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field2d_earth.py
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# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.mlab import griddata
import numpy.ma as ma
import scipy.ndimage.filters
from scipy.ndimage import convolve
import matplotlib
import multiprocessing
from functools import partial
import os
from subprocess import call
import obspy.geodetics
from mpl_toolkits.basemap import Basemap, shiftgrid, cm
from pyproj import Geod
import random
import copy
import colormaps
import pyasdf
import math
import numba
lon_diff_weight_2 = np.array([[1., 0., -1.]])/2.
lat_diff_weight_2 = lon_diff_weight_2.T
lon_diff_weight_4 = np.array([[-1., 8., 0., -8., 1.]])/12.
lat_diff_weight_4 = lon_diff_weight_4.T
lon_diff_weight_6 = np.array([[1./60., -3./20., 3./4., 0., -3./4., 3./20., -1./60.]])
lat_diff_weight_6 = lon_diff_weight_6.T
lon_diff2_weight_2 = np.array([[1., -2., 1.]])
lat_diff2_weight_2 = lon_diff2_weight_2.T
lon_diff2_weight_4 = np.array([[-1., 16., -30., 16., -1.]])/12.
lat_diff2_weight_4 = lon_diff2_weight_4.T
lon_diff2_weight_6 = np.array([[1./90., -3./20., 3./2., -49./18., 3./2., -3./20., 1./90.]])
lat_diff2_weight_6 = lon_diff2_weight_6.T
geodist = Geod(ellps='WGS84')
def discrete_cmap(N, base_cmap=None):
"""Create an N-bin discrete colormap from the specified input map"""
# Note that if base_cmap is a string or None, you can simply do
# return plt.cm.get_cmap(base_cmap, N)
# The following works for string, None, or a colormap instance:
base = plt.cm.get_cmap(base_cmap)
color_list = base(np.linspace(0, 1, N))
cmap_name = base.name + str(N)
return base.from_list(cmap_name, color_list, N)
def _write_txt(fname, outlon, outlat, outZ):
outArr = np.append(outlon, outlat)
outArr = np.append(outArr, outZ)
outArr = outArr.reshape((3,outZ.size))
outArr = outArr.T
np.savetxt(fname, outArr, fmt='%g')
return
def determine_interval(minlat=None, maxlat=None, dlon=0.2, dlat=0.2, verbose=True):
# if (medlat is None) and (minlat is None and maxlat is None):
# raise ValueError('medlat or minlat/maxlat need to be specified!')
# if minlat is not None and maxlat is not None:
medlat = (minlat + maxlat)/2.
dist_lon_max,az,baz = obspy.geodetics.gps2dist_azimuth(minlat, 0., minlat, dlon)
dist_lon_min,az,baz = obspy.geodetics.gps2dist_azimuth(maxlat, 0., maxlat, dlon)
dist_lon_med,az,baz = obspy.geodetics.gps2dist_azimuth(medlat, 0., medlat, dlon)
dist_lat, az, baz = obspy.geodetics.gps2dist_azimuth(medlat, 0., medlat+dlat, 0.)
ratio_min = dist_lat / dist_lon_max
ratio_max = dist_lat / dist_lon_min
index = np.floor(np.log2((ratio_min+ratio_max)/2.))
final_ratio = 2**index
if verbose:
print 'ratio_min =',ratio_min,',ration_max =',ratio_max,',final_ratio =',final_ratio
return final_ratio
class Field2d(object):
"""
An object to analyze 2D spherical field data on Earth
===========================================================================
::: parameters :::
dlon, dlat - grid interval
Nlon, Nlat - grid number in longitude, latitude
lonArr, latArr - arrays for grid location
fieldtype - field type (Tph, Tgr, Amp)
---------------------------------------------------------------------------
Note: meshgrid's default indexing is 'xy', which means:
lons, lats = np.meshgrid[lon, lat]
in lons[i, j] or lats[i, j], i->lat, j->lon
===========================================================================
"""
def __init__(self, minlon, maxlon, dlon, minlat, maxlat, dlat, period, evlo=float('inf'), evla=float('inf'), fieldtype='Tph',\
evid='', nlat_grad=1, nlon_grad=1, nlat_lplc=2, nlon_lplc=2):
self.Nlon = int(round((maxlon-minlon)/dlon)+1)
self.Nlat = int(round((maxlat-minlat)/dlat)+1)
self.dlon = dlon
self.dlat = dlat
self.lon = np.arange(self.Nlon)*self.dlon+minlon
self.lat = np.arange(self.Nlat)*self.dlat+minlat
self.lonArr, self.latArr= np.meshgrid(self.lon, self.lat)
self.minlon = minlon
self.maxlon = self.lon.max()
self.minlat = minlat
self.maxlat = self.lat.max()
self._get_dlon_dlat_km()
self.period = period
self.evid = evid
self.fieldtype = fieldtype
self.Zarr = np.zeros((self.Nlat, self.Nlon), dtype=np.float64)
self.evlo = evlo
self.evla = evla
#-----------------------------------------------------------
# parameters indicate edge cutting for gradient/lplc arrays
#-----------------------------------------------------------
self.nlon_grad = nlon_grad
self.nlat_grad = nlat_grad
self.nlon_lplc = nlon_lplc
self.nlat_lplc = nlat_lplc
return
def copy(self):
return copy.deepcopy(self)
def _get_dlon_dlat_km_slow(self):
"""Get longitude and latitude interval in km
"""
self.dlon_km = np.array([])
self.dlat_km = np.array([])
for lat in self.lat:
dist_lon, az, baz = obspy.geodetics.gps2dist_azimuth(lat, 0., lat, self.dlon)
dist_lat, az, baz = obspy.geodetics.gps2dist_azimuth(lat, 0., lat+self.dlat, 0.)
self.dlon_km = np.append(self.dlon_km, dist_lon/1000.)
self.dlat_km = np.append(self.dlat_km, dist_lat/1000.)
self.dlon_kmArr = (np.tile(self.dlon_km, self.Nlon).reshape(self.Nlon, self.Nlat)).T
self.dlat_kmArr = (np.tile(self.dlat_km, self.Nlon).reshape(self.Nlon, self.Nlat)).T
return
def _get_dlon_dlat_km(self):
"""Get longitude and latitude interval in km
"""
az, baz, dist_lon = geodist.inv(np.zeros(self.lat.size), self.lat, np.ones(self.lat.size)*self.dlon, self.lat)
az, baz, dist_lat = geodist.inv(np.zeros(self.lat.size), self.lat, np.zeros(self.lat.size), self.lat+self.dlat)
self.dlon_km = dist_lon/1000.
self.dlat_km = dist_lat/1000.
self.dlon_kmArr = (np.tile(self.dlon_km, self.Nlon).reshape(self.Nlon, self.Nlat)).T
self.dlat_kmArr = (np.tile(self.dlat_km, self.Nlon).reshape(self.Nlon, self.Nlat)).T
return
def read(self, fname):
"""read field file
"""
try:
Inarray = np.loadtxt(fname)
with open(fname) as f:
inline = f.readline()
if inline.split()[0] =='#':
evlostr = inline.split()[1]
evlastr = inline.split()[2]
if evlostr.split('=')[0] =='evlo':
self.evlo = float(evlostr.split('=')[1])
if evlastr.split('=')[0] =='evla':
self.evla = float(evlastr.split('=')[1])
except:
Inarray = np.load(fname)
self.lonArrIn = Inarray[:,0]
self.latArrIn = Inarray[:,1]
self.ZarrIn = Inarray[:,2]
return
def read_HD(self, fname):
Inarray = np.loadtxt(fname)
# self.lplc_gmt = (Inarray[:, 2].reshape(self.lonArr.shape))[self.nlat_lplc:-self.nlat_lplc, self.nlon_lplc:-self.nlon_lplc]
# data = Inarray[:, 2].reshape((self.lonArr.shape[0]-2*self.nlat_grad, self.lonArr.shape[1]-2*self.nlon_grad))
data = Inarray[:, 2].reshape((self.lonArr.shape[1]-2*self.nlon_grad, self.lonArr.shape[0]-2*self.nlat_grad))
data = data.T
self.appV = data.copy()
self.mask = np.zeros(self.lonArr.shape, dtype=bool)
mask = data == 0.
self.mask[self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad] \
= mask.copy()
self.appV[np.logical_not(mask)] \
= 1./self.appV[np.logical_not(mask)]
return
def synthetic_field(self, lat0, lon0, v=3.0):
az, baz, distevent = geodist.inv( np.ones(self.lonArrIn.size)*lon0, np.ones(self.lonArrIn.size)*lat0, self.lonArrIn, self.latArrIn)
self.ZarrIn = distevent/v/1000.
return
def diff_debug(self, lat0, lon0, v=3.0):
lonArr = self.lonArr[self.nlat_lplc:-self.nlat_lplc, self.nlon_lplc:-self.nlon_lplc]
latArr = self.latArr[self.nlat_lplc:-self.nlat_lplc, self.nlon_lplc:-self.nlon_lplc]
az, baz, distevent = geodist.inv( np.ones(lonArr.shape)*lon0, np.ones(lonArr.shape)*lat0, lonArr, latArr)
self.lplc_diff = 1./distevent*1000./v - self.lplc
self.lplc_theo = 1./distevent*1000./v
def read_ind(self, fname, zindex=2, dindex=None):
"""read field file
"""
try:
Inarray = np.loadtxt(fname)
with open(fname) as f:
inline = f.readline()
if inline.split()[0] =='#':
evlostr = inline.split()[1]
evlastr = inline.split()[2]
if evlostr.split('=')[0] =='evlo':
self.evlo = float(evlostr.split('=')[1])
if evlastr.split('=')[0] =='evla':
self.evla = float(evlastr.split('=')[1])
except:
Inarray = np.load(fname)
self.lonArrIn = Inarray[:,0]
self.latArrIn = Inarray[:,1]
self.ZarrIn = Inarray[:,zindex]*1e9
if dindex is not None:
darrIn = Inarray[:,dindex]
self.ZarrIn = darrIn/Inarray[:,zindex]
return
def read_array(self, lonArr, latArr, ZarrIn):
"""read field file
"""
self.lonArrIn = lonArr
self.latArrIn = latArr
self.ZarrIn = ZarrIn
return
def add_noise(self, sigma=0.5):
"""Add Gaussian noise with standard deviation = sigma to the input data
"""
for i in xrange(self.ZarrIn.size):
self.ZarrIn[i] = self.ZarrIn[i] + random.gauss(0, sigma)
return
def load_field(self, inField):
"""Load field data from an input object
"""
self.lonArrIn = inField.lonArr
self.latArrIn = inField.latArr
self.ZarrIn = inField.Zarr
return
def write(self, fname, fmt='npy'):
"""Save field file
"""
OutArr = np.append(self.lonArr, self.latArr)
OutArr = np.append(OutArr, self.Zarr)
OutArr = OutArr.reshape(3, self.Nlon*self.Nlat)
OutArr = OutArr.T
if fmt is 'npy':
np.save(fname, OutArr)
elif fmt is 'txt':
np.savetxt(fname, OutArr)
else:
raise TypeError('Wrong output format!')
return
def np2ma(self):
"""Convert all the data array to masked array according to reason_n array.
"""
try:
reason_n = self.reason_n
except:
raise AttrictError('No reason_n array!')
self.Zarr = ma.masked_array(self.Zarr, mask=np.zeros(reason_n.shape) )
self.Zarr.mask[reason_n!=0] = 1
try:
self.diffaArr = ma.masked_array(self.diffaArr, mask=np.zeros(reason_n.shape) )
self.diffaArr.mask[reason_n!=0] = 1
except:
pass
try:
self.appV = ma.masked_array(self.appV, mask=np.zeros(reason_n.shape) )
self.appV.mask[reason_n!=0] = 1
except:
pass
try:
self.grad[0] = ma.masked_array(self.grad[0], mask=np.zeros(reason_n.shape) )
self.grad[0].mask[reason_n!=0] = 1
self.grad[1] = ma.masked_array(self.grad[1], mask=np.zeros(reason_n.shape) )
self.grad[1].mask[reason_n!=0] = 1
except:
pass
try:
self.lplc = ma.masked_array(self.lplc, mask=np.zeros(reason_n.shape) )
self.lplc.mask[reason_n!=0] = 1
except:
print 'No Laplacian array!'
pass
return
def ma2np(self):
"""Convert all the maksed data array to numpy array
"""
self.Zarr = ma.getdata(self.Zarr)
try:
self.diffaArr = ma.getdata(self.diffaArr)
except:
pass
try:
self.appV = ma.getdata(self.appV)
except:
pass
try:
self.lplc = ma.getdata(self.lplc)
except:
pass
return
def cut_edge(self, nlon, nlat):
"""Cut edge
=======================================================================================
::: input parameters :::
nlon, nlon - number of edge point in longitude/latitude to be cutted
=======================================================================================
"""
self.Nlon = self.Nlon-2*nlon
self.Nlat = self.Nlat-2*nlat
self.minlon = self.minlon + nlon*self.dlon
self.maxlon = self.maxlon - nlon*self.dlon
self.minlat = self.minlat + nlat*self.dlat
self.maxlat = self.maxlat - nlat*self.dlat
self.lon = np.arange(self.Nlon)*self.dlon+self.minlon
self.lat = np.arange(self.Nlat)*self.dlat+self.minlat
self.lonArr,self.latArr = np.meshgrid(self.lon, self.lat)
self.Zarr = self.Zarr[nlat:-nlat, nlon:-nlon]
try:
self.reason_n = self.reason_n[nlat:-nlat, nlon:-nlon]
except:
pass
self._get_dlon_dlat_km()
return
def interp_surface(self, workingdir, outfname, tension=0.0):
"""Interpolate input data to grid point with gmt surface command
=======================================================================================
::: input parameters :::
workingdir - working directory
outfname - output file name for interpolation
tension - input tension for gmt surface(0.0-1.0)
---------------------------------------------------------------------------------------
::: output :::
self.Zarr - interpolated field data
---------------------------------------------------------------------------------------
version history
- 2018/07/06 : added the capability of interpolation for dlon != dlat
=======================================================================================
"""
if not os.path.isdir(workingdir):
os.makedirs(workingdir)
OutArr = np.append(self.lonArrIn, self.latArrIn)
OutArr = np.append(OutArr, self.ZarrIn)
OutArr = OutArr.reshape(3, self.lonArrIn.size)
OutArr = OutArr.T
np.savetxt(workingdir+'/'+outfname, OutArr, fmt='%g')
fnameHD = workingdir+'/'+outfname+'.HD'
tempGMT = workingdir+'/'+outfname+'_GMT.sh'
grdfile = workingdir+'/'+outfname+'.grd'
with open(tempGMT,'wb') as f:
REG = '-R'+str(self.minlon)+'/'+str(self.maxlon)+'/'+str(self.minlat)+'/'+str(self.maxlat)
# f.writelines('gmtset MAP_FRAME_TYPE fancy \n')
# f.writelines('surface %s -T%g -G%s -I%g %s \n' %( workingdir+'/'+outfname, tension, grdfile, self.dlon, REG ))
# f.writelines('grd2xyz %s %s > %s \n' %( grdfile, REG, fnameHD ))
f.writelines('gmt gmtset MAP_FRAME_TYPE fancy \n')
if self.dlon == self.dlat:
f.writelines('gmt surface %s -T%g -G%s -I%g %s \n' %( workingdir+'/'+outfname, tension, grdfile, self.dlon, REG ))
else:
f.writelines('gmt surface %s -T%g -G%s -I%g/%g %s \n' %( workingdir+'/'+outfname, tension, grdfile, self.dlon, self.dlat, REG ))
f.writelines('gmt grd2xyz %s %s > %s \n' %( grdfile, REG, fnameHD ))
call(['bash', tempGMT])
os.remove(grdfile)
os.remove(tempGMT)
inArr = np.loadtxt(fnameHD)
ZarrIn = inArr[:, 2]
self.Zarr = (ZarrIn.reshape(self.Nlat, self.Nlon))[::-1, :]
return
def gauss_smoothing(self, workingdir, outfname, tension=0.0, sigma=50.):
"""
Perform a Gaussian smoothing
"""
if not os.path.isdir(workingdir):
os.makedirs(workingdir)
OutArr = np.append(self.lonArrIn, self.latArrIn)
OutArr = np.append(OutArr, self.ZarrIn)
OutArr = OutArr.reshape(3, self.lonArrIn.size)
OutArr = OutArr.T
np.savetxt(workingdir+'/'+outfname, OutArr, fmt='%g')
fnameHD = workingdir+'/'+outfname+'.HD'
tempGMT = workingdir+'/'+outfname+'_GMT.sh'
grdfile = workingdir+'/'+outfname+'.grd'
outgrd = workingdir+'/'+outfname+'_filtered.grd'
# http://gmt.soest.hawaii.edu/doc/5.3.2/grdfilter.html
# Gaussian: Weights are given by the Gaussian function, where width is 6 times the conventional Gaussian sigma.
width = 6.*(sigma/2.)
