def preconstrain(self, x):
d = self.get_parameter_dict(x)
m6 = num.array([d.rmnn, d.rmee, d.rmdd, d.rmne, d.rmnd, d.rmed],
dtype=num.float)
m9 = mtm.symmat6(*m6)
if self.mt_type == 'deviatoric':
trace_m = num.trace(m9)
m_iso = num.diag([trace_m / 3., trace_m / 3., trace_m / 3.])
m9 -= m_iso
elif self.mt_type == 'dc':
mt = mtm.MomentTensor(m=m9)
m9 = mt.standard_decomposition()[1][2]
m0_unscaled = math.sqrt(num.sum(m9.A**2)) / math.sqrt(2.)
m9 /= m0_unscaled
m6 = mtm.to6(m9)
d.rmnn, d.rmee, d.rmdd, d.rmne, d.rmnd, d.rmed = m6
x = self.get_parameter_array(d)
source = self.get_source(x)
for t in self.targets:
if (self.distance_min > num.asarray(t.distance_to(source))).any():
raise Forbidden()
return x
def add_map_artists(self, automap):
from pyrocko import gmtpy
for source in self.get_sources():
event = source.pyrocko_event()
mt = event.moment_tensor.m_up_south_east()
xx = num.trace(mt) / 3.
mc = num.matrix([[xx, 0., 0.], [0., xx, 0.], [0., 0., xx]])
mc = mt - mc
mc = mc / event.moment_tensor.scalar_moment() * \
moment_tensor.magnitude_to_moment(5.0)
m6 = tuple(moment_tensor.to6(mc))
symbol_size = 20.
automap.gmt.psmeca(
S='%s%g' % ('d', symbol_size / gmtpy.cm),
in_rows=[(source.lon, source.lat, 10) + m6 + (1, 0, 0)],
M=True,
*automap.jxyr)
def add_map_artists(self, automap):
from pyrocko import gmtpy
for source in self.get_sources():
event = source.pyrocko_event()
mt = event.moment_tensor.m_up_south_east()
xx = num.trace(mt) / 3.
mc = num.matrix([[xx, 0., 0.], [0., xx, 0.], [0., 0., xx]])
mc = mt - mc
mc = mc / event.moment_tensor.scalar_moment() * \
moment_tensor.magnitude_to_moment(5.0)
m6 = tuple(moment_tensor.to6(mc))
symbol_size = 20.
automap.gmt.psmeca(S='%s%g' % ('d', symbol_size / gmtpy.cm),
in_rows=[(source.lon, source.lat, 10) + m6 +
(1, 0, 0)],
M=True,
*automap.jxyr)
def plot_gnss(gnss_target, result, ifig, vertical=False):
campaign = gnss_target.campaign
item = PlotItem(
name='fig_%i' % ifig,
attributes={'targets': gnss_target.path},
title=u'Static GNSS Surface Displacements - Campaign %s' %
campaign.name,
description=u'''
Static surface displacement from GNSS campaign %s (black vectors) and
displacements derived from best model (red).
''' % campaign.name)
event = source.pyrocko_event()
locations = campaign.stations + [event]
lat, lon = od.geographic_midpoint_locations(locations)
if self.radius is None:
coords = num.array([loc.effective_latlon for loc in locations])
radius = od.distance_accurate50m_numpy(lat[num.newaxis],
lon[num.newaxis],
coords[:, 0].max(),
coords[:, 1]).max()
radius *= 1.1
if radius < 30. * km:
logger.warn('Radius of GNSS campaign %s too small, defaulting'
' to 30 km' % campaign.name)
radius = 30 * km
model_camp = gnss.GNSSCampaign(stations=copy.deepcopy(
campaign.stations),
name='grond model')
for ista, sta in enumerate(model_camp.stations):
sta.north.shift = result.statics_syn['displacement.n'][ista]
sta.north.sigma = 0.
sta.east.shift = result.statics_syn['displacement.e'][ista]
sta.east.sigma = 0.
if sta.up:
sta.up.shift = -result.statics_syn['displacement.d'][ista]
sta.up.sigma = 0.
m = automap.Map(width=self.size_cm[0],
height=self.size_cm[1],
lat=lat,
lon=lon,
radius=radius,
show_topo=self.show_topo,
show_grid=self.show_grid,
show_rivers=self.show_rivers,
color_wet=(216, 242, 254),
color_dry=(238, 236, 230))
all_stations = campaign.stations + model_camp.stations
offset_scale = num.zeros(len(all_stations))
for ista, sta in enumerate(all_stations):
for comp in sta.components.values():
offset_scale[ista] += comp.shift
offset_scale = num.sqrt(offset_scale**2).max()
m.add_gnss_campaign(campaign,
psxy_style={
'G': 'black',
'W': '0.8p,black',
},
offset_scale=offset_scale,
vertical=vertical)
m.add_gnss_campaign(model_camp,
psxy_style={
'G': 'red',
'W': '0.8p,red',
't': 30,
},
offset_scale=offset_scale,
vertical=vertical,
labels=False)
if isinstance(problem, CMTProblem) \
or isinstance(problem, VLVDProblem):
from pyrocko import moment_tensor
from pyrocko.plot import gmtpy
mt = event.moment_tensor.m_up_south_east()
ev_lat, ev_lon = event.effective_latlon
xx = num.trace(mt) / 3.
mc = num.matrix([[xx, 0., 0.], [0., xx, 0.], [0., 0., xx]])
mc = mt - mc
mc = mc / event.moment_tensor.scalar_moment() * \
moment_tensor.magnitude_to_moment(5.0)
m6 = tuple(moment_tensor.to6(mc))
symbol_size = 20.
m.gmt.psmeca(S='%s%g' % ('d', symbol_size / gmtpy.cm),
in_rows=[(ev_lon, ev_lat, 10) + m6 + (1, 0, 0)],
M=True,
*m.jxyr)
elif isinstance(problem, RectangularProblem):
m.gmt.psxy(in_rows=source.outline(cs='lonlat'),
L='+p2p,black',
W='1p,black',
G='black',
t=60,
*m.jxyr)
elif isinstance(problem, VolumePointProblem):
ev_lat, ev_lon = event.effective_latlon
dV = abs(source.volume_change)
sphere_radius = num.cbrt(dV / (4. / 3. * num.pi))
volcanic_circle = [ev_lon, ev_lat, '%fe' % sphere_radius]
m.gmt.psxy(S='E-',
in_rows=[volcanic_circle],
W='1p,black',
G='orange3',
*m.jxyr)
return (item, m)
def m6(self):
return mtm.to6(self.m9)
for ev in events:
mag = ev.magnitude
if ev.moment_tensor is None:
ev_symb = 'c' + str(mag * factor_symbl_size) + 'p'
m.gmt.psxy(in_rows=[[ev.lon, ev.lat]],
S=ev_symb,
G=gmtpy.color('scarletred2'),
W='1p,black',
*m.jxyr)
else:
devi = ev.moment_tensor.deviatoric()
beachball_size = mag * factor_symbl_size
mt = devi.m_up_south_east()
mt = mt / ev.moment_tensor.scalar_moment() \
* pmt.magnitude_to_moment(5.0)
m6 = pmt.to6(mt)
data = (ev.lon, ev.lat, 10) + tuple(m6) + (1, 0, 0)
if m.gmt.is_gmt5():
kwargs = dict(M=True,
S='%s%g' % (beachball_symbol[0],
(beachball_size) / gmtpy.cm))
else:
kwargs = dict(S='%s%g' % (beachball_symbol[0],
(beachball_size) * 2 / gmtpy.cm))
m.gmt.psmeca(in_rows=[data],
G=gmtpy.color('chocolate1'),
E='white',
W='1p,%s' % gmtpy.color('chocolate3'),
*m.jxyr,
def main(args=None):
if args is None:
args = sys.argv[1:]
parser = OptionParser(
usage=usage,
description=description)
parser.add_option(
'--width',
dest='width',
type='float',
default=20.0,
metavar='FLOAT',
help='set width of output image [cm] (%default)')
parser.add_option(
'--height',
dest='height',
type='float',
default=15.0,
metavar='FLOAT',
help='set height of output image [cm] (%default)')
parser.add_option(
'--topo-resolution-min',
dest='topo_resolution_min',
type='float',
default=40.0,
metavar='FLOAT',
help='minimum resolution of topography [dpi] (%default)')
parser.add_option(
'--topo-resolution-max',
dest='topo_resolution_max',
type='float',
