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Start working on mag integral
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D Fournier committed
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@@ -0,0 +1,806 @@
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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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"source": [
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"from SimPEG import *\n",
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"import matplotlib.pyplot as plt\n",
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"import simpegPF as PF\n",
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"import matplotlib\n",
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"#from get_UBC_mesh import get_UBC_mesh\n",
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"from read_MAG_obs import read_MAG_obs\n",
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"from get_T_mat import get_T_mat"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Writing magnetostatic problem with total field formulation\n",
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"\n",
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"## - Here we focus on cell-centered system with FVM-Weak form"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step:1 Generating mesh and operators"
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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": 26,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"#mesh = Utils.meshutils.readUBCTensorMesh(\"Tile1.msh\")\n",
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"cs = 25.\n",
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"hxind = [(cs,5,-1.3), (cs/2.0, 21),(cs,5,1.3)]\n",
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"hyind = [(cs,5,-1.3), (cs/2.0, 21),(cs,5,1.3)]\n",
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"hzind = [(cs,5,-1.3),(cs/2.0, 20)]\n",
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"\n",
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"mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')\n",
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"\n",
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"xn = mesh.vectorNx\n",
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"yn = mesh.vectorNy\n",
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"zn = mesh.vectorNz\n",
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"\n",
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"\n",
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"mcell = (xn.size-1) * (yn.size-1) * (zn.size-1)\n",
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"\n",
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"N = mesh.gridN\n",
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"\n",
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"Utils.meshutils.writeUBCTensorMesh('Mesh.msh',mesh)\n"
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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": 28,
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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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"[-425.15075 -425.15075 -271.950375] [ 425.15075 425.15075 271.950375]\n"
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]
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}
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],
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"source": [
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"aa.size\n",
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"print N[0],N[-1]\n",
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"\n",
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"Utils.mkvc?"
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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": 25,
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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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"sph_ind = PF.MagAnalytics.spheremodel(mesh, 0, 0, 175, 50)\n",
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"\n",
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"Utils.meshutils.writeUBCTensorModel('Mesh.dat',mesh,sph_ind)"
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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": 30,
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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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"# Load in obsfile\n",
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"#Decl, Incl, B0, Mdec, Minc, obsx, obsy, obsz, data, unct = read_MAG_obs('Obs_RAW_REM_GRID_TMI.obs')\n",
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"\n",
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"Incl = 90.\n",
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"Decl = 00.\n",
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"B0 = 50000\n",
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" \n",
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"# Or create juste a plane grid\n",
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"xr = np.linspace(-125, 125, 25)\n",
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"yr = np.linspace(-125, 125, 25)\n",
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"X, Y = np.meshgrid(xr, yr)\n",
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"Z = np.ones((xr.size, yr.size))*280\n",
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"rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n",
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" \n",
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"# Write obsfile in UBC format\n",
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"fid = open('Obs_loc.dat','w')\n",
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"fid.write('%6.2f %6.2f %6.2f\\n' %(Incl, Decl, B0) )\n",
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"fid.write('%6.2f %6.2f %6.2f\\n' %(Incl, Decl, 1) )\n",
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"fid.close();"
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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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"# Create magnetization matrix\n",
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"mx = np.cos(np.deg2rad(Incl)) * np.cos(np.deg2rad(Decl))\n",
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"my = np.cos(np.deg2rad(Incl)) * np.sin(np.deg2rad(Decl))\n",
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"mz = np.sin(np.deg2rad(Incl))"
