diff --git a/SimPEG/Examples/DC_PseudoSection_Simulation.py b/SimPEG/Examples/DC_PseudoSection_Simulation.py new file mode 100644 index 00000000..3b9c4c05 --- /dev/null +++ b/SimPEG/Examples/DC_PseudoSection_Simulation.py @@ -0,0 +1,220 @@ +from SimPEG import * +# import simpegDCIP as DC +from SimPEG import DCIP as DC +import scipy.interpolate as interpolation +import matplotlib.pyplot as plt +import time +import re + +def run(loc=np.c_[[-50.,0.,-50.],[50.,0.,-50.]], sig=np.r_[1e-2,1e-1,1e-3], radi=np.r_[25.,25.], param = np.r_[30.,30.,5], stype = 'dpdp', plotIt=True): + """ + + DC Forward Simulation + ===================== + + Forward model conductive spheres in a half-space and plot a pseudo-section + + Created on Mon Feb 01 19:28:06 2016 + + @fourndo + + """ + + def getIndicesSphere(center,radius,ccMesh): + """ + Creates a vector containing the sphere indices in the cell centers mesh. + Returns a tuple + + The sphere is defined by the points + + p0, describe the position of the center of the cell + + r, describe the radius of the sphere. + + ccMesh represents the cell-centered mesh + + The points p0 must live in the the same dimensional space as the mesh. + + """ + + # Validation: mesh and point (p0) live in the same dimensional space + dimMesh = np.size(ccMesh[0,:]) + assert len(center) == dimMesh, "Dimension mismatch. len(p0) != dimMesh" + + if dimMesh == 1: + # Define the reference points + + ind = np.abs(center[0] - ccMesh[:,0]) < radius + + elif dimMesh == 2: + # Define the reference points + + ind = np.sqrt( ( center[0] - ccMesh[:,0] )**2 + ( center[1] - ccMesh[:,1] )**2 ) < radius + + elif dimMesh == 3: + # Define the points + ind = np.sqrt( ( center[0] - ccMesh[:,0] )**2 + ( center[1] - ccMesh[:,1] )**2 + ( center[2] - ccMesh[:,2] )**2 ) < radius + + # Return a tuple + return ind + # First we need to create a mesh and a model. + + # This is our mesh + dx = 5. + + hxind = [(dx,15,-1.3), (dx, 75), (dx,15,1.3)] + hyind = [(dx,15,-1.3), (dx, 10), (dx,15,1.3)] + hzind = [(dx,15,-1.3),(dx, 15)] + + mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN') + + + # Set background conductivity + model = np.ones(mesh.nC) * sig[0] + + # First anomaly + ind = getIndicesSphere(loc[:,0],radi[0],mesh.gridCC) + model[ind] = sig[1] + + # Second anomaly + ind = getIndicesSphere(loc[:,1],radi[1],mesh.gridCC) + model[ind] = sig[2] + + # Get index of the center + indy = int(mesh.nCy/2) + + + # Plot the model for reference + # Define core mesh extent + xlim = 200 + zlim = 125 + + # Specify the survey type: "pdp" | "dpdp" + + + # Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh + ends = [(-175,0),(175,0)] + ends = np.c_[np.asarray(ends),np.ones(2).T*mesh.vectorNz[-1]] + + # Snap the endpoints to the grid. Easier to create 2D section. + indx = Utils.closestPoints(mesh, ends ) + locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]] + + # We will handle the geometry of the survey for you and create all the combination of tx-rx along line + [Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1], param[2]) + + # Define some global geometry + dl_len = np.sqrt( np.sum((locs[0,:] - locs[1,:])**2) ) + dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len + dl_y = ( Tx[-1][1,1] - Tx[0][1,0] ) / dl_len + azm = np.arctan(dl_y/dl_x) + + #Set boundary conditions + mesh.setCellGradBC('neumann') + + # Define the differential operators needed for the DC problem + Div = mesh.faceDiv + Grad = mesh.cellGrad + Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model))) + + A = Div*Msig*Grad + + # Change one corner to deal with nullspace + A[0,0] = 1 + A = sp.csc_matrix(A) + + # We will solve the system iteratively, so a pre-conditioner is helpful + # This is simply a Jacobi preconditioner (inverse of the main diagonal) + dA = A.diagonal() + P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0]) + + # Now we can solve the system for all the transmitters + # We want to store the data + data = [] + + # There is probably a more elegant way to do this, but we can just for-loop through the transmitters + for ii in range(len(Tx)): + + start_time = time.time() # Let's time the calculations + + #print("Transmitter %i / %i\r" % (ii+1,len(Tx))) + + # Select dipole locations for receiver + rxloc_M = np.asarray(Rx[ii][:,0:3]) + rxloc_N = np.asarray(Rx[ii][:,3:]) + + + # For usual cases "dpdp" or "gradient" + if not re.match(stype,'pdp'): + inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T ) + RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] ) + + else: + + # Create an "inifinity" pole + tx = np.squeeze(Tx[ii][:,0:1]) + tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2 + inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T) + RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] ) + + + # Iterative Solve + Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5) + + # We now have the potential everywhere + phi = mkvc(Ainvb[0]) + + # Solve for phi on pole locations + P1 = mesh.getInterpolationMat(rxloc_M, 'CC') + P2 = mesh.getInterpolationMat(rxloc_N, 'CC') + + # Compute the potential difference + dtemp = (P1*phi - P2*phi)*np.pi + + data.append( dtemp ) + print '\rTransmitter {0} of {1} -> Time:{2} sec'.format(ii,len(Tx),time.time()- start_time), + + print 'Transmitter {0} of {1}'.format(ii,len(Tx)) + print 'Forward completed' + + + # Let's just convert the 3D format into 2D (distance along line) and plot + [Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx) + + + # Here is an example for the first tx-rx array + if plotIt: + fig = plt.figure() + ax = plt.subplot(2,1,1, aspect='equal') + mesh.plotSlice(np.log10(model), ax =ax, normal = 'Y', ind = indy,grid=True) + ax.set_title('E-W section at '+str(mesh.vectorCCy[indy])+' m') + plt.gca().set_aspect('equal', adjustable='box') + + plt.scatter(Tx[0][0,:],Tx[0][2,:],s=40,c='g', marker='v') + plt.scatter(Rx[0][:,0::3],Rx[0][:,2::3],s=40,c='y') + plt.xlim([-xlim,xlim]) + plt.ylim([-zlim,mesh.vectorNz[-1]+dx]) + + + ax = plt.subplot(2,1,2, aspect='equal') + + # Plot the location of the spheres for reference + circle1=plt.Circle((loc[0,0]-Tx[0][0,0],loc[2,0]),radi[0],color='w',fill=False, lw=3) + circle2=plt.Circle((loc[0,1]-Tx[0][0,0],loc[2,1]),radi[1],color='k',fill=False, lw=3) + ax.add_artist(circle1) + ax.add_artist(circle2) + + # Add the speudo section + DC.plot_pseudoSection(Tx2d,Rx2d,data,mesh.vectorNz[-1],stype) + + plt.scatter(Tx2d[0][:],Tx[0][2,:],s=40,c='g', marker='v') + plt.scatter(Rx2d[0][:],Rx[0][:,2::3],s=40,c='y') + plt.plot(np.r_[Tx2d[0][0],Rx2d[-1][-1,-1]],np.ones(2)*mesh.vectorNz[-1], color='k') + plt.ylim([-zlim,mesh.vectorNz[-1]+dx]) + + plt.show() + + return fig, ax + +if __name__ == '__main__': + run() diff --git a/SimPEG/Examples/EM_FDEM_SusEffects.py b/SimPEG/Examples/EM_FDEM_SusEffects.py new file mode 100644 index 00000000..1abbb16f --- /dev/null +++ b/SimPEG/Examples/EM_FDEM_SusEffects.py @@ -0,0 +1,148 @@ +from SimPEG import * +from SimPEG import EM +from pymatsolver import MumpsSolver +from scipy.constants import mu_0 + +def run(plotIt=True): + """ + EM: FDEM: Effects of susceptibility + =================================== + + When airborne freqeuncy domain EM (AFEM) survey is flown over + the earth including significantly susceptible bodies (magnetite-rich rocks), + negative data is often observed in the real part of the lowest frequency + (e.g. Dighem system 900 Hz). This phenomenon mostly based upon magnetization + occurs due to a susceptible body when the magnetic field is applied. + + To clarify what is happening in the earth when we are exciting the earth with + a loop source in the frequency domain we run three forward modelling: + + - F[:math:`\sigma`, :math:`\mu`]: Anomalous conductivity and susceptibility + - F[:math:`\sigma`, :math:`\mu_0`]: Anomalous conductivity + - F[:math:`\sigma_{air}`, :math:`\mu_0`]: primary field + + We plot vector magnetic fields in the earth. For secondary fields we provide + F[:math:`\sigma`, :math:`\mu`]-F[:math:`\sigma`, :math:`\mu_0`]. Following + figure show both real and parts. + + """ + # Generate Cylindrical mesh + cs, ncx, ncz, npad = 5, 25, 24, 20. + hx = [(cs,ncx), (cs,npad,1.3)] + hz = [(cs,npad,-1.3), (cs,ncz), (cs,npad,1.3)] + mesh = Mesh.CylMesh([hx,1,hz], '00C') + sighalf = 1e-3 + sigma = np.ones(mesh.nC)*1e-8 + sigmahomo = sigma.copy() + mu = np.ones(mesh.nC)*mu_0 + sigma[mesh.gridCC[:,-1]<0.] = sighalf + blkind = np.logical_and(mesh.gridCC[:,0]<30., (mesh.gridCC[:,2]<0)&(mesh.gridCC[:,2]>-150)&(mesh.gridCC[:,2]<-50)) + sigma[blkind] = 1e-1 + mu[blkind] = mu_0*1.1 + offset = 0. + frequency = np.r_[10., 100., 1000.] + rx0 = EM.FDEM.Rx(np.array([[8., 0., 30.]]), 'bzr') + rx1 = EM.FDEM.Rx(np.array([[8., 0., 30.]]), 'bzi') + srcLists = [] + nfreq = frequency.size + for ifreq in range(nfreq): + src = EM.FDEM.Src.CircularLoop([rx0, rx1], frequency[ifreq], np.array([[0., 0., 30.]]), radius=5.) + srcLists.append(src) + survey = EM.FDEM.Survey(srcLists) + iMap = Maps.IdentityMap(nP=int(mesh.nC)) + # Use PhysPropMap + maps = [('sigma', iMap), ('mu', iMap)] + prob = EM.FDEM.Problem_b(mesh, mapping=maps) + prob.Solver = MumpsSolver + survey.pair(prob) + m = np.r_[sigma, mu] + survey0 = EM.FDEM.Survey(srcLists) + prob0 = EM.FDEM.Problem_b(mesh, mapping=maps) + prob0.Solver = MumpsSolver + survey0.pair(prob0) + m = np.r_[sigma, mu] + m0 = np.r_[sigma, np.ones(mesh.nC)*mu_0] + m00 = np.r_[np.ones(mesh.nC)*1e-8, np.ones(mesh.nC)*mu_0] + # Anomalous conductivity and susceptibility + F = prob.fields(m) + # Only anomalous conductivity + F0 = prob.fields(m0) + # Primary field + F00 = prob.fields(m00) + + if plotIt: + import matplotlib.pyplot as plt + def vizfields(ifreq=0, primsec="secondary",realimag="real"): + + titles = ["F[$\sigma$, $\mu$]", "F[$\sigma$, $\mu_0$]", "F[$\sigma$, $\mu$]-F[$\sigma$, $\mu_0$]"] + actind = np.logical_and(mesh.gridCC[:,0]<200., (mesh.gridCC[:,2]>-400)&(mesh.gridCC[:,2]<200)) + + if primsec=="secondary": + bCCprim = (mesh.aveF2CCV*F00[:,'b'][:,ifreq]).reshape(mesh.nC, 2, order='F') + bCC = (mesh.aveF2CCV*F[:,'b'][:,ifreq]).reshape(mesh.nC, 2, order='F')-bCCprim + bCC0 = (mesh.aveF2CCV*F0[:,'b'][:,ifreq]).reshape(mesh.nC, 2, order='F')-bCCprim + elif primsec=="primary": + bCC = (mesh.aveF2CCV*F[:,'b'][:,ifreq]).reshape(mesh.nC, 2, order='F') + bCC0 = (mesh.aveF2CCV*F0[:,'b'][:,ifreq]).reshape(mesh.nC, 2, order='F') + + XYZ = mesh.gridCC[actind,:] + X = XYZ[:,0].reshape((31,43), order='F') + Z = XYZ[:,2].reshape((31,43), order='F') + bx = bCC[actind,0].reshape((31,43), order='F') + bz = bCC[actind,1].reshape((31,43), order='F') + bx0 = bCC0[actind,0].reshape((31,43), order='F') + bz0 = bCC0[actind,1].reshape((31,43), order='F') + + bxsec = (bCC[actind,0]-bCC0[actind,0]).reshape((31,43), order='F') + bzsec = (bCC[actind,1]-bCC0[actind,1]).reshape((31,43), order='F') + + absbreal = np.sqrt(bx.real**2+bz.real**2) + absbimag = np.sqrt(bx.imag**2+bz.imag**2) + absb0real = np.sqrt(bx0.real**2+bz0.real**2) + absb0imag = np.sqrt(bx0.imag**2+bz0.imag**2) + + absbrealsec = np.sqrt(bxsec.real**2+bzsec.real**2) + absbimagsec = np.sqrt(bxsec.imag**2+bzsec.imag**2) + + fig = plt.figure(figsize=(15,5)) + ax1 = plt.subplot(131) + ax2 = plt.subplot(132) + ax3 = plt.subplot(133) + typefield="real" + scale=20 + if realimag=="real": + ax1.contourf(X, Z,np.log10(absbreal), 100) + ax1.quiver(X, Z,bx.real/absbreal,bz.real/absbreal,scale=scale,width=0.005, alpha = 0.5) + ax2.contourf(X, Z,np.log10(absb0real), 100) + ax2.quiver(X, Z,bx0.real/absb0real,bz0.real/absb0real,scale=scale,width=0.005, alpha = 0.5) + ax3.contourf(X, Z,np.log10(absbrealsec), 100) + ax3.quiver(X, Z,bxsec.real/absbrealsec,bzsec.real/absbrealsec,scale=scale,width=0.005, alpha = 0.5) + elif realimag=="imag": + ax1.contourf(X, Z,np.log10(absbimag), 100) + ax1.quiver(X, Z,bx.imag/absbimag,bz.imag/absbimag,scale=scale,width=0.005, alpha = 0.5) + ax2.contourf(X, Z,np.log10(absb0imag), 100) + ax2.quiver(X, Z,bx0.imag/absb0imag,bz0.imag/absb0imag,scale=scale,width=0.005, alpha = 0.5) + ax3.contourf(X, Z,np.log10(absbimagsec), 100) + ax3.quiver(X, Z,bxsec.imag/absbimagsec,bzsec.imag/absbimagsec,scale=scale,width=0.005, alpha = 0.5) + + ax = [ax1, ax2, ax3] + ax3.text(30, 50, ("Frequency=%5.2f Hz")%(frequency[ifreq]), color="k", fontsize=18) + ax2.text(30, 50, primsec, color="k", fontsize=18) + ax1.text(30, 50, realimag, color="k", fontsize=18) + for i, axtemp in enumerate(ax): + axtemp.plot(np.r_[0, 29.75], np.r_[-50, -50], 'w', lw=3) + + axtemp.plot(np.r_[29.5, 29.5], np.r_[-50, -142.5], 'w', lw=3) + axtemp.plot(np.r_[0, 29.5], np.r_[-142.5, -142.5], 'w', lw=3) + axtemp.plot(np.r_[0, 100.], np.r_[0, 0], 'w', lw=3) + axtemp.set_ylim(-200, 100.) + axtemp.set_xlim(10, 100.) + axtemp.set_title(titles[i]) + plt.show() + return fig, ax + fig1, ax1 = vizfields(1, primsec="primary", realimag="real") + fig2, ax2 = vizfields(1, primsec="secondary", realimag="real") + fig4, ax4 = vizfields(1, primsec="secondary", realimag="imag") + +if __name__ == '__main__': + run() diff --git a/SimPEG/Examples/Inversion_IRLS.py b/SimPEG/Examples/Inversion_IRLS.py new file mode 100644 index 00000000..a7f5bd54 --- /dev/null +++ b/SimPEG/Examples/Inversion_IRLS.py @@ -0,0 +1,90 @@ +from SimPEG import * + + +def run(N=100, plotIt=True): + """ + Inversion: Linear Problem + ========================= + + Here we go over the basics of creating a linear problem and inversion. + + """ + + class LinearSurvey(Survey.BaseSurvey): + def projectFields(self, u): + return u + + class LinearProblem(Problem.BaseProblem): + + surveyPair = LinearSurvey + + def __init__(self, mesh, G, **kwargs): + Problem.BaseProblem.__init__(self, mesh, **kwargs) + self.G = G + + def fields(self, m, u=None): + return self.G.dot(m) + + def Jvec(self, m, v, u=None): + return self.G.dot(v) + + def Jtvec(self, m, v, u=None): + return self.G.T.dot(v) + + + np.random.seed(1) + + mesh = Mesh.TensorMesh([N]) + + nk = 20 + jk = np.linspace(1.,20.,nk) + p = -0.25 + q = 0.25 + + g = lambda k: np.exp(p*jk[k]*mesh.vectorCCx)*np.cos(2*np.pi*q*jk[k]*mesh.vectorCCx) + + G = np.empty((nk, mesh.nC)) + + for i in range(nk): + G[i,:] = g(i) + + mtrue = np.zeros(mesh.nC) + mtrue[mesh.vectorCCx > 0.3] = 1. + mtrue[mesh.vectorCCx > 0.45] = -0.5 + mtrue[mesh.vectorCCx > 0.6] = 0 + + prob = LinearProblem(mesh, G) + survey = LinearSurvey() + survey.pair(prob) + survey.makeSyntheticData(mtrue, std=0.01) + + M = prob.mesh + + reg = Regularization.Tikhonov(mesh) + dmis = DataMisfit.l2_DataMisfit(survey) + opt = Optimization.ProjectedGNCG(maxIter=20,lower=-1.,upper=1., maxIterCG= 20, tolCG = 1e-3) + invProb = InvProblem.BaseInvProblem(dmis, reg, opt) + beta = Directives.BetaSchedule() + betaest = Directives.BetaEstimate_ByEig() + inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest]) + m0 = np.zeros_like(survey.mtrue) + + mrec = inv.run(m0) + + if plotIt: + import matplotlib.pyplot as plt + + fig, axes = plt.subplots(1,2,figsize=(12*1.2,4*1.2)) + for i in range(prob.G.shape[0]): + axes[0].plot(prob.G[i,:]) + axes[0].set_title('Columns of matrix G') + + axes[1].plot(M.vectorCCx, survey.mtrue, 'b-') + axes[1].plot(M.vectorCCx, mrec, 'r-') + axes[1].legend(('True Model', 'Recovered Model')) + plt.show() + + return prob, survey, mesh, mrec + +if __name__ == '__main__': + run() diff --git a/SimPEG/Examples/MT_1D_analytic_nlayer_Earth.py b/SimPEG/Examples/MT_1D_analytic_nlayer_Earth.py new file mode 100644 index 00000000..0cccc5f1 --- /dev/null +++ b/SimPEG/Examples/MT_1D_analytic_nlayer_Earth.py @@ -0,0 +1,443 @@ +from scipy.constants import epsilon_0, mu_0 +import matplotlib.pyplot as plt +import numpy as np +from ipywidgets import * +#from SimPEG.EM.Utils import k, omega + +""" +MT1D: n layered earth problem +***************************** + +Author: Thibaut Astic +Contact: thast@eos.ubc.ca +Date: January 2016 + +This code compute the analytic response of a n-layered Earth to a plane wave (Magneto-Tellurics). + +We start by looking at Maxwell's equations in the electric +field \\\(\\\mathbf{E}\\) and the magnetic flux +\\\(\\\mathbf{H}\\) to write the wave equations +\\(\\ \nabla ^2 \mathbf{E_x} + k^2 \mathbf{E_x} = 0 \\) & +\\(\\ \nabla ^2 \mathbf{H_y} + k^2 \mathbf{H_y} = 0 \\) + +Then solving the equations in each layer "j" between z_{j-1} and z_j in the form of +\\(\\ E_{x,j} (z) = U_j e^{i k (z-z_{j-1})} + D_j e^{-i k (z-z_{j-1})} \\) +\\(\\ H_{y,j} (z) = \frac{1}{Z_j} (D_j e^{-i k (z-z_{j-1})} - U_j e^{i k (z-z_{j-1})}) \\) + +With U and D the Up and Down components of the E-field. + +The iteration from one layer to another is ensure by: + +\\(\\ \left(\begin{matrix} E_{x,j} \\ H_{y,j} \end{matrix} \right) = + P_j T_j P^{-1}_J \left(\begin{matrix} E_{x,j+1} \\ H_{y,j+1} \end{matrix} \right) \\) + +And the Boundary Condition is set for the E-field in the last layer, with no Up component (=0) +and only a down component (=1 then normalized by the highest amplitude to ensure numeric stability) + +The layer 0 is assumed to be the air layer. + +""" + +#Frequency conversion +omega = lambda f: 2.*np.pi*f + +#Evaluate k wavenumber +k = lambda mu,sig,eps,f: np.sqrt(mu*mu_0*eps*epsilon_0*(2.*np.pi*f)**2.-1.j*mu*mu_0*sig*omega(f)) + +#Define a frquency range for a survey +frange = lambda minfreq, maxfreq, step: np.logspace(minfreq,maxfreq,num = step, base = 10.) + +#Functions to create random physical Perties for a n-layered earth +thick = lambda minthick, maxthick, nlayer: np.append(np.array([1.2*10.**5]), + np.ndarray.round(minthick + (maxthick-minthick)* np.random.rand(nlayer-1,1) + ,decimals =1)) + +sig = lambda minsig, maxsig, nlayer: np.append(np.array([0.]), + np.ndarray.round(10.**minsig + (10.**maxsig-10.**minsig)* np.random.rand(nlayer,1) + ,decimals=3)) + +mu = lambda minmu, maxmu, nlayer: np.append(np.array([1.]), + np.ndarray.round(minmu + (maxmu-minmu)* np.random.rand(nlayer,1) + ,decimals=1)) + +eps = lambda mineps, maxeps, nlayer: np.append(np.array([1.]), + np.ndarray.round(mineps + (maxeps-mineps)* np.random.rand(nlayer,1) + ,decimals=1)) + +#Evaluate Impedance Z of a layer +ImpZ = lambda f, mu, k: omega(f)*mu*mu_0/k + +#Complex Cole-Cole Conductivity - EM utils +PCC= lambda siginf,m,t,c,f: siginf*(1.-(m/(1.+(1j*omega(f)*t)**c))) + + +#Converted thickness array into top of layer array +def top(thick): + topv= np.zeros(len(thick)+1) + + topv[0]=-thick[0] + + for i in range(1,len(topv),1): + topv[i] = topv[i-1] + thick[i-1] + + return topv + +#Propagation Matrix and theirs inverses + +#matrix T for transition of Up and Down components accross a layer +T = lambda h,k: np.matrix([[np.exp(1j*k*h),0.],[0.,np.exp(-1j*k*h)]],dtype='complex_') + +Tinv = lambda h,k: np.matrix([[np.exp(-1j*k*h),0.],[0.,np.exp(1j*k*h)]],dtype='complex_') + +#transition of Up and Down components accross a layer +UD_Z = lambda UD,z,zj,k : T((z-zj),k)*UD + + +#matrix P relating Up and Down components with E and H fields +P = lambda z: np.matrix([[1.,1,],[-1./z,1./z]],dtype='complex_') + +Pinv = lambda z: np.matrix([[1.,-z],[1.,z]],dtype='complex_')/2. + + +#Time Variation of E and H +E_ZT = lambda U,D,f,t : np.exp(1j*omega(f)*t)*(U+D) +H_ZT = lambda U,D,Z,f,t : (1./Z)*np.exp(1j*omega(f)*t)*(D-U) + +#Plot the configuration of the problem +def PlotConfiguration(thick,sig,eps,mu,ax,widthg,z): + + topn = top(thick) + widthn = np.arange(-widthg,widthg+widthg/10.,widthg/10.) + + ax.set_ylim([z.min(),z.max()]) + ax.set_xlim([-widthg,widthg]) + + ax.set_ylabel("Depth (m)", fontsize=16.) + ax.yaxis.tick_right() + ax.yaxis.set_label_position("right") + + #define filling for the different layers + hatches=['/' , '+', 'x', '|' , '\\', '-' , 'o' , 'O' , '.' , '*' ] + + #Write the physical properties of air + ax.annotate(("Air, $\sigma$ =%1.0f mS/m")%(sig[0]*10**(3)), + xy=(-widthg/2., -np.abs(z.max())/2.), xycoords='data', + xytext=(-widthg/2., -np.abs(z.max())/2.), textcoords='data', + fontsize=14.) + + ax.annotate(("$\epsilon_r$= %1i")%(eps[0]), + xy=(-widthg/2., -np.abs(z.max())/3.), xycoords='data', + xytext=(-widthg/2., -np.abs(z.max())/3.), textcoords='data', + fontsize=14.) + + ax.annotate(("$\mu_r$= %1i")%(mu[0]), + xy=(-widthg/2., -np.abs(z.max())/3.), xycoords='data', + xytext=(0, -np.abs(z.max())/3.), textcoords='data', + fontsize=14.) + + #Write the physical properties of the differents layers up to the (n-1)-th and fill it with pattern + for i in range(1,len(topn)-1,1): + if topn[i] == topn[i+1]: + pass + else: + ax.annotate(("$\sigma$ =%3.3f mS/m")%(sig[i]*10**(3)), + xy=(0., (2.*topn[i]+topn[i+1])/3), xycoords='data', + xytext=(0., (2.*topn[i]+topn[i+1])/3), textcoords='data', + fontsize=14.) + + ax.annotate(("$\epsilon_r$= %1i")%(eps[i]), + xy=(-widthg/1.1, (2.*topn[i]+topn[i+1])/3), xycoords='data', + xytext=(-widthg/1.1, (2.*topn[i]+topn[i+1])/3), textcoords='data', + fontsize=14.) + + ax.annotate(("$\mu_r$= %1.2f")%(mu[i]), + xy=(-widthg/2., (2.*topn[i]+topn[i+1])/3), xycoords='data', + xytext=(-widthg/2., (2.*topn[i]+topn[i+1])/3), textcoords='data', + fontsize=14.) + + ax.plot(widthn,topn[i]*np.ones_like(widthn),color='black') + ax.fill_between(widthn,topn[i],topn[i+1],alpha=0.3,color="none",edgecolor='black', hatch=hatches[(i-1)%10]) + + #Write the physical properties of the n-th layer and fill it with pattern + ax.plot(widthn,topn[-1]*np.ones_like(widthn),color='black') + ax.fill_between(widthn,topn[-1],z.max(),alpha=0.3,color="none",edgecolor='black', hatch=hatches[(len(topn)-2)%10]) + + ax.annotate(("$\sigma$ =%3.3f mS/m")%(sig[-1]*10**(3)), + xy=(0., (2.*topn[-1]+z.max())/3), xycoords='data', + xytext=(0., (2.*topn[-1]+z.max())/3), textcoords='data', + fontsize=14.) + + ax.annotate(("$\epsilon_r$= %1i")%(eps[-1]), + xy=(-widthg/1.1, (2.*topn[-1]+z.max())/3), xycoords='data', + xytext=(-widthg/1.1, (2.*topn[-1]+z.max())/3), textcoords='data', + fontsize=14.) + + ax.annotate(("$\mu_r$= %1.2f")%(mu[-1]), + xy=(-widthg/2., (2.*topn[-1]+z.max())/3), xycoords='data', + xytext=(-widthg/2., (2.*topn[-1]+z.max())/3), textcoords='data', + fontsize=14.) + + #plot Trees! + ax.annotate("", + xy=(widthg/2., -1.*z.max()/5.), xycoords='data', + xytext=(widthg/2., 0.), textcoords='data', + arrowprops=dict(arrowstyle='->, head_width=1.2,head_length=1.2',color='green',linewidth=2.) + ) + + ax.annotate("", + xy=(widthg/2., -3./4.*z.max()/5.), xycoords='data', + xytext=(widthg/2., 0.), textcoords='data', + arrowprops=dict(arrowstyle='->, head_width=1.4,head_length=1.4',color='green',linewidth=2.) + ) + + ax.annotate("", + xy=(widthg/2., -1./2.*z.max()/5.), xycoords='data', + xytext=(widthg/2., 0.), textcoords='data', + arrowprops=dict(arrowstyle='->, head_width=1.6,head_length=1.6',color='green',linewidth=2.) + ) + + ax.annotate("", + xy=(1.2*widthg/2., -1.*z.max()/5.), xycoords='data', + xytext=(1.2*widthg/2., 0.), textcoords='data', + arrowprops=dict(arrowstyle='->, head_width=1.2,head_length=1.2',color='green',linewidth=2.) + ) + + ax.annotate("", + xy=(1.2*widthg/2., -3./4.*z.max()/5.), xycoords='data', + xytext=(1.2*widthg/2., 0.), textcoords='data', + arrowprops=dict(arrowstyle='->, head_width=1.4,head_length=1.4',color='green',linewidth=2.) + ) + + ax.annotate("", + xy=(1.2*widthg/2., -1./2.*z.max()/5.), xycoords='data', + xytext=(1.2*widthg/2., 0.), textcoords='data', + arrowprops=dict(arrowstyle='->, head_width=1.6,head_length=1.6',color='green',linewidth=2.) + ) + + ax.annotate("", + xy=(1.5*widthg/2., -1.*z.max()/5.), xycoords='data', + xytext=(1.5*widthg/2., 0.), textcoords='data', + arrowprops=dict(arrowstyle='->, head_width=1.2,head_length=1.2',color='green',linewidth=2.) + ) + + ax.annotate("", + xy=(1.5*widthg/2., -3./4.