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
def run_smoothing(grid_lims,
smoothing_config,
catalogue,
completeness_table,
map_config,
run,
overwrite=True):
"""Run all the smoothing
"""
ystart = completeness_table[-1][0]
yend = catalogue.end_year
catalogue_comp = deepcopy(catalogue)
# Ensuring that catalogue is cleaned of earthquakes outside of
# completeness period
index = catalogue_comp.data['year'] >= ystart
catalogue_comp.purge_catalogue(index)
completeness_string = 'comp'
for ym in completeness_table:
completeness_string += '_%i_%.1f' % (ym[0], ym[1])
smoother_filename = 'Australia_Fixed_%i_%i_b%.3f_mmin_%.1f_0.1%s.csv' % (
smoothing_config["BandWidth"], smoothing_config["Length_Limit"],
bvalue, completeness_table[0][1], completeness_string)
filename = smoother_filename[:-4] + '.xml'
if os.path.exists(filename) and not overwrite:
print '%s already created, not overwriting!' % filename
return
smoother = SmoothedSeismicity(
[105., 160., 0.1, -47., -5, 0.1, 0., 20., 20.],
bvalue=smoothing_config['bvalue'])
print 'Running smoothing'
smoothed_grid = smoother.run_analysis(
catalogue_comp,
smoothing_config,
completeness_table=completeness_table)
smoother.write_to_csv(smoother_filename)
from openquake.hazardlib.nrml import SourceModelParser, write, NAMESPACE
from openquake.baselib.node import Node
from openquake.hazardlib import nrml
from openquake.hazardlib.sourcewriter import obj_to_node
# Build nrml input file of point sources
source_list = []
#i=0
min_mag = 4.5
max_mag = 7.8
bval = bvalue # just define as 1 for time being
# Read in data again to solve number fomatting issue in smoother.data
# For some reason it just returns 0 for all a values
try:
data = np.genfromtxt(smoother_filename, delimiter=',', skip_header=1)
except ValueError:
print 'Something wrong with file %s' % smoother_filename
sys.exit()
tom = PoissonTOM(
50) # Dummy temporal occurence model for building pt sources
msr = Leonard2014_SCR()
for j in range(len(data[:, 4])):
# print smoother.data[j,:]
identifier = 'FSS' + str(j) + '_' + str(run)
name = 'Frankel' + str(j) + '_' + str(run)
point = Point(data[j, 0], data[j, 1], data[j, 2])
annual_rate = data[j, 4] / (yend - ystart + 1)
aval = np.log10(annual_rate) + smoothing_config[
'bvalue'] * completeness_table[0][1]
mfd = TruncatedGRMFD(min_mag, max_mag, 0.1, aval, bval)
hypo_depth_dist = PMF([(0.5, 10.0), (0.25, 5.0), (0.25, 15.0)])
nodal_plane_dist = PMF([(0.3, NodalPlane(0, 30, 90)),
(0.2, NodalPlane(90, 30, 90)),
(0.3, NodalPlane(180, 30, 90)),
(0.2, NodalPlane(270, 30, 90))])
point_source = PointSource(identifier, name, 'Non_cratonic', mfd, 2,
msr, 2.0, tom, 0.1, 20.0, point,
nodal_plane_dist, hypo_depth_dist)
source_list.append(point_source)
nodes = list(map(obj_to_node, sorted(source_list)))
source_model = Node("sourceModel", {"name": name}, nodes=nodes)
with open(filename, 'wb') as f:
nrml.write([source_model], f, '%s', xmlns=NAMESPACE)
# Creating a basemap - input a cconfiguration and (if desired) a title
title = 'Smoothed seismicity rate for learning \nperiod %i 2017, Mmin = %.1f' % (
completeness_table[0][0], completeness_table[0][1])
basemap1 = HMTKBaseMap(map_config, 'Smoothed seismicity rate')
# Adding the smoothed grip to the basemap
sym = (2., 3., 'cx')
x, y = basemap1.m(smoother.data[:, 0], smoother.data[:, 1])
basemap1.m.scatter(x,
y,
marker='s',
c=np.log10(smoother.data[:, 4]),
cmap=plt.cm.coolwarm,
zorder=10,
lw=0,
vmin=-6.5,
vmax=1.5)
basemap1.m.drawcoastlines(linewidth=1, zorder=50) # Add coastline on top
basemap1.m.drawmeridians(
np.arange(map_config['min_lat'], map_config['max_lat'], 5))
basemap1.m.drawparallels(
np.arange(map_config['min_lon'], map_config['max_lon'], 5))
plt.colorbar(label='log10(Smoothed rate per cell)')
plt.legend()
figname = smoother_filename[:-4] + '_smoothed_rates_map.png'
plt.savefig(figname)
catalogue.sort_catalogue_chronologically()
print 'Catalogue sorted chronologically!'
# In[ ]:
# 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'
}
# Create a hmtk basemap
basemap1 = HMTKBaseMap(map_config, 'Earthquake Catalogue')
# Add a catalogue
basemap1.add_catalogue(catalogue)
# In[ ]:
# Limit the catalogue to the time period 1960 - 2012
valid_time = np.logical_and(catalogue.data['year'] >= 1900,
catalogue.data['year'] <= 2014)
catalogue.select_catalogue_events(valid_time)
plot_magnitude_time_density(catalogue, 0.2, 2.0)
print 'Catalogue now contains %s events' % catalogue.get_number_events()
# In[ ]:
# Show distribution of magnitudes with time
# Map configuration
llon, ulon, llat, ulat = source.catalogue.get_bounding_box()
#map_config = {'min_lon': np.floor(llon), 'max_lon': np.ceil(ulon),
# 'min_lat': np.floor(llat), 'max_lat': np.ceil(ulat), 'resolution':'c'}
#map_config = {'min_lon': np.floor(105), 'max_lon': np.ceil(155),
# 'min_lat': np.floor(-45), 'max_lat': np.ceil(-9), 'resolution':'c'}
map_config = {
'min_lon': np.floor(100),
'max_lon': np.ceil(160),
'min_lat': np.floor(-45),
'max_lat': np.ceil(-4),
'resolution': 'c'
}
# Creating a basemap - input a cconfiguration and (if desired) a title
basemap1 = HMTKBaseMap(map_config, 'Smoothed seismicity rate')
basemap1.m.drawmeridians(np.arange(llat, ulat, 5))
basemap1.m.drawparallels(np.arange(llon, ulon, 5))
#print smoother.data[:,0]
#print smoother.data[:,1]
# Adding the smoothed grip to the basemap
sym = (2., 3., 'cx')
x, y = basemap1.m(smoother.data[:, 0], smoother.data[:, 1])
#print data[:,4]
basemap1.m.scatter(x,
y,
marker='s',
c=np.log10(smoother.data[:, 4] -
bvalue * completeness_table_a[0][1]),
cmap=plt.cm.coolwarm,
zorder=10,
writer.write_file(catalogue)
#exit()
print 'File %s written' % output_file_name
f=open(input_catalogue_file + ".pkl",'wb')
pickle.dump(catalogue, f)
f.close()
###
### Mapa
###
# Create a hmtk basemap
basemap1 = HMTKBaseMap(map_config, map_title, dpi=map_dpi)
###
### Geologia
###
if add_geology:
wms_cprm = "http://onegeology.cprm.gov.br/cgi-bin/BRA_GSB_EN_Bedrock_Geology/wms?"
