Source code for eqcorrscan.core.match_filter.tribe

"""
Functions for network matched-filter detection of seismic data.

Designed to cross-correlate templates generated by template_gen function
with data and output the detections.

:copyright:
    EQcorrscan developers.

:license:
    GNU Lesser General Public License, Version 3
    (https://www.gnu.org/copyleft/lesser.html)
"""
import copy
import getpass
import glob
import os
import shutil
import tarfile
import tempfile
import logging

import numpy as np
from obspy import Catalog, Stream, read, read_events
from obspy.core.event import Comment, CreationInfo

from eqcorrscan.core.match_filter.template import Template, group_templates
from eqcorrscan.core.match_filter.party import Party
from eqcorrscan.core.match_filter.helpers import (
    _safemembers, _par_read, get_waveform_client)
from eqcorrscan.core.match_filter.matched_filter import (
    _group_detect, MatchFilterError)
from eqcorrscan.core import template_gen
from eqcorrscan.utils.pre_processing import _check_daylong

Logger = logging.getLogger(__name__)


[docs]class Tribe(object): """ Holder for multiple templates. :type templates: List of Template :param templates: The templates within the Tribe. """
[docs] def __init__(self, templates=None): self.templates = [] if isinstance(templates, Template): templates = [templates] if templates: self.templates.extend(templates)
def __repr__(self): """ Print information about the tribe. .. rubric:: Example >>> tribe = Tribe(templates=[Template(name='a')]) >>> print(tribe) Tribe of 1 templates """ return 'Tribe of %i templates' % self.__len__() def __add__(self, other): """ Add two Tribes or a Tribe and a Template together. '+' .. rubric:: Example >>> tribe = Tribe(templates=[Template(name='a')]) >>> tribe_ab = tribe + Tribe(templates=[Template(name='b')]) >>> print(tribe_ab) Tribe of 2 templates >>> tribe_abc = tribe_ab + Template(name='c') >>> print(tribe_abc) Tribe of 3 templates """ return self.copy().__iadd__(other) def __iadd__(self, other): """ Add in place: '+=' .. rubric:: Example >>> tribe = Tribe(templates=[Template(name='a')]) >>> tribe += Tribe(templates=[Template(name='b')]) >>> print(tribe) Tribe of 2 templates >>> tribe += Template(name='c') >>> print(tribe) Tribe of 3 templates """ if isinstance(other, Tribe): self.templates += other.templates elif isinstance(other, Template): self.templates.append(other) else: raise TypeError('Must be either Template or Tribe') return self def __eq__(self, other): """ Test for equality. Rich comparison operator '==' .. rubric:: Example >>> tribe_a = Tribe(templates=[Template(name='a')]) >>> tribe_b = Tribe(templates=[Template(name='b')]) >>> tribe_a == tribe_b False >>> tribe_a == tribe_a True """ if self.sort().templates != other.sort().templates: return False return True def __ne__(self, other): """ Test for inequality. Rich comparison operator '!=' .. rubric:: Example >>> tribe_a = Tribe(templates=[Template(name='a')]) >>> tribe_b = Tribe(templates=[Template(name='b')]) >>> tribe_a != tribe_b True >>> tribe_a != tribe_a False """ return not self.__eq__(other) def __len__(self): """ Number of Templates in Tribe. len(tribe) .. rubric:: Example >>> tribe_a = Tribe(templates=[Template(name='a')]) >>> len(tribe_a) 1 """ return len(self.templates) def __iter__(self): """ Iterator for the Tribe. """ return list(self.templates).__iter__() def __getitem__(self, index): """ Support slicing to get Templates from Tribe. .. rubric:: Example >>> tribe = Tribe(templates=[Template(name='a'), Template(name='b'), ... Template(name='c')]) >>> tribe[1] # doctest: +NORMALIZE_WHITESPACE Template b: 0 channels; lowcut: None Hz; highcut: None Hz; sampling rate None Hz; filter order: None; process length: None s >>> tribe[0:2] Tribe of 2 templates """ if isinstance(index, slice): return self.__class__(templates=self.templates.__getitem__(index)) elif isinstance(index, int): return self.templates.__getitem__(index) else: _index = [i for i, t in enumerate(self.templates) if t.name == index] try: return self.templates.__getitem__(_index[0]) except IndexError: Logger.warning('Template: %s not in tribe' % index) return []
