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Work on Example and mesh refinement for MinSIM
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D Fournier committed
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-127994
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+27
-4
@@ -417,13 +417,14 @@ def read_GOCAD_ts(tsfile):
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return vrtx, trgl
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def gocad2vtk(gcFile,mesh):
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def gocad2vtk(gcFile,mesh,bcflag,inflag):
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""""
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Function to read gocad polystructure file and output indexes of mesh with in the structure.
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"""
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import vtk, vtk.util.numpy_support as npsup
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print "Reading GOCAD ts file..."
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vrtx, trgl = read_GOCAD_ts(gcFile)
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# Adjust the index
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trgl = trgl - 1
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@@ -464,12 +465,34 @@ def gocad2vtk(gcFile,mesh):
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extractImpDistRectGridFilt = vtk.vtkExtractGeometry() # Object constructor
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extractImpDistRectGridFilt.SetImplicitFunction(ImpDistFunc) #
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extractImpDistRectGridFilt.SetInputData(vtkMesh)
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extractImpDistRectGridFilt.ExtractBoundaryCellsOn()
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extractImpDistRectGridFilt.ExtractInsideOn()
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if bcflag is True:
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extractImpDistRectGridFilt.ExtractBoundaryCellsOn()
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else:
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extractImpDistRectGridFilt.ExtractBoundaryCellsOff()
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if inflag is True:
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extractImpDistRectGridFilt.ExtractInsideOn()
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else:
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extractImpDistRectGridFilt.ExtractInsideOff()
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print "Extracting indices from grid..."
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# Executing the pipe
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extractImpDistRectGridFilt.Update()
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# Get index inside
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insideGrid = extractImpDistRectGridFilt.GetOutput()
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insideGrid = npsup.vtk_to_numpy(insideGrid.GetCellData().GetArray('Index'))
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# Get index surface intersect
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extractImpDistRectGridFilt.ExtractBoundaryCellsOn()
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extractImpDistRectGridFilt.ExtractInsideOff()
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extractImpDistRectGridFilt.Update()
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bcGrid = extractImpDistRectGridFilt.GetOutput()
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bcGrid = npsup.vtk_to_numpy(bcGrid.GetCellData().GetArray('Index'))
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# Return the indexes inside
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return npsup.vtk_to_numpy(insideGrid.GetCellData().GetArray('Index'))
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return insideGrid, bcGrid
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@@ -104,7 +104,7 @@ for ii in range(d_iter):
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Utils.writeUBCTensorMesh('Mesh.msh',mesh)
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Utils.writeUBCTensorModel('Model.sus',mesh,model)
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actv = np.ones(mesh.nC)
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#actv = np.ones(mesh.nC)
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#%% Forward mode ldata
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start_time = time.time()
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@@ -1,76 +0,0 @@
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import os
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home_dir = '.\Test_tria_2_grid'
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inpfile = 'PYMAG3C_fwr.inp'
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dsep = '\\'
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os.chdir(home_dir)
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#%%
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from SimPEG import np, sp, Utils, mkvc, Maps
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import vtk, vtk.util.numpy_support as npsup
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import simpegPF as PF
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import pylab as plt
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## New scripts to be added to basecode
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#from fwr_MAG_data import fwr_MAG_data
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#from read_MAGfwr_inp import read_MAGfwr_inp
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#%%
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# Read input file
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# Load mesh file
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mesh = Utils.meshutils.readUBCTensorMesh('Mesh_2p5m.msh')
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# Load GOCAD surf
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tsfile = 'Crown.ts'
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#[vrtx, trgl] = PF.BaseMag.read_GOCAD_ts(tsfile)
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indx = PF.BaseMag.gocad2vtk(tsfile,mesh)
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model= np.zeros(mesh.nC)
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model[indx]=1
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Utils.meshutils.writeUBCTensorModel('VTKout.dat',mesh,model)
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# Load model file
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#model = Utils.meshutils.readUBCTensorModel(modfile,mesh)
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# Load in topofile or create flat surface
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#==============================================================================
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# if topofile == 'null':
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#
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# actv = np.ones(mesh.nC)
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#
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# else:
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# topo = np.genfromtxt(topofile,skip_header=1)
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# actv = PF.Magnetics.getActiveTopo(mesh,topo,'N')
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#
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#
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# Utils.writeUBCTensorModel('nullcell.dat',mesh,actv)
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#
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# # Load in observation file
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# [B,M,dobs] = PF.BaseMag.readUBCmagObs(obsfile)
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#
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# rxLoc = dobs[:,0:3]
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# #rxLoc[:,2] += 5 # Temporary change for test
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# ndata = rxLoc.shape[0]
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#==============================================================================
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#%% Run forward modeling
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# Compute forward model using integral equation
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#==============================================================================
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# d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi')
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#
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# # Form data object with coordinates and write to file
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# wd = np.zeros((ndata,1))
