def test_real_near_repeat_magnitudes_bad_corr(self):
relative_magnitudes = relative_magnitude(
st1=self.st1,
st2=self.st2,
event1=self.event1,
event2=self.event2,
correlations={tr.id: 0.2
for tr in self.st1})
self.assertEqual(len(relative_magnitudes), 0)
def test_real_near_repeat_magnitudes(self):
relative_magnitudes, correlations = relative_magnitude(
st1=self.st1,
st2=self.st2,
event1=self.event1,
event2=self.event2,
return_correlations=True)
for seed_id, mag_diff in relative_magnitudes.items():
self.assertLess(abs(mag_diff + 0.15), 0.1)
def test_real_near_repeat_magnitudes_corr_provided(self):
relative_magnitudes = relative_magnitude(
st1=self.st1,
st2=self.st2,
event1=self.event1,
event2=self.event2,
correlations={tr.id: 1.0
for tr in self.st1})
for seed_id, mag_diff in relative_magnitudes.items():
self.assertLess(abs(mag_diff + 0.15), 0.1)
def relative_magnitudes(self,
stream,
pre_processed,
process_cores=1,
ignore_bad_data=False,
parallel=False,
min_cc=0.4,
**kwargs):
"""
Compute relative magnitudes for the detections.
Works in place on events in the Family
:type stream: obspy.core.stream.Stream
:param stream:
All the data needed to cut from - can be a gappy Stream.
:type pre_processed: bool
:param pre_processed:
Whether the stream has been pre-processed or not to match the
templates. See note below.
:param parallel: Turn parallel processing on or off.
:type process_cores: int
:param process_cores:
Number of processes to use for pre-processing (if different to
`cores`).
:type ignore_bad_data: bool
:param ignore_bad_data:
If False (default), errors will be raised if data are excessively
gappy or are mostly zeros. If True then no error will be raised,
but an empty trace will be returned (and not used in detection).
:type min_cc: float
:param min_cc: Minimum correlation for magnitude to be computed.
:param kwargs:
Keyword arguments passed to `utils.mag_calc.relative_mags`
.. Note::
Note on pre-processing: You can provide a pre-processed stream,
which may be beneficial for detections over large time periods
(the stream can have gaps, which reduces memory usage). However,
in this case the processing steps are not checked, so you must
ensure that the template in the Family has the same sampling
rate and filtering as the stream.
If pre-processing has not be done then the data will be processed
according to the parameters in the template.
"""
processed_stream = self._process_streams(
stream=stream,
pre_processed=pre_processed,
process_cores=process_cores,
parallel=parallel,
ignore_bad_data=ignore_bad_data)
for detection in self.detections:
event = detection.event
if event is None:
continue
corr_dict = {
p.waveform_id.get_seed_string():
float(p.comments[0].text.split("=")[-1])
for p in event.picks
}
template = self.template
try:
t_mag = (template.event.preferred_magnitude()
or template.event.magnitudes[0])
except IndexError:
Logger.info("No template magnitude, relative magnitudes cannot"
" be computed for {0}".format(event.resource_id))
continue
# Set the signal-window to be the template length
signal_window = (
-template.prepick,
min([tr.stats.npts * tr.stats.delta
for tr in template.st]) - template.prepick)
delta_mag = relative_magnitude(st1=template.st,
st2=processed_stream,
event1=template.event,
event2=event,
correlations=corr_dict,
min_cc=min_cc,
signal_window=signal_window,
**kwargs)
# Add station magnitudes
sta_contrib = []
av_mag = 0.0
for seed_id, _delta_mag in delta_mag.items():
sta_mag = StationMagnitude(
mag=t_mag.mag + _delta_mag,
magnitude_type=t_mag.magnitude_type,
method_id=ResourceIdentifier("relative"),
waveform_id=WaveformStreamID(seed_string=seed_id),
creation_info=CreationInfo(author="EQcorrscan",
creation_time=UTCDateTime()))
event.station_magnitudes.append(sta_mag)
sta_contrib.append(
StationMagnitudeContribution(
station_magnitude_id=sta_mag.resource_id, weight=1.))
av_mag += sta_mag.mag
if len(delta_mag) > 0:
av_mag /= len(delta_mag)
# Compute average magnitude
event.magnitudes.append(
Magnitude(mag=av_mag,
magnitude_type=t_mag.magnitude_type,
method_id=ResourceIdentifier("relative"),
station_count=len(delta_mag),
evaluation_mode="manual",
station_magnitude_contributions=sta_contrib,
creation_info=CreationInfo(
author="EQcorrscan",
creation_time=UTCDateTime())))
return self.catalog