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
result[empty_spots:empty_spots + spots] = diff
# Merge the seperately treated data values.
if samples_before_start and samples_before_start > result[0]:
result[0] = samples_before_start
if samples_after_end and samples_after_end > result[-1]:
result[-1] = samples_after_end
if lost_samples and first_spot > result[empty_spots]:
result[empty_spots] = first_spot
if free_samples and end_samples > result[empty_spots + spots]:
result[empty_spots + spots] = end_samples
# Check for masked values.
if is_masked(result):
result.fill_value = 0.0
result = result.filled()
# Create new stream.
tr = Trace(data=result)
stats = st[0].stats
stats.starttime = day_starttime
# Hard code the sampling rate for safe handling of leap seconds and
# floating point inaccuracies. This will result in 1000 samples with the
# last sample exactly one delta step away from the beginning of the next
# day.
stats.sampling_rate = 0.0115740740741
stats.npts = 1000
tr.stats = stats
out_stream = Stream(traces=[tr])
# Write the stream.
out_stream.write(file + '_index.mseed', format='MSEED')
if samples_before_start and samples_before_start > result[0]:
result[0] = samples_before_start
if samples_after_end and samples_after_end > result[-1]:
result[-1] = samples_after_end
if lost_samples and first_spot > result[empty_spots]:
result[empty_spots] = first_spot
if free_samples and end_samples > result[empty_spots + spots]:
result[empty_spots + spots] = end_samples
# Check for masked values.
if is_masked(result):
result.fill_value = 0.0
result = result.filled()
# Create new stream.
tr = Trace(data = result)
stats = st[0].stats
stats.starttime = day_starttime
# Hard code the sampling rate for safe handling of leap seconds and
# floating point inaccuracies. This will result in 1000 samples with the
# last sample exactly one delta step away from the beginning of the next
# day.
stats.sampling_rate = 0.0115740740741
stats.npts = 1000
tr.stats = stats
out_stream = Stream(traces = [tr])
# Write the stream.
out_stream.write (file + '_index.mseed', format = 'MSEED')
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
