Select Git revision
test_2d_plot.py
core.py 41.65 KiB
""" The core tools used in pyfstat """
import os
import logging
import copy
import glob
import numpy as np
import scipy.special
import scipy.optimize
import lal
import lalpulsar
import helper_functions
# workaround for matplotlib on X-less remote logins
if 'DISPLAY' in os.environ:
import matplotlib.pyplot as plt
else:
logging.info('No $DISPLAY environment variable found, so importing \
matplotlib.pyplot with non-interactive "Agg" backend.')
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
helper_functions.set_up_matplotlib_defaults()
args, tqdm = helper_functions.set_up_command_line_arguments()
earth_ephem, sun_ephem = helper_functions.set_up_ephemeris_configuration()
detector_colors = {'h1': 'C0', 'l1': 'C1'}
class Bunch(object):
""" Turns dictionary into object with attribute-style access
Parameter
---------
dict
Input dictionary
Examples
--------
>>> data = Bunch(dict(x=1, y=[1, 2, 3], z=True))
>>> print(data.x)
1
>>> print(data.y)
[1, 2, 3]
>>> print(data.z)
True
"""
def __init__(self, dictionary):
self.__dict__.update(dictionary)
def read_par(filename=None, label=None, outdir=None, suffix='par',
return_type='dict', comments=['%', '#'], raise_error=False):
""" Read in a .par or .loudest file, returns a dict or Bunch of the data
Parameters
----------
filename : str
Filename (path) containing rows of `key=val` data to read in.
label, outdir, suffix : str, optional
If filename is None, form the file to read as `outdir/label.suffix`.
return_type : {'dict', 'bunch'}, optional
If `dict`, return a dictionary, if 'bunch' return a Bunch
comments : str or list of strings, optional
Characters denoting that a row is a comment.
raise_error : bool, optional
If True, raise an error for lines which are not comments, but cannot
be read.
Notes
-----
This can also be used to read in .loudest files, or any file which has
rows of `key=val` data (in which the val can be understood using eval(val)
Returns
-------
d: Bunch or dict
The par values as either a `Bunch` or dict type
"""
if filename is None:
filename = '{}/{}.{}'.format(outdir, label, suffix)
if os.path.isfile(filename) is False:
raise ValueError("No file {} found".format(filename))
d = {}
with open(filename, 'r') as f:
d = _get_dictionary_from_lines(f, comments, raise_error)
if return_type in ['bunch', 'Bunch']:
return Bunch(d)
elif return_type in ['dict', 'dictionary']:
return d
else:
raise ValueError('return_type {} not understood'.format(return_type))
def _get_dictionary_from_lines(lines, comments, raise_error):
""" Return dictionary of key=val pairs for each line in lines
Parameters
----------
comments : str or list of strings
Characters denoting that a row is a comment.
raise_error : bool
If True, raise an error for lines which are not comments, but cannot
be read.
Returns
-------
d: Bunch or dict
The par values as either a `Bunch` or dict type
"""
d = {}
for line in lines:
if line[0] not in comments and len(line.split('=')) == 2:
try:
key, val = line.rstrip('\n').split('=')
key = key.strip()
d[key] = np.float64(eval(val.rstrip('; ')))
except SyntaxError:
if raise_error:
raise IOError('Line {} not understood'.format(line))
pass
return d
def predict_fstat(h0, cosi, psi, Alpha, Delta, Freq, sftfilepattern,
minStartTime, maxStartTime, IFO=None, assumeSqrtSX=None,
**kwargs):
""" Wrapper to lalapps_PredictFstat
Parameters
----------
h0, cosi, psi, Alpha, Delta, Freq : float
Signal properties, see `lalapps_PredictFstat --help` for more info.
sftfilepattern : str
Pattern matching the sftfiles to use.