#
with open(tempGMT,'wb') as f:
REG = '-R'+str(self.minlon)+'/'+str(self.maxlon)+'/'+str(self.minlat)+'/'+str(self.maxlat)
f.writelines('gmt gmtset MAP_FRAME_TYPE fancy \n')
if self.dlon == self.dlat:
f.writelines('gmt surface %s -T%g -G%s -I%g %s \n' %( workingdir+'/'+outfname, tension, grdfile, self.dlon, REG ))
else:
f.writelines('gmt surface %s -T%g -G%s -I%g/%g %s \n' %( workingdir+'/'+outfname, tension, grdfile, self.dlon, self.dlat, REG ))
f.writelines('gmt grdfilter %s -D4 -Fg%g -G%s %s \n' %( grdfile, width, outgrd, REG))
# f.writelines('gmt grdfilter %s -D4 -Fc%g -G%s %s \n' %( grdfile, width, outgrd, REG))
f.writelines('gmt grd2xyz %s %s > %s \n' %( outgrd, REG, fnameHD ))
call(['bash', tempGMT])
os.remove(grdfile)
os.remove(outgrd)
os.remove(tempGMT)
inArr = np.loadtxt(fnameHD)
ZarrIn = inArr[:, 2]
self.Zarr = (ZarrIn.reshape(self.Nlat, self.Nlon))[::-1, :]
return
def gradient(self, method='diff', edge_order=1, order=2):
"""Compute gradient of the field
=============================================================================================
::: input parameters :::
edge_order - edge_order : {1, 2}, optional, only has effect when method='default'
Gradient is calculated using Nth order accurate differences at the boundaries
method - method: 'default' : use numpy.gradient 'convolve': use convolution
order - order of finite difference scheme, only has effect when method='convolve'
::: note :::
gradient arrays are of shape Nlat-1, Nlon-1
=============================================================================================
"""
Zarr = self.Zarr
if method=='diff':
# self.dlat_kmArr : dx here in numpy gradient since Zarr is Z[ilat, ilon]
self.grad = np.gradient( self.Zarr, self.dlat_kmArr, self.dlon_kmArr, edge_order=edge_order)
self.grad[0]= self.grad[0][self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad]
self.grad[1]= self.grad[1][self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad]
elif method == 'convolve':
dlat_km = self.dlat_kmArr
dlon_km = self.dlon_kmArr
if order==2:
diff_lon= convolve(Zarr, lon_diff_weight_2)/dlon_km
diff_lat= convolve(Zarr, lat_diff_weight_2)/dlat_km
elif order==4:
diff_lon= convolve(Zarr, lon_diff_weight_4)/dlon_km
diff_lat= convolve(Zarr, lat_diff_weight_4)/dlat_km
elif order==6:
diff_lon= convolve(Zarr, lon_diff_weight_6)/dlon_km
diff_lat= convolve(Zarr, lat_diff_weight_6)/dlat_km
self.grad = []
self.grad.append(diff_lat[self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad])
self.grad.append(diff_lon[self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad])
self.proAngle = np.arctan2(self.grad[0], self.grad[1])/np.pi*180.
return
def Laplacian(self, method='green', order=4, verbose=False):
"""Compute Laplacian of the field
=============================================================================================================
::: input parameters :::
method - method: 'diff' : use central finite difference scheme, similar to convolve with order =2
'convolve': use convolution
'green' : use Green's theorem( 2D Gauss's theorem )
order - order of finite difference scheme, only has effect when method='convolve'
=============================================================================================================
"""
Zarr = self.Zarr
if method == 'diff':
dlat_km = self.dlat_kmArr[1:-1, 1:-1]
dlon_km = self.dlon_kmArr[1:-1, 1:-1]
Zarr_latp = Zarr[2:, 1:-1]
Zarr_latn = Zarr[:-2, 1:-1]
Zarr_lonp = Zarr[1:-1, 2:]
Zarr_lonn = Zarr[1:-1, :-2]
Zarr = Zarr[1:-1, 1:-1]
lplc = (Zarr_latp+Zarr_latn-2*Zarr) / (dlat_km**2) + (Zarr_lonp+Zarr_lonn-2*Zarr) / (dlon_km**2)
dnlat = self.nlat_lplc - 1
dnlon = self.nlon_lplc - 1
if dnlat == 0 and dnlon == 0:
self.lplc = lplc
elif dnlat == 0 and dnlon != 0:
self.lplc = lplc[:, dnlon:-dnlon]
elif dnlat != 0 and dnlon == 0:
self.lplc = lplc[dnlat:-dnlat, :]
else:
self.lplc = lplc[dnlat:-dnlat, dnlon:-dnlon]
# # # elif method == 'diff2':
# # # dlat_km = self.dlat_kmArr[2:-2, 2:-2]
# # # dlon_km = self.dlon_kmArr[2:-2, 2:-2]
# # # Zarr_latp = Zarr[4:, 2:-2]
# # # Zarr_latn = Zarr[:-4, 2:-2]
# # # Zarr_lonp = Zarr[2:-2, 4:]
# # # Zarr_lonn = Zarr[2:-2, :-4]
# # # Zarr = Zarr[2:-2, 2:-2]
# # # lplc = (Zarr_latp+Zarr_latn-2*Zarr) / (4.*dlat_km**2) + (Zarr_lonp+Zarr_lonn-2*Zarr) / (4.*dlon_km**2)
# # # dnlat = self.nlat_lplc - 2
# # # dnlon = self.nlon_lplc - 2
# # # if dnlat == 0 and dnlon == 0:
# # # self.lplc = lplc
# # # elif dnlat == 0 and dnlon != 0:
# # # self.lplc = lplc[:, dnlon:-dnlon]
# # # elif dnlat != 0 and dnlon == 0:
# # # self.lplc = lplc[dnlat:-dnlat, :]
# # # else:
# # # self.lplc = lplc[dnlat:-dnlat, dnlon:-dnlon]
# # # elif method == 'diff3':
# # # self.gradient('diff')
# # # grad_y = self.grad[0]
# # # grad_x = self.grad[1]
# # # grad_xp = grad_x[1:-1, 2:]
# # # grad_xn = grad_x[1:-1, :-2]
# # # grad_yp = grad_y[2:, 1:-1]
# # # grad_yn = grad_y[:-2, 1:-1]
# # # dlat_km = self.dlat_kmArr[self.nlat_grad+1:-self.nlat_grad-1, self.nlon_grad+1:-self.nlon_grad-1]
# # # dlon_km = self.dlon_kmArr[self.nlat_grad+1:-self.nlat_grad-1, self.nlon_grad+1:-self.nlon_grad-1]
# # # temp1 = (grad_xp - grad_xn)/2./dlon_km
# # # temp2 = (grad_yp - grad_yn)/2./dlat_km
# # # lplc = temp1 + temp2
# # # #-----------------------------------------------
# # # # cut edges according to nlat_lplc, nlon_lplc
# # # #-----------------------------------------------
# # # dnlat = self.nlat_lplc - self.nlat_grad - 1
# # # if dnlat < 0:
# # # self.nlat_lplc = self.nlat_grad + 1
# # # dnlon = self.nlon_lplc - self.nlon_grad - 1
# # # if dnlon < 0:
# # # self.nlon_lplc = self.nlon_grad + 1
# # # if dnlat == 0 and dnlon == 0:
# # # self.lplc = lplc
# # # elif dnlat == 0 and dnlon != 0:
# # # self.lplc = lplc[:, dnlon:-dnlon]
# # # elif dnlat != 0 and dnlon == 0:
# # # self.lplc = lplc[dnlat:-dnlat, :]
# # # else:
# # # self.lplc = lplc[dnlat:-dnlat, dnlon:-dnlon]
# # # elif method == 'diff4':
# # # dlat_km = self.dlat_kmArr[1:-1, 1:-1]
# # # dlon_km = self.dlon_kmArr[1:-1, 1:-1]
# # # Zarr_latp = Zarr[2:, 1:-1]
# # # Zarr_latn = Zarr[:-2, 1:-1]
# # # Zarr_lonp = Zarr[1:-1, 2:]
# # # Zarr_lonn = Zarr[1:-1, :-2]
# # # grad_y = (Zarr_latp - Zarr_latn)/dlat_km/2.
# # # grad_x = (Zarr_lonp - Zarr_lonn)/dlon_km/2.
# # # grad_xp = grad_x[1:-1, 2:]
# # # grad_xn = grad_x[1:-1, :-2]
# # # grad_yp = grad_y[2:, 1:-1]
# # # grad_yn = grad_y[:-2, 1:-1]
# # # dlat_km = self.dlat_kmArr[self.nlat_grad+1:-self.nlat_grad-1, self.nlon_grad+1:-self.nlon_grad-1]
# # # dlon_km = self.dlon_kmArr[self.nlat_grad+1:-self.nlat_grad-1, self.nlon_grad+1:-self.nlon_grad-1]
# # # temp1 = (grad_xp - grad_xn)/2./dlon_km
# # # temp2 = (grad_yp - grad_yn)/2./dlat_km
# # # self.lplc = temp1 + temp2
elif method == 'convolve':
dlat_km = self.dlat_kmArr
dlon_km = self.dlon_kmArr
if order==2:
diff2_lon = convolve(Zarr, lon_diff2_weight_2)/dlon_km/dlon_km
diff2_lat = convolve(Zarr, lat_diff2_weight_2)/dlat_km/dlat_km
elif order==4:
diff2_lon = convolve(Zarr, lon_diff2_weight_4)/dlon_km/dlon_km
diff2_lat = convolve(Zarr, lat_diff2_weight_4)/dlat_km/dlat_km
elif order==6:
diff2_lon = convolve(Zarr, lon_diff2_weight_6)/dlon_km/dlon_km
diff2_lat = convolve(Zarr, lat_diff2_weight_6)/dlat_km/dlat_km
self.lplc = diff2_lon+diff2_lat
self.lplc = self.lplc[self.nlat_lplc:-self.nlat_lplc, self.nlon_lplc:-self.nlon_lplc]
elif method=='green':
#---------------
# gradient arrays
#---------------
try:
grad_y = self.grad[0]
grad_x = self.grad[1]
except:
self.gradient('diff')
grad_y = self.grad[0]
grad_x = self.grad[1]
grad_xp = grad_x[1:-1, 2:]
grad_xn = grad_x[1:-1, :-2]
grad_yp = grad_y[2:, 1:-1]
grad_yn = grad_y[:-2, 1:-1]
dlat_km = self.dlat_kmArr[self.nlat_grad+1:-self.nlat_grad-1, self.nlon_grad+1:-self.nlon_grad-1]
dlon_km = self.dlon_kmArr[self.nlat_grad+1:-self.nlat_grad-1, self.nlon_grad+1:-self.nlon_grad-1]
#------------------
# Green's theorem
#------------------
loopsum = (grad_xp - grad_xn)*dlat_km + (grad_yp - grad_yn)*dlon_km
area = dlat_km*dlon_km
lplc = loopsum/area
#-----------------------------------------------
# cut edges according to nlat_lplc, nlon_lplc
#-----------------------------------------------
dnlat = self.nlat_lplc - self.nlat_grad - 1
if dnlat < 0:
self.nlat_lplc = self.nlat_grad + 1
dnlon = self.nlon_lplc - self.nlon_grad - 1
if dnlon < 0:
self.nlon_lplc = self.nlon_grad + 1
if dnlat == 0 and dnlon == 0:
self.lplc = lplc
elif dnlat == 0 and dnlon != 0:
self.lplc = lplc[:, dnlon:-dnlon]
elif dnlat != 0 and dnlon == 0:
self.lplc = lplc[dnlat:-dnlat, :]
else:
self.lplc = lplc[dnlat:-dnlat, dnlon:-dnlon]
if verbose:
print 'max lplc:',self.lplc.max(), 'min lplc:',self.lplc.min()
return
def get_appV(self):
"""Get the apparent velocity from gradient
"""
slowness = np.sqrt ( self.grad[0] ** 2 + self.grad[1] ** 2)
slowness[slowness==0] = 0.3
self.appV = 1./slowness
return
def check_curvature(self, workingdir, outpfx='', threshold=0.005):
"""
Check and discard data points with large curvatures.
Points at boundaries will be discarded.
Two interpolation schemes with different tension (0, 0.2) will be applied to the quality controlled field data file.
