From 203640c8033b219d765699bbc63d54ec1b5e8bf3 Mon Sep 17 00:00:00 2001 From: Lindsey Date: Thu, 6 Aug 2015 16:40:19 -0700 Subject: [PATCH 01/31] for the sphere problem, the magnetic field values need to be calculated wrt the center of the sphere. analytics abbreviate to Ana --- simpegPF/MagAnalytics.py | 24 +++++++++++++----------- simpegPF/Tests/test_forward_PFproblem.py | 4 ++-- 2 files changed, 15 insertions(+), 13 deletions(-) diff --git a/simpegPF/MagAnalytics.py b/simpegPF/MagAnalytics.py index c737e43f..703a63a8 100644 --- a/simpegPF/MagAnalytics.py +++ b/simpegPF/MagAnalytics.py @@ -15,11 +15,11 @@ def spheremodel(mesh, x0, y0, z0, r): -def MagSphereAnalFun(x, y, z, R, x0, y0, z0, mu1, mu2, H0, flag): +def MagSphereAnaFun(x, y, z, R, x0, y0, z0, mu1, mu2, H0, flag='total'): """ test Analytic function for Magnetics problem. The set up here is - magnetic sphere in whole-space. + magnetic sphere in whole-space assuming that the inducing field is oriented in the x-direction. * (x0,y0,z0) * (x0, y0, z0 ): is the center location of sphere @@ -31,6 +31,8 @@ def MagSphereAnalFun(x, y, z, R, x0, y0, z0, mu1, mu2, H0, flag): """ + print H0 + if (~np.size(x)==np.size(y)==np.size(z)): print "Specify same size of x, y, z" return @@ -47,7 +49,7 @@ def MagSphereAnalFun(x, y, z, R, x0, y0, z0, mu1, mu2, H0, flag): # Inside of the sphere rf2 = 3*mu1/(mu2+2*mu1) - if (flag == 'total'): + if flag is 'total' and any(ind): Bx[ind] = mu2*H0*(rf2) elif (flag == 'secondary'): Bx[ind] = mu2*H0*(rf2)-mu1*H0 @@ -57,12 +59,12 @@ def MagSphereAnalFun(x, y, z, R, x0, y0, z0, mu1, mu2, H0, flag): # Outside of the sphere rf1 = (mu2-mu1)/(mu2+2*mu1) if (flag == 'total'): - Bx[~ind] = mu1*(H0+H0/r[~ind]**5*(R**3)*rf1*(2*x[~ind]**2-y[~ind]**2-z[~ind]**2)) + Bx[~ind] = mu1*(H0+H0/r[~ind]**5*(R**3)*rf1*(2*(x[~ind]-x0)**2-(y[~ind]-y0)**2-(z[~ind]-z0)**2)) elif (flag == 'secondary'): - Bx[~ind] = mu1*(H0/r[~ind]**5*(R**3)*rf1*(2*x[~ind]**2-y[~ind]**2-z[~ind]**2)) + Bx[~ind] = mu1*(H0/r[~ind]**5*(R**3)*rf1*(2*(x[~ind]-x0)**2-(y[~ind]-y0)**2-(z[~ind]-z0)**2)) - By[~ind] = mu1*(H0/r[~ind]**5*(R**3)*rf1*(3*x[~ind]*y[~ind])) - Bz[~ind] = mu1*(H0/r[~ind]**5*(R**3)*rf1*(3*x[~ind]*z[~ind])) + By[~ind] = mu1*(H0/r[~ind]**5*(R**3)*rf1*(3*(x[~ind]-x0)*(y[~ind]-y0))) + Bz[~ind] = mu1*(H0/r[~ind]**5*(R**3)*rf1*(3*(x[~ind]-x0)*(z[~ind]-z0))) return np.reshape(Bx, x.shape, order='F'), np.reshape(By, x.shape, order='F'), np.reshape(Bz, x.shape, order='F') @@ -124,7 +126,7 @@ def CongruousMagBC(mesh, Bo, chi): return np.r_[Bbcx, Bbcy, Bbcz], (1/gamma-1/(3+gamma))*1/V -def MagSphereAnalFunA(x, y, z, R, xc, yc, zc, chi, Bo, flag): +def MagSphereAnaFunA(x, y, z, R, xc, yc, zc, chi, Bo, flag): """ Computing boundary condition using Congrous sphere method. This is designed for secondary field formulation. @@ -210,9 +212,9 @@ if __name__ == '__main__': flag = 'secondary' Box = 1. H0 = Box/mu_0 - Bbcxx, Bbcxy, Bbcxz = MagSphereAnalFun(M3.gridFx[(indxd|indxu),0], M3.gridFx[(indxd|indxu),1], M3.gridFx[(indxd|indxu),2], 100, 0., 0., 0., mu_0, mu_0*(1+chiblk), H0, flag) - Bbcyx, Bbcyy, Bbcyz = MagSphereAnalFun(M3.gridFy[(indyd|indyu),0], M3.gridFy[(indyd|indyu),1], M3.gridFy[(indyd|indyu),2], 100, 0., 0., 0., mu_0, mu_0*(1+chiblk), H0, flag) - Bbczx, Bbczy, Bbczz = MagSphereAnalFun(M3.gridFz[(indzd|indzu),0], M3.gridFz[(indzd|indzu),1], M3.gridFz[(indzd|indzu),2], 100, 0., 0., 0., mu_0, mu_0*(1+chiblk), H0, flag) + Bbcxx, Bbcxy, Bbcxz = MagSphereAnaFun(M3.gridFx[(indxd|indxu),0], M3.gridFx[(indxd|indxu),1], M3.gridFx[(indxd|indxu),2], 100, 0., 0., 0., mu_0, mu_0*(1+chiblk), H0, flag) + Bbcyx, Bbcyy, Bbcyz = MagSphereAnaFun(M3.gridFy[(indyd|indyu),0], M3.gridFy[(indyd|indyu),1], M3.gridFy[(indyd|indyu),2], 100, 0., 0., 0., mu_0, mu_0*(1+chiblk), H0, flag) + Bbczx, Bbczy, Bbczz = MagSphereAnaFun(M3.gridFz[(indzd|indzu),0], M3.gridFz[(indzd|indzu),1], M3.gridFz[(indzd|indzu),2], 100, 0., 0., 0., mu_0, mu_0*(1+chiblk), H0, flag) Bbc_anal = np.r_[Bbcxx, Bbcyy, Bbczz] # fig, ax = plt.subplots(1,1, figsize = (10, 10)) diff --git a/simpegPF/Tests/test_forward_PFproblem.py b/simpegPF/Tests/test_forward_PFproblem.py index 8be7a75c..09e2c30d 100644 --- a/simpegPF/Tests/test_forward_PFproblem.py +++ b/simpegPF/Tests/test_forward_PFproblem.py @@ -26,7 +26,7 @@ class MagFwdProblemTests(unittest.TestCase): self.chi = chi - def test_anal_forward(self): + def test_ana_forward(self): survey = PF.BaseMag.BaseMagSurvey() @@ -47,7 +47,7 @@ class MagFwdProblemTests(unittest.TestCase): u = self.prob.fields(self.chi) B = u['B'] - bxa,bya,bza = PF.MagAnalytics.MagSphereAnalFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],100.,0.,0.,0.,0.01, b0,'secondary') + bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],100.,0.,0.,0.,0.01, b0,'secondary') dpred = survey.projectFieldsAsVector(B) err = np.linalg.norm(dpred-np.r_[bxa, bya, bza])/np.linalg.norm(np.r_[bxa, bya, bza]) From f416dfacf8146c9a81d10ea2b9ba26f32e199c08 Mon Sep 17 00:00:00 2001 From: Lindsey Heagy Date: Thu, 6 Aug 2015 20:45:18 -0700 Subject: [PATCH 02/31] added myself to travis --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 9683c710..f43c84bc 100644 --- a/.travis.yml +++ b/.travis.yml @@ -35,3 +35,4 @@ notifications: email: - rowanc1@gmail.com - sgkang09@gmail.com + - lindseyheagy@gmail.com From 80c839390c46993fcf2b9f4ffeb220ce83b4380c Mon Sep 17 00:00:00 2001 From: Lindsey Date: Fri, 7 Aug 2015 09:51:22 -0700 Subject: [PATCH 03/31] updated travis --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index f43c84bc..ee6b575e 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,7 +4,7 @@ python: # Setup anaconda before_install: - - if [ ${TRAVIS_PYTHON_VERSION:0:1} == "2" ]; then wget http://repo.continuum.io/miniconda/Miniconda-3.3.0-Linux-x86_64.sh -O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-3.3.0-Linux-x86_64.sh -O miniconda.sh; fi + - if [ ${TRAVIS_PYTHON_VERSION:0:1} == "2" ]; then wget http://repo.continuum.io/miniconda/Miniconda-3.8.3-Linux-x86_64.sh -O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-3.8.3-Linux-x86_64.sh -O miniconda.sh; fi - chmod +x miniconda.sh - ./miniconda.sh -b - export PATH=/home/travis/anaconda/bin:/home/travis/miniconda/bin:$PATH From 7d516d2879367299bc511f7308a01051ea9b1f0e Mon Sep 17 00:00:00 2001 From: D Fournier Date: Sun, 27 Sep 2015 09:25:53 -0700 Subject: [PATCH 04/31] Change functions in the header and start a development branch. --- .../Tutorial_1_Mag forward modeling.ipynb | 895 +++++++++--------- 1 file changed, 462 insertions(+), 433 deletions(-) diff --git a/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb b/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb index 2f383f33..c58a50b7 100644 --- a/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb +++ b/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb @@ -1,443 +1,472 @@ { - "metadata": { - "name": "", - "signature": "sha256:550ca3df316c026a67f55f9de3baa38df81944304bfe8103f9a2d9e8a43c20c7" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from SimPEG import *\n", - "from simpegPF.MagAnalytics import spheremodel, MagSphereAnalFun, CongruousMagBC, MagSphereAnalFunA\n", - "from simpegPF.Magnetics import MagneticsDiffSecondary, MagneticsDiffSecondaryInv, BaseMag\n", - "%pylab inline" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import matplotlib\n", - "matplotlib.rcParams.update({'font.size': 16, 'text.usetex': True})" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Forward problem: Magnetics" + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a tutorial for Mag forward problem using simpegPF package. We first start with analytic solution for susceptible sphere in a whole space. Then we solve steady-state Maxwell's equatoins for Mag problem (See Doc) using simpegPF package. " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Step1: Discretize the earth" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use TensorMesh class in SimPEG to discretize the 3D earth (See Doc). Let's visualize discretized mesh on section views:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "cs = 12.5\n", - "ncx, ncy, ncz, npad = 41, 41, 40, 5\n", - "hx = [(cs,npad,-1.4), (cs,ncx), (cs,npad,1.4)]\n", - "hy = [(cs,npad,-1.4), (cs,ncy), (cs,npad,1.4)]\n", - "hz = [(cs,npad,-1.4), (cs,ncz), (cs,npad,1.4)]\n", - "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCC')\n", - "fig, ax = plt.subplots(1,2, figsize=(12, 5))\n", - "dat0 = mesh.plotSlice(np.zeros(mesh.nC), grid=True, ax=ax[0]); ax[0].set_title('XY plane')\n", - "dat1 = mesh.plotSlice(np.zeros(mesh.nC), grid=True, normal='X', ax=ax[1]); ax[1].set_title('YZ plane')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 17, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Step2: Compose suceptibility model: susceptible sphere in whole space" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- $\\mu = \\mu_0(1+\\chi)$\n", - "- $\\mu$: magnetic permeability\n", - "- $\\mu_0$: magnetic permeability of vacuum space\n", - "- $\\chi$: magnetic susceptibility" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.constants import mu_0\n", - "mu0 = 4*np.pi*1e-7\n", - "chibkg = 0. # Background susceptibility\n", - "chiblk = 0.01 # Susceptibility for a sphere\n", - "chi = np.ones(mesh.nC)*chibkg" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sph_ind = spheremodel(mesh, 0, 0, -100, 80) # A sphere is located at (0, 0, 0) and radius of the sphere is 100 m\n", - "chi[sph_ind] = chiblk # Assign susceptibility value for the sphere\n", - "mu = (1.+chi)*mu0" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 20 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(1,2, figsize=(12, 7))\n", - "indz = int(np.argmin(abs(mesh.vectorCCz-(-100.)))); indx = int(np.argmin(abs(mesh.vectorCCx-0.)))\n", - "dat0 = mesh.plotSlice(chi, grid=True, ind=indz, ax=ax[0]); ax[0].set_title(('XY plane at z=%5.2f')%(mesh.vectorCCz[indz]))\n", - "dat1 = mesh.plotSlice(chi, grid=True, normal='X', ind=indx, ax=ax[1]); ax[1].set_title(('YZ plane at x=%5.2f')%(mesh.vectorCCx[indx]))\n", - "cb0 = plt.colorbar(dat0[0], orientation='horizontal', ax=ax[0], ticks = linspace(0, 0.01, 5)); \n", - "cb1 = plt.colorbar(dat1[0], orientation='horizontal', ax=ax[1], ticks = linspace(0, 0.01, 5)); \n", - "cb0.set_label(\"Suceptibility\")\n", - "cb1.set_label(\"Suceptibility\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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q5DGK4WsUP7ZMPZKpavjaQ+N984fu76u9FiLnbABAiNBF/5uSbkl6knAsKZxv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\nrOGqyzX9dfUF/7ak17U4gr+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- "text": [ - "" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Step3: Set up an airborne MAG survey" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have discretized 3D earth and generated suceptibility model, which means that we have discretized earth and physical property distribution. We can compute magnetic fields everywhere in our domain by solving parial differental equation (PDE), but our measurements are confined to finite locations. Therefore, we need to project computed fields $\\mathbf{u}$, which is defined everywhere in our domain to certain locations where we have receiving points. For instance in airborne mag survey these are the points where a plane or helicopter measure earth magnetic fields. This projection can be expressed as:\n", - "\n", - "$$ \\mathbf{d} = P(\\mathbf{u})$$\n", - "\n", - "where $P(\\cdot)$ is a projection from computed field to the measured data, and $d$ is the measure data. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's assume that we have a survey area: 400 m $\\times$ 400 m. We have 21 lines of airborne MAG survey, and we measure magnetic fields for every 20 m on each line. A pilot for this helicopter is really talented so that the flight height is constant for 30 m above the surface. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "xr = np.linspace(-200, 200, 21)\n", - "yr = np.linspace(-200, 200, 21)\n", - "X, Y = np.meshgrid(xr, yr)\n", - "Z = np.ones((size(xr), size(yr)))*30." - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 22 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(1,1, figsize=(5, 5))\n", - "indz = int(np.argmin(abs(mesh.vectorCCz-(0.))));\n", - "dat0 = mesh.plotSlice(chi, grid=True, ind=indz, ax=ax); ax.set_title(('XY plane at z=%5.2f')%(mesh.vectorCCz[indz]))\n", - "ax.plot(X.flatten(), Y.flatten(), 'w.', ms=5)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "[]" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Step4: Analytic solution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have an analytic solution when we have a sphere in a whole-space. simpegPF provides this function so that you can compute magnetic field on your receiving locations. Another input you need to put is direction of the earth magnetic fieds, and the strength, you can easily get this information from NOAA's website. We assume that we have veritcal earth fields and the strength is 1. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "Bxra, Byra, Bzra = MagSphereAnalFunA(X, Y, Z, 80., 0., 0., -100, chiblk, np.array([0., 0., 1.]), flag)\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')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 25 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(1,3, figsize=(18, 4))\n", - "dat0=ax[0].contourf(X, Y, Bxra, 30); ax[0].set_title('Bx'); cb0 = plt.colorbar(dat0, ax=ax[0])\n", - "dat1=ax[1].contourf(X, Y, Byra, 30); ax[1].set_title('By'); cb1 = plt.colorbar(dat0, ax=ax[1])\n", - "dat2=ax[2].contourf(X, Y, Bzra, 30); ax[2].set_title('Bz'); cb2 = plt.colorbar(dat0, ax=ax[2])\n", - "for i in range(3):\n", - " ax[i].plot(X.flatten(), Y.flatten(), 'k.', ms=3)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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hOQQQyn5CRZKY6JEk4ggLI0z75BBF1gM4G8A7ATyjab8ZwFcAfL15vQ312sX3\nE9tbIacg0rUYokOMiSqOdF07JCelCiIyOcURFkYGwSD8cJuQf/dtRYm49hMihqQqsGfqh3rhQBFH\n2ooYyZhCE8tK7JvJwu4DYwZ8cQ8EkRAxJEYIaUsEcZFCJAmNHlnyhp4IIwxTc1yGPh9BvWzOE9BX\nu92ERecOAA8DeL9He3ZyTUJ+9oNTixREZHzG5zpObU3mUkWJnPzzF3ohiMjkGu8g7rLPNr33w4Om\nbUHkKaRdmjHVfmKPQ4p9MExeBu6L2xBEzkSrgsjpCBNEzpAepXIGwscZclyWvCGykG3KpZZNtFkL\nhymZHKKIjbM1770I4Hxie3ZyCiJ9oU9jdeEjiPSVXGLOKuxlcWSYFO+He43rIrxNQaQtMUS3Xypd\nRMz47qPB18+y/2Qc9NwXtyWIBBJyQe570Z9DCFnreKSkTXEkGBZGmHZou9DqCgD7lfcONM+vJrS/\nlG9oeQSRvgoM1HSa0KKqqdJvYvtIKiaoE+uWQ7JzpdRwOs3gKNoPd0Gy1JlSBJEuhBAVMQbK5NmV\nBuNKcXFtz4VZmTLpsS/ugSDiQ4gYEkIKUYPShy5Ry4b4PD7pNafD71wTlU7TRioN1xeZddoWRZZj\nsVCUQDj8FYT2LCeAwYghTyuvT4vv8mc/ODVaGElBSP+uu3TRgojrAoYa+p1YPMkhjrAwMiiK9MMp\n6eUd+hSCSCIx5DnF5Z8cczqjiiO5hREXBdcWYQbL4H1xOIUKIiFiSOroDt99+ggkvuKIb72RqCKs\nLIwweWlbFDmgeU84/P2E9mCerDYDAM4Y7Zh43ySI7K/eW+989AVn37ItSQy5tKqfPzNy2wr7fwfw\ncaL9tU3/qr1JJHGMR40a0R0bkzDyZLUZTwJ45+jPSUPfWW3HPI7gqtFGkv3mqvbcO0aTZyrTRdGG\n6iDmXzmK0UOk7lFdUD+PthNsL29s7yX2rdo7JuQLYyGOfeO5L+DI8UvwwOhEkv2G6iAAGO1VYcR0\n7HUIW6YIOvPDW6rdAICbRudZ7YQv8fmOyfZ/P1pGshff+W89QDB+1vEbV0SLaktje5O+farvLzX2\n73H3DQDVY439uc0bjklp9dPG/o2T76sCiGBD8/yAwUYWSkx9TyCJI1NjFxiEjYljQxAupo69pW9A\n+r9+Xd+u4vKVMqHfYao99Tel2jNF0JkvBm5tnq8JsHdFiVzZPN9B7Fu1dwkiFzbPD0436QSRo80c\nd4kyxzWQl+alAAAgAElEQVSJIXsa+9WSve3nuLuxP0+yNwkhn25s30ecz6ewtwkkurEDZnFEd2wA\nfdSI6bgDhqgRy/91AVkYif2euYj5jVBtmZJoWxTZj1r5lhGvXyK0T/EvW/924e/fWHcWfmOdLgVT\nT+oIkSzRIU+jFkRS9aXiEU1CiRppk5DokflXjtKNnwVwyHsX4ch3ihPdsZx/5WjSpSR9Ikae2PUL\nfHvXLwAAz+87Er9zDn9PRXI/DACf27p4B+ct616Ht6x7ffAAh7y6VRY8IkRMIogvcj9HAMzPEzd0\njTUm4oPTZLT8866f4593/SsA4IV9CSYUfIxTkcEXy3dNQiuG2sidNtNShAj1sFAjQ5YiTUSI+vFP\nMLwfGjChjtGlkcqf33VvKyRqpMiIkVzRInKRrxfju2M/nBxdJexUbEPtvDcr7+/HZDjg+ajXYH83\nsV1mfM74G0GDK1oQ0YkXufEQR2zCiOlCRvd+rK1pe1OUSNIlN9skkUCSMp0mJJXmHXOPA+E+Zzy+\nj2Y4dxli9jM02vDDADC+b3xR9GBlYkQR3/QZkm/IWU/E1rdtuw7EEBfkVBvbRYlrsmfzibZtY/pt\n8PWjsWmHOdJ7L5v7IsC+uG1amRMDtycZrJlCRRGqIJJaDPEVQtqoGSrw1Q2oaTaUwF+fVM7gOiO5\nU2lyp9FcDbAfLoqckSKmf8DdmFxj/XwAd3m0F0XvxRB13wRxJFXESI67wdG1BEoTRIDFMUWKI11F\njBTEdaiPpphg3hNpH9O+HPVSiwewOK26MXK8OmbCDxfPjAgiYl8kYeQpWMLXUexdsJR+1EVby9p3\nAPviRXrkiwsURFJHh6QWQ9oUQWz7pugH4jO5xJEzkDZqJKoAKxPBLPphKzmW5D0L9cAvBrCx+fss\nqf1G1Jd3FzdtTwP4vEd7NKkmGkkEkaelRwkQx2L67KZjm3py5yumOO8EP4syBRGZBGPs89LDkdwM\n4HHUE8t7UDvdiyPsY9v/FMDHmrYbUU90N0WMV6V4P8zk57kftSuIyPslEVoYNtQPpli+l4mFfTH7\nYg0ZBRFqFhFFEKEsi3um9AjhNMcjBJ8xUT4jVTyiRuYELdmbW3Ea9DK9s+aHSfQ9nMY7faYYQaQU\nEcQGwfmaIkZiUmDU96jbBafNxIohtol2zrudEZEjqe50+kSLdJw+o4YgrwdwA4B3Gbpx2ce2P406\nnFqU7Pxs8/wHgePtmqLSZwC/qLHo9JnQ1JmMUSJdiCEqpIiR0DSanqTQxETU5YoU6Th9hn1xPjKm\nz+S8QMwsiFBwXeRThBAqCVaGdOJzjeGKInFFjqRMpwmKGMmZRpMzhabT9Bn2wxraLrTaKYMXREz9\nhjpgj5SaXuIrhoTcZdRtk0ooiVhCMlUIeE/SaHTVl19ErUSH2Me2o/l7r/R6LQBxivMdL1MyqaMT\nMgsiuull6P04UipNT9NomCDYF/eSAQsibYkhbc+j5f25rjnEZzBpC660Gmo6TbZUmpyFVwe5RC/7\nYQMzI4oUIYikFEN8+ooVS54225rqi+jqheRcUcI7SoQiiOQKtVb7jZn0szBCYQWmly8USx2+GtNV\n/F32se0vYdL5nw3gGIC/DhzvIJnpFWgifI+vIEKZSqo2Ppcx0cKIiQjfx3QG+2ImjrYEkRRiSIQQ\nctxv/lL7/rHvv8q/M6pA4tIW1oKFkWHAftjATIgigxBEckSXyH26nLclaqTtpXqpF0paQYQaHdJm\n7rnYV6g4ElGIdUaEkeWYDLsDFh3sCkw7VJd9bLvY34moQwM3ArgiYrxMbnLUGwrp0zGhpAoisVNH\neXvKtQG5+KqO1NEiHH3SJeyLe0dhUSIUYgSRWDGEKISYRI8U21mFEzE+0zVFTNSIOKY2caS3wsig\nYD9sYCZEkRQECyIliiG2/VDEEaLTb+tOry5KJLigaJeF+FKIIx0KI9kxHJddjwO7nrBueUDznnCw\nqvpMsY9tFxxEXTDqHtQFpHY0f/uOlxkSgT7IJYjkmipSBRKnMJIyWmQAwkfRK8+wLzaNd2AUJohQ\nokRcPiQ0OsQ2XMKcOFQECUHel1EgcUWPUMSR0KiRrMJILgqMFmE/bBpvMIMXRVJMLDoRRLoqxEoR\nPTQ2odEirYbIu+7MlrIyQYw40qEw0lW0yLpz6ofgI387ZbIftdIsI17rFGaXfWy7eC07+h2ol1m8\nJ2C8TJfY/EZIgVUTlklkV4KIbj8uYQQIiBoZgMgxC7AvZmgMRBDJKIasfIN+Xrzveb9rGrH/4OgR\nmzjiihrpRBjhNBr2w+EMWhSRBZH91XsBACtGXyBtK+yP/t3jbuNLq/r5M6PF92yixrWN/cdH0226\n7W5r7D+ssdcRa++KGhHjf2Cyf50w8k/V1ViKIzhjtAOAWwTZWW3HPI7gqtFGbbu67Yeqf2qGcuLC\ne7a0mery+nl0r9RmuaiptjT2N5ltFmyvb2xvcduS+lbEEe3YdYjPelVj/xBtPBvPfQFHjl8ycSxt\nbKgOApg89iZhZHNFKU+ejScwrTSvAPBwoH1s+/kAvoraqQuHLiqDvzpgvL1hS7UbAHDT6DySvfje\n7BjR1v/ztRff4W89QDKn/wbR/L4PAaMPEPv+UmN/rqZRM3msflo/7zxi71dMDz/UPH+CNhwve9nW\nJYwAwNt+BMzPA6M3Kg2GaJHqMQDfAUbvIQwGQHUngGU0vw34/V8Bve8zEfod/vMR7eLH9zcl7DuC\nfXEx3No8X2NoV6NErmye7yD0bbPVeYcLm+cH9d2pgsjRZg66RJqD2gSRPRVwAoAzDHNiVRD5dNP/\nX1jm0PL8+E8a+7+p7W1CyLEL19c2Dz5iFD5kflLVS4e8aVTXnHRt88Nz/2ihfxlj9Ig8dpc48j0s\nHpv3ScfGFDXyfAW8DGC14Tiqwoju/2rF9L0xCSM+3+EQe9dvSmfbCeyHDRyXusNSSBEhcvTQUv+N\nnkZYlEfodjkpYDzZo0h8okP2OB4muxRjDOnnkP8m868cDdjRJD5LobbI3Zhc0/x81Cq0YI3S7rKP\naX+s+VtWuN8JYKf0nqv/meAw5rseAsMEU3CdpS5hX8zQSREhcoLh/bUwR4iYtjkN+huGv34Mx/3m\nL62CyMo37MP88Ycxf/xhkiASgty/aR9inMaxmm6Ingn7sdRhshdQUiapxXVbIWdKWauwH9YQuj5y\nKYzPGX9D2xArigSlzISKISkI6cenMrbJVvO+Gi2iChs+r1226gU4ubgqRWTIlU4TGwbuu31AOk1s\nKo3uYuAdc48DMWuyf5NmOPc7MO3nOiwmGL2IxfXQAWATgEsAvJtoH9t+FhaXE3stgDGAP/XsvyTG\n940vytJxqDDqI845axDZ0l1Sps+Y7HuQOqPiihYxptCYJskmv2fybzY/aWtz+Esf3xgqiuSuJ3LZ\n3BcB9sWCIfniMXB7gm5yXfhlSJsJTZkxXcR71g5xpcf4iB+rA24o7SH6GFfajTG9xnRtYTqxmOqM\nuIKFKak03vVFcp39UqXQXA2wHxYU4YcHKYrMhCCSOoqDIpAQhRFdbREfoYNqSyqwWpogoiNEJOmh\nMFKAKMLkozhRBKALI6TCzCHCCIsiRlgUMTMDogiTh2GJIrFRIqkEEcPc1ySI2ISQEOEjBJtYYhNI\ntOJIKcIIiyIy7IczMOiaIkXjK2rkTmWhrD5DXKHGVXQ1V3FV0oWNS+zoothqSBFB320CC7AyTNe0\nWoy5Z5QqiBRNhCDiA6fOMP2EBZEFPMQQV0SIjxjiOt9RRFN5f6pAIo9VFUiO+81fTgsjplojptId\nphojruKrLooputqPgquMP4MTRYqPEukyxYa6L9/VZzyW6Y3B+8JIvRtboiCi7ttH6Mi8IkPsijRd\nrUbDML489/rXhi/jbWM1ylnVyhNKwVQmPUUvxcswpVCIIBIaFRIj9Nu21fkPMQ5d9IgYvyyOaIUR\nQC+OpBRGKCvSFLVMLzM0BieKxFCcINLlsrxAlNChRovkvtsbdUETc9FiCoMPvePoK3T42AdEi6RY\nqjcZHOkys4T6j71YVWbh3zUIW5a3Jb4nPbMwskgxvjCCJKIL+2LGi8RRIpTinCqRgohPdIhJDAk5\nh8nnL8pNJnkf6m/dFT2iCiOAIZ3mNHQrjHgx4GgR9sPJGZQoEnOyH5Qgots2ROCwRYB0FC0icF7o\nUKNEXIJI6IWLvJ2v4/KNGsksjMTA0SLMYChcyMhFqDDSqaCSMYKOQqm1RBjGzgBW1tBFiVAFkYh0\nmZioEKpwb7Iz+RuKQCKLI95RI10JIxwtwmRiMKJI65OJXIJIqBji2s7VbhM/TO0OYcRVW0QQG0Xi\nLK6qEz5yiSGuvnwECR+xI6MwUlS0CMOUSI/TZFRmtg7JDNx1Y9GFaZ8Wo0QSCyI5xJAckYu6PlWh\nRIyFKo6QokZihZHWGHC0CJOU47oeQAl4R4mUJIg8Hbid774Tp/J0OjlrUxDR9e3T/x7QL7Z8Lso8\nP2NMelKR6QtM7wj1GdS79kUIfwkjHEKmgKZtihVKZkDIYJh2KCBKZACCyErsW3jIrMJe0lxI3t71\nsGHan2lb9bOsfMO+qc+rXW1HPXYmDUz3vzDVfgHcKVKUQrwTcCIo42YQkSKtps2UIIjkqjXiqiWi\na/eIFklVV0R29N5RIjbhoM3QeN/oEWokCEeMMB3Bq8QMm0HUF4kQnai+r9TUGY4SYdonoccIKayq\nI0AQ0dUOmRIRDOc+kzCRAlM/8m/dVJNEFzkSHDUSEzHS+zQajhYZEjMTKbK/ei/2V++lb3BpVT8o\nXFvVDxmbcHFbVT+EnUvk2FkB/6OiR4U8WtUPKqq9ax8flcbv4tLK67jvrLZje7Vz6n2T868uqB9a\nNIJIdWf9mMIgiFRfqh8UfGwn7IliTHU9UG0hGDafu7q8fpDHYzuWGjZUB7GhOkiy/VD1T/SOmcGy\npdqNLdXubPabqyexuaKv+efzHQaA6ir6b6raQvy9Cntf//FTYAPdHB9qHibUOeytzSNF3yobALzN\nnVm5QPWY57HxPfaX5/N9vt/J3L8R3fmVmUXkXzglSuTK5kHBxxYALgTgMT/fU9UPgLbSzKer+kEU\nRI5duB7HLlwPQB8dIgsiK7EP362uxJerbRN2ukiNldiHT1d3Y1v1ZcOgJ21XYh9uqx7AbdUD5AiR\nbdWX8enqbmPqjjqm71ZX4rvV5P9KFzUiEMdmKmpEFzEijruMLWJE/r8KbGLY0Qr1d4fKdfD7Xvp+\nj33OmlQ7pk26iBS5BPX96Z0AXgSwCcDnMHkJex3qS8UVzet7TJ0VGSWS2u4Q0S4lPivQZCq6Gr0E\nb4ptxLGnpKbItr53JKlRG9TvAjWVhqNFZpmkvhjIHy2SexWaLEvzDqjeSErm57segcSybnfPUSIz\nTXI/XAYnpOvKd7UZ3YU3YTiuCBFKdIhPZEjouZISISLbqe+LMaqRI2rUiC1iRIsaMXICgJftmyxw\nBgLPk8eHbMQwWuY62OcVAHY0fx8AcDmAz0vtNwP4CoCvN6+3AXgMwP2avsYnjX8YNIjeCCIhqTIh\ny1nZTjomgUN9/zRzm0ihkZ25629TuzF9RhY4KGkzLkEkxYWMrzhCFSgo/frs20MYCRVFTpl7AQj3\nOePxz2iGcychZj+zRFJffMv4gwsvcqfRxC5taIMkiph8h+8qVz79GPz6c5aIi9BaIPJ2oQHwtu1O\nNp1+TechnS8z+SyT37P5Q4f/63PqjK7v6+c+CbAvLoWkfhi4PWAIOeqJeHiO0FoilCgR01CUOWqs\nIEIVQ6jnrlWGk8Zej4md+ts3+RnZb6k26tK9qjAyVXxVd82iOxHpCq/aAups1zVeKTQ5KmSFps9c\nDbAfLoouIkXGAJajVrz3ato3AbhBev1w81p3AugnuQSRmLW9n4L5xOMTNSJvo7FPeSfZKIhQsNmn\nvKvru7wuNXKDEo0SErFCoKtoEfo+jRe1vnfbXPax7UB9l/BtAG7UvO+6exhLNl9cYsRIK9EipqgQ\n0/s+y/wa8qtPPtUsjITW3I+tBJBdEDHBgkgrfbMvTuqLeU5sI0WUiEpCQSSFGGISQHxsdWKJGv1h\nixwR/ss3YmRqyV41WgSgn4hs9UVseNUWybESTTd1RdgPJ58Td1ZT5CXonf/ZmvdeBHB+1tG4SCli\n5BBEnkKcICL3Y0MdU66CrxaCVjKRfzKu1V9yhbn7rCJDvUii9Jd6n/3lZgCPo55I3oN66nRxhH1s\n+3rUJ4grAJyo2f8K1HcEn0H933kGeb6d2XxxiSkByVaisV1Ih1yYU20NFwhGoQH1FLDNAqmtCCK6\n4x8iALMg0gXsi6fpcE5ceJSICepqM4Q6IjIxgoiu5of63irsmXoYx3J438TDhqlPypjUz6K22WqM\nAJpVaSg3UCnilYyvOMa4YD+soStRZBPqg3Ex6oMgWAFgv2J7oHl+daqde6fOUEgpEFD7SiWG+PRp\nG5unaJJt0hb6M2kj758qjgxfpGibTVgMPwbqu23vj7CPbX8EwMcAPAF9WKO4e7gGtV/8vMYmBVl9\ncYkXZq0IIyZSpIFYhBGXOJITl/hiG5sWH0EkpJ9EsCDiDfviaTqdExdL7IUwwenJF/ShgghdeKAL\nIDoRRGdjEktCxJEYYWQK6lK9Kj4rCcl4iWu9X0ctBeyHNXQhinwNtUp0f/NYi/pgAYshhDLihKC+\n3w5tix0UmxxiiG4fJp42/G0hixAloEZ+2PL32y6ESNknRRjpKFokeTHKvPjebXPZx7ZTMd09TEUr\nvrjEi7/QC1kyqWoJeQojAC1qJFX0CLUvqyCi+yy+gkiHaTMhlPibaAn2xdP0a07spKAoERVL2kyM\nIKKiE0MmBAqisPGqp45NPEzY+lOFGJM4QvlsUyvvSMdsKlpEh/rV4GiRrmA/bKCLmiLqJdrDqMNq\n7sGiAi4jHL+qlgMA/m3rbQt/z6/7Hcyve7t1551FiaSwyS2E6Pbn64QSrTzjwnhR7itu2Oxjjjf1\nuLlqjlBqjHRYX8TGaNcreHTXK+3uVI/rbttLnvax7er+TGyS+lmDWkVPSVJf/NWt/7Dw99p1J2Pt\nulMWXuesMZJzRRpnfRFbTZBU9UUCVq+x1RmRkeeo1CxrHzHFGR2SYpLbszoiXQgiz+z6MZ7Z9Vy2\n/XrAvniapH4YeEj6+3T09krSt7iqiiNtxnYRHyqIuCJDbCkwNtHDZvPL0/X3tlce3od9S1dOjEOu\nO6LWF5FridhqjEztR6oxQqovorIW00VXW6ktkhpKXZE27miTYD9soG1RZDnqD7QciwfhIBanKKJN\n3QYwHLT/ZeuHEw9RIlWUSNuCiK6qMwWdaiv2q56gZPHDJoQkEEmsFzwxUSK5BBF5ex9xJLcwQsFj\niV5XwdVq3fGo1i0ul3bbR/49cnDBuO62qb7FZR/bTjkBfA2T39AdqE8IqZZiTO6L37X1t607LFEY\noVCsMGIovCqgCiMCk0ASGlESLIikSpsJZGiCCACsXXfKhEj5tY/8Y7ZxOGBfPElyPwxc4LH7HPVE\niIRGieigrDZjQY54CBFE9EvzTkaGyFAEkAUcN85sQonYr0sc8RVG1OKrMk5hJEed0yC6GIgqUv59\ny/tfgP2wgbbTZ8YAbsHkAViDxcv4JzCtjK9ArZyXSQrhJJUg8gzCBRGxfewYgPJridhSV1ILuT79\n2cafosZI2ylC5eB7t81lH9tOQXf38AadYSCd+OLS0gaKqy/ii+NGsHcdj4bYFJvWBJEMUSIU+iSI\nFAb74kkGNidOVKshNkpEhZg2k0IQ0aXKCFxpMAtzU/lBaVNQ90NJqdH9Ta0x4iy86kJ3Q9b0v3bd\naEwptg0X9sMG2hZFDmJ6baBLMPnB7sZkRdrzAdyVYudeqTNtpcSkEERixRC1L+pYAmqLxOC18owp\nSiRndIiNNoSRVKLH8GqL+N5tc9nHtrtYDuAYJgvpyXcPU9CZLy7t4jCZMGIid30RIIswEkqfBZEu\nlhqfMdgXT9LpnLgz2owSaUkQMdUNEWjFEIrIYbux5hBKdOKIcbxEYURuswkjE4QWXc1OMQNpG/bD\nBrqoKXI36uraB1C7sx2YrCJ7Y9N+MeoP/DQSVJndX70XOLQU+MyItsG1Vf38cYL9bY3thxVbk1Cw\ns7E/y9G3cIQ/buxP0djrRIxxYz9H/KyqvejTlE7zr4392y39i7QZcRwfGOFnPzgVJ73ZHs+9s9qO\neRzBVaONRhv5Qry6vH4e/bm120X7xxr7c5UGw0mn+mlj/0ZC3y5bJaXGOBZDKkz1pcb+P1sGIW1b\nbWnsb3L3DUjH8l5L/7J9E6W78zG37YbqIK1TC6aL1yd2/QLf3vUL26a+d9tc9rHtLlx3D1PRui/e\nXu0EAFw12khKd9lS7QYA3DQ6j9T/lmo3luIIdoxotxQ3V3Xi8o7RGSTRVXznRw9pGpXUl4nfEyGN\nprqzsf/AdF9ae9V/WNJpTj619k9HjgAP6E2m2NA8U+yF7beIBVWnxu4QRCaOjc1e9K/zZZbpU3UB\ncOT4JXiAcMrci1UT3xsXm6sncRjzuGlEE+x8v/M3VN8E8E3rOVNG/AZjYF+c1Bd3MicGbgVwAoA7\niPZXNs8me/kC88Lm+UFa10ebeeKSES1KZHdj/78T57h/Utsf98jiv912Ef/d6kp8F8DvjW4kCSJ/\nWtXjuG/0JmeqTNX8TEe3GHau+PDq9sb+ao2t5lgt9C/9zF/11DFjSo0Y+0dHFSmV5svVNhzBUpw9\nuhXAZCrNVH2R9e+sB/A3I3cazVoA/735v57X/F9NtUXEuU7+3sgYa4t4fi+d33mVHQBeBnANwfZW\nYp9m2A8nnxN3IoochLs4SvLiKUcPLaUbtxEBcsjR7oosSP5VMOxDJ4wcArCs+ZtaW6RNfKNELMf6\nyJH62Zab730nNqSArYxH3Y+i95GIs9f9Bs5e9xsLrz/1kZ/ozMTdtvub1+rdtjUAzpLaXfax7QLd\n0mOUu4cp6MQXC3LVATmM+eR9Co4cvwTzrxw1G9jqi5hIWXjVUWdkfh44uRFtfeqNmBC+b/6nDkOb\nv0tZQyQwJenI8bSpUEjaTM7vY33R8s1s/fvCvjiIjvzwCem7pJAzvcERJWJitSJ8fLf5mxohsvC+\nq3aIzm/HRCjL26o+VqlFIgsjwGQhVhmKMDKPw7Tx/fox4N+7WOR0tmE/HI5u531ifNL4hyTD1lNn\nbO2xKTMhgsj4x/b2uVPMbTphRHbAp9H/FpEi4qSiO9GYnsUJaiFSxCR4+IgilmMdc9FAEkpcwkhM\nXrzr4oBy8eBxUUIJOz9l7gUg3OeMvzE+h2T4jrnHTfu5Dotyz4sA5PvIm1A72XcT7WPbz0J9Ung/\ngNcA2Ia6kNS3m/YTAVyBxbuH/4hM67InYnzL+INBG+YqkBraNzVNz5o6Ri0ATXnf1p8rZS5iwi38\nX3D6Tah/s/kdXmkmSb/Xz30SYF8sGJIvHgO3E01TF1klpiK4RBGfeiIeqTNqnQtT6oxP2kywIELx\nyzbfTpm/ycdRsldXq5GFEbkAq+xj5L9lPyi/LxdeFdEiACaLrgLT1z9qrVPd9Y0uWsR1DL1WoUlZ\ncNW1Ao3M1QD7YUERfphFEZU2aonY2lIJIi4RREdOYaREUSSTICKIFkZcJ76Qiwdqu6t/hZ6IIkw+\ngkURQS5xpDNhBAgTR2xtof0B7awEmEvoDRFDHH32UQxJ1XcBogiTh45EEY/aDCGiSKQgAtBqiYTU\nEQESCSKhNeGotaY8hZHiRRHAfk5jUWQB9sN0ukifaR2vKJEU5Co6mlMQEduZhBFTKk0JFCiIiH6c\nwogtlSbVMrsM0wNypdPIocBUxMTPJY6IC2ujOCIuyk1pMIC51oiujdKfqU+XYEERTULS/nKJu5mj\nQ4YohjBM0cSkFSekNUGEIoTYUmTUPlSfKM8vpfkkJZXGd6lea20RVRhpA2NdEYYxw8lebRMaJZJb\nEKFsn6GOSScTvRYEEa/+Qu/gxtw1ppyMUywDzDAe7Gvq2+fq25ekS/aGrCqz2tBu60/ezkdUPZ3w\noELZv+kzuLa1tTmOy3Ovf23vBJGcvwmGaZWOlkv1jRJxoS5pK5gQRNTVYOR5HmW1Gd2qM6b31X5t\n+5XGqluZxrQiTTJCVqIJXYq5E1KnpTFtwqKIL7EFVnMSK4hQ+pGFEdnZEj+3iNppdZJHzOVMLYgk\n6TfVMruZ6cnSvExPyHUhGNLvXqwiXSSTLrhtwkiMOOISXXSPFPj07Rpr6DGQ+zbgI4b4CiKlCXkM\n46ZHF26UC+LAAqs6KFEiC++Zltw1RSabxBCX2KGDIpCo+9eMTbdUsE70oaQWmVDruVjpJCJ9Zpfm\nZRRmIn2GTG5BI2eUSCpBRO7PVmPEhm5FmjZWpjGlzhDIJYjI/VtTaXKk0XD6DdNzZjalBvBLq1H7\nNvVv2lcuKHWJYuofEfrvY+0QFkMYxoLHhbPpgtwnSsSVNkMWRHTvmzD5b53PM6XYyHNAQyoNsJhO\n45NGo8OUQjOFujxvKI7V1hjGl8GLIq3XE0lNF4KI3K9OGCmhvog4YfhEUhicp0kQsZVeCtGVo4QR\nEzFL51JEk4KW5qVfKDyedRxMu4QIGD59+/brI45YI6hs4ghAqzliapf7F7SRDkf1FbGFoFkM6RT2\nxcwihd1lt9x8k1NndLgiIHQRFEZBxBSdYbuIp/ho2cYmkOgEEIswIhDCyCrsWSi6qhNDTLVFgjgT\n7lqnZ8BccNXEwOuKsB9Oz+BFkaTkWoY3RukMEkR0nsUQo+iKGAm5kA9gauUZKg7RJEQQsbW7pgek\n4qs6ehD1cfLPXyBfiDCML7nEkdB+KeKI/HsIihwB6NEjNht5PzZsk/IYcZTqu1gMYZhhkmrVGZUA\nTSY0SmThvSZKJIkg4hBCnpe2eYN6jGwCiUkAIRZfBTAhjAhCo0W8Cq6uRZYahu3xVvitQsOUAosi\nglo1+1YAACAASURBVC5rgZhwOQWnIOIjqz6JYGFEoEubiSToAoh4RzRUELEhtrWdp63CSOpokR6I\nKQzjQx/FESAyrUbgSp+RbQQ+0XQpo8JSCSEAiyEMw9QEps6kihJRV5sB4C+IeAghuvenxBG1T+Ev\nA4URVxqNICpaJFUKDcMkZNCiyETqzKVV/fyZEW3jaxv7jxPsb2tsf99gq/7wH23s3z4yR4nIgsi4\nsZ+T+rcKIu9unj9hsZH5kGRPEEbGVSN6EI/NMtCOI4Cd1XbM4wiuGm0k2Vdb6ufRHzsMm+Nc/bTZ\nzxFS9xNHxsf2e3ALIxvn679Hb6SNpfosgGXA6D1E+zub/m+xGEknxOryxv5excYgulQXNPYPucey\noTroNmIGz/ZqJwCQf982e52IsaXaDQC4aXQeqX+dvU0c2VzVQvOO0bSfVMUR8Z1/YHTihJ1JHNH+\nnizRIxO/V4JIsuArb2red4glC/7jAwF9O6j+srFXfY2M5HNMvsYkhOiOvU0MMf1fTZN8n++Z7Tum\nI+VvxGbPzCqiyOqVzfMdxO187C9snh+kdX20auYixPn5p5s58fsUe8ONuGMXrsdPjj+MN43um3hf\nGwWCfdhWfRkAcMfotwA40mb2ANX19Z+jTZKBQRBp3AFGigswCSG6I2mLHqn2AdgHLLgbhzCyMHbF\nLejSaAAsHJv3ja7QDxiL0SI/qS7DsVeW4rgHHwHgiBY5E8B/lf6vumgRNYVmT1VfxR4lfm+s30td\nDk/O38itxD6ZNhm0KNIqh1rcl1UQ8U26023vGTHiim5o89jIGCb9RyyCSEyUSDLari3CMD2m1MgR\n4J+sds7IEYBeODUkOsQVobGMaEeB2gfRh/mk6XFkCMPMHpQCq75oo0RcOCJETGKI4OXm2XSjzRg9\nIs8JbcKIgi6NRqCPoNlr9bHzxx/GUWMrw5TFXNcDiGR80viHxkavIquxS+2G1BMJWXEmacqMDYMw\nIosichij7JBPU57F383rk95c560IB2t7Fn9P1RRRC63qVp4xKPW2lWZSiyK2aBFrbRGTKBKyEkNs\nUUNb3wq2i5VT5l4Awn3O+L7xRSTDy+a+GLMfJozxLeMPdj2GBXIUZY3p15VaI0OumxRaODXHMt+h\nwomHkJszPQaYLSHk+rlPAuyLh8gYuN1hknI5Xo+CHilqiuhSZ+QhSHNOVRQR6TO6eiK61BnbijPa\nWiIBaTMuQUQ3H7Ud8SlhRPavcttq+/tCFJFTaES0iPBnsl+Tfa54f4/8nrQKzUSkiHptpH5g3XWQ\n7hLHVZeRXGw15RUApabI1QD74aLgSJES6XWBof7SdpRIcNFVhmGsqBeiqUSS0H7VC3VqcVaBViix\nCQo+USW5CYhgyx0JIsghWJQogjBM0WQusuqqJ9IFNkHENhe1pWY//5QijJiiRRIQveIMwHVFmOJg\nUQTI+6Ns9QefKkrEgpxC47s0b6Liq1Ycd09tUSK9IKRwKhdbZWYcefKWMoqkDZEE8BBKBKGpdK7o\nkwwper4rVpUmguTsl2GYPFB9ta6eyAKuqDtDlEioICLbkIURHaaiqwq6gqsMM2RYFOkTQcvvhmKp\nLUIlQgTJFf7eNq6Cq0Mj57K8fOHBpCBXFInat0+/ugv9EKFExXsJ88SiR6wviBFAABZBcjHrn58Z\nJjafba0nYkqd8cQnWjlaGLFgqytCRV6al8kD++H0lCqKXIdaV13RvL6nw7EwfceVb4juCqwaU2gS\nhzoyAPz9isu+6/Y2KGEMWdFNLFIIJaYJS2g0iQy1RkkugTIlscIHkHdyyBPPLLAv9qPr/cfhqieS\ngtxRyDEQokRC5qCkm26JU2hcS/O6sK5Aw7QN+2GFEkWRmwF8BcDXm9fbAFwM4P7ORuQiJEWGcKHu\nRwupMza6uIjPUTBwFpmN9Bpfv+Ky77q9DUoYQyfkEkpMffv27yMk+BR5TUUKoUNHboGCBZBWYF/s\nR9f7ZyQmiqza8Jjj2wQRUa7TVB7XtiqNNVpEN++zzAXVpXkZF28FrdhqZ7Af1hAXH5WHTVj80ADw\nMID3dzSW9gledSYHeYQWr1WBKESKI66iVrFRJEUs88v4+hWXfdftbVDCGIph38J6WNOP3P3H7Gsv\nVrX+yPX5U5C7f8YJ+2I/ut5/7/BZjlcnGpMEat280zEX1UWJUAQR9W+fPgC4a0Ulv0k7SXChW5+a\nhYwv7Ic1lBYpcrbmvRcBnN/2QGYLz8oXcrHVGUGcdIquDyKHSTIyvn7FZd91exuUMIbe4Lqgzlnc\nNYbYcZUoJJQ4JmYB9sV+dL3/mcZaZFXgEhQsggRVEJHf840YmcIzqttWbFWXSpNkVRpfTkd2YWdg\nsB82UJoosgLAfuW9A83zqwG8RO0oeTRC0cREdAw0jiFBao3uyMxa4dQF+i24+PoVl33X7WQ/GEEJ\nYxgMoZPE3AWnSxYQSh4bEwz7Yj+63n/vSbEcr7XIqo7IOna2qBAfYSQohaYhRbFVb86E+5LkDHRe\nLWAAsB82UJooshyLBVQE4kCsQMwHv7Sqnz8zotlf29h/fOSuGbKzsd1I7PvRCjgE4BSi/bgCcATA\nF2n2+FDz/AmLjex5bgVwgsFetwrNRcB4HpgzjF9edea25tjcQfusO6vtmMcR3Dz6HbORpLxXWwAc\nAkYfcPf93I+ADc3fDzTPLv97a/N8jfSeKWrEdtR1YsrCWIjFVqvH6ucRQKoBUt3Z2H8ApLoh1eWN\n/b3uvgGguqCxf8htu6E6SOs0D75+xWXfdXsbE+EsY9he7QQAXDXaOHj7FH3bhIGhfdah2pc0Ftm+\nI9gX+5F4//Ll9JXN8x3EbX3sL2yeH6R1vaeZJ662zBPlVIpPN/Z/QZtX/qS6rOn+owD0YrP83jXV\nEwCAz49eM2FjqidS3Q7gMDC6xDwGkTpzIYCXYT6KqiCim4Pa6oz8LurZ/NSRN9zYqm6vn0efbN5w\nzBXrY/MEPjzaYDZqEMf9TaP7nLYAgL+q6oPzPuK1EeV7M4Hre6mqMjl/I7e6TfLBfthAaaLIAc17\n4kCoKhEA4N+23rbw9/y638H8urdnGFYgIQVYW0EnBbwc1tUz4Lw/Rsto1yt4dNcrAIB/2ferbPt5\nZteP8cyu52wmvn7FZd91ext4j+GrW/9h4e+1607G2nWzlWLHMKUi+8gD+37Ryn4MsC/2I2D/8l2K\n01H8MnanY7hF8121PBRSleV0zuZ1KTSRiyX4rEAzwWko+FopNU9hMYzoxWx7YT8czlzKzhJwNoBv\nYbIArO49wfik8Q+1HXmlz7h+kDHtpjZTeF1QodWQWDJVGLElhaiRIlisKSILIrJDPc3+fNKbf7Sg\nzOue1fdEMayTf/7C4olmj/IMTLcBC8f6uR9NfwxXkVUbvmk0NnttpAhgPkmZlHxTiosrsoRSVJyY\nPmNaBvSUuReAcJ8z/uD4FpLhJ+euV/fj61dc9l23t4G3L6b+fxiG6RaNj/SBfXF7vtjbDwO3W7oz\nJV6EQpwFuZbk1c1z1Gmn7uabvHtpSV650KqcPiMKrcpRIas07y3OO+uJpEifmYgUEXPMp5TX8nvA\nwpxULrIamjojY/tPyodlIn1GzOHk91ab3xPpM6KmiLz6jIhiFM+yICLe2yO/9/xi1OPEkrzqNZJ6\ncHTXRLpLHlvK0tHnLY22ncfi+m9eDbAfLsoPl7b6zBOYVoRWoK4y2x0lr38ezJmgndA0goiL3Mcr\nsLaFTngIqQ9CPXLU/SQTRBgTvn7FZd91exuUMAaGYYYF+2I/Eu+/6CVC7ZhuGAagS0uk1DCy1thw\nzMtkccI2H6TIVlRBZALqvJkwv+R6T72H/bCB0tJnAOBuTK49fD6Au3w7OenNP5qhYqsxlYcGWjZ0\nNaJDMtXswoEeKRr9LbIqcPmVNQDOktpd9l23t0EJY5hJeNK5SO5is0zrsC/2o+v995p9z6+MLra6\nb+lKv2KrhNVQbDVF3wqzfOUjiFiLrAJWAaT1IqsALVCDi6ymgv2whhJFkRsBXIf6w69BHVz1+U5H\nxEwyY8vxAj0RRPovXOTE5VfWA7gEiw7XZd91exuUMIZBUILI0cYYcggYIeNmIaVo2Bf70fX+e8ex\n779qIoUmlL1Y7V6W1yWArIGxtoivMBIUIaLiWTfEtBwvEB5xkxxejjcE9sMaShRFAOBjXQ+gM9ZC\nHyY4d4qjrkgOAlJnCJz0Zk1xjxgio0JsJ6YUYkgvBJXZwOZX7mkeVPsS2tughDEUT1sTwRLEFRsx\n40spZLjGwaJJ57Av9qPr/Q+CPVi1UFdEsBerFuqKCPZhpdtH6OadjrnoG06frC0C0IURX0FEW0vE\nBEEo2RuRty3XE/EiYcoUo4X9sEKpoki/CKmeTAiv86Pjxbu7KHCeIEWGAdcoYRgCuQWJHP13KaL4\nCg+UsaYSM0z7YrGEYRgbvzz9OOPSvBPIc3xLtAjgFkZsBN900837eC6YkB7X75lhWBRhhk9kfmdO\nvIusMgyTnZxiQoq+S48YAehj9BEicosZuv5ZKGGYSI4+716BJpankbXIv3ddEQO6aBEgbA5KEkTk\nKJEE80qdj/RZindi5RmGKQwWRfpEqyk0CVJnIk5QpPDFHpA8daZwJd+0HC/DlEqpAkjsuEooNE5J\nlbR9Tuo5IKdYwkIJwwwP3RwzeN4p33iLiGAW80WKOGKbWzoLrDpIUWR1j4dQwjClwKIIEJb+UkLf\nU7SQQiMXWdWtG2+jjaWNHWGKJ58KPJe4pEnxFC6kUOnD3XGmH+T6LoX2G7JdMtEj5PxE9OWuMbpE\nk1hBQt0+V0TJrIkk7IuZWcFabNUlgBhSaEzRIgJX1Ei0ICLPCS32tiKrTPewH04PiyIlYiq22hp5\nCqyWTtspNMbUGYZhktJ3EcRbAMktxPv0bxFQdJ/LVygJFUlyFXadNYGEYaJ5CtMX50/CPRX9Hkjh\nuCmW5U1NqDDiJYj4pM543jxLck5t7YYxw9CYHVHk0qp+/syIZn9tY//xxt4W8XFbBRwCsJHY96NN\n328f0Yqtjhv7uREhheYMAO9u/v4EbTz4EM1eRImI8UDzedUJ8G2N7R20Y7Oz2o55HMFVo40k+2pL\nM5I/Jpmj+ilwBMADNHPqkdHaus7V1U/r59EbaWOpHgPwHWD0HqL9nU3/txDtL2/s7yXaX9DYP+S2\n3VAdpHXKDJqd1XYAwMbRVdntKZO23Y0DOW90E6n/h6u/BACcMdrhtH2y2rxgSxnL/uq9AICjf/c4\naSzYoJyjXFzbnKc+TLQXvptif1sFLDOMRXfeFOfXB6btdULJkj86BwCwYvSFqTadSCIfexPydk9W\nmzGPw+Tvge17o47nm9UNANr5zvvYM7POlc3zHRH2JlXiwub5QVrXR6s64mK1w9c8g/qm4acb//E+\nmi87duF6/OT4w3jT6D5tu7oCzbbqywCAO0a/pbVXi61W19fPo03NG3IEiVLTrmq0mdFKmjDyu83f\nd4AmiMj9U1gY+06zjbzyjDg27xtdMWWn+r6fVJfVf3zhG7TBeP5fscdyLSI4+rz0wvN7meQ3YuJW\nYp9MmwxaFDnpzT9qL696WeL+bNEiTmHk1wH8e+COA6JEXAp06mNDxRDaOD+PWhnR0Fa0yMmnAvip\noTEkH9S15FoMOftmmIQcxlIAaSNDQvs6jHny9j/7wanAoaV2I5+7ajrbQx7bh3DIsF9bqo1qb7A9\n2hwbcT63RZPsw8qFY+/DESxd+F+ljPYQ30mGYdrj2PdfheN+85dT7+uW5aWiLbbqE2GxBlBdixAz\nTOLICc2zSRAxpsuYokQcqTO2eiK1f/zuxHuuIqtHXlkKUoUSdeKtu/7RVQc4hIFfyTJtMtf1ACIZ\nnzT+odWALIpQJpwuG1u7rc2kFLtSaEhFV31qjFgEEbmWCDBZT0R2rKdp/laexYRWTDxtz+JvoeKf\n/PMX6k5E3RBZ9BB/P6t5D5g6zrbaIrHCiE3Rd6bNmE5ytpOvTbiwbUc5oXuIIrZCq6fMvQCE+5zx\nRWP9XR6VL85dFrMfJozxB8fEkKQMlJQeQ92m1XNTrL2NkFpRlG0INpRCrkC4yJEjFaaE9JpPzl0P\nsC8eImPgdoKZa6FXH4jl5F2rz+jmPbopqVrLTt295DdkUUROn5FFEXWOKb832V5PJmVRZCFaxDTP\ntMw/bbXvbNEjAKFuiK8g0rwvCyKinogcJSLObfI5ThZFxPtykdV9zy/aTq08I5+HQkURV7T9RKSI\njdS3QylL8l4NsB8uCtbXBJSCqK0WTYW7tghpNRqT0KF6Fw9BxIROEIkkqBq4o9iqwFZ0NdfyaMGC\niI1QQSQxvPIM0zaDFUNSCiFd1Bdx+X95G5Pt04520CJHAARHgOSIHMnRJ8MUT6pleUUKjcBSV0SO\nFpHriuiiReQUGt28UxRclaNFFtJo1HQZoL5Yt6TRTMzblPmqLHoIgcRLCBF4CCIyugKrrqV4vc+Z\nXE+EKRAWRVJiE01sbaqz9CF4mV5imoxLEEmw7jkFccJ67vWvXYwWoeCoDh4ijIQusxsliAxkBRmG\nScHMiiE5okZ8tqWK3cS0mAlblzhisWFxhGF6Tmix1UhcN960S/ea0mjkuaaY19uEEQFRILFup+Ip\niOjSZkSUiHyOc53vyFEiMpQokV5BiRJhSmTwokirdUVyQFmJJlgYIfSrw3cp3hyIaBCfNeENJySX\nMJKCqJVmcqTNUNpd/TNMy7AYErCtr11MHxRhw2SXIHpEPtauuiMAiyMMEw9xCZi2eBpG/+BahYYa\nLQIsCiMTRVdtwgiaNjmSRIdFIJlq16GKKJ6CiC5tRkfrUSI+lQAE5NQZhqkZvCjiRYoUmhzRIl0I\nI9SUGYEpdUatK5IT+YRETKER2ISRWEiCSEsRN32G12SfXWZSDOk6aiQEaoSISwChRI84zik/+8Gp\nWSNHUosYfRJH2BcznRKYQiMjp9D4RIvIwohgShipd1Ajz+11aTYCVwSJDUodOg9BRCZFlEg2QiPs\np2hjeYX0sB9OD4siQyKVMGITREwFVomoRVZbgRK+iDzCSFSECNCbtBmuJ8LkIOdJv1NBJJcYQhVB\nkk0mYT8PxEZ/2LbvuOZILhGjT+II01e+g7TFVjMSkkJDEE1NmKJFXGk06jK9xnmnKbpZ9qMU/+ya\nf0cIIrooEVdxVRuzlTrD9BkWRdomZ7QIEC+MUAWRRHQy8WtJGCELIqFRIpw6k5PrUMcarWhe3xNp\nH9sOAJcAeBuAGzXvrwGwE8CLADYB+BzoiWVFMsjokFBBI1YISSmCUPrW+TRXFElodAhRHKGsVBMS\nAZIjakT0C7A4AvbFwyFVsVVPKAVXJ6NB/NNoAE9hBDB/K3QCCWWeqJvXedQQAfSrzZgIOke3lTrT\nGYOtJzITfpi0fHTfoS7bl4xcqSJUUcI39YWyXQl1REysNvy9xvA+YD3BREd3+PTBxVVL5GYAjwO4\nH7UjXgvg4gj72Pb1qE8QVwA4UbP/FQC2oZZNn22eezsJ39csxl1Cv9RtfvaDU+2CyNMwTwZztAH1\nZFo8KDxDfFCg7Ns0/kzHw/k/asj5PQlhxkOk2Rf3gkTpBznF2wRMRkvoJ2hTYoMpYkO0yQ8dp2u2\nM/Vh284iiPikzchRIjJBBVZzwvVEUjIzfngmRBEvKIJGrOhh296lBvsII+rDZe+zT3mcrnoipZJJ\nGMkeIQLMehRHbjYB+Lr0+mEA74+wj21/BMDHADwB/VrzYwDLUX8rVgD4vGWsxVLahWWS6JDUF/hP\nW9oAuxiRQuxw9eM7JqBoccSXnN/fGRVH2Bcz+kgB1d+ouozFB8gX7XsMqSCmFBHd71AVFbyEEdXO\nJpCoNjYhxbB/myDiKq46sV3X/qhwAW1gzIwfnp30mUur+vkzI5r9tY39xwn2tzW2Hyb2vbMCDgF4\nu8FeTe/4cdP/KY29K5Vm3NjPKf2bhI9xVX+FVHuxL5UfV8C/wjx+GXEcH7DbivDEndV2zOMIrhpt\ndPe9Bqh+t/5z9OducwCoHmvsz23ecKTSvK0JMnrA0D7R90+bvikDOV0zFhnNuan6UmP/HkffzbbV\nlsb+JnffAFBd3tjfC5LoUl1QP+98zF1PZEN10N1hGZytee9FAOcH2se2U3mpeRTNzmo7AGDj6KqJ\n900TrN3Nl/i8qS+xHtXeNXF7stoMADhjtMM5FgDYX70XALBi9AX3hfUGyzlEN2G/rTkvbCTaP9rY\nn2LxOPJ5wnRe0DH+MYCLGvtvEeybvp+R+tadO4Sv/XEFLMPkOcSUBvM06nPmMkyfY03byOdvy0o1\nIopU/r/KmFJYdN8b0zax32HXeEy/KRPCvgewL87Klc3zHRnsL2yeH6R1fbT5vS4hzqF3N/ZrifZ/\nUuEYgOMeeZhk/uVqGwDgA6NLrXYijeay6icAluAb/8/RhTZTKk11ff1ydEvzvuniXszjbm/sr3YM\nuhFCpvp3CCL12IGPjqrF9y1RIuLY/NZo8XtgjBJZ/876j79p/k/qeUwVsv57M4bzpP+rLXXG93uj\n/V7aopxCfiMvA7iGYHsrsc/OmSk/PDOiyJJlh3H00FKaMTVaJHQVGqCe4Nmw1RcBFiecuYoS2SJS\n5LFTIkN+PX44Ms+9/rU4+ecvmA1Mq9CsBvCYxt5yrOfn6+eT3xgwUBOxK81wlEhOVgDYr7x3oHl+\nNaadrMs+tp3q1DdJ/axBraIXzyALqYbW/zjkuY3JHqCdF6i1p2x2tghDeQy688kh1H5X9YcmoeMQ\n7HVFAlaqEf9H10SopFojufsuCPbFWXkZwAnt7zZnXRF1FRri8rxqbREBpehqHV1RCwsi6sJaY0SF\nsgqNDp9Cq7BHiByWJvWUtJkjIF5LMUNgpvywLuwkJ5TiJz7FXMYnjX9I3jlpiURBquUOY5dV9MkH\nT4VNEFEdMSV1pvlbru0iTijqs61NnJwALIoi8rK7ewx/m2xUcofjUcUQWwSjSxBJUWCVsh8J6soz\np8y9AIT7nPE5429oG36x6wn8Yte3F17/5COfitnPJQDuxqL/AeowvP2oj8peT/u3RbbL+9vWtG1W\nxqDWs9+BOh/T5DtT+2EAGH9wfIvDZJJBCiJAmCji+77NV1mjCBMu267iStH0Oa8ITEKH7/uONmrd\nsRAhIqd4Edr3J+euB9gX+7bL+yvVF4+B2y3NKqlXoDGsi6viEkVM/kC3Co3qV9QhKL97eXleIYoI\nVkv/Yvm3tcrwvvr7k5fqFcKIYEIcEVCrzejEYx2a+Z2rfoipsCp1tRlyLRFXlIjuvGWKEnHN1b3q\niaRcjte3yOrVAPth33Z5f6n88AJtR4qI4ifbUCs/l2Ny4DcD+AoWc4m2oS6ucn+KnZ/05h/RhRFX\npEcqG1e7K2JEQF2dhtKPbSwyLdcPkVV7J7ZoEVvF71zCSGjlcJlYQYRKBkEkBcaL1nUrgXUbFl/X\nJwCV5aiTxEyI/J4DmjbhnFX1mmIf205B/UY/jNqXmk4Anfrh3OQUW4oWRFz+P6cgIvp3RY7kLtgd\nuBRnH1enyd23a79a2BezL6aQMlpE9SuOaBHTSjQqlNVo1N+fuioNMBk1AsAdOeJarlfGMeczLbkr\nxirTqSDSGcUMJAj2w0Z8/fACbYsiovjJCkyrS0Ctkt8gvX64eV3uCYAijMT24SOMAGHiSMhdPBOO\nSWm2SZxN8FDtYLAVnzWVOJIiOgRII4jM7mo2FwN4p8PmAOplvfaj9lEy4rUubM9lH9vuQijoyyX7\ng7B/Yzr3w50XaVNIMp5ZFkTk/YQII6Y7oSHpMiYc21CFkRCGKIwEwr54ks59cVpURSIQkz94Evpo\nEdcwiMKInEYDhAsjAPzEERl1nrbH8L4B3VK7rmKqKQWRIDqLEplZ2A8b6KKmiKn4SariKulIIXhQ\n+0kljABp78ZRwpoDQ5apWCd9chSIiilaRNeuEhs14iMkxQoiKSk0SiSS+0GfRD6BaaV6BerJaIh9\nbLuLMYBbMOlT18B9mdyZHx5s2owJ33NIEYKIPCOlXIVo9mcSR3ILI4H1RQCaMBIqQrAwAoB9sY4O\n58TfQfoUGiKh0SI6YSQyCi2FMAJgShyR02n2LV05kVKjEy+0QollfqjrY+JzeUSHqK+pgogKR4n0\nAvbDBroQRUzFT4KKq/zb1tsW/p5f9zuYX/d26869UmiotJVqkzqKwUVIQdCWluE1FlulRotQ7GML\nolL3b4MiUhQaJTLa9Qoe3fVK+zuO525MhiifD+AuqX0NgLOkdpd9bLtAlxN6EID6Q7gEk3cXdST1\nwwDwD1u/uvD3yevW4pR107PUwQoiIeJ5CsE9qSBiujUXKJDYoka6FEYSUKIIYRvTj3c9g+d25arK\nnhX2xZMQfPFD0t+no52JTAaodTRMeESLAPHCiHgNQLK1R42o2IQSlwAi4xJD5LHqXvsIItFpM4OJ\nEqHUE7GtTV80s+CHjR3mxFb8xLeYC+BZaFXgLYqkKrqauq+u6l/YokQMbboiq/Lf1Pe0xVYBezFV\nU9FVFR8xJRaKSNG2IJI5SiS20Cr1t/6zuf8Ysx+BKG63BvXduXultk2o/dW7ifax7WehPim8H8Br\nUOeVfw2AqKR1IoArUE+Y1wL4R9jXZU/thwFCodWZFERyps0kKahqW+/QBVEgsaXTmO7u+hReTVx0\nFehv4VVq/7GFVtkXF+uLPQutAnkiRTxSaGzRIra5KKXoqm4ogYVXAXPxVbXN9N4qyyTTJJS4UAUQ\nFVd0iPpeMkEECCuuCrQgiuSIEvEtsgrEFlplP5zMDy+QQhShFmvRcTHq4ienof6An8XkCWAN6p+V\nnBskEySKABlWoklt53MXMaU44iOIqK8tbbaVZ0x/B4siAG0lGh25hZGUAgW1r1QCjMQMiCJ9o0s/\nDDhEkdIEEep2wxREYoQQGxaRxFcY6Xg1GmDYwkjPRJG+0emceFCiCBC3Eo1uKJrffSnCiA5ZcSjZ\n4wAAIABJREFULHEJIDK+Yki9zSpjm00QAVqIEgHs1zqdrTgjGLwoMhPEps/4FGtxFT+JLa6SD2pt\nkVQ1SHz7SpVWYxNEfO7QZcRrBRqBWlsEoNUhSYlP+krKGiKzW1x1lijaD/dVEIkidx0RE1ZBJJcY\nIvdvEEZ8U2l6Ul8klNzpNyWm98wIRftiPTnqingUXM1dW8SRRqOirkhDTaURbcCkEGJKqZGxCSUU\nIUTXp7p/13tFCCI2epl1wvSNWFHEp1iLq/hJbHEVL7LUFqGSS2QJFUd8o0MoNoZtUk7UJuqKqIVU\nXeKGq0ArHNtT8RUlqIJIR2kzQK8KrM4KxfrhPgsirdURSVVYtVNBRN5PR8KIiULri7AwMkiK9cW9\nxbe2CKXoqkd9EcAtjADwFkdkTKKGKpbYxA+VEDFEZxMtiPgQeprqZZQIUyJtFlqlFD+hFlfxZn/1\nXgDAitEXaBtcWtXPnxnVzzaB4trG9uMjmpBxW2P/+yP3OE4D8D8a+7cT7AHgXz3tH7XY6yaTOytg\nGYAPE/q/tMKSZYcB4nHfWW3HPI7gqtFGkn11Qf08eshuJ0SS6s7G/gMgRY1Un23szyWM5THJlnDu\nqr7U2L+necMhTiyM3V66YdH+8sZezdQz2VOPZcOGqo4CfmB0Itk2hs5EzGHRmh/WTcB2V1sAAOeN\nbiL14Wv/ZLUZAHDGaAdpTKbzgvG7Js4Lf2HxfcL/Cz8v/KTrvCD7YYogMm7s50ZEQeRDzfMnHAMR\nbGqe7yHYyn07hBEAwB/UT3OOc4i4IFLPUTqhQ35PPfYmxDbq+b7BFC2ifm9sAoTtO6nbLuVvRNf/\nzmo7qV8b7IuT0OmcGLi1eb6GaH9l83wH0fYEAA/Suj56LoDjgSXEOeue5veKEa200X+t6uH8mdS/\nRRg5duF6/BDAf3zs7xZ3aRFGvlxtAwD83uhGpzhyZfVdAMCNo9+balPZi9W4pnoCAHDrSLcY0SJi\nX9uqLzf965fZXex71dTYKYLIsQvXAwCOe/ARfR0Rmb+qgJcBvE/5v5qEffF/Xa3Ym86HR8X3gHhd\nhwtRD4jyHQb8vvOA32/qVreJA/bD6Wl79Zm7URdPEcVPdmCy+MmNTfvFWMydtBZHWYl9wXcMvaNF\nUkd4UO2WAThEsEuJz501YpSIL953umzRH6HbLWueKQK9EItDUlao0RrL3CZeY+AokVkkuR9WyZ2e\nUuTSu0C6OiI6WosQUe+kidcedQJswogJ24o0VELTaCxQ02g4YoQJILsv7o6X03WVIlqEMJyQiBEV\nauSIrQ+Trc3ex1aNDtHZuiJEtKi+1OcrEBzM2MsVDplC6XvhlfE5429ETcCDJsCpC6/mtA3BJWq4\nRBDltWvlGd+/jcVWAXvBVd1r27ZtkTJlxsfOZ98NoaLIXqzCO+YeByKKSuH7ttp1Er85F7MfJoyF\nQqslCiI+2/WuuGp2UQTwE0QEAYVXfVajGUjR1ZjtQvuPLbTKvrhYAgqtCjouuAqkXYkGSFJ4FZgu\nvgrYC7DKmGrg5frN+wghJnvvlBmAVkcESFtcFeh56kxcoVX2w+lpO1IkC61Gi/jgUxPE11aQUiDx\nFUN07xEFkRjk4lYTdUUAd20RW60RVzpNSnzECB+Ro0BBhGGYrjgD/sLImZicPIYIIrNJqVEZpY6L\nGToeBVdd2KJFTIFpgYVXdREjAMhRI/JvTRc5otrLUH+n1GuelGIIwIIIM2yO63oAJeBdYT4mtcRl\n65t6chrCttP149sekTbT6QTNJRykXP1F7Vc8qAxAEDGdlJnhkX1Vl0BKHZeWIqrs90AISbXCD4Hc\nudu9+n4yA6WAYpGui1ybb/TRftXr46cx5R+Off9VUwKAKhKoIsKCHVZql7ulzIXEtq6HC9P+TNsP\nWxBhGBqDiBQB4qJFgIz1RXxtQ+zl7aDZNkYwidg2d8ixwDtaRLwHzftyH0B81EiowOJbl6RQQYRh\nUpL74jFLPZFeEyqOeNYT6ZrMK9SUAEeLMN3gGS3iWqLXN2LEVKtIN6yAqBFZTFBTanS1REzCiCnN\nhoJLbLGdN3XCDildBogTRFolV5RIAUIik5TBiCJAvDDiTUjKSy573bax+ORgtzyhVNeHn4K6RC9l\n6V4XOuEkJtqkIEEkFo4SmR34bvfQ6EG0iI6OBY5SC64yjJvvIE9tkRbJJIwAsBZhBRYFBoo4omKb\nK63C3qC5lOmcbIxwoUaHAPGCCEeJMAXC6TMS3mk0QFi6S077FCRIx1GPZe7JXlQ0w2qErRgjWKN5\nxIwll33AuDhKhCmF3AVWSaSMBhlkZAlTMixcMt3geac+Jo0G0F9wmy7OdUMz+GZXOo1gD1bpIzA8\nU2AEPoKIrX/TuAB9dIgxXaZLQcQbjhJh6AwqUgToII0GyJ8eE5pOEwJFDOkoSiR6iV5XVIirPSch\nokzhgkjyKBG+iGQYpiOoS/POBOyLma7xXaYX8I8YAcjpNMD0KjW21BrALlKmWJJXHYO2v5joEKB9\nQYSjRBZhP5ycwYkivSFEGJFJ/WMIEUMM77kmjrmiRqZqi+igCCNw2KQmtyDCMIyTbMU0c09c5k5x\nLMsbsgLNQLGl1iRMuyk5hYajRRg7uVJoEtcWAdKtSGMbHjGdBtCvUiMwpdaYiP2d2sQQU4SLlxgC\npBVEKHgLIhwlwvgxSFGkF9EiodvI28qE7DunvUSuSZ6zrggwHS0C0CJCckaNxAgaIdsOIUqEmWmK\nSJ1hiPSsyCrDMGWSUxgB3Mv1CohRIwJq9IgKVTCh9GUbk47OBZEiVl1jZp1BiiI69lfvBQCsGH2B\nZL/kj87B0UNLgc+M3MaXVvXzZ0Y0ceLaxv7jI1pB1dsa+w9bxiI7a4o9tX+dGCLG/8C0vRolsr96\nL36JIzhjtIM0lJ3VdszjCK4abSTZb6gONkM5EYAhWkQSRqrL6+fRvU2bI2qk2tLY3+S2r+5sbD8w\n3Y/WXu3bQfWXjf29drsF+8sBLANGDxHtL6ifdz5GE0TUYy/QCSKbq97dqb4O9bdmRfP6nkj7mPbl\nADYBOIDFadyNkePthN3Nl/484pde2K8ZfYpk/2S1GQDI/sbrvPA0Jn232qYi/OrvE/3wo43964j2\n48Z+jmiPDzXPn8hg79m379jFsXl7gnOaDss5TUak0Ph8b3y/k6G/EV/7HsG+OAu3Ns/XaNp00SJX\nNs93EPo22ZpUhwub5wcJfQM42vxelxB+308CeL6xP0+x10WNfA/ApyvgBAB/pvHzuqiRP6n7P+6R\nh6d2rwokP6kuAwC8aXTflK1O5LDZ65DtXUIIABxb/876j78hnNMA4K8q4GUA79Mce1UQ2d0c9zc4\n/k9CELH9X7VRIrbvjU618fkOh9jbflMm294wE354sIVWU0QnLFl22H+j0CKlCYqbJqWQ8WS/0+tb\nZHW15bGseajvtz1GwTL/TY4cPzM6qY2bATwO4H7UjnQtgIsj7GPb/xTAx5q2GwGcj/qEEDreXnEE\nS7seQruY7nrqQr4Fc6c4Om0reoOwH+dYZ5uZ+77bYV88OBIXXQXcUQYvW9pMkQ2mbXSFRhuMxUkb\n9j2/EkdeWYojryzFvudXkkQLH9T+TYhxkgupCr4H83HxPY6C3tURmcnUmZnxw3MUo4IZnzP+htUg\nxUV1cK55TC55lwV0AuuL6GqJqOKUTqxy2bhe61JotLVFdEvnynRVZNVEqKASuPpNipVmbGkz75h7\nHAj3OWP832Oa5f86F7MfANiPRXUZANYDuAHAuwLtY9ufBrANgIgR+mzz/AeB483B+KIx7U6WLzE+\n3Gdbp593+WRbu6nN9L5pomiaeAqstUWAvLVFIgURk+hjK6ZoOlf5LCdPbWvwKbYaenMmd12RL85d\nBrAvDmkv3RePgdsTdpdred6A5b5daTSAu/CqzUXZRGfbcB0+Q5deQ0VNvYkRUWxizQK+qTKA/ZyU\nKmVmsLVErgbYD4e0Z/PDg78tHFtfBAisMQLQUmNybBtDQkGEQqtFV3X1RWS6KLKqI3Z54AByCyI9\n4mzNey+iVqJD7GPb0fy9V3q9FoBQIHzHy/SB06GfMK6FfRJKKroqSCGQECNQXNEhtgsSEwVEMjJZ\nYV/cOYUUXQXi64sAiy7PpwArsHiNTSzEKiOLEb4CSYpIkigxBGBBhJkpPzx4USQVwcIIkKagam5x\nhDrB9JiI5r7bJSAVXPWhq6V5Y1NtOhREOuX/2wV8Z1eq3lagVpllDjTPrwbwkqd9bPtLmHT+ZwM4\nBuCvA8fLlETIuSFaGBGoVwZUkcQzFScmXcZ3yU0gOhKEiYB9MftiMh0JI4D/yjQCVyFWgCyQAHFR\nJNR9GHGdd1zawswJIj1Km2E/HOyHZ0IUSREtAnQojIjtZWJFkoSrz4RGiYRCXbbQGC0CuFNp2owa\n6UgMAdIJIq1EiZi+8yesA3573eLr/+sjMXtZjsmwO2DRwa7AtEN12ce2i/2diDo0cCOAKyLGy4QS\n68N9MUWLUCALIzKmKJKIeiQUQYSjRKZoY2neKNgXz5AvzhUtAhQrjABhUSMAWSAB3AKGKpqQBQ8T\nlPNXTjEEyCiIzCDsh5P74ZkQRYCChBEgzcTa5GzVvlNMHgMEEUrtkJSYokW0wgjgTqURqIJFrEiS\novCqzKwIIvEsB2BLwDzYPB/QtAkHq6rPFPvYdnl89zSPxwHsaP72HS/TBakFFVe0CBAojAgSFGaN\nFURCokSYPsC+mMlPjDACxIsjgJdAoiNaBFHH4CI0VQYoQBDhKBFP2A9ryCWKXALgbZheIgeIX6Yn\nmCKEESDvHceUd9AcffkIIkVCjRqRMYkaOrEktQAiEyGGADMniFwM4J0OmwOo/dV+1CcLGfFapzC7\n7GPbxWvZ0e8AcBdq3+javkhfTCX76lMpCfXrtu1s0SLZhZEIulphJub8N/Dok0KYVV/8rv+/vbON\ntas68/vP1AlkpODL5UXqaKwBQxDlGzBUmqORQGOTqplIA7XdgCq1nTZ2XMGgJtQBOiMr4UNjwoRI\njKOat06kqoJwMWGqKG2AyVxL1SEKAUZFxR4BhsHJVBVwbRMNDB40tx/WXr777Lv32Wvt9br3fn7S\n0bnnrOeste4+5/z32v/zrLXosQ73MlsEzI0RCGuOgLNBYoUvIwTazzEghkj/GKsOt2br+TZFtqLm\n99xA/VfpXuBHwI+Lx/tRb84hw3JnfBoj4LAzTaqFVE3IbHBomkpsnS2i6WKOVAlpgFQRQ8SWQ5hr\nyEusd5oXgWc7xruWbwOeQYm6FnS9ivi5c17/Imowna0Wh6ZXhkpItEERyxwxNUS6Zolkdn4SrBib\nFm9FXQ7upvc6nKExAn6m0sD8rBEwN0fAziBpwjQbvAsmHoIvMwR6bIgMlrHp8Lz+znCWSZAFf4ba\nO/gl6rf/2cWauIPq5Jcsyr3gM5PBeT2Ny0q3HDDsh22WiO/skaYLnqYLdSMjwNFsCM4WxBCJw0PM\n7mm+DeVCa7ZUytviXcpfKP4uO9w3AEul5+pe/w16oMVCwTzdnTegt1mPI0b2RmhDxIVczrGCDX3X\n4vOBuxEdNqDjxa3JhfRrmF2Ym1zkv0G7YXCkcuvC6w23rpj2x+T/A/PskF4bIoPMEulC33W4Wn8j\nMdcU8bENjzd8ZYyAh+k0mpTZIxYDRl8Lq2Y5zcZH1ohPPBo1vd9lJh53obIstqPegdeBp0rlW1HT\nUg4ZxruUn0IJ/N7i8flF+d0W9VfJSotHQ6hpk9pgMBnIhsgasTVbuiysqklsbMReVFwYjBb/45r/\nrYc6HDJbBDpljEDc6TSatsyRMjZZJL6w9QtMzh8an9NlQBZVzZ+h6HArMU0RH9vwdOboZA8AV0wP\nGsWvTG4CYHH6faPYjcDH333RrDM3T9T949P68vLA73XgjiL+Ww3xVUzjdTtt/Slx0eVvNx6bOpND\nH/fN0z9orRtgaXKAT3Ka26Y7jeL3TJQ6H5yuncHmTaPZea2aRjP9YUvFxUKsky+qh9NH2vtiE9sa\nX2OGTD5XxLf1vRK/9IKZGfK7E7Wu0p9ON82N01kidce+CR3bI+6bU6YXdzKNdy1/ubi51F8mmRYf\nnuwD4LrpPUbxtrodUucBOy3+dhH7ZUPdfr6I/82a+Lq1RX5exP9aEd+2xshqEb9hut7IqDVJbizu\nn15ftO71pbrnoS8gqn3XNF2w6GNz2Zz6y+dN22N/xwQ+hdE5EOw+N10/w0wPGv1wYPud0vE9Yqha\nnHRMDPcX91+xjP8Tg9hbi/vvGNZdjjcxRj5f3P9g7al5xsjHhR5snK7p6Dxz5M2JWifukqmZOXK4\nqP/fGOjHHxax/6IlVh+C/1TE/8dS/DzD478Z1q/5L0X8dS3xegj3ZhF/yZx4fYzLx72JGUOk5n1t\n5Ahun7M2XqH7d8Qk/v72kLwYqg7PENMU8bUNzwx//bVHz/z96euv4tPX15nr9fjMFgGPGSNlLkMN\n1nzV5UBuv5Kp987uYvv0J/RH/uP24C3AOba9ciDA9J21/9cPNtNmXlr+JS8v/xKA/3f8tNd+CE4E\n0eIjX3vyzN8XXH8lF15/pXNHs+NTwIdzytuyQeaVnwP87ZzXmmzRa7L4ah112R6rc8psMfk1te0X\n3JhaPDDeWX6Vd5dfBeDD43PW1xJiEkSHofyryWfwPx8tdLaIA6YZI2C31giYbchV1l6XjDhYMz4+\nqDx2xfb8YDPEtt1CvnOGSF+nzZTnE50I1IbgQt0cxyqm2/aU2V+8bk/puW3AE8yKvE5rWUClF84r\nrzsBrF6z+r9aum+G70X5vJsjpugBt8c0YxMzZN4vWrbrjNhs5zuv3bpskTJzF181wccUm4DrmPie\nLuO6jshvbXgRzDSnjlV+f54MlfjjDS7t5EzWWnzj6mMt3bfHVZdtXm+s2W1TYEKXmw48u5gjvjG9\nMDC5OJl3Tms737mWF9j+MOA6RTTUFNOnN9wCosVdyVqH4YGW7vsitDHiMM/E1BjR2PhGXXcrdzVJ\nXOhyLrBN7o02XSbGwqqx1hK5HUSHs6LtZ2SbbXva8LENTzBCZI1AAnPE85xrV0PEF6Y70JRpmkaj\nad2Vpo2qoXFsTllEQqwdMvCFVfvAaLQ4FV7XhuqaLWJSbpIxAnZrjfjG5gKg7YIkkuEhCAaIDp8h\n0/VFwC5jBMyzRsAuc6SMzywS2/ZsCGmGgBgiQta0mSI22/a04boNT3B8GyOQ0BxxxPQXsTajIvRu\nNF3MkjLaQHDOGoHku9eIGTJoRqXFWWOyYGouxgjEN0dyMkRMyDRLRMgS0eGo6IvggIuvakzWGinT\n1RwBMy1u0tFQOt5l2TcxRISB4XtLXk1Tmo7rNj3BCTWQuejyt7Nbk6OJvvTTBNOL+j7vzPKLC88X\nQ0RoordaPHhcL/htlwq4lHC/UF6Kff2uhogJkiUi5MFAdTjWRaTDdr22F+K2F/pHSzefvNFw80nX\nvttstasRQ0ToAb4XWr0KJdrbgfNQX+HnWFsl1nWbniiEyBjRBFmM1RO+fwmL9UtZW7ZI2zQajdes\nkQiENHLEEOk9g9Di2Bjrs49sEZMYk4wRsBuguvwC6cNUcV0/xCZGENIyAh2OtfBq5Ok0GhvzuWou\ndF1/JCQu5o2tEQIettsVQ0SIh29TRG+TM28rHNdteqIQ2hjR5GKQ9NUQMcXUGAEPa41EQAwRoYXB\naPHoMTFXupgjVULPcze9uPBliHg0VlJkT+Z2DhU6MRId7okxAvaLsNpOqylTNiBSGSSuGSxdzynO\nZgiIISLEJuaWvL1DD0pCmSOQ1iDp2zSZeRkhrmuLVMkxayTGFB8xRIQhEdLcjpYtYhoDfswR39hc\nTEiGiCD0lB4YI5DGHIFmc8KXWeJ7+o7LOaQ3hoggzCKmiAFBB9YlqiaFb5PEhwliYjykyiLxNY2m\nTNmISGGQxFrrRMwQQUiIqTGCQRzMDtxTGSS2Fw8+zY7E5opkeQjjpCfGCLibI9DdICnj28xwZVRm\niGSJCLOIKWJIjKyRKm0mxjzTJEQWyBAGel2MEU3VoAhhkqRY8LU3hojJxaAgOGK17pPPLA/fcZrY\n2SNdLhRMDQrJJMkD0WIhOR6MEbBfb6SMa/ZILrieG7yYISCGiCWiw94ZjSlydLIHgCumB53i67JG\nViY3AbA4/b5R3b7im4yPlclNrHjsT9UMmXcsq7GHJ/sAuG56j1FfliYH+CSnuW260yh+3+QwAPdM\nrwPas0X2TJQtf3B6hZE58ruTUwD86XTTurKqgbHzWmWSTH9YX1c1fl7dtn3pEl81Q8rHxgSbeB3b\nI/YCx1BbIAI87BjvWl7mILCn9HgHahG+JeAEsAt4Enizpc/RsdUDX7rdhK0Wc/NE3T8+bY+9o4j9\n1tTMyPj2BP4W2GlQ92XAfy3q/02DeIB3i/gLDON/XsT/mkH8u5Z9eb4Ub2JiLE3gHODLhvWXj30d\n1TbnvK9151mbz03Xz/Dm6R8Yxdt+p3R8jxAtDsL9xf1XPMe/gjosAN8xrPtWx/g2c+Tzxf0P6our\nWSMfF3qw0VBvjtTENxklbxaxlxjW7Tu+aoDY/q/l+FYzpOW4z3AE98+BafyeuVFrhPqOlGN7wyh0\nONSWvNHYzPHoGQwp2kyFzf8Z45j4zNTxnSFx+hMbOf2JjWe2yK3ecqI32SFpuBd4ETiEEuJLmd0W\n0Tbetbza1m9UnlsE9qN2NjhW3Gc4CE9DF12yyrTzmeVwjkV95xjGVfnMnFuI1zUR4n+VLJGhIVrc\nSz6I3J6nDIMuW/g28VrNLQVB+vB3nrNDYmaIxP5sDoLR6HDT3ul9YfXG1cfOPIg5taVMqnZDY3sx\n4WO9EZMYX+2U6Tqlpm+kMkOOs5lbNjwN3TVnlX+6ahb5Pza4tAOoRKvS463AncBnO8a7lmu2ALtR\nWzyWTwK7gO8VdbzV0MfQzGixT3zoa5c6rNZ0skljNY0NUWcO2BgXIWIt6uwyDdXXjwMhf2R4esMt\nIFrcpVyTqxavwgMJmm0ixvoiZTxMpynTdVpNF7qayzHNFm9GiCb2Yqq5TZm5HUSHu5RrvOvwoKbP\nxFoQNZd2Q9FlMJZb5oztbjQua430gZRmSM+4uua5EyjR7RLvWl5mK/BsQ9n7xU3whPe1RWxjbevU\n5GiQdMngSGyIpCS382kiRIt7T6yFVzX6ItuTOVI2AUIbJDntGFbFuxkCYoj0hlHpcO+nz1RJNZjQ\nU2r6PpjpgyES6kJ7qFNKxBCxYhHlUpc5Wdyf2yHetVyzFXiCZrd/Fyq9cDtqLqZQIopGhbqIt72Q\n7/KaUITuv22sBSmzRARAtHggpLgYDXDB7XNqTR/Q/2+Q7BAxRHrEqHR4UJkimtSZGyl2qnElxmAu\nhXli26Y2EPqeNZLa4OnTZ7/CArNpe7Am0Iusd57b4l3LdXsLwKmGPj/H7HzJg6gTQttCWEILVtki\nECZjxDa2/BpN7OyRrqZMqKk1loTYvU2wRrR4MMTOGAHvWSOarlv59oWgxk9sMwTEEHFmVDo8SFME\n0hsjug+a1H2Zh4tZkfsvY12MEejvdJrUZggk+Ky/twwry21RC8C8CZhaXE/WlGmBrrrXJvGu5aCc\n7kM1cZrqAlLPohafGsRA3JeWd60nK2MEi3hfr7VtI8ZrQ8cL3RAtHrQWu5HCGAFvW/dWiTm1JgaD\nM0NgtIaI6HBnHR6sKQJ5ZWzk2Jdc6umCjdnhYoxA/lkjORghmqCf78Y5t9cXN83XqwHbgRtaaj8J\n3IUS3YVKmX5cNz+xLd61fAv1J4lyrK5D9+9U8TqhD9hmgXTJGim/dh7z6g1lLGRmiHTNEhnCedUY\n0eI6RItbSWmMQBBzBNYbCn0wSaJMBUplhsAoDBHR4TqcdHjQpogmh6wRTarsEd8DrRA70+RK2XTI\nySDJyQyBPAy/Bg4x31Uu8xLrBXcR5TR3iXctvwol5nrxqWtRYv8fUP/TCvBNZk9OW1BbkAkVsswW\n6RqP5Wts6o1B6MVXO8TLtJngiBaPnlTGCAQ3RzS5mSTR10JJaYbAKAwRN0SHGxiFKQJ5GSOaJqPA\nVz/7bET4pmu2SJWqERHbJMnNCIGszZCuPMRset424MFS+RaUMB8yjHcpr564dhft/1HpufcqMTtQ\n25cJHsnOGNGvocPrUhJjvRGXdjog59pgiBYPkpTGCEQzRzRNpoRvsyT5QrCpzRAQQyQIo9Fhl32L\nc2D1xtXHrF4wwAu46ITeoSZUbJd4G3waJDmaH3XYfJ9u2fA0uOzJfpnhnuyvO+/JDmq16mMosT0B\nPFIq24US2X9iGO+jXLe7E7gG+AZqfuQpYBPqxHASuBT4KfCU6T/qCWsttsWXdrvUY2WKaGwNC1eD\nI0eDxNWgiGCIuGSJ+DyvxDBYnt5wC4gWD1GLV+GBiM25ktIYKRPJHBksOZgh0D9D5HYQHc5Kh0dn\nimjEHLGn62AtpHHRhy2Eh4rtd6hnpohgR29MEde6ohgjXV8Too6u+MjUiDHFhnEZItA7U0Qwp2em\niCYXcwTEILEhFzME+meIQM9MkVEQavrMDuA3UIu0VJ/fAiyhnJ9dwJPMrhSr3SG92qyXVbsPT/YB\ncN30HqB9Os3RyR4ArpgebK3bJrav8eXBWvVYzuPwZB9n8xE7p7cZ9WVpcoBPcprbpjuN4vdNDgNw\nz/Q6q/g/mZqtfbZnchSAg9MrvMb2OV5/b2yOvY4VopOVFttoR5d4W+1bmdwEwOL0+8bxG4GPv/ti\ne/DNE3X/+NRsaswdRfy3puq+bWrMt4v4L0+b6ywbBN8o4nfOiS+zZBGvY+82rLut71Vjo3psmtCv\nKx97A7p8DkzjbT+Tob8jOl6ISlY6rLi/uP9KgHjbun8P+BXgO4bxtxb3IeKPFPG/AvzAsP7PF/cm\n8TaxOcb/dnFvcixDvk/l+D2G8SE/87bx97eHCNE5y3N9W1ECvhuVvlJlEdiPWvDkWHGbJyv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- "text": [ - "" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Step5: Solve PDE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that data for this case magnetic fields projected to earth field direction, which is typical data type for airborne mag survey. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, set survey class" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "survey = BaseMag.BaseMagSurvey() # survey class for mag problem\n", - "Inc = 90.\n", - "Dec = 0.\n", - "Btot = 1\n", - "survey.setBackgroundField(Inc, Dec, Btot) # set inclination, declination, and strength of magnetic field\n", - "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n", - "survey.rxLoc = rxLoc # set receiver locations" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Second, set problem class then pair with survey" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prob = MagneticsDiffSecondary(mesh)\n", - "prob.pair(survey)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 28 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Third, run forward modeling" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "data = survey.dpred(mu)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 29 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we viualize computed solution and compare with analytic solutions! Note that you may always need to make sure that your numerical solution is reasonable enough. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(1,3, figsize=(18, 4))\n", - "vmin = Bzra.min()\n", - "vmax = Bzra.max()\n", - "residual = data.reshape((xr.size, yr.size), order='F')-Bzra\n", - "dat0=ax[0].contourf(X, Y, Bzra, 30, vmin=vmin, vmax=vmax)\n", - "dat1=ax[1].contourf(X, Y, data.reshape((xr.size, yr.size), order='F'), 30, vmin=vmin, vmax=vmax)\n", - "dat2=ax[2].contourf(X, Y, residual, 30)\n", - "cb0 = plt.colorbar(dat0, ax=ax[0])\n", - "cb1 = plt.colorbar(dat0, ax=ax[1])\n", - "cb2 = plt.colorbar(dat2, ax=ax[2])\n", - "ax[0].set_title('Bz (analytic)')\n", - "ax[1].set_title('Bz (simpegPF)')\n", - "ax[2].set_title('Residual')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 30, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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qZkoFE2VKOl3NyIbumtuHgS2aKgjlLO1/J/R+Ss81pqnSxUxZ2mftQowVYVz6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- "text": [ - "" - ] - } - ], - "prompt_number": 30 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Creative
This work is licensed under a Creative Commons Attribution 4.0 International License." + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: pylab import has clobbered these variables: ['linalg']\n", + "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], - "metadata": {} + "source": [ + "from SimPEG import *\n", + "from simpegPF.MagAnalytics import spheremodel, MagSphereAnaFun, CongruousMagBC, MagSphereAnaFunA\n", + "from simpegPF.Magnetics import MagneticsDiffSecondary, MagneticsDiffSecondaryInv, BaseMag\n", + "%pylab inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.rcParams.update({'font.size': 16, 'text.usetex': True})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Forward problem: Magnetics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a tutorial for Mag forward problem using simpegPF package. We first start with analytic solution for susceptible sphere in a whole space. Then we solve steady-state Maxwell's equatoins for Mag problem (See Doc) using simpegPF package. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step1: Discretize the earth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use TensorMesh class in SimPEG to discretize the 3D earth (See Doc). Let's visualize discretized mesh on section views:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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HDDJSZ4TG+9bH9k89vzubZ84BYD545hwAsIGKhLljsmPnh/rb6vXxpr5QDxlk5JDRNR7b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\nz5DelBn98cx5Bc+cA5gznjm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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cs = 12.5\n", + "ncx, ncy, ncz, npad = 41, 41, 40, 5\n", + "hx = [(cs,npad,-1.4), (cs,ncx), (cs,npad,1.4)]\n", + "hy = [(cs,npad,-1.4), (cs,ncy), (cs,npad,1.4)]\n", + "hz = [(cs,npad,-1.4), (cs,ncz), (cs,npad,1.4)]\n", + "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCC')\n", + "fig, ax = plt.subplots(1,2, figsize=(12, 5))\n", + "dat0 = mesh.plotSlice(np.zeros(mesh.nC), grid=True, ax=ax[0]); ax[0].set_title('XY plane')\n", + "dat1 = mesh.plotSlice(np.zeros(mesh.nC), grid=True, normal='X', ax=ax[1]); ax[1].set_title('YZ plane')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step2: Compose suceptibility model: susceptible sphere in whole space" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- $\\mu = \\mu_0(1+\\chi)$\n", + "- $\\mu$: magnetic permeability\n", + "- $\\mu_0$: magnetic permeability of vacuum space\n", + "- $\\chi$: magnetic susceptibility" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy.constants import mu_0\n", + "mu0 = 4*np.pi*1e-7\n", + "chibkg = 0. # Background susceptibility\n", + "chiblk = 0.01 # Susceptibility for a sphere\n", + "chi = np.ones(mesh.nC)*chibkg" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sph_ind = spheremodel(mesh, 0, 0, -100, 80) # A sphere is located at (0, 0, 0) and radius of the sphere is 100 m\n", + "chi[sph_ind] = chiblk # Assign susceptibility value for the sphere\n", + "mu = (1.+chi)*mu0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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8uni5PXbcpc/21Bh+FFmrrKU+HsupEWtvG++bP3R/AADSseg/Jhuxbp/aqf1j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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2, figsize=(12, 7))\n", + "indz = int(np.argmin(abs(mesh.vectorCCz-(-100.)))); indx = int(np.argmin(abs(mesh.vectorCCx-0.)))\n", + "dat0 = mesh.plotSlice(chi, grid=True, ind=indz, ax=ax[0]); ax[0].set_title(('XY plane at z=%5.2f')%(mesh.vectorCCz[indz]))\n", + "dat1 = mesh.plotSlice(chi, grid=True, normal='X', ind=indx, ax=ax[1]); ax[1].set_title(('YZ plane at x=%5.2f')%(mesh.vectorCCx[indx]))\n", + "cb0 = plt.colorbar(dat0[0], orientation='horizontal', ax=ax[0], ticks = linspace(0, 0.01, 5)); \n", + "cb1 = plt.colorbar(dat1[0], orientation='horizontal', ax=ax[1], ticks = linspace(0, 0.01, 5)); \n", + "cb0.set_label(\"Suceptibility\")\n", + "cb1.set_label(\"Suceptibility\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step3: Set up an airborne MAG survey" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have discretized 3D earth and generated suceptibility model, which means that we have discretized earth and physical property distribution. We can compute magnetic fields everywhere in our domain by solving parial differental equation (PDE), but our measurements are confined to finite locations. Therefore, we need to project computed fields $\\mathbf{u}$, which is defined everywhere in our domain to certain locations where we have receiving points. For instance in airborne mag survey these are the points where a plane or helicopter measure earth magnetic fields. This projection can be expressed as:\n", + "\n", + "$$ \\mathbf{d} = P(\\mathbf{u})$$\n", + "\n", + "where $P(\\cdot)$ is a projection from computed field to the measured data, and $d$ is the measure data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's assume that we have a survey area: 400 m $\\times$ 400 m. We have 21 lines of airborne MAG survey, and we measure magnetic fields for every 20 m on each line. A pilot for this helicopter is really talented so that the flight height is constant for 30 m above the surface. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "xr = np.linspace(-200, 200, 21)\n", + "yr = np.linspace(-200, 200, 21)\n", + "X, Y = np.meshgrid(xr, yr)\n", + "Z = np.ones((size(xr), size(yr)))*30." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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wBEwawb0guBcEaQ/ngC04B0wI6QvOAUcx6sFfU5+xfUN2Pr0rS9nOKZecc8sp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\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1, figsize=(5, 5))\n", + "indz = int(np.argmin(abs(mesh.vectorCCz-(0.))));\n", + "dat0 = mesh.plotSlice(chi, grid=True, ind=indz, ax=ax); ax.set_title(('XY plane at z=%5.2f')%(mesh.vectorCCz[indz]))\n", + "ax.plot(X.flatten(), Y.flatten(), 'w.', ms=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step4: Analytic solution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have an analytic solution when we have a sphere in a whole-space. simpegPF provides this function so that you can compute magnetic field on your receiving locations. Another input you need to put is direction of the earth magnetic fieds, and the strength, you can easily get this information from NOAA's website. We assume that we have veritcal earth fields and the strength is 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Bxra, Byra, Bzra = MagSphereAnaFunA(X, Y, Z, 80., 0., 0., -100, chiblk, np.array([0., 0., 1.]), flag)\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": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ox6ZGePS9Gk2XgkjbfHPd/l0KJX0WYmWYwTMEQaSvZWtDnobGLILqU3yVAK9E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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,3, figsize=(18, 4))\n", + "dat0=ax[0].contourf(X, Y, Bxra, 30); ax[0].set_title('Bx'); cb0 = plt.colorbar(dat0, ax=ax[0])\n", + "dat1=ax[1].contourf(X, Y, Byra, 30); ax[1].set_title('By'); cb1 = plt.colorbar(dat0, ax=ax[1])\n", + "dat2=ax[2].contourf(X, Y, Bzra, 30); ax[2].set_title('Bz'); cb2 = plt.colorbar(dat0, ax=ax[2])\n", + "for i in range(3):\n", + " ax[i].plot(X.flatten(), Y.flatten(), 'k.', ms=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step5: Solve PDE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that data for this case magnetic fields projected to earth field direction, which is typical data type for airborne mag survey. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, set survey class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "survey = BaseMag.BaseMagSurvey() # survey class for mag problem\n", + "Inc = 90.\n", + "Dec = 0.\n", + "Btot = 1\n", + "survey.setBackgroundField(Inc, Dec, Btot) # set inclination, declination, and strength of magnetic field\n", + "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n", + "survey.rxLoc = rxLoc # set receiver locations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, set problem class then pair with survey" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "prob = MagneticsDiffSecondary(mesh)\n", + "prob.pair(survey)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Third, run forward modeling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = survey.dpred(mu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we viualize computed solution and compare with analytic solutions! Note that you may always need to make sure that your numerical solution is reasonable enough. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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\u001b[0m in \u001b[0;36m\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 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,3, figsize=(18, 4))\n", + "vmin = Bzra.min()\n", + "vmax = Bzra.max()\n", + "residual = data.reshape((xr.size, yr.size), order='F')-Bzra\n", + "dat0=ax[0].contourf(X, Y, Bzra, 30, vmin=vmin, vmax=vmax)\n", + "dat1=ax[1].contourf(X, Y, data.reshape((xr.size, yr.size), order='F'), 30, vmin=vmin, vmax=vmax)\n", + "dat2=ax[2].contourf(X, Y, residual, 30)\n", + "cb0 = plt.colorbar(dat0, ax=ax[0])\n", + "cb1 = plt.colorbar(dat0, ax=ax[1])\n", + "cb2 = plt.colorbar(dat2, ax=ax[2])\n", + "ax[0].set_title('Bz (analytic)')\n", + "ax[1].set_title('Bz (simpegPF)')\n", + "ax[2].set_title('Residual')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Creative
This work is licensed under a Creative Commons Attribution 4.0 International License." + ] } - ] -} \ No newline at end of file + ], + "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 +} From 6174c6feceb51ebe10f3fce5dee49737cf8e4fd6 Mon Sep 17 00:00:00 2001 From: D Fournier Date: Fri, 23 Oct 2015 12:08:19 -0700 Subject: [PATCH 05/31] Start working on mag integral --- simpegPF/notebooks/MagForward_Intergral.ipynb | 806 + simpegPF/notebooks/MagInversion.ipynb | 5530 ++-- simpegPF/notebooks/Mesh.dat | 24025 ++++++++++++++++ simpegPF/notebooks/Mesh.msh | 5 + simpegPF/notebooks/Obs_RAW_REM_GRID_TMI.obs | 345 + simpegPF/notebooks/Obs_loc.dat | 2 + simpegPF/notebooks/Tile1.msh | 5 + simpegPF/notebooks/get_T_mat.py | 127 + simpegPF/notebooks/get_UBC_mesh.py | 117 + simpegPF/notebooks/read_MAG_obs.py | 58 + .../Tutorial_1_Mag forward modeling.ipynb | 65 +- 11 files changed, 28280 insertions(+), 2805 deletions(-) create mode 100644 simpegPF/notebooks/MagForward_Intergral.ipynb create mode 100644 simpegPF/notebooks/Mesh.dat create mode 100644 simpegPF/notebooks/Mesh.msh create mode 100644 simpegPF/notebooks/Obs_RAW_REM_GRID_TMI.obs create mode 100644 simpegPF/notebooks/Obs_loc.dat create mode 100644 simpegPF/notebooks/Tile1.msh create mode 100644 simpegPF/notebooks/get_T_mat.py create mode 100644 simpegPF/notebooks/get_UBC_mesh.py create mode 100644 simpegPF/notebooks/read_MAG_obs.py diff --git a/simpegPF/notebooks/MagForward_Intergral.ipynb b/simpegPF/notebooks/MagForward_Intergral.ipynb new file mode 100644 index 00000000..a50642c8 --- /dev/null +++ b/simpegPF/notebooks/MagForward_Intergral.ipynb @@ -0,0 +1,806 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from SimPEG import *\n", + "import matplotlib.pyplot as plt\n", + "import simpegPF as PF\n", + "import matplotlib\n", + "#from get_UBC_mesh import get_UBC_mesh\n", + "from read_MAG_obs import read_MAG_obs\n", + "from get_T_mat import get_T_mat" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Writing magnetostatic problem with total field formulation\n", + "\n", + "## - Here we focus on cell-centered system with FVM-Weak form" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step:1 Generating mesh and operators" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#mesh = Utils.meshutils.readUBCTensorMesh(\"Tile1.msh\")\n", + "cs = 25.\n", + "hxind = [(cs,5,-1.3), (cs/2.0, 21),(cs,5,1.3)]\n", + "hyind = [(cs,5,-1.3), (cs/2.0, 21),(cs,5,1.3)]\n", + "hzind = [(cs,5,-1.3),(cs/2.0, 20)]\n", + "\n", + "mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')\n", + "\n", + "xn = mesh.vectorNx\n", + "yn = mesh.vectorNy\n", + "zn = mesh.vectorNz\n", + "\n", + "\n", + "mcell = (xn.size-1) * (yn.size-1) * (zn.size-1)\n", + "\n", + "N = mesh.gridN\n", + "\n", + "Utils.meshutils.writeUBCTensorMesh('Mesh.msh',mesh)\n" + ] + }, + { + "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, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sph_ind = PF.MagAnalytics.spheremodel(mesh, 0, 0, 175, 50)\n", + "\n", + "Utils.meshutils.writeUBCTensorModel('Mesh.dat',mesh,sph_ind)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "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", + "\n", + "Incl = 90.\n", + "Decl = 00.\n", + "B0 = 50000\n", + " \n", + "# Or create juste a plane grid\n", + "xr = np.linspace(-125, 125, 25)\n", + "yr = np.linspace(-125, 125, 25)\n", + "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", + "# 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();" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create magnetization matrix\n", + "mx = np.cos(np.deg2rad(Incl)) * np.cos(np.deg2rad(Decl))\n", + "my = np.cos(np.deg2rad(Incl)) * np.sin(np.deg2rad(Decl))\n", + "mz = np.sin(np.deg2rad(Incl))" + ] + }, + { + "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, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Mx = Utils.sdiag(np.ones([mcell])*mx*B0)\n", + "My = Utils.sdiag(np.ones([mcell])*my*B0)\n", + "Mz = Utils.sdiag(np.ones([mcell])*mz*B0)\n", + "\n", + "#matplotlib.pyplot.spy(scipy.sparse.csr_matrix(Mx))\n", + "#plt.show()\n", + "M = sp.vstack((Mx,My,Mz));\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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": "", + "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 \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 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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: " + ] + } + ], + "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", + "print Tx[0,0],Ty[0,0],Tz[0,0]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For sparse matrix\n", + "b = A*x\n", + "\n", + "For dense matrix \n", + "b = A.dot(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x = np.ones(10)\n", + "y = np.ones(10)*3" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "30.0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.dot(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Gx = Tx*M" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cs = 25.