[refactor] rewrote/simplified getQualitiesfromxml code, used function already implemented in phases.py
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PyLoT.py
6
PyLoT.py
@ -89,7 +89,7 @@ from pylot.core.util.structure import DATASTRUCTURE
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from pylot.core.util.thread import Thread, Worker
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from pylot.core.util.version import get_git_version as _getVersionString
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from pylot.core.io.getEventListFromXML import geteventlistfromxml
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from pylot.core.io.getQualitiesfromxml import getQualitiesfromxml
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from pylot.core.io.phases import getQualitiesfromxml
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from pylot.styles import style_settings
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@ -1669,8 +1669,8 @@ class MainWindow(QMainWindow):
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self.cmpw.show()
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def pickQualities(self):
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path = self._inputs['rootpath'] + '/' + self._inputs['datapath'] + '/' + self._inputs['database']
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getQualitiesfromxml(path)
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path = self.get_current_event_path()
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getQualitiesfromxml(path, self._inputs.get('timeerrorsP'), self._inputs.get('timeerrorsS'), plotflag=1)
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return
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def eventlistXml(self):
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@ -1,138 +0,0 @@
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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Script to get onset uncertainties from Quakeml.xml files created by PyLoT.
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Uncertainties are tranformed into quality classes and visualized via histogram if desired.
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Ludger Küperkoch, BESTEC GmbH, 07/2017
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rev.: Ludger Küperkoch, igem, 10/2020
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Edited for usage in PyLoT: Jeldrik Gaal, igem, 01/2022
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"""
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import glob
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import matplotlib.pyplot as plt
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import numpy as np
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from obspy.core.event import read_events
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def getQualitiesfromxml(path):
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# uncertainties
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ErrorsP = [0.02, 0.04, 0.08, 0.16]
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ErrorsS = [0.04, 0.08, 0.16, 0.32]
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Pw0 = []
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Pw1 = []
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Pw2 = []
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Pw3 = []
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Pw4 = []
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Sw0 = []
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Sw1 = []
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Sw2 = []
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Sw3 = []
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Sw4 = []
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# data path
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dp = path + '/e*/*.xml'
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# list of all available xml-files
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xmlnames = glob.glob(dp)
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# read all onset weights
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for names in xmlnames:
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print("Getting onset weights from {}".format(names))
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cat = read_events(names)
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arrivals = cat.events[0].picks
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for Pick in arrivals:
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if Pick.phase_hint[0] == 'P':
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if Pick.time_errors.uncertainty <= ErrorsP[0]:
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Pw0.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsP[0] and \
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Pick.time_errors.uncertainty <= ErrorsP[1]:
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Pw1.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsP[1] and \
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Pick.time_errors.uncertainty <= ErrorsP[2]:
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Pw2.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsP[2] and \
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Pick.time_errors.uncertainty <= ErrorsP[3]:
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Pw3.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsP[3]:
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Pw4.append(Pick.time_errors.uncertainty)
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else:
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pass
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elif Pick.phase_hint[0] == 'S':
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if Pick.time_errors.uncertainty <= ErrorsS[0]:
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Sw0.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsS[0] and \
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Pick.time_errors.uncertainty <= ErrorsS[1]:
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Sw1.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsS[1] and \
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Pick.time_errors.uncertainty <= ErrorsS[2]:
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Sw2.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsS[2] and \
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Pick.time_errors.uncertainty <= ErrorsS[3]:
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Sw3.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsS[3]:
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Sw4.append(Pick.time_errors.uncertainty)
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else:
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pass
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else:
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print("Phase hint not defined for picking!")
