def doCompute():
xs = xa.SI['nrinl']
ys = xa.SI['nrcrl']
zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
wf = xa.params['Par_0']['Value']
kernel = lpa3D_init(xs, ys, zs, wf)
gam = 1 / (8 * ((min(xs, ys, zs) - 1) * wf)**2)
while True:
xa.doInput()
r = np.zeros((10, xa.TI['nrsamp']))
for i in range(0, 10):
r[i, :] = xl.sconvolve(xa.Input, kernel[i])
A = np.rollaxis(
np.array([[r[4], r[7] / 2, r[8] / 2], [r[7] / 2, r[5], r[9] / 2],
[r[8] / 2, r[9] / 2, r[6]]]), 2)
AAT = np.einsum('...ij,...jk->...ik', A, np.swapaxes(A, 1, 2))
B = np.rollaxis(np.array([[r[1]], [r[2]], [r[3]]]), 2)
BBT = np.einsum('...ij,...jk->...ik', B, np.swapaxes(B, 1, 2))
T = AAT + gam * BBT
w = np.linalg.eigvalsh(T)
v = np.rollaxis(np.sort(w), 1)
xa.Output['e1'] = v[2, :]
xa.Output['e2'] = v[1, :]
xa.Output['e3'] = v[0, :]
xa.doOutput()
def doCompute(): # # Compute the filter kernel # nil = xa.SI['nrinl'] nxl = xa.SI['nrcrl'] centre_trace_x = nil//2 centre_trace_y = nxl//2 freq = xa.params['Par_0']['Value'] type = xa.params['Select']['Selection'] kernelFunc = lpKernel if type==0 else hpKernel if type==1 else bpKernel if type==2 else brKernel kernel = np.zeros((nil,nil,1)) N = nil//2 if (N%2 == 0): N=N-1 kernel[1:2*N+2,1:2*N+2,1] = kernelFunc(N, freq) else: kernel = kernelFunc(N, freq) # # This is the trace processing loop # while True: xa.doInput() # # Get the input # indata = xa.Input['Input'] # # Apply the kernel # outdata = np.sum(kernel * indata, axis=(0,1)) #------------------------------------------------------------------------------------ # xa.Output = outdata 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'] while True: xa.doInput() s = xa.Input['Input'] sh = np.imag( hilbert(s) ) # # Compute partial derivatives sx = xl.kroon3( s, axis=0 )[hxs,hys,:] sy = xl.kroon3( s, axis=1 )[hxs,hys,:] sz = xl.kroon3( s, axis=2 )[hxs,hys,:] shx = xl.kroon3( sh, axis=0 )[hxs,hys,:] shy = xl.kroon3( sh, axis=1 )[hxs,hys,:] shz = xl.kroon3( sh, axis=2 )[hxs,hys,:] px = s[hxs,hys,:] * shx - sh[hxs,hys,:] * sx py = s[hxs,hys,:] * shy - sh[hxs,hys,:] * sy pz = s[hxs,hys,:] * shz - sh[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():
inlFactor = xa.SI['zstep'] / xa.SI['inldist'] * xa.SI['dipFactor']
crlFactor = xa.SI['zstep'] / xa.SI['crldist'] * xa.SI['dipFactor']
zw = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
filt = xa.params['Select']['Selection']
filtFunc = jit(xl.vecmean) if filt == 0 else jit(
xl.vmf_l1) if filt == 1 else jit(xl.vmf_l2)
while True:
xa.doInput()
dx = -xa.Input['Inl_dip'] / inlFactor
dy = -xa.Input['Crl_dip'] / crlFactor
dz = np.ones(dx.shape)
s = np.sqrt(dx * dx + dy * dy + dz * dz)
#
# Apply the Vector Filter
out = np.empty((3, xa.TI['nrsamp']))
xl.vecFilter(np.array([dx / s, dy / s, dz / s]), 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():
xs = xa.SI['nrinl']
ys = xa.SI['nrcrl']
zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1
wf = xa.params['Par_0']['Value']
kernel = lpa3D_init(xs, ys, zs, wf)
gam = 1/(8*((min(xs,ys,zs)-1)*wf)**2)
while True:
xa.doInput()
r = np.zeros((10,xa.TI['nrsamp']))
for i in range(0,10):
r[i,:] = xl.sconvolve(xa.Input,kernel[i])
A = np.rollaxis(np.array([[r[4],r[7]/2, r[8]/2], [r[7]/2, r[5], r[9]/2], [r[8]/2, r[9]/2, r[6]]]),2)
AAT = np.einsum('...ij,...jk->...ik', A, np.swapaxes(A,1,2))
B = np.rollaxis(np.array([[r[1]],[r[2]],[r[3]]]),2)
BBT = np.einsum('...ij,...jk->...ik', B, np.swapaxes(B,1,2))
T = AAT+gam*BBT
p = np.rollaxis(T,0,3)
xa.Output['t1x'] = p[0,0,:]*np.sign(p[0,2,:])
xa.Output['t1y'] = p[0,1,:]*np.sign(p[0,2,:])
xa.Output['t1z'] = p[0,2,:]*np.sign(p[0,2,:])
xa.Output['t2x'] = p[1,0,:]*np.sign(p[1,2,:])
xa.Output['t2y'] = p[1,1,:]*np.sign(p[1,2,:])
xa.Output['t2z'] = p[1,2,:]*np.sign(p[1,2,:])
xa.Output['t3x'] = p[2,0,:]*np.sign(p[2,2,:])
xa.Output['t3y'] = p[2,1,:]*np.sign(p[2,2,:])
xa.Output['t3z'] = p[2,2,:]*np.sign(p[2,2,:])
xa.doOutput()
def doCompute():
xs = xa.SI["nrinl"]
ys = xa.SI["nrcrl"]
zs = xa.params["ZSampMargin"]["Value"][1] - xa.params["ZSampMargin"]["Value"][0] + 1
wf = xa.params["Par_0"]["Value"]
kernel = lpa3D_init(xs, ys, zs, wf)
gam = 1 / (8 * ((min(xs, ys, zs) - 1) * wf) ** 2)
while True:
xa.doInput()
r = np.zeros((10, xa.TI["nrsamp"]))
for i in range(0, 10):
r[i, :] = xl.sconvolve(xa.Input, kernel[i])
A = np.rollaxis(
np.array([[r[4], r[7] / 2, r[8] / 2], [r[7] / 2, r[5], r[9] / 2], [r[8] / 2, r[9] / 2, r[6]]]), 2
)
AAT = np.einsum("...ij,...jk->...ik", A, np.swapaxes(A, 1, 2))
B = np.rollaxis(np.array([[r[1]], [r[2]], [r[3]]]), 2)
BBT = np.einsum("...ij,...jk->...ik", B, np.swapaxes(B, 1, 2))
T = AAT + gam * BBT
w = np.linalg.eigvalsh(T)
v = np.rollaxis(np.sort(w), 1)
xa.Output["e1"] = v[2, :]
xa.Output["e2"] = v[1, :]
xa.Output["e3"] = v[0, :]
xa.doOutput()
def doCompute():
near_ang = xa.params['Par_0']['Value']
mid_ang = xa.params['Par_1']['Value']
far_ang = xa.params['Par_2']['Value']
ufar_ang = xa.params['Par_3']['Value']
angs = np.array([near_ang, mid_ang, far_ang, ufar_ang])
while True:
xa.doInput()
near = xa.Input['Near'][0, 0, :]
mid = xa.Input['Mid'][0, 0, :]
far = xa.Input['Far'][0, 0, :]
ufar = xa.Input['UltraFar'][0, 0, :]
refl = np.array([near, mid, far, ufar])
ns = near.shape[0]
G = np.zeros(ns)
I = np.zeros(ns)
Q = np.zeros(ns)
avo_IG_numpy(refl, angs, I, G, Q)
#
# Get the output
xa.Output['Intercept'] = I
xa.Output['Gradient'] = G
xa.Output['Quality'] = Q
xa.doOutput()
def doCompute():
xs = xa.SI['nrinl']
ys = xa.SI['nrcrl']
zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1
wf = xa.params['Par_0']['Value']
maxDip = xa.params['Par_1']['Value']
kernel = lpa3D_init(xs, ys, zs, wf)
gam = 1/(8*((min(xs,ys,zs)-1)*wf)**2)
inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor']
crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor']
smb,sma = butter(4, 0.3, btype='lowpass', analog=False)
while True:
xa.doInput()
r = np.zeros((10,xa.TI['nrsamp']))
for i in range(0,10):
r[i,:] = xl.sconvolve(xa.Input,kernel[i])
A = np.rollaxis(np.array([[r[4],r[7]/2, r[8]/2], [r[7]/2, r[5], r[9]/2], [r[8]/2, r[9]/2, r[6]]]),2)
AAT = np.einsum('...ij,...jk->...ik', A, np.swapaxes(A,1,2))
B = np.rollaxis(np.array([[r[1]],[r[2]],[r[3]]]),2)
BBT = np.einsum('...ij,...jk->...ik', B, np.swapaxes(B,1,2))
T = AAT+gam*BBT
evals, evecs = np.linalg.eigh(T)
ndx = evals.argsort()[:,-1]
evecs = evecs[np.arange(0,T.shape[0],1),:,ndx]
dip = -evecs[:,1]/evecs[:,2]*crlFactor
dip[dip>maxDip] = 0.0
dip[dip<-maxDip] = 0.0
xa.Output['Crl_dip'] = filtfilt(smb, sma, dip)
dip = -evecs[:,0]/evecs[:,2]*inlFactor
dip[dip>maxDip] = 0.0
dip[dip<-maxDip] = 0.0
xa.Output['Inl_dip'] = filtfilt(smb, sma, dip)
xa.doOutput()
def doCompute():
xs = xa.SI['nrinl']
ys = xa.SI['nrcrl']
zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
wf = xa.params['Par_0']['Value']
kernel = lpa3D_init(xs, ys, zs, wf)
gam = 1 / (8 * ((min(xs, ys, zs) - 1) * wf)**2)
while True:
xa.doInput()
r = np.zeros((10, xa.TI['nrsamp']))
for i in range(0, 10):
r[i, :] = xl.sconvolve(xa.Input, kernel[i])
A = np.rollaxis(
np.array([[r[4], r[7] / 2, r[8] / 2], [r[7] / 2, r[5], r[9] / 2],
[r[8] / 2, r[9] / 2, r[6]]]), 2)
AAT = np.einsum('...ij,...jk->...ik', A, np.swapaxes(A, 1, 2))
B = np.rollaxis(np.array([[r[1]], [r[2]], [r[3]]]), 2)
BBT = np.einsum('...ij,...jk->...ik', B, np.swapaxes(B, 1, 2))
T = AAT + gam * BBT
p = np.rollaxis(T, 0, 3)
xa.Output['t1x'] = p[0, 0, :] * np.sign(p[0, 2, :])
xa.Output['t1y'] = p[0, 1, :] * np.sign(p[0, 2, :])
xa.Output['t1z'] = p[0, 2, :] * np.sign(p[0, 2, :])
xa.Output['t2x'] = p[1, 0, :] * np.sign(p[1, 2, :])
xa.Output['t2y'] = p[1, 1, :] * np.sign(p[1, 2, :])
xa.Output['t2z'] = p[1, 2, :] * np.sign(p[1, 2, :])
xa.Output['t3x'] = p[2, 0, :] * np.sign(p[2, 2, :])
xa.Output['t3y'] = p[2, 1, :] * np.sign(p[2, 2, :])
xa.Output['t3z'] = p[2, 2, :] * np.sign(p[2, 2, :])
xa.doOutput()
def doCompute(): # # Compute the filter kernel # nil = xa.SI['nrinl'] nxl = xa.SI['nrcrl'] centre_trace_x = nil//2 centre_trace_y = nxl//2 freq = xa.params['Par_0']['Value'] type = xa.params['Select']['Selection'] kernelFunc = lpKernel if type==0 else hpKernel if type==1 else bpKernel if type==2 else brKernel kernel = np.zeros((nil,nil,1)) N = nil//2 if (N%2 == 0): N=N-1 kernel[1:2*N+2,1:2*N+2,1] = kernelFunc(N, freq) else: kernel = kernelFunc(N, freq) # # This is the trace processing loop # while True: xa.doInput() # # Get the input # indata = xa.Input['Input'] # # Apply the kernel # outdata = np.sum(kernel * indata, axis=(0,1)) #------------------------------------------------------------------------------------ # xa.Output = outdata xa.doOutput()
def doCompute():
near_ang = xa.params['Par_0']['Value']
mid_ang = xa.params['Par_1']['Value']
far_ang = xa.params['Par_2']['Value']
ufar_ang = xa.params['Par_3']['Value']
angs = np.array([near_ang, mid_ang, far_ang, ufar_ang])
while True:
xa.doInput()
near = xa.Input['Near'][0,0,:]
mid = xa.Input['Mid'][0,0,:]
far = xa.Input['Far'][0,0,:]
ufar = xa.Input['UltraFar'][0,0,:]
refl = np.array([near,mid,far,ufar])
ns = near.shape[0]
G=np.zeros(ns)
I=np.zeros(ns)
Q=np.zeros(ns)
avo_IG_numpy( refl, angs, I, G, Q)
#
# Get the output
xa.Output['Intercept'] = I
xa.Output['Gradient'] = G
xa.Output['Quality'] = Q
xa.doOutput()
def doCompute(): inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor'] crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor'] while True: xa.doInput() gx = xa.Input['In-line gradient'][0,0,:] gy = xa.Input['Cross-line gradient'][0,0,:] gz = xa.Input['Z gradient'][0,0,:] # # Inner product of gradients gx2 = gx * gx gy2 = gy * gy gz2 = gz * gz gxgy = gx * gy gxgz = gx * gz gygz = gy * gz # # Form the structure tensor T = np.rollaxis(np.array([ [gx2, gxgy, gxgz], [gxgy, gy2, gygz], [gxgz, gygz, gz2 ]]), 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[:,-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.doOutput()
def doCompute():
endpoint_name = "endpoint-from-saved-model"
predictor = TensorFlowPredictor(endpoint_name)
myfile = open(
r"C:\Users\Polash-Dell\coder_guy\PycharmProjects\aws_dl\1_aws_dl.txt",
'w+')
myfile.write("Norm Amp : Porosity")
while True:
xa.doInput()
x_test_list = xa.Input['Input'][0, 0, :].tolist()
x_test = pd.DataFrame(x_test_list)
stats = x_test.describe()
stats = stats.transpose()
x_test = (x_test - stats['mean']) / stats['std']
for a in x_test.values:
myfile.write(str(a) + ": ")
try:
test_predictions = predictor.predict(a)
myfile.write(str(test_predictions['predictions'][0][0]) + "\n")
except:
e = sys.exc_info()[0]
myfile.write("\nError:" + str(e))
xa.Output['Output'] = xa.Input['Input']
xa.doOutput()
myfile.close()
def doCompute():
#
# Initialise some constants from the attribute parameters or the SeismicInfo, xa.SI, array for use in the calculations
# These are just some examples - keep/add what you need
#
nyquist = 1.0 / (2.0 * xa.SI['zstep'])
par0 = xa.params['Par_0']['Value']
zw = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
select = xa.params['Select']['Selection']
#
# This is the trace processing loop
#
while True:
xa.doInput()
#
# After doInput the TraceInfo, xa.TI, array contains information specific to this trace segment - keep what you need
#
number_of_samples = xa.TI['nrsamp']
start_time = xa.TI['z0']
current_inline = xa.TI['inl']
current_crossline = xa.TI['crl']
#
# Get the input
#
indata = xa.Input['Input'][0, 0, :]
#
# Your attribute calculation goes here
#
outdata = indata
#------------------------------------------------------------------------------------
#
xa.Output = outdata
xa.doOutput()
def doCompute(): dz = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 kernel = lpa3D_init(xa.SI['nrinl'], xa.SI['nrcrl'], dz, xa.params['Par_0']['Value'])[0] while True: xa.doInput() xa.Output = xl.sconvolve(xa.Input, kernel) xa.doOutput()
def doCompute(): # # Initialise some constants from the attribute parameters or the SeismicInfo, xa.SI, array for use in the calculations # These are just some examples - keep/add what you need # nyquist = 1.0/(2.0*xa.SI['zstep']) par0 = xa.params['Par_0']['Value'] zw = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 select = xa.params['Select']['Selection'] # # This is the trace processing loop # while True: xa.doInput() # # After doInput the TraceInfo, xa.TI, array contains information specific to this trace segment - keep what you need # number_of_samples = xa.TI['nrsamp'] start_time = xa.TI['z0'] current_inline = xa.TI['inl'] current_crossline = xa.TI['crl'] # # Get the input # indata = xa.Input['Input'][0,0,:] # # Your attribute calculation goes here # outdata = indata #------------------------------------------------------------------------------------ # xa.Output = outdata xa.doOutput()
def doCompute():
xs = xa.SI['nrinl']
ys = xa.SI['nrcrl']
zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1
wf = xa.params['Par_0']['Value']
kernel = lpa3D_init(xs, ys, zs, wf)
gam = 1/(8*((min(xs,ys,zs)-1)*wf)**2)
while True:
xa.doInput()
r = np.zeros((10,xa.TI['nrsamp']))
for i in range(0,10):
r[i,:] = xl.sconvolve(xa.Input,kernel[i])
A = np.rollaxis(np.array([[r[4],r[7]/2, r[8]/2], [r[7]/2, r[5], r[9]/2], [r[8]/2, r[9]/2, r[6]]]),2)
AAT = np.einsum('...ij,...jk->...ik', A, np.swapaxes(A,1,2))
B = np.rollaxis(np.array([[r[1]],[r[2]],[r[3]]]),2)
BBT = np.einsum('...ij,...jk->...ik', B, np.swapaxes(B,1,2))
T = AAT+gam*BBT
w = np.linalg.eigvalsh(T)
v=np.rollaxis(np.sort(w),1)
e1 = v[2,:]
e2 = v[1,:]
e3 = v[0,:]
e1me2 = e1-e2
e1me3 = e1-e3
e2me3 = e2-e3
e1pe3 = e1+e3
xa.Output['Cline'] = e2me3/(e2+e3)
xa.Output['Cplane'] = e1me2/(e1+e2)
xa.Output['Cfault'] = xa.Output['Cline']*(1.0 - xa.Output['Cplane'])
xa.Output['Cchaos'] = 4.0 * e1 * e3 * (e1 + e2 + e3)/(e1pe3*e1pe3)
xa.Output['Ctype'] = (e1me2*e1me2 + e1me3*e1me3 + e2me3*e2me3)/(e1*e1 + e2*e2 + e3*e3)
xa.doOutput()
def doCompute():
inlpos = xa.SI['nrinl'] // 2
crlpos = xa.SI['nrcrl'] // 2
filt = xa.params['Select']['Selection']
while True:
xa.doInput()
indata = xa.Input['Input']
if filt == 0:
xa.Output['In-line gradient'] = xl.scharr3_dx(indata, full=False)
xa.Output['Cross-line gradient'] = xl.scharr3_dy(indata,
full=False)
xa.Output['Z gradient'] = xl.scharr3_dz(indata, full=False)
else:
