def get_corner_coordinates(self):
inc = self.inclination
llLat, llLon = self.get_ll_anchor()
urLat, urLon = self.get_ur_anchor()
if self.orbital_node == 'Ascending':
ulLat, ulLon = od.ne_to_latlon(
self.lat_center, self.lon_center,
self.track_length/2,
-num.tan(inc*d2r) * self.width/2)
lrLat, lrLon = od.ne_to_latlon(
self.lat_center, self.lon_center,
-self.track_length/2,
num.tan(inc*d2r) * self.width/2)
elif self.orbital_node == 'Descending':
urLat, urLon = od.ne_to_latlon(
self.lat_center, self.lon_center,
self.track_length/2,
num.tan(inc*d2r) * self.width/2)
llLat, llLon = od.ne_to_latlon(
self.lat_center, self.lon_center,
-self.track_length/2,
-num.tan(inc*d2r) * self.width/2)
return ((llLat, llLon), (ulLat, ulLon),
(urLat, urLon), (lrLat, lrLon))
def test_midpoint(self):
center_lons = num.linspace(0., 180., 5)
center_lats = [0., 89.]
npoints = 10000
half_side_length = 1000000.
distance_error_max = 50000.
for lat in center_lats:
for lon in center_lons:
n = num.random.uniform(
-half_side_length, half_side_length, npoints)
e = num.random.uniform(
-half_side_length, half_side_length, npoints)
dlats, dlons = orthodrome.ne_to_latlon(lat, lon, n, e)
clat, clon = orthodrome.geographic_midpoint(dlats, dlons)
d = orthodrome.distance_accurate50m_numpy(
clat, clon, lat, lon)[0]
if plot:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(n, e)
c_n, c_e = orthodrome.latlon_to_ne_numpy(
lat, lon, clat, clon)
ax.plot(c_n, c_e, 'ro')
plt.show()
self.assertTrue(d < distance_error_max, 'Distance %s > %s' %
(d, distance_error_max) +
'(maximum error)\n tested lat/lon: %s/%s' %
(lat, lon))
def testGridDistances(self):
for i in range(100):
w,h = 20,15
km = 1000.
gsize = random.uniform(0.,1.)*2.*10.**random.uniform(4.,7.)
north_grid, east_grid = num.meshgrid(num.linspace(-gsize/2.,gsize/2.,11) ,
num.linspace(-gsize/2.,gsize/2.,11) )
north_grid = north_grid.flatten()
east_grid = east_grid.flatten()
lat_delta = gsize/config.earthradius*r2d*2.
lon = random.uniform(-180.,180.)
lat = random.uniform(-90.,90.)
lat_grid, lon_grid = orthodrome.ne_to_latlon(lat, lon, north_grid, east_grid)
lat_grid_alt, lon_grid_alt = orthodrome.ne_to_latlon_alternative_method(lat, lon, north_grid, east_grid)
for la, lo, no, ea in zip(lat_grid, lon_grid, north_grid, east_grid):
a = Loc()
a.lat = la
a.lon = lo
b = Loc()
b.lat = lat
b.lon = lon
cd = orthodrome.cosdelta(a,b)
assert cd <= 1.0
d = num.arccos(cd)*config.earthradius
d2 = math.sqrt(no**2+ea**2)
assert not (abs(d-d2) > 1.0e-3 and d2 > 1.)
def testProjectionsZOnly(self):
km = 1000.
ev = model.Event(lat=-10, lon=150., depth=0.0)
for azi in num.linspace(0., 360., 37):
lat, lon = orthodrome.ne_to_latlon(
ev.lat, ev.lon, 10.*km * math.cos(azi), 10.*km * math.sin(azi))
sta = model.Station(lat=lat, lon=lon)
sta.set_event_relative_data(ev)
sta.set_channels_by_name('BHZ', 'BHN', 'BHE')
traces = [
trace.Trace(
channel='BHZ',
ydata=num.array([1.0])),
]
projected = []
for m, in_channels, out_channels in sta.guess_projections_to_enu():
projected.extend(trace.project(
traces, m, in_channels, out_channels))
def g(traces, cha):
for tr in traces:
if tr.channel == cha:
return tr
z = g(projected, 'U')
assert(near(z.ydata[0], 1.0, 0.001))
def testGridDistances(self):
for i in range(100):
w, h = 20, 15
km = 1000.0
gsize = random.uniform(0.0, 1.0) * 2.0 * 10.0 ** random.uniform(4.0, 7.0)
north_grid, east_grid = num.meshgrid(
num.linspace(-gsize / 2.0, gsize / 2.0, 11), num.linspace(-gsize / 2.0, gsize / 2.0, 11)
)
north_grid = north_grid.flatten()
east_grid = east_grid.flatten()
lat_delta = gsize / config.earthradius * r2d * 2.0
lon = random.uniform(-180.0, 180.0)
lat = random.uniform(-90.0, 90.0)
lat_grid, lon_grid = orthodrome.ne_to_latlon(lat, lon, north_grid, east_grid)
lat_grid_alt, lon_grid_alt = orthodrome.ne_to_latlon_alternative_method(lat, lon, north_grid, east_grid)
for la, lo, no, ea in zip(lat_grid, lon_grid, north_grid, east_grid):
a = Loc()
a.lat = la
a.lon = lo
b = Loc()
b.lat = lat
b.lon = lon
d = num.arccos(orthodrome.cosdelta(a, b)) * config.earthradius
d2 = math.sqrt(no ** 2 + ea ** 2)
if abs(d - d2) > 1.0e-3 and d2 > 1.0:
print "x", a.lat, a.lon, b.lat, b.lon
print "y", d, d2, d - d2
def testProjections(self):
km = 1000.
ev = model.Event(lat=-10, lon=150., depth=0.0)
for azi in num.linspace(0., 360., 37):
lat,lon = orthodrome.ne_to_latlon(ev.lat, ev.lon, 10.*km * math.cos(azi), 10.*km * math.sin(azi) )
sta = model.Station(lat=lat, lon=lon)
sta.set_event_relative_data(ev)
sta.set_channels_by_name('BHZ', 'BHE', 'BHN')
r = 1.
t = 1.
traces = [
trace.Trace(channel='BHE', ydata=num.array([math.sin(azi)*r+math.cos(azi)*t])),
trace.Trace(channel='BHN', ydata=num.array([math.cos(azi)*r-math.sin(azi)*t])),
]
for m, in_channels, out_channels in sta.guess_projections_to_rtu():
projected = trace.project(traces, m, in_channels, out_channels)
def g(traces, cha):
for tr in traces:
if tr.channel == cha:
return tr
r = g(projected, 'R')
t = g(projected, 'T')
assert( near(r.ydata[0], 1.0, 0.001) )
assert( near(t.ydata[0], 1.0, 0.001) )
def effective_latlons(self):
if self._latlons is None:
if self.lats is not None and self.lons is not None:
if (self.north_shifts is not None and
self.east_shifts is not None):
self._latlons = orthodrome.ne_to_latlon(
self.lats, self.lons,
self.north_shifts, self.east_shifts)
else:
self._latlons = self.lats, self.lons
else:
lat = g(self.lat, 0.0)
lon = g(self.lon, 0.0)
self._latlons = orthodrome.ne_to_latlon(
lat, lon, self.north_shifts, self.east_shifts)
return self._latlons
def add_gnss_campaign(self, campaign, psxy_style=dict(), offset_scale=None,
labels=True, vertical=False):
if offset_scale is None:
offset_scale = num.array(
[math.sqrt(s.east.shift**2 + s.north.shift**2 + s.up.shift**2)
for s in campaign.stations]).max()
size = math.sqrt(self.height**2 + self.width**2)
scale = (size/50.) / offset_scale
lats, lons = zip(
*[od.ne_to_latlon(s.lat, s.lon, s.north_shift, s.east_shift)
for s in campaign.stations])
if vertical:
rows = [[lons[ista], lats[ista],
0., s.up.shift,
0., s.up.sigma, 0.]
for ista, s in enumerate(campaign.stations)
if s.up is not None]
default_psxy_style = {
'h': 0,
'W': '0.5p,black',
'G': 'black',
'L': True,
'S': 'e%ec/0.95/8' % scale,
}
else:
rows = [[lons[ista], lats[ista],
s.east.shift, s.north.shift,
s.east.sigma, s.north.sigma, s.correlation_ne]
for ista, s in enumerate(campaign.stations)
if s.east is not None or s.north is not None]
default_psxy_style = {
'h': 0,
'W': '0.5p,black',
'G': 'black',
'L': True,
'S': 'e%ec/0.95/8' % scale,
}
if labels:
for row, sta in zip(rows, campaign.stations):
if vertical and sta.up is None:
continue
row.append(sta.code)
default_psxy_style.update(psxy_style)
self.gmt.psvelo(
in_rows=rows,
*self.jxyr,
**default_psxy_style)
def extent(lon, lat, w, h, n):
x = num.linspace(-0.5 * w, 0.5 * w, n)
y = num.linspace(-0.5 * h, 0.5 * h, n)
slats, slons = od.ne_to_latlon(lat, lon, y[0], x)
nlats, nlons = od.ne_to_latlon(lat, lon, y[-1], x)
south = slats.min()
north = nlats.max()
wlats, wlons = od.ne_to_latlon(lat, lon, y, x[0])
elats, elons = od.ne_to_latlon(lat, lon, y, x[-1])
elons = num.where(elons < wlons, elons + 360.0, elons)
if elons.max() - elons.min() > 180 or wlons.max() - wlons.min() > 180.0:
west = -180.0
east = 180.0
else:
west = wlons.min()
east = elons.max()
return topo.positive_region((west, east, south, north))
def extent(lon, lat, w, h, n):
x = num.linspace(-0.5 * w, 0.5 * w, n)
y = num.linspace(-0.5 * h, 0.5 * h, n)
slats, slons = od.ne_to_latlon(lat, lon, y[0], x)
nlats, nlons = od.ne_to_latlon(lat, lon, y[-1], x)
south = slats.min()
north = nlats.max()
wlats, wlons = od.ne_to_latlon(lat, lon, y, x[0])
elats, elons = od.ne_to_latlon(lat, lon, y, x[-1])
elons = num.where(elons < wlons, elons + 360., elons)
if elons.max() - elons.min() > 180 or wlons.max() - wlons.min() > 180.:
west = -180.
east = 180.
else:
west = wlons.min()
east = elons.max()
return topo.positive_region((west, east, south, north))
def effective_latlon(self):
'''
Property holding the offset-corrected lat/lon pair of the location.
'''
if self._latlon is None:
if self.north_shift == 0.0 and self.east_shift == 0.0:
self._latlon = self.lat, self.lon
else:
self._latlon = tuple(float(x) for x in orthodrome.ne_to_latlon(
self.lat, self.lon, self.north_shift, self.east_shift))
return self._latlon
def effective_latlon(self):
'''
Property holding the offset-corrected lat/lon pair of the location.