default=200.0,
metavar='FLOAT',
help='maximum resolution of topography [dpi] (%default)')
parser.add_option(
'--no-grid',
dest='show_grid',
default=True,
action='store_false',
help='don\'t show grid lines')
parser.add_option(
'--no-topo',
dest='show_topo',
default=True,
action='store_false',
help='don\'t show topography')
parser.add_option(
'--no-cities',
dest='show_cities',
default=True,
action='store_false',
help='don\'t show cities')
parser.add_option(
'--no-illuminate',
dest='illuminate',
default=True,
action='store_false',
help='deactivate artificial illumination of topography')
parser.add_option(
'--illuminate-factor-land',
dest='illuminate_factor_land',
type='float',
metavar='FLOAT',
help='set factor for artificial illumination of land (0.5)')
parser.add_option(
'--illuminate-factor-ocean',
dest='illuminate_factor_ocean',
type='float',
metavar='FLOAT',
help='set factor for artificial illumination of ocean (0.25)')
parser.add_option(
'--theme',
choices=['topo', 'soft'],
default='topo',
help='select color theme, available: topo, soft (topo)"')
parser.add_option(
'--download-etopo1',
dest='download_etopo1',
action='store_true',
help='download full ETOPO1 topography dataset')
parser.add_option(
'--download-srtmgl3',
dest='download_srtmgl3',
action='store_true',
help='download full SRTMGL3 topography dataset')
parser.add_option(
'--make-decimated-topo',
dest='make_decimated',
action='store_true',
help='pre-make all decimated topography datasets')
parser.add_option(
'--stations',
dest='stations_fn',
metavar='FILENAME',
help='load station coordinates from FILENAME')
parser.add_option(
'--events',
dest='events_fn',
metavar='FILENAME',
help='load event coordinates from FILENAME')
parser.add_option(
'--debug',
dest='debug',
action='store_true',
default=False,
help='print debugging information to stderr')
(options, args) = parser.parse_args(args)
if options.debug:
util.setup_logging(program_name, 'debug')
else:
util.setup_logging(program_name, 'info')
if options.download_etopo1:
import pyrocko.datasets.topo.etopo1
pyrocko.datasets.topo.etopo1.download()
if options.download_srtmgl3:
import pyrocko.datasets.topo.srtmgl3
pyrocko.datasets.topo.srtmgl3.download()
if options.make_decimated:
import pyrocko.datasets.topo
pyrocko.datasets.topo.make_all_missing_decimated()
if (options.download_etopo1 or options.download_srtmgl3 or
options.make_decimated) and len(args) == 0:
sys.exit(0)
if options.theme == 'soft':
color_kwargs = {
'illuminate_factor_land': options.illuminate_factor_land or 0.2,
'illuminate_factor_ocean': options.illuminate_factor_ocean or 0.15,
'color_wet': (216, 242, 254),
'color_dry': (238, 236, 230),
'topo_cpt_wet': 'light_sea_uniform',
'topo_cpt_dry': 'light_land_uniform'}
elif options.theme == 'topo':
color_kwargs = {
'illuminate_factor_land': options.illuminate_factor_land or 0.5,
'illuminate_factor_ocean': options.illuminate_factor_ocean or 0.25}
events = []
if options.events_fn:
events = model.load_events(options.events_fn)
stations = []
if options.stations_fn:
stations = model.load_stations(options.stations_fn)
if not (len(args) == 4 or (
len(args) == 1 and (events or stations))):
parser.print_help()
sys.exit(1)
if len(args) == 4:
try:
lat = float(args[0])
lon = float(args[1])
radius = float(args[2])*km
except Exception:
parser.print_help()
sys.exit(1)
else:
lats, lons = latlon_arrays(stations+events)
