def main():
stations = 'PB03 PB04'
stations2 = None
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2006-01-01')
t1 = UTC('2007-11-10')
t2 = UTC()
t2 = UTC('2007-11-20')
shift = 500
correlations = get_correlations(stations, components, stations2, only_cross=True)
method = 'filter4-6_water_env2_whitening_1bit'
# method = 'filter0.01-1_1bit_whitening0.01'
# method = 'filter0.005_rm20'
# method = 'filter0.005_1bit'
data = IPOC(xcorr_append='/Tocopilla/tests/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
# prepare(data, stations.split(), t1, t2, component=components,
# filter=(4, 6), downsample=None,
# eventremoval='waterlevel_env2', param_removal=(10, 0),
# #whitening=True,
# normalize='1bit', param_norm=None)
correlations = get_correlations(stations, components, stations2, only_auto=True)
# noisexcorr(data, correlations, t1, t2, shift)
plotXcorrs(data, correlations, t1, t2, start= -150, end=150, plot_overview=True, plot_years=False, use_dlognorm=True,
plot_stack=True, plot_psd=True, add_to_title=method, show=True)
def main():
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 HMBCX MNMCX PATCX PSGCX'
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2006-07-01')
t2 = UTC('2008-12-31')
shift = 500
correlations = get_correlations(stations, components)
method = 'FINAL_filter0.005-10_1bit_Tocopilla'
data = IPOC(xcorr_append='/' + method, use_local_LVC=False)
data.setXLogger('_' + method)
pool = Pool()
prepare(data, stations.split(), t1, t2, component=components,
filter=(0.005, 10, 2, True), downsample=20,
whitening=False,
normalize='1bit', param_norm=None,
pool=pool)
noisexcorrf(data, correlations, t1, t2, shift, pool=pool)
pool.close()
pool.join()
stack(data, correlations, dt=10 * 24 * 3600, shift=5 * 24 * 3600)
stack(data, correlations, dt= -1)
filters = None
#filters = getFilters((0.005, 0.01, 0.1, 1, 5, 10), zerophase=True, corners=2)
# plotXcorrs(data, correlations, t1, t2, start=None, end=None, filters=filters, plot_overview=True, plot_years=False, use_dlognorm=False,
# plot_stack=True, plot_psd=True, add_to_title='', downsample=None)
plotXcorrs(data, correlations, t1=None, t2=None, start=None, end=None, filters=filters, plot_overview=True, plot_years=False, use_dlognorm=False,
plot_stack=True, plot_psd=True, add_to_title='', downsample=None, stack=('10days', '5days'))
def main():
stations = 'PB03 PB04'
component = 'Z'
t1 = UTC('2006-01-01')
t2 = UTC()
# t1 = UTC('2007-01-01')
# t2 = UTC('2007-01-03')
method1 = 'filter0.01-1_water_env2_whitening_1bit'
method2 = 'filter0.01-1_water_env2_whitening_1bit_fft'
data1 = IPOC(xcorr_append='/Tocopilla/' + method1, use_local_LVC=True)
data2 = IPOC(xcorr_append='/Tocopilla/' + method2, use_local_LVC=True)
for station in stations.split():
for day in daygen(t1, t2):
try:
stream = data1.getStream(day, station, component)
except:
log.warning('Could not read stream for day %s station %s' % (day, station))
else:
if len(stream) != 1:
log.warning('Stream for day %s station %s has wrong length %d' % (day, station, len(stream)))
elif stream[0].stats.npts / stream[0].stats.sampling_rate < 24 * 3600 * 0.5:
log.warning('Stream for day %s station %s has only a coverage of %f -> discard' % (day, station, 1. * stream[0].stats.npts / stream[0].stats.sampling_rate / 24 / 3600))
else:
stream.fft()
stream.write(data2.getDay(station, day), 'Q')
def main():
#stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16 HMBCX MNMCX PATCX PSGCX LVC TAIQ'
stations = 'PB01 PB02 PB03 PB04 PB05'
stations2 = None
components = 'Z'
# TOcopilla earthquake:
#t_Toco=UTC('2007-11-14 15:14:00')
t1 = UTC('2006-01-01')
#t2 = UTC('2011-09-01')
#t1 = UTC('2007-01-01')
#t2 = UTC('2009-01-01')
t2 = UTC('2012-01-01')
shift = 500
correlations = get_correlations(stations, components, stations2, only_cross=True)
# method = 'FINAL_filter0.005-5_1bit_whitening_2011+2012'
# method = 'filter0.01-1_1bit_whitening0.01'
# method = 'filter0.005_rm20'
# method = 'filter0.005_1bit'
method = 'filter0.01-1_water_env2_whitening_1bit_fft'
data = IPOC(xcorr_append='/Tocopilla/' + method, use_local_LVC=False)
data.setXLogger('_' + method)
# pool = Pool()
# prepare(data, stations.split(), t1, t2, component=components,
# filter=(0.005, 5, 2, True), downsample=20,
## filter=(1, 10), downsample=None,
## eventremoval=None, #'waterlevel_env2', param_removal=(10, 0),
# whitening=True,
# use_this_filter_after_whitening=(0.005, 5, 2),
# normalize='1bit', param_norm=None,
# pool=pool)
# noisexcorrf(data, correlations, t1, t2, shift, pool=pool)
# pool.close()
# pool.join()
#
# stack(data, correlations, dt=10 * 24 * 3600, shift=5 * 24 * 3600)
# stack(data, correlations, dt= -1)
t1p, t2p = t1, t2
# t1p, t2p = None, None
filters = None
filters = getFilters((0.025, 0.05, 0.1, 0.25, 0.5, 1))
plotXcorrs(data, correlations, t1=t1p, t2=t2p, start=None, end=None, plot_overview=True, plot_years=False, use_dlognorm=False,
plot_stack=True, plot_psd=False, add_to_title='', downsample=None, filters=filters, filter_now=False)
plotXcorrs(data, correlations, t1=t1p, t2=t2p, start=None, end=None, plot_overview=True, plot_years=False, use_dlognorm=False,
plot_stack=True, plot_psd=False, add_to_title='', downsample=None, stack=('10days', 'day'), filters=filters, filter_now=False)
plotXcorrs(data, correlations, t1=t1p, t2=t2p, start=None, end=None, plot_overview=True, plot_years=False, use_dlognorm=False,
plot_stack=True, plot_psd=False, add_to_title='', downsample=None, stack=('50days', '5days'), filters=filters, filter_now=False)
def main():
stations = 'PB01 PB02 PB03 PB04 PB04 PB05 PB06'
stations2 = 'PB01 PB02 PB03 PB04 PB04 PB05 PB06 PB07 PB08 HMBCX PATCX'
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2007-09-01')
t2 = UTC('2008-01-31')
shift = 200
correlations = get_correlations(stations, components, stations2)
# method = 'filter0.01-1_1bit'
# method = 'filter0.01-1_1bit_whitening0.01'
# method = 'filter2-20_1bit'
# method = 'filter0.005_1bit'
# period = 'day'
#
# data = IPOC(xcorr_append='/Tocopilla/' + method, use_local_LVC=True)
# data.setXLogger('_' + method)
# prepare(data, stations.split(), t1, t2, filter=(0.005, 1.), downsample=5, whitening=True,
# component=components, normalize='1bit', norm_param=None)
# noisexcorr(data, correlations, t1, t2, shift_sec=shift, period=period)
# correlations = (('PB03Z', 'PB04Z'),)
# data.x_plot_day = data.x_res + '/plots2/%s_day_%s'
# plotXcorrs(data, correlations, t1, t2, start=9, end=15, plot_overview=True, filter=(2, None, 2, True), stack_lim=(-0.01, 0.01), downsample=None, plot_years=False,
# plot_stack=True, plot_psd=True, add_to_title=method + '_filter2_9-15', add_to_file='_filter2_9-15.png', show=False)
method = 'filter4-6_1bit'
period = 'day'
data = IPOC(xcorr_append='/Tocopilla/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
# prepare(data, stations.split(), t1, t2, filter=(4, 6), downsample=None, whitening=None,
# component=components, normalize='1bit', norm_param=None)
# noisexcorr(data, correlations, t1, t2, shift_sec=shift, period=period)
plotXcorrs(data,
correlations,
t1,
t2,
start=-50,
end=50,
plot_overview=True,
filter=None,
stack_lim=(-0.1, 0.1),
plot_years=False,
plot_stack=True,
plot_psd=False,
add_to_title=method + '_wodlognorm_50s',
add_to_file='_wodlognorm_50s.png',
show=True,
landscape=True,
use_dlognorm=False)
def main():
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 HMBCX MNMCX PATCX PSGCX LVC'
stations = 'PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16'
stations = 'PB02 PB03 PB04 PB05 HMBCX MNMCX PSGCX'
stations = 'PATCX'
stations2 = None
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2007-01-01')
#t1 = UTC('2007-12-01')
#t2 = UTC('2008-12-31')
#t2 = UTC('2012-10-01')
t2 = UTC('2011-12-31')
#t2 = UTC('2007-02-03')
# t1 = UTC('2009-05-01')
# t2 = UTC('2009-05-03')
shift = 100
shift = 60
correlations = get_correlations(stations, components, stations2, only_auto=True)
#correlations = get_correlations(stations, components, stations2)
print correlations
method = 'FINAL_filter4-6_1bit_auto'
method = 'FINAL_filter4-6_1bit_auto_3C'
method = 'FINAL_filter4-6_1bit_auto_hour2'
data = IPOC(xcorr_append='/' + method, use_local_LVC=False)
data.setXLogger('_' + method)
# pool = Pool()
pool = None
prepare(data, stations.split(), t1, t2, component=components,
filter=(4, 6, 2, True), downsample=50,
eventremoval='waterlevel_env2', param_removal=(10, 0),
whitening=False,
normalize='1bit', param_norm=None,
pool=pool, discard=0.1 * 24 * 3600, freq_domain=False, trim='day')
noisexcorrf(data, correlations, t1, t2, shift, period=3600, pool=pool, overlap=1800)
# noisexcorrf(data, correlations, t1, t2, shift, period=5 * 60, pool=pool,
# max_preload=1000)
# pool.close()
# pool.join()
# plotXcorrs(data, correlations, t1, t2, start=None, end=None, plot_overview=True, plot_years=False, use_dlognorm=False,
# plot_stack=True, plot_psd=False, add_to_title='', downsample=None)
plotXcorrs(data, correlations, t1, t2, start= -20, end=20, plot_overview=True, plot_years=True, use_dlognorm=False,
plot_stack=True, plot_psd=False, add_to_title='', downsample=None, ext='_hg.png', vmax=0.1,
period=3600)
def analyze():
#stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16 HMBCX MNMCX PATCX PSGCX LVC TAIQ'
stations = 'PB01 PB02 PATCX'
stations = 'PATCX'
t1 = UTC('2009-01-01')
t2 = UTC('2010-01-01')
data = IPOC()
for station in stations.split():
hours = [[] for i in range(24)]
times = []
levels = []
for t_day in daygen(t1, t2):
try:
stream = data.getRawStreamFromClient(t_day, t_day + 24 * 3600, station, component='Z')
except ValueError:
continue
for tr in stream:
tr.stats.filter = ''
stream.demean()
stream.detrend()
stream.filter2(4, 6)
stream.downsample2(5)
stream.merge()
tr = stream[0]
startt = tr.stats.starttime
endt = tr.stats.endtime
if endt - startt < 12 * 3600:
continue
tr.data = obspy.signal.cpxtrace.envelope(tr.data)[1][:len(tr.data)]
for hour in range(24):
tr2 = tr.slice(t_day + hour * 3600, t_day + (hour + 1) * 3600)
if tr2.stats.endtime - tr2.stats.starttime < 1800:
continue
num_stds = 60 # =^ every minute
len_parts = len(tr2.data) // 60 # =^ 1min
len_stds = len_parts // 6 # =^ 10s
stds = np.array([np.std(tr2.data[i:i + len_stds]) for i in np.arange(num_stds) * len_parts])
stds = stds[stds != 0.]
num_stds = len(stds)
if num_stds < 50:
continue
stds = np.sort(stds)[num_stds // 5:-num_stds // 5]
stds = stds[stds < np.min(stds) * 2.]
val = np.mean(stds)
levels.append(val)
times.append(date2num(t_day + (0.5 + hour) * 3600))
hours[hour].append(val)
errors = np.array([np.std(hours[i], ddof=1) / len(hours[i]) ** 0.5 for i in range(24)])
hours = np.array([np.mean(hours[i]) for i in range(24)])
times = np.array(times)
levels = np.array(levels)
np.savez('/home/richter/Results/IPOC/xcorr/noise_apmlitudes_%s_4-6Hz.npz' % station,
hours=hours, errors=errors, times=times, levels=levels)
def main():
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 HMBCX MNMCX PATCX PSGCX'
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2006-07-01')
t2 = UTC('2008-12-31')
shift = 500
correlations = get_correlations(stations, components)
method = 'FINAL_filter0.005-10_1bit_Tocopilla'
data = IPOC(xcorr_append='/' + method, use_local_LVC=False)
data.setXLogger('_' + method)
pool = Pool()
prepare(data,
stations.split(),
t1,
t2,
component=components,
filter=(0.005, 10, 2, True),
downsample=20,
whitening=False,
normalize='1bit',
param_norm=None,
pool=pool)
noisexcorrf(data, correlations, t1, t2, shift, pool=pool)
pool.close()
pool.join()
stack(data, correlations, dt=10 * 24 * 3600, shift=5 * 24 * 3600)
stack(data, correlations, dt=-1)
filters = None
#filters = getFilters((0.005, 0.01, 0.1, 1, 5, 10), zerophase=True, corners=2)
# plotXcorrs(data, correlations, t1, t2, start=None, end=None, filters=filters, plot_overview=True, plot_years=False, use_dlognorm=False,
# plot_stack=True, plot_psd=True, add_to_title='', downsample=None)
plotXcorrs(data,
correlations,
t1=None,
t2=None,
start=None,
end=None,
filters=filters,
plot_overview=True,
plot_years=False,
use_dlognorm=False,
plot_stack=True,
plot_psd=True,
add_to_title='',
downsample=None,
stack=('10days', '5days'))
def main():
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 HMBCX MNMCX PATCX PSGCX LVC'
#stations = 'PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16'
stations2 = None
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2006-02-01')
t2 = UTC('2012-10-01')
shift = 100
correlations = get_correlations(stations,
components,
stations2,
only_auto=True)
method = 'FINAL_filter1-3_1bit_auto'
data = IPOC(xcorr_append='/' + method, use_local_LVC=False)
data.setXLogger('_' + method)
# pool = Pool()
# prepare(data, stations.split(), t1, t2, component=components,
# filter=(1, 3, 2, True), downsample=20,
# eventremoval='waterlevel_env2', param_removal=(10, 0),
# whitening=False,
# normalize='1bit', param_norm=None,
# pool=pool)
# noisexcorrf(data, correlations, t1, t2, shift, pool=pool)
# pool.close()
# pool.join()
# plotXcorrs(data, correlations, t1, t2, start=None, end=None, plot_overview=True, plot_years=False, use_dlognorm=False,
# plot_stack=True, plot_psd=False, add_to_title='', downsample=None)
plt.rc('font', size=16)
plotXcorrs(data,
correlations,
t1,
t2,
start=0,
end=20,
plot_overview=True,
plot_years=False,
use_dlognorm=False,
plot_stack=True,
plot_psd=False,
downsample=None,
ext='_hg_dis.pdf',
