def doCompute(): xs = xa.SI['nrinl'] ys = xa.SI['nrcrl'] zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 filt = xa.params['Select']['Selection'] filtFunc = autojit(xl.vecmean) if filt==0 else autojit(xl.vmf_l1) if filt==1 else autojit(xl.vmf_l2) inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor'] crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor'] band = xa.params['Par_1']['Value'] zw = min(2*int(xa.params['Par_0']['Value'])+1,3) N = xa.params['ZSampMargin']['Value'][1] kernel = xl.hilbert_kernel(N, band) while True: xa.doInput() indata = xa.Input['Input'] # # Analytic Signal # ansig = np.apply_along_axis(np.convolve,-1, indata, kernel, mode="same") sr = np.real(ansig) si = np.imag(ansig) # # Compute partial derivatives sx = xl.kroon3( sr, axis=0 ) sy = xl.kroon3( sr, axis=1 ) sz = xl.kroon3( sr, axis=2 ) shx = xl.kroon3( si, axis=0 ) shy = xl.kroon3( si, axis=1 ) shz = xl.kroon3( si, axis=2 ) px = sr[1:xs-1,1:ys-1,:] * shx[1:xs-1,1:ys-1,:] - si[1:xs-1,1:ys-1,:] * sx[1:xs-1,1:ys-1,:] py = sr[1:xs-1,1:ys-1,:] * shy[1:xs-1,1:ys-1,:] - si[1:xs-1,1:ys-1,:] * sy[1:xs-1,1:ys-1,:] pz = sr[1:xs-1,1:ys-1,:] * shz[1:xs-1,1:ys-1,:] - si[1:xs-1,1:ys-1,:] * sz[1:xs-1,1:ys-1,:] # # Normalise the gradients so Z component is positive p = np.sign(pz)/np.sqrt(px*px+py*py+pz*pz) px *= p py *= p pz *= p # # Filter out = np.empty((3,xa.TI['nrsamp'])) xl.vecFilter(np.array([px,py,pz]), zw, filtFunc, out) # # Get the output xa.Output['Crl_dip'] = -out[1,:]/out[2,:]*crlFactor xa.Output['Inl_dip'] = -out[0,:]/out[2,:]*inlFactor xa.Output['True Dip'] = np.sqrt(xa.Output['Crl_dip']*xa.Output['Crl_dip']+xa.Output['Inl_dip']*xa.Output['Inl_dip']) xa.Output['Dip Azimuth'] = np.degrees(np.arctan2(xa.Output['Inl_dip'],xa.Output['Crl_dip'])) xa.doOutput()
def doCompute():
hxs = xa.SI['nrinl'] // 2
hys = xa.SI['nrcrl'] // 2
inlFactor = xa.SI['zstep'] / xa.SI['inldist'] * xa.SI['dipFactor']
crlFactor = xa.SI['zstep'] / xa.SI['crldist'] * xa.SI['dipFactor']
band = xa.params['Par_0']['Value']
N = xa.params['ZSampMargin']['Value'][1]
kernel = xl.hilbert_kernel(N, band)
while True:
xa.doInput()
indata = xa.Input['Input']
#
# Analytic Signal
#
ansig = np.apply_along_axis(np.convolve,
-1,
indata,
kernel,
mode="same")
sr = np.real(ansig)
si = np.imag(ansig)
#
# Compute partial derivatives
sx = xl.kroon3(sr, axis=0)[hxs, hys, :]
sy = xl.kroon3(sr, axis=1)[hxs, hys, :]
sz = xl.kroon3(sr, axis=2)[hxs, hys, :]
shx = xl.kroon3(si, axis=0)[hxs, hys, :]
shy = xl.kroon3(si, axis=1)[hxs, hys, :]
shz = xl.kroon3(si, axis=2)[hxs, hys, :]
px = sr[hxs, hys, :] * shx - si[hxs, hys, :] * sx
py = sr[hxs, hys, :] * shy - si[hxs, hys, :] * sy
pz = sr[hxs, hys, :] * shz - si[hxs, hys, :] * sz
#
# Calculate dips and output
xa.Output['Crl_dip'] = -py / pz * crlFactor
xa.Output['Inl_dip'] = -px / pz * inlFactor
xa.Output['True Dip'] = np.sqrt(
xa.Output['Crl_dip'] * xa.Output['Crl_dip'] +
xa.Output['Inl_dip'] * xa.Output['Inl_dip'])
xa.Output['Dip Azimuth'] = np.degrees(
np.arctan2(xa.Output['Inl_dip'], xa.Output['Crl_dip']))
xa.doOutput()
def doCompute(): xs = xa.SI['nrinl'] ys = xa.SI['nrcrl'] zs = min(2*int(xa.params['Par_0']['Value'])+1,3) kernel = xl.getGaussian(xs-2, ys-2, zs-2) hxs = xs//2 hys = ys//2 inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor'] crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor'] N = xa.params['ZSampMargin']['Value'][1] band = xa.params['Par_1']['Value'] hilbkernel = xl.hilbert_kernel(N, band) while True: xa.doInput() indata = xa.Input['Input'] # # Analytic Signal # ansig = np.apply_along_axis(np.convolve,-1, indata, hilbkernel, mode="same") sr = np.real(ansig) si = np.imag(ansig) # # Compute partial derivatives # sx = xl.kroon3( sr, axis=0 ) sy = xl.kroon3( sr, axis=1 ) sz = xl.kroon3( sr, axis=2 ) shx = xl.kroon3( si, axis=0 ) shy = xl.kroon3( si, axis=1 ) shz = xl.kroon3( si, axis=2 ) px = sr[1:xs-1,1:ys-1,:] * shx[1:xs-1,1:ys-1,:] - si[1:xs-1,1:ys-1,:] * sx[1:xs-1,1:ys-1,:] py = sr[1:xs-1,1:ys-1,:] * shy[1:xs-1,1:ys-1,:] - si[1:xs-1,1:ys-1,:] * sy[1:xs-1,1:ys-1,:] pz = sr[1:xs-1,1:ys-1,:] * shz[1:xs-1,1:ys-1,:] - si[1:xs-1,1:ys-1,:] * sz[1:xs-1,1:ys-1,:] # # Inner product of gradients px2 = px * px py2 = py * py pz2 = pz * pz pxpy = px * py pxpz = px * pz pypz = py * pz # # Outer smoothing rgx2 = xl.sconvolve(px2, kernel) rgy2 = xl.sconvolve(py2, kernel) rgz2 = xl.sconvolve(pz2, kernel) rgxgy = xl.sconvolve(pxpy, kernel) rgxgz = xl.sconvolve(pxpz, kernel) rgygz = xl.sconvolve(pypz, kernel) # # Form the structure tensor T = np.rollaxis(np.array([ [rgx2, rgxgy, rgxgz], [rgxgy, rgy2, rgygz], [rgxgz, rgygz, rgz2 ]]), 2) # # Get the eigenvalues and eigen vectors and calculate the dips evals, evecs = np.linalg.eigh(T) ndx = evals.argsort() evec = evecs[np.arange(0,T.shape[0],1),:,ndx[:,2]] e1 = evals[np.arange(0,T.shape[0],1),ndx[:,2]] e2 = evals[np.arange(0,T.shape[0],1),ndx[:,1]] xa.Output['Crl_dip'] = -evec[:,1]/evec[:,2]*crlFactor xa.Output['Inl_dip'] = -evec[:,0]/evec[:,2]*inlFactor xa.Output['True Dip'] = np.sqrt(xa.Output['Crl_dip']*xa.Output['Crl_dip']+xa.Output['Inl_dip']*xa.Output['Inl_dip']) xa.Output['Dip Azimuth'] = np.degrees(np.arctan2(xa.Output['Inl_dip'],xa.Output['Crl_dip'])) xa.Output['Cplane'] = (e1-e2)/(e1+e2) xa.doOutput()