class ReportIndexEntry(Object):
path = String.T()
problem_name = String.T()
event_reference = Event.T(optional=True)
event_best = Event.T(optional=True)
silvertine_version = String.T(optional=True)
run_info = RunInfo.T(optional=True)
def setup(self):
self.data_pile = pile.make_pile(
self.data_paths, fileformat=self.data_format)
if self.data_pile.is_empty():
sys.exit('Data pile is empty!')
self.deltat_want = self.config.deltat_want or \
min(self.data_pile.deltats.keys())
self.n_samples = int(
(self.config.sample_length + self.config.tpad) / self.deltat_want)
logger.debug('loading marker file %s' % self.fn_markers)
# loads just plain markers:
markers = marker.load_markers(self.fn_markers)
if self.fn_events:
markers.extend(
[marker.EventMarker(e) for e in
load_events(self.fn_events)])
if self.sort_markers:
logger.info('sorting markers!')
markers.sort(key=lambda x: x.tmin)
marker.associate_phases_to_events(markers)
markers_by_nsl = {}
for m in markers:
if not m.match_nsl(self.config.reference_target.codes[:3]):
continue
if m.get_phasename().upper() != self.align_phase:
continue
markers_by_nsl.setdefault(m.one_nslc()[:3], []).append(m)
assert(len(markers_by_nsl) == 1)
# filter markers that do not have an event assigned:
self.markers = list(markers_by_nsl.values())[0]
if not self.labeled:
dummy_event = Event(lat=0., lon=0., depth=0.)
for m in self.markers:
if not m.get_event():
m.set_event(dummy_event)
self.markers = [m for m in self.markers if m.get_event() is not None]
if not len(self.markers):
raise Exception('No markers left in dataset')
self.config.channels = list(self.data_pile.nslc_ids.keys())
self.config.channels.sort()
def setup(self):
self.data_pile = pile.make_pile(
self.data_paths, fileformat=self.data_format)
if self.data_pile.is_empty():
sys.exit('Data pile is empty!')
self.deltat_want = self.config.deltat_want or \
min(self.data_pile.deltats.keys())
self.n_samples = int(
(self.config.sample_length + self.config.tpad) / self.deltat_want)
logger.debug('loading marker file %s' % self.fn_markers)
# loads just plain markers:
markers = marker.load_markers(self.fn_markers)
if self.fn_events:
markers.extend(
[marker.EventMarker(e) for e in
load_events(self.fn_events)])
marker.associate_phases_to_events(markers)
markers = [m for m in markers if isinstance(m, marker.PhaseMarker)]
markers_dict = defaultdict(list)
for m in markers:
if m.get_phasename().upper() != self.align_phase:
continue
markers_dict[m.get_event()].append(m)
self.markers = []
for e, _markers in markers_dict.items():
first = min(_markers, key=lambda x: x.tmin)
self.markers.append(first)
if not self.labeled:
dummy_event = Event(lat=0., lon=0., depth=0.)
for m in self.markers:
if not m.get_event():
m.set_event(dummy_event)
self.markers = [m for m in self.markers if m.get_event() is not None]
if not len(self.markers):
raise Exception('No markers left in dataset')
self.config.channels = list(self.data_pile.nslc_ids.keys())
self.config.channels.sort()
if True:
_map.gmt.psmeca(
S='%s%g' % ('m', size_cm * 2.0),
#G=gmtpy.color(colors[e.cluster]),
#G=colors[i_e],
G='red',
C='3p,0/0/0',
#W='thinnest,%i/%i/%i' % (255, 255, 255),
#L='thinnest,%i/%i/%i' % (255, 255, 255),
in_rows=[data],
*_map.jxyr)
_map.save(outpath=outfn)
if __name__ == '__main__':
e = list(Event.load_catalog(filename='event.pf'))[0]
#stations = model.load_stations('arrays.pf')
stations = model.load_stations('array_center.pf')
color_wet = [200, 200, 200]
color_dry = [253, 253, 253]
params = MapParameters(lat=e.lat,
lon=e.lon,
radius=8000000,
outfn='array-map-new.pdf',
stations=stations,
events=[e],
show_topo=False,
show_grid=False,
color_wet=color_wet,
color_dry=color_dry)
make_map(map_parameters=params)
data = (e.lon, e.lat, 10, 1,1,1,0,0,0, 1, e.lon, e.lat, 'Test site')
if True:
_map.gmt.psmeca(
S='%s%g' % ('m', size_cm*2.0),
#G=gmtpy.color(colors[e.cluster]),
#G=colors[i_e],
G='red',
C='3p,0/0/0',
#W='thinnest,%i/%i/%i' % (255, 255, 255),
#L='thinnest,%i/%i/%i' % (255, 255, 255),
in_rows=[data],
*_map.jxyr)
_map.save(outpath=outfn)
if __name__=='__main__':
e = list(Event.load_catalog(filename='event.pf'))[0]
#stations = model.load_stations('arrays.pf')
stations = model.load_stations('array_center.pf')
color_wet = [200, 200, 200]
color_dry = [253, 253, 253]
params = MapParameters(lat=e.lat, lon=e.lon, radius=8000000, outfn='array-map-new.pdf',stations=stations, events=[e],
show_topo=False,
show_grid=False,
color_wet=color_wet,
color_dry=color_dry)
make_map(map_parameters=params)
print '.'*40
fdomain_station_locs = []
#with open('northkoreaplot/stations.table.mec', 'r') as f:
# for line in f.readlines():
# lat, lon, c = line.split()
config_ids = [c.id for c in configs]
return config_ids
if __name__ == "__main__":
import argparse
from pyrocko.model import Event, load_stations
parser = argparse.ArgumentParser('suggest a store for P phases only')
parser.add_argument('--stations', help='stations file')
parser.add_argument('--events', help='event file')
parser.add_argument('--force', action='store_true', help='force_overwrite')
parser.add_argument('--superdir',
default='.',
help='directory where to put the store')
parser.add_argument(
'--number_of_distances',
help='number of distances between outer grid nodes in GFDB',
default=2)
args = parser.parse_args()
stations = load_stations(args.stations)
if len(stations) == 1:
s = stations[0]
events = list(Event.load_catalog(args.events))
propose_store(s, events, superdir=args.superdir, force=args.force)
tmin = str_to_time('2013-01-01 00:00:00')
tmax = str_to_time('2014-01-01 00:00:00')
print 'download event data....'
all_events = map(lambda x: x.get_events((tmin, tmax)), catalogs)
print 'done'
# unravel lists and filter lists with more than two entries:
# TODO: die Fehlerbalken muessen unterschieden werden, um keine Wertigkeit in
# Abhaengigkeit von der Anzahl der Verfuegbaren Kataloge zu haben.
all_events = [item for sublist in all_events for item in sublist]
all_events_shallow = filter(lambda x: x.depth<33000, all_events)
magmin = 0
magmax = 6.
grouped_events = Event.grouped(all_events)
grouped_events = filter(lambda x: len(x)>=2, grouped_events)
grouped_events = filter_by_attribute(grouped_events, attr='magnitude',
maxlim=magmax, minlim=magmin)
remove_duplicates(grouped_events)
grouped_events_shallow = Event.grouped(all_events_shallow)
grouped_events_shallow = filter(lambda x: len(x)>=2, grouped_events_shallow)
grouped_events_shallow = filter_by_attribute(grouped_events_shallow,
attr='magnitude', maxlim=8., minlim=0.)
remove_duplicates(grouped_events_shallow)
grp_mean = group_mean(grouped_events_shallow)
fig = plt.figure(figsize=(4,3), dpi=160)
class ReportIndexEntry(Object):
path = String.T()
problem_name = String.T()
event_reference = Event.T(optional=True)
event_best = Event.T(optional=True)
grond_version = String.T(optional=True)
)
_map.gmt.psmeca(
S="%s%g" % ("m", size_cm * 2.0),
# G = gmtpy.color(colors[e.cluster]),
# G = colors[i_e],
G="red",
C="3p,0/0/0",
# W = 'thinnest,%i/%i/%i' % (255, 255, 255),
# L = 'thinnest,%i/%i/%i' % (255, 255, 255),
in_rows=[data],
*_map.jxyr)
_map.save(outpath=outfn)
if __name__ == "__main__":
e = list(Event.load_catalog(filename="event.pf"))[0]
stations = model.load_stations("array_center.pf")
color_wet = [200, 200, 200]
color_dry = [253, 253, 253]
params = MapParameters(
lat=e.lat,
lon=e.lon,
radius=8000000,
outfn="array-map-new.pdf",
stations=stations,
events=[e],
show_topo=False,
show_grid=False,
color_wet=color_wet,
color_dry=color_dry,
)
output[tuple(d)] = e
return output
def readnsplit(fn):
''' read and split file content....'''
with open(fn, 'r') as f:
return [l.split() for l in f.readlines()]
def convert_time(t):
return util.str_to_time('%s-%s-%s %s:%s:%s.' %
(t[0:4], t[4:6], t[6:8], t[8:10], t[10:12], t[12:14]))
fn_loki = 'catalogue_loki.dat'
fn_mts = 'qresults_definitivo_ok.dat'
events = list(Event.load_catalog(fn_loki))
mts_data = readnsplit(fn_mts)
# check pattern
pattern = ['time', 'misfit', 'lat', 'lon', 'depth', 'magnitude', 'misfit2', '_',
'moment', 'strike', 'dip', 'rake', 'dz', 'dx', 'dy', 'rt']
wanted_arguments = ['moment', 'strike', 'dip', 'rake']
mts = classify(MomentTensor, mts_data, pattern, wanted_arguments)
time_threshold = 80.
print 'number of events: ', len(events)
done = []
print 'number of mts: ', len(mts.keys())
for e in events:
for d, mt in mts.items():
# specific to this pattern because time is at first place:
if abs(e.time-convert_time(d[0])) < time_threshold:
event_id = int(line[:4])
date = line[5:27]
try:
t = datetime.strptime(date, '%d-%b-%Y %H:%M:%S.%f')
except ValueError:
continue
t = (t - datetime(1970, 1, 1)).total_seconds()
lat = float(line[28:34])
lon = float(line[35:41])
depth = float(line[43:47])
print line[56:60]
if year == 'temp':
mag = float(line[56:60])
else:
print line[60:64]
mag = float(line[60:64])
name = line[69::]
e = Event(lat=lat,
lon=lon,
depth=depth,
magnitude=mag,
name=name,
time=t,
catalog='Colm')
print e
m = EventMarker(e)
markers.append(m)
EventMarker.save_markers(markers, 'event_markers_Colm%s.txt' % year)
if __name__=="__main__":
import argparse
from pyrocko.model import Event, load_stations
parser = argparse.ArgumentParser('suggest a store for P phases only')
parser.add_argument('--stations',
help='stations file')
parser.add_argument('--events',
help='event file')
parser.add_argument('--force',
action='store_true',
help='force_overwrite')
parser.add_argument('--superdir',
default='.',
help='directory where to put the store')
parser.add_argument('--number_of_distances',
help='number of distances between outer grid nodes in GFDB',
default=2)
args = parser.parse_args()
stations = load_stations(args.stations)
if len(stations)==1:
s = stations[0]
events = list(Event.load_catalog(args.events))
propose_store(s, events, superdir=args.superdir, force=args.force)