lat, lon = map(float, od.geographic_midpoint(lats, lons))
radius = float(
num.max(od.distance_accurate50m_numpy(lat, lon, lats, lons)))
radius *= 1.1
m = automap.Map(
width=options.width,
height=options.height,
lat=lat,
lon=lon,
radius=radius,
topo_resolution_max=options.topo_resolution_max,
topo_resolution_min=options.topo_resolution_min,
show_topo=options.show_topo,
show_grid=options.show_grid,
illuminate=options.illuminate,
**color_kwargs)
logger.debug('map configuration:\n%s' % str(m))
if options.show_cities:
m.draw_cities()
if stations:
lats = [s.lat for s in stations]
lons = [s.lon for s in stations]
m.gmt.psxy(
in_columns=(lons, lats),
S='t8p',
G='black',
*m.jxyr)
for s in stations:
m.add_label(s.lat, s.lon, '%s' % '.'.join(
x for x in s.nsl() if x))
if events:
beachball_symbol = 'mt'
beachball_size = 20.0
for ev in events:
if ev.moment_tensor is None:
m.gmt.psxy(
in_rows=[[ev.lon, ev.lat]],
S='c12p',
G=gmtpy.color('scarletred2'),
W='1p,black',
*m.jxyr)
else:
devi = ev.moment_tensor.deviatoric()
mt = devi.m_up_south_east()
mt = mt / ev.moment_tensor.scalar_moment() \
* pmt.magnitude_to_moment(5.0)
m6 = pmt.to6(mt)
data = (ev.lon, ev.lat, 10) + tuple(m6) + (1, 0, 0)
if m.gmt.is_gmt5():
kwargs = dict(
M=True,
S='%s%g' % (
beachball_symbol[0], beachball_size / gmtpy.cm))
else:
kwargs = dict(
S='%s%g' % (
beachball_symbol[0], beachball_size*2 / gmtpy.cm))
m.gmt.psmeca(
in_rows=[data],
G=gmtpy.color('chocolate1'),
E='white',
W='1p,%s' % gmtpy.color('chocolate3'),
*m.jxyr,
**kwargs)
m.save(args[-1])
def scaled_m6(self):
m9 = mtm.symmat6(*self.m6)
m0_unscaled = math.sqrt(num.sum(m9.A ** 2)) / math.sqrt(2.)
m9 /= m0_unscaled
m6 = mtm.to6(m9)
return m6
def plot_spatial_with_dcs(events, eventsclusters, clusters, conf, plotdir):
'''
Plot map of seismicity clusters, with focal mechanisms
'''
lats = [ev.lat for ev in events]
lons = [ev.lon for ev in events]
# deps = [ev.depth for ev in events]
# colors = [cluster_to_color(clid) for clid in eventsclusters]
if conf.sw_filterevent:
latmin, latmax = conf.latmin, conf.latmax
lonmin, lonmax = conf.lonmin, conf.lonmax
# depmin, depmax = conf.depthmin, conf.depthmax
if latmin > latmax:
print('cases over lon +-180 still to be implemented')
sys.exit()
center = model.event(0.5 * (latmin + latmax), 0.5 * (lonmin + lonmax))
else:
latmin, latmax = min(lats) - 0.1, max(lats) + 0.1
lonmin, lonmax = min(lons) - 0.1, max(lons) + 0.1
# depmin = 0.*km
# depmax = 1.05*max(deps)
center = od.Loc(0.5 * (latmin + latmax), 0.5 * (lonmin + lonmax))
# Map size
if conf.sw_manual_radius:
safe_radius = conf.map_radius
else:
corners = [od.Loc(latmin, lonmin), od.Loc(latmin, lonmax)]
dist1 = od.distance_accurate50m(center, corners[0])
dist2 = od.distance_accurate50m(center, corners[1])
safe_radius = max(dist1, dist2)
# Generate the basic map
m = Map(lat=center.lat,
lon=center.lon,
radius=safe_radius,
width=30.,
height=30.,
show_grid=False,
show_topo=False,
color_dry=(238, 236, 230),
topo_cpt_wet='light_sea_uniform',
topo_cpt_dry='light_land_uniform',
illuminate=True,
illuminate_factor_ocean=0.15,
show_rivers=False,
show_plates=False)
if conf.sw_filterevent:
rectlons = [lonmin, lonmin, lonmax, lonmax, lonmin]
rectlats = [latmin, latmax, latmax, latmin, latmin]
m.gmt.psxy(in_columns=(rectlons, rectlats),
W='thin,0/0/0,dashed',
*m.jxyr)
# Draw some larger cities covered by the map area
m.draw_cities()
# Events in clusters
factor_symbl_size = 5.
