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https://github.com/wassname/simpeg.git
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Rowan Cockett committed
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85 files changed
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@@ -0,0 +1,556 @@
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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": 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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]
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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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"from numpy.polynomial import polynomial"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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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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"%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": 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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"cs = 0.5\n",
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"mesh = Mesh.TensorMesh([np.ones(100)*cs, np.ones(50)*cs], \"CN\")\n",
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"x = mesh.vectorCCx"
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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": true
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},
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"outputs": [],
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"source": [
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"actx = (x>-15.)&(x<15.)"
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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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"source": [
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"order = 3\n",
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"Vobs = polynomial.polyvander(x, order)\n",
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"dobs = 0.05*x-4\n",
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"H = np.dot(Vobs.T, Vobs)\n",
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"g = np.dot(Vobs.T, dobs)\n",
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"mest = np.linalg.solve(H, g)"
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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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"dpred = Vobs.dot(mest)"
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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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"V = polynomial.polyvander(x, order)\n",
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"m1D = Mesh.TensorMesh([V.shape[1]+2])\n",
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"weight = (1./(V**2).sum(axis=0))**0.5\n",
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"weight = weight / weight.max()\n",
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"weightmap = Maps.Weighting(m1D, weights=np.r_[1., 1., weight])\n",
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"m0_poly = np.r_[np.log(1e-3), np.log(1e-3), np.r_[-3., np.zeros(V.shape[1]-1)] / weight]\n",
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"mtrue_poly = np.r_[np.log(1e-3), np.log(1e0), mest / weight]"
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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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"polymap = Maps.PolyMap(mesh, V.shape[1]-1, logSigma=True, normal='Y')\n",
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"mappingfwd = polymap*weightmap\n",
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"mapping = Maps.ExpMap(mesh)"
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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": 9,
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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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"sigma = mappingfwd*mtrue_poly"
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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": 10,
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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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"xr = np.linspace(-15, 15, 20)\n",
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"xz_A = Utils.ndgrid(xr, np.r_[-0.25])\n",
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"xz_B = Utils.ndgrid(np.ones_like(xr)*22, np.r_[-0.25])\n",
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"xz_M = Utils.ndgrid(xr, np.r_[-0.25])\n",
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"xz_N = Utils.ndgrid(np.ones_like(xr)*-22, np.r_[-0.25])\n",
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"\n",
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"ntx = xz_A.shape[0]\n",
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"txList = []\n",
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"for i in range(ntx):\n",
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" offset = abs(xz_A[i,0]-xz_M[:,0])\n",
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" actrx = offset > 5.\n",
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" rx = DC.RxDipole(xz_M[actrx,:], xz_N[actrx,:])\n",
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" src = DC.SrcDipole([rx], xz_A[i,:], xz_B[i,:])\n",
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" txList.append(src)"
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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": 11,
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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.lines.Line2D at 0x15be0278>]"
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]
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},
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"execution_count": 11,
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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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truncated
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"text/plain": [
|
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"<matplotlib.figure.Figure at 0x15968710>"
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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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"source": [
|
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"dat = mesh.plotImage(np.log10(sigma), clim=(-2, 0))\n",
|
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"plt.colorbar(dat[0])\n",
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"plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
|
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"plot(xz_B[:,0], xz_B[:,1], 'y.')\n",
|
||||
"plot(xz_N[:,0], xz_N[:,1], 'r.')"
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||||
]
|
||||
},
|
||||
{
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||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x15e72518>]"
|
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]
|
||||
},
|
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"execution_count": 12,
|
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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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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15968198>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dat = mesh.plotImage(np.log10(mappingfwd*m0_poly), clim=(-2, 0))\n",
|
||||
"plt.colorbar(dat[0])\n",
|
||||
"plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
|
||||
"plot(xz_B[:,0], xz_B[:,1], 'y.')\n",
|
||||
"plot(xz_N[:,0], xz_N[:,1], 'r.')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from SimPEG import SolverLU"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x160b5eb8>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x160c1278>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mtrue = np.log(sigma)\n",
|
||||
"m0 = np.log(np.ones(mesh.nC)*1e-3)\n",
|
||||
"survey = DC.SurveyDC(txList)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
|
||||
"problem.pair(survey)\n",
|
||||
"problem.Solver = SolverLU\n",
|
||||
"dtrue = survey.dpred(mtrue)\n",
|
||||
"d0 = survey.dpred(m0)\n",
|
||||
"j = problem.fields\n",
|
||||
"plot(dtrue)\n",
|
||||
"plot(d0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.0046885259087758868"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"abs(dtrue).min()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(array([ 1., 2., 3., 4., 5., 4., 6., 7., 8., 9., 8.,\n",
|
||||
" 10., 17., 17., 18., 33., 41., 30., 32., 17.]),\n",
|
||||
" array([-2.32896368, -2.11250236, -1.89604103, -1.67957971, -1.46311839,\n",
|
||||
" -1.24665706, -1.03019574, -0.81373441, -0.59727309, -0.38081177,\n",
|
||||
" -0.16435044, 0.05211088, 0.2685722 , 0.48503353, 0.70149485,\n",
|
||||
" 0.91795617, 1.1344175 , 1.35087882, 1.56734014, 1.78380147,\n",
|
||||
" 2.00026279]),\n",
|
||||
" <a list of 20 Patch objects>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15ca0828>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"hist(np.log10(abs(dtrue)), bins = 20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"noise = 0.05*abs(dtrue)*np.random.randn(dtrue.shape[0])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 1.00e+00 1.40e+08 0.00e+00 1.40e+08 8.32e+06 0 \n",
|
||||
" 1 1.00e+00 1.88e+07 6.18e-03 1.88e+07 1.13e+06 0 \n",
|
||||
" 2 1.00e+00 2.52e+06 2.45e-02 2.52e+06 1.55e+05 0 Skip BFGS \n",
|
||||
" 3 1.25e-01 3.49e+05 5.43e-02 3.49e+05 2.17e+04 0 Skip BFGS \n",
|
||||
" 4 1.25e-01 6.57e+04 9.31e-02 6.57e+04 3.25e+03 0 Skip BFGS \n",
|
||||
" 5 1.25e-01 1.53e+04 7.19e+00 1.53e+04 4.83e+02 1 Skip BFGS \n",
|
||||
" 6 1.56e-02 4.40e+03 1.81e+01 4.40e+03 5.23e+02 1 \n",
|
||||
" 7 1.56e-02 2.56e+03 4.02e+01 2.56e+03 2.82e+03 0 Skip BFGS \n",
|
||||
" 8 1.56e-02 5.47e+02 3.69e+01 5.47e+02 2.89e+02 0 \n",
|
||||
" 9 1.95e-03 3.14e+02 3.71e+01 3.14e+02 3.22e+02 0 Skip BFGS \n",
|
||||
" 10 1.95e-03 2.18e+02 3.92e+01 2.18e+02 1.07e+02 0 \n",
|
||||
" 11 1.95e-03 1.77e+02 3.86e+01 1.77e+02 8.57e+01 0 \n",
|
||||
" 12 2.44e-04 1.44e+02 4.09e+01 1.44e+02 9.30e+01 0 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.3960e+07\n",
|
||||
"1 : |xc-x_last| = 3.1882e+00 <= tolX*(1+|x0|) = 4.8945e+01\n",
|
||||
"0 : |proj(x-g)-x| = 9.3043e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 9.3043e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 30 <= iter = 13\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"survey.dobs = dtrue +noise\n",
|
||||
"dmis = DataMisfit.l2_DataMisfit(survey)\n",
|
||||
"dmis.Wd = 1./(0.05*abs(dtrue)+ 0.1)\n",
|
||||
"reg = Regularization.Tikhonov(mesh)\n",
|
||||
"opt = Optimization.InexactGaussNewton(maxIter=30,maxIterLS=20)\n",
|
||||
"opt.remember('xc')\n",
|
||||
"invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
|
||||
"betaSched = Directives.BetaSchedule(coolingFactor=8, coolingRate=3)\n",
|
||||
"targetmis = Directives.TargetMisfit()\n",
|
||||
"savemodel = Directives.SaveModelEveryIteration()\n",
|
||||
"inv = Inversion.BaseInversion(invProb, directiveList=[betaSched,targetmis])\n",
|
||||
"reg.alpha_s = 1e-5\n",
|
||||
"reg.mref = m0\n",
|
||||
"mopt = inv.run(m0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"XC = opt.recall('xc')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from ipywidgets import interact, IntSlider\n",
|
||||
"def viewInv(iteration):\n",
|
||||
" fig = plt.figure(num=0,figsize = (10,5))\n",
|
||||
" ax = plt.subplot(111)\n",
|
||||
" dat = mesh.plotImage(np.log10(mapping*XC[iteration]), grid=True, ax=ax, clim=(-3, 0), gridOpts={'alpha':0.2}, pcolorOpts={'cmap':cm.RdPu})\n",
|
||||
"# ax.set_xlim(mesh.vectorNx.min(), mesh.vectorNx.max())\n",
|
||||
"# ax.set_ylim(mesh.vectorNy.min(), mesh.vectorNy.max())\n",
|
||||
" ax.plot(mesh.vectorCCx, dpred, 'r--')\n",
|
||||
" ax.plot(xz_A[:,0], xz_A[:,1], 'k.')\n",
|
||||
" ax.plot(xz_B[:,0], xz_B[:,1], 'r.')\n",
|
||||
" ax.plot(xz_N[:,0], xz_N[:,1], 'b.') \n",
|
||||
"# plt.colorbar(dat[0], ax=ax)\n",
|
||||
"# ax.set_ylim (-15, 0.)\n",
|
||||
"# ax.set_xlim (-15, 15.)\n",
|
||||
" plt.show()\n",
|
||||
" return True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1ab1e9b0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"interact(viewInv, iteration = IntSlider(min=0, max=opt.iter-1,step=1, value=0))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x1ba516d8>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1ef46908>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.plot(survey.dobs)\n",
|
||||
"plt.plot(invProb.dpred)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"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
|
||||
}
|
||||
@@ -0,0 +1,402 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 372,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"from ipywidgets import interact, IntSlider, FloatSlider, FloatText, ToggleButtons"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 221,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 230,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"matplotlib.rcParams['font.size'] = 14"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 231,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"npad = 8\n",
|
||||
"cs = 1.\n",
|
||||
"hx = [(cs,npad, -1.3),(cs,100),(cs,npad, 1.3)]\n",
|
||||
"hy = [(cs,npad, -1.3),(cs,50)]\n",
|
||||
"mesh = Mesh.TensorMesh([hx, hy], \"CN\")\n",
|
||||
"x = mesh.vectorCCx"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 232,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"circmap = Maps.CircleMap(mesh)\n",
|
||||
"circmap.slope = 1e5\n",
|
||||
"mapping = circmap\n",
|
||||
"sighalf = 1e-3\n",
|
||||
"rhohalf = 1./sighalf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 233,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xr = np.linspace(-40, 40, 20)\n",
|
||||
"xz_A = Utils.ndgrid(xr, np.r_[-2.5])\n",
|
||||
"xz_B = Utils.ndgrid(np.ones_like(xr)*19, np.r_[-2.5])\n",
|
||||
"xz_M = Utils.ndgrid(xr, np.r_[-2.5])\n",
|
||||
"xz_N = Utils.ndgrid(np.ones_like(xr)*-19, np.r_[-2.5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 364,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def DipoleDipolefun(i):\n",
|
||||
" plt.figure(figsize=(10, 3))\n",
|
||||
" nmax = 8\n",
|
||||
" ntx = xr.size-2\n",
|
||||
" dxr = np.diff(xr)\n",
|
||||
" plt.plot(xr[:-1]+dxr*0.5, np.zeros_like(xr[:-1]), 'ko')\n",
|
||||
" plt.plot(xr[i]+dxr[i]*0.5, np.zeros(1), 'ro')\n",
|
||||
" # for i in range(ntx):\n",
|
||||
" if i < ntx-nmax+1:\n",
|
||||
" txmid = xr[i]+dxr[i]*0.5\n",
|
||||
" rxmid = xr[i+1:i+1+nmax]+dxr[i+1:i+1+nmax]*0.5\n",
|
||||
" mid = (txmid+rxmid)*0.5\n",
|
||||
" plt.plot(rxmid, np.zeros(rxmid.size), 'go')\n",
|
||||
" plt.plot(mid, np.arange(nmax)+1., 'bo')\n",
|
||||
" plt.plot(np.r_[txmid, mid[-1]], np.r_[0, nmax], 'k:') \n",
|
||||
" for j in range(nmax):\n",
|
||||
" plt.plot(np.r_[rxmid[j], mid[j]], np.r_[0, j+1], 'k:') \n",
|
||||
"\n",
|
||||
" else:\n",
|
||||
" txmid = xr[i]+dxr[i]*0.5\n",
|
||||
" rxmid = xr[i+1:ntx+1]+dxr[i+1:ntx+1]*0.5\n",
|
||||
" mid = (txmid+rxmid)*0.5 \n",
|
||||
" plt.plot((txmid+rxmid)*0.5, np.arange(mid.size)+1., 'bo')\n",
|
||||
" plt.plot(rxmid, np.zeros(rxmid.size), 'go')\n",
|
||||
" plt.plot(np.r_[txmid, mid[-1]], np.r_[0, mid.size], 'k:') \n",
|
||||
" for j in range(ntx-i):\n",
|
||||
" plt.plot(np.r_[rxmid[j], mid[j]], np.r_[0, j+1], 'k:') \n",
|
||||
" plt.xlabel(\"X (m)\")\n",
|
||||
" plt.ylabel(\"N-spacing\") \n",
|
||||
" xlim(xr.min(), xr.max())\n",
|
||||
" ylim(nmax+1, -1)\n",
|
||||
" plt.show()\n",
|
||||
" return "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 365,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def getPseudoLocs(xr, ntx, nmax):\n",
|
||||
" dxr = np.diff(xr)\n",
|
||||
" xloc = []\n",
|
||||
" yloc = [] \n",
|
||||
" for i in range(ntx):\n",
|
||||
" if i < ntx-nmax+1:\n",
|
||||
" txmid = xr[i]+dxr[i]*0.5\n",
|
||||
" rxmid = xr[i+1:i+1+nmax]+dxr[i+1:i+1+nmax]*0.5\n",
|
||||
" mid = (txmid+rxmid)*0.5\n",
|
||||
" xloc.append(mid)\n",
|
||||
" yloc.append(np.arange(nmax)+1.)\n",
|
||||
" else:\n",
|
||||
" txmid = xr[i]+dxr[i]*0.5\n",
|
||||
" rxmid = xr[i+1:ntx+1]+dxr[i+1:ntx+1]*0.5\n",
|
||||
" mid = (txmid+rxmid)*0.5 \n",
|
||||
" xloc.append(mid)\n",
|
||||
" yloc.append(np.arange(mid.size)+1.)\n",
|
||||
" xlocvec = np.hstack(xloc)\n",
|
||||
" ylocvec = np.hstack(yloc)\n",
|
||||
" return np.c_[xlocvec, ylocvec]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 366,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dxr = np.diff(xr)\n",
|
||||
"ntx, nmax = xr.size-2, 8\n",
|
||||
"xzlocs = getPseudoLocs(xr, ntx, nmax)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 367,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10a35a610>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"interact(DipoleDipolefun, i=IntSlider(min=0, max=ntx-1, step = 1, value=0))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 238,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"txList = []\n",
|
||||
"zloc = -2.5\n",
|
||||
"for i in range(ntx):\n",
|
||||
" A = np.r_[xr[i]+dxr[i]*0.5, zloc]\n",
|
||||
" B = np.r_[mesh.vectorCCx.min(), zloc] \n",
|
||||
" if i < ntx-nmax+1:\n",
|
||||
" M = np.c_[xr[i+1:i+1+nmax], np.ones(nmax)*zloc] \n",
|
||||
" N = np.c_[xr[i+2:i+2+nmax], np.ones(nmax)*zloc] \n",
|
||||
" else:\n",
|
||||
" M = np.c_[xr[i+1:ntx+1], np.ones(ntx-i)*zloc] \n",
|
||||
" N = np.c_[xr[i+2:i+2+nmax], np.ones(ntx-i)*zloc] \n",
|
||||
" rx = DC.RxDipole(M, N)\n",
|
||||
" src = DC.SrcDipole([rx], A, B)\n",
|
||||
" txList.append(src)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 239,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"survey = DC.SurveyDC(txList)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
|
||||
"problem.pair(survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 240,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pymatsolver import MumpsSolver\n",
|
||||
"problem.Solver = MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 422,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sigblk, sighalf = 2e-2, 2e-3\n",
|
||||
"xc, yc, r = -15, -8, 4\n",
|
||||
"mtrue = np.r_[np.log(sigblk), np.log(sighalf), xc, yc, r]\n",
|
||||
"dtrue = survey.dpred(mtrue) \n",
|
||||
"perc = 0.1\n",
|
||||
"floor = np.linalg.norm(dtrue)*1e-3\n",
|
||||
"uncert = np.random.randn(survey.nD)*perc + floor\n",
|
||||
"dobs = dtrue + uncert "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 429,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def DC2Dfwdfun(mesh, rhohalf, rhoblk, xc, yc, r, dobs, uncert, predmis):\n",
|
||||
" sighalf, sigblk = 1./rhohalf, 1./rhoblk\n",
|
||||
" m0 = np.r_[np.log(sighalf), np.log(sighalf), xc, yc, r]\n",
|
||||
" dini = survey.dpred(m0)\n",
|
||||
" mtrue = np.r_[np.log(sigblk), np.log(sighalf), xc, yc, r]\n",
|
||||
" dpred = survey.dpred(mtrue)\n",
|
||||
" xi, yi = np.meshgrid(np.linspace(xr.min(), xr.max(), 120), np.linspace(1., nmax, 100))\n",
|
||||
" appres = dpred/dini/sighalf\n",
|
||||
" appresobs = dobs/dini/sighalf\n",
|
||||
" pred = griddata(xzlocs[:,0], xzlocs[:,1], appres, xi, yi, interp='linear')\n",
|
||||
" obs = griddata(xzlocs[:,0], xzlocs[:,1], appresobs, xi, yi, interp='linear')\n",
|
||||
" fig = plt.figure(figsize = (12, 8))\n",
|
||||
" ax1 = plt.subplot(311)\n",
|
||||
" dat1 = mesh.plotImage(np.log10(1./(mapping*mtrue)), ax=ax1, clim=(0, 3), grid=True, gridOpts={'color':'k', 'alpha':0.5})\n",
|
||||
" cb1 = plt.colorbar(dat1[0], ticks=np.linspace(0, 3, 5), ax=ax1, format=\"$10^{%4.1f}$\")\n",
|
||||
" cb1.set_label(\"Resistivity (ohm-m)\")\n",
|
||||
" ax1.set_ylim(-30, 0.)\n",
|
||||
" ax1.set_xlim(-40, 40)\n",
|
||||
" ax1.set_xlabel(\"\")\n",
|
||||
" ax1.set_ylabel(\"Depth (m)\") \n",
|
||||
" ax2 = plt.subplot(312)\n",
|
||||
" dat2 = ax2.contourf(xi, yi, obs, 10)\n",
|
||||
" ax2.contour(xi, yi, obs, 10, colors='k', alpha=0.5)\n",
|
||||
" ax2.plot(xzlocs[:,0], xzlocs[:,1],'k.', ms = 3)\n",
|
||||
" cb2 = plt.colorbar(dat2, ax=ax2, ticks=np.linspace(appresobs.min(), appresobs.max(), 5), format=\"%4.1f\")\n",
|
||||
" \n",
|
||||
" cb2.set_label(\"Apparent Resistivity \\n (ohm-m)\")\n",
|
||||
" ax2.set_ylim(nmax+1, 0.)\n",
|
||||
" ax2.set_ylabel(\"N-spacing\") \n",
|
||||
" ax2.text(-38, 7, \"Observed\")\n",
|
||||
" \n",
|
||||
" ax3 = plt.subplot(313)\n",
|
||||
" if predmis==\"pred\":\n",
|
||||
" dat3 = ax3.contourf(xi, yi, pred, 10)\n",
|
||||
" ax3.contour(xi, yi, pred, 10, colors='k', alpha=0.5)\n",
|
||||
" ax3.plot(xzlocs[:,0], xzlocs[:,1],'k.', ms = 3)\n",
|
||||
" cb3 = plt.colorbar(dat3, ax=ax3, ticks=np.linspace(appres.min(), appres.max(), 5),format=\"%4.0f\")\n",
|
||||
" cb3.set_label(\"Apparent Resistivity \\n (ohm-m)\")\n",
|
||||
" ax3.text(-38, 7, \"Predicted\")\n",
|
||||
" elif predmis==\"mis\":\n",
|
||||
" mis = (appresobs-appres)/(0.1*appresobs)\n",
|
||||
" Mis = griddata(xzlocs[:,0], xzlocs[:,1], mis, xi, yi, interp='linear') \n",
|
||||
" dat3 = ax3.contourf(xi, yi, Mis, 10)\n",
|
||||
" ax3.contour(xi, yi, Mis, 10, colors='k', alpha=0.5)\n",
|
||||
" ax3.plot(xzlocs[:,0], xzlocs[:,1],'k.', ms = 3)\n",
|
||||
" cb3 = plt.colorbar(dat3, ax=ax3, ticks=np.linspace(mis.min(), mis.max(), 5), format=\"%4.2f\")\n",
|
||||
" cb3.set_label(\"Normalized misfit\")\n",
|
||||
" ax3.text(-38, 7, \"Misifit\") \n",
|
||||
" ax3.set_ylim(nmax+1, 0.)\n",
|
||||
" ax3.set_ylabel(\"N-spacing\") \n",
|
||||
" ax3.set_xlabel(\"Distance (m)\") \n",
|
||||
" plt.show()\n",
|
||||
" return True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 430,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"DC2Dfwd = lambda rhohalf, rhosph, xc, zc, r, predmis: DC2Dfwdfun(mesh, rhohalf, rhosph, xc, zc, r, dobs, uncert, predmis)\n",
|
||||
"# DC2Dfwdfun(mesh, 1000., 1e1, 0., -9, 4, dobs, uncert, \"pred\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 431,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x109567310>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"interact(DC2Dfwd,\n",
|
||||
" rhohalf = FloatSlider(min=0, max=1000, step=1, value = 500),\n",
|
||||
" rhosph = FloatSlider(min=0, max=1000, step=1, value = 50),\n",
|
||||
" xc = FloatSlider(min=-40, max=40, step=1, value = -15),\n",
|
||||
" zc = FloatSlider(min= -20, max=0, step=1, value = -8),\n",
|
||||
" r = FloatSlider(min= 0, max=15, step=0.5, value = 4),\n",
|
||||
" predmis = ToggleButtons(options=['pred','mis']) \n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 2",
|
||||
"language": "python",
|
||||
"name": "python2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -0,0 +1,506 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from SimPEG import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING: pylab import has clobbered these variables: ['linalg']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cs = 0.5\n",
|
||||
"mesh = Mesh.TensorMesh([np.ones(100)*cs, np.ones(50)*cs], \"CN\")\n",
|
||||
"x = mesh.vectorCCx"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"actind = mesh.gridCC[:,1] < -1.\n",
|
||||
"meshact = Mesh.TensorMesh([mesh.hx, mesh.hy[:-2]], x0=mesh.x0)\n",
|
||||
"actmap = Maps.ActiveCells(mesh, actind, 1e-2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"circmap = Maps.CircleMap(meshact)\n",
|
||||
"circmap.slope = 1e5"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mapping = actmap*circmap"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"# mapping = Maps.CircleMap(mesh)\n",
|
||||
"mtrue = np.r_[np.log(1e0), np.log(1e-3), 0., -3., 2.]\n",
|
||||
"m0 = np.r_[np.log(1e-3), np.log(1e-3), -3, -5., 1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import simpegDCIP as DC"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xr = np.linspace(-15, 15, 20)\n",
|
||||
"xz_A = Utils.ndgrid(xr, np.r_[-0.25])\n",
|
||||
"xz_B = Utils.ndgrid(np.ones_like(xr)*19, np.r_[-0.25])\n",
|
||||
"xz_M = Utils.ndgrid(xr, np.r_[-0.25])\n",
|
||||
"xz_N = Utils.ndgrid(np.ones_like(xr)*-19, np.r_[-0.25])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ntx = xz_A.shape[0]\n",
|
||||
"txList = []\n",
|
||||
"for i in range(ntx):\n",
|
||||
" offset = abs(xz_A[i,0]-xz_M[:,0])\n",
|
||||
" actrx = offset > 5.\n",
|
||||
" rx = DC.RxDipole(xz_M[actrx,:], xz_N[actrx,:])\n",
|
||||
" src = DC.SrcDipole([rx], xz_A[i,:], xz_B[i,:])\n",
|
||||
" txList.append(src)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"survey = DC.SurveyDC(txList)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
|
||||
"problem.pair(survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pymatsolver import MumpsSolver\n",
|
||||
"problem.Solver = MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dini = survey.dpred(m0)\n",
|
||||
"dtrue = survey.dpred(mtrue)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x107614750>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plot(dini)\n",
|
||||
"plot(dtrue)\n",
|
||||
"figsize(12, 5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(array([ 1., 2., 5., 4., 10., 11., 13., 20., 17., 20., 23.,\n",
|
||||
" 10., 0., 0., 4., 17., 29., 36., 32., 18.]),\n",
|
||||
" array([-0.21911748, -0.05445749, 0.1102025 , 0.27486249, 0.43952249,\n",
|
||||
" 0.60418248, 0.76884247, 0.93350246, 1.09816245, 1.26282245,\n",
|
||||
" 1.42748244, 1.59214243, 1.75680242, 1.92146242, 2.08612241,\n",
|
||||
" 2.2507824 , 2.41544239, 2.58010239, 2.74476238, 2.90942237,\n",
|
||||
" 3.07408236]),\n",
|
||||
" <a list of 20 Patch objects>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10a2ad990>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"hist(np.log10(abs(dtrue)), bins = 20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1D = Mesh.TensorMesh([5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x10a7b12d8>"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10a698690>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"figsize(14*0.5,7*0.5)\n",
|
||||
"circmodelest = mapping*mtrue\n",
|
||||
"dat = mesh.plotImage(np.log10(circmodelest), clim=(-4, 1), grid=True, gridOpts={'alpha':0.5})\n",
|
||||
"plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
|
||||
"plot(xz_B[:,0], xz_B[:,1], 'k.')\n",
|
||||
"plot(xz_N[:,0], xz_N[:,1], 'r.')\n",
|
||||
"# plot(temp.rxList[0].locs[0][:,0], temp.rxList[0].locs[0][:,1], 'bo')\n",
|
||||
"plt.colorbar(dat[0])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"SimPEG.SaveModelEveryIteration will save your models as: '###-InversionModel-2015-11-10-16-33.npy'\n",
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 0.00e+00 4.69e+03 0.00e+00 4.69e+03 3.97e+04 0 \n",
|
||||
" 1 0.00e+00 2.80e+03 4.41e-03 2.80e+03 1.09e+03 0 \n",
|
||||
" 2 0.00e+00 2.59e+03 3.14e+01 2.59e+03 1.43e+04 1 \n",
|
||||
" 3 0.00e+00 1.31e+03 4.23e+01 1.31e+03 1.48e+04 3 \n",
|
||||
" 4 0.00e+00 1.22e+03 5.74e+01 1.22e+03 1.49e+04 4 \n",
|
||||
" 5 0.00e+00 8.55e+02 3.58e+01 8.55e+02 2.20e+04 0 \n",
|
||||
" 6 0.00e+00 6.43e+02 5.20e+01 6.43e+02 1.71e+04 3 \n",
|
||||
" 7 0.00e+00 4.57e+02 4.43e+01 4.57e+02 5.20e+03 0 \n",
|
||||
" 8 0.00e+00 3.83e+02 6.55e+01 3.83e+02 1.54e+04 2 \n",
|
||||
" 9 0.00e+00 3.67e+02 5.78e+01 3.67e+02 5.76e+03 1 \n",
|
||||
" 10 0.00e+00 3.31e+02 6.61e+01 3.31e+02 1.20e+04 1 \n",
|
||||
" 11 0.00e+00 3.30e+02 6.15e+01 3.30e+02 1.76e+04 2 \n",
|
||||
" 12 0.00e+00 2.01e+02 4.84e+01 2.01e+02 8.75e+03 0 \n",
|
||||
" 13 0.00e+00 1.54e+02 6.25e+01 1.54e+02 8.56e+03 2 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 4.6952e+02\n",
|
||||
"0 : |xc-x_last| = 5.6930e-01 <= tolX*(1+|x0|) = 1.2421e-19\n",
|
||||
"0 : |proj(x-g)-x| = 8.5645e+03 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 8.5645e+03 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 30 <= iter = 14\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"survey.dobs = dtrue\n",
|
||||
"dmis = DataMisfit.l2_DataMisfit(survey)\n",
|
||||
"dmis.Wd = 1./(0.01*abs(dtrue)+1.)\n",
|
||||
"reg = Regularization.BaseRegularization(m1D)\n",
|
||||
"opt = Optimization.InexactGaussNewton(maxIter=30,tolX=1e-20, maxIterLS=20)\n",
|
||||
"opt.remember('xc')\n",
|
||||
"invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
|
||||
"invProb.beta = 0.\n",
|
||||
"betaSched = Directives.BetaSchedule(coolingFactor=1, coolingRate=1)\n",
|
||||
"targetmis = Directives.TargetMisfit()\n",
|
||||
"savemodel = Directives.SaveModelEveryIteration()\n",
|
||||
"inv = Inversion.BaseInversion(invProb, directiveList=[betaSched,targetmis, savemodel])\n",
|
||||
"reg.mref = m0\n",
|
||||
"mopt = inv.run(m0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"XC = opt.recall('xc')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from ipywidgets import interact, IntSlider"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def viewinv(iteration):\n",
|
||||
"# iteration = 15\n",
|
||||
" figsize(10,6)\n",
|
||||
" ax1 = plt.subplot(211)\n",
|
||||
" circmodelest = mapping*mtrue\n",
|
||||
" if iteration > opt.iter-1:\n",
|
||||
" circmodeltrue = mapping*mopt\n",
|
||||
" else:\n",
|
||||
" circmodeltrue = mapping*XC[iteration]\n",
|
||||
" mesh.plotImage(np.log10(circmodelest), ax=ax1, clim=(-3, 0), grid=True, gridOpts={'alpha':0.5})\n",
|
||||
" ax2 = plt.subplot(212)\n",
|
||||
" mesh.plotImage(np.log10(circmodeltrue), ax=ax2, clim=(-3, 0), grid=True, gridOpts={'alpha':0.5})\n",
|
||||
" plt.show()\n",
|
||||
" return True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10c979dd0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"interact(viewinv, iteration=IntSlider(min=0, max=opt.iter, step = 1, value=0))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x10c8ed390>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10c8ed2d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plot(invProb.dpred, '.')\n",
|
||||
"plot(dtrue)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"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
|
||||
}
|
||||
@@ -0,0 +1,426 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from SimPEG import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING: pylab import has clobbered these variables: ['linalg']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cs = 25.\n",
|
||||
"hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
|
||||
"hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
|
||||
"hz = [(cs,7, -1.3),(cs,20)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 72,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mesh = Mesh.TensorMesh([hx, hy, hz], \"CCN\")\n",
|
||||
"sigma = np.ones(mesh.nC)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 79,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" ---- 3-D TensorMesh ---- \n",
|
||||
" x0: -833.94\n",
|
||||
" y0: -833.94\n",
|
||||
" z0: -1071.44\n",
|
||||
" nCx: 35\n",
|
||||
" nCy: 35\n",
|
||||
" nCz: 27\n",
|
||||
" hx: 156.87, 120.67, 92.82, 71.40, 54.93, 42.25, 32.50, 21*25.00, 32.50, 42.25, 54.93, 71.40, 92.82, 120.67, 156.87\n",
|
||||
" hy: 156.87, 120.67, 92.82, 71.40, 54.93, 42.25, 32.50, 21*25.00, 32.50, 42.25, 54.93, 71.40, 92.82, 120.67, 156.87\n",
|
||||
" hz: 156.87, 120.67, 92.82, 71.40, 54.93, 42.25, 32.50, 20*25.00\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print mesh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 81,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"33075"
|
||||
]
|
||||
},
|
||||
"execution_count": 81,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.nC"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 82,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Div = mesh.faceDiv"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 83,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[['neumann', 'neumann'], ['neumann', 'neumann'], ['neumann', 'neumann']]"
|
||||
]
|
||||
},
|
||||
"execution_count": 83,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.setCellGradBC(\"neumann\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 84,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Grad = mesh.cellGrad"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 101,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Afc = mesh.aveF2CC\n",
|
||||
"Msig = Utils.sdiag(1./(Afc.T*(1./sigma)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 102,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"A = Div*Msig*Grad"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 103,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# A[-1, -1] / mesh.vol[-1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 104,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"A[0,0] /= mesh.vol[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 105,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"inds = Utils.closestPoints(mesh, np.r_[0., 0., 0.])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 106,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"q = np.zeros(mesh.nC)\n",
|
||||
"q[inds] = 1."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 109,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pymatsolver import MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 110,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Ainv = MumpsSolver(A)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 111,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"phi = Ainv*q"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 115,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(27,)"
|
||||
]
|
||||
},
|
||||
"execution_count": 115,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.vectorCCz.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 117,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<matplotlib.collections.QuadMesh at 0x10e663790>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x10e663c10>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 117,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10da13090>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.plotSlice(phi,ind=26, grid=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 120,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xyzM = Utils.ndgrid(mesh.vectorCCx+5., mesh.vectorCCy, np.r_[0.])\n",
|
||||
"xyzN = Utils.ndgrid(mesh.vectorCCx-5., mesh.vectorCCy, np.r_[0.])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 123,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"PM = mesh.getInterpolationMat(xyzM, \"CC\")\n",
|
||||
"PN = mesh.getInterpolationMat(xyzN, \"CC\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"PM = mesh.getInterpolationMat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 124,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = PM*phi-PN*phi"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 126,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.image.AxesImage at 0x108812c10>"
|
||||
]
|
||||
},
|
||||
"execution_count": 126,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10ceae110>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.imshow(data.reshape(mesh.nCx, mesh.nCy))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"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
|
||||
}
|
||||
@@ -0,0 +1,167 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# DCR Pseudo-section Simulation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"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"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from SimPEG import *\n",
|
||||
"from SimPEG.Examples import DC_PseudoSection_Simulation\n",
|
||||
"\n",
|
||||
"from ipywidgets import interactive, FloatText, FloatSlider, ToggleButtons #interactive plots!\n",
|
||||
"\n",
|
||||
"%matplotlib inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Transmitter 8 of 9 -> Time:0.976000070572 sec Transmitter 8 of 9\n",
|
||||
"Forward completed\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x154fecc0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = DC_PseudoSection_Simulation.run()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that we have a tool to forward model data in 3D space, we can experiment with different survey configurations.\n",
|
||||
"\n",
|
||||
"We here give the two options:\n",
|
||||
"pole-dipole (pdp) or dipole-dipole (dpdp).\n",
|
||||
"\n",
|
||||
"In both cases we need to specify three important parameter.\n",
|
||||
"\n",
|
||||
"a: Transmitter and receivers seperation (m)\n",
|
||||
"\n",
|
||||
"b: Dipole seperation (m)\n",
|
||||
"\n",
|
||||
"n: Number of receiver dipoles along line\n",
|
||||
"\n",
|
||||
"We can also specify values of conductivity for the background (sig0) and the two sphere anomalies (sig1 and sig2)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Transmitter 8 of 9 -> Time:0.976999998093 sec Transmitter 8 of 9\n",
|
||||
"Forward completed\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x2183a90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<matplotlib.figure.Figure at 0x2183a90>,\n",
|
||||
" <matplotlib.axes._subplots.AxesSubplot at 0x187ccba8>)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Simul_Fct = lambda a,b,n, sig0, sig1, sig2, stype: DC_PseudoSection_Simulation.run(param = np.r_[a,b,n], sig=np.r_[sig0,sig1,sig2], stype = stype)\n",
|
||||
"\n",
|
||||
"interactive(Simul_Fct, a=FloatText(min=10.,max=40.,step=5.,value=30.),\n",
|
||||
" b=FloatText(min=10.,max=40.,step=5.,value=30.),\n",
|
||||
" n=FloatText(min=1,max=30,step=5,value=10.),\n",
|
||||
" sig0=FloatText(min=1e-4,max=1e+4,step=1e+1,value=1e-2),\n",
|
||||
" sig1=FloatText(min=1e-4,max=1e+4,step=1e+1,value=1e-1),\n",
|
||||
" sig2=FloatText(min=1e-4,max=1e+4,step=1e+1,value=1e-3),\n",
|
||||
" stype=ToggleButtons(options=['pdp','dpdp'],value='dpdp'))\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"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
|
||||
}
|
||||
@@ -0,0 +1,534 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING: pylab import has clobbered these variables: ['linalg']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cs = 25.\n",
|
||||
"hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
|
||||
"hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
|
||||
"hz = [(cs,7, -1.3),(cs,20)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"blk1 = Utils.ModelBuilder.getIndicesBlock(np.r_[-50, 75, -50], np.r_[75, -50, -150], mesh.gridCC)\n",
|
||||
"sighalf = 1e-3\n",
|
||||
"sigma = np.ones(mesh.nC)*sighalf\n",
|
||||
"sigma[blk1] = 1e-1\n",
|
||||
"sigmahomo = np.ones(mesh.nC)*sighalf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<matplotlib.collections.QuadMesh at 0x157f41d0>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x15631320>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15482e10>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"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": 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truncated
|
||||
"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
|
||||
}
|
||||
+474
-433
@@ -1,443 +1,484 @@
|
||||
{
|
||||
"metadata": {
|
||||
"name": "",
|
||||
"signature": "sha256:b7556968258042b90534fa310f5e2522502708190e2655f8d1be84dd18c34ec2"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
"worksheets": [
|
||||
"cells": [
|
||||
{
|
||||
"cells": [
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDC as DC\n",
|
||||
"from simpegem1d import Utils1D\n",
|
||||
"%pylab inline"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 12
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 1,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"DC Forward Modeling of Schlumber array"
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Here we test the accuracy of DC forward modeling using analytic solution."
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Vendor: Continuum Analytics, Inc.\n",
|
||||
"Package: mkl\n",
|
||||
"Message: trial mode expires in 29 days\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 2,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Step1: Generate mesh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"cs = 25.\n",
|
||||
"npad = 11\n",
|
||||
"hx = [(cs,npad, -1.3),(cs,41),(cs,npad, 1.3)]\n",
|
||||
"hy = [(cs,npad, -1.3),(cs,17),(cs,npad, 1.3)]\n",
|
||||
"hz = [(cs,npad, -1.3),(cs,20)]"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 13
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 14
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"mesh.plotGrid()"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x110fe1850>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 15
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 2,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Step2: Generating model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"sighalf = 1e-2\n",
|
||||
"sigma = np.ones(mesh.nC)*sighalf"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 16
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 2,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Step3: Design survey: Schulumberger array"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<img src=\"http://www.landrinstruments.com/_/rsrc/1271695892678/home/ultra-minires/additional-information-1/schlumberger-soundings/schlum%20array.JPG\"> </img>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 3,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 4,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let $b=na$, then we rewrite above equation as:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 3,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$ \\rho_a = \\frac{V}{I}\\pi na(n+1)$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 4,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Since AB/2 can be a good measure for depth of investigation, we express "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 3,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$AB/2 = \\frac{(2n+1)a}{2}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 17
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"ntx = 16"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 18
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"xtemp_txP = np.arange(ntx)*(25.)-500.\n",
|
||||
"xtemp_txN = -xtemp_txP\n",
|
||||
"ytemp_tx = np.zeros(ntx)\n",
|
||||
"xtemp_rxP = -50.\n",
|
||||
"xtemp_rxN = 50.\n",
|
||||
"ytemp_rx = 0.\n",
|
||||
"abhalf = abs(xtemp_txP-xtemp_txN)*0.5\n",
|
||||
"a = xtemp_rxN-xtemp_rxP\n",
|
||||
"b = ((xtemp_txN-xtemp_txP)-a)*0.5"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 19
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"fig, ax = plt.subplots(1,1, figsize = (12,3))\n",
|
||||
"for i in range(ntx):\n",
|
||||
" ax.plot(np.r_[xtemp_txP[i], xtemp_txP[i]], np.r_[0., 0.4-0.01*(i-1)], 'k-', lw = 1)\n",
|
||||
" ax.plot(np.r_[xtemp_txN[i], xtemp_txN[i]], np.r_[0., 0.4-0.01*(i-1)], 'k-', lw = 1)\n",
|
||||
" ax.plot(xtemp_txP[i], ytemp_tx[i], 'bo')\n",
|
||||
" ax.plot(xtemp_txN[i], ytemp_tx[i], 'ro')\n",
|
||||
" ax.plot(np.r_[xtemp_txP[i], xtemp_txN[i]], np.r_[0.4-0.01*(i-1), 0.4-0.01*(i-1)], 'k-', lw = 1) \n",
|
||||
"\n",
|
||||
"ax.plot(np.r_[xtemp_rxP, xtemp_rxP], np.r_[0., 0.2], 'k-', lw = 1)\n",
|
||||
"ax.plot(np.r_[xtemp_rxN, xtemp_rxN], np.r_[0., 0.2], 'k-', lw = 1)\n",
|
||||
"ax.plot(xtemp_rxP, ytemp_rx, 'ko')\n",
|
||||
"ax.plot(xtemp_rxN, ytemp_rx, 'go')\n",
|
||||
"ax.plot(np.r_[xtemp_rxP, xtemp_rxN], np.r_[0.2, 0.2], 'k-', lw = 1) \n",
|
||||
"\n",
|
||||
"ax.grid(True) \n",
|
||||
"ax.set_ylim(-0.2,0.6)\n",
|
||||
"ax.set_xlim(-600,600)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 20,
|
||||
"text": [
|
||||
"(-600, 600)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10d9c1f90>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 20
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"fig, ax = plt.subplots(1,1, figsize = (6,4))\n",
|
||||
"ax.plot(xtemp_txP, ytemp_tx, 'bo')\n",
|
||||
"ax.plot(xtemp_txN, ytemp_tx, 'ro')\n",
|
||||
"ax.plot(xtemp_rxP, ytemp_rx, 'ko')\n",
|
||||
"ax.plot(xtemp_rxN, ytemp_rx, 'go')\n",
|
||||
"ax.legend(('A (C+)', 'B (C-)', 'M (P+)', 'N (C-)'), fontsize = 14)\n",
|
||||
"mesh.plotSlice(sigma, grid=True, ax = ax, pcolorOpts={'cmap':'binary'})\n",
|
||||
"ax.set_xlim(-600, 600)\n",
|
||||
"ax.set_ylim(-200, 200)\n",
|
||||
"ax.set_title('Survey geometry (Plan view)')\n",
|
||||
"ax.set_xlabel('x (m)')\n",
|
||||
"ax.set_ylabel('y (m)')\n",
|
||||
"ax.text(-600, 210, '(a)', fontsize = 16)\n",
|
||||
"# fig.savefig('DCsurvey.png', dpi = 200)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 21,
|
||||
"text": [
|
||||
"<matplotlib.text.Text at 0x10da73a50>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x112b59190>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 21
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"txlist = []\n",
|
||||
"rx = DC.RxDipole(np.r_[xtemp_rxP, ytemp_rx, -12.5], np.r_[xtemp_rxN, ytemp_rx, -12.5])\n",
|
||||
"for i in range(ntx): \n",
|
||||
" tx = DC.SrcDipole([rx], [xtemp_txP[i], ytemp_tx[i], -12.5],[xtemp_txN[i], ytemp_tx[i], -12.5])\n",
|
||||
" txlist.append(tx)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 22
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"survey = DC.SurveyDC(txlist)\n",
|
||||
"problem = DC.ProblemDC(mesh)\n",
|
||||
"problem.pair(survey)\n",
|
||||
"try:\n",
|
||||
" from pymatsolver import MumpsSolver\n",
|
||||
" problem.Solver = MumpsSolver\n",
|
||||
"except Exception, e:\n",
|
||||
" problem.Solver = SolverLU"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 23
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 2,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Step4: Run DC forward modeling"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"%%time\n",
|
||||
"data = survey.dpred(sigma)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"CPU times: user 4.93 s, sys: 612 ms, total: 5.54 s\n",
|
||||
"Wall time: 5 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 25
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"appres = data*np.pi*b*(b+a)/a\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots(1,1, figsize = (6, 4))\n",
|
||||
"ax.semilogx(np.r_[100., 500.], np.r_[100., 100.], 'k--')\n",
|
||||
"ax.semilogx(abhalf, appres, 'k.-')\n",
|
||||
"ax.set_ylim(90., 120.)\n",
|
||||
"ax.set_xscale('log')\n",
|
||||
"ax.set_xlabel('AB/2')\n",
|
||||
"ax.set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
|
||||
"ax.grid(True)\n",
|
||||
"ax.text(100, 122, '(b)', fontsize = 16)\n",
|
||||
"ax.legend(('True', 'simpegDC'), loc = 1, fontsize = 14)\n",
|
||||
"# fig.savefig('comp_dc.png')"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 26,
|
||||
"text": [
|
||||
"<matplotlib.legend.Legend at 0x10d9ed410>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x112b6ca50>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 26
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 21
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 21
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": []
|
||||
}
|
||||
],
|
||||
"metadata": {}
|
||||
"source": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"from simpegem1d import Utils1D\n",
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# DC Forward Modeling of Schlumber array"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Here we test the accuracy of DC forward modeling using analytic solution."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step1: Generate mesh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cs = 25.\n",
|
||||
"npad = 11\n",
|
||||
"hx = [(cs,npad, -1.3),(cs,41),(cs,npad, 1.3)]\n",
|
||||
"hy = [(cs,npad, -1.3),(cs,17),(cs,npad, 1.3)]\n",
|
||||
"hz = [(cs,npad, -1.3),(cs,20)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x104395510>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.plotGrid()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step2: Generating model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sighalf = 1e-2\n",
|
||||
"sigma = np.ones(mesh.nC)*sighalf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step3: Design survey: Schulumberger array"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<img src=\"http://www.landrinstruments.com/_/rsrc/1271695892678/home/ultra-minires/additional-information-1/schlumberger-soundings/schlum%20array.JPG\"> </img>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### $$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Let $b=na$, then we rewrite above equation as:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### $$ \\rho_a = \\frac{V}{I}\\pi na(n+1)$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Since AB/2 can be a good measure for depth of investigation, we express "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### $$AB/2 = \\frac{(2n+1)a}{2}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ntx = 16"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xtemp_txP = np.arange(ntx)*(25.)-500.\n",
|
||||
"xtemp_txN = -xtemp_txP\n",
|
||||
"ytemp_tx = np.zeros(ntx)\n",
|
||||
"xtemp_rxP = -50.\n",
|
||||
"xtemp_rxN = 50.\n",
|
||||
"ytemp_rx = 0.\n",
|
||||
"abhalf = abs(xtemp_txP-xtemp_txN)*0.5\n",
|
||||
"a = xtemp_rxN-xtemp_rxP\n",
|
||||
"b = ((xtemp_txN-xtemp_txP)-a)*0.5"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(-600, 600)"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10e08b5d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots(1,1, figsize = (12,3))\n",
|
||||
"for i in range(ntx):\n",
|
||||
" ax.plot(np.r_[xtemp_txP[i], xtemp_txP[i]], np.r_[0., 0.4-0.01*(i-1)], 'k-', lw = 1)\n",
|
||||
" ax.plot(np.r_[xtemp_txN[i], xtemp_txN[i]], np.r_[0., 0.4-0.01*(i-1)], 'k-', lw = 1)\n",
|
||||
" ax.plot(xtemp_txP[i], ytemp_tx[i], 'bo')\n",
|
||||
" ax.plot(xtemp_txN[i], ytemp_tx[i], 'ro')\n",
|
||||
" ax.plot(np.r_[xtemp_txP[i], xtemp_txN[i]], np.r_[0.4-0.01*(i-1), 0.4-0.01*(i-1)], 'k-', lw = 1) \n",
|
||||
"\n",
|
||||
"ax.plot(np.r_[xtemp_rxP, xtemp_rxP], np.r_[0., 0.2], 'k-', lw = 1)\n",
|
||||
"ax.plot(np.r_[xtemp_rxN, xtemp_rxN], np.r_[0., 0.2], 'k-', lw = 1)\n",
|
||||
"ax.plot(xtemp_rxP, ytemp_rx, 'ko')\n",
|
||||
"ax.plot(xtemp_rxN, ytemp_rx, 'go')\n",
|
||||
"ax.plot(np.r_[xtemp_rxP, xtemp_rxN], np.r_[0.2, 0.2], 'k-', lw = 1) \n",
|
||||
"\n",
|
||||
"ax.grid(True) \n",
|
||||
"ax.set_ylim(-0.2,0.6)\n",
|
||||
"ax.set_xlim(-600,600)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.text.Text at 0x10ee9dcd0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10e069890>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots(1,1, figsize = (6,4))\n",
|
||||
"ax.plot(xtemp_txP, ytemp_tx, 'bo')\n",
|
||||
"ax.plot(xtemp_txN, ytemp_tx, 'ro')\n",
|
||||
"ax.plot(xtemp_rxP, ytemp_rx, 'ko')\n",
|
||||
"ax.plot(xtemp_rxN, ytemp_rx, 'go')\n",
|
||||
"ax.legend(('A (C+)', 'B (C-)', 'M (P+)', 'N (C-)'), fontsize = 14)\n",
|
||||
"mesh.plotSlice(sigma, grid=True, ax = ax, pcolorOpts={'cmap':'binary'})\n",
|
||||
"ax.set_xlim(-600, 600)\n",
|
||||
"ax.set_ylim(-200, 200)\n",
|
||||
"ax.set_title('Survey geometry (Plan view)')\n",
|
||||
"ax.set_xlabel('x (m)')\n",
|
||||
"ax.set_ylabel('y (m)')\n",
|
||||
"ax.text(-600, 210, '(a)', fontsize = 16)\n",
|
||||
"# fig.savefig('DCsurvey.png', dpi = 200)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"txlist = []\n",
|
||||
"rx = DC.RxDipole(np.r_[xtemp_rxP, ytemp_rx, -12.5], np.r_[xtemp_rxN, ytemp_rx, -12.5])\n",
|
||||
"for i in range(ntx): \n",
|
||||
" tx = DC.SrcDipole([rx], [xtemp_txP[i], ytemp_tx[i], -12.5],[xtemp_txN[i], ytemp_tx[i], -12.5])\n",
|
||||
" txlist.append(tx)\n",
|
||||
"survey = DC.SurveyDC(txlist)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh)\n",
|
||||
"problem.pair(survey)\n",
|
||||
"try:\n",
|
||||
" from pymatsolver import MumpsSolver\n",
|
||||
" problem.Solver = MumpsSolver\n",
|
||||
"except Exception, e:\n",
|
||||
" problem.Solver = SolverLU "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step4: Run DC forward modeling"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 5.26 s, sys: 554 ms, total: 5.81 s\n",
|
||||
"Wall time: 4.06 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"data = survey.dpred(sigma)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.legend.Legend at 0x10f990a10>"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10ea722d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"appres = data*np.pi*b*(b+a)/a\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots(1,1, figsize = (6, 4))\n",
|
||||
"ax.semilogx(np.r_[100., 500.], np.r_[100., 100.], 'k--')\n",
|
||||
"ax.semilogx(abhalf, appres, 'k.-')\n",
|
||||
"ax.set_ylim(90., 120.)\n",
|
||||
"ax.set_xscale('log')\n",
|
||||
"ax.set_xlabel('AB/2')\n",
|
||||
"ax.set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
|
||||
"ax.grid(True)\n",
|
||||
"ax.text(100, 122, '(b)', fontsize = 16)\n",
|
||||
"ax.legend(('True', 'simpegDC'), loc = 1, fontsize = 14)\n",
|
||||
"# fig.savefig('comp_dc.png')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"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
|
||||
}
|
||||
@@ -1,7 +1,7 @@
|
||||
{
|
||||
"metadata": {
|
||||
"name": "",
|
||||
"signature": "sha256:420f2b8a60ef08ab473ad159b33d15f48d4551a725f31a27a51cc03264657fbc"
|
||||
"signature": "sha256:29c8b78e5b8f768bcf861afb186d1d85bff67ec768eba74cf6c3e85da81d0c01"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
@@ -13,7 +13,7 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDC as DC\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"from simpegem1d import Utils1D\n",
|
||||
"%pylab inline"
|
||||
],
|
||||
@@ -26,18 +26,9 @@
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stderr",
|
||||
"text": [
|
||||
"Vendor: Continuum Analytics, Inc.\n",
|
||||
"Package: mkl\n",
|
||||
"Message: trial mode expires in 20 days\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 1
|
||||
"prompt_number": 4
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -51,7 +42,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 2
|
||||
"prompt_number": 5
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
@@ -97,7 +88,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 3
|
||||
"prompt_number": 6
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -108,7 +99,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 4
|
||||
"prompt_number": 7
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -124,11 +115,11 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10439a3d0>"
|
||||
"<matplotlib.figure.Figure at 0x10c83c990>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 5
|
||||
"prompt_number": 8
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
@@ -154,7 +145,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 6
|
||||
"prompt_number": 9
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -187,11 +178,11 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10db67550>"
|
||||
"<matplotlib.figure.Figure at 0x10e1d9710>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 7
|
||||
"prompt_number": 10
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -212,7 +203,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 8
|
||||
"prompt_number": 11
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
@@ -278,7 +269,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 15
|
||||
"prompt_number": 12
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -297,7 +288,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 16
|
||||
"prompt_number": 13
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -319,7 +310,7 @@
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 17
|
||||
"prompt_number": 14
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -349,7 +340,7 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 18,
|
||||
"prompt_number": 15,
|
||||
"text": [
|
||||
"(-0.2, 0.6)"
|
||||
]
|
||||
@@ -359,11 +350,11 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10e28ea50>"
|
||||
"<matplotlib.figure.Figure at 0x1104a8b50>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 18
|
||||
"prompt_number": 15
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -392,11 +383,11 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x114a31710>"
|
||||
"<matplotlib.figure.Figure at 0x10e2f0790>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 19
|
||||
"prompt_number": 16
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -419,7 +410,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 22
|
||||
"prompt_number": 17
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
@@ -433,7 +424,7 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"problem = DC.ProblemDC(mesh, mapping=mapping)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh, mapping=mapping)\n",
|
||||
"problem.pair(survey)\n",
|
||||
"try:\n",
|
||||
" from pymatsolver import MumpsSolver\n",
|
||||
@@ -444,7 +435,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 29
|
||||
"prompt_number": 19
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
@@ -468,12 +459,12 @@
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"CPU times: user 4.7 s, sys: 582 ms, total: 5.28 s\n",
|
||||
"Wall time: 4.41 s\n"
|
||||
"CPU times: user 6.05 s, sys: 708 ms, total: 6.76 s\n",
|
||||
"Wall time: 6.01 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 31
|
||||
"prompt_number": 20
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -497,8 +488,20 @@
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 32
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 21,
|
||||
"text": [
|
||||
"array([ 0.0186522 , 0.0211157 , 0.02374998, 0.02677094, 0.02987307,\n",
|
||||
" 0.03446037, 0.03920482, 0.04541832, 0.05344991, 0.062497 ,\n",
|
||||
" 0.07516488, 0.09013051, 0.11364832, 0.1407387 , 0.18918521,\n",
|
||||
" 0.27485483])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 21
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -533,7 +536,7 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 34,
|
||||
"prompt_number": 22,
|
||||
"text": [
|
||||
"(-500.0, 0.0)"
|
||||
]
|
||||
@@ -543,11 +546,11 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x110aad290>"
|
||||
"<matplotlib.figure.Figure at 0x10e2d5d10>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 34
|
||||
"prompt_number": 22
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
@@ -573,7 +576,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 35
|
||||
"prompt_number": 23
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -610,7 +613,7 @@
|
||||
"\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 4.33e-01 6.26e+03 4.48e+00 6.27e+03 8.79e+03 0 "
|
||||
" 0 2.61e-01 6.27e+03 4.48e+00 6.27e+03 8.80e+03 0 "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -618,7 +621,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 1 4.33e-01 6.16e+02 3.99e+00 6.18e+02 2.57e+03 0 "
|
||||
" 1 2.61e-01 5.22e+02 3.96e+00 5.23e+02 2.50e+03 0 "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -626,7 +629,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 2 8.65e-02 2.66e+02 3.93e+00 2.66e+02 1.05e+03 0 "
|
||||
" 2 5.23e-02 1.67e+02 3.91e+00 1.67e+02 8.21e+02 0 "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -634,7 +637,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 3 8.65e-02 9.60e+01 3.92e+00 9.64e+01 3.61e+02 0 Skip BFGS "
|
||||
" 3 5.23e-02 7.93e+01 4.08e+00 7.95e+01 2.62e+02 0 Skip BFGS "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -642,7 +645,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 4 1.73e-02 7.46e+01 4.01e+00 7.46e+01 2.16e+02 0 Skip BFGS "
|
||||
" 4 1.05e-02 6.35e+01 4.25e+00 6.36e+01 1.71e+02 0 Skip BFGS "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -650,7 +653,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 5 1.73e-02 4.24e+01 4.41e+00 4.24e+01 1.20e+02 0 Skip BFGS "
|
||||
" 5 1.05e-02 3.01e+01 5.08e+00 3.02e+01 2.36e+02 0 Skip BFGS "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -658,7 +661,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 6 3.46e-03 2.73e+01 4.78e+00 2.73e+01 8.23e+01 0 Skip BFGS "
|
||||
" 6 2.09e-03 2.33e+01 5.32e+00 2.33e+01 1.57e+02 0 Skip BFGS "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -666,7 +669,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 7 3.46e-03 1.10e+01 5.73e+00 1.10e+01 2.42e+01 0 Skip BFGS "
|
||||
" 7 2.09e-03 1.01e+01 6.69e+00 1.01e+01 1.34e+02 0 Skip BFGS "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -675,16 +678,16 @@
|
||||
"text": [
|
||||
"\n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 1.6315e+01 <= tolF*(1+|f0|) = 6.2674e+02\n",
|
||||
"0 : |xc-x_last| = 7.2794e-01 <= tolX*(1+|x0|) = 2.6641e-14\n",
|
||||
"0 : |proj(x-g)-x| = 2.4186e+01 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 2.4186e+01 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : |fc-fOld| = 1.3195e+01 <= tolF*(1+|f0|) = 6.2702e+02\n",
|
||||
"0 : |xc-x_last| = 7.9764e-01 <= tolX*(1+|x0|) = 2.6641e-14\n",
|
||||
"0 : |proj(x-g)-x| = 1.3417e+02 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 1.3417e+02 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 7 <= iter = 7\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 36
|
||||
"prompt_number": 24
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -695,7 +698,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 37
|
||||
"prompt_number": 25
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -730,14 +733,22 @@
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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||||
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|
||||
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|
||||
"text": [
|
||||
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|
||||
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||||
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||||
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|
||||
{
|
||||
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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x114071f10>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 38
|
||||
"prompt_number": 26
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -758,7 +769,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 39
|
||||
"prompt_number": 27
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
|
||||
+648
-423
File diff suppressed because it is too large.
Load diff
+64
-28
@@ -1,7 +1,7 @@
|
||||
{
|
||||
"metadata": {
|
||||
"name": "",
|
||||
"signature": "sha256:8f8d2c689741d8d56146a511dd2c67e7956c3d1fe0dcef0cf550c0b5c5f9c52d"
|
||||
"signature": "sha256:9107d99fbfb21822b71a94de16f078e82c12cf73d2e85893fd26dfda06c8ce14"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
@@ -13,7 +13,7 @@
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDC as DC\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"%pylab inline\n",
|
||||
"from pymatsolver import MumpsSolver"
|
||||
],
|
||||
@@ -33,7 +33,7 @@
|
||||
"text": [
|
||||
"Vendor: Continuum Analytics, Inc.\n",
|
||||
"Package: mkl\n",
|
||||
"Message: trial mode expires in 20 days\n"
|
||||
"Message: trial mode expires in 29 days\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -156,9 +156,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 8,
|
||||
"prompt_number": 7,
|
||||
"text": [
|
||||
"[<matplotlib.lines.Line2D at 0x10ea54b90>]"
|
||||
"[<matplotlib.lines.Line2D at 0x10db0fa50>]"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -166,11 +166,11 @@
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10e8888d0>"
|
||||
"<matplotlib.figure.Figure at 0x10d9ac710>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 8
|
||||
"prompt_number": 7
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
@@ -179,7 +179,7 @@
|
||||
"expmap = Maps.ExpMap(mesh)\n",
|
||||
"m2to3 = Maps.Map2Dto3D(mesh,normal='Y')\n",
|
||||
"imap = Maps.IdentityMap(mesh)\n",
|
||||
"problem = DC.ProblemDC(mesh, mapping= imap )\n",
|
||||
"problem = DC.ProblemDC_CC(mesh, mapping= imap )\n",
|
||||
"problem.Solver = MumpsSolver\n",
|
||||
"problem.pair(survey)"
|
||||
],
|
||||
@@ -204,16 +204,16 @@
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 10,
|
||||
"text": [
|
||||
"[<matplotlib.lines.Line2D at 0x1106dd910>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x1106ddb90>]"
|
||||
"[<matplotlib.lines.Line2D at 0x1038621d0>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x103862450>]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10d9bb3d0>"
|
||||
"<matplotlib.figure.Figure at 0x10e82c610>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -283,7 +283,7 @@
|
||||
"\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 6.59e+00 1.84e+03 1.05e+04 7.11e+04 2.15e+03 0 "
|
||||
" 0 7.28e+00 1.86e+03 1.05e+04 7.84e+04 2.20e+03 0 "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -291,7 +291,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 1 6.59e+00 1.98e+02 1.07e+04 7.10e+04 1.89e+02 0 "
|
||||
" 1 7.28e+00 6.57e+02 1.06e+04 7.80e+04 1.15e+03 1 "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -299,7 +299,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 2 1.32e+00 1.70e+02 1.07e+04 1.43e+04 2.63e+02 0 "
|
||||
" 2 1.46e+00 5.58e+02 1.06e+04 1.61e+04 1.03e+03 3 Skip BFGS "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -307,7 +307,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 3 1.32e+00 9.16e+01 1.07e+04 1.42e+04 1.81e+02 0 "
|
||||
" 3 1.46e+00 1.36e+02 1.08e+04 1.59e+04 1.69e+02 0 Skip BFGS "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -315,7 +315,7 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
"------------------------------------------------------------------"
|
||||
" 4 2.91e-01 8.24e+01 1.07e+04 3.20e+03 2.05e+02 0 "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -323,10 +323,37 @@
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
"0 : ft = 1.4181e+04 <= alp*descent = 1.4181e+04\n",
|
||||
"1 : maxIterLS = 10 <= iterLS = 10\n",
|
||||
"------------------------- End Linesearch -------------------------\n",
|
||||
"The linesearch got broken. Boo.\n"
|
||||
" 5 2.91e-01 4.61e+01 1.07e+04 3.16e+03 1.35e+02 0 Skip BFGS "
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 6 5.83e-02 4.40e+01 1.07e+04 6.65e+02 1.16e+02 0 "
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
" 7 5.83e-02 3.99e+01 1.07e+04 6.61e+02 1.35e+02 0 "
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"\n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 4.7528e+00 <= tolF*(1+|f0|) = 7.8397e+03\n",
|
||||
"0 : |xc-x_last| = 4.6314e-01 <= tolX*(1+|x0|) = 1.8172e-13\n",
|
||||
"0 : |proj(x-g)-x| = 1.3494e+02 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 1.3494e+02 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 7 <= iter = 7\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -346,15 +373,15 @@
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 14,
|
||||
"text": [
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x10daf85a8>"
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x10fcbaa28>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10eeb9190>"
|
||||
"<matplotlib.figure.Figure at 0x103879a90>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -385,21 +412,30 @@
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 16,
|
||||
"text": [
|
||||
"[<matplotlib.lines.Line2D at 0x110194e10>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x1101a10d0>]"
|
||||
"[<matplotlib.lines.Line2D at 0x10f42a790>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x10f42aa10>]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10ed69dd0>"
|
||||
"<matplotlib.figure.Figure at 0x10fa4d110>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 16
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 16
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
|
||||
@@ -0,0 +1,703 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING: pylab import has clobbered these variables: ['linalg']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"%pylab inline\n",
|
||||
"from pymatsolver import MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cs = 12.5\n",
|
||||
"nc = 500/cs+1\n",
|
||||
"hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]\n",
|
||||
"hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]\n",
|
||||
"hz = [(cs,7, -1.3),(cs,int(nc/2+1))]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" ---- 3-D TensorMesh ---- \n",
|
||||
" x0: -541.97\n",
|
||||
" y0: -416.97\n",
|
||||
" z0: -548.22\n",
|
||||
" nCx: 55\n",
|
||||
" nCy: 35\n",
|
||||
" nCz: 28\n",
|
||||
" hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
|
||||
" hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
|
||||
" hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n",
|
||||
"print mesh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sighalf = 1e-2\n",
|
||||
"sigma = np.ones(mesh.nC)*sighalf\n",
|
||||
"p0 = np.r_[-50., 50., -50.]\n",
|
||||
"p1 = np.r_[ 50.,-50., -150.]\n",
|
||||
"blk_ind = Utils.ModelBuilder.getIndicesBlock(p0, p1, mesh.gridCC)\n",
|
||||
"sigma[blk_ind] = 1e-3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"eta = np.zeros_like(sigma)\n",
|
||||
"eta[blk_ind] = 0.1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sigmaInf = sigma.copy()\n",
|
||||
"sigma0 = sigma*(1-eta)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(-600.0, 600.0, -600.0, 0.0)"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10986eb90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dat = mesh.plotSlice(eta, normal='Y')\n",
|
||||
"plt.colorbar(dat[0])\n",
|
||||
"axis('equal')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"nElecs = 21\n",
|
||||
"x_temp = np.linspace(-250, 250, nElecs)\n",
|
||||
"aSpacing = x_temp[1]-x_temp[0]\n",
|
||||
"y_temp = 0.\n",
|
||||
"xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"63\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)\n",
|
||||
"survey = DC.SurveyDC(srcList)\n",
|
||||
"print len(survey.srcList)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"DATA = Survey.Data(survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tx0 = srcList[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'c5f9fd3e-9e73-4561-b2c2-52a2747439e8'"
|
||||
]
|
||||
},
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tx0.uid"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import SimPEG"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"SimPEG.Survey.Data??"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x1089f8550>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1087a6750>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
|
||||
"indz = 22\n",
|
||||
"dat = mesh.plotSlice(sigma, grid=True, ax = ax, ind=indz)\n",
|
||||
"ax.set_xlim(-300, 300)\n",
|
||||
"ax.set_ylim(-300, 300)\n",
|
||||
"cb = plt.colorbar(dat[0])\n",
|
||||
"ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
|
||||
"\n",
|
||||
"txLocs = np.array([[tx.loc[0][0], tx.loc[1][0]] for tx in survey.srcList]).flatten()\n",
|
||||
"ax.plot(txLocs, txLocs*0, 'w.', ms = 3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"expmap = Maps.ExpMap(mesh)\n",
|
||||
"m2to3 = Maps.Map2Dto3D(mesh,normal='Y')\n",
|
||||
"imap = Maps.IdentityMap(mesh)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh, mapping= imap )\n",
|
||||
"problem.Solver = MumpsSolver\n",
|
||||
"try:\n",
|
||||
" problem.pair(survey)\n",
|
||||
"except Exception, e:\n",
|
||||
" survey.unpair()\n",
|
||||
" problem.pair(survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 6.49 s, sys: 705 ms, total: 7.2 s\n",
|
||||
"Wall time: 4.94 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"phi0 = survey.dpred(sigma0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" ---- 3-D TensorMesh ---- \n",
|
||||
" x0: -541.97\n",
|
||||
" y0: -416.97\n",
|
||||
" z0: -548.22\n",
|
||||
" nCx: 55\n",
|
||||
" nCy: 35\n",
|
||||
" nCz: 28\n",
|
||||
" hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
|
||||
" hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
|
||||
" hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print mesh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"phiInf = survey.dpred(sigmaInf)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"phiIP_true = phi0-phiInf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# F = problem.fields(mesh, survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"surveyIP = DC.SurveyIP(srcList)\n",
|
||||
"problemIP = DC.ProblemIP(mesh, sigma=sigma)\n",
|
||||
"problemIP.pair(surveyIP)\n",
|
||||
"problemIP.Solver = MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x10984ab90>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x10984ae10>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1089dcf90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"datasyn = surveyIP.dpred(eta)\n",
|
||||
"surveyIP.makeSyntheticData(eta,std=0.02,force=True)\n",
|
||||
"plot(np.c_[surveyIP.dobs,datasyn])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"problemIP.mapping = imap"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dmis = DataMisfit.l2_DataMisfit(surveyIP)\n",
|
||||
"dmis.Wd = 1./abs(phi0)*1e3\n",
|
||||
"reg = Regularization.Tikhonov(mesh,mapping=imap)\n",
|
||||
"# opt = Optimization.InexactGaussNewton(maxIter=4,tolX=1e-15)\n",
|
||||
"opt = Optimization.ProjectedGNCG(maxIter=3,tolX=1e-15)\n",
|
||||
"opt.remember('xc')\n",
|
||||
"invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
|
||||
"beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e-1)\n",
|
||||
"betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=4)\n",
|
||||
"inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"reg.alpha_s = 1e-4\n",
|
||||
"reg.alpha_x = 1.\n",
|
||||
"reg.alpha_z = 1.\n",
|
||||
"reg.alpha_y = 1."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"opt.upper = Inf\n",
|
||||
"opt.lower = 0."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"SimPEG.InvProblem will set Regularization.mref to m0.\n",
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"=============================== Projected GNCG ===============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 5.84e+00 9.43e+01 0.00e+00 9.43e+01 1.69e+03 0 \n",
|
||||
" 1 5.84e+00 7.43e+00 6.13e-03 7.46e+00 3.04e+02 0 \n",
|
||||
" 2 5.84e+00 3.10e+00 1.33e-02 3.18e+00 2.65e+02 0 Skip BFGS \n",
|
||||
" 3 5.84e+00 2.13e+00 1.82e-02 2.24e+00 2.49e+02 0 Skip BFGS \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 9.4407e-01 <= tolF*(1+|f0|) = 9.5287e+00\n",
|
||||
"0 : |xc-x_last| = 4.4997e-02 <= tolX*(1+|x0|) = 1.0000e-15\n",
|
||||
"0 : |proj(x-g)-x| = 2.4936e+02 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 2.4936e+02 <= 1e3*eps = 1.0000e-02\n",
|
||||
"1 : maxIter = 3 <= iter = 3\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"m0 = np.ones(problemIP.mapping.nP)*1e-10\n",
|
||||
"mopt = inv.run(m0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" ---- 3-D TensorMesh ---- \n",
|
||||
" x0: -541.97\n",
|
||||
" y0: -416.97\n",
|
||||
" z0: -548.22\n",
|
||||
" nCx: 55\n",
|
||||
" nCy: 35\n",
|
||||
" nCz: 28\n",
|
||||
" hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
|
||||
" hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
|
||||
" hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print mesh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(-600.0, 600.0, -600.0, 0.0)"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEZCAYAAACNebLAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10faf04d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"colorbar(mesh.plotSlice(mopt, normal='Y', ind=17, grid=True, gridOpts={'alpha': 0.2, 'color':'black'})[0])\n",
|
||||
"axis('equal')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# sigma_est = expmap * m2to3 * mopt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dpred = surveyIP.dpred(mopt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x11345b510>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x11345b750>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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|
||||
"<matplotlib.figure.Figure at 0x10f219190>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plot(np.c_[dpred,surveyIP.dobs], '.-')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"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
|
||||
}
|
||||
@@ -0,0 +1,441 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Objective:** \n",
|
||||
"\n",
|
||||
"In this tutorial we will create a simple two-sphere model and simulate DC Resistivity data for various transmitter-receiver configurations.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"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"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"import scipy.interpolate as interpolation\n",
|
||||
"import time\n",
|
||||
"import re"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\dominiquef.MIRAGEOSCIENCE\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
|
||||
" \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n",
|
||||
"WARNING: "
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"pylab import has clobbered these variables: ['linalg']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%matplotlib notebook\n",
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(-200, 200)"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15e3dd30>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# First we need to create a mesh and a model.\n",
|
||||
"\n",
|
||||
"# This is our mesh\n",
|
||||
"dx = 5.\n",
|
||||
"\n",
|
||||
"hxind = [(dx,15,-1.3), (dx, 75), (dx,15,1.3)]\n",
|
||||
"hyind = [(dx,15,-1.3), (dx, 10), (dx,15,1.3)]\n",
|
||||
"hzind = [(dx,15,-1.3),(dx, 15)]\n",
|
||||
"\n",
|
||||
"mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')\n",
|
||||
"\n",
|
||||
"# Define our model\n",
|
||||
"bckgr = 1e-2\n",
|
||||
"cond = 1e-1\n",
|
||||
"resis = 1e-3\n",
|
||||
"zloc = -50.\n",
|
||||
"xloc = 50.\n",
|
||||
"yloc = 0.\n",
|
||||
"radi = 25.\n",
|
||||
"\n",
|
||||
"# Set background conductivity\n",
|
||||
"model = np.ones(mesh.nC) * bckgr\n",
|
||||
"\n",
|
||||
"# First anomaly (conductor)\n",
|
||||
"ind = Utils.ModelBuilder.getIndicesSphere([-xloc,yloc,zloc],radi,mesh.gridCC)\n",
|
||||
"model[ind] = cond\n",
|
||||
"\n",
|
||||
"# Second anomaly (resistor)\n",
|
||||
"ind = Utils.ModelBuilder.getIndicesSphere([xloc,yloc,zloc],radi,mesh.gridCC)\n",
|
||||
"model[ind] = resis\n",
|
||||
"\n",
|
||||
"# Get index of the center\n",
|
||||
"indy = int(mesh.nCy/2)\n",
|
||||
"indz = int(np.argmin( np.abs(mesh.vectorCCz - zloc) ))\n",
|
||||
"\n",
|
||||
"# Plot the model for reference\n",
|
||||
"# Define core mesh extent\n",
|
||||
"xlim = 200\n",
|
||||
"zlim = 200\n",
|
||||
"\n",
|
||||
"plt.figure()\n",
|
||||
"ax = plt.subplot(1,2,1, aspect='equal')\n",
|
||||
"mesh.plotSlice(np.log10(model), ax =ax, normal = 'Y', ind = indy,grid=True)\n",
|
||||
"ax.set_title('E-W section at '+str(mesh.vectorCCy[indy])+' m')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"plt.xlim([-xlim,xlim])\n",
|
||||
"plt.ylim([-zlim,0])\n",
|
||||
"\n",
|
||||
"ax = plt.subplot(1,2,2, aspect='equal')\n",
|
||||
"mesh.plotSlice(np.log10(model), ax =ax, normal = 'Z', ind = indz,grid=True)\n",
|
||||
"ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"plt.xlim([-xlim,xlim])\n",
|
||||
"plt.ylim([-xlim,xlim])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that we have model in 3D, we can define a survey.\n",
|
||||
"There area various ground DC configurations used in the field.\n",
|
||||
"We can explore two survey types here: pole-dipole (pdp) and dipole-dipole (dpdp).\n",
|
||||
"In both cases we need to specify three important parameter.\n",
|
||||
"\n",
|
||||
"a: Seperation (m) between the transmitter and receivers\n",
|
||||
"\n",
|
||||
"b: Dipole seperation (m) of the receiver (and transmitter for dpdp)\n",
|
||||
"\n",
|
||||
"n: Number of receiver dipoles along line\n",
|
||||
"\n",
|
||||
"We also need to give a starting and end point for the survey.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(-200, 200)"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15e3d240>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Survey parameters\n",
|
||||
"a = 30.\n",
|
||||
"b = 30.\n",
|
||||
"n = 20 # Integer number of rx dipoles\n",
|
||||
"\n",
|
||||
"# Specify the survey type: \"pdp\" | \"dpdp\"\n",
|
||||
"stype = 'dpdp'\n",
|
||||
"\n",
|
||||
"# Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh\n",
|
||||
"ends = [(-175,0),(175,0)]\n",
|
||||
"ends = np.c_[np.asarray(ends),np.ones(2).T*mesh.vectorNz[-1]]\n",
|
||||
"\n",
|
||||
"# Snap the endpoints to the grid. Easier to create 2D section.\n",
|
||||
"indx = Utils.closestPoints(mesh, ends )\n",
|
||||
"locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]]\n",
|
||||
"\n",
|
||||
"# We will handle the geometry of the survey for you and create all the combination of tx-rx along line\n",
|
||||
"[Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, a, b, n)\n",
|
||||
"\n",
|
||||
"# Here is an example for the first tx-rx array\n",
|
||||
"fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
|
||||
"mesh.plotSlice(np.log10(model), ax =ax, normal = 'Z', ind = indz,grid=True)\n",
|
||||
"ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"\n",
|
||||
"plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')\n",
|
||||
"plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')\n",
|
||||
"plt.xlim([-xlim,xlim])\n",
|
||||
"plt.ylim([-xlim,xlim])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The next section will specify all the parameters used to forward model the data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Set boundary conditions\n",
|
||||
"mesh.setCellGradBC('neumann')\n",
|
||||
"\n",
|
||||
"# Define the differential operators needed for the DC problem\n",
|
||||
"Div = mesh.faceDiv\n",
|
||||
"Grad = mesh.cellGrad\n",
|
||||
"Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))\n",
|
||||
"\n",
|
||||
"A = Div*Msig*Grad\n",
|
||||
"\n",
|
||||
"# Change one corner to deal with nullspace\n",
|
||||
"A[0,0] = 1\n",
|
||||
"A = sp.csc_matrix(A)\n",
|
||||
"\n",
|
||||
"# We will solve the system iteratively, so a pre-conditioner is helpful\n",
|
||||
"# This is simply a Jacobi preconditioner (inverse of the main diagonal)\n",
|
||||
"dA = A.diagonal()\n",
|
||||
"P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Transmitter 8 of 9 -> Time:0.990999937057 sec Transmitter 8 of 9\n",
|
||||
"Forward completed\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Now we can solve the system for all the transmitters\n",
|
||||
"# We want to store the data\n",
|
||||
"data = []\n",
|
||||
"\n",
|
||||
"# There is probably a more elegant way to do this, but we can just for-loop through the transmitters\n",
|
||||
"for ii in range(len(Tx)):\n",
|
||||
" \n",
|
||||
" start_time = time.time() # Let's time the calculations\n",
|
||||
" \n",
|
||||
" #print(\"Transmitter %i / %i\\r\" % (ii+1,len(Tx)))\n",
|
||||
" \n",
|
||||
" # Select dipole locations for receiver\n",
|
||||
" rxloc_M = np.asarray(Rx[ii][:,0:3])\n",
|
||||
" rxloc_N = np.asarray(Rx[ii][:,3:])\n",
|
||||
" \n",
|
||||
" # Number of receivers\n",
|
||||
" nrx = rxloc_M.shape[0]\n",
|
||||
" \n",
|
||||
" # For usual cases \"dpdp\" or \"gradient\"\n",
|
||||
" if not re.match(stype,'pdp'): \n",
|
||||
" inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )\n",
|
||||
" RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] ) \n",
|
||||
" \n",
|
||||
" else: \n",
|
||||
" \n",
|
||||
" # Create an \"inifinity\" pole\n",
|
||||
" tx = np.squeeze(Tx[ii][:,0:1])\n",
|
||||
" tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2\n",
|
||||
" inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)\n",
|
||||
" RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] ) \n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Iterative Solve\n",
|
||||
" Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)\n",
|
||||
"\n",
|
||||
" # We now have the potential everywhere\n",
|
||||
" phi = mkvc(Ainvb[0])\n",
|
||||
" \n",
|
||||
" # Solve for phi on pole locations\n",
|
||||
" P1 = mesh.getInterpolationMat(rxloc_M, 'CC')\n",
|
||||
" P2 = mesh.getInterpolationMat(rxloc_N, 'CC')\n",
|
||||
" \n",
|
||||
" # Compute the potential difference\n",
|
||||
" dtemp = (P1*phi - P2*phi)*np.pi\n",
|
||||
" \n",
|
||||
" data.append( dtemp ) \n",
|
||||
" print '\\rTransmitter {0} of {1} -> Time:{2} sec'.format(ii,len(Tx),time.time()- start_time),\n",
|
||||
" \n",
|
||||
"print 'Transmitter {0} of {1}'.format(ii,len(Tx))\n",
|
||||
"print 'Forward completed'\n",
|
||||
" \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Once we have our problem, we can use the inversion tools in SimPEG to run our inversion:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgcAAADwCAYAAACUqcezAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x18d372e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Let's just convert the 3D format into 2D (distance along line) and plot\n",
|
||||
"[Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots(1,1, figsize = (8,7))\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"\n",
|
||||
"# Plot the location of the spheres for reference\n",
|
||||
"circle1=plt.Circle((-xloc-Tx[0][0,0],zloc),radi,color='w',fill=False, lw=3)\n",
|
||||
"circle2=plt.Circle((xloc-Tx[0][0,0],zloc),radi,color='k',fill=False, lw=3)\n",
|
||||
"ax.add_artist(circle1)\n",
|
||||
"ax.add_artist(circle2)\n",
|
||||
"\n",
|
||||
"# Add the speudo section\n",
|
||||
"DC.plot_pseudoSection(Tx2d,Rx2d,data,mesh.vectorNz[-1],stype)\n",
|
||||
"\n",
|
||||
"plt.xlim([0,2*xlim])\n",
|
||||
"plt.ylim([-zlim,0])\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Back in the days (not so long ago really), geophysicists used to interpret DCR data directly from pseudo-section. Hopefully this example will convince you that interpretating speudo-section is really tricky, arguably impossible.\n",
|
||||
"Fortunately for us, we now have inversion techniques to make sense of the data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"125.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print -xloc-Tx[0][0,0]"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 103 KiB |
@@ -1,233 +0,0 @@
|
||||
from SimPEG import *
|
||||
|
||||
|
||||
class SrcDipole(Survey.BaseSrc):
|
||||
"""A dipole source, locA and locB are moved to the closest cell-centers"""
|
||||
|
||||
current = 1
|
||||
loc = None
|
||||
_rhsDict = None
|
||||
|
||||
def __init__(self, rxList, locA, locB, **kwargs):
|
||||
self.loc = (locA, locB)
|
||||
super(SrcDipole, self).__init__(rxList, **kwargs)
|
||||
|
||||
def getRhs(self, mesh):
|
||||
if getattr(self, '_rhsDict', None) is None:
|
||||
self._rhsDict = {}
|
||||
if mesh not in self._rhsDict:
|
||||
pts = [self.loc[0], self.loc[1]]
|
||||
inds = Utils.closestPoints(mesh, pts)
|
||||
q = np.zeros(mesh.nC)
|
||||
q[inds] = - self.current * ( np.r_[1., -1.] / mesh.vol[inds] )
|
||||
self._rhsDict[mesh] = q
|
||||
return self._rhsDict[mesh]
|
||||
|
||||
|
||||
class RxDipole(Survey.BaseRx):
|
||||
"""A dipole source, locA and locB are moved to the closest cell-centers"""
|
||||
def __init__(self, locsM, locsN, **kwargs):
|
||||
locs = (locsM, locsN)
|
||||
assert locsM.shape == locsN.shape, 'locs must be the same shape.'
|
||||
super(RxDipole, self).__init__(locs, 'dipole', storeProjections=False, **kwargs)
|
||||
|
||||
@property
|
||||
def nD(self):
|
||||
"""Number of data in the receiver."""
|
||||
return self.locs[0].shape[0]
|
||||
|
||||
def getP(self, mesh):
|
||||
P0 = mesh.getInterpolationMat(self.locs[0], self.projGLoc)
|
||||
P1 = mesh.getInterpolationMat(self.locs[1], self.projGLoc)
|
||||
return P0 - P1
|
||||
|
||||
|
||||
class SurveyDC(Survey.BaseSurvey):
|
||||
"""
|
||||
**SurveyDC**
|
||||
|
||||
Geophysical DC resistivity data.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, srcList, **kwargs):
|
||||
self.srcList = srcList
|
||||
Survey.BaseSurvey.__init__(self, **kwargs)
|
||||
self._rhsDict = {}
|
||||
self._Ps = {}
|
||||
|
||||
def projectFields(self, u):
|
||||
"""
|
||||
Predicted data.
|
||||
|
||||
.. math::
|
||||
d_\\text{pred} = Pu(m)
|
||||
"""
|
||||
P = self.getP(self.prob.mesh)
|
||||
return P*mkvc(u)
|
||||
|
||||
def getRhs(self, mesh):
|
||||
if mesh not in self._rhsDict:
|
||||
RHS = np.array([src.getRhs(mesh) for src in self.srcList]).T
|
||||
self._rhsDict[mesh] = RHS
|
||||
return self._rhsDict[mesh]
|
||||
|
||||
def getP(self, mesh):
|
||||
if mesh in self._Ps:
|
||||
return self._Ps[mesh]
|
||||
|
||||
P_src = [sp.vstack([rx.getP(mesh) for rx in src.rxList]) for src in self.srcList]
|
||||
|
||||
self._Ps[mesh] = sp.block_diag(P_src)
|
||||
return self._Ps[mesh]
|
||||
|
||||
|
||||
|
||||
|
||||
class ProblemDC(Problem.BaseProblem):
|
||||
"""
|
||||
**ProblemDC**
|
||||
|
||||
Geophysical DC resistivity problem.
|
||||
|
||||
"""
|
||||
|
||||
surveyPair = SurveyDC
|
||||
Solver = Solver
|
||||
|
||||
def __init__(self, mesh, **kwargs):
|
||||
Problem.BaseProblem.__init__(self, mesh)
|
||||
self.mesh.setCellGradBC('neumann')
|
||||
Utils.setKwargs(self, **kwargs)
|
||||
|
||||
|
||||
deleteTheseOnModelUpdate = ['_A', '_Msig', '_dMdsig']
|
||||
|
||||
@property
|
||||
def Msig(self):
|
||||
if getattr(self, '_Msig', None) is None:
|
||||
sigma = self.curModel.transform
|
||||
Av = self.mesh.aveF2CC
|
||||
self._Msig = Utils.sdiag(1/(self.mesh.dim * Av.T * (1/sigma)))
|
||||
return self._Msig
|
||||
|
||||
@property
|
||||
def dMdsig(self):
|
||||
if getattr(self, '_dMdsig', None) is None:
|
||||
sigma = self.curModel.transform
|
||||
Av = self.mesh.aveF2CC
|
||||
dMdprop = self.mesh.dim * Utils.sdiag(self.Msig.diagonal()**2) * Av.T * Utils.sdiag(1./sigma**2)
|
||||
self._dMdsig = lambda Gu: Utils.sdiag(Gu) * dMdprop
|
||||
return self._dMdsig
|
||||
|
||||
@property
|
||||
def A(self):
|
||||
"""
|
||||
Makes the matrix A(m) for the DC resistivity problem.
|
||||
|
||||
:param numpy.array m: model
|
||||
:rtype: scipy.csc_matrix
|
||||
:return: A(m)
|
||||
|
||||
.. math::
|
||||
c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
|
||||
|
||||
Where M() is the mass matrix and mT is the model transform.
|
||||
"""
|
||||
if getattr(self, '_A', None) is None:
|
||||
D = self.mesh.faceDiv
|
||||
G = self.mesh.cellGrad
|
||||
self._A = D*self.Msig*G
|
||||
# Remove the null space from the matrix.
|
||||
self._A[-1,-1] /= self.mesh.vol[-1]
|
||||
self._A = self._A.tocsc()
|
||||
return self._A
|
||||
|
||||
def fields(self, m):
|
||||
self.curModel = m
|
||||
A = self.A
|
||||
Ainv = self.Solver(A, **self.solverOpts)
|
||||
Q = self.survey.getRhs(self.mesh)
|
||||
Phi = Ainv * Q
|
||||
return Phi
|
||||
|
||||
def Jvec(self, m, v, u=None):
|
||||
"""
|
||||
:param numpy.array m: model
|
||||
:param numpy.array v: vector to multiply
|
||||
:param numpy.array u: fields
|
||||
:rtype: numpy.array
|
||||
:return: Jv
|
||||
|
||||
.. math::
|
||||
c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
|
||||
|
||||
\\nabla_u (A(m)u - q) = A(m)
|
||||
|
||||
\\nabla_m (A(m)u - q) = G\\text{sdiag}(Du)\\nabla_m(M(mT(m)))
|
||||
|
||||
Where M() is the mass matrix and mT is the model transform.
|
||||
|
||||
.. math::
|
||||
J = - P \left( \\nabla_u c(m, u) \\right)^{-1} \\nabla_m c(m, u)
|
||||
|
||||
J(v) = - P ( A(m)^{-1} ( G\\text{sdiag}(Du)\\nabla_m(M(mT(m))) v ) )
|
||||
"""
|
||||
# Set current model; clear dependent property $\mathbf{A(m)}$
|
||||
self.curModel = m
|
||||
sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
|
||||
if u is None:
|
||||
# Run forward simulation if $u$ not provided
|
||||
u = self.fields(self.curModel)
|
||||
else:
|
||||
shp = (self.mesh.nC, self.survey.nSrc)
|
||||
u = u.reshape(shp, order='F')
|
||||
|
||||
D = self.mesh.faceDiv
|
||||
G = self.mesh.cellGrad
|
||||
# Derivative of model transform, $\deriv{\sigma}{\m}$
|
||||
dsigdm_x_v = self.curModel.transformDeriv * v
|
||||
|
||||
# Take derivative of $C(m,u)$ w.r.t. $m$
|
||||
dCdm_x_v = np.empty_like(u)
|
||||
# loop over fields for each source
|
||||
for i in range(self.survey.nSrc):
|
||||
# Derivative of inner product, $\left(\mathbf{M}_{1/\sigma}^f\right)^{-1}$
|
||||
dAdsig = D * self.dMdsig( G * u[:,i] )
|
||||
dCdm_x_v[:, i] = dAdsig * dsigdm_x_v
|
||||
|
||||
# Take derivative of $C(m,u)$ w.r.t. $u$
|
||||
dCdu = self.A
|
||||
# Solve for $\deriv{u}{m}$
|
||||
dCdu_inv = self.Solver(dCdu, **self.solverOpts)
|
||||
P = self.survey.getP(self.mesh)
|
||||
J_x_v = - P * mkvc( dCdu_inv * dCdm_x_v )
|
||||
return J_x_v
|
||||
|
||||
def Jtvec(self, m, v, u=None):
|
||||
|
||||
self.curModel = m
|
||||
sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
|
||||
if u is None:
|
||||
u = self.fields(self.curModel)
|
||||
|
||||
shp = (self.mesh.nC, self.survey.nSrc)
|
||||
u = u.reshape(shp, order='F')
|
||||
P = self.survey.getP(self.mesh)
|
||||
PT_x_v = (P.T*v).reshape(shp, order='F')
|
||||
|
||||
D = self.mesh.faceDiv
|
||||
G = self.mesh.cellGrad
|
||||
A = self.A
|
||||
mT_dm = self.mapping.deriv(m)
|
||||
|
||||
dCdu = A.T
|
||||
Ainv = self.Solver(dCdu, **self.solverOpts)
|
||||
w = Ainv * PT_x_v
|
||||
|
||||
Jtv = 0
|
||||
for i, ui in enumerate(u.T): # loop over each column
|
||||
Jtv += self.dMdsig( G * ui ).T * ( D.T * w[:,i] )
|
||||
|
||||
Jtv = - mT_dm.T * ( Jtv )
|
||||
return Jtv
|
||||
@@ -0,0 +1,853 @@
|
||||
from SimPEG import *
|
||||
|
||||
|
||||
|
||||
class FieldsDC_CC(Problem.Fields):
|
||||
knownFields = {'phi_sol':'CC'}
|
||||
aliasFields = {
|
||||
'phi' : ['phi_sol','CC','_phi'],
|
||||
'e' : ['phi_sol','F','_e'],
|
||||
'j' : ['phi_sol','F','_j']
|
||||
}
|
||||
|
||||
def __init__(self,mesh,survey,**kwargs):
|
||||
super(FieldsDC_CC, self).__init__(mesh, survey, **kwargs)
|
||||
|
||||
def startup(self):
|
||||
self._cellGrad = self.survey.prob.mesh.cellGrad
|
||||
self._Mfinv = self.survey.prob.mesh.getFaceInnerProduct(invMat=True)
|
||||
|
||||
def _phi(self, phi_sol, srcList):
|
||||
phi = phi_sol
|
||||
# for i, src in enumerate(srcList):
|
||||
# phi_p = src.phi_p(self.survey.prob)
|
||||
# if phi_p is not None:
|
||||
# phi[:,i] += phi_p
|
||||
return phi
|
||||
|
||||
def _e(self, phi_sol, srcList):
|
||||
e = -self._cellGrad*phi_sol
|
||||
# for i, src in enumerate(srcList):
|
||||
# e_p = src.e_p(self.survey.prob)
|
||||
# if e_p is not None:
|
||||
# e[:,i] += e_p
|
||||
return e
|
||||
|
||||
def _j(self, phi_sol, srcList):
|
||||
|
||||
j = -self._Mfinv*self.survey.prob.Msig*self._cellGrad*phi_sol
|
||||
# for i, src in enumerate(srcList):
|
||||
# j_p = src.j_p(self.survey.prob)
|
||||
# if j_p is not None:
|
||||
# j[:,i] += j_p
|
||||
return j
|
||||
|
||||
|
||||
|
||||
class SrcDipole(Survey.BaseSrc):
|
||||
"""A dipole source, locA and locB are moved to the closest cell-centers"""
|
||||
|
||||
current = 1
|
||||
loc = None
|
||||
# _rhsDict = None
|
||||
|
||||
def __init__(self, rxList, locA, locB, **kwargs):
|
||||
self.loc = (locA, locB)
|
||||
super(SrcDipole, self).__init__(rxList, **kwargs)
|
||||
|
||||
def eval(self, prob):
|
||||
# Recompute rhs
|
||||
# if getattr(self, '_rhsDict', None) is None:
|
||||
# self._rhsDict = {}
|
||||
# if mesh not in self._rhsDict:
|
||||
pts = [self.loc[0], self.loc[1]]
|
||||
inds = Utils.closestPoints(prob.mesh, pts)
|
||||
q = np.zeros(prob.mesh.nC)
|
||||
q[inds] = - self.current * ( np.r_[1., -1.] / prob.mesh.vol[inds] )
|
||||
# self._rhsDict[mesh] = q
|
||||
# return self._rhsDict[mesh]
|
||||
return q
|
||||
|
||||
|
||||
class RxDipole(Survey.BaseRx):
|
||||
"""A dipole source, locA and locB are moved to the closest cell-centers"""
|
||||
def __init__(self, locsM, locsN, **kwargs):
|
||||
locs = (locsM, locsN)
|
||||
assert locsM.shape == locsN.shape, 'locs must be the same shape.'
|
||||
super(RxDipole, self).__init__(locs, 'dipole', storeProjections=False, **kwargs)
|
||||
|
||||
@property
|
||||
def nD(self):
|
||||
"""Number of data in the receiver."""
|
||||
return self.locs[0].shape[0]
|
||||
|
||||
def getP(self, mesh):
|
||||
P0 = mesh.getInterpolationMat(self.locs[0], self.projGLoc)
|
||||
P1 = mesh.getInterpolationMat(self.locs[1], self.projGLoc)
|
||||
return P0 - P1
|
||||
|
||||
|
||||
class SurveyDC(Survey.BaseSurvey):
|
||||
"""
|
||||
**SurveyDC**
|
||||
|
||||
Geophysical DC resistivity data.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, srcList, **kwargs):
|
||||
self.srcList = srcList
|
||||
Survey.BaseSurvey.__init__(self, **kwargs)
|
||||
# self._rhsDict = {}
|
||||
self._Ps = {}
|
||||
|
||||
def projectFields(self, u):
|
||||
"""
|
||||
Predicted data.
|
||||
|
||||
.. math::
|
||||
d_\\text{pred} = Pu(m)
|
||||
"""
|
||||
P = self.getP(self.prob.mesh)
|
||||
return P*mkvc(u[self.srcList, 'phi_sol'])
|
||||
|
||||
def getP(self, mesh):
|
||||
if mesh in self._Ps:
|
||||
return self._Ps[mesh]
|
||||
|
||||
P_src = [sp.vstack([rx.getP(mesh) for rx in src.rxList]) for src in self.srcList]
|
||||
|
||||
self._Ps[mesh] = sp.block_diag(P_src)
|
||||
return self._Ps[mesh]
|
||||
|
||||
|
||||
class ProblemDC_CC(Problem.BaseProblem):
|
||||
"""
|
||||
**ProblemDC**
|
||||
|
||||
Geophysical DC resistivity problem.
|
||||
|
||||
"""
|
||||
|
||||
surveyPair = SurveyDC
|
||||
Solver = Solver
|
||||
fieldsPair = FieldsDC_CC
|
||||
Ainv = None
|
||||
|
||||
def __init__(self, mesh, **kwargs):
|
||||
Problem.BaseProblem.__init__(self, mesh)
|
||||
self.mesh.setCellGradBC('neumann')
|
||||
Utils.setKwargs(self, **kwargs)
|
||||
|
||||
|
||||
deleteTheseOnModelUpdate = ['_A', '_Msig', '_dMdsig']
|
||||
|
||||
@property
|
||||
def Msig(self):
|
||||
if getattr(self, '_Msig', None) is None:
|
||||
sigma = self.curModel.transform
|
||||
Av = self.mesh.aveF2CC
|
||||
self._Msig = Utils.sdiag(1/(self.mesh.dim * Av.T * (1/sigma)))
|
||||
return self._Msig
|
||||
|
||||
@property
|
||||
def dMdsig(self):
|
||||
if getattr(self, '_dMdsig', None) is None:
|
||||
sigma = self.curModel.transform
|
||||
Av = self.mesh.aveF2CC
|
||||
dMdprop = self.mesh.dim * Utils.sdiag(self.Msig.diagonal()**2) * Av.T * Utils.sdiag(1./sigma**2)
|
||||
self._dMdsig = lambda Gu: Utils.sdiag(Gu) * dMdprop
|
||||
return self._dMdsig
|
||||
|
||||
@property
|
||||
def A(self):
|
||||
"""
|
||||
Makes the matrix A(m) for the DC resistivity problem.
|
||||
|
||||
:param numpy.array m: model
|
||||
:rtype: scipy.csc_matrix
|
||||
:return: A(m)
|
||||
|
||||
.. math::
|
||||
c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
|
||||
|
||||
Where M() is the mass matrix and mT is the model transform.
|
||||
"""
|
||||
if getattr(self, '_A', None) is None:
|
||||
D = self.mesh.faceDiv
|
||||
G = self.mesh.cellGrad
|
||||
self._A = D*self.Msig*G
|
||||
# Remove the null space from the matrix.
|
||||
self._A[0,0] /= self.mesh.vol[0]
|
||||
self._A = self._A.tocsc()
|
||||
return self._A
|
||||
|
||||
def getRHS(self):
|
||||
# if self.mesh not in self._rhsDict:
|
||||
RHS = np.array([src.eval(self) for src in self.survey.srcList]).T
|
||||
# self._rhsDict[mesh] = RHS
|
||||
# return self._rhsDict[mesh]
|
||||
return RHS
|
||||
|
||||
def fields(self, m):
|
||||
|
||||
F = self.fieldsPair(self.mesh, self.survey)
|
||||
self.curModel = m
|
||||
A = self.A
|
||||
self.Ainv = self.Solver(A, **self.solverOpts)
|
||||
RHS = self.getRHS()
|
||||
Phi = self.Ainv * RHS
|
||||
Srcs = self.survey.srcList
|
||||
F[Srcs, 'phi_sol'] = Phi
|
||||
|
||||
return F
|
||||
|
||||
def Jvec(self, m, v, u=None):
|
||||
"""
|
||||
:param numpy.array m: model
|
||||
:param numpy.array v: vector to multiply
|
||||
:param numpy.array u: fields
|
||||
:rtype: numpy.array
|
||||
:return: Jv
|
||||
|
||||
.. math::
|
||||
c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
|
||||
|
||||
\\nabla_u (A(m)u - q) = A(m)
|
||||
|
||||
\\nabla_m (A(m)u - q) = G\\text{sdiag}(Du)\\nabla_m(M(mT(m)))
|
||||
|
||||
Where M() is the mass matrix and mT is the model transform.
|
||||
|
||||
.. math::
|
||||
J = - P \left( \\nabla_u c(m, u) \\right)^{-1} \\nabla_m c(m, u)
|
||||
|
||||
J(v) = - P ( A(m)^{-1} ( G\\text{sdiag}(Du)\\nabla_m(M(mT(m))) v ) )
|
||||
"""
|
||||
# Set current model; clear dependent property $\mathbf{A(m)}$
|
||||
self.curModel = m
|
||||
sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
|
||||
if u is None:
|
||||
# Run forward simulation if $u$ not provided
|
||||
u = self.fields(self.curModel)[self.survey.srcList, 'phi_sol']
|
||||
else:
|
||||
u = u[self.survey.srcList, 'phi_sol']
|
||||
|
||||
D = self.mesh.faceDiv
|
||||
G = self.mesh.cellGrad
|
||||
# Derivative of model transform, $\deriv{\sigma}{\m}$
|
||||
dsigdm_x_v = self.curModel.transformDeriv * v
|
||||
|
||||
# Take derivative of $C(m,u)$ w.r.t. $m$
|
||||
dCdm_x_v = np.empty_like(u)
|
||||
# loop over fields for each source
|
||||
for i in range(self.survey.nSrc):
|
||||
# Derivative of inner product, $\left(\mathbf{M}_{1/\sigma}^f\right)^{-1}$
|
||||
dAdsig = D * self.dMdsig( G * u[:,i] )
|
||||
dCdm_x_v[:, i] = dAdsig * dsigdm_x_v
|
||||
|
||||
# Take derivative of $C(m,u)$ w.r.t. $u$
|
||||
dA_du = self.A
|
||||
# Solve for $\deriv{u}{m}$
|
||||
# dCdu_inv = self.Solver(dCdu, **self.solverOpts)
|
||||
if self.Ainv is None:
|
||||
self.Ainv = self.Solver(dA_du, **self.solverOpts)
|
||||
|
||||
P = self.survey.getP(self.mesh)
|
||||
Jv = - P * mkvc( self.Ainv * dCdm_x_v )
|
||||
return Jv
|
||||
|
||||
def Jtvec(self, m, v, u=None):
|
||||
|
||||
self.curModel = m
|
||||
sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
|
||||
if u is None:
|
||||
# Run forward simulation if $u$ not provided
|
||||
u = self.fields(self.curModel)[self.survey.srcList, 'phi_sol']
|
||||
else:
|
||||
u = u[self.survey.srcList, 'phi_sol']
|
||||
|
||||
shp = u.shape
|
||||
P = self.survey.getP(self.mesh)
|
||||
PT_x_v = (P.T*v).reshape(shp, order='F')
|
||||
|
||||
D = self.mesh.faceDiv
|
||||
G = self.mesh.cellGrad
|
||||
dA_du = self.A
|
||||
mT_dm = self.mapping.deriv(m)
|
||||
|
||||
# We probably always need this due to the linesearch .. (?)
|
||||
self.Ainv = self.Solver(dA_du.T, **self.solverOpts)
|
||||
# if self.Ainv is None:
|
||||
# self.Ainv = self.Solver(dCdu, **self.solverOpts)
|
||||
|
||||
w = self.Ainv * PT_x_v
|
||||
|
||||
Jtv = 0
|
||||
for i, ui in enumerate(u.T): # loop over each column
|
||||
Jtv += self.dMdsig( G * ui ).T * ( D.T * w[:,i] )
|
||||
|
||||
Jtv = - mT_dm.T * ( Jtv )
|
||||
return Jtv
|
||||
|
||||
def readUBC_DC2DModel(fileName):
|
||||
|
||||
from SimPEG import np, mkvc
|
||||
"""
|
||||
Read UBC GIF 2DTensor model and generate 2D Tensor model in simpeg
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF 2D model file
|
||||
|
||||
Output:
|
||||
:param SimPEG TensorMesh 2D object
|
||||
:return
|
||||
|
||||
Created on Thu Nov 12 13:14:10 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
# Open fileand skip header... assume that we know the mesh already
|
||||
|
||||
obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
|
||||
|
||||
dim = np.array(obsfile[0].split(),dtype=float)
|
||||
|
||||
temp = np.array(obsfile[1].split(),dtype=float)
|
||||
|
||||
if len(temp) > 1:
|
||||
model = np.zeros(dim)
|
||||
|
||||
for ii in range(len(obsfile)-1):
|
||||
mm = np.array(obsfile[ii+1].split(),dtype=float)
|
||||
model[:,ii] = mm
|
||||
|
||||
model = model[:,::-1]
|
||||
|
||||
else:
|
||||
|
||||
if len(obsfile[1:])==1:
|
||||
mm = np.array(obsfile[1:].split(),dtype=float)
|
||||
|
||||
else:
|
||||
mm = np.array(obsfile[1:],dtype=float)
|
||||
|
||||
# Permute the second dimension to flip the order
|
||||
model = mm.reshape(dim[1],dim[0])
|
||||
|
||||
model = model[::-1,:]
|
||||
model = np.transpose(model, (1, 0))
|
||||
|
||||
model = mkvc(model)
|
||||
|
||||
|
||||
return model
|
||||
|
||||
def plot_pseudoSection(Tx,Rx,data,z0, stype):
|
||||
|
||||
from SimPEG import np, mkvc
|
||||
from scipy.interpolate import griddata
|
||||
from matplotlib.colors import LogNorm
|
||||
import pylab as plt
|
||||
import re
|
||||
"""
|
||||
Read list of 2D tx-rx location and plot a speudo-section of apparent
|
||||
resistivity.
|
||||
|
||||
Assumes flat topo for now...
|
||||
|
||||
Input:
|
||||
:param d2D, z0
|
||||
:switch stype -> Either 'pdp' (pole-dipole) | 'dpdp' (dipole-dipole)
|
||||
|
||||
Output:
|
||||
:figure scatter plot overlayed on image
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
#d2D = np.asarray(d2D)
|
||||
|
||||
midl = []
|
||||
midz = []
|
||||
rho = []
|
||||
|
||||
for ii in range(len(Tx)):
|
||||
# Get distances between each poles
|
||||
rC1P1 = np.abs(Tx[ii][0] - Rx[ii][:,0])
|
||||
rC2P1 = np.abs(Tx[ii][1] - Rx[ii][:,0])
|
||||
rC1P2 = np.abs(Tx[ii][1] - Rx[ii][:,1])
|
||||
rC2P2 = np.abs(Tx[ii][0] - Rx[ii][:,1])
|
||||
rP1P2 = np.abs(Rx[ii][:,1] - Rx[ii][:,0])
|
||||
|
||||
# Compute apparent resistivity
|
||||
if re.match(stype,'pdp'):
|
||||
rho = np.hstack([rho, data[ii] * 2*np.pi * rC1P1 * ( rC1P1 + rP1P2 ) / rP1P2] )
|
||||
|
||||
elif re.match(stype,'dpdp'):
|
||||
rho = np.hstack([rho, data[ii] * 2*np.pi / ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 ) ])
|
||||
|
||||
Cmid = (Tx[ii][0] + Tx[ii][1])/2
|
||||
Pmid = (Rx[ii][:,0] + Rx[ii][:,1])/2
|
||||
|
||||
midl = np.hstack([midl, ( Cmid + Pmid )/2 ])
|
||||
midz = np.hstack([midz, -np.abs(Cmid-Pmid)/2 + z0 ])
|
||||
|
||||
|
||||
# Grid points
|
||||
grid_x, grid_z = np.mgrid[np.min(midl):np.max(midl), np.min(midz):np.max(midz)]
|
||||
grid_rho = griddata(np.c_[midl,midz], np.log10(abs(1/rho.T)), (grid_x, grid_z), method='linear')
|
||||
|
||||
|
||||
#plt.subplot(2,1,2)
|
||||
plt.imshow(grid_rho.T, extent = (np.min(midl),np.max(midl),np.min(midz),np.max(midz)), origin='lower', alpha=0.8)
|
||||
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
|
||||
cmin,cmax = cbar.get_clim()
|
||||
ticks = np.linspace(cmin,cmax,3)
|
||||
cbar.set_ticks(ticks)
|
||||
|
||||
# Plot apparent resistivity
|
||||
plt.scatter(midl,midz,s=50,c=np.log10(abs(1/rho.T)))
|
||||
|
||||
def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
|
||||
|
||||
from SimPEG import np
|
||||
import re
|
||||
"""
|
||||
Load in endpoints and survey specifications to generate Tx, Rx location
|
||||
stations.
|
||||
|
||||
Assumes flat topo for now...
|
||||
|
||||
Input:
|
||||
:param endl -> input endpoints [x1, y1, z1, x2, y2, z2]
|
||||
:object mesh -> SimPEG mesh object
|
||||
:switch stype -> "dpdp" (dipole-dipole) | "pdp" (pole-dipole) | 'gradient'
|
||||
: param a, n -> pole seperation, number of rx dipoles per tx
|
||||
|
||||
Output:
|
||||
:param Tx, Rx -> List objects for each tx location
|
||||
Lines: P1x, P1y, P1z, P2x, P2y, P2z
|
||||
|
||||
Created on Wed December 9th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
def xy_2_r(x1,x2,y1,y2):
|
||||
r = np.sqrt( np.sum((x2 - x1)**2 + (y2 - y1)**2) )
|
||||
return r
|
||||
|
||||
## Evenly distribute electrodes and put on surface
|
||||
# Mesure survey length and direction
|
||||
dl_len = xy_2_r(endl[0,0],endl[1,0],endl[0,1],endl[1,1])
|
||||
|
||||
dl_x = ( endl[1,0] - endl[0,0] ) / dl_len
|
||||
dl_y = ( endl[1,1] - endl[0,1] ) / dl_len
|
||||
|
||||
nstn = np.floor( dl_len / a )
|
||||
|
||||
# Compute discrete pole location along line
|
||||
stn_x = endl[0,0] + np.array(range(int(nstn)))*dl_x*a
|
||||
stn_y = endl[0,1] + np.array(range(int(nstn)))*dl_y*a
|
||||
|
||||
# Create line of P1 locations
|
||||
M = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
# Create line of P2 locations
|
||||
N = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
## Build list of Tx-Rx locations depending on survey type
|
||||
# Dipole-dipole: Moving tx with [a] spacing -> [AB a MN1 a MN2 ... a MNn]
|
||||
# Pole-dipole: Moving pole on one end -> [A a MN1 a MN2 ... MNn a B]
|
||||
Tx = []
|
||||
Rx = []
|
||||
|
||||
if not re.match(stype,'gradient'):
|
||||
|
||||
for ii in range(0, int(nstn)-1):
|
||||
|
||||
|
||||
if re.match(stype,'dpdp'):
|
||||
tx = np.c_[M[ii,:],N[ii,:]]
|
||||
elif re.match(stype,'pdp'):
|
||||
tx = np.c_[M[ii,:],M[ii,:]]
|
||||
|
||||
#Rx.append(np.c_[M[ii+1:indx,:],N[ii+1:indx,:]])
|
||||
|
||||
# Current elctrode seperation
|
||||
AB = xy_2_r(tx[0,1],endl[1,0],tx[1,1],endl[1,1])
|
||||
|
||||
# Number of receivers to fit
|
||||
nstn = np.min([np.floor( (AB - b) / a ) , n])
|
||||
|
||||
# Check if there is enough space, else break the loop
|
||||
if nstn <= 0:
|
||||
continue
|
||||
|
||||
# Compute discrete pole location along line
|
||||
stn_x = N[ii,0] + dl_x*b + np.array(range(int(nstn)))*dl_x*a
|
||||
stn_y = N[ii,1] + dl_y*b + np.array(range(int(nstn)))*dl_y*a
|
||||
|
||||
# Create receiver poles
|
||||
# Create line of P1 locations
|
||||
P1 = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
# Create line of P2 locations
|
||||
P2 = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
Rx.append(np.c_[P1,P2])
|
||||
Tx.append(tx)
|
||||
|
||||
#==============================================================================
|
||||
# elif re.match(stype,'dpdp'):
|
||||
#
|
||||
# for ii in range(0, int(nstn)-2):
|
||||
#
|
||||
# indx = np.min([ii+n+1,nstn])
|
||||
# Tx.append(np.c_[M[ii,:],N[ii,:]])
|
||||
# Rx.append(np.c_[M[ii+2:indx,:],N[ii+2:indx,:]])
|
||||
#==============================================================================
|
||||
|
||||
elif re.match(stype,'gradient'):
|
||||
|
||||
# Gradient survey only requires Tx at end of line and creates a square
|
||||
# grid of receivers at in the middle at a pre-set minimum distance
|
||||
Tx.append(np.c_[M[0,:],N[-1,:]])
|
||||
|
||||
# Get the edge limit of survey area
|
||||
min_x = endl[0,0] + dl_x * b
|
||||
min_y = endl[0,1] + dl_y * b
|
||||
|
||||
max_x = endl[1,0] - dl_x * b
|
||||
max_y = endl[1,1] - dl_y * b
|
||||
|
||||
box_l = np.sqrt( (min_x - max_x)**2 + (min_y - max_y)**2 )
|
||||
box_w = box_l/2.
|
||||
|
||||
nstn = np.floor( box_l / a )
|
||||
|
||||
# Compute discrete pole location along line
|
||||
stn_x = min_x + np.array(range(int(nstn)))*dl_x*a
|
||||
stn_y = min_y + np.array(range(int(nstn)))*dl_y*a
|
||||
|
||||
# Define number of cross lines
|
||||
nlin = int(np.floor( box_w / a ))
|
||||
lind = range(-nlin,nlin+1)
|
||||
|
||||
ngrad = nstn * len(lind)
|
||||
|
||||
rx = np.zeros([ngrad,6])
|
||||
for ii in range( len(lind) ):
|
||||
|
||||
# Move line in perpendicular direction by dipole spacing
|
||||
lxx = stn_x - lind[ii]*a*dl_y
|
||||
lyy = stn_y + lind[ii]*a*dl_x
|
||||
|
||||
|
||||
M = np.c_[ lxx, lyy , np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
N = np.c_[ lxx+a*dl_x, lyy+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
rx[(ii*nstn):((ii+1)*nstn),:] = np.c_[M,N]
|
||||
|
||||
Rx.append(rx)
|
||||
|
||||
else:
|
||||
print """stype must be either 'pdp', 'dpdp' or 'gradient'. """
|
||||
|
||||
|
||||
|
||||
return Tx, Rx
|
||||
|
||||
def writeUBC_DCobs(fileName,Tx,Rx,d,wd, dtype):
|
||||
|
||||
from SimPEG import np, mkvc
|
||||
import re
|
||||
"""
|
||||
Read UBC GIF DCIP 3D observation file and generate arrays for tx-rx location
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF 3D obs file
|
||||
|
||||
Output:
|
||||
:param rx, tx, d, wd
|
||||
:return
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
fid = open(fileName,'w')
|
||||
fid.write('! GENERAL FORMAT\n')
|
||||
|
||||
for ii in range(len(Tx)):
|
||||
|
||||
tx = np.asarray(Tx[ii])
|
||||
rx = np.asarray(Rx[ii])
|
||||
nrx = rx.shape[0]
|
||||
|
||||
fid.write('\n')
|
||||
|
||||
if re.match(dtype,'2D'):
|
||||
|
||||
for jj in range(nrx):
|
||||
|
||||
fid.writelines("%e " % ii for ii in mkvc(tx))
|
||||
fid.writelines("%e " % ii for ii in mkvc(rx[jj]))
|
||||
fid.write('%e %e\n'% (d[ii][jj],wd[ii][jj]))
|
||||
#np.savetxt(fid, np.c_[ rx ,np.asarray(d[ii]), np.asarray(wd[ii]) ], fmt='%e',delimiter=' ',newline='\n')
|
||||
|
||||
elif re.match(dtype,'3D'):
|
||||
|
||||
fid.write('\n')
|
||||
fid.writelines("%e " % ii for ii in mkvc(tx))
|
||||
fid.write('%i\n'% nrx)
|
||||
np.savetxt(fid, np.c_[ rx ,np.asarray(d[ii]), np.asarray(wd[ii]) ], fmt='%e',delimiter=' ',newline='\n')
|
||||
|
||||
|
||||
fid.close()
|
||||
|
||||
def convertObs_DC3D_to_2D(Tx,Rx):
|
||||
|
||||
from SimPEG import np
|
||||
import numpy.matlib as npm
|
||||
"""
|
||||
Read list of 3D Tx Rx location and change coordinate system to distance
|
||||
along line assuming all data is acquired along line
|
||||
First transmitter pole is assumed to be at the origin
|
||||
|
||||
Assumes flat topo for now...
|
||||
|
||||
Input:
|
||||
:param Tx, Rx
|
||||
|
||||
Output:
|
||||
:figure Tx2d, Rx2d
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
|
||||
Tx2d = []
|
||||
Rx2d = []
|
||||
|
||||
for ii in range(len(Tx)):
|
||||
|
||||
if ii == 0:
|
||||
endp = Tx[0][0:2,0]
|
||||
|
||||
nrx = Rx[ii].shape[0]
|
||||
|
||||
rP1 = np.sqrt( np.sum( ( endp - Tx[ii][0:2,0] )**2 , axis=0))
|
||||
rP2 = np.sqrt( np.sum( ( endp - Tx[ii][0:2,1] )**2 , axis=0))
|
||||
rC1 = np.sqrt( np.sum( ( npm.repmat(endp.T,nrx,1) - Rx[ii][:,0:2] )**2 , axis=1))
|
||||
rC2 = np.sqrt( np.sum( ( npm.repmat(endp.T,nrx,1) - Rx[ii][:,3:5] )**2 , axis=1))
|
||||
|
||||
Tx2d.append( np.r_[rP1, rP2] )
|
||||
Rx2d.append( np.c_[rC1, rC2] )
|
||||
#np.savetxt(fid, data, fmt='%e',delimiter=' ',newline='\n')
|
||||
|
||||
return Tx2d, Rx2d
|
||||
|
||||
def readUBC_DC3Dobs(fileName):
|
||||
|
||||
from SimPEG import np
|
||||
"""
|
||||
Read UBC GIF DCIP 3D observation file and generate arrays for tx-rx location
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF 3D obs file
|
||||
|
||||
Output:
|
||||
:param rx, tx, d, wd
|
||||
:return
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
# Load file
|
||||
obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
|
||||
|
||||
# Pre-allocate
|
||||
Tx = []
|
||||
Rx = []
|
||||
d = []
|
||||
wd = []
|
||||
|
||||
# Countdown for number of obs/tx
|
||||
count = 0
|
||||
for ii in range(obsfile.shape[0]):
|
||||
|
||||
if not obsfile[ii]:
|
||||
continue
|
||||
|
||||
# First line is transmitter with number of receivers
|
||||
if count==0:
|
||||
|
||||
temp = (np.fromstring(obsfile[ii], dtype=float,sep=' ').T)
|
||||
count = int(temp[-1])
|
||||
temp = np.reshape(temp[0:-1],[2,3]).T
|
||||
|
||||
Tx.append(temp)
|
||||
rx = []
|
||||
continue
|
||||
|
||||
temp = np.fromstring(obsfile[ii], dtype=float,sep=' ')
|
||||
|
||||
|
||||
rx.append(temp)
|
||||
|
||||
count = count -1
|
||||
|
||||
# Reach the end of
|
||||
if count == 0:
|
||||
temp = np.asarray(rx)
|
||||
Rx.append(temp[:,0:6])
|
||||
|
||||
# Check for data + uncertainties
|
||||
if temp.shape[1]==8:
|
||||
d.append(temp[:,6])
|
||||
wd.append(temp[:,7])
|
||||
|
||||
# Check for data only
|
||||
elif temp.shape[1]==7:
|
||||
d.append(temp[:,6])
|
||||
|
||||
return Tx, Rx, d, wd
|
||||
|
||||
def readUBC_DC2DLoc(fileName):
|
||||
|
||||
from SimPEG import np
|
||||
"""
|
||||
Read UBC GIF 2D observation file and generate arrays for tx-rx location
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF 2D model file
|
||||
|
||||
Output:
|
||||
:param rx, tx
|
||||
:return
|
||||
|
||||
Created on Thu Nov 12 13:14:10 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
# Open fileand skip header... assume that we know the mesh already
|
||||
#==============================================================================
|
||||
# fopen = open(fileName,'r')
|
||||
# lines = fopen.readlines()
|
||||
# fopen.close()
|
||||
#==============================================================================
|
||||
|
||||
# Load file
|
||||
obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
|
||||
|
||||
# Check first line and figure out if 2D or 3D file format
|
||||
line = np.array(obsfile[0].split(),dtype=float)
|
||||
|
||||
tx_A = []
|
||||
tx_B = []
|
||||
rx_M = []
|
||||
rx_N = []
|
||||
d = []
|
||||
wd = []
|
||||
|
||||
for ii in range(obsfile.shape[0]):
|
||||
|
||||
# If len==3, then simple format where tx-rx is listed on each line
|
||||
if len(line) == 4:
|
||||
|
||||
temp = np.fromstring(obsfile[ii], dtype=float,sep=' ')
|
||||
tx_A = np.hstack((tx_A,temp[0]))
|
||||
tx_B = np.hstack((tx_B,temp[1]))
|
||||
rx_M = np.hstack((rx_M,temp[2]))
|
||||
rx_N = np.hstack((rx_N,temp[3]))
|
||||
|
||||
|
||||
rx = np.transpose(np.array((rx_M,rx_N)))
|
||||
tx = np.transpose(np.array((tx_A,tx_B)))
|
||||
|
||||
return tx, rx, d, wd
|
||||
|
||||
def readUBC_DC2DMesh(fileName):
|
||||
|
||||
from SimPEG import np
|
||||
"""
|
||||
Read UBC GIF 2DTensor mesh and generate 2D Tensor mesh in simpeg
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF mesh file
|
||||
|
||||
Output:
|
||||
:param SimPEG TensorMesh 2D object
|
||||
:return
|
||||
|
||||
Created on Thu Nov 12 13:14:10 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
# Open file
|
||||
fopen = open(fileName,'r')
|
||||
|
||||
# Read down the file and unpack dx vector
|
||||
def unpackdx(fid,nrows):
|
||||
for ii in range(nrows):
|
||||
|
||||
line = fid.readline()
|
||||
var = np.array(line.split(),dtype=float)
|
||||
|
||||
if ii==0:
|
||||
x0= var[0]
|
||||
xvec = np.ones(int(var[2])) * (var[1] - var[0]) / int(var[2])
|
||||
xend = var[1]
|
||||
|
||||
else:
|
||||
xvec = np.hstack((xvec,np.ones(int(var[1])) * (var[0] - xend) / int(var[1])))
|
||||
xend = var[0]
|
||||
|
||||
return x0, xvec
|
||||
|
||||
#%% Start with dx block
|
||||
# First line specifies the number of rows for x-cells
|
||||
line = fopen.readline()
|
||||
nl = np.array(line.split(),dtype=float)
|
||||
|
||||
[x0, dx] = unpackdx(fopen,nl)
|
||||
|
||||
|
||||
#%% Move down the file until reaching the z-block
|
||||
line = fopen.readline()
|
||||
if not line:
|
||||
line = fopen.readline()
|
||||
|
||||
#%% End with dz block
|
||||
# First line specifies the number of rows for z-cells
|
||||
line = fopen.readline()
|
||||
nl = np.array(line.split(),dtype=float)
|
||||
|
||||
[z0, dz] = unpackdx(fopen,nl)
|
||||
|
||||
# Flip z0 to be the bottom of the mesh for SimPEG
|
||||
z0 = z0 - sum(dz)
|
||||
dz = dz[::-1]
|
||||
#%% Make the mesh using SimPEG
|
||||
|
||||
from SimPEG import Mesh
|
||||
tensMsh = Mesh.TensorMesh([dx,dz],(x0, z0))
|
||||
return tensMsh
|
||||
|
||||
@@ -0,0 +1,182 @@
|
||||
from SimPEG import *
|
||||
from BaseDC import SurveyDC, FieldsDC_CC
|
||||
|
||||
class SurveyIP(SurveyDC):
|
||||
"""
|
||||
**SurveyDC**
|
||||
|
||||
Geophysical DC resistivity data.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, srcList, **kwargs):
|
||||
self.srcList = srcList
|
||||
Survey.BaseSurvey.__init__(self, **kwargs)
|
||||
self._Ps = {}
|
||||
|
||||
def dpred(self, m, u=None):
|
||||
"""
|
||||
Predicted data.
