def _plot( self, run_id='current' ):
saved = self.load('source_receivers', run_id=run_id)
conf = self.in_config.get_config()
#
# Station plot
#
receivers = saved['receivers']
source_location = saved['source_location']
source = saved['source']
shifts = saved['shifts']
fails = saved['fails']
lats = num.array( [ r.lat for r in receivers ], dtype='float' )
lons = num.array( [ r.lon for r in receivers ], dtype='float' )
dists = num.array( [ r.distance_deg for r in receivers ], dtype='float' )
rnames = [ ' '.join(r.name.split('.')) for r in receivers ]
slat, slon = source_location[:2]
station_color = []
for s,f in zip(shifts,fails):
if f:
station_color.append(num.NaN)
else:
station_color.append(s)
station_size = [ 1. ] * len(receivers)
plotdir = self.make_plotdir_path(run_id)
fn = pjoin(plotdir, 'stations.pdf')
plotting.station_plot( slat, slon, lats, lons, rnames, station_color, station_size,
source, num.amax(dists)*1.05, fn, {}, zexpand=1.02, nsets=conf['nsets'])
return [ 'stations.pdf' ]
def _plot(self, run_id='current'):
saved = self.load('source_receivers', run_id=run_id)
conf = self.in_config.get_config()
#
# Station map
#
receivers = saved['receivers']
source_location = saved['source_location']
lats = num.array( [ r.lat for r in receivers ], dtype='float' )
lons = num.array( [ r.lon for r in receivers ], dtype='float' )
dists = num.array( [ r.distance_deg for r in receivers ], dtype='float' )
rnames = [ ' '.join(r.name.split('.')) for r in receivers ]
slat, slon = source_location[:2]
station_size = [ 0.05 ] * len(receivers)
station_color = [ (0.,1.)[r.enabled] for r in receivers ]
plotdir = self.make_plotdir_path(run_id)
source = None
fn = pjoin(plotdir, 'stations.pdf')
plotting.station_plot( slat, slon, lats, lons, rnames, station_color, station_size,
source, num.amax(dists)*1.05, fn, {}, zexpand=1.02, nsets=conf['nsets'], symbols=('t','t'))
#
# Region map
#
fn = pjoin(plotdir, 'region.pdf')
plotting.location_map( fn, slat, slon, 5., {}, receivers=(lats, lons, rnames))
return [ 'stations.pdf', 'region.pdf' ]
def plot(self, dirname, nsets=1, source_model_infos=None, conf_overrides=None):
best_source = self.best_source
bootstrap_sources = self.bootstrap_sources
plot_files = []
param_min_misfits = {}
for iparam, param in enumerate(self.sourceparams):
#
# 1D misfit cross section
#
xy = []
for s,m in zip(self.sources, self.misfits_by_s):
if not num.isnan(m):
xy.append((s[param], m))
xdata, ydata = num.array(xy,dtype=num.float).transpose()
conf = dict( xlabel = param.title(),
xunit = self.base_source.sourceinfo(param).unit )
if conf_overrides:
conf.update(conf_overrides)
plotting.km_hack(conf)
fn = 'misfit-%s.pdf' % param
plotting.misfit_plot_1d( [(xdata,ydata)],
pjoin(dirname, fn),
conf, apply_moment_to_magnitude_hack=True )
plot_files.append(fn)
#
# 1D histogram
#
gvalues = self.param_values[iparam][1]
gedges = values_to_bin_edges(gvalues)
kwargs = {}
if util.cmp_version(numpy_version(), '1.3.0') < 0:
kwargs = {'new': True}
hist, edges = num.histogram(self.stats[param].distribution,
bins=gedges, **kwargs)
hist = hist/float(len(bootstrap_sources))
conf = dict( xlabel = param.title(),
xunit = self.base_source.sourceinfo(param).unit)
if conf_overrides:
conf.update(conf_overrides)
plotting.km_hack(conf)
fn = 'histogram-%s.pdf' % param
plotting.histogram_plot_1d( edges, hist,
pjoin(dirname, fn),
conf, apply_moment_to_magnitude_hack=True)
plot_files.append( fn )
#
#
# gather data for 1D projected misfit cross sections
indi = num.digitize(xdata, gedges) - 1
for_minmisfits = []
for ival in range(gvalues.size):
vydata = num.extract(indi == ival, ydata)
if vydata.size != 0:
for_minmisfits.append((gvalues[ival], vydata.min()))
param_min_misfits[param] = num.array(for_minmisfits,dtype=num.float).transpose()
# data range for projected misfit cross section
mi, ma = num.inf, -num.inf
for xdata,ydata in param_min_misfits.values():
mi = min(mi, ydata.min())
ma = max(ma, ydata.max())
param_min_misfits_range = (mi,ma)
# 1D projected misfit cross sections
for iparam, param in enumerate(self.sourceparams):
mini, maxi = param_min_misfits_range
if mini < 0.95:
maxi = 1.
