mirror of
https://github.com/wassname/simpeg.git
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Merge branch 'dev' into feat/sparse-regularization
# Conflicts: # SimPEG/Regularization.py # SimPEG/Survey.py
This commit is contained in:
Lindsey Heagy
@@ -0,0 +1,68 @@
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from SimPEG import *
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import SimPEG.DCIP as DC
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def run(plotIt=False):
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cs = 25.
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hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
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hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
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hz = [(cs,7, -1.3),(cs,20)]
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mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')
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sighalf = 1e-2
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sigma = np.ones(mesh.nC)*sighalf
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xtemp = np.linspace(-150, 150, 21)
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ytemp = np.linspace(-150, 150, 21)
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xyz_rxP = Utils.ndgrid(xtemp-10., ytemp, np.r_[0.])
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xyz_rxN = Utils.ndgrid(xtemp+10., ytemp, np.r_[0.])
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xyz_rxM = Utils.ndgrid(xtemp, ytemp, np.r_[0.])
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# if plotIt:
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# fig, ax = plt.subplots(1,1, figsize = (5,5))
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# mesh.plotSlice(sigma, grid=True, ax = ax)
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# ax.plot(xyz_rxP[:,0],xyz_rxP[:,1], 'w.')
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# ax.plot(xyz_rxN[:,0],xyz_rxN[:,1], 'r.', ms = 3)
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rx = DC.RxDipole(xyz_rxP, xyz_rxN)
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src = DC.SrcDipole([rx], [-200, 0, -12.5], [+200, 0, -12.5])
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survey = DC.SurveyDC([src])
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problem = DC.ProblemDC_CC(mesh)
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problem.pair(survey)
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try:
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from pymatsolver import MumpsSolver
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problem.Solver = MumpsSolver
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except Exception, e:
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pass
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data = survey.dpred(sigma)
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def DChalf(srclocP, srclocN, rxloc, sigma, I=1.):
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rp = (srclocP.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)
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rn = (srclocN.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)
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rP = np.sqrt(((rxloc-rp)**2).sum(axis=1))
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rN = np.sqrt(((rxloc-rn)**2).sum(axis=1))
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return I/(sigma*2.*np.pi)*(1/rP-1/rN)
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data_anaP = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxP, sighalf)
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data_anaN = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxN, sighalf)
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data_ana = data_anaP-data_anaN
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Data_ana = data_ana.reshape((21, 21), order = 'F')
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Data = data.reshape((21, 21), order = 'F')
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X = xyz_rxM[:,0].reshape((21, 21), order = 'F')
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Y = xyz_rxM[:,1].reshape((21, 21), order = 'F')
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if plotIt:
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots(1,2, figsize = (12, 5))
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vmin = np.r_[data, data_ana].min()
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vmax = np.r_[data, data_ana].max()
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dat1 = ax[1].contourf(X, Y, Data, 60, vmin = vmin, vmax = vmax)
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dat0 = ax[0].contourf(X, Y, Data_ana, 60, vmin = vmin, vmax = vmax)
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cb0 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[0])
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cb1 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[1])
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ax[1].set_title('Analytic')
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ax[0].set_title('Computed')
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plt.show()
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return np.linalg.norm(data-data_ana)/np.linalg.norm(data_ana)
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if __name__ == '__main__':
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print run(plotIt=True)
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@@ -0,0 +1,187 @@
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from SimPEG import Mesh, Utils, np, sp
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import SimPEG.DCIP as DC
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import time
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def run(loc=None, sig=None, radi=None, param=None, stype='dpdp', plotIt=True):
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"""
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DC Forward Simulation
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=====================
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Forward model conductive spheres in a half-space and plot a pseudo-section
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Created by @fourndo on Mon Feb 01 19:28:06 2016
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"""
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assert stype in ['pdp', 'dpdp'], "Source type (stype) must be pdp or dpdp (pole dipole or dipole dipole)"
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if loc is None:
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loc = np.c_[[-50.,0.,-50.],[50.,0.,-50.]]
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if sig is None:
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sig = np.r_[1e-2,1e-1,1e-3]
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if radi is None:
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radi = np.r_[25.,25.]
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if param is None:
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param = np.r_[30.,30.,5]
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# First we need to create a mesh and a model.
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# This is our mesh
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dx = 5.