*z.max()/5.), xycoords='data', + xytext=(1.5*widthg/2., 0.), textcoords='data', + arrowprops=dict(arrowstyle='->, head_width=1.4,head_length=1.4',color='green',linewidth=2.) + ) + + ax.annotate("", + xy=(1.5*widthg/2., -1./2.*z.max()/5.), xycoords='data', + xytext=(1.5*widthg/2., 0.), textcoords='data', + arrowprops=dict(arrowstyle='->, head_width=1.6,head_length=1.6',color='green',linewidth=2.) + ) + + + ax.invert_yaxis() + + return ax + +#Propagate Up and Down component for a certain frequency & evaluate E and H field + +def Propagate(f,H,sig,chg,taux,c,mu,eps,n): + + sigcm = np.zeros_like(sig,dtype='complex_') + + for j in range(1,len(sig)): + sigcm[j]=PCC(sig[j],chg[j],taux[j],c[j],f) + + K = k(mu,sigcm,eps,f) + Z = ImpZ(f,mu,K) + + EH = np.matrix(np.zeros((2,n+1),dtype = 'complex_'),dtype = 'complex_') + UD = np.matrix(np.zeros((2,n+1),dtype = 'complex_'),dtype = 'complex_') + + UD[1,-1] = 1. + + for i in range(-2,-(n+2),-1): + + UD[:,i] = Tinv(H[i+1],K[i])*Pinv(Z[i])*P(Z[i+1])*UD[:,i+1] + UD = UD/((np.abs(UD[0,:]+UD[1,:])).max()) + + for j in range(0,n+1): + EH[:,j] = np.matrix([[1.,1,],[-1./Z[j],1./Z[j]]])*UD[:,j] + + return UD, EH, Z ,K + + +#Evaluate the apparent resistivity and phase for a frequency range +def appres(F,H,sig,chg,taux,c,mu,eps,n): + + Res = np.zeros_like(F) + Phase = np.zeros_like(F) + App_ImpZ= np.zeros_like(F,dtype='complex_') + + for i in range(0,len(F)): + + UD,EH,Z ,K = Propagate(F[i],H,sig,chg,taux,c,mu,eps,n) + + App_ImpZ[i] = EH[0,1]/EH[1,1] + + Res[i] = np.abs(App_ImpZ[i])**2./(mu_0*omega(F[i])) + Phase[i] = np.angle(App_ImpZ[i], deg = True) + + return Res,Phase + +#Evaluate Up, Down components, E and H field, for a frequency range, +#a discretized depth range and a time range (use to calculate envelope) +def calculateEHzt(F,H,sig,chg,taux,c,mu,eps,n,zsample,tsample): + + topc = top(H) + + layer = np.zeros(len(zsample),dtype=np.int)-1 + + Exzt = np.matrix(np.zeros((len(zsample),len(tsample)),dtype = 'complex_'),dtype = 'complex_') + Hyzt = np.matrix(np.zeros((len(zsample),len(tsample)),dtype = 'complex_'),dtype = 'complex_') + Uz = np.matrix(np.zeros((len(zsample),len(tsample)),dtype = 'complex_'),dtype = 'complex_') + Dz = np.matrix(np.zeros((len(zsample),len(tsample)),dtype = 'complex_'),dtype = 'complex_') + UDaux = np.matrix(np.zeros((2,len(zsample)),dtype = 'complex_'),dtype = 'complex_') + + for i in range(0,n+1,1): + layer = layer+(zsample>=topc[i])*1 + + for j in range(0,len(F)): + + UD,EH,Z ,K = Propagate(F[j],H,sig,chg,taux,c,mu,eps,n) + + for p in range(0,len(zsample)): + + UDaux[:,p] = UD_Z(UD[:,layer[p]],zsample[p],topc[layer[p]],K[layer[p]]) + + for q in range(0,len(tsample)): + + Exzt[p,q] = Exzt[p,q] + E_ZT(UDaux[0,p],UDaux[1,p],F[j],tsample[q])/len(F) + Hyzt[p,q] = Hyzt[p,q] + H_ZT(UDaux[0,p],UDaux[1,p],Z[layer[p]],F[j],tsample[q])/len(F) + Uz[p,q] = Uz[p,q] + UDaux[0,p]*np.exp(1j*omega(F[j])*tsample[q])/len(F) + Dz[p,q] = Dz[p,q] + UDaux[1,p]*np.exp(1j*omega(F[j])*tsample[q])/len(F) + + return Exzt,Hyzt,Uz,Dz,UDaux,layer + + +#Function to Plot Apparent Resistivity and Phase +def PlotAppRes(F,H,sig,chg,taux,c,mu,eps,n,fenvelope,PlotEnvelope): + + Res, Phase = appres(F,H,sig,chg,taux,c,mu,eps,n) + + fig,ax = plt.subplots(1,2,figsize=(16,10)) + + ax[0].scatter(Res,F,color='black') + ax[0].set_xscale('Log') + ax[0].set_yscale('Log') + ax[0].set_xlim([10.**(np.log10(Res.min())-1.),10.**(np.log10(Res.max())+1.)]) + ax[0].set_ylim([F.min(),F.max()]) + ax[0].set_xlabel('Apparent Resistivity (Ohm*m)',fontsize=16.,color="black") + ax[0].set_ylabel('Frequency (Hz)',fontsize=16.) + ax[0].grid(which='major') + + ax0 = ax[0].twiny() + + ax0.set_xlim([0.,90.]) + ax0.set_ylim([F.min(),F.max()]) + ax0.scatter(Phase,F,color='purple') + ax0.set_xlabel('Phase (Degrees)',fontsize=16.,color="purple") + + zc=np.arange(-(H[1:].max()+10)*n,(H[1:].max()+10)*n,10.) + + ax[0].tick_params(labelsize=16) + ax[1].tick_params(labelsize=16) + ax0.tick_params(labelsize=16) + + if PlotEnvelope: + + widthn=np.logspace(np.log10(Res.min())-1., np.log10(Res.max())+1., num=100, endpoint=True, base=10.0) + fenvelope1n=np.ones(100)*fenvelope + ax[0].plot(widthn,fenvelope1n,linestyle='dashed',color='black') + + tc=np.arange(0.,1./fenvelope,0.01/(fenvelope)) + Exzt,Hyzt,Uz,Dz,UDaux,layer = calculateEHzt(np.array([fenvelope]),H,sig,chg,taux,c,mu,eps,n,zc,tc) + + ax1=ax[1].twiny() + + ax[1].tick_params(labelsize=16) + ax1.tick_params(labelsize=16) + + ax[1].set_xlabel('Amplitude Electric Field E (V/m)',color='blue',fontsize=16) + + ax1.set_xlabel('Amplitude Magnetic Field H (A/m)',color='red',fontsize=16) + + ax[1].fill_betweenx(zc,np.squeeze(np.asarray(np.real(Exzt.min(axis=1)))), + np.squeeze(np.asarray(np.real(Exzt.max(axis=1)))), + color='blue', alpha=0.1) + + ax1.fill_betweenx(zc,np.squeeze(np.asarray(np.real(Hyzt.min(axis=1)))), + np.squeeze(np.asarray(np.real(Hyzt.max(axis=1)))), + color='red', alpha=0.1) + + ax[1] = PlotConfiguration(H,sig,eps,mu,ax[1],(1.5*np.abs(Exzt).max()),zc) + ax1.set_xlim([-1.5*np.abs(Hyzt).max(),1.5*np.abs(Hyzt).max()]) + ax1.set_xlim([-1.5*np.abs(Hyzt).max(),1.5*np.abs(Hyzt).max()]) + else: + print 'No envelop (if True, might be slow)' + ax[1] = PlotConfiguration(H,sig,eps,mu,ax[1],1.,zc) + ax[1].get_xaxis().set_ticks([]) + + plt.show() + +#Interactive MT for Notebook +def PlotAppRes3LayersInteract(h1,h2,sigl1,sigl2,sigl3,mul1,mul2,mul3,epsl1,epsl2,epsl3,PlotEnvelope,F_Envelope): + + frangn=frange(-5,5,100.) + sig3= np.array([0.,0.001,0.1, 0.001]) + thick3 = np.array([120000.,50.,50.]) + eps3=np.array([1.,1.,1.,1]) + mu3=np.array([1.,1.,1.,1]) + chg3=np.array([0.,0.1,0.,0.2]) + chg3_0=np.array([0.,0.1,0.,0.]) + taux3=np.array([0.,0.1,0.,0.1]) + c3=np.array([1.,1.,1.,1.]) + + sig3[1]=sigl1 + sig3[1]=10.**sig3[1] + sig3[2]=sigl2 + sig3[2]=10.**sig3[2] + sig3[3]=sigl3 + sig3[3]=10.**sig3[3] + mu3[1]=mul1 + mu3[2]=mul2 + mu3[3]=mul3 + eps3[1]=epsl1 + eps3[2]=epsl2 + eps3[3]=epsl3 + thick3[1]=h1 + thick3[2]=h2 + + PlotAppRes(frangn,thick3,sig3,chg3_0,taux3,c3,mu3,eps3,3,F_Envelope,PlotEnvelope) + + +def run(n,plotIt=True): + # something to make a plot + + F = frange(-5.,5.,20) + H = thick(50.,100.,n) + sign = sig(-5.,0.,n) + mun = mu(1.,2.,n) + epsn = eps(1.,9.,n) + chg = np.zeros_like(sign) + taux = np.zeros_like(sign) + c = np.zeros_like(sign) + + Res, Phase = appres(F,H,sign,chg,taux,c,mun,epsn,n) + + if plotIt: + + PlotAppRes(F, H, sign, chg, taux, c, mun, epsn, n, fenvelope=1000., PlotEnvelope=True) + + return Res, Phase + +if __name__ == '__main__': + run(3) + + + + + diff --git a/SimPEG/Examples/__init__.py b/SimPEG/Examples/__init__.py index cce22296..73665644 100644 --- a/SimPEG/Examples/__init__.py +++ b/SimPEG/Examples/__init__.py @@ -1,10 +1,10 @@ # Run this file to add imports. ##### AUTOIMPORTS ##### -import DC_Analytic_Dipole -import DC_Forward_PseudoSection +#import DC_PseudoSection_Simulation import EM_FDEM_1D_Inversion import EM_FDEM_Analytic_MagDipoleWholespace +#import EM_FDEM_SusEffects import EM_TDEM_1D_Inversion import FLOW_Richards_1D_Celia1990 import Forward_BasicDirectCurrent @@ -16,10 +16,8 @@ import Mesh_QuadTree_Creation import Mesh_QuadTree_FaceDiv import Mesh_QuadTree_HangingNodes import Mesh_Tensor_Creation -import MT_1D_ForwardAndInversion -import MT_3D_Foward -__examples__ = ["DC_Analytic_Dipole", "DC_Forward_PseudoSection", "EM_FDEM_1D_Inversion", "EM_FDEM_Analytic_MagDipoleWholespace", "EM_TDEM_1D_Inversion", "FLOW_Richards_1D_Celia1990", "Forward_BasicDirectCurrent", "Inversion_Linear", "Mesh_Basic_PlotImage", "Mesh_Basic_Types", "Mesh_Operators_CahnHilliard", "Mesh_QuadTree_Creation", "Mesh_QuadTree_FaceDiv", "Mesh_QuadTree_HangingNodes", "Mesh_Tensor_Creation", "MT_1D_ForwardAndInversion", "MT_3D_Foward"] +__examples__ = ["DC_PseudoSection_Simulation", "EM_FDEM_1D_Inversion", "EM_FDEM_Analytic_MagDipoleWholespace", "EM_FDEM_SusEffects", "EM_TDEM_1D_Inversion", "FLOW_Richards_1D_Celia1990", "Forward_BasicDirectCurrent", "Inversion_Linear", "Mesh_Basic_PlotImage", "Mesh_Basic_Types", "Mesh_Operators_CahnHilliard", "Mesh_QuadTree_Creation", "Mesh_QuadTree_FaceDiv", "Mesh_QuadTree_HangingNodes", "Mesh_Tensor_Creation"] ##### AUTOIMPORTS ##### diff --git a/SimPEG/Examples/sphereElectrostatic_example.py b/SimPEG/Examples/sphereElectrostatic_example.py new file mode 100644 index 00000000..7ff1ead1 --- /dev/null +++ b/SimPEG/Examples/sphereElectrostatic_example.py @@ -0,0 +1,775 @@ +from scipy.constants import epsilon_0 +import matplotlib.pyplot as plt +import matplotlib.colors as colors +import numpy as np +from SimPEG.Utils import ndgrid, mkvc + +''' +Authors: Thibaut Astic, Lindsey Heagy, Sanna Tyrvainen, Ronghua Peng +Date: December 2015 + +This code defines function to resolve analytically the electrostatic sphere problem. +We first define a problem configuration, with a conductive or resistive sphere in a +wholespace background. +We then calculate the potential, then the electric field, then the current density and +finally the charges accumulation. + +Several plotting functions are defined for data visualisation. + + +''' + +# Plot options +ftsize_title = 18 #font size for titles +ftsize_axis = 14 #font size for axis ticks +ftsize_label = 14 #font size for axis labels + +# Radius function, useful sigma ratio, and log scale converter +r = lambda x,y,z: np.sqrt(x**2.+y**2.+z**2.) +sigf = lambda sig0,sig1: (sig1-sig0)/(sig1+2.*sig0) + +#tools to convert log conductivity in conductivity +def conductivity_log_wrapper(log_sig0,log_sig1): + sig0 = 10.**log_sig0 + sig1 = 10.**log_sig1 + + return sig0,sig1 + +# Examples +#Plot the configuration. Label=False is used to generate a general case figure +def get_Setup(XYZ,sig0,sig1,R,E0,ax,label,colorsphere): + ''' + XYZ: ndgrid + sig0: conductivity of the background + sig1: conductivity of the sphere + R: radius of the sphere + E0: Amplitude of the uniform electrostatic field + ax: ax where to plot the configuration + label: True: plot real values, False: plot general case + colorsphere: color of the sphere, format [x,x,x] + ''' + + xplt = np.linspace(-R, R, num=100) + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + dx = xr[1]-xr[0] + top = np.sqrt(R**2-xplt**2) + bot = -np.sqrt(R**2-xplt**2) + + if R != 0: + ax.plot(xplt, top, xplt, bot, color=colorsphere,linewidth=1.5) + ax.fill_between(xplt,bot,top,color=colorsphere,alpha=0.5 ) + ax.arrow(0.,0.,np.sqrt(2.)*R/2.,np.sqrt(2.)*R/2.,head_width=0.,head_length=0.) + + if label: + ax.annotate(("$\sigma_1$=%3.3f mS/m")%(sig1*10.**(3.)), + xy=(0.,-R/2.), xycoords='data', + xytext=(0.,-R/2.), textcoords='data', + fontsize=14.) + ax.annotate(("$\sigma_0$= %3.3f mS/m")%(sig0*10.**(3.)), + xy=(0.,-1.5*R), xycoords='data', + xytext=(0.,-1.5*R), textcoords='data', + fontsize=14.) + ax.annotate(('$\mathbf{E_0} = %1i \mathbf{\hat{x}}$ V/m')%(E0), + xy=(xr.min()+np.abs(xr.max()-xr.min())/20.,0), xycoords='data', + xytext=(xr.min()+np.abs(xr.max()-xr.min())/20.,0), textcoords='data', + fontsize=14.) + ax.annotate(('$R$ = %1i m')%(R), + xy=(R/4.+(xr[1]-xr[0]),R/4.), xycoords='data', + xytext=(R/4.+(xr[1]-xr[0]),R/4.), textcoords='data', + fontsize=14.) + ax.set_ylabel('Y coordinate ($m$)',fontsize = ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize = ftsize_label) + ax.tick_params(labelsize=ftsize_axis) + + else: + ax.set_xticklabels([]) + ax.set_yticklabels([]) + ax.text(-1.,-np.sqrt(R)/2.-10.,'$\sigma_1$',fontsize=14) + ax.text(-0.05,-R-10,'$\sigma_0$',fontsize=14) + ax.annotate(('$\mathbf{E_0} = E_0 \mathbf{\hat{x}}$ V/m'), + xy=(xr.min()+np.abs(xr.max()-xr.min())/20.,0), xycoords='data', + xytext=(xr.min()+np.abs(xr.max()-xr.min())/20.,0), textcoords='data', + fontsize=14.) + ax.annotate(('$R$'), + xy=(R/4.+(xr[1]-xr[0]),R/4.), xycoords='data', + xytext=(R/4.+(xr[1]-xr[0]),R/4.), textcoords='data', + fontsize=14.) + ax.set_xlabel('x',fontsize=12) + ax.set_ylabel('y',fontsize=12) + + else: + if label: + ax.annotate(("$\sigma_0$= %3.3f mS/m")%(sig0*10.**(3.)), + xy=(0.,-1.5*R), xycoords='data', + xytext=(0.,-1.5*R), textcoords='data', + fontsize=14.) + ax.annotate(('$\mathbf{E_0} = %1i \mathbf{\hat{x}}$ V/m')%(E0), + xy=(xr.min()+np.abs(xr.max()-xr.min())/20.,0), xycoords='data', + xytext=(xr.min()+np.abs(xr.max()-xr.min())/20.,0), textcoords='data', + fontsize=14.) + ax.set_ylabel('Y coordinate ($m$)',fontsize = ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize = ftsize_label) + ax.tick_params(labelsize=ftsize_axis) + + else: + ax.set_xticklabels([]) + ax.set_yticklabels([]) + ax.text(-0.05,-10,'$\sigma_0$',fontsize=14) + ax.text(xr.min()+np.abs(xr.max()-xr.min())/20., 0, '$\mathbf{E_0} = E_0 \mathbf{\hat{x}}$ V/m', fontsize=14) + ax.set_xlabel('x',fontsize=12) + ax.set_ylabel('y',fontsize=12) + + + ax.set_xlim([xr.min(),xr.max()]) + ax.set_ylim([yr.min(),yr.max()]) + [ax.arrow(xr.min(),_,np.abs(xr.max()-xr.min())/20.,0.,head_width=5.,head_length=2.,color='k') for _ in np.linspace(yr.min(),yr.max(),num=10)] + ax.patch.set_facecolor([0.4,0.7,0.4]) + ax.patch.set_alpha(0.2) + + ax.set_aspect('equal') + + + + return ax + +def get_Conductivity(XYZ,sig0,sig1,R): + ''' + Define the conductivity for each point of the space + ''' + x,y,z = XYZ[:,0],XYZ[:,1],XYZ[:,2] + r_view=r(x,y,z) + + ind0= (r_view>R) + ind1= (r_view<=R) + + assert (ind0 + ind1).all(), 'Some indicies not included' + + Sigma = np.zeros_like(x) + + Sigma[ind0] = sig0 + Sigma[ind1] = sig1 + + return Sigma + + +def get_Potential(XYZ,sig0,sig1,R,E0): + + ''' + Function that returns the total, the primary and the secondary potentials, assumes an x-oriented inducing field and that the sphere is at the origin + :input: grid, outer sigma, inner sigma, radius of the sphere, strength of the electric field + ''' + + x,y,z = XYZ[:,0],XYZ[:,1],XYZ[:,2] + + sig_cur = sigf(sig0,sig1) + + r_cur = r(x,y,z) # current radius + + ind0 = (r_cur > R) + ind1 = (r_cur <= R) + + assert (ind0 + ind1).all(), 'Some indicies not included' + + Vt = np.zeros_like(x) + Vp = np.zeros_like(x) + Vs = np.zeros_like(x) + + Vt[ind0] = -E0*x[ind0]*(1.-sig_cur*R**3./r_cur[ind0]**3.) # total potential outside the sphere + Vt[ind1] = -E0*x[ind1]*3.*sig0/(sig1+2.*sig0) # inside the sphere + + + Vp = - E0*x # primary potential + + Vs = Vt - Vp # secondary potential + + return Vt,Vp,Vs + +#plot the primary potential on ax +def Plot_Primary_Potential(XYZ,sig0,sig1,R,E0,ax): + + Vt,Vp,Vs = get_Potential(XYZ,sig0,sig1,R,E0) + + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + + xcirc = xr[np.abs(xr) <= R] + + Pplot = ax.pcolor(xr,yr,Vp.reshape(xr.size,yr.size)) + ax.plot(xcirc,np.sqrt(R**2-xcirc**2),'--k',xcirc,-np.sqrt(R**2-xcirc**2),'--k') + ax.set_title('Primary Potential',fontsize=ftsize_title) + cb = plt.colorbar(Pplot,ax=ax) + cb.set_label(label= 'Potential ($V$)',size=ftsize_label) + cb.ax.tick_params(labelsize=ftsize_axis) + ax.set_xlim([xr.min(),xr.max()]) + ax.set_ylim([yr.min(),yr.max()]) + ax.set_ylabel('Y coordinate ($m$)',fontsize = ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize = ftsize_label) + ax.set_aspect('equal') + ax.tick_params(labelsize=ftsize_axis) + + return ax + +#plot the total potential on ax +def Plot_Total_Potential(XYZ,sig0,sig1,R,E0,ax): + + Vt,Vp,Vs = get_Potential(XYZ,sig0,sig1,R,E0) + + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + + xcirc = xr[np.abs(xr) <= R] + + + Pplot = ax.pcolor(xr,yr,Vt.reshape(xr.size,yr.size)) + ax.plot(xcirc,np.sqrt(R**2-xcirc**2),'--k',xcirc,-np.sqrt(R**2-xcirc**2),'--k') + ax.set_title('Total Potential',fontsize=ftsize_title) + cb = plt.colorbar(Pplot,ax=ax) + cb.set_label(label= 'Potential ($V$)',size=ftsize_label) + cb.ax.tick_params(labelsize=ftsize_axis) + ax.set_xlim([xr.min(),xr.max()]) + ax.set_ylim([yr.min(),yr.max()]) + ax.set_ylabel('Y coordinate ($m$)',fontsize = ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize = ftsize_label) + ax.set_aspect('equal') + ax.tick_params(labelsize=ftsize_axis) + + return ax + +#plot the secondary potential on ax +def Plot_Secondary_Potential(XYZ,sig0,sig1,R,E0,ax): + + Vt,Vp,Vs = get_Potential(XYZ,sig0,sig1,R,E0) + + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + + xcirc = xr[np.abs(xr) <= R] + + Pplot = ax.pcolor(xr,yr,Vs.reshape(xr.size,yr.size)) + ax.plot(xcirc,np.sqrt(R**2-xcirc**2),'--k',xcirc,-np.sqrt(R**2-xcirc**2),'--k') + ax.set_title('Secondary Potential',fontsize=ftsize_title) + cb = plt.colorbar(Pplot,ax=ax) + cb.set_label(label= 'Potential ($V$)',size=ftsize_label) + cb.ax.tick_params(labelsize=ftsize_axis) + ax.set_xlim([xr.min(),xr.max()]) + ax.set_ylim([yr.min(),yr.max()]) + ax.set_ylabel('Y coordinate ($m$)',fontsize = ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize = ftsize_label) + ax.set_aspect('equal') + ax.tick_params(labelsize=ftsize_axis) + + return ax + + +def get_ElectricField(XYZ,sig0,sig1,R,E0): + ''' + Function that returns the total, the primary and the secondary electric fields, + input: grid, outer sigma, inner sigma, radius of the sphere, strength of the electric field + ''' + + x,y,z= XYZ[:,0], XYZ[:,1], XYZ[:,2] + + r_cur=r(x,y,z) # current radius + + ind0= (r_cur>R) + ind1= (r_cur<=R) + + assert (ind0 + ind1).all(), 'Some indicies not included' + + Ep = np.zeros(shape=(len(x),3)) + Ep[:,0] = E0 + + Et = np.zeros(shape=(len(x),3)) + + Et[ind0,0] = E0 + E0*R**3./(r_cur[ind0]**5.)*sigf(sig0,sig1)*(2.*x[ind0]**2.-y[ind0]**2.-z[ind0]**2.); + Et[ind0,1] = E0*R**3./(r_cur[ind0]**5.)*3.*x[ind0]*y[ind0]*sigf(sig0,sig1); + Et[ind0,2] = E0*R**3./(r_cur[ind0]**5.)*3.*x[ind0]*z[ind0]*sigf(sig0,sig1); + + Et[ind1,0] = 3.*sig0/(sig1+2.*sig0)*E0; + Et[ind1,1] = 0.; + Et[ind1,2] = 0.; + + Es = Et - Ep + + return Et, Ep, Es + +#plot the total electric field on ax +def Plot_Total_ElectricField(XYZ,sig0,sig1,R,E0,ax): + + Et, Ep, Es = get_ElectricField(XYZ,sig0,sig1,R,E0) + + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + + xcirc = xr[np.abs(xr) <= R] + + EtXr = Et[:,0].reshape(xr.size, yr.size) + EtYr = Et[:,1].reshape(xr.size, yr.size) + EtAmp = np.sqrt(Et[:,0]**2+Et[:,1]**2 + Et[:,2]**2).reshape(xr.size, yr.size) + + ax.set_xlim([xr.min(),xr.max()]) + ax.set_ylim([yr.min(),yr.max()]) + ax.set_ylabel('Y coordinate ($m$)',fontsize = ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize = ftsize_label) + ax.plot(xcirc,np.sqrt(R**2-xcirc**2),'--k',xcirc,-np.sqrt(R**2-xcirc**2),'--k') + ax.tick_params(labelsize=ftsize_axis) + ax.set_aspect('equal') + + Eplot = ax.pcolor(xr,yr,EtAmp) + cb = plt.colorbar(Eplot,ax=ax) + cb.set_label(label= 'Amplitude ($V/m$)',size=ftsize_label) #weight='bold') + cb.ax.tick_params(labelsize=ftsize_axis) + ax.streamplot(xr,yr,EtXr,EtYr,color='gray',linewidth=2.,density=0.75)#angles='xy',scale_units='xy',scale=0.05) + ax.set_title('Total Field',fontsize=ftsize_title) + + + return ax + +#plot the secondary electric field on ax +def Plot_Secondary_ElectricField(XYZ,sig0,sig1,R,E0,ax): + + Et, Ep, Es = get_ElectricField(XYZ,sig0,sig1,R,E0) + + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + + xcirc = xr[np.abs(xr) <= R] + + EsXr = Es[:,0].reshape(xr.size, yr.size) + EsYr = Es[:,1].reshape(xr.size, yr.size) + EsAmp = np.sqrt(Es[:,0]**2+Es[:,1]**2+Es[:,2]**2).reshape(xr.size, yr.size) + + ax.set_xlim([xr.min(),xr.max()]) + ax.set_ylim([yr.min(),yr.max()]) + ax.set_ylabel('Y coordinate ($m$)',fontsize = ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize = ftsize_label) + ax.plot(xcirc,np.sqrt(R**2-xcirc**2),'--k',xcirc,-np.sqrt(R**2-xcirc**2),'--k') + ax.tick_params(labelsize=ftsize_axis) + ax.set_aspect('equal') + + Eplot = ax.pcolor(xr,yr,EsAmp) + cb = plt.colorbar(Eplot,ax=ax) + cb.set_label(label= 'Amplitude ($V/m$)',size=ftsize_label) #weight='bold') + cb.ax.tick_params(labelsize=ftsize_axis) + ax.streamplot(xr,yr,EsXr,EsYr,color='gray',linewidth=2.,density=0.75)#,angles='xy',scale_units='xy',scale=0.05) + ax.plot(xcirc,np.sqrt(R**2-xcirc**2),'--k',xcirc,-np.sqrt(R**2-xcirc**2),'--k') + ax.set_title('Secondary Field',fontsize=ftsize_title) + + return ax + + +def get_Current(XYZ,sig0,sig1,R,Et,Ep,Es): + ''' + Function that returns the total, the primary and the secondary current densities, + :input: grid, outer sigma, inner sigma, radius of the sphere, total, the primary and the seconadry electric fields, + ''' + + x,y,z= XYZ[:,0], XYZ[:,1], XYZ[:,2] + + r_cur=r(x,y,z) + + ind0= (r_cur>R) + ind1= (r_cur<=R) + + assert (ind0 + ind1).all(), 'Some indicies not included' + + Jt = np.zeros(shape=(len(x),3)) + J0 = np.zeros(shape=(len(x),3)) + Js = np.zeros(shape=(len(x),3)) + + + Jp = sig0*Ep + + Jt[ind0,:] = sig0*Et[ind0,:] + Jt[ind1,:] = sig1*Et[ind1,:] + + Js[ind0,:] = sig0*(Et[ind0,:]-Ep[ind0,:]) + Js[ind1,:] = sig1*Et[ind1,:]-sig0*Ep[ind1,:] + + return Jt,Jp,Js + +#plot the total currents density on ax +def Plot_Total_Currents(XYZ,sig0,sig1,R,E0,ax): + + Et,Ep,Es = get_ElectricField(XYZ,sig0,sig1,R,E0) + Jt,Jp,Js = get_Current(XYZ,sig0,sig1,R,Et,Ep,Es) + + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + xcirc = xr[np.abs(xr) <= R] + + JtXr = Jt[:,0].reshape(xr.size, yr.size) + JtYr = Jt[:,1].reshape(xr.size, yr.size) + JtAmp = np.sqrt(Jt[:,0]**2+Jt[:,1]**2+Jt[:,2]**2).reshape(xr.size, yr.size) + + ax.set_xlim([xr.min(),xr.max()]) + ax.set_ylim([yr.min(),yr.max()]) + ax.plot(xcirc,np.sqrt(R**2-xcirc**2),'--k',xcirc,-np.sqrt(R**2-xcirc**2),'--k') + ax.set_ylabel('Y coordinate ($m$)',fontsize=ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize=ftsize_label) + ax.tick_params(labelsize=ftsize_axis) + ax.set_aspect('equal') + + Jplot = ax.pcolor(xr,yr,JtAmp.reshape(xr.size,yr.size)) + cb = plt.colorbar(Jplot,ax=ax) + cb.set_label(label= 'Current Density ($A/m^2$)',size=ftsize_label) #weight='bold') + cb.ax.tick_params(labelsize=ftsize_axis) + ax.streamplot(xr,yr,JtXr,JtYr,color='gray',linewidth=2.,density=0.75)#,angles='xy',scale_units='xy',scale=1) + ax.set_title('Total Current Density',fontsize=ftsize_title) + + return ax + + +#plot the secondary currents density on ax +def Plot_Secondary_Currents(XYZ,sig0,sig1,R,E0,ax): + + Et,Ep,Es = get_ElectricField(XYZ,sig0,sig1,R,E0) + Jt,Jp,Js = get_Current(XYZ,sig0,sig1,R,Et,Ep,Es) + + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + xcirc = xr[np.abs(xr) <= R] + + JsXr = Js[:,0].reshape(xr.size, yr.size) + JsYr = Js[:,1].reshape(xr.size, yr.size) + JsAmp = np.sqrt(Js[:,1]**2+Js[:,0]**2+Jt[:,2]**2).reshape(xr.size,yr.size) + + ax.set_xlim([xr.min(),xr.max()]) + ax.set_ylim([yr.min(),yr.max()]) + ax.plot(xcirc,np.sqrt(R**2-xcirc**2),'--k',xcirc,-np.sqrt(R**2-xcirc**2),'--k') + ax.set_ylabel('Y coordinate ($m$)',fontsize=ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize=ftsize_label) + ax.tick_params(labelsize=ftsize_axis) + ax.set_aspect('equal') + + Jplot = ax.pcolor(xr,yr,JsAmp.reshape(xr.size,yr.size)) + cb = plt.colorbar(Jplot,ax=ax) + cb.set_label(label= 'Current Density ($A/m^2$)',size=ftsize_label) #weight='bold') + cb.ax.tick_params(labelsize=ftsize_axis) + ax.streamplot(xr,yr,JsXr,JsYr,color='gray',linewidth=2.,density=0.75)#,angles='xy',scale_units='xy',scale=1) + ax.set_title('Secondary Current Density',fontsize=ftsize_title) + + return ax + + +def get_ChargesDensity(XYZ,sig0,sig1,R,Et,Ep): + ''' + Function that returns the charges accumulation at the background/sphere interface, + :input: grid, outer sigma, inner sigma, radius of the sphere, total and the primary electric fields, + ''' + + x,y,z= XYZ[:,0], XYZ[:,1], XYZ[:,2] + + dx = x[1]-x[0] + + r_cur=r(x,y,z) + + ind0 = (r_cur > R) + ind1 = (r_cur < R) + ind2 = ((r_cur < (R+dx/2)) & (r_cur > (R-dx/2)) ) + + assert (ind0 + ind1 + ind2).all(), 'Some indicies not included' + + rho = np.zeros_like(x) + + rho[ind0] = 0 + rho[ind1] = 0 + rho[ind2] = epsilon_0*3.