def coords_for_profile(lon0,lat0,lon1,lat1):
'''
Determine the minpoint coordinates and the two azimuths for input into program to determine the tomography slice
'''
d1, az1, bz1 = gps2DistAzimuth(lat0,lon0,lat1,lon1)
return az1
def calc_sta_dist_azi(self):
for i in range(len(self.sta_lat)):
dist, az, baz = gps2DistAzimuth(self.cmtsource.latitude,
self.cmtsource.longitude,
self.sta_lat[i], self.sta_lon[i])
self.sta_azi.append(az)
self.sta_theta.append(az/180.0*np.pi)
if self.mode == "regional":
# if regional, then use original distance(in km)
self.sta_dist.append(dist/1000.0)
elif self.mode == "global":
# if global, then use degree as unit
self.sta_dist.append(dist/EARTH_HC)
def moment_tensor(sta_lat, sta_lon, sta_alt, n, e, u, eq_lat, eq_lon, eq_alt,timer,effhypodist,repi):
l1=len(sta_lat)
l2=1
xrs = numpy.zeros([l1,1])
yrs = numpy.zeros([l1,1])
zrs = numpy.zeros([l1,1])
azi = numpy.zeros([l1,1])
backazi = numpy.zeros([l1,1])
theta = numpy.zeros([l1,1])
for j in range (0, l1):
(x1,y1) = ll2utm(sta_lon[j],sta_lat[j], -71)
(x2,y2) = ll2utm(eq_lon,eq_lat, -71)
result = gps2DistAzimuth(sta_lat[j],sta_lon[j],eq_lat,eq_lon)
backazi[j,0]=result[1]
azi[j,0]=result[2]
theta[j,0] = 90 - backazi[j,0] + 180
xrs[j,0] = (x1-x2)
yrs[j,0] = (y1-y2)
zrs[j,0] = (sta_alt[j]+eq_alt*1000)
U = numpy.zeros([3*l1,1])
for i in range (0, l1):
efftime = math.ceil(effhypodist[i])
NN = n[i,efftime:efftime+10]
EE = e[i,efftime:efftime+10]
UU = u[i,efftime:efftime+10]
a1 = numpy.where(n[i,efftime:efftime+10] > -999)[0]
a1 = numpy.array(a1)
if (len(a1) > 1):
N = numpy.nanmean(NN[a1])
E = numpy.nanmean(EE[a1])
Z = numpy.nanmean(UU[a1])
U[3*i,0]= N
U[3*i+1,0]= E
U[3*i+2,0]= -Z
else:
U[3*i,0]= 0
U[3*i+1,0]= 0
U[3*i+2,0]= 0
G = okadapoint.greenF(yrs, xrs, -zrs) #Compute Green's function
S = numpy.linalg.lstsq(G,U)[0]
M12 = S[0,0]
M13 = S[1,0]
M33 = S[2,0]
M23 = S[4,0]
M11 = S[3,0]-0.5*S[2,0]
M22 = -S[3,0]-0.5*S[2,0]
M_devi = numpy.array([[M11,M12,M13],[M12,M22,M23],[M13,M23,M33]])
eigenwtot, eigenvtot = numpy.linalg.eig(M_devi)
eigenw1, eigenv1 = numpy.linalg.eig(M_devi)
eigenw = numpy.real(numpy.take(eigenw1, numpy.argsort(abs(eigenwtot))))
eigenv = numpy.real(numpy.take(eigenv1, numpy.argsort(abs(eigenwtot)), 1))
eigenw_devi = numpy.real(numpy.take(eigenw1, numpy.argsort(abs(eigenw1))))
eigenv_devi = numpy.real(numpy.take(eigenv1, numpy.argsort(abs(eigenw1)), 1))
M0_devi = max(abs(eigenw_devi))
F = -eigenw_devi[0] / eigenw_devi[2]
M_DC_percentage = (1 - 2 * abs(F)) * 100
Mo = math.pow(math.pow(M11,2)+math.pow(M22,2)+math.pow(M33,2)+2*math.pow(M12,2)+2*math.pow(M13,2)+2*math.pow(M23,2),0.5)/math.pow(2,0.5)
if (Mo == 0):
Mw = 0
else:
Mw = 2*math.log10(Mo)/3-6.03
UP = numpy.dot(G,S)
dms = U-UP
VR = (1-numpy.sum(numpy.sqrt(numpy.power(dms,2)))/numpy.sum(numpy.sqrt(numpy.power(U,2))))*M_DC_percentage
#VR = numpy.linalg.norm(dms)/M_DC_percentage*1000
mt = obspy.imaging.beachball.MomentTensor(M33,M11,M22,M13,-M23,-M12,26)
axes = obspy.imaging.beachball.MT2Axes(mt)
plane1 = obspy.imaging.beachball.MT2Plane(mt)
plane2 = obspy.imaging.beachball.AuxPlane(plane1.strike,plane1.dip,plane1.rake)
T = {'azimuth':axes[0].strike,'plunge':axes[0].dip}
N = {'azimuth':axes[1].strike,'plunge':axes[1].dip}
P = {'azimuth':axes[2].strike,'plunge':axes[2].dip}
NP1 = {'strike':plane1.strike,'dip':plane1.dip,'rake':plane1.rake}
NP2 = {'strike':plane2[0],'dip':plane2[1],'rake':plane2[2]}
return(S, VR, Mw, NP1, NP2)
m.fillcontinents()
m.drawparallels(np.arange(-90., 120., 30.))
m.drawmeridians(np.arange(0., 420., 60.))
m.drawmapboundary()
x, y = m(lons, lats)
m.scatter(x, y, 30, color="r", marker="^", edgecolor="k", linewidth='0.3', zorder=3)
focmecs = [moment_tensor,]
ax = plt.gca()
for i in range(len(focmecs)):
b = Beach(focmecs[i], xy=(cmt_lon, cmt_lat), width=10, linewidth=1)
b.set_zorder(10)
ax.add_collection(b)
earth_hc, _ , _ = gps2DistAzimuth(0,0,0, 180)
##plot azimuth and distance distribution
theta=[]
radius=[]
for i in range(len(lons)):
geo_info = gps2DistAzimuth(cmt_lat, cmt_lon, lats[i], lons[i])
#geo_info = gps2DistAzimuth(lats[i], lons[i], cmt_loc[0], cmt_loc[1])
#print geo_info
theta.append(geo_info[1]/180.0*np.pi)
radius.append(geo_info[0]/earth_hc)
ax = plt.subplot(223, polar=True)
c = plt.scatter(theta, radius, marker=u'^', c='r', s=10, edgecolor='k', linewidth='0.3')
c.set_alpha(0.75)
color="r",
marker="^",
edgecolor="k",
linewidth='0.3',
zorder=3)
focmecs = [
moment_tensor,
]
ax = plt.gca()
for i in range(len(focmecs)):
b = Beach(focmecs[i], xy=(cmt_lon, cmt_lat), width=10, linewidth=1)
b.set_zorder(10)
ax.add_collection(b)
earth_hc, _, _ = gps2DistAzimuth(0, 0, 0, 180)
##plot azimuth and distance distribution
theta = []
radius = []
for i in range(len(lons)):
geo_info = gps2DistAzimuth(cmt_lat, cmt_lon, lats[i], lons[i])
#geo_info = gps2DistAzimuth(lats[i], lons[i], cmt_loc[0], cmt_loc[1])
#print geo_info
theta.append(geo_info[1] / 180.0 * np.pi)
radius.append(geo_info[0] / earth_hc)
ax = plt.subplot(223, polar=True)
c = plt.scatter(theta,
radius,
marker=u'^',
c='r',
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from obspy.core.util.geodetics.base import gps2DistAzimuth
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
import math
from obspy.imaging.beachball import Beach
import matplotlib.gridspec as gridspec
# earth half circle
EARTH_HC, _, _ = gps2DistAzimuth(0, 0, 0, 180)
class PlotUtil(object):
def __init__(self, data_container=None, cmtsource=None, config=None,
nregions=12, new_cmtsource=None, bootstrap_mean=None,
bootstrap_std=None, var_reduction=0.0, mode="regional"):
self.data_container = data_container
self.cmtsource = cmtsource
self.window = data_container.window
self.config = config
self.nregions = nregions
self.new_cmtsource = new_cmtsource
self.bootstrap_mean = bootstrap_mean
self.bootstrap_std = bootstrap_std
self.var_reduction = var_reduction
self.moment_tensor = [cmtsource.m_rr, cmtsource.m_tt, cmtsource.m_pp,