beachball_symbol = 'd'
for id_cluster in clusters:
col = cluster_to_color(id_cluster)
g_col = color2rgb(col)
for iev, evcl in enumerate(eventsclusters):
if evcl == id_cluster:
ev = events[iev]
if ev.moment_tensor is not None:
factor_symbl_size = ev.magnitude
devi = ev.moment_tensor.deviatoric()
beachball_size = 3. * factor_symbl_size
mt = devi.m_up_south_east()
mt = mt / ev.moment_tensor.scalar_moment() \
* pmt.magnitude_to_moment(5.0)
m6 = pmt.to6(mt)
if m.gmt.is_gmt5():
kwargs = dict(M=True,
S='%s%g' % (beachball_symbol[0],
(beachball_size) / gmtpy.cm))
else:
kwargs = dict(S='%s%g' %
(beachball_symbol[0],
(beachball_size) * 2 / gmtpy.cm))
data = (ev.lon, ev.lat, 10) + tuple(m6) + (1, 0, 0)
m.gmt.psmeca(in_rows=[data],
G=g_col,
E='white',
W='1p,%s' % g_col,
*m.jxyr,
**kwargs)
figname = os.path.join(plotdir, 'plot_map_with_dcs.' + conf.figure_format)
m.save(figname)
def main(args=None):
if args is None:
args = sys.argv[1:]
parser = OptionParser(
usage=usage,
description=description)
parser.add_option(
'--width',
dest='width',
type='float',
default=20.0,
metavar='FLOAT',
help='set width of output image [cm] (%default)')
parser.add_option(
'--height',
dest='height',
type='float',
default=15.0,
metavar='FLOAT',
help='set height of output image [cm] (%default)')
parser.add_option(
'--topo-resolution-min',
dest='topo_resolution_min',
type='float',
default=40.0,
metavar='FLOAT',
help='minimum resolution of topography [dpi] (%default)')
parser.add_option(
'--topo-resolution-max',
dest='topo_resolution_max',
type='float',
default=200.0,
metavar='FLOAT',
help='maximum resolution of topography [dpi] (%default)')
parser.add_option(
'--no-grid',
dest='show_grid',
default=True,
action='store_false',
help='don\'t show grid lines')
parser.add_option(
'--no-topo',
dest='show_topo',
default=True,
action='store_false',
help='don\'t show topography')
parser.add_option(
'--no-cities',
dest='show_cities',
default=True,
action='store_false',
help='don\'t show cities')
parser.add_option(
'--no-illuminate',
dest='illuminate',
default=True,
action='store_false',
help='deactivate artificial illumination of topography')
parser.add_option(
'--illuminate-factor-land',
dest='illuminate_factor_land',
type='float',
metavar='FLOAT',
help='set factor for artificial illumination of land (0.5)')
parser.add_option(
'--illuminate-factor-ocean',
dest='illuminate_factor_ocean',
type='float',
metavar='FLOAT',
help='set factor for artificial illumination of ocean (0.25)')
parser.add_option(
'--theme',
choices=['topo', 'soft'],
default='topo',
help='select color theme, available: topo, soft (topo)"')
parser.add_option(
'--download-etopo1',
dest='download_etopo1',
action='store_true',
help='download full ETOPO1 topography dataset')
parser.add_option(
'--download-srtmgl3',
dest='download_srtmgl3',
action='store_true',
help='download full SRTMGL3 topography dataset')
parser.add_option(
'--make-decimated-topo',
dest='make_decimated',
action='store_true',
help='pre-make all decimated topography datasets')
parser.add_option(
'--stations',
dest='stations_fn',
metavar='FILENAME',
help='load station coordinates from FILENAME')
parser.add_option(
'--events',
dest='events_fn',
metavar='FILENAME',
help='load event coordinates from FILENAME')
parser.add_option(
'--debug',
dest='debug',
action='store_true',
default=False,
help='print debugging information to stderr')
(options, args) = parser.parse_args(args)
if options.debug:
util.setup_logging(program_name, 'debug')
else:
util.setup_logging(program_name, 'info')
if options.download_etopo1:
import pyrocko.datasets.topo.etopo1
pyrocko.datasets.topo.etopo1.download()
if options.download_srtmgl3:
import pyrocko.datasets.topo.srtmgl3
pyrocko.datasets.topo.srtmgl3.download()
if options.make_decimated:
import pyrocko.datasets.topo
pyrocko.datasets.topo.make_all_missing_decimated()
if (options.download_etopo1 or options.download_srtmgl3 or
options.make_decimated) and len(args) == 0:
sys.exit(0)
if options.theme == 'soft':
color_kwargs = {
'illuminate_factor_land': options.illuminate_factor_land or 0.2,
'illuminate_factor_ocean': options.illuminate_factor_ocean or 0.15,
'color_wet': (216, 242, 254),
'color_dry': (238, 236, 230),
'topo_cpt_wet': 'light_sea_uniform',
'topo_cpt_dry': 'light_land_uniform'}
elif options.theme == 'topo':
color_kwargs = {
'illuminate_factor_land': options.illuminate_factor_land or 0.5,
'illuminate_factor_ocean': options.illuminate_factor_ocean or 0.25}
events = []
if options.events_fn:
events = model.load_events(options.events_fn)
stations = []
if options.stations_fn:
stations = model.load_stations(options.stations_fn)
if not (len(args) == 4 or (
len(args) == 1 and (events or stations))):
parser.print_help()
sys.exit(1)
if len(args) == 4:
try:
lat = float(args[0])
lon = float(args[1])
radius = float(args[2])*km
except Exception:
parser.print_help()
sys.exit(1)
else:
lats, lons = latlon_arrays(stations+events)
lat, lon = map(float, od.geographic_midpoint(lats, lons))
radius = float(
num.max(od.distance_accurate50m_numpy(lat, lon, lats, lons)))
radius *= 1.1
m = automap.Map(
width=options.width,
height=options.height,
lat=lat,
lon=lon,
radius=radius,
topo_resolution_max=options.topo_resolution_max,
topo_resolution_min=options.topo_resolution_min,
show_topo=options.show_topo,
show_grid=options.show_grid,
illuminate=options.illuminate,
**color_kwargs)
logger.debug('map configuration:\n%s' % str(m))
if options.show_cities:
m.draw_cities()
if stations:
lats = [s.lat for s in stations]
lons = [s.lon for s in stations]
m.gmt.psxy(
in_columns=(lons, lats),
S='t8p',
G='black',
*m.jxyr)
for s in stations:
m.add_label(s.lat, s.lon, '%s' % '.'.join(
x for x in s.nsl() if x))
if events:
beachball_symbol = 'mt'
beachball_size = 20.0
for ev in events:
