pyfstat.py 78.5 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
""" Classes for various types of searches using ComputeFstatistic """
import os
import sys
import itertools
import logging
import argparse
import copy
import glob
import inspect
from functools import wraps
11
import subprocess
12
from collections import OrderedDict
13
14
15

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

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

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

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

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

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

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

72
73

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

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

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

        func(self, *args, **kargs)

    return wrapper


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


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

    earth_ephem_default = earth_ephem
    sun_ephem_default = sun_ephem

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

120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
    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
145
            lowest degree e.g [phi, F0, F1,...].
146
        dT: float
147
            difference between the two reference times as tref_new - tref_old.
148
149
150
151

        Returns
        -------
        theta_new: array-like shape (n,)
152
            vector of the coefficients as evaluate as the new reference time.
153
154
155
156
157
        """
        n = len(theta)
        m = self.shift_matrix(n, dT)
        return np.dot(m, theta)

158
    def calculate_thetas(self, theta, delta_thetas, tbounds, theta0_idx=0):
159
160
161
        """ Calculates the set of coefficients for the post-glitch signal """
        thetas = [theta]
        for i, dt in enumerate(delta_thetas):
162
163
164
165
166
167
168
169
170
171
172
173
174
            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]))
175
176
177
        return thetas


Gregory Ashton's avatar
Gregory Ashton committed
178
179
180
181
182
183
184
class ComputeFstat(object):
    """ Base class providing interface to lalpulsar.ComputeFstat """

    earth_ephem_default = earth_ephem
    sun_ephem_default = sun_ephem

    @initializer
185
    def __init__(self, tref, sftfilepath=None,
186
                 minStartTime=None, maxStartTime=None,
Gregory Ashton's avatar
Gregory Ashton committed
187
                 minCoverFreq=None, maxCoverFreq=None,
188
                 detector=None, earth_ephem=None, sun_ephem=None,
189
                 binary=False, transient=True, BSGL=False,
Gregory Ashton's avatar
Gregory Ashton committed
190
                 BSGL_PREFACTOR=1):
191
192
193
194
195
        """
        Parameters
        ----------
        tref: int
            GPS seconds of the reference time.
196
197
        sftfilepath: str
            File patern to match SFTs
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
        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.
213
214
        BSGL: bool
            If true, compute the BSGL rather than the twoF value.
215
216

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

        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
232
233
234
235
236
        if self.minStartTime:
            constraints.minStartTime = lal.LIGOTimeGPS(self.minStartTime)
        if self.maxStartTime:
            constraints.maxStartTime = lal.LIGOTimeGPS(self.maxStartTime)

237
        logging.info('Loading data matching pattern {}'.format(
238
239
                     self.sftfilepath))
        SFTCatalog = lalpulsar.SFTdataFind(self.sftfilepath, constraints)
Gregory Ashton's avatar
Gregory Ashton committed
240
        names = list(set([d.header.name for d in SFTCatalog.data]))
241
        epochs = [d.header.epoch for d in SFTCatalog.data]
242
        logging.info(
243
244
            'Loaded {} data files from detectors {} spanning {} to {}'.format(
                len(epochs), names, int(epochs[0]), int(epochs[-1])))
Gregory Ashton's avatar
Gregory Ashton committed
245
246
247
248
249
250

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

        logging.info('Initialising FstatInput')
        dFreq = 0
251
252
253
254
255
        if self.transient:
            self.whatToCompute = lalpulsar.FSTATQ_ATOMS_PER_DET
        else:
            self.whatToCompute = lalpulsar.FSTATQ_2F

Gregory Ashton's avatar
Gregory Ashton committed
256
257
258
259
260
261
262
263
        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
264
265
266
            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
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286

        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()