minStartTime, maxStartTime : int
IFO : str
See `lalapps_PredictFstat --help`
assumeSqrtSX : float or None
See `lalapps_PredictFstat --help`, if None this option is not used
Returns
-------
twoF_expected, twoF_sigma : float
The expectation and standard deviation of 2F
"""
cl_pfs = []
cl_pfs.append("lalapps_PredictFstat")
cl_pfs.append("--h0={}".format(h0))
cl_pfs.append("--cosi={}".format(cosi))
cl_pfs.append("--psi={}".format(psi))
cl_pfs.append("--Alpha={}".format(Alpha))
cl_pfs.append("--Delta={}".format(Delta))
cl_pfs.append("--Freq={}".format(Freq))
cl_pfs.append("--DataFiles='{}'".format(sftfilepattern))
if assumeSqrtSX:
cl_pfs.append("--assumeSqrtSX={}".format(assumeSqrtSX))
if IFO:
cl_pfs.append("--IFO={}".format(IFO))
cl_pfs.append("--minStartTime={}".format(int(minStartTime)))
cl_pfs.append("--maxStartTime={}".format(int(maxStartTime)))
cl_pfs.append("--outputFstat=/tmp/fs")
cl_pfs = " ".join(cl_pfs)
helper_functions.run_commandline(cl_pfs)
d = read_par(filename='/tmp/fs')
return float(d['twoF_expected']), float(d['twoF_sigma'])
class BaseSearchClass(object):
""" The base search class providing parent methods to other searches
Attributes
----------
earth_ephem_default, sun_ephem_default : str
Paths to the earth and sun ephemeris files to use if not specified
elsewhere
"""
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 : ndarray, shape (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 : ndarray, shape (n,)
Vector of the coefficients as evaluated 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 thetas given delta_thetas, the jumps
This is used when generating data containing glitches or timing noise.
Specifically, the source parameters of the signal are not constant in
time, but jump by `delta_theta` at `tbounds`.
Parameters
----------
theta : array_like
The source parameters of size (n,).
delta_thetas : array_like
The jumps in the source parameters of size (m, n) where m is the
number of jumps.
tbounds : array_like
Time boundaries of the jumps of size (m+2,).
theta0_idx : int
Index of the segment for which the theta are defined.
Returns
-------
ndarray
The set of thetas, shape (m+1, n).
"""
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]))
self.thetas_at_tref = thetas
return thetas
def _get_list_of_matching_sfts(self):
""" Returns a list of sfts matching the attribute sftfilepattern """
sftfilepatternlist = np.atleast_1d(self.sftfilepattern.split(';'))
matches = [glob.glob(p) for p in sftfilepatternlist]
matches = [item for sublist in matches for item in sublist]
if len(matches) > 0:
return matches
else:
raise IOError('No sfts found matching {}'.format(
self.sftfilepattern))
class ComputeFstat(object):
""" Base class providing interface to `lalpulsar.ComputeFstat` """
earth_ephem_default = earth_ephem
sun_ephem_default = sun_ephem
@helper_functions.initializer
def __init__(self, tref, sftfilepattern=None, minStartTime=None,
maxStartTime=None, binary=False, transient=True, BSGL=False,
detectors=None, minCoverFreq=None, maxCoverFreq=None,
earth_ephem=None, sun_ephem=None, injectSources=None,
injectSqrtSX=None, assumeSqrtSX=None, SSBprec=None):
"""
Parameters
----------
tref : int
GPS seconds of the reference time.
sftfilepattern : str
Pattern to match SFTs using wildcards (*?) and ranges [0-9];
mutiple patterns can be given separated by colons.
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.
detectors : str
Two character reference to the data to use, specify None for no
contraint. If multiple-separate by comma.
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 will be used.
injectSources : dict or str
Either a dictionary of the values to inject, or a string pointing
to the .cff file to inject
injectSqrtSX :
Not yet implemented
assumeSqrtSX : float
Don't estimate noise-floors but assume (stationary) per-IFO
sqrt{SX} (if single value: use for all IFOs). If signal only,
set sqrtSX=1
SSBprec : int
Flag to set the SSB calculation: 0=Newtonian, 1=relativistic,
2=relativisitic optimised, 3=DMoff, 4=NO_SPIN
"""
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 _get_SFTCatalog(self):
""" Load the SFTCatalog
If sftfilepattern is specified, load the data. If not, attempt to
create data on the fly.
