def test_sac_template_gen(self):
"""Test template generation."""
samp_rate = 20
length = 8
for event in ['2014p611252', 'No_head']:
test_files = os.path.join(
os.path.abspath(os.path.dirname(__file__)), 'test_data', 'SAC',
event, '*')
# Test with various input types
filelist = glob.glob(test_files)
streamlist = [read(f) for f in glob.glob(test_files)]
stream = read(test_files)
for sac_files in [filelist, streamlist, stream]:
templates = from_sac(sac_files,
lowcut=2.0,
highcut=8.0,
samp_rate=samp_rate,
filt_order=4,
length=length,
swin='all',
prepick=0.1,
debug=0,
plot=False)
self.assertEqual(len(templates), 1)
template = templates[0]
self.assertEqual(len(template), len(sactoevent(stream).picks))
for tr in template:
self.assertEqual(len(tr.data), length * samp_rate)
def template_gen(method,
lowcut,
highcut,
samp_rate,
filt_order,
length,
prepick,
swin="all",
process_len=86400,
all_horiz=False,
delayed=True,
plot=False,
plotdir=None,
return_event=False,
min_snr=None,
parallel=False,
num_cores=False,
save_progress=False,
skip_short_chans=False,
**kwargs):
"""
Generate processed and cut waveforms for use as templates.
:type method: str
:param method:
Template generation method, must be one of ('from_client',
'from_seishub', 'from_sac', 'from_meta_file'). - Each method requires
associated arguments, see note below.
:type lowcut: float
:param lowcut: Low cut (Hz), if set to None will not apply a lowcut.
:type highcut: float
:param highcut: High cut (Hz), if set to None will not apply a highcut.
:type samp_rate: float
:param samp_rate: New sampling rate in Hz.
:type filt_order: int
:param filt_order: Filter level (number of corners).
:type length: float
:param length: Length of template waveform in seconds.
:type prepick: float
:param prepick: Pre-pick time in seconds
:type swin: str
:param swin:
P, S, P_all, S_all or all, defaults to all: see note in
:func:`eqcorrscan.core.template_gen.template_gen`
:type process_len: int
:param process_len: Length of data in seconds to download and process.
:type all_horiz: bool
:param all_horiz:
To use both horizontal channels even if there is only a pick on one of
them. Defaults to False.
:type delayed: bool
:param delayed: If True, each channel will begin relative to it's own \
pick-time, if set to False, each channel will begin at the same time.
:type plot: bool
:param plot: Plot templates or not.
:type plotdir: str
 :param plotdir:
The path to save plots to. If `plotdir=None` (default) then the figure
will be shown on screen.
:type return_event: bool
:param return_event: Whether to return the event and process length or not.
:type min_snr: float
:param min_snr:
Minimum signal-to-noise ratio for a channel to be included in the
template, where signal-to-noise ratio is calculated as the ratio of
the maximum amplitude in the template window to the rms amplitude in
the whole window given.
:type parallel: bool
:param parallel: Whether to process data in parallel or not.
:type num_cores: int
:param num_cores:
Number of cores to try and use, if False and parallel=True, will use
either all your cores, or as many traces as in the data (whichever is
smaller).
:type save_progress: bool
:param save_progress:
Whether to save the resulting templates at every data step or not.
Useful for long-running processes.
:type skip_short_chans: bool
:param skip_short_chans:
Whether to ignore channels that have insufficient length data or not.
Useful when the quality of data is not known, e.g. when downloading
old, possibly triggered data from a datacentre
:returns: List of :class:`obspy.core.stream.Stream` Templates
:rtype: list
.. note::
*Method specific arguments:*
- `from_client` requires:
:param str client_id:
string passable by obspy to generate Client, or a Client
instance
:param `obspy.core.event.Catalog` catalog:
Catalog of events to generate template for
:param float data_pad: Pad length for data-downloads in seconds
- `from_seishub` requires:
:param str url: url to seishub database
:param `obspy.core.event.Catalog` catalog:
Catalog of events to generate template for
:param float data_pad: Pad length for data-downloads in seconds
- `from_sac` requires:
:param list sac_files:
osbpy.core.stream.Stream of sac waveforms, or list of paths to
sac waveforms.
.. note::
See `eqcorrscan.utils.sac_util.sactoevent` for details on
how pick information is collected.
- `from_meta_file` requires:
:param str meta_file:
Path to obspy-readable event file, or an obspy Catalog
:param `obspy.core.stream.Stream` st:
Stream containing waveform data for template. Note that this
should be the same length of stream as you will use for the
continuous detection, e.g. if you detect in day-long files,
give this a day-long file!
:param bool process:
Whether to process the data or not, defaults to True.
.. note::
process_len should be set to the same length as used when computing
detections using match_filter.match_filter, e.g. if you read
in day-long data for match_filter, process_len should be 86400.
.. rubric:: Example
>>> from obspy.clients.fdsn import Client
>>> from eqcorrscan.core.template_gen import template_gen
>>> client = Client('NCEDC')
>>> catalog = client.get_events(eventid='72572665', includearrivals=True)
>>> # We are only taking two picks for this example to speed up the
>>> # example, note that you don't have to!
>>> catalog[0].picks = catalog[0].picks[0:2]
>>> templates = template_gen(
... method='from_client', catalog=catalog, client_id='NCEDC',
... lowcut=2.0, highcut=9.0, samp_rate=20.0, filt_order=4, length=3.0,
... prepick=0.15, swin='all', process_len=300, all_horiz=True)
>>> templates[0].plot(equal_scale=False, size=(800,600)) # doctest: +SKIP
.. figure:: ../../plots/template_gen.from_client.png
.. rubric:: Example
>>> from obspy import read
>>> from eqcorrscan.core.template_gen import template_gen
>>> # Get the path to the test data
>>> import eqcorrscan
>>> import os
>>> TEST_PATH = os.path.dirname(eqcorrscan.__file__) + '/tests/test_data'
>>> st = read(TEST_PATH + '/WAV/TEST_/' +
... '2013-09-01-0410-35.DFDPC_024_00')
>>> quakeml = TEST_PATH + '/20130901T041115.xml'
>>> templates = template_gen(
... method='from_meta_file', meta_file=quakeml, st=st, lowcut=2.0,
... highcut=9.0, samp_rate=20.0, filt_order=3, length=2, prepick=0.1,
... swin='S', all_horiz=True)
>>> print(len(templates[0]))
10
>>> templates = template_gen(
... method='from_meta_file', meta_file=quakeml, st=st, lowcut=2.0,
... highcut=9.0, samp_rate=20.0, filt_order=3, length=2, prepick=0.1,
... swin='S_all', all_horiz=True)
>>> print(len(templates[0]))
15
.. rubric:: Example
>>> from eqcorrscan.core.template_gen import template_gen
>>> import glob
>>> # Get all the SAC-files associated with one event.
>>> sac_files = glob.glob(TEST_PATH + '/SAC/2014p611252/*')
>>> templates = template_gen(
... method='from_sac', sac_files=sac_files, lowcut=2.0, highcut=10.0,
... samp_rate=25.0, filt_order=4, length=2.0, swin='all', prepick=0.1,
... all_horiz=True)
>>> print(templates[0][0].stats.sampling_rate)
25.0
>>> print(len(templates[0]))
15
"""
client_map = {'from_client': 'fdsn', 'from_seishub': 'seishub'}
assert method in ('from_client', 'from_seishub', 'from_meta_file',
'from_sac')
if not isinstance(swin, list):
swin = [swin]
process = True
if method in ['from_client', 'from_seishub']:
catalog = kwargs.get('catalog', Catalog())
data_pad = kwargs.get('data_pad', 90)
# Group catalog into days and only download the data once per day
sub_catalogs = _group_events(catalog=catalog,
process_len=process_len,
template_length=length,
data_pad=data_pad)
if method == 'from_client':
if isinstance(kwargs.get('client_id'), str):
client = FDSNClient(kwargs.get('client_id', None))
else:
client = kwargs.get('client_id', None)
available_stations = []
else:
client = SeisHubClient(kwargs.get('url', None), timeout=10)
available_stations = client.waveform.get_station_ids()
elif method == 'from_meta_file':
if isinstance(kwargs.get('meta_file'), Catalog):
catalog = kwargs.get('meta_file')
elif kwargs.get('meta_file'):
catalog = read_events(kwargs.get('meta_file'))
elif kwargs.get('catalog'):
catalog = kwargs.get('catalog')
sub_catalogs = [catalog]
st = kwargs.get('st', Stream())
process = kwargs.get('process', True)
elif method == 'from_sac':
sac_files = kwargs.get('sac_files')
if isinstance(sac_files, list):
if isinstance(sac_files[0], (Stream, Trace)):