vmax=0.1,
ylabel=None,
add_to_title='1-3Hz')
def main2():
data = IPOC(xcorr_append='/1bit', use_local_LVC=True)
t1 = UTC('2010-01-01')
stream0_1 = data.getRawStream(t1, 'PB01', component='Z')
stream0_2 = data.getRawStream(t1, 'PB02', component='Z')
stream2_1 = data.getStream(t1, 'PB01', component='Z')
stream2_2 = data.getStream(t1, 'PB02', component='Z')
plot_streams([stream0_1, stream0_2, stream2_1, stream2_2], [None, None, 0.1, 0.1])
plotPSD([stream0_1, stream0_2, stream2_1, stream2_2], 4096)
ipshell()
def main():
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 HMBCX MNMCX PATCX PSGCX LVC'
stations = 'PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16'
stations = 'PATCX'
stations2 = None
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2007-10-01')
t2 = UTC('2007-11-30')
#t2 = UTC('2012-10-01')
#t2 = UTC('2011-12-31')
# t1 = UTC('2009-05-01')
# t2 = UTC('2009-05-03')
shift = 100
shift = 60
#correlations = get_correlations(stations, components, stations2, only_auto=True)
correlations = get_correlations(stations, components, stations2)
print correlations
method = 'zerotest_nozero'
#method = 'FINAL_filter4-6_1bit_auto_3C'
#method = 'FINAL_filter3-5'
data = IPOC(xcorr_append='/' + method, use_local_LVC=False)
data.setXLogger('_' + method)
pool = Pool()
prepare(data, stations.split(), t1, t2, component=components,
filter=(4, 6, 2, True), downsample=50,
#eventremoval='waterlevel_env2', param_removal=(10, 0),
eventremoval=None, param_removal=None,
whitening=False,
normalize='1bit', param_norm=None,
pool=pool)
noisexcorrf(data, correlations, t1, t2, shift, period=24 * 3600, pool=pool)
# noisexcorrf(data, correlations, t1, t2, shift, period=5 * 60, pool=pool,
# max_preload=1000)
pool.close()
pool.join()
# plotXcorrs(data, correlations, t1, t2, start=None, end=None, plot_overview=True, plot_years=False, use_dlognorm=False,
# plot_stack=True, plot_psd=False, add_to_title='', downsample=None)
#plt.rc('font', size=16)
plotXcorrs(data, correlations, t1, t2, start=-20, end=20, plot_overview=True, plot_years=False, use_dlognorm=False,
plot_stack=True, plot_psd=False, downsample=None, ext='_hg0.02_dis.pdf', vmax=0.02,
add_to_title='4-6Hz', ylabel=None)
def main():
stations = 'PB01 PB02 PB03 PB04 PB05'
component = 'Z'
t1 = UTC('2009-06-01')
t2 = UTC('2009-07-01')
data = IPOC('test', use_local_LVC=False)
data.setXLogger('_test')
period = 24 * 3600
ax = None
plt.ion()
for station in stations.split():
pxxs = []
freqs_old = None
i = 0
for t in timegen(t1, t2, period):
st = data.getRawStreamFromClient(t, t + period, station, component)
st.merge(method=1,
interpolation_samples=10,
fill_value='interpolate')
print st
pxx, freqs = st.plotPSD(just_calculate=True)
assert np.all(freqs == freqs_old) or not freqs_old
freqs_old = freqs
if max(pxx[4:]) > 1e7:
print 'discard'
i += 1
continue
pxxs.append(pxx)
pxx = sum(pxxs) / len(pxxs)
del pxxs
tr = Trace(data=pxx,
header=dict(is_fft=True,
sampling_rate=2 * max(freqs),
freq_min=min(freqs),
freq_max=max(freqs)))
ax = tr.plotPSD(ax=ax,
label='%s-%d' % (st[0].stats.station, i),
figtitle=None)
plt.draw()
# embed()
ax.legend()
fig = ax.get_figure()
fig.suptitle('%s %s %s to %s' %
(stations, component, t1.strftime('%Y-%m-%d'),
t2.strftime('%Y-%m-%d')))
plt.ioff()
plt.show()
def main():
stations = 'PB03 PB04'
stations2 = None
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2006-01-01')
t1 = UTC('2007-11-10')
t2 = UTC()
t2 = UTC('2007-11-20')
shift = 500
correlations = get_correlations(stations,
components,
stations2,
only_cross=True)
method = 'filter4-6_water_env2_whitening_1bit'
# method = 'filter0.01-1_1bit_whitening0.01'
# method = 'filter0.005_rm20'
# method = 'filter0.005_1bit'
data = IPOC(xcorr_append='/Tocopilla/tests/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
# prepare(data, stations.split(), t1, t2, component=components,
# filter=(4, 6), downsample=None,
# eventremoval='waterlevel_env2', param_removal=(10, 0),
# #whitening=True,
# normalize='1bit', param_norm=None)
correlations = get_correlations(stations,
components,
stations2,
only_auto=True)
# noisexcorr(data, correlations, t1, t2, shift)
plotXcorrs(data,
correlations,
t1,
t2,
start=-150,
end=150,
plot_overview=True,
plot_years=False,
use_dlognorm=True,
plot_stack=True,
plot_psd=True,
add_to_title=method,
show=True)
def main():
stations = 'PB01 PB02 PB03 PB04 PB04 PB05 PB06'
stations2 = 'PB01 PB02 PB03 PB04 PB04 PB05 PB06 PB07 PB08 HMBCX PATCX'
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2007-09-01')
t2 = UTC('2008-01-31')
shift = 200
correlations = get_correlations(stations, components, stations2)
# method = 'filter0.01-1_1bit'
# method = 'filter0.01-1_1bit_whitening0.01'
# method = 'filter2-20_1bit'
# method = 'filter0.005_1bit'
# period = 'day'
#
# data = IPOC(xcorr_append='/Tocopilla/' + method, use_local_LVC=True)
# data.setXLogger('_' + method)
# prepare(data, stations.split(), t1, t2, filter=(0.005, 1.), downsample=5, whitening=True,
# component=components, normalize='1bit', norm_param=None)
# noisexcorr(data, correlations, t1, t2, shift_sec=shift, period=period)
# correlations = (('PB03Z', 'PB04Z'),)
# data.x_plot_day = data.x_res + '/plots2/%s_day_%s'
# plotXcorrs(data, correlations, t1, t2, start=9, end=15, plot_overview=True, filter=(2, None, 2, True), stack_lim=(-0.01, 0.01), downsample=None, plot_years=False,
# plot_stack=True, plot_psd=True, add_to_title=method + '_filter2_9-15', add_to_file='_filter2_9-15.png', show=False)
method = 'filter4-6_1bit'
period = 'day'
data = IPOC(xcorr_append='/Tocopilla/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
# prepare(data, stations.split(), t1, t2, filter=(4, 6), downsample=None, whitening=None,
# component=components, normalize='1bit', norm_param=None)
# noisexcorr(data, correlations, t1, t2, shift_sec=shift, period=period)
plotXcorrs(data, correlations, t1, t2, start= -50, end=50, plot_overview=True, filter=None, stack_lim=(-0.1, 0.1), plot_years=False,
plot_stack=True, plot_psd=False, add_to_title=method + '_wodlognorm_50s', add_to_file='_wodlognorm_50s.png', show=True, landscape=True, use_dlognorm=False)
def main():
data = IPOC(xcorr_append='/tests/1bit_filter0.01', use_local_LVC=True)
data.setXLogger('_1bit0.01Hz')
stations = 'PB01 PB03'
stations2 = 'PB03'
components = 'Z'
t1 = UTC('2010-01-01')
t2 = UTC('2010-01-02')
shift = 500
# prepare(data, stations.split(), t1, t2, filter=(0.01, None), downsample=None,
# component=components, normalize='1bit', norm_param=None,
# use_floating_stream=True)
correlations = get_correlations(stations, components, stations2)
# xcorr_day(data, correlations, t1, t2, shift, use_floating_stream=True)
plotXcorrs(data, correlations, t1, t2, plot_overview=False, plot_stack=True, plot_psd=True)
