pyfstat.py 105 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
""" Classes for various types of searches using ComputeFstatistic """
import os
import sys
import itertools
import logging
import argparse
import copy
import glob
import inspect
from functools import wraps
11
import subprocess
12
from collections import OrderedDict
13
14
15

import numpy as np
import matplotlib
16
matplotlib.use('Agg')
17
18
19
20
import matplotlib.pyplot as plt
import emcee
import corner
import dill as pickle
21
import lal
22
23
import lalpulsar

24
25
26
27
28
29
try:
    from tqdm import tqdm
except ImportError:
    def tqdm(x):
        return x

30
plt.rcParams['text.usetex'] = True
31
plt.rcParams['axes.formatter.useoffset'] = False
32

33
34
35
36
37
38
39
config_file = os.path.expanduser('~')+'/.pyfstat.conf'
if os.path.isfile(config_file):
    d = {}
    with open(config_file, 'r') as f:
        for line in f:
            k, v = line.split('=')
            k = k.replace(' ', '')
40
            v = v.replace(' ', '').replace("'", "").replace('"', '').replace('\n', '')
41
42
43
44
45
46
            d[k] = v
    earth_ephem = d['earth_ephem']
    sun_ephem = d['sun_ephem']
else:
    logging.warning('No ~/.pyfstat.conf file found please provide the paths '
                    'when initialising searches')
47
48
49
    earth_ephem = None
    sun_ephem = None

50
51
52
53
54
parser = argparse.ArgumentParser()
parser.add_argument("-q", "--quite", help="Decrease output verbosity",
                    action="store_true")
parser.add_argument("-c", "--clean", help="Don't use cached data",
                    action="store_true")
55
parser.add_argument("-u", "--use-old-data", action="store_true")
56
57
58
59
parser.add_argument('unittest_args', nargs='*')
args, unknown = parser.parse_known_args()
sys.argv[1:] = args.unittest_args

Gregory Ashton's avatar
Gregory Ashton committed
60
61
62
63

logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
stream_handler = logging.StreamHandler()
64
if args.quite:
Gregory Ashton's avatar
Gregory Ashton committed
65
    stream_handler.setLevel(logging.WARNING)
66
else:
Gregory Ashton's avatar
Gregory Ashton committed
67
68
69
70
    stream_handler.setLevel(logging.DEBUG)
stream_handler.setFormatter(logging.Formatter(
    '%(asctime)s %(levelname)-8s: %(message)s', datefmt='%H:%M'))
logger.addHandler(stream_handler)
71

72
73

def initializer(func):
74
    """ Decorator function to automatically assign the parameters to self """
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
    names, varargs, keywords, defaults = inspect.getargspec(func)

    @wraps(func)
    def wrapper(self, *args, **kargs):
        for name, arg in list(zip(names[1:], args)) + list(kargs.items()):
            setattr(self, name, arg)

        for name, default in zip(reversed(names), reversed(defaults)):
            if not hasattr(self, name):
                setattr(self, name, default)

        func(self, *args, **kargs)

    return wrapper


def read_par(label, outdir):
92
    """ Read in a .par file, returns a dictionary of the values """
93
94
95
96
    filename = '{}/{}.par'.format(outdir, label)
    d = {}
    with open(filename, 'r') as f:
        for line in f:
97
98
99
            if len(line.split('=')) > 1:
                key, val = line.rstrip('\n').split(' = ')
                key = key.strip()
100
                d[key] = np.float64(eval(val.rstrip('; ')))
101
102
103
104
    return d


class BaseSearchClass(object):
105
    """ The base search class, provides general functions """
106
107
108
109

    earth_ephem_default = earth_ephem
    sun_ephem_default = sun_ephem

110
    def add_log_file(self):
111
        """ Log output to a file, requires class to have outdir and label """
112
113
        logfilename = '{}/{}.log'.format(self.outdir, self.label)
        fh = logging.FileHandler(logfilename)
Gregory Ashton's avatar
Gregory Ashton committed
114
        fh.setLevel(logging.INFO)
115
116
117
118
119
        fh.setFormatter(logging.Formatter(
            '%(asctime)s %(levelname)-8s: %(message)s',
            datefmt='%y-%m-%d %H:%M'))
        logging.getLogger().addHandler(fh)

120
    def shift_matrix(self, n, dT):
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
        """ Generate the shift matrix

        Parameters
        ----------
        n: int
            The dimension of the shift-matrix to generate
        dT: float
            The time delta of the shift matrix

        Returns
        -------
        m: array (n, n)
            The shift matrix
        """

136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
        m = np.zeros((n, n))
        factorial = np.math.factorial
        for i in range(n):
            for j in range(n):
                if i == j:
                    m[i, j] = 1.0
                elif i > j:
                    m[i, j] = 0.0
                else:
                    if i == 0:
                        m[i, j] = 2*np.pi*float(dT)**(j-i) / factorial(j-i)
                    else:
                        m[i, j] = float(dT)**(j-i) / factorial(j-i)
        return m

    def shift_coefficients(self, theta, dT):
        """ Shift a set of coefficients by dT

        Parameters
        ----------
        theta: array-like, shape (n,)
            vector of the expansion coefficients to transform starting from the
158
            lowest degree e.g [phi, F0, F1,...].
159
        dT: float
160
            difference between the two reference times as tref_new - tref_old.
161
162
163
164

        Returns
        -------
        theta_new: array-like shape (n,)
165
            vector of the coefficients as evaluate as the new reference time.
166
        """
167

168
169
170
171
        n = len(theta)
        m = self.shift_matrix(n, dT)
        return np.dot(m, theta)

172
    def calculate_thetas(self, theta, delta_thetas, tbounds, theta0_idx=0):
173
174
175
        """ Calculates the set of coefficients for the post-glitch signal """
        thetas = [theta]
        for i, dt in enumerate(delta_thetas):
176
177
178
179
180
181
182
183
184
185
186
187
188
            if i < theta0_idx:
                pre_theta_at_ith_glitch = self.shift_coefficients(
                    thetas[0], tbounds[i+1] - self.tref)
                post_theta_at_ith_glitch = pre_theta_at_ith_glitch - dt
                thetas.insert(0, self.shift_coefficients(
                    post_theta_at_ith_glitch, self.tref - tbounds[i+1]))

            elif i >= theta0_idx:
                pre_theta_at_ith_glitch = self.shift_coefficients(
                    thetas[i], tbounds[i+1] - self.tref)
                post_theta_at_ith_glitch = pre_theta_at_ith_glitch + dt
                thetas.append(self.shift_coefficients(
                    post_theta_at_ith_glitch, self.tref - tbounds[i+1]))
189
190
191
        return thetas


Gregory Ashton's avatar
Gregory Ashton committed
192
class ComputeFstat(object):
193
    """ Base class providing interface to `lalpulsar.ComputeFstat` """
Gregory Ashton's avatar
Gregory Ashton committed
194
195
196
197
198

    earth_ephem_default = earth_ephem
    sun_ephem_default = sun_ephem

    @initializer
199
200
    def __init__(self, tref, sftfilepath=None, minStartTime=None,
                 maxStartTime=None, binary=False, transient=True, BSGL=False,
201
                 detector=None, minCoverFreq=None, maxCoverFreq=None,
202
                 earth_ephem=None, sun_ephem=None, injectSources=None
203
                 ):
204
205
206
207
208
        """
        Parameters
        ----------
        tref: int
            GPS seconds of the reference time.
209
210
        sftfilepath: str
            File patern to match SFTs
211
212
213
214
215
216
217
218
219
220
221
222
        minStartTime, maxStartTime: float GPStime
            Only use SFTs with timestemps starting from (including, excluding)
            this epoch
        binary: bool
            If true, search of binary parameters.
        transient: bool
            If true, allow for the Fstat to be computed over a transient range.
        BSGL: bool
            If true, compute the BSGL rather than the twoF value.
        detector: str
            Two character reference to the data to use, specify None for no
            contraint.
223
224
225
226
227
228
229
230
231
232
        minCoverFreq, maxCoverFreq: float
            The min and max cover frequency passed to CreateFstatInput, if
            either is None the range of frequencies in the SFT less 1Hz is
            used.
        earth_ephem, sun_ephem: str
            Paths of the two files containing positions of Earth and Sun,
            respectively at evenly spaced times, as passed to CreateFstatInput.
            If None defaults defined in BaseSearchClass will be used.

        """
Gregory Ashton's avatar
Gregory Ashton committed
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247

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

        self.init_computefstatistic_single_point()

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

        logging.info('Initialising SFTCatalog')
        constraints = lalpulsar.SFTConstraints()
        if self.detector:
            constraints.detector = self.detector
248
249
250
251
252
        if self.minStartTime:
            constraints.minStartTime = lal.LIGOTimeGPS(self.minStartTime)
        if self.maxStartTime:
            constraints.maxStartTime = lal.LIGOTimeGPS(self.maxStartTime)

253
        logging.info('Loading data matching pattern {}'.format(
254
255
                     self.sftfilepath))
        SFTCatalog = lalpulsar.SFTdataFind(self.sftfilepath, constraints)
Gregory Ashton's avatar
Gregory Ashton committed
256
        names = list(set([d.header.name for d in SFTCatalog.data]))
257
        SFT_timestamps = [d.header.epoch for d in SFTCatalog.data]
258
        logging.info(
259
            'Loaded {} data files from detectors {} spanning {} to {}'.format(
260
261
                len(SFT_timestamps), names, int(SFT_timestamps[0]),
                int(SFT_timestamps[-1])))
Gregory Ashton's avatar
Gregory Ashton committed
262
263
264
265
266
267

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

        logging.info('Initialising FstatInput')
        dFreq = 0
268
269
270
271
272
        if self.transient:
            self.whatToCompute = lalpulsar.FSTATQ_ATOMS_PER_DET
        else:
            self.whatToCompute = lalpulsar.FSTATQ_2F

273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
        FstatOAs = lalpulsar.FstatOptionalArgs()
        FstatOAs.randSeed = lalpulsar.FstatOptionalArgsDefaults.randSeed
        FstatOAs.SSBprec = lalpulsar.FstatOptionalArgsDefaults.SSBprec
        FstatOAs.Dterms = lalpulsar.FstatOptionalArgsDefaults.Dterms
        FstatOAs.runningMedianWindow = lalpulsar.FstatOptionalArgsDefaults.runningMedianWindow
        FstatOAs.FstatMethod = lalpulsar.FstatOptionalArgsDefaults.FstatMethod
        FstatOAs.InjectSqrtSX = lalpulsar.FstatOptionalArgsDefaults.injectSqrtSX
        FstatOAs.assumeSqrtSX = lalpulsar.FstatOptionalArgsDefaults.assumeSqrtSX
        FstatOAs.prevInput = lalpulsar.FstatOptionalArgsDefaults.prevInput
        FstatOAs.collectTiming = lalpulsar.FstatOptionalArgsDefaults.collectTiming

        if type(self.injectSources) == dict:
            logging.info('Injecting source with params: {}'.format(
                self.injectSources))
            PPV = lalpulsar.CreatePulsarParamsVector(1)
            PP = PPV.data[0]
            PP.Amp.h0 = self.injectSources['h0']
            PP.Amp.cosi = self.injectSources['cosi']
            PP.Amp.phi0 = self.injectSources['phi0']
            PP.Amp.psi = self.injectSources['psi']
            PP.Doppler.Alpha = self.injectSources['Alpha']
            PP.Doppler.Delta = self.injectSources['Delta']
            PP.Doppler.fkdot = np.array(self.injectSources['fkdot'])
            PP.Doppler.refTime = self.tref
            if 't0' not in self.injectSources:
                #PP.Transient.t0 = int(self.minStartTime)
                #PP.Transient.tau = int(self.maxStartTime - self.minStartTime)
                PP.Transient.type = lalpulsar.TRANSIENT_NONE
            FstatOAs.injectSources = PPV
        else:
            FstatOAs.injectSources = lalpulsar.FstatOptionalArgsDefaults.injectSources
Gregory Ashton's avatar
Gregory Ashton committed
304
305
306
307
308
309
310

        if self.minCoverFreq is None or self.maxCoverFreq is None:
            fA = SFTCatalog.data[0].header.f0
            numBins = SFTCatalog.data[0].numBins
            fB = fA + (numBins-1)*SFTCatalog.data[0].header.deltaF
            self.minCoverFreq = fA + 0.5
            self.maxCoverFreq = fB - 0.5
311
312
313
            logging.info('Min/max cover freqs not provided, using '
                         '{} and {}, est. from SFTs'.format(
                             self.minCoverFreq, self.maxCoverFreq))
Gregory Ashton's avatar
Gregory Ashton committed
314
315
316
317
318
319

        self.FstatInput = lalpulsar.CreateFstatInput(SFTCatalog,
                                                     self.minCoverFreq,
                                                     self.maxCoverFreq,
                                                     dFreq,
                                                     ephems,
320
                                                     FstatOAs
Gregory Ashton's avatar
Gregory Ashton committed
321
322
323
324
325
326
327
328
329
330
331
332
333
                                                     )

        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()

334
        if self.BSGL:
Gregory Ashton's avatar
Gregory Ashton committed
335
336
            if len(names) < 2:
                raise ValueError("Can't use BSGL with single detector data")
337
            else:
338
                logging.info('Initialising BSGL')
339

340
341
            # Tuning parameters - to be reviewed
            numDetectors = 2
Gregory Ashton's avatar
Gregory Ashton committed
342
            Fstar0sc = 15.
343
            oLGX = np.zeros(10)
Gregory Ashton's avatar
Gregory Ashton committed
344
            oLGX[:numDetectors] = 1./numDetectors
345
346
347
            self.BSGLSetup = lalpulsar.CreateBSGLSetup(numDetectors,
                                                       Fstar0sc,
                                                       oLGX,
348
                                                       True,
349
350
                                                       1)
            self.twoFX = np.zeros(10)
Gregory Ashton's avatar
Gregory Ashton committed
351
            self.whatToCompute = (self.whatToCompute +
352
353
                                  lalpulsar.FSTATQ_2F_PER_DET)

354
        if self.transient:
355
            logging.info('Initialising transient parameters')
356
357
358
359
360
361
            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
362

363
364
365
366
367
368
369
370
371
    def compute_fullycoherent_det_stat_single_point(
            self, F0, F1, F2, Alpha, Delta, asini=None, period=None, ecc=None,
            tp=None, argp=None):
        """ Compute the fully-coherent det. statistic at a single point """

        return self.run_computefstatistic_single_point(
            self.minStartTime, self.maxStartTime, F0, F1, F2, Alpha, Delta,
            asini, period, ecc, tp, argp)

Gregory Ashton's avatar
Gregory Ashton committed
372
    def run_computefstatistic_single_point(self, tstart, tend, F0, F1,
373
374
375
                                           F2, Alpha, Delta, asini=None,
                                           period=None, ecc=None, tp=None,
                                           argp=None):
376
        """ Returns twoF or ln(BSGL) fully-coherently at a single point """
Gregory Ashton's avatar
Gregory Ashton committed
377
378
379
380

        self.PulsarDopplerParams.fkdot = np.array([F0, F1, F2, 0, 0, 0, 0])
        self.PulsarDopplerParams.Alpha = Alpha
        self.PulsarDopplerParams.Delta = Delta
381
382
383
384
385
386
        if self.binary:
            self.PulsarDopplerParams.asini = asini
            self.PulsarDopplerParams.period = period
            self.PulsarDopplerParams.ecc = ecc
            self.PulsarDopplerParams.tp = tp
            self.PulsarDopplerParams.argp = argp
Gregory Ashton's avatar
Gregory Ashton committed
387
388
389
390

        lalpulsar.ComputeFstat(self.FstatResults,
                               self.FstatInput,
                               self.PulsarDopplerParams,
391
                               1,
Gregory Ashton's avatar
Gregory Ashton committed
392
393
394
                               self.whatToCompute
                               )

395
        if self.transient is False:
396
397
398
399
400
401
            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)
402
403
404
            log10_BSGL = lalpulsar.ComputeBSGL(twoF, self.twoFX,
                                               self.BSGLSetup)
            return log10_BSGL/np.log10(np.exp(1))
405

406
407
        self.windowRange.t0 = int(tstart)  # TYPE UINT4
        self.windowRange.tau = int(tend - tstart)  # TYPE UINT4
408

Gregory Ashton's avatar
Gregory Ashton committed
409
        FS = lalpulsar.ComputeTransientFstatMap(
410
            self.FstatResults.multiFatoms[0], self.windowRange, False)
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425

        if self.BSGL is False:
            return 2*FS.F_mn.data[0][0]

        FstatResults_single = copy.copy(self.FstatResults)
        FstatResults_single.lenth = 1
        FstatResults_single.data = self.FstatResults.multiFatoms[0].data[0]
        FS0 = lalpulsar.ComputeTransientFstatMap(
            FstatResults_single.multiFatoms[0], self.windowRange, False)
        FstatResults_single.data = self.FstatResults.multiFatoms[0].data[1]
        FS1 = lalpulsar.ComputeTransientFstatMap(
            FstatResults_single.multiFatoms[0], self.windowRange, False)

        self.twoFX[0] = 2*FS0.F_mn.data[0][0]
        self.twoFX[1] = 2*FS1.F_mn.data[0][0]
426
427
        log10_BSGL = lalpulsar.ComputeBSGL(
                2*FS.F_mn.data[0][0], self.twoFX, self.BSGLSetup)
428

429
        return log10_BSGL/np.log10(np.exp(1))
Gregory Ashton's avatar
Gregory Ashton committed
430

431
432
    def calculate_twoF_cumulative(self, F0, F1, F2, Alpha, Delta, asini=None,
                                  period=None, ecc=None, tp=None, argp=None,
433
434
                                  tstart=None, tend=None, npoints=1000,
                                  minfraction=0.01, maxfraction=1):
435
436
        """ Calculate the cumulative twoF along the obseration span """
        duration = tend - tstart
437
438
        tstart = tstart + minfraction*duration
        taus = np.linspace(minfraction*duration, maxfraction*duration, npoints)
439
        twoFs = []
Gregory Ashton's avatar
Gregory Ashton committed
440
441
442
        if self.transient is False:
            self.transient = True
            self.init_computefstatistic_single_point()
443
444
445
446
447
448
449
450
451
        for tau in taus:
            twoFs.append(self.run_computefstatistic_single_point(
                tstart=tstart, tend=tstart+tau, F0=F0, F1=F1, F2=F2,
                Alpha=Alpha, Delta=Delta, asini=asini, period=period, ecc=ecc,
                tp=tp, argp=argp))

        return taus, np.array(twoFs)

    def plot_twoF_cumulative(self, label, outdir, ax=None, c='k', savefig=True,
452
                             title=None, **kwargs):
453

454
455
456
457
458
459
        taus, twoFs = self.calculate_twoF_cumulative(**kwargs)
        if ax is None:
            fig, ax = plt.subplots()
        ax.plot(taus/86400., twoFs, label=label, color=c)
        ax.set_xlabel(r'Days from $t_{{\rm start}}={:.0f}$'.format(
            kwargs['tstart']))
Gregory Ashton's avatar
Gregory Ashton committed
460
461
462
463
        if self.BSGL:
            ax.set_ylabel(r'$\log_{10}(\mathrm{BSGL})_{\rm cumulative}$')
        else:
            ax.set_ylabel(r'$\widetilde{2\mathcal{F}}_{\rm cumulative}$')
464
        ax.set_xlim(0, taus[-1]/86400)
465
        ax.set_title(title)
466
467
        if savefig:
            plt.savefig('{}/{}_twoFcumulative.png'.format(outdir, label))
Gregory Ashton's avatar
Gregory Ashton committed
468
            return taus, twoFs
469
470
471
        else:
            return ax

Gregory Ashton's avatar
Gregory Ashton committed
472

473
474
475
476
477
478
479
class SemiCoherentSearch(BaseSearchClass, ComputeFstat):
    """ A semi-coherent search """

    @initializer
    def __init__(self, label, outdir, tref, nsegs=None, sftfilepath=None,
                 binary=False, BSGL=False, minStartTime=None,
                 maxStartTime=None, minCoverFreq=None, maxCoverFreq=None,
480
481
                 detector=None, earth_ephem=None, sun_ephem=None,
                 injectSources=None):
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
        """
        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to.
        tref, tstart, tend: int
            GPS seconds of the reference time, and start and end of the data.
        nsegs: int
            The (fixed) number of segments
        sftfilepath: str
            File patern to match SFTs

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

        self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)
        if self.earth_ephem is None:
            self.earth_ephem = self.earth_ephem_default
        if self.sun_ephem is None:
            self.sun_ephem = self.sun_ephem_default
        self.transient = True
        self.init_computefstatistic_single_point()
        self.init_semicoherent_parameters()

    def init_semicoherent_parameters(self):
507
508
509
        logging.info(('Initialising semicoherent parameters from {} to {} in'
                      ' {} segments').format(
            self.minStartTime, self.maxStartTime, self.nsegs))
510
511
        self.transient = True
        self.whatToCompute = lalpulsar.FSTATQ_2F+lalpulsar.FSTATQ_ATOMS_PER_DET
512
513
514
        self.tboundaries = np.linspace(self.minStartTime, self.maxStartTime,
                                       self.nsegs+1)

Gregory Ashton's avatar
Gregory Ashton committed
515
516
517
518
    def run_semi_coherent_computefstatistic_single_point(
            self, F0, F1, F2, Alpha, Delta, asini=None,
            period=None, ecc=None, tp=None, argp=None):
        """ Returns twoF or ln(BSGL) semi-coherently at a single point """
519

Gregory Ashton's avatar
Gregory Ashton committed
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
        self.PulsarDopplerParams.fkdot = np.array([F0, F1, F2, 0, 0, 0, 0])
        self.PulsarDopplerParams.Alpha = Alpha
        self.PulsarDopplerParams.Delta = Delta
        if self.binary:
            self.PulsarDopplerParams.asini = asini
            self.PulsarDopplerParams.period = period
            self.PulsarDopplerParams.ecc = ecc
            self.PulsarDopplerParams.tp = tp
            self.PulsarDopplerParams.argp = argp

        lalpulsar.ComputeFstat(self.FstatResults,
                               self.FstatInput,
                               self.PulsarDopplerParams,
                               1,
                               self.whatToCompute
                               )

        if self.transient is False:
            if self.BSGL is False:
                return self.FstatResults.twoF[0]

            twoF = np.float(self.FstatResults.twoF[0])
            self.twoFX[0] = self.FstatResults.twoFPerDet(0)
            self.twoFX[1] = self.FstatResults.twoFPerDet(1)
            log10_BSGL = lalpulsar.ComputeBSGL(twoF, self.twoFX,
                                               self.BSGLSetup)
            return log10_BSGL/np.log10(np.exp(1))

        detStat = 0
        for tstart, tend in zip(self.tboundaries[:-1], self.tboundaries[1:]):
            self.windowRange.t0 = int(tstart)  # TYPE UINT4
            self.windowRange.tau = int(tend - tstart)  # TYPE UINT4

            FS = lalpulsar.ComputeTransientFstatMap(
                self.FstatResults.multiFatoms[0], self.windowRange, False)

            if self.BSGL is False:
                detStat += 2*FS.F_mn.data[0][0]
                continue
559

Gregory Ashton's avatar
Gregory Ashton committed
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
            FstatResults_single = copy.copy(self.FstatResults)
            FstatResults_single.lenth = 1
            FstatResults_single.data = self.FstatResults.multiFatoms[0].data[0]
            FS0 = lalpulsar.ComputeTransientFstatMap(
                FstatResults_single.multiFatoms[0], self.windowRange, False)
            FstatResults_single.data = self.FstatResults.multiFatoms[0].data[1]
            FS1 = lalpulsar.ComputeTransientFstatMap(
                FstatResults_single.multiFatoms[0], self.windowRange, False)

            self.twoFX[0] = 2*FS0.F_mn.data[0][0]
            self.twoFX[1] = 2*FS1.F_mn.data[0][0]
            log10_BSGL = lalpulsar.ComputeBSGL(
                    2*FS.F_mn.data[0][0], self.twoFX, self.BSGLSetup)

            detStat += log10_BSGL/np.log10(np.exp(1))

        return detStat
577
578


Gregory Ashton's avatar
Gregory Ashton committed
579
class SemiCoherentGlitchSearch(BaseSearchClass, ComputeFstat):
580
581
582
    """ A semi-coherent glitch search

    This implements a basic `semi-coherent glitch F-stat in which the data
583
584
    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
585
586
587
588
    F-stat
    """

    @initializer
Gregory Ashton's avatar
Gregory Ashton committed
589
    def __init__(self, label, outdir, tref, tstart, tend, nglitch=0,
590
591
592
                 sftfilepath=None, theta0_idx=0, BSGL=False, minStartTime=None,
                 maxStartTime=None, minCoverFreq=None, maxCoverFreq=None,
                 detector=None, earth_ephem=None, sun_ephem=None):
593
594
595
596
        """
        Parameters
        ----------
        label, outdir: str
597
598
599
600
601
602
            A label and directory to read/write data from/to.
        tref, tstart, tend: int
            GPS seconds of the reference time, and start and end of the data.
        nglitch: int
            The (fixed) number of glitches; this can zero, but occasionally
            this causes issue (in which case just use ComputeFstat).
603
604
        sftfilepath: str
            File patern to match SFTs
605
606
607
608
        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)
609
610

        For all other parameters, see pyfstat.ComputeFStat.
611
612
613
614
615
616
617
        """

        self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)
        if self.earth_ephem is None:
            self.earth_ephem = self.earth_ephem_default
        if self.sun_ephem is None:
            self.sun_ephem = self.sun_ephem_default
618
619
        self.transient = True
        self.binary = False
620
621
622
        self.init_computefstatistic_single_point()

    def compute_nglitch_fstat(self, F0, F1, F2, Alpha, Delta, *args):
623
        """ Returns the semi-coherent glitch summed twoF """
624
625
626

        args = list(args)
        tboundaries = [self.tstart] + args[-self.nglitch:] + [self.tend]
627
628
629
630
631
632
633
634
        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)

635
636
        thetas = self.calculate_thetas(theta, delta_thetas, tboundaries,
                                       theta0_idx=self.theta0_idx)
637
638

        twoFSum = 0
639
        for i, theta_i_at_tref in enumerate(thetas):
640
641
642
            ts, te = tboundaries[i], tboundaries[i+1]

            twoFVal = self.run_computefstatistic_single_point(
643
644
                ts, te, theta_i_at_tref[1], theta_i_at_tref[2],
                theta_i_at_tref[3], Alpha, Delta)
645
646
            twoFSum += twoFVal

647
648
649
        if np.isfinite(twoFSum):
            return twoFSum
        else:
650
            return -np.inf
651
652
653

    def compute_glitch_fstat_single(self, F0, F1, F2, Alpha, Delta, delta_F0,
                                    delta_F1, tglitch):
654
655
656
657
        """ Returns the semi-coherent glitch summed twoF for nglitch=1

        Note: used for testing
        """
658
659
660
661
662
663
664
665
666
667
668

        theta = [F0, F1, F2]
        delta_theta = [delta_F0, delta_F1, 0]
        tref = self.tref

        theta_at_glitch = self.shift_coefficients(theta, tglitch - tref)
        theta_post_glitch_at_glitch = theta_at_glitch + delta_theta
        theta_post_glitch = self.shift_coefficients(
            theta_post_glitch_at_glitch, tref - tglitch)

        twoFsegA = self.run_computefstatistic_single_point(
Gregory Ashton's avatar
Gregory Ashton committed
669
            self.tstart, tglitch, theta[0], theta[1], theta[2], Alpha,
670
671
672
673
674
675
            Delta)

        if tglitch == self.tend:
            return twoFsegA

        twoFsegB = self.run_computefstatistic_single_point(
Gregory Ashton's avatar
Gregory Ashton committed
676
            tglitch, self.tend, theta_post_glitch[0],
677
678
679
680
681
682
            theta_post_glitch[1], theta_post_glitch[2], Alpha,
            Delta)

        return twoFsegA + twoFsegB


Gregory Ashton's avatar
Gregory Ashton committed
683
684
class MCMCSearch(BaseSearchClass):
    """ MCMC search using ComputeFstat"""
685
    @initializer
686
    def __init__(self, label, outdir, sftfilepath, theta_prior, tref,
687
                 tstart, tend, nsteps=[100, 100, 100], nwalkers=100, ntemps=1,
688
                 log10temperature_min=-5, theta_initial=None, scatter_val=1e-10,
689
690
                 binary=False, BSGL=False, minCoverFreq=None,
                 maxCoverFreq=None, detector=None, earth_ephem=None,
691
                 sun_ephem=None, injectSource=None):
692
693
694
695
        """
        Parameters
        label, outdir: str
            A label and directory to read/write data from/to
696
697
        sftfilepath: str
            File patern to match SFTs
698
        theta_prior: dict
699
700
701
702
            Dictionary of priors and fixed values for the search parameters.
            For each parameters (key of the dict), if it is to be held fixed
            the value should be the constant float, if it is be searched, the
            value should be a dictionary of the prior.
703
704
705
706
        theta_initial: dict, array, (None)
            Either a dictionary of distribution about which to distribute the
            initial walkers about, an array (from which the walkers will be
            scattered by scatter_val, or  None in which case the prior is used.
707
708
709
710
711
712
713
        tref, tstart, tend: int
            GPS seconds of the reference time, start time and end time
        nsteps: list (m,)
            List specifying the number of steps to take, the last two entries
            give the nburn and nprod of the 'production' run, all entries
            before are for iterative initialisation steps (usually just one)
            e.g. [1000, 1000, 500].
714
715
716
717
718
719
720
721
722
723
724
        nwalkers, ntemps: int,
            The number of walkers and temperates to use in the parallel
            tempered PTSampler.
        log10temperature_min float < 0
            The  log_10(tmin) value, the set of betas passed to PTSampler are
            generated from np.logspace(0, log10temperature_min, ntemps).
        binary: Bool
            If true, search over binary parameters
        detector: str
            Two character reference to the data to use, specify None for no
            contraint.
725
726
727
728
729
730
731
732
733
734
        minCoverFreq, maxCoverFreq: float
            Minimum and maximum instantaneous frequency which will be covered
            over the SFT time span as passed to CreateFstatInput
        earth_ephem, sun_ephem: str
            Paths of the two files containing positions of Earth and Sun,
            respectively at evenly spaced times, as passed to CreateFstatInput
            If None defaults defined in BaseSearchClass will be used

        """

735
736
737
        self.minStartTime = tstart
        self.maxStartTime = tend

Gregory Ashton's avatar
Gregory Ashton committed
738
739
        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
740
        self.add_log_file()
Gregory Ashton's avatar
Gregory Ashton committed
741
742
        logging.info(
            'Set-up MCMC search for model {} on data {}'.format(
743
                self.label, self.sftfilepath))
744
745
746
        self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
        self.unpack_input_theta()
        self.ndim = len(self.theta_keys)
747
748
749
750
        if self.log10temperature_min:
            self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
        else:
            self.betas = None
751

752
753
754
755
756
757
758
759
        if earth_ephem is None:
            self.earth_ephem = self.earth_ephem_default
        if sun_ephem is None:
            self.sun_ephem = self.sun_ephem_default

        if args.clean and os.path.isfile(self.pickle_path):
            os.rename(self.pickle_path, self.pickle_path+".old")

760
761
762
        self.log_input()

    def log_input(self):
763
        logging.info('theta_prior = {}'.format(self.theta_prior))
764
        logging.info('nwalkers={}'.format(self.nwalkers))
765
766
767
768
        logging.info('scatter_val = {}'.format(self.scatter_val))
        logging.info('nsteps = {}'.format(self.nsteps))
        logging.info('ntemps = {}'.format(self.ntemps))
        logging.info('log10temperature_min = {}'.format(
769
            self.log10temperature_min))
770
771
772

    def inititate_search_object(self):
        logging.info('Setting up search object')
Gregory Ashton's avatar
Gregory Ashton committed
773
        self.search = ComputeFstat(
774
775
776
777
            tref=self.tref, sftfilepath=self.sftfilepath,
            minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
            earth_ephem=self.earth_ephem, sun_ephem=self.sun_ephem,
            detector=self.detector, BSGL=self.BSGL, transient=False,
778
            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
779
            binary=self.binary, injectSources=self.injectSources)
780
781

    def logp(self, theta_vals, theta_prior, theta_keys, search):
Gregory Ashton's avatar
Gregory Ashton committed
782
        H = [self.generic_lnprior(**theta_prior[key])(p) for p, key in
783
784
785
786
787
788
             zip(theta_vals, theta_keys)]
        return np.sum(H)

    def logl(self, theta, search):
        for j, theta_i in enumerate(self.theta_idxs):
            self.fixed_theta[theta_i] = theta[j]
789
790
        FS = search.compute_fullycoherent_det_stat_single_point(
            *self.fixed_theta)
791
792
793
        return FS

    def unpack_input_theta(self):
794
        full_theta_keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta']
795
796
797
        if self.binary:
            full_theta_keys += [
                'asini', 'period', 'ecc', 'tp', 'argp']
798
799
        full_theta_keys_copy = copy.copy(full_theta_keys)

800
801
        full_theta_symbols = ['$f$', '$\dot{f}$', '$\ddot{f}$', r'$\alpha$',
                              r'$\delta$']
802
803
804
805
        if self.binary:
            full_theta_symbols += [
                'asini', 'period', 'period', 'ecc', 'tp', 'argp']

806
807
        self.theta_keys = []
        fixed_theta_dict = {}
808
        for key, val in self.theta_prior.iteritems():
809
810
            if type(val) is dict:
                fixed_theta_dict[key] = 0
Gregory Ashton's avatar
Gregory Ashton committed
811
                self.theta_keys.append(key)
812
813
814
815
816
817
            elif type(val) in [float, int, np.float64]:
                fixed_theta_dict[key] = val
            else:
                raise ValueError(
                    'Type {} of {} in theta not recognised'.format(
                        type(val), key))
Gregory Ashton's avatar
Gregory Ashton committed
818
            full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834

        if len(full_theta_keys_copy) > 0:
            raise ValueError(('Input dictionary `theta` is missing the'
                              'following keys: {}').format(
                                  full_theta_keys_copy))

        self.fixed_theta = [fixed_theta_dict[key] for key in full_theta_keys]
        self.theta_idxs = [full_theta_keys.index(k) for k in self.theta_keys]
        self.theta_symbols = [full_theta_symbols[i] for i in self.theta_idxs]

        idxs = np.argsort(self.theta_idxs)
        self.theta_idxs = [self.theta_idxs[i] for i in idxs]
        self.theta_symbols = [self.theta_symbols[i] for i in idxs]
        self.theta_keys = [self.theta_keys[i] for i in idxs]

    def check_initial_points(self, p0):
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
        for nt in range(self.ntemps):
            logging.info('Checking temperature {} chains'.format(nt))
            initial_priors = np.array([
                self.logp(p, self.theta_prior, self.theta_keys, self.search)
                for p in p0[nt]])
            number_of_initial_out_of_bounds = sum(initial_priors == -np.inf)

            if number_of_initial_out_of_bounds > 0:
                logging.warning(
                    'Of {} initial values, {} are -np.inf due to the prior'
                    .format(len(initial_priors),
                            number_of_initial_out_of_bounds))

                p0 = self.generate_new_p0_to_fix_initial_points(
                    p0, nt, initial_priors)

