plot.py 44.2 KB
Newer Older
Sebastian Heimann's avatar
Sebastian Heimann committed
1
import math
2
import re
Sebastian Heimann's avatar
Sebastian Heimann committed
3
import random
4
import logging
5
import os
Sebastian Heimann's avatar
Sebastian Heimann committed
6
7
8
import os.path as op
import numpy as num
from scipy import signal
9
from pyrocko import beachball, guts, trace, util, gf
10
from pyrocko import hudson
Sebastian Heimann's avatar
Sebastian Heimann committed
11
12
13
from grond import core
from matplotlib import pyplot as plt
from matplotlib import cm, patches
14
from pyrocko.cake_plot import colors, \
Sebastian Heimann's avatar
Sebastian Heimann committed
15
16
    str_to_mpl_color as scolor, light

17
18
from pyrocko.plot import mpl_init, mpl_papersize, mpl_margins

19
20
logger = logging.getLogger('grond.plot')

Sebastian Heimann's avatar
Sebastian Heimann committed
21
22
23
km = 1000.


24
25
26
27
28
29
30
31
32
33
34
35
36
def amp_spec_max(spec_trs, key):
    amaxs = {}
    for spec_tr in spec_trs:
        amax = num.max(num.abs(spec_tr.ydata))
        k = key(spec_tr)
        if k not in amaxs:
            amaxs[k] = amax
        else:
            amaxs[k] = max(amaxs[k], amax)

    return amaxs


Sebastian Heimann's avatar
Sebastian Heimann committed
37
38
39
40
41
42
43
def ordersort(x):
    isort = num.argsort(x)
    iorder = num.empty(isort.size)
    iorder[isort] = num.arange(isort.size)
    return iorder


Sebastian Heimann's avatar
Sebastian Heimann committed
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
def nextpow2(i):
    return 2**int(math.ceil(math.log(i)/math.log(2.)))


def fixlim(lo, hi):
    if lo == hi:
        return lo - 1.0, hi + 1.0
    else:
        return lo, hi


def str_dist(dist):
    if dist < 10.0:
        return '%g m' % dist
    elif 10. <= dist < 1.*km:
        return '%.0f m' % dist
    elif 1.*km <= dist < 10.*km:
        return '%.1f km' % (dist / km)
    else:
        return '%.0f km' % (dist / km)


def str_duration(t):
Sebastian Heimann's avatar
Sebastian Heimann committed
67
68
69
    s = ''
    if t < 0.:
        s = '-'
Sebastian Heimann's avatar
Sebastian Heimann committed
70

Sebastian Heimann's avatar
Sebastian Heimann committed
71
    t = abs(t)
Sebastian Heimann's avatar
Sebastian Heimann committed
72

Sebastian Heimann's avatar
Sebastian Heimann committed
73
74
    if t < 10.0:
        return s + '%.2g s' % t
Sebastian Heimann's avatar
Sebastian Heimann committed
75
    elif 10.0 <= t < 3600.:
Sebastian Heimann's avatar
Sebastian Heimann committed
76
77
78
79
80
        return s + util.time_to_str(t, format='%M:%S min')
    elif 3600. <= t < 24*3600.:
        return s + util.time_to_str(t, format='%H:%M h')
    else:
        return s + '%.1f d' % (t / (24.*3600.))
Sebastian Heimann's avatar
Sebastian Heimann committed
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


def eigh_sorted(mat):
    evals, evecs = num.linalg.eigh(mat)
    iorder = num.argsort(evals)
    return evals[iorder], evecs[:, iorder]


class GrondModel(object):
    def __init__(self, **kwargs):
        self.listeners = []
        self.set_problem(None)

    def add_listener(self, listener):
        self.listeners.append(listener)

    def set_problem(self, problem):

        self.problem = problem
        if problem:
            nparameters = problem.nparameters
            ntargets = problem.ntargets
        else:
            nparameters = 0
            ntargets = 0

        nmodels = 0
        nmodels_capacity = 1024

        self._xs_buffer = num.zeros(
            (nmodels_capacity, nparameters), dtype=num.float)
        self._misfits_buffer = num.zeros(
            (nmodels_capacity, ntargets, 2), dtype=num.float)

        self.xs = self._xs_buffer[:nmodels, :]
        self.misfits = self._misfits_buffer[:nmodels, :, :]

        self.data_changed()

    @property
    def nmodels(self):
        return self.xs.shape[0]

