mcmc_based_searches.py 106 KB
Newer Older
Gregory Ashton's avatar
Gregory Ashton committed
1
""" Searches using MCMC-based methods """
2
from __future__ import division, absolute_import, print_function
Gregory Ashton's avatar
Gregory Ashton committed
3

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

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
Gregory Ashton's avatar
Gregory Ashton committed
14
from ptemcee import Sampler as PTSampler
15
16
17
import corner
import dill as pickle

18
import pyfstat.core as core
19
20
from pyfstat.core import tqdm, args, read_par
import pyfstat.optimal_setup_functions as optimal_setup_functions
21
import pyfstat.helper_functions as helper_functions
22
23


24
class MCMCSearch(core.BaseSearchClass):
Gregory Ashton's avatar
Gregory Ashton committed
25
    """MCMC search using ComputeFstat
26
27
28
29
30
31
32
33
34

    Parameters
    ----------
    theta_prior: dict
        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.
    tref, minStartTime, maxStartTime: int
35
36
37
38
39
40
41
        GPS seconds of the reference time, start time and end time. While tref
        is requirede, minStartTime and maxStartTime default to None in which
        case all available data is used.
    label, outdir: str
        A label and output directory (optional, defaults is `'data'`) to
        name files
    sftfilepattern: str, optional
Gregory Ashton's avatar
Gregory Ashton committed
42
43
        Pattern to match SFTs using wildcards (*?) and ranges [0-9];
        mutiple patterns can be given separated by colons.
44
    detectors: str, optional
Gregory Ashton's avatar
Gregory Ashton committed
45
46
        Two character reference to the detectors to use, specify None for no
        contraint and comma separate for multiple references.
47
    nsteps: list (2,), optional
48
49
50
        Number of burn-in and production steps to take, [nburn, nprod]. See
        `pyfstat.MCMCSearch.setup_initialisation()` for details on adding
        initialisation steps.
51
    nwalkers, ntemps: int, optional
52
53
        The number of walkers and temperates to use in the parallel
        tempered PTSampler.
54
    log10beta_min float < 0, optional
55
56
        The  log_10(beta) value, if given the set of betas passed to PTSampler
        are generated from `np.logspace(0, log10beta_min, ntemps)` (given
Gregory Ashton's avatar
Gregory Ashton committed
57
        in descending order to ptemcee).
58
    theta_initial: dict, array, optional
59
60
        A dictionary of distribution about which to distribute the
        initial walkers about
61
    rhohatmax: float, optional
62
63
64
        Upper bound for the SNR scale parameter (required to normalise the
        Bayes factor) - this needs to be carefully set when using the
        evidence.
65
    binary: bool, optional
66
        If true, search over binary parameters
67
    BSGL: bool, optional
Gregory Ashton's avatar
Gregory Ashton committed
68
        If true, use the BSGL statistic
69
    SSBPrec: int, optional
Gregory Ashton's avatar
Gregory Ashton committed
70
        SSBPrec (SSB precision) to use when calling ComputeFstat
71
    minCoverFreq, maxCoverFreq: float, optional
72
73
        Minimum and maximum instantaneous frequency which will be covered
        over the SFT time span as passed to CreateFstatInput
74
    injectSources: dict, optional
Gregory Ashton's avatar
Gregory Ashton committed
75
76
        If given, inject these properties into the SFT files before running
        the search
77
    assumeSqrtSX: float, optional
Gregory Ashton's avatar
Gregory Ashton committed
78
        Don't estimate noise-floors, but assume (stationary) per-IFO sqrt{SX}
David Keitel's avatar
David Keitel committed
79
80
81
82
83
84
    transientWindowType: str
        If 'rect' or 'exp',
        compute atoms so that a transient (t0,tau) map can later be computed.
        ('none' instead of None explicitly calls the transient-window function,
        but with the full range, for debugging)
        Currently only supported for nsegs=1.
85
86
87
    tCWFstatMapVersion: str
        Choose between standard 'lal' implementation,
        'pycuda' for gpu, and some others for devel/debug.
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102

    Attributes
    ----------
    symbol_dictionary: dict
        Key, val pairs of the parameters (i.e. `F0`, `F1`), to Latex math
        symbols for plots
    unit_dictionary: dict
        Key, val pairs of the parameters (i.e. `F0`, `F1`), and the
        units (i.e. `Hz`)
    transform_dictionary: dict
        Key, val pairs of the parameters (i.e. `F0`, `F1`), where the key is
        itself a dictionary which can item `multiplier`, `subtractor`, or
        `unit` by which to transform by and update the units.

