pylot/pylot/core/io/getQualitiesfromxml.py

139 lines
4.8 KiB
Python

#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Script to get onset uncertainties from Quakeml.xml files created by PyLoT.
Uncertainties are tranformed into quality classes and visualized via histogram if desired.
Ludger Küperkoch, BESTEC GmbH, 07/2017
rev.: Ludger Küperkoch, igem, 10/2020
Edited for usage in PyLoT: Jeldrik Gaal, igem, 01/2022
"""
import glob
import matplotlib.pyplot as plt
import numpy as np
from obspy.core.event import read_events
def getQualitiesfromxml(path):
# uncertainties
ErrorsP = [0.02, 0.04, 0.08, 0.16]
ErrorsS = [0.04, 0.08, 0.16, 0.32]
Pw0 = []
Pw1 = []
Pw2 = []
Pw3 = []
Pw4 = []
Sw0 = []
Sw1 = []
Sw2 = []
Sw3 = []
Sw4 = []
# data path
dp = path + '/e*/*.xml'
# list of all available xml-files
xmlnames = glob.glob(dp)
# read all onset weights
for names in xmlnames:
print("Getting onset weights from {}".format(names))
cat = read_events(names)
arrivals = cat.events[0].picks
for Pick in arrivals:
if Pick.phase_hint[0] == 'P':
if Pick.time_errors.uncertainty <= ErrorsP[0]:
Pw0.append(Pick.time_errors.uncertainty)
elif Pick.time_errors.uncertainty > ErrorsP[0] and \
Pick.time_errors.uncertainty <= ErrorsP[1]:
Pw1.append(Pick.time_errors.uncertainty)
elif Pick.time_errors.uncertainty > ErrorsP[1] and \
Pick.time_errors.uncertainty <= ErrorsP[2]:
Pw2.append(Pick.time_errors.uncertainty)
elif Pick.time_errors.uncertainty > ErrorsP[2] and \
Pick.time_errors.uncertainty <= ErrorsP[3]:
Pw3.append(Pick.time_errors.uncertainty)
elif Pick.time_errors.uncertainty > ErrorsP[3]:
Pw4.append(Pick.time_errors.uncertainty)
else:
pass
elif Pick.phase_hint[0] == 'S':
if Pick.time_errors.uncertainty <= ErrorsS[0]:
Sw0.append(Pick.time_errors.uncertainty)
elif Pick.time_errors.uncertainty > ErrorsS[0] and \
Pick.time_errors.uncertainty <= ErrorsS[1]:
Sw1.append(Pick.time_errors.uncertainty)
elif Pick.time_errors.uncertainty > ErrorsS[1] and \
Pick.time_errors.uncertainty <= ErrorsS[2]:
Sw2.append(Pick.time_errors.uncertainty)
elif Pick.time_errors.uncertainty > ErrorsS[2] and \
Pick.time_errors.uncertainty <= ErrorsS[3]:
Sw3.append(Pick.time_errors.uncertainty)
elif Pick.time_errors.uncertainty > ErrorsS[3]:
Sw4.append(Pick.time_errors.uncertainty)
else:
pass
else:
print("Phase hint not defined for picking!")
pass
# get percentage of weights
numPweights = np.sum([len(Pw0), len(Pw1), len(Pw2), len(Pw3), len(Pw4)])
numSweights = np.sum([len(Sw0), len(Sw1), len(Sw2), len(Sw3), len(Sw4)])
try:
P0perc = 100.0 / numPweights * len(Pw0)
except:
P0perc = 0
try:
P1perc = 100.0 / numPweights * len(Pw1)
except:
P1perc = 0
try:
P2perc = 100.0 / numPweights * len(Pw2)
except:
P2perc = 0
try:
P3perc = 100.0 / numPweights * len(Pw3)
except:
P3perc = 0
try:
P4perc = 100.0 / numPweights * len(Pw4)
except:
P4perc = 0
try:
S0perc = 100.0 / numSweights * len(Sw0)
except:
Soperc = 0
try:
S1perc = 100.0 / numSweights * len(Sw1)
except:
S1perc = 0
try:
S2perc = 100.0 / numSweights * len(Sw2)
except:
S2perc = 0
try:
S3perc = 100.0 / numSweights * len(Sw3)
except:
S3perc = 0
try:
S4perc = 100.0 / numSweights * len(Sw4)
except:
S4perc = 0
weights = ('0', '1', '2', '3', '4')
y_pos = np.arange(len(weights))
width = 0.34
p1, = plt.bar(0 - width, P0perc, width, color='black')
p2, = plt.bar(0, S0perc, width, color='red')
plt.bar(y_pos - width, [P0perc, P1perc, P2perc, P3perc, P4perc], width, color='black')
plt.bar(y_pos, [S0perc, S1perc, S2perc, S3perc, S4perc], width, color='red')
plt.ylabel('%')
plt.xticks(y_pos, weights)
plt.xlim([-0.5, 4.5])
plt.xlabel('Qualities')
plt.title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights))
plt.legend([p1, p2], ['P-Weights', 'S-Weights'])
plt.show()