    @property
    def nmodels_capacity(self):
        return self._xs_buffer.shape[0]

    def append(self, xs, misfits):
        assert xs.shape[0] == misfits.shape[0]

        nmodels_add = xs.shape[0]

        nmodels = self.nmodels
        nmodels_new = nmodels + nmodels_add
        nmodels_capacity_new = max(1024, nextpow2(nmodels_new))

        nmodels_capacity = self.nmodels_capacity
        if nmodels_capacity_new > nmodels_capacity:
            xs_buffer = num.zeros(
                (nmodels_capacity_new, self.problem.nparameters),
                dtype=num.float)

            misfits_buffer = num.zeros(
                (nmodels_capacity_new, self.problem.ntargets, 2),
                dtype=num.float)

            xs_buffer[:nmodels, :] = self._xs_buffer[:nmodels]
            misfits_buffer[:nmodels, :] = self._misfits_buffer[:nmodels]
            self._xs_buffer = xs_buffer
            self._misfits_buffer = misfits_buffer

        self._xs_buffer[nmodels:nmodels+nmodels_add, :] = xs
        self._misfits_buffer[nmodels:nmodels+nmodels_add, :, :] = misfits

        nmodels = nmodels_new

        self.xs = self._xs_buffer[:nmodels, :]
        self.misfits = self._misfits_buffer[:nmodels, :, :]

        self.data_changed()

    def data_changed(self):
        for listener in self.listeners:
            listener()


def draw_sequence_figures(model, plt, misfit_cutoff=None):
    problem = model.problem

    imodels = num.arange(model.nmodels)
    bounds = problem.bounds() + problem.dependant_bounds()

    xref = problem.pack(problem.base_source)

    xs = model.xs

    npar = problem.nparameters
    ndep = problem.ndependants

    gms = problem.global_misfits(model.misfits)
    gms_softclip = num.where(gms > 1.0, 0.2 * num.log10(gms) + 1.0, gms)

    isort = num.argsort(gms)[::-1]

    imodels = imodels[isort]
    gms = gms[isort]
    gms_softclip = gms_softclip[isort]
    xs = xs[isort, :]

    iorder = num.empty_like(isort)
    iorder = num.arange(iorder.size)

    if misfit_cutoff is None:
        ibest = num.ones(gms.size, dtype=num.bool)
    else:
        ibest = gms < misfit_cutoff

198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
    def config_axes(axes, nfx, nfy, impl, iplot, nplots):
        if (impl - 1) % nfx != nfx - 1:
            axes.get_yaxis().tick_left()

        if (impl - 1) >= (nfx * (nfy-1)) or iplot >= nplots - nfx:
            axes.set_xlabel('Iteration')
            if not (impl - 1) / nfx == 0:
                axes.get_xaxis().tick_bottom()
        elif (impl - 1) / nfx == 0:
            axes.get_xaxis().tick_top()
            axes.set_xticklabels([])
        else:
            axes.get_xaxis().set_visible(False)

    fontsize = 10.0

Sebastian Heimann's avatar
Sebastian Heimann committed
214
    nfx = 2
215
    nfy = 3
Sebastian Heimann's avatar
Sebastian Heimann committed
216
217
218
    # nfz = (npar + ndep + 1 - 1) / (nfx*nfy) + 1
    cmap = cm.YlOrRd
    cmap = cm.jet
219
    msize = 1.5
Sebastian Heimann's avatar
Sebastian Heimann committed
220
    axes = None
221
    figs = []
Sebastian Heimann's avatar
Sebastian Heimann committed
222
223
224
225
226
227
    fig = None
    alpha = 0.5
    for ipar in xrange(npar):
        impl = ipar % (nfx*nfy) + 1

        if impl == 1:
228
229
230
            fig = plt.figure(figsize=mpl_papersize('a5', 'landscape'))
            labelpos = mpl_margins(fig, nw=nfx, nh=nfy, w=7., h=5., wspace=7.,
                                   hspace=2., units=fontsize)
231
            figs.append(fig)
Sebastian Heimann's avatar
Sebastian Heimann committed
232
233
234

        par = problem.parameters[ipar]

235
236
237
        axes = fig.add_subplot(nfy, nfx, impl)
        labelpos(axes, 2.5, 2.0)

Sebastian Heimann's avatar
Sebastian Heimann committed
238
239
        axes.set_ylabel(par.get_label())
        axes.get_yaxis().set_major_locator(plt.MaxNLocator(4))
240
241

        config_axes(axes, nfx, nfy, impl, ipar, npar+ndep+1)
Sebastian Heimann's avatar
Sebastian Heimann committed
242
243
244
245
246

        axes.set_ylim(*fixlim(*par.scaled(bounds[ipar])))
        axes.set_xlim(0, model.nmodels)

        axes.scatter(
247
248
249
250
            imodels[ibest], par.scaled(xs[ibest, ipar]), s=msize,
            c=iorder[ibest], edgecolors='none', cmap=cmap, alpha=alpha)

        axes.axhline(par.scaled(xref[ipar]), color='black', alpha=0.3)
Sebastian Heimann's avatar
Sebastian Heimann committed
251
252
253
254
255
256

    for idep in xrange(ndep):
        # ifz, ify, ifx = num.unravel_index(ipar, (nfz, nfy, nfx))
        impl = (npar+idep) % (nfx*nfy) + 1

        if impl == 1:
257
258
259
            fig = plt.figure(figsize=mpl_papersize('a5', 'landscape'))
            labelpos = mpl_margins(fig, nw=nfx, nh=nfy, w=7., h=5., wspace=7.,
                                   hspace=2., units=fontsize)
260
            figs.append(fig)
Sebastian Heimann's avatar
Sebastian Heimann committed
261
262
263

        par = problem.dependants[idep]