|
||||
|
||||
.. math::
|
||||
d_\\text{pred} = Pu(m)
|
||||
"""
|
||||
|
||||
return self.prob.forward(m)
|
||||
|
||||
|
||||
class ProblemIP(Problem.BaseProblem):
|
||||
"""
|
||||
**ProblemIP**
|
||||
|
||||
Geophysical IP resistivity problem.
|
||||
|
||||
"""
|
||||
|
||||
surveyPair = SurveyDC
|
||||
Solver = Solver
|
||||
sigma = None
|
||||
Ainv = None
|
||||
u = None
|
||||
|
||||
def __init__(self, mesh, **kwargs):
|
||||
Problem.BaseProblem.__init__(self, mesh)
|
||||
self.mesh.setCellGradBC('neumann')
|
||||
Utils.setKwargs(self, **kwargs)
|
||||
|
||||
# deleteTheseOnModelUpdate = ['_A', '_Msig', '_dMdsig']
|
||||
|
||||
@property
|
||||
def Msig(self):
|
||||
if getattr(self, '_Msig', None) is None:
|
||||
# sigma = self.curModel.transform
|
||||
sigma = self.sigma
|
||||
Av = self.mesh.aveF2CC
|
||||
self._Msig = Utils.sdiag(1/(self.mesh.dim * Av.T * (1/sigma)))
|
||||
return self._Msig
|
||||
|
||||
@property
|
||||
def dMdsig(self):
|
||||
if getattr(self, '_dMdsig', None) is None:
|
||||
# sigma = self.curModel.transform
|
||||
sigma = self.sigma
|
||||
Av = self.mesh.aveF2CC
|
||||
dMdprop = self.mesh.dim * Utils.sdiag(self.Msig.diagonal()**2) * Av.T * Utils.sdiag(1./sigma**2)
|
||||
self._dMdsig = lambda Gu: Utils.sdiag(Gu) * dMdprop
|
||||
return self._dMdsig
|
||||
|
||||
@property
|
||||
def A(self):
|
||||
"""
|
||||
Makes the matrix A(m) for the DC resistivity problem.
|
||||
|
||||
:param numpy.array m: model
|
||||
:rtype: scipy.csc_matrix
|
||||
:return: A(m)
|
||||
|
||||
.. math::
|
||||
c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
|
||||
|
||||
Where M() is the mass matrix and mT is the model transform.
|
||||
"""
|
||||
if getattr(self, '_A', None) is None:
|
||||
D = self.mesh.faceDiv
|
||||
G = self.mesh.cellGrad
|
||||
self._A = D*self.Msig*G
|
||||
# Remove the null space from the matrix.
|
||||
self._A[-1,-1] /= self.mesh.vol[-1]
|
||||
self._A = self._A.tocsc()
|
||||
return self._A
|
||||
|
||||
def getRHS(self):
|
||||
# if self.mesh not in self._rhsDict:
|
||||
RHS = np.array([src.eval(self) for src in self.survey.srcList]).T
|
||||
# self._rhsDict[mesh] = RHS
|
||||
# return self._rhsDict[mesh]
|
||||
return RHS
|
||||
|
||||
def fields(self, m):
|
||||
if self.u is None:
|
||||
A = self.A
|
||||
if self.Ainv == None:
|
||||
self.Ainv = self.Solver(A, **self.solverOpts)
|
||||
Q = self.getRHS()
|
||||
self.u = self.Ainv * Q
|
||||
return self.u
|
||||
|
||||
def forward(self, m, u=None):
|
||||
# Set current model; clear dependent property $\mathbf{A(m)}$
|
||||
self.curModel = m
|
||||
# sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
|
||||
sigma = self.sigma
|
||||
if self.u is None:
|
||||
# Run forward simulation if $u$ not provided
|
||||
u = self.fields(sigma)
|
||||
|
||||
shp = (self.mesh.nC, self.survey.nSrc)
|
||||
u = self.u.reshape(shp, order='F')
|
||||
|
||||
D = self.mesh.faceDiv
|
||||
G = self.mesh.cellGrad
|
||||
# Derivative of model transform, $\deriv{\sigma}{\m}$
|
||||
# dsigdm_x_v = self.curModel.transformDeriv * v
|
||||
|
||||
dsigdm_x_v = Utils.sdiag(sigma) * self.curModel.transformDeriv * m
|
||||
|
||||
# Take derivative of $C(m,u)$ w.r.t. $m$
|
||||
dCdm_x_v = np.empty_like(u)
|
||||
# loop over fields for each source
|
||||
for i in range(self.survey.nSrc):
|
||||
# Derivative of inner product, $\left(\mathbf{M}_{1/\sigma}^f\right)^{-1}$
|
||||
dAdsig = D * self.dMdsig( G * u[:,i] )
|
||||
dCdm_x_v[:, i] = dAdsig * dsigdm_x_v
|
||||
|
||||
# Take derivative of $C(m,u)$ w.r.t. $u$
|
||||
|
||||
if self.Ainv == None:
|
||||
self.Ainv = self.Solver(A, **self.solverOpts)
|
||||
|
||||
# dCdu = self.A
|
||||
# Solve for $\deriv{u}{m}$
|
||||
# dCdu_inv = self.Solver(dCdu, **self.solverOpts)
|
||||
P = self.survey.getP(self.mesh)
|
||||
J_x_v = - P * mkvc( self.Ainv * dCdm_x_v )
|
||||
return -J_x_v
|
||||
|
||||
def Jvec(self, m, v, u=None):
|
||||
return self.forward(v)
|
||||
|
||||
def Jtvec(self, m, v, u=None):
|
||||
|
||||
self.curModel = m
|
||||
# sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
|
||||
sigma = self.sigma
|
||||
if self.u is None:
|
||||
u = self.fields(sigma)
|
||||
else:
|
||||
u = self.u
|
||||
shp = (self.mesh.nC, self.survey.nSrc)
|
||||
u = u.reshape(shp, order='F')
|
||||
P = self.survey.getP(self.mesh)
|
||||
PT_x_v = (P.T*v).reshape(shp, order='F')
|
||||
|
||||
D = self.mesh.faceDiv
|
||||
G = self.mesh.cellGrad
|
||||
A = self.A
|
||||
mT_dm = Utils.sdiag(sigma)*self.mapping.deriv(m)
|
||||
# mT_dm = self.mapping.deriv(m)
|
||||
|
||||
# dCdu = A.T
|
||||
# Ainv = self.Solver(dCdu, **self.solverOpts)
|
||||
# if self.Ainv == None:
|
||||
self.Ainv = self.Solver(A.T, **self.solverOpts)
|
||||
|
||||
w = self.Ainv * PT_x_v
|
||||
|
||||
Jtv = 0
|
||||
for i, ui in enumerate(u.T): # loop over each column
|
||||
Jtv += self.dMdsig( G * ui ).T * ( D.T * w[:,i] )
|
||||
|
||||
Jtv = - mT_dm.T * ( Jtv )
|
||||
return -Jtv
|
||||
|
||||
@@ -0,0 +1,216 @@
|
||||
import os
|
||||
from SimPEG import *
|
||||
import simpegDCIP as DC
|
||||
import pylab as plt
|
||||
from matplotlib import animation
|
||||
from JSAnimation import HTMLWriter
|
||||
import time
|
||||
import re
|
||||
#from readUBC_DC2DMesh import readUBC_DC2DMesh
|
||||
#from readUBC_DC2DModel import readUBC_DC2DModel
|
||||
#from readUBC_DC2DLoc import readUBC_DC2DLoc
|
||||
#from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
|
||||
#from readUBC_DC3Dobs import readUBC_DC3Dobs
|
||||
|
||||
#%%
|
||||
home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
|
||||
msh_file = 'Mesh_2D.msh'
|
||||
mod_file = 'Model_2D.con'
|
||||
obs_file = 'FWR_data3D.dat'
|
||||
|
||||
dsep = '\\'
|
||||
|
||||
# Forward solver
|
||||
slvr = 'BiCGStab' #'LU'
|
||||
|
||||
# Preconditioner
|
||||
pcdr = 'Jacobi' #'Gauss-Seidel'#
|
||||
|
||||
# Number of padding cells to remove from plotting
|
||||
padc = 15
|
||||
|
||||
# Load UBC mesh 2D
|
||||
mesh = DC.readUBC_DC2DMesh(home_dir + dsep + msh_file)
|
||||
|
||||
# Load model
|
||||
model = DC.readUBC_DC2DModel(home_dir + dsep + mod_file)
|
||||
|
||||
# load obs file
|
||||
[Tx,Rx,d,wd] = DC.readUBC_DC3Dobs(home_dir + dsep + obs_file)
|
||||
[Tx, Rx] = DC.convertObs_DC3D_to_2D(Tx,Rx)
|
||||
#%% 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
|
||||
A = sp.csc_matrix(A)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
if re.match(slvr,'BiCGStab'):
|
||||
# Create Jacobi Preconditioner
|
||||
if re.match(pcdr,'Jacobi'):
|
||||
dA = A.diagonal()
|
||||
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
|
||||
|
||||
# Create Gauss-Seidel Preconditioner
|
||||
elif re.match(pcdr,'Gauss-Seidel'):
|
||||
LD = sp.tril(A,k=0)
|
||||
#LDinv = sp.linalg.splu(LD)
|
||||
|
||||
elif re.match(slvr,'LU'):
|
||||
# Factor A matrix
|
||||
Ainv = sp.linalg.splu(A)
|
||||
print("LU DECOMP--- %s seconds ---" % (time.time() - start_time))
|
||||
|
||||
#%% Create SimPEG objects
|
||||
|
||||
|
||||
# Create sub-mesh for plotting
|
||||
hx = mesh.hx
|
||||
hy = mesh.hy
|
||||
|
||||
hx_sub = hx[padc:-padc]
|
||||
hy_sub = hy[padc:]
|
||||
mesh_sub = Mesh.TensorMesh([hx_sub,hy_sub],(mesh.vectorNx[padc], mesh.vectorNy[padc]))
|
||||
model_sub = model.reshape(mesh.nCy,mesh.nCx)
|
||||
model_sub = mkvc(model_sub[padc:,padc:-padc].T)
|
||||
|
||||
xx = mesh_sub.vectorCCx
|
||||
yy = mesh_sub.vectorCCy
|
||||
|
||||
#%% Solve
|
||||
#txii = range(50,1950,100)
|
||||
#jx_CC_sub = np.zeros((len(txii),mesh_sub.nCx,mesh_sub.nCy))
|
||||
#jy_CC_sub = np.zeros((len(txii),mesh_sub.nCx,mesh_sub.nCy))
|
||||
|
||||
fig = plt.figure(figsize=(10,5))
|
||||
axs = plt.axes(ylim = (yy[0],yy[-1]+mesh.hy[-1]*2), xlim = (xx[0],xx[-1]))#
|
||||
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
|
||||
plt.ylim(yy[0],yy[-1]+mesh.hy[-1]*2)
|
||||
plt.xlim(xx[0],xx[-1])
|
||||
#im1 = axs.pcolormesh([],[],[], alpha=0.75,extent = (xx[0],xx[-1],yy[-1],yy[0]),interpolation='nearest',vmin=-1e-2, vmax=1e-2)
|
||||
#im2 = axs.pcolormesh([],[],[],alpha=0.2,extent = (xx[0],xx[-1],yy[-1],yy[0]),interpolation='nearest',cmap='gray')
|
||||
im1 = axs.pcolormesh(mesh_sub.vectorCCx,mesh_sub.vectorCCy,np.zeros((mesh_sub.nCy,mesh_sub.nCx)), alpha=0.75,vmin=-1e-2, vmax=1e-2)
|
||||
im2 = axs.pcolormesh(mesh_sub.vectorCCx,mesh_sub.vectorCCy,np.zeros((mesh_sub.nCy,mesh_sub.nCx)), alpha=0.75,vmin=-1e-2, vmax=1e-2)
|
||||
im3 = axs.streamplot(xx, yy, np.zeros((mesh_sub.nCy,mesh_sub.nCx)), np.zeros((mesh_sub.nCy,mesh_sub.nCx)),color='k')
|
||||
im4 = axs.scatter([],[], c='r', s=200)
|
||||
im5 = axs.scatter([],[], c='r', s=200)
|
||||
|
||||
|
||||
|
||||
#==============================================================================
|
||||
# def init():
|
||||
# im1.set_data([[],[],[]])
|
||||
# im2.set_data([[],[],[]])
|
||||
#
|
||||
# return [im1]+[im2]
|
||||
#==============================================================================
|
||||
|
||||
def animate(ii):
|
||||
|
||||
|
||||
|
||||
#for ii in range(len(txii)):
|
||||
|
||||
removeStream()
|
||||
tx = np.asarray(np.c_[Tx[ii],np.ones(Tx[ii].shape[0])*mesh.vectorNy[-1]-1])
|
||||
inds = Utils.closestPoints(mesh, tx )
|
||||
RHS = mesh.getInterpolationMat( tx , 'CC').T*( [-1,1] / mesh.vol[inds] )
|
||||
|
||||
if re.match(slvr,'BiCGStab'):
|
||||
|
||||
if re.match(pcdr,'Jacobi'):
|
||||
dA = A.diagonal()
|
||||
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
|
||||
|
||||
# Iterative Solve
|
||||
phi = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
|
||||
phi = mkvc(phi[0])
|
||||
|
||||
elif re.match(slvr,'LU'):
|
||||
#Direct Solve
|
||||
phi = Ainv.solve(RHS)
|
||||
|
||||
|
||||
j = -Msig*Grad*phi
|
||||
j_CC = mesh.aveF2CCV*j
|
||||
|
||||
# Compute charge density solving div*grad*phi
|
||||
Q = -mesh.faceDiv*mesh.cellGrad*phi
|
||||
|
||||
jx_CC = j_CC[0:mesh.nC].reshape(mesh.nCy,mesh.nCx)
|
||||
jy_CC = j_CC[mesh.nC:].reshape(mesh.nCy,mesh.nCx)
|
||||
|
||||
#%% Grab only the core for presentation
|
||||
jx_CC_sub = jx_CC[padc:,padc:-padc]
|
||||
|
||||
jy_CC_sub = jy_CC[padc:,padc:-padc]
|
||||
|
||||
Q_sub = Q.reshape(mesh.nCy,mesh.nCx)
|
||||
Q_sub = Q_sub[padc:,padc:-padc]
|
||||
|
||||
J_rho = np.sqrt(jx_CC_sub**2 + jy_CC_sub**2)
|
||||
lw = np.log10(J_rho/J_rho.min())
|
||||
|
||||
|
||||
#axs.imshow(Q_sub,alpha=0.75,extent = (xx[0],xx[-1],yy[-1],yy[0]),interpolation='nearest',vmin=-1e-2, vmax=1e-2)
|
||||
#axs.imshow(np.log10(model_sub.reshape(mesh_sub.nCy,mesh_sub.nCx)),alpha=0.2,extent = (xx[0],xx[-1],yy[-1],yy[0]),interpolation='nearest',cmap='gray')
|
||||
global im1
|
||||
im1 = axs.pcolormesh(mesh_sub.vectorCCx,mesh_sub.vectorCCy,Q_sub, alpha=0.75,vmin=-1e-2, vmax=1e-2)
|
||||
|
||||
global im2
|
||||
im2 = axs.pcolormesh(mesh_sub.vectorCCx,mesh_sub.vectorCCy,np.log10(model_sub.reshape(mesh_sub.nCy,mesh_sub.nCx)), alpha=0.25)
|
||||
|
||||
global im3
|
||||
im3 = axs.streamplot(xx, yy, jx_CC_sub, jy_CC_sub,color='k',linewidth = lw,density=0.5)
|
||||
|
||||
global im4
|
||||
im4 = axs.scatter(tx[0,0],mesh.vectorNy[-1], c='r', s=75, marker='v' )
|
||||
|
||||
global im5
|
||||
im5 = axs.scatter(tx[1,0],mesh.vectorNy[-1], c='b', s=75, marker='v' )
|
||||
|
||||
|
||||
#plt.show()
|
||||
#im1.set_array(Q_sub)
|
||||
#im2.set_array(np.log10(model_sub.reshape(mesh_sub.nCy,mesh_sub.nCx)))
|
||||
#im2.set_array(mesh_sub.vectorCCx, mesh_sub.vectorCCy,jx_CC_sub.T,jy_CC_sub.T)
|
||||
|
||||
#return [im1] + [im2]
|
||||
#%% Create widget
|
||||
def removeStream():
|
||||
global im1
|
||||
im1.remove()
|
||||
|
||||
global im2
|
||||
im2.remove()
|
||||
|
||||
global im3
|
||||
im3.lines.remove()
|
||||
axs.patches = []
|
||||
|
||||
global im4
|
||||
im4.remove()
|
||||
|
||||
global im5
|
||||
im5.remove()
|
||||
#def viewInv(msh,iteration):
|
||||
|
||||
|
||||
|
||||
#, linewidth=lw.T
|
||||
#%%
|
||||
#interact(viewInv,msh = mesh_sub, iteration = IntSlider(min=0, max=len(txii)-1 ,step=1, value=0))
|
||||
# set embed_frames=True to embed base64-encoded frames directly in the HTML
|
||||
anim = animation.FuncAnimation(fig, animate,
|
||||
frames=len(Tx), interval=5)
|
||||
|
||||
anim.save(home_dir + '\\animation.html', writer=HTMLWriter(embed_frames=True))
|
||||
@@ -0,0 +1,478 @@
|
||||
"""
|
||||
Experimental script for the forward modeling of DC resistivity data
|
||||
along survey lines defined by the user. The program loads in a 3D mesh
|
||||
and model which is used to design pole-dipole or dipole-dipole survey
|
||||
lines.
|
||||
|
||||
Uses SimPEG to generate the forward problem and compute the LU
|
||||
factorization.
|
||||
|
||||
Calls DCIP2D for the inversion of a projected 2D section from the full
|
||||
3D model.
|
||||
|
||||
Assumes flat topo for now...
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
|
||||
#%%
|
||||
from SimPEG import *
|
||||
import simpegDCIP as DC
|
||||
import pylab as plt
|
||||
from pylab import get_current_fig_manager
|
||||
from scipy.interpolate import griddata
|
||||
import time
|
||||
import re
|
||||
import numpy.matlib as npm
|
||||
import scipy.interpolate as interpolation
|
||||
#==============================================================================
|
||||
# from readUBC_DC3Dobs import readUBC_DC3Dobs
|
||||
# from readUBC_DC2DModel import readUBC_DC2DModel
|
||||
# from writeUBC_DCobs import writeUBC_DCobs
|
||||
|
||||
# from plot_pseudoSection import plot_pseudoSection
|
||||
# from gen_DCIPsurvey import gen_DCIPsurvey
|
||||
# from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
|
||||
#==============================================================================
|
||||
from matplotlib.colors import LogNorm
|
||||
import os
|
||||
|
||||
home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
|
||||
dsep = '\\'
|
||||
#from scipy.linalg import solve_banded
|
||||
|
||||
# Load UBC mesh 3D
|
||||
mesh = Mesh.TensorMesh.readUBC(home_dir + '\Mesh_5m.msh')
|
||||
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
|
||||
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
|
||||
|
||||
# Load model
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_3D.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Synthetic.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
|
||||
model = Mesh.TensorMesh.readModelUBC(mesh,home_dir + '\TwoSpheres.con')
|
||||
|
||||
#model = model**0 * 1e-2
|
||||
# Specify survey type
|
||||
stype = 'dpdp'
|
||||
|
||||
# Survey parameters
|
||||
a = 30
|
||||
b = 30
|
||||
n = 20
|
||||
|
||||
# Forward solver
|
||||
slvr = 'BiCGStab' #'LU'
|
||||
|
||||
# Preconditioner
|
||||
pcdr = 'Jacobi'#
|
||||
|
||||
# Inversion parameter
|
||||
pct = 0.01
|
||||
flr = 1e-4
|
||||
chifact = 100
|
||||
ref_mod = 1e-2
|
||||
|
||||
# DOI threshold
|
||||
cutoff = 0.8
|
||||
|
||||
#%% 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
|
||||
A = sp.csc_matrix(A)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
if re.match(slvr,'BiCGStab'):
|
||||
# Create Jacobi Preconditioner
|
||||
if re.match(pcdr,'Jacobi'):
|
||||
dA = A.diagonal()
|
||||
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
|
||||
|
||||
#LDinv = sp.linalg.splu(LD)
|
||||
|
||||
elif re.match(slvr,'LU'):
|
||||
# Factor A matrix
|
||||
Ainv = sp.linalg.splu(A)
|
||||
print("LU DECOMP--- %s seconds ---" % (time.time() - start_time))
|
||||
|
||||
#%% Create survey
|
||||
# Display top section
|
||||
top = int(mesh.nCz)-1
|
||||
|
||||
plt.figure()
|
||||
ax_prim = plt.subplot(1,1,1)
|
||||
mesh.plotSlice(model, ind=top, normal='Z', grid=False, pcolorOpts={'alpha':0.5}, ax =ax_prim)
|
||||
plt.xlim([423200,423750])
|
||||
plt.ylim([546350,546650])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
plt.show()
|
||||
cfm1=get_current_fig_manager().window
|
||||
gin=[1]
|
||||
|
||||
# Keep creating sections until returns an empty ginput (press enter on figure)
|
||||
#while bool(gin)==True:
|
||||
|
||||
# Bring back the plan view figure and pick points
|
||||
cfm1.activateWindow()
|
||||
plt.sca(ax_prim)
|
||||
|
||||
# Takes two points from ginput and create survey
|
||||
#if re.match(stype,'gradient'):
|
||||
gin = [(423230. , 546440.), (423715. , 546440.)]
|
||||
#else:
|
||||
#gin = plt.ginput(2, timeout = 0)
|
||||
|
||||
|
||||
|
||||
#==============================================================================
|
||||
# if not gin:
|
||||
# print 'SimPED - Simulation has ended with return'
|
||||
# break
|
||||
#==============================================================================
|
||||
|
||||
# Add z coordinate to all survey... assume flat
|
||||
nz = mesh.vectorNz
|
||||
var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
|
||||
|
||||
# Snap the endpoints to the grid. Easier to create 2D section.
|
||||
indx = Utils.closestPoints(mesh, var )
|
||||
endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
|
||||
|
||||
[Tx, Rx] = DC.gen_DCIPsurvey(endl, mesh, stype, a, b, n)
|
||||
|
||||
dl_len = np.sqrt( np.sum((endl[0,:] - endl[1,:])**2) )
|
||||
dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
|
||||
dl_y = ( Tx[-1][1,1] - Tx[0][1,0] ) / dl_len
|
||||
azm = np.arctan(dl_y/dl_x)
|
||||
|
||||
# Plot stations along line
|
||||
plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')
|
||||
plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')
|
||||
|
||||
#%% Forward model data
|
||||
data = []#np.zeros( nstn*nrx )
|
||||
unct = []
|
||||
problem = DC.ProblemDC_CC(mesh)
|
||||
|
||||
for ii in range(len(Tx)):
|
||||
start_time = time.time()
|
||||
|
||||
# Select dipole locations for receiver
|
||||
rxloc_M = np.asarray(Rx[ii][:,0:3])
|
||||
rxloc_N = np.asarray(Rx[ii][:,3:])
|
||||
|
||||
# Number of receivers
|
||||
nrx = rxloc_M.shape[0]
|
||||
|
||||
|
||||
|
||||
if not re.match(stype,'pdp'):
|
||||
inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
|
||||
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )
|
||||
|
||||
else:
|
||||
|
||||
# Create an "inifinity" pole
|
||||
tx = np.squeeze(Tx[ii][:,0:1])
|
||||
tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
|
||||
inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
|
||||
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )
|
||||
|
||||
# Solve for phi on pole locations
|
||||
P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
|
||||
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
|
||||
|
||||
if re.match(slvr,'BiCGStab'):
|
||||
|
||||
if re.match(pcdr,'Jacobi'):
|
||||
dA = A.diagonal()
|
||||
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
|
||||
|
||||
# Iterative Solve
|
||||
Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
|
||||
|
||||
|
||||
phi = mkvc(Ainvb[0])
|
||||
|
||||
elif re.match(slvr,'LU'):
|
||||
#Direct Solve
|
||||
phi = Ainv.solve(RHS)
|
||||
|
||||
|
||||
|
||||
# Compute potential at each electrode
|
||||
dtemp = (P1*phi - P2*phi)*np.pi
|
||||
|
||||
data.append( dtemp )
|
||||
unct.append( np.abs(dtemp) * pct + flr)
|
||||
|
||||
print("--- %s seconds ---" % (time.time() - start_time))
|
||||
|
||||
|
||||
#%% Run 2D inversion if pdp or dpdp survey
|
||||
# Otherwise just plot and apparent susceptibility map
|
||||
if not re.match(stype,'gradient'):
|
||||
|
||||
#%% Write data file in UBC-DCIP3D format
|
||||
DC.writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')
|
||||
|
||||
|
||||
#%% Load 3D data
|
||||
[Tx, Rx, data, wd] = DC.readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
|
||||
|
||||
|
||||
#%% Convert 3D obs to 2D and write to file
|
||||
[Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)
|
||||
|
||||
DC.writeUBC_DCobs(home_dir+'\FWR_3D_2_2D.dat',Tx2d,Rx2d,data,unct,'2D')
|
||||
|
||||
#%% Create a 2D mesh along axis of Tx end points and keep z-discretization
|
||||
dx = np.min( [ np.min(mesh.hx), np.min(mesh.hy) ])
|
||||
nc = np.ceil(dl_len/dx)+3
|
||||
|
||||
padx = dx*np.power(1.4,range(1,15))
|
||||
|
||||
# Creating padding cells
|
||||
h1 = np.r_[padx[::-1], np.ones(nc)*dx , padx]
|
||||
|
||||
# Create mesh with 0 coordinate centerer on the ginput points in cell center
|
||||
mesh2d = Mesh.TensorMesh([h1, mesh.hz], x0=(-np.sum(padx)-dx/2,mesh.x0[2]))
|
||||
|
||||
# Create array of points for interpolating from 3D to 2D mesh
|
||||
xx = Tx[0][0,0] + mesh2d.vectorCCx * np.cos(azm)
|
||||
yy = Tx[0][1,0] + mesh2d.vectorCCx * np.sin(azm)
|
||||
zz = mesh2d.vectorCCy
|
||||
|
||||
[XX,ZZ] = np.meshgrid(xx,zz)
|
||||
[YY,ZZ] = np.meshgrid(yy,zz)
|
||||
|
||||
xyz2d = np.c_[mkvc(XX),mkvc(YY),mkvc(ZZ)]
|
||||
|
||||
#plt.scatter(xx,yy,s=20,c='y')
|
||||
|
||||
|
||||
F = interpolation.NearestNDInterpolator(mesh.gridCC,model)
|
||||
m2D = np.reshape(F(xyz2d),[mesh2d.nCx,mesh2d.nCy]).T
|
||||
|
||||
|
||||
#==============================================================================
|
||||
# mesh2d = Mesh.TensorMesh([mesh.hx, mesh.hz], x0=(mesh.x0[0]-endl[0,0],mesh.x0[2]))
|
||||
# m3D = np.reshape(model, (mesh.nCz, mesh.nCy, mesh.nCx))
|
||||
# m2D = m3D[:,1,:]
|
||||
#==============================================================================
|
||||
#%%
|
||||
plt.figure()
|
||||
axs = plt.subplot(1,1,1)
|
||||
|
||||
plt.xlim([-dx,nc*dx+dx])
|
||||
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
circle1=plt.Circle((144,1500),50,color='w',fill=False, lw=3)
|
||||
circle2=plt.Circle((344,1500),50,color='k',fill=False, lw=3)
|
||||
axs.add_artist(circle1)
|
||||
axs.add_artist(circle2)
|
||||
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D))#axes = [mesh2d.vectorNx[0],mesh2d.vectorNx[-1],mesh2d.vectorNy[0],mesh2d.vectorNy[-1]])
|
||||
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
|
||||
cmin,cmax = cbar.get_clim()
|
||||
ticks = np.linspace(cmin,cmax,3)
|
||||
cbar.set_ticks(ticks)
|
||||
|
||||
# Plot poles
|
||||
plt.scatter(Tx2d[0][0],mesh2d.vectorNy[-1]+dx,s=50,c='r',marker='v')
|
||||
plt.scatter(Tx2d[0][1],mesh2d.vectorNy[-1]+dx,s=50,c='b',marker='v')
|
||||
plt.scatter(Rx2d[0][:,0],np.ones(Rx2d[0].shape[0])*mesh2d.vectorNy[-1]+dx,s=50,c='g')
|
||||
#mesh2d.plotImage(mkvc(m2D), grid=True, ax=axs)
|
||||
|
||||
#%% Plot pseudo section
|
||||
plt.figure()
|
||||
axs = plt.subplot(1,1,1)
|
||||
plt.xlim([-dx,nc*dx+dx])
|
||||
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
circle1=plt.Circle((144,1500),50,color='w',fill=False, lw=3)
|
||||
circle2=plt.Circle((344,1500),50,color='k',fill=False, lw=3)
|
||||
axs.add_artist(circle1)
|
||||
axs.add_artist(circle2)
|
||||
|
||||
DC.plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
|
||||
plt.show()
|
||||
|
||||
#%% Run two inversions with different reference models and compute a DOI
|
||||
|
||||
invmod = []
|
||||
refmod = []
|
||||
plt.figure()
|
||||
|
||||
for jj in range(2):
|
||||
|
||||
# Create dcin2d inversion files and run
|
||||
inv_dir = home_dir + '\Inv2D'
|
||||
if not os.path.exists(inv_dir):
|
||||
os.makedirs(inv_dir)
|
||||
|
||||
mshfile2d = 'Mesh_2D.msh'
|
||||
modfile2d = 'Model_2D.con'
|
||||
obsfile2d = 'FWR_3D_2_2D.dat'
|
||||
inp_file = 'dcinv2d.inp'
|
||||
|
||||
|
||||
# Export 2D mesh
|
||||
fid = open(inv_dir + dsep + mshfile2d,'w')
|
||||
fid.write('%i\n'% mesh2d.nCx)
|
||||
fid.write('%f %f 1\n'% (mesh2d.vectorNx[0],mesh2d.vectorNx[1]))
|
||||
np.savetxt(fid, np.c_[mesh2d.vectorNx[2:],np.ones(mesh2d.nCx-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
|
||||
fid.write('\n')
|
||||
fid.write('%i\n'% mesh2d.nCy)
|
||||
fid.write('%f %f 1\n'%( 0,mesh2d.hy[-1]))
|
||||
np.savetxt(fid, np.c_[np.cumsum(mesh2d.hy[-2::-1])+mesh2d.hy[-1],np.ones(mesh2d.nCy-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
|
||||
fid.close()
|
||||
|
||||
# Export 2D model
|
||||
fid = open(inv_dir + dsep + modfile2d,'w')
|
||||
fid.write('%i %i\n'% (mesh2d.nCx,mesh2d.nCy))
|
||||
np.savetxt(fid, mkvc(m2D[::-1,:].T), fmt='%e',delimiter=' ',newline='\n')
|
||||
fid.close()
|
||||
|
||||
# Export data file
|
||||
DC.writeUBC_DCobs(inv_dir + dsep + obsfile2d,Tx2d,Rx2d,data,unct,'2D')
|
||||
|
||||
# Write input file
|
||||
fid = open(inv_dir + dsep + inp_file,'w')
|
||||
fid.write('OBS LOC_X %s \n'% obsfile2d)
|
||||
fid.write('MESH FILE %s \n'% mshfile2d)
|
||||
fid.write('CHIFACT 1 %f\n'% chifact)
|
||||
fid.write('TOPO DEFAULT %s \n')
|
||||
fid.write('INIT_MOD DEFAULT\n')
|
||||
fid.write('REF_MOD VALUE %e\n'% (ref_mod*(jj+1)))
|
||||
fid.write('ALPHA DEFAULT\n')
|
||||
fid.write('WEIGHT DEFAULT\n')
|
||||
fid.write('STORE_ALL_MODELS FALSE\n')
|
||||
fid.write('INVMODE SVD\n')
|
||||
fid.write('USE_MREF TRUE\n')
|
||||
fid.close()
|
||||
|
||||
os.chdir(inv_dir)
|
||||
os.system('dcinv2d ' + inp_file)
|
||||
|
||||
|
||||
#Load model
|
||||
minv = DC.readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
|
||||
|
||||
axs = plt.subplot(2,1,jj+1)
|
||||
|
||||
plt.xlim([-dx,nc*dx+dx])
|
||||
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
minv = np.reshape(minv,(mesh2d.nCy,mesh2d.nCx))
|
||||
#plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')
|
||||
|
||||
circle1=plt.Circle((144,1500),50,color='w',fill=False, lw=3)
|
||||
circle2=plt.Circle((344,1500),50,color='k',fill=False, lw=3)
|
||||
axs.add_artist(circle1)
|
||||
axs.add_artist(circle2)
|
||||
|
||||
|
||||
axp = plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(minv),alpha=1,vmin = -2.25, vmax = -1.5)
|
||||
|
||||
plt.show()
|
||||
|
||||
if jj == 1:
|
||||
|
||||
plt.ylabel('(b)',rotation=360)
|
||||
plt.xlabel('Distance (m)')
|
||||
|
||||
else:
|
||||
plt.ylabel('(a)',rotation=360)
|
||||
|
||||
|
||||
cbar = plt.colorbar(format = '%.2f',fraction=0.05,orientation='vertical',pad=0.02)
|
||||
cmin,cmax = cbar.get_clim()
|
||||
ticks = np.linspace(cmin,cmax,3)
|
||||
cbar.set_ticks(ticks)
|
||||
#cbar.set_ticklabels('%.2f')
|
||||
|
||||
invmod.append(minv)
|
||||
refmod.append(ref_mod*(jj+1))
|
||||
|
||||
#%% Compute DOI
|
||||
DOI = np.abs(invmod[0] - invmod[1]) / np.abs(refmod[0] - refmod[1])
|
||||
# Normalize between [0 1]
|
||||
DOI = DOI - np.min(DOI)
|
||||
DOI = (1.- DOI/np.max(DOI))
|
||||
DOI[DOI > cutoff] = 1
|
||||
|
||||
plt.figure()
|
||||
plt.xlim([-dx,nc*dx+dx])
|
||||
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,DOI,alpha=1)
|
||||
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
|
||||
|
||||
#%% Replace alpha values from inversion
|
||||
#rgba_plt = axp.get_facecolor()
|
||||
#rgba_plt[:,3] = mkvc(DOI)/2
|
||||
plt.figure()
|
||||
axs = plt.subplot(1,1,1)
|
||||
|
||||
plt.xlim([-dx,nc*dx+dx])
|
||||
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]+2*dx])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
circle1=plt.Circle((144,1500),50,color='w',fill=False, lw=3)
|
||||
circle2=plt.Circle((344,1500),50,color='k',fill=False, lw=3)
|
||||
axs.add_artist(circle1)
|
||||
axs.add_artist(circle2)
|
||||
|
||||
axs = plt.pcolor(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(invmod[0]),edgecolor="none")
|
||||
plt.draw()
|
||||
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
|
||||
aa = axs.get_facecolors()
|
||||
aa[:,3] = mkvc(DOI.T)
|
||||
axs.set_facecolor(aa)
|
||||
|
||||
plt.draw()
|
||||
|
||||
#%% Othrwise it is a gradient array, plot surface of apparent resisitivty
|
||||
elif re.match(stype,'gradient'):
|
||||
|
||||
rC1P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
|
||||
rC2P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
|
||||
rC1P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
|
||||
rC2P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
|
||||
|
||||
rC1C2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0]-Tx[0][0:2,1],Rx[0].shape[0], 1) )**2, axis=1 ))
|
||||
rP1P2 = np.sqrt( np.sum( (Rx[0][:,0:2] - Rx[0][:,3:5])**2, axis=1 ))
|
||||
|
||||
rho = np.abs(data[0]) * np.pi *((rC1P1)**2 / rP1P2)#/ ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 )
|
||||
|
||||
Pmid = (Rx[0][:,0:2] + Rx[0][:,3:5])/2
|
||||
|
||||
# Grid points
|
||||
grid_x, grid_z = np.mgrid[np.min(Rx[0][:,[0,3]]):np.max(Rx[0][:,[0,3]]):a/10, np.min(Rx[0][:,[1,4]]):np.max(Rx[0][:,[1,4]]):a/10]
|
||||
grid_rho = griddata(np.c_[Pmid[:,0],Pmid[:,1]], (abs(rho.T)), (grid_x, grid_z), method='linear')
|
||||
|
||||
|
||||
#plt.subplot(2,1,2)
|
||||
plt.imshow(grid_rho.T, extent = (np.min(grid_x),np.max(grid_x),np.min(grid_z),np.max(grid_z)) ,origin='lower')
|
||||
var = 'Gradient Array - a-spacing: ' + str(a) + ' m'
|
||||
plt.title(var)
|
||||
plt.colorbar()
|
||||
@@ -0,0 +1,403 @@
|
||||
"""
|
||||
Experimental script for the forward modeling of DC resistivity data
|
||||
along survey lines defined by the user. The program loads in a 3D mesh
|
||||
and model which is used to design pole-dipole or dipole-dipole survey
|
||||
lines.
|
||||
|
||||
Uses SimPEG to generate the forward problem and compute the LU
|
||||
factorization.
|
||||
|
||||
Calls DCIP2D for the inversion of a projected 2D section from the full
|
||||
3D model.
|
||||
|
||||
Assumes flat topo for now...
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
|
||||
#%%
|
||||
from SimPEG import np, Utils, Mesh, mkvc, sp
|
||||
import simpegDCIP as DC
|
||||
import pylab as plt
|
||||
from pylab import get_current_fig_manager
|
||||
from scipy.interpolate import griddata
|
||||
import time
|
||||
import re
|
||||
import numpy.matlib as npm
|
||||
#from readUBC_DC3Dobs import readUBC_DC3Dobs
|
||||
#from readUBC_DC2DModel import readUBC_DC2DModel
|
||||
#from writeUBC_DCobs import writeUBC_DCobs
|
||||
import scipy.interpolate as interpolation
|
||||
#from plot_pseudoSection import plot_pseudoSection
|
||||
#from gen_DCIPsurvey import gen_DCIPsurvey
|
||||
#from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
|
||||
import os
|
||||
|
||||
#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
|
||||
home_dir ='C:\Users\dominiquef.MIRAGEOSCIENCE\ownCloud\Research\MtIsa\Modeling'
|
||||
dsep = '\\'
|
||||
#from scipy.linalg import solve_banded
|
||||
|
||||
# Load UBC mesh 3D
|
||||
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_10m.msh')
|
||||
mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
|
||||
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
|
||||
|
||||
# Load model
|
||||
model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_20m.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Synthetic.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\TwoSpheres.con',mesh)
|
||||
|
||||
#model[model>1] = 0.08
|
||||
|
||||
#model = model**0 * 1e-2
|
||||
# Specify survey type
|
||||
stype = 'pdp'
|
||||
|
||||
# Survey parameters
|
||||
a = 100
|
||||
b = 100
|
||||
n = 15
|
||||
|
||||
# Forward solver
|
||||
slvr = 'BiCGStab' #'LU'
|
||||
|
||||
# Preconditioner
|
||||
pcdr = 'Jacobi'#'Gauss-Seidel'#
|
||||
|
||||
# Inversion parameter
|
||||
pct = 0.01
|
||||
flr = 1e-4
|
||||
chifact = 100
|
||||
ref_mod = 1e-2
|
||||
|
||||
#%% 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
|
||||
A = sp.csc_matrix(A)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
if re.match(slvr,'BiCGStab'):
|
||||
# Create Jacobi Preconditioner
|
||||
if re.match(pcdr,'Jacobi'):
|
||||
dA = A.diagonal()
|
||||
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
|
||||
|
||||
# Create Gauss-Seidel Preconditioner
|
||||
elif re.match(pcdr,'Gauss-Seidel'):
|
||||
LD = sp.tril(A,k=0)
|
||||
#LDinv = sp.linalg.splu(LD)
|
||||
|
||||
elif re.match(slvr,'LU'):
|
||||
# Factor A matrix
|
||||
Ainv = sp.linalg.splu(A)
|
||||
print("LU DECOMP--- %s seconds ---" % (time.time() - start_time))
|
||||
|
||||
#%% Create survey
|
||||
# Display top section
|
||||
top = int(mesh.nCz)-1
|
||||
|
||||
plt.figure()
|
||||
ax_prim = plt.subplot(1,1,1)
|
||||
mesh.plotSlice(model, ind=top, normal='Z', grid=False, pcolorOpts={'alpha':0.5}, ax =ax_prim)
|
||||
#plt.xlim([423000,424000])
|
||||
#plt.ylim([546200,547000])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
plt.show()
|
||||
cfm1=get_current_fig_manager().window
|
||||
gin=[1]
|
||||
|
||||
# Keep creating sections until returns an empty ginput (press enter on figure)
|
||||
#while bool(gin)==True:
|
||||
|
||||
# Bring back the plan view figure and pick points
|
||||
cfm1.activateWindow()
|
||||
plt.sca(ax_prim)
|
||||
|
||||
# Takes two points from ginput and create survey
|
||||
#if re.match(stype,'gradient'):
|
||||
gin = [(400.,12200.), (1800.,12200.)]
|
||||
#else:
|
||||
#gin = plt.ginput(2, timeout = 0)
|
||||
|
||||
|
||||
|
||||
#==============================================================================
|
||||
# if not gin:
|
||||
# print 'SimPED - Simulation has ended with return'
|
||||
# break
|
||||
#==============================================================================
|
||||
|
||||
# Add z coordinate to all survey... assume flat
|
||||
nz = mesh.vectorNz
|
||||
var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
|
||||
|
||||
# Snap the endpoints to the grid. Easier to create 2D section.
|
||||
indx = Utils.closestPoints(mesh, var )
|
||||
endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
|
||||
|
||||
[Tx, Rx] = DC.gen_DCIPsurvey(endl, mesh, stype, a, b, n)
|
||||
|
||||
dl_len = np.sqrt( np.sum((endl[0,:] - endl[1,:])**2) )
|
||||
dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
|
||||
dl_y = ( Tx[-1][1,1] - Tx[0][1,0] ) / dl_len
|
||||
azm = np.arctan(dl_y/dl_x)
|
||||
|
||||
# Plot stations along line
|
||||
plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')
|
||||
plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')
|
||||
|
||||
#%% Forward model data
|
||||
data = []#np.zeros( nstn*nrx )
|
||||
unct = []
|
||||
problem = DC.ProblemDC_CC(mesh)
|
||||
|
||||
for ii in range(len(Tx)):
|
||||
start_time = time.time()
|
||||
|
||||
# Select dipole locations for receiver
|
||||
rxloc_M = np.asarray(Rx[ii][:,0:3])
|
||||
rxloc_N = np.asarray(Rx[ii][:,3:])
|
||||
|
||||
# Number of receivers
|
||||
nrx = rxloc_M.shape[0]
|
||||
|
||||
|
||||
|
||||
if not re.match(stype,'pdp'):
|
||||
inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
|
||||
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )
|
||||
|
||||
else:
|
||||
|
||||
# Create an "inifinity" pole
|
||||
tx = np.squeeze(Tx[ii][:,0:1])
|
||||
tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
|
||||
inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
|
||||
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )
|
||||
|
||||
# Solve for phi on pole locations
|
||||
P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
|
||||
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
|
||||
|
||||
if re.match(slvr,'BiCGStab'):
|
||||
|
||||
if re.match(pcdr,'Jacobi'):
|
||||
dA = A.diagonal()
|
||||
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
|
||||
|
||||
# Iterative Solve
|
||||
Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
|
||||
|
||||
# Create Gauss-Seidel Preconditioner
|
||||
elif re.match(pcdr,'Gauss-Seidel'):
|
||||
LD = sp.tril(A,k=0)
|
||||
|
||||
|
||||
phi = mkvc(Ainvb[0])
|
||||
|
||||
elif re.match(slvr,'LU'):
|
||||
#Direct Solve
|
||||
phi = Ainv.solve(RHS)
|
||||
|
||||
|
||||
|
||||
# Compute potential at each electrode
|
||||
dtemp = (P1*phi - P2*phi)*np.pi
|
||||
|
||||
data.append( dtemp )
|
||||
unct.append( np.abs(dtemp) * pct + flr)
|
||||
|
||||
print("--- %s seconds ---" % (time.time() - start_time))
|
||||
|
||||
|
||||
#%% Run 2D inversion if pdp or dpdp survey
|
||||
# Otherwise just plot and apparent susceptibility map
|
||||
if not re.match(stype,'gradient'):
|
||||
|
||||
#%% Write data file in UBC-DCIP3D format
|
||||
DC.writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')
|
||||
|
||||
|
||||
#%% Load 3D data
|
||||
[Tx, Rx, data, wd] = DC.readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
|
||||
|
||||
|
||||
#%% Convert 3D obs to 2D and write to file
|
||||
[Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)
|
||||
|
||||
DC.writeUBC_DCobs(home_dir+'\FWR_3D_2_2D.dat',Tx2d,Rx2d,data,unct,'2D')
|
||||
|
||||
#%% Create a 2D mesh along axis of Tx end points and keep z-discretization
|
||||
dx = np.min( [ np.min(mesh.hx), np.min(mesh.hy) ])
|
||||
nc = np.ceil(dl_len/dx)+3
|
||||
|
||||
padx = dx*np.power(1.4,range(1,15))
|
||||
|
||||
# Creating padding cells
|
||||
h1 = np.r_[padx[::-1], np.ones(nc)*dx , padx]
|
||||
|
||||
# Create mesh with 0 coordinate centerer on the ginput points in cell center
|
||||
mesh2d = Mesh.TensorMesh([h1, mesh.hz], x0=(-np.sum(padx)-dx/2,mesh.x0[2]))
|
||||
|
||||
# Create array of points for interpolating from 3D to 2D mesh
|
||||
xx = Tx[0][0,0] + mesh2d.vectorCCx * np.cos(azm)
|
||||
yy = Tx[0][1,0] + mesh2d.vectorCCx * np.sin(azm)
|
||||
zz = mesh2d.vectorCCy
|
||||
|
||||
[XX,ZZ] = np.meshgrid(xx,zz)
|
||||
[YY,ZZ] = np.meshgrid(yy,zz)
|
||||
|
||||
xyz2d = np.c_[mkvc(XX),mkvc(YY),mkvc(ZZ)]
|
||||
|
||||
#plt.scatter(xx,yy,s=20,c='y')
|
||||
|
||||
|
||||
F = interpolation.NearestNDInterpolator(mesh.gridCC,model)
|
||||
m2D = np.reshape(F(xyz2d),[mesh2d.nCx,mesh2d.nCy]).T
|
||||
|
||||
|
||||
#==============================================================================
|
||||
# mesh2d = Mesh.TensorMesh([mesh.hx, mesh.hz], x0=(mesh.x0[0]-endl[0,0],mesh.x0[2]))
|
||||
# m3D = np.reshape(model, (mesh.nCz, mesh.nCy, mesh.nCx))
|
||||
# m2D = m3D[:,1,:]
|
||||
#==============================================================================
|
||||
|
||||
plt.figure()
|
||||
axs = plt.subplot(2,1,1)
|
||||
|
||||
plt.xlim([0,nc*dx])
|
||||
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')#axes = [mesh2d.vectorNx[0],mesh2d.vectorNx[-1],mesh2d.vectorNy[0],mesh2d.vectorNy[-1]])
|
||||
#mesh2d.plotImage(mkvc(m2D), grid=True, ax=axs)
|
||||
|
||||
#%% Plot pseudo section
|
||||
|
||||
DC.plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
|
||||
plt.colorbar
|
||||
plt.show()
|
||||
|
||||
#%% Create dcin2d inversion files and run
|
||||
inv_dir = home_dir + '\Inv2D'
|
||||
if not os.path.exists(inv_dir):
|
||||
os.makedirs(inv_dir)
|
||||
|
||||
mshfile2d = 'Mesh_2D.msh'
|
||||
modfile2d = 'MtIsa_2D.con'
|
||||
obsfile2d = 'FWR_3D_2_2D.dat'
|
||||
inp_file = 'dcinv2d.inp'
|
||||
|
||||
|
||||
# Export 2D mesh
|
||||
fid = open(inv_dir + dsep + mshfile2d,'w')
|
||||
fid.write('%i\n'% mesh2d.nCx)
|
||||
fid.write('%f %f 1\n'% (mesh2d.vectorNx[0],mesh2d.vectorNx[1]))
|
||||
np.savetxt(fid, np.c_[mesh2d.vectorNx[2:],np.ones(mesh2d.nCx-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
|
||||
fid.write('\n')
|
||||
fid.write('%i\n'% mesh2d.nCy)
|
||||
fid.write('%f %f 1\n'%( 0,mesh2d.hy[-1]))
|
||||
np.savetxt(fid, np.c_[np.cumsum(mesh2d.hy[-2::-1])+mesh2d.hy[-1],np.ones(mesh2d.nCy-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
|
||||
fid.close()
|
||||
|
||||
# Export 2D model
|
||||
fid = open(inv_dir + dsep + modfile2d,'w')
|
||||
fid.write('%i %i\n'% (mesh2d.nCx,mesh2d.nCy))
|
||||
np.savetxt(fid, mkvc(m2D[::-1,:].T), fmt='%e',delimiter=' ',newline='\n')
|
||||
fid.close()
|
||||
|
||||
# Export data file
|
||||
DC.writeUBC_DCobs(inv_dir + dsep + obsfile2d,Tx2d,Rx2d,data,unct,'2D')
|
||||
|
||||
# Write input file
|
||||
fid = open(inv_dir + dsep + inp_file,'w')
|
||||
fid.write('OBS LOC_X %s \n'% obsfile2d)
|
||||
fid.write('MESH FILE %s \n'% mshfile2d)
|
||||
fid.write('CHIFACT 1 %f\n'% chifact)
|
||||
fid.write('TOPO DEFAULT %s \n')
|
||||
fid.write('INIT_MOD DEFAULT\n')
|
||||
fid.write('REF_MOD VALUE %e\n'% ref_mod)
|
||||
fid.write('ALPHA DEFAULT\n')
|
||||
fid.write('WEIGHT DEFAULT\n')
|
||||
fid.write('STORE_ALL_MODELS FALSE\n')
|
||||
fid.write('INVMODE SVD\n')
|
||||
fid.write('USE_MREF TRUE\n')
|
||||
fid.close()
|
||||
|
||||
os.chdir(inv_dir)
|
||||
os.system('dcinv2d ' + inp_file)
|
||||
|
||||
#%%
|
||||
#Load model
|
||||
minv = DC.readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
|
||||
#plt.figure()
|
||||
axs = plt.subplot(2,1,2)
|
||||
|
||||
plt.xlim([0,nc*dx])
|
||||
plt.ylim([mesh2d.vectorNy[-1]-dl_len/2,mesh2d.vectorNy[-1]])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
minv = np.reshape(minv,(mesh2d.nCy,mesh2d.nCx))
|
||||
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')
|
||||
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(minv),alpha=0.5, clim=(np.min(np.log10(m2D)),np.max(np.log10(m2D))))
|
||||
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
|
||||
cmin,cmax = cbar.get_clim()
|
||||
ticks = np.linspace(cmin,cmax,3)
|
||||
cbar.set_ticks(ticks)
|
||||
|
||||
#%% Othrwise it is a gradient array, plot surface of apparent resisitivty
|
||||
elif re.match(stype,'gradient'):
|
||||
|
||||
rC1P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
|
||||
rC2P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
|
||||
rC1P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
|
||||
rC2P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
|
||||
|
||||
rC1C2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0]-Tx[0][0:2,1],Rx[0].shape[0], 1) )**2, axis=1 ))
|
||||
rP1P2 = np.sqrt( np.sum( (Rx[0][:,0:2] - Rx[0][:,3:5])**2, axis=1 ))
|
||||
|
||||
rho = np.abs(data[0]) *np.pi *2. / ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 )#*((rC1P1)**2 / rP1P2)#
|
||||
|
||||
Pmid = (Rx[0][:,0:2] + Rx[0][:,3:5])/2
|
||||
|
||||
# Grid points
|
||||
grid_x, grid_z = np.mgrid[np.min(Rx[0][:,[0,3]]):np.max(Rx[0][:,[0,3]]):a/10, np.min(Rx[0][:,[1,4]]):np.max(Rx[0][:,[1,4]]):a/10]
|
||||
grid_rho = griddata(np.c_[Pmid[:,0],Pmid[:,1]], (abs(rho.T)), (grid_x, grid_z), method='linear')
|
||||
|
||||
|
||||
#plt.subplot(2,1,2)
|
||||
plt.figure()
|
||||
plt.imshow(grid_rho.T, extent = (np.min(grid_x),np.max(grid_x),np.min(grid_z),np.max(grid_z)) ,origin='lower')
|
||||
|
||||
var = 'Gradient Array - a-spacing: ' + str(a) + ' m'
|
||||
plt.title(var)
|
||||
plt.colorbar()
|
||||
plt.contour(grid_x,grid_z,grid_rho, colors='k')
|
||||
|
||||
#%% Load tight model and plot
|
||||
mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_5m.msh')
|
||||
|
||||
# Load model
|
||||
model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_5m.con',mesh)
|
||||
model = model.reshape((mesh.nCz,mesh.nCx))
|
||||
plt.figure()
|
||||
plt.imshow(np.log10(model),extent = (125,375,0,75),origin='lower')
|
||||
plt.colorbar(fraction=0.015)
|
||||
|
||||
@@ -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 +1,390 @@
|
||||
"""
|
||||
Experimental script for the forward modeling of DC resistivity data
|
||||
along survey lines defined by the user. The program loads in a 3D mesh
|
||||
and model which is used to design pole-dipole or dipole-dipole survey
|
||||
lines.
|
||||
|
||||
Uses SimPEG to generate the forward problem and compute the LU
|
||||
factorization.
|
||||
|
||||
Calls DCIP2D for the inversion of a projected 2D section from the full
|
||||
3D model.
|
||||
|
||||
Assumes flat topo for now...
