid_based_searches.py
+++ b/pyfstat/grid_based_searches.py
@@ -31,7 +31,8 @@ class GridSearch(BaseSearchClass):
Deltas, tref=None, minStartTime=None, maxStartTime=None,
nsegs=1, BSGL=False, minCoverFreq=None, maxCoverFreq=None,
detectors=None, SSBprec=None, injectSources=None,
- input_arrays=False, assumeSqrtSX=None):
+ input_arrays=False, assumeSqrtSX=None,
+ transientWindowType=None, t0Band=None, tauBand=None):
"""
Parameters
----------
@@ -48,6 +49,16 @@ class GridSearch(BaseSearchClass):
GPS seconds of the reference time, start time and end time
input_arrays: bool
if true, use the F0s, F1s, etc as is
+ transientWindowType: str
+ If 'rect' or 'exp', compute atoms so that a transient (t0,tau) map
+ can later be computed. ('none' instead of None explicitly calls
+ the transient-window function, but with the full range, for
+ debugging). Currently only supported for nsegs=1.
+ t0Band, tauBand: int
+ if >0, search t0 in (minStartTime,minStartTime+t0Band)
+ and tau in (2*Tsft,2*Tsft+tauBand).
+ if =0, only compute CW Fstat with t0=minStartTime,
+ tau=maxStartTime-minStartTime.
For all other parameters, see `pyfstat.ComputeFStat` for details
"""
@@ -66,7 +77,9 @@ class GridSearch(BaseSearchClass):
self.search = ComputeFstat(
tref=self.tref, sftfilepattern=self.sftfilepattern,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
- detectors=self.detectors, transient=False,
+ detectors=self.detectors,
+ transientWindowType=self.transientWindowType,
+ t0Band=self.t0Band, tauBand=self.tauBand,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
BSGL=self.BSGL, SSBprec=self.SSBprec,
injectSources=self.injectSources,
@@ -573,7 +586,7 @@ class FrequencySlidingWindow(GridSearch):
self.search = ComputeFstat(
tref=self.tref, sftfilepattern=self.sftfilepattern,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
- detectors=self.detectors, transient=True,
+ detectors=self.detectors, transientWindowType=self.transientWindowType,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
BSGL=self.BSGL, SSBprec=self.SSBprec,
injectSources=self.injectSources)
@@ -779,10 +792,15 @@ class EarthTest(GridSearch):
r'$\Delta P_\mathrm{spin}$ [min]',
r'$2\mathcal{F}$']
- from projection_matrix import projection_matrix
+ 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")
- fig, axes = projection_matrix(data, xyz, projection=projection,
- factor=1.6, labels=labels)
+ fig, axes = gridcorner(data, xyz, projection=projection, factor=1.6,
+ labels=labels)
axes[-1][-1].axvline((lal.DAYJUL_SI - lal.DAYSID_SI)/60.0, color='C3')
plt.suptitle(
'T={:.1f} days, $f$={:.2f} Hz, $\log\mathcal{{B}}_{{S/A}}$={:.1f},'
diff --git a/pyfstat/make_sfts.py b/pyfstat/make_sfts.py
index bf4e258faafca5a30ed042b1da221de3d4a9a963..5e47b56c6f0a5c56e71697326303cf6307f483cb 100644
--- a/pyfstat/make_sfts.py
+++ b/pyfstat/make_sfts.py
@@ -25,7 +25,8 @@ class Writer(BaseSearchClass):
tref=None, F0=30, F1=1e-10, F2=0, Alpha=5e-3,
Delta=6e-2, h0=0.1, cosi=0.0, psi=0.0, phi=0, Tsft=1800,
outdir=".", sqrtSX=1, Band=4, detectors='H1',
- minStartTime=None, maxStartTime=None, add_noise=True):
+ minStartTime=None, maxStartTime=None, add_noise=True,
+ transientWindowType='none'):
"""
Parameters
----------
@@ -91,9 +92,9 @@ class Writer(BaseSearchClass):
self.make_cff()
self.run_makefakedata()