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hxind = [(dx,15,-1.3), (dx, 75), (dx,15,1.3)]
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hyind = [(dx,15,-1.3), (dx, 10), (dx,15,1.3)]
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hzind = [(dx,15,-1.3),(dx, 15)]
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mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')
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# Set background conductivity
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model = np.ones(mesh.nC) * sig[0]
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# First anomaly
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ind = Utils.ModelBuilder.getIndicesSphere(loc[:,0],radi[0],mesh.gridCC)
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model[ind] = sig[1]
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# Second anomaly
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ind = Utils.ModelBuilder.getIndicesSphere(loc[:,1],radi[1],mesh.gridCC)
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model[ind] = sig[2]
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# Get index of the center
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indy = int(mesh.nCy/2)
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# Plot the model for reference
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# Define core mesh extent
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xlim = 200
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zlim = 125
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# Specify the survey type: "pdp" | "dpdp"
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# Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh
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ends = [(-175,0),(175,0)]
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ends = np.c_[np.asarray(ends),np.ones(2).T*mesh.vectorNz[-1]]
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# Snap the endpoints to the grid. Easier to create 2D section.
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indx = Utils.closestPoints(mesh, ends )
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locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]]
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# We will handle the geometry of the survey for you and create all the combination of tx-rx along line
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# [Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1], param[2])
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survey, Tx, Rx = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1], param[2])
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# Define some global geometry
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dl_len = np.sqrt( np.sum((locs[0,:] - locs[1,:])**2) )
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dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
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dl_y = ( Tx[-1][1,1] - Tx[0][1,0] ) / dl_len
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azm = np.arctan(dl_y/dl_x)
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#Set boundary conditions
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mesh.setCellGradBC('neumann')
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# Define the differential operators needed for the DC problem
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Div = mesh.faceDiv
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Grad = mesh.cellGrad
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Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
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A = Div*Msig*Grad
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# Change one corner to deal with nullspace
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A[0,0] = 1
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A = sp.csc_matrix(A)
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# We will solve the system iteratively, so a pre-conditioner is helpful
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# This is simply a Jacobi preconditioner (inverse of the main diagonal)
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dA = A.diagonal()
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P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
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# Now we can solve the system for all the transmitters
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# We want to store the data
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data = []
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# There is probably a more elegant way to do this, but we can just for-loop through the transmitters
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for ii in range(len(Tx)):
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start_time = time.time() # Let's time the calculations
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#print("Transmitter %i / %i\r" % (ii+1,len(Tx)))
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# Select dipole locations for receiver
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rxloc_M = np.asarray(Rx[ii][:,0:3])
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rxloc_N = np.asarray(Rx[ii][:,3:])
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# For usual cases "dpdp" or "gradient"
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if stype == 'pdp':
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# Create an "inifinity" pole
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tx = np.squeeze(Tx[ii][:,0:1])
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tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
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inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
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RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )
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else:
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inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
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RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )
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# Iterative Solve
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Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
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# We now have the potential everywhere
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phi = Utils.mkvc(Ainvb[0])
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# Solve for phi on pole locations
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P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
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P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
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# Compute the potential difference
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dtemp = (P1*phi - P2*phi)*np.pi
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data.append( dtemp )
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print '\rTransmitter {0} of {1} -> Time:{2} sec'.format(ii,len(Tx),time.time()- start_time),
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print 'Transmitter {0} of {1}'.format(ii,len(Tx))
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print 'Forward completed'
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# Let's just convert the 3D format into 2D (distance along line) and plot
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# [Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(survey, np.ones(survey.nSrc))
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survey2D = DC.convertObs_DC3D_to_2D(survey, np.ones(survey.nSrc))
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survey2D.dobs =np.hstack(data)
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# Here is an example for the first tx-rx array
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if plotIt:
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import matplotlib.pyplot as plt
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fig = plt.figure()
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ax = plt.subplot(2,1,1, aspect='equal')
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mesh.plotSlice(np.log10(model), ax =ax, normal = 'Y', ind = indy,grid=True)
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ax.set_title('E-W section at '+str(mesh.vectorCCy[indy])+' m')
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plt.gca().set_aspect('equal', adjustable='box')
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plt.scatter(Tx[0][0,:],Tx[0][2,:],s=40,c='g', marker='v')
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plt.scatter(Rx[0][:,0::3],Rx[0][:,2::3],s=40,c='y')
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plt.xlim([-xlim,xlim])
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plt.ylim([-zlim,mesh.vectorNz[-1]+dx])
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ax = plt.subplot(2,1,2, aspect='equal')
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# Plot the location of the spheres for reference
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circle1=plt.Circle((loc[0,0]-Tx[0][0,0],loc[2,0]),radi[0],color='w',fill=False, lw=3)
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circle2=plt.Circle((loc[0,1]-Tx[0][0,0],loc[2,1]),radi[1],color='k',fill=False, lw=3)
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ax.add_artist(circle1)
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ax.add_artist(circle2)
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# Add the speudo section
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DC.plot_pseudoSection(survey2D,ax,stype)
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# plt.scatter(Tx2d[0][:],Tx[0][2,:],s=40,c='g', marker='v')
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# plt.scatter(Rx2d[0][:],Rx[0][:,2::3],s=40,c='y')
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# plt.plot(np.r_[Tx2d[0][0],Rx2d[-1][-1,-1]],np.ones(2)*mesh.vectorNz[-1], color='k')
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plt.ylim([-zlim,mesh.vectorNz[-1]+dx])
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plt.show()
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return fig, ax
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if __name__ == '__main__':
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run()
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@@ -21,8 +21,8 @@ def run(plotIt=True):
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active = mesh.vectorCCz<0.
