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https://github.com/wassname/simpeg.git
synced 2026-06-27 22:08:38 +08:00
Fix bug for poly map
Working Spline map
This commit is contained in:
seogi_macbook
+191
-16
@@ -752,19 +752,19 @@ class PolyMap(IdentityMap):
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raise(Exception("Input for normal = X or Y or Z"))
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#3D
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elif self.mesh.dim == 3:
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X = self.mesh.gridCC[:,0]
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Y = self.mesh.gridCC[:,1]
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Z = self.mesh.gridCC[:,1]
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if self.normal =='X':
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f = polynomial.polyval2d(Y, Z, c.reshape((self.order[0]+1,self.order[1]+1))) - X
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elif self.normal =='Y':
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f = polynomial.polyval2d(X, Z, c.reshape((self.order[0]+1,self.order[1]+1))) - Y
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elif self.normal =='Z':
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f = polynomial.polyval2d(X, Y, c.reshape((self.order[0]+1,self.order[1]+1))) - Z
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else:
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raise(Exception("Input for normal = X or Y or Z"))
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else:
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raise(Exception("Only supports 2D and 3D"))
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# X = self.mesh.gridCC[:,0]
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# Y = self.mesh.gridCC[:,1]
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# Z = self.mesh.gridCC[:,1]
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# if self.normal =='X':
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# f = polynomial.polyval2d(Y, Z, c.reshape((self.order[0]+1,self.order[1]+1))) - X
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# elif self.normal =='Y':
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# f = polynomial.polyval2d(X, Z, c.reshape((self.order[0]+1,self.order[1]+1))) - Y
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# elif self.normal =='Z':
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# f = polynomial.polyval2d(X, Y, c.reshape((self.order[0]+1,self.order[1]+1))) - Z
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# else:
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# raise(Exception("Input for normal = X or Y or Z"))
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# else:
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raise(Exception("Only supports 2D"))
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return sig1+(sig2-sig1)*(np.arctan(alpha*f)/np.pi+0.5)
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@@ -791,10 +791,9 @@ class PolyMap(IdentityMap):
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elif self.mesh.dim == 3:
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X = self.mesh.gridCC[:,0]
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Y = self.mesh.gridCC[:,1]
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Z = self.mesh.gridCC[:,1]
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Z = self.mesh.gridCC[:,2]
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if self.normal =='X':
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f = polynomial.polyval2d(Y, Z, c.reshape((self.order[0]+1,self.order[1]+1))) - X
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V = polynomial.polyvander2d(Y, Z, self.order)
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elif self.normal =='Y':
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@@ -815,4 +814,180 @@ class PolyMap(IdentityMap):
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g3 = Utils.sdiag(alpha*(sig2-sig1)/(1.+(alpha*f)**2)/np.pi)*V
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return sp.csr_matrix(np.c_[g1,g2,g3])
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return sp.csr_matrix(np.c_[g1,g2,g3])
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class SplineMap(IdentityMap):
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"""SplineMap
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Parameterize the boundary of two geological units using a spline interpolation
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..math::
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g = f(x)-y
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Define the model as:
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..math::
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m = [\sigma_1, \sigma_2, y]
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"""
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def __init__(self, mesh, pts, ptsv=None,order=3, logSigma=True, normal='X'):
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IdentityMap.__init__(self, mesh)
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self.logSigma = logSigma
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self.order = order
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self.normal = normal
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self.pts= pts
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self.npts = np.size(pts)
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self.ptsv = ptsv
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self.spl = None
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slope = 1e4
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@property
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def nP(self):
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if self.mesh.dim == 2:
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return np.size(self.pts)+2
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elif self.mesh.dim == 3:
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return np.size(self.pts)*2+2
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else:
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raise(Exception("Only supports 2D and 3D"))
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def _transform(self, m):
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# Set model parameters
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alpha = self.slope
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sig1,sig2 = m[0],m[1]
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c = m[2:]
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if self.logSigma:
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sig1, sig2 = np.exp(sig1), np.exp(sig2)
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#2D
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if self.mesh.dim == 2:
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X = self.mesh.gridCC[:,0]
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Y = self.mesh.gridCC[:,1]
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self.spl = UnivariateSpline(self.pts, c, k=self.order, s=0)
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if self.normal =='X':
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f = self.spl(Y) - X
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elif self.normal =='Y':
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f = self.spl(X) - Y
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else:
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raise(Exception("Input for normal = X or Y or Z"))
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# 3D:
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# Comments:
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# Make two spline functions and link them using linear interpolation.
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# This is not quite direct extension of 2D to 3D case
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# Using 2D interpolation is possible
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elif self.mesh.dim == 3:
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X = self.mesh.gridCC[:,0]
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Y = self.mesh.gridCC[:,1]
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Z = self.mesh.gridCC[:,2]
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npts = np.size(self.pts)
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if np.mod(c.size, 2):
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raise(Exception("Put even points!"))