[docs] def sort(self): """ Sort the tribe, sorts by template name. .. rubric:: Example >>> tribe = Tribe(templates=[Template(name='c'), Template(name='b'), ... Template(name='a')]) >>> tribe.sort() Tribe of 3 templates >>> tribe[0] # doctest: +NORMALIZE_WHITESPACE Template a: 0 channels; lowcut: None Hz; highcut: None Hz; sampling rate None Hz; filter order: None; process length: None s """ self.templates = sorted(self.templates, key=lambda x: x.name) return self
[docs] def select(self, template_name): """ Select a particular template from the tribe. :type template_name: str :param template_name: Template name to look-up :return: Template .. rubric:: Example >>> tribe = Tribe(templates=[Template(name='c'), Template(name='b'), ... Template(name='a')]) >>> tribe.select('b') # doctest: +NORMALIZE_WHITESPACE Template b: 0 channels; lowcut: None Hz; highcut: None Hz; sampling rate None Hz; filter order: None; process length: None s """ return [t for t in self.templates if t.name == template_name][0]
[docs] def remove(self, template): """ Remove a template from the tribe. :type template: :class:`eqcorrscan.core.match_filter.Template` :param template: Template to remove from tribe .. rubric:: Example >>> tribe = Tribe(templates=[Template(name='c'), Template(name='b'), ... Template(name='a')]) >>> tribe.remove(tribe.templates[0]) Tribe of 2 templates """ self.templates = [t for t in self.templates if t != template] return self
[docs] def copy(self): """ Copy the Tribe. .. rubric:: Example >>> tribe_a = Tribe(templates=[Template(name='a')]) >>> tribe_b = tribe_a.copy() >>> tribe_a == tribe_b True """ return copy.deepcopy(self)
[docs] def write(self, filename, compress=True, catalog_format="QUAKEML"): """ Write the tribe to a file using tar archive formatting. :type filename: str :param filename: Filename to write to, if it exists it will be appended to. :type compress: bool :param compress: Whether to compress the tar archive or not, if False then will just be files in a folder. :type catalog_format: str :param catalog_format: What format to write the detection-catalog with. Only Nordic, SC3ML, QUAKEML are supported. Note that not all information is written for all formats (QUAKEML is the most complete, but is slow for IO). .. rubric:: Example >>> tribe = Tribe(templates=[Template(name='c', st=read())]) >>> tribe.write('test_tribe') Tribe of 1 templates """ from eqcorrscan.core.match_filter import CAT_EXT_MAP if catalog_format not in CAT_EXT_MAP.keys(): raise TypeError("{0} is not supported".format(catalog_format)) dirname, ext = os.path.splitext(filename) if not os.path.isdir(dirname): os.makedirs(dirname) self._par_write(dirname) tribe_cat = Catalog() for t in self.templates: if t.event is not None: # Check that the name in the comment matches the template name for comment in t.event.comments: if comment.text and comment.text.startswith( "eqcorrscan_template_"): comment.text = "eqcorrscan_template_{0}".format(t.name) tribe_cat.append(t.event) if len(tribe_cat) > 0: tribe_cat.write( os.path.join(dirname, 'tribe_cat.{0}'.format( CAT_EXT_MAP[catalog_format])), format=catalog_format) for template in self.templates: template.st.write( os.path.join(dirname, '{0}.ms'.format(template.name)), format='MSEED') if compress: if not filename.endswith(".tgz"): Logger.info("Appending '.tgz' to filename.") filename += ".tgz" with tarfile.open(filename, "w:gz") as tar: tar.add(dirname, arcname=os.path.basename(dirname)) shutil.rmtree(dirname) return self
def _par_write(self, dirname): """ Internal write function to write a formatted parameter file. :type dirname: str :param dirname: Directory to write the parameter file to. """ filename = dirname + '/' + 'template_parameters.csv' with open(filename, 'w') as parfile: for template in self.templates: for key in template.__dict__.keys(): if key not in ['st', 'event']: parfile.write(key + ': ' + str(template.__dict__[key]) + ', ') parfile.write('\n') return self
[docs] def read(self, filename): """ Read a tribe of templates from a tar formatted file. :type filename: str :param filename: File to read templates from. .. rubric:: Example >>> tribe = Tribe(templates=[Template(name='c', st=read())]) >>> tribe.write('test_tribe') Tribe of 1 templates >>> tribe_back = Tribe().read('test_tribe.tgz') >>> tribe_back == tribe True """ with tarfile.open(filename, "r:*") as arc: temp_dir = tempfile.mkdtemp() arc.extractall(path=temp_dir, members=_safemembers(arc)) tribe_dir = glob.glob(temp_dir + os.sep + '*')[0] self._read_from_folder(dirname=tribe_dir) shutil.rmtree(temp_dir) return self