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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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"Mx = scipy.sparse.diags(np.ones([mcell])*mx*B0,0)"
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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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{
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"data": {
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"text/plain": [
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"scipy.sparse.dia.dia_matrix"
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]
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},
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"execution_count": 8,
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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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"source": [
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"type(Mx)"
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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": true
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},
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"outputs": [],
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"source": [
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"Mx1 = Utils.sdiag(np.ones([mcell])*mx*B0)"
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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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{
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"data": {
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"text/plain": [
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"scipy.sparse.csr.csr_matrix"
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]
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},
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"execution_count": 10,
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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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"source": [
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"type(Mx1)"
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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": true
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},
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"outputs": [],
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"source": [
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"Mx = Utils.sdiag(np.ones([mcell])*mx*B0)\n",
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"My = Utils.sdiag(np.ones([mcell])*my*B0)\n",
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"Mz = Utils.sdiag(np.ones([mcell])*mz*B0)\n",
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"\n",
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"#matplotlib.pyplot.spy(scipy.sparse.csr_matrix(Mx))\n",
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"#plt.show()\n",
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"M = sp.vstack((Mx,My,Mz));\n"
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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": 13,
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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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"0 24\n",
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"1 24\n",
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"2 24\n",
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"3 24\n",
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"4 24\n",
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"5 24\n",
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"6 24\n",
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"7 24\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-13-0ffb93de4fd3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Call the function to build tensor matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mTx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_T_mat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mzn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mobsx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mobsy\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mobsz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\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 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mTx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTy\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32mC:\\Users\\dominiquef.MIRAGEOSCIENCE\\Documents\\GIT\\SimPEG\\Source\\SimPEGpf\\simpegPF\\notebooks\\get_T_mat.py\u001b[0m in \u001b[0;36mget_T_mat\u001b[1;34m(xn, yn, zn, obsx, obsy, obsz)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[0mR1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdx2\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdz1\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 105\u001b[1;33m \u001b[0mR2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdx2\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdz2\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\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 106\u001b[0m \u001b[0mR3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdx1\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdz1\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 107\u001b[0m \u001b[0mR4\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdx1\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdz2\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
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]
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}
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],
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"source": [
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"# Call the function to build tensor matrix\n",
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"Tx, Ty, Tz = get_T_mat(xn,yn,zn,obsx[0],obsy[0],obsz[0])\n",
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"\n",
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"print Tx[0,0],Ty[0,0],Tz[0,0]\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For sparse matrix\n",
|
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"b = A*x\n",
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"\n",
|
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"For dense matrix \n",
|
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"b = A.dot(x)"
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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": 16,
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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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"x = np.ones(10)\n",