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
|
||||
#%%
|
||||
from SimPEG import np, Utils, Mesh, mkvc, sp
|
||||
import simpegDCIP as DC
|
||||
import pylab as plt
|
||||
from pylab import get_current_fig_manager
|
||||
from scipy.interpolate import griddata
|
||||
import time
|
||||
import re
|
||||
import numpy.matlib as npm
|
||||
#from readUBC_DC3Dobs import readUBC_DC3Dobs
|
||||
#from readUBC_DC2DModel import readUBC_DC2DModel
|
||||
#from writeUBC_DCobs import writeUBC_DCobs
|
||||
import scipy.interpolate as interpolation
|
||||
#from plot_pseudoSection import plot_pseudoSection
|
||||
#from gen_DCIPsurvey import gen_DCIPsurvey
|
||||
#from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
|
||||
import os
|
||||
|
||||
#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
|
||||
home_dir ='C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegdc\simpegDCIP\Dev'
|
||||
dsep = '\\'
|
||||
#from scipy.linalg import solve_banded
|
||||
|
||||
# Load UBC mesh 3D
|
||||
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_10m.msh')
|
||||
mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
|
||||
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
|
||||
|
||||
# Load model
|
||||
model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_3D.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Synthetic.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\TwoSpheres.con',mesh)
|
||||
|
||||
#model = model**0 * 1e-2
|
||||
# Specify survey type
|
||||
stype = 'pdp'
|
||||
|
||||
# Survey parameters
|
||||
a = 150
|
||||
b = 150
|
||||
n = 40
|
||||
|
||||
# Forward solver
|
||||
slvr = 'BiCGStab' #'LU'
|
||||
|
||||
# Preconditioner
|
||||
pcdr = 'Jacobi'#'Gauss-Seidel'#
|
||||
|
||||
# Inversion parameter
|
||||
pct = 0.01
|
||||
flr = 1e-4
|
||||
chifact = 100
|
||||
ref_mod = 1e-3
|
||||
|
||||
#%% 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
|
||||
A = sp.csc_matrix(A)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
if re.match(slvr,'BiCGStab'):
|
||||
# Create Jacobi Preconditioner
|
||||
if re.match(pcdr,'Jacobi'):
|
||||
dA = A.diagonal()
|
||||
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
|
||||
|
||||
# Create Gauss-Seidel Preconditioner
|
||||
elif re.match(pcdr,'Gauss-Seidel'):
|
||||
LD = sp.tril(A,k=0)
|
||||
#LDinv = sp.linalg.splu(LD)
|
||||
|
||||
elif re.match(slvr,'LU'):
|
||||
# Factor A matrix
|
||||
Ainv = sp.linalg.splu(A)
|
||||
print("LU DECOMP--- %s seconds ---" % (time.time() - start_time))
|
||||
|
||||
#%% Create survey
|
||||
# Display top section
|
||||
top = int(mesh.nCz)-1
|
||||
|
||||
plt.figure()
|
||||
ax_prim = plt.subplot(1,1,1)
|
||||
mesh.plotSlice(model, ind=top, normal='Z', grid=False, pcolorOpts={'alpha':0.5}, ax =ax_prim)
|
||||
#plt.xlim([423000,424000])
|
||||
#plt.ylim([546200,547000])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
plt.show()
|
||||
cfm1=get_current_fig_manager().window
|
||||
gin=[1]
|
||||
|
||||
# Keep creating sections until returns an empty ginput (press enter on figure)
|
||||
#while bool(gin)==True:
|
||||
|
||||
# Bring back the plan view figure and pick points
|
||||
cfm1.activateWindow()
|
||||
plt.sca(ax_prim)
|
||||
|
||||
# Takes two points from ginput and create survey
|
||||
#if re.match(stype,'gradient'):
|
||||
#gin = [(425347, 6079766), (427792, 6081806)]
|
||||
#else:
|
||||
gin = plt.ginput(2, timeout = 0)
|
||||
|
||||
|
||||
|
||||
#==============================================================================
|
||||
# if not gin:
|
||||
# print 'SimPED - Simulation has ended with return'
|
||||
# break
|
||||
#==============================================================================
|
||||
|
||||
# Add z coordinate to all survey... assume flat
|
||||
nz = mesh.vectorNz
|
||||
var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
|
||||
|
||||
# Snap the endpoints to the grid. Easier to create 2D section.
|
||||
indx = Utils.closestPoints(mesh, var )
|
||||
endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
|
||||
|
||||
[Tx, Rx] = gen_DCIPsurvey(endl, mesh, stype, a, b, n)
|
||||
|
||||
dl_len = np.sqrt( np.sum((endl[0,:] - endl[1,:])**2) )
|
||||
dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
|
||||
dl_y = ( Tx[-1][1,1] - Tx[0][1,0] ) / dl_len
|
||||
azm = np.arctan(dl_y/dl_x)
|
||||
|
||||
# Plot stations along line
|
||||
plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')
|
||||
plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')
|
||||
|
||||
#%% Forward model data
|
||||
data = []#np.zeros( nstn*nrx )
|
||||
unct = []
|
||||
problem = DC.ProblemDC_CC(mesh)
|
||||
|
||||
for ii in range(len(Tx)):
|
||||
start_time = time.time()
|
||||
|
||||
# Select dipole locations for receiver
|
||||
rxloc_M = np.asarray(Rx[ii][:,0:3])
|
||||
rxloc_N = np.asarray(Rx[ii][:,3:])
|
||||
|
||||
# Number of receivers
|
||||
nrx = rxloc_M.shape[0]
|
||||
|
||||
|
||||
|
||||
if not re.match(stype,'pdp'):
|
||||
inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
|
||||
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )
|
||||
|
||||
else:
|
||||
|
||||
# Create an "inifinity" pole
|
||||
tx = np.squeeze(Tx[ii][:,0:1])
|
||||
tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
|
||||
inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
|
||||
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )
|
||||
|
||||
# Solve for phi on pole locations
|
||||
P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
|
||||
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
|
||||
|
||||
if re.match(slvr,'BiCGStab'):
|
||||
|
||||
if re.match(pcdr,'Jacobi'):
|
||||
dA = A.diagonal()
|
||||
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
|
||||
|
||||
# Iterative Solve
|
||||
Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
|
||||
|
||||
# Create Gauss-Seidel Preconditioner
|
||||
elif re.match(pcdr,'Gauss-Seidel'):
|
||||
LD = sp.tril(A,k=0)
|
||||
|
||||
|
||||
phi = mkvc(Ainvb[0])
|
||||
|
||||
elif re.match(slvr,'LU'):
|
||||
#Direct Solve
|
||||
phi = Ainv.solve(RHS)
|
||||
|
||||
|
||||
|
||||
# Compute potential at each electrode
|
||||
dtemp = (P1*phi - P2*phi)*np.pi
|
||||
|
||||
data.append( dtemp )
|
||||
unct.append( np.abs(dtemp) * pct + flr)
|
||||
|
||||
print("--- %s seconds ---" % (time.time() - start_time))
|
||||
|
||||
|
||||
#%% Run 2D inversion if pdp or dpdp survey
|
||||
# Otherwise just plot and apparent susceptibility map
|
||||
if not re.match(stype,'gradient'):
|
||||
|
||||
#%% Write data file in UBC-DCIP3D format
|
||||
writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')
|
||||
|
||||
|
||||
#%% Load 3D data
|
||||
[Tx, Rx, data, wd] = readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
|
||||
|
||||
|
||||
#%% Convert 3D obs to 2D and write to file
|
||||
[Tx2d, Rx2d] = convertObs_DC3D_to_2D(Tx,Rx)
|
||||
|
||||
writeUBC_DCobs(home_dir+'\FWR_3D_2_2D.dat',Tx2d,Rx2d,data,unct,'2D')
|
||||
|
||||
#%% Create a 2D mesh along axis of Tx end points and keep z-discretization
|
||||
dx = np.min( [ np.min(mesh.hx), np.min(mesh.hy) ])
|
||||
nc = np.ceil(dl_len/dx)+3
|
||||
|
||||
padx = dx*np.power(1.4,range(1,15))
|
||||
|
||||
# Creating padding cells
|
||||
h1 = np.r_[padx[::-1], np.ones(nc)*dx , padx]
|
||||
|
||||
# Create mesh with 0 coordinate centerer on the ginput points in cell center
|
||||
mesh2d = Mesh.TensorMesh([h1, mesh.hz], x0=(-np.sum(padx)-dx/2,mesh.x0[2]))
|
||||
|
||||
# Create array of points for interpolating from 3D to 2D mesh
|
||||
xx = Tx[0][0,0] + mesh2d.vectorCCx * np.cos(azm)
|
||||
yy = Tx[0][1,0] + mesh2d.vectorCCx * np.sin(azm)
|
||||
zz = mesh2d.vectorCCy
|
||||
|
||||
[XX,ZZ] = np.meshgrid(xx,zz)
|
||||
[YY,ZZ] = np.meshgrid(yy,zz)
|
||||
|
||||
xyz2d = np.c_[mkvc(XX),mkvc(YY),mkvc(ZZ)]
|
||||
|
||||
#plt.scatter(xx,yy,s=20,c='y')
|
||||
|
||||
|
||||
F = interpolation.NearestNDInterpolator(mesh.gridCC,model)
|
||||
m2D = np.reshape(F(xyz2d),[mesh2d.nCx,mesh2d.nCy]).T
|
||||
|
||||
|
||||
#==============================================================================
|
||||
# mesh2d = Mesh.TensorMesh([mesh.hx, mesh.hz], x0=(mesh.x0[0]-endl[0,0],mesh.x0[2]))
|
||||
# m3D = np.reshape(model, (mesh.nCz, mesh.nCy, mesh.nCx))
|
||||
# m2D = m3D[:,1,:]
|
||||
#==============================================================================
|
||||
|
||||
plt.figure()
|
||||
axs = plt.subplot(2,1,1)
|
||||
|
||||
plt.xlim([0,nc*dx])
|
||||
plt.ylim([mesh2d.vectorNy[-1]-dl_len,mesh2d.vectorNy[-1]])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')#axes = [mesh2d.vectorNx[0],mesh2d.vectorNx[-1],mesh2d.vectorNy[0],mesh2d.vectorNy[-1]])
|
||||
#mesh2d.plotImage(mkvc(m2D), grid=True, ax=axs)
|
||||
|
||||
#%% Plot pseudo section
|
||||
|
||||
plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
|
||||
plt.colorbar
|
||||
plt.show()
|
||||
|
||||
#%% Create dcin2d inversion files and run
|
||||
inv_dir = home_dir + '\Inv2D'
|
||||
if not os.path.exists(inv_dir):
|
||||
os.makedirs(inv_dir)
|
||||
|
||||
mshfile2d = 'Mesh_2D.msh'
|
||||
modfile2d = 'MtIsa_2D.con'
|
||||
obsfile2d = 'FWR_3D_2_2D.dat'
|
||||
inp_file = 'dcinv2d.inp'
|
||||
|
||||
|
||||
# Export 2D mesh
|
||||
fid = open(inv_dir + dsep + mshfile2d,'w')
|
||||
fid.write('%i\n'% mesh2d.nCx)
|
||||
fid.write('%f %f 1\n'% (mesh2d.vectorNx[0],mesh2d.vectorNx[1]))
|
||||
np.savetxt(fid, np.c_[mesh2d.vectorNx[2:],np.ones(mesh2d.nCx-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
|
||||
fid.write('\n')
|
||||
fid.write('%i\n'% mesh2d.nCy)
|
||||
fid.write('%f %f 1\n'%( 0,mesh2d.hy[-1]))
|
||||
np.savetxt(fid, np.c_[np.cumsum(mesh2d.hy[-2::-1])+mesh2d.hy[-1],np.ones(mesh2d.nCy-1)], fmt='\t %e %i',delimiter=' ',newline='\n')
|
||||
fid.close()
|
||||
|
||||
# Export 2D model
|
||||
fid = open(inv_dir + dsep + modfile2d,'w')
|
||||
fid.write('%i %i\n'% (mesh2d.nCx,mesh2d.nCy))
|
||||
np.savetxt(fid, mkvc(m2D[::-1,:].T), fmt='%e',delimiter=' ',newline='\n')
|
||||
fid.close()
|
||||
|
||||
# Export data file
|
||||
writeUBC_DCobs(inv_dir + dsep + obsfile2d,Tx2d,Rx2d,data,unct,'2D')
|
||||
|
||||
# Write input file
|
||||
fid = open(inv_dir + dsep + inp_file,'w')
|
||||
fid.write('OBS LOC_X %s \n'% obsfile2d)
|
||||
fid.write('MESH FILE %s \n'% mshfile2d)
|
||||
fid.write('CHIFACT 1 %f\n'% chifact)
|
||||
fid.write('TOPO DEFAULT %s \n')
|
||||
fid.write('INIT_MOD DEFAULT\n')
|
||||
fid.write('REF_MOD VALUE %e\n'% ref_mod)
|
||||
fid.write('ALPHA DEFAULT\n')
|
||||
fid.write('WEIGHT DEFAULT\n')
|
||||
fid.write('STORE_ALL_MODELS FALSE\n')
|
||||
fid.write('INVMODE SVD\n')
|
||||
fid.write('USE_MREF TRUE\n')
|
||||
fid.close()
|
||||
|
||||
os.chdir(inv_dir)
|
||||
os.system('dcinv2d ' + inp_file)
|
||||
|
||||
#%%
|
||||
#Load model
|
||||
minv = readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
|
||||
#plt.figure()
|
||||
axs = plt.subplot(2,1,2)
|
||||
|
||||
plt.xlim([0,nc*dx])
|
||||
plt.ylim([mesh2d.vectorNy[-1]-dl_len,mesh2d.vectorNy[-1]])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
minv = np.reshape(minv,(mesh2d.nCy,mesh2d.nCx))
|
||||
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(m2D),alpha=0.5, cmap='gray')
|
||||
plt.pcolormesh(mesh2d.vectorNx,mesh2d.vectorNy,np.log10(minv),alpha=0.5, clim=(np.min(np.log10(m2D)),np.max(np.log10(m2D))))
|
||||
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
|
||||
cmin,cmax = cbar.get_clim()
|
||||
ticks = np.linspace(cmin,cmax,3)
|
||||
cbar.set_ticks(ticks)
|
||||
|
||||
#%% Othrwise it is a gradient array, plot surface of apparent resisitivty
|
||||
elif re.match(stype,'gradient'):
|
||||
|
||||
rC1P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
|
||||
rC2P1 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,0:2])**2, axis=1 ))
|
||||
rC1P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
|
||||
rC2P2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,1],Rx[0].shape[0], 1) - Rx[0][:,3:5])**2, axis=1 ))
|
||||
|
||||
rC1C2 = np.sqrt( np.sum( (npm.repmat(Tx[0][0:2,0]-Tx[0][0:2,1],Rx[0].shape[0], 1) )**2, axis=1 ))
|
||||
rP1P2 = np.sqrt( np.sum( (Rx[0][:,0:2] - Rx[0][:,3:5])**2, axis=1 ))
|
||||
|
||||
rho = np.abs(data[0]) *np.pi *2. / ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 )#*((rC1P1)**2 / rP1P2)#
|
||||
|
||||
Pmid = (Rx[0][:,0:2] + Rx[0][:,3:5])/2
|
||||
|
||||
# Grid points
|
||||
grid_x, grid_z = np.mgrid[np.min(Rx[0][:,[0,3]]):np.max(Rx[0][:,[0,3]]):a/10, np.min(Rx[0][:,[1,4]]):np.max(Rx[0][:,[1,4]]):a/10]
|
||||
grid_rho = griddata(np.c_[Pmid[:,0],Pmid[:,1]], (abs(rho.T)), (grid_x, grid_z), method='linear')
|
||||
|
||||
|
||||
#plt.subplot(2,1,2)
|
||||
plt.figure()
|
||||
plt.imshow(grid_rho.T, extent = (np.min(grid_x),np.max(grid_x),np.min(grid_z),np.max(grid_z)) ,origin='lower')
|
||||
|
||||
var = 'Gradient Array - a-spacing: ' + str(a) + ' m'
|
||||
plt.title(var)
|
||||
plt.colorbar()
|
||||
plt.contour(grid_x,grid_z,grid_rho, colors='k')
|
||||
@@ -0,0 +1,245 @@
|
||||
! GENERAL FORMAT
|
||||
|
||||
0.000000e+00 0.000000e+00 4.000000e+01 8.000000e+01 4.536103e-01 4.636103e-03
|
||||
0.000000e+00 0.000000e+00 8.000000e+01 1.200000e+02 1.956283e-01 2.056283e-03
|
||||
0.000000e+00 0.000000e+00 1.200000e+02 1.600000e+02 9.661533e-02 1.066153e-03
|
||||
0.000000e+00 0.000000e+00 1.600000e+02 2.000000e+02 5.443205e-03 1.544321e-04
|
||||
0.000000e+00 0.000000e+00 2.000000e+02 2.400000e+02 2.977518e-03 1.297752e-04
|
||||
0.000000e+00 0.000000e+00 2.400000e+02 2.800000e+02 3.113318e-03 1.311332e-04
|
||||
0.000000e+00 0.000000e+00 2.800000e+02 3.200000e+02 7.216380e-03 1.721638e-04
|
||||
0.000000e+00 0.000000e+00 3.200000e+02 3.600000e+02 6.475000e-03 1.647500e-04
|
||||
0.000000e+00 0.000000e+00 3.600000e+02 4.000000e+02 4.750858e-03 1.475086e-04
|
||||
|
||||
4.000000e+01 4.000000e+01 8.000000e+01 1.200000e+02 4.737093e-01 4.837093e-03
|
||||
4.000000e+01 4.000000e+01 1.200000e+02 1.600000e+02 1.933017e-01 2.033017e-03
|
||||
4.000000e+01 4.000000e+01 1.600000e+02 2.000000e+02 9.625111e-03 1.962511e-04
|
||||
4.000000e+01 4.000000e+01 2.000000e+02 2.400000e+02 4.710170e-03 1.471017e-04
|
||||
4.000000e+01 4.000000e+01 2.400000e+02 2.800000e+02 4.146907e-03 1.414691e-04
|
||||
4.000000e+01 4.000000e+01 2.800000e+02 3.200000e+02 9.014465e-03 1.901446e-04
|
||||
4.000000e+01 4.000000e+01 3.200000e+02 3.600000e+02 7.875920e-03 1.787592e-04
|
||||
4.000000e+01 4.000000e+01 3.600000e+02 4.000000e+02 5.711976e-03 1.571198e-04
|
||||
4.000000e+01 4.000000e+01 4.000000e+02 4.400000e+02 6.259850e-04 1.062599e-04
|
||||
|
||||
8.000000e+01 8.000000e+01 1.200000e+02 1.600000e+02 4.956306e-01 5.056306e-03
|
||||
8.000000e+01 8.000000e+01 1.600000e+02 2.000000e+02 2.048511e-02 3.048511e-04
|
||||
8.000000e+01 8.000000e+01 2.000000e+02 2.400000e+02 8.575637e-03 1.857564e-04
|
||||
8.000000e+01 8.000000e+01 2.400000e+02 2.800000e+02 6.032524e-03 1.603252e-04
|
||||
8.000000e+01 8.000000e+01 2.800000e+02 3.200000e+02 1.189394e-02 2.189394e-04
|
||||
8.000000e+01 8.000000e+01 3.200000e+02 3.600000e+02 9.982800e-03 1.998280e-04
|
||||
8.000000e+01 8.000000e+01 3.600000e+02 4.000000e+02 7.117008e-03 1.711701e-04
|
||||
8.000000e+01 8.000000e+01 4.000000e+02 4.400000e+02 7.649935e-04 1.076499e-04
|
||||
8.000000e+01 8.000000e+01 4.400000e+02 4.800000e+02 6.193333e-04 1.061933e-04
|
||||
|
||||
1.200000e+02 1.200000e+02 1.600000e+02 2.000000e+02 5.597631e-02 6.597631e-04
|
||||
1.200000e+02 1.200000e+02 2.000000e+02 2.400000e+02 1.877787e-02 2.877787e-04
|
||||
1.200000e+02 1.200000e+02 2.400000e+02 2.800000e+02 9.908609e-03 1.990861e-04
|
||||
1.200000e+02 1.200000e+02 2.800000e+02 3.200000e+02 1.676287e-02 2.676287e-04
|
||||
1.200000e+02 1.200000e+02 3.200000e+02 3.600000e+02 1.319428e-02 2.319428e-04
|
||||
1.200000e+02 1.200000e+02 3.600000e+02 4.000000e+02 9.156663e-03 1.915666e-04
|
||||
1.200000e+02 1.200000e+02 4.000000e+02 4.400000e+02 9.579451e-04 1.095795e-04
|
||||
1.200000e+02 1.200000e+02 4.400000e+02 4.800000e+02 7.568666e-04 1.075687e-04
|
||||
1.200000e+02 1.200000e+02 4.800000e+02 5.200000e+02 6.080823e-04 1.060808e-04
|
||||
|
||||
1.600000e+02 1.600000e+02 2.000000e+02 2.400000e+02 4.265271e-02 5.265271e-04
|
||||
1.600000e+02 1.600000e+02 2.400000e+02 2.800000e+02 1.691741e-02 2.691741e-04
|
||||
1.600000e+02 1.600000e+02 2.800000e+02 3.200000e+02 2.380462e-02 3.380462e-04
|
||||
1.600000e+02 1.600000e+02 3.200000e+02 3.600000e+02 1.727048e-02 2.727048e-04
|
||||
1.600000e+02 1.600000e+02 3.600000e+02 4.000000e+02 1.158363e-02 2.158363e-04
|
||||
1.600000e+02 1.600000e+02 4.000000e+02 4.400000e+02 1.175287e-03 1.117529e-04
|
||||
1.600000e+02 1.600000e+02 4.400000e+02 4.800000e+02 9.042510e-04 1.090425e-04
|
||||
1.600000e+02 1.600000e+02 4.800000e+02 5.200000e+02 7.129196e-04 1.071292e-04
|
||||
1.600000e+02 1.600000e+02 5.200000e+02 5.600000e+02 5.684553e-04 1.056846e-04
|
||||
|
||||
2.000000e+02 2.000000e+02 2.400000e+02 2.800000e+02 3.101272e-02 4.101272e-04
|
||||
2.000000e+02 2.000000e+02 2.800000e+02 3.200000e+02 3.293247e-02 4.293247e-04
|
||||
2.000000e+02 2.000000e+02 3.200000e+02 3.600000e+02 2.118536e-02 3.118536e-04
|
||||
2.000000e+02 2.000000e+02 3.600000e+02 4.000000e+02 1.354928e-02 2.354928e-04
|
||||
2.000000e+02 2.000000e+02 4.000000e+02 4.400000e+02 1.328240e-03 1.132824e-04
|
||||
2.000000e+02 2.000000e+02 4.400000e+02 4.800000e+02 9.951781e-04 1.099518e-04
|
||||
2.000000e+02 2.000000e+02 4.800000e+02 5.200000e+02 7.713268e-04 1.077133e-04
|
||||
2.000000e+02 2.000000e+02 5.200000e+02 5.600000e+02 6.080769e-04 1.060808e-04
|
||||
2.000000e+02 2.000000e+02 5.600000e+02 6.000000e+02 4.818454e-04 1.048185e-04
|
||||
|
||||
2.400000e+02 2.400000e+02 2.800000e+02 3.200000e+02 6.649140e-02 7.649140e-04
|
||||
2.400000e+02 2.400000e+02 3.200000e+02 3.600000e+02 3.258335e-02 4.258335e-04
|
||||
2.400000e+02 2.400000e+02 3.600000e+02 4.000000e+02 1.854898e-02 2.854898e-04
|
||||
2.400000e+02 2.400000e+02 4.000000e+02 4.400000e+02 1.680085e-03 1.168008e-04
|
||||
2.400000e+02 2.400000e+02 4.400000e+02 4.800000e+02 1.185559e-03 1.118556e-04
|
||||
2.400000e+02 2.400000e+02 4.800000e+02 5.200000e+02 8.847441e-04 1.088474e-04
|
||||
2.400000e+02 2.400000e+02 5.200000e+02 5.600000e+02 6.804673e-04 1.068047e-04
|
||||
2.400000e+02 2.400000e+02 5.600000e+02 6.000000e+02 5.302990e-04 1.053030e-04
|
||||
2.400000e+02 2.400000e+02 6.000000e+02 6.400000e+02 4.148104e-04 1.041481e-04
|
||||
|
||||
2.800000e+02 2.800000e+02 3.200000e+02 3.600000e+02 1.578256e-01 1.678256e-03
|
||||
2.800000e+02 2.800000e+02 3.600000e+02 4.000000e+02 6.200859e-02 7.200859e-04
|
||||
2.800000e+02 2.800000e+02 4.000000e+02 4.400000e+02 4.339120e-03 1.433912e-04
|
||||
2.800000e+02 2.800000e+02 4.400000e+02 4.800000e+02 2.478341e-03 1.247834e-04
|
||||
2.800000e+02 2.800000e+02 4.800000e+02 5.200000e+02 1.605665e-03 1.160567e-04
|
||||
2.800000e+02 2.800000e+02 5.200000e+02 5.600000e+02 1.123502e-03 1.112350e-04
|
||||
2.800000e+02 2.800000e+02 5.600000e+02 6.000000e+02 8.210704e-04 1.082107e-04
|
||||
2.800000e+02 2.800000e+02 6.000000e+02 6.400000e+02 6.139691e-04 1.061397e-04
|
||||
2.800000e+02 2.800000e+02 6.400000e+02 6.800000e+02 4.652759e-04 1.046528e-04
|
||||
|
||||
3.200000e+02 3.200000e+02 3.600000e+02 4.000000e+02 2.808898e-01 2.908898e-03
|
||||
3.200000e+02 3.200000e+02 4.000000e+02 4.400000e+02 1.592369e-02 2.592369e-04
|
||||
3.200000e+02 3.200000e+02 4.400000e+02 4.800000e+02 7.543011e-03 1.754301e-04
|
||||
3.200000e+02 3.200000e+02 4.800000e+02 5.200000e+02 4.240833e-03 1.424083e-04
|
||||
3.200000e+02 3.200000e+02 5.200000e+02 5.600000e+02 2.671738e-03 1.267174e-04
|
||||
3.200000e+02 3.200000e+02 5.600000e+02 6.000000e+02 1.808136e-03 1.180814e-04
|
||||
3.200000e+02 3.200000e+02 6.000000e+02 6.400000e+02 1.277382e-03 1.127738e-04
|
||||
3.200000e+02 3.200000e+02 6.400000e+02 6.800000e+02 9.267469e-04 1.092675e-04
|
||||
3.200000e+02 3.200000e+02 6.800000e+02 7.200000e+02 6.861838e-04 1.068618e-04
|
||||
|
||||
3.600000e+02 3.600000e+02 4.000000e+02 4.400000e+02 4.641363e-02 5.641363e-04
|
||||
3.600000e+02 3.600000e+02 4.400000e+02 4.800000e+02 1.769581e-02 2.769581e-04
|
||||
3.600000e+02 3.600000e+02 4.800000e+02 5.200000e+02 8.621103e-03 1.862110e-04
|
||||
3.600000e+02 3.600000e+02 5.200000e+02 5.600000e+02 4.936600e-03 1.493660e-04
|
||||
3.600000e+02 3.600000e+02 5.600000e+02 6.000000e+02 3.134506e-03 1.313451e-04
|
||||
3.600000e+02 3.600000e+02 6.000000e+02 6.400000e+02 2.120500e-03 1.212050e-04
|
||||
3.600000e+02 3.600000e+02 6.400000e+02 6.800000e+02 1.491948e-03 1.149195e-04
|
||||
3.600000e+02 3.600000e+02 6.800000e+02 7.200000e+02 1.079403e-03 1.107940e-04
|
||||
3.600000e+02 3.600000e+02 7.200000e+02 7.600000e+02 8.007966e-04 1.080080e-04
|
||||
|
||||
4.000000e+02 4.000000e+02 4.400000e+02 4.800000e+02 3.878789e-02 4.878789e-04
|
||||
4.000000e+02 4.000000e+02 4.800000e+02 5.200000e+02 1.570287e-02 2.570287e-04
|
||||
4.000000e+02 4.000000e+02 5.200000e+02 5.600000e+02 7.998542e-03 1.799854e-04
|
||||
4.000000e+02 4.000000e+02 5.600000e+02 6.000000e+02 4.715645e-03 1.471565e-04
|
||||
4.000000e+02 4.000000e+02 6.000000e+02 6.400000e+02 3.043004e-03 1.304300e-04
|
||||
4.000000e+02 4.000000e+02 6.400000e+02 6.800000e+02 2.075749e-03 1.207575e-04
|
||||
4.000000e+02 4.000000e+02 6.800000e+02 7.200000e+02 1.469917e-03 1.146992e-04
|
||||
4.000000e+02 4.000000e+02 7.200000e+02 7.600000e+02 1.073330e-03 1.107333e-04
|
||||
4.000000e+02 4.000000e+02 7.600000e+02 8.000000e+02 8.070689e-04 1.080707e-04
|
||||
|
||||
4.400000e+02 4.400000e+02 4.800000e+02 5.200000e+02 2.913833e-02 3.913833e-04
|
||||
4.400000e+02 4.400000e+02 5.200000e+02 5.600000e+02 1.196402e-02 2.196402e-04
|
||||
4.400000e+02 4.400000e+02 5.600000e+02 6.000000e+02 6.215752e-03 1.621575e-04
|
||||
4.400000e+02 4.400000e+02 6.000000e+02 6.400000e+02 3.724292e-03 1.372429e-04
|
||||
4.400000e+02 4.400000e+02 6.400000e+02 6.800000e+02 2.430992e-03 1.243099e-04
|
||||
4.400000e+02 4.400000e+02 6.800000e+02 7.200000e+02 1.675270e-03 1.167527e-04
|
||||
4.400000e+02 4.400000e+02 7.200000e+02 7.600000e+02 1.201692e-03 1.120169e-04
|
||||
4.400000e+02 4.400000e+02 7.600000e+02 8.000000e+02 8.925612e-04 1.089256e-04
|
||||
4.400000e+02 4.400000e+02 8.000000e+02 8.400000e+02 6.833048e-04 1.068330e-04
|
||||
|
||||
4.800000e+02 4.800000e+02 5.200000e+02 5.600000e+02 2.703944e-02 3.703944e-04
|
||||
4.800000e+02 4.800000e+02 5.600000e+02 6.000000e+02 1.085234e-02 2.085234e-04
|
||||
4.800000e+02 4.800000e+02 6.000000e+02 6.400000e+02 5.538480e-03 1.553848e-04
|
||||
4.800000e+02 4.800000e+02 6.400000e+02 6.800000e+02 3.276154e-03 1.327615e-04
|
||||
4.800000e+02 4.800000e+02 6.800000e+02 7.200000e+02 2.123985e-03 1.212398e-04
|
||||
4.800000e+02 4.800000e+02 7.200000e+02 7.600000e+02 1.464765e-03 1.146476e-04
|
||||
4.800000e+02 4.800000e+02 7.600000e+02 8.000000e+02 1.059450e-03 1.105945e-04
|
||||
4.800000e+02 4.800000e+02 8.000000e+02 8.400000e+02 7.964537e-04 1.079645e-04
|
||||
4.800000e+02 4.800000e+02 8.400000e+02 8.800000e+02 6.110337e-04 1.061103e-04
|
||||
|
||||
5.200000e+02 5.200000e+02 5.600000e+02 6.000000e+02 2.645607e-02 3.645607e-04
|
||||
5.200000e+02 5.200000e+02 6.000000e+02 6.400000e+02 1.045640e-02 2.045640e-04
|
||||
5.200000e+02 5.200000e+02 6.400000e+02 6.800000e+02 5.256994e-03 1.525699e-04
|
||||
5.200000e+02 5.200000e+02 6.800000e+02 7.200000e+02 3.076076e-03 1.307608e-04
|
||||
5.200000e+02 5.200000e+02 7.200000e+02 7.600000e+02 1.987042e-03 1.198704e-04
|
||||
5.200000e+02 5.200000e+02 7.600000e+02 8.000000e+02 1.376699e-03 1.137670e-04
|
||||
5.200000e+02 5.200000e+02 8.000000e+02 8.400000e+02 1.005689e-03 1.100569e-04
|
||||
5.200000e+02 5.200000e+02 8.400000e+02 8.800000e+02 7.576795e-04 1.075768e-04
|
||||
5.200000e+02 5.200000e+02 8.800000e+02 9.200000e+02 5.186000e-04 1.051860e-04
|
||||
|
||||
5.600000e+02 5.600000e+02 6.000000e+02 6.400000e+02 2.618374e-02 3.618374e-04
|
||||
5.600000e+02 5.600000e+02 6.400000e+02 6.800000e+02 1.025489e-02 2.025489e-04
|
||||
5.600000e+02 5.600000e+02 6.800000e+02 7.200000e+02 5.112650e-03 1.511265e-04
|
||||
5.600000e+02 5.600000e+02 7.200000e+02 7.600000e+02 2.981344e-03 1.298134e-04
|
||||
5.600000e+02 5.600000e+02 7.600000e+02 8.000000e+02 1.933940e-03 1.193394e-04
|
||||
5.600000e+02 5.600000e+02 8.000000e+02 8.400000e+02 1.353895e-03 1.135390e-04
|
||||
5.600000e+02 5.600000e+02 8.400000e+02 8.800000e+02 9.936591e-04 1.099366e-04
|
||||
5.600000e+02 5.600000e+02 8.800000e+02 9.200000e+02 6.711771e-04 1.067118e-04
|
||||
5.600000e+02 5.600000e+02 9.200000e+02 9.600000e+02 1.196590e-04 1.011966e-04
|
||||
|
||||
6.000000e+02 6.000000e+02 6.400000e+02 6.800000e+02 2.604130e-02 3.604130e-04
|
||||
6.000000e+02 6.000000e+02 6.800000e+02 7.200000e+02 1.015618e-02 2.015618e-04
|
||||
6.000000e+02 6.000000e+02 7.200000e+02 7.600000e+02 5.056610e-03 1.505661e-04
|
||||
6.000000e+02 6.000000e+02 7.600000e+02 8.000000e+02 2.964264e-03 1.296426e-04
|
||||
6.000000e+02 6.000000e+02 8.000000e+02 8.400000e+02 1.946604e-03 1.194660e-04
|
||||
6.000000e+02 6.000000e+02 8.400000e+02 8.800000e+02 1.375056e-03 1.137506e-04
|
||||
6.000000e+02 6.000000e+02 8.800000e+02 9.200000e+02 9.115939e-04 1.091159e-04
|
||||
6.000000e+02 6.000000e+02 9.200000e+02 9.600000e+02 1.606589e-04 1.016066e-04
|
||||
6.000000e+02 6.000000e+02 9.600000e+02 1.000000e+03 1.525385e-04 1.015254e-04
|
||||
|
||||
6.400000e+02 6.400000e+02 6.800000e+02 7.200000e+02 2.598448e-02 3.598448e-04
|
||||
6.400000e+02 6.400000e+02 7.200000e+02 7.600000e+02 1.014015e-02 2.014015e-04
|
||||
6.400000e+02 6.400000e+02 7.600000e+02 8.000000e+02 5.081253e-03 1.508125e-04
|
||||
6.400000e+02 6.400000e+02 8.000000e+02 8.400000e+02 3.022289e-03 1.302229e-04
|
||||
6.400000e+02 6.400000e+02 8.400000e+02 8.800000e+02 2.013954e-03 1.201395e-04
|
||||
6.400000e+02 6.400000e+02 8.800000e+02 9.200000e+02 1.298628e-03 1.129863e-04
|
||||
6.400000e+02 6.400000e+02 9.200000e+02 9.600000e+02 2.256474e-04 1.022565e-04
|
||||
6.400000e+02 6.400000e+02 9.600000e+02 1.000000e+03 1.990072e-04 1.019901e-04
|
||||
6.400000e+02 6.400000e+02 1.000000e+03 1.040000e+03 1.962003e-04 1.019620e-04
|
||||
|
||||
6.800000e+02 6.800000e+02 7.200000e+02 7.600000e+02 2.601381e-02 3.601381e-04
|
||||
6.800000e+02 6.800000e+02 7.600000e+02 8.000000e+02 1.021743e-02 2.021743e-04
|
||||
6.800000e+02 6.800000e+02 8.000000e+02 8.400000e+02 5.202320e-03 1.520232e-04
|
||||
6.800000e+02 6.800000e+02 8.400000e+02 8.800000e+02 3.160360e-03 1.316036e-04
|
||||
6.800000e+02 6.800000e+02 8.800000e+02 9.200000e+02 1.950870e-03 1.195087e-04
|
||||
6.800000e+02 6.800000e+02 9.200000e+02 9.600000e+02 3.326250e-04 1.033263e-04
|
||||
6.800000e+02 6.800000e+02 9.600000e+02 1.000000e+03 2.688421e-04 1.026884e-04
|
||||
6.800000e+02 6.800000e+02 1.000000e+03 1.040000e+03 2.591515e-04 1.025915e-04
|
||||
6.800000e+02 6.800000e+02 1.040000e+03 1.080000e+03 1.904861e-04 1.019049e-04
|
||||
|
||||
7.200000e+02 7.200000e+02 7.600000e+02 8.000000e+02 2.616280e-02 3.616280e-04
|
||||
7.200000e+02 7.200000e+02 8.000000e+02 8.400000e+02 1.043353e-02 2.043353e-04
|
||||
7.200000e+02 7.200000e+02 8.400000e+02 8.800000e+02 5.460042e-03 1.546004e-04
|
||||
7.200000e+02 7.200000e+02 8.800000e+02 9.200000e+02 3.137251e-03 1.313725e-04
|
||||
7.200000e+02 7.200000e+02 9.200000e+02 9.600000e+02 5.203728e-04 1.052037e-04
|
||||
7.200000e+02 7.200000e+02 9.600000e+02 1.000000e+03 3.766559e-04 1.037666e-04
|
||||
7.200000e+02 7.200000e+02 1.000000e+03 1.040000e+03 3.529782e-04 1.035298e-04
|
||||
7.200000e+02 7.200000e+02 1.040000e+03 1.080000e+03 2.532919e-04 1.025329e-04
|
||||
7.200000e+02 7.200000e+02 1.080000e+03 1.120000e+03 1.807361e-04 1.018074e-04
|
||||
|
||||
7.600000e+02 7.600000e+02 8.000000e+02 8.400000e+02 2.629864e-02 3.629864e-04
|
||||
7.600000e+02 7.600000e+02 8.400000e+02 8.800000e+02 1.081983e-02 2.081983e-04
|
||||
7.600000e+02 7.600000e+02 8.800000e+02 9.200000e+02 5.511974e-03 1.551197e-04
|
||||
7.600000e+02 7.600000e+02 9.200000e+02 9.600000e+02 8.761073e-04 1.087611e-04
|
||||
7.600000e+02 7.600000e+02 9.600000e+02 1.000000e+03 5.463970e-04 1.054640e-04
|
||||
7.600000e+02 7.600000e+02 1.000000e+03 1.040000e+03 4.928757e-04 1.049288e-04
|
||||
7.600000e+02 7.600000e+02 1.040000e+03 1.080000e+03 3.429987e-04 1.034300e-04
|
||||
7.600000e+02 7.600000e+02 1.080000e+03 1.120000e+03 2.390966e-04 1.023910e-04
|
||||
7.600000e+02 7.600000e+02 1.120000e+03 1.160000e+03 1.621222e-04 1.016212e-04
|
||||
|
||||
8.000000e+02 8.000000e+02 8.400000e+02 8.800000e+02 2.725269e-02 3.725269e-04
|
||||
8.000000e+02 8.000000e+02 8.800000e+02 9.200000e+02 1.131742e-02 2.131742e-04
|
||||
8.000000e+02 8.000000e+02 9.200000e+02 9.600000e+02 1.680339e-03 1.168034e-04
|
||||
8.000000e+02 8.000000e+02 9.600000e+02 1.000000e+03 8.496814e-04 1.084968e-04
|
||||
8.000000e+02 8.000000e+02 1.000000e+03 1.040000e+03 7.251771e-04 1.072518e-04
|
||||
8.000000e+02 8.000000e+02 1.040000e+03 1.080000e+03 4.838564e-04 1.048386e-04
|
||||
8.000000e+02 8.000000e+02 1.080000e+03 1.120000e+03 3.272610e-04 1.032726e-04
|
||||
8.000000e+02 8.000000e+02 1.120000e+03 1.160000e+03 2.168282e-04 1.021683e-04
|
||||
|
||||
8.400000e+02 8.400000e+02 8.800000e+02 9.200000e+02 2.912738e-02 3.912738e-04
|
||||
8.400000e+02 8.400000e+02 9.200000e+02 9.600000e+02 3.864081e-03 1.386408e-04
|
||||
8.400000e+02 8.400000e+02 9.600000e+02 1.000000e+03 1.447001e-03 1.144700e-04
|
||||
8.400000e+02 8.400000e+02 1.000000e+03 1.040000e+03 1.135173e-03 1.113517e-04
|
||||
8.400000e+02 8.400000e+02 1.040000e+03 1.080000e+03 7.123625e-04 1.071236e-04
|
||||
8.400000e+02 8.400000e+02 1.080000e+03 1.120000e+03 4.621593e-04 1.046216e-04
|
||||
8.400000e+02 8.400000e+02 1.120000e+03 1.160000e+03 2.972157e-04 1.029722e-04
|
||||
|
||||
8.800000e+02 8.800000e+02 9.200000e+02 9.600000e+02 1.171587e-02 2.171587e-04
|
||||
8.800000e+02 8.800000e+02 9.600000e+02 1.000000e+03 2.827625e-03 1.282763e-04
|
||||
8.800000e+02 8.800000e+02 1.000000e+03 1.040000e+03 1.944627e-03 1.194463e-04
|
||||
8.800000e+02 8.800000e+02 1.040000e+03 1.080000e+03 1.112595e-03 1.111259e-04
|
||||
8.800000e+02 8.800000e+02 1.080000e+03 1.120000e+03 6.796523e-04 1.067965e-04
|
||||
8.800000e+02 8.800000e+02 1.120000e+03 1.160000e+03 4.197216e-04 1.041972e-04
|
||||
|
||||
9.200000e+02 9.200000e+02 9.600000e+02 1.000000e+03 6.722928e-03 1.672293e-04
|
||||
9.200000e+02 9.200000e+02 1.000000e+03 1.040000e+03 3.711820e-03 1.371182e-04
|
||||
9.200000e+02 9.200000e+02 1.040000e+03 1.080000e+03 1.840319e-03 1.184032e-04
|
||||
9.200000e+02 9.200000e+02 1.080000e+03 1.120000e+03 1.028874e-03 1.102887e-04
|
||||
9.200000e+02 9.200000e+02 1.120000e+03 1.160000e+03 6.007261e-04 1.060073e-04
|
||||
|
||||
9.600000e+02 9.600000e+02 1.000000e+03 1.040000e+03 7.044588e-03 1.704459e-04
|
||||
9.600000e+02 9.600000e+02 1.040000e+03 1.080000e+03 2.863830e-03 1.286383e-04
|
||||
9.600000e+02 9.600000e+02 1.080000e+03 1.120000e+03 1.427305e-03 1.142730e-04
|
||||
9.600000e+02 9.600000e+02 1.120000e+03 1.160000e+03 7.789635e-04 1.077896e-04
|
||||
|
||||
1.000000e+03 1.000000e+03 1.040000e+03 1.080000e+03 2.057350e-02 3.057350e-04
|
||||
1.000000e+03 1.000000e+03 1.080000e+03 1.120000e+03 7.101947e-03 1.710195e-04
|
||||
1.000000e+03 1.000000e+03 1.120000e+03 1.160000e+03 3.009493e-03 1.300949e-04
|
||||
|
||||
1.040000e+03 1.040000e+03 1.080000e+03 1.120000e+03 2.354771e-02 3.354771e-04
|
||||
1.040000e+03 1.040000e+03 1.120000e+03 1.160000e+03 8.232986e-03 1.823299e-04
|
||||
|
||||
1.080000e+03 1.080000e+03 1.120000e+03 1.160000e+03 2.379402e-02 3.379402e-04
|
||||
@@ -0,0 +1,133 @@
|
||||
85
|
||||
-7718.404778 -5496.003413 1
|
||||
-3.908574e+03 1
|
||||
-2.774696e+03 1
|
||||
-1.964783e+03 1
|
||||
-1.386273e+03 1
|
||||
-9.730523e+02 1
|
||||
-6.778945e+02 1
|
||||
-4.670675e+02 1
|
||||
-3.164768e+02 1
|
||||
-2.089120e+02 1
|
||||
-1.320800e+02 1
|
||||
-7.720000e+01 1
|
||||
-3.800000e+01 1
|
||||
-1.000000e+01 1
|
||||
1.000000e+01 1
|
||||
3.000000e+01 1
|
||||
5.000000e+01 1
|
||||
7.000000e+01 1
|
||||
9.000000e+01 1
|
||||
1.100000e+02 1
|
||||
1.300000e+02 1
|
||||
1.500000e+02 1
|
||||
1.700000e+02 1
|
||||
1.900000e+02 1
|
||||
2.100000e+02 1
|
||||
2.300000e+02 1
|
||||
2.500000e+02 1
|
||||
2.700000e+02 1
|
||||
2.900000e+02 1
|
||||
3.100000e+02 1
|
||||
3.300000e+02 1
|
||||
3.500000e+02 1
|
||||
3.700000e+02 1
|
||||
3.900000e+02 1
|
||||
4.100000e+02 1
|
||||
4.300000e+02 1
|
||||
4.500000e+02 1
|
||||
4.700000e+02 1
|
||||
4.900000e+02 1
|
||||
5.100000e+02 1
|
||||
5.300000e+02 1
|
||||
5.500000e+02 1
|
||||
5.700000e+02 1
|
||||
5.900000e+02 1
|
||||
6.100000e+02 1
|
||||
6.300000e+02 1
|
||||
6.500000e+02 1
|
||||
6.700000e+02 1
|
||||
6.900000e+02 1
|
||||
7.100000e+02 1
|
||||
7.300000e+02 1
|
||||
7.500000e+02 1
|
||||
7.700000e+02 1
|
||||
7.900000e+02 1
|
||||
8.100000e+02 1
|
||||
8.300000e+02 1
|
||||
8.500000e+02 1
|
||||
8.700000e+02 1
|
||||
8.900000e+02 1
|
||||
9.100000e+02 1
|
||||
9.300000e+02 1
|
||||
9.500000e+02 1
|
||||
9.700000e+02 1
|
||||
9.900000e+02 1
|
||||
1.010000e+03 1
|
||||
1.030000e+03 1
|
||||
1.050000e+03 1
|
||||
1.070000e+03 1
|
||||
1.090000e+03 1
|
||||
1.110000e+03 1
|
||||
1.130000e+03 1
|
||||
1.158000e+03 1
|
||||
1.197200e+03 1
|
||||
1.252080e+03 1
|
||||
1.328912e+03 1
|
||||
1.436477e+03 1
|
||||
1.587068e+03 1
|
||||
1.797895e+03 1
|
||||
2.093052e+03 1
|
||||
2.506273e+03 1
|
||||
3.084783e+03 1
|
||||
3.894696e+03 1
|
||||
5.028574e+03 1
|
||||
6.616003e+03 1
|
||||
8.838405e+03 1
|
||||
|
||||
45
|
||||
0.000000 20.000000 1
|
||||
4.000000e+01 1
|
||||
6.000000e+01 1
|
||||
8.000000e+01 1
|
||||
1.000000e+02 1
|
||||
1.200000e+02 1
|
||||
1.400000e+02 1
|
||||
1.600000e+02 1
|
||||
1.800000e+02 1
|
||||
2.000000e+02 1
|
||||
2.200000e+02 1
|
||||
2.400000e+02 1
|
||||
2.600000e+02 1
|
||||
2.800000e+02 1
|
||||
3.000000e+02 1
|
||||
3.200000e+02 1
|
||||
3.400000e+02 1
|
||||
3.600000e+02 1
|
||||
3.800000e+02 1
|
||||
4.000000e+02 1
|
||||
4.200000e+02 1
|
||||
4.400000e+02 1
|
||||
4.600000e+02 1
|
||||
4.800000e+02 1
|
||||
5.000000e+02 1
|
||||
5.200000e+02 1
|
||||
5.400000e+02 1
|
||||
5.600000e+02 1
|
||||
5.800000e+02 1
|
||||
6.000000e+02 1
|
||||
6.240000e+02 1
|
||||
6.540000e+02 1
|
||||
6.890000e+02 1
|
||||
7.290000e+02 1
|
||||
7.790000e+02 1
|
||||
8.390000e+02 1
|
||||
9.110000e+02 1
|
||||
9.970000e+02 1
|
||||
1.100000e+03 1
|
||||
1.250000e+03 1
|
||||
1.425000e+03 1
|
||||
1.625000e+03 1
|
||||
1.875000e+03 1
|
||||
2.175000e+03 1
|
||||
2.525000e+03 1
|
||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,48 @@
|
||||
|
||||
Parallelized with OpenMP. # of threads: 4
|
||||
|
||||
DCIP2D - Version 5 (BETA) 20110811: DCIPF2D
|
||||
|
||||
Developed by University of British Columbia
|
||||
Geophysical Inversion Facility (UBC-GIF)
|
||||
|
||||
(C) Copyright 1992 - 2011, UBC-GIF,
|
||||
Department of Earth and Ocean Sciences, UBC
|
||||
http://www.eos.ubc.ca/research/ubcgif/
|
||||
|
||||
Distributed by:
|
||||
Mira Geoscience Ltd.
|
||||
|
||||
|
||||
DCIPF2D started on:12/09/2015 17:56:14
|
||||
|
||||
|
||||
Reading input file: dcipf2d.inp
|
||||
|
||||
----------------------------------------------
|
||||
FWD DC
|
||||
MESH FILE Mesh_2D.msh
|
||||
LOC LOC_X FWR_3D_2_2D.dat
|
||||
TOPO DEFAULT
|
||||
COND FILE MtIsa_2D.con
|
||||
----------------------------------------------
|
||||
|
||||
electrode locations were read from: FWR_3D_2_2D.dat
|
||||
# of current locations: 28
|
||||
# of data: 216
|
||||
|
||||
mesh was read from: Mesh_2D.msh
|
||||
# of cells: 85 x 45
|
||||
total # of cells: 3825
|
||||
# of active cells: 3825
|
||||
|
||||
# of wave values: 13
|
||||
2.5000E-04 4.9901E-04 9.9606E-04 1.9882E-03 3.9685E-03 7.9213E-03 1.5811E-02 3.1560E-02 6.2996E-02 1.2574E-01 2.5099E-01 5.0099E-01 1.0000E+00
|
||||
|
||||
conductivity was read from file: MtIsa_2D.con
|
||||
dc fwd cpu time: 0:00:00.19
|
||||
|
||||
total cpu time: 0:00:00.19
|
||||
|
||||
DCIPF2D ended on:12/09/2015 17:56:14
|
||||
|
||||
@@ -0,0 +1,217 @@
|
||||
! Predicted data ! GENERAL FORMAT Last column are apparent conductivities.