=====================================================================================================================
::: input parameters :::
workingdir - working directory
threshold - threshold value for Laplacian
---------------------------------------------------------------------------------------------------------------------
::: output :::
workingdir/outpfx+fieldtype_per_v1.lst - output field file with data point passing curvature checking
workingdir/outpfx+fieldtype_per_v1.lst.HD - interpolated travel time file
workingdir/outpfx+fieldtype_per_v1.lst.HD_0.2 - interpolated travel time file with tension=0.2
---------------------------------------------------------------------------------------------------------------------
version history
- 2018/07/06 : added the capability of dealing with dlon != dlat
=====================================================================================================================
"""
# Compute Laplacian
self.Laplacian(method='green')
tfield = self.copy()
tfield.cut_edge(nlon=self.nlon_lplc, nlat=self.nlat_lplc)
#--------------------
# quality control
#--------------------
LonLst = tfield.lonArr.reshape(tfield.lonArr.size)
LatLst = tfield.latArr.reshape(tfield.latArr.size)
TLst = tfield.Zarr.reshape(tfield.Zarr.size)
lplc = self.lplc.reshape(self.lplc.size)
index = np.where((lplc>-threshold)*(lplc<threshold))[0]
# Sep 24th, 2018
if index.size == 0:
return False
LonLst = LonLst[index]
LatLst = LatLst[index]
TLst = TLst[index]
# output to txt file
outfname = workingdir+'/'+outpfx+self.fieldtype+'_'+str(self.period)+'_v1.lst'
TfnameHD = outfname+'.HD'
_write_txt(fname=outfname, outlon=LonLst, outlat=LatLst, outZ=TLst)
# interpolate with gmt surface
tempGMT = workingdir+'/'+outpfx+self.fieldtype+'_'+str(self.period)+'_v1_GMT.sh'
grdfile = workingdir+'/'+outpfx+self.fieldtype+'_'+str(self.period)+'_v1.grd'
with open(tempGMT,'wb') as f:
REG = '-R'+str(self.minlon)+'/'+str(self.maxlon)+'/'+str(self.minlat)+'/'+str(self.maxlat)
# f.writelines('gmtset MAP_FRAME_TYPE fancy \n')
# f.writelines('surface %s -T0.0 -G%s -I%g %s \n' %( outfname, grdfile, self.dlon, REG ))
# f.writelines('grd2xyz %s %s > %s \n' %( grdfile, REG, TfnameHD ))
# f.writelines('surface %s -T0.2 -G%s -I%g %s \n' %( outfname, grdfile+'.T0.2', self.dlon, REG ))
# f.writelines('grd2xyz %s %s > %s \n' %( grdfile+'.T0.2', REG, TfnameHD+'_0.2' ))
f.writelines('gmt gmtset MAP_FRAME_TYPE fancy \n')
if self.dlon == self.dlat:
f.writelines('gmt surface %s -T0.0 -G%s -I%g %s \n' %( outfname, grdfile, self.dlon, REG ))
else:
f.writelines('gmt surface %s -T0.0 -G%s -I%g/%g %s \n' %( outfname, grdfile, self.dlon, self.dlat, REG ))
f.writelines('gmt grd2xyz %s %s > %s \n' %( grdfile, REG, TfnameHD ))
if self.dlon == self.dlat:
f.writelines('gmt surface %s -T0.2 -G%s -I%g %s \n' %( outfname, grdfile+'.T0.2', self.dlon, REG ))
else:
f.writelines('gmt surface %s -T0.2 -G%s -I%g/%g %s \n' %( outfname, grdfile+'.T0.2', self.dlon, self.dlat, REG ))
f.writelines('gmt grd2xyz %s %s > %s \n' %( grdfile+'.T0.2', REG, TfnameHD+'_0.2' ))
call(['bash', tempGMT])
os.remove(grdfile+'.T0.2')
os.remove(grdfile)
os.remove(tempGMT)
return True
def check_curvature_amp(self, workingdir, outpfx='', threshold=0.2):
"""
Check and discard data points with large curvatures.
Points at boundaries will be discarded.
Two interpolation schemes with different tension (0, 0.2) will be applied to the quality controlled field data file.
=====================================================================================================================
::: input parameters :::
workingdir - working directory
threshold - threshold value for Laplacian
---------------------------------------------------------------------------------------------------------------------
::: output :::
workingdir/outpfx+fieldtype_per_v1.lst - output field file with data point passing curvature checking
workingdir/outpfx+fieldtype_per_v1.lst.HD - interpolated travel time file
workingdir/outpfx+fieldtype_per_v1.lst.HD_0.2 - interpolated travel time file with tension=0.2
---------------------------------------------------------------------------------------------------------------------
version history
- 2018/07/06 : added the capability of dealing with dlon != dlat
=====================================================================================================================
"""
# Compute Laplacian
self.Laplacian(method='green')
tfield = self.copy()
tfield.cut_edge(nlon=self.nlon_lplc, nlat=self.nlat_lplc)
threshold = threshold*2./(3.**2)
#--------------------
# quality control
#--------------------
LonLst = tfield.lonArr.reshape(tfield.lonArr.size)
LatLst = tfield.latArr.reshape(tfield.latArr.size)
ampLst = tfield.Zarr.reshape(tfield.Zarr.size)
lplc = self.lplc.reshape(self.lplc.size)
lplc_corr = lplc.copy()
lplc_corr[ampLst!=0.]\
= lplc[ampLst!=0.]/ampLst[ampLst!=0.]
lplc_corr[ampLst==0.]\
= 0.
omega = 2.*np.pi/self.period
lplc_corr = lplc_corr/(omega**2)
index = np.where((lplc_corr>-threshold)*(lplc_corr<threshold))[0]
LonLst = LonLst[index]
LatLst = LatLst[index]
ampLst = ampLst[index]
# output to txt file
outfname = workingdir+'/'+outpfx+self.fieldtype+'_'+str(self.period)+'_v1.lst'
AfnameHD = outfname+'.HD'
_write_txt(fname=outfname, outlon=LonLst, outlat=LatLst, outZ=ampLst)
# interpolate with gmt surface
tempGMT = workingdir+'/'+outpfx+self.fieldtype+'_'+str(self.period)+'_v1_GMT.sh'
grdfile = workingdir+'/'+outpfx+self.fieldtype+'_'+str(self.period)+'_v1.grd'
with open(tempGMT,'wb') as f:
REG = '-R'+str(self.minlon)+'/'+str(self.maxlon)+'/'+str(self.minlat)+'/'+str(self.maxlat)
f.writelines('gmt gmtset MAP_FRAME_TYPE fancy \n')
if self.dlon == self.dlat:
f.writelines('gmt surface %s -T0.0 -G%s -I%g %s \n' %( outfname, grdfile, self.dlon, REG ))
else:
f.writelines('gmt surface %s -T0.0 -G%s -I%g/%g %s \n' %( outfname, grdfile, self.dlon, self.dlat, REG ))
f.writelines('gmt grd2xyz %s %s > %s \n' %( grdfile, REG, AfnameHD ))
if self.dlon == self.dlat:
f.writelines('gmt surface %s -T0.2 -G%s -I%g %s \n' %( outfname, grdfile+'.T0.2', self.dlon, REG ))
else:
f.writelines('gmt surface %s -T0.2 -G%s -I%g/%g %s \n' %( outfname, grdfile+'.T0.2', self.dlon, self.dlat, REG ))
f.writelines('gmt grd2xyz %s %s > %s \n' %( grdfile+'.T0.2', REG, AfnameHD+'_0.2' ))
call(['bash', tempGMT])
os.remove(grdfile+'.T0.2')
os.remove(grdfile)
os.remove(tempGMT)
return
def eikonal_operator(self, workingdir, inpfx='', nearneighbor=True, cdist=150., lplcthresh=0.005, lplcnearneighbor=False):
"""
Generate slowness maps from travel time maps using eikonal equation
Two interpolated travel time file with different tension will be used for quality control.