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pass
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# get percentage of weights
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numPweights = np.sum([len(Pw0), len(Pw1), len(Pw2), len(Pw3), len(Pw4)])
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numSweights = np.sum([len(Sw0), len(Sw1), len(Sw2), len(Sw3), len(Sw4)])
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try:
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P0perc = 100.0 / numPweights * len(Pw0)
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except:
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P0perc = 0
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try:
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P1perc = 100.0 / numPweights * len(Pw1)
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except:
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P1perc = 0
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try:
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P2perc = 100.0 / numPweights * len(Pw2)
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except:
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P2perc = 0
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try:
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P3perc = 100.0 / numPweights * len(Pw3)
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except:
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P3perc = 0
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try:
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P4perc = 100.0 / numPweights * len(Pw4)
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except:
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P4perc = 0
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try:
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S0perc = 100.0 / numSweights * len(Sw0)
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except:
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Soperc = 0
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try:
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S1perc = 100.0 / numSweights * len(Sw1)
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except:
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S1perc = 0
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try:
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S2perc = 100.0 / numSweights * len(Sw2)
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except:
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S2perc = 0
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try:
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S3perc = 100.0 / numSweights * len(Sw3)
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except:
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S3perc = 0
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try:
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S4perc = 100.0 / numSweights * len(Sw4)
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except:
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S4perc = 0
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weights = ('0', '1', '2', '3', '4')
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y_pos = np.arange(len(weights))
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width = 0.34
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p1, = plt.bar(0 - width, P0perc, width, color='black')
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p2, = plt.bar(0, S0perc, width, color='red')
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plt.bar(y_pos - width, [P0perc, P1perc, P2perc, P3perc, P4perc], width, color='black')
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plt.bar(y_pos, [S0perc, S1perc, S2perc, S3perc, S4perc], width, color='red')
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plt.ylabel('%')
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plt.xticks(y_pos, weights)
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plt.xlim([-0.5, 4.5])
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plt.xlabel('Qualities')
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plt.title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights))
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plt.legend([p1, p2], ['P-Weights', 'S-Weights'])
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plt.show()
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@ -17,7 +17,7 @@ from pylot.core.io.location import create_event, \
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create_magnitude
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from pylot.core.pick.utils import select_for_phase, get_quality_class
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from pylot.core.util.utils import getOwner, full_range, four_digits, transformFilterString4Export, \
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backtransformFilterString
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backtransformFilterString, loopIdentifyPhase, identifyPhase
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def add_amplitudes(event, amplitudes):
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@ -375,7 +375,6 @@ def picks_from_picksdict(picks, creation_info=None):
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def reassess_pilot_db(root_dir, db_dir, out_dir=None, fn_param=None, verbosity=0):
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import glob
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# TODO: change root to datapath
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db_root = os.path.join(root_dir, db_dir)
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evt_list = glob.glob1(db_root, 'e????.???.??')
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@ -1056,37 +1055,60 @@ def merge_picks(event, picks):
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return event
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def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
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def getQualitiesfromxml(path, errorsP, errorsS, plotflag=1, figure=None, verbosity=0):
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"""
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Script to get onset uncertainties from Quakeml.xml files created by PyLoT.
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Uncertainties are tranformed into quality classes and visualized via histogram if desired.
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Ludger Küperkoch, BESTEC GmbH, 07/2017
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:param xmlnames: list of xml obspy event files containing picks
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:type xmlnames: list
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:param ErrorsP: time errors of P waves for the four discrete quality classes
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:type ErrorsP:
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:param ErrorsS: time errors of S waves for the four discrete quality classes
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:type ErrorsS:
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:param path: path containing xml files
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:type path: str
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:param errorsP: time errors of P waves for the four discrete quality classes
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:type errorsP:
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:param errorsS: time errors of S waves for the four discrete quality classes
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:type errorsS:
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:param plotflag:
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:type plotflag:
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:return:
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:rtype:
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"""
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from pylot.core.pick.utils import get_quality_class
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from pylot.core.util.utils import loopIdentifyPhase, identifyPhase
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def calc_perc(uncertainties, ntotal):
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if len(uncertainties) == 0:
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return 0
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else:
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return 100 / ntotal * len(uncertainties)
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def calc_weight_perc(psweights, weight_ids):
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# count total number of list items for this phase
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numWeights = np.sum([len(weight) for weight in psweights.values()])
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# iterate over all available weights to return a list with percentages for plotting
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plot_list = []
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for weight_id in weight_ids:
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plot_list.append(calc_perc(psweights[weight_id], numWeights))
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return plot_list, numWeights
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xmlnames = glob.glob(os.path.join(path, '*.xml'))
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if len(xmlnames) == 0:
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print(f'No files found in path {path}.')
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return False
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# first define possible phases here
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phases = ['P', 'S']
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# define possible weights (0-4)
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weight_ids = list(range(5))
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# put both error lists in a dictionary with P/S key so that amount of code can be halfed by simply using P/S as key
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errors = dict(P=errorsP, S=errorsS)
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# create dictionaries for each phase (P/S) with a dictionary of empty list for each weight defined in weights
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# tuple above
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weights = {}
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for phase in phases:
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weights[phase] = {weight_id: [] for weight_id in weight_ids}
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# read all onset weights
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Pw0 = []
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Pw1 = []
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Pw2 = []
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Pw3 = []
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Pw4 = []
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Sw0 = []
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Sw1 = []
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Sw2 = []
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Sw3 = []
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Sw4 = []
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for names in xmlnames:
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print("Getting onset weights from {}".format(names))
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cat = read_events(names)
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@ -1094,119 +1116,60 @@ def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
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arrivals = cat.events[0].picks
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arrivals_copy = cat_copy.events[0].picks
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# Prefere manual picks if qualities are sufficient!
