diff --git a/simpegPF/BaseMag.py b/simpegPF/BaseMag.py index a70f255f..bd78009d 100644 --- a/simpegPF/BaseMag.py +++ b/simpegPF/BaseMag.py @@ -141,4 +141,50 @@ class WeightMap(Maps.IdentityMap): return m*self.weight def deriv(self, m): - return Utils.sdiag(self.weight) \ No newline at end of file + return Utils.sdiag(self.weight) + + + + +def readUBCmagObs(obs_file): + + """ + Read and write UBC mag file format + + INPUT: + :param fileName, path to the UBC obs mag file + + OUTPUT: + :param dobs, observation in (x y z [data] [wd]) + :param B, primary field information (BI, BD, B0) + :param M, magnetization orentiaton (MI, MD) + + """ + + fid = open(obs_file,'r') + + # First line has the inclination,declination and amplitude of B0 + line = fid.readline() + B = np.array(line.split(),dtype=float) + + # Second line has the magnetization orientation and a flag + line = fid.readline() + M = np.array(line.split(),dtype=float) + + # Third line has the number of rows + line = fid.readline() + ndat = np.array(line.split(),dtype=int) + + # Pre-allocate space for obsx, obsy, obsz, data, uncert + line = fid.readline() + temp = np.array(line.split(),dtype=float) + + dobs = np.zeros((ndat,len(temp)), dtype=float) + + + for ii in range(ndat): + + dobs[ii,:] = np.array(line.split(),dtype=float) + line = fid.readline() + + return B, M, dobs \ No newline at end of file diff --git a/simpegPF/Dev/FWR_data.dat b/simpegPF/Dev/FWR_data.dat new file mode 100644 index 00000000..dc88c9d4 --- /dev/null +++ b/simpegPF/Dev/FWR_data.dat @@ -0,0 +1,628 @@ + 45.00 135.00 50000.00 + 45.00 135.00 1.00 +625 +-5.000000e-01 -5.000000e-01 5.500000e-01 -1.659610e+02 0.000000e+00 +-5.000000e-01 -4.583330e-01 5.500000e-01 -1.399730e+02 0.000000e+00 +-5.000000e-01 -4.166670e-01 5.500000e-01 -1.038361e+02 0.000000e+00 +-5.000000e-01 -3.750000e-01 5.500000e-01 -5.615031e+01 0.000000e+00 +-5.000000e-01 -3.333330e-01 5.500000e-01 4.169920e+00 0.000000e+00 +-5.000000e-01 -2.916670e-01 5.500000e-01 7.767438e+01 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Binary files /dev/null and b/simpegPF/Dev/G.mat differ diff --git a/simpegPF/Dev/MAG3Cfwr.inp b/simpegPF/Dev/MAG3Cfwr.inp new file mode 100644 index 00000000..c90b0c01 --- /dev/null +++ b/simpegPF/Dev/MAG3Cfwr.inp @@ -0,0 +1,5 @@ +Mesh.msh ! Mesh file +Obs_loc.dat ! Obsfile +Model.dat ! Susceptibility model +null ! M_azm_dip.dat ! Magnetization model | null +null ! Topography file | null \ No newline at end of file diff --git a/simpegPF/Dev/MAG3Csen.inp b/simpegPF/Dev/MAG3Csen.inp new file mode 100644 index 00000000..210a1da1 --- /dev/null +++ b/simpegPF/Dev/MAG3Csen.inp @@ -0,0 +1,5 @@ +Mesh.msh ! Mesh file +Obs_loc.dat ! Obsfile +null ! Topofile | null +DISTANCE ! weighting flag DISTANCE | DEPTH +G ! Define output TxTyTz | G diff --git a/simpegPF/Dev/MAG3Csen.log b/simpegPF/Dev/MAG3Csen.log new file mode 100644 index 00000000..fc67eff0 --- /dev/null +++ b/simpegPF/Dev/MAG3Csen.log @@ -0,0 +1,35 @@ +MAG3Csen +Generates sparse matrices for magnetostatic forward modeling: Tx, Ty, Tz +Topographic model: nullcell.dat +DISTANCE | DEPTH weighting: wr.dat + +Written by: Dominique Fournier +Last update: July 14th, 2014 + +INPUT FILES: +Mesh: Mesh.msh +Obsfile: Obs_loc.dat +Topography: +Weighting: DISTANCE +Computed 0 pct of data in 0.04128 sec +Computed 5 pct of data in 0.04634 sec +Computed 10 pct of data in 0.05386 sec +Computed 15 pct of data in 0.06035 sec +Computed 20 pct of data in 0.06507 sec +Computed 25 pct of data in 0.07160 sec +Computed 30 pct of data in 0.08211 sec +Computed 35 pct of data in 0.08901 sec +Computed 40 pct of data in 0.09365 sec +Computed 45 pct of data in 0.10179 sec +Computed 50 pct of data in 0.10963 sec +Computed 55 pct of data in 0.11704 sec +Computed 60 pct of data in 0.12449 sec +Computed 65 pct of data in 0.13205 sec +Computed 70 pct of data in 0.13838 sec +Computed 75 pct of data in 0.14311 sec +Computed 80 pct of data in 0.14761 sec +Computed 85 pct of data in 0.15447 sec +Computed 90 pct of data in 0.15941 sec +Computed 95 pct of data in 0.16424 sec +Computed 100 pct of data in 0.17136 sec +Sensitivity calculation completed in: 0.002860 min diff --git a/simpegPF/Dev/MAG3D_fwr_Driver.py b/simpegPF/Dev/MAG3D_fwr_Driver.py