    def generate_new_p0_to_fix_initial_points(self, p0, nt, initial_priors):
        logging.info('Attempting to correct intial values')
        idxs = np.arange(self.nwalkers)[initial_priors == -np.inf]
        count = 0
        while sum(initial_priors == -np.inf) > 0 and count < 100:
            for j in idxs:
                p0[nt][j] = (p0[nt][np.random.randint(0, self.nwalkers)]*(
                             1+np.random.normal(0, 1e-10, self.ndim)))
            initial_priors = np.array([
                self.logp(p, self.theta_prior, self.theta_keys,
                          self.search)
                for p in p0[nt]])
            count += 1

        if sum(initial_priors == -np.inf) > 0:
            logging.info('Failed to fix initial priors')
        else:
            logging.info('Suceeded to fix initial priors')

        return p0
871

Gregory Ashton's avatar
Gregory Ashton committed
872
    def run_sampler_with_progress_bar(self, sampler, ns, p0):
873
874
        for result in tqdm(sampler.sample(p0, iterations=ns), total=ns):
            pass
Gregory Ashton's avatar
Gregory Ashton committed
875
876
877
        return sampler

    def run(self, proposal_scale_factor=2):
878

Gregory Ashton's avatar
Gregory Ashton committed
879
        self.old_data_is_okay_to_use = self.check_old_data_is_okay_to_use()
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
        if self.old_data_is_okay_to_use is True:
            logging.warning('Using saved data from {}'.format(
                self.pickle_path))
            d = self.get_saved_data()
            self.sampler = d['sampler']
            self.samples = d['samples']
            self.lnprobs = d['lnprobs']
            self.lnlikes = d['lnlikes']
            return

        self.inititate_search_object()

        sampler = emcee.PTSampler(
            self.ntemps, self.nwalkers, self.ndim, self.logl, self.logp,
            logpargs=(self.theta_prior, self.theta_keys, self.search),
895
            loglargs=(self.search,), betas=self.betas, a=proposal_scale_factor)
896

Gregory Ashton's avatar
Gregory Ashton committed
897
898
        p0 = self.generate_initial_p0()
        p0 = self.apply_corrections_to_p0(p0)
899
900
901
902
903
        self.check_initial_points(p0)

        ninit_steps = len(self.nsteps) - 2
        for j, n in enumerate(self.nsteps[:-2]):
            logging.info('Running {}/{} initialisation with {} steps'.format(
Gregory Ashton's avatar
Gregory Ashton committed
904
                j, ninit_steps, n))
Gregory Ashton's avatar
Gregory Ashton committed
905
            sampler = self.run_sampler_with_progress_bar(sampler, n, p0)
906
907
            logging.info("Mean acceptance fraction: {}"
                         .format(np.mean(sampler.acceptance_fraction, axis=1)))
908
909
910
            if self.ntemps > 1:
                logging.info("Tswap acceptance fraction: {}"
                             .format(sampler.tswap_acceptance_fraction))
Gregory Ashton's avatar
Gregory Ashton committed
911
            fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols)
912
913
914
            fig.savefig('{}/{}_init_{}_walkers.png'.format(
                self.outdir, self.label, j))

915
            p0 = self.get_new_p0(sampler)
Gregory Ashton's avatar
Gregory Ashton committed
916
            p0 = self.apply_corrections_to_p0(p0)
917
918
919
            self.check_initial_points(p0)
            sampler.reset()

Gregory Ashton's avatar
Gregory Ashton committed
920
921
922
923
        if len(self.nsteps) > 1:
            nburn = self.nsteps[-2]
        else:
            nburn = 0
924
925
926
        nprod = self.nsteps[-1]
        logging.info('Running final burn and prod with {} steps'.format(
            nburn+nprod))
Gregory Ashton's avatar
Gregory Ashton committed
927
        sampler = self.run_sampler_with_progress_bar(sampler, nburn+nprod, p0)
928
929
        logging.info("Mean acceptance fraction: {}"
                     .format(np.mean(sampler.acceptance_fraction, axis=1)))
930
931
932
        if self.ntemps > 1:
            logging.info("Tswap acceptance fraction: {}"
                         .format(sampler.tswap_acceptance_fraction))
933

Gregory Ashton's avatar
Gregory Ashton committed
934
935
        fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols,
                                      burnin_idx=nburn)
936
937
938
939
940
941
942
943
944
945
946
        fig.savefig('{}/{}_walkers.png'.format(self.outdir, self.label))

        samples = sampler.chain[0, :, nburn:, :].reshape((-1, self.ndim))
        lnprobs = sampler.lnprobability[0, :, nburn:].reshape((-1))
        lnlikes = sampler.lnlikelihood[0, :, nburn:].reshape((-1))
        self.sampler = sampler
        self.samples = samples
        self.lnprobs = lnprobs
        self.lnlikes = lnlikes
        self.save_data(sampler, samples, lnprobs, lnlikes)

947
    def plot_corner(self, figsize=(7, 7),  tglitch_ratio=False,
948
949
950
                    add_prior=False, nstds=None, label_offset=0.4,
                    dpi=300, rc_context={}, **kwargs):

Gregory Ashton's avatar
Gregory Ashton committed
951
952
953
954
955
956
957
958
959
960
        if self.ndim < 2:
            with plt.rc_context(rc_context):
                fig, ax = plt.subplots(figsize=figsize)
                ax.hist(self.samples, bins=50, histtype='stepfilled')
                ax.set_xlabel(self.theta_symbols[0])

            fig.savefig('{}/{}_corner.png'.format(
                self.outdir, self.label), dpi=dpi)
            return

961
962
963
964
965
966
        with plt.rc_context(rc_context):
            fig, axes = plt.subplots(self.ndim, self.ndim,
                                     figsize=figsize)

            samples_plt = copy.copy(self.samples)
            theta_symbols_plt = copy.copy(self.theta_symbols)
967
968
            theta_symbols_plt = [s.replace('_{glitch}', r'_\textrm{glitch}')
                                 for s in theta_symbols_plt]
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017

            if tglitch_ratio:
                for j, k in enumerate(self.theta_keys):
                    if k == 'tglitch':
                        s = samples_plt[:, j]
                        samples_plt[:, j] = (s - self.tstart)/(
                                             self.tend - self.tstart)
                        theta_symbols_plt[j] = r'$R_{\textrm{glitch}}$'

            if type(nstds) is int and 'range' not in kwargs:
                _range = []
                for j, s in enumerate(samples_plt.T):
                    median = np.median(s)
                    std = np.std(s)
                    _range.append((median - nstds*std, median + nstds*std))
            else:
                _range = None

            fig_triangle = corner.corner(samples_plt,
                                         labels=theta_symbols_plt,
                                         fig=fig,
                                         bins=50,
                                         max_n_ticks=4,
                                         plot_contours=True,
                                         plot_datapoints=True,
                                         label_kwargs={'fontsize': 8},
                                         data_kwargs={'alpha': 0.1,
                                                      'ms': 0.5},
                                         range=_range,
                                         **kwargs)

            axes_list = fig_triangle.get_axes()
            axes = np.array(axes_list).reshape(self.ndim, self.ndim)
            plt.draw()
            for ax in axes[:, 0]:
                ax.yaxis.set_label_coords(-label_offset, 0.5)
            for ax in axes[-1, :]:
                ax.xaxis.set_label_coords(0.5, -label_offset)
            for ax in axes_list:
                ax.set_rasterized(True)
                ax.set_rasterization_zorder(-10)
            plt.tight_layout(h_pad=0.0, w_pad=0.0)
            fig.subplots_adjust(hspace=0.05, wspace=0.05)

            if add_prior:
                self.add_prior_to_corner(axes, samples_plt)

            fig_triangle.savefig('{}/{}_corner.png'.format(
                self.outdir, self.label), dpi=dpi)
1018
1019
1020
1021
1022
1023

    def add_prior_to_corner(self, axes, samples):
        for i, key in enumerate(self.theta_keys):
            ax = axes[i][i]
            xlim = ax.get_xlim()
            s = samples[:, i]
Gregory Ashton's avatar
Gregory Ashton committed
1024
            prior = self.generic_lnprior(**self.theta_prior[key])
1025
1026
1027
1028
1029
1030
            x = np.linspace(s.min(), s.max(), 100)
            ax2 = ax.twinx()
            ax2.get_yaxis().set_visible(False)
            ax2.plot(x, [prior(xi) for xi in x], '-r')
            ax.set_xlim(xlim)

1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
    def plot_prior_posterior(self, normal_stds=2):
        """ Plot the posterior in the context of the prior """
        fig, axes = plt.subplots(nrows=self.ndim, figsize=(8, 4*self.ndim))
        N = 1000
        from scipy.stats import gaussian_kde

        for i, (ax, key) in enumerate(zip(axes, self.theta_keys)):
            prior_dict = self.theta_prior[key]
            prior_func = self.generic_lnprior(**prior_dict)
            if prior_dict['type'] == 'unif':
                x = np.linspace(prior_dict['lower'], prior_dict['upper'], N)
                prior = prior_func(x)
                prior[0] = 0
                prior[-1] = 0
            elif prior_dict['type'] == 'norm':
                lower = prior_dict['loc'] - normal_stds * prior_dict['scale']
                upper = prior_dict['loc'] + normal_stds * prior_dict['scale']
                x = np.linspace(lower, upper, N)
                prior = prior_func(x)
1050
1051
1052
1053
1054
            elif prior_dict['type'] == 'halfnorm':
                lower = prior_dict['loc']
                upper = prior_dict['loc'] + normal_stds * prior_dict['scale']
                x = np.linspace(lower, upper, N)
                prior = [prior_func(xi) for xi in x]
Gregory Ashton's avatar
Gregory Ashton committed
1055
1056
1057
1058
1059
            elif prior_dict['type'] == 'neghalfnorm':
                upper = prior_dict['loc']
                lower = prior_dict['loc'] - normal_stds * prior_dict['scale']
                x = np.linspace(lower, upper, N)
                prior = [prior_func(xi) for xi in x]
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
            else:
                raise ValueError('Not implemented for prior type {}'.format(
                    prior_dict['type']))
            priorln = ax.plot(x, prior, 'r', label='prior')
            ax.set_xlabel(self.theta_symbols[i])

            s = self.samples[:, i]
            while len(s) > 10**4:
                # random downsample to avoid slow calculation of kde
                s = np.random.choice(s, size=int(len(s)/2.))
            kde = gaussian_kde(s)
            ax2 = ax.twinx()
            postln = ax2.plot(x, kde.pdf(x), 'k', label='posterior')
            ax2.set_yticklabels([])
            ax.set_yticklabels([])

        lns = priorln + postln
        labs = [l.get_label() for l in lns]
        axes[0].legend(lns, labs, loc=1, framealpha=0.8)

        fig.savefig('{}/{}_prior_posterior.png'.format(
            self.outdir, self.label))

1083
    def plot_cumulative_max(self, **kwargs):
Gregory Ashton's avatar
Gregory Ashton committed
1084
1085
1086
1087
        d, maxtwoF = self.get_max_twoF()
        for key, val in self.theta_prior.iteritems():
            if key not in d:
                d[key] = val
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101

        if hasattr(self, 'search') is False:
            self.inititate_search_object()
        if self.binary is False:
            self.search.plot_twoF_cumulative(
                self.label, self.outdir, F0=d['F0'], F1=d['F1'], F2=d['F2'],
                Alpha=d['Alpha'], Delta=d['Delta'], tstart=self.tstart,
                tend=self.tend, **kwargs)
        else:
            self.search.plot_twoF_cumulative(
                self.label, self.outdir, F0=d['F0'], F1=d['F1'], F2=d['F2'],
                Alpha=d['Alpha'], Delta=d['Delta'], asini=d['asini'],
                period=d['period'], ecc=d['ecc'], argp=d['argp'], tp=d['argp'],
                tstart=self.tstart, tend=self.tend, **kwargs)
Gregory Ashton's avatar
Gregory Ashton committed
1102

Gregory Ashton's avatar
Gregory Ashton committed
1103
    def generic_lnprior(self, **kwargs):
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
        """ Return a lambda function of the pdf

        Parameters
        ----------
        kwargs: dict
            A dictionary containing 'type' of pdf and shape parameters

        """

        def logunif(x, a, b):
            above = x < b
            below = x > a
            if type(above) is not np.ndarray:
                if above and below:
                    return -np.log(b-a)
                else:
                    return -np.inf
            else:
                idxs = np.array([all(tup) for tup in zip(above, below)])
                p = np.zeros(len(x)) - np.inf
                p[idxs] = -np.log(b-a)
                return p

        def halfnorm(x, loc, scale):
            if x < 0:
                return -np.inf
            else:
                return -0.5*((x-loc)**2/scale**2+np.log(0.5*np.pi*scale**2))

        def cauchy(x, x0, gamma):
            return 1.0/(np.pi*gamma*(1+((x-x0)/gamma)**2))

        def exp(x, x0, gamma):
            if x > x0:
                return np.log(gamma) - gamma*(x - x0)
            else:
                return -np.inf

        if kwargs['type'] == 'unif':
            return lambda x: logunif(x, kwargs['lower'], kwargs['upper'])
        elif kwargs['type'] == 'halfnorm':
            return lambda x: halfnorm(x, kwargs['loc'], kwargs['scale'])
1146
1147
        elif kwargs['type'] == 'neghalfnorm':
            return lambda x: halfnorm(-x, kwargs['loc'], kwargs['scale'])
1148
1149
1150
1151
1152
1153
1154
        elif kwargs['type'] == 'norm':
            return lambda x: -0.5*((x - kwargs['loc'])**2/kwargs['scale']**2
                                   + np.log(2*np.pi*kwargs['scale']**2))
        else:
            logging.info("kwargs:", kwargs)
            raise ValueError("Print unrecognise distribution")

Gregory Ashton's avatar
Gregory Ashton committed
1155
    def generate_rv(self, **kwargs):
1156
1157
1158
1159
1160
1161
1162
1163
        dist_type = kwargs.pop('type')
        if dist_type == "unif":
            return np.random.uniform(low=kwargs['lower'], high=kwargs['upper'])
        if dist_type == "norm":
            return np.random.normal(loc=kwargs['loc'], scale=kwargs['scale'])
        if dist_type == "halfnorm":
            return np.abs(np.random.normal(loc=kwargs['loc'],
                                           scale=kwargs['scale']))
1164
1165
1166
        if dist_type == "neghalfnorm":
            return -1 * np.abs(np.random.normal(loc=kwargs['loc'],
                                                scale=kwargs['scale']))
1167
1168
1169
1170
1171
1172
        if dist_type == "lognorm":
            return np.random.lognormal(
                mean=kwargs['loc'], sigma=kwargs['scale'])
        else:
            raise ValueError("dist_type {} unknown".format(dist_type))

