# http://pyrocko.org - GPLv3
#
# The Pyrocko Developers, 21st Century
# ---|P------/S----------~Lg----------
'''This module provides basic signal processing for seismic traces.'''
from __future__ import division, absolute_import
import time
import math
import copy
import logging
import numpy as num
from scipy import signal
from . import util, evalresp, orthodrome, pchain, model
from .util import reuse, hpfloat, UnavailableDecimation
from .guts import Object, Float, Int, String, Complex, Tuple, List, \
StringChoice, Timestamp
from .guts_array import Array
try:
newstr = unicode
except NameError:
newstr = str
UnavailableDecimation # noqa
guts_prefix = 'pf'
logger = logging.getLogger('pyrocko.trace')
[docs]class Trace(Object):
'''
Create new trace object.
A ``Trace`` object represents a single continuous strip of evenly sampled
time series data. It is built from a 1D NumPy array containing the data
samples and some attributes describing its beginning and ending time, its
sampling rate and four string identifiers (its network, station, location
and channel code).
:param network: network code
:param station: station code
:param location: location code
:param channel: channel code
:param tmin: system time of first sample in [s]
:param tmax: system time of last sample in [s] (if set to ``None`` it is
computed from length of ``ydata``)
:param deltat: sampling interval in [s]
:param ydata: 1D numpy array with data samples (can be ``None`` when
``tmax`` is not ``None``)
:param mtime: optional modification time
:param meta: additional meta information (not used, but maintained by the
library)
The length of the network, station, location and channel codes is not
resricted by this software, but data formats like SAC, Mini-SEED or GSE
have different limits on the lengths of these codes. The codes set here are
silently truncated when the trace is stored
'''
network = String.T(default='')
station = String.T(default='STA')
location = String.T(default='')
channel = String.T(default='')
tmin = Timestamp.T(default=0.0)
tmax = Timestamp.T()
deltat = Float.T(default=1.0)
ydata = Array.T(optional=True, shape=(None,), serialize_as='base64+meta')
mtime = Timestamp.T(optional=True)
cached_frequencies = {}
def __init__(self, network='', station='STA', location='', channel='',
tmin=0., tmax=None, deltat=1., ydata=None, mtime=None,
meta=None):
Object.__init__(self, init_props=False)
if not isinstance(tmin, float):
tmin = Trace.tmin.regularize_extra(tmin)
if tmax is not None and not isinstance(tmax, float):
tmax = Trace.tmax.regularize_extra(tmax)
if mtime is not None and not isinstance(mtime, float):
mtime = Trace.mtime.regularize_extra(mtime)
self._growbuffer = None
if deltat < 0.001:
tmin = hpfloat(tmin)
if tmax is not None:
tmax = hpfloat(tmax)
if mtime is None:
mtime = time.time()
self.network, self.station, self.location, self.channel = [
reuse(x) for x in (network, station, location, channel)]
self.tmin = tmin
self.deltat = deltat
if tmax is None:
if ydata is not None:
self.tmax = self.tmin + (ydata.size-1)*self.deltat
else:
raise Exception(
'fixme: trace must be created with tmax or ydata')
else:
n = int(round((tmax - self.tmin) / self.deltat)) + 1
self.tmax = self.tmin + (n - 1) * self.deltat
self.meta = meta
self.ydata = ydata
self.mtime = mtime
self._update_ids()
self.file = None
self._pchain = None
def __str__(self):
fmt = min(9, max(0, -int(math.floor(math.log10(self.deltat)))))
s = 'Trace (%s, %s, %s, %s)\n' % self.nslc_id
s += ' timerange: %s - %s\n' % (
util.time_to_str(self.tmin, format=fmt),
util.time_to_str(self.tmax, format=fmt))
s += ' delta t: %g\n' % self.deltat
if self.meta:
for k in sorted(self.meta.keys()):
s += ' %s: %s\n' % (k, self.meta[k])
return s
def __getstate__(self):
return (self.network, self.station, self.location, self.channel,
self.tmin, self.tmax, self.deltat, self.mtime,
self.ydata, self.meta)
def __setstate__(self, state):
if len(state) == 10:
self.network, self.station, self.location, self.channel, \
self.tmin, self.tmax, self.deltat, self.mtime, \
self.ydata, self.meta = state
else:
# backward compatibility with old behaviour
self.network, self.station, self.location, self.channel, \
self.tmin, self.tmax, self.deltat, self.mtime = state
self.ydata = None
self.meta = None
self._growbuffer = None
self._update_ids()
[docs] def name(self):
'''
Get a short string description.
'''
s = '%s.%s.%s.%s, %s, %s' % (self.nslc_id + (
util.time_to_str(self.tmin),
util.time_to_str(self.tmax)))
return s
def __eq__(self, other):
return (
isinstance(other, Trace)
and self.network == other.network
and self.station == other.station
and self.location == other.location
and self.channel == other.channel
and (abs(self.deltat - other.deltat)
< (self.deltat + other.deltat)*1e-6)
and abs(self.tmin-other.tmin) < self.deltat*0.01
and abs(self.tmax-other.tmax) < self.deltat*0.01
and num.all(self.ydata == other.ydata))
def almost_equal(self, other, rtol=1e-5, atol=0.0):
return (
self.network == other.network
and self.station == other.station
and self.location == other.location
and self.channel == other.channel
and (abs(self.deltat - other.deltat)
< (self.deltat + other.deltat)*1e-6)
and abs(self.tmin-other.tmin) < self.deltat*0.01
and abs(self.tmax-other.tmax) < self.deltat*0.01
and num.allclose(self.ydata, other.ydata, rtol=rtol, atol=atol))
def assert_almost_equal(self, other, rtol=1e-5, atol=0.0):
assert self.network == other.network, \
'network codes differ: %s, %s' % (self.network, other.network)
assert self.station == other.station, \
'station codes differ: %s, %s' % (self.station, other.station)
assert self.location == other.location, \
'location codes differ: %s, %s' % (self.location, other.location)
assert self.channel == other.channel, 'channel codes differ'
assert (abs(self.deltat - other.deltat)
< (self.deltat + other.deltat)*1e-6), \
'sampling intervals differ %g, %g' % (self.deltat, other.delta)
assert abs(self.tmin-other.tmin) < self.deltat*0.01, \
'start times differ: %s, %s' % (
util.time_to_str(self.tmin), util.time_to_str(other.tmin))
assert abs(self.tmax-other.tmax) < self.deltat*0.01, \
'end times differ: %s, %s' % (
util.time_to_str(self.tmax), util.time_to_str(other.tmax))
assert num.allclose(self.ydata, other.ydata, rtol=rtol, atol=atol), \
'trace values differ'
def __hash__(self):
return id(self)
def __call__(self, t, clip=False, snap=round):
it = int(snap((t-self.tmin)/self.deltat))
if clip:
it = max(0, min(it, self.ydata.size-1))
else:
if it < 0 or self.ydata.size <= it:
raise IndexError()
return self.tmin+it*self.deltat, self.ydata[it]
[docs] def interpolate(self, t, clip=False):
'''
Value of trace between supporting points through linear interpolation.
:param t: time instant
:param clip: whether to clip indices to trace ends
'''
t0, y0 = self(t, clip=clip, snap=math.floor)
t1, y1 = self(t, clip=clip, snap=math.ceil)
if t0 == t1:
return y0
else:
return y0+(t-t0)/(t1-t0)*(y1-y0)
[docs] def index_clip(self, i):
'''
Clip index to valid range.
'''
return min(max(0, i), self.ydata.size)
[docs] def add(self, other, interpolate=True, left=0., right=0.):
'''
Add values of other trace (self += other).
Add values of ``other`` trace to the values of ``self``, where it
intersects with ``other``. This method does not change the extent of
``self``. If ``interpolate`` is ``True`` (the default), the values of
``other`` to be added are interpolated at sampling instants of
``self``. Linear interpolation is performed. In this case the sampling
rate of ``other`` must be equal to or lower than that of ``self``. If
``interpolate`` is ``False``, the sampling rates of the two traces must
match.
'''
if interpolate:
assert self.deltat <= other.deltat \
or same_sampling_rate(self, other)
other_xdata = other.get_xdata()
xdata = self.get_xdata()
self.ydata += num.interp(
xdata, other_xdata, other.ydata, left=left, right=left)
else:
assert self.deltat == other.deltat
ioff = int(round((other.tmin-self.tmin)/self.deltat))
ibeg = max(0, ioff)
iend = min(self.data_len(), ioff+other.data_len())
self.ydata[ibeg:iend] += other.ydata[ibeg-ioff:iend-ioff]
[docs] def mult(self, other, interpolate=True):
'''
Muliply with values of other trace ``(self *= other)``.
Multiply values of ``other`` trace to the values of ``self``, where it
intersects with ``other``. This method does not change the extent of
``self``. If ``interpolate`` is ``True`` (the default), the values of
``other`` to be multiplied are interpolated at sampling instants of
``self``. Linear interpolation is performed. In this case the sampling
rate of ``other`` must be equal to or lower than that of ``self``. If
``interpolate`` is ``False``, the sampling rates of the two traces must
match.
'''
if interpolate:
assert self.deltat <= other.deltat or \
same_sampling_rate(self, other)
other_xdata = other.get_xdata()
xdata = self.get_xdata()
self.ydata *= num.interp(
xdata, other_xdata, other.ydata, left=0., right=0.)
else:
assert self.deltat == other.deltat
ibeg1 = int(round((other.tmin-self.tmin)/self.deltat))
ibeg2 = int(round((self.tmin-other.tmin)/self.deltat))
iend1 = int(round((other.tmax-self.tmin)/self.deltat))+1
iend2 = int(round((self.tmax-other.tmin)/self.deltat))+1
ibeg1 = self.index_clip(ibeg1)
iend1 = self.index_clip(iend1)
ibeg2 = self.index_clip(ibeg2)
iend2 = self.index_clip(iend2)
self.ydata[ibeg1:iend1] *= other.ydata[ibeg2:iend2]
[docs] def max(self):
'''
Get time and value of data maximum.
'''
i = num.argmax(self.ydata)
return self.tmin + i*self.deltat, self.ydata[i]
[docs] def min(self):
'''
Get time and value of data minimum.
'''
i = num.argmin(self.ydata)
return self.tmin + i*self.deltat, self.ydata[i]
[docs] def absmax(self):
'''
Get time and value of maximum of the absolute of data.
'''
tmi, mi = self.min()
tma, ma = self.max()
if abs(mi) > abs(ma):
return tmi, abs(mi)
else:
return tma, abs(ma)
[docs] def set_codes(
self, network=None, station=None, location=None, channel=None):
'''
Set network, station, location, and channel codes.
'''
if network is not None:
self.network = network
if station is not None:
self.station = station
if location is not None:
self.location = location
if channel is not None:
self.channel = channel
self._update_ids()
def set_network(self, network):
self.network = network
self._update_ids()
def set_station(self, station):
self.station = station
self._update_ids()
def set_location(self, location):
self.location = location
self._update_ids()
def set_channel(self, channel):
self.channel = channel
self._update_ids()
[docs] def overlaps(self, tmin, tmax):
'''
Check if trace has overlap with a given time span.
'''
return not (tmax < self.tmin or self.tmax < tmin)
[docs] def is_relevant(self, tmin, tmax, selector=None):
'''
Check if trace has overlap with a given time span and matches a
condition callback. (internal use)
'''
return not (tmax <= self.tmin or self.tmax < tmin) \
and (selector is None or selector(self))
def _update_ids(self):
'''
Update dependent ids.
'''
self.full_id = (
self.network, self.station, self.location, self.channel, self.tmin)
self.nslc_id = reuse(
(self.network, self.station, self.location, self.channel))
def prune_from_reuse_cache(self):
util.deuse(self.nslc_id)
util.deuse(self.network)
util.deuse(self.station)
util.deuse(self.location)
util.deuse(self.channel)
[docs] def set_mtime(self, mtime):
'''
Set modification time of the trace.
'''
self.mtime = mtime
[docs] def get_xdata(self):
'''
Create array for time axis.
'''
if self.ydata is None:
raise NoData()
return self.tmin \
+ num.arange(len(self.ydata), dtype=num.float64) * self.deltat
[docs] def get_ydata(self):
'''
Get data array.
'''
if self.ydata is None:
raise NoData()
return self.ydata
[docs] def set_ydata(self, new_ydata):
'''
Replace data array.
'''
self.drop_growbuffer()
self.ydata = new_ydata
self.tmax = self.tmin+(len(self.ydata)-1)*self.deltat
def data_len(self):
if self.ydata is not None:
return self.ydata.size
else:
return int(round((self.tmax-self.tmin)/self.deltat)) + 1
[docs] def drop_data(self):
'''
Forget data, make dataless trace.
'''
self.drop_growbuffer()
self.ydata = None
[docs] def drop_growbuffer(self):
'''
Detach the traces grow buffer.
'''
self._growbuffer = None
self._pchain = None
[docs] def copy(self, data=True):
'''
Make a deep copy of the trace.
'''
tracecopy = copy.copy(self)
tracecopy.drop_growbuffer()
if data:
tracecopy.ydata = self.ydata.copy()
tracecopy.meta = copy.deepcopy(self.meta)
return tracecopy
[docs] def crop_zeros(self):
'''
Remove any zeros at beginning and end.