'''
if self._latlon is None:
if self.north_shift == 0.0 and self.east_shift == 0.0:
self._latlon = self.lat, self.lon
else:
self._latlon = tuple(float(x) for x in orthodrome.ne_to_latlon(
self.lat, self.lon, self.north_shift, self.east_shift))
return self._latlon
def get_mask_water(self):
if self._mask_water is None:
east_shifts, north_shifts = self.get_grid()
east_shifts -= east_shifts[0, -1]/2
north_shifts -= north_shifts[-1, -1]/2
latlon = od.ne_to_latlon(self.lat_center, self.lon_center,
north_shifts.ravel(), east_shifts.ravel())
points = num.array(latlon).T
self._mask_water = get_gsshg().get_land_mask(points)\
.reshape(*east_shifts.shape)
return self._mask_water
def get_mask_water(self):
if self._mask_water is None:
east_shifts, north_shifts = self.get_grid()
east_shifts -= east_shifts[0, -1] / 2
north_shifts -= north_shifts[-1, -1] / 2
latlon = od.ne_to_latlon(self.lat_center, self.lon_center,
north_shifts.ravel(), east_shifts.ravel())
points = num.array(latlon).T
self._mask_water = get_gsshg().get_land_mask(points)\
.reshape(*east_shifts.shape)
return self._mask_water
def get_corner_coordinates(self):
inc = self.inclination
llLat, llLon = self.get_ll_anchor()
urLat, urLon = self.get_ur_anchor()
if self.orbital_node == 'Ascending':
ulLat, ulLon = od.ne_to_latlon(
self.lat_center, self.lon_center, self.track_length / 2,
-num.tan(inc * d2r) * self.width / 2)
lrLat, lrLon = od.ne_to_latlon(self.lat_center, self.lon_center,
-self.track_length / 2,
num.tan(inc * d2r) * self.width / 2)
elif self.orbital_node == 'Descending':
urLat, urLon = od.ne_to_latlon(self.lat_center, self.lon_center,
self.track_length / 2,
num.tan(inc * d2r) * self.width / 2)
llLat, llLon = od.ne_to_latlon(
self.lat_center, self.lon_center, -self.track_length / 2,
-num.tan(inc * d2r) * self.width / 2)
return ((llLat, llLon), (ulLat, ulLon), (urLat, urLon), (lrLat, lrLon))
def add_gnss_campaign(self,
campaign,
psxy_style=dict(),
offset_scale=None,
labels=True,
vertical=False):
if offset_scale is None:
offset_scale = num.array([
math.sqrt(s.east.shift**2 + s.north.shift**2 + s.up.shift**2)
for s in campaign.stations
]).max()
size = math.sqrt(self.height**2 + self.width**2)
scale = (size / 10.) / offset_scale
default_psxy_style = {
'h': 0,
'W': '0.5p,black',
'G': 'black',
'L': True,
'S': 'e%dc/0.95/8' % scale,
}
default_psxy_style.update(psxy_style)
lats, lons = zip(*[
od.ne_to_latlon(s.lat, s.lon, s.north_shift, s.east_shift)
for s in campaign.stations
])
if vertical:
rows = [[
lons[ista], lats[ista], 0., s.up.shift, s.east.sigma,
s.north.sigma, s.correlation_ne
] for ista, s in enumerate(campaign.stations) if s.up is not None]
else:
rows = [[
lons[ista], lats[ista], s.east.shift, s.north.shift,
s.east.sigma, s.north.sigma, s.correlation_ne
] for ista, s in enumerate(campaign.stations)]
if labels:
for row, sta in zip(rows, campaign.stations):
if vertical and sta.up is None:
continue
row.append(sta.code)
self.gmt.psvelo(in_rows=rows, *self.jxyr, **default_psxy_style)
def call(self):
self.mycleanup()
self.detections = []
i_detection = 0
zpeak = 0.
lat = 0.
lon = 0.
for traces in self.chopper_selected_traces(
mode='all',
trace_selector=lambda x: x.station == "SMAX",
fallback=True):
tr_smax = [tr for tr in traces if tr.location == '']
tr_i = [tr for tr in traces if tr.location == 'i']
if not tr_i:
tr_i = [None] * len(tr_smax)
for tr_i, tr_stackmax in zip(tr_i, tr_smax):
tpeaks, apeaks = tr_stackmax.peaks(self.detector_threshold,
self.tsearch)
if self.level_trace:
ltrace = tr_stackmax.copy(data=False)
ltrace.set_ydata(
num.ones(tr_stackmax.data_len()) *
self.detector_threshold)
self.add_trace(ltrace)
for t, a in zip(tpeaks, apeaks):
if tr_i:
lat, lon, xpeak, ypeak, zpeak = \
self.grid.index_to_location(tr_i(t)[1])
lat, lon = orthodrome.ne_to_latlon(
lat, lon, xpeak, ypeak)
e = model.Event(time=t,
name="%s-%s" % (i_detection, a),
lat=lat,
lon=lon,
depth=zpeak)
self.detections.append(
gui_util.EventMarker(event=e,
kind=int(self.marker_kind[0])))
i_detection += 1
self.add_markers(self.detections)
if self.hold_figure:
self.show_comparison()
def export_geojson(self, filename):
import geojson
self._log.debug("Exporting GeoJSON Quadtree to %s", filename)
features = []
for lf in self.leaves:
llN, llE, urN, urE = (lf.llN, lf.llE, lf.urN, lf.urE)
if self.frame.isDegree():
llN += self.frame.llLat
llE += self.frame.llLon
urN += self.frame.llLat
urE += self.frame.llLon
coords = num.array(
[(llN, llE), (llN, urE), (urN, urE), (urN, llE), (llN, llE)]
)
if self.frame.isMeter():
coords = od.ne_to_latlon(self.frame.llLat, self.frame.llLon, *coords.T)
coords = num.array(coords).T
coords = coords[:, [1, 0]].tolist()
feature = geojson.Feature(
geometry=geojson.Polygon(coordinates=[coords]),
id=lf.id,
properties={
"mean": float(lf.mean),
"median": float(lf.median),
"std": float(lf.std),
"var": float(lf.var),
"phi": float(lf.phi),
"theta": float(lf.theta),
"unitE": float(lf.unitE),
"unitN": float(lf.unitN),
"unitU": float(lf.unitU),
},
)
features.append(feature)
collection = geojson.FeatureCollection(features)
with open(filename, "w") as f:
geojson.dump(collection, f)
def add_stations(self, stations, psxy_style=dict()):
default_psxy_style = {'S': 't8p', 'G': 'black'}
default_psxy_style.update(psxy_style)
lats, lons = zip(*[
od.ne_to_latlon(s.lat, s.lon, s.north_shift, s.east_shift)
for s in stations
])
self.gmt.psxy(in_columns=(lons, lats),
*self.jxyr,
**default_psxy_style)
for station in stations:
self.add_label(
station.lat, station.lon,
'.'.join(x for x in (station.network, station.station) if x))
def coordinates(self):
"""Local east and north coordinates of all pixels in
``Nx2`` matrix.
:type: :class:`numpy.ndarray`, size ``Nx2``
"""
coords = num.empty((self.rows * self.cols, 2))
coords[:, 0] = num.repeat(self.E[num.newaxis, :], self.rows, axis=0).flatten()
coords[:, 1] = num.repeat(self.N[:, num.newaxis], self.cols, axis=1).flatten()
if self.isMeter():
coords = ne_to_latlon(self.llLat, self.llLon, *coords.T)
coords = num.array(coords).T
else:
coords[:, 0] += self.llLon
coords[:, 1] += self.llLat
return coords
def add_stations(self, stations, psxy_style=dict()):
default_psxy_style = {
'S': 't8p',
'G': 'black'
}
default_psxy_style.update(psxy_style)
lats, lons = zip(*[od.ne_to_latlon(
s.lat, s.lon, s.north_shift, s.east_shift)
for s in stations])
self.gmt.psxy(
in_columns=(lons, lats),
*self.jxyr, **default_psxy_style)
for station in stations:
self.add_label(station.lat, station.lon, '.'.join(
x for x in (station.network, station.station) if x))
def outline(self, cs='xyz'):
points = outline_rect_source(self.strike, self.dip, self.length,
self.width)
center = self.center(self.width)
points[:, 0] += center[0]
points[:, 1] += center[1]
points[:, 2] += center[2]
if cs == 'xyz':
return points
elif cs == 'xy':
return points[:, :2]
elif cs in ('latlon', 'lonlat'):
latlon = ne_to_latlon(self.lat, self.lon, points[:, 0], points[:,
1])
latlon = num.array(latlon).T
if cs == 'latlon':
return latlon
else:
return latlon[:, ::-1]
def outline(self, cs='xyz'):
points = gf.seismosizer.outline_rect_source(self.strike, self.dip,
self.length, self.width,
'center')
points[:, 0] += self.north_shift
points[:, 1] += self.east_shift
points[:, 2] += self.depth
if cs == 'xyz':
return points
elif cs == 'xy':
return points[:, :2]
elif cs in ('latlon', 'lonlat'):
latlon = ne_to_latlon(self.lat, self.lon, points[:, 0], points[:,
1])
latlon = num.array(latlon).T
if cs == 'latlon':
return latlon
else:
return latlon[:, ::-1]
def detections_to_event_markers(fn_detections):
markers = []
if fn_detections:
with open(fn_detections, 'r') as f:
for line in f.readlines():
data = line.split()
i, t_d, t_t, apeak, latpeak, lonpeak, xpeak, ypeak, zpeak = \
data
lat, lon = orthodrome.ne_to_latlon(float(latpeak),
float(lonpeak),
float(xpeak), float(ypeak))
t = util.str_to_time("%s %s" % (t_d, t_t))
label = "%s-%s" % (apeak, i)
e = model.Event(lat=lat,
lon=lon,
depth=float(zpeak),
name=label,
time=t)
m = gui_util.EventMarker(e, kind=int(kind_default[0]))
markers.append(m)
return markers
def export_geojson(self, filename):
import geojson
self._log.debug('Exporting GeoJSON Quadtree to %s', filename)
features = []
for lf in self.leaves:
llN, llE, urN, urE = (lf.llN, lf.llE, lf.urN, lf.urE)
if self.frame.isDegree():
llN += self.frame.llLat
llE += self.frame.llLon
urN += self.frame.llLat
urE += self.frame.llLon
coords = num.array([(llN, llE), (llN, urE), (urN, urE), (urN, llE),
(llN, llE)])
if self.frame.isMeter():
coords = od.ne_to_latlon(self.frame.llLat, self.frame.llLon,
*coords.T)
coords = num.array(coords).T
coords = coords[:, [1, 0]].tolist()
feature = geojson.Feature(
geometry=geojson.Polygon(coordinates=[coords]),
id=lf.id,
properties={
'mean': lf.mean,
'median': lf.median,
'std': lf.std,
'var': lf.var
})
features.append(feature)
collection = geojson.FeatureCollection(features)
with open(filename, 'w') as f:
geojson.dump(collection, f)
def testProjections(self):
km = 1000.