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self.spl = {"splb":UnivariateSpline(self.pts, c[:npts], k=self.order, s=0),
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"splt":UnivariateSpline(self.pts, c[npts:], k=self.order, s=0)}
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if self.normal =='X':
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zb = self.ptsv[0]
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zt = self.ptsv[1]
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flines = (self.spl["splt"](Y)-self.spl["splb"](Y))*(Z-zb)/(zt-zb) + self.spl["splb"](Y)
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f = flines - X
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# elif self.normal =='Y':
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# elif self.normal =='Z':
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else:
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raise(Exception("Input for normal = X or Y or Z"))
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else:
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raise(Exception("Only supports 2D and 3D"))
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return sig1+(sig2-sig1)*(np.arctan(alpha*f)/np.pi+0.5)
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def deriv(self, m):
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alpha = self.slope
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sig1,sig2, c = m[0],m[1],m[2:]
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if self.logSigma:
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sig1, sig2 = np.exp(sig1), np.exp(sig2)
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#2D
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if self.mesh.dim == 2:
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X = self.mesh.gridCC[:,0]
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Y = self.mesh.gridCC[:,1]
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if self.normal =='X':
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f = self.spl(Y) - X
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elif self.normal =='Y':
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f = self.spl(X) - Y
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else:
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raise(Exception("Input for normal = X or Y or Z"))
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#3D
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elif self.mesh.dim == 3:
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X = self.mesh.gridCC[:,0]
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Y = self.mesh.gridCC[:,1]
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Z = self.mesh.gridCC[:,2]
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if self.normal =='X':
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zb = self.ptsv[0]
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zt = self.ptsv[1]
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flines = (self.spl["splt"](Y)-self.spl["splb"](Y))*(Z-zb)/(zt-zb) + self.spl["splb"](Y)
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f = flines - X
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# elif self.normal =='Y':
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# elif self.normal =='Z':
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else:
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raise(Exception("Not Implemented for Y and Z, your turn :)"))
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if self.logSigma:
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g1 = -(np.arctan(alpha*f)/np.pi + 0.5)*sig1 + sig1
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g2 = (np.arctan(alpha*f)/np.pi + 0.5)*sig2
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else:
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g1 = -(np.arctan(alpha*f)/np.pi + 0.5) + 1.0
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g2 = (np.arctan(alpha*f)/np.pi + 0.5)
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if self.mesh.dim ==2:
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g3 = np.zeros((self.mesh.nC, self.npts))
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if self.normal =='Y':
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# Here we use perturbation to compute sensitivity
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# TODO: bit more generalization of this ...
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# Modfications for X and Z directions ...
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for i in range(np.size(self.pts)):
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ctemp = c[i]
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ind = np.argmin(abs(self.mesh.vectorCCy-ctemp))
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ca = c.copy()
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cb = c.copy()
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dy = self.mesh.hy[ind]*1.5
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ca[i] = ctemp+dy
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cb[i] = ctemp-dy
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spla = UnivariateSpline(self.pts, ca, k=self.order, s=0)
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splb = UnivariateSpline(self.pts, cb, k=self.order, s=0)
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fderiv = (spla(X)-splb(X))/(2*dy)
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g3[:,i] = Utils.sdiag(alpha*(sig2-sig1)/(1.+(alpha*f)**2)/np.pi)*fderiv
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elif self.mesh.dim==3:
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g3 = np.zeros((self.mesh.nC, self.npts*2))
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if self.normal =='X':
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# Here we use perturbation to compute sensitivity
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for i in range(self.npts*2):
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ctemp = c[i]
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ind = np.argmin(abs(self.mesh.vectorCCy-ctemp))
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ca = c.copy()
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cb = c.copy()
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dy = self.mesh.hy[ind]*1.5
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ca[i] = ctemp+dy
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cb[i] = ctemp-dy
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#treat bottom boundary
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if i< self.npts:
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splba = UnivariateSpline(self.pts, ca[:self.npts], k=self.order, s=0)
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splbb = UnivariateSpline(self.pts, cb[:self.npts], k=self.order, s=0)
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flinesa = (self.spl["splt"](Y)-splba(Y))*(Z-zb)/(zt-zb) + splba(Y) - X
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flinesb = (self.spl["splt"](Y)-splbb(Y))*(Z-zb)/(zt-zb) + splbb(Y) - X
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#treat top boundary
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else:
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splta = UnivariateSpline(self.pts, ca[self.npts:], k=self.order, s=0)
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spltb = UnivariateSpline(self.pts, ca[self.npts:], k=self.order, s=0)
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flinesa = (self.spl["splt"](Y)-splta(Y))*(Z-zb)/(zt-zb) + splta(Y) - X
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flinesb = (self.spl["splt"](Y)-spltb(Y))*(Z-zb)/(zt-zb) + spltb(Y) - X
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fderiv = (flinesa-flinesb)/(2*dy)
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g3[:,i] = Utils.sdiag(alpha*(sig2-sig1)/(1.+(alpha*f)**2)/np.pi)*fderiv
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else :
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raise(Exception("Not Implemented for Y and Z, your turn :)"))
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