mcmc_based_searches.py 91.3 KB
Newer Older
Gregory Ashton's avatar
Gregory Ashton committed
1
2
""" Searches using MCMC-based methods """

3
import sys
Gregory Ashton's avatar
Gregory Ashton committed
4
import os
5
import copy
Gregory Ashton's avatar
Gregory Ashton committed
6
import logging
7
from collections import OrderedDict
8
9
10
11
12
13
14
15

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import emcee
import corner
import dill as pickle

16
import core
Gregory Ashton's avatar
Gregory Ashton committed
17
from core import tqdm, args, earth_ephem, sun_ephem
18
from optimal_setup_functions import get_V_estimate
Gregory Ashton's avatar
Gregory Ashton committed
19
20
from optimal_setup_functions import get_optimal_setup
import helper_functions
21
22


23
class MCMCSearch(core.BaseSearchClass):
Gregory Ashton's avatar
Gregory Ashton committed
24
    """ MCMC search using ComputeFstat"""
25
26

    symbol_dictionary = dict(
27
        F0='$f$', F1='$\dot{f}$', F2='$\ddot{f}$', Alpha=r'$\alpha$',
28
29
        Delta='$\delta$', asini='asini', period='P', ecc='ecc', tp='tp',
        argp='argp')
30
    unit_dictionary = dict(
31
32
        F0='Hz', F1='Hz/s', F2='Hz/s$^2$', Alpha=r'rad', Delta='rad',
        asini='', period='s', ecc='', tp='', argp='')
33
34
35
    rescale_dictionary = {}


Gregory Ashton's avatar
Gregory Ashton committed
36
    @helper_functions.initializer
Gregory Ashton's avatar
Gregory Ashton committed
37
38
    def __init__(self, label, outdir, theta_prior, tref, minStartTime,
                 maxStartTime, sftfilepath=None, nsteps=[100, 100],
39
40
                 nwalkers=100, ntemps=1, log10temperature_min=-5,
                 theta_initial=None, scatter_val=1e-10,
41
                 binary=False, BSGL=False, minCoverFreq=None,
42
                 maxCoverFreq=None, detectors=None, earth_ephem=None,
43
                 sun_ephem=None, injectSources=None, assumeSqrtSX=None):
44
45
46
47
        """
        Parameters
        label, outdir: str
            A label and directory to read/write data from/to
48
        sftfilepath: str
49
50
            Pattern to match SFTs using wildcards (*?) and ranges [0-9];
            mutiple patterns can be given separated by colons.
51
        theta_prior: dict
52
53
54
55
            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.
56
57
58
59
        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.
60
        tref, minStartTime, maxStartTime: int
61
62
63
64
65
66
            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].
67
68
69
70
71
72
73
74
        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
75
        detectors: str
76
77
            Two character reference to the data to use, specify None for no
            contraint.
78
79
80
81
82
83
84
85
86
87
        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

        """

Gregory Ashton's avatar
Gregory Ashton committed
88
89
        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
90
        self._add_log_file()
Gregory Ashton's avatar
Gregory Ashton committed
91
92
        logging.info(
            'Set-up MCMC search for model {} on data {}'.format(
93
                self.label, self.sftfilepath))
94
        self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
95
        self._unpack_input_theta()
96
        self.ndim = len(self.theta_keys)
97
98
99
100
        if self.log10temperature_min:
            self.betas = np.logspace(0, self.log10temperature_min, self.ntemps)
        else:
            self.betas = None
101

102
103
104
105
106
107
108
109
        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")

110
        self._log_input()
111

112
    def _log_input(self):
113
        logging.info('theta_prior = {}'.format(self.theta_prior))
114
        logging.info('nwalkers={}'.format(self.nwalkers))
115
116
117
118
        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(
119
            self.log10temperature_min))
120

121
    def _initiate_search_object(self):
122
        logging.info('Setting up search object')
123
        self.search = core.ComputeFstat(
124
125
126
            tref=self.tref, sftfilepath=self.sftfilepath,
            minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
            earth_ephem=self.earth_ephem, sun_ephem=self.sun_ephem,
127
            detectors=self.detectors, BSGL=self.BSGL, transient=False,
128
            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
129
130
            binary=self.binary, injectSources=self.injectSources,
            assumeSqrtSX=self.assumeSqrtSX)
131
132

    def logp(self, theta_vals, theta_prior, theta_keys, search):
133
        H = [self._generic_lnprior(**theta_prior[key])(p) for p, key in
134
135
136
137
138
139
             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]
140
141
        FS = search.compute_fullycoherent_det_stat_single_point(
            *self.fixed_theta)
142
143
        return FS

144
    def _unpack_input_theta(self):
145
        full_theta_keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta']
146
147
148
        if self.binary:
            full_theta_keys += [
                'asini', 'period', 'ecc', 'tp', 'argp']
149
150
        full_theta_keys_copy = copy.copy(full_theta_keys)

151
152
        full_theta_symbols = ['$f$', '$\dot{f}$', '$\ddot{f}$', r'$\alpha$',
                              r'$\delta$']
153
154
        if self.binary:
            full_theta_symbols += [
155
                'asini', 'period', 'ecc', 'tp', 'argp']
156

157
158
        self.theta_keys = []
        fixed_theta_dict = {}
159
        for key, val in self.theta_prior.iteritems():
160
161
            if type(val) is dict:
                fixed_theta_dict[key] = 0
Gregory Ashton's avatar
Gregory Ashton committed
162
                self.theta_keys.append(key)
163
164
165
166
167
168
            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
169
            full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184

        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]

185
    def _check_initial_points(self, p0):
186
187
188
189
190
191
192
193
194
195
196
197
198
        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))

199
                p0 = self._generate_new_p0_to_fix_initial_points(
200
201
                    p0, nt, initial_priors)

202
    def _generate_new_p0_to_fix_initial_points(self, p0, nt, initial_priors):
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
        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
222

223
    def _OLD_run_sampler_with_progress_bar(self, sampler, ns, p0):
224
225
        for result in tqdm(sampler.sample(p0, iterations=ns), total=ns):
            pass
Gregory Ashton's avatar
Gregory Ashton committed
226
227
        return sampler

228
229
    def setup_convergence_testing(
            self, convergence_period=10, convergence_length=10,
230
            convergence_burnin_fraction=0.25, convergence_threshold_number=10,
231
            convergence_threshold=1.2, convergence_prod_threshold=2,
232
            convergence_plot_upper_lim=2, convergence_early_stopping=True):
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
        """
        If called, convergence testing is used during the MCMC simulation

        This uses the Gelmanr-Rubin statistic based on the ratio of between and
        within walkers variance. The original statistic was developed for
        multiple (independent) MCMC simulations, in this context we simply use
        the walkers

        Parameters
        ----------
        convergence_period: int
            period (in number of steps) at which to test convergence
        convergence_length: int
            number of steps to use in testing convergence - this should be
            large enough to measure the variance, but if it is too long
            this will result in incorect early convergence tests
        convergence_burnin_fraction: float [0, 1]
            the fraction of the burn-in period after which to start testing
        convergence_threshold_number: int
            the number of consecutive times where the test passes after which
            to break the burn-in and go to production
        convergence_threshold: float
            the threshold to use in diagnosing convergence. Gelman & Rubin
            recomend a value of 1.2, 1.1 for strict convergence
        convergence_prod_threshold: float
            the threshold to test the production values with
259
260
        convergence_plot_upper_lim: float
            the upper limit to use in the diagnostic plot
261
262
        convergence_early_stopping: bool
            if true, stop the burnin early if convergence is reached
263
        """
264
265
266
267
268
269
270

        if convergence_length > convergence_period:
            raise ValueError('convergence_length must be < convergence_period')
        logging.info('Setting up convergence testing')
        self.convergence_length = convergence_length
        self.convergence_period = convergence_period
        self.convergence_burnin_fraction = convergence_burnin_fraction
271
        self.convergence_prod_threshold = convergence_prod_threshold
272
273
274
275
276
        self.convergence_diagnostic = []
        self.convergence_diagnosticx = []
        self.convergence_threshold_number = convergence_threshold_number
        self.convergence_threshold = convergence_threshold
        self.convergence_number = 0
277
        self.convergence_plot_upper_lim = convergence_plot_upper_lim
278
        self.convergence_early_stopping = convergence_early_stopping
279

280
    def _get_convergence_statistic(self, i, sampler):
281
        s = sampler.chain[0, :, i-self.convergence_length+1:i+1, :]
282
283
284
        N = float(self.convergence_length)
        M = float(self.nwalkers)
        W = np.mean(np.var(s, axis=1), axis=0)
285
286
        per_walker_mean = np.mean(s, axis=1)
        mean = np.mean(per_walker_mean, axis=0)
287
288
289
        B = N / (M-1.) * np.sum((per_walker_mean-mean)**2, axis=0)
        Vhat = (N-1)/N * W + (M+1)/(M*N) * B
        c = Vhat/W
290
        self.convergence_diagnostic.append(c)
291
        self.convergence_diagnosticx.append(i - self.convergence_length/2)
292
293
        return c

