def stream_collapse_tr(st):
if not isinstance(st, Stream):
raise InputError("'st' must be a 'obspy.core.stream.Stream' object")
stream_new = Stream()
# Generate sorted list of traces (no copy)
# Sort order, id, starttime, endtime
ids = []
for tr in st:
if not tr.id in ids:
ids.append(tr.id)
for id in ids:
print "new_trace id: %s" % id
tr_new = Trace()
tr_new.data = np.zeros(st[0].data.shape)
# tr_new.stats = {}
tr_new.stats_tr1 = {}
tr_new.stats_tr2 = {}
starttime1_list = []
starttime2_list = []
endtime1_list = []
endtime2_list = []
n_tr = 0
for tr in st:
if tr.id == id:
print tr.id
if len(tr_new.data) != len(tr.data):
lp = len(tr_new.data) - len(tr.data)
print "lp: %d" % lp
if lp > 0:
left = np.ceil(lp / 2)
right = lp - left
cdata = np.append(np.zeros(left, dtype=tr.data.dtype),
tr.data)
tr.data = np.append(cdata,
np.zeros(right,
dtype=tr.data.dtype))
else:
lp = -lp
left = np.ceil(lp / 2)
right = lp - left
tr.data = tr.data[left:-right]
print "len tr: %d" % len(tr)
print "len tr_new: % d" % len(tr_new)
tr_new.data += tr.data
n_tr += 1
starttime1_list.append(tr.stats_tr1.starttime)
starttime2_list.append(tr.stats_tr2.starttime)
endtime1_list.append(tr.stats_tr1.endtime)
endtime2_list.append(tr.stats_tr2.endtime)
tr_new.stats.update(tr.stats)
tr_new.stats_tr1.update(tr.stats_tr1)
tr_new.stats_tr2.update(tr.stats_tr2)
tr_new.data /= n_tr
tr_new.stats['starttime1'] = starttime1_list
tr_new.stats['starttime2'] = starttime2_list
tr_new.stats['endtime1'] = endtime1_list
tr_new.stats['endtime2'] = endtime2_list
stream_new.append(tr_new)
return stream_new
def conv_traces(tr1, tr2, normal=True):
""" Convolve two Traces and merge their meta-information
It convolves the data stored in two :class:`~obspy.core.trace.Trace`
Objects in frequency domain. If ``normal==True`` the resulting correlation
data are normalized by a factor of
:func:`sqrt(||tr1.data||^2 x ||tr2.data||^2)`
Meta-informations associated to the resulting Trace are obtained through:
- Merging the original meta-informations of the two input traces
according to the :func:`~miic.core.corr_fun.combine_stats` function.
- Adding the original two `Stats` objects to the newly
created :class:`~obspy.core.trace.Trace` object as:
>>> conv_tr.stats_tr1 = tr1.stats
>>> conv_tr.stats_tr2 = tr2.stats
- Fixing:
>>> conv_tr.stats['npts'] = '...number of correlation points...'
>>> conv_tr.stats['starttime'] = tr2.stats['starttime'] -
tr1.stats['starttime']
:type tr1: :class:`~obspy.core.trace.Trace`
:param tr1: First Trace
:type tr2: :class:`~obspy.core.trace.Trace`
:param tr2: Second Trace
:type normal: bool
:param normal: Normalization flag
:rtype: :class:`~obspy.core.trace.Trace`
:return: **conv_tr**: Trace that stores convolved data and meta-information
"""
if not isinstance(tr1, Trace):
raise TypeError("tr1 must be an obspy Trace object.")
if not isinstance(tr2, Trace):
raise TypeError("tr2 must be an obspy Trace object.")
zerotime = UTCDateTime(1971, 1, 1, 0, 0, 0)
conv_tr = Trace()
# extend traces to the next power of 2 of the longest trace
lt = pow(2, np.ceil(np.log2(np.max([tr1.stats['npts'],
tr2.stats['npts']]))))
s1 = extend(tr1.data, method='zeros', length='fixed',size=lt)
s2 = extend(tr2.data, method='zeros', length='fixed',size=lt)
# create the combined stats
conv_tr.stats = combine_stats(tr1, tr2)
conv_tr.stats_tr1 = tr1.stats
conv_tr.stats_tr2 = tr2.stats
conv_tr.stats_tr1.npts = min(tr1.stats.npts, tr2.stats.npts)
conv_tr.stats_tr2.npts = min(tr1.stats.npts, tr2.stats.npts)
if normal:
denom = np.sqrt(np.dot(s1.astype(np.float64), s1.T) *
np.dot(s2.astype(np.float64), s2.T))
else:
denom = 1.
