mirror of
https://github.com/wassname/simpeg.git
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191 lines
50 KiB
Plaintext
191 lines
50 KiB
Plaintext
{
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"metadata": {
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"name": "",
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"signature": "sha256:f84d7f6fac432ae58377d9da7313463a2286c599c51cd1ac6b9a6ad5cb6ebd91"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Test 1D solution of MT problem and compare to a analytic solution\n"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 1
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# import the simpegMT module\n",
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"from simpegMT.Utils import MT1Danalytic, MT1Dsolutions\n",
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"\n",
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"def omega(freq):\n",
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" \"\"\"Change frequency to angular frequency, omega\"\"\"\n",
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" return 2.*np.pi*freq"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 2
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Set up the mesh.\n",
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"freq = 10.0\n",
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"hz = [(100.,50)]\n",
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"M = simpeg.Mesh.TensorMesh([hz],'C')\n",
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"sig = np.zeros(M.nC) + 1e-8\n",
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"sig[M.vectorCCx<=0] = 1.0\n"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 3
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Get the fields\n",
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"anaEd, anaEu, anaHd, anaHu = MT1Danalytic.getEHfields(M,sig,freq,M.vectorNx)\n",
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"anaEtemp = anaEd+anaEu\n",
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"# Scale the solution\n",
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"anaE = anaEtemp/anaEtemp[-1]\n",
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"\n",
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"solE = MT1Dsolutions.get1DEfields(M,sig,freq)\n"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 4
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"plot(solE.real,M.vectorNx,'r*--',anaE.real,M.vectorNx,'b+:')"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 5,
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"text": [
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"[<matplotlib.lines.Line2D at 0x7f5b370f1a50>,\n",
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" <matplotlib.lines.Line2D at 0x7f5b370f1cd0>]"
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]
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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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"png": 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B3MiGWo2UgxCR0FAOIgSyIxGaDxvG9vzDlYMQkdDQCCLJYjmIXd/8msN31VAO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\nIiIJKUCIiEhC/w8ZM2f6EBPGOgAAAABJRU5ErkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x7f5b379ae790>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 5
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"plot(solE.imag,M.vectorNx,'r*--',-anaE.imag,M.vectorNx,'b+:')"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 6,
|
|
"text": [
|
|
"[<matplotlib.lines.Line2D at 0x7f5b36fded50>,\n",
|
|
" <matplotlib.lines.Line2D at 0x7f5b36fdefd0>]"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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E7Gzeu/IlPq7fN2/Wzuuvw4wZ8NoFY+H113l3WyKfbjuNx856C2rWJPDbW1gQ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\nYII=\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x7f5b379c6810>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 6
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"semilogx(abs(solE),M.vectorNx,'r*--',abs(anaE),M.vectorNx,'b+:')"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 7,
|
|
"text": [
|
|
"[<matplotlib.lines.Line2D at 0x7f5b36fcda50>,\n",
|
|
" <matplotlib.lines.Line2D at 0x7f5b36f1fd50>]"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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UKCRuSqs7AOR+2ICPnv2cr3PmwqBBrFizmt/c0ZJ/fPQ2vbKzyc/Lo5ZeuSKV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\ngg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x7f5b370013d0>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 7
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"anaE[1].conj()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 8,
|
|
"text": [
|
|
"(-1.2220068301411678e-08-1.5509245597740526e-09j)"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 8
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 8
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |