pyfstat.py 50.6 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
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
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
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
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
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
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
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
""" Classes for various types of searches using ComputeFstatistic """
import os
import sys
import itertools
import logging
import argparse
import copy
import glob
import inspect
from functools import wraps

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

try:
    from ephemParams import earth_ephem, sun_ephem
except (IOError, ImportError):
    logging.warning('No ephemParams.py file found, or it does not contain '
                    'earth_ephem and sun_ephem, please provide the paths when '
                    'initialising searches')
    earth_ephem = None
    sun_ephem = None

plt.style.use('paper')


def initializer(func):
    """ Automatically assigns the parameters to self"""
    names, varargs, keywords, defaults = inspect.getargspec(func)

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

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

        func(self, *args, **kargs)

    return wrapper


def read_par(label, outdir):
    filename = '{}/{}.par'.format(outdir, label)
    d = {}
    with open(filename, 'r') as f:
        for line in f:
            key, val = line.rstrip('\n').split(' = ')
            d[key] = np.float64(val)
    return d

parser = argparse.ArgumentParser()
parser.add_argument("-q", "--quite", help="Decrease output verbosity",
                    action="store_true")
parser.add_argument("-c", "--clean", help="Don't use cached data",
                    action="store_true")
parser.add_argument('unittest_args', nargs='*')
args, unknown = parser.parse_known_args()
sys.argv[1:] = args.unittest_args

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

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


class BaseSearchClass(object):

    earth_ephem_default = earth_ephem
    sun_ephem_default = sun_ephem

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

        return m

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

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

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

    # Rewrite this to generalise to N glitches, then use everywhere!
    def calculate_thetas(self, theta, delta_thetas, tbounds):
        """ Calculates the set of coefficients for the post-glitch signal """
        thetas = [theta]
        for i, dt in enumerate(delta_thetas):
            pre_theta_at_ith_glitch = self.shift_coefficients(
                thetas[i], tbounds[i+1] - self.tref)
            post_theta_at_ith_glitch = pre_theta_at_ith_glitch + dt
            thetas.append(self.shift_coefficients(
                post_theta_at_ith_glitch, self.tref - tbounds[i+1]))
        return thetas


class FullyCoherentNarrowBandSearch(BaseSearchClass):
    """ Search over a narrow band of F0, F1, and F2 """

    @initializer
    def __init__(self, label, outdir, sftlabel=None, sftdir=None,
                 tglitch=None, tref=None, tstart=None, Alpha=None, Delta=None,
                 duration=None, Writer=None):
        """
        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to
        sftlabel, sftdir: str
            A label and directory in which to find the relevant sft file. If
            None use label and outdir
        """
        if self.sftlabel is None:
            self.sftlabel = self.label
        if self.sftdir is None:
            self.sftdir = self.outdir
        self.tend = self.tstart + self.duration
        self.calculate_best_fit_F0_and_F1(Writer)
        self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)

    def calculate_best_fit_F0_and_F1(self, writer):
        R = (writer.tglitch - writer.tstart) / float(writer.duration)
        self.F0_min = (writer.F0 + writer.delta_F0*(1-R)**2*(2*R+1) +
                       3*writer.delta_phi*R*(1-R)/np.pi/writer.duration +
                       # .5*writer.duration*writer.F1*(1-R) +
                       .5*writer.duration*writer.delta_F1*(1-R)**3*(1+R))

        self.F1_min = (writer.F1 + writer.delta_F1*(1-R)**3*(6*R**2+3*R+1) +
                       30*writer.delta_F0*R**2*(1-R)**2/writer.duration +
                       30*writer.delta_phi*R*(1-R)*(2*R-1)/np.pi/writer.duration**2
                       )

    def get_grid_setup(self, m, n, searchF2=False):
        """ Calc. the grid parameters of bands given the metric-mismatch

        Parameters
        ----------
        m: float in [0, 1]
            The mismatch spacing between adjacent grid points
        n: int
            Number of grid points to search

        """
        DeltaF0 = np.sqrt(12 * m) / (np.pi * self.duration)
        DeltaF1 = np.sqrt(720 * m) / (np.pi * self.duration**2.0)
        DeltaF2 = np.sqrt(100800 * m) / (np.pi * self.duration**3.0)

        # Calculate the width of bands given n
        F0Band = n * DeltaF0
        F1Band = n * DeltaF1
        F2Band = n * DeltaF2