\n", + "hxind = [(cs,5,-1.3), (cs/2.0, 41),(cs,5,1.3)]\n", + "hyind = [(cs,5,-1.3), (cs/2.0, 41),(cs,5,1.3)]\n", + "hzind = [(cs,5,-1.3), (cs/2.0, 40),(cs,5,1.3)]\n", + "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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\nEREJxoJFREREJBgLFhEREZFg/w+en7T8iAv/xAAAAABJRU5ErkJggg==\n", + "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\nmrS6fGkyOn6w3VlDfS6EYwiB9joPRldFQMypIAjCFUQRDtMVNmWZ1QbVX+VI33j5roBUBIKd1CnQ\nykdFMTjhHI8Z6j6NzqWZ8xWuiWcqItpYosQGJV4o8U6NOpYo36n3Y0XM85XB1dOk1aX3Xbg0qRsu\nzT4X1MutiMdGDQyBGnmrn1d6ZRZIA6+aQHUFY+T9NcBdLUa+YtYQBEEQrnCC6d4WKYwqawrS7c6Y\nq7FVPFikrCoW6hinrWhrTaoWbbzRmtRgTY6RWVZTZuq8e4joxUG1JqN9hqrLQZFPI69dbqWZN2vk\nPY1zZIy8WlMgRt4qXXrazPSKMjLyYF1jr1UZeSs0iTkVBEGIQmJwXlEGtTwprxlQyyPTFUzmNBjC\n2aW0omYErSyzilpGVqBUZM2Mb3NQRIzGEJbtx2Fp3NTTVXJNlCxXV94VXWqt4dCk1qVdZtbIK8tC\n1RSIkS/5v36Dgt4+w2XkzTaUqrWVl5GPFL4y8sFmmUMtJ5kQSRAEoQITagumXiXQ3wPL6intrySC\n7e5khlBn7gyHCbbivJnVFeoMocEQTHc27Sy/FeXatoqS6dpKykRrAJXyUv/V4r2GpyksM4mBTQyn\nl8nVHt/X+dQbZ6/VpOgKFrUubRnpxXq1YdaWVyDjQs3q0ltepNJY8r8iw2NbqUlPl/JZz5AZZZmt\n0KS+5tValL9GmrS61H/V34erd1LZfeo9Q3U4e0RJ5lQQBCEK0MsGhtq6rd/CHtwrMbTbqbvR+Rtz\nqrTqW6XHly6rKC9dZjOnVv3OaM0Gaq9/MxNGqdcNpwEMxtSLIS0jkHtBG++UTKUTJ3EUehjPQI9j\ndP2Y0aWnSR339LKmVmjy1fVZWV89CV0R3l1n1eZde8/EuLfxzFQGosvXemYmLTPaVvkNnplP32Mq\nPfdnbJgDyTIry43NY+DXlZ5p9hxP6vlcCCb7bSZuRWuWWcypIAhCFKGtpAfSauvrQRCuCWL09OkZ\nLquNqVqXr30Zdf8L96Q1vipRvo4dbAU6WG1mKE+j5Uub0XkTI3hlos0CarOpJZVb48lY1A1k2oq9\nFd1W1VlJvX2VHNdBHAU4VL9C/fvCMQRBm731zGppZzH2zOaGR4//btlm0BrmQLHCMOt97719aGM9\nfW2n/IY4Cr2up1AIxTAHMpbZCsNcsh9BEAQh6gjEdJVXpd3XQ9tXBag8xoheqYarPMyoIFwp6I/z\n9Iwz6nFuCqQJAAAgAElEQVSn4RpnqkZtmpXKurYrccn/yyrsimFW709vZuFQ0TMOThzEUei1bnm9\nWkyvR4N6AiL1mGG9Z4UVMdDf79RmiH11NVaw2jSX6HCoykA/o+vZhdezfNSayvvVcUZYcT/KmFNB\nEIQoRm98lZER9TdWK5zatFr0tAneSLmYJxxjZoXIonf9q8efGqFnDrXfWUmZafZd+VeMhNVjJ8t0\n6Gdwy773NsNgNNtreMeg+8tY6jVolgdmrit/y6zUYfYZ4OuaDxe+rvdwXeMl+xYEQRDKHaseMOHO\njgZLOMYjhko0lpXVxr0ilxVcHdeVIFREzI6vFwTJnAqCIEQxkjkVhBLkGqp4+MpU+Rt/rP5X7zsr\nKZtcyPcYQCWLGa5XhuiViVGG1MxkSlbjKxvq6zkRLcMsyut6Uuswa9gDzbRaga/rPZyvxZHMqSAI\nQhRiZtIGfw8rozGh4dCkN65Uz6Sa3Z9V2syUTbg0Ge1Te14C6QonCBUZZdycdmypQsknz6qrlZVk\nvclutDPaGnVnVI9J1e7Dap1qbVqUmKI1FuU1HlFrULU6ysZIeo8vtW5COt/jL7VjTPXeXasl2PNn\ndqZfIyOoHlesLR9/DRCRwIrrPDp+iSAIggDotTbrvTJBP3RrHwoxmkqd3v6D1aXWp9WjV+kwmrgj\nVPMViIlXH9eIUMvJjC6jhgVfrfZXm5EPpJIq3QWvTNRGVEE7865RLxG1CSxZpthY6zKX6mtROymN\nMrFP2bGN76lwdpFXX/v+JsWzarIhbz1l5zGU/Yd67owmnLJ6srzQ3x1ubJzVE32p1w8VM3HV+7U9\nxo0xRjP5+jt3Zq8PMaeCIAhRgH73ttAeSkaG0Oh4wWrTPtSMWneVf60yzeE0JKEYnkDL1swD2yoD\nZmWZWWkKjboAmmlI0P7f6utCawbMEI5GhSsVsw0OvhrAlPtEmSnX+7UX5u4hq3SVZVg93y8aqDnz\np0n7uhy9hkD1v9psoHqWXL37xSjLa0aXnhHx12U20HvJly5/58/IBPrrqqpnxvzNuOu5/+CNoHYf\nvnRZYQS15zGYOofxK3yCt5gy5lQQBKECE46uZP4eYFdzhTzchjmUzES4Z+cMlmi+XkLVJuNkA8O7\nS6jx+Hr1Z/W/weJvdls9jVpdRljd/VJvbKee5iKdsgnnmFxfy7UZ8vLAqveEhoOSppYi0xqLPLao\n2HFFMqeCIAhRSLQ8VIOpnAdT+QkkC1deZqY8sl/lZV6iNcscDsKZZTaTHdPrRh5N5RNptNlANdpu\nqkZjA7XodQm1ovulv+VmDFe4xsSaGa+oHD9G57q12uD46haqzSYG0yXULGaygf66qhoR7DVl5h23\n/sYyW61JfVwjXTGqe7HseGW9F4x0ha5JEARBEELA35hTK17KLQjhGjNXHohBNUcghsvMskDRmgh/\nhks7NtCX4Qqlwn6lGC6tLrOTR1mlSX3caDNcvnT5m4/BSJdVkyDpnUsz7zHV02VNOfkhJSWFGjVq\n4HA4iI2NZfXq1eTl5TFy5EjWr19P586dmTVrFtWqVQPgrbfeYsqUKcTGxvLOO+/Qq1cvALKzs7nn\nnns4e/YsI0aM4KWXXgpZ/JijR5k7YADH161j2NKl/DJtGttnzwagSb9+XDdpErVatMAeG8vh5cvZ\n//33bJgyxb19o969ueuHH7z2u2DUKHgvMpoU2o8dS5vf/IY6bdpw+fx5ds+bB2NyghNlga6b33uP\n9Pvu89qvy+Xi5fpvcOrUxXLXBJDSvz+t77mHxn36cP7oUQ58/z3rp0yBwwHLsVbXLbeQOmwYTQcM\n4HxODpvfe4/1f/97WDSpqd26NfesWYM9Lo6/x8V5fFc1MZE+U6eS1LMnhXl57Jo7l+XPPAPFQcsK\nWVeV+vXp/cYbJHTowDWpqRxasoTP+vULXlAIRFusUwf9J5jIbN7lKIe5l9+xlp/4hc0AtKAFN3Ej\ntamNAwd7OMBO9rKCNe59NaMJo7hfcwQXc5nHOtYHpW0c492afsMY1vITm/kFgKakemjax362s89D\nk0J3utKFDtSnHpcpJJttfMGXAWtSGMd4PuJ9clS6jMpqH/vZyR5Wsdq9/V3cTic6aPbqAuAVXuMC\nFwyPbWQ+fGlKpSmZ9KUOtbEbaAJoRQs60Y7mNCWPfHazix/J4hznAi0iQ12rWc0mNgJlZeVPVyot\naE8bUmlBHvmsYx2r+MnU8fXKy5cmtRFIIIFHGIMDB+P5s8c+qlON2xlACskUUMBWsvmOBbhKz2Mw\n2hRdhzjKA4xijUqXGiNd1ajKrfSjIfWpS132spf3mGlaj5VES6xTV9SVWDeRJ3iX2RzmKI9wL1ms\n9bh/b6QPtamDAwcH2MNedrKGFe59NqE5Ixnjdawv+ZyfA4h1am0v8KihplSaciM3uWOKniYoiZld\n6U4HulCP+hRSyHa2Mi+AWKc1Nuqy+h338hNr3TG4pKx86xrIcNrTxes4LuA1XvEZ68zoOsZhxvAb\nty4njtLy8n0OncTQgla0oxNNaU4+eexmJ6tZxVnOB1ReUGK4jDTpldV+9rGP7TqaUmlDe1qSSh55\nbGAtWaw0rUerazzjeJ+POEyOlyZ1prk+dXiIR3Hg4M+M99hXNaozgNtJJoUCCtjGFhYyP6BYp9Xm\nS5eautTT1VWVavTjVhqQSB0S2MceZvHvAHX4wWazsWTJEmrXru1eNm3aNJKTk5kzZw7jxo3jn//8\nJ0899RTHjx/n7bffZtGiRezdu5fHHnuMdevWATBu3DieeeYZ+vbty+23387atWvp2rVrQGLV1Gre\nnNgqVTi+fj322Fjqd+3K4RVlF1JBbi7r/vY3Tm7eTHFREUm9etHnH//g0smTbPv4Y499fdCpE+dz\nyoxf4blzwLMR03TT22/TbOBAtrz/PgsefBCbzUbNZs2AzgFrskrX4sceY9nTT7u3sdlsDPriCy7n\n5wdlTK3QVKl2bW6bM4dN//oXn/fvT7WGDen50ktUa9QIRu6KWFnV79KF2+fNY+X48fz8xhs07NWL\nnn/+MzGVK8Or1mtSiKlcmdvmzOHAokWk9O/v8Z09Npa7V66k+PJlFowaRe20NLqPH098rVrwUHBO\n3gpdjvh4Lp06xc9vvEGrYcOwOSKXFYnGWOckhrrUJJZYjnIEF/E0JIkD7HOvU8AlVrKKHE5STDGN\nSeEO+nOeC6xni0fr51tMJ488ABwUU0BBULquoQ6xxHGY49hVmhTzcJkLrGQVxzhOEZBCMoMYwEXy\n3ZU6gMEMoDWp/MwGPuNLHBRTm9oGRzWvK4cj2HH4LStF13kuuHV9yXy+5Xv3NjZs3MdwCikwXVkL\nRNMlCnTLSq2pCpW5h6GsZh0zmEUNqnMzfahBDT7lM8vKaj/7vcrKl64kErmPu/mOH1jJCprQhF9x\nEzHEsozlIWtK1GhSMlWxxDKCYexmDy1p6bEPB3Ye5gGcFPMZX1Kf2mTSm8pUYi7zQi4rB7FeZaXg\nS1cMMVzgIitYRTvaYIvglB+RinV6E8WoDUQdriGWWI5wFAd2GtGQTzjg3u4SBaxiFSfJKb1/G9Of\nO7jAebaUGk9l39N5yx3rinH4jHVGE9jE4KQmdXU1KZRoWunWlEwKAxik0RTDAAaTSms28DNf8hnF\nOHzGOl+aAOpwDXHEcpzDxGMniYbsM6Ern4tuo/EtX7OA/7m3sWFjOPdRQKFhrAtEl6NU10H2urfV\nnkM9XZWpwlDuYR2rmcUMqlKLvvyK6tTiMz41rUmhPjUMNanL6jjHgCJdTYkkcTf38QPf8SPLSCaF\nG7kZB3EsZ1lAZaUupyPkuDWpz5+SOY0llrv4NXvZRQtaeezDjoMHeJhinHzJZ9QlgevpQzxVmMfc\ngDUZ6VKXVdmxKxnqiiGGi1zgR5aTTnvs2ALu8WIq9+pyeTrw1atXM2HCBOLj43nggQd45ZVXAMjK\nyqJ///4kJyeTnJyMy+UiPz+fatWqsX37doYPHw7AkCFDyMrKCsmcNuzZk5ysLHC5aJCRwaVTp8g/\ndMj9/dGsLI5mZbk/n925k8aZmbR76CEvc3rp5EkunjgRtBYrNdXv0oV2o0cz7/bb2fvNN+51T27e\nTLDm1ApdhXl5kJfnXqdWy5Ykdu/OV3fdBbSPiKbmgwZhczhY9vTTuJxOTm7aRJX69ek7fToxv/kr\nRUWBpwSt0NXp8cfZN38+a//6V6Dk3NVq3pyu48YR/7d/UlAQWNcuf5oU+kydyqFlyzialUXKLbd4\nfNdq2DBqNGnCPxs04OKJE+z56ivyDx+m34wZJP7pH+Tk5AekySpdeQcOsPjxx4GSngzVkpIC1mEl\n0RjrkmjOYQ7ixEFjGnGBC5wjF0qD/UEOcZBD7uB/ktO0JJlr6eiV2bnABc6XVjpC6WKYTBMOcwBw\nkaSjaT9HgCNAyUPwJKdpRgrd6OxhbLrRhZnMZhs73ZqOcdxSXWcou7aVsvKlq4BCCih0b1OX2jQi\nidnMCYumQxzEWVpWgK6m1qRiw858vqcYF0c5Tg2qcDsD+Yy5FAfR/cH4HJagLisjXT3pznZ2sYxV\n7nNXm9r0oger+JEiP+OmtWN6tZouajQp6w/kVvayn0McopXGBLajDbWoxau8xnnOswM4Rx5DGMxC\nfnAbFn+ou9UpupzYaUySly4FX7rOksu3lDzTm5JCDaqb0hEuoi3WxeCkOUkc5DAOnDShIRe4QC7n\n3NXaQ6VXpXJeTnOSZFrSkWvZyCaP/V0ojXYQ2rhuY01OD01Qch2f5iQpNPPQlEgSXejGbGayk21u\nTSVmKDiakMwBDuMCGpHEBS6Qzxn390a6OtPNbbgKS6OdQm3qkkQj5uDd+ykUXbmlvTucOHTPoVZX\nKq2xY+N75uOiGCcnqEY1BnI7c4NojPOlCcyVVXd6sovtrCo1osc5Ri3q0oNe/Mgqv7HOjCb1+VPK\n51YGso99HOEALUj12Ecb2lGLWrzOS1zgPDvIJpd8BjOEH1hoOtb505Wr0zvHl65czvI/virdXzOq\nUyNgHaYyp3369KFp06Y88MADDBo0iDVr1pCWlgZAWloaq1eXdPPJysqidevW7m1TU1PJysqiSZMm\n1KtXz708PT2dDz/8kN/97nc6R1wCwHdAc6CF5ttHzpwBlwtHfDw2u51HTp/GHhuLIz6eR06fxuVy\nMa1OHY9tlKxOSv/+rC4NuGqGl2Z7ds2dy/ZPPuHo6tVe6/jCSk0thw6l6OJFqjduzD0//4yruJit\n779P9ocfwtmAZIWlrBTajxnD+aNH2f3FFwRiTq3UtH/BAnC56PDII2x9/32q1KtH63vvZe833wRs\nTK3UFVejBoX5nmavMC+PSrVrk56ewPr1Ry3X1Pree6nfpQsfZWSQOmKE176SevYk//Bhj0aY4+vX\n44iNpVu3JObN2x6WsvKnS49dwG5AiQXlRXnHuqWTyjJLTTKTaZ7ZyOP7Z5iICxcxxGDDxtP8CQcO\nHMTwdOl3r1LWja7kYRZLEg1pRQuW4J3FHstvAdjCNjazkUMB9n/3relPuIBXecmd4SrRZdfV1I50\nLlNETWrye0bjwsV61rGBjVzikmW6nmWCV1n50qWlG13IJ5+tZPvVoTY0ZjV5jllzkEQjL0072Q24\nuJYMfmYDNahMRzqwnR0BG9PgykpfVyXiKVQZeYACCqhMZRJIIAdzQ1H0NNlxEKOjqSMdSKIhbzOd\n9rTz2lcKjTnHOc6ruv8dIQc7dhqRRHapOTDD00yEUl1g41kmYMduWpfnfeDdGLR/yT4OLPHOwIab\n8o51yyctdf8/ObMJjTKbe3w/kWc8zv+feLr0inQwkadx4eIlVbejkuw5NCSJFrRihc6z4reMBWAb\nW9jIZg7j3XCqRX2+AtVUQhyNaOClKZ12FHGZmtRkNL+nGBfrWc9GNviNddpMly9dE3hWV5fSM8Ko\nrJT9d+Za8sknm60BlZVZXeq44usc7mYnLiCDa1nHBmpSlQ50ZAfbfcY7K8rK6BzGU8kd65T9m411\nZq8rraYOdKQhSUznbdrp1LMbk8I5znk0wpT0OrGTRCO2+XhmmSurklgXqC41Lmy4sLFvyX72Lzng\nc101fs3pypUrSUxMJDs7m4EDB9KtWzevFjdf2Gw2b7E+t88E4GaDG+iD9u3BZmPETz+xaOxYjm/Y\nwK2zZ7Pto49KxmVqeOjgQSonJOCIjWXZ00+z7s033d/lHznCwrFjObZ2LZUTEkgdPpzhK1aw8rnn\n4HXTP9FSTde0bInN4aDjo4+y5tVXKbp4kYw//pFWw4bxaO/F5kVZrEuNIy6ONvffz8bp03EVB1Yx\nsvr8zercmeHLl5P5//4fNrudHf/9L9/cfTcwMWK6Nv3rX9z26ae0GDKE/d99R+J119Fu1CgAGjeu\nadqcmtVUOy2N3q+/zpzMTJyFhbr7qpaUxPH1nuNtzmzfzuULF2jUKLBWLSt16dGi9O+C0lhQXia1\nvGNd70nXa5Z4Vl6n8SY2bDzII3zDXI6Sw52MYDMb2Ip3Y8LTjKMqVbHjYD7fs0I17u8ceXzB1xzi\nCFWpSnvaMJpRLGAhKwIYM2NWk/LA86WpZCyjjR5ksISVXOYymfSkLW15lxk+dWgr/Ea6NrJR9wHt\nS5eaOKALHVjD2oDH8ASvyc58Fnqdv7d4h7H8llu5GRuwha18otPFzQjFOGt1HeY4wxjORn7xq+s7\nFrCCsp4jq1nHPdxFe1LZyS4a05iupWPYalHTtDmdyhQcFPMgj/AVX5JDjq6m+tTmFm7mXd7Dqbpf\n1BWtmlT3Ou5JTnKZy9QMsAX/baYANkYzlq+YZ6grgbq6utTa9GiSmUKTzBT35+WT9bsHWk15x7rr\nJ/XWLPGciOZNpmHDxiM8yFy+IYejjOBONrJRtzFhHE9Tlao4sPM98/lJ1WCSxzm+5gt3rGtLe0Yx\nmoUsYKWPRigF5XwFq8mOnYUaTbWpgw07GfRgOcu4zGWu5wba0pYZvOtXk6LLqKx+zZ38EoQuBWdp\n81RHOrGWNaZjnfq+0+o6zmGGMywoXXmcYxpTeZCH6Met2LCxlS18yicB6QpF0wK+I0ulaR2ruYt7\nSKU9u9hJYxrThZJeAjWp5TfWKZqmMBUbMIbRfMlX5JCjq6kuCdzMLbzHux4xRf3sq0ENcsjxaCRQ\nYl0NagZUVoqusYxmXgi6jEjJbEJKZhP352WTfQ/78GtOExMTAWjdujWDBg3iq6++IiMjg+zsbDp1\n6kR2djYZGRkAdO/enYULF7q33bZtGxkZGVSvXp1jx8q6L2zdupVrr73W36F1yTt4kLrt2uGIjWX3\nV18RV60aCR07Mm/QIC6ePOm1/uyePalcty7JN91E1z/8gZjKlcn6c8kkBWd37uTszp3udfcvWEBs\ntWp0e+45eP2tiGiyx8YSEx/P0v/7v5LMIJC7dy+/Xr2axo1/5uBB85NfWKlLTcuhQ4m/5ho2vvOO\naS3h0FQnPZ07Fy5k++zZ7Pj0U6onJ9P5yScZ+OmnjBu62Wtf5aVr77ffkvXii1z7/PPcNmcO548c\nYf2UKVz/6qsBZXTNaHLExXHbp5+ycsIETmcbt5K5XC5sdmvGOFmpK5qIdKzTGq5z5FKPBjhwsJ1t\nxBFHAxryIbN0xwO9w7tUoQpNacEN9CCWWH4o7YJ0ktOc5LR7/3vYQSViyeQGv+ZUnQ00q0mpSCma\nmtOMG+jpoUnJ1n3NAnayGwdOcjnFw4yhJjXJ1ek2aaRNqyuGyqbKSk+X+je0pT2Vqcwa1vrVotYE\ncIZ8EknAgYNsdpo+f81pRm96EoeDRaWVo3ok8BD3spFNbGYztahFD67jbobxsckKm6LtDPnUpz52\nD12JfMgHfnVdTy9iiGUJJZmwXWxnMUu4kd7czTDOkcdP/EQ/foUzgIxuLrluTWXXlacmBw6GM4Lv\nWcQJ9IfhOHDiwoUNb/MUDGW67D51jWC4T13RRqRjnRoHTvI5Q33q48DOTrKJI46GNGAWH+pek+/y\nDlWoQjOa05PeOIhjaWkj5gnOcIKf3evuYhdxxHEDmabMqYKVmuzE4MDB//iWXZTMh3GGM4zhYdOx\nzldZJdKAD4LQpaYNbalMZdbqTFpnlS61ffGlK4F6/IYH2MxGd7y7jh4M424+4WNvAQaEUla9uJ5Y\nYtyatrOLJSymNzcyjLvJ4xw/8RO/oh/FAQyRUWKKAzvb2G5QTiWxbhHfG8YUJw6KwaJI5xnrQtFl\nFT7N6YULF3A6nVSvXp0TJ07w3Xff8eSTT3LmzBlmzJjBa6+9xowZM9wBqVu3bvzhD3/gwIED7Nmz\nB7vdTvXqJeMq0tLSmD17Nn379mXu3Lm8aZCV88V9mzdTIzkZW0wMjthYHs3NxWa3ExMfz4N79gDw\nfuvW5B8u66qWd+AAeQcOcHzdOrDZyHj6aVa/8goup/7FtPOzz2g1dCjXXFOJM2f8dy2zWlNe6Zi9\nw8vLWhWOr1vH5fPn6du3Ge+9tyHiZdV+7Fj2f/cdeQfMp+jDoandQw9x8fhx95hFgLO7dzNi1SrS\n0o6ybZu3qSyXsnK5WP3KK6x+5RXia9akIDeX5oMGAbBjxylLNdljYqiTnk6fqVPpM3UqUNKqbbPb\nebywkFXPP8+av/yF80eO0GzgQI9jXJOaSmyVKhw6ZL7Bw2pd0UK0xbqHeZKa1MKOHQd2/sgkbNiI\nwcGTPAXAFN70mKn1LLmcJZeDHMeGjRvowRKWU2zQCr6FrbQhncpU5iL+JzUz0uQwoekIObhweGhS\nxrHsU00sc4QcCimkOc1MzyJstS413chgJ7s4a7LyqPAoj3loeo7nA9IENm6gF4tZSTEuutGZfM7z\nDd8CsJ8DnOY0oxlFAnU5gblYp9ZlD0HXMpZTTDEuXCxlOUtZTiUqcYlLtKaka+gpizXZsZNAAoO4\njUHc5t7eho0XmMhCFrGMFeSRR5pmDFTd0klt9MZOlZeuaCHaYt1jPEotarrL+Xmec5fzUzwJwJtM\n8bgmc0uvyhyOYAN6cQPLWWbY5XMrW0injelYZ7UmZXyyehKtHI5QSCHNaM561kW8rDLoxi52khvo\n+LEAdJ1Xjan0pasrXTlPvnuM9gH2c5rTjGI0dUmISFm5cJVGuqXuWJdGSXf3k5ir15nVpMSU20qj\nioINGxN5gUUsZAXLyCOP1NJ4q6DEOr0x8eWlywp8mtNjx45xxx13AFCnTh3GjRtH48aNefjhhxk5\nciSpqal07tyZv5RWMuvXr8/DDz9Mnz59iIuLY/r06e59vf7664wcOZJnn32Wu+++O6hB85/3748j\nLo5+M2awb/58dsyZw7UTJ+IsKGDNqyV9odWz7mpxxMURW60aNrvd0Jw2HziQgtxcU8bUKk1x1au7\nNR1etowOY8fSsGdPDpS2ViZ07EhslSosWLDblCardOmVVe3WrUnq2ZMvS6+LQLC6rIqdToqLPAeh\nK1oD6aIUzuuqILckQLT57W85vmEDu3adtlaTzcZ/2rb12LbF4MFcN3kyH3TowIXjJZPLHF6xgvZj\nxlA5IcE97rRep04UFxWRlWV+3KHVurQEct6sJJpinRMHM/kPDhzcwRB2sIMtbCKTPjhxsrw0a2U0\n2YEDJzHYiCeOGFxe3QwV0kilgAJTlbVQNQFemvazl+50oSmN2F06yjiRBsQSyy7MxzqrdSkkkEAy\njfkoiMlBAtGk9+5OBw7iPDQ5vSqTyudA7phQy0rRpZeZVMbOdaYTRznKKczFOrOabNiYylseZZVO\nGjfRhym87R5jup8DZNCVqlR1L2tIIsUUe0zwZKWut5jqsa2eLi2RiXTRFesA/sNMHDgYwh3sYAeb\n2EIfMnHiZGnpjM/BXpMKqaSZjnWBatJ7f61W0z720ZVuJJPsjnUNSCSWWHZj/s0C4SqrBBJoTDKz\n+ci0lmB0GfXd0uoqxmUY78zeOeG8rpRY14nOHOUop02aU7OalFinJo10+nATbzPFHVMOsJ+uZHjE\nukQaUkyxe4KnSOjyJvBo59OcNm3alA0bvDN11atXZ57OODyAxx9/nMdVmSyF9PR09/TjwZJ/6BA2\nu52E9u1ZOHo0uXv3UrddO36cNIncvZ5THXf5v//jVHY2Z3fuJL5WLZJ69aLzE0+wffZsii9fBqDz\nE09wbv9+Tm3dSqXatWk1dCgthw7lpxdfpGS0Uflo2vbxx25NOz79lOsmTeL6v/yFrBdfpOjSJbpP\nmMCBH37g8GHzM29ZXVYK7ceMIf/IEXZ/9ZVpLeEqq43TptH5iSfo9fLLJd16Gzcm45lnOPDDD2zf\nbi5YhKOsajZrRlKvXhz58UfqdepE16eeok56Op/37w/0tVyTtttsfrduXst3zJlDjxdfZPiyZSx7\n+mlqp6bSbfx4tv7nPxw9an6mXqt1ASR0KHmnZKXatYmrXp267duXjGnyfq1W2Ii2WHeOXGzYqE8D\n5vEFZzhDfRqwmEWcUbU+A/SkByc4wSlOUYlKNKEJPbmOjWxym60eXMdZznKcE1ShMm1oQxvSWcxS\nvcOHrKkHPTnGKZ+aNrOFm7iR/vyKxcRxmSJupDd72BvQuzutLiuFbnQljzy26YzvLe+yymINPbiO\nfvRlE1uoRU2upxd72MtJkxnKcJRVba6hCU04wEEakkgvelKPBN4P4P2dZjW5cHGCEx7m9Bwls3ur\nu5htYjN96cNDPMD/WEBd6pLJDaxnA/mYj3WB6vLc1lsXQAMaAFCZysQTTwPqAzaOYm4eAiuItliX\nyzls2GhAfb5gHmc4QwPqs4jFuveK9pq8jp5sYqP7mryOHpzlLCc4TmWqlEa6NizF/JwdgWjqSQ9O\nld7BRpq2sJkbuYlf0Z84FlPEZXpzI3vZE1Css7qsFLrSjTzy2B7AZGHh1LWGLK6jB33pxxY2UZNa\n9OJ69rLHdLyzWtM11KYJTTjIARJpSE96kUA9ZvK+5eWkF1OSSq8T9fLNbKIPfXmAh1jA/6hLXW4g\nk8zYGE8AACAASURBVA2sDyjWWa0LvGNdfRpgA9OxztSrZKKJep064Swo4MyOHcTVqEGdNm04vMw7\njWyLieGG116jRkoKF0+e5OAPP7D48cfZMafsdQA2h4NeL79M9caNOX/sGHu+/JK5AwZwcPFiYFJE\nNLmKi5ndqxd9pkyhz9SpnD96lC3vv8+2WbMA74dDeekCiKlUidb33suGKVMgyAyXlZrO7t7Nl4MH\n0+KOO7j9q684n5PD3q+/ZuvMmcD9EdNls9vp+Oij9Jk6lcK8PI6tWcN3v/kNp7Zuxaw5DUSTLprz\nU1xUxCc9e9Jn6lT6vfsuhXl5bPrXv1jxxz8CfzKtyWpdACNVlRuXy8W969fjcrm4L8Z7vPPVRCIN\nKaKIU5wknnjqUY/9qvdjKtix059+1KIWF7jAHvbyNd+ymS2qdWz0oy81qVk6G+N2ZjKLPTrvLysv\nTS5cvMO/uY0BDORW8slnHevZoHn9TXnrgpL3tHWkAz+RFfTLzK3UdJrTfMjHpNOae7mntCK5g/WY\nG+YRLl02bFxLdwZyK4UUcojDfMbnHA9wPJJZTXpoz08xxUznXQZxG3dwOwUUspaf+U717tpI6AL4\nXekMsmWfHwbg+QDqGxWRhiRSRBEnOeUu530675G1Y6cf/d3X5F728C1fs0X17mQbdvrSzx3rtpPN\nLGaylz1h0RQDfjW5cPFv3mEAt3ErA8knn/WsY2MQ96+VZVWiP4YOdCSLn4KOdVbrOs1pPuZDWpPO\nPdxLHnnsYDsbTA71CIcmGza6cy23MpBCCjnMIT7nM04E+Oozs5r00It17zKd2xjE7dxBIQX8zFq+\n57uANFmtC2AsnrN2P1z6eRLPm9qnzRWpfnQ62Gy2iHXrEwQhOqmIcaEi/iZBEEKjIsaFivibBEEI\nDX9xwZrpOwVBEARBEARBEAQhBKKuW6/NNhmANyLczWVc6fFdrpL3Zdpsk6NSE0hZ6SFlZZ5oL6uK\nyvgAu1Mb4es9ioHwQukL0qNJVzRqgujUFY2awFpd0agJrNNVUYm2cpZr0jxSVuaRsrIOyZwKgiAI\ngiAIgiAIEUfMqSAIgiAIgiAIghBxoq5bryAIwtWI9l2XRvjrVmO2243Z41mhKxo1mfk+0OOZWc/M\nMc2sY1aTmfXNloOv9QLVE4wmvXdKhkOX3nbBdmfTbhesHkEQhKsFyZwKgiAIgiAIgiAIEUcyp4Ig\nCBEk2AyYNiMTaGZHWV/v+MFmwZR9qrUEosuXpmB0GWXarNRl1fmzUpPe8YLVZFaXWT1mdfnTFIgu\nKzSpvwslI6/+TrKogiAI3kjmVBAEQRAEQRAEQYg4kjkVBEGIMor8ZFRiDDNcRT63c4YY8n3piinN\nUOllTY10harHl64YVSbPV5ZZq80qTXrajM6bHmpdwWgKNftdpqPiZ+RD0WR0LME8Rucl2Ay1Fec5\n2DHa4Rj/HKomf99HSlc0avL3vZSV+e9D0SXmVBAEIYrQGi2tMXFQ5F5HXWlXzIzaAGn35aAoaPPl\nzzAX4dA1X74Mcyh6/OlSlmtNsz/DrCwPhy49TWZ0WVFOvnQpmnyhp6u8jLxSVtoy09NWUYz81UCw\nXbvNVpitnqRLvY76GHqNJ+WtK5ThHoF0fQ9Ek7JOMEMYKlJZBTKsIJSyCvRYoWgKly4FMaeCIAhR\nil5F20mMobHSGgzlsz9j6Q9/hhm0BsHzgaZnfMoMtjVmUKtN2a+eaVZrtaqM1ESrkTejKVAjHy7T\nrH/e/JeZ1eWk1WU2+x2qYa7IGFVUAy1rf70erBoHrddoYtS4pNWlHytDHwd9JZaV9lhaXeEqK7Wu\nQO9fI03+jhcsvsoqUljREBcoEjEFQRCiEOWhaKbLjYMiYnQySk7VA9kq86V+WKtboNXL4yhEyebG\n4FSt41lxsdI0aysRWpOqRc/Iq/dnlckJ1ciXp2kOhnBnv/UyqOpjhxOj7HegRiDcDQsVAV89DUC/\nl4iWcDdMqJeZbVyyuvHN371itnGpPHRFY1kF2qMmHI1MwfReCffQGPA9PMaIcOmSaCkIghBlaI2p\nnkH1foh6mlN1V0iwxgwaGWat6VSOrTXM4Wpx1jPMyvHKTKqnkVcvU7YLt2HWW65n5NWoNVlZeTPq\nLm6UZQ6nadbT5ivzrdalaAtHw4IRkc4yVySU82bU6KXg6/5V1i/CEdQ9oheP9HTpocRdo/tXrUu9\nr2BmJjcTW/Tu3/LSZdQQp9ZdEu/0Y4o/TUbHD7Ssgo114TyHRpqMdPnTZHR8f5r0vgs01vnSZfbZ\nL5FSEAQhiimp9ug9ZEoeGXEUuk2gd9ctT4NqtS4tjlKToNail80NtyYzr/1QKpWe2V9rDKqRLn+a\ntN+FI/Ndst/QuouHk7IGEM/MtxOH28jr6QqljIJpNPHXXdwMV9tYU+3v1ZoaJYqp1y87N9pY52lc\n1dtpDU4g51drHnzFFO31p9dAqNWk/r2B4E+XLyOv1qX/e4JrONEzWr4aUrX3rt6zQbmPrSwrI11q\nTf4aLZX9WlFWZnXp9T4y0qTeZ7C6fG0faKOHsk6wDXJiTgVBEKIQ7+xkjGpZAQ5NZclBEfEUem1r\nfZbSs8KlxlH6oPTXBS+clXKj325UuQ2XUTbSps1m62WZ1ZP/hFubXtdsM1mqYPDfqq9vmkE7iZRx\nxUgPK8pQm+Xwha9JyQRPtMa0SHU3aOMalFxDcRS47xdt451VDUxqXVoTXWb6iogr/axkBPWMTDg0\naX+30lgSh2esU2cqFe3a+8xqXep9KmcSCtxlFa/qLaLe3kpNRrqUz8rxlUYv8O595Lkv6xsHtboU\nfD0XtJqsarg06n2kxt8wIis1iTkVBEGIUrSVtbLlJY8vBWWNSKCusDlLlYBny3g4Jh3y1qGflSnR\n5j2Zg7piZKUJ9PdgVmeDjLQZb2vtI9soS6UlGiblECo22lin/HXgmU1z4HSbG719WK2p7P/abGWB\nJtZ5mmhQZ5CCjy/+YqZ3l1XvRkv97qCemgLNvnlnAj2NqVFXU/XxPMeT68fhQLJvRhlTrWFWr6No\n0Ot9pNVkRVbXTC8BPfR6CoSj8dJXRt6XLm0PpFARcyoIghCFKEFf/UAtJM79nTJGpaTCVuTu8mj0\nILZSl3dWV3nQFpS23Jd169HL5lqF53hM49+u1yXPCLNjzfT25+s36mV0i0or33EUuKvjvjQZ7c+X\nrvLKCiuElln1XRE1a5LDMbYzmC5qZrvMXW3odectwkEh8ThxUEAchcQDZedcyQRW4QLVyXMvV9ZV\n8kzK/rTGx6wubQZJq0v9XYxKWxUuEkeB+3tFU4HO+oFo0q6vxNqCUk1qk1NWVgWlz4QCqnDRIxYo\nlqbI/X/rsm/axoUSTZ7aSrK5BR4NDGpNik4r0DPLahNoZOSNhnhY1cCgzXyrG3jjKaQIhzvjre4p\noN1XeRr5ku+9ex8ZDSVS9hGska+Q0TK29N/LEVUhCILgHz0ToX7IlORIy0K1nnlSt/oaPUB9GTYj\nTcp66hZjXw8Zz+545rK5ZroTaXUF8lAuwrtFWu+BatZYmkGvvIxalH1nSq01N1pdwbZyW2G69DIS\nJcuNz6Wv7IJR5TqQ36e9vvQmWjKD0fiyYDRVdMqquSWx4wJVuEBltyFUrhPFkNbirPv/JV0y47iI\nkwtUwTPrFWpX1ZLtC4mn0G2n4tzntpBCd3fQeAqoTp7bnCoZuYJSg33Bot4ZalPjxOHWpTanJce5\nSAwlGeYqXPC4rpXfUfI5tAYsran5/+yde3wU5b3/35vZ7JIlC4GEBAIJ4aJcFBQEkYuCaJFyKiq1\nXqr29KJtpa2Xau3psa22Peo5bbWe+rNWa22PlyMHb/Ve7wgoghcQkJtcIoGExAQSNmzYZTf5/THz\nzDwzO3ufmIjz6cuy2Z195rPfeeaZ5/P9fp/vI66hbCcBv5Q66ydKEWFTO1FJ+MuiMV0WSjJecgQw\nMRqv/n+yiLy4Vpk+S+3Sbe04peIpEMFc/0BOYYfkz5F0vFLZyvq5Wcx78WvOU2vbfskZY27Tfvy0\nckqFo06cFqY/pEfgCmYXLlxkAmvqENgXlLCD+SEsS9bsooGZ8rJCLl4D5oel1atujVJkA3mylYpX\nsuqHoviP1ctvF9HJlleyFLBUv8HuXKk92PkLLlmgpoN14qK+l7/gsruO6vupbSbzMHPI3NGRjpds\nL7vzpepXyTjly+tohhEx9dFBgBBBXXDGUSinER8RSmillGa8miANESSOV4tw+vIa46wQIlC0rQrn\nAID2ShVYRXQQJKT/LY4R0clUTkM7O6Q/xmsSmkKgioipEO9FhCkijJ+ofrw4R6aOwEx5CVtZo81y\nNFdwFJzEfaYKsswyfjIVN4KzzEtEvsVzQdgIVAeDvD5XIaZH750tRqck8BLvq1FS47V4Ylqzopzm\nJHOzW0Yk+rGA/AtkOMnpqBOnvR2F9C6B6opmFy56B1I9dNXJhYiMJu7fGUOhgyL9tZgY5Cu2ZF5C\nQFg/E7ENu+PVh5x9EY98OMnnsvKSRac8MRKI4NMna+L8doIrV16p7GU+Tk4hsy8I4pTYyoZXutTD\n7hBbdtcRzM4Fu4hBsiirU5zktuy3eDBHJpJdS1eQJod5ou4lgl8XnSGC+nFVBAjQQSnNlNECGH0i\nqolaOc3QmSiliAT6dV4dFOliMEiIOIqeahwklCAARRQ1btN/c+Ekp77aCVQxtolIYIAO/ERMvKwp\nmU72T8NeZkeBcITJEV0wO7vEGBC1bTl/XtaigvL1EfaQl6GIaybu/e64j+VrancOs+Q3F9hK9p1c\nOFj/jps42de4SO8sz53XUS1Oe5sQdOHChYtMYE7hSR4t8hMlip+Q5ftOCL9knFLxkoVERJvQWdec\nOP2AT+SVbK84Y3IY/gyiWVZe6nnsCyEZE1nnJhzZ8EqGTCIsTsGOSzJ+yaLN3YFsIzUucoNxDxhR\nylA4SDym4OsX0UVVkBAltOrHqtHV9BPlZOfM9DghgMMEtPMWmYReEWFThMtHVBeEqZALbzBHKuVU\nXXldrE8bhX2S4DJEqc+u2bxgdfTJ96l4rUhXWiAK2je7Z7yxinorP8FLFlyqrbqTU2KRQ8BSs8Fc\nHdeJAluZ8DJem4suyZlAmaQZ54OjWpy6cOHCRW9FppMSH/I2CuaUQREhbaXE9L7dfm2fBS/xYBMT\nObAXrt3FycpLhjzxzaf9fL4nczNPTMQ6Nnnd1WfDKRm604mQD7rL4ZIPXFH6xYV77V24cB5HtTh1\no6YuXLj4PMLqMZVTe+Q1cCGglRJ93ZX4jnUfuXyEoZWTzEu8b/aC+0wFROTvO83JjpdoW35fRBNE\noY6oFNGw49SdvARE2q66Ti5xDa51qwWnhJQdLxnWiEe6tZbdwUvmZxdxlz356dJtu4OXlUeyv7uD\nz9EKOaLm09I+4wHVnnIBpBBBPfUyTEC/f3IRidbU7VTH+bSRLUCYCH69Cq7g1UFAL4IkxsBMOOUq\nbuXngihM49fOLiDSkQFLdd/umfqL6KOIhMrvCzvJ/0FiWm938pLX2FvXTIqxTl5b2b2czFHZVHtK\nf5br1uX10YqFj1yc0RpVdRpHtTjtjehtgrm38XHh4ouKVCJLVOwTaWOAvsYU1IeX+NtHFKPCXv57\nV6biZSeYFemhJQsuQ2grloedYnuubLjZ8RLvq7aI6qm88nrcZGsvBa98o7yZCHF1uwmf7UTIrsBO\nsnby4WWFPHFMNpG1rsNywvFh/1uN92QhLyC/ljk56Vyw42atdmydbMuc8unjRzvkCbDqWIuowhQj\nBbWMFooIE6aIFsoA9IJIYQJE8JvWrToxYVYwRjCfvj5RbbeIDr0gkkgtVh2EYtlAQE+oNVpxch20\nN0GYCp5iW6q4Nq6I9GOR/msWiM6LL8FD5Wk8v4QVYhiFiWSeye4fJ3mJdZtgrnBvXvNsIJNx0Ble\nxvMmUZgqptcCdoWL8uGQaozKTMibiynly+uoE6dH6J0Ve10R6MKFi0xgJ7SsYjD1tin2D3nxEM5k\ne5dMecmRXQE5qTaTh2cufDIRzPLnVg4xGxupD2ivyU7ZCq90UVxrO9ZCGFaeMicZ2YivZCLLTjAn\nu1aJvGIJ6dq5iNRkUVy7wkji+GTRBCunfERzuuiyeN/KwW5ibb3nnBDORyNUoaU61oq07U9ExDRI\nSF9f30qJbkMhbKLaXqdOCxtx7wrHIBjF1dTtUNToaQdFhAjqosvIiDBayMYOahvJf4uQBrIwFf+K\niK4czRWZIrJATQe7sSgVL1H0SbxGknlqUSbVflH8dGjVjI1zZS5oshE9Bmf1X7lYlCzsAa0+guE8\ntUZ4nYJ5vDVnxsgOEfVz4YjzZTROO8lNhvUZb9wViXUnnMJRJ07BFYIuXLj4/MDugWCtPitDPMit\n+y+mm/BaRU6qyXs6QZMMcoTAENKpH1rZCIpUD89ksJsYWvdfS3fe7EVq5mnCqewjC5l8HAvJeOUq\nkuS+lCuvTIVpLpzM7+cv5K3c0k3ErBW17QSq3fm+iJCj5gqKti9njKi0L6ac1iuLU0ATpyIi6EzU\nRpw3jqKLUavTy68lzaoc/IQI4pOWMQhOhmB1LnKqtpe4xYgcpRQRSkUrGCU4mCVQfray3h/ib5WX\nlZMh+OTIqcxLjrxlInaSCWa5CrF83cwRSuNsxtY8Pr0HWaOmMkcrMhHy1roM4j1rBX6/tp2NiDKr\njgVDRFvToFPZyi4aamcraxaIPFYlq8orHB2pCh7msw3aUSlOXbhw4eLzDsXmoRCVJjpimxYvhmf/\ns5j0JntQmTfulh9yEVuB6AwX87omg4+9EBfHpRaE5uhpeg6Z2VqeUMsxcWOClDwykSx6mur8PSV8\n8umD6eyd6cTGCSFvRS7tOSHij0ZYtysS94AYK0QUFUQ0MKzfk2IPUXthY4itXFIx7XipiFj+Rkuh\nFdFAI33W+L5ZbAlOou1sIYsIUNeX2q1T9xPVBZeImNqtdRccZV75QjyLrMs57AWX2dnTXRXm49p9\nJ3glq0sgR7jttjvLh5fVGWodG631G4xsFPVKqo4O83eSPSey4QRGBV5r/5a5yndYJgJdPibXZ78r\nTl24cOGilyLZA9G6hsnq1U+WitgdsHpyjRRM9bEnozsn59YImTyxSIwy23u6nUAq29tPKJJzsfKy\nptLmC6tgToZ0gj5TpLOznYBLTL02+lemEx8nnDaCW2YRHTHZ++zuw88rrBNjq42F480QXD79ewLd\nIbbsouby/SePucbe0sY1twqbXDmlExHW7Ayr4EonbHIVEbI97Pq5neCSX1l5xXUeZls5KbisrxMF\nly/h2SBzy1dwqedNJwTNUd9MoqJOwBpBVUzXz1ycL9nvd5KTK05duHDhohfCLBzMm5lbPbki0hDX\nHmb5pm1mwsv6IEoVDZTR3RE9O17iX/GwTzYxUP91XlDYiUDrOkprpDdZ2qsTkPuIdaIq9zXZg98T\nkVi7FDbBQ56AQn42yvS3pZs0p5uwW/FFT++1RrOsE2PxXiYiwikBYRU4MpJN2EUhHVHwzcrLCVFj\ncEgvblJFuAQncHadoH0xM/sx1bBHZs4xJznJn2UiuATytVUmkcpkmUDJzu1Ev7KOWYmCPrkNk62x\nF8iP1+cY39u3j6cWLKDpgw+44M03+fCee9i6ZAkAx33rW4y/7DJKjz+eeCTCrhde4OMnnuCTl182\ntVEzfz7jLrmEqrlzObRvH7tfeYW1d90Fe3uQ05e/zJgLLmDEggUcamhg49/+xtr//u/cCGXAa/i8\neUy/+WZKRo+moLCQvStW8Mkrr7DurrtMbfQdMoS5d9/N0JkziYZCbH/qKVb89KfQ2XO8AhUVzL79\ndgadcAIDxoxhz7JlPDFvXu6EHOA0auFCJn7vewyaNIkCr5dPXnqJHc8+y7alS3uM06ATT2TOH/7A\nwLFjKSwupuGdd9j96qus/eMfoSNnWo70K4FARQWXrVtHoLyc+4YNg4bceX3eYS3Is5j/4Cn+RAP1\nXMRVfMDbbGA9cRSmMpETmMRgyokRZyO7WM9WtlALGA+58QxnMsdzDDWEaOdjdvA2azhEa9b84ij8\niF/xBPfSSJ2JE8AkJjORyVRonD5iFx+yLYHTWEZxAuMZy2jaCfEe63iL1XnZ60f8mif4M/XU83V+\nyPusYj0bARjNaOZyOqUMREFhB3Vso5YVvG9qrx/FnMd8RlBFhAgfsYUXeTVnXgCLuYWl3Ecje7iY\nH/EBb+u8jmEUZzCHMgYk5RWkL2dzJkOooJxSdlLLX3k4a04yt2u5KSmnVLYSE5bjOJZTmMxQKiig\ngI/ZwWa2sZ6P8uJ1DTexhPvZx14uYzHvsZoP2YhCnBGMMfHayW4+ZhcreVdvo5IKzuVLDKIMHz52\ns4ft7GQ1qziSY1UKO04b+dDWVnacZBTTl6v5Pn3py23cQZi2nDh93mAnRGSBGsfLLSzmPpayh0Z+\nyNdZxftslMaVEzmRciqIE2Mnm9nKh+xiK2BMjEcwnglMYgSjaCfEdnawmlUc5GDGvIzPYvyaHyVw\nWqf18Uw5jWIs45jEMYwhRIgP+IB3eDtjW9k5PmReP+Ji3uYD3VajGc3pzGUgpSgo1LGDWrbxPitM\nAqKYfpzFIqqpIUKEzWziZV4CupJyE7CuwbbyamAvi7mM1bzHRj7U7187XqslW/QlyBksZDBDKGUQ\ntezkYf6alo/My5otA3AT13A/S9jLPhMvgFEcwxzOYABlKCjsZie7+Jh3Wam3cyzHMYnpDKaSAgrY\nwcdsY3NW453VIZOKk9VWdpwqqORLnEsZg/DhYw+72cl2VvM28Qwn59a+ZcfpQ+m5kI4TGMK0L8V8\nn6vpS1/u4DbaCGfIKQPE43GmTJnCsGHDePbZZwmFQlx66aWsXbuWyZMn8/DDD1NcXAzAH//4R+66\n6y4KCwu57777mDVrFgCbN2/mkksuobW1lYsvvphbbrklI4LJUDJqFIWBAE1r11JQWEjFlCnsXWkY\np+r00/n4qad48/rr6YzFGHfJJZz73HP8fcwY2nbtAqDPwIF8ZelSNvzlLzw5fz7FlZXMvOUWiocN\ng0u39winipNO4pynn+atG2/k/dtvp3LWLGb+x3/gLSqC/+weW0Xa2vjgD3+geeNGOmMxhs6axdz/\n9/843NzMlkcfBaCgsJCL3nqLziNHePnyyxk4dizTbrwRf0kJXJGbkneCl+L3c7ilhfdvv51jL7gA\nj5Kfd8sJTsNmz2bvW2+x6le/InrwICMXLmTBI48Q3rcPlvcMp/jhw2x84AE+XbuWI+EwQ6ZN47Tb\nb8dTUAC39ZytdHg8LHjkERpWr2bU2WfnRsgB9MaxLkgFhfhooIEufAymmk94VB/8RzKCTWzmWV4l\nRgGTOJ7vsYhbuY/92qQ3SCGXsYjVrOUBHqYfQeYxl/7043Eez5iLEFsllOHFRz0NFEicBGoYxUds\n4Rleo5NOJjExgdNwBvEtLuBFlvEm7zCKYczjdAopZAVvZm2nGAplDMBLocZLYQhV1PG/+jERDrOS\nd9hLC510MpzhfJV5HKKDD9ikxRoK+AHfpJM4T/AMgyhjLqfShz48zT/S8rCmc8ZQGEApXgrZyz4K\n8FHJMJ6kTp94HiHMW6zSeY2givOYTwftrNUmPgpeOgizklVMZDweCrK2kYxknKy2aqSJKB5bTiOp\nppY6XmcZh4kwnjFcyCJCtLObnTnzKqSQfdRTgEIlw9jN/6XldYiwzquLKO+ylnr2cYQjVDGUf2Ee\nCl28yYqMuYhrmYyTuH4RDvM2b9NAM510UkVNAicxkfcAF7GIOvYwljE52cgJ9KaxTkyOywlSiJd9\n7MVHAVUMYanm0AIYwUi28BGv8AIQ5Xgmcz5XcA//RSv7AfDRj/O5hA9Yw8M8QJB+zOUs+tGPJ3gs\nK04A/Smz5SRi+SMYySY28wov0EmciZyQwKmc4VzAt3idl1jFcqqp4XTOohAvK7KYFMhisJQBJl7D\nqKSOJ/VjDxPhHVbSRCMeolQxgvmcRzsdfMRaALrw86/8kE7iPMMTlDGIU5lLH4p4mqdsOSRzMNjx\nUjRe/8du/TuZ8PLgp4Mwq1jBeCbioSBlZDUdJ0DjVUg9+/DTxTAqeZxd+vFhjrCKtxJ4hTnER6wl\njpcqRlPHJ7zJa0Q4zDEcx3lcSBsP8InUT1PxEtwMW5k5CVtZr2EnndRQlcApShdreZd91HOEIwxh\nOPNZQBeK7XM0FScQ/T2Rk/jeYSK8zds000AnnVRTwwIWmjgZ8LCIi9hDHccyls4sos8ZidP//u//\nZvz48YRCIQDuueceqqurWbp0Kddddx1//vOfuf7662lqauJPf/oTr732Grt27eKqq67igw8+AOC6\n667jpz/9KWeeeSbnnHMO7733HlOmTMmYqBWVM2fSsHo1dHUxeOpUDre00L5nj/75P7/xDdPxK9av\nZ9icOZx03XW8/sMfAmqEy6MoLL/hBrricZo3bCBQUcGZ996L95u/IxbLLiToBKdJV19N7Ysv8t7v\nfgdA88aNlIwaxZTrrsP/hz8TiWSf/pOO177Vq9m32ohWtH78MVVz5jDhiit0EXHsBRfQb/hw/jx4\nMB2ffsrOZ5+lfe9e5j3wAEN++f9oaGjvEV6h3bt54+qrAVUUFg8dmjUPpzm9ed11pjb3b9lCxUkn\nMeWGG2D5ez3Caf+WLezfskU/pm3nTgaMHcuEK66A2x7MmpNTvARO+cUviB0+zAd/+EOPitPeNNYJ\nITiMEdSzmzgFDKWKDg5xkDbE8P04T0rJXF7qeZ1jGMbpnMwTvALAcRxDAQU8z2sUEGMfTRRTzHl8\nhScpoDOL9IcYCsMYQQO7gS6GSJxE+uVTPKanYcZQ2MMyRlFt4nQqU9nCDpaxCoBm9lHKAE5lOqt4\ni1hGaZFmIVjJSJ1XOTWECZtsVcce4jToE5Zm9jOaak7hBD7UIg4nMJ4B9OcWfs8hwmxmGwcJPldz\n8AAAIABJREFUcT4LeZ3XCBHK2FYCVdRQr/GqpEripSTlNYrhTGOSLm5aaeNZ/gnASIbTj2DWPGR7\nJeeEiROo19yO07O8akqNe5NmhjKE2czgoSzEaTJeg03XUO1PMi+Ad214fUozn9KsH7OfA5RTxhSm\nZCVOBVJxErZSedlfP9lGc5lNjBgreadHxWlvGutAnRzXUMVu6ukCqqgkTJg2DuoT4yc1R5r4u4nn\nGc4oTmE2/+Qp4ngZwzgK8PAKL9JFJ03sI0A/zuYcnuKJrMY6gFEMteUk8BhPIa/1fIV9CZymMZPt\nbOVtTYg20UgJZcxgFqt4O6OxLltbqT3SsNV+3mU4o5jENF1ETGACJZTwe24hzCG2sZk22jmXRbzO\nq1mPdVZeNQyWeKlc9lBHg3Q/yLzWswGANlr5J88CMJyRFNM/Kx5WToDOSyHOMG28k69jMl4ncorO\n62WeN7XdyAoqGcosTrMVp+l4jWIodexN4CQkXCKnZqo5xsSpWRvtBJppYxCDmMKUnJy8mXDaQ53U\nr5qpYaSJk8Bs5hIjxjus5FjGZsUjrTjds2cPL7zwAjfeeCN33HEHAGvWrOHnP/85fr+fb3/729x2\nmxp6Wb16NfPnz6e6uprq6mq6urpob2+nuLiYrVu3cuGFFwKwaNEiVq9endMgtvjAAejqQvH78RQU\nsHj/fgoKC1H8fhbv309XVxf3lJbaftfj8aiRIg2fvPwydHVxwuLFbPr73wmUlzPussvY9fzzWQlT\nJzn5+vUj2m4WetFQiD4DBzJ+/CDWrt3XrbxEBKxm/nzW3GaE1IbOnEn73r10fGrcBE1r16IUFnLy\nyUN5+umtPcLLKXQ3J+t17klOnoICBp1wAseefz47nnkmK07dwWvYnDlMuPxyHpo0ibLjj8+aj1Po\nbWPd9fyGLrrw4sWDh2u4hQIUFLxcx68BuA1zpEKkxnm0/4kJ/1Z20UUXM5jCWj6gmL5MYiJb2JbR\nZE2IByunq7kFJQ0nwasAKMBIsfLjJ6IVOhGIEKWIIgYxiIYs8rqz5eUlTheFDKOSsYzkdd7SP6uh\nioMc5JCUfiQiscMYymYMJ0+29rqOX+u8buCXdAH/mYTXGEaZeDlVBCkbTmLtmp8uBlOVMScPnm7h\ndYslfUihgGqGmHhZOXnwMIQKjmc8WzK4djJ+yk1ZXT/BaSiVjGUUy1hp4jOSGqYyiT9yL4Mpz4qL\nk+jpsc4avbmJn5rs/Guu06ys8EtuALoSrr36fS/goQtjrfQOPqYLmMopfMA6+tKXEziRbWxNO9bJ\na+mz5WReK2vm5KePvjWOODZCJKOxzglbqVkk1YxiLCtZpvOqooaDHCTMIb39BuopoIChDGMLmzOy\nVTpeN3EDXXTp94qwlZWXHbqyGEvsooJWXr/kBp3Xz/mZiVcye1lhjTpmO96l4pTKVpUMZTTHJrWV\nOtYNYTzHZz3WZcLJ2t8LISmnGkYyiancyx8pZ3BWXCADcXrttdfyu9/9joMHDQ/Du+++y9ixqgoe\nO3Ysa9asAdRBbNy4cfpxY8aMYfXq1QwfPpzycmMgHj9+PI888gg/+MEPsib80MSJ4PFw8Tvv8Nr3\nv0/TunX8y5IlbPnf/2XH008n/d6E736X0uOO48XLLtPfa6+v5+HJk7lwxQrm3HEHnoICtj3+OM9f\ndBFwU49w2vCXv/CVxx5j9KJFfPLSSwyZPp0Jl18OQFVV/6zEaba8rqiro2jQIJTCQpbfcAMf3Hmn\n/lnx0KE0rV1rOv7A1q0cCYcZNqxfxpyc5uUUupNTzfz5jDr3XG0t7Jwe5XTRW29RPnkyis/Hurvv\nZtk11wA3Z8zJaV6B8nK+/NBD/PMb3+BwS0tWPJxGbxnrhLC5j9vxAN/kKl7kCRrZy3lcykbWspkt\nphQZuUjMTCYwmDIelVJQDxLiTu7nB/wrZ/MlPHjYyCYe5fGMk0PjeNNyMo41p1dNYQoVDOJ/MfrH\natbyDb7KJsaxje1UU8VUJgFQQv+MxWkyXudyGRtZxxY2JdjqBq6nLwEKUHie11jBGv3zEoqpxzzO\nfkoLRzhCfzIf6+J4uYc78RLXedXTyFe5mI2sYxNmh14qXk5WOb6HO/Hg4Tss5nmeYh8NOierrQB+\nxrU6pxd5hVWsSjpxOJbRHMfYnNbCpuIlbCUXzJFtlYzXlXybSoagoPAOa3ieF23PnawwUSacZPyM\na+hLX53TSt7RPyumLxdyHkt5irC+0L8LJZ+iDTmit4x1wt53cg8ePCzmOzzF8zSwj4v5KutZb+sM\nEtfrZE6inAqewqjnEOIg93A33+EK5vEvePCwiY94TEoNz4fTOjaylU22342j2HL6gDV8jUsYw0S2\n8zE1VHESqojvT0lGY53o+9na6hp+Rl/6olDAK7zIO9LawH70Yx/1pvG6mWaOcIR+GUQrZTHoJC/R\nZq6QhbMdr6/zVT604RVH4TpuyJjXMRzDOMbzIH9Ly0f+fnfY6nK+yxAqUVBYw2petER5neaUyk59\nKeY8LuQJHqedCGU5LEFJKU6fe+45ysvLmTRpEsuWLdPf7+pKv1BawONJ9Cik/r56npeAUcBoy6eh\nujrKJkxAKSxkx7PP4isuZtCJJ/L0woV0NDdjh1ELFzLnD3/glcsv58BW44FSOn48X331VbYuWcK2\nxx4jWF3N5Guv5ezHHuO68zdm/Bud5LTrhRdY/ZvfcMovfsFXli7lUH09a++6i1P/8z+zTjPOlteS\nmTMpKiuj+owzmPKTn+AtKmL1f/wHoF6zbCN/nwUvp9BdnAZPm8aCRx/lrZ//nD3LlpGNOO0OTs9d\ncAF9Bgxg2Jw5TL7mGrx9+sB36zPm5DSvLz/yCJsefJC6N97Qj+8C3vR4IIl3sDvQE2Pdmzer6YUF\ndFIzp5qaOdX6Z3G8HKCdcgajoLCZbfjwUcFQHuUhk6dbxgRGs5B5PMEzHKBJH+DLGcTlXMaHbGQD\nmyihP7M4ha/zNZawJOPfmC0nhTjHMt6W03a28hrLmctpfJ3zOUiIt1nDfM4k00IOqXgNppIlPEiI\nwwnH38df6EOQUYxgDtPxo/C6vvarK6fInx0O0paWl2yze3iAvgSS8MoOyQSXzGkrW7Ky1WnMoJBC\nW05VDOVivsorvMYn7EjJKRtej/AwYS2KLX8/E16P8BhFFDGaamYwnUIW8g8yzxbJhJPZVvcTIMAI\nRidwupBFfMCH7JDT/7pgzR3v0Byynyd0B3pirFtxs5FeWD1nOMPn1Jg+b+cAFVSgUMDHbMaHj0oG\n8zCP2NoZYCzjmMdX+AdP0cR+RIr1IMr5Jt9mI+vZyEZKKGE6M7iAi/g/HrVtyw6ZcpL7cTJOW9nO\nMt5gNqdzARcR4iDv8A5fYh6dWTieFOJZ2+p+7iNAgJGMYiazUfDxpvZs7SS3LIfceRnR5VS8nIA4\njx2vIQzmIYmX/NTKlNcwqjifC3mNV9glrV3NhFcmtsqW01KW0IciRjCC6czAy7k8k0GNhGw4yUjF\n6TwuYh1r2SUv7eiCd+5Yk/FYl1Kcvv322zzzzDO88MILHD58mIMHD3LZZZcxdepUNm/ezKRJk9i8\neTNTp04FYNq0abz6qlHRcMuWLUydOpVgMEhjY6P+/qZNmzjllFOSnHUOAGfZdIZvbNxIv+pqPF4v\nSmEhP2xrw1NQgNfv5zs7VSP8fdw42vcaBXrGXHgh8x54gFds1rlNuOIKOpqa9DWLAK07dnDx228z\nduw+tmxJb0SnOdHVxZrbbmPNbbfh79+fSFsboxYuBGDbtswjS7nwCu3eTWj3bpo++AA8HqbecANr\nbruNrnicQ/X1jLSsBRwwZgyFgQB79thXwPsseDmB7uI0bPZsznnmGdbceivv/fa3vYJT+969tO/d\nS/PGjYSbmvjyww8z6MY7+fTTzCqoOc2reu5chs2ezZSf/ATQ0p+Bh2prOfX+dSxe/AKfhUjtibFu\n9s2nAvbbSFzJtfSnhAIKUCjg37gZDx68KFzFDQDcxZ2m6pMTOY7zOYcneVavrCdwMpM5xCF9zSJA\nC/u5ku8wiDLT+rxkyIXTBI5nEefZcuoClrGSZaykD34OE2GctgavJQM++fBqpY047eylAQ8eTmMG\ny1hBJ10cJKTzEBikFcSR1yg5xesAh0y8Wmmz5eUEnLaVwEiG8w0u5g1WsNLi1c+X17Vcr/Oy2iod\nr4OEOEiIZho4xCG+polnJzlZbdVKG3U0JXAaxQhGMpzTmKEd7QEPPHbdUt7jfZ7hOVb8KjdHRDbo\nibHu1JtnJ+VzFT+khP4UaP/7Bf+uLUlQuJ5rAbiTu0x2Pp4JnMsinuEfeoVwgSlM4RDtvKBFjnbz\nCfvZz+V8lzIGmdbnfVacuuhiBW+ygjfpQx8Oc5ixqNHoZjKf1+XCq03rlQ3U4wFmcRorWE4nnYQI\nMcayFrBMK4gjrz//rHk5hUx5HeJAVrxqGMHXuZTlvMnKLNewdxeng9r/mmjkEIf4Kl/jNa2+g1Oc\nMr1+IxhJDSOYyanGSTyw9LrHeJ/3eI5nWP6r1HZLKU5vvfVWbr31VgDefPNNfv/73/PQQw/x29/+\nlgceeED/VwxIJ598Mj/5yU/YvXs3O3fupKCggGBQLdowduxYlixZwplnnslTTz3FnTmkZj45fz6K\nz8e8Bx6g9sUX2bZ0KafcdBPxSIR3/1PNhT7UYKRHHH/55Zz+xz/y4qWXsv3JJxPa64zH6YyZF6KL\niXymXkSnOcmItKmDw3Hf+hZN69axffv+jDjlwssKxefDFwziKSigKx5n78qVTPze9ygaNEhfd1o+\naRKdsRirV2derdcJXoXFxTovK7Lx/nYnpxELFvCVpUtZ+e//rm7X0gs42R1ToCj07evLWJw63a/+\nx7LGdPDJJ3PWAw/wxLx5/Grzv2TEyQn05FhnF+F6hAdQUFjI+WxnKx+xgdmcQZwYy7WHoVysYgon\n8RUW8BhPsN4m3bCTzoRopIcY6rQpOSeZVypOK3mTTgqy4iTjMBEApnIC+2igBfuxzglbyb9LTRFV\n8OPXIghd1FLHyUyhLwF93ekwKugkrpUYSW+rVLxidOqFKlReBQnfN3j5dF6J6LJ1bKSyWeacEm2l\n/pvIaQzHcAnn8zKvskpKY82EV6q+dRpnEidu4qXQZWvvVLaSj/HgwUdhWlvZcdrAR8xhbgInOyja\nym+Z0538CVAzJQCGMZRFnMvfeJBPMxBMTqG3zev+hwdRUFjEeWxjGxv4iLnMIU5cL14l2/kkprCA\nr/AEj7HJZguPTroSBI7xd2bzA6c5yTisZSdMYjL72Mf+LMRptrysUFDw6X1SFe5TmEpf+nJIc/wM\noZJOOtkjVe52iley8crKKxG5Oecy5ZUsJ9CO17EcywVczKu8zDtaMb+e5mR3jAcPhfgc5ZTqfDKn\nuzHPeYcyjHNZxIP8LeOxLqt9TkUqx5VXXsmll17KmDFjmDx5Mv/1X/8FQEVFBVdeeSVz587F5/Nx\n77336t/9/e9/z6WXXsrPfvYzLrroopwKhLTv2aMWc5k4kVe/+13adu2ibMIEVt18s74Vi8Dka67h\n1N/+ltcWL6b+rbcIVFQA0BmNcviA6pFYf889TL7mGmbdequa1ltVxdSf/pTdr7/O1q2ZDRhOc+o/\nciRDZ82iftUqyidNYsr111M6fjxPzp8PnNkttjrpxz+mZfNmWj/+GH9JCUNnzWLyNdew5dFH6Tyi\n7gm3belSZvzmN1y4fDnLb7iBgWPGcPKNN7Lpf/6Hffsyr9TrBK+tS5bovAAGnXACoG4N5AsGKZs4\nkRM8FXz4YSOZwGlOx5x/PgseeYTVt9zC1iVL9OvcFY+TaUDIaU7Hf+c7HD5wgP2bNlFQWMjQWbM4\n6frr2bN8ObW1me9z6XS/2r/ZXHAhoK1hOrB1K42NczLm5TR6eqw7SBsePFQwmOd4klb2U8FglvEq\nBySPKsAMpjOfeTzDc3zCborpC0CcOB3aRGg17zGLUziLuWxgE/3pzxxmsJNdNGfYKVNxamW/aWKf\nCaeBDKCGanZTRyVDOJXpVDCIv5Nd9ehsbDWTGXzKp7TQQh/6UM0IZjKND9mgi/f1fMSXOJ3v8S1e\n5BUGUcbpzGIt62gn87EuGa/XeT0LXhtNToUhqGNJEUX48TOYCsDDPjKrRZAPp+EMT+A0gfFcyCLe\nYAXr2UAx6tYjnXQmTcfMhtcbvJYTr6lMooPDNPIphUAN1cxiJrV8woEM9/W1cjrAgSw5TWe91K+a\ntPtMTNDFPdFMc1b9ymn09FjXxkE8eBhMBf/gaQ5wgMFU8BpvJNh5OjOYx3ye4xl284ne39RxRV3H\n+y6rmc4MzmQeH7GB/pQwi1PZxc6MxzqnOQ1gIMMZTh27GUIlM5nFIMp5kL93m61mMDOhT05nprY/\nttoHN7KBuZzJt7mCl/knZZRxGnNYx9qs+qTTvAAGa0V0xFhXwWA8kPFY1x28juN4vsrXWM4yNrA+\np/HOaU6TOYnDHKaJJtQCcTXMZBafUEurpb3PipNVgPbV7JTNWJexOJ09ezazZ6upGcFgkKeTFPq5\n+uqruVpKkxUYP368Xn48H5RPmkQ8EuHAtm34+vWj9Ljj2Ls8MRVm0lVX4Sko4Ev33gvSYFq3bBmP\nn3EGoKbwPnPuuYw+7zzOefZZDjU0sOu559j04IPAv/YIJ09BASf+8IfMvftuoqEQje++y0vf/CYt\nmzaRjTjNhpfH6+W03/6WfjU1dDQ3U/f667xx9dVsW2os6O+Mxfi/mTOZe/fdzLv/fqKhEBv+8hdW\n/tu/Ab/sMV4Al0r9qquri8vWruWSLvB6f9MjnE5cvBiPojD9ppuYfpNRWKuttpbFox7qEU6dsRjT\nbryRklGjiHV0sPv111lz663aMdcltPlZ8bJDLtFvJ9FTY511vWYlQ4kRp4kD+AkwiAp2SXugCUzn\nFDx4OIezOQcj9X4XtTygTX5aaeYRHmU84/hXLiZEiK1sYy3rsuKVjJOV+wympeXkpZMZTOUcvkyU\nKHvYy5M8SVMGntVcbVVAAfOZRwklhAmzk108zwts5CPJqx/nPv7CQr7CVzmbCFHe431eyiBFKhNe\ndlsOZMYLfsR3Td/7AVcC8IsMipoJbk5yms5JFODhDE7jDE7Tv9tKK7eTPoqWqm+VU54zry5izGUW\nAxnIEY6wk10sYzkb00S1rNzMnPxZcXrO5vr1NvSWeV0lQ4gRo5kW3c61fJJw3ClMx4NHG1XO0d+v\nZRd/5wEA9rOfR3mEcYznEi4jRIhtbGUdaxPa+6w4efAwjVP4F84mSpS97OFJnuBTmrLilA2vAgqY\nx3y9T+5iJy/wHB9Jyys66eR+7uUrLOQcziNKhPd5j1d4qUd5AXwfc2GtK7W/b+YXPcZrKtMooIA5\nzGUOc/X3W2nlTm7vEU6ddHIac/Sxbhc7Wc6yBHt+lpycgKerp2eCEjweT49PTF24cNG7cDSOC0fj\nb3LhwkV+OBrHhaPxN7lw4SI/pBsXnCm/6sKFCxcuXLhw4cKFCxcuXOSBrNacfhbweH4FwO1Z7r3o\nNK7Tzt/VpaZlejy/6pWcwLWVHVxbZY7ebqujFTdmmQ6fDE6lDf5a2/TbCV69kRMc3bx6Iydw+5WL\n3mdnt09mjt7IqzdyArdfOQk3curChQsXLly4cOHChQsXLnocrjh14cKFCxcuXLhw4cKFCxc9jl6X\n1uvChQsXXyRYK7xmguT7xWWfcmN3/lw4JTu/U5xSvZ8tp1Tvp4Jrq8zhlK1SnTtbXunOnw0/J2yV\na99x4eLzCDf93UWmcCOnLly4cOHChQsXLly4cOGix+FGTl24cOGilyGWQUTF6oVWiKMQS/mdeB5D\nfjpOXuLEUVCkfwXHVLzy4ZSOl8xJRk/y8mrnTrSRwdGOV758MuGVi626k5fgJPNIfJ2/rVJFMGVe\nXum8qWxl5ZaMjyL1BRcuXLgQsBtfvkhwI6cuXLhw4cKFCxcuXLhw4aLH4UZOXbhw4aIHkCxaIiI1\nmUR/5KibQswU2bFrUyGW0K7VO2vHS44epeIlR9/kiJsdL5mTtd1MOGXDS25TjjBbecntZWKrdJxS\n8bJGUMU5rbysdkrVZq68FGLEUBKip8l4We2UK6dk3Kzvp7KVHeyunROcrFHUVLay8hFwKtLsonfg\nix7hssIu0yEdujuDwNpuOm52nzvNza69ZNkhyTg5yStZO7nYKlV7mcIdJV24cOGiF8BOaKUb4K0C\nMNlDVRYV2YoJO7GcipfMIxkvKydxbLacjPZS28sqQr2YBUUqTnL7VqSyQ6b2EkJeFvHyZM1OEFq/\nnyknKy+796y2snMupONkx8sOmTgXhHAG8BFN6VyQ28xHNKfjJYt56/t2vNI5PVyYkY3gE8sIupOL\n3TntXtv9nawNpzklO3eq953kZW0rburzmRV/c1Kkph6b7QV0JsXX8uWWjle2hfLy7f+ZFonLxlby\n57lyc0dIFy5cuOiFEIN68olIjDgKPqLa3/Yi0OnJWzpe8sRcCNPunLBZhamdlzxGYsRN5iVPCoQg\nzGTdrxXphLyVmyzczaI+npJTPkI+GRcZ2Tg9nBCD6YS8XTQyldMjl2uXjpdoX702XltOgpfK2bjv\nnOR0tCIbcZPs789KBGYrIpwagzMREr1NcMm8rFk1qXh1h+CyZmIkP3/ysSxXbukykqzZIXa2SpaB\n4XT/T7bO3g7WcdApx5srTl24cOGiF8E6UY+hJAz4RoQmDICfKD4iCemgsojJZ+JuJwCtvAwho57Z\nR0SLdBmRt8SIZ/rIYGb8FJ2bLBDFuX1AnDg+otrfBi/ZTk6LeTteMayRbNVWcgQV6BZO8nV0yumR\nr5CX25T/tSKd0yMZJycilam4JRPydlF5V6DaI9nE3T6d2zxZ765JutxGJlkL8r1rbserf+6k4Mok\nayEZJ5lXPtwy5WSXqZKOl1PXMl16fipbOZWOn4mdZOEsxt9Mlgrkeh3TiWW79zLpV1ZeuV5HV5y6\ncOHCRS+DIVNUARjFZxrcxYCvipoYPiIE6DA9CITf1fjbmcmR4BXFbxI5ii764viJav9F9PesnKzt\n5sbJLJojGifjPZ8usvxEADU1VPBSeZs55RvtsgpAWcQb3IxPFDo0wRwxiZtknMAZIW8X5c3U6SHa\n6Y6ovPW3xVDwE00Q8j7telq/nw+nZKn1soPBms4rOz2sDgYrJ9kZIp/ji4xkIlB1zBgOJhnJ1rE7\nIQSTCYlUFZetafnyd2UB7bTgSlcFOtV6/3ydNunsJD8XZF6yraz2sOOVj+BKxknmlSpLJBmvfK5j\nYh9P5OTFLOIz5ZUrUi0psl4/mZP8nvU+zpeXOzK6cOHCRS+ELEwjkqQCw7sa0IRNkHaChPQHgvod\nVVqIlFanogmyMBW8xARciOUS7XUxIfyaOJU5qW058/iRH9RxFP03i/YjmrAvIoxCjABhigjrD1dD\n0Pps202FZHaNkzgBEfYC9Oitn6huO9UyUV24RPFnxMn6XrKJU2KKqllOAbpgl50egpfRpk9qI7mQ\nz6a/WYV88giliHyrvGREpLacglWYGhM3VTTJHET/txdNbsTUDtYJrZ1DQIxefmkUlKPmic4lZyKV\nqTiJ86Ld0aIv2AkIK69skUpwWR1NqbJE5O/Y8XJCcCXjJGwlnEvCVpnwyhV2nOT3M7GVXR9y4rll\n58AUnJL1q2QOrWwdlamiuHa2MjsR7B1xsq2sTrhc+9dRJ04LpddHeoxFIgSv3sQJei8vFy6OdmQy\nWAtB00GAMAFCBIniQyFOKS0UE6KIMGU0U0oLCnFCFBMiqH9fFbLJh3o7cZOakzeBV5gAAAFN9FXQ\nRJB2ymghoEXhQgT136z+hljS82QquARkUSPkVJgAcdT0qDhegppQDhIiSEh7XyFEkDABFBuBlY5X\numPlKGOYgC6cAT3SXUIrAcIEtWupHi+iRnH9b6cgx2xlBwOoQj0IxDWnh7CX4CGEvtOcrPyskzZA\nc8KEdFFvtlX6fp4P5P4liyZ5EiYcDMki305HmY8mWO0rHGACwlmiEKOIsMkpoPZJeUFB7hEbuyiu\nlZMcUQqCniUi3yfivkrGK9e+IAsuMZbIGSwKcd0JJ2wlxhn5fnfKXlZhIzsGo5ITS9gqQEeCrQDT\n92Reot10yETAR0jM9DHuXaNfyddQfE8+RzaCy64/yZ/J10JcP9GmsFWAsP6++H+7pT7i96ZykCRD\nsmwf1dmLNrZFErKPZNFq7gv5L2M46sSpjEJ6h+gqtLzuDZzAzKs3wXUwZIfe1KdcOIu49pCI4KeV\nElWChsuIxxTK+zVSwgFKaOVYtlJBE3EU6qjSvqt+z/BuOhtRiuInTEDnFcWvp1rWUEsFjYxiO36i\nmh+4khgieuhL2X6unFRePk1+BnXRXEqzLgLLaaSMFgDCBHTB1V0CxxCAPl3Ix1Ho0CaRAMWEKNWE\nvCxkrdGafGA3CRXCVHASkw0xeRRCXp5MG5Fyo0/lMgmxj7YakyRrlN1HRD+vEKkqH58+gbTr59lc\nz1Rp06JvmaMccV0kGynsxqQ6WebCFz2dN5UIjOCnQ5NVYvIuHEt+opTRQpCQLsxU+VVEBH9CxCbf\nNFrZ4SXOEaZIH+viKJTQSgmtlNKsj3UdBPBJ33FivbFVcMlOOPkesLMVoB+vSMfbRbjS28Tu3jBE\nZVTiJa6fEDUltBLUxjo/EdM1jEqJ+lZ75Sq45D4lMpA6COgRQdlW1uwjcZ2F7azXIVfIzt2ozstw\neghHjHgtso/Ebzd+iwEn1rLLwrRDex5Yx1UfUf05apd9ZHWY5IMv9gjpQkdvETifB8HcW2wFBq/e\nxAl6r5D/PEIWXHWNVXS+3xdq4ZPSAZRd2MJodjD29U9gP1AFkWlNepRVfagUdctkWIioZkpVXrV9\nAYhO8zODtxnPJkZsbwDgUFUBrf4B2kQysy0iMoF4aHqJE8VYEyjEfGO4gnCoiIqKEiqTC5yGAAAg\nAElEQVSpp5gQo9lBeaSRDn+ARipopYQOTcTKD/lcBZcMa3tCzIcp0tNl4ygMoJVK6vER0bmI6LeA\nORLrjOACVaCGKdLPG0ehlGZ8RPSJN0CYIm0iK45LTBPOF+J6xrQ+LyY9MRQCBIAWTYqE9ZToVL+t\nO2CNKMTx6imBIktAOJWM7zjnZDgaYRWBByihhTIaKae9M0hxQYiJ+DiVFUxgPUHaCVFMExU0Uyq1\nk39/sBOBHRQRIsgBSmjSxgyAKuqooZbxbKKKOgB13KGcVgYk8JJ/by6QBZcQgK2U0EIpByghip8S\nWvET4UTWMZ5NlNBKBB+tDKBFspVYwy3/5nxgOCyLdOegGFt9RIngp4o6Kqmnijp8RAlTRCsl+LTj\nQaTnO+e8jEm2Erw6tLGsjBZt5AtTST2lNBPHq19veQyO5CG47NLDhfNKOFGFg1CMIRU0UUIrFTSZ\nnJbid6icjCilQKZ9K1kqtlg+1CFxUpflGI5K4WQQTrewJldV+IjbPOOz7fNHnTg9AoRQf1i/HuZi\nhRdIvm24CxfZwSrke4tA7a1CvrfDKmysFUkj+FQB+BxwzxF4q5AJbGADE5hZ+T5HFkHhdRCcEkJR\nEtNB5XSdfHjJSaGgRtI6a/vC80AHNBwaAXNhxIcN8Brgh75ndeIfHZE4mFO4rFwz5SRHpUSLXumB\n2V5bBtuhoWwEoRlrCRJixJYGaIG+I9sJDTEmH3HMEZxMOaXy6qtc4jpflVcRTQcriMcUAgPViUg5\nTdTEa4krCo2UEyJommjbRytST5SSRY2M6ya35TWl90bx49e85EFCeuRXtCdHu7LhlcxWCvYC2kh9\nU4hokyGj+FfMlPopfkeydlLBaqtUTgb5OMFN8LJP6fWa2rHek19kWJ03YrLeRAWbDo6n49UBsA5a\npsCkheuIozD2rU9gHTChhYrTmtjEeJOQtEu/zBViLAgT4AAl1DKCPetHwzKgA5omVLNgwQvMXP0+\nbAb6QPXMJrZXhdmF4VjMJY031fEiAyVEkEbKqQ2PoP09bawbDJUL6qmgkUmrN0MTMAKaj28xOX+E\nmLQil2iz7FDq0MRTA5XsaqzRnZat00qYwntMYIPJaVnrH0E9xlIGcT/lIpjtBJcY20IEaaGURipo\n2l9OtD1A6bBGSgtaKKGVCWyg4mAL4b4+mpRyGqnQ2xQZGwrOOBliUr8KE9CdC+2dQYoKOgAYwzYq\naGI02ymigxDFusPD/Kxysrq8arcOSciL5Til2tKhAGFKaaZMX0ZkLNcRnJzI1CrI98f0BsS0/0La\nf+K9/dJnPYmDGpcmDE69AUdQ+ezvaSIuXHyBIQs9QJJsYiIe1tcOUQS0A28WsnjGHVRSTz2V3DF2\nMYUrgUMQVoro0PzB1sJImUZzFOnRbuUkokSCm55g1AGMgzvnfg+A751wJxyDXqlGTfNSk6jEw9lo\nMf0WJ6lsJVfgFREsndceIAZltNBCmToAq3MjIlqql0hBi0qRulwjX7KtFIlfQPNF+4kSjylE2wO6\nuFaIETgURYnH9TQ3cd3iCdbPbUJk5SVEnuBl9r6b90tU09wCep+SU1ZzcXjInGRefr1fdeh9zK6q\npzzpFBNiIUxE0alcbJWszxtJgeYK1NaIhRAjchqhHJXItJ9/kWAICUNE1FNJx7IB8HPgRPjRwt8x\nhq1sZQy/m/kjmADcCgPe6qCSei3NO2K6S5yCiLq1UMaenaNgCXD1epgAty24FoBrp90G42D9xcBT\nMGr/Hn29vUg/dwLmqJtfF/Lt75TB/cB3aqFVXfsfJgBtULsQeBjKPmzXagEYReqs42k2sBuP5KyV\nuv1VdL7VF+4Ffg4NfxqBQpwRjzfALcDfoO/qTqridXq0V1w/MI8NuUIW4SJKu6e+iujKfvAPLy3/\nGEoj5ZTTyNDlLXhfgH7vRKkK76GEVtM6VKf6lOyYFf29mTI+qR9By7tD2bN+NLuoIY5CDbsYXbeH\nodtbGBH+hDKaE/p6PvaROYnWxPNQdXxUUB+upPZgDXVU0UERRXToGQOVqE6QICH9GaJId3Q+OCrE\nqUCqx1BvEYS9CR3S694W3TrqQvouXKSBdaIgD/IBOtS1QyUx6IDFp92hr3UDNeXyjuMXw2E1rSxE\nUCtf4NMfPALZTozNnAwhKIo1BAnBoC4ohMu+/RdVAKKKwb+cfRl4oWsgWvpSkSYfE1OksuUkT2LE\ne7KYDxKioPQQHFbt5SNKFJ9up0MDC7QUr4DpkZqPiEglbPxENCdDmJKBrfQf3MymxvHEURgf2URz\nv/60KiV6ilQ6x0KutjKXu1ALXQQIU6xNMHzaujkfESkdrlgXzNZIbr62SsYroK8mlBwNGuRU5LB+\n/ewj3vmKQLNTRhaoRtVYce5IgjBN7FMu7GEIVD/haBHUAptrmb7oDUpo1e3cSglvnDad2peAOjQB\nYV8lOV8YzhfV6cCnHtgI0MTJC5brKcWlNLN82sk0AWwGz350B4YouJMv7JxlUXyEwwHVAfceQC2M\nUjMxmiiHkaoZWQfUo9/fVuHghO2EUBXje7Q1qJ58HfBqB8zSDpwGHY+h2nE3BNvEysuoo9fQPEYZ\na1tp9qv22gDsgXaCBGmHetTodwP4W9AdifL4ma/gsvIT9uroLIJWPzSqnFqjJYj0WeqBRvC3QZHm\ntBPCvbsgxrIoPtpbg3Q0q1HdCH78RAjSTsn+DoLhdt2JmK+zw4rP/Wgpbvkj2AvQDtAzoT9LxJK8\nBrMQ7KkLYI0o95Sd7HAQlZuX3sPJhYvPCsaEN64LGpFgU1LdSuixoE21Qz9xvLxx53R2U6Wv9bEK\nrXw5qe0Y0cAgIUpopWpkHfFbvZTTqKex+YjSSDnPXDWPGAoNVJqKZOTLSfACeQ/TiC7ky2hhdMUO\nIj9WBYOIMldSz5OXflmPPLRS0i2cxDWUeYk1RMeylQ5fgGCFeh3/z38RQUImwWWtYJkvrLzkvlVC\nK+U06aJqNDvYwWgAU9EOq1B2ylZ2vOJ4KaFVn96LiaJY82ddK+UUJ5mX3OdFAZe45tYV0WU/ESL4\naaFUF1CQuM7MRXoIwdXWPABWAHRwPo/TSonJto9zPj9kFbwDAxa24QskCq5sz5vqPZGCSS3wLMAE\nzuKPeoSyhTJe4iyuYg28AHwNAqPDCf3brshPur5hTW+NSf0rgo/25hLYDmxuBCr4zrS7qWEXu6jh\n7tHfYS5/pfElqDgbSg624+tnjrqJ8zux9664F8MEVCG/GVirzuj6j20jjsK+qv74Im0ceRn6nagK\n+cDAMHJWh5OQo4LhaBHsA7YAbwHLoPGqCipoVCPPXqAPMAKKqsK6jboLYlwNtwdUXrVAB7QNHgyT\nYfCuNj1dnGIIDAlj3qPbeVvF8eprT0MHg6qYb4cWbwXh6iKKCFNxsAVPE/j7QjAQ0u7PmKN8Pvfi\n1IULFy4+j0g3kBsPoBii/L5IYVvOaVKUSFRR9XEjt1DFbq2inrouT44sOsVLtCsEainNeImzngm6\naBBFJ+7mByjEqKRBT7ftDk7qcWZeIgWqgUrqqUQhzlom8TwLdJuW0JoTp1x5yWnaIYLsYBSvcQYh\ngvr7RoQjcQP2fDkl42WkRUfYwWjiKLyg2cmczpp9n8qVl+GNV9eXtlJCHVVE8CV83l2c7HiJ76oR\nBrVgjiqYjXToXDm5cHG0wr0PXGSKz704FUWGRBEWq4+jpyJvXstrmVchPW94L2ZevSlC2Q+DW29L\nN3bhorshT7aFmAkSooo6Zhxcg/eXsPbOSZZS9+q6u1VfO536x7Ywhm2U0ayXofdpUZ9c14LIHmRZ\n/JXQSg27mLJ/I56HVS90LTV65LSeGl7+94VweRdzRr5EOY0EaZf2bstvAi9HTo203g5KaaaKOsZH\nNtH3sU7uuHQxldSzlkn4ifDiHYsouOQQ4ys2MYZt+pqZfMSXlZMssHwiFYoDVGpS2UeEYCTEf/n/\njSWNFxIcEMLvi1JJPUOo16s0Wjk5xUsIziIt0lzCAYK006E9DV5gAXVU00ERxYQYoBVHEn3Kyetn\nx0sW8oAe5RYFowCTkLfrU07YCow+LxxF4nVUi5iKiqlCNMvrZZ26fl8EiHGjf9kB2k4dDI8X8Tjn\ncyav6sI/jsL5PK720lPgQKA/YtulfM4L9kW6xOsAYagBzgae3cBLnMWpLMdPlFKaOY0VbAAqFgDV\n6OnmArkuYzCKqXn1v6Pav36iFJe10j66DMZVwObN/HX1D/jOtLuZxFp+sP2vLAPGnQWMhNZ+xXqW\njYiSGXycqLatthwgrC71GOeBSf1gbQdtWwbCRBhc10aHH4rOAI5Rl3yotjLcTU7CtFTA10HbYGAs\narrgBKigkUYqGHF5gxqlPAYox7Tco7sgxrpAcZiOwQPUehJe6H/8PgD2jejP4KY2dTJcLtdtMIrT\nOQnz3CNCsF+IjrIAFPsoHdZIgA46CNDYr5TKWAvRPmhLiIwlDE6hpzWSI5B/RMzyfm/4gf0wUlWD\naY79LCFEIPQ+IeiuEXbxRUNy8RDW0i4b8Tapx/544p+4Y/1i9DUrBPhTvx/DWdB0sIKyfi3Im2TL\n7Sf7OxUvayQrINZOcoAyWvBo62KuuOYhlt95Mq9yJi9xFmu+dpo66O3x0Dyy1LQ3miiZnysn2Vay\nMA1qQqqMFvrWd0If+PHDf+LJS7/M8yzgxTsWQR/o3NuXlooyQjSYUpJ8qBM1OZ0rW1sB2AnAAbRS\nwgFKI834I51s6ncscRRGV+xg2+7xFPgjFFWI5N4OqV0FvbJUjraS7WUnTEW0HYxqvWJLBTm1UEQR\nxd574rca6YHZ8xJ9VaTJiuRmEfmO4zVVhRQp62LtckDbM9bKCRKLO2VrK5mXWQRbI6jqfagQJ4Kf\nAErS9X3Z2uqLAGHjICEqfE20nTkYLq9h1Wk1jF++SX1fK75y+tdXwS+B2dBEhb7G3mkhIQRgQBuD\niyc0035JGTScyZqFEH6miPP4B2fxElOv3AiLgK9B8+hibcuogC4G84U5BddwEFYEGmk/pQy+D7w6\nB+6HrdOO5UKWwEMw5/vAbGAKNGq2sop5J7aSUe8/Y6lAaU09LXOGQjFwuEhNTQXoA0X3A37gRKgf\nWKrXI8hnu5ZEPkZKvk+6hqHRzbRTBqOBGhhCPfUMgTPW6cK0uUq+fkbdBqeWDBj1CNR9TEsLWoiN\nVmgrGYCvT4TRvh0A7GA0gSkb8EeiHAj0p4VSQhRrFQz82u9zxqkgivMZzud2ovhRKlU7VtCop7HX\nUUV0oCqQQ9oOsfK+3E7YqjdoN8fgBQZijgj2FsElb2vTWziBKwJduOhp2E1exRAvb9MNQF9givry\nx9f8iTfunM6N3MKqr50OZwHTQfHGTQ8IsbmK3D5AqglysvfkSboozBBHgVLUCVAEpoXX8GrgTNbU\nT4Mvo05K1BpJyJV6I/i0CJwxCqWbtKd6XxapumCoAP9JcKiyQBdcBZcconNvXxisHidXnxXbBVjP\nk62trKshRVEUlZeXVv8A4n5FX6dYSgvl1fXEOxXdpkaRHbUIhVUwZ2srs9gy81Lb82p7FBYTxYeP\nKMWE9Ig7GNt9qL/DvqhKNrayOgDMvMzVg8UVFr9B3s7GWJtrfy2yF87JeJkFqbz1heAlR/iskSCF\nmMmeX3TIdlD7uKIvX4iPV9jxizHQ4OWv639A/7H72OIby+C6Nvg1xMpha79R1FOpCy7IfcssO4gr\nWESYUlqoCeyi7rwYbeMGQwxqwyMgACOohRsAL+yr6k8dVbRQSpiihD6QLx/135gu5MtpInasQkNZ\nJdH5/SgafIABtNLKAA78qogBn3ZwcKCPemUITVrWgRCB+XCz9mG5eF8JrVQV1OGbHKV1dAmKN86Y\nwFYA3hp0EmMu2opCXMuEMPaato4AkL/4km0VQ0EJxGid2Ep8oqJvidJCGe8OOp6yQS3EUTRXXYnF\nVs7KJdnBEMeLzxehorJJ33MVUJeiKHECgbC6vlhbrCILQacgZ8AIx7FCzLS3aRFhdSs0yvXngFwn\nQY7q5s/nKIQruFx8EXAE856ivcXpIfPqLZw+D0hWeEEtEqIW79k3pD+Dz2iDU9TJWQi1OBK3x6DB\nC0EoDTTr0S07D6Z46Gc6QbamJFrbChFk75BSSgYeIOr3s5UxxFE4sXItW88fQ6TDx4BBrXoqqPUh\nL/+dDSdxvFXMyFtS1AWGoYyI00ypbqvxFZtoqShDIZ5QWCqVwMmUV7LUaZmXqIIr9tKr0ApJhQsC\neiXYxO8n7qGaCxKFt9iWQkUrA3RBPIBW/Ti/pZqm8LQ7AdGOdTsbUW1aRDDkCZ1CIEHE5soplYPB\nygvQJ2DyREytcuxPaEvmlK2D4YsEI/vBsElpdQvxalWwjmIHT3A+o6p2aBN6RU/1NgqtOTdZF5FK\ncS/G8VIJBH3tdEysxUeUChqJo/AGp1MxohGflG0gKoGLrY2yjU4afSZxn2J5aQWowr5iYBPxgYpe\n3KyJct5mBiWDWvV7KCSJm1z3T07FV942RyFOMSGi/fz4iOiCazujCFOEn6iebaBGTo1tqnJFsjHR\nR1RfHiD2b/Zq43+QECGC1DKCFsoQ6frmqun5OT7shDxSXxdcRBZIKS0AtGjVoAOEEY5K8ewQxZTy\nFfLWeYecHSJEveAn0nqbKdPGuhjCsWstmCfzygVHpTgV6C0TY6uI6I1wbZUavVVw9SYuMnorr88j\n9KqMBKmjitYhJYCaziaqz55c/TaN1eWAuoWL2MvOif3G7KAKZnVi0YqaxtjkL9dTfgBqqEXpFyfc\nL6A/5MRedt2BOEYkTX2AquJdeMKFOK2ijiDt+sRTpIRCcgdBLkgUvYo2MfTrHmfBT42ANOppob4k\nAjU3HoaQt0JMLAB93ZCYvMqTXxHts0tTFe0IZOtgkCGEnCrkjWsa0SaLYvImhKpoJ9U5nYhUGrwU\nKZ1OvS/Vz2K6sLITnK4INcMqvERfEGnZYmJcSjOAnq6viogaXWgYE2PzHsUCuQgtOUtA5aFyCoI+\njgnOYoubeoYQpkj/rrydkLxtlhPCS3VwGBkiIrIr2hbp8GK/2FZK9N8TlQSEqFeQrdiyE81CyPul\nMUtE3QA9fRXQK22LY2VeVrElkI9gBvSx1CvZSiGm76ccxadHur3EdZtYt4RyCgYvsUQiYhLPInIZ\n1kaUsFYHQN5LWd5JOxfI/VzmJDK2xD0oPivSnpOqQ7oYebmEkeWTaKvc98A+yiBERG+bHPc2PtA7\nOYHLy8UXA5lMVEU6UQcBQH1YtVKiVy41C64QEXwE6NCjb5meIxteIvFVRInE1gFi4i62fRDbgIgJ\nhyhu408icFJxSv8bvHr6ZkQXW37dCy4LrjJadK+9EFzdIeLjmmgRD25hIzFZjWi8RARXtVGH/tCX\no8L/n70zj2+qyvv/O7lpQkOLpS0NLbQUyl62sqqgLPqgMuKKMm48biOOu4M64zKjjqPO6DD6DD91\ndBQZFcdRAQHFEVEWRS2yby3Q0kKhpbWlpSkJTZvm98e95+Zma5M2aQvm4wvTJHf55HvPPfd8znc5\nkYA2bFg78y6qLEs0EqvkoGq97YJTpEJTxeBLyEHxXthFHijZVbEohZFTc7bWTn6IV7G2o1h30IjD\na9CuvY6eHl4Iz/qSpzu8bSAm1bSTMyYlR1rcM+IeB/C8U8IXfukZcuzmJO4J7ZIedqWgj1ZwRYqX\nVshrl/Vyc633sJXWu++PV1vgLeTF+b05CSEoJg4BtfCaP14QmqgJLJgN6pHxqcHQ6LddeUeHNGev\n1ghDbR8jbKX9rdp25VAibUzNXMNwQGsrbd6+4CVsJT9jDerEhrfADW/bOgMRFRFRRBHF6Q4RYioG\nF41IyuPdqYZD1WOUq+phVYWYeDRoq4qGn5vk4XGzKyJVXegcbSiSETFT7a/KazAIRjjKD0MRfuW5\nYL3NQ3DZFH7uGWPxryWRGqyQF6LJzUPkabr31+YNaQWX9tje74PhFAjCg+i5vfBAusPWRGEZuRhM\nvfp5czzC0ca8f4f2vTZHTjuj3x7Q8hA21HpWBDxD6Q1hscnPDe4IBqdH5po7X9sd6u2NcAhA73vU\nm5NcmMzTuwt4eNMFxN/+Bult8XaJfkzLGY2g0Ip6kfPq7/zhyukUvLR8hJ28J4/EZKZdjTFw21tr\nr7Zy8raTETw4CZ5aW7lDdz2Frvd1bK1o9hTyntdPnFPYRORfa9uVtwAPZK+W2pb3cbS28haoInMe\n8KzloKY1eN4zzdkq1DZ/WovTuceOsWzGDCq2buXa9evZ8dpr7PvgAwCyb7mFoTfdRNKwYTjr6yla\ntYoDS5ZwaPVqdX+zxcLk+fPpMXIk3QcN4si6dSyZPr3DeWVddhkj5s6lR04OeoOBQ198QeHKlfBh\nx3HqMWoUU156icTBg4mJi6Pshx84vGYN2/7+dzRFJduVkxZmi4Wbtm/HnJLCG717Q1nrOIWDV+/J\nk7nm6699jrv69tvh7Y7hJDDizjvJvvlmkrKzaTh5ksLly2Fu643VVl4Xvf02Q+fM8Tmuy+XiOct8\nqqpa2bjOEIhO/pe8yipeopqDXMpj7GIDu9mBEwNnM5wRjCaVZBpwsoMStlJIHodUEZiAiSuZSi96\nkEIihRxiIe+GxMXfw0XwquQQl/EoO9jADvbgRGISgxjNCNIUXjs5zDYKyKdIFYEj6cc5jKI3FvTo\nOEAheexnJ3tazesWFvApf+c4B1VOu9mpsVWOYqsmH04AvUjhaqaSQiImjBziKIUU8h25NAQhOLRh\npILbr3iRT/k/fuIQV/IIO9hAHtuox8hYRjKcMVhIoVGx01YKyacI8Mx9lEPluvIg9xBHV57nb1ip\n8zh/KN7yX/Eiy1lABYeZxTy28y272AXAWEYxklH0pAeNONlNMVspYL/CS8LJQHpxF9f5HH8pK9jM\n9hZ5aPmIQc5cnmcZr1JFkcppL9tpRFJt1ZMUGgJwEr//HEYzlpH0pAcOHOxlHytYHjSnQLwqKOFa\nHmAbG9nBbpxIjGMEI8lRee2hiO0cUO5Bmc91/IKxDPNzFhfP8wI2P9Wrz3RoB7binnmSB3iTDzjK\nMe7iJr5jK7vZCUAOoxnFKFKw4KSRAvLJZycH2a8OhLsSx3R+gYU0kkmmiCLeaeWDVztwD8TLicQY\nchjJKHrQ04cXyIP0QQxhDBPoSRp6JAo4wD7y2a3cb83ZSD6Gr7f9j9zDG3zEEcq5h+v5ni3sZicS\nTkYwzsNWB8ljHzs4QIG6fw8yuIhLSaYHMRg5QgmFFJLL9zS04OYJJOSf5AGV0/3M5nu2sJ09GHD6\nXD/BqYh9fkV8V+KYy/10pSvzeQGrmhnfMrzF4B+5lzf4MCRbaXn1IYsbmetxDhewnGVsY2tIvAJx\n2sg2dqr978iAnMTvEhjLBEYyhhQsOGggnzxW8EmrOAEBbQW+96A/XjOZzQjG+NgK4AWeD6qvO23F\naUJWFjFmMxXbtqGPicEydixHv/1W/T596lQOLFvG+oceoqmxkSE33MAVn37KokGDOFGkPMhMJk5V\nVbFl/nwGXnstOqntLvJw8Oo9eTJHN27k+6efxlFbS7/LLmPG4sWcf+w9Nmw41CGcnKdOsXvhQn7a\nto0Gm43UCRM4f/58dHo9PN8xdlKh0zFj8WLKcnPJmjkzdDIR4vVuTg4ny9zCz1FbCzzaYZwuePVV\n+s2cyZ5Fi1h9223odDrO6tcPGB0yp3DxWnvffWx45BF1H51Ox2WffEJDXd3PWphqH/rx9MSAiaMc\nQ08cyfSjmHdVr00/MtlNPksppwEd4xnEA1zCH3iHGqqVeVUDNmysYxM5DEKHPiQ+/rxunrxi6UEm\nh3hPDfEaSDq72M8y1qu87uMXPM0iajgOQBbpHOIwa1jPKeoZzgBmcxVW6iii5b7OWwgmkIwBE2Uc\n9eAkeMu22sdy1tEAjGGoyqmKE0g4acJBLjsp4ygNNJBBL2ZyITp0rOPbZth4QgibeCwYMHKEcvSY\nVU6i+Edf+rGXPJazlgZ0jCHbh5MYoOqA67mCwxxlKAOD5tIcrzLK0GMihUyK+beay5RFH/aQzwq+\nVnndw2X8ibdUXgIv8U9VJMue/dA9m04kkuhOjMrJrHIS3vdMsthLHitZQyN6chjml9M1/A9DGcBm\ndvARnyLRRCLdg+DgDmPTtnlPXkYs9OEw76v79aOv0q7W0oSLHIbxa67gT7yltvWlfMkqvlRtpEPH\nTczGgeNnKUwFtOIrie4YiaGCo5jQ05s0SliqbiPulS9ZRRNOhpPDbG7j/zFftbMOEzbsfMe3ZDMc\nPbpW8wF54N4Srz5kkcdeVvN5QF596MchDvM1a6mnnsEM5mquwYqVQxQHxUvb/1roRgwxHOMoRvSk\nk8qHmuP0pR/57FFtNYKRzOJXvMZfqKIWgEYa2MaPHKWcBhroTW8uZgY6dHzD+pDsJuzkj5OYYPO+\nflpONRzH0yup4yp+yRFKGMTgoDj4C+814OQskonBEJStwMEwRvvwEsd8nb9zQnO/BtPXeV+7QJwO\n8YlfW/njJP9OAzO4gkEMYTtb+IRlgI5EEoOylxbiOdpWWwGs4lNW8191Hx06ZjOH+hD6utNWnKZN\nnEhZbi64XPQcN45TVVXUHTmifv9fL2/MNzt30nvKFMbMm8fX99wDgPXwYdbefz8gC8K4Xr06Ba/1\n8+Z5bHM8Px/LmDE88si5rRKn4eB0PD+f4/n56jYnDh6k++DBDP/Vr+D5dzqEk8DZv/89jadOsfWl\nl9osTsPJ61RlJfaffmoTn3BxsowZw/A77mD55ZdT9Nln6raVu3fTWnEaDl4OqxWs7tnQhAEDSJ0w\ngZXXXAOMaBWv0x3eQrAnAyjlEA6MpNEXOyc5jk0VEUtY4lGI4DNKGUwvLmIUH7MGCSe1VLOMNUg0\n0p/enNWGFZeb41WpFJtwIvERyzzyYwSvCxnNMr4AYDlfe4Q4raeCXqQymXODEnUX+FwAACAASURB\nVKfCXgL+ONVgVcNCl7AEbbjUZ5T7cKqgiirK1WNWUUNPEhnP6BbFqQjn1F7DVPpTyiGc6LFoOAne\nS/nYo8rhKo4xkN4enEAWTBdyHg008h3fBy1OAwkumddhHMTQi0zsnKRaWdYA4GOWos2Xk3n14gLG\nsEQjsgBOYudUCF4Nra3A3eYFJyd6emo4CSzjI9VWAJ9RwQB6e3DqQw/OJoe3+ZA8CtRBVzkVIQaW\nBeZlwxbQVv54ybZvVH3cBpwkk0g6vXmfj0JkdWZCHpD3poQjuIAMemLDRh3V6jZL+dhjn1JWk0k/\nzuZ8PmMlACeoYRXyMy6TvsR7LOQXGh+Q+7s+ZHCYo7iA3vTy4CXh9OAl4aScz8mkH+dwHp+zHCcS\nn2sG6wDf8hNp9GIS5wclTv1xKuEoEk76kKZyEm3cm9OXHKMP/RnPVD5XIgjKOU65IigAqjlOD3ow\nlrFBiVPtBFUgTieoVbcLhpM41hSm0oCT7/kuaHGqPbZWDGbRKyhe4n0ZX/jlBWClnpOcDImP9tiC\nlz9OJ5XJZC0nsa8/Tqn0YgzjeZ/32K94LgEqNM+vYPkIhMNWTupxaFJCkkiiF735kA+Ctlez4vTU\nqVNMnjyZ+vp6unTpwuzZs3nwwQexWq3ceOONbNu2jdGjR/Pee+8RFxcHwN///ncWLFhATEwMb7zx\nBpMmTQIgLy+PG264gZqaGq677jqeffbZoElqcVd1NbhcSCYTOr2eu44fRx8Tg2Qycdfx47hcLl5L\nSvK7r06nkz19EUCkeel0OvT60GYAI8VJp9fTY+RIBs6aReGKFR3KqfeUKQy//XbezckheZi/kKmO\n4QUwW/EiFixbxr7//IdjmzZ1GKcBs2bRaLcTn57ODVu24GpqYu+iReQtXoxmtYh25+WNEXPncvLY\nMQo/+YT2FKedoa/zFhEP8QzgQsKADh338mf0SEgYuB/5mM8rr975mzpAwqU+QLQCUI8LnRpkExwv\nwQloltc8/qjy0ubZePPy5uT9G3QteDu0tgJ4gOcCcvK2lfe5m+MUQxOpWBjGUPI0D383j8BLlzzA\nc7hwYWiBk79j+OOURR8mMIq/8Sa9cd9f3vtqc6qC4fUAz/q9fgLe10WHzsdOd/O/6HCxh3x2skcd\n3LQEcR1/y5MenO7nWaQAnMRxRRiaHtBr7DSCwTTQSCLxPMhtuHCxhe1sZxeOIAeVos0/xDNB8xK2\nCsTL257jGUMddewhP8Q4hrahM/R13nhCiSiSkNCh40keQY8eAwae4FFcuHiWPwfYW9dif9FaBMPr\nz3j+ZvdgXwfofQb/3giVe2tsFQwnHTp60pOhDCOffJ/vg+ek5w88gqRwepJHWuQkbCA+60tfxjCW\n13iFFGW5rVAh4dTwku9fmZeEAckvL+++U3ttmtDjAm7nVwDksZdd7OIo7gn5YPAkjwTFyTvP1B+n\noQyngQbO4izu5G5cNLGNbexkO6c4FbSdAJ7gMY++ri220n4PMJbx1FFHHnuDtlOz4rRLly6sXbsW\ns9lMfX09Y8aM4dJLL2XZsmVkZGTw4YcfMm/ePP7xj3/w0EMPUVFRwauvvspXX31FUVER9913H1u3\nyrHY8+bN47e//S0XXnghl19+OZs3b2bs2LFBExV4d8QI0Om47ocf+OrOO6nYvp1ffPAB+e+/L+fP\nBcDwO+4gKTubz2+6KeRzdjSvzIsvJuuKK5g7/f2A27QXp19u3EjK6NFIRiPbX3mFdQ88ADzVIZzM\nKSlc8u67/HfOHE5VVQXNIdK86kpLWXPnnZRv3kxsjx4Mmj2b2d9+y8bHHoO/dgyn7gMGoJMkRt1z\nDz/++c802u2M+93vGHjttdwzeW3wpMLMSwvJaCT7f/+Xna+/jqupKSRObUVn7OsW8gKgYw4P8AUf\nUc5RLmcOe9lCHnkBBz3nMIqeJLNY8SSEG4F47WIbec0MaoLhNZD+ZDOYt3gvJE5vMB8DTX457Vdy\nYEPldA//Sy9SkZD4gU2s1Hgxg+WkA27hvpA4nU2OD6c4unIdl/MBy7G1Nsm/GV6XMYfdLVw/f7xO\nYGUJqzhCGd3owgiyuZNb+IKv2cg3IXO6mfv4nCWUc5QruZHdQdjKQg/e13g3kklEj45zGcc6NtJA\nA1OYxAiyeZO3gubUEq+WbOXNSwsJiTGMIpfNuEKYKAoHOmNft4BX0QF3cgfLWUkZZczmWnaws1k7\nj2UcPUhhSYS8z5HkNYABDGFoyPmwkeB0O3eQShoSEpvI5XM+83OE4Dgt5TPKOMb1XB00p2X8R/2s\nK125imtYysdtDncXvOZyB8sUXtdxNTtb4DWG8aRgYZmm0IuVWlaynFKO0pWuDGMEt3MHa1jNxhDS\nPQJx2s5u9jUj3vxxSiQJPXomcDbfsIEGGjiP8xnGMBbyZtCcAF7mNXTouIvb2mwrLSQkRpHDZn4M\nqa9rMazXbJYrL9bV1dHY2IjJZGLTpk088cQTmEwmbr31Vp5/Xk46zM3N5eKLLyYjI4OMjAxcLhd1\ndXXExcWxb98+Zs+eDcBVV11Fbm5uqzoxa0kJycOHI8XEULhyJca4OHqMGsXyyy7DXlnpd5+syy5j\nyksv8eXtt1O9z3f2OxyIFK+eEyYw49//ZuMTT7BunbnDOX167bV06d6d3lOmMPqBBzB06QJ3lHYI\np0sWL2bvO+9QstZTXOl0oc+ihpNXzYED1Bw4oL4/tHo1MXFxjH/sMfjr3zuEkz4mBoPJxPrf/EYt\nSHSiqIjrN20iPX0LJSW1HcJLiwGzZmHq3p2db7wRNJdwor37ukAeLuG1OY6VFFLRI5FHPkaMpNCL\n//A2VqWan4A4RjYDuYIL+IhPqfITxuivQq4/z1sgTkBAXrt4Fxsn0VZVFd7T5ngJTun04jquZjVr\nOUShxzYt2aqWmhY5ef/eIQxq1lbv8xGxxNKPTCYxgaswsDyIgjrCG9gSJ8FGG4I2nP5cxnQfTtdz\nBdvYQTEHvbKygu/rmuNloRf/DmArJ5IPL4FKjlODnLpQDhygEBNGpjApJHFaTR2p9EBCYj97fTi5\n2bi5aTn9hHtyUng2P2U1ByhUjl/D3dzOWZzFCU4EbS9vXhLmoG21hBUqL+/7bjhDiaULm0IopBJO\ndLZx3QlOYMGCHj357MOIkVR68i6LAwqUwQzhYmawnGVU4v+501YEx8uzvwyGV2/SmcVsvuJLiijy\nu03bOHmiJU4f8gFdiKUvfTmHczFwRUgFdbScDpAXMqcKjoNyL83iWnawjSIOeu0R+rhO8JI0vNLo\nyXst8JrOpXzixatK+U+ggAKMGDmfKSGJ02A5ebcpf5z0GJCQ+C+rKFAKXVVTzVx+HVJfB1BHddhs\npUU2w4glls38GDQXCEKcNjU1kZOTw549e3j55ZfJyMjgxx9/ZPBgOQZ88ODBbFLCFXNzcxkyZIi6\n76BBg8jNzaVPnz6kpKSonw8dOpTFixdz9913+znjOgC+ALKA/ppv5uzeTbeMDHQGA1JMDPecOIFO\nr8dgMnHbQbkhLxoyhLqjR90cZs9m+sKFfPmrX5H/738HZ5UQESlevSdP5vIVK9j03HNsfuEFQvFQ\nRopT3dGj1B09SuXu3dgqKrjkvffo8fjL/PRTyzNc4eaUMW0avSdPZuzDDwNuUXpbcTHON7dz112r\nOtRWWhxYsoSBs2bRvXsXqqtbDrcINyerkgt69Bv3gLFi61YaTp7kwgv78fbbwVXWjKStRtx5J4e+\n+IJthw8rQ8p1QXEKF9q7r1v/lHwt9DSROSWDzCkZ6ndzeYhuJCjhlnoe5hl06DAgcTePAjoW8DK1\nuCcVRpDNLC7nIz5jWwjVbkNBc7zuU8KVFvAy1ZrwyWB49aMPc7iOtXzDeja2EAjXOk6h2qoWK7VY\nKaeCk5xkNleyhjVB5Rv9mgc5i7Oa5fQK86nxY6elrGSb1wx6fzLJIoPzOVf5RO7rfsv9bGYLK/g0\nKFsFw6ut7Wo3eQxjKLHEYg/CyytzSkCPPiy2OqFwL9bkLB+lDAcNZNGPrWxrkVNgXvqgbLWUlexg\nN/4GawATGMN+Ctm5bgeH1xWjp30jRdq7r/vmKXf+YsaUPvSZkqm+v497SOAs9Mp/v+cxJXRc4iEe\nBOBlFnjYeRjDuYKrWMEn7FKqiIYbkeKVSV+u50Y2sJ5vQ5jAiSSnWuW/Cso5yUmu5hq+4sug+rpw\nc+pLPzLpy0TO8/j8NzzEFjbzKcGlkrVHu9rLHoaSHXRfF25OtYr4PKTp68ooxYGDfmQFXUU4krYa\nx3gKOMCOdTs4tO5wUHwgCHGq1+vZsWMHxcXFzJgxg4kTJ+JyBe+a9efFan7/KQBc5GdguvTii5GM\nRqYvXEjx55+z/8MPOfvJJ3HW1/Pjn+VYaG111GG3387Uv/+dz2+8kYKlS1vkGsrvijSvvjNmcOmH\nH/LtY4/Jy7V0Ak7ekIxG9JJE167GoMRpuDn9yyvHtOf48Vy0cCFLpk/n6bxfBPUbIsHLH7JmzqT+\nxImghGkkOB3dsIGRd95J2sSJHF6zBpCXB4oxm1m9ujAoTpHgJZA4ZAi9Jk5kxZVX0h95Umq10he0\nl0ht775u8lPyw9dfbt57vI2ExGXMooB97GEXk7kAJ418y3qa0HuU1R/LGC5lBv9hGbvJa7Fjb01P\n58QQAi85i24Co1rkNYgB3MAs/svXfEduyLwibStw51IZiQkqa3ExC5vlBHhUfGyJ08u86vE+nV5c\nzeW8xXsexZvayiscthrCQE5xKqjBWrCchK0knOQwnkuZwUcsYSf7fDgVU8zZjKEPGRQo3pc0ehJD\nDAX47+v8eebbaivwH6mQQjJ9yOBd/kPfKen0nZKufvfN0xuCsllb0d593XlPTQ743b94BwmJq7iS\n/exnF3uYxhScOFmviDetnccwlhlcyhI+Ym9Qk3CtG9cFy0u0mWB4DWQg13Ida1jND3wfMU4CodvK\nncsagxGC6O3CzekVPMe7vejNFVzFO7zNTwRfYLI9bDWIwdRTH3RfF25OxRQzlvFkkEGh0rf1JJUY\nYijULBnU3rwEetCDdDL4gPfJnNKHzCl91O82PN38xEzQ1XozMzOZMWMGubm5jBs3jry8PHJycsjL\ny2PcuHEATJgwgTXKwBcgPz+fcePGER8fT3m5+wG6d+9ezj777GBPraLuyBG5GM+IEay54w5OFBWR\nPHw43z/1lM8yHqMfeIDzXniBr+66i9KNGzFb5KTqJoeDU9Xu6m89Ro4EoEtiIsb4eJJHjJA73h0d\nx2vArFnMWLyY3GefZd8HH6jbJDvNVFYGF4Mfbk7DbruNU9XVHN+7F31MDL0mTWLMQw9xZMMGiouD\nq6gTbk7H8/I89jErs7jV+/ZRXj4lKE6R4DX6gQeoPXSIqr176ZKYyMBZsxgwaxY/PPMMYrH79ua0\n/6OPOOeppzjvL38h95lnaDx1iglPPMHhr7/m6NHgq2xG4h4EuRBSXWmpvJ5vB6Mz9HW1nECHDgs9\n+ZSl1HAcCz1ZxxqltL3bI3Mu53Ax01nBpxRzmDi6AvIC2nZNUYRUpbBELLGYMNITCzqgguDD8lvD\n6xNWBeQ1nKHM5irW8g072a1uo6Mx6HyjcNtqHDnYOUU5PyGhJ5MMzuNcijlEdZDVw1riBHL5JoBz\nOJfpXNwspwqvUDyxzU9UYvNa57Q9bTWJs6mmhgp+IpZYhjOUYQzla4IXWaFw0trqUABOu9jLhUzh\nEi7kazbQQCPTOI9Cijxm/tvbVgITGIMVK/nsa9dCSP7QGfq6E9QqxXgsfMJyqqmmJxa+Yi3VeD4n\nxPX/lBUc5hBxyAWbZDu7BUJPegKirzNhoSc64BjHOoxXNsO4mmvYwDp2sVPdpommoPu6cHMazRhO\ncYoKKpDQk0EmE5nEIYqp8Tpee3HyFqBdlW0qqaQuhL4u3LzO4VxqqOEnKojFTDbZDCWb9QRfsyPc\nnPawm6lcwP9wMUbW0kgDk5lKEQdD6usicQ+CXAjJipV9IRbYghbEaWVlJQaDgYSEBKqqqli9ejXz\n5s2jtraWhQsX8sILL7Bw4UK1Qxo/fjwPP/wwhw8f5uDBg+j1euLj5SULBg8ezAcffMCFF17IsmXL\nePnll0MmC5CSk4Ozvp7q/fsxdutGUnY2Rzf4Pghz7rsPnV7P/7z+Orz+uvp5ybp1fHzBBer7G7e6\n3d4ul4ubtm3D5XIxx/CnDuM16q670EkS5zz5JOc8+aS6zfTiE2RlBe9FDSenpsZGJjz+OAlZWTTa\n7Rz++ms2Pfcc+z/8EJjnc8z24OQPrfV+h5OXTpKY9NxzxKenc7K8nIMrVrBsxgwlN/apDuHkamri\ng0mTmLZgAdNeeYWTx46xZ9Ei8t97D7g/aE7h5gVg6NKFITfdxPYFC6CV16+t6Mi+LlCxlzR60YiT\nCqoxYaYHFoo47LP9OZyNDh2XM5PLNZ8XUcxCFqnv7+UOj/3uU97/3k+bbK7CZDC8JJwqryv5BVfi\njmTQ8jqHMejRcQHncwHnq9vUUMN8fO3WHrZy0cg0JpFIIg00cJAi1rOe3QFmiNvK6WzOCer6aSGH\ngbqQmgkHbQ9bSbi4mGmcxVlKNcZ9vMO7HAyQRxdeW7mXDfO21T95k0uZweVcQh11bGUb24MI0/M+\nX7jvQQMGchjBD+Si9+NVbQ90xnFdGqk00kglVZgwkUKKR1i2gLj+M70sXUwRi1iovr8Tz9DiXyvv\nn+L3HcZrHBPQo2cK05jCNHWbGmp4mfkdwqmJJs5nitrXFXGQDaxjD7uD5hNuTuFEOHnp0HMh09W+\nbh95vMc7fnJj24+TCxdv8QYzuJRfMJM66tjGVnYSXJpWpHiB3NeNZBS5/NC6om+uZrBz505XTk6O\na8SIEa7p06e7/vWvf7lcLpertrbWddlll7nS09Ndl19+uctqtar7vPzyy66srCzXkCFDXBs2bFA/\n37NnjysnJ8eVmZnp+t3vfuf3fC3QiSKKKH6GaI9+IdrXRRFFFB2NaF8XRRRR/BzQUr+gUzbqFNDp\ndK32fEURRRRnJs7EfuFM/E1RRBFF23Am9gtn4m+KIooo2oaW+oWgc07bCzrd0wDMDyH8MRKYp5zf\n5ZLDanW6pzslJ4jayh+itgoend1WZyoe5w9hOY6/gkqtwR+VxeTDwaszcoIzm1dn5ASdk1dn5HQm\n4w88HpbjNJduEAqe5Y9A5+LVGTlBeHl1FKeWztvS/Rss70C8WvO7W9uneJ9LcDrd+rqOzsePIooo\noogiiiiiiCKKKKKIIoqoOI0iiiiiiCKKKKKIIoooooii49HpwnqjiCKKKH5OCGfIT2tCbvydv7Xh\nV+L8TiT173BxCgevQO+DRSRspeUStVVwnAJ91hpOzX0eKqfmPm8O4Qp3jCKKzgh/6whHos1H6j4K\ndE+Hu+8NB86UFIOo5zSKKKKIIooooogiiiiiiCKKDkfUcxpFFFFE0cnQGOTMq7f3TaKx2e2dbejy\nW+JkwOnhMQ2WV1s4tYZX1FbB82oPW7XEq7PaypdL+9gqis6FqNfbjVBsEao3MlznDSe0kUJRhBdR\nz2kUUUQRRRRRRBFFFFFEEUUUHY7oFF4UUUQRRQcg0Gyr8Ni05GEx+OQGNvp85n1MiUaf43rPYPvj\nFSwnASMOJJyqF6k5XmIb7bGD4RQKr1BspeXVHrYCYa+fr62C4QWnd7sK5R6M4vSBpHj2f84I5++P\neiOjgKg4jSKKKKLoFPA3UHeqg2T3d4HCGw04lYF74Id7oEF7MLy8OWn/FgM0CafH4Nyg4ePNy4mE\nAWezg/aWOPnj1VZbaXlFwlYyh8C28lc05Ey2ldins7cr8XegglFaW2m5azmJc4Rqqyii6IyIpIAM\nRqR2FgEbnVwKP6I9YxRRRBFFJ4R2YBso/82JEyMODwHhnfMWzge4t6hp1Lx6Dr4ljNQ3yykcHget\ngPDmJP424VC28bSVkXq/v0/w0gqc1nLSvvo7ViOS6jUNZCstN62NW4twtqtwe406S7vy5qPlpeUk\nf9eyrbTHiCKKnyv8Te5E4R8daSvvvirYyuTh7OOi4jSKKKKIohPBqRnmAopscXuWHICRelV0AarY\n0n7mDCCuWiu4vEWNr7fL/Y2EHYPCSetNcnvEfL1ZrRVcbjkg+YhVWagYMGNTt5dwYsamCgnx/0bN\n72urwGnJVuIaaoWw7/XzFcht42TwsJX2M8FJ2EZAayv3cXxtBa1vV+I4zbUrE/U4MajtSlI+0/Ly\nnWqgzbwEN29eoq37uwc927vv9WvtpMfPAf4GwYHavL/PO2Iw/3OddGiv330mh00314ZbWmIsEjZp\n7l4LZomxcIZkR8VpFFFEEUUnRT0mdeBej1EdNJsw4sSuCJxGzNiIxY6Jeq+BtFE9VjgeGNqBugOj\nwssEoA7WRXVVIw7MCkcxINfuF+6QRvGbHRg9hKrwYmltFYfVwyPnwESkbaW9fgBmRajEY1VtpRWG\nslQ1qr8tnAhkK5Mi8fy1K2g/W2nfSzTiwEg8VoWbQ7GZZ7uSj+NU22M4OAkeDs19KISlEKbCVkbq\n1fYu7+97D4rjRuGLlrwz3l5sf4iktymQl8g7fLu5faLoWLR0fSLpCWyJi/bzloSp+DwSUSsQeEJP\nK5z9R4cY1O/byi0qTqPolIgBGjqaRBRRdCCED9Kh+NRsxKqDZCMOEqjGjA0TDuKw0p0ajDhwYMSG\nmXp1UOwpitoKrdiyY/YQUbGKly2JKszYSKISM3YAlT+AAyOy9zScwkby+O12zGpYr/CemnCQQA0J\n1GBUvHFW4jVHMyqCKDwPfm9b2TCrx7URi4STBGp8bOVEUnmF01aAX1uJdqXlo7WVsEc9RkUiQiTb\nlQOTh63MXu1KcJT3k7BhBiJlK4N6DnEvyvdgPfEYMeFQbRWPVWlvknpfuIVLdLgVCJ6Dcc9iV1ov\nved7g9/twy1QgxUH/tqcVsBG0XF28L42IjRf21a0ueNakeVv/3Dx8ZfjLtqzNkXAXzqHeE6Fg5+3\nMPVOTfFnI38RUd7c2sLrjOstYzR/dyZx0xl5dUZO4ObVmQRqjNf7zsirM3KCzsPrdILwLIpBro1Y\nauiOlTjsykA8hQriqcOMjQxKSKECgBoSMOKghgT1IeEuwNK2UFVtZqQdM1biqSEBK/HUY1K9W+mU\nkEI5fSkmHiv1mKgiSeEk7y8PDsInAt22MiuSKkEVLWmUEo+VeKykU0IapQDYMFNKmsdvDKWATmA+\nblsJYSpsVU0CDj+26k+hYisjNXRX7RROW/m2K89rKNGIhQpV/AluBpzqdvL+4WtXgEe7cmDy4ORE\nIlYRommUkkQVmRRhxq6KeHd4dmi2am5w5w53lgWzjVisxFOntHWJRpKpUu9BCxUkUYkB2XtrJU69\nL+zENmurn7M3VTvgBd/CYEZQRL5TeTWok2Hyfm6BESmB6s1PwDOyxdTstpGEZ9i9t1DwX/Qs0vDn\npRTXpSM8lL7F6gxqLy3zc4tAbeh9OCcrtXzc7z3TeCQkUKaWRTSSm6M7eiMczylvPtrJOG30CshR\nNXL6iRzlo42m0aY8aLm1xW5nnDjVorOIG+/BemdE1FanH7xt1VmuYRTBIZhZT/GQsBJPFUmU1KZj\nL+4OdVCUlUmypZIUKhhfn0vXoiboCkXpqZSSpg6qvcMK/Z3Dm1dLD5R6TNRjooYESprSqcrvBUeA\nOHCcaySH7QxiP9n5hXASSIM9qfIjV+wbKid/n3vv48CIlXjKSeFQaV/IN0EdHBmWhaVfOUlUcd7x\nTejyZK71A8BmNqv7tjR4C9VWQgwKwVzSlE7V7l5QDGVx4JwmuW21oxDqgAw4ml6HndiI28pGLBWk\nUHI8HUd+N6iDQ4P7Y8koJ4UKzq3dhOEA0BVqBxgpkdKxEo8Do4dACIetQB7YCFuVkkbZ4XQoM0As\n2EeYGcV2hrKX7JJCqIfGFCjplgqgitRQOLUEMTBzYMKu8CprSqPqiAVOGTgr8xgJxhqSqGIU2+j5\n0wkaTVDeLYlSUhGeeYlonqk/eF8vbbEyk9LKQBanlSTRiEQNCWrbM6q+bIfHMSA8AtWfp0hbSK0R\nd2i8jVj1c62g8PbQhRve4h58c97DySOYe8ifZ1ArSj25tiximhPfwcBbdIk+1YxNFVwmHGo0hjz5\nJGnsGF6BKjiJiBUxgSU8ugnUKHzsSuSKLPbEM8GbW7j4uCcuRfSKO1pLpJ+IaJp4rOq2dk0EjjhW\nW3FGi9MooogiitMR3g9jByaqmpKwb+4OHwM/QlNsVwo29Kc/BXS9pUmuCDMK0u8vw9pN9jwZ2vhQ\n9+akfegIIViV30vmtAgogsIfsmECZBcUwgXAAOA6sMwtV71h4eLkjUYk7MRSUWuBb0zwOrAZ6K/D\nujWeeKzobpF5MgpMN0PCtGpqSAgrD284kagmQRamHwD/BCobKNyTjTTUSXZZIQ2TIWYAcBP0urKK\nknS7xyAuHNC2K9lWZsptFhzfdpOv3w9AsoHKnckkU4nhOuA4MBa6/dKBZWI5JaT7/LZwQQyOrMTL\nwvS/BvgUqIHCC7Nx/kEi+/VCWA+cBYbpkH6B3N7bOoD1hvfx5MmY7vI1XAfkw4nknnxy82yWJlyH\n7t/AcTAMgF4Tq7Cnx1LnETL+8/aQNgdv8Sf7p60kU0ksdnlih3TM2NlCJjUkEIudZCqVCASrIggb\nfaoph4ubPPSuJw4rZuR7cwtjkXBixs5ehmLASSw24qnDpEmsiJQHVSv2hECvB3WiTSsYUDxfkRTK\n4FtATJxPm7IgxLtT+b936HY4ucivbtFVp8TQNCIp0Q5V2InFQjkplKsTSlbiNLZruwgM5KEU0StV\nJFFJEgBTWafaZzg7AagimSqSsBLvkfIAbQ8/9i7aJiJ9KkmiAgs2zMRj5Vy+oz8F5LCNeKzYMFNO\nClUka9JQwmMzfav37IRoRH6Oin+1HUuHRnw5HQesmu86ipPdD6+O4qTllsO2DgAAIABJREFU5c3J\n3oG8xHmtfnh1pK0Eaulc7V1Ay6uzcDodYFAfVyLnpNHjoWOrM8N24DU7bIF7N7zILD7mY2bx4vv3\nwnaw/wUMpaiz++5wSYPmvSGkh4aWkxhkiff1mGSP6SKgaBHnuNZy74QXkXDyYv97WXv0HBatB/4N\n8bY65YErjuieXdeGD4VqK20AkoC9Jh5ygbVPw+0wf+vdDGUvexnK3cvnw43w9LvAZvCukOt55NbZ\nSjs0E3BggmIUYbqIma5PuG3oKziReCX1Nj6pmcmizcC7wGH3Mb1/ZVtsBXjwcyJhs8bKAn7ZazAF\nntn5EGPZzGbG8tBnz8BEeO3/Adshtl72LHiHyLXNVtq1VDWhr2WKMF25GP4Mb/xhDhJO5sx9A/4A\ni/8BrAZDhefgLFztSthKfpWPb2+KhQLgH8BrR+j9xwLe63cN1yS+R8HdvTn6B9RJmlh1MC5+Xett\n9XOAEKayt6ia/hQwteh7EqimmExsmDFjI5Niqkng+61TKaA/1SSonptw2lTr+ZSQq0MnUENfirnm\np5Ws4heqSDBj4xesYuX+ayimLzUkaEJ+fdfLDSc/uXCZXfVkacWBEIFW4tUc/EhwEdAeV0wyVZKk\nFjADlLz1eJKUiQVtHxlO0eztvRXCtBwLGxzn4cDEIPZhop56TOxjECYcnMcGLJQTT53PcmPh5lev\npDCUksZm21h2PzeOqawjiwKcSBSQxTqmMu6t3Yxls5qeIiqVa/uq1pzf83q5e2QbZipJIq9pKPtX\njeDIw/05jw0kUE01CWzgPPr/7QgjcvczlDylXoLNI2JAwF8YdzA47cWpVmzZ/XzXUUJQnC9QmGWD\n13btAS0nf+fVcmovXtpz+bNVIx1vK3/oCE7ifNp27f1dR4l57X3Y6PV5R04wnA7w13nLgyE5rE2e\nibdijrPJsS7Jscxr+pM6sype/1Q4j9ghQFfU2VWHEhbq/SAC34eTP07eAwdtyJ3gRRxQBOe4+jCc\nnR6DkJ0Mp4/rHDgAVnMcVuKUUCGj+jAMllNzttLmwsRiJzbBKje4e5/k+r8tpIR04rFiJZ50Slj4\nyPU8+RzgFLaKVYsDhdtWZuXoqq0qG5jp6koqpSRQDUA1CZSSSlfXTBoOAHEov8QcMVvFKlWLzfF2\n2VY3/Zrr319IFcmqrZKpYuFfr+fXv5VtZTeZ1VCvcNlKfG5S2pWwF7FADbDxBu4990WKycSJRCbF\nvDj4Xm7IA04AJpQ9YpU7xhQWW2lDS+VSVjZi9Xb5HjwG011buYHF7GMQg9jHYm5gi2s6lAOSfP20\nRZTcYfbB8/o5QDu4FksqdaeGgfX74Dh8wUWUY1HbpIVyLuILqIR95QOpobuPMA13CLWk3DMJ1JBJ\nMYt7XK0KBSH+Sknj6oGLKVY8u9piWJGC8JjKSy25C4NpQ1BFMbF6ZctIh5eLyReRp51MFRJO9b4U\nBcOqSCYWm2b5pciNEMQkgQ0zJaTjbDRwHt+oefeysKrnG87DgJN0StSQ30h4md3pArKNSkml7uNk\nej9WQBJVVJGMhJNkqqgiiYLbepP8eR2plCo28xWB4eEl7BRLBRaqvu0F98BtL75CdyXU2KFEkLzy\nm9vgt9BrdxUWKtRiiN4Txa3FGTF1F0hsab+PUbZpjx/ckgjUbtNeOZadkZMWwfJqr2sInY9TS4JZ\nu017XkNvMerv+xjN32dEpxMBaDt0bUEG4Vlq1Et8e4WF1PtKMGP3yPkTg9+iH1OxYaaYvlSRpA6O\n/VXgC5WX4OTtFXSca6Twh2zGslkdaIiHnJV4NjOWqeu/ZxfDKSVNGbiZfAaUofDytpW3b5FusOWp\nMWQmFpNChTrTLGarK0hh56MDyXQWU0CWGroUaVs5p0kU7slWillZ1e+ErSpwEpMHe1KzOEx6xG0F\n4DQbyL1/POmWElIp9Sn4Ukoq+X/uQ3p9CbsYHnZbiTM5MXh4UuwjzBRemM0b586hmEyPfazEM2fw\nG7wz+Q72pGep3qpw2krw8vbolk5IpWm7iamsVQs2iba1lqnMWL+aepOeAvpTjkXdpi22+jlATBCI\n6uNdDzTROEAOZ/TeLpkqGNxI076uWC1xivCxK9cifCJH60EXE01mbBQp7dEbmRTxhe0ibGZ5oisW\nCSPu6rCRggjhDfx9o4dHFSJTNEp7bNHWY7Gp59JOqNqIJQm3ZzqSoln2KBuVq2emv7lAKVLm7puF\n4CugP8mKJ1AUNHPnMbd99KL1XosQ2ipbMqyEu+e84pM7b8TBK9zNS188SvIlVZixU6P2KFLY7aZy\nakqCb4FLIYFqj+sH8oQqFwG5kDSsilLSsFLvkXvdFkTHiZ0A0QF750LUw9c6eEcuaCEmiKJww3tg\nIDp9ozKzLIo0JODEQgVZFHJ+xjfqtqJYiHag+za3+Aw6tN9rHy7aUMiWeLlDyORHF0ASVaRRyli2\n4JzgHqBrj+tQiiU83v/3fs/vvX0gToG4ic+MoNoqmSoGsZ8LE9f4/AZxXBtm/sNsJCnwubXbNwd/\nXkD5VQJlTc4EakijlBy2Iw31tQGgev0eT/29z3FDGUg2167kV9RKt8JWUy1r/e7rUApqvcscJJPT\nx47a39ASP3/21LYrFA+lsNVwdsEfUHNcvc+TTgmPz/293/MG05Za4gpum8VjVe/Bcy3fIbxl3m3X\njI0/dnvMb7sOtV39XCEmdOgKtq5GzNhUj6CADTPGOBuOuG74iyoINx8tyrGQTBXlWDzOK7yBFnO5\nx/bhFsz+IMSgv7blxF3Bur0hlvFCeaZpxaABp49gbg+YFc+jyKP0/k6I2PaAmnrQKMGVkMsEBrFP\n/V7YawK5MMO9vmikvcyNSNSfMkE10NOzwJfYxoEJUoATKJO/7tQfh/9Dh4SoJuoEiF6EzgUDUYHa\nGsQSWKBGhWnL8BSA8iNCBAPGU0c6h5la+y0l3VJ5k9t9hI0RB29yO2ZsDGWvuiakUSkU4l0MI9gB\nnXZAoS0OYsZGCuVypdmCQl7sf6/qtRIhbaIi4YLchzlnwlrSOaxWItR6WcV5QhGl4BYPWlslUEMG\nJYxxbqFYymQVM9TQZmGrekysYgbFxzO5IHENFiqatVVL18zfZ+L3+diqrJBXUm+jWl2WxV3xs4bu\nvLX3brKG7mEMm31s1ZI9muOovYbethpfn0uJKZ2PmYXb1+seCn3MLErK05lg2cQg9vnYKlQuvtw8\n21USVXJV3tcLmTP3DTIpVrcX1zGTYu744ztk/WEPQ9lLKqUB21WwfALxEkVu0ijlvPoNmOqbeLjb\n8x6DWydyRctHD7+I3lTP+ZZv1KVvhCXboyDN6Q7R5jgJ5pMObN18hYIZG446M9R55uZHio8WFsqp\nJMnvREsSlZTbLKSYKzw+jzRE+/LXfwqvZEdMiNQr6w4LHuCerGlEXiu4parf4YYI4xWVeb2/E31Q\ne0DNcTc4YRlMuD7XZ9ILZNH6y1Wf4Jwupm4ipxrEGUxd6qnrDhyTr6PnkjZK1eoKIEUsqeTOqQ8H\nTvucU5AHvs1dKjEwbi8RKM7THC8D7Ttg74yctAiWV3sK+c7GSXsNm9umva+hIcDfwX4fhQxPUerO\nvxOepKHkYVgFfceVEavkyAiItVDLevSlcGc2RWSqlf2ER65eyTdyhjhQ8eYk8ifFOqbZ+YUwGcay\nWQnLlAcjjUhqWC9nwy6bO6zXhlkpYmLSFK4JlZObl3w0G8lUkUEJo9hOtxccjHh+f8Cw3v33jcDx\nVDcK6U85KWruaaRs1Z9CsncU0jAENaxX/HJhqwpSILuBwq+zKSGDKpIjZiuth7Lr600M/t0h0ijz\naVdlpHFozmCa/q8ru2zDw24rt1iWvcxiHdPsErkq7zv5d/h4OKzEc8d378Aa1PYu2pWWU1tsJXjF\nK6ubWiiXK2NvbsIwGaayVm1T4nUtU2GUgabcrh5hvSJvWMsp6j31hLCJKFxzcoAewwFIotJD4DmR\n5Kqm+Qb0g06qhWu0k2jh4+ReE9Oh5CfaMNNXmSzxRjF9MZttanEY7bqZkYTwjgb+3hBwYigSEPcQ\ngB2zz7mcSGr13vYQzpJm4suMjQJbf2Kxq5ObgBLlE0t/CtTrrJ1kDZcgdBcVlNtrLDaSzJUwE17h\nbg9OgtfdvAIXQSVJ2IhFeMojYTeVk74KJgGf4rHutuDdnRr4ApiAkkIUG1ZP+GkvTg3Kv1jln/d3\n8Zpt2psXBBYKHSG2WhKoWk7tKbias1VHCdPOeP3E+bTt2vu7WDquvWvP3xk4nS7wN6gSM5OiyE88\nVnk2txHsefBE1nyfgfF8/RNQaYc6d0Eksa6ov8FwcwNk9/DZt8iP8E4JXpwEBsAh3feMYJc6w+rA\nxC5G8L3uEPSFuhp5gQhR5MffoKSlQXsgW8k5YQ6lLI6d+Fo7SPD0Y3DrC+/7FER6/ze3woKnwYBa\nyVIIm3DbyqzIy3isUCcvF3NSt5I0yuiuZA/V0J0y0lipOwnJMVAnCurERsxWMqc6uQKvBK/9BW59\n6H21uqaoqPn+9bfCu6+BAWzWWI817cJhK/G5SWmtwl7UA2fB4iHwcP4Cj4JIC757GCYuhgTALg+A\nRbsShZraait3CSNNQSTs4AQsMEa32qcg0mrdaOiJfJ82+RZE8pf393MXqd6FjByYqCaB/aZBkAgX\n8QUWyrEiLwVVjoUvuAiSYZBlPwlUq8vICIRbDIrohhoSKCaTG35aouTYyfnE8VhJo5Ql+28gk2IS\nqIlY0RpPXga1j5dwqh4/rXdeVBQW91ekhbLIaxfrdAovs7ZQnvAy25U+N5zizx9Ev2fGRjolSIZG\nvuE8ZbLOpjwrTZzHNzQiUUI6NqW6cWQEoNxWRT+cRhlxsyo58lx/qkgiiUpAFqNJVNH/rSNUXhJH\nGWmaInmREaai+nMK5SRNOgr/D956+G6qSaAeoxJxU83df3sL/gJHhyVRTgp2pZ2FSzSf9uJUCzFo\nF39360AugoOWE3gKi44YrAcSER3JCfzbCjpW2AQSggY6h9jqpuHQGdq7gODVmTidDtAuqwHuQhEC\nTiRIgthJsO4gPLxxAaPYznZGsWDFw+BaB0NiIc694Lr3g0LrvQmFl/cSMh4DnK7AELmC9NTffc8M\nVgGwihl8P2cqYIVhYOzif3kbEaYUKqfmbFVv0kMKPNgFNv4WXimah4VyyrEwb+sr8NJG4EGwuMPe\nBLTLpITDVuJvJxKcJdvqEHDN8yuZwjoA1jGFlfddAxyC4aBddjVStlKL/BgMYIFfZ8DG+fDX3b8n\nnRJKSOf3G/4K/94I/Bp6giHGszBIOGyl9SR4XEcT0BduSAKug+d5FAknjzY9D5cC3AD98ZiV9m7r\nbbWVN9RrOAp6nQ39HzjCLbzN29zCkWv6Q2pvGAskgKSXj+E9wG2NrX4O0PZXdmUpi4K+vYmnjtn8\nh10MZzi7+E/tbOqa4uk9ukBZwsKu7hkJaJcmEktsbOwxhsd5ll0MB2A4u3iWxxkzcKO6rIb3ckSR\nhJhGMSp/CXFar3wiJlkiDe8JOjGJWUmSen2cGFRh6j2RFE7BJWn6O/EqojMGGfdjJZ59DFIn4gax\nj3is7GeQR7SDFuHmJ6JZ4rGSbi7BeGctC7Y+TAI1ANSQwMNFC6i92UgJ6WqBpnD0Hb4Tqu5nu4iw\nSdFXYDy7Fm6CN767nwosWKjg/vw34BqoHWukQhGmrZkEbw4dPb4OO2KAxI4m4QVvTs1VW21PeAuH\nzsAraqvQ0FnFX2fldbrAe8ZdzNonj62D+2HKL4F61HXHsAN/ngKpEJtZrZZ1F3BX/m39AM5faJaV\neE721dP1xibuHSJ/nllfhNMkUVSeCb2BR++FsyEhscZngKSd4Q8XLwdGqkzJdJ1UQbfXYGIJsBsS\n+tZQQ4K8VuyvJ0IfYArqkgGCTzh4aXNz5OPJyxjU9wPTzfDkMMAJ6c4SJMlJyXG56A8PPgkTgP6+\ng8lw20rkBldJSXSbUAbzYeJhoAgShim2KgAemgh9gUmQlFjltx20nZd7AO9EwkYslelxJM+ogwz5\n87TjVZAIVcVp8JSy8SiI618pLz2j+V1iaqatMHjdQ1biOTm4kq7XNcF0IA11cMuDwCygB+izT3qE\nuIv9I+1JO10hRICEezkUkYMt0YiVeKawlrVMYWa3FViJR+QCizB1MdgPl0jVtmttJVwnFqpIZi9D\nmcJaHJhYyxQuZI3yG9zLbWnXyQx/yLGImpFtJ2wCskdO5HLKESXe61RH3nsKKNfGrvKTcBKPVY10\nAPfSM5GEHC0i/nZ7mI3UU0oaaZRSj5EC+gNy1Iq4guHi534WuKvwywWj7Gp14PTEEqTERr7jXJKp\n4ly+Y2nfSzDgVEON7Ur4rDbcuK2iWVtdHuQ1mpOpRKKRpMRKnIkGJb1B7utWDJ5OPFa1T6yhu2qz\ncIVBn3HiNIoooojiTIGYdZRn7JOhByRcUoOEk1LSKCGDDErImr2H0to0JIOTNHOpJg8rMrP2jUjq\nYurFpr5YJpYTn1OH1RynehMmWDax64nh1NXEE5cg512KXKxIQeSFlZMC/SEpvRKDs4kSc2/KsWCh\nnN63FnDk4nT0UiPJlioSqFHEfPgLq4gHtY1YakigyNwHy7QK4sfasXaLZTujcCIxJnEL2/80CntN\nPLEJVlK6Rd5W7naVBOmQlFaF1NhIiSldHbD1uTWfkvJ0DDFOkhKrSKKyXdpVnbJuZP2wKszD5LC7\nvQzBicSkfmvIu2cotjoz5jg5NyqeOh9PQFvhHbVgx0wVSWCC+JFyDmw5KWpYr+1ceW1AQM2dNWP3\nEEvhEPJnErztIQbZIiWhTomlEgVXDMjVuOOpA9ze90Ch4uHhaECiUfXyBcqrE94ucW6/USZhhrdA\n9RYE2sgELbdIQqSZ+Du3QL0fG0YqTFWbj2vEnUIg2owccuxQQ4zFZ+HONdVCKwaFlz0WOwnUKJ/J\n0QB2zJSRpnISYe+R4ibarKg7YMQh55YiC9ZYbNiIpYR0OcUB1BBjbdHBcOCME6faJSs6i3erM3KC\nzsmrsy45cjrYqjPy6iycOiNaGiTIDy9JydGRZ5ntxKpLGFSSRA0JJFDDQPaR1K0KJ5JahMeM3acE\nvD94P1CC2V4UCKlR8lBqSEAyO7ESR6nyME3nMJK5kTqze8Y8HqtavCScnMQ+QjCDnIdYakrDiaQU\nh4ojDitD2YslrVwdlIiKuMEMIlvHy20rkV9KN/laHiZdtRXdwN5NjlGNVXJU29NW5ZIFpyTbSrSr\nQewn2VKl2kpb6CUS7cqBCTvucNgaEpCQl20R7SqDEgx6J9Zu8aqXSmur5oRzKJzEAFBSBl5aL5qB\nFNWzJ3Ih+yq5hkLMeHr0nM3a7OeedwqeoeLyvWj0GJQLxCqVorXCNlJC0Fv8CX7enOXz1nvs29rq\n2q3hGAiROK+3B7A15w21vbfl/vAWqN65yeJ7+R737Dtau15yKHzAvbSX+N67qKC3zcMpTLV8xMqp\nRs3nbi+zQ4lJMajhxQJaMS+/b/t6zqe1OJ177BjLZsygYutWrl2/nh2vvca+Dz6gAUgaOpRznnqK\nHiNHclZWFnsWLuTLO+4IeCyzxcJN27djTknhjd69oSz8vAiSV+/Jk7nm6699jrv79pW8/fb2sHIK\n1VYj7ryT7JtvJik7m4aTJylcvhzmtt5YbbXVRW+/zdA5c3yOe78LLJa/hpVTKLbKvPhihtxwA+nT\npnHy2DEOf/kl2xYsgKOtohQeTpdcwqBrr6XvjBmcLCtj99tvs+3//q91hILglX3LLQy96SaShg3D\nWV9P0apVHFiyhEOrV3sco2tqKtNeeYVeEyfisFopWLaMb377W2hqE7XTGk7lISEG7NfyOht4hkoO\nMIXnOMAXVLEDB0Yy6Mb/MoreJGMhnq84yBv8oJkdbvQYGMfTlQf5FXF05Xn+hlXxQgTDCcQ6ZnKQ\n1OW8zbc8TTn7OI8XyOMratgLwBD0PMAAMkiiJ3F8xUHe4lu1NIxEI4NI4x5m+5xrKSvYTHB9nfuh\nKHNy2yqP8/kL+1lNOTuwY6YfXbmNYaqtvqSYNylRioU4PETEueQwnmH0pAcOHOxlH8v4NCRbOf3Y\nqpR9nM9fyGcNNeTjRGIIeu6nHxkkkkocayhiIYc9bDWH6Ywj28/ZXPyJ4Pq6QLYq5wBTeVa1lbtd\nuW21hiL+wSafZWSGkcE4BjOAPlipo4BCvmMTtV4Vdpvn5Q7rvIq3WMezVHGAC3iGQj6nnM1YiSOJ\nnlzOeaSSgoVuKifZG+KeiBlOBqMZxBD6YaWOLWxjI7lB8/G2mROJy1mk8prGn9jPagrZiQMjkxjE\nKEaRRhINNLGVUnI5TL5SaVYMNrvRlWu5kL70oh4He8njc9bgwtUqbmcCtINyJwbm8Tgf8CbHOMp1\n3MtWvmMnu3EikUoiU5lGKj1Jojub2MHHfBZQCMYRxz38mq505QXmYw2hTWr5STiZx+O8zyLKOMrN\nzGUzP7AjAK/NbGOp0lcIPn3J5FZu9jn+MpazlW0h8wJ4hEd5l39RRim3cjs/sold7AQghR5MYyqp\n9CSRRLawlU9Y4fc44xnHaHJIoQcNNJBHfsBtg8GjPMK/eJdSyridW9nEj+xkFwA9SGEq0+hJKokk\nspUtrOATj/2v5GpGMsrvsZ/nhZD5SDj5HY+wiPc5ShlzuZkf2MxudnhwSqUn3UliG5v5lKWAp8Aa\nwABGMIq+9KOOOgopIJfvqaU2ZE4gP0uf5AHe5AOOcoy7uIlcNrOD3QAkksZkLqQnqXQniR1s4jM+\n9hHNAxjIMIYzgIHUYWUrW/mB71rFB1A5HaaSe7ie79nCdvYg4WQE48hhJD3oiZNGDpLHXnZxkP0K\nH5lbHPFcwhVk0AcH9eSxl9V8EXRfd9qK04SsLGLMZiq2bUMfE4Nl7FiOfvut+r0hNpYTxcUULF/O\nmN/8BperGYPodMxYvJiy3FyyZs7sNLzezcnhZJlb+L1fe1+Hcrrg1VfpN3MmexYtYvVtt6HT6Tir\nXz9gdIfxWnvffWx45BH1vU6n47JPPmFPXQpVVYFW3Ywspy6JiVz64Yfs+uc/WXrxxcSlpTHx2WeJ\n690bbvz/7J15fFTV/f7fM3cywwzZdxIS9i2ABEGURQmKu4IoSlu1tXWtWpfqr7WFVqu1dqNo/Vrr\nUrTWHZEiKkJVkEVEgSB7WAOBQEKGTJgwYYaZzO+Pe8+dO5OZZGYykAj34cUrycxdnvu55557nvNZ\nzs4O4ZQ3YgST589n5fTprJ05k4Jx4xj7+99jslrhjzFTiopX0YQJ7Jg3jy8efphmr5dBN97INR9+\nyKsDBtCwZw8AxqQkvrdyJc0nTrD4ttvIHDiQc6dPx5KeDrfHqeRPE4hOPo1CJCzso44k0smgH7t4\nBScpeJFIxkADdtazh4sZhokTET02BuAHXMM+DlBC/7h4idnRLnRHwkIVhzGSTQb92MO/1bCjFPw0\nUM8C9nApQxVeTUGCWWAmr3BMCWkCcId4IWK11QFqMJJNOv3Zxau4sKq2clCv2kpUh5XwBYmIG7iY\nIfRmDd8yhw+RaCaTjITZSub1mhoi2BU/RznCh+zmUoaShCdImJrw8R6f8SHypKX8WTM3Mw0PHlwR\nVxiO1lbpqq0itaskxXOqrULalS78mEl8xXpe5i1SSeZyykgjlXeYFwMn2U7JZCJhZj81JJFCBv3Y\nzcs0koIHCylIOKinnEomMlzlJHtQZVv1JpvbmcJHLGMpX9OHblxGGUkksZQVbTBpzVYyLyPpZNKX\nPcxWq3wOoDubqOAdjnACA2Poy68o49e8RQNOpRiLnwf5AT6amcOH5JLNRMbShS68z4KYeJ2uyCCL\nJMwcoBYjFgrozvtUqe0jiSQaqGcLFZzPaAw0RxSmBgzcwHVUsZ+BDGgXrzRyFF41GDHTjUL2UKV+\nL3htYyvjGIMBf0Tv4XM8HzQhGGtfJ5BJJkkkcYiDSEgUUMhe9gZxcuBgK9sYx5iIx5nEVQxkAOtY\nz/v8F4Ny7HgheB3kEBIShRSE5bWNrYxhXNhjfMyHLOYT9W8fEjfxAzy4ccWxBmkmmZhJooYDmDFS\nSDeq2BORkx9DC6+fFSs38H3W8g3/4VVSSGUiF5NKKnOZExMf0TbSycZMErUcwIKR7hTwDvvU7410\nwUE9FWxhNOfTjKGFMC2gkB9wE5/yP1aygh704CIuJgkTy1kWEx8fEllkqJzMJFFEN96lUn3OetFb\nEZoLAQ9DOJtp3Mr/MRMHRxTeEj/hp/hoZj7zyCaH8ZTRBSvzo3wvfGfFacHYsRxcvRr8fvLPOYfj\ndjuN+/er39esXUvN2rUADL311laPdd5vfoP3+HHWzZrVbnGaSF7H6+poOnxY/dsdZ4hGIjjljRjB\n0DvuYP7kyez56CP187pNm4hXnCaCl8fpBGdgNjS9Xz+6nXsuP7v+vQ7j1GfSJAySxLJf/AK/z0fd\nxo3Y8vKY+MILmG75C15vbC7BRHAafv/9VC5cyJq//AWQ71t6nz6MfOghLLP+idsde9tqi9cnIR7t\n5Rs20L2sjBEPPcTn994LQP8bbiC1Rw/+mZ9P0+HD7F6wgMYDB7hk9my6/fb/OHgwOq/e6QbtizGX\n/hyiEg9JZNKHJo5xmONqNcFD7Gcvh/FgZjz9gwbqoQO3ixnLCbws5+uYxWlwVUUT2Qykhj000YV8\nhVctbrV4SA37qKIGN2bK6IcIwYRAOJVRcY8fw0WjpjBRe2wVykm2la2FrS5gQFhb9SSbMZzFv5hL\nBRUq3xpqE2orOy7cWJHwUcM+KqnDi6SxVYCXhA8vHnVIK+ElnzSK6M6bMQyMwrWrlrZq2a6Eraw0\nBdlqGP0xYmABSzBwgoPUkoaNKVzFHObTHGP4QzaDwrT1JjxKDqKDQ+xkMR4snM9AlZOwiQkfEyhl\nK7tZonhKazlIDumcz2hWsApvlG1Maystr1BbAbzLf4MKqMzjECXxdB+ZAAAgAElEQVTkcwVDmMNn\nSPgYTgkZpPEoz9LEUbawg0YamMok/seSqCMYTlf4kOhOL/ZTBfgppDsuXNRr7HKAag5QDcAojWct\nnBAsYzxevHzJqnaL02J6sJ8q/PgpoJAmXBylQT23ltfINsZDLlwc41i7+LTFCYiKUyEFnMNIXudN\nKhTPFxBVXyfCPkPRg2Kq2I8fP4UU4KKJBo1nsVphBnA2I8Me2638AyGYsuhOIW/zbpu8wiERnAYy\nCAMGFrOIZpqpoYZkkrmaycxjbsx9HUAvulPFfoz4KKYAFy4aqQ/Lq5RR+DG2sPl5jGYH21nJcgBq\nqSGTTMYwjlUxek8lfPSgmH0cwA/0JB8XLho4qj5j7/Oeui3AQRbRg76M5nwWMh8fEoMZRhrp/IU/\ncoxjVLANJ0e5hmv5nE+jimD4zonTu+vrwe9HslgwGI3cfeQIxqQkJIuFu48cwe/383xWVtTH615W\nxtDbbuM/w4eTPWRIp+EFME3xQu2cN4+Kd96BrzuOU7+pU/E2NZFSVMSNa9fib25my6uvsvWNN9DU\nATjlvEJx1p13cuzQIf77320dxmnv4sXg9zPs7rvZ8uqr2HJzGXTzzez56KOYhGkiOZlTU/E0Bg9+\nPE4nXTIzKSnJobz80CnhZTAYMBgDK1gVjh1L44EDQZMwteXlSElJjBpVyPz5FVHzOl0gcq5+xp8A\nPxImDBi4lb9jRELCxN38BT/wFE+qHj8zqDP2lpBcRQkffSlmNGfxN16mG7lxcfIh8TPF1R6O18/4\no8pL3l4OtzTS3MKLq+X3M24CYAtb2cBmqqKMf2+PrQSnUFsNpx8n8JJJVx7gVvz4Wcd61rORJo4n\nzFZaXmKZAwkJY5h7GJpbBzCKETTSyGai6+tOhq12sAs/F3E+pXzDtyTTleGcxTa2RzVYE57lB/hD\nRE53Mgs/fv7ME2ob8uHDgB+TshSD1kZWknCHrHvpxoMVK7nkUE3bfV17bCUhAR6M+DEp91LCRx+6\n0YCTpqBB8UGMSBRRyBbOvL5O4FfMAGTbGTDwCx7FiBETJn7FDPz4+SNPtthPPCuh6EUvRjKC53ie\nvBj7utZ4/YoZLXg9GWPo0e3cBsh93UY2sj/GXJ9oOIWzVTgMZjAnOEEaadzDXTTjp5xy1rOB41H0\ndVrM4FdBvGbwK5XXDH4VE69QjGIkjTSyha0J5xTt/duJHPV2DueynnV0pSvDKGU7FTEL02h5RZMz\nbMGCJ6Rwnhs3VqzkkBM3p0f5hcrpUX7RwlbBItkAGuFcTA+cHA2ahDlINUaMFNKdbVHcx++cOP3P\nWWeBwcD3v/qKz+66i9r167ny7bfZ9uabcv5jDLDl5nL5f/7DJz/8Icft9k7Dq7G6mk/vuouaNWuw\n5uQwYNo0pq1YQfmvl/LXv0Y/E5JIThn9+mGQJErvvZdv/vhHvE1NnPPII/S/4QbuHb8kpmMlkpcW\nktnM4B/9iA0vvEBzc2w5PIm+f6+ffTbTli+n7G9/w2A0sv299/joe98DHu0QThtfeomr5syh77XX\nsnfRIrqNHs3Q2+SXZFFRWkziNF5eQ++4g6zBg1l4883qZ8mFhdSWB+fb1FdUcMLlonv3M2tBmkDe\nnRcvErP5M2DghzzAIuZQwwEm80O2sJatbA16OQhPlixOm1ssQ5KGhRu5ireZj4dGJKKf/BEFXsSA\nfTZ/CctrI+VsZ7MaICuuRcIbkVcjDubyMfs5SCpdOIvB3MWPWcTnLGtl1vdk2iqHTIwYGMs5fMEK\nTnCCMsZxFoN5gVdPga2aw/LSii0LMIJSVrOmzRyek2mrBhp5mpe5mx9xNRdjwMAmtvAWbUeuCFsB\nvMhMTDRH5BTYJ2ArI80Y8bfg9BXfcgvXsImBVLCL3hRwDsMBSCOtVXGaCFsBjKaUbmTxJh+oA8x0\nktVzi20PY1fEwZnV14XiHzwLGLiDu1jAfA5ykBuYxga+jWogq0VXunI91/Ie78cVAhorr0gexFAc\nxcl8FnCAarrSlbMYwh3cxmI+ZQUrE8opWmSRiREj53Euy1jOCU5wAeczhCG8zOw299eGgz7LPzAA\nd3EH81nAQQ4yjRv4lg1sVSbQYqlWHXjWJYZTyjdh+rq2ijNFwylaODnK8zzHrdzOZVyOAQNb2Mwc\n3onq/sfKK1o7rWUN0/g+JQxmJzsooogRivc3TbtgdgI4hcNIziGH3KDQ5lRSORhSuKeOOk5wglTS\nouJjbO3LqqoqJkyYwODBgykrK+PNN98EwOl0MnnyZIqLi7nmmmto1Hhk/v73v9OvXz9KSkpYock/\n27p1K2effTa9e/dm+vTpUZELB2dVFZa0NKSkJHYtWIC7vp6c0lIq3n4bZ1UVzqqqtg+i4PI33mDL\na69RtSRYXBkMhg7l5dixg40vvURteTl7Fy9m8a23snPePH796/Dx+aeCkzEpCZPFwhc//zlbX3+d\nHXPn8umdd1IwdixFRbG9WBPJS4t+U6diychgw4svxrxvIjlllZRw/dKlVLz9NnMmTGDhzTeT1rs3\nV8+JLS8hkZz2fPwxq594gvN+8xvuaWjg0tmz5QJNEHOYcTy8+kyaRNmsWfzvttuorwh4CPx+P8Tx\nvCUanbGvO4oDC10wYmQHmzmOi1wK2EI5R3EEhXC1hR9wDWvZwE5N7g/IeVmJ47U+Zl51HGE15Rzg\nEDvYxVw+YDPbKIuQixQ7p9htJWFEwsQH/I9yNrCJrczjQ3pQFLOISKSttBhKCVa68DXrYt43kbbK\nJ5ufcjPr2cxL/Jt3mUcmGfyA6xPMKfrwnK3s4n+sYCLj+D3/j6lM4ksl7ChWD0c8thpCP67hIt5h\nIbVKDhbQaYoedca+roEGumDBiJEKtnGcJvLpxkY20KD8ixY3MJVyvmW3JqcQiKOnSywvO3bWsJaD\nHGQnO3mf/7KFrZRxQYdxkpR/H/MJ6/mWzWxhPh/Qg2LSohQRWl7iWdlGBU0cpxv5bGBjzLy0GMJg\nrFj5hjUx75tITjnk8hNuYxMbeIV/8T7vkUEGN/C9DuW1g+0sZQnjmcCv+Q3XcC1f8RUAzTGkycTD\naSCDuIwrmM886qhTP09EX9eq5zQpKYlZs2ZRWlpKXV0do0aN4uqrr+b555+nuLiYd999l4ceeoh/\n/vOfPPzww9TW1vKPf/yDzz77jD179nDfffexbp388nzooYf45S9/ycSJE5k8eTJr1qxh5Mjwsd2R\n8MNNm0gtLsZgMiElJXFvQwMGoxGTxcKtu3cD8OqgQTQeiC5MovjCC+k+fjwj/9//AwKi9NbKSnwv\nr+fuuz/uEF7hsGPuXPpNvZ6MjC4dwsmp5BIeWL5c/ax23TpOHDvGxIm9o64ifDJtddZdd7F30SKc\n+/bFtF+iOQ29/XaaamtZcv/96meOXbv4/pdfMnDgIbZtq2tl75PDCb+fr596iq+fegpLWhruhgb6\nTJoEwPbt0UcNxMNrwLRpXDJ7Nv+7/Xa2vfVW0PGOVVfTOyTPO2PAAJJsNvbvj68CXjzoiL5OW6Uy\n+HMTd/MAqaQrIYRGHuQPGDBgwsRdzAAMPMdMHJqwGZMS6ih73oJL4velJ30opozRyidyX/dL7mcN\na/lAU4U23Oy28HLdyi8j8rqHXwPwLE/jC6leGImX4K3FJrYyhEEkBy18f+psJQSHtmDGAQ7i4QT9\n6RlUWTMRtvJwLOgY0djqXEawnV04aAhz/lNnq9EMpREXH7FQ/czOEX7KreSTwWHNgCWSrX7Kg6SR\nFhWnaO30OStZxjK6YOE4bgYpOYf11AYdI9G2OpuBTGUSc/iIb9kU5E85SiMl9Atq7zlkkUQSTk0x\nsJONzjauu5f7SCMdo/Lv1/wGAwYkJB7kYUB+TqKtiNqbXvSiJ+czNujzh/l5i77uVPIKh81sYTAl\nWLF2CCeRd6ktWFTNQTx46EPvqKsI3889Qbx+w69VXg/zIABP8yzHNPmUkRDqhRzFOexgJ44Yxe19\n3Es6aW1yitZWIxnJMRr5GLnmyj72coQj3MYd5JAd1Nclglc0tgJZCC7nC5bzBV3ownGOM5BBANQR\n3dguHlsNYSjXcC0f8F+1QrSAEycDGBj0WTbZJJEU9eRnq+I0Pz+f/Px8+cDZ2QwePJhvvvmGr7/+\nmhkzZmCxWPjJT37CU089BcDq1au57LLLKC4upri4GL/fT2NjI8nJyVRUVDBtmrxkwLXXXsvq1atj\n7sTev+wyJLOZS2bPpnLhQra/+y7nPfooPrebb/4ox0Jrq9u2hX+H5JjmjxrFpbNnM/eSS/jd1is7\njFc49Ln6ahoajlNfH10eQKI5HVi2jGF33UXB2LHs+/RTAHJKS0my2Vi8eFfUxzlZtsocNIjCsWP5\nYMqUmPdNNKdmn49mb/CAye+TBx6tVo0+iZy0cDfIncPgH/+Y2vXr2bnzSBt7xM9ryG23MeHvf2fh\nTTex8/33WxzvwIoVnHXnnVhzctS809zhw2n2elm9+tRV6+1sfd3rvIKExCSmspMKNrOR8VyEDy8r\n+IJmjDRoQtYC1Xj9Si5e8ED3af4R9HcRhVzHZP7F69ipiYqTD1NUvLTFDkTIWyRe4TCI/rhx0xRl\nBdq2OAEtbNUap0oqOY8R9KCYncgTLgXkk0QSO4mur4vNVsaoeAnkkk0PivkP70TFRYtE2wp8LWbn\nxax5tHPnbzA7Jk6CF9Bmmzqu5KOOpJSDHMJO9H1drM/guZRyFVfwDvPYxNYWg6t97OU8zqYrNo4p\n+xXQjWaaqWI/pwqdra97jX8jITGFa9nOdjazkTIuxIeP5cr9j2UJmL/zXNDf3SnkWq7hFV7jMIcj\n7HXyeYXDQAbE1NclmlMllYxiJMUUs0vp27rF2NdFw8uHhBMnfs0ElRCh4SrjCuSQQzFFvMnbrZ4/\nXHjvv3kNeWmqKWxnOxvZzIWU4cPHF0rxoFhs1Yy/ReSFGOXF4ieMllerYa0RIPKEh3M2hzjEkSjF\naay2GsFIruAq5jKHLWxucbx97GUk59CVrmreaTcKaKZZKXjWNqLOOd25cyebN29m1KhR/PjHP2bg\nQFkVDxw4kK+/lkNmVq9ezaBBg9R9BgwYwOrVq+nRowe5uYGk9JKSEt544w3uueeeMGdaCsAioA/Q\nV/NN4/79GIxGcs46i0/vuIOGPXvIHjqUVY89pi5NIWA0mcgaLK8Jl5SSgjUri5xhw/B5PBzZKsfl\ni58CNoVjfUUFNTVl0Zom4bzOfuABju7di33LFrpkZtJ/6lT6TZ3Ko09En2+aaE7b58xh9GOPcf6f\n/sTqJ57Ae/w4586Ywb7PP+fAgegf8ETzEjjrzjtprK5m14LYS/InmtOG55/n7AceYNwf/sD2OXNI\nKSrinF/+kn2ff05FRXSdRaI5pfXuTeG4cVSvWkXu8OGMfPhhskpKeP+yy4CJJ8VWZz/wAOf/+c98\ndvfdVK9ciS0vD4Bmj4fj9fKs4PZ332XME08wbdkylv3iF2QOGMDI6dNZ/O9/c+hQdDPcicap6uu+\neEzu9I0007OsmJ5lxep3R2nAgIE88vmQ93FwhDzyWcqnODgS9BI2YiRXKfphwYINK93Iw4ePWmU2\ntzZkVjeZrgAcpg5XDFVC4+HVjDEir3GcRz0OajmMFStDKWEIJSxR3gOJ4ARySZtoOW1kCxMp43Im\n8jnLOIGXCzmfXeyJyUuSaFsJnMsInDjZFkcBnUTbajVrGMd5XMqFbGQLaaRRxlh2sScozCuRnHKQ\nhVUkTplk0JNi9lFFAd04n9HkkcNs3kiorbT3bwyjuYxL+IAPqWSf+nz58KlFtDawmYuZwJ38mIX8\njxyyGe8dy8ffLmThgo/CcjjZONV9HUCPsmJ6lvVQ/9baeT7/pZ568shnCZ9RH+I90vZ1ZixYsZJP\nPj58qvAMFaDiXtRRR2OcfV0ieI1hNA6lr7NhZTCDGUwJSxRR2RGcNrGZi5jAZVzMEsycwMsExrO7\nHX1dOF7h+jofUkReAqMYGXdf18BRDBjIJ4//Mp966sknj89Y0qqtLJjDcvqG1YxmDBO5hM1sJI10\nxnIBu2Po604Grwwy6UEPqthHNwoYyzhyyOW1NuojxMtpNGO4hMv4kA/Yx16SSQZEXydPsmxiIxcy\nkZ9wO4v5hGyyGee9gIXfLuSjBQtbnD8cohKnTqeTadOmMWvWLJKTk6P2/kD4/M3W9y8D4NIIg5Pc\n4cPxud3Ub9+OOTWVrMGDObCs5Vo+yYWF3KSEnvj9fvLOPpu+U6bQUFnJ7D59Ip49lms7WbwMksS4\nP/yBlKIijtXUsPuDD5h3xRX8Ycn4DuPkb27m7XHjuPDZZ7nwuec4dugQm199lW2vvw7c3+KYp4oX\ngKlLFwbdfDPrn30WOsH9c+zaxQfXXEPfKVOYvGABxw4eZM+HH7LltdeAH3UIJ4PRSOm993Lhc8/h\ncTqp+eYbFt1yC/YtW4hFnMbCa/h992EwGrn4hRfghRfUz6uWLuW9iy4CoNnr5Z2xY7nwuee45OWX\n8TidbH7pJbY98gjwW2WPpTHxaw9OZV83/rHzgciFD/IpwIcPO3VYsJBDHntD8qhALj5wD3epfxfQ\njcEMoh4Hf+HvrTCO71mJnZcB8IflZcDApVxEOqk4OcZWKniVN6iMYdY+0Zz8+Pkns5nE5UzmCpw0\nspb1lIeELp1qXgAmTAxnGF+yOu6snkRyslPPa7zDYAbyI76Pk0a2sZ11yuL2J4+TjEhtajTnMJkr\ncONmP9XMYT61HI6xbEn0vEZzHgYMTOZqJmuyG3dTycu8Bsj5rv9kNpO5guuYhBs3a0zlrBqxjvNH\nyO/25b+Lbl3CRKAj+rpI6EYBXryqnXPJZS+VLbYLd/9LGIQDBzN5Omr+0SKRvIwYuISJpJFGI41s\npYLXeL1Fbuyp5OTHz4v8i6u4gqu5kkYaWadU640VsfC6i8AERqR7aMJEKcP4itVx5zAW0A0vXuqw\nq5wqQ+ouhOPUjQIGUYIDB08zE4AjHOEt3mAQJdzIzYpo3kE50aW0nSxeBgycy3lcydV48HCA/bzP\nXA7HsPRZLJzOYzQGDFyt9HYClezhVaWIVjPNvMwLXMUkJjMFD27KTetYM2I140fIfcGy3y1vcWwt\nDP42eqQTJ05w5ZVXcsUVV/DAAw8AcN111zFjxgyGDx/O2rVreeqpp3jvvfdYsGABn376Kc888wwA\npaWlLF++nJSUFHr37s1uJSdt5syZdOnSpcUMm9zhPSZvo/zsKDyknN/vl6urGgy/65ScQLdVOOi2\nih6d3VbwWNyTRrHgVPd1v/H/OiG8Y60UGAlP8jgAvyX+wiZaJIJXZ+QEnZNXZ+QEnZNXZ+QE8AfD\nE3pf1wr0Nhk9dFtFf8628r2j5RyJVzzXHG8Oeui5BKfp6mR/+5Co3PgnDH9ova/zt4Lm5mb/zTff\n7H/wwQeDPv/Tn/7kv/fee/0ul8t/9913+//yl7/4/X6//9ChQ/4BAwb49+7d61+yZIl/+PDh6j6X\nX365/6233vIfPnzYP3bsWP8333zT4nxt0NGhQ8cZiFPRL+h9nQ4dOjoael+nQ4eOMwFt9Qutfrt8\n+XK/wWDwDxs2zF9aWuovLS31L1y40H/06FH/pEmT/EVFRf7Jkyf7nU6nus/TTz/t79Onj3/QoEH+\nZcuWqZ9v3rzZP3z4cH/Pnj39jzzySFxkdejQcebhVPQLel+nQ4eOjobe1+nQoeNMQFv9QpthvacS\nelhv6+js4Ze6rdqGbqvocarDek8lDAYDv/b/JiHHSlSYzeM8CXSu8J/OyAk6J6/OyAkSy6szcoJT\nGOr2HYQe1hsdOiMn6Jy8OiMnSCyvzsgJTl0KQzzVinXo0KFDhw4dOnTo0KFDh46EQhenOnTo0KFD\nhw4dOnTo0KGjwxH1Oqc6dOjQoePkIZZwmUhhhLGEF7Z2vvZWFwzlEWvYY6TzJ7LqYTyhmOHOH2+Y\nkzh/a3aLl1O8vDq7rXxIYe3WHk7x8jrZttKhQ4eOMxW651SHDh06dOjQoUOHDh06dHQ4dM+pDh06\ndHQgInlNvGE+N4XxIAkEPEreMOdo2dVrvVHRIhwnwUscJ5InMFpe8aAtXrHYKhG8IvFpjVdbnE42\nr3ht1RG8OpqTFqGe3NY4JYKXDh3fVegRAtFD0rxTz0TonlMdOnTo0KFDhw4dOnTo0NHh0KfwdOjQ\noaOTIZLXRnwe6k1qy2sjPo/XaxOOjziWhDeIl/yd1IKTSeP5Ettr+cbDLZRXa8fQ2iocp3C8Tian\nSLYK5RQrr2g88W1dVzS2CuUVesxQD2w4XtFy0rb3tmwljhvOVtFw0vJqy06h52+Lk9imLVudaYh0\n/W15jiJ9nyh7xsPrZHOKdLx4bHWmtzsdnRe6ONWhQ4eOToRYBuxaxDswjhWh+0cKGRbCJhwn8Vm0\noZNtD7xMmt+Dt40k2E0aoSP2a4tXWwPCSPeuNf5mPEG2CncOE76gY2vvY1sDzGg5RZrsCMdJy0uc\nI1ZBH45Xa3bS8oiGVzxtvi1ObYU9B3MMhOW111anK9pqu5FSD6IVYvGIr2j2iRRyGa1ojVcUtrZf\nPLZqj51i4aY9j/aZiOdYieIVa3G0kyHkQ98/bSERbei7gjO7Z9ShQ4eOTgqfMtRtDfEM1ts+b3jB\nFWmwHspFy0P8Hu4l3JqIiEdwiTOFXouEBLiDbGVRBGGk6xe84hUR4TiFer3NyrYSEmbcmPBhxqNy\nDHfOaO9h69wC9g89nkU5v0/hIu6fGXeLa9MeK34uwW0qHCeTchfleyjbyqIR9JF4xToBEomXlpM2\nWkC+X8G2kicavEHnDscrEffxu4xwz15r+d/hRE1rOfmBfWMTX7Hw0h5f/N5a7nvofrGKi5aTIuFz\n+OOxVTx8wnEKxyuUU2u8tNEkrZ0jXm6h33cmIR9JqEYjojtCzJ/syQVdnOrQoUNHJ0Co4Io0wAge\nVASLCO2A3acO630JEVxahBuIeZGw4FHOIYuIUGGj3T+RBR8iiQgAC248WFTxIISNRcPLq7F1e3m1\nJmx8SHgIvLzNgESThltARPs0IkbwSYTgEscTwil4IGTChkv9W8KHDVfIwMkX0lYTI7hCOWkHg6It\nBWzlDppg8Cl3LZRXe/lE4gRm9dyiHZnxtLCVhKS2skS3+dMJrYWOQ8u+KlT0hyLScRLNC8CDOaid\nhDuGFyluTq0JwHDHjMZW4TglQny1fR8j99Ph0kPi59G2kNdyEj+jmVyIV8iH49ZakUBtPxHNREx7\n0ZrNopn0SKSN5OPp0KFDh45OjfAv85bhhcJrEyxCYxsMtzaAbjl7Ghri25JTawOVSIiWc7jBYKjg\ncmMJElxAkAiMdO5Eerhaihv5npo124jhbbz5d9HxCC+4xLHNeNTvtXzMqke15aAkUYIrHCftANqi\ncJMnFjyqlzKakOD2IpQToIrkgK28Kid5H5O6nfY4OoIRTtBEigCB4HYSafv2iMFIvKINwQzlJQ/s\n2ydQo+UVyiGSrRLBKZKYCT1epH40XHpIeLHWXjHYtmCGyBMeibp3rV1D+JD/yFEbYttQT3OivMyh\n9yFcJE+4yelEClRdnOrQoUNHJ0U4ISH8LxDstbEEha7GFrLUNo/wXjfhNRDnFD9tNGFROImcylBx\nFos3MFI+lfgsHKfQWXjZ++bFhgsrTUEiwqPIRG1YcaRzRYNInNxYNALLrAnx9ZBCo3oPxbYezJqw\n50R6moM5iesX4lR441NwYsOlilMPZlzYsOAJ8gi2n09gRt6j+kUD126jCQkf6Tiw4SIFJ1ZlwkF7\n/1DvY2Ls5EXCo9wzFzaVm2wjtxotYMFDOg5ScKoiWlxD4Nr04Vbbg+DA5EywyDfjw6fY0xTUNloW\nh4t98B7p89AcYi0n4RWXf5rVdhLJ+9cy3DV2QaEVWomyVaKFfCgvra2EfbS2iiSy4+HUmmAOP/HX\n0lbhjhdqp/aLwWBe4liSclzxVhe2EueUNPfvZIlmbVpC6Hs30qRHpHvXHoGq95Y6OiWSgBMdTSIE\nScrPzsQrSfN7Z+GVFPJ3Z+H1XYT2JRA6yACw4kLOoXSrgkv70jhZuW3hBBeghDXKL16zwkl4LcUA\nTvBKdIijuG5xHrcy4BACPgUnJnxYaVJEhCwumrABsvCK1dsczTWIYZELmypwQL53ZkXU2HCRTj02\nmlQuYt9weVihxw/l1Nr2AX9ogJMLKx4smHGToYgswS2dekWMmmnCpg6Y5IFu9FWD27JV4P5ZcJKi\nTCHY8CEFTSYk46SAaqw0KddgVXm1FpoXq620AzI3ZlxYcZJCIym4lXacRw3pODDjJo9a8qgBUK4h\nGaeybRPWVtvWmexR1Q6ERe61Vkho+zKv0g+6lHYh9hN3XuRMy5+3vt5sJIQXzCLHOHB88Yx6lOkJ\n7bMNotBZcC2AWMJWoxFaYjJL/BfPg7CV9hmSQvbTHjcRwkubnx7+HqaotnKSEvH+tTccVHtdbfEK\nZ6twnEKvM3YuLfcJxwsC9m/CprarYFsFOIW7d+31MofGrohjy+95+b3fhE3tA1sWgkuMaD4txaku\nbKJDZxQ2EODVme5jUsjvnYFXqAjsLLx0xIfWPIhaEagdaPgwqYMmke8mRIcQNtFU120L2rAebR6k\nG4sqbEAWoGIAJ3uT6jUv9xQAdQAgkEgBLVvFFCS4xDlkESFzysau8vVhUjiYWzlyvHxMGjvZcJKC\nk2Q8SqixCR8FVJOFnQIOYsOFG7MqgNxYkDg5BXS8iuhykoKdLHXAmKsIrhSc9GQPedTiQ1K3k9tW\nYocOWsHsUYRgLXnYm7NwOlKwJrvwmSX6soteVNKfCmy+JpySzKmGXJykJGxgKyCu04OFJmw4SOdg\ncwH2/Xlw3ER1z0Okmx1MYCmllJN/uAGvBWpSs6imGz5MKq8zvQiSFuEEoLYomDanWPR3wrslP0Mp\n6ucBP3sgrSAeMRiKwCDdq4aRy1EEsrATfYuDdFzYsJOFW8uquI8AACAASURBVOlDLOq2HkQhr3DX\nHQ8ncX0iKkWcR0y2mTUi0EG6YitT0PYyRxGlET6cNhK0z1g4Aai1lXgnuZUcbScpOEinjiw8St8m\nttO+N0KvWTt5E80zHomXFZcS1SPfQ2GbgxRgJxun8o6SJ3td6mShVjS3V3hpxVw4Xm7NfXMo04Nu\nLGE5aa+1vYjES5xHTL4ITi7lPW7GjY0mdZ9EcTrtxKkubOJDZ+EVKrh06DjToZ3H1HofAdJxIOFT\nhmtOQH6JiKzBRHhkQnOGtIJXFhPyTLgHszL3LHu0UnCSgQMrTcpw06wOUsJfZ/Svo0hVObUCp5EU\n6klXZ3vzqMWGiwIOkk0dXiQcZKgDEu2xhAeuvSJMfkmbVU5OkjnYXED94XRMST7MmR6GspE8aijx\nbUHyenFYMqgG6klX9z8ZkO0ie/hqPLk0bMuHOqjtW0Cv4krScTD86CZMe4BMOFQkC+c6shPORW5T\nPtWL0YQNe3MW9m8KYS14TKl8PTGPS3sv4vwjX2P4AvBBaqkd+nJShKn2eGKCwU429k2FsBBYAw2j\n8/nv1KnMSf8+pg/kz0ylUHiRHU+R3Aa1OJM9pOGg9dJoJ9msSui2CJE241b7mDqycJChfp6CE58y\nYA+XKxcLtNsHvIwB8SD6WbNSZA2ghjzsZFFFEW7MWPAonCQ19DwgKluGHMcK0TuZlfOk4FQnkyR8\n2MmiSZkIq6QXdrLUd0Q6DrVv1l5jIqC9hyk4yaaOLOyk4MSNmUp6UUlPqihiDz3xYCEFJ1nYyaZO\n5e9LEKdgIR+wVS61ZFOHlSYcpLOGEaxmFOspxdGcjtXYRDZ15FKLD5P6BCdeDMrpLzZcZGEnjxqy\nsONDooY8qihiI0OpIQ8fUtA2NqUfEZza680NbfeirWdRR7ZyD52k8CVj2ElfyhmOk5QW22g5CV7x\ne+JPY3QWwaXj9IB4WBLTNSUGR5H5mIDUDuYCMhdnyGdWTvOOpp0IF1ooeyolddCu9ZQKb58Zd5D3\nwIZLCU+yqcfTeqRCPTetDZRDB+baz8O9bDyKB86FTR1kiIGHmDEPPV484Y2RwjC1clLAjQWHJ52G\nugya8q30N1ZgxUURVWS77NTb0oK8uGKIHA7RiorQQUJgikD+3IOF+sPpNH/bFU89VE0rAqCIKlK/\n8EAaMKQeuyVLOa+JwNA9fJXLWDkF2lQgT62hLgO+Aj4BhpgwP+7BgxnTf4BtwIWQkdXAQVuBao/Q\nthWrkA/mZQ76zIeE05ECa4G/Aukw+o6l+JBYmjmaCa+uggbgXkjp5USStE9JYmwlP1vy5IB6/5rN\nsBV4BjjYRI85e3mSGXyP1/n9TdPpcdderEOBYjAXeYKOpY080PNPW0L0Y1ZcZOAgC7s6+VZLrurZ\nqqQXtc25SEafIrYChWJErnR7EFyYyasI5ibSqSePWnKpIftoA26LESywkaFUUUR5TSnNbgtp+XXk\nmeXtxPHMGi+lOG70dmkZziuOKYRNEVUUVtnhCHh77aI6tQAH6Xy9bwzsNEE2ZA05QJGxSrVzpDD7\n9thPTC5kU0cRVfQ9vB/KgWOQPsXBcs6n3FNKw6v5kAwHR0L3/juV6wz0JrFOx0USPlohb8VFLrWU\n+LaQutIDa6Aw107lTT2xk82uvw2Gg8BoqC3LhUzZ9gEPc0vRHIvwankfvZhxk46DIqoYXLUL5gEW\nqLtT7v+9SBx8vBcch9rvFeM5a3PQNQlOiahyrOUlt6s6BrCd4rW18BU8c88dNCnv9/NZxou/vR/G\nQe4l+4CKkN43EZMLOnR0MmgFl7WDuUB4wZUSbsNTBPFac4Z8doSAvU71g+1FnggK98ptouN4fRcR\nqLYbXPRIeN9kL4486BbV/cQ+HjWoykK4Ih3xDoq1Lz3twEGEoVppUkOJBUdtIaQmxacajld7Bupa\nXlrPi+zBcGMy+eC4mfrD6ZAnb5fursd8HHw2k5IPaAvKWYyVV6iQ13ISs8gWzIr3xYUpyYenHjiA\nmqNYQx69qg/KxzCZFD9NoACPyDeOZY3RUCEf8CRIiFBFNaSuixvPcQtkwR2PPwNAHVk8c88d3P/o\ni+AGt8WsCZc2B4nAaHlFspW2GrBNaS3WZBceUyqkwx3rnlEnPVYyhg3zh3L/5BdBApdkpQmrEgYd\nflmPeCY9zOpzZwpk5hqb5Fnv43Cd/33yqGELJZSwhY+5krzGGm4cPhdM0KT622wtCjxFy+tMgPaJ\ns6hC0EEB1WRXNcIxyC62s9PWFwfpbK0ugUoLJIOrrw3J5lMC4D34aFJtnCjvmxAoKTSSSw352xpg\nE5i8zZROKWeR5VJZmL7TFUzQUJoPI8Fmdilt2aW0RU+b54sWWu9WHjUU7rHDx8BqMFkg76UaasiD\nu0xwHBgHdm8h5rM9WJXw0dDw2dg5BBfOEWJLzp930PPofvgI+DOQBvYp2Uj4yDbbaXgsHxzAo7D/\n+z1JKQ54pEVRNi3a42U24VPun5yrnrrSA78FquGZ7XfgwUIJW+jz8528mHQ/zAXPX1OpvraAZJxK\nyGri7p2Wl40msrAz4OgueBWYA3/c8ID6LrqURUz87Wc8nfoIOGDXIwNIKZaL1FmUnGbRnyeSlxUX\nedRSvK0WpsPfPrkbF1ZEeLiEl7sf/xv/KPk5tS8Xkz7GoRbPE4zaH8Ku44zFCQICJ7MjiWgghCnK\nTyFSOwKCRzjvu/iuI0QgoAQxtcQJ5LHTqbSb4JRE217ljryfnR2hgykhOMUaY9pQLjkXURYYHizU\nKzkqovhKvIImGl6AUkYiwEkMlDyanFMPZqooUr2qIncmlFciOGllknagm2x0UtvbzvbqAWo4ssnX\nzMbM/tQo3hiRPxMqatrLSzvJIISzCTmUt2paEXnUUMp6JHyspxRuksVqFUXUkBeWV2LunztohlvC\nB5mw864+DDBvV+6QHEZpwcOy342iPxXsoi9VFOEgHW3BqfbYKuB58GralHzFPrPE1xPzGH3HUjVk\nTGzvJIUl80dTdmQV2xnAPpVX+9p8JFtpixnVluUiVfooYQsuzfSpCytbKKG+/GMkn4+d9KWGPDU/\nsr1t63RHIIxWFhJZRxphD7APLPsg/ZJ66kmHpRY4BPSExuQsnL0darhqIu0q2kJQqOrRBtgBfAHY\noWttM9wHzc90hRVANtAFGrpn4yy2B3l2RZRL+3l51Z9WRcizA/gcFrwPV78ipwSkUw/jgUcWgPNq\nGAiOvuk0pVqDespEhfWaNP1cCk45HeALeHIrTH8fZvEgedTwILO497GX4c4n4dPpcI4JZ3FK0IRX\nIqC9LvGOynPVwhp49Qv4kR01PFwWVhJU+KHPv2HVLdSel0dRQZU62Zoo0aXlJ9q6aQ/wPvCnQIVw\n8Q5zA/wf8AAwVbaVtghhoiHOm45Djqa5ArUPE9+L9wfXAMvBMSZdnXBOFPRx4imEic4VEqqj/dDF\nloxIXlPt93o+cesIN0jQziKnhGwnBjuieIKA+Dw0HFd819pn4vNw24nfhedU5JZq99Huu4aRYfmE\nnje6kKjIXMVPCUktFtFiIFggf1ZJTx63/aYFZy0iXXtb3LT7CoEuF0LxqCHORVSFPX4VRVRTEPHe\nRXtPw/EK9gZ6lAGWfO+Eh6qELVxqDt5PFAH6lIks4tI2bRHp/kaCNtxZLJAkBkU+JAawnUt7L2px\n7CZsSPj4lIksyZwQsf2GPifRcNLaSXj/xeA1Gzs9qeT8zOXqcYT3SY4OkHn9lYfVMONw9y7cs6AD\nZfgfmKAwuJFDtxsAE+pEHHXIXjcHcNygmc44eYN1wc0kONmRQ5WqlTa1H5kXgpcp6GoSyUX8FBNy\nZrdbntW3QyVAluztN+GDHIBKma8D3E1mvKny3trCdolsh2LCkkbgoDIuyJX7uAKqKaIK8gG8cABo\nJGxUQeL4iOgMN+bjQLV82o2Z/XGSjKjgLuGlf++NbOeAvMFxs1rg6mRBCFTssGA99Lu8B3ay1fgi\nIfR7/HAbe3+0Aw5fHSRME+GRDye25fe80q5SA5WpxXfqzzSgAdweMz6zdgJO5tSe3Gp9XH0KoQvT\n0w/6AyQjSfkfyaOrC9OWaG1AEBA3LSvoaSsz1iqFE2rIRYTciLAt7VIG4QbD0YZfhuMEqB42bbEJ\nM3Ku4hpGsppzVU+qKMYRyisRNhO8AvZxBxV0yKOWAa7tvGH7AZ9xEW9W30j/ggo5dwy7UmDCpQ5C\ntaFq0YaDhv6tHRBpw41FUYsiqqghj/WUUk0B1RSwnlJqyKOIKvKoIVcp4KTlFc39i8RJ/B7KSeQ8\n9WEn2xnAas7FrVRl9GBhNedS4elPX/MuiqgilxrVzuHWKIyWV6idIFDlNBknvajk/CNfszRzNCsZ\nA6CEyMoDpS8Zy6rdZYzqvZye7CEbe8Q2Hwsn8bnW7nKBnkYKqGaM70t8ksRfeRgrLtVj4MaMjSZm\nHX0Qn1diTOaXaiVm7bHiEctnAnyq7022lt8ChjTkAXCmqATulr2TXiAd6OJX71V7c00j85JUbl4L\nmNKALOXLAqUNdUdWhtmClzfoahLJRdQjUNdQtljomtoEWdATwC730V4kOAzQU+abDharRxW14Z7d\nxHBU1gtNBropY6RaOa8eZJHKIQATFALJJCRXODIf+RnzYMHTBSwF8mmHHtlOSmYjPkQqipXtu4cC\n6+QNunjaHfocDTc3cjrF1aXAwr1kXV6n9ik+JJwks/e1gZAxEHICVZYhMcWZwlVrlm1ikQuZHA2e\nJA/62QCkgcXsCZowEWhfDqyOMxZJdJ5wXgHlWVA9kh3ZQLXnDu0COoqX8L5bCS8EkzTbnSoITtEU\nH9M7nMgIFhHB640J0SnyOsy41aUtZCGRgQur6nkSFS8tatGE8LOjsfDSFnQQAtmqFAoRBUwkfPLg\ng0BYr4SPLGXplrZ4xcpJa6tQTgUcpIgqCo7YcXU1qgM2o+Rl++6h2HtmqQVCZC+ZO2G8QicVUmhU\nOZX4tpD6hUfOMb1JHqytp5T1fzsPCsF+cRYlmVvUAYF8v1u3QSy20gp44Tkd6ttI6hwP+Vu/pvx3\npZhxq/fx69svgKGw8SYbZKJej/AsJuIeasWyyA3rTwWGL2DCq6vYMH9oUEVeJymsOnsC3AUVt/RH\nMssDo2Slwisa4RyLZyi0rQuxbKWJPGroy045Z+1+KCnfQg15atXWJmxU0oumnhnwMuy8tq9y/z3t\nvodnAkRvJ9a5tWcmk92rEXLBXSyHYGbggDK3mnOanG9X1yzWeuITxQdEmKWcmlCXmkZ+vwZwA144\nNsUIgPH+YzTnyzmnDIS0/Dp1si4QhpsYbsIz5cNEE1YcpJPRrwkuhKu7Aish4xYHDjLk8OMJV8M4\nmVd6qlw9XVtmLVHQrkPrJAVvLzCNh+mrgT/Dg1Nm8Q7TmMWD8BhgnQ4Tgb7eIFslzk6B514sT1Vj\ny6V4ZC23jAfOg/Tt9XiwqJWWGWCAXrfAaMgtqFHXog4Ir8S2L9HWvb3AdC3wSzBd7gPl/a6mTtwL\n3IRqK61ATTTERJuDdIrPq4UHIOU+p5pzKrzcTdjgv8DLqGs9J5KTPlbU0emgrTrbGaotm5BFvLZa\nb0fyEjwyaVmtt6N4iYkELy1Fs16tNzZoBzNCmArhZVEWZNHmo4rZSrmSr/wi1r7kIy3YHSsnbfEa\n7Zp58lILTarIi1QQyYU1LC+B9vASnqnA+npNauEGTxdwWDLUfTJyHNh9JrxeCbfZEsTLF8IhGk4t\nPZUtvd0WRQzacCF5vbI3iOCCSBQCGeA9IamFh7SDEK24iaZEf2u8tNU+bbiwuD1gAY7A/c+9yBv3\nXIedbOb+9kY5JLALeI5b1AI/2mIcobZqjVc4TvK1BdqUVRGDNl8T+IAGuH/yiyyZP5olTJA9pmdP\nkMMnvdDUaKMpU86zFh4Ybc5pPLaCQGSAds1GK01yJ9cFrk2ey97GHszgSaYwj+n8nr2GHtANOAFN\nzVZcRpvSvgLhy8JL3JatzhRoPTFuRUQ4SEfCh6soUK3XQTrpOBhUsIXa/EC13nQciogIzqVOBEQf\n61aWXKolDwZCdoFcrXe9ZTgAw/PWUz4tuFpvsiIiQr1JiYAPCRdWnKRQQx7mXh4KJ9lhHHh7yf1J\nHjXwT29Qtd48akjBiVWJyGgfh5brZ3uQ+wgH6VSmdqfvlfuhADgGWUrcc50nSxanycBI6K4sWxVc\n5Cdgs/YIaOFhFiKwmgLSxzpIfVyu1nv/6y/y0U0XsYhL+exvV8p5naPBXHaUAqpPmhAUvFxYsZNF\nRWofBt+yC9LgkRee5ps7h7CASSziUjY9fo4sTr8HfYoryMKuiuaTIZiF8Kwhl30DHRQ/WcvPn/uH\nWq1XroVglav1Pg25Y/aRSw02ZQygV+uNAp1B2Og4fdAZw7I7w/IxWgghr4X+HLaOcB25dl28QIih\ntiIvauiPLGADi6sLBGRIsOdM+32k80cziNf+LmbLm5QBE6DmWTZhDap2qB2Mhp4nHhEhPg99KYrF\nzBttKdSRTRM2bDRRZKzCWtCEqMKpPXekAiHRiohQj7fYR/zuxiwL5SH1+Ewm1bNcRBX2i7PwnpDI\nyHGoAia4cEl8FSzDLWGg9Zp4kai3pZE/sgF6y4NbEZ7NbYHBbVp2vXqscG0rHl6h22qP75RS5HVM\n7wUkKDuyik8zJ7Jqdxnchdwhj4CUdLE+YmJtpf2p9cDQD7gTrOth4Ot7eXvSTSQ5TsB7JpgKjAQG\ngdkY3OZF2woVzjpkiFDCJk27dJCOD0kt+JaCk57sId3oQEQ7pCgVVYWwaS+01X6Fp0gUvvJhwkkK\nValu1eMGSshqHkHrnAoRqA1ZjYdfuKrh4j3gwoadLHxI2IuykIrkdU6FkB9V/CX24uB1TgMi0Kf5\nH1+aRShkW9nUtZBdOTZSLgmscwow3LyePXe0XOfUFmKrWBCpfxb9lAczTdioJRckyL6gDusF8jqn\ndWSRRR19fr65xTqnolJvJI9uLCIskph3kC4XDiwyk3WfXdO3yhPJ3X67Bx+BdU7l9JhgTom4d9qK\n/nK7yqYCsI/IImWEkwtYrq5z+iVj6P74zqB1Tm2aCaJE4LQTp6LwSmcaEGuLwXQmXlp0Fl564Rwd\nOoKhHULIglR+cYkKemLgIbZNtPegZdhmsMCVcwBlQepWRGpgMFSviuaABzc8r1i8uqGCS8tHeG+d\nJKuFHOrIwoUNGy66UR2Upxp6znB5gPEXdQh4Ez2YaSSFasBukfmIBdbzqKEkcwsubGrYrMhZO1nw\nKveukRRqyaOpyIZU5KNG46UaUlxObXGeej9TaEy4FwECkzAit094FOgrr2PqkqxsZwAAo3ovp+KW\n/jQ12khJd5JltCfEE9SSU3C7cmOhjizMRR7yrrVjuhIO5aSxlAlck/oeS+4to+GqfOjiJat7DVnU\nBU1+iOOcyV7SUGiL4Mi/m9XfRcVS0deJ/PVs7Er4fWCNZ5GLr50YilcIaidIADXHWfSqQqh6sMht\nFNS0BbGtmCw0K9730JDj9noDheAS7wAXNrUOgVN5UoWQz6IOUY/ArOGVaI+gmIDR8hLvAjdm7GTj\nQ6KIKjVXW8IXWM4qwWG94n2ifUcJL2otuYCcHmAnm2zslLIep1F+j8npIS5FnLo1Yj5xLgrRloTd\nnKQo4cUWNXJgKBspogo3loRz0gr60HYveIn7BuDCprar4ZSr66mLFJFQTu3NaT7txCl0HqGlhc4p\neugTDNEhVMh3Fl464kNrLxkxOA68QAK5H9pwVvFCDs2d0yKeBbu124pQYrWYA6h5dy41T0b2amTg\nCHqJhSswEc/C9JEQCJeyKeFJPjWkSwxuM3BgUj4XtjtZIlB48dzKQAxQbSYEmISck6vm8QCiUmOi\n8+i0EDP3TmXQps1fFoOQXsghdx4sQYOQxIe5yS1KiHgRVuYkBUny0YSVfYqXqid7kMw+mjLlcFkx\nYEvkwFbAi4QZ1HBTiXR8mKhNlQe3NUrpqlxqGG5eT01/eTFdC26SFc9ZokPvTgeEPvNi4k1O5jRr\n/haDZZva14n1brWDa21EQKJEoNaT5CYw8SUm4oSXEFAn4LRVTbXFuRIlbIIFV8A+4rqFsBDFo4SX\nTbuNSBVp7R3RGkJFjRbaeyZEqvhbCNV0HBHvXzhxI7aJjaMstsQ91PISVbUBpQKCFSsushTvbSRO\nAu19V2htFspL/C7alZzL6QmxlfckieVgXnK7N6vX61YSiyxKu7IqCVyh0UKJ4nRailMd3310RqGl\nc4oOnZHTdxXBLwxz0BDDrbxAxCLj8vaxLYMSL4SXw4wnaJAkagmLUGOfJhdVOzg6Gfy0AxHxYpV5\nyYVDxOBWvFStykBEFOOJx+MczbbiqsWapcIebiUsz4dc9CgDh7o8j3YA2VbIXainty1OWiEozh+Y\nZJAH3ELIi8GRGHSblaDyaAYhsfLSCnlAnVQAudhQo+qRl6vmCg9WoBiWJ4prj56Tj0BlVBFuKrcl\neXArogQ8WLDgIZeaII+QdjkIwTPS+cJ5688UaL2n8rMbLJrERJzo64Q9QyfZEjU4DiechfTTCtaA\nR05bbdodxCv0fscibEJDVUMFFwQmuiLZyqYUP2rLVu0RggLB/W/gHoazVQqBicRQTolCOIEamoYQ\nzlZatGan2Li0jJjQ8vIQXFVcO8Eb7hiJvneBcwSvDy0h4SFwrwQHEa4e2kYTkScscNqL0zsPHWLe\nFVdQu24dN3zxBd8+/zwVb78NwPVLltD9ggta7HPC5eKhlJkdwglg4Pe/T7/rrqPwggtw7NjB3sWL\nWff003LZ5pOIVnkZDPS77jr6X389xRddRMPu3aybNYttb711Us6XVVLC6MceI2fYMNL69GHz7Nn8\n7447Whyja7duXPjccxSOHYvH6WTnvHks/+UvoTluWu3mZcvLY/zMmeQMG0bGgAHsX7qUuZdcEj+h\nBPHqM2kSZ915JznDh2M0mdi7aBG7FiyAdzuOU05pKWWzZpE5cCBJyckc/Oor9n36KeV//3vkdWnO\nUIgXgfZFISDh42/8mGf4kL0c5hdMYRnfUs5WJHz8jGn0UTxPWpzgBI/z+3Zx0goubYaseIE+y4+Z\nySKqqOFXTFJ5CZxNCWcxgD50x84RdrCLVXzJcY4HXV9rHEJDMEP5iUEcyAPImQqnXdTzWy7jCzax\nVuFkAIbTl2EMpD89OIKDFaziWzbFJCLEtiZ83MizLGIm9eziCmawjc/Yxlp8SOSSw1QuoRt55JLK\nEnYymxUtBmupdOUGJtKLQtx42MJWFvIpfvxRcxIQQvBGnuUznuIoO5nI42zlczazHi8SBaRzIxMo\nJIdcUljCbl5iJRC4HynYmMoFyjaZ7GIvs/lPzHy0NvMhcS0vs5Q/0MAOJvAkW/mcGuX+9CSZixlD\nEVnkkazyEpxM+BhKX8ZyFt3Jw4iBHexiK9vZwOa4OEn4uJZ/sZQnaWAHF/EEFfyPWjbgQ6KQDG5m\nHIXkkEcKn7GbF/lKnTSS8NKdHKYynjwysWBmLwfYxS6+ZDUnOmVFg5OPSMtYBEK8AxVzBSS8PMXP\neIG5VFLHA9zASspZxzYAfsrN9KK4xbla6+tCB9kCwfmnLfsVLSR8/J67+QfzqKKWB7meFaznWzar\n1ziMIQxhEL3ogR07O9nFl6wK6uuiQaQ1QUUfHOAk2yocJ5D7uqEMopSB9KEXR6jnS75kAxtj4qO9\nj3fzJHP5JzVUcT0Psp4VbOZbfJjIIZfxTCSfbmSTzlds5G0Wq/YTnDOwMpkr6EkxbtxsYSuLWBxz\nXxeJ1/e4j3V8yUbl+e1GJmVcRDfyg3hp20MKXZmkbJNDFrup5FX+HROfUF5eJH7G47zLi9Sxl2nc\nTzkrWc8WJHwUkMmFXEgBuWSTzmo28C6fBD0zgxjAuYyggHwkjOxgJ9uoYCObouQS+v408QCP8jYv\nU8c+fsC9rGUVm1kPQC45lHERBeSRRQar2cBcPlT3N+GjgHyu4lJyyMKMmSr2s4tdrGI1J6J0X5zW\n4jS9Tx+SbDZqy8sxJiWRN3IkB1asUL//YMoUjEmBwEiD0ciN33xD5SefdBinzIEDuey111gxfTor\nZ8wgY8AAymbNknnOOHkvsLZ49bvuOi5+6SVWTp/OV48/Ts/LLuOSV17B7/fD260cOM7zmaxWGior\n2Tl/PiN+/nP5PCEwJiXxvZUraT5xgsW33UbmwIGcO306lvR0uP3ASbFDNLwki4XjdjtrZ86k/w03\nYJDaPyueCF7dx4/nwMqVrPrd7/AcPUrvSZO44o03uODQ6yxbtrdDOPmOH2fT7NkcLi/nhMtFt3PP\n5YKZMzEYjfBUzJROOwQEaeBlpl0LUCCPFMyYqGE/ZpLoSQ7/pkp96bzCXEWOyH2IAQP38xO2s7PF\nOcMJQW04WejnWniwqIVfJLwUYsOCiVr2YcXYglcuWfyAq/iEJSzic/LJ4CouxYiRxSxp0z7heIXm\nropFy8UMeR4pWDBxkGqSFU6vUKVadRj9mcalLGQJn7GMAfTheiYD8G0cL/yu5GPCQjUHScJKFr3Z\ny2zVa5lEEvU4+JZdTOAcRKix1mtqxs+D/AAfzbzDQvLJYCJj6UIX3mdBVJwEL4Eu5GDCwgFqMZJO\nJn3YyysKLxNdMNLAEdazh4mUYsKjVmQUvCwYcOFiKV8znAEYMEbNJRKvNAqRsLCfGpXXLl5Tq0Db\n8NPAEb5lN5cwDIkTQbwA+tCdPexnESs4gYuh9GMa1+KkkT1E39cJT4LMyaxyyqAfexRbubGQSTMO\njlBOJZcwjCQlzF4isK5sMx6+YiMHOYSPJoop5GomYsDAUla0weT0QDgRGM4jGPo8iz5PDoHPwKw8\nv1aM9CCPN9mn3v9XmYNZI2IMGLiX29nBjhi5Rs/LpOFVy36sGCkmn73sU9tkNzK4gSks5nMWs4Q8\nMriCy5Ew8j8+i5qTbL/wvCLZKhKnUgZwLVeyiM/5pAJSygAAIABJREFUjC/oT1+uYwpAmwI1nKj5\n/+2deXwU5f3H33tkQ8INCQlXiAQICUcIEAKictZSyqWgSL1+nngBWqjVQsXan9Zi/Wm1WrW/oq1W\nKVX54VHrgQKKGhFQJCQckkAIZ8hBICEhm/n9sTuzM5PZ3Zk9koDPOy9e7M7OzPPZZ2a++3yf7/d5\nns50xUkMhziMHRfJpFBEiaJVtnU72MMl5GJD0jilnv/t3M6NuGnkDd4iiS6MZxxxtGENa4Nq8pzD\nWNcBjmEnjmRS2M9rSn3GEEMlFeSzm0vIxe59bmV8tq6WT/mCLDIs2zqjSHgCHXESQylHsBNHd3rz\nBqXKvh4bfIKdFHrrqxF9dLIvfTjAfj7hY+qoYyADuYLZVFNNsUlbp76WnelKDC6OcAg7LrrTm2JW\nN6mrAgq5iDHYvJEg9W/uWRrYwhYOc5iznKUXvZjKFGzY2MCnpjSd185pj7FjOZyXB5JEck4OZ06c\n4NTBg8rndZWVmv1TJk+mXc+efPvcc8CMFtGUftVVVBUX8/WKFQCUFxaSmJXFoBtugGWh9dJEQlf2\nwoUU/P3vfPvsswCcyM+n++jR5C5bBqtej3h5R7ds4eiWLQAMuekmw3MMuPJKOvTpw3PJydQeP86+\nt9/mVGkpl65cSfcH/sThw6daRFf1gQN8smgR4HEI2/XsaVlHNHRtWLxY8768sJCkESO4994LQ3JO\nI6GpvLCQ8sJC5X3Vvn10HjiQIbfcAr/7u2VN5zJGDQ91j72+516dZtOXHhzgEHbcpJHEaWqp9qZa\nOHB7k0V9P2r96EsHOpDHlhC1OgwaJ3Ksz9dg60d39nPIqzGJ05zR6BpOBhVU8glfAFDOMbqTzEiG\nmXJO1WVr32vTk+Q0KQdu+pPEfg7hoo6+9KDGq0n+LuMYzmZ28Lm3bo5wnFR6MJGLTTmneoc5mQEc\nppgG7CSSxhlqOEGNMma4lEPs805aciFDcNKgmajJgZtsMulMRx7iCU5RQyFwiirmMIMP+YRqgts6\ntdPuxkEyAzhCEXXE0J00znCaY9Qpuks5zD7KcePgYgZqHC1Z10kqWMNHOGigH73o6E3DDQW53G4M\n4AjF1NDGq6uGcmpowIUDN4c4SDHHceNgHAMM6+ttVWPfQQMbOEZPujOOC007p+p7StZUTwzJpFHL\naY5zRkl3luuqHhfjGICdxiZjDo9SThnHFU0nqCSZLoxi+A/GOfWHkSOo/kxNH1IooRQ7DaTSixpq\nqKYCBx7bdpbTmtiMx9a15ys2R1yX2gam0dNrg7W68OrKYhAVVLLBm31QzlGSSWYE2QGdU7MOvbxd\njb+6kjVdyCi28i1fshkHbso4Qh96Mp5xlqOnAD1J4xAHaMBOL1KpoYYKTiMnOZdwhBKO4MZBLlka\nZ0vWNIxMOtGJh/kDp6lhN25OUs3lzOIjPqbaO/lfIPS/TT1Jo5QS3DjoQR9Fl8/WHaKUQypdUpPU\n1EqqeJv/4MBNGil0CNHWqbUZ6TpJFah0HfAuN+ZP17u6CO9xPqMnPbiEi0w7p7IugBT6UMqBJpqM\n6iqHbENN5Ryl3Kvb876CRBIZycgftnN6R0UFSBKO2Fhsdjt3lJdjj4nBERvLHeXlSJLEn7t2bXJc\n1m23cXTrVo5t3UqknVOzmorefZec++6j/+zZFL37Lp369aP/7NnseeONiOqxqiu2QwfqT2kbQPXV\n1XTNyKBz5zZUVJhLSwn12hjRc+xYTpWWUnv8uLLt2LZtOGJiGDWqJ2vX7op4PTQ30dZls9mw222t\nQpPNbicxK4sBc+bw/VtvWT7+fMNf6pb8mRsn97MMAAdObNh4lAU4sOPEwaMsQEJiGX/UNAAAchnB\nIQ5TyuEmpRj1iPtvoBlHUpdxPxLg9Op6jDsMdRWym0mMZggD2cMuutKNwWSQT75GQ9NytY687Awa\nOcz+6kqt6THuUDS1wcVZb5qdbzKIOhJJJI421JpIwWvAwRJ+C0hKefN5AjsOHDi5g8eQgBU8pLjz\nADYasSFpJo7yNIa6c5KTnFItF+SJTjjoTU92Yt7WLeD3fnUt4FEk4Hc8jDoSaUPCTmMTXeGivqfu\n5hG/uu7iUQD+m0e9dVWHG2dAXUZjD22Ys3XyPaWvq5t4qsk1VNeVC7yNtQbdpDPqcWEN2LDRgyQG\nk0mBhWt3vuDP4Qq0v+/5dWDDzkMs9l4JBw/h6Xh9iBWa4xy4Gc1wDnGYQxw2pUt/X/vTJX8Hs7oK\n2cN4LmIwGeyh0GvrMslnZ1BdRtoC6TKrqQ2x1OvGantsXQJxxFEbZGyNtg4kxd4v5iEc3hLv5QEk\n4FHvc6L+HkaOTSq9OclJzlCtWAePrbPTi54UUIgZfPXg03UvD6h0LUdC4lEeVo4JZNMiOR72XpYD\n4MSh6LLjwGmgS11XVvSYtXUy2nvGxr0sx47dUJNcvsf+SgG12LCRTDKDyaTQ5LWD89Q5fXnoULDZ\nmPfll6y77TaOffMNP121isJXX+X7tcZpAW2Tk+k7fTrr7ryzRTUd2byZ1ydOZPaHH+JwubDZ7Wx5\n4gk2LlkCPNhiur77y18YtXQpBzdsoPSzz+jzox8xYM4cJEmid++Opp3TUK6NP9r17Mmxbds02yp2\n7eJsTQ29ellbATSSuiJJNHWlTplC2qxZzL/01RbXdNWmTXQbPhyHy8U3zzzD+rvvJhr3+7mAepyM\n/7FFHtP9LE/TiIP53Moa3qWE41zDZWxjB/nsBrSNdCdu2tOODNJZy79D0qefXEObXubpIX+aZ7EB\n87mVN3iPUo5yDZezhZ3sYI/XZWyghEM8xyvcytXKj+JnfMH7vK/7voHXGVVr0qfeyajrykiTXFdf\nsY1JjOV7DlBCMf3pyxAyAYkutOMIpzW6jBx5gBd4HBtwAwt5n39xlFJmch072UIBBYozrf5e/n7s\nO9GOUlVPtBM3xznBWc7SmXaWHPkXeBwnjVzH3Rpd37GN3eRr6k++xnYaDXWp7y07UpMGVCD09/lK\nVgA2Q11yg1Ru3DqD1Jf+nh9APwYxkL/ySlBN6rryp0l9DfV1JWtSr+2r1nQX19OT7jhw8CVf8bbu\nXv8hEey5dhs8v7dxK2t5m8Mc5kqu5Bt2sFPX6JXP1452DCSdt3nXsqZQdR3kGPOY3UTXYUr4Ky9y\nA9crtu5zvuA9C9c/kLZQ6uprvmY84yimiP3spx9pDGYQAB3pENQ5VZcHNm7lNt5mrbe8uWznWwpV\n8wuo8ffsdqQ9h3UdCWWUcZazdLS4snskdUWSUHQBpnT1pz+ZZPAi1jLPQtUUiFu5mR5eW5fHV7zL\ne6aPPS+d0+qSEhKGDMERE8P3b7+Nq107EocNY+2MGdSWlRkeM+jGG2moraXwVWsN9Uhr6j1hAtNe\nf52vV6yg6D//IWHIEHJ+8QtsNhssDp7OEC1d+S+95Jl86Omn6ZiWRuWePWz5n/9h9K9/TUOD+dmH\nQrk2/pAkyTM2MQJEUlckiZau5Nxcpr72GpuWLWP9+vgW1/TOlVfSpnNneo0fz/C778bZpg3ceiik\nc50PGE0UIm/3vXZQRRUJ9MCBnUJ24cJFT5J4kdWcVkXZ1L3TI8mmgbN8YyFty2i5F71TqqaKKpJI\nwoGD3RR4dXXjRf6p0ZVOb67mSjayiV3sJZkkLuFC7MB7BB/7r9flL81NX1eBNG3mWzoSz2X8mK50\npoxyPuULJjKORhMzrcnX7iSVdKM7dhwUUIgLF93oyT95kWplPJN27LANqUmqG4CEhA1bkyiDcfn+\nFqQPrOs7XqZGleqmPoc/XTJGuowaz/6iUg4aKKfalC61gxpIl6ypNz2Zx2w+4BP2873mDglWV/40\n6a+husxgdfUq/yKOOPqSykXkcjlO1gYZS3e+Y2aiMfn5tatsXQ+SeYV/UKMsAaJlBMNpoIFv2R41\nXeWcIomkoLr6cgHzmMunbGIPe0giiYsZC9hM2Tor2szW1Va+oT3tmc5UutCFE5xgE58z3qStU5cn\n18Eu73OSTHf+wctKeWaRbV0kiKSuSBItXb3pxVzm8CHrKKKoxTWtYjVxtOECLuBCxjALJ/+Huay4\ngM7pjTfeyLvvvku3bt347jtPQ6a6upprrrmGbdu2MXz4cF555RXatWsHwFNPPcXTTz9NTEwML7zw\nAhdddBEABQUFXH311VRWVjJv3jwefvhhv2WGy3U7dtAhJQWb04kjJoa7qqqw2e04Y2O5ad8+AF7K\nyOBUqWrCHJuNIbfcQsE//kFDTeRvWCuashcu5NBnn/HFb34DwJG8PM5WV/Pjl16iw2+e5OTJukBF\nRU3X2dOn2bRsGZuWLSO2Y0fqqqrIXrgQSZLYu7c84uWZ4fShQ/SdPl2zrXN6OjHx8Rw8eDIq9dCc\nREtXr3HjmPnWW3z1yCPesc0PtrimU6WlnCotpWzHDmqOHeMnr7xC4tInOX68eX5AzkVbdxcL6Ugn\n7N6/B7kPGzYcOLkfTwbIYzxHlWqMjg0YxXC28Z3pWfPC0+UIqOtCcinmAB+xAYASSqmjjiuYyccW\nxpxa0xS4ruo5ywd8wgd8QhtiOUMdF5ILSJzAnK2bzxI60MmbAmrnF/zW61w6uJP7ARvP8DiVqihs\nIE5yikz6a7Yl0pUYYqjCvK0LpGsh9wLwNE9y0sI5I0G0dPWlD9cxj0/4lA1sMuFqmNMUyjWUOUk1\nJ6nmKMc4zWnmchkf8RGnLZ4nHM41e6d/fn/Nr7zPr4Ml3APAkzytuT9s2MhhBN+wvZlsXWBdYxjN\nfg4odq2Eg9RRx2wui6qtC6Spnno+ZB0fso42tOEMZxjDaADTtk5f3q/4tVLePSwBrD271VQzkHTN\ntgQSLNu6SOuKFNHSdQGpXMPP2MBGPrU4hj1amk56/2RbdwWz+ZB1pmxdQOf0hhtuYMGCBVx33XXK\ntj//+c+kpKSwevVqFi9ezHPPPceSJUs4duwYzz77LOvWraOoqIiFCxeydetWABYvXswvf/lLJk+e\nzMyZM/n6668ZOXKkpS9pljenTMHhcnHpypUUv/ceu1evZvTy5bjr6tj8qGfcyunD2pSB1ClT6JCS\nwvbnn29xTZLbjeTW9uI2ut1IjY3YItOZFJIuNXVVnglNMq+/nn3vvEN9vbn0h1DL80fpZ58xdP58\n4hITlXGn3bKzaWxoIC/PvHMUaV16jGapbSldF0ydyrTVq/nsV7/yLNfSCjTpcbhc2B0O2rZ1NZtz\n2pK2LlD0JtAxf+dvOHAwi9nsZjffkc8EJtCAmw3eH6caqnCqUi0H0I+OdCSPLSGlLQXTBFjSZdQ7\nL3n/rPSeR6OuAM54o2MjyKKQ3ejXyfPHK7yIAwczmMNedpHPd4xjEm4a+MzriFfpeqMDjcM6wH5G\nM5y2xCtR3h50p5FGSjiIWYLpasTeZMKRQLr0hGbpzOvSR09t3lRiI13p9Odq5vAfPuZz8qKiSX0N\ntVHm4HXlOcYzJtBFTDO6pude287o+Z3IeNy4lQlW9Pdtf6+t2xzCREhWdNmI4XIuC6orUrbOjCar\ndQUoy9kMZxiF7DJt6+TyLuNydrObfL5jPBNx4+ZTr60zM4mRzH4OkMNI2tJWcWJCsXWR1qUnVFsX\nDV0DGMA8ruQDPuILvmwVmvTIqexmbV3AnMiLL76Yzp07a7Z99dVX3HTTTcTGxnLjjTeSl+cx+nl5\neUyZMoWUlBTGjRuHJEmc8k6gs2vXLubOnUvXrl25/PLLlWOiwamDBzlZXEzi0KHsXbOGqqIiEoYM\nYd8771BVVERVURFSo9ZIDJ0/nyNffUXZ9tBSPyKp6ZtnnqHvjBmMXLKEhCFDSL/qKsY8+CCFr75K\nVVXkoqZWdSWNGEH/OXPolJbG4Jtv5r8KCuiQkuIdCxv58uxOJ4lZWSRmZRHTvj1xXbuSmJVFl4wM\n5Xy7V6+mqriYuRs30nf6dEYuWcLEZ55h59/+xpEj5mfqjbQuQNmnTZcuuNq3J2HoUBKzskxrioau\n/nPmMGPNGjavWMGuVauIT0oiPimJhATzab2R1jT4ppvod/nldBk4kIQhQ8i6/XbG/OY3HNy4keLi\nSn8yIs65aOtOUkUlFSSTxE4KqPC+LmQ35VRSTmWTdeFyGcFBSjmiGr/Ykrq+ZDOZpHMJF5JMN7IY\nzGTvTJFW1/6LlKaedGcImXSlMzkM5+fcSSc68i4fWi4viWQKyaeScpJIZjeF3tLKlfLs2En2/sUS\nSzxx3jVPE5TzbSefCiqZzw1kMIBLuJCZTGUr33DKxEy9ZnVVUGFJF0B3kuhOEnHEEYuLZO97K4Si\nqztJfnUNIZNrmcsGPmc7O2hHW9rRlnjM27pI11UO2Qwmg0QSSKYboxnJZMZTzH4qaD5bB+eevfP/\n/O6iwvunt3U5jGwWW1dBpSldeXxFBgO5iLEkkcRQhjCRCc1o65pq6kkPBjOILnRhJCO4mwV0pCP/\nsTAOVv2cFLCTCu/rXRQ2KU/9nLiIJY44kkkmkUTlfN+xg0oquYUbGUg6FzGW6fyUbWHYukjoApR9\nPLYulmSSSCbZtKZo6BrMIK7mKjbyGdv5jnbev1BtXSQ0jWA4g8gkkQSSSCKXHCYxwZKtszzmdPPm\nzQwcOBCAgQMH8tVXXwEeA5ahanSmp6eTl5dHnz596Natm7I9MzOTf/zjH9zpd+Kh9QC8D6QB/awK\nxBM9c9fVUbF7N64OHeg6aBClGzca7tuuRw8umDqVj269NYSSIq+p5JNPeP+GG+j7058yYskSygsL\n2fn3v7PzpZeAO1pMlyM2ltEPPECntDRqjx/n0BdfsHHJEsvpm2bLa9ezJ9d4e2clSSJp+HD6XXYZ\nVcXFrExLA6CxoYF/jh3LxGee4dL//V/qq6v57i9/4bP77gMeaDFdgLKPvN+127YhSRLXOY0XAW8O\nXcPuuAObw8GY5csZs3y5cuylxVWkpZmPokb6GuYuXUqntDQaams58PHHvPPII7yxejUwwrSmaBBt\nW7fhQd+U6n3Gp5A6vo9ljd3pQQMNlHGCWGJJIpEDFBlGRTvQnnT683+8FTRqamasVSR0FfM9b7CG\ndAZwCWM4Thnb+IatbDM8bzi6zGpyYWMSl9CFzpymhhJKeI/3/aY0+dPUg5404OYYFcQSTyJJFHmn\n6FfTmbbcyW2q45IZxEAqqeRxnlRKeYG/MINpzGY6ddTzNV/zfgCHOVxdHehgQhcsQPvbudD7/tcG\nwwUCXb9I6hrDCOze6ziJS5R99dqD6YqkJokGJnIRXejCWc6yjyLW7HqT1998nZq6lhv/JhNNexcN\nW9eNbn6XyvDYugGmx7eFQy+STOnaR5Fi6y5mbFBbFw5m68qBgwmMN23rgpV3gjKlvP0UN9mv6XPS\nnUwyNM9JI408z/8yg2lcxkyvrdsS0NY1hy5As4/n/e2Asa1rLl255GDHzkTGM5Hxyr7+bF1zaGqk\nkfFcorF1b+5aY83WSUEoKiqSBg8erLzv3bu3VFtbK0mSJJ0+fVpKSUmRJEmSli5dKj333HPKfnPn\nzpXWrVsn7dmzRxo9erSy/d///rd0zTXXGJZlQo5AIPiB0Vx2Qdg6gUDQkjSnXWgueydsnUAg0BPM\nLlie6jQnJ4eCAs+0wgUFBeTk5ACQm5vLzp2+9ZoKCwvJycmhX79+HD3qS6vYuXMno0ePtlqsQCAQ\nNCvC1gkEgh8Kwt4JBILWguW03tzcXFauXMmKFStYuXKlYoxGjRrFL37xCw4cOMC+ffuw2+20b98e\n8KSIrFq1ismTJ7NmzRqefNJ/qNlm88xS+3gLr3G42Fu+JHnSH22237RKTSDqyghRV+Zp7XXVUkTb\n1i21mHruj0itx/aQd4HtSOhqjZqgdepqjZrg/NbVGjW1NNG0dw+wNCIawx1uIPMwDwGtS1dr1ASt\nU1dr1ASR1dUaNUHkdAUjYOR03rx5XHjhhezevZvevXvz4osvcvvtt3PgwAHS09MpLS3ltts8ucdJ\nSUncfvvtTJw4kTvuuIM//vGPynn+8Ic/sGLFCnJycrj44oujNlOvQCAQhIKwdQKB4IeCsHcCgaA1\nEzBy+tprrxluX7vWeMHoRYsWsWjRoibbMzMzlanHBQKBoLUhbJ1AIPihIOydQCBozVhO6xUIBAKB\nIBBuHOdFqmMoBEp7MqoTf3UVqP6spFaZ3TfY9TJ7Pc2WF4ouvYaW0hSobCv3fXOlyAkEgnMLeS3n\nHyqWJ0QSCAQCgUAgEAgEAoEg0gjnVCAQCAQCgUAgEAgELY5I6xUIBALBOUmwtKdwU1WtplUF21/+\nPFC5ZtJCzaR8haI9Eqmq8r5G5YeSphZK2rMVTcF0Nag+c6rKNNLV9H2Dd1/jplYwXQKBQPBDRDin\nAoFAIAiKvwa0FcfB35jLSGkx2i8ch8uM89BaHS5/NPjZ3+l1eP05XLKjpcef4xUJTf50BdMUCV1G\nmvSOqlpXIE3qbZGoL0H0CddWGe0bzjj8cDvSwu3IC/WYcG1nc+uKliYzx4ZaV+HO7xDqvRWNe0pG\npPUKBAKBQCAQCAQCgaDFEV14AoFAIAhIsB5dK1FIf9ujldpoJv0yWhhF35yq7+vA3STy5nndNPoW\n7QhlAw5L0UAZBw0ha9Pr0Z/HQYOiy6hc0Kbaqs8XLKXWrC6j4/3pMtJkpEtET1snZocBhBrhcmvu\nA3M2yMp+4UQDzQw5iKSuSGS/hKrJny6rs3qHkuliVZMZXaHoCVeX2Xsq1N9aYSEFAoFAEBb6HyF/\naZjRxt/4wECE48SY0eHvM72TqtZiRLhOTTCHS8bI4TKqy0g7XOrjAzV8XNRrNKkdBnlboPoPRZOZ\nlDojTdHSJWhZwnW4zJYR+POmqeLBnAgz9jGYoxN4vLxxp5pel/w60BADI03ByjeryUhXME1Gusxr\nMO7E8oe+rsxqCteRj1Rd6TX5Ky8QwjkVCAQCQURpiTVO9T+U/iJuatQ/xtFwUmWMIoK+11qNgaJu\nkdaib/SpG7myzkAOVzSdQH0jW10vTrQRZ7lRFq5zYOTA6xtk8muHt9moRh0J16Ous2jeawLrmHEE\nQ43Ah+oEBtLib5sVJ0LfSRYOek3qzhh/ddegaJRtToPmMyu6gjla/q+BVlcDDkOt8rn0ukJx5APp\n0toG2cJo68mspkC6Amky0mVUfqC6isS9JSyjQCAQCMJG3WtrlBoaTfw5SaGmqkYSox9w/TZ9NFBN\nJJ1ArYamTlegRpb+2kbeQW3aoFY3vBpwKPUk6zGO1EROl1qTupGodgbcuHFRrzjM8nXUOroiYnou\nYtR5BdaigdFyAv05NkZOhKzZ6FxWHUF/eow0GUVA3coT7NR95nsfroNjVE+BOo3A52zpn1Vtp53/\nYQaR0CTfU2odRpr057VSV4GiuHpd6o6/OrR1VU+sV5+/yf20dWW1M0Y4pwKBQCAQRIFAUUp/6COU\n0dEVOLLS1OFyKw3fSEQoA+nSN6bdOImlTmmkyZpiqdMd6z+1MRKatA1GTymAosNFPbHUNWnYRUvX\n+UQwx8Ef/j6PRIdYKCn2ZsZ0R0KP1U4sfx1kkXBQ1eXrOwC1zrtT81pvg6LRmal2ttSdWVotbupx\nKZrUNkhdJ5HuZPXXwVZPrFeBgzpcuL3/G30v37nC72hVXz+X16bphyO4cVKvWDqXxmlX7x+JuhLO\nqUAgEAgCEm2HJJoEmrimufA3DkzdIFC/bk5NDbr/jXrz5fpqrsl81A1Fh7fRrW6eqaOnoU5qYkSg\nsaFaN91BrDeKK0esHDTgoh6XypGWtep1iXReH8E6avxdz0DXOZyJWIJFA81E5ANFKY3Oqd03sK0y\n4wAEiwbKx+sdVPX5jSKI/nSpO7HUn6nthee1Pu8hcERQ1uUvbdXfMWpN4HG2tPVWjxsHNcRrzltD\nvMZZlutH66Raj+r6qyu5M0tdt/I564mlnlhqiKdeZf08ujy1F+v9HuF2Mug7IT22TZstA1CHiwav\ns1yrqyvPvtoOw3B+Z4WFFAgEESfG+//ZFlUhaCmae8xptFJfzRAo0hnNaGMkzmeU8udQnVedpqre\nP9qom0ry91Q3mGK9/fdqoqlNPf60jljkiKjnutbi9GqKp0ZpPMr7qb+TQEs07YQZBzXY54EilGbH\nBsrb/E82Y/6+0EcCjWjwfm/ZoVE/Q3r0DqpVAs1W7XNsPM+CEzf1+Dqe1M+HcXpr07GoRvqD6fK5\n525lWAB4nD/Z0ZL/93QtuZRz6G2Mv/oycw2Norgur01TZ1zUEO+9l9p7ncA4xYlW7+sC5NwMM3Wl\nx2hmcdnW+2Kj9ZrfMI9D6uQU7ammveIMy/t7qNPUlf77m0U4pwLBOUyM6nVrcQRjdK9biy5BeBil\nahl9ridQIzHUBruRFn8Oqr/JHcws1xKqPivOsr/60Uc2rUbb9HWk1qQuTz8JkJumDTt/GGkLHNmw\nlrLs0+tr0jVN0dOOzTKKyAQry+h+kiM96tfqqI8bhzcd0KE0fNWNWY8+X7qg+vtYqYMfOpGOjgcu\ny7hxr79v/T3fRhN7GT3boUS4jCKmRnqdKqdAvvf0Wp2qOg3XQZXPIevyF3VT/1/n7V7SRt4cSjpp\nOJFvvSa9Eyg7XPXeZ1V2uGqJp9rrEKodU9kR1M9dEKzswJ/LTmCd0qkllxdPDW4cnPBqKyNB6Whw\neSOlLq+D6i/bRcbscyLXlfy946khnhraU62UGUsdlXSiHhdlJFBJJ6+WetpTrSurDkeY11A4pwKB\nQCAwjZkoYLAf8Ug1LvVawomgRsIxVetRR1giHT0N5zzhpuYGisRYxd8SNfq6CpZiaKQnnO/ou3ba\niLEc/VFr9WnSR1OMyxcpvecWxlG9wCnF+vfRiBAbRb7M3HPhTOoTDG100P+M5EadSNoIYHTqSz8u\n3IVLuZ513qhpPbG4le/RoLEBkdTlcwbrFIfQgZsa4hQnvQ4XNcRRS7xStqxJdiejcR3lzjZPyTWK\nlljqqCOWGuKopBPgcaZjqSOWuibDL8JBWEnGcB2oAAAgAElEQVSBwCQiVVUg8GD1RzqaESIjB9Vo\nH7UWfcqRmnC1+nNQm+7XoOwP/mf5NBuVDKRHPtafHnm7ftyrHOVQO7T+UhitatNfN1lTLPXUgdLI\nUTfujaIx9d60PHWDN1Rd+rqSidWl98lpdnrHQC5LHicmT2ii1yUipq2fSDf6w41OQtNJg/TPqzyR\njq9MX7Rfe54GzT6RcLr0o0l92nwRSLm8elyaVFXP/9ox2uHr0eqI9TqmcdR447YuTaehHDmtJ1Zx\nDmUH0o3cyRiZiK46zTieWm+SbDUu6oj36gCopj0nSKCGOGV8KeAdRlCnXF8zDmEg3WoL5dFQQycq\nSaCMTlTictfjctRRQgq1xFFCbw6X98DVpp5O8ZWqc7i9OuVa858RFAzhnAoE5ygxBu+F4yxoDUSj\n5zuS5UXbOTAzFkreTxshNBqvFrkos16L2sl0Kfv5eublFDfZVWw6k274jryRJrl89Yy8ctpbPS6l\n8SZriLTzJ5fv9DazZE0ex9lFHLVKQ9VFnTI2TE5VlJdaCNdZPl9pLvugThsNF7Vz5XttbgbaaKAf\nVynfr+q0fLfumdDrjk6E0pceKndqeaKVvkl76lTPhmdf/fjJ+oBlmNGgfu1zvDzOoBytlGfGbcAz\nGdIp7zhKuS7laKADX5RSdlTD0aTWFatyBuOpoYZayugKyM5pV2pq4omPr1G+Qxw1GqdUPfwgHNTR\nXMVBPX4KToGjRwntY6uppj0lR3vTWNqW+nZAL4iPr6Ed1d74bqSyagQCQVDEOEpBpNFHgdTbfdG9\npmOXjD7zt7++kRTKmBn1uc2k6ur1B/ueRmWa0WW2fH1ZRtuMygmnl1z9neUxO/7qQ35fr0oxs9Lj\nbNToMUI+Xq3HqMddXb6cTqbXZFTXwfQa1be+EamvL6OyarwjoozKsnqf+0OvS90I7GTwnWuJV5xR\nf+fRa/X3uUBgllDun2ilGftDHnvtmwXY43Y05wR2vpG3TZc8kcfoajuS6mnQvY8mctTR5a6nweFQ\nMjXqiaWu3kX9GRexbepw2yNfZ0b3gmb25VPASYjv2Igj1uPIN56OB0/AFHeDvKdxx2rok20JBIKg\nnEWk9Qqig9GPgz59K9BneqdLRu+8Gr02oyeQFn/HGTmDegLpDabJTPmBNAUrL5h2s7rkn+1AetRl\nGZUbrAyzPdVqDb7XTcetqaN+6rXs/O0figY96slH1Kl46v3liKkn9U67tIL6OHmbGU2B6lY/uZEv\nFa9Oee3GQQWdqKQTlXRWGv7a5Rj0S1E0dfIFglAIxdFs7g4RvTOqtiHN5aD6ltRx6t7LidC+LTJO\n1R7RRrZg9Q7fPLngWQIn1lWPu40Th904TTYSZevPq07Qpp1nW01bO3L02N62hsZObaEdOJw++6sn\nHL3CORUIzlHUDrP8XiBoDTR3o9uqAxeuI2xFj36NOxlfiqw68aypIxgpbUYOqd5R9YzB8uiQF1tX\np995phAxjiKEos2fJtkBlGfZdONQUnllJ9UX/a1XnEG3QSMpXF1qTfISNvIkIXIUt55YzZIP8dRQ\n7x11qtZlZcbP8xl9pDtaRPL8+o4tvWOlnj26Oa6velIjWZt+fKlsWfSa9B0mkcRXNy7kcaSeNF6P\nbVOPl1R3LOltQbga1J23sptZ751gSO54q/HO1OvETTw1yoRIctqv70ifXQn12hpld7hxeicY8mSo\nyJMfyfatPdV05QRx8TXKrL6xXtvor8MrHOQrIa+vWkknSARXl3oOObpTTXvaU03vpBIOx/jGnMZ7\nx/HqOxTDQTinAoFJhPMnEHiwknZq9FkkG29NI71NZ001iuQaLa+g/jxUjUaOqdEyEHpt6jFrRuiX\nfwhXj1HE0YlnTUKfPofyXp7QxWGgMRRt+v2MHFMXdYbH+pL05FRlz5g1/fnkfUPRFMgxNWpIqx0D\nddqx3gkI9ToKmpdIz2gbSadLTpdsujSR1ob40laNO8vkfSKBehkqfYRSv2yM3MGlngU7ko6NR4O2\nM6HOO2wCUKKTsqPqwK2M46zXLDtTp9EVCX2+aK3PWZa3yw5qNe1x4PY6p2XEe53XeGqI8y47I+sy\ne48Gsjm+30a34pjK+9UQDw44SjdqiSOOWnpTQmwX39I38nhZX301+K0rs3UonFOB4BxGjp62JsdZ\npECfvwT6YVE7gEbOoJpINc79RSi1+zRdOsUonUzfGA0nZc4oWmr1XL6GqFNpiIbicOlRz/BpvNZr\n4JTicK6dv46EplFcbUNVX6YD9WROvuaQ+hh1wzeYZiNdak1Og/ryRGJilWsR651gRd354UK7BIU6\nemT0vQRNMbIfavsS2bKazqhrZNMCoY6M+buuoTi98vOvtgt67f7qSp7YR122PmsiHGRNchn1qqip\n7BhqnVN5NlzfNnVHWSQjgXLkuN7bySaP7fdMYhZPHS7vBEC13s996fj+UvPNlCt/L+PPPVdS3fEn\nD1WQJ2byRE5PkcAJarzaYpXYrs8JDITVepSdZrXDLK9nWk17aognnhoSOKHMHqx2mmNV0eZwEc6p\nQHCO0xodwNaoSRAe0WhAh+pkRasxb+SghlNeqI5pJAgWUTbSZOS0B1oX1Z/j7O/8VlB3bsiRF3Uq\nr/yZkRMr6440njQ833g5eRkbeQ1FF3VKnUTyPjpfiWYKbCTOa3Rfy9v1z4R+wh8zHWehonWYfbPx\n+mJWvrHPRkSqzo06gPQa5bpSjzn11FWdpg71jqmZOvP3XDXo6qcOlM6jetWx6gwHOVVfvWyUOpND\n7UDrMVOfRkuFyXZNXjsU8DrMsUqnQicqFWderUOrK/T7S6+rTvV9ZIe5Ac9SO/J9JWvy3esNSmaJ\nfkmhUBHOqUAgEAgCEkrUz+q+4TSYAv0IGjlWkZyMw4xuIydKHfnznKdB897MOc2WH/j4BpUW47TA\n5kbtmKob3LImjzOorYNI6PR3H+mjtPVoU54BZRxdsMiz+nwtUbfnEoGehUhET808Q2oHtWkabdOo\nqu85dmre+0Nftl6T+jz+Os/0r4OVod7mb5IzM7r0mQS+11qbq57ADOTJxlxByzSjS6tRW+dqTfIA\nAdn2+9KNncowAvA50/qsjkCagmEUkZcjufpJ3OQBBKCdKE79G+Jv0jhr0V3/daVOw5Y/qyOWOmKV\npW/0z5/sTIdbV77jBAKBQCCIINFKuzvXMErF1deL0Y98NCNKevRrcDZotDQdYxptXUZLTdQTq6TH\nyr30dRhHg6MZVUfVyPbFB5yasan+okEiamqMPwfRbAqt0fMUnp6mTqXR2E4j1J1eRk5pJDojAnWq\n6SO66miusYMamYiuOuVYj69zSZvSru5c8umJTJqxkSZ1OrQ2quuzKw5lH23GS6R1yXiGAWivi5yN\nIZdntNyXXlckNKnrSu50k7Nm9J1u6onq1L9xkUwVt4d19DnA/CNH6DZ8OABXbthA+lVXaT7vMnAg\nsz/4gNtPnOD6/HxyfvnLFtfVNTOTaatXc8OuXdzd0MCPXnihxTUNuuEGrvj4Y247doxbSkqY/Pzz\n9Ln00hbX1efSS7nq88+57dgx7qioYOZbbzFswYIW1aSmS0YGC06dYlF9dNfJMqMr8/rrucftbvJv\nwoTUFtMEYLPbyV26lGu2bmXB6dPcUlLCmOXLo6rpfOR+7qUH3QG4mRsZyhDN54kkcAPXsZT7WMRd\nXMJFzaJrKYvp6dU1n/8ii8HKZ91I5GfMYTF38VseZBYzmkUTwHLupifJANzBtQxjkPLZSIZxC9ex\njCXczz3MYgb96Bd2mepGkX4bwEPcRS+ScODmLn5GNhnKj/wA+nI7N7KMJSznl1zPVVzIqLB0qJPV\n1A0QtXP8MHeQSgIO3NzDFQxnoGGjqBsJPMT9PMyykDX5yncq/+RxnOrGrQM3j3EzF9AVJ26WMJsc\n+ivnymEIK1jKCpbyCA8o//pyQUiaZC3yJEye6IZ2XVNZUz864qCBe5lFLv00dWXDxiQu4h5u5hF+\nyf3cw2TGhVRf5xtG96E/1LZuPteTTaamYRwJW6deekS/3R9LWUwfugFam+LETQ+6KLbuEX7N5Uyz\nrEeNfC+q//nLEnmIu0ghAQcNLGAeI0hX7EoOWdzO1Yqtu4xp9CfNtCZ9Z5L+Ovpzph/iLvp47coi\n5jKSAYDHqRlAXxZwPQ9yT0i2Tq1JnWRa702+90QCtctkOXHzMLfThwRc1PNz5jCSAU2cLSfukGyd\nmbqSHVPZIZSXsnmU+RobLOty0EAumYa2Lo3UkOtKrqN6bxTXKF38Ye6gL52JpZ57uIJc0jR1ZcPG\nj7iQBdzKQ/zKsq07ryOnndLSiImP59i2bdhjYkgaOZLSzz5TPo9LSOCqL76gcs8e3rniClIvvZQx\nDz6I3emEh1tOlzMujqriYvauXcuIn/8cSZKiJ8akpt4TJrBnzRo2LFlCY0MDGVdfzax33uGl9HQo\najlddVVVbH3iCcp27KCxoYGeF13ExD/9iXll7/HaaztaRJOMMy6OaatXc2DdOlKnTImKFqu6Gt1u\nXujRA2w2ZdunFdFz5s1omvX223Tq148dK1ey5803iWnblvjERGBs1HSdb3ShCzHEcJgjOHDQkx7s\nZ7/yeTzxzOcWTlDOa6ymP2lMZAJ27KxnY0Q0GKWYdaELLmI4xGEc2OlJD4o5oOwXg5MKKtnJLi5h\ndBhlN02bkrfrtXnGzCR4dR3BgZ1e9KCItUoPcF9SyaeQf/MhbhoZzmCu42qe4CkqqLCkTZ/CqU+h\nlX/Qu9KZGGI4QikOXPSmO/v5P+U8Z6jjM77kKMdopJFUUpjBVGo5xXa+s6THX5RD38BNogMxODnM\nIVzYSSGZ/arrJ9d5DE5+xhXspYgBYTjxgSIv6s+70R4XTg5zGBd2+pDEy5QqdWnHjUQjD/FHTbSq\nntNByjdOoTS6t/UNNrWmeOyk0o1XKPGez3OeW5hNAp35mm3kU4ALF21V494EwWluW+d/vLX2Xk2k\no2LrYr02pYQ3lXtIbesuZgyhtOoCpQpbtXXgsUn9SdHYumyG8F/Ms2Tr1GPP/X2ujpCrbZ0Lu2Lr\n5O91lhpDW3eaGnbwrSVNEDjaLBNME3jqy4qtM8oK8FdXgX8XnBzjAC5cpJDMa+zX6JJo5BH+x1tW\nIwC13kmUrGhS6zDSJSP/LnjqyljTTVxJV7ootq4NDtrSNmB9qTmvndMeY8dyOC8PJInknBzOnDjB\nqYMHlc+Hzp+PzW7n1VGeHpmSjz+m7uRJRtxzD67HnqO+PjppOMF0Hd2yhaNbtgAw5KaboqLBqqb/\nXHedZv9Pt2+n1/jxjFi8GO4qazFdR/LyOJKXp7yv3LOH3uPHc8stF0fNOQ2mSWbiM89wcONGjuTl\nkfqTn0RFSyi6asu016vBa8xaQlO/yy8ndcoU/j50KCfy83VHC+fULH1IoYSDSEj0pAc11FLFSeXz\nUYzEho0/8zwA+9jHGeoYyxg+ZRP61KJwkVN+fLqgFz2poUajq5TDlHLYq3FYWGUGimjo6UMKB7zO\nTB96UEMNp1UNsdUqpxDgfQ5xAalcxIW8zbumtOgbkNpoaVOtnroqRQJS6dakrg5xgEOq/csopy+p\n5DDCknMqa9GnGBo14Dz1dAg7DfSmD6eppZJqQNtAvoyfUMR+SiglPQTn1J8jqNcl31ep9PbqcpNK\nMqeppZrKJrpOU6P5no4QbJ1ejzYl0Hcd0+hpqEk+ehj9SCeN/+EFyjiiOadIgDdPNGyd1fHbRp0o\nVmxdDtnYkQKWF0hTJGydfAa9rfuAQ/Slj19b5z8N22imY2Odsq1z4KYXvTR15cRNCaWUUKrsX8Fx\n0uhDLtmGzmkgTZ7txrrU1/ECegXUJDOTqRpbF0pqrb4TTL9dTaDfBY8b67Fpp70TOoU+B4G2rvRO\nqf+6aqppMBkMoB9/5M8c5XhIus5L5/SOigqQJByxsdjsdu4oL8ceE4MjNpY7ysuRJIk/d+1Kj7Fj\nOf6t9kY/tm0bbbp0ISMjgW+/PdoiupqTcDTZbDZs9uhkhoeiS47MpU6Zwl9+t61FNWVcey1JI0bw\nak4O6fPmRVxLqLrsDgc37t2Lu76ePa+/zq5//hP0PmEzahpwxRVUFRXRd9o0pq1eTW1ZGTtWrmT3\n6tX46fgTqLjfm1bkwIENG8u4Hzt2nDhZxv1ISDzMo/QhhSNo7dlhDhNHHIkkckTXWLaKvrGm17Wc\ne7HjwImDB7gXgIdYEVaZobCM+726nNiw8QD34jCpy+b9M0uwaK6Mr648mh5isaLpIRYD8ACPa46R\no9AD6McGk9EgvR4jZ1TWFkjTwyzSaBrBEHqTzJ/4C8NUKdtWCeQwy9EEva6HWaTSdTcgKbps2LmP\nO2mkge/YyXbymziFVjUZpWUH0yQhsZzHGUoG5VSSQX9Gcjk11PA129hOPo0R7hw6H2ktti6YLis2\nJdKo7bDP1jmwYY+qrfOVHzjKrK0rrSbZ1uk1ObCTQg/6048NfGpZUyBdVjVlM5Se9LBs6/zNIWCc\nNm5cV0a/C7IuG3Z+wQLcuMknn+3s4BjHgmrynN/YoTfCbF0NIZMKKhjIAK5mDqepYQtb2UE+Z02u\n5XBeOqcvDx0KNhvzvvySdbfdxrFvvuGnq1ZR+OqrfL92rbJfu549Obh+vebY49s8Tk2vXh0i7pya\n1dWchKppyK230nXQIN679log8s6XVV23lJQQl5iIIyaGjffey5NPtm8xTV0GDmTcH/7A6vHjcTfD\nWFOzusoLC3nvuuso276dTv37kz53Ltdt3847c9/g9dd3toimzv3707Z7d9JmzOCz+++nTdeu5P7q\nV6RMnAjXRzFf/DzhWZ6mEQe3cStreZvDHGYuV/It2ymgUNmvAx3YR7Hm2EPeXvyOdAjaYAs0QY9R\nFOFpnsUGTXRtYwc7VbqgOSbY8elT63qTdznMEeYxm28MdKnJYSTdSORfvGGxbP8/sbIuM5rUDtL9\n3ENb4rHj4D0+5HO+sKTH30QV6h5y9X0la7qKOWxjB/nsVo7pRlemM5kXeCmk6Lu/3nqjfcBXV/O5\nlTW8SwnHuYbLmug6xglWs4YjHCWBrgxlEIu4nX+ymh0meuPMpATKuuS6CqYpkS50oB2D6c9/WEdb\n4hjPxaRxAW9YvK9+iDSHrdOnxAb6PJiuQLbOZiGp12xUV69Nb4MPciygrZPPb9bWmZ20Ta1LX1fB\nNPlsnZ33+SCorbMSAZf3NaspkQR+yqW8wEtAPTaLWRhmtIVSV8co43XeUGzdEAazgDv4J/8yaeuC\nX0erddWVLrSnPZkM4AM+Io44xnMJafTldd4MqgnOU+e0uqSEhCFDcMTE8P3bb+Nq147EYcNYO2OG\nNqWxGcZyhqSrlWtKmzGD8U88wYc330zFrl2tQteqsWOJS0ggZdIkRv7iFyyL28l//3dkxtJZ0eRw\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": {}, + "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": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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T76W8/B0qKvT1mvV/TSd7cloikZwcZPAmkZxkJvvg8/nAaITk5OeJiIjWtN+8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\nX3xxwDVfj3vHjh2sWrWKlJQUAMaNGwf4ftyRkZE0NTXR09NDU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+ "text/plain": [ + "" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WSzHN2p8FWxkN2gMQ7PdmUvw17PEmyV8wMn8dPHgQCHsJvvbaazz11FMsLy8n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CFGNTUEhky5woF4GwfifqZiBxXF1e9ARo5XwmCV5WixeQYqi8lymyelImSckyyZJ2ETxoZOaHcN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VZWVlYfFy3zW3X3/MTpHrpzf0jmA5yzZfdLoPPbXr1sCodM+ckPxXxp+QxquvTL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\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+ "text/plain": [ + "" + ] + }, + "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. 