if ev.moment_tensor is None:
m.gmt.psxy(
in_rows=[[ev.lon, ev.lat]],
S='c12p',
G=gmtpy.color('scarletred2'),
W='1p,black',
*m.jxyr)
else:
devi = ev.moment_tensor.deviatoric()
mt = devi.m_up_south_east()
mt = mt / ev.moment_tensor.scalar_moment() \
* pmt.magnitude_to_moment(5.0)
m6 = pmt.to6(mt)
data = (ev.lon, ev.lat, 10) + tuple(m6) + (1, 0, 0)
if m.gmt.is_gmt5():
kwargs = dict(
M=True,
S='%s%g' % (
beachball_symbol[0], beachball_size / gmtpy.cm))
else:
kwargs = dict(
S='%s%g' % (
beachball_symbol[0], beachball_size*2 / gmtpy.cm))
m.gmt.psmeca(
in_rows=[data],
G=gmtpy.color('chocolate1'),
E='white',
W='1p,%s' % gmtpy.color('chocolate3'),
*m.jxyr,
**kwargs)
m.save(args[-1])
def plot_map(stations, center, events=None, savename=None):
from pyrocko.plot.automap import Map
from pyrocko.example import get_example_data
from pyrocko import model, gmtpy
from pyrocko import moment_tensor as pmt
gmtpy.check_have_gmt()
# Generate the basic map
m = Map(lat=center[0],
lon=center[1],
radius=150000.,
width=30.,
height=30.,
show_grid=False,
show_topo=True,
color_dry=(238, 236, 230),
topo_cpt_wet='light_sea_uniform',
topo_cpt_dry='light_land_uniform',
illuminate=True,
illuminate_factor_ocean=0.15,
show_rivers=False,
show_plates=False)
# Draw some larger cities covered by the map area
m.draw_cities()
# Generate with latitute, longitude and labels of the stations
lats = [s.lat for s in stations]
lons = [s.lon for s in stations]
labels = ['.'.join(s.nsl()) for s in stations]
# Stations as black triangles.
m.gmt.psxy(in_columns=(lons, lats), S='t20p', G='black', *m.jxyr)
# Station labels
for i in range(len(stations)):
m.add_label(lats[i], lons[i], labels[i])
beachball_symbol = 'd'
factor_symbl_size = 5.0
if events is not None:
for ev in events:
mag = ev.magnitude
if ev.moment_tensor is None:
ev_symb = 'c' + str(mag * factor_symbl_size) + 'p'
m.gmt.psxy(in_rows=[[ev.lon, ev.lat]],
S=ev_symb,
G=gmtpy.color('scarletred2'),
W='1p,black',
*m.jxyr)
else:
devi = ev.moment_tensor.deviatoric()
beachball_size = mag * factor_symbl_size
mt = devi.m_up_south_east()
mt = mt / ev.moment_tensor.scalar_moment() \
* pmt.magnitude_to_moment(5.0)
m6 = pmt.to6(mt)
data = (ev.lon, ev.lat, 10) + tuple(m6) + (1, 0, 0)
if m.gmt.is_gmt5():
kwargs = dict(M=True,
S='%s%g' % (beachball_symbol[0],
(beachball_size) / gmtpy.cm))
else:
kwargs = dict(S='%s%g' % (beachball_symbol[0],
(beachball_size) * 2 / gmtpy.cm))
m.gmt.psmeca(in_rows=[data],
G=gmtpy.color('chocolate1'),
E='white',
W='1p,%s' % gmtpy.color('chocolate3'),
*m.jxyr,
**kwargs)
if savename is None:
if events is None:
m.save('pics/stations_ridgecrest.png')
else:
m.save('pics/mechanisms_scedc_ridgecrest.png')
else:
m.save(savename)
mag = ev.magnitude
if ev.moment_tensor is None:
ev_symb = 'c'+str(mag*factor_symbl_size)+'p'
m.gmt.psxy(
in_rows=[[ev.lon, ev.lat]],
S=ev_symb,
G=gmtpy.color('scarletred2'),
W='1p,black',
*m.jxyr)