new file mode 100644 index 00000000..7e1ff802 --- /dev/null +++ b/simpegPF/Dev/MAG3D_fwr_Driver.py @@ -0,0 +1,47 @@ +import os + +home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\Research\SimPeg' + +inpfile = 'MAG3Cfwr.inp' + +os.chdir(home_dir) + +#%% +from SimPEG import np, Utils +from simpegPF import BaseMag + +## New scripts to be added to basecode +from fwr_MAG_obs import fwr_MAG_obs +from read_MAGfwr_inp import read_MAGfwr_inp + +#%% +# Read input file +[mshfile, obsfile, modfile, magfile, topofile] = read_MAGfwr_inp(inpfile) + +# Load mesh file +mesh = Utils.meshutils.readUBCTensorMesh(mshfile) + +# Load model file +model = Utils.meshutils.readUBCTensorModel(modfile,mesh) + +# Load in observation file +[B,M,dobs] = BaseMag.readUBCmagObs(obsfile) + +rxLoc = dobs[:,0:3] + +# Compute forward model using integral equation +d = fwr_MAG_obs(xn,yn,zn,B,M,rxLoc,model) + +# Form data object with coordinates and write to file +data = np.c_[rxLoc , d , np.zeros((ndata,1))] + +# Save forward data to file +with file('FWR_data.dat','w') as fid: + fid.write('%6.2f %6.2f %6.2f\n' %(B[0], B[1], B[2]) ) + fid.write('%6.2f %6.2f %6.2f\n' %(M[0], M[1], 1) ) + fid.write('%i\n' %(ndata) ) + np.savetxt(fid, data, fmt='%e',delimiter=' ',newline='\n') + +print "Observation file saved to " + home_dir + '\FWR_data.dat' + + diff --git a/simpegPF/Dev/MAG3D_fwr_Test.py b/simpegPF/Dev/MAG3D_fwr_Test.py new file mode 100644 index 00000000..03665e86 --- /dev/null +++ b/simpegPF/Dev/MAG3D_fwr_Test.py @@ -0,0 +1,100 @@ +import os + +home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\Research\SimPeg' + +os.chdir(home_dir) + +#%% +from SimPEG import * +import matplotlib.pyplot as plt +import simpegPF as PF +from simpegPF import BaseMag +import matplotlib + +from fwr_MAG_obs import fwr_MAG_obs + +#%% Create survey + +B[0] = 90. +B[1] = 00. +B[2] = 50000. + +M[0] = 90. +M[1] = 0. + +# # Or create juste a plane grid +xr = np.linspace(-1./2., 1./2., 10) +yr = np.linspace(-1./2., 1./2., 10) +X, Y = np.meshgrid(xr, yr) +Z = np.ones((xr.size, yr.size))*.75 +rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)] + +ndata = rxLoc.shape[0] + +#%% +# Create mesh using simpeg and write out in GIF format +nc = 30. + +hxind = [(1./nc, nc)] +hyind = [(1./nc, nc)] +hzind = [(1./nc, nc)] + +mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC') + +xn = mesh.vectorNx +yn = mesh.vectorNy +zn = mesh.vectorNz + +mcell = mesh.nC + +sph_ind = PF.MagAnalytics.spheremodel(mesh, 0, 0, 0, 0.25) + +chibkg = 0. +chiblk = 0.01 +model = np.ones(mcell)*chibkg +model[sph_ind] = chiblk + +#%% Forward mode ldata +d = fwr_MAG_obs(xn,yn,zn,B,M,rxLoc,model) + +#%% Get the analystical answer and compute the residual +bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],.25,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') + +r_Bz = mkvc(d) - bza +lrl = sum( dBz**2 ) **0.5 + +print "Residual between analytical and integral= " + str(lrl) +#%% Plot fields + +plt.figure(1) +ax = plt.subplot(131) +plt.imshow(np.reshape(bxa,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()]) +plt.contour(X,Y, np.reshape(bxa,X.shape),10) +plt.scatter(X,Y, c='k', s=5) +ax.set_title('Sphere Ana Bx') + +ax = plt.subplot(132) +plt.imshow(np.reshape(bya,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()]) +plt.contour(X,Y, np.reshape(bya,X.shape),10) +plt.scatter(X,Y, c='k', s=5) +ax.set_title('Sphere Ana By') + +ax = plt.subplot(133) +plt.imshow(np.reshape(bza,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()]) +plt.contour(X,Y, np.reshape(bza,X.shape),10) +plt.scatter(X,Y, c='k', s=5) +ax.set_title('Sphere Ana Bz') +#%% Plot foward data +plt.figure(2) +plt.subplot(121) +d2D = np.reshape(d,X.shape) +plt.imshow(d2D, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()]) +plt.contour(X,Y, d2D,10) +plt.scatter(X,Y, c='k', s=5) + +#%% Compare fields + +plt.subplot(122) +plt.imshow(np.reshape(dBz,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()]) +plt.contour(X,Y, np.reshape(dBz,X.shape),10) +plt.scatter(X,Y, c='k', s=5) \ No newline at end of file diff --git a/simpegPF/notebooks/MagForward_Intergral.ipynb b/simpegPF/Dev/MagForward_Intergral.ipynb similarity index 52% rename from simpegPF/notebooks/MagForward_Intergral.ipynb rename to simpegPF/Dev/MagForward_Intergral.ipynb index a50642c8..4e7f6242 100644 --- a/simpegPF/notebooks/MagForward_Intergral.ipynb +++ b/simpegPF/Dev/MagForward_Intergral.ipynb @@ -6,7 +6,26 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Efficiency Warning: Interpolation will be slow, use setup.py!