|
||||
0.0000000E+00 0.0000000E+00 4.0000000E+01 8.0000000E+01 4.37963E-01 4.54248E-03
|
||||
0.0000000E+00 0.0000000E+00 8.0000000E+01 1.2000000E+02 1.62622E-01 4.07785E-03
|
||||
0.0000000E+00 0.0000000E+00 1.2000000E+02 1.6000000E+02 7.69824E-02 4.30712E-03
|
||||
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7.2000000E+02 7.2000000E+02 9.2000000E+02 9.6000000E+02 4.23001E-04 3.13543E-01
|
||||
7.2000000E+02 7.2000000E+02 9.6000000E+02 1.0000000E+03 3.40352E-04 2.78345E-01
|
||||
7.2000000E+02 7.2000000E+02 1.0000000E+03 1.0400000E+03 3.23938E-04 2.19336E-01
|
||||
7.2000000E+02 7.2000000E+02 1.0400000E+03 1.0800000E+03 2.37881E-04 2.32310E-01
|
||||
7.2000000E+02 7.2000000E+02 1.0800000E+03 1.1200000E+03 1.75459E-04 2.51965E-01
|
||||
7.6000000E+02 7.6000000E+02 8.0000000E+02 8.4000000E+02 2.72284E-02 7.30647E-02
|
||||
7.6000000E+02 7.6000000E+02 8.4000000E+02 8.8000000E+02 9.66569E-03 6.86082E-02
|
||||
7.6000000E+02 7.6000000E+02 8.8000000E+02 9.2000000E+02 4.59764E-03 7.21180E-02
|
||||
7.6000000E+02 7.6000000E+02 9.2000000E+02 9.6000000E+02 7.18689E-04 2.76815E-01
|
||||
7.6000000E+02 7.6000000E+02 9.6000000E+02 1.0000000E+03 4.92200E-04 2.69462E-01
|
||||
7.6000000E+02 7.6000000E+02 1.0000000E+03 1.0400000E+03 4.50063E-04 2.10493E-01
|
||||
7.6000000E+02 7.6000000E+02 1.0400000E+03 1.0800000E+03 3.19985E-04 2.22045E-01
|
||||
7.6000000E+02 7.6000000E+02 1.0800000E+03 1.1200000E+03 2.30214E-04 2.40047E-01
|
||||
7.6000000E+02 7.6000000E+02 1.1200000E+03 1.1600000E+03 1.64278E-04 2.69115E-01
|
||||
8.0000000E+02 8.0000000E+02 8.4000000E+02 8.8000000E+02 2.81224E-02 7.07421E-02
|
||||
8.0000000E+02 8.0000000E+02 8.8000000E+02 9.2000000E+02 9.73736E-03 6.81032E-02
|
||||
8.0000000E+02 8.0000000E+02 9.2000000E+02 9.6000000E+02 1.39849E-03 2.37094E-01
|
||||
8.0000000E+02 8.0000000E+02 9.6000000E+02 1.0000000E+03 7.58522E-04 2.62278E-01
|
||||
8.0000000E+02 8.0000000E+02 1.0000000E+03 1.0400000E+03 6.54056E-04 2.02779E-01
|
||||
8.0000000E+02 8.0000000E+02 1.0400000E+03 1.0800000E+03 4.44836E-04 2.12966E-01
|
||||
8.0000000E+02 8.0000000E+02 1.0800000E+03 1.1200000E+03 3.10020E-04 2.29183E-01
|
||||
8.0000000E+02 8.0000000E+02 1.1200000E+03 1.1600000E+03 2.15819E-04 2.56057E-01
|
||||
8.4000000E+02 8.4000000E+02 8.8000000E+02 9.2000000E+02 2.90585E-02 6.84630E-02
|
||||
8.4000000E+02 8.4000000E+02 9.2000000E+02 9.6000000E+02 3.44112E-03 1.92712E-01
|
||||
8.4000000E+02 8.4000000E+02 9.6000000E+02 1.0000000E+03 1.28993E-03 2.57048E-01
|
||||
8.4000000E+02 8.4000000E+02 1.0000000E+03 1.0400000E+03 1.01463E-03 1.96075E-01
|
||||
8.4000000E+02 8.4000000E+02 1.0400000E+03 1.0800000E+03 6.46364E-04 2.05193E-01
|
||||
8.4000000E+02 8.4000000E+02 1.0800000E+03 1.1200000E+03 4.31094E-04 2.19755E-01
|
||||
8.4000000E+02 8.4000000E+02 1.1200000E+03 1.1600000E+03 2.90763E-04 2.44361E-01
|
||||
8.8000000E+02 8.8000000E+02 9.2000000E+02 9.6000000E+02 1.37818E-02 1.44352E-01
|
||||
8.8000000E+02 8.8000000E+02 9.6000000E+02 1.0000000E+03 2.58571E-03 2.56466E-01
|
||||
8.8000000E+02 8.8000000E+02 1.0000000E+03 1.0400000E+03 1.73659E-03 1.90933E-01
|
||||
8.8000000E+02 8.8000000E+02 1.0400000E+03 1.0800000E+03 9.99448E-04 1.99054E-01
|
||||
8.8000000E+02 8.8000000E+02 1.0800000E+03 1.1200000E+03 6.25061E-04 2.12186E-01
|
||||
8.8000000E+02 8.8000000E+02 1.1200000E+03 1.1600000E+03 4.03769E-04 2.34627E-01
|
||||
9.2000000E+02 9.2000000E+02 9.6000000E+02 1.0000000E+03 7.20306E-03 2.76193E-01
|
||||
9.2000000E+02 9.2000000E+02 1.0000000E+03 1.0400000E+03 3.38580E-03 1.95861E-01
|
||||
9.2000000E+02 9.2000000E+02 1.0400000E+03 1.0800000E+03 1.63263E-03 2.03091E-01
|
||||
9.2000000E+02 9.2000000E+02 1.0800000E+03 1.1200000E+03 9.26167E-04 2.14803E-01
|
||||
9.2000000E+02 9.2000000E+02 1.1200000E+03 1.1600000E+03 5.63508E-04 2.35363E-01
|
||||
9.6000000E+02 9.6000000E+02 1.0000000E+03 1.0400000E+03 7.04738E-03 2.82295E-01
|
||||
9.6000000E+02 9.6000000E+02 1.0400000E+03 1.0800000E+03 2.52957E-03 2.62158E-01
|
||||
9.6000000E+02 9.6000000E+02 1.0800000E+03 1.1200000E+03 1.25737E-03 2.63704E-01
|
||||
9.6000000E+02 9.6000000E+02 1.1200000E+03 1.1600000E+03 7.12741E-04 2.79125E-01
|
||||
1.0000000E+03 1.0000000E+03 1.0400000E+03 1.0800000E+03 2.22317E-02 8.94866E-02
|
||||
1.0000000E+03 1.0000000E+03 1.0800000E+03 1.1200000E+03 6.47719E-03 1.02382E-01
|
||||
1.0000000E+03 1.0000000E+03 1.1200000E+03 1.1600000E+03 2.70624E-03 1.22522E-01
|
||||
1.0400000E+03 1.0400000E+03 1.0800000E+03 1.1200000E+03 2.49914E-02 7.96048E-02
|
||||
1.0400000E+03 1.0400000E+03 1.1200000E+03 1.1600000E+03 7.57627E-03 8.75293E-02
|
||||
1.0800000E+03 1.0800000E+03 1.1200000E+03 1.1600000E+03 2.53783E-02 7.83913E-02
|
||||
@@ -0,0 +1,5 @@
|
||||
FWD DC
|
||||
MESH FILE Mesh_2D.msh
|
||||
LOC LOC_X FWR_3D_2_2D.dat
|
||||
TOPO DEFAULT
|
||||
COND FILE MtIsa_2D.con
|
||||
@@ -0,0 +1,5 @@
|
||||
104 21 45
|
||||
-1565.00 10725 0
|
||||
350 300 250 200 175 150 103.00 86.00 72.00 60.00 50.00 40.00 35.00 30.00 24.00 74*20.00 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
|
||||
300 250 200 175 150 103.00 86.00 72.00 5*60 72.00 86.00 103.00 150 175 200 250 300
|
||||
30*20 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
|
||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,245 @@
|
||||
! GENERAL FORMAT
|
||||
|
||||
0.000000e+00 0.000000e+00 4.000000e+01 8.000000e+01 3.134690e-01 4.636103e-03
|
||||
0.000000e+00 0.000000e+00 8.000000e+01 1.200000e+02 1.388270e-01 2.056283e-03
|
||||
0.000000e+00 0.000000e+00 1.200000e+02 1.600000e+02 7.065850e-02 1.066153e-03
|
||||
0.000000e+00 0.000000e+00 1.600000e+02 2.000000e+02 4.188140e-03 1.544321e-04
|
||||
0.000000e+00 0.000000e+00 2.000000e+02 2.400000e+02 2.415160e-03 1.297752e-04
|
||||
0.000000e+00 0.000000e+00 2.400000e+02 2.800000e+02 2.739890e-03 1.311332e-04
|
||||
0.000000e+00 0.000000e+00 2.800000e+02 3.200000e+02 6.530410e-03 1.721638e-04
|
||||
0.000000e+00 0.000000e+00 3.200000e+02 3.600000e+02 5.932600e-03 1.647500e-04
|
||||
0.000000e+00 0.000000e+00 3.600000e+02 4.000000e+02 4.378410e-03 1.475086e-04
|
||||
|
||||
4.000000e+01 4.000000e+01 8.000000e+01 1.200000e+02 3.311530e-01 4.837093e-03
|
||||
4.000000e+01 4.000000e+01 1.200000e+02 1.600000e+02 1.346040e-01 2.033017e-03
|
||||
4.000000e+01 4.000000e+01 1.600000e+02 2.000000e+02 7.114680e-03 1.962511e-04
|
||||
4.000000e+01 4.000000e+01 2.000000e+02 2.400000e+02 3.712820e-03 1.471017e-04
|
||||
4.000000e+01 4.000000e+01 2.400000e+02 2.800000e+02 3.590020e-03 1.414691e-04
|
||||
4.000000e+01 4.000000e+01 2.800000e+02 3.200000e+02 8.088730e-03 1.901446e-04
|
||||
4.000000e+01 4.000000e+01 3.200000e+02 3.600000e+02 7.176080e-03 1.787592e-04
|
||||
4.000000e+01 4.000000e+01 3.600000e+02 4.000000e+02 5.240720e-03 1.571198e-04
|
||||
4.000000e+01 4.000000e+01 4.000000e+02 4.400000e+02 5.803160e-04 1.062599e-04
|
||||
|
||||
8.000000e+01 8.000000e+01 1.200000e+02 1.600000e+02 3.387770e-01 5.056306e-03
|
||||
8.000000e+01 8.000000e+01 1.600000e+02 2.000000e+02 1.417790e-02 3.048511e-04
|
||||
8.000000e+01 8.000000e+01 2.000000e+02 2.400000e+02 6.363520e-03 1.857564e-04
|
||||
8.000000e+01 8.000000e+01 2.400000e+02 2.800000e+02 5.008630e-03 1.603252e-04
|
||||
8.000000e+01 8.000000e+01 2.800000e+02 3.200000e+02 1.039040e-02 2.189394e-04
|
||||
8.000000e+01 8.000000e+01 3.200000e+02 3.600000e+02 8.905940e-03 1.998280e-04
|
||||
8.000000e+01 8.000000e+01 3.600000e+02 4.000000e+02 6.407310e-03 1.711701e-04
|
||||
8.000000e+01 8.000000e+01 4.000000e+02 4.400000e+02 6.967590e-04 1.076499e-04
|
||||
8.000000e+01 8.000000e+01 4.400000e+02 4.800000e+02 5.706700e-04 1.061933e-04
|
||||
|
||||
1.200000e+02 1.200000e+02 1.600000e+02 2.000000e+02 3.847470e-02 6.597631e-04
|
||||
1.200000e+02 1.200000e+02 2.000000e+02 2.400000e+02 1.295120e-02 2.877787e-04
|
||||
1.200000e+02 1.200000e+02 2.400000e+02 2.800000e+02 7.717090e-03 1.990861e-04
|
||||
1.200000e+02 1.200000e+02 2.800000e+02 3.200000e+02 1.408500e-02 2.676287e-04
|
||||
1.200000e+02 1.200000e+02 3.200000e+02 3.600000e+02 1.144910e-02 2.319428e-04
|
||||
1.200000e+02 1.200000e+02 3.600000e+02 4.000000e+02 8.052980e-03 1.915666e-04
|
||||
1.200000e+02 1.200000e+02 4.000000e+02 4.400000e+02 8.545430e-04 1.095795e-04
|
||||
1.200000e+02 1.200000e+02 4.400000e+02 4.800000e+02 6.842970e-04 1.075687e-04
|
||||
1.200000e+02 1.200000e+02 4.800000e+02 5.200000e+02 5.552720e-04 1.060808e-04
|
||||
|
||||
1.600000e+02 1.600000e+02 2.000000e+02 2.400000e+02 2.893830e-02 5.265271e-04
|
||||
1.600000e+02 1.600000e+02 2.400000e+02 2.800000e+02 1.227910e-02 2.691741e-04
|
||||
1.600000e+02 1.600000e+02 2.800000e+02 3.200000e+02 1.906980e-02 3.380462e-04
|
||||
1.600000e+02 1.600000e+02 3.200000e+02 3.600000e+02 1.451740e-02 2.727048e-04
|
||||
1.600000e+02 1.600000e+02 3.600000e+02 4.000000e+02 9.935060e-03 2.158363e-04
|
||||
1.600000e+02 1.600000e+02 4.000000e+02 4.400000e+02 1.026530e-03 1.117529e-04
|
||||
1.600000e+02 1.600000e+02 4.400000e+02 4.800000e+02 8.026950e-04 1.090425e-04
|
||||
1.600000e+02 1.600000e+02 4.800000e+02 5.200000e+02 6.402360e-04 1.071292e-04
|
||||
1.600000e+02 1.600000e+02 5.200000e+02 5.600000e+02 5.145720e-04 1.056846e-04
|
||||
|
||||
2.000000e+02 2.000000e+02 2.400000e+02 2.800000e+02 2.209580e-02 4.101272e-04
|
||||
2.000000e+02 2.000000e+02 2.800000e+02 3.200000e+02 2.501490e-02 4.293247e-04
|
||||
2.000000e+02 2.000000e+02 3.200000e+02 3.600000e+02 1.726610e-02 3.118536e-04
|
||||
2.000000e+02 2.000000e+02 3.600000e+02 4.000000e+02 1.139970e-02 2.354928e-04
|
||||
2.000000e+02 2.000000e+02 4.000000e+02 4.400000e+02 1.145600e-03 1.132824e-04
|
||||
2.000000e+02 2.000000e+02 4.400000e+02 4.800000e+02 8.759600e-04 1.099518e-04
|
||||
2.000000e+02 2.000000e+02 4.800000e+02 5.200000e+02 6.883230e-04 1.077133e-04
|
||||
2.000000e+02 2.000000e+02 5.200000e+02 5.600000e+02 5.476180e-04 1.060808e-04
|
||||
2.000000e+02 2.000000e+02 5.600000e+02 6.000000e+02 4.364850e-04 1.048185e-04
|
||||
|
||||
2.400000e+02 2.400000e+02 2.800000e+02 3.200000e+02 4.885300e-02 7.649140e-04
|
||||
2.400000e+02 2.400000e+02 3.200000e+02 3.600000e+02 2.527390e-02 4.258335e-04
|
||||
2.400000e+02 2.400000e+02 3.600000e+02 4.000000e+02 1.518430e-02 2.854898e-04
|
||||
2.400000e+02 2.400000e+02 4.000000e+02 4.400000e+02 1.432570e-03 1.168008e-04
|
||||
2.400000e+02 2.400000e+02 4.400000e+02 4.800000e+02 1.042790e-03 1.118556e-04
|
||||
2.400000e+02 2.400000e+02 4.800000e+02 5.200000e+02 7.934300e-04 1.088474e-04
|
||||
2.400000e+02 2.400000e+02 5.200000e+02 5.600000e+02 6.177110e-04 1.068047e-04
|
||||
2.400000e+02 2.400000e+02 5.600000e+02 6.000000e+02 4.850580e-04 1.053030e-04
|
||||
2.400000e+02 2.400000e+02 6.000000e+02 6.400000e+02 3.813010e-04 1.041481e-04
|
||||
|
||||
2.800000e+02 2.800000e+02 3.200000e+02 3.600000e+02 1.141790e-01 1.678256e-03
|
||||
2.800000e+02 2.800000e+02 3.600000e+02 4.000000e+02 4.444100e-02 7.200859e-04
|
||||
2.800000e+02 2.800000e+02 4.000000e+02 4.400000e+02 3.327290e-03 1.433912e-04
|
||||
2.800000e+02 2.800000e+02 4.400000e+02 4.800000e+02 2.028060e-03 1.247834e-04
|
||||
2.800000e+02 2.800000e+02 4.800000e+02 5.200000e+02 1.369420e-03 1.160567e-04
|
||||
2.800000e+02 2.800000e+02 5.200000e+02 5.600000e+02 9.822340e-04 1.112350e-04
|
||||
2.800000e+02 2.800000e+02 5.600000e+02 6.000000e+02 7.284700e-04 1.082107e-04
|
||||
2.800000e+02 2.800000e+02 6.000000e+02 6.400000e+02 5.496900e-04 1.061397e-04
|
||||
2.800000e+02 2.800000e+02 6.400000e+02 6.800000e+02 4.191740e-04 1.046528e-04
|
||||
|
||||
3.200000e+02 3.200000e+02 3.600000e+02 4.000000e+02 1.962910e-01 2.908898e-03
|
||||
3.200000e+02 3.200000e+02 4.000000e+02 4.400000e+02 1.127790e-02 2.592369e-04
|
||||
3.200000e+02 3.200000e+02 4.400000e+02 4.800000e+02 5.753480e-03 1.754301e-04
|
||||
3.200000e+02 3.200000e+02 4.800000e+02 5.200000e+02 3.426860e-03 1.424083e-04
|
||||
3.200000e+02 3.200000e+02 5.200000e+02 5.600000e+02 2.238850e-03 1.267174e-04
|
||||
3.200000e+02 3.200000e+02 5.600000e+02 6.000000e+02 1.548240e-03 1.180814e-04
|
||||
3.200000e+02 3.200000e+02 6.000000e+02 6.400000e+02 1.107970e-03 1.127738e-04
|
||||
3.200000e+02 3.200000e+02 6.400000e+02 6.800000e+02 8.105190e-04 1.092675e-04
|
||||
3.200000e+02 3.200000e+02 6.800000e+02 7.200000e+02 6.037130e-04 1.068618e-04
|
||||
|
||||
3.600000e+02 3.600000e+02 4.000000e+02 4.400000e+02 3.262940e-02 5.641363e-04
|
||||
3.600000e+02 3.600000e+02 4.400000e+02 4.800000e+02 1.268600e-02 2.769581e-04
|
||||
3.600000e+02 3.600000e+02 4.800000e+02 5.200000e+02 6.634070e-03 1.862110e-04
|
||||
3.600000e+02 3.600000e+02 5.200000e+02 5.600000e+02 4.002550e-03 1.493660e-04
|
||||
3.600000e+02 3.600000e+02 5.600000e+02 6.000000e+02 2.623140e-03 1.313451e-04
|
||||
3.600000e+02 3.600000e+02 6.000000e+02 6.400000e+02 1.807470e-03 1.212050e-04
|
||||
3.600000e+02 3.600000e+02 6.400000e+02 6.800000e+02 1.285890e-03 1.149195e-04
|
||||
3.600000e+02 3.600000e+02 6.800000e+02 7.200000e+02 9.371730e-04 1.107940e-04
|
||||
3.600000e+02 3.600000e+02 7.200000e+02 7.600000e+02 6.989270e-04 1.080080e-04
|
||||
|
||||
4.000000e+02 4.000000e+02 4.400000e+02 4.800000e+02 2.729790e-02 4.878789e-04
|
||||
4.000000e+02 4.000000e+02 4.800000e+02 5.200000e+02 1.146310e-02 2.570287e-04
|
||||
4.000000e+02 4.000000e+02 5.200000e+02 5.600000e+02 6.243480e-03 1.799854e-04
|
||||
4.000000e+02 4.000000e+02 5.600000e+02 6.000000e+02 3.853410e-03 1.471565e-04
|
||||
4.000000e+02 4.000000e+02 6.000000e+02 6.400000e+02 2.554350e-03 1.304300e-04
|
||||
4.000000e+02 4.000000e+02 6.400000e+02 6.800000e+02 1.769940e-03 1.207575e-04
|
||||
4.000000e+02 4.000000e+02 6.800000e+02 7.200000e+02 1.265610e-03 1.146992e-04
|
||||
4.000000e+02 4.000000e+02 7.200000e+02 7.600000e+02 9.301620e-04 1.107333e-04
|
||||
4.000000e+02 4.000000e+02 7.600000e+02 8.000000e+02 7.024960e-04 1.080707e-04
|
||||
|
||||
4.400000e+02 4.400000e+02 4.800000e+02 5.200000e+02 2.082870e-02 3.913833e-04
|
||||
4.400000e+02 4.400000e+02 5.200000e+02 5.600000e+02 8.821230e-03 2.196402e-04
|
||||
4.400000e+02 4.400000e+02 5.600000e+02 6.000000e+02 4.889580e-03 1.621575e-04
|
||||
4.400000e+02 4.400000e+02 6.000000e+02 6.400000e+02 3.059170e-03 1.372429e-04
|
||||
4.400000e+02 4.400000e+02 6.400000e+02 6.800000e+02 2.047550e-03 1.243099e-04
|
||||
4.400000e+02 4.400000e+02 6.800000e+02 7.200000e+02 1.431960e-03 1.167527e-04
|
||||
4.400000e+02 4.400000e+02 7.200000e+02 7.600000e+02 1.036630e-03 1.120169e-04
|
||||
4.400000e+02 4.400000e+02 7.600000e+02 8.000000e+02 7.745150e-04 1.089256e-04
|
||||
4.400000e+02 4.400000e+02 8.000000e+02 8.400000e+02 5.950870e-04 1.068330e-04
|
||||
|
||||
4.800000e+02 4.800000e+02 5.200000e+02 5.600000e+02 1.949380e-02 3.703944e-04
|
||||
4.800000e+02 4.800000e+02 5.600000e+02 6.000000e+02 8.039150e-03 2.085234e-04
|
||||
4.800000e+02 4.800000e+02 6.000000e+02 6.400000e+02 4.384660e-03 1.553848e-04
|
||||
4.800000e+02 4.800000e+02 6.400000e+02 6.800000e+02 2.714300e-03 1.327615e-04
|
||||
4.800000e+02 4.800000e+02 6.800000e+02 7.200000e+02 1.807600e-03 1.212398e-04
|
||||
4.800000e+02 4.800000e+02 7.200000e+02 7.600000e+02 1.266500e-03 1.146476e-04
|
||||
4.800000e+02 4.800000e+02 7.600000e+02 8.000000e+02 9.249070e-04 1.105945e-04
|
||||
4.800000e+02 4.800000e+02 8.000000e+02 8.400000e+02 6.993340e-04 1.079645e-04
|
||||
4.800000e+02 4.800000e+02 8.400000e+02 8.800000e+02 5.382050e-04 1.061103e-04
|
||||
|
||||
5.200000e+02 5.200000e+02 5.600000e+02 6.000000e+02 1.900290e-02 3.645607e-04
|
||||
5.200000e+02 5.200000e+02 6.000000e+02 6.400000e+02 7.698270e-03 2.045640e-04
|
||||
5.200000e+02 5.200000e+02 6.400000e+02 6.800000e+02 4.139850e-03 1.525699e-04
|
||||
5.200000e+02 5.200000e+02 6.800000e+02 7.200000e+02 2.539690e-03 1.307608e-04
|
||||
5.200000e+02 5.200000e+02 7.200000e+02 7.600000e+02 1.687820e-03 1.198704e-04
|
||||
5.200000e+02 5.200000e+02 7.600000e+02 8.000000e+02 1.189110e-03 1.137670e-04
|
||||
5.200000e+02 5.200000e+02 8.000000e+02 8.400000e+02 8.771970e-04 1.100569e-04
|
||||
5.200000e+02 5.200000e+02 8.400000e+02 8.800000e+02 6.644400e-04 1.075768e-04
|
||||
5.200000e+02 5.200000e+02 8.800000e+02 9.200000e+02 4.327750e-04 1.051860e-04
|
||||
|
||||
5.600000e+02 5.600000e+02 6.000000e+02 6.400000e+02 1.876480e-02 3.618374e-04
|
||||
5.600000e+02 5.600000e+02 6.400000e+02 6.800000e+02 7.521690e-03 2.025489e-04
|
||||
5.600000e+02 5.600000e+02 6.800000e+02 7.200000e+02 4.013680e-03 1.511265e-04
|
||||
5.600000e+02 5.600000e+02 7.200000e+02 7.600000e+02 2.457010e-03 1.298134e-04
|
||||
5.600000e+02 5.600000e+02 7.600000e+02 8.000000e+02 1.641300e-03 1.193394e-04
|
||||
5.600000e+02 5.600000e+02 8.000000e+02 8.400000e+02 1.168710e-03 1.135390e-04
|
||||
5.600000e+02 5.600000e+02 8.400000e+02 8.800000e+02 8.657950e-04 1.099366e-04
|
||||
5.600000e+02 5.600000e+02 8.800000e+02 9.200000e+02 5.575880e-04 1.067118e-04
|
||||
5.600000e+02 5.600000e+02 9.200000e+02 9.600000e+02 9.830600e-05 1.011966e-04
|
||||
|
||||
6.000000e+02 6.000000e+02 6.400000e+02 6.800000e+02 1.863960e-02 3.604130e-04
|
||||
6.000000e+02 6.000000e+02 6.800000e+02 7.200000e+02 7.435780e-03 2.015618e-04
|
||||
6.000000e+02 6.000000e+02 7.200000e+02 7.600000e+02 3.965310e-03 1.505661e-04
|
||||
6.000000e+02 6.000000e+02 7.600000e+02 8.000000e+02 2.442400e-03 1.296426e-04
|
||||
6.000000e+02 6.000000e+02 8.000000e+02 8.400000e+02 1.652150e-03 1.194660e-04
|
||||
6.000000e+02 6.000000e+02 8.400000e+02 8.800000e+02 1.186320e-03 1.137506e-04
|
||||
6.000000e+02 6.000000e+02 8.800000e+02 9.200000e+02 7.524330e-04 1.091159e-04
|
||||
6.000000e+02 6.000000e+02 9.200000e+02 9.600000e+02 1.311600e-04 1.016066e-04
|
||||
6.000000e+02 6.000000e+02 9.600000e+02 1.000000e+03 1.370860e-04 1.015254e-04
|
||||
|
||||
6.400000e+02 6.400000e+02 6.800000e+02 7.200000e+02 1.859090e-02 3.598448e-04
|
||||
6.400000e+02 6.400000e+02 7.200000e+02 7.600000e+02 7.422920e-03 2.014015e-04
|
||||
6.400000e+02 6.400000e+02 7.600000e+02 8.000000e+02 3.987550e-03 1.508125e-04
|
||||
6.400000e+02 6.400000e+02 8.000000e+02 8.400000e+02 2.493010e-03 1.302229e-04
|
||||
6.400000e+02 6.400000e+02 8.400000e+02 8.800000e+02 1.709810e-03 1.201395e-04
|
||||
6.400000e+02 6.400000e+02 8.800000e+02 9.200000e+02 1.061310e-03 1.129863e-04
|
||||
6.400000e+02 6.400000e+02 9.200000e+02 9.600000e+02 1.825910e-04 1.022565e-04
|
||||
6.400000e+02 6.400000e+02 9.600000e+02 1.000000e+03 1.772200e-04 1.019901e-04
|
||||
6.400000e+02 6.400000e+02 1.000000e+03 1.040000e+03 1.758080e-04 1.019620e-04
|
||||
|
||||
6.800000e+02 6.800000e+02 7.200000e+02 7.600000e+02 1.861820e-02 3.601381e-04
|
||||
6.800000e+02 6.800000e+02 7.600000e+02 8.000000e+02 7.491490e-03 2.021743e-04
|
||||
6.800000e+02 6.800000e+02 8.000000e+02 8.400000e+02 4.093150e-03 1.520232e-04
|
||||
6.800000e+02 6.800000e+02 8.400000e+02 8.800000e+02 2.611450e-03 1.316036e-04
|
||||
6.800000e+02 6.800000e+02 8.800000e+02 9.200000e+02 1.569020e-03 1.195087e-04
|
||||
6.800000e+02 6.800000e+02 9.200000e+02 9.600000e+02 2.655830e-04 1.033263e-04
|
||||
6.800000e+02 6.800000e+02 9.600000e+02 1.000000e+03 2.370450e-04 1.026884e-04
|
||||
6.800000e+02 6.800000e+02 1.000000e+03 1.040000e+03 2.300430e-04 1.025915e-04
|
||||
6.800000e+02 6.800000e+02 1.040000e+03 1.080000e+03 1.703430e-04 1.019049e-04
|
||||
|
||||
7.200000e+02 7.200000e+02 7.600000e+02 8.000000e+02 1.874970e-02 3.616280e-04
|
||||
7.200000e+02 7.200000e+02 8.000000e+02 8.400000e+02 7.679470e-03 2.043353e-04
|
||||
7.200000e+02 7.200000e+02 8.400000e+02 8.800000e+02 4.313090e-03 1.546004e-04
|
||||
7.200000e+02 7.200000e+02 8.800000e+02 9.200000e+02 2.456700e-03 1.313725e-04
|
||||
7.200000e+02 7.200000e+02 9.200000e+02 9.600000e+02 4.067210e-04 1.052037e-04
|
||||
7.200000e+02 7.200000e+02 9.600000e+02 1.000000e+03 3.281940e-04 1.037666e-04
|
||||
7.200000e+02 7.200000e+02 1.000000e+03 1.040000e+03 3.102060e-04 1.035298e-04
|
||||
7.200000e+02 7.200000e+02 1.040000e+03 1.080000e+03 2.243930e-04 1.025329e-04
|
||||
7.200000e+02 7.200000e+02 1.080000e+03 1.120000e+03 1.613910e-04 1.018074e-04
|
||||
|
||||
7.600000e+02 7.600000e+02 8.000000e+02 8.400000e+02 1.907180e-02 3.629864e-04
|
||||
7.600000e+02 7.600000e+02 8.400000e+02 8.800000e+02 8.087110e-03 2.081983e-04
|
||||
7.600000e+02 7.600000e+02 8.800000e+02 9.200000e+02 4.169100e-03 1.551197e-04
|
||||
7.600000e+02 7.600000e+02 9.200000e+02 9.600000e+02 6.673730e-04 1.087611e-04
|
||||
7.600000e+02 7.600000e+02 9.600000e+02 1.000000e+03 4.726200e-04 1.054640e-04
|
||||
7.600000e+02 7.600000e+02 1.000000e+03 1.040000e+03 4.318260e-04 1.049288e-04
|
||||
7.600000e+02 7.600000e+02 1.040000e+03 1.080000e+03 3.035780e-04 1.034300e-04
|
||||
7.600000e+02 7.600000e+02 1.080000e+03 1.120000e+03 2.134420e-04 1.023910e-04
|
||||
7.600000e+02 7.600000e+02 1.120000e+03 1.160000e+03 1.459980e-04 1.016212e-04
|
||||
|
||||
8.000000e+02 8.000000e+02 8.400000e+02 8.800000e+02 1.986530e-02 3.725269e-04
|
||||
8.000000e+02 8.000000e+02 8.800000e+02 9.200000e+02 8.060720e-03 2.131742e-04
|
||||
8.000000e+02 8.000000e+02 9.200000e+02 9.600000e+02 1.214790e-03 1.168034e-04
|
||||
8.000000e+02 8.000000e+02 9.600000e+02 1.000000e+03 7.165130e-04 1.084968e-04
|
||||
8.000000e+02 8.000000e+02 1.000000e+03 1.040000e+03 6.247460e-04 1.072518e-04
|
||||
8.000000e+02 8.000000e+02 1.040000e+03 1.080000e+03 4.231240e-04 1.048386e-04
|
||||
8.000000e+02 8.000000e+02 1.080000e+03 1.120000e+03 2.892700e-04 1.032726e-04
|
||||
8.000000e+02 8.000000e+02 1.120000e+03 1.160000e+03 1.934370e-04 1.021683e-04
|
||||
|
||||
8.400000e+02 8.400000e+02 8.800000e+02 9.200000e+02 2.038510e-02 3.912738e-04
|
||||
8.400000e+02 8.400000e+02 9.200000e+02 9.600000e+02 2.654280e-03 1.386408e-04
|
||||
8.400000e+02 8.400000e+02 9.600000e+02 1.000000e+03 1.170870e-03 1.144700e-04
|
||||
8.400000e+02 8.400000e+02 1.000000e+03 1.040000e+03 9.524550e-04 1.113517e-04
|
||||
8.400000e+02 8.400000e+02 1.040000e+03 1.080000e+03 6.124800e-04 1.071236e-04
|
||||
8.400000e+02 8.400000e+02 1.080000e+03 1.120000e+03 4.035790e-04 1.046216e-04
|
||||
8.400000e+02 8.400000e+02 1.120000e+03 1.160000e+03 2.624710e-04 1.029722e-04
|
||||
|
||||
8.800000e+02 8.800000e+02 9.200000e+02 9.600000e+02 8.804060e-03 2.171587e-04
|
||||
8.800000e+02 8.800000e+02 9.600000e+02 1.000000e+03 2.158930e-03 1.282763e-04
|
||||
8.800000e+02 8.800000e+02 1.000000e+03 1.040000e+03 1.563470e-03 1.194463e-04
|
||||
8.800000e+02 8.800000e+02 1.040000e+03 1.080000e+03 9.314900e-04 1.111259e-04
|
||||
8.800000e+02 8.800000e+02 1.080000e+03 1.120000e+03 5.834130e-04 1.067965e-04
|
||||
8.800000e+02 8.800000e+02 1.120000e+03 1.160000e+03 3.660430e-04 1.041972e-04
|
||||
|
||||
9.200000e+02 9.200000e+02 9.600000e+02 1.000000e+03 5.100800e-03 1.672293e-04
|
||||
9.200000e+02 9.200000e+02 1.000000e+03 1.040000e+03 2.798410e-03 1.371182e-04
|
||||
9.200000e+02 9.200000e+02 1.040000e+03 1.080000e+03 1.463010e-03 1.184032e-04
|
||||
9.200000e+02 9.200000e+02 1.080000e+03 1.120000e+03 8.516570e-04 1.102887e-04
|
||||
9.200000e+02 9.200000e+02 1.120000e+03 1.160000e+03 5.099430e-04 1.060073e-04
|
||||
|
||||
9.600000e+02 9.600000e+02 1.000000e+03 1.040000e+03 5.065130e-03 1.704459e-04
|
||||
9.600000e+02 9.600000e+02 1.040000e+03 1.080000e+03 2.120460e-03 1.286383e-04
|
||||
9.600000e+02 9.600000e+02 1.080000e+03 1.120000e+03 1.122450e-03 1.142730e-04
|
||||
9.600000e+02 9.600000e+02 1.120000e+03 1.160000e+03 6.382340e-04 1.077896e-04
|
||||
|
||||
1.000000e+03 1.000000e+03 1.040000e+03 1.080000e+03 1.460590e-02 3.057350e-04
|
||||
1.000000e+03 1.000000e+03 1.080000e+03 1.120000e+03 5.002950e-03 1.710195e-04
|
||||
1.000000e+03 1.000000e+03 1.120000e+03 1.160000e+03 2.257870e-03 1.300949e-04
|
||||
|
||||
1.040000e+03 1.040000e+03 1.080000e+03 1.120000e+03 1.670000e-02 3.354771e-04
|
||||
1.040000e+03 1.040000e+03 1.120000e+03 1.160000e+03 5.840630e-03 1.823299e-04
|
||||
|
||||
1.080000e+03 1.080000e+03 1.120000e+03 1.160000e+03 1.688390e-02 3.379402e-04
|
||||
@@ -0,0 +1,301 @@
|
||||
! GENERAL FORMAT
|
||||
|
||||
|
||||
5.100000e+02 1.215100e+04 0.000000e+00 5.100000e+02 1.215100e+04 0.000000e+00 9
|
||||
5.500000e+02 1.215100e+04 0.000000e+00 5.900000e+02 1.215100e+04 0.000000e+00 4.536103e-01 4.636103e-03
|
||||
5.900000e+02 1.215100e+04 0.000000e+00 6.300000e+02 1.215100e+04 0.000000e+00 1.956283e-01 2.056283e-03
|
||||
6.300000e+02 1.215100e+04 0.000000e+00 6.700000e+02 1.215100e+04 0.000000e+00 9.661533e-02 1.066153e-03
|
||||
6.700000e+02 1.215100e+04 0.000000e+00 7.100000e+02 1.215100e+04 0.000000e+00 5.443205e-03 1.544321e-04
|
||||
7.100000e+02 1.215100e+04 0.000000e+00 7.500000e+02 1.215100e+04 0.000000e+00 2.977518e-03 1.297752e-04
|
||||
7.500000e+02 1.215100e+04 0.000000e+00 7.900000e+02 1.215100e+04 0.000000e+00 3.113318e-03 1.311332e-04
|
||||
7.900000e+02 1.215100e+04 0.000000e+00 8.300000e+02 1.215100e+04 0.000000e+00 7.216380e-03 1.721638e-04
|
||||
8.300000e+02 1.215100e+04 0.000000e+00 8.700000e+02 1.215100e+04 0.000000e+00 6.475000e-03 1.647500e-04
|
||||
8.700000e+02 1.215100e+04 0.000000e+00 9.100000e+02 1.215100e+04 0.000000e+00 4.750858e-03 1.475086e-04
|
||||
|
||||
|
||||
5.500000e+02 1.215100e+04 0.000000e+00 5.500000e+02 1.215100e+04 0.000000e+00 9
|
||||
5.900000e+02 1.215100e+04 0.000000e+00 6.300000e+02 1.215100e+04 0.000000e+00 4.737093e-01 4.837093e-03
|
||||
6.300000e+02 1.215100e+04 0.000000e+00 6.700000e+02 1.215100e+04 0.000000e+00 1.933017e-01 2.033017e-03
|
||||
6.700000e+02 1.215100e+04 0.000000e+00 7.100000e+02 1.215100e+04 0.000000e+00 9.625111e-03 1.962511e-04
|
||||
7.100000e+02 1.215100e+04 0.000000e+00 7.500000e+02 1.215100e+04 0.000000e+00 4.710170e-03 1.471017e-04
|
||||
7.500000e+02 1.215100e+04 0.000000e+00 7.900000e+02 1.215100e+04 0.000000e+00 4.146907e-03 1.414691e-04
|
||||
7.900000e+02 1.215100e+04 0.000000e+00 8.300000e+02 1.215100e+04 0.000000e+00 9.014465e-03 1.901446e-04
|
||||
8.300000e+02 1.215100e+04 0.000000e+00 8.700000e+02 1.215100e+04 0.000000e+00 7.875920e-03 1.787592e-04
|
||||
8.700000e+02 1.215100e+04 0.000000e+00 9.100000e+02 1.215100e+04 0.000000e+00 5.711976e-03 1.571198e-04
|
||||
9.100000e+02 1.215100e+04 0.000000e+00 9.500000e+02 1.215100e+04 0.000000e+00 6.259850e-04 1.062599e-04
|
||||
|
||||
|
||||
5.900000e+02 1.215100e+04 0.000000e+00 5.900000e+02 1.215100e+04 0.000000e+00 9
|
||||
6.300000e+02 1.215100e+04 0.000000e+00 6.700000e+02 1.215100e+04 0.000000e+00 4.956306e-01 5.056306e-03
|
||||
6.700000e+02 1.215100e+04 0.000000e+00 7.100000e+02 1.215100e+04 0.000000e+00 2.048511e-02 3.048511e-04
|
||||
7.100000e+02 1.215100e+04 0.000000e+00 7.500000e+02 1.215100e+04 0.000000e+00 8.575637e-03 1.857564e-04
|
||||
7.500000e+02 1.215100e+04 0.000000e+00 7.900000e+02 1.215100e+04 0.000000e+00 6.032524e-03 1.603252e-04
|
||||
7.900000e+02 1.215100e+04 0.000000e+00 8.300000e+02 1.215100e+04 0.000000e+00 1.189394e-02 2.189394e-04
|
||||
8.300000e+02 1.215100e+04 0.000000e+00 8.700000e+02 1.215100e+04 0.000000e+00 9.982800e-03 1.998280e-04
|
||||
8.700000e+02 1.215100e+04 0.000000e+00 9.100000e+02 1.215100e+04 0.000000e+00 7.117008e-03 1.711701e-04
|
||||
9.100000e+02 1.215100e+04 0.000000e+00 9.500000e+02 1.215100e+04 0.000000e+00 7.649935e-04 1.076499e-04
|
||||
9.500000e+02 1.215100e+04 0.000000e+00 9.900000e+02 1.215100e+04 0.000000e+00 6.193333e-04 1.061933e-04
|
||||
|
||||
|
||||
6.300000e+02 1.215100e+04 0.000000e+00 6.300000e+02 1.215100e+04 0.000000e+00 9
|
||||
6.700000e+02 1.215100e+04 0.000000e+00 7.100000e+02 1.215100e+04 0.000000e+00 5.597631e-02 6.597631e-04
|
||||
7.100000e+02 1.215100e+04 0.000000e+00 7.500000e+02 1.215100e+04 0.000000e+00 1.877787e-02 2.877787e-04
|
||||
7.500000e+02 1.215100e+04 0.000000e+00 7.900000e+02 1.215100e+04 0.000000e+00 9.908609e-03 1.990861e-04
|
||||
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7.500000e+02 1.215100e+04 0.000000e+00 7.500000e+02 1.215100e+04 0.000000e+00 9
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7.900000e+02 1.215100e+04 0.000000e+00 7.900000e+02 1.215100e+04 0.000000e+00 9
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8.300000e+02 1.215100e+04 0.000000e+00 8.300000e+02 1.215100e+04 0.000000e+00 9
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8.700000e+02 1.215100e+04 0.000000e+00 8.700000e+02 1.215100e+04 0.000000e+00 9
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1.030000e+03 1.215100e+04 0.000000e+00 1.030000e+03 1.215100e+04 0.000000e+00 9
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1.070000e+03 1.215100e+04 0.000000e+00 1.070000e+03 1.215100e+04 0.000000e+00 9
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1.110000e+03 1.215100e+04 0.000000e+00 1.110000e+03 1.215100e+04 0.000000e+00 9
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1.150000e+03 1.215100e+04 0.000000e+00 1.190000e+03 1.215100e+04 0.000000e+00 2.604130e-02 3.604130e-04
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|
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|
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1.150000e+03 1.215100e+04 0.000000e+00 1.150000e+03 1.215100e+04 0.000000e+00 9
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1.190000e+03 1.215100e+04 0.000000e+00 1.230000e+03 1.215100e+04 0.000000e+00 2.598448e-02 3.598448e-04
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
1.470000e+03 1.215100e+04 0.000000e+00 1.510000e+03 1.215100e+04 0.000000e+00 1.990072e-04 1.019901e-04
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 1.962003e-04 1.019620e-04
|
||||
|
||||
|
||||
1.190000e+03 1.215100e+04 0.000000e+00 1.190000e+03 1.215100e+04 0.000000e+00 9
|
||||
1.230000e+03 1.215100e+04 0.000000e+00 1.270000e+03 1.215100e+04 0.000000e+00 2.601381e-02 3.601381e-04
|
||||
1.270000e+03 1.215100e+04 0.000000e+00 1.310000e+03 1.215100e+04 0.000000e+00 1.021743e-02 2.021743e-04
|
||||
1.310000e+03 1.215100e+04 0.000000e+00 1.350000e+03 1.215100e+04 0.000000e+00 5.202320e-03 1.520232e-04
|
||||
1.350000e+03 1.215100e+04 0.000000e+00 1.390000e+03 1.215100e+04 0.000000e+00 3.160360e-03 1.316036e-04
|
||||
1.390000e+03 1.215100e+04 0.000000e+00 1.430000e+03 1.215100e+04 0.000000e+00 1.950870e-03 1.195087e-04
|
||||
1.430000e+03 1.215100e+04 0.000000e+00 1.470000e+03 1.215100e+04 0.000000e+00 3.326250e-04 1.033263e-04
|
||||
1.470000e+03 1.215100e+04 0.000000e+00 1.510000e+03 1.215100e+04 0.000000e+00 2.688421e-04 1.026884e-04
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 2.591515e-04 1.025915e-04
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 1.904861e-04 1.019049e-04
|
||||
|
||||
|
||||
1.230000e+03 1.215100e+04 0.000000e+00 1.230000e+03 1.215100e+04 0.000000e+00 9
|
||||
1.270000e+03 1.215100e+04 0.000000e+00 1.310000e+03 1.215100e+04 0.000000e+00 2.616280e-02 3.616280e-04
|
||||
1.310000e+03 1.215100e+04 0.000000e+00 1.350000e+03 1.215100e+04 0.000000e+00 1.043353e-02 2.043353e-04
|
||||
1.350000e+03 1.215100e+04 0.000000e+00 1.390000e+03 1.215100e+04 0.000000e+00 5.460042e-03 1.546004e-04
|
||||
1.390000e+03 1.215100e+04 0.000000e+00 1.430000e+03 1.215100e+04 0.000000e+00 3.137251e-03 1.313725e-04
|
||||
1.430000e+03 1.215100e+04 0.000000e+00 1.470000e+03 1.215100e+04 0.000000e+00 5.203728e-04 1.052037e-04
|
||||
1.470000e+03 1.215100e+04 0.000000e+00 1.510000e+03 1.215100e+04 0.000000e+00 3.766559e-04 1.037666e-04
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 3.529782e-04 1.035298e-04
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 2.532919e-04 1.025329e-04
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.630000e+03 1.215100e+04 0.000000e+00 1.807361e-04 1.018074e-04
|
||||
|
||||
|
||||
1.270000e+03 1.215100e+04 0.000000e+00 1.270000e+03 1.215100e+04 0.000000e+00 9
|
||||
1.310000e+03 1.215100e+04 0.000000e+00 1.350000e+03 1.215100e+04 0.000000e+00 2.629864e-02 3.629864e-04
|
||||
1.350000e+03 1.215100e+04 0.000000e+00 1.390000e+03 1.215100e+04 0.000000e+00 1.081983e-02 2.081983e-04
|
||||
1.390000e+03 1.215100e+04 0.000000e+00 1.430000e+03 1.215100e+04 0.000000e+00 5.511974e-03 1.551197e-04
|
||||
1.430000e+03 1.215100e+04 0.000000e+00 1.470000e+03 1.215100e+04 0.000000e+00 8.761073e-04 1.087611e-04
|
||||
1.470000e+03 1.215100e+04 0.000000e+00 1.510000e+03 1.215100e+04 0.000000e+00 5.463970e-04 1.054640e-04
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 4.928757e-04 1.049288e-04
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 3.429987e-04 1.034300e-04
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.630000e+03 1.215100e+04 0.000000e+00 2.390966e-04 1.023910e-04
|
||||
1.630000e+03 1.215100e+04 0.000000e+00 1.670000e+03 1.215100e+04 0.000000e+00 1.621222e-04 1.016212e-04
|
||||
|
||||
|
||||
1.310000e+03 1.215100e+04 0.000000e+00 1.310000e+03 1.215100e+04 0.000000e+00 8
|
||||
1.350000e+03 1.215100e+04 0.000000e+00 1.390000e+03 1.215100e+04 0.000000e+00 2.725269e-02 3.725269e-04
|
||||
1.390000e+03 1.215100e+04 0.000000e+00 1.430000e+03 1.215100e+04 0.000000e+00 1.131742e-02 2.131742e-04
|
||||
1.430000e+03 1.215100e+04 0.000000e+00 1.470000e+03 1.215100e+04 0.000000e+00 1.680339e-03 1.168034e-04
|
||||
1.470000e+03 1.215100e+04 0.000000e+00 1.510000e+03 1.215100e+04 0.000000e+00 8.496814e-04 1.084968e-04
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 7.251771e-04 1.072518e-04
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 4.838564e-04 1.048386e-04
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.630000e+03 1.215100e+04 0.000000e+00 3.272610e-04 1.032726e-04
|
||||
1.630000e+03 1.215100e+04 0.000000e+00 1.670000e+03 1.215100e+04 0.000000e+00 2.168282e-04 1.021683e-04
|
||||
|
||||
|
||||
1.350000e+03 1.215100e+04 0.000000e+00 1.350000e+03 1.215100e+04 0.000000e+00 7
|
||||
1.390000e+03 1.215100e+04 0.000000e+00 1.430000e+03 1.215100e+04 0.000000e+00 2.912738e-02 3.912738e-04
|
||||
1.430000e+03 1.215100e+04 0.000000e+00 1.470000e+03 1.215100e+04 0.000000e+00 3.864081e-03 1.386408e-04
|
||||
1.470000e+03 1.215100e+04 0.000000e+00 1.510000e+03 1.215100e+04 0.000000e+00 1.447001e-03 1.144700e-04
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 1.135173e-03 1.113517e-04
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 7.123625e-04 1.071236e-04
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.630000e+03 1.215100e+04 0.000000e+00 4.621593e-04 1.046216e-04
|
||||
1.630000e+03 1.215100e+04 0.000000e+00 1.670000e+03 1.215100e+04 0.000000e+00 2.972157e-04 1.029722e-04
|
||||
|
||||
|
||||
1.390000e+03 1.215100e+04 0.000000e+00 1.390000e+03 1.215100e+04 0.000000e+00 6
|
||||
1.430000e+03 1.215100e+04 0.000000e+00 1.470000e+03 1.215100e+04 0.000000e+00 1.171587e-02 2.171587e-04
|
||||
1.470000e+03 1.215100e+04 0.000000e+00 1.510000e+03 1.215100e+04 0.000000e+00 2.827625e-03 1.282763e-04
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 1.944627e-03 1.194463e-04
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 1.112595e-03 1.111259e-04
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.630000e+03 1.215100e+04 0.000000e+00 6.796523e-04 1.067965e-04
|
||||
1.630000e+03 1.215100e+04 0.000000e+00 1.670000e+03 1.215100e+04 0.000000e+00 4.197216e-04 1.041972e-04
|
||||
|
||||
|
||||
1.430000e+03 1.215100e+04 0.000000e+00 1.430000e+03 1.215100e+04 0.000000e+00 5
|
||||
1.470000e+03 1.215100e+04 0.000000e+00 1.510000e+03 1.215100e+04 0.000000e+00 6.722928e-03 1.672293e-04
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 3.711820e-03 1.371182e-04
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 1.840319e-03 1.184032e-04
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.630000e+03 1.215100e+04 0.000000e+00 1.028874e-03 1.102887e-04
|
||||
1.630000e+03 1.215100e+04 0.000000e+00 1.670000e+03 1.215100e+04 0.000000e+00 6.007261e-04 1.060073e-04
|
||||
|
||||
|
||||
1.470000e+03 1.215100e+04 0.000000e+00 1.470000e+03 1.215100e+04 0.000000e+00 4
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 7.044588e-03 1.704459e-04
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 2.863830e-03 1.286383e-04
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.630000e+03 1.215100e+04 0.000000e+00 1.427305e-03 1.142730e-04
|
||||
1.630000e+03 1.215100e+04 0.000000e+00 1.670000e+03 1.215100e+04 0.000000e+00 7.789635e-04 1.077896e-04
|
||||
|
||||
|
||||
1.510000e+03 1.215100e+04 0.000000e+00 1.510000e+03 1.215100e+04 0.000000e+00 3
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 2.057350e-02 3.057350e-04
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.630000e+03 1.215100e+04 0.000000e+00 7.101947e-03 1.710195e-04
|
||||
1.630000e+03 1.215100e+04 0.000000e+00 1.670000e+03 1.215100e+04 0.000000e+00 3.009493e-03 1.300949e-04
|
||||
|
||||
|
||||
1.550000e+03 1.215100e+04 0.000000e+00 1.550000e+03 1.215100e+04 0.000000e+00 2
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.630000e+03 1.215100e+04 0.000000e+00 2.354771e-02 3.354771e-04
|
||||
1.630000e+03 1.215100e+04 0.000000e+00 1.670000e+03 1.215100e+04 0.000000e+00 8.232986e-03 1.823299e-04
|
||||
|
||||
|
||||
1.590000e+03 1.215100e+04 0.000000e+00 1.590000e+03 1.215100e+04 0.000000e+00 1
|
||||
1.630000e+03 1.215100e+04 0.000000e+00 1.670000e+03 1.215100e+04 0.000000e+00 2.379402e-02 3.379402e-04
|
||||
@@ -0,0 +1,273 @@
|
||||
! dc data
|
||||
|
||||
5.100000E+02 1.215100E+04 0.000000E+00 5.100000E+02 1.215100E+04 0.000000E+00 9
|
||||
5.500000E+02 1.215100E+04 0.000000E+00 5.900000E+02 1.215100E+04 0.000000E+00 3.13469E-01 6.34653E-03
|
||||
5.900000E+02 1.215100E+04 0.000000E+00 6.300000E+02 1.215100E+04 0.000000E+00 1.38827E-01 4.77678E-03
|
||||
6.300000E+02 1.215100E+04 0.000000E+00 6.700000E+02 1.215100E+04 0.000000E+00 7.06585E-02 4.69261E-03
|
||||
6.700000E+02 1.215100E+04 0.000000E+00 7.100000E+02 1.215100E+04 0.000000E+00 4.18814E-03 4.75017E-02
|
||||
7.100000E+02 1.215100E+04 0.000000E+00 7.500000E+02 1.215100E+04 0.000000E+00 2.41516E-03 5.49152E-02
|
||||
7.500000E+02 1.215100E+04 0.000000E+00 7.900000E+02 1.215100E+04 0.000000E+00 2.73989E-03 3.45762E-02
|
||||
7.900000E+02 1.215100E+04 0.000000E+00 8.300000E+02 1.215100E+04 0.000000E+00 6.53041E-03 1.08801E-02
|
||||
8.300000E+02 1.215100E+04 0.000000E+00 8.700000E+02 1.215100E+04 0.000000E+00 5.93260E-03 9.31499E-03
|
||||
8.700000E+02 1.215100E+04 0.000000E+00 9.100000E+02 1.215100E+04 0.000000E+00 4.37841E-03 1.00972E-02
|
||||
|
||||
5.500000E+02 1.215100E+04 0.000000E+00 5.500000E+02 1.215100E+04 0.000000E+00 9
|
||||
5.900000E+02 1.215100E+04 0.000000E+00 6.300000E+02 1.215100E+04 0.000000E+00 3.31153E-01 6.00761E-03
|
||||
6.300000E+02 1.215100E+04 0.000000E+00 6.700000E+02 1.215100E+04 0.000000E+00 1.34604E-01 4.92665E-03
|
||||
6.700000E+02 1.215100E+04 0.000000E+00 7.100000E+02 1.215100E+04 0.000000E+00 7.11468E-03 4.66040E-02
|
||||
7.100000E+02 1.215100E+04 0.000000E+00 7.500000E+02 1.215100E+04 0.000000E+00 3.71282E-03 5.35829E-02
|
||||
7.500000E+02 1.215100E+04 0.000000E+00 7.900000E+02 1.215100E+04 0.000000E+00 3.59002E-03 3.69439E-02
|
||||
7.900000E+02 1.215100E+04 0.000000E+00 8.300000E+02 1.215100E+04 0.000000E+00 8.08873E-03 1.17120E-02
|
||||
8.300000E+02 1.215100E+04 0.000000E+00 8.700000E+02 1.215100E+04 0.000000E+00 7.17608E-03 9.90113E-03
|
||||
8.700000E+02 1.215100E+04 0.000000E+00 9.100000E+02 1.215100E+04 0.000000E+00 5.24072E-03 1.05448E-02
|
||||
9.100000E+02 1.215100E+04 0.000000E+00 9.500000E+02 1.215100E+04 0.000000E+00 5.80316E-04 7.61821E-02
|
||||
|
||||
5.900000E+02 1.215100E+04 0.000000E+00 5.900000E+02 1.215100E+04 0.000000E+00 9
|
||||
6.300000E+02 1.215100E+04 0.000000E+00 6.700000E+02 1.215100E+04 0.000000E+00 3.38777E-01 5.87241E-03
|
||||
6.700000E+02 1.215100E+04 0.000000E+00 7.100000E+02 1.215100E+04 0.000000E+00 1.41779E-02 4.67732E-02
|
||||
7.100000E+02 1.215100E+04 0.000000E+00 7.500000E+02 1.215100E+04 0.000000E+00 6.36352E-03 5.21052E-02
|
||||
7.500000E+02 1.215100E+04 0.000000E+00 7.900000E+02 1.215100E+04 0.000000E+00 5.00863E-03 3.97201E-02
|
||||
7.900000E+02 1.215100E+04 0.000000E+00 8.300000E+02 1.215100E+04 0.000000E+00 1.03904E-02 1.27645E-02
|
||||
8.300000E+02 1.215100E+04 0.000000E+00 8.700000E+02 1.215100E+04 0.000000E+00 8.90594E-03 1.06373E-02
|
||||
8.700000E+02 1.215100E+04 0.000000E+00 9.100000E+02 1.215100E+04 0.000000E+00 6.40731E-03 1.10891E-02
|
||||
9.100000E+02 1.215100E+04 0.000000E+00 9.500000E+02 1.215100E+04 0.000000E+00 6.96759E-04 7.93131E-02
|
||||
9.500000E+02 1.215100E+04 0.000000E+00 9.900000E+02 1.215100E+04 0.000000E+00 5.70670E-04 7.74698E-02
|
||||
|
||||
6.300000E+02 1.215100E+04 0.000000E+00 6.300000E+02 1.215100E+04 0.000000E+00 9
|
||||
6.700000E+02 1.215100E+04 0.000000E+00 7.100000E+02 1.215100E+04 0.000000E+00 3.84747E-02 5.17076E-02
|
||||
7.100000E+02 1.215100E+04 0.000000E+00 7.500000E+02 1.215100E+04 0.000000E+00 1.29512E-02 5.12036E-02
|
||||
7.500000E+02 1.215100E+04 0.000000E+00 7.900000E+02 1.215100E+04 0.000000E+00 7.71709E-03 4.29660E-02
|
||||
7.900000E+02 1.215100E+04 0.000000E+00 8.300000E+02 1.215100E+04 0.000000E+00 1.40850E-02 1.41245E-02
|
||||
8.300000E+02 1.215100E+04 0.000000E+00 8.700000E+02 1.215100E+04 0.000000E+00 1.14491E-02 1.15842E-02
|
||||
8.700000E+02 1.215100E+04 0.000000E+00 9.100000E+02 1.215100E+04 0.000000E+00 8.05298E-03 1.17640E-02
|
||||
9.100000E+02 1.215100E+04 0.000000E+00 9.500000E+02 1.215100E+04 0.000000E+00 8.54543E-04 8.31454E-02
|
||||
9.500000E+02 1.215100E+04 0.000000E+00 9.900000E+02 1.215100E+04 0.000000E+00 6.84297E-04 8.07575E-02
|
||||
9.900000E+02 1.215100E+04 0.000000E+00 1.030000E+03 1.215100E+04 0.000000E+00 5.55272E-04 7.96181E-02
|
||||
|
||||
6.700000E+02 1.215100E+04 0.000000E+00 6.700000E+02 1.215100E+04 0.000000E+00 9
|
||||
7.100000E+02 1.215100E+04 0.000000E+00 7.500000E+02 1.215100E+04 0.000000E+00 2.89383E-02 6.87475E-02
|
||||
7.500000E+02 1.215100E+04 0.000000E+00 7.900000E+02 1.215100E+04 0.000000E+00 1.22791E-02 5.40059E-02
|
||||
7.900000E+02 1.215100E+04 0.000000E+00 8.300000E+02 1.215100E+04 0.000000E+00 1.90698E-02 1.73873E-02
|
||||
8.300000E+02 1.215100E+04 0.000000E+00 8.700000E+02 1.215100E+04 0.000000E+00 1.45174E-02 1.37038E-02
|
||||
8.700000E+02 1.215100E+04 0.000000E+00 9.100000E+02 1.215100E+04 0.000000E+00 9.93506E-03 1.33496E-02
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|
||||
1.470000E+03 1.215100E+04 0.000000E+00 1.510000E+03 1.215100E+04 0.000000E+00 2.37045E-04 2.99738E-01
|
||||
1.510000E+03 1.215100E+04 0.000000E+00 1.550000E+03 1.215100E+04 0.000000E+00 2.30043E-04 2.40225E-01
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 1.70343E-04 2.59534E-01
|
||||
|
||||
1.230000E+03 1.215100E+04 0.000000E+00 1.230000E+03 1.215100E+04 0.000000E+00 9
|
||||
1.270000E+03 1.215100E+04 0.000000E+00 1.310000E+03 1.215100E+04 0.000000E+00 1.87497E-02 1.06105E-01
|
||||
1.310000E+03 1.215100E+04 0.000000E+00 1.350000E+03 1.215100E+04 0.000000E+00 7.67947E-03 8.63531E-02
|
||||
1.350000E+03 1.215100E+04 0.000000E+00 1.390000E+03 1.215100E+04 0.000000E+00 4.31309E-03 7.68759E-02
|
||||
1.390000E+03 1.215100E+04 0.000000E+00 1.430000E+03 1.215100E+04 0.000000E+00 2.45670E-03 8.09801E-02
|
||||
1.430000E+03 1.215100E+04 0.000000E+00 1.470000E+03 1.215100E+04 0.000000E+00 4.06721E-04 3.26093E-01
|
||||
1.470000E+03 1.215100E+04 0.000000E+00 1.510000E+03 1.215100E+04 0.000000E+00 3.28194E-04 2.88656E-01
|
||||
1.510000E+03 1.215100E+04 0.000000E+00 1.550000E+03 1.215100E+04 0.000000E+00 3.10206E-04 2.29046E-01
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 2.24393E-04 2.46273E-01
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.630000E+03 1.215100E+04 0.000000E+00 1.61391E-04 2.73929E-01
|
||||
|
||||
1.270000E+03 1.215100E+04 0.000000E+00 1.270000E+03 1.215100E+04 0.000000E+00 9
|
||||
1.310000E+03 1.215100E+04 0.000000E+00 1.350000E+03 1.215100E+04 0.000000E+00 1.90718E-02 1.04313E-01
|
||||
1.350000E+03 1.215100E+04 0.000000E+00 1.390000E+03 1.215100E+04 0.000000E+00 8.08711E-03 8.20003E-02
|
||||
1.390000E+03 1.215100E+04 0.000000E+00 1.430000E+03 1.215100E+04 0.000000E+00 4.16910E-03 7.95310E-02