|
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"y = np.ones(10)*3"
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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": 19,
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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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"30.0"
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]
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},
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"execution_count": 19,
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"metadata": {},
|
||||
"output_type": "execute_result"
|
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}
|
||||
],
|
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"source": [
|
||||
"x.dot(y)"
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]
|
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},
|
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
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||||
"metadata": {
|
||||
"collapsed": false
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||||
},
|
||||
"outputs": [],
|
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"source": [
|
||||
"Gx = Tx*M"
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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": {
|
||||
"collapsed": false
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},
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"outputs": [],
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"source": [
|
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"cs = 25.\n",
|
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"hxind = [(cs,5,-1.3), (cs/2.0, 41),(cs,5,1.3)]\n",
|
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"hyind = [(cs,5,-1.3), (cs/2.0, 41),(cs,5,1.3)]\n",
|
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"hzind = [(cs,5,-1.3), (cs/2.0, 40),(cs,5,1.3)]\n",
|
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"M3 = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
||||
"### Step2: Compute Boundary indicies and set $\\mathbf{B}_{bc}$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Step3: Generating model $\\mu$\n",
|
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"\n",
|
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"$\\mu = \\mu_0(1+\\chi)$"
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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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"source": [
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||||
"mu0 = 4*np.pi*1e-7\n",
|
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"chibkg = 0.\n",
|
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"chiblk = 0.01\n",
|
||||
"chi = np.ones(M3.nC)*chibkg"
|
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]
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||||
},
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'spheremodel' 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-12-72465b2c3978>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msph_ind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mspheremodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mM3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mchi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msph_ind\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mchiblk\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mmu\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1.\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mchi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmu0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mNameError\u001b[0m: name 'spheremodel' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sph_ind = spheremodel(M3, 0, 0, 0, 100)\n",
|
||||
"chi[sph_ind] = chiblk\n",
|
||||
"mu = (1.+chi)*mu0"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\SEOGI\\AppData\\Local\\Enthought\\Canopy\\App\\appdata\\canopy-1.0.1.1189.win-x86_64\\lib\\site-packages\\matplotlib\\lines.py:483: RuntimeWarning: invalid value encountered in greater_equal\n",
|
||||
" return np.alltrue(x[1:]-x[0:-1]>=0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x57d1828>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"figsize(10,10)\n",
|
||||
"M3.plotGrid()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.collections.QuadMesh at 0xaa7f390>"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xa0f8ef0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"M3.plotImage(np.log10(mu), imageType='CC')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0\n",
|
||||
"119646\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print info\n",
|
||||
"print M3.nC"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0\n",
|
||||
"7.65372604673e-07\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"B = -MfmuI*D.T*phi\n",
|
||||
"rhsa = A*phi\n",
|
||||
"print info\n",
|
||||
"print np.linalg.norm(rhs-rhsa)/np.linalg.norm(rhs)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### BICG, qmr or BICGstab with Jacobi preconditioner works well (scipy), minres does not work ... Need to play with some other iterative solvers. Remeber that our system has null-space, which is constant"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xaa72208>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"figsize(16,5)\n",
|
||||
"M3.plotImage(B, imageType='F')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Step4: Compute analytic function (sphere in whole space)\n",
|
||||
"Outside of the sphere $(r>R)$\n",
|
||||
"\n",
|
||||
"$$\\mathbf{H}_1 = H_0 \\hat{x} + H_0\\frac{R}{r^5}\\frac{\\mu_2-\\mu_1}{\\mu_2+2\\mu_1}[(2x^2-y^2-z^2)\\hat{x}+(3xy)\\hat{y}+(3xz)\\hat{z}]$$\n",
|
||||
"\n",
|
||||
"$$H_{x1} = H_0 + H_0\\frac{R}{r^5}\\frac{\\mu_2-\\mu_1}{\\mu_2+2\\mu_1}(2x^2-y^2-z^2)$$\n",
|
||||
"\n",
|
||||
"$$H_{y1} = H_0\\frac{R}{r^5}\\frac{\\mu_2-\\mu_1}{\\mu_2+2\\mu_1}(3xy)$$\n",
|
||||
"\n",
|
||||
"$$H_{z1} = H_0\\frac{R}{r^5}\\frac{\\mu_2-\\mu_1}{\\mu_2+2\\mu_1}(3xz)$$\n",
|
||||
"\n",
|
||||
"Inside of the sphere $(r\\le R)$\n",
|
||||
"\n",
|
||||
"$$\\mathbf{H}_2 = H_0\\frac{3\\mu_1}{\\mu_2+2\\mu_1}\\hat{x}$$\n",
|
||||
"\n",
|
||||
"$$H_{x2} = H_0\\frac{3\\mu_1}{\\mu_2+2\\mu_1}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Step5: Projection to receiver plane"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xr = np.linspace(-300, 300, 41)\n",
|
||||
"yr = np.linspace(-300, 300, 41)\n",
|
||||
"X, Y = np.meshgrid(xr, yr)\n",
|
||||
"Z = np.ones((size(xr), size(yr)))*0."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n",
|
||||
"Qfx = M3.getInterpolationMat(rxLoc,'Fx')\n",
|
||||
"Qfy = M3.getInterpolationMat(rxLoc,'Fy')\n",
