def testGlobalCMT(self):
def is_the_haiti_event(ev):
assert near(ev.magnitude, 7.0, 0.1)
assert near(ev.lat, 18.61, 0.01)
assert near(ev.lon, -72.62, 0.01)
assert near(ev.depth, 12000., 1.)
assert ev.region.lower() == 'haiti region'
cat = catalog.GlobalCMT()
tmin = util.str_to_time('2010-01-12 21:50:00')
tmax = util.str_to_time('2010-01-13 03:17:00')
names = cat.get_event_names(time_range=(tmin, tmax), magmin=5.)
ident = None
for name in names:
ev = cat.get_event(name)
if ev.magnitude > 7:
is_the_haiti_event(ev)
ident = ev.name
assert ident is not None
cat.flush()
ev = cat.get_event(ident)
is_the_haiti_event(ev)
def command_init(args):
'''
Execution of command init
'''
def setup(parser):
parser.add_option('--force',
dest='force',
action='store_true',
help='overwrite existing project directory')
parser, options, args = cl_parse('init', args, setup)
if len(args) != 1:
help_and_die(parser, 'missing argument')
else:
fn_config = args[0]
if not os.path.isfile(fn_config):
die('config file missing: %s' % fn_config)
conf = load(filename=fn_config)
config.check(conf)
if ((not options.force) and (os.path.isdir(conf.project_dir))):
die('project dir exists: %s; use force option' % conf.project_dir)
else:
if os.path.isdir(conf.project_dir):
shutil.rmtree(conf.project_dir)
os.mkdir(conf.project_dir)
conf.dump(filename=os.path.join(conf.project_dir, 'seiscloud.config'))
dst = os.path.join(conf.project_dir, 'catalog.pf')
if conf.catalog_origin == 'file':
src = conf.catalog_fn
shutil.copyfile(src, dst)
else:
if conf.catalog_origin == 'globalcmt':
orig_catalog = catalog.GlobalCMT()
else: # geofon
orig_catalog = catalog.Geofon()
events = orig_catalog.get_events(time_range=(util.str_to_time(
conf.tmin), util.str_to_time(conf.tmax)),
magmin=conf.magmin,
latmin=conf.latmin,
latmax=conf.latmax,
lonmin=conf.lonmin,
lonmax=conf.lonmax)
selevents = [ev for ev in events if ev.magnitude <= conf.magmax]
model.dump_events(selevents, dst)
print('Project directory prepared "%s"' % conf.project_dir)
def setup(self):
self.catalogs = {
'Geofon': catalog.Geofon(),
'USGS/NEIC US': catalog.USGS('us'),
'Global-CMT': catalog.GlobalCMT(),
'Kinherd': catalog.Kinherd(),
'Saxony (Uni-Leipzig)': catalog.Saxony(),
}
catkeys = sorted(self.catalogs.keys())
self.set_name('Catalog Search')
self.add_parameter(Choice('Catalog', 'catalog', catkeys[0], catkeys))
self.add_parameter(Param('Min Magnitude', 'magmin', 0, 0, 10))
self.set_live_update(False)
def setup(self):
self.catalogs = {
'Geofon': catalog.Geofon(),
'USGS/NEIC US': catalog.USGS('us'),
'Global-CMT': catalog.GlobalCMT(),
'Saxony (Uni-Leipzig)': catalog.Saxony(),
}
fdsn_has_events = ['ISC', 'SCEDC', 'NCEDC', 'IRIS', 'GEONET']
catkeys = sorted(self.catalogs.keys())
catkeys.extend(fdsn_has_events)
self.set_name('Catalog Search')
self.add_parameter(Choice('Catalog', 'catalog', catkeys[0], catkeys))
self.add_parameter(Param('Min Magnitude', 'magmin', 0, 0, 10))
self.set_live_update(False)
from pyrocko import util, model
from pyrocko.client import catalog
tmin = util.str_to_time('2011-01-01 00:00:00') # beginning time of query
tmax = util.str_to_time('2011-12-31 23:59:59')
# create an instance of the global CMT catalog
global_cmt_catalog = catalog.GlobalCMT()
# query the catalog
events = global_cmt_catalog.get_events(time_range=(tmin, tmax),
magmin=2.,
latmin=-35.,
latmax=-20.,
lonmin=-76.,
lonmax=-65.)
print('Downloaded %s events' % len(events))
print('The last one is:')
print(events[-1])
# dump events to catalog
model.dump_events(events, 'northern_chile_events.txt')
def seismic_noise_variance(traces, engine, source, targets, nwindows,
pre_event_noise_duration, check_events, phase_def):
"""
Calculate variance of noise in a given time before P-Phase onset.
Optionally check the gCMT earthquake catalogue for M>5 events interfering.
Parameters
----------
data_traces : list
of :class:`pyrocko.trace.Trace` containing observed data
engine : :class:`pyrocko.gf.seismosizer.LocalEngine`
processing object for synthetics calculation
source : :class:`pyrocko.gf.Source`
reference source
targets : list
of :class:`pyrocko.gf.seismosizer.Targets`
nwindows : integer
number of windows in which the noise trace is split. If not 1, the
variance is calculated for each window separately and a mean
variance is returned. Else, the variance is calculated on the
entire pre-event noise window.
pre_event_noise_duration : Time before the first arrival to include in the
noise analysis
phase_def : :class:'pyrocko.gf.Timing'
arrivals : list
of :class'pyrocko.gf.Timing' arrivals of waveforms
at station
Returns
-------
:class:`numpy.ndarray`
"""
var_ds = []
global_cmt_catalog = catalog.GlobalCMT()
var_ds = []
ev_ws = []
for tr, target in zip(traces, targets):
stat_w = 1.
if tr is None:
var_ds.append(num.nan)
ev_ws.append(num.nan)
else:
arrival_time = get_phase_arrival_time(engine=engine,
source=source,
target=target,
wavename=phase_def)
if check_events:
events = global_cmt_catalog.get_events(
time_range=(arrival_time - pre_event_noise_duration -
50. * 60., arrival_time),
magmin=5.,
)
for ev in events:
try:
arrival_time_pre = get_phase_arrival_time(
engine=engine,
source=ev,
target=target,
wavename=phase_def)
if arrival_time_pre > arrival_time \
- pre_event_noise_duration \
and arrival_time_pre < arrival_time:
stat_w = 0.
logger.info(
'Noise analyser found event "%s" phase onset '
'of "%s" for target "%s".' %
(ev.name, phase_def, target.name))
if arrival_time_pre > arrival_time-30.*60.\
and arrival_time_pre < arrival_time - \
pre_event_noise_duration:
stat_w *= 1.
logger.info(
'Noise analyser found event "%s" possibly '
'contaminating the noise.' % ev.name)
# this should be magnitude dependent
except Exception:
pass
ev_ws.append(stat_w)
if nwindows == 1:
vtrace_var = num.nanvar(tr.ydata)
var_ds.append(vtrace_var)
else:
win = arrival_time - (arrival_time - pre_event_noise_duration)
win_len = win / nwindows
v_traces_w = []
for i in range(0, nwindows):
vtrace_w = tr.chop(tmin=win + win_len * i,
tmax=arrival_time + win_len * i + 1,
inplace=False)
v_traces_w.append(vtrace_w.ydata)
v_traces_w = num.nanmean(v_traces_w)
var_ds.append(v_traces_w)
var_ds = num.array(var_ds, dtype=num.float)
ev_ws = num.array(ev_ws, dtype=num.float)
return var_ds, ev_ws
def main():
if len(sys.argv) < 2:
print(
"input: asc_path dsc_path minlat minlon maxlat maxlon --workdir=name m"
)
try:
x0 = float(sys.argv[3])
y0 = float(sys.argv[4])
x1 = float(sys.argv[5])
y1 = float(sys.argv[6])
