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DC FWR 3D demo started
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D Fournier committed
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Efficiency Warning: Interpolation will be slow, use setup.py!\n",
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"\n",
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" python setup.py build_ext --inplace\n",
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" \n",
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING: pylab import has clobbered these variables: ['linalg']\n",
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"`%matplotlib` prevents importing * from pylab and numpy\n"
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]
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}
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],
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"source": [
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"from SimPEG import *\n",
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"import simpegDCIP as DC\n",
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"%pylab inline"
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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": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"cs = 25.\n",
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"hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
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"hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
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"hz = [(cs,7, -1.3),(cs,20)]"
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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": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
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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": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"blk1 = Utils.ModelBuilder.getIndicesBlock(np.r_[-50, 75, -50], np.r_[75, -50, -150], mesh.gridCC)\n",
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"sighalf = 1e-3\n",
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"sigma = np.ones(mesh.nC)*sighalf\n",
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"sigma[blk1] = 1e-1\n",
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"sigmahomo = np.ones(mesh.nC)*sighalf"
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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": 33,
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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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"(<matplotlib.collections.QuadMesh at 0x1b15fda0>,\n",
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" <matplotlib.lines.Line2D at 0x1b170240>)"
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]
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},
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"execution_count": 33,
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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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"data": {
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"metadata": {},
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{
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1b0d3a58>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.plotSlice(sigma, normal='X', grid=True)\n",
|
||||
"mesh.plotSlice(sigma, ind=22, normal='Z', grid=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xtemp = np.linspace(-150, 150, 21)\n",
|
||||
"ytemp = np.linspace(-150, 150, 21)\n",
|
||||
"xyz_rxP = Utils.ndgrid(xtemp-10., ytemp, np.r_[0.])\n",
|
||||
"xyz_rxN = Utils.ndgrid(xtemp+10., ytemp, np.r_[0.])\n",
|
||||
"xyz_rxM = Utils.ndgrid(xtemp, ytemp, np.r_[0.])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x15a0fda0>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15c45550>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots(1,1, figsize = (5,5))\n",
|
||||
"mesh.plotSlice(sigma, grid=True, ax = ax)\n",
|
||||
"ax.plot(xyz_rxP[:,0],xyz_rxP[:,1], 'w.')\n",
|
||||
"ax.plot(xyz_rxN[:,0],xyz_rxN[:,1], 'r.', ms = 3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"1323\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"rx = DC.RxDipole(xyz_rxP, xyz_rxN)\n",
|
||||
"tx = DC.SrcDipole([rx], [-200, 0, -12.5],[+200, 0, -12.5])\n",
|
||||
"print xyz_rxP.size"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"survey = DC.SurveyDC([tx])\n",
|
||||
"problem = DC.ProblemDC_CC(mesh)\n",
|
||||
"problem.pair(survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" from pymatsolver import MumpsSolver\n",
|
||||
" problem.Solver = MumpsSolver\n",
|
||||
"except Exception, e:\n",
|
||||
" problem.Solver = SolverLU"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ImportError",
|
||||
"evalue": "No module named pymatsolver",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-10-d6799536a06f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpymatsolver\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMumpsSolver\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;31mImportError\u001b[0m: No module named pymatsolver"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from pymatsolver import MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"problem.Solver = SolverLU\n",
|
||||
"\n",
|
||||
"data = survey.dpred(sigmahomo)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"441"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Plot pseudo section\n",
|
||||
"for ii in range(data):\n",
|
||||
" \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"u1 = problem.fields(sigma)\n",
|
||||
"u2 = problem.fields(sigmahomo)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Msig1 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigma)))\n",
