core.py 41.5 KB
Newer Older
Gregory Ashton's avatar
Gregory Ashton committed
1
""" The core tools used in pyfstat """
2
3
from __future__ import division, absolute_import, print_function

Gregory Ashton's avatar
Gregory Ashton committed
4
5
6
7
import os
import logging
import copy

8
import glob
Gregory Ashton's avatar
Gregory Ashton committed
9
import numpy as np
10
11
12
13
14
import scipy.special
import scipy.optimize

import lal
import lalpulsar
15
import pyfstat.helper_functions as helper_functions
16
17

# workaround for matplotlib on X-less remote logins
18
if 'DISPLAY' in os.environ:
19
20
    import matplotlib.pyplot as plt
else:
21
22
    logging.info('No $DISPLAY environment variable found, so importing \
                  matplotlib.pyplot with non-interactive "Agg" backend.')
23
24
25
26
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt

Gregory Ashton's avatar
Gregory Ashton committed
27
helper_functions.set_up_matplotlib_defaults()
28
args, tqdm = helper_functions.set_up_command_line_arguments()
29
detector_colors = {'h1': 'C0', 'l1': 'C1'}
Gregory Ashton's avatar
Gregory Ashton committed
30
31


Gregory Ashton's avatar
Gregory Ashton committed
32
class Bunch(object):
33
34
    """ Turns dictionary into object with attribute-style access

35
36
    Parameters
    ----------
37
38
39
40
41
42
43
44
45
46
47
48
49
50
    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

    """
Gregory Ashton's avatar
Gregory Ashton committed
51
52
53
54
55
    def __init__(self, dictionary):
        self.__dict__.update(dictionary)


def read_par(filename=None, label=None, outdir=None, suffix='par',
56
57
             return_type='dict', comments=['%', '#'], raise_error=False):
    """ Read in a .par or .loudest file, returns a dict or Bunch of the data
58

Gregory Ashton's avatar
Gregory Ashton committed
59
60
    Parameters
    ----------
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
    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)
Gregory Ashton's avatar
Gregory Ashton committed
77
78
79
80
81

    Returns
    -------
    d: Bunch or dict
        The par values as either a `Bunch` or dict type
82

83
84
85
86
    """
    if filename is None:
        filename = '{}/{}.{}'.format(outdir, label, suffix)
    if os.path.isfile(filename) is False:
87
        raise ValueError("No file {} found".format(filename))
Gregory Ashton's avatar
Gregory Ashton committed
88
89
    d = {}
    with open(filename, 'r') as f:
90
        d = _get_dictionary_from_lines(f, comments, raise_error)
Gregory Ashton's avatar
Gregory Ashton committed
91
92
93
94
95
96
    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))
Gregory Ashton's avatar
Gregory Ashton committed
97
98


99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
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

    """
116
117
    d = {}
    for line in lines:
118
        if line[0] not in comments and len(line.split('=')) == 2:
119
120
121
            try:
                key, val = line.rstrip('\n').split('=')
                key = key.strip()
Gregory Ashton's avatar
Gregory Ashton committed
122
123
124
125
                try:
                    d[key] = np.float64(eval(val.rstrip('; ')))
                except NameError:
                    d[key] = val.rstrip('; ')
126
            except SyntaxError:
127
128
                if raise_error:
                    raise IOError('Line {} not understood'.format(line))
129
130
131
132
133
                pass
    return d


def predict_fstat(h0, cosi, psi, Alpha, Delta, Freq, sftfilepattern,
Gregory Ashton's avatar
Gregory Ashton committed
134
135
                  minStartTime, maxStartTime, IFO=None, assumeSqrtSX=None,
                  **kwargs):
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
    """ 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

    """
156
157
    tempory_filename = 'fs.tmp'

158
159
160
161
162
163
164
165
166
167
    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))
168
    if assumeSqrtSX:
169
        cl_pfs.append("--assumeSqrtSX={}".format(assumeSqrtSX))
170
    if IFO:
171
172
173
174
175
        if ',' in IFO:
            logging.warning('Multiple detector selection not available, using'
                            ' all available data')
        else:
            cl_pfs.append("--IFO={}".format(IFO))
176

177
178
    cl_pfs.append("--minStartTime={}".format(int(minStartTime)))
    cl_pfs.append("--maxStartTime={}".format(int(maxStartTime)))
179
    cl_pfs.append("--outputFstat={}".format(tempory_filename))
180

181
182
    cl_pfs = " ".join(cl_pfs)
    helper_functions.run_commandline(cl_pfs)
183
184
    d = read_par(filename=tempory_filename)
    os.remove(tempory_filename)
185
186
187
    return float(d['twoF_expected']), float(d['twoF_sigma'])


Gregory Ashton's avatar
Gregory Ashton committed
188
class BaseSearchClass(object):
189
    """ The base search class providing parent methods to other searches """
Gregory Ashton's avatar
Gregory Ashton committed
190

191
    def _add_log_file(self):
Gregory Ashton's avatar
Gregory Ashton committed
192
193
194
195
196
197
198
199
200
        """ 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)

201
    def _shift_matrix(self, n, dT):
Gregory Ashton's avatar
Gregory Ashton committed
202
203
204
205
        """ Generate the shift matrix

        Parameters
        ----------
206
        n : int
Gregory Ashton's avatar
Gregory Ashton committed
207
            The dimension of the shift-matrix to generate
208
        dT : float
Gregory Ashton's avatar
Gregory Ashton committed
209
210
211
212
            The time delta of the shift matrix

        Returns
        -------
213
214
        m : ndarray, shape (n,)
            The shift matrix.
Gregory Ashton's avatar
Gregory Ashton committed
215

216
        """
Gregory Ashton's avatar
Gregory Ashton committed
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
        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

232
    def _shift_coefficients(self, theta, dT):
Gregory Ashton's avatar
Gregory Ashton committed
233
234
235
236
        """ Shift a set of coefficients by dT

        Parameters
        ----------
237
238
        theta : array-like, shape (n,)
            Vector of the expansion coefficients to transform starting from the
Gregory Ashton's avatar
Gregory Ashton committed
239
            lowest degree e.g [phi, F0, F1,...].
240
241
        dT : float
            Difference between the two reference times as tref_new - tref_old.
Gregory Ashton's avatar
Gregory Ashton committed
242
243
244

        Returns
        -------
245
246
        theta_new : ndarray, shape (n,)
            Vector of the coefficients as evaluated as the new reference time.
Gregory Ashton's avatar
Gregory Ashton committed
247
248
        """
        n = len(theta)
249
        m = self._shift_matrix(n, dT)
Gregory Ashton's avatar
Gregory Ashton committed
250
251
        return np.dot(m, theta)

252
    def _calculate_thetas(self, theta, delta_thetas, tbounds, theta0_idx=0):
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
        """ 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).

        """
Gregory Ashton's avatar
Gregory Ashton committed
277
278
279
        thetas = [theta]
        for i, dt in enumerate(delta_thetas):
            if i < theta0_idx:
280
                pre_theta_at_ith_glitch = self._shift_coefficients(
Gregory Ashton's avatar
Gregory Ashton committed
281
282
                    thetas[0], tbounds[i+1] - self.tref)
                post_theta_at_ith_glitch = pre_theta_at_ith_glitch - dt
283
                thetas.insert(0, self._shift_coefficients(
Gregory Ashton's avatar
Gregory Ashton committed
284
285
286
                    post_theta_at_ith_glitch, self.tref - tbounds[i+1]))

            elif i >= theta0_idx:
287
                pre_theta_at_ith_glitch = self._shift_coefficients(
Gregory Ashton's avatar
Gregory Ashton committed
288
289
                    thetas[i], tbounds[i+1] - self.tref)
                post_theta_at_ith_glitch = pre_theta_at_ith_glitch + dt
290
                thetas.append(self._shift_coefficients(
Gregory Ashton's avatar
Gregory Ashton committed
291
                    post_theta_at_ith_glitch, self.tref - tbounds[i+1]))
292
        self.thetas_at_tref = thetas
Gregory Ashton's avatar
Gregory Ashton committed
293
294
        return thetas

295
    def _get_list_of_matching_sfts(self):
296
        """ Returns a list of sfts matching the attribute sftfilepattern """
297
298
        sftfilepatternlist = np.atleast_1d(self.sftfilepattern.split(';'))
        matches = [glob.glob(p) for p in sftfilepatternlist]
299
        matches = [item for sublist in matches for item in sublist]
300
301
302
303
        if len(matches) > 0:
            return matches
        else:
            raise IOError('No sfts found matching {}'.format(
304
                self.sftfilepattern))
305

306
307
    def set_ephemeris_files(self, earth_ephem=None, sun_ephem=None):
        """ Set the ephemeris files to use for the Earth and Sun
Gregory Ashton's avatar
Gregory Ashton committed
308

309
310
311
312
313
        Parameters
        ----------
        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
Gregory Ashton's avatar
Gregory Ashton committed
314

315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
        Note: If not manually set, default values in ~/.pyfstat are used

        """

        earth_ephem_default, sun_ephem_default = (
                helper_functions.get_ephemeris_files())

        if earth_ephem is None:
            self.earth_ephem = earth_ephem_default
        if sun_ephem is None:
            self.sun_ephem = sun_ephem_default


class ComputeFstat(BaseSearchClass):
    """ Base class providing interface to `lalpulsar.ComputeFstat` """
Gregory Ashton's avatar
Gregory Ashton committed
330
331

    @helper_functions.initializer
332
    def __init__(self, tref, sftfilepattern=None, minStartTime=None,
Gregory Ashton's avatar
Gregory Ashton committed
333
                 maxStartTime=None, binary=False, transient=True, BSGL=False,
334
                 detectors=None, minCoverFreq=None, maxCoverFreq=None,
335
336
                 injectSources=None, injectSqrtSX=None, assumeSqrtSX=None,
                 SSBprec=None):
Gregory Ashton's avatar
Gregory Ashton committed
337
338
339
        """
        Parameters
        ----------
340
        tref : int
Gregory Ashton's avatar
Gregory Ashton committed
341
            GPS seconds of the reference time.
342
        sftfilepattern : str
343
344
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
345
        minStartTime, maxStartTime : float GPStime
Gregory Ashton's avatar
Gregory Ashton committed
346
347
            Only use SFTs with timestemps starting from (including, excluding)
            this epoch
348
        binary : bool
Gregory Ashton's avatar
Gregory Ashton committed
349
            If true, search of binary parameters.
350
        transient : bool
Gregory Ashton's avatar
Gregory Ashton committed
351
            If true, allow for the Fstat to be computed over a transient range.
352
        BSGL : bool
Gregory Ashton's avatar
Gregory Ashton committed
353
            If true, compute the BSGL rather than the twoF value.
354
        detectors : str
Gregory Ashton's avatar
Gregory Ashton committed
355
            Two character reference to the data to use, specify None for no
356
            contraint. If multiple-separate by comma.
357
        minCoverFreq, maxCoverFreq : float
Gregory Ashton's avatar
Gregory Ashton committed
358
359
360
            The min and max cover frequency passed to CreateFstatInput, if
            either is None the range of frequencies in the SFT less 1Hz is
            used.
361
        injectSources : dict or str
362
363
            Either a dictionary of the values to inject, or a string pointing
            to the .cff file to inject
364
        injectSqrtSX :
365
            Not yet implemented
366
        assumeSqrtSX : float
367
368
369
            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
370
        SSBprec : int
371
372
            Flag to set the SSB calculation: 0=Newtonian, 1=relativistic,
            2=relativisitic optimised, 3=DMoff, 4=NO_SPIN
Gregory Ashton's avatar
Gregory Ashton committed
373
374
375

        """

376
        self.set_ephemeris_files()
Gregory Ashton's avatar
Gregory Ashton committed
377
378
        self.init_computefstatistic_single_point()

379
380
381
382
383
384
385
386
387
388
389
    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

        """
Gregory Ashton's avatar
Gregory Ashton committed
390
391
        if hasattr(self, 'SFTCatalog'):
            return
392
        if self.sftfilepattern is None:
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
            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

Gregory Ashton's avatar
Gregory Ashton committed
414
415
        logging.info('Initialising SFTCatalog')
        constraints = lalpulsar.SFTConstraints()
416
        if self.detectors:
417
            if ',' in self.detectors:
418
419
                logging.warning('Multiple detector selection not available,'
                                ' using all available data')
420
421
            else:
                constraints.detector = self.detectors
Gregory Ashton's avatar
Gregory Ashton committed
422
423
424
425
426
427
        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(
428
429
                     self.sftfilepattern))
        SFTCatalog = lalpulsar.SFTdataFind(self.sftfilepattern, constraints)
Gregory Ashton's avatar
Gregory Ashton committed
430
431
432
        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]
Gregory Ashton's avatar
Gregory Ashton committed
433
        self.SFT_timestamps = [float(s) for s in SFT_timestamps]
434
435
        if len(SFT_timestamps) == 0:
            raise ValueError('Failed to load any data')
Gregory Ashton's avatar
Gregory Ashton committed
436
437
438
439
440
        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)
Gregory Ashton's avatar
Gregory Ashton committed
441
            except ImportError:
Gregory Ashton's avatar
Gregory Ashton committed
442
443
444
445
446
                pass
        if len(detector_names) == 0:
            raise ValueError('No data loaded.')
        logging.info('Loaded {} data files from detectors {}'.format(
            len(SFT_timestamps), detector_names))
447
        cl_tconv1 = 'lalapps_tconvert {}'.format(int(SFT_timestamps[0]))
448
        output = helper_functions.run_commandline(cl_tconv1)
449
450
        tconvert1 = output.rstrip('\n')
        cl_tconv2 = 'lalapps_tconvert {}'.format(int(SFT_timestamps[-1]))
451
        output = helper_functions.run_commandline(cl_tconv2)
452
        tconvert2 = output.rstrip('\n')
Gregory Ashton's avatar
Gregory Ashton committed
453
454
        logging.info('Data spans from {} ({}) to {} ({})'.format(
            int(SFT_timestamps[0]),
455
            tconvert1,
Gregory Ashton's avatar
Gregory Ashton committed
456
            int(SFT_timestamps[-1]),
457
            tconvert2))
458
        return SFTCatalog
Gregory Ashton's avatar
Gregory Ashton committed
459
460
461
462

    def init_computefstatistic_single_point(self):
        """ Initilisation step of run_computefstatistic for a single point """

463
        SFTCatalog = self._get_SFTCatalog()
Gregory Ashton's avatar
Gregory Ashton committed
464
465
466
467
468
469
470
471
472
473
474
475
476

        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
477
478
479
480
481
        if self.SSBprec:
            logging.info('Using SSBprec={}'.format(self.SSBprec))
            FstatOAs.SSBprec = self.SSBprec
        else:
            FstatOAs.SSBprec = lalpulsar.FstatOptionalArgsDefaults.SSBprec
Gregory Ashton's avatar
Gregory Ashton committed
482
483
484
        FstatOAs.Dterms = lalpulsar.FstatOptionalArgsDefaults.Dterms
        FstatOAs.runningMedianWindow = lalpulsar.FstatOptionalArgsDefaults.runningMedianWindow
        FstatOAs.FstatMethod = lalpulsar.FstatOptionalArgsDefaults.FstatMethod
485
486
487
488
489
490
491
492
        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
Gregory Ashton's avatar
Gregory Ashton committed
493
494
495
        FstatOAs.prevInput = lalpulsar.FstatOptionalArgsDefaults.prevInput
        FstatOAs.collectTiming = lalpulsar.FstatOptionalArgsDefaults.collectTiming

Gregory Ashton's avatar
Gregory Ashton committed
496
        if hasattr(self, 'injectSources') and type(self.injectSources) == dict:
Gregory Ashton's avatar
Gregory Ashton committed
497
498
499
500
501
502
503
504
505
506
            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']
Gregory Ashton's avatar
Gregory Ashton committed
507
508
509
510
511
512
            if 'fkdot' in self.injectSources:
                PP.Doppler.fkdot = np.array(self.injectSources['fkdot'])
            else:
                PP.Doppler.fkdot = np.zeros(lalpulsar.PULSAR_MAX_SPINS)
                for i, key in enumerate(['F0', 'F1', 'F2']):
                    PP.Doppler.fkdot[i] = self.injectSources[key]
Gregory Ashton's avatar
Gregory Ashton committed
513
514
515
516
            PP.Doppler.refTime = self.tref
            if 't0' not in self.injectSources:
                PP.Transient.type = lalpulsar.TRANSIENT_NONE
            FstatOAs.injectSources = PPV
Gregory Ashton's avatar
Gregory Ashton committed
517
        elif hasattr(self, 'injectSources') and type(self.injectSources) == str:
518
519
520
521
            logging.info('Injecting source from param file: {}'.format(
                self.injectSources))
            PPV = lalpulsar.PulsarParamsFromFile(self.injectSources, self.tref)
            FstatOAs.injectSources = PPV
Gregory Ashton's avatar
Gregory Ashton committed
522
523
        else:
            FstatOAs.injectSources = lalpulsar.FstatOptionalArgsDefaults.injectSources
524
525
526
527
        if hasattr(self, 'injectSqrtSX') and self.injectSqrtSX is not None:
            raise ValueError('injectSqrtSX not implemented')
        else:
            FstatOAs.InjectSqrtSX = lalpulsar.FstatOptionalArgsDefaults.injectSqrtSX
Gregory Ashton's avatar
Gregory Ashton committed
528
        if self.minCoverFreq is None or self.maxCoverFreq is None:
529
            fAs = [d.header.f0 for d in SFTCatalog.data]
Gregory Ashton's avatar
Gregory Ashton committed
530
            fBs = [d.header.f0 + (d.numBins-1)*d.header.deltaF
531
                   for d in SFTCatalog.data]
Gregory Ashton's avatar
Gregory Ashton committed
532
533
534
535
536
537
            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))

538
        self.FstatInput = lalpulsar.CreateFstatInput(SFTCatalog,
Gregory Ashton's avatar
Gregory Ashton committed
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
                                                     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:
559
                raise ValueError("Can't use BSGL with single detectors data")
Gregory Ashton's avatar
Gregory Ashton committed
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
            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

595
596
597
    def get_fullycoherent_twoF(self, tstart, tend, F0, F1, F2, Alpha, Delta,
                               asini=None, period=None, ecc=None, tp=None,
                               argp=None):
Gregory Ashton's avatar
Gregory Ashton committed
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
        """ 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:
635
636
637
638
639
            twoF = 2*FS.F_mn.data[0][0]
            if np.isnan(twoF):
                return 0
            else:
                return twoF
Gregory Ashton's avatar
Gregory Ashton committed
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659

        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,
660
661
                                  ):
        """ Calculate the cumulative twoF along the obseration span
662
663
664

        Parameters
        ----------
665
666
        F0, F1, F2, Alpha, Delta: float
            Parameters at which to compute the cumulative twoF
667
668
        asini, period, ecc, tp, argp: float, optional
            Binary parameters at which to compute the cumulative 2F
669
670
671
672
673
674
        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

675
676
677
        Notes
        -----
        The minimum cumulatibe twoF is hard-coded to be computed over
678
679
680
681
682
683
        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]
Gregory Ashton's avatar
Gregory Ashton committed
684
        tstart = np.max([SFTminStartTime, tstart])
685
686
687
        min_tau = np.max([SFTminStartTime - tstart, 0]) + 3600*6
        max_tau = SFTmaxStartTime - tstart
        taus = np.linspace(min_tau, max_tau, npoints)
Gregory Ashton's avatar
Gregory Ashton committed
688
689
690
691
692
        twoFs = []
        if self.transient is False:
            self.transient = True
            self.init_computefstatistic_single_point()
        for tau in taus:
693
            twoFs.append(self.get_fullycoherent_twoF(
Gregory Ashton's avatar
Gregory Ashton committed
694
695
696
697
698
699
                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)

700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
    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,)

        """
Gregory Ashton's avatar
Gregory Ashton committed
720
721
722
723
724

        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')
725
            loudest = read_par(label=label, outdir=outdir, suffix='loudest')
Gregory Ashton's avatar
Gregory Ashton committed
726
727
            pfs_input = {key: loudest[key] for key in
                         ['h0', 'cosi', 'psi', 'Alpha', 'Delta', 'Freq']}
728
729
730
        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,
731
                             sftfilepattern=self.sftfilepattern, IFO=IFO,
732
733
734
735
                             **pfs_input) for t in times]
        pfs, pfs_sigma = np.array(out).T
        return times, pfs, pfs_sigma

736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
    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

        """
Gregory Ashton's avatar
Gregory Ashton committed
767
768
        if ax is None:
            fig, ax = plt.subplots()
Gregory Ashton's avatar
Gregory Ashton committed
769
770
771
772
773
774
775
        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
776
777
778
779

        taus, twoFs = self.calculate_twoF_cumulative(**kwargs)
        ax.plot(taus/86400., twoFs, label='All detectors', color=c)
        if len(self.detector_names) > 1:
780
781
            detector_names = self.detector_names
            detectors = self.detectors
782
783
784
785
            for d in self.detector_names:
                self.detectors = d
                self.init_computefstatistic_single_point()
                taus, twoFs = self.calculate_twoF_cumulative(**kwargs)
786
787
788
789
790
791
                ax.plot(taus/86400., twoFs, label='{}'.format(d),
                        color=detector_colors[d.lower()])
            self.detectors = detectors
            self.detector_names = detector_names

        if add_pfs:
792
793
            times, pfs, pfs_sigma = self._calculate_predict_fstat_cumulative(
                N=N, label=label, outdir=outdir, pfs_input=pfs_input)
794
795
            ax.fill_between(
                (times-self.minStartTime)/86400., pfs-pfs_sigma, pfs+pfs_sigma,
Gregory Ashton's avatar
Gregory Ashton committed
796
                color=c,
797
798
                label=(r'Predicted $\langle 2\mathcal{F} '
                       r'\rangle\pm $ 1-$\sigma$ band'),
799
800
801
                zorder=-10, alpha=0.2)
            if len(self.detector_names) > 1:
                for d in self.detector_names:
802
803
804
805
                    out = self._calculate_predict_fstat_cumulative(
                        N=N, label=label, outdir=outdir, IFO=d.upper(),
                        pfs_input=pfs_input)
                    times, pfs, pfs_sigma = out
806
807
808
809
810
811
812
813
                    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)
814

Gregory Ashton's avatar
Gregory Ashton committed
815
816
817
818
819
820
821
        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)
Gregory Ashton's avatar
Gregory Ashton committed
822
        ax.legend(frameon=False, loc=2, fontsize=6)
Gregory Ashton's avatar
Gregory Ashton committed
823
824
825
826
827
828
829
830
831
832
        if title:
            ax.set_title(title)
        if savefig:
            plt.tight_layout()
            plt.savefig('{}/{}_twoFcumulative.png'.format(outdir, label))
            return taus, twoFs
        else:
            return ax


833
class SemiCoherentSearch(ComputeFstat):
Gregory Ashton's avatar
Gregory Ashton committed
834
835
836
    """ A semi-coherent search """

    @helper_functions.initializer
837
    def __init__(self, label, outdir, tref, nsegs=None, sftfilepattern=None,
Gregory Ashton's avatar
Gregory Ashton committed
838
839
                 binary=False, BSGL=False, minStartTime=None,
                 maxStartTime=None, minCoverFreq=None, maxCoverFreq=None,
840
841
                 detectors=None, injectSources=None, assumeSqrtSX=None,
                 SSBprec=None):
Gregory Ashton's avatar
Gregory Ashton committed
842
843
844
845
846
847
848
849
850
        """
        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
851
852
853
        sftfilepattern: str
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
Gregory Ashton's avatar
Gregory Ashton committed
854
855
856
857
858

        For all other parameters, see pyfstat.ComputeFStat.
        """

        self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)
859
        self.set_ephemeris_files()
Gregory Ashton's avatar
Gregory Ashton committed
860
861
862
863
864
865
866
867
868
869
870
871
        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)
872
        self.Tcoh = self.tboundaries[1] - self.tboundaries[0]
Gregory Ashton's avatar
Gregory Ashton committed
873

874
875
876
877
878
879
880
881
882
        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]))
Gregory Ashton's avatar
Gregory Ashton committed
883

884
    def get_semicoherent_twoF(
885
886
887
888
889
            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 """

Gregory Ashton's avatar
Gregory Ashton committed
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
        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
                               )

907
908
909
910
911
912
913
914
915
        #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))
Gregory Ashton's avatar
Gregory Ashton committed
916
917

        detStat = 0
918
919
        if record_segments:
            self.detStat_per_segment = []
Gregory Ashton's avatar
Gregory Ashton committed
920

921
922
923
        self.windowRange.tau = int(self.Tcoh)  # TYPE UINT4
        for tstart in self.tboundaries[:-1]:
            d_detStat = self._get_per_segment_det_stat(tstart)
924
925
926
            detStat += d_detStat
            if record_segments:
                self.detStat_per_segment.append(d_detStat)
Gregory Ashton's avatar
Gregory Ashton committed
927
928
929

        return detStat

930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
    def _get_per_segment_det_stat(self, tstart):
        self.windowRange.t0 = int(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

        return d_detStat

Gregory Ashton's avatar
Gregory Ashton committed
959

960
class SemiCoherentGlitchSearch(ComputeFstat):
Gregory Ashton's avatar
Gregory Ashton committed
961
962
963
964
965
966
967
968
969
970
    """ 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,
971
                 nglitch=0, sftfilepattern=None, theta0_idx=0, BSGL=False,
972
                 minCoverFreq=None, maxCoverFreq=None, assumeSqrtSX=None,
973
                 detectors=None, SSBprec=None, injectSources=None):
Gregory Ashton's avatar
Gregory Ashton committed
974
975
976
977
978
979
980
981
982
983
        """
        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).
984
985
986
        sftfilepattern: str
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
Gregory Ashton's avatar
Gregory Ashton committed
987
988
989
990
991
992
993
994
995
        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)
996
        self.set_ephemeris_files()
Gregory Ashton's avatar
Gregory Ashton committed
997
998
999
1000
        self.transient = True
        self.binary = False
        self.init_computefstatistic_single_point()

1001
    def get_semicoherent_nglitch_twoF(self, F0, F1, F2, Alpha, Delta, *args):
Gregory Ashton's avatar
Gregory Ashton committed
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
        """ 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)

1015
        thetas = self._calculate_thetas(theta, delta_thetas, tboundaries,
1016
                                        theta0_idx=self.theta0_idx)
Gregory Ashton's avatar
Gregory Ashton committed
1017
1018
1019
1020
1021

        twoFSum = 0
        for i, theta_i_at_tref in enumerate(thetas):
            ts, te = tboundaries[i], tboundaries[i+1]

1022
            twoFVal = self.get_fullycoherent_twoF(
Gregory Ashton's avatar
Gregory Ashton committed
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
                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

1043
        theta_at_glitch = self._shift_coefficients(theta, tglitch - tref)
Gregory Ashton's avatar
Gregory Ashton committed
1044
        theta_post_glitch_at_glitch = theta_at_glitch + delta_theta
1045
        theta_post_glitch = self._shift_coefficients(
Gregory Ashton's avatar
Gregory Ashton committed
1046
1047
            theta_post_glitch_at_glitch, tref - tglitch)

1048
        twoFsegA = self.get_fullycoherent_twoF(
Gregory Ashton's avatar
Gregory Ashton committed
1049
1050
1051
1052
1053
1054
            self.minStartTime, tglitch, theta[0], theta[1], theta[2], Alpha,
            Delta)

        if tglitch == self.maxStartTime:
            return twoFsegA

1055
        twoFsegB = self.get_fullycoherent_twoF(
Gregory Ashton's avatar
Gregory Ashton committed
1056
1057
1058
1059
1060
            tglitch, self.maxStartTime, theta_post_glitch[0],
            theta_post_glitch[1], theta_post_glitch[2], Alpha,
            Delta)

        return twoFsegA + twoFsegB