# This is a list of streams...
st = Stream(sac_files[0])
for sac_file in sac_files[1:]:
st += sac_file
else:
sac_files = [read(sac_file)[0] for sac_file in sac_files]
st = Stream(sac_files)
else:
st = sac_files
# Make an event object...
catalog = Catalog([sactoevent(st)])
sub_catalogs = [catalog]
temp_list = []
process_lengths = []
if "P_all" in swin or "S_all" in swin or all_horiz:
all_channels = True
else:
all_channels = False
for sub_catalog in sub_catalogs:
if method in ['from_seishub', 'from_client']:
Logger.info("Downloading data")
st = _download_from_client(client=client,
client_type=client_map[method],
catalog=sub_catalog,
data_pad=data_pad,
process_len=process_len,
available_stations=available_stations,
all_channels=all_channels)
Logger.info('Pre-processing data')
st.merge()
if len(st) == 0:
Logger.info("No data")
continue
if process:
data_len = max(
[len(tr.data) / tr.stats.sampling_rate for tr in st])
if 80000 < data_len < 90000:
daylong = True
starttime = min([tr.stats.starttime for tr in st])
min_delta = min([tr.stats.delta for tr in st])
# Cope with the common starttime less than 1 sample before the
# start of day.
if (starttime + min_delta).date > starttime.date:
starttime = (starttime + min_delta)
# Check if this is stupid:
if abs(starttime - UTCDateTime(starttime.date)) > 600:
print(abs(starttime - UTCDateTime(starttime.date)))
daylong = False
starttime = starttime.date
else:
daylong = False
# Check if the required amount of data have been downloaded - skip
# channels if arg set.
if skip_short_chans:
_st = Stream()
for tr in st:
if np.ma.is_masked(tr.data):
_len = np.ma.count(tr.data) * tr.stats.delta
else:
_len = tr.stats.npts * tr.stats.delta
if _len < process_len * .8:
Logger.info(
"Data for {0} are too short, skipping".format(
tr.id))
else:
_st += tr
st = _st
if len(st) == 0:
Logger.info("No data")
continue
if daylong:
st = pre_processing.dayproc(st=st,
lowcut=lowcut,
highcut=highcut,
filt_order=filt_order,
samp_rate=samp_rate,
parallel=parallel,
starttime=UTCDateTime(starttime),
num_cores=num_cores)
else:
st = pre_processing.shortproc(st=st,
lowcut=lowcut,
highcut=highcut,
filt_order=filt_order,
parallel=parallel,
samp_rate=samp_rate,
num_cores=num_cores)
data_start = min([tr.stats.starttime for tr in st])
data_end = max([tr.stats.endtime for tr in st])
for event in sub_catalog:
stations, channels, st_stachans = ([], [], [])
if len(event.picks) == 0:
Logger.warning('No picks for event {0}'.format(
event.resource_id))
continue
use_event = True
# Check that the event is within the data
for pick in event.picks:
if not data_start < pick.time < data_end:
Logger.warning(
"Pick outside of data span: Pick time {0} Start "
"time {1} End time: {2}".format(
str(pick.time), str(data_start), str(data_end)))
use_event = False
if not use_event:
Logger.error('Event is not within data time-span')
continue
# Read in pick info
Logger.debug("I have found the following picks")
for pick in event.picks:
if not pick.waveform_id:
Logger.warning(
'Pick not associated with waveforms, will not use:'
' {0}'.format(pick))
continue
Logger.debug(pick)
stations.append(pick.waveform_id.station_code)
channels.append(pick.waveform_id.channel_code)
# Check to see if all picks have a corresponding waveform
for tr in st:
st_stachans.append('.'.join(
[tr.stats.station, tr.stats.channel]))
# Cut and extract the templates
template = _template_gen(event.picks,
st,
length,
swin,
prepick=prepick,
plot=plot,
all_horiz=all_horiz,
delayed=delayed,
min_snr=min_snr,
plotdir=plotdir)
process_lengths.append(len(st[0].data) / samp_rate)
temp_list.append(template)
if save_progress:
if not os.path.isdir("eqcorrscan_temporary_templates"):
os.makedirs("eqcorrscan_temporary_templates")
for template in temp_list:
template.write(
"eqcorrscan_temporary_templates{0}{1}.ms".format(
os.path.sep, template[0].stats.starttime),
format="MSEED")
del st
if return_event:
return temp_list, catalog, process_lengths
return temp_list
def from_sac(sac_files, lowcut, highcut, samp_rate, filt_order, length, swin,
prepick=0.05, debug=0, plot=False):
"""Function to read picks and waveforms from SAC data, and generate a \
template from these.