287
        if self.BSGL:
288
289
            logging.info('Initialising BSGL with prefactor {:2.2f}, this will'
                         ' fail if numDet < 2'.format(self.BSGL_PREFACTOR))
290
291
            # Tuning parameters - to be reviewed
            numDetectors = 2
Gregory Ashton's avatar
Gregory Ashton committed
292
            Fstar0sc = 15.
293
            oLGX = np.zeros(10)
Gregory Ashton's avatar
Gregory Ashton committed
294
            oLGX[:numDetectors] = 1./numDetectors
295
296
297
            self.BSGLSetup = lalpulsar.CreateBSGLSetup(numDetectors,
                                                       Fstar0sc,
                                                       oLGX,
298
                                                       True,
299
300
                                                       1)
            self.twoFX = np.zeros(10)
Gregory Ashton's avatar
Gregory Ashton committed
301
            self.whatToCompute = (self.whatToCompute +
302
303
                                  lalpulsar.FSTATQ_2F_PER_DET)

304
        if self.transient:
305
            logging.info('Initialising transient parameters')
306
307
308
309
310
311
            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
312

Gregory Ashton's avatar
Gregory Ashton committed
313
    def run_computefstatistic_single_point(self, tstart, tend, F0, F1,
314
315
316
                                           F2, Alpha, Delta, asini=None,
                                           period=None, ecc=None, tp=None,
                                           argp=None):
317
        """ Returns the twoF fully-coherently at a single point """
Gregory Ashton's avatar
Gregory Ashton committed
318

319

Gregory Ashton's avatar
Gregory Ashton committed
320
321
322
        self.PulsarDopplerParams.fkdot = np.array([F0, F1, F2, 0, 0, 0, 0])
        self.PulsarDopplerParams.Alpha = Alpha
        self.PulsarDopplerParams.Delta = Delta
323
324
325
326
327
328
        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
329
330
331
332

        lalpulsar.ComputeFstat(self.FstatResults,
                               self.FstatInput,
                               self.PulsarDopplerParams,
333
                               1,
Gregory Ashton's avatar
Gregory Ashton committed
334
335
336
                               self.whatToCompute
                               )

337
        if self.transient is False:
338
339
340
341
342
343
344
345
            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)
Gregory Ashton's avatar
Gregory Ashton committed
346
            return self.BSGL_PREFACTOR * BSGL/np.log10(np.exp(1))
347

348
349
        self.windowRange.t0 = int(tstart)  # TYPE UINT4
        self.windowRange.tau = int(tend - tstart)  # TYPE UINT4
350

Gregory Ashton's avatar
Gregory Ashton committed
351
        FS = lalpulsar.ComputeTransientFstatMap(
352
            self.FstatResults.multiFatoms[0], self.windowRange, False)
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370

        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)

Gregory Ashton's avatar
Gregory Ashton committed
371
        return self.BSGL_PREFACTOR * BSGL/np.log10(np.exp(1))
Gregory Ashton's avatar
Gregory Ashton committed
372
373
374


class SemiCoherentGlitchSearch(BaseSearchClass, ComputeFstat):
375
376
377
378
379
380
381
382
383
    """ 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
384
    def __init__(self, label, outdir, tref, tstart, tend, nglitch=0,
385
                 sftfilepath=None, theta0_idx=0, BSGL=False,
386
387
                 minCoverFreq=None, maxCoverFreq=None, minStartTime=None,
                 maxStartTime=None, detector=None, earth_ephem=None,
Gregory Ashton's avatar
Gregory Ashton committed
388
                 sun_ephem=None, BSGL_PREFACTOR=1):
389
390
391
392
        """
        Parameters
        ----------
        label, outdir: str
393
394
395
396
397
398
            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).
399
400
        sftfilepath: str
            File patern to match SFTs
401
402
403
404
        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)
405
        minCoverFreq, maxCoverFreq: float
406
407
408
            The min and max cover frequency passed to CreateFstatInput, if
            either is None the range of frequencies in the SFT less 1Hz is
            used.
409
410
        detector: str
            Two character reference to the data to use, specify None for no
411
            contraint.
412
413
        earth_ephem, sun_ephem: str
            Paths of the two files containing positions of Earth and Sun,
414
415
            respectively at evenly spaced times, as passed to CreateFstatInput.
            If None defaults defined in BaseSearchClass will be used.
416
417
418
419
420
421
422
        """