else:
if mini < 1.95:
maxi = 2.
conf = dict( xlabel = param.title(),
xunit = self.base_source.sourceinfo(param).unit,
ylimits = (mini, maxi),
ymasking=False)
if conf_overrides:
conf.update(conf_overrides)
plotting.km_hack(conf)
fn = 'min-misfit-%s.pdf' % param
plotting.misfit_plot_1d( [param_min_misfits[param]],
pjoin(dirname, fn),
conf,apply_moment_to_magnitude_hack=True )
plot_files.append(fn)
fn_table = 'min-misfit-%s.table' % param
num.savetxt(pjoin(dirname, fn_table), param_min_misfits[param].transpose())
for ixparam, xparam in enumerate(self.sourceparams):
for iyparam, yparam in enumerate(self.sourceparams):
if ixparam == iyparam: continue
#
# 2D misfogram plot
#
vmisfits = {}
maxmisfit = num.nanmax(self.misfits_by_s)
for (source, misfit) in zip(self.sources, self.misfits_by_s):
if num.isnan(misfit): continue
vx = source[xparam]
vy = source[yparam]
vmisfits[(vx,vy)] = min(vmisfits.get((vx,vy), maxmisfit), misfit)
vcounts = {}
for source in bootstrap_sources:
vx = source[xparam]
vy = source[yparam]
vcounts[(vx,vy)] = vcounts.get((vx,vy), 0) + 1
lx, ly, lz = [], [], []
for ((vx,vy),vmisfit) in vmisfits.iteritems():
lx.append(vx)
ly.append(vy)
lz.append(vmisfit)
lxc, lyc, lc = [], [], []
for ((vx,vy),vcount) in vcounts.iteritems():
lxc.append(vx)
lyc.append(vy)
lc.append(vcount)
ax = num.array(lx,dtype=num.float)
ay = num.array(ly,dtype=num.float)
az = num.array(lz,dtype=num.float)
axc = num.array(lxc,dtype=num.float)
ayc = num.array(lyc,dtype=num.float)
ac = num.array(lc,dtype=num.float)
ac = ac/len(bootstrap_sources)
if len(bootstrap_sources) > 1:
bootstrap_data = (axc, ayc, ac)
else:
bootstrap_data = ([],[],[])
if az.min() < 0.95:
zlimits = (az.min(),min(1.,az.max()))
else:
zlimits = (az.min(),az.max())
if 'misfit_limits' in dir(config):
zlimits = config.misfit_limits
conf = dict( xlabel = xparam.title(),
xunit = self.base_source.sourceinfo(xparam).unit,
ylabel = yparam.title(),
yunit = self.base_source.sourceinfo(yparam).unit,
zlimits = zlimits,
zmasking=False,
zsnap = True
)
if conf_overrides:
for k in conf_overrides:
if not k.endswith('snap'):
conf[k] = conf_overrides[k]
best_loc = (num.array([ best_source[xparam]]), num.array([best_source[yparam]]))
plotting.km_hack(conf)
plotting.nukl_hack(conf)
fn = 'misfogram-%s-%s.pdf' % (xparam, yparam)
plotting.misfogram_plot_2d_gmtpy( [(ax, ay, az), best_loc, bootstrap_data],
pjoin(dirname, fn),
conf, apply_moment_to_magnitude_hack=True )
plot_files.append(fn)
#
# Station plot
#
lats = num.array( [ r.lat for r in self.receivers ], dtype='float' )
lons = num.array( [ r.lon for r in self.receivers ], dtype='float' )
dists = num.array( [ r.distance_deg for r in self.receivers ], dtype='float' )
rnames = [ ' '.join(r.name.split('.')) for r in self.receivers ]
slat, slon = self.source_location[:2]
if len(self.misfits_by_r) == 0:
station_misfits = self.ref_misfits_by_r
else:
station_misfits = self.misfits_by_r-self.ref_misfits_by_r
#station_varia = self.variability_by_r / num.sum(self.variability_by_r) * len(self.receivers)
station_varia = self.misfits_by_r / num.sum(self.misfits_by_r) * len(self.receivers)
plotting.station_plot( slat, slon, lats, lons, rnames, station_misfits, station_varia,
best_source, num.amax(dists)*1.05, pjoin(dirname, 'stations.pdf'), {}, nsets=nsets)
plot_files.append('stations.pdf')
indicate_plane = 0
if source_model_infos:
source_size = 0.
