#!/usr/bin/python
"""
Functions to generate template waveforms and information to go with them
for the application of cross-correlation of seismic data for the detection of
repeating events.
.. note::
All functions use obspy filters, which are implemented such that
if both highcut and lowcut are set a bandpass filter will be used,
but of highcut is not set (None) then a highpass filter will be used and
if only the highcut is set then a lowpass filter will be used.
:copyright:
EQcorrscan developers.
:license:
GNU Lesser General Public License, Version 3
(https://www.gnu.org/copyleft/lesser.html)
"""
import warnings
import numpy as np
import logging
import os
from obspy import Stream, read, Trace, UTCDateTime, read_events
from obspy.core.event import Catalog
from obspy.clients.fdsn import Client as FDSNClient
from eqcorrscan.utils.sac_util import sactoevent
from eqcorrscan.utils import pre_processing
# from eqcorrscan.core import EQcorrscanDeprecationWarning
Logger = logging.getLogger(__name__)
class TemplateGenError(Exception):
"""
Default error for template generation errors.
"""
def __init__(self, value):
"""
Raise error.
"""
self.value = value
def __repr__(self):
return self.value
def __str__(self):
return 'TemplateGenError: ' + self.value
[docs]
def template_gen(method, lowcut, highcut, samp_rate, filt_order,
length, prepick, swin="all", process_len=86400,
all_vert=False, 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, vertical_chans=['Z'],
horizontal_chans=['E', 'N', '1', '2'], **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_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_vert: bool
:param all_vert:
To use all channels defined in vertical_chans for P-arrivals even if
there is only a pick on one of them. Defaults to False.
: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
:type vertical_chans: list
:param vertical_chans:
List of channel endings on which P-picks are accepted.
:type horizontal_chans: list
:param horizontal_chans:
List of channel endings for horizontal channels, on which S-picks are
accepted.
:returns: List of :class:`obspy.core.stream.Stream` Templates
:rtype: list
.. note::
By convention templates are generated with P-phases on the
vertical channel [can be multiple, e.g., Z (vertical) and H
(hydrophone) for an ocean bottom seismometer] and S-phases on the
horizontal channels. By default, normal seismograph naming conventions
are assumed, where Z denotes vertical and N, E, 1 and 2 denote
horizontal channels, either oriented or not. To this end we will
**only** use vertical channels if they have a P-pick, and will use one
or other horizontal channels **only** if there is an S-pick on it.
.. warning::
If there is no phase_hint included in picks, and swin=all, all channels
with picks will be used.
.. note::
If swin=all, then all picks will be used, not just phase-picks (e.g. it
will use amplitude picks). If you do not want this then we suggest
that you remove any picks you do not want to use in your templates
before using the event.
.. note::
*Method specific arguments:*
- `from_client` requires:
:param str client_id:
string passable by obspy to generate Client, or any object
with a `get_waveforms` method, including 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_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'}
assert method in ('from_client', 'from_meta_file', 'from_sac')
if not isinstance(swin, list):
swin = [swin]
process = True
if method in ['from_client']:
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':
client_id = kwargs.get('client_id', None)
if hasattr(client_id, 'get_waveforms'):
client = client_id
elif isinstance(client_id, str):
client = FDSNClient(client_id)
else:
raise NotImplementedError(
"client_id must be an FDSN client string, or a Client "
"with a get_waveforms method"
)
available_stations = []
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_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
kwargs = dict(
st=st, lowcut=lowcut, highcut=highcut,
filt_order=filt_order, samp_rate=samp_rate,
parallel=parallel, num_cores=num_cores, daylong=daylong)
if daylong:
kwargs.update(dict(starttime=UTCDateTime(starttime)))
st = pre_processing.multi_process(**kwargs)
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_vert=all_vert, all_horiz=all_horiz, delayed=delayed,
min_snr=min_snr, vertical_chans=vertical_chans,
horizontal_chans=horizontal_chans, 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 _download_from_client(client, client_type, catalog, data_pad, process_len,
available_stations=[], all_channels=False):
"""
Internal function to handle downloading from fdsn client
"""
st = Stream()
catalog = Catalog(sorted(catalog, key=lambda e: e.origins[0].time))
all_waveform_info = []
for event in catalog:
for pick in event.picks:
if not pick.waveform_id:
Logger.warning(
"Pick not associated with waveforms, will not use:"
" {0}".format(pick))
continue
if all_channels:
channel_code = pick.waveform_id.channel_code[0:2] + "?"
else:
channel_code = pick.waveform_id.channel_code
if pick.waveform_id.station_code is None:
Logger.error("No station code for pick, skipping")
continue
all_waveform_info.append((
pick.waveform_id.network_code or "*",
pick.waveform_id.station_code,
channel_code, pick.waveform_id.location_code or "*"))
starttime = UTCDateTime(
catalog[0].origins[0].time - data_pad)
endtime = starttime + process_len
# Check that endtime is after the last event
if not endtime > catalog[-1].origins[0].time + data_pad:
raise TemplateGenError(
'Events do not fit in processing window')
all_waveform_info = sorted(list(set(all_waveform_info)))
dropped_pick_stations = 0
for waveform_info in all_waveform_info:
net, sta, chan, loc = waveform_info
Logger.info('Downloading for start-time: {0} end-time: {1}'.format(
starttime, endtime))
Logger.debug('.'.join([net, sta, loc, chan]))
query_params = dict(
network=net, station=sta, location=loc, channel=chan,
starttime=starttime, endtime=endtime)
try:
st += client.get_waveforms(**query_params)
except Exception as e:
Logger.error(e)
Logger.error('Found no data for this station: {0}'.format(
query_params))
dropped_pick_stations += 1
if not st and dropped_pick_stations == len(event.picks):
raise Exception('No data available, is the server down?')
st.merge()
# clients download chunks, we need to check that the data are
# the desired length
final_channels = []
for tr in st:
tr.trim(starttime, endtime)
if len(tr.data) == (process_len * tr.stats.sampling_rate) + 1:
tr.data = tr.data[1:len(tr.data)]
if tr.stats.endtime - tr.stats.starttime < 0.8 * process_len:
Logger.warning(
"Data for {0}.{1} is {2} hours long, which is less than 80 "
"percent of the desired length, will not use".format(
tr.stats.station, tr.stats.channel,
(tr.stats.endtime - tr.stats.starttime) / 3600))
elif not pre_processing._check_daylong(tr.data):
Logger.warning(
"Data are mostly zeros, removing trace: {0}".format(tr.id))
else:
final_channels.append(tr)
st.traces = final_channels
return st
def _rms(array):
"""
Calculate RMS of array.
:type array: numpy.ndarray
:param array: Array to calculate the RMS for.
:returns: RMS of array
:rtype: float
"""
return np.sqrt(np.mean(np.square(array)))
def _template_gen(picks, st, length, swin='all', prepick=0.05, all_vert=False,
all_horiz=False, delayed=True, plot=False, min_snr=None,
plotdir=None, vertical_chans=['Z'],
horizontal_chans=['E', 'N', '1', '2']):
"""
Master function to generate a multiplexed template for a single event.
Function to generate a cut template as :class:`obspy.core.stream.Stream`
from a given set of picks and data. Should be given pre-processed
data (downsampled and filtered).
:type picks: list
:param picks: Picks to extract data around, where each pick in the \
list is an obspy.core.event.origin.Pick object.
:type st: obspy.core.stream.Stream
:param st: Stream to extract templates from
:type length: float
:param length: Length of template 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 prepick: float
:param prepick:
Length in seconds to extract before the pick time default is 0.05
seconds.
:type all_vert: bool
:param all_vert:
To use all channels defined in vertical_chans for P-arrivals even if
there is only a pick on one of them. Defaults to False.
: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:
To plot the template or not, default is False. Plots are saved as
`template-starttime_template.png` and `template-starttime_noise.png`,
where `template-starttime` is the start-time of the template
: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 plotdir: str
:param plotdir:
The path to save plots to. If `plotdir=None` (default) then the figure
will be shown on screen.
:type vertical_chans: list
:param vertical_chans:
List of channel endings on which P-picks are accepted.
:type horizontal_chans: list
:param horizontal_chans:
List of channel endings for horizontal channels, on which S-picks are
accepted.
:returns: Newly cut template.
:rtype: :class:`obspy.core.stream.Stream`
.. note::
By convention templates are generated with P-phases on the
vertical channel [can be multiple, e.g., Z (vertical) and H
(hydrophone) for an ocean bottom seismometer] and S-phases on the
horizontal channels. By default, normal seismograph naming conventions
are assumed, where Z denotes vertical and N, E, 1 and 2 denote
horizontal channels, either oriented or not. To this end we will
**only** use vertical channels if they have a P-pick, and will use one
or other horizontal channels **only** if there is an S-pick on it.