ev = model.Event(lat=-10, lon=150., depth=0.0)
for azi in num.linspace(0., 360., 37):
lat, lon = orthodrome.ne_to_latlon(ev.lat, ev.lon,
10. * km * math.cos(azi),
10. * km * math.sin(azi))
sta = model.Station(lat=lat, lon=lon)
sta.set_event_relative_data(ev)
sta.set_channels_by_name('BHZ', 'BHE', 'BHN')
r = 1.
t = 1.
traces = [
trace.Trace(channel='BHE',
ydata=num.array(
[math.sin(azi) * r + math.cos(azi) * t])),
trace.Trace(channel='BHN',
ydata=num.array(
[math.cos(azi) * r - math.sin(azi) * t])),
]
for m, in_channels, out_channels in sta.guess_projections_to_rtu():
projected = trace.project(traces, m, in_channels, out_channels)
def g(traces, cha):
for tr in traces:
if tr.channel == cha:
return tr
r = g(projected, 'R')
t = g(projected, 'T')
assert (near(r.ydata[0], 1.0, 0.001))
assert (near(t.ydata[0], 1.0, 0.001))
def get_latlon(self, i):
rstate = self.get_rstate(i)
for itry in range(self.ntries):
radius = self.get_radius()
if radius is None:
lat = random_lat(rstate)
lon = rstate.uniform(-180., 180.)
else:
lat_center, lon_center = self.get_center_latlon()
while True:
north = rstate.uniform(-radius, radius)
east = rstate.uniform(-radius, radius)
if math.sqrt(north**2 + east**2) <= radius:
break
lat, lon = od.ne_to_latlon(lat_center, lon_center, north, east)
if not self.avoid_water or is_on_land(lat, lon):
return lat, lon
if self.avoid_water:
sadd = ' (avoiding water)'
raise ScenarioError('could not generate location%s' % sadd)
def get_latlon(self, i):
rstate = self.get_rstate(i)
for itry in range(self.ntries):
radius = self.get_radius()
if radius is None:
lat = random_lat(rstate)
lon = rstate.uniform(-180., 180.)
else:
lat_center, lon_center = self.get_center_latlon()
while True:
north = rstate.uniform(-radius, radius)
east = rstate.uniform(-radius, radius)
if math.sqrt(north**2 + east**2) <= radius:
break
lat, lon = od.ne_to_latlon(lat_center, lon_center, north, east)
if not self.avoid_water or is_on_land(lat, lon):
return lat, lon
if self.avoid_water:
sadd = ' (avoiding water)'
raise ScenarioError('could not generate location%s' % sadd)
def testGridDistances(self):
for i in range(100):
w, h = 20, 15
km = 1000.
gsize = random.uniform(0., 1.) * 2. * 10.**random.uniform(4., 7.)
north_grid, east_grid = num.meshgrid(
num.linspace(-gsize / 2., gsize / 2., 11),
num.linspace(-gsize / 2., gsize / 2., 11))
north_grid = north_grid.flatten()
east_grid = east_grid.flatten()
lat_delta = gsize / config.earthradius * r2d * 2.
lon = random.uniform(-180., 180.)
lat = random.uniform(-90., 90.)
lat_grid, lon_grid = orthodrome.ne_to_latlon(
lat, lon, north_grid, east_grid)
lat_grid_alt, lon_grid_alt = orthodrome.ne_to_latlon_alternative_method(
lat, lon, north_grid, east_grid)
for la, lo, no, ea in zip(lat_grid, lon_grid, north_grid,
east_grid):
a = Loc()
a.lat = la
a.lon = lo
b = Loc()
b.lat = lat
b.lon = lon
cd = orthodrome.cosdelta(a, b)
assert cd <= 1.0
d = num.arccos(cd) * config.earthradius
d2 = math.sqrt(no**2 + ea**2)
assert not (abs(d - d2) > 1.0e-3 and d2 > 1.)
def corners(lon, lat, w, h):
ll_lat, ll_lon = od.ne_to_latlon(lat, lon, -0.5 * h, -0.5 * w)
ur_lat, ur_lon = od.ne_to_latlon(lat, lon, 0.5 * h, 0.5 * w)
return ll_lon, ll_lat, ur_lon, ur_lat
def test_fomosto_vs_psgrn_pscmp(self):
store_dir, c = self.get_pscmp_store_info()
origin = gf.Location(lat=10., lon=-15.)
# test GF store
TestRF = dict(lat=origin.lat,
lon=origin.lon,
depth=2. * km,
width=0.2 * km,
length=0.5 * km,
rake=90.,
dip=45.,
strike=45.,
slip=num.random.uniform(1., 5.))
source_plain = gf.RectangularSource(**TestRF)
source_with_time = gf.RectangularSource(time=123.5, **TestRF)
neast = 40
nnorth = 40
N, E = num.meshgrid(num.linspace(-20. * km, 20. * km, nnorth),
num.linspace(-20. * km, 20. * km, neast))
# direct pscmp output
lats, lons = ortd.ne_to_latlon(origin.lat, origin.lon, N.flatten(),
E.flatten())
pscmp_sources = [psgrn_pscmp.PsCmpRectangularSource(**TestRF)]
cc = c.pscmp_config
cc.observation = psgrn_pscmp.PsCmpScatter(lats=lats, lons=lons)
cc.rectangular_source_patches = pscmp_sources
ccf = psgrn_pscmp.PsCmpConfigFull(**cc.items())
ccf.psgrn_outdir = os.path.join(store_dir, c.gf_outdir) + '/'
t2 = time()
runner = psgrn_pscmp.PsCmpRunner(keep_tmp=False)
runner.run(ccf)
ps2du = runner.get_results(component='displ')[0]
t3 = time()
logger.info('pscmp stacking time %f' % (t3 - t2))
un_pscmp = ps2du[:, 0]
ue_pscmp = ps2du[:, 1]
ud_pscmp = ps2du[:, 2]
# test against engine
starget_nn = gf.StaticTarget(lats=num.full(N.size, origin.lat),
lons=num.full(N.size, origin.lon),
north_shifts=N.flatten(),
east_shifts=E.flatten(),
interpolation='nearest_neighbor')
starget_ml = gf.StaticTarget(lats=num.full(N.size, origin.lat),
lons=num.full(N.size, origin.lon),
north_shifts=N.flatten(),
east_shifts=E.flatten(),
interpolation='multilinear')
engine = gf.LocalEngine(store_dirs=[store_dir])
for source in [source_plain, source_with_time]:
t0 = time()
r = engine.process(source, [starget_nn, starget_ml])
t1 = time()
logger.info('pyrocko stacking time %f' % (t1 - t0))
for static_result in r.static_results():
un_fomosto = static_result.result['displacement.n']
ue_fomosto = static_result.result['displacement.e']
ud_fomosto = static_result.result['displacement.d']
num.testing.assert_allclose(un_fomosto, un_pscmp, atol=1 * mm)
num.testing.assert_allclose(ue_fomosto, ue_pscmp, atol=1 * mm)
num.testing.assert_allclose(ud_fomosto, ud_pscmp, atol=1 * mm)
def fomosto_vs_psgrn_pscmp(self, pscmp_sources, gf_sources, atol=2 * mm):
def plot_components_compare(fomosto_comps, psgrn_comps):
import matplotlib.pyplot as plt
fig, axes = plt.subplots(4, 3)
for i, (fcomp, pscomp, cname) in enumerate(
zip(fomosto_comps, psgrn_comps, ['N', 'E', 'D'])):
fdispl = fcomp.reshape(nnorth, neast)
pdispl = pscomp.reshape(nnorth, neast)
pcbound = num.max([num.abs(pdispl.min()), pdispl.max()])
# fcbound = num.max([num.abs(fdispl.min()), fdispl.max()])
axes[0, i].imshow(pdispl,
cmap='seismic',
vmin=-pcbound,
vmax=pcbound)
axes[1, i].imshow(fdispl,
cmap='seismic',
vmin=-pcbound,
vmax=pcbound)
diff = pdispl - fdispl
rdiff = pdispl / fdispl
axes[2, i].imshow(diff, cmap='seismic')
axes[3, i].imshow(rdiff, cmap='seismic')
axes[0, i].set_title('PSCMP %s' % cname)
axes[1, i].set_title('Fomosto %s' % cname)
axes[2, i].set_title('abs diff min max %f, %f' %
(diff.min(), diff.max()))
axes[3, i].set_title('rel diff min max %f, %f' %
(rdiff.min(), rdiff.max()))
plt.show()
store_dir, c = self.get_pscmp_store_info()
origin = gf.Location(lat=10., lon=-15.)
N, E = num.meshgrid(num.linspace(-20. * km, 20. * km, nnorth),
num.linspace(-20. * km, 20. * km, neast))
# direct pscmp output
lats, lons = ortd.ne_to_latlon(origin.lat, origin.lon, N.flatten(),
E.flatten())
cc = c.pscmp_config
cc.observation = psgrn_pscmp.PsCmpScatter(lats=lats, lons=lons)
cc.rectangular_source_patches = pscmp_sources
cc.snapshots = psgrn_pscmp.PsCmpSnapshots(tmin=0.,
tmax=1.,
deltatdays=1.)
ccf = psgrn_pscmp.PsCmpConfigFull(**cc.items())
ccf.psgrn_outdir = os.path.join(store_dir, c.gf_outdir) + '/'
t2 = time()
runner = psgrn_pscmp.PsCmpRunner(keep_tmp=False)
runner.run(ccf)
ps2du = runner.get_results(component='displ')[0]
logger.info('pscmp stacking time %f s' % (time() - t2))
un_pscmp = ps2du[:, 0]
ue_pscmp = ps2du[:, 1]
ud_pscmp = ps2du[:, 2]
# test against engine
starget_nn = gf.StaticTarget(lats=num.full(N.size, origin.lat),
lons=num.full(N.size, origin.lon),
north_shifts=N.flatten(),
east_shifts=E.flatten(),
interpolation='nearest_neighbor')
starget_ml = gf.StaticTarget(lats=num.full(N.size, origin.lat),
lons=num.full(N.size, origin.lon),
north_shifts=N.flatten(),
east_shifts=E.flatten(),
interpolation='multilinear')
engine = gf.LocalEngine(store_dirs=[store_dir])
for source in gf_sources:
t0 = time()
r = engine.process(source, [starget_nn, starget_ml])
logger.info('pyrocko stacking time %f' % (time() - t0))
for i, static_result in enumerate(r.static_results()):
un_fomosto = static_result.result['displacement.n']
ue_fomosto = static_result.result['displacement.e']
ud_fomosto = static_result.result['displacement.d']
if show_plot:
fomosto_comps = [un_fomosto, ue_fomosto, ud_fomosto]
psgrn_comps = [un_pscmp, ue_pscmp, ud_pscmp]
plot_components_compare(fomosto_comps, psgrn_comps)
num.testing.assert_allclose(un_fomosto, un_pscmp, atol=atol)
num.testing.assert_allclose(ue_fomosto, ue_pscmp, atol=atol)
num.testing.assert_allclose(ud_fomosto, ud_pscmp, atol=atol)
def call(self):
self.cleanup()
viewer = self.get_viewer()
master = viewer.get_active_event()
if master is None:
self.fail('no master event selected')
stations = list(viewer.stations.values())
stations.sort(key=lambda s: (s.network,s.station))
if not stations:
self.fail('no station information available')
# gather events to be processed
events = []
for m in viewer.markers:
if isinstance(m, EventMarker):
if m.kind == 0:
events.append( m.get_event() )
events.sort(key=lambda ev: ev.time)
event_to_number = {}
for iev, ev in enumerate(events):
event_to_number[ev] = iev
if self.model_select.startswith('Global'):
model_key = 'global'
else:
model_key = master.lat, master.lon
if model_key != self.model_key:
if self.model_select.startswith('Global'):
self.model = cake.load_model()
else:
latlon = master.lat, master.lon
profile = crust2x2.get_profile(*latlon)
profile.set_layer_thickness(crust2x2.LWATER, 0.0)
self.model = cake.LayeredModel.from_scanlines(
cake.from_crust2x2_profile(profile))
self.model_key = model_key
phases = {
'P': ([ cake.PhaseDef(x) for x in 'P p'.split() ], 'Z'),
'S': ([ cake.PhaseDef(x) for x in 'S s'.split() ], 'NE'),
}
phasenames = phases.keys()
phasenames.sort()
# synthetic arrivals and ray geometry for master event
master_depth = master.depth
if self.master_depth_km is not None:
master_depth = self.master_depth_km * km
tt = {}
g = {}
for iphase, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
dist = orthodrome.distance_accurate50m(master, station)
azi = orthodrome.azimuth(master, station)
arrivals = self.model.arrivals(
phases=phases[phasename][0],
distances=[ dist*cake.m2d ],
zstart = master_depth,
zstop = 0.0)
if arrivals:
first = arrivals[0]
tt[station.network, station.station, phasename] = first.t
takeoff = first.takeoff_angle()
u = first.path.first_straight().u_in(first.endgaps)
g[iphase, istation] = num.array([
math.cos(azi*d2r) * math.sin(takeoff*d2r) * u,
math.sin(azi*d2r) * math.sin(takeoff*d2r) * u,
math.cos(takeoff*d2r) * u ])
# gather picks for each event
for ev in events:
picks = {}
for m2 in viewer.markers:
if isinstance(m2, PhaseMarker) and m2.kind == 0:
if m2.get_event() == ev:
net, sta, _, _ = m2.one_nslc()
picks[net,sta,m2.get_phasename()] = (m2.tmax + m2.tmin) / 2.0
ev.picks = picks
# time corrections for extraction windows
dataobs = []
datasyn = []
for phasename in phasenames:
for station in stations:
nsp = station.network, station.station, phasename
datasyn.append(tt.get(nsp,None))
for ev in events:
if nsp in ev.picks:
ttobs = ev.picks[nsp] - ev.time
else:
ttobs = None
dataobs.append(ttobs)
ttsyn = num.array(datasyn, dtype=num.float).reshape((
len(phasenames),
len(stations)))
ttobs = num.array(dataobs, dtype=num.float).reshape((
len(phasenames),
len(stations),
len(events)))
ttres = ttobs - ttsyn[:,:,num.newaxis]
tt_corr_event = num.nansum( ttres, axis=1) / \
num.nansum( num.isfinite(ttres), axis=1 )
tt_corr_event = num.where(num.isfinite(tt_corr_event), tt_corr_event, 0.)
ttres -= tt_corr_event[:,num.newaxis,:]
tt_corr_station = num.nansum( ttres, axis=2) / \
num.nansum( num.isfinite(ttres), axis=2 )
tt_corr_station = num.where(num.isfinite(tt_corr_station), tt_corr_station, 0.)
ttres -= tt_corr_station[:,:, num.newaxis]
tevents_raw = num.array( [ ev.time for ev in events ] )
tevents_corr = tevents_raw + num.mean(tt_corr_event, axis=0)
# print timing information
print 'timing stats'
for iphasename, phasename in enumerate(phasenames):
data = []
for ev in events:
iev = event_to_number[ev]
for istation, station in enumerate(stations):
nsp = station.network, station.station, phasename
if nsp in tt and nsp in ev.picks:
tarr = ev.time + tt[nsp]
tarr_ec = tarr + tt_corr_event[iphasename, iev]
tarr_ec_sc = tarr_ec + tt_corr_station[iphasename, istation]
tobs = ev.picks[nsp]
data.append((tobs-tarr, tobs-tarr_ec, tobs-tarr_ec_sc))
if data:
data = num.array(data, dtype=num.float).T
print 'event %10s %3s %3i %15.2g %15.2g %15.2g' % (
(ev.name, phasename, data.shape[1]) +
tuple( num.mean(num.abs(x)) for x in data ))
else:
print 'event %10s %3s no picks' % (ev.name, phasename)
# extract and preprocess waveforms
tpad = 0.0
for f in self.corner_highpass, self.corner_lowpass:
if f is not None:
tpad = max(tpad, 1.0/f)
pile = self.get_pile()
waveforms = {}
for ev in events:
iev = event_to_number[ev]
markers = []
for iphasename, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
nsp = station.network, station.station, phasename
if nsp in tt:
tarr = ev.time + tt[nsp]
nslcs = [ ( station.network, station.station, '*', '*' ) ]
marker = PhaseMarker( nslcs, tarr, tarr, 1, event=ev,
phasename=phasename)
markers.append(marker)
tarr2 = tarr + tt_corr_station[iphasename, istation] + \
tt_corr_event[iphasename, iev]
marker = PhaseMarker( nslcs, tarr2, tarr2, 2, event=ev,
phasename=phasename)
markers.append(marker)
tmin = tarr2+self.tstart
tmax = tarr2+self.tend
marker = PhaseMarker( nslcs,
tmin, tmax, 3, event=ev,
phasename=phasename)
markers.append(marker)
trs = pile.all(tmin, tmax, tpad=tpad, trace_selector=
lambda tr: tr.nslc_id[:2] == nsp[:2],
want_incomplete=False)
trok = []
for tr in trs:
if num.all(tr.ydata[0] == tr.ydata):
continue
if self.corner_highpass:
tr.highpass(4, self.corner_highpass)
if self.corner_lowpass:
tr.lowpass(4, self.corner_lowpass)
tr.chop(tmin, tmax)
tr.set_location(ev.name)
#tr.shift( - (tmin - master.time) )
if num.all(num.isfinite(tr.ydata)):
trok.append(tr)
waveforms[nsp+(iev,)] = trok
self.add_markers(markers)
def get_channel(trs, cha):
for tr in trs:
if tr.channel == cha:
return tr
return None
nevents = len(events)
nstations = len(stations)
nphases = len(phasenames)
# correlate waveforms
coefs = num.zeros((nphases, nstations, nevents, nevents))
coefs.fill(num.nan)
tshifts = coefs.copy()
tshifts_picked = coefs.copy()
for iphase, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
nsp = station.network, station.station, phasename
for a in events:
ia = event_to_number[a]
for b in events:
ib = event_to_number[b]
if ia == ib:
continue
if nsp in a.picks and nsp in b.picks:
tshifts_picked[iphase,istation,ia,ib] = \
b.picks[nsp] - a.picks[nsp]
wa = waveforms[nsp+(ia,)]
wb = waveforms[nsp+(ib,)]
channels = list(set([ tr.channel for tr in wa + wb ]))
channels.sort()
tccs = []
for cha in channels:
if cha[-1] not in phases[phasename][1]:
continue
ta = get_channel(wa, cha)
tb = get_channel(wb, cha)
if ta is None or tb is None:
continue
tcc = trace.correlate(ta,tb, mode='full', normalization='normal',
use_fft=True)
tccs.append(tcc)
if not tccs:
continue
tc = None
for tcc in tccs:
if tc is None:
tc = tcc
else:
tc.add(tcc)
tc.ydata *= 1./len(tccs)
tmid = tc.tmin*0.5 + tc.tmax*0.5
tlen = (tc.tmax - tc.tmin)*0.5
tc_cut = tc.chop(tmid-tlen*0.5, tmid+tlen*0.5, inplace=False)
tshift, coef = tc_cut.max()
if (tshift < tc.tmin + 0.5*tc.deltat or
tc.tmax - 0.5*tc.deltat < tshift):
continue
coefs[iphase,istation,ia,ib] = coef
tshifts[iphase,istation,ia,ib] = tshift
if self.show_correlation_traces:
tc.shift(master.time - (tc.tmax + tc.tmin)/2.)
self.add_trace(tc)
#tshifts = tshifts_picked
coefssum_sta = num.nansum(coefs, axis=2) / num.sum(num.isfinite(coefs), axis=2)
csum_sta = num.nansum(coefssum_sta, axis=2) / num.sum(num.isfinite(coefssum_sta), axis=2)
for iphase, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
print 'station %-5s %s %15.2g' % (station.station, phasename, csum_sta[iphase,istation])
coefssum = num.nansum(coefs, axis=1) / num.sum(num.isfinite(coefs), axis=1)
csumevent = num.nansum(coefssum, axis=2) / num.sum(num.isfinite(coefssum), axis=2)
above = num.where(num.isfinite(coefs), coefs >= self.min_corr, 0)
csumabove = num.sum(num.sum(above, axis=1), axis=2)
coefssum = num.ma.masked_invalid(coefssum)
print 'correlation stats'
for iphase, phasename in enumerate(phasenames):
for ievent, event in enumerate(events):
print 'event %10s %3s %8i %15.2g' % (
event.name, phasename,
csumabove[iphase,ievent], csumevent[iphase,ievent])
# plot event correlation matrix
fframe = self.figure_frame()
fig = fframe.gcf()
for iphase, phasename in enumerate(phasenames):
p = fig.add_subplot(1,nphases,iphase+1)
p.set_xlabel('Event number')
p.set_ylabel('Event number')
mesh = p.pcolormesh(coefssum[iphase])
cb = fig.colorbar(mesh, ax=p)
cb.set_label('Max correlation coefficient')
if self.save:
fig.savefig(self.output_filename(dir='correlation.pdf'))
fig.canvas.draw()
# setup and solve linear system
data = []
rows = []
weights = []
for iphase in xrange(nphases):
for istation in xrange(nstations):
for ia in xrange(nevents):
for ib in xrange(ia+1,nevents):
k = iphase, istation, ia, ib
w = coefs[k]
if not num.isfinite(tshifts[k]) \
or not num.isfinite(w) or w < self.min_corr:
continue
row = num.zeros(nevents*4)
row[ia*4:ia*4+3] = g[iphase,istation]
row[ia*4+3] = -1.0
row[ib*4:ib*4+3] = -g[iphase,istation]
row[ib*4+3] = 1.0
weights.append(w)
rows.append(row)
data.append(tshifts[iphase,istation,ia,ib])
nsamp = len(data)
for i in range(4):
row = num.zeros(nevents*4)
row[i::4] = 1.