- def get_single_config_line(self, i, Alpha, Delta, h0, cosi, psi, phi, F0,
- F1, F2, tref, tstart, duration_days):
- template = (
+ def get_base_template(self, i, Alpha, Delta, h0, cosi, psi, phi, F0,
+ F1, F2, tref):
+ return (
"""[TS{}]
Alpha = {:1.18e}
Delta = {:1.18e}
@@ -104,12 +105,35 @@ phi0 = {:1.18e}
Freq = {:1.18e}
f1dot = {:1.18e}
f2dot = {:1.18e}
-refTime = {:10.6f}
-transientWindowType=rect
-transientStartTime={:10.3f}
-transientTauDays={:1.3f}\n""")
+refTime = {:10.6f}""")
+
+ def get_single_config_line_cw(
+ self, i, Alpha, Delta, h0, cosi, psi, phi, F0, F1, F2, tref):
+ template = (self.get_base_template(
+ i, Alpha, Delta, h0, cosi, psi, phi, F0, F1, F2, tref) + """\n""")
+ return template.format(
+ i, Alpha, Delta, h0, cosi, psi, phi, F0, F1, F2, tref)
+
+ def get_single_config_line_tcw(
+ self, i, Alpha, Delta, h0, cosi, psi, phi, F0, F1, F2, tref,
+ window, tstart, duration_days):
+ template = (self.get_base_template(
+ i, Alpha, Delta, h0, cosi, psi, phi, F0, F1, F2, tref) + """
+transientWindowType = {:s}
+transientStartTime = {:10.3f}
+transientTauDays = {:1.3f}\n""")
return template.format(i, Alpha, Delta, h0, cosi, psi, phi, F0, F1,
- F2, tref, tstart, duration_days)
+ F2, tref, window, tstart, duration_days)
+
+ def get_single_config_line(self, i, Alpha, Delta, h0, cosi, psi, phi, F0,
+ F1, F2, tref, window, tstart, duration_days):
+ if window == 'none':
+ return self.get_single_config_line_cw(
+ i, Alpha, Delta, h0, cosi, psi, phi, F0, F1, F2, tref)
+ else:
+ return self.get_single_config_line_tcw(
+ i, Alpha, Delta, h0, cosi, psi, phi, F0, F1, F2, tref, window,
+ tstart, duration_days)
def make_cff(self):
"""
@@ -119,8 +143,8 @@ transientTauDays={:1.3f}\n""")
content = self.get_single_config_line(
0, self.Alpha, self.Delta, self.h0, self.cosi, self.psi,
- self.phi, self.F0, self.F1, self.F2, self.tref, self.tstart,
- self.duration_days)
+ self.phi, self.F0, self.F1, self.F2, self.tref,
+ self.transientWindowType, self.tstart, self.duration_days)
if self.check_if_cff_file_needs_rewritting(content):
config_file = open(self.config_file_name, "w+")
@@ -247,7 +271,8 @@ class GlitchWriter(Writer):
delta_F2=0, tref=None, F0=30, F1=1e-10, F2=0, Alpha=5e-3,
Delta=6e-2, h0=0.1, cosi=0.0, psi=0.0, phi=0, Tsft=1800,
outdir=".", sqrtSX=1, Band=4, detectors='H1',
- minStartTime=None, maxStartTime=None, add_noise=True):
+ minStartTime=None, maxStartTime=None, add_noise=True,
+ transientWindowType='rect'):
"""
Parameters
----------
@@ -317,7 +342,8 @@ class GlitchWriter(Writer):
self.tbounds[:-1])):
line = self.get_single_config_line(
i, self.Alpha, self.Delta, self.h0, self.cosi, self.psi,
- t[0], t[1], t[2], t[3], self.tref, ts, d)
+ t[0], t[1], t[2], t[3], self.tref, self.transientWindowType,
+ ts, d)
content += line
diff --git a/pyfstat/mcmc_based_searches.py b/pyfstat/mcmc_based_searches.py
index 912445e33f2e2846710b8edaf70df7490f262144..3da12b601920b9336acaa6a328664f9808acef8b 100644
--- a/pyfstat/mcmc_based_searches.py
+++ b/pyfstat/mcmc_based_searches.py
@@ -76,6 +76,12 @@ class MCMCSearch(core.BaseSearchClass):
the search
assumeSqrtSX: float, optional
Don't estimate noise-floors, but assume (stationary) per-IFO sqrt{SX}
+ transientWindowType: str
+ If 'rect' or 'exp',
+ compute atoms so that a transient (t0,tau) map can later be computed.