except:
x0 = "eins"
y0 = "eins"
x1 = "eins"
y1 = "eins"
sharp = False
loading = False
plot = True
topo = False
synthetic = False
calc_statistics = False
subsample = False
dump_grid = False
for argv in sys.argv:
if argv == "--sharp":
sharp = True
if argv == "--basic":
sharp = "basic"
if argv == "--ss":
sharp = "ss"
if argv == "--loading=True":
loading = True
if argv == "--loading=true":
loading = True
if argv == "--plot=False":
plot = False
if argv[0:10] == "--workdir=":
name = argv[10:]
if argv == "--topography":
topo = True
if argv == "--synthetic":
synthetic = True
if argv == "--statistics":
calc_statistics = True
if argv == "--subsample":
subsample = True
if argv == "--grond_export":
dump_grid = True
strikes = []
lengths = []
widths = []
if loading is False:
img_asc, coh_asc, scene_asc, dates_asc = load(sys.argv[1],
kite_scene=True)
try:
os.mkdir('work-%s' % name)
except:
pass
files = glob.glob('work-%s/*' % name)
for f in files:
os.remove(f)
fname = 'work-%s/asc.mod.tif' % name
writeout(img_asc, fname, sc=scene_asc)
longs_asc, lats_asc = to_latlon(fname)
try:
global_cmt_catalog = catalog.GlobalCMT()
events = global_cmt_catalog.get_events(
time_range=(num.min(dates_asc), num.max(dates_asc)),
magmin=2.,
latmin=num.min(lats_asc),
latmax=num.max(lats_asc),
lonmin=num.min(longs_asc),
lonmax=num.max(longs_asc))
areas = []
for ev in events:
areas.append(num.cbrt(ev.moment_tensor.moment) / 1000)
area = num.max(areas)
except:
area = 400
fname = 'work-%s/asc-' % name
img_asc = process(img_asc,
coh_asc,
longs_asc,
lats_asc,
scene_asc,
x0,
y0,
x1,
y1,
fname,
plot=plot,
mode=sharp,
loading=loading,
topo=topo,
synthetic=synthetic,
calc_statistics=calc_statistics,
subsample=subsample)
fname = 'work-%s/asc.mod.tif' % name
writeout(img_asc, fname, sc=scene_asc)
db = 1
dates = []
img_asc, coh_asc, scene_asc, dates_asc = load(sys.argv[1],
kite_scene=True)
dates.append(dates_asc)
snr_asc = aoi_snr(img_asc, area)
img_dsc, coh_dsc, scene_dsc, dates_dsc = load(sys.argv[2],
kite_scene=True)
dates.append(dates_dsc)
fname = 'work-%s/dsc.mod.tif' % name
writeout(img_dsc, fname, sc=scene_dsc)
longs_dsc, lats_dsc = to_latlon(fname)
fname = 'work-%s/dsc-' % name
img_dsc = process(img_dsc,
coh_dsc,
longs_dsc,
lats_dsc,
scene_dsc,
x0,
y0,
x1,
y1,
fname,
plot=plot,
mode=sharp,
loading=loading,
topo=topo,
synthetic=synthetic,
calc_statistics=calc_statistics,
subsample=subsample)
fname = 'work-%s/dsc.mod.tif' % name
writeout(img_dsc, fname, sc=scene_dsc)
db = 1
img_dsc, coh_dsc, scene_dsc, dates = load(sys.argv[2], kite_scene=True)
snr_dsc = aoi_snr(img_dsc, area)
minda = num.min(scene_asc.displacement)
mindd = num.min(scene_dsc.displacement)
mind = num.min([minda, mindd])
maxa = num.max(scene_asc.displacement)
maxdd = num.max(scene_dsc.displacement)
maxd = num.max([maxa, maxdd])
max_cum = num.max([abs(maxd), abs(mind)])
minda = -max_cum
mindd = -max_cum
mind = -max_cum
maxa = max_cum
maxdd = max_cum
maxd = max_cum
if plot is True:
fname = 'work-%s/asc' % name
plot_on_map(db,
scene_asc,
longs_asc,
lats_asc,
x0,
y0,
x1,
y1,
minda,
maxa,
fname,
synthetic=synthetic,
topo=topo,
kite_scene=True)
fname = 'work-%s/dsc' % name
plot_on_map(db,
scene_dsc,
longs_dsc,
lats_dsc,
x0,
y0,
x1,
y1,
mindd,
maxdd,
fname,
synthetic=synthetic,
topo=topo,
kite_scene=True)
fname = 'work-%s/asc.mod.tif' % name
comb = rasterio.open(fname)
longs_comb, lats_comb = to_latlon(fname)
comb_img = comb.read(1)
centers_bounding, coords_out, coords_box, strike, ellipses, max_bound = bounding_box(
comb_img, 400, sharp)
for st in strike:
strikes.append(st)
print("Strike(s) of moment weighted centerline(s) are :%s" % strike)
if plot is True:
fname = 'work-%s/asc-comb-' % name
plot_on_kite_box(coords_box,
coords_out,
scene_asc,
longs_asc,
lats_asc,
longs_comb,
lats_comb,
x0,
y0,
x1,
y1,
name,
ellipses,
minda,
maxa,
fname,
synthetic=synthetic,
topo=topo)
fname = 'work-%s/dsc.mod.tif' % name
comb = rasterio.open(fname)
longs_comb, lats_comb = to_latlon(fname)
comb_img = comb.read(1)
centers_bounding, coords_out, coords_box, strike, ellipses, max_bound = bounding_box(
comb_img, 400, sharp)
for st in strike:
strikes.append(st)
print("Strike(s) of moment weighted centerline(s) are :%s" % strike)
if plot is True:
fname = 'work-%s/dsc-comb-' % name
plot_on_kite_box(coords_box,
coords_out,
scene_dsc,
longs_dsc,
lats_dsc,
longs_comb,
lats_comb,
x0,
y0,
x1,
y1,
name,
ellipses,
mindd,
maxdd,
fname,
synthetic=synthetic,
topo=topo)
comb_img = combine('work-%s/asc.mod.tif' % name,
'work-%s/dsc.mod.tif' % name,
name,
weight_asc=snr_asc,
weight_dsc=snr_dsc,
plot=False)
longs_comb, lats_comb = to_latlon("work-%s/merged.tiff" % name)
else:
fname = 'work-%s/merged.tiff' % name
comb = rasterio.open(fname)
longs, lats = to_latlon(fname)
comb_img = comb.read(1)
easts, norths = get_coords_from_geotiff(fname, comb_img)
dE = easts[1] - easts[0]
dN = norths[1] - norths[0]
ll_long = num.min(longs)
ll_lat = num.min(lats)
dates = []
img_asc, coh_asc, scene_asc, dates_asc = load(sys.argv[1],
kite_scene=True)
img_dsc, coh_dsc, scene_dsc, dates_dsc = load(sys.argv[2],
kite_scene=True)
minda = num.min(scene_asc.displacement)
mindd = num.min(scene_dsc.displacement)
mind = num.min([minda, mindd])
maxa = num.max(scene_asc.displacement)
maxdd = num.max(scene_dsc.displacement)
maxd = num.max([maxa, maxdd])
if plot is True:
plt.figure(figsize=(sz1, sz2))
plt.title('Loaded combined image')
xr = plt.imshow(comb_img)
plt.close()
if subsample is True:
# Define the scene's frame
frame = FrameConfig(
# Lower left geographical reference [deg]
llLat=ll_lat,
llLon=ll_long,
# Pixel spacing [m] or [degrees]
spacing='meter',
dE=dE,
dN=dN)
displacement = comb_img
# Look vectors
# Theta is elevation angle from horizon
theta = num.full_like(displacement, 48. * d2r)
# Phi is azimuth towards the satellite, counter-clockwise from East
phi = num.full_like(displacement, 23. * d2r)
kite_comb_scene = Scene(displacement=displacement,
phi=phi,
theta=theta,
frame=frame)
kite_comb_scene.spool()
# For convenience we set an abbreviation to the quadtree
qt = kite_comb_scene.quadtree
# Parametrisation of the quadtree
qt.epsilon = 0.024 # Variance threshold
qt.nan_allowed = 0.9 # Percentage of NaN values allowed per tile/leave
qt.tile_size_max = 12000 # Maximum leave edge length in [m] or [deg]
qt.tile_size_min = 250 # Minimum leave edge length in [m] or [deg]
# We save the scene in kite's format
# sc.save('kite_scene')
# Or export the quadtree to CSV file
# qt.export('/tmp/tree.csv')
# statistical output
# img_asc, coh_asc, scene_asc = load('muji_kite/asc', kite_scene=True)
# comb_img = process(img_asc, coh_asc, plot=True)
# use quadtree subsampling on gradient
img_asc, coh_asc, scene_asc, dates = load(sys.argv[1], kite_scene=True)
fname = 'work-%s/asc.mod.tif' % name
longs_asc, lats_asc = to_latlon(fname)
db = 1
longs_comb, lats_comb = to_latlon("work-%s/merged.tiff" % name)
mindc = num.min(comb_img)
maxdc = num.max(comb_img)
try:
global_cmt_catalog = catalog.GlobalCMT()
events = global_cmt_catalog.get_events(time_range=(num.min(dates),
num.max(dates)),
magmin=2.,
latmin=num.min(lats_comb),
latmax=num.max(lats_comb),
lonmin=num.min(longs_comb),
lonmax=num.max(longs_comb))
areas = []
for ev in events:
areas.append(num.cbrt(ev.moment_tensor.moment) / 1000)
area = num.max(areas)
except:
area = 400
if dump_grid is True:
from scipy import signal
es = longs_comb.flatten()
es_resamp = signal.decimate(es, 20)
ns = lats_comb.flatten()
ns_resamp = signal.decimate(ns, 20)
comb_img_grid = comb_img.flatten()
comb_img_grid_resamp = signal.decimate(comb_img_grid, 20)
fobj_cum = open(os.path.join('work-%s/grad_grid.ASC' % name), 'w')
for x, y, sembcums in zip(es, ns, comb_img_grid.flatten()):
fobj_cum.write('%.2f %.2f %.20f\n' % (x, y, sembcums))
fobj_cum.close()
fobj_cum = open(os.path.join('work-%s/grad_grid_resam.ASC' % name),
'w')
for x, y, sembcums in zip(es_resamp, ns_resamp, comb_img_grid_resamp):
fobj_cum.write('%.2f %.2f %.20f\n' % (x, y, sembcums))
fobj_cum.close()
if plot is True:
fname = 'work-%s/comb-' % name
plot_on_map(db,
comb_img.copy(),
longs_comb,
lats_comb,
x0,
y0,
x1,
y1,
mindc,
maxdc,
fname,
synthetic=synthetic,
topo=topo,
comb=True)
centers_bounding, coords_out, coords_box, strike, ellipses, max_bound = bounding_box(
comb_img, area, sharp)
for st in strike:
strikes.append(st)
print("Strike(s) of moment weighted centerline(s) are :%s" % strike)
if plot is True:
fname = 'work-%s/comb-' % name
lengths, widths = plot_on_kite_box(coords_box,
coords_out,
scene_asc,
longs_asc,
lats_asc,
longs_comb,
lats_comb,
x0,
y0,
x1,
y1,
name,
ellipses,
mind,
maxd,
fname,
synthetic=synthetic,
topo=topo)
fobj_cum = open(os.path.join('work-%s/priors.ASC' % name), 'w')
for lens, wid in zip(lengths, widths):
fobj_cum.write('%.2f %.2f\n' % (lens, wid))
fobj_cum.close()
fobj_cum = open(os.path.join('work-%s/priors_strike.ASC' % name), 'w')
for st in zip(strikes):
fobj_cum.write('%.2f\n' % (st))
fobj_cum.close()
plot_on_kite_line(coords_out,
scene_asc,
longs_asc,
lats_asc,
longs_comb,
lats_comb,
x0,
y0,
x1,
y1,
mind,
maxd,
fname,
synthetic=synthetic,
topo=topo)
simp_fault, comp_fault = simplify(centers_bounding)
db = dump_geojson(simp_fault, longs_comb, lats_comb, name)
if plot is True:
plot_on_kite_scatter(
db,
scene_asc,
longs_asc,
lats_asc,
x0,
y0,
x1,
y1,
mind,
maxd,
fname,
synthetic=synthetic,
topo=topo,
)
img_dsc, coh_dsc, scene_dsc, dates = load(sys.argv[2], kite_scene=True)
fname = 'work-%s/dsc.mod.tif' % name
longs_dsc, lats_dsc = to_latlon(fname)
fname = 'work-%s/comb-' % name
if plot is True:
plot_on_kite_scatter(
db,
scene_dsc,
longs_dsc,
lats_dsc,
x0,
y0,
x1,
y1,
mind,
maxd,
fname,
synthetic=synthetic,
topo=topo,
)
centers = skelotonize(comb_img)
simp_fault, comp_fault = simplify(centers)
if calc_statistics is True:
res_faults = rup_prop(db)
y = l1tf_prep(res_faults)
run_l1tf(y)
def seismic_noise_variance(traces, engine, event, stations, nwindows,
pre_event_noise_duration, check_events, phase_def,
store_id):
'''
Calculate variance of noise (half an hour) before P-Phase onset, and check
for other events interfering
Parameters
----------
data_traces : list
of :class:`pyrocko.trace.Trace` containing observed data
engine : :class:`pyrocko.gf.seismosizer.LocalEngine`
processing object for synthetics calculation
event : :class:`pyrocko.meta.Event`
reference event from catalog
targets : list
of :class:`pyrocko.gf.seismosizer.Targets`
nwindows : integer
if given, windowed analysis of noise, else
variance is calculated on the entire pre-event
noise
phase_def : :class:'pyrocko.gf.Timing'
Returns
-------
:class:`numpy.ndarray`
'''
var_ds = []
global_cmt_catalog = catalog.GlobalCMT()
var_ds = []
ev_ws = []
for tr, station in zip(traces, stations):
stat_w = 1.
if tr is None:
var_ds.append(0.)
ev_ws.append(0.)
else:
arrival_time = get_phase_arrival_time(engine=engine,
source=event,
station=station,
wavename=phase_def,
store_id=store_id)
if check_events:
events = global_cmt_catalog.get_events(
time_range=(arrival_time - pre_event_noise_duration -
50. * 60., arrival_time),
magmin=4.)
ev_sum = 0.
for ev in events:
ev_sum += ev.magnitude
for ev in events:
try:
arrival_time_pre = get_phase_arrival_time(
engine=engine,
source=ev,
station=station,
wavename=phase_def,
store_id=store_id)
if arrival_time_pre > arrival_time \
- pre_event_noise_duration \
and arrival_time_pre < arrival_time:
stat_w = 0.
logger.info(
'Noise analyser found event %s phase onset of '
'%s for %s' % (ev.name, phase_def, station))
if arrival_time_pre > arrival_time-30.*60.\
and arrival_time_pre < arrival_time - \
pre_event_noise_duration:
stat_w *= 0.5 * (ev.magnitude / ev.sum)
logger.info(
'Noise analyser found event %s possibly '
'contaminating the noise' % ev.name)
# 0.5 arbitrary
except Exception:
pass
ev_ws.append(stat_w)
if nwindows == 1:
vtrace_var = num.nanvar(tr.ydata)
var_ds.append(vtrace_var)
else:
win = arrival_time - (arrival_time - pre_event_noise_duration)
win_len = win / nwindows
v_traces_w = []
for i in range(0, nwindows):
vtrace_w = tr.chop(tmin=win + win_len * i,
tmax=arrival_time + win_len * i + 1,
inplace=False)
v_traces_w.append(vtrace_w.ydata)
v_traces_w = num.nanmean(v_traces_w)
var_ds.append(v_traces_w)
var_ds = num.array(var_ds, dtype=num.float)
ev_ws = num.array(ev_ws, dtype=num.float)
return var_ds, ev_ws
