1# http://pyrocko.org - GPLv3
2#
3# The Pyrocko Developers, 21st Century
4# ---|P------/S----------~Lg----------
5'''
6This module provides basic signal processing for seismic traces.
7'''
9from __future__ import division, absolute_import
11import time
12import math
13import copy
14import logging
15import hashlib
16from collections import defaultdict
18import numpy as num
19from scipy import signal
21from pyrocko import util, orthodrome, pchain, model
22from pyrocko.util import reuse
23from pyrocko.guts import Object, Float, Int, String, List, \
24 StringChoice, Timestamp
25from pyrocko.guts_array import Array
26from pyrocko.model import squirrel_content
28# backward compatibility
29from pyrocko.util import UnavailableDecimation # noqa
30from pyrocko.response import ( # noqa
31 FrequencyResponse, Evalresp, InverseEvalresp, PoleZeroResponse,
32 ButterworthResponse, SampledResponse, IntegrationResponse,
33 DifferentiationResponse, MultiplyResponse)
36guts_prefix = 'pf'
38logger = logging.getLogger('pyrocko.trace')
41@squirrel_content
42class Trace(Object):
44 '''
45 Create new trace object.
47 A ``Trace`` object represents a single continuous strip of evenly sampled
48 time series data. It is built from a 1D NumPy array containing the data
49 samples and some attributes describing its beginning and ending time, its
50 sampling rate and four string identifiers (its network, station, location
51 and channel code).
53 :param network: network code
54 :param station: station code
55 :param location: location code
56 :param channel: channel code
57 :param extra: extra code
58 :param tmin: system time of first sample in [s]
59 :param tmax: system time of last sample in [s] (if set to ``None`` it is
60 computed from length of ``ydata``)
61 :param deltat: sampling interval in [s]
62 :param ydata: 1D numpy array with data samples (can be ``None`` when
63 ``tmax`` is not ``None``)
64 :param mtime: optional modification time
66 :param meta: additional meta information (not used, but maintained by the
67 library)
69 The length of the network, station, location and channel codes is not
70 resricted by this software, but data formats like SAC, Mini-SEED or GSE
71 have different limits on the lengths of these codes. The codes set here are
72 silently truncated when the trace is stored
73 '''
75 network = String.T(default='', help='Deployment/network code (1-8)')
76 station = String.T(default='STA', help='Station code (1-5)')
77 location = String.T(default='', help='Location code (0-2)')
78 channel = String.T(default='', help='Channel code (3)')
79 extra = String.T(default='', help='Extra/custom code')
81 tmin = Timestamp.T(default=Timestamp.D('1970-01-01 00:00:00'))
82 tmax = Timestamp.T()
84 deltat = Float.T(default=1.0)
85 ydata = Array.T(optional=True, shape=(None,), serialize_as='base64+meta')
87 mtime = Timestamp.T(optional=True)
89 cached_frequencies = {}
91 def __init__(self, network='', station='STA', location='', channel='',
92 tmin=0., tmax=None, deltat=1., ydata=None, mtime=None,
93 meta=None, extra=''):
95 Object.__init__(self, init_props=False)
97 time_float = util.get_time_float()
99 if not isinstance(tmin, time_float):
100 tmin = Trace.tmin.regularize_extra(tmin)
102 if tmax is not None and not isinstance(tmax, time_float):
103 tmax = Trace.tmax.regularize_extra(tmax)
105 if mtime is not None and not isinstance(mtime, time_float):
106 mtime = Trace.mtime.regularize_extra(mtime)
108 self._growbuffer = None
110 tmin = time_float(tmin)
111 if tmax is not None:
112 tmax = time_float(tmax)
114 if mtime is None:
115 mtime = time_float(time.time())
117 self.network, self.station, self.location, self.channel, \
118 self.extra = [
119 reuse(x) for x in (
120 network, station, location, channel, extra)]
122 self.tmin = tmin
123 self.deltat = deltat
125 if tmax is None:
126 if ydata is not None:
127 self.tmax = self.tmin + (ydata.size-1)*self.deltat
128 else:
129 raise Exception(
130 'fixme: trace must be created with tmax or ydata')
131 else:
132 n = int(round((tmax - self.tmin) / self.deltat)) + 1
133 self.tmax = self.tmin + (n - 1) * self.deltat
135 self.meta = meta
136 self.ydata = ydata
137 self.mtime = mtime
138 self._update_ids()
139 self.file = None
140 self._pchain = None
142 def __str__(self):
143 fmt = min(9, max(0, -int(math.floor(math.log10(self.deltat)))))
144 s = 'Trace (%s, %s, %s, %s)\n' % self.nslc_id
145 s += ' timerange: %s - %s\n' % (
146 util.time_to_str(self.tmin, format=fmt),
147 util.time_to_str(self.tmax, format=fmt))
149 s += ' delta t: %g\n' % self.deltat
150 if self.meta:
151 for k in sorted(self.meta.keys()):
152 s += ' %s: %s\n' % (k, self.meta[k])
153 return s
155 @property
156 def codes(self):
157 from pyrocko.squirrel import model
158 return model.CodesNSLCE(
159 self.network, self.station, self.location, self.channel,
160 self.extra)
162 @property
163 def time_span(self):
164 return self.tmin, self.tmax
166 @property
167 def summary(self):
168 return '%s %-16s %s %g' % (
169 self.__class__.__name__, self.str_codes, self.str_time_span,
170 self.deltat)
172 def __getstate__(self):
173 return (self.network, self.station, self.location, self.channel,
174 self.tmin, self.tmax, self.deltat, self.mtime,
175 self.ydata, self.meta, self.extra)
177 def __setstate__(self, state):
178 if len(state) == 11:
179 self.network, self.station, self.location, self.channel, \
180 self.tmin, self.tmax, self.deltat, self.mtime, \
181 self.ydata, self.meta, self.extra = state
183 elif len(state) == 12:
184 # backward compatibility 0
185 self.network, self.station, self.location, self.channel, \
186 self.tmin, self.tmax, self.deltat, self.mtime, \
187 self.ydata, self.meta, _, self.extra = state
189 elif len(state) == 10:
190 # backward compatibility 1
191 self.network, self.station, self.location, self.channel, \
192 self.tmin, self.tmax, self.deltat, self.mtime, \
193 self.ydata, self.meta = state
195 self.extra = ''
197 else:
198 # backward compatibility 2
199 self.network, self.station, self.location, self.channel, \
200 self.tmin, self.tmax, self.deltat, self.mtime = state
201 self.ydata = None
202 self.meta = None
203 self.extra = ''
205 self._growbuffer = None
206 self._update_ids()
208 def hash(self, unsafe=False):
209 sha1 = hashlib.sha1()
210 for code in self.nslc_id:
211 sha1.update(code.encode())
212 sha1.update(self.tmin.hex().encode())
213 sha1.update(self.tmax.hex().encode())
214 sha1.update(self.deltat.hex().encode())
216 if self.ydata is not None:
217 if not unsafe:
218 sha1.update(memoryview(self.ydata))
219 else:
220 sha1.update(memoryview(self.ydata[:32]))
221 sha1.update(memoryview(self.ydata[-32:]))
223 return sha1.hexdigest()
225 def name(self):
226 '''
227 Get a short string description.
228 '''
230 s = '%s.%s.%s.%s, %s, %s' % (self.nslc_id + (
231 util.time_to_str(self.tmin),
232 util.time_to_str(self.tmax)))
234 return s
236 def __eq__(self, other):
237 return (
238 isinstance(other, Trace)
239 and self.network == other.network
240 and self.station == other.station
241 and self.location == other.location
242 and self.channel == other.channel
243 and (abs(self.deltat - other.deltat)
244 < (self.deltat + other.deltat)*1e-6)
245 and abs(self.tmin-other.tmin) < self.deltat*0.01
246 and abs(self.tmax-other.tmax) < self.deltat*0.01
247 and num.all(self.ydata == other.ydata))
249 def almost_equal(self, other, rtol=1e-5, atol=0.0):
250 return (
251 self.network == other.network
252 and self.station == other.station
253 and self.location == other.location
254 and self.channel == other.channel
255 and (abs(self.deltat - other.deltat)
256 < (self.deltat + other.deltat)*1e-6)
257 and abs(self.tmin-other.tmin) < self.deltat*0.01
258 and abs(self.tmax-other.tmax) < self.deltat*0.01
259 and num.allclose(self.ydata, other.ydata, rtol=rtol, atol=atol))
261 def assert_almost_equal(self, other, rtol=1e-5, atol=0.0):
263 assert self.network == other.network, \
264 'network codes differ: %s, %s' % (self.network, other.network)
265 assert self.station == other.station, \
266 'station codes differ: %s, %s' % (self.station, other.station)
267 assert self.location == other.location, \
268 'location codes differ: %s, %s' % (self.location, other.location)
269 assert self.channel == other.channel, 'channel codes differ'
270 assert (abs(self.deltat - other.deltat)
271 < (self.deltat + other.deltat)*1e-6), \
272 'sampling intervals differ %g, %g' % (self.deltat, other.delta)
273 assert abs(self.tmin-other.tmin) < self.deltat*0.01, \
274 'start times differ: %s, %s' % (
275 util.time_to_str(self.tmin), util.time_to_str(other.tmin))
276 assert abs(self.tmax-other.tmax) < self.deltat*0.01, \
277 'end times differ: %s, %s' % (
278 util.time_to_str(self.tmax), util.time_to_str(other.tmax))
280 assert num.allclose(self.ydata, other.ydata, rtol=rtol, atol=atol), \
281 'trace values differ'
283 def __hash__(self):
284 return id(self)
286 def __call__(self, t, clip=False, snap=round):
287 it = int(snap((t-self.tmin)/self.deltat))
288 if clip:
289 it = max(0, min(it, self.ydata.size-1))
290 else:
291 if it < 0 or self.ydata.size <= it:
292 raise IndexError()
294 return self.tmin+it*self.deltat, self.ydata[it]
296 def interpolate(self, t, clip=False):
297 '''
298 Value of trace between supporting points through linear interpolation.
300 :param t: time instant
301 :param clip: whether to clip indices to trace ends
302 '''
304 t0, y0 = self(t, clip=clip, snap=math.floor)
305 t1, y1 = self(t, clip=clip, snap=math.ceil)
306 if t0 == t1:
307 return y0
308 else:
309 return y0+(t-t0)/(t1-t0)*(y1-y0)
311 def index_clip(self, i):
312 '''
313 Clip index to valid range.
314 '''
316 return min(max(0, i), self.ydata.size)
318 def add(self, other, interpolate=True, left=0., right=0.):
319 '''
320 Add values of other trace (self += other).
322 Add values of ``other`` trace to the values of ``self``, where it
323 intersects with ``other``. This method does not change the extent of
324 ``self``. If ``interpolate`` is ``True`` (the default), the values of
325 ``other`` to be added are interpolated at sampling instants of
326 ``self``. Linear interpolation is performed. In this case the sampling
327 rate of ``other`` must be equal to or lower than that of ``self``. If
328 ``interpolate`` is ``False``, the sampling rates of the two traces must
329 match.
330 '''
332 if interpolate:
333 assert self.deltat <= other.deltat \
334 or same_sampling_rate(self, other)
336 other_xdata = other.get_xdata()
337 xdata = self.get_xdata()
338 self.ydata += num.interp(
339 xdata, other_xdata, other.ydata, left=left, right=left)
340 else:
341 assert self.deltat == other.deltat
342 ioff = int(round((other.tmin-self.tmin)/self.deltat))
343 ibeg = max(0, ioff)
344 iend = min(self.data_len(), ioff+other.data_len())
345 self.ydata[ibeg:iend] += other.ydata[ibeg-ioff:iend-ioff]
347 def mult(self, other, interpolate=True):
348 '''
349 Muliply with values of other trace ``(self *= other)``.
351 Multiply values of ``other`` trace to the values of ``self``, where it
352 intersects with ``other``. This method does not change the extent of
353 ``self``. If ``interpolate`` is ``True`` (the default), the values of
354 ``other`` to be multiplied are interpolated at sampling instants of
355 ``self``. Linear interpolation is performed. In this case the sampling
356 rate of ``other`` must be equal to or lower than that of ``self``. If
357 ``interpolate`` is ``False``, the sampling rates of the two traces must
358 match.
359 '''
361 if interpolate:
362 assert self.deltat <= other.deltat or \
363 same_sampling_rate(self, other)
365 other_xdata = other.get_xdata()
366 xdata = self.get_xdata()
367 self.ydata *= num.interp(
368 xdata, other_xdata, other.ydata, left=0., right=0.)
369 else:
370 assert self.deltat == other.deltat
371 ibeg1 = int(round((other.tmin-self.tmin)/self.deltat))
372 ibeg2 = int(round((self.tmin-other.tmin)/self.deltat))
373 iend1 = int(round((other.tmax-self.tmin)/self.deltat))+1
374 iend2 = int(round((self.tmax-other.tmin)/self.deltat))+1
376 ibeg1 = self.index_clip(ibeg1)
377 iend1 = self.index_clip(iend1)
378 ibeg2 = self.index_clip(ibeg2)
379 iend2 = self.index_clip(iend2)
381 self.ydata[ibeg1:iend1] *= other.ydata[ibeg2:iend2]
383 def max(self):
384 '''
385 Get time and value of data maximum.
386 '''
388 i = num.argmax(self.ydata)
389 return self.tmin + i*self.deltat, self.ydata[i]
391 def min(self):
392 '''
393 Get time and value of data minimum.
394 '''
396 i = num.argmin(self.ydata)
397 return self.tmin + i*self.deltat, self.ydata[i]
399 def absmax(self):
400 '''
401 Get time and value of maximum of the absolute of data.
402 '''
404 tmi, mi = self.min()
405 tma, ma = self.max()
406 if abs(mi) > abs(ma):
407 return tmi, abs(mi)
408 else:
409 return tma, abs(ma)
411 def set_codes(
412 self, network=None, station=None, location=None, channel=None,
413 extra=None):
415 '''
416 Set network, station, location, and channel codes.
417 '''
419 if network is not None:
420 self.network = network
421 if station is not None:
422 self.station = station
423 if location is not None:
424 self.location = location
425 if channel is not None:
426 self.channel = channel
427 if extra is not None:
428 self.extra = extra
430 self._update_ids()
432 def set_network(self, network):
433 self.network = network
434 self._update_ids()
436 def set_station(self, station):
437 self.station = station
438 self._update_ids()
440 def set_location(self, location):
441 self.location = location
442 self._update_ids()
444 def set_channel(self, channel):
445 self.channel = channel
446 self._update_ids()
448 def overlaps(self, tmin, tmax):
449 '''
450 Check if trace has overlap with a given time span.
451 '''
452 return not (tmax < self.tmin or self.tmax < tmin)
454 def is_relevant(self, tmin, tmax, selector=None):
455 '''
456 Check if trace has overlap with a given time span and matches a
457 condition callback. (internal use)
458 '''
460 return not (tmax <= self.tmin or self.tmax < tmin) \
461 and (selector is None or selector(self))
463 def _update_ids(self):
464 '''