wms_oneg = "http://mapdmzrec.brgm.fr/cgi-bin/mapserv54?map=/carto/ogg/mapFiles/CGMW_Bedrock_and_Structural_Geology.map&"
wmsl_oneg = {'server_url': wms_oneg,
'layers': ['World_CGMW_50M_Geology'],
}
wmsl_br_blt = {'server_url': wms_cprm,
'layers': ['BRA_GSB_EN_1M_BLT'],
'styles': ['default'],
likelihood_filename = os.path.join('llh_results',
smoother_filename[:-4] + '_llh.csv')
f_out = open(likelihood_filename, 'w')
line = '%.10f,%.10f,%.10f,%.10f,%.10f' % (poiss_llh, kagan_i0, kagan_i1,
uniform_llh, prob_gain)
f_out.write(line)
f_out.close()
###
#sys.exit()
###
# Creating a basemap - input a cconfiguration and (if desired) a title
title = 'Smoothed seismicity rate for learning \nperiod %i %i, K=%i, Mmin=%.1f' % (
learning_start, learning_end, smoother.config['k'],
smoother.config['mmin'])
basemap1 = HMTKBaseMap(map_config, title)
basemap1.m.drawmeridians(np.arange(llat, ulat, 5))
basemap1.m.drawparallels(np.arange(llon, ulon, 5))
# Adding the smoothed grip to the basemap
sym = (2., 3., 'cx')
x, y = basemap1.m(smoother.grid[:, 0], smoother.grid[:, 1])
if smoother.config['mmin'] == 3.5:
vmax = -1.0
elif smoother.config['mmin'] == 4.0:
vmax = -2.5
else:
vmax = -1.0
basemap1.m.scatter(x,
y,
marker='s',
c=np.log10(smoother.rates),
source_model = parser.read_file(
"Aus Source Model 1") # You need to supply a name for the source model
# Map configuration
llon, ulon, llat, ulat = catalogue_clean.get_bounding_box()
#map_config = {'min_lon': np.floor(llon), 'max_lon': np.ceil(ulon),
# 'min_lat': np.floor(llat), 'max_lat': np.ceil(ulat), 'resolution':'c'}
map_config = {
'min_lon': np.floor(100),
'max_lon': np.ceil(160),
'min_lat': np.floor(-45),
'max_lat': np.ceil(-4),
'resolution': 'c'
}
# Creating a basemap - input a cconfiguration and (if desired) a title
basemap1 = HMTKBaseMap(map_config, 'Earthquake Catalogue')
# Adding the seismic sources
basemap1.add_source_model(source_model,
area_border='r-',
border_width=1.5,
alpha=0.5)
# Select catalogue from within sourcezone
selector1 = CatalogueSelector(catalogue_depth_clean, create_copy=True)
for source in source_model.sources:
source.select_catalogue(selector1)
llon, ulon, llat, ulat = source.catalogue.get_bounding_box()
print llon, ulon, llat, ulat
# Map the Source
def run_smoothing(grid_lims, config, catalogue, completeness_table, map_config,
run):
"""Run all the smoothing
:params config:
Dictionary of configuration parameters.
For more info see helmstetter_werner_2012 code
and docs.
"""
completeness_string = 'comp'
for ym in completeness_table:
completeness_string += '_%i_%.1f' % (ym[0], ym[1])
smoother_filename = "Australia_Adaptive_K%i_b%.3f_mmin%.1f_%s.csv" % (
config['k'], config['bvalue'], config['mmin'], completeness_string)
filename = smoother_filename[:-4] + '.xml'
if os.path.exists(filename) and not overwrite:
print '%s already created, not overwriting!' % filename
return
smoother = h_w.HelmstetterEtAl2007(grid_lims,
config,
catalogue,
storage_file=("Aus1_tmp2%.3f_%s.hdf5" %
(config['bvalue'], run)))
smoother._get_catalogue_completeness_weights(completeness_table)
smoother.build_distance_arrays()
smoother.build_catalogue_2_grid_array()
# Exhaustive smoothing
exhaustive = False
if exhaustive == True:
params, poiss_llh = smoother.exhaustive_smoothing(
np.arange(2, 10, 1), np.arange(1.0e-6, 1.0e-5, 2.0e-6))
print params, poiss_llh
smoother.config["k"] = params[0]
smoother.config["r_min"] = params[1]
#print 'Exiting now, re-run using optimised parameters'
#sys.exit()
d_i = smoother.optimise_bandwidths()
smoother.run_smoothing(config["r_min"], d_i)
data = np.column_stack([smoother.grid, smoother.rates])
np.savetxt(
smoother_filename,
data,
# np.column_stack([smoother.grid, smoother.rates]),
delimiter=",",
fmt=["%.4f", "%.4f", "%.8e"],
header="longitude,latitude,rate")
# Creating a basemap - input a cconfiguration and (if desired) a title
title = 'Smoothed seismicity rate for learning \nperiod %i %i, K=%i, Mmin=%.1f' % (
config['learning_start'], config['learning_end'], smoother.config['k'],
smoother.config['mmin'])
basemap1 = HMTKBaseMap(map_config, title)
basemap1.m.drawmeridians(
np.arange(map_config['min_lat'], map_config['max_lat'], 5))
basemap1.m.drawparallels(
np.arange(map_config['min_lon'], map_config['max_lon'], 5))
# Adding the smoothed grip to the basemap
sym = (2., 3., 'cx')
x, y = basemap1.m(smoother.grid[:, 0], smoother.grid[:, 1])
if smoother.config['mmin'] == 3.5:
vmax = -1.0
elif smoother.config['mmin'] == 4.0:
vmax = -2.5
else:
vmax = -1.0
basemap1.m.scatter(x,
y,
marker='s',
c=np.log10(smoother.rates),
cmap=plt.cm.coolwarm,
zorder=10,
lw=0,
vmin=-7.0,
vmax=vmax)
basemap1.m.drawcoastlines(linewidth=1, zorder=50) # Add coastline on top
#basemap1.m.drawmeridians(np.arange(llat, ulat, 5))
#basemap1.m.drawparallels(np.arange(llon, ulon, 5))
plt.colorbar(label='Log10(Smoothed rate per cell)')
#plt.colorbar()#label='log10(Smoothed rate per cell)')
plt.legend()
#basemap1.m.scatter(x, y, marker = 's', c = smoother.data[:,4], cmap = plt.cm.coolwarm, zorder=10)
#basemap1.m.scatter([150],[22], marker='o')
#basemap1.fig.show()
#(smoother.data[0], smoother.data[1])
#basemap1.add_catalogue(catalogue_depth_clean, erlay=False)
figname = smoother_filename[:-4] + '_smoothed_rates_map.png'
plt.savefig(figname)
source_list = []
#i=0
min_mag = 4.5
max_mag = 7.2
# Read in data again to solve number fomatting issue in smoother.data
# For some reason it just returns 0 for all a values
#data = np.genfromtxt(smoother_filename, delimiter = ',', skip_header = 1)
tom = PoissonTOM(
50) # Dummy temporal occurence model for building pt sources
msr = Leonard2014_SCR()
for j in range(len(data[:, 2])):
identifier = 'ASS' + str(j) + '_' + str(run)
name = 'Helmstetter' + str(j) + '_' + str(run)
point = Point(data[j, 0], data[j, 1], 10)
rate = data[j, 2]
# Convert rate to a value
aval = np.log10(rate) + config['bvalue'] * config["mmin"]
mfd = TruncatedGRMFD(min_mag, max_mag, 0.1, aval, config['bvalue'])
hypo_depth_dist = PMF([(0.5, 10.0), (0.25, 5.0), (0.25, 15.0)])
nodal_plane_dist = PMF([(0.3, NodalPlane(0, 30, 90)),
(0.2, NodalPlane(90, 30, 90)),
(0.3, NodalPlane(180, 30, 90)),
(0.2, NodalPlane(270, 30, 90))])
point_source = PointSource(identifier, name, 'Non_cratonic', mfd, 2,
msr, 2.0, tom, 0.1, 20.0, point,
nodal_plane_dist, hypo_depth_dist)
source_list.append(point_source)
mod_name = "Australia_Adaptive_K%i_b%.3f" % (smoother.config['k'],
smoother.config['bvalue'])
nodes = list(map(obj_to_node, sorted(source_list)))
source_model = Node("sourceModel", {"name": name}, nodes=nodes)
with open(filename, 'wb') as f:
nrml.write([source_model], f, '%s', xmlns=NAMESPACE)
# In[ ]:
# Sort catalogue chronologically
catalogue.sort_catalogue_chronologically()
print 'Catalogue sorted chronologically!'
# In[ ]:
# 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'}
# Create a hmtk basemap
basemap1 = HMTKBaseMap(map_config, 'Earthquake Catalogue')
# Add a catalogue
basemap1.add_catalogue(catalogue)
# In[ ]:
# Limit the catalogue to the time period 1960 - 2012
valid_time = np.logical_and(catalogue.data['year'] >= 1900,
catalogue.data['year'] <= 2014)
catalogue.select_catalogue_events(valid_time)
plot_magnitude_time_density(catalogue, 0.2, 2.0)
print 'Catalogue now contains %s events' % catalogue.get_number_events()
# In[ ]:
parser = nrmlSourceModelParser(area_source_file)
area_model = parser.read_file()
# <codecell>
# 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'
}
# Create a hmtk basemap
basemap1 = HMTKBaseMap(map_config, 'Source Models')
# Add fault sources
#basemap1.add_source_model(fault_model, overlay=True)
# Add area sources
basemap1.add_source_model(area_model, area_border='b-')
# <codecell>
# Load in the catalogue
from hmtk.parsers.catalogue.csv_catalogue_parser import CsvCatalogueParser
input_file = 'data_input/hmtk_bsb2013.csv'
parser = CsvCatalogueParser(input_file)
catalogue = parser.read_file()
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year,
# <codecell>
# Import an Area Source Model
area_source_file = 'snippets/s03.xml'
parser = nrmlSourceModelParser(area_source_file)
area_model = parser.read_file()
# <codecell>
# 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'}
# Create a hmtk basemap
basemap1 = HMTKBaseMap(map_config, 'Source Models')
# Add fault sources
#basemap1.add_source_model(fault_model, overlay=True)
# Add area sources
basemap1.add_source_model(area_model, area_border='b-')
# <codecell>
# Load in the catalogue
from hmtk.parsers.catalogue.csv_catalogue_parser import CsvCatalogueParser
input_file = 'data_input/hmtk_bsb2013.csv'
parser = CsvCatalogueParser(input_file)
catalogue = parser.read_file()
print 'Input complete: %s events in catalogue' % catalogue.get_number_events()
print 'Catalogue Covers the Period: %s to %s' % (catalogue.start_year, catalogue.end_year)
import numpy as np
import matplotlib.pyplot as plt
plt.xkcd()
from hmtk.plotting.mapping import HMTKBaseMap
dpi = 90
map_config = {'min_lon': -80.0, 'max_lon': -30.0, 'min_lat': -37.0, 'max_lat': 14.0, 'resolution':'l'}
basemap1 = HMTKBaseMap(map_config, '\gls{bsb2013} helmstetter2012 catalogues', dpi=dpi)
X = np.genfromtxt('cat', skip_header=True)
x = X[:,1]
y = X[:,2]
z = X[:,3]
#print min(z), max(z)
basemap1.add_size_scaled_points(y, x, z, alpha=0.3,
colour='k', smin=0.5, sscale=2,
facecolor='none', overlay=True,
label='learning')
Y = np.genfromtxt('TARG', skip_header=True)
#print Y[:10]
x = Y[:,1]
y = Y[:,2]
z = Y[:,3]
