mirror of
https://github.com/wassname/simpeg.git
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rowanc1
395 lines
27 KiB
Plaintext
395 lines
27 KiB
Plaintext
{
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"metadata": {
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"name": "InterpolationMatrix"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import numpy as np\n",
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"import scipy.sparse as sp\n",
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"from SimPEG.utils import spzeros, mkvc"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 1
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"#x = unique(mesh.gridEx[:,0])\n",
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"#y = unique(mesh.gridEx[:,1])\n",
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"#z = unique(mesh.gridEx[:,2])\n",
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"x = np.linspace(-1000,1000,5)\n",
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"y = np.linspace(-1000,1000,5)\n",
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"z = np.linspace(-1000,1000,5)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 2
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"xr1 = np.linspace(-500,500,5)\n",
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"yr1 = np.linspace(-500,500,5)\n",
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"zr1 = 0"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 3
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"xr, yr = meshgrid(xr1, yr1, indexing='ij')\n",
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"zr = np.ones((xr.shape[0],xr.shape[1]))*zr1"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 4
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"xr = mkvc(xr)\n",
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"yr = mkvc(yr)\n",
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"zr = mkvc(zr)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 5
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"nx = max(x.shape)\n",
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"ny = max(y.shape)\n",
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"nz = max(z.shape)\n",
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"npts = max(xr.shape)\n",
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"\n",
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"Q = np.zeros((npts, nx*ny*nz))\n",
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"\n",
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"for i in range(npts):\n",
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"\t# in x-direction \n",
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" im = np.argmin(abs(x-xr[i]))\n",
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" if xr[i] - x[im] >= 0: # Point on the left\n",
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" ind_x1 = im\n",
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" ind_x2 = im+1\n",
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" elif xr[i] - x[im] < 0: # Point on the right\n",
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" ind_x1 = im-1\n",
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" ind_x2 = im \n",
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" dx1 = xr[i] - x[ind_x1]\n",
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" dx2 = x[ind_x2] - xr[i]\n",
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" # in y-direction\n",
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" im = np.argmin(abs(y-yr[i]))\n",
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" if yr[i] - y[im] >= 0: # Point on the left\n",
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" ind_y1 = im\n",
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" ind_y2 = im+1\n",
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" elif yr[i] - y[im] < 0: # Point on the right\n",
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" ind_y1 = im-1\n",
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" ind_y2 = im \n",
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" dy1 = yr[i] - y[ind_y1]\n",
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" dy2 = y[ind_y2] - yr[i] \n",
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" # in z-direction\n",
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" im = np.argmin(abs(z-zr[i]))\n",
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" if zr[i] - z[im] >= 0: # Point on the left\n",
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" ind_z1 = im\n",
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" ind_z2 = im+1\n",
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" elif zr[i] - z[im] < 0: # Point on the right\n",
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" ind_z1 = im-1\n",
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" ind_z2 = im \n",
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" dz1 = zr[i] - z[ind_z1]\n",
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" dz2 = z[ind_z2] - zr[i] \n",
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" dv = (x[ind_x2] - x[ind_x1]) * (y[ind_y2] - y[ind_y1]) *(z[ind_z2] - z[ind_z1])\n",
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"\n",
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" Dx = x[ind_x2] - x[ind_x1]\n",
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" Dy = y[ind_y2] - y[ind_y1]\n",
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" Dz = z[ind_z2] - z[ind_z1]\n",
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"\n",
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" # Get the row in the matrix\n",
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"\n",
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" v = np.zeros((nx,ny,nz));\n",
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" \n",
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" v[ ind_x1, ind_y1, ind_z1] = (1-dx1/Dx)*(1-dy1/Dy)*(1-dz1/Dz);\n",
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" v[ ind_x1, ind_y2, ind_z1] = (1-dx1/Dx)*(1-dy2/Dy)*(1-dz1/Dz);\n",
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" v[ ind_x2, ind_y1, ind_z1] = (1-dx2/Dx)*(1-dy1/Dy)*(1-dz1/Dz);\n",
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" v[ ind_x2, ind_y2, ind_z1] = (1-dx2/Dx)*(1-dy2/Dy)*(1-dz1/Dz);\n",
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" v[ ind_x1, ind_y1, ind_z2] = (1-dx1/Dx)*(1-dy1/Dy)*(1-dz2/Dz);\n",
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" v[ ind_x1, ind_y2, ind_z2] = (1-dx1/Dx)*(1-dy2/Dy)*(1-dz2/Dz);\n",
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" v[ ind_x2, ind_y1, ind_z2] = (1-dx2/Dx)*(1-dy1/Dy)*(1-dz2/Dz);\n",
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" v[ ind_x2, ind_y2, ind_z2] = (1-dx2/Dx)*(1-dy2/Dy)*(1-dz2/Dz);\n",
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" \n",
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" \n",
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" Q[i,:] = mkvc(v)\n",
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"Q = sp.csr_matrix(Q)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 6
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import scipy.io\n",
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"\n",
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"# import file into a dictionary\n",
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"f = scipy.io.loadmat('q.mat')\n",
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" \n",
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"# read in the structure\n",
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"Qmat= f['q']\n",
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"Qmat = Qmat.todense()\n",
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"Q = Q.todense()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 7
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"norm(Q-Qmat)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 13,
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"text": [
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"0.0"
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]
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}
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],
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"prompt_number": 13
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"plt.subplot(131)\n",
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"spy(Q)\n",
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"plt.subplot(132)\n",
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"spy(Qmat)\n",
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"plt.subplot(133)\n",
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"spy(Qmat-Q)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "pyout",
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"prompt_number": 15,
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"text": [
|
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"<matplotlib.image.AxesImage at 0x8469588>"
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]
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},
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{
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"output_type": "display_data",
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"png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAAuCAYAAADeKCkyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4gAAAABJRU5ErkJggg==\n",
|
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"text": [
|
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"<matplotlib.figure.Figure at 0x84d4cc0>"
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]
|
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}
|
|
],
|
|
"prompt_number": 15
|
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},
|
|
{
|
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"cell_type": "code",
|
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"collapsed": false,
|
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"input": [
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"a = np.random.rand(5)\n",
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"ind = np.logical_and(a>0.1,a<0.5)\n",
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"print ind"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[False False False False False]\n"