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for Pick in arrivals:
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if Pick.method_id.id.split('/')[1] == 'manual':
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mstation = Pick.waveform_id.station_code
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for pick in arrivals:
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if pick.method_id.id.split('/')[1] == 'manual':
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mstation = pick.waveform_id.station_code
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mstation_ext = mstation + '_'
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for mpick in arrivals_copy:
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phase = identifyPhase(loopIdentifyPhase(Pick.phase_hint))
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if phase == 'P':
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phase = identifyPhase(loopIdentifyPhase(pick.phase_hint)) # MP MP catch if this fails?
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if ((mpick.waveform_id.station_code == mstation) or
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(mpick.waveform_id.station_code == mstation_ext)) and \
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(mpick.method_id.id.split('/')[1] == 'auto') and \
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(mpick.time_errors['uncertainty'] <= ErrorsP[3]):
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del mpick
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break
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elif phase == 'S':
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if ((mpick.waveform_id.station_code == mstation) or
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(mpick.waveform_id.station_code == mstation_ext)) and \
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(mpick.method_id.id.split('/')[1] == 'auto') and \
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(mpick.time_errors['uncertainty'] <= ErrorsS[3]):
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(mpick.time_errors['uncertainty'] <= errors[phase][3]):
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del mpick
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break
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lendiff = len(arrivals) - len(arrivals_copy)
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if lendiff != 0:
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print("Found manual as well as automatic picks, prefered the {} manual ones!".format(lendiff))
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for Pick in arrivals_copy:
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phase = identifyPhase(loopIdentifyPhase(Pick.phase_hint))
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if phase == 'P':
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Pqual = get_quality_class(Pick.time_errors.uncertainty, ErrorsP)
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if Pqual == 0:
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Pw0.append(Pick.time_errors.uncertainty)
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elif Pqual == 1:
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Pw1.append(Pick.time_errors.uncertainty)
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elif Pqual == 2:
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Pw2.append(Pick.time_errors.uncertainty)
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elif Pqual == 3:
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Pw3.append(Pick.time_errors.uncertainty)
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elif Pqual == 4:
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Pw4.append(Pick.time_errors.uncertainty)
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elif phase == 'S':
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Squal = get_quality_class(Pick.time_errors.uncertainty, ErrorsS)
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if Squal == 0:
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Sw0.append(Pick.time_errors.uncertainty)
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elif Squal == 1:
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Sw1.append(Pick.time_errors.uncertainty)
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elif Squal == 2:
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Sw2.append(Pick.time_errors.uncertainty)
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elif Squal == 3:
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Sw3.append(Pick.time_errors.uncertainty)
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elif Squal == 4:
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Sw4.append(Pick.time_errors.uncertainty)
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else:
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for pick in arrivals_copy:
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phase = identifyPhase(loopIdentifyPhase(pick.phase_hint))
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uncertainty = pick.time_errors.uncertainty
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if not uncertainty:
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if verbosity > 0:
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print('No uncertainty, pick {} invalid!'.format(pick.method_id.id))
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continue
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# check P/S phase
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if phase not in phases:
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print("Phase hint not defined for picking!")
