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import numpy as num
from pyrocko import orthodrome as od
def get_mean_x(xs):
return num.mean(xs, axis=0)
def get_mean_x_and_gm(problem, xs, misfits):
gms = problem.combine_misfits(misfits)
return num.mean(xs, axis=0), num.mean(gms)
def get_best_x(problem, xs, misfits):
gms = problem.combine_misfits(misfits)
ibest = num.argmin(gms)
return xs[ibest, :]
def get_best_x_and_gm(problem, xs, misfits):
gms = problem.combine_misfits(misfits)
ibest = num.argmin(gms)
return xs[ibest, :], gms[ibest]
def get_mean_source(problem, xs):
x_mean = get_mean_x(xs)
source = problem.get_source(x_mean)
return source
def get_best_source(problem, xs, misfits):
x_best = get_best_x(problem, xs, misfits)
source = problem.get_source(x_best)
return source
def mean_latlondist(lats, lons):
if len(lats) == 0:
return 0., 0., 1000.
else:
ns, es = od.latlon_to_ne_numpy(lats[0], lons[0], lats, lons)
n, e = num.mean(ns), num.mean(es)
dists = num.sqrt((ns-n)**2 + (es-e)**2)
lat, lon = od.ne_to_latlon(lats[0], lons[0], n, e)
return float(lat), float(lon), float(num.max(dists))
def stations_mean_latlondist(stations):
lats = num.array([s.lat for s in stations])
lons = num.array([s.lon for s in stations])
return mean_latlondist(lats, lons)
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