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fmLyNrfO3N0fPfwDYunUrYmJiEBcXhyNHjsjbzWYzZs6ciejoaKxbt07e3tnZ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\n", + "text/plain": [ + "" + ] + }, + "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": [ + "[]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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kpAT5+fk4d+4cGhoacP36dXR3d+Ppp5/GBx984DZhkyAI4qRPwkMTQj287iDFTO7Ez8Xg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\n5XHiY7LMS8g3biIl8zQv2U2OiEhqrFb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EePdInggh+mLKlCkYPnx4j/5vvvkGP/74o0ff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\nkBBCCCGEEGJA+j8B48lch4brPQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "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 +} diff --git a/simpegPF/notebooks/MagInversion.ipynb b/simpegPF/notebooks/MagInversion.ipynb index b18124c2..5972e2e8 100644 --- a/simpegPF/notebooks/MagInversion.ipynb +++ b/simpegPF/notebooks/MagInversion.ipynb @@ -1,2779 +1,2765 @@ { - "metadata": { - "name": "", - "signature": "sha256:ea5924570857853319c35ba53aa8dd01074562c5d5f8e317e0328471779ef10d" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "from SimPEG import *\n", - "from simpegPF import BaseMag\n", - "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", - "%pylab inline" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Mag Inversion\n", - "\n", - "## Step1: Generating mesh" + "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" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "cs = 25.\n", - "hxind = [(cs,5,-1.3), (cs, 31),(cs,5,1.3)]\n", - "hyind = [(cs,5,-1.3), (cs, 31),(cs,5,1.3)]\n", - "hzind = [(cs,5,-1.3), (cs, 30),(cs,5,1.3)]\n", - "mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step2: Generating Model: Use Combo model\n", - "\n", - "### Here we combined $\\mu$ model$^1$, Depth model$^2$ and Active model$^3$" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "chibkg = 1e-5\n", - "chiblk = 0.1\n", - "chi = np.ones(mesh.nC)*chibkg\n", - "sph_ind = spheremodel(mesh, 0., 0., -150., 80)\n", - "chi[sph_ind] = chiblk\n", - "active = mesh.gridCC[:,2]<0\n", - "actMap = Maps.ActiveCells(mesh, active, chibkg)\n", - "dweight = np.ones(mesh.nC)\n", - "dweight[active] = (1/abs(mesh.gridCC[active, 2]-13.)**1.5)\n", - "baseMap = BaseMag.BaseMagMap(mesh)\n", - "depthMap = BaseMag.WeightMap(mesh, dweight)\n", - "dmap = baseMap*actMap\n", - "rmap = depthMap*actMap\n", - "model = (chi)[active]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "sph_ind_ini = spheremodel(mesh, 0., 0., -200., 150)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 22 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "chi_ini = np.ones_like(chi)*chibkg\n", - "chi_ini[sph_ind_ini] = chiblk*0.1" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 35 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(1,1, figsize = (5, 5))\n", - "dat1 = mesh.plotSlice(rmap*test, ax = ax, normal = 'X')\n", - "plt.colorbar(dat1[0], orientation=\"horizontal\", ax = ax)\n", - "ax.set_ylim(-500, 0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 29, - "text": [ - "(-500, 0)" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 29 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print model.shape\n", - "print chi.shape" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "(33620,)\n", - "(67240,)\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step3: Generating Data" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "survey = BaseMag.BaseMagSurvey()\n", - "const = 20\n", - "Inc = 90.\n", - "Dec = 0.\n", - "Btot = 51000\n", - "survey.setBackgroundField(Inc, Dec, Btot)\n", - "xr = np.linspace(-300, 300, 81)\n", - "yr = np.linspace(-300, 300, 81)\n", - "X, Y = np.meshgrid(xr, yr)\n", - "Z = np.ones((xr.size, yr.size))*(0.)\n", - "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n", - "survey.rxLoc = rxLoc\n", - "prob = MagneticsDiffSecondary(mesh, mapping = dmap)\n", - "prob.pair(survey)\n", - "prob.Solver = Utils.SolverUtils.SolverWrapD(Mumps, factorize=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "dsyn = survey.dpred(model)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "survey.dtrue = Utils.mkvc(dsyn)\n", - "std = 0.05\n", - "noise = std*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)\n", - "survey.dobs = survey.dtrue+noise\n", - "survey.std = survey.dobs*0 + std" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "fig, ax = plt.subplots(1,2, figsize = (8,5) )\n", - "dat = ax[0].imshow(np.reshape(noise, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n", - "plt.colorbar(dat, ax = ax[0], orientation=\"horizontal\")\n", - "dat2 = ax[1].imshow(np.reshape(survey.dobs, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n", - "plt.colorbar(dat2, ax = ax[1], orientation=\"horizontal\")\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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nrfNa1TweS/ck4WOsLOsIH5Pnx+zmJaX0CHha07Z+lkUKk01jWbHALXF1Wrrd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\n6yaDWjHtWgoudaSd1bqUICmg7esWGC1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HlHUOucoTxgx5yo6yNrStJTK1MaXnXwfh2GFr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\nOdclxezpyTc1xVLMFG5TRzz3qNt+glOPqHoDmrUObeu0cIE36BdkUz1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A93+NH4B1RGIa+svcXlb9/kdO0cyZULDOMmD4J96rUlqssZX1l+g51zd4jWbW7WLrJ7bou96hbW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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# m0 = (1e-5*np.ones(mesh.nC))[active]\n", - "m0 = chi_ini[active]/dweight[active] \n", - "dmisfit = DataMisfit.l2_DataMisfit(survey)\n", - "valmin = abs(survey.dobs).max()\n", - "dmisfit.Wd = 1/(np.ones(survey.dobs.size)*valmin)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 36 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "d_ini = survey.dpred(m0)\n", - "fig, ax = plt.subplots(1,2, figsize = (8,5) )\n", - "dat1 = ax[0].imshow(np.reshape(d_ini, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n", - "vmin = d_ini.min()\n", - "vmax = d_ini.max()\n", - "plt.colorbar(dat1, ax = ax[0], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 3)], format = FormatStrFormatter('$%5.5f$'))\n", - "dat2 = ax[1].imshow(np.reshape(survey.dobs, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n", - "vmin = survey.dobs.min()\n", - "vmax = survey.dobs.max()\n", - "plt.colorbar(dat2, ax = ax[1], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)])\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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MGNlix/CQDLJtEkHhMbQeYzklF0y+MHLJPuDWed4qH30fKWPv4ylI3UbNa56X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\ntUwhOvz8KXkSETgM0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- "text": [ - "" - ] - } - ], - "prompt_number": 37 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "reg = Regularization.Tikhonov(mesh, mapping = rmap)\n", - "opt = Optimization.ProjectedGNCG(maxIter = 30)\n", - "opt.lower = 1e-10\n", - "opt.maxIterLS = 50\n", - "invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)\n", - "beta = Directives.BetaSchedule(coolingFactor=8, coolingRate=2)\n", - "betaest = Directives.BetaEstimate_ByEig(beta0_ratio=10**0)\n", - "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaest])\n", - "opt.tolG = 1e-20\n", - "opt.eps = 1e-20\n", - "reg.alpha_s = 1e-9\n", - "reg.alpha_x = 1.\n", - "reg.alpha_y = 1.\n", - "reg.alpha_z = 1.\n", - "prob.counter = opt.counter = Utils.Counter()\n", - "opt.LSshorten = 0.1\n", - "opt.remember('xc')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 40 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mopt = inv.run(m0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "SimPEG.InvProblem will set Regularization.mref to m0.\n", - "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", - " ***Done using same solver as the problem***\n", - "=============================== Projected GNCG ===============================" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", - "-----------------------------------------------------------------------------\n", - " 0 -8.78e+04 1.08e+06 2.70e-01 1.06e+06 1.38e+05 0 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 1 -8.78e+04 3.89e+05 1.52e+09 -1.34e+14 1.57e+08 0 " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 2 -1.10e+04 2.49e+05 1.52e+09 -1.67e+13 1.96e+07 15 Skip BFGS " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 3 -1.10e+04 2.49e+05 1.52e+09 -1.67e+13 1.96e+07 19 Skip BFGS " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " 4 -1.37e+03 2.49e+05 1.52e+09 -2.09e+12 2.45e+06 19 Skip BFGS " - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "pyerr", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\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[1;32m----> 1\u001b[1;33m \u001b[0mmopt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m/home/seogi/Documents/simpeg/SimPEG/Utils/CounterUtils.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'counter'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mCounter\u001b[0m\u001b[1;33m:\u001b[0m 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"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.pyc\u001b[0m in \u001b[0;36m_mul_vector\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 445\u001b[0m \u001b[1;31m# csr_matvec or csc_matvec\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 446\u001b[0m \u001b[0mfn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_sparsetools\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'_matvec'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m,\u001b[0m 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MagneticsDiffSecondary.Jtvec : 3.10e-01, 5.64e+01, 182x\n", - " MagneticsDiffSecondary.Jtvec_approx : 3.48e-01, 5.26e+01, 151x\n", - " MagneticsDiffSecondary.Jvec : 3.48e-01, 5.26e+01, 151x\n", - " MagneticsDiffSecondary.Jvec_approx : 3.48e-01, 5.26e+01, 151x\n", - " ProjectedGNCG.findSearchDirection : 3.66e+00, 1.10e+02, 30x\n", - " ProjectedGNCG.minimize : 2.57e+02, 2.57e+02, 1x\n", - " ProjectedGNCG.modifySearchDirection : 4.71e+00, 1.41e+02, 30x\n" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from JSAnimation import IPython_display\n", - "from matplotlib import animation\n", - "from SimPEG import *\n", - "\n", - "fig, ax = subplots(1,2, figsize = (16, 5))\n", - "ax[0].set_xlabel('Easting (m)')\n", - "ax[0].set_ylabel('Depth (m)')\n", - "ax[1].set_xlabel('Easting (m)')\n", - "ax[1].set_ylabel('Depth (m)')\n", - "\n", - "\n", - "def animate(i_id):\n", - " indx = 18\n", - " temp = dmap*(xc[i_id])\n", - " minval = (temp).min()\n", - " maxval = (temp).max()\n", - " \n", - " frame1 = mesh.plotSlice(temp, vType='CC', ind=indx, normal='X',ax = ax[1], grid=False, gridOpts={'color':'b','lw':0.3, 'alpha':0.5}, )\n", - " frame2 = mesh.plotSlice(chi, vType='CC', ind=indx, normal='X',ax = ax[0], grid=False, gridOpts={'color':'b','lw':0.3, 'alpha':0.5}, );\n", - " ax[0].set_title('True model', fontsize = 16)\n", - " ax[1].set_title('Estimated model at iteration = ' + str(i_id+1), fontsize = 16)\n", - " ax[0].set_ylim(-500, 0)\n", - " ax[1].set_ylim(-500, 0)\n", - " return frame1[0]\n", - "\n", - "animation.FuncAnimation(fig, animate, frames=10, interval=40, blit=True)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "\n", - "\n", - "
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\n", - "
\n", - "\n", - "\n", - "\n" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import matplotlib\n", - "matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "indx = 18\n", - "iteration = 9\n", - "fig, axes = subplots(1,2, figsize = (12, 5))\n", - "vmin = chi.min()\n", - "vmax = chi.max()\n", - "ps1 = mesh.plotSlice(chi, vType='CC', ind=indx, normal='X',ax = axes[0], grid=True, gridOpts={'color':'b','lw':0.3, 'alpha':0.5});\n", - "axes[0].set_title('$\\chi_{true}$', fontsize = 16)\n", - "axes[0].set_ylim(-500, 0.)\n", - "cb1 = colorbar(ps1[0], ax = axes[0], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)], format = FormatStrFormatter('$%5.3f$'))\n", - "axes[0].set_xlabel('Easting (m)')\n", - "axes[0].set_ylabel('Depth (m)')\n", - "\n", - "vmin = (actMap*xc[iteration]).min()\n", - "vmax = (actMap*xc[iteration]).max()\n", - "ps2 = mesh.plotSlice(actMap*xc[iteration], vType='CC', ind=indx, normal='X', ax = axes[1], grid=True, gridOpts={'color':'b','lw':0.3, 'alpha':0.5});\n", - "axes[1].set_title('$\\chi_{pred}$', fontsize = 16)\n", - "axes[1].set_ylim(-500, 0.)\n", - "cb2 = colorbar(ps2[0], ax = axes[1], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)], format = FormatStrFormatter('$%5.3f$'))\n", - "cb1.set_label('Susceptibility (dimensionless)')\n", - "cb2.set_label('Susceptibility (dimensionless)')\n", - "axes[1].set_xlabel('Easting (m)')\n", - "axes[1].set_ylabel('Depth (m)')\n", - "fig.savefig('model.png', dpi = 200)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stderr", - "text": [ - "/usr/local/lib/python2.7/dist-packages/matplotlib/lines.py:503: RuntimeWarning: invalid value encountered in greater_equal\n", - " return np.alltrue(x[1:] - x[0:-1] >= 0)\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "dpred_xc = survey.dpred(xc[iteration])\n", - "fig, ax = plt.subplots(1,2, figsize = (12,7) )\n", - "vmin = survey.dobs.min()\n", - "vmax = survey.dobs.max()\n", - "dat2 = ax[0].imshow(np.reshape(survey.dobs, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)], vmin = vmin, vmax = vmax)\n", - "cb1 = plt.colorbar(dat2, ax = ax[0], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)])\n", - "dat = ax[1].imshow(np.reshape(dpred_xc, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)], vmin = vmin, vmax = vmax)\n", - "cb2 = plt.colorbar(dat, ax = ax[1], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)])\n", - "ax[0].plot(rxLoc[:,0],rxLoc[:,1],'w.', ms=1)\n", - "ax[1].plot(rxLoc[:,0],rxLoc[:,1],'w.', ms=1)\n", - "ax[0].set_title('Observed', fontsize = 16)\n", - "ax[1].set_title('Predicted', fontsize = 16)\n", - "ax[0].set_xlabel('Easting (m)')\n", - "ax[0].set_ylabel('Northing (m)')\n", - "ax[1].set_xlabel('Easting (m)')\n", - "ax[1].set_ylabel('Northing (m)')\n", - "cb1.set_label('Total magnetic intensity (nT)')\n", - "cb2.set_label('Total magnetic intensity (nT)')\n", - "fig.savefig('obspred.png', dpi = 200)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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jnvasX8cwEhor2+gZouxDP9PHyQk/5nQUzT0cLYfw5WeiGtCSk7ZLo1w+r7pp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\nX9b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- "text": [ - "" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 18 } ], - "metadata": {} + "source": [ + "from SimPEG import *\n", + "from simpegPF import BaseMag\n", + "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", + "%pylab inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mag Inversion\n", + "\n", + "## Step1: Generating mesh" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cs = 25.\n", + "hxind = [(cs,5,-1.3), (cs, 31),(cs,5,1.3)]\n", + "hyind = [(cs,5,-1.3), (cs, 31),(cs,5,1.3)]\n", + "hzind = [(cs,5,-1.3), (cs, 30),(cs,5,1.3)]\n", + "mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step2: Generating Model: Use Combo model\n", + "\n", + "### Here we combined $\\mu$ model$^1$, Depth model$^2$ and Active model$^3$" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "chibkg = 1e-5\n", + "chiblk = 0.1\n", + "chi = np.ones(mesh.nC)*chibkg\n", + "sph_ind = spheremodel(mesh, 0., 0., -150., 80)\n", + "chi[sph_ind] = chiblk\n", + "active = mesh.gridCC[:,2]<0\n", + "actMap = Maps.ActiveCells(mesh, active, chibkg)\n", + "dweight = np.ones(mesh.nC)\n", + "dweight[active] = (1/abs(mesh.gridCC[active, 2]-13.)**1.5)\n", + "baseMap = BaseMag.BaseMagMap(mesh)\n", + "depthMap = BaseMag.WeightMap(mesh, dweight)\n", + "dmap = baseMap*actMap\n", + "rmap = depthMap*actMap\n", + "model = (chi)[active]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sph_ind_ini = spheremodel(mesh, 0., 0., -200., 150)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "chi_ini = np.ones_like(chi)*chibkg\n", + "chi_ini[sph_ind_ini] = chiblk*0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-500, 0)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,1, figsize = (5, 5))\n", + "dat1 = mesh.plotSlice(rmap*test, 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, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(33620,)\n", + "(67240,)\n" + ] + } + ], + "source": [ + "print model.shape\n", + "print chi.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step3: Generating Data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "survey = BaseMag.BaseMagSurvey()\n", + "const = 20\n", + "Inc = 90.\n", + "Dec = 0.\n", + "Btot = 51000\n", + "survey.setBackgroundField(Inc, Dec, Btot)\n", + "xr = np.linspace(-300, 300, 81)\n", + "yr = np.linspace(-300, 300, 81)\n", + "X, Y = np.meshgrid(xr, yr)\n", + "Z = np.ones((xr.size, yr.size))*(0.)\n", + "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n", + "survey.rxLoc = rxLoc\n", + "prob = MagneticsDiffSecondary(mesh, mapping = dmap)\n", + "prob.pair(survey)\n", + "prob.Solver = Utils.SolverUtils.SolverWrapD(Mumps, factorize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dsyn = survey.dpred(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "survey.dtrue = Utils.mkvc(dsyn)\n", + "std = 0.05\n", + "noise = std*abs(survey.dtrue)*np.random.randn(*survey.dtrue.shape)\n", + "survey.dobs = survey.dtrue+noise\n", + "survey.std = survey.dobs*0 + std" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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nrfNa1TweS/ck4WOsLOsIH5Pnx+zmJaX0CHha07Z+lkUKk01jWbHALXF1Wrrd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\n6yaDWjHtWgoudaSd1bqUICmg7esWGC1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HlHUOucoTxgx5yo6yNrStJTK1MaXnXwfh2GFr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\nOdclxezpyTc1xVLMFG5TRzz3qNt+glOPqHoDmrUObeu0cIE36BdkUz1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A93+NH4B1RGIa+svcXlb9/kdO0cyZULDOMmD4J96rUlqssZX1l+g51zd4jWbW7WLrJ7bou96hbW\nWoztx1xMb9MUQ74rfkZmZ2SOxLdCAjkmSEMqaUQlm9C1G3ScFolls80DAsZ5JfAaY6vC0K7Rsxr0\nrQZ9oai4S3SoFCQdBzdn4TskKqyAi0Oc6x2rFynCMzwS5vP75G3qBZJ5VG9acMTsxw9XiZslp00n\nGfKJJg/PfjN9oeZ780kJY3p/87ifpplIUh6c54h3yNKuZRev6RoPmap5heRWpCAZumBVsWoSp5oy\n8SdF2aARVTosUSGkRZeAMZadEeHi+Cl+JcGqpUq2xkHFsnWsvMcUwIUik1l1EOeUVW6FINbAyo17\nNkG8iopZ1/KfWQGqMGl6jJYC+s0avaDBiACQVBnRoM8EnzEBVUaMCYhsj9h3SROHxEJZ2jBlyo+Y\nGso6zm/GrzVxb64MeDmJu3yznwZIUzqW2U/L25Sb6fX57GLszzRgz3M3ajfyomaSyeaR0sy/H7WN\n2UzA1vWXy9+ZxCPtPjLZ5Ume7qRcv0o7eIkOdQZFgRDtahpQL9bp6lMhFe5ynus8R4M+TXp5Fasx\nKTa3ucgdLvAfHPyI7//6Z1yNbnLstzi52EKgiHGHlXX+3erv8kvnG6Qdl+yuwz+y/xV/Evxf/E7w\nIxXz7Sf8r50/5Yenv8/IqRBu+zgvTehcOUfy1fOMnHUetIdUGw1qdsZXrTfYcR8wcW1uiou8VXuF\nt7Ze4Tn7OtecD7k4usW3xeusWF1Cy2biOAgrw81i3DTBTTLcJOM97xpvWS8xsVyucoOL3OZ9vsQH\nvEAoKgztqiLuCfXrAdp0yOjlhL56kTZnkRWzfjcHbG1RmzXM5+XtP2krh2j08c2CCCYRrdxntE9G\nTwMxrkcT1n77m2Zgm7FH+OTuQj2YPg3XOMyC/jz3+BzALl+CiR8a+2NUfFgDdk6wkhNBKjzSxMXK\nJK4bE/ljA7ADTlkiYMQOlgoV2RmR5WB7LnZF4tZTdcwVFBBqxvgN4IBpelYdxBKINoihUjWjBqKa\nX+sGyrVeYwrYNaABkyWPzlKDTq3BUNQYE6C87irlUgulaNCO7JxgmrlgOaRJfoM81CRAMBunNhnj\nkqnF/RABbdENLz+3pwXY5WOZk81HudvL1/P02zML2ClWIeWpW9kiLqfAaHLRfECdVu0ySUJmZa9F\nbR4pqKz9PAUA0Dxw7RLXpTNj3CLOCtClxYB6QZLSx/OIchZ0wmp2zGp6TJ0RfbuBtJSe9rI8ZUiV\nQ7FeWN0BY45ry/x86xvcSC4zalQY4xcErNCpYFdjaqLP2Koxdmy6doNbwXm2xSW2B7tsHe5x0b7N\nN4KfE1YrXLVvsjY55rX0LSQ+sW+z3DykudwvNMbGWYU301eIpUdsuZxz73I+ucdOtEstHXPqtzlq\nreEHI3x7RM0aKGCVGYf2Bods8sDeoicaCLLC5VYhZItdLDJCUeGAdRIcWnSK+69T2hRzQBVaEWTF\n4KcXnYIyfeZmkt/si/5J+A3lpdw3Zz05Zl62cvFlxbazwP7Fa/PIZ+WB9nG/29y/fIyyK3Pe8ctt\nHunM9OeWSU4lEp12546ZFrrQIL0glj31/00nfCG+ygrJ+0CfJvts5OJAI6piRMMd0KoOSBnixglu\nnGAFUgGijj1LFHu8m587L0oidAxZFyhJUYIuTRQjvAlZ02Lc8glbPv16lVElYGL7hPnI1afOKcuc\nsEyXFkNqBZE2jjySkUfS88l6NoyEuifas6BvXZqv0yIt5mMoDFvz/pvMPfM5yAWfzf0XPdMnAfiz\n9qPPvj2zgJ2hdcEfJuOo7y20SpBtDHI6dmzGqzMEDlpAdNYqMkF3USvLkWpiSIY1k86liW26pIey\nrNUtNtOWPCIiPE5pF6InbU6L76uMWOaENqe00x7tqMtYqLztDWuftjxhWZ7yAS9wk6t8LC7Tossm\nexyvLvPXL/0BSebgrEW4RKxzwAYHTGyfleCYa+4HHAVrHC2v0rHqvG6/Rjh0+d7ez9i4fsC18+/j\nXRmSNiy2wgNWD05odse82nmH07RJb7nGkAo6DPBR+jxvRy8TS5fXvF/xTfsXnEt22Ql36WRLvBO8\nxEeN59h277Fj3WOTXWwrBSF4236R1+U3SYWNbaXU6XPEahGbv8AdIryCmdrmlHUOmOAXnghdfEWF\nItR16apHMS6TPMddp6JoyDbDKGcJjcxrjwZrbQ/MWtbz+9U0B9/sw1+8ZpqgupkD61lDAWXLujxQ\nl9ctanoA19U0HGYHdtNVroGjFIzNIBfTm62PrcVTdNi1KB2Zf3YlwpFYluoHIQFaeMkjokeDe+ww\nJsipXR2WnROSqov0oDYZY09ShC0RZvXJBrCHkrLR6mswZZa7TF3PLkrmNJc7TVsW/WaNk2aLiesR\nOQ4xDnGuMNhliT02OWK1IKANqKvYdhgQntSYHFXJTmzkUCjrf8gUnPVEQQO5vu0mNkvz/pcp95mx\noQm8psvc3L/s4s7mbHuW9qTbfzbtmQVs7QI3B7lpZNoE24ebKL2oi+C4PDg/DrRNLWo9MJvkOL2/\nJjVp1/08DWudytWlhVYKAvL8bBXVrjHElTFSWthS0qCPRCog55RbXKInmxyzyqbc5zl5g1u1S9z2\nLtCRS7TdU5bjU1LLAQuaaZ/K+CYb0T53gvNUlkdUrJAxHnvROh8fX6D9TodGpcOL197BqksmccAg\nrmMfpbTvnFCpj2jGFfrjKt1Wk16rSZcWu3KLOPV4LroBiWA8rnEyWuXYWeGoucZhbRmLGOXUypSF\nLTIOWOMWF2jJHpvZPjXGTCyffbHBeq7+JhFFTnaDPlVGCCTDnHin77mapKnkPZ2pr8ubzrqY1Zbz\n7Lqzz7nFQ59n+6ow1k63WeQx0p9NsaAvVhx7UYzRfFcfNVla9F0ZtMvu0/K6RVbYIvKSBm0TKOzp\n8fVq7cY1xUA8ZrFG5yHruHYCMhWkiU2UeCTCYSwq+Exoij6hCOjQJsoJXRKBYyV4XoTnhjgioZp7\n66ShLSJqkPoWqW9DF+w0xU6zQnEt8ywS1ybzLexailNNkXVB0rIJl3z6tRon1RaR5ediT5U8CNfM\nR542HZYKbYmRrDLKqoRRQDTwSbqeAmQtmDLKF21lxxTEthmjuZT69jDhbB5gl13Yi571vO+Y8/+8\ntqjvPqrNI0Z+erB/ZgFbWx/TNKop71t/ZxdPd9alPRsR1APgbLpN2Wp+nDVTJg+ZsVB1XdOynyIH\nYJ1nXZZJneCjpTbrDJAIenlcdurmlUzwwRYITwFPaHkkOdD3RIM9uUlXtiCFc8kDvpO+zgVxnzXn\nkJGsci55wPZkF8+b4Pohbi/BvZWSHrt8ePEqK5eOqfsDRfIKR9y5c45fvv4a32j9ku++8DO8dsQ7\n1Zd4X3yJ7httej9dYju8z1fWf8P2hV2Ovr3G9W89R99rsO4dEE4q3Opf4WC4TRCFVOMJ7eCYjcoD\nvl/7Mae0OWANgSQgpEGfGiMucYvtZI8r8W08InbdDQ7cVUCR9SJ8TmkX4jI6fjakSphb1HUGeEyo\nMMmd3eT10kRBupvGkWVuI83m+D9pmwXnxd/rrcqubjOObvIdpp6hLwpg63frUXG9x0mUwuLBbx5o\na/+rmegrjP/1d48C8NTYZw5bOZM5EIsp6zljCtgTFDCPUTFmzYjW26SCRLiM7CqRtLHtFMtOaTld\nUntW+lTxI5TefJzzYeoyREqBtEEGIPPca5HBZMdjuFlBjgS1k5DayVgJibcgqjn0/Tpjx6cxHtIY\nD0lsh0FQpVev0XMbhCIoVAV7tPK/W4yoFgTZMQFd2WScVBnHAWFUUYIpgqKUKDAVedFWdcisYIop\nrPJIxTP9HBflzC2anC1K9ZoH6k+jzRsP9HV8uvbMArYuoagtpcwY5CRm+s7Drka9Hcymh5VVpc7a\n5h1bu8HNuGiW+5m0+pletPteTz5CKkim+cARXlGdSoOKcj+1GNlVhnatcL97RPREgxYtduUmvawJ\nCZyLHvDt6HWO/DYb3i6JtLk2uclzw1t06zV6bg27l9K6PsT+WLJiH1PbGdDyO1zmJqOwyr+48xr/\n4vV/yuhCwIvHH1K9PObX1a/wV8EfsHt8nr2fnOP37v+Qa7WP2L58wC8Dn4++8hwEgjX7kHFc5d3B\nq7y3+0rxjnyr9VP+i9b/xPf5MT/hu3zAC8R4tOgiUIzTy3zM8+nHvDT5QKVqWXXed6+pSUpuXfRo\n5bP9oGCn6viZJpvpNLlEC0/kVkmMW4Qu5hEWPw3Z7Gzb6aS/2f5aVuwre2++OIANjx4YywSicit/\nZ1rQ80hnpmU8b+AuH6+cJnTGJROQ5MfRnnTTktZqmlrpTGO/BiYgthxip4YQPo6bYLuJkim2ZgmH\nalKndopxGcsKsXTVNViQuky1w1OYNF267TpZYmF/nFG7NVaiKJsQt1y6QZ2O3UTckdTujolsl25Q\n46S+VLi4T2mzyxaHrBX51npMSzVg02KSVBRYRz5Z5kzvh65cNkG5xTVYa+CeMI0yaMBeqHhmPp+Z\n3K8535et6nJ8u7zPZwHY8/rkF9jCLrcpAD8cbyy7m2e3zx46xuMso0VgXrbiTcsITPCe3U6iUr1G\nVLFyd7B2mweMi21NkpSW53SJWeGYGLfIeyzitaKKZ0UsiQ5BOMbuZlgNiXAloVfhvr/NiCqOF+FY\nEfVshEgkTpTQTLtsyV2ORiv83eB3ORquMnm5yjf/y9fZ+No+3fN1xhWfwBlxkduEX6px8MebZNcF\n8gB8f8J59y5fF29wN7rA7mSHbrhEq9LhW5s/YaV3wkrvlHZ8xGGyyv+Z/sfcs85xKNawSYuiJwAB\nYxw7IvMlyJSG3WODveK+jfN610qjvckRq4rkkseptVtc8wa0Z8PMtVbPgSKMUbZ2P0ns+nH9xdx2\nSnab9s95egDlsMsXo5UtWd1K5K1nopmgvehvHceWyqzN7NmfVyYL52UqmaBIXilTq9OxilE4q1jg\n28TCZ+JUCG01sVd14VVfDvFVfQLhELgxgQxxRExmQyotkrpH4ngkFYvUE7h2jFyVShK5ZZM2bQaV\ngLFTYWJ5xA2HZF0QBxZRRY1Rp7Q5Qikpag+Wl3sDh9SUYEra5jRaZhw1iCcuSeiSDR3kSEzBWC/6\n9mlinvm9nuBopviMd6Pk0XjIG/K0mOH6eJ/W4n7Uvn+PANt0cc8jCJnkMYxt9KBYdn+X9513rkXN\nJKmVY5AmD928DhOwA8bYpPiEhZvLy5O4TMuxT2OGkKbX92gWIDYSVXxrohIrxmPEqUQKgawLRpUq\nA7/OLfcCG9Yem9YuVRkiU4GVZDSyPpvscnt0iR/u/4C7wwtceeU63/v6T9la2qW3XEdUJIE15Kq8\nwfGL69xsTOA3GfxM4h1FXHDvIEXGJAp4u/dVDpJNXqi+y5fa73JNfsQL3RscxSv8bfI7/CL7Q2Xh\nWik1MWRAnRFVtHvcdhISS0m91kWPLXaLWf2IaiGU0qXFEatImIlNZ1hU8nuqZVoVYGuJG60BPz/F\nal5feFx7ku3LVrVeZ6ZuSeNadV//YjTtBy67sUvkrc+9zQNoM35txk9l/nPsh3fTP02v1xa3rlSl\nWdICpW5i5xP9RJJJh9ipMPH9YpKq+3mER5ine0phUXEmVKwxrlCiQTEOA6fBMKgT2EMadh/HjpEr\nGXHdYuJ6TDyPvh0wslQt+ajukNiC2LGZ+GqMOmGZXbaK8sACJfJkk9KlySFrHKdrDMZNRsMGcmKR\nTSwYiClg60UX9jBj15PS/dCWdQHYqbHMCUUUz+ppgbZ5rM/qGE/nXf6tAGwAMyVrEau77Fqc10xL\n/JNaMOYEwQRlaUyty9engUTHVF1sPCYFqExy0X+9rWY4aze6R0QzLySij9XmVOkQWympa3Hdv8LQ\nCYiEixQwsGv07QYjWeGENuv+ETure6yFx8gmLNGhQR/HSiAAf3NCa7NHVYxwZIyfRGxPdmlEQ3Yb\nO9x84Qp1+rjjGPtBSmN9wIa9zyb7rFmHWHbGc/51vlL/FddqN3mhdoPb9nnedg4w0UgAACAASURB\nVL6Mz4Tl9JR22mHNOsCyJAOrXjyDqhhRs4e4RIUoTJ1BDr6KUDYmAGBEkKfNUbi59aKfz2xceBqy\nWERAfFxb1F/M52+e25xAmvtNreyHa3B/cSzqxzVRWh7XTNP107ZFcc5Fcc95pDOm28gMpKQodKGJ\nVjpe7RiX7TEFLF0MZAKEAnyBdCT4ECYBnbgNlqRih1SsMBcCmorwRvhISyAtCo9dhsXArjNw60SF\njx6ywCat2ETCIxIeQ6r5UqPnN6nYIWMroGPrmLVadEgpLUJ606I8IxkQZR5pYkNoIQfWVLSlz9T1\nPWR2opKV/tdFQGYA27SyTaWVRz2jRz3LJ+kX5XVPsm/ZFQ+z/Iu/dxa2fOhvc7A03YlmlSRY7M7+\nNHFCE7QXtTR3B/o5yUog8zrNIn8NskKTV1neqkCGrtqj3OiqXOQ2D7jCzVy2sPL/s/eusZYsWZ3f\nLzP3Pvs861TVrXur+j7oexsamqbHDDDQljUjNULgZjQ28AFjf0DC8A0J5oM1ZgBL7tHYHnuQ7Zmx\nBLZkzVMCazTGzKDBmAZRBmmAxkB30+++9L3d99G37q13ndd+ZKY/RK6MlWtH5M59zj6Pqs7/0dbe\nJzMyIjIiM/5rrVixoib0WTbg0aUtfnv43WyPHnF5dI8RY/bZIifj5fIbuFtc5drOHT7wTZ/mG5/7\nIleu3uFKepeXRl/i33/633GjfJN0a8bt5GmeLm5zLb/N0wd3KO4POHq4yZu7z/Hl3XfzzAu32MwP\n4GFC+R4ohwnXk6/yXTt/wLgc8d7RF3gvX+D65h3Wro25lt7hOzc/xuX0NlcnD7gyecBRNuLe6BL3\n08u1ufuQDSYM2aqiJo0ZcZkHPMcb7LPFW9zgLldr3wFIGFTTCusc1XHIpe9F0GkSd6p63gc1sc+H\nhe1jO/2ij2thzubtzeB+udeAGTMyiupVdBq4W1f+ZBK41awXIWRGPwlCmlCb9ibEIeQhDKzM/GUB\nRerms49oTrNq48IQZwYfVtlItDEhrird/mybtw5vsF9ssDt6wKXRg8r50pH1HtvK8rRZr5wYMq0D\nLrlQvGvsseMWRCZ79XtxUMcE34UUjobOifNueoW7XGWfrTqeudPwh1WkfqeBF6QkSUEymJGuTSkf\nDij3EniQuL21H+I17Id4D/ESJ6jYlXL1vVvC0/bymMbd9lmFeXvR9TqdNq0I9LGvEcKG2BKYpnkR\noLEBPE0NSI75JUDHX+vaxZQu85YAIyaklIyrtcLgTMEDch6wy5s8yxoTdnngHNXKlEGZM02GPEou\nsc6Yp3mH53m92h3nEiUJA2bsZdv86c6386c73867+TIf4FNc4mEtkLxRPsenig9wdesuyaWCjWSf\nwWzMtdltrg9u8S2bn2Qnu89XeIHX+Dom5ZDtYp9nxu+Q3k+Y3hnx/PqbvGvtTXafvU96OedoOmKy\nucZ0MOSp9DZ/eej2n77OLa5zi/XNGWlSsJM85AMbn+Kl5GV2830ujQ/48vAFPj78AG/wLPe5zD2u\n1GvQZTtSgE3e4Xne4Ij1ymmrrNzKRrgwoE7A2WKfbfaYVJMLYiLXTnxW411WULPPWbi/9StZosXK\nphlc7XteaUait/ltOVm6jhcfmqyXIV5p2VVs+NFG2Jq0Qxq2kHYglnVRQp747SNzmsu6wTugDXFz\n2XpKXPHSwXiTg2LEfrHJLB3AqKw16wM2a+dTWS1xhXvs8oBNDhrPvljmdpMNJtVi7JKkWk7qppuO\n0nXnOFY5vsouXC5+wagqY1SFbtngoNxkRkZSlo60kxlMUpK9kvJB4oK1aMLer+5ZtGzdfXLvM1z7\nzfWRELZdgx0i5UWkfZxxvithy7Mkz7Z+jvSxkwvgF5qw7fywJWloajVWo4pBBmxN4m3z3Lo+1uzu\nN+30Gr4uw2+S68y32vv3iPV63fV1btXmpmEx4/rkba5P3+H+8BL3hpcpspQ9trnDU0DCOkfc4wq3\nuM7bPMMjdrjG7ZqohQBHjHkheY21bMIV7vGe5EvsJg+4m17h3uAK95IrvJM8zW2ucZtr7LPFV9Nn\n+Uz2zdzavM7k6Q32N7f55JUP8NbwOnky4JOjb2E8HDIernGUDOtdxtY5YsyIt7hBlpUMRgVJUlJm\nJUWS8JXh8+xt7nI/3WU/26zjhQ+ZssMjrnOLHR5xjytubTfvqgePB+zykEt1FLQt9utY7eKx6pxj\nXChXccxrOnrNC35dIX2mSdQKkDJU6jly61gmGrTUxT5/kqc28T/+0IMWzGskx4UerGOeufq91kRr\nvc6tY5mdu9ZadtZMX1Ye41P8blQlTqvcx42yW9XxGd5UPKLJCXkCRyVsJrCZkW8POVzb5GG+yyQZ\nMU5GjJJxvdGNOIDNGLDHdu1kKZD3QQjaa9ibdZRF2VxoytARMpuVvcp/DkpnAj8oN9mb7rA32+Fo\nf4PZ/SHl/SHFncxp2LLe+hCvNcu8tfYSf1SlG5eerMuyagctwSwyg3eZ3w6Retf3364yCCHkh7Ho\n2PFx4QlbzIgSSDKhmAtZqsOEAjWpa83aaiuW5OU6fV5/6/roa5sBLuc/RaXlDaplRXqp2mEV0m/E\nmJ0qwtebPMtaMeWZ8W0+ePAnvLz5Ep/Pvp6DbIM9dkgp61jiY0b8BV/Pq7zILg94iju12V0T9vPp\n6zxfvs4uD3g2eZNLPODV9EVeTV/iHpfZS7Y5YKuOC/zV9F1kSc7a5oQHo13uP3WF17PnuDW4zph1\nNtY/wL1yl7V0wjCZcIO3uMx9Njlgny3ucpVykFJkCeuM2U0esJaMeW3teT43eB9lAjvJIzY4rElX\nCHuLfbfhBxs8YofXeb7u35yM/aqeKQVXuVu1acYhWTVRcEhKUS+dmxfmlidrO9csz4KFfu7sdrCh\nNPLsNs328izmLBI8Hy9oM7Lc1yrigGuyjkU/C5E2Jp11NmsjbaPtlQXkqdeqZbtI8ZDeormxhV6X\nDd5iegg8TGA3gbykGKxxuLFFmZeM0xHjbFSHKZWptYy8fh9Cn2G1ba3eZMgJvZuV5cpZdNyqlLVq\nKm5YEbjb+3qfLQ6KTfaLLcZjF9Fsdm9IeSuDd1LnaLZvCFs4TjZA2Vcf2YN7DMwKpV1Dc52X7rdY\nH4UIm5a0XbXtkPAXS2fTxo6dHBeWsK0Zu+nS5aEfTz086wFT5g3doB02r4e09i611HnYuue1IVcE\nDQlq6q4bGGcp0TTTpGQ/2+KV4bu5m12mTJI61rh4iAMcsFk5sLmlX8/zOuscue04yXmqvMNT5R32\nky32k02mDHmTd/F28TSzwzWuHdwlGcJ0c8TBGrV2usMjdpMHDLIZR9k6GTOen7zJuw7eZpoMeDTc\n4dPZ+9lmz0VjY8ozvM0aEy90JBPWErc++pB1Bsx4mOxQZs7SMKoc7rbY54h1LuWPuJy73cs2siOG\n2ZQR49qsL8u4wO0tvlERc163alb3QqFCx9q+WSUJtpG3fRasBUgEUfG1kLzyKiSvu/ZJQmm+F6WD\n8KBprxe2CxF1rPyQ1gZhErAOZzKPLf9X8UDLShAQK64UleDncmc4Mh/hNwURGUPCdspnlFCsZ0zX\nRs4RfT2jHKUUWcqsHNTTP3vJNuvJkdvgJpnWz5mMimuKsCVSwRQXgGlW7Qgo79a0dGpFkbirD6Zb\n7E+3OZhucZSPGOfrTPeHzPbWKB5kcD+FR4knYB0YRbpDnMq0Y5mWfUrtTi8quf5oN3IrMFntN6QN\nh/p72Wex7XlC3eyi/J5gk7gM1DLo6tWp0NR6ZEND0A5lqTovNE1FoXmdn53H1mRtSXte02rqcDq9\n1NQJErL1Yk5WhYQZMGWTQ9aY1M4jA2Y8w9vkacabo+u8MniR9eyQ9fSg9gQF6mVf+2yxyQHP8ibv\n5lXew5fIK0e1rMy5Utxjt3jAl9KXeJDuciu5zj0uMyuGfNv9T/Jttz7JrZ2nSZ8pma35iHLP8zrf\nyBdYY8I2e2yWB9w4eofrj27zSvYiv7/1H/DZjfdxOb3P5eQ+l3jI87wOwBs8y+d5X7U9wF1GjJ3G\nXbXN07xTO4oB7PCIgpTLs4dsjw8ZlWM214/YzA7cJifcY8qAe1ypPFcHlZDivMMnDPEhUTKOGFGS\nVGu0RcSTuPTzFpRl0NXxDLzp3P/v12H7KH6pmw/EE7tYE/Se6E8G7IAZujd7PpRGC1yJ+ZY85LuL\nmRzCS7c0SSfqO6R1q8nqPPEOVkl1eB/HN1u4TTbAkZr4rxW4a6q9rNkAxlAepuTJGuUsc7G+U5iV\nGYflBlmZM0ynDJIZu+l9nkrvVrtoubnupBpvJMSx16xdXH3ZbuYBl7jPFcblyDk/lpXAmJQcjbfY\nf3SJ8cEGeZ4xyzPKRynlo8wR9R4+1Kpo1rKUS5pec21Cc8qgTKrukkbQ7D5hnu0lo1ikM+imRXch\n7UWm8NBzHHq2YLkVEe248IRttRJoDrbexOyeAk/APq2PkiYpmp7nGou069B5Tdo2baHqogfsYaUl\nrjGpvaLXOWKnCopwe/gUrwzfw3VucYO36g1DfPw058G5ywPWmHCDW1zjDntsVTtWlWyUh1wu77NW\nTpgy4CGXuMUNxuU63zH+BC/svU6Wzfhy/oKKFHbEDd7iOreghDvFNbK84OrkHi+NX+VwsEG2XrBX\nbNcatAQwEQ/Uu1xBr4UeM6Ig5Tq3eJp3Ks9o54Uq+3mPyjGzmXubi8JFddtin6vcqTft0GFqZRPL\nGcP6tyyFaz4jCfqls4LVsgiRdrf/558QIWg9MSPHtPb9+MNqsvZcaNBLI+csYsQv53QeMW0rZP6O\nmcX1ZhSSb0o931rg5rPBO5zpjS40aaGyVNHPZElUmSXk0wH5eEBCAVnJdG3Nj2dZQZKVzLIBg2RG\nmcBBuclhuUmaFGSJc8oUbfygcHPRlAlryYSUgvvJFd7hGmPWyYuUskxJk4I0yRmPtzh8tMP00cgr\nvI9oen5PqvsTc7gsW5Pmtrt1xZTdOe3ahj+Ladi6zxeRLOpcF7N4LC+bh9Wwuz6ny+PCEnbI09sd\nd7Kj3S5T69FW6/WBD8NauuRxXITmNqUc5wWc1KYn7ZAk6661+dZFQhvzNLcBF4JVHK0mrJGT1Q5e\nOzxilwdssU9CyZu8i6PKcaQkYZyOeDt5mlvJdQpSruCk8CzN2b18j3vJDu+sP8X99V0mrPEMb/M8\nr7HNngtQkj/Nnx7+FT519O/xMu/jEzuvMR6usT464K9k/x9XErcVwNfzF1znFiPG1TKsTQqyavnZ\nqPbm3uVB7Vz3Dk+zxzYjxlzhHvuDTf584/3MygH7g00OWCcjd/HUgUM2ajKW50MIfEBOWkU5kyVR\nbqlUNtff1jzdpd9jmnXsWi1oSigUiWGuLUUuNvQMP23i5q7Feegkz+TFhdY2tJayjENOm+Yc02b0\n+TatqStpj1U+4lwqDmmp0x7FPC5rrgt8hDNZry1e5WWVRszl9/CR0dYhnw3gaINkVFAOEhhAuj4j\nWZ+xv7bF3fQpDpNNjvJ1jmYj1rIpo+yIaeq8vR+xw/50m73xDnmRkSUzkqTgaLDOUbbBrByQFxlF\nkdbdMjsYURylnpDF41t7fcvmHg/xjmVC4jJ3LU5mwrsivBQlnoD1gmwr2Ol+Kc0xu2uXva6MnNPn\nF5G8hRbUrFOZLjMh/jweDxeWsENarDd/Z5WhWRx1miZz69gj19uB25az7ACpB39bV9DbKnqHDr9m\n2BP1jEGdR4pbs32N22yzxz2u1HvPipf0M7xdf57ndS5zn9d4gdd5nglDCjLyJOWd5FpdRk7GVe65\nDfGyR2xf3uPupUu8k1zlfuoIe5f7fAMvV96nl3l59l7+5OA7+YNHf42ru29z9dI7vDD8Cu9J/oJv\nSj7HZe6zy/16KVdKwXO8QUHCW9zgLd5FTlZtzDHhEg+5wVt1JCUx6V/lLm8OnuUr2bvZY6tyfTtg\nzIh7XGVQGcJlOZ7M/4p2OmDGGpPagUY8xCVKk54vDmmtXfpdx6R3QqAIB/PPgIuollVWBh8DOiFp\nmOWdpaWsQtFm9b3Y1QRPFjQ5W2162Xys9q3Jv+1cG6yDUoi06ziaKs8BPhB20iTsRGU9Vt96fXKK\nI+tcHT/ErdXegPxoQLE3cGbzdWC9JN2ZkCYJB+kmxSAlK3OmsyGTyZCN4SHbaVYHP4GEh9PLPDzY\nZTpdg7QgSQuy0YzB2oyyTMjzjDzPKPKUIk/hcEA5zlw9HlQfiVAmPmEzHCk/pLlD18Mq/SHNyGai\nadfbaMo/WrsOEbYl1phwZbGIsJfxGtcIkbAcK5gn8tXgwhI2xJfAaGcyq9GG5rjlvA1HqTWtZcyk\nVuPS85DWfC//y5ykzEMLBUkeGxwyYMaUIROG9ZpI2ctZPKrBOYeJx/Q9rtRtJTHHZ8getsMqJOpW\nHdZwwCZDZuwkD9lJ99weu9xlrZhwbXqXZyZ3uTcsmA5HXMoe8MLoK9wvP8Wl0T0uDe4z4ojb02sc\nlJu8O3uVtWzM7eQa+2xXRJPwNLcrsnFt6ojdra/+Eu+p1pFvV3PO6zzkktvXOhGHtCM2OcC+SLpt\npf/BCT5iuciVaVyOZzVhNp+BmJYc0qJDpJyYeWpdTx11TRzJtPeuyyOtiVrioMsOYvr+Hn/IQBbz\nCrcazqLNQLpC52nN4tZzXIhD17c5peEHY+01JVr1pJlfmTaDollLrjSHxNcWLTzFz2UX+vqEUsjw\n0J0vxwOKI5huJbCVkawV5OMB+SQjHUFalEyGI4oipSgyDg+3mB6NmM2qHbUoKPcGlIWLGlgmKUWZ\nUI5TykkKR6mLwibz04c0dyKTaGYPcFYD0ZzHNE3h1mesDpAi7afnqEXD1iSuz1mt2WrfbR7hJzWZ\n63MxK441k0N8vn15nISwfxj4CPA+4DuBP1Xnfhb4cVyr/zTwW9Xx7wD+Ke6R/A3gb8YyF3JLyWsa\ntOfzStMJ7XrkXi1PynYOPKZldyXueRN9c/2tTSsaojaHy9zvOkdssU9JUq/FlnXRMq8sS7k2OKxD\nBz7gEkOuM2XINnvc4KtVZKJRZYJ3qyxvkVakuF7N+WY8U7zN5eI+h8kG17NbLlDK4R2e2rsPmylH\n2yOeGbzN+zY/zdb6Q7ayPbbSfd6a3eCzk2/m7uwqs9GAS9kj7nOFI9w+vi/wGs/zGgklIyYMmPEU\nd9hmj6/wdXyG93OkAp8csFkTt6wJ3eER2/jITEKATsN2LS5ug3Je96Ym7GFtKvfCVMxhTD8bIQHQ\nPid6ZYKGaN/iaFaS1rPwPpKZu5MZg9ozwd1Xc67+DHGq73NzgLNm6UXm7OPC9q+UGxpA08hxfW2i\n0iU0SVvKq7TtsnQOaKKFyhpsGetl5JV12VJEgXNM26YpY0hREtZ0mFAeZpT7CeVORjFZg/WScpw4\nst1MKJOEpMjJZwOKWUZ+uEZ+NIC82s+7TMkfZRR7VTutQZkkcJC4OhWVpUCEBO0LdoiPaqbN5BI3\n/ZAwWddCiJ5eCM1Vz1QGOhNpkBA5hzTu0lzTxfytJaXYOQhbcfRzrI+vBich7D8Hfgj438zx9wM/\nUn0/B/w28F5crX8J+AngY7gX/MPAb7YVklRPuJZb9CCZqGGxqYFT/7aw2rm/Jj6Qd6mbfGvNSAZ/\nreUllOjQfzJYU1/vtPYJw2r22hP2JgdVwJWUI9Z5xA6ydeRmRfpS5hpTBuS1Q5nsFCZz4BKv+2p5\nl3XGbJd7DMsZElxhlqZcWbtTb1QCJXe5yrQcsl9u1SbnCWvc4woDZlzhHjOGDJixzV7tEZ5ScMAm\n7/A0JQmXeMA6E0qondKElAfVohNLsI6ckznSLfF7Xkv7h/r9uHPCoWfDEqqdfrHXNnW9Uv1yJBLa\n9e2MCfuU3+fQ3HGbWXEZCAvaYzZfez5G0ELGdg6yWsJV/68JWwbmak/NUpZ6GStJhtem9ZacEiNE\nT8vOcJr2uPpepxGjvBwnMM4o84yiKN35iSPY2bR0nDgsKCYZ+WQAkxQmWSVIJDCD8gGUD6vbWa/y\n3sOZuaVZxUyvHc32cJr1fZpat3y0di18W5RVt+i11iFWt1p1aCmXnbuOkbU1o7cRqE3bdk4/z23P\n8MUh7M9Fjv8A8Cu4Vn8