264
265
266
        axes = fig.add_subplot(nfy, nfx, impl)
        labelpos(axes, 2.5, 2.0)

Sebastian Heimann's avatar
Sebastian Heimann committed
267
268
        axes.set_ylabel(par.get_label())
        axes.get_yaxis().set_major_locator(plt.MaxNLocator(4))
269
270
271

        config_axes(axes, nfx, nfy, impl, npar+idep, npar+ndep+1)

Sebastian Heimann's avatar
Sebastian Heimann committed
272
273
274
275
276
        axes.set_ylim(*fixlim(*par.scaled(bounds[npar+idep])))
        axes.set_xlim(0, model.nmodels)

        ys = problem.make_dependant(xs[ibest, :], par.name)
        axes.scatter(
277
278
279
280
281
            imodels[ibest], par.scaled(ys), s=msize, c=iorder[ibest],
            edgecolors='none', cmap=cmap, alpha=alpha)

        y = problem.make_dependant(xref, par.name)
        axes.axhline(par.scaled(y), color='black', alpha=0.3)
Sebastian Heimann's avatar
Sebastian Heimann committed
282
283
284

    impl = (npar+ndep) % (nfx*nfy) + 1
    if impl == 1:
285
286
287
        fig = plt.figure(figsize=mpl_papersize('a5', 'landscape'))
        labelpos = mpl_margins(fig, nw=nfx, nh=nfy, w=7., h=5., wspace=7.,
                               hspace=2., units=fontsize)
288
        figs.append(fig)
Sebastian Heimann's avatar
Sebastian Heimann committed
289

290
291
292
293
    axes = fig.add_subplot(nfy, nfx, impl)
    labelpos(axes, 2.5, 2.0)

    config_axes(axes, nfx, nfy, impl, npar+ndep, npar+ndep+1)
Sebastian Heimann's avatar
Sebastian Heimann committed
294
295
296
297
298
299
300

    axes.set_ylim(0., 1.5)
    axes.set_yticks([0., 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4])
    axes.set_yticklabels(['0.0', '0.2', '0.4', '0.6', '0.8', '1', '10', '100'])

    axes.scatter(
        imodels[ibest], gms_softclip[ibest], c=iorder[ibest],
301
302
303
304
        s=msize, edgecolors='none', cmap=cmap, alpha=alpha)

    axes.axhspan(1.0, 1.5, color=(0.8, 0.8, 0.8), alpha=0.2)
    axes.axhline(1.0, color=(0.5, 0.5, 0.5), zorder=2)
Sebastian Heimann's avatar
Sebastian Heimann committed
305
306
307
308
309
310

    axes.set_xlim(0, model.nmodels)
    axes.set_xlabel('Iteration')

    axes.set_ylabel('Misfit')

311
    return figs
Sebastian Heimann's avatar
Sebastian Heimann committed
312
313
314


def draw_jointpar_figures(
315
        model, plt, misfit_cutoff=None, ibootstrap=None, color=None,
316
        exclude=None, include=None):
317

318
    color = 'misfit'
Sebastian Heimann's avatar
Sebastian Heimann committed
319
    # exclude = ['duration']
320
    # include = ['magnitude', 'rel_moment_iso', 'rel_moment_clvd', 'depth']
321
322
    neach = 6
    figsize = (8, 8)
Sebastian Heimann's avatar
Sebastian Heimann committed
323
324
    # cmap = cm.YlOrRd
    # cmap = cm.jet
325
    cmap = cm.coolwarm
Sebastian Heimann's avatar
Sebastian Heimann committed
326
327
328

    problem = model.problem
    if not problem:
329
        return []
Sebastian Heimann's avatar
Sebastian Heimann committed
330
331
332
333

    xs = model.xs

    bounds = problem.bounds() + problem.dependant_bounds()
Sebastian Heimann's avatar
wip    
Sebastian Heimann committed
334
335
336
337
338
339
340
341
    for ipar in xrange(problem.ncombined):
        par = problem.combined[ipar]
        lo, hi = bounds[ipar]
        if lo == hi:
            if exclude is None:
                exclude = []

            exclude.append(par.name)
Sebastian Heimann's avatar
Sebastian Heimann committed
342
343
344
345
346
347
348
349
350
351
352
353
354

    xref = problem.pack(problem.base_source)

    if ibootstrap is not None:
        gms = problem.bootstrap_misfits(model.misfits, ibootstrap)
    else:
        gms = problem.global_misfits(model.misfits)

    isort = num.argsort(gms)[::-1]

    gms = gms[isort]
    xs = xs[isort, :]

