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@sgkang
Created March 3, 2015 20:01
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DCfwdtest
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{
"metadata": {
"language": "Julia",
"name": "",
"signature": "sha256:902a18b5542ca93e1de35d64ef1ff99f85c021c5b3a25e774117ee5920af51c3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"using DCIP\n",
"using Mesh\n",
"using Base.Test\n",
"using Utils\n",
"using LinearSolvers\n",
"using MUMPS"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"using PyPlot"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"DC foward modeling test"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Step1: Generate Mesh"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cs, npad, rate = 100., 10, 1.3\n",
"hpad = cs*(1:npad).^rate;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hx = [hpad, cs*ones(40), hpad[end:-1:1]]; \n",
"hy = [hpad, cs*ones(40), hpad[end:-1:1]]; \n",
"hz = [hpad, cs*ones(20)]; \n",
"x0 = [-sum(hx)/2., -sum(hy)/2., -sum(hz)];\n",
"M = getTensorMesh3D(hx, hy, hz, x0);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xc, yc, zc = getNodalAxes(M);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"display(M)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Tensor mesh of size 60 x 60 x 30\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of cells: 108000\n",
"Number of faces: 109800 + 109800 + 111600 = 331200\n",
"Number of edges: 113460 + 113460 + 111630 = 338550\n",
"Number of nodes: 115351\n",
"Coordinate origin: (-11689.954389991177m, -11689.954389991177m, -11689.954389991177m)\n",
"Domain size: 23379.908779982354m x 23379.908779982354m x 11689.954389991177m\n",
"Minimum cell size: 100.0m x 100.0m x 100.0m\n",
"Maximum cell size: 1995.2623149688798m x 1995.2623149688798m x 1995.2623149688798m\n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Step2: Generate DC survey (gradient array)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"XYZN = getNodalGrid(M)\n",
"xc1_p, yc1_p, zc1_p = -1500., 0., 0.; \n",
"xc1_n, yc1_n, zc1_n = 1500., 0., 0.; \n",
"xc2_p, yc2_p, zc2_p = 0., -1500., 0.; \n",
"xc2_n, yc2_n, zc2_n = 0., 1500., 0.; \n",
"(dum, indp1) = findmin(abs(XYZN[:,1]-xc1_p)+abs(XYZN[:,2]-yc1_p)+abs(XYZN[:,3]-zc1_p));\n",
"(dum, indn1) = findmin(abs(XYZN[:,1]-xc1_n)+abs(XYZN[:,2]-yc1_n)+abs(XYZN[:,3]-zc1_n));\n",
"(dum, indp2) = findmin(abs(XYZN[:,1]-xc2_p)+abs(XYZN[:,2]-yc2_p)+abs(XYZN[:,3]-zc2_p));\n",
"(dum, indn2) = findmin(abs(XYZN[:,1]-xc2_n)+abs(XYZN[:,2]-yc2_n)+abs(XYZN[:,3]-zc2_n));"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Sources = zeros(Float64, M.nn, 2);\n",
"Sources[indp1,1] = 1./3;\n",
"Sources[indn1,1] = -1./3;\n",
"Sources[indp2,2] = 1./3;\n",
"Sources[indn2,2] = -1./3;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xr = linspace(-1000., 1000., 11);\n",
"(XrP, YrP, ZrP) = ndgrid(xr, xr, -1e-4*ones(1));\n",
"(XrN, YrN, ZrN) = ndgrid(xr+100., xr, -1e0*ones(1));\n",
"xyzrP = [XrP[:] YrP[:] ZrP[:]];\n",
"xyzrN = [XrN[:] YrN[:] ZrN[:]];\n",
"QP = getNodalInterpolationMatrix(M, xyzrP);\n",
"QN = getNodalInterpolationMatrix(M, xyzrN);\n",
"Q = blkdiag(QP-QN, QP-QN);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = figure(\"pyplot_annotation\",figsize=(4,4))\n",
"# ax = axes([-1100, 1100, -1100, 1100])\n",
"ax = gca();\n",
"xlim(-1600, 1600);\n",
"ylim(-1600, 1600);\n",
"plot(XrP[:], YrP[:], linestyle=\"None\", marker=\"o\", color=\"black\")\n",
"plot(XrN[:], YrN[:], linestyle=\"None\", marker=\"o\", color=\"red\")\n",
"plot([xc1_p, xc1_n], [yc1_p, yc1_n], linestyle=\"None\", marker=\"*\", color=\"blue\")\n",
"plot([xc2_p, xc2_n], [yc2_p, yc2_n], linestyle=\"None\", marker=\"*\", color=\"green\")\n",
"legend((\"M\", \"N\",\"Tx1\",\"Tx2\"),loc=1, fontsize=\"8\")\n",
"grid(\"on\")\n",
"title(\"DC survey geometry\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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",
"text": [
"Figure(PyObject <matplotlib.figure.Figure object at 0x114f04850>)"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 38,
"text": [
"PyObject <matplotlib.text.Text object at 0x116a4fdd0>"
]
}
],
"prompt_number": 38
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Step3: Generate DCIPParam"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mumps = getMUMPSsolver([],1);\n",
"param = getDCIPParam(M, Sources, Q', [], mumps);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Step4: Compute forward response"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sigma = ones(M.nc);\n",
"@time data, param = DCIPfwd(sigma, param);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"elapsed time: 3"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
".872142183 seconds\n",
"(230702,)(230702,242)\n",
"elapsed time: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4.640110579 seconds (407636672 bytes allocated, 4.72% gc time)\n"
]
}
],
"prompt_number": 40
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Step5: Compare with analytics"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"function analyticDC(txlocs::Array{Float64,2}, rxlocsP::Array{Float64,2}, rxlocsN::Array{Float64,2}, sig)\n",
" nrx = size(rxlocsP, 1)\n",
" rP1 = sqrt((rxlocsP[:,1]-txlocs[1,1]).^2 + (rxlocsP[:,2]-txlocs[1,2]).^2)\n",
" rN1 = sqrt((rxlocsN[:,1]-txlocs[1,1]).^2 + (rxlocsN[:,2]-txlocs[1,2]).^2)\n",
" rP2 = sqrt((rxlocsP[:,1]-txlocs[2,1]).^2 + (rxlocsP[:,2]-txlocs[2,2]).^2)\n",
" rN2 = sqrt((rxlocsN[:,1]-txlocs[2,1]).^2 + (rxlocsN[:,2]-txlocs[2,2]).^2)\n",
"\n",
" phiP1 = 1./(2*pi*rP1)\n",
" phiN1 = 1./(2*pi*rN1) \n",
" phiP2 = 1./(2*pi*rP2)\n",
" phiN2 = 1./(2*pi*rN2) \n",
" delphi = phiP1 - phiN1 - (phiP2 - phiN2)\n",
" return delphi/sig\n",
"end"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"analyticDC (generic function with 1 method)"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"trxlocs = [[xc1_p yc1_p zc1_p], [xc1_n yc1_n zc1_n]];"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_anal = analyticDC(trxlocs, xyzrP, xyzrN, 1.);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nrx = prod(size(Xr));\n",
"data = reshape(data, nrx, 2);"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(Xr, Yr) = ndgrid(xr, xr);\n",
"fig = figure(\"pyplot_subplot_mixed\",figsize=(8,3))\n",
"subplot(121)\n",
"contourf(Xr+50., Yr, reshape(data[:,1], size(Xr)), 20)\n",
"title(\"DC julia\")\n",
"subplot(122)\n",
"contourf(Xr+50., Yr, reshape(data_anal, size(Xr)), 20)\n",
"title(\"DC analytic\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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",
"text": [
"Figure(PyObject <matplotlib.figure.Figure object at 0x116658250>)"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"PyObject <matplotlib.text.Text object at 0x1140a4d10>"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = figure(figsize=(8,4))\n",
"plot(data[:,1], linestyle=\"None\", marker=\"o\", color=\"black\")\n",
"plot(data_anal, color=\"black\")\n",
"legend((\"DC julia\", \"DC analytic\"))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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",
"text": [
"Figure(PyObject <matplotlib.figure.Figure object at 0x116a01b10>)"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 54,
"text": [
"PyObject <matplotlib.legend.Legend object at 0x11892c090>"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = figure(figsize=(5,3))\n",
"plot(data_anal./data[:,1])\n",
"title(\"ratio\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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",
"text": [
"Figure(PyObject <matplotlib.figure.Figure object at 0x1111d67d0>)"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 55,
"text": [
"PyObject <matplotlib.text.Text object at 0x118994e90>"
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Grad = getNodalGradientMatrix(param.M);\n",
"# Msig = getEdgeMassMatrix(param.M,vec(sigma));\n",
"# A = Grad'*Msig*Grad;\n",
"# A[1,1] = A[1,1] + 1.;"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 51
}
],
"metadata": {}
}
]
}
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