config,
completeness_table=comp_table,
smoothing_kernel=IsotropicGaussian(),
#increment = False,
)
x = o[:, 0]
y = o[:, 1]
r = o[:, 4] #/ (res**2)
r = np.array([np.log10(r) if r > 0 else np.NaN for r in r])
#r = np.log10(r)
r[r < 0] = 0.
#r =
#print np.sqrt(len(x))
from map import rate_map
m = rate_map(x,
y,
r,
"a-value [frankel1995]", (nx, ny),
catalogue=catalogue,
origin='lower')
m.show()
#print output_data
# export results
model.write_to_csv(OUTPUT_FILE)
import numpy as np
smooth = np.genfromtxt("../data_output/hmtk_bsb2013_pp_decluster_woo1996.csv", delimiter=",", skip_header=True)
smooth = np.genfromtxt("../data_output/hmtk_bsb2013_decluster_frankel1995.csv", delimiter=",", skip_header=True)
# smooth = np.genfromtxt("../data_output/hmtk_bsb2013_pp_decluster_woo1996.csv", delimiter=",",skip_header=True)
smooth = np.genfromtxt(
"/Users/pirchiner/dev/helmstetter/output/conan/rates_2_280.csv", delimiter=",", skip_header=True, skiprows=2
)
# filename = "/Users/pirchiner/dev/helmstetter/output/conan/rates_1_235.csv"
o = np.array(smooth)
x = o[:, 0]
y = o[:, 1]
r = o[:, 2]
# r = o[:, 2] / (res**2)
# r = np.array([ np.log10(r) if r >= 1 else 0 for r in r ])
# r = np.array([ np.log10(r) if r >= 1 else 0 for r in r ])
# print np.sqrt(len(x))
from map import rate_map
# print len(x), len(y), len(r), sqrt()
# m = rate_map(x, y, r, "a-value [Woo1996] $h(m) = %.2f e^{(%.2f m)}$ km"%(model.c, model.d),
m = rate_map(x, y, r, "a-value [Frankel1995]", (100, 100), origin="lower")
m.show()
b_val=1.0),
nodal_plane_dist=None,
hypo_depth_dist=None)
sources.append(p)
s = source_model.mtkSourceModel(identifier="04",
name="PSHAB-Smoothed Helmstetter2012",
sources=sources)
s.serialise_to_nrml(
filename="helmstetter2012/source_model_pshab_helmstetter2012.xml",
use_defaults=True)
o = np.array(o)
x = o[:, 0]
y = o[:, 1]
r = o[:, 2]
from map import rate_map
#print len(x), len(y), len(r), sqrt()
m = rate_map(x,
y,
r,
"a-value [Helmstetter2012]", (n, n),
catalogue=catalogue,
origin='lower')
m.show()
# config
config = {"Length_Limit": 3.0, "BandWidth": 150.0, "increment": True, "magnitude_increment": 0.5}
# smoothing
o = model.run_analysis(
catalogue,
config,
completeness_table=comp_table,
smoothing_kernel=IsotropicGaussian(),
# increment = False,
)
x = o[:, 0]
y = o[:, 1]
r = o[:, 4] # / (res**2)
r = np.array([np.log10(r) if r > 0 else np.NaN for r in r])
# r = np.log10(r)
r[r < 0] = 0.0
# r =
# print np.sqrt(len(x))
from map import rate_map
m = rate_map(x, y, r, "a-value [frankel1995]", (nx, ny), catalogue=catalogue, origin="lower")
m.show()
# print output_data
# export results
model.write_to_csv(OUTPUT_FILE)
# ax.set_xlabel("distances [km]")
# ax.set_ylabel("times [days]")
#
#
# plt.scatter(_d, _h, s=np.exp(_m), cmap=plt.cm.RdYlGn,
# marker='o', facecolors='none', edgecolors=color, #alpha=0.3,
# )
# #plt.colorbar(cs)
# plt.show()
exit()
from map import rate_map
m = rate_map(x,
y,
r,
"alpha [Helmstetter2012]", (nx, ny),
s.learning_catalogue,
origin='lower')
m.show()
# m.m.scatter(_x, _y, s=_h,
# marker='o', facecolors='none', edgecolors='k', alpha=0.1
# )
# m.m.scatter(_x, _y, s=_d,
# marker='o', facecolors='none', edgecolors='b', alpha=0.1
# )
m = rate_map(x,
y,
a,
"a-value [Helmstetter2012]", (nx, ny),
a_val= _a ,
b_val=1.0),
nodal_plane_dist=None,
hypo_depth_dist=None)
sources.append(p)
s = source_model.mtkSourceModel(identifier="04",
name = "PSHAB-Smoothed Helmstetter2012",