*Ep[ind2,0]*sigf(sig0,sig1)*x[ind2]/(np.sqrt(x[ind2]**2.+y[ind2]**2.)) + + return rho + +#Plot charges density on ax +def Plot_ChargesDensity(XYZ,sig0,sig1,R,E0,ax): + + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + xcirc = xr[np.abs(xr) <= R] + + Et, Ep, Es = get_ElectricField(XYZ,sig0,sig1,R,E0) + rho = get_ChargesDensity(XYZ,sig0,sig1,R,Et,Ep) + + ax.set_xlim([xr.min(),xr.max()]) + ax.set_ylim([yr.min(),yr.max()]) + ax.set_aspect('equal') + Cplot = ax.pcolor(xr,yr,rho.reshape(xr.size, yr.size)) + cb1 = plt.colorbar(Cplot,ax=ax) + cb1.set_label(label= 'Charge Density ($C/m^2$)',size=ftsize_label) #weight='bold') + cb1.ax.tick_params(labelsize=ftsize_axis) + ax.plot(xcirc,np.sqrt(R**2-xcirc**2),'--k',xcirc,-np.sqrt(R**2-xcirc**2),'--k') + ax.set_ylabel('Y coordinate ($m$)',fontsize=ftsize_label) + ax.set_xlabel('X coordinate ($m$)',fontsize=ftsize_label) + ax.tick_params(labelsize=ftsize_axis) + ax.set_title('Charges Density', fontsize=ftsize_title) + + return ax + +def MN_Potential_total(sig0,sig1,R,E0,start,end,nbmp,mn): + + ''' + Function that return array of midpoints electrodes, electrodes positions, + potentials differences for total and secondary potentials fields, unormalized and + normalized to electrodes distances. + sig0: background conductivity + sig1: sphere conductivity + R: Sphere's radius + E0: uniform E field value + start: start point for the profile start.shape = (2,) + end: end point for the profile end.shape = (2,) + nbmp: number of dipoles + mn: Space between the M and N electrodes + ''' + + #D: total distance from start to end + D = np.sqrt((start[0]-end[0])**2.+(start[1]-end[1])**2.) + + #MP: dipoles'midpoint positions (x,y) + MP = np.zeros(shape=(nbmp,2)) + MP[:,0] = np.linspace(start[0],end[0],nbmp) + MP[:,1] = np.linspace(start[1],end[1],nbmp) + + #Dipoles'Electrodes positions around each midpoints + EL = np.zeros(shape=(2*nbmp,2)) + for n in range(0,len(EL),2): + EL[n,0] = MP[n/2,0] - ((end[0]-start[0])/D)*mn/2. + EL[n+1,0] = MP[n/2,0] + ((end[0]-start[0])/D)*mn/2. + EL[n,1] = MP[n/2,1] - ((end[1]-start[1])/D)*mn/2. + EL[n+1,1] = MP[n/2,1] + ((end[1]-start[1])/D)*mn/2. + + VtEL = np.zeros(2*nbmp) #Total Potential (Vt-) at each electrode (-EL) + VsEL = np.zeros(2*nbmp) #Secondary Potential (Vt-) at each electrode (-EL) + dVtMP = np.zeros(nbmp) #Diffence (d-) of Total Potential (Vt-) at each dipole (-MP) + dVtMPn = np.zeros(nbmp) #Diffence (d-) of Total Potential (Vt-) at each dipole (-MP) normalized for the mn spacing (n) + dVsMP = np.zeros(nbmp) #Diffence (d-) of Secondaty Potential (Vt-) at each dipole (-MP) + dVsMPn = np.zeros(nbmp) #Diffence (d-) of Secondary Potential (Vt-) at each dipole (-MP) normalized for the mn spacing (n) + dVpMP = np.zeros(nbmp) #Diffence (d-) of Primary Potential (Vt-) at each dipole (-MP) + dVpMPn = np.zeros(nbmp) #Diffence (d-) of Primary Potential (Vt-) at each dipole (-MP) normalized for the mn spacing (n) + + #Computing VtEL + for m in range(0,2*nbmp): + if (r(EL[m,0],EL[m,1],0) > R): + VtEL[m] = -E0*EL[m,0]*(1.-sigf(sig0,sig1)*R**3./r(EL[m,0],EL[m,1],0)**3.) + else: + VtEL[m] = -E0*EL[m,0]*3.*sig0/(sig1+2.*sig0) + + #Computing VsEL + VsEL = VtEL + E0*EL[:,0] + + #Computing dVtMP, dVsMP + for p in range(0,nbmp): + dVtMP[p] = VtEL[2*p]-VtEL[2*p+1] + dVtMPn[p] = dVtMP[p]/mn + dVsMP[p] = VsEL[2*p]-VsEL[2*p+1] + dVsMPn[p] = dVsMP[p]/mn + + return MP,EL,dVtMP,dVtMPn,dVsMP,dVsMPn + +#Compare the DC response of two configurations +def two_configurations_comparison(XYZ,sig0,sig1,sig2,R0,R1,E0,xstart,ystart,xend,yend,nb_dipole,electrode_spacing,PlotOpt):#,linearcolor): + + #Define the mesh + xr,yr,zr = np.unique(XYZ[:,0]),np.unique(XYZ[:,1]),np.unique(XYZ[:,2]) + + #Defining the Profile + start = np.array([xstart,ystart]) + end = np.array([xend,yend]) + + #Calculating the data from the defined survey line for Configuration 0 and 1 + MP0,EL0,VtdMP0,VtdMPn0,VsdMP0,VsdMPn0 = MN_Potential_total(sig0,sig1,R0,E0,start,end,nb_dipole,electrode_spacing) + MP1,EL1,VtdMP1,VtdMPn1,VsdMP1,VsdMPn1 = MN_Potential_total(sig0,sig2,R1,E0,start,end,nb_dipole,electrode_spacing) + + + # Initializing the figure + fig = plt.figure(figsize=(20,20)) + ax0 = plt.subplot2grid((20,12), (0, 0),colspan=6,rowspan=6) + ax1 = plt.subplot2grid((20,12), (0, 6),colspan=6,rowspan=6) + ax2 = plt.subplot2grid((20,12), (16, 2), colspan=9,rowspan=4) + ax3 = plt.subplot2grid((20,12), (8, 0),colspan=6,rowspan=6) + ax4 = plt.subplot2grid((20,12), (8, 6),colspan=6,rowspan=6) + + #Plotting the Configuration 0 + ax0 = get_Setup(XYZ,sig0,sig1,R0,E0,ax0,True,[0.6,0.1,0.1]) + + #Plotting the Configuration 1 + ax1 = get_Setup(XYZ,sig0,sig2,R1,E0,ax1,True,[0.1,0.1,0.6]) + + #Plotting the Data (Legends) + ax2.set_title('Potential Differences',fontsize=ftsize_title) + ax2.set_ylabel('Potential difference ($V$)',fontsize=ftsize_label) + ax2.set_xlabel('Distance from start point ($m$)',fontsize=ftsize_label) + ax2.tick_params(labelsize=ftsize_axis) + ax2.grid() + + if PlotOpt == 'Total': + ax3= Plot_Total_Potential(XYZ,sig0,sig1,R0,E0,ax3) + ax4= Plot_Total_Potential(XYZ,sig0,sig2,R1,E0,ax4) + + #Plot the Data (from Configuration 0) + gphy0 = ax2.plot(np.sqrt((MP0[0,0]-MP0[:,0])**2+(MP0[:,1]-MP0[0,1])**2),VtdMP0 + ,marker='o',color='blue',linewidth=3.,label ='Left Model Response' ) + + #Plot the Data (from Configuration 1) + gphy1 = ax2.plot(np.sqrt((MP1[0,0]-MP1[:,0])**2+(MP1[:,1]-MP1[0,1])**2),VtdMP1 + ,marker='o',color='red',linewidth=2.,label ='Right Model Response' ) + ax2.legend(('Left Model Response','Right Model Response'),loc=4) + + elif PlotOpt == 'Secondary': + #plot the secondary potentials + ax3= Plot_Secondary_Potential(XYZ,sig0,sig1,R0,E0,ax3) + ax4= Plot_Secondary_Potential(XYZ,sig0,sig2,R1,E0,ax4) + + #Plot the data(from configuration 0) + gphy0 = ax2.plot(np.sqrt((MP0[0,0]-MP0[:,0])**2+(MP0[:,1]-MP0[0,1])**2),VsdMP0,color='blue' + ,marker='o',linewidth=3.,label ='Left Model Response' ) + + + #Plot the Data (from Configuration 1) + gphy1 = ax2.plot(np.sqrt((MP1[0,0]-MP1[:,0])**2+(MP1[:,1]-MP1[0,1])**2),VsdMP1 + ,marker='o',color='red',linewidth=2.,label ='Right Model Response' ) + ax2.legend(('Left Model Response','Right Model Response'),loc=4 ) + + else: + print('What dont you get? Total or Secondary?') + + #Legends + ax3.plot(MP0[:,0],MP0[:,1],color='gray') + Dip_Midpoint0 = ax3.scatter(MP0[:,0],MP0[:,1],color='black') + Electrodes0 = ax3.scatter(EL0[:,0],EL0[:,1],color='red') + ax3.legend([Dip_Midpoint0,Electrodes0], ["Dipole Midpoint", "Electrodes"],scatterpoints=1) + + ax4.plot(MP1[:,0],MP1[:,1],color='gray') + Dip_Midpoint1 = ax4.scatter(MP1[:,0],MP1[:,1],color='black') + Electrodes1 = ax4.scatter(EL1[:,0],EL1[:,1],color='red') + ax4.legend([Dip_Midpoint1,Electrodes1], ["Dipole Midpoint", "Electrodes"],scatterpoints=1) + + return fig + +#Function to visualise and compare any two meaningful plots for the sphere in a uniform backgound with an unifom Electric Field +def interact_conductiveSphere(R,log_sig0,log_sig1,Figure1a,Figure1b,Figure2a,Figure2b): + + sig0,sig1 = conductivity_log_wrapper(log_sig0,log_sig1) + E0 = 1. # inducing field strength in V/m + n = 100 #level of discretisation + xr = np.linspace(-200., 200., n) # X-axis discretization + yr = xr.copy() # Y-axis discretization + zr = np.r_[0] # identical to saying `zr = np.array([0])` + XYZ = ndgrid(xr,yr,zr) # Space Definition + + fig, ax = plt.subplots(1,2,figsize=(18,6)) + + #Setup figure 1 with options Configuration, Total or Secondary, + #then Potential, ElectricField, Current Density or Charges Density + if Figure1a == 'Configuration': + ax[0] = get_Setup(XYZ,sig0,sig1,R,E0,ax[0],True,[0.1,0.1,0.6]) + + elif