        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
423
424
        self.transient = True
        self.binary = False
425
426
427
        self.init_computefstatistic_single_point()

    def compute_nglitch_fstat(self, F0, F1, F2, Alpha, Delta, *args):
428
        """ Returns the semi-coherent glitch summed twoF """
429
430
431

        args = list(args)
        tboundaries = [self.tstart] + args[-self.nglitch:] + [self.tend]
432
433
434
435
436
437
438
439
        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)

440
441
        thetas = self.calculate_thetas(theta, delta_thetas, tboundaries,
                                       theta0_idx=self.theta0_idx)
442
443

        twoFSum = 0
444
        for i, theta_i_at_tref in enumerate(thetas):
445
446
447
            ts, te = tboundaries[i], tboundaries[i+1]

            twoFVal = self.run_computefstatistic_single_point(
448
449
                ts, te, theta_i_at_tref[1], theta_i_at_tref[2],
                theta_i_at_tref[3], Alpha, Delta)
450
451
            twoFSum += twoFVal

452
453
454
        if np.isfinite(twoFSum):
            return twoFSum
        else:
455
            return -np.inf
456
457
458

    def compute_glitch_fstat_single(self, F0, F1, F2, Alpha, Delta, delta_F0,
                                    delta_F1, tglitch):
459
460
461
462
        """ Returns the semi-coherent glitch summed twoF for nglitch=1

        Note: used for testing
        """
463
464
465
466
467
468
469
470
471
472
473

        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
474
            self.tstart, tglitch, theta[0], theta[1], theta[2], Alpha,
475
476
477
478
479
480
            Delta)

        if tglitch == self.tend:
            return twoFsegA

        twoFsegB = self.run_computefstatistic_single_point(
Gregory Ashton's avatar
Gregory Ashton committed
481
            tglitch, self.tend, theta_post_glitch[0],
482
483
484
485
486
487
            theta_post_glitch[1], theta_post_glitch[2], Alpha,
            Delta)

        return twoFsegA + twoFsegB


Gregory Ashton's avatar
Gregory Ashton committed
488
489
class MCMCSearch(BaseSearchClass):
    """ MCMC search using ComputeFstat"""
490
    @initializer
491
    def __init__(self, label, outdir, sftfilepath, theta_prior, tref,
492
                 tstart, tend, nsteps=[100, 100, 100], nwalkers=100, ntemps=1,
493
                 log10temperature_min=-5, theta_initial=None, scatter_val=1e-4,
494
495
                 binary=False, BSGL=False, minCoverFreq=None,
                 maxCoverFreq=None, detector=None, earth_ephem=None,
496
                 sun_ephem=None, theta0_idx=0,
Gregory Ashton's avatar
Gregory Ashton committed
497
                 BSGL_PREFACTOR=1):
498
499
500
501
        """
        Parameters
        label, outdir: str
            A label and directory to read/write data from/to
502
503
        sftfilepath: str
            File patern to match SFTs
504
        theta_prior: dict
505
506
507
508
            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.
509
510
511
512
        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.
513
514
515
516
517
518
519
        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].
520
521
522
523
524
525
526
527
528
529
530
        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.
531
532
533
534
535
536
537
538
539
540
        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

        """

541
542
543
        self.minStartTime = tstart
        self.maxStartTime = tend

Gregory Ashton's avatar
Gregory Ashton committed
544
545
        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
546
        self.add_log_file()
Gregory Ashton's avatar
Gregory Ashton committed
547
548
        logging.info(
            'Set-up MCMC search for model {} on data {}'.format(
549
                self.label, self.sftfilepath))
550
        self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
Gregory Ashton's avatar
Gregory Ashton committed
551
552
        self.theta_prior['tstart'] = self.tstart
        self.theta_prior['tend'] = self.tend
553
554
        self.unpack_input_theta()
        self.ndim = len(self.theta_keys)
555
556
557
558
        if self.log10temperature_min:
            self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
        else:
            self.betas = None
559

560
561
562
563
564
565
566
567
568
        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()
569
570
571
        self.log_input()

    def log_input(self):
572
        logging.info('theta_prior = {}'.format(self.theta_prior))
573
        logging.info('nwalkers={}'.format(self.nwalkers))
574
575
576
577
        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(
578
            self.log10temperature_min))
579
580
581

    def inititate_search_object(self):
        logging.info('Setting up search object')
Gregory Ashton's avatar
Gregory Ashton committed
582
        self.search = ComputeFstat(
583
584
585
586
            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,
587
588
            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
            BSGL_PREFACTOR=self.BSGL_PREFACTOR)
589
590

    def logp(self, theta_vals, theta_prior, theta_keys, search):
Gregory Ashton's avatar
Gregory Ashton committed
591
        H = [self.generic_lnprior(**theta_prior[key])(p) for p, key in
592
593
594
595
596
597
             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
598
        FS = search.run_computefstatistic_single_point(*self.fixed_theta)
599
600
601
        return FS

    def unpack_input_theta(self):
Gregory Ashton's avatar
Gregory Ashton committed
602
603
        full_theta_keys = ['tstart', 'tend', 'F0', 'F1', 'F2', 'Alpha',
                           'Delta']
604
605
606
        if self.binary:
            full_theta_keys += [
                'asini', 'period', 'ecc', 'tp', 'argp']
607
608
        full_theta_keys_copy = copy.copy(full_theta_keys)

Gregory Ashton's avatar
Gregory Ashton committed
609
610
        full_theta_symbols = ['_', '_', '$f$', '$\dot{f}$', '$\ddot{f}$',
                              r'$\alpha$', r'$\delta$']
611
612
613
614
        if self.binary:
            full_theta_symbols += [
                'asini', 'period', 'period', 'ecc', 'tp', 'argp']

615
616
        self.theta_keys = []
        fixed_theta_dict = {}
617
        for key, val in self.theta_prior.iteritems():
618
619
            if type(val) is dict:
                fixed_theta_dict[key] = 0
Gregory Ashton's avatar
Gregory Ashton committed
620
                self.theta_keys.append(key)
621
622
623
624
625
626
            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
627
            full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643

        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):
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
        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
680

Gregory Ashton's avatar
Gregory Ashton committed
681
    def run_sampler_with_progress_bar(self, sampler, ns, p0):
682
683
        for result in tqdm(sampler.sample(p0, iterations=ns), total=ns):
            pass
Gregory Ashton's avatar
Gregory Ashton committed
684
685
686
        return sampler

    def run(self, proposal_scale_factor=2):
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702

        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),
703
            loglargs=(self.search,), betas=self.betas, a=proposal_scale_factor)
704

Gregory Ashton's avatar
Gregory Ashton committed
705
706
        p0 = self.generate_initial_p0()
        p0 = self.apply_corrections_to_p0(p0)
707
708
709
710
711
        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(
712
                j+1, ninit_steps, n))
Gregory Ashton's avatar
Gregory Ashton committed
713
            sampler = self.run_sampler_with_progress_bar(sampler, n, p0)
714
715
            logging.info("Mean acceptance fraction: {}"
                         .format(np.mean(sampler.acceptance_fraction, axis=1)))
716
717
718
            if self.ntemps > 1:
                logging.info("Tswap acceptance fraction: {}"
                             .format(sampler.tswap_acceptance_fraction))
Gregory Ashton's avatar
Gregory Ashton committed
719
            fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols)
720
721
722
            fig.savefig('{}/{}_init_{}_walkers.png'.format(
                self.outdir, self.label, j))

723
            p0 = self.get_new_p0(sampler)
Gregory Ashton's avatar
Gregory Ashton committed
724
            p0 = self.apply_corrections_to_p0(p0)
725
726
727
            self.check_initial_points(p0)
            sampler.reset()

Gregory Ashton's avatar
Gregory Ashton committed
728
729
730
731
        if len(self.nsteps) > 1:
            nburn = self.nsteps[-2]
        else:
            nburn = 0
732
733
734
        nprod = self.nsteps[-1]
        logging.info('Running final burn and prod with {} steps'.format(
            nburn+nprod))
Gregory Ashton's avatar
Gregory Ashton committed
735
        sampler = self.run_sampler_with_progress_bar(sampler, nburn+nprod, p0)
736
737
        logging.info("Mean acceptance fraction: {}"
                     .format(np.mean(sampler.acceptance_fraction, axis=1)))
738
739
740
        if self.ntemps > 1:
            logging.info("Tswap acceptance fraction: {}"
                         .format(sampler.tswap_acceptance_fraction))
741

Gregory Ashton's avatar
Gregory Ashton committed
742
743
        fig, axes = self.plot_walkers(sampler, symbols=self.theta_symbols,
                                      burnin_idx=nburn)
744
745
746
747
748
749
750
751
752
753
754
        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)

755
    def plot_corner(self, figsize=(7, 7),  tglitch_ratio=False,
756
757
758
759
760
761
762
763
764
                    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)
765
766
            theta_symbols_plt = [s.replace('_{glitch}', r'_\textrm{glitch}')
                                 for s in theta_symbols_plt]
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
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

            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)
816
817
818
819
820
821

    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
822
            prior = self.generic_lnprior(**self.theta_prior[key])
823
824
825
826
827
828
            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)

829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
    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)
848
849
850
851
852
            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]
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
            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))

Gregory Ashton's avatar
Gregory Ashton committed
876
    def generic_lnprior(self, **kwargs):
877
878
879
880
881
882
883
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
        """ 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'])
919
920
        elif kwargs['type'] == 'neghalfnorm':
            return lambda x: halfnorm(-x, kwargs['loc'], kwargs['scale'])
921
922
923
924
925
926
927
        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
928
    def generate_rv(self, **kwargs):
929
930
931
932
933
934
935
936
        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']))
937
938
939
        if dist_type == "neghalfnorm":
            return -1 * np.abs(np.random.normal(loc=kwargs['loc'],
                                                scale=kwargs['scale']))
940
941
942
943
944
945
        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
946
    def plot_walkers(self, sampler, symbols=None, alpha=0.4, color="k", temp=0,
Gregory Ashton's avatar
Gregory Ashton committed
947
                     burnin_idx=None):
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
        """ Plot all the chains from a sampler """

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

        with plt.style.context(('classic')):
Gregory Ashton's avatar
Gregory Ashton committed
964
965
966
967
            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)]
968

Gregory Ashton's avatar
Gregory Ashton committed
969
            idxs = np.arange(chain.shape[1])
970
971
            if ndim > 1:
                for i in range(ndim):
972
                    axes[i].ticklabel_format(useOffset=False, axis='y')
Gregory Ashton's avatar
Gregory Ashton committed
973
974
975
976
977
978
                    cs = chain[:, :, i].T
                    if burnin_idx:
                        axes[i].plot(idxs[:burnin_idx], cs[:burnin_idx],
                                     color="r", alpha=alpha)
                    axes[i].plot(idxs[burnin_idx:], cs[burnin_idx:], color="k",
                                 alpha=alpha)
979
980
                    if symbols:
                        axes[i].set_ylabel(symbols[i])
981
            else:
Gregory Ashton's avatar
Gregory Ashton committed
982
                cs = chain[:, :, temp].T
983
984
                axes.plot(cs, color='k', alpha=alpha)
                axes.ticklabel_format(useOffset=False, axis='y')
985

