pyfstat.py 113 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
parser.add_argument('-s', "--setup-only", action="store_true")
57
parser.add_argument('-n', "--no-template-counting", action="store_true")
58
59
60
61
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
62
63
64
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
stream_handler = logging.StreamHandler()
65
if args.quite:
Gregory Ashton's avatar
Gregory Ashton committed
66
    stream_handler.setLevel(logging.WARNING)
67
else:
Gregory Ashton's avatar
Gregory Ashton committed
68
69
70
71
    stream_handler.setLevel(logging.DEBUG)
stream_handler.setFormatter(logging.Formatter(
    '%(asctime)s %(levelname)-8s: %(message)s', datefmt='%H:%M'))
logger.addHandler(stream_handler)
72

73

74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
def round_to_n(x, n):
    if not x:
        return 0
    power = -int(np.floor(np.log10(abs(x)))) + (n - 1)
    factor = (10 ** power)
    return round(x * factor) / factor


def texify_float(x, d=1):
    x = round_to_n(x, d)
    if 0.01 < abs(x) < 100:
        return str(x)
    else:
        power = int(np.floor(np.log10(abs(x))))
        stem = np.round(x / 10**power, d)
        if d == 1:
            stem = int(stem)
        return r'${}{{\times}}10^{{{}}}$'.format(stem, power)


94
def initializer(func):
95
    """ Decorator function to automatically assign the parameters to self """
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
    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):
113
    """ Read in a .par file, returns a dictionary of the values """
114
115
116
117
    filename = '{}/{}.par'.format(outdir, label)
    d = {}
    with open(filename, 'r') as f:
        for line in f:
118
119
120
            if len(line.split('=')) > 1:
                key, val = line.rstrip('\n').split(' = ')
                key = key.strip()
121
                d[key] = np.float64(eval(val.rstrip('; ')))
122
123
124
125
    return d


class BaseSearchClass(object):
126
    """ The base search class, provides general functions """
127
128
129
130

    earth_ephem_default = earth_ephem
    sun_ephem_default = sun_ephem

131
    def add_log_file(self):
132
        """ Log output to a file, requires class to have outdir and label """
133
134
        logfilename = '{}/{}.log'.format(self.outdir, self.label)
        fh = logging.FileHandler(logfilename)
Gregory Ashton's avatar
Gregory Ashton committed
135
        fh.setLevel(logging.INFO)
136
137
138
139
140
        fh.setFormatter(logging.Formatter(
            '%(asctime)s %(levelname)-8s: %(message)s',
            datefmt='%y-%m-%d %H:%M'))
        logging.getLogger().addHandler(fh)

141
    def shift_matrix(self, n, dT):
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
        """ 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
        """

157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
        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
179
            lowest degree e.g [phi, F0, F1,...].
180
        dT: float
181
            difference between the two reference times as tref_new - tref_old.
182
183
184
185

        Returns
        -------
        theta_new: array-like shape (n,)
186
            vector of the coefficients as evaluate as the new reference time.
187
        """
188

189
190
191
192
        n = len(theta)
        m = self.shift_matrix(n, dT)
        return np.dot(m, theta)

193
    def calculate_thetas(self, theta, delta_thetas, tbounds, theta0_idx=0):
194
195
196
        """ Calculates the set of coefficients for the post-glitch signal """
        thetas = [theta]
        for i, dt in enumerate(delta_thetas):
197
198
199
200
201
202
203
204
205
206
207
208
209
            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]))
210
211
        return thetas

Gregory Ashton's avatar
Gregory Ashton committed
212
213
214
215
216
217
218
219
220
221
222
223
224
225
    def generate_loudest(self):
        params = read_par(self.label, self.outdir)
        for key in ['Alpha', 'Delta', 'F0', 'F1']:
            if key not in params:
                params[key] = self.theta_prior[key]
        cmd = ('lalapps_ComputeFstatistic_v2 -a {} -d {} -f {} -s {} -D "{}"'
               ' --refTime={} --outputLoudest="{}/{}.loudest" '
               '--minStartTime={} --maxStartTime={}').format(
                    params['Alpha'], params['Delta'], params['F0'],
                    params['F1'], self.sftfilepath, params['tref'],
                    self.outdir, self.label, self.minStartTime,
                    self.maxStartTime)
        subprocess.call([cmd], shell=True)

