def xcorr(tr1, tr2, shift_len, full_xcorr=False):
from obspy.core.util.deprecation_helpers import ObsPyDeprecationWarning
if min(len(tr1), len(tr2)) - 2 * shift_len <= 0:
msg = "shift_len too large. The underlying C code would silently " + \
"use shift_len/2 which we want to avoid."
raise ValueError(msg)
tr1 = np.ascontiguousarray(tr1, np.float32)
tr2 = np.ascontiguousarray(tr2, np.float32)
corp = np.empty(2 * shift_len + 1, dtype=np.float64, order='C')
shift = C.c_int()
coe_p = C.c_double()
res = clibsignal.X_corr(tr1, tr2, corp, shift_len, len(tr1), len(tr2),
C.byref(shift), C.byref(coe_p))
if res:
raise MemoryError
if full_xcorr:
return shift.value, coe_p.value, corp
else:
return shift.value, coe_p.value
def xcorr(tr1, tr2, shift_len, full_xcorr=False):
"""
Cross correlation of tr1 and tr2 in the time domain using window_len.
.. note::
Please use the :func:`~obspy.signal.cross_correlation.correlate`
function for new code.
::
Mid Sample
|
|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|
|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|
|<-shift_len/2->| <- region of support -> |<-shift_len/2->|
:type tr1: :class:`~numpy.ndarray`, :class:`~obspy.core.trace.Trace`
:param tr1: Trace 1
:type tr2: :class:`~numpy.ndarray`, :class:`~obspy.core.trace.Trace`
:param tr2: Trace 2 to correlate with trace 1
:type shift_len: int
:param shift_len: Total length of samples to shift for cross correlation.
:type full_xcorr: bool
:param full_xcorr: If ``True``, the complete xcorr function will be
returned as :class:`~numpy.ndarray`
:return: **index, value[, fct]** - Index of maximum xcorr value and the
value itself. The complete xcorr function is returned only if
``full_xcorr=True``.
.. note::
As shift_len gets higher the window supporting the cross correlation
actually gets smaller. So with shift_len=0 you get the correlation
coefficient of both traces as a whole without any shift applied. As the
xcorr function works in time domain and does not zero pad at all, with
higher shifts allowed the window of support gets smaller so that the
moving windows shifted against each other do not run out of the
timeseries bounds at high time shifts. Of course there are other
possibilities to do cross correlations e.g. in frequency domain.
.. seealso::
`ObsPy-users mailing list
<http://lists.obspy.org/pipermail/obspy-users/2011-March/000056.html>`_
and `issue #249 <https://github.com/obspy/obspy/issues/249>`_.
"""
from obspy.core.util.deprecation_helpers import ObsPyDeprecationWarning
msg = ('Call to deprecated function xcorr(). Please use the correlate and '
'xcorr_max functions.')
warnings.warn(msg, ObsPyDeprecationWarning)
# if we get Trace objects, use their data arrays
for tr in [tr1, tr2]:
if isinstance(tr, Trace):
tr = tr.data
# check if shift_len parameter is in an acceptable range.
# if not the underlying c code tampers with shift_len and uses shift_len/2
# instead. we want to avoid this silent automagic and raise an error in the
# python layer right here.
# see ticket #249 and src/xcorr.c lines 43-57
if min(len(tr1), len(tr2)) - 2 * shift_len <= 0:
msg = "shift_len too large. The underlying C code would silently " + \
"use shift_len/2 which we want to avoid."
raise ValueError(msg)
# be nice and adapt type if necessary
tr1 = np.ascontiguousarray(tr1, np.float32)
tr2 = np.ascontiguousarray(tr2, np.float32)
corp = np.empty(2 * shift_len + 1, dtype=np.float64, order='C')
shift = C.c_int()
coe_p = C.c_double()
res = clibsignal.X_corr(tr1, tr2, corp, shift_len, len(tr1), len(tr2),
C.byref(shift), C.byref(coe_p))
if res:
raise MemoryError
if full_xcorr:
return shift.value, coe_p.value, corp
else:
return shift.value, coe_p.value
def xcorr(tr1, tr2, shift_len, full_xcorr=False):
"""
Cross correlation of tr1 and tr2 in the time domain using window_len.
::
Mid Sample
|
|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|AAAAAAAAAAAAAAA|
|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|BBBBBBBBBBBBBBB|
|<-shift_len/2->| <- region of support -> |<-shift_len/2->|
:type tr1: :class:`~numpy.ndarray`, :class:`~obspy.core.trace.Trace`
:param tr1: Trace 1
:type tr2: :class:`~numpy.ndarray`, :class:`~obspy.core.trace.Trace`
:param tr2: Trace 2 to correlate with trace 1
:type shift_len: int
:param shift_len: Total length of samples to shift for cross correlation.
:type full_xcorr: bool
:param full_xcorr: If ``True``, the complete xcorr function will be
returned as :class:`~numpy.ndarray`
:return: **index, value[, fct]** - Index of maximum xcorr value and the
value itself. The complete xcorr function is returned only if
``full_xcorr=True``.
.. note::
As shift_len gets higher the window supporting the cross correlation
actually gets smaller. So with shift_len=0 you get the correlation
coefficient of both traces as a whole without any shift applied. As the
xcorr function works in time domain and does not zero pad at all, with
higher shifts allowed the window of support gets smaller so that the
moving windows shifted against each other do not run out of the
timeseries bounds at high time shifts. Of course there are other
possibilities to do cross correlations e.g. in frequency domain.
.. seealso::
`ObsPy-users mailing list
<http://lists.obspy.org/pipermail/obspy-users/2011-March/000056.html>`_
and
`ticket #249 <https://obspy.org/ticket/249>`_.
.. rubric:: Example
>>> tr1 = np.random.randn(10000).astype('float32')
>>> tr2 = tr1.copy()
>>> a, b = xcorr(tr1, tr2, 1000)
>>> a
0
>>> round(b, 7)
1.0
"""
# if we get Trace objects, use their data arrays
for tr in [tr1, tr2]:
if isinstance(tr, Trace):
tr = tr.data
# check if shift_len parameter is in an acceptable range.
# if not the underlying c code tampers with shift_len and uses shift_len/2
# instead. we want to avoid this silent automagic and raise an error in the
# python layer right here.
# see ticket #249 and src/xcorr.c lines 43-57
if min(len(tr1), len(tr2)) - 2 * shift_len <= 0:
msg = "shift_len too large. The underlying C code would silently " + \
"use shift_len/2 which we want to avoid."
raise ValueError(msg)
# be nice and adapt type if necessary
tr1 = np.require(tr1, 'float32', ['C_CONTIGUOUS'])
tr2 = np.require(tr2, 'float32', ['C_CONTIGUOUS'])
corp = np.empty(2 * shift_len + 1, dtype='float64', order='C')
shift = C.c_int()
coe_p = C.c_double()
clibsignal.X_corr(tr1, tr2, corp, shift_len, len(tr1), len(tr2),
C.byref(shift), C.byref(coe_p))
if full_xcorr:
return shift.value, coe_p.value, corp
else:
return shift.value, coe_p.value