'''
indices = num.where(self.ydata != 0.0)[0]
if indices.size == 0:
raise NoData()
ibeg = indices[0]
iend = indices[-1]+1
if ibeg == 0 and iend == self.ydata.size-1:
return
self.drop_growbuffer()
self.ydata = self.ydata[ibeg:iend].copy()
self.tmin = self.tmin+ibeg*self.deltat
self.tmax = self.tmin+(len(self.ydata)-1)*self.deltat
self._update_ids()
[docs] def append(self, data):
'''
Append data to the end of the trace.
To make this method efficient when successively very few or even single
samples are appended, a larger grow buffer is allocated upon first
invocation. The traces data is then changed to be a view into the
currently filled portion of the grow buffer array.
'''
assert self.ydata.dtype == data.dtype
newlen = data.size + self.ydata.size
if self._growbuffer is None or self._growbuffer.size < newlen:
self._growbuffer = num.empty(newlen*2, dtype=self.ydata.dtype)
self._growbuffer[:self.ydata.size] = self.ydata
self._growbuffer[self.ydata.size:newlen] = data
self.ydata = self._growbuffer[:newlen]
self.tmax = self.tmin + (newlen-1)*self.deltat
[docs] def chop(
self, tmin, tmax, inplace=True, include_last=False,
snap=(round, round), want_incomplete=True):
'''
Cut the trace to given time span.
If the ``inplace`` argument is True (the default) the trace is cut in
place, otherwise a new trace with the cut part is returned. By
default, the indices where to start and end the trace data array are
determined by rounding of ``tmin`` and ``tmax`` to sampling instances
using Python's :py:func:`round` function. This behaviour can be changed
with the ``snap`` argument, which takes a tuple of two functions (one
for the lower and one for the upper end) to be used instead of
:py:func:`round`. The last sample is by default not included unless
``include_last`` is set to True. If the given time span exceeds the
available time span of the trace, the available part is returned,
unless ``want_incomplete`` is set to False - in that case, a
:py:exc:`NoData` exception is raised. This exception is always raised,
when the requested time span does dot overlap with the trace's time
span.
'''
if want_incomplete:
if tmax <= self.tmin-self.deltat or self.tmax+self.deltat < tmin:
raise NoData()
else:
if tmin < self.tmin or self.tmax < tmax:
raise NoData()
ibeg = max(0, t2ind(tmin-self.tmin, self.deltat, snap[0]))
iplus = 0
if include_last:
iplus = 1
iend = min(
self.data_len(),
t2ind(tmax-self.tmin, self.deltat, snap[1])+iplus)
if ibeg >= iend:
raise NoData()
obj = self
if not inplace:
obj = self.copy(data=False)
self.drop_growbuffer()
if self.ydata is not None:
obj.ydata = self.ydata[ibeg:iend].copy()
else:
obj.ydata = None
obj.tmin = obj.tmin+ibeg*obj.deltat
obj.tmax = obj.tmin+((iend-ibeg)-1)*obj.deltat
obj._update_ids()
return obj
[docs] def downsample(self, ndecimate, snap=False, initials=None, demean=False):
'''
Downsample trace by a given integer factor.
:param ndecimate: decimation factor, avoid values larger than 8
:param snap: whether to put the new sampling instances closest to
multiples of the sampling rate.
:param initials: ``None``, ``True``, or initial conditions for the
anti-aliasing filter, obtained from a previous run. In the latter
two cases the final state of the filter is returned instead of
``None``.
:param demean: whether to demean the signal before filtering.
'''
newdeltat = self.deltat*ndecimate
if snap:
ilag = int(round(
(math.ceil(self.tmin / newdeltat) * newdeltat - self.tmin)
/ self.deltat))
else:
ilag = 0
if snap and ilag > 0 and ilag < self.ydata.size:
data = self.ydata.astype(num.float64)
self.tmin += ilag*self.deltat
else:
data = self.ydata.astype(num.float64)
if demean:
data -= num.mean(data)
if data.size != 0:
result = util.decimate(
data, ndecimate, ftype='fir', zi=initials, ioff=ilag)
else:
result = data
if initials is None:
self.ydata, finals = result, None
else:
self.ydata, finals = result
self.deltat = reuse(self.deltat*ndecimate)
self.tmax = self.tmin+(len(self.ydata)-1)*self.deltat
self._update_ids()
return finals
[docs] def downsample_to(self, deltat, snap=False, allow_upsample_max=1,
initials=None, demean=False):
'''
Downsample to given sampling rate.
Tries to downsample the trace to a target sampling interval of
``deltat``. This runs the :py:meth:`Trace.downsample` one or several
times. If allow_upsample_max is set to a value larger than 1,
intermediate upsampling steps are allowed, in order to increase the
number of possible downsampling ratios.
If the requested ratio is not supported, an exception of type
:py:exc:`pyrocko.util.UnavailableDecimation` is raised.
'''
ratio = deltat/self.deltat
rratio = round(ratio)
ok = False
for upsratio in range(1, allow_upsample_max+1):
dratio = (upsratio/self.deltat) / (1./deltat)
if abs(dratio - round(dratio)) / dratio < 0.0001 and \
util.decitab(int(round(dratio))):
ok = True
break
if not ok:
raise util.UnavailableDecimation('ratio = %g' % ratio)
if upsratio > 1:
self.drop_growbuffer()
ydata = self.ydata
self.ydata = num.zeros(
ydata.size*upsratio-(upsratio-1), ydata.dtype)
self.ydata[::upsratio] = ydata
for i in range(1, upsratio):
self.ydata[i::upsratio] = \
float(i)/upsratio * ydata[:-1] \
+ float(upsratio-i)/upsratio * ydata[1:]
self.deltat = self.deltat/upsratio
ratio = deltat/self.deltat
rratio = round(ratio)
deci_seq = util.decitab(int(rratio))
finals = []
for i, ndecimate in enumerate(deci_seq):
if ndecimate != 1:
xinitials = None
if initials is not None:
xinitials = initials[i]
finals.append(self.downsample(
ndecimate, snap=snap, initials=xinitials, demean=demean))
if initials is not None:
return finals
[docs] def resample(self, deltat):
'''
Resample to given sampling rate ``deltat``.
Resampling is performed in the frequency domain.
'''
ndata = self.ydata.size
ntrans = nextpow2(ndata)
fntrans2 = ntrans * self.deltat/deltat
ntrans2 = int(round(fntrans2))
deltat2 = self.deltat * float(ntrans)/float(ntrans2)
ndata2 = int(round(ndata*self.deltat/deltat2))
if abs(fntrans2 - ntrans2) > 1e-7:
logger.warning(
'resample: requested deltat %g could not be matched exactly: '
'%g' % (deltat, deltat2))
data = self.ydata
data_pad = num.zeros(ntrans, dtype=num.float)
data_pad[:ndata] = data
fdata = num.fft.rfft(data_pad)
fdata2 = num.zeros((ntrans2+1)//2, dtype=fdata.dtype)
n = min(fdata.size, fdata2.size)
fdata2[:n] = fdata[:n]
data2 = num.fft.irfft(fdata2)
data2 = data2[:ndata2]
data2 *= float(ntrans2) / float(ntrans)
self.deltat = deltat2
self.set_ydata(data2)
def resample_simple(self, deltat):
tyear = 3600*24*365.
if deltat == self.deltat:
return
if abs(self.deltat - deltat) * tyear/deltat < deltat:
logger.warning(
'resample_simple: less than one sample would have to be '
'inserted/deleted per year. Doing nothing.')
return
ninterval = int(round(deltat / (self.deltat - deltat)))
if abs(ninterval) < 20:
logger.error(
'resample_simple: sample insertion/deletion interval less '
'than 20. results would be erroneous.')
raise ResamplingFailed()
delete = False
if ninterval < 0:
ninterval = - ninterval
delete = True
tyearbegin = util.year_start(self.tmin)
nmin = int(round((self.tmin - tyearbegin)/deltat))
ibegin = (((nmin-1)//ninterval)+1) * ninterval - nmin
nindices = (len(self.ydata) - ibegin - 1) / ninterval + 1
if nindices > 0:
indices = ibegin + num.arange(nindices) * ninterval
data_split = num.split(self.ydata, indices)
data = []
for ln, h in zip(data_split[:-1], data_split[1:]):
if delete:
ln = ln[:-1]
data.append(ln)
if not delete:
if ln.size == 0:
v = h[0]
else:
v = 0.5*(ln[-1] + h[0])
data.append(num.array([v], dtype=ln.dtype))
data.append(data_split[-1])
ydata_new = num.concatenate(data)
self.tmin = tyearbegin + nmin * deltat
self.deltat = deltat
self.set_ydata(ydata_new)
[docs] def stretch(self, tmin_new, tmax_new):
'''
Stretch signal while preserving sample rate using sinc interpolation.
:param tmin_new: new time of first sample
:param tmax_new: new time of last sample
This method can be used to correct for a small linear time drift or to
introduce sub-sample time shifts. The amount of stretching is limited
to 10% by the implementation and is expected to be much smaller than
that by the approximations used.
'''
from pyrocko import signal_ext
i_control = num.array([0, self.ydata.size-1], dtype=num.int64)
t_control = num.array([tmin_new, tmax_new], dtype=num.float)
r = (tmax_new - tmin_new) / self.deltat + 1.0
n_new = int(round(r))
if abs(n_new - r) > 0.001:
n_new = int(math.floor(r))
assert n_new >= 2
tmax_new = tmin_new + (n_new-1) * self.deltat
ydata_new = num.empty(n_new, dtype=num.float)
signal_ext.antidrift(i_control, t_control,
self.ydata.astype(num.float),
tmin_new, self.deltat, ydata_new)
self.tmin = tmin_new
self.set_ydata(ydata_new)
self._update_ids()
[docs] def nyquist_check(self, frequency, intro='Corner frequency', warn=True,
raise_exception=False):
'''
Check if a given frequency is above the Nyquist frequency of the trace.
:param intro: string used to introduce the warning/error message
:param warn: whether to emit a warning
:param raise_exception: whether to raise an :py:exc:`AboveNyquist`
exception.
'''
if frequency >= 0.5/self.deltat:
message = '%s (%g Hz) is equal to or higher than nyquist ' \
'frequency (%g Hz). (Trace %s)' \
% (intro, frequency, 0.5/self.deltat, self.name())
if warn:
logger.warning(message)
if raise_exception:
raise AboveNyquist(message)
[docs] def lowpass(self, order, corner, nyquist_warn=True,
nyquist_exception=False, demean=True):
'''
Apply Butterworth lowpass to the trace.
:param order: order of the filter
:param corner: corner frequency of the filter
Mean is removed before filtering.
'''
self.nyquist_check(
corner, 'Corner frequency of lowpass', nyquist_warn,
nyquist_exception)
(b, a) = _get_cached_filter_coefs(
order, [corner*2.0*self.deltat], btype='low')
if len(a) != order+1 or len(b) != order+1:
logger.warning(
'Erroneous filter coefficients returned by '
'scipy.signal.butter(). You may need to downsample the '
'signal before filtering.')
data = self.ydata.astype(num.float64)
if demean:
data -= num.mean(data)
self.drop_growbuffer()
self.ydata = signal.lfilter(b, a, data)
[docs] def highpass(self, order, corner, nyquist_warn=True,
nyquist_exception=False, demean=True):
'''
Apply butterworth highpass to the trace.
:param order: order of the filter
:param corner: corner frequency of the filter
Mean is removed before filtering.
'''
self.nyquist_check(
corner, 'Corner frequency of highpass', nyquist_warn,
nyquist_exception)
(b, a) = _get_cached_filter_coefs(
order, [corner*2.0*self.deltat], btype='high')
data = self.ydata.astype(num.float64)
if len(a) != order+1 or len(b) != order+1:
logger.warning(
'Erroneous filter coefficients returned by '
'scipy.signal.butter(). You may need to downsample the '
'signal before filtering.')
if demean:
data -= num.mean(data)
self.drop_growbuffer()
self.ydata = signal.lfilter(b, a, data)
[docs] def bandpass(self, order, corner_hp, corner_lp, demean=True):
'''
Apply butterworth bandpass to the trace.
:param order: order of the filter
:param corner_hp: lower corner frequency of the filter
:param corner_lp: upper corner frequency of the filter
Mean is removed before filtering.
'''
self.nyquist_check(corner_hp, 'Lower corner frequency of bandpass')
self.nyquist_check(corner_lp, 'Higher corner frequency of bandpass')
(b, a) = _get_cached_filter_coefs(
order,
[corner*2.0*self.deltat for corner in (corner_hp, corner_lp)],
btype='band')
data = self.ydata.astype(num.float64)
if demean:
data -= num.mean(data)
self.drop_growbuffer()
self.ydata = signal.lfilter(b, a, data)
[docs] def bandstop(self, order, corner_hp, corner_lp, demean=True):
'''
Apply bandstop (attenuates frequencies in band) to the trace.
:param order: order of the filter
:param corner_hp: lower corner frequency of the filter
:param corner_lp: upper corner frequency of the filter
Mean is removed before filtering.
'''
self.nyquist_check(corner_hp, 'Lower corner frequency of bandstop')
self.nyquist_check(corner_lp, 'Higher corner frequency of bandstop')
(b, a) = _get_cached_filter_coefs(
order,
[corner*2.0*self.deltat for corner in (corner_hp, corner_lp)],
btype='bandstop')
data = self.ydata.astype(num.float64)
if demean:
data -= num.mean(data)
self.drop_growbuffer()
self.ydata = signal.lfilter(b, a, data)
def abshilbert(self):
self.drop_growbuffer()
self.ydata = num.abs(hilbert(self.ydata))
[docs] def envelope(self, inplace=True):
'''
Calculate the envelope of the trace.