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pass
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continue
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qual = get_quality_class(uncertainty, errors[phase])
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weights[phase][qual].append(uncertainty)
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if plotflag == 0:
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Punc = [Pw0, Pw1, Pw2, Pw3, Pw4]
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Sunc = [Sw0, Sw1, Sw2, Sw3, Sw4]
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return Punc, Sunc
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p_unc = [weights['P'][weight_id] for weight_id in weight_ids]
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s_unc = [weights['S'][weight_id] for weight_id in weight_ids]
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return p_unc, s_unc
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else:
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if not figure:
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fig = plt.figure()
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ax = fig.add_subplot(111)
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# get percentage of weights
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numPweights = np.sum([len(Pw0), len(Pw1), len(Pw2), len(Pw3), len(Pw4)])
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numSweights = np.sum([len(Sw0), len(Sw1), len(Sw2), len(Sw3), len(Sw4)])
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if len(Pw0) > 0:
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P0perc = 100 / numPweights * len(Pw0)
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else:
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P0perc = 0
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if len(Pw1) > 0:
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P1perc = 100 / numPweights * len(Pw1)
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else:
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P1perc = 0
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if len(Pw2) > 0:
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P2perc = 100 / numPweights * len(Pw2)
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else:
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P2perc = 0
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if len(Pw3) > 0:
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P3perc = 100 / numPweights * len(Pw3)
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else:
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P3perc = 0
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if len(Pw4) > 0:
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P4perc = 100 / numPweights * len(Pw4)
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else:
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P4perc = 0
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if len(Sw0) > 0:
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S0perc = 100 / numSweights * len(Sw0)
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else:
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S0perc = 0
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if len(Sw1) > 0:
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S1perc = 100 / numSweights * len(Sw1)
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else:
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S1perc = 0
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if len(Sw2) > 0:
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S2perc = 100 / numSweights * len(Sw2)
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else:
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S2perc = 0
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if len(Sw3) > 0:
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S3perc = 100 / numSweights * len(Sw3)
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else:
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S3perc = 0
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if len(Sw4) > 0:
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S4perc = 100 / numSweights * len(Sw4)
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else:
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S4perc = 0
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listP, numPweights = calc_weight_perc(weights['P'], weight_ids)
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listS, numSweights = calc_weight_perc(weights['S'], weight_ids)
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weights = ('0', '1', '2', '3', '4')
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y_pos = np.arange(len(weights))
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y_pos = np.arange(len(weight_ids))
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width = 0.34
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plt.bar(y_pos - width, [P0perc, P1perc, P2perc, P3perc, P4perc], width, color='black')
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plt.bar(y_pos, [S0perc, S1perc, S2perc, S3perc, S4perc], width, color='red')
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plt.ylabel('%')
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plt.xticks(y_pos, weights)
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plt.xlim([-0.5, 4.5])
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plt.xlabel('Qualities')
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plt.title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights))
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plt.show()
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ax.bar(y_pos - width, listP, width, color='black')
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ax.bar(y_pos, listS, width, color='red')
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ax.set_ylabel('%')
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ax.set_xticks(y_pos, weight_ids)
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ax.set_xlim([-0.5, 4.5])
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ax.set_xlabel('Qualities')
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ax.set_title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights))
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return [P0perc, P1perc, P2perc, P3perc, P4perc], [S0perc, S1perc, S2perc, S3perc, S4perc]
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if not figure:
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fig.show()
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return listP, listS
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|
||||
@ -1320,7 +1320,7 @@ def get_quality_class(uncertainty, weight_classes):
|
||||
:return: quality of pick (0-4)
|
||||
:rtype: int
|
||||
"""
|
||||
if not uncertainty: return max(weight_classes)
|
||||
if not uncertainty: return len(weight_classes)
|
||||
try:
|
||||
# create generator expression containing all indices of values in weight classes that are >= than uncertainty.
|
||||
# call next on it once to receive first value
|
||||
|
||||
@ -5,7 +5,7 @@ from pylot.core.io.phases import getQualitiesfromxml
|
||||
|
||||
class TestQualityFromXML(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.xmlpaths = ['PyLoT_e0019.048.13.xml']
|
||||
self.path = '.'
|
||||
self.ErrorsP = [0.02, 0.04, 0.08, 0.16]
|
||||
self.ErrorsS = [0.04, 0.08, 0.16, 0.32]
|
||||
self.test0_result = [[0.0136956521739, 0.0126, 0.0101612903226, 0.00734848484849, 0.0135069444444,
|
||||
@ -23,10 +23,10 @@ class TestQualityFromXML(unittest.TestCase):
|
||||
[92.0, 4.0, 4.0, 0, 0]
|
||||
|
||||
def test_result_plotflag0(self):
|
||||
self.assertEqual(getQualitiesfromxml(self.xmlpaths, self.ErrorsP, self.ErrorsS, 0), self.test0_result)
|
||||
self.assertEqual(getQualitiesfromxml(self.path, self.ErrorsP, self.ErrorsS, 0), self.test0_result)
|
||||
|
||||
def test_result_plotflag1(self):
|
||||
self.assertEqual(getQualitiesfromxml(self.xmlpaths, self.ErrorsP, self.ErrorsS, 1), self.test1_result)
|
||||
self.assertEqual(getQualitiesfromxml(self.path, self.ErrorsP, self.ErrorsS, 1), self.test1_result)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
Loading…
Reference in New Issue
Block a user