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#
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# # Save forward data to file
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# PF.Magnetics.writeUBCobs(home_dir + dsep + 'FWR_data.dat',B,M,rxLoc,d,wd)
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#==============================================================================
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@@ -0,0 +1,139 @@
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import os
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home_dir = '.\Test_tria_2_grid'
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inpfile = 'PYMAG3C_fwr.inp'
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dsep = '\\'
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os.chdir(home_dir)
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#%%
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from SimPEG import np, sp, Utils, Mesh
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import scipy.interpolate as interpolation
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import simpegPF as PF
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import pylab as plt
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import time as tm
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## New scripts to be added to basecode
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#from fwr_MAG_data import fwr_MAG_data
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#from read_MAGfwr_inp import read_MAGfwr_inp
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#%%
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chibkg = 0.001
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# Read in topo surface
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tsfile = 'Topo_Gaussian.ts'
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# For now just read both
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topofile = 'Gaussian.topo'
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topo = np.genfromtxt(topofile,skip_header=1)
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# Offset data above topo
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zoffset = 2
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# Load mesh file
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B = np.array(([90.,0.,50000.]))
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M = np.array(([90.,0.,315.]))
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# Sphere radius
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R = 25.
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#%% Script starts here
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# # Create a grid of observations and offset the z from topo
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xr = np.linspace(-99., 99., 40)
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yr = np.linspace(-49., 49., 20)
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X, Y = np.meshgrid(xr, yr)
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F = interpolation.NearestNDInterpolator(topo[:,0:2],topo[:,2])
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Z = F(X,Y) + zoffset
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rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)]
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ndata = rxLoc.shape[0]
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sclx = 100.
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dx = 10
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nc = int(sclx/dx)
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hxind = [(dx, 2*nc)]
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hyind = [(dx, nc)]
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hzind = [(dx, nc)]
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mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')
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# Load GOCAD surf
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#[vrtx, trgl] = PF.BaseMag.read_GOCAD_ts(tsfile)
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# Find active cells from surface
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tin = tm.time()
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print "Computing indices with VTK: "
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[indx, bc] = PF.BaseMag.gocad2vtk(tsfile,mesh, bcflag = False, inflag = True)
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print "VTK operation completed in " + str(tm.time() - tin)
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actv = np.zeros(mesh.nC)
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actv[indx] = 1
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model= np.zeros(mesh.nC)
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model[indx]= chibkg
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Utils.meshutils.writeUBCTensorModel('VTKout.dat',mesh,model)
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Utils.meshutils.writeUBCTensorMesh('Mesh_temp.msh',mesh)
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start_time = tm.time()
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d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi')
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timer = (tm.time() - start_time)
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#%% Plot data
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plt.figure(1)
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ax = plt.subplot()
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plt.imshow(np.reshape(d,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower')
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plt.colorbar(fraction=0.02)
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plt.contour(X,Y, np.reshape(d,X.shape),10)
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plt.scatter(X,Y, c=np.reshape(d,X.shape), s=20)
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ax.set_title('Forward data')
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# Load model file
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#model = Utils.meshutils.readUBCTensorModel(modfile,mesh)
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# Load in topofile or create flat surface
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#==============================================================================
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# if topofile == 'null':
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#
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# actv = np.ones(mesh.nC)
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#
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# else:
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# topo = np.genfromtxt(topofile,skip_header=1)
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# actv = PF.Magnetics.getActiveTopo(mesh,topo,'N')
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#
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#
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# Utils.writeUBCTensorModel('nullcell.dat',mesh,actv)
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#
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# # Load in observation file
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# [B,M,dobs] = PF.BaseMag.readUBCmagObs(obsfile)
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#
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# rxLoc = dobs[:,0:3]
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# #rxLoc[:,2] += 5 # Temporary change for test
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# ndata = rxLoc.shape[0]
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#==============================================================================
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#%% Run forward modeling
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# Compute forward model using integral equation
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#==============================================================================
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# d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi')
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#
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# # Form data object with coordinates and write to file
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# wd = np.zeros((ndata,1))
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#
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# # Save forward data to file
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# PF.Magnetics.writeUBCobs(home_dir + dsep + 'FWR_data.dat',B,M,rxLoc,d,wd)
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#==============================================================================
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File diff suppressed because it is too large.