:param inplace: calculate envelope in place
The calculation follows:
.. math::
Y' = \\sqrt{Y^2+H(Y)^2}
where H is the Hilbert-Transform of the signal Y.
'''
if inplace:
self.drop_growbuffer()
self.ydata = num.sqrt(self.ydata**2 + hilbert(self.ydata)**2)
else:
tr = self.copy(data=False)
tr.ydata = num.sqrt(self.ydata**2 + hilbert(self.ydata)**2)
return tr
[docs] def taper(self, taperer, inplace=True, chop=False):
'''
Apply a :py:class:`Taper` to the trace.
:param taperer: instance of :py:class:`Taper` subclass
:param inplace: apply taper inplace
:param chop: if ``True``: exclude tapered parts from the resulting
trace
'''
if not inplace:
tr = self.copy()
else:
tr = self
if chop:
i, n = taperer.span(tr.ydata, tr.tmin, tr.deltat)
tr.shift(i*tr.deltat)
tr.set_ydata(tr.ydata[i:i+n])
taperer(tr.ydata, tr.tmin, tr.deltat)
if not inplace:
return tr
[docs] def whiten(self, order=6):
'''
Whiten signal in time domain using autoregression and recursive filter.
:param order: order of the autoregression process
'''
b, a = self.whitening_coefficients(order)
self.drop_growbuffer()
self.ydata = signal.lfilter(b, a, self.ydata)
def whitening_coefficients(self, order=6):
ar = yulewalker(self.ydata, order)
b, a = [1.] + ar.tolist(), [1.]
return b, a
[docs] def ampspec_whiten(
self,
width,
td_taper='auto',
fd_taper='auto',
pad_to_pow2=True,
demean=True):
'''
Whiten signal via frequency domain using moving average on amplitude
spectra.
:param width: width of smoothing kernel [Hz]
:param td_taper: time domain taper, object of type :py:class:`Taper` or
``None`` or ``'auto'``.
:param fd_taper: frequency domain taper, object of type
:py:class:`Taper` or ``None`` or ``'auto'``.
:param pad_to_pow2: whether to pad the signal with zeros up to a length
of 2^n
:param demean: whether to demean the signal before tapering
The signal is first demeaned and then tapered using ``td_taper``. Then,
the spectrum is calculated and inversely weighted with a smoothed
version of its amplitude spectrum. A moving average is used for the
smoothing. The smoothed spectrum is then tapered using ``fd_taper``.
Finally, the smoothed and tapered spectrum is back-transformed into the
time domain.
If ``td_taper`` is set to ``'auto'``, ``CosFader(1.0/width)`` is used.
If ``fd_taper`` is set to ``'auto'``, ``CosFader(width)`` is used.
'''
ndata = self.data_len()
if pad_to_pow2:
ntrans = nextpow2(ndata)
else:
ntrans = ndata
df = 1./(ntrans*self.deltat)
nw = int(round(width/df))
if ndata//2+1 <= nw:
raise TraceTooShort(
'Samples in trace: %s, samples needed: %s' % (ndata, nw))
if td_taper == 'auto':
td_taper = CosFader(1./width)
if fd_taper == 'auto':
fd_taper = CosFader(width)
if td_taper:
self.taper(td_taper)
ydata = self.get_ydata().astype(num.float)
if demean:
ydata -= ydata.mean()
spec = num.fft.rfft(ydata, ntrans)
amp = num.abs(spec)
nspec = amp.size
csamp = num.cumsum(amp)
amp_smoothed = num.empty(nspec, dtype=csamp.dtype)
n1, n2 = nw//2, nw//2 + nspec - nw
amp_smoothed[n1:n2] = (csamp[nw:] - csamp[:-nw]) / nw
amp_smoothed[:n1] = amp_smoothed[n1]
amp_smoothed[n2:] = amp_smoothed[n2-1]
denom = amp_smoothed * amp
numer = amp
eps = num.mean(denom) * 1e-9
if eps == 0.0:
eps = 1e-9
numer += eps
denom += eps
spec *= numer/denom
if fd_taper:
fd_taper(spec, 0., df)
ydata = num.fft.irfft(spec)
self.set_ydata(ydata[:ndata])
def _get_cached_freqs(self, nf, deltaf):
ck = (nf, deltaf)
if ck not in Trace.cached_frequencies:
Trace.cached_frequencies[ck] = deltaf * num.arange(
nf, dtype=num.float)
return Trace.cached_frequencies[ck]
[docs] def bandpass_fft(self, corner_hp, corner_lp):
'''
Apply boxcar bandbpass to trace (in spectral domain).
'''
n = len(self.ydata)
n2 = nextpow2(n)
data = num.zeros(n2, dtype=num.float64)
data[:n] = self.ydata
fdata = num.fft.rfft(data)
freqs = self._get_cached_freqs(len(fdata), 1./(self.deltat*n2))
fdata[0] = 0.0
fdata *= num.logical_and(corner_hp < freqs, freqs < corner_lp)
data = num.fft.irfft(fdata)
self.drop_growbuffer()
self.ydata = data[:n]
[docs] def shift(self, tshift):
'''
Time shift the trace.
'''
self.tmin += tshift
self.tmax += tshift
self._update_ids()
[docs] def snap(self, inplace=True, interpolate=False):
'''
Shift trace samples to nearest even multiples of the sampling rate.
:param inplace: (boolean) snap traces inplace
If ``inplace`` is ``False`` and the difference of tmin and tmax of
both, the snapped and the original trace is smaller than 0.01 x deltat,
:py:func:`snap` returns the unsnapped instance of the original trace.
'''
tmin = round(self.tmin/self.deltat)*self.deltat
tmax = tmin + (self.ydata.size-1)*self.deltat
if inplace:
xself = self
else:
if abs(self.tmin - tmin) < 1e-2*self.deltat and \
abs(self.tmax - tmax) < 1e-2*self.deltat:
return self
xself = self.copy()
if interpolate:
from pyrocko import signal_ext
n = xself.data_len()
ydata_new = num.empty(n, dtype=num.float)
i_control = num.array([0, n-1], dtype=num.int64)
t_control = num.array([xself.tmin, xself.tmax])
signal_ext.antidrift(i_control, t_control,
xself.ydata.astype(num.float),
tmin, xself.deltat, ydata_new)
xself.ydata = ydata_new
xself.tmin = tmin
xself.tmax = tmax
xself._update_ids()
return xself
[docs] def fix_deltat_rounding_errors(self):
'''
Try to undo sampling rate rounding errors.
See :py:func:`fix_deltat_rounding_errors`.
'''
self.deltat = fix_deltat_rounding_errors(self.deltat)
self.tmax = self.tmin + (self.data_len() - 1) * self.deltat
[docs] def sta_lta_centered(self, tshort, tlong, quad=True, scalingmethod=1):
'''
Run special STA/LTA filter where the short time window is centered on
the long time window.
:param tshort: length of short time window in [s]
:param tlong: length of long time window in [s]
:param quad: whether to square the data prior to applying the STA/LTA
filter
:param scalingmethod: integer key to select how output values are
scaled / normalized (``1``, ``2``, or ``3``)
============= ====================================== ===========
Scalingmethod Implementation Range
============= ====================================== ===========
``1`` As/Al* Tl/Ts [0,1]
``2`` (As/Al - 1) / (Tl/Ts - 1) [-Ts/Tl,1]
``3`` Like ``2`` but clipping range at zero [0,1]
============= ====================================== ===========
'''
nshort = int(round(tshort/self.deltat))
nlong = int(round(tlong/self.deltat))
assert nshort < nlong
if nlong > len(self.ydata):
raise TraceTooShort(
'Samples in trace: %s, samples needed: %s'
% (len(self.ydata), nlong))
if quad:
sqrdata = self.ydata**2
else:
sqrdata = self.ydata
mavg_short = moving_avg(sqrdata, nshort)
mavg_long = moving_avg(sqrdata, nlong)
self.drop_growbuffer()
if scalingmethod not in (1, 2, 3):
raise Exception('Invalid argument to scalingrange argument.')
if scalingmethod == 1:
self.ydata = mavg_short/mavg_long * float(nshort)/float(nlong)
elif scalingmethod in (2, 3):
self.ydata = (mavg_short/mavg_long - 1.) \
/ ((float(nlong)/float(nshort)) - 1)
if scalingmethod == 3:
self.ydata = num.maximum(self.ydata, 0.)
[docs] def sta_lta_right(self, tshort, tlong, quad=True, scalingmethod=1):
'''
Run special STA/LTA filter where the short time window is overlapping
with the last part of the long time window.
:param tshort: length of short time window in [s]
:param tlong: length of long time window in [s]
:param quad: whether to square the data prior to applying the STA/LTA
filter
:param scalingmethod: integer key to select how output values are
scaled / normalized (``1``, ``2``, or ``3``)
============= ====================================== ===========
Scalingmethod Implementation Range
============= ====================================== ===========
``1`` As/Al* Tl/Ts [0,1]
``2`` (As/Al - 1) / (Tl/Ts - 1) [-Ts/Tl,1]
``3`` Like ``2`` but clipping range at zero [0,1]
============= ====================================== ===========
With ``scalingmethod=1``, the values produced by this variant of the
STA/LTA are equivalent to
.. math::
s_i = \\frac{s}{l} \\frac{\\frac{1}{s}\\sum_{j=i}^{i+s-1} f_j}
{\\frac{1}{l}\\sum_{j=i+s-l}^{i+s-1} f_j}
where :math:`f_j` are the input samples, :math:`s` are the number of
samples in the short time window and :math:`l` are the number of
samples in the long time window.
'''
n = self.data_len()
tmin = self.tmin
nshort = max(1, int(round(tshort/self.deltat)))
nlong = max(1, int(round(tlong/self.deltat)))
assert nshort < nlong
if nlong > len(self.ydata):
raise TraceTooShort(
'Samples in trace: %s, samples needed: %s'
% (len(self.ydata), nlong))
if quad:
sqrdata = self.ydata**2
else:
sqrdata = self.ydata
nshift = int(math.floor(0.5 * (nlong - nshort)))
if nlong % 2 != 0 and nshort % 2 == 0:
nshift += 1
mavg_short = moving_avg(sqrdata, nshort)[nshift:]
mavg_long = moving_avg(sqrdata, nlong)[:sqrdata.size-nshift]
self.drop_growbuffer()
if scalingmethod not in (1, 2, 3):
raise Exception('Invalid argument to scalingrange argument.')
if scalingmethod == 1:
ydata = mavg_short/mavg_long * nshort/nlong
elif scalingmethod in (2, 3):
ydata = (mavg_short/mavg_long - 1.) \
/ ((float(nlong)/float(nshort)) - 1)
if scalingmethod == 3:
ydata = num.maximum(self.ydata, 0.)
self.set_ydata(ydata)
self.shift((math.ceil(0.5*nlong) - nshort + 1) * self.deltat)
self.chop(
tmin + (nlong - nshort) * self.deltat,
tmin + (n - nshort) * self.deltat)
[docs] def peaks(self, threshold, tsearch,
deadtime=False,
nblock_duration_detection=100):
'''
Detect peaks above a given threshold (method 1).
From every instant, where the signal rises above ``threshold``, a time
length of ``tsearch`` seconds is searched for a maximum. A list with
tuples (time, value) for each detected peak is returned. The
``deadtime`` argument turns on a special deadtime duration detection
algorithm useful in combination with recursive STA/LTA filters.
'''
y = self.ydata
above = num.where(y > threshold, 1, 0)
deriv = num.zeros(y.size, dtype=num.int8)
deriv[1:] = above[1:]-above[:-1]
itrig_positions = num.nonzero(deriv > 0)[0]
tpeaks = []
apeaks = []
tzeros = []
tzero = self.tmin
for itrig_pos in itrig_positions:
ibeg = itrig_pos
iend = min(
len(self.ydata),
itrig_pos + int(math.ceil(tsearch/self.deltat)))
ipeak = num.argmax(y[ibeg:iend])
tpeak = self.tmin + (ipeak+ibeg)*self.deltat
apeak = y[ibeg+ipeak]
if tpeak < tzero:
continue
if deadtime:
ibeg = itrig_pos
iblock = 0
nblock = nblock_duration_detection
totalsum = 0.
while True:
if ibeg+iblock*nblock >= len(y):
tzero = self.tmin + (len(y)-1) * self.deltat
break
logy = num.log(
y[ibeg+iblock*nblock:ibeg+(iblock+1)*nblock])
logy[0] += totalsum
ysum = num.cumsum(logy)
totalsum = ysum[-1]
below = num.where(ysum <= 0., 1, 0)
deriv = num.zeros(ysum.size, dtype=num.int8)
deriv[1:] = below[1:]-below[:-1]
izero_positions = num.nonzero(deriv > 0)[0] + iblock*nblock
if len(izero_positions) > 0:
tzero = self.tmin + self.deltat * (
ibeg + izero_positions[0])
break
iblock += 1
else:
tzero = ibeg*self.deltat + self.tmin + tsearch
tpeaks.append(tpeak)
apeaks.append(apeak)
tzeros.append(tzero)
if deadtime:
return tpeaks, apeaks, tzeros
else:
return tpeaks, apeaks
[docs] def peaks2(self, threshold, tsearch):
'''
Detect peaks above a given threshold (method 2).
This variant of peak detection is a bit more robust (and slower) than
the one implemented in :py:meth:`Trace.peaks`. First all samples with
``a[i-1] < a[i] > a[i+1]`` are masked as potential peaks. From these,
iteratively the one with the maximum amplitude ``a[j]`` and time
``t[j]`` is choosen and potential peaks within
``t[j] - tsearch, t[j] + tsearch``
are discarded. The algorithm stops, when ``a[j] < threshold`` or when
no more potential peaks are left.
'''
a = num.copy(self.ydata)
amin = num.min(a)
a[0] = amin
a[1: -1][num.diff(a, 2) <= 0.] = amin
a[-1] = amin
data = []
while True:
imax = num.argmax(a)
amax = a[imax]
if amax < threshold or amax == amin:
break
data.append((self.tmin + imax * self.deltat, amax))
ntsearch = int(round(tsearch / self.deltat))
a[max(imax-ntsearch//2, 0):min(imax+ntsearch//2, a.size)] = amin
if data:
data.sort()
tpeaks, apeaks = list(zip(*data))
else:
tpeaks, apeaks = [], []
return tpeaks, apeaks
[docs] def extend(self, tmin=None, tmax=None, fillmethod='zeros'):
'''
Extend trace to given span.
:param tmin: begin time of new span
:param tmax: end time of new span
:param fillmethod: ``'zeros'``, ``'repeat'``, ``'mean'``, or
``'median'``
'''
nold = self.ydata.size
if tmin is not None:
nl = min(0, int(round((tmin-self.tmin)/self.deltat)))
else:
nl = 0
if tmax is not None:
nh = max(nold - 1, int(round((tmax-self.tmin)/self.deltat)))
else:
nh = nold - 1
n = nh - nl + 1
data = num.zeros(n, dtype=self.ydata.dtype)
data[-nl:-nl + nold] = self.ydata
if self.ydata.size >= 1:
if fillmethod == 'repeat':
data[:-nl] = self.ydata[0]
data[-nl + nold:] = self.ydata[-1]
elif fillmethod == 'median':
v = num.median(self.ydata)
data[:-nl] = v
data[-nl + nold:] = v
elif fillmethod == 'mean':
v = num.mean(self.ydata)
data[:-nl] = v
data[-nl + nold:] = v
self.drop_growbuffer()
self.ydata = data
self.tmin += nl * self.deltat
self.tmax = self.tmin + (self.ydata.size - 1) * self.deltat
self._update_ids()
[docs] def transfer(self,
tfade=0.,
freqlimits=None,
transfer_function=None,
cut_off_fading=True,
demean=True,
invert=False):
'''
Return new trace with transfer function applied (convolution).
:param tfade: rise/fall time in seconds of taper applied in timedomain
at both ends of trace.
:param freqlimits: 4-tuple with corner frequencies in Hz.
:param transfer_function: FrequencyResponse object; must provide a
method 'evaluate(freqs)', which returns the transfer function
coefficients at the frequencies 'freqs'.
:param cut_off_fading: whether to cut off rise/fall interval in output
trace.
:param demean: remove mean before applying transfer function
:param invert: set to True to do a deconvolution
'''
if transfer_function is None:
transfer_function = FrequencyResponse()
if self.tmax - self.tmin <= tfade*2.:
raise TraceTooShort(
'Trace %s.%s.%s.%s too short for fading length setting. '
'trace length = %g, fading length = %g'
% (self.nslc_id + (self.tmax-self.tmin, tfade)))
if freqlimits is None and (
transfer_function is None or transfer_function.is_scalar()):
# special case for flat responses
output = self.copy()
data = self.ydata
ndata = data.size
if transfer_function is not None:
c = num.abs(transfer_function.evaluate(num.ones(1))[0])
if invert:
c = 1.0/c
data *= c
if tfade != 0.0:
data *= costaper(
0., tfade, self.deltat*(ndata-1)-tfade, self.deltat*ndata,
ndata, self.deltat)
output.ydata = data
else:
ndata = self.ydata.size
ntrans = nextpow2(ndata*1.2)
coefs = self._get_tapered_coefs(
ntrans, freqlimits, transfer_function, invert=invert)
data = self.ydata
data_pad = num.zeros(ntrans, dtype=num.float)
data_pad[:ndata] = data
if demean:
data_pad[:ndata] -= data.mean()
if tfade != 0.0:
data_pad[:ndata] *= costaper(
0., tfade, self.deltat*(ndata-1)-tfade, self.deltat*ndata,
ndata, self.deltat)
fdata = num.fft.rfft(data_pad)
fdata *= coefs
ddata = num.fft.irfft(fdata)
output = self.copy()
output.ydata = ddata[:ndata]
if cut_off_fading and tfade != 0.0:
try:
output.chop(output.tmin+tfade, output.tmax-tfade, inplace=True)
except NoData:
raise TraceTooShort(
'Trace %s.%s.%s.%s too short for fading length setting. '
'trace length = %g, fading length = %g'
% (self.nslc_id + (self.tmax-self.tmin, tfade)))
else:
output.ydata = output.ydata.copy()
return output
[docs] def differentiate(self, n=1, order=4, inplace=True):
'''
Approximate first or second derivative of the trace.
:param n: 1 for first derivative, 2 for second
:param order: order of the approximation 2 and 4 are supported
:param inplace: if ``True`` the trace is differentiated in place,
otherwise a new trace object with the derivative is returned.
Raises :py:exc:`ValueError` for unsupported `n` or `order`.
See :py:func:`~pyrocko.util.diff_fd` for implementation details.
'''
ddata = util.diff_fd(n, order, self.deltat, self.ydata)
if inplace:
self.ydata = ddata
else:
output = self.copy(data=False)
output.set_ydata(ddata)
return output
def drop_chain_cache(self):
if self._pchain:
self._pchain.clear()
def init_chain(self):
self._pchain = pchain.Chain(
do_downsample,
do_extend,
do_pre_taper,
do_fft,
do_filter,
do_ifft)
def run_chain(self, tmin, tmax, deltat, setup, nocache):
if setup.domain == 'frequency_domain':
_, _, data = self._pchain(
(self, deltat),
(tmin, tmax),
(setup.taper,),
(setup.filter,),
(setup.filter,),
nocache=nocache)
return num.abs(data), num.abs(data)
else:
processed = self._pchain(
(self, deltat),
(tmin, tmax),
(setup.taper,),
(setup.filter,),
(setup.filter,),
(),
nocache=nocache)
if setup.domain == 'time_domain':
data = processed.get_ydata()
elif setup.domain == 'envelope':
processed = processed.envelope(inplace=False)
elif setup.domain == 'absolute':
processed.set_ydata(num.abs(processed.get_ydata()))
return processed.get_ydata(), processed
[docs] def misfit(self, candidate, setup, nocache=False, debug=False):
"""
Calculate misfit and normalization factor against candidate trace.
:param candidate: :py:class:`Trace` object
:param setup: :py:class:`MisfitSetup` object
:returns: tuple ``(m, n)``, where m is the misfit value and n is the
normalization divisor
If the sampling rates of ``self`` and ``candidate`` differ, the trace
with the higher sampling rate will be downsampled.
"""
a = self
b = candidate
for tr in (a, b):
if not tr._pchain:
tr.init_chain()
deltat = max(a.deltat, b.deltat)
tmin = min(a.tmin, b.tmin) - deltat
tmax = max(a.tmax, b.tmax) + deltat
adata, aproc = a.run_chain(tmin, tmax, deltat, setup, nocache)
bdata, bproc = b.run_chain(tmin, tmax, deltat, setup, nocache)
if setup.domain != 'cc_max_norm':
m, n = Lx_norm(bdata, adata, norm=setup.norm)
else:
ctr = correlate(aproc, bproc, mode='full', normalization='normal')
ccmax = ctr.max()[1]
m = 0.5 - 0.5 * ccmax
n = 0.5
if debug:
return m, n, aproc, bproc
else:
return m, n
[docs] def spectrum(self, pad_to_pow2=False, tfade=None):
'''
Get FFT spectrum of trace.
:param pad_to_pow2: whether to zero-pad the data to next larger
power-of-two length
:param tfade: ``None`` or a time length in seconds, to apply cosine
shaped tapers to both
:returns: a tuple with (frequencies, values)
'''
ndata = self.ydata.size
if pad_to_pow2:
ntrans = nextpow2(ndata)
else:
ntrans = ndata
if tfade is None:
ydata = self.ydata
else:
ydata = self.ydata * costaper(
0., tfade, self.deltat*(ndata-1)-tfade, self.deltat*ndata,
ndata, self.deltat)
fydata = num.fft.rfft(ydata, ntrans)
df = 1./(ntrans*self.deltat)
fxdata = num.arange(len(fydata))*df
return fxdata, fydata
def multi_filter(self, filter_freqs, bandwidth):
class Gauss(FrequencyResponse):
def __init__(self, f0, a=1.0):
self._omega0 = 2.*math.pi*f0
self._a = a
def evaluate(self, freqs):
omega = 2.*math.pi*freqs
return num.exp(-((omega-self._omega0)
/ (self._a*self._omega0))**2)
freqs, coefs = self.spectrum()
n = self.data_len()
nfilt = len(filter_freqs)
signal_tf = num.zeros((nfilt, n))
centroid_freqs = num.zeros(nfilt)
for ifilt, f0 in enumerate(filter_freqs):
taper = Gauss(f0, a=bandwidth)
weights = taper.evaluate(freqs)
nhalf = freqs.size
analytic_spec = num.zeros(n, dtype=num.complex)
analytic_spec[:nhalf] = coefs*weights
enorm = num.abs(analytic_spec[:nhalf])**2
enorm /= num.sum(enorm)
if n % 2 == 0:
analytic_spec[1:nhalf-1] *= 2.
else:
analytic_spec[1:nhalf] *= 2.
analytic = num.fft.ifft(analytic_spec)
signal_tf[ifilt, :] = num.abs(analytic)
enorm = num.abs(analytic_spec[:nhalf])**2
enorm /= num.sum(enorm)
centroid_freqs[ifilt] = num.sum(freqs*enorm)
return centroid_freqs, signal_tf
def _get_tapered_coefs(
self, ntrans, freqlimits, transfer_function, invert=False):
deltaf = 1./(self.deltat*ntrans)
nfreqs = ntrans//2 + 1
transfer = num.ones(nfreqs, dtype=num.complex)
hi = snapper(nfreqs, deltaf)
if freqlimits is not None:
a, b, c, d = freqlimits
freqs = num.arange(hi(d)-hi(a), dtype=num.float)*deltaf \
+ hi(a)*deltaf
if invert:
coeffs = transfer_function.evaluate(freqs)
if num.any(coeffs == 0.0):
raise InfiniteResponse('%s.%s.%s.%s' % self.nslc_id)
transfer[hi(a):hi(d)] = 1.0 / transfer_function.evaluate(freqs)
else:
transfer[hi(a):hi(d)] = transfer_function.evaluate(freqs)
tapered_transfer = costaper(a, b, c, d, nfreqs, deltaf)*transfer
else:
if invert:
raise Exception(
'transfer: `freqlimits` must be given when `invert` is '
'set to `True`')
freqs = num.arange(nfreqs) * deltaf
tapered_transfer = transfer_function.evaluate(freqs)
tapered_transfer[0] = 0.0 # don't introduce static offsets
return tapered_transfer
[docs] def fill_template(self, template, **additional):
'''
Fill string template with trace metadata.
Uses normal python '%(placeholder)s' string templates. The following
placeholders are considered: ``network``, ``station``, ``location``,
``channel``, ``tmin`` (time of first sample), ``tmax`` (time of last
sample), ``tmin_ms``, ``tmax_ms``, ``tmin_us``, ``tmax_us``,
``tmin_year``, ``tmax_year``, ``tmin_month``, ``tmax_month``,
``tmin_day``, ``tmax_day``, ``julianday``. The variants ending with
``'_ms'`` include milliseconds, those with ``'_us'`` include
microseconds, those with ``'_year'`` contain only the year.
'''
template = template.replace('%n', '%(network)s')\
.replace('%s', '%(station)s')\
.replace('%l', '%(location)s')\
.replace('%c', '%(channel)s')\
.replace('%b', '%(tmin)s')\
.replace('%e', '%(tmax)s')\
.replace('%j', '%(julianday)s')
params = dict(
zip(('network', 'station', 'location', 'channel'), self.nslc_id))
params['tmin'] = util.time_to_str(
self.tmin, format='%Y-%m-%d_%H-%M-%S')
params['tmax'] = util.time_to_str(
self.tmax, format='%Y-%m-%d_%H-%M-%S')
params['tmin_ms'] = util.time_to_str(
self.tmin, format='%Y-%m-%d_%H-%M-%S.3FRAC')
params['tmax_ms'] = util.time_to_str(
self.tmax, format='%Y-%m-%d_%H-%M-%S.3FRAC')
params['tmin_us'] = util.time_to_str(
self.tmin, format='%Y-%m-%d_%H-%M-%S.6FRAC')
params['tmax_us'] = util.time_to_str(
self.tmax, format='%Y-%m-%d_%H-%M-%S.6FRAC')
params['tmin_year'], params['tmin_month'], params['tmin_day'] \
= util.time_to_str(self.tmin, format='%Y-%m-%d').split('-')
params['tmax_year'], params['tmax_month'], params['tmax_day'] \
= util.time_to_str(self.tmax, format='%Y-%m-%d').split('-')
params['julianday'] = util.julian_day_of_year(self.tmin)
params.update(additional)
return template % params
[docs] def plot(self):
'''
Show trace with matplotlib.
See also: :py:meth:`Trace.snuffle`.
'''
import pylab
pylab.plot(self.get_xdata(), self.get_ydata())
name = '%s %s %s - %s' % (
self.channel,
self.station,
time.strftime("%d-%m-%y %H:%M:%S", time.gmtime(self.tmin)),
time.strftime("%d-%m-%y %H:%M:%S", time.gmtime(self.tmax)))
pylab.title(name)
pylab.show()
[docs] def snuffle(self, **kwargs):
'''
Show trace in a snuffler window.
:param stations: list of `pyrocko.model.Station` objects or ``None``
:param events: list of `pyrocko.model.Event` objects or ``None``
:param markers: list of `pyrocko.gui.util.Marker` objects or ``None``
:param ntracks: float, number of tracks to be shown initially (default:
12)
:param follow: time interval (in seconds) for real time follow mode or
``None``
:param controls: bool, whether to show the main controls (default:
``True``)
:param opengl: bool, whether to use opengl (default: ``False``)
'''
return snuffle([self], **kwargs)
[docs]def snuffle(traces, **kwargs):
'''
Show traces in a snuffler window.
:param stations: list of `pyrocko.model.Station` objects or ``None``
:param events: list of `pyrocko.model.Event` objects or ``None``
:param markers: list of `pyrocko.gui.util.Marker` objects or ``None``
:param ntracks: float, number of tracks to be shown initially (default: 12)
:param follow: time interval (in seconds) for real time follow mode or
``None``
:param controls: bool, whether to show the main controls (default:
``True``)
:param opengl: bool, whether to use opengl (default: ``False``)
'''
from pyrocko import pile
from pyrocko.gui import snuffler
p = pile.Pile()
if traces:
trf = pile.MemTracesFile(None, traces)
p.add_file(trf)
return snuffler.snuffle(p, **kwargs)
[docs]class InfiniteResponse(Exception):
'''
This exception is raised by :py:class:`Trace` operations when deconvolution
of a frequency response (instrument response transfer function) would
result in a division by zero.
'''
[docs]class MisalignedTraces(Exception):
'''
This exception is raised by some :py:class:`Trace` operations when tmin,
tmax or number of samples do not match.
'''
pass
[docs]class NoData(Exception):
'''
This exception is raised by some :py:class:`Trace` operations when no or
not enough data is available.
'''
pass
[docs]class AboveNyquist(Exception):
'''
This exception is raised by some :py:class:`Trace` operations when given
frequencies are above the Nyquist frequency.
'''
pass
[docs]class TraceTooShort(Exception):
'''
This exception is raised by some :py:class:`Trace` operations when the
trace is too short.
'''
pass
[docs]class ResamplingFailed(Exception):
pass
[docs]def minmax(traces, key=None, mode='minmax'):
'''
Get data range given traces grouped by selected pattern.
:param key: a callable which takes as single argument a trace and returns a
key for the grouping of the results. If this is ``None``, the default,
``lambda tr: (tr.network, tr.station, tr.location, tr.channel)`` is
used.
:param mode: 'minmax' or floating point number. If this is 'minmax',
minimum and maximum of the traces are used, if it is a number, mean +-
standard deviation times ``mode`` is used.
:returns: a dict with the combined data ranges.
Examples::
ranges = minmax(traces, lambda tr: tr.channel)
print ranges['N'] # print min & max of all traces with channel == 'N'
print ranges['E'] # print min & max of all traces with channel == 'E'
ranges = minmax(traces, lambda tr: (tr.network, tr.station))
print ranges['GR', 'HAM3'] # print min & max of all traces with
# network == 'GR' and station == 'HAM3'
ranges = minmax(traces, lambda tr: None)
print ranges[None] # prints min & max of all traces
'''
if key is None:
key = _default_key
ranges = {}
for trace in traces:
if isinstance(mode, str) and mode == 'minmax':
mi, ma = trace.ydata.min(), trace.ydata.max()
else:
mean = trace.ydata.mean()
std = trace.ydata.std()
mi, ma = mean-std*mode, mean+std*mode
k = key(trace)
if k not in ranges:
ranges[k] = mi, ma
else:
tmi, tma = ranges[k]
ranges[k] = min(tmi, mi), max(tma, ma)
return ranges
[docs]def minmaxtime(traces, key=None):
'''
Get time range given traces grouped by selected pattern.
:param key: a callable which takes as single argument a trace and returns a
key for the grouping of the results. If this is ``None``, the default,
``lambda tr: (tr.network, tr.station, tr.location, tr.channel)`` is
used.
:returns: a dict with the combined data ranges.
'''
if key is None:
key = _default_key
ranges = {}
for trace in traces:
mi, ma = trace.tmin, trace.tmax
k = key(trace)
if k not in ranges:
ranges[k] = mi, ma
else:
tmi, tma = ranges[k]
ranges[k] = min(tmi, mi), max(tma, ma)
return ranges
[docs]def degapper(
traces,
maxgap=5,
fillmethod='interpolate',
deoverlap='use_second',
maxlap=None):
'''
Try to connect traces and remove gaps.
This method will combine adjacent traces, which match in their network,
station, location and channel attributes. Overlapping parts are handled
according to the ``deoverlap`` argument.
:param traces: input traces, must be sorted by their full_id attribute.
:param maxgap: maximum number of samples to interpolate.
:param fillmethod: what to put into the gaps: 'interpolate' or 'zeros'.
:param deoverlap: how to handle overlaps: 'use_second' to use data from
second trace (default), 'use_first' to use data from first trace,
'crossfade_cos' to crossfade with cosine taper, 'add' to add amplitude
values.
:param maxlap: maximum number of samples of overlap which are removed
:returns: list of traces
'''
in_traces = traces
out_traces = []
if not in_traces:
return out_traces
out_traces.append(in_traces.pop(0))
while in_traces:
a = out_traces[-1]
b = in_traces.pop(0)
avirt, bvirt = a.ydata is None, b.ydata is None
assert avirt == bvirt, \
'traces given to degapper() must either all have data or have ' \
'no data.'
virtual = avirt and bvirt
if (a.nslc_id == b.nslc_id and a.deltat == b.deltat
and a.data_len() >= 1 and b.data_len() >= 1
and (virtual or a.ydata.dtype == b.ydata.dtype)):
dist = (b.tmin-(a.tmin+(a.data_len()-1)*a.deltat))/a.deltat
idist = int(round(dist))
if abs(dist - idist) > 0.05 and idist <= maxgap:
# logger.warning('Cannot degap traces with displaced sampling '
# '(%s, %s, %s, %s)' % a.nslc_id)
pass
else:
if 1 < idist <= maxgap:
if not virtual:
if fillmethod == 'interpolate':
filler = a.ydata[-1] + (
((1.0 + num.arange(idist-1, dtype=num.float))
/ idist) * (b.ydata[0]-a.ydata[-1])
).astype(a.ydata.dtype)
elif fillmethod == 'zeros':
filler = num.zeros(idist-1, dtype=a.ydist.dtype)
a.ydata = num.concatenate((a.ydata, filler, b.ydata))
a.tmax = b.tmax
if a.mtime and b.mtime:
a.mtime = max(a.mtime, b.mtime)
continue
elif idist == 1:
if not virtual:
a.ydata = num.concatenate((a.ydata, b.ydata))
a.tmax = b.tmax
if a.mtime and b.mtime:
a.mtime = max(a.mtime, b.mtime)
continue
elif idist <= 0 and (maxlap is None or -maxlap < idist):
if b.tmax > a.tmax:
if not virtual:
na = a.ydata.size
n = -idist+1
if deoverlap == 'use_second':
a.ydata = num.concatenate(
(a.ydata[:-n], b.ydata))
elif deoverlap in ('use_first', 'crossfade_cos'):
a.ydata = num.concatenate(
(a.ydata, b.ydata[n:]))
elif deoverlap == 'add':
a.ydata[-n:] += b.ydata[:n]
a.ydata = num.concatenate(
(a.ydata, b.ydata[n:]))
else:
assert False, 'unknown deoverlap method'
if deoverlap == 'crossfade_cos':
n = -idist+1
taper = 0.5-0.5*num.cos(
(1.+num.arange(n))/(1.+n)*num.pi)
a.ydata[na-n:na] *= 1.-taper
a.ydata[na-n:na] += b.ydata[:n] * taper
a.tmax = b.tmax
if a.mtime and b.mtime:
a.mtime = max(a.mtime, b.mtime)
continue
else:
# make short second trace vanish
continue
if b.data_len() >= 1:
out_traces.append(b)
for tr in out_traces:
tr._update_ids()
return out_traces
[docs]def rotate(traces, azimuth, in_channels, out_channels):
'''
2D rotation of traces.
:param traces: list of input traces
:param azimuth: difference of the azimuths of the component directions
(azimuth of out_channels[0]) - (azimuth of in_channels[0])
:param in_channels: names of the input channels (e.g. 'N', 'E')
:param out_channels: names of the output channels (e.g. 'R', 'T')
:returns: list of rotated traces
'''
phi = azimuth/180.*math.pi
cphi = math.cos(phi)
sphi = math.sin(phi)
rotated = []
in_channels = tuple(_channels_to_names(in_channels))
out_channels = tuple(_channels_to_names(out_channels))
for a in traces:
for b in traces:
if ((a.channel, b.channel) == in_channels and
a.nslc_id[:3] == b.nslc_id[:3] and
abs(a.deltat-b.deltat) < a.deltat*0.001):
tmin = max(a.tmin, b.tmin)
tmax = min(a.tmax, b.tmax)
if tmin < tmax:
ac = a.chop(tmin, tmax, inplace=False, include_last=True)
bc = b.chop(tmin, tmax, inplace=False, include_last=True)
if abs(ac.tmin - bc.tmin) > ac.deltat*0.01:
logger.warning(
'Cannot rotate traces with displaced sampling '
'(%s, %s, %s, %s)' % a.nslc_id)
continue
acydata = ac.get_ydata()*cphi+bc.get_ydata()*sphi
bcydata = -ac.get_ydata()*sphi+bc.get_ydata()*cphi
ac.set_ydata(acydata)
bc.set_ydata(bcydata)
ac.set_codes(channel=out_channels[0])
bc.set_codes(channel=out_channels[1])
rotated.append(ac)
rotated.append(bc)
return rotated
def rotate_to_rt(n, e, source, receiver, out_channels=('R', 'T')):
azimuth = orthodrome.azimuth(receiver, source) + 180.
in_channels = n.channel, e.channel
out = rotate(
[n, e], azimuth,
in_channels=in_channels,
out_channels=out_channels)
assert len(out) == 2
for tr in out:
if tr.channel == 'R':
r = tr
elif tr.channel == 'T':
t = tr
return r, t
[docs]def rotate_to_lqt(traces, backazimuth, incidence, in_channels,
out_channels=('L', 'Q', 'T')):
'''Rotate traces from ZNE to LQT system.
:param traces: list of traces in arbitrary order
:param backazimuth: backazimuth in degrees clockwise from north
:param incidence: incidence angle in degrees from vertical
:param in_channels: input channel names
:param out_channels: output channel names (default: ('L', 'Q', 'T'))
:returns: list of transformed traces
'''
i = incidence/180.*num.pi
b = backazimuth/180.*num.pi
ci = num.cos(i)
cb = num.cos(b)
si = num.sin(i)
sb = num.sin(b)
rotmat = num.array(
[[ci, -cb*si, -sb*si], [si, cb*ci, sb*ci], [0., sb, -cb]])
return project(traces, rotmat, in_channels, out_channels)
def _decompose(a):
'''
Decompose matrix into independent submatrices.
'''
def depends(iout, a):
row = a[iout, :]
return set(num.arange(row.size).compress(row != 0.0))
def provides(iin, a):
col = a[:, iin]
return set(num.arange(col.size).compress(col != 0.0))
a = num.asarray(a)
outs = set(range(a.shape[0]))
systems = []
while outs:
iout = outs.pop()
gout = set()
for iin in depends(iout, a):
gout.update(provides(iin, a))
if not gout:
continue
gin = set()
for iout2 in gout:
gin.update(depends(iout2, a))
if not gin:
continue
for iout2 in gout:
if iout2 in outs:
outs.remove(iout2)
gin = list(gin)
gin.sort()
gout = list(gout)
gout.sort()
systems.append((gin, gout, a[gout, :][:, gin]))
return systems
def _channels_to_names(channels):
names = []
for ch in channels:
if isinstance(ch, model.Channel):
names.append(ch.name)
else:
names.append(ch)
return names
[docs]def project(traces, matrix, in_channels, out_channels):
'''
Affine transform of three-component traces.
Compute matrix-vector product of three-component traces, to e.g. rotate
traces into a different basis. The traces are distinguished and ordered by
their channel attribute. The tranform is applied to overlapping parts of
any appropriate combinations of the input traces. This should allow this
function to be robust with data gaps. It also tries to apply the
tranformation to subsets of the channels, if this is possible, so that, if
for example a vertical compontent is missing, horizontal components can
still be rotated.
:param traces: list of traces in arbitrary order
:param matrix: tranformation matrix
:param in_channels: input channel names
:param out_channels: output channel names
:returns: list of transformed traces
'''
in_channels = tuple(_channels_to_names(in_channels))
out_channels = tuple(_channels_to_names(out_channels))
systems = _decompose(matrix)
# fallback to full matrix if some are not quadratic
for iins, iouts, submatrix in systems:
if submatrix.shape[0] != submatrix.shape[1]:
return _project3(traces, matrix, in_channels, out_channels)
projected = []
for iins, iouts, submatrix in systems:
in_cha = tuple([in_channels[iin] for iin in iins])
out_cha = tuple([out_channels[iout] for iout in iouts])
if submatrix.shape[0] == 1:
projected.extend(_project1(traces, submatrix, in_cha, out_cha))
elif submatrix.shape[1] == 2:
projected.extend(_project2(traces, submatrix, in_cha, out_cha))
else:
projected.extend(_project3(traces, submatrix, in_cha, out_cha))
return projected
[docs]def project_dependencies(matrix, in_channels, out_channels):
'''
Figure out what dependencies project() would produce.
'''
in_channels = tuple(_channels_to_names(in_channels))
out_channels = tuple(_channels_to_names(out_channels))
systems = _decompose(matrix)
subpro = []
for iins, iouts, submatrix in systems:
if submatrix.shape[0] != submatrix.shape[1]:
subpro.append((matrix, in_channels, out_channels))
if not subpro:
for iins, iouts, submatrix in systems:
in_cha = tuple([in_channels[iin] for iin in iins])
out_cha = tuple([out_channels[iout] for iout in iouts])
subpro.append((submatrix, in_cha, out_cha))
deps = {}
for mat, in_cha, out_cha in subpro:
for oc in out_cha:
if oc not in deps:
deps[oc] = []
for ic in in_cha:
deps[oc].append(ic)
return deps
def _project1(traces, matrix, in_channels, out_channels):
assert len(in_channels) == 1
assert len(out_channels) == 1
assert matrix.shape == (1, 1)
projected = []
for a in traces:
if not (a.channel,) == in_channels:
continue
ac = a.copy()
ac.set_ydata(matrix[0, 0]*a.get_ydata())
ac.set_codes(channel=out_channels[0])
projected.append(ac)
return projected
def _project2(traces, matrix, in_channels, out_channels):
assert len(in_channels) == 2
assert len(out_channels) == 2
assert matrix.shape == (2, 2)
projected = []
for a in traces:
for b in traces:
if not ((a.channel, b.channel) == in_channels and
a.nslc_id[:3] == b.nslc_id[:3] and
abs(a.deltat-b.deltat) < a.deltat*0.001):
continue
tmin = max(a.tmin, b.tmin)
tmax = min(a.tmax, b.tmax)
if tmin > tmax:
continue
ac = a.chop(tmin, tmax, inplace=False, include_last=True)
bc = b.chop(tmin, tmax, inplace=False, include_last=True)
if abs(ac.tmin - bc.tmin) > ac.deltat*0.01:
logger.warning(
'Cannot project traces with displaced sampling '
'(%s, %s, %s, %s)' % a.nslc_id)
continue
acydata = num.dot(matrix[0], (ac.get_ydata(), bc.get_ydata()))
bcydata = num.dot(matrix[1], (ac.get_ydata(), bc.get_ydata()))
ac.set_ydata(acydata)
bc.set_ydata(bcydata)
ac.set_codes(channel=out_channels[0])
bc.set_codes(channel=out_channels[1])
projected.append(ac)
projected.append(bc)
return projected
def _project3(traces, matrix, in_channels, out_channels):
assert len(in_channels) == 3
assert len(out_channels) == 3
assert matrix.shape == (3, 3)
projected = []
for a in traces:
for b in traces:
for c in traces:
if not ((a.channel, b.channel, c.channel) == in_channels
and a.nslc_id[:3] == b.nslc_id[:3]
and b.nslc_id[:3] == c.nslc_id[:3]
and abs(a.deltat-b.deltat) < a.deltat*0.001
and abs(b.deltat-c.deltat) < b.deltat*0.001):
continue
tmin = max(a.tmin, b.tmin, c.tmin)
tmax = min(a.tmax, b.tmax, c.tmax)
if tmin >= tmax:
continue
ac = a.chop(tmin, tmax, inplace=False, include_last=True)
bc = b.chop(tmin, tmax, inplace=False, include_last=True)
cc = c.chop(tmin, tmax, inplace=False, include_last=True)
if (abs(ac.tmin - bc.tmin) > ac.deltat*0.01
or abs(bc.tmin - cc.tmin) > bc.deltat*0.01):
logger.warning(
'Cannot project traces with displaced sampling '
'(%s, %s, %s, %s)' % a.nslc_id)
continue
acydata = num.dot(
matrix[0],
(ac.get_ydata(), bc.get_ydata(), cc.get_ydata()))
bcydata = num.dot(
matrix[1],
(ac.get_ydata(), bc.get_ydata(), cc.get_ydata()))
ccydata = num.dot(
matrix[2],
(ac.get_ydata(), bc.get_ydata(), cc.get_ydata()))
ac.set_ydata(acydata)
bc.set_ydata(bcydata)
cc.set_ydata(ccydata)
ac.set_codes(channel=out_channels[0])
bc.set_codes(channel=out_channels[1])
cc.set_codes(channel=out_channels[2])
projected.append(ac)
projected.append(bc)
projected.append(cc)
return projected
[docs]def correlate(a, b, mode='valid', normalization=None, use_fft=False):
'''
Cross correlation of two traces.
:param a,b: input traces
:param mode: ``'valid'``, ``'full'``, or ``'same'``
:param normalization: ``'normal'``, ``'gliding'``, or ``None``
:param use_fft: bool, whether to do cross correlation in spectral domain
:returns: trace containing cross correlation coefficients
This function computes the cross correlation between two traces. It
evaluates the discrete equivalent of
.. math::
c(t) = \\int_{-\\infty}^{\\infty} a^{\\ast}(\\tau) b(t+\\tau) d\\tau
where the star denotes complex conjugate. Note, that the arguments here are
swapped when compared with the :py:func:`numpy.correlate` function,
which is internally called. This function should be safe even with older
versions of NumPy, where the correlate function has some problems.
A trace containing the cross correlation coefficients is returned. The time
information of the output trace is set so that the returned cross
correlation can be viewed directly as a function of time lag.
Example::
# align two traces a and b containing a time shifted similar signal:
c = pyrocko.trace.correlate(a,b)
t, coef = c.max() # get time and value of maximum
b.shift(-t) # align b with a
'''
assert_same_sampling_rate(a, b)
ya, yb = a.ydata, b.ydata
# need reversed order here:
yc = numpy_correlate_fixed(yb, ya, mode=mode, use_fft=use_fft)
kmin, kmax = numpy_correlate_lag_range(yb, ya, mode=mode, use_fft=use_fft)
if normalization == 'normal':
normfac = num.sqrt(num.sum(ya**2))*num.sqrt(num.sum(yb**2))
yc = yc/normfac
elif normalization == 'gliding':
if mode != 'valid':
assert False, 'gliding normalization currently only available ' \
'with "valid" mode.'
if ya.size < yb.size:
yshort, ylong = ya, yb
else:
yshort, ylong = yb, ya
epsilon = 0.00001
normfac_short = num.sqrt(num.sum(yshort**2))
normfac = normfac_short * num.sqrt(
moving_sum(ylong**2, yshort.size, mode='valid')) \
+ normfac_short*epsilon
if yb.size <= ya.size:
normfac = normfac[::-1]
yc /= normfac
c = a.copy()
c.set_ydata(yc)
c.set_codes(*merge_codes(a, b, '~'))
c.shift(-c.tmin + b.tmin-a.tmin + kmin * c.deltat)
return c
def deconvolve(
a, b, waterlevel,
tshift=0.,
pad=0.5,
fd_taper=None,
pad_to_pow2=True):
same_sampling_rate(a, b)
assert abs(a.tmin - b.tmin) < a.deltat * 0.001
deltat = a.deltat
npad = int(round(a.data_len()*pad + tshift / deltat))
ndata = max(a.data_len(), b.data_len())
ndata_pad = ndata + npad
if pad_to_pow2:
ntrans = nextpow2(ndata_pad)
else:
ntrans = ndata
aspec = num.fft.rfft(a.ydata, ntrans)
bspec = num.fft.rfft(b.ydata, ntrans)
out = aspec * num.conj(bspec)
bautocorr = bspec*num.conj(bspec)
denom = num.maximum(bautocorr, waterlevel * bautocorr.max())
out /= denom
df = 1/(ntrans*deltat)
if fd_taper is not None:
fd_taper(out, 0.0, df)
ydata = num.roll(num.fft.irfft(out), int(round(tshift/deltat)))
c = a.copy(data=False)
c.set_ydata(ydata[:ndata])
c.set_codes(*merge_codes(a, b, '/'))
return c
def assert_same_sampling_rate(a, b, eps=1.0e-6):
assert same_sampling_rate(a, b, eps), \
'Sampling rates differ: %g != %g' % (a.deltat, b.deltat)
[docs]def same_sampling_rate(a, b, eps=1.0e-6):
'''
Check if two traces have the same sampling rate.
:param a,b: input traces
:param eps: relative tolerance
'''
return abs(a.deltat - b.deltat) < (a.deltat + b.deltat)*eps
[docs]def fix_deltat_rounding_errors(deltat):
'''
Try to undo sampling rate rounding errors.
Fix rounding errors of sampling intervals when these are read from single
precision floating point values.
Assumes that the true sampling rate or sampling interval was an integer
value. No correction will be applied if this would change the sampling
rate by more than 0.001%.
'''
if deltat <= 1.0:
deltat_new = 1.0 / round(1.0 / deltat)
else:
deltat_new = round(deltat)
if abs(deltat_new - deltat) / deltat > 1e-5:
deltat_new = deltat
return deltat_new
[docs]def merge_codes(a, b, sep='-'):
'''
Merge network-station-location-channel codes of a pair of traces.
'''
o = []
for xa, xb in zip(a.nslc_id, b.nslc_id):
if xa == xb:
o.append(xa)
else:
o.append(sep.join((xa, xb)))
return o
[docs]class Taper(Object):
'''
Base class for tapers.
Does nothing by default.
'''
def __call__(self, y, x0, dx):
pass
[docs]class CosTaper(Taper):
'''
Cosine Taper.
:param a: start of fading in
:param b: end of fading in
:param c: start of fading out
:param d: end of fading out
'''
a = Float.T()
b = Float.T()
c = Float.T()
d = Float.T()
def __init__(self, a, b, c, d):
Taper.__init__(self, a=a, b=b, c=c, d=d)
def __call__(self, y, x0, dx):
apply_costaper(self.a, self.b, self.c, self.d, y, x0, dx)
def span(self, y, x0, dx):
return span_costaper(self.a, self.b, self.c, self.d, y, x0, dx)
def time_span(self):
return self.a, self.d
[docs]class CosFader(Taper):
'''
Cosine Fader.
:param xfade: fade in and fade out time in seconds (optional)
:param xfrac: fade in and fade out as fraction between 0. and 1. (optional)
Only one argument can be set. The other should to be ``None``.
'''
xfade = Float.T(optional=True)
xfrac = Float.T(optional=True)
def __init__(self, xfade=None, xfrac=None):
Taper.__init__(self, xfade=xfade, xfrac=xfrac)
assert (xfade is None) != (xfrac is None)
self._xfade = xfade
self._xfrac = xfrac
def __call__(self, y, x0, dx):
xfade = self._xfade
xlen = (y.size - 1)*dx
if xfade is None:
xfade = xlen * self._xfrac
a = x0
b = x0 + xfade
c = x0 + xlen - xfade
d = x0 + xlen
apply_costaper(a, b, c, d, y, x0, dx)
def span(self, y, x0, dx):
return 0, y.size
def time_span(self):
return None, None
def none_min(li):
if None in li:
return None
else:
return min(x for x in li if x is not None)
def none_max(li):
if None in li:
return None
else:
return max(x for x in li if x is not None)
[docs]class MultiplyTaper(Taper):
'''
Multiplication of several tapers.
'''
tapers = List.T(Taper.T())
def __init__(self, tapers=None):
if tapers is None:
tapers = []
Taper.__init__(self, tapers=tapers)
def __call__(self, y, x0, dx):
for taper in self.tapers:
taper(y, x0, dx)
def span(self, y, x0, dx):
spans = []
for taper in self.tapers:
spans.append(taper.span(y, x0, dx))
mins, maxs = list(zip(*spans))
return min(mins), max(maxs)
def time_span(self):
spans = []
for taper in self.tapers:
spans.append(taper.time_span())
mins, maxs = list(zip(*spans))
return none_min(mins), none_max(maxs)
[docs]class GaussTaper(Taper):
'''
Frequency domain Gaussian filter.
'''
alpha = Float.T()
def __init__(self, alpha):
Taper.__init__(self, alpha=alpha)
self._alpha = alpha
def __call__(self, y, x0, dx):
f = x0 + num.arange(y.size)*dx
y *= num.exp(-num.pi**2 / (self._alpha**2) * f**2)
[docs]class FrequencyResponse(Object):
'''
Evaluates frequency response at given frequencies.