Figure1a == 'Total': + + if Figure1b == 'Potential': + ax[0] = Plot_Total_Potential(XYZ,sig0,sig1,R,E0,ax[0]) + + elif Figure1b == 'ElectricField': + ax[0] = Plot_Total_ElectricField(XYZ,sig0,sig1,R,E0,ax[0]) + + elif Figure1b == 'CurrentDensity': + ax[0] = Plot_Total_Currents(XYZ,sig0,sig1,R,E0,ax[0]) + + elif Figure1b == 'ChargesDensity': + ax[0] = Plot_ChargesDensity(XYZ,sig0,sig1,R,E0,ax[0]) + + elif Figure1a == 'Secondary': + + if Figure1b == 'Potential': + ax[0] = Plot_Secondary_Potential(XYZ,sig0,sig1,R,E0,ax[0]) + + elif Figure1b == 'ElectricField': + ax[0] = Plot_Secondary_ElectricField(XYZ,sig0,sig1,R,E0,ax[0]) + + elif Figure1b == 'CurrentDensity': + ax[0] = Plot_Secondary_Currents(XYZ,sig0,sig1,R,E0,ax[0]) + + elif Figure1b == 'ChargesDensity': + ax[0] = Plot_ChargesDensity(XYZ,sig0,sig1,R,E0,ax[0]) + + + if Figure1a== 'Configuration': + ax[1] = Plot_Primary_Potential(XYZ,sig0,sig1,R,E0,ax[1]) + print 'While figure1 is plotting Configuration, figure2 plots the primary field' + + elif Figure2a == 'Total': + if Figure2b == 'Potential': + ax[1] = Plot_Total_Potential(XYZ,sig0,sig1,R,E0,ax[1]) + + elif Figure2b == 'ElectricField': + ax[1] = Plot_Total_ElectricField(XYZ,sig0,sig1,R,E0,ax[1]) + + elif Figure2b == 'CurrentDensity': + ax[1]=Plot_Total_Currents(XYZ,sig0,sig1,R,E0,ax[1]) + + elif Figure2b == 'ChargesDensity': + ax[1] = Plot_ChargesDensity(XYZ,sig0,sig1,R,E0,ax[1]) + + + elif Figure2a == 'Secondary': + if Figure2b == 'Potential': + ax[1] = Plot_Secondary_Potential(XYZ,sig0,sig1,R,E0,ax[1]) + + elif Figure2b == 'ElectricField': + ax[1] = Plot_Secondary_ElectricField(XYZ,sig0,sig1,R,E0,ax[1]) + + elif Figure2b == 'CurrentDensity': + ax[1] = Plot_Secondary_Currents(XYZ,sig0,sig1,R,E0,ax[1]) + + elif Figure2b == 'ChargesDensity': + ax[1] = Plot_ChargesDensity(XYZ,sig0,sig1,R,E0,ax[1]) + + plt.tight_layout(True) + plt.show() + +#Interactive Visualisation of the responses of two configurations to a (pseudo) DC resistivity survey +def interactive_two_configurations_comparison(log_sig0,log_sig1,log_sig2,R0,R1,xstart,ystart,xend,yend,dipole_number,electrode_spacing,matching_spheres_example): + + sig0,sig1 = conductivity_log_wrapper(log_sig0,log_sig1) + sig2 = 10.**log_sig2 + E0 = 1. # inducing field strength in V/m + n = 100 #level of discretisation + xr = np.linspace(-200., 200., n) # X-axis discretization + yr = xr.copy() # Y-axis discretization + zr = np.r_[0] # identical to saying `zr = np.array([0])` + XYZ = ndgrid(xr,yr,zr) # Space Definition + PlotOpt = 'Total' + + if matching_spheres_example: + sig0 = 10.**(-3) + sig1 = 10.**(-2) + sig2 = 1.310344828 * 10**(-3) + R0 = 20. + R1 = 40. + + two_configurations_comparison(XYZ,sig0,sig1,sig2,R0,R1,E0,xstart,ystart,xend,yend,dipole_number,electrode_spacing,PlotOpt) + + else: + two_configurations_comparison(XYZ,sig0,sig1,sig2,R0,R1,E0,xstart,ystart,xend,yend,dipole_number,electrode_spacing,PlotOpt) + + plt.tight_layout(True) + plt.show() + + + +if __name__ == '__main__': + sig0 = -3. # conductivity of the wholespace + sig1 = -1. # conductivity of the sphere + sig0, sig1 = conductivity_log_wrapper(sig0,sig1) + R = 50. # radius of the sphere + E0 = 1. # inducing field strength + n = 100 #level of discretisation + xr = np.linspace(-2.*R, 2.*R, n) # X-axis discretization + yr = xr.copy() # Y-axis discretization + zr = np.r_[0] # identical to saying `zr = np.array([0])` + XYZ = ndgrid(xr,yr,zr) # Space Definition + + fig, ax = plt.subplots(2,5,figsize=(50,10)) + ax[0,0] = get_Setup(XYZ,sig0,sig1,R,E0,ax[0,0],True,[0.6,0.1,0.1]) + ax[1,0] = Plot_Primary_Potential(XYZ,sig0,sig1,R,E0,ax[1,0]) + ax[0,1] = Plot_Total_Potential(XYZ,sig0,sig1,R,E0,ax[0,1]) + ax[1,1] = Plot_Secondary_Potential(XYZ,sig0,sig1,R,E0,ax[1,1]) + ax[0,2] = Plot_Total_ElectricField(XYZ,sig0,sig1,R,E0,ax[0,2]) + ax[1,2] = Plot_Secondary_ElectricField(XYZ,sig0,sig1,R,E0,ax[1,2]) + ax[0,3] = Plot_Total_Currents(XYZ,sig0,sig1,R,E0,ax[0,3]) + ax[1,3] = Plot_Secondary_Currents(XYZ,sig0,sig1,R,E0,ax[1,3]) + ax[0,4] = Plot_Primary_Potential(XYZ,sig0,sig1,R,E0,ax[0,4]) + ax[1,4] = Plot_ChargesDensity(XYZ,sig0,sig1,R,E0,ax[1,4]) + + + plt.show() + diff --git a/SimPEG/Optimization.py b/SimPEG/Optimization.py index 4f2cb062..54f6c4ff 100644 --- a/SimPEG/Optimization.py +++ b/SimPEG/Optimization.py @@ -990,4 +990,18 @@ class ProjectedGNCG(BFGS, Minimize, Remember): cgFlag = 1 # End CG Iterations + # Take a gradient step on the active cells if exist + if temp != self.xc.size: + + rhs_a = (Active) * -self.g + + dm_i = max( abs( delx ) ) + dm_a = max( abs(rhs_a) ) + + delx = delx + rhs_a * dm_i / dm_a /10. + + # Only keep gradients going in the right direction on the active set + indx = ((self.xc<=self.lower) & (delx < 0)) | ((self.xc>=self.upper) & (delx > 0)) + delx[indx] = 0. + return delx diff --git a/SimPEG/Regularization.py b/SimPEG/Regularization.py index e43670d2..ed75ada6 100644 --- a/SimPEG/Regularization.py +++ b/SimPEG/Regularization.py @@ -646,7 +646,7 @@ class Sparse(Simple): eps = 1e-1 curModel = None # use a model to compute the weights gamma = 1. - p = 0. + norms = [0., .2, 2., 2., 1.] qx = 2. qy = 2. qz = 2. @@ -666,7 +666,7 @@ class Sparse(Simple): else: f_m = self.curModel - self.reg.mref - self.rs = self.R(f_m , self.p) + self.rs = self.R(f_m , self.norms[0]) #print "Min rs: " + str(np.max(self.rs)) + "Max rs: " + str(np.min(self.rs)) self.Rs = Utils.sdiag( self.rs ) diff --git a/docs/examples/DC_PseudoSection_Simulation.rst b/docs/examples/DC_PseudoSection_Simulation.rst new file mode 100644 index 00000000..1d4330a1 --- /dev/null +++ b/docs/examples/DC_PseudoSection_Simulation.rst @@ -0,0 +1,31 @@ +.. _examples_DC_PseudoSection_Simulation: + +.. --------------------------------- .. +.. .. +.. THIS FILE IS AUTO GENEREATED .. +.. .. +.. SimPEG/Examples/__init__.py .. +.. .. +.. --------------------------------- .. + + + +DC Forward Simulation +===================== + +Forward model conductive spheres in a half-space and plot a pseudo-section + +Created on Mon Feb 01 19:28:06 2016 + +@fourndo + + + +.. plot:: + + from SimPEG import Examples + Examples.DC_PseudoSection_Simulation.run() + +.. literalinclude:: ../../SimPEG/Examples/DC_PseudoSection_Simulation.py + :language: python + :linenos: diff --git a/docs/examples/EM_FDEM_SusEffects.rst b/docs/examples/EM_FDEM_SusEffects.rst new file mode 100644 index 00000000..3e7dea80 --- /dev/null +++ b/docs/examples/EM_FDEM_SusEffects.rst @@ -0,0 +1,41 @@ +.. _examples_EM_FDEM_SusEffects: + +.. --------------------------------- .. +.. .. +.. THIS FILE IS AUTO GENEREATED .. +.. .. +.. SimPEG/Examples/__init__.py .. +.. .. +.. --------------------------------- .. + + +EM: FDEM: Effects of susceptibility +=================================== + +When airborne freqeuncy domain EM (AFEM) survey is flown over +the earth including significantly susceptible bodies (magnetite-rich rocks), +negative data is often observed in the real part of the lowest frequency +(e.g. Dighem system 900 Hz). This phenomenon mostly based upon magnetization +occurs due to a susceptible body when the magnetic field is applied. + +To clarify what is happening in the earth when we are exciting the earth with +a loop source in the frequency domain we run three forward modelling: + + - F[:math:`\sigma`, :math:`\mu`]: Anomalous conductivity and susceptibility + - F[:math:`\sigma`, :math:`\mu_0`]: Anomalous conductivity + - F[:math:`\sigma_{air}`, :math:`\mu_0`]: primary field + +We plot vector magnetic fields in the earth. For secondary fields we provide +F[:math:`\sigma`, :math:`\mu`]-F[:math:`\sigma`, :math:`\mu_0`]. Following +figure show both real and parts. + + + +.. plot:: + + from SimPEG import Examples + Examples.EM_FDEM_SusEffects.run() + +.. literalinclude:: ../../SimPEG/Examples/EM_FDEM_SusEffects.py + :language: python + :linenos: