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Oliver Bock authored* Resets animation parameters and camera settings to initial/default values * Considered as activity: mouse press events * Inactivity timeout: 10 minutes * Currently only enabled in demo mode
Oliver Bock authored* Resets animation parameters and camera settings to initial/default values * Considered as activity: mouse press events * Inactivity timeout: 10 minutes * Currently only enabled in demo mode
pyfstat.py 112.68 KiB
""" Classes for various types of searches using ComputeFstatistic """
import os
import sys
import itertools
import logging
import argparse
import copy
import glob
import inspect
from functools import wraps
import subprocess
from collections import OrderedDict
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import emcee
import corner
import dill as pickle
import lal
import lalpulsar
try:
from tqdm import tqdm
except ImportError:
def tqdm(x):
return x
plt.rcParams['text.usetex'] = True
plt.rcParams['axes.formatter.useoffset'] = False
config_file = os.path.expanduser('~')+'/.pyfstat.conf'
if os.path.isfile(config_file):
d = {}
with open(config_file, 'r') as f:
for line in f:
k, v = line.split('=')
k = k.replace(' ', '')
v = v.replace(' ', '').replace("'", "").replace('"', '').replace('\n', '')
d[k] = v
earth_ephem = d['earth_ephem']
sun_ephem = d['sun_ephem']
else:
logging.warning('No ~/.pyfstat.conf file found please provide the paths '
'when initialising searches')
earth_ephem = None
sun_ephem = None
parser = argparse.ArgumentParser()
parser.add_argument("-q", "--quite", help="Decrease output verbosity",
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")
parser.add_argument('-s', "--setup-only", action="store_true")
parser.add_argument('unittest_args', nargs='*')
args, unknown = parser.parse_known_args()
sys.argv[1:] = args.unittest_args
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
stream_handler = logging.StreamHandler()
if args.quite:
stream_handler.setLevel(logging.WARNING)
else:
stream_handler.setLevel(logging.DEBUG)
stream_handler.setFormatter(logging.Formatter(
'%(asctime)s %(levelname)-8s: %(message)s', datefmt='%H:%M'))
logger.addHandler(stream_handler)
def round_to_n(x, n):
if not x:
return 0
power = -int(np.floor(np.log10(abs(x)))) + (n - 1)
factor = (10 ** power)
return round(x * factor) / factor
def texify_float(x, d=1):
x = round_to_n(x, d)
if 0.01 < abs(x) < 100:
return str(x)
else:
power = int(np.floor(np.log10(abs(x))))
stem = np.round(x / 10**power, d)
if d == 1:
stem = int(stem)
return r'${}{{\times}}10^{{{}}}$'.format(stem, power)
def initializer(func):
""" Decorator function to automatically assign the parameters to self """
names, varargs, keywords, defaults = inspect.getargspec(func)
@wraps(func)
def wrapper(self, *args, **kargs):
for name, arg in list(zip(names[1:], args)) + list(kargs.items()):
setattr(self, name, arg)
for name, default in zip(reversed(names), reversed(defaults)):
if not hasattr(self, name):
setattr(self, name, default)
func(self, *args, **kargs)
return wrapper
def read_par(label, outdir):
""" Read in a .par file, returns a dictionary of the values """
filename = '{}/{}.par'.format(outdir, label)
d = {}
with open(filename, 'r') as f:
for line in f:
if len(line.split('=')) > 1:
key, val = line.rstrip('\n').split(' = ')
key = key.strip()
d[key] = np.float64(eval(val.rstrip('; ')))
return d
class BaseSearchClass(object):
""" The base search class, provides general functions """
earth_ephem_default = earth_ephem
sun_ephem_default = sun_ephem
def add_log_file(self):
""" Log output to a file, requires class to have outdir and label """
logfilename = '{}/{}.log'.format(self.outdir, self.label)
fh = logging.FileHandler(logfilename)
fh.setLevel(logging.INFO)
fh.setFormatter(logging.Formatter(
'%(asctime)s %(levelname)-8s: %(message)s',
datefmt='%y-%m-%d %H:%M'))
logging.getLogger().addHandler(fh)
def shift_matrix(self, n, dT):
""" Generate the shift matrix
Parameters
----------
n: int
The dimension of the shift-matrix to generate
dT: float
The time delta of the shift matrix
Returns
-------
m: array (n, n)
The shift matrix
"""
m = np.zeros((n, n))
factorial = np.math.factorial
for i in range(n):
for j in range(n):
if i == j:
m[i, j] = 1.0
elif i > j:
m[i, j] = 0.0
else:
if i == 0:
m[i, j] = 2*np.pi*float(dT)**(j-i) / factorial(j-i)
else:
m[i, j] = float(dT)**(j-i) / factorial(j-i)
return m
def shift_coefficients(self, theta, dT):
""" Shift a set of coefficients by dT
Parameters
----------
theta: array-like, shape (n,)
vector of the expansion coefficients to transform starting from the
lowest degree e.g [phi, F0, F1,...].
dT: float
difference between the two reference times as tref_new - tref_old.
Returns
-------
theta_new: array-like shape (n,)
vector of the coefficients as evaluate as the new reference time.
"""
n = len(theta)
m = self.shift_matrix(n, dT)
return np.dot(m, theta)
def calculate_thetas(self, theta, delta_thetas, tbounds, theta0_idx=0):
""" Calculates the set of coefficients for the post-glitch signal """
thetas = [theta]
for i, dt in enumerate(delta_thetas):
if i < theta0_idx:
pre_theta_at_ith_glitch = self.shift_coefficients(
thetas[0], tbounds[i+1] - self.tref)
post_theta_at_ith_glitch = pre_theta_at_ith_glitch - dt
thetas.insert(0, self.shift_coefficients(
post_theta_at_ith_glitch, self.tref - tbounds[i+1]))
elif i >= theta0_idx:
pre_theta_at_ith_glitch = self.shift_coefficients(
thetas[i], tbounds[i+1] - self.tref)
post_theta_at_ith_glitch = pre_theta_at_ith_glitch + dt
thetas.append(self.shift_coefficients(
post_theta_at_ith_glitch, self.tref - tbounds[i+1]))
return thetas
def generate_loudest(self):
params = read_par(self.label, self.outdir)
for key in ['Alpha', 'Delta', 'F0', 'F1']:
if key not in params:
params[key] = self.theta_prior[key]
cmd = ('lalapps_ComputeFstatistic_v2 -a {} -d {} -f {} -s {} -D "{}"'
' --refTime={} --outputLoudest="{}/{}.loudest" '
'--minStartTime={} --maxStartTime={}').format(
params['Alpha'], params['Delta'], params['F0'],
params['F1'], self.sftfilepath, params['tref'],
self.outdir, self.label, self.minStartTime,
self.maxStartTime)
subprocess.call([cmd], shell=True)
class ComputeFstat(object):
""" Base class providing interface to `lalpulsar.ComputeFstat` """
earth_ephem_default = earth_ephem
sun_ephem_default = sun_ephem
@initializer
def __init__(self, tref, sftfilepath=None, minStartTime=None,
maxStartTime=None, binary=False, transient=True, BSGL=False,
detector=None, minCoverFreq=None, maxCoverFreq=None,
earth_ephem=None, sun_ephem=None, injectSources=None
):
"""
Parameters
----------
tref: int
GPS seconds of the reference time.
sftfilepath: str
File patern to match SFTs
minStartTime, maxStartTime: float GPStime
Only use SFTs with timestemps starting from (including, excluding)
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.
detector: str
Two character reference to the data to use, specify None for no
contraint.
minCoverFreq, maxCoverFreq: float
The min and max cover frequency passed to CreateFstatInput, if
either is None the range of frequencies in the SFT less 1Hz is
used.
earth_ephem, sun_ephem: str
Paths of the two files containing positions of Earth and Sun,
respectively at evenly spaced times, as passed to CreateFstatInput.
If None defaults defined in BaseSearchClass will be used.
"""
if earth_ephem is None:
self.earth_ephem = self.earth_ephem_default
if sun_ephem is None:
self.sun_ephem = self.sun_ephem_default
self.init_computefstatistic_single_point()
def init_computefstatistic_single_point(self):
""" Initilisation step of run_computefstatistic for a single point """
logging.info('Initialising SFTCatalog')
constraints = lalpulsar.SFTConstraints()
if self.detector:
constraints.detector = self.detector
if self.minStartTime:
constraints.minStartTime = lal.LIGOTimeGPS(self.minStartTime)
if self.maxStartTime:
constraints.maxStartTime = lal.LIGOTimeGPS(self.maxStartTime)
logging.info('Loading data matching pattern {}'.format(
self.sftfilepath))
SFTCatalog = lalpulsar.SFTdataFind(self.sftfilepath, constraints)
names = list(set([d.header.name for d in SFTCatalog.data]))
self.names = names
SFT_timestamps = [d.header.epoch for d in SFTCatalog.data]
try:
from bashplotlib.histogram import plot_hist
print('Data timestamps histogram:')
plot_hist(SFT_timestamps, height=5, bincount=50)
except IOError:
pass
if len(names) == 0:
raise ValueError('No data loaded.')
logging.info('Loaded {} data files from detectors {}'.format(
len(SFT_timestamps), names))
logging.info('Data spans from {} ({}) to {} ({})'.format(
int(SFT_timestamps[0]),
subprocess.check_output('lalapps_tconvert {}'.format(
int(SFT_timestamps[0])), shell=True).rstrip('\n'),
int(SFT_timestamps[-1]),
subprocess.check_output('lalapps_tconvert {}'.format(
int(SFT_timestamps[-1])), shell=True).rstrip('\n')))
logging.info('Initialising ephems')
ephems = lalpulsar.InitBarycenter(self.earth_ephem, self.sun_ephem)
logging.info('Initialising FstatInput')
dFreq = 0
if self.transient:
self.whatToCompute = lalpulsar.FSTATQ_ATOMS_PER_DET
else:
self.whatToCompute = lalpulsar.FSTATQ_2F
FstatOAs = lalpulsar.FstatOptionalArgs()
FstatOAs.randSeed = lalpulsar.FstatOptionalArgsDefaults.randSeed
FstatOAs.SSBprec = lalpulsar.FstatOptionalArgsDefaults.SSBprec
FstatOAs.Dterms = lalpulsar.FstatOptionalArgsDefaults.Dterms
FstatOAs.runningMedianWindow = lalpulsar.FstatOptionalArgsDefaults.runningMedianWindow
FstatOAs.FstatMethod = lalpulsar.FstatOptionalArgsDefaults.FstatMethod
FstatOAs.InjectSqrtSX = lalpulsar.FstatOptionalArgsDefaults.injectSqrtSX
FstatOAs.assumeSqrtSX = lalpulsar.FstatOptionalArgsDefaults.assumeSqrtSX
FstatOAs.prevInput = lalpulsar.FstatOptionalArgsDefaults.prevInput
FstatOAs.collectTiming = lalpulsar.FstatOptionalArgsDefaults.collectTiming
if hasattr(self, 'injectSource') and type(self.injectSources) == dict:
logging.info('Injecting source with params: {}'.format(
self.injectSources))
PPV = lalpulsar.CreatePulsarParamsVector(1)
PP = PPV.data[0]
PP.Amp.h0 = self.injectSources['h0']
PP.Amp.cosi = self.injectSources['cosi']
PP.Amp.phi0 = self.injectSources['phi0']
PP.Amp.psi = self.injectSources['psi']
PP.Doppler.Alpha = self.injectSources['Alpha']
PP.Doppler.Delta = self.injectSources['Delta']
PP.Doppler.fkdot = np.array(self.injectSources['fkdot'])
PP.Doppler.refTime = self.tref
if 't0' not in self.injectSources:
#PP.Transient.t0 = int(self.minStartTime)
#PP.Transient.tau = int(self.maxStartTime - self.minStartTime)
PP.Transient.type = lalpulsar.TRANSIENT_NONE
FstatOAs.injectSources = PPV
else:
FstatOAs.injectSources = lalpulsar.FstatOptionalArgsDefaults.injectSources
if self.minCoverFreq is None or self.maxCoverFreq is None:
fAs = [d.header.f0 for d in SFTCatalog.data]
fBs = [d.header.f0 + (d.numBins-1)*d.header.deltaF
for d in SFTCatalog.data]
self.minCoverFreq = np.min(fAs) + 0.5
self.maxCoverFreq = np.max(fBs) - 0.5
logging.info('Min/max cover freqs not provided, using '
'{} and {}, est. from SFTs'.format(
self.minCoverFreq, self.maxCoverFreq))
self.FstatInput = lalpulsar.CreateFstatInput(SFTCatalog,
self.minCoverFreq,
self.maxCoverFreq,
dFreq,
ephems,
FstatOAs
)
logging.info('Initialising PulsarDoplerParams')
PulsarDopplerParams = lalpulsar.PulsarDopplerParams()
PulsarDopplerParams.refTime = self.tref
PulsarDopplerParams.Alpha = 1
PulsarDopplerParams.Delta = 1
PulsarDopplerParams.fkdot = np.array([0, 0, 0, 0, 0, 0, 0])
self.PulsarDopplerParams = PulsarDopplerParams
logging.info('Initialising FstatResults')
self.FstatResults = lalpulsar.FstatResults()
if self.BSGL:
if len(names) < 2:
raise ValueError("Can't use BSGL with single detector data")
else:
logging.info('Initialising BSGL')
# Tuning parameters - to be reviewed
numDetectors = 2
Fstar0sc = 15.
oLGX = np.zeros(10)
oLGX[:numDetectors] = 1./numDetectors
self.BSGLSetup = lalpulsar.CreateBSGLSetup(numDetectors,
Fstar0sc,
oLGX,
True,
1)
self.twoFX = np.zeros(10)
self.whatToCompute = (self.whatToCompute +
lalpulsar.FSTATQ_2F_PER_DET)
if self.transient:
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
def compute_fullycoherent_det_stat_single_point(
self, F0, F1, F2, Alpha, Delta, asini=None, period=None, ecc=None,
tp=None, argp=None):
""" Compute the fully-coherent det. statistic at a single point """
return self.run_computefstatistic_single_point(
self.minStartTime, self.maxStartTime, F0, F1, F2, Alpha, Delta,
asini, period, ecc, tp, argp)
def run_computefstatistic_single_point(self, tstart, tend, F0, F1,
F2, Alpha, Delta, asini=None,
period=None, ecc=None, tp=None,
argp=None):
""" Returns twoF or ln(BSGL) fully-coherently at a single point """
self.PulsarDopplerParams.fkdot = np.array([F0, F1, F2, 0, 0, 0, 0])
self.PulsarDopplerParams.Alpha = Alpha
self.PulsarDopplerParams.Delta = Delta
if self.binary:
self.PulsarDopplerParams.asini = asini
self.PulsarDopplerParams.period = period
self.PulsarDopplerParams.ecc = ecc
self.PulsarDopplerParams.tp = tp
self.PulsarDopplerParams.argp = argp
lalpulsar.ComputeFstat(self.FstatResults,
self.FstatInput,
self.PulsarDopplerParams,
1,
self.whatToCompute
)
if self.transient is False:
if self.BSGL is False:
return self.FstatResults.twoF[0]
twoF = np.float(self.FstatResults.twoF[0])
self.twoFX[0] = self.FstatResults.twoFPerDet(0)
self.twoFX[1] = self.FstatResults.twoFPerDet(1)
log10_BSGL = lalpulsar.ComputeBSGL(twoF, self.twoFX,
self.BSGLSetup)
return log10_BSGL/np.log10(np.exp(1))
self.windowRange.t0 = int(tstart) # TYPE UINT4
self.windowRange.tau = int(tend - tstart) # TYPE UINT4
FS = lalpulsar.ComputeTransientFstatMap(
self.FstatResults.multiFatoms[0], self.windowRange, False)
if self.BSGL is False:
return 2*FS.F_mn.data[0][0]
FstatResults_single = copy.copy(self.FstatResults)
FstatResults_single.lenth = 1
FstatResults_single.data = self.FstatResults.multiFatoms[0].data[0]
FS0 = lalpulsar.ComputeTransientFstatMap(
FstatResults_single.multiFatoms[0], self.windowRange, False)
FstatResults_single.data = self.FstatResults.multiFatoms[0].data[1]
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]
log10_BSGL = lalpulsar.ComputeBSGL(
2*FS.F_mn.data[0][0], self.twoFX, self.BSGLSetup)
return log10_BSGL/np.log10(np.exp(1))
def calculate_twoF_cumulative(self, F0, F1, F2, Alpha, Delta, asini=None,
period=None, ecc=None, tp=None, argp=None,
tstart=None, tend=None, npoints=1000,
minfraction=0.01, maxfraction=1):
""" Calculate the cumulative twoF along the obseration span """
duration = tend - tstart
tstart = tstart + minfraction*duration
taus = np.linspace(minfraction*duration, maxfraction*duration, npoints)
twoFs = []
if self.transient is False:
self.transient = True
self.init_computefstatistic_single_point()
for tau in taus:
twoFs.append(self.run_computefstatistic_single_point(
tstart=tstart, tend=tstart+tau, F0=F0, F1=F1, F2=F2,
Alpha=Alpha, Delta=Delta, asini=asini, period=period, ecc=ecc,
tp=tp, argp=argp))
return taus, np.array(twoFs)
def plot_twoF_cumulative(self, label, outdir, ax=None, c='k', savefig=True,
title=None, **kwargs):
taus, twoFs = self.calculate_twoF_cumulative(**kwargs)
if ax is None:
fig, ax = plt.subplots()
ax.plot(taus/86400., twoFs, label=label, color=c)
ax.set_xlabel(r'Days from $t_{{\rm start}}={:.0f}$'.format(
kwargs['tstart']))
if self.BSGL:
ax.set_ylabel(r'$\log_{10}(\mathrm{BSGL})_{\rm cumulative}$')
else:
ax.set_ylabel(r'$\widetilde{2\mathcal{F}}_{\rm cumulative}$')
ax.set_xlim(0, taus[-1]/86400)
ax.set_title(title)
if savefig:
plt.savefig('{}/{}_twoFcumulative.png'.format(outdir, label))
return taus, twoFs
else:
return ax
class SemiCoherentSearch(BaseSearchClass, ComputeFstat):
""" A semi-coherent search """
@initializer
def __init__(self, label, outdir, tref, nsegs=None, sftfilepath=None,
binary=False, BSGL=False, minStartTime=None,
maxStartTime=None, minCoverFreq=None, maxCoverFreq=None,
detector=None, earth_ephem=None, sun_ephem=None,
injectSources=None):
"""
Parameters
----------
label, outdir: str
A label and directory to read/write data from/to.
tref, tstart, tend: int
GPS seconds of the reference time, and start and end of the data.
nsegs: int
The (fixed) number of segments
sftfilepath: str
File patern to match SFTs
For all other parameters, see pyfstat.ComputeFStat.
"""
self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)
if self.earth_ephem is None:
self.earth_ephem = self.earth_ephem_default
if self.sun_ephem is None:
self.sun_ephem = self.sun_ephem_default
self.transient = True
self.init_computefstatistic_single_point()
self.init_semicoherent_parameters()
def init_semicoherent_parameters(self):
logging.info(('Initialising semicoherent parameters from {} to {} in'
' {} segments').format(
self.minStartTime, self.maxStartTime, self.nsegs))
self.transient = True
self.whatToCompute = lalpulsar.FSTATQ_2F+lalpulsar.FSTATQ_ATOMS_PER_DET
self.tboundaries = np.linspace(self.minStartTime, self.maxStartTime,
self.nsegs+1)
def run_semi_coherent_computefstatistic_single_point(
self, F0, F1, F2, Alpha, Delta, asini=None,
period=None, ecc=None, tp=None, argp=None):
""" Returns twoF or ln(BSGL) semi-coherently at a single point """
self.PulsarDopplerParams.fkdot = np.array([F0, F1, F2, 0, 0, 0, 0])
self.PulsarDopplerParams.Alpha = Alpha
self.PulsarDopplerParams.Delta = Delta
if self.binary:
self.PulsarDopplerParams.asini = asini
self.PulsarDopplerParams.period = period
self.PulsarDopplerParams.ecc = ecc
self.PulsarDopplerParams.tp = tp
self.PulsarDopplerParams.argp = argp
lalpulsar.ComputeFstat(self.FstatResults,
self.FstatInput,
self.PulsarDopplerParams,
1,
self.whatToCompute
)
if self.transient is False:
if self.BSGL is False:
return self.FstatResults.twoF[0]
twoF = np.float(self.FstatResults.twoF[0])
self.twoFX[0] = self.FstatResults.twoFPerDet(0)
self.twoFX[1] = self.FstatResults.twoFPerDet(1)
log10_BSGL = lalpulsar.ComputeBSGL(twoF, self.twoFX,
self.BSGLSetup)
return log10_BSGL/np.log10(np.exp(1))
detStat = 0
for tstart, tend in zip(self.tboundaries[:-1], self.tboundaries[1:]):
self.windowRange.t0 = int(tstart) # TYPE UINT4
self.windowRange.tau = int(tend - tstart) # TYPE UINT4
FS = lalpulsar.ComputeTransientFstatMap(
self.FstatResults.multiFatoms[0], self.windowRange, False)
if self.BSGL is False:
detStat += 2*FS.F_mn.data[0][0]
continue
FstatResults_single = copy.copy(self.FstatResults)
FstatResults_single.lenth = 1
FstatResults_single.data = self.FstatResults.multiFatoms[0].data[0]
FS0 = lalpulsar.ComputeTransientFstatMap(
FstatResults_single.multiFatoms[0], self.windowRange, False)
FstatResults_single.data = self.FstatResults.multiFatoms[0].data[1]
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]
log10_BSGL = lalpulsar.ComputeBSGL(
2*FS.F_mn.data[0][0], self.twoFX, self.BSGLSetup)
detStat += log10_BSGL/np.log10(np.exp(1))
return detStat
class SemiCoherentGlitchSearch(BaseSearchClass, ComputeFstat):
""" A semi-coherent glitch search
This implements a basic `semi-coherent glitch F-stat in which the data
is divided into segments either side of the proposed glitches and the
fully-coherent F-stat in each segment is summed to give the semi-coherent
F-stat
"""
@initializer
def __init__(self, label, outdir, tref, tstart, tend, nglitch=0,
sftfilepath=None, theta0_idx=0, BSGL=False, minStartTime=None,
maxStartTime=None, minCoverFreq=None, maxCoverFreq=None,
detector=None, earth_ephem=None, sun_ephem=None):
"""
Parameters
----------
label, outdir: str
A label and directory to read/write data from/to.
tref, tstart, tend: int
GPS seconds of the reference time, and start and end of the data.
nglitch: int
The (fixed) number of glitches; this can zero, but occasionally
this causes issue (in which case just use ComputeFstat).
sftfilepath: str
File patern to match SFTs
theta0_idx, int
Index (zero-based) of which segment the theta refers to - uyseful
if providing a tight prior on theta to allow the signal to jump
too theta (and not just from)
For all other parameters, see pyfstat.ComputeFStat.
"""
self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)
if self.earth_ephem is None:
self.earth_ephem = self.earth_ephem_default
if self.sun_ephem is None:
self.sun_ephem = self.sun_ephem_default
self.transient = True
self.binary = False
self.init_computefstatistic_single_point()
def compute_nglitch_fstat(self, F0, F1, F2, Alpha, Delta, *args):
""" Returns the semi-coherent glitch summed twoF """
args = list(args)
tboundaries = [self.tstart] + args[-self.nglitch:] + [self.tend]
delta_F0s = args[-3*self.nglitch:-2*self.nglitch]
delta_F1s = args[-2*self.nglitch:-self.nglitch]
delta_F2 = np.zeros(len(delta_F0s))
delta_phi = np.zeros(len(delta_F0s))
theta = [0, F0, F1, F2]
delta_thetas = np.atleast_2d(
np.array([delta_phi, delta_F0s, delta_F1s, delta_F2]).T)
thetas = self.calculate_thetas(theta, delta_thetas, tboundaries,
theta0_idx=self.theta0_idx)
twoFSum = 0
for i, theta_i_at_tref in enumerate(thetas):
ts, te = tboundaries[i], tboundaries[i+1]
twoFVal = self.run_computefstatistic_single_point(
ts, te, theta_i_at_tref[1], theta_i_at_tref[2],
theta_i_at_tref[3], Alpha, Delta)
twoFSum += twoFVal
if np.isfinite(twoFSum):
return twoFSum
else:
return -np.inf
def compute_glitch_fstat_single(self, F0, F1, F2, Alpha, Delta, delta_F0,
delta_F1, tglitch):
""" Returns the semi-coherent glitch summed twoF for nglitch=1
Note: used for testing
"""
theta = [F0, F1, F2]
delta_theta = [delta_F0, delta_F1, 0]
tref = self.tref
theta_at_glitch = self.shift_coefficients(theta, tglitch - tref)
theta_post_glitch_at_glitch = theta_at_glitch + delta_theta
theta_post_glitch = self.shift_coefficients(
theta_post_glitch_at_glitch, tref - tglitch)
twoFsegA = self.run_computefstatistic_single_point(
self.tstart, tglitch, theta[0], theta[1], theta[2], Alpha,
Delta)
if tglitch == self.tend:
return twoFsegA
twoFsegB = self.run_computefstatistic_single_point(
tglitch, self.tend, theta_post_glitch[0],
theta_post_glitch[1], theta_post_glitch[2], Alpha,
Delta)
return twoFsegA + twoFsegB
class MCMCSearch(BaseSearchClass):
""" MCMC search using ComputeFstat"""
@initializer
def __init__(self, label, outdir, sftfilepath, theta_prior, tref,
tstart, tend, nsteps=[100, 100, 100], nwalkers=100, ntemps=1,
log10temperature_min=-5, theta_initial=None, scatter_val=1e-10,
binary=False, BSGL=False, minCoverFreq=None,
maxCoverFreq=None, detector=None, earth_ephem=None,
sun_ephem=None, injectSources=None):
"""
Parameters
label, outdir: str
A label and directory to read/write data from/to
sftfilepath: str
File patern to match SFTs
theta_prior: dict
Dictionary of priors and fixed values for the search parameters.
For each parameters (key of the dict), if it is to be held fixed
the value should be the constant float, if it is be searched, the
value should be a dictionary of the prior.
theta_initial: dict, array, (None)
Either a dictionary of distribution about which to distribute the
initial walkers about, an array (from which the walkers will be
scattered by scatter_val, or None in which case the prior is used.
tref, tstart, tend: int
GPS seconds of the reference time, start time and end time
nsteps: list (m,)
List specifying the number of steps to take, the last two entries
give the nburn and nprod of the 'production' run, all entries
before are for iterative initialisation steps (usually just one)
e.g. [1000, 1000, 500].
nwalkers, ntemps: int,
The number of walkers and temperates to use in the parallel
tempered PTSampler.
log10temperature_min float < 0
The log_10(tmin) value, the set of betas passed to PTSampler are
generated from np.logspace(0, log10temperature_min, ntemps).
binary: Bool
If true, search over binary parameters
detector: str
Two character reference to the data to use, specify None for no
contraint.
minCoverFreq, maxCoverFreq: float
Minimum and maximum instantaneous frequency which will be covered
over the SFT time span as passed to CreateFstatInput
earth_ephem, sun_ephem: str
Paths of the two files containing positions of Earth and Sun,
respectively at evenly spaced times, as passed to CreateFstatInput
If None defaults defined in BaseSearchClass will be used
"""
self.minStartTime = tstart
self.maxStartTime = tend
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self.add_log_file()
logging.info(
'Set-up MCMC search for model {} on data {}'.format(
self.label, self.sftfilepath))
self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
self.unpack_input_theta()
self.ndim = len(self.theta_keys)
if self.log10temperature_min:
self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
else:
self.betas = None
if earth_ephem is None:
self.earth_ephem = self.earth_ephem_default
if sun_ephem is None:
self.sun_ephem = self.sun_ephem_default
if args.clean and os.path.isfile(self.pickle_path):
os.rename(self.pickle_path, self.pickle_path+".old")
self.log_input()
def log_input(self):
logging.info('theta_prior = {}'.format(self.theta_prior))
logging.info('nwalkers={}'.format(self.nwalkers))
logging.info('scatter_val = {}'.format(self.scatter_val))
logging.info('nsteps = {}'.format(self.nsteps))
logging.info('ntemps = {}'.format(self.ntemps))
logging.info('log10temperature_min = {}'.format(
self.log10temperature_min))
def inititate_search_object(self):
logging.info('Setting up search object')
self.search = ComputeFstat(
tref=self.tref, sftfilepath=self.sftfilepath,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
earth_ephem=self.earth_ephem, sun_ephem=self.sun_ephem,
detector=self.detector, BSGL=self.BSGL, transient=False,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
binary=self.binary, injectSources=self.injectSources)
def logp(self, theta_vals, theta_prior, theta_keys, search):
H = [self.generic_lnprior(**theta_prior[key])(p) for p, key in
zip(theta_vals, theta_keys)]
return np.sum(H)
def logl(self, theta, search):
for j, theta_i in enumerate(self.theta_idxs):
self.fixed_theta[theta_i] = theta[j]
FS = search.compute_fullycoherent_det_stat_single_point(
*self.fixed_theta)
return FS
def unpack_input_theta(self):
full_theta_keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta']
if self.binary:
full_theta_keys += [
'asini', 'period', 'ecc', 'tp', 'argp']
full_theta_keys_copy = copy.copy(full_theta_keys)
full_theta_symbols = ['$f$', '$\dot{f}$', '$\ddot{f}$', r'$\alpha$',
r'$\delta$']
if self.binary:
full_theta_symbols += [
'asini', 'period', 'period', 'ecc', 'tp', 'argp']
self.theta_keys = []
fixed_theta_dict = {}
for key, val in self.theta_prior.iteritems():
if type(val) is dict:
fixed_theta_dict[key] = 0
self.theta_keys.append(key)
elif type(val) in [float, int, np.float64]:
fixed_theta_dict[key] = val
else:
raise ValueError(
'Type {} of {} in theta not recognised'.format(
type(val), key))
full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
if len(full_theta_keys_copy) > 0:
raise ValueError(('Input dictionary `theta` is missing the'
'following keys: {}').format(
full_theta_keys_copy))
self.fixed_theta = [fixed_theta_dict[key] for key in full_theta_keys]
self.theta_idxs = [full_theta_keys.index(k) for k in self.theta_keys]
self.theta_symbols = [full_theta_symbols[i] for i in self.theta_idxs]
idxs = np.argsort(self.theta_idxs)
self.theta_idxs = [self.theta_idxs[i] for i in idxs]
self.theta_symbols = [self.theta_symbols[i] for i in idxs]
self.theta_keys = [self.theta_keys[i] for i in idxs]
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:
logging.warning(
'Of {} initial values, {} are -np.inf due to the prior'
.format(len(initial_priors),
number_of_initial_out_of_bounds))
p0 = self.generate_new_p0_to_fix_initial_points(
p0, nt, initial_priors)
def generate_new_p0_to_fix_initial_points(self, p0, nt, initial_priors):
logging.info('Attempting to correct intial values')
idxs = np.arange(self.nwalkers)[initial_priors == -np.inf]
count = 0
while sum(initial_priors == -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]])
count += 1
if sum(initial_priors == -np.inf) > 0:
logging.info('Failed to fix initial priors')
else:
logging.info('Suceeded to fix initial priors')
return p0
def run_sampler_with_progress_bar(self, sampler, ns, p0):
for result in tqdm(sampler.sample(p0, iterations=ns), total=ns):
pass
return sampler
def run(self, proposal_scale_factor=2, **kwargs):
self.old_data_is_okay_to_use = self.check_old_data_is_okay_to_use()
if self.old_data_is_okay_to_use is True:
logging.warning('Using saved data from {}'.format(
self.pickle_path))
d = self.get_saved_data()
self.sampler = d['sampler']
self.samples = d['samples']
self.lnprobs = d['lnprobs']
self.lnlikes = d['lnlikes']
return
self.inititate_search_object()
sampler = emcee.PTSampler(
self.ntemps, self.nwalkers, self.ndim, self.logl, self.logp,
logpargs=(self.theta_prior, self.theta_keys, self.search),
loglargs=(self.search,), betas=self.betas, a=proposal_scale_factor)
p0 = self.generate_initial_p0()
p0 = self.apply_corrections_to_p0(p0)
self.check_initial_points(p0)
ninit_steps = len(self.nsteps) - 2
for j, n in enumerate(self.nsteps[:-2]):
logging.info('Running {}/{} initialisation with {} steps'.format(
j, ninit_steps, n))
sampler = self.run_sampler_with_progress_bar(sampler, n, p0)
logging.info("Mean acceptance fraction: {}"
.format(np.mean(sampler.acceptance_fraction, axis=1)))
if self.ntemps > 1:
logging.info("Tswap acceptance fraction: {}"
.format(sampler.tswap_acceptance_fraction))
fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols,
**kwargs)
fig.savefig('{}/{}_init_{}_walkers.png'.format(
self.outdir, self.label, j), dpi=200)
p0 = self.get_new_p0(sampler)
p0 = self.apply_corrections_to_p0(p0)
self.check_initial_points(p0)
sampler.reset()
if len(self.nsteps) > 1:
nburn = self.nsteps[-2]
else:
nburn = 0
nprod = self.nsteps[-1]
logging.info('Running final burn and prod with {} steps'.format(
nburn+nprod))
sampler = self.run_sampler_with_progress_bar(sampler, nburn+nprod, p0)
logging.info("Mean acceptance fraction: {}"
.format(np.mean(sampler.acceptance_fraction, axis=1)))
if self.ntemps > 1:
logging.info("Tswap acceptance fraction: {}"
.format(sampler.tswap_acceptance_fraction))
fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols,
burnin_idx=nburn, **kwargs)
fig.savefig('{}/{}_walkers.png'.format(self.outdir, self.label),
dpi=200)
samples = sampler.chain[0, :, nburn:, :].reshape((-1, self.ndim))
lnprobs = sampler.lnprobability[0, :, nburn:].reshape((-1))
lnlikes = sampler.lnlikelihood[0, :, nburn:].reshape((-1))
self.sampler = sampler
self.samples = samples
self.lnprobs = lnprobs
self.lnlikes = lnlikes
self.save_data(sampler, samples, lnprobs, lnlikes)
def plot_corner(self, figsize=(7, 7), tglitch_ratio=False,
add_prior=False, nstds=None, label_offset=0.4,
dpi=300, rc_context={}, **kwargs):
if self.ndim < 2:
with plt.rc_context(rc_context):
fig, ax = plt.subplots(figsize=figsize)
ax.hist(self.samples, bins=50, histtype='stepfilled')
ax.set_xlabel(self.theta_symbols[0])
fig.savefig('{}/{}_corner.png'.format(
self.outdir, self.label), dpi=dpi)
return
with plt.rc_context(rc_context):
fig, axes = plt.subplots(self.ndim, self.ndim,
figsize=figsize)
samples_plt = copy.copy(self.samples)
theta_symbols_plt = copy.copy(self.theta_symbols)
theta_symbols_plt = [s.replace('_{glitch}', r'_\textrm{glitch}')
for s in theta_symbols_plt]
if tglitch_ratio:
for j, k in enumerate(self.theta_keys):
if k == 'tglitch':
s = samples_plt[:, j]
samples_plt[:, j] = (s - self.tstart)/(
self.tend - self.tstart)
theta_symbols_plt[j] = r'$R_{\textrm{glitch}}$'
if type(nstds) is int and 'range' not in kwargs:
_range = []
for j, s in enumerate(samples_plt.T):
median = np.median(s)
std = np.std(s)
_range.append((median - nstds*std, median + nstds*std))
else:
_range = None
fig_triangle = corner.corner(samples_plt,
labels=theta_symbols_plt,
fig=fig,
bins=50,
max_n_ticks=4,
plot_contours=True,
plot_datapoints=True,
label_kwargs={'fontsize': 8},
data_kwargs={'alpha': 0.1,
'ms': 0.5},
range=_range,
**kwargs)
axes_list = fig_triangle.get_axes()
axes = np.array(axes_list).reshape(self.ndim, self.ndim)
plt.draw()
for ax in axes[:, 0]:
ax.yaxis.set_label_coords(-label_offset, 0.5)
for ax in axes[-1, :]:
ax.xaxis.set_label_coords(0.5, -label_offset)
for ax in axes_list:
ax.set_rasterized(True)
ax.set_rasterization_zorder(-10)
plt.tight_layout(h_pad=0.0, w_pad=0.0)
fig.subplots_adjust(hspace=0.05, wspace=0.05)
if add_prior:
self.add_prior_to_corner(axes, samples_plt)
fig_triangle.savefig('{}/{}_corner.png'.format(
self.outdir, self.label), dpi=dpi)
def add_prior_to_corner(self, axes, samples):
for i, key in enumerate(self.theta_keys):
ax = axes[i][i]
xlim = ax.get_xlim()
s = samples[:, i]
prior = self.generic_lnprior(**self.theta_prior[key])
x = np.linspace(s.min(), s.max(), 100)
ax2 = ax.twinx()
ax2.get_yaxis().set_visible(False)
ax2.plot(x, [prior(xi) for xi in x], '-r')
ax.set_xlim(xlim)
def plot_prior_posterior(self, normal_stds=2):
""" Plot the posterior in the context of the prior """
fig, axes = plt.subplots(nrows=self.ndim, figsize=(8, 4*self.ndim))
N = 1000
from scipy.stats import gaussian_kde
for i, (ax, key) in enumerate(zip(axes, self.theta_keys)):
prior_dict = self.theta_prior[key]
prior_func = self.generic_lnprior(**prior_dict)
if prior_dict['type'] == 'unif':
x = np.linspace(prior_dict['lower'], prior_dict['upper'], N)
prior = prior_func(x)
prior[0] = 0
prior[-1] = 0
elif prior_dict['type'] == 'norm':
lower = prior_dict['loc'] - normal_stds * prior_dict['scale']
upper = prior_dict['loc'] + normal_stds * prior_dict['scale']
x = np.linspace(lower, upper, N)
prior = prior_func(x)
elif prior_dict['type'] == 'halfnorm':
lower = prior_dict['loc']
upper = prior_dict['loc'] + normal_stds * prior_dict['scale']
x = np.linspace(lower, upper, N)
prior = [prior_func(xi) for xi in x]
elif prior_dict['type'] == 'neghalfnorm':
upper = prior_dict['loc']
lower = prior_dict['loc'] - normal_stds * prior_dict['scale']
x = np.linspace(lower, upper, N)
prior = [prior_func(xi) for xi in x]
else:
raise ValueError('Not implemented for prior type {}'.format(
prior_dict['type']))
priorln = ax.plot(x, prior, 'r', label='prior')
ax.set_xlabel(self.theta_symbols[i])
s = self.samples[:, i]
while len(s) > 10**4:
# random downsample to avoid slow calculation of kde
s = np.random.choice(s, size=int(len(s)/2.))