rows.append(row)
data.append(0.0)
if self.fix_depth:
for ievent in range(nevents):
row = num.zeros(nevents*4)
row[ievent*4+2] = 1.0
rows.append(row)
data.append(0.0)
a = num.array(rows, dtype=num.float)
d = num.array(data, dtype=num.float)
w = num.array(weights, dtype=num.float)
if self.weighting == 'equal':
w[:nsamp] = 1.0
elif self.weighting == 'linear':
pass
elif self.weighting == 'quadratic':
w[:nsamp] = w[:nsamp]**2
a[:nsamp,:] *= w[:,num.newaxis]
d[:nsamp] *= w[:nsamp]
x, residuals, rank, singular = num.linalg.lstsq(a,d)
x0 = num.zeros(nevents*4)
x0[3::4] = tevents_corr
mean_abs_residual0 = num.mean(
num.abs((num.dot(a[:nsamp], x0) - d[:nsamp])/w[:nsamp]))
mean_abs_residual = num.mean(
num.abs((num.dot(a[:nsamp],x) - d[:nsamp])/w[:nsamp]))
print mean_abs_residual0, mean_abs_residual
# distorted solutions
npermutations = 100
noiseamount = mean_abs_residual
xdistorteds = []
for i in range(npermutations):
dnoisy = d.copy()
dnoisy[:nsamp] += num.random.normal(size=nsamp)*noiseamount*w[:nsamp]
xdistorted, residuals, rank, singular = num.linalg.lstsq(a,dnoisy)
xdistorteds.append(xdistorted)
mean_abs_residual = num.mean(num.abs(num.dot(a,xdistorted)[:nsamp] - dnoisy[:nsamp]))
tmean = num.mean([ e.time for e in events ])
north = x[0::4]
east = x[1::4]
down = x[2::4]
etime = x[3::4] + tmean
def plot_range(x):
mi, ma = num.percentile(x, [10., 90.])
ext = (ma-mi)/5.
mi -= ext
ma += ext
return mi, ma
lat, lon = orthodrome.ne_to_latlon(master.lat, master.lon, north, east)
events_out = []
for ievent, event in enumerate(events):
event_out = model.Event(time=etime[ievent],
lat=lat[ievent],
lon=lon[ievent],
depth=down[ievent] + master_depth,
name = event.name)
mark = EventMarker(event_out, kind=4)
self.add_marker(mark)
events_out.append(event_out)
model.Event.dump_catalog(events_out, 'events.relocated.txt')
# plot results
ned_orig = []
for event in events:
n, e = orthodrome.latlon_to_ne(master, event)
d = event.depth
ned_orig.append((n,e,d))
ned_orig = num.array(ned_orig)
ned_orig[:,0] -= num.mean(ned_orig[:,0])
ned_orig[:,1] -= num.mean(ned_orig[:,1])
ned_orig[:,2] -= num.mean(ned_orig[:,2])
north0, east0, down0 = ned_orig.T
north2, east2, down2, time2 = num.hstack(xdistorteds).reshape((-1,4)).T
fframe = self.figure_frame()
fig = fframe.gcf()
color_sym = (0.1,0.1,0.0)
color_scat = (0.3,0.5,1.0,0.2)
d = u'\u0394 '
if not self.fix_depth:
p = fig.add_subplot(2,2,1, aspect=1.0)
else:
p = fig.add_subplot(1,1,1, aspect=1.0)
mi_north, ma_north = plot_range(north)
mi_east, ma_east = plot_range(east)
mi_down, ma_down = plot_range(down)
p.set_xlabel(d+'East [km]')
p.set_ylabel(d+'North [km]')
p.plot(east2/km, north2/km, '.', color=color_scat, markersize=2)
p.plot(east/km, north/km, '+', color=color_sym)
p.plot(east0/km, north0/km, 'x', color=color_sym)
p0 = p
for i,ev in enumerate(events):
p.text(east[i]/km, north[i]/km, ev.name, clip_on=True)
if not self.fix_depth:
p = fig.add_subplot(2,2,2, sharey=p0, aspect=1.0)
p.set_xlabel(d+'Depth [km]')
p.set_ylabel(d+'North [km]')
p.plot(down2/km, north2/km, '.', color=color_scat, markersize=2)
p.plot(down/km, north/km, '+', color=color_sym)
for i,ev in enumerate(events):
p.text(down[i]/km, north[i]/km, ev.name, clip_on=True)
p1 = p
p = fig.add_subplot(2,2,3, sharex=p0, aspect=1.0)
p.set_xlabel(d+'East [km]')
p.set_ylabel(d+'Depth [km]')
p.plot(east2/km, down2/km, '.', color=color_scat, markersize=2)
p.plot(east/km, down/km, '+', color=color_sym)
for i,ev in enumerate(events):
p.text(east[i]/km, down[i]/km, ev.name, clip_on=True)
p.invert_yaxis()
p2 = p
p0.set_xlim(mi_east/km, ma_east/km)
p0.set_ylim(mi_north/km, ma_north/km)
if not self.fix_depth:
p1.set_xlim(mi_down/km, ma_down/km)
p2.set_ylim(mi_down/km, ma_down/km)
if self.save:
fig.savefig(self.output_filename(dir='locations.pdf'))
fig.canvas.draw()
def plot_erroneous_ne_to_latlon():
import gmtpy
import random
import subprocess
import time
while True:
w, h = 20, 15
gsize = random.uniform(0., 1.) * 4. * 10.**random.uniform(4., 7.)
north_grid, east_grid = num.meshgrid(
num.linspace(-gsize / 2., gsize / 2., 11),
num.linspace(-gsize / 2., gsize / 2., 11))
north_grid = north_grid.flatten()
east_grid = east_grid.flatten()
lat_delta = gsize / earthradius * r2d * 2.
lon = random.uniform(-180., 180.)
lat = random.uniform(-90., 90.)
print(gsize / 1000.)
lat_grid, lon_grid = orthodrome.ne_to_latlon(lat, lon, north_grid,
east_grid)
lat_grid_alt, lon_grid_alt = \
orthodrome.ne_to_latlon_alternative_method(
lat, lon, north_grid, east_grid)
maxerrlat = num.max(num.abs(lat_grid - lat_grid_alt))
maxerrlon = num.max(num.abs(lon_grid - lon_grid_alt))
eps = 1.0e-8
if maxerrlon > eps or maxerrlat > eps:
print(lat, lon, maxerrlat, maxerrlon)
gmt = gmtpy.GMT(
config={
'PLOT_DEGREE_FORMAT': 'ddd.xxxF',
'PAPER_MEDIA': 'Custom_%ix%i' %
(w * gmtpy.cm, h * gmtpy.cm),
'GRID_PEN_PRIMARY': 'thinnest/0/50/0'
})
south = max(-85., lat - 0.5 * lat_delta)
north = min(85., lat + 0.5 * lat_delta)
lon_delta = lat_delta / math.cos(lat * d2r)
delta = lat_delta / 360. * earthradius * 2. * math.pi
scale_km = gmtpy.nice_value(delta / 10.) / 1000.
west = lon - 0.5 * lon_delta
east = lon + 0.5 * lon_delta
x, y = (west, east), (south, north)
xax = gmtpy.Ax(mode='min-max', approx_ticks=4.)
yax = gmtpy.Ax(mode='min-max', approx_ticks=4.)
scaler = gmtpy.ScaleGuru(data_tuples=[(x, y)], axes=(xax, yax))
scaler['R'] = '-Rg'
layout = gmt.default_layout()
mw = 2.5 * gmtpy.cm
layout.set_fixed_margins(mw, mw, mw / gmtpy.golden_ratio,
mw / gmtpy.golden_ratio)
widget = layout.get_widget()
# widget['J'] = ('-JT%g/%g' % (lon, lat)) + '/%(width)gp'
widget['J'] = (
'-JE%g/%g/%g' % (lon, lat, min(lat_delta/2., 180.)))\
+ '/%(width)gp'
aspect = gmtpy.aspect_for_projection(*(widget.J() + scaler.R()))
widget.set_aspect(aspect)
gmt.psbasemap(B='5g5',
L=('x%gp/%gp/%g/%g/%gk' %
(widget.width() / 2., widget.height() / 7., lon,
lat, scale_km)),
*(widget.JXY() + scaler.R()))
gmt.psxy(in_columns=(lon_grid, lat_grid),
S='x10p',
W='1p/200/0/0',
*(widget.JXY() + scaler.R()))
gmt.psxy(in_columns=(lon_grid_alt, lat_grid_alt),
S='c10p',
W='1p/0/0/200',
*(widget.JXY() + scaler.R()))
gmt.save('orthodrome.pdf')
subprocess.call(['xpdf', '-remote', 'ortho', '-reload'])
time.sleep(2)
else:
print('ok', gsize, lat, lon)
def get_ll_anchor(self):
return od.ne_to_latlon(self.lat_center, self.lon_center,
-self.track_length / 2, -self.width / 2)
sab_los = d['LOS'][0:n_pix, :]
los = num.zeros_like(sab_los)
los[:, 0] = sab_los[:, 1]
los[:, 1] = sab_los[:, 0]
los[:, 2] = sab_los[:, 2]
## init fault geometry
n_sources = source_params.shape[1]
sources = []
for sps in range(n_sources):
Length, Width, Depth, Dip, Strike, Xloc, Yloc, strsl, dipsl, _ = source_params[:,
sps]
print(Xloc, Yloc)
lat, lon = otd.ne_to_latlon(event.lat, event.lon,
(Yloc - y_shift) * km,
(Xloc - x_shift) * km)
rake = math.atan2(dipsl, strsl)
print('d,s,r', dipsl, strsl, rake)
slip = math.sqrt(strsl**2 + dipsl**2)
print('lat,lon', lat, lon)
rf = RectangularSource(
lat=lat,
lon=lon,
east_shift=0.,
north_shift=0.,
depth=Depth * km,
length=Length * km,
width=Width * km,
dip=Dip + 180., # no negative dip!