294
    def _burnin_convergence_test(self, i, sampler, nburn):
295
296
        if i < self.convergence_burnin_fraction*nburn:
            return False
297
        if np.mod(i+1, self.convergence_period) != 0:
298
            return False
299
        c = self._get_convergence_statistic(i, sampler)
300
301
        if np.all(c < self.convergence_threshold):
            self.convergence_number += 1
302
303
        else:
            self.convergence_number = 0
304
305
        if self.convergence_early_stopping:
            return self.convergence_number > self.convergence_threshold_number
306

307
    def _prod_convergence_test(self, i, sampler, nburn):
308
309
310
        testA = i > nburn + self.convergence_length
        testB = np.mod(i+1, self.convergence_period) == 0
        if testA and testB:
311
            self._get_convergence_statistic(i, sampler)
312

313
    def _check_production_convergence(self, k):
314
315
316
317
318
319
320
321
        bools = np.any(
            np.array(self.convergence_diagnostic)[k:, :]
            > self.convergence_prod_threshold, axis=1)
        if np.any(bools):
            logging.warning(
                '{} convergence tests in the production run of {} failed'
                .format(np.sum(bools), len(bools)))

322
    def _run_sampler(self, sampler, p0, nprod=0, nburn=0):
323
        if hasattr(self, 'convergence_period'):
324
325
326
327
            logging.info('Running {} burn-in steps with convergence testing'
                         .format(nburn))
            iterator = tqdm(sampler.sample(p0, iterations=nburn), total=nburn)
            for i, output in enumerate(iterator):
328
                if self._burnin_convergence_test(i, sampler, nburn):
329
330
331
332
333
334
335
336
337
338
339
                    logging.info(
                        'Converged at {} before max number {} of steps reached'
                        .format(i, nburn))
                    self.convergence_idx = i
                    break
            iterator.close()
            logging.info('Running {} production steps'.format(nprod))
            j = nburn
            k = len(self.convergence_diagnostic)
            for result in tqdm(sampler.sample(output[0], iterations=nprod),
                               total=nprod):
340
                self._prod_convergence_test(j, sampler, nburn)
341
                j += 1
342
            self._check_production_convergence(k)
343
344
345
346
347
348
            return sampler
        else:
            for result in tqdm(sampler.sample(p0, iterations=nburn+nprod),
                               total=nburn+nprod):
                pass
            return sampler
349

350
    def run(self, proposal_scale_factor=2, create_plots=True, **kwargs):
351
        """ Run the MCMC simulatation """
352

353
        self.old_data_is_okay_to_use = self._check_old_data_is_okay_to_use()
354
355
356
        if self.old_data_is_okay_to_use is True:
            logging.warning('Using saved data from {}'.format(
                self.pickle_path))
357
            d = self.get_saved_data_dictionary()
358
359
360
            self.samples = d['samples']
            self.lnprobs = d['lnprobs']
            self.lnlikes = d['lnlikes']
361
            self.all_lnlikelihood = d['all_lnlikelihood']
362
363
            return

364
        self._initiate_search_object()
365
366
367
368

        sampler = emcee.PTSampler(
            self.ntemps, self.nwalkers, self.ndim, self.logl, self.logp,
            logpargs=(self.theta_prior, self.theta_keys, self.search),
369
            loglargs=(self.search,), betas=self.betas, a=proposal_scale_factor)
370

371
372
373
        p0 = self._generate_initial_p0()
        p0 = self._apply_corrections_to_p0(p0)
        self._check_initial_points(p0)
374
375
376
377

        ninit_steps = len(self.nsteps) - 2
        for j, n in enumerate(self.nsteps[:-2]):
            logging.info('Running {}/{} initialisation with {} steps'.format(
Gregory Ashton's avatar
Gregory Ashton committed
378
                j, ninit_steps, n))
379
            sampler = self._run_sampler(sampler, p0, nburn=n)
380
381
            logging.info("Mean acceptance fraction: {}"
                         .format(np.mean(sampler.acceptance_fraction, axis=1)))
382
383
384
            if self.ntemps > 1:
                logging.info("Tswap acceptance fraction: {}"
                             .format(sampler.tswap_acceptance_fraction))
385
            if create_plots:
386
                fig, axes = self._plot_walkers(sampler,
387
388
389
390
                                              symbols=self.theta_symbols,
                                              **kwargs)
                fig.tight_layout()
                fig.savefig('{}/{}_init_{}_walkers.png'.format(
391
                    self.outdir, self.label, j), dpi=400)
392

393
394
395
            p0 = self._get_new_p0(sampler)
            p0 = self._apply_corrections_to_p0(p0)
            self._check_initial_points(p0)
396
397
            sampler.reset()