VeBn4IPBlYAf3cgP8c+AHibzgQqqlii4gA7hz6/Ap9EBtB2s9n5xWL6Hf\nEamsB3zReHR+XSD1cGXNa++6DloLHzCrB+kJa+yxXS3IGHO5ckQaVJ7SjvgmzBhwHxd/O2dQOS0l\nTBhym2vc5zJ+jt+Rs6zLvspdhky5gnMUu5zed05vCWwnewzTKclGzqNsgzeH1/l89o3cZ9ct62K/\nWqR1lUfpNtdGt7kyvMdLg1e4wVu1k5vcx6u8WM/Fyl7fUnfRoi9V8Y+1RgnU889pFQFNIplNGDGt\n7lk0bbvhR6iNdR/rdG2w0yr6uDapx9LIygV/jXvBZwwqQUSeFzHX+9j3ui3OGKf6Pjdtwajvk5r9\nrRlyGW3dknbIbK4/Oc38Qmb+CX5Oflh9qvNCwjZJhtsYRFuKJUraGo6sq7ls1vD7bOuqzxJ3rOK4\n8iglP1gjSUuKwwQOVT2lDppkxTyf4AOcaN6U9HpTD3Emk3SarCVdw7Fb2lXU8JAGbT96blr3iT4f\n29VrGWezNg1cPyfyTEm60POs06wWpzGH/Szwh+r/13GS+bT6LXijOh5FUUm0TY26rAftmJnbkqWf\nEZSY0n5Q9POhonUvN98QMok2vYS9KdbXP681SqD2IL/EQ3Z4VDtQ+ZCfh+RkPOISj9ipCDur72rK\nGg/Z5ZCN+r7WOeIK92oPbYlT/i6+ylPccTv3ZM4hZYt9RskY1nMerm/yVa7zMt/APpu8hy9xlbsc\nsc5b3GCaDXkqu8MV7vFuvsx1btWELOEOH3KJ3SpacU7GXa7ykEuACw6zzR47PKqjL7lgDk5bF8IG\nam2bak5e1qoPyGvhRuatZ+pRTtXzoeeCu2itti/Dz1I5Z0XR10pcAHlWdUwB97zlNZFn1XMhhB2z\nDpwjVvA+awK0nrUnuVebr+Qpx+3SsJgGpQdj6xBnyboLYcv1CT7sWeIVQwmsIjFYBuqYZHGAI2ch\n7G3cOu4RPvgK+BVlhzTCWJf7GeUoc/8LAQvRi8Z8pMrU0/D7+DlpHWZU5yXXH9E012uyPlLXlbqv\nJF6pXV+t21of0/0mUk9Iow7Na1vo823HQtdYzXnVz/NiLCLsjwI3Asd/Dvj11VfHw2kj8+EZveNR\nczANmSyhaZ4WLV2cfJr5yrXzUpE1cbZB6LtQL7+ew9bm+HWOakctp1HPkPlXCW4gu+9MGdTrtcFv\nqSdLncQcPapC9meVln5URRkTs7tovPeqTTemlVd5SsGsKiMn5Slus8UGQ2Ycsc5V7rLBUe19LR7b\nEipUlqxtsc9GNSknQRyccDKrJgCc1it7XWsfgy32G45ZYiGYMmStInUd8EZHCbN9ZOO6d+k36Sv7\nDOlnScrSZYZ8IryxO/7MOG9yWZvvBTtt8l8xzul9lr5ZRShSm6fOd5E2Ld/Wi9em02NOKJ67TDYL\nuUj5wp7aEU3M4hnOc7yaF87xpnHRliVb8QSXWxtQzSlXWa7R9NKWhQ/aqCDKPXhlVqpoOXFmzon5\n2wYdm9IMkKI9v/Vv7exdE7W2/euCrfnbknfbxh9dPLxXYZK2RK2/u5R1dl7i33uMPN8AXlD/P4+T\nxN+ofuvjb8Qy+dcf+WStnbzvQ8/wTR+6DjA3oNno0PMDtx+C/Typ09JkxlQG2LbhRLSkbhCdPquJ\nV7Q+IRO3pMc7Gs1wMdDERCykLmuZZbtNFyvNac1ihp4xcGZu7nOJB+zygJKEO1zjDk+xyUFNsFOG\nPGKbt7jBG1V3iAf6ARsM2KEg5Rp3ak13zDpXucdlvsSY9XqTECjZqwjbkWnBJR5yjXcqQr5SWwNc\nnd38+SGb1faAG7Uw4awL+6xX24uWVb0kNrgEjilr0coTtvSPJc5lBC1JYy0mIdLX1h6BdnBMVC1l\nCib0jPjpi5ISZ3n5ws23+PzNW3UJK8Y5vc+/gx/0XgLec4xqaFiNpwtZW007ML+MJpe2+W1Zd201\nb8nTrueqmLOoSDvBk6Ssz9bWYilaipE0YpJex5OiBCmRa0Rj117nluO0YUCIVluRxSNcc6YOOyrE\nLB89vx1UjDVZC1HLxzqcaRN3m4m8K2kvmrfuAmmwLmbz0lz3avVZDVZlEtdvy78Bfhn4n3Amsvfi\n5rlKnE/hB6v/fxT4R7EM/8ZHvq0ezPVA2DRwN8ORQpNYrVFsfm56/iWPzV93Iet586nfGkTOa81P\nSNSRefP+xClNE7iee5f/ZK54xBFrFYnLnDKUbLHPWuWtXZBywAYlm+yzzSEbtfPZJvt13XfYY7My\nV4tWv8Ehl3jEIbOaoEuSel9umf/3Ebqcl7/zVXe2trXKzW6C9/Z2IlPOWuWcNmJc6eJZ9Qo403+G\nCygyq+7YTWzM91lo/jlE4LbfQlMqbZq5No1rJzH3zLm119oK1MzfjaJy/1KzgpT3fegZvuVDT9X5\n/uu/8+n2h+50sOL3+btpEmTb4NlVSFmGrCW9Jm5rTrdErTVwYTkdnjQ3aUL1sObTAZQDKLNmelsd\nvWe0cL+Q8Bqe58QbG1VFMaOLc5uulg7QJmUIAWuulGPCPzPCRK3ntY9oms5rCOOHtGhN2nZOOkTU\ndprCEmhpjgu0VrwMcVvy1cdCc9T6mHy/BHy9+v93lyh/Hich7B/CvaDXgH8L/Bnw/cBngH9Zfc+A\nn8TX9idxy0A2cF6lUQ9xvd5WYMlaL/nxAUrd0+K0WPfL62TiIJbURNAFmqzbBn35dnWSNbXzO4dp\nUtFOTE5LdOW53XNcXOwh0+qOvfgiy8HcLrWHtYn/Hld5xA4pBde4zXVu1Zr5lCFHFcFOGbDOEbs8\n4Gne5hIPa4uAtPGYEQ+5xB7bSNQx2bLStUtWCRVpTU77bCG+AOKR7mNnuR63kwAAIABJREFUO9uG\n7M61UU0JDJhV22VOyCiq1zmrzOlZ3Way/ly0UnEssz4Dtj9CZuwY7Jy37netWVshzIlm/sigMqU2\nydyJKTlpNZ66uxEhRKfLOfPQpKf6Pnu0aa/QzWzehailLBHb5TqNInBM1yM08Gt7sq6PrZ98i3aZ\n4TTtNXxs0ox6W06RI1K8+XqG01wzvNOZTAGPq2wGqjjZDUxuWSv+CcpbW1VNSFcrv/rYzJzT4b21\nGVzybbxecu82HriuiCXlrhq1Je9FGvciy0ksvbbkwLymLbDnY/mdDCch7P+r+oTw31Ufiz8B/lKX\nzEX7DGlKYg5trsH1Wqcm7abe5KXnmHNPaD7UEu0i0rZmVXteBI5YRDa5v2m1PGrIlKxeOiKPXUJK\nzgaH9RrukpQH7PI6zzEgr0KvPGKPbST4v6zNLnD7cG+yzyUecpkHc8QxZFpr95pYgMa68qwiTpkD\nP2CzXoYm8/QpBXts174Jm5XdTaYLZJtN3x6JKs8JP3JeTOa6b9vM2aF+jcFq1fO6d7hfdfkiTNj8\nyvp/J8rJFppaQJCALOcQ6exU32ePNhOlsEgXQborYcO8c1DInBkb0EPhJoVYQtfbY1K2XeddMW1R\nfcrEm8CliLG6REhdK/li/pZb0+Z1q/yLF7gQsFRNLNKWlOUjxC3QpC1OZnJ9Ye/fXhCLC67Z3k6w\nW+16EVHrfggJW7ExoI1kdWPqcyFBTTovpuGfDBc20pl2wJHgE+DnsK3DjzjvaE1Wn3fI6ry1CR28\nJiS/Y4NzW33bzslHtF1dvggfYvYVq4HsaOXygGFFfDL3PmHEnUqjFq9pcVibknCfXd7kXXWglYyi\n9kQXb/MDtrhfOZ9N6/1wRe/15ni3+iSvzedDJlUI0UMO2WCPbfYqM/thNTctRC05C+GKQKKFFrd8\nK6sFlJSicjlz+1r7sKTU5yUfQcyEvUjQkmvFrK+fLw1xJCzNdVn19FkBQluDoGRQEbS3Hvl94xyF\nO9FkVr344kH++COmNcsAZwfZgsWEHNJwuhB9SCuKpdOTvsJ6miB0Og09D1vi7dMyr63JoyLzInVL\ns46AaeJN1tYCK6vEJJTpCG9ASPEhT/UtWsu85rWQg7aQsXxrY4Kk0SuzZiU+zKjVgLWUIBfpTHQE\nM03WWrLQeet2Fdgbg+ZNxrDovLXk6PQhEtfX6c6z1xwfjwVha93YUZUz/+o5YbvRg0CbIp0W1Fz2\ns2hd7bJ1XnQ/QE3YOurZhLVqCRM10Y0YI/Gx3HHnE+6cr7zJuiRhlwdc4mE1t5wyq7TtjJz7lUva\npTrg6aN6M419thgwq8KzrFdx1ZyveULJBoeVNkzDa7+EKrr5A/aq4C0ScMUvv3L7Xos5W5axheJ6\ny/y0jgsuu5IJkcv1rn3KikB9v2vCbtOIQyiRaZTZHGE3bTh+jTXIfLVOHzKXe2dHrX3r+W+99Ksg\nQSKjPRkIWQpkUNMkqDWamBkbwgNtW/rQtV3y1/PWOp2ur2iB+lodCaXER0ix5CPnMvdzmsAshbSE\nNGkaHGQqWEbsFLfMa5MmN4jW3SYfaVgrssxhy5y1aNC2eWTd9aQ6UIbM21JpHRBlbM5NmZcYLIm3\nmb1DlpGYtSSENmtP27Ohg+jErtWChH6mj48LS9iWSP3g6zVT0brERNokeK/ZiJuP1sm1kdOW2SzP\nD7yCruZVDclDtOcmyfgytZncmlmb9+vbBag1WLdh/UFNdPtsM64IHpyWWFRa4TpHzBhwm2sklPWS\nMImEllFUkc2dPW5cBS+R+OZiEZB5cSjZZq9eqnbEer10a6yEAGfiz+v7kHtxEc+SOlSnEJluBwma\n4u59OQHLtplGWg20esbZGsN9H5WN60KmeE/Gcoz6edTprKBY1tf7WOVPBtq0k9icdMiEHevvrpqz\nTR8ygUselmQFuUor9ZblXaW6NqU5cFvipnm8TKtT6fz0umwSIjJuqrK1vnzaZB7iFSs36FsTU7yE\nGdXTzaUqJC+9TDJHtCGzt7azS+HW7F2Y/20lbduF/sccC8GmXwTdV7Eyrd+CrZ8+fnxcaMLWRKmH\nTaFfS3wSUMVtrZjUpKKJ2M5r2nlwgZCnJlp7fXftrTkPrteAe63fh+XU5brXvrlxhNfEp2xW7SDR\nwzc4YIdhtV7bLQsD6ohnBS5qmOw3fZer3OYaU4Y8y5tc5j5uc8vDygHM6baybecBm+yzVZvNc9xO\nZPtsUpJyiQc8wy1u8zTv8DT7bHHIRr3391pF3bLYSe5qxqDW8J23+FGj/7QWm1bPQEjoipm/Y+n0\ns2XJ2goU3irSTg7N/nZHvPnbCyCh9JLfBQygckJYwrb3FhNONFm2mb3tu9h1fhvaSTt03qbVBKS9\n4VNzPsSQ+lzljCabY5TqHmQuWZpAiFv82YQPhCsl4IpERpNVaJoTNV/qWxEfMU3sM5zpWw6WpWpC\n6/E9YV57Fm82S87WUhEj61C7WfJEpVlE2MuQdqgsW67WoLukPx4uLGFDUyvVmo0MbNqsrAdBr+Ek\npPVgKwP+/ECsNThBSNM97gCqyxLLADjTuMu7qL2GrZk+QZYI+YkBue8BM9Y5ZE0JAEPcEi/wg77T\nTGXryQFHjMjxgVr22aqjjYkZe6MOzDKovcslIpsIURKKdFoJCBLs5Ar3KrP4FcaM6vx3eFjlNjXz\n8Vk9LeCnOJrTHFqAcmNTc118qA8X9Umov60QoNNpC4jvV6vlNzXm5jMpz21TaKRxddNV8smAHvlD\nQU609hFSBeVjI5eFytGw6ewAu4i0tZbdRtraEqAJOxSVRGtcco2wqZr7LI1anCfukzST1Yq9JBXf\nrhGeQ8VMrslaa86WB4VrtWV3VlZz2aE5fJ2p3iFECFpnaqOUhZZwxcjaEmCb5q2/Y1iGQG3a0LNU\nBs6HrETHx4UlbIngJWZRN5iC1rXtcish6ZxBrblJ9zsh082LuKZtOiv5wdRRxnHM3jGE8gmRuOxM\nJveVNu7Cz/2K45rz8nbLuo6qOWi/7KmoNOWjWjMuSBizRsZWna4grZeFZeT1Rh73uVwLBLKtJ8A2\ne1znFmtMVBQ231dCvBsccYO3ag1byFhHK8tI8NuBDuv6jFmr5rdFNHOe6FbrtAJV1z4Iadoh83SC\n90zXz5/0hSVUbUWxQldZEbUWyEQ80/PaMpifg5f4KcJaJSxC2kpIe5JBOmZCh/ly9IBpywgReoyY\nFwlPIoxk6n/t0i35ym+ZoxUVWRN2aG5U5roTn/2UZnOA5zz5X8sLuqqiXUuVrcO2Nmo0mkWzvRZm\nSpre4Nor3JrMtVZto5vFyLrLudAnBCsULoKWkOw1+lnUZdp3d9E70A0XmLAHiFZtzdjgyUsPjnp9\ntqzflRAdKTJ7K85KNPKU2UsXwCRfuXYTmuOUAV6WRWkND6hJ3JGVX8omS74cKR+ywyNyBuxXy6ac\nx3nBNntc4R53ucqkIsQJI0rSOcJ25c0YV8u/DtlkjQnP8PYcYT/P6+zygLe4wVsq0qUQtgRa2WaP\nfbZ4R21MMmENmYuGvE4v3u1SrwllJZrMaoufnipoelt300ZD5CzHNVl7q0RzS88Sv+WInU7R11or\nidXSdZ1dHrIzWV5bkWx89Mcbi5x/NCPYeV9L2CVhQrP5STp9TMMu8wpp+BoyKIfuRTRmbUoXkpKy\npDwhZ01kQuIy8Ryby6/yEHN0gfMst1WRbyFz+S3O6qjiR9WxEs+xrbyS4ye4rfOXXcKlQ5+FzN0h\nEo8Rctv5kKC1SPjSfbmISHX6kECoTeExKxIt9emOCzsiZMoByJqnwZOzHli1xhqay5QB0EamkoZ1\nz7Yjb7k2NL+tESOKtvQhpzFNQvK/mKhlXYUmcjGrH7BZr3+W4T7BDfgSxGTMqPayFkuEaPUuxvek\nJhXZCUzaeo9tZCtP2av7HleYsMYDdqvys7ruBRmHbNSU6MKbJpWXuCcxIT3RsBOcJ7k2VSaN1vLt\naf0J2sg6pE1b8gz1V1PASqshfX4+W5vqddAZby5PG+l1ndyr7msoOQnSFbzgFwOaOJcVhBPzLdCm\n5S4m8mXLtQO+kGosHzmfB9Jo5pS8U/XRwcALwlq2kGHWPF9qG7lKX5bOMQxcmlKIXpF9ifNMr3mw\nwMf+lnpaLVoHDLea7Uyda3MkiwVHaSNkey40vnbRrllwLpRf6Dr93HUps+057Y4LS9iydCdEllo7\nySqjt5vndQ+/dtDy5vOkDqeZNPJxkKHfNXuGmGK9zBzvjPl5yPa02olNOzjpOudkdaQz3wauHDFf\nQ8kjtqvXXIZ+l37GgIfs8JBLtfd3QoksE1urZqSlTk5rX+OAzXq+G2CfLfbYniMrF/l7mwM2SSiq\nOsnOWRu1OCA9MWKihoG0PifOaz5AjF8iExJsLJEWNIl3Uf90JWmdTpun9fF5b39tLpfnK22ktWgT\nPp5MwoblB66Ym/Oi/ELaUBeETJ9d8rDmbgtNPpqsNSEJKev4pKj/ZbcQ0cStk5tUXRG21Dsvqbe4\nlFPT1M2NlwUUIfKsVXmakVVsFDJL2DGStfPWMUJepFkTaONlCLtLmpBgoIVEHVSnC2E/wcu6BFqr\ntsc13dqtO+YHZa8r+2s9CVoDqWhHElrURqTS3wJLLm1ozm36QXs+lKW3JDTNtpBXXtpaY9ZpxLws\na6BFu4am1idWihkDisqcLp7oMkctacXRLGWDaXVurYpSNiCvNGYfAx38XLwIC0LU+v69WNXUMK2f\ngm63LnKyJ9K4Hm6tKDpnLTTEcrDCjH92Epp9ltRndLmhvLvd3eMGrZHo/+1vPQC2pemCGGmHBtiY\nNqWhB2ktUMUGbj3A23xSPHFpzVnv3CHQhG3nxzXBV+WUun5yrnDErOtYJlBoE74lUKsNh0zcmrBl\n3lruXV9TqmNdPMLbiFy3b+gTQmmuiWHRs3FcPMEatgthqXfl8uZu7WkN1NqzJTs7bHq9R6+QbRKl\n9QKWpUXWJK/JPxQtLYbElCvHhEx1XWSJly4PnEOe37mKxvlc3UdKzholQxW0RMfzmlXbZGbVC+ba\nVeaNZU59njycf8GgmjpwZO82AJ3VZCyBU/TgLPeVq7bzpvCSotLtdeuEzNdayLFtauFe96Qayubn\nvENatk6j+9g6guk+czqTzM2LYCcDsoM4mOl6tGHR+ccHYuaF5qAamusLDbpiUtZC+SKPcY0YaYdM\n3CFnoVie1tQvE8U6jSZje63cjyZqIeWcecLWa7NsEHG5XtrTErZcqzXHNrJL8A5m2nNbE3Cbx7cm\nZ03Y0M0MvojIdVtagg+hixas85M2kLYMCXM2XcxUvzpcWMLO64evSY7y0Rqjo6bmEh/5X5t39Vy4\n/sggrDVVHYXKEkRoLtOmaUNssBatWOqjHdF0GvGqdveX1+XL2vOyulchXgnlWdRt6H7LnL6sLZZr\nfJxwNyCK459fM+33604rPV/M2X4Hsryasy4p65QZQol59WtYLfPKK6LXhDi/T1mzLbq1t7/Stn2I\ntEOmcCuwyb1LGu/RPlNCptv4RVCo61z7uBXZsXt5csgamtqyHhC19qkHRqtBEUgP3cg6lI+dT7bQ\n+WoP71ia2Dl9n5n6HXJMSvALpoX0LGFr7XqmrtOkb9d+o/JtI0cNyUOvnRbY3basadySrXYqE4RM\n5KG6tR0T6DSL0JWwtdCifRfss6iFTgJpVo8LS9iy25HVQkHInMZxPaj7KFFumZa9XvINzTXqfIUI\npcxQXUIk0mbq1Ok0YkQU0/iEROYDw7idotzQ5kllWnV1QlkRspjQqfywnXDjPMaTSu/2e3rbe3IW\ngCnrlTlcvNOlb/x1HiluX+uQZUEEg0QNDpYgl9FIpa76uQBvUo/ND2sLh0BbPrxtRltWksbQq+sg\n5Yt3QnOJYjOdbtsnC5bANEmH0obm+mLpBXagDZnSF+WxCJosYqZx1HkpT55prVlr7VpraHoe3Grl\nUlYWyCMl3G6ovKwmHSJA3VbWg1s0ZmsOD2nZmoxDJm5Lxvac1ZyPizbNOvTM6Odv/o0OHzs7XFjC\n1qEr9cBntW1oDs4y41tUg6ROY5d/ac1ctB6BJeSQGdVqayHzrZwLoc08HoJo0bI5qA5XavMo8c51\nXlP0C6fc7PesWlblzOvinCbxyGTnLmlbFz/OE9iQGRscAEmtHXvC9nPvep5cT2UICeq5bnu/Ol0X\njdqSto9M1uwDLwA0j7ebvcUN0V3pLTf+GdKQ8puWHREg59NJ+U8eYQsJWY06NiDG+nkRYWsTdyjv\nkzj9aCtBl3pJWj0VYLVgbdbX5m5JH9Ikc3Vtpj6W4HQbh8zIArkmZOkIOZbFiNqaxGNpYpqzvc+Y\nCXxZdCVs6Ga9iQmDp6tZCy4sYYcIOWbKDGlWIS0qnG4e1gQdK7uNyDWZd5mvDEELAfMamZ8L9QSt\nqW1eqGnW1KcWKhZNl7lUZZ2qxG3r6R3wvPaprRAWcbNz0rjPk5qGw/kkwTQu//irqVvMar5uWEkb\nLazJtqnZ2/aXcr3A1dYGjz9imm2o9eP91p6uy2C7SBAIDeCh/0NaacgMHhIQrIariVugNWddrtWq\nhSS1w5kmbOs3EKqLJkxL8DFNOEbCMcJuu0YTckizDp2zwsei96Wt32OCTGm+uwhqbZaj1eDCErYm\nHK/ZxAczoalYOpuH1b7KyEfnZc3SckzS6OOxOixz/7Z8nW+KD/+iy/WvvjfuStAYIRUh1pKkMmWL\nMXpQO+81tUJvvQAXoSuvrpMNPSy0Zq2tIz5viS0HsReqrS1j6W2/2D5fJDxJfUUcErN+0/IhscG1\nRi7WD7EkhB3c9HMyH+Y03N9PBqz2FiI0fa5t4NOEqfOIadB28LX10t+ibVpztE5vScKaULWmb2Gt\nDZq0rblbC9AhU3puzmnBQ18be540yYbOxTRhre3ngWMhM3eMsNvKs+dCwtJJ3xU7VRCDFepimnbI\nkXJ1uLCEDX6A0w5geuAVWC0oNNBrwtZORFq2tfmENHJBERgcdFlaWzqudt0s3x9NsXHVw3X0q6DF\nMa25RMk7ebkHzEUYW2uQtCYWoS9ZPlaSMGWgeqdZp5CFQ4gwVQNfW+ssQ15tz4XNaxFpW+EsJgBK\n+2k9urkNaTM6uK2PdnAM3cOTg5AWG9JetOYZs39YravLALmoTdu0rVg6q2Xr0SQ0xx0SVISo08Dv\n0By11b4xafSxULtYE6714NYIacf6nFwraCNnTeCWIENk3ZXM29Clz20ZIei+WaRxn552DReYsPVg\nr+ecwQ1qsuQpNLjpwTYU9UyTdwh2YNXHQxpRjNz1ea1ddSFwa04FT8AJzN1320AvYWUSfJQ03T6y\nZMkR8LC+TrzGfQCarL4XmQfPqmDFXp/3JBe6TxEFxEu8JJ27z0Xt0sxrMew1bW2lLRDuPv3Tp+/R\nOjmCCGnlnHbdNKX79mmbsrH1fvIgA7DWIGPp9CBpTd+nNUBqctUkp+usjxE4J+e1Jh3KQ5OBJeyU\n+XbSWpz9aIS0QFtv/W3vXZNkSMsVjVpf12YKt9frMhdp38tACwIhk3/oPtuepbZnLPR8nh4uNGHr\nAU8vd5Lz0DQzCvTAmjRyaR/sQ+lC5+V6S/xWo7O/Y4JArKz5sh01hMynTVNuc1rAk0zOsFqypYlJ\n/tcbeQBzmqLo92JAlzXbbvV2VpejTer6Wt1XshTKE113aI/xRW0Z6vNFhC3L3fzyNr/UT+fhl/Pp\nfOfX+Jf1oDvfFhZakHryoUmx7Rmw5HnaA6PVtmJe6zZdSPjQwoUm467pQppzyGTeBTqvmKa6SNtt\nI9YuZI06t+jak8DWIdROupw2obENOg8t3J0OLixhCyzhykCpB+2YuTG0fjek4QrlAYgRWPLQ12gS\n0uk0qcXMp4t+LyKtsqaFuDARusam8s5i8+2go6WVqj30Mjqfk8/PO661+xmEar0srCYv5Vlijlk9\nYlYObf6XNtABbJwm7V39rCjon82QUOhTWiFP8vXr58v62JOPEPnqgdOSIeb4ss/ZIvN6KE3MVNo2\nT20HcTlm7xNzro209XUhLTtUdwtttrf3Zc3TVrsWtJFw6Lq2PGLHbHvbMixi6QV2GkAf09fYKQ6b\nTrdf7FmK5RGr+3K4sIQtgyDIYOqGMB2cw5oXZTDUpG4HcjvY+zNl9Zq4F0E7WWmNMK1Tu5nhzKRr\nux/7bbXkLm3SpZyQlpZUD5leKx3S4iVamSx3KyuNM9aWIE5oaa1ZxzbrWIWJN0bGVrhqI2X7DGhr\njTxbEuPcWlCE1nPiVhrUOSs4grd4CLzw5/c+133wZMJqo3ZgXGRqtAP+orIWtaUm4FBZIUEipIWX\nxMlcH5OBXxzHQqZtTdr2XnS6rpp2iKT0b004i0zXIaK2n0WauD0WEiTseYvYNfbZ0elCpnH9LdfZ\nZ2YZwc/m8TVA2JagvFm4OSjqa3QUKnCD4byeLum95qpL8tGgC5XHvNYcI1xrotfX2DRaMOmCGFm3\nlefby92VtQQ0BSOdNkx+ul+aDoHyKszfz7JkHWvD2L3K8RBRt+VtnxmtZcuUQZPgw3nrllpkAdB9\n39TaZThZzct98dB2T6EBN6T1WlLQ52Na8jL18r0wf640aUJ56HMh8o6Zeq0Ao/OywkRh0q3CM9kS\nttTVPouLCNuma9PE7bE2E30bKcf+h/CzE7N0tKHt2VokCIWepePhwhK2aDl+gYyYaMUhqAmhHpfG\n6YrUA7MfMt0r4YZJ0QolhdfKdYk+Qq/VWK2ndkiQsOThNfm4Y9ZxschMbL+1sKC/5d5ce4X3nLaC\nRqYIT3/b8rvAasyh87F7D91/SNMWQnZatPZc92X4MKtNMtf5608z4pwVxuT5K6tIdHl9NA/MfYlH\nwJODmAYVIzA9uFrSs9qTJVBLXsu8Y1017bZB2hKtXCNpQvcsaXT5ViO0pK3vfVWEHfpoxJzQFqUj\nch2R6+X4Is1a0IUUrTm7TWMO1SOUPnQsdD9drDyLcWEJG6iXGYEnjry6cdFL/JmkJmJvWPQzk35N\nshMB3KApJk8/4Er8bRAdOw0OnpYQNFEt0vL8BhyrJWtdt9g5fcybsJsmW+9M5clIm8BtvkKAcqxN\ngFkEawcJCTwhWMK2nt06L9nwpKz+8wvlmgLZgFm0/OY69XnhyPaHDhCTqv3NYxYWPSXz+COmQXWx\nJJTmW0OToR7Yjzs4hgbZWD2sX4fcizbx62OlOWYhZcUc8EJatF1/vQpIP8WevZBDWSydPhd7BtoI\nuS1/3Y76GYD29rVk26XtbFk235BgF3qWTt5PJ6H8XwA+C3wC+FVgV537WeCLwOeA71PHvwP48+rc\nP2zLXM9FZ/WMov8IWVutWchjWG16IZ6+Q2aMGFdbTbqBfECujjnCESFhylrD81nqpOeAZT7dekSH\nECMhr3ctNuWuGotIJmTqt6ZdfU+hKYpl7mne/BwuY9EnVAe9Il0sK+D9H6QM3cf+moxptWUoNJ0M\n9ZJDXXf9EZEoV2l0G6bmcxxBZwU4xfdZE2voYwfRRemslms9fbW21ebIZM27VpPtQiYxE7E9bz+h\nzTPaNtSIbUkp18Q++pq8Y/q2/LuEHl3mE2urLlq3/nTp51ifHQdt2r9+hlf7Dp+EsH8L+BbgW4Ev\n4F5qgPcDP1J9fxj4RXytfwn4CeC91efDscx1kJNB4CMm27KSWoWsRUse1ISdV4Q9rWJlT2rNKyNn\nxIQ1JrUnsCfsIbLXtDYjC4mjytPLn+yADPPmU61ZhcjoLKCJKkbWun46fYi0bf3bSDQES3BW+zwO\nWdvrckXGMj/t4rE3Ba6ZWaamCdu3w7wwoOtuCbz5pISWHOqpGN+mZ4hTfZ/jJGy9nRelE2gzswyc\nVgvtqqXpQdcSfhfCDpl3Q4TSRsD2mCVFuxd16FjoY7fA7JLeEnxbvbrca1v6RWltu4f6LHRMn1um\njC6wdY9p06vRqjVOQtgfxb8JfwQ8X/3+AeBXcBupvgq8DHwQeBewA3ysSvfPgR9sK8CShCVIf3ze\nLKmJJkaMflCNa0c2v5BRdhExhYjM5eQ94GPmVX0Pp4llCTZW11XhpMuaNOHrUKj+nF+CZgnSPi9y\nzNVLwrv6vcdtmaFjeq7Rb4PqSTtTT6Au9wxx6u/z/EDW9rHXhWDN1JY0266NIUa4i8ij7RPSJGNE\nskiDDZHpIhJe5mPr0EbWy2rSMaJua+9Y+9t+bRuHrAC26EPH9DadLXP17/Cq5rB/HPdSAzwL/KE6\n9zrwHO6Ff10df6M6HoRE34LmXKgm70wNynI+pJmB3x4zwa97LUjrQCGaeLLGQ2Q1zgLpjGW14pAA\nEPu29xvK4zSwKP/TIujQPcocu+7PkJBgBSJ9XMjaBUTxgoAlYv2/zF2LIKfnk4sGtSaIoTtmZdD3\nl6G9w5vLFnWdtSZ/Tlj5+9yO0OAm71/sebRtE0vfNnCGhMLQ8y3vPITzKjuk0/W1afR1ofOYc20a\nXQiWXNoQI8xQupiGqs8te21bXULz/IvyWETssbYPla/TW0tOyUUInPJR4Ebg+M8Bv179/nncLue/\nvMJ68Wsf+VQ9RH/Th57hfR+6Xv1nSRSkQUVrDml/OrSpQOYp3WDa1NottDbvH5VmLdo0zhARxrTU\nc9Culir3NElbk501C2uLi8DWWVtPJK1ft++sKYnK316rndVymnPcMgeu904vaU4L2Dl4a/puBknx\n9yXXf/bmO3z25tutz9IJcE7v8++o3y8B7zHnFxFNF23KDuCLzJJtxBs71vZ+tA3aIe3cCgs6Tczx\nLFZHO3d/EmgN+LjpFuUR05pD6Qrzv9xn13dD+l/yCQkvtu3tlIpNr/O1wo3tgy8Br3Ss62IsIuzv\nXXD+x4C/DnyPOvYG8IL6/3mcJP4G3swmx9+IZfw3PvKtNPVYP0esHYKg6VFrv91v16hWE9fLdrJG\nzOx5yXsROYcGaH8ORFvTAocNsXmSAfq0tfCu+R+3HiFiXvYaaXUTr/D0AAAgAElEQVTpPxv0xLfx\nfEtrq4ve5Swlr3+Bt754Ipfrs8q9MUMTtCZjKX9Acw2Drus3f+hpvvlDT9fnf+3vfLZzG3bAOb3P\n3xM+HBy07Xx2CCHtJnROD6iWuK3m1uVZtdpYqE5lIF3MgqC/F+XRVqbksyoBr23eX1AuSBcTfo5T\nz9A1bfm0ndOR3kL90iYQhJ4hfTzUX++hKaD+bqRe3XCSicIPA38LN8d1pI7/G+A/BdZw4vR7cfNc\nbwEPcfNfCfCjwK/FMpf119DUrFzM6yFThvU6bUui80turC+v/zgHtWmV41SZwz0kB422QVnXWQZ1\n8RKWeU+tjcW8jJdFm4VgFYhZBGwaqwV3hSa1RXnY6Y+m57aOdN6MWOb6iUZ7a+/xWe1g5oh7yLRh\nlpcVB24dwaQOYyoObTPjqKiXzXnCnlXP2nysdv3cnDFO9X2ehx7wQ85bQtghzVETekx7lve1zfTa\n1TQraUN1DjmddXWgWjaPWJ5d05/lp8v9LoNlrw+lt8+Mfo66enVLev3snI5HeAgnmcP+X3Av8Uer\n//8A+EngM8C/rL5n1TFp3Z8E/imwAfwG8JuxzDV9iaOZ16BA6EOQkzUGOj0PKqZOuUrgtV3d1Frn\nbZra7bxn08TZ1JAt4Yq1wLq2+ZKSRh7LEq/Mfdpj8/dazp1bDt20a/k/Vk7MlB3LK5bOHrftaMuy\n7aCPa+3b69fNTWd8nvNWF03Asf7ThKwFN78ZCo1zZ4hTfZ+bCM3RxjQX+d+aMrUWFAohmah0obxD\n2nVI2+8CWx99DHWuTWuL5RFCzGS7KsQ02kVp2o53ydf2w3H7Q9KG+jpm0Ylp/zbynNWmz/QdPePS\nuqP8J+V/glTPLe2SZVfePJng103nZHU6aJJsURG23vZQQwQCcU4qlFmzJGmYOrUQAdQaXEwzsqSs\nd3OyQoWQQEJTM7N5zDUWQjBJo46avHQ+1hrQFYvIPmRtaCNXDR/gpqzXzofKCi2JkjTSFyV+T+qQ\neT1E5vNilCX1puYvvaWnOmTBmF6rL/lI3v5ZLuprZ9VRoBYRJP1PJL/isnh8UcJ/o/7Vg53WfLQp\nWGtDVgsOmSFjWnaIDHReIfO5PCvLakwxK8Aic7td1talnGXSHxeWuGJas/1/kRBhBRJtnk7NMQLf\n0O2+dT5SHyu46Tjttt6xNg4Jd8v0w3+17AUNXNhIZzJoSehQaJpbhZjkuItI5jcFmTcxl/ihoUk8\nkl66LqVQZ10JOn874GvNW+oS0pZDJKOv9ceWNRgl6h78i+UJm/qY1foWabXeBrEYOq8uAoG1WISu\nDZGrdigTWAuICGEh6D7Q37psazmxQpYleiHsIdO6PtojvSkOlFUaPVsudfdP3xlr2GcIq52EyDmm\nbdpBN6Yhl8SfW32d1eJ12cu0/3JvrEfB/KYiGm11OG6Zy0ITn62PbfM2z/G2vK2W2+a93QXSt5JP\nm0UgpHnrPFDnQ89hKL/TwYUlbD1ozpBm8I5FQrw66pgfdPWA5zXiLDDIy6CqHdgScjTNyly3npO2\nA3XMBGpJxGr+TnOzeiMNggjlESaM5v2FhAv922rB4b2c/cYUIS1dU6olQV2PmJnXC2BeqNL9ERMw\ndF7a6qIdCReZlucFMy28FY3nw7afrpt8u2d1gI6UJteI9u2GIud3ITttS3/b/go9T08GYgO/1rS0\n1i2Qc5hj+nqdPjb/bfPQJKNDi54Ey5hzY2RiBZtQvqeJMvCJacKL8oids78t6Z+0H9rqq8tss1jY\na2P9Ytto9biwhB1yEQsRgfzfNEf7oc81XTMSGswTlgyOfqD3Wo81T+vr2hyEdF2101xT+6dyUxIv\n+OaDo+unB3Rb/zZzfKhO84KDREduaqXikKWpqyn0NOtqoa0i80ZmL0xJWVrDXeR4pdO6KRG98Kpb\nuFgJVKrvV+5Wm71DFhH7LfWfVbH4RER0z6BvAxEwJI2Ywb82yNpqKvJbD4ah/rJEHso3pN2FIqSF\nBtqC9vyPA8m7TQhoIzR9z12vWSWkXfR7aNdAd5lDX4bYdT+uwpFLa9q2Dppg20g2JETE+kWeo9PR\ntC8sYc9U1SxJybHQoGlpWDeZXqMtGpQlakmnIzsLtPZoB3BbD/utCbJZfxEC3FEa5FwgW5XEtOMQ\nYqZmSy7+3n0bWIGEuVYgmGcMzb5rHo+RoL5H25by2/a/t0g4ccver0VTYPGbsNp+cuvum5uhgDdn\nzz9zvh6A2muubJjIyzkiD7XXk2oSXxYhDbOtbRLzCUGTTuj3ojKOizZNOpY+RorWCmHrbq0PyyJ0\nndW4Q9foOhwHzVG7uxClCVjy6SIMdi1L57tIqDwdXGjC9s5gzQdDa5SWvOSYXooj12jnIB8gsqiv\nmyfs5qBpBQZ9XGCJRJcv5nupV3OwbpaV1ibUpJoSSOfKCg3ots627SxBhKwAOh9tgLboogV2IWY9\nJ22tKJJeX6P7zPV1iZjTy0q61W1sNXXJ39sMfHDaUPs1BQNngXDLCl29B6ruVqvPVD+GlgZar/Ke\nsC30IBlbnmPTdzVJxpZTnZZJM0R0IfOqTq+/LewGKDbfmEPVMggRdtFyzmqty2LZ/o6Vb++9TYAL\nXRcrK2aFsf14Ou/uhSVsPeDK/21ptb5q089rbvPQWpA2tdo87LfVmGPadahe9h5CdWqmsca9boQZ\nz2/evGvrGyIhDSHNtnsJabq6Paw2HaqvPa+JXZ6Tebor5q6191KS1EdL9Lx1c+cs2xbUgkbMuuM+\n4q2utXabXj85Kd369cnFqjS0kLkylmdsEF+laVxre7H+bSPtWL5tGm/McnBSxDRrS9jHLddqyLGy\nu/aP7ftFpN2mYXfRwE8PF5aw9ex1SEMTQpXZZTdX6FI3d2MKzTGLEVXPVTYRIi2vgZb1UfCm9tC8\naZu5V9beapOuT5M24p83F3817ySklepzofuSjUFd2WGS0PdtCcfmbwlYw/aBFops3UJ1kHO6r2xa\nvU5fa9Z5tWTK9k+bAOZN103ydYbzrLqH+MuZUtTe4pbAbbtJvrbMr00NO6ShhY4tq71ZDUqubQsB\nuuq2j2m4tvxl76stiMgiDX1VCAkN9vdxENKsLakmJr09pqGtJ8u08+mburviwhK23mtam5M1sXiy\n9quj8/p4OGCK1p61ZhczL8u1krtfHxza17i5zjtG2r4ObuDJ6p1wMOczoOmhHdL4vPGo3ZSs7107\n8jk3qDC00DQvLIQFkZAmqQk7ZG3Q+dt21/W28+zQDFgi1+l19Tlpoy1CGr+uhxi4/VMlA6NrCW3J\nCSE0jaPvZ16Qmi/zaxe+recH/5OYqkPED2czCC/qz+NaFdo02FVr1V3qUZpjJ4UVzKQM7SC4KL0c\nt3VblrDPn6zhAhN2yHM4TAQulQQN0aZIISUJqiJk6xzavPdvU6sSrdkPq0mdxmu8iamHvlaOh/Ju\nDtbz0dNCZCb1tlpgiORs+2hrhP4GMddKfebzLlUdtXAj0BqnravWyPUSLd0u85aLZttp4Uf6VKex\nwpgIcHKdePtL/iGnOr+0zpfvYgC4nKlbpSlsJVALBVK2FV5CiGnOOliKy7+5xO5rB1qbkYFWt5de\nEtbFzHxchDR9aJYTqsdxB3fJd5GDma5b6Fws7zZLQtv9xcrsmv9JEMs3pnXH0KU/7HN3MXGhCbs5\nsM57WDe1Jv/QaQcmiRI+ZFoP2n5gjxOrX9blUvgBubk5BCoP64XezFs0qGJucJY8BPqcXlIm0PHT\nQ1qqLj+huZRMa/VWEyzr+/VEJrPAIZKxJK5Lkbjaum01Yet+tvUImcztcjx9f/q4d+zzlgG9pE/3\niSyv0pqtCAZuCEjn6t0UeJr3Y9uorc00fJkuql5BykDd89cOQiStv0NR0KxZeBWDrc5Ta/Qxxy5d\nj5OYT7uSXoygQmV2EWhOSsqnJVhKm2pYUl00LSDRzEJ5CWLC4cXDhSVsa/acJ3BvHvPHw05iTboS\nKkqDXaP9falyl3y0GdkbWueXfnmhQNfHnbVrhO28rNU+Y+bRkLNXU4ApIx95LJsaeOz+9f5Wtj6e\niL02aO8hdE+2vvr+/XEfutPWXNLaey1Jaq8AWxcrXOhWkzyaQoBPqYlaa9ieIuIx030/yz3EBwRd\n3lkaNE8f1iTZfLe6p28zY+tzNv9Ya3YhoGU1t5MO+F16fZmnI0bYIQ31rLToRc9CYn6HhDEr1FkL\ngZ32sH1q89TXX1zSvrCEraEH9bzWa9Iqarik8MuxtE4usZu1RinrajWpamO2RJ9q6psSV1xiPqe1\nyVWIQIQCu3xHk6ueb7UOdVb7mtd+fX5Ws7PEZ4nRWwOa5KnziDmDSZlay22mdS+E1qZ12aF6WoFB\nW1Bko8q8UTdvutbTJd7cneP9tu0GmdmcRi6iltRa9vYKLenTxmlpGTlmBTCN5jH3XMUEMdv23TYY\nfVygB0UrtCwiiBAR2zzsBg02jxhZt7VwiPhtuaF6nKZJNWS2bjseQ6I+uh1i+Z4GbMz2WH/HyNYK\nb9aB0Ct0TUgaG20vlu/FwoUlbEtcIbLwQS3lscvUrl2QkDNQG4eEtDmtSTvyaGpOmr5k8HeanHNp\nEuGhGRpVa6jNckMDtt7fWxO7ratuD722N6Yt62uFVKwznq1vDLoPvNnZaY4pmmi8QBDy8JdzOu67\nfPKqVTNE4PD3L0TqiFlbBJpruL3A5ALMagFC0up+sOvwtZ4t96i16uZzIr+a7RT637dVUwDVbaaf\nGd1mjz9CxKf/j8GmD5FhjCBD2rU1/cbI3JKFHsi77NF9GujaVqvO+zRIW7d7zJphBTR9Xey5sJvJ\nxPpD8tBTF6F8Lx4eC8K2Wps4lEkcqnky9CZwKBukJunk2y8HSioia+ruAinfD/B5PcDLnk0hE62+\nVhOmvVeptY3wFjMZl+Zu3Xl/d2Id0GRmSdSiqfk2H3g9v+9ng+fjbLur4qFa5TUJOWkJZUk75FVc\nbu0EJysB9BFJ40net5N22JN2s2RdkDJljTEjlS91Kn+9jz+uXe28gOJqoo/7XchA77XetOxowUHa\naF4Lf3xhibHLfcU0pK5lSRk2j66mXf0s6TzCG8rEsYg82srW168Sy/ZFLI9l6xgSkkJ9JVp/yKKx\naCneojxi19lP6HxXS4a1zoQsS8fDY0PY1pyqz/nBTozd82uiQxqv1shk0M8qdylL/3ltJi8qIhCN\nrEkCugzRGq2WOT8QuyPaUUucsUJtIuVBU+vXa4OFONP6nsJLjWwtPKk1W0qbbO2KcPkthBkrR876\nqNrzZcs9SdtJlG2BcxbM6jtPSGpf91Df+tZttp0VhCYVYa8xYY0JEPJmL+fK8I584kA3U3VNKas4\naELo1m4j5GwN4DEz++MJfW9dCfMkA5wdJLtq1qHrYd6xbBlI+cs4oulyTmv9b9d+6JJHlw1T5J5s\n+9l+kjp1XboVOidlxPIIoU041PXr0v/2mbPHjo8LS9jQNA3awVNgl/ZYU29Ms5bz8h1yKrJ5Jkor\n0uRmNVZNPIUhEykrJHxojVCu17YFW5ZuE6mXbUFnhpVQrPE8POkmc2lCdSzVtZbIbFv4+slkRXPP\nbg25Hy/cFKSNulBfb3cW02VbPwKpr9a+bT21b0Cijif1lT5ymW57P12TIvumJ6DuoZwrO/TshNrv\nyYHu57O4RysSSh2WqYfOQ5NJV4QGbptXzLwbMgfbvM8bVvvVCGmWbVaTkGCl87Jp29pDzlshK6Sx\nx/pIY9l+t319HCtLGBeasCFO2vqc/Zb0ciymmcsnV4ZjNyPrnZT80qCCWaVlo+qgc5ZHUeZKbQAX\nS9h+HtflqR3SnMadVvJhc0mTWAN0G3kxg/r65j7L89YG/b943etWckfm10h7U7L7D+YFpfm6NfvA\nLtOKXZc2Bs0mecZIT/rAz28X1WvblLZ1nTJy1pg02snft6+zbLMq1hBbpvehaAZyCRG8nR6JCZRP\nBpY1K552PWKOVqsux27tqE22IbOyvk4jZDo+T1iBwsKS7qL0sfwFIdLVVogYYtYWyattL3KbT5d0\nOs2y97wYF5awrcakB7yQpi2wWltTA0rmvtvmlOcFAq/h+fy9D7GmFe/NHv7YOgu8Y5cne1kz7onV\nXWX3ULbtVZiW0OfsvTbpy58N1c+LN6l6jeZJRn5bpzCbp71Ot8t8e8eFNJufX8CnNwb1tWi2naVz\ne1/NHEJp5pfyhZ/P+RYOlTf/nDz+WKRtds1DD56xNoppNDGNeVFbL2NCDmnHdsDXml+oDiFLQNv9\nLsKy11rS0ccInLNprDCybF5l4BM6F8ordC6kTXe1nhxHqNPC1dcAYWsnr5i2rMk5PLgJJefMqgAq\n89qubHMo/zU1IJuThduzSTy2ZUdm7yil6yne1XZ+3QoVoisn6s6du5Jd8+11+FJdK/mkioQkbnmp\n2jQklLj7Fs15niilXeQR11aDZv/5PKQuoT7S13YxB9t2jRO/nLOTAa4N8kaZWpCaF4C0JUZ2kdNO\nc/o5DAku0r/zdZU+DQs7Tw5hdyHVLoOiTp+b4/q33o+4Sxvq5Vghkl1m3lrnYwlHNPuuQoolppM6\n4YU099g1NqJcKGiM/g6VFWv/EAnHrAq6XNsf1lrRFTrPNie2i4cLS9h6QNUDlzU3Qoys5RFw5s4J\na0wZkpMxqNfcuqFUKFbPH2uxoM10OWTKGhMK0rqMkqQe2GWgFgeqTBngdX42MAxV3XVd9HIjuT/3\nXRDy2PZLlZqkGLJCSPqkEgNyc07O67rYyGHzAorf0EIvv9J11ARsy7Pw4pefUkhVXrZtnBOia92k\n0RfOPSxUktRYe6FLTDMRuPQe1xrWwqGP2fTaeiL3YAXQkID4eCKk3WnTb1fztKTP8YP0so5JoTwl\nX00aMqjbLTi75Bfb1jKmEbZB8jjJGm+dxyJNW9LqMkvm70W3mb1+kXBgiRjCxBlq91C/h6wVMcTu\n5fHAhSVsC6vtNAc29yBrTVVQVOQp5KoJ0w3ZeZ3PrHIY0vsiyzCrQ5kKSmDKoB589e5alpj1el+t\ncdlB3loTmm2g55hdbUImfZu31M2Wqwk23MbyWvigMda64YWDvH61qcm82V9eg/WIea9bEm62hxi2\n5y0tvt9TxGlPymi2nNbF5+/e9oenV20Fma+3FWDkmBae9PHmorWw8PH4o810ugwh6oG2iwYXOh/S\n7vS53KTTZdk8NNrq00a0msRjxBMKx7mMBSFWrs7DChOWjLXlYhGxLuqXUBpbj9CxmObeVreYFeek\npmprRbHn9Pfq8FgQdsjsabXfkvDgLzGwEqi3PNQDbnNZV1YR67wGX6oHR+txsypKua2vBGuxx/Xg\nbYOZhAZ4XQdLBlqLlaubS7i8sGHXous6zWvKTdoCS4A+opsshRuo3bEKMjLcgrcC7ySXVH+aIp1g\n09wBrY2sZVPQLpp4QcpAEXYzP2/yE4MfaKFMr6HW8/Zlo4z2dvTPzYCy1un186c3EAnd+5OBmFa3\nrMZpB24C+WrtLbTcSJOirZc9J9daTTFkHtbpYsQTawNd5qLr7P2dROO2eYSOWWIMCRSSX0L8XstI\nujZoItbXhfpV1822Ycjx76SkGuoXjS5L3ZbHhSVsLxPNk2GTrJuvlqSRayWd3q1LexBrJ6/YsOGJ\n1urdeuD2w23TDOqJIUaa9j41sVsi12k00YfyLet84w+N1L+NAJs5hq8tazK2JBs37fp+k4GzaW0I\n/fbpUeWGtWILSZ/W9hKff4L4M4hbn6+T9KN9JkOafbNt9BOhnQj1NEDzmdH5P1mkDXHtLIaQZqQ1\nwNj1ljDb8g8NunbAx6QLaYah98yeCxG5zc+OZphzi+4rlH9XaFKzba/rGqvDcawaMXQhdJ1Wf8fy\naRP0VgGbd6gdToaTTJL9XeATwMeB3wFeUOd+Fvgi8Dng+9Tx7wD+vDr3D9synzfPNs2MPp0ErCgC\nKefz0Pnrgd/NdTc1XzmXUTTikWsNWWKVD9Q5KVM0KK1FaehBXNfJ1r8IpEvVUV2XhLJecqSJwd6X\nwEX5GjJhrZ6j9RTj00r+0s5SuszNu13RBo1AMZJHMzq31kB9ze3dWMKWe/LBVObbzd2PEO98+FO5\nD1meFfpkNKcxdEk2H3kmrMNgUx/3wlozalsyV3/b72eMU32fm1rdMmTahQhD52JzvnIuDaSx5xbN\nGy9bD61lWvOsjYeurynUR0gnpl13aWd9n2152HKPAysE6HnxUP66bpjztv1CpuhQ/wpO4gcQui9b\nj7bnblmBJYyTEPbfB74V+MvArwH/dXX8/cCPVN8fBn4RX/tfAn4CeG/1+XAs8zay1Wk8KTQ3xLCE\n7REeFDWhfe7mrcbg6QbnJlnrc1mDRjQp+u0nrAYlGljIoU3qYO8/TNhCc54Mm2eSufuzRDJjUDvk\nSf5fuPnVRjuJ4KLJyZJ2roQErfmGiEiTmyYyTXSfvfl2nd55aPsesFYHd1/ULSFtJPdo2y5Ttfa1\nz03f+naw9bZCUEzAFMjUgF1Db/s9/tyeOk71fV5ey4oRYZfzltC+FDgXGtQXDfj6Pmz6tnq8Yq63\nBGXvxeYZIra2uoUI9tVI3ULaZ4hMT0LYkscrzDvkhcqwAo61sMQEiEV9+wrxvloWtm72GYqlPxlO\nQtiP1O9t4Hb1+weAXwGmuKfkZeCDwLuAHeBjVbp/Dvxgt0r6AbapuzRJoKgG9ZnRZARJYHC15OaI\n4p26XKs9Weh6aFLIqjCULtDliFzNPniiSmvNUQhT10EP7ppErLAhGqjNw5I8UJepLQCDaiZe5qUB\nvnDzrYZVwV3bbNuMnBHjhjOfbitLbiFC0qRt71fqkNbBR0V4mtdsnbCRoPVb3W663XW/z4JWAf9U\nfL4S3mx/hwQWbdGQTWdECEhVfexzpYUW7ytw5vt1ndH73IUU4Xhm1BheWZxkITTBaNN5SBu16V4J\nHFu2rC7pQpqztPeXmSdCXVdBjPC6IpSv1OtVk9Y+C1rzLs15K8xg0nbBcZ+DRRYbfbxNmDi5oHDS\nOez/FvhR4BD4rurYs8AfqjSvA8/hXvjX1fE3quMLoc2b4Ac5C+1cJSuBQ2ZH/7r5o838miRXqnxD\n5kpdTklSD7vTau23ni/36aR8r1nKLlWSn3hlSxkhk7auny5Ha9LSdoVKJ/qw5Dtf/2aY1FzRptRa\nyFxr67qtrFOW1dh1WpvOe/LPEFc6SOtyrdab1/deNu5d96Ouj9yzXhsf0pb1K2bL9GTtXmZd70XC\ni85DBKVmueeyweaZvM/zRBByFloVWa8SVojQpl4LTTDLEovOtwtht2nnqP/1GBdznGtaI5frg1B7\nLLKUhIhQ8hEi1/km6phdK35a0PULtUdsPXioHU6GRRr2R3FzVPbzH1Xnfx74OuCfAP/gxLVpgSXL\nkJZpDYvzeUiTzZs4fR7xMkNpQnXRx6xpOpS7DPHNeoc15Bi09thmSvXnm4LMPDlogi0XXmvbKlaH\nmMAR6jfdo/r6kHVF6tXWF7ZusWelmT4spOkzOoWQbaye9h7a73/lhHVh3ufHF6E+KSOf0yirC7oS\ng61vyDx/HITMxW35LXMuRO5nga5tctGEzDC+DvhU9ftvVx/Bb+JMaDeAz6rj/xnwv0bye5n4W9B/\n+s/X0udlzh6rfJ/7d7n/9B//OY/3GXBOJoKfAv5F9fv9OE/TNeAl4C/woskf4V72BPgNWp1UevTo\ncYbo3+cePZ5g/CucOe3jwP8JPKPO/RxOkvgc8B+q47IM5GXgH51NNXv06NEB/fvco0ePHj169OjR\no8fjjlMO1tAJv4Cbi/sE8KvA7hnX4YeBT+OCGH+7OXdWbWDx4arMLwI/cwr5A/xj4BbuPgRXcY5R\nXwB+C7iszsXa4iR4AfhdXPt/CvjpM67HOs6s/HHgM8DfO+PyV43zfp/P+12Gi/c+n8W7DOf/Pp/3\nuwxP3vs8hx31+6eA/736LfNmQ+BFnMlN5s0+hl9ysop5s+/Fe8v/99XnLOvwPuAbcQ+bfsHPsg00\nsqqsF6uyPw588wrzF/w14NtovuB/H/gvq98/Q3tfrGIrqxu4QCHg1h5/HnevZ1mPzep7gFs+9VfP\nuPxV4rzf5/N+l+Fivc9n9S7D+b/PF+FdhlN+n8/7ZT+z4Cst+Ch+IegfAc+fcR0+h5O8LM6yDTS+\nqyrr1ars/6Oqy6rx+8A9c+w/Bv5Z9fuf4e8r1BbfxcnxFu6FAdjDaWfPnXE9DqrvNdwAe++My18l\nzvt9Pu93GS7W+3xW7zKc//t8Ed5lOOX3+bwJG1ywhq8AP4Y3ITxLMyiDBGuwx5cI1tAJP46TcM+z\nDoLzKv854LVAuWeB6zizGtX39ep3rC1WiRdxGsIfnXE9UtxAcwtv0jvPdjgpLsr7fJHe5fOqw3m+\ny3B+z/GLnM+7DKf8Pp/Fbl0fxZkrLH4O+HVcsIafx631/AfAf34OdaCqwwT45XMq/6KgPO8KVJB1\ni23nV4VtnGf036SpJZ5FPQqcKW8X+H+A7z7j8pfFeb/P5/0ud63DRcBFeZfh7J7j83yX4ZTf57Mg\n7O/tmO6X8RLxGzQdVp7HSR9v4M1ccvyNFdThx4C/DnyPOrbKOnRtA41Vt8Fxy32BphR4mriFGwjf\nwpkKZfePUFus6p6HuBf8X+A2vTivejwA/i3OAek8yu+K836fz/td7lKHEM7jfT7PdxnO/jm+KO8y\nPD7v81K4CMEaPowzW1wzx886YMTv4jr3vMoXDKqyXqzKPk1HlReZd1IRT9a/zbxzRqgtToIEN2f4\nP5vjZ1WPa3iP0Q3g93BEc9btsCqc9/t8Ud5luBjv81m+y3C+7/N5v8vw5L3Pc7gIwRq+iNvK5s+q\nzy+ecR1+CDfPdIiTwP7vMy4/hO/HeVm+jFt2cBr4FeBNnOnyNZzp9Crw24SXP8Ta4iT4qzgT1sfx\n/f/hM6zHXwL+tCr/k8Dfqo6fdTusCuf9Pp/3uwwX730+iwbejMQAAAnRSURBVHcZzv99Pu93GZ68\n97lHjx49evTo0aNHjx49evTo0aNHjx49evTo0aNHjx49evTo0aNHjx49evTo0aNHj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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2, figsize = (8,5) )\n", + "dat = ax[0].imshow(np.reshape(noise, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n", + "plt.colorbar(dat, ax = ax[0], orientation=\"horizontal\")\n", + "dat2 = ax[1].imshow(np.reshape(survey.dobs, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n", + "plt.colorbar(dat2, ax = ax[1], orientation=\"horizontal\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# m0 = (1e-5*np.ones(mesh.nC))[active]\n", + "m0 = chi_ini[active]/dweight[active] \n", + "dmisfit = DataMisfit.l2_DataMisfit(survey)\n", + "valmin = abs(survey.dobs).max()\n", + "dmisfit.Wd = 1/(np.ones(survey.dobs.size)*valmin)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MGNlix/CQDLJtEkHhMbQeYzklF0y+MHLJPuDWed4qH30fKWPv4ylI3UbNa56X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\ntUwhOvz8KXkSETgM0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "d_ini = survey.dpred(m0)\n", + "fig, ax = plt.subplots(1,2, figsize = (8,5) )\n", + "dat1 = ax[0].imshow(np.reshape(d_ini, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n", + "vmin = d_ini.min()\n", + "vmax = d_ini.max()\n", + "plt.colorbar(dat1, ax = ax[0], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 3)], format = FormatStrFormatter('$%5.5f$'))\n", + "dat2 = ax[1].imshow(np.reshape(survey.dobs, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)])\n", + "vmin = survey.dobs.min()\n", + "vmax = survey.dobs.max()\n", + "plt.colorbar(dat2, ax = ax[1], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "reg = Regularization.Tikhonov(mesh, mapping = rmap)\n", + "opt = Optimization.ProjectedGNCG(maxIter = 30)\n", + "opt.lower = 1e-10\n", + "opt.maxIterLS = 50\n", + "invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)\n", + "beta = Directives.BetaSchedule(coolingFactor=8, coolingRate=2)\n", + "betaest = Directives.BetaEstimate_ByEig(beta0_ratio=10**0)\n", + "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaest])\n", + "opt.tolG = 1e-20\n", + "opt.eps = 1e-20\n", + "reg.alpha_s = 1e-9\n", + "reg.alpha_x = 1.\n", + "reg.alpha_y = 1.\n", + "reg.alpha_z = 1.\n", + "prob.counter = opt.counter = Utils.Counter()\n", + "opt.LSshorten = 0.1\n", + "opt.remember('xc')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SimPEG.InvProblem will set Regularization.mref to m0.\n", + "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", + " ***Done using same solver as the problem***\n", + "=============================== Projected GNCG ===============================\n", + " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", + "-----------------------------------------------------------------------------\n", + " 0 -8.78e+04 1.08e+06 2.70e-01 1.06e+06 1.38e+05 0 \n", + " 1 -8.78e+04 3.89e+05 1.52e+09 -1.34e+14 1.57e+08 0 \n", + " 2 -1.10e+04 2.49e+05 1.52e+09 -1.67e+13 1.96e+07 15 Skip BFGS \n", + " 3 -1.10e+04 2.49e+05 1.52e+09 -1.67e+13 1.96e+07 19 Skip BFGS \n", + " 4 -1.37e+03 2.49e+05 1.52e+09 -2.09e+12 2.45e+06 19 Skip BFGS \n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\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[1;32m----> 1\u001b[1;33m \u001b[0mmopt\u001b[0m \u001b[1;33m=\u001b[0m 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"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.pyc\u001b[0m in \u001b[0;36m_mul_vector\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m 445\u001b[0m \u001b[1;31m# csr_matvec or csc_matvec\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 446\u001b[0m \u001b[0mfn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_sparsetools\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'_matvec'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m,\u001b[0m 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MagneticsDiffSecondary.Jtvec_approx : 3.48e-01, 5.26e+01, 151x\n", + " MagneticsDiffSecondary.Jvec : 3.48e-01, 5.26e+01, 151x\n", + " MagneticsDiffSecondary.Jvec_approx : 3.48e-01, 5.26e+01, 151x\n", + " ProjectedGNCG.findSearchDirection : 3.66e+00, 1.10e+02, 30x\n", + " ProjectedGNCG.minimize : 2.57e+02, 2.57e+02, 1x\n", + " ProjectedGNCG.modifySearchDirection : 4.71e+00, 1.41e+02, 30x\n" + ] + } + ], + "source": [ + "opt.counter.summary()\n", + "xc = opt.recall('xc')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from JSAnimation import IPython_display\n", + "from matplotlib import animation\n", + "from SimPEG import *\n", + "\n", + "fig, ax = subplots(1,2, figsize = (16, 5))\n", + "ax[0].set_xlabel('Easting (m)')\n", + "ax[0].set_ylabel('Depth (m)')\n", + "ax[1].set_xlabel('Easting (m)')\n", + "ax[1].set_ylabel('Depth (m)')\n", + "\n", + "\n", + "def animate(i_id):\n", + " indx = 18\n", + " temp = dmap*(xc[i_id])\n", + " minval = (temp).min()\n", + " maxval = (temp).max()\n", + " \n", + " frame1 = mesh.plotSlice(temp, vType='CC', ind=indx, normal='X',ax = ax[1], grid=False, gridOpts={'color':'b','lw':0.3, 'alpha':0.5}, )\n", + " frame2 = mesh.plotSlice(chi, vType='CC', ind=indx, normal='X',ax = ax[0], grid=False, gridOpts={'color':'b','lw':0.3, 'alpha':0.5}, );\n", + " ax[0].set_title('True model', fontsize = 16)\n", + " ax[1].set_title('Estimated model at iteration = ' + str(i_id+1), fontsize = 16)\n", + " ax[0].set_ylim(-500, 0)\n", + " ax[1].set_ylim(-500, 0)\n", + " return frame1[0]\n", + "\n", + "animation.FuncAnimation(fig, animate, frames=10, interval=40, blit=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/matplotlib/lines.py:503: 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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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "indx = 18\n", + "iteration = 9\n", + "fig, axes = subplots(1,2, figsize = (12, 5))\n", + "vmin = chi.min()\n", + "vmax = chi.max()\n", + "ps1 = mesh.plotSlice(chi, vType='CC', ind=indx, normal='X',ax = axes[0], grid=True, gridOpts={'color':'b','lw':0.3, 'alpha':0.5});\n", + "axes[0].set_title('$\\chi_{true}$', fontsize = 16)\n", + "axes[0].set_ylim(-500, 0.)\n", + "cb1 = colorbar(ps1[0], ax = axes[0], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)], format = FormatStrFormatter('$%5.3f$'))\n", + "axes[0].set_xlabel('Easting (m)')\n", + "axes[0].set_ylabel('Depth (m)')\n", + "\n", + "vmin = (actMap*xc[iteration]).min()\n", + "vmax = (actMap*xc[iteration]).max()\n", + "ps2 = mesh.plotSlice(actMap*xc[iteration], vType='CC', ind=indx, normal='X', ax = axes[1], grid=True, gridOpts={'color':'b','lw':0.3, 'alpha':0.5});\n", + "axes[1].set_title('$\\chi_{pred}$', fontsize = 16)\n", + "axes[1].set_ylim(-500, 0.)