:type sac_files: list or stream
:param sac_files: List or stream of sac waveforms, or list of paths to \
sac waveforms.
:type lowcut: float
:param lowcut: Low cut (Hz), if set to None will look in template \
defaults file
:type highcut: float
:param highcut: High cut (Hz), if set to None will look in template \
defaults file
:type samp_rate: float
:param samp_rate: New sampling rate in Hz, if set to None will look in \
template defaults file
:type filt_order: int
:param filt_order: Filter level, if set to None will look in \
template defaults file
:type swin: str
:param swin: Either 'all', 'P' or 'S', to select which phases to output.
:type length: float
:param length: Extract length in seconds, if None will look in template \
defaults file.
:type prepick: float
:param prepick: Length to extract prior to the pick in seconds.
:type debug: int
:param debug: Debug level, higher number=more output.
:type plot: bool
:param plot: Turns template plotting on or off.
:returns: obspy.Stream Newly cut template
.. note:: This functionality is not supported for obspy versions below \
1.0.0 as references times are not read in by SACIO, which are needed \
for defining pick times.
"""
from obspy import read, Stream
from eqcorrscan.utils.sac_util import sactoevent
from eqcorrscan.utils import pre_processing
# Check whether sac_files is a stream or a list
if isinstance(sac_files, list):
if isinstance(sac_files[0], str) or isinstance(sac_files[0], unicode):
sac_files = [read(sac_file)[0] for sac_file in sac_files]
if isinstance(sac_files[0], Stream):
# This is a list of streams...
st = sac_files[0]
for sac_file in sac_files[1:]:
st += sac_file
st = Stream(sac_files)
elif isinstance(sac_files, Stream):
st = sac_files
# Make an event object...
event = sactoevent(st)
# Process the data
st.merge(fill_value='interpolate')
st = pre_processing.shortproc(st, lowcut, highcut, filt_order,
samp_rate, debug)
template = _template_gen(picks=event.picks, st=st, length=length,
swin=swin, prepick=prepick, plot=plot)
return template
def custom_template_gen(method,
lowcut,
highcut,
samp_rate,
filt_order,
length,
prepick,
swin="all",
process_len=86400,
all_horiz=False,
delayed=True,
plot=False,
plotdir=None,
return_event=False,
min_snr=None,
parallel=False,
num_cores=False,
save_progress=False,
skip_short_chans=False,
**kwargs):
"""
Generate processed and cut waveforms for use as templates.
:type method: str
:param method:
Template generation method, must be one of ('from_client',
'from_seishub', 'from_sac', 'from_meta_file'). - Each method requires
associated arguments, see note below.
:type lowcut: float
:param lowcut: Low cut (Hz), if set to None will not apply a lowcut.
:type highcut: float
:param highcut: High cut (Hz), if set to None will not apply a highcut.
:type samp_rate: float
:param samp_rate: New sampling rate in Hz.
:type filt_order: int
:param filt_order: Filter level (number of corners).
:type length: float
:param length: Length of template waveform in seconds.
:type prepick: float
:param prepick: Pre-pick time in seconds
:type swin: str
:param swin:
P, S, P_all, S_all or all, defaults to all: see note in
:func:`eqcorrscan.core.template_gen.template_gen`
:type process_len: int
:param process_len: Length of data in seconds to download and process.
:type all_horiz: bool
:param all_horiz:
To use both horizontal channels even if there is only a pick on one of
them. Defaults to False.
