sys.path.append('/Users/dmelgar/code/python/neic_tools')
from neic_tools import neic_catalog
from matplotlib import pyplot as plt
import matplotlib as mpl
from numpy import where, logspace, cov, array, argsort, log, histogram, log10, median, arange
path_to_files = '/Users/dmelgar/Downloads/PARAM_FILES/'
catalog_file = '/Users/dmelgar/USGSFF/catalog.txt'
mpl.rcParams['xtick.labelsize'] = 14
mpl.rcParams['ytick.labelsize'] = 14
run_regression = True
Nbins = 500
#Get the catalog
neic = neic_catalog(path_to_files,
catalog_file,
percent_cutoff=0.2,
get_stfs=True)
#Run regression
if run_regression:
A, k, mcmc = neic.run_regression(inversion_type='bayesian',
prior='uninformative',
Niter=1000e3,
burn=100e3,
fix_exponent=False,
dependent_variable='slip')
print '\n'
print A
print k
else:
A = 9.05037048766e-06
from neic_tools import neic_catalog
from matplotlib import pyplot as plt
import matplotlib as mpl
from numpy import where, logspace, cov, array, argsort, log, histogram, log10, median, arange
path_to_files = '/Users/dmelgar/USGSFF/finite_faults/param/'
catalog_file = '/Users/dmelgar/USGSFF/catalog.txt'
mpl.rcParams['xtick.labelsize'] = 14
mpl.rcParams['ytick.labelsize'] = 14
run_regression = True
Nbins = 500
#Get the catalog
neic = neic_catalog(path_to_files,
catalog_file,
percent_cutoff=0.2,
percent_cutoff_vrup=0.3,
duration_cutoff=0.9)
#Run regression
if run_regression:
A, k, mcmc = neic.run_regression(inversion_type='bayesian',
prior='uninformative',
Niter=1000e3,
burn=100e3,
fix_exponent=False)
print '\n'
print A
print k
else:
A = 9.05037048766e-06
import sys
sys.path.append('/Users/dmelgar/code/python/neic_tools')
from neic_tools import neic_catalog
from matplotlib import pyplot as plt
import matplotlib as mpl
from numpy import where, logspace, cov, array, argsort, log, histogram, log10, median, arange
path_to_files = '/Users/dmelgar/Downloads/PARAM_FILES/'
catalog_file = '/Users/dmelgar/USGSFF/catalog.txt'
mpl.rcParams['xtick.labelsize'] = 14
mpl.rcParams['ytick.labelsize'] = 14
run_regression = True
Nbins = 500
#Get the catalog
neic = neic_catalog(path_to_files, catalog_file, percent_cutoff=0.2)
#Run regression
if run_regression:
Ai, ki, mcmci = neic.run_regression(inversion_type='bayesian',
prior='uninformative',
Niter=100e3,
burn=20e3,
fix_exponent=False,
select_event_type='i',
dependent_variable='slip')
Au, ku, mcmcu = neic.run_regression(inversion_type='bayesian',
prior='uninformative',
Niter=100e3,
burn=20e3,
fix_exponent=False,
import cPickle as pickle
path_to_files = '/Users/dmelgar/USGSFF/param/'
catalog_file = '/Users/dmelgar/USGSFF/catalog.txt'
mpl.rcParams['xtick.labelsize'] = 14
mpl.rcParams['ytick.labelsize'] = 14
run_regression = True
Nbins = 500
#Get the catalog
#Run regression
Niter = 100e3
neic = neic_catalog(path_to_files,
catalog_file,
percent_cutoff=0.2,
get_stfs=True,
percent_cutoff_vrup=0.3,
median=False)
Atr, ktr, mcmctr = neic.run_regression(inversion_type='bayesian',
prior='uninformative',
Niter=Niter,
burn=50e3,
fix_exponent=False,
dependent_variable='rise_time')
neic_med = neic_catalog(path_to_files,
catalog_file,
percent_cutoff=0.2,
get_stfs=True,
percent_cutoff_vrup=0.3,
median=True)
