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)