465 Update dependent ids.
466 '''
468 self.full_id = (
469 self.network, self.station, self.location, self.channel, self.tmin)
470 self.nslc_id = reuse(
471 (self.network, self.station, self.location, self.channel))
473 def prune_from_reuse_cache(self):
474 util.deuse(self.nslc_id)
475 util.deuse(self.network)
476 util.deuse(self.station)
477 util.deuse(self.location)
478 util.deuse(self.channel)
480 def set_mtime(self, mtime):
481 '''
482 Set modification time of the trace.
483 '''
485 self.mtime = mtime
487 def get_xdata(self):
488 '''
489 Create array for time axis.
490 '''
492 if self.ydata is None:
493 raise NoData()
495 return self.tmin \
496 + num.arange(len(self.ydata), dtype=num.float64) * self.deltat
498 def get_ydata(self):
499 '''
500 Get data array.
501 '''
503 if self.ydata is None:
504 raise NoData()
506 return self.ydata
508 def set_ydata(self, new_ydata):
509 '''
510 Replace data array.
511 '''
513 self.drop_growbuffer()
514 self.ydata = new_ydata
515 self.tmax = self.tmin+(len(self.ydata)-1)*self.deltat
517 def data_len(self):
518 if self.ydata is not None:
519 return self.ydata.size
520 else:
521 return int(round((self.tmax-self.tmin)/self.deltat)) + 1
523 def drop_data(self):
524 '''
525 Forget data, make dataless trace.
526 '''
528 self.drop_growbuffer()
529 self.ydata = None
531 def drop_growbuffer(self):
532 '''
533 Detach the traces grow buffer.
534 '''
536 self._growbuffer = None
537 self._pchain = None
539 def copy(self, data=True):
540 '''
541 Make a deep copy of the trace.
542 '''
544 tracecopy = copy.copy(self)
545 tracecopy.drop_growbuffer()
546 if data:
547 tracecopy.ydata = self.ydata.copy()
548 tracecopy.meta = copy.deepcopy(self.meta)
549 return tracecopy
551 def crop_zeros(self):
552 '''
553 Remove any zeros at beginning and end.
554 '''
556 indices = num.where(self.ydata != 0.0)[0]
557 if indices.size == 0:
558 raise NoData()
560 ibeg = indices[0]
561 iend = indices[-1]+1
562 if ibeg == 0 and iend == self.ydata.size-1:
563 return
565 self.drop_growbuffer()
566 self.ydata = self.ydata[ibeg:iend].copy()
567 self.tmin = self.tmin+ibeg*self.deltat
568 self.tmax = self.tmin+(len(self.ydata)-1)*self.deltat
569 self._update_ids()
571 def append(self, data):
572 '''
573 Append data to the end of the trace.
575 To make this method efficient when successively very few or even single
576 samples are appended, a larger grow buffer is allocated upon first
577 invocation. The traces data is then changed to be a view into the
578 currently filled portion of the grow buffer array.
579 '''
581 assert self.ydata.dtype == data.dtype
582 newlen = data.size + self.ydata.size
583 if self._growbuffer is None or self._growbuffer.size < newlen:
584 self._growbuffer = num.empty(newlen*2, dtype=self.ydata.dtype)
585 self._growbuffer[:self.ydata.size] = self.ydata
586 self._growbuffer[self.ydata.size:newlen] = data
587 self.ydata = self._growbuffer[:newlen]
588 self.tmax = self.tmin + (newlen-1)*self.deltat
590 def chop(
591 self, tmin, tmax, inplace=True, include_last=False,
592 snap=(round, round), want_incomplete=True):
594 '''
595 Cut the trace to given time span.
597 If the ``inplace`` argument is True (the default) the trace is cut in
598 place, otherwise a new trace with the cut part is returned. By
599 default, the indices where to start and end the trace data array are
600 determined by rounding of ``tmin`` and ``tmax`` to sampling instances
601 using Python's :py:func:`round` function. This behaviour can be changed
602 with the ``snap`` argument, which takes a tuple of two functions (one
603 for the lower and one for the upper end) to be used instead of
604 :py:func:`round`. The last sample is by default not included unless
605 ``include_last`` is set to True. If the given time span exceeds the
606 available time span of the trace, the available part is returned,
607 unless ``want_incomplete`` is set to False - in that case, a
608 :py:exc:`NoData` exception is raised. This exception is always raised,
609 when the requested time span does dot overlap with the trace's time
610 span.
611 '''
613 if want_incomplete:
614 if tmax <= self.tmin-self.deltat or self.tmax+self.deltat < tmin:
615 raise NoData()
616 else:
617 if tmin < self.tmin or self.tmax < tmax:
618 raise NoData()
620 ibeg = max(0, t2ind(tmin-self.tmin, self.deltat, snap[0]))
621 iplus = 0
622 if include_last:
623 iplus = 1
625 iend = min(
626 self.data_len(),
627 t2ind(tmax-self.tmin, self.deltat, snap[1])+iplus)
629 if ibeg >= iend:
630 raise NoData()
632 obj = self
633 if not inplace:
634 obj = self.copy(data=False)
636 self.drop_growbuffer()
637 if self.ydata is not None:
638 obj.ydata = self.ydata[ibeg:iend].copy()
639 else:
640 obj.ydata = None
642 obj.tmin = obj.tmin+ibeg*obj.deltat
643 obj.tmax = obj.tmin+((iend-ibeg)-1)*obj.deltat
645 obj._update_ids()
647 return obj
649 def downsample(self, ndecimate, snap=False, initials=None, demean=False,
650 ftype='fir-remez'):
651 '''
652 Downsample trace by a given integer factor.
654 Comparison of the available FIR filters `fir` and `fir-remez`:
657 :param ndecimate: decimation factor, avoid values larger than 8
658 :param snap: whether to put the new sampling instances closest to
659 multiples of the sampling rate.
660 :param initials: ``None``, ``True``, or initial conditions for the
661 anti-aliasing filter, obtained from a previous run. In the latter
662 two cases the final state of the filter is returned instead of
663 ``None``.
664 :param demean: whether to demean the signal before filtering.
665 :param ftype: which FIR filter to use, choose from `fir, fir-remez`.
666 Default is `fir-remez`.
667 '''
668 newdeltat = self.deltat*ndecimate
669 if snap:
670 ilag = int(round(
671 (math.ceil(self.tmin / newdeltat) * newdeltat - self.tmin)
672 / self.deltat))
673 else:
674 ilag = 0
676 if snap and ilag > 0 and ilag < self.ydata.size:
677 data = self.ydata.astype(num.float64)
678 self.tmin += ilag*self.deltat
679 else:
680 data = self.ydata.astype(num.float64)
682 if demean:
683 data -= num.mean(data)
685 if data.size != 0:
686 result = util.decimate(
687 data, ndecimate, ftype=ftype, zi=initials, ioff=ilag)
688 else:
689 result = data
691 if initials is None:
692 self.ydata, finals = result, None
693 else:
694 self.ydata, finals = result
696 self.deltat = reuse(self.deltat*ndecimate)
697 self.tmax = self.tmin+(len(self.ydata)-1)*self.deltat
698 self._update_ids()
700 return finals
702 def downsample_to(self, deltat, snap=False, allow_upsample_max=1,
703 initials=None, demean=False, ftype='fir-remez'):
705 '''
706 Downsample to given sampling rate.
708 Tries to downsample the trace to a target sampling interval of
709 ``deltat``. This runs the :py:meth:`Trace.downsample` one or several
710 times. If allow_upsample_max is set to a value larger than 1,
711 intermediate upsampling steps are allowed, in order to increase the
712 number of possible downsampling ratios.
714 If the requested ratio is not supported, an exception of type
715 :py:exc:`pyrocko.util.UnavailableDecimation` is raised.
717 :param deltat: desired deltat in [s]
718 :param allow_upsample_max: maximum upsampling of the signal to archive
719 the desired deltat. Default is `1`.
720 :param snap: whether to put the new sampling instances closest to
721 multiples of the sampling rate.
722 :param initials: ``None``, ``True``, or initial conditions for the
723 anti-aliasing filter, obtained from a previous run. In the latter
724 two cases the final state of the filter is returned instead of
725 ``None``.
726 :param demean: whether to demean the signal before filtering.
727 :param ftype: which FIR filter to use, choose from `fir, fir-remez`.
728 Default is `fir-remez`.
729 '''
731 ratio = deltat/self.deltat
732 rratio = round(ratio)
734 ok = False
735 for upsratio in range(1, allow_upsample_max+1):
736 dratio = (upsratio/self.deltat) / (1./deltat)
737 if abs(dratio - round(dratio)) / dratio < 0.0001 and \
738 util.decitab(int(round(dratio))):
740 ok = True
741 break
743 if not ok:
744 raise util.UnavailableDecimation('ratio = %g' % ratio)
746 if upsratio > 1:
747 self.drop_growbuffer()
748 ydata = self.ydata
749 self.ydata = num.zeros(
750 ydata.size*upsratio-(upsratio-1), ydata.dtype)
751 self.ydata[::upsratio] = ydata
752 for i in range(1, upsratio):
753 self.ydata[i::upsratio] = \
754 float(i)/upsratio * ydata[:-1] \
755 + float(upsratio-i)/upsratio * ydata[1:]
756 self.deltat = self.deltat/upsratio
758 ratio = deltat/self.deltat
759 rratio = round(ratio)
761 deci_seq = util.decitab(int(rratio))
762 finals = []
763 for i, ndecimate in enumerate(deci_seq):
764 if ndecimate != 1:
765 xinitials = None
766 if initials is not None:
767 xinitials = initials[i]
768 finals.append(self.downsample(
769 ndecimate, snap=snap, initials=xinitials, demean=demean,
770 ftype=ftype))
772 if initials is not None:
773 return finals
775 def resample(self, deltat):
776 '''
777 Resample to given sampling rate ``deltat``.
779 Resampling is performed in the frequency domain.
780 '''
782 ndata = self.ydata.size
783 ntrans = nextpow2(ndata)
784 fntrans2 = ntrans * self.deltat/deltat
785 ntrans2 = int(round(fntrans2))
786 deltat2 = self.deltat * float(ntrans)/float(ntrans2)
787 ndata2 = int(round(ndata*self.deltat/deltat2))
788 if abs(fntrans2 - ntrans2) > 1e-7:
789 logger.warning(
790 'resample: requested deltat %g could not be matched exactly: '
791 '%g' % (deltat, deltat2))
793 data = self.ydata
794 data_pad = num.zeros(ntrans, dtype=float)
795 data_pad[:ndata] = data
796 fdata = num.fft.rfft(data_pad)
797 fdata2 = num.zeros((ntrans2+1)//2, dtype=fdata.dtype)
798 n = min(fdata.size, fdata2.size)
799 fdata2[:n] = fdata[:n]
800 data2 = num.fft.irfft(fdata2)
801 data2 = data2[:ndata2]
802 data2 *= float(ntrans2) / float(ntrans)
803 self.deltat = deltat2
804 self.set_ydata(data2)
806 def resample_simple(self, deltat):
807 tyear = 3600*24*365.
809 if deltat == self.deltat:
810 return
812 if abs(self.deltat - deltat) * tyear/deltat < deltat:
813 logger.warning(
814 'resample_simple: less than one sample would have to be '
815 'inserted/deleted per year. Doing nothing.')
816 return
818 ninterval = int(round(deltat / (self.deltat - deltat)))
819 if abs(ninterval) < 20:
820 logger.error(
821 'resample_simple: sample insertion/deletion interval less '
822 'than 20. results would be erroneous.')
823 raise ResamplingFailed()
825 delete = False
826 if ninterval < 0:
827 ninterval = - ninterval
828 delete = True
830 tyearbegin = util.year_start(self.tmin)
832 nmin = int(round((self.tmin - tyearbegin)/deltat))
834 ibegin = (((nmin-1)//ninterval)+1) * ninterval - nmin
835 nindices = (len(self.ydata) - ibegin - 1) / ninterval + 1
836 if nindices > 0:
837 indices = ibegin + num.arange(nindices) * ninterval
838 data_split = num.split(self.ydata, indices)
839 data = []
840 for ln, h in zip(data_split[:-1], data_split[1:]):
841 if delete:
842 ln = ln[:-1]
844 data.append(ln)
845 if not delete:
846 if ln.size == 0:
847 v = h[0]
848 else:
849 v = 0.5*(ln[-1] + h[0])
850 data.append(num.array([v], dtype=ln.dtype))
852 data.append(data_split[-1])
854 ydata_new = num.concatenate(data)
856 self.tmin = tyearbegin + nmin * deltat
857 self.deltat = deltat
858 self.set_ydata(ydata_new)
860 def stretch(self, tmin_new, tmax_new):
861 '''
862 Stretch signal while preserving sample rate using sinc interpolation.
864 :param tmin_new: new time of first sample
865 :param tmax_new: new time of last sample
867 This method can be used to correct for a small linear time drift or to
868 introduce sub-sample time shifts. The amount of stretching is limited
869 to 10% by the implementation and is expected to be much smaller than
870 that by the approximations used.
871 '''
873 from pyrocko import signal_ext
875 i_control = num.array([0, self.ydata.size-1], dtype=num.int64)
876 t_control = num.array([tmin_new, tmax_new], dtype=float)
878 r = (tmax_new - tmin_new) / self.deltat + 1.0
879 n_new = int(round(r))
880 if abs(n_new - r) > 0.001:
881 n_new = int(math.floor(r))
883 assert n_new >= 2
885 tmax_new = tmin_new + (n_new-1) * self.deltat
887 ydata_new = num.empty(n_new, dtype=float)
888 signal_ext.antidrift(i_control, t_control,
889 self.ydata.astype(float),
890 tmin_new, self.deltat, ydata_new)
892 self.tmin = tmin_new
893 self.set_ydata(ydata_new)
894 self._update_ids()
896 def nyquist_check(self, frequency, intro='Corner frequency', warn=True,
897 raise_exception=False):
899 '''
900 Check if a given frequency is above the Nyquist frequency of the trace.
902 :param intro: string used to introduce the warning/error message
903 :param warn: whether to emit a warning
904 :param raise_exception: whether to raise an :py:exc:`AboveNyquist`
905 exception.
906 '''
908 if frequency >= 0.5/self.deltat:
909 message = '%s (%g Hz) is equal to or higher than nyquist ' \
910 'frequency (%g Hz). (Trace %s)' \
911 % (intro, frequency, 0.5/self.deltat, self.name())
912 if warn:
913 logger.warning(message)
914 if raise_exception:
915 raise AboveNyquist(message)
917 def lowpass(self, order, corner, nyquist_warn=True,
918 nyquist_exception=False, demean=True):
920 '''
921 Apply Butterworth lowpass to the trace.
923 :param order: order of the filter
924 :param corner: corner frequency of the filter
926 Mean is removed before filtering.
927 '''
929 self.nyquist_check(
930 corner, 'Corner frequency of lowpass', nyquist_warn,
931 nyquist_exception)
933 (b, a) = _get_cached_filter_coefs(
934 order, [corner*2.0*self.deltat], btype='low')
936 if len(a) != order+1 or len(b) != order+1:
937 logger.warning(
938 'Erroneous filter coefficients returned by '
939 'scipy.signal.butter(). You may need to downsample the '
940 'signal before filtering.')
