mirror of
https://github.com/wassname/simpeg.git
synced 2026-07-05 15:19:01 +08:00
D Fournier
535 lines
38 KiB
Plaintext
535 lines
38 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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": 2,
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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": 3,
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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": 4,
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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": 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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"data": {
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"text/plain": [
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"(<matplotlib.collections.QuadMesh at 0x157f41d0>,\n",
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" <matplotlib.lines.Line2D at 0x15631320>)"
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]
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},
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"execution_count": 5,
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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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"image/png": 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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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f8CdJYl19wN/i+QF/i53HD/gzNik2Kd43Z5bceYxb9FxDzD/NOrpOQpJ0cLBISJKaLBKS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\nSUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x1577eba8>"
|
|
]
|
|
},
|
|
"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": 6,
|
|
"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": 7,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x15be3940>]"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAFRCAYAAAAmQSVBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x155f4a20>"
|
|
]
|
|
},
|
|
"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": 8,
|
|
"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": 9,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"survey = DC.SurveyDC([tx])\n",
|
|
"problem = DC.ProblemDC_CC(mesh)\n",
|
|
"problem.pair(survey)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"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": 11,
|
|
"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-11-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": 12,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"problem.Solver = SolverLU\n",
|
|
"\n",
|
|
"data = survey.dpred(sigmahomo)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "IndentationError",
|
|
"evalue": "expected an indented block (<ipython-input-13-ff092c0e869a>, line 3)",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-13-ff092c0e869a>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m \u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
|
|
]
|
|
}
|
|
],
|
|
"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": "2.7.11"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|