\n", + "\n", + " python setup.py build_ext --inplace\n", + " \n" + ] + }, + { + "ename": "SyntaxError", + "evalue": "'return' outside function (get_T_mat.py, line 131)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"get_T_mat.py\"\u001b[1;36m, line \u001b[1;32m131\u001b[0m\n\u001b[1;33m return Tx,Ty,Tz\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m 'return' outside function\n" + ] + } + ], "source": [ "from SimPEG import *\n", "import matplotlib.pyplot as plt\n", @@ -21,9 +40,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Writing magnetostatic problem with total field formulation\n", + "# Writing magnetostatic problem with integral formulation\n", "\n", - "## - Here we focus on cell-centered system with FVM-Weak form" + "## - Create tensor matrix TxTyTz" ] }, { @@ -35,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -63,29 +82,7 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-425.15075 -425.15075 -271.950375] [ 425.15075 425.15075 271.950375]\n" - ] - } - ], - "source": [ - "aa.size\n", - "print N[0],N[-1]\n", - "\n", - "Utils.mkvc?" - ] - }, - { - "cell_type": "code", - "execution_count": 25, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -98,11 +95,20 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 5, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 0 0]\n", + " [1 1 1]]\n" + ] + } + ], "source": [ "# Load in obsfile\n", "#Decl, Incl, B0, Mdec, Minc, obsx, obsy, obsz, data, unct = read_MAG_obs('Obs_RAW_REM_GRID_TMI.obs')\n", @@ -117,12 +123,18 @@ "X, Y = np.meshgrid(xr, yr)\n", "Z = np.ones((xr.size, yr.size))*280\n", "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n", - " \n", + " \n", + "aa = np.array([[0,0,0],[0,0,0]])\n", + "\n", + "aa[1,0:3] = 1\n", + "print aa\n", + "ndata = rxLoc.shape[0]\n", + "\n", "# Write obsfile in UBC format\n", - "fid = open('Obs_loc.dat','w')\n", - "fid.write('%6.2f %6.2f %6.2f\\n' %(Incl, Decl, B0) )\n", - "fid.write('%6.2f %6.2f %6.2f\\n' %(Incl, Decl, 1) )\n", - "fid.close();" + "with file('Obs_loc.dat','w') as fid:\n", + " fid.write('%6.2f %6.2f %6.2f\\n' %(Incl, Decl, B0) )\n", + " fid.write('%6.2f %6.2f %6.2f\\n' %(Incl, Decl, 1) ) \n", + " np.savetxt(fid, rxLoc, fmt='%-7.2f',delimiter=' ',newline='\\n')\n" ] }, { @@ -141,73 +153,7 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "Mx = scipy.sparse.diags(np.ones([mcell])*mx*B0,0)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "scipy.sparse.dia.dia_matrix" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(Mx)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "Mx1 = Utils.sdiag(np.ones([mcell])*mx*B0)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "scipy.sparse.csr.csr_matrix" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(Mx1)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": { "collapsed": true }, @@ -224,41 +170,31 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 24\n", - "1 24\n", - "2 24\n", - "3 24\n", - "4 24\n", - "5 24\n", - "6 24\n", - "7 24\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", + "ename": "NameError", + "evalue": "name 'empty' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\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 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\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[1;31mKeyboardInterrupt\u001b[0m: " + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\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[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mTx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mndata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmcell\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mTy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mndata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmcell\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mTz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mndata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmcell\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'empty' is not defined" ] } ], "source": [ "# Call the function to build tensor matrix\n", - "Tx, Ty, Tz = get_T_mat(xn,yn,zn,obsx[0],obsy[0],obsz[0])\n", + "\n", + "Tx = empty([ndata,3*mcell], dtype=float)\n", + "Ty = empty([ndata,3*mcell], dtype=float)\n", + "Tz = empty([ndata,3*mcell], dtype=float)\n", + " \n", + "Tx, Ty, Tz = get_T_mat(xn,yn,zn,rxLoc)\n", "\n", "print Tx[0,0],Ty[0,0],Tz[0,0]\n" ] @@ -334,198 +270,6 @@ "M3 = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step2: Compute Boundary indicies and set $\\mathbf{B}_{bc}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step3: Generating model $\\mu$\n", - "\n", - "$\\mu = \\mu_0(1+\\chi)$" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "mu0 = 4*np.pi*1e-7\n", - "chibkg = 0.\n", - "chiblk = 0.01\n", - "chi = np.ones(M3.nC)*chibkg" - ] - }, - { - "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\u001b[0m in \u001b[0;36m\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": { - "image/png": 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v8Pl8IisrSxw6dKhQVZ3ff/eL9u2jKxGJiaYYN86q8FSvbohBg3SRkqKLK64w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WJ07NiR9PRgIvbEiRMpU6YMFSpUuC1DaFLw3ACqChUqmGzZEuDdd3XOn1do2tTJyJF2SpQQJCfD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\n1u65xLx5w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PHWW15RoIAh4uLSRWpqqoiLS8+0XvnyeqZtUlNTxY4daaJoUUO89ZZXFC+u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\nvw9SsaJ1QRcqBJs3q1SqZFKkiAgLJ7BS2B980E6jRiY7d15fxOzerbJ79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zsLFmhcuUK4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AGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyj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\ndnaWEhIS1MuTk5MlT09Pyc7OTlq6dKkhVsWgfvjhB0kmk0nu7u6Sh4e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- "text/plain": [ - "" - ] - }, - "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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1hlT9OUYVTwDVv56GVP1ZG/P8XQtmdRlp8qXLak16uvw9E8qO6alN79lglS6z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\nuZj2r3+xadkyygsKIlp+OLpAmwJd9t137H3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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "figsize(16,5)\n", - "M3.plotImage(B, imageType='F')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -555,20 +299,6 @@ "### 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, diff --git a/simpegPF/Dev/Mesh.msh b/simpegPF/Dev/Mesh.msh new file mode 100644 index 00000000..24664d59 --- /dev/null +++ b/simpegPF/Dev/Mesh.msh @@ -0,0 +1,5 @@ +20 20 20 +-0.50 -0.50 0.50 +0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 +0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 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2.50000000e+01 8.921462e+01 0.000000e+00 +2.55000000e+01 1.91250000e+01 2.50000000e+01 8.025329e+01 0.000000e+00 +2.55000000e+01 2.12500000e+01 2.50000000e+01 7.193299e+01 0.000000e+00 +2.55000000e+01 2.33750000e+01 2.50000000e+01 6.432307e+01 0.000000e+00 +2.55000000e+01 2.55000000e+01 2.50000000e+01 5.744580e+01 0.000000e+00 diff --git a/simpegPF/notebooks/Tile1.msh b/simpegPF/Dev/Tile1.msh similarity index 100% rename from simpegPF/notebooks/Tile1.msh rename to simpegPF/Dev/Tile1.msh diff --git a/simpegPF/Dev/fwr_MAG_obs.py b/simpegPF/Dev/fwr_MAG_obs.py new file mode 100644 index 00000000..6b65ffe5 --- /dev/null +++ b/simpegPF/Dev/fwr_MAG_obs.py @@ -0,0 +1,80 @@ +def fwr_MAG_obs(xn,yn,zn,B,M,rxLoc,model): + """ + Forward model magnetic data using integral equation + + INPUT: + xn, yn, zn = Mesh nodes location + B = Inducing field parameter [Binc, Bdecl, B0] + M = Magnetization matrix [Minc, Mdecl] + rxLox = Observation location informat [obsx, obsy, obsz] + model = Model associated with mesh + + OUTPUT: + dobs =Observation array in format [obsx, obsy, obsz, data] + + Created on Oct 7, 2015 + + @author: dominiquef + """ + + #%% + from SimPEG import np, Utils, sp, mkvc + from get_T_mat import get_T_mat + + mcell = (len(xn)-1) * (len(yn)-1) * (len(zn)-1) + + ndata = rxLoc.shape[0] + + # Convert declination from north to cartesian + Md = (450.-float(M[1]))%360. + + # Create magnetization matrix + mx = np.cos(np.deg2rad(M[0])) * np.cos(np.deg2rad(Md)) + my = np.cos(np.deg2rad(M[0])) * np.sin(np.deg2rad(Md)) + mz = np.sin(np.deg2rad(M[0])) + + Mx = Utils.sdiag(np.ones([mcell])*mx*B[2]) + My = Utils.sdiag(np.ones([mcell])*my*B[2]) + Mz = Utils.sdiag(np.ones([mcell])*mz*B[2]) + + #matplotlib.pyplot.spy(scipy.sparse.csr_matrix(Mx)) + #plt.show() + Mxyz = sp.vstack((Mx,My,Mz)); + + #%% Create TMI projector + + # Convert Bdecination from north to cartesian + D = (450.-float(B[1]))%360. + + Ptmi = mkvc(np.r_[np.cos(np.deg2rad(B[0]))*np.cos(np.deg2rad(D)),np.cos(np.deg2rad(B[0]))*np.sin(np.deg2rad(D)),np.sin(np.deg2rad(B[0]))],2).T; + + d = np.zeros((ndata,1)) + + # Loop through all observations and create forward operator (ndata-by-mcell) + print "Begin forward modeling " +str(int(ndata)) + " data points..." + + # Add counter to dsiplay progress. Good for large problems + progress = -1; + for ii in range(ndata): + + tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) + + G = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz + #%% + # Forward operator + d[ii,0] = G.dot(model) + + d_iter = np.floor(float(ii)/float(ndata)*10.); + + if d_iter > progress: + + arg = "Done " + str(d_iter*10) + " %" + print arg + progress = d_iter; + + + print "Done 100% ...forward modeling completed!!\n" + + return d + + diff --git a/simpegPF/Dev/get_T_mat.py b/simpegPF/Dev/get_T_mat.py new file mode 100644 index 00000000..3b710892 --- /dev/null +++ b/simpegPF/Dev/get_T_mat.py @@ -0,0 +1,133 @@ +''' +Created on Sep 27, 2015 + +@author: dominiquef +''' +def get_T_mat(xn,yn,zn,rxLoc): + """ + 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 SimPEG import np, mkvc + + ncx = len(xn)-1 + ncy = len(yn)-1 + ncz = len(zn)-1 + + mcell = ncx*ncy*ncz + + # Pre-allocate space for 1D array + Tx = np.zeros((1,3*mcell)) + Ty = np.zeros((1,3*mcell)) + Tz = np.zeros((1,3*mcell)) + + yn2,xn2,zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:]) + yn1,xn1,zn1 = np.meshgrid(yn[0:ncy], xn[0:ncx], zn[0:ncz]) + + yn2 = mkvc(yn2) + yn1 = mkvc(yn1) + + zn2 = mkvc(zn2) + zn1 = mkvc(zn1) + + xn2 = mkvc(xn2) + xn1 = mkvc(xn1) + #%% + #============================================================================== + + + dz2 = rxLoc[2] - zn1; + dz1 = rxLoc[2] - zn2; + + + dy2 = yn2 - rxLoc[1]; + dy1 = yn1 - rxLoc[1]; + + + dx2 = xn2 - rxLoc[0]; + dx1 = xn1 - rxLoc[0]; + + R1 = ( dy2**2 + dx2**2 ); + R2 = ( dy2**2 + dx1**2 ); + R3 = ( dy1**2 + dx2**2 ); + R4 = ( dy1**2 + dx1**2 ); + + + arg1 = np.sqrt( dz2**2 + R2 ); + arg2 = np.sqrt( dz2**2 + R1 ); + arg3 = np.sqrt( dz1**2 + R1 ); + arg4 = np.sqrt( dz1**2 + R2 ); + arg5 = np.sqrt( dz2**2 + R3 ); + arg6 = np.sqrt( dz2**2 + R4 ); + arg7 = np.sqrt( dz1**2 + R4 ); + arg8 = np.sqrt( dz1**2 + R3 ); + + + + Tx[0,0:mcell] = np.arctan2( dy1 * dz2 , ( dx2 * arg5 ) ) +\ + - np.arctan2( dy2 * dz2 , ( dx2 * arg2 ) ) +\ + np.arctan2( dy2 * dz1 , ( dx2 * arg3 ) ) +\ + - np.arctan2( dy1 * dz1 , ( dx2 * arg8 ) ) +\ + np.arctan2( dy2 * dz2 , ( dx1 * arg1 ) ) +\ + - np.arctan2( dy1 * dz2 , ( dx1 * arg6 ) ) +\ + np.arctan2( dy1 * dz1 , ( dx1 * arg7 ) ) +\ + - np.arctan2( dy2 * dz1 , ( dx1 * arg4 ) ); + + + Ty[0,0:mcell] = np.log( ( dz2 + arg2 ) / (dz1 + arg3 ) ) +\ + -np.log( ( dz2 + arg1 ) / (dz1 + arg4 ) ) +\ + np.log( ( dz2 + arg6 ) / (dz1 + arg7 ) ) +\ + -np.log( ( dz2 + arg5 ) / (dz1 + arg8 ) ); + + Ty[0,mcell:2*mcell] = np.arctan2( dx1 * dz2 , ( dy2 * arg1 ) ) +\ + - np.arctan2( dx2 * dz2 , ( dy2 * arg2 ) ) +\ + np.arctan2( dx2 * dz1 , ( dy2 * arg3 ) ) +\ + - np.arctan2( dx1 * dz1 , ( dy2 * arg4 ) ) +\ + np.arctan2( dx2 * dz2 , ( dy1 * arg5 ) ) +\ + - np.arctan2( dx1 * dz2 , ( dy1 * arg6 ) ) +\ + np.arctan2( dx1 * dz1 , ( dy1 * arg7 ) ) +\ + - np.arctan2( 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:] = np.log( ( dx1 + np.sqrt( dx1**2 + R1 ) ) / (dx2 + np.sqrt( dx2**2 + R1 ) ) ) +\ + -np.log( ( dx1 + np.sqrt( dx1**2 + R2 ) ) / (dx2 + np.sqrt( dx2**2 + R2 ) ) ) +\ + np.log( ( dx1 + np.sqrt( dx1**2 + R4 ) ) / (dx2 + np.sqrt( dx2**2 + R4 ) ) ) +\ + -np.log( ( dx1 + np.sqrt( dx1**2 + R3 ) ) / (dx2 + np.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:] = np.log( ( dy1 + np.sqrt( dy1**2 + R1 ) ) / (dy2 + np.sqrt( dy2**2 + R1 ) ) ) +\ + -np.log( ( dy1 + np.sqrt( dy1**2 + R2 ) ) / (dy2 + np.sqrt( dy2**2 + R2 ) ) ) +\ + np.log( ( dy1 + np.sqrt( dy1**2 + R4 ) ) / (dy2 + np.sqrt( dy2**2 + R4 ) ) ) +\ + -np.log( ( dy1 + np.sqrt( dy1**2 + R3 ) ) / (dy2 + np.sqrt( dy2**2 + R3 ) ) ); + + Tz[0,2*mcell:] = -( Ty[0,mcell:2*mcell] + Tx[0,0:mcell] ); + Tz[0,mcell:2*mcell] = Ty[0,2*mcell:]; + Tx[0,mcell:2*mcell] = Ty[0,0:mcell]; + Tz[0,0:mcell] = Tx[0,2*mcell:]; + + + + Tx = Tx/(4*np.pi); + Ty = Ty/(4*np.pi); + Tz = Tz/(4*np.pi); + + + return Tx,Ty,Tz diff --git a/simpegPF/notebooks/get_UBC_mesh.py b/simpegPF/Dev/get_UBC_mesh.py similarity index 100% rename from simpegPF/notebooks/get_UBC_mesh.py rename to simpegPF/Dev/get_UBC_mesh.py diff --git a/simpegPF/Dev/magfor3d.log b/simpegPF/Dev/magfor3d.log new file mode 100644 index 00000000..231d442c --- /dev/null +++ b/simpegPF/Dev/magfor3d.log @@ -0,0 +1,31 @@ + +Parallelized with OpenMP. # of threads: 4 + +MAG3D - Version 5.0 : MAGFOR3D + +Developed by University of British Columbia +Geophysical Inversion Facility (UBC-GIF) + +(C) Copyright 1992 - 2013, UBC-GIF, +Department of Earth and Ocean Sciences, UBC +http://gif.eos.ubc.ca/ + +This program is licensed to: + + For internal use within UBC-GIF. + + +MAGFOR3D started on: 11/05/2015 14:43:03 + + +magfor3d Mesh.msh Obs_loc.dat Model.dat + + # of surface data: 625 + # of borehole data: 0 + + model was read from file: Model.dat + + TOTAL cpu time: 0:00:01.49 + +MAGFOR3D ended on: 11/05/2015 14:43:04 + diff --git a/simpegPF/Dev/magfor3d.mag b/simpegPF/Dev/magfor3d.mag new file mode 100644 index 00000000..1fd05ead --- /dev/null +++ b/simpegPF/Dev/magfor3d.mag @@ -0,0 +1,628 @@ + 90.00 0.00 50000.00 !! incl, decl, geomag + 90.00 0.00 1 !! aincl, adecl, idir + 625 !! # of data + -2.550000000E+01 -2.550000000E+01 2.500000000E+01 -1.236074E+00 + -2.550000000E+01 -2.337500000E+01 2.500000000E+01 1.500213E+00 + -2.550000000E+01 -2.125000000E+01 2.500000000E+01 4.762224E+00 + -2.550000000E+01 -1.912500000E+01 2.500000000E+01 8.540956E+00 + -2.550000000E+01 -1.700000000E+01 2.500000000E+01 1.278550E+01 + 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2.337500000E+01 1.912500000E+01 2.500000000E+01 1.452843E+01 + 2.337500000E+01 2.125000000E+01 2.500000000E+01 9.462184E+00 + 2.337500000E+01 2.337500000E+01 2.500000000E+01 5.120495E+00 + 2.337500000E+01 2.550000000E+01 2.500000000E+01 1.500213E+00 + 2.550000000E+01 -2.550000000E+01 2.500000000E+01 -1.236074E+00 + 2.550000000E+01 -2.337500000E+01 2.500000000E+01 1.500213E+00 + 2.550000000E+01 -2.125000000E+01 2.500000000E+01 4.762224E+00 + 2.550000000E+01 -1.912500000E+01 2.500000000E+01 8.540956E+00 + 2.550000000E+01 -1.700000000E+01 2.500000000E+01 1.278550E+01 + 2.550000000E+01 -1.487500000E+01 2.500000000E+01 1.739269E+01 + 2.550000000E+01 -1.275000000E+01 2.500000000E+01 2.220054E+01 + 2.550000000E+01 -1.062500000E+01 2.500000000E+01 2.698887E+01 + 2.550000000E+01 -8.500000000E+00 2.500000000E+01 3.148978E+01 + 2.550000000E+01 -6.375000000E+00 2.500000000E+01 3.540971E+01 + 2.550000000E+01 -4.250000000E+00 2.500000000E+01 3.846152E+01 + 2.550000000E+01 -2.125000000E+00 2.500000000E+01 4.040207E+01 + 2.550000000E+01 0.000000000E+00 2.500000000E+01 4.106811E+01 + 2.550000000E+01 2.125000000E+00 2.500000000E+01 4.040207E+01 + 2.550000000E+01 4.250000000E+00 2.500000000E+01 3.846152E+01 + 2.550000000E+01 6.375000000E+00 2.500000000E+01 3.540971E+01 + 2.550000000E+01 8.500000000E+00 2.500000000E+01 3.148978E+01 + 2.550000000E+01 1.062500000E+01 2.500000000E+01 2.698887E+01 + 2.550000000E+01 1.275000000E+01 2.500000000E+01 2.220054E+01 + 2.550000000E+01 1.487500000E+01 2.500000000E+01 1.739269E+01 + 2.550000000E+01 1.700000000E+01 2.500000000E+01 1.278550E+01 + 2.550000000E+01 1.912500000E+01 2.500000000E+01 8.540956E+00 + 2.550000000E+01 2.125000000E+01 2.500000000E+01 4.762224E+00 + 2.550000000E+01 2.337500000E+01 2.500000000E+01 1.500213E+00 + 2.550000000E+01 2.550000000E+01 2.500000000E+01 -1.236074E+00 diff --git a/simpegPF/notebooks/model.png b/simpegPF/Dev/model.png similarity index 100% rename from simpegPF/notebooks/model.png rename to simpegPF/Dev/model.png diff --git a/simpegPF/Dev/nullcell.dat b/simpegPF/Dev/nullcell.dat new file mode 100644 index 00000000..e99eb1d1 --- /dev/null +++ b/simpegPF/Dev/nullcell.dat @@ -0,0 +1,27 @@ + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 + 1.0000000e+00 diff --git a/simpegPF/notebooks/obspred.png b/simpegPF/Dev/obspred.png similarity index 100% rename from simpegPF/notebooks/obspred.png rename to simpegPF/Dev/obspred.png diff --git a/simpegPF/notebooks/read_MAG_obs.py b/simpegPF/Dev/read_MAG_obs.py similarity index 91% rename from simpegPF/notebooks/read_MAG_obs.py rename to simpegPF/Dev/read_MAG_obs.py index b53182c1..9031147a 100644 --- a/simpegPF/notebooks/read_MAG_obs.py +++ b/simpegPF/Dev/read_MAG_obs.py @@ -12,21 +12,21 @@ def read_MAG_obs(obs_file): # First line has the declination, inclination and amplitude of B0 line = fid.readline() - line = line.split(' ') + 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(' ') + 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(' ') + line = line.split() ndat = int(line[0]) # Pre-allocate space for obsx, obsy, obsz, data, uncert @@ -39,7 +39,7 @@ def read_MAG_obs(obs_file): for ii in range(ndat): line = fid.readline() - line = line.split(' ') + line = line.split() obsx[ii] = line[0] obsy[ii] = line[1] diff --git a/simpegPF/Dev/read_MAGfwr_inp.py b/simpegPF/Dev/read_MAGfwr_inp.py new file mode 100644 index 00000000..3f2453a6 --- /dev/null +++ b/simpegPF/Dev/read_MAGfwr_inp.py @@ -0,0 +1,52 @@ +def read_MAGfwr_inp(input_file): + """Read input files for forward modeling MAG data with integral form + INPUT: + input_file: File name containing the forward parameter + + OUTPUT: + mshfile + obsfile + modfile + magfile + topofile + # All files should be in the working directory, otherwise the path must + # be specified. + + Created on Jul 17, 2013 + + @author: dominiquef + """ + + + fid = open(input_file,'r') + + line = fid.readline() + l_input = line.split('!') + mshfile = l_input[0].rstrip() + + line = fid.readline() + l_input = line.split('!') + obsfile = l_input[0].rstrip() + + line = fid.readline() + l_input = line.split('!') + modfile = l_input[0].rstrip() + + line = fid.readline() + l_input = line.split('!') + if l_input=='null': + magfile = [] + + else: + magfile = l_input[0].rstrip() + + + line = fid.readline() + l_input = line.split('!') + if l_input=='null': + topofile = [] + + else: + topofile = l_input[0].rstrip() + + return mshfile, obsfile, modfile, magfile, topofile \ No newline at end of file diff --git a/simpegPF/Dev/test.out b/simpegPF/Dev/test.out new file mode 100644 index 00000000..d015c5b4 --- /dev/null +++ b/simpegPF/Dev/test.out @@ -0,0 +1,3 @@ +0.000000000000000000e+00 1.000000000000000000e+00 2.000000000000000000e+00 3.000000000000000000e+00 4.000000000000000000e+00 +0.000000000000000000e+00 1.000000000000000000e+00 2.000000000000000000e+00 3.000000000000000000e+00 4.000000000000000000e+00 +0.000000000000000000e+00 1.000000000000000000e+00 2.000000000000000000e+00 3.000000000000000000e+00 4.000000000000000000e+00 diff --git a/simpegPF/Dev/wr.dat b/simpegPF/Dev/wr.dat new file mode 100644 index 00000000..590efee4 --- /dev/null +++ b/simpegPF/Dev/wr.dat @@ -0,0 +1,27 @@ + 9.8795652e-01 + 5.5014643e-01 + 3.6763740e-01 + 9.9389003e-01 + 5.5768086e-01 + 3.7348300e-01 + 9.8795652e-01 + 5.5014643e-01 + 3.6763740e-01 + 9.9389003e-01 + 5.5768086e-01 + 3.7348300e-01 + 1.0000000e+00 + 5.6536871e-01 + 3.7943638e-01 + 9.9389003e-01 + 5.5768086e-01 + 3.7348300e-01 + 9.8795652e-01 + 5.5014643e-01 + 3.6763740e-01 + 9.9389003e-01 + 5.5768086e-01 + 3.7348300e-01 + 9.8795652e-01 + 5.5014643e-01 + 3.6763740e-01 diff --git a/simpegPF/MagSurveyIE.py b/simpegPF/MagSurveyIE.py new file mode 100644 index 00000000..f0a0a678 --- /dev/null +++ b/simpegPF/MagSurveyIE.py @@ -0,0 +1,18 @@ +from BaseMag import BaseMagSurvey + +class MagSurveyIE(BaseMagSurvey): + """Base Magnetics Survey""" + + rxLoc = None #: receiver locations + rxType = None #: receiver type + + def __init__(self, **kwargs): + Survey.BaseSurvey.__init__(self, **kwargs) + + def setBackgroundField(self, Inc, Dec, Btot): + + Bx = Btot*np.cos(Inc/180.*np.pi)*np.sin(Dec/180.*np.pi) + By = Btot*np.cos(Inc/180.*np.pi)*np.cos(Dec/180.*np.pi) + Bz = -Btot*np.sin(Inc/180.*np.pi) + + self.B0 = np.r_[Bx,By,Bz] \ No newline at end of file diff --git a/simpegPF/notebooks/MagInversion.ipynb b/simpegPF/notebooks/MagInversion.ipynb index 5972e2e8..0f1ed012 100644 --- a/simpegPF/notebooks/MagInversion.ipynb +++ b/simpegPF/notebooks/MagInversion.ipynb @@ -2,20 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "ImportError", - "evalue": "No module named SeogiUtils", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msimpegPF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMagAnalytics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mspheremodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCongruousMagBC\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msimpegPF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMagnetics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMagneticsDiffSecondary\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMagneticsDiffSecondaryInv\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mSeogiUtils\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mSeUtils\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msimpegEM\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSolver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMumps\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mMumps\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'pylab inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mImportError\u001b[0m: No module named SeogiUtils" + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: pylab import has clobbered these variables: ['linalg']\n", + "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], @@ -25,8 +29,8 @@ "from scipy.constants import mu_0\n", "from simpegPF.MagAnalytics import spheremodel, CongruousMagBC\n", "from simpegPF.Magnetics import MagneticsDiffSecondary, MagneticsDiffSecondaryInv\n", - "import SeogiUtils as SeUtils\n", - "import simpegEM.Utils.Solver.Mumps as Mumps\n", + "# import SeogiUtils as SeUtils\n", + "# import simpegEM.Utils.Solver.Mumps as Mumps\n", "%pylab inline" ] }, @@ -41,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -65,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -89,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -100,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -112,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -123,15 +127,15 @@ "(-500, 0)" ] }, - "execution_count": 29, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -140,14 +144,14 @@ ], "source": [ "fig, ax = plt.subplots(1,1, figsize = (5, 5))\n", - "dat1 = mesh.plotSlice(rmap*test, ax = ax, normal = 'X')\n", + "dat1 = mesh.plotSlice(chi, ax = ax, normal = 'X')\n", "plt.colorbar(dat1[0], orientation=\"horizontal\", ax = ax)\n", "ax.set_ylim(-500, 0)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -156,8 +160,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "(33620,)\n", - "(67240,)\n" + "(33620L,)\n", + "(67240L,)\n" ] } ], @@ -175,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "metadata": { "collapsed": false }, @@ -186,7 +190,17 @@ "Inc = 90.\n", "Dec = 0.\n", "Btot = 51000\n", - "survey.setBackgroundField(Inc, Dec, Btot)\n", + "survey.setBackgroundField(Inc, Dec, Btot)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + 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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 diff --git a/simpegPF/notebooks/Obs_loc.dat b/simpegPF/notebooks/Obs_loc.dat deleted file mode 100644 index 73adf2a4..00000000 --- a/simpegPF/notebooks/Obs_loc.dat +++ /dev/null @@ -1,2 +0,0 @@ - 90.00 0.00 50000.00 - 90.00 0.00 1.00 diff --git a/simpegPF/notebooks/Untitled.ipynb b/simpegPF/notebooks/Untitled.ipynb new file mode 100644 index 00000000..6956dbcd --- /dev/null +++ b/simpegPF/notebooks/Untitled.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "obs = BaseMag.UBCmagObs('Obs_loc.dat')" + ] + } + ], + "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 +} diff --git a/simpegPF/notebooks/get_T_mat.py b/simpegPF/notebooks/get_T_mat.py deleted file mode 100644 index f130e7d4..00000000 --- a/simpegPF/notebooks/get_T_mat.py +++ /dev/null @@ -1,127 +0,0 @@ -''' -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