pyfstat.py 70.7 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
16
17
18
19
20

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

24
plt.rcParams['text.usetex'] = True
25
plt.rcParams['axes.formatter.useoffset'] = False
26

27
28
29
30
31
32
33
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(' ', '')
34
            v = v.replace(' ', '').replace("'", "").replace('"', '').replace('\n', '')
35
36
37
38
39
40
            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')
41
42
43
    earth_ephem = None
    sun_ephem = None

44
45
46
47
48
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")
49
parser.add_argument("-u", "--use-old-data", action="store_true")
50
51
52
53
54
55
56
57
58
59
60
61
62
parser.add_argument('unittest_args', nargs='*')
args, unknown = parser.parse_known_args()
sys.argv[1:] = args.unittest_args

if args.quite:
    log_level = logging.WARNING
else:
    log_level = logging.DEBUG

logging.basicConfig(level=log_level,
                    format='%(asctime)s %(levelname)-8s: %(message)s',
                    datefmt='%H:%M')

63
64

def initializer(func):
65
    """ Automatically assigns the parameters to self """
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
    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):
83
    """ Read in a .par file, returns a dictionary of the values """
84
85
86
87
    filename = '{}/{}.par'.format(outdir, label)
    d = {}
    with open(filename, 'r') as f:
        for line in f:
88
89
90
91
            if len(line.split('=')) > 1:
                key, val = line.rstrip('\n').split(' = ')
                key = key.strip()
                d[key] = np.float64(val.rstrip('; '))
92
93
94
95
    return d


class BaseSearchClass(object):
96
    """ The base search class, provides ephemeris and general utilities """
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125

    earth_ephem_default = earth_ephem
    sun_ephem_default = sun_ephem

    def shift_matrix(self, n, dT):
        """ Generate the shift matrix """
        m = np.zeros((n, n))
        factorial = np.math.factorial
        for i in range(n):
            for j in range(n):
                if i == j:
                    m[i, j] = 1.0
                elif i > j:
                    m[i, j] = 0.0
                else:
                    if i == 0:
                        m[i, j] = 2*np.pi*float(dT)**(j-i) / factorial(j-i)
                    else:
                        m[i, j] = float(dT)**(j-i) / factorial(j-i)

        return m

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

        Parameters
        ----------
        theta: array-like, shape (n,)
            vector of the expansion coefficients to transform starting from the
126
            lowest degree e.g [phi, F0, F1,...].
127
        dT: float
128
            difference between the two reference times as tref_new - tref_old.
129
130
131
132

        Returns
        -------
        theta_new: array-like shape (n,)
133
            vector of the coefficients as evaluate as the new reference time.
134
135
136
137
138
        """
        n = len(theta)
        m = self.shift_matrix(n, dT)
        return np.dot(m, theta)

139
    def calculate_thetas(self, theta, delta_thetas, tbounds, theta0_idx=0):
140
141
142
        """ Calculates the set of coefficients for the post-glitch signal """
        thetas = [theta]
        for i, dt in enumerate(delta_thetas):
143
144
145
146
147
148
149
150
151
152
153
154
155
            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]))
156
157
158
        return thetas


Gregory Ashton's avatar
Gregory Ashton committed
159
160
161
162
163
164
165
166
class ComputeFstat(object):
    """ Base class providing interface to lalpulsar.ComputeFstat """

    earth_ephem_default = earth_ephem
    sun_ephem_default = sun_ephem

    @initializer
    def __init__(self, tref, sftlabel=None, sftdir=None,
167
                 minStartTime=None, maxStartTime=None,
Gregory Ashton's avatar
Gregory Ashton committed
168
                 minCoverFreq=None, maxCoverFreq=None,
169
                 detector=None, earth_ephem=None, sun_ephem=None,
170
                 binary=False, transient=True, BSGL=False):
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
        """
        Parameters
        ----------
        tref: int
            GPS seconds of the reference time.
        sftlabel, sftdir: str
            A label and directory in which to find the relevant sft file
        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.
        detector: str
            Two character reference to the data to use, specify None for no
            contraint.
        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.
        binary: bool
            If true, search of binary parameters.
        transient: bool
            If true, allow for the Fstat to be computed over a transient range.
193
194
        BSGL: bool
            If true, compute the BSGL rather than the twoF value.
195
196

        """
Gregory Ashton's avatar
Gregory Ashton committed
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211

        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
212
213
214
215
216
        if self.minStartTime:
            constraints.minStartTime = lal.LIGOTimeGPS(self.minStartTime)
        if self.maxStartTime:
            constraints.maxStartTime = lal.LIGOTimeGPS(self.maxStartTime)

Gregory Ashton's avatar
Gregory Ashton committed
217
218
219
        self.sft_filepath = self.sftdir+'/*_'+self.sftlabel+"*sft"
        SFTCatalog = lalpulsar.SFTdataFind(self.sft_filepath, constraints)
        names = list(set([d.header.name for d in SFTCatalog.data]))
220
221
222
        logging.info(
            'Loaded data from detectors {} matching pattern {}'.format(
                names, self.sft_filepath))
Gregory Ashton's avatar
Gregory Ashton committed
223
224
225
226
227
228

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

        logging.info('Initialising FstatInput')
        dFreq = 0
229
230
231
232
233
        if self.transient:
            self.whatToCompute = lalpulsar.FSTATQ_ATOMS_PER_DET
        else:
            self.whatToCompute = lalpulsar.FSTATQ_2F

Gregory Ashton's avatar
Gregory Ashton committed
234
235
236
237
238
239
240
241
        FstatOptionalArgs = lalpulsar.FstatOptionalArgsDefaults

        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
242
243
244
            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
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264

        self.FstatInput = lalpulsar.CreateFstatInput(SFTCatalog,
                                                     self.minCoverFreq,
                                                     self.maxCoverFreq,
                                                     dFreq,
                                                     ephems,
                                                     FstatOptionalArgs
                                                     )

        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()

265
266
267
268
        if self.BSGL:
            logging.info('Initialising BSGL: this will fail if numDet < 2')
            # Tuning parameters - to be reviewed
            numDetectors = 2
Gregory Ashton's avatar
Gregory Ashton committed
269
            Fstar0sc = 15.
270
            oLGX = np.zeros(10)
Gregory Ashton's avatar
Gregory Ashton committed
271
            oLGX[:numDetectors] = 1./numDetectors
272
273
274
275
276
277
278
279
280
            self.BSGLSetup = lalpulsar.CreateBSGLSetup(numDetectors,
                                                       Fstar0sc,
                                                       oLGX,
                                                       False,
                                                       1)
            self.twoFX = np.zeros(10)
            self.whatToCompute = (lalpulsar.FSTATQ_2F +
                                  lalpulsar.FSTATQ_2F_PER_DET)

281
        if self.transient:
282
            logging.info('Initialising transient parameters')
283
284
285
286
287
288
            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
289

Gregory Ashton's avatar
Gregory Ashton committed
290
    def run_computefstatistic_single_point(self, tstart, tend, F0, F1,
291
292
293
                                           F2, Alpha, Delta, asini=None,
                                           period=None, ecc=None, tp=None,
                                           argp=None):
294
        """ Returns the twoF fully-coherently at a single point """
Gregory Ashton's avatar
Gregory Ashton committed
295
296
297
298

        self.PulsarDopplerParams.fkdot = np.array([F0, F1, F2, 0, 0, 0, 0])
        self.PulsarDopplerParams.Alpha = Alpha
        self.PulsarDopplerParams.Delta = Delta
299
300
301
302
303
304
        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
305
306
307
308

        lalpulsar.ComputeFstat(self.FstatResults,
                               self.FstatInput,
                               self.PulsarDopplerParams,
309
                               1,
Gregory Ashton's avatar
Gregory Ashton committed
310
311
312
                               self.whatToCompute
                               )

313
        if self.transient is False:
314
315
316
317
318
319
320
321
322
            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)
            BSGL = lalpulsar.ComputeBSGL(twoF, self.twoFX,
                                         self.BSGLSetup)
            return BSGL
323

324
325
        self.windowRange.t0 = int(tstart)  # TYPE UINT4
        self.windowRange.tau = int(tend - tstart)  # TYPE UINT4
326