:type delayed: bool
:param delayed: If True, each channel will begin relative to it's own \
pick-time, if set to False, each channel will begin at the same time.
:type plot: bool
:param plot: Plot templates or not.
:type plotdir: str
:param plotdir:
The path to save plots to. If `plotdir=None` (default) then the figure
will be shown on screen.
:type return_event: bool
:param return_event: Whether to return the event and process length or not.
:type min_snr: float
:param min_snr:
Minimum signal-to-noise ratio for a channel to be included in the
template, where signal-to-noise ratio is calculated as the ratio of
the maximum amplitude in the template window to the rms amplitude in
the whole window given.
:type parallel: bool
:param parallel: Whether to process data in parallel or not.
:type num_cores: int
:param num_cores:
Number of cores to try and use, if False and parallel=True, will use
either all your cores, or as many traces as in the data (whichever is
smaller).
:type save_progress: bool
:param save_progress:
Whether to save the resulting templates at every data step or not.
Useful for long-running processes.
:type skip_short_chans: bool
:param skip_short_chans:
Whether to ignore channels that have insufficient length data or not.
Useful when the quality of data is not known, e.g. when downloading
old, possibly triggered data from a datacentre
:returns: List of :class:`obspy.core.stream.Stream` Templates
:rtype: list
"""
client_map = {'from_client': 'fdsn', 'from_seishub': 'seishub'}
assert method in ('from_client', 'from_seishub', 'from_meta_file',
'from_sac')
if not isinstance(swin, list):
swin = [swin]
process = True
if method in ['from_client', 'from_seishub']:
catalog = kwargs.get('catalog', Catalog())
data_pad = kwargs.get('data_pad', 90)
# Group catalog into days and only download the data once per day
sub_catalogs = _group_events(catalog=catalog,
process_len=process_len,
template_length=length,
data_pad=data_pad)
if method == 'from_client':
if isinstance(kwargs.get('client_id'), str):
client = FDSNClient(kwargs.get('client_id', None))
else:
client = kwargs.get('client_id', None)
available_stations = []
else:
client = SeisHubClient(kwargs.get('url', None), timeout=10)
available_stations = client.waveform.get_station_ids()
elif method == 'from_meta_file':
if isinstance(kwargs.get('meta_file'), Catalog):
catalog = kwargs.get('meta_file')
elif kwargs.get('meta_file'):
catalog = read_events(kwargs.get('meta_file'))
else:
catalog = kwargs.get('catalog')
sub_catalogs = [catalog]
st = kwargs.get('st', Stream())
process = kwargs.get('process', True)
elif method == 'from_sac':
sac_files = kwargs.get('sac_files')
if isinstance(sac_files, list):
if isinstance(sac_files[0], (Stream, Trace)):
# This is a list of streams...
st = Stream(sac_files[0])
for sac_file in sac_files[1:]:
st += sac_file
else:
sac_files = [read(sac_file)[0] for sac_file in sac_files]
st = Stream(sac_files)
else:
st = sac_files
# Make an event object...
catalog = Catalog([sactoevent(st)])
sub_catalogs = [catalog]
temp_list = []
process_lengths = []
catalog_out = Catalog()
if "P_all" in swin or "S_all" in swin or all_horiz:
all_channels = True
else:
all_channels = False
for sub_catalog in sub_catalogs:
if method in ['from_seishub', 'from_client']:
Logger.info("Downloading data")
st = _download_from_client(client=client,
client_type=client_map[method],
catalog=sub_catalog,
data_pad=data_pad,
process_len=process_len,
available_stations=available_stations,
all_channels=all_channels)
Logger.info('Pre-processing data')
st.merge()
if len(st) == 0:
Logger.info("No data")
continue
if process:
data_len = max(
[len(tr.data) / tr.stats.sampling_rate for tr in st])
if 80000 < data_len < 90000:
daylong = True
starttime = min([tr.stats.starttime for tr in st])
min_delta = min([tr.stats.delta for tr in st])
# Cope with the common starttime less than 1 sample before the
# start of day.
if (starttime + min_delta).date > starttime.date:
starttime = (starttime + min_delta)
# Check if this is stupid:
if abs(starttime - UTCDateTime(starttime.date)) > 600:
daylong = False
starttime = starttime.date
else:
daylong = False
# Check if the required amount of data have been downloaded - skip
# channels if arg set.
for tr in st:
if np.ma.is_masked(tr.data):
_len = np.ma.count(tr.data) * tr.stats.delta
else:
_len = tr.stats.npts * tr.stats.delta
if _len < process_len * .8:
Logger.info("Data for {0} are too short, skipping".format(
tr.id))
if skip_short_chans:
continue
# Trim to enforce process-len
tr.data = tr.data[0:int(process_len * tr.stats.sampling_rate)]
if len(st) == 0:
Logger.info("No data")
continue
if daylong:
st = pre_processing.dayproc(st=st,
lowcut=lowcut,
highcut=highcut,
filt_order=filt_order,
samp_rate=samp_rate,
parallel=parallel,
starttime=UTCDateTime(starttime),
num_cores=num_cores)
else:
st = pre_processing.shortproc(st=st,
lowcut=lowcut,
highcut=highcut,
filt_order=filt_order,
parallel=parallel,
samp_rate=samp_rate,
num_cores=num_cores)
data_start = min([tr.stats.starttime for tr in st])
data_end = max([tr.stats.endtime for tr in st])
for event in sub_catalog:
stations, channels, st_stachans = ([], [], [])
if len(event.picks) == 0:
Logger.warning('No picks for event {0}'.format(
event.resource_id))
continue
use_event = True
# Check that the event is within the data
for pick in event.picks:
if not data_start < pick.time < data_end:
Logger.warning(
"Pick outside of data span: Pick time {0} Start "
"time {1} End time: {2}".format(
str(pick.time), str(data_start), str(data_end)))
use_event = False
if not use_event:
Logger.error('Event is not within data time-span')
continue
# Read in pick info
Logger.debug("I have found the following picks")
for pick in event.picks:
if not pick.waveform_id:
Logger.warning(
'Pick not associated with waveforms, will not use:'
' {0}'.format(pick))
continue
Logger.debug(pick)
stations.append(pick.waveform_id.station_code)
channels.append(pick.waveform_id.channel_code)
# Check to see if all picks have a corresponding waveform
for tr in st:
st_stachans.append('.'.join(
[tr.stats.station, tr.stats.channel]))
# Cut and extract the templates
template = _template_gen(event.picks,
st,
length,
swin,
prepick=prepick,
plot=plot,
all_horiz=all_horiz,
delayed=delayed,
min_snr=min_snr,
plotdir=plotdir)
process_lengths.append(len(st[0].data) / samp_rate)
temp_list.append(template)
catalog_out += event
if save_progress:
if not os.path.isdir("eqcorrscan_temporary_templates"):
os.makedirs("eqcorrscan_temporary_templates")
for template in temp_list:
template.write(
"eqcorrscan_temporary_templates{0}{1}.ms".format(
os.path.sep, template[0].stats.starttime.strftime(
"%Y-%m-%dT%H%M%S")),
format="MSEED")
del st
if return_event:
return temp_list, catalog_out, process_lengths
return temp_list
def from_sac(sac_files, lowcut, highcut, samp_rate, filt_order, length, swin,
prepick=0.05, debug=0, plot=False):
"""Function to read picks and waveforms from SAC data, and generate a \
template from these. Usually sac_files is a list of all single-channel \
SAC files for a given event, a single, multi-channel template will be \
created from these traces.
:type sac_files: list or stream
:param sac_files: List or stream of sac waveforms, or list of paths to \
sac waveforms.
:type lowcut: float
:param lowcut: Low cut (Hz), if set to None will look in template \
defaults file
:type highcut: float
:param highcut: High cut (Hz), if set to None will look in template \
defaults file
:type samp_rate: float
:param samp_rate: New sampling rate in Hz, if set to None will look in \
template defaults file
:type filt_order: int
:param filt_order: Filter level, if set to None will look in \
template defaults file
:type swin: str
:param swin: Either 'all', 'P' or 'S', to select which phases to output.
:type length: float
:param length: Extract length in seconds, if None will look in template \
defaults file.
:type prepick: float
:param prepick: Length to extract prior to the pick in seconds.
:type debug: int
:param debug: Debug level, higher number=more output.
:type plot: bool
:param plot: Turns template plotting on or off.
:returns: obspy.Stream Newly cut template
.. note:: This functionality is not supported for obspy versions below \
1.0.0 as references times are not read in by SACIO, which are needed \
for defining pick times.
"""
from obspy import read, Stream
from eqcorrscan.utils.sac_util import sactoevent
from eqcorrscan.utils import pre_processing
# Check whether sac_files is a stream or a list
if isinstance(sac_files, list):
if isinstance(sac_files[0], str) or isinstance(sac_files[0], unicode):
sac_files = [read(sac_file)[0] for sac_file in sac_files]
if isinstance(sac_files[0], Stream):
# This is a list of streams...
st = sac_files[0]
for sac_file in sac_files[1:]:
st += sac_file
st = Stream(sac_files)
elif isinstance(sac_files, Stream):
st = sac_files
# Make an event object...
event = sactoevent(st, debug=debug)
# Process the data
st.merge(fill_value='interpolate')
st = pre_processing.shortproc(st, lowcut, highcut, filt_order,
samp_rate, debug)
template = _template_gen(picks=event.picks, st=st, length=length,
swin=swin, prepick=prepick, plot=plot,
debug=debug)
return template
def from_sac(sac_files, lowcut, highcut, samp_rate, filt_order, length, swin,
prepick=0.05, debug=0, plot=False):
"""
Generate a multiplexed template from a list of SAC files.
Function to read picks and waveforms from SAC data, and generate a \
template from these. Usually sac_files is a list of all single-channel \
SAC files for a given event, a single, multi-channel template will be \
created from these traces.
**All files listed in sac_files should be associated with a single event.**
:type sac_files: list
:param sac_files: osbpy.core.stream.Stream of sac waveforms, or
list of paths to sac waveforms.
:type lowcut: float
:param lowcut: Low cut (Hz), if set to None will look in template \
defaults file
:type highcut: float
:param highcut: High cut (Hz), if set to None will look in template \
defaults file
:type samp_rate: float
:param samp_rate: New sampling rate in Hz, if set to None will look in \
template defaults file
:type filt_order: int
:param filt_order: Filter level, if set to None will look in \
template defaults file
:type swin: str
:param swin: Either 'all', 'P' or 'S', to select which phases to output.
:type length: float
:param length: Extract length in seconds, if None will look in template \
defaults file.
:type prepick: float
:param prepick: Length to extract prior to the pick in seconds.
:type debug: int
:param debug: Debug level, higher number=more output.
:type plot: bool
:param plot: Turns template plotting on or off.
:returns: obspy.core.stream.Stream Newly cut template
.. note:: This functionality is not supported for obspy versions below \
1.0.0 as references times are not read in by SACIO, which are needed \
for defining pick times.
.. rubric:: Example
>>> from eqcorrscan.core.template_gen import from_sac
>>> import glob
>>> # Get all the SAC-files associated with one event.
>>> sac_files = glob.glob('eqcorrscan/tests/test_data/SAC/2014p611252/*')
>>> template = from_sac(sac_files=sac_files, lowcut=2.0, highcut=10.0,
... samp_rate=25.0, filt_order=4, length=2.0,
... swin='all', prepick=0.1)
>>> print(template[0].stats.sampling_rate)
25.0
>>> print(len(template))
15
"""
from obspy import read, Stream
from eqcorrscan.utils.sac_util import sactoevent
from eqcorrscan.utils import pre_processing
# Check whether sac_files is a stream or a list
if isinstance(sac_files, list):
if isinstance(sac_files[0], str) or isinstance(sac_files[0], unicode):
sac_files = [read(sac_file)[0] for sac_file in sac_files]
if isinstance(sac_files[0], Stream):
# This is a list of streams...
st = sac_files[0]
for sac_file in sac_files[1:]:
st += sac_file
st = Stream(sac_files)
elif isinstance(sac_files, Stream):
st = sac_files
# Make an event object...
event = sactoevent(st, debug=debug)
# Process the data
st.merge(fill_value='interpolate')
st = pre_processing.shortproc(st, lowcut, highcut, filt_order,
samp_rate, debug)
template = _template_gen(picks=event.picks, st=st, length=length,
swin=swin, prepick=prepick, plot=plot,
debug=debug)
return template