Returns
-------
SFTCatalog: lalpulsar.SFTCatalog
"""
if hasattr(self, 'SFTCatalog'):
return
if self.sftfilepattern is None:
for k in ['minStartTime', 'maxStartTime', 'detectors']:
if getattr(self, k) is None:
raise ValueError('You must provide "{}" to injectSources'
.format(k))
C1 = getattr(self, 'injectSources', None) is None
C2 = getattr(self, 'injectSqrtSX', None) is None
if C1 and C2:
raise ValueError('You must specify either one of injectSources'
' or injectSqrtSX')
SFTCatalog = lalpulsar.SFTCatalog()
Tsft = 1800
Toverlap = 0
Tspan = self.maxStartTime - self.minStartTime
detNames = lal.CreateStringVector(
*[d for d in self.detectors.split(',')])
multiTimestamps = lalpulsar.MakeMultiTimestamps(
self.minStartTime, Tspan, Tsft, Toverlap, detNames.length)
SFTCatalog = lalpulsar.MultiAddToFakeSFTCatalog(
SFTCatalog, detNames, multiTimestamps)
return SFTCatalog
logging.info('Initialising SFTCatalog')
constraints = lalpulsar.SFTConstraints()
if self.detectors:
if ',' in self.detectors:
logging.info('Using all detector data')
else:
constraints.detector = self.detectors
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.sftfilepattern))
SFTCatalog = lalpulsar.SFTdataFind(self.sftfilepattern, constraints)
detector_names = list(set([d.header.name for d in SFTCatalog.data]))
self.detector_names = detector_names
SFT_timestamps = [d.header.epoch for d in SFTCatalog.data]
self.SFT_timestamps = [float(s) for s in SFT_timestamps]
if len(SFT_timestamps) == 0:
raise ValueError('Failed to load any data')
if args.quite is False and args.no_interactive is False:
try:
from bashplotlib.histogram import plot_hist
print('Data timestamps histogram:')
plot_hist(SFT_timestamps, height=5, bincount=50)
except ImportError:
pass
if len(detector_names) == 0:
raise ValueError('No data loaded.')
logging.info('Loaded {} data files from detectors {}'.format(
len(SFT_timestamps), detector_names))
cl_tconv1 = 'lalapps_tconvert {}'.format(int(SFT_timestamps[0]))
output = helper_functions.run_commandline(cl_tconv1)
tconvert1 = output.rstrip('\n')
cl_tconv2 = 'lalapps_tconvert {}'.format(int(SFT_timestamps[-1]))
output = helper_functions.run_commandline(cl_tconv2)
tconvert2 = output.rstrip('\n')
logging.info('Data spans from {} ({}) to {} ({})'.format(
int(SFT_timestamps[0]),
tconvert1,
int(SFT_timestamps[-1]),
tconvert2))
return SFTCatalog
def init_computefstatistic_single_point(self):
""" Initilisation step of run_computefstatistic for a single point """
SFTCatalog = self._get_SFTCatalog()
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
if self.SSBprec:
logging.info('Using SSBprec={}'.format(self.SSBprec))
FstatOAs.SSBprec = self.SSBprec
else:
FstatOAs.SSBprec = lalpulsar.FstatOptionalArgsDefaults.SSBprec
FstatOAs.Dterms = lalpulsar.FstatOptionalArgsDefaults.Dterms
FstatOAs.runningMedianWindow = lalpulsar.FstatOptionalArgsDefaults.runningMedianWindow
FstatOAs.FstatMethod = lalpulsar.FstatOptionalArgsDefaults.FstatMethod
if self.assumeSqrtSX is None:
FstatOAs.assumeSqrtSX = lalpulsar.FstatOptionalArgsDefaults.assumeSqrtSX
else:
mnf = lalpulsar.MultiNoiseFloor()
assumeSqrtSX = np.atleast_1d(self.assumeSqrtSX)
mnf.sqrtSn[:len(assumeSqrtSX)] = assumeSqrtSX
mnf.length = len(assumeSqrtSX)
FstatOAs.assumeSqrtSX = mnf
FstatOAs.prevInput = lalpulsar.FstatOptionalArgsDefaults.prevInput
FstatOAs.collectTiming = lalpulsar.FstatOptionalArgsDefaults.collectTiming
if hasattr(self, 'injectSources') 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.type = lalpulsar.TRANSIENT_NONE
FstatOAs.injectSources = PPV
elif hasattr(self, 'injectSources') and type(self.injectSources) == str:
logging.info('Injecting source from param file: {}'.format(
self.injectSources))
PPV = lalpulsar.PulsarParamsFromFile(self.injectSources, self.tref)
FstatOAs.injectSources = PPV
else:
FstatOAs.injectSources = lalpulsar.FstatOptionalArgsDefaults.injectSources
if hasattr(self, 'injectSqrtSX') and self.injectSqrtSX is not None:
raise ValueError('injectSqrtSX not implemented')
else:
FstatOAs.InjectSqrtSX = lalpulsar.FstatOptionalArgsDefaults.injectSqrtSX
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(self.detector_names) < 2:
raise ValueError("Can't use BSGL with single detectors data")
else:
logging.info('Initialising BSGL')
# Tuning parameters - to be reviewed
numDetectors = 2
if hasattr(self, 'nsegs'):
p_val_threshold = 1e-6
Fstar0s = np.linspace(0, 1000, 10000)
p_vals = scipy.special.gammaincc(2*self.nsegs, Fstar0s)
Fstar0 = Fstar0s[np.argmin(np.abs(p_vals - p_val_threshold))]
if Fstar0 == Fstar0s[-1]:
raise ValueError('Max Fstar0 exceeded')
else:
Fstar0 = 15.
logging.info('Using Fstar0 of {:1.2f}'.format(Fstar0))
oLGX = np.zeros(10)
oLGX[:numDetectors] = 1./numDetectors
self.BSGLSetup = lalpulsar.CreateBSGLSetup(numDetectors,
Fstar0,
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:
twoF = 2*FS.F_mn.data[0][0]
if np.isnan(twoF):
return 0
else:
return twoF
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,
):
""" Calculate the cumulative twoF along the obseration span
Parameters
----------
F0, F1, F2, Alpha, Delta: float
Parameters at which to compute the cumulative twoF
asini, period, ecc, tp, argp: float, optional
Binary parameters at which to compute the cumulative 2F
tstart, tend: int
GPS times to restrict the range of data used - automatically
truncated to the span of data available
npoints: int
Number of points to compute twoF along the span
Notes
-----
The minimum cumulatibe twoF is hard-coded to be computed over
the first 6 hours from either the first timestampe in the data (if
tstart is smaller than it) or tstart.
"""
SFTminStartTime = self.SFT_timestamps[0]
SFTmaxStartTime = self.SFT_timestamps[-1]
tstart = np.max([SFTminStartTime, tstart])
min_tau = np.max([SFTminStartTime - tstart, 0]) + 3600*6
max_tau = SFTmaxStartTime - tstart
taus = np.linspace(min_tau, max_tau, 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 _calculate_predict_fstat_cumulative(self, N, label=None, outdir=None,
IFO=None, pfs_input=None):
""" Calculates the predicted 2F and standard deviation cumulatively
Parameters
----------
N : int
Number of timesteps to use between minStartTime and maxStartTime.
label, outdir : str, optional
The label and directory to read in the .loudest file from
IFO : str
pfs_input : dict, optional
Input kwargs to predict_fstat (alternative to giving label and
outdir).
Returns
-------
times, pfs, pfs_sigma : ndarray, size (N,)
"""
if pfs_input is None:
if os.path.isfile('{}/{}.loudest'.format(outdir, label)) is False:
raise ValueError(
'Need a loudest file to add the predicted Fstat')
loudest = read_par(label=label, outdir=outdir, suffix='loudest')
pfs_input = {key: loudest[key] for key in
['h0', 'cosi', 'psi', 'Alpha', 'Delta', 'Freq']}
times = np.linspace(self.minStartTime, self.maxStartTime, N+1)[1:]
times = np.insert(times, 0, self.minStartTime + 86400/2.)
out = [predict_fstat(minStartTime=self.minStartTime, maxStartTime=t,
sftfilepattern=self.sftfilepattern, IFO=IFO,
**pfs_input) for t in times]
pfs, pfs_sigma = np.array(out).T
return times, pfs, pfs_sigma
def plot_twoF_cumulative(self, label, outdir, add_pfs=False, N=15,
injectSources=None, ax=None, c='k', savefig=True,
title=None, **kwargs):
""" Plot the twoF value cumulatively
Parameters
----------
label, outdir : str
add_pfs : bool
If true, plot the predicted 2F and standard deviation
N : int
Number of points to use
injectSources : dict
See `ComputeFstat`
ax : matplotlib.axes._subplots_AxesSubplot, optional
Axis to add the plot to.
c : str
Colour
savefig : bool
If true, save the figure in outdir
title: str
Figure title
Returns
-------
tauS, tauF : ndarray shape (N,)
If savefig, the times and twoF (cumulative) values
ax : matplotlib.axes._subplots_AxesSubplot, optional
If savefig is False
"""
if ax is None:
fig, ax = plt.subplots()
if injectSources:
pfs_input = dict(
h0=injectSources['h0'], cosi=injectSources['cosi'],
psi=injectSources['psi'], Alpha=injectSources['Alpha'],
Delta=injectSources['Delta'], Freq=injectSources['fkdot'][0])
else:
pfs_input = None
taus, twoFs = self.calculate_twoF_cumulative(**kwargs)
ax.plot(taus/86400., twoFs, label='All detectors', color=c)
if len(self.detector_names) > 1:
detector_names = self.detector_names
detectors = self.detectors
for d in self.detector_names:
self.detectors = d
self.init_computefstatistic_single_point()
taus, twoFs = self.calculate_twoF_cumulative(**kwargs)
ax.plot(taus/86400., twoFs, label='{}'.format(d),
color=detector_colors[d.lower()])
self.detectors = detectors
self.detector_names = detector_names
if add_pfs:
times, pfs, pfs_sigma = self._calculate_predict_fstat_cumulative(
N=N, label=label, outdir=outdir, pfs_input=pfs_input)
ax.fill_between(
(times-self.minStartTime)/86400., pfs-pfs_sigma, pfs+pfs_sigma,
color=c,
label=(r'Predicted $\langle 2\mathcal{F} '
r'\rangle\pm $ 1-$\sigma$ band'),
zorder=-10, alpha=0.2)
if len(self.detector_names) > 1:
for d in self.detector_names:
out = self._calculate_predict_fstat_cumulative(
N=N, label=label, outdir=outdir, IFO=d.upper(),
pfs_input=pfs_input)
times, pfs, pfs_sigma = out
ax.fill_between(
(times-self.minStartTime)/86400., pfs-pfs_sigma,
pfs+pfs_sigma, color=detector_colors[d.lower()],
alpha=0.5,
label=(
'Predicted $2\mathcal{{F}}$ 1-$\sigma$ band ({})'
.format(d.upper())),
zorder=-10)
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.legend(frameon=False, loc=2, fontsize=6)
if title:
ax.set_title(title)
if savefig:
plt.tight_layout()
plt.savefig('{}/{}_twoFcumulative.png'.format(outdir, label))
return taus, twoFs
else:
return ax
class SemiCoherentSearch(BaseSearchClass, ComputeFstat):
""" A semi-coherent search """
@helper_functions.initializer
def __init__(self, label, outdir, tref, nsegs=None, sftfilepattern=None,
binary=False, BSGL=False, minStartTime=None,
maxStartTime=None, minCoverFreq=None, maxCoverFreq=None,
detectors=None, earth_ephem=None, sun_ephem=None,
injectSources=None, assumeSqrtSX=None, SSBprec=None):
"""
Parameters
----------
label, outdir: str
A label and directory to read/write data from/to.
tref, minStartTime, maxStartTime: int
GPS seconds of the reference time, and start and end of the data.
nsegs: int
The (fixed) number of segments
sftfilepattern: str
Pattern to match SFTs using wildcards (*?) and ranges [0-9];
mutiple patterns can be given separated by colons.
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,
record_segments=False):
""" Returns twoF or ln(BSGL) semi-coherently at a single point """
if hasattr(self, 'SFT_timestamps'):
if self.tboundaries[0] < self.SFT_timestamps[0]:
logging.debug(
'Semi-coherent start time {} before first SFT timestamp {}'
.format(self.tboundaries[0], self.SFT_timestamps[0]))
if self.tboundaries[-1] > self.SFT_timestamps[-1]:
logging.debug(
'Semi-coherent end time {} after last SFT timestamp {}'
.format(self.tboundaries[-1], self.SFT_timestamps[-1]))
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
if record_segments:
self.detStat_per_segment = []
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:
d_detStat = 2*FS.F_mn.data[0][0]
else:
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)
d_detStat = log10_BSGL/np.log10(np.exp(1))
if np.isnan(d_detStat):
logging.debug('NaNs in semi-coherent twoF treated as zero')
d_detStat = 0
detStat += d_detStat
if record_segments:
self.detStat_per_segment.append(d_detStat)
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
"""
@helper_functions.initializer
def __init__(self, label, outdir, tref, minStartTime, maxStartTime,
nglitch=0, sftfilepattern=None, theta0_idx=0, BSGL=False,
minCoverFreq=None, maxCoverFreq=None, assumeSqrtSX=None,
detectors=None, earth_ephem=None, sun_ephem=None,
SSBprec=None, injectSources=None):
"""
Parameters
----------
label, outdir: str
A label and directory to read/write data from/to.
tref, minStartTime, maxStartTime: 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).
sftfilepattern: str
Pattern to match SFTs using wildcards (*?) and ranges [0-9];
mutiple patterns can be given separated by colons.
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.minStartTime] + args[-self.nglitch:]
+ [self.maxStartTime])
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: OBSOLETE, used only 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.minStartTime, tglitch, theta[0], theta[1], theta[2], Alpha,
Delta)
if tglitch == self.maxStartTime:
return twoFsegA
twoFsegB = self.run_computefstatistic_single_point(
tglitch, self.maxStartTime, theta_post_glitch[0],
theta_post_glitch[1], theta_post_glitch[2], Alpha,
Delta)
return twoFsegA + twoFsegB