226

Gregory Ashton's avatar
Gregory Ashton committed
227
class ComputeFstat(object):
228
    """ Base class providing interface to `lalpulsar.ComputeFstat` """
Gregory Ashton's avatar
Gregory Ashton committed
229
230
231
232
233

    earth_ephem_default = earth_ephem
    sun_ephem_default = sun_ephem

    @initializer
234
235
    def __init__(self, tref, sftfilepath=None, minStartTime=None,
                 maxStartTime=None, binary=False, transient=True, BSGL=False,
236
                 detector=None, minCoverFreq=None, maxCoverFreq=None,
237
                 earth_ephem=None, sun_ephem=None, injectSources=None
238
                 ):
239
240
241
242
243
        """
        Parameters
        ----------
        tref: int
            GPS seconds of the reference time.
244
245
        sftfilepath: str
            File patern to match SFTs
246
247
248
249
250
251
252
253
254
255
256
257
        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.
258
259
260
261
262
263
264
265
266
267
        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
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282

        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
283
284
285
286
287
        if self.minStartTime:
            constraints.minStartTime = lal.LIGOTimeGPS(self.minStartTime)
        if self.maxStartTime:
            constraints.maxStartTime = lal.LIGOTimeGPS(self.maxStartTime)

288
        logging.info('Loading data matching pattern {}'.format(
289
290
                     self.sftfilepath))
        SFTCatalog = lalpulsar.SFTdataFind(self.sftfilepath, constraints)
Gregory Ashton's avatar
Gregory Ashton committed
291
        names = list(set([d.header.name for d in SFTCatalog.data]))
292
        self.names = names
293
        SFT_timestamps = [d.header.epoch for d in SFTCatalog.data]
Gregory Ashton's avatar
Gregory Ashton committed
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
        try:
            from bashplotlib.histogram import plot_hist
            print('Data timestamps histogram:')
            plot_hist(SFT_timestamps, height=5, bincount=50)
        except IOError:
            pass
        if len(names) == 0:
            raise ValueError('No data loaded.')
        logging.info('Loaded {} data files from detectors {}'.format(
            len(SFT_timestamps), names))
        logging.info('Data spans from {} ({}) to {} ({})'.format(
            int(SFT_timestamps[0]),
            subprocess.check_output('lalapps_tconvert {}'.format(
                int(SFT_timestamps[0])), shell=True).rstrip('\n'),
            int(SFT_timestamps[-1]),
            subprocess.check_output('lalapps_tconvert {}'.format(
310
                int(SFT_timestamps[-1])), shell=True).rstrip('\n')))
Gregory Ashton's avatar
Gregory Ashton committed
311
312
313
314
315
316

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

        logging.info('Initialising FstatInput')
        dFreq = 0
317
318
319
320
321
        if self.transient:
            self.whatToCompute = lalpulsar.FSTATQ_ATOMS_PER_DET
        else:
            self.whatToCompute = lalpulsar.FSTATQ_2F

322
323
324
325
326
327
328
329
330
331
332
        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

333
        if hasattr(self, 'injectSource') and type(self.injectSources) == dict:
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
            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
353
354

        if self.minCoverFreq is None or self.maxCoverFreq is None:
Gregory Ashton's avatar
Gregory Ashton committed
355
356
357
358
359
            fAs = [d.header.f0 for d in SFTCatalog.data]
            fBs = [d.header.f0 + (d.numBins-1)*d.header.deltaF
                   for d in SFTCatalog.data]
            self.minCoverFreq = np.min(fAs) + 0.5
            self.maxCoverFreq = np.max(fBs) - 0.5
360
361
362
            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
363
364
365
366
367
368

        self.FstatInput = lalpulsar.CreateFstatInput(SFTCatalog,
                                                     self.minCoverFreq,
                                                     self.maxCoverFreq,
                                                     dFreq,
                                                     ephems,
369
                                                     FstatOAs
Gregory Ashton's avatar
Gregory Ashton committed
370
371
372
373
374
375
376
377
378
379
380
381
382
                                                     )