    """
103
104

    symbol_dictionary = dict(
105
        F0='$f$', F1='$\dot{f}$', F2='$\ddot{f}$', Alpha=r'$\alpha$',
106
107
        Delta='$\delta$', asini='asini', period='P', ecc='ecc', tp='tp',
        argp='argp')
108
    unit_dictionary = dict(
109
110
        F0='Hz', F1='Hz/s', F2='Hz/s$^2$', Alpha=r'rad', Delta='rad',
        asini='', period='s', ecc='', tp='', argp='')
111
    transform_dictionary = {}
112

113
114
115
    def __init__(self, theta_prior, tref, label, outdir='data',
                 minStartTime=None, maxStartTime=None, sftfilepattern=None,
                 detectors=None, nsteps=[100, 100], nwalkers=100, ntemps=1,
116
                 log10beta_min=-5, theta_initial=None,
117
                 rhohatmax=1000, binary=False, BSGL=False,
Gregory Ashton's avatar
Gregory Ashton committed
118
                 SSBprec=None, minCoverFreq=None, maxCoverFreq=None,
David Keitel's avatar
David Keitel committed
119
                 injectSources=None, assumeSqrtSX=None,
120
                 transientWindowType=None, tCWFstatMapVersion='lal'):
121

Gregory Ashton's avatar
Gregory Ashton committed
122
123
124
125
126
127
128
129
130
131
132
133
        self.theta_prior = theta_prior
        self.tref = tref
        self.label = label
        self.outdir = outdir
        self.minStartTime = minStartTime
        self.maxStartTime = maxStartTime
        self.sftfilepattern = sftfilepattern
        self.detectors = detectors
        self.nsteps = nsteps
        self.nwalkers = nwalkers
        self.ntemps = ntemps
        self.log10beta_min = log10beta_min
Gregory Ashton's avatar
Gregory Ashton committed
134
        self.theta_initial = theta_initial
Gregory Ashton's avatar
Gregory Ashton committed
135
136
137
138
139
140
141
142
143
144
145
        self.rhohatmax = rhohatmax
        self.binary = binary
        self.BSGL = BSGL
        self.SSBprec = SSBprec
        self.minCoverFreq = minCoverFreq
        self.maxCoverFreq = maxCoverFreq
        self.injectSources = injectSources
        self.assumeSqrtSX = assumeSqrtSX
        self.transientWindowType = transientWindowType
        self.tCWFstatMapVersion = tCWFstatMapVersion

Gregory Ashton's avatar
Gregory Ashton committed
146
147
        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
148
        self._add_log_file()
149
        logging.info('Set-up MCMC search for model {}'.format(self.label))
150
151
        if sftfilepattern:
            logging.info('Using data {}'.format(self.sftfilepattern))
152
        else:
153
            logging.info('No sftfilepattern given')
154
155
        if injectSources:
            logging.info('Inject sources: {}'.format(injectSources))
156
        self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
157
        self._unpack_input_theta()
158
        self.ndim = len(self.theta_keys)
159
160
        if self.log10beta_min:
            self.betas = np.logspace(0, self.log10beta_min, self.ntemps)
161
162
        else:
            self.betas = None
163

164
165
166
        if args.clean and os.path.isfile(self.pickle_path):
            os.rename(self.pickle_path, self.pickle_path+".old")

167
        self._set_likelihoodcoef()
168
        self._log_input()
169
170
171

    def _set_likelihoodcoef(self):
        self.likelihoodcoef = np.log(70./self.rhohatmax**4)
172

173
    def _log_input(self):
174
        logging.info('theta_prior = {}'.format(self.theta_prior))
175
        logging.info('nwalkers={}'.format(self.nwalkers))
176
177
        logging.info('nsteps = {}'.format(self.nsteps))
        logging.info('ntemps = {}'.format(self.ntemps))
178
179
        logging.info('log10beta_min = {}'.format(
            self.log10beta_min))
180

181
    def _initiate_search_object(self):
182
        logging.info('Setting up search object')
183
        self.search = core.ComputeFstat(
184
            tref=self.tref, sftfilepattern=self.sftfilepattern,
185
            minCoverFreq=self.minCoverFreq, maxCoverFreq=self.maxCoverFreq,
David Keitel's avatar
David Keitel committed
186
187
            detectors=self.detectors, BSGL=self.BSGL,
            transientWindowType=self.transientWindowType,
188
            minStartTime=self.minStartTime, maxStartTime=self.maxStartTime,
189
            binary=self.binary, injectSources=self.injectSources,
190
191
            assumeSqrtSX=self.assumeSqrtSX, SSBprec=self.SSBprec,
            tCWFstatMapVersion=self.tCWFstatMapVersion)
192
193
194
195
        if self.minStartTime is None:
            self.minStartTime = self.search.minStartTime
        if self.maxStartTime is None:
            self.maxStartTime = self.search.maxStartTime
196
197

    def logp(self, theta_vals, theta_prior, theta_keys, search):
198
        H = [self._generic_lnprior(**theta_prior[key])(p) for p, key in
199
200
201
202
203
204
             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]
205
206
207
        twoF = search.get_fullycoherent_twoF(
            self.minStartTime, self.maxStartTime, *self.fixed_theta)
        return twoF/2.0 + self.likelihoodcoef
208

209
    def _unpack_input_theta(self):
210
        full_theta_keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta']
211
212
213
        if self.binary:
            full_theta_keys += [
                'asini', 'period', 'ecc', 'tp', 'argp']
214
215
        full_theta_keys_copy = copy.copy(full_theta_keys)

216
217
        full_theta_symbols = ['$f$', '$\dot{f}$', '$\ddot{f}$', r'$\alpha$',
                              r'$\delta$']
218
219
        if self.binary:
            full_theta_symbols += [
220
                'asini', 'period', 'ecc', 'tp', 'argp']
221

222
223
        self.theta_keys = []
        fixed_theta_dict = {}
224
        for key, val in self.theta_prior.iteritems():
225
226
            if type(val) is dict:
                fixed_theta_dict[key] = 0
Gregory Ashton's avatar
Gregory Ashton committed
227
                self.theta_keys.append(key)
228
229
230
231
232
233
            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
234
            full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249

        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]

250
251
252
253
254
255
256
257
258
    def _evaluate_logpost(self, p0vec):
        init_logp = np.array([
            self.logp(p, self.theta_prior, self.theta_keys, self.search)
            for p in p0vec])
        init_logl = np.array([
            self.logl(p, self.search)
            for p in p0vec])
        return init_logl + init_logp

259
    def _check_initial_points(self, p0):
260
261
        for nt in range(self.ntemps):
            logging.info('Checking temperature {} chains'.format(nt))
262
263
            num = sum(self._evaluate_logpost(p0[nt]) == -np.inf)
            if num > 0:
264
265
                logging.warning(
                    'Of {} initial values, {} are -np.inf due to the prior'
266
                    .format(len(p0[0]), num))
267
                p0 = self._generate_new_p0_to_fix_initial_points(
268
                    p0, nt)
269

270
    def _generate_new_p0_to_fix_initial_points(self, p0, nt):
271
        logging.info('Attempting to correct intial values')
272
273
        init_logpost = self._evaluate_logpost(p0[nt])
        idxs = np.arange(self.nwalkers)[init_logpost == -np.inf]
274
        count = 0
275
        while sum(init_logpost == -np.inf) > 0 and count < 100:
276
277
278
            for j in idxs:
                p0[nt][j] = (p0[nt][np.random.randint(0, self.nwalkers)]*(
                             1+np.random.normal(0, 1e-10, self.ndim)))
279
            init_logpost = self._evaluate_logpost(p0[nt])
280
281
            count += 1

282
        if sum(init_logpost == -np.inf) > 0:
283
284
285
286
287
            logging.info('Failed to fix initial priors')
        else:
            logging.info('Suceeded to fix initial priors')

        return p0
288

289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
    def setup_initialisation(self, nburn0, scatter_val=1e-10):
        """ Add an initialisation step to the MCMC run

        If called prior to `run()`, adds an intial step in which the MCMC
        simulation is run for `nburn0` steps. After this, the MCMC simulation
        continues in the usual manner (i.e. for nburn and nprod steps), but the
        walkers are reset scattered around the maximum likelihood position
        of the initialisation step.

        Parameters
        ----------
        nburn0: int
            Number of initialisation steps to take
        scatter_val: float
            Relative number to scatter walkers around the maximum likelihood
            position after the initialisation step

        """

        logging.info('Setting up initialisation with nburn0={}, scatter_val={}'
                     .format(nburn0, scatter_val))
        self.nsteps = [nburn0] + self.nsteps
        self.scatter_val = scatter_val