.. note::
swin argument: Setting to `P` will return only data for channels
with P picks, starting at the pick time (minus the prepick).
Setting to `S` will return only data for channels with
S picks, starting at the S-pick time (minus the prepick)
(except if `all_horiz=True` when all horizontal channels will
be returned if there is an S pick on one of them). Setting to `all`
will return channels with either a P or S pick (including both
horizontals if `all_horiz=True`) - with this option vertical channels
will start at the P-pick (minus the prepick) and horizontal channels
will start at the S-pick time (minus the prepick).
`P_all` will return cut traces starting at the P-pick time for all
channels. `S_all` will return cut traces starting at the S-pick
time for all channels.
.. warning::
If there is no phase_hint included in picks, and swin=all, all
channels with picks will be used.
"""
from eqcorrscan.utils.plotting import pretty_template_plot as tplot
from eqcorrscan.utils.plotting import noise_plot
# the users picks intact.
if not isinstance(swin, list):
swin = [swin]
for _swin in swin:
assert _swin in ['P', 'all', 'S', 'P_all', 'S_all']
picks_copy = []
for pick in picks:
if not pick.waveform_id:
Logger.warning(
"Pick not associated with waveform, will not use it: "
"{0}".format(pick))
continue
if not pick.waveform_id.station_code or not \
pick.waveform_id.channel_code:
Logger.warning(
"Pick not associated with a channel, will not use it:"
" {0}".format(pick))
continue
picks_copy.append(pick)
if len(picks_copy) == 0:
return Stream()
st_copy = Stream()
for tr in st:
# Check that the data can be represented by float16, and check they
# are not all zeros
# Catch RuntimeWarning for overflow in casting
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
if np.all(tr.data.astype(np.float16) == 0):
Logger.error(
"Trace is all zeros at float16 level, either gain or "
f"check. Not using in template: {tr}")
continue
st_copy += tr
st = st_copy
if len(st) == 0:
return st
# Get the earliest pick-time and use that if we are not using delayed.
picks_copy.sort(key=lambda p: p.time)
first_pick = picks_copy[0]
if plot:
stplot = st.slice(first_pick.time - 20,
first_pick.time + length + 90).copy()
noise = stplot.copy()
# Work out starttimes
starttimes = []
for _swin in swin:
for tr in st:
starttime = {'station': tr.stats.station,
'channel': tr.stats.channel, 'picks': []}
station_picks = [pick for pick in picks_copy
if pick.waveform_id.station_code ==
tr.stats.station]
# Cope with missing phase_hints
if _swin != "all":
station_picks = [p for p in station_picks if p.phase_hint]
if _swin == 'P_all':
p_pick = [pick for pick in station_picks
if pick.phase_hint.upper()[0] == 'P']
if len(p_pick) == 0:
Logger.debug(f"No picks with phase_hint P "
f"found for {tr.stats.station}")
continue
starttime.update({'picks': p_pick})
elif _swin == 'S_all':
s_pick = [pick for pick in station_picks
if pick.phase_hint.upper()[0] == 'S']
if len(s_pick) == 0:
Logger.debug(f"No picks with phase_hint S "
f"found for {tr.stats.station}")
continue
starttime.update({'picks': s_pick})
elif _swin == 'all':
if all_vert and tr.stats.channel[-1] in vertical_chans:
# Get all picks on vertical channels
channel_pick = [
pick for pick in station_picks
if pick.waveform_id.channel_code[-1] in
vertical_chans]
elif all_horiz and tr.stats.channel[-1] in horizontal_chans:
# Get all picks on horizontal channels
channel_pick = [
pick for pick in station_picks
if pick.waveform_id.channel_code[-1] in
horizontal_chans]
else:
channel_pick = [
pick for pick in station_picks
if pick.waveform_id.channel_code == tr.stats.channel]
if len(channel_pick) == 0:
continue
starttime.update({'picks': channel_pick})
elif _swin == 'P':
p_pick = [pick for pick in station_picks
if pick.phase_hint.upper()[0] == 'P']
if not all_vert:
p_pick = [pick for pick in p_pick
if pick.waveform_id.channel_code ==
tr.stats.channel]
if len(p_pick) == 0:
Logger.debug(
f"No picks with phase_hint P "
f"found for {tr.stats.station}.{tr.stats.channel}")
continue
starttime.update({'picks': p_pick})
elif _swin == 'S':
if tr.stats.channel[-1] in vertical_chans:
continue
s_pick = [pick for pick in station_picks
if pick.phase_hint.upper()[0] == 'S']
if not all_horiz:
s_pick = [pick for pick in s_pick
if pick.waveform_id.channel_code ==
tr.stats.channel]
starttime.update({'picks': s_pick})
if len(starttime['picks']) == 0:
Logger.debug(
f"No picks with phase_hint S "
f"found for {tr.stats.station}.{tr.stats.channel}")
continue
if not delayed:
starttime.update({'picks': [first_pick]})
starttimes.append(starttime)
# Cut the data
st1 = Stream()
for _starttime in starttimes:
Logger.info(f"Working on channel {_starttime['station']}."