Gregory Ashton's avatar
Gregory Ashton committed
398
399
400
401
        if len(self.nsteps) > 1:
            nburn = self.nsteps[-2]
        else:
            nburn = 0
402
403
404
        nprod = self.nsteps[-1]
        logging.info('Running final burn and prod with {} steps'.format(
            nburn+nprod))
405
        sampler = self._run_sampler(sampler, p0, nburn=nburn, nprod=nprod)
406
407
        logging.info("Mean acceptance fraction: {}"
                     .format(np.mean(sampler.acceptance_fraction, axis=1)))
408
409
410
        if self.ntemps > 1:
            logging.info("Tswap acceptance fraction: {}"
                         .format(sampler.tswap_acceptance_fraction))
411

412
        if create_plots:
413
            fig, axes = self._plot_walkers(sampler, symbols=self.theta_symbols,
414
                                          nprod=nprod, **kwargs)
415
416
417
            fig.tight_layout()
            fig.savefig('{}/{}_walkers.png'.format(self.outdir, self.label),
                        dpi=200)
418
419
420
421

        samples = sampler.chain[0, :, nburn:, :].reshape((-1, self.ndim))
        lnprobs = sampler.lnprobability[0, :, nburn:].reshape((-1))
        lnlikes = sampler.lnlikelihood[0, :, nburn:].reshape((-1))
422
        all_lnlikelihood = sampler.lnlikelihood[:, :, nburn:]
423
424
425
        self.samples = samples
        self.lnprobs = lnprobs
        self.lnlikes = lnlikes
426
427
        self.all_lnlikelihood = all_lnlikelihood
        self._save_data(sampler, samples, lnprobs, lnlikes, all_lnlikelihood)
428

429
    def _get_rescale_multiplier_for_key(self, key):
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
        """ Get the rescale multiplier from the rescale_dictionary

        Can either be a float, a string (in which case it is interpretted as
        a attribute of the MCMCSearch class, e.g. minStartTime, or non-existent
        in which case 0 is returned
        """
        if key not in self.rescale_dictionary:
            return 1

        if 'multiplier' in self.rescale_dictionary[key]:
            val = self.rescale_dictionary[key]['multiplier']
            if type(val) == str:
                if hasattr(self, val):
                    multiplier = getattr(
                        self, self.rescale_dictionary[key]['multiplier'])
                else:
                    raise ValueError(
                        "multiplier {} not a class attribute".format(val))
            else:
                multiplier = val
        else:
            multiplier = 1
        return multiplier

454
    def _get_rescale_subtractor_for_key(self, key):
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
        """ Get the rescale subtractor from the rescale_dictionary

        Can either be a float, a string (in which case it is interpretted as
        a attribute of the MCMCSearch class, e.g. minStartTime, or non-existent
        in which case 0 is returned
        """
        if key not in self.rescale_dictionary:
            return 0

        if 'subtractor' in self.rescale_dictionary[key]:
            val = self.rescale_dictionary[key]['subtractor']
            if type(val) == str:
                if hasattr(self, val):
                    subtractor = getattr(
                        self, self.rescale_dictionary[key]['subtractor'])
                else:
                    raise ValueError(
                        "subtractor {} not a class attribute".format(val))
            else:
                subtractor = val
        else:
            subtractor = 0
        return subtractor

479
    def _scale_samples(self, samples, theta_keys):
480
        """ Scale the samples using the rescale_dictionary """
481
482
483
484
        for key in theta_keys:
            if key in self.rescale_dictionary:
                idx = theta_keys.index(key)
                s = samples[:, idx]
485
                subtractor = self._get_rescale_subtractor_for_key(key)
486
                s = s - subtractor
487
                multiplier = self._get_rescale_multiplier_for_key(key)
488
                s *= multiplier
489
490
                samples[:, idx] = s

491
492
        return samples

493
    def _get_labels(self):
494
        """ Combine the units, symbols and rescaling to give labels """
495

496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
        labels = []
        for key in self.theta_keys:
            label = None
            s = self.symbol_dictionary[key]
            s.replace('_{glitch}', r'_\textrm{glitch}')
            u = self.unit_dictionary[key]
            if key in self.rescale_dictionary:
                if 'symbol' in self.rescale_dictionary[key]:
                    s = self.rescale_dictionary[key]['symbol']
                if 'label' in self.rescale_dictionary[key]:
                    label = self.rescale_dictionary[key]['label']
                if 'unit' in self.rescale_dictionary[key]:
                    u = self.rescale_dictionary[key]['unit']
            if label is None:
                label = '{} \n [{}]'.format(s, u)
            labels.append(label)
        return labels
513

514
515
    def plot_corner(self, figsize=(7, 7), add_prior=False, nstds=None,
                    label_offset=0.4, dpi=300, rc_context={},
516
                    tglitch_ratio=False, fig_and_axes=None, save_fig=True,
517
                    **kwargs):
518
519
520
521
522
523
524
525
526
        """ Generate a corner plot of the posterior

        Using the `corner` package (https://pypi.python.org/pypi/corner/),
        generate estimates of the posterior from the production samples.

        Parameters
        ----------
        figsize: tuple (7, 7)
            Figure size in inches (passed to plt.subplots)
527
528
529
        add_prior: bool, str
            If true, plot the prior as a red line. If 'full' then for uniform
            priors plot the full extent of the prior.
530
531
532
533
534
535
536
537
538
539
540
541
542
543
        nstds: float
            The number of standard deviations to plot centered on the mean
        label_offset: float
            Offset the labels from the plot: useful to precent overlapping the
            tick labels with the axis labels
        dpi: int
            Passed to plt.savefig
        rc_context: dict
            Dictionary of rc values to set while generating the figure (see
            matplotlib rc for more details)
        tglitch_ratio: bool
            If true, and tglitch is a parameter, plot posteriors as the
            fractional time at which the glitch occurs instead of the actual
            time
544
545
546
547
548
        fig_and_axes: tuple
            fig and axes to plot on, the axes must be of the right shape,
            namely (ndim, ndim)
        save_fig: bool
            If true, save the figure, else return the fig, axes
549

550
        Note: kwargs are passed on to corner.corner
551
552

        """
553

554
555
556
557
        if 'truths' in kwargs and len(kwargs['truths']) != self.ndim:
            logging.warning('len(Truths) != ndim, Truths will be ignored')
            kwargs['truths'] = None

Gregory Ashton's avatar
Gregory Ashton committed
558
559
        if self.ndim < 2:
            with plt.rc_context(rc_context):
560
561
562
563
                if fig_and_axes is None:
                    fig, ax = plt.subplots(figsize=figsize)
                else:
                    fig, ax = fig_and_axes
Gregory Ashton's avatar
Gregory Ashton committed
564
565
566
567
568
569
570
                ax.hist(self.samples, bins=50, histtype='stepfilled')
                ax.set_xlabel(self.theta_symbols[0])

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

571
        with plt.rc_context(rc_context):
572
573
574
575
576
            if fig_and_axes is None:
                fig, axes = plt.subplots(self.ndim, self.ndim,
                                         figsize=figsize)
            else:
                fig, axes = fig_and_axes
577
578

            samples_plt = copy.copy(self.samples)
579
            labels = self._get_labels()
580

581
            samples_plt = self._scale_samples(samples_plt, self.theta_keys)
582
583
584
585
586

            if tglitch_ratio:
                for j, k in enumerate(self.theta_keys):
                    if k == 'tglitch':
                        s = samples_plt[:, j]
587
588
589
                        samples_plt[:, j] = (
                            s - self.minStartTime)/(
                                self.maxStartTime - self.minStartTime)
590
                        labels[j] = r'$R_{\textrm{glitch}}$'
591
592
593
594
595
596
597

            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))
598
599
            elif 'range' in kwargs:
                _range = kwargs.pop('range')
600
601
602
            else:
                _range = None

603
604
605
606
            hist_kwargs = kwargs.pop('hist_kwargs', dict())
            if 'normed' not in hist_kwargs:
                hist_kwargs['normed'] = True

607
            fig_triangle = corner.corner(samples_plt,
608
                                         labels=labels,
609
610
611
612
613
614
615
616
617
                                         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,
618
                                         hist_kwargs=hist_kwargs,
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
                                         **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:
635
                self._add_prior_to_corner(axes, self.samples, add_prior)
636

637
638
639
640
641
            if save_fig:
                fig_triangle.savefig('{}/{}_corner.png'.format(
                    self.outdir, self.label), dpi=dpi)
            else:
                return fig, axes
642

643
    def _add_prior_to_corner(self, axes, samples, add_prior):
644
645
646
        for i, key in enumerate(self.theta_keys):
            ax = axes[i][i]
            s = samples[:, i]
647
648
649
650
651
652
653
654
655
656
            lnprior = self._generic_lnprior(**self.theta_prior[key])
            if add_prior == 'full' and self.theta_prior[key]['type'] == 'unif':
                lower = self.theta_prior[key]['lower']
                upper = self.theta_prior[key]['upper']
                r = upper-lower
                xlim = [lower-0.05*r, upper+0.05*r]
                x = np.linspace(xlim[0], xlim[1], 1000)
            else:
                xlim = ax.get_xlim()
                x = np.linspace(s.min(), s.max(), 1000)
657
658
            multiplier = self._get_rescale_multiplier_for_key(key)
            subtractor = self._get_rescale_subtractor_for_key(key)
659
660
661
662
663
664
665
666
            ax.plot((x-subtractor)*multiplier,
                    [np.exp(lnprior(xi)) for xi in x], '-C3',
                    label='prior')

            for j in range(i, self.ndim):
                axes[j][i].set_xlim(xlim[0], xlim[1])
            for k in range(0, i):
                axes[i][k].set_ylim(xlim[0], xlim[1])
667

668
669
670
671
672
673
674
675
    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]
676
            prior_func = self._generic_lnprior(**prior_dict)
677
678
679
680
681
            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
Gregory Ashton's avatar
Gregory Ashton committed
682
683
684
685
686
            elif prior_dict['type'] == 'log10unif':
                upper = prior_dict['log10upper']
                lower = prior_dict['log10lower']
                x = np.linspace(lower, upper, N)
                prior = [prior_func(xi) for xi in x]
687
688
689
690
691
            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)
692
693
694
695
696
            elif prior_dict['type'] == 'halfnorm':
                lower = prior_dict['loc']
                upper = prior_dict['loc'] + normal_stds * prior_dict['scale']
                x = np.linspace(lower, upper, N)
                prior = [prior_func(xi) for xi in x]
Gregory Ashton's avatar
Gregory Ashton committed
697
698
699
700
701
            elif prior_dict['type'] == 'neghalfnorm':
                upper = prior_dict['loc']
                lower = prior_dict['loc'] - normal_stds * prior_dict['scale']
                x = np.linspace(lower, upper, N)
                prior = [prior_func(xi) for xi in x]
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
            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))

725
    def plot_cumulative_max(self, **kwargs):
726
727
728
729
        """ Plot the cumulative twoF for the maximum posterior estimate

        See the pyfstat.core.plot_twoF_cumulative function for further details
        """
Gregory Ashton's avatar
Gregory Ashton committed
730
731
732
733
        d, maxtwoF = self.get_max_twoF()
        for key, val in self.theta_prior.iteritems():
            if key not in d:
                d[key] = val
734
735

        if hasattr(self, 'search') is False:
736
            self._initiate_search_object()
737
738
739
        if self.binary is False:
            self.search.plot_twoF_cumulative(
                self.label, self.outdir, F0=d['F0'], F1=d['F1'], F2=d['F2'],
740
                Alpha=d['Alpha'], Delta=d['Delta'],
741
                tstart=self.minStartTime, tend=self.maxStartTime,
742
                **kwargs)
743
744
745
746
747
        else:
            self.search.plot_twoF_cumulative(
                self.label, self.outdir, F0=d['F0'], F1=d['F1'], F2=d['F2'],
                Alpha=d['Alpha'], Delta=d['Delta'], asini=d['asini'],
                period=d['period'], ecc=d['ecc'], argp=d['argp'], tp=d['argp'],
748
                tstart=self.minStartTime, tend=self.maxStartTime, **kwargs)
Gregory Ashton's avatar
Gregory Ashton committed
749

750
    def _generic_lnprior(self, **kwargs):
751
752
753
754
755
756
757
758
759
        """ Return a lambda function of the pdf

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

        """

Gregory Ashton's avatar
Gregory Ashton committed
760
        def log_of_unif(x, a, b):
761
762
763
764
765
766
767
768
769
770
771
772
773
            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

Gregory Ashton's avatar
Gregory Ashton committed
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
        def log_of_log10unif(x, log10lower, log10upper):
            log10x = np.log10(x)
            above = log10x < log10upper
            below = log10x > log10lower
            if type(above) is not np.ndarray:
                if above and below:
                    return -np.log(x*np.log(10)*(log10upper-log10lower))
                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(x*np.log(10)*(log10upper-log10lower))
                return p

        def log_of_halfnorm(x, loc, scale):
790
            if x < loc:
791
792
793
794
795
796
797
798
799
800
801
802
803
804
                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':
Gregory Ashton's avatar
Gregory Ashton committed
805
806
807
808
            return lambda x: log_of_unif(x, kwargs['lower'], kwargs['upper'])
        if kwargs['type'] == 'log10unif':
            return lambda x: log_of_log10unif(
                x, kwargs['log10lower'], kwargs['log10upper'])
809
        elif kwargs['type'] == 'halfnorm':
Gregory Ashton's avatar
Gregory Ashton committed
810
            return lambda x: log_of_halfnorm(x, kwargs['loc'], kwargs['scale'])
811
        elif kwargs['type'] == 'neghalfnorm':
Gregory Ashton's avatar
Gregory Ashton committed
812
813
            return lambda x: log_of_halfnorm(
                -x, kwargs['loc'], kwargs['scale'])
814
815
816
817
818
819
820
        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")

821
    def _generate_rv(self, **kwargs):
822
823
824
        dist_type = kwargs.pop('type')
        if dist_type == "unif":
            return np.random.uniform(low=kwargs['lower'], high=kwargs['upper'])
Gregory Ashton's avatar
Gregory Ashton committed
825
826
827
        if dist_type == "log10unif":
            return 10**(np.random.uniform(low=kwargs['log10lower'],
                                          high=kwargs['log10upper']))
828
829
830
831
832
        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']))
833
834
835
        if dist_type == "neghalfnorm":
            return -1 * np.abs(np.random.normal(loc=kwargs['loc'],
                                                scale=kwargs['scale']))
836
837
838
839
840
841
        if dist_type == "lognorm":
            return np.random.lognormal(
                mean=kwargs['loc'], sigma=kwargs['scale'])
        else:
            raise ValueError("dist_type {} unknown".format(dist_type))

842
843
844
    def _plot_walkers(self, sampler, symbols=None, alpha=0.4, color="k",
                      temp=0, lw=0.1, nprod=0, add_det_stat_burnin=False,
                      fig=None, axes=None, xoffset=0, plot_det_stat=False,
845
                      context='ggplot', subtractions=None, labelpad=0.05):
846
847
        """ Plot all the chains from a sampler """

848
849
850
851
852
        if context not in plt.style.available:
            raise ValueError((
                'The requested context {} is not available; please select a'
                ' context from `plt.style.available`').format(context))

853
854
855
        if np.ndim(axes) > 1:
            axes = axes.flatten()

856
857
858
859
860
861
862
863
864
865
866
867
868
        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, :, :, :]

869
870
        if subtractions is None:
            subtractions = [0 for i in range(ndim)]
871
872
873
        else:
            if len(subtractions) != self.ndim:
                raise ValueError('subtractions must be of length ndim')
874

875
876
877
878
        if plot_det_stat:
            extra_subplots = 1
        else:
            extra_subplots = 0
879
        with plt.style.context((context)):
Gregory Ashton's avatar
Gregory Ashton committed
880
            plt.rcParams['text.usetex'] = True
Gregory Ashton's avatar
Gregory Ashton committed
881
            if fig is None and axes is None:
882
                fig = plt.figure(figsize=(4, 3.0*ndim))
883
884
                ax = fig.add_subplot(ndim+extra_subplots, 1, 1)
                axes = [ax] + [fig.add_subplot(ndim+extra_subplots, 1, i)
Gregory Ashton's avatar
Gregory Ashton committed
885
                               for i in range(2, ndim+1)]
886

Gregory Ashton's avatar
Gregory Ashton committed
887
            idxs = np.arange(chain.shape[1])
888
889
890
891
892
            burnin_idx = chain.shape[1] - nprod
            if hasattr(self, 'convergence_idx'):
                convergence_idx = self.convergence_idx
            else:
                convergence_idx = burnin_idx
893
894
            if ndim > 1:
                for i in range(ndim):
895
                    axes[i].ticklabel_format(useOffset=False, axis='y')
Gregory Ashton's avatar
Gregory Ashton committed
896
                    cs = chain[:, :, i].T
897
                    if burnin_idx > 0:
898
899
                        axes[i].plot(xoffset+idxs[:convergence_idx+1],
                                     cs[:convergence_idx+1]-subtractions[i],
900
                                     color="r", alpha=alpha,
Gregory Ashton's avatar
Gregory Ashton committed
901
                                     lw=lw)
902
                        axes[i].axvline(xoffset+convergence_idx,
903
                                        color='k', ls='--', lw=0.25)
904
905
                    axes[i].plot(xoffset+idxs[burnin_idx:],
                                 cs[burnin_idx:]-subtractions[i],
Gregory Ashton's avatar
Gregory Ashton committed
906
                                 color="k", alpha=alpha, lw=lw)
907
                    if symbols:
908
                        if subtractions[i] == 0:
909
                            axes[i].set_ylabel(symbols[i], labelpad=labelpad)
910
911
                        else:
                            axes[i].set_ylabel(
912
913
                                symbols[i]+'$-$'+symbols[i]+'$_0$',
                                labelpad=labelpad)
914