|
||||
"Qfz = M3.getInterpolationMat(rxLoc,'Fz')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100.0 kang\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Bxr = np.reshape(Qfx*B, (size(xr), size(yr)), order='F')\n",
|
||||
"Byr = np.reshape(Qfy*B, (size(xr), size(yr)), order='F')\n",
|
||||
"Bzr = np.reshape(Qfz*B, (size(xr), size(yr)), order='F')\n",
|
||||
"H0 = Box/mu0\n",
|
||||
"flag = 'secondary'\n",
|
||||
"if flag=='secondary':\n",
|
||||
" Bxr = Bxr-Box\n",
|
||||
"\n",
|
||||
"# Bxra, Byra, Bzra = MagSphereAnalFun(X, Y, Z, 100, 0., 0., 0., mu0, mu0*(1+chiblk), H0, flag)\n",
|
||||
"Bxra, Byra, Bzra = MagSphereAnalFunA(X, Y, Z, 100., 0., 0., 0., chiblk, np.array([1., 0., 0.]), flag)\n",
|
||||
"\n",
|
||||
"Bxra = np.reshape(Bxra, (size(xr), size(yr)), order='F')\n",
|
||||
"Byra = np.reshape(Byra, (size(xr), size(yr)), order='F')\n",
|
||||
"Bzra = np.reshape(Bzra, (size(xr), size(yr)), order='F')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0xa11f550>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1826c630>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"figsize(10, 4)\n",
|
||||
"plot(Utils.mkvc(Bxra))\n",
|
||||
"plot(Utils.mkvc(Bxr), 'k:')\n",
|
||||
"plot(Utils.mkvc(Byra))\n",
|
||||
"plot(Utils.mkvc(Byr), 'k:')\n",
|
||||
"plot(Utils.mkvc(Bzra))\n",
|
||||
"plot(Utils.mkvc(Bzr), 'k:')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x000000001244C608>"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x12fb62e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = subplots(3,2, figsize = (10,15))\n",
|
||||
"dat1 = ax[0,0].imshow(Bxr); fig.colorbar(dat1, ax=ax[0,0])\n",
|
||||
"dat2 = ax[0,1].imshow(Bxra); fig.colorbar(dat2, ax=ax[0,1])\n",
|
||||
"dat3 = ax[1,0].imshow(Byr); fig.colorbar(dat3, ax=ax[1,0])\n",
|
||||
"dat4 = ax[1,1].imshow(Byra); fig.colorbar(dat4, ax=ax[1,1])\n",
|
||||
"dat5 = ax[2,0].imshow(Bzr); fig.colorbar(dat5, ax=ax[2,0])\n",
|
||||
"dat6 = ax[2,1].imshow(Bzra); fig.colorbar(dat6, ax=ax[2,1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[-285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285.\n",
|
||||
" -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285.\n",
|
||||
" -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285.\n",
|
||||
" -285. -285. -285. -285. -285.]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0xaa70e80>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"id = 21\n",
|
||||
"fig, axes = subplots(1,3, figsize=(14,5))\n",
|
||||
"epsx = np.linalg.norm(Utils.mkvc(Bxr))*1e-6\n",
|
||||
"epsy = np.linalg.norm(Utils.mkvc(Byr))*1e-6\n",
|
||||
"epsz = np.linalg.norm(Utils.mkvc(Bzr))*1e-6\n",
|
||||
"axes[0].plot(Y[:,id], Bxra[:,id], 'b', Y[:,id], Bxr[:,id], 'r.')\n",
|
||||
"axes[1].plot(Y[:,id], Byra[:,id], 'b', Y[:,id], Byr[:,id], 'r.')\n",
|
||||
"axes[2].plot(Y[:,id], Bzra[:,id], 'b', Y[:,id], Bzr[:,id], 'r.')\n",
|
||||
"print X[:,1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x139014e0>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x12897198>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, axes = subplots(1,3, figsize=(14,5))\n",
|
||||
"epsx = np.linalg.norm(Utils.mkvc(Bxr))*1e-6\n",
|
||||
"epsy = np.linalg.norm(Utils.mkvc(Byr))*1e-6\n",
|
||||
"epsz = np.linalg.norm(Utils.mkvc(Bzr))*1e-6\n",
|
||||
"axes[0].plot(Y[:,1], abs((Bxr[:,1]-Bxra[:,1])/(Bxra[:,1]+epsx)), 'r.')\n",
|
||||
"axes[1].plot(Y[:,1], abs((Byr[:,1]-Byra[:,1])/(Byra[:,1]+epsy)), 'r.')\n",
|
||||
"axes[2].plot(Y[:,1], abs((Bzr[:,1]-Bzra[:,1])/(Bzra[:,1]+epsz)), 'r.')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Thoughts\n",
|
||||
"\n",
|
||||
"- It works well with non-uniform mesh!!\n",
|
||||
"- Actual accuray is ~10% relative error in secondary fields. As we pad more we can get better accuracy, since we did not consider secondary field at the boudnary ($\\partial\\Omega$). \n",
|
||||
"- Here, we can try primary secondary field approach to get better accuracy. \n",
|
||||
"- In addition, we can use the congruous sphere method to handle this secondary fields at boundaries"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
+2758
-2772
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,5 @@
|
||||
31 31 25
|
||||
-425.15 -425.15 271.95
|
||||
92.82 71.40 54.93 42.25 32.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 32.50 42.25 54.93 71.40 92.82
|
||||
92.82 71.40 54.93 42.25 32.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 32.50 42.25 54.93 71.40 92.82
|
||||
12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 12.50 32.50 42.25 54.93 71.40 92.82
|
||||
@@ -0,0 +1,345 @@
|
||||
90.00 0.00 50000.00
|
||||
90.00 0.00 1.00
|
||||
342
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4.22510000e+05 5.45450000e+05 1.62000000e+03 6.73233900e+00 1.00000000e+00
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4.22750000e+05 5.45450000e+05 1.62000000e+03 3.58074714e+00 1.00000000e+00
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4.22990000e+05 5.45450000e+05 1.62000000e+03 4.50486079e+00 1.00000000e+00
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4.23110000e+05 5.45450000e+05 1.62000000e+03 8.54060168e+00 1.00000000e+00
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4.23230000e+05 5.45450000e+05 1.62000000e+03 7.56521324e+00 1.00000000e+00
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4.22270000e+05 5.45490000e+05 1.62000000e+03 6.07795504e+00 1.00000000e+00
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4.22390000e+05 5.45490000e+05 1.62000000e+03 1.48368791e+01 1.00000000e+00
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4.22510000e+05 5.45490000e+05 1.62000000e+03 1.09771592e+01 1.00000000e+00
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4.22630000e+05 5.45490000e+05 1.62000000e+03 6.96072957e+00 1.00000000e+00
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4.22750000e+05 5.45490000e+05 1.62000000e+03 9.41411677e+00 1.00000000e+00
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4.22870000e+05 5.45490000e+05 1.62000000e+03 7.73682208e+00 1.00000000e+00
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4.22990000e+05 5.45490000e+05 1.62000000e+03 7.92529254e+00 1.00000000e+00
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4.23110000e+05 5.45490000e+05 1.62000000e+03 1.36806576e+01 1.00000000e+00
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4.23230000e+05 5.45490000e+05 1.62000000e+03 1.15837327e+01 1.00000000e+00
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4.22270000e+05 5.45530000e+05 1.62000000e+03 7.22666342e+00 1.00000000e+00
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4.22390000e+05 5.45530000e+05 1.62000000e+03 2.10115904e+01 1.00000000e+00
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4.22510000e+05 5.45530000e+05 1.62000000e+03 1.67571228e+01 1.00000000e+00
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4.22630000e+05 5.45530000e+05 1.62000000e+03 1.24744034e+01 1.00000000e+00
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4.22750000e+05 5.45530000e+05 1.62000000e+03 2.09131341e+01 1.00000000e+00
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4.22870000e+05 5.45530000e+05 1.62000000e+03 1.59182943e+01 1.00000000e+00
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4.22990000e+05 5.45530000e+05 1.62000000e+03 1.28178898e+01 1.00000000e+00
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4.23110000e+05 5.45530000e+05 1.62000000e+03 2.02870303e+01 1.00000000e+00
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4.23230000e+05 5.45530000e+05 1.62000000e+03 1.54671112e+01 1.00000000e+00
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4.22630000e+05 5.45570000e+05 1.62000000e+03 2.04680600e+01 1.00000000e+00
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4.22750000e+05 5.45570000e+05 1.62000000e+03 4.16660828e+01 1.00000000e+00
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4.22870000e+05 5.45570000e+05 1.62000000e+03 2.93966109e+01 1.00000000e+00
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4.23110000e+05 5.45570000e+05 1.62000000e+03 2.76112641e+01 1.00000000e+00
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4.23230000e+05 5.45570000e+05 1.62000000e+03 1.72850506e+01 1.00000000e+00
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4.22270000e+05 5.45610000e+05 1.62000000e+03 3.79541661e+00 1.00000000e+00
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4.22390000e+05 5.45610000e+05 1.62000000e+03 2.83221180e+01 1.00000000e+00
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4.22510000e+05 5.45610000e+05 1.62000000e+03 3.24444989e+01 1.00000000e+00
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4.22630000e+05 5.45610000e+05 1.62000000e+03 3.05874074e+01 1.00000000e+00
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4.22750000e+05 5.45610000e+05 1.62000000e+03 7.25067339e+01 1.00000000e+00
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4.22870000e+05 5.45610000e+05 1.62000000e+03 4.79655353e+01 1.00000000e+00
|
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4.22990000e+05 5.45610000e+05 1.62000000e+03 2.75464261e+01 1.00000000e+00
|
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4.23110000e+05 5.45610000e+05 1.62000000e+03 3.38679334e+01 1.00000000e+00
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4.23230000e+05 5.45610000e+05 1.62000000e+03 1.51846258e+01 1.00000000e+00
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4.22270000e+05 5.45650000e+05 1.62000000e+03 -4.39065820e-01 1.00000000e+00
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4.22390000e+05 5.45650000e+05 1.62000000e+03 2.50202337e+01 1.00000000e+00
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4.22510000e+05 5.45650000e+05 1.62000000e+03 4.05253645e+01 1.00000000e+00
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4.22630000e+05 5.45650000e+05 1.62000000e+03 4.10457762e+01 1.00000000e+00
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4.22870000e+05 5.45650000e+05 1.62000000e+03 6.65102889e+01 1.00000000e+00
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4.23230000e+05 5.45650000e+05 1.62000000e+03 9.24227489e+00 1.00000000e+00
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4.22390000e+05 5.45690000e+05 1.62000000e+03 1.71125012e+01 1.00000000e+00
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4.22510000e+05 5.45690000e+05 1.62000000e+03 4.58784547e+01 1.00000000e+00
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4.22750000e+05 5.45690000e+05 1.62000000e+03 1.18854047e+02 1.00000000e+00
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4.22870000e+05 5.45690000e+05 1.62000000e+03 7.67155817e+01 1.00000000e+00
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|
||||
4.23050000e+05 5.45730000e+05 1.62000000e+03 4.42417162e+01 1.00000000e+00
|
||||
4.23170000e+05 5.45730000e+05 1.62000000e+03 5.67356246e+00 1.00000000e+00
|
||||
4.23290000e+05 5.45730000e+05 1.62000000e+03 -8.76983083e+00 1.00000000e+00
|
||||
4.22330000e+05 5.45770000e+05 1.62000000e+03 -8.29599646e+00 1.00000000e+00
|
||||
4.22450000e+05 5.45770000e+05 1.62000000e+03 1.55377257e+01 1.00000000e+00
|
||||
4.22570000e+05 5.45770000e+05 1.62000000e+03 5.63461753e+01 1.00000000e+00
|
||||
4.22690000e+05 5.45770000e+05 1.62000000e+03 6.58920602e+01 1.00000000e+00
|
||||
4.22810000e+05 5.45770000e+05 1.62000000e+03 8.34938010e+01 1.00000000e+00
|
||||
4.22930000e+05 5.45770000e+05 1.62000000e+03 5.68291435e+01 1.00000000e+00
|
||||
4.23050000e+05 5.45770000e+05 1.62000000e+03 3.80726539e+01 1.00000000e+00
|
||||
4.23170000e+05 5.45770000e+05 1.62000000e+03 -3.02070849e+00 1.00000000e+00
|
||||
4.23290000e+05 5.45770000e+05 1.62000000e+03 -1.06200100e+01 1.00000000e+00
|
||||
4.22330000e+05 5.45810000e+05 1.62000000e+03 -1.09840526e+01 1.00000000e+00
|
||||
4.22450000e+05 5.45810000e+05 1.62000000e+03 5.13214242e+00 1.00000000e+00
|
||||
4.22570000e+05 5.45810000e+05 1.62000000e+03 5.75920636e+01 1.00000000e+00
|
||||
4.22690000e+05 5.45810000e+05 1.62000000e+03 5.79853733e+01 1.00000000e+00
|
||||
4.22810000e+05 5.45810000e+05 1.62000000e+03 6.18017494e+01 1.00000000e+00
|
||||
4.22930000e+05 5.45810000e+05 1.62000000e+03 6.02618603e+01 1.00000000e+00
|
||||
4.23050000e+05 5.45810000e+05 1.62000000e+03 2.84743717e+01 1.00000000e+00
|
||||
4.23170000e+05 5.45810000e+05 1.62000000e+03 -9.10456112e+00 1.00000000e+00
|
||||
4.23290000e+05 5.45810000e+05 1.62000000e+03 -1.13731721e+01 1.00000000e+00
|
||||
4.22330000e+05 5.45850000e+05 1.62000000e+03 -1.22148188e+01 1.00000000e+00
|
||||
4.22450000e+05 5.45850000e+05 1.62000000e+03 -3.10495166e+00 1.00000000e+00
|
||||
4.22570000e+05 5.45850000e+05 1.62000000e+03 5.58506516e+01 1.00000000e+00
|
||||
4.22690000e+05 5.45850000e+05 1.62000000e+03 5.82957567e+01 1.00000000e+00
|
||||
4.22810000e+05 5.45850000e+05 1.62000000e+03 5.30224805e+01 1.00000000e+00
|
||||
4.22930000e+05 5.45850000e+05 1.62000000e+03 6.63035303e+01 1.00000000e+00
|
||||
4.23050000e+05 5.45850000e+05 1.62000000e+03 1.73587657e+01 1.00000000e+00
|
||||
4.23170000e+05 5.45850000e+05 1.62000000e+03 -1.27463067e+01 1.00000000e+00
|
||||
4.23290000e+05 5.45850000e+05 1.62000000e+03 -1.13912348e+01 1.00000000e+00
|
||||
4.22330000e+05 5.45890000e+05 1.62000000e+03 -1.25008925e+01 1.00000000e+00
|
||||
4.22450000e+05 5.45890000e+05 1.62000000e+03 -9.23370082e+00 1.00000000e+00
|
||||
4.22570000e+05 5.45890000e+05 1.62000000e+03 4.85451545e+01 1.00000000e+00
|
||||
4.22690000e+05 5.45890000e+05 1.62000000e+03 6.63625434e+01 1.00000000e+00
|
||||