942 data = self.ydata.astype(num.float64)
943 if demean:
944 data -= num.mean(data)
945 self.drop_growbuffer()
946 self.ydata = signal.lfilter(b, a, data)
948 def highpass(self, order, corner, nyquist_warn=True,
949 nyquist_exception=False, demean=True):
951 '''
952 Apply butterworth highpass to the trace.
954 :param order: order of the filter
955 :param corner: corner frequency of the filter
957 Mean is removed before filtering.
958 '''
960 self.nyquist_check(
961 corner, 'Corner frequency of highpass', nyquist_warn,
962 nyquist_exception)
964 (b, a) = _get_cached_filter_coefs(
965 order, [corner*2.0*self.deltat], btype='high')
967 data = self.ydata.astype(num.float64)
968 if len(a) != order+1 or len(b) != order+1:
969 logger.warning(
970 'Erroneous filter coefficients returned by '
971 'scipy.signal.butter(). You may need to downsample the '
972 'signal before filtering.')
973 if demean:
974 data -= num.mean(data)
975 self.drop_growbuffer()
976 self.ydata = signal.lfilter(b, a, data)
978 def bandpass(self, order, corner_hp, corner_lp, demean=True):
979 '''
980 Apply butterworth bandpass to the trace.
982 :param order: order of the filter
983 :param corner_hp: lower corner frequency of the filter
984 :param corner_lp: upper corner frequency of the filter
986 Mean is removed before filtering.
987 '''
989 self.nyquist_check(corner_hp, 'Lower corner frequency of bandpass')
990 self.nyquist_check(corner_lp, 'Higher corner frequency of bandpass')
991 (b, a) = _get_cached_filter_coefs(
992 order,
993 [corner*2.0*self.deltat for corner in (corner_hp, corner_lp)],
994 btype='band')
995 data = self.ydata.astype(num.float64)
996 if demean:
997 data -= num.mean(data)
998 self.drop_growbuffer()
999 self.ydata = signal.lfilter(b, a, data)
1001 def bandstop(self, order, corner_hp, corner_lp, demean=True):
1002 '''
1003 Apply bandstop (attenuates frequencies in band) to the trace.
1005 :param order: order of the filter
1006 :param corner_hp: lower corner frequency of the filter
1007 :param corner_lp: upper corner frequency of the filter
1009 Mean is removed before filtering.
1010 '''
1012 self.nyquist_check(corner_hp, 'Lower corner frequency of bandstop')
1013 self.nyquist_check(corner_lp, 'Higher corner frequency of bandstop')
1014 (b, a) = _get_cached_filter_coefs(
1015 order,
1016 [corner*2.0*self.deltat for corner in (corner_hp, corner_lp)],
1017 btype='bandstop')
1018 data = self.ydata.astype(num.float64)
1019 if demean:
1020 data -= num.mean(data)
1021 self.drop_growbuffer()
1022 self.ydata = signal.lfilter(b, a, data)
1024 def envelope(self, inplace=True):
1025 '''
1026 Calculate the envelope of the trace.
1028 :param inplace: calculate envelope in place
1030 The calculation follows:
1032 .. math::
1034 Y' = \\sqrt{Y^2+H(Y)^2}
1036 where H is the Hilbert-Transform of the signal Y.
1037 '''
1039 y = self.ydata.astype(float)
1040 env = num.abs(hilbert(y))
1041 if inplace:
1042 self.drop_growbuffer()
1043 self.ydata = env
1044 else:
1045 tr = self.copy(data=False)
1046 tr.ydata = env
1047 return tr
1049 def taper(self, taperer, inplace=True, chop=False):
1050 '''
1051 Apply a :py:class:`Taper` to the trace.
1053 :param taperer: instance of :py:class:`Taper` subclass
1054 :param inplace: apply taper inplace
1055 :param chop: if ``True``: exclude tapered parts from the resulting
1056 trace
1057 '''
1059 if not inplace:
1060 tr = self.copy()
1061 else:
1062 tr = self
1064 if chop:
1065 i, n = taperer.span(tr.ydata, tr.tmin, tr.deltat)
1066 tr.shift(i*tr.deltat)
1067 tr.set_ydata(tr.ydata[i:i+n])
1069 taperer(tr.ydata, tr.tmin, tr.deltat)
1071 if not inplace:
1072 return tr
1074 def whiten(self, order=6):
1075 '''
1076 Whiten signal in time domain using autoregression and recursive filter.
1078 :param order: order of the autoregression process
1079 '''
1081 b, a = self.whitening_coefficients(order)
1082 self.drop_growbuffer()
1083 self.ydata = signal.lfilter(b, a, self.ydata)
1085 def whitening_coefficients(self, order=6):
1086 ar = yulewalker(self.ydata, order)
1087 b, a = [1.] + ar.tolist(), [1.]
1088 return b, a
1090 def ampspec_whiten(
1091 self,
1092 width,
1093 td_taper='auto',
1094 fd_taper='auto',
1095 pad_to_pow2=True,
1096 demean=True):
1098 '''
1099 Whiten signal via frequency domain using moving average on amplitude
1100 spectra.
1102 :param width: width of smoothing kernel [Hz]
1103 :param td_taper: time domain taper, object of type :py:class:`Taper` or
1104 ``None`` or ``'auto'``.
1105 :param fd_taper: frequency domain taper, object of type
1106 :py:class:`Taper` or ``None`` or ``'auto'``.
1107 :param pad_to_pow2: whether to pad the signal with zeros up to a length
1108 of 2^n
1109 :param demean: whether to demean the signal before tapering
1111 The signal is first demeaned and then tapered using ``td_taper``. Then,
1112 the spectrum is calculated and inversely weighted with a smoothed
1113 version of its amplitude spectrum. A moving average is used for the
1114 smoothing. The smoothed spectrum is then tapered using ``fd_taper``.
1115 Finally, the smoothed and tapered spectrum is back-transformed into the
1116 time domain.
1118 If ``td_taper`` is set to ``'auto'``, ``CosFader(1.0/width)`` is used.
1119 If ``fd_taper`` is set to ``'auto'``, ``CosFader(width)`` is used.
1120 '''
1122 ndata = self.data_len()
1124 if pad_to_pow2:
1125 ntrans = nextpow2(ndata)
1126 else:
1127 ntrans = ndata
1129 df = 1./(ntrans*self.deltat)
1130 nw = int(round(width/df))
1131 if ndata//2+1 <= nw:
1132 raise TraceTooShort(
1133 'Samples in trace: %s, samples needed: %s' % (ndata, nw))
1135 if td_taper == 'auto':
1136 td_taper = CosFader(1./width)
1138 if fd_taper == 'auto':
1139 fd_taper = CosFader(width)
1141 if td_taper:
1142 self.taper(td_taper)
1144 ydata = self.get_ydata().astype(float)
1145 if demean:
1146 ydata -= ydata.mean()
1148 spec = num.fft.rfft(ydata, ntrans)
1150 amp = num.abs(spec)
1151 nspec = amp.size
1152 csamp = num.cumsum(amp)
1153 amp_smoothed = num.empty(nspec, dtype=csamp.dtype)
1154 n1, n2 = nw//2, nw//2 + nspec - nw
1155 amp_smoothed[n1:n2] = (csamp[nw:] - csamp[:-nw]) / nw
1156 amp_smoothed[:n1] = amp_smoothed[n1]
1157 amp_smoothed[n2:] = amp_smoothed[n2-1]
1159 denom = amp_smoothed * amp
1160 numer = amp
1161 eps = num.mean(denom) * 1e-9
1162 if eps == 0.0:
1163 eps = 1e-9
1165 numer += eps
1166 denom += eps
1167 spec *= numer/denom
1169 if fd_taper:
1170 fd_taper(spec, 0., df)
1172 ydata = num.fft.irfft(spec)
1173 self.set_ydata(ydata[:ndata])
1175 def _get_cached_freqs(self, nf, deltaf):
1176 ck = (nf, deltaf)
1177 if ck not in Trace.cached_frequencies:
1178 Trace.cached_frequencies[ck] = deltaf * num.arange(
1179 nf, dtype=float)
1181 return Trace.cached_frequencies[ck]
1183 def bandpass_fft(self, corner_hp, corner_lp):
1184 '''
1185 Apply boxcar bandbpass to trace (in spectral domain).
1186 '''
1188 n = len(self.ydata)
1189 n2 = nextpow2(n)
1190 data = num.zeros(n2, dtype=num.float64)
1191 data[:n] = self.ydata
1192 fdata = num.fft.rfft(data)
1193 freqs = self._get_cached_freqs(len(fdata), 1./(self.deltat*n2))
1194 fdata[0] = 0.0
1195 fdata *= num.logical_and(corner_hp < freqs, freqs < corner_lp)
1196 data = num.fft.irfft(fdata)
1197 self.drop_growbuffer()
1198 self.ydata = data[:n]
1200 def shift(self, tshift):
1201 '''
1202 Time shift the trace.
1203 '''
1205 self.tmin += tshift
1206 self.tmax += tshift
1207 self._update_ids()
1209 def snap(self, inplace=True, interpolate=False):
1210 '''
1211 Shift trace samples to nearest even multiples of the sampling rate.
1213 :param inplace: (boolean) snap traces inplace
1215 If ``inplace`` is ``False`` and the difference of tmin and tmax of
1216 both, the snapped and the original trace is smaller than 0.01 x deltat,
1217 :py:func:`snap` returns the unsnapped instance of the original trace.
1218 '''
1220 tmin = round(self.tmin/self.deltat)*self.deltat
1221 tmax = tmin + (self.ydata.size-1)*self.deltat
1223 if inplace:
1224 xself = self
1225 else:
1226 if abs(self.tmin - tmin) < 1e-2*self.deltat and \
1227 abs(self.tmax - tmax) < 1e-2*self.deltat:
1228 return self
1230 xself = self.copy()
1232 if interpolate:
1233 from pyrocko import signal_ext
1234 n = xself.data_len()
1235 ydata_new = num.empty(n, dtype=float)
1236 i_control = num.array([0, n-1], dtype=num.int64)
1237 tref = tmin
1238 t_control = num.array(
1239 [float(xself.tmin-tref), float(xself.tmax-tref)],
1240 dtype=float)
1242 signal_ext.antidrift(i_control, t_control,
1243 xself.ydata.astype(float),
1244 float(tmin-tref), xself.deltat, ydata_new)
1246 xself.ydata = ydata_new
1248 xself.tmin = tmin
1249 xself.tmax = tmax
1250 xself._update_ids()
1252 return xself
1254 def fix_deltat_rounding_errors(self):
1255 '''
1256 Try to undo sampling rate rounding errors.
1258 See :py:func:`fix_deltat_rounding_errors`.
1259 '''
1261 self.deltat = fix_deltat_rounding_errors(self.deltat)
1262 self.tmax = self.tmin + (self.data_len() - 1) * self.deltat
1264 def sta_lta_centered(self, tshort, tlong, quad=True, scalingmethod=1):
1265 '''
1266 Run special STA/LTA filter where the short time window is centered on
1267 the long time window.
1269 :param tshort: length of short time window in [s]
1270 :param tlong: length of long time window in [s]
1271 :param quad: whether to square the data prior to applying the STA/LTA
1272 filter
1273 :param scalingmethod: integer key to select how output values are
1274 scaled / normalized (``1``, ``2``, or ``3``)
1276 ============= ====================================== ===========
1277 Scalingmethod Implementation Range
1278 ============= ====================================== ===========
1279 ``1`` As/Al* Ts/Tl [0,1]
1280 ``2`` (As/Al - 1) / (Tl/Ts - 1) [-Ts/Tl,1]
1281 ``3`` Like ``2`` but clipping range at zero [0,1]
1282 ============= ====================================== ===========
1284 '''
1286 nshort = int(round(tshort/self.deltat))
1287 nlong = int(round(tlong/self.deltat))
1289 assert nshort < nlong
1290 if nlong > len(self.ydata):
1291 raise TraceTooShort(
1292 'Samples in trace: %s, samples needed: %s'
1293 % (len(self.ydata), nlong))
1295 if quad:
1296 sqrdata = self.ydata**2
1297 else:
1298 sqrdata = self.ydata
1300 mavg_short = moving_avg(sqrdata, nshort)
1301 mavg_long = moving_avg(sqrdata, nlong)
1303 self.drop_growbuffer()
1305 if scalingmethod not in (1, 2, 3):
1306 raise Exception('Invalid argument to scalingrange argument.')
1308 if scalingmethod == 1:
1309 self.ydata = mavg_short/mavg_long * float(nshort)/float(nlong)
1310 elif scalingmethod in (2, 3):
1311 self.ydata = (mavg_short/mavg_long - 1.) \
1312 / ((float(nlong)/float(nshort)) - 1)
1314 if scalingmethod == 3:
1315 self.ydata = num.maximum(self.ydata, 0.)
1317 def sta_lta_right(self, tshort, tlong, quad=True, scalingmethod=1):
1318 '''
1319 Run special STA/LTA filter where the short time window is overlapping
1320 with the last part of the long time window.
1322 :param tshort: length of short time window in [s]
1323 :param tlong: length of long time window in [s]
1324 :param quad: whether to square the data prior to applying the STA/LTA
1325 filter
1326 :param scalingmethod: integer key to select how output values are
1327 scaled / normalized (``1``, ``2``, or ``3``)
1329 ============= ====================================== ===========
1330 Scalingmethod Implementation Range
1331 ============= ====================================== ===========
1332 ``1`` As/Al* Ts/Tl [0,1]
1333 ``2`` (As/Al - 1) / (Tl/Ts - 1) [-Ts/Tl,1]
1334 ``3`` Like ``2`` but clipping range at zero [0,1]
1335 ============= ====================================== ===========
1337 With ``scalingmethod=1``, the values produced by this variant of the
1338 STA/LTA are equivalent to
1340 .. math::
1341 s_i = \\frac{s}{l} \\frac{\\frac{1}{s}\\sum_{j=i}^{i+s-1} f_j}
1342 {\\frac{1}{l}\\sum_{j=i+s-l}^{i+s-1} f_j}
1344 where :math:`f_j` are the input samples, :math:`s` are the number of
1345 samples in the short time window and :math:`l` are the number of
1346 samples in the long time window.
1347 '''
1349 n = self.data_len()
1350 tmin = self.tmin
1352 nshort = max(1, int(round(tshort/self.deltat)))
1353 nlong = max(1, int(round(tlong/self.deltat)))
1355 assert nshort < nlong
1357 if nlong > len(self.ydata):
1358 raise TraceTooShort(
1359 'Samples in trace: %s, samples needed: %s'
1360 % (len(self.ydata), nlong))
1362 if quad:
1363 sqrdata = self.ydata**2
1364 else:
1365 sqrdata = self.ydata
1367 nshift = int(math.floor(0.5 * (nlong - nshort)))
1368 if nlong % 2 != 0 and nshort % 2 == 0:
1369 nshift += 1
1371 mavg_short = moving_avg(sqrdata, nshort)[nshift:]
1372 mavg_long = moving_avg(sqrdata, nlong)[:sqrdata.size-nshift]
1374 self.drop_growbuffer()
1376 if scalingmethod not in (1, 2, 3):
1377 raise Exception('Invalid argument to scalingrange argument.')
1379 if scalingmethod == 1:
1380 ydata = mavg_short/mavg_long * nshort/nlong
1381 elif scalingmethod in (2, 3):
1382 ydata = (mavg_short/mavg_long - 1.) \
1383 / ((float(nlong)/float(nshort)) - 1)
1385 if scalingmethod == 3:
1386 ydata = num.maximum(self.ydata, 0.)
1388 self.set_ydata(ydata)
1390 self.shift((math.ceil(0.5*nlong) - nshort + 1) * self.deltat)
1392 self.chop(
1393 tmin + (nlong - nshort) * self.deltat,
1394 tmin + (n - nshort) * self.deltat)
1396 def peaks(self, threshold, tsearch,
1397 deadtime=False,
1398 nblock_duration_detection=100):
1400 '''
1401 Detect peaks above a given threshold (method 1).
1403 From every instant, where the signal rises above ``threshold``, a time
1404 length of ``tsearch`` seconds is searched for a maximum. A list with
1405 tuples (time, value) for each detected peak is returned. The
1406 ``deadtime`` argument turns on a special deadtime duration detection
1407 algorithm useful in combination with recursive STA/LTA filters.
1408 '''
1410 y = self.ydata
1411 above = num.where(y > threshold, 1, 0)
1412 deriv = num.zeros(y.size, dtype=num.int8)
1413 deriv[1:] = above[1:]-above[:-1]
1414 itrig_positions = num.nonzero(deriv > 0)[0]
1415 tpeaks = []
1416 apeaks = []
1417 tzeros = []
1418 tzero = self.tmin
1420 for itrig_pos in itrig_positions:
1421 ibeg = itrig_pos
1422 iend = min(
1423 len(self.ydata),
1424 itrig_pos + int(math.ceil(tsearch/self.deltat)))
1425 ipeak = num.argmax(y[ibeg:iend])
1426 tpeak = self.tmin + (ipeak+ibeg)*self.deltat
1427 apeak = y[ibeg+ipeak]
1429 if tpeak < tzero:
1430 continue
1432 if deadtime:
1433 ibeg = itrig_pos
1434 iblock = 0
1435 nblock = nblock_duration_detection
1436 totalsum = 0.
1437 while True:
1438 if ibeg+iblock*nblock >= len(y):
1439 tzero = self.tmin + (len(y)-1) * self.deltat
1440 break
1442 logy = num.log(
1443 y[ibeg+iblock*nblock:ibeg+(iblock+1)*nblock])
1444 logy[0] += totalsum
1445 ysum = num.cumsum(logy)
1446 totalsum = ysum[-1]
1447 below = num.where(ysum <= 0., 1, 0)
1448 deriv = num.zeros(ysum.size, dtype=num.int8)
1449 deriv[1:] = below[1:]-below[:-1]
1450 izero_positions = num.nonzero(deriv > 0)[0] + iblock*nblock
1451 if len(izero_positions) > 0:
1452 tzero = self.tmin + self.deltat * (
1453 ibeg + izero_positions[0])
1454 break
1455 iblock += 1
1456 else:
1457 tzero = ibeg*self.deltat + self.tmin + tsearch
1459 tpeaks.append(tpeak)
1460 apeaks.append(apeak)
1461 tzeros.append(tzero)
1463 if deadtime:
1464 return tpeaks, apeaks, tzeros
1465 else:
1466 return tpeaks, apeaks
1468 def peaks2(self, threshold, tsearch):
1470 '''
1471 Detect peaks above a given threshold (method 2).
1473 This variant of peak detection is a bit more robust (and slower) than
1474 the one implemented in :py:meth:`Trace.peaks`. First all samples with
1475 ``a[i-1] < a[i] > a[i+1]`` are masked as potential peaks. From these,
1476 iteratively the one with the maximum amplitude ``a[j]`` and time
1477 ``t[j]`` is choosen and potential peaks within
1478 ``t[j] - tsearch, t[j] + tsearch``
1479 are discarded. The algorithm stops, when ``a[j] < threshold`` or when
1480 no more potential peaks are left.
1481 '''
1483 a = num.copy(self.ydata)
1485 amin = num.min(a)
1487 a[0] = amin
1488 a[1: -1][num.diff(a, 2) <= 0.] = amin
1489 a[-1] = amin
1491 data = []
1492 while True:
1493 imax = num.argmax(a)
1494 amax = a[imax]
1496 if amax < threshold or amax == amin:
1497 break
1499 data.append((self.tmin + imax * self.deltat, amax))
1501 ntsearch = int(round(tsearch / self.deltat))
1502 a[max(imax-ntsearch//2, 0):min(imax+ntsearch//2, a.size)] = amin
1504 if data:
1505 data.sort()
1506 tpeaks, apeaks = list(zip(*data))
1507 else:
1508 tpeaks, apeaks = [], []
1510 return tpeaks, apeaks
1512 def extend(self, tmin=None, tmax=None, fillmethod='zeros'):
1513 '''
1514 Extend trace to given span.
1516 :param tmin: begin time of new span
1517 :param tmax: end time of new span
1518 :param fillmethod: ``'zeros'``, ``'repeat'``, ``'mean'``, or
1519 ``'median'``
1520 '''
1522 nold = self.ydata.size
1524 if tmin is not None:
1525 nl = min(0, int(round((tmin-self.tmin)/self.deltat)))
1526 else:
1527 nl = 0
1529 if tmax is not None:
1530 nh = max(nold - 1, int(round((tmax-self.tmin)/self.deltat)))
1531 else:
1532 nh = nold - 1
1534 n = nh - nl + 1
1535 data = num.zeros(n, dtype=self.ydata.dtype)
1536 data[-nl:-nl + nold] = self.ydata
1537 if self.ydata.size >= 1:
1538 if fillmethod == 'repeat':
1539 data[:-nl] = self.ydata[0]
1540 data[-nl + nold:] = self.ydata[-1]
1541 elif fillmethod == 'median':
1542 v = num.median(self.ydata)
1543 data[:-nl] = v
1544 data[-nl + nold:] = v
1545 elif fillmethod == 'mean':
1546 v = num.mean(self.ydata)
1547 data[:-nl] = v
1548 data[-nl + nold:] = v
1550 self.drop_growbuffer()
1551 self.ydata = data
1553 self.tmin += nl * self.deltat
1554 self.tmax = self.tmin + (self.ydata.size - 1) * self.deltat
1556 self._update_ids()
1558 def transfer(self,
1559 tfade=0.,
1560 freqlimits=None,
1561 transfer_function=None,
1562 cut_off_fading=True,
1563 demean=True,
1564 invert=False):
1566 '''
1567 Return new trace with transfer function applied (convolution).
1569 :param tfade: rise/fall time in seconds of taper applied in timedomain
1570 at both ends of trace.
1571 :param freqlimits: 4-tuple with corner frequencies in Hz.
1572 :param transfer_function: FrequencyResponse object; must provide a
1573 method 'evaluate(freqs)', which returns the transfer function
1574 coefficients at the frequencies 'freqs'.
1575 :param cut_off_fading: whether to cut off rise/fall interval in output
1576 trace.
1577 :param demean: remove mean before applying transfer function
1578 :param invert: set to True to do a deconvolution
1579 '''
1581 if transfer_function is None:
1582 transfer_function = FrequencyResponse()
1584 if self.tmax - self.tmin <= tfade*2.:
1585 raise TraceTooShort(
1586 'Trace %s.%s.%s.%s too short for fading length setting. '
1587 'trace length = %g, fading length = %g'
1588 % (self.nslc_id + (self.tmax-self.tmin, tfade)))
1590 if freqlimits is None and (
1591 transfer_function is None or transfer_function.is_scalar()):
1593 # special case for flat responses
1595 output = self.copy()
1596 data = self.ydata
1597 ndata = data.size
1599 if transfer_function is not None:
1600 c = num.abs(transfer_function.evaluate(num.ones(1))[0])
1602 if invert:
1603 c = 1.0/c
1605 data *= c
1607 if tfade != 0.0:
1608 data *= costaper(
1609 0., tfade, self.deltat*(ndata-1)-tfade, self.deltat*ndata,
1610 ndata, self.deltat)
1612 output.ydata = data
1614 else:
1615 ndata = self.ydata.size
1616 ntrans = nextpow2(ndata*1.2)
1617 coefs = self._get_tapered_coefs(
1618 ntrans, freqlimits, transfer_function, invert=invert)
1620 data = self.ydata
1622 data_pad = num.zeros(ntrans, dtype=float)
1623 data_pad[:ndata] = data
1624 if demean:
1625 data_pad[:ndata] -= data.mean()
1627 if tfade != 0.0:
1628 data_pad[:ndata] *= costaper(
1629 0., tfade, self.deltat*(ndata-1)-tfade, self.deltat*ndata,
1630 ndata, self.deltat)
1632 fdata = num.fft.rfft(data_pad)
1633 fdata *= coefs
1634 ddata = num.fft.irfft(fdata)
1635 output = self.copy()
1636 output.ydata = ddata[:ndata]
1638 if cut_off_fading and tfade != 0.0:
1639 try:
1640 output.chop(output.tmin+tfade, output.tmax-tfade, inplace=True)
1641 except NoData:
1642 raise TraceTooShort(
1643 'Trace %s.%s.%s.%s too short for fading length setting. '
1644 'trace length = %g, fading length = %g'
1645 % (self.nslc_id + (self.tmax-self.tmin, tfade)))
1646 else:
1647 output.ydata = output.ydata.copy()
1649 return output
1651 def differentiate(self, n=1, order=4, inplace=True):
1652 '''
1653 Approximate first or second derivative of the trace.
1655 :param n: 1 for first derivative, 2 for second
1656 :param order: order of the approximation 2 and 4 are supported
1657 :param inplace: if ``True`` the trace is differentiated in place,
1658 otherwise a new trace object with the derivative is returned.
1660 Raises :py:exc:`ValueError` for unsupported `n` or `order`.
1662 See :py:func:`~pyrocko.util.diff_fd` for implementation details.
1663 '''
1665 ddata = util.diff_fd(n, order, self.deltat, self.ydata)
1667 if inplace:
1668 self.ydata = ddata
1669 else:
1670 output = self.copy(data=False)
1671 output.set_ydata(ddata)
1672 return output
1674 def drop_chain_cache(self):
1675 if self._pchain:
1676 self._pchain.clear()
1678 def init_chain(self):
1679 self._pchain = pchain.Chain(
1680 do_downsample,
1681 do_extend,
1682 do_pre_taper,
1683 do_fft,
1684 do_filter,
1685 do_ifft)
1687 def run_chain(self, tmin, tmax, deltat, setup, nocache):
1688 if setup.domain == 'frequency_domain':
1689 _, _, data = self._pchain(
1690 (self, deltat),
1691 (tmin, tmax),
1692 (setup.taper,),
1693 (setup.filter,),
1694 (setup.filter,),
1695 nocache=nocache)
1697 return num.abs(data), num.abs(data)
1699 else:
1700 processed = self._pchain(
1701 (self, deltat),
1702 (tmin, tmax),
1703 (setup.taper,),
1704 (setup.filter,),
1705 (setup.filter,),
1706 (),
1707 nocache=nocache)
1709 if setup.domain == 'time_domain':
1710 data = processed.get_ydata()
1712 elif setup.domain == 'envelope':
1713 processed = processed.envelope(inplace=False)
1715 elif setup.domain == 'absolute':
1716 processed.set_ydata(num.abs(processed.get_ydata()))
1718 return processed.get_ydata(), processed
1720 def misfit(self, candidate, setup, nocache=False, debug=False):
1721 '''
1722 Calculate misfit and normalization factor against candidate trace.
1724 :param candidate: :py:class:`Trace` object
1725 :param setup: :py:class:`MisfitSetup` object
1726 :returns: tuple ``(m, n)``, where m is the misfit value and n is the
1727 normalization divisor
1729 If the sampling rates of ``self`` and ``candidate`` differ, the trace
1730 with the higher sampling rate will be downsampled.
1731 '''
1733 a = self
1734 b = candidate
1736 for tr in (a, b):
1737 if not tr._pchain:
1738 tr.init_chain()
1740 deltat = max(a.deltat, b.deltat)
1741 tmin = min(a.tmin, b.tmin) - deltat
1742 tmax = max(a.tmax, b.tmax) + deltat
1744 adata, aproc = a.run_chain(tmin, tmax, deltat, setup, nocache)
1745 bdata, bproc = b.run_chain(tmin, tmax, deltat, setup, nocache)
1747 if setup.domain != 'cc_max_norm':
1748 m, n = Lx_norm(bdata, adata, norm=setup.norm)
1749 else:
1750 ctr = correlate(aproc, bproc, mode='full', normalization='normal')
1751 ccmax = ctr.max()[1]
1752 m = 0.5 - 0.5 * ccmax
1753 n = 0.5
1755 if debug:
1756 return m, n, aproc, bproc
1757 else:
1758 return m, n
1760 def spectrum(self, pad_to_pow2=False, tfade=None):
1761 '''
1762 Get FFT spectrum of trace.
1764 :param pad_to_pow2: whether to zero-pad the data to next larger
1765 power-of-two length
1766 :param tfade: ``None`` or a time length in seconds, to apply cosine
1767 shaped tapers to both
1769 :returns: a tuple with (frequencies, values)
1770 '''
1772 ndata = self.ydata.size
1774 if pad_to_pow2:
1775 ntrans = nextpow2(ndata)
1776 else:
1777 ntrans = ndata
1779 if tfade is None:
1780 ydata = self.ydata
1781 else:
1782 ydata = self.ydata * costaper(
1783 0., tfade, self.deltat*(ndata-1)-tfade, self.deltat*ndata,
1784 ndata, self.deltat)
1786 fydata = num.fft.rfft(ydata, ntrans)
1787 df = 1./(ntrans*self.deltat)
1788 fxdata = num.arange(len(fydata))*df
1789 return fxdata, fydata
1791 def multi_filter(self, filter_freqs, bandwidth):
1793 class Gauss(FrequencyResponse):
1794 f0 = Float.T()
1795 a = Float.T(default=1.0)
1797 def __init__(self, f0, a=1.0, **kwargs):
1798 FrequencyResponse.__init__(self, f0=f0, a=a, **kwargs)
1800 def evaluate(self, freqs):
1801 omega0 = 2.*math.pi*self.f0
1802 omega = 2.*math.pi*freqs
1803 return num.exp(-((omega-omega0)
1804 / (self.a*omega0))**2)
1806 freqs, coefs = self.spectrum()
1807 n = self.data_len()
1808 nfilt = len(filter_freqs)
1809 signal_tf = num.zeros((nfilt, n))
1810 centroid_freqs = num.zeros(nfilt)
1811 for ifilt, f0 in enumerate(filter_freqs):
1812 taper = Gauss(f0, a=bandwidth)
1813 weights = taper.evaluate(freqs)
1814 nhalf = freqs.size
1815 analytic_spec = num.zeros(n, dtype=complex)
1816 analytic_spec[:nhalf] = coefs*weights
1818 enorm = num.abs(analytic_spec[:nhalf])**2
1819 enorm /= num.sum(enorm)
1821 if n % 2 == 0:
1822 analytic_spec[1:nhalf-1] *= 2.