+ ('none' instead of None explicitly calls the transient-window function,
+ but with the full range, for debugging)
+ Currently only supported for nsegs=1.
Attributes
----------
@@ -108,7 +114,8 @@ class MCMCSearch(core.BaseSearchClass):
log10beta_min=-5, theta_initial=None,
rhohatmax=1000, binary=False, BSGL=False,
SSBprec=None, minCoverFreq=None, maxCoverFreq=None,
- injectSources=None, assumeSqrtSX=None):
+ injectSources=None, assumeSqrtSX=None,
+ transientWindowType=None):
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
@@ -150,7 +157,8 @@ class MCMCSearch(core.BaseSearchClass):
self.search = core.ComputeFstat(
tref=self.tref, sftfilepattern=self.sftfilepattern,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
- detectors=self.detectors, BSGL=self.BSGL, transient=False,
+ detectors=self.detectors, BSGL=self.BSGL,
+ transientWindowType=self.transientWindowType,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
binary=self.binary, injectSources=self.injectSources,
assumeSqrtSX=self.assumeSqrtSX, SSBprec=self.SSBprec)
@@ -212,38 +220,39 @@ class MCMCSearch(core.BaseSearchClass):
self.theta_symbols = [self.theta_symbols[i] for i in idxs]
self.theta_keys = [self.theta_keys[i] for i in idxs]
+ def _evaluate_logpost(self, p0vec):
+ init_logp = np.array([
+ self.logp(p, self.theta_prior, self.theta_keys, self.search)
+ for p in p0vec])
+ init_logl = np.array([
+ self.logl(p, self.search)
+ for p in p0vec])
+ return init_logl + init_logp
+
def _check_initial_points(self, p0):
for nt in range(self.ntemps):
logging.info('Checking temperature {} chains'.format(nt))
- initial_priors = np.array([
- self.logp(p, self.theta_prior, self.theta_keys, self.search)
- for p in p0[nt]])
- number_of_initial_out_of_bounds = sum(initial_priors == -np.inf)
-
- if number_of_initial_out_of_bounds > 0:
+ num = sum(self._evaluate_logpost(p0[nt]) == -np.inf)
+ if num > 0:
logging.warning(
'Of {} initial values, {} are -np.inf due to the prior'
- .format(len(initial_priors),
- number_of_initial_out_of_bounds))
-
+ .format(len(p0[0]), num))
p0 = self._generate_new_p0_to_fix_initial_points(
- p0, nt, initial_priors)
+ p0, nt)
- def _generate_new_p0_to_fix_initial_points(self, p0, nt, initial_priors):
+ def _generate_new_p0_to_fix_initial_points(self, p0, nt):
logging.info('Attempting to correct intial values')
- idxs = np.arange(self.nwalkers)[initial_priors == -np.inf]
+ init_logpost = self._evaluate_logpost(p0[nt])
+ idxs = np.arange(self.nwalkers)[init_logpost == -np.inf]
count = 0
- while sum(initial_priors == -np.inf) > 0 and count < 100:
+ while sum(init_logpost == -np.inf) > 0 and count < 100:
for j in idxs:
p0[nt][j] = (p0[nt][np.random.randint(0, self.nwalkers)]*(
1+np.random.normal(0, 1e-10, self.ndim)))
- initial_priors = np.array([
- self.logp(p, self.theta_prior, self.theta_keys,
- self.search)
- for p in p0[nt]])
+ init_logpost = self._evaluate_logpost(p0[nt])
count += 1
- if sum(initial_priors == -np.inf) > 0:
+ if sum(init_logpost == -np.inf) > 0:
logging.info('Failed to fix initial priors')
else:
logging.info('Suceeded to fix initial priors')
@@ -2194,10 +2203,13 @@ class MCMCTransientSearch(MCMCSearch):
def _initiate_search_object(self):
logging.info('Setting up search object')
+ if not self.transientWindowType:
+ self.transientWindowType = 'rect'
self.search = core.ComputeFstat(
tref=self.tref, sftfilepattern=self.sftfilepattern,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
- detectors=self.detectors, transient=True,
+ detectors=self.detectors,
+ transientWindowType=self.transientWindowType,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
BSGL=self.BSGL, binary=self.binary,
injectSources=self.injectSources)