marcel/pylot
3
0
Fork 0
Code Issues 17 Pull Requests 1 Releases Wiki Activity

Merge branch 'develop'

Browse Source
This commit is contained in:
Sebastian Wehling-Benatelli 2015-01-27 05:01:45 +01:00
commit 1accc92421
3 changed files with 24 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
pylot/core/pick/CharFuns.py
View File

@ -155,7 +155,7 @@ class CharacteristicFunction(object):
stop = self.cut[1] / self.dt
zz = self.orig_data.copy()
z1 = zz[0].copy()
zz[0].data = z1.data[start:stop]
zz[0].data = z1.data[int(start):int(stop)]
data = zz
return data
elif len(self.orig_data) == 2:
@ -171,8 +171,8 @@ class CharacteristicFunction(object):
hh = self.orig_data.copy()
h1 = hh[0].copy()
h2 = hh[1].copy()
hh[0].data = h1.data[start:stop]
hh[1].data = h2.data[start:stop]
hh[0].data = h1.data[int(start):int(stop)]
hh[1].data = h2.data[int(start):int(stop)]
data = hh
return data
elif len(self.orig_data) == 3:
@ -189,9 +189,9 @@ class CharacteristicFunction(object):
h1 = hh[0].copy()
h2 = hh[1].copy()
h3 = hh[2].copy()
hh[0].data = h1.data[start:stop]
hh[1].data = h2.data[start:stop]
hh[2].data = h3.data[start:stop]
hh[0].data = h1.data[int(start):int(stop)]
hh[1].data = h2.data[int(start):int(stop)]
hh[2].data = h3.data[int(start):int(stop)]
data = hh
return data
else:
@ -263,10 +263,9 @@ class HOScf(CharacteristicFunction):
#Initialisation
#t2: long term moving window
ilta = round(self.getTime2() / self.getIncrement())
ilta = int(round(self.getTime2() / self.getIncrement()))
lta = y[0]
lta1 = y1[0]
#moving windows
LTA = np.zeros(len(xnp))
for j in range(0, len(xnp)):

3
pylot/core/pick/Picker.py
View File

@ -17,7 +17,6 @@ autoregressive prediction: application ot local and regional distances, Geophys.
import numpy as np
import matplotlib.pyplot as plt
from pylot.core.pick.CharFuns import CharacteristicFunction
import pdb
class AutoPicking(object):
'''
@ -173,7 +172,7 @@ class PragPicker(AutoPicking):
self.Pick = -1
#smooth CF
ismooth = round(self.Tsmooth / self.dt);
ismooth = int(round(self.Tsmooth / self.dt))
cfsmooth = np.zeros(len(self.cf))
if len(self.cf) < ismooth:
print 'PragPicker: Tsmooth larger than CF!'

33
pylot/core/pick/run_makeCF.py
View File

@ -13,13 +13,12 @@ from CharFuns import *
from Picker import *
import glob
import argparse
import pdb
def run_makeCF(project, database, event, iplot, station=None):
#parameters for CF calculation
t2 = 7 #length of moving window for HOS calculation [sec]
p = 4 #order of statistics
cuttimes = [5, 40] #start and end time for CF calculation
cuttimes = [10, 40] #start and end time for CF calculation
bpz = [2, 30] #corner frequencies of bandpass filter, vertical component
bph = [2, 15] #corner frequencies of bandpass filter, horizontal components
tdetz= 1.2 #length of AR-determination window [sec], vertical component
@ -151,11 +150,11 @@ def run_makeCF(project, database, event, iplot, station=None):
plt.figure()
tr = st[0]
tdata = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)
p1 = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p2 = plt.plot(hoscf.getTimeArray(), hoscf.getCF() / max(hoscf.getCF()), 'r')
p3 = plt.plot(aiccf.getTimeArray(), aiccf.getCF()/max(aiccf.getCF()), 'b')
p4 = plt.plot(arzcf.getTimeArray(), arzcf.getCF()/max(arzcf.getCF()), 'g')
p5 = plt.plot(araiccf.getTimeArray(), araiccf.getCF()/max(araiccf.getCF()), 'y')
p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p2, = plt.plot(hoscf.getTimeArray(), hoscf.getCF() / max(hoscf.getCF()), 'r')
p3, = plt.plot(aiccf.getTimeArray(), aiccf.getCF()/max(aiccf.getCF()), 'b')
p4, = plt.plot(arzcf.getTimeArray(), arzcf.getCF()/max(arzcf.getCF()), 'g')
p5, = plt.plot(araiccf.getTimeArray(), araiccf.getCF()/max(araiccf.getCF()), 'y')
plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'b--')
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'b')
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'b')
@ -176,14 +175,14 @@ def run_makeCF(project, database, event, iplot, station=None):
plt.legend([p1, p2, p3, p4, p5], ['Data', 'HOS-CF', 'HOSAIC-CF', 'ARZ-CF', 'ARZAIC-CF'])
#plot horizontal traces
plt.figure(2)
plt.subplot(211)
plt.subplot(2,1,1)
tsteph = tpredh / 4
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
tarhcf = np.arange(0, len(arhcf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdeth +tpredh
p21 = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
p22 = plt.plot(arhcf.getTimeArray(), arhcf.getCF()/max(arhcf.getCF()), 'r')
p23 = plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF()/max(arhaiccf.getCF()))
p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
p22, = plt.plot(arhcf.getTimeArray(), arhcf.getCF()/max(arhcf.getCF()), 'r')
p23, = plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF()/max(arhaiccf.getCF()))
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b--')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [1, 1], 'b')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [-1, -1], 'b')
@ -192,7 +191,7 @@ def run_makeCF(project, database, event, iplot, station=None):
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
plt.suptitle(trH1_filt.stats.starttime)
plt.legend([p21, p22, p23], ['Data', 'ARH-CF', 'ARHAIC-CF'])
plt.subplot(212)
plt.subplot(2,1,2)
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF()/max(arhaiccf.getCF()))
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b--')
@ -205,22 +204,22 @@ def run_makeCF(project, database, event, iplot, station=None):
#plot 3-component window
plt.figure(3)
tar3ccf = np.arange(0, len(ar3ccf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdetz +tpredz
plt.subplot(311)
p31 = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p32 = plt.plot(tar3ccf, ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.subplot(3,1,1)
p31, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p32, = plt.plot(tar3ccf, ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.yticks([])
plt.xticks([])
plt.ylabel('Normalized Counts')
plt.title([tr.stats.station, tr.stats.channel])
plt.legend([p31, p32], ['Data', 'AR3C-CF'])
plt.subplot(312)
plt.subplot(3,1,2)
plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
plt.plot(tar3ccf, ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.yticks([])
plt.xticks([])
plt.ylabel('Normalized Counts')
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
plt.subplot(313)
plt.subplot(3,1,3)
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
plt.plot(tar3ccf, ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.yticks([])