313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
#    def setup_burnin_convergence_testing(
#            self, n=10, test_type='autocorr', windowed=False, **kwargs):
#        """ Set up convergence testing during the MCMC simulation
#
#        Parameters
#        ----------
#        n: int
#            Number of steps after which to test convergence
#        test_type: str ['autocorr', 'GR']
#            If 'autocorr' use the exponential autocorrelation time (kwargs
#            passed to `get_autocorr_convergence`). If 'GR' use the Gelman-Rubin
#            statistic (kwargs passed to `get_GR_convergence`)
#        windowed: bool
#            If True, only calculate the convergence test in a window of length
#            `n`
#        **kwargs:
#            Passed to either `_test_autocorr_convergence()` or
#            `_test_GR_convergence()` depending on `test_type`.
#
#        """
#        logging.info('Setting up convergence testing')
#        self.convergence_n = n
#        self.convergence_windowed = windowed
#        self.convergence_test_type = test_type
#        self.convergence_kwargs = kwargs
#        self.convergence_diagnostic = []
#        self.convergence_diagnosticx = []
#        if test_type in ['autocorr']:
#            self._get_convergence_test = self._test_autocorr_convergence
#        elif test_type in ['GR']:
#            self._get_convergence_test = self._test_GR_convergence
#        else:
#            raise ValueError('test_type {} not understood'.format(test_type))
#
#
#    def _test_autocorr_convergence(self, i, sampler, test=True, n_cut=5):
#        try:
#            acors = np.zeros((self.ntemps, self.ndim))
#            for temp in range(self.ntemps):
#                if self.convergence_windowed:
#                    j = i-self.convergence_n
#                else:
#                    j = 0
#                x = np.mean(sampler.chain[temp, :, j:i, :], axis=0)
#                acors[temp, :] = emcee.autocorr.exponential_time(x)
#            c = np.max(acors, axis=0)
#        except emcee.autocorr.AutocorrError:
#            logging.info('Failed to calculate exponential autocorrelation')
#            c = np.zeros(self.ndim) + np.nan
#        except AttributeError:
#            logging.info('Unable to calculate exponential autocorrelation')
#            c = np.zeros(self.ndim) + np.nan
#
#        self.convergence_diagnosticx.append(i - self.convergence_n/2.)
#        self.convergence_diagnostic.append(list(c))
#
#        if test:
#            return i > n_cut * np.max(c)
#
#    def _test_GR_convergence(self, i, sampler, test=True, R=1.1):
#        if self.convergence_windowed:
#            s = sampler.chain[0, :, i-self.convergence_n+1:i+1, :]
#        else:
#            s = sampler.chain[0, :, :i+1, :]
#        N = float(self.convergence_n)
#        M = float(self.nwalkers)
#        W = np.mean(np.var(s, axis=1), axis=0)
#        per_walker_mean = np.mean(s, axis=1)
#        mean = np.mean(per_walker_mean, axis=0)
#        B = N / (M-1.) * np.sum((per_walker_mean-mean)**2, axis=0)
#        Vhat = (N-1)/N * W + (M+1)/(M*N) * B
#        c = np.sqrt(Vhat/W)
#        self.convergence_diagnostic.append(c)
#        self.convergence_diagnosticx.append(i - self.convergence_n/2.)
#
#        if test and np.max(c) < R:
#            return True
#        else:
#            return False
#
#    def _test_convergence(self, i, sampler, **kwargs):
#        if np.mod(i+1, self.convergence_n) == 0:
#            return self._get_convergence_test(i, sampler, **kwargs)
#        else:
#            return False
#
#    def _run_sampler_with_conv_test(self, sampler, p0, nprod=0, nburn=0):
#        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):
#            if self._test_convergence(i, sampler, test=True,
#                                      **self.convergence_kwargs):
#                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
#        iterator = tqdm(sampler.sample(output[0], iterations=nprod),
#                        total=nprod)
#        for result in iterator:
#            self._test_convergence(j, sampler, test=False,
#                                   **self.convergence_kwargs)
#            j += 1
#        return sampler

    def _run_sampler(self, sampler, p0, nprod=0, nburn=0, window=50):
        for result in tqdm(sampler.sample(p0, iterations=nburn+nprod),
                           total=nburn+nprod):
            pass
426

427
428
        self.mean_acceptance_fraction = np.mean(
            sampler.acceptance_fraction, axis=1)
429
        logging.info("Mean acceptance fraction: {}"
430
                     .format(self.mean_acceptance_fraction))
431
        if self.ntemps > 1:
432
            self.tswap_acceptance_fraction = sampler.tswap_acceptance_fraction
433
434
            logging.info("Tswap acceptance fraction: {}"
                         .format(sampler.tswap_acceptance_fraction))
Gregory Ashton's avatar
Gregory Ashton committed
435
436
437
        self.autocorr_time = sampler.get_autocorr_time(window=window)
        logging.info("Autocorrelation length: {}".format(
            self.autocorr_time))
438
439
440

        return sampler

441
    def _estimate_run_time(self):
442
443
444
445
446
447
448
449
450
451
        """ Print the estimated run time

        Uses timing coefficients based on a Lenovo T460p Intel(R)
        Core(TM) i5-6300HQ CPU @ 2.30GHz.

        """
        # Todo: add option to time on a machine, and move coefficients to
        # ~/.pyfstat.conf
        if (type(self.theta_prior['Alpha']) == dict or
                type(self.theta_prior['Delta']) == dict):
Gregory Ashton's avatar
Gregory Ashton committed
452
453
454
455
            tau0LD = 5.2e-7
            tau0T = 1.5e-8
            tau0S = 1.2e-4
            tau0C = 5.8e-6
456
        else:
Gregory Ashton's avatar
Gregory Ashton committed
457
            tau0LD = 1.3e-7
458
            tau0T = 1.5e-8
Gregory Ashton's avatar
Gregory Ashton committed
459
460
            tau0S = 9.1e-5
            tau0C = 5.5e-6
461
        Nsfts = (self.maxStartTime - self.minStartTime) / 1800.
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
        if hasattr(self, 'run_setup'):
            ts = []
            for row in self.run_setup:
                nsteps = row[0]
                nsegs = row[1]
                numb_evals = np.sum(nsteps)*self.nwalkers*self.ntemps
                t = (tau0S + tau0LD*Nsfts) * numb_evals
                if nsegs > 1:
                    t += (tau0C + tau0T*Nsfts)*nsegs*numb_evals
                ts.append(t)
            time = np.sum(ts)
        else:
            numb_evals = np.sum(self.nsteps)*self.nwalkers*self.ntemps
            time = (tau0S + tau0LD*Nsfts) * numb_evals
            if getattr(self, 'nsegs', 1) > 1:
                time += (tau0C + tau0T*Nsfts)*self.nsegs*numb_evals

479
        logging.info('Estimated run-time = {} s = {:1.0f}:{:1.0f} m'.format(
480
            time, *divmod(time, 60)))
481

Gregory Ashton's avatar
Gregory Ashton committed
482
483
    def run(self, proposal_scale_factor=2, create_plots=True, window=50,
            **kwargs):
484
485
486
487
488
489
490
491
492
493
494
        """ Run the MCMC simulatation

        Parameters
        ----------
        proposal_scale_factor: float
            The proposal scale factor used by the sampler, see Goodman & Weare
            (2010). If the acceptance fraction is too low, you can raise it by
            decreasing the a parameter; and if it is too high, you can reduce
            it by increasing the a parameter [Foreman-Mackay (2013)].
        create_plots: bool
            If true, save trace plots of the walkers
Gregory Ashton's avatar
Gregory Ashton committed
495
        window: int
496
497
            The minimum number of autocorrelation times needed to trust the
            result when estimating the autocorrelation time (see
Gregory Ashton's avatar
Gregory Ashton committed
498
            ptemcee.Sampler.get_autocorr_time for further details.
499
500
501
        **kwargs:
            Passed to _plot_walkers to control the figures

502
503
        Returns
        -------
Gregory Ashton's avatar
Gregory Ashton committed
504
505
        sampler: ptemcee.Sampler
            The ptemcee ptsampler object
506

507
        """
508

509
        self.old_data_is_okay_to_use = self._check_old_data_is_okay_to_use()
510
511
512
        if self.old_data_is_okay_to_use is True:
            logging.warning('Using saved data from {}'.format(
                self.pickle_path))
513
            d = self.get_saved_data_dictionary()
514
515
516
            self.samples = d['samples']
            self.lnprobs = d['lnprobs']
            self.lnlikes = d['lnlikes']
517
            self.all_lnlikelihood = d['all_lnlikelihood']
518
            self.chain = d['chain']
519
520
            return

521
        self._initiate_search_object()
522
        self._estimate_run_time()
523

Gregory Ashton's avatar
Gregory Ashton committed
524
525
526
        sampler = PTSampler(
            ntemps=self.ntemps, nwalkers=self.nwalkers, dim=self.ndim,
            logl=self.logl, logp=self.logp,
527
            logpargs=(self.theta_prior, self.theta_keys, self.search),
528
            loglargs=(self.search,), betas=self.betas, a=proposal_scale_factor)
529

530
531
532
        p0 = self._generate_initial_p0()
        p0 = self._apply_corrections_to_p0(p0)
        self._check_initial_points(p0)
533

534
        # Run initialisation steps if required
535
536
537
        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
538
                j, ninit_steps, n))
Gregory Ashton's avatar
Gregory Ashton committed
539
            sampler = self._run_sampler(sampler, p0, nburn=n, window=window)
540
            if create_plots:
541
                fig, axes = self._plot_walkers(sampler,
542
                                               **kwargs)
543
544
                fig.tight_layout()
                fig.savefig('{}/{}_init_{}_walkers.png'.format(
Gregory Ashton's avatar
Gregory Ashton committed
545
                    self.outdir, self.label, j))
546

547
548
549
            p0 = self._get_new_p0(sampler)
            p0 = self._apply_corrections_to_p0(p0)
            self._check_initial_points(p0)
550
551
            sampler.reset()

Gregory Ashton's avatar
Gregory Ashton committed
552
553
554
555
        if len(self.nsteps) > 1:
            nburn = self.nsteps[-2]
        else:
            nburn = 0
556
557
558
        nprod = self.nsteps[-1]
        logging.info('Running final burn and prod with {} steps'.format(
            nburn+nprod))
559
        sampler = self._run_sampler(sampler, p0, nburn=nburn, nprod=nprod)
560

561
        if create_plots:
562
563
564
565
566
567
568
            try:
                fig, axes = self._plot_walkers(sampler, nprod=nprod, **kwargs)
                fig.tight_layout()
                fig.savefig('{}/{}_walkers.png'.format(self.outdir, self.label))
            except RuntimeError as e:
                logging.warning("Failed to save walker plots due to Erro {}"
                                .format(e))
569
570

        samples = sampler.chain[0, :, nburn:, :].reshape((-1, self.ndim))
Gregory Ashton's avatar
Gregory Ashton committed
571
572
573
        lnprobs = sampler.logprobability[0, :, nburn:].reshape((-1))
        lnlikes = sampler.loglikelihood[0, :, nburn:].reshape((-1))
        all_lnlikelihood = sampler.loglikelihood[:, :, nburn:]
574
        self.samples = samples
575
        self.chain = sampler.chain
576
577
        self.lnprobs = lnprobs
        self.lnlikes = lnlikes
578
        self.all_lnlikelihood = all_lnlikelihood
579
580
        self._save_data(sampler, samples, lnprobs, lnlikes, all_lnlikelihood,
                        sampler.chain)
Gregory Ashton's avatar
Gregory Ashton committed
581
        return sampler
582