        # Search takes the lowest frequency in the band
        F0_bottom = self.F0_min - .5 * F0Band
        F1_bottom = self.F1_min - .5 * F1Band
        if searchF2:
            F2_bottom = self.F2_min-.5*self.F2Band  # Not yet implemented
        else:
            F2_bottom = 0  # Broken functionality
            F2Band = 0

        Messg = ["Automated search for {}:".format(self.label),
                 "Grid parameters : m={}, n={}".format(m, n),
                 "Reference time: {}".format(self.tref),
                 "Analytic best-fit values : {}, {}".format(
                     self.F0_min, self.F1_min),
                 "F0Band : {} -- {}".format(F0_bottom,
                                            F0_bottom + F0Band),
                 "F1Band : {} -- {}".format(F1_bottom,
                                            F1_bottom + F1Band),
                 "F2Band : {} -- {}".format(F2_bottom,
                                            F2_bottom + F2Band),
                 ]
        logging.info("\n  ".join(Messg))

        return (F0_bottom, DeltaF0, F0Band,
                F1_bottom, DeltaF1, F1Band,
                F2_bottom, DeltaF2, F2Band)

    def run_computefstatistic_slow(self, m, n, search_F2=False):
        """ Compute the f statistic fully-coherently over a grid """

        (F0_bottom, DeltaF0, F0Band,
         F1_bottom, DeltaF1, F1Band,
         F2_bottom, DeltaF2, F2Band) = self.get_grid_setup(m, n)

        c_l = []
        c_l.append("lalapps_ComputeFstatistic_v2")
        c_l.append("--Freq={}".format(F0_bottom))
        c_l.append("--dFreq={}".format(DeltaF0))
        c_l.append("--FreqBand={}".format(F0Band))

        c_l.append("--f1dot={}".format(F1_bottom))
        c_l.append("--df1dot={}".format(DeltaF1))
        c_l.append("--f1dotBand={}".format(F1Band))

        if search_F2:
            c_l.append("--f2dot={}".format(F2_bottom))
            c_l.append("--df2dot={}".format(DeltaF2))
            c_l.append("--f2dotBand={}".format(F2Band))
        else:
            c_l.append("--f2dot={}".format(F2_bottom))

        c_l.append("--DataFiles='{}'".format(
            self.outdir+"/*SFT_"+self.label+"*sft"))

        c_l.append("--refTime={:10.6f}".format(self.tref))
        c_l.append("--outputFstat='{}'".format(self.fs_file_name))

        c_l.append("--Alpha={}".format(self.Alpha))
        c_l.append("--Delta={}".format(self.Delta))

        c_l.append("--minStartTime={}".format(self.tstart))
        c_l.append("--maxStartTime={}".format(self.tend))

        logging.info("Executing: " + " ".join(c_l) + "\n")
        os.system(" ".join(c_l))

        self.read_in_fstat()

    def run_computefstatistic(self, dFreq=0, numFreqBins=1):
        """ Compute the f statistic fully-coherently over a grid """

        constraints = lalpulsar.SFTConstraints()
        FstatOptionalArgs = lalpulsar.FstatOptionalArgsDefaults
        minCoverFreq = 29
        maxCoverFreq = 31

        SFTCatalog = lalpulsar.SFTdataFind(
                self.sftdir+'/*_'+self.sftlabel+"*sft", constraints)

        ephemerides = lalpulsar.InitBarycenter(
            '~/lalsuite-install/share/lalpulsar/earth00-19-DE421.dat.gz',
            '~/lalsuite-install/share/lalpulsar/sun00-19-DE421.dat.gz')

        whatToCompute = lalpulsar.FSTATQ_2F
        FstatInput = lalpulsar.CreateFstatInput(SFTCatalog,
                                                minCoverFreq,
                                                maxCoverFreq,
                                                dFreq,
                                                ephemerides,
                                                FstatOptionalArgs
                                                )

        PulsarDopplerParams = lalpulsar.PulsarDopplerParams()
        PulsarDopplerParams.refTime = self.tref
        PulsarDopplerParams.Alpha = self.Alpha
        PulsarDopplerParams.Delta = self.Delta
        PulsarDopplerParams.fkdot = np.array([self.F0_min-dFreq*numFreqBins/2.,
                                              self.F1_min, 0, 0, 0, 0, 0])

        FstatResults = lalpulsar.FstatResults()
        lalpulsar.ComputeFstat(FstatResults,
                               FstatInput,
                               PulsarDopplerParams,
                               numFreqBins,
                               whatToCompute
                               )
        self.search_F0 = (np.linspace(0, dFreq * numFreqBins, numFreqBins)
                          + FstatResults.doppler.fkdot[0])
        self.search_F1 = np.zeros(numFreqBins) + self.F1_min
        self.search_F2 = np.zeros(numFreqBins) + 0
        self.search_FS = FstatResults.twoF

    def read_in_fstat(self):
        """
        Read in data from *_FS.dat file as produced by ComputeFStatistic_v2
        """

        data = np.genfromtxt(self.fs_file_name, comments="%")

        # If none of the components are varying:
        if data.ndim == 1:
            self.search_F0 = data[0]
            self.search_F1 = data[3]
            self.search_F2 = data[4]
            self.search_FS = data[6]
            return

        search_F0 = data[:, 0]
        search_F1 = data[:, 3]
        search_F2 = data[:, 4]
        search_FS = data[:, 6]

        NF0 = len(np.unique(search_F0))
        NF1 = len(np.unique(search_F1))
        NF2 = len(np.unique(search_F2))

        shape = (NF2, NF1, NF0)
        self.data_shape = shape
        self.search_F0 = np.squeeze(np.reshape(search_F0,
                                    newshape=shape).transpose())
        self.search_F1 = np.squeeze(np.reshape(search_F1,
                                    newshape=shape).transpose())
        self.search_F2 = np.squeeze(np.reshape(search_F2,
                                    newshape=shape).transpose())
        self.search_FS = np.squeeze(np.reshape(search_FS,
                                    newshape=shape).transpose())

    def get_FS_max(self):
        """ Returns the maximum FS and the corresponding F0, F1, and F2 """

        if np.shape(self.search_FS) == ():
            return self.search_F0, self.search_F1, self.search_F2, self.search_FS
        else:
            max_idx = np.unravel_index(self.search_FS.argmax(), self.search_FS.shape)
            return (self.search_F0[max_idx], self.search_F1[max_idx],
                    self.search_F2[max_idx], self.search_FS[max_idx])

    def plot_output(self, output_type='FS', perfectlymatched_FS=None,
                    fig=None, ax=None, savefig=False, title=None):
        """
        Plot the output of the *_FS.dat file as a contour plot

        Parameters
        ----------
        output_type: str
            one of 'FS', 'rho', or 'mismatch'
        perfectlymatched_FS: float
            the 2F of a perfectly matched signal against which to
            compute the mismatch

        """

        resF0 = self.search_F0 - self.F0_min
        resF1 = self.search_F1 - self.F1_min

        if output_type == 'FS':
            Z = self.search_FS
            zlabel = r'$2\bar{\mathcal{F}}$'
        elif output_type == 'rho':
            Z = self.search_FS - 4
            zlabel = r'\rho^{2}'
        elif output_type == 'mismatch':
            rho2 = self.search_FS - 4
            perfectlymatched_rho2 = perfectlymatched_FS - 4
            if perfectlymatched_FS:
                Z = 1 - (rho2) / (perfectlymatched_rho2)
            else:
                raise ValueError('Plotting the mismatch requires a value for'
                                 ' the parameter perfectlymatched_rho2')
            zlabel = 'mismatch'

        if ax is None:
            fig, ax = plt.subplots()
        plt.rcParams['font.size'] = 12

        pax = ax.pcolormesh(resF0, resF1, Z, cmap=plt.cm.viridis,
                            vmin=0, vmax=1)
        fig.colorbar(pax, label=zlabel, ax=ax)
        ax.set_xlim(np.min(resF0), np.max(resF0))
        ax.set_ylim(np.min(resF1), np.max(resF1))

        ax.set_xlabel(r'$f_0 - f_\textrm{min}$')
        ax.set_ylabel(r'$\dot{f}_0 - \dot{f}_\textrm{min}$')
        ax.set_title(self.label)

        plt.tight_layout()
        if savefig:
            fig.savefig('output_{}.png'.format(self.label))


class SemiCoherentGlitchSearch(BaseSearchClass):
    """ A semi-coherent glitch search

