except OSError:
pass
config = {
"k": 4,
"r_min": 1.0E-6,
"bvalue": bvalue,
"mmin": 3.0,
"learning_start": 1965,
"learning_end": 2009,
"target_start": 2009,
"target_end": 2010
}
smoother = h_w.HelmstetterEtAl2007(grid_lims,
config,
source.catalogue,
storage_file=("Aus1_tmp2%.3f.hdf5" %
bvalue))
smoother._get_catalogue_completeness_weights(completeness_table_a)
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(3, 6, 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]
d_i = smoother.optimise_bandwidths()
os.remove("Aus1_tmp2.hdf5")
except OSError:
pass
config = {
"k": 3,
"r_min": 1.0E-7,
"bvalue": 1.0,
"mmin": 3.0,
"learning_start": 1990,
"learning_end": 2003,
"target_start": 2004,
"target_end": 2013
}
smoother = h_w.HelmstetterEtAl2007(grid_lims,
config,
source.catalogue,
storage_file="Aus1_tmp2.hdf5")
smoother._get_catalogue_completeness_weights(completeness_table_a)
smoother.build_distance_arrays()
smoother.build_catalogue_2_grid_array()
# Exhaustive smoothing
params, poiss_llh = smoother.exhaustive_smoothing(
np.arange(3, 6, 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]
d_i = smoother.optimise_bandwidths()
smoother.run_smoothing(config["r_min"], d_i)
np.savetxt("Australia_Adaptive_K3.csv",
np.column_stack([smoother.grid, smoother.rates]),
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)
os.remove(hdf_filename)
except OSError:
pass
config = {
"k": 3,
"r_min": 1.0E-7,
"bvalue": bvalue,
"mmin": 4.0,
"learning_start": learning_start,
"learning_end": learning_end,
"target_start": target_end,
"target_end": target_end
}
smoother = h_w.HelmstetterEtAl2007(grid_lims,
config,
source.catalogue,
storage_file=hdf_filename)
smoother._get_catalogue_completeness_weights(completeness_table_a)
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(3, 6, 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]
#d_i = smoother.optimise_bandwidths()
#poiss_llh, kagan_i0, kagan_i1 = smoother.run_smoothing(config["r_min"], d_i)
mmin = completeness_table_a[0][1]
print 'mmin', mmin
config = {
"k": 3,
"r_min": 1.0E-6,
"bvalue": bvalue,
"mmin": mmin,
"learning_start": 1900,
"learning_end": 2017,
"target_start": 2018,
"target_end": 2019
} # using already optimised parameters
smoother = h_w.HelmstetterEtAl2007(grid_lims,
config,
catalogue_depth_clean,
storage_file=("Aus1_tmp2%.3f.hdf5" %
bvalue))
smoother._get_catalogue_completeness_weights(completeness_table_a)
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'