583
    def _get_rescale_multiplier_for_key(self, key):
584
        """ Get the rescale multiplier from the transform_dictionary
585
586
587
588
589

        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
        """
590
        if key not in self.transform_dictionary:
591
592
            return 1

593
594
        if 'multiplier' in self.transform_dictionary[key]:
            val = self.transform_dictionary[key]['multiplier']
595
596
597
            if type(val) == str:
                if hasattr(self, val):
                    multiplier = getattr(
598
                        self, self.transform_dictionary[key]['multiplier'])
599
600
601
602
603
604
605
606
607
                else:
                    raise ValueError(
                        "multiplier {} not a class attribute".format(val))
            else:
                multiplier = val
        else:
            multiplier = 1
        return multiplier

608
    def _get_rescale_subtractor_for_key(self, key):
609
        """ Get the rescale subtractor from the transform_dictionary
610
611
612
613
614

        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
        """
615
        if key not in self.transform_dictionary:
616
617
            return 0

618
619
        if 'subtractor' in self.transform_dictionary[key]:
            val = self.transform_dictionary[key]['subtractor']
620
621
622
            if type(val) == str:
                if hasattr(self, val):
                    subtractor = getattr(
623
                        self, self.transform_dictionary[key]['subtractor'])
624
625
626
627
628
629
630
631
632
                else:
                    raise ValueError(
                        "subtractor {} not a class attribute".format(val))
            else:
                subtractor = val
        else:
            subtractor = 0
        return subtractor

633
    def _scale_samples(self, samples, theta_keys):
634
        """ Scale the samples using the transform_dictionary """
635
        for key in theta_keys:
636
            if key in self.transform_dictionary:
637
638
                idx = theta_keys.index(key)
                s = samples[:, idx]
639
                subtractor = self._get_rescale_subtractor_for_key(key)
640
                s = s - subtractor
641
                multiplier = self._get_rescale_multiplier_for_key(key)
642
                s *= multiplier
643
644
                samples[:, idx] = s

645
646
        return samples

647
    def _get_labels(self, newline_units=False):
648
        """ Combine the units, symbols and rescaling to give labels """
649

650
651
652
653
654
655
        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]
656
657
658
659
660
661
662
            if key in self.transform_dictionary:
                if 'symbol' in self.transform_dictionary[key]:
                    s = self.transform_dictionary[key]['symbol']
                if 'label' in self.transform_dictionary[key]:
                    label = self.transform_dictionary[key]['label']
                if 'unit' in self.transform_dictionary[key]:
                    u = self.transform_dictionary[key]['unit']
663
            if label is None:
664
665
666
667
                if newline_units:
                    label = '{} \n [{}]'.format(s, u)
                else:
                    label = '{} [{}]'.format(s, u)
668
669
            labels.append(label)
        return labels
670

671
672
    def plot_corner(self, figsize=(7, 7), add_prior=False, nstds=None,
                    label_offset=0.4, dpi=300, rc_context={},
673
                    tglitch_ratio=False, fig_and_axes=None, save_fig=True,
674
                    **kwargs):
675
676
677
678
679
680
681
682
683
        """ 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)
684
685
686
        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.
687
688
689
690
691
692
693
694
695
696
697
698
699
700
        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
701
702
703
704
705
        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
706
707
        **kwargs:
            Passed to corner.corner
708

709
710
711
712
        Returns
        -------
        fig, axes:
            The matplotlib figure and axes, only returned if save_fig = False
713
714

        """
715

716
717
718
719
        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
720
721
        if self.ndim < 2:
            with plt.rc_context(rc_context):
722
723
724
725
                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
726
727
728
729
730
731
732
                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

733
        with plt.rc_context(rc_context):
734
735
736
737
738
            if fig_and_axes is None:
                fig, axes = plt.subplots(self.ndim, self.ndim,
                                         figsize=figsize)
            else:
                fig, axes = fig_and_axes
739
740

            samples_plt = copy.copy(self.samples)
741
            labels = self._get_labels(newline_units=True)
742

743
            samples_plt = self._scale_samples(samples_plt, self.theta_keys)
744
745
746
747
748

            if tglitch_ratio:
                for j, k in enumerate(self.theta_keys):
                    if k == 'tglitch':
                        s = samples_plt[:, j]
749
750
751
                        samples_plt[:, j] = (
                            s - self.minStartTime)/(
                                self.maxStartTime - self.minStartTime)
752
                        labels[j] = r'$R_{\textrm{glitch}}$'
753
754
755
756
757
758
759

            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))
760
761
            elif 'range' in kwargs:
                _range = kwargs.pop('range')
762
763
764
            else:
                _range = None

765
766
767
768
            hist_kwargs = kwargs.pop('hist_kwargs', dict())
            if 'normed' not in hist_kwargs:
                hist_kwargs['normed'] = True

769
            fig_triangle = corner.corner(samples_plt,
770
                                         labels=labels,
771
772
773
774
775
                                         fig=fig,
                                         bins=50,
                                         max_n_ticks=4,
                                         plot_contours=True,
                                         plot_datapoints=True,
776
                                         #label_kwargs={'fontsize': 12},
777
778
779
                                         data_kwargs={'alpha': 0.1,
                                                      'ms': 0.5},
                                         range=_range,
780
                                         hist_kwargs=hist_kwargs,
781
782
783
784
785
786
787
788
789
790
791
792
                                         **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)
793
794

                for tick in ax.xaxis.get_major_ticks():
795
                    #tick.label.set_fontsize(8)
796
797
                    tick.label.set_rotation('horizontal')
                for tick in ax.yaxis.get_major_ticks():
798
                    #tick.label.set_fontsize(8)
799
800
                    tick.label.set_rotation('vertical')

801
802
803
804
            plt.tight_layout(h_pad=0.0, w_pad=0.0)
            fig.subplots_adjust(hspace=0.05, wspace=0.05)

            if add_prior:
805
                self._add_prior_to_corner(axes, self.samples, add_prior)
806

807
808
809
810
811
            if save_fig:
                fig_triangle.savefig('{}/{}_corner.png'.format(
                    self.outdir, self.label), dpi=dpi)
            else:
                return fig, axes
812

813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
    def plot_chainconsumer(
            self, save_fig=True, label_offset=0.25, dpi=300, **kwargs):
        """ Generate a corner plot of the posterior using chainconsumer

        Parameters
        ----------
        dpi: int
            Passed to plt.savefig
        **kwargs:
            Passed to chainconsumer.plotter.plot

        """

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

        samples_plt = copy.copy(self.samples)
        labels = self._get_labels(newline_units=True)

        samples_plt = self._scale_samples(samples_plt, self.theta_keys)

        import chainconsumer
        c = chainconsumer.ChainConsumer()
        c.add_chain(samples_plt, parameters=labels)
        c.configure(smooth=0, summary=False, sigma2d=True)
        fig = c.plotter.plot(**kwargs)

        axes_list = fig.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)

            #for tick in ax.xaxis.get_major_ticks():
            #    #tick.label.set_fontsize(8)
            #    tick.label.set_rotation('horizontal')
            #for tick in ax.yaxis.get_major_ticks():
            #    #tick.label.set_fontsize(8)
            #    tick.label.set_rotation('vertical')

            plt.tight_layout(h_pad=0.0, w_pad=0.0)
            fig.subplots_adjust(hspace=0.05, wspace=0.05)

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

868
    def _add_prior_to_corner(self, axes, samples, add_prior):
869
870
871
        for i, key in enumerate(self.theta_keys):
            ax = axes[i][i]
            s = samples[:, i]
872
873
874
875
876
877
878
879
880
881
            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)
882
883
            multiplier = self._get_rescale_multiplier_for_key(key)
            subtractor = self._get_rescale_subtractor_for_key(key)
884
885
886
887
888
889
890
891
            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])
892

893
894
895
896
897
898
899
900
    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]
901
            prior_func = self._generic_lnprior(**prior_dict)
902
903
904
905
906
            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
907
908
909
910
911
            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]
912
913
914
915
916
            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)
917
918
919
920
921
            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
922
923
924
925
926
            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]
927
928
929
            else:
                raise ValueError('Not implemented for prior type {}'.format(
                    prior_dict['type']))
930
            priorln = ax.plot(x, prior, 'C3', label='prior')
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
            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))

950
    def plot_cumulative_max(self, **kwargs):
951
952
953
954
        """ 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
955
956
957
958
        d, maxtwoF = self.get_max_twoF()
        for key, val in self.theta_prior.iteritems():
            if key not in d:
                d[key] = val
959

960
961
962
        if 'add_pfs' in kwargs:
            self.generate_loudest()

963
        if hasattr(self, 'search') is False:
964
            self._initiate_search_object()
965
966
967
        if self.binary is False:
            self.search.plot_twoF_cumulative(
                self.label, self.outdir, F0=d['F0'], F1=d['F1'], F2=d['F2'],
968
                Alpha=d['Alpha'], Delta=d['Delta'],
969
                tstart=self.minStartTime, tend=self.maxStartTime,
970
                **kwargs)
971
972
973
974
975
        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'],
976
                tstart=self.minStartTime, tend=self.maxStartTime, **kwargs)
Gregory Ashton's avatar
Gregory Ashton committed
977

978
    def _generic_lnprior(self, **kwargs):
979
980
981
982
        """ Return a lambda function of the pdf

        Parameters
        ----------
983
        **kwargs:
984
985
986
987
            A dictionary containing 'type' of pdf and shape parameters

        """

Gregory Ashton's avatar
Gregory Ashton committed
988
        def log_of_unif(x, a, b):
989
990
991
992
993
994
995
996
997
998
999
1000
1001
            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
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
        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):
1018
            if x < loc:
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
                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
1033
1034
1035
1036
            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'])
1037
        elif kwargs['type'] == 'halfnorm':
Gregory Ashton's avatar
Gregory Ashton committed
1038
            return lambda x: log_of_halfnorm(x, kwargs['loc'], kwargs['scale'])
1039
        elif kwargs['type'] == 'neghalfnorm':
Gregory Ashton's avatar
Gregory Ashton committed
1040
1041
            return lambda x: log_of_halfnorm(
                -x, kwargs['loc'], kwargs['scale'])
1042
1043
1044
1045
1046
1047
1048
        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")

1049
    def _generate_rv(self, **kwargs):
1050
1051
1052
        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
1053
1054
1055
        if dist_type == "log10unif":
            return 10**(np.random.uniform(low=kwargs['log10lower'],
                                          high=kwargs['log10upper']))
1056
1057
1058
1059
1060
        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']))
1061
1062
1063
        if dist_type == "neghalfnorm":
            return -1 * np.abs(np.random.normal(loc=kwargs['loc'],
                                                scale=kwargs['scale']))
1064
1065
1066
1067
1068
1069
        if dist_type == "lognorm":
            return np.random.lognormal(
                mean=kwargs['loc'], sigma=kwargs['scale'])
        else:
            raise ValueError("dist_type {} unknown".format(dist_type))

1070
    def _plot_walkers(self, sampler, symbols=None, alpha=0.8, color="k",
1071
1072
                      temp=0, lw=0.1, nprod=0, add_det_stat_burnin=False,
                      fig=None, axes=None, xoffset=0, plot_det_stat=False,
1073
                      context='ggplot', labelpad=5):
1074
1075
        """ Plot all the chains from a sampler """

1076
1077
        if symbols is None:
            symbols = self._get_labels()
1078
1079
1080
1081
1082
        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))

1083
1084
1085
        if np.ndim(axes) > 1:
            axes = axes.flatten()

1086
1087
1088
        shape = sampler.chain.shape
        if len(shape) == 3:
            nwalkers, nsteps, ndim = shape
1089
            chain = sampler.chain[:, :, :].copy()
1090
1091
1092
1093
1094
1095
1096
        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))
1097
            chain = sampler.chain[temp, :, :, :].copy()
1098

1099
1100
1101
        samples = chain.reshape((nwalkers*nsteps, ndim))
        samples = self._scale_samples(samples, self.theta_keys)
        chain = chain.reshape((nwalkers, nsteps, ndim))
1102

1103
1104
1105
1106
        if plot_det_stat:
            extra_subplots = 1
        else:
            extra_subplots = 0
1107
        with plt.style.context((context)):
Gregory Ashton's avatar
Gregory Ashton committed
1108
            plt.rcParams['text.usetex'] = True
Gregory Ashton's avatar
Gregory Ashton committed
1109
            if fig is None and axes is None:
1110
                fig = plt.figure(figsize=(4, 3.0*ndim))
1111
1112
                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
1113
                               for i in range(2, ndim+1)]
1114

Gregory Ashton's avatar
Gregory Ashton committed
1115
            idxs = np.arange(chain.shape[1])
1116
            burnin_idx = chain.shape[1] - nprod
1117
1118
1119
1120
            #if hasattr(self, 'convergence_idx'):
            #    last_idx = self.convergence_idx
            #else:
            last_idx = burnin_idx
1121
1122
            if ndim > 1:
                for i in range(ndim):
1123
                    axes[i].ticklabel_format(useOffset=False, axis='y')
Gregory Ashton's avatar
Gregory Ashton committed
1124
                    cs = chain[:, :, i].T
1125
                    if burnin_idx > 0:
1126
                        axes[i].plot(xoffset+idxs[:last_idx+1],
1127
                                     cs[:last_idx+1],
1128
                                     color="C3", alpha=alpha,
Gregory Ashton's avatar
Gregory Ashton committed
1129
                                     lw=lw)
1130
                        axes[i].axvline(xoffset+last_idx,
1131
                                        color='k', ls='--', lw=0.5)
1132
                    axes[i].plot(xoffset+idxs[burnin_idx:],
1133
                                 cs[burnin_idx:],
Gregory Ashton's avatar
Gregory Ashton committed
1134
                                 color="k", alpha=alpha, lw=lw)
Gregory Ashton's avatar
Gregory Ashton committed
1135
1136

                    axes[i].set_xlim(0, xoffset+idxs[-1])