kde = gaussian_kde(s)
ax2 = ax.twinx()
postln = ax2.plot(x, kde.pdf(x), 'k', label='posterior')
ax2.set_yticklabels([])
ax.set_yticklabels([])
lns = priorln + postln
labs = [l.get_label() for l in lns]
axes[0].legend(lns, labs, loc=1, framealpha=0.8)
fig.savefig('{}/{}_prior_posterior.png'.format(
self.outdir, self.label))
def plot_cumulative_max(self, **kwargs):
d, maxtwoF = self.get_max_twoF()
for key, val in self.theta_prior.iteritems():
if key not in d:
d[key] = val
if hasattr(self, 'search') is False:
self.inititate_search_object()
if self.binary is False:
self.search.plot_twoF_cumulative(
self.label, self.outdir, F0=d['F0'], F1=d['F1'], F2=d['F2'],
Alpha=d['Alpha'], Delta=d['Delta'], tstart=self.tstart,
tend=self.tend, **kwargs)
else:
self.search.plot_twoF_cumulative(
self.label, self.outdir, F0=d['F0'], F1=d['F1'], F2=d['F2'],
Alpha=d['Alpha'], Delta=d['Delta'], asini=d['asini'],
period=d['period'], ecc=d['ecc'], argp=d['argp'], tp=d['argp'],
tstart=self.tstart, tend=self.tend, **kwargs)
def generic_lnprior(self, **kwargs):
""" Return a lambda function of the pdf
Parameters
----------
kwargs: dict
A dictionary containing 'type' of pdf and shape parameters
"""
def logunif(x, a, b):
above = x < b
below = x > a
if type(above) is not np.ndarray:
if above and below:
return -np.log(b-a)
else:
return -np.inf
else:
idxs = np.array([all(tup) for tup in zip(above, below)])
p = np.zeros(len(x)) - np.inf
p[idxs] = -np.log(b-a)
return p
def halfnorm(x, loc, scale):
if x < 0:
return -np.inf
else:
return -0.5*((x-loc)**2/scale**2+np.log(0.5*np.pi*scale**2))
def cauchy(x, x0, gamma):
return 1.0/(np.pi*gamma*(1+((x-x0)/gamma)**2))
def exp(x, x0, gamma):
if x > x0:
return np.log(gamma) - gamma*(x - x0)
else:
return -np.inf
if kwargs['type'] == 'unif':
return lambda x: logunif(x, kwargs['lower'], kwargs['upper'])
elif kwargs['type'] == 'halfnorm':
return lambda x: halfnorm(x, kwargs['loc'], kwargs['scale'])
elif kwargs['type'] == 'neghalfnorm':
return lambda x: halfnorm(-x, kwargs['loc'], kwargs['scale'])
elif kwargs['type'] == 'norm':
return lambda x: -0.5*((x - kwargs['loc'])**2/kwargs['scale']**2
+ np.log(2*np.pi*kwargs['scale']**2))
else:
logging.info("kwargs:", kwargs)
raise ValueError("Print unrecognise distribution")
def generate_rv(self, **kwargs):
dist_type = kwargs.pop('type')
if dist_type == "unif":
return np.random.uniform(low=kwargs['lower'], high=kwargs['upper'])
if dist_type == "norm":
return np.random.normal(loc=kwargs['loc'], scale=kwargs['scale'])
if dist_type == "halfnorm":
return np.abs(np.random.normal(loc=kwargs['loc'],
scale=kwargs['scale']))
if dist_type == "neghalfnorm":
return -1 * np.abs(np.random.normal(loc=kwargs['loc'],
scale=kwargs['scale']))
if dist_type == "lognorm":
return np.random.lognormal(
mean=kwargs['loc'], sigma=kwargs['scale'])
else:
raise ValueError("dist_type {} unknown".format(dist_type))
def plot_walkers(self, sampler, symbols=None, alpha=0.4, color="k", temp=0,
lw=0.1, burnin_idx=None, add_det_stat_burnin=False,
fig=None, axes=None, xoffset=0, plot_det_stat=True,
context='classic'):
""" Plot all the chains from a sampler """
shape = sampler.chain.shape
if len(shape) == 3:
nwalkers, nsteps, ndim = shape
chain = sampler.chain[:, :, :]
if len(shape) == 4:
ntemps, nwalkers, nsteps, ndim = shape
if temp < ntemps:
logging.info("Plotting temperature {} chains".format(temp))
else:
raise ValueError(("Requested temperature {} outside of"
"available range").format(temp))
chain = sampler.chain[temp, :, :, :]
with plt.style.context((context)):
if fig is None and axes is None:
fig = plt.figure(figsize=(8, 4*ndim))
ax = fig.add_subplot(ndim+1, 1, 1)
axes = [ax] + [fig.add_subplot(ndim+1, 1, i, sharex=ax)
for i in range(2, ndim+1)]
idxs = np.arange(chain.shape[1])
if ndim > 1:
for i in range(ndim):
axes[i].ticklabel_format(useOffset=False, axis='y')
if i < ndim:
axes[i].set_xticklabels([])
cs = chain[:, :, i].T
if burnin_idx:
axes[i].plot(xoffset+idxs[:burnin_idx],
cs[:burnin_idx], color="r", alpha=alpha,
lw=lw)
axes[i].plot(xoffset+idxs[burnin_idx:], cs[burnin_idx:],
color="k", alpha=alpha, lw=lw)
if symbols:
axes[i].set_ylabel(symbols[i])
else:
axes[0].ticklabel_format(useOffset=False, axis='y')
cs = chain[:, :, temp].T
if burnin_idx:
axes[0].plot(idxs[:burnin_idx], cs[:burnin_idx],
color="r", alpha=alpha, lw=lw)
axes[0].plot(idxs[burnin_idx:], cs[burnin_idx:], color="k",
alpha=alpha, lw=lw)
if symbols:
axes[0].set_ylabel(symbols[0])
if len(axes) == ndim:
axes.append(fig.add_subplot(ndim+1, 1, ndim+1))
if plot_det_stat:
lnl = sampler.lnlikelihood[temp, :, :]
if burnin_idx and add_det_stat_burnin:
burn_in_vals = lnl[:, :burnin_idx].flatten()
axes[-1].hist(burn_in_vals[~np.isnan(burn_in_vals)], bins=50,
histtype='step', color='r')
else:
burn_in_vals = []
prod_vals = lnl[:, burnin_idx:].flatten()
axes[-1].hist(prod_vals[~np.isnan(prod_vals)], bins=50,
histtype='step', color='k')
if self.BSGL:
axes[-1].set_xlabel(r'$\mathcal{B}_\mathrm{S/GL}$')
else:
axes[-1].set_xlabel(r'$\widetilde{2\mathcal{F}}$')
axes[-1].set_ylabel(r'$\textrm{Counts}$')
combined_vals = np.append(burn_in_vals, prod_vals)
if len(combined_vals) > 0:
minv = np.min(combined_vals)
maxv = np.max(combined_vals)
Range = abs(maxv-minv)
axes[-1].set_xlim(minv-0.1*Range, maxv+0.1*Range)
axes[-2].set_xlabel(r'$\textrm{Number of steps}$', labelpad=0.1)
return fig, axes
def apply_corrections_to_p0(self, p0):
""" Apply any correction to the initial p0 values """
return p0
def generate_scattered_p0(self, p):
""" Generate a set of p0s scattered about p """
p0 = [[p + self.scatter_val * p * np.random.randn(self.ndim)
for i in xrange(self.nwalkers)]
for j in xrange(self.ntemps)]
return p0
def generate_initial_p0(self):
""" Generate a set of init vals for the walkers """
if type(self.theta_initial) == dict:
logging.info('Generate initial values from initial dictionary')
if hasattr(self, 'nglitch') and self.nglitch > 1:
raise ValueError('Initial dict not implemented for nglitch>1')
p0 = [[[self.generate_rv(**self.theta_initial[key])
for key in self.theta_keys]
for i in range(self.nwalkers)]
for j in range(self.ntemps)]
elif type(self.theta_initial) == list:
logging.info('Generate initial values from list of theta_initial')
p0 = [[[self.generate_rv(**val)
for val in self.theta_initial]
for i in range(self.nwalkers)]
for j in range(self.ntemps)]
elif self.theta_initial is None:
logging.info('Generate initial values from prior dictionary')
p0 = [[[self.generate_rv(**self.theta_prior[key])
for key in self.theta_keys]
for i in range(self.nwalkers)]
for j in range(self.ntemps)]
elif len(self.theta_initial) == self.ndim:
p0 = self.generate_scattered_p0(self.theta_initial)
else:
raise ValueError('theta_initial not understood')
return p0
def get_new_p0(self, sampler):
""" Returns new initial positions for walkers are burn0 stage
This returns new positions for all walkers by scattering points about
the maximum posterior with scale `scatter_val`.