strike=Strike,
def test_against_kiwi(self):
engine = gf.get_engine()
store_id = 'chile_70km_crust'
try:
store = engine.get_store(store_id)
except gf.NoSuchStore:
logger.warn('GF Store %s not available - skipping test' % store_id)
return
base_source = gf.RectangularSource(
depth=15*km,
strike=0.,
dip=90.,
rake=0.,
magnitude=4.5,
nucleation_x=-1.,
length=10*km,
width=0*km,
stf=gf.BoxcarSTF(duration=1.0))
base_event = base_source.pyrocko_event()
channels = 'NEZ'
nstations = 10
stations = []
targets = []
for istation in xrange(nstations):
dist = rand(40.*km, 900*km)
azi = rand(-180., 180.)
north_shift = dist * math.cos(azi*d2r)
east_shift = dist * math.sin(azi*d2r)
lat, lon = od.ne_to_latlon(0., 0., north_shift, east_shift)
sta = 'S%02i' % istation
station = model.Station(
'', sta, '',
lat=lat,
lon=lon)
station.set_channels_by_name('N', 'E', 'Z')
stations.append(station)
for cha in channels:
target = gf.Target(
codes=station.nsl() + (cha,),
lat=lat,
lon=lon,
quantity='displacement',
interpolation='multilinear',
optimization='enable',
store_id=store_id)
targets.append(target)
from tunguska import glue
nsources = 10
# nprocs_max = multiprocessing.cpu_count()
nprocs = 1
try:
seis = glue.start_seismosizer(
gfdb_path=op.join(store.store_dir, 'db'),
event=base_event,
stations=stations,
hosts=['localhost']*nprocs,
balance_method='123321',
effective_dt=0.5,
verbose=False)
ksource = to_kiwi_source(base_source)
seis.set_source(ksource)
recs = seis.get_receivers_snapshot(('syn',), (), 'plain')
trs = []
for rec in recs:
for tr in rec.get_traces():
tr.set_codes(channel=transchan[tr.channel])
trs.append(tr)
trs2 = engine.process(base_source, targets).pyrocko_traces()
trace.snuffle(trs + trs2)
seis.set_synthetic_reference()
for sourcetype in ['point', 'rect']:
sources = []
for isource in xrange(nsources):
m = pmt.MomentTensor.random_dc()
strike, dip, rake = map(float, m.both_strike_dip_rake()[0])
if sourcetype == 'point':
source = gf.RectangularSource(
north_shift=rand(-20.*km, 20*km),
east_shift=rand(-20.*km, 20*km),
depth=rand(10*km, 20*km),
nucleation_x=0.0,
nucleation_y=0.0,
strike=strike,
dip=dip,
rake=rake,
magnitude=rand(4.0, 5.0),
stf=gf.BoxcarSTF(duration=1.0))
elif sourcetype == 'rect':
source = gf.RectangularSource(
north_shift=rand(-20.*km, 20*km),
east_shift=rand(-20.*km, 20*km),
depth=rand(10*km, 20*km),
length=10*km,
width=5*km,
nucleation_x=-1.,
nucleation_y=0,
strike=strike,
dip=dip,
rake=rake,
magnitude=rand(4.0, 5.0),
stf=gf.BoxcarSTF(duration=1.0))
else:
assert False
sources.append(source)
for temperature in ['cold', 'hot']:
t0 = time.time()
resp = engine.process(sources, targets, nprocs=nprocs)
t1 = time.time()
if temperature == 'hot':
dur_pyrocko = t1 - t0
del resp
ksources = map(to_kiwi_source, sources)
for temperature in ['cold', 'hot']:
t0 = time.time()
seis.make_misfits_for_sources(
ksources, show_progress=False)
t1 = time.time()
if temperature == 'hot':
dur_kiwi = t1 - t0
print 'pyrocko %-5s %5.2fs %5.1fx' % (
sourcetype, dur_pyrocko, 1.0)
print 'kiwi %-5s %5.2fs %5.1fx' % (
sourcetype, dur_kiwi, dur_pyrocko/dur_kiwi)
finally:
seis.close()
del seis
def get_lr_anchor(self):
return od.ne_to_latlon(self.lat_center, self.lon_center,
-self.track_length/2, self.width/2)
def plot_erroneous_ne_to_latlon():
import gmtpy
import random
import subprocess
import time
while True:
w, h = 20, 15
gsize = random.uniform(0., 1.)*4.*10.**random.uniform(4., 7.)
north_grid, east_grid = num.meshgrid(
num.linspace(-gsize/2., gsize/2., 11),
num.linspace(-gsize/2., gsize/2., 11))
north_grid = north_grid.flatten()
east_grid = east_grid.flatten()
lat_delta = gsize/earthradius*r2d*2.
lon = random.uniform(-180., 180.)
lat = random.uniform(-90., 90.)
print(gsize/1000.)
lat_grid, lon_grid = orthodrome.ne_to_latlon(
lat, lon, north_grid, east_grid)
lat_grid_alt, lon_grid_alt = \
orthodrome.ne_to_latlon_alternative_method(
lat, lon, north_grid, east_grid)
maxerrlat = num.max(num.abs(lat_grid-lat_grid_alt))
maxerrlon = num.max(num.abs(lon_grid-lon_grid_alt))
eps = 1.0e-8
if maxerrlon > eps or maxerrlat > eps:
print(lat, lon, maxerrlat, maxerrlon)
gmt = gmtpy.GMT(
config={
'PLOT_DEGREE_FORMAT': 'ddd.xxxF',
'PAPER_MEDIA': 'Custom_%ix%i' % (w*gmtpy.cm, h*gmtpy.cm),
'GRID_PEN_PRIMARY': 'thinnest/0/50/0'})
south = max(-85., lat - 0.5*lat_delta)
north = min(85., lat + 0.5*lat_delta)
lon_delta = lat_delta/math.cos(lat*d2r)
delta = lat_delta/360.*earthradius*2.*math.pi
scale_km = gmtpy.nice_value(delta/10.)/1000.
west = lon - 0.5*lon_delta
east = lon + 0.5*lon_delta
x, y = (west, east), (south, north)
xax = gmtpy.Ax(mode='min-max', approx_ticks=4.)
yax = gmtpy.Ax(mode='min-max', approx_ticks=4.)
scaler = gmtpy.ScaleGuru(data_tuples=[(x, y)], axes=(xax, yax))
scaler['R'] = '-Rg'
layout = gmt.default_layout()
mw = 2.5*gmtpy.cm
layout.set_fixed_margins(
mw, mw, mw/gmtpy.golden_ratio, mw/gmtpy.golden_ratio)
widget = layout.get_widget()
# widget['J'] = ('-JT%g/%g' % (lon, lat)) + '/%(width)gp'
widget['J'] = (
'-JE%g/%g/%g' % (lon, lat, min(lat_delta/2., 180.)))\
+ '/%(width)gp'
aspect = gmtpy.aspect_for_projection(*(widget.J() + scaler.R()))
widget.set_aspect(aspect)
gmt.psbasemap(
B='5g5',
L=('x%gp/%gp/%g/%g/%gk' % (
widget.width()/2., widget.height()/7.,
lon, lat, scale_km)),
*(widget.JXY()+scaler.R()))
gmt.psxy(
in_columns=(lon_grid, lat_grid),
S='x10p', W='1p/200/0/0', *(widget.JXY()+scaler.R()))
gmt.psxy(
in_columns=(lon_grid_alt, lat_grid_alt),
S='c10p', W='1p/0/0/200', *(widget.JXY()+scaler.R()))
gmt.save('orthodrome.pdf')
subprocess.call(['xpdf', '-remote', 'ortho', '-reload'])
time.sleep(2)
else:
print('ok', gsize, lat, lon)
def test_fomosto_vs_psgrn_pscmp(self):
mod = cake.LayeredModel.from_scanlines(cake.read_nd_model_str('''
0. 5.8 3.46 2.6 1264. 600.
20. 5.8 3.46 2.6 1264. 600.
20. 6.5 3.85 2.9 1283. 600.
35. 6.5 3.85 2.9 1283. 600.
mantle
35. 8.04 4.48 3.58 1449. 600.
77.5 8.045 4.49 3.5 1445. 600.
77.5 8.045 4.49 3.5 180.6 75.
120. 8.05 4.5 3.427 180. 75.
120. 8.05 4.5 3.427 182.6 76.06
165. 8.175 4.509 3.371 188.7 76.55
210. 8.301 4.518 3.324 201. 79.4
210. 8.3 4.52 3.321 336.9 133.3
410. 9.03 4.871 3.504 376.5 146.1
410. 9.36 5.08 3.929 414.1 162.7
660. 10.2 5.611 3.918 428.5 172.9
660. 10.79 5.965 4.229 1349. 549.6'''.lstrip()))
store_dir = mkdtemp(prefix='gfstore')
self.tempdirs.append(store_dir)
store_id = 'psgrn_pscmp_test'
version = '2008a'
c = psgrn_pscmp.PsGrnPsCmpConfig()
c.psgrn_config.sampling_interval = 1.
c.psgrn_config.version = version
c.pscmp_config.version = version
config = gf.meta.ConfigTypeA(
id=store_id,
ncomponents=10,
sample_rate=1. / (3600. * 24.),
receiver_depth=0. * km,
source_depth_min=0. * km,
source_depth_max=5. * km,
source_depth_delta=0.1 * km,
distance_min=0. * km,
distance_max=40. * km,
distance_delta=0.1 * km,
modelling_code_id='psgrn_pscmp.%s' % version,
earthmodel_1d=mod,
tabulated_phases=[])
config.validate()
gf.store.Store.create_editables(
store_dir, config=config, extra={'psgrn_pscmp': c})
store = gf.store.Store(store_dir, 'r')
store.close()
# build store
try:
psgrn_pscmp.build(store_dir, nworkers=1)
except psgrn_pscmp.PsCmpError as e:
if str(e).find('could not start psgrn/pscmp') != -1:
logger.warn('psgrn/pscmp not installed; '
'skipping test_pyrocko_gf_vs_pscmp')
return
else:
raise
origin = gf.Location(
lat=10.,
lon=-15.)