4.22810000e+05 5.45890000e+05 1.62000000e+03 5.66170758e+01 1.00000000e+00
|
||||
4.22930000e+05 5.45890000e+05 1.62000000e+03 7.21862948e+01 1.00000000e+00
|
||||
4.23050000e+05 5.45890000e+05 1.62000000e+03 5.60977667e+00 1.00000000e+00
|
||||
4.23170000e+05 5.45890000e+05 1.62000000e+03 -1.46073579e+01 1.00000000e+00
|
||||
4.23290000e+05 5.45890000e+05 1.62000000e+03 -1.09712490e+01 1.00000000e+00
|
||||
4.22330000e+05 5.45930000e+05 1.62000000e+03 -1.22053845e+01 1.00000000e+00
|
||||
4.22450000e+05 5.45930000e+05 1.62000000e+03 -1.34718185e+01 1.00000000e+00
|
||||
4.22570000e+05 5.45930000e+05 1.62000000e+03 3.04668037e+01 1.00000000e+00
|
||||
4.22690000e+05 5.45930000e+05 1.62000000e+03 7.34007776e+01 1.00000000e+00
|
||||
4.22810000e+05 5.45930000e+05 1.62000000e+03 6.56540653e+01 1.00000000e+00
|
||||
4.22930000e+05 5.45930000e+05 1.62000000e+03 6.55157969e+01 1.00000000e+00
|
||||
4.23050000e+05 5.45930000e+05 1.62000000e+03 -5.79900602e+00 1.00000000e+00
|
||||
4.23170000e+05 5.45930000e+05 1.62000000e+03 -1.52326254e+01 1.00000000e+00
|
||||
4.23290000e+05 5.45930000e+05 1.62000000e+03 -1.03089067e+01 1.00000000e+00
|
||||
4.22330000e+05 5.45970000e+05 1.62000000e+03 -1.15473617e+01 1.00000000e+00
|
||||
4.22450000e+05 5.45970000e+05 1.62000000e+03 -1.57941112e+01 1.00000000e+00
|
||||
4.22570000e+05 5.45970000e+05 1.62000000e+03 6.54966187e+00 1.00000000e+00
|
||||
4.22690000e+05 5.45970000e+05 1.62000000e+03 6.19188314e+01 1.00000000e+00
|
||||
4.22810000e+05 5.45970000e+05 1.62000000e+03 6.39595126e+01 1.00000000e+00
|
||||
4.22930000e+05 5.45970000e+05 1.62000000e+03 3.81395360e+01 1.00000000e+00
|
||||
4.23050000e+05 5.45970000e+05 1.62000000e+03 -1.45013343e+01 1.00000000e+00
|
||||
4.23170000e+05 5.45970000e+05 1.62000000e+03 -1.49641121e+01 1.00000000e+00
|
||||
4.23290000e+05 5.45970000e+05 1.62000000e+03 -9.52064246e+00 1.00000000e+00
|
||||
4.22330000e+05 5.46010000e+05 1.62000000e+03 -1.06635244e+01 1.00000000e+00
|
||||
4.22450000e+05 5.46010000e+05 1.62000000e+03 -1.62980506e+01 1.00000000e+00
|
||||
4.22570000e+05 5.46010000e+05 1.62000000e+03 -1.11314626e+01 1.00000000e+00
|
||||
4.22690000e+05 5.46010000e+05 1.62000000e+03 2.72308836e+01 1.00000000e+00
|
||||
4.22810000e+05 5.46010000e+05 1.62000000e+03 3.64117975e+01 1.00000000e+00
|
||||
4.22930000e+05 5.46010000e+05 1.62000000e+03 5.17965912e+00 1.00000000e+00
|
||||
4.23050000e+05 5.46010000e+05 1.62000000e+03 -1.87774433e+01 1.00000000e+00
|
||||
4.23170000e+05 5.46010000e+05 1.62000000e+03 -1.40550957e+01 1.00000000e+00
|
||||
4.23290000e+05 5.46010000e+05 1.62000000e+03 -8.67570798e+00 1.00000000e+00
|
||||
4.22330000e+05 5.46050000e+05 1.62000000e+03 -9.65498711e+00 1.00000000e+00
|
||||
4.22450000e+05 5.46050000e+05 1.62000000e+03 -1.54412681e+01 1.00000000e+00
|
||||
4.22570000e+05 5.46050000e+05 1.62000000e+03 -1.87575430e+01 1.00000000e+00
|
||||
4.22690000e+05 5.46050000e+05 1.62000000e+03 -4.50885928e+00 1.00000000e+00
|
||||
4.22810000e+05 5.46050000e+05 1.62000000e+03 1.81151715e+00 1.00000000e+00
|
||||
4.22930000e+05 5.46050000e+05 1.62000000e+03 -1.45206672e+01 1.00000000e+00
|
||||
4.23050000e+05 5.46050000e+05 1.62000000e+03 -1.92485357e+01 1.00000000e+00
|
||||
4.23170000e+05 5.46050000e+05 1.62000000e+03 -1.27463859e+01 1.00000000e+00
|
||||
4.23290000e+05 5.46050000e+05 1.62000000e+03 -7.81923059e+00 1.00000000e+00
|
||||
4.22330000e+05 5.46090000e+05 1.62000000e+03 -8.60449402e+00 1.00000000e+00
|
||||
4.22450000e+05 5.46090000e+05 1.62000000e+03 -1.38293662e+01 1.00000000e+00
|
||||
4.22570000e+05 5.46090000e+05 1.62000000e+03 -1.95693404e+01 1.00000000e+00
|
||||
4.22690000e+05 5.46090000e+05 1.62000000e+03 -1.80506180e+01 1.00000000e+00
|
||||
4.22810000e+05 5.46090000e+05 1.62000000e+03 -1.59731684e+01 1.00000000e+00
|
||||
4.22930000e+05 5.46090000e+05 1.62000000e+03 -2.03813111e+01 1.00000000e+00
|
||||
4.23050000e+05 5.46090000e+05 1.62000000e+03 -1.75595717e+01 1.00000000e+00
|
||||
4.23170000e+05 5.46090000e+05 1.62000000e+03 -1.12597736e+01 1.00000000e+00
|
||||
4.23290000e+05 5.46090000e+05 1.62000000e+03 -6.98367651e+00 1.00000000e+00
|
||||
4.22330000e+05 5.46130000e+05 1.62000000e+03 -7.57673512e+00 1.00000000e+00
|
||||
4.22450000e+05 5.46130000e+05 1.62000000e+03 -1.19604913e+01 1.00000000e+00
|
||||
4.22570000e+05 5.46130000e+05 1.62000000e+03 -1.74356213e+01 1.00000000e+00
|
||||
4.22690000e+05 5.46130000e+05 1.62000000e+03 -1.98507647e+01 1.00000000e+00
|
||||
4.22810000e+05 5.46130000e+05 1.62000000e+03 -1.97082556e+01 1.00000000e+00
|
||||
4.22930000e+05 5.46130000e+05 1.62000000e+03 -1.94681936e+01 1.00000000e+00
|
||||
4.23050000e+05 5.46130000e+05 1.62000000e+03 -1.50733842e+01 1.00000000e+00
|
||||
4.23170000e+05 5.46130000e+05 1.62000000e+03 -9.76662679e+00 1.00000000e+00
|
||||
4.23290000e+05 5.46130000e+05 1.62000000e+03 -6.19275271e+00 1.00000000e+00
|
||||
4.22330000e+05 5.46170000e+05 1.62000000e+03 -6.61601124e+00 1.00000000e+00
|
||||
4.22450000e+05 5.46170000e+05 1.62000000e+03 -1.01410111e+01 1.00000000e+00
|
||||
4.22570000e+05 5.46170000e+05 1.62000000e+03 -1.45716750e+01 1.00000000e+00
|
||||
4.22690000e+05 5.46170000e+05 1.62000000e+03 -1.74665180e+01 1.00000000e+00
|
||||
4.22810000e+05 5.46170000e+05 1.62000000e+03 -1.78787936e+01 1.00000000e+00
|
||||
4.22930000e+05 5.46170000e+05 1.62000000e+03 -1.64978743e+01 1.00000000e+00
|
||||
4.23050000e+05 5.46170000e+05 1.62000000e+03 -1.25568643e+01 1.00000000e+00
|
||||
4.23170000e+05 5.46170000e+05 1.62000000e+03 -8.37505092e+00 1.00000000e+00
|
||||
4.23290000e+05 5.46170000e+05 1.62000000e+03 -5.46233759e+00 1.00000000e+00
|
||||
@@ -0,0 +1,2 @@
|
||||
90.00 0.00 50000.00
|
||||
90.00 0.00 1.00
|
||||
@@ -0,0 +1,5 @@
|
||||
66 52 24
|
||||
421780 544950 1600
|
||||
151.00 108.00 77.00 55.00 40.00 28.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 28.00 40.00 55.00 77.00 108.00 151.00
|
||||
151.00 108.00 77.00 55.00 40.00 28.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 28.00 40.00 55.00 77.00 108.00 151.00
|
||||
20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 28.00 39.00 55.00 75.00 105.00 130.00
|
||||
@@ -0,0 +1,127 @@
|
||||
'''
|
||||
Created on Sep 27, 2015
|
||||
|
||||
@author: dominiquef
|
||||
'''
|
||||
def get_T_mat(xn,yn,zn,obsx,obsy,obsz):
|
||||
"""
|
||||
Load in the nodes of a tensor mesh and computes the magnetic tensor
|
||||
for a given observation location [obsx, obsy, obsz]
|
||||
OUTPUT:
|
||||
Tx = [Txx Txy Txz]
|
||||
Ty = [Tyx Tyy Tyz]
|
||||
Tz = [Tzx Tzy Tzz]
|
||||
|
||||
where each elements have dimension 1-by-mcell.
|
||||
Only the upper half 5 elements have to be computed since symetric.
|
||||
Currently done as for-loops but will eventually be changed to vector
|
||||
indexing, once the topography has been figured out.