def _read_from_folder(self, dirname): """ Internal folder reader. :type dirname: str :param dirname: Folder to read from. """ templates = _par_read(dirname=dirname, compressed=False) t_files = glob.glob(dirname + os.sep + '*.ms') tribe_cat_file = glob.glob(os.path.join(dirname, "tribe_cat.*")) if len(tribe_cat_file) != 0: tribe_cat = read_events(tribe_cat_file[0]) else: tribe_cat = Catalog() previous_template_names = [t.name for t in self.templates] for template in templates: if template.name in previous_template_names: # Don't read in for templates that we already have. continue for event in tribe_cat: for comment in event.comments: if comment.text == 'eqcorrscan_template_' + template.name: template.event = event t_file = [t for t in t_files if t.split(os.sep)[-1] == template.name + '.ms'] if len(t_file) == 0: Logger.error('No waveform for template: ' + template.name) templates.remove(template) continue elif len(t_file) > 1: Logger.warning('Multiple waveforms found, using: ' + t_file[0]) template.st = read(t_file[0]) self.templates.extend(templates) return
[docs] def cluster(self, method, **kwargs): """ Cluster the tribe. Cluster templates within a tribe: returns multiple tribes each of which could be stacked. :type method: str :param method: Method of stacking, see :mod:`eqcorrscan.utils.clustering` :return: List of tribes. .. rubric:: Example """ from eqcorrscan.utils import clustering tribes = [] func = getattr(clustering, method) if method in ['space_cluster', 'space_time_cluster']: cat = Catalog([t.event for t in self.templates]) groups = func(cat, **kwargs) for group in groups: new_tribe = Tribe() for event in group: new_tribe.templates.extend([t for t in self.templates if t.event == event]) tribes.append(new_tribe) return tribes
[docs] def detect(self, stream, threshold, threshold_type, trig_int, plot=False, plotdir=None, daylong=False, parallel_process=True, xcorr_func=None, concurrency=None, cores=None, ignore_length=False, ignore_bad_data=False, group_size=None, overlap="calculate", full_peaks=False, save_progress=False, process_cores=None, **kwargs): """ Detect using a Tribe of templates within a continuous stream. :type stream: `obspy.core.stream.Stream` :param stream: Continuous data to detect within using the Template. :type threshold: float :param threshold: Threshold level, if using `threshold_type='MAD'` then this will be the multiple of the median absolute deviation. :type threshold_type: str :param threshold_type: The type of threshold to be used, can be MAD, absolute or av_chan_corr. See Note on thresholding below. :type trig_int: float :param trig_int: Minimum gap between detections from one template in seconds. If multiple detections occur within trig_int of one-another, the one with the highest cross-correlation sum will be selected. :type plot: bool :param plot: Turn plotting on or off. :type plotdir: str :param plotdir: The path to save plots to. If `plotdir=None` (default) then the figure will be shown on screen. :type daylong: bool :param daylong: Set to True to use the :func:`eqcorrscan.utils.pre_processing.dayproc` routine, which preforms additional checks and is more efficient for day-long data over other methods. :type parallel_process: bool :param parallel_process: :type xcorr_func: str or callable :param xcorr_func: A str of a registered xcorr function or a callable for implementing a custom xcorr function. For more information see: :func:`eqcorrscan.utils.correlate.register_array_xcorr` :type concurrency: str :param concurrency: The type of concurrency to apply to the xcorr function. Options are 'multithread', 'multiprocess', 'concurrent'. For more details see :func:`eqcorrscan.utils.correlate.get_stream_xcorr` :type cores: int :param cores: Number of workers for procesisng and detection. :type ignore_length: bool :param ignore_length: If using daylong=True, then dayproc will try check that the data are there for at least 80% of the day, if you don't want this check (which will raise an error if too much data are missing) then set ignore_length=True. This is not recommended! :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 group_size: int :param group_size: Maximum number of templates to run at once, use to reduce memory consumption, if unset will use all templates. :type overlap: float :param overlap: Either None, "calculate" or a float of number of seconds to overlap detection streams by. This is to counter the effects of the delay-and-stack in calculating cross-correlation sums. Setting overlap = "calculate" will work out the appropriate overlap based on the maximum lags within templates. :type full_peaks: bool :param full_peaks: See `eqcorrscan.utils.findpeak.find_peaks2_short` :type save_progress: bool :param save_progress: Whether to save the resulting party at every data step or not. Useful for long-running processes. :type process_cores: int :param process_cores: Number of processes to use for pre-processing (if different to `cores`). :return: :class:`eqcorrscan.core.match_filter.Party` of Families of detections. .. Note:: When using the "fftw" correlation backend the length of the fft can be set. See :mod:`eqcorrscan.utils.correlate` for more info. .. Note:: `stream` must not be pre-processed. If your data contain gaps you should *NOT* fill those gaps before using this method. The pre-process functions (called within) will fill the gaps internally prior to processing, process the data, then re-fill the gaps with zeros to ensure correlations are not incorrectly calculated within gaps. If your data have gaps you should pass a merged stream without the `fill_value` argument (e.g.: `stream = stream.merge()`). .. note:: **Data overlap:** Internally this routine shifts and trims the data according to the offsets in the template (e.g. if trace 2 starts 2 seconds after trace 1 in the template then the continuous data will be shifted by 2 seconds to align peak correlations prior to summing). Because of this, detections at the start and end of continuous data streams **may be missed**. The maximum time-period that might be missing detections is the maximum offset in the template. To work around this, if you are conducting matched-filter detections through long-duration continuous data, we suggest using some overlap (a few seconds, on the order of the maximum offset in the templates) in the continuous data. You will then need to post-process the detections (which should be done anyway to remove duplicates). See below note for how `overlap` argument affects data internally if `stream` is longer than the processing length. .. Note:: If `stream` is longer than processing length, this routine will ensure that data overlap between loops, which will lead to no missed detections at data start-stop points (see above note). This will result in end-time not being strictly honoured, so detections may occur after the end-time set. This is because data must be run in the correct process-length. .. note:: **Thresholding:** **MAD** threshold is calculated as the: .. math:: threshold {\\times} (median(abs(cccsum))) where :math:`cccsum` is the cross-correlation sum for a given template. **absolute** threshold is a true absolute threshold based on the cccsum value. **av_chan_corr** is based on the mean values of single-channel cross-correlations assuming all data are present as required for the template, e.g: .. math:: av\_chan\_corr\_thresh=threshold \\times (cccsum / len(template)) where :math:`template` is a single template from the input and the length is the number of channels within this template. """ party = Party() template_groups = group_templates(self.templates) # now we can compute the detections for each group for group in template_groups: group_party = _group_detect( templates=group, stream=stream.copy(), threshold=threshold, threshold_type=threshold_type, trig_int=trig_int, plot=plot, group_size=group_size, pre_processed=False, daylong=daylong, parallel_process=parallel_process, xcorr_func=xcorr_func, concurrency=concurrency, cores=cores, ignore_length=ignore_length, overlap=overlap, plotdir=plotdir, full_peaks=full_peaks, process_cores=process_cores, ignore_bad_data=ignore_bad_data, arg_check=False, **kwargs) party += group_party if save_progress: party.write("eqcorrscan_temporary_party") if len(party) > 0: for family in party: if family is not None: family.detections = family._uniq().detections return party
[docs] def client_detect(self, client, starttime, endtime, threshold, threshold_type, trig_int, plot=False, plotdir=None, min_gap=None, daylong=False, parallel_process=True, xcorr_func=None, concurrency=None, cores=None, ignore_length=False, ignore_bad_data=False, group_size=None, return_stream=False, full_peaks=False, save_progress=False, process_cores=None, retries=3, **kwargs): """ Detect using a Tribe of templates within a continuous stream. :type client: `obspy.clients.*.Client` :param client: Any obspy client with a dataselect service. :type starttime: :class:`obspy.core.UTCDateTime` :param starttime: Start-time for detections. :type endtime: :class:`obspy.core.UTCDateTime` :param endtime: End-time for detections :type threshold: float :param threshold: Threshold level, if using `threshold_type='MAD'` then this will be the multiple of the median absolute deviation. :type threshold_type: str :param threshold_type: The type of threshold to be used, can be MAD, absolute or av_chan_corr. See Note on thresholding below. :type trig_int: float :param trig_int: Minimum gap between detections from one template in seconds. If multiple detections occur within trig_int of one-another, the one with the highest cross-correlation sum will be selected. :type plot: bool :param plot: Turn plotting on or off. :type plotdir: str :param plotdir: The path to save plots to. If `plotdir=None` (default) then the figure will be shown on screen. :type min_gap: float :param min_gap: Minimum gap allowed in data - use to remove traces with known issues :type daylong: bool :param daylong: Set to True to use the :func:`eqcorrscan.utils.pre_processing.dayproc` routine, which preforms additional checks and is more efficient for day-long data over other methods. :type parallel_process: bool :param parallel_process: :type xcorr_func: str or callable :param xcorr_func: A str of a registered xcorr function or a callable for implementing a custom xcorr function. For more information see: :func:`eqcorrscan.utils.correlate.register_array_xcorr` :type concurrency: str :param concurrency: The type of concurrency to apply to the xcorr function. Options are 'multithread', 'multiprocess', 'concurrent'. For more details see :func:`eqcorrscan.utils.correlate.get_stream_xcorr` :type cores: int :param cores: Number of workers for processing and detection. :type ignore_length: bool :param ignore_length: If using daylong=True, then dayproc will try check that the data are there for at least 80% of the day, if you don't want this check (which will raise an error if too much data are missing) then set ignore_length=True. This is not recommended! :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 group_size: int :param group_size: Maximum number of templates to run at once, use to reduce memory consumption, if unset will use all templates. :type full_peaks: bool :param full_peaks: See `eqcorrscan.utils.findpeaks.find_peaks2_short` :type save_progress: bool :param save_progress: Whether to save the resulting party at every data step or not. Useful for long-running processes. :type process_cores: int :param process_cores: Number of processes to use for pre-processing (if different to `cores`). :type return_stream: bool :param return_stream: Whether to also output the stream downloaded, useful if you plan to use the stream for something else, e.g. lag_calc. :type retries: int :param retries: Number of attempts allowed for downloading - allows for transient server issues. :return: :class:`eqcorrscan.core.match_filter.Party` of Families of detections. .. Note:: When using the "fftw" correlation backend the length of the fft can be set. See :mod:`eqcorrscan.utils.correlate` for more info. .. Note:: Ensures that data overlap between loops, which will lead to no missed detections at data start-stop points (see note for :meth:`eqcorrscan.core.match_filter.Tribe.detect` method). This will result in end-time not being strictly honoured, so detections may occur after the end-time set. This is because data must be run in the correct process-length. .. warning:: Plotting within the match-filter routine uses the Agg backend with interactive plotting turned off. This is because the function is designed to work in bulk. If you wish to turn interactive plotting on you must import matplotlib in your script first, when you then import match_filter you will get the warning that this call to matplotlib has no effect, which will mean that match_filter has not changed the plotting behaviour. .. note:: **Thresholding:** **MAD** threshold is calculated as the: .. math:: threshold {\\times} (median(abs(cccsum))) where :math:`cccsum` is the cross-correlation sum for a given template. **absolute** threshold is a true absolute threshold based on the cccsum value. **av_chan_corr** is based on the mean values of single-channel cross-correlations assuming all data are present as required for the template, e.g: .. math:: av\_chan\_corr\_thresh=threshold \\times (cccsum / len(template)) where :math:`template` is a single template from the input and the length is the number of channels within this template. """ from obspy.clients.fdsn.client import FDSNException # This uses get_waveforms_bulk to get data - not all client types have # this, so we check and monkey patch here. if not hasattr(client, "get_waveforms_bulk"): assert hasattr(client, "get_waveforms"), ( f"client {client} must have at least a get_waveforms method") Logger.info(f"Client {client} does not have a get_waveforms_bulk " "method, monkey-patching this") client = get_waveform_client(client) party = Party() buff = 300 # Apply a buffer, often data downloaded is not the correct length data_length = max([t.process_length for t in self.templates]) pad = 0 for template in self.templates: max_delay = (template.st.sort(['starttime'])[-1].stats.starttime - template.st.sort(['starttime'])[0].stats.starttime) if max_delay > pad: pad = max_delay download_groups = int(endtime - starttime) / data_length template_channel_ids = [] for template in self.templates: for tr in template.st: if tr.stats.network not in [None, '']: chan_id = (tr.stats.network,) else: chan_id = ('*',) if tr.stats.station not in [None, '']: chan_id += (tr.stats.station,) else: chan_id += ('*',) if tr.stats.location not in [None, '']: chan_id += (tr.stats.location,) else: chan_id += ('*',) if tr.stats.channel not in [None, '']: if len(tr.stats.channel) == 2: chan_id += (tr.stats.channel[0] + '?' + tr.stats.channel[-1],) else: chan_id += (tr.stats.channel,) else: chan_id += ('*',) template_channel_ids.append(chan_id) template_channel_ids = list(set(template_channel_ids)) if return_stream: stream = Stream() if int(download_groups) < download_groups: download_groups = int(download_groups) + 1 else: download_groups = int(download_groups) for i in range(download_groups): bulk_info = [] for chan_id in template_channel_ids: bulk_info.append(( chan_id[0], chan_id[1], chan_id[2], chan_id[3], starttime + (i * data_length) - (pad + buff), starttime + ((i + 1) * data_length) + (pad + buff))) for retry_attempt in range(retries): try: Logger.info("Downloading data") st = client.get_waveforms_bulk(bulk_info) Logger.info( "Downloaded data for {0} traces".format(len(st))) break except FDSNException as e: if "Split the request in smaller" in " ".join(e.args): Logger.warning( "Datacentre does not support large requests: " "splitting request into smaller chunks") st = Stream() for _bulk in bulk_info: try: st += client.get_waveforms_bulk([_bulk]) except Exception as e: Logger.error("No data for {0}".format(_bulk)) Logger.error(e) continue Logger.info("Downloaded data for {0} traces".format( len(st))) break except Exception as e: Logger.error(e) continue else: raise MatchFilterError( "Could not download data after {0} attempts".format( retries)) # Get gaps and remove traces as necessary if min_gap: gaps = st.get_gaps(min_gap=min_gap) if len(gaps) > 0: Logger.warning("Large gaps in downloaded data") st.merge() gappy_channels = list( set([(gap[0], gap[1], gap[2], gap[3]) for gap in gaps])) _st = Stream() for tr in st: tr_stats = (tr.stats.network, tr.stats.station, tr.stats.location, tr.stats.channel) if tr_stats in