if 'bord-radius' in best_source.keys():
source_size = best_source['bord-radius']
delta_lat = 1.5*max(source_size*2.,50000)/(20000.*1000.)*180
si = None
if 'bord-radius' in best_source.keys() and best_source['bord-radius'] > 0.0:
si = source_model_infos
indicate_plane = 1
plotting.location_map(pjoin(dirname, 'location.pdf'), slat, slon, delta_lat, {}, source=best_source,
source_model_infos=si, receivers=(lats,lons,rnames))
plot_files.append('location.pdf')
fns = plotting.beachball(best_source, pjoin(dirname, 'beachball.pdf'), indicate_plane=indicate_plane)
if fns:
plot_files.append('beachball.pdf')
return plot_files
def plot(self,
dirname,
nsets=1,
source_model_infos=None,
conf_overrides=None):
best_source = self.best_source
bootstrap_sources = self.bootstrap_sources
plot_files = []
param_min_misfits = {}
for iparam, param in enumerate(self.sourceparams):
#
# 1D misfit cross section
#
xy = []
for s, m in zip(self.sources, self.misfits_by_s):
if not num.isnan(m):
xy.append((s[param], m))
xdata, ydata = num.array(xy, dtype=num.float).transpose()
conf = dict(xlabel=param.title(),
xunit=self.base_source.sourceinfo(param).unit)
if conf_overrides:
conf.update(conf_overrides)
plotting.km_hack(conf)
fn = 'misfit-%s.pdf' % param
plotting.misfit_plot_1d([(xdata, ydata)],
pjoin(dirname, fn),
conf,
apply_moment_to_magnitude_hack=True)
plot_files.append(fn)
#
# 1D histogram
#
gvalues = self.param_values[iparam][1]
gedges = values_to_bin_edges(gvalues)
kwargs = {}
if util.cmp_version(numpy_version(), '1.3.0') < 0:
kwargs = {'new': True}
hist, edges = num.histogram(self.stats[param].distribution,
bins=gedges,
**kwargs)
hist = hist / float(len(bootstrap_sources))
conf = dict(xlabel=param.title(),
xunit=self.base_source.sourceinfo(param).unit)
if conf_overrides:
conf.update(conf_overrides)
plotting.km_hack(conf)
fn = 'histogram-%s.pdf' % param
plotting.histogram_plot_1d(edges,
hist,
pjoin(dirname, fn),
conf,
apply_moment_to_magnitude_hack=True)
plot_files.append(fn)
#
#
# gather data for 1D projected misfit cross sections
indi = num.digitize(xdata, gedges) - 1
for_minmisfits = []
for ival in range(gvalues.size):
vydata = num.extract(indi == ival, ydata)
if vydata.size != 0:
for_minmisfits.append((gvalues[ival], vydata.min()))
param_min_misfits[param] = num.array(for_minmisfits,
dtype=num.float).transpose()
# data range for projected misfit cross section
mi, ma = num.inf, -num.inf
for xdata, ydata in param_min_misfits.values():
mi = min(mi, ydata.min())
ma = max(ma, ydata.max())
param_min_misfits_range = (mi, ma)
# 1D projected misfit cross sections
for iparam, param in enumerate(self.sourceparams):
mini, maxi = param_min_misfits_range
if mini < 0.95:
maxi = 1.
else:
if mini < 1.95:
maxi = 2.
