Python vecFilter Examples

Example #1

File: ex_vector_filter_dip.py Project: heinerrene/OpendTect-Plugins

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()

Example #2

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()

Example #3

File: ex_vf_phase3_dip.py Project: waynegm/OpendTect-External-Attributes

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()

Example #4

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) if filt == 2 else autojit(xl.vmf_x3)
    inlFactor = xa.SI['zstep'] / xa.SI['inldist'] * xa.SI['dipFactor']
    crlFactor = xa.SI['zstep'] / xa.SI['crldist'] * xa.SI['dipFactor']
    while True:
        xa.doInput()

        p = xa.Input['Input']
        #
        #	Compute partial derivatives
        px = xl.kroon3(p, axis=0)[1:xs - 1, 1:ys - 1, :]
        py = xl.kroon3(p, axis=1)[1:xs - 1, 1:ys - 1, :]
        pz = xl.kroon3(p, axis=2)[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()

Example #5

File: ex_vector_filter_dip.py Project: waynegm/OpendTect-External-Attributes

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 = autojit(xl.vecmean) if filt==0 else  autojit(xl.vmf_l1) if filt==1 else autojit(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()