Gregory Ashton's avatar
Gregory Ashton committed
986
987
988
        axes.append(fig.add_subplot(ndim+1, 1, ndim+1))
        lnl = sampler.lnlikelihood[temp, :, :]
        if burnin_idx:
Gregory Ashton's avatar
Gregory Ashton committed
989
990
            axes[-1].hist(lnl[:, :burnin_idx].flatten(), bins=50,
                          histtype='step', color='r')
Gregory Ashton's avatar
Gregory Ashton committed
991
992
        axes[-1].hist(lnl[:, burnin_idx:].flatten(), bins=50, histtype='step',
                      color='k')
Gregory Ashton's avatar
Gregory Ashton committed
993
994
995
996
        if self.BSGL:
            axes[-1].set_xlabel(r'$\mathcal{B}_\mathrm{S/GL}$')
        else:
            axes[-1].set_xlabel(r'$2\mathcal{F}$')
Gregory Ashton's avatar
Gregory Ashton committed
997

998
999
        return fig, axes

Gregory Ashton's avatar
Gregory Ashton committed
1000
1001
1002
1003
1004
    def apply_corrections_to_p0(self, p0):
        """ Apply any correction to the initial p0 values """
        return p0

    def generate_scattered_p0(self, p):
1005
        """ Generate a set of p0s scattered about p """
Gregory Ashton's avatar
Gregory Ashton committed
1006
        p0 = [[p + self.scatter_val * p * np.random.randn(self.ndim)
1007
1008
1009
1010
               for i in xrange(self.nwalkers)]
              for j in xrange(self.ntemps)]
        return p0

Gregory Ashton's avatar
Gregory Ashton committed
1011
    def generate_initial_p0(self):
1012
1013
1014
        """ Generate a set of init vals for the walkers """

        if type(self.theta_initial) == dict:
1015
            logging.info('Generate initial values from initial dictionary')
1016
            if hasattr(self, 'nglitch') and self.nglitch > 1:
1017
                raise ValueError('Initial dict not implemented for nglitch>1')
Gregory Ashton's avatar
Gregory Ashton committed
1018
            p0 = [[[self.generate_rv(**self.theta_initial[key])
1019
1020
1021
                    for key in self.theta_keys]
                   for i in range(self.nwalkers)]
                  for j in range(self.ntemps)]
1022
1023
1024
1025
1026
1027
        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)]
1028
        elif self.theta_initial is None:
1029
            logging.info('Generate initial values from prior dictionary')
Gregory Ashton's avatar
Gregory Ashton committed
1030
            p0 = [[[self.generate_rv(**self.theta_prior[key])
1031
1032
1033
1034
                    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
1035
            p0 = self.generate_scattered_p0(self.theta_initial)
1036
1037
1038
1039
1040
        else:
            raise ValueError('theta_initial not understood')

        return p0

1041
    def get_new_p0(self, sampler):
1042
1043
1044
1045
1046
1047
        """ Returns new initial positions for walkers are burn0 stage

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

        """
Gregory Ashton's avatar
Gregory Ashton committed
1048
1049
1050
1051
        temp_idx = 0
        pF = sampler.chain[temp_idx, :, :, :]
        lnl = sampler.lnlikelihood[temp_idx, :, :]
        lnp = sampler.lnprobability[temp_idx, :, :]
1052
1053

        # General warnings about the state of lnp
Gregory Ashton's avatar
Gregory Ashton committed
1054
        if np.any(np.isnan(lnp)):
1055
1056
            logging.warning(
                "Of {} lnprobs {} are nan".format(
Gregory Ashton's avatar
Gregory Ashton committed
1057
1058
                    np.shape(lnp), np.sum(np.isnan(lnp))))
        if np.any(np.isposinf(lnp)):
1059
1060
            logging.warning(
                "Of {} lnprobs {} are +np.inf".format(
Gregory Ashton's avatar
Gregory Ashton committed
1061
1062
                    np.shape(lnp), np.sum(np.isposinf(lnp))))
        if np.any(np.isneginf(lnp)):
1063
1064
            logging.warning(
                "Of {} lnprobs {} are -np.inf".format(
Gregory Ashton's avatar
Gregory Ashton committed
1065
                    np.shape(lnp), np.sum(np.isneginf(lnp))))
1066

1067
1068
        lnp_finite = copy.copy(lnp)
        lnp_finite[np.isinf(lnp)] = np.nan
Gregory Ashton's avatar
Gregory Ashton committed
1069
1070
        idx = np.unravel_index(np.nanargmax(lnp_finite), lnp_finite.shape)
        p = pF[idx]
1071
        p0 = self.generate_scattered_p0(p)
1072

1073
1074
1075
1076
1077
1078
1079
1080
        self.search.BSGL = False
        twoF = self.logl(p, self.search)
        self.search.BSGL = self.BSGL

        logging.info(('Gen. new p0 from pos {} which had det. stat.={:2.1f},'
                      ' twoF={:2.1f} and lnp={:2.1f}')
                     .format(idx[1], lnl[idx], twoF, lnp_finite[idx]))

1081
1082
1083
1084
1085
        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
1086
                 theta_prior=self.theta_prior, scatter_val=self.scatter_val,
1087
                 log10temperature_min=self.log10temperature_min,
1088
1089
                 theta0_idx=self.theta0_idx, BSGL=self.BSGL,
                 BSGL_PREFACTOR=self.BSGL_PREFACTOR)
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
        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)

    def get_list_of_matching_sfts(self):
1107
        matches = glob.glob(self.sftfilepath)
1108
1109
1110
1111
        if len(matches) > 0:
            return matches
        else:
            raise IOError('No sfts found matching {}'.format(
1112
                self.sftfilepath))
1113
1114
1115
1116
1117
1118
1119

    def get_saved_data(self):
        with open(self.pickle_path, "r") as File:
            d = pickle.load(File)
        return d

    def check_old_data_is_okay_to_use(self):
1120
1121
1122
1123
        if args.use_old_data:
            logging.info("Forcing use of old data")
            return True

1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
        if os.path.isfile(self.pickle_path) is False:
            logging.info('No pickled data found')
            return False

        oldest_sft = min([os.path.getmtime(f) for f in
                          self.get_list_of_matching_sfts()])
        if os.path.getmtime(self.pickle_path) < oldest_sft:
            logging.info('Pickled data outdates sft files')
            return False

        old_d = self.get_saved_data().copy()
        new_d = self.get_save_data_dictionary().copy()

        old_d.pop('samples')
        old_d.pop('sampler')
        old_d.pop('lnprobs')
        old_d.pop('lnlikes')

        mod_keys = []
        for key in new_d.keys():
            if key in old_d:
                if new_d[key] != old_d[key]:
                    mod_keys.append((key, old_d[key], new_d[key]))
            else:
1148
                raise ValueError('Keys {} not in old dictionary'.format(key))
1149
1150
1151
1152
1153
1154
1155
1156
1157

        if len(mod_keys) == 0:
            return True
        else:
            logging.warning("Saved data differs from requested")
            logging.info("Differences found in following keys:")
            for key in mod_keys:
                if len(key) == 3:
                    if np.isscalar(key[1]) or key[0] == 'nsteps':
1158
                        logging.info("    {} : {} -> {}".format(*key))
1159
                    else:
1160
                        logging.info("    " + key[0])
1161
1162
1163
1164
1165
                else:
                    logging.info(key)
            return False

    def get_max_twoF(self, threshold=0.05):
1166
        """ Returns the max likelihood sample and the corresponding 2F value
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180

        Note: the sample is returned as a dictionary along with an estimate of
        the standard deviation calculated from the std of all samples with a
        twoF within `threshold` (relative) to the max twoF

        """
        if any(np.isposinf(self.lnlikes)):
            logging.info('twoF values contain positive infinite values')
        if any(np.isneginf(self.lnlikes)):
            logging.info('twoF values contain negative infinite values')
        if any(np.isnan(self.lnlikes)):
            logging.info('twoF values contain nan')
        idxs = np.isfinite(self.lnlikes)
        jmax = np.nanargmax(self.lnlikes[idxs])
1181
        maxlogl = self.lnlikes[jmax]
1182
        d = OrderedDict()
1183

1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
        if self.BSGL:
            if hasattr(self, 'search') is False:
                self.inititate_search_object()
            p = self.samples[jmax]
            self.search.BSGL = False
            maxtwoF = self.logl(p, self.search)
            self.search.BSGL = self.BSGL
        else:
            maxtwoF = maxlogl

Gregory Ashton's avatar
Gregory Ashton committed
1194
        repeats = []
1195
        for i, k in enumerate(self.theta_keys):
Gregory Ashton's avatar
Gregory Ashton committed
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
            if k in d and k not in repeats:
                d[k+'_0'] = d[k]  # relabel the old key
                d.pop(k)
                repeats.append(k)
            if k in repeats:
                k = k + '_0'
                count = 1
                while k in d:
                    k = k.replace('_{}'.format(count-1), '_{}'.format(count))
                    count += 1
1206
1207
1208
1209
1210
            d[k] = self.samples[jmax][i]
        return d, maxtwoF

    def get_median_stds(self):
        """ Returns a dict of the median and std of all production samples """
1211
        d = OrderedDict()
Gregory Ashton's avatar
Gregory Ashton committed
1212
        repeats = []
1213
        for s, k in zip(self.samples.T, self.theta_keys):
Gregory Ashton's avatar
Gregory Ashton committed
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
            if k in d and k not in repeats:
                d[k+'_0'] = d[k]  # relabel the old key
                d[k+'_0_std'] = d[k+'_std']
                d.pop(k)
                d.pop(k+'_std')
                repeats.append(k)
            if k in repeats:
                k = k + '_0'
                count = 1
                while k in d:
                    k = k.replace('_{}'.format(count-1), '_{}'.format(count))
                    count += 1

1227
1228
1229
1230
1231
1232
1233
1234
            d[k] = np.median(s)
            d[k+'_std'] = np.std(s)
        return d

    def write_par(self, method='med'):
        """ Writes a .par of the best-fit params with an estimated std """
        logging.info('Writing {}/{}.par using the {} method'.format(
            self.outdir, self.label, method))
1235
1236
1237
1238

        median_std_d = self.get_median_stds()
        max_twoF_d, max_twoF = self.get_max_twoF()

Gregory Ashton's avatar
Gregory Ashton committed
1239
        logging.info('Writing par file with max twoF = {}'.format(max_twoF))
1240
1241
1242
        filename = '{}/{}.par'.format(self.outdir, self.label)
        with open(filename, 'w+') as f:
            f.write('MaxtwoF = {}\n'.format(max_twoF))
1243
            f.write('theta0_index = {}\n'.format(self.theta0_idx))
1244
            if method == 'med':
1245
1246
                for key, val in median_std_d.iteritems():
                    f.write('{} = {:1.16e}\n'.format(key, val))
1247
            if method == 'twoFmax':
1248
1249
1250
1251
                for key, val in max_twoF_d.iteritems():
                    f.write('{} = {:1.16e}\n'.format(key, val))

    def print_summary(self):
Gregory Ashton's avatar
Gregory Ashton committed
1252
        max_twoFd, max_twoF = self.get_max_twoF()
1253
        median_std_d = self.get_median_stds()
Gregory Ashton's avatar
Gregory Ashton committed
1254
        print('\nSummary:')
1255
        print('theta0 index: {}'.format(self.theta0_idx))
Gregory Ashton's avatar
Gregory Ashton committed
1256
1257
1258
1259
        print('Max twoF: {} with parameters:'.format(max_twoF))
        for k in np.sort(max_twoFd.keys()):
            print('  {:10s} = {:1.9e}'.format(k, max_twoFd[k]))
        print('\nMedian +/- std for production values')
1260
        for k in np.sort(median_std_d.keys()):
1261
            if 'std' not in k:
Gregory Ashton's avatar
Gregory Ashton committed
1262
                print('  {:10s} = {:1.9e} +/- {:1.9e}'.format(
12