# test GF store
TestRF = dict(
lat=origin.lat,
lon=origin.lon,
depth=2. * km,
width=2. * km,
length=5. * km,
rake=90., dip=45., strike=45.,
slip=1.)
source = gf.RectangularSource(**TestRF)
neast = 40
nnorth = 40
N, E = num.meshgrid(num.linspace(-20. * km, 20. * km, nnorth),
num.linspace(-20. * km, 20. * km, neast))
starget = gf.StaticTarget(
lats=num.array([origin.lat] * N.size),
lons=num.array([origin.lon] * N.size),
north_shifts=N.flatten(),
east_shifts=E.flatten(),
interpolation='nearest_neighbor')
engine = gf.LocalEngine(store_dirs=[store_dir])
t0 = time()
r = engine.process(source, starget)
t1 = time()
logger.info('pyrocko stacking time %f' % (t1 - t0))
un_fomosto = r.static_results()[0].result['displacement.n']
ue_fomosto = r.static_results()[0].result['displacement.e']
ud_fomosto = r.static_results()[0].result['displacement.d']
# test against direct pscmp output
lats, lons = ortd.ne_to_latlon(
origin.lat, origin.lon, N.flatten(), E.flatten())
pscmp_sources = [psgrn_pscmp.PsCmpRectangularSource(**TestRF)]
cc = c.pscmp_config
cc.observation = psgrn_pscmp.PsCmpScatter(lats=lats, lons=lons)
cc.rectangular_source_patches = pscmp_sources
ccf = psgrn_pscmp.PsCmpConfigFull(**cc.items())
ccf.psgrn_outdir = os.path.join(store_dir, c.gf_outdir) + '/'
t2 = time()
runner = psgrn_pscmp.PsCmpRunner(keep_tmp=False)
runner.run(ccf)
ps2du = runner.get_results(component='displ')[0]
t3 = time()
logger.info('pscmp stacking time %f' % (t3 - t2))
un_pscmp = ps2du[:, 0]
ue_pscmp = ps2du[:, 1]
ud_pscmp = ps2du[:, 2]
num.testing.assert_allclose(un_fomosto, un_pscmp, atol=0.002)
num.testing.assert_allclose(ue_fomosto, ue_pscmp, atol=0.002)
num.testing.assert_allclose(ud_fomosto, ud_pscmp, atol=0.002)
def test_point_in_polygon(self):
if plot:
from pyrocko.plot import mpl_graph_color
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
axes = plt.gca()
nip = 100
for i in range(1):
np = 3
points = num.zeros((np, 2))
points[:, 0] = random_lat(size=3)
points[:, 1] = random_lon(size=3)
points_ip = num.zeros((nip*points.shape[0], 2))
for ip in range(points.shape[0]):
n, e = orthodrome.latlon_to_ne_numpy(
points[ip % np, 0], points[ip % np, 1],
points[(ip+1) % np, 0], points[(ip+1) % np, 1])
ns = num.arange(nip) * n / nip
es = num.arange(nip) * e / nip
lats, lons = orthodrome.ne_to_latlon(
points[ip % np, 0], points[ip % np, 1], ns, es)
points_ip[ip*nip:(ip+1)*nip, 0] = lats
points_ip[ip*nip:(ip+1)*nip, 1] = lons
if plot:
color = mpl_graph_color(i)
axes.add_patch(
Polygon(
num.fliplr(points_ip),
facecolor=light(color),
edgecolor=color,
alpha=0.5))
points_xyz = orthodrome.latlon_to_xyz(points_ip.T)
center_xyz = num.mean(points_xyz, axis=0)
assert num.all(
orthodrome.distances3d(
points_xyz, center_xyz[num.newaxis, :]) < 1.0)
lat, lon = orthodrome.xyz_to_latlon(center_xyz)
rot = orthodrome.rot_to_00(lat, lon)
points_rot_xyz = num.dot(rot, points_xyz.T).T
points_rot_pro = orthodrome.stereographic(points_rot_xyz) # noqa
poly_xyz = orthodrome.latlon_to_xyz(points_ip)
poly_rot_xyz = num.dot(rot, poly_xyz.T).T
groups = orthodrome.spoly_cut([poly_rot_xyz], axis=0)
num.zeros(points.shape[0], dtype=num.int)
if plot:
for group in groups:
for poly_rot_group_xyz in group:
axes.set_xlim(-180., 180.)
axes.set_ylim(-90., 90.)
plt.show()
class GFPsgrnPscmpTestCase(unittest.TestCase):
def __init__(self, *args, **kwargs):
unittest.TestCase.__init__(self, *args, **kwargs)
self.tempdirs = []
def __del__(self):
import shutil
for d in self.tempdirs:
shutil.rmtree(d)
def test_fomosto_vs_psgrn_pscmp(self):
mod = cake.LayeredModel.from_scanlines(
cake.read_nd_model_str('''
0. 5.8 3.46 2.6 1264. 600.
20. 5.8 3.46 2.6 1264. 600.
20. 6.5 3.85 2.9 1283. 600.
35. 6.5 3.85 2.9 1283. 600.
mantle
35. 8.04 4.48 3.58 1449. 600.
77.5 8.045 4.49 3.5 1445. 600.
77.5 8.045 4.49 3.5 180.6 75.
120. 8.05 4.5 3.427 180. 75.
120. 8.05 4.5 3.427 182.6 76.06
165. 8.175 4.509 3.371 188.7 76.55
210. 8.301 4.518 3.324 201. 79.4
210. 8.3 4.52 3.321 336.9 133.3
410. 9.03 4.871 3.504 376.5 146.1
410. 9.36 5.08 3.929 414.1 162.7
660. 10.2 5.611 3.918 428.5 172.9
660. 10.79 5.965 4.229 1349. 549.6'''.lstrip()))
store_dir = mkdtemp(prefix='gfstore')
self.tempdirs.append(store_dir)
store_id = 'psgrn_pscmp_test'
version = '2008a'
c = psgrn_pscmp.PsGrnPsCmpConfig()
c.psgrn_config.sampling_interval = 1.
c.psgrn_config.version = version
c.pscmp_config.version = version
config = gf.meta.ConfigTypeA(id=store_id,
ncomponents=10,
sample_rate=1. / (3600. * 24.),
receiver_depth=0. * km,
source_depth_min=0. * km,
source_depth_max=5. * km,
source_depth_delta=0.1 * km,
distance_min=0. * km,
distance_max=40. * km,
distance_delta=0.1 * km,
modelling_code_id='psgrn_pscmp.%s' %
version,
earthmodel_1d=mod,
tabulated_phases=[])
config.validate()
gf.store.Store.create_editables(store_dir,
config=config,
extra={'psgrn_pscmp': c})
store = gf.store.Store(store_dir, 'r')
store.close()
# build store
try:
psgrn_pscmp.build(store_dir, nworkers=1)
except psgrn_pscmp.PsCmpError, e:
if str(e).find('could not start psgrn/pscmp') != -1:
logger.warn('psgrn/pscmp not installed; '
'skipping test_pyrocko_gf_vs_pscmp')
return
else:
raise
origin = gf.Location(lat=10., lon=-15.)
# test GF store
TestRF = dict(lat=origin.lat,
lon=origin.lon,
depth=2. * km,
width=2. * km,
length=5. * km,
rake=90.,
dip=45.,
strike=45.,
slip=1.)
source = gf.RectangularSource(**TestRF)
neast = 40
nnorth = 40
N, E = num.meshgrid(num.linspace(-20. * km, 20. * km, nnorth),
num.linspace(-20. * km, 20. * km, neast))
starget = gf.StaticTarget(lats=num.array([origin.lat] * N.size),
lons=num.array([origin.lon] * N.size),
north_shifts=N.flatten(),
east_shifts=E.flatten(),
interpolation='nearest_neighbor')
engine = gf.LocalEngine(store_dirs=[store_dir])
t0 = time()
r = engine.process(source, starget)
t1 = time()
logger.info('pyrocko stacking time %f' % (t1 - t0))
un_fomosto = r.static_results()[0].result['displacement.n']
ue_fomosto = r.static_results()[0].result['displacement.e']
ud_fomosto = r.static_results()[0].result['displacement.d']
# test against direct pscmp output
lats, lons = ortd.ne_to_latlon(origin.lat, origin.lon, N.flatten(),
E.flatten())
pscmp_sources = [psgrn_pscmp.PsCmpRectangularSource(**TestRF)]
cc = c.pscmp_config
cc.observation = psgrn_pscmp.PsCmpScatter(lats=lats, lons=lons)
cc.rectangular_source_patches = pscmp_sources
ccf = psgrn_pscmp.PsCmpConfigFull(**cc.items())
ccf.psgrn_outdir = os.path.join(store_dir, c.gf_outdir) + '/'
t2 = time()
runner = psgrn_pscmp.PsCmpRunner(keep_tmp=False)
runner.run(ccf)
ps2du = runner.get_results(component='displ')[0]
t3 = time()
logger.info('pscmp stacking time %f' % (t3 - t2))
un_pscmp = ps2du[:, 0]
ue_pscmp = ps2du[:, 1]
ud_pscmp = ps2du[:, 2]
num.testing.assert_allclose(un_fomosto, un_pscmp, atol=0.002)
num.testing.assert_allclose(ue_fomosto, ue_pscmp, atol=0.002)
num.testing.assert_allclose(ud_fomosto, ud_pscmp, atol=0.002)
def draw_plates(self):
from pyrocko.dataset import tectonics
neast = 20
nnorth = max(1, int(round(num.round(self._hreg/self._wreg * neast))))
norths = num.linspace(-self._hreg*0.5, self._hreg*0.5, nnorth)
easts = num.linspace(-self._wreg*0.5, self._wreg*0.5, neast)
norths2 = num.repeat(norths, neast)
easts2 = num.tile(easts, nnorth)
lats, lons = od.ne_to_latlon(
self.lat, self.lon, norths2, easts2)
bird = tectonics.PeterBird2003()
plates = bird.get_plates()
color_plates = gmtpy.color('aluminium5')
color_velocities = gmtpy.color('skyblue1')
color_velocities_lab = gmtpy.color(darken(gmtpy.color_tup('skyblue1')))
points = num.vstack((lats, lons)).T
used = []
for plate in plates:
mask = plate.contains_points(points)
if num.any(mask):
used.append((plate, mask))
if len(used) > 1:
candi_fixed = {}
label_data = []
for plate, mask in used:
mean_north = num.mean(norths2[mask])
mean_east = num.mean(easts2[mask])
iorder = num.argsort(num.sqrt(
(norths2[mask] - mean_north)**2 +
(easts2[mask] - mean_east)**2))
lat_candis = lats[mask][iorder]
lon_candis = lons[mask][iorder]
candi_fixed[plate.name] = lat_candis.size
label_data.append((
lat_candis, lon_candis, plate, color_plates))
boundaries = bird.get_boundaries()
size = 2
psxy_kwargs = []
for boundary in boundaries:
if num.any(points_in_region(boundary.points, self._wesn)):
for typ, part in boundary.split_types(
[['SUB'],
['OSR', 'OTF', 'OCB', 'CTF', 'CCB', 'CRB']]):
lats, lons = part.T
kwargs = {}
if typ[0] == 'SUB':
if boundary.kind == '\\':
kwargs['S'] = 'f%g/%gp+t+r' % (
0.45*size, 3.*size)