=====================================================================================================================
::: input parameters :::
workingdir - working directory
evlo, evla - event location
nearneighbor - do near neighbor quality control or not
cdist - distance for quality control, default is 12*period
::: output format :::
outdir/slow_azi_stacode.pflag.txt.HD.2.v2 - Slowness map
---------------------------------------------------------------------------------------------------------------------
Note: edge has been cutting twice, one in check_curvature
=====================================================================================================================
"""
if cdist is None:
cdist = max(12.*self.period/3., 150.)
evlo = self.evlo
evla = self.evla
# Read data
# v1: data that passes check_curvature criterion
# v1HD and v1HD02: interpolated v1 data with tension = 0. and 0.2
fnamev1 = workingdir+'/'+inpfx+self.fieldtype+'_'+str(self.period)+'_v1.lst'
fnamev1HD = fnamev1+'.HD'
fnamev1HD02 = fnamev1HD+'_0.2'
InarrayV1 = np.loadtxt(fnamev1)
loninV1 = InarrayV1[:,0]
latinV1 = InarrayV1[:,1]
fieldin = InarrayV1[:,2]
Inv1HD = np.loadtxt(fnamev1HD)
lonv1HD = Inv1HD[:,0]
latv1HD = Inv1HD[:,1]
fieldv1HD = Inv1HD[:,2]
Inv1HD02 = np.loadtxt(fnamev1HD02)
lonv1HD02 = Inv1HD02[:,0]
latv1HD02 = Inv1HD02[:,1]
fieldv1HD02 = Inv1HD02[:,2]
# Set field value to be zero if there is large difference between v1HD and v1HD02
diffArr = fieldv1HD-fieldv1HD02
# fieldArr = fieldv1HD*((diffArr<1.)*(diffArr>-1.))
# old
fieldArr = fieldv1HD*((diffArr<2.)*(diffArr>-2.))
fieldArr = (fieldArr.reshape(self.Nlat, self.Nlon))[::-1, :]
# reason_n
# 0: accepted point
# 1: data point the has large difference between v1HD and v1HD02
# 2: data point that does not have near neighbor points at all E/W/N/S directions
# 3: slowness is too large/small
# 4: near a zero field data point
# 5: epicentral distance is too small
reason_n = np.ones(fieldArr.size, dtype=np.int32)
# reason_n1 = np.int32(reason_n*(diffArr>1.))
# reason_n2 = np.int32(reason_n*(diffArr<-1.))
# old
reason_n1 = np.int32(reason_n*(diffArr>2.))
reason_n2 = np.int32(reason_n*(diffArr<-2.))
reason_n = reason_n1+reason_n2
reason_n = (reason_n.reshape(self.Nlat, self.Nlon))[::-1,:]
#-------------------------------------------------------------------------------------------------------
# check each data point if there are close-by four stations located at E/W/N/S directions respectively
#-------------------------------------------------------------------------------------------------------
if nearneighbor:
for ilat in range(self.Nlat):
for ilon in range(self.Nlon):
if reason_n[ilat, ilon]==1:
continue
lon = self.lon[ilon]
lat = self.lat[ilat]
dlon_km = self.dlon_km[ilat]
dlat_km = self.dlat_km[ilat]
difflon = abs(self.lonArrIn-lon)/self.dlon*dlon_km
difflat = abs(self.latArrIn-lat)/self.dlat*dlat_km
index = np.where((difflon<cdist)*(difflat<cdist))[0]
marker_EN = np.zeros((2,2), dtype=np.bool)
marker_nn = 4
tflag = False
for iv1 in index:
lon2 = self.lonArrIn[iv1]
lat2 = self.latArrIn[iv1]
if lon2-lon<0:
marker_E = 0
else:
marker_E = 1
if lat2-lat<0:
marker_N = 0
else:
marker_N = 1
if marker_EN[marker_E , marker_N]:
continue
az, baz, dist = geodist.inv(lon, lat, lon2, lat2) # loninArr/latinArr are initial points
dist = dist/1000.
if dist< cdist*2 and dist >= 1:
marker_nn = marker_nn-1
if marker_nn==0:
tflag = True
break
marker_EN[marker_E, marker_N] = True
if not tflag:
fieldArr[ilat, ilon] = 0
reason_n[ilat, ilon] = 2
# Start to Compute Gradient
tfield = self.copy()
tfield.Zarr = fieldArr
tfield.gradient('diff')
# if one field point has zero value, reason_n for four near neighbor points will all be set to 4
tempZarr = tfield.Zarr[self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad]
index0 = np.where(tempZarr==0.)
ilatArr = index0[0] + 1
ilonArr = index0[1] + 1
reason_n[ilatArr+1, ilonArr]= 4
reason_n[ilatArr-1, ilonArr]= 4
reason_n[ilatArr, ilonArr+1]= 4
reason_n[ilatArr, ilonArr-1]= 4
# reduce size of reason_n to be the same shape as gradient arrays
reason_n = reason_n[self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad]
# if slowness is too large/small, reason_n will be set to 3
slowness = np.sqrt(tfield.grad[0]**2 + tfield.grad[1]**2)
if self.fieldtype=='Tph' or self.fieldtype=='Tgr':
reason_n[(slowness>0.5)*(reason_n==0)] = 3
reason_n[(slowness<0.2)*(reason_n==0)] = 3
#-------------------------------------
# computing propagation deflection
#-------------------------------------
indexvalid = np.where(reason_n==0)
diffaArr = np.zeros(reason_n.shape, dtype = np.float64)
latinArr = self.lat[indexvalid[0] + 1]
loninArr = self.lon[indexvalid[1] + 1]
evloArr = np.ones(loninArr.size, dtype=np.float64)*evlo
evlaArr = np.ones(latinArr.size, dtype=np.float64)*evla
az, baz, distevent = geodist.inv(loninArr, latinArr, evloArr, evlaArr) # loninArr/latinArr are initial points
distevent = distevent/1000.
az = az + 180.
az = 90.-az
baz = 90.-baz
az[az>180.] = az[az>180.] - 360.
az[az<-180.] = az[az<-180.] + 360.
baz[baz>180.] = baz[baz>180.] - 360.
baz[baz<-180.] = baz[baz<-180.] + 360.