Sebastian Heimann's avatar
Sebastian Heimann committed
355
    if misfit_cutoff is not None:
Sebastian Heimann's avatar
Sebastian Heimann committed
356
        ibest = gms < misfit_cutoff
Sebastian Heimann's avatar
Sebastian Heimann committed
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
        gms = gms[ibest]
        xs = xs[ibest]

    nmodels = xs.shape[0]

    if color == 'dist':
        mx = num.mean(xs, axis=0)
        cov = num.cov(xs.T)
        mdists = core.mahalanobis_distance(xs, mx, cov)
        color = ordersort(mdists)

    elif color == 'misfit':
        iorder = num.arange(nmodels)
        color = iorder

    elif color in problem.parameter_names:
        ind = problem.name_to_index(color)
        color = ordersort(problem.extract(xs, ind))
Sebastian Heimann's avatar
Sebastian Heimann committed
375

376
377
378
379
    smap = {}
    iselected = 0
    for ipar in xrange(problem.ncombined):
        par = problem.combined[ipar]
Sebastian Heimann's avatar
wip    
Sebastian Heimann committed
380
381
        if exclude and par.name in exclude or \
                include and par.name not in include:
382
            continue
Sebastian Heimann's avatar
Sebastian Heimann committed
383

384
385
386
387
        smap[iselected] = ipar
        iselected += 1

    nselected = iselected
Sebastian Heimann's avatar
Sebastian Heimann committed
388

389
390
391
392
    if nselected < 2:
        logger.warn('cannot draw joinpar figures with less than two '
                    'parameters selected')
        return []
393
394

    nfig = (nselected-2) / neach + 1
Sebastian Heimann's avatar
Sebastian Heimann committed
395
396
397
398
399
400

    figs = []
    for ifig in xrange(nfig):
        figs_row = []
        for jfig in xrange(nfig):
            if ifig >= jfig:
401
                figs_row.append(plt.figure(figsize=figsize))
Sebastian Heimann's avatar
Sebastian Heimann committed
402
403
404
405
406
            else:
                figs_row.append(None)

        figs.append(figs_row)

407
408
    for iselected in xrange(nselected):
        ipar = smap[iselected]
Sebastian Heimann's avatar
Sebastian Heimann committed
409
        ypar = problem.combined[ipar]
410
411
        for jselected in xrange(iselected):
            jpar = smap[jselected]
Sebastian Heimann's avatar
Sebastian Heimann committed
412
413
            xpar = problem.combined[jpar]

414
415
            ixg = (iselected - 1)
            iyg = jselected
Sebastian Heimann's avatar
Sebastian Heimann committed
416
417
418
419
420
421
422
423
424
425
426
427
428

            ix = ixg % neach
            iy = iyg % neach

            ifig = ixg/neach
            jfig = iyg/neach

            aind = (neach, neach, (ix * neach) + iy + 1)

            fig = figs[ifig][jfig]

            axes = fig.add_subplot(*aind)

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
            axes.axvline(0., color=scolor('aluminium3'), lw=0.5)
            axes.axhline(0., color=scolor('aluminium3'), lw=0.5)
            for spine in axes.spines.values():
                spine.set_edgecolor(scolor('aluminium5'))
                spine.set_linewidth(0.5)

            xmin, xmax = fixlim(*xpar.scaled(bounds[jpar]))
            ymin, ymax = fixlim(*ypar.scaled(bounds[ipar]))

            if ix == 0 or jselected + 1 == iselected:
                for (xpos, xoff, x) in [(0.0, 10., xmin), (1.0, -10., xmax)]:
                    axes.annotate(
                        '%.2g%s' % (x, xpar.get_unit_suffix()),
                        xy=(xpos, 1.05),
                        xycoords='axes fraction',
                        xytext=(xoff, 5.),
                        textcoords='offset points',
                        verticalalignment='bottom',
                        horizontalalignment='left',
                        rotation=45.)

            if iy == neach - 1 or jselected + 1 == iselected:
                for (ypos, yoff, y) in [(0., 10., ymin), (1.0, -10., ymax)]:
                    axes.annotate(
                        '%.2g%s' % (y, ypar.get_unit_suffix()),
                        xy=(1.0, ypos),
                        xycoords='axes fraction',
                        xytext=(5., yoff),
                        textcoords='offset points',
                        verticalalignment='bottom',
                        horizontalalignment='left',
                        rotation=45.)

            axes.set_xlim(xmin, xmax)
            axes.set_ylim(ymin, ymax)
Sebastian Heimann's avatar
Sebastian Heimann committed
464
465
466
467

            axes.get_xaxis().set_ticks([])
            axes.get_yaxis().set_ticks([])

468
            if iselected == nselected - 1 or ix == neach - 1:
Sebastian Heimann's avatar
Sebastian Heimann committed
469
                axes.annotate(
470
                    xpar.get_label(with_unit=False),
Sebastian Heimann's avatar
Sebastian Heimann committed
471
472
473
474
475
476
477
478
                    xy=(0.5, -0.05),
                    xycoords='axes fraction',
                    verticalalignment='top',
                    horizontalalignment='right',
                    rotation=45.)

            if iy == 0:
                axes.annotate(
479
                    ypar.get_label(with_unit=False),
Sebastian Heimann's avatar
Sebastian Heimann committed
480
481
                    xy=(-0.05, 0.5),
                    xycoords='axes fraction',
482
483
484
                    verticalalignment='top',
                    horizontalalignment='right',
                    rotation=45.)
Sebastian Heimann's avatar
Sebastian Heimann committed
485

Sebastian Heimann's avatar
Sebastian Heimann committed
486
487
            fx = problem.extract(xs, jpar)
            fy = problem.extract(xs, ipar)
Sebastian Heimann's avatar
Sebastian Heimann committed
488
489
490
491
492

            axes.scatter(
                xpar.scaled(fx),
                ypar.scaled(fy),
                c=color,
493
                s=3, alpha=0.5, cmap=cmap, edgecolors='none')
Sebastian Heimann's avatar
Sebastian Heimann committed
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508

            cov = num.cov((xpar.scaled(fx), ypar.scaled(fy)))
            evals, evecs = eigh_sorted(cov)
            evals = num.sqrt(evals)
            ell = patches.Ellipse(
                xy=(num.mean(xpar.scaled(fx)), num.mean(ypar.scaled(fy))),
                width=evals[0]*2,
                height=evals[1]*2,
                angle=num.rad2deg(num.arctan2(evecs[1][0], evecs[0][0])))

            ell.set_facecolor('none')
            axes.add_artist(ell)

            fx = problem.extract(xref, jpar)
            fy = problem.extract(xref, ipar)
509
510
511
512
513
514
515

            ref_color = scolor('aluminium6')
            ref_color_light = 'none'
            axes.plot(
                xpar.scaled(fx), ypar.scaled(fy), 's',
                mew=1.5, ms=5, color=ref_color_light, mec=ref_color)

516
517
518
519
520
521
    figs_flat = []
    for figs_row in figs:
        figs_flat.extend(fig for fig in figs_row if fig is not None)

    return figs_flat

Sebastian Heimann's avatar
Sebastian Heimann committed
522
523
524
525

def draw_solution_figure(
        model, plt, misfit_cutoff=None, beachball_type='full'):

Sebastian Heimann's avatar
Sebastian Heimann committed
526
527
528
529
530
    fontsize = 10.

    fig = plt.figure(figsize=(6, 2))
    axes = fig.add_subplot(1, 1, 1, aspect=1.0)
    fig.subplots_adjust(left=0., right=1., bottom=0., top=1.)
Sebastian Heimann's avatar
Sebastian Heimann committed
531
532
533

    problem = model.problem
    if not problem:
534
535
        logger.warn('problem not set')
        return []
Sebastian Heimann's avatar
Sebastian Heimann committed
536
537
538
539

    xs = model.xs

    if xs.size == 0:
540
541
        logger.warn('empty models vector')
        return []
Sebastian Heimann's avatar
Sebastian Heimann committed
542
543
544
545
546
547

    gms = problem.global_misfits(model.misfits)
    isort = num.argsort(gms)
    iorder = num.empty_like(isort)
    iorder[isort] = num.arange(iorder.size)[::-1]

Sebastian Heimann's avatar
Sebastian Heimann committed
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
    mean_source = core.get_mean_source(problem, model.xs)
    best_source = core.get_best_source(problem, model.xs, model.misfits)
    ref_source = problem.base_source

    for xpos, label in [
            (0., 'Full'),
            (2., 'Isotropic'),
            (4., 'Deviatoric'),
            (6., 'CLVD'),
            (8., 'DC')]:

        axes.annotate(
            label,
            xy=(1+xpos, 3),
            xycoords='data',
            xytext=(0., 0.),
            textcoords='offset points',
            ha='center',
            va='center',
            color='black',
            fontsize=fontsize)

    decos = []
    for source in [best_source, mean_source, ref_source]:
        mt = source.pyrocko_moment_tensor()
        deco = mt.standard_decomposition()
        decos.append(deco)

    moment_full_max = max(deco[-1][0] for deco in decos)

    for ypos, label, deco, color_t in [
            (2., 'Ensemble best', decos[0], scolor('aluminium5')),
            (1., 'Ensemble mean', decos[1], scolor('scarletred1')),
            (0., 'Reference', decos[2], scolor('aluminium3'))]:

        [(moment_iso, ratio_iso, m_iso),
         (moment_dc, ratio_dc, m_dc),
         (moment_clvd, ratio_clvd, m_clvd),
         (moment_devi, ratio_devi, m_devi),
         (moment_full, ratio_full, m_full)] = deco

        size0 = moment_full / moment_full_max

        axes.annotate(
            label,
            xy=(-2., ypos),
            xycoords='data',
            xytext=(0., 0.),
            textcoords='offset points',
            ha='left',
            va='center',
            color='black',
            fontsize=fontsize)

        for xpos, mt_part, ratio, ops in [
                (0., m_full, ratio_full, '-'),
                (2., m_iso, ratio_iso, '='),
                (4., m_devi, ratio_devi, '='),
                (6., m_clvd, ratio_clvd, '+'),
                (8., m_dc, ratio_dc, None)]:

609
            if ratio > 1e-4:
610
611
612
613
614
615
616
617
618
619
620
621
                try:
                    beachball.plot_beachball_mpl(
                        mt_part, axes,
                        beachball_type='full',
                        position=(1.+xpos, ypos),
                        size=0.9*size0*math.sqrt(ratio),
                        size_units='data',
                        color_t=color_t,
                        linewidth=1.0)

                except beachball.BeachballError, e:
                    logger.warn(str(e))
Sebastian Heimann's avatar
Sebastian Heimann committed
622

623
624
625
626
627
628
629
630
                    axes.annotate(
                        'ERROR',
                        xy=(1.+xpos, ypos),
                        ha='center',
                        va='center',
                        color='red',
                        fontsize=fontsize)

Sebastian Heimann's avatar
Sebastian Heimann committed
631
632
633
            else:
                axes.annotate(
                    'N/A',
Sebastian Heimann's avatar
Sebastian Heimann committed
634
                    xy=(1.+xpos, ypos),
Sebastian Heimann's avatar
Sebastian Heimann committed
635
636
637
638
639
640
641
642
643
644
645
646
647
                    ha='center',
                    va='center',
                    color='black',
                    fontsize=fontsize)

            if ops is not None:
                axes.annotate(
                    ops,
                    xy=(2. + xpos, ypos),
                    ha='center',
                    va='center',
                    color='black',
                    fontsize=fontsize)
Sebastian Heimann's avatar
Sebastian Heimann committed
648
649

    axes.axison = False
Sebastian Heimann's avatar
Sebastian Heimann committed
650
651
    axes.set_xlim(-2.25, 9.75)
    axes.set_ylim(-0.5, 3.5)
Sebastian Heimann's avatar
Sebastian Heimann committed
652

653
654
    return [fig]

Sebastian Heimann's avatar
Sebastian Heimann committed
655
656
657

def draw_contributions_figure(model, plt):

658
659
660
661
662
    fontsize = 10.

    fig = plt.figure(figsize=mpl_papersize('a5', 'landscape'))
    labelpos = mpl_margins(fig, nw=2, nh=2, w=7., h=5., wspace=2.,
                           hspace=5., units=fontsize)
Sebastian Heimann's avatar
Sebastian Heimann committed
663
664
665

    problem = model.problem
    if not problem:
666
667
        logger.warn('problem not set')
        return []
Sebastian Heimann's avatar
Sebastian Heimann committed
668
669
670
671

    xs = model.xs

    if xs.size == 0:
672
673
        logger.warn('empty models vector')
        return []
Sebastian Heimann's avatar
Sebastian Heimann committed
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693

    imodels = num.arange(model.nmodels)

    gms = problem.global_misfits(model.misfits)**2

    isort = num.argsort(gms)[::-1]

    gms = gms[isort]

    gms_softclip = num.where(gms > 1.0, 0.1 * num.log10(gms) + 1.0, gms)

    gcms = problem.global_contributions(model.misfits)
    gcms = gcms[isort, :]

    jsort = num.argsort(gcms[-1, :])[::-1]

    # ncols = 4
    # nrows = ((problem.ntargets + 1) - 1) / ncols + 1

    axes = fig.add_subplot(2, 2, 1)
694
695
    labelpos(axes, 2.5, 2.0)

Sebastian Heimann's avatar
Sebastian Heimann committed
696
697
698
699
    axes.set_ylabel('Relative contribution (smoothed)')
    axes.set_ylim(0.0, 1.0)

    axes2 = fig.add_subplot(2, 2, 3, sharex=axes)
700
701
    labelpos(axes2, 2.5, 2.0)

Sebastian Heimann's avatar
Sebastian Heimann committed
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
    axes2.set_xlabel('Tested model, sorted descending by global misfit value')

    axes2.set_ylabel('Square of misfit')

    axes2.set_ylim(0., 1.5)
    axes2.axhspan(1.0, 1.5, color=(0.8, 0.8, 0.8))
    axes2.set_yticks([0., 0.2, 0.4, 0.6, 0.8, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5])
    axes2.set_yticklabels(
        ['0.0', '0.2', '0.4', '0.6', '0.8', '1', '10', '100', '1000', '10000',
         '100000'])

    axes2.set_xlim(imodels[0], imodels[-1])

    rel_ms_sum = num.zeros(model.nmodels)
    rel_ms_smooth_sum = num.zeros(model.nmodels)
    ms_smooth_sum = num.zeros(model.nmodels)
    b = num.hanning(100)
    b /= num.sum(b)
    a = [1]
    ii = 0
    for itarget in jsort:
        target = problem.targets[itarget]
        ms = gcms[:, itarget]
        ms = num.where(num.isfinite(ms), ms, 0.0)
        if num.all(ms == 0.0):
            continue

        rel_ms = ms / gms

        rel_ms_smooth = signal.filtfilt(b, a, rel_ms)

        ms_smooth = rel_ms_smooth * gms_softclip

        rel_poly_y = num.concatenate(
            [rel_ms_smooth_sum[::-1], rel_ms_smooth_sum + rel_ms_smooth])
        poly_x = num.concatenate([imodels[::-1], imodels])