|
||||
"""
|
||||
|
||||
from numpy import empty, pi, log, arctan, sqrt, shape
|
||||
|
||||
ncx = len(xn)-1
|
||||
ncy = len(yn)-1
|
||||
ncz = len(zn)-1
|
||||
|
||||
mcell = ncx*ncy*ncz;
|
||||
|
||||
Tx = empty([1,3*mcell], dtype=float)
|
||||
Ty = empty([1,3*mcell], dtype=float)
|
||||
Tz = empty([1,3*mcell], dtype=float)
|
||||
|
||||
count = 0
|
||||
|
||||
|
||||
for ii in range(ncz):
|
||||
|
||||
print ii, ncz
|
||||
|
||||
dz2 = zn[ii] - obsz;
|
||||
dz1 = zn[ii+1] - obsz;
|
||||
|
||||
for jj in range(ncy):
|
||||
|
||||
dy2 = yn[jj] - obsy;
|
||||
dy1 = yn[jj+1] - obsy;
|
||||
|
||||
for kk in range(ncx):
|
||||
|
||||
dx2 = xn[kk] - obsx;
|
||||
dx1 = xn[kk+1] - obsx;
|
||||
|
||||
R1 = ( dy2**2 + dx2**2 );
|
||||
R2 = ( dy2**2 + dx1**2 );
|
||||
R3 = ( dy1**2 + dx2**2 );
|
||||
R4 = ( dy1**2 + dx1**2 );
|
||||
|
||||
arg1 = sqrt( dz2**2 + R2 );
|
||||
arg2 = sqrt( dz2**2 + R1 );
|
||||
arg3 = sqrt( dz1**2 + R1 );
|
||||
arg4 = sqrt( dz1**2 + R2 );
|
||||
arg5 = sqrt( dz2**2 + R3 );
|
||||
arg6 = sqrt( dz2**2 + R4 );
|
||||
arg7 = sqrt( dz1**2 + R4 );
|
||||
arg8 = sqrt( dz1**2 + R3 );
|
||||
|
||||
|
||||
|
||||
Tx[0,count] = arctan( dy1 * dz2 / ( dx2 * arg5 ) ) +\
|
||||
- arctan( dy2 * dz2 / ( dx2 * arg2 ) ) +\
|
||||
arctan( dy2 * dz1 / ( dx2 * arg3 ) ) +\
|
||||
- arctan( dy1 * dz1 / ( dx2 * arg8 ) ) +\
|
||||
arctan( dy2 * dz2 / ( dx1 * arg1 ) ) +\
|
||||
- arctan( dy1 * dz2 / ( dx1 * arg6 ) ) +\
|
||||
arctan( dy1 * dz1 / ( dx1 * arg7 ) ) +\
|
||||
- arctan( dy2 * dz1 / ( dx1 * arg4 ) );
|
||||
|
||||
|
||||
Ty[0,count] = log( ( dz2 + arg2 ) / (dz1 + arg3 ) ) +\
|
||||
-log( ( dz2 + arg1 ) / (dz1 + arg4 ) ) +\
|
||||
log( ( dz2 + arg6 ) / (dz1 + arg7 ) ) +\
|
||||
-log( ( dz2 + arg5 ) / (dz1 + arg8 ) );
|
||||
|
||||
|
||||
Ty[0,mcell+count] = arctan( dx1 * dz2 / ( dy2 * arg1 ) ) +\
|
||||
- arctan( dx2 * dz2 / ( dy2 * arg2 ) ) +\
|
||||
arctan( dx2 * dz1 / ( dy2 * arg3 ) ) +\
|
||||
- arctan( dx1 * dz1 / ( dy2 * arg4 ) ) +\
|
||||
arctan( dx2 * dz2 / ( dy1 * arg5 ) ) +\
|
||||
- arctan( dx1 * dz2 / ( dy1 * arg6 ) ) +\
|
||||
arctan( dx1 * dz1 / ( dy1 * arg7 ) ) +\
|
||||
- arctan( dx2 * dz1 / ( dy1 * arg8 ) );
|
||||
|
||||
R1 = (dy2**2 + dz1**2);
|
||||
R2 = (dy2**2 + dz2**2);
|
||||
R3 = (dy1**2 + dz1**2);
|
||||
R4 = (dy1**2 + dz2**2);
|
||||
|
||||
Ty[0,2*mcell+count] = log( ( dx1 + sqrt( dx1**2 + R1 ) ) / (dx2 + sqrt( dx2**2 + R1 ) ) ) +\
|
||||
-log( ( dx1 + sqrt( dx1**2 + R2 ) ) / (dx2 + sqrt( dx2**2 + R2 ) ) ) +\
|
||||
log( ( dx1 + sqrt( dx1**2 + R4 ) ) / (dx2 + sqrt( dx2**2 + R4 ) ) ) +\
|
||||
-log( ( dx1 + sqrt( dx1**2 + R3 ) ) / (dx2 + sqrt( dx2**2 + R3 ) ) );
|
||||
|
||||
R1 = (dx2**2 + dz1**2);
|
||||
R2 = (dx2**2 + dz2**2);
|
||||
R3 = (dx1**2 + dz1**2);
|
||||
R4 = (dx1**2 + dz2**2);
|
||||
|
||||
Tx[0,2*mcell+count] = log( ( dy1 + sqrt( dy1**2 + R1 ) ) / (dy2 + sqrt( dy2**2 + R1 ) ) ) +\
|
||||
-log( ( dy1 + sqrt( dy1**2 + R2 ) ) / (dy2 + sqrt( dy2**2 + R2 ) ) ) +\
|
||||
log( ( dy1 + sqrt( dy1**2 + R4 ) ) / (dy2 + sqrt( dy2**2 + R4 ) ) ) +\
|
||||
-log( ( dy1 + sqrt( dy1**2 + R3 ) ) / (dy2 + sqrt( dy2**2 + R3 ) ) );
|
||||
|
||||
Tz[0,2*mcell+count] = -( Ty[0,mcell+count] + Tx[0,count] );
|
||||
Tz[0,mcell+count] = Ty[0,2*mcell+count];
|
||||
Tx[0,mcell+count] = Ty[0,count];
|
||||
Tz[0,count] = Tx[0,2*mcell+count];
|
||||
|
||||
|
||||
|
||||
count = count + 1
|
||||
|
||||
Tx = Tx/(4*pi);
|
||||
Ty = Ty/(4*pi);
|
||||
Tz = Tz/(4*pi);
|
||||
|
||||
return Tx,Ty,Tz
|
||||
@@ -0,0 +1,117 @@
|
||||
'''
|
||||
Created on Jul 17, 2013
|
||||
|
||||
@author: dominiquef
|
||||
'''
|
||||
def get_UBC_mesh(meshfile):
|
||||
""" Read UBC mesh file and extract parameters
|
||||
Works for the condenced version (20 * 3) --> [20 20 20] """
|
||||
|
||||
fid = open(meshfile,'r')
|
||||
from numpy import zeros
|
||||
|
||||
# Go through the log file and extract data and the last achieved misfit
|
||||
for ii in range (1, 6):
|
||||
|
||||
line = fid.readline()
|
||||
line = line.split(' ')
|
||||
|
||||
# First line: number of cells in i, j, k
|
||||
if ii == 1:
|
||||
|
||||
numcell=[]
|
||||
|
||||
for jj in range(len(line)):
|
||||
t = int(line[jj])
|
||||
numcell.append(t)
|
||||
|
||||
nX = numcell[0]
|
||||
nY = numcell[1]
|
||||
nZ = numcell[2]
|
||||
# Second line: origin coordinate (X,Y,Z)
|
||||
elif ii==2:
|
||||
|
||||
origin = []
|
||||
|
||||
for jj in range(len(line)):
|
||||
t = float(line[jj])
|
||||
origin.append(t)
|
||||
|
||||
|
||||
# Other lines for the xn, yn, zn (nodes location)
|
||||
elif ii==3:
|
||||
|
||||
xn=zeros((nX+1,1), dtype=float)
|
||||
xn[0] = origin[0]
|
||||
|
||||
count_entry = 0;
|
||||
count = 0;
|
||||
while (count<nX):
|
||||
|
||||
if line[count_entry].find('*') != -1:
|
||||
|
||||
ndx = line[count_entry].split('*')
|
||||
|
||||
for kk in range(int(ndx[0])):
|
||||
xn[count+1] = xn[count] + (ndx[1])
|
||||
count = count+1
|
||||
count_entry=count_entry+1
|
||||
|
||||
else:
|
||||
|
||||
t = float(line[count_entry])
|
||||
xn[count+1]= xn[count] +t
|
||||
count = count+1;
|
||||
count_entry=count_entry+1
|
||||
|
||||
elif ii==4:
|
||||
|
||||
yn=zeros((nY+1,1), dtype=float)
|
||||
yn[0] = origin[0]
|
||||
|
||||
count_entry = 0;
|
||||
count = 0;
|
||||
while (count<nY):
|
||||
|
||||
if line[count_entry].find('*') != -1:
|
||||
|
||||
ndx = line[count_entry].split('*')
|
||||
|
||||
for kk in range(int(ndx[0])):
|
||||
yn[count+1] = yn[count] + (ndx[1])
|
||||
count = count+1