# az azimuth receiver -> source
diffaArr[indexvalid[0], indexvalid[1]] = tfield.proAngle[indexvalid[0], indexvalid[1]] - az
self.gradient('diff')
self.az = np.zeros(self.proAngle.shape, dtype=np.float64)
self.az[indexvalid[0], indexvalid[1]] = az
self.baz = np.zeros(self.proAngle.shape, dtype=np.float64)
self.baz[indexvalid[0], indexvalid[1]] = baz
# if epicentral distance is too small, reason_n will be set to 5, and diffaArr will be 0.
tempArr = diffaArr[indexvalid[0], indexvalid[1]]
tempArr[distevent<cdist+50.] = 0.
diffaArr[indexvalid[0], indexvalid[1]] = tempArr
diffaArr[diffaArr>180.] = diffaArr[diffaArr>180.]-360.
diffaArr[diffaArr<-180.] = diffaArr[diffaArr<-180.]+360.
tempArr = reason_n[indexvalid[0], indexvalid[1]]
tempArr[distevent<cdist+50.] = 5
reason_n[indexvalid[0], indexvalid[1]] = tempArr
#------------------------------------------------------------------------
# final check of curvature, discard grid points with large curvature
#------------------------------------------------------------------------
self.Laplacian(method='green')
dnlat = self.nlat_lplc - self.nlat_grad
dnlon = self.nlon_lplc - self.nlon_grad
tempind = (self.lplc > lplcthresh) + (self.lplc < -lplcthresh)
if dnlat == 0 and dnlon == 0:
reason_n[tempind] = 6
elif dnlat == 0 and dnlon != 0:
(reason_n[:, dnlon:-dnlon])[tempind]= 6
elif dnlat != 0 and dnlon == 0:
(reason_n[dnlat:-dnlat, :])[tempind]= 6
else:
(reason_n[dnlat:-dnlat, dnlon:-dnlon])[tempind]\
= 6
# near neighbor discard for large curvature
if lplcnearneighbor:
indexlplc = np.where(reason_n==6.)
ilatArr = indexlplc[0]
ilonArr = indexlplc[1]
reason_n_temp = np.zeros(self.lonArr.shape)
reason_n_temp[self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad] \
= reason_n.copy()
reason_n_temp[ilatArr+1, ilonArr] = 6
reason_n_temp[ilatArr-1, ilonArr] = 6
reason_n_temp[ilatArr, ilonArr+1] = 6
reason_n_temp[ilatArr, ilonArr-1] = 6
reason_n = reason_n_temp[self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad]
# store final data
self.diffaArr = diffaArr
self.grad = tfield.grad
self.get_appV()
self.reason_n = reason_n
self.mask = np.ones((self.Nlat, self.Nlon), dtype=np.bool)
tempmask = reason_n != 0
self.mask[self.nlat_grad:-self.nlat_grad, self.nlon_grad:-self.nlon_grad]\
= tempmask
# added 04/05/2018
self.Nvalid_grd = (np.where(reason_n==0.)[0]).size
self.Ntotal_grd = reason_n.size
return
def helmholtz_operator(self, workingdir, inpfx='', lplcthresh=0.2):
"""
quality control for helmholtz tomography
Two interpolated amplitude file with different tension will be used for quality control.
=====================================================================================================================
::: input parameters :::
workingdir - working directory
::: output format :::
---------------------------------------------------------------------------------------------------------------------
Note: edge has been cutting twice, one in check_curvature
=====================================================================================================================
"""
# Read data,
# v1: data that pass check_curvature criterion
# v1HD and v1HD02: interpolated v1 data with tension = 0. and 0.2
fnamev1 = workingdir+'/'+inpfx+self.fieldtype+'_'+str(self.period)+'_v1.lst'
fnamev1HD = fnamev1+'.HD'
fnamev1HD02 = fnamev1HD+'_0.2'
InarrayV1 = np.loadtxt(fnamev1)
loninV1 = InarrayV1[:,0]
latinV1 = InarrayV1[:,1]
fieldin = InarrayV1[:,2]
Inv1HD = np.loadtxt(fnamev1HD)
lonv1HD = Inv1HD[:,0]
latv1HD = Inv1HD[:,1]
fieldv1HD = Inv1HD[:,2]
Inv1HD02 = np.loadtxt(fnamev1HD02)
lonv1HD02 = Inv1HD02[:,0]
latv1HD02 = Inv1HD02[:,1]
fieldv1HD02 = Inv1HD02[:,2]
# Set field value to be zero if there is large difference between v1HD and v1HD02
diffArr = fieldv1HD-fieldv1HD02
med_amp = np.median(self.Zarr)
# # fieldArr = fieldv1HD*((diffArr<0.01*med_amp)*(diffArr>-0.01*med_amp))
fieldArr = fieldv1HD*((diffArr<0.01*med_amp)*(diffArr>-0.01*med_amp))
# # fieldArr = fieldv1HD.copy()
fieldArr = (fieldArr.reshape(self.Nlat, self.Nlon))[::-1, :]
# reason_n
# 0: accepted point
# 1: data point the has large difference between v1HD and v1HD02
reason_n = np.ones(fieldArr.size, dtype=np.int32)
reason_n1 = np.int32(reason_n*(diffArr>0.01*med_amp))
reason_n2 = np.int32(reason_n*(diffArr<-0.01*med_amp))
reason_n = reason_n1+reason_n2
reason_n = (reason_n.reshape(self.Nlat, self.Nlon))[::-1,:]
# Start to compute Laplacian
tfield = self.copy()
tfield.Zarr = fieldArr
tfield.Laplacian(method='green') ## schemes
# if one field point has zero value, reason_n for four near neighbor points will all be set to 4
tempZarr = tfield.Zarr[self.nlat_lplc:-self.nlat_lplc, self.nlon_lplc:-self.nlon_lplc]