core.py 48.4 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
import pyfstat.tcw_fstat_map_funcs as tcw
17
18

# workaround for matplotlib on X-less remote logins
19
if 'DISPLAY' in os.environ:
20
21
    import matplotlib.pyplot as plt
else:
22
23
    logging.info('No $DISPLAY environment variable found, so importing \
                  matplotlib.pyplot with non-interactive "Agg" backend.')
24
25
26
27
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt

Gregory Ashton's avatar
Gregory Ashton committed
28
helper_functions.set_up_matplotlib_defaults()
29
args, tqdm = helper_functions.set_up_command_line_arguments()
30
detector_colors = {'h1': 'C0', 'l1': 'C1'}
Gregory Ashton's avatar
Gregory Ashton committed
31
32


Gregory Ashton's avatar
Gregory Ashton committed
33
class Bunch(object):
34
35
    """ Turns dictionary into object with attribute-style access

36
37
    Parameters
    ----------
38
39
40
41
42
43
44
45
46
47
48
49
50
51
    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
52
53
54
55
56
    def __init__(self, dictionary):
        self.__dict__.update(dictionary)


def read_par(filename=None, label=None, outdir=None, suffix='par',
57
58
             return_type='dict', comments=['%', '#'], raise_error=False):
    """ Read in a .par or .loudest file, returns a dict or Bunch of the data
59

Gregory Ashton's avatar
Gregory Ashton committed
60
61
    Parameters
    ----------
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
    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
78
79
80
81
82

    Returns
    -------
    d: Bunch or dict
        The par values as either a `Bunch` or dict type
83

84
85
86
87
    """
    if filename is None:
        filename = '{}/{}.{}'.format(outdir, label, suffix)
    if os.path.isfile(filename) is False:
88
        raise ValueError("No file {} found".format(filename))
Gregory Ashton's avatar
Gregory Ashton committed
89
90
    d = {}
    with open(filename, 'r') as f:
91
        d = _get_dictionary_from_lines(f, comments, raise_error)
Gregory Ashton's avatar
Gregory Ashton committed
92
93
94
95
96
97
    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
98
99


100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
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

    """
117
118
    d = {}
    for line in lines:
119
        if line[0] not in comments and len(line.split('=')) == 2:
120
121
122
            try:
                key, val = line.rstrip('\n').split('=')
                key = key.strip()
123
124
125
126
127
128
129
130
                val = val.strip()
                if (val[0] in ["'", '"']) and (val[-1] in ["'", '"']):
                    d[key] = val.lstrip('"').lstrip("'").rstrip('"').rstrip("'")
                else:
                    try:
                        d[key] = np.float64(eval(val.rstrip('; ')))
                    except NameError:
                        d[key] = val.rstrip('; ')
131
            except SyntaxError:
132
133
                if raise_error:
                    raise IOError('Line {} not understood'.format(line))
134
135
136
137
138
                pass
    return d


def predict_fstat(h0, cosi, psi, Alpha, Delta, Freq, sftfilepattern,
Gregory Ashton's avatar
Gregory Ashton committed
139
                  minStartTime, maxStartTime, IFO=None, assumeSqrtSX=None,
140
                  tempory_filename='fs.tmp', **kwargs):
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
    """ 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

    """
161

162
163
164
165
166
167
168
169
170
171
    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))
172
    if assumeSqrtSX:
173
        cl_pfs.append("--assumeSqrtSX={}".format(assumeSqrtSX))
174
    if IFO:
175
176
177
178
179
        if ',' in IFO:
            logging.warning('Multiple detector selection not available, using'
                            ' all available data')
        else:
            cl_pfs.append("--IFO={}".format(IFO))
180

181
182
    cl_pfs.append("--minStartTime={}".format(int(minStartTime)))
    cl_pfs.append("--maxStartTime={}".format(int(maxStartTime)))
183
    cl_pfs.append("--outputFstat={}".format(tempory_filename))
184

185
186
    cl_pfs = " ".join(cl_pfs)
    helper_functions.run_commandline(cl_pfs)
187
188
    d = read_par(filename=tempory_filename)
    os.remove(tempory_filename)
189
190
191
    return float(d['twoF_expected']), float(d['twoF_sigma'])


Gregory Ashton's avatar
Gregory Ashton committed
192
class BaseSearchClass(object):
193
    """ The base search class providing parent methods to other searches """
Gregory Ashton's avatar
Gregory Ashton committed
194

195
    def _add_log_file(self):
Gregory Ashton's avatar
Gregory Ashton committed
196
197
198
199
200
201
202
203
204
        """ 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)

205
    def _shift_matrix(self, n, dT):
Gregory Ashton's avatar
Gregory Ashton committed
206
207
208
209
        """ Generate the shift matrix

        Parameters
        ----------
210
        n : int
Gregory Ashton's avatar
Gregory Ashton committed
211
            The dimension of the shift-matrix to generate
212
        dT : float
Gregory Ashton's avatar
Gregory Ashton committed
213
214
215
216
            The time delta of the shift matrix

        Returns
        -------
217
218
        m : ndarray, shape (n,)
            The shift matrix.
Gregory Ashton's avatar
Gregory Ashton committed
219

220
        """
Gregory Ashton's avatar
Gregory Ashton committed
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
        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

236
    def _shift_coefficients(self, theta, dT):
Gregory Ashton's avatar
Gregory Ashton committed
237
238
239
240
        """ Shift a set of coefficients by dT

        Parameters
        ----------
241
242
        theta : array-like, shape (n,)
            Vector of the expansion coefficients to transform starting from the
Gregory Ashton's avatar
Gregory Ashton committed
243
            lowest degree e.g [phi, F0, F1,...].
244
245
        dT : float
            Difference between the two reference times as tref_new - tref_old.
Gregory Ashton's avatar
Gregory Ashton committed
246
247
248

        Returns
        -------
249
250
        theta_new : ndarray, shape (n,)
            Vector of the coefficients as evaluated as the new reference time.
Gregory Ashton's avatar
Gregory Ashton committed
251
252
        """
        n = len(theta)
253
        m = self._shift_matrix(n, dT)
Gregory Ashton's avatar
Gregory Ashton committed
254
255
        return np.dot(m, theta)

256
    def _calculate_thetas(self, theta, delta_thetas, tbounds, theta0_idx=0):
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
        """ 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
281
282
283
        thetas = [theta]
        for i, dt in enumerate(delta_thetas):
            if i < theta0_idx:
284
                pre_theta_at_ith_glitch = self._shift_coefficients(
Gregory Ashton's avatar
Gregory Ashton committed
285
286
                    thetas[0], tbounds[i+1] - self.tref)
                post_theta_at_ith_glitch = pre_theta_at_ith_glitch - dt
287
                thetas.insert(0, self._shift_coefficients(
Gregory Ashton's avatar
Gregory Ashton committed
288
289
290
                    post_theta_at_ith_glitch, self.tref - tbounds[i+1]))

            elif i >= theta0_idx:
291
                pre_theta_at_ith_glitch = self._shift_coefficients(
Gregory Ashton's avatar
Gregory Ashton committed
292
293
                    thetas[i], tbounds[i+1] - self.tref)
                post_theta_at_ith_glitch = pre_theta_at_ith_glitch + dt
294
                thetas.append(self._shift_coefficients(
Gregory Ashton's avatar
Gregory Ashton committed
295
                    post_theta_at_ith_glitch, self.tref - tbounds[i+1]))
296
        self.thetas_at_tref = thetas
Gregory Ashton's avatar
Gregory Ashton committed
297
298
        return thetas

299
    def _get_list_of_matching_sfts(self):
300
        """ Returns a list of sfts matching the attribute sftfilepattern """
301
302
        sftfilepatternlist = np.atleast_1d(self.sftfilepattern.split(';'))
        matches = [glob.glob(p) for p in sftfilepatternlist]
303
        matches = [item for sublist in matches for item in sublist]
304
305
306
307
        if len(matches) > 0:
            return matches
        else:
            raise IOError('No sfts found matching {}'.format(
308
                self.sftfilepattern))
309

310
311
    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
312

313
314
315
316
317
        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
318

319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
        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
334
335

    @helper_functions.initializer
336
    def __init__(self, tref, sftfilepattern=None, minStartTime=None,
David Keitel's avatar
David Keitel committed
337
338
                 maxStartTime=None, binary=False, BSGL=False,
                 transientWindowType=None, t0Band=None, tauBand=None,
339
                 dt0=None, dtau=None,
340
                 detectors=None, minCoverFreq=None, maxCoverFreq=None,
341
                 injectSources=None, injectSqrtSX=None, assumeSqrtSX=None,
342
                 SSBprec=None,
343
                 tCWFstatMapVersion='lal', cudaDeviceName=None):
Gregory Ashton's avatar
Gregory Ashton committed
344
345
346
        """
        Parameters
        ----------
347
        tref : int
Gregory Ashton's avatar
Gregory Ashton committed
348
            GPS seconds of the reference time.
349
        sftfilepattern : str
350
351
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
352
        minStartTime, maxStartTime : float GPStime
Gregory Ashton's avatar
Gregory Ashton committed
353
354
            Only use SFTs with timestemps starting from (including, excluding)
            this epoch
355
        binary : bool
Gregory Ashton's avatar
Gregory Ashton committed
356
            If true, search of binary parameters.
357
        BSGL : bool
Gregory Ashton's avatar
Gregory Ashton committed
358
            If true, compute the BSGL rather than the twoF value.
David Keitel's avatar
David Keitel committed
359
360
361
        transientWindowType: str
            If 'rect' or 'exp',
            allow for the Fstat to be computed over a transient range.
Gregory Ashton's avatar
Gregory Ashton committed
362
363
            ('none' instead of None explicitly calls the transient-window
            function, but with the full range, for debugging)
364
365
366
367
368
        t0Band, tauBand: int
            if >0, search t0 in (minStartTime,minStartTime+t0Band)
                   and tau in (2*Tsft,2*Tsft+tauBand).
            if =0, only compute CW Fstat with t0=minStartTime,
                   tau=maxStartTime-minStartTime.
369
370
371
        dt0, dtau: int
            grid resolutions in transient start-time and duration,
            both default to Tsft
372
        detectors : str
Gregory Ashton's avatar
Gregory Ashton committed
373
            Two character reference to the data to use, specify None for no
374
            contraint. If multiple-separate by comma.
375
        minCoverFreq, maxCoverFreq : float
Gregory Ashton's avatar
Gregory Ashton committed
376
377
378
            The min and max cover frequency passed to CreateFstatInput, if
            either is None the range of frequencies in the SFT less 1Hz is
            used.
379
        injectSources : dict or str
380
381
            Either a dictionary of the values to inject, or a string pointing
            to the .cff file to inject
382
        injectSqrtSX :
383
            Not yet implemented
384
        assumeSqrtSX : float
385
386
387
            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
388
        SSBprec : int
389
390
            Flag to set the SSB calculation: 0=Newtonian, 1=relativistic,
            2=relativisitic optimised, 3=DMoff, 4=NO_SPIN
391
392
393
        tCWFstatMapVersion: str
            Choose between standard 'lal' implementation,
            'pycuda' for gpu, and some others for devel/debug.
394
395
        cudaDeviceName: str
            GPU name to be matched against drv.Device output.
Gregory Ashton's avatar
Gregory Ashton committed
396
397
398

        """

399
        self.set_ephemeris_files()
Gregory Ashton's avatar
Gregory Ashton committed
400
401
        self.init_computefstatistic_single_point()

402
403
404
405
406
407
408
409
410
411
412
    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
413
414
        if hasattr(self, 'SFTCatalog'):
            return
415
        if self.sftfilepattern is None:
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
            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
437
438
        logging.info('Initialising SFTCatalog')
        constraints = lalpulsar.SFTConstraints()
439
        if self.detectors:
440
            if ',' in self.detectors:
441
442
                logging.warning('Multiple detector selection not available,'
                                ' using all available data')
443
444
            else:
                constraints.detector = self.detectors
Gregory Ashton's avatar
Gregory Ashton committed
445
446
447
448
449
        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(
450
451
                     self.sftfilepattern))
        SFTCatalog = lalpulsar.SFTdataFind(self.sftfilepattern, constraints)
452

Gregory Ashton's avatar
Gregory Ashton committed
453
        SFT_timestamps = [d.header.epoch for d in SFTCatalog.data]
Gregory Ashton's avatar
Gregory Ashton committed
454
        self.SFT_timestamps = [float(s) for s in SFT_timestamps]
455
456
        if len(SFT_timestamps) == 0:
            raise ValueError('Failed to load any data')
Gregory Ashton's avatar
Gregory Ashton committed
457
458
459
460
461
        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
462
            except ImportError:
Gregory Ashton's avatar
Gregory Ashton committed
463
                pass
464

465
        cl_tconv1 = 'lalapps_tconvert {}'.format(int(SFT_timestamps[0]))
466
467
        output = helper_functions.run_commandline(cl_tconv1,
                                                  log_level=logging.DEBUG)
468
469
        tconvert1 = output.rstrip('\n')
        cl_tconv2 = 'lalapps_tconvert {}'.format(int(SFT_timestamps[-1]))
470
471
        output = helper_functions.run_commandline(cl_tconv2,
                                                  log_level=logging.DEBUG)
472
        tconvert2 = output.rstrip('\n')
Gregory Ashton's avatar
Gregory Ashton committed
473
474
        logging.info('Data spans from {} ({}) to {} ({})'.format(
            int(SFT_timestamps[0]),
475
            tconvert1,
Gregory Ashton's avatar
Gregory Ashton committed
476
            int(SFT_timestamps[-1]),
477
            tconvert2))
478
479
480
481
482
483
484
485
486
487
488
489
490

        if self.minStartTime is None:
            self.minStartTime = int(SFT_timestamps[0])
        if self.maxStartTime is None:
            self.maxStartTime = int(SFT_timestamps[-1])

        detector_names = list(set([d.header.name for d in SFTCatalog.data]))
        self.detector_names = detector_names
        if len(detector_names) == 0:
            raise ValueError('No data loaded.')
        logging.info('Loaded {} data files from detectors {}'.format(
            len(SFT_timestamps), detector_names))

491
        return SFTCatalog
Gregory Ashton's avatar
Gregory Ashton committed
492
493
494
495

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

496
        SFTCatalog = self._get_SFTCatalog()
Gregory Ashton's avatar
Gregory Ashton committed
497
498
499
500
501
502

        logging.info('Initialising ephems')
        ephems = lalpulsar.InitBarycenter(self.earth_ephem, self.sun_ephem)

        logging.info('Initialising FstatInput')
        dFreq = 0
David Keitel's avatar
David Keitel committed
503
        if self.transientWindowType:
Gregory Ashton's avatar
Gregory Ashton committed
504
505
506
507
508
509
            self.whatToCompute = lalpulsar.FSTATQ_ATOMS_PER_DET
        else:
            self.whatToCompute = lalpulsar.FSTATQ_2F

        FstatOAs = lalpulsar.FstatOptionalArgs()
        FstatOAs.randSeed = lalpulsar.FstatOptionalArgsDefaults.randSeed
510
511
512
513
514
        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
515
516
517
        FstatOAs.Dterms = lalpulsar.FstatOptionalArgsDefaults.Dterms
        FstatOAs.runningMedianWindow = lalpulsar.FstatOptionalArgsDefaults.runningMedianWindow
        FstatOAs.FstatMethod = lalpulsar.FstatOptionalArgsDefaults.FstatMethod
518
519
520
521
522
523
524
525
        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
526
527
528
        FstatOAs.prevInput = lalpulsar.FstatOptionalArgsDefaults.prevInput
        FstatOAs.collectTiming = lalpulsar.FstatOptionalArgsDefaults.collectTiming

Gregory Ashton's avatar
Gregory Ashton committed
529
        if hasattr(self, 'injectSources') and type(self.injectSources) == dict:
Gregory Ashton's avatar
Gregory Ashton committed
530
531
532
533
534
535
536
537
538
539
            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
540
541
542
543
544
545
            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
546
547
548
549
            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
550
        elif hasattr(self, 'injectSources') and type(self.injectSources) == str:
551
552
553
554
            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
555
556
        else:
            FstatOAs.injectSources = lalpulsar.FstatOptionalArgsDefaults.injectSources
557
558
559
560
        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
561
        if self.minCoverFreq is None or self.maxCoverFreq is None:
562
            fAs = [d.header.f0 for d in SFTCatalog.data]
Gregory Ashton's avatar
Gregory Ashton committed
563
            fBs = [d.header.f0 + (d.numBins-1)*d.header.deltaF
564
                   for d in SFTCatalog.data]
Gregory Ashton's avatar
Gregory Ashton committed
565
566
567
568
569
570
            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))

571
        self.FstatInput = lalpulsar.CreateFstatInput(SFTCatalog,
Gregory Ashton's avatar
Gregory Ashton committed
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
                                                     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:
592
                raise ValueError("Can't use BSGL with single detectors data")
Gregory Ashton's avatar
Gregory Ashton committed
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
            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)

David Keitel's avatar
David Keitel committed
619
        if self.transientWindowType:
Gregory Ashton's avatar
Gregory Ashton committed
620
621
            logging.info('Initialising transient parameters')
            self.windowRange = lalpulsar.transientWindowRange_t()
David Keitel's avatar
David Keitel committed
622
623
624
625
626
627
            transientWindowTypes = {'none': lalpulsar.TRANSIENT_NONE,
                                    'rect': lalpulsar.TRANSIENT_RECTANGULAR,
                                    'exp':  lalpulsar.TRANSIENT_EXPONENTIAL}
            if self.transientWindowType in transientWindowTypes:
                self.windowRange.type = transientWindowTypes[self.transientWindowType]
            else:
Gregory Ashton's avatar
Gregory Ashton committed
628
629
630
631
                raise ValueError(
                    'Unknown window-type ({}) passed as input, [{}] allows.'
                    .format(self.transientWindowType,
                            ', '.join(transientWindowTypes)))
David Keitel's avatar
David Keitel committed
632

633
            # default spacing
David Keitel's avatar
David Keitel committed
634
            self.Tsft = int(1.0/SFTCatalog.data[0].header.deltaF)
635
636
637
            self.windowRange.dt0 = self.Tsft
            self.windowRange.dtau = self.Tsft

David Keitel's avatar
David Keitel committed
638
639
            # special treatment of window_type = none
            # ==> replace by rectangular window spanning all the data
640
641
            if self.windowRange.type == lalpulsar.TRANSIENT_NONE:
                self.windowRange.t0 = int(self.minStartTime)
Gregory Ashton's avatar
Gregory Ashton committed
642
                self.windowRange.t0Band = 0
643
                self.windowRange.tau = int(self.maxStartTime-self.minStartTime)
David Keitel's avatar
David Keitel committed
644
                self.windowRange.tauBand = 0
Gregory Ashton's avatar
Gregory Ashton committed
645
            else:  # user-set bands and spacings
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
                if self.t0Band is None:
                    self.windowRange.t0Band = 0
                else:
                    if not isinstance(self.t0Band, int):
                        logging.warn('Casting non-integer t0Band={} to int...'
                                     .format(self.t0Band))
                        self.t0Band = int(self.t0Band)
                    self.windowRange.t0Band = self.t0Band
                    if self.dt0:
                        self.windowRange.dt0 = self.dt0
                if self.tauBand is None:
                    self.windowRange.tauBand = 0
                else:
                    if not isinstance(self.tauBand, int):
                        logging.warn('Casting non-integer tauBand={} to int...'
                                     .format(self.tauBand))
                        self.tauBand = int(self.tauBand)
                    self.windowRange.tauBand = self.tauBand
                    if self.dtau:
                        self.windowRange.dtau = self.dtau
Gregory Ashton's avatar
Gregory Ashton committed
666

David Keitel's avatar
David Keitel committed
667
            logging.info('Initialising transient FstatMap features...')
Gregory Ashton's avatar
Gregory Ashton committed
668
669
670
            self.tCWFstatMapFeatures, self.gpu_context = (
                tcw.init_transient_fstat_map_features(
                    self.tCWFstatMapVersion == 'pycuda', self.cudaDeviceName))
671

672
673
674
    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
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
        """ 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
                               )

David Keitel's avatar
David Keitel committed
693
        if not self.transientWindowType:
Gregory Ashton's avatar
Gregory Ashton committed
694
695
696
697
698
699
700
701
702
703
704
            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
David Keitel's avatar
David Keitel committed
705
706
707
708
709
        if self.windowRange.tauBand == 0:
            # true single-template search also in transient params:
            # actual (t0,tau) window was set with tstart, tend before
            self.windowRange.tau = int(tend - tstart)  # TYPE UINT4
        else:
Gregory Ashton's avatar
Gregory Ashton committed
710
711
            # grid search: start at minimum tau required for nondegenerate
            # F-stat computation
David Keitel's avatar
David Keitel committed
712
            self.windowRange.tau = int(2*self.Tsft)
Gregory Ashton's avatar
Gregory Ashton committed
713

Gregory Ashton's avatar
Gregory Ashton committed
714
715
716
        self.FstatMap = tcw.call_compute_transient_fstat_map(
            self.tCWFstatMapVersion, self.tCWFstatMapFeatures,
            self.FstatResults.multiFatoms[0], self.windowRange)
717
718
719
720
        if self.tCWFstatMapVersion == 'lal':
            F_mn = self.FstatMap.F_mn.data
        else:
            F_mn = self.FstatMap.F_mn
Gregory Ashton's avatar
Gregory Ashton committed
721

722
        twoF = 2*np.max(F_mn)
Gregory Ashton's avatar
Gregory Ashton committed
723
        if self.BSGL is False:
724
725
726
727
            if np.isnan(twoF):
                return 0
            else:
                return twoF
Gregory Ashton's avatar
Gregory Ashton committed
728
729
730
731
732
733
734
735
736
737

        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)

738
739
740
741
742
        # for now, use the Doppler parameter with
        # multi-detector F maximised over t0,tau
        # to return BSGL
        # FIXME: should we instead compute BSGL over the whole F_mn
        # and return the maximum of that?
743
        idx_maxTwoF = np.argmax(F_mn)
744
745
746

        self.twoFX[0] = 2*FS0.F_mn.data[idx_maxTwoF]
        self.twoFX[1] = 2*FS1.F_mn.data[idx_maxTwoF]
Gregory Ashton's avatar
Gregory Ashton committed
747
        log10_BSGL = lalpulsar.ComputeBSGL(
748
                twoF, self.twoFX, self.BSGLSetup)
Gregory Ashton's avatar
Gregory Ashton committed
749
750
751
752
753
754

        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,
755
756
                                  ):
        """ Calculate the cumulative twoF along the obseration span
757
758
759

        Parameters
        ----------
760
761
        F0, F1, F2, Alpha, Delta: float
            Parameters at which to compute the cumulative twoF
762
763
        asini, period, ecc, tp, argp: float, optional
            Binary parameters at which to compute the cumulative 2F
764
765
766
767
768
769
        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

770
771
772
        Notes
        -----
        The minimum cumulatibe twoF is hard-coded to be computed over
773
774
775
776
777
778
        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
779
        tstart = np.max([SFTminStartTime, tstart])
780
781
782
        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
783
        twoFs = []
David Keitel's avatar
David Keitel committed
784
785
786
        if not self.transientWindowType:
            # still call the transient-Fstat-map function, but using the full range
            self.transientWindowType = 'none'
Gregory Ashton's avatar
Gregory Ashton committed
787
788
            self.init_computefstatistic_single_point()
        for tau in taus:
789
            detstat = self.get_fullycoherent_twoF(
Gregory Ashton's avatar
Gregory Ashton committed
790
791
                tstart=tstart, tend=tstart+tau, F0=F0, F1=F1, F2=F2,
                Alpha=Alpha, Delta=Delta, asini=asini, period=period, ecc=ecc,
792
793
                tp=tp, argp=argp)
            twoFs.append(detstat)
Gregory Ashton's avatar
Gregory Ashton committed
794
795
796

        return taus, np.array(twoFs)

797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
    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
817
818
819
820
821

        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')
822
            loudest = read_par(label=label, outdir=outdir, suffix='loudest')
Gregory Ashton's avatar
Gregory Ashton committed
823
824
            pfs_input = {key: loudest[key] for key in
                         ['h0', 'cosi', 'psi', 'Alpha', 'Delta', 'Freq']}
825
826
827
        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,
828
                             sftfilepattern=self.sftfilepattern, IFO=IFO,
829
830
831
832
                             **pfs_input) for t in times]
        pfs, pfs_sigma = np.array(out).T
        return times, pfs, pfs_sigma

833
834
    def plot_twoF_cumulative(self, label, outdir, add_pfs=False, N=15,
                             injectSources=None, ax=None, c='k', savefig=True,
835
                             title=None, plt_label=None, **kwargs):
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
        """ 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
853
854
        title, plt_label: str
            Figure title and label
855
856
857
858
859
860
861
862
863

        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
864
865
        if ax is None:
            fig, ax = plt.subplots()
Gregory Ashton's avatar
Gregory Ashton committed
866
867
868
869
870
871
872
        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
873
874

        taus, twoFs = self.calculate_twoF_cumulative(**kwargs)
875
        ax.plot(taus/86400., twoFs, label=plt_label, color=c)
876
        if len(self.detector_names) > 1:
877
878
            detector_names = self.detector_names
            detectors = self.detectors
879
880
881
882
            for d in self.detector_names:
                self.detectors = d
                self.init_computefstatistic_single_point()
                taus, twoFs = self.calculate_twoF_cumulative(**kwargs)
883
884
885
886
887
888
                ax.plot(taus/86400., twoFs, label='{}'.format(d),
                        color=detector_colors[d.lower()])
            self.detectors = detectors
            self.detector_names = detector_names

        if add_pfs:
889
890
            times, pfs, pfs_sigma = self._calculate_predict_fstat_cumulative(
                N=N, label=label, outdir=outdir, pfs_input=pfs_input)
891
892
            ax.fill_between(
                (times-self.minStartTime)/86400., pfs-pfs_sigma, pfs+pfs_sigma,
Gregory Ashton's avatar
Gregory Ashton committed
893
                color=c,
894
895
                label=(r'Predicted $\langle 2\mathcal{F} '
                       r'\rangle\pm $ 1-$\sigma$ band'),
896
897
898
                zorder=-10, alpha=0.2)
            if len(self.detector_names) > 1:
                for d in self.detector_names:
899
900
901
902
                    out = self._calculate_predict_fstat_cumulative(
                        N=N, label=label, outdir=outdir, IFO=d.upper(),
                        pfs_input=pfs_input)
                    times, pfs, pfs_sigma = out
903
904
905
906
907
908
909
910
                    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)
911

Gregory Ashton's avatar
Gregory Ashton committed
912
913
914
915
916
917
918
        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)
919
920
        if plt_label:
            ax.legend(frameon=False, loc=2, fontsize=6)
Gregory Ashton's avatar
Gregory Ashton committed
921
922
923
924
925
926
927
928
929
        if title:
            ax.set_title(title)
        if savefig:
            plt.tight_layout()
            plt.savefig('{}/{}_twoFcumulative.png'.format(outdir, label))
            return taus, twoFs
        else:
            return ax

930
931
932
933
934
935
936
937
938
939
940
941
    def get_full_CFSv2_output(self, tstart, tend, F0, F1, F2, Alpha, Delta,
                              tref):
        """ Basic wrapper around CFSv2 to get the full (h0..) output """
        cl_CFSv2 = "lalapps_ComputeFstatistic_v2 --minStartTime={} --maxStartTime={} --Freq={} --f1dot={} --f2dot={} --Alpha={} --Delta={} --refTime={} --DataFiles='{}' --outputLoudest='{}' --ephemEarth={} --ephemSun={}"
        LoudestFile = "loudest.temp"
        helper_functions.run_commandline(cl_CFSv2.format(
            tstart, tend, F0, F1, F2, Alpha, Delta, tref, self.sftfilepattern,
            LoudestFile, self.earth_ephem, self.sun_ephem))
        loudest = read_par(LoudestFile, return_type='dict')
        os.remove(LoudestFile)
        return loudest

942
943
944
945
946
947
948
949
950
951
952
953
    def write_atoms_to_file(self, fnamebase=''):
        multiFatoms = getattr(self.FstatResults, 'multiFatoms', None)
        if multiFatoms and multiFatoms[0]:
            dopplerName = lalpulsar.PulsarDopplerParams2String ( self.PulsarDopplerParams )
            #fnameAtoms = os.path.join(self.outdir,'Fstatatoms_%s.dat' % dopplerName)
            fnameAtoms = fnamebase + '_Fstatatoms_%s.dat' % dopplerName
            fo = lal.FileOpen(fnameAtoms, 'w')
            lalpulsar.write_MultiFstatAtoms_to_fp ( fo, multiFatoms[0] )
            del fo # instead of lal.FileClose() which is not SWIG-exported
        else:
            raise RuntimeError('Cannot print atoms vector to file: no FstatResults.multiFatoms, or it is None!')

Gregory Ashton's avatar
Gregory Ashton committed
954

955
956
957
958
959
960
961
962
963
    def __del__(self):
        """
        In pyCuda case without autoinit,
        we need to make sure the context is removed at the end
        """
        if hasattr(self,'gpu_context') and self.gpu_context:
            self.gpu_context.detach()


964
class SemiCoherentSearch(ComputeFstat):
Gregory Ashton's avatar
Gregory Ashton committed
965
966
967
    """ A semi-coherent search """

    @helper_functions.initializer
968
    def __init__(self, label, outdir, tref, nsegs=None, sftfilepattern=None,
Gregory Ashton's avatar
Gregory Ashton committed
969
970
                 binary=False, BSGL=False, minStartTime=None,
                 maxStartTime=None, minCoverFreq=None, maxCoverFreq=None,
971
972
                 detectors=None, injectSources=None, assumeSqrtSX=None,
                 SSBprec=None):
Gregory Ashton's avatar
Gregory Ashton committed
973
974
975
976
977
978
979
980
981
        """
        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
982
983
984
        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
985
986
987
988
989

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

        self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)
990
        self.set_ephemeris_files()
David Keitel's avatar
David Keitel committed
991
992
993
        self.transientWindowType = 'rect'
        self.t0Band  = None
        self.tauBand = None
994
        self.tCWFstatMapVersion = 'lal'
995
        self.cudaDeviceName = None
Gregory Ashton's avatar
Gregory Ashton committed
996
997
998
999
1000
        self.init_computefstatistic_single_point()
        self.init_semicoherent_parameters()

    def init_semicoherent_parameters(self):
        logging.info(('Initialising semicoherent parameters from {} to {} in'
For faster browsing, not all history is shown. View entire blame