Load diff
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20 10 10
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-100.00 -50.00 0.00
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10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00
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10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00
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10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00
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File diff suppressed because it is too large.
Load diff
+1851
-127851
File diff suppressed because it is too large.
Load diff
@@ -100,7 +100,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 59,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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@@ -117,12 +117,18 @@
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"M = np.array([90,0])\n",
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"\n",
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"# Create a mesh\n",
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"hxind = [(5, 20)]\n",
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"hyind = [(5, 20)]\n",
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"hzind = [(5, 10)]\n",
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"dx = 5.\n",
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"\n",
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"hxind = [(dx,5,-1.3), (dx, 20), (dx,5,1.3)]\n",
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"hyind = [(dx,5,-1.3), (dx, 20), (dx,5,1.3)]\n",
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"hzind = [(dx,5,-1.3),(5, 10)]\n",
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"\n",
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"mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')\n",
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"\n",
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"# Get index of the center\n",
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"midx = int(mesh.nCx/2)\n",
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"midy = int(mesh.nCy/2)\n",
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"\n",
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"# Assume flat topo for now, so all cells are active\n",
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"nC = mesh.nC \n",
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"actv = np.ones(nC)\n",
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@@ -132,11 +138,33 @@
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"xr = np.linspace(-20., 20., 20)\n",
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"yr = np.linspace(-20., 20., 20)\n",
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"X, Y = np.meshgrid(xr, yr)\n",
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"Z = np.ones(X.size)*(mesh.vectorNz[-1]+1.) # Let just put the observation flat\n",
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"Z = np.ones(X.size)*(mesh.vectorNz[-1]+dx) # Let just put the observation flat\n",
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"\n",
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"rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"59.390075000000003"
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]
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},