def main():
data = IPOC(xcorr_append='/tests/1bit_filter0.1-1', use_local_LVC=True)
data.setXLogger('_1bit')
stations = 'PB01 PB03'
stations2 = 'PB03'
components = 'Z'
t1 = UTC('2010-01-01')
t2 = UTC('2010-12-31')
shift = 500
prepare(data, stations.split(), t1, t2, filter=(0.1, 1.), downsample=10,
component=components, normalize='1bit', param_norm=None,
use_floating_stream=True)
correlations = get_correlations(stations, components, stations2)
xcorr_day(data, correlations, t1, t2, shift, use_floating_stream=True)
plotXcorrs(data, correlations, t1, t2)
def main():
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16 HMBCX MNMCX PATCX PSGCX LVC'
stations2 = None
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2006-02-01')
t2 = UTC('2012-10-01')
shift = 500
correlations = get_correlations(stations, components, stations2, only_auto=True)
method = 'FINAL_filter0.01-0.5_1bit_auto'
data = IPOC(xcorr_append='/' + method, use_local_LVC=False)
data.setXLogger('_' + method)
# pool = Pool()
# prepare(data, stations.split(), t1, t2, component=components,
# filter=(0.01, 0.5, 2, True), downsample=5,
# eventremoval='waterlevel_env2', param_removal=(10, 0),
# whitening=False,
# normalize='1bit', param_norm=None,
# pool=pool)
# noisexcorrf(data, correlations, t1, t2, shift, pool=pool)
# pool.close()
# pool.join()
# plotXcorrs(data, correlations, t1, t2, start=None, end=None, plot_overview=True, plot_years=False, use_dlognorm=False,
# plot_stack=True, plot_psd=False, add_to_title='', downsample=None)
plotXcorrs(data, correlations, t1, t2, start=0, end=200, plot_overview=True, plot_years=False, use_dlognorm=False,
plot_stack=True, plot_psd=False, add_to_title='', downsample=None, ext='_hg.png', vmax=0.1)
# stack(data, correlations, dt= -1)
# stack(data, correlations, dt=10 * 24 * 3600, shift=2 * 24 * 3600)
# plotXcorrs(data, correlations, t1=None, t2=None, start=None, end=None, plot_overview=True, plot_years=False, use_dlognorm=False,
# plot_stack=True, plot_psd=False, add_to_title='', downsample=None,
# stack=('10days', '2days'))
plotXcorrs(data, correlations, t1=None, t2=None, start=0, end=200, plot_overview=True, plot_years=False, use_dlognorm=False,
plot_stack=True, plot_psd=False, add_to_title='', downsample=None,
stack=('10days', '2days'), ext='_hg.png', vmax=0.1)
def main():
stations = 'PB01 PB02 PB03 PB04 PB05'
component = 'Z'
t1 = UTC('2009-06-01')
t2 = UTC('2009-07-01')
data = IPOC('test', use_local_LVC=False)
data.setXLogger('_test')
period = 24 * 3600
ax = None
plt.ion()
for station in stations.split():
pxxs = []
freqs_old = None
i = 0
for t in timegen(t1, t2, period):
st = data.getRawStreamFromClient(t, t + period, station, component)
st.merge(method=1, interpolation_samples=10, fill_value='interpolate')
print st
pxx, freqs = st.plotPSD(just_calculate=True)
assert np.all(freqs == freqs_old) or not freqs_old
freqs_old = freqs
if max(pxx[4:]) > 1e7:
print 'discard'
i += 1
continue
pxxs.append(pxx)
pxx = sum(pxxs) / len(pxxs)
del pxxs
tr = Trace(data=pxx,
header=dict(is_fft=True, sampling_rate=2 * max(freqs),
freq_min=min(freqs), freq_max=max(freqs)))
ax = tr.plotPSD(ax=ax, label='%s-%d' % (st[0].stats.station, i), figtitle=None)
plt.draw()
# embed()
ax.legend()
fig = ax.get_figure()
fig.suptitle('%s %s %s to %s' % (stations, component, t1.strftime('%Y-%m-%d'),
t2.strftime('%Y-%m-%d')))
plt.ioff()
plt.show()
def calc_temp():
station = "PB12"
channel = "WKI"
from sito.data import IPOC
ipoc = IPOC()
stream = ipoc.getChannelFromClient("2006-01-01", "2012-01-01", station=station, channel=channel)
stream2 = stream.copy()
day = stream[0].stats.starttime
day2 = UTC(day.year, day.month, day.day)
if day2 < day:
day = day2 + 24 * 3600
stream2.trim(day, day + 24 * 3600)
stream2.merge()
data = []
while day < stream[-1].stats.endtime:
st = stream.slice(day, day + 24 * 3600)
st.merge()
if len(st) == 0 or len(st[0].data) < 8640:
print "skip %s" % day.date
day = day + 24 * 3600
continue
a = st[0].data[:8640]
if channel == "WKI":
a = a / 100.0
a = np.ma.masked_outside(a, 0, 50)
elif channel == "WDI":
a = a / 10000.0
a = np.ma.masked_outside(a, 0, 5)
else:
a = np.ma.masked_outside(a, 0, 100)
a = np.ma.masked_invalid(a)
data.append(a)
day = day + 24 * 3600
# from IPython import embed
# embed()
stream2[0].data = np.ma.mean(data, axis=0)
stream2.write(path + "/climate/%s_%s" % (station, channel), "Q")
stream2.plot(method="full")
plt.show()
def calc_strong_motion_Toco():
t = UTC('2007-11-14')
data = IPOC()
channel = 'BHZ'
acs = {}
vels = {}
for station in 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PATCX HMBCX PSGCX MNMCX'.split(
):
for channel in ('BLZ', 'BHZ'):
try:
ms = data.getChannelFromClient(
t - 60,
t + 24 * 3600 + 60,
network='GE' if station == 'LVC' else 'CX',
location='10' if station == 'LVC' else '',
station=station,
channel=channel)
except Exception as ex:
print station, channel, 'sucks'
continue
for tr in ms:
if tr.stats.endtime - tr.stats.starttime < 1000:
ms.remove(tr)
continue
tr.trim(tr.stats.starttime + 10, tr.stats.endtime - 10)
tr.stats.filter = ''
tr.detrend()
tr.filter('highpass', freq=0.2)
tr.trim(tr.stats.starttime + 10, tr.stats.endtime - 10)
ms.merge(fill_value=0)
if len(ms) == 0:
continue
maxi = float(np.max(np.abs(ms[0].data)))
if 'BH' in channel:
vels[station] = maxi / 629145000.0 * 100
elif 'BL' in channel:
acs[station] = maxi / 427566.942
with open(GM_TOCO, 'w') as f:
yaml.dump({'vels': vels, 'acs': acs}, f, default_flow_style=False)
def main():
data = IPOC(xcorr_append='/tests/1bit_filter0.01', use_local_LVC=True)
data.setXLogger('_1bit0.01Hz')
stations = 'PB01 PB03'
stations2 = 'PB03'
components = 'Z'
t1 = UTC('2010-01-01')
t2 = UTC('2010-01-02')
shift = 500
# prepare(data, stations.split(), t1, t2, filter=(0.01, None), downsample=None,
# component=components, normalize='1bit', norm_param=None,
# use_floating_stream=True)
correlations = get_correlations(stations, components, stations2)
# xcorr_day(data, correlations, t1, t2, shift, use_floating_stream=True)
plotXcorrs(data,
correlations,
t1,
t2,
plot_overview=False,
plot_stack=True,
plot_psd=True)
def main():
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 HMBCX MNMCX PATCX PSGCX LVC'
#stations = 'PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16'
stations2 = None
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2006-02-01')
t2 = UTC('2012-10-01')
shift = 100
correlations = get_correlations(stations, components, stations2, only_auto=True)
method = 'FINAL_filter1-3_1bit_auto'
data = IPOC(xcorr_append='/' + method, use_local_LVC=False)
data.setXLogger('_' + method)