sources = sources)
s.serialise_to_nrml(filename = "helmstetter2012/source_model_pshab_helmstetter2012.xml",
use_defaults = True)
o = np.array(o)
x = o[:, 0]
y = o[:, 1]
r = o[:, 2]
from map import rate_map
#print len(x), len(y), len(r), sqrt()
m = rate_map(x, y, r, "a-value [Helmstetter2012]",
(n,n), catalogue=catalogue, origin='lower')
m.show()
delimiter=",",
skip_header=True)
smooth = np.genfromtxt("../data_output/hmtk_bsb2013_decluster_frankel1995.csv",
delimiter=",",
skip_header=True)
#smooth = np.genfromtxt("../data_output/hmtk_bsb2013_pp_decluster_woo1996.csv", delimiter=",",skip_header=True)
smooth = np.genfromtxt(
"/Users/pirchiner/dev/helmstetter/output/conan/rates_2_280.csv",
delimiter=",",
skip_header=True,
skiprows=2)
#filename = "/Users/pirchiner/dev/helmstetter/output/conan/rates_1_235.csv"
o = np.array(smooth)
x = o[:, 0]
y = o[:, 1]
r = o[:, 2]
#r = o[:, 2] / (res**2)
#r = np.array([ np.log10(r) if r >= 1 else 0 for r in r ])
#r = np.array([ np.log10(r) if r >= 1 else 0 for r in r ])
#print np.sqrt(len(x))
from map import rate_map
#print len(x), len(y), len(r), sqrt()
#m = rate_map(x, y, r, "a-value [Woo1996] $h(m) = %.2f e^{(%.2f m)}$ km"%(model.c, model.d),
m = rate_map(x, y, r, "a-value [Frankel1995]", (100, 100), origin='lower')
m.show()
y = d[:,1]
h = d[:,2]
# d2 = np.genfromtxt(fname=filename2,
# #comments='#',
# delimiter=',',
# skiprows = 2,
# #skip_header = 1,
# #skip_footer,
# #converters, missing, missing_values, filling_values, usecols, names,
# #excludelist, deletechars, replace_space, autostrip,
# #case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise,
# )
#
# x2 = d2[:,0]
# y2 = d2[:,1]
# h2 = d2[:,2]
#print len(x), np.sqrt(len(x))
from map import rate_map
title = "PGA (poe 10%, 50 years) [ helmstetter2012 ]"
# m = hazard_map(x, y, h2-h, title,
# (50, 50), origin='lower')
m = rate_map(x, y, h*1000, title,
# (50, 50), origin='lower',
(100, 100), origin='lower',
catalogue=catalogue)
m.show()
# plt.colorbar()
# plt.show()
#r = o[:, 2] / (res**2)
#r = np.array([ np.log10(r) if r >= 1 else 0 for r in r ])
#r = np.array([ np.log10(r) if r >= 1 else 0 for r in r ])
#print np.sqrt(len(x))
from map import rate_map
#print len(x), len(y), len(r), sqrt()
#print model.c, model.d
#m = rate_map(x, y, r, "a-value [Woo1996] $h(m) = %.2f e^{(%.2f m)}$ km"%(model.c, model.d),
print nx, ny, len(x), len(y)
m = rate_map(x, y, r, "a-value [Woo1996]",
(nx,ny), catalogue=model.catalogue, origin='lower')
m.show()
# export results
model.write_to_csv(OUTPUT_FILE)
#model.write_rates(OUTPUT_FILE)
#print model.grid
#print model.catalogue
#print model.beta
#print model.data
#print model.kernel
#print output_data
# ax.set_title("DistanceTime Bandwidth Distribution")
# ax.set_xlabel("distances [km]")
# ax.set_ylabel("times [days]")
#
#
# plt.scatter(_d, _h, s=np.exp(_m), cmap=plt.cm.RdYlGn,
# marker='o', facecolors='none', edgecolors=color, #alpha=0.3,
# )
# #plt.colorbar(cs)
# plt.show()
exit()
from map import rate_map
m = rate_map(x, y, r, "alpha [Helmstetter2012]",
(nx,ny), s.learning_catalogue, origin='lower')
m.show()
# m.m.scatter(_x, _y, s=_h,
# marker='o', facecolors='none', edgecolors='k', alpha=0.1
# )
# m.m.scatter(_x, _y, s=_d,
# marker='o', facecolors='none', edgecolors='b', alpha=0.1
# )
m = rate_map(x, y, a, "a-value [Helmstetter2012]",
(nx,ny), s.learning_catalogue, origin='lower')
m.show()
#print output_data