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"execution_count": 60,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Z.max()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@@ -172,7 +200,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 61,
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"metadata": {
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"collapsed": false
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},
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@@ -209,7 +237,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 91,
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"metadata": {
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"collapsed": false
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},
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@@ -228,7 +256,9 @@
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"Done 60.0 %\n",
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"Done 70.0 %\n",
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"Done 80.0 %\n",
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"Done 90.0 %\n"
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"Done 90.0 %\n",
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"Done 100% ...distance weighting completed!!\n",
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"\n"
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]
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},
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{
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@@ -970,7 +1000,7 @@
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{
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"data": {
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"text/html": [
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||||
"<img 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truncated
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truncated
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -983,11 +1013,11 @@
|
||||
"source": [
|
||||
"# Generate a distance weighting\n",
|
||||
"wr = PF.Magnetics.get_dist_wgt(mesh,rxLoc,3.,np.min(mesh.hx)/4)\n",
|
||||
"wrMap = PF.BaseMag.WeightMap(mesh, wr**-1)\n",
|
||||
"wrMap = PF.BaseMag.WeightMap(mesh, wr)\n",
|
||||
"\n",
|
||||
"plt.figure()\n",
|
||||
"ax = subplot()\n",
|
||||
"mesh.plotSlice(wr, ax = ax, normal = 'Y', ind=10)\n",
|
||||
"mesh.plotSlice(wr, ax = ax, normal = 'Y', ind=midx)\n",
|
||||
"title('Distance weighting')\n",
|
||||
"xlabel('x');ylabel('z')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')"
|
||||
@@ -995,7 +1025,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"execution_count": 92,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -1739,7 +1769,7 @@
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAgAElEQVR4nOydd5hU1f3G3+27tKVKVdaKBsESEUP8KWqssWuMEVTUJGoSW2LsZSyoMbbE3nuJWLALKk2jYsOGovS6lN1lge07M+/vj3Ouc/ZyZwsMcyfs+3mezxPmzr137hzI7uv3NEAIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghRGbyIIA4gNtSeM8Se89TUnjP1jIFwOQU3WskzPfYvxXnxgFcnaLPDbr3dZvo3puKMTDPvdUGXLsp21IIIYRo9xQBWAOgGsByADkpum8+gD0B9EjR/drCZACTUnSvkWhbALwqRZ8bdO9rN9G9NxVjsHEBcFO1pRBCCNHu+R3ML9u/2P/9dbiPkxKmQAEwExgDBUAhhBAiI3kbwEwAWQCWAHi+ldf1AfA4gKUA6gAsA/AagF72/RKYX+KnOtc8BmAxgGEAPgJQA2AWEqHzYgALAVQCeBlAT99nxgFcD+By+6w1AKYC2MV33hSsHwB7AbjPXlcH4HsAf2jF9xxpP/dY+/wVMBXTpwB0D3g+f2g5BInv6n2vHQI+5xgA/wWwzt5/OoAjfPd2A2AHmPZeBmBIM88fsdfuCOAdmErvAgCn2fdPA/Cj/dxJALbxXZ8H0+YLANQDmA/TFZ3rO28bAG/Y+68EcAeAMxEcAP8I4CsAtQBWAXgIQDffOQqAQgghxCaiH4BGJH7R3gbzS7lrK659Bya8/Q7A3gCOB3APgIH2/RKsPwbwMZhwMxOmOnQwgGn2M+8E8CqAQ2FCyRoA//F9ZhzAIgDvAzgSwAn2GcrQNEBMQdMA2AXADzAh5gyYat7NAKIwlc/mGOl87sMADrLXrMX6IdMfWg4BEAMwAcDhMG01GyYg9XPOO8de+yJMEDwQwCW+Z3MDYHcAH8J894Fonoi99ht7vwMAvGSP3QoTOo+E+ftbCuBj3/XPwPwbiQD4Fcy4vAYATzvn5AOYCxOuTwVwGIBXYMK+PwDeZK//p73fGHvdxwCyfd9XAVAIIYTYBFwE84vWq0jtaV+f2Ypr16H58FSC4AAYhwmMHkPsse9hqpAet8IEBfdYHCY8FTnHBtrz3OrYFDQNZ1fChMxtfc/4AEwFKhvJGWk/903f8ZOwftewP7R8BhM83fuX2Oe91b7uAtOWLzTzDN69r4UJU9/DVAhbM74yYq8d7RzrChN+VwHo5Bz3guiW9vXOCA5il9vjXuXxD/b1ns45WQC+hQnAXgAssZ97he9+I+z1RznHFACFEEKITcRMADN8x+bCVJdaYhJMVexcmCCQ5Xu/BMEBcK3vvHx73p2+43+0x91KWdzew880mIqkxxQ0DYD/hZkYkgPTdel5vL3nzgH39BhpzxkT8NxRAJf5ns8LLR1hwk/QuL3JMOEQMFXCOExlsTm8CuESmG77Di2c7xGx1/rD4lKYLmSXg+y5I+zrP9nX/m7hgfb4n+3rR2Cqq36uRtMKoBcUt0HTv4dcmH8XtzrXKgAKIYQQm4A9kFhapKvjHfb49i1c3wvAXTCBJA4TKK5EIgiWIDgALgq4V9AEhzFYP3zEAfwj4PoXYMKsxxQ0DYCz7bVBxgDsG3BPj5H2vEMD3lsF4G7f83mhZYB9fXbAdc/BBG0AGGXP+1kzz+Dde5V93hNaONclYq/1VznnA3jCd2wkmlY1r7Cvi3znFdrjV9rXb8FUJP2chaYB0KscJvt7eNS5VgFQCCGE2ATcieS/jL1g2Fq2BzDWXneWPVaC4AC4OOD6tgTAxwKufx/AROf1FDQNgB/Zc3ZPotsN6mek/dzTfMdbWwG8JuCeUwB8av98MFpfAbwO5u+tHmZSSmuIYMMDYLIKYAnWrwAuDPhsfwXQC4QHIPjvwR3PqAAohBBCpJh8mIkTHwLYx+e+AL5AcJdeS5Qj0ZVbgk1TAVyJpt2fJTCTFNygNQVNA+DVMN+3F9rOSPu5b/mOe5W7/XzP54aWT2DG67nhyxuz+E/7ujNM92drxwACwO32Hse34vkj2PAAONi+vsx3nlfJG2xf/96+Hu6ckw1TlXXHAG4DE5pPb8VzKwAKIYQQKeYYmF+wJyd531u+Y2SS94thKljnwYxhOwDAv+013tIlJdg0FcBFAD6AmTDwW5iZsKvQdObyFDTdCaQLgO/suWfChLbDAVwIYHyS7+gx0n7uQphK18EwkyXWAngv4Hu4oeVgmMDzJky7/A5myZUVMMvoePzZXvsCTGXvQAB/R/JZwABwC0wIbKk7OILgALgALQdAwMz2bbDf60D7vw0wy+B45AGYAzMMwJsF/CrM35VbAQRMpbgGpiv/1zD/dsbY+410zvO35b4wbZns36wQQgghWuBlmDXpCpO83wVmPbdHkryfD7Om3rdoum7dic45JVg/AD6KtlUAY1g/AF4H4FKYIFkLsw7gUN+1QTuBdIVZ5mYeTBfqCnvtuUFf0GGkfY6j7fOvhgl/rV0H8GCYSqu7DmDQ+MrjYJZCqYFpz49ggpR7b38b3QRT/fxtM89/tX3+1lYAY2gaAPNg2nwBTPCbb5/Dv2PM1mi6DuDtMBN53Aqgx2iY71cF8+/nO5j/gOjvnONvS+/Z3H9PQgghhGgHBIUgIYQQQgixGaMAKIQQQgjRzlAAFEIIIYQQQgghhBBCCCGEEOJ/kiyYGYFdpJRSps3+WH9bRyGESBv9AVBKKWXadZfjEUKItNIFAIELCFySAQ7LgGfIRNUuahe1y+bTLhfQ/NxFl5B//gsh2jE2AF5CIJIB7pUBz5CJql3ULmqXzaddLiEUAIUQIaMA+D+h2kXtonbZfNpFAVAIET4ZFgBHZ8AzZKJqF7WL2mXzaRcFQCFE+GRYAJRSys1dBUAhRPgoAEopZVpVABRChI8CoJRSplUFQCFE+CgASillWlUAFEKEjwKglFKmVQVAIUT4KABKKWVaVQAUQoSPAqCUUqZVBUAhxIZzE4CvAawBsAzAMwAG+M4ZCmAagCoASwFcHXAfBUAppUyrCoBCiA3nBgC7AcgFUAzgaQAznPc7AygFMBZAAYCdASwGcL7vPgqAUkqZVhUAhRCpY1cAcZgwCACnAlgBINs551wAc3zXKQBKKWVaVQAUQqSOiwDMc17fDuAt3zkjYEJiJ+eYAqCUUqZVBUAhRGr4Fcw4v4OcYw8DeM533k4wAbCfc0wBUEop06oCoBBi4zkcwGoAR/mO3wbgbd+xZiqAwwjsZc2UDdOllHJzcTQTP2OHEQqAQoiNYBSASpgKoJ9TYMYA5jjHzgMw23eeKoCyTUaAzcqw21O2R1UBFEJsOH8BUAHgl0ne7wSzPMz1AAoBDAGwCJoFLDfSSAaEtlQadnvK9qgCoBBiw4kDqAewzqcbCIfArANYDbMkzFUB91EAlG0ykgGhLZWG3Z6yPaoAKIQIHwVA2SYjGRDaUmnY7SnbowqAQojwUQCUbTKSAaEtlYbdnrI9qgAohAgfBUDZJiMZENpSadjtKdujCoBCiPBRAJRtMpIBoS2Vht2esj2qACiECB8FQNkmIxkQ2lJp2O0p26MKgEKI8FEAlG0ykgGhLZWG3Z6yPaoAKIQIHwVA2SYjGRDaUmnY7SnbowqAQojwUQCUbTKSAaEtlYbdnrI9qgAohAgfBUDZJiMZENpSadjtKdujCoBCiPBRAJRtMpIBoS2Vht2esj2qACiECB8FQNkmIxkQ2lJp2O0p26MKgEKI8FEAlG0ykgGhLZWG3Z6yPaoAKIQIHwVA2SYjGRDaUmnY7SnbowqAQojwUQCUbTKSAaEtlYbdnrI9qgAohAgfBUDZJiMZENpSadjtKdujCoBCiPBRAJRtMpIBoS2Vht2esj2qACiECB8FQNkmIxkQ2lJp2O0p26MKgEKI8FEAlG0ykgGhLZWG3Z6yPaoAKIQIHwVA2SYjGRDaUmnY7SnbowqAQojwUQCUbTKSAaEtlYbdnrI9qgAohAgfBUDZJiMZENpSadjtKdujCoBCiPBRAJRtMpIBoS2Vht2esj2qACiECB8FQNkmIxkQ2lJp2O0p26MKgEKI8FEAlG0ykgGhLZWG3Z6yPaoAKIQIHwVA2SYjGRDaUmnY7SnbowqAQojwUQCUbTKSAaEtlYbdnrI9qgAohNhwTgTwPoA1AOIAsn3vDwUwDUAVgKUArk5yHwVA2SYjGRDaUmnY7SnbowqAQogN5yAAvwVwGtYPgJ0BlAIYC6AAwM4AFgM4P+A+CoCyTUYyILSl0rDbU7ZHFQCFEBvPSKwfAE8FsMJ37FwAcwKuVwCUbTKSAaEtlYbdnrI9qgAohNh4RmL9AHg7gLd8542w53XyHVcAlG0ykgGhLZWG3Z6yPaoAKITYeEZi/QD4MIDnfOftZM/r5zuuACjbZCQDQlsqDbs9ZXtUAVAIsfGMxPoB8DYAb/vOa6ECOIzAXtbRGfADUmaqkQwIbak07PaU7cXRTPyMHUYoAAohNpKRWD8AngIzBjDHOXYegNkB16sCKNtkJANCWyoNuz1le1QVQCHEhpMNoBBmNnAcQAf7OgumyrcMwPX22BAAi6BZwDIFRjIgtKXSsNtTtkcVAIUQG84YmOAXBxBz/ryPfX8IzDqA1TBLwlyV5D4KgLJNRjIgtKXSsNtTtkcVAIUQ4aMAKNtkJANCWyoNuz1le1QBUAgRPgqAsk1GMiC0pdKw21O2RxUAhRDhowAo22QkA0JbKg27PWV7VAFQCBE+CoCyTULine truncated
|
||||
"<img src=\"data:image/png;base64,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 truncated
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -1753,21 +1783,21 @@
|
||||
"# We can now create a susceptibility model and generate data\n",
|
||||
"# Lets start with a simple block in half-space\n",
|
||||
"model = np.zeros((mesh.nCx,mesh.nCy,mesh.nCz))\n",
|
||||
"model[8:11,8:11,6:9] = 0.01\n",
|
||||
"model[(midx-2):(midx+2),(midy-2):(midy+2),-6:-2] = 0.01\n",
|
||||
"model = mkvc(model)\n",
|
||||
"\n",
|
||||
"# Create a few models\n",
|
||||
"figure()\n",
|
||||
"ax = subplot(211)\n",
|
||||
"mesh.plotSlice(model, ax = ax, normal = 'Y', ind=10)\n",
|
||||
"mesh.plotSlice(model, ax = ax, normal = 'Y', ind=midx, grid=True)\n",
|
||||
"title('A simple block model.')\n",
|
||||
"xlabel('x');ylabel('y')\n",
|
||||
"xlabel('x');ylabel('z')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"\n",
|
||||
"# We can now generate data\n",
|
||||
"data = F.dot(model) #: this is matrix multiplication!!\n",
|
||||
"subplot(212)\n",
|
||||
"imshow(data.reshape(X.shape))\n",
|
||||
"imshow(data.reshape(X.shape), extent=[xr.min(), xr.max(), yr.min(), yr.max()])\n",
|
||||
"title('Predicted data.')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n"
|
||||
]
|
||||
@@ -1779,7 +1809,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"execution_count": 93,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -1820,7 +1850,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"execution_count": 97,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -1832,17 +1862,20 @@
|
||||
"survey.pair(prob)\n",
|
||||
"#survey.makeSyntheticData(data, std=0.01)\n",
|
||||
"survey.dobs=data\n",
|
||||
"survey.std = np.ones(len(data))*1e-1\n",
|
||||
"survey.mtrue = model\n",
|
||||
"\n",
|
||||
"reg = Regularization.Tikhonov(mesh, mapping = wrMap)\n",
|
||||
"\n",
|
||||
"reg = Regularization.Tikhonov(mesh, mapping=wrMap)\n",
|
||||
"dmis = DataMisfit.l2_DataMisfit(survey)\n",
|
||||
"opt = Optimization.ProjectedGNCG(maxIter=35,lower=0.,upper=1.)\n",
|
||||
"invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = 1e-2)\n",
|
||||
"dmis.Wd = np.ones(len(data))*1.\n",
|
||||
"opt = Optimization.ProjectedGNCG(maxIter=6,lower=0.,upper=1.)\n",
|
||||
"# opt = Optimization.InexactGaussNewton(maxIter=6)\n",
|
||||
"invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = 1e+4)\n",
|
||||
"beta = Directives.BetaSchedule()\n",
|
||||
"#betaest = Directives.BetaEstimate_ByEig()\n",
|
||||
"target = Directives.TargetMisfit()\n",
|
||||
"inv = Inversion.BaseInversion(invProb, directiveList=[beta, target])\n",