Gregory Ashton's avatar
Gregory Ashton committed
1173
    def plot_walkers(self, sampler, symbols=None, alpha=0.4, color="k", temp=0,
Gregory Ashton's avatar
Gregory Ashton committed
1174
1175
                     lw=0.1, burnin_idx=None, add_det_stat_burnin=False,
                     fig=None, axes=None, xoffset=0, plot_det_stat=True):
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
        """ Plot all the chains from a sampler """

        shape = sampler.chain.shape
        if len(shape) == 3:
            nwalkers, nsteps, ndim = shape
            chain = sampler.chain[:, :, :]
        if len(shape) == 4:
            ntemps, nwalkers, nsteps, ndim = shape
            if temp < ntemps:
                logging.info("Plotting temperature {} chains".format(temp))
            else:
                raise ValueError(("Requested temperature {} outside of"
                                  "available range").format(temp))
            chain = sampler.chain[temp, :, :, :]

        with plt.style.context(('classic')):
Gregory Ashton's avatar
Gregory Ashton committed
1192
1193
1194
1195
1196
            if fig is None and axes is None:
                fig = plt.figure(figsize=(8, 4*ndim))
                ax = fig.add_subplot(ndim+1, 1, 1)
                axes = [ax] + [fig.add_subplot(ndim+1, 1, i, sharex=ax)
                               for i in range(2, ndim+1)]
1197

Gregory Ashton's avatar
Gregory Ashton committed
1198
            idxs = np.arange(chain.shape[1])
1199
1200
            if ndim > 1:
                for i in range(ndim):
1201
                    axes[i].ticklabel_format(useOffset=False, axis='y')
Gregory Ashton's avatar
Gregory Ashton committed
1202
1203
                    cs = chain[:, :, i].T
                    if burnin_idx:
Gregory Ashton's avatar
Gregory Ashton committed
1204
1205
1206
1207
1208
                        axes[i].plot(xoffset+idxs[:burnin_idx],
                                     cs[:burnin_idx], color="r", alpha=alpha,
                                     lw=lw)
                    axes[i].plot(xoffset+idxs[burnin_idx:], cs[burnin_idx:],
                                 color="k", alpha=alpha, lw=lw)
1209
1210
                    if symbols:
                        axes[i].set_ylabel(symbols[i])
1211
            else:
Gregory Ashton's avatar
Gregory Ashton committed
1212
                axes[0].ticklabel_format(useOffset=False, axis='y')
Gregory Ashton's avatar
Gregory Ashton committed
1213
                cs = chain[:, :, temp].T
Gregory Ashton's avatar
Gregory Ashton committed
1214
1215
1216
1217
1218
1219
1220
                if burnin_idx:
                    axes[0].plot(idxs[:burnin_idx], cs[:burnin_idx],
                                 color="r", alpha=alpha, lw=lw)
                axes[0].plot(idxs[burnin_idx:], cs[burnin_idx:], color="k",
                             alpha=alpha, lw=lw)
                if symbols:
                    axes[0].set_ylabel(symbols[0])
1221

Gregory Ashton's avatar
Gregory Ashton committed
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
        if len(axes) == ndim:
            axes.append(fig.add_subplot(ndim+1, 1, ndim+1))

        if plot_det_stat:
            lnl = sampler.lnlikelihood[temp, :, :]
            if burnin_idx and add_det_stat_burnin:
                burn_in_vals = lnl[:, :burnin_idx].flatten()
                axes[-1].hist(burn_in_vals[~np.isnan(burn_in_vals)], bins=50,
                              histtype='step', color='r')
            else:
                burn_in_vals = []
            prod_vals = lnl[:, burnin_idx:].flatten()
            axes[-1].hist(prod_vals[~np.isnan(prod_vals)], bins=50,
                          histtype='step', color='k')
            if self.BSGL:
                axes[-1].set_xlabel(r'$\mathcal{B}_\mathrm{S/GL}$')
            else:
                axes[-1].set_xlabel(r'$2\mathcal{F}$')
            combined_vals = np.append(burn_in_vals, prod_vals)
            if len(combined_vals) > 0:
                minv = np.min(combined_vals)
                maxv = np.max(combined_vals)
                Range = abs(maxv-minv)
                axes[-1].set_xlim(minv-0.1*Range, maxv+0.1*Range)
Gregory Ashton's avatar
Gregory Ashton committed
1246

1247
1248
        return fig, axes

Gregory Ashton's avatar
Gregory Ashton committed
1249
1250
1251
1252
1253
    def apply_corrections_to_p0(self, p0):
        """ Apply any correction to the initial p0 values """
        return p0

    def generate_scattered_p0(self, p):
1254
        """ Generate a set of p0s scattered about p """
Gregory Ashton's avatar
Gregory Ashton committed
1255
        p0 = [[p + self.scatter_val * p * np.random.randn(self.ndim)
1256
1257
1258
1259
               for i in xrange(self.nwalkers)]
              for j in xrange(self.ntemps)]
        return p0

Gregory Ashton's avatar
Gregory Ashton committed
1260
    def generate_initial_p0(self):
1261
1262
1263
        """ Generate a set of init vals for the walkers """

        if type(self.theta_initial) == dict:
1264
            logging.info('Generate initial values from initial dictionary')
1265
            if hasattr(self, 'nglitch') and self.nglitch > 1:
1266
                raise ValueError('Initial dict not implemented for nglitch>1')
Gregory Ashton's avatar
Gregory Ashton committed
1267
            p0 = [[[self.generate_rv(**self.theta_initial[key])
1268
1269
1270
                    for key in self.theta_keys]
                   for i in range(self.nwalkers)]
                  for j in range(self.ntemps)]
1271
1272
1273
1274
1275
1276
        elif type(self.theta_initial) == list:
            logging.info('Generate initial values from list of theta_initial')
            p0 = [[[self.generate_rv(**val)
                    for val in self.theta_initial]
                   for i in range(self.nwalkers)]
                  for j in range(self.ntemps)]
1277
        elif self.theta_initial is None:
1278
            logging.info('Generate initial values from prior dictionary')
Gregory Ashton's avatar
Gregory Ashton committed
1279
            p0 = [[[self.generate_rv(**self.theta_prior[key])
1280
1281
1282
1283
                    for key in self.theta_keys]
                   for i in range(self.nwalkers)]
                  for j in range(self.ntemps)]
        elif len(self.theta_initial) == self.ndim:
Gregory Ashton's avatar
Gregory Ashton committed
1284
            p0 = self.generate_scattered_p0(self.theta_initial)
1285
1286
1287
1288
1289
        else:
            raise ValueError('theta_initial not understood')

        return p0

1290
    def get_new_p0(self, sampler):
1291
1292
1293
1294
1295
1296
        """ Returns new initial positions for walkers are burn0 stage

        This returns new positions for all walkers by scattering points about
        the maximum posterior with scale `scatter_val`.

        """
Gregory Ashton's avatar
Gregory Ashton committed
1297
1298
1299
1300
        temp_idx = 0
        pF = sampler.chain[temp_idx, :, :, :]
        lnl = sampler.lnlikelihood[temp_idx, :, :]
        lnp = sampler.lnprobability