gappy_channels: Logger.warning( "Removing gappy channel: {0}".format(tr)) else: _st += tr st = _st st.split() st.detrend("simple").merge() st.trim(starttime=starttime + (i * data_length) - pad, endtime=starttime + ((i + 1) * data_length) + pad) for tr in st: if not _check_daylong(tr): st.remove(tr) Logger.warning( "{0} contains more zeros than non-zero, " "removed".format(tr.id)) for tr in st: if tr.stats.endtime - tr.stats.starttime < \ 0.8 * data_length: st.remove(tr) Logger.warning( "{0} is less than 80% of the required length" ", removed".format(tr.id)) if return_stream: stream += st try: party += self.detect( stream=st, threshold=threshold, threshold_type=threshold_type, trig_int=trig_int, plot=plot, plotdir=plotdir, daylong=daylong, parallel_process=parallel_process, xcorr_func=xcorr_func, concurrency=concurrency, cores=cores, ignore_length=ignore_length, ignore_bad_data=ignore_bad_data, group_size=group_size, overlap=None, full_peaks=full_peaks, process_cores=process_cores, **kwargs) if save_progress: party.write("eqcorrscan_temporary_party") except Exception as e: Logger.critical( 'Error, routine incomplete, returning incomplete Party') Logger.error('Error: {0}'.format(e)) if return_stream: return party, stream else: return party for family in party: if family is not None: family.detections = family._uniq().detections if return_stream: return party, stream else: return party
[docs] def construct(self, method, lowcut, highcut, samp_rate, filt_order, length, prepick, swin="all", process_len=86400, all_horiz=False, delayed=True, plot=False, plotdir=None, min_snr=None, parallel=False, num_cores=False, skip_short_chans=False, save_progress=False, **kwargs): """ Generate a Tribe of Templates. :type method: str :param method: Method of Tribe generation. Possible options are: `from_client`, `from_seishub`, `from_meta_file`. See below on the additional required arguments for each method. :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 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 template set 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 save_progress: bool :param save_progress: Whether to save the resulting party at every data step or not. Useful for long-running processes. .. 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_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_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:: Method: `from_sac` is not supported by Tribe.construct and must use Template.construct. .. Note:: Templates will be named according to their start-time. """ templates, catalog, process_lengths = template_gen.template_gen( method=method, lowcut=lowcut, highcut=highcut, length=length, filt_order=filt_order, samp_rate=samp_rate, prepick=prepick, return_event=True, save_progress=save_progress, swin=swin, process_len=process_len, all_horiz=all_horiz, plotdir=plotdir, delayed=delayed, plot=plot, min_snr=min_snr, parallel=parallel, num_cores=num_cores, skip_short_chans=skip_short_chans, **kwargs) for template, event, process_len in zip(templates, catalog, process_lengths): t = Template() for tr in template: if not np.any(tr.data.astype(np.float16)): Logger.warning('Data are zero in float16, missing data,' ' will not use: {0}'.format(tr.id)) template.remove(tr) if len(template) == 0: Logger.error('Empty Template') continue t.st = template t.name = template.sort(['starttime'])[0]. \ stats.starttime.strftime('%Y_%m_%dt%H_%M_%S') t.lowcut = lowcut t.highcut = highcut t.filt_order = filt_order t.samp_rate = samp_rate t.process_length = process_len t.prepick = prepick event.comments.append(Comment( text="eqcorrscan_template_" + t.name, creation_info=CreationInfo(agency='eqcorrscan', author=getpass.getuser()))) t.event = event self.templates.append(t) return self
[docs]def read_tribe(fname): """ Read a Tribe of templates from a tar archive. :param fname: Filename to read from :return: :class:`eqcorrscan.core.match_filter.Tribe` """ tribe = Tribe() tribe.read(filename=fname) return tribe
if __name__ == "__main__": import doctest doctest.testmod() # List files to be removed after doctest cleanup = ['test_tribe.tgz'] for f in cleanup: if os.path.isfile(f): os.remove(f) elif os.path.isdir(f): shutil.rmtree(f)