elif boundary.kind == '/':
kwargs['S'] = 'f%g/%gp+t+l' % (
0.45*size, 3.*size)
kwargs['G'] = color_plates
kwargs['in_columns'] = (lons, lats)
kwargs['W'] = '%gp,%s' % (size, color_plates),
psxy_kwargs.append(kwargs)
if boundary.kind == '\\':
if boundary.name2 in candi_fixed:
candi_fixed[boundary.name2] += neast*nnorth
elif boundary.kind == '/':
if boundary.name1 in candi_fixed:
candi_fixed[boundary.name1] += neast*nnorth
candi_fixed = [name for name in sorted(
list(candi_fixed.keys()), key=lambda name: -candi_fixed[name])]
candi_fixed.append(None)
gsrm = tectonics.GSRM1()
for name in candi_fixed:
if name not in gsrm.plate_names() \
and name not in gsrm.plate_alt_names():
continue
lats, lons, vnorth, veast, vnorth_err, veast_err, corr = \
gsrm.get_velocities(name, region=self._wesn)
fixed_plate_name = name
self.gmt.psvelo(
in_columns=(
lons, lats, veast, vnorth, veast_err, vnorth_err,
corr),
W='0.25p,%s' % color_velocities,
A='9p+e+g%s' % color_velocities,
S='e0.2p/0.95/10',
*self.jxyr)
for _ in range(len(lons) // 50 + 1):
ii = random.randint(0, len(lons)-1)
v = math.sqrt(vnorth[ii]**2 + veast[ii]**2)
self.add_label(
lats[ii], lons[ii], '%.0f' % v,
font_size=0.7*self.gmt.label_font_size(),
style=dict(
G=color_velocities_lab))
break
for (lat_candis, lon_candis, plate, color) in label_data:
full_name = bird.full_name(plate.name)
if plate.name == fixed_plate_name:
full_name = '@_' + full_name + '@_'
self.add_area_label(
lat_candis, lon_candis,
full_name,
color=color,
font='3')
for kwargs in psxy_kwargs:
self.gmt.psxy(*self.jxyr, **kwargs)
def ellipse_lat_lon(major, minor, azimuth, lat, lon):
points = ellipse(major, minor, azimuth)
return orthodrome.ne_to_latlon(lat, lon, points[:, 0], points[:, 1])
def test_point_in_polygon(self):
if plot:
from pyrocko.plot import mpl_graph_color
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
axes = plt.gca()
nip = 100
for i in range(1):
np = 3
points = num.zeros((np, 2))
points[:, 0] = random_lat(size=3)
points[:, 1] = random_lon(size=3)
points_ip = num.zeros((nip*points.shape[0], 2))
for ip in range(points.shape[0]):
n, e = orthodrome.latlon_to_ne_numpy(
points[ip % np, 0], points[ip % np, 1],
points[(ip+1) % np, 0], points[(ip+1) % np, 1])
ns = num.arange(nip) * n / nip
es = num.arange(nip) * e / nip
lats, lons = orthodrome.ne_to_latlon(
points[ip % np, 0], points[ip % np, 1], ns, es)
points_ip[ip*nip:(ip+1)*nip, 0] = lats
points_ip[ip*nip:(ip+1)*nip, 1] = lons
if plot:
color = mpl_graph_color(i)
axes.add_patch(
Polygon(
num.fliplr(points_ip),
facecolor=light(color),
edgecolor=color,
alpha=0.5))
points_xyz = orthodrome.latlon_to_xyz(points_ip.T)
center_xyz = num.mean(points_xyz, axis=0)
assert num.all(
orthodrome.distances3d(
points_xyz, center_xyz[num.newaxis, :]) < 1.0)
lat, lon = orthodrome.xyz_to_latlon(center_xyz)
rot = orthodrome.rot_to_00(lat, lon)
points_rot_xyz = num.dot(rot, points_xyz.T).T
points_rot_pro = orthodrome.stereographic(points_rot_xyz) # noqa
poly_xyz = orthodrome.latlon_to_xyz(points_ip)
poly_rot_xyz = num.dot(rot, poly_xyz.T).T
groups = orthodrome.spoly_cut([poly_rot_xyz], axis=0)
num.zeros(points.shape[0], dtype=num.int)
if plot:
for group in groups:
for poly_rot_group_xyz in group:
axes.set_xlim(-180., 180.)
axes.set_ylim(-90., 90.)
plt.show()
z = db.firstz + iz*db.dz
p.stdin.write("%g %g T T\n" % (x,z))
p.stdin.flush()
p.stdin.close()
p.wait()
elif command == 'syntheseis':
olat, olon = 30., 70.
receivers = []
distances = num.linspace(3000, 4000, 10)
for dist in distances:
lat, lon = orthodrome.ne_to_latlon(olat, olon, dist, 0.)
r = receiver.Receiver(lat,lon, components='ned')
receivers.append(r)
db = gfdb.Gfdb('benchdb')
seis = seismosizer.Seismosizer(hosts=['localhost']*4)
seis.set_database(db)
seis.set_effective_dt(0.1)
seis.set_local_interpolation('bilinear')
seis.set_receivers(receivers)
seis.set_source_location( olat, olon, 0.0)
s = source.Source('bilateral',
sourceparams_str='0 0 0 5000 1e12 91 87 164 0 0 0 0 2500 0.2')
def draw_plates(self):
from pyrocko.dataset import tectonics
neast = 20
nnorth = max(1, int(round(num.round(self._hreg/self._wreg * neast))))
norths = num.linspace(-self._hreg*0.5, self._hreg*0.5, nnorth)
easts = num.linspace(-self._wreg*0.5, self._wreg*0.5, neast)
norths2 = num.repeat(norths, neast)
easts2 = num.tile(easts, nnorth)
lats, lons = od.ne_to_latlon(
self.lat, self.lon, norths2, easts2)
bird = tectonics.PeterBird2003()
plates = bird.get_plates()
color_plates = gmtpy.color('aluminium5')
color_velocities = gmtpy.color('skyblue1')
color_velocities_lab = gmtpy.color(darken(gmtpy.color_tup('skyblue1')))
points = num.vstack((lats, lons)).T
used = []
for plate in plates:
mask = plate.contains_points(points)
if num.any(mask):
used.append((plate, mask))
if len(used) > 1:
candi_fixed = {}
label_data = []
for plate, mask in used:
mean_north = num.mean(norths2[mask])
mean_east = num.mean(easts2[mask])
iorder = num.argsort(num.sqrt(
(norths2[mask] - mean_north)**2 +
(easts2[mask] - mean_east)**2))
lat_candis = lats[mask][iorder]
lon_candis = lons[mask][iorder]
candi_fixed[plate.name] = lat_candis.size
label_data.append((
lat_candis, lon_candis, plate, color_plates))
boundaries = bird.get_boundaries()
size = 2
psxy_kwargs = []
for boundary in boundaries:
if num.any(points_in_region(boundary.points, self._wesn)):
for typ, part in boundary.split_types(
[['SUB'],
['OSR', 'OTF', 'OCB', 'CTF', 'CCB', 'CRB']]):
lats, lons = part.T
kwargs = {}
if typ[0] == 'SUB':
if boundary.kind == '\\':
kwargs['S'] = 'f%g/%gp+t+r' % (
0.45*size, 3.*size)
elif boundary.kind == '/':
kwargs['S'] = 'f%g/%gp+t+l' % (
0.45*size, 3.*size)
kwargs['G'] = color_plates
kwargs['in_columns'] = (lons, lats)
kwargs['W'] = '%gp,%s' % (size, color_plates),
psxy_kwargs.append(kwargs)
if boundary.kind == '\\':
if boundary.name2 in candi_fixed:
candi_fixed[boundary.name2] += neast*nnorth
elif boundary.kind == '/':
if boundary.name1 in candi_fixed:
candi_fixed[boundary.name1] += neast*nnorth
candi_fixed = [name for name in sorted(
list(candi_fixed.keys()), key=lambda name: -candi_fixed[name])]
candi_fixed.append(None)
gsrm = tectonics.GSRM1()
for name in candi_fixed:
if name not in gsrm.plate_names() \
and name not in gsrm.plate_alt_names():
continue
lats, lons, vnorth, veast, vnorth_err, veast_err, corr = \
gsrm.get_velocities(name, region=self._wesn)
fixed_plate_name = name
self.gmt.psvelo(
in_columns=(
lons, lats, veast, vnorth, veast_err, vnorth_err,
corr),
W='0.25p,%s' % color_velocities,
A='9p+e+g%s' % color_velocities,
S='e0.2p/0.95/10',
*self.jxyr)
for _ in range(len(lons) // 50 + 1):
ii = random.randint(0, len(lons)-1)
v = math.sqrt(vnorth[ii]**2 + veast[ii]**2)
self.add_label(
lats[ii], lons[ii], '%.0f' % v,
font_size=0.7*self.gmt.label_font_size(),
style=dict(
G=color_velocities_lab))
break
for (lat_candis, lon_candis, plate, color) in label_data:
full_name = bird.full_name(plate.name)
if plate.name == fixed_plate_name:
full_name = '@_' + full_name + '@_'
self.add_area_label(
lat_candis, lon_candis,
full_name,
color=color,
font='3')
for kwargs in psxy_kwargs:
self.gmt.psxy(*self.jxyr, **kwargs)
def corners(lon, lat, w, h):
ll_lat, ll_lon = od.ne_to_latlon(lat, lon, -0.5*h, -0.5*w)
ur_lat, ur_lon = od.ne_to_latlon(lat, lon, 0.5*h, 0.5*w)
return ll_lon, ll_lat, ur_lon, ur_lat
from pyrocko import orthodrome # arguments: origin lat, origin lon, north [m], east [m] lat, lon = orthodrome.ne_to_latlon(37.58, 57.11, 3000.0, 10000.0) print(lat, lon)
x = db.firstx + ix * db.dx
z = db.firstz + iz * db.dz
p.stdin.write("%g %g T T\n" % (x, z))
p.stdin.flush()
p.stdin.close()
p.wait()
elif command == 'syntheseis':
olat, olon = 30., 70.
receivers = []
distances = num.linspace(3000, 4000, 10)
for dist in distances:
lat, lon = orthodrome.ne_to_latlon(olat, olon, dist, 0.)
r = receiver.Receiver(lat, lon, components='ned')
receivers.append(r)
db = gfdb.Gfdb('benchdb')
seis = seismosizer.Seismosizer(hosts=['localhost'] * 4)
seis.set_database(db)
seis.set_effective_dt(0.1)
seis.set_local_interpolation('bilinear')
seis.set_receivers(receivers)
seis.set_source_location(olat, olon, 0.0)
s = source.Source(
'bilateral',
sourceparams_str='0 0 0 5000 1e12 91 87 164 0 0 0 0 2500 0.2')
from pyrocko import orthodrome
# arguments: origin lat, origin lon, north [m], east [m]
lat, lon = orthodrome.ne_to_latlon(10.3, 12.4, 22200., 21821.)
print("latitude: %s, longitude: %s " % (lat, lon))
# >>> latitude: 10.4995878932, longitude: 12.5995823469