# pool = Pool()
# prepare(data, stations.split(), t1, t2, component=components,
# filter=(1, 3, 2, True), downsample=20,
# eventremoval='waterlevel_env2', param_removal=(10, 0),
# whitening=False,
# normalize='1bit', param_norm=None,
# pool=pool)
# noisexcorrf(data, correlations, t1, t2, shift, pool=pool)
# pool.close()
# pool.join()
# plotXcorrs(data, correlations, t1, t2, start=None, end=None, plot_overview=True, plot_years=False, use_dlognorm=False,
# plot_stack=True, plot_psd=False, add_to_title='', downsample=None)
plt.rc('font', size=16)
plotXcorrs(data, correlations, t1, t2, start=0, end=20, plot_overview=True, plot_years=False, use_dlognorm=False,
plot_stack=True, plot_psd=False, downsample=None, ext='_hg_dis.pdf', vmax=0.1, ylabel=None,
add_to_title='1-3Hz')
def calc_strong_motion_Toco():
t = UTC('2007-11-14')
data = IPOC()
channel = 'BHZ'
acs = {}
vels = {}
for station in 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PATCX HMBCX PSGCX MNMCX'.split():
for channel in ('BLZ', 'BHZ'):
try:
ms = data.getChannelFromClient(t - 60, t + 24 * 3600 + 60,
network='GE' if station == 'LVC' else 'CX',
location='10' if station == 'LVC' else '',
station=station, channel=channel)
except Exception as ex:
print station, channel, 'sucks'
continue
for tr in ms:
if tr.stats.endtime - tr.stats.starttime < 1000:
ms.remove(tr)
continue
tr.trim(tr.stats.starttime + 10, tr.stats.endtime - 10)
tr.stats.filter = ''
tr.detrend()
tr.filter('highpass', freq=0.2)
tr.trim(tr.stats.starttime + 10, tr.stats.endtime - 10)
ms.merge(fill_value=0)
if len(ms) == 0:
continue
maxi = float(np.max(np.abs(ms[0].data)))
if 'BH' in channel:
vels[station] = maxi / 629145000.0 * 100
elif 'BL' in channel:
acs[station] = maxi / 427566.942
with open(GM_TOCO, 'w') as f:
yaml.dump({'vels': vels, 'acs':acs}, f, default_flow_style=False)
def main():
stations = 'PB01 PB03'
stations2 = 'PB03'
components = 'Z'
t1 = UTC('2010-01-01')
t2 = UTC('2010-12-31')
shift = 500
correlations = get_correlations(stations, components, stations2)
method = 'filter0.1-1_1bit_whitening0.01'
data = IPOC(xcorr_append='/tests/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
prepare(data, stations.split(), t1, t2, filter=(0.1, 1), downsample=10, whitening=0.01,
component=components, normalize='1bit', param_norm=None,
use_floating_stream=True)
xcorr_day(data, correlations, t1, t2, shift, use_floating_stream=True)
plotXcorrs(data, correlations, t1, t2, plot_overview=False, plot_stack=True, plot_psd=True, add_to_title=method)
method = 'filter0.1-1_1bit_whitening0.001'
data = IPOC(xcorr_append='/tests/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
prepare(data, stations.split(), t1, t2, filter=(0.1, 1), downsample=10, whitening=0.001,
component=components, normalize='1bit', param_norm=None,
use_floating_stream=True)
xcorr_day(data, correlations, t1, t2, shift, use_floating_stream=True)
plotXcorrs(data, correlations, t1, t2, plot_overview=False, plot_stack=True, plot_psd=True, add_to_title=method)
method = 'filter0.1-1_1bit_whitening0.1'
data = IPOC(xcorr_append='/tests/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
prepare(data, stations.split(), t1, t2, filter=(0.1, 1), downsample=10, whitening=0.1,
component=components, normalize='1bit', param_norm=None,
use_floating_stream=True)
xcorr_day(data, correlations, t1, t2, shift, use_floating_stream=True)
plotXcorrs(data, correlations, t1, t2, plot_overview=False, plot_stack=True, plot_psd=True, add_to_title=method)
def plot_salar_map():
map_dic = dict(show=False, ll=(-21.4, -70.3), ur=(-20.7, -69.8),
figsize=(fw, 1.61 * fw * 0.7),
margin=(0.05, 0.05, 0.9, 0.9), lw=2,
station_markersize=4,
grid=0.2, grid_labels=True, grid_lw=0.2, slip=None, earthquake=None,
countries=None, coastlines=None,
elevation_args=(1592 * 2, None, False), elevation_offset=1000,
shaded=True, shaded_args=(90, 45, 0.7),
colormap=cm.binary,
elevation='/home/richter/Data/map/salar_90m.tif',
elev_oceans=False,
stations=None,
spines_lw=2,
loffset=1000)
from sito.data import IPOC
m = map.createIPOCMap(**map_dic)
chos = [('CHO1', -21.094050, -70.102000, 653),
('CHO2', -21.105933, -70.096900, 620),
('CHO3', -21.106233, -70.097517, 625)]
kw = dict(bbox=dict(boxstyle="round", fc="w", alpha=0.5, ec='none'))
IPOC().stations.plot(m, mfc='w', ms=4, zorder=10, lsize='small', kwargs_an=kw)
#ASTER GDEM is a product of METI and NASA.
for station, lat, lon, height in chos[1:2]:
x, y = m(lon, lat)
m.plot((x,), (y,), marker='o', mfc='w', ms=4, zorder=10)
plt.annotate(station, (x, y), xytext=(3, 3), textcoords='offset points', size='small', **kw)
#plt.annotate('ASTER GDEM is a product of METI and NASA.', (1, 0), xycoords='axes fraction', ha='right', va='bottom', size='xx-small')
plt.annotate('Salar Grande', (0.62, 0.56), rotation=-80, xycoords='axes fraction',
ha='center', va='center', size='small', color='k')
path = svg2path('/home/richter/Documents/pics/maps/ipoc/salar/salar.svg')[0]
lat21, lon70, px2deg = 862 - 15, 557 + 15, (1.177425 - 0.869511) / 1055
for i in range(len(path)):
x, y = path.vertices[i, :]
lon = -70 + (x - lon70) * px2deg / cos(21. / 180 * pi)
lat = -21 - (y - lat21) * px2deg
path.vertices[i, :] = m(lon, lat)
patch = patches.PathPatch(path, facecolor='none', lw=0.8, ec='r', alpha=0.5, zorder=50)
plt.gca().add_patch(patch)
mpl.rcParams.update({'lines.linewidth':1.})
m.drawmapscale(-70.2, -21.35, -70, -21, 10, fontsize=7, yoffset=0.005 * (m.ymax - m.ymin))
plt.gcf().savefig('/home/richter/Documents/pics/maps/ipoc/salar_map.pdf', dpi=1200)
def psd(station, parser):
data = IPOC()
ppsd_length = 6 * 3600
overlap = 0.5
dt = 3 * 24 * 3600
t1 = UTC('2006-01-01')
t2 = UTC('2013-11-01')
ppsd = None
print t1, t2
while t1 < t2:
try:
if station != 'LVC':
stream = data.client.getWaveform(
'CX', station, '', 'HHZ', t1,
t1 + dt + overlap * ppsd_length)
else:
stream = data.client.getWaveform(
'GE', 'LVC', '00', 'BHZ', t1,
t1 + dt + overlap * ppsd_length)
except:
t1 += dt
continue
if ppsd is None:
ppsd = PPSD(stream[0].stats,
parser=parser,
skip_on_gaps=True,
db_bins=(-200, -50, 0.5),
ppsd_length=ppsd_length,
overlap=overlap)
print t1
ppsd.add(stream)
t1 += dt
if ppsd is not None:
print 'station %s: %d segments' % (station, len(ppsd.times))
ppsd.save("/home/richter/Results/IPOC/PPSD/ppsd_%s_6h.pkl.bz2" %
station,
compress=True)
return True
else:
return False
def main():
stations = 'PB03 PB04'
component = 'Z'
t1 = UTC('2006-01-01')
t2 = UTC()
# t1 = UTC('2007-01-01')
# t2 = UTC('2007-01-03')
method1 = 'filter0.01-1_water_env2_whitening_1bit'
method2 = 'filter0.01-1_water_env2_whitening_1bit_fft'
data1 = IPOC(xcorr_append='/Tocopilla/' + method1, use_local_LVC=True)
data2 = IPOC(xcorr_append='/Tocopilla/' + method2, use_local_LVC=True)
for station in stations.split():
for day in daygen(t1, t2):
try:
stream = data1.getStream(day, station, component)
except:
log.warning('Could not read stream for day %s station %s' %
(day, station))
else:
if len(stream) != 1:
log.warning(
'Stream for day %s station %s has wrong length %d' %
(day, station, len(stream)))
elif stream[0].stats.npts / stream[
0].stats.sampling_rate < 24 * 3600 * 0.5:
log.warning(
'Stream for day %s station %s has only a coverage of %f -> discard'
% (day, station, 1. * stream[0].stats.npts /
stream[0].stats.sampling_rate / 24 / 3600))
else:
stream.fft()
stream.write(data2.getDay(station, day), 'Q')
def analyze():
#stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16 HMBCX MNMCX PATCX PSGCX LVC TAIQ'
stations = 'PB01 PB02 PATCX'
stations = 'PATCX'
t1 = UTC('2009-01-01')
t2 = UTC('2010-01-01')
data = IPOC()
for station in stations.split():
hours = [[] for i in range(24)]
times = []
levels = []
for t_day in daygen(t1, t2):
try:
stream = data.getRawStreamFromClient(t_day,
t_day + 24 * 3600,
station,
component='Z')
except ValueError:
continue
for tr in stream:
tr.stats.filter = ''
stream.demean()
stream.detrend()
stream.filter2(4, 6)
stream.downsample2(5)
stream.merge()
tr = stream[0]
startt = tr.stats.starttime
endt = tr.stats.endtime
if endt - startt < 12 * 3600:
continue
tr.data = obspy.signal.cpxtrace.envelope(tr.data)[1][:len(tr.data)]
for hour in range(24):
tr2 = tr.slice(t_day + hour * 3600, t_day + (hour + 1) * 3600)
if tr2.stats.endtime - tr2.stats.starttime < 1800:
continue
num_stds = 60 # =^ every minute
len_parts = len(tr2.data) // 60 # =^ 1min
len_stds = len_parts // 6 # =^ 10s
stds = np.array([
np.std(tr2.data[i:i + len_stds])
for i in np.arange(num_stds) * len_parts
])
stds = stds[stds != 0.]
num_stds = len(stds)
if num_stds < 50:
continue
stds = np.sort(stds)[num_stds // 5:-num_stds // 5]
stds = stds[stds < np.min(stds) * 2.]
val = np.mean(stds)
levels.append(val)
times.append(date2num(t_day + (0.5 + hour) * 3600))
hours[hour].append(val)
errors = np.array(
[np.std(hours[i], ddof=1) / len(hours[i])**0.5 for i in range(24)])
hours = np.array([np.mean(hours[i]) for i in range(24)])
times = np.array(times)
levels = np.array(levels)
np.savez(
'/home/richter/Results/IPOC/xcorr/noise_apmlitudes_%s_4-6Hz.npz' %
station,
hours=hours,
errors=errors,
times=times,
levels=levels)
scale=args.relative_scale,
downsample=args.downsample,
save=args.save, show=args.save is None))
print kwargs
if args.date is None:
from sito import read
if not '.' in args.file_station:
args.file_station = args.file_station + '.QHD'
stream = read(args.file_station)
if args.absolute_scale is None and args.relative_scale is None:
kwargs['scale'] = 1.
else:
station = args.file_station
if station.startswith('PB') or station == 'LVC' or station.endswith('CX'):
from sito.data import IPOC
data = IPOC(xcorr_append=args.xcorr_append)
elif station == 'PKD':
from sito.data import Parkfield
data = Parkfield(xcorr_append=args.xcorr_append)
else:
raise argparse.ArgumentError('Not a valid station name')
day = UTC(args.date)
if args.xcorr_append is None:
#stream = data.getRawStream(day, station, component=args.component)
stream = data.getRawStreamFromClient(day, day + 24 * 3600, station, component=args.component, channel=args.channel)
else:
stream = data.getStream(day, station, component=args.component)
if args.absolute_scale is None and args.relative_scale is None:
kwargs['absolutescale'] = 0.0005
tr = stream[0]
scale=args.relative_scale,
downsample=args.downsample,
save=args.save, show=args.save is None))
print kwargs
if args.date is None:
from sito import read
from sito.imaging import plotTrace
stream = read(args.file_station)
plotTrace(stream, **kwargs)
else:
from sito.imaging import plotTrace2
station = args.file_station
if station.startswith('PB') or station == 'LVC':
from sito.data import IPOC
data = IPOC(xcorr_append=args.xcorr_append)
elif station == 'PKD':
from sito.data import Parkfield
data = Parkfield(xcorr_append=args.xcorr_append)
else:
raise argparse.ArgumentError('Not a valid station name')
day = UTCDateTime(args.date)
if args.xcorr_append is None:
stream = data.getRawStream(day, station, component=args.component)
else:
stream = data.getStream(day, station, component=args.component)
if stream[0].stats.is_fft:
stream.ifft()
plotTrace(stream, component=args.component, **kwargs)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# by TR
from sito.data import IPOC
from sito.noisexcorr import removeBad
import glob
from sito import read
import os.path
method = 'filter0.01-1_water_env2_whitening_1bit_fft'
data = IPOC(xcorr_append='/Tocopilla/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
#path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter4-6_water_env2_1bit/stretch_t/'
#path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter0.01-1_water_env2_whitening_1bit/stretch2/'
path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter0.01-1_water_env2_whitening_1bit_fft/xcorr/'
for file in glob.glob(path + '*.QHD'): #@ReservedAssignment
if not 'filter' in os.path.basename(file):
print file
ms = read(file)
ms.normalize()
removeBad(ms, 0.8)
ms.write(os.path.splitext(file)[0], 'Q')
# min_longitude=lon_Toc - 1., max_longitude=lon_Toc + 1,
# max_results=1000000,
# min_magnitude=None, max_magnitude=None)
#events.write('/home/richter/Data/events/events_Tocopilla.txt')
events = Events.read('/home/richter/Data/events/events_Tocopilla.txt')
events.pick(latitude=lat_Toc,
longitude=lon_Toc,
minval=0,
maxval=100.,
indegree=False)
#events.plot(lat_Toc, lon_Toc, circles=(1,))
method = 'filter2-20_1bit'
#method = 'filter0.005_1bit'
data = IPOC(xcorr_append='/Tocopilla/' + method, use_local_LVC=True)
t1 = UTC('2007-11-01')
t2 = UTC('2007-12-01')
period = 1800
correlation = ('PB03Z', 'PB03Z')
stream = data.readX(correlation, t1, t2, period=period)
#stream.filter2(2, 20)
stream.setHIForHist(events, period=period)
figsize = (8.267, 11.693)[::-1]
add_to_title = '_againfilter_zoom1'
#save = data.getPlotXCorr(correlation, 'all') + '_againfilter_zoom1 + events.png'
save = False
stream.plotXcorr(
def main():
stations = (
"PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16 HMBCX MNMCX PATCX PSGCX LVC"
)
# TAIQ
stations2 = None
components = "Z"
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC("2006-01-01")
# t2 = UTC('2011-09-01')
# t1 = UTC('2007-01-01')
# t2 = UTC('2009-01-01')
t2 = UTC("2012-09-01")
shift = 500
correlations = get_correlations(stations, components, stations2)
method = "FINAL_filter0.01-1_1bit"
data = IPOC(xcorr_append="/" + method, use_local_LVC=False)
data.setXLogger("_" + method)
pool = Pool()
prepare(
data,
stations.split(),
t1,
t2,
component=components,
filter=(0.01, 1, 2, True),
downsample=10,
eventremoval="waterlevel_env2",
param_removal=(10, 0),
whitening=False,
normalize="1bit",
param_norm=None,
pool=pool,
)
noisexcorrf(data, correlations, t1, t2, shift, pool=pool)
pool.close()
pool.join()
stack(data, correlations, dt=10 * 24 * 3600, shift=5 * 24 * 3600)
stack(data, correlations, dt=50 * 24 * 3600, shift=5 * 24 * 3600)
stack(data, correlations, dt=-1)
plotXcorrs(
data,
correlations,
t1,
t2,
start=None,
end=None,
plot_overview=True,
plot_years=False,
use_dlognorm=False,
plot_stack=True,
plot_psd=False,
add_to_title="",
downsample=None,
)
plotXcorrs(
data,
correlations,
t1=None,
t2=None,
start=None,
end=None,
plot_overview=True,
plot_years=False,
use_dlognorm=False,
plot_stack=True,
plot_psd=False,
add_to_title="",
downsample=None,
stack=("10days", "5days"),
)
plotXcorrs(
data,
correlations,
t1=None,
t2=None,
start=None,
end=None,
plot_overview=True,
plot_years=False,
use_dlognorm=False,
plot_stack=True,
plot_psd=False,
add_to_title="",
downsample=None,
stack=("50days", "5days"),
)
def main():
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 PB16 HMBCX MNMCX PATCX PSGCX LVC'
# TAIQ
stations2 = None
components = 'Z'
# TOcopilla earthquake: 2007-11-14 15:14
t1 = UTC('2006-01-01')
#t2 = UTC('2011-09-01')
#t1 = UTC('2007-01-01')
#t2 = UTC('2009-01-01')
t2 = UTC('2012-09-01')
shift = 500
correlations = get_correlations(stations, components, stations2)
method = 'FINAL_filter0.01-1_1bit'
data = IPOC(xcorr_append='/' + method, use_local_LVC=False)
data.setXLogger('_' + method)
pool = Pool()
prepare(data,
stations.split(),
t1,
t2,
component=components,
filter=(0.01, 1, 2, True),
downsample=10,
eventremoval='waterlevel_env2',
param_removal=(10, 0),
whitening=False,
normalize='1bit',
param_norm=None,
pool=pool)
noisexcorrf(data, correlations, t1, t2, shift, pool=pool)
pool.close()
pool.join()
stack(data, correlations, dt=10 * 24 * 3600, shift=5 * 24 * 3600)
stack(data, correlations, dt=50 * 24 * 3600, shift=5 * 24 * 3600)
stack(data, correlations, dt=-1)
plotXcorrs(data,
correlations,
t1,
t2,
start=None,
end=None,
plot_overview=True,
plot_years=False,
use_dlognorm=False,
plot_stack=True,
plot_psd=False,
add_to_title='',
downsample=None)
plotXcorrs(data,
correlations,
t1=None,
t2=None,
start=None,
end=None,
plot_overview=True,
plot_years=False,
use_dlognorm=False,
plot_stack=True,
plot_psd=False,
add_to_title='',
downsample=None,
stack=('10days', '5days'))
plotXcorrs(data,
correlations,
t1=None,
t2=None,
start=None,
end=None,
plot_overview=True,
plot_years=False,
use_dlognorm=False,
plot_stack=True,
plot_psd=False,
add_to_title='',
downsample=None,
stack=('50days', '5days'))
from sito.data import IPOC
import numpy as np
from sito.util.main import streamdaygen
import pylab as plt
stations = 'PB01' # PB04 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB16'
channels = 'WKI WDI WII'
#channels = 'WDI_10'
datafile = '/home/richter/Data/climate/2006-2012_%s_%s.npz'
output = '/home/richter/Results/IPOC/climate/%s.pdf'
calculate = False
show = False
if calculate:
ipoc = IPOC()
for station in stations.split():
for channel in channels.split():
stream = ipoc.getChannelFromClient('2006-01-01', '2013-01-01',
station=station, channel=channel)
data = []
dates = []
for day in streamdaygen(stream):
day.merge()
data.append(np.mean(day[0].data))
st = day[0].stats.starttime
et = day[0].stats.endtime
dates.append(st + (et - st) / 2.)
np.savez(datafile % (station, channel), dates=dates, data=data)
else:
#http://stackoverflow.com/questions/7733693/matplotlib-overlay-plots-with-different-scales
from sito.data import IPOC
import numpy as np
from sito.util.main import streamdaygen
import pylab as plt
stations = 'PB01' # PB04 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB16'
channels = 'WKI WDI WII'
#channels = 'WDI_10'
datafile = '/home/richter/Data/climate/2006-2012_%s_%s.npz'
output = '/home/richter/Results/IPOC/climate/%s.pdf'
calculate = False
show = False
if calculate:
ipoc = IPOC()
for station in stations.split():
for channel in channels.split():
stream = ipoc.getChannelFromClient('2006-01-01',
'2013-01-01',
station=station,
channel=channel)
data = []
dates = []
for day in streamdaygen(stream):
day.merge()
data.append(np.mean(day[0].data))
st = day[0].stats.starttime
et = day[0].stats.endtime
dates.append(st + (et - st) / 2.)
np.savez(datafile % (station, channel), dates=dates, data=data)
#!/usr/bin/env python
# by TR
from sito.data import IPOC
from obspy.core import UTCDateTime as UTC
import matplotlib.pyplot as plt
from sito.util.main import streamtimegen
from sito.stream import Stream
from progressbar import ProgressBar
data = IPOC()
t_day = UTC('2008-01-01')
station = 'PB01'
stream = data.getRawStreamFromClient(
t_day, t_day + 24 * 3600, station, component='Z')
stream.setHI('filter', '')
stream.demean()
stream.filter2(0.5, 5)
stream.trim2(0, 5 * 3600)
auto = Stream()
for st in streamtimegen(stream, dt=60, start=None, shift=30, use_slice=True):
tr = st[0].copy()
tr.addZeros(60)
tr.acorr(60)
auto.append(tr)
print auto
auto.plotXcorr()
stream.plot(type='dayplot')
(2012, ) * 7)
stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 HMBCX MNMCX PATCX PSGCX LVC'
# 20 stations, 10 days, 2008-2012
# days = '2008-01-01 2008-07-01 2009-01-01 2009-07-01 2010-01-01 2010-07-01 2011-01-01 2011-07-01 2012-01-01 2012-07-01'
# stations = 'PB01 PB02 PB03 PB04 PB05 PB06 PB07 PB08 PB09 PB10 PB11 PB12 PB13 PB14 PB15 HMBCX MNMCX PATCX PSGCX LVC'
pic_dir = '/home/richter/Data/IPOC/raw_pictures/'
output = '/home/richter/Results/IPOC/raw_pics/test.pdf'
output = '/home/richter/Results/IPOC/raw_pics/13stations_7days_2012.pdf'
scale_range = 2000
scale_LVC = 20000
days = days.split()
stations = stations.split()
dx = 0.98 / len(days)
dy = 0.98 / len(stations)
data = IPOC()
figsize = (11.69, 16.53) # A3 # 8.27, 11.69 # A4
fig = plt.figure(figsize=figsize)
for i, station in ProgressBar(len(stations))(enumerate(stations)):
for j, day in enumerate(days):
x0 = 0.01 + dx * (j + 0.02)
y0 = 0.99 - dy * (1 + i)
if i == 0:
fig.text(x0 + 0.98 * dx / 2, 0.98, day, ha='center', va='center')
if j == 0:
fig.text(0.02,
y0 + (0.98 / 2) * dy,
station,
va='center',
ha='center',
rotation=90)
#method = 'filter4-6_water_env2_1bit' #method = 'filter4-6_water_env2_1bit_fft' #method = 'filter0.01-1_water_env2_whitening_1bit_fft' #method = '/Tocopilla/filter0.01-1_water_env2_whitening_1bit_fft' method = 'FINAL_filter0.01-0.5_1bit_whitening' method = 'FINAL_filter0.01-0.5_1bit_auto' method = 'FINAL_filter1-3_1bit_auto' method = 'FINAL_filter4-6_1bit_auto' method = 'FINAL_filter4-6_1bit_auto_3C' method = 'FINAL_filter3-5' method = 'PAT_filter9-11' method = 'zerotest_nozero' data = IPOC(xcorr_append='/' + method, use_local_LVC=False) #path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter4-6_water_env2_1bit/stretch_t/' #path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter4-6_water_env2_1bit_fft/stretch/' #path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter0.01-1_water_env2_whitening_1bit/stretch2/' #path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter0.01-1_water_env2_whitening_1bit_fft/stretch_Toco/' path = '/home/richter/Results/IPOC/xcorr/FINAL_filter0.01-0.5_1bit_whitening/stretch_Toco/swcoda/' path = '/home/richter/Results/IPOC/xcorr/FINAL_filter0.01-0.5_1bit_auto/stretch_Toco/' path = '/home/richter/Results/IPOC/xcorr/FINAL_filter1-3_1bit_auto/stretch/' path = '/home/richter/Results/IPOC/xcorr/FINAL_filter4-6_1bit_auto/stretch/' path = '/home/richter/Results/IPOC/xcorr/FINAL_filter4-6_1bit_auto_3C/stretch3_10s/' path = '/home/richter/Results/IPOC/xcorr/FINAL_filter3-5/stretch/' path = '/home/richter/Results/IPOC/xcorr/PAT_filter9-11/stretch3/' path = '/home/richter/Results/IPOC/xcorr/zerotest_nozero/stretch/' #path = '/home/richter/Results/IPOC/xcorr/FINAL_filter4-6_1bit_auto/stretch2/' #path = '/home/richter/Results/IPOC/xcorr/FINAL_filter4-6_1bit_auto/stretch_Toco_PATCX/' #path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter4-6_water_env2_1bit_fft/stretch/'
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# by TR
from sito.data import IPOC
from sito.noisexcorr import removeBad
import glob
from sito import read
import os.path
method = 'filter0.01-1_water_env2_whitening_1bit_fft'
data = IPOC(xcorr_append='/Tocopilla/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
#path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter4-6_water_env2_1bit/stretch_t/'
#path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter0.01-1_water_env2_whitening_1bit/stretch2/'
path = '/home/richter/Results/IPOC/xcorr/Tocopilla/filter0.01-1_water_env2_whitening_1bit_fft/xcorr/'
for file in glob.glob(path + '*.QHD'): #@ReservedAssignment
if not 'filter' in os.path.basename(file):
print file
ms = read(file)
ms.normalize()
removeBad(ms, 0.8)
ms.write(os.path.splitext(file)[0], 'Q')
def main():
stations = 'PB01 PB03'
stations2 = 'PB03'
components = 'Z'
t1 = UTC('2010-01-01')
t2 = UTC('2010-12-31')
shift = 500
correlations = get_correlations(stations, components, stations2)
method = 'filter0.1-1_1bit_whitening0.01'
data = IPOC(xcorr_append='/tests/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
prepare(data,
stations.split(),
t1,
t2,
filter=(0.1, 1),
downsample=10,
whitening=0.01,
component=components,
normalize='1bit',
param_norm=None,
use_floating_stream=True)
xcorr_day(data, correlations, t1, t2, shift, use_floating_stream=True)
plotXcorrs(data,
correlations,
t1,
t2,
plot_overview=False,
plot_stack=True,
plot_psd=True,
add_to_title=method)
method = 'filter0.1-1_1bit_whitening0.001'
data = IPOC(xcorr_append='/tests/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
prepare(data,
stations.split(),
t1,
t2,
filter=(0.1, 1),
downsample=10,
whitening=0.001,
component=components,
normalize='1bit',
param_norm=None,
use_floating_stream=True)
xcorr_day(data, correlations, t1, t2, shift, use_floating_stream=True)
plotXcorrs(data,
correlations,
t1,
t2,
plot_overview=False,
plot_stack=True,
plot_psd=True,
add_to_title=method)
method = 'filter0.1-1_1bit_whitening0.1'
data = IPOC(xcorr_append='/tests/' + method, use_local_LVC=True)
data.setXLogger('_' + method)
prepare(data,
stations.split(),
t1,
t2,
filter=(0.1, 1),
downsample=10,
whitening=0.1,
component=components,
normalize='1bit',
param_norm=None,
use_floating_stream=True)
xcorr_day(data, correlations, t1, t2, shift, use_floating_stream=True)
plotXcorrs(data,
correlations,
t1,
t2,
plot_overview=False,
plot_stack=True,
plot_psd=True,
add_to_title=method)
downsample=args.downsample,
save=args.save,
show=args.save is None))
print kwargs
if args.date is None:
from sito import read
from sito.imaging import plotTrace
stream = read(args.file_station)
plotTrace(stream, **kwargs)
else:
from sito.imaging import plotTrace2
station = args.file_station
if station.startswith('PB') or station == 'LVC':
from sito.data import IPOC
data = IPOC(xcorr_append=args.xcorr_append)
elif station == 'PKD':
from sito.data import Parkfield
data = Parkfield(xcorr_append=args.xcorr_append)
else:
raise argparse.ArgumentError('Not a valid station name')
day = UTCDateTime(args.date)
if args.xcorr_append is None:
stream = data.getRawStream(day, station, component=args.component)
else:
stream = data.getStream(day, station, component=args.component)
if stream[0].stats.is_fft:
stream.ifft()
plotTrace(stream, component=args.component, **kwargs)
#import numpy as np
#import os.path
#import warnings
#from obspy.core.util.decorator import deprecated
#import itertools
import logging
from sito import Stream, read
from obspy.core.event import readEvents
from sito.data import IPOC
from obspy.core.util.attribdict import AttribDict
import numpy as np
import os.path
import glob
from progressbar import ProgressBar
log = logging.getLogger(__name__)
data = IPOC()
def get_event_id(expr):
if '/' in expr:
expr = expr.split('/', 1)[1]
expr = expr.replace('NLL.', '').replace('Origin#', '')
return expr
def cut_events(in_, out):
print 'read events...'
catalog = readEvents(in_, 'QUAKEML')
print 'cut events...'
for event in ProgressBar()(catalog):
oid = get_event_id(event.origins[0].resource_id.getQuakeMLURI())
lon_Toc = -69.971
#events = Events.load(min_datetime="2007-01-01", max_datetime="2008-12-31",
# min_latitude=lat_Toc - 1, max_latitude=lat_Toc + 1,
# min_longitude=lon_Toc - 1., max_longitude=lon_Toc + 1,
# max_results=1000000,
# min_magnitude=None, max_magnitude=None)
#events.write('/home/richter/Data/events/events_Tocopilla.txt')
events = Events.read('/home/richter/Data/events/events_Tocopilla.txt')
events.pick(latitude=lat_Toc, longitude=lon_Toc, minval=0, maxval=100., indegree=False)
#events.plot(lat_Toc, lon_Toc, circles=(1,))
method = 'filter2-20_1bit'
#method = 'filter0.005_1bit'
data = IPOC(xcorr_append='/Tocopilla/' + method, use_local_LVC=True)
t1 = UTC('2007-11-01')
t2 = UTC('2007-12-01')
period = 1800
correlation = ('PB03Z', 'PB03Z')
stream = data.readX(correlation, t1, t2, period=period)
#stream.filter2(2, 20)
stream.setHIForHist(events, period=period)
figsize = (8.267, 11.693)[::-1]
add_to_title = '_againfilter_zoom1'
#save = data.getPlotXCorr(correlation, 'all') + '_againfilter_zoom1 + events.png'
save = False
stream.plotXcorr(0, 50, imshow=True, use_dlognorm=True, filter=(2, 20),