|
||||
1.430000E+03 1.215100E+04 0.000000E+00 1.470000E+03 1.215100E+04 0.000000E+00 6.67373E-04 2.98100E-01
|
||||
1.470000E+03 1.215100E+04 0.000000E+00 1.510000E+03 1.215100E+04 0.000000E+00 4.72620E-04 2.80625E-01
|
||||
1.510000E+03 1.215100E+04 0.000000E+00 1.550000E+03 1.215100E+04 0.000000E+00 4.31826E-04 2.19383E-01
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 3.03578E-04 2.34046E-01
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.630000E+03 1.215100E+04 0.000000E+00 2.13442E-04 2.58909E-01
|
||||
1.630000E+03 1.215100E+04 0.000000E+00 1.670000E+03 1.215100E+04 0.000000E+00 1.45998E-04 3.02811E-01
|
||||
|
||||
1.310000E+03 1.215100E+04 0.000000E+00 1.310000E+03 1.215100E+04 0.000000E+00 8
|
||||
1.350000E+03 1.215100E+04 0.000000E+00 1.390000E+03 1.215100E+04 0.000000E+00 1.98653E-02 1.00147E-01
|
||||
1.390000E+03 1.215100E+04 0.000000E+00 1.430000E+03 1.215100E+04 0.000000E+00 8.06072E-03 8.22687E-02
|
||||
1.430000E+03 1.215100E+04 0.000000E+00 1.470000E+03 1.215100E+04 0.000000E+00 1.21479E-03 2.72946E-01
|
||||
1.470000E+03 1.215100E+04 0.000000E+00 1.510000E+03 1.215100E+04 0.000000E+00 7.16513E-04 2.77655E-01
|
||||
1.510000E+03 1.215100E+04 0.000000E+00 1.550000E+03 1.215100E+04 0.000000E+00 6.24746E-04 2.12293E-01
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 4.23124E-04 2.23894E-01
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.630000E+03 1.215100E+04 0.000000E+00 2.89270E-04 2.45622E-01
|
||||
1.630000E+03 1.215100E+04 0.000000E+00 1.670000E+03 1.215100E+04 0.000000E+00 1.93437E-04 2.85686E-01
|
||||
|
||||
1.350000E+03 1.215100E+04 0.000000E+00 1.350000E+03 1.215100E+04 0.000000E+00 7
|
||||
1.390000E+03 1.215100E+04 0.000000E+00 1.430000E+03 1.215100E+04 0.000000E+00 2.03851E-02 9.75925E-02
|
||||
1.430000E+03 1.215100E+04 0.000000E+00 1.470000E+03 1.215100E+04 0.000000E+00 2.65428E-03 2.49840E-01
|
||||
1.470000E+03 1.215100E+04 0.000000E+00 1.510000E+03 1.215100E+04 0.000000E+00 1.17087E-03 2.83186E-01
|
||||
1.510000E+03 1.215100E+04 0.000000E+00 1.550000E+03 1.215100E+04 0.000000E+00 9.52455E-04 2.08875E-01
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 6.12480E-04 2.16545E-01
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.630000E+03 1.215100E+04 0.000000E+00 4.03579E-04 2.34738E-01
|
||||
1.630000E+03 1.215100E+04 0.000000E+00 1.670000E+03 1.215100E+04 0.000000E+00 2.62471E-04 2.70702E-01
|
||||
|
||||
1.390000E+03 1.215100E+04 0.000000E+00 1.390000E+03 1.215100E+04 0.000000E+00 6
|
||||
1.430000E+03 1.215100E+04 0.000000E+00 1.470000E+03 1.215100E+04 0.000000E+00 8.80406E-03 2.25968E-01
|
||||
1.470000E+03 1.215100E+04 0.000000E+00 1.510000E+03 1.215100E+04 0.000000E+00 2.15893E-03 3.07164E-01
|
||||
1.510000E+03 1.215100E+04 0.000000E+00 1.550000E+03 1.215100E+04 0.000000E+00 1.56347E-03 2.12075E-01
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 9.31490E-04 2.13576E-01
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.630000E+03 1.215100E+04 0.000000E+00 5.83413E-04 2.27333E-01
|
||||
1.630000E+03 1.215100E+04 0.000000E+00 1.670000E+03 1.215100E+04 0.000000E+00 3.66043E-04 2.58809E-01
|
||||
|
||||
1.430000E+03 1.215100E+04 0.000000E+00 1.430000E+03 1.215100E+04 0.000000E+00 5
|
||||
1.470000E+03 1.215100E+04 0.000000E+00 1.510000E+03 1.215100E+04 0.000000E+00 5.10080E-03 3.90025E-01
|
||||
1.510000E+03 1.215100E+04 0.000000E+00 1.550000E+03 1.215100E+04 0.000000E+00 2.79841E-03 2.36972E-01
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 1.46301E-03 2.26638E-01
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.630000E+03 1.215100E+04 0.000000E+00 8.51657E-04 2.33596E-01
|
||||
1.630000E+03 1.215100E+04 0.000000E+00 1.670000E+03 1.215100E+04 0.000000E+00 5.09943E-04 2.60086E-01
|
||||
|
||||
1.470000E+03 1.215100E+04 0.000000E+00 1.470000E+03 1.215100E+04 0.000000E+00 4
|
||||
1.510000E+03 1.215100E+04 0.000000E+00 1.550000E+03 1.215100E+04 0.000000E+00 5.06513E-03 3.92771E-01
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 2.12046E-03 3.12736E-01
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.630000E+03 1.215100E+04 0.000000E+00 1.12245E-03 2.95402E-01
|
||||
1.630000E+03 1.215100E+04 0.000000E+00 1.670000E+03 1.215100E+04 0.000000E+00 6.38234E-04 3.11709E-01
|
||||
|
||||
1.510000E+03 1.215100E+04 0.000000E+00 1.510000E+03 1.215100E+04 0.000000E+00 3
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 1.46059E-02 1.36208E-01
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.630000E+03 1.215100E+04 0.000000E+00 5.00295E-03 1.32551E-01
|
||||
1.630000E+03 1.215100E+04 0.000000E+00 1.670000E+03 1.215100E+04 0.000000E+00 2.25787E-03 1.46852E-01
|
||||
|
||||
1.550000E+03 1.215100E+04 0.000000E+00 1.550000E+03 1.215100E+04 0.000000E+00 2
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.630000E+03 1.215100E+04 0.000000E+00 1.67000E-02 1.19128E-01
|
||||
1.630000E+03 1.215100E+04 0.000000E+00 1.670000E+03 1.215100E+04 0.000000E+00 5.84063E-03 1.13540E-01
|
||||
|
||||
1.590000E+03 1.215100E+04 0.000000E+00 1.590000E+03 1.215100E+04 0.000000E+00 1
|
||||
1.630000E+03 1.215100E+04 0.000000E+00 1.670000E+03 1.215100E+04 0.000000E+00 1.68839E-02 1.17831E-01
|
||||
@@ -0,0 +1,9 @@
|
||||
dc
|
||||
Mesh_20m.msh
|
||||
OBS_LOC_3D.dat
|
||||
MtIsa_3D.con
|
||||
VALUE 0.0
|
||||
null
|
||||
0
|
||||
1e-5
|
||||
-1
|
||||
@@ -0,0 +1,55 @@
|
||||
! GENERAL FORMAT
|
||||
|
||||
0.000000e+00 0.000000e+00 2.000000e+02 3.000000e+02 8.353735e-02 9.353735e-04
|
||||
0.000000e+00 0.000000e+00 3.000000e+02 4.000000e+02 4.392504e-03 1.439250e-04
|
||||
0.000000e+00 0.000000e+00 4.000000e+02 5.000000e+02 1.243627e-02 2.243627e-04
|
||||
0.000000e+00 0.000000e+00 5.000000e+02 6.000000e+02 2.058920e-03 1.205892e-04
|
||||
0.000000e+00 0.000000e+00 6.000000e+02 7.000000e+02 6.812879e-04 1.068129e-04
|
||||
0.000000e+00 0.000000e+00 7.000000e+02 8.000000e+02 4.313826e-04 1.043138e-04
|
||||
0.000000e+00 0.000000e+00 8.000000e+02 9.000000e+02 2.625458e-04 1.026255e-04
|
||||
0.000000e+00 0.000000e+00 9.000000e+02 1.000000e+03 1.651300e-04 1.016513e-04
|
||||
0.000000e+00 0.000000e+00 1.000000e+03 1.100000e+03 5.231997e-05 1.005232e-04
|
||||
|
||||
1.000000e+02 1.000000e+02 3.000000e+02 4.000000e+02 8.045914e-03 1.804591e-04
|
||||
1.000000e+02 1.000000e+02 4.000000e+02 5.000000e+02 1.749001e-02 2.749001e-04
|
||||
1.000000e+02 1.000000e+02 5.000000e+02 6.000000e+02 2.785547e-03 1.278555e-04
|
||||
1.000000e+02 1.000000e+02 6.000000e+02 7.000000e+02 8.714611e-04 1.087146e-04
|
||||
1.000000e+02 1.000000e+02 7.000000e+02 8.000000e+02 5.383535e-04 1.053835e-04
|
||||
1.000000e+02 1.000000e+02 8.000000e+02 9.000000e+02 3.220988e-04 1.032210e-04
|
||||
1.000000e+02 1.000000e+02 9.000000e+02 1.000000e+03 2.004558e-04 1.020046e-04
|
||||
1.000000e+02 1.000000e+02 1.000000e+03 1.100000e+03 6.438889e-05 1.006439e-04
|
||||
|
||||
2.000000e+02 2.000000e+02 4.000000e+02 5.000000e+02 2.987416e-02 3.987416e-04
|
||||
2.000000e+02 2.000000e+02 5.000000e+02 6.000000e+02 4.374492e-03 1.437449e-04
|
||||
2.000000e+02 2.000000e+02 6.000000e+02 7.000000e+02 1.244707e-03 1.124471e-04
|
||||
2.000000e+02 2.000000e+02 7.000000e+02 8.000000e+02 7.390806e-04 1.073908e-04
|
||||
2.000000e+02 2.000000e+02 8.000000e+02 9.000000e+02 4.306959e-04 1.043070e-04
|
||||
2.000000e+02 2.000000e+02 9.000000e+02 1.000000e+03 2.629704e-04 1.026297e-04
|
||||
2.000000e+02 2.000000e+02 1.000000e+03 1.100000e+03 8.356921e-05 1.008357e-04
|
||||
|
||||
3.000000e+02 3.000000e+02 5.000000e+02 6.000000e+02 7.102682e-03 1.710268e-04
|
||||
3.000000e+02 3.000000e+02 6.000000e+02 7.000000e+02 1.756322e-03 1.175632e-04
|
||||
3.000000e+02 3.000000e+02 7.000000e+02 8.000000e+02 9.969346e-04 1.099693e-04
|
||||
3.000000e+02 3.000000e+02 8.000000e+02 9.000000e+02 5.678287e-04 1.056783e-04
|
||||
3.000000e+02 3.000000e+02 9.000000e+02 1.000000e+03 3.423076e-04 1.034231e-04
|
||||
3.000000e+02 3.000000e+02 1.000000e+03 1.100000e+03 1.100753e-04 1.011008e-04
|
||||
|
||||
4.000000e+02 4.000000e+02 6.000000e+02 7.000000e+02 3.330609e-03 1.333061e-04
|
||||
4.000000e+02 4.000000e+02 7.000000e+02 8.000000e+02 1.534079e-03 1.153408e-04
|
||||
4.000000e+02 4.000000e+02 8.000000e+02 9.000000e+02 7.882245e-04 1.078822e-04
|
||||
4.000000e+02 4.000000e+02 9.000000e+02 1.000000e+03 4.439992e-04 1.044400e-04
|
||||
4.000000e+02 4.000000e+02 1.000000e+03 1.100000e+03 1.345726e-04 1.013457e-04
|
||||
|
||||
5.000000e+02 5.000000e+02 7.000000e+02 8.000000e+02 6.629023e-03 1.662902e-04
|
||||
5.000000e+02 5.000000e+02 8.000000e+02 9.000000e+02 2.814898e-03 1.281490e-04
|
||||
5.000000e+02 5.000000e+02 9.000000e+02 1.000000e+03 1.424089e-03 1.142409e-04
|
||||
5.000000e+02 5.000000e+02 1.000000e+03 1.100000e+03 4.141629e-04 1.041416e-04
|
||||
|
||||
6.000000e+02 6.000000e+02 8.000000e+02 9.000000e+02 4.338127e-03 1.433813e-04
|
||||
6.000000e+02 6.000000e+02 9.000000e+02 1.000000e+03 1.980291e-03 1.198029e-04
|
||||
6.000000e+02 6.000000e+02 1.000000e+03 1.100000e+03 5.550423e-04 1.055504e-04
|
||||
|
||||
7.000000e+02 7.000000e+02 9.000000e+02 1.000000e+03 3.989883e-03 1.398988e-04
|
||||
7.000000e+02 7.000000e+02 1.000000e+03 1.100000e+03 1.014215e-03 1.101421e-04
|
||||
|
||||
8.000000e+02 8.000000e+02 1.000000e+03 1.100000e+03 2.476501e-03 1.247650e-04
|
||||
@@ -0,0 +1,73 @@
|
||||
! GENERAL FORMAT
|
||||
|
||||
|
||||
3.900000e+02 1.221100e+04 0.000000e+00 3.900000e+02 1.221100e+04 0.000000e+00 9
|
||||
5.900000e+02 1.221100e+04 0.000000e+00 6.900000e+02 1.221100e+04 0.000000e+00 8.353735e-02 9.353735e-04
|
||||
6.900000e+02 1.221100e+04 0.000000e+00 7.900000e+02 1.221100e+04 0.000000e+00 4.392504e-03 1.439250e-04
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.900000e+02 1.221100e+04 0.000000e+00 1.243627e-02 2.243627e-04
|
||||
8.900000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 2.058920e-03 1.205892e-04
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.090000e+03 1.221100e+04 0.000000e+00 6.812879e-04 1.068129e-04
|
||||
1.090000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 4.313826e-04 1.043138e-04
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.290000e+03 1.221100e+04 0.000000e+00 2.625458e-04 1.026255e-04
|
||||
1.290000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 1.651300e-04 1.016513e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.490000e+03 1.221100e+04 0.000000e+00 5.231997e-05 1.005232e-04
|
||||
|
||||
|
||||
4.900000e+02 1.221100e+04 0.000000e+00 4.900000e+02 1.221100e+04 0.000000e+00 8
|
||||
6.900000e+02 1.221100e+04 0.000000e+00 7.900000e+02 1.221100e+04 0.000000e+00 8.045914e-03 1.804591e-04
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.900000e+02 1.221100e+04 0.000000e+00 1.749001e-02 2.749001e-04
|
||||
8.900000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 2.785547e-03 1.278555e-04
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.090000e+03 1.221100e+04 0.000000e+00 8.714611e-04 1.087146e-04
|
||||
1.090000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 5.383535e-04 1.053835e-04
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.290000e+03 1.221100e+04 0.000000e+00 3.220988e-04 1.032210e-04
|
||||
1.290000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 2.004558e-04 1.020046e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.490000e+03 1.221100e+04 0.000000e+00 6.438889e-05 1.006439e-04
|
||||
|
||||
|
||||
5.900000e+02 1.221100e+04 0.000000e+00 5.900000e+02 1.221100e+04 0.000000e+00 7
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.900000e+02 1.221100e+04 0.000000e+00 2.987416e-02 3.987416e-04
|
||||
8.900000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 4.374492e-03 1.437449e-04
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.090000e+03 1.221100e+04 0.000000e+00 1.244707e-03 1.124471e-04
|
||||
1.090000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 7.390806e-04 1.073908e-04
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.290000e+03 1.221100e+04 0.000000e+00 4.306959e-04 1.043070e-04
|
||||
1.290000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 2.629704e-04 1.026297e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.490000e+03 1.221100e+04 0.000000e+00 8.356921e-05 1.008357e-04
|
||||
|
||||
|
||||
6.900000e+02 1.221100e+04 0.000000e+00 6.900000e+02 1.221100e+04 0.000000e+00 6
|
||||
8.900000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 7.102682e-03 1.710268e-04
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.090000e+03 1.221100e+04 0.000000e+00 1.756322e-03 1.175632e-04
|
||||
1.090000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 9.969346e-04 1.099693e-04
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.290000e+03 1.221100e+04 0.000000e+00 5.678287e-04 1.056783e-04
|
||||
1.290000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 3.423076e-04 1.034231e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.490000e+03 1.221100e+04 0.000000e+00 1.100753e-04 1.011008e-04
|
||||
|
||||
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 7.900000e+02 1.221100e+04 0.000000e+00 5
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.090000e+03 1.221100e+04 0.000000e+00 3.330609e-03 1.333061e-04
|
||||
1.090000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 1.534079e-03 1.153408e-04
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.290000e+03 1.221100e+04 0.000000e+00 7.882245e-04 1.078822e-04
|
||||
1.290000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 4.439992e-04 1.044400e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.490000e+03 1.221100e+04 0.000000e+00 1.345726e-04 1.013457e-04
|
||||
|
||||
|
||||
8.900000e+02 1.221100e+04 0.000000e+00 8.900000e+02 1.221100e+04 0.000000e+00 4
|
||||
1.090000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 6.629023e-03 1.662902e-04
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.290000e+03 1.221100e+04 0.000000e+00 2.814898e-03 1.281490e-04
|
||||
1.290000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 1.424089e-03 1.142409e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.490000e+03 1.221100e+04 0.000000e+00 4.141629e-04 1.041416e-04
|
||||
|
||||
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 3
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.290000e+03 1.221100e+04 0.000000e+00 4.338127e-03 1.433813e-04
|
||||
1.290000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 1.980291e-03 1.198029e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.490000e+03 1.221100e+04 0.000000e+00 5.550423e-04 1.055504e-04
|
||||
|
||||
|
||||
1.090000e+03 1.221100e+04 0.000000e+00 1.090000e+03 1.221100e+04 0.000000e+00 2
|
||||
1.290000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 3.989883e-03 1.398988e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.490000e+03 1.221100e+04 0.000000e+00 1.014215e-03 1.101421e-04
|
||||
|
||||
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 1
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.490000e+03 1.221100e+04 0.000000e+00 2.476501e-03 1.247650e-04
|
||||
@@ -0,0 +1,55 @@
|
||||
! GENERAL FORMAT
|
||||
|
||||
0.000000e+00 0.000000e+00 2.000000e+02 3.000000e+02 8.353735e-02 9.353735e-04
|
||||
0.000000e+00 0.000000e+00 3.000000e+02 4.000000e+02 4.392504e-03 1.439250e-04
|
||||
0.000000e+00 0.000000e+00 4.000000e+02 5.000000e+02 1.243627e-02 2.243627e-04
|
||||
0.000000e+00 0.000000e+00 5.000000e+02 6.000000e+02 2.058920e-03 1.205892e-04
|
||||
0.000000e+00 0.000000e+00 6.000000e+02 7.000000e+02 6.812879e-04 1.068129e-04
|
||||
0.000000e+00 0.000000e+00 7.000000e+02 8.000000e+02 4.313826e-04 1.043138e-04
|
||||
0.000000e+00 0.000000e+00 8.000000e+02 9.000000e+02 2.625458e-04 1.026255e-04
|
||||
0.000000e+00 0.000000e+00 9.000000e+02 1.000000e+03 1.651300e-04 1.016513e-04
|
||||
0.000000e+00 0.000000e+00 1.000000e+03 1.100000e+03 5.231997e-05 1.005232e-04
|
||||
|
||||
1.000000e+02 1.000000e+02 3.000000e+02 4.000000e+02 8.045914e-03 1.804591e-04
|
||||
1.000000e+02 1.000000e+02 4.000000e+02 5.000000e+02 1.749001e-02 2.749001e-04
|
||||
1.000000e+02 1.000000e+02 5.000000e+02 6.000000e+02 2.785547e-03 1.278555e-04
|
||||
1.000000e+02 1.000000e+02 6.000000e+02 7.000000e+02 8.714611e-04 1.087146e-04
|
||||
1.000000e+02 1.000000e+02 7.000000e+02 8.000000e+02 5.383535e-04 1.053835e-04
|
||||
1.000000e+02 1.000000e+02 8.000000e+02 9.000000e+02 3.220988e-04 1.032210e-04
|
||||
1.000000e+02 1.000000e+02 9.000000e+02 1.000000e+03 2.004558e-04 1.020046e-04
|
||||
1.000000e+02 1.000000e+02 1.000000e+03 1.100000e+03 6.438889e-05 1.006439e-04
|
||||
|
||||
2.000000e+02 2.000000e+02 4.000000e+02 5.000000e+02 2.987416e-02 3.987416e-04
|
||||
2.000000e+02 2.000000e+02 5.000000e+02 6.000000e+02 4.374492e-03 1.437449e-04
|
||||
2.000000e+02 2.000000e+02 6.000000e+02 7.000000e+02 1.244707e-03 1.124471e-04
|
||||
2.000000e+02 2.000000e+02 7.000000e+02 8.000000e+02 7.390806e-04 1.073908e-04
|
||||
2.000000e+02 2.000000e+02 8.000000e+02 9.000000e+02 4.306959e-04 1.043070e-04
|
||||
2.000000e+02 2.000000e+02 9.000000e+02 1.000000e+03 2.629704e-04 1.026297e-04
|
||||
2.000000e+02 2.000000e+02 1.000000e+03 1.100000e+03 8.356921e-05 1.008357e-04
|
||||
|
||||
3.000000e+02 3.000000e+02 5.000000e+02 6.000000e+02 7.102682e-03 1.710268e-04
|
||||
3.000000e+02 3.000000e+02 6.000000e+02 7.000000e+02 1.756322e-03 1.175632e-04
|
||||
3.000000e+02 3.000000e+02 7.000000e+02 8.000000e+02 9.969346e-04 1.099693e-04
|
||||
3.000000e+02 3.000000e+02 8.000000e+02 9.000000e+02 5.678287e-04 1.056783e-04
|
||||
3.000000e+02 3.000000e+02 9.000000e+02 1.000000e+03 3.423076e-04 1.034231e-04
|
||||
3.000000e+02 3.000000e+02 1.000000e+03 1.100000e+03 1.100753e-04 1.011008e-04
|
||||
|
||||
4.000000e+02 4.000000e+02 6.000000e+02 7.000000e+02 3.330609e-03 1.333061e-04
|
||||
4.000000e+02 4.000000e+02 7.000000e+02 8.000000e+02 1.534079e-03 1.153408e-04
|
||||
4.000000e+02 4.000000e+02 8.000000e+02 9.000000e+02 7.882245e-04 1.078822e-04
|
||||
4.000000e+02 4.000000e+02 9.000000e+02 1.000000e+03 4.439992e-04 1.044400e-04
|
||||
4.000000e+02 4.000000e+02 1.000000e+03 1.100000e+03 1.345726e-04 1.013457e-04
|
||||
|
||||
5.000000e+02 5.000000e+02 7.000000e+02 8.000000e+02 6.629023e-03 1.662902e-04
|
||||
5.000000e+02 5.000000e+02 8.000000e+02 9.000000e+02 2.814898e-03 1.281490e-04
|
||||
5.000000e+02 5.000000e+02 9.000000e+02 1.000000e+03 1.424089e-03 1.142409e-04
|
||||
5.000000e+02 5.000000e+02 1.000000e+03 1.100000e+03 4.141629e-04 1.041416e-04
|
||||
|
||||
6.000000e+02 6.000000e+02 8.000000e+02 9.000000e+02 4.338127e-03 1.433813e-04
|
||||
6.000000e+02 6.000000e+02 9.000000e+02 1.000000e+03 1.980291e-03 1.198029e-04
|
||||
6.000000e+02 6.000000e+02 1.000000e+03 1.100000e+03 5.550423e-04 1.055504e-04
|
||||
|
||||
7.000000e+02 7.000000e+02 9.000000e+02 1.000000e+03 3.989883e-03 1.398988e-04
|
||||
7.000000e+02 7.000000e+02 1.000000e+03 1.100000e+03 1.014215e-03 1.101421e-04
|
||||
|
||||
8.000000e+02 8.000000e+02 1.000000e+03 1.100000e+03 2.476501e-03 1.247650e-04
|
||||
@@ -0,0 +1,129 @@
|
||||
81
|
||||
-7718.404778 -5496.003413 1
|
||||
-3.908574e+03 1
|
||||
-2.774696e+03 1
|
||||
-1.964783e+03 1
|
||||
-1.386273e+03 1
|
||||
-9.730523e+02 1
|
||||
-6.778945e+02 1
|
||||
-4.670675e+02 1
|
||||
-3.164768e+02 1
|
||||
-2.089120e+02 1
|
||||
-1.320800e+02 1
|
||||
-7.720000e+01 1
|
||||
-3.800000e+01 1
|
||||
-1.000000e+01 1
|
||||
1.000000e+01 1
|
||||
3.000000e+01 1
|
||||
5.000000e+01 1
|
||||
7.000000e+01 1
|
||||
9.000000e+01 1
|
||||
1.100000e+02 1
|
||||
1.300000e+02 1
|
||||
1.500000e+02 1
|
||||
1.700000e+02 1
|
||||
1.900000e+02 1
|
||||
2.100000e+02 1
|
||||
2.300000e+02 1
|
||||
2.500000e+02 1
|
||||
2.700000e+02 1
|
||||
2.900000e+02 1
|
||||
3.100000e+02 1
|
||||
3.300000e+02 1
|
||||
3.500000e+02 1
|
||||
3.700000e+02 1
|
||||
3.900000e+02 1
|
||||
4.100000e+02 1
|
||||
4.300000e+02 1
|
||||
4.500000e+02 1
|
||||
4.700000e+02 1
|
||||
4.900000e+02 1
|
||||
5.100000e+02 1
|
||||
5.300000e+02 1
|
||||
5.500000e+02 1
|
||||
5.700000e+02 1
|
||||
5.900000e+02 1
|
||||
6.100000e+02 1
|
||||
6.300000e+02 1
|
||||
6.500000e+02 1
|
||||
6.700000e+02 1
|
||||
6.900000e+02 1
|
||||
7.100000e+02 1
|
||||
7.300000e+02 1
|
||||
7.500000e+02 1
|
||||
7.700000e+02 1
|
||||
7.900000e+02 1
|
||||
8.100000e+02 1
|
||||
8.300000e+02 1
|
||||
8.500000e+02 1
|
||||
8.700000e+02 1
|
||||
8.900000e+02 1
|
||||
9.100000e+02 1
|
||||
9.300000e+02 1
|
||||
9.500000e+02 1
|
||||
9.700000e+02 1
|
||||
9.900000e+02 1
|
||||
1.010000e+03 1
|
||||
1.030000e+03 1
|
||||
1.050000e+03 1
|
||||
1.078000e+03 1
|
||||
1.117200e+03 1
|
||||
1.172080e+03 1
|
||||
1.248912e+03 1
|
||||
1.356477e+03 1
|
||||
1.507068e+03 1
|
||||
1.717895e+03 1
|
||||
2.013052e+03 1
|
||||
2.426273e+03 1
|
||||
3.004783e+03 1
|
||||
3.814696e+03 1
|
||||
4.948574e+03 1
|
||||
6.536003e+03 1
|
||||
8.758405e+03 1
|
||||
|
||||
45
|
||||
0.000000 20.000000 1
|
||||
4.000000e+01 1
|
||||
6.000000e+01 1
|
||||
8.000000e+01 1
|
||||
1.000000e+02 1
|
||||
1.200000e+02 1
|
||||
1.400000e+02 1
|
||||
1.600000e+02 1
|
||||
1.800000e+02 1
|
||||
2.000000e+02 1
|
||||
2.200000e+02 1
|
||||
2.400000e+02 1
|
||||
2.600000e+02 1
|
||||
2.800000e+02 1
|
||||
3.000000e+02 1
|
||||
3.200000e+02 1
|
||||
3.400000e+02 1
|
||||
3.600000e+02 1
|
||||
3.800000e+02 1
|
||||
4.000000e+02 1
|
||||
4.200000e+02 1
|
||||
4.400000e+02 1
|
||||
4.600000e+02 1
|
||||
4.800000e+02 1
|
||||
5.000000e+02 1
|
||||
5.200000e+02 1
|
||||
5.400000e+02 1
|
||||
5.600000e+02 1
|
||||
5.800000e+02 1
|
||||
6.000000e+02 1
|
||||
6.240000e+02 1
|
||||
6.540000e+02 1
|
||||
6.890000e+02 1
|
||||
7.290000e+02 1
|
||||
7.790000e+02 1
|
||||
8.390000e+02 1
|
||||
9.110000e+02 1
|
||||
9.970000e+02 1
|
||||
1.100000e+03 1
|
||||
1.250000e+03 1
|
||||
1.425000e+03 1
|
||||
1.625000e+03 1
|
||||
1.875000e+03 1
|
||||
2.175000e+03 1
|
||||
2.525000e+03 1
|
||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,46 @@
|
||||
81 45
|
||||
1.00032E-02 1.00043E-02 1.00076E-02 1.00170E-02 1.00422E-02 1.01036E-02 1.02324E-02 1.04313E-02 1.05054E-02 9.84770E-03 7.88758E-03 5.21056E-03 3.16670E-03 2.48044E-03 4.75998E-03 8.77166E-03 1.15206E-02 1.31677E-02 1.39808E-02 1.43267E-02 1.45905E-02 1.40787E-02 1.25439E-02 9.39334E-03 4.25422E-03 1.94679E-03 3.74368E-03 9.57644E-03 2.12550E-02 2.66451E-02 2.18952E-02 1.83729E-02 1.30285E-02 8.01519E-03 4.19143E-03 1.75255E-03 1.06087E-03 1.20231E-03 2.65494E-03 9.73502E-03 1.56866E-02 1.41785E-02 1.32011E-02 1.92029E-02 6.02855E-02 1.15763E-01 8.41411E-02 3.94050E-02 2.42574E-02 7.98561E-02 1.75843E-01 1.64939E-01 1.09164E-01 7.05542E-02 9.83501E-02 1.72424E-01 2.07011E-01 2.15154E-01 1.99924E-01 1.48401E-01 1.24690E-01 1.34185E-01 1.41765E-01 1.43873E-01 1.43735E-01 1.58018E-01 1.52491E-01 1.19719E-01 4.71725E-02 2.31558E-03 1.58827E-03 3.30763E-03 6.13008E-03 8.30077E-03 9.35467E-03 9.77369E-03 9.92676E-03 9.97879E-03 9.99530E-03 1.00003E-02 1.00019E-02
|
||||
1.00032E-02 1.00043E-02 1.00076E-02 1.00170E-02 1.00422E-02 1.01035E-02 1.02323E-02 1.04314E-02 1.05094E-02 9.87256E-03 7.96995E-03 5.37823E-03 3.41942E-03 2.79696E-03 3.94421E-03 5.76043E-03 7.56965E-03 8.90061E-03 9.58321E-03 9.67705E-03 9.29751E-03 8.32677E-03 6.73106E-03 4.74795E-03 3.01345E-03 2.42512E-03 4.49935E-03 1.05101E-02 2.20729E-02 3.25799E-02 3.31792E-02 2.74602E-02 1.83159E-02 1.04396E-02 5.20156E-03 2.37644E-03 1.45881E-03 1.60773E-03 3.22847E-03 8.60311E-03 1.46341E-02 1.58886E-02 1.65150E-02 2.37386E-02 5.24371E-02 9.00341E-02 8.25175E-02 4.98898E-02 3.41591E-02 5.47403E-02 8.43579E-02 9.22769E-02 7.65582E-02 5.95454E-02 6.32497E-02 8.12883E-02 9.90527E-02 1.09008E-01 1.09162E-01 1.02647E-01 1.01153E-01 1.07025E-01 1.11720E-01 1.14580E-01 1.18622E-01 1.20448E-01 1.04621E-01 7.01147E-02 2.51409E-02 2.48372E-03 1.67636E-03 3.36908E-03 6.15785E-03 8.30813E-03 9.35617E-03 9.77398E-03 9.92682E-03 9.97880E-03 9.99531E-03 1.00003E-02 1.00019E-02
|
||||
1.00032E-02 1.00043E-02 1.00076E-02 1.00170E-02 1.00421E-02 1.01034E-02 1.02319E-02 1.04315E-02 1.05172E-02 9.92060E-03 8.12690E-03 5.68973E-03 3.84958E-03 3.20542E-03 3.62160E-03 4.34385E-03 5.14488E-03 5.77386E-03 6.06720E-03 5.98219E-03 5.56410E-03 4.87831E-03 4.04280E-03 3.27009E-03 2.85106E-03 3.27915E-03 5.99386E-03 1.29046E-02 2.62345E-02 4.26231E-02 5.00437E-02 4.24503E-02 2.71828E-02 1.45784E-02 7.11443E-03 3.49180E-03 2.21726E-03 2.30495E-03 3.91320E-03 8.14376E-03 1.38028E-02 1.76511E-02 2.08780E-02 2.92305E-02 5.06542E-02 7.80191E-02 8.31830E-02 6.39127E-02 4.79202E-02 5.01202E-02 5.63749E-02 5.76996E-02 5.19335E-02 4.44732E-02 4.25197E-02 4.62284E-02 5.25981E-02 5.89998E-02 6.43205E-02 6.92673E-02 7.51651E-02 8.14398E-02 8.61647E-02 8.90957E-02 8.97058E-02 8.31005E-02 6.58880E-02 4.08223E-02 1.51413E-02 2.71282E-03 1.84397E-03 3.48862E-03 6.21228E-03 8.32284E-03 9.35912E-03 9.77452E-03 9.92693E-03 9.97882E-03 9.99531E-03 1.00003E-02 1.00019E-02
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
1.00031E-02 1.00040E-02 1.00060E-02 1.00099E-02 1.00162E-02 1.00243E-02 1.00328E-02 1.00402E-02 1.00458E-02 1.00497E-02 1.00521E-02 1.00537E-02 1.00546E-02 1.00552E-02 1.00556E-02 1.00559E-02 1.00561E-02 1.00563E-02 1.00565E-02 1.00566E-02 1.00567E-02 1.00568E-02 1.00568E-02 1.00568E-02 1.00567E-02 1.00567E-02 1.00565E-02 1.00563E-02 1.00561E-02 1.00558E-02 1.00555E-02 1.00551E-02 1.00547E-02 1.00542E-02 1.00537E-02 1.00532E-02 1.00526E-02 1.00519E-02 1.00512E-02 1.00505E-02 1.00497E-02 1.00489E-02 1.00480E-02 1.00471E-02 1.00461E-02 1.00451E-02 1.00441E-02 1.00431E-02 1.00420E-02 1.00408E-02 1.00396E-02 1.00384E-02 1.00372E-02 1.00359E-02 1.00346E-02 1.00333E-02 1.00320E-02 1.00307E-02 1.00293E-02 1.00280E-02 1.00266E-02 1.00252E-02 1.00239E-02 1.00225E-02 1.00211E-02 1.00197E-02 1.00184E-02 1.00168E-02 1.00145E-02 1.00114E-02 1.00074E-02 1.00021E-02 9.99590E-03 9.98953E-03 9.98484E-03 9.98408E-03 9.98784E-03 9.99362E-03 9.99826E-03 1.00008E-02 1.00019E-02
|
||||
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|
||||
1.00031E-02 1.00038E-02 1.00053E-02 1.00074E-02 1.00099E-02 1.00120E-02 1.00136E-02 1.00146E-02 1.00152E-02 1.00155E-02 1.00156E-02 1.00156E-02 1.00157E-02 1.00157E-02 1.00156E-02 1.00156E-02 1.00156E-02 1.00156E-02 1.00156E-02 1.00155E-02 1.00155E-02 1.00155E-02 1.00154E-02 1.00154E-02 1.00153E-02 1.00152E-02 1.00152E-02 1.00151E-02 1.00150E-02 1.00149E-02 1.00149E-02 1.00148E-02 1.00147E-02 1.00146E-02 1.00145E-02 1.00144E-02 1.00143E-02 1.00142E-02 1.00140E-02 1.00139E-02 1.00138E-02 1.00136E-02 1.00135E-02 1.00133E-02 1.00132E-02 1.00130E-02 1.00129E-02 1.00127E-02 1.00125E-02 1.00123E-02 1.00122E-02 1.00120E-02 1.00118E-02 1.00116E-02 1.00114E-02 1.00112E-02 1.00110E-02 1.00108E-02 1.00106E-02 1.00103E-02 1.00101E-02 1.00099E-02 1.00097E-02 1.00094E-02 1.00092E-02 1.00090E-02 1.00087E-02 1.00084E-02 1.00080E-02 1.00075E-02 1.00067E-02 1.00056E-02 1.00040E-02 1.00021E-02 9.99987E-03 9.99783E-03 9.99688E-03 9.99765E-03 9.99947E-03 1.00011E-02 1.00020E-02
|
||||
@@ -0,0 +1,11 @@
|
||||
OBS LOC_X FWR_3D_2_2D.dat
|
||||
MESH FILE Mesh_2D.msh
|
||||
CHIFACT 1 100.000000
|
||||
TOPO DEFAULT %s
|
||||
INIT_MOD DEFAULT
|
||||
REF_MOD VALUE 1.000000e-02
|
||||
ALPHA DEFAULT
|
||||
WEIGHT DEFAULT
|
||||
STORE_ALL_MODELS FALSE
|
||||
INVMODE SVD
|
||||
USE_MREF TRUE
|
||||
@@ -0,0 +1,410 @@
|
||||
|
||||
Parallelized with OpenMP. # of threads: 4
|
||||
|
||||
DCIP2D - Version 5 (BETA) 20110811: DCINV2D
|
||||
|
||||
Developed by University of British Columbia
|
||||
Geophysical Inversion Facility (UBC-GIF)
|
||||
|
||||
(C) Copyright 1992 - 2011, UBC-GIF,
|
||||
Department of Earth and Ocean Sciences, UBC
|
||||
http://www.eos.ubc.ca/research/ubcgif/
|
||||
|
||||
Distributed by:
|
||||
Mira Geoscience Ltd.
|
||||
|
||||
|
||||
DCINV2D started on: 1/12/2016 21:07:02
|
||||
|
||||
|
||||
Reading input file: dcinv2d.inp
|
||||
|
||||
----------------------------------------------
|
||||
OBS LOC_X FWR_3D_2_2D.dat
|
||||
MESH FILE Mesh_2D.msh
|
||||
CHIFACT 1 100.000000
|
||||
TOPO DEFAULT %s
|
||||
INIT_MOD DEFAULT
|
||||
REF_MOD VALUE 1.000000e-02
|
||||
ALPHA DEFAULT
|
||||
WEIGHT DEFAULT
|
||||
STORE_ALL_MODELS FALSE
|
||||
INVMODE SVD
|
||||
USE_MREF TRUE
|
||||
----------------------------------------------
|
||||
|
||||
maximum # of iterations: 100
|
||||
data were read from: FWR_3D_2_2D.dat
|
||||
# of current locations: 9
|
||||
# of data: 45
|
||||
chifact: 1.00000E+00
|
||||
target misfit: 4.50000E+01
|
||||
|
||||
mesh was read from: Mesh_2D.msh
|
||||
# of cells: 81 x 45
|
||||
total # of cells: 3645
|
||||
# of active cells: 3645
|
||||
|
||||
# of unique data locations: 9
|
||||
# of wave values: 13
|
||||
2.5000E-04 4.9901E-04 9.9606E-04 1.9882E-03 3.9685E-03 7.9213E-03 1.5811E-02 3.1560E-02 6.2996E-02 1.2574E-01 2.5099E-01 5.0099E-01 1.0000E+00
|
||||
|
||||
reference conductivity model is set to a constant: 1.000000E-02
|
||||
initial model is set to the reference model.
|
||||
|
||||
using default length scales (Lx, Lz): ( 8.00000E+01, 8.00000E+01)
|
||||
corresponding alpha (a_s, a_x, a_z): ( 1.56250E-04, 1.0000E+00, 1.0000E+00)
|
||||
|
||||
Using basis vectors and SVD.
|
||||
reference model will be used in the derivative terms.
|
||||
number of basis vectors: 17 + 3 + 1 = 21
|
||||
|
||||
init cpu time: 0:00:00.18
|
||||
|
||||
|
||||
initial misfit = 2.57080E+05
|
||||
init. model norm = 0.00000E+00
|
||||
norm comp Ws = 0.00000E+00
|
||||
norm comp Wx = 0.00000E+00
|
||||
norm comp Wz = 0.00000E+00
|
||||
|
||||
|
||||
Iteration 1
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
1.11493E+03 4.74485E+04
|
||||
2.22985E+03 5.68364E+04
|
||||
5.57463E+03 8.02516E+04
|
||||
1.39366E+04 1.21526E+05
|
||||
1.57750E+04 1.28279E+05
|
||||
1.58488E+04 1.28536E+05
|
||||
1.58499E+04 1.28540E+05
|
||||
3.96247E+04 1.79392E+05
|
||||
|
||||
chosen beta = 1.58499E+04
|
||||
target misfit = 1.28540E+05
|
||||
achieved misfit = 1.28540E+05
|
||||
model norm = 3.63297E+00
|
||||
misfit change = 5.00000E-01
|
||||
model norm change = 0.00000E+00
|
||||
|
||||
norm comp Ws = 2.88514E+00
|
||||
norm comp Wx = 3.70226E-01
|
||||
norm comp Wz = 3.77603E-01
|
||||
|
||||
iter cpu time: 0:00:01.81
|
||||
|
||||
|
||||
Iteration 2
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
3.96247E+03 6.15053E+04
|
||||
7.92494E+03 9.46707E+04
|
||||
|
||||
chosen beta = 4.25263E+03
|
||||
target misfit = 6.42701E+04
|
||||
achieved misfit = 6.42774E+04
|
||||
model norm = 1.37169E+01
|
||||
misfit change = 4.99943E-01
|
||||
model norm change = 2.77567E+00
|
||||
|
||||
norm comp Ws = 1.06979E+01
|
||||
norm comp Wx = 1.61088E+00
|
||||
norm comp Wz = 1.40814E+00
|
||||
|
||||
iter cpu time: 0:00:00.83
|
||||
|
||||
|
||||
Iteration 3
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
1.06316E+03 2.49833E+04
|
||||
2.12631E+03 4.10044E+04
|
||||
|
||||
chosen beta = 1.51222E+03
|
||||
target misfit = 3.21387E+04
|
||||
achieved misfit = 3.17674E+04
|
||||
model norm = 2.91306E+01
|
||||
misfit change = 5.05777E-01
|
||||
model norm change = 1.12370E+00
|
||||
|
||||
norm comp Ws = 2.19928E+01
|
||||
norm comp Wx = 4.11507E+00
|
||||
norm comp Wz = 3.02271E+00
|
||||
|
||||
iter cpu time: 0:00:00.64
|
||||
|
||||
|
||||
Iteration 4
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
3.78054E+02 1.14051E+04
|
||||
7.56108E+02 1.87488E+04
|
||||
|
||||
chosen beta = 6.00000E+02
|
||||
target misfit = 1.58837E+04
|
||||
achieved misfit = 1.56569E+04
|
||||
model norm = 4.84312E+01
|
||||
misfit change = 5.07137E-01
|
||||
model norm change = 6.62554E-01
|
||||
|
||||
norm comp Ws = 3.49213E+01
|
||||
norm comp Wx = 8.32898E+00
|
||||
norm comp Wz = 5.18098E+00
|
||||
|
||||
iter cpu time: 0:00:01.47
|
||||
|
||||
|
||||
Iteration 5
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
1.50000E+02 5.05151E+03
|
||||
3.00000E+02 8.76679E+03
|
||||
|
||||
chosen beta = 2.60199E+02
|
||||
target misfit = 7.82847E+03
|
||||
achieved misfit = 7.79602E+03
|
||||
model norm = 6.97577E+01
|
||||
misfit change = 5.02073E-01
|
||||
model norm change = 4.40344E-01
|
||||
|
||||
norm comp Ws = 4.74916E+01
|
||||
norm comp Wx = 1.43993E+01
|
||||
norm comp Wz = 7.86671E+00
|
||||
|
||||
iter cpu time: 0:00:01.41
|
||||
|
||||
|
||||
Iteration 6
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
6.50498E+01 1.83324E+03
|
||||
1.30100E+02 3.68412E+03
|
||||
1.37599E+02 3.90387E+03
|
||||
|
||||
chosen beta = 1.37400E+02
|
||||
target misfit = 3.89801E+03
|
||||
achieved misfit = 3.89800E+03
|
||||
model norm = 8.95751E+01
|
||||
misfit change = 5.00001E-01
|
||||
model norm change = 2.84090E-01
|
||||
|
||||
norm comp Ws = 5.75580E+01
|
||||
norm comp Wx = 2.11440E+01
|
||||
norm comp Wz = 1.08731E+01
|
||||
|
||||
iter cpu time: 0:00:01.02
|
||||
|
||||
|
||||
Iteration 7
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
3.43499E+01 8.99667E+02
|
||||
6.86999E+01 1.65819E+03
|
||||
8.25106E+01 2.00613E+03
|
||||
|
||||
chosen beta = 8.02499E+01
|
||||
target misfit = 1.94900E+03
|
||||
achieved misfit = 1.94758E+03
|
||||
model norm = 1.06566E+02
|
||||
misfit change = 5.00364E-01
|
||||
model norm change = 1.89686E-01
|
||||
|
||||
norm comp Ws = 6.57720E+01
|
||||
norm comp Wx = 2.69488E+01
|
||||
norm comp Wz = 1.38454E+01
|
||||
|
||||
iter cpu time: 0:00:01.00
|
||||
|
||||
|
||||
Iteration 8
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
2.00625E+01 5.63946E+02
|
||||
4.01250E+01 9.43655E+02
|
||||
4.18598E+01 9.78372E+02
|
||||
|
||||
chosen beta = 4.16303E+01
|
||||
target misfit = 9.73791E+02
|
||||
achieved misfit = 9.73755E+02
|
||||
model norm = 1.22904E+02
|
||||
misfit change = 5.00019E-01
|
||||
model norm change = 1.53314E-01
|
||||
|
||||
norm comp Ws = 7.39851E+01
|
||||
norm comp Wx = 3.20341E+01
|
||||
norm comp Wz = 1.68852E+01
|
||||
|
||||
iter cpu time: 0:00:01.08
|
||||
|
||||
|
||||
Iteration 9
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
1.04076E+01 3.54740E+02
|
||||
2.08152E+01 5.47348E+02
|
||||
|
||||
chosen beta = 1.72632E+01
|
||||
target misfit = 4.86877E+02
|
||||
achieved misfit = 4.81217E+02
|
||||
model norm = 1.41185E+02
|
||||
misfit change = 5.05813E-01
|
||||
model norm change = 1.48742E-01
|
||||
|
||||
norm comp Ws = 8.27069E+01
|
||||
norm comp Wx = 3.78831E+01
|
||||
norm comp Wz = 2.05954E+01
|
||||
|
||||
iter cpu time: 0:00:00.62
|
||||
|
||||
|
||||
Iteration 10
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
4.31579E+00 2.09965E+02
|
||||
8.63158E+00 2.93092E+02
|
||||
|
||||
chosen beta = 5.72809E+00
|
||||
target misfit = 2.40608E+02
|
||||
achieved misfit = 2.34998E+02
|
||||
model norm = 1.66836E+02
|
||||
misfit change = 5.11659E-01
|
||||
model norm change = 1.81682E-01
|
||||
|
||||
norm comp Ws = 9.43748E+01
|
||||
norm comp Wx = 4.67077E+01
|
||||
norm comp Wz = 2.57538E+01
|
||||
|
||||
iter cpu time: 0:00:01.14
|
||||
|
||||
|
||||
Iteration 11
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
1.43202E+00 1.08765E+02
|
||||
2.86405E+00 1.48776E+02
|
||||
|
||||
chosen beta = 1.69894E+00
|
||||
target misfit = 1.17499E+02
|
||||
achieved misfit = 1.13855E+02
|
||||
model norm = 2.07214E+02
|
||||
misfit change = 5.15505E-01
|
||||
model norm change = 2.42021E-01
|
||||
|
||||
norm comp Ws = 1.10709E+02
|
||||
norm comp Wx = 6.50107E+01
|
||||
norm comp Wz = 3.14941E+01
|
||||
|
||||
iter cpu time: 0:00:01.18
|
||||
|
||||
|
||||
Iteration 12
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
4.24735E-01 4.69288E+01
|
||||
8.49470E-01 5.82083E+01
|
||||
|
||||
chosen beta = 7.90778E-01
|
||||
target misfit = 5.69276E+01
|
||||
achieved misfit = 5.61534E+01
|
||||
model norm = 2.39426E+02
|
||||
misfit change = 5.06799E-01
|
||||
model norm change = 1.55454E-01
|
||||
|
||||
norm comp Ws = 1.22208E+02
|
||||
norm comp Wx = 7.90861E+01
|
||||
norm comp Wz = 3.81319E+01
|
||||
|
||||
iter cpu time: 0:00:01.10
|
||||
|
||||
|
||||
Iteration 13
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
5.07840E-01 2.85576E+01
|
||||
6.33710E-01 3.23824E+01
|
||||
1.13145E+00 5.17204E+01
|
||||
|
||||
chosen beta = 9.52357E-01
|
||||
target misfit = 4.50000E+01
|
||||
achieved misfit = 4.40177E+01
|
||||
model norm = 2.37754E+02
|
||||
misfit change = 2.16117E-01
|
||||
model norm change = -6.98309E-03
|
||||
|
||||
norm comp Ws = 1.26074E+02
|
||||
norm comp Wx = 7.63269E+01
|
||||
norm comp Wz = 3.53536E+01
|
||||
|
||||
iter cpu time: 0:00:00.88
|
||||
|
||||
|
||||
Iteration 14
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
9.73609E-01 3.64390E+01
|
||||
9.95336E-01 3.71506E+01
|
||||
1.23871E+00 4.57613E+01
|
||||
|
||||
chosen beta = 1.21709E+00
|
||||
target misfit = 4.50000E+01
|
||||
achieved misfit = 4.49504E+01
|
||||
model norm = 2.30537E+02
|
||||
misfit change = -2.11895E-02
|
||||
model norm change = -3.03571E-02
|
||||
|
||||
norm comp Ws = 1.26466E+02
|
||||
norm comp Wx = 7.21243E+01
|
||||
norm comp Wz = 3.19461E+01
|
||||
|
||||
iter cpu time: 0:00:00.89
|
||||
|
||||
|
||||
Target misfit achieved. Minimizing model norm.
|
||||
|
||||
|
||||
Iteration 15
|
||||
|
||||
beta vs. misfit:
|
||||
beta misfit
|
||||
1.21843E+00 3.86348E+01
|
||||
1.21978E+00 3.86736E+01
|
||||
1.44018E+00 4.53382E+01
|
||||
|
||||
chosen beta = 1.42896E+00
|
||||
target misfit = 4.50000E+01
|
||||
achieved misfit = 4.49850E+01
|
||||
model norm = 2.28950E+02
|
||||
misfit change = -7.69307E-04
|
||||
model norm change = -6.88525E-03
|
||||
|
||||
norm comp Ws = 1.25759E+02
|
||||
norm comp Wx = 6.90749E+01
|
||||
norm comp Wz = 3.41159E+01
|
||||
|
||||
iter cpu time: 0:00:00.95
|
||||
|
||||
|
||||
Exit at convergence.
|
||||
|
||||
|
||||
Iterations performed: 15
|
||||
|
||||
total cpu time: 0:00:16.25
|
||||
|
||||
DCINV2D ended on: 1/12/2016 21:07:18
|
||||
|
||||
@@ -0,0 +1,19 @@
|
||||
15 iter data misfit model norm beta
|
||||
0 2.57080E+05 0.00000E+00 0.00000E+00
|
||||
1 1.28540E+05 3.63297E+00 1.58499E+04
|
||||
2 6.42774E+04 1.37169E+01 4.25263E+03
|
||||
3 3.17674E+04 2.91306E+01 1.51222E+03
|
||||
4 1.56569E+04 4.84312E+01 6.00000E+02
|
||||
5 7.79602E+03 6.97577E+01 2.60199E+02
|
||||
6 3.89800E+03 8.95751E+01 1.37400E+02
|
||||
7 1.94758E+03 1.06566E+02 8.02499E+01
|
||||
8 9.73755E+02 1.22904E+02 4.16303E+01
|
||||
9 4.81217E+02 1.41185E+02 1.72632E+01
|
||||
10 2.34998E+02 1.66836E+02 5.72809E+00
|
||||
11 1.13855E+02 2.07214E+02 1.69894E+00
|
||||
12 5.61534E+01 2.39426E+02 7.90778E-01
|
||||
13 4.40177E+01 2.37754E+02 9.52357E-01
|
||||
14 4.49504E+01 2.30537E+02 1.21709E+00
|
||||
15 4.49850E+01 2.28950E+02 1.42896E+00
|
||||
4.50000E+01 target misfit
|
||||
45 number of data
|
||||
@@ -0,0 +1,46 @@
|
||||
! Predicted data ! GENERAL FORMAT
|
||||
0.0000000E+00 0.0000000E+00 2.0000000E+02 3.0000000E+02 8.31819E-02
|
||||
0.0000000E+00 0.0000000E+00 3.0000000E+02 4.0000000E+02 4.41288E-03
|
||||
0.0000000E+00 0.0000000E+00 4.0000000E+02 5.0000000E+02 1.23515E-02
|
||||
0.0000000E+00 0.0000000E+00 5.0000000E+02 6.0000000E+02 2.09881E-03
|
||||
0.0000000E+00 0.0000000E+00 6.0000000E+02 7.0000000E+02 7.28316E-04
|
||||
0.0000000E+00 0.0000000E+00 7.0000000E+02 8.0000000E+02 5.24885E-04
|
||||
0.0000000E+00 0.0000000E+00 8.0000000E+02 9.0000000E+02 3.56979E-04
|
||||
0.0000000E+00 0.0000000E+00 9.0000000E+02 1.0000000E+03 2.71240E-04
|
||||
0.0000000E+00 0.0000000E+00 1.0000000E+03 1.1000000E+03 1.71849E-04
|
||||
1.0000000E+02 1.0000000E+02 3.0000000E+02 4.0000000E+02 8.00012E-03
|
||||
1.0000000E+02 1.0000000E+02 4.0000000E+02 5.0000000E+02 1.75080E-02
|
||||
1.0000000E+02 1.0000000E+02 5.0000000E+02 6.0000000E+02 2.81046E-03
|
||||
1.0000000E+02 1.0000000E+02 6.0000000E+02 7.0000000E+02 9.20650E-04
|
||||
1.0000000E+02 1.0000000E+02 7.0000000E+02 8.0000000E+02 6.40887E-04
|
||||
1.0000000E+02 1.0000000E+02 8.0000000E+02 9.0000000E+02 4.28101E-04
|
||||
1.0000000E+02 1.0000000E+02 9.0000000E+02 1.0000000E+03 3.19325E-04
|
||||
1.0000000E+02 1.0000000E+02 1.0000000E+03 1.1000000E+03 1.97133E-04
|
||||
2.0000000E+02 2.0000000E+02 4.0000000E+02 5.0000000E+02 3.00094E-02
|
||||
2.0000000E+02 2.0000000E+02 5.0000000E+02 6.0000000E+02 4.17179E-03
|
||||
2.0000000E+02 2.0000000E+02 6.0000000E+02 7.0000000E+02 1.23020E-03
|
||||
2.0000000E+02 2.0000000E+02 7.0000000E+02 8.0000000E+02 8.09705E-04
|
||||
2.0000000E+02 2.0000000E+02 8.0000000E+02 9.0000000E+02 5.26566E-04
|
||||
2.0000000E+02 2.0000000E+02 9.0000000E+02 1.0000000E+03 3.82914E-04
|
||||
2.0000000E+02 2.0000000E+02 1.0000000E+03 1.1000000E+03 2.28587E-04
|
||||
3.0000000E+02 3.0000000E+02 5.0000000E+02 6.0000000E+02 7.27481E-03
|
||||
3.0000000E+02 3.0000000E+02 6.0000000E+02 7.0000000E+02 1.78177E-03
|
||||
3.0000000E+02 3.0000000E+02 7.0000000E+02 8.0000000E+02 1.07707E-03
|
||||
3.0000000E+02 3.0000000E+02 8.0000000E+02 9.0000000E+02 6.74696E-04
|
||||
3.0000000E+02 3.0000000E+02 9.0000000E+02 1.0000000E+03 4.74671E-04
|
||||
3.0000000E+02 3.0000000E+02 1.0000000E+03 1.1000000E+03 2.71808E-04
|
||||
4.0000000E+02 4.0000000E+02 6.0000000E+02 7.0000000E+02 3.37547E-03
|
||||
4.0000000E+02 4.0000000E+02 7.0000000E+02 8.0000000E+02 1.52487E-03
|
||||
4.0000000E+02 4.0000000E+02 8.0000000E+02 9.0000000E+02 8.67080E-04
|
||||
4.0000000E+02 4.0000000E+02 9.0000000E+02 1.0000000E+03 5.71693E-04
|
||||
4.0000000E+02 4.0000000E+02 1.0000000E+03 1.1000000E+03 3.07672E-04
|
||||
5.0000000E+02 5.0000000E+02 7.0000000E+02 8.0000000E+02 6.64946E-03
|
||||
5.0000000E+02 5.0000000E+02 8.0000000E+02 9.0000000E+02 2.83925E-03
|
||||
5.0000000E+02 5.0000000E+02 9.0000000E+02 1.0000000E+03 1.50341E-03
|
||||
5.0000000E+02 5.0000000E+02 1.0000000E+03 1.1000000E+03 6.31242E-04
|
||||
6.0000000E+02 6.0000000E+02 8.0000000E+02 9.0000000E+02 4.28770E-03
|
||||
6.0000000E+02 6.0000000E+02 9.0000000E+02 1.0000000E+03 2.02291E-03
|
||||
6.0000000E+02 6.0000000E+02 1.0000000E+03 1.1000000E+03 7.75855E-04
|
||||
7.0000000E+02 7.0000000E+02 9.0000000E+02 1.0000000E+03 3.85090E-03
|
||||
7.0000000E+02 7.0000000E+02 1.0000000E+03 1.1000000E+03 1.15357E-03
|
||||
8.0000000E+02 8.0000000E+02 1.0000000E+03 1.1000000E+03 2.22667E-03
|
||||
@@ -0,0 +1,46 @@
|
||||
! Predicted data ! GENERAL FORMAT Last column are apparent conductivities.
|
||||
0.0000000E+00 0.0000000E+00 2.0000000E+02 3.0000000E+02 2.63497E-02 1.00668E-02
|
||||
0.0000000E+00 0.0000000E+00 3.0000000E+02 4.0000000E+02 1.31837E-02 1.00601E-02
|
||||
0.0000000E+00 0.0000000E+00 4.0000000E+02 5.0000000E+02 7.93784E-03 1.00251E-02
|
||||
0.0000000E+00 0.0000000E+00 5.0000000E+02 6.0000000E+02 5.30187E-03 1.00062E-02
|
||||
0.0000000E+00 0.0000000E+00 6.0000000E+02 7.0000000E+02 3.79075E-03 9.99646E-03
|
||||
0.0000000E+00 0.0000000E+00 7.0000000E+02 8.0000000E+02 2.84529E-03 9.98863E-03
|
||||
0.0000000E+00 0.0000000E+00 8.0000000E+02 9.0000000E+02 2.21518E-03 9.97881E-03
|
||||
0.0000000E+00 0.0000000E+00 9.0000000E+02 1.0000000E+03 1.77462E-03 9.96491E-03
|
||||
0.0000000E+00 0.0000000E+00 1.0000000E+03 1.1000000E+03 1.45107E-03 9.97103E-03
|
||||
1.0000000E+02 1.0000000E+02 3.0000000E+02 4.0000000E+02 2.64919E-02 1.00128E-02
|
||||
1.0000000E+02 1.0000000E+02 4.0000000E+02 5.0000000E+02 1.32352E-02 1.00209E-02
|
||||
1.0000000E+02 1.0000000E+02 5.0000000E+02 6.0000000E+02 7.96229E-03 9.99429E-03
|
||||
1.0000000E+02 1.0000000E+02 6.0000000E+02 7.0000000E+02 5.31543E-03 9.98068E-03
|
||||
1.0000000E+02 1.0000000E+02 7.0000000E+02 8.0000000E+02 3.79921E-03 9.97417E-03
|
||||
1.0000000E+02 1.0000000E+02 8.0000000E+02 9.0000000E+02 2.85122E-03 9.96784E-03
|
||||
1.0000000E+02 1.0000000E+02 9.0000000E+02 1.0000000E+03 2.21994E-03 9.95742E-03
|
||||
1.0000000E+02 1.0000000E+02 1.0000000E+03 1.1000000E+03 1.77401E-03 9.96831E-03
|
||||
2.0000000E+02 2.0000000E+02 4.0000000E+02 5.0000000E+02 2.65164E-02 1.00036E-02
|
||||
2.0000000E+02 2.0000000E+02 5.0000000E+02 6.0000000E+02 1.32488E-02 1.00106E-02
|
||||
2.0000000E+02 2.0000000E+02 6.0000000E+02 7.0000000E+02 7.97076E-03 9.98368E-03
|
||||
2.0000000E+02 2.0000000E+02 7.0000000E+02 8.0000000E+02 5.32136E-03 9.96956E-03
|
||||
2.0000000E+02 2.0000000E+02 8.0000000E+02 9.0000000E+02 3.80396E-03 9.96173E-03
|
||||
2.0000000E+02 2.0000000E+02 9.0000000E+02 1.0000000E+03 2.85567E-03 9.95231E-03
|
||||
2.0000000E+02 2.0000000E+02 1.0000000E+03 1.1000000E+03 2.21779E-03 9.96706E-03
|
||||
3.0000000E+02 3.0000000E+02 5.0000000E+02 6.0000000E+02 2.65249E-02 1.00004E-02
|
||||
3.0000000E+02 3.0000000E+02 6.0000000E+02 7.0000000E+02 1.32547E-02 1.00062E-02
|
||||
3.0000000E+02 3.0000000E+02 7.0000000E+02 8.0000000E+02 7.97549E-03 9.97775E-03
|
||||
3.0000000E+02 3.0000000E+02 8.0000000E+02 9.0000000E+02 5.32580E-03 9.96125E-03
|
||||
3.0000000E+02 3.0000000E+02 9.0000000E+02 1.0000000E+03 3.80888E-03 9.94888E-03
|
||||
3.0000000E+02 3.0000000E+02 1.0000000E+03 1.1000000E+03 2.85172E-03 9.96611E-03
|
||||
4.0000000E+02 4.0000000E+02 6.0000000E+02 7.0000000E+02 2.65296E-02 9.99858E-03
|
||||
4.0000000E+02 4.0000000E+02 7.0000000E+02 8.0000000E+02 1.32592E-02 1.00028E-02
|
||||
4.0000000E+02 4.0000000E+02 8.0000000E+02 9.0000000E+02 7.98040E-03 9.97162E-03
|
||||
4.0000000E+02 4.0000000E+02 9.0000000E+02 1.0000000E+03 5.33212E-03 9.94944E-03
|
||||
4.0000000E+02 4.0000000E+02 1.0000000E+03 1.1000000E+03 3.80236E-03 9.96593E-03
|
||||
5.0000000E+02 5.0000000E+02 7.0000000E+02 8.0000000E+02 2.65345E-02 9.99673E-03
|
||||
5.0000000E+02 5.0000000E+02 8.0000000E+02 9.0000000E+02 1.32655E-02 9.99807E-03
|
||||
5.0000000E+02 5.0000000E+02 9.0000000E+02 1.0000000E+03 7.98964E-03 9.96008E-03
|
||||
5.0000000E+02 5.0000000E+02 1.0000000E+03 1.1000000E+03 5.32134E-03 9.96960E-03
|
||||
6.0000000E+02 6.0000000E+02 8.0000000E+02 9.0000000E+02 2.65437E-02 9.99326E-03
|
||||
6.0000000E+02 6.0000000E+02 9.0000000E+02 1.0000000E+03 1.32806E-02 9.98668E-03
|
||||
6.0000000E+02 6.0000000E+02 1.0000000E+03 1.1000000E+03 7.97063E-03 9.98384E-03
|
||||
7.0000000E+02 7.0000000E+02 9.0000000E+02 1.0000000E+03 2.65717E-02 9.98273E-03
|
||||
7.0000000E+02 7.0000000E+02 1.0000000E+03 1.1000000E+03 1.32427E-02 1.00153E-02
|
||||
8.0000000E+02 8.0000000E+02 1.0000000E+03 1.1000000E+03 2.64774E-02 1.00183E-02
|
||||
@@ -0,0 +1,46 @@
|
||||
81 45
|
||||
1.00411E-13 3.10408E-13 9.74717E-13 3.09564E-12 9.68504E-12 2.91605E-11 8.35615E-11 2.28263E-10 6.04123E-10 1.61401E-09 4.67374E-09 1.58647E-08 6.23317E-08 2.35358E-07 1.71041E-08 2.08590E-07 1.56029E-07 1.46650E-07 1.72752E-07 1.54926E-07 1.77390E-07 2.03028E-07 2.69140E-07 4.10395E-07 1.29687E-07 7.93598E-07 2.97657E-07 1.28094E-07 7.80834E-08 7.94358E-08 1.06544E-07 6.15537E-08 7.23303E-08 1.38411E-07 8.59111E-08 1.68916E-07 1.64240E-07 1.58717E-07 1.44871E-07 1.00497E-07 1.53571E-07 1.14875E-07 1.03632E-07 9.31407E-08 4.54508E-08 5.91715E-08 3.66679E-08 6.10459E-08 1.23422E-07 6.11907E-08 8.25315E-08 6.19628E-08 8.25261E-08 1.49285E-07 7.23998E-08 1.14387E-07 8.11571E-08 6.93066E-08 7.27982E-08 5.00166E-08 1.01158E-07 5.96975E-08 4.37654E-08 4.14493E-08 3.33156E-08 6.30583E-08 3.86673E-08 2.63025E-08 1.28371E-08 7.03143E-09 2.55569E-09 6.86395E-10 2.47006E-10 1.07284E-10 4.64772E-11 1.87670E-11 6.99064E-12 2.43083E-12 8.12035E-13 2.70167E-13 9.04729E-14
|
||||
1.00408E-13 3.10389E-13 9.74598E-13 3.09493E-12 9.68107E-12 2.91394E-11 8.34535E-11 2.27725E-10 6.01411E-10 1.59939E-09 4.58076E-09 1.50821E-08 5.37357E-08 1.45015E-07 9.49187E-08 3.64320E-08 2.63448E-08 4.94770E-08 6.85655E-08 7.00921E-08 5.92742E-08 6.09382E-08 4.59596E-08 9.87180E-08 2.28836E-07 4.66832E-07 1.96472E-07 6.17388E-08 3.68928E-08 7.81857E-08 6.83590E-08 3.70845E-08 2.61880E-08 3.78832E-08 3.43796E-08 8.14050E-08 1.25510E-07 1.28215E-07 8.53512E-08 2.97777E-08 6.74204E-08 7.99932E-08 7.90362E-08 5.79363E-08 2.71734E-08 3.44196E-08 2.90110E-08 4.29354E-08 7.81656E-08 3.22896E-08 2.56389E-08 2.96368E-08 4.22860E-08 6.50714E-08 3.97379E-08 3.15747E-08 3.02920E-08 2.68593E-08 2.63743E-08 3.31479E-08 4.64930E-08 3.35157E-08 2.40778E-08 1.98269E-08 2.25180E-08 2.99855E-08 2.16250E-08 1.19611E-08 3.79368E-09 5.30861E-09 2.42064E-09 6.74309E-10 2.45568E-10 1.07010E-10 4.64139E-11 1.87527E-11 6.98765E-12 2.43026E-12 8.11935E-13 2.70151E-13 9.04706E-14
|
||||
1.00403E-13 3.10352E-13 9.74361E-13 3.09352E-12 9.67313E-12 2.90971E-11 8.32379E-11 2.26652E-10 5.96028E-10 1.57061E-09 4.40228E-09 1.37248E-08 4.29042E-08 9.30852E-08 9.29476E-08 7.55456E-08 5.93963E-08 4.76051E-08 4.12935E-08 3.65352E-08 3.71970E-08 5.21912E-08 8.02057E-08 1.21892E-07 1.86496E-07 2.35135E-07 1.04732E-07 4.22783E-08 4.65762E-08 7.58073E-08 7.79331E-08 5.75407E-08 3.61509E-08 2.56255E-08 3.04573E-08 5.36578E-08 9.24811E-08 9.80266E-08 6.17923E-08 2.36365E-08 3.82806E-08 5.03284E-08 5.12491E-08 3.73919E-08 2.34194E-08 2.60215E-08 2.90466E-08 2.45768E-08 3.72154E-08 2.46155E-08 1.95316E-08 1.83760E-08 1.99613E-08 2.36885E-08 2.06672E-08 1.50154E-08 1.45427E-08 1.58183E-08 1.70564E-08 1.86277E-08 2.16690E-08 1.97339E-08 1.67292E-08 1.43288E-08 1.38128E-08 1.49270E-08 1.10184E-08 5.71202E-09 3.08046E-09 4.21637E-09 2.22842E-09 6.53757E-10 2.42850E-10 1.06473E-10 4.62878E-11 1.87241E-11 6.98166E-12 2.42911E-12 8.11735E-13 2.70119E-13 9.04659E-14
|
||||
1.00395E-13 3.10297E-13 9.74007E-13 3.09140E-12 9.66126E-12 2.90340E-11 8.29162E-11 2.25056E-10 5.88072E-10 1.52870E-09 4.15246E-09 1.20281E-08 3.25763E-08 6.12733E-08 7.24227E-08 7.24351E-08 6.89739E-08 6.44213E-08 6.06043E-08 5.88049E-08 6.12468E-08 7.02526E-08 8.55137E-08 1.01379E-07 1.17435E-07 1.10049E-07 5.93222E-08 3.57569E-08 4.43427E-08 6.79194E-08 7.87981E-08 6.57704E-08 4.31389E-08 2.76129E-08 2.75646E-08 4.19311E-08 6.75913E-08 7.21313E-08 4.69404E-08 2.17775E-08 2.38212E-08 3.41662E-08 3.39607E-08 2.71160E-08 2.20754E-08 2.36058E-08 2.62335E-08 2.39947E-08 2.08291E-08 1.86009E-08 1.69637E-08 1.56636E-08 1.45648E-08 1.45218E-08 1.31646E-08 1.11608E-08 1.04547E-08 1.10253E-08 1.18592E-08 1.22029E-08 1.25574E-08 1.22845E-08 1.09972E-08 9.27076E-09 8.52098E-09 8.26660E-09 6.25092E-09 3.63958E-09 2.74981E-09 3.36581E-09 2.00653E-09 6.28282E-10 2.39092E-10 1.05685E-10 4.60999E-11 1.86812E-11 6.97267E-12 2.42739E-12 8.11435E-13 2.70071E-13 9.04589E-14
|
||||
1.00384E-13 3.10223E-13 9.73535E-13 3.08858E-12 9.64549E-12 2.89501E-11 8.24901E-11 2.22953E-10 5.77686E-10 1.47517E-09 3.85028E-09 1.02301E-08 2.40819E-08 4.09946E-08 5.15113E-08 5.66371E-08 5.89699E-08 5.94047E-08 5.89331E-08 5.84650E-08 5.93855E-08 6.22206E-08 6.78377E-08 7.13474E-08 6.93776E-08 5.69048E-08 3.70412E-08 2.95279E-08 3.94982E-08 5.92464E-08 7.22061E-08 6.40885E-08 4.40738E-08 2.78255E-08 2.51527E-08 3.40307E-08 4.93936E-08 5.18882E-08 3.56867E-08 2.04985E-08 1.83801E-08 2.24585E-08 2.41321E-08 2.17185E-08 1.94928E-08 2.06288E-08 2.24118E-08 2.28724E-08 2.02549E-08 1.76004E-08 1.56231E-08 1.40811E-08 1.27921E-08 1.17500E-08 1.06340E-08 9.42140E-09 8.66731E-09 8.58213E-09 8.57312E-09 8.46942E-09 8.19420E-09 7.86826E-09 7.17032E-09 5.95046E-09 5.55749E-09 5.19157E-09 4.13317E-09 2.91236E-09 2.33485E-09 2.69307E-09 1.77544E-09 6.00266E-10 2.34548E-10 1.04670E-10 4.58521E-11 1.86242E-11 6.96069E-12 2.42509E-12 8.11034E-13 2.70007E-13 9.04495E-14
|
||||
1.00371E-13 3.10131E-13 9.72945E-13 3.08506E-12 9.62578E-12 2.88456E-11 8.19609E-11 2.20358E-10 5.65059E-10 1.41203E-09 3.51764E-09 8.52308E-09 1.76652E-08 2.78171E-08 3.52435E-08 4.02390E-08 4.36436E-08 4.55593E-08 4.62885E-08 4.61935E-08 4.58570E-08 4.59609E-08 4.64599E-08 4.56144E-08 4.14404E-08 3.34164E-08 2.49965E-08 2.53810E-08 3.47950E-08 5.06486E-08 6.17827E-08 5.69160E-08 4.16721E-08 2.78329E-08 2.33998E-08 2.81598E-08 3.63162E-08 3.71243E-08 2.70011E-08 1.91329E-08 1.63397E-08 1.47793E-08 1.66401E-08 1.65319E-08 1.60060E-08 1.71740E-08 1.88635E-08 1.96484E-08 1.89244E-08 1.69428E-08 1.49295E-08 1.32093E-08 1.17339E-08 1.04676E-08 9.36066E-09 8.39593E-09 7.61931E-09 7.12654E-09 6.73969E-09 6.24094E-09 5.73430E-09 5.35322E-09 4.79423E-09 4.16788E-09 3.88162E-09 3.63303E-09 3.07894E-09 2.47285E-09 2.04204E-09 2.17776E-09 1.55221E-09 5.71028E-10 2.29430E-10 1.03448E-10 4.55470E-11 1.85534E-11 6.94577E-12 2.42222E-12 8.10534E-13 2.69927E-13 9.04378E-14
|
||||
1.00355E-13 3.10020E-13 9.72237E-13 3.08084E-12 9.60217E-12 2.87206E-11 8.13304E-11 2.17294E-10 5.50419E-10 1.34158E-09 3.17567E-09 7.02033E-09 1.30320E-08 1.92181E-08 2.39653E-08 2.75259E-08 3.02282E-08 3.19461E-08 3.27113E-08 3.26200E-08 3.18885E-08 3.12732E-08 3.00593E-08 2.86405E-08 2.56339E-08 2.14299E-08 1.89580E-08 2.23895E-08 3.04660E-08 4.24723E-08 5.07654E-08 4.80954E-08 3.77129E-08 2.73072E-08 2.24444E-08 2.34653E-08 2.72261E-08 2.68281E-08 2.14528E-08 1.75685E-08 1.46768E-08 1.25037E-08 1.15821E-08 1.20603E-08 1.24725E-08 1.36978E-08 1.52171E-08 1.60684E-08 1.62655E-08 1.53328E-08 1.38668E-08 1.23503E-08 1.09593E-08 9.72435E-09 8.63431E-09 7.70079E-09 6.89380E-09 6.18921E-09 5.61042E-09 4.95624E-09 4.26757E-09 3.87139E-09 3.35373E-09 3.04604E-09 2.88718E-09 2.74890E-09 2.46405E-09 2.14088E-09 1.79987E-09 1.78719E-09 1.34762E-09 5.41150E-10 2.23890E-10 1.02046E-10 4.51874E-11 1.84692E-11 6.92794E-12 2.41879E-12 8.09934E-13 2.69832E-13 9.04238E-14
|
||||
1.00336E-13 3.09891E-13 9.71411E-13 3.07592E-12 9.57467E-12 2.85754E-11 8.06014E-11 2.13788E-10 5.34038E-10 1.26628E-09 2.84198E-09 5.76246E-09 9.75193E-09 1.35929E-08 1.65370E-08 1.88158E-08 2.06103E-08 2.17898E-08 2.23262E-08 2.22280E-08 2.16421E-08 2.09737E-08 1.99993E-08 1.85755E-08 1.69017E-08 1.55254E-08 1.60218E-08 1.99564E-08 2.63817E-08 3.50897E-08 4.09329E-08 3.97264E-08 3.32442E-08 2.60457E-08 2.20879E-08 2.11235E-08 2.17143E-08 2.10861E-08 1.89413E-08 1.58676E-08 1.32122E-08 1.10786E-08 9.92740E-09 9.16885E-09 9.39976E-09 1.05910E-08 1.19192E-08 1.28577E-08 1.32372E-08 1.30813E-08 1.22734E-08 1.11880E-08 1.00543E-08 8.97649E-09 7.98457E-09 7.07801E-09 6.25073E-09 5.47228E-09 4.73144E-09 4.05594E-09 3.31529E-09 2.91552E-09 2.49766E-09 2.34118E-09 2.25926E-09 2.19007E-09 2.04976E-09 1.86300E-09 1.58773E-09 1.48800E-09 1.16650E-09 5.10906E-10 2.18020E-10 1.00482E-10 4.47762E-11 1.83718E-11 6.90724E-12 2.41479E-12 8.09235E-13 2.69720E-13 9.04074E-14
|
||||
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||||
1.00291E-13 3.09578E-13 9.69410E-13 3.06400E-12 9.50825E-12 2.82261E-11 7.88633E-11 2.05589E-10 4.97255E-10 1.11022E-09 2.24351E-09 3.92819E-09 5.79462E-09 7.40172E-09 8.57749E-09 9.47480E-09 1.01960E-08 1.06898E-08 1.09436E-08 1.09611E-08 1.07838E-08 1.05014E-08 1.01811E-08 9.97381E-09 1.01025E-08 1.08935E-08 1.26925E-08 1.55304E-08 1.90388E-08 2.33962E-08 2.62872E-08 2.65280E-08 2.45516E-08 2.16871E-08 1.94958E-08 1.82962E-08 1.75802E-08 1.65498E-08 1.49138E-08 1.28468E-08 1.07956E-08 9.17292E-09 7.92457E-09 6.97593E-09 6.48324E-09 6.38049E-09 6.97941E-09 7.72720E-09 8.17911E-09 8.37416E-09 8.36571E-09 8.03001E-09 7.48958E-09 6.84088E-09 6.14292E-09 5.42532E-09 4.69983E-09 3.97609E-09 3.28197E-09 2.69547E-09 2.13889E-09 1.82781E-09 1.61833E-09 1.54897E-09 1.53551E-09 1.51804E-09 1.49590E-09 1.41467E-09 1.25122E-09 1.06965E-09 8.75561E-10 4.50144E-10 2.05449E-10 9.69446E-11 4.38100E-11 1.81387E-11 6.85724E-12 2.40511E-12 8.07539E-13 2.69449E-13 9.03676E-14
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||||
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||||
1.00235E-13 3.09192E-13 9.66943E-13 3.04934E-12 9.42683E-12 2.78009E-11 7.67795E-11 1.96064E-10 4.57164E-10 9.58990E-10 1.76439E-09 2.77256E-09 3.74623E-09 4.52289E-09 5.07087E-09 5.48475E-09 5.83364E-09 6.10339E-09 6.29388E-09 6.42125E-09 6.50841E-09 6.60258E-09 6.76419E-09 7.07637E-09 7.64147E-09 8.55714E-09 9.86160E-09 1.14924E-08 1.32588E-08 1.54522E-08 1.69788E-08 1.75410E-08 1.72095E-08 1.63021E-08 1.52743E-08 1.44030E-08 1.35638E-08 1.25885E-08 1.14083E-08 1.00855E-08 8.73594E-09 7.49606E-09 6.41313E-09 5.59220E-09 5.01660E-09 4.67212E-09 4.49966E-09 4.52794E-09 4.85228E-09 5.01107E-09 5.05876E-09 5.01417E-09 4.80398E-09 4.47401E-09 4.06323E-09 3.60170E-09 3.11249E-09 2.61681E-09 2.15325E-09 1.78634E-09 1.45530E-09 1.27314E-09 1.15976E-09 1.12687E-09 1.13279E-09 1.12049E-09 1.12614E-09 1.08168E-09 9.85176E-10 8.28335E-10 6.65411E-10 3.90865E-10 1.91875E-10 9.29542E-11 4.26729E-11 1.78574E-11 6.79615E-12 2.39322E-12 8.05450E-13 2.69114E-13 9.03185E-14
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||||
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|
||||
1.00168E-13 3.08733E-13 9.64012E-13 3.03196E-12 9.33072E-12 2.73036E-11 7.43870E-11 1.85541E-10 4.15982E-10 8.21522E-10 1.39817E-09 2.03742E-09 2.60712E-09 3.04531E-09 3.35431E-09 3.59470E-09 3.81484E-09 4.01301E-09 4.19243E-09 4.36345E-09 4.54408E-09 4.75822E-09 5.03651E-09 5.41168E-09 5.91420E-09 6.56080E-09 7.34406E-09 8.22083E-09 9.11269E-09 1.02332E-08 1.10631E-08 1.15274E-08 1.16224E-08 1.14070E-08 1.09838E-08 1.04535E-08 9.84755E-09 9.15475E-09 8.36350E-09 7.49511E-09 6.61228E-09 5.77709E-09 5.03408E-09 4.40694E-09 3.91684E-09 3.54052E-09 3.25045E-09 3.02482E-09 2.91338E-09 3.00252E-09 3.01630E-09 2.99737E-09 2.91922E-09 2.75949E-09 2.53942E-09 2.27838E-09 1.99349E-09 1.70371E-09 1.44640E-09 1.23856E-09 1.05492E-09 9.54876E-10 8.87081E-10 8.64440E-10 8.71160E-10 8.63043E-10 8.60880E-10 8.40253E-10 7.72680E-10 6.53785E-10 5.14391E-10 3.35476E-10 1.77469E-10 8.85993E-11 4.13898E-11 1.75318E-11 6.72442E-12 2.37915E-12 8.02971E-13 2.68717E-13 9.02601E-14
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||||
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||||
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|
||||
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|
||||
9.99052E-14 3.06921E-13 9.52494E-13 2.96405E-12 8.95987E-12 2.54320E-11 6.58239E-11 1.51233E-10 3.00841E-10 5.11282E-10 7.48055E-10 9.69775E-10 1.15326E-09 1.29466E-09 1.40005E-09 1.48995E-09 1.58240E-09 1.67793E-09 1.77740E-09 1.88190E-09 1.99246E-09 2.11005E-09 2.23517E-09 2.36768E-09 2.50638E-09 2.64860E-09 2.79025E-09 2.92603E-09 3.04971E-09 3.19393E-09 3.31226E-09 3.39108E-09 3.42658E-09 3.41788E-09 3.36528E-09 3.27074E-09 3.13853E-09 2.97440E-09 2.78567E-09 2.57977E-09 2.36485E-09 2.14868E-09 1.93770E-09 1.73673E-09 1.54945E-09 1.37726E-09 1.22096E-09 1.07991E-09 9.54922E-10 8.71828E-10 8.56381E-10 8.52938E-10 8.47336E-10 8.29767E-10 7.96630E-10 7.51505E-10 6.97646E-10 6.44078E-10 6.01609E-10 5.58706E-10 5.17495E-10 4.88313E-10 4.66815E-10 4.46501E-10 4.35678E-10 4.31304E-10 4.22618E-10 4.05940E-10 3.83107E-10 3.38796E-10 2.73771E-10 2.05291E-10 1.32853E-10 7.40656E-11 3.68765E-11 1.63267E-11 6.44997E-12 2.32436E-12 7.93233E-13 2.67149E-13 9.00290E-14
|
||||
9.98521E-14 3.06557E-13 9.50183E-13 2.95051E-12 8.88685E-12 2.50726E-11 6.42584E-11 1.45482E-10 2.83946E-10 4.72901E-10 6.80235E-10 8.71597E-10 1.02910E-09 1.15060E-09 1.24134E-09 1.31903E-09 1.39901E-09 1.48165E-09 1.56736E-09 1.65678E-09 1.75042E-09 1.84844E-09 1.95091E-09 2.05718E-09 2.16594E-09 2.27512E-09 2.38159E-09 2.48145E-09 2.57042E-09 2.67104E-09 2.75417E-09 2.80738E-09 2.82813E-09 2.81513E-09 2.76930E-09 2.69218E-09 2.58615E-09 2.45553E-09 2.30560E-09 2.14229E-09 1.97113E-09 1.79772E-09 1.62708E-09 1.46317E-09 1.30857E-09 1.16525E-09 1.03357E-09 9.13598E-10 8.07038E-10 7.40119E-10 7.27680E-10 7.27128E-10 7.24478E-10 7.12025E-10 6.87039E-10 6.52027E-10 6.10086E-10 5.68063E-10 5.34735E-10 5.00419E-10 4.66470E-10 4.40945E-10 4.21826E-10 4.03108E-10 3.91128E-10 3.85915E-10 3.78327E-10 3.63250E-10 3.41899E-10 3.04907E-10 2.48358E-10 1.88963E-10 1.25752E-10 7.15300E-11 3.60540E-11 1.60980E-11 6.39631E-12 2.31348E-12 7.91282E-13 2.66834E-13 8.99824E-14
|
||||
9.97963E-14 3.06174E-13 9.47762E-13 2.93636E-12 8.81084E-12 2.47019E-11 6.26693E-11 1.39795E-10 2.67821E-10 4.37624E-10 6.19771E-10 7.85716E-10 9.21536E-10 1.02622E-09 1.10452E-09 1.17153E-09 1.24048E-09 1.31158E-09 1.38500E-09 1.46089E-09 1.53954E-09 1.62075E-09 1.70421E-09 1.78922E-09 1.87442E-09 1.95814E-09 2.03818E-09 2.11157E-09 2.17525E-09 2.24500E-09 2.30277E-09 2.33752E-09 2.34747E-09 2.33180E-09 2.29080E-09 2.22622E-09 2.14007E-09 2.03483E-09 1.91453E-09 1.78353E-09 1.64623E-09 1.50660E-09 1.36823E-09 1.23422E-09 1.10690E-09 9.87476E-10 8.76972E-10 7.75313E-10 6.85182E-10 6.34491E-10 6.25972E-10 6.26315E-10 6.25672E-10 6.16694E-10 5.97544E-10 5.70171E-10 5.36964E-10 5.03644E-10 4.76927E-10 4.49169E-10 4.20883E-10 3.98317E-10 3.81042E-10 3.64677E-10 3.52169E-10 3.45600E-10 3.38972E-10 3.25844E-10 3.05749E-10 2.74619E-10 2.25801E-10 1.73982E-10 1.18859E-10 6.89887E-11 3.52178E-11 1.58626E-11 6.34062E-12 2.30211E-12 7.89239E-13 2.66503E-13 8.99334E-14
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|
||||
9.78819E-14 2.93262E-13 8.67847E-13 2.48897E-12 6.59922E-12 1.53374E-11 3.00989E-11 4.95772E-11 7.01394E-11 8.83045E-11 1.02385E-10 1.12401E-10 1.19184E-10 1.23679E-10 1.26645E-10 1.28907E-10 1.30975E-10 1.32828E-10 1.34442E-10 1.35797E-10 1.36871E-10 1.37645E-10 1.38099E-10 1.38217E-10 1.37985E-10 1.37389E-10 1.36421E-10 1.35074E-10 1.33811E-10 1.32552E-10 1.30875E-10 1.28786E-10 1.26292E-10 1.23406E-10 1.20145E-10 1.16532E-10 1.12591E-10 1.08352E-10 1.03842E-10 9.90961E-11 9.41480E-11 8.90335E-11 8.37883E-11 7.84469E-11 7.30432E-11 6.77996E-11 6.26579E-11 5.83120E-11 5.50300E-11 5.34055E-11 5.29755E-11 5.29159E-11 5.28504E-11 5.27637E-11 5.24773E-11 5.19157E-11 5.10981E-11 5.00457E-11 4.89525E-11 4.78152E-11 4.66595E-11 4.56012E-11 4.44813E-11 4.32984E-11 4.21304E-11 4.10822E-11 4.00388E-11 3.89027E-11 3.79095E-11 3.66340E-11 3.47793E-11 3.30504E-11 3.01604E-11 2.48716E-11 1.72995E-11 9.93254E-12 4.74524E-12 1.94695E-12 7.22147E-13 2.55345E-13 8.82505E-14
|
||||
9.74101E-14 2.90149E-13 8.49164E-13 2.39027E-12 6.16106E-12 1.37821E-11 2.58747E-11 4.08183E-11 5.56653E-11 6.80912E-11 7.72716E-11 8.35284E-11 8.76057E-11 9.02150E-11 9.18830E-11 9.31152E-11 9.42055E-11 9.51414E-11 9.59106E-11 9.65014E-11 9.69029E-11 9.71051E-11 9.70987E-11 9.68753E-11 9.64283E-11 9.57523E-11 9.48440E-11 9.37014E-11 9.26813E-11 9.16426E-11 9.03526E-11 8.88147E-11 8.70344E-11 8.50191E-11 8.27788E-11 8.03258E-11 7.76745E-11 7.48404E-11 7.18402E-11 6.86917E-11 6.54140E-11 6.20271E-11 5.85514E-11 5.50069E-11 5.14133E-11 4.79098E-11 4.44512E-11 4.12415E-11 3.89822E-11 3.74683E-11 3.67294E-11 3.66300E-11 3.65187E-11 3.64046E-11 3.62898E-11 3.60055E-11 3.55619E-11 3.49701E-11 3.42833E-11 3.36184E-11 3.29028E-11 3.22326E-11 3.15669E-11 3.08723E-11 3.01831E-11 2.95184E-11 2.89063E-11 2.81975E-11 2.75621E-11 2.69120E-11 2.59717E-11 2.52548E-11 2.37272E-11 2.03732E-11 1.48602E-11 8.93202E-12 4.43078E-12 1.86925E-12 7.06535E-13 2.52658E-13 8.78354E-14
|
||||
9.67954E-14 2.86136E-13 8.25433E-13 2.26817E-12 5.64280E-12 1.20557E-11 2.15158E-11 3.23754E-11 4.24575E-11 5.03912E-11 5.59325E-11 5.95158E-11 6.17371E-11 6.30912E-11 6.39170E-11 6.44950E-11 6.49769E-11 6.53563E-11 6.56272E-11 6.57838E-11 6.58209E-11 6.57340E-11 6.55190E-11 6.51725E-11 6.46915E-11 6.40744E-11 6.33201E-11 6.24286E-11 6.16342E-11 6.08391E-11 5.99002E-11 5.88199E-11 5.76019E-11 5.62504E-11 5.47708E-11 5.31696E-11 5.14544E-11 4.96335E-11 4.77160E-11 4.57111E-11 4.36288E-11 4.14796E-11 3.92743E-11 3.70239E-11 3.47393E-11 3.24922E-11 3.02597E-11 2.81016E-11 2.64803E-11 2.51406E-11 2.44148E-11 2.41525E-11 2.40547E-11 2.39713E-11 2.38789E-11 2.37625E-11 2.35530E-11 2.32557E-11 2.28767E-11 2.24940E-11 2.21028E-11 2.17027E-11 2.13416E-11 2.09601E-11 2.05958E-11 2.02350E-11 1.98970E-11 1.95079E-11 1.91277E-11 1.88866E-11 1.85338E-11 1.84627E-11 1.78929E-11 1.60375E-11 1.23240E-11 7.81380E-12 4.05832E-12 1.77328E-12 6.86734E-13 2.49196E-13 8.72946E-14
|
||||
9.58839E-14 2.80293E-13 7.91737E-13 2.10183E-12 4.98034E-12 1.00232E-11 1.68067E-11 2.39038E-11 2.99328E-11 3.42920E-11 3.70977E-11 3.87690E-11 3.97184E-11 4.02437E-11 4.05308E-11 4.07035E-11 4.08200E-11 4.08777E-11 4.08744E-11 4.08080E-11 4.06765E-11 4.04784E-11 4.02124E-11 3.98776E-11 3.94733E-11 3.89990E-11 3.84548E-11 3.78412E-11 3.72416E-11 3.67037E-11 3.60948E-11 3.54162E-11 3.46696E-11 3.38573E-11 3.29816E-11 3.20455E-11 3.10522E-11 3.00054E-11 2.89091E-11 2.77677E-11 2.65853E-11 2.53668E-11 2.41169E-11 2.28408E-11 2.15434E-11 2.02363E-11 1.89620E-11 1.76951E-11 1.64574E-11 1.56028E-11 1.48611E-11 1.44316E-11 1.42477E-11 1.41781E-11 1.41204E-11 1.40565E-11 1.40025E-11 1.39031E-11 1.37606E-11 1.35773E-11 1.34008E-11 1.32200E-11 1.30392E-11 1.28748E-11 1.26993E-11 1.25560E-11 1.24137E-11 1.22520E-11 1.20784E-11 1.20205E-11 1.20131E-11 1.23052E-11 1.23939E-11 1.17070E-11 9.57610E-12 6.48913E-12 3.58096E-12 1.64266E-12 6.58659E-13 2.44158E-13 8.64923E-14
|
||||
9.45853E-14 2.72125E-13 7.45882E-13 1.88587E-12 4.18208E-12 7.79999E-12 1.21503E-11 1.62390E-11 1.93759E-11 2.14256E-11 2.26089E-11 2.32274E-11 2.35214E-11 2.36446E-11 2.36846E-11 2.36824E-11 2.36510E-11 2.35896E-11 2.34974E-11 2.33739E-11 2.32184E-11 2.30306E-11 2.28102E-11 2.25570E-11 2.22711E-11 2.19526E-11 2.16015E-11 2.12185E-11 2.08098E-11 2.04768E-11 2.01119E-11 1.97157E-11 1.92889E-11 1.88326E-11 1.83480E-11 1.78363E-11 1.72988E-11 1.67370E-11 1.61527E-11 1.55476E-11 1.49235E-11 1.42823E-11 1.36260E-11 1.29566E-11 1.22763E-11 1.15871E-11 1.09034E-11 1.02289E-11 9.56282E-12 8.93731E-12 8.47900E-12 8.08176E-12 7.84549E-12 7.69854E-12 7.65803E-12 7.62033E-12 7.58228E-12 7.55754E-12 7.51848E-12 7.46030E-12 7.38393E-12 7.31582E-12 7.24615E-12 7.17996E-12 7.11762E-12 7.06398E-12 7.01976E-12 6.97063E-12 6.92949E-12 6.95132E-12 7.06851E-12 7.44475E-12 7.81153E-12 7.79631E-12 6.84191E-12 5.02678E-12 3.00325E-12 1.47319E-12 6.20516E-13 2.37118E-13 8.53487E-14
|
||||
9.29105E-14 2.61903E-13 6.90857E-13 1.64355E-12 3.36904E-12 5.77987E-12 8.35514E-12 1.05216E-11 1.20107E-11 1.28718E-11 1.32952E-11 1.34641E-11 1.35040E-11 1.34871E-11 1.34506E-11 1.34024E-11 1.33400E-11 1.32630E-11 1.31712E-11 1.30646E-11 1.29431E-11 1.28064E-11 1.26547E-11 1.24880E-11 1.23064E-11 1.21100E-11 1.18991E-11 1.16737E-11 1.14343E-11 1.12059E-11 1.09970E-11 1.07744E-11 1.05384E-11 1.02894E-11 1.00281E-11 9.75498E-12 9.47048E-12 9.17526E-12 8.87002E-12 8.55550E-12 8.23245E-12 7.90159E-12 7.56370E-12 7.21963E-12 6.87023E-12 6.51634E-12 6.15882E-12 5.80525E-12 5.45640E-12 5.10952E-12 4.77615E-12 4.53732E-12 4.30485E-12 4.17421E-12 4.04644E-12 4.01937E-12 3.99302E-12 3.97821E-12 3.95837E-12 3.94710E-12 3.92761E-12 3.90021E-12 3.87555E-12 3.85423E-12 3.83325E-12 3.81803E-12 3.80569E-12 3.79848E-12 3.79650E-12 3.84150E-12 3.95655E-12 4.26273E-12 4.64846E-12 4.89623E-12 4.61056E-12 3.68796E-12 2.41122E-12 1.28282E-12 5.74791E-13 2.28312E-13 8.38727E-14
|
||||
9.06671E-14 2.48805E-13 6.24443E-13 1.37534E-12 2.56476E-12 4.00741E-12 5.35705E-12 6.35734E-12 6.95622E-12 7.24368E-12 7.34172E-12 7.34416E-12 7.30730E-12 7.26003E-12 7.21523E-12 7.16992E-12 7.11836E-12 7.06049E-12 6.99628E-12 6.92573E-12 6.84883E-12 6.76561E-12 6.67607E-12 6.58027E-12 6.47830E-12 6.37025E-12 6.25618E-12 6.13622E-12 6.01050E-12 5.87921E-12 5.74374E-12 5.62936E-12 5.50959E-12 5.38460E-12 5.25460E-12 5.11977E-12 4.98031E-12 4.83645E-12 4.68844E-12 4.53655E-12 4.38104E-12 4.22217E-12 4.06024E-12 3.89553E-12 3.72835E-12 3.55900E-12 3.38781E-12 3.21509E-12 3.04115E-12 2.87034E-12 2.70128E-12 2.53193E-12 2.37434E-12 2.25785E-12 2.14781E-12 2.07037E-12 2.00737E-12 1.96590E-12 1.95374E-12 1.94469E-12 1.93651E-12 1.92894E-12 1.92424E-12 1.91643E-12 1.91099E-12 1.90857E-12 1.90707E-12 1.90928E-12 1.91667E-12 1.94195E-12 2.01744E-12 2.19623E-12 2.49199E-12 2.79163E-12 2.84879E-12 2.50718E-12 1.82182E-12 1.07161E-12 5.19628E-13 2.17034E-13 8.18944E-14
|
||||
8.76274E-14 2.32052E-13 5.45719E-13 1.08871E-12 1.80554E-12 2.52935E-12 3.10601E-12 3.47319E-12 3.65506E-12 3.71442E-12 3.70935E-12 3.67777E-12 3.63984E-12 3.60461E-12 3.57520E-12 3.54764E-12 3.51773E-12 3.48549E-12 3.45089E-12 3.41395E-12 3.37468E-12 3.33309E-12 3.28920E-12 3.24301E-12 3.19457E-12 3.14392E-12 3.09106E-12 3.03606E-12 2.97895E-12 2.91978E-12 2.85862E-12 2.79551E-12 2.73051E-12 2.66370E-12 2.60454E-12 2.54603E-12 2.48580E-12 2.42392E-12 2.36046E-12 2.29551E-12 2.22913E-12 2.16141E-12 2.09243E-12 2.02228E-12 1.95106E-12 1.87886E-12 1.80576E-12 1.73188E-12 1.65730E-12 1.58213E-12 1.50647E-12 1.43040E-12 1.35405E-12 1.27941E-12 1.20552E-12 1.13158E-12 1.06482E-12 1.01509E-12 9.68017E-13 9.24674E-13 8.96954E-13 8.72307E-13 8.54023E-13 8.48478E-13 8.45922E-13 8.42764E-13 8.39459E-13 8.38982E-13 8.44741E-13 8.55990E-13 8.83681E-13 9.55281E-13 1.10925E-12 1.33704E-12 1.52295E-12 1.51613E-12 1.26106E-12 8.44907E-13 4.54188E-13 2.02613E-13 7.92114E-14
|
||||
8.36871E-14 2.11997E-13 4.60601E-13 8.15381E-13 1.17142E-12 1.43328E-12 1.58758E-12 1.66071E-12 1.68239E-12 1.67669E-12 1.65967E-12 1.64019E-12 1.62233E-12 1.60750E-12 1.59582E-12 1.58528E-12 1.57415E-12 1.56245E-12 1.55015E-12 1.53727E-12 1.52380E-12 1.50973E-12 1.49507E-12 1.47981E-12 1.46395E-12 1.44749E-12 1.43044E-12 1.41279E-12 1.39454E-12 1.37571E-12 1.35628E-12 1.33627E-12 1.31567E-12 1.29450E-12 1.27277E-12 1.25046E-12 1.22761E-12 1.20420E-12 1.18027E-12 1.15580E-12 1.13082E-12 1.10534E-12 1.07936E-12 1.05292E-12 1.02601E-12 1.00195E-12 9.78502E-13 9.54632E-13 9.30349E-13 9.05668E-13 8.80605E-13 8.55175E-13 8.29397E-13 8.03290E-13 7.76874E-13 7.50167E-13 7.23191E-13 6.95967E-13 6.68518E-13 6.40865E-13 6.13031E-13 5.85039E-13 5.56915E-13 5.28682E-13 5.00433E-13 4.73117E-13 4.45755E-13 4.13820E-13 3.84114E-13 3.45355E-13 3.18491E-13 3.24929E-13 3.61710E-13 4.47700E-13 6.15733E-13 7.72106E-13 7.87687E-13 6.27249E-13 3.83284E-13 1.85351E-13 7.57304E-14
|
||||
@@ -0,0 +1,92 @@
|
||||
from .html_writer import HTMLWriter
|
||||
from matplotlib.animation import Animation
|
||||
import matplotlib.pyplot as plt
|
||||
import tempfile
|
||||
import random
|
||||
import os
|
||||
|
||||
|
||||
__all__ = ['anim_to_html', 'display_animation']
|
||||
|
||||
|
||||
class _NameOnlyTemporaryFile(object):
|
||||
"""A context-managed temporary file which is not opened.
|
||||
|
||||
The file should be accessible by name on any system.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
suffix : string
|
||||
The suffix of the temporary file (default = '')
|
||||
prefix : string
|
||||
The prefix of the temporary file (default = '_tmp_')
|
||||
hash_length : string
|
||||
The length of the random hash. The size of the hash space will
|
||||
be 16 ** hash_length (default=8)
|
||||
seed : integer
|
||||
the seed for the random number generator. If not specified, the
|
||||
system time will be used as a seed.
|
||||
absolute : boolean
|
||||
If true, return an absolute path to a temporary file in the current
|
||||
working directory.
|
||||
|
||||
Example
|
||||
-------
|
||||
|
||||
>>> with _NameOnlyTemporaryFile(seed=0, absolute=False) as f:
|
||||
... print(f)
|
||||
...
|
||||
_tmp_d82c07cd
|
||||
>>> os.path.exists('_tmp_d82c07cd') # file removed after context
|
||||
False
|
||||
|
||||
"""
|
||||
def __init__(self, prefix='_tmp_', suffix='', hash_length=8,
|
||||
seed=None, absolute=True):
|
||||
rng = random.Random(seed)
|
||||
self.name = '%s%0*x%s' % (prefix, hash_length,
|
||||
rng.getrandbits(4 * hash_length), suffix)
|
||||
if absolute:
|
||||
self.name = os.path.abspath(self.name)
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, *exc_info):
|
||||
if os.path.exists(self.name):
|
||||
os.remove(self.name)
|
||||
|
||||
|
||||
def anim_to_html(anim, fps=None, embed_frames=True, default_mode='loop'):
|
||||
"""Generate HTML representation of the animation"""
|
||||
if fps is None and hasattr(anim, '_interval'):
|
||||
# Convert interval in ms to frames per second
|
||||
fps = 1000. / anim._interval
|
||||
|
||||
plt.close(anim._fig)
|
||||
if hasattr(anim, "_html_representation"):
|
||||
return anim._html_representation
|
||||
else:
|
||||
# tempfile can't be used here: we need a filename, and this
|
||||
# fails on windows. Instead, we use a custom filename generator
|
||||
#with tempfile.NamedTemporaryFile(suffix='.html') as f:
|
||||
with _NameOnlyTemporaryFile(suffix='.html') as f:
|
||||
anim.save(f.name, writer=HTMLWriter(fps=fps,
|
||||
embed_frames=embed_frames,
|
||||
default_mode=default_mode))
|
||||
html = open(f.name).read()
|
||||
|
||||
anim._html_representation = html
|
||||
return html
|
||||
|
||||
|
||||
def display_animation(anim, **kwargs):
|
||||
"""Display the animation with an IPython HTML object"""
|
||||
from IPython.display import HTML
|
||||
return HTML(anim_to_html(anim, **kwargs))
|
||||
|
||||
|
||||
# This is the magic that makes animations display automatically in the
|
||||
# IPython notebook. The _repr_html_ method is a special method recognized
|
||||
# by IPython.
|
||||
Animation._repr_html_ = anim_to_html
|
||||
@@ -0,0 +1 @@
|
||||
from .html_writer import HTMLWriter
|
||||
@@ -0,0 +1,97 @@
|
||||
import numpy as np
|
||||
from matplotlib import pyplot as plt
|
||||
from matplotlib import animation
|
||||
from JSAnimation import IPython_display
|
||||
|
||||
def basic_animation(frames=100, interval=30):
|
||||
"""Plot a basic sine wave with oscillating amplitude"""
|
||||
fig = plt.figure()
|
||||
ax = plt.axes(xlim=(0, 10), ylim=(-2, 2))
|
||||
line, = ax.plot([], [], lw=2)
|
||||
|
||||
x = np.linspace(0, 10, 1000)
|
||||
|
||||
def init():
|
||||
line.set_data([], [])
|
||||
return line,
|
||||
|
||||
def animate(i):
|
||||
y = np.cos(i * 0.02 * np.pi) * np.sin(x - i * 0.02 * np.pi)
|
||||
line.set_data(x, y)
|
||||
return line,
|
||||
|
||||
return animation.FuncAnimation(fig, animate, init_func=init,
|
||||
frames=frames, interval=interval)
|
||||
|
||||
|
||||
def lorenz_animation(N_trajectories=20, rseed=1, frames=200, interval=30):
|
||||
"""Plot a 3D visualization of the dynamics of the Lorenz system"""
|
||||
from scipy import integrate
|
||||
from mpl_toolkits.mplot3d import Axes3D
|
||||
from matplotlib.colors import cnames
|
||||
|
||||
def lorentz_deriv(coords, t0, sigma=10., beta=8./3, rho=28.0):
|
||||
"""Compute the time-derivative of a Lorentz system."""
|
||||
x, y, z = coords
|
||||
return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]
|
||||
|
||||
# Choose random starting points, uniformly distributed from -15 to 15
|
||||
np.random.seed(rseed)
|
||||
x0 = -15 + 30 * np.random.random((N_trajectories, 3))
|
||||
|
||||
# Solve for the trajectories
|
||||
t = np.linspace(0, 2, 500)
|
||||
x_t = np.asarray([integrate.odeint(lorentz_deriv, x0i, t)
|
||||
for x0i in x0])
|
||||
|
||||
# Set up figure & 3D axis for animation
|
||||
fig = plt.figure()
|
||||
ax = fig.add_axes([0, 0, 1, 1], projection='3d')
|
||||
ax.axis('off')
|
||||
|
||||
# choose a different color for each trajectory
|
||||
colors = plt.cm.jet(np.linspace(0, 1, N_trajectories))
|
||||
|
||||
# set up lines and points
|
||||
lines = sum([ax.plot([], [], [], '-', c=c)
|
||||
for c in colors], [])
|
||||
pts = sum([ax.plot([], [], [], 'o', c=c, ms=4)
|
||||
for c in colors], [])
|
||||
|
||||
# prepare the axes limits
|
||||
ax.set_xlim((-25, 25))
|
||||
ax.set_ylim((-35, 35))
|
||||
ax.set_zlim((5, 55))
|
||||
|
||||
# set point-of-view: specified by (altitude degrees, azimuth degrees)
|
||||
ax.view_init(30, 0)
|
||||
|
||||
# initialization function: plot the background of each frame
|
||||
def init():
|
||||
for line, pt in zip(lines, pts):
|
||||
line.set_data([], [])
|
||||
line.set_3d_properties([])
|
||||
|
||||
pt.set_data([], [])
|
||||
pt.set_3d_properties([])
|
||||
return lines + pts
|
||||
|
||||
# animation function: called sequentially
|
||||
def animate(i):
|
||||
# we'll step two time-steps per frame. This leads to nice results.
|
||||
i = (2 * i) % x_t.shape[1]
|
||||
|
||||
for line, pt, xi in zip(lines, pts, x_t):
|
||||
x, y, z = xi[:i + 1].T
|
||||
line.set_data(x, y)
|
||||
line.set_3d_properties(z)
|
||||
|
||||
pt.set_data(x[-1:], y[-1:])
|
||||
pt.set_3d_properties(z[-1:])
|
||||
|
||||
ax.view_init(30, 0.3 * i)
|
||||
fig.canvas.draw()
|
||||
return lines + pts
|
||||
|
||||
return animation.FuncAnimation(fig, animate, init_func=init,
|
||||
frames=frames, interval=interval)
|
||||
@@ -0,0 +1,327 @@
|
||||
import os
|
||||
import sys
|
||||
import random
|
||||
import string
|
||||
import warnings
|
||||
if sys.version_info < (3, 0):
|
||||
from cStringIO import StringIO as InMemory
|
||||
else:
|
||||
from io import BytesIO as InMemory
|
||||
from matplotlib.animation import writers, FileMovieWriter
|
||||
from base64 import b64encode
|
||||
|
||||
|
||||
ICON_DIR = os.path.join(os.path.dirname(__file__), 'icons')
|
||||
|
||||
|
||||
class _Icons(object):
|
||||
"""This class is a container for base64 representations of the icons"""
|
||||
icons = ['first', 'prev', 'reverse', 'pause', 'play', 'next', 'last']
|
||||
|
||||
def __init__(self, icon_dir=ICON_DIR, extension='png'):
|
||||
self.icon_dir = icon_dir
|
||||
self.extension = extension
|
||||
for icon in self.icons:
|
||||
setattr(self, icon,
|
||||
self._load_base64('{0}.{1}'.format(icon, extension)))
|
||||
|
||||
def _load_base64(self, filename):
|
||||
data = open(os.path.join(self.icon_dir, filename), 'rb').read()
|
||||
return 'data:image/{0};base64,{1}'.format(self.extension,
|
||||
b64encode(data).decode('ascii'))
|
||||
|
||||
|
||||
JS_INCLUDE = """
|
||||
<script language="javascript">
|
||||
/* Define the Animation class */
|
||||
function Animation(frames, img_id, slider_id, interval, loop_select_id){
|
||||
this.img_id = img_id;
|
||||
this.slider_id = slider_id;
|
||||
this.loop_select_id = loop_select_id;
|
||||
this.interval = interval;
|
||||
this.current_frame = 0;
|
||||
this.direction = 0;
|
||||
this.timer = null;
|
||||
this.frames = new Array(frames.length);
|
||||
|
||||
for (var i=0; i<frames.length; i++)
|
||||
{
|
||||
this.frames[i] = new Image();
|
||||
this.frames[i].src = frames[i];
|
||||
}
|
||||
document.getElementById(this.slider_id).max = this.frames.length - 1;
|
||||
this.set_frame(this.current_frame);
|
||||
}
|
||||
|
||||
Animation.prototype.get_loop_state = function(){
|
||||
var button_group = document[this.loop_select_id].state;
|
||||
for (var i = 0; i < button_group.length; i++) {
|
||||
var button = button_group[i];
|
||||
if (button.checked) {
|
||||
return button.value;
|
||||
}
|
||||
}
|
||||
return undefined;
|
||||
}
|
||||
|
||||
Animation.prototype.set_frame = function(frame){
|
||||
this.current_frame = frame;
|
||||
document.getElementById(this.img_id).src = this.frames[this.current_frame].src;
|
||||
document.getElementById(this.slider_id).value = this.current_frame;
|
||||
}
|
||||
|
||||
Animation.prototype.next_frame = function()
|
||||
{
|
||||
this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));
|
||||
}
|
||||
|
||||
Animation.prototype.previous_frame = function()
|
||||
{
|
||||
this.set_frame(Math.max(0, this.current_frame - 1));
|
||||
}
|
||||
|
||||
Animation.prototype.first_frame = function()
|
||||
{
|
||||
this.set_frame(0);
|
||||
}
|
||||
|
||||
Animation.prototype.last_frame = function()
|
||||
{
|
||||
this.set_frame(this.frames.length - 1);
|
||||
}
|
||||
|
||||
Animation.prototype.slower = function()
|
||||
{
|
||||
this.interval /= 0.7;
|
||||
if(this.direction > 0){this.play_animation();}
|
||||
else if(this.direction < 0){this.reverse_animation();}
|
||||
}
|
||||
|
||||
Animation.prototype.faster = function()
|
||||
{
|
||||
this.interval *= 0.7;
|
||||
if(this.direction > 0){this.play_animation();}
|
||||
else if(this.direction < 0){this.reverse_animation();}
|
||||
}
|
||||
|
||||
Animation.prototype.anim_step_forward = function()
|
||||
{
|
||||
this.current_frame += 1;
|
||||
if(this.current_frame < this.frames.length){
|
||||
this.set_frame(this.current_frame);
|
||||
}else{
|
||||
var loop_state = this.get_loop_state();
|
||||
if(loop_state == "loop"){
|
||||
this.first_frame();
|
||||
}else if(loop_state == "reflect"){
|
||||
this.last_frame();
|
||||
this.reverse_animation();
|
||||
}else{
|
||||
this.pause_animation();
|
||||
this.last_frame();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Animation.prototype.anim_step_reverse = function()
|
||||
{
|
||||
this.current_frame -= 1;
|
||||
if(this.current_frame >= 0){
|
||||
this.set_frame(this.current_frame);
|
||||
}else{
|
||||
var loop_state = this.get_loop_state();
|
||||
if(loop_state == "loop"){
|
||||
this.last_frame();
|
||||
}else if(loop_state == "reflect"){
|
||||
this.first_frame();
|
||||
this.play_animation();
|
||||
}else{
|
||||
this.pause_animation();
|
||||
this.first_frame();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Animation.prototype.pause_animation = function()
|
||||
{
|
||||
this.direction = 0;
|
||||
if (this.timer){
|
||||
clearInterval(this.timer);
|
||||
this.timer = null;
|
||||
}
|
||||
}
|
||||
|
||||
Animation.prototype.play_animation = function()
|
||||
{
|
||||
this.pause_animation();
|
||||
this.direction = 1;
|
||||
var t = this;
|
||||
if (!this.timer) this.timer = setInterval(function(){t.anim_step_forward();}, this.interval);
|
||||
}
|
||||
|
||||
Animation.prototype.reverse_animation = function()
|
||||
{
|
||||
this.pause_animation();
|
||||
this.direction = -1;
|
||||
var t = this;
|
||||
if (!this.timer) this.timer = setInterval(function(){t.anim_step_reverse();}, this.interval);
|
||||
}
|
||||
</script>
|
||||
"""
|
||||
|
||||
|
||||
DISPLAY_TEMPLATE = """
|
||||
<div class="animation" align="center">
|
||||
<img id="_anim_img{id}">
|
||||
<br>
|
||||
<input id="_anim_slider{id}" type="range" style="width:350px" name="points" min="0" max="1" step="1" value="0" onchange="anim{id}.set_frame(parseInt(this.value));"></input>
|
||||
<br>
|
||||
<button onclick="anim{id}.slower()">–</button>
|
||||
<button onclick="anim{id}.first_frame()"><img class="anim_icon" src="{icons.first}"></button>
|
||||
<button onclick="anim{id}.previous_frame()"><img class="anim_icon" src="{icons.prev}"></button>
|
||||
<button onclick="anim{id}.reverse_animation()"><img class="anim_icon" src="{icons.reverse}"></button>
|
||||
<button onclick="anim{id}.pause_animation()"><img class="anim_icon" src="{icons.pause}"></button>
|
||||
<button onclick="anim{id}.play_animation()"><img class="anim_icon" src="{icons.play}"></button>
|
||||
<button onclick="anim{id}.next_frame()"><img class="anim_icon" src="{icons.next}"></button>
|
||||
<button onclick="anim{id}.last_frame()"><img class="anim_icon" src="{icons.last}"></button>
|
||||
<button onclick="anim{id}.faster()">+</button>
|
||||
<form action="#n" name="_anim_loop_select{id}" class="anim_control">
|
||||
<input type="radio" name="state" value="once" {once_checked}> Once </input>
|
||||
<input type="radio" name="state" value="loop" {loop_checked}> Loop </input>
|
||||
<input type="radio" name="state" value="reflect" {reflect_checked}> Reflect </input>
|
||||
</form>
|
||||
</div>
|
||||
|
||||
|
||||
<script language="javascript">
|
||||
/* Instantiate the Animation class. */
|
||||
/* The IDs given should match those used in the template above. */
|
||||
(function() {{
|
||||
var img_id = "_anim_img{id}";
|
||||
var slider_id = "_anim_slider{id}";
|
||||
var loop_select_id = "_anim_loop_select{id}";
|
||||
var frames = new Array({Nframes});
|
||||
{fill_frames}
|
||||
|
||||
/* set a timeout to make sure all the above elements are created before
|
||||
the object is initialized. */
|
||||
setTimeout(function() {{
|
||||
anim{id} = new Animation(frames, img_id, slider_id, {interval}, loop_select_id);
|
||||
}}, 0);
|
||||
}})()
|
||||
</script>
|
||||
"""
|
||||
|
||||
INCLUDED_FRAMES = """
|
||||
for (var i=0; i<{Nframes}; i++){{
|
||||
frames[i] = "{frame_dir}/frame" + ("0000000" + i).slice(-7) + ".{frame_format}";
|
||||
}}
|
||||
"""
|
||||
|
||||
|
||||
def _included_frames(frame_list, frame_format):
|
||||
"""frame_list should be a list of filenames"""
|
||||
return INCLUDED_FRAMES.format(Nframes=len(frame_list),
|
||||
frame_dir=os.path.dirname(frame_list[0]),
|
||||
frame_format=frame_format)
|
||||
|
||||
|
||||
def _embedded_frames(frame_list, frame_format):
|
||||
"""frame_list should be a list of base64-encoded png files"""
|
||||
template = ' frames[{0}] = "data:image/{1};base64,{2}"\n'
|
||||
embedded = "\n"
|
||||
for i, frame_data in enumerate(frame_list):
|
||||
embedded += template.format(i, frame_format,
|
||||
frame_data.replace('\n', '\\\n'))
|
||||
return embedded
|
||||
|
||||
|
||||
@writers.register('html')
|
||||
class HTMLWriter(FileMovieWriter):
|
||||
# we start the animation id count at a random number: this way, if two
|
||||
# animations are meant to be included on one HTML page, there is a
|
||||
# very small chance of conflict.
|
||||
rng = random.Random()
|
||||
exec_key = 'animation.ffmpeg_path'
|
||||
args_key = 'animation.ffmpeg_args'
|
||||
supported_formats = ['png', 'jpeg', 'tiff', 'svg']
|
||||
|
||||
@classmethod
|
||||
def new_id(cls):
|
||||
#return '%16x' % cls.rng.getrandbits(64)
|
||||
return ''.join(cls.rng.choice(string.ascii_uppercase)
|
||||
for x in range(16))
|
||||
|
||||
def __init__(self, fps=30, codec=None, bitrate=None, extra_args=None,
|
||||
metadata=None, embed_frames=False, default_mode='loop'):
|
||||
self.embed_frames = embed_frames
|
||||
self.default_mode = default_mode.lower()
|
||||
|
||||
if self.default_mode not in ['loop', 'once', 'reflect']:
|
||||
self.default_mode = 'loop'
|
||||
warnings.warn("unrecognized default_mode: using 'loop'")
|
||||
|
||||
self._saved_frames = list()
|
||||
super(HTMLWriter, self).__init__(fps, codec, bitrate,
|
||||
extra_args, metadata)
|
||||
|
||||
def setup(self, fig, outfile, dpi, frame_dir=None):
|
||||
if os.path.splitext(outfile)[-1] not in ['.html', '.htm']:
|
||||
raise ValueError("outfile must be *.htm or *.html")
|
||||
|
||||
if not self.embed_frames:
|
||||
if frame_dir is None:
|
||||
frame_dir = outfile.rstrip('.html') + '_frames'
|
||||
if not os.path.exists(frame_dir):
|
||||
os.makedirs(frame_dir)
|
||||
frame_prefix = os.path.join(frame_dir, 'frame')
|
||||
else:
|
||||
frame_prefix = None
|
||||
|
||||
super(HTMLWriter, self).setup(fig, outfile, dpi,
|
||||
frame_prefix, clear_temp=False)
|
||||
|
||||
def grab_frame(self, **savefig_kwargs):
|
||||
if self.embed_frames:
|
||||
suffix = '.' + self.frame_format
|
||||
f = InMemory()
|
||||
self.fig.savefig(f, format=self.frame_format,
|
||||
dpi=self.dpi, **savefig_kwargs)
|
||||
f.seek(0)
|
||||
self._saved_frames.append(b64encode(f.read()).decode('ascii'))
|
||||
else:
|
||||
return super(HTMLWriter, self).grab_frame(**savefig_kwargs)
|
||||
|
||||
def _run(self):
|
||||
# make a ducktyped subprocess standin
|
||||
# this is called by the MovieWriter base class, but not used here.
|
||||
class ProcessStandin(object):
|
||||
returncode = 0
|
||||
def communicate(self):
|
||||
return ('', '')
|
||||
self._proc = ProcessStandin()
|
||||
|
||||
# save the frames to an html file
|
||||
if self.embed_frames:
|
||||
fill_frames = _embedded_frames(self._saved_frames,
|
||||
self.frame_format)
|
||||
else:
|
||||
# temp names is filled by FileMovieWriter
|
||||
fill_frames = _included_frames(self._temp_names,
|
||||
self.frame_format)
|
||||
|
||||
mode_dict = dict(once_checked='',
|
||||
loop_checked='',
|
||||
reflect_checked='')
|
||||
mode_dict[self.default_mode + '_checked'] = 'checked'
|
||||
|
||||
interval = int(1000. / self.fps)
|
||||
|
||||
with open(self.outfile, 'w') as of:
|
||||
of.write(JS_INCLUDE)
|
||||
of.write(DISPLAY_TEMPLATE.format(id=self.new_id(),
|
||||
Nframes=len(self._temp_names),
|
||||
fill_frames=fill_frames,
|
||||
interval=interval,
|
||||
icons=_Icons(),
|
||||
**mode_dict))
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 421 B |
Binary file not shown.
|
After Width: | Height: | Size: 427 B |
Binary file not shown.
|
After Width: | Height: | Size: 324 B |
Binary file not shown.
|
After Width: | Height: | Size: 295 B |
Binary file not shown.
|
After Width: | Height: | Size: 427 B |
Binary file not shown.
|
After Width: | Height: | Size: 336 B |
Binary file not shown.
|
After Width: | Height: | Size: 461 B |
@@ -0,0 +1,5 @@
|
||||
53 53 30
|
||||
422045.00 545169.00 1584.88
|
||||
300.00 250.00 193.00 114.00 88.00 67.00 52.00 40.00 28.00 35*20.00 28.00 40.00 52.00 67.00 88.00 114.00 193.00 250.00 300.00
|
||||
300.00 250.00 193.00 114.00 88.00 67.00 52.00 40.00 28.00 35*20.00 28.00 40.00 52.00 67.00 88.00 114.00 193.00 250.00 300.00
|
||||
19*20.00 28.00 40.00 52.00 67.00 52.00 67.00 88.00 114.00 193.00 250.00 300.00
|
||||
@@ -0,0 +1,5 @@
|
||||
104 21 45
|
||||
-1565.00 10725 0
|
||||
350 300 250 200 175 150 103.00 86.00 72.00 60.00 50.00 40.00 35.00 30.00 24.00 74*20.00 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
|
||||
300 250 200 175 150 103.00 86.00 72.00 5*60 72.00 86.00 103.00 150 175 200 250 300
|
||||
30*20 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
|
||||
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,184 @@
|
||||
SIMPEG FORWARD
|
||||
|
||||
0.000000e+00 4.000000e+01 8.000000e+01 1.200000e+02 -2.494158e-01 1.000000e+00
|
||||
0.000000e+00 4.000000e+01 1.200000e+02 1.600000e+02 -9.336097e-02 1.000000e+00
|
||||
0.000000e+00 4.000000e+01 1.600000e+02 2.000000e+02 -1.922888e-02 1.000000e+00
|
||||
0.000000e+00 4.000000e+01 2.000000e+02 2.400000e+02 -1.744448e-03 1.000000e+00
|
||||
0.000000e+00 4.000000e+01 2.400000e+02 2.800000e+02 -7.248272e-04 1.000000e+00
|
||||
0.000000e+00 4.000000e+01 2.800000e+02 3.200000e+02 -1.261533e-03 1.000000e+00
|
||||
0.000000e+00 4.000000e+01 3.200000e+02 3.600000e+02 -1.264605e-03 1.000000e+00
|
||||
4.000000e+01 8.000000e+01 1.200000e+02 1.600000e+02 -2.789362e-01 1.000000e+00
|
||||
4.000000e+01 8.000000e+01 1.600000e+02 2.000000e+02 -4.996821e-02 1.000000e+00
|
||||
4.000000e+01 8.000000e+01 2.000000e+02 2.400000e+02 -3.773255e-03 1.000000e+00
|
||||
4.000000e+01 8.000000e+01 2.400000e+02 2.800000e+02 -1.389521e-03 1.000000e+00
|
||||
4.000000e+01 8.000000e+01 2.800000e+02 3.200000e+02 -1.919248e-03 1.000000e+00
|
||||
4.000000e+01 8.000000e+01 3.200000e+02 3.600000e+02 -1.796881e-03 1.000000e+00
|
||||
4.000000e+01 8.000000e+01 3.600000e+02 4.000000e+02 -1.461358e-03 1.000000e+00
|
||||
8.000000e+01 1.200000e+02 1.600000e+02 2.000000e+02 -1.664943e-01 1.000000e+00
|
||||
8.000000e+01 1.200000e+02 2.000000e+02 2.400000e+02 -9.795789e-03 1.000000e+00
|
||||
8.000000e+01 1.200000e+02 2.400000e+02 2.800000e+02 -3.101796e-03 1.000000e+00
|
||||
8.000000e+01 1.200000e+02 2.800000e+02 3.200000e+02 -3.228048e-03 1.000000e+00
|
||||
8.000000e+01 1.200000e+02 3.200000e+02 3.600000e+02 -2.735533e-03 1.000000e+00
|
||||
8.000000e+01 1.200000e+02 3.600000e+02 4.000000e+02 -2.085615e-03 1.000000e+00
|
||||
8.000000e+01 1.200000e+02 4.000000e+02 4.400000e+02 -6.052930e-04 1.000000e+00
|
||||
1.200000e+02 1.600000e+02 2.000000e+02 2.400000e+02 -3.238350e-02 1.000000e+00
|
||||
1.200000e+02 1.600000e+02 2.400000e+02 2.800000e+02 -8.425264e-03 1.000000e+00
|
||||
1.200000e+02 1.600000e+02 2.800000e+02 3.200000e+02 -6.336856e-03 1.000000e+00
|
||||
1.200000e+02 1.600000e+02 3.200000e+02 3.600000e+02 -4.668494e-03 1.000000e+00
|
||||
1.200000e+02 1.600000e+02 3.600000e+02 4.000000e+02 -3.233326e-03 1.000000e+00
|
||||
1.200000e+02 1.600000e+02 4.000000e+02 4.400000e+02 -9.075322e-04 1.000000e+00
|
||||
1.200000e+02 1.600000e+02 4.400000e+02 4.800000e+02 -1.757728e-04 1.000000e+00
|
||||
1.600000e+02 2.000000e+02 2.400000e+02 2.800000e+02 -1.289155e-02 1.000000e+00
|
||||
1.600000e+02 2.000000e+02 2.800000e+02 3.200000e+02 -6.345002e-03 1.000000e+00
|
||||
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1.470000e+03 1.510000e+03 1.550000e+03 1.590000e+03 -1.782789e-02 1.000000e-02
|
||||
1.470000e+03 1.510000e+03 1.590000e+03 1.630000e+03 -5.476754e-03 1.000000e-02
|
||||
1.470000e+03 1.510000e+03 1.630000e+03 1.670000e+03 -2.129383e-03 1.000000e-02
|
||||
1.470000e+03 1.510000e+03 1.670000e+03 1.710000e+03 -8.830290e-04 1.000000e-02
|
||||
1.470000e+03 1.510000e+03 1.710000e+03 1.750000e+03 -4.593613e-04 1.000000e-02
|
||||
1.510000e+03 1.550000e+03 1.590000e+03 1.630000e+03 -1.658220e-02 1.000000e-02
|
||||
1.510000e+03 1.550000e+03 1.630000e+03 1.670000e+03 -5.041717e-03 1.000000e-02
|
||||
1.510000e+03 1.550000e+03 1.670000e+03 1.710000e+03 -1.828697e-03 1.000000e-02
|
||||
1.510000e+03 1.550000e+03 1.710000e+03 1.750000e+03 -8.334506e-04 1.000000e-02
|
||||
1.550000e+03 1.590000e+03 1.630000e+03 1.670000e+03 -1.573884e-02 1.000000e-02
|
||||
1.550000e+03 1.590000e+03 1.670000e+03 1.710000e+03 -4.395610e-03 1.000000e-02
|
||||
1.550000e+03 1.590000e+03 1.710000e+03 1.750000e+03 -1.596948e-03 1.000000e-02
|
||||
1.590000e+03 1.630000e+03 1.670000e+03 1.710000e+03 -1.485953e-02 1.000000e-02
|
||||
1.590000e+03 1.630000e+03 1.710000e+03 1.750000e+03 -3.965492e-03 1.000000e-02
|
||||
1.630000e+03 1.670000e+03 1.710000e+03 1.750000e+03 -1.352088e-02 1.000000e-02
|
||||
@@ -0,0 +1,212 @@
|
||||
GENERAL FORMAT
|
||||
5.100000e+02 1.221100e+04 0.000000e+00 5.500000e+02 1.221100e+04 0.000000e+00 7
|
||||
5.900000e+02 1.221100e+04 0.000000e+00 6.300000e+02 1.221100e+04 0.000000e+00 -2.714530e-01
|
||||
6.300000e+02 1.221100e+04 0.000000e+00 6.700000e+02 1.221100e+04 0.000000e+00 -9.078871e-02
|
||||
6.700000e+02 1.221100e+04 0.000000e+00 7.100000e+02 1.221100e+04 0.000000e+00 -3.904038e-03
|
||||
7.100000e+02 1.221100e+04 0.000000e+00 7.500000e+02 1.221100e+04 0.000000e+00 -1.618037e-03
|
||||
7.500000e+02 1.221100e+04 0.000000e+00 7.900000e+02 1.221100e+04 0.000000e+00 -9.804391e-04
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.300000e+02 1.221100e+04 0.000000e+00 -1.727176e-03
|
||||
8.300000e+02 1.221100e+04 0.000000e+00 8.700000e+02 1.221100e+04 0.000000e+00 -1.355919e-03
|
||||
5.500000e+02 1.221100e+04 0.000000e+00 5.900000e+02 1.221100e+04 0.000000e+00 7
|
||||
6.300000e+02 1.221100e+04 0.000000e+00 6.700000e+02 1.221100e+04 0.000000e+00 -2.921238e-01
|
||||
6.700000e+02 1.221100e+04 0.000000e+00 7.100000e+02 1.221100e+04 0.000000e+00 -1.009492e-02
|
||||
7.100000e+02 1.221100e+04 0.000000e+00 7.500000e+02 1.221100e+04 0.000000e+00 -3.548991e-03
|
||||
7.500000e+02 1.221100e+04 0.000000e+00 7.900000e+02 1.221100e+04 0.000000e+00 -1.723986e-03
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.300000e+02 1.221100e+04 0.000000e+00 -2.630518e-03
|
||||
8.300000e+02 1.221100e+04 0.000000e+00 8.700000e+02 1.221100e+04 0.000000e+00 -1.925165e-03
|
||||
8.700000e+02 1.221100e+04 0.000000e+00 9.100000e+02 1.221100e+04 0.000000e+00 -1.284180e-03
|
||||
5.900000e+02 1.221100e+04 0.000000e+00 6.300000e+02 1.221100e+04 0.000000e+00 7
|
||||
6.700000e+02 1.221100e+04 0.000000e+00 7.100000e+02 1.221100e+04 0.000000e+00 -3.457665e-02
|
||||
7.100000e+02 1.221100e+04 0.000000e+00 7.500000e+02 1.221100e+04 0.000000e+00 -9.501139e-03
|
||||
7.500000e+02 1.221100e+04 0.000000e+00 7.900000e+02 1.221100e+04 0.000000e+00 -3.561828e-03
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.300000e+02 1.221100e+04 0.000000e+00 -4.451255e-03
|
||||
8.300000e+02 1.221100e+04 0.000000e+00 8.700000e+02 1.221100e+04 0.000000e+00 -2.932679e-03
|
||||
8.700000e+02 1.221100e+04 0.000000e+00 9.100000e+02 1.221100e+04 0.000000e+00 -1.862391e-03
|
||||
9.100000e+02 1.221100e+04 0.000000e+00 9.500000e+02 1.221100e+04 0.000000e+00 -1.762776e-04
|
||||
6.300000e+02 1.221100e+04 0.000000e+00 6.700000e+02 1.221100e+04 0.000000e+00 7
|
||||
7.100000e+02 1.221100e+04 0.000000e+00 7.500000e+02 1.221100e+04 0.000000e+00 -2.311407e-02
|
||||
7.500000e+02 1.221100e+04 0.000000e+00 7.900000e+02 1.221100e+04 0.000000e+00 -6.497353e-03
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.300000e+02 1.221100e+04 0.000000e+00 -6.444456e-03
|
||||
8.300000e+02 1.221100e+04 0.000000e+00 8.700000e+02 1.221100e+04 0.000000e+00 -3.717098e-03
|
||||
8.700000e+02 1.221100e+04 0.000000e+00 9.100000e+02 1.221100e+04 0.000000e+00 -2.211079e-03
|
||||
9.100000e+02 1.221100e+04 0.000000e+00 9.500000e+02 1.221100e+04 0.000000e+00 -1.980605e-04
|
||||
9.500000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 -1.344407e-04
|
||||
6.700000e+02 1.221100e+04 0.000000e+00 7.100000e+02 1.221100e+04 0.000000e+00 7
|
||||
7.500000e+02 1.221100e+04 0.000000e+00 7.900000e+02 1.221100e+04 0.000000e+00 -1.392702e-02
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.300000e+02 1.221100e+04 0.000000e+00 -8.492591e-03
|
||||
8.300000e+02 1.221100e+04 0.000000e+00 8.700000e+02 1.221100e+04 0.000000e+00 -3.590565e-03
|
||||
8.700000e+02 1.221100e+04 0.000000e+00 9.100000e+02 1.221100e+04 0.000000e+00 -1.793323e-03
|
||||
9.100000e+02 1.221100e+04 0.000000e+00 9.500000e+02 1.221100e+04 0.000000e+00 -1.392059e-04
|
||||
9.500000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 -8.266499e-05
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.030000e+03 1.221100e+04 0.000000e+00 -5.310599e-05
|
||||
7.100000e+02 1.221100e+04 0.000000e+00 7.500000e+02 1.221100e+04 0.000000e+00 7
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.300000e+02 1.221100e+04 0.000000e+00 -3.379942e-02
|
||||
8.300000e+02 1.221100e+04 0.000000e+00 8.700000e+02 1.221100e+04 0.000000e+00 -1.112369e-02
|
||||
8.700000e+02 1.221100e+04 0.000000e+00 9.100000e+02 1.221100e+04 0.000000e+00 -4.897810e-03
|
||||
9.100000e+02 1.221100e+04 0.000000e+00 9.500000e+02 1.221100e+04 0.000000e+00 -3.495618e-04
|
||||
9.500000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 -1.934129e-04
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.030000e+03 1.221100e+04 0.000000e+00 -1.181884e-04
|
||||
1.030000e+03 1.221100e+04 0.000000e+00 1.070000e+03 1.221100e+04 0.000000e+00 -7.744231e-05
|
||||
7.500000e+02 1.221100e+04 0.000000e+00 7.900000e+02 1.221100e+04 0.000000e+00 7
|
||||
8.300000e+02 1.221100e+04 0.000000e+00 8.700000e+02 1.221100e+04 0.000000e+00 -1.245106e-01
|
||||
8.700000e+02 1.221100e+04 0.000000e+00 9.100000e+02 1.221100e+04 0.000000e+00 -4.108718e-02
|
||||
9.100000e+02 1.221100e+04 0.000000e+00 9.500000e+02 1.221100e+04 0.000000e+00 -2.485836e-03
|
||||
9.500000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 -1.202480e-03
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.030000e+03 1.221100e+04 0.000000e+00 -6.693235e-04
|
||||
1.030000e+03 1.221100e+04 0.000000e+00 1.070000e+03 1.221100e+04 0.000000e+00 -4.113179e-04
|
||||
1.070000e+03 1.221100e+04 0.000000e+00 1.110000e+03 1.221100e+04 0.000000e+00 -2.702122e-04
|
||||
7.900000e+02 1.221100e+04 0.000000e+00 8.300000e+02 1.221100e+04 0.000000e+00 7
|
||||
8.700000e+02 1.221100e+04 0.000000e+00 9.100000e+02 1.221100e+04 0.000000e+00 -2.137886e-01
|
||||
9.100000e+02 1.221100e+04 0.000000e+00 9.500000e+02 1.221100e+04 0.000000e+00 -1.091859e-02
|
||||
9.500000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 -4.724633e-03
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.030000e+03 1.221100e+04 0.000000e+00 -2.449333e-03
|
||||
1.030000e+03 1.221100e+04 0.000000e+00 1.070000e+03 1.221100e+04 0.000000e+00 -1.437846e-03
|
||||
1.070000e+03 1.221100e+04 0.000000e+00 1.110000e+03 1.221100e+04 0.000000e+00 -9.172139e-04
|
||||
1.110000e+03 1.221100e+04 0.000000e+00 1.150000e+03 1.221100e+04 0.000000e+00 -6.169701e-04
|
||||
8.300000e+02 1.221100e+04 0.000000e+00 8.700000e+02 1.221100e+04 0.000000e+00 7
|
||||
9.100000e+02 1.221100e+04 0.000000e+00 9.500000e+02 1.221100e+04 0.000000e+00 -3.008740e-02
|
||||
9.500000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 -9.582880e-03
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.030000e+03 1.221100e+04 0.000000e+00 -4.089211e-03
|
||||
1.030000e+03 1.221100e+04 0.000000e+00 1.070000e+03 1.221100e+04 0.000000e+00 -2.106531e-03
|
||||
1.070000e+03 1.221100e+04 0.000000e+00 1.110000e+03 1.221100e+04 0.000000e+00 -1.232643e-03
|
||||
1.110000e+03 1.221100e+04 0.000000e+00 1.150000e+03 1.221100e+04 0.000000e+00 -7.837170e-04
|
||||
1.150000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 -5.252836e-04
|
||||
8.700000e+02 1.221100e+04 0.000000e+00 9.100000e+02 1.221100e+04 0.000000e+00 7
|
||||
9.500000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 -2.072588e-02
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.030000e+03 1.221100e+04 0.000000e+00 -6.680527e-03
|
||||
1.030000e+03 1.221100e+04 0.000000e+00 1.070000e+03 1.221100e+04 0.000000e+00 -2.858761e-03
|
||||
1.070000e+03 1.221100e+04 0.000000e+00 1.110000e+03 1.221100e+04 0.000000e+00 -1.471032e-03
|
||||
1.110000e+03 1.221100e+04 0.000000e+00 1.150000e+03 1.221100e+04 0.000000e+00 -8.573545e-04
|
||||
1.150000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 -5.422200e-04
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.230000e+03 1.221100e+04 0.000000e+00 -3.622185e-04
|
||||
9.100000e+02 1.221100e+04 0.000000e+00 9.500000e+02 1.221100e+04 0.000000e+00 7
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.030000e+03 1.221100e+04 0.000000e+00 -1.342708e-02
|
||||
1.030000e+03 1.221100e+04 0.000000e+00 1.070000e+03 1.221100e+04 0.000000e+00 -3.764640e-03
|
||||
1.070000e+03 1.221100e+04 0.000000e+00 1.110000e+03 1.221100e+04 0.000000e+00 -1.404981e-03
|
||||
1.110000e+03 1.221100e+04 0.000000e+00 1.150000e+03 1.221100e+04 0.000000e+00 -6.344059e-04
|
||||
1.150000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 -3.297554e-04
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.230000e+03 1.221100e+04 0.000000e+00 -1.901888e-04
|
||||
1.230000e+03 1.221100e+04 0.000000e+00 1.270000e+03 1.221100e+04 0.000000e+00 -1.186449e-04
|
||||
9.500000e+02 1.221100e+04 0.000000e+00 9.900000e+02 1.221100e+04 0.000000e+00 7
|
||||
1.030000e+03 1.221100e+04 0.000000e+00 1.070000e+03 1.221100e+04 0.000000e+00 -1.528939e-02
|
||||
1.070000e+03 1.221100e+04 0.000000e+00 1.110000e+03 1.221100e+04 0.000000e+00 -4.517699e-03
|
||||
1.110000e+03 1.221100e+04 0.000000e+00 1.150000e+03 1.221100e+04 0.000000e+00 -1.761066e-03
|
||||
1.150000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 -8.239729e-04
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.230000e+03 1.221100e+04 0.000000e+00 -4.404471e-04
|
||||
1.230000e+03 1.221100e+04 0.000000e+00 1.270000e+03 1.221100e+04 0.000000e+00 -2.601287e-04
|
||||
1.270000e+03 1.221100e+04 0.000000e+00 1.310000e+03 1.221100e+04 0.000000e+00 -1.662365e-04
|
||||
9.900000e+02 1.221100e+04 0.000000e+00 1.030000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.070000e+03 1.221100e+04 0.000000e+00 1.110000e+03 1.221100e+04 0.000000e+00 -1.564742e-02
|
||||
1.110000e+03 1.221100e+04 0.000000e+00 1.150000e+03 1.221100e+04 0.000000e+00 -4.709421e-03
|
||||
1.150000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 -1.873639e-03
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.230000e+03 1.221100e+04 0.000000e+00 -8.954006e-04
|
||||
1.230000e+03 1.221100e+04 0.000000e+00 1.270000e+03 1.221100e+04 0.000000e+00 -4.894624e-04
|
||||
1.270000e+03 1.221100e+04 0.000000e+00 1.310000e+03 1.221100e+04 0.000000e+00 -2.966754e-04
|
||||
1.310000e+03 1.221100e+04 0.000000e+00 1.350000e+03 1.221100e+04 0.000000e+00 -1.954672e-04
|
||||
1.030000e+03 1.221100e+04 0.000000e+00 1.070000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.110000e+03 1.221100e+04 0.000000e+00 1.150000e+03 1.221100e+04 0.000000e+00 -1.576273e-02
|
||||
1.150000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 -4.783585e-03
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.230000e+03 1.221100e+04 0.000000e+00 -1.925094e-03
|
||||
1.230000e+03 1.221100e+04 0.000000e+00 1.270000e+03 1.221100e+04 0.000000e+00 -9.344398e-04
|
||||
1.270000e+03 1.221100e+04 0.000000e+00 1.310000e+03 1.221100e+04 0.000000e+00 -5.220443e-04
|
||||
1.310000e+03 1.221100e+04 0.000000e+00 1.350000e+03 1.221100e+04 0.000000e+00 -3.257533e-04
|
||||
1.350000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 -2.206926e-04
|
||||
1.070000e+03 1.221100e+04 0.000000e+00 1.110000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.150000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 -1.581724e-02
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.230000e+03 1.221100e+04 0.000000e+00 -4.825705e-03
|
||||
1.230000e+03 1.221100e+04 0.000000e+00 1.270000e+03 1.221100e+04 0.000000e+00 -1.961115e-03
|
||||
1.270000e+03 1.221100e+04 0.000000e+00 1.310000e+03 1.221100e+04 0.000000e+00 -9.683840e-04
|
||||
1.310000e+03 1.221100e+04 0.000000e+00 1.350000e+03 1.221100e+04 0.000000e+00 -5.557387e-04
|
||||
1.350000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 -3.573570e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.430000e+03 1.221100e+04 0.000000e+00 -2.237379e-04
|
||||
1.110000e+03 1.221100e+04 0.000000e+00 1.150000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.230000e+03 1.221100e+04 0.000000e+00 -1.585629e-02
|
||||
1.230000e+03 1.221100e+04 0.000000e+00 1.270000e+03 1.221100e+04 0.000000e+00 -4.863527e-03
|
||||
1.270000e+03 1.221100e+04 0.000000e+00 1.310000e+03 1.221100e+04 0.000000e+00 -2.001001e-03
|
||||
1.310000e+03 1.221100e+04 0.000000e+00 1.350000e+03 1.221100e+04 0.000000e+00 -1.011908e-03
|
||||
1.350000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 -5.999730e-04
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.430000e+03 1.221100e+04 0.000000e+00 -3.608492e-04
|
||||
1.430000e+03 1.221100e+04 0.000000e+00 1.470000e+03 1.221100e+04 0.000000e+00 -6.233112e-05
|
||||
1.150000e+03 1.221100e+04 0.000000e+00 1.190000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.230000e+03 1.221100e+04 0.000000e+00 1.270000e+03 1.221100e+04 0.000000e+00 -1.589981e-02
|
||||
1.270000e+03 1.221100e+04 0.000000e+00 1.310000e+03 1.221100e+04 0.000000e+00 -4.914252e-03
|
||||
1.310000e+03 1.221100e+04 0.000000e+00 1.350000e+03 1.221100e+04 0.000000e+00 -2.061606e-03
|
||||
1.350000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 -1.079518e-03
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.430000e+03 1.221100e+04 0.000000e+00 -6.091457e-04
|
||||
1.430000e+03 1.221100e+04 0.000000e+00 1.470000e+03 1.221100e+04 0.000000e+00 -1.027616e-04
|
||||
1.470000e+03 1.221100e+04 0.000000e+00 1.510000e+03 1.221100e+04 0.000000e+00 -6.569923e-05
|
||||
1.190000e+03 1.221100e+04 0.000000e+00 1.230000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.270000e+03 1.221100e+04 0.000000e+00 1.310000e+03 1.221100e+04 0.000000e+00 -1.596873e-02
|
||||
1.310000e+03 1.221100e+04 0.000000e+00 1.350000e+03 1.221100e+04 0.000000e+00 -5.005094e-03
|
||||
1.350000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 -2.175347e-03
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.430000e+03 1.221100e+04 0.000000e+00 -1.110316e-03
|
||||
1.430000e+03 1.221100e+04 0.000000e+00 1.470000e+03 1.221100e+04 0.000000e+00 -1.806351e-04
|
||||
1.470000e+03 1.221100e+04 0.000000e+00 1.510000e+03 1.221100e+04 0.000000e+00 -1.013643e-04
|
||||
1.510000e+03 1.221100e+04 0.000000e+00 1.550000e+03 1.221100e+04 0.000000e+00 -8.848532e-05
|
||||
1.230000e+03 1.221100e+04 0.000000e+00 1.270000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.310000e+03 1.221100e+04 0.000000e+00 1.350000e+03 1.221100e+04 0.000000e+00 -1.611766e-02
|
||||
1.350000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 -5.219583e-03
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.430000e+03 1.221100e+04 0.000000e+00 -2.278612e-03
|
||||
1.430000e+03 1.221100e+04 0.000000e+00 1.470000e+03 1.221100e+04 0.000000e+00 -3.508786e-04
|
||||
1.470000e+03 1.221100e+04 0.000000e+00 1.510000e+03 1.221100e+04 0.000000e+00 -1.640165e-04
|
||||
1.510000e+03 1.221100e+04 0.000000e+00 1.550000e+03 1.221100e+04 0.000000e+00 -1.359041e-04
|
||||
1.550000e+03 1.221100e+04 0.000000e+00 1.590000e+03 1.221100e+04 0.000000e+00 -8.751714e-05
|
||||
1.270000e+03 1.221100e+04 0.000000e+00 1.310000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.350000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 -1.657478e-02
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.430000e+03 1.221100e+04 0.000000e+00 -5.565939e-03
|
||||
1.430000e+03 1.221100e+04 0.000000e+00 1.470000e+03 1.221100e+04 0.000000e+00 -7.880475e-04
|
||||
1.470000e+03 1.221100e+04 0.000000e+00 1.510000e+03 1.221100e+04 0.000000e+00 -2.872745e-04
|
||||
1.510000e+03 1.221100e+04 0.000000e+00 1.550000e+03 1.221100e+04 0.000000e+00 -2.204123e-04
|
||||
1.550000e+03 1.221100e+04 0.000000e+00 1.590000e+03 1.221100e+04 0.000000e+00 -1.337666e-04
|
||||
1.590000e+03 1.221100e+04 0.000000e+00 1.630000e+03 1.221100e+04 0.000000e+00 -8.382002e-05
|
||||
1.310000e+03 1.221100e+04 0.000000e+00 1.350000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.430000e+03 1.221100e+04 0.000000e+00 -1.781077e-02
|
||||
1.430000e+03 1.221100e+04 0.000000e+00 1.470000e+03 1.221100e+04 0.000000e+00 -2.175030e-03
|
||||
1.470000e+03 1.221100e+04 0.000000e+00 1.510000e+03 1.221100e+04 0.000000e+00 -5.670577e-04
|
||||
1.510000e+03 1.221100e+04 0.000000e+00 1.550000e+03 1.221100e+04 0.000000e+00 -3.891514e-04
|
||||
1.550000e+03 1.221100e+04 0.000000e+00 1.590000e+03 1.221100e+04 0.000000e+00 -2.167667e-04
|
||||
1.590000e+03 1.221100e+04 0.000000e+00 1.630000e+03 1.221100e+04 0.000000e+00 -1.280080e-04
|
||||
1.630000e+03 1.221100e+04 0.000000e+00 1.670000e+03 1.221100e+04 0.000000e+00 -7.633646e-05
|
||||
1.350000e+03 1.221100e+04 0.000000e+00 1.390000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.430000e+03 1.221100e+04 0.000000e+00 1.470000e+03 1.221100e+04 0.000000e+00 -8.397906e-03
|
||||
1.470000e+03 1.221100e+04 0.000000e+00 1.510000e+03 1.221100e+04 0.000000e+00 -1.320713e-03
|
||||
1.510000e+03 1.221100e+04 0.000000e+00 1.550000e+03 1.221100e+04 0.000000e+00 -7.699112e-04
|
||||
1.550000e+03 1.221100e+04 0.000000e+00 1.590000e+03 1.221100e+04 0.000000e+00 -3.795939e-04
|
||||
1.590000e+03 1.221100e+04 0.000000e+00 1.630000e+03 1.221100e+04 0.000000e+00 -2.060814e-04
|
||||
1.630000e+03 1.221100e+04 0.000000e+00 1.670000e+03 1.221100e+04 0.000000e+00 -1.160841e-04
|
||||
1.670000e+03 1.221100e+04 0.000000e+00 1.710000e+03 1.221100e+04 0.000000e+00 -6.159250e-05
|
||||
1.390000e+03 1.221100e+04 0.000000e+00 1.430000e+03 1.221100e+04 0.000000e+00 7
|
||||
1.470000e+03 1.221100e+04 0.000000e+00 1.510000e+03 1.221100e+04 0.000000e+00 -4.023143e-03
|
||||
1.510000e+03 1.221100e+04 0.000000e+00 1.550000e+03 1.221100e+04 0.000000e+00 -1.729697e-03
|
||||
1.550000e+03 1.221100e+04 0.000000e+00 1.590000e+03 1.221100e+04 0.000000e+00 -7.003067e-04
|
||||
1.590000e+03 1.221100e+04 0.000000e+00 1.630000e+03 1.221100e+04 0.000000e+00 -3.336371e-04
|
||||
1.630000e+03 1.221100e+04 0.000000e+00 1.670000e+03 1.221100e+04 0.000000e+00 -1.723402e-04
|
||||
1.670000e+03 1.221100e+04 0.000000e+00 1.710000e+03 1.221100e+04 0.000000e+00 -8.628177e-05
|
||||
1.710000e+03 1.221100e+04 0.000000e+00 1.750000e+03 1.221100e+04 0.000000e+00 -5.826410e-05
|
||||
1.430000e+03 1.221100e+04 0.000000e+00 1.470000e+03 1.221100e+04 0.000000e+00 6
|
||||
1.510000e+03 1.221100e+04 0.000000e+00 1.550000e+03 1.221100e+04 0.000000e+00 -3.340181e-03
|
||||
1.550000e+03 1.221100e+04 0.000000e+00 1.590000e+03 1.221100e+04 0.000000e+00 -9.789342e-04
|
||||
1.590000e+03 1.221100e+04 0.000000e+00 1.630000e+03 1.221100e+04 0.000000e+00 -3.766422e-04
|
||||
1.630000e+03 1.221100e+04 0.000000e+00 1.670000e+03 1.221100e+04 0.000000e+00 -1.677244e-04
|
||||
1.670000e+03 1.221100e+04 0.000000e+00 1.710000e+03 1.221100e+04 0.000000e+00 -7.590380e-05
|
||||
1.710000e+03 1.221100e+04 0.000000e+00 1.750000e+03 1.221100e+04 0.000000e+00 -4.410703e-05
|
||||
1.470000e+03 1.221100e+04 0.000000e+00 1.510000e+03 1.221100e+04 0.000000e+00 5
|
||||
1.550000e+03 1.221100e+04 0.000000e+00 1.590000e+03 1.221100e+04 0.000000e+00 -1.782789e-02
|
||||
1.590000e+03 1.221100e+04 0.000000e+00 1.630000e+03 1.221100e+04 0.000000e+00 -5.476754e-03
|
||||
1.630000e+03 1.221100e+04 0.000000e+00 1.670000e+03 1.221100e+04 0.000000e+00 -2.129383e-03
|
||||
1.670000e+03 1.221100e+04 0.000000e+00 1.710000e+03 1.221100e+04 0.000000e+00 -8.830290e-04
|
||||
1.710000e+03 1.221100e+04 0.000000e+00 1.750000e+03 1.221100e+04 0.000000e+00 -4.593613e-04
|
||||
1.510000e+03 1.221100e+04 0.000000e+00 1.550000e+03 1.221100e+04 0.000000e+00 4
|
||||
1.590000e+03 1.221100e+04 0.000000e+00 1.630000e+03 1.221100e+04 0.000000e+00 -1.658220e-02
|
||||
1.630000e+03 1.221100e+04 0.000000e+00 1.670000e+03 1.221100e+04 0.000000e+00 -5.041717e-03
|
||||
1.670000e+03 1.221100e+04 0.000000e+00 1.710000e+03 1.221100e+04 0.000000e+00 -1.828697e-03
|
||||
1.710000e+03 1.221100e+04 0.000000e+00 1.750000e+03 1.221100e+04 0.000000e+00 -8.334506e-04
|
||||
1.550000e+03 1.221100e+04 0.000000e+00 1.590000e+03 1.221100e+04 0.000000e+00 3
|
||||
1.630000e+03 1.221100e+04 0.000000e+00 1.670000e+03 1.221100e+04 0.000000e+00 -1.573884e-02
|
||||
1.670000e+03 1.221100e+04 0.000000e+00 1.710000e+03 1.221100e+04 0.000000e+00 -4.395610e-03
|
||||
1.710000e+03 1.221100e+04 0.000000e+00 1.750000e+03 1.221100e+04 0.000000e+00 -1.596948e-03
|
||||
1.590000e+03 1.221100e+04 0.000000e+00 1.630000e+03 1.221100e+04 0.000000e+00 2
|
||||
1.670000e+03 1.221100e+04 0.000000e+00 1.710000e+03 1.221100e+04 0.000000e+00 -1.485953e-02
|
||||
1.710000e+03 1.221100e+04 0.000000e+00 1.750000e+03 1.221100e+04 0.000000e+00 -3.965492e-03
|
||||
1.630000e+03 1.221100e+04 0.000000e+00 1.670000e+03 1.221100e+04 0.000000e+00 1
|
||||
1.710000e+03 1.221100e+04 0.000000e+00 1.750000e+03 1.221100e+04 0.000000e+00 -1.352088e-02
|
||||
@@ -0,0 +1,5 @@
|
||||
104 21 57
|
||||
-1565.00 10725 0
|
||||
350 300 250 200 175 150 103.00 86.00 72.00 60.00 50.00 40.00 35.00 30.00 24.00 74*20.00 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
|
||||
300 250 200 175 150 103.00 86.00 72.00 5*60 72.00 86.00 103.00 150 175 200 250 300
|
||||
20*10.00 12 16 20*20 24.00 30.00 35.00 40.00 50.00 60.00 72.00 86.00 103.00 150 175 200 250 300 350
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,44 @@
|
||||
def convertObs_DC3D_to_2D(Tx,Rx):
|
||||
|
||||
from SimPEG import np
|
||||
import numpy.matlib as npm
|
||||
"""
|
||||
Read list of 3D Tx Rx location and change coordinate system to distance
|
||||
along line assuming all data is acquired along line
|
||||
First transmitter pole is assumed to be at the origin
|
||||
|
||||
Assumes flat topo for now...
|
||||
|
||||
Input:
|
||||
:param Tx, Rx
|
||||
|
||||
Output:
|
||||
:figure Tx2d, Rx2d
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
|
||||
Tx2d = []
|
||||
Rx2d = []
|
||||
|
||||
for ii in range(len(Tx)):
|
||||
|
||||
if ii == 0:
|
||||
endp = Tx[0][0:2,0]
|
||||
|
||||
nrx = Rx[ii].shape[0]
|
||||
|
||||
rP1 = np.sqrt( np.sum( ( endp - Tx[ii][0:2,0] )**2 , axis=0))
|
||||
rP2 = np.sqrt( np.sum( ( endp - Tx[ii][0:2,1] )**2 , axis=0))
|
||||
rC1 = np.sqrt( np.sum( ( npm.repmat(endp.T,nrx,1) - Rx[ii][:,0:2] )**2 , axis=1))
|
||||
rC2 = np.sqrt( np.sum( ( npm.repmat(endp.T,nrx,1) - Rx[ii][:,3:5] )**2 , axis=1))
|
||||
|
||||
Tx2d.append( np.r_[rP1, rP2] )
|
||||
Rx2d.append( np.c_[rC1, rC2] )
|
||||
#np.savetxt(fid, data, fmt='%e',delimiter=' ',newline='\n')
|
||||
|
||||
return Tx2d, Rx2d
|
||||
@@ -0,0 +1,149 @@
|
||||
def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
|
||||
|
||||
from SimPEG import np
|
||||
import re
|
||||
"""
|
||||
Load in endpoints and survey specifications to generate Tx, Rx location
|
||||
stations.
|
||||
|
||||
Assumes flat topo for now...
|
||||
|
||||
Input:
|
||||
:param endl -> input endpoints [x1, y1, z1, x2, y2, z2]
|
||||
:object mesh -> SimPEG mesh object
|
||||
:switch stype -> "dpdp" (dipole-dipole) | "pdp" (pole-dipole)
|
||||
: param a, n -> pole seperation, number of rx dipoles per tx
|
||||
|
||||
Output:
|
||||
:param Tx, Rx -> List objects for each tx location
|
||||
Lines: P1x, P1y, P1z, P2x, P2y, P2z
|
||||
|
||||
Created on Wed December 9th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
def xy_2_r(x1,x2,y1,y2):
|
||||
r = np.sqrt( np.sum((x2 - x1)**2 + (y2 - y1)**2) )
|
||||
return r
|
||||
|
||||
## Evenly distribute electrodes and put on surface
|
||||
# Mesure survey length and direction
|
||||
dl_len = xy_2_r(endl[0,0],endl[1,0],endl[0,1],endl[1,1])
|
||||
|
||||
dl_x = ( endl[1,0] - endl[0,0] ) / dl_len
|
||||
dl_y = ( endl[1,1] - endl[0,1] ) / dl_len
|
||||
|
||||
nstn = np.floor( dl_len / a )
|
||||
|
||||
# Compute discrete pole location along line
|
||||
stn_x = endl[0,0] + np.array(range(int(nstn)))*dl_x*a
|
||||
stn_y = endl[0,1] + np.array(range(int(nstn)))*dl_y*a
|
||||
|
||||
# Create line of P1 locations
|
||||
M = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
# Create line of P2 locations
|
||||
N = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
## Build list of Tx-Rx locations depending on survey type
|
||||
# Dipole-dipole: Moving tx with [a] spacing -> [AB a MN1 a MN2 ... a MNn]
|
||||
# Pole-dipole: Moving pole on one end -> [A a MN1 a MN2 ... MNn a B]
|
||||
Tx = []
|
||||
Rx = []
|
||||
|
||||
if not re.match(stype,'gradient'):
|
||||
|
||||
for ii in range(0, int(nstn)-1):
|
||||
|
||||
|
||||
if re.match(stype,'dpdp'):
|
||||
tx = np.c_[M[ii,:],N[ii,:]]
|
||||
elif re.match(stype,'pdp'):
|
||||
tx = np.c_[M[ii,:],M[ii,:]]
|
||||
|
||||
#Rx.append(np.c_[M[ii+1:indx,:],N[ii+1:indx,:]])
|
||||
|
||||
# Current elctrode seperation
|
||||
AB = xy_2_r(tx[0,1],endl[1,0],tx[1,1],endl[1,1])
|
||||
|
||||
# Number of receivers to fit
|
||||
nstn = np.min([np.floor( (AB - b) / a ) , n])
|
||||
|
||||
# Check if there is enough space, else break the loop
|
||||
if nstn <= 0:
|
||||
continue
|
||||
|
||||
# Compute discrete pole location along line
|
||||
stn_x = N[ii,0] + dl_x*b + np.array(range(int(nstn)))*dl_x*a
|
||||
stn_y = N[ii,1] + dl_y*b + np.array(range(int(nstn)))*dl_y*a
|
||||
|
||||
# Create receiver poles
|
||||
# Create line of P1 locations
|
||||
P1 = np.c_[stn_x, stn_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
# Create line of P2 locations
|
||||
P2 = np.c_[stn_x+a*dl_x, stn_y+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
Rx.append(np.c_[P1,P2])
|
||||
Tx.append(tx)
|
||||
|
||||
#==============================================================================
|
||||
# elif re.match(stype,'dpdp'):
|
||||
#
|
||||
# for ii in range(0, int(nstn)-2):
|
||||
#
|
||||
# indx = np.min([ii+n+1,nstn])
|
||||
# Tx.append(np.c_[M[ii,:],N[ii,:]])
|
||||
# Rx.append(np.c_[M[ii+2:indx,:],N[ii+2:indx,:]])
|
||||
#==============================================================================
|
||||
|
||||
elif re.match(stype,'gradient'):
|
||||
|
||||
# Gradient survey only requires Tx at end of line and creates a square
|
||||
# grid of receivers at in the middle at a pre-set minimum distance
|
||||
Tx.append(np.c_[M[0,:],N[-1,:]])
|
||||
|
||||
# Get the edge limit of survey area
|
||||
min_x = endl[0,0] + dl_x * b
|
||||
min_y = endl[0,1] + dl_y * b
|
||||
|
||||
max_x = endl[1,0] - dl_x * b
|
||||
max_y = endl[1,1] - dl_y * b
|
||||
|
||||
box_l = np.sqrt( (min_x - max_x)**2 + (min_y - max_y)**2 )
|
||||
box_w = box_l/2.
|
||||
|
||||
nstn = np.floor( box_l / a )
|
||||
|
||||
# Compute discrete pole location along line
|
||||
stn_x = min_x + np.array(range(int(nstn)))*dl_x*a
|
||||
stn_y = min_y + np.array(range(int(nstn)))*dl_y*a
|
||||
|
||||
# Define number of cross lines
|
||||
nlin = int(np.floor( box_w / a ))
|
||||
lind = range(-nlin,nlin+1)
|
||||
|
||||
ngrad = nstn * len(lind)
|
||||
|
||||
rx = np.zeros([ngrad,6])
|
||||
for ii in range( len(lind) ):
|
||||
|
||||
# Move line in perpendicular direction by dipole spacing
|
||||
lxx = stn_x - lind[ii]*a*dl_y
|
||||
lyy = stn_y + lind[ii]*a*dl_x
|
||||
|
||||
|
||||
M = np.c_[ lxx, lyy , np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
N = np.c_[ lxx+a*dl_x, lyy+a*dl_y, np.ones(nstn).T*mesh.vectorNz[-1]]
|
||||
|
||||
rx[(ii*nstn):((ii+1)*nstn),:] = np.c_[M,N]
|
||||
|
||||
Rx.append(rx)
|
||||
|
||||
else:
|
||||
print """stype must be either 'pdp', 'dpdp' or 'gradient'. """
|
||||
|
||||
|
||||
|
||||
return Tx, Rx
|
||||
@@ -0,0 +1,68 @@
|
||||
def plot_pseudoSection(Tx,Rx,data,z0, stype):
|
||||
|
||||
from SimPEG import np, mkvc
|
||||
from scipy.interpolate import griddata
|
||||
from matplotlib.colors import LogNorm
|
||||
import pylab as plt
|
||||
import re
|
||||
"""
|
||||
Read list of 2D tx-rx location and plot a speudo-section of apparent
|
||||
resistivity.
|
||||
|
||||
Assumes flat topo for now...
|
||||
|
||||
Input:
|
||||
:param d2D, z0
|
||||
:switch stype -> Either 'pdp' (pole-dipole) | 'dpdp' (dipole-dipole)
|
||||
|
||||
Output:
|
||||
:figure scatter plot overlayed on image
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
#d2D = np.asarray(d2D)
|
||||
|
||||
midl = []
|
||||
midz = []
|
||||
rho = []
|
||||
|
||||
for ii in range(len(Tx)):
|
||||
# Get distances between each poles
|
||||
rC1P1 = np.abs(Tx[ii][0] - Rx[ii][:,0])
|
||||
rC2P1 = np.abs(Tx[ii][1] - Rx[ii][:,0])
|
||||
rC1P2 = np.abs(Tx[ii][1] - Rx[ii][:,1])
|
||||
rC2P2 = np.abs(Tx[ii][0] - Rx[ii][:,1])
|
||||
rP1P2 = np.abs(Rx[ii][:,1] - Rx[ii][:,0])
|
||||
|
||||
# Compute apparent resistivity
|
||||
if re.match(stype,'pdp'):
|
||||
rho = np.hstack([rho, data[ii] * 2*np.pi * rC1P1 * ( rC1P1 + rP1P2 ) / rP1P2] )
|
||||
|
||||
elif re.match(stype,'dpdp'):
|
||||
rho = np.hstack([rho, data[ii] * 2*np.pi / ( 1/rC1P1 - 1/rC2P1 - 1/rC1P2 + 1/rC2P2 ) ])
|
||||
|
||||
Cmid = (Tx[ii][0] + Tx[ii][1])/2
|
||||
Pmid = (Rx[ii][:,0] + Rx[ii][:,1])/2
|
||||
|
||||
midl = np.hstack([midl, ( Cmid + Pmid )/2 ])
|
||||
midz = np.hstack([midz, -np.abs(Cmid-Pmid)/2 + z0 ])
|
||||
|
||||
|
||||
# Grid points
|
||||
grid_x, grid_z = np.mgrid[np.min(midl):np.max(midl), np.min(midz):np.max(midz)]
|
||||
grid_rho = griddata(np.c_[midl,midz], np.log10(abs(1/rho.T)), (grid_x, grid_z), method='linear')
|
||||
|
||||
|
||||
#plt.subplot(2,1,2)
|
||||
plt.imshow(grid_rho.T, extent = (np.min(midl),np.max(midl),np.min(midz),np.max(midz)), origin='lower', alpha=0.8)
|
||||
cbar = plt.colorbar(format = '%.2f',fraction=0.02)
|
||||
cmin,cmax = cbar.get_clim()
|
||||
ticks = np.linspace(cmin,cmax,3)
|
||||
cbar.set_ticks(ticks)
|
||||
|
||||
# Plot apparent resistivity
|
||||
plt.scatter(midl,midz,s=50,c=np.log10(abs(1/rho.T)))
|
||||
|
||||
@@ -0,0 +1,57 @@
|
||||
def readUBC_DC2DLoc(fileName):
|
||||
|
||||
from SimPEG import np
|
||||
"""
|
||||
Read UBC GIF 2D observation file and generate arrays for tx-rx location
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF 2D model file
|
||||
|
||||
Output:
|
||||
:param rx, tx
|
||||
:return
|
||||
|
||||
Created on Thu Nov 12 13:14:10 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
# Open fileand skip header... assume that we know the mesh already
|
||||
#==============================================================================
|
||||
# fopen = open(fileName,'r')
|
||||
# lines = fopen.readlines()
|
||||
# fopen.close()
|
||||
#==============================================================================
|
||||
|
||||
# Load file
|
||||
obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
|
||||
|
||||
# Check first line and figure out if 2D or 3D file format
|
||||
line = np.array(obsfile[0].split(),dtype=float)
|
||||
|
||||
tx_A = []
|
||||
tx_B = []
|
||||
rx_M = []
|
||||
rx_N = []
|
||||
d = []
|
||||
wd = []
|
||||
|
||||
for ii in range(obsfile.shape[0]):
|
||||
|
||||
# If len==3, then simple format where tx-rx is listed on each line
|
||||
if len(line) == 4:
|
||||
|
||||
temp = np.fromstring(obsfile[ii], dtype=float,sep=' ')
|
||||
tx_A = np.hstack((tx_A,temp[0]))
|
||||
tx_B = np.hstack((tx_B,temp[1]))
|
||||
rx_M = np.hstack((rx_M,temp[2]))
|
||||
rx_N = np.hstack((rx_N,temp[3]))
|
||||
|
||||
|
||||
rx = np.transpose(np.array((rx_M,rx_N)))
|
||||
tx = np.transpose(np.array((tx_A,tx_B)))
|
||||
|
||||
return tx, rx, d, wd
|
||||
|
||||
|
||||
@@ -0,0 +1,70 @@
|
||||
def readUBC_DC2DMesh(fileName):
|
||||
|
||||
from SimPEG import np
|
||||
"""
|
||||
Read UBC GIF 2DTensor mesh and generate 2D Tensor mesh in simpeg
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF mesh file
|
||||
|
||||
Output:
|
||||
:param SimPEG TensorMesh 2D object
|
||||
:return
|
||||
|
||||
Created on Thu Nov 12 13:14:10 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
# Open file
|
||||
fopen = open(fileName,'r')
|
||||
|
||||
# Read down the file and unpack dx vector
|
||||
def unpackdx(fid,nrows):
|
||||
for ii in range(nrows):
|
||||
|
||||
line = fid.readline()
|
||||
var = np.array(line.split(),dtype=float)
|
||||
|
||||
if ii==0:
|
||||
x0= var[0]
|
||||
xvec = np.ones(int(var[2])) * (var[1] - var[0]) / int(var[2])
|
||||
xend = var[1]
|
||||
|
||||
else:
|
||||
xvec = np.hstack((xvec,np.ones(int(var[1])) * (var[0] - xend) / int(var[1])))
|
||||
xend = var[0]
|
||||
|
||||
return x0, xvec
|
||||
|
||||
#%% Start with dx block
|
||||
# First line specifies the number of rows for x-cells
|
||||
line = fopen.readline()
|
||||
nl = np.array(line.split(),dtype=float)
|
||||
|
||||
[x0, dx] = unpackdx(fopen,nl)
|
||||
|
||||
|
||||
#%% Move down the file until reaching the z-block
|
||||
line = fopen.readline()
|
||||
if not line:
|
||||
line = fopen.readline()
|
||||
|
||||
#%% End with dz block
|
||||
# First line specifies the number of rows for z-cells
|
||||
line = fopen.readline()
|
||||
nl = np.array(line.split(),dtype=float)
|
||||
|
||||
[z0, dz] = unpackdx(fopen,nl)
|
||||
|
||||
# Flip z0 to be the bottom of the mesh for SimPEG
|
||||
z0 = z0 - sum(dz)
|
||||
dz = dz[::-1]
|
||||
#%% Make the mesh using SimPEG
|
||||
|
||||
from SimPEG import Mesh
|
||||
tensMsh = Mesh.TensorMesh([dx,dz],(x0, z0))
|
||||
return tensMsh
|
||||
|
||||
|
||||
@@ -0,0 +1,56 @@
|
||||
def readUBC_DC2DModel(fileName):
|
||||
|
||||
from SimPEG import np, mkvc
|
||||
"""
|
||||
Read UBC GIF 2DTensor model and generate 2D Tensor model in simpeg
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF 2D model file
|
||||
|
||||
Output:
|
||||
:param SimPEG TensorMesh 2D object
|
||||
:return
|
||||
|
||||
Created on Thu Nov 12 13:14:10 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
# Open fileand skip header... assume that we know the mesh already
|
||||
|
||||
obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
|
||||
|
||||
dim = np.array(obsfile[0].split(),dtype=float)
|
||||
|
||||
temp = np.array(obsfile[1].split(),dtype=float)
|
||||
|
||||
if len(temp) > 1:
|
||||
model = np.zeros(dim)
|
||||
|
||||
for ii in range(len(obsfile)-1):
|
||||
mm = np.array(obsfile[ii+1].split(),dtype=float)
|
||||
model[:,ii] = mm
|
||||
|
||||
model = model[:,::-1]
|
||||
|
||||
else:
|
||||
|
||||
if len(obsfile[1:])==1:
|
||||
mm = np.array(obsfile[1:].split(),dtype=float)
|
||||
|
||||
else:
|
||||
mm = np.array(obsfile[1:],dtype=float)
|
||||
|
||||
# Permute the second dimension to flip the order
|
||||
model = mm.reshape(dim[1],dim[0])
|
||||
|
||||
model = model[::-1,:]
|
||||
model = np.transpose(model, (1, 0))
|
||||
|
||||
model = mkvc(model)
|
||||
|
||||
|
||||
return model
|
||||
|
||||
|
||||
@@ -0,0 +1,69 @@
|
||||
def readUBC_DC3Dobs(fileName):
|
||||
|
||||
from SimPEG import np
|
||||
"""
|
||||
Read UBC GIF DCIP 3D observation file and generate arrays for tx-rx location
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF 3D obs file
|
||||
|
||||
Output:
|
||||
:param rx, tx, d, wd
|
||||
:return
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
|
||||
# Load file
|
||||
obsfile = np.genfromtxt(fileName,delimiter=' \n',dtype=np.str,comments='!')
|
||||
|
||||
# Pre-allocate
|
||||
Tx = []
|
||||
Rx = []
|
||||
d = []
|
||||
wd = []
|
||||
|
||||
# Countdown for number of obs/tx
|
||||
count = 0
|
||||
for ii in range(obsfile.shape[0]):
|
||||
|
||||
if not obsfile[ii]:
|
||||
continue
|
||||
|
||||
# First line is transmitter with number of receivers
|
||||
if count==0:
|
||||
|
||||
temp = (np.fromstring(obsfile[ii], dtype=float,sep=' ').T)
|
||||
count = int(temp[-1])
|
||||
temp = np.reshape(temp[0:-1],[2,3]).T
|
||||
|
||||
Tx.append(temp)
|
||||
rx = []
|
||||
continue
|
||||
|
||||
temp = np.fromstring(obsfile[ii], dtype=float,sep=' ')
|
||||
|
||||
|
||||
rx.append(temp)
|
||||
|
||||
count = count -1
|
||||
|
||||
# Reach the end of
|
||||
if count == 0:
|
||||
temp = np.asarray(rx)
|
||||
Rx.append(temp[:,0:6])
|
||||
|
||||
# Check for data + uncertainties
|
||||
if temp.shape[1]==8:
|
||||
d.append(temp[:,6])
|
||||
wd.append(temp[:,7])
|
||||
|
||||
# Check for data only
|
||||
elif temp.shape[1]==7:
|
||||
d.append(temp[:,6])
|
||||
|
||||
return Tx, Rx, d, wd
|
||||
|
||||
@@ -0,0 +1,49 @@
|
||||
def writeUBC_DCobs(fileName,Tx,Rx,d,wd, dtype):
|
||||
|
||||
from SimPEG import np, mkvc
|
||||
import re
|
||||
"""
|
||||
Read UBC GIF DCIP 3D observation file and generate arrays for tx-rx location
|
||||
|
||||
Input:
|
||||
:param fileName, path to the UBC GIF 3D obs file
|
||||
|
||||
Output:
|
||||
:param rx, tx, d, wd
|
||||
:return
|
||||
|
||||
Created on Mon December 7th, 2015
|
||||
|
||||
@author: dominiquef
|
||||
|
||||
"""
|
||||
fid = open(fileName,'w')
|
||||
fid.write('! GENERAL FORMAT\n')
|
||||
|
||||
for ii in range(len(Tx)):
|
||||
|
||||
tx = np.asarray(Tx[ii])
|
||||
rx = np.asarray(Rx[ii])
|
||||
nrx = rx.shape[0]
|
||||
|
||||
fid.write('\n')
|
||||
|
||||
if re.match(dtype,'2D'):
|
||||
|
||||
for jj in range(nrx):
|
||||
|
||||
fid.writelines("%e " % ii for ii in mkvc(tx))
|
||||
fid.writelines("%e " % ii for ii in mkvc(rx[jj]))
|
||||
fid.write('%e %e\n'% (d[ii][jj],wd[ii][jj]))
|
||||
#np.savetxt(fid, np.c_[ rx ,np.asarray(d[ii]), np.asarray(wd[ii]) ], fmt='%e',delimiter=' ',newline='\n')
|
||||
|
||||
elif re.match(dtype,'3D'):
|
||||
|
||||
fid.write('\n')
|
||||
fid.writelines("%e " % ii for ii in mkvc(tx))
|
||||
fid.write('%i\n'% nrx)
|
||||
np.savetxt(fid, np.c_[ rx ,np.asarray(d[ii]), np.asarray(wd[ii]) ], fmt='%e',delimiter=' ',newline='\n')
|
||||
|
||||
|
||||
fid.close()
|
||||
|
||||
@@ -0,0 +1,191 @@
|
||||
|
||||
<script language="javascript">
|
||||
/* Define the Animation class */
|
||||
function Animation(frames, img_id, slider_id, interval, loop_select_id){
|
||||
this.img_id = img_id;
|
||||
this.slider_id = slider_id;
|
||||
this.loop_select_id = loop_select_id;
|
||||
this.interval = interval;
|
||||
this.current_frame = 0;
|
||||
this.direction = 0;
|
||||
this.timer = null;
|
||||
this.frames = new Array(frames.length);
|
||||
|
||||
for (var i=0; i<frames.length; i++)
|
||||
{
|
||||
this.frames[i] = new Image();
|
||||
this.frames[i].src = frames[i];
|
||||
}
|
||||
document.getElementById(this.slider_id).max = this.frames.length - 1;
|
||||
this.set_frame(this.current_frame);
|
||||
}
|
||||
|
||||
Animation.prototype.get_loop_state = function(){
|
||||
var button_group = document[this.loop_select_id].state;
|
||||
for (var i = 0; i < button_group.length; i++) {
|
||||
var button = button_group[i];
|
||||
if (button.checked) {
|
||||
return button.value;
|
||||
}
|
||||
}
|
||||
return undefined;
|
||||
}
|
||||
|
||||
Animation.prototype.set_frame = function(frame){
|
||||
this.current_frame = frame;
|
||||
document.getElementById(this.img_id).src = this.frames[this.current_frame].src;
|
||||
document.getElementById(this.slider_id).value = this.current_frame;
|
||||
}
|
||||
|
||||
Animation.prototype.next_frame = function()
|
||||
{
|
||||
this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));
|
||||
}
|
||||
|
||||
Animation.prototype.previous_frame = function()
|
||||
{
|
||||
this.set_frame(Math.max(0, this.current_frame - 1));
|
||||
}
|
||||
|
||||
Animation.prototype.first_frame = function()
|
||||
{
|
||||
this.set_frame(0);
|
||||
}
|
||||
|
||||
Animation.prototype.last_frame = function()
|
||||
{
|
||||
this.set_frame(this.frames.length - 1);
|
||||
}
|
||||
|
||||
Animation.prototype.slower = function()
|
||||
{
|
||||
this.interval /= 0.7;
|
||||
if(this.direction > 0){this.play_animation();}
|
||||
else if(this.direction < 0){this.reverse_animation();}
|
||||
}
|
||||
|
||||
Animation.prototype.faster = function()
|
||||
{
|
||||
this.interval *= 0.7;
|
||||
if(this.direction > 0){this.play_animation();}
|
||||
else if(this.direction < 0){this.reverse_animation();}
|
||||
}
|
||||
|
||||
Animation.prototype.anim_step_forward = function()
|
||||
{
|
||||
this.current_frame += 1;
|
||||
if(this.current_frame < this.frames.length){
|
||||
this.set_frame(this.current_frame);
|
||||
}else{
|
||||
var loop_state = this.get_loop_state();
|
||||
if(loop_state == "loop"){
|
||||
this.first_frame();
|
||||
}else if(loop_state == "reflect"){
|
||||
this.last_frame();
|
||||
this.reverse_animation();
|
||||
}else{
|
||||
this.pause_animation();
|
||||
this.last_frame();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Animation.prototype.anim_step_reverse = function()
|
||||
{
|
||||
this.current_frame -= 1;
|
||||
if(this.current_frame >= 0){
|
||||
this.set_frame(this.current_frame);
|
||||
}else{
|
||||
var loop_state = this.get_loop_state();
|
||||
if(loop_state == "loop"){
|
||||
this.last_frame();
|
||||
}else if(loop_state == "reflect"){
|
||||
this.first_frame();
|
||||
this.play_animation();
|
||||
}else{
|
||||
this.pause_animation();
|
||||
this.first_frame();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Animation.prototype.pause_animation = function()
|
||||
{
|
||||
this.direction = 0;
|
||||
if (this.timer){
|
||||
clearInterval(this.timer);
|
||||
this.timer = null;
|
||||
}
|
||||
}
|
||||
|
||||
Animation.prototype.play_animation = function()
|
||||
{
|
||||
this.pause_animation();
|
||||
this.direction = 1;
|
||||
var t = this;
|
||||
if (!this.timer) this.timer = setInterval(function(){t.anim_step_forward();}, this.interval);
|
||||
}
|
||||
|
||||
Animation.prototype.reverse_animation = function()
|
||||
{
|
||||
this.pause_animation();
|
||||
this.direction = -1;
|
||||
var t = this;
|
||||
if (!this.timer) this.timer = setInterval(function(){t.anim_step_reverse();}, this.interval);
|
||||
}
|
||||
</script>
|
||||
|
||||
<div class="animation" align="center">
|
||||
<img id="_anim_imgBYBDWVXULSOEHXUW">
|
||||
<br>
|
||||
<input id="_anim_sliderBYBDWVXULSOEHXUW" type="range" style="width:350px" name="points" min="0" max="1" step="1" value="0" onchange="animBYBDWVXULSOEHXUW.set_frame(parseInt(this.value));"></input>
|
||||
<br>
|
||||
<button onclick="animBYBDWVXULSOEHXUW.slower()">–</button>
|
||||
<button onclick="animBYBDWVXULSOEHXUW.first_frame()"><img class="anim_icon" src="data:image/png;base64,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"></button>
|
||||
<button onclick="animBYBDWVXULSOEHXUW.previous_frame()"><img class="anim_icon" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAgyTCyQ6wAAANRJREFUKM9jYBjO4AiUfgzFGGAp4+yayUvX6jMwMDCsYmBgOCS4OAOrSYmMgcc8/pd5Q3irC+Neh/1AlmeBMVgZmP8yMLD8/c/cqv9r90whzv/MX7Eq/MfAwMDIwCuZdfSV8U8WDgZGRmYGrAoZGRgY/jO8b3sj/J2F6T8j4z80pzEhmIwMjAxsSbqqlkeZGP//Z8SlkJnhPwMjwx/Guoe1NhmRwk+YGH5jV8jOwMPHzcDBysAwh8FrxQwtPU99HrwBXsnAwMDAsJiBgYGBoZ1xmKYqALHhMpn1o7igAAAAAElFTkSuQmCC"></button>
|
||||
<button onclick="animBYBDWVXULSOEHXUW.reverse_animation()"><img class="anim_icon" src="data:image/png;base64,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"></button>
|
||||
<button onclick="animBYBDWVXULSOEHXUW.pause_animation()"><img class="anim_icon" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkR91DQ2AAAAKtJREFUKM9jYCANTEVib2K4jcRbzQihGWEC00JuNjN8Z2Q0Zo3VYWA4lL005venH9+c3ZK5IfIsMIXMBtc12Bj+MMgxMDAwMPzWe2TBzPCf4SLcZCYY4/9/RgZGBiaYFf8gljFhKiQERhUOeoX/Gf8y/GX4y/APmlj+Mfxj+MfwH64Qnnq0zr9fyfLrPzP3eQYGBobvk5x4GX4xMIij23gdib0cRWYHiVmAAQDK5ircshCbHQAAAABJRU5ErkJggg=="></button>
|
||||
<button onclick="animBYBDWVXULSOEHXUW.play_animation()"><img class="anim_icon" src="data:image/png;base64,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"></button>
|
||||
<button onclick="animBYBDWVXULSOEHXUW.next_frame()"><img class="anim_icon" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkd/uac8wAAAMhJREFUKM9jYBie4DEUQ8B+fEq3+3UrMzAwMFxjYGBgYJizYubaOUxYFUaXh/6vWfRfEMIL/+//P5gZJoei4/f/7wxnY1PeNUXdE2RgYGZgYoCrY2BBVsjKwMDAwvCS4f3SG/dXxm5gYESSQ1HIwvCPgZmB8f8Pxv+Kxxb/YfiPJIdi9T8GJgaG/38ZFd4Fx0xUYsZt4h8GBgb2D2bLy7KnMTAwMEIxFoVCXIYr1IoDnkF4XAysqNIwUMDAwMDAsADKS2NkGL4AAIARMlfNIfZMAAAAAElFTkSuQmCC"></button>
|
||||
<button onclick="animBYBDWVXULSOEHXUW.last_frame()"><img class="anim_icon" src="data:image/png;base64,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"></button>
|
||||
<button onclick="animBYBDWVXULSOEHXUW.faster()">+</button>
|
||||
<form action="#n" name="_anim_loop_selectBYBDWVXULSOEHXUW" class="anim_control">
|
||||
<input type="radio" name="state" value="once" > Once </input>
|
||||
<input type="radio" name="state" value="loop" checked> Loop </input>
|
||||
<input type="radio" name="state" value="reflect" > Reflect </input>
|
||||
</form>
|
||||
</div>
|
||||
|
||||
|
||||
<script language="javascript">
|
||||
/* Instantiate the Animation class. */
|
||||
/* The IDs given should match those used in the template above. */
|
||||
(function() {
|
||||
var img_id = "_anim_imgBYBDWVXULSOEHXUW";
|
||||
var slider_id = "_anim_sliderBYBDWVXULSOEHXUW";
|
||||
var loop_select_id = "_anim_loop_selectBYBDWVXULSOEHXUW";
|
||||
var frames = new Array(0);
|
||||
|
||||
frames[0] = "data:image/png;base64,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 truncated
|
||||
frames[1] = 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|
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||||
frames[13] = 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truncated
|
||||
|
||||
|
||||
/* set a timeout to make sure all the above elements are created before
|
||||
the object is initialized. */
|
||||
setTimeout(function() {
|
||||
animBYBDWVXULSOEHXUW = new Animation(frames, img_id, slider_id, 33, loop_select_id);
|
||||
}, 0);
|
||||
})()
|
||||
</script>
|
||||
@@ -0,0 +1,5 @@
|
||||
FWD DC
|
||||
MESH FILE Mesh_2D.msh
|
||||
LOC LOC_X OBS_LOC.dat
|
||||
TOPO DEFAULT
|
||||
COND FILE MtIsa_2D.con
|
||||
@@ -0,0 +1,325 @@
|
||||
0 0 75 150
|
||||
0 0 150 225
|
||||
0 0 225 300
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
0 0 1875 1950
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
75 75 1350 1425
|
||||
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|
||||
75 75 1500 1575
|
||||
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|
||||
75 75 1650 1725
|
||||
75 75 1725 1800
|
||||
75 75 1800 1875
|
||||
75 75 1875 1950
|
||||
150 150 225 300
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
150 150 1800 1875
|
||||
150 150 1875 1950
|
||||
225 225 300 375
|
||||
225 225 375 450
|
||||
225 225 450 525
|
||||
225 225 525 600
|
||||
225 225 600 675
|
||||
225 225 675 750
|
||||
225 225 750 825
|
||||
225 225 825 900
|
||||
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|
||||
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|
||||
225 225 1050 1125
|
||||
225 225 1125 1200
|
||||
225 225 1200 1275
|
||||
225 225 1275 1350
|
||||
225 225 1350 1425
|
||||
225 225 1425 1500
|
||||
225 225 1500 1575
|
||||
225 225 1575 1650
|
||||
225 225 1650 1725
|
||||
225 225 1725 1800
|
||||
225 225 1800 1875
|
||||
225 225 1875 1950
|
||||
300 300 375 450
|
||||
300 300 450 525
|
||||
300 300 525 600
|
||||
300 300 600 675
|
||||
300 300 675 750
|
||||
300 300 750 825
|
||||
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|
||||
300 300 900 975
|
||||
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|
||||
300 300 1050 1125
|
||||
300 300 1125 1200
|
||||
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|
||||
300 300 1275 1350
|
||||
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|
||||
300 300 1425 1500
|
||||
300 300 1500 1575
|
||||
300 300 1575 1650
|
||||
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|
||||
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|
||||
300 300 1800 1875
|
||||
300 300 1875 1950
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
375 375 1350 1425
|
||||
375 375 1425 1500
|
||||
375 375 1500 1575
|
||||
375 375 1575 1650
|
||||
375 375 1650 1725
|
||||
375 375 1725 1800
|
||||
375 375 1800 1875
|
||||
375 375 1875 1950
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
450 450 1500 1575
|
||||
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|
||||
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|
||||
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|
||||
450 450 1800 1875
|
||||
450 450 1875 1950
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
525 525 1125 1200
|
||||
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|
||||
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|
||||
525 525 1350 1425
|
||||
525 525 1425 1500
|
||||
525 525 1500 1575
|
||||
525 525 1575 1650
|
||||
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|
||||
525 525 1725 1800
|
||||
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|
||||
525 525 1875 1950
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
675 675 1725 1800
|
||||
675 675 1800 1875
|
||||
675 675 1875 1950
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
900 900 1500 1575
|
||||
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|
||||
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|
||||
900 900 1725 1800
|
||||
900 900 1800 1875
|
||||
900 900 1875 1950
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
975 975 1500 1575
|
||||
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|
||||
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|
||||
975 975 1725 1800
|
||||
975 975 1800 1875
|
||||
975 975 1875 1950
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
1350 1350 1425 1500
|
||||
1350 1350 1500 1575
|
||||
1350 1350 1575 1650
|
||||
1350 1350 1650 1725
|
||||
1350 1350 1725 1800
|
||||
1350 1350 1800 1875
|
||||
1350 1350 1875 1950
|
||||
1425 1425 1500 1575
|
||||
1425 1425 1575 1650
|
||||
1425 1425 1650 1725
|
||||
1425 1425 1725 1800
|
||||
1425 1425 1800 1875
|
||||
1425 1425 1875 1950
|
||||
1500 1500 1575 1650
|
||||
1500 1500 1650 1725
|
||||
1500 1500 1725 1800
|
||||
1500 1500 1800 1875
|
||||
1500 1500 1875 1950
|
||||
1575 1575 1650 1725
|
||||
1575 1575 1725 1800
|
||||
1575 1575 1800 1875
|
||||
1575 1575 1875 1950
|
||||
1650 1650 1725 1800
|
||||
1650 1650 1800 1875
|
||||
1650 1650 1875 1950
|
||||
1725 1725 1800 1875
|
||||
1725 1725 1875 1950
|
||||
1800 1800 1875 1950
|
||||
@@ -1,5 +1,5 @@
|
||||
from SimPEG import *
|
||||
import simpegDC as DC
|
||||
import simpegDCIP as DC
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
@@ -26,7 +26,7 @@ def run(plotIt=False):
|
||||
rx = DC.RxDipole(xyz_rxP, xyz_rxN)
|
||||
src = DC.SrcDipole([rx], [-200, 0, -12.5], [+200, 0, -12.5])
|
||||
survey = DC.SurveyDC([src])
|
||||
problem = DC.ProblemDC(mesh)
|
||||
problem = DC.ProblemDC_CC(mesh)
|
||||
problem.pair(survey)
|
||||
try:
|
||||
from pymatsolver import MumpsSolver
|
||||
@@ -1,5 +1,5 @@
|
||||
from SimPEG import *
|
||||
import simpegDC as DC
|
||||
import simpegDCIP as DC
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
@@ -55,7 +55,7 @@ def example(aSpacing=2.5, nElecs=10, plotIt=False):
|
||||
|
||||
srcList = getSrcList(nElecs, aSpacing, in2D=True)
|
||||
survey = DC.SurveyDC(srcList)
|
||||
problem = DC.ProblemDC(mesh)
|
||||
problem = DC.ProblemDC_CC(mesh)
|
||||
problem.pair(survey)
|
||||
|
||||
return mesh, survey, problem
|
||||
File renamed without changes.
File renamed without changes.
+3
-3
@@ -1,6 +1,6 @@
|
||||
import unittest
|
||||
from SimPEG import *
|
||||
import simpegDC as DC
|
||||
import simpegDCIP as DC
|
||||
|
||||
|
||||
class DCProblemTests(unittest.TestCase):
|
||||
@@ -38,8 +38,8 @@ class DCProblemTests(unittest.TestCase):
|
||||
u = np.random.rand(self.mesh.nC*self.survey.nSrc)
|
||||
v = np.random.rand(self.mesh.nC)
|
||||
w = np.random.rand(self.survey.dobs.shape[0])
|
||||
wtJv = w.dot(self.p.Jvec(self.m0, v, u=u))
|
||||
vtJtw = v.dot(self.p.Jtvec(self.m0, w, u=u))
|
||||
wtJv = w.dot(self.p.Jvec(self.m0, v))
|
||||
vtJtw = v.dot(self.p.Jtvec(self.m0, w))
|
||||
passed = np.abs(wtJv - vtJtw) < 1e-10
|
||||
print 'Adjoint Test', np.abs(wtJv - vtJtw), passed
|
||||
self.assertTrue(passed)
|
||||
+1
-2
@@ -1,5 +1,5 @@
|
||||
import unittest
|
||||
import simpegDC as DC
|
||||
import simpegDCIP as DC
|
||||
|
||||
|
||||
class DCAnalyticTests(unittest.TestCase):
|
||||
@@ -8,6 +8,5 @@ class DCAnalyticTests(unittest.TestCase):
|
||||
self.assertTrue(DC.Examples.Verification.run() < 0.1)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@@ -0,0 +1,57 @@
|
||||
import unittest
|
||||
import simpegDCIP as DC
|
||||
from SimPEG import *
|
||||
from pymatsolver import MumpsSolver
|
||||
|
||||
class IPforwardTests(unittest.TestCase):
|
||||
|
||||
def test_IPforward(self):
|
||||
|
||||
cs = 12.5
|
||||
nc = 500/cs+1
|
||||
hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]
|
||||
hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]
|
||||
hz = [(cs,7, -1.3),(cs,int(nc/2+1))]
|
||||
mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')
|
||||
sighalf = 1e-2
|
||||
sigma = np.ones(mesh.nC)*sighalf
|
||||
p0 = np.r_[-50., 50., -50.]
|
||||
p1 = np.r_[ 50.,-50., -150.]
|
||||
blk_ind = Utils.ModelBuilder.getIndicesBlock(p0, p1, mesh.gridCC)
|
||||
sigma[blk_ind] = 1e-3
|
||||
eta = np.zeros_like(sigma)
|
||||
eta[blk_ind] = 0.1
|
||||
sigmaInf = sigma.copy()
|
||||
sigma0 = sigma*(1-eta)
|
||||
|
||||
nElecs = 11
|
||||
x_temp = np.linspace(-250, 250, nElecs)
|
||||
aSpacing = x_temp[1]-x_temp[0]
|
||||
y_temp = 0.
|
||||
xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])
|
||||
srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)
|
||||
survey = DC.SurveyDC(srcList)
|
||||
|
||||
imap = Maps.IdentityMap(mesh)
|
||||
problem = DC.ProblemDC_CC(mesh, mapping= imap )
|
||||
problem.Solver = MumpsSolver
|
||||
problem.pair(survey)
|
||||
|
||||
phi0 = survey.dpred(sigma0)
|
||||
phiInf = survey.dpred(sigmaInf)
|
||||
|
||||
phiIP_true = phi0-phiInf
|
||||
|
||||
surveyIP = DC.SurveyIP(srcList)
|
||||
problemIP = DC.ProblemIP(mesh, sigma=sigma)
|
||||
problemIP.pair(surveyIP)
|
||||
problemIP.Solver = MumpsSolver
|
||||
phiIP_approx = surveyIP.dpred(eta)
|
||||
|
||||
err = np.linalg.norm(phiIP_true-phiIP_approx) / np.linalg.norm(phiIP_true)
|
||||
|
||||
self.assertTrue(err < 0.02)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@@ -0,0 +1,80 @@
|
||||
import unittest
|
||||
from SimPEG import *
|
||||
import simpegDCIP as DC
|
||||
from pymatsolver import MumpsSolver
|
||||
|
||||
|
||||
|
||||
class IPProblemTests(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
|
||||
cs = 12.5
|
||||
nc = 500/cs+1
|
||||
hx = [(cs,0, -1.3),(cs,nc),(cs,0, 1.3)]
|
||||
hy = [(cs,0, -1.3),(cs,int(nc/2+1)),(cs,0, 1.3)]
|
||||
hz = [(cs,0, -1.3),(cs,int(nc/2+1))]
|
||||
mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')
|
||||
sighalf = 1e-2
|
||||
sigma = np.ones(mesh.nC)*sighalf
|
||||
p0 = np.r_[-50., 50., -50.]
|
||||
p1 = np.r_[ 50.,-50., -150.]
|
||||
blk_ind = Utils.ModelBuilder.getIndicesBlock(p0, p1, mesh.gridCC)
|
||||
sigma[blk_ind] = 1e-3
|
||||
eta = np.zeros_like(sigma)
|
||||
eta[blk_ind] = 0.1
|
||||
|
||||
nElecs = 5
|
||||
x_temp = np.linspace(-250, 250, nElecs)
|
||||
aSpacing = x_temp[1]-x_temp[0]
|
||||
y_temp = 0.
|
||||
xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])
|
||||
srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)
|
||||
survey = DC.SurveyIP(srcList)
|
||||
imap = Maps.IdentityMap(mesh)
|
||||
problem = DC.ProblemIP(mesh, sigma=sigma, mapping= imap)
|
||||
problem.pair(survey)
|
||||
problem.Solver = MumpsSolver
|
||||
|
||||
mSynth = eta
|
||||
survey.makeSyntheticData(mSynth)
|
||||
|
||||
# Now set up the problem to do some minimization
|
||||
dmis = DataMisfit.l2_DataMisfit(survey)
|
||||
reg = Regularization.Tikhonov(mesh)
|
||||
opt = Optimization.InexactGaussNewton(maxIterLS=20, maxIter=10, tolF=1e-6, tolX=1e-6, tolG=1e-6, maxIterCG=6)
|
||||
invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=1e4)
|
||||
inv = Inversion.BaseInversion(invProb)
|
||||
|
||||
self.inv = inv
|
||||
self.reg = reg
|
||||
self.p = problem
|
||||
self.mesh = mesh
|
||||
self.m0 = mSynth
|
||||
self.survey = survey
|
||||
self.dmis = dmis
|
||||
|
||||
def test_misfit(self):
|
||||
derChk = lambda m: [self.survey.dpred(m), lambda mx: self.p.Jvec(self.m0, mx)]
|
||||
passed = Tests.checkDerivative(derChk, self.m0*0, plotIt=False)
|
||||
self.assertTrue(passed)
|
||||
|
||||
def test_adjoint(self):
|
||||
# Adjoint Test
|
||||
u = np.random.rand(self.mesh.nC*self.survey.nSrc)
|
||||
v = np.random.rand(self.mesh.nC)
|
||||
w = np.random.rand(self.survey.dobs.shape[0])
|
||||
wtJv = w.dot(self.p.Jvec(self.m0, v))
|
||||
vtJtw = v.dot(self.p.Jtvec(self.m0, w))
|
||||
passed = np.abs(wtJv - vtJtw) < 1e-10
|
||||
print 'Adjoint Test', np.abs(wtJv - vtJtw), passed
|
||||
self.assertTrue(passed)
|
||||
|
||||
def test_dataObj(self):
|
||||
derChk = lambda m: [self.dmis.eval(m), self.dmis.evalDeriv(m)]
|
||||
passed = Tests.checkDerivative(derChk, self.m0, plotIt=False)
|
||||
self.assertTrue(passed)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@@ -1,2 +1,3 @@
|
||||
from BaseDC import *
|
||||
from BaseIP import *
|
||||
import Examples
|
||||
Reference in new issue
Block a user