Gregory Ashton's avatar
Gregory Ashton committed
327
        FS = lalpulsar.ComputeTransientFstatMap(
328
            self.FstatResults.multiFatoms[0], self.windowRange, False)
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347

        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]
        BSGL = lalpulsar.ComputeBSGL(2*FS.F_mn.data[0][0], self.twoFX,
                                     self.BSGLSetup)

        return BSGL
Gregory Ashton's avatar
Gregory Ashton committed
348
349
350


class SemiCoherentGlitchSearch(BaseSearchClass, ComputeFstat):
351
352
353
354
355
356
357
358
359
    """ A semi-coherent glitch search

    This implements a basic `semi-coherent glitch F-stat in which the data
    is divided into two segments either side of the proposed glitch and the
    fully-coherent F-stat in each segment is averaged to give the semi-coherent
    F-stat
    """

    @initializer
Gregory Ashton's avatar
Gregory Ashton committed
360
    def __init__(self, label, outdir, tref, tstart, tend, nglitch=0,
361
362
363
364
                 sftlabel=None, sftdir=None, theta0_idx=0, BSGL=False,
                 minCoverFreq=None, maxCoverFreq=None, minStartTime=None,
                 maxStartTime=None, detector=None, earth_ephem=None,
                 sun_ephem=None):
365
366
367
368
        """
        Parameters
        ----------
        label, outdir: str
369
370
371
372
373
374
            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).
375
376
        sftlabel, sftdir: str
            A label and directory in which to find the relevant sft file. If
377
            None use label and outdir.
378
379
380
381
        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)
382
        minCoverFreq, maxCoverFreq: float
383
384
385
            The min and max cover frequency passed to CreateFstatInput, if
            either is None the range of frequencies in the SFT less 1Hz is
            used.
386
387
        detector: str
            Two character reference to the data to use, specify None for no
388
            contraint.
389
390
        earth_ephem, sun_ephem: str
            Paths of the two files containing positions of Earth and Sun,
391
392
            respectively at evenly spaced times, as passed to CreateFstatInput.
            If None defaults defined in BaseSearchClass will be used.
393
394
395
396
397
398
399
400
401
402
403
        """

        if self.sftlabel is None:
            self.sftlabel = self.label
        if self.sftdir is None:
            self.sftdir = self.outdir
        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
404
405
        self.transient = True
        self.binary = False
406
407
408
        self.init_computefstatistic_single_point()

    def compute_nglitch_fstat(self, F0, F1, F2, Alpha, Delta, *args):
409
        """ Returns the semi-coherent glitch summed twoF """
410
411
412

        args = list(args)
        tboundaries = [self.tstart] + args[-self.nglitch:] + [self.tend]
413
414
415
416
417
418
419
420
        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)

421
422
        thetas = self.calculate_thetas(theta, delta_thetas, tboundaries,
                                       theta0_idx=self.theta0_idx)
423
424

        twoFSum = 0
425
        for i, theta_i_at_tref in enumerate(thetas):
426
427
428
            ts, te = tboundaries[i], tboundaries[i+1]

            twoFVal = self.run_computefstatistic_single_point(
429
430
                ts, te, theta_i_at_tref[1], theta_i_at_tref[2],
                theta_i_at_tref[3], Alpha, Delta)
431
432
            twoFSum += twoFVal

433
434
435
        if np.isfinite(twoFSum):
            return twoFSum
        else:
436
            return -np.inf
437
438
439

    def compute_glitch_fstat_single(self, F0, F1, F2, Alpha, Delta, delta_F0,
                                    delta_F1, tglitch):
440
441
442
443
        """ Returns the semi-coherent glitch summed twoF for nglitch=1

        Note: used for testing
        """
444
445
446
447
448
449
450
451
452
453
454

        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
455
            self.tstart, tglitch, theta[0], theta[1], theta[2], Alpha,
456
457
458
459
460
461
            Delta)

        if tglitch == self.tend:
            return twoFsegA

        twoFsegB = self.run_computefstatistic_single_point(
Gregory Ashton's avatar
Gregory Ashton committed
462
            tglitch, self.tend, theta_post_glitch[0],
463
464
465
466
467
468
            theta_post_glitch[1], theta_post_glitch[2], Alpha,
            Delta)

        return twoFsegA + twoFsegB


Gregory Ashton's avatar
Gregory Ashton committed
469
470
class MCMCSearch(BaseSearchClass):
    """ MCMC search using ComputeFstat"""
471
    @initializer
472
473
    def __init__(self, label, outdir, sftlabel, sftdir, theta_prior, tref,
                 tstart, tend, nsteps=[100, 100, 100], nwalkers=100, ntemps=1,
474
                 log10temperature_min=-5, theta_initial=None, scatter_val=1e-4,
475
476
                 binary=False, BSGL=False, minCoverFreq=None, maxCoverFreq=None,
                 detector=None, earth_ephem=None, sun_ephem=None, theta0_idx=0):
477
478
479
480
481
482
        """
        Parameters
        label, outdir: str
            A label and directory to read/write data from/to
        sftlabel, sftdir: str
            A label and directory in which to find the relevant sft file
483
        theta_prior: dict
484
485
486
487
            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.
488
489
490
491
        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.
492
493
494
495
496
497
498
        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].
499
500
501
502
503
504
505
506
507
508
509
        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.
510
511
512
513
514
515
516
517
518
519
        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

        """

520
521
522
        self.minStartTime = tstart
        self.maxStartTime = tend

Gregory Ashton's avatar
Gregory Ashton committed
523
524
525
        logging.info(
            'Set-up MCMC search for model {} on data {}'.format(
                self.label, self.sftlabel))
526
527
528
        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
        self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
Gregory Ashton's avatar
Gregory Ashton committed
529
530
        self.theta_prior['tstart'] = self.tstart
        self.theta_prior['tend'] = self.tend
531
532
        self.unpack_input_theta()
        self.ndim = len(self.theta_keys)
533
        self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
534
        self.sft_filepath = self.sftdir+'/*_'+self.sftlabel+"*sft"
535

536
537
538
539
540
541
542
543
544
        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")

        self.old_data_is_okay_to_use = self.check_old_data_is_okay_to_use()
545
546
547
548
549
550
551
552
553
554
        self.log_input()

    def log_input(self):
        logging.info('Input prior dictionary: {}'.format(self.theta_prior))
        logging.info('nwalkers={}'.format(self.nwalkers))
        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(
            self.log10temperature_min))
555
556
557

    def inititate_search_object(self):
        logging.info('Setting up search object')
Gregory Ashton's avatar
Gregory Ashton committed
558
559
560
        self.search = ComputeFstat(
            tref=self.tref, sftlabel=self.sftlabel,
            sftdir=self.sftdir, minCoverFreq=self.minCoverFreq,
561
            maxCoverFreq=self.maxCoverFreq, earth_ephem=self.earth_ephem,
562
            sun_ephem=self.sun_ephem, detector=self.detector,
563
            BSGL=self.BSGL, transient=False,
564
            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime)
565
566

    def logp(self, theta_vals, theta_prior, theta_keys, search):
Gregory Ashton's avatar
Gregory Ashton committed
567
        H = [self.generic_lnprior(**theta_prior[key])(p) for p, key in
568
569
570
571
572
573
             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]
Gregory Ashton's avatar
Gregory Ashton committed
574
        FS = search.run_computefstatistic_single_point(*self.fixed_theta)
575
576
577
        return FS

    def unpack_input_theta(self):
Gregory Ashton's avatar
Gregory Ashton committed
578
579
        full_theta_keys = ['tstart', 'tend', 'F0', 'F1', 'F2', 'Alpha',
                           'Delta']
580
581
582
        if self.binary:
            full_theta_keys += [
                'asini', 'period', 'ecc', 'tp', 'argp']
583
584
        full_theta_keys_copy = copy.copy(full_theta_keys)

Gregory Ashton's avatar
Gregory Ashton committed
585
586
        full_theta_symbols = ['_', '_', '$f$', '$\dot{f}$', '$\ddot{f}$',
                              r'$\alpha$', r'$\delta$']
587
588
589
590
        if self.binary:
            full_theta_symbols += [
                'asini', 'period', 'period', 'ecc', 'tp', 'argp']