"""
temp_idx = 0
pF = sampler.chain[temp_idx, :, :, :]
lnl = sampler.lnlikelihood[temp_idx, :, :]
lnp = sampler.lnprobability[temp_idx, :, :]
# General warnings about the state of lnp
if np.any(np.isnan(lnp)):
logging.warning(
"Of {} lnprobs {} are nan".format(
np.shape(lnp), np.sum(np.isnan(lnp))))
if np.any(np.isposinf(lnp)):
logging.warning(
"Of {} lnprobs {} are +np.inf".format(
np.shape(lnp), np.sum(np.isposinf(lnp))))
if np.any(np.isneginf(lnp)):
logging.warning(
"Of {} lnprobs {} are -np.inf".format(
np.shape(lnp), np.sum(np.isneginf(lnp))))
lnp_finite = copy.copy(lnp)
lnp_finite[np.isinf(lnp)] = np.nan
idx = np.unravel_index(np.nanargmax(lnp_finite), lnp_finite.shape)
p = pF[idx]
p0 = self.generate_scattered_p0(p)
self.search.BSGL = False
twoF = self.logl(p, self.search)
self.search.BSGL = self.BSGL
logging.info(('Gen. new p0 from pos {} which had det. stat.={:2.1f},'
' twoF={:2.1f} and lnp={:2.1f}')
.format(idx[1], lnl[idx], twoF, lnp_finite[idx]))
return p0
def get_save_data_dictionary(self):
d = dict(nsteps=self.nsteps, nwalkers=self.nwalkers,
ntemps=self.ntemps, theta_keys=self.theta_keys,
theta_prior=self.theta_prior, scatter_val=self.scatter_val,
log10temperature_min=self.log10temperature_min,
BSGL=self.BSGL)
return d
def save_data(self, sampler, samples, lnprobs, lnlikes):
d = self.get_save_data_dictionary()
d['sampler'] = sampler
d['samples'] = samples
d['lnprobs'] = lnprobs
d['lnlikes'] = lnlikes
if os.path.isfile(self.pickle_path):
logging.info('Saving backup of {} as {}.old'.format(
self.pickle_path, self.pickle_path))
os.rename(self.pickle_path, self.pickle_path+".old")
with open(self.pickle_path, "wb") as File:
pickle.dump(d, File)
def get_list_of_matching_sfts(self):
matches = glob.glob(self.sftfilepath)
if len(matches) > 0:
return matches
else:
raise IOError('No sfts found matching {}'.format(
self.sftfilepath))
def get_saved_data(self):
with open(self.pickle_path, "r") as File:
d = pickle.load(File)
return d
def check_old_data_is_okay_to_use(self):
if args.use_old_data:
logging.info("Forcing use of old data")
return True
if os.path.isfile(self.pickle_path) is False:
logging.info('No pickled data found')
return False
oldest_sft = min([os.path.getmtime(f) for f in
self.get_list_of_matching_sfts()])
if os.path.getmtime(self.pickle_path) < oldest_sft:
logging.info('Pickled data outdates sft files')
return False
old_d = self.get_saved_data().copy()
new_d = self.get_save_data_dictionary().copy()
old_d.pop('samples')
old_d.pop('sampler')
old_d.pop('lnprobs')
old_d.pop('lnlikes')
mod_keys = []
for key in new_d.keys():
if key in old_d:
if new_d[key] != old_d[key]:
mod_keys.append((key, old_d[key], new_d[key]))
else:
raise ValueError('Keys {} not in old dictionary'.format(key))
if len(mod_keys) == 0:
return True
else:
logging.warning("Saved data differs from requested")
logging.info("Differences found in following keys:")
for key in mod_keys:
if len(key) == 3:
if np.isscalar(key[1]) or key[0] == 'nsteps':
logging.info(" {} : {} -> {}".format(*key))
else:
logging.info(" " + key[0])
else:
logging.info(key)
return False
def get_max_twoF(self, threshold=0.05):
""" Returns the max likelihood sample and the corresponding 2F value
Note: the sample is returned as a dictionary along with an estimate of
the standard deviation calculated from the std of all samples with a
twoF within `threshold` (relative) to the max twoF
"""
if any(np.isposinf(self.lnlikes)):
logging.info('twoF values contain positive infinite values')
if any(np.isneginf(self.lnlikes)):
logging.info('twoF values contain negative infinite values')
if any(np.isnan(self.lnlikes)):
logging.info('twoF values contain nan')
idxs = np.isfinite(self.lnlikes)
jmax = np.nanargmax(self.lnlikes[idxs])
maxlogl = self.lnlikes[jmax]
d = OrderedDict()
if self.BSGL:
if hasattr(self, 'search') is False:
self.inititate_search_object()
p = self.samples[jmax]
self.search.BSGL = False
maxtwoF = self.logl(p, self.search)
self.search.BSGL = self.BSGL
else:
maxtwoF = maxlogl
repeats = []
for i, k in enumerate(self.theta_keys):
if k in d and k not in repeats:
d[k+'_0'] = d[k] # relabel the old key
d.pop(k)
repeats.append(k)
if k in repeats:
k = k + '_0'
count = 1
while k in d:
k = k.replace('_{}'.format(count-1), '_{}'.format(count))
count += 1
d[k] = self.samples[jmax][i]
return d, maxtwoF
def get_median_stds(self):
""" Returns a dict of the median and std of all production samples """
d = OrderedDict()
repeats = []
for s, k in zip(self.samples.T, self.theta_keys):
if k in d and k not in repeats:
d[k+'_0'] = d[k] # relabel the old key
d[k+'_0_std'] = d[k+'_std']
d.pop(k)
d.pop(k+'_std')
repeats.append(k)
if k in repeats:
k = k + '_0'
count = 1
while k in d:
k = k.replace('_{}'.format(count-1), '_{}'.format(count))
count += 1
d[k] = np.median(s)
d[k+'_std'] = np.std(s)
return d
def write_par(self, method='med'):
""" Writes a .par of the best-fit params with an estimated std """
logging.info('Writing {}/{}.par using the {} method'.format(
self.outdir, self.label, method))
median_std_d = self.get_median_stds()
max_twoF_d, max_twoF = self.get_max_twoF()
logging.info('Writing par file with max twoF = {}'.format(max_twoF))
filename = '{}/{}.par'.format(self.outdir, self.label)
with open(filename, 'w+') as f:
f.write('MaxtwoF = {}\n'.format(max_twoF))
f.write('tref = {}\n'.format(self.tref))
if hasattr(self, 'theta0_index'):
f.write('theta0_index = {}\n'.format(self.theta0_idx))
if method == 'med':
for key, val in median_std_d.iteritems():
f.write('{} = {:1.16e}\n'.format(key, val))
if method == 'twoFmax':
for key, val in max_twoF_d.iteritems():
f.write('{} = {:1.16e}\n'.format(key, val))
def print_summary(self):
max_twoFd, max_twoF = self.get_max_twoF()
median_std_d = self.get_median_stds()
print('\nSummary:')
if hasattr(self, 'theta0_idx'):
print('theta0 index: {}'.format(self.theta0_idx))
print('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')
for k in np.sort(median_std_d.keys()):
if 'std' not in k:
print(' {:10s} = {:1.9e} +/- {:1.9e}'.format(
k, median_std_d[k], median_std_d[k+'_std']))
def CF_twoFmax(self, theta, twoFmax, ntrials):
Fmax = twoFmax/2.0
return (np.exp(1j*theta*twoFmax)*ntrials/2.0
* Fmax*np.exp(-Fmax)*(1-(1+Fmax)*np.exp(-Fmax))**(ntrials-1))
def pdf_twoFhat(self, twoFhat, nglitch, ntrials, twoFmax=100, dtwoF=0.1):
if np.ndim(ntrials) == 0:
ntrials = np.zeros(nglitch+1) + ntrials
twoFmax_int = np.arange(0, twoFmax, dtwoF)
theta_int = np.arange(-1/dtwoF, 1./dtwoF, 1./twoFmax)
CF_twoFmax_theta = np.array(
[[np.trapz(self.CF_twoFmax(t, twoFmax_int, ntrial), twoFmax_int)
for t in theta_int]
for ntrial in ntrials])
CF_twoFhat_theta = np.prod(CF_twoFmax_theta, axis=0)
pdf = (1/(2*np.pi)) * np.array(
[np.trapz(np.exp(-1j*theta_int*twoFhat_val)
* CF_twoFhat_theta, theta_int) for twoFhat_val in twoFhat])
return pdf.real
def p_val_twoFhat(self, twoFhat, ntrials, twoFhatmax=500, Npoints=1000):
""" Caluculate the p-value for the given twoFhat in Gaussian noise
Parameters
----------
twoFhat: float
The observed twoFhat value
ntrials: int, array of len Nglitch+1
The number of trials for each glitch+1
"""
twoFhats = np.linspace(twoFhat, twoFhatmax, Npoints)
pdf = self.pdf_twoFhat(twoFhats, self.nglitch, ntrials)
return np.trapz(pdf, twoFhats)
def get_p_value(self, delta_F0, time_trials=0):
""" Get's the p-value for the maximum twoFhat value """
d, max_twoF = self.get_max_twoF()
if self.nglitch == 1:
tglitches = [d['tglitch']]
else:
tglitches = [d['tglitch_{}'.format(i)] for i in range(self.nglitch)]
tboundaries = [self.tstart] + tglitches + [self.tend]
deltaTs = np.diff(tboundaries)
ntrials = [time_trials + delta_F0 * dT for dT in deltaTs]
p_val = self.p_val_twoFhat(max_twoF, ntrials)
print('p-value = {}'.format(p_val))
return p_val
def compute_evidence(self):
""" Computes the evidence/marginal likelihood for the model """
fburnin = float(self.nsteps[-2])/np.sum(self.nsteps[-2:])
lnev, lnev_err = self.sampler.thermodynamic_integration_log_evidence(
fburnin=fburnin)
log10evidence = lnev/np.log(10)
log10evidence_err = lnev_err/np.log(10)
betas = self.betas
alllnlikes = self.sampler.lnlikelihood[:, :, self.nsteps[-2]:]
mean_lnlikes = np.mean(np.mean(alllnlikes, axis=1), axis=1)
mean_lnlikes = mean_lnlikes[::-1]
betas = betas[::-1]
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 8))
if any(np.isinf(mean_lnlikes)):
print("WARNING mean_lnlikes contains inf: recalculating without"
" the {} infs".format(len(betas[np.isinf(mean_lnlikes)])))
idxs = np.isinf(mean_lnlikes)
mean_lnlikes = mean_lnlikes[~idxs]
betas = betas[~idxs]
log10evidence = np.trapz(mean_lnlikes, betas)/np.log(10)
z1 = np.trapz(mean_lnlikes, betas)
z2 = np.trapz(mean_lnlikes[::-1][::2][::-1],
betas[::-1][::2][::-1])
log10evidence_err = np.abs(z1 - z2) / np.log(10)
ax1.semilogx(betas, mean_lnlikes, "-o")
ax1.set_xlabel(r"$\beta$")
ax1.set_ylabel(r"$\langle \log(\mathcal{L}) \rangle$")
print("log10 evidence for {} = {} +/- {}".format(
self.label, log10evidence, log10evidence_err))
min_betas = []
evidence = []
for i in range(len(betas)/2):
min_betas.append(betas[i])
lnZ = np.trapz(mean_lnlikes[i:], betas[i:])
evidence.append(lnZ/np.log(10))
ax2.semilogx(min_betas, evidence, "-o")
ax2.set_ylabel(r"$\int_{\beta_{\textrm{Min}}}^{\beta=1}" +
r"\langle \log(\mathcal{L})\rangle d\beta$", size=16)
ax2.set_xlabel(r"$\beta_{\textrm{min}}$")
plt.tight_layout()
fig.savefig("{}/{}_beta_lnl.png".format(self.outdir, self.label))
class MCMCGlitchSearch(MCMCSearch):
""" MCMC search using the SemiCoherentGlitchSearch """
@initializer
def __init__(self, label, outdir, sftfilepath, theta_prior, tref,
tstart, tend, nglitch=1, nsteps=[100, 100, 100], nwalkers=100,
ntemps=1, log10temperature_min=-5, theta_initial=None,
scatter_val=1e-10, dtglitchmin=1*86400, theta0_idx=0,
detector=None, BSGL=False,
minCoverFreq=None, maxCoverFreq=None, earth_ephem=None,
sun_ephem=None):
"""
Parameters
label, outdir: str
A label and directory to read/write data from/to
_ sftfilepath: str
File patern to match SFTs
theta_prior: dict
Dictionary of priors and fixed values for the search parameters.
For each parameters (key of the dict), if it is to be held fixed
the value should be the constant float, if it is be searched, the
value should be a dictionary of the prior.
theta_initial: dict, array, (None)
Either a dictionary of distribution about which to distribute the
initial walkers about, an array (from which the walkers will be
scattered by scatter_val), or None in which case the prior is used.
scatter_val, float or ndim array
Size of scatter to use about the initialisation step, if given as
an array it must be of length ndim and the order is given by
theta_keys
nglitch: int
The number of glitches to allow
tref, tstart, tend: int
GPS seconds of the reference time, start time and end time
nsteps: list (m,)
List specifying the number of steps to take, the last two entries
give the nburn and nprod of the 'production' run, all entries
before are for iterative initialisation steps (usually just one)
e.g. [1000, 1000, 500].
dtglitchmin: int
The minimum duration (in seconds) of a segment between two glitches
or a glitch and the start/end of the data
nwalkers, ntemps: int,
The number of walkers and temperates to use in the parallel
tempered PTSampler.
log10temperature_min float < 0
The log_10(tmin) value, the set of betas passed to PTSampler are
generated from np.logspace(0, log10temperature_min, ntemps).
theta0_idx, int
Index (zero-based) of which segment the theta refers to - uyseful
if providing a tight prior on theta to allow the signal to jump
too theta (and not just from)
detector: str
Two character reference to the data to use, specify None for no
contraint.