'''
def evaluate(self, freqs):
coefs = num.ones(freqs.size, dtype=num.complex)
return coefs
[docs] def is_scalar(self):
'''
Check if this is a flat response.
'''
if type(self) == FrequencyResponse:
return True
else:
return False # default for derived classes
[docs]class Evalresp(FrequencyResponse):
'''
Calls evalresp and generates values of the instrument response transfer
function.
:param respfile: response file in evalresp format
:param trace: trace for which the response is to be extracted from the file
:param target: ``'dis'`` for displacement or ``'vel'`` for velocity
'''
respfile = String.T()
nslc_id = Tuple.T(4, String.T())
target = String.T(default='dis')
instant = Float.T()
def __init__(
self, respfile, trace=None, target='dis', nslc_id=None, time=None):
if trace is not None:
nslc_id = trace.nslc_id
time = (trace.tmin + trace.tmax) / 2.
FrequencyResponse.__init__(
self,
respfile=respfile,
nslc_id=nslc_id,
instant=time,
target=target)
def evaluate(self, freqs):
network, station, location, channel = self.nslc_id
x = evalresp.evalresp(
sta_list=station,
cha_list=channel,
net_code=network,
locid=location,
instant=self.instant,
freqs=freqs,
units=self.target.upper(),
file=self.respfile,
rtype='CS')
transfer = x[0][4]
return transfer
[docs]class InverseEvalresp(FrequencyResponse):
'''
Calls evalresp and generates values of the inverse instrument response for
deconvolution of instrument response.
:param respfile: response file in evalresp format
:param trace: trace for which the response is to be extracted from the file
:param target: ``'dis'`` for displacement or ``'vel'`` for velocity
'''
respfile = String.T()
nslc_id = Tuple.T(4, String.T())
target = String.T(default='dis')
instant = Float.T()
def __init__(self, respfile, trace, target='dis'):
FrequencyResponse.__init__(
self,
respfile=respfile,
nslc_id=trace.nslc_id,
instant=(trace.tmin + trace.tmax)/2.,
target=target)
def evaluate(self, freqs):
network, station, location, channel = self.nslc_id
x = evalresp.evalresp(sta_list=station,
cha_list=channel,
net_code=network,
locid=location,
instant=self.instant,
freqs=freqs,
units=self.target.upper(),
file=self.respfile,
rtype='CS')
transfer = x[0][4]
return 1./transfer
[docs]class PoleZeroResponse(FrequencyResponse):
'''
Evaluates frequency response from pole-zero representation.
:param zeros: :py:class:`numpy.array` containing complex positions of zeros
:param poles: :py:class:`numpy.array` containing complex positions of poles
:param constant: gain as floating point number
::
(j*2*pi*f - zeros[0]) * (j*2*pi*f - zeros[1]) * ...
T(f) = constant * ----------------------------------------------------
(j*2*pi*f - poles[0]) * (j*2*pi*f - poles[1]) * ...
The poles and zeros should be given as angular frequencies, not in Hz.
'''
zeros = List.T(Complex.T())
poles = List.T(Complex.T())
constant = Complex.T(default=1.0+0j)
def __init__(self, zeros=None, poles=None, constant=1.0+0j):
if zeros is None:
zeros = []
if poles is None:
poles = []
FrequencyResponse.__init__(
self, zeros=zeros, poles=poles, constant=constant)
def evaluate(self, freqs):
jomeg = 1.0j * 2.*num.pi*freqs
a = num.ones(freqs.size, dtype=num.complex)*self.constant
for z in self.zeros:
a *= jomeg-z
for p in self.poles:
a /= jomeg-p
return a
[docs] def is_scalar(self):
return len(self.zeros) == 0 and len(self.poles) == 0
[docs]class ButterworthResponse(FrequencyResponse):
'''
Butterworth frequency response.
:param corner: corner frequency of the response
:param order: order of the response
:param type: either ``high`` or ``low``
'''
corner = Float.T(default=1.0)
order = Int.T(default=4)
type = StringChoice.T(choices=['low', 'high'], default='low')
def evaluate(self, freqs):
b, a = signal.butter(
int(self.order), float(self.corner), self.type, analog=True)
w, h = signal.freqs(b, a, freqs)
return h
[docs]class SampledResponse(FrequencyResponse):
'''
Interpolates frequency response given at a set of sampled frequencies.
:param frequencies,values: frequencies and values of the sampled response
function.
:param left,right: values to return when input is out of range. If set to
``None`` (the default) the endpoints are returned.
'''
frequencies = Array.T(shape=(None,), dtype=num.float, serialize_as='list')
values = Array.T(shape=(None,), dtype=num.complex, serialize_as='list')
left = Complex.T(optional=True)
right = Complex.T(optional=True)
def __init__(self, frequencies, values, left=None, right=None):
FrequencyResponse.__init__(
self,
frequencies=asarray_1d(frequencies, num.float),
values=asarray_1d(values, num.complex))
def evaluate(self, freqs):
ereal = num.interp(
freqs, self.frequencies, num.real(self.values),
left=self.left, right=self.right)
eimag = num.interp(
freqs, self.frequencies, num.imag(self.values),
left=self.left, right=self.right)
transfer = ereal + 1.0j*eimag
return transfer
[docs] def inverse(self):
'''
Get inverse as a new :py:class:`SampledResponse` object.
'''
def inv_or_none(x):
if x is not None:
return 1./x
return SampledResponse(
self.frequencies, 1./self.values,
left=inv_or_none(self.left),
right=inv_or_none(self.right))
[docs]class IntegrationResponse(FrequencyResponse):
'''
The integration response, optionally multiplied by a constant gain.
:param n: exponent (integer)
:param gain: gain factor (float)
::
gain
T(f) = --------------
(j*2*pi * f)^n
'''
n = Int.T(optional=True, default=1)
gain = Float.T(optional=True, default=1.0)
def __init__(self, n=1, gain=1.0):
FrequencyResponse.__init__(self, n=n, gain=gain)
def evaluate(self, freqs):
nonzero = freqs != 0.0
resp = num.empty(freqs.size, dtype=num.complex)
resp[nonzero] = self.gain / (1.0j * 2. * num.pi*freqs[nonzero])**self.n
resp[num.logical_not(nonzero)] = 0.0
return resp
[docs]class DifferentiationResponse(FrequencyResponse):
'''
The differentiation response, optionally multiplied by a constant gain.
:param n: exponent (integer)
:param gain: gain factor (float)
::
T(f) = gain * (j*2*pi * f)^n
'''
n = Int.T(optional=True, default=1)
gain = Float.T(optional=True, default=1.0)
def __init__(self, n=1, gain=1.0):
FrequencyResponse.__init__(self, n=n, gain=gain)
def evaluate(self, freqs):
return self.gain * (1.0j * 2. * num.pi * freqs)**self.n
[docs]class AnalogFilterResponse(FrequencyResponse):
'''
Frequency response of an analog filter.
(see :py:func:`scipy.signal.freqs`).
'''
b = List.T(Float.T())
a = List.T(Float.T())
def __init__(self, b, a):
FrequencyResponse.__init__(self, b=b, a=a)
def evaluate(self, freqs):
return signal.freqs(self.b, self.a, freqs/(2.*num.pi))[1]
[docs]class MultiplyResponse(FrequencyResponse):
'''
Multiplication of several :py:class:`FrequencyResponse` objects.
'''
responses = List.T(FrequencyResponse.T())
def __init__(self, responses=None):
if responses is None:
responses = []
FrequencyResponse.__init__(self, responses=responses)
def evaluate(self, freqs):
a = num.ones(freqs.size, dtype=num.complex)
for resp in self.responses:
a *= resp.evaluate(freqs)
return a
[docs] def is_scalar(self):
return all(resp.is_scalar() for resp in self.responses)
def asarray_1d(x, dtype):
if isinstance(x, (list, tuple)) and x and isinstance(x[0], (str, newstr)):
return num.asarray(list(map(dtype, x)), dtype=dtype)
else:
a = num.asarray(x, dtype=dtype)
if not a.ndim == 1:
raise ValueError('could not convert to 1D array')
return a
cached_coefficients = {}
def _get_cached_filter_coefs(order, corners, btype):
ck = (order, tuple(corners), btype)
if ck not in cached_coefficients:
if len(corners) == 0:
cached_coefficients[ck] = signal.butter(
order, corners[0], btype=btype)
else:
cached_coefficients[ck] = signal.butter(
order, corners, btype=btype)
return cached_coefficients[ck]
class _globals(object):
_numpy_has_correlate_flip_bug = None
def _default_key(tr):
return (tr.network, tr.station, tr.location, tr.channel)
[docs]def numpy_has_correlate_flip_bug():
'''
Check if NumPy's correlate function reveals old behaviour
'''
if _globals._numpy_has_correlate_flip_bug is None:
a = num.array([0, 0, 1, 0, 0, 0, 0])
b = num.array([0, 0, 0, 0, 1, 0, 0, 0])
ab = num.correlate(a, b, mode='same')
ba = num.correlate(b, a, mode='same')
_globals._numpy_has_correlate_flip_bug = num.all(ab == ba)
return _globals._numpy_has_correlate_flip_bug
[docs]def numpy_correlate_fixed(a, b, mode='valid', use_fft=False):
'''
Call :py:func:`numpy.correlate` with fixes.
c[k] = sum_i a[i+k] * conj(b[i])
Note that the result produced by newer numpy.correlate is always flipped
with respect to the formula given in its documentation (if ascending k
assumed for the output).
'''
if use_fft:
if a.size < b.size:
c = signal.fftconvolve(b[::-1], a, mode=mode)
else:
c = signal.fftconvolve(a, b[::-1], mode=mode)
return c
else:
buggy = numpy_has_correlate_flip_bug()
a = num.asarray(a)
b = num.asarray(b)
if buggy:
b = num.conj(b)
c = num.correlate(a, b, mode=mode)
if buggy and a.size < b.size:
return c[::-1]
else:
return c
[docs]def numpy_correlate_emulate(a, b, mode='valid'):
'''
Slow version of :py:func:`numpy.correlate` for comparison.
'''
a = num.asarray(a)
b = num.asarray(b)
kmin = -(b.size-1)
klen = a.size-kmin
kmin, kmax = numpy_correlate_lag_range(a, b, mode=mode)
kmin = int(kmin)
kmax = int(kmax)
klen = kmax - kmin + 1
c = num.zeros(klen, dtype=num.find_common_type((b.dtype, a.dtype), ()))
for k in range(kmin, kmin+klen):
imin = max(0, -k)
ilen = min(b.size, a.size-k) - imin
c[k-kmin] = num.sum(
a[imin+k:imin+ilen+k] * num.conj(b[imin:imin+ilen]))
return c
[docs]def numpy_correlate_lag_range(a, b, mode='valid', use_fft=False):
'''
Get range of lags for which :py:func:`numpy.correlate` produces values.
'''
a = num.asarray(a)
b = num.asarray(b)
kmin = -(b.size-1)
if mode == 'full':
klen = a.size-kmin
elif mode == 'same':
klen = max(a.size, b.size)
kmin += (a.size+b.size-1 - max(a.size, b.size)) // 2 + \
int(not use_fft and a.size % 2 == 0 and b.size > a.size)
elif mode == 'valid':
klen = abs(a.size - b.size) + 1
kmin += min(a.size, b.size) - 1
return kmin, kmin + klen - 1
[docs]def autocorr(x, nshifts):
'''
Compute biased estimate of the first autocorrelation coefficients.
:param x: input array
:param nshifts: number of coefficients to calculate
'''
mean = num.mean(x)
std = num.std(x)
n = x.size
xdm = x - mean
r = num.zeros(nshifts)
for k in range(nshifts):
r[k] = 1./((n-num.abs(k))*std) * num.sum(xdm[:n-k] * xdm[k:])
return r
[docs]def yulewalker(x, order):
'''
Compute autoregression coefficients using Yule-Walker method.
:param x: input array
:param order: number of coefficients to produce
A biased estimate of the autocorrelation is used. The Yule-Walker equations
are solved by :py:func:`numpy.linalg.inv` instead of Levinson-Durbin
recursion which is normally used.
'''
gamma = autocorr(x, order+1)
d = gamma[1:1+order]
a = num.zeros((order, order))
gamma2 = num.concatenate((gamma[::-1], gamma[1:order]))
for i in range(order):
ioff = order-i
a[i, :] = gamma2[ioff:ioff+order]
return num.dot(num.linalg.inv(a), -d)
def moving_avg(x, n):
n = int(n)
cx = x.cumsum()
nn = len(x)
y = num.zeros(nn, dtype=cx.dtype)
y[n//2:n//2+(nn-n)] = (cx[n:]-cx[:-n])/n
y[:n//2] = y[n//2]
y[n//2+(nn-n):] = y[n//2+(nn-n)-1]
return y
def moving_sum(x, n, mode='valid'):
n = int(n)
cx = x.cumsum()
nn = len(x)
if mode == 'valid':
if nn-n+1 <= 0:
return num.zeros(0, dtype=cx.dtype)
y = num.zeros(nn-n+1, dtype=cx.dtype)
y[0] = cx[n-1]
y[1:nn-n+1] = cx[n:nn]-cx[0:nn-n]
if mode == 'full':
y = num.zeros(nn+n-1, dtype=cx.dtype)
if n <= nn:
y[0:n] = cx[0:n]
y[n:nn] = cx[n:nn]-cx[0:nn-n]
y[nn:nn+n-1] = cx[-1]-cx[nn-n:nn-1]
else:
y[0:nn] = cx[0:nn]
y[nn:n] = cx[nn-1]
y[n:nn+n-1] = cx[nn-1] - cx[0:nn-1]
if mode == 'same':
n1 = (n-1)//2
y = num.zeros(nn, dtype=cx.dtype)
if n <= nn:
y[0:n-n1] = cx[n1:n]
y[n-n1:nn-n1] = cx[n:nn]-cx[0:nn-n]
y[nn-n1:nn] = cx[nn-1] - cx[nn-n:nn-n+n1]
else:
y[0:max(0, nn-n1)] = cx[min(n1, nn):nn]
y[max(nn-n1, 0):min(n-n1, nn)] = cx[nn-1]
y[min(n-n1, nn):nn] = cx[nn-1] - cx[0:max(0, nn-(n-n1))]
return y
def nextpow2(i):
return 2**int(math.ceil(math.log(i)/math.log(2.)))
def snapper_w_offset(nmax, offset, delta, snapfun=math.ceil):
def snap(x):
return max(0, min(int(snapfun((x-offset)/delta)), nmax))
return snap
def snapper(nmax, delta, snapfun=math.ceil):
def snap(x):
return max(0, min(int(snapfun(x/delta)), nmax))
return snap
def apply_costaper(a, b, c, d, y, x0, dx):
hi = snapper_w_offset(y.size, x0, dx)
y[:hi(a)] = 0.
y[hi(a):hi(b)] *= 0.5 \
- 0.5*num.cos((dx*num.arange(hi(a), hi(b))-(a-x0))/(b-a)*num.pi)
y[hi(c):hi(d)] *= 0.5 \
+ 0.5*num.cos((dx*num.arange(hi(c), hi(d))-(c-x0))/(d-c)*num.pi)
y[hi(d):] = 0.
def span_costaper(a, b, c, d, y, x0, dx):
hi = snapper_w_offset(y.size, x0, dx)
return hi(a), hi(d) - hi(a)
def costaper(a, b, c, d, nfreqs, deltaf):
hi = snapper(nfreqs, deltaf)
tap = num.zeros(nfreqs)
tap[hi(a):hi(b)] = 0.5 \
- 0.5*num.cos((deltaf*num.arange(hi(a), hi(b))-a)/(b-a)*num.pi)
tap[hi(b):hi(c)] = 1.
tap[hi(c):hi(d)] = 0.5 \
+ 0.5*num.cos((deltaf*num.arange(hi(c), hi(d))-c)/(d-c)*num.pi)
return tap
def t2ind(t, tdelta, snap=round):
return int(snap(t/tdelta))
[docs]def hilbert(x, N=None):
'''
Return the hilbert transform of x of length N.
(from scipy.signal, but changed to use fft and ifft from numpy.fft)
'''
x = num.asarray(x)
if N is None:
N = len(x)
if N <= 0:
raise ValueError("N must be positive.")
if num.iscomplexobj(x):
logger.warning('imaginary part of x ignored.')
x = num.real(x)
Xf = num.fft.fft(x, N, axis=0)
h = num.zeros(N)
if N % 2 == 0:
h[0] = h[N//2] = 1
h[1:N//2] = 2
else:
h[0] = 1
h[1:(N+1)//2] = 2
if len(x.shape) > 1:
h = h[:, num.newaxis]
x = num.fft.ifft(Xf*h)
return x
def near(a, b, eps):
return abs(a-b) < eps
def coroutine(func):
def wrapper(*args, **kwargs):
gen = func(*args, **kwargs)
next(gen)
return gen
wrapper.__name__ = func.__name__
wrapper.__dict__ = func.__dict__
wrapper.__doc__ = func.__doc__
return wrapper
[docs]class States(object):
'''
Utility to store channel-specific state in coroutines.
'''
def __init__(self):
self._states = {}
def get(self, tr):
k = tr.nslc_id
if k in self._states:
tmin, deltat, dtype, value = self._states[k]
if (near(tmin, tr.tmin, deltat/100.)
and near(deltat, tr.deltat, deltat/10000.)
and dtype == tr.ydata.dtype):
return value
return None
def set(self, tr, value):
k = tr.nslc_id
if k in self._states and self._states[k][-1] is not value:
self.free(self._states[k][-1])
self._states[k] = (tr.tmax+tr.deltat, tr.deltat, tr.ydata.dtype, value)
def free(self, value):
pass
@coroutine
def co_list_append(list):
while True:
list.append((yield))
[docs]class ScipyBug(Exception):
pass
[docs]@coroutine
def co_lfilter(target, b, a):
'''
Successively filter broken continuous trace data (coroutine).
Create coroutine which takes :py:class:`Trace` objects, filters their data
through :py:func:`scipy.signal.lfilter` and sends new :py:class:`Trace`
objects containing the filtered data to target. This is useful, if one
wants to filter a long continuous time series, which is split into many
successive traces without producing filter artifacts at trace boundaries.
Filter states are kept *per channel*, specifically, for each (network,
station, location, channel) combination occuring in the input traces, a
separate state is created and maintained. This makes it possible to filter
multichannel or multistation data with only one :py:func:`co_lfilter`
instance.
Filter state is reset, when gaps occur.
Use it like this::
from pyrocko.trace import co_lfilter, co_list_append
filtered_traces = []
pipe = co_lfilter(co_list_append(filtered_traces), a, b)
for trace in traces:
pipe.send(trace)
pipe.close()
'''
try:
states = States()
output = None
while True:
input = (yield)
zi = states.get(input)
if zi is None:
zi = num.zeros(max(len(a), len(b))-1, dtype=num.float)
output = input.copy(data=False)
try:
ydata, zf = signal.lfilter(b, a, input.get_ydata(), zi=zi)
except ValueError:
raise ScipyBug(
'signal.lfilter failed: could be related to a bug '
'in some older scipy versions, e.g. on opensuse42.1')
output.set_ydata(ydata)
states.set(input, zf)
target.send(output)
except GeneratorExit:
target.close()
def co_antialias(target, q, n=None, ftype='fir'):
b, a, n = util.decimate_coeffs(q, n, ftype)
anti = co_lfilter(target, b, a)
return anti
@coroutine
def co_dropsamples(target, q, nfir):
try:
states = States()
while True:
tr = (yield)
newdeltat = q * tr.deltat
ioffset = states.get(tr)
if ioffset is None:
# for fir filter, the first nfir samples are pulluted by
# boundary effects; cut it off.
# for iir this may be (much) more, we do not correct for that.
# put sample instances to a time which is a multiple of the
# new sampling interval.
newtmin_want = math.ceil(
(tr.tmin+(nfir+1)*tr.deltat) / newdeltat) * newdeltat \
- (nfir/2*tr.deltat)
ioffset = int(round((newtmin_want - tr.tmin)/tr.deltat))
if ioffset < 0:
ioffset = ioffset % q
newtmin_have = tr.tmin + ioffset * tr.deltat
newtr = tr.copy(data=False)
newtr.deltat = newdeltat
# because the fir kernel shifts data by nfir/2 samples:
newtr.tmin = newtmin_have - (nfir/2*tr.deltat)
newtr.set_ydata(tr.get_ydata()[ioffset::q].copy())
states.set(tr, (ioffset % q - tr.data_len() % q) % q)
target.send(newtr)
except GeneratorExit:
target.close()
[docs]def co_downsample(target, q, n=None, ftype='fir'):
'''
Successively downsample broken continuous trace data (coroutine).
Create coroutine which takes :py:class:`Trace` objects, downsamples their
data and sends new :py:class:`Trace` objects containing the downsampled
data to target. This is useful, if one wants to downsample a long
continuous time series, which is split into many successive traces without
producing filter artifacts and gaps at trace boundaries.
Filter states are kept *per channel*, specifically, for each (network,
station, location, channel) combination occuring in the input traces, a
separate state is created and maintained. This makes it possible to filter
multichannel or multistation data with only one :py:func:`co_lfilter`
instance.
Filter state is reset, when gaps occur. The sampling instances are choosen
so that they occur at (or as close as possible) to even multiples of the
sampling interval of the downsampled trace (based on system time).
'''
b, a, n = util.decimate_coeffs(q, n, ftype)
return co_antialias(co_dropsamples(target, q, n), q, n, ftype)
@coroutine
def co_downsample_to(target, deltat):
decimators = {}
try:
while True:
tr = (yield)
ratio = deltat / tr.deltat
rratio = round(ratio)
if abs(rratio - ratio)/ratio > 0.0001:
raise util.UnavailableDecimation('ratio = %g' % ratio)
deci_seq = tuple(x for x in util.decitab(int(rratio)) if x != 1)
if deci_seq not in decimators:
pipe = target
for q in deci_seq[::-1]:
pipe = co_downsample(pipe, q)
decimators[deci_seq] = pipe
decimators[deci_seq].send(tr)
except GeneratorExit:
for g in decimators.values():
g.close()
[docs]class DomainChoice(StringChoice):
choices = [
'time_domain',
'frequency_domain',
'envelope',
'absolute',
'cc_max_norm']
[docs]class MisfitSetup(Object):
'''
Contains misfit setup to be used in :py:func:`trace.misfit`
:param description: Description of the setup
:param norm: L-norm classifier
:param taper: Object of :py:class:`Taper`
:param filter: Object of :py:class:`FrequencyResponse`
:param domain: ['time_domain', 'frequency_domain', 'envelope', 'absolute',
'cc_max_norm']
Can be dumped to a yaml file.
'''
xmltagname = 'misfitsetup'
description = String.T(optional=True)
norm = Int.T(optional=False)
taper = Taper.T(optional=False)
filter = FrequencyResponse.T(optional=True)
domain = DomainChoice.T(default='time_domain')
[docs]def equalize_sampling_rates(trace_1, trace_2):
'''
Equalize sampling rates of two traces (reduce higher sampling rate to
lower).
:param trace_1: :py:class:`Trace` object
:param trace_2: :py:class:`Trace` object
Returns a copy of the resampled trace if resampling is needed.
'''
if same_sampling_rate(trace_1, trace_2):
return trace_1, trace_2
if trace_1.deltat < trace_2.deltat:
t1_out = trace_1.copy()
t1_out.downsample_to(deltat=trace_2.deltat, snap=True)
logger.debug('Trace downsampled (return copy of trace): %s'
% '.'.join(t1_out.nslc_id))
return t1_out, trace_2
elif trace_1.deltat > trace_2.deltat:
t2_out = trace_2.copy()
t2_out.downsample_to(deltat=trace_1.deltat, snap=True)
logger.debug('Trace downsampled (return copy of trace): %s'
% '.'.join(t2_out.nslc_id))
return trace_1, t2_out
[docs]def Lx_norm(u, v, norm=2):
'''
Calculate the misfit denominator *m* and the normalization devisor *n*
according to norm.
The normalization divisor *n* is calculated from ``v``.
:param u: :py:class:`numpy.array`
:param v: :py:class:`numpy.array`
:param norm: (default = 2)
``u`` and ``v`` must be of same size.
'''
if norm == 1:
return (
num.sum(num.abs(v-u)),
num.sum(num.abs(v)))
elif norm == 2:
return (
num.sqrt(num.sum((v-u)**2)),
num.sqrt(num.sum(v**2)))
else:
return (
num.power(num.sum(num.abs(num.power(v - u, norm))), 1./norm),
num.power(num.sum(num.abs(num.power(v, norm))), 1./norm))
def do_downsample(tr, deltat):
if abs(tr.deltat - deltat) / tr.deltat > 1e-6:
tr = tr.copy()
tr.downsample_to(deltat, snap=True, demean=False)
else:
if tr.tmin/tr.deltat > 1e-6 or tr.tmax/tr.deltat > 1e-6:
tr = tr.copy()
tr.snap()
return tr
def do_extend(tr, tmin, tmax):
if tmin < tr.tmin or tmax > tr.tmax:
tr = tr.copy()
tr.extend(tmin=tmin, tmax=tmax, fillmethod='repeat')
return tr
def do_pre_taper(tr, taper):
return tr.taper(taper, inplace=False, chop=True)
def do_fft(tr, filter):
if filter is None:
return tr
else:
ndata = tr.ydata.size
nfft = nextpow2(ndata)
padded = num.zeros(nfft, dtype=num.float)
padded[:ndata] = tr.ydata
spectrum = num.fft.rfft(padded)
df = 1.0 / (tr.deltat * nfft)
frequencies = num.arange(spectrum.size)*df
return [tr, frequencies, spectrum]
def do_filter(inp, filter):
if filter is None:
return inp
else:
tr, frequencies, spectrum = inp
spectrum *= filter.evaluate(frequencies)
return [tr, frequencies, spectrum]
def do_ifft(inp):
if isinstance(inp, Trace):
return inp
else:
tr, _, spectrum = inp
ndata = tr.ydata.size
tr = tr.copy(data=False)
tr.set_ydata(num.fft.irfft(spectrum)[:ndata])
return tr
def check_alignment(t1, t2):
if abs(t1.tmin-t2.tmin) > t1.deltat * 1e-4 or \
abs(t1.tmax - t2.tmax) > t1.deltat * 1e-4 or \
t1.ydata.shape != t2.ydata.shape:
raise MisalignedTraces(
'Cannot calculate misfit of %s and %s due to misaligned '
'traces.' % ('.'.join(t1.nslc_id), '.'.join(t2.nslc_id)))