        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()

383
        if self.BSGL:
Gregory Ashton's avatar
Gregory Ashton committed
384
385
            if len(names) < 2:
                raise ValueError("Can't use BSGL with single detector data")
386
            else:
387
                logging.info('Initialising BSGL')
388

389
390
            # Tuning parameters - to be reviewed
            numDetectors = 2
Gregory Ashton's avatar
Gregory Ashton committed
391
            Fstar0sc = 15.
392
            oLGX = np.zeros(10)
Gregory Ashton's avatar
Gregory Ashton committed
393
            oLGX[:numDetectors] = 1./numDetectors
394
395
396
            self.BSGLSetup = lalpulsar.CreateBSGLSetup(numDetectors,
                                                       Fstar0sc,
                                                       oLGX,
397
                                                       True,
398
399
                                                       1)
            self.twoFX = np.zeros(10)
Gregory Ashton's avatar
Gregory Ashton committed
400
            self.whatToCompute = (self.whatToCompute +
401
402
                                  lalpulsar.FSTATQ_2F_PER_DET)

403
        if self.transient:
404
            logging.info('Initialising transient parameters')
405
406
407
408
409
410
            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
411

412
413
414
415
416
417
418
419
420
    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
421
    def run_computefstatistic_single_point(self, tstart, tend, F0, F1,
422
423
424
                                           F2, Alpha, Delta, asini=None,
                                           period=None, ecc=None, tp=None,
                                           argp=None):
425
        """ Returns twoF or ln(BSGL) fully-coherently at a single point """
Gregory Ashton's avatar
Gregory Ashton committed
426
427
428
429

        self.PulsarDopplerParams.fkdot = np.array([F0, F1, F2, 0, 0, 0, 0])
        self.PulsarDopplerParams.Alpha = Alpha
        self.PulsarDopplerParams.Delta = Delta
430
431
432
433
434
435
        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
436
437
438
439

        lalpulsar.ComputeFstat(self.FstatResults,
                               self.FstatInput,
                               self.PulsarDopplerParams,
440
                               1,
Gregory Ashton's avatar
Gregory Ashton committed
441
442
443
                               self.whatToCompute
                               )

444
        if self.transient is False:
445
446
447
448
449
450
            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)
451
452
453
            log10_BSGL = lalpulsar.ComputeBSGL(twoF, self.twoFX,
                                               self.BSGLSetup)
            return log10_BSGL/np.log10(np.exp(1))
454

455
456
        self.windowRange.t0 = int(tstart)  # TYPE UINT4
        self.windowRange.tau = int(tend - tstart)  # TYPE UINT4
457

Gregory Ashton's avatar
Gregory Ashton committed
458
        FS = lalpulsar.ComputeTransientFstatMap(
459
            self.FstatResults.multiFatoms[0], self.windowRange, False)
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474

        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]
475
476
        log10_BSGL = lalpulsar.ComputeBSGL(
                2*FS.F_mn.data[0][0], self.twoFX, self.BSGLSetup)
477

478
        return log10_BSGL/np.log10(np.exp(1))
Gregory Ashton's avatar
Gregory Ashton committed
479

480
481
    def calculate_twoF_cumulative(self, F0, F1, F2, Alpha, Delta, asini=None,
                                  period=None, ecc=None, tp=None, argp=None,
482
483
                                  tstart=None, tend=None, npoints=1000,
                                  minfraction=0.01, maxfraction=1):
484
485
        """ Calculate the cumulative twoF along the obseration span """
        duration = tend - tstart
486
487
        tstart = tstart + minfraction*duration
        taus = np.linspace(minfraction*duration, maxfraction*duration, npoints)
488
        twoFs = []
Gregory Ashton's avatar
Gregory Ashton committed
489
490
491
        if self.transient is False:
            self.transient = True
            self.init_computefstatistic_single_point()
492
493
494
495
496
497
498
499
500
        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,
501
                             title=None, **kwargs):
502

503
504
505
506
507
508
        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
509
510
511
512
        if self.BSGL:
            ax.set_ylabel(r'$\log_{10}(\mathrm{BSGL})_{\rm cumulative}$')
        else:
            ax.set_ylabel(r'$\widetilde{2\mathcal{F}}_{\rm cumulative}$')
513
        ax.set_xlim(0, taus[-1]/86400)
514
        ax.set_title(title)
515
516
        if savefig:
            plt.savefig('{}/{}_twoFcumulative.png'.format(outdir, label))
Gregory Ashton's avatar
Gregory Ashton committed
517
            return taus, twoFs
518
519
520
        else:
            return ax

Gregory Ashton's avatar
Gregory Ashton committed
521

522
523
524
525
526
527
528
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,
529
530
                 detector=None, earth_ephem=None, sun_ephem=None,
                 injectSources=None):
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
        """
        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):
556
557
558
        logging.info(('Initialising semicoherent parameters from {} to {} in'
                      ' {} segments').format(
            self.minStartTime, self.maxStartTime, self.nsegs))
559
560
        self.transient = True
        self.whatToCompute = lalpulsar.FSTATQ_2F+lalpulsar.FSTATQ_ATOMS_PER_DET
561
562
563
        self.tboundaries = np.linspace(self.minStartTime, self.maxStartTime,
                                       self.nsegs+1)

Gregory Ashton's avatar
Gregory Ashton committed
564
565
566
567
    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 """
568

Gregory Ashton's avatar
Gregory Ashton committed
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
        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
608

Gregory Ashton's avatar
Gregory Ashton committed
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
            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
626
627


Gregory Ashton's avatar
Gregory Ashton committed
628
class SemiCoherentGlitchSearch(BaseSearchClass, ComputeFstat):
629
630
631
    """ A semi-coherent glitch search

    This implements a basic `semi-coherent glitch F-stat in which the data
632
633
    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
634
635
636
637
    F-stat
    """

    @initializer
Gregory Ashton's avatar
Gregory Ashton committed
638
    def __init__(self, label, outdir, tref, tstart, tend, nglitch=0,
639
640
641
                 sftfilepath=None, theta0_idx=0, BSGL=False, minStartTime=None,
                 maxStartTime=None, minCoverFreq=None, maxCoverFreq=None,
                 detector=None, earth_ephem=None, sun_ephem=None):
642
643
644
645
        """
        Parameters
        ----------
        label, outdir: str
646
647
648
649
650
651
            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).
652
653
        sftfilepath: str
            File patern to match SFTs
654
655
656
657
        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)
658
659

        For all other parameters, see pyfstat.ComputeFStat.
660
661
662
663
664
665
666
        """

        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
667
668
        self.transient = True
        self.binary = False
669
670
671
        self.init_computefstatistic_single_point()

    def compute_nglitch_fstat(self, F0, F1, F2, Alpha, Delta, *args):
672
        """ Returns the semi-coherent glitch summed twoF """
673
674
675

        args = list(args)
        tboundaries = [self.tstart] + args[-self.nglitch:] + [self.tend]
676
677
678
679
680
681
682
683
        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)

684
685
        thetas = self.calculate_thetas(theta, delta_thetas, tboundaries,
                                       theta0_idx=self.theta0_idx)
686
687

        twoFSum = 0
688
        for i, theta_i_at_tref in enumerate(thetas):
689
690
691
            ts, te = tboundaries[i], tboundaries[i+1]

            twoFVal = self.run_computefstatistic_single_point(
692
693
                ts, te, theta_i_at_tref[1], theta_i_at_tref[2],
                theta_i_at_tref[3], Alpha, Delta)
694
695
            twoFSum += twoFVal

696
697
698
        if np.isfinite(twoFSum):
            return twoFSum
        else:
699
            return -np.inf
700
701
702

    def compute_glitch_fstat_single(self, F0, F1, F2, Alpha, Delta, delta_F0,
                                    delta_F1, tglitch):
703
704
705
706
        """ Returns the semi-coherent glitch summed twoF for nglitch=1

        Note: used for testing
        """
707
708
709
710
711
712
713
714
715
716
717

        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
718
            self.tstart, tglitch, theta[0], theta[1], theta[2], Alpha,
719
720
721
722
723
724
            Delta)

        if tglitch == self.tend:
            return twoFsegA

        twoFsegB = self.run_computefstatistic_single_point(
Gregory Ashton's avatar
Gregory Ashton committed
725
            tglitch, self.tend, theta_post_glitch[0],
726
727
728
729
730
731
            theta_post_glitch[1], theta_post_glitch[2], Alpha,
            Delta)

        return twoFsegA + twoFsegB


Gregory Ashton's avatar
Gregory Ashton committed
732
733
class MCMCSearch(BaseSearchClass):
    """ MCMC search using ComputeFstat"""
734
    @initializer
735
    def __init__(self, label, outdir, sftfilepath, theta_prior, tref,
736
                 tstart, tend, nsteps=[100, 100, 100], nwalkers=100, ntemps=1,
737
                 log10temperature_min=-5, theta_initial=None, scatter_val=1e-10,
738
739
                 binary=False, BSGL=False, minCoverFreq=None,
                 maxCoverFreq=None, detector=None, earth_ephem=None,
740
                 sun_ephem=None, injectSources=None):
741
742
743
744
        """
        Parameters
        label, outdir: str
            A label and directory to read/write data from/to
745
746
        sftfilepath: str
            File patern to match SFTs
747
        theta_prior: dict
748
749
750
751
            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.
752
753
754
755
        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.
756
757
758
759
760
761
762
        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].
763
764
765
766
767
768
769
770
771
772
773
        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.
774
775
776
777
778
779
780
781
782
783
        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

        """

784
785
786
        self.minStartTime = tstart
        self.maxStartTime = tend

Gregory Ashton's avatar
Gregory Ashton committed
787
788
        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
789
        self.add_log_file()
Gregory Ashton's avatar
Gregory Ashton committed
790
791
        logging.info(
            'Set-up MCMC search for model {} on data {}'.format(
792
                self.label, self.sftfilepath))
793
794
795
        self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
        self.unpack_input_theta()
        self.ndim = len(self.theta_keys)
796
797
798
799
        if self.log10temperature_min:
            self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
        else:
            self.betas = None
800

801
802
803
804
805
806
807
808
        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")

809
810
811
        self.log_input()

    def log_input(self):
812
        logging.info('theta_prior = {}'.format(self.theta_prior))
813
        logging.info('nwalkers={}'.format(self.nwalkers))
814
815
816
817
        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(
818
            self.log10temperature_min))
819
820
821

    def inititate_search_object(self):
        logging.info('Setting up search object')
Gregory Ashton's avatar
Gregory Ashton committed
822
        self.search = ComputeFstat(
823
824
825
826
            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,
827
            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
828
            binary=self.binary, injectSources=self.injectSources)
829
830

    def logp(self, theta_vals, theta_prior, theta_keys, search):
Gregory Ashton's avatar
Gregory Ashton committed
831
        H = [self.generic_lnprior(**theta_prior[key])(p) for p, key in
832
833
834
835
836
837
             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]
838
839
        FS = search.compute_fullycoherent_det_stat_single_point(
            *self.fixed_theta)
840
841
842
        return FS

    def unpack_input_theta(self):
843
        full_theta_keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta']
844
845
846
        if self.binary:
            full_theta_keys += [
                'asini', 'period', 'ecc', 'tp', 'argp']
847
848
        full_theta_keys_copy = copy.copy(full_theta_keys)

849
850
        full_theta_symbols = ['$f$', '$\dot{f}$', '$\ddot{f}$', r'$\alpha$',
                              r'$\delta$']
851
852
853
854
        if self.binary:
            full_theta_symbols += [
                'asini', 'period', 'period', 'ecc', 'tp', 'argp']

855
856
        self.theta_keys = []
        fixed_theta_dict = {}
857
        for key, val in self.theta_prior.iteritems():
858
859
            if type(val) is dict:
                fixed_theta_dict[key] = 0
Gregory Ashton's avatar
Gregory Ashton committed
860
                self.theta_keys.append(key)
861
862
863
864
865
866
            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
867
            full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883

        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):
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
        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
920

Gregory Ashton's avatar
Gregory Ashton committed
921
    def run_sampler_with_progress_bar(self, sampler, ns, p0):
922
923
        for result in tqdm(sampler.sample(p0, iterations=ns), total=ns):
            pass
Gregory Ashton's avatar
Gregory Ashton committed
924
925
        return sampler

926
    def run(self, proposal_scale_factor=2, **kwargs):
927

Gregory Ashton's avatar
Gregory Ashton committed
928
        self.old_data_is_okay_to_use = self.check_old_data_is_okay_to_use()
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
        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),
944
            loglargs=(self.search,), betas=self.betas, a=proposal_scale_factor)
945

Gregory Ashton's avatar
Gregory Ashton committed
946
947
        p0 = self.generate_initial_p0()
        p0 = self.apply_corrections_to_p0(p0)
948
949
950
951
952
        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
953
                j, ninit_steps, n))
Gregory Ashton's avatar
Gregory Ashton committed
954
            sampler = self.run_sampler_with_progress_bar(sampler, n, p0)
955
956
            logging.info("Mean acceptance fraction: {}"
                         .format(np.mean(sampler.acceptance_fraction, axis=1)))
957
958
959
            if self.ntemps > 1:
                logging.info("Tswap acceptance fraction: {}"
                             .format(sampler.tswap_acceptance_fraction))
960
961
            fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols,
                                          **kwargs)
962
            fig.savefig('{}/{}_init_{}_walkers.png'.format(
963
                self.outdir, self.label, j), dpi=200)
964

965
            p0 = self.get_new_p0(sampler)
Gregory Ashton's avatar
Gregory Ashton committed
966
            p0 = self.apply_corrections_to_p0(p0)
967
968
969
            self.check_initial_points(p0)
            sampler.reset()

Gregory Ashton's avatar
Gregory Ashton committed
970
971
972
973
        if len(self.nsteps) > 1:
            nburn = self.nsteps[-2]
        else:
            nburn = 0
974
975
976
        nprod = self.nsteps[-1]
        logging.info('Running final burn and prod with {} steps'.format(
            nburn+nprod))
Gregory Ashton's avatar
Gregory Ashton committed
977
        sampler = self.run_sampler_with_progress_bar(sampler, nburn+nprod, p0)
978
979
        logging.info("Mean acceptance fraction: {}"
                     .format(np.mean(sampler.acceptance_fraction, axis=1)))
980
981
982
        if self.ntemps > 1:
            logging.info("Tswap acceptance fraction: {}"
                         .format(sampler.tswap_acceptance_fraction))
983

Gregory Ashton's avatar
Gregory Ashton committed
984
        fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols,
985
986
987
                                      burnin_idx=nburn, **kwargs)
        fig.savefig('{}/{}_walkers.png'.format(self.outdir, self.label),
                    dpi=200)
988
989
990
991
992
993
994
995
996
997

        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)

998
    def plot_corner(self, figsize=(7, 7),  tglitch_ratio=False,
999
1000
1001
                    add_prior=False, nstds=None, label_offset=0.4,
                    dpi=300, rc_context={}, **kwargs):

Gregory Ashton's avatar
Gregory Ashton committed
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
        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

1012
1013
1014
1015
1016
1017
        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)
1018
1019
            theta_symbols_plt = [s.replace('_{glitch}', r'_\textrm{glitch}')
                                 for s in theta_symbols_plt]
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068

            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)
1069
1070
1071
1072
1073
1074

    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
1075
            prior = self.generic_lnprior(**self.theta_prior[key])
1076
1077
1078
1079
1080
1081
            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)

1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
    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)
1101
1102
1103
1104
1105
            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
1106
1107
1108
1109
1110
            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]
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
            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))

1134
    def plot_cumulative_max(self, **kwargs):
Gregory Ashton's avatar
Gregory Ashton committed
1135
1136
1137
1138
        d, maxtwoF = self.get_max_twoF()
        for key, val in self.theta_prior.iteritems():
            if key not in d:
                d[key] = val
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152

        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
1153

Gregory Ashton's avatar
Gregory Ashton committed
1154
    def generic_lnprior(self, **kwargs):
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
        """ 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'])
1197
1198
        elif kwargs['type'] == 'neghalfnorm':
            return lambda x: halfnorm(-x, kwargs['loc'], kwargs['scale'])
1199
1200
1201
1202
1203
1204
1205
        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
1206
    def generate_rv(self, **kwargs):
1207
1208
1209
1210
1211
1212
1213
1214
        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']))
1215
1216
1217
        if dist_type == "neghalfnorm":
            return -1 * np.abs(np.random.normal(loc=kwargs['loc'],
                                                scale=kwargs['scale']))
1218
1219
1220
1221
1222
1223
        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
1224
    def plot_walkers(self, sampler, symbols=None, alpha=0.4, color="k", temp=0,
Gregory Ashton's avatar
Gregory Ashton committed
1225
                     lw=0.1, burnin_idx=None, add_det_stat_burnin=False,
1226
1227
                     fig=None, axes=None, xoffset=0, plot_det_stat=True,
                     context='classic'):
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
        """ 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, :, :, :]

1243
        with plt.style.context((context)):
Gregory Ashton's avatar
Gregory Ashton committed
1244
1245
1246
1247
1248
            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)]
1249

Gregory Ashton's avatar
Gregory Ashton committed
1250
            idxs = np.arange(chain.shape[1])
1251
1252
            if ndim > 1:
                for i in range(ndim):
1253
                    axes[i].ticklabel_format(useOffset=False, axis='y')
1254
1255
                    if i < ndim:
                        axes[i].set_xticklabels([])
Gregory Ashton's avatar
Gregory Ashton committed
1256
1257
                    cs = chain[:, :, i].T
                    if burnin_idx:
Gregory Ashton's avatar
Gregory Ashton committed
1258
1259
1260
1261
1262
                        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)
1263
1264
                    if symbols:
                        axes[i].set_ylabel(symbols[i])
1265
            else:
Gregory Ashton's avatar
Gregory Ashton committed
1266
                axes[0].ticklabel_format(useOffset=False, axis='y')
Gregory Ashton's avatar
Gregory Ashton committed
1267
                cs = chain[:, :, temp].T
Gregory Ashton's avatar
Gregory Ashton committed
1268
1269
1270
1271
1272
1273
1274
                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])
1275

1276
1277
            if len(axes) == ndim:
                axes.append(fig.add_subplot(ndim+1, 1, ndim+1))
Gregory Ashton's avatar
Gregory Ashton committed
1278

1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
            if plot_det_stat:
                lnl = sampler.lnlikelihood[temp, :, :]
                if burnin_idx and add_det_stat_burnin:
                    burn_in_vals = lnl[:, :burnin_idx].flatten()
                    axes[-1].hist(burn_in_vals[~np.isnan(burn_in_vals)], bins=50,
                                  histtype='step', color='r')
                else:
                    burn_in_vals = []
                prod_vals = lnl[:, burnin_idx:].flatten()
                axes[-1].hist(prod_vals[~np.isnan(prod_vals)], bins=50,
                              histtype='step', color='k')
                if self.BSGL:
                    axes[-1].set_xlabel(r'$\mathcal{B}_\mathrm{S/GL}$')
                else:
                    axes[-1].set_xlabel(r'$\widetilde{2\mathcal{F}}$')
                axes[-1].set_ylabel(r'$\textrm{Counts}$')
                combined_vals = np.append(burn_in_vals, prod_vals)
                if len(combined_vals) > 0:
                    minv = np.min(combined_vals)
                    maxv = np.max(combined_vals)
                    Range = abs(maxv-minv)
                    axes[-1].set_xlim(minv-0.1*Range, maxv+0.1*Range)

            axes[-2].set_xlabel(r'$\textrm{Number of steps}$', labelpad=0.1)
1303
1304
        return fig, axes

Gregory Ashton's avatar
Gregory Ashton committed
1305
1306
1307
1308
1309