def ffpscatter(passed_staev, all_events=False):
"""
Plot dT and observed A for all the stations in one event
"""
if not all_events:
for i in range(len(passed_staev)):
for j in range(len(passed_staev[i])):
plt.plot(passed_staev[i][j][5], passed_staev[i][j][2], 'ko')
plt.show()
else:
ls_all_passed_band = []
ls_all_passed_dt = []
for k in range(len(passed_staev)):
for i in range(len(passed_staev[k])):
for j in range(len(passed_staev[k][i])):
plt.plot(passed_staev[k][i][j][5], passed_staev[k][i][j][2], 'ko')
ls_all_passed_band.append(passed_staev[k][i][j][5])
ls_all_passed_dt.append(passed_staev[k][i][j][2])
plt.ion()
import py2mat_mod
py2mat_mod.py2mat(ls_all_passed_band, 'dispersion_all_passed_band', 'dispersion_all_passed_band')
py2mat_mod.py2mat(ls_all_passed_dt, 'dispersion_all_passed_dt', 'dispersion_all_passed_dt')
plt.show()
# [0] in all_passed_staev[j][0] shows the current band
# in general we have a loop over bands and in each step there is just
# one band that we are working with which is accessible by [0]
for k in range(len(all_passed_staev[j][0])):
if not all_passed_staev[j][0][k] == []:
if not nr_cc:
t_shift_array.append(all_passed_staev[j][0][k][2])
else:
# keep the name as t_shift_array to not change the whole script!
# However, if nr_cc is selected, it will be number of stations vs
# cross correlation coefficient
t_shift_array.append(all_passed_staev[j][0][k][4])
print 'Length of all passed data: %s' % len(t_shift_array)
import py2mat_mod
py2mat_mod.py2mat(t_shift_array, 't_shift_array_%s' % bands[i], 't_shift_array_%s' % bands[i])
nr_dt(t_shift_array, num_bands=len(bands), enum=i, leg=str(band_period[str(bands[i])]) + 's', line_plot=line_plot)
if nr_cc:
#Pdiff
#plt.vlines(x=0.8, ymin=0.0, ymax=80000, lw=2, linestyle='--')
#plt.xlim(-1.1, 1.1)
#plt.ylim(ymax=80000)
plt.vlines(x=0.8, ymin=0.0, ymax=215000, lw=2, linestyle='--')
plt.xlim(-1.1, 1.1)
plt.ylim(ymax=10000)
plt.xlabel('xcorrelation factor', fontsize = 'xx-large', weight = 'bold')
plt.ylabel('nr of data', fontsize = 'xx-large', weight = 'bold')
plt.xticks(np.arange(-1.0, 1.1, 0.2), fontsize = 'xx-large', weight = 'bold')
plt.yticks(fontsize = 'xx-large', weight = 'bold')
# ------------------ IMPORT ---------------------------------
import numpy as np
from py2mat_mod import py2mat
# ------------------- INPUT ---------------------------------
ell_corr_file ='ell_ccor.dataset1_wri_example'
# -----------------------------------------------------------
data = np.loadtxt(ell_corr_file, delimiter=',')
lat = []
lon = []
rtarg = []
ecorr = []
tau = []
telev = []
for i in range(len(data)):
lat.append(data[i][0])
lon.append(data[i][1])
rtarg.append(data[i][2])
ecorr.append(data[i][3])
tau.append(data[i][4])
telev.append(data[i][5])
py2mat(lat, 'lat', 'lat')
py2mat(lon, 'lon', 'lon')
py2mat(rtarg, 'rtarg', 'rtarg')
py2mat(ecorr, 'ecorr', 'ecorr')
py2mat(tau, 'tau', 'tau')
py2mat(telev, 'telev', 'telev')
counter[str(-int(events[i]['depth']))] += 1
counting += 1
print 'Number of all used events: %s' % counting
for i in counter.keys():
#if 100 <= counter[i]:
#plt.bar(int(i)-0.5, counter[i], 1, log=True)
plt.bar(int(i)-0.5, counter[i], 1)
counter_list = []
for i in counter.keys():
counter_list.append([int(i), counter[i]])
counter_list.sort()
import py2mat_mod
py2mat_mod.py2mat(counter_list, 'counter_list', 'counter_list')
plt.ion()
plt.xlim(xmin=-2, xmax=100)
plt.ylim(ymax=1100)
plt.xlabel('Depth (km)', size=36, weight='bold')
plt.ylabel('Number of events', size=36, weight='bold')
plt.xticks(size=32, weight='bold')
plt.yticks(size=32, weight='bold')
plt.title('Depth=10km (1002 events) Depth=33km (254 events)\n', size=36, weight='bold')
plt.show()
# --------------------- TRASH --------------------------
#for i in range(len(events)):
counting += 1
print 'Number of all used events: %s' % counting
for i in counter.keys():
#if 100 <= counter[i]:
#plt.bar(int(i)-0.5, counter[i], 1, log=True)
plt.bar(int(i) - 0.5, counter[i], 1)
counter_list = []
for i in counter.keys():
counter_list.append([int(i), counter[i]])
counter_list.sort()
import py2mat_mod
py2mat_mod.py2mat(counter_list, 'counter_list', 'counter_list')
plt.ion()
plt.xlim(xmin=-2, xmax=100)
plt.ylim(ymax=1100)
plt.xlabel('Depth (km)', size=36, weight='bold')
plt.ylabel('Number of events', size=36, weight='bold')
plt.xticks(size=32, weight='bold')
plt.yticks(size=32, weight='bold')
plt.title('Depth=10km (1002 events) Depth=33km (254 events)\n',
size=36,
weight='bold')
plt.show()
# --------------------- TRASH --------------------------
#for i in range(len(events)):
all_passed_staev.append(passed_staev)
t_shift_array = []
for j in range(len(all_passed_staev)):
# [0] in all_passed_staev[j][0] shows the current band
# in general we have a loop over bands and in each step there is just
# one band that we are working with which is accessible by [0]
# [6]: epicentral distance
for k in range(len(all_passed_staev[j][0])):
if not all_passed_staev[j][0][k] == []:
t_shift_array.append(all_passed_staev[j][0][k][6])
print 'Length of all passed data: %s' % len(t_shift_array)
import py2mat_mod
py2mat_mod.py2mat(t_shift_array, 't_shift_array_%s' % bands[i], 'xcorr_epi_t_shift_array_%s' % bands[i])
nr_dt(t_shift_array, num_bands=len(bands), enum=i, leg=str(band_period[str(bands[i])]) + 's')
#plt.xlim(97.0, 160.0)
plt.xlim(30.0, 90.0)
plt.xlabel('Epicentral Distance / degree', fontsize = 'xx-large', weight = 'bold')
#plt.ylabel('nr of data', fontsize = 'xx-large', weight = 'bold')
plt.ylabel('% of data (xcorr>=0.8)', fontsize = 'xx-large', weight = 'bold')
plt.xticks(fontsize = 'xx-large', weight = 'bold')
plt.yticks(fontsize = 'xx-large', weight = 'bold')
#plt.title(fontsize = 'xx-large', weight = 'bold')
#plt.legend(prop={'size':22})
#plt.legend(prop={'size':22}, loc=8)
plt.show()
nw_all = np.append(nw_all, len(passed_staev_epi))
for j in range(len(mag_all)):
for k in mag_dic:
if abs(uf.round_to(mag_all[j], 0.5) - float(k)) < 0.1:
mag_dic[k][0] += nw_all[j]
mag_dic[k][1] += 1
break
mag_sta_list = []
for md in mag_dic:
mag_sta_list.append([float(md), mag_dic[md][0], mag_dic[md][1]])
mag_sta_list.sort()
import py2mat_mod
py2mat_mod.py2mat(mag_sta_list, 'mag_sta_%s' % bands[i], 'mag_sta_%s' % bands[i])
plt.ion()
plt.figure()
plt.subplot(2, 1, 2)
for j in mag_dic:
if mag_dic[j][1] > 0.1:
plt.bar(left=float(j)-0.05, width=0.1, height=mag_dic[j][0]/mag_dic[j][1])
#plt.bar(left=float(j)-0.05, width=0.1, height=mag_dic[j][0])
plt.xlabel('Magnitude', fontsize='xx-large', weight='bold')
plt.ylabel('#waveforms/#events', fontsize='xx-large', weight='bold')
plt.xticks(fontsize='xx-large', weight='bold')
plt.yticks(fontsize='xx-large', weight='bold')
plt.legend()
plt.show()
def meanall_ffplot(per, all_dt_mean, all_a_mean, all_tt_single):
"""
Plot mean dT and observed A for all the stations in all events
"""
meanall_dt = []
meanall_a = []
for j in range(len(all_dt_mean[0][0])):
weight_tt = 0
weight_aa = 0
tt = 0
aa = 0
for i in range(len(all_dt_mean)):
tt += all_dt_mean[i][0][j] * all_dt_mean[i][1]
weight_tt += all_dt_mean[i][1]
# some strange observed A exist...
# this is a very simple way and naive to go around it!
if not all_a_mean[i][0][j] > 10:
aa += all_a_mean[i][0][j] * all_a_mean[i][1]
weight_aa += all_a_mean[i][1]
else:
print 'Error: %s for observed A' % all_a_mean[i][0][j]
meanall_dt.append(tt/weight_tt)
#meanall_a.append(aa/weight_aa)
### A TEST
plt.ion()
plt.figure()
plt.subplot(1, 1, 1)
all_tt_mean = []
all_tt_std = []
for i in range(len(all_tt_single)):
all_tt_mean.append(np.mean(all_tt_single[i]))
all_tt_std.append(np.std(all_tt_single[i]))
plt.xlabel('Dominant Period', fontsize='x-large', weight='bold')
x = [2.7, 3.7, 5.3, 7.5, 10.6, 15.0, 21.2, 30.0]
plt.xlim(xmin=0.0)
plt.vlines(x, 0.0, 1.6, linestyle='--')
# !!! change these according to your case!
plt.xlim(1.7, 31)
plt.ylim(0.0, 1.6)
plt.xticks(x, fontsize='x-large', weight='bold')
plt.yticks(fontsize='x-large', weight='bold')
plt.plot(per, all_tt_mean, lw=3, color='black', label='Mean')
plt.plot(per, all_tt_std, lw=3, color='red', label='STD')
plt.legend(loc=5, prop={'size': 32})
plt.show()
### FINISH A TEST
plt.figure()
plt.subplot(1, 1, 1)
plt.ylabel('Time difference (dT)', fontsize='x-large', weight='bold')
plt.xlabel('Dominant Period', fontsize='x-large', weight='bold')
x = [2.7, 3.7, 5.3, 7.5, 10.6, 15.0, 21.2, 30.0]
plt.xlim(xmin=0.0)
#plt.ylim(ymin=0.25, ymax=0.65)
plt.vlines(x, 0.0, 0.65, linestyle='--')
plt.ylim(0.0, 0.65)
plt.xlim(1.7, 31.)
plt.xticks(x, fontsize='x-large', weight='bold')
plt.yticks(fontsize='x-large', weight='bold')
#pltitle = evname
#pltitle += '\nxcorr >= %s' %(xcorr_limit)
#pltitle = '#station-event pairs (dT): %s\n' %(weight_tt)
#pltitle += '#station-event pairs (A): %s' %(weight_aa)
pltitle = '#station-event pairs (dT): %s' % weight_tt
plt.title(pltitle, fontsize='x-large', weight='bold')
# writing meanall_dt for later usages!
import pickle
meanall_file = open(os.path.join('.', 'statistics', 'meanall_dt'), 'w')
print "start pickling the meanall_file in %s..." % meanall_file
pickle.dump(meanall_dt, meanall_file)
print "DONE"
print '\n\n=========='
print 'bands:'
print per
print 'mean values:'
print meanall_dt
import py2mat_mod
py2mat_mod.py2mat(per, 'dispersion_period', 'dispersion_period')
py2mat_mod.py2mat(meanall_dt, 'dispersion_meanall_dt', 'dispersion_meanall_dt')
plt.plot(per, meanall_dt, linewidth=3)
#plt.subplot(2, 1, 2)
#plt.ylabel('Observed Amplitude', fontsize = 'x-large', weight = 'bold')
#plt.xlabel('Dominant Period', fontsize = 'x-large', weight = 'bold')
#plt.xticks(fontsize = 'x-large', weight = 'bold')
#plt.yticks(fontsize = 'x-large', weight = 'bold')
#plt.plot(per, meanall_a, linewidth=3)
plt.show()