|
||||
"reg.alpha_s =0.0025\n",
|
||||
"m0 = np.ones_like(survey.mtrue)*1e-4"
|
||||
]
|
||||
},
|
||||
@@ -1855,7 +1888,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"execution_count": 98,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -1870,18 +1903,14 @@
|
||||
"=============================== Projected GNCG ===============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
"SimPEG.l2_DataMisfit is creating default weightings for Wd.\n",
|
||||
" 0 1.00e-02 3.06e+05 1.89e-04 3.06e+05 3.98e+01 0 \n",
|
||||
" 1 1.00e-02 5.42e+04 1.86e-04 5.42e+04 3.93e+01 0 \n",
|
||||
" 2 1.00e-02 2.72e+04 1.78e-04 2.72e+04 3.89e+01 0 Skip BFGS \n",
|
||||
" 3 1.25e-03 1.98e+04 1.42e-04 1.98e+04 3.81e+01 0 Skip BFGS \n",
|
||||
" 4 1.25e-03 1.97e+04 1.63e-04 1.97e+04 3.80e+01 3 \n",
|
||||
" 5 1.25e-03 1.86e+04 5.30e-05 1.86e+04 3.72e+01 0 \n",
|
||||
"------------------------------------------------------------------\n",
|
||||
"0 : ft = 1.9170e+04 <= alp*descent = 1.8567e+04\n",
|
||||
"1 : maxIterLS = 10 <= iterLS = 10\n",
|
||||
"------------------------- End Linesearch -------------------------\n",
|
||||
"The linesearch got broken. Boo.\n"
|
||||
" 0 1.00e+04 5.58e+04 2.22e-07 5.58e+04 8.41e+01 0 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 5.5847e+03\n",
|
||||
"1 : |xc-x_last| = 4.1645e-02 <= tolX*(1+|x0|) = 1.0116e-01\n",
|
||||
"0 : |proj(x-g)-x| = 8.4094e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 8.4094e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 6 <= iter = 1\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1891,29 +1920,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"min(mrec)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"execution_count": 99,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -2657,7 +2664,7 @@
|
||||
{
|
||||
"data": {
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truncated
|
||||
"<img src=\"data:image/png;base64,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 truncated
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -2665,15 +2672,47 @@
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x000000002CCA78C8>"
|
||||
]
|
||||
},
|
||||
"execution_count": 99,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Here is the recovered susceptibility model\n",
|
||||
"plt.figure()\n",
|
||||
"ax = subplot()\n",
|
||||
"ax = subplot(211)\n",
|
||||
"mesh.plotSlice(mrec, ax = ax, normal = 'Y', ind=10)\n",
|
||||
"title('Recovered model.')\n",
|
||||
"xlabel('x');ylabel('y')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')"
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"\n",
|
||||
"# Plot predicted data and residual\n",
|
||||
"pred = F.dot(mrec) #: this is matrix multiplication!!\n",
|
||||
"\n",
|
||||
"subplot(234)\n",
|
||||
"imshow(data.reshape(X.shape))\n",
|
||||
"title('Predicted data.')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"colorbar()\n",
|
||||
"\n",
|
||||
"subplot(235)\n",
|
||||
"imshow(pred.reshape(X.shape))\n",
|
||||
"title('Predicted data.')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"colorbar()\n",
|
||||
"\n",
|
||||
"subplot(236)\n",
|
||||
"imshow(data.reshape(X.shape) - pred.reshape(X.shape))\n",
|
||||
"title('Residual data.')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"colorbar()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
Reference in new issue
Block a user