xa.Output['In-line gradient'] = xl.kroon3(indata,
axis=0)[inlpos,
crlpos, :]
xa.Output['Cross-line gradient'] = xl.kroon3(indata,
axis=1)[inlpos,
crlpos, :]
xa.Output['Z gradient'] = xl.kroon3(indata, axis=-1)[inlpos,
crlpos, :]
xa.Output['Average Gradient'] = (xa.Output['In-line gradient'] +
xa.Output['Cross-line gradient'] +
xa.Output['Z gradient']) / 3
xa.doOutput()
def doCompute():
while True:
xa.doInput()
#
# Get the output
xa.Output['Output'] = xa.Input['Input'] * 5
xa.doOutput()
def doCompute(): zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 kernel = lpa3D_init(xa.SI['nrinl'], xa.SI['nrcrl'], zs, xa.params['Par_0']['Value']) while True: xa.doInput() for i in range(0,10): xa.Output['r'+str(i)] = xl.sconvolve(xa.Input,kernel[i]) xa.doOutput()
def doCompute():
while True:
xa.doInput()
mylist=xa.Input['Input'][0, 0, :].tolist()
myfile = open(r"C:\Users\Polash-Dell\coder_guy\PycharmProjects\snippets\123pol.txt", 'a')
myfile.write(str(xa.TI['z0'])+"\n"+str(mylist)+"\n")
myfile.close()
xa.doOutput()
def doCompute(): while True: xa.doInput() xa.Output['A'] = xa.Input['A'] xa.Output['B'] = xa.Input['B'] xa.Output['C'] = xa.Input['C'] xa.Output['A+B-2C'] = xa.Input['A'] + xa.Input['B'] - 2.0 * xa.Input['C'] xa.doOutput()
def doCompute():
dz = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
kernel = lpa3D_init(xa.SI['nrinl'], xa.SI['nrcrl'], dz,
xa.params['Par_0']['Value'])[0]
while True:
xa.doInput()
xa.Output = xl.sconvolve(xa.Input['Input'], kernel)
xa.doOutput()
def doCompute(): order = xa.params['Par_1']['Value'] nyquist = 1.0/(2.0*xa.SI['zstep']) cutoff = xa.params['Par_0']['Value']/nyquist b, a = sig.butter(order, cutoff, 'low', analog=False) while True: xa.doInput() xa.Output = sig.filtfilt(b, a, xa.Input[0,0,:], padtype=None, padlen=0) xa.doOutput()
def doCompute(): hxs = xa.SI['nrinl']//2 hys = xa.SI['nrcrl']//2 while True: xa.doInput() xa.Output['A'] = np.mean(xa.Input['A'],axis=(0,1)) xa.Output['B'] = np.mean(xa.Input['B'],axis=(0,1)) xa.Output['C'] = np.mean(xa.Input['C'],axis=(0,1)) xa.Output['A+B-2C'] = xa.Input['A'][hxs,hys,:] + xa.Input['B'][hxs,hys,:] - 2.0 * xa.Input['C'][hxs,hys,:] xa.doOutput()
def doCompute():
#
# index of current trace position in Input numpy array
#
ilndx = xa.SI['nrinl'] // 2
crldx = xa.SI['nrcrl'] // 2
while True:
xa.doInput()
xa.Output = xa.Input[ilndx, crldx, :]
xa.doOutput()
def doCompute(): # # index of current trace position in Input numpy array # ilndx = xa.SI['nrinl']//2 crldx = xa.SI['nrcrl']//2 while True: xa.doInput() xa.Output = xa.Input[ilndx,crldx,:] xa.doOutput()
def doCompute():
zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
kernel = lpa3D_init(xa.SI['nrinl'], xa.SI['nrcrl'], zs,
xa.params['Par_0']['Value'])
while True:
xa.doInput()
for i in range(0, 10):
xa.Output['r' + str(i)] = xl.sconvolve(xa.Input, kernel[i])
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']
while True:
xa.doInput()
U = -xa.Input['Inl_dip'] / inlFactor
V = -xa.Input['Crl_dip'] / crlFactor
W = np.ones(U.shape)
#
# Calculate the derivatives of the components and some intermediate results
ux = xl.kroon3(U, axis=0)[hxs, hys, :]
uy = xl.kroon3(U, axis=1)[hxs, hys, :]
uz = xl.kroon3(U, axis=2)[hxs, hys, :]
vx = xl.kroon3(V, axis=0)[hxs, hys, :]
vy = xl.kroon3(V, axis=1)[hxs, hys, :]
vz = xl.kroon3(V, axis=2)[hxs, hys, :]
wx = xl.kroon3(W, axis=0)[hxs, hys, :]
wy = xl.kroon3(W, axis=1)[hxs, hys, :]
wz = xl.kroon3(W, axis=2)[hxs, hys, :]
u = U[hxs, hys, :]
v = V[hxs, hys, :]
w = W[hxs, hys, :]
uv = u * v
vw = v * w
u2 = u * u
v2 = v * v
w2 = w * w
u2pv2 = u2 + v2
v2pw2 = v2 + w2
s = np.sqrt(u2pv2 + w2)
#
# Compute the basic measures of Gauss's theory of surfaces
E = np.ones(u.shape)
F = -(u * w) / (np.sqrt(u2pv2) * np.sqrt(v2pw2))
G = np.ones(u.shape)
D = -(-uv * vx + u2 * vy + v2 * ux - uv * uy) / (u2pv2 * s)
Di = -(vw * (uy + vx) - 2 * u * w * vy - v2 * (uz + wx) + uv *
(vz + wy)) / (2 * np.sqrt(u2pv2) * np.sqrt(v2pw2) * s)
Dii = -(-vw * wy + v2 * wz + w2 * vy - vw * vz) / (v2pw2 * s)
H = (E * Dii - 2 * F * Di + G * D) / (2 * (E * G - F * F))
K = (D * Dii - Di * Di) / (E * G - F * F)
Kmin = H - np.sqrt(H * H - K)
Kmax = H + np.sqrt(H * H - K)
#
# Get the output
xa.Output['Mean curvature'] = H
xa.Output['Gaussian curvature'] = K
xa.Output['Maximum curvature'] = Kmax
xa.Output['Minimum curvature'] = Kmin
xa.Output['Most pos curvature'] = np.maximum(Kmax, Kmin)
xa.Output['Most neg curvature'] = np.minimum(Kmax, Kmin)
xa.doOutput()
def doCompute():
xs = xa.SI['nrinl']
ys = xa.SI['nrcrl']
zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
kernel = xl.getGaussian(xs - 4, ys - 4, zs - 4)
inlFactor = xa.SI['zstep'] / xa.SI['inldist'] * xa.SI['dipFactor']
crlFactor = xa.SI['zstep'] / xa.SI['crldist'] * xa.SI['dipFactor']
while True:
xa.doInput()
g = xa.Input['Input']
#
# Compute gradients
gx = xl.farid5(g, axis=0)
gy = xl.farid5(g, axis=1)
gz = xl.farid5(g, axis=2)
#
# Inner product of gradients
gx2 = gx[2:xs - 2, 2:ys - 2, :] * gx[2:xs - 2, 2:ys - 2, :]
gy2 = gy[2:xs - 2, 2:ys - 2, :] * gy[2:xs - 2, 2:ys - 2, :]
gz2 = gz[2:xs - 2, 2:ys - 2, :] * gz[2:xs - 2, 2:ys - 2, :]
gxgy = gx[2:xs - 2, 2:ys - 2, :] * gy[2:xs - 2, 2:ys - 2, :]
gxgz = gx[2:xs - 2, 2:ys - 2, :] * gz[2:xs - 2, 2:ys - 2, :]
gygz = gy[2:xs - 2, 2:ys - 2, :] * gz[2:xs - 2, 2:ys - 2, :]
#
# Outer gaussian smoothing
rgx2 = xl.sconvolve(gx2, kernel)
rgy2 = xl.sconvolve(gy2, kernel)
rgz2 = xl.sconvolve(gz2, kernel)
rgxgy = xl.sconvolve(gxgy, kernel)
rgxgz = xl.sconvolve(gxgz, kernel)
rgygz = xl.sconvolve(gygz, 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()
evecs = 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'] = -evecs[:, 1] / evecs[:, 2] * crlFactor
xa.Output['Inl_dip'] = -evecs[:, 0] / evecs[:, 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']))
coh = (e1 - e2) / (e1 + e2)
xa.Output['Coherency'] = coh * coh
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'] while True: xa.doInput() U = -xa.Input['Inl_dip']/inlFactor V = -xa.Input['Crl_dip']/crlFactor W = np.ones(U.shape) # # Calculate the derivatives of the components and some intermediate results ux = xl.kroon3( U, axis=0 )[hxs,hys,:] uy = xl.kroon3( U, axis=1 )[hxs,hys,:] uz = xl.kroon3( U, axis=2 )[hxs,hys,:] vx = xl.kroon3( V, axis=0 )[hxs,hys,:] vy = xl.kroon3( V, axis=1 )[hxs,hys,:] vz = xl.kroon3( V, axis=2 )[hxs,hys,:] wx = xl.kroon3( W, axis=0 )[hxs,hys,:] wy = xl.kroon3( W, axis=1 )[hxs,hys,:] wz = xl.kroon3( W, axis=2 )[hxs,hys,:] u = U[hxs,hys,:] v = V[hxs,hys,:] w = W[hxs,hys,:] uv = u*v vw = v*w u2 = u*u v2 = v*v w2 = w*w u2pv2 = u2+v2 v2pw2 = v2+w2 s = np.sqrt(u2pv2+w2) # # Compute the basic measures of Gauss's theory of surfaces E = np.ones(u.shape) F = -(u*w)/(np.sqrt(u2pv2)*np.sqrt(v2pw2)) G = np.ones(u.shape) D = -(-uv*vx+u2*vy+v2*ux-uv*uy)/(u2pv2*s) Di = -(vw*(uy+vx)-2*u*w*vy-v2*(uz+wx)+uv*(vz+wy))/(2*np.sqrt(u2pv2)*np.sqrt(v2pw2)*s) Dii = -(-vw*wy+v2*wz+w2*vy-vw*vz)/(v2pw2*s) H = (E*Dii - 2*F*Di + G*D)/(2*(E*G - F*F)) K = (D*Dii - Di*Di)/(E*G - F*F) Kmin = H - np.sqrt(H*H - K) Kmax = H + np.sqrt(H*H - K) # # Get the output xa.Output['Mean curvature'] = H xa.Output['Gaussian curvature'] = K xa.Output['Maximum curvature'] = Kmax xa.Output['Minimum curvature'] = Kmin xa.Output['Most pos curvature'] = np.maximum(Kmax,Kmin) xa.Output['Most neg curvature'] = np.minimum(Kmax,Kmin) xa.doOutput()
def doCompute(): inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor'] crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor'] while True: xa.doInput() # # Get the output xa.Output['True Dip'] = np.sqrt(xa.Input['Crl_dip']*xa.Input['Crl_dip']+xa.Input['Inl_dip']*xa.Input['Inl_dip']) xa.Output['Dip Azimuth'] = np.degrees(np.arctan2(xa.Input['Inl_dip'],xa.Input['Crl_dip'])) xa.doOutput()
def doCompute():
hxs = xa.SI['nrinl'] // 2
hys = xa.SI['nrcrl'] // 2
while True:
xa.doInput()
xa.Output['A'] = np.mean(xa.Input['A'], axis=(0, 1))
xa.Output['B'] = np.mean(xa.Input['B'], axis=(0, 1))
xa.Output['C'] = np.mean(xa.Input['C'], axis=(0, 1))
xa.Output['A+B-2C'] = xa.Input['A'][hxs, hys, :] + xa.Input['B'][
hxs, hys, :] - 2.0 * xa.Input['C'][hxs, hys, :]
xa.doOutput()
def doCompute():
while True:
xa.doInput()
inp = xa.Input['Input'][0, 0, :]
outp = np.zeros(inp.shape)
response(inp, outp)
#
# Get the output
xa.Output = outp
xa.doOutput()
def doCompute(): # # index of current trace position in Input numpy array # ilndx = xa.SI['nrinl']//2 crldx = xa.SI['nrcrl']//2 dz = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 kernel = lpa3D_smooth(xa.SI['nrinl'], xa.SI['nrcrl'], dz, xa.params['Par_0']['Value']) while True: xa.doInput() xa.Output = convolve(xa.Input, kernel)[ilndx,crldx,:] xa.doOutput()
def doCompute(): while True: xa.doInput() inp = xa.Input['Input'][0,0,:] outp = np.zeros(inp.shape) response( inp, outp) # # Get the output xa.Output = outp xa.doOutput()
def doCompute(): xs = xa.SI['nrinl'] ys = xa.SI['nrcrl'] zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 kernel = xl.getGaussian(xs, ys, zs) while True: xa.doInput() gx = xa.Input['In-line gradient'] gy = xa.Input['Cross-line gradient'] gz = xa.Input['Z gradient'] # # Inner product of gradients gx2 = gx * gx gy2 = gy * gy gz2 = gz * gz gxgy = gx * gy gxgz = gx * gz gygz = gy * gz # # Outer gaussian smoothing rgx2 = xl.sconvolve(gx2, kernel) rgy2 = xl.sconvolve(gy2, kernel) rgz2 = xl.sconvolve(gz2, kernel) rgxgy = xl.sconvolve(gxgy, kernel) rgxgz = xl.sconvolve(gxgz, kernel) rgygz = xl.sconvolve(gygz, kernel) # # Form the structure tensor T = np.rollaxis(np.array([ [rgx2, rgxgy, rgxgz], [rgxgy, rgy2, rgygz], [rgxgz, rgygz, rgz2 ]]), 2) # # Get the eigenvalues w = np.linalg.eigvalsh(T) v = np.rollaxis(np.sort(w),1) e1 = v[2,:] e2 = v[1,:] e3 = v[0,:] # # Calculate the attributes e1me2 = e1-e2 e1me3 = e1-e3 e2me3 = e2-e3 e1pe3 = e1+e3 xa.Output['Cline'] = e2me3/(e2+e3) xa.Output['Cplane'] = e1me2/(e1+e2) xa.Output['Cfault'] = xa.Output['Cline']*(1.0 - xa.Output['Cplane']) xa.Output['Cchaos'] = 4.0 * e1 * e3 * (e1 + e2 + e3)/(e1pe3*e1pe3) xa.Output['Ctype'] = (e1me2*e1me2 + e1me3*e1me3 + e2me3*e2me3)/(e1*e1 + e2*e2 + e3*e3) xa.doOutput()
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():
#
# Initialise some constants from the attribute parameters or the SeismicInfo, xa.SI, array for use in the calculations
# These are just some examples - keep/add what you need
#
number_inline = xa.SI['nrinl']
number_xline = xa.SI['nrcrl']
centre_trace_x = xa.SI['nrinl'] // 2
centre_trace_y = xa.SI['nrcrl'] // 2
nyquist = 1.0 / (2.0 * xa.SI['zstep'])
par0 = xa.params['Par_0']['Value']
zw = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
select = xa.params['Select']['Selection']
#
# This is the trace processing loop
#
while True:
xa.doInput()
#
# After doInput the TraceInfo, xa.TI, array contains information specific to this trace segment - keep what you need
#
number_of_samples = xa.TI['nrsamp']
start_time = xa.TI['z0']
current_inline = xa.TI['inl']
current_crossline = xa.TI['crl']
#
# Get the input
#
indata = xa.Input['Input']
#
# Your attribute calculation goes here
#
# Warning Python loops can be slow - this is contrived just to show how to access traces in the analysis data cube
#
outdata = np.zeros(number_of_samples)
for inline in range(number_inline):
for xline in range(number_xline):
if (inline != centre_trace_x and xline != centre_trace_y):
outdata += indata[inline, xline, :]
out_1 = indata[centre_trace_x, centre_trace_y, :]
out_2 = outdata
out_3 = outdata / (number_inline * number_xline - 1)
#------------------------------------------------------------------------------------
#
# Replace Output1-Output3 with the names in the xa.params Output array
#
xa.Output['Output1'] = out_1
xa.Output['Output2'] = out_2
xa.Output['Output3'] = out_3
xa.doOutput()
def doCompute():
endpoint_name = "endpoint-from-saved-model"
predictor = TensorFlowPredictor(endpoint_name)
while True:
xa.doInput()
x_test_list = xa.Input['Input'][0, 0, :].tolist()
x_test = pd.DataFrame(x_test_list)
stats = x_test.describe()
stats = stats.transpose()
x_test = (x_test - stats['mean']) / stats['std']
y_test = predictor.predict(np.array(x_test))
xa.Output['Output'] = np.array(y_test['predictions'])
xa.doOutput()
def doCompute():
order = xa.params['Par_1']['Value']
nyquist = 1.0 / (2.0 * xa.SI['zstep'])
cutoff = xa.params['Par_0']['Value'] / nyquist
b, a = sig.butter(order, cutoff, 'low', analog=False)
while True:
xa.doInput()
xa.Output = sig.filtfilt(b,
a,
xa.Input[0, 0, :],
padtype=None,
padlen=0)
xa.doOutput()
def doCompute():
xa.doInput()
mylist = xa.Input['Input'][0, 0, :].tolist()
myz0 = xa.TI['z0']
myfile = open(
r"C:\Users\Polash-Dell\coder_guy\PycharmProjects\snippets\321pol.csv",
'a')
for samp in mylist:
myfile.write(str(myz0) + ", " + str(samp) + "\n")
myz0 = myz0 + 1
myfile.close()
xa.doOutput()
def doCompute():
xs = xa.SI["nrinl"]
ys = xa.SI["nrcrl"]
zs = xa.params["ZSampMargin"]["Value"][1] - xa.params["ZSampMargin"]["Value"][0] + 1
kernel = xl.getGaussian(xs - 4, ys - 4, zs - 4)
inlFactor = xa.SI["zstep"] / xa.SI["inldist"] * xa.SI["dipFactor"]
crlFactor = xa.SI["zstep"] / xa.SI["crldist"] * xa.SI["dipFactor"]
while True:
xa.doInput()
g = xa.Input["Input"]
#
# Compute gradients
gx = xl.farid5(g, axis=0)
gy = xl.farid5(g, axis=1)
gz = xl.farid5(g, axis=2)
#
# Inner product of gradients
gx2 = gx[2 : xs - 2, 2 : ys - 2, :] * gx[2 : xs - 2, 2 : ys - 2, :]
gy2 = gy[2 : xs - 2, 2 : ys - 2, :] * gy[2 : xs - 2, 2 : ys - 2, :]
gz2 = gz[2 : xs - 2, 2 : ys - 2, :] * gz[2 : xs - 2, 2 : ys - 2, :]
gxgy = gx[2 : xs - 2, 2 : ys - 2, :] * gy[2 : xs - 2, 2 : ys - 2, :]
gxgz = gx[2 : xs - 2, 2 : ys - 2, :] * gz[2 : xs - 2, 2 : ys - 2, :]
gygz = gy[2 : xs - 2, 2 : ys - 2, :] * gz[2 : xs - 2, 2 : ys - 2, :]
#
# Outer gaussian smoothing
rgx2 = xl.sconvolve(gx2, kernel)
rgy2 = xl.sconvolve(gy2, kernel)
rgz2 = xl.sconvolve(gz2, kernel)
rgxgy = xl.sconvolve(gxgy, kernel)
rgxgz = xl.sconvolve(gxgz, kernel)
rgygz = xl.sconvolve(gygz, 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()
evecs = 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"] = -evecs[:, 1] / evecs[:, 2] * crlFactor
xa.Output["Inl_dip"] = -evecs[:, 0] / evecs[:, 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"]))
coh = (e1 - e2) / (e1 + e2)
xa.Output["Coherency"] = coh * coh
xa.doOutput()
def doCompute(): # # Initialise some constants from the attribute parameters or the SeismicInfo, xa.SI, array for use in the calculations # These are just some examples - keep/add what you need # number_inline = xa.SI['nrinl'] number_xline = xa.SI['nrcrl'] centre_trace_x = xa.SI['nrinl']//2 centre_trace_y = xa.SI['nrcrl']//2 nyquist = 1.0/(2.0*xa.SI['zstep']) par0 = xa.params['Par_0']['Value'] zw = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 select = xa.params['Select']['Selection'] # # This is the trace processing loop # while True: xa.doInput() # # After doInput the TraceInfo, xa.TI, array contains information specific to this trace segment - keep what you need # number_of_samples = xa.TI['nrsamp'] start_time = xa.TI['z0'] current_inline = xa.TI['inl'] current_crossline = xa.TI['crl'] # # Get the input # indata = xa.Input['Input'] # # Your attribute calculation goes here # # Warning Python loops can be slow - this is contrived just to show how to access traces in the analysis data cube # outdata = np.zeros(number_of_samples) for inline in range(number_inline): for xline in range(number_xline): if (inline != centre_trace_x and xline != centre_trace_y): outdata += indata[inline,xline,:] out_1 = indata[centre_trace_x,centre_trace_y,:] out_2 = outdata out_3 = outdata / (number_inline * number_xline - 1) #------------------------------------------------------------------------------------ # # Replace Output1-Output3 with the names in the xa.params Output array # xa.Output['Output1'] = out_1 xa.Output['Output2'] = out_2 xa.Output['Output3'] = out_3 xa.doOutput()
def doCompute(): inlpos = xa.SI['nrinl']//2 crlpos = xa.SI['nrcrl']//2 while True: xa.doInput() indata = xa.Input['Input'] xa.Output['In-line gradient'] = prewitt(indata, axis=0)[inlpos,crlpos,:] xa.Output['Cross-line gradient'] = prewitt(indata, axis=1)[inlpos,crlpos,:] xa.Output['Z gradient'] = prewitt(indata, axis=2)[inlpos,crlpos,:] xa.Output['Average Gradient'] = ( xa.Output['In-line gradient'] + xa.Output['Cross-line gradient'] + xa.Output['Z gradient'] )/3 xa.doOutput()
def doCompute():
#
# index of current trace position in Input numpy array
#
ilndx = xa.SI["nrinl"] // 2
crldx = xa.SI["nrcrl"] // 2
while True:
xa.doInput()
xa.Output["In-line gradient"] = prewitt(xa.Input, axis=0)[ilndx, crldx, :]
xa.Output["Cross-line gradient"] = prewitt(xa.Input, axis=1)[ilndx, crldx, :]
xa.Output["Z gradient"] = prewitt(xa.Input, axis=2)[ilndx, crldx, :]
xa.Output["Average gradient"] = (
xa.Output["In-line gradient"] + xa.Output["Cross-line gradient"] + xa.Output["Z gradient"]
) / 3
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']
while True:
xa.doInput()
s = xa.Input['Input']
sh = np.imag(hilbert(s))
#
# Compute partial derivatives
sx = xl.kroon3(s, axis=0)[hxs, hys, :]
sy = xl.kroon3(s, axis=1)[hxs, hys, :]
sz = xl.kroon3(s, axis=2)[hxs, hys, :]
shx = xl.kroon3(sh, axis=0)[hxs, hys, :]
shy = xl.kroon3(sh, axis=1)[hxs, hys, :]
shz = xl.kroon3(sh, axis=2)[hxs, hys, :]
a = s[hxs, hys, :] * s[hxs, hys, :] + sh[hxs, hys, :] * sh[hxs, hys, :]
px = (s[hxs, hys, :] * shx - sh[hxs, hys, :] * sx) / a
py = (s[hxs, hys, :] * shy - sh[hxs, hys, :] * sy) / a
pz = (s[hxs, hys, :] * shz - sh[hxs, hys, :] * sz) / a
#
# Form the structure tensor
px2 = px * px
py2 = py * py
pz2 = pz * pz
pxpy = px * py
pxpz = px * pz
pypz = py * pz
T = np.rollaxis(
np.array([[px2, pxpy, pxpz], [pxpy, py2, pypz], [pxpz, pypz,
pz2]]), 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[:, -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.doOutput()
def doCompute(): xs = xa.SI['nrinl'] ys = xa.SI['nrcrl'] zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 kernel = xl.getGaussian(xs, ys, zs) inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor'] crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor'] while True: xa.doInput() gx = xa.Input['In-line gradient'] gy = xa.Input['Cross-line gradient'] gz = xa.Input['Z gradient'] # # Inner product of gradients gx2 = gx * gx gy2 = gy * gy gz2 = gz * gz gxgy = gx * gy gxgz = gx * gz gygz = gy * gz # # Outer gaussian smoothing rgx2 = xl.sconvolve(gx2, kernel) rgy2 = xl.sconvolve(gy2, kernel) rgz2 = xl.sconvolve(gz2, kernel) rgxgy = xl.sconvolve(gxgy, kernel) rgxgz = xl.sconvolve(gxgz, kernel) rgygz = xl.sconvolve(gygz, 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() evecs = evecs[np.arange(0,T.shape[0],1),:,ndx[:,2]] eval2 = evals[np.arange(0,T.shape[0],1),ndx[:,2]] eval1 = evals[np.arange(0,T.shape[0],1),ndx[:,1]] xa.Output['Crl_dip'] = -evecs[:,1]/evecs[:,2]*crlFactor xa.Output['Inl_dip'] = -evecs[:,0]/evecs[:,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'])) coh = (eval2-eval1)/(eval2+eval1) xa.Output['Coherency'] = coh * coh xa.doOutput()
def doCompute():
inlpos = xa.SI['nrinl'] // 2
crlpos = xa.SI['nrcrl'] // 2
while True:
xa.doInput()
indata = xa.Input['Input']
xa.Output['In-line gradient'] = prewitt(indata, axis=0)[inlpos,
crlpos, :]
xa.Output['Cross-line gradient'] = prewitt(indata, axis=1)[inlpos,
crlpos, :]
xa.Output['Z gradient'] = prewitt(indata, axis=2)[inlpos, crlpos, :]
xa.Output['Average Gradient'] = (xa.Output['In-line gradient'] +
xa.Output['Cross-line gradient'] +
xa.Output['Z gradient']) / 3
xa.doOutput()
def doCompute(): zw = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 width = np.arange(1,zw) while True: xa.doInput() data = xa.Input['Input'][0,0,:] # # Find signal peaks peakidx = signal.find_peaks_cwt(data, width) out = np.zeros(data.shape) out[peakidx] = data[peakidx] xa.Output['Find Peaks'] = out # # Output xa.doOutput()
def doCompute(): inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor'] crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor'] while True: xa.doInput() gx = xa.Input['In-line gradient'][0,0,:] gy = xa.Input['Cross-line gradient'][0,0,:] gz = xa.Input['Z gradient'][0,0,:] # # Calculate the dips and output xa.Output['Crl_dip'] = -gy/gz*crlFactor xa.Output['Inl_dip'] = -gx/gz*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(): # # Start debugging before computation starts # web_pdb.set_trace() # while True: xa.doInput() inp = xa.Input['Input1'][0,0,:] # # Add some more local variables # inline = xa.TI['inl'] crossline = xa.TI['crl'] # xa.Output = inp 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'] while True: xa.doInput() s = xa.Input['Input'] sh = np.imag( hilbert(s) ) # # Compute partial derivatives sx = xl.kroon3( s, axis=0 )[hxs,hys,:] sy = xl.kroon3( s, axis=1 )[hxs,hys,:] sz = xl.kroon3( s, axis=2 )[hxs,hys,:] shx = xl.kroon3( sh, axis=0 )[hxs,hys,:] shy = xl.kroon3( sh, axis=1 )[hxs,hys,:] shz = xl.kroon3( sh, axis=2 )[hxs,hys,:] a = s[hxs,hys,:]*s[hxs,hys,:] + sh[hxs,hys,:]*sh[hxs,hys,:] px = (s[hxs,hys,:] * shx - sh[hxs,hys,:] * sx) /a py = (s[hxs,hys,:] * shy - sh[hxs,hys,:] * sy) /a pz = (s[hxs,hys,:] * shz - sh[hxs,hys,:] * sz) /a # # Form the structure tensor px2 = px * px py2 = py * py pz2 = pz * pz pxpy = px * py pxpz = px * pz pypz = py * pz T = np.rollaxis(np.array([ [px2, pxpy, pxpz], [pxpy, py2, pypz], [pxpz, pypz, pz2 ]]), 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[:,-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.doOutput()
def doCompute(): zw = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 nlag = int(xa.params['Par_0']['Value']) while True: xa.doInput() ref = xa.Input['Reference'][0,0,:] mat = xa.Input['Match'][0,0,:] ns = ref.shape[0] lag = np.zeros(ns) qual = np.zeros(ns) localCorr( ref, mat, zw, nlag, lag, qual) # # Get the output xa.Output['Shift'] = lag*xa.SI['zstep'] xa.Output['Quality'] = qual xa.doOutput()
def doCompute(): xs = xa.SI['nrinl'] ys = xa.SI['nrcrl'] zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 zw = zs-2 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'] while True: xa.doInput() s = xa.Input['Input'] sh = np.imag( hilbert(s) ) # # Compute partial derivatives sx = xl.kroon3( s, axis=0 ) sy = xl.kroon3( s, axis=1 ) sz = xl.kroon3( s, axis=2 ) shx = xl.kroon3( sh, axis=0 ) shy = xl.kroon3( sh, axis=1 ) shz = xl.kroon3( sh, axis=2 ) px = s[1:xs-1,1:ys-1,:] * shx[1:xs-1,1:ys-1,:] - sh[1:xs-1,1:ys-1,:] * sx[1:xs-1,1:ys-1,:] py = s[1:xs-1,1:ys-1,:] * shy[1:xs-1,1:ys-1,:] - sh[1:xs-1,1:ys-1,:] * sy[1:xs-1,1:ys-1,:] pz = s[1:xs-1,1:ys-1,:] * shz[1:xs-1,1:ys-1,:] - sh[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(): # # Compute the filter kernel # nil = xa.SI['nrinl'] nxl = xa.SI['nrcrl'] centre_trace_x = nil//2 centre_trace_y = nxl//2 ifreq = xa.params['Par_0']['Value'] xfreq = xa.params['Par_1']['Value'] type = xa.params['Select']['Selection'] kernelFunc = lpKernel if type==0 else hpKernel if type==1 else bpKernel if type==2 else brKernel ikernel = np.ones((nil,1)) xkernel = np.ones((nxl,1)) Ni = nil//2 Nx = nxl//2 if (nil != 1): if (Ni%2 == 0): ikernel[1:2*Ni+2] = kernelFunc(Ni, ifreq) else: ikernel = kernelFunc(Ni, ifreq) if (nxl != 1): if (Nx%2 == 0): xkernel[1:2*Nx+2] = kernelFunc(Nx,xfreq) else: xkernel = kernelFunc(Nx,xfreq) kernel = np.dot(ikernel, xkernel.T).reshape(nil,nxl,1) # # This is the trace processing loop # while True: xa.doInput() # # Get the input # indata = xa.Input['Input'] # # Apply the kernel # outdata = np.sum(kernel * indata, axis=(0,1)) #------------------------------------------------------------------------------------ # xa.Output = outdata xa.doOutput()