else:
devi = ev.moment_tensor.deviatoric()
beachball_size = mag*factor_symbl_size
mt = devi.m_up_south_east()
mt = mt / ev.moment_tensor.scalar_moment() \
* pmt.magnitude_to_moment(5.0)
m6 = pmt.to6(mt)
data = (ev.lon, ev.lat, 10) + tuple(m6) + (1, 0, 0)#
if m.gmt.is_gmt5():
kwargs = dict(
M=True,
S='%s%g' % (beachball_symbol[0], (beachball_size) / gmtpy.cm))
else:
kwargs = dict(
S='%s%g' % (beachball_symbol[0],
(beachball_size)*2 / gmtpy.cm))
m.gmt.psmeca(
in_rows=[data],
G=gmtpy.color('chocolate1'),
E='white',
def plot_gnss(gnss_target, result, ifig, vertical=False):
campaign = gnss_target.campaign
item = PlotItem(
name='fig_%i' % ifig,
attributes={'targets': gnss_target.path},
title=u'Static GNSS Surface Displacements - Campaign %s' %
campaign.name,
description=u'Static surface displacement from GNSS campaign '
u'%s (black vectors) and displacements derived '
u'from best rupture model (red).' % campaign.name)
lat, lon = campaign.get_center_latlon()
if self.radius is None:
radius = campaign.get_radius()
if radius == 0.:
logger.warn('Radius of GNSS campaign %s too small, defaulting'
' to 30 km' % campaign.name)
radius = 30 * km
model_camp = gnss.GNSSCampaign(stations=copy.deepcopy(
campaign.stations),
name='grond model')
for ista, sta in enumerate(model_camp.stations):
sta.north.shift = result.statics_syn['displacement.n'][ista]
sta.north.sigma = 0.
sta.east.shift = result.statics_syn['displacement.e'][ista]
sta.east.sigma = 0.
sta.up.shift = -result.statics_syn['displacement.d'][ista]
sta.up.sigma = 0.
m = automap.Map(width=self.size_cm[0],
height=self.size_cm[1],
lat=lat,
lon=lon,
radius=radius,
show_topo=self.show_topo,
show_grid=self.show_grid,
show_rivers=self.show_rivers,
color_wet=(216, 242, 254),
color_dry=(238, 236, 230))
if vertical:
m.add_gnss_campaign(campaign,
psxy_style={
'G': 'black',
'W': '0.8p,black',
},
vertical=True)
m.add_gnss_campaign(model_camp,
psxy_style={
'G': 'red',
'W': '0.8p,red',
't': 30,
},
vertical=True,
labels=False)
else:
m.add_gnss_campaign(campaign,
psxy_style={
'G': 'black',
'W': '0.8p,black',
})
m.add_gnss_campaign(model_camp,
psxy_style={
'G': 'red',
'W': '0.8p,red',
't': 30,
},
labels=False)
if isinstance(problem, CMTProblem):
from pyrocko import moment_tensor
from pyrocko.plot import gmtpy
event = source.pyrocko_event()
mt = event.moment_tensor.m_up_south_east()
xx = num.trace(mt) / 3.
mc = num.matrix([[xx, 0., 0.], [0., xx, 0.], [0., 0., xx]])
mc = mt - mc
mc = mc / event.moment_tensor.scalar_moment() * \
moment_tensor.magnitude_to_moment(5.0)
m6 = tuple(moment_tensor.to6(mc))
symbol_size = 20.
m.gmt.psmeca(S='%s%g' % ('d', symbol_size / gmtpy.cm),
in_rows=[
(source.lon, source.lat, 10) + m6 + (1, 0, 0)
],
M=True,
*m.jxyr)
elif isinstance(problem, RectangularProblem):
m.gmt.psxy(in_rows=source.outline(cs='lonlat'),
L='+p2p,black',
W='1p,black',
G='black',
t=60,
*m.jxyr)
return (item, m)