739
740
741
742
743
        add_args = {}
        if ii < 20:
            add_args['label'] = '%s (%.2g)' % (
                target.string_id(), num.mean(rel_ms[-1]))

Sebastian Heimann's avatar
Sebastian Heimann committed
744
745
746
747
        axes.fill(
            poly_x, rel_poly_y,
            alpha=0.5,
            color=colors[ii % len(colors)],
748
            **add_args)
Sebastian Heimann's avatar
Sebastian Heimann committed
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763

        poly_y = num.concatenate(
            [ms_smooth_sum[::-1], ms_smooth_sum + ms_smooth])

        axes2.fill(poly_x, poly_y, alpha=0.5, color=colors[ii % len(colors)])

        rel_ms_sum += rel_ms

        # axes.plot(imodels, rel_ms_sum, color='black', alpha=0.1, zorder=-1)

        ms_smooth_sum += ms_smooth
        rel_ms_smooth_sum += rel_ms_smooth
        ii += 1

    axes.legend(
764
        title='Contributions (top twenty)',
Sebastian Heimann's avatar
Sebastian Heimann committed
765
766
        bbox_to_anchor=(1.05, 0.0, 1.0, 1.0),
        loc='upper left',
767
        ncol=1, borderaxespad=0., prop={'size': 9})
Sebastian Heimann's avatar
Sebastian Heimann committed
768
769
770
771

    axes2.plot(imodels, gms_softclip, color='black')
    axes2.axhline(1.0, color=(0.5, 0.5, 0.5))

772
773
    return [fig]

Sebastian Heimann's avatar
Sebastian Heimann committed
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797

def draw_bootstrap_figure(model, plt):

    fig = plt.figure()

    problem = model.problem
    gms = problem.global_misfits(model.misfits)

    imodels = num.arange(model.nmodels)

    axes = fig.add_subplot(1, 1, 1)

    gms_softclip = num.where(gms > 1.0, 0.1 * num.log10(gms) + 1.0, gms)

    ibests = []
    for ibootstrap in xrange(problem.nbootstrap):
        bms = problem.bootstrap_misfits(model.misfits, ibootstrap)
        isort_bms = num.argsort(bms)[::-1]

        ibests.append(isort_bms[-1])

        bms_softclip = num.where(bms > 1.0, 0.1 * num.log10(bms) + 1.0, bms)
        axes.plot(imodels, bms_softclip[isort_bms], color='red', alpha=0.2)

Sebastian Heimann's avatar
Sebastian Heimann committed
798
799
800
801
802
803
804
805
806
807
808
809
810
    isort = num.argsort(gms)[::-1]
    iorder = num.empty(isort.size)
    iorder[isort] = imodels

    axes.plot(iorder[ibests], gms_softclip[ibests], 'x', color='black')

    m = num.median(gms[ibests])
    s = num.std(gms[ibests])

    axes.axhline(m+s, color='black', alpha=0.5)
    axes.axhline(m, color='black')
    axes.axhline(m-s, color='black', alpha=0.5)

Sebastian Heimann's avatar
Sebastian Heimann committed
811
812
    axes.plot(imodels, gms_softclip[isort], color='black')

Sebastian Heimann's avatar
Sebastian Heimann committed
813
814
    axes.set_xlim(imodels[0], imodels[-1])
    axes.set_xlabel('Tested model, sorted descending by global misfit value')
Sebastian Heimann's avatar
Sebastian Heimann committed
815

816
817
    return [fig]

818

Sebastian Heimann's avatar
Sebastian Heimann committed
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
def gather(l, key, sort=None, filter=None):
    d = {}
    for x in l:
        if filter is not None and not filter(x):
            continue

        k = key(x)
        if k not in d:
            d[k] = []

        d[k].append(x)

    if sort is not None:
        for v in d.itervalues():
            v.sort(key=sort)

    return d


def plot_trace(axes, tr, **kwargs):
    return axes.plot(tr.get_xdata(), tr.get_ydata(), **kwargs)


def plot_taper(axes, t, taper, **kwargs):
    y = num.ones(t.size) * 0.9
    taper(y, t[0], t[1] - t[0])
    y2 = num.concatenate((y, -y[::-1]))
    t2 = num.concatenate((t, t[::-1]))
    axes.fill(t2, y2, **kwargs)


850
def plot_dtrace(axes, tr, space, mi, ma, **kwargs):
Sebastian Heimann's avatar
Sebastian Heimann committed
851
852
    t = tr.get_xdata()
    y = tr.get_ydata()
853
854
    y2 = (num.concatenate((y, num.zeros(y.size))) - mi) / \
        (ma-mi) * space - (1.0 + space)
Sebastian Heimann's avatar
Sebastian Heimann committed
855
    t2 = num.concatenate((t, t[::-1]))
856
    axes.fill(
Sebastian Heimann's avatar
Sebastian Heimann committed
857
858
859
860
        t2, y2,
        clip_on=False,
        **kwargs)

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
def plot_spectrum(
        axes, spec_syn, spec_obs, fmin, fmax, space, mi, ma,
        syn_color='red', obs_color='black',
        syn_lw=1.5, obs_lw=1.0, color_vline='gray', fontsize=9.):

    fpad = (fmax - fmin) / 6.

    for spec, color, lw in [
            (spec_syn, syn_color, syn_lw),
            (spec_obs, obs_color, obs_lw)]:

        f = spec.get_xdata()
        mask = num.logical_and(fmin - fpad <= f, f <= fmax + fpad)

        f = f[mask]
        y = num.abs(spec.get_ydata())[mask]

        y2 = (num.concatenate((y, num.zeros(y.size))) - mi) / \
            (ma-mi) * space - (1.0 + space)
        f2 = num.concatenate((f, f[::-1]))
        axes2 = axes.twiny()
        axes2.set_axis_off()

        axes2.set_xlim(fmin - fpad * 5, fmax + fpad * 5)

        axes2.plot(f2, y2, clip_on=False, color=color, lw=lw)
        axes2.fill(f2, y2, alpha=0.1, clip_on=False, color=color)

    axes2.plot([fmin, fmin], [-1.0 - space, -1.0], color=color_vline)
    axes2.plot([fmax, fmax], [-1.0 - space, -1.0], color=color_vline)

    for (text, fx, ha) in [
            ('%.3g Hz' % fmin, fmin, 'right'),
            ('%.3g Hz' % fmax, fmax, 'left')]:

        axes2.annotate(
            text,
            xy=(fx, -1.0),
            xycoords='data',
            xytext=(
                fontsize*0.4 * [-1, 1][ha == 'left'],
                -fontsize*0.2),
            textcoords='offset points',
            ha=ha,
            va='top',
            color=color_vline,
            fontsize=fontsize)

Sebastian Heimann's avatar
Sebastian Heimann committed
910

911
912
913
914
def plot_dtrace_vline(axes, t, space, **kwargs):
    axes.plot([t, t], [-1.0 - space, -1.0], **kwargs)


Sebastian Heimann's avatar
Sebastian Heimann committed
915
def draw_fits_figures(ds, model, plt):
916
917
    fontsize = 8
    fontsize_title = 10
Sebastian Heimann's avatar
Sebastian Heimann committed
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

    problem = model.problem

    for target in problem.targets:
        target.set_dataset(ds)

    target_index = dict(
        (target, i) for (i, target) in enumerate(problem.targets))

    gms = problem.global_misfits(model.misfits)
    isort = num.argsort(gms)
    gms = gms[isort]
    xs = model.xs[isort, :]
    misfits = model.misfits[isort, :]

    xbest = xs[0, :]

    ws = problem.get_target_weights()
    gcms = problem.global_contributions(misfits[:1])[0]

    w_max = num.nanmax(ws)
    gcm_max = num.nanmax(gcms)

    source = problem.unpack(xbest)

    target_to_result = {}
    all_syn_trs = []
945
    all_syn_specs = []
946
    ms, ns, results = problem.evaluate(xbest, result_mode='full')
Sebastian Heimann's avatar
Sebastian Heimann committed
947
948
949

    dtraces = []
    for target, result in zip(problem.targets, results):
950
        if isinstance(result, gf.SeismosizerError):
Sebastian Heimann's avatar
Sebastian Heimann committed
951
952
953
954
955
956
            dtraces.append(None)
            continue

        itarget = target_index[target]
        w = target.get_combined_weight(problem.apply_balancing_weights)

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
        if target.misfit_config.domain == 'cc_max_norm':
            tref = (result.filtered_obs.tmin + result.filtered_obs.tmax) * 0.5
            for tr_filt, tr_proc, tshift in (
                    (result.filtered_obs,
                     result.processed_obs,
                     0.),
                    (result.filtered_syn,
                     result.processed_syn,
                     result.cc_shift)):

                norm = num.sum(num.abs(tr_proc.ydata)) / tr_proc.data_len()
                tr_filt.ydata /= norm
                tr_proc.ydata /= norm

                tr_filt.shift(tshift)
                tr_proc.shift(tshift)

            ctr = result.cc
            ctr.shift(tref)

            dtrace = ctr

        else:
            for tr in (
                    result.filtered_obs,
                    result.filtered_syn,
                    result.processed_obs,
                    result.processed_syn):
Sebastian Heimann's avatar
Sebastian Heimann committed
985

986
                tr.ydata *= w
Sebastian Heimann's avatar
Sebastian Heimann committed
987

988
989
990
991
992
993
994
            for spec in (
                    result.spectrum_obs,
                    result.spectrum_syn):

                if spec is not None:
                    spec.ydata *= w

995
996
997
998
999
            dtrace = result.processed_syn.copy()
            dtrace.set_ydata(
                (
                    (result.processed_syn.get_ydata() -
                     result.processed_obs.get_ydata())**2))
Sebastian Heimann's avatar
Sebastian Heimann committed
1000