minCoverFreq, maxCoverFreq: float
Minimum and maximum instantaneous frequency which will be covered
over the SFT time span as passed to CreateFstatInput
earth_ephem, sun_ephem: str
Paths of the two files containing positions of Earth and Sun,
respectively at evenly spaced times, as passed to CreateFstatInput
If None defaults defined in BaseSearchClass will be used
"""
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self.add_log_file()
logging.info(('Set-up MCMC glitch search with {} glitches for model {}'
' on data {}').format(self.nglitch, self.label,
self.sftfilepath))
self.minStartTime = tstart
self.maxStartTime = tend
self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
self.unpack_input_theta()
self.ndim = len(self.theta_keys)
if self.log10temperature_min:
self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
else:
self.betas = None
if earth_ephem is None:
self.earth_ephem = self.earth_ephem_default
if sun_ephem is None:
self.sun_ephem = self.sun_ephem_default
if args.clean and os.path.isfile(self.pickle_path):
os.rename(self.pickle_path, self.pickle_path+".old")
self.old_data_is_okay_to_use = self.check_old_data_is_okay_to_use()
self.log_input()
def inititate_search_object(self):
logging.info('Setting up search object')
self.search = SemiCoherentGlitchSearch(
label=self.label, outdir=self.outdir, sftfilepath=self.sftfilepath,
tref=self.tref, tstart=self.tstart,
tend=self.tend, minCoverFreq=self.minCoverFreq,
maxCoverFreq=self.maxCoverFreq, earth_ephem=self.earth_ephem,
sun_ephem=self.sun_ephem, detector=self.detector, BSGL=self.BSGL,
nglitch=self.nglitch, theta0_idx=self.theta0_idx,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime)
def logp(self, theta_vals, theta_prior, theta_keys, search):
if self.nglitch > 1:
ts = [self.tstart] + list(theta_vals[-self.nglitch:]) + [self.tend]
if np.array_equal(ts, np.sort(ts)) is False:
return -np.inf
if any(np.diff(ts) < self.dtglitchmin):
return -np.inf
H = [self.generic_lnprior(**theta_prior[key])(p) for p, key in
zip(theta_vals, theta_keys)]
return np.sum(H)
def logl(self, theta, search):
if self.nglitch > 1:
ts = [self.tstart] + list(theta[-self.nglitch:]) + [self.tend]
if np.array_equal(ts, np.sort(ts)) is False:
return -np.inf
for j, theta_i in enumerate(self.theta_idxs):
self.fixed_theta[theta_i] = theta[j]
FS = search.compute_nglitch_fstat(*self.fixed_theta)
return FS
def unpack_input_theta(self):
glitch_keys = ['delta_F0', 'delta_F1', 'tglitch']
full_glitch_keys = list(np.array(
[[gk]*self.nglitch for gk in glitch_keys]).flatten())
if 'tglitch_0' in self.theta_prior:
full_glitch_keys[-self.nglitch:] = [
'tglitch_{}'.format(i) for i in range(self.nglitch)]
full_glitch_keys[-2*self.nglitch:-1*self.nglitch] = [
'delta_F1_{}'.format(i) for i in range(self.nglitch)]
full_glitch_keys[-4*self.nglitch:-2*self.nglitch] = [
'delta_F0_{}'.format(i) for i in range(self.nglitch)]
full_theta_keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta']+full_glitch_keys
full_theta_keys_copy = copy.copy(full_theta_keys)
glitch_symbols = ['$\delta f$', '$\delta \dot{f}$', r'$t_{glitch}$']
full_glitch_symbols = list(np.array(
[[gs]*self.nglitch for gs in glitch_symbols]).flatten())
full_theta_symbols = (['$f$', '$\dot{f}$', '$\ddot{f}$', r'$\alpha$',
r'$\delta$'] + full_glitch_symbols)
self.theta_keys = []
fixed_theta_dict = {}
for key, val in self.theta_prior.iteritems():
if type(val) is dict:
fixed_theta_dict[key] = 0
if key in glitch_keys:
for i in range(self.nglitch):
self.theta_keys.append(key)
else:
self.theta_keys.append(key)
elif type(val) in [float, int, np.float64]:
fixed_theta_dict[key] = val
else:
raise ValueError(
'Type {} of {} in theta not recognised'.format(
type(val), key))
if key in glitch_keys:
for i in range(self.nglitch):
full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
else:
full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
if len(full_theta_keys_copy) > 0:
raise ValueError(('Input dictionary `theta` is missing the'
'following keys: {}').format(
full_theta_keys_copy))
self.fixed_theta = [fixed_theta_dict[key] for key in full_theta_keys]
self.theta_idxs = [full_theta_keys.index(k) for k in self.theta_keys]
self.theta_symbols = [full_theta_symbols[i] for i in self.theta_idxs]
idxs = np.argsort(self.theta_idxs)
self.theta_idxs = [self.theta_idxs[i] for i in idxs]
self.theta_symbols = [self.theta_symbols[i] for i in idxs]
self.theta_keys = [self.theta_keys[i] for i in idxs]
# Correct for number of glitches in the idxs
self.theta_idxs = np.array(self.theta_idxs)
while np.sum(self.theta_idxs[:-1] == self.theta_idxs[1:]) > 0:
for i, idx in enumerate(self.theta_idxs):
if idx in self.theta_idxs[:i]:
self.theta_idxs[i] += 1
def get_save_data_dictionary(self):
d = dict(nsteps=self.nsteps, nwalkers=self.nwalkers,
ntemps=self.ntemps, theta_keys=self.theta_keys,
theta_prior=self.theta_prior, scatter_val=self.scatter_val,
log10temperature_min=self.log10temperature_min,
theta0_idx=self.theta0_idx, BSGL=self.BSGL)
return d
def apply_corrections_to_p0(self, p0):
p0 = np.array(p0)
if self.nglitch > 1:
p0[:, :, -self.nglitch:] = np.sort(p0[:, :, -self.nglitch:],
axis=2)
return p0
def plot_cumulative_max(self):
fig, ax = plt.subplots()
d, maxtwoF = self.get_max_twoF()
for key, val in self.theta_prior.iteritems():
if key not in d:
d[key] = val
if self.nglitch > 1:
delta_F0s = [d['delta_F0_{}'.format(i)] for i in
range(self.nglitch)]
delta_F0s.insert(self.theta0_idx, 0)
delta_F0s = np.array(delta_F0s)
delta_F0s[:self.theta0_idx] *= -1
tglitches = [d['tglitch_{}'.format(i)] for i in
range(self.nglitch)]
elif self.nglitch == 1:
delta_F0s = [d['delta_F0']]
delta_F0s.insert(self.theta0_idx, 0)
delta_F0s = np.array(delta_F0s)
delta_F0s[:self.theta0_idx] *= -1
tglitches = [d['tglitch']]
tboundaries = [self.tstart] + tglitches + [self.tend]
for j in range(self.nglitch+1):
ts = tboundaries[j]
te = tboundaries[j+1]
if (te - ts)/86400 < 5:
logging.info('Period too short to perform cumulative search')
continue
if j < self.theta0_idx:
summed_deltaF0 = np.sum(delta_F0s[j:self.theta0_idx])
F0_j = d['F0'] - summed_deltaF0
taus, twoFs = self.search.calculate_twoF_cumulative(
F0_j, F1=d['F1'], F2=d['F2'], Alpha=d['Alpha'],
Delta=d['Delta'], tstart=ts, tend=te)
elif j >= self.theta0_idx:
summed_deltaF0 = np.sum(delta_F0s[self.theta0_idx:j+1])
F0_j = d['F0'] + summed_deltaF0
taus, twoFs = self.search.calculate_twoF_cumulative(
F0_j, F1=d['F1'], F2=d['F2'], Alpha=d['Alpha'],
Delta=d['Delta'], tstart=ts, tend=te)
ax.plot(ts+taus, twoFs)
ax.set_xlabel('GPS time')
fig.savefig('{}/{}_twoFcumulative.png'.format(self.outdir, self.label))
class MCMCSemiCoherentSearch(MCMCSearch):
""" MCMC search for a signal using the semi-coherent ComputeFstat """
@initializer
def __init__(self, label, outdir, sftfilepath, theta_prior, tref,
nsegs=None, nsteps=[100, 100, 100], nwalkers=100, binary=False,
ntemps=1, log10temperature_min=-5, theta_initial=None,
scatter_val=1e-10, detector=None, BSGL=False,
minStartTime=None, maxStartTime=None, minCoverFreq=None,
maxCoverFreq=None, earth_ephem=None, sun_ephem=None,
injectSources=None):
"""
"""
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self.add_log_file()
logging.info(('Set-up MCMC semi-coherent search for model {} on data'
'{}').format(
self.label, self.sftfilepath))
self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
self.unpack_input_theta()
self.ndim = len(self.theta_keys)
if self.log10temperature_min:
self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
else:
self.betas = None
if earth_ephem is None:
self.earth_ephem = self.earth_ephem_default
if sun_ephem is None:
self.sun_ephem = self.sun_ephem_default
if args.clean and os.path.isfile(self.pickle_path):
os.rename(self.pickle_path, self.pickle_path+".old")
self.log_input()
def inititate_search_object(self):
logging.info('Setting up search object')
self.search = SemiCoherentSearch(
label=self.label, outdir=self.outdir, tref=self.tref,
nsegs=self.nsegs, sftfilepath=self.sftfilepath, binary=self.binary,
BSGL=self.BSGL, minStartTime=self.minStartTime,
maxStartTime=self.maxStartTime, minCoverFreq=self.minCoverFreq,
maxCoverFreq=self.maxCoverFreq, detector=self.detector,
earth_ephem=self.earth_ephem, sun_ephem=self.sun_ephem,
injectSources=self.injectSources)
def logp(self, theta_vals, theta_prior, theta_keys, search):
H = [self.generic_lnprior(**theta_prior[key])(p) for p, key in
zip(theta_vals, theta_keys)]
return np.sum(H)
def logl(self, theta, search):
for j, theta_i in enumerate(self.theta_idxs):
self.fixed_theta[theta_i] = theta[j]
FS = search.run_semi_coherent_computefstatistic_single_point(
*self.fixed_theta)
return FS
class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
""" A follow up procudure increasing the coherence time in a zoom """
def get_save_data_dictionary(self):
d = dict(nwalkers=self.nwalkers, ntemps=self.ntemps,
theta_keys=self.theta_keys, theta_prior=self.theta_prior,
scatter_val=self.scatter_val,
log10temperature_min=self.log10temperature_min,
BSGL=self.BSGL, run_setup=self.run_setup)
return d
def get_width_from_prior(self, prior, key):
if prior[key]['type'] == 'unif':
return prior[key]['upper'] - prior[key]['lower']
def get_mid_from_prior(self, prior, key):
if prior[key]['type'] == 'unif':
return .5*(prior[key]['upper'] + prior[key]['lower'])
def get_number_of_templates_estimate(self, nsegs):
""" Returns V, Vsky, Vf estimated from the super-sky metric """
tboundaries = np.linspace(self.minStartTime, self.maxStartTime,
nsegs+1)
if 'Alpha' in self.theta_keys:
DeltaAlpha = self.get_width_from_prior(self.theta_prior, 'Alpha')
DeltaDelta = self.get_width_from_prior(self.theta_prior, 'Delta')
DeltaMid = self.get_mid_from_prior(self.theta_prior, 'Delta')
DeltaOmega = np.sin(DeltaMid) * DeltaDelta * DeltaAlpha
if 'F0' in self.theta_keys:
DeltaF0 = self.get_width_from_prior(self.theta_prior, 'F0')
else:
DeltaF0 = 1
if 'F1' in self.theta_keys:
DeltaF1 = self.get_width_from_prior(self.theta_prior, 'F1')
spindowns = 1
else:
DeltaF1 = 1
spindowns = 0
ref_time = lal.LIGOTimeGPS(self.tref)
segments = lal.SegListCreate()
for j in range(len(tboundaries)-1):
seg = lal.SegCreate(lal.LIGOTimeGPS(tboundaries[j]),
lal.LIGOTimeGPS(tboundaries[j+1]),
j)
lal.SegListAppend(segments, seg)
if type(self.theta_prior['F0']) == dict:
fiducial_freq = self.get_mid_from_prior(self.theta_prior, 'F0')
else:
fiducial_freq = self.theta_prior['F0']
detector_names = self.search.names
detNames = lal.CreateStringVector(*detector_names)
detectors = lalpulsar.MultiLALDetector()
lalpulsar.ParseMultiLALDetector(detectors, detNames)
detector_weights = None
detector_motion = (lalpulsar.DETMOTION_SPIN
+ lalpulsar.DETMOTION_ORBIT)
ephemeris = lalpulsar.InitBarycenter(self.earth_ephem,
self.sun_ephem)
try:
SSkyMetric = lalpulsar.ComputeSuperskyMetrics(
spindowns, ref_time, segments, fiducial_freq, detectors,
detector_weights, detector_motion, ephemeris)
sqrtdetG_SKY = np.sqrt(np.linalg.det(
SSkyMetric.semi_rssky_metric.data[:2, :2]))
sqrtdetG_F = np.sqrt(np.linalg.det(
SSkyMetric.semi_rssky_metric.data[2:, 2:]))
return (.5*sqrtdetG_SKY*sqrtdetG_F*DeltaOmega*DeltaF1*DeltaF0,
.5*sqrtdetG_SKY*DeltaOmega,
sqrtdetG_F*DeltaF1*DeltaF0)
except RuntimeError:
return 'N/A'
elif self.theta_keys == ['F0', 'F1']:
return 'N/A'
def init_run_setup(self, run_setup, log_table=True, gen_tex_table=True):
logging.info('Calculating the number of templates for this setup..')
number_of_templates = []
for i, rs in enumerate(run_setup):
rs = list(rs)
if len(rs) == 2:
rs.append(False)
if np.shape(rs[0]) == ():
rs[0] = (rs[0], 0)
run_setup[i] = rs
number_of_templates.append(
self.get_number_of_templates_estimate(rs[1]))
if log_table:
logging.info('Using run-setup as follow:')
logging.info('Stage | nburn | nprod | nsegs | resetp0 |'
'# templates = # sky x # Freq')
for i, rs in enumerate(run_setup):
if number_of_templates[i] != 'N/A':
vtext = '{:1.0e} = {:1.0e} x {:1.0e}'.format(
*number_of_templates[i])
else:
vtext = number_of_templates[i]
logging.info('{} | {} | {} | {} | {} | {}'.format(
str(i).ljust(5), str(rs[0][0]).ljust(5),
str(rs[0][1]).ljust(5), str(rs[1]).ljust(5),
str(rs[2]).ljust(7), vtext))
if gen_tex_table:
filename = '{}/{}_run_setup.tex'.format(self.outdir, self.label)
with open(filename, 'w+') as f:
f.write(r'\begin{tabular}{c|cccccc}' + '\n')
f.write(r'Stage & $\Nseg$ & $\Tcoh^{\rm days}$ &'
r'$\Nsteps$ & $\V$ & $\Vsky$ & $\Vf$ \\ \hline'
'\n')
for i, rs in enumerate(run_setup):
Tcoh = (self.maxStartTime - self.minStartTime) / rs[1] / 86400
line = r'{} & {} & {} & {} & {} & {} & {} \\' + '\n'
if number_of_templates[i] == 'N/A':
V = Vsky = Vf = 'N/A'
else:
V, Vsky, Vf = number_of_templates[i]
if rs[0][-1] == 0:
nsteps = rs[0][0]
else:
nsteps = '{},{}'.format(*rs[0])
line = line.format(i, rs[1], Tcoh, nsteps,
texify_float(V), texify_float(Vsky),
texify_float(Vf))
f.write(line)
f.write(r'\end{tabular}' + '\n')
if args.setup_only:
logging.info("Exit as requested by setup_only flag")
sys.exit()
else:
return run_setup
def run(self, run_setup, proposal_scale_factor=2, **kwargs):
""" Run the follow-up with the given run_setup
Parameters
----------
run_setup: list of tuples
"""
self.nsegs = 1
self.inititate_search_object()
self.run_setup = self.init_run_setup(run_setup)
self.old_data_is_okay_to_use = self.check_old_data_is_okay_to_use()
if self.old_data_is_okay_to_use is True:
logging.warning('Using saved data from {}'.format(
self.pickle_path))
d = self.get_saved_data()
self.sampler = d['sampler']
self.samples = d['samples']
self.lnprobs = d['lnprobs']
self.lnlikes = d['lnlikes']
self.nsegs = run_setup[-1][1]
return
fig = None
axes = None
nsteps_total = 0
for j, ((nburn, nprod), nseg, reset_p0) in enumerate(run_setup):
if j == 0:
p0 = self.generate_initial_p0()
p0 = self.apply_corrections_to_p0(p0)
elif reset_p0:
p0 = self.get_new_p0(sampler)
p0 = self.apply_corrections_to_p0(p0)
# self.check_initial_points(p0)
else:
p0 = sampler.chain[:, :, -1, :]
self.nsegs = nseg
self.search.nsegs = nseg
self.search.init_semicoherent_parameters()
sampler = emcee.PTSampler(
self.ntemps, self.nwalkers, self.ndim, self.logl, self.logp,
logpargs=(self.theta_prior, self.theta_keys, self.search),
loglargs=(self.search,), betas=self.betas,
a=proposal_scale_factor)
Tcoh = (self.maxStartTime-self.minStartTime)/nseg/86400.
logging.info(('Running {}/{} with {} steps and {} nsegs '
'(Tcoh={:1.2f} days)').format(
j+1, len(run_setup), (nburn, nprod), nseg, Tcoh))
sampler = self.run_sampler_with_progress_bar(
sampler, nburn+nprod, p0)
logging.info("Mean acceptance fraction: {}"
.format(np.mean(sampler.acceptance_fraction, axis=1)))
if self.ntemps > 1:
logging.info("Tswap acceptance fraction: {}"
.format(sampler.tswap_acceptance_fraction))
logging.info('Max detection statistic of run was {}'.format(
np.max(sampler.lnlikelihood)))
fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols,
fig=fig, axes=axes, burnin_idx=nburn,
xoffset=nsteps_total, **kwargs)
#yvals = axes[0].get_ylim()
#axes[0].annotate(
# #r'$T_{{\rm coh}}^{{\rm (days)}}{{=}}{:1.1f}$'.format(Tcoh),
# r'{}'.format(j),
# xy=(nsteps_total, yvals[0]*(1+1e-2*(yvals[1]-yvals[0])/yvals[1])),
# fontsize=6)
for ax in axes[:-1]:
ax.axvline(nsteps_total, color='k', ls='--')
nsteps_total += nburn+nprod
fig.savefig('{}/{}_walkers.png'.format(
self.outdir, self.label), dpi=200)
samples = sampler.chain[0, :, nburn:, :].reshape((-1, self.ndim))
lnprobs = sampler.lnprobability[0, :, nburn:].reshape((-1))
lnlikes = sampler.lnlikelihood[0, :, nburn:].reshape((-1))
self.sampler = sampler
self.samples = samples
self.lnprobs = lnprobs
self.lnlikes = lnlikes
self.save_data(sampler, samples, lnprobs, lnlikes)
class MCMCTransientSearch(MCMCSearch):
""" MCMC search for a transient signal using the ComputeFstat """
def inititate_search_object(self):
logging.info('Setting up search object')
self.search = ComputeFstat(
tref=self.tref, sftfilepath=self.sftfilepath,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
earth_ephem=self.earth_ephem, sun_ephem=self.sun_ephem,
detector=self.detector, transient=True,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
BSGL=self.BSGL, binary=self.binary)
def logl(self, theta, search):
for j, theta_i in enumerate(self.theta_idxs):
self.fixed_theta[theta_i] = theta[j]
if self.fixed_theta[1] < 86400:
return -np.inf
self.fixed_theta[1] += self.fixed_theta[0]
if self.fixed_theta[1] > self.tend:
return -np.inf
FS = search.run_computefstatistic_single_point(*self.fixed_theta)
return FS
def unpack_input_theta(self):
full_theta_keys = ['transient_tstart',
'transient_duration', 'F0', 'F1', 'F2', 'Alpha',
'Delta']
if self.binary:
full_theta_keys += [
'asini', 'period', 'ecc', 'tp', 'argp']
full_theta_keys_copy = copy.copy(full_theta_keys)
full_theta_symbols = [r'$t_{\rm start}$', r'$\Delta T$',
'$f$', '$\dot{f}$', '$\ddot{f}$',
r'$\alpha$', r'$\delta$']
if self.binary:
full_theta_symbols += [
'asini', 'period', 'period', 'ecc', 'tp', 'argp']
self.theta_keys = []
fixed_theta_dict = {}
for key, val in self.theta_prior.iteritems():
if type(val) is dict:
fixed_theta_dict[key] = 0
self.theta_keys.append(key)
elif type(val) in [float, int, np.float64]:
fixed_theta_dict[key] = val
else:
raise ValueError(
'Type {} of {} in theta not recognised'.format(
type(val), key))
full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
if len(full_theta_keys_copy) > 0:
raise ValueError(('Input dictionary `theta` is missing the'
'following keys: {}').format(
full_theta_keys_copy))
self.fixed_theta = [fixed_theta_dict[key] for key in full_theta_keys]
self.theta_idxs = [full_theta_keys.index(k) for k in self.theta_keys]
self.theta_symbols = [full_theta_symbols[i] for i in self.theta_idxs]
idxs = np.argsort(self.theta_idxs)
self.theta_idxs = [self.theta_idxs[i] for i in idxs]
self.theta_symbols = [self.theta_symbols[i] for i in idxs]
self.theta_keys = [self.theta_keys[i] for i in idxs]
class GridSearch(BaseSearchClass):
""" Gridded search using ComputeFstat """
@initializer
def __init__(self, label, outdir, sftfilepath, F0s=[0], F1s=[0], F2s=[0],
Alphas=[0], Deltas=[0], tref=None, tstart=None, tend=None,
BSGL=False, minCoverFreq=None, maxCoverFreq=None,
earth_ephem=None, sun_ephem=None, detector=None):
"""
Parameters
----------
label, outdir: str
A label and directory to read/write data from/to
sftfilepath: str
File patern to match SFTs
F0s, F1s, F2s, delta_F0s, delta_F1s, tglitchs, Alphas, Deltas: tuple
Length 3 tuple describing the grid for each parameter, e.g
[F0min, F0max, dF0], for a fixed value simply give [F0].
tref, tstart, tend: int
GPS seconds of the reference time, start time and end time
For all other parameters, see `pyfstat.ComputeFStat` for details
"""
self.minStartTime = tstart
self.maxStartTime = tend
if earth_ephem is None:
self.earth_ephem = self.earth_ephem_default
if sun_ephem is None:
self.sun_ephem = self.sun_ephem_default
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self.out_file = '{}/{}_gridFS.txt'.format(self.outdir, self.label)
self.keys = ['_', '_', 'F0', 'F1', 'F2', 'Alpha', 'Delta']
def inititate_search_object(self):
logging.info('Setting up search object')
self.search = ComputeFstat(
tref=self.tref, sftfilepath=self.sftfilepath,
minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
earth_ephem=self.earth_ephem, sun_ephem=self.sun_ephem,
detector=self.detector, transient=False,
minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
BSGL=self.BSGL)
def get_array_from_tuple(self, x):
if len(x) == 1:
return np.array(x)
else:
return np.arange(x[0], x[1]*(1+1e-15), x[2])
def get_input_data_array(self):
arrays = []
for tup in ([self.tstart], [self.tend], self.F0s, self.F1s, self.F2s,
self.Alphas, self.Deltas):
arrays.append(self.get_array_from_tuple(tup))
input_data = []
for vals in itertools.product(*arrays):
input_data.append(vals)
self.arrays = arrays
self.input_data = np.array(input_data)
def check_old_data_is_okay_to_use(self):
if args.clean:
return False
if os.path.isfile(self.out_file) is False:
logging.info('No old data found, continuing with grid search')
return False
data = np.atleast_2d(np.genfromtxt(self.out_file, delimiter=' '))
if np.all(data[:, 0:-1] == self.input_data):
logging.info(
'Old data found with matching input, no search performed')
return data
else:
logging.info(
'Old data found, input differs, continuing with grid search')
return False
def run(self, return_data=False):
self.get_input_data_array()
old_data = self.check_old_data_is_okay_to_use()
if old_data is not False:
self.data = old_data
return
self.inititate_search_object()
logging.info('Total number of grid points is {}'.format(
len(self.input_data)))
data = []
for vals in tqdm(self.input_data):
FS = self.search.run_computefstatistic_single_point(*vals)
data.append(list(vals) + [FS])
data = np.array(data)
if return_data:
return data
else:
logging.info('Saving data to {}'.format(self.out_file))
np.savetxt(self.out_file, data, delimiter=' ')
self.data = data
def convert_F0_to_mismatch(self, F0, F0hat, Tseg):
DeltaF0 = F0[1] - F0[0]
m_spacing = (np.pi*Tseg*DeltaF0)**2 / 12.
N = len(F0)
return np.arange(-N*m_spacing/2., N*m_spacing/2., m_spacing)
def convert_F1_to_mismatch(self, F1, F1hat, Tseg):
DeltaF1 = F1[1] - F1[0]
m_spacing = (np.pi*Tseg**2*DeltaF1)**2 / 720.
N = len(F1)
return np.arange(-N*m_spacing/2., N*m_spacing/2., m_spacing)
def add_mismatch_to_ax(self, ax, x, y, xkey, ykey, xhat, yhat, Tseg):
axX = ax.twiny()
axX.zorder = -10
axY = ax.twinx()
axY.zorder = -10
if xkey == 'F0':
m = self.convert_F0_to_mismatch(x, xhat, Tseg)
axX.set_xlim(m[0], m[-1])
if ykey == 'F1':
m = self.convert_F1_to_mismatch(y, yhat, Tseg)
axY.set_ylim(m[0], m[-1])
def plot_1D(self, xkey):
fig, ax = plt.subplots()
xidx = self.keys.index(xkey)
x = np.unique(self.data[:, xidx])
z = self.data[:, -1]
plt.plot(x, z)
fig.savefig('{}/{}_1D.png'.format(self.outdir, self.label))
def plot_2D(self, xkey, ykey, ax=None, save=True, vmin=None, vmax=None,
add_mismatch=None, xN=None, yN=None, flat_keys=[],
rel_flat_idxs=[], flatten_method=np.max,
predicted_twoF=None, cm=None, cbarkwargs={}):
""" Plots a 2D grid of 2F values
Parameters
----------
add_mismatch: tuple (xhat, yhat, Tseg)
If not None, add a secondary axis with the metric mismatch from the
point xhat, yhat with duration Tseg
flatten_method: np.max
Function to use in flattening the flat_keys
"""
if ax is None:
fig, ax = plt.subplots()
xidx = self.keys.index(xkey)
yidx = self.keys.index(ykey)
flat_idxs = [self.keys.index(k) for k in flat_keys]
x = np.unique(self.data[:, xidx])
y = np.unique(self.data[:, yidx])
flat_vals = [np.unique(self.data[:, j]) for j in flat_idxs]
z = self.data[:, -1]
Y, X = np.meshgrid(y, x)
shape = [len(x), len(y)] + [len(v) for v in flat_vals]
Z = z.reshape(shape)
if len(rel_flat_idxs) > 0:
Z = flatten_method(Z, axis=tuple(rel_flat_idxs))
if predicted_twoF:
Z = (predicted_twoF - Z) / (predicted_twoF + 4)
if cm is None:
cm = plt.cm.viridis_r
else:
if cm is None:
cm = plt.cm.viridis
pax = ax.pcolormesh(X, Y, Z, cmap=cm, vmin=vmin, vmax=vmax)
cb = plt.colorbar(pax, ax=ax, **cbarkwargs)
cb.set_label('$2\mathcal{F}$')
if add_mismatch:
self.add_mismatch_to_ax(ax, x, y, xkey, ykey, *add_mismatch)
ax.set_xlim(x[0], x[-1])
ax.set_ylim(y[0], y[-1])
ax.set_xlabel(xkey)
ax.set_ylabel(ykey)
if xN:
ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(xN))
if yN:
ax.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(yN))
if save:
fig.tight_layout()
fig.savefig('{}/{}_2D.png'.format(self.outdir, self.label))
else:
return ax
def get_max_twoF(self):
twoF = self.data[:, -1]
idx = np.argmax(twoF)
v = self.data[idx, :]
d = OrderedDict(tstart=v[0], tend=v[1], F0=v[2], F1=v[3], F2=v[4],
Alpha=v[5], Delta=v[6], twoF=v[7])
return d
def print_max_twoF(self):
d = self.get_max_twoF()
print('Max twoF values for {}:'.format(self.label))
for k, v in d.iteritems():
print(' {}={}'.format(k, v))
class GridGlitchSearch(GridSearch):
""" Grid search using the SemiCoherentGlitchSearch """
@initializer
def __init__(self, label, outdir, sftfilepath=None, F0s=[0],
F1s=[0], F2s=[0], delta_F0s=[0], delta_F1s=[0], tglitchs=None,
Alphas=[0], Deltas=[0], tref=None, tstart=None, tend=None,
minCoverFreq=None, maxCoverFreq=None, write_after=1000,
earth_ephem=None, sun_ephem=None):
"""
Parameters
----------
label, outdir: str
A label and directory to read/write data from/to
sftfilepath: str
File patern to match SFTs
F0s, F1s, F2s, delta_F0s, delta_F1s, tglitchs, Alphas, Deltas: tuple
Length 3 tuple describing the grid for each parameter, e.g
[F0min, F0max, dF0], for a fixed value simply give [F0].
tref, tstart, tend: int
GPS seconds of the reference time, start time and end time
For all other parameters, see pyfstat.ComputeFStat.
"""
if tglitchs is None:
self.tglitchs = [self.tend]
if earth_ephem is None:
self.earth_ephem = self.earth_ephem_default
if sun_ephem is None:
self.sun_ephem = self.sun_ephem_default
self.search = SemiCoherentGlitchSearch(
label=label, outdir=outdir, sftfilepath=self.sftfilepath,
tref=tref, tstart=tstart, tend=tend, minCoverFreq=minCoverFreq,
maxCoverFreq=maxCoverFreq, earth_ephem=self.earth_ephem,
sun_ephem=self.sun_ephem, BSGL=self.BSGL)
if os.path.isdir(outdir) is False:
os.mkdir(outdir)
self.out_file = '{}/{}_gridFS.txt'.format(self.outdir, self.label)
self.keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta', 'delta_F0',
'delta_F1', 'tglitch']
def get_input_data_array(self):
arrays = []
for tup in (self.F0s, self.F1s, self.F2s, self.Alphas, self.Deltas,
self.delta_F0s, self.delta_F1s, self.tglitchs):
arrays.append(self.get_array_from_tuple(tup))
input_data = []
for vals in itertools.product(*arrays):
input_data.append(vals)
self.arrays = arrays
self.input_data = np.array(input_data)
class Writer(BaseSearchClass):
""" Instance object for generating SFTs containing glitch signals """
@initializer
def __init__(self, label='Test', tstart=700000000, duration=100*86400,
dtglitch=None,
delta_phi=0, delta_F0=0, delta_F1=0, delta_F2=0,
tref=None, phi=0, F0=30, F1=1e-10, F2=0, Alpha=5e-3,
Delta=6e-2, h0=0.1, cosi=0.0, psi=0.0, Tsft=1800, outdir=".",
sqrtSX=1, Band=4, detector='H1', data_tstart=None,
data_duration=None):
"""
Parameters
----------
label: string
a human-readable label to be used in naming the output files
tstart, tend : float
start and end times (in gps seconds) of the total observation span
dtglitch: float
time (in gps seconds) of the glitch after tstart. To create data
without a glitch, set dtglitch=tend-tstart or leave as None
delta_phi, delta_F0, delta_F1: float
instanteneous glitch magnitudes in rad, Hz, and Hz/s respectively
tref: float or None
reference time (default is None, which sets the reference time to
tstart)
phil, F0, F1, F2, Alpha, Delta, h0, cosi, psi: float
pre-glitch phase, frequency, sky-position, and signal properties
Tsft: float
the sft duration
data_tstart, data_duration: float
if not None, the total span of data, this can be used to generate
transient signals
see `lalapps_Makefakedata_v5 --help` for help with the other paramaters
"""
for d in self.delta_phi, self.delta_F0, self.delta_F1, self.delta_F2:
if np.size(d) == 1:
d = [d]
self.tend = self.tstart + self.duration
if self.dtglitch is None or self.dtglitch == self.duration:
self.tbounds = [self.tstart, self.tend]
elif np.size(self.dtglitch) == 1:
self.tbounds = [self.tstart, self.tstart+self.dtglitch, self.tend]
else:
self.tglitch = self.tstart + np.array(self.dtglitch)
self.tbounds = [self.tstart] + list(self.tglitch) + [self.tend]
if os.path.isdir(self.outdir) is False:
os.makedirs(self.outdir)
if self.tref is None:
self.tref = self.tstart
self.tend = self.tstart + self.duration
tbs = np.array(self.tbounds)
self.durations_days = (tbs[1:] - tbs[:-1]) / 86400
self.config_file_name = "{}/{}.cff".format(outdir, label)
self.theta = np.array([phi, F0, F1, F2])
self.delta_thetas = np.atleast_2d(
np.array([delta_phi, delta_F0, delta_F1, delta_F2]).T)
numSFTs = int(float(self.duration) / self.Tsft)
self.sftfilename = lalpulsar.OfficialSFTFilename(
'H', '1', numSFTs, self.Tsft, self.tstart, self.duration,
self.label)
self.sftfilepath = '{}/{}'.format(self.outdir, self.sftfilename)
self.calculate_fmin_Band()
def make_data(self):
''' A convienience wrapper to generate a cff file then sfts '''
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 = (
"""[TS{}]
Alpha = {:1.18e}
Delta = {:1.18e}
h0 = {:1.18e}
cosi = {:1.18e}
psi = {:1.18e}
phi0 = {:1.18e}
Freq = {:1.18e}
f1dot = {:1.18e}
f2dot = {:1.18e}
refTime = {:10.6f}
transientWindowType=rect
transientStartTime={:10.3f}
transientTauDays={:1.3f}\n""")
return template.format(i, Alpha, Delta, h0, cosi, psi, phi, F0, F1,
F2, tref, tstart, duration_days)
def make_cff(self):
"""
Generates an .cff file for a 'glitching' signal
"""
thetas = self.calculate_thetas(self.theta, self.delta_thetas,
self.tbounds)
content = ''
for i, (t, d, ts) in enumerate(zip(thetas, self.durations_days,
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)
content += line
if self.check_if_cff_file_needs_rewritting(content):
config_file = open(self.config_file_name, "w+")
config_file.write(content)
config_file.close()
def calculate_fmin_Band(self):
self.fmin = self.F0 - .5 * self.Band
def check_cached_data_okay_to_use(self, cl):
""" Check if cached data exists and, if it does, if it can be used """
getmtime = os.path.getmtime
if os.path.isfile(self.sftfilepath) is False:
logging.info('No SFT file matching {} found'.format(
self.sftfilepath))
return False
else:
logging.info('Matching SFT file found')
if getmtime(self.sftfilepath) < getmtime(self.config_file_name):
logging.info(
('The config file {} has been modified since the sft file {} '
+ 'was created').format(
self.config_file_name, self.sftfilepath))
return False
logging.info(
'The config file {} is older than the sft file {}'.format(
self.config_file_name, self.sftfilepath))
logging.info('Checking contents of cff file')
logging.info('Execute: {}'.format(
'lalapps_SFTdumpheader {} | head -n 20'.format(self.sftfilepath)))
output = subprocess.check_output(
'lalapps_SFTdumpheader {} | head -n 20'.format(self.sftfilepath),
shell=True)
calls = [line for line in output.split('\n') if line[:3] == 'lal']
if calls[0] == cl:
logging.info('Contents matched, use old sft file')
return True
else:
logging.info('Contents unmatched, create new sft file')
return False
def check_if_cff_file_needs_rewritting(self, content):
""" Check if the .cff file has changed
Returns True if the file should be overwritten - where possible avoid
overwriting to allow cached data to be used
"""
if os.path.isfile(self.config_file_name) is False:
logging.info('No config file {} found'.format(
self.config_file_name))
return True
else:
logging.info('Config file {} already exists'.format(
self.config_file_name))
with open(self.config_file_name, 'r') as f:
file_content = f.read()
if file_content == content:
logging.info(
'File contents match, no update of {} required'.format(
self.config_file_name))
return False
else:
logging.info(
'File contents unmatched, updating {}'.format(
self.config_file_name))
return True
def run_makefakedata(self):
""" Generate the sft data from the configuration file """
# Remove old data:
try:
os.unlink("{}/*{}*.sft".format(self.outdir, self.label))
except OSError:
pass
cl = []
cl.append('lalapps_Makefakedata_v5')
cl.append('--outSingleSFT=TRUE')
cl.append('--outSFTdir="{}"'.format(self.outdir))
cl.append('--outLabel="{}"'.format(self.label))
cl.append('--IFOs="{}"'.format(self.detector))
cl.append('--sqrtSX="{}"'.format(self.sqrtSX))
if self.data_tstart is None:
cl.append('--startTime={:10.9f}'.format(float(self.tstart)))
else:
cl.append('--startTime={:10.9f}'.format(float(self.data_tstart)))
if self.data_duration is None:
cl.append('--duration={}'.format(int(self.duration)))
else:
cl.append('--duration={}'.format(int(self.data_duration)))
cl.append('--fmin={}'.format(int(self.fmin)))
cl.append('--Band={}'.format(self.Band))
cl.append('--Tsft={}'.format(self.Tsft))
cl.append('--injectionSources="./{}"'.format(self.config_file_name))
cl = " ".join(cl)
if self.check_cached_data_okay_to_use(cl) is False:
logging.info("Executing: " + cl)
os.system(cl)
os.system('\n')
def predict_fstat(self):
""" Wrapper to lalapps_PredictFstat """
c_l = []
c_l.append("lalapps_PredictFstat")
c_l.append("--h0={}".format(self.h0))
c_l.append("--cosi={}".format(self.cosi))
c_l.append("--psi={}".format(self.psi))
c_l.append("--Alpha={}".format(self.Alpha))
c_l.append("--Delta={}".format(self.Delta))
c_l.append("--Freq={}".format(self.F0))
c_l.append("--DataFiles='{}'".format(
self.outdir+"/*SFT_"+self.label+"*sft"))
c_l.append("--assumeSqrtSX={}".format(self.sqrtSX))
c_l.append("--minStartTime={}".format(self.tstart))
c_l.append("--maxStartTime={}".format(self.tend))
logging.info("Executing: " + " ".join(c_l) + "\n")
output = subprocess.check_output(" ".join(c_l), shell=True)
twoF = float(output.split('\n')[-2])
return float(twoF)