def well_geometry_sparrow_export(file, folder, name):
import utm
from pyproj import Proj
from pyrocko.model import Geometry
data = num.loadtxt(file, delimiter=",")
utmx = data[:, 0]
utmy = data[:, 1]
z = data[:, 2]
proj_gk4 = Proj(init="epsg:31467") # GK-projection
lons, lats = proj_gk4(utmx, utmy, inverse=True)
ev = event.Event(lat=num.mean(lats), lon=num.mean(lons), depth=num.mean(z),
time=util.str_to_time("2000-01-01 01:01:01"))
ncorners = 4
verts = []
xs = []
ys = []
dist = 200.
for i in range(0, len(z)):
try:
x = lats[i]
y = lons[i]
x1 = lats[i+1]
y1 = lons[i+1]
depth = z[i]
depth1 = z[i+1]
xyz = ([dist/2.8, dist/2.8, depth], [dist/2.8, dist/2.8, depth1],
[dist/2.8, -dist/2.8, depth1], [dist/2.8, -dist/2.8, depth])
latlon = ([x, y], [x1, y1], [x1, y1], [x, y])
patchverts = num.hstack((latlon, xyz))
verts.append(patchverts)
except Exception:
pass
vertices = num.vstack(verts)
npatches = int(len(vertices))
faces1 = num.arange(ncorners * npatches, dtype='int64').reshape(
npatches, ncorners)
faces2 = num.fliplr(faces1)
faces = num.hstack((faces2, faces1))
srf_semblance_list = []
srf_semblance = num.ones(num.shape(data[:, 2]))
srf_semblance = duplicate_property(srf_semblance)
srf_semblance_list.append(srf_semblance)
srf_semblance = num.asarray(srf_semblance_list).T
srf_times = num.linspace(0, 1, 1)
geom = Geometry(times=srf_times, event=ev)
geom.setup(vertices, faces)
sub_headers = tuple([str(i) for i in srf_times])
geom.add_property((('semblance', 'float64', sub_headers)), srf_semblance)
dump(geom, filename='geom_gtla1.yaml')
def get_pyrocko_event(self):
lat, lon, depth = self.position_values()
otime = self.time.value
if self.creation_info:
cat = self.creation_info.agency_id
else:
cat = None
return event.Event(name=self.public_id,
lat=lat,
lon=lon,
time=otime,
depth=depth,
catalog=cat,
region=self.region)
def pyrocko_event(self):
'''Considers only the *preferred* origin and magnitude'''
lat, lon, depth = self.preferred_origin.position_values()
otime = self.preferred_origin.time.value
reg = self.region
foc_mech = self.preferred_focal_mechanism
if foc_mech is not None:
mrr = foc_mech.moment_tensor_list[0].tensor.mrr.value
mtt = foc_mech.moment_tensor_list[0].tensor.mtt.value
mpp = foc_mech.moment_tensor_list[0].tensor.mpp.value
mrt = foc_mech.moment_tensor_list[0].tensor.mrt.value
mrp = foc_mech.moment_tensor_list[0].tensor.mrp.value
mtp = foc_mech.moment_tensor_list[0].tensor.mtp.value
mt = moment_tensor.MomentTensor(m_up_south_east=num.matrix(
[[mrr, mrt, mrp], [mrt, mtt, mtp], [mrp, mtp, mpp]]))
else:
mt = None
pref_mag = self.preferred_magnitude
if pref_mag is None:
mag = self.magnitude_list[0].mag.value
else:
mag = pref_mag.mag.value
cat = None
if self.preferred_origin.creation_info is not None:
cat = self.preferred_origin.creation_info.agency_id
reg = None
if self.description_list:
reg = self.description_list[0].text
return event.Event(name=self.public_id,
lat=lat,
lon=lon,
time=otime,
depth=depth,
magnitude=mag,
region=reg,
moment_tensor=mt,
catalog=cat)
def from_palantiri():
km = 1000.
try:
path = sys.argv[3]
evpath = path
except:
path = None
evpath = 'events/' + str(sys.argv[1])
C = config.Config(evpath)
Origin = C.parseConfig('origin')
Config = C.parseConfig('config')
cfg = ConfigObj(dict=Config)
step = cfg.UInt('step')
step2 = cfg.UInt('step_f2')
duration = cfg.UInt('duration')
forerun = cfg.UInt('forerun')
deltat = step
deltat2 = step2
rel = 'events/' + str(sys.argv[1]) + '/work/semblance/'
dimx = int(Config['dimx'])
dimy = int(Config['dimy'])
origin = OriginCfg(Origin)
depth = origin.depth() * 1000.
ev = event.Event(lat=origin.lat(),
lon=origin.lon(),
depth=depth,
time=util.str_to_time(origin.time()))
data, data_int, data_boot, data_int_boot, path_in_str, maxs, datamax, n_files = load(
0, path=path)
values_orig = data[:, 2]
values_orig = num.append(values_orig, num.array([0., 0.]))
lat_orig = data[:, 1]
lon_orig = data[:, 0]
ncorners = 4
lon_grid_orig = num.linspace(num.min(lat_orig), num.max(lat_orig), (dimy))
lat_grid_orig = num.linspace(num.min(lon_orig), num.max(lon_orig), dimx)
if path is None:
ntimes = int((forerun + duration) / step)
else:
ntimes = n_files
verts = []
lon_diff = ((lon_orig)[dimy + 1] - (lon_orig)[0]) / 4.
lat_diff = ((lat_orig)[1] - (lat_orig)[0]) / 4.
dist = orthodrome.distance_accurate50m(lat_grid_orig[1], lon_grid_orig[1],
lat_grid_orig[0], lon_grid_orig[0])
for x, y in zip(lon_orig, lat_orig):
xyz = ([dist / 2., dist / 2.,
depth], [-dist / 2., dist / 2.,
depth], [-dist / 2., -dist / 2.,
depth], [dist / 2., -dist / 2., depth])
latlon = ([x, y], [x, y], [x, y], [x, y])
patchverts = num.hstack((latlon, xyz))
verts.append(patchverts)
vertices = num.vstack(verts)
npatches = int(len(vertices)) #*2?
faces1 = num.arange(ncorners * npatches,
dtype='int64').reshape(npatches, ncorners)
faces2 = num.fliplr(faces1)
faces = num.vstack((faces2, faces1))
srf_semblance_list = []
for i in range(0, ntimes):
if len(sys.argv) < 4:
print("missing input arrayname")
else:
data, data_int, data_boot, data_int_boot, path_in_str, maxsb, datamaxb, n_files = load(
0, step=i, path=path)
srf_semblance = data[:, 2]
srf_semblance = num.append(srf_semblance, num.array([0., 0.]))
srf_semblance = duplicate_property(srf_semblance)
srf_semblance_list.append(srf_semblance)
srf_semblance = num.asarray(srf_semblance_list).T
srf_times = num.linspace(0, forerun + duration, ntimes)
geom = Geometry(times=srf_times, event=ev)
geom.setup(vertices, faces)
sub_headers = tuple([str(i) for i in srf_times])
geom.add_property((('semblance', 'float64', sub_headers)), srf_semblance)
dump(geom, filename='geom.yaml')