|
||||
"Msig2 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigmahomo)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"j1 = Msig1*mesh.cellGrad*u1[tx, 'phi_sol']\n",
|
||||
"j2 = Msig2*mesh.cellGrad*u2[tx, 'phi_sol']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# us = u1-u2\n",
|
||||
"# js = j1-j2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'mesh' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-2-cb76a57fca1d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotSlice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maveF2CCV\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mj1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvType\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'CCv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Y'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mview\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'vec'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstreamOpts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"density\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"color\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;31m#xlim(-300, 300)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#ylim(-300, 0)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mNameError\u001b[0m: name 'mesh' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.plotSlice(mesh.aveF2CCV*j1, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
|
||||
"#xlim(-300, 300)\n",
|
||||
"#ylim(-300, 0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'js' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-23-575f23801c4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maveF2CCV\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mjs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvType\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CCv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mview\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'vec'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstreamOpts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"density\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"color\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mNameError\u001b[0m: name 'js' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.plotSlice(mesh.aveF2CCV*js, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
|
||||
"xlim(-300, 300)\n",
|
||||
"ylim(-300, 0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"a = np.random.randn(3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print (a.reshape([1,-1])).repeat(3, axis = 0)\n",
|
||||
"print (a.reshape([1,-1])).repeat(3, axis = 0).sum(axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def DChalf(txlocP, txlocN, rxloc, sigma, I=1.):\n",
|
||||
" rp = (txlocP.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)\n",
|
||||
" rn = (txlocN.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)\n",
|
||||
" rP = np.sqrt(((rxloc-rp)**2).sum(axis=1))\n",
|
||||
" rN = np.sqrt(((rxloc-rn)**2).sum(axis=1))\n",
|
||||
" return I/(sigma*2.*np.pi)*(1/rP-1/rN)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data_analP = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxP, sighalf)\n",
|
||||
"data_analN = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxN, sighalf)\n",
|
||||
"data_anal = data_analP-data_analN"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Data_anal = data_anal.reshape((21, 21), order = 'F')\n",
|
||||
"Data = data.reshape((21, 21), order = 'F')\n",
|
||||
"X = xyz_rxM[:,0].reshape((21, 21), order = 'F')\n",
|
||||
"Y = xyz_rxM[:,1].reshape((21, 21), order = 'F')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
|
||||
"vmin = np.r_[data, data_anal].min()\n",
|
||||
"vmax = np.r_[data, data_anal].max()\n",
|
||||
"dat0 = ax[0].contourf(X, Y, Data, 60, vmin = vmin, vmax = vmax)\n",
|
||||
"dat1 = ax[1].contourf(X, Y, Data_anal, 60, vmin = vmin, vmax = vmax)\n",
|
||||
"cb0 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[0])\n",
|
||||
"cb1 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 2",
|
||||
"language": "python",
|
||||
"name": "python2"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
+69
-38
@@ -11,6 +11,10 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Efficiency Warning: Interpolation will be slow, use setup.py!\n",
|
||||
"\n",
|
||||
" python setup.py build_ext --inplace\n",
|
||||
" \n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
@@ -56,7 +60,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -71,7 +75,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -79,19 +83,19 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<matplotlib.collections.QuadMesh at 0x1077619d0>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x107761e90>)"
|
||||
"(<matplotlib.collections.QuadMesh at 0x15c5eac8>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x15c5ef60>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x107575a50>"
|
||||
"<matplotlib.figure.Figure at 0x15940f98>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -104,7 +108,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -119,7 +123,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -127,18 +131,18 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x10820a490>]"
|
||||
"[<matplotlib.lines.Line2D at 0x159e1048>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10820a390>"
|
||||
"<matplotlib.figure.Figure at 0x15b93f60>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -154,7 +158,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -166,7 +170,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -179,7 +183,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -194,28 +198,43 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 1.15 s, sys: 155 ms, total: 1.3 s\n",
|
||||
"Wall time: 941 ms\n"
|
||||
"ename": "ImportError",
|
||||
"evalue": "No module named pymatsolver",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-10-d6799536a06f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpymatsolver\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMumpsSolver\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;31mImportError\u001b[0m: No module named pymatsolver"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"from pymatsolver import MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"problem.Solver = SolverLU\n",
|
||||
"\n",
|
||||
"data = survey.dpred(sigmahomo)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -227,7 +246,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -239,7 +258,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -251,19 +270,31 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "TypeError",
|
||||
"evalue": "unsupported operand type(s) for -: 'FieldsDC_CC' and 'FieldsDC_CC'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-22-fd20083b1fc4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mu1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mu2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mjs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mj1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mj2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'FieldsDC_CC' and 'FieldsDC_CC'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# us = u1-u2\n",
|
||||
"# js = j1-j2"
|
||||
"us = u1-u2\n",
|
||||
"js = j1-j2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -274,15 +305,15 @@
|
||||
"(-300, 0)"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEZCAYAAAC99aPhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x108233510>"
|
||||
"<matplotlib.figure.Figure at 0x15fa7860>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -307,10 +338,10 @@
|
||||
"evalue": "name 'js' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-23-575f23801c4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maveF2CCV\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mjs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvType\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CCv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mview\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'vec'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstreamOpts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"density\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"color\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mNameError\u001b[0m: name 'js' is not defined"
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-23-575f23801c4a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotSlice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maveF2CCV\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mjs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvType\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'CCv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Y'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mview\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'vec'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstreamOpts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"density\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"color\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mxlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mNameError\u001b[0m: name 'js' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -0,0 +1,85 @@
|
||||
import os
|
||||
|
||||
home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegdc\simpegDCIP\Dev'
|
||||
|
||||
os.chdir(home_dir)
|
||||
|
||||
|
||||
#%%
|
||||
from SimPEG import np, Utils, Mesh, mkvc, SolverLU
|
||||
import simpegDCIP as DC
|
||||
import pylab as plt
|
||||
|
||||
# Load UBC mesh 3D
|
||||
mesh = Utils.meshutils.readUBCTensorMesh('Mesh_40m.msh')
|
||||
|
||||
# Load model
|
||||
model = Utils.meshutils.readUBCTensorModel('Synthetic.con',mesh)
|
||||
|
||||
#%%
|
||||
# Display top section
|
||||
top = int(mesh.nCz)-1
|
||||
mesh.plotSlice(model, ind=top, normal='Z', grid=True, pcolorOpts={'alpha':0.8})
|
||||
ylim=(546000,546750)
|
||||
xlim=(422900,423675)
|
||||
# Takes two points from ginput and create survey
|
||||
temp = plt.ginput(2)
|
||||
|
||||
# Add z coordinate
|
||||
nz = mesh.vectorNz
|
||||
endp = np.c_[np.asarray(temp),np.ones(2).T*nz[-1]]
|
||||
|
||||
# Create dipole survey receivers and plot
|
||||
nrx = 10
|
||||
ab = 40
|
||||
a = 20
|
||||
|
||||
# Evenly distribute transmitters for now and put on surface
|
||||
dplen = np.sqrt( np.sum((endp[1,:] - endp[0,:])**2) )
|
||||
dp_x = ( endp[1,0] - endp[0,0] ) / dplen
|
||||
dp_y = ( endp[1,1] - endp[0,1] ) / dplen
|
||||
|
||||
nstn = np.floor( dplen / ab )
|
||||
|
||||
stn_x = endp[0,0] + np.cumsum( np.ones(nstn)*dp_x*ab )
|
||||
stn_y = endp[0,1] + np.cumsum( np.ones(nstn)*dp_y*ab )
|
||||
|
||||
plt.scatter(stn_x,stn_y,s=100, c='w')
|
||||
|
||||
M = np.c_[stn_x-a*dp_x, stn_y-a*dp_y, np.ones(nstn).T*nz[-1]]
|
||||
N = np.c_[stn_x+a*dp_x, stn_y+a*dp_y, np.ones(nstn).T*nz[-1]]
|
||||
|
||||
plt.scatter(M[:,0],M[:,1],s=10,c='r')
|
||||
plt.scatter(N[:,0],N[:,1],s=10,c='b')
|
||||
|
||||
#%% Create inversion parameter
|
||||
|
||||
Rx = DC.RxDipole(M,N)
|
||||
Tx = DC.SrcDipole([Rx], tx[0,:],tx[1,:])
|
||||
survey = DC.SurveyDC([Tx])
|
||||
|
||||
problem = DC.ProblemDC_CC(mesh)
|
||||
problem.pair(survey)
|
||||
|
||||
problem.Solver = SolverLU
|
||||
|
||||
data = survey.dpred(model)
|
||||
|
||||
#Set boundary conditions
|
||||
mesh.setCellGradBC('neumann')
|
||||
|
||||
Div = mesh.faceDiv
|
||||
Grad = mesh.cellGradBC
|
||||
Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
|
||||
|
||||
A = Div*Msig*Grad
|
||||
|
||||
# Change one corner to deal with nullspace
|
||||
A[0,0] = 1.
|
||||
|
||||
# Get the righthand side
|
||||
RHS = problem.getRHS
|
||||
|
||||
# Solve for phi
|
||||
phi = SolverLU(A)*-RHS
|
||||
|
||||
@@ -0,0 +1,122 @@
|
||||
import os
|
||||
|
||||
home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegdc\simpegDCIP\Dev'
|
||||
|
||||
os.chdir(home_dir)
|
||||
|
||||
|
||||
#%%
|
||||
from SimPEG import np, Utils, Mesh, mkvc, SolverLU, sp
|
||||
import simpegDCIP as DC
|
||||
import pylab as plt
|
||||
import time
|
||||
|
||||
# Load UBC mesh 3D
|
||||
mesh = Utils.meshutils.readUBCTensorMesh('Mesh_40m.msh')
|
||||
|
||||
# Load model
|
||||
model = Utils.meshutils.readUBCTensorModel('Synthetic.con',mesh)
|
||||
|
||||
#%%
|
||||
# Display top section
|
||||
top = int(mesh.nCz)-1
|
||||
mesh.plotSlice(model, ind=top, normal='Z', grid=True, pcolorOpts={'alpha':0.8})
|
||||
ylim=(546000,546750)
|
||||
xlim=(422900,423675)
|
||||
# Takes two points from ginput and create survey
|
||||
temp = plt.ginput(2)
|
||||
|
||||
# Add z coordinate
|
||||
nz = mesh.vectorNz
|
||||
endp = np.c_[np.asarray(temp),np.ones(2).T*nz[-1]]
|
||||
|
||||
# Create dipole survey receivers and plot
|
||||
ab = 40
|
||||
a = 20
|
||||
|
||||
# Evenly distribute transmitters for now and put on surface
|
||||
dplen = np.sqrt( np.sum((endp[1,:] - endp[0,:])**2) )
|
||||
dp_x = ( endp[1,0] - endp[0,0] ) / dplen
|
||||
dp_y = ( endp[1,1] - endp[0,1] ) / dplen
|
||||
|
||||
nstn = np.floor( dplen / ab )
|
||||
nrx = nstn-1
|
||||
|
||||
stn_x = endp[0,0] + np.cumsum( np.ones(nstn)*dp_x*ab )
|
||||
stn_y = endp[0,1] + np.cumsum( np.ones(nstn)*dp_y*ab )
|
||||
|
||||
plt.scatter(stn_x,stn_y,s=100, c='w')
|
||||
|
||||
M = np.c_[stn_x-a*dp_x, stn_y-a*dp_y, np.ones(nstn).T*nz[-1]]
|
||||
N = np.c_[stn_x+a*dp_x, stn_y+a*dp_y, np.ones(nstn).T*nz[-1]]
|
||||
|
||||
plt.scatter(M[:,0],M[:,1],s=10,c='r')
|
||||
plt.scatter(N[:,0],N[:,1],s=10,c='b')
|
||||
|
||||
#%% Create system
|
||||
#Set boundary conditions
|
||||
mesh.setCellGradBC('neumann')
|
||||
|
||||
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.
|
||||
|
||||
# Factor A matrix
|
||||
Ainv = sp.linalg.splu(A)
|
||||
|
||||
#%% Forward model data
|
||||
data = np.zeros( nstn*nrx )
|
||||
problem = DC.ProblemDC_CC(mesh)
|
||||
fig = plt.figure()
|
||||
|
||||
|
||||
|
||||
|
||||
for ii in range(0, int(nstn)):
|
||||
start_time = time.time()
|
||||
|
||||
rxloc_M = np.r_[M[0:ii,:],M[ii+1:,:]]
|
||||
rxloc_N = np.r_[N[0:ii,:],N[ii+1:,:]]
|
||||
|
||||
|
||||
Rx = DC.RxDipole(rxloc_M,rxloc_N)
|
||||
|
||||
Tx = DC.SrcDipole([Rx], M[ii,:],N[ii,:])
|
||||
survey = DC.SurveyDC([Tx])
|
||||
|
||||
problem.pair(survey)
|
||||
|
||||
# Get the righthand side
|
||||
RHS = problem.getRHS()
|
||||
|
||||
# Solve for phi
|
||||
P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
|
||||
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
|
||||
|
||||
phi = Ainv.solve(RHS)
|
||||
d = P1*phi - P2*phi
|
||||
|
||||
data[(nrx*(ii)):nrx+(nrx*(ii))] = d.T#survey.dpred(model)
|
||||
|
||||
# Plot pseudo section along line
|
||||
txmidx = endp[0,0] - np.mean(np.c_[M[ii,0],N[ii,0]])
|
||||
rxmidx = endp[0,0] - np.mean( np.c_[rxloc_M[:,0], rxloc_N[:,0]], axis=1 )
|
||||
|
||||
txmidy = endp[0,1] - np.mean(np.c_[M[ii,1],N[ii,1]])
|
||||
rxmidy = endp[0,1] - np.mean( np.c_[rxloc_M[:,1], rxloc_N[:,1]], axis=1 )
|
||||
|
||||
rxmid = np.sqrt(rxmidx**2 + rxmidy**2)
|
||||
txmid = np.sqrt(txmidx**2 + txmidy**2)
|
||||
|
||||
midp = ( rxmid + txmid )/2
|
||||
|
||||
print("--- %s seconds ---" % (time.time() - start_time))
|
||||
|
||||
plt.scatter(midp,-np.abs(txmid-midp),s=50,c=data[(nrx*(ii)):nrx+(nrx*(ii))])
|
||||
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
31 31 15
|
||||
422145.00 545269.00 1584.88
|
||||
250 193 114 88 67 52 19*40.00 52.00 67.00 88.00 114 193 250
|
||||
250 193 114 88 67 52 19*40.00 52.00 67.00 88.00 114 193 250
|
||||
9*40.00 52.00 67.00 88.00 114 193 250
|
||||
File diff suppressed because it is too large.
Load diff
Reference in new issue
Block a user