591
592
        self.theta_keys = []
        fixed_theta_dict = {}
593
        for key, val in self.theta_prior.iteritems():
594
595
            if type(val) is dict:
                fixed_theta_dict[key] = 0
Gregory Ashton's avatar
Gregory Ashton committed
596
                self.theta_keys.append(key)
597
598
599
600
601
602
            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
603
            full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619

        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):
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
        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
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675

    def run(self):

        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),
            loglargs=(self.search,), betas=self.betas)

Gregory Ashton's avatar
Gregory Ashton committed
676
677
        p0 = self.generate_initial_p0()
        p0 = self.apply_corrections_to_p0(p0)
678
679
680
681
682
683
684
        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(
                j, ninit_steps, n))
            sampler.run_mcmc(p0, n)
685
686
            logging.info("Mean acceptance fraction: {0:.3f}"
                         .format(np.mean(sampler.acceptance_fraction)))
Gregory Ashton's avatar
Gregory Ashton committed
687
            fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols)
688
689
690
            fig.savefig('{}/{}_init_{}_walkers.png'.format(
                self.outdir, self.label, j))

691
            p0 = self.get_new_p0(sampler)
Gregory Ashton's avatar
Gregory Ashton committed
692
            p0 = self.apply_corrections_to_p0(p0)
693
694
695
696
697
698
699
700
            self.check_initial_points(p0)
            sampler.reset()

        nburn = self.nsteps[-2]
        nprod = self.nsteps[-1]
        logging.info('Running final burn and prod with {} steps'.format(
            nburn+nprod))
        sampler.run_mcmc(p0, nburn+nprod)
701
702
        logging.info("Mean acceptance fraction: {0:.3f}"
                     .format(np.mean(sampler.acceptance_fraction)))
703

Gregory Ashton's avatar
Gregory Ashton committed
704
        fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols)
705
706
707
708
709
710
711
712
713
714
715
        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)

716
    def plot_corner(self, figsize=(7, 7),  tglitch_ratio=False,
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
                    add_prior=False, nstds=None, label_offset=0.4,
                    dpi=300, rc_context={}, **kwargs):

        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)
            theta_symbols_plt = [s.replace('_{glitch}', r'_\textrm{glitch}') for s
                                 in theta_symbols_plt]

            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)
777
778
779
780
781
782

    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
783
            prior = self.generic_lnprior(**self.theta_prior[key])
784
785
786
787
788
789
            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)

Gregory Ashton's avatar
Gregory Ashton committed
790
    def generic_lnprior(self, **kwargs):
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
        """ 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'])
833
834
        elif kwargs['type'] == 'neghalfnorm':
            return lambda x: halfnorm(-x, kwargs['loc'], kwargs['scale'])
835
836
837
838
839
840
841
        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
842
    def generate_rv(self, **kwargs):
843
844
845
846
847
848
849
850
        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']))
851
852
853
        if dist_type == "neghalfnorm":
            return -1 * np.abs(np.random.normal(loc=kwargs['loc'],
                                                scale=kwargs['scale']))
854
855
856
857
858
859
        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
860
861
    def plot_walkers(self, sampler, symbols=None, alpha=0.4, color="k", temp=0,
                     start=None, stop=None, draw_vline=None):
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
        """ 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')):
            fig, axes = plt.subplots(ndim, 1, sharex=True, figsize=(8, 4*ndim))

            if ndim > 1:
                for i in range(ndim):
882
                    axes[i].ticklabel_format(useOffset=False, axis='y')
883
884
                    cs = chain[:, start:stop, i].T
                    axes[i].plot(cs, color="k", alpha=alpha)
885
886
887
888
                    if symbols:
                        axes[i].set_ylabel(symbols[i])
                    if draw_vline is not None:
                        axes[i].axvline(draw_vline, lw=2, ls="--")
889
890
891
892
893

            else:
                cs = chain[:, start:stop, 0].T
                axes.plot(cs, color='k', alpha=alpha)
                axes.ticklabel_format(useOffset=False, axis='y')
894
895
896

        return fig, axes

Gregory Ashton's avatar
Gregory Ashton committed
897
898
899
900
901
    def apply_corrections_to_p0(self, p0):
        """ Apply any correction to the initial p0 values """
        return p0

    def generate_scattered_p0(self, p):
902
        """ Generate a set of p0s scattered about p """
Gregory Ashton's avatar
Gregory Ashton committed
903
        p0 = [[p + self.scatter_val * p * np.random.randn(self.ndim)
904
905
906
907
               for i in xrange(self.nwalkers)]
              for j in xrange(self.ntemps)]
        return p0

Gregory Ashton's avatar
Gregory Ashton committed
908
    def generate_initial_p0(self):
909
910
911
        """ Generate a set of init vals for the walkers """

        if type(self.theta_initial) == dict:
912
913
914
            logging.info('Generate initial values from initial dictionary')
            if self.nglitch > 1:
                raise ValueError('Initial dict not implemented for nglitch>1')
Gregory Ashton's avatar
Gregory Ashton committed
915
            p0 = [[[self.generate_rv(**self.theta_initial[key])
916
917
918
                    for key in self.theta_keys]
                   for i in range(self.nwalkers)]
                  for j in range(self.ntemps)]
919
920
921
922
923
924
        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)]
925
        elif self.theta_initial is None:
926
            logging.info('Generate initial values from prior dictionary')
Gregory Ashton's avatar
Gregory Ashton committed
927
            p0 = [[[self.generate_rv(**self.theta_prior[key])
928
929
930
931
                    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
932
            p0 = self.generate_scattered_p0(self.theta_initial)
933
934
935
936
937
        else:
            raise ValueError('theta_initial not understood')

        return p0

938
    def get_new_p0(self, sampler):
939
940
941
942
943
944
945
946
947
948
949
950
        """ 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`.

        """
        if sampler.chain[:, :, -1, :].shape[0] == 1:
            ntemps_temp = 1
        else:
            ntemps_temp = self.ntemps
        pF = sampler.chain[:, :, -1, :].reshape(
            ntemps_temp, self.nwalkers, self.ndim)[0, :, :]
951
952
        lnl = sampler.lnlikelihood[:, :, -1].reshape(
            self.ntemps, self.nwalkers)[0, :]
953
954
        lnp = sampler.lnprobability[:, :, -1].reshape(
            self.ntemps, self.nwalkers)[0, :]
955
956

        # General warnings about the state of lnp
957
        if any(np.isnan(lnp)):
958
959
960
961
962
963
964
965
966
967
968
            logging.warning(
                "Of {} lnprobs {} are nan".format(
                    len(lnp), np.sum(np.isnan(lnp))))
        if any(np.isposinf(lnp)):
            logging.warning(
                "Of {} lnprobs {} are +np.inf".format(
                    len(lnp), np.sum(np.isposinf(lnp))))
        if any(np.isneginf(lnp)):
            logging.warning(
                "Of {} lnprobs {} are -np.inf".format(
                    len(lnp), np.sum(np.isneginf(lnp))))
969

970
971
972
        lnp_finite = copy.copy(lnp)
        lnp_finite[np.isinf(lnp)] = np.nan
        p = pF[np.nanargmax(lnp_finite)]
973
974
        logging.info('Generating new p0 from max lnp which had twoF={}'
                     .format(lnl[np.nanargmax(lnp_finite)]))
975
        p0 = self.generate_scattered_p0(p)
976
977
978
979
980
981

        return p0

    def get_save_data_dictionary(self):
        d = dict(nsteps=self.nsteps, nwalkers=self.nwalkers,
                 ntemps=self.ntemps, theta_keys=self.theta_keys,
Gregory Ashton's avatar
Gregory Ashton committed
982
                 theta_prior=self.theta_prior, scatter_val=self.scatter_val,
983
984
                 log10temperature_min=self.log10temperature_min,
                 theta0_idx=self.theta0_idx)
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
        return d

    def save_data(self, sampler, samples, lnprobs, lnlikes):
        d = self.get_save_data_dictionary()
        d['sampler'] = sampler
        d['samples'] = samples
        d['lnprobs'] = lnprobs
        d['lnlikes'] = lnlikes

        if os.path.isfile(self.pickle_path):
            logging.info('Saving backup of {} as {}.old'.format(
                self.pickle_path, self.pickle_path))
            os.rename(self.pickle_path, self.pickle_path+".old")
        with open(self.pickle_path, "wb") as File:
            pickle.dump(d, File)

For faster browsing, not all history is shown. View entire blame