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]
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}
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],
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"prompt_number": 10
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},
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{
|
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"def interpmat(x,y,z,xr,yr,zr):\n",
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"\n",
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" \"\"\" Local nterpolation computed for each receiver point in turn \"\"\"\n",
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"\n",
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" nx = max(x.shape)\n",
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" ny = max(y.shape)\n",
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" nz = max(z.shape)\n",
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" npts = max(xr.shape)\n",
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"\n",
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" Q = np.zeros((npts, nx*ny*nz))\n",
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"\n",
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" for i in range(npts):\n",
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" \t# in x-direction \n",
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" im = np.argmin(abs(x-xr[i]))\n",
|
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" if xr[i] - x[im] >= 0: # Point on the left\n",
|
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" ind_x1 = im\n",
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" ind_x2 = im+1\n",
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" elif xr[i] - x[im] < 0: # Point on the right\n",
|
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" ind_x1 = im-1\n",
|
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" ind_x2 = im \n",
|
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" dx1 = xr[i] - x[ind_x1]\n",
|
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" dx2 = x[ind_x2] - xr[i]\n",
|
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" # in y-direction\n",
|
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" im = np.argmin(abs(y-yr[i]))\n",
|
|
" if yr[i] - y[im] >= 0: # Point on the left\n",
|
|
" ind_y1 = im\n",
|
|
" ind_y2 = im+1\n",
|
|
" elif yr[i] - y[im] < 0: # Point on the right\n",
|
|
" ind_y1 = im-1\n",
|
|
" ind_y2 = im \n",
|
|
" dy1 = yr[i] - y[ind_y1]\n",
|
|
" dy2 = y[ind_y2] - yr[i] \n",
|
|
" # in z-direction\n",
|
|
" im = np.argmin(abs(z-zr[i]))\n",
|
|
" if zr[i] - z[im] >= 0: # Point on the left\n",
|
|
" ind_z1 = im\n",
|
|
" ind_z2 = im+1\n",
|
|
" elif zr[i] - z[im] < 0: # Point on the right\n",
|
|
" ind_z1 = im-1\n",
|
|
" ind_z2 = im \n",
|
|
" dz1 = zr[i] - z[ind_z1]\n",
|
|
" dz2 = z[ind_z2] - zr[i] \n",
|
|
" dv = (x[ind_x2] - x[ind_x1]) * (y[ind_y2] - y[ind_y1]) *(z[ind_z2] - z[ind_z1])\n",
|
|
"\n",
|
|
" Dx = x[ind_x2] - x[ind_x1]\n",
|
|
" Dy = y[ind_y2] - y[ind_y1]\n",
|
|
" Dz = z[ind_z2] - z[ind_z1]\n",
|
|
"\n",
|
|
" # Get the row in the matrix\n",
|
|
"\n",
|
|
" v = np.zeros((nx,ny,nz));\n",
|
|
" \n",
|
|
" v[ ind_x1, ind_y1, ind_z1] = (1-dx1/Dx)*(1-dy1/Dy)*(1-dz1/Dz);\n",
|
|
" v[ ind_x1, ind_y2, ind_z1] = (1-dx1/Dx)*(1-dy2/Dy)*(1-dz1/Dz);\n",
|
|
" v[ ind_x2, ind_y1, ind_z1] = (1-dx2/Dx)*(1-dy1/Dy)*(1-dz1/Dz);\n",
|
|
" v[ ind_x2, ind_y2, ind_z1] = (1-dx2/Dx)*(1-dy2/Dy)*(1-dz1/Dz);\n",
|
|
" v[ ind_x1, ind_y1, ind_z2] = (1-dx1/Dx)*(1-dy1/Dy)*(1-dz2/Dz);\n",
|
|
" v[ ind_x1, ind_y2, ind_z2] = (1-dx1/Dx)*(1-dy2/Dy)*(1-dz2/Dz);\n",
|
|
" v[ ind_x2, ind_y1, ind_z2] = (1-dx2/Dx)*(1-dy1/Dy)*(1-dz2/Dz);\n",
|
|
" v[ ind_x2, ind_y2, ind_z2] = (1-dx2/Dx)*(1-dy2/Dy)*(1-dz2/Dz);\n",
|
|
" \n",
|
|
" \n",
|
|
" Q[i,:] = mkvc(v)\n",
|
|
" Q = sp.csr_matrix(Q)\n",
|
|
" return Q"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 16
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"Q = interpmat(x,y,z,xr,yr,zr)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 17
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"Q = Q.todense()\n",
|
|
"norm(Q-Qmat)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "pyout",
|
|
"prompt_number": 18,
|
|
"text": [
|
|
"0.0"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 18
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"plt.subplot(131)\n",
|
|
"plt.plot(mkvc(Qmat))\n",
|
|
"plt.subplot(132)\n",
|
|
"plt.plot(mkvc(Q))\n",
|
|
"plt.subplot(133)\n",
|
|
"plt.plot(mkvc(Qmat-Q))"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "pyout",
|
|
"prompt_number": 29,
|
|
"text": [
|
|
"[<matplotlib.lines.Line2D at 0xcfc4ef0>]"
|
|
]
|
|
},
|
|
{
|
|
"output_type": "display_data",
|
|
"png": 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CBNHa2mr33M9//nOxfPly0dnZKR5//HGxceNGYTQaRVVVlbjhhhvEiRMnhMFg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\n7w0VJIln3wAAAABJRU5ErkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0xca3aac8>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 29
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 16
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": []
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |