def test_fidshi(self):
x = num.linspace(178., 182., 101)
y = num.linspace(-17.5, -14.5, 101)
z = num.sin((x[num.newaxis, :]-x[0])/(x[-1]-x[0])*2.*math.pi*5) * \
num.sin((y[:, num.newaxis]-y[0])/(y[-1]-y[0])*2.*math.pi*5) * 5000.
tile = automap.FloatTile(xmin=x[0],
ymin=y[0],
dx=x[1] - x[0],
dy=y[1] - y[0],
data=z)
for version in gmtpy.all_installed_gmt_versions():
m = automap.Map(gmtversion=version,
lat=-16.5,
lon=180.,
radius=100. * km,
width=20.,
height=20.,
margins=[2., 2., 2., 4.5],
show_grid=True,
show_topo=True,
show_topo_scale=True,
replace_topo_color_only=tile,
illuminate=True)
m.draw_cities()
fname = 'automap_test_fidshi.png'
fpath = self.fpath(fname)
m.save(fpath)
self.compare_with_ref(fname, 0.01, show=False)
def test_napoli(self):
for version in gmtpy.all_installed_gmt_versions():
m = automap.Map(gmtversion=version,
lat=40.85,
lon=14.27,
radius=50. * km,
width=20.,
height=20.,
margins=[2., 2., 2., 4.5],
show_grid=True,
show_topo=True,
show_topo_scale=True,
illuminate=True,
custom_cities=[
automap.City('Pompeji',
40.7506,
14.4897,
population=9000)
],
comment='This is output from test_napoli!')
assert m.have_coastlines()
m.draw_cities(include=['Pompeji', 'Capri'])
fname = 'automap_test_napoli.png'
fpath = self.fpath(fname)
m.save(fpath)
self.compare_with_ref(fname, 0.01, show=False)
def draw_map(self, fn):
from pyrocko.plot import automap
lat, lon = self.get_center_latlon()
radius = self.get_radius()
m = automap.Map(width=30.,
height=30.,
lat=lat,
lon=lon,
radius=radius,
show_topo=True,
show_grid=True,
show_rivers=True,
color_wet=(216, 242, 254),
color_dry=(238, 236, 230))
self.source_generator.add_map_artists(m)
sources = self.get_sources()
for gen in self.target_generators:
gen.add_map_artists(self.get_engine(), sources, m)
# for patch in self.get_insar_patches():
# symbol_size = 50.
# coords = num.array(patch.get_corner_coordinates())
# m.gmt.psxy(in_rows=num.fliplr(coords),
# L=True,
# *m.jxyr)
m.save(fn)
def test_new_zealand(self):
m = automap.Map(lat=-42.57,
lon=173.01,
radius=1000. * km,
width=20.,
height=20.,
color_dry=gmtpy.color_tup('aluminium1'),
show_topo=False,
show_rivers=False,
show_plates=True)
m.draw_cities()
fname = 'new_zealand.pdf'
fpath = self.fpath(fname)
m.save(fpath)
def map_gmt(receivers,
grid,
center_lat,
center_lon,
radius,
output_path,
width=25.,
height=25.,
show_station_labels=False):
station_lats = num.array([r.lat for r in receivers])
station_lons = num.array([r.lon for r in receivers])
map = automap.Map(width=width,
height=height,
lat=center_lat,
lon=center_lon,
radius=radius,
show_rivers=False,
show_topo=False,
illuminate_factor_land=0.35,
color_dry=(240, 240, 235),
topo_cpt_wet='white_sea_land',
topo_cpt_dry='white_sea_land')
map.gmt.psxy(in_columns=(station_lons, station_lats),
S='t8p',
G='black',
*map.jxyr)
if grid:
surf_points = grid.surface_points()
map.gmt.psxy(in_columns=(surf_points[1], surf_points[0]),
S='c1p',
G='black',
*map.jxyr)
if show_station_labels:
for r in receivers:
map.add_label(r.lat, r.lon, '%s' % r.station)
map.save(output_path)
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 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_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)
