def plot_sources(point_source, fault_source):
llon, ulon, llat, ulat = 105, 155, -45, -5,
map_config = {'min_lon': np.floor(llon), 'max_lon': np.ceil(ulon),
'min_lat': np.floor(llat), 'max_lat': np.ceil(ulat), 'resolution':'i'}
basemap1 = HMTKBaseMap(map_config, 'Point and fault sources')
for pt_source in point_source:
x,y = basemap1.m(pt_source.location.longitude,
pt_source.location.latitude)
basemap1.m.plot(x, y, 'rs',
markersize = 2.0)
for simplefault in fault_source:
trace_lons = np.array([pnt.longitude
for pnt in simplefault.fault_trace.points])
trace_lats = np.array([pnt.latitude
for pnt in simplefault.fault_trace.points])
x, y = basemap1.m(trace_lons, trace_lats)
basemap1.m.plot(x, y, 'b', linewidth=1.3)
basemap1.savemap('Point and fault sources.png')
def decluster_catalogue(catalogue, config):
###
### Catalogue cache or read/cache
###
# Set up the declustering algorithm
# Step 1 - set-up the tool
if config['decluster_method'] == 'afteran':
decluster_method = Afteran()
elif config['decluster_method'] == 'gardner_knopoff':
decluster_method = GardnerKnopoffType1()
else:
print "invalid decluster_method configuration: use [afteran|gardner_knopoff]"
return None
print 'Running declustering ...'
cluster_vector, flag_vector = decluster_method.decluster(catalogue, config)
print 'done!'
print '%s clusters found' % np.max(cluster_vector)
print '%s Non-poissionian events identified' % np.sum(flag_vector != 0)
if config['plot']:
###
### Map Config
###
map_dpi = 90
add_geology = True
add_sourcemodel = True
savefig=False
#map_title = 'Brazilian Seismic Zones'
map_title = 'Clusters'
#map_title = 'ISC-GEM Catalogue'
#map_title = 'South-American Lithology'
# Configure the limits of the map and the coastline resolution
map_config = {'min_lon': -80.0, 'max_lon': -30.0, 'min_lat': -37.0, 'max_lat': 14.0, 'resolution':'l'}
#map_config = {'min_lon': -72.0, 'max_lon': -68.0, 'min_lat': -22.0, 'max_lat': -18.0, 'resolution':'l'}
#map_config = {'min_lon': -95.0, 'max_lon': -25.0, 'min_lat': -65.0, 'max_lat': 25.0, 'resolution':'l'}
basemap = HMTKBaseMap(map_config, map_title, dpi=map_dpi)
#basemap.add_catalogue(catalogue, linewidth=0.2, alpha=0.1, overlay=True)
idx = cluster_vector != 0
x = catalogue.data['longitude'][idx]
y = catalogue.data['latitude'][idx]
c = cluster_vector[idx]
basemap.add_colour_scaled_points(x, y, c,
overlay=True,
shape='s', alpha=0.5, size=100,
linewidth=0.5, facecolor='none',
cmap=plt.cm.get_cmap('Paired'),
)
plt.show()
if config['figname']:
basemap.savemap(config['figname'])
print 'Original catalogue had %s events' % catalogue.get_number_events()
catalogue.select_catalogue_events(flag_vector == 0)
print 'Purged catalogue now contains %s events' % catalogue.get_number_events()
if config['filename']:
writer = CsvCatalogueWriter(config['filename'])
writer.write_file(catalogue)
return catalogue
# add source model
#basemap1.add_source_model(source_model, area_border, border_width, point_marker, point_size, overlay)
basemap1.add_source_model(source_model, overlay=True)
###
### Catálogo
###
x = catalogue.data['longitude']
y = catalogue.data['latitude']
z = catalogue.data['depth']
_idx = np.argsort(z)
catalogue.select_catalogue_events(_idx)
basemap1.add_catalogue(catalogue, alpha=0.1)
#basemap1.add_colour_scaled_points(x, y, np.log(z+1), overlay=True)
if savefig: basemap1.savemap("/Users/pirchiner/Desktop/teste.png")
plt.show()
#exit()
# Limit the catalogue to the time period 1960 - 2012
#valid_time = np.logical_and(catalogue.data['year'] >= 1960,
# catalogue.data['year'] <= 2014)
#catalogue.select_catalogue_events(valid_time)