conf = dict(xlabel=param.title(),
xunit=self.base_source.sourceinfo(param).unit,
ylimits=(mini, maxi),
ymasking=False)
if conf_overrides:
conf.update(conf_overrides)
plotting.km_hack(conf)
fn = 'min-misfit-%s.pdf' % param
plotting.misfit_plot_1d([param_min_misfits[param]],
pjoin(dirname, fn),
conf,
apply_moment_to_magnitude_hack=True)
plot_files.append(fn)
fn_table = 'min-misfit-%s.table' % param
num.savetxt(pjoin(dirname, fn_table),
param_min_misfits[param].transpose())
for ixparam, xparam in enumerate(self.sourceparams):
for iyparam, yparam in enumerate(self.sourceparams):
if ixparam == iyparam: continue
#
# 2D misfogram plot
#
vmisfits = {}
maxmisfit = num.nanmax(self.misfits_by_s)
for (source, misfit) in zip(self.sources, self.misfits_by_s):
if num.isnan(misfit): continue
vx = source[xparam]
vy = source[yparam]
vmisfits[(vx, vy)] = min(vmisfits.get((vx, vy), maxmisfit),
misfit)
vcounts = {}
for source in bootstrap_sources:
vx = source[xparam]
vy = source[yparam]
vcounts[(vx, vy)] = vcounts.get((vx, vy), 0) + 1
lx, ly, lz = [], [], []
for ((vx, vy), vmisfit) in vmisfits.iteritems():
lx.append(vx)
ly.append(vy)
lz.append(vmisfit)
lxc, lyc, lc = [], [], []
for ((vx, vy), vcount) in vcounts.iteritems():
lxc.append(vx)
lyc.append(vy)
lc.append(vcount)
ax = num.array(lx, dtype=num.float)
ay = num.array(ly, dtype=num.float)
az = num.array(lz, dtype=num.float)
axc = num.array(lxc, dtype=num.float)
ayc = num.array(lyc, dtype=num.float)
ac = num.array(lc, dtype=num.float)
ac = ac / len(bootstrap_sources)
if len(bootstrap_sources) > 1:
bootstrap_data = (axc, ayc, ac)
else:
bootstrap_data = ([], [], [])
if az.min() < 0.95:
zlimits = (az.min(), min(1., az.max()))
else:
zlimits = (az.min(), az.max())
if 'misfit_limits' in dir(config):
zlimits = config.misfit_limits
conf = dict(xlabel=xparam.title(),
xunit=self.base_source.sourceinfo(xparam).unit,
ylabel=yparam.title(),
yunit=self.base_source.sourceinfo(yparam).unit,
zlimits=zlimits,
zmasking=False,
zsnap=True)
if conf_overrides:
for k in conf_overrides:
if not k.endswith('snap'):
conf[k] = conf_overrides[k]
best_loc = (num.array([best_source[xparam]]),
num.array([best_source[yparam]]))
plotting.km_hack(conf)
plotting.nukl_hack(conf)
fn = 'misfogram-%s-%s.pdf' % (xparam, yparam)
plotting.misfogram_plot_2d_gmtpy(
[(ax, ay, az), best_loc, bootstrap_data],
pjoin(dirname, fn),
conf,
apply_moment_to_magnitude_hack=True)
plot_files.append(fn)
#
# Station plot
#
lats = num.array([r.lat for r in self.receivers], dtype='float')
lons = num.array([r.lon for r in self.receivers], dtype='float')
dists = num.array([r.distance_deg for r in self.receivers],
dtype='float')
rnames = [' '.join(r.name.split('.')) for r in self.receivers]
slat, slon = self.source_location[:2]
if len(self.misfits_by_r) == 0:
station_misfits = self.ref_misfits_by_r
else:
station_misfits = self.misfits_by_r - self.ref_misfits_by_r
#station_varia = self.variability_by_r / num.sum(self.variability_by_r) * len(self.receivers)
station_varia = self.misfits_by_r / num.sum(self.misfits_by_r) * len(
self.receivers)
plotting.station_plot(slat,
slon,
lats,
lons,
rnames,
station_misfits,
station_varia,
best_source,
num.amax(dists) * 1.05,
pjoin(dirname, 'stations.pdf'), {},
nsets=nsets)
plot_files.append('stations.pdf')
indicate_plane = 0
if source_model_infos:
source_size = 0.
if 'bord-radius' in best_source.keys():
source_size = best_source['bord-radius']
delta_lat = 1.5 * max(source_size * 2.,
50000) / (20000. * 1000.) * 180
si = None
if 'bord-radius' in best_source.keys(
) and best_source['bord-radius'] > 0.0:
si = source_model_infos
indicate_plane = 1
plotting.location_map(pjoin(dirname, 'location.pdf'),
slat,
slon,
delta_lat, {},
source=best_source,
source_model_infos=si,
receivers=(lats, lons, rnames))
plot_files.append('location.pdf')
fns = plotting.beachball(best_source,
pjoin(dirname, 'beachball.pdf'),
indicate_plane=indicate_plane)
if fns:
plot_files.append('beachball.pdf')
return plot_files