# remaining offset in samples (just remove fractions of samples)
roffset = np.round((tr2.stats.starttime - tr1.stats.starttime) *
tr1.stats.sampling_rate)
offset = (tr2.stats.starttime - tr1.stats.starttime) * \
tr1.stats.sampling_rate - roffset
# remaining offset in seconds
roffset /= tr1.stats.sampling_rate
convData = _fftconvolve(s1[::-1], s2, offset)
convData = np.multiply(convData, (1 / denom))
# set number of samples
conv_tr.stats['npts'] = convData.shape[0]
# time lag of the zero position, i.e. lag time of alignent
t_offset_zeroleg = (float(convData.shape[0]) - 1.) / \
(2. * tr1.stats.sampling_rate)
# set starttime
conv_tr.stats['starttime'] = zerotime - t_offset_zeroleg + \
roffset
conv_tr.data = convData
return conv_tr
def conv_traces(tr1, tr2, normal=True):
""" Convolve two Traces and merge their meta-information
It convolves the data stored in two :class:`~obspy.core.trace.Trace`
Objects in frequency domain. If ``normal==True`` the resulting correlation
data are normalized by a factor of
:func:`sqrt(||tr1.data||^2 x ||tr2.data||^2)`
Meta-informations associated to the resulting Trace are obtained through:
- Merging the original meta-informations of the two input traces
according to the :func:`~miic.core.corr_fun.combine_stats` function.
- Adding the original two `Stats` objects to the newly
created :class:`~obspy.core.trace.Trace` object as:
>>> conv_tr.stats_tr1 = tr1.stats
>>> conv_tr.stats_tr2 = tr2.stats
- Fixing:
>>> conv_tr.stats['npts'] = '...number of correlation points...'
>>> conv_tr.stats['starttime'] = tr2.stats['starttime'] -
tr1.stats['starttime']
:type tr1: :class:`~obspy.core.trace.Trace`
:param tr1: First Trace
:type tr2: :class:`~obspy.core.trace.Trace`
:param tr2: Second Trace
:type normal: bool
:param normal: Normalization flag
:rtype: :class:`~obspy.core.trace.Trace`
:return: **conv_tr**: Trace that stores convolved data and meta-information
"""
if not isinstance(tr1, Trace):
raise TypeError("tr1 must be an obspy Trace object.")
if not isinstance(tr2, Trace):
raise TypeError("tr2 must be an obspy Trace object.")
zerotime = UTCDateTime(1971, 1, 1, 0, 0, 0)
conv_tr = Trace()
# extend traces to the next power of 2 of the longest trace
lt = pow(2, np.ceil(np.log2(np.max([tr1.stats['npts'],
tr2.stats['npts']]))))
s1 = extend(tr1.data, method='zeros', length='fixed',size=lt)
s2 = extend(tr2.data, method='zeros', length='fixed',size=lt)
# create the combined stats
conv_tr.stats = combine_stats(tr1, tr2)
conv_tr.stats_tr1 = tr1.stats
conv_tr.stats_tr2 = tr2.stats
conv_tr.stats_tr1.npts = min(tr1.stats.npts, tr2.stats.npts)
conv_tr.stats_tr2.npts = min(tr1.stats.npts, tr2.stats.npts)
if normal:
denom = np.sqrt(np.dot(s1.astype(np.float64), s1.T) *
np.dot(s2.astype(np.float64), s2.T))
else:
denom = 1.
# remaining offset in samples (just remove fractions of samples)
roffset = np.round((tr2.stats.starttime - tr1.stats.starttime) *
tr1.stats.sampling_rate)
offset = (tr2.stats.starttime - tr1.stats.starttime) * \
tr1.stats.sampling_rate - roffset
# remaining offset in seconds
roffset /= tr1.stats.sampling_rate
convData = _fftconvolve(s1[::-1], s2, offset)
convData = np.multiply(convData, (1 / denom))
# set number of samples
conv_tr.stats['npts'] = convData.shape[0]
# time lag of the zero position, i.e. lag time of alignent
t_offset_zeroleg = (float(convData.shape[0]) - 1.) / \
(2. * tr1.stats.sampling_rate)
# set starttime
conv_tr.stats['starttime'] = zerotime - t_offset_zeroleg + \
roffset
conv_tr.data = convData
return conv_tr