1823 else:
1824 analytic_spec[1:nhalf] *= 2.
1826 analytic = num.fft.ifft(analytic_spec)
1827 signal_tf[ifilt, :] = num.abs(analytic)
1829 enorm = num.abs(analytic_spec[:nhalf])**2
1830 enorm /= num.sum(enorm)
1831 centroid_freqs[ifilt] = num.sum(freqs*enorm)
1833 return centroid_freqs, signal_tf
1835 def _get_tapered_coefs(
1836 self, ntrans, freqlimits, transfer_function, invert=False):
1838 deltaf = 1./(self.deltat*ntrans)
1839 nfreqs = ntrans//2 + 1
1840 transfer = num.ones(nfreqs, dtype=complex)
1841 hi = snapper(nfreqs, deltaf)
1842 if freqlimits is not None:
1843 a, b, c, d = freqlimits
1844 freqs = num.arange(hi(d)-hi(a), dtype=float)*deltaf \
1845 + hi(a)*deltaf
1847 if invert:
1848 coeffs = transfer_function.evaluate(freqs)
1849 if num.any(coeffs == 0.0):
1850 raise InfiniteResponse('%s.%s.%s.%s' % self.nslc_id)
1852 transfer[hi(a):hi(d)] = 1.0 / transfer_function.evaluate(freqs)
1853 else:
1854 transfer[hi(a):hi(d)] = transfer_function.evaluate(freqs)
1856 tapered_transfer = costaper(a, b, c, d, nfreqs, deltaf)*transfer
1857 else:
1858 if invert:
1859 raise Exception(
1860 'transfer: `freqlimits` must be given when `invert` is '
1861 'set to `True`')
1863 freqs = num.arange(nfreqs) * deltaf
1864 tapered_transfer = transfer_function.evaluate(freqs)
1866 tapered_transfer[0] = 0.0 # don't introduce static offsets
1867 return tapered_transfer
1869 def fill_template(self, template, **additional):
1870 '''
1871 Fill string template with trace metadata.
1873 Uses normal python '%(placeholder)s' string templates. The following
1874 placeholders are considered: ``network``, ``station``, ``location``,
1875 ``channel``, ``tmin`` (time of first sample), ``tmax`` (time of last
1876 sample), ``tmin_ms``, ``tmax_ms``, ``tmin_us``, ``tmax_us``,
1877 ``tmin_year``, ``tmax_year``, ``tmin_month``, ``tmax_month``,
1878 ``tmin_day``, ``tmax_day``, ``julianday``. The variants ending with
1879 ``'_ms'`` include milliseconds, those with ``'_us'`` include
1880 microseconds, those with ``'_year'`` contain only the year.
1881 '''
1883 template = template.replace('%n', '%(network)s')\
1884 .replace('%s', '%(station)s')\
1885 .replace('%l', '%(location)s')\
1886 .replace('%c', '%(channel)s')\
1887 .replace('%b', '%(tmin)s')\
1888 .replace('%e', '%(tmax)s')\
1889 .replace('%j', '%(julianday)s')
1891 params = dict(
1892 zip(('network', 'station', 'location', 'channel'), self.nslc_id))
1893 params['tmin'] = util.time_to_str(
1894 self.tmin, format='%Y-%m-%d_%H-%M-%S')
1895 params['tmax'] = util.time_to_str(
1896 self.tmax, format='%Y-%m-%d_%H-%M-%S')
1897 params['tmin_ms'] = util.time_to_str(
1898 self.tmin, format='%Y-%m-%d_%H-%M-%S.3FRAC')
1899 params['tmax_ms'] = util.time_to_str(
1900 self.tmax, format='%Y-%m-%d_%H-%M-%S.3FRAC')
1901 params['tmin_us'] = util.time_to_str(
1902 self.tmin, format='%Y-%m-%d_%H-%M-%S.6FRAC')
1903 params['tmax_us'] = util.time_to_str(
1904 self.tmax, format='%Y-%m-%d_%H-%M-%S.6FRAC')
1905 params['tmin_year'], params['tmin_month'], params['tmin_day'] \
1906 = util.time_to_str(self.tmin, format='%Y-%m-%d').split('-')
1907 params['tmax_year'], params['tmax_month'], params['tmax_day'] \
1908 = util.time_to_str(self.tmax, format='%Y-%m-%d').split('-')
1909 params['julianday'] = util.julian_day_of_year(self.tmin)
1910 params.update(additional)
1911 return template % params
1913 def plot(self):
1914 '''
1915 Show trace with matplotlib.
1917 See also: :py:meth:`Trace.snuffle`.
1918 '''
1920 import pylab
1921 pylab.plot(self.get_xdata(), self.get_ydata())
1922 name = '%s %s %s - %s' % (
1923 self.channel,
1924 self.station,
1925 time.strftime("%d-%m-%y %H:%M:%S", time.gmtime(self.tmin)),
1926 time.strftime("%d-%m-%y %H:%M:%S", time.gmtime(self.tmax)))
1928 pylab.title(name)
1929 pylab.show()
1931 def snuffle(self, **kwargs):
1932 '''
1933 Show trace in a snuffler window.
1935 :param stations: list of `pyrocko.model.Station` objects or ``None``
1936 :param events: list of `pyrocko.model.Event` objects or ``None``
1937 :param markers: list of `pyrocko.gui.util.Marker` objects or ``None``
1938 :param ntracks: float, number of tracks to be shown initially (default:
1939 12)
1940 :param follow: time interval (in seconds) for real time follow mode or
1941 ``None``
1942 :param controls: bool, whether to show the main controls (default:
1943 ``True``)
1944 :param opengl: bool, whether to use opengl (default: ``False``)
1945 '''
1947 return snuffle([self], **kwargs)
1950def snuffle(traces, **kwargs):
1951 '''
1952 Show traces in a snuffler window.
1954 :param stations: list of `pyrocko.model.Station` objects or ``None``
1955 :param events: list of `pyrocko.model.Event` objects or ``None``
1956 :param markers: list of `pyrocko.gui.util.Marker` objects or ``None``
1957 :param ntracks: float, number of tracks to be shown initially (default: 12)
1958 :param follow: time interval (in seconds) for real time follow mode or
1959 ``None``
1960 :param controls: bool, whether to show the main controls (default:
1961 ``True``)
1962 :param opengl: bool, whether to use opengl (default: ``False``)
1963 '''
1965 from pyrocko import pile
1966 from pyrocko.gui import snuffler
1967 p = pile.Pile()
1968 if traces:
1969 trf = pile.MemTracesFile(None, traces)
1970 p.add_file(trf)
1971 return snuffler.snuffle(p, **kwargs)
1974class InfiniteResponse(Exception):
1975 '''
1976 This exception is raised by :py:class:`Trace` operations when deconvolution
1977 of a frequency response (instrument response transfer function) would
1978 result in a division by zero.
1979 '''
1982class MisalignedTraces(Exception):
1983 '''
1984 This exception is raised by some :py:class:`Trace` operations when tmin,
1985 tmax or number of samples do not match.
1986 '''
1988 pass
1991class NoData(Exception):
1992 '''
1993 This exception is raised by some :py:class:`Trace` operations when no or
1994 not enough data is available.
1995 '''
1997 pass
2000class AboveNyquist(Exception):
2001 '''
2002 This exception is raised by some :py:class:`Trace` operations when given
2003 frequencies are above the Nyquist frequency.
2004 '''
2006 pass
2009class TraceTooShort(Exception):
2010 '''
2011 This exception is raised by some :py:class:`Trace` operations when the
2012 trace is too short.
2013 '''
2015 pass
2018class ResamplingFailed(Exception):
2019 pass
2022def minmax(traces, key=None, mode='minmax', outer_mode='minmax'):
2024 '''
2025 Get data range given traces grouped by selected pattern.
2027 :param key: a callable which takes as single argument a trace and returns a
2028 key for the grouping of the results. If this is ``None``, the default,
2029 ``lambda tr: (tr.network, tr.station, tr.location, tr.channel)`` is
2030 used.
2031 :param mode: ``'minmax'`` or floating point number. If this is
2032 ``'minmax'``, minimum and maximum of the traces are used, if it is a
2033 number, mean +- standard deviation times ``mode`` is used.
2035 param outer_mode: ``'minmax'`` to use mininum and maximum of the
2036 single-trace ranges, or ``'robust'`` to use the interval to discard 10%
2037 extreme values on either end.
2039 :returns: a dict with the combined data ranges.
2041 Examples::
2043 ranges = minmax(traces, lambda tr: tr.channel)
2044 print ranges['N'] # print min & max of all traces with channel == 'N'
2045 print ranges['E'] # print min & max of all traces with channel == 'E'
2047 ranges = minmax(traces, lambda tr: (tr.network, tr.station))
2048 print ranges['GR', 'HAM3'] # print min & max of all traces with
2049 # network == 'GR' and station == 'HAM3'
2051 ranges = minmax(traces, lambda tr: None)
2052 print ranges[None] # prints min & max of all traces
2053 '''
2055 if key is None:
2056 key = _default_key
2058 ranges = defaultdict(list)
2059 for trace in traces:
2060 if isinstance(mode, str) and mode == 'minmax':
2061 mi, ma = num.nanmin(trace.ydata), num.nanmax(trace.ydata)
2062 else:
2063 mean = trace.ydata.mean()
2064 std = trace.ydata.std()
2065 mi, ma = mean-std*mode, mean+std*mode
2067 k = key(trace)
2068 ranges[k].append((mi, ma))
2070 for k in ranges:
2071 mins, maxs = num.array(ranges[k]).T
2072 if outer_mode == 'minmax':
2073 ranges[k] = num.nanmin(mins), num.nanmax(maxs)
2074 elif outer_mode == 'robust':
2075 ranges[k] = num.percentile(mins, 10.), num.percentile(maxs, 90.)
2077 return ranges
2080def minmaxtime(traces, key=None):
2082 '''
2083 Get time range given traces grouped by selected pattern.
2085 :param key: a callable which takes as single argument a trace and returns a
2086 key for the grouping of the results. If this is ``None``, the default,
2087 ``lambda tr: (tr.network, tr.station, tr.location, tr.channel)`` is
2088 used.
2090 :returns: a dict with the combined data ranges.
2091 '''
2093 if key is None:
2094 key = _default_key
2096 ranges = {}
2097 for trace in traces:
2098 mi, ma = trace.tmin, trace.tmax
2099 k = key(trace)
2100 if k not in ranges:
2101 ranges[k] = mi, ma
2102 else:
2103 tmi, tma = ranges[k]
2104 ranges[k] = min(tmi, mi), max(tma, ma)
2106 return ranges
2109def degapper(
2110 traces,
2111 maxgap=5,
2112 fillmethod='interpolate',
2113 deoverlap='use_second',
2114 maxlap=None):
2116 '''
2117 Try to connect traces and remove gaps.
2119 This method will combine adjacent traces, which match in their network,
2120 station, location and channel attributes. Overlapping parts are handled
2121 according to the ``deoverlap`` argument.
2123 :param traces: input traces, must be sorted by their full_id attribute.
2124 :param maxgap: maximum number of samples to interpolate.
2125 :param fillmethod: what to put into the gaps: 'interpolate' or 'zeros'.
2126 :param deoverlap: how to handle overlaps: 'use_second' to use data from
2127 second trace (default), 'use_first' to use data from first trace,
2128 'crossfade_cos' to crossfade with cosine taper, 'add' to add amplitude
2129 values.
2130 :param maxlap: maximum number of samples of overlap which are removed
2132 :returns: list of traces
2133 '''
2135 in_traces = traces
2136 out_traces = []
2137 if not in_traces:
2138 return out_traces
2139 out_traces.append(in_traces.pop(0))
2140 while in_traces:
2142 a = out_traces[-1]
2143 b = in_traces.pop(0)
2145 avirt, bvirt = a.ydata is None, b.ydata is None
2146 assert avirt == bvirt, \
2147 'traces given to degapper() must either all have data or have ' \
2148 'no data.'
2150 virtual = avirt and bvirt
2152 if (a.nslc_id == b.nslc_id and a.deltat == b.deltat
2153 and a.data_len() >= 1 and b.data_len() >= 1
2154 and (virtual or a.ydata.dtype == b.ydata.dtype)):
2156 dist = (b.tmin-(a.tmin+(a.data_len()-1)*a.deltat))/a.deltat
2157 idist = int(round(dist))
2158 if abs(dist - idist) > 0.05 and idist <= maxgap:
2159 # logger.warning('Cannot degap traces with displaced sampling '
2160 # '(%s, %s, %s, %s)' % a.nslc_id)
2161 pass
2162 else:
2163 if 1 < idist <= maxgap:
2164 if not virtual:
2165 if fillmethod == 'interpolate':
2166 filler = a.ydata[-1] + (
2167 ((1.0 + num.arange(idist-1, dtype=float))
2168 / idist) * (b.ydata[0]-a.ydata[-1])
2169 ).astype(a.ydata.dtype)
2170 elif fillmethod == 'zeros':
2171 filler = num.zeros(idist-1, dtype=a.ydist.dtype)
2172 a.ydata = num.concatenate((a.ydata, filler, b.ydata))
2173 a.tmax = b.tmax
2174 if a.mtime and b.mtime:
2175 a.mtime = max(a.mtime, b.mtime)
2176 continue
2178 elif idist == 1:
2179 if not virtual:
2180 a.ydata = num.concatenate((a.ydata, b.ydata))
2181 a.tmax = b.tmax
2182 if a.mtime and b.mtime:
2183 a.mtime = max(a.mtime, b.mtime)
2184 continue
2186 elif idist <= 0 and (maxlap is None or -maxlap < idist):
2187 if b.tmax > a.tmax:
2188 if not virtual:
2189 na = a.ydata.size
2190 n = -idist+1
2191 if deoverlap == 'use_second':
2192 a.ydata = num.concatenate(
2193 (a.ydata[:-n], b.ydata))
2194 elif deoverlap in ('use_first', 'crossfade_cos'):
2195 a.ydata = num.concatenate(
2196 (a.ydata, b.ydata[n:]))
2197 elif deoverlap == 'add':
2198 a.ydata[-n:] += b.ydata[:n]
2199 a.ydata = num.concatenate(
2200 (a.ydata, b.ydata[n:]))
2201 else:
2202 assert False, 'unknown deoverlap method'
2204 if deoverlap == 'crossfade_cos':
2205 n = -idist+1
2206 taper = 0.5-0.5*num.cos(
2207 (1.+num.arange(n))/(1.+n)*num.pi)
2208 a.ydata[na-n:na] *= 1.-taper
2209 a.ydata[na-n:na] += b.ydata[:n] * taper
2211 a.tmax = b.tmax
2212 if a.mtime and b.mtime:
2213 a.mtime = max(a.mtime, b.mtime)
2214 continue
2215 else:
2216 # make short second trace vanish
2217 continue
2219 if b.data_len() >= 1:
2220 out_traces.append(b)
2222 for tr in out_traces:
2223 tr._update_ids()
2225 return out_traces
2228def rotate(traces, azimuth, in_channels, out_channels):
2229 '''
2230 2D rotation of traces.