915
916
                    if hasattr(self, 'convergence_diagnostic'):
                        ax = axes[i].twinx()
917
918
                        c_x = np.array(self.convergence_diagnosticx)
                        c_y = np.array(self.convergence_diagnostic)
919
920
921
922
                        break_idx = np.argmin(np.abs(c_x - burnin_idx))
                        ax.plot(c_x[:break_idx], c_y[:break_idx, i], '-b')
                        ax.plot(c_x[break_idx:], c_y[break_idx:, i], '-b')
                        ax.set_ylabel('PSRF')
923
                        ax.ticklabel_format(useOffset=False)
924
                        ax.set_ylim(0.5, self.convergence_plot_upper_lim)
925
            else:
Gregory Ashton's avatar
Gregory Ashton committed
926
                axes[0].ticklabel_format(useOffset=False, axis='y')
Gregory Ashton's avatar
Gregory Ashton committed
927
                cs = chain[:, :, temp].T
Gregory Ashton's avatar
Gregory Ashton committed
928
929
930
931
932
933
                if burnin_idx:
                    axes[0].plot(idxs[:burnin_idx], cs[:burnin_idx],
                                 color="r", alpha=alpha, lw=lw)
                axes[0].plot(idxs[burnin_idx:], cs[burnin_idx:], color="k",
                             alpha=alpha, lw=lw)
                if symbols:
934
                    axes[0].set_ylabel(symbols[0], labelpad=labelpad)
935

936
            if plot_det_stat:
937
938
939
                if len(axes) == ndim:
                    axes.append(fig.add_subplot(ndim+1, 1, ndim+1))

940
941
942
                lnl = sampler.lnlikelihood[temp, :, :]
                if burnin_idx and add_det_stat_burnin:
                    burn_in_vals = lnl[:, :burnin_idx].flatten()
943
944
945
946
947
948
949
                    try:
                        axes[-1].hist(burn_in_vals[~np.isnan(burn_in_vals)],
                                      bins=50, histtype='step', color='r')
                    except ValueError:
                        logging.info('Det. Stat. hist failed, most likely all '
                                     'values where the same')
                        pass
950
951
952
                else:
                    burn_in_vals = []
                prod_vals = lnl[:, burnin_idx:].flatten()
953
954
955
956
957
958
959
                try:
                    axes[-1].hist(prod_vals[~np.isnan(prod_vals)], bins=50,
                                  histtype='step', color='k')
                except ValueError:
                    logging.info('Det. Stat. hist failed, most likely all '
                                 'values where the same')
                    pass
960
961
962
963
964
965
966
967
968
969
970
971
                if self.BSGL:
                    axes[-1].set_xlabel(r'$\mathcal{B}_\mathrm{S/GL}$')
                else:
                    axes[-1].set_xlabel(r'$\widetilde{2\mathcal{F}}$')
                axes[-1].set_ylabel(r'$\textrm{Counts}$')
                combined_vals = np.append(burn_in_vals, prod_vals)
                if len(combined_vals) > 0:
                    minv = np.min(combined_vals)
                    maxv = np.max(combined_vals)
                    Range = abs(maxv-minv)
                    axes[-1].set_xlim(minv-0.1*Range, maxv+0.1*Range)

972
                xfmt = matplotlib.ticker.ScalarFormatter()
973
                xfmt.set_powerlimits((-4, 4))
974
975
                axes[-1].xaxis.set_major_formatter(xfmt)

976
            axes[-2].set_xlabel(r'$\textrm{Number of steps}$', labelpad=0.2)
977
978
        return fig, axes

979
    def _apply_corrections_to_p0(self, p0):
Gregory Ashton's avatar
Gregory Ashton committed
980
981
982
        """ Apply any correction to the initial p0 values """
        return p0

983
    def _generate_scattered_p0(self, p):
984
        """ Generate a set of p0s scattered about p """
Gregory Ashton's avatar
Gregory Ashton committed
985
        p0 = [[p + self.scatter_val * p * np.random.randn(self.ndim)
986
987
988
989
               for i in xrange(self.nwalkers)]
              for j in xrange(self.ntemps)]
        return p0

990
    def _generate_initial_p0(self):
991
992
993
        """ Generate a set of init vals for the walkers """

        if type(self.theta_initial) == dict:
994
            logging.info('Generate initial values from initial dictionary')
995
            if hasattr(self, 'nglitch') and self.nglitch > 1:
996
                raise ValueError('Initial dict not implemented for nglitch>1')
997
            p0 = [[[self._generate_rv(**self.theta_initial[key])
998
999
1000
                    for key in self.theta_keys]
                   for i in range(self.nwalkers)]
                  for j in range(self.ntemps)]