def setUp(self):
self.path = os.path.dirname(__file__)
# Parkfield
self.pkd = ParkfieldTest(os.path.join(self.path, 'data'),
os.path.join(self.path, 'temp'))
self.t1_pkd = UTC('1997-09-03')
self.t2_pkd = UTC('1997-09-03')
self.ipoc = IPOCTest(os.path.join(self.path, 'data'),
os.path.join(self.path, 'temp'))
self.ipoc2 = IPOCTest(os.path.join(self.path, 'data'),
os.path.join(self.path, 'temp/test2'))
# ## Copy test files from location of raw files
# print os.path.isfile(self.pkd.getDay('PKD', self.t1_pkd) + '.QHD')
try:
self.pkd.getRawStream(self.t1_pkd, 'PKD', 'Z', checkfile=True)
except ValueError:
print('Copy missing raw files for Parkfield...')
self.pkd.copyTestDataFiles(self.t1_pkd, self.t2_pkd, component='Z')
self.t1_ipoc = UTC('2010-05-04')
self.t1b_ipoc = UTC('2010-05-05')
self.t2_ipoc = UTC('2010-05-06')
try:
self.ipoc.getRawStream(self.t1_ipoc, 'PB01', 'Z', checkfile=True)
except ValueError:
print('Copy missing raw files for IPOC...')
self.ipoc.copyTestDataFiles(self.t1_ipoc,
self.t2_ipoc,
component='Z')
def mean_hour_file():
fname = data_path + 'climate_hour_IQ'
#fname = data_path + 'climate_CHO'
sens = False
data = get_dict2(np.load(fname + '.npz'))
dates = data['date']
temp = data['temp' + '_sens' * sens]
preselect = (UTC('2007-01-01').toordinal() <= dates) * (UTC('2012-01-01').toordinal() >= dates)
dates = dates[preselect]
temp = temp[preselect]
date0 = int(dates[0] + 1)
temps = []
for i in range(int(dates[-1] - date0)):
select = (date0 + i <= dates) * (dates <= date0 + i + 1)
if i == 0 or len(plotdates) != 25:
plotdates = dates[select]
temps.append(temp[select])
if i % 1000 == 0:
print i
print len(temps)
print [len(i) for i in temps]
temps = [t for t in temps if len(t) == 25]
print len(temps)
temps = np.ma.mean(temps, axis=0)
np.savez(fname + '_sens' * sens + '_mean', date=plotdates, temp=np.array(temps))
def get_events(individual=True):
endtime = UTC('2015-09-01')
kw = {
'starttime': UTC('2005-04-01'),
'endtime': endtime,
'minmagnitude': 1.5,
'maxmagnitude': 3.5,
'minlatitude': 25,
'maxlatitude': 49.5,
'minlongitude': -124,
'maxlongitude': -67,
'maxdepth': 40,
'includearrivals': individual,
'catalog': 'ANF'
}
client = FSDNClient()
if individual:
for t in range(6, 17):
kw['endtime'] = UTC('20%02d-01-01' % t) if t < 16 else endtime
events = client.get_events(**kw)
for ev in events:
id_ = _extract_eventid(ev)
ev.resource_id = ResourceIdentifier(id_)
ev.write(EVENT_PICKS_FNAME + id_ + '.xml', 'QUAKEML')
print('fetched events of year %d' % kw['starttime'].year)
kw['starttime'] = kw['endtime']
else:
events = client.get_events(**kw)
events.plot(projection='local', outfile=EVENT_FNAME + '.png')
for ev in events:
id_ = _extract_eventid(ev)
ev.resource_id = ResourceIdentifier(id_)
events.write(EVENT_FNAME + '.xml', 'QUAKEML')
return events
def set_dates(self):
try:
self.startdate = self.date = UTC(UTC(str(self.ui.t1.text())).date)
self.enddate = UTC(UTC(str(self.ui.t2.text())).date)
self.daystream = None
except:
log.exception('')
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 synrf(mod, p=6.4, wave='P', gauss=2., nsamp=1024, fsamp=20., \
tshft=10., nsv=(3.5, 0.25)):
"""
Computes a synthetic teleseismic P or SV receiver function.
Method from seispy.
Parameters:
mod velocity model
Optional parameters:
p horizontal slowness of r.f. is; this value *must*
be the angular slowness in seconds/degree and will
stored in the extra[10] entry of the trace header
wave "P" or "SV".
gauss Gauss lowpass parameter used to compute r.f.
nsamp number of samples to be used in synthetic computation
fsamp sampling frequency in Hz
tshft time shift used in the computation of the r.f., i.e.
zero delay time corresponds to the sample at the
index tshft*fsamp. This value is stored in the 'sec'
field of the trace starting time, e.g. a time of
0/0/0 0:0:-10 corresponds to tshft of +10 sec.
nsv tuple containing the near-surface S velocity and
Poisson's ratio
"""
import seis._rf
import sito
from obspy.core import UTCDateTime as UTC
if type(nsv) is tuple: nsvs, nspr = nsv
else: nsvs, nspr = nsv, 0.25
if not wave in ["P", "SV"]:
raise ValueError, "wave type must be either 'P' or 'SV'"
if wave == "P": c = "RFQ"
else: c = "RFL"
# Response of L component, Response of Q component, RF of Q component
fzz, frr, frf = seis._rf.synrf(mod.z, mod.vp, mod.vs, mod.rh, mod.qp, mod.qs, \
p, gauss, nsamp, fsamp, tshft, nsvs, nspr, wave)
stream = sito.Stream(traces=[
sito.Trace(data=fzz),
sito.Trace(data=frr),
sito.Trace(data=frf)
])
for i, tr in enumerate(stream):
tr.stats.starttime = UTC('2000-01-01') - tshft
tr.stats.ponset = UTC('2000-01-01')
tr.stats.sampling_rate = fsamp
tr.stats.channel = ('LRSP', 'QRSP', c)[i]
tr.stats.slowness = p
return stream
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 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 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 pick(self,
latitude=None,
longitude=None,
minval=0,
maxval=180,
indegree=True,
after='1900-01-01',
before='3000-01-01',
bigger=0.,
smaller=10.,
replace=True):
"""
Pick events fullfilling the given conditions.
:param latitude, longitude: coordinates for distance condition
:param minval, maxval: distance of event has to be between this values
:param indegree: True if minval and maxval in deg, False if in km
:param after, before: UTCDateTime objects or strings with time range
:param bigger, smaller: magnitude range
:param replace: if True the data in the event list is overwritten
:return: picked Events instance
"""
if indegree:
degorkm = 'deg'
else:
degorkm = 'km'
newdata = []
dist = 50
for event in self[::-1]:
if latitude != None and longitude != None:
if not indegree:
dist = gps2DistAzimuth(event.latitude, event.longitude,
latitude, longitude)[0] / 1000.
else:
dist = gps2DistDegree(event.latitude, event.longitude,
latitude, longitude)
if bigger <= event.magnitude and smaller >= event.magnitude and \
dist >= minval and dist <= maxval and \
UTC(after) <= event.datetime and UTC(before) >= event.datetime:
newdata.append(event)
elif replace:
self.remove(event)
if latitude == None:
latitude = 0
if longitude == None:
longitude = 0
log.info(
'Pick %d events with distance between %d%s and %d%s from coordinates lat:%5.2f lon:%5.2f; between the dates %s and %s and between the magnitudes %3.1f and %3.1f'
% (len(newdata), minval, degorkm, maxval, degorkm, latitude,
longitude, after, before, bigger, smaller))
return self.__class__(newdata[::-1])
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 get_stations(level='station'):
fname = STATION_FNAME + '_bh' * (level == 'channel') + '.xml'
kw = {
'network': '_US-REF',
'location': '',
'channel': 'BH?',
'starttime': UTC('2005-04-01'),
'endtime': UTC('2015-09-01'),
'minlatitude': 20,
'maxlatitude': 50,
'minlongitude': -130,
'maxlongitude': -65,
'level': level
}
client = FSDNClient()
stations = client.get_stations(**kw)
print('num stations:', len(stations.get_contents()['stations']))
kw['network'] = '_US-TA'
stations2 = client.get_stations(**kw)
merge_stations(stations, stations2)
print('num stations:', len(stations.get_contents()['stations']))
# use HH channel only if BH channel not availlable
kw['network'] = '_US-REF'
kw['channel'] = 'HH?'
stations2 = client.get_stations(**kw)
merge_stations(stations, stations2)
print('num stations:', len(stations.get_contents()['stations']))
kw['network'] = '_US-TA'
stations2 = client.get_stations(**kw)
merge_stations(stations, stations2)
print('num stations:', len(stations.get_contents()['stations']))
# get stations with loc code 00 if no other station
kw['network'] = '_US-REF'
kw['channel'] = 'BH?'
kw['location'] = '00'
stations2 = client.get_stations(**kw)
merge_stations(stations, stations2)
print('num stations:', len(stations.get_contents()['stations']))
kw['network'] = '_US-TA'
stations2 = client.get_stations(**kw)
merge_stations(stations, stations2)
print('final num stations:', len(stations.get_contents()['stations']))
stations.write(fname, 'STATIONXML')
if level == 'station':
stations.plot(projection='local',
size=1,
outfile=STATION_FNAME + '.pdf')
return stations
def plot_some_events():
from obspy.core.event import Catalog, Event, Origin, Magnitude
from obspy.core import UTCDateTime as UTC
eqs = """2008-09-10T16:12:03 6.0 -20.40 -69.40 40
2008-03-24T20:39:06 5.9 -20.10 -69.20 85
2008-03-01T19:51:59 5.7 -20.10 -69.60 15
2008-02-15T16:54:04 5.5 -23.00 -70.20 32
2008-02-04T17:01:30 6.6 -20.20 -70.00 36
2007-12-16T08:09:16 7.1 -22.80 -70.00 14
2007-11-14T15:40:51 7.8 -22.34 -70.06 37""" #GEOFON:-22.30 -69.80
events = []
for eq in eqs.split('\n'):
time, mag, lat, lon, depth = eq.split()
ev = Event(event_type='earthquake', creation_info='GEOFON',
origins=[Origin(time=UTC(time), latitude=float(lat),
longitude=float(lon), depth=float(depth))],
magnitudes=[Magnitude(mag=float(mag), magnitude_type='M')])
events.append(ev)
cat = Catalog(events[::-1])
#print cat
#cat.plot(projection='local')
lons = [ev.origins[0].longitude for ev in cat]
lats = [ev.origins[0].latitude for ev in cat]
dates = [ev.origins[0].time for ev in cat]
mags = [ev.magnitudes[0].mag for ev in cat]
def extract_cdo_file():
file = data_path + 'CDO_CAL.txt' #AF, IQ
regex = """
^
(?P<stn>\d+)\s+
(?P<wban>\d+)\s+
(?P<date>\d+)\s+
(?P<temp>[-\d.]+)\s+
(?P<temp_count>\d+)\s+
(?P<dewp>[-\d.]+)\s+
(?P<dewp_count>\d+)\s+
(?P<slp>[\d.]+)\s+
(?P<slp_count>\d+)\s+
(?P<stp>[\d.]+)\s+
(?P<stp_count>\d+)\s+
(?P<visib>[\d.]+)\s+
(?P<visib_count>\d+)\s+
(?P<wdsp>[\d.]+)\s+
(?P<wdsp_count>\d+)\s+
(?P<mxspd>[\d.]+)\s+
(?P<gust>[\d.]+)\s+
(?P<max>[-\d.]+)\*?\s+
(?P<min>[-\d.]+)\*?\s+
(?P<prcp>[\d.]+)
(?P<prcp_flag>.)\s+
(?P<sndp>[\d.]+)\s+
(?P<frshtt>\d+)
"""
with open(file, 'r') as f:
filedata = f.read()
matches = re.finditer(regex, filedata, re.VERBOSE + re.MULTILINE)
data_list = [i.groupdict() for i in matches]
data = {}
for i, dp in enumerate(data_list): # convert numbers to float and int types
for key, item in dp.iteritems():
if key == 'date':
dp[key] = UTC(item).toordinal()
elif item is not None and key != 'frshtt':
if isint(item):
dp[key] = int(item)
elif isfloat(item):
dp[key] = float(item)
if dp['stn'] == 854420:
dp['station'] = 'Antofagasta'
elif dp['stn'] == 854320:
dp['station'] = 'Calama'
elif dp['stn'] == 854180:
dp['station'] = 'Iquique'
else:
dp['station'] = 'other'
if i == 0:
for key, item in dp.iteritems():
data[key] = []
for key, item in dp.iteritems():
#ipshell()
data[key].append(item)
np.savez(data_path + 'climate_CAL', **data) #AF, IQ
def main2():
# dis
fwd = 155 / 25.4
mpl.rcParams.update({
'font.size': 9,
'lines.linewidth': 1,
'xtick.major.width': 1.
})
pathav = '/home/richter/Results/IPOC/avail'
args = [
'--nox', '-l', pathav + '/avail_IPOC_2012_03.npz', '-o',
pathav + '/avail_IPOC_2012_09.png'
]
fig = main(args)
fig.set_size_inches(fwd, 0.9 * fwd / 1.61, forward=True)
ax = fig.axes[0]
ax.set_xticklabels([])
ax.set_ylim([-0.6, ax.get_ylim()[1] + 0.1])
for y in range(2006, 2012):
ax.annotate(str(y),
xy=(date2num(UTC(year=y, month=7, day=1)), -0.6),
xycoords='data',
xytext=(0, -5),
textcoords='offset points',
ha='center',
va='top',
clip_on=False)
plt.tight_layout()
plt.subplots_adjust(bottom=0.08)
#from IPython import embed
#embed()
fig.savefig(pathav + '/avail_IPOC_2012_09.png', dpi=300)
def _load_data(seedid, day, data, data_format, key='data', **prep_kw):
"""Load preprocessed or raw data"""
from obspy import UTCDateTime as UTC
from yam.util import _seedid2meta
from yam.correlate import get_data, preprocess
smeta = _seedid2meta(seedid)
day = UTC(day)
if key == 'data':
obj = get_data(smeta,
data,
data_format,
day,
overlap=0,
edge=0,
trim_and_merge=True)
return obj
stream = get_data(smeta,
data,
data_format,
day,
overlap=0,
edge=60,
trim_and_merge=False)
preprocess(stream, day, **prep_kw)
return stream
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():
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 _get_data_glob(data):
"""
Construct a glob expression from the data expression
"""
from obspy import UTCDateTime as UTC
kw = dict(network='*', station='*', location='*', channel='*',
t=UTC('2211-11-11 11:11:11'))
dataglob = data.format(**kw)
dataglob = dataglob.replace('22', '*').replace('11', '*')
return dataglob
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 mean_hour_file2():
#fname = data_path + 'climate_hour_IQ'
fname = data_path + 'climate_CHO'
sens = False
data = get_dict2(np.load(fname + '.npz'))
dates = data['date']
temp = data['temp' + '_sens' * sens]
preselect = (UTC('2007-01-01').toordinal() <= dates) * (UTC('2012-01-01').toordinal() >= dates)
dates = dates[preselect]
temp = temp[preselect]
date0 = int(dates[0] + 1)
temps = []
dates2 = []
for i in range(int(dates[-1] - date0)):
select = (date0 + i <= dates) * (dates <= date0 + i + 1)
if i == 0 or len(plotdates) != 1441:
plotdates = dates[select]
if len(temp[select]) >= 144:
temps.append(temp[select])
dates2.append(dates[select])
if i % 1000 == 0:
print i
print len(temps)
print [len(i) for i in temps]
for i in range(len(temps)):
#print i
#print plotdates
#print dates2[i] - dates2[i][0] + plotdates[0]
#print temps[i]
temps[i] = np.interp(plotdates, dates2[i] - dates2[i][0] + plotdates[0], temps[i])
temps = [t for t in temps if len(t) == 1441]
print len(temps)
temps_mean = np.ma.mean(temps, axis=0)
temps2 = np.array(temps) - np.tile(np.ma.mean(temps, axis=1)[:, np.newaxis], len(temps[0]))
#temps_mean = np.ma.mean(temps2, axis=0) + np.ma.mean(temps2)
#from IPython import embed
#embed()
temps_std = np.ma.std(temps2, axis=0)
np.savez(fname + '_sens' * sens + '_hui_mean', date=plotdates, temp=np.array(temps_mean), temp_std=np.array(temps_std))
def extract_hour_file():
fname = data_path + 'HOUR_ALL.txt'
data = defaultdict(lambda : defaultdict(list))
with open(fname, 'r') as f:
for line in f:
if line[:2] in ('AN', 'IQ'):
ls = line.split()
st = ls[0]
time = date2num(UTC(ls[3] + ls[4]))
data[st]['date'].append(time)
data[st]['temp'].append(float(ls[20]))
np.savez(data_path + 'climate_hour_IQ', **data['IQUIQUE'])
np.savez(data_path + 'climate_hour_AF', **data['ANTOFAGASTA'])
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():
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 setUp(self):
self.stream = read().sort()
# add processing info
self.stream.decimate(2)
self.stream.differentiate()
self.stream[0].stats.onset = UTC()
self.stream[0].stats.header = 42
self.stream[0].stats.header2 = 'Test entry'
self.stream[0].stats.header3 = u'Test entry unicode'
stack = dict(group='all', count=5, type=['pw', 2])
self.stream[0].stats.stack = stack
for tr in self.stream:
if 'response' in tr.stats:
del tr.stats.response
def produceHist(self, start=None, end=None, period=24 * 3600):
"""
produce Histogramm Information and saves into stream
"""
self.sort()
if len(self) == 0:
return
if start is None:
start = UTC(self[0].datetime.date)
if end is None:
end = UTC(self[-1].datetime)
entries = int((end - start) / period) + 1
hist_list = np.zeros(entries)
mag_list = np.zeros(entries)
for event in self:
#date = UTC(event.datetime.date)
entry = int((event.datetime - start) / period)
print entry
try:
hist_list[entry] += 1
mag_list[entry] = max(event.magnitude, mag_list[entry])
except IndexError:
pass
return hist_list, mag_list
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')