2232 :param traces: list of input traces
2233 :param azimuth: difference of the azimuths of the component directions
2234 (azimuth of out_channels[0]) - (azimuth of in_channels[0])
2235 :param in_channels: names of the input channels (e.g. 'N', 'E')
2236 :param out_channels: names of the output channels (e.g. 'R', 'T')
2237 :returns: list of rotated traces
2238 '''
2240 phi = azimuth/180.*math.pi
2241 cphi = math.cos(phi)
2242 sphi = math.sin(phi)
2243 rotated = []
2244 in_channels = tuple(_channels_to_names(in_channels))
2245 out_channels = tuple(_channels_to_names(out_channels))
2246 for a in traces:
2247 for b in traces:
2248 if ((a.channel, b.channel) == in_channels and
2249 a.nslc_id[:3] == b.nslc_id[:3] and
2250 abs(a.deltat-b.deltat) < a.deltat*0.001):
2251 tmin = max(a.tmin, b.tmin)
2252 tmax = min(a.tmax, b.tmax)
2254 if tmin < tmax:
2255 ac = a.chop(tmin, tmax, inplace=False, include_last=True)
2256 bc = b.chop(tmin, tmax, inplace=False, include_last=True)
2257 if abs(ac.tmin - bc.tmin) > ac.deltat*0.01:
2258 logger.warning(
2259 'Cannot rotate traces with displaced sampling '
2260 '(%s, %s, %s, %s)' % a.nslc_id)
2261 continue
2263 acydata = ac.get_ydata()*cphi+bc.get_ydata()*sphi
2264 bcydata = -ac.get_ydata()*sphi+bc.get_ydata()*cphi
2265 ac.set_ydata(acydata)
2266 bc.set_ydata(bcydata)
2268 ac.set_codes(channel=out_channels[0])
2269 bc.set_codes(channel=out_channels[1])
2270 rotated.append(ac)
2271 rotated.append(bc)
2273 return rotated
2276def rotate_to_rt(n, e, source, receiver, out_channels=('R', 'T')):
2277 '''
2278 Rotate traces from NE to RT system.
2280 :param n:
2281 North trace.
2282 :type n:
2283 :py:class:`~pyrocko.trace.Trace`
2285 :param e:
2286 East trace.
2287 :type e:
2288 :py:class:`~pyrocko.trace.Trace`
2290 :param source:
2291 Source of the recorded signal.
2292 :type source:
2293 :py:class:`pyrocko.gf.seismosizer.Source`
2295 :param receiver:
2296 Receiver of the recorded signal.
2297 :type receiver:
2298 :py:class:`pyrocko.model.location.Location`
2300 :param out_channels:
2301 Output channel names (default: ('R', 'T')).
2302 :type out_channels:
2303 optional, tuple of str
2305 :returns:
2306 Rotated traces (radial and transversal).
2307 :rtype:
2308 :py:class:`~pyrocko.trace.Trace`,
2309 :py:class:`~pyrocko.trace.Trace`
2310 '''
2311 azimuth = orthodrome.azimuth(receiver, source) + 180.
2312 in_channels = n.channel, e.channel
2313 out = rotate(
2314 [n, e], azimuth,
2315 in_channels=in_channels,
2316 out_channels=out_channels)
2318 assert len(out) == 2
2319 for tr in out:
2320 if tr.channel == 'R':
2321 r = tr
2322 elif tr.channel == 'T':
2323 t = tr
2325 return r, t
2328def rotate_to_lqt(traces, backazimuth, incidence, in_channels,
2329 out_channels=('L', 'Q', 'T')):
2330 '''
2331 Rotate traces from ZNE to LQT system.
2333 :param traces: list of traces in arbitrary order
2334 :param backazimuth: backazimuth in degrees clockwise from north
2335 :param incidence: incidence angle in degrees from vertical
2336 :param in_channels: input channel names
2337 :param out_channels: output channel names (default: ('L', 'Q', 'T'))
2338 :returns: list of transformed traces
2339 '''
2340 i = incidence/180.*num.pi
2341 b = backazimuth/180.*num.pi
2343 ci = num.cos(i)
2344 cb = num.cos(b)
2345 si = num.sin(i)
2346 sb = num.sin(b)
2348 rotmat = num.array(
2349 [[ci, -cb*si, -sb*si], [si, cb*ci, sb*ci], [0., sb, -cb]])
2350 return project(traces, rotmat, in_channels, out_channels)
2353def _decompose(a):
2354 '''
2355 Decompose matrix into independent submatrices.
2356 '''
2358 def depends(iout, a):
2359 row = a[iout, :]
2360 return set(num.arange(row.size).compress(row != 0.0))
2362 def provides(iin, a):
2363 col = a[:, iin]
2364 return set(num.arange(col.size).compress(col != 0.0))
2366 a = num.asarray(a)
2367 outs = set(range(a.shape[0]))
2368 systems = []
2369 while outs:
2370 iout = outs.pop()
2372 gout = set()
2373 for iin in depends(iout, a):
2374 gout.update(provides(iin, a))
2376 if not gout:
2377 continue
2379 gin = set()
2380 for iout2 in gout:
2381 gin.update(depends(iout2, a))
2383 if not gin:
2384 continue
2386 for iout2 in gout:
2387 if iout2 in outs:
2388 outs.remove(iout2)
2390 gin = list(gin)
2391 gin.sort()
2392 gout = list(gout)
2393 gout.sort()
2395 systems.append((gin, gout, a[gout, :][:, gin]))
2397 return systems
2400def _channels_to_names(channels):
2401 names = []
2402 for ch in channels:
2403 if isinstance(ch, model.Channel):
2404 names.append(ch.name)
2405 else:
2406 names.append(ch)
2407 return names
2410def project(traces, matrix, in_channels, out_channels):
2411 '''
2412 Affine transform of three-component traces.
2414 Compute matrix-vector product of three-component traces, to e.g. rotate
2415 traces into a different basis. The traces are distinguished and ordered by
2416 their channel attribute. The tranform is applied to overlapping parts of
2417 any appropriate combinations of the input traces. This should allow this
2418 function to be robust with data gaps. It also tries to apply the
2419 tranformation to subsets of the channels, if this is possible, so that, if
2420 for example a vertical compontent is missing, horizontal components can
2421 still be rotated.
2423 :param traces: list of traces in arbitrary order
2424 :param matrix: tranformation matrix
2425 :param in_channels: input channel names
2426 :param out_channels: output channel names
2427 :returns: list of transformed traces
2428 '''
2430 in_channels = tuple(_channels_to_names(in_channels))
2431 out_channels = tuple(_channels_to_names(out_channels))
2432 systems = _decompose(matrix)
2434 # fallback to full matrix if some are not quadratic
2435 for iins, iouts, submatrix in systems:
2436 if submatrix.shape[0] != submatrix.shape[1]:
2437 return _project3(traces, matrix, in_channels, out_channels)
2439 projected = []
2440 for iins, iouts, submatrix in systems:
2441 in_cha = tuple([in_channels[iin] for iin in iins])
2442 out_cha = tuple([out_channels[iout] for iout in iouts])
2443 if submatrix.shape[0] == 1:
2444 projected.extend(_project1(traces, submatrix, in_cha, out_cha))
2445 elif submatrix.shape[1] == 2:
2446 projected.extend(_project2(traces, submatrix, in_cha, out_cha))
2447 else:
2448 projected.extend(_project3(traces, submatrix, in_cha, out_cha))
2450 return projected
2453def project_dependencies(matrix, in_channels, out_channels):
2454 '''
2455 Figure out what dependencies project() would produce.
2456 '''
2458 in_channels = tuple(_channels_to_names(in_channels))
2459 out_channels = tuple(_channels_to_names(out_channels))
2460 systems = _decompose(matrix)
2462 subpro = []
2463 for iins, iouts, submatrix in systems:
2464 if submatrix.shape[0] != submatrix.shape[1]:
2465 subpro.append((matrix, in_channels, out_channels))
2467 if not subpro:
2468 for iins, iouts, submatrix in systems:
2469 in_cha = tuple([in_channels[iin] for iin in iins])
2470 out_cha = tuple([out_channels[iout] for iout in iouts])
2471 subpro.append((submatrix, in_cha, out_cha))
2473 deps = {}
2474 for mat, in_cha, out_cha in subpro:
2475 for oc in out_cha:
2476 if oc not in deps:
2477 deps[oc] = []
2479 for ic in in_cha:
2480 deps[oc].append(ic)
2482 return deps
2485def _project1(traces, matrix, in_channels, out_channels):
2486 assert len(in_channels) == 1
2487 assert len(out_channels) == 1
2488 assert matrix.shape == (1, 1)
2490 projected = []
2491 for a in traces:
2492 if not (a.channel,) == in_channels:
2493 continue
2495 ac = a.copy()
2496 ac.set_ydata(matrix[0, 0]*a.get_ydata())
2497 ac.set_codes(channel=out_channels[0])
2498 projected.append(ac)
2500 return projected
2503def _project2(traces, matrix, in_channels, out_channels):
2504 assert len(in_channels) == 2
2505 assert len(out_channels) == 2
2506 assert matrix.shape == (2, 2)
2507 projected = []
2508 for a in traces:
2509 for b in traces:
2510 if not ((a.channel, b.channel) == in_channels and
2511 a.nslc_id[:3] == b.nslc_id[:3] and
2512 abs(a.deltat-b.deltat) < a.deltat*0.001):
2513 continue
2515 tmin = max(a.tmin, b.tmin)
2516 tmax = min(a.tmax, b.tmax)
2518 if tmin > tmax:
2519 continue
2521 ac = a.chop(tmin, tmax, inplace=False, include_last=True)
2522 bc = b.chop(tmin, tmax, inplace=False, include_last=True)
2523 if abs(ac.tmin - bc.tmin) > ac.deltat*0.01:
2524 logger.warning(
2525 'Cannot project traces with displaced sampling '
2526 '(%s, %s, %s, %s)' % a.nslc_id)
2527 continue
2529 acydata = num.dot(matrix[0], (ac.get_ydata(), bc.get_ydata()))
2530 bcydata = num.dot(matrix[1], (ac.get_ydata(), bc.get_ydata()))
2532 ac.set_ydata(acydata)
2533 bc.set_ydata(bcydata)
2535 ac.set_codes(channel=out_channels[0])
2536 bc.set_codes(channel=out_channels[1])
2538 projected.append(ac)
2539 projected.append(bc)
2541 return projected
2544def _project3(traces, matrix, in_channels, out_channels):
2545 assert len(in_channels) == 3
2546 assert len(out_channels) == 3
2547 assert matrix.shape == (3, 3)
2548 projected = []
2549 for a in traces:
2550 for b in traces:
2551 for c in traces:
2552 if not ((a.channel, b.channel, c.channel) == in_channels
2553 and a.nslc_id[:3] == b.nslc_id[:3]
2554 and b.nslc_id[:3] == c.nslc_id[:3]
2555 and abs(a.deltat-b.deltat) < a.deltat*0.001
2556 and abs(b.deltat-c.deltat) < b.deltat*0.001):
2558 continue
2560 tmin = max(a.tmin, b.tmin, c.tmin)
2561 tmax = min(a.tmax, b.tmax, c.tmax)
2563 if tmin >= tmax:
2564 continue
2566 ac = a.chop(tmin, tmax, inplace=False, include_last=True)
2567 bc = b.chop(tmin, tmax, inplace=False, include_last=True)
2568 cc = c.chop(tmin, tmax, inplace=False, include_last=True)
2569 if (abs(ac.tmin - bc.tmin) > ac.deltat*0.01
2570 or abs(bc.tmin - cc.tmin) > bc.deltat*0.01):
2572 logger.warning(
2573 'Cannot project traces with displaced sampling '
2574 '(%s, %s, %s, %s)' % a.nslc_id)
2575 continue
2577 acydata = num.dot(
2578 matrix[0],
2579 (ac.get_ydata(), bc.get_ydata(), cc.get_ydata()))
2580 bcydata = num.dot(
2581 matrix[1],
2582 (ac.get_ydata(), bc.get_ydata(), cc.get_ydata()))
2583 ccydata = num.dot(
2584 matrix[2],
2585 (ac.get_ydata(), bc.get_ydata(), cc.get_ydata()))
2587 ac.set_ydata(acydata)
2588 bc.set_ydata(bcydata)
2589 cc.set_ydata(ccydata)
2591 ac.set_codes(channel=out_channels[0])
2592 bc.set_codes(channel=out_channels[1])
2593 cc.set_codes(channel=out_channels[2])
2595 projected.append(ac)
2596 projected.append(bc)
2597 projected.append(cc)
2599 return projected
2602def correlate(a, b, mode='valid', normalization=None, use_fft=False):
2603 '''
2604 Cross correlation of two traces.
2606 :param a,b: input traces
2607 :param mode: ``'valid'``, ``'full'``, or ``'same'``
2608 :param normalization: ``'normal'``, ``'gliding'``, or ``None``
2609 :param use_fft: bool, whether to do cross correlation in spectral domain
2611 :returns: trace containing cross correlation coefficients
2613 This function computes the cross correlation between two traces. It
2614 evaluates the discrete equivalent of
2616 .. math::
2618 c(t) = \\int_{-\\infty}^{\\infty} a^{\\ast}(\\tau) b(t+\\tau) d\\tau
2620 where the star denotes complex conjugate. Note, that the arguments here are
2621 swapped when compared with the :py:func:`numpy.correlate` function,
2622 which is internally called. This function should be safe even with older
2623 versions of NumPy, where the correlate function has some problems.
2625 A trace containing the cross correlation coefficients is returned. The time
2626 information of the output trace is set so that the returned cross
2627 correlation can be viewed directly as a function of time lag.
2629 Example::
2631 # align two traces a and b containing a time shifted similar signal:
2632 c = pyrocko.trace.correlate(a,b)
2633 t, coef = c.max() # get time and value of maximum
2634 b.shift(-t) # align b with a
2636 '''
2638 assert_same_sampling_rate(a, b)
2640 ya, yb = a.ydata, b.ydata
2642 # need reversed order here:
2643 yc = numpy_correlate_fixed(yb, ya, mode=mode, use_fft=use_fft)
2644 kmin, kmax = numpy_correlate_lag_range(yb, ya, mode=mode, use_fft=use_fft)
2646 if normalization == 'normal':
2647 normfac = num.sqrt(num.sum(ya**2))*num.sqrt(num.sum(yb**2))
2648 yc = yc/normfac
2650 elif normalization == 'gliding':
2651 if mode != 'valid':
2652 assert False, 'gliding normalization currently only available ' \
2653 'with "valid" mode.'
2655 if ya.size < yb.size:
2656 yshort, ylong = ya, yb
2657 else:
2658 yshort, ylong = yb, ya
2660 epsilon = 0.00001
2661 normfac_short = num.sqrt(num.sum(yshort**2))
2662 normfac = normfac_short * num.sqrt(
2663 moving_sum(ylong**2, yshort.size, mode='valid')) \
2664 + normfac_short*epsilon
2666 if yb.size <= ya.size:
2667 normfac = normfac[::-1]
2669 yc /= normfac
2671 c = a.copy()
2672 c.set_ydata(yc)
2673 c.set_codes(*merge_codes(a, b, '~'))
2674 c.shift(-c.tmin + b.tmin-a.tmin + kmin * c.deltat)
2676 return c
2679def deconvolve(
2680 a, b, waterlevel,
2681 tshift=0.,
2682 pad=0.5,
2683 fd_taper=None,
2684 pad_to_pow2=True):
2686 same_sampling_rate(a, b)
2687 assert abs(a.tmin - b.tmin) < a.deltat * 0.001
2688 deltat = a.deltat
2689 npad = int(round(a.data_len()*pad + tshift / deltat))
2691 ndata = max(a.data_len(), b.data_len())
2692 ndata_pad = ndata + npad
2694 if pad_to_pow2:
2695 ntrans = nextpow2(ndata_pad)
2696 else:
2697 ntrans = ndata
2699 aspec = num.fft.rfft(a.ydata, ntrans)
2700 bspec = num.fft.rfft(b.ydata, ntrans)
2702 out = aspec * num.conj(bspec)
2704 bautocorr = bspec*num.conj(bspec)
2705 denom = num.maximum(bautocorr, waterlevel * bautocorr.max())
2707 out /= denom
2708 df = 1/(ntrans*deltat)
2710 if fd_taper is not None:
2711 fd_taper(out, 0.0, df)
2713 ydata = num.roll(num.fft.irfft(out), int(round(tshift/deltat)))
2714 c = a.copy(data=False)
2715 c.set_ydata(ydata[:ndata])
2716 c.set_codes(*merge_codes(a, b, '/'))
2717 return c
2720def assert_same_sampling_rate(a, b, eps=1.0e-6):
2721 assert same_sampling_rate(a, b, eps), \
2722 'Sampling rates differ: %g != %g' % (a.deltat, b.deltat)
2725def same_sampling_rate(a, b, eps=1.0e-6):
2726 '''
2727 Check if two traces have the same sampling rate.
2729 :param a,b: input traces
2730 :param eps: relative tolerance
2731 '''
2733 return abs(a.deltat - b.deltat) < (a.deltat + b.deltat)*eps
2736def fix_deltat_rounding_errors(deltat):
2737 '''
2738 Try to undo sampling rate rounding errors.
2740 Fix rounding errors of sampling intervals when these are read from single
2741 precision floating point values.
2743 Assumes that the true sampling rate or sampling interval was an integer
2744 value. No correction will be applied if this would change the sampling
2745 rate by more than 0.001%.
2746 '''
2748 if deltat <= 1.0:
2749 deltat_new = 1.0 / round(1.0 / deltat)
2750 else:
2751 deltat_new = round(deltat)
2753 if abs(deltat_new - deltat) / deltat > 1e-5:
2754 deltat_new = deltat
2756 return deltat_new
2759def merge_codes(a, b, sep='-'):
2760 '''
2761 Merge network-station-location-channel codes of a pair of traces.
2762 '''
2764 o = []
2765 for xa, xb in zip(a.nslc_id, b.nslc_id):
2766 if xa == xb:
2767 o.append(xa)
2768 else:
2769 o.append(sep.join((xa, xb)))
2770 return o
2773class Taper(Object):
2774 '''
2775 Base class for tapers.
2777 Does nothing by default.
2778 '''
2780 def __call__(self, y, x0, dx):
2781 pass
2784class CosTaper(Taper):
2785 '''
2786 Cosine Taper.
2788 :param a: start of fading in
2789 :param b: end of fading in
2790 :param c: start of fading out
2791 :param d: end of fading out
2792 '''
2794 a = Float.T()
2795 b = Float.T()
2796 c = Float.T()
2797 d = Float.T()
2799 def __init__(self, a, b, c, d):
2800 Taper.__init__(self, a=a, b=b, c=c, d=d)
2802 def __call__(self, y, x0, dx):
2803 apply_costaper(self.a, self.b, self.c, self.d, y, x0, dx)
2805 def span(self, y, x0, dx):
2806 return span_costaper(self.a, self.b, self.c, self.d, y, x0, dx)
2808 def time_span(self):
2809 return self.a, self.d
2812class CosFader(Taper):
2813 '''
2814 Cosine Fader.
2816 :param xfade: fade in and fade out time in seconds (optional)
2817 :param xfrac: fade in and fade out as fraction between 0. and 1. (optional)
2819 Only one argument can be set. The other should to be ``None``.
2820 '''
2822 xfade = Float.T(optional=True)
2823 xfrac = Float.T(optional=True)
2825 def __init__(self, xfade=None, xfrac=None):
2826 Taper.__init__(self, xfade=xfade, xfrac=xfrac)
2827 assert (xfade is None) != (xfrac is None)
2828 self._xfade = xfade
2829 self._xfrac = xfrac
2831 def __call__(self, y, x0, dx):
2833 xfade = self._xfade
2835 xlen = (y.size - 1)*dx
2836 if xfade is None:
2837 xfade = xlen * self._xfrac
2839 a = x0
2840 b = x0 + xfade
2841 c = x0 + xlen - xfade
2842 d = x0 + xlen
2844 apply_costaper(a, b, c, d, y, x0, dx)
2846 def span(self, y, x0, dx):
2847 return 0, y.size
2849 def time_span(self):
2850 return None, None
2853def none_min(li):
2854 if None in li:
2855 return None
2856 else:
2857 return min(x for x in li if x is not None)
2860def none_max(li):
2861 if None in li:
2862 return None
2863 else:
2864 return max(x for x in li if x is not None)
2867class MultiplyTaper(Taper):
2868 '''
2869 Multiplication of several tapers.
2870 '''
2872 tapers = List.T(Taper.T())
2874 def __init__(self, tapers=None):
2875 if tapers is None:
2876 tapers = []
2878 Taper.__init__(self, tapers=tapers)
2880 def __call__(self, y, x0, dx):
2881 for taper in self.tapers:
2882 taper(y, x0, dx)
2884 def span(self, y, x0, dx):
2885 spans = []
2886 for taper in self.tapers:
2887 spans.append(taper.span(y, x0, dx))
2889 mins, maxs = list(zip(*spans))
2890 return min(mins), max(maxs)
2892 def time_span(self):
2893 spans = []
2894 for taper in self.tapers:
2895 spans.append(taper.time_span())
2897 mins, maxs = list(zip(*spans))
2898 return none_min(mins), none_max(maxs)
2901class GaussTaper(Taper):
2902 '''
2903 Frequency domain Gaussian filter.
2904 '''
2906 alpha = Float.T()
2908 def __init__(self, alpha):
2909 Taper.__init__(self, alpha=alpha)
2910 self._alpha = alpha
2912 def __call__(self, y, x0, dx):
2913 f = x0 + num.arange(y.size)*dx
2914 y *= num.exp(-num.pi**2 / (self._alpha**2) * f**2)
2917cached_coefficients = {}
2920def _get_cached_filter_coefs(order, corners, btype):
2921 ck = (order, tuple(corners), btype)
2922 if ck not in cached_coefficients:
2923 if len(corners) == 0:
2924 cached_coefficients[ck] = signal.butter(
2925 order, corners[0], btype=btype)
2926 else:
2927 cached_coefficients[ck] = signal.butter(
2928 order, corners, btype=btype)
2930 return cached_coefficients[ck]
2933class _globals(object):
2934 _numpy_has_correlate_flip_bug = None
2937def _default_key(tr):
2938 return (tr.network, tr.station, tr.location, tr.channel)
2941def numpy_has_correlate_flip_bug():
2942 '''
2943 Check if NumPy's correlate function reveals old behaviour.
2944 '''
2946 if _globals._numpy_has_correlate_flip_bug is None:
2947 a = num.array([0, 0, 1, 0, 0, 0, 0])
2948 b = num.array([0, 0, 0, 0, 1, 0, 0, 0])
2949 ab = num.correlate(a, b, mode='same')
2950 ba = num.correlate(b, a, mode='same')
2951 _globals._numpy_has_correlate_flip_bug = num.all(ab == ba)
2953 return _globals._numpy_has_correlate_flip_bug
2956def numpy_correlate_fixed(a, b, mode='valid', use_fft=False):
2957 '''
2958 Call :py:func:`numpy.correlate` with fixes.
2960 c[k] = sum_i a[i+k] * conj(b[i])
2962 Note that the result produced by newer numpy.correlate is always flipped
2963 with respect to the formula given in its documentation (if ascending k
2964 assumed for the output).
2965 '''
2967 if use_fft:
2968 if a.size < b.size:
2969 c = signal.fftconvolve(b[::-1], a, mode=mode)
2970 else:
2971 c = signal.fftconvolve(a, b[::-1], mode=mode)
2972 return c
2974 else:
2975 buggy = numpy_has_correlate_flip_bug()
2977 a = num.asarray(a)
2978 b = num.asarray(b)
2980 if buggy:
2981 b = num.conj(b)
2983 c = num.correlate(a, b, mode=mode)
2985 if buggy and a.size < b.size:
2986 return c[::-1]
2987 else:
2988 return c
2991def numpy_correlate_emulate(a, b, mode='valid'):
2992 '''
2993 Slow version of :py:func:`numpy.correlate` for comparison.
2994 '''
2996 a = num.asarray(a)
2997 b = num.asarray(b)
2998 kmin = -(b.size-1)
2999 klen = a.size-kmin
3000 kmin, kmax = numpy_correlate_lag_range(a, b, mode=mode)
3001 kmin = int(kmin)
3002 kmax = int(kmax)
3003 klen = kmax - kmin + 1
3004 c = num.zeros(klen, dtype=num.find_common_type((b.dtype, a.dtype), ()))
3005 for k in range(kmin, kmin+klen):
3006 imin = max(0, -k)
3007 ilen = min(b.size, a.size-k) - imin
3008 c[k-kmin] = num.sum(
3009 a[imin+k:imin+ilen+k] * num.conj(b[imin:imin+ilen]))
3011 return c
3014def numpy_correlate_lag_range(a, b, mode='valid', use_fft=False):
3015 '''
3016 Get range of lags for which :py:func:`numpy.correlate` produces values.
3017 '''
3019 a = num.asarray(a)
3020 b = num.asarray(b)
3022 kmin = -(b.size-1)
3023 if mode == 'full':
3024 klen = a.size-kmin
3025 elif mode == 'same':
3026 klen = max(a.size, b.size)
3027 kmin += (a.size+b.size-1 - max(a.size, b.size)) // 2 + \
3028 int(not use_fft and a.size % 2 == 0 and b.size > a.size)
3029 elif mode == 'valid':
3030 klen = abs(a.size - b.size) + 1
3031 kmin += min(a.size, b.size) - 1
3033 return kmin, kmin + klen - 1
3036def autocorr(x, nshifts):
3037 '''
3038 Compute biased estimate of the first autocorrelation coefficients.
3040 :param x: input array
3041 :param nshifts: number of coefficients to calculate
3042 '''
3044 mean = num.mean(x)
3045 std = num.std(x)
3046 n = x.size
3047 xdm = x - mean
3048 r = num.zeros(nshifts)
3049 for k in range(nshifts):
3050 r[k] = 1./((n-num.abs(k))*std) * num.sum(xdm[:n-k] * xdm[k:])
3052 return r
3055def yulewalker(x, order):
3056 '''
3057 Compute autoregression coefficients using Yule-Walker method.
3059 :param x: input array
3060 :param order: number of coefficients to produce
3062 A biased estimate of the autocorrelation is used. The Yule-Walker equations
3063 are solved by :py:func:`numpy.linalg.inv` instead of Levinson-Durbin
3064 recursion which is normally used.
3065 '''
3067 gamma = autocorr(x, order+1)
3068 d = gamma[1:1+order]
3069 a = num.zeros((order, order))
3070 gamma2 = num.concatenate((gamma[::-1], gamma[1:order]))
3071 for i in range(order):
3072 ioff = order-i
3073 a[i, :] = gamma2[ioff:ioff+order]
3075 return num.dot(num.linalg.inv(a), -d)
3078def moving_avg(x, n):
3079 n = int(n)
3080 cx = x.cumsum()
3081 nn = len(x)
3082 y = num.zeros(nn, dtype=cx.dtype)
3083 y[n//2:n//2+(nn-n)] = (cx[n:]-cx[:-n])/n
3084 y[:n//2] = y[n//2]
3085 y[n//2+(nn-n):] = y[n//2+(nn-n)-1]
3086 return y
3089def moving_sum(x, n, mode='valid'):
3090 n = int(n)
3091 cx = x.cumsum()
3092 nn = len(x)
3094 if mode == 'valid':
3095 if nn-n+1 <= 0:
3096 return num.zeros(0, dtype=cx.dtype)
3097 y = num.zeros(nn-n+1, dtype=cx.dtype)
3098 y[0] = cx[n-1]
3099 y[1:nn-n+1] = cx[n:nn]-cx[0:nn-n]
3101 if mode == 'full':
3102 y = num.zeros(nn+n-1, dtype=cx.dtype)
3103 if n <= nn:
3104 y[0:n] = cx[0:n]
3105 y[n:nn] = cx[n:nn]-cx[0:nn-n]
3106 y[nn:nn+n-1] = cx[-1]-cx[nn-n:nn-1]
3107 else:
3108 y[0:nn] = cx[0:nn]
3109 y[nn:n] = cx[nn-1]
3110 y[n:nn+n-1] = cx[nn-1] - cx[0:nn-1]
3112 if mode == 'same':
3113 n1 = (n-1)//2
3114 y = num.zeros(nn, dtype=cx.dtype)
3115 if n <= nn:
3116 y[0:n-n1] = cx[n1:n]
3117 y[n-n1:nn-n1] = cx[n:nn]-cx[0:nn-n]
3118 y[nn-n1:nn] = cx[nn-1] - cx[nn-n:nn-n+n1]
3119 else:
3120 y[0:max(0, nn-n1)] = cx[min(n1, nn):nn]
3121 y[max(nn-n1, 0):min(n-n1, nn)] = cx[nn-1]
3122 y[min(n-n1, nn):nn] = cx[nn-1] - cx[0:max(0, nn-(n-n1))]
3124 return y
3127def nextpow2(i):
3128 return 2**int(math.ceil(math.log(i)/math.log(2.)))