    This implements a basic `semi-coherent glitch F-stat in which the data
    is divided into two segments either side of the proposed glitch and the
    fully-coherent F-stat in each segment is averaged to give the semi-coherent
    F-stat
    """
    # Copy methods
    read_in_fstat = FullyCoherentNarrowBandSearch.__dict__['read_in_fstat']

    @initializer
    def __init__(self, label, outdir, tref, tstart, tend, sftlabel=None,
                 nglitch=0, sftdir=None, minCoverFreq=29, maxCoverFreq=31,
                 detector=None, earth_ephem=None, sun_ephem=None):
        """
        Parameters
        ----------
        label, outdir: str
            A label and directory to read/write data from/to
        sftlabel, sftdir: str
            A label and directory in which to find the relevant sft file. If
            None use label and outdir
        tref: int
            GPS seconds of the reference time
        minCoverFreq, maxCoverFreq: float
            The min and max cover frequency passed to CreateFstatInput
        detector: str
            Two character reference to the data to use, specify None for no
            contraint
        earth_ephem, sun_ephem: str
            Paths of the two files containing positions of Earth and Sun,
            respectively at evenly spaced times, as passed to CreateFstatInput
            If None defaults defined in BaseSearchClass will be used
        """

        if self.sftlabel is None:
            self.sftlabel = self.label
        if self.sftdir is None:
            self.sftdir = self.outdir
        self.fs_file_name = "{}/{}_FS.dat".format(self.outdir, self.label)
        if self.earth_ephem is None:
            self.earth_ephem = self.earth_ephem_default
        if self.sun_ephem is None:
            self.sun_ephem = self.sun_ephem_default
        self.init_computefstatistic_single_point()

    def compute_nglitch_fstat(self, F0, F1, F2, Alpha, Delta, *args):
        """ Compute the semi-coherent glitch F-stat """

        args = list(args)
        tboundaries = [self.tstart] + args[-self.nglitch:] + [self.tend]
        delta_F0s = [0] + args[-3*self.nglitch:-2*self.nglitch]
        delta_F1s = [0] + args[-2*self.nglitch:-self.nglitch]
        theta = [F0, F1, F2]
        tref = self.tref

        twoFSum = 0
        for i in range(self.nglitch+1):
            ts, te = tboundaries[i], tboundaries[i+1]

            if i == 0:
                theta_at_tref = theta
            else:
                # Issue here - are these correct?
                delta_theta = np.array([delta_F0s[i], delta_F1s[i], 0])
                theta_at_glitch = self.shift_coefficients(theta_at_tref,
                                                          te - tref)
                theta_post_glitch_at_glitch = theta_at_glitch + delta_theta
                theta_at_tref = self.shift_coefficients(
                    theta_post_glitch_at_glitch, tref - te)

            twoFVal = self.run_computefstatistic_single_point(
                tref, ts, te, theta_at_tref[0], theta_at_tref[1],
                theta_at_tref[2], Alpha, Delta)
            twoFSum += twoFVal

        return twoFSum

    def compute_glitch_fstat_single(self, F0, F1, F2, Alpha, Delta, delta_F0,
                                    delta_F1, tglitch):
        """ Compute the semi-coherent glitch F-stat """

        theta = [F0, F1, F2]
        delta_theta = [delta_F0, delta_F1, 0]
        tref = self.tref

        theta_at_glitch = self.shift_coefficients(theta, tglitch - tref)
        theta_post_glitch_at_glitch = theta_at_glitch + delta_theta
        theta_post_glitch = self.shift_coefficients(
            theta_post_glitch_at_glitch, tref - tglitch)

        twoFsegA = self.run_computefstatistic_single_point(
            tref, self.tstart, tglitch, theta[0], theta[1], theta[2], Alpha,
            Delta)

        if tglitch == self.tend:
            return twoFsegA

        twoFsegB = self.run_computefstatistic_single_point(
            tref, tglitch, self.tend, theta_post_glitch[0],
            theta_post_glitch[1], theta_post_glitch[2], Alpha,
            Delta)

        return twoFsegA + twoFsegB

    def init_computefstatistic_single_point(self):
        """ Initilisation step of run_computefstatistic for a single point """

        logging.info('Initialising SFTCatalog')
        constraints = lalpulsar.SFTConstraints()
        if self.detector:
            constraints.detector = self.detector
        self.sft_filepath = self.sftdir+'/*_'+self.sftlabel+"*sft"
        SFTCatalog = lalpulsar.SFTdataFind(self.sft_filepath, constraints)
        names = list(set([d.header.name for d in SFTCatalog.data]))
        logging.info('Loaded data from detectors {}'.format(names))

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

        logging.info('Initialising FstatInput')
        dFreq = 0
        self.whatToCompute = lalpulsar.FSTATQ_ATOMS_PER_DET
        FstatOptionalArgs = lalpulsar.FstatOptionalArgsDefaults
        self.FstatInput = lalpulsar.CreateFstatInput(SFTCatalog,
                                                     self.minCoverFreq,
                                                     self.maxCoverFreq,
                                                     dFreq,
                                                     ephems,
                                                     FstatOptionalArgs
                                                     )

        logging.info('Initialising PulsarDoplerParams')
        PulsarDopplerParams = lalpulsar.PulsarDopplerParams()
        PulsarDopplerParams.refTime = self.tref
        PulsarDopplerParams.Alpha = 1
        PulsarDopplerParams.Delta = 1
        PulsarDopplerParams.fkdot = np.array([0, 0, 0, 0, 0, 0, 0])
        self.PulsarDopplerParams = PulsarDopplerParams

        logging.info('Initialising FstatResults')
        self.FstatResults = lalpulsar.FstatResults()

    def run_computefstatistic_single_point(self, tref, tstart, tend, F0, F1,
                                           F2, Alpha, Delta):
        """ Compute the F-stat fully-coherently at a single point """

        numFreqBins = 1
        self.PulsarDopplerParams.fkdot = np.array([F0, F1, F2, 0, 0, 0, 0])
        self.PulsarDopplerParams.Alpha = Alpha
        self.PulsarDopplerParams.Delta = Delta

        lalpulsar.ComputeFstat(self.FstatResults,
                               self.FstatInput,
                               self.PulsarDopplerParams,
                               numFreqBins,
                               self.whatToCompute
                               )

        windowRange = lalpulsar.transientWindowRange_t()
        windowRange.type = lalpulsar.TRANSIENT_RECTANGULAR
        windowRange.t0 = int(tstart)  # TYPE UINT4
        windowRange.t0Band = 0
        windowRange.dt0 = 1
        windowRange.tau = int(tend - tstart)  # TYPE UINT4
        windowRange.tauBand = 0
        windowRange.dtau = 1
        useFReg = False
        FS = lalpulsar.ComputeTransientFstatMap(self.FstatResults.multiFatoms[0],
                                                windowRange,
                                                useFReg)
        return 2*FS.F_mn.data[0][0]

    def compute_glitch_fstat_slow(self, F0, F1, F2, Alpha, Delta, delta_F0,
                                  delta_F1, tglitch):
        """ Compute the semi-coherent F-stat """

        theta = [F0, F1, F2]
        delta_theta = [delta_F0, delta_F1, 0]
        tref = self.tref

        theta_at_glitch = self.shift_coefficients(theta, tglitch - tref)
        theta_post_glitch_at_glitch = theta_at_glitch + delta_theta
        theta_post_glitch = self.shift_coefficients(
            theta_post_glitch_at_glitch, tref - tglitch)

        FsegA = self.run_computefstatistic_single_point_slow(
            tref, self.tstart, tglitch, theta[0], theta[1], theta[2], Alpha,
            Delta)
        FsegB = self.run_computefstatistic_single_point_slow(
            tref, tglitch, self.tend, theta_post_glitch[0],
            theta_post_glitch[1], theta_post_glitch[2], Alpha,
            Delta)

        return (FsegA + FsegB) / 2.

    def run_computefstatistic_single_point_slow(self, tref, tstart, tend, F0,
                                                F1, F2, Alpha, Delta):
        """ Compute the f statistic fully-coherently at a single point """

        c_l = []
        c_l.append("lalapps_ComputeFstatistic_v2")
        c_l.append("--Freq={}".format(F0))
        c_l.append("--f1dot={}".format(F1))
        c_l.append("--f2dot={}".format(F2))

        c_l.append("--DataFiles='{}'".format(
            self.outdir+"/*SFT_"+self.label+"*sft"))

        c_l.append("--refTime={:10.6f}".format(tref))
        c_l.append("--outputFstat='{}'".format(self.fs_file_name))

        c_l.append("--Alpha={}".format(Alpha))
        c_l.append("--Delta={}".format(Delta))

        c_l.append("--minStartTime={}".format(tstart))
        c_l.append("--maxStartTime={}".format(tend))

        logging.info("Executing: " + " ".join(c_l) + "\n")
        os.system(" ".join(c_l))

        self.read_in_fstat()

        return self.search_FS


class MCMCGlitchSearch(BaseSearchClass):
    """ MCMC search using the SemiCoherentGlitchSearch """
    @initializer
    def __init__(self, label, outdir, sftlabel, sftdir, theta, tref, tstart,
                 tend, nsteps=[100, 100, 100], nwalkers=100, ntemps=1,
                 nglitch=0, minCoverFreq=29, maxCoverFreq=31, scatter_val=1e-4,
                 betas=None, detector=None, dtglitchmin=20*86400,
                 earth_ephem=None, sun_ephem=None):
        """
        Parameters
        label, outdir: str
            A label and directory to read/write data from/to
        sftlabel, sftdir: str
            A label and directory in which to find the relevant sft file
        theta: 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.
        nglitch: int
            The number of glitches to allow
        tref, tstart, tend: int
            GPS seconds of the reference time, start time and end time
        nsteps: list (m,)
            List specifying the number of steps to take, the last two entries
            give the nburn and nprod of the 'production' run, all entries
            before are for iterative initialisation steps (usually just one)
            e.g. [1000, 1000, 500].
        dtglitchmin: int
            The minimum duration (in seconds) of a segment between two glitches
            or a glitch and the start/end of the data
        nwalkers, ntemps: int
            Number of walkers and temperatures
        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

        """

        logging.info(('Set-up MCMC search with {} glitches for model {} on'
                      ' data {}').format(self.nglitch, self.label,
                                         self.sftlabel))
        if os.path.isdir(outdir) is False:
            os.mkdir(outdir)
        self.pickle_path = '{}/{}_saved_data.p'.format(self.outdir, self.label)
        self.unpack_input_theta()
        self.ndim = len(self.theta_keys)
        self.sft_filepath = self.sftdir+'/*_'+self.sftlabel+"*sft"
        if earth_ephem is None:
            self.earth_ephem = self.earth_ephem_default
        if sun_ephem is None:
            self.sun_ephem = self.sun_ephem_default

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

        self.old_data_is_okay_to_use = self.check_old_data_is_okay_to_use()

    def inititate_search_object(self):
        logging.info('Setting up search object')
        self.search = SemiCoherentGlitchSearch(
            label=self.label, outdir=self.outdir, sftlabel=self.sftlabel,
            sftdir=self.sftdir, tref=self.tref, tstart=self.tstart,
            tend=self.tend, minCoverFreq=self.minCoverFreq,
            maxCoverFreq=self.maxCoverFreq, earth_ephem=self.earth_ephem,
            sun_ephem=self.sun_ephem, detector=self.detector,
            nglitch=self.nglitch)

    def logp(self, theta_vals, theta_prior, theta_keys, search):
        if self.nglitch > 1:
            ts = [self.tstart] + theta_vals[-self.nglitch:] + [self.tend]
            if np.array_equal(ts, np.sort(ts)) is False:
                return -np.inf
            if any(np.diff(ts) < self.dtglitchmin):
                return -np.inf

        H = [self.Generic_lnprior(**theta_prior[key])(p) for p, key in
             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]
        FS = search.compute_nglitch_fstat(*self.fixed_theta)
        return FS

    def unpack_input_theta(self):
        glitch_keys = ['delta_F0', 'delta_F1', 'tglitch']
        full_glitch_keys = list(np.array(
            [[gk]*self.nglitch for gk in glitch_keys]).flatten())
        full_theta_keys = ['F0', 'F1', 'F2', 'Alpha', 'Delta']+full_glitch_keys
        full_theta_keys_copy = copy.copy(full_theta_keys)

        glitch_symbols = ['$\delta f$', '$\delta \dot{f}$', r'$t_{glitch}$']
        full_glitch_symbols = list(np.array(
            [[gs]*self.nglitch for gs in glitch_symbols]).flatten())
        full_theta_symbols = (['$f$', '$\dot{f}$', '$\ddot{f}$', r'$\alpha$',
                               r'$\delta$'] + full_glitch_symbols)
        self.theta_prior = {}
        self.theta_keys = []
        fixed_theta_dict = {}
        for key, val in self.theta.iteritems():
            if type(val) is dict:
                self.theta_prior[key] = val
                fixed_theta_dict[key] = 0
                if key in glitch_keys:
                    for i in range(self.nglitch):
                        self.theta_keys.append(key)
                else:
                    self.theta_keys.append(key)
            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))
            if key in glitch_keys:
                for i in range(self.nglitch):
                    full_theta_keys_copy.pop(full_theta_keys_copy.index(key))
            else:
                full_theta_keys_copy.pop(full_theta_keys_copy.index(key))

        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]

        # Correct for number of glitches in the idxs
        self.theta_idxs = np.array(self.theta_idxs)
        while np.sum(self.theta_idxs[:-1] == self.theta_idxs[1:]) > 0:
            for i, idx in enumerate(self.theta_idxs):
                if idx in self.theta_idxs[:i]:
                    self.theta_idxs[i] += 1

    def check_initial_points(self, p0):
        initial_priors = np.array([
            self.logp(p, self.theta_prior, self.theta_keys, self.search)
            for p in p0[0]])
        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))

    def run(self):

        if self.old_data_is_okay_to_use is True:
            logging.warning('Using saved data from {}'.format(
                self.pickle_path))
            d = self.get_saved_data()
            self.sampler = d['sampler']
            self.samples = d['samples']
            self.lnprobs = d['lnprobs']
            self.lnlikes = d['lnlikes']
            return

        self.inititate_search_object()

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

        p0 = self.GenerateInitial()
        self.check_initial_points(p0)

        ninit_steps = len(self.nsteps) - 2
        for j, n in enumerate(self.nsteps[:-2]):
            logging.info('Running {}/{} initialisation with {} steps'.format(
                j, ninit_steps, n))
            sampler.run_mcmc(p0, n)

            fig, axes = self.PlotWalkers(sampler, symbols=self.theta_symbols)
            fig.savefig('{}/{}_init_{}_walkers.png'.format(
                self.outdir, self.label, j))

            p0 = self.get_new_p0(sampler, scatter_val=self.scatter_val)
            self.check_initial_points(p0)
            sampler.reset()

        nburn = self.nsteps[-2]
        nprod = self.nsteps[-1]
        logging.info('Running final burn and prod with {} steps'.format(
            nburn+nprod))
        sampler.run_mcmc(p0, nburn+nprod)

        fig, axes = self.PlotWalkers(sampler, symbols=self.theta_symbols)
        fig.savefig('{}/{}_walkers.png'.format(self.outdir, self.label))

        samples = sampler.chain[0, :, nburn:, :].reshape((-1, self.ndim))
        lnprobs = sampler.lnprobability[0, :, nburn:].reshape((-1))
        lnlikes = sampler.lnlikelihood[0, :, nburn:].reshape((-1))
        self.sampler = sampler
        self.samples = samples
        self.lnprobs = lnprobs
        self.lnlikes = lnlikes
        self.save_data(sampler, samples, lnprobs, lnlikes)

    def plot_corner(self, corner_figsize=(7, 7),  deltat=False,
                    add_prior=False, nstds=None, label_offset=0.4, **kwargs):

        fig, axes = plt.subplots(self.ndim, self.ndim,
                                 figsize=corner_figsize)

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

        if deltat:
            samples_plt[:, self.theta_keys.index('tglitch')] -= self.tref
            theta_symbols_plt[self.theta_keys.index('tglitch')] = (
                r'$t_{\textrm{glitch}} - t_{\textrm{ref}}$')

        if type(nstds) is int and 'range' not in kwargs:
            _range = []
            for j, s in enumerate(samples_plt.T):
                median = np.median(s)
                std = np.std(s)
                _range.append((median - nstds*std, median + nstds*std))
        else:
            _range = None

        fig_triangle = corner.corner(samples_plt,
                                     labels=theta_symbols_plt,
                                     fig=fig,
                                     bins=50,
                                     max_n_ticks=4,
                                     plot_contours=True,
                                     plot_datapoints=True,
                                     label_kwargs={'fontsize': 8},
                                     data_kwargs={'alpha': 0.1,
                                                  'ms': 0.5},
                                     range=_range,
                                     **kwargs)

        axes_list = fig_triangle.get_axes()
        axes = np.array(axes_list).reshape(self.ndim, self.ndim)
        plt.draw()
        for ax in axes[:, 0]:
            ax.yaxis.set_label_coords(-label_offset, 0.5)
        for ax in axes[-1, :]:
            ax.xaxis.set_label_coords(0.5, -label_offset)
        for ax in axes_list:
            ax.set_rasterized(True)
            ax.set_rasterization_zorder(-10)
        plt.tight_layout(h_pad=0.0, w_pad=0.0)
        fig.subplots_adjust(hspace=0.05, wspace=0.05)

        if add_prior:
            self.add_prior_to_corner(axes, samples_plt)

        fig_triangle.savefig('{}/{}_corner.png'.format(
            self.outdir, self.label))

    def add_prior_to_corner(self, axes, samples):
        for i, key in enumerate(self.theta_keys):
            ax = axes[i][i]
            xlim = ax.get_xlim()
            s = samples[:, i]
            prior = self.Generic_lnprior(**self.theta_prior[key])
            x = np.linspace(s.min(), s.max(), 100)
            ax2 = ax.twinx()
            ax2.get_yaxis().set_visible(False)
            ax2.plot(x, [prior(xi) for xi in x], '-r')
            ax.set_xlim(xlim)

    def get_new_p0(self, sampler, scatter_val=1e-3):
        """ Returns new initial positions for walkers are burn0 stage

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

        """
        if sampler.chain[:, :, -1, :].shape[0] == 1:
            ntemps_temp = 1
        else:
            ntemps_temp = self.ntemps
        pF = sampler.chain[:, :, -1, :].reshape(
            ntemps_temp, self.nwalkers, self.ndim)[0, :, :]
        lnp = sampler.lnprobability[:, :, -1].reshape(
            self.ntemps, self.nwalkers)[0, :]
        if any(np.isnan(lnp)):
            logging.warning("The sampler has produced nan's")

        p = pF[np.nanargmax(lnp)]
        p0 = [[p + scatter_val * p * np.random.randn(self.ndim)
              for i in xrange(self.nwalkers)] for j in xrange(self.ntemps)]
        if self.nglitch > 1:
            p0 = np.array(p0)
            p0[:, :, -self.nglitch:] = np.sort(p0[:, :, -self.nglitch:],
                                               axis=2)
        return p0

    def Generic_lnprior(self, **kwargs):
        """ Return a lambda function of the pdf

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

        """

        def logunif(x, a, b):
            above = x < b
            below = x > a
            if type(above) is not np.ndarray:
                if above and below:
                    return -np.log(b-a)
                else:
                    return -np.inf
            else:
                idxs = np.array([all(tup) for tup in zip(above, below)])
                p = np.zeros(len(x)) - np.inf
                p[idxs] = -np.log(b-a)
                return p

        def halfnorm(x, loc, scale):
            if x < 0:
                return -np.inf
            else:
                return -0.5*((x-loc)**2/scale**2+np.log(0.5*np.pi*scale**2))

        def cauchy(x, x0, gamma):
            return 1.0/(np.pi*gamma*(1+((x-x0)/gamma)**2))

        def exp(x, x0, gamma):
            if x > x0:
                return np.log(gamma) - gamma*(x - x0)
            else:
                return -np.inf

        if kwargs['type'] == 'unif':
            return lambda x: logunif(x, kwargs['lower'], kwargs['upper'])
        elif kwargs['type'] == 'halfnorm':
            return lambda x: halfnorm(x, kwargs['loc'], kwargs['scale'])
        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")

    def GenerateRV(self, **kwargs):
        dist_type = kwargs.pop('type')
        if dist_type == "unif":
            return np.random.uniform(low=kwargs['lower'], high=kwargs['upper'])
        if dist_type == "norm":
            return np.random.normal(loc=kwargs['loc'], scale=kwargs['scale'])
        if dist_type == "halfnorm":
            return np.abs(np.random.normal(loc=kwargs['loc'],
                                           scale=kwargs['scale']))
        if dist_type == "lognorm":
            return np.random.lognormal(
                mean=kwargs['loc'], sigma=kwargs['scale'])
        else:
            raise ValueError("dist_type {} unknown".format(dist_type))

    def PlotWalkers(self, sampler, symbols=None, alpha=0.4, color="k", temp=0,