|
||||
count_entry=count_entry+1
|
||||
|
||||
else:
|
||||
|
||||
t = float(line[count_entry])
|
||||
yn[count+1]= yn[count] +t
|
||||
count = count+1;
|
||||
count_entry=count_entry+1
|
||||
|
||||
elif ii==5:
|
||||
|
||||
zn=zeros((nZ+1,1), dtype=float)
|
||||
zn[0] = origin[0]
|
||||
|
||||
count_entry = 0;
|
||||
count = 0;
|
||||
while (count<nZ):
|
||||
|
||||
if line[count_entry].find('*') != -1:
|
||||
|
||||
ndx = line[count_entry].split('*')
|
||||
|
||||
for kk in range(int(ndx[0])):
|
||||
zn[count+1] = zn[count] + (ndx[1])
|
||||
count = count+1
|
||||
count_entry=count_entry+1
|
||||
|
||||
else:
|
||||
|
||||
t = float(line[count_entry])
|
||||
zn[count+1]= zn[count] +t
|
||||
count = count+1;
|
||||
count_entry=count_entry+1
|
||||
fid.close();
|
||||
return xn,yn,zn
|
||||
@@ -0,0 +1,58 @@
|
||||
'''
|
||||
Created on Jul 17, 2013
|
||||
|
||||
@author: dominiquef
|
||||
'''
|
||||
def read_MAG_obs(obs_file):
|
||||
"""Read input files for the lp_norm script"""
|
||||
from numpy import zeros
|
||||
|
||||
fid = open(obs_file,'r')
|
||||
|
||||
|
||||
# First line has the declination, inclination and amplitude of B0
|
||||
line = fid.readline()
|
||||
line = line.split(' ')
|
||||
Incl = float(line[0])
|
||||
Decl = float(line[1])
|
||||
B0 = float(line[2])
|
||||
|
||||
# Second line has the magnetization orientation and a flag
|
||||
line = fid.readline()
|
||||
line = line.split(' ')
|
||||
Minc = float(line[0])
|
||||
Mdec = float(line[1])
|
||||
FLAG = float(line[2])
|
||||
|
||||
# Third line has the number of rows
|
||||
line = fid.readline()
|
||||
line = line.split(' ')
|
||||
ndat = int(line[0])
|
||||
|
||||
# Pre-allocate space for obsx, obsy, obsz, data, uncert
|
||||
obsx = zeros((ndat,1), dtype=float)
|
||||
obsy = zeros((ndat,1), dtype=float)
|
||||
obsz = zeros((ndat,1), dtype=float)
|
||||
data = zeros((ndat,1), dtype=float)
|
||||
unct = zeros((ndat,1), dtype=float)
|
||||
|
||||
for ii in range(ndat):
|
||||
|
||||
line = fid.readline()
|
||||
line = line.split(' ')
|
||||
|
||||
obsx[ii] = line[0]
|
||||
obsy[ii] = line[1]
|
||||
obsz[ii] = line[2]
|
||||
|
||||
if len(line)>3:
|
||||
|
||||
data[ii] = line[3]
|
||||
|
||||
if len(line)>4:
|
||||
|
||||
unct[ii] = line[4]
|
||||
|
||||
|
||||
|
||||
return Decl, Incl, B0, Mdec, Minc, obsx, obsy, obsz, data, unct
|
||||
@@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -32,7 +32,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -72,7 +72,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -80,10 +80,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.text.Text at 0x15eb8588>"
|
||||
"<matplotlib.text.Text at 0x13415908>"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
@@ -91,7 +91,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15cca3c8>"
|
||||
"<matplotlib.figure.Figure at 0x13302320>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -129,7 +129,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -144,7 +144,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -157,7 +157,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -166,7 +166,7 @@
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x161cc710>"
|
||||
"<matplotlib.figure.Figure at 0x13547e48>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -211,7 +211,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -225,7 +225,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -233,10 +233,10 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x1794ee10>]"
|
||||
"[<matplotlib.lines.Line2D at 0x17706b70>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
@@ -244,7 +244,7 @@
|
||||
"data": {
|
||||
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||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -274,7 +274,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -288,7 +288,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -297,7 +297,7 @@
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17ede9b0>"
|
||||
"<matplotlib.figure.Figure at 0x132b8d30>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -336,7 +336,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -360,7 +360,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -379,7 +379,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -397,27 +397,26 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'Bzra' 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-11-0e249253afbe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mvmin\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBzra\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\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 3\u001b[0m \u001b[0mvmax\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBzra\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mresidual\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'F'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mBzra\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdat0\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontourf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBzra\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mNameError\u001b[0m: name 'Bzra' is not defined"
|
||||
]
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.text.Text at 0x13826278>"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x17684eb8>"
|
||||
"<matplotlib.figure.Figure at 0x1a54e9e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
Reference in new issue
Block a user