3131def snapper_w_offset(nmax, offset, delta, snapfun=math.ceil):
3132 def snap(x):
3133 return max(0, min(int(snapfun((x-offset)/delta)), nmax))
3134 return snap
3137def snapper(nmax, delta, snapfun=math.ceil):
3138 def snap(x):
3139 return max(0, min(int(snapfun(x/delta)), nmax))
3140 return snap
3143def apply_costaper(a, b, c, d, y, x0, dx):
3144 hi = snapper_w_offset(y.size, x0, dx)
3145 y[:hi(a)] = 0.
3146 y[hi(a):hi(b)] *= 0.5 \
3147 - 0.5*num.cos((dx*num.arange(hi(a), hi(b))-(a-x0))/(b-a)*num.pi)
3148 y[hi(c):hi(d)] *= 0.5 \
3149 + 0.5*num.cos((dx*num.arange(hi(c), hi(d))-(c-x0))/(d-c)*num.pi)
3150 y[hi(d):] = 0.
3153def span_costaper(a, b, c, d, y, x0, dx):
3154 hi = snapper_w_offset(y.size, x0, dx)
3155 return hi(a), hi(d) - hi(a)
3158def costaper(a, b, c, d, nfreqs, deltaf):
3159 hi = snapper(nfreqs, deltaf)
3160 tap = num.zeros(nfreqs)
3161 tap[hi(a):hi(b)] = 0.5 \
3162 - 0.5*num.cos((deltaf*num.arange(hi(a), hi(b))-a)/(b-a)*num.pi)
3163 tap[hi(b):hi(c)] = 1.
3164 tap[hi(c):hi(d)] = 0.5 \
3165 + 0.5*num.cos((deltaf*num.arange(hi(c), hi(d))-c)/(d-c)*num.pi)
3167 return tap
3170def t2ind(t, tdelta, snap=round):
3171 return int(snap(t/tdelta))
3174def hilbert(x, N=None):
3175 '''
3176 Return the hilbert transform of x of length N.
3178 (from scipy.signal, but changed to use fft and ifft from numpy.fft)
3179 '''
3181 x = num.asarray(x)
3182 if N is None:
3183 N = len(x)
3184 if N <= 0:
3185 raise ValueError("N must be positive.")
3186 if num.iscomplexobj(x):
3187 logger.warning('imaginary part of x ignored.')
3188 x = num.real(x)
3190 Xf = num.fft.fft(x, N, axis=0)
3191 h = num.zeros(N)
3192 if N % 2 == 0:
3193 h[0] = h[N//2] = 1
3194 h[1:N//2] = 2
3195 else:
3196 h[0] = 1
3197 h[1:(N+1)//2] = 2
3199 if len(x.shape) > 1:
3200 h = h[:, num.newaxis]
3201 x = num.fft.ifft(Xf*h)
3202 return x
3205def near(a, b, eps):
3206 return abs(a-b) < eps
3209def coroutine(func):
3210 def wrapper(*args, **kwargs):
3211 gen = func(*args, **kwargs)
3212 next(gen)
3213 return gen
3215 wrapper.__name__ = func.__name__
3216 wrapper.__dict__ = func.__dict__
3217 wrapper.__doc__ = func.__doc__
3218 return wrapper
3221class States(object):
3222 '''
3223 Utility to store channel-specific state in coroutines.
3224 '''
3226 def __init__(self):
3227 self._states = {}
3229 def get(self, tr):
3230 k = tr.nslc_id
3231 if k in self._states:
3232 tmin, deltat, dtype, value = self._states[k]
3233 if (near(tmin, tr.tmin, deltat/100.)
3234 and near(deltat, tr.deltat, deltat/10000.)
3235 and dtype == tr.ydata.dtype):
3237 return value
3239 return None
3241 def set(self, tr, value):
3242 k = tr.nslc_id
3243 if k in self._states and self._states[k][-1] is not value:
3244 self.free(self._states[k][-1])
3246 self._states[k] = (tr.tmax+tr.deltat, tr.deltat, tr.ydata.dtype, value)
3248 def free(self, value):
3249 pass
3252@coroutine
3253def co_list_append(list):
3254 while True:
3255 list.append((yield))
3258class ScipyBug(Exception):
3259 pass
3262@coroutine
3263def co_lfilter(target, b, a):
3264 '''
3265 Successively filter broken continuous trace data (coroutine).
3267 Create coroutine which takes :py:class:`Trace` objects, filters their data
3268 through :py:func:`scipy.signal.lfilter` and sends new :py:class:`Trace`
3269 objects containing the filtered data to target. This is useful, if one
3270 wants to filter a long continuous time series, which is split into many
3271 successive traces without producing filter artifacts at trace boundaries.
3273 Filter states are kept *per channel*, specifically, for each (network,
3274 station, location, channel) combination occuring in the input traces, a
3275 separate state is created and maintained. This makes it possible to filter
3276 multichannel or multistation data with only one :py:func:`co_lfilter`
3277 instance.
3279 Filter state is reset, when gaps occur.
3281 Use it like this::
3283 from pyrocko.trace import co_lfilter, co_list_append
3285 filtered_traces = []
3286 pipe = co_lfilter(co_list_append(filtered_traces), a, b)
3287 for trace in traces:
3288 pipe.send(trace)
3290 pipe.close()
3292 '''
3294 try:
3295 states = States()
3296 output = None
3297 while True:
3298 input = (yield)
3300 zi = states.get(input)
3301 if zi is None:
3302 zi = num.zeros(max(len(a), len(b))-1, dtype=float)
3304 output = input.copy(data=False)
3305 try:
3306 ydata, zf = signal.lfilter(b, a, input.get_ydata(), zi=zi)
3307 except ValueError:
3308 raise ScipyBug(
3309 'signal.lfilter failed: could be related to a bug '
3310 'in some older scipy versions, e.g. on opensuse42.1')
3312 output.set_ydata(ydata)
3313 states.set(input, zf)
3314 target.send(output)
3316 except GeneratorExit:
3317 target.close()
3320def co_antialias(target, q, n=None, ftype='fir'):
3321 b, a, n = util.decimate_coeffs(q, n, ftype)
3322 anti = co_lfilter(target, b, a)
3323 return anti
3326@coroutine
3327def co_dropsamples(target, q, nfir):
3328 try:
3329 states = States()
3330 while True:
3331 tr = (yield)
3332 newdeltat = q * tr.deltat
3333 ioffset = states.get(tr)
3334 if ioffset is None:
3335 # for fir filter, the first nfir samples are pulluted by
3336 # boundary effects; cut it off.
3337 # for iir this may be (much) more, we do not correct for that.
3338 # put sample instances to a time which is a multiple of the
3339 # new sampling interval.
3340 newtmin_want = math.ceil(
3341 (tr.tmin+(nfir+1)*tr.deltat) / newdeltat) * newdeltat \
3342 - (nfir/2*tr.deltat)
3343 ioffset = int(round((newtmin_want - tr.tmin)/tr.deltat))
3344 if ioffset < 0:
3345 ioffset = ioffset % q
3347 newtmin_have = tr.tmin + ioffset * tr.deltat
3348 newtr = tr.copy(data=False)
3349 newtr.deltat = newdeltat
3350 # because the fir kernel shifts data by nfir/2 samples:
3351 newtr.tmin = newtmin_have - (nfir/2*tr.deltat)
3352 newtr.set_ydata(tr.get_ydata()[ioffset::q].copy())
3353 states.set(tr, (ioffset % q - tr.data_len() % q) % q)
3354 target.send(newtr)
3356 except GeneratorExit:
3357 target.close()
3360def co_downsample(target, q, n=None, ftype='fir'):
3361 '''
3362 Successively downsample broken continuous trace data (coroutine).
3364 Create coroutine which takes :py:class:`Trace` objects, downsamples their
3365 data and sends new :py:class:`Trace` objects containing the downsampled
3366 data to target. This is useful, if one wants to downsample a long
3367 continuous time series, which is split into many successive traces without
3368 producing filter artifacts and gaps at trace boundaries.
3370 Filter states are kept *per channel*, specifically, for each (network,
3371 station, location, channel) combination occuring in the input traces, a
3372 separate state is created and maintained. This makes it possible to filter
3373 multichannel or multistation data with only one :py:func:`co_lfilter`
3374 instance.
3376 Filter state is reset, when gaps occur. The sampling instances are choosen
3377 so that they occur at (or as close as possible) to even multiples of the
3378 sampling interval of the downsampled trace (based on system time).
3379 '''
3381 b, a, n = util.decimate_coeffs(q, n, ftype)
3382 return co_antialias(co_dropsamples(target, q, n), q, n, ftype)
3385@coroutine
3386def co_downsample_to(target, deltat):
3388 decimators = {}
3389 try:
3390 while True:
3391 tr = (yield)
3392 ratio = deltat / tr.deltat
3393 rratio = round(ratio)
3394 if abs(rratio - ratio)/ratio > 0.0001:
3395 raise util.UnavailableDecimation('ratio = %g' % ratio)
3397 deci_seq = tuple(x for x in util.decitab(int(rratio)) if x != 1)
3398 if deci_seq not in decimators:
3399 pipe = target
3400 for q in deci_seq[::-1]:
3401 pipe = co_downsample(pipe, q)
3403 decimators[deci_seq] = pipe
3405 decimators[deci_seq].send(tr)
3407 except GeneratorExit:
3408 for g in decimators.values():
3409 g.close()
3412class DomainChoice(StringChoice):
3413 choices = [
3414 'time_domain',
3415 'frequency_domain',
3416 'envelope',
3417 'absolute',
3418 'cc_max_norm']
3421class MisfitSetup(Object):
3422 '''
3423 Contains misfit setup to be used in :py:func:`trace.misfit`
3425 :param description: Description of the setup
3426 :param norm: L-norm classifier
3427 :param taper: Object of :py:class:`Taper`
3428 :param filter: Object of :py:class:`FrequencyResponse`
3429 :param domain: ['time_domain', 'frequency_domain', 'envelope', 'absolute',
3430 'cc_max_norm']
3432 Can be dumped to a yaml file.
3433 '''
3435 xmltagname = 'misfitsetup'
3436 description = String.T(optional=True)
3437 norm = Int.T(optional=False)
3438 taper = Taper.T(optional=False)
3439 filter = FrequencyResponse.T(optional=True)
3440 domain = DomainChoice.T(default='time_domain')
3443def equalize_sampling_rates(trace_1, trace_2):
3444 '''
3445 Equalize sampling rates of two traces (reduce higher sampling rate to
3446 lower).
3448 :param trace_1: :py:class:`Trace` object
3449 :param trace_2: :py:class:`Trace` object
3451 Returns a copy of the resampled trace if resampling is needed.
3452 '''
3454 if same_sampling_rate(trace_1, trace_2):
3455 return trace_1, trace_2
3457 if trace_1.deltat < trace_2.deltat:
3458 t1_out = trace_1.copy()
3459 t1_out.downsample_to(deltat=trace_2.deltat, snap=True)
3460 logger.debug('Trace downsampled (return copy of trace): %s'
3461 % '.'.join(t1_out.nslc_id))
3462 return t1_out, trace_2
3464 elif trace_1.deltat > trace_2.deltat:
3465 t2_out = trace_2.copy()
3466 t2_out.downsample_to(deltat=trace_1.deltat, snap=True)
3467 logger.debug('Trace downsampled (return copy of trace): %s'
3468 % '.'.join(t2_out.nslc_id))
3469 return trace_1, t2_out
3472def Lx_norm(u, v, norm=2):
3473 '''
3474 Calculate the misfit denominator *m* and the normalization devisor *n*
3475 according to norm.
3477 The normalization divisor *n* is calculated from ``v``.
3479 :param u: :py:class:`numpy.array`
3480 :param v: :py:class:`numpy.array`
3481 :param norm: (default = 2)
3483 ``u`` and ``v`` must be of same size.
3484 '''
3486 if norm == 1:
3487 return (
3488 num.sum(num.abs(v-u)),
3489 num.sum(num.abs(v)))
3491 elif norm == 2:
3492 return (
3493 num.sqrt(num.sum((v-u)**2)),
3494 num.sqrt(num.sum(v**2)))
3496 else:
3497 return (
3498 num.power(num.sum(num.abs(num.power(v - u, norm))), 1./norm),
3499 num.power(num.sum(num.abs(num.power(v, norm))), 1./norm))
3502def do_downsample(tr, deltat):
3503 if abs(tr.deltat - deltat) / tr.deltat > 1e-6:
3504 tr = tr.copy()
3505 tr.downsample_to(deltat, snap=True, demean=False)
3506 else:
3507 if tr.tmin/tr.deltat > 1e-6 or tr.tmax/tr.deltat > 1e-6:
3508 tr = tr.copy()
3509 tr.snap()
3510 return tr
3513def do_extend(tr, tmin, tmax):
3514 if tmin < tr.tmin or tmax > tr.tmax:
3515 tr = tr.copy()
3516 tr.extend(tmin=tmin, tmax=tmax, fillmethod='repeat')
3518 return tr
3521def do_pre_taper(tr, taper):
3522 return tr.taper(taper, inplace=False, chop=True)
3525def do_fft(tr, filter):
3526 if filter is None:
3527 return tr
3528 else:
3529 ndata = tr.ydata.size
3530 nfft = nextpow2(ndata)
3531 padded = num.zeros(nfft, dtype=float)
3532 padded[:ndata] = tr.ydata
3533 spectrum = num.fft.rfft(padded)
3534 df = 1.0 / (tr.deltat * nfft)
3535 frequencies = num.arange(spectrum.size)*df
3536 return [tr, frequencies, spectrum]
3539def do_filter(inp, filter):
3540 if filter is None:
3541 return inp
3542 else:
3543 tr, frequencies, spectrum = inp
3544 spectrum *= filter.evaluate(frequencies)
3545 return [tr, frequencies, spectrum]
3548def do_ifft(inp):
3549 if isinstance(inp, Trace):
3550 return inp
3551 else:
3552 tr, _, spectrum = inp
3553 ndata = tr.ydata.size
3554 tr = tr.copy(data=False)
3555 tr.set_ydata(num.fft.irfft(spectrum)[:ndata])
3556 return tr
3559def check_alignment(t1, t2):
3560 if abs(t1.tmin-t2.tmin) > t1.deltat * 1e-4 or \
3561 abs(t1.tmax - t2.tmax) > t1.deltat * 1e-4 or \
3562 t1.ydata.shape != t2.ydata.shape:
3563 raise MisalignedTraces(
3564 'Cannot calculate misfit of %s and %s due to misaligned '
3565 'traces.' % ('.'.join(t1.nslc_id), '.'.join(t2.nslc_id)))