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layer = (mesh.vectorCCz<0.) & (mesh.vectorCCz>=layerz)
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actMap = Maps.ActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)
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mapping = Maps.ExpMap(mesh) * Maps.Vertical1DMap(mesh) * actMap
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actMap = Maps.InjectActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)
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mapping = Maps.ExpMap(mesh) * Maps.SurjectVertical1D(mesh) * actMap
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sig_half = 2e-2
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sig_air = 1e-8
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sig_layer = 1e-2
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@@ -19,8 +19,8 @@ def run(plotIt=True):
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active = mesh.vectorCCz<0.
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layer = (mesh.vectorCCz<0.) & (mesh.vectorCCz>=-100.)
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actMap = Maps.ActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)
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mapping = Maps.ExpMap(mesh) * Maps.Vertical1DMap(mesh) * actMap
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actMap = Maps.InjectActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)
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mapping = Maps.ExpMap(mesh) * Maps.SurjectVertical1D(mesh) * actMap
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sig_half = 2e-3
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sig_air = 1e-8
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sig_layer = 1e-3
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@@ -10,28 +10,6 @@ def run(N=100, plotIt=True):
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"""
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class LinearSurvey(Survey.BaseSurvey):
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def projectFields(self, u):
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return u
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class LinearProblem(Problem.BaseProblem):
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surveyPair = LinearSurvey
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def __init__(self, mesh, G, **kwargs):
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Problem.BaseProblem.__init__(self, mesh, **kwargs)
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self.G = G
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def fields(self, m, u=None):
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return self.G.dot(m)
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def Jvec(self, m, v, u=None):
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return self.G.dot(v)
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def Jtvec(self, m, v, u=None):
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return self.G.T.dot(v)
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np.random.seed(1)
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mesh = Mesh.TensorMesh([N])
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@@ -53,8 +31,8 @@ def run(N=100, plotIt=True):
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mtrue[mesh.vectorCCx > 0.45] = -0.5
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mtrue[mesh.vectorCCx > 0.6] = 0
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prob = LinearProblem(mesh, G)
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survey = LinearSurvey()
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prob = Problem.LinearProblem(mesh, G)
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survey = Survey.LinearSurvey()
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survey.pair(prob)
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survey.makeSyntheticData(mtrue, std=0.01)
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@@ -52,7 +52,7 @@ def run(plotIt=True, nFreq=1):
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# Calculate the data
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fields = problem.fields(sig)
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dataVec = survey.projectFields(fields)
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dataVec = survey.eval(fields)
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# Make the data
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mtData = MT.Data(survey,dataVec)
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@@ -1,6 +1,8 @@
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# Run this file to add imports.
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##### AUTOIMPORTS #####
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import DC_Analytic_Dipole
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import DC_Forward_PseudoSection
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import EM_FDEM_1D_Inversion
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import EM_FDEM_Analytic_MagDipoleWholespace
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import EM_TDEM_1D_Inversion
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@@ -17,7 +19,7 @@ import Mesh_Tensor_Creation
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import MT_1D_ForwardAndInversion
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import MT_3D_Foward
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__examples__ = ["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"]
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__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"]
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##### AUTOIMPORTS #####
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