f"{_starttime['channel']}")
tr = st.select(
station=_starttime['station'], channel=_starttime['channel'])[0]
Logger.info(f"Found Trace {tr}")
used_tr = False
for pick in _starttime['picks']:
if not pick.phase_hint:
Logger.warning(
"Pick for {0}.{1} has no phase hint given, you should not "
"use this template for cross-correlation"
" re-picking!".format(
pick.waveform_id.station_code,
pick.waveform_id.channel_code))
starttime = pick.time - prepick
Logger.debug("Cutting {0}".format(tr.id))
noise_amp = _rms(
tr.slice(starttime=starttime - 100, endtime=starttime).data)
tr_cut = tr.slice(
starttime=starttime, endtime=starttime + length,
nearest_sample=False).copy()
if plot:
noise.select(
station=_starttime['station'],
channel=_starttime['channel']).trim(
noise[0].stats.starttime, starttime)
if len(tr_cut.data) == 0:
Logger.warning(
"No data provided for {0}.{1} starting at {2}".format(
tr.stats.station, tr.stats.channel, starttime))
continue
# Ensure that the template is the correct length
if len(tr_cut.data) == (tr_cut.stats.sampling_rate *
length) + 1:
tr_cut.data = tr_cut.data[0:-1]
Logger.debug(
'Cut starttime = %s\nCut endtime %s' %
(str(tr_cut.stats.starttime), str(tr_cut.stats.endtime)))
if min_snr is not None and \
max(tr_cut.data) / noise_amp < min_snr:
Logger.warning(
"Signal-to-noise ratio {0} below threshold for {1}.{2}, "
"not using".format(
max(tr_cut.data) / noise_amp, tr_cut.stats.station,
tr_cut.stats.channel))
continue
st1 += tr_cut
used_tr = True
if not used_tr:
Logger.warning('No pick for {0}'.format(tr.id))
if plot and len(st1) > 0:
plot_kwargs = dict(show=True)
if plotdir is not None:
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
plot_kwargs.update(dict(show=False, save=True))
tplot(st1, background=stplot, picks=picks_copy,
title='Template for ' + str(st1[0].stats.starttime),
savefile="{0}/{1}_template.png".format(
plotdir, st1[0].stats.starttime.strftime(
"%Y-%m-%dT%H%M%S")),
**plot_kwargs)
noise_plot(signal=st1, noise=noise,
savefile="{0}/{1}_noise.png".format(
plotdir, st1[0].stats.starttime.strftime(
"%Y-%m-%dT%H%M%S")),
**plot_kwargs)
del stplot
return st1
def _group_events(catalog, process_len, template_length, data_pad):
"""
Internal function to group events into sub-catalogs based on process_len.
:param catalog: Catalog to groups into sub-catalogs
:type catalog: obspy.core.event.Catalog
:param process_len: Length in seconds that data will be processed in
:type process_len: int
:return: List of catalogs
:rtype: list
"""
# case for catalog only containing one event
assert len(catalog), "No events to group"
if len(catalog) == 1:
return [catalog]
sub_catalogs = []
# Sort catalog by date
catalog.events = sorted(
catalog.events,
key=lambda e: (e.preferred_origin() or e.origins[0]).time)
sub_catalog = Catalog([catalog[0]])
for event in catalog[1:]:
origin_time = (event.preferred_origin() or event.origins[0]).time
last_pick = sorted(event.picks, key=lambda p: p.time)[-1]
max_diff = (
process_len - (last_pick.time - origin_time) - template_length)
max_diff -= 2 * data_pad
if origin_time - sub_catalog[0].origins[0].time < max_diff:
sub_catalog.append(event)
else:
sub_catalogs.append(sub_catalog)
sub_catalog = Catalog([event])
sub_catalogs.append(sub_catalog)
return sub_catalogs
if __name__ == "__main__":
import doctest
doctest.testmod()