\n", + "cb2 = colorbar(ps2[0], ax = axes[1], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)], format = FormatStrFormatter('$%5.3f$'))\n", + "cb1.set_label('Susceptibility (dimensionless)')\n", + "cb2.set_label('Susceptibility (dimensionless)')\n", + "axes[1].set_xlabel('Easting (m)')\n", + "axes[1].set_ylabel('Depth (m)')\n", + "fig.savefig('model.png', dpi = 200)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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jnvasX8cwEhor2+gZouxDP9PHyQk/5nQUzT0cLYfw5WeiGtCSk7ZLo1w+r7pp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\nX9b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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dpred_xc = survey.dpred(xc[iteration])\n", + "fig, ax = plt.subplots(1,2, figsize = (12,7) )\n", + "vmin = survey.dobs.min()\n", + "vmax = survey.dobs.max()\n", + "dat2 = ax[0].imshow(np.reshape(survey.dobs, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)], vmin = vmin, vmax = vmax)\n", + "cb1 = plt.colorbar(dat2, ax = ax[0], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)])\n", + "dat = ax[1].imshow(np.reshape(dpred_xc, (xr.size, yr.size), order='F'), extent=[min(xr), max(xr), min(yr), max(yr)], vmin = vmin, vmax = vmax)\n", + "cb2 = plt.colorbar(dat, ax = ax[1], orientation=\"horizontal\", ticks=[np.linspace(vmin, vmax, 5)])\n", + "ax[0].plot(rxLoc[:,0],rxLoc[:,1],'w.', ms=1)\n", + "ax[1].plot(rxLoc[:,0],rxLoc[:,1],'w.', ms=1)\n", + "ax[0].set_title('Observed', fontsize = 16)\n", + "ax[1].set_title('Predicted', fontsize = 16)\n", + "ax[0].set_xlabel('Easting (m)')\n", + "ax[0].set_ylabel('Northing (m)')\n", + "ax[1].set_xlabel('Easting (m)')\n", + "ax[1].set_ylabel('Northing (m)')\n", + "cb1.set_label('Total magnetic intensity (nT)')\n", + "cb2.set_label('Total magnetic intensity (nT)')\n", + "fig.savefig('obspred.png', dpi = 200)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] } - ] -} \ No newline at end of file + ], + "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/Mesh.dat b/simpegPF/notebooks/Mesh.dat new file mode 100644 index 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b/simpegPF/notebooks/Tile1.msh @@ -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 \ No newline at end of file diff --git a/simpegPF/notebooks/get_T_mat.py b/simpegPF/notebooks/get_T_mat.py new file mode 100644 index 00000000..f130e7d4 --- /dev/null +++ b/simpegPF/notebooks/get_T_mat.py @@ -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 diff --git a/simpegPF/notebooks/get_UBC_mesh.py b/simpegPF/notebooks/get_UBC_mesh.py new file mode 100644 index 00000000..ebdbbbac --- /dev/null +++ b/simpegPF/notebooks/get_UBC_mesh.py @@ -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 (count3: + + data[ii] = line[3] + + if len(line)>4: + + unct[ii] = line[4] + + + + return Decl, Incl, B0, Mdec, Minc, obsx, obsy, obsz, data, unct \ No newline at end of file diff --git a/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb b/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb index c58a50b7..e8d344f5 100644 --- a/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb +++ b/simpegPF/notebooks/tutorials/Tutorial_1_Mag forward modeling.ipynb @@ -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": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, @@ -91,7 +91,7 @@ "data": { "image/png": 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HDDJSZ4TG+9bH9k89vzubZ84BYD545hwAsIGKhLljsmPnh/rb6vXxpr5QDxlk5JDRNR7b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\nz5DelBn98cx5Bc+cA5gznjm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"text/plain": [ - "" + "" ] }, "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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8uni5PXbcpc/21Bh+FFmrrKU+HsupEWtvG++bP3R/AADSseg/Jhuxbp/aqf1j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\n", "text/plain": [ - "" + "" ] }, "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": [ - "[]" + "[]" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -244,7 +244,7 @@ "data": { "image/png": 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wBEwawb0guBcEaQ/ngC04B0wI6QvOAUcx6sFfU5+xfUN2Pr0rS9nOKZecc8sp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\nExF5BeCBqr6dOhcy2/AiHCE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"text/plain": [ - "" + "" ] }, "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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ox6ZGePS9Gk2XgkjbfHPd/l0KJX0WYmWYwTMEQaSvZWtDnobGLILqU3yVAK9E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"text/plain": [ - "" + "" ] }, "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\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m 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0V1O4vc3MP1evjzLzRyqv/wERHWHm26p70UWCUMpUzZSx6XXXBktfxspmaWop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"text/plain": [ - "" + "" ] }, "metadata": {}, From 6c712373287329ab437ca9537dd0d135f1ad62d2 Mon Sep 17 00:00:00 2001 From: D Fournier Date: Tue, 10 Nov 2015 14:23:29 -0800 Subject: [PATCH 06/31] Scripts added for the magnetic integral formulation --- simpegPF/BaseMag.py | 48 +- simpegPF/Dev/FWR_data.dat | 628 + simpegPF/Dev/FWR_test.dat | 103 + simpegPF/Dev/G.mat | Bin 0 -> 321119 bytes simpegPF/Dev/MAG3Cfwr.inp | 5 + simpegPF/Dev/MAG3Csen.inp | 5 + simpegPF/Dev/MAG3Csen.log | 35 + simpegPF/Dev/MAG3D_fwr_Driver.py | 47 + simpegPF/Dev/MAG3D_fwr_Test.py | 100 + .../MagForward_Intergral.ipynb | 388 +- simpegPF/Dev/Mesh.msh | 5 + simpegPF/Dev/Model.dat | 8000 +++++ .../Obs_RAW_REM_GRID_TMI.obs | 0 simpegPF/Dev/Obs_loc.dat | 628 + simpegPF/Dev/Obs_loc_3C.obs | 1878 ++ simpegPF/Dev/Obs_loc_TMI.obs | 628 + simpegPF/Dev/Obs_loc_lBl.obs | 628 + simpegPF/{notebooks => Dev}/Tile1.msh | 0 simpegPF/Dev/fwr_MAG_obs.py | 80 + simpegPF/Dev/get_T_mat.py | 133 + simpegPF/{notebooks => Dev}/get_UBC_mesh.py | 0 simpegPF/Dev/magfor3d.log | 31 + simpegPF/Dev/magfor3d.mag | 628 + simpegPF/{notebooks => Dev}/model.png | Bin simpegPF/Dev/nullcell.dat | 27 + simpegPF/{notebooks => Dev}/obspred.png | Bin simpegPF/{notebooks => Dev}/read_MAG_obs.py | 8 +- simpegPF/Dev/read_MAGfwr_inp.py | 52 + simpegPF/Dev/test.out | 3 + simpegPF/Dev/wr.dat | 27 + simpegPF/MagSurveyIE.py | 18 + simpegPF/notebooks/MagInversion.ipynb | 64 +- simpegPF/notebooks/Mesh.dat | 24025 ---------------- simpegPF/notebooks/Mesh.msh | 5 - simpegPF/notebooks/Obs_loc.dat | 2 - simpegPF/notebooks/Untitled.ipynb | 36 + simpegPF/notebooks/get_T_mat.py | 127 - 37 files changed, 13874 insertions(+), 24518 deletions(-) create mode 100644 simpegPF/Dev/FWR_data.dat create mode 100644 simpegPF/Dev/FWR_test.dat create mode 100644 simpegPF/Dev/G.mat create mode 100644 simpegPF/Dev/MAG3Cfwr.inp create mode 100644 simpegPF/Dev/MAG3Csen.inp create mode 100644 simpegPF/Dev/MAG3Csen.log create mode 100644 simpegPF/Dev/MAG3D_fwr_Driver.py create mode 100644 simpegPF/Dev/MAG3D_fwr_Test.py rename simpegPF/{notebooks => Dev}/MagForward_Intergral.ipynb (52%) create mode 100644 simpegPF/Dev/Mesh.msh create mode 100644 simpegPF/Dev/Model.dat rename simpegPF/{notebooks => Dev}/Obs_RAW_REM_GRID_TMI.obs (100%) create mode 100644 simpegPF/Dev/Obs_loc.dat create mode 100644 simpegPF/Dev/Obs_loc_3C.obs create mode 100644 simpegPF/Dev/Obs_loc_TMI.obs create mode 100644 simpegPF/Dev/Obs_loc_lBl.obs rename simpegPF/{notebooks => Dev}/Tile1.msh (100%) create mode 100644 simpegPF/Dev/fwr_MAG_obs.py create mode 100644 simpegPF/Dev/get_T_mat.py rename simpegPF/{notebooks => Dev}/get_UBC_mesh.py (100%) create mode 100644 simpegPF/Dev/magfor3d.log create mode 100644 simpegPF/Dev/magfor3d.mag rename simpegPF/{notebooks => Dev}/model.png (100%) create mode 100644 simpegPF/Dev/nullcell.dat rename simpegPF/{notebooks => Dev}/obspred.png (100%) rename simpegPF/{notebooks => Dev}/read_MAG_obs.py (91%) create mode 100644 simpegPF/Dev/read_MAGfwr_inp.py create mode 100644 simpegPF/Dev/test.out create mode 100644 simpegPF/Dev/wr.dat create mode 100644 simpegPF/MagSurveyIE.py delete mode 100644 simpegPF/notebooks/Mesh.dat delete mode 100644 simpegPF/notebooks/Mesh.msh delete mode 100644 simpegPF/notebooks/Obs_loc.dat create mode 100644 simpegPF/notebooks/Untitled.ipynb delete mode 100644 simpegPF/notebooks/get_T_mat.py 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 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simpegPF/Dev/get_UBC_mesh.py rename to simpegPF/Dev/functions/get_UBC_mesh.py diff --git a/simpegPF/Dev/read_MAG_obs.py b/simpegPF/Dev/functions/read_MAG_obs.py similarity index 100% rename from simpegPF/Dev/read_MAG_obs.py rename to simpegPF/Dev/functions/read_MAG_obs.py diff --git a/simpegPF/Dev/read_MAGfwr_inp.py b/simpegPF/Dev/functions/read_MAGfwr_inp.py similarity index 100% rename from simpegPF/Dev/read_MAGfwr_inp.py rename to simpegPF/Dev/functions/read_MAGfwr_inp.py diff --git a/simpegPF/Dev/magfor3d.log b/simpegPF/Dev/magfor3d.log index 231d442c..40af1270 100644 --- a/simpegPF/Dev/magfor3d.log +++ b/simpegPF/Dev/magfor3d.log @@ -15,7 +15,7 @@ This program is licensed to: For internal use within UBC-GIF. -MAGFOR3D started on: 11/05/2015 14:43:03 +MAGFOR3D started on: 12/21/2015 08:35:38 magfor3d Mesh.msh Obs_loc.dat Model.dat @@ -25,7 +25,7 @@ magfor3d Mesh.msh Obs_loc.dat Model.dat model was read from file: Model.dat - TOTAL cpu time: 0:00:01.49 + TOTAL cpu time: 0:00:00.16 -MAGFOR3D ended on: 11/05/2015 14:43:04 +MAGFOR3D ended on: 12/21/2015 08:35:39 diff --git a/simpegPF/Dev/magfor3d.mag b/simpegPF/Dev/magfor3d.mag index 1fd05ead..4fb32d9f 100644 --- a/simpegPF/Dev/magfor3d.mag +++ b/simpegPF/Dev/magfor3d.mag @@ -1,628 +1,628 @@ - 90.00 0.00 50000.00 !! incl, decl, geomag - 90.00 0.00 1 !! aincl, adecl, idir + 45.00 315.00 50000.00 !! incl, decl, geomag + -45.00 135.00 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 - -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 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5.500000000E-01 -1.051072E+03 + 5.000000000E-01 -2.083330000E-01 5.500000000E-01 -1.062073E+03 + 5.000000000E-01 -1.666670000E-01 5.500000000E-01 -1.053680E+03 + 5.000000000E-01 -1.250000000E-01 5.500000000E-01 -1.024210E+03 + 5.000000000E-01 -8.333330000E-02 5.500000000E-01 -9.732772E+02 + 5.000000000E-01 -4.166670000E-02 5.500000000E-01 -9.020055E+02 + 5.000000000E-01 0.000000000E+00 5.500000000E-01 -8.130400E+02 + 5.000000000E-01 4.166670000E-02 5.500000000E-01 -7.103443E+02 + 5.000000000E-01 8.333330000E-02 5.500000000E-01 -5.987970E+02 + 5.000000000E-01 1.250000000E-01 5.500000000E-01 -4.836628E+02 + 5.000000000E-01 1.666670000E-01 5.500000000E-01 -3.700392E+02 + 5.000000000E-01 2.083330000E-01 5.500000000E-01 -2.623794E+02 + 5.000000000E-01 2.500000000E-01 5.500000000E-01 -1.641466E+02 + 5.000000000E-01 2.916670000E-01 5.500000000E-01 -7.767438E+01 + 5.000000000E-01 3.333330000E-01 5.500000000E-01 -4.169920E+00 + 5.000000000E-01 3.750000000E-01 5.500000000E-01 5.615031E+01 + 5.000000000E-01 4.166670000E-01 5.500000000E-01 1.038361E+02 + 5.000000000E-01 4.583330000E-01 5.500000000E-01 1.399730E+02 + 5.000000000E-01 5.000000000E-01 5.500000000E-01 1.659610E+02 From cbe3b3de42e98aa69634478d79b2e5971e7129fe Mon Sep 17 00:00:00 2001 From: D Fournier Date: Mon, 21 Dec 2015 13:11:46 -0800 Subject: [PATCH 10/31] Move sub functions to PF.Magnetics Add Mag Integral forward operator Begin Inversion script for magnetic integral --- simpegPF/BaseMag.py | 55 ++- .../{functions => Archives}/fwr_MAG_data.py | 4 +- .../Dev/{functions => Archives}/get_MAG_F.py | 6 +- .../Dev/{functions => Archives}/get_T_mat.py | 0 .../{functions => Archives}/get_UBC_mesh.py | 0 .../{functions => Archives}/read_MAG_obs.py | 0 .../read_MAGfwr_inp.py | 0 ...fwr_Driver.py => Intgrl_MAG_Fwr_Driver.py} | 13 +- ...r_Test.py => Intgrl_MAG_Fwr_SphereTest.py} | 56 +-- simpegPF/Dev/Intgrl_MAG_Inv_Driver.py | 37 ++ simpegPF/Magnetics.py | 345 +++++++++++++++++- 11 files changed, 473 insertions(+), 43 deletions(-) rename simpegPF/Dev/{functions => Archives}/fwr_MAG_data.py (96%) rename simpegPF/Dev/{functions => Archives}/get_MAG_F.py (96%) rename simpegPF/Dev/{functions => Archives}/get_T_mat.py (100%) rename simpegPF/Dev/{functions => Archives}/get_UBC_mesh.py (100%) rename simpegPF/Dev/{functions => Archives}/read_MAG_obs.py (100%) rename simpegPF/Dev/{functions => Archives}/read_MAGfwr_inp.py (100%) rename simpegPF/Dev/{MAG3D_fwr_Driver.py => Intgrl_MAG_Fwr_Driver.py} (76%) rename simpegPF/Dev/{MAG3D_fwr_Test.py => Intgrl_MAG_Fwr_SphereTest.py} (85%) create mode 100644 simpegPF/Dev/Intgrl_MAG_Inv_Driver.py diff --git a/simpegPF/BaseMag.py b/simpegPF/BaseMag.py index bd78009d..df75013b 100644 --- a/simpegPF/BaseMag.py +++ b/simpegPF/BaseMag.py @@ -187,4 +187,57 @@ def readUBCmagObs(obs_file): dobs[ii,:] = np.array(line.split(),dtype=float) line = fid.readline() - return B, M, dobs \ No newline at end of file + return B, M, dobs + +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/functions/fwr_MAG_data.py b/simpegPF/Dev/Archives/fwr_MAG_data.py similarity index 96% rename from simpegPF/Dev/functions/fwr_MAG_data.py rename to simpegPF/Dev/Archives/fwr_MAG_data.py index 88ba50c2..6bd3dbe1 100644 --- a/simpegPF/Dev/functions/fwr_MAG_data.py +++ b/simpegPF/Dev/Archives/fwr_MAG_data.py @@ -57,7 +57,7 @@ def fwr_MAG_data(mesh,B,M,rxLoc,model,flag): d = np.zeros(ndata) elif flag=='xyz': - d = np.zeros((3,ndata)) + d = np.zeros(int(3*ndata)) # Loop through all observations and create forward operator (ndata-by-mcell) print "Begin forward modeling " +str(int(ndata)) + " data points..." @@ -70,7 +70,7 @@ def fwr_MAG_data(mesh,B,M,rxLoc,model,flag): Gxyz = np.vstack((tx,ty,tz))*Mxyz if flag=='xyz': - d[:,ii] = Gxyz.dot(model) + d[ii:ndata:] = mkvc(Gxyz.dot(model)) elif flag=='tmi': d[ii] = Ptmi.dot(Gxyz.dot(model)) diff --git a/simpegPF/Dev/functions/get_MAG_F.py b/simpegPF/Dev/Archives/get_MAG_F.py similarity index 96% rename from simpegPF/Dev/functions/get_MAG_F.py rename to simpegPF/Dev/Archives/get_MAG_F.py index d9431b16..54172d71 100644 --- a/simpegPF/Dev/functions/get_MAG_F.py +++ b/simpegPF/Dev/Archives/get_MAG_F.py @@ -79,13 +79,13 @@ def fwr_MAG_F(mesh,B,M,rxLoc,flag): Mxyz = sp.spdiags(M,0,mesh.nC * 3,mesh.nC * 3) if flag == 'tmi': - F = np.zeros(ndata, mesh.nC) + F = np.zeros((ndata, mesh.nC)) elif flag == 'xyz': - F = np.zeros(int(3*ndata), mesh.nC) + F = np.zeros((int(3*ndata), mesh.nC)) elif flag == 'full': - F = np.zeros(int(3*ndata), int(3*mesh.nC)) + F = np.zeros((int(3*ndata), int(3*mesh.nC))) else: print """Flag must be either 'tmi' | 'xyz' | 'full', please revised""" diff --git a/simpegPF/Dev/functions/get_T_mat.py b/simpegPF/Dev/Archives/get_T_mat.py similarity index 100% rename from simpegPF/Dev/functions/get_T_mat.py rename to simpegPF/Dev/Archives/get_T_mat.py diff --git a/simpegPF/Dev/functions/get_UBC_mesh.py b/simpegPF/Dev/Archives/get_UBC_mesh.py similarity index 100% rename from simpegPF/Dev/functions/get_UBC_mesh.py rename to simpegPF/Dev/Archives/get_UBC_mesh.py diff --git a/simpegPF/Dev/functions/read_MAG_obs.py b/simpegPF/Dev/Archives/read_MAG_obs.py similarity index 100% rename from simpegPF/Dev/functions/read_MAG_obs.py rename to simpegPF/Dev/Archives/read_MAG_obs.py diff --git a/simpegPF/Dev/functions/read_MAGfwr_inp.py b/simpegPF/Dev/Archives/read_MAGfwr_inp.py similarity index 100% rename from simpegPF/Dev/functions/read_MAGfwr_inp.py rename to simpegPF/Dev/Archives/read_MAGfwr_inp.py diff --git a/simpegPF/Dev/MAG3D_fwr_Driver.py b/simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py similarity index 76% rename from simpegPF/Dev/MAG3D_fwr_Driver.py rename to simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py index 4870159e..448e6b83 100644 --- a/simpegPF/Dev/MAG3D_fwr_Driver.py +++ b/simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py @@ -8,15 +8,15 @@ os.chdir(home_dir) #%% from SimPEG import np, Utils -from simpegPF import BaseMag +import simpegPF as PF ## New scripts to be added to basecode -from fwr_MAG_data import fwr_MAG_data -from read_MAGfwr_inp import read_MAGfwr_inp +#from fwr_MAG_data import fwr_MAG_data +#from read_MAGfwr_inp import read_MAGfwr_inp #%% # Read input file -[mshfile, obsfile, modfile, magfile, topofile] = read_MAGfwr_inp(inpfile) +[mshfile, obsfile, modfile, magfile, topofile] = PF.BaseMag.read_MAGfwr_inp(inpfile) # Load mesh file mesh = Utils.meshutils.readUBCTensorMesh(mshfile) @@ -25,13 +25,13 @@ mesh = Utils.meshutils.readUBCTensorMesh(mshfile) model = Utils.meshutils.readUBCTensorModel(modfile,mesh) # Load in observation file -[B,M,dobs] = BaseMag.readUBCmagObs(obsfile) +[B,M,dobs] = PF.BaseMag.readUBCmagObs(obsfile) rxLoc = dobs[:,0:3] ndata = rxLoc.shape[0] # Compute forward model using integral equation -d = fwr_MAG_data(mesh,B,M,rxLoc,model,'tmi') +d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,'tmi') # Form data object with coordinates and write to file data = np.c_[rxLoc , d , np.zeros((ndata,1))] @@ -43,6 +43,7 @@ with file('FWR_data.dat','w') as fid: 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/Intgrl_MAG_Fwr_SphereTest.py similarity index 85% rename from simpegPF/Dev/MAG3D_fwr_Test.py rename to simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py index 1d30dbb6..ddea3760 100644 --- a/simpegPF/Dev/MAG3D_fwr_Test.py +++ b/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py @@ -8,10 +8,8 @@ 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 +#from fwr_MAG_data import fwr_MAG_data plt.close('all') @@ -39,7 +37,7 @@ lrl = np.zeros(d_iter) # Create mesh using simpeg and write out in GIF format for ii in range(d_iter): - + nc = 3**(ii+1) hxind = [(1./nc, nc)] @@ -54,6 +52,8 @@ for ii in range(d_iter): mcell = mesh.nC + print 'Mesh size: ' + str(mcell) + sph_ind = PF.MagAnalytics.spheremodel(mesh, 0, 0, 0, R) chibkg = 0. @@ -62,10 +62,10 @@ for ii in range(d_iter): model[sph_ind] = chiblk #%% Forward mode ldata - d = fwr_MAG_obs(mesh,B,M,rxLoc,model) - fwr_x = mkvc(d[0,:]) - fwr_y = mkvc(d[1,:]) - fwr_z = mkvc(d[2,:]) + d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,'xyz') + fwr_x = d[0:ndata] + fwr_y = d[ndata:2*ndata] + fwr_z = d[2*ndata:] #%% Get the analystical answer and compute the residual bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') @@ -90,30 +90,32 @@ for ii in range(d_iter): #%% Plot results +print 'Residual between analytical sphere and integral forward' for ii in range(d_iter): nc = 3**(ii+1) - print "Residual= " + str(lrl[ii]) + "\t dx= " + str(1./nc) + + print "||r||= " + str(lrl[ii]) + "\t dx= " + str(1./nc) #%% Plot fields plt.figure(1) -ax = plt.subplot(131) +ax = plt.subplot(221) plt.imshow(np.reshape(bxa,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar() +plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(bxa,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(bxa,X.shape).T, s=20) ax.set_title('Sphere Ana Bx') -ax = plt.subplot(132) +ax = plt.subplot(222) plt.imshow(np.reshape(bya,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar() +plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(bya,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(bya,X.shape).T, s=20) ax.set_title('Sphere Ana By') -ax = plt.subplot(133) +ax = plt.subplot(212) plt.imshow(np.reshape(bza,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar() +plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(bza,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(bza,X.shape).T, s=20) ax.set_title('Sphere Ana Bz') @@ -121,23 +123,23 @@ ax.set_title('Sphere Ana Bz') #%% Plot the forward solution from integral plt.figure(2) -ax = plt.subplot(131) +ax = plt.subplot(221) plt.imshow(np.reshape(fwr_x,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') -plt.colorbar() +plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(fwr_x,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(fwr_x,X.shape).T, s=20) ax.set_title('Sphere Ana Bx') -ax = plt.subplot(132) +ax = plt.subplot(222) plt.imshow(np.reshape(fwr_y,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar() +plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(fwr_y,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(fwr_y,X.shape).T, s=20) ax.set_title('Sphere Ana By') -ax = plt.subplot(133) +ax = plt.subplot(212) plt.imshow(np.reshape(fwr_z,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar() +plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(fwr_z,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(fwr_z,X.shape).T, s=20) ax.set_title('Sphere Ana Bz') @@ -145,23 +147,23 @@ ax.set_title('Sphere Ana Bz') #%% Plot foward data plt.figure(3) -ax = plt.subplot(131) +ax = plt.subplot(221) plt.imshow(np.reshape(r_Bx,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar() +plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(r_Bx,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(r_Bx,X.shape).T, s=20) ax.set_title('Sphere Ana Bx') -ax = plt.subplot(132) +ax = plt.subplot(222) plt.imshow(np.reshape(r_By,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar() +plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(r_By,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(r_By,X.shape).T, s=20) ax.set_title('Sphere Ana By') -ax = plt.subplot(133) +ax = plt.subplot(212) plt.imshow(np.reshape(r_Bz,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar() +plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(r_Bz,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(r_Bz,X.shape).T, s=20) ax.set_title('Sphere Ana Bz') \ No newline at end of file diff --git a/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py b/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py new file mode 100644 index 00000000..296d0a1b --- /dev/null +++ b/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py @@ -0,0 +1,37 @@ +import os + +home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' + +inpfile = 'MAG3Cinv.inp' + +os.chdir(home_dir) + +#%% +from SimPEG import np, Utils +import simpegPF as PF + +## New scripts to be added to basecode +#from fwr_MAG_data import fwr_MAG_data +#from read_MAGfwr_inp import read_MAGfwr_inp + +#%% +# Read input file +[mshfile, obsfile, modfile, magfile, topofile] = PF.BaseMag.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] = PF.BaseMag.readUBCmagObs(obsfile) + +rxLoc = dobs[:,0:3] +ndata = rxLoc.shape[0] + +# Create forward operator +F = PF.Magnetics.Intrgl_Fwr_Op(mesh,B,M,rxLoc,'tmi') + + + diff --git a/simpegPF/Magnetics.py b/simpegPF/Magnetics.py index dc96baa8..8b8e88cd 100644 --- a/simpegPF/Magnetics.py +++ b/simpegPF/Magnetics.py @@ -409,10 +409,347 @@ if __name__ == '__main__': # plt.show() - - - - +def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,flag): + """ + 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 + flag = Data type "tmi" | "xyz" + + OUTPUT: + dobs =Observation array in format [obsx, obsy, obsz, data] + + Created on Oct 7, 2015 + + @author: dominiquef + """ + + xn = mesh.vectorNx; + yn = mesh.vectorNy; + zn = mesh.vectorNz; + + 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; + + if flag=='tmi': + d = np.zeros(ndata) + + elif flag=='xyz': + d = np.zeros(int(3*ndata)) + + # 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. + count = -1 + + for ii in range(ndata): + + tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) + Gxyz = np.vstack((tx,ty,tz))*Mxyz + + if flag=='xyz': + d[ii::ndata] = mkvc(Gxyz.dot(model)) + + elif flag=='tmi': + d[ii] = Ptmi.dot(Gxyz.dot(model)) + + # Display progress + count = progress(ii,count,ndata) + + + print "Done 100% ...forward modeling completed!!\n" + + return d + +def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): + """ + Magnetic forward operator in integral form + + INPUT: + mesh = Mesh in SimPEG format + B = Inducing field parameter [Binc, Bdecl, B0] + M = Magnetization information + [OPTIONS] + 1- [Minc, Mdecl] : Assumes uniform magnetization orientation + 2- [mx1,mx2,..., my1,...,mz1] : cell-based defined magnetization direction + 3- diag(M): Block diagonal matrix with [Mx, My, Mz] along the diagonal + + rxLox = Observation location informat [obsx, obsy, obsz] + + flag = 'tmi' | 'xyz' | 'full' + [OPTIONS] + 1- tmi : Magnetization direction used and data are projected onto the + inducing field direction F.shape([ndata, nc]) + + 2- xyz : Magnetization direction used and data are given in 3-components + F.shape([3*ndata, nc]) + + 3- full: Full tensor matrix stored with shape([3*ndata, 3*nc]) + + OUTPUT: + F = Linear forward modeling operation + + Created on Dec, 20th 2015 + + @author: dominiquef + """ + + xn = mesh.vectorNx; + yn = mesh.vectorNy; + zn = mesh.vectorNz; + + mcell = (len(xn)-1) * (len(yn)-1) * (len(zn)-1) + + ndata = rxLoc.shape[0] + + + # Convert Bdecination from north to cartesian + D = (450.-float(B[1]))%360. + + + # Pre-allocate space and create magnetization matrix if required + if (flag=='tmi') | (flag == 'xyz'): + + # If assumes uniform magnetization direction + if len(M) == 3: + + # 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]) + + Mxyz = sp.vstack((Mx,My,Mz)); + + # Otherwise if given a vector 3*ncells + elif len(M) == mesh.nC * 3: + + Mxyz = sp.spdiags(M,0,mesh.nC * 3,mesh.nC * 3) + + if flag == 'tmi': + F = np.zeros((ndata, mesh.nC)) + + # Projection matrix + 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; + + elif flag == 'xyz': + F = np.zeros((int(3*ndata), mesh.nC)) + + elif flag == 'full': + F = np.zeros((int(3*ndata), int(3*mesh.nC))) + + else: + print """Flag must be either 'tmi' | 'xyz' | 'full', please revised""" + return + + + # Loop through all observations and create forward operator (ndata-by-mcell) + print "Begin calculation of forward operator: " + flag + + # Add counter to dsiplay progress. Good for large problems + count = -1; + for ii in range(ndata): + + tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) + + if flag=='tmi': + F[ii,:] = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz + + elif flag == 'xyz': + F[ii,:] = tx*Mxyz + F[ii+ndata,:] = ty*Mxyz + F[ii+2*ndata,:] = tz*Mxyz + + elif flag == 'full': + F[ii,:] = tx + F[ii+ndata,:] = ty + F[ii+2*ndata,:] = tz + + + # Display progress + count = progress(ii,count,ndata) + + + print "Done 100% ...forward modeling completed!!\n" + + return F + +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 rxLoc[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. + """ + + 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 + +def progress(iter,prog,final): + + arg = np.floor(float(iter)/float(final)*10.); + + if arg > prog: + + strg = "Done " + str(arg*10) + " %" + print strg + prog = arg; + + return prog From 1a7656c34dc6bf1fe448875a80bb8aed194e6f8d Mon Sep 17 00:00:00 2001 From: D Fournier Date: Sun, 27 Dec 2015 11:29:01 -0800 Subject: [PATCH 11/31] Implement distance weighting for potential fields in BaseMag --- simpegPF/BaseMag.py | 124 +- simpegPF/Dev/Intgrl_MAG_Inv_Driver.py | 47 +- simpegPF/Dev/MAG3D_inv.inp | 11 + simpegPF/Dev/wr.dat | 8027 ++++++++++++++++++++++++- simpegPF/Magnetics.py | 157 +- 5 files changed, 8297 insertions(+), 69 deletions(-) create mode 100644 simpegPF/Dev/MAG3D_inv.inp diff --git a/simpegPF/BaseMag.py b/simpegPF/BaseMag.py index df75013b..a39f4ee8 100644 --- a/simpegPF/BaseMag.py +++ b/simpegPF/BaseMag.py @@ -1,6 +1,6 @@ from SimPEG import Maps, Survey, Utils, np, sp from scipy.constants import mu_0 - +import re class BaseMagSurvey(Survey.BaseSurvey): """Base Magnetics Survey""" @@ -190,6 +190,7 @@ def readUBCmagObs(obs_file): return B, M, dobs 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 @@ -240,4 +241,123 @@ def read_MAGfwr_inp(input_file): else: topofile = l_input[0].rstrip() - return mshfile, obsfile, modfile, magfile, topofile \ No newline at end of file + return mshfile, obsfile, modfile, magfile, topofile + +def read_MAGinv_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 + topofile + start model + ref model + mag model + weightfile + chi_target + as, ax ,ay, az + upper, lower bounds + lp, lqx, lqy, lqz + + # All files should be in the working directory, otherwise the path must + # be specified. + + Created on Dec 21th, 2015 + + @author: dominiquef + """ + + + fid = open(input_file,'r') + + # Line 1 + line = fid.readline() + l_input = line.split('!') + mshfile = l_input[0].rstrip() + + # Line 2 + line = fid.readline() + l_input = line.split('!') + obsfile = l_input[0].rstrip() + + # Line 3 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input=='null': + topofile = [] + + else: + topofile = l_input[0].rstrip() + + + # Line 4 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + mstart = float(l_input[1]) + + else: + mstart = l_input[0].rstrip() + + # Line 5 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + mref = float(l_input[1]) + + else: + mref = l_input[0].rstrip() + + + # Line 6 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input=='DEFAULT': + magfile = [] + + else: + magfile = l_input[0].rstrip() + + # Line 7 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input=='DEFAULT': + wgtfile = [] + + else: + wgtfile = l_input[0].rstrip() + + # Line 8 + line = fid.readline() + l_input = re.split('[!\s]',line) + chi = float(l_input[0]) + + # Line 9 + line = fid.readline() + l_input = re.split('[!\s]',line) + val = np.array(l_input[0:4]) + alphas = val.astype(np.float) + + # Line 10 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + val = np.array(l_input[1:3]) + bounds = val.astype(np.float) + + else: + bounds = l_input[0].rstrip() + + # Line 11 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + val = np.array(l_input[1:6]) + lpnorms = val.astype(np.float) + + else: + lpnorms = l_input[0].rstrip() + + return mshfile, obsfile, topofile, mstart, mref, magfile, wgtfile, chi, alphas, bounds, lpnorms \ No newline at end of file diff --git a/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py b/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py index 296d0a1b..b1dfa137 100644 --- a/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py +++ b/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py @@ -2,13 +2,15 @@ import os home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' -inpfile = 'MAG3Cinv.inp' +inpfile = 'MAG3D_inv.inp' +dsep = '\\' os.chdir(home_dir) #%% -from SimPEG import np, Utils +from SimPEG import np, Utils, mkvc import simpegPF as PF +import pylab as plt ## New scripts to be added to basecode #from fwr_MAG_data import fwr_MAG_data @@ -16,13 +18,10 @@ import simpegPF as PF #%% # Read input file -[mshfile, obsfile, modfile, magfile, topofile] = PF.BaseMag.read_MAGfwr_inp(inpfile) +[mshfile, obsfile, topofile, mstart, mref, magfile, wgtfile, chi, alphas, bounds, lpnorms] = PF.BaseMag.read_MAGinv_inp(home_dir + dsep + 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] = PF.BaseMag.readUBCmagObs(obsfile) @@ -30,8 +29,38 @@ model = Utils.meshutils.readUBCTensorModel(modfile,mesh) rxLoc = dobs[:,0:3] ndata = rxLoc.shape[0] + +# Load in topofile or create flat surface +#============================================================================== +# if topofile == 'null': +# +# Nx,Ny = np.meshgrid(mesh.vectorNx,mesh.vectorNy) +# Nz = np.ones(Nx.shape) * mesh.vectorNz[-1] +# +# topo = np.c_[mkvc(Nx),mkvc(Ny),mkvc(Nz)] +#============================================================================== +# Work with flat topogrphy for now +nullcell = np.ones(mesh.nC) + +# Load model file +if isinstance(mstart, float): + mstart = np.ones(mesh.nC) * mstart +else: + mstart = Utils.meshutils.readUBCTensorModel(mstart,mesh) + + +# Get magnetization vector for MOF +if magfile=='DEFAULT': + + M_xyz = PF.Magnetics.dipazm_2_xyz(np.ones(mesh.nC) * M[0], np.ones(mesh.nC) * M[1]) + +else: + M_xyz = np.genfromtxt(magfile,delimiter=' \n',dtype=np.str,comments='!') + + # Create forward operator -F = PF.Magnetics.Intrgl_Fwr_Op(mesh,B,M,rxLoc,'tmi') - - +F = PF.Magnetics.Intrgl_Fwr_Op(mesh,B,M_xyz,rxLoc,'tmi') +# Get distance weighting function +wr = PF.Magnetics.get_dist_wgt(mesh,rxLoc,3.,np.min(mesh.hx)/4) +Utils.writeUBCTensorModel(home_dir+dsep+'wr.dat',mesh,wr) \ No newline at end of file diff --git a/simpegPF/Dev/MAG3D_inv.inp b/simpegPF/Dev/MAG3D_inv.inp new file mode 100644 index 00000000..9023ae01 --- /dev/null +++ b/simpegPF/Dev/MAG3D_inv.inp @@ -0,0 +1,11 @@ +Mesh.msh ! Mesh file +FWR_data.dat ! Obsfile +null ! Topofile +Model.dat ! Starting model +VALUE 0.0 ! Reference model +DEFAULT !..\AzmDip.dat ! Magnetization vector model +DEFAULT ! Cell based weight file +1 ! target chi factor | DEFAULT=1 +1 1 1 1 ! alpha s, x ,y ,z +VALUE 0 1 ! Lower and Upper Bounds for p-component +VALUE 0 2 2 2 1 ! lp-norm for amplitude inversion FILE pqxqyqzr.dat ! Norms VALUE p, qx, qy, qz, r | FILE m-by-5 matrix \ No newline at end of file diff --git a/simpegPF/Dev/wr.dat b/simpegPF/Dev/wr.dat index 590efee4..b31718e7 100644 --- a/simpegPF/Dev/wr.dat +++ b/simpegPF/Dev/wr.dat @@ -1,27 +1,8000 @@ - 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 +9.201237682702764209e-01 +5.319047179091668243e-01 +3.697075645889145168e-01 +2.822469846862549758e-01 +2.279063805064212300e-01 +1.909392091251238821e-01 +1.641655914131082417e-01 +1.438666425493223822e-01 +1.279302275870793371e-01 +1.150705976388446405e-01 +1.044616427912537643e-01 +9.554913529386012405e-02 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+8.794783256894180001e-02 +8.138187720366046873e-02 +7.564866028145778021e-02 +7.059605560814800895e-02 +6.610760411266616843e-02 +6.209259971626260310e-02 +5.847929271967589049e-02 +5.521013035301709782e-02 diff --git a/simpegPF/Magnetics.py b/simpegPF/Magnetics.py index 8b8e88cd..213e10b9 100644 --- a/simpegPF/Magnetics.py +++ b/simpegPF/Magnetics.py @@ -527,9 +527,7 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): xn = mesh.vectorNx; yn = mesh.vectorNy; zn = mesh.vectorNz; - - mcell = (len(xn)-1) * (len(yn)-1) * (len(zn)-1) - + ndata = rxLoc.shape[0] @@ -539,28 +537,20 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): # Pre-allocate space and create magnetization matrix if required if (flag=='tmi') | (flag == 'xyz'): - # If assumes uniform magnetization direction - if len(M) == 3: + if M.shape != (mesh.nC,3): + + print 'Magnetization vector must be 3*Ncells' + return - # 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]) - - Mxyz = sp.vstack((Mx,My,Mz)); - # Otherwise if given a vector 3*ncells - elif len(M) == mesh.nC * 3: - - Mxyz = sp.spdiags(M,0,mesh.nC * 3,mesh.nC * 3) + Mx = Utils.sdiag(M[:,0]*B[2]) + My = Utils.sdiag(M[:,1]*B[2]) + Mz = Utils.sdiag(M[:,2]*B[2]) + + Mxyz = sp.vstack((Mx,My,Mz)) + + if flag == 'tmi': F = np.zeros((ndata, mesh.nC)) @@ -614,7 +604,7 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): def get_T_mat(xn,yn,zn,rxLoc): """ - Load in the nodes of a tensor mesh and computes the magnetic tensor + Load in the active nodes of a tensor mesh and computes the magnetic tensor for a given observation location rxLoc[obsx, obsy, obsz] OUTPUT: Tx = [Txx Txy Txz] @@ -625,6 +615,10 @@ def get_T_mat(xn,yn,zn,rxLoc): 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. + + Created on Oct, 20th 2015 + + @author: dominiquef """ ncx = len(xn)-1 @@ -649,18 +643,13 @@ def get_T_mat(xn,yn,zn,rxLoc): 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]; @@ -740,7 +729,16 @@ def get_T_mat(xn,yn,zn,rxLoc): return Tx,Ty,Tz def progress(iter,prog,final): + """ + progress(iter,prog,final) + + Function measuring the progress of a process and print to screen the %. + Useful to estimate the remaining runtime of a large problem. + + Created on Dec, 20th 2015 + @author: dominiquef + """ arg = np.floor(float(iter)/float(final)*10.); if arg > prog: @@ -751,6 +749,103 @@ def progress(iter,prog,final): return prog +def dipazm_2_xyz(dip,azm_N): + """ + dipazm_2_xyz(dip,azm_N) + + Function converting degree angles for dip and azimuth from north to a + 3-components in cartesian coordinates. + + INPUT + dip : Value or vector of dip from horizontal in DEGREE + azm_N : Value or vector of azimuth from north in DEGREE + + OUTPUT + M : [n-by-3] Array of xyz components of a unit vector in cartesian + + Created on Dec, 20th 2015 + @author: dominiquef + """ + mcell = len(azm_N) + + M = np.zeros((mcell,3)) + + # Modify azimuth from North to Cartesian-X + azm_X = (450.- azm_N) % 360. + + D = np.deg2rad(dip) + I = np.deg2rad(azm_X) + + M[:,0] = np.cos(D) * np.cos(I) ; + M[:,1] = np.cos(D) * np.sin(I) ; + M[:,2] = np.sin(D) ; + + return M +def get_dist_wgt(mesh,rxLoc,R,R0): + """ + get_dist_wgt(xn,yn,zn,rxLoc,R,R0) + + Function creating a distance weighting function required for the magnetic + inverse problem. + + INPUT + xn, yn, zn : Node location + rxLoc : Observation locations [obsx, obsy, obsz] + R : Decay factor (mag=3, grav =2) + R0 : Small factor added (default=dx/4) + + OUTPUT + wr : [mcell] Vector of distance weighting + + Created on Dec, 20th 2015 + @author: dominiquef + """ + + p = 1/np.sqrt(3); + + # Create cell center location + Ym,Xm,Zm = np.meshgrid(mesh.vectorCCy, mesh.vectorCCx, mesh.vectorCCz) + hY,hX,hZ = np.meshgrid(mesh.hy, mesh.hx, mesh.hz) + + V = np.reshape(mesh.vol,hY.shape) + wr = np.zeros(hY.shape) + + ndata = rxLoc.shape[0] + count = -1; + print "Begin calculation of distance weighting for R= " + str(R) + + for dd in range(ndata): + + nx1 = (Xm - hX * p - rxLoc[dd,0])**2 + nx2 = (Xm + hX * p - rxLoc[dd,0])**2 + + ny1 = (Ym - hY * p - rxLoc[dd,1])**2 + ny2 = (Ym + hY * p - rxLoc[dd,1])**2 + + nz1 = (Zm - hZ * p - rxLoc[dd,2])**2 + nz2 = (Zm + hZ * p - rxLoc[dd,2])**2 + + R1 = np.sqrt(nx1 + ny1 + nz1) + R2 = np.sqrt(nx1 + ny1 + nz2) + R3 = np.sqrt(nx2 + ny1 + nz1) + R4 = np.sqrt(nx2 + ny1 + nz2) + R5 = np.sqrt(nx1 + ny2 + nz1) + R6 = np.sqrt(nx1 + ny2 + nz2) + R7 = np.sqrt(nx2 + ny2 + nz1) + R8 = np.sqrt(nx2 + ny2 + nz2) + + temp = (R1 + R0)**-R + (R2 + R0)**-R + (R3 + R0)**-R + (R4 + R0)**-R + (R5 + R0)**-R + (R6 + R0)**-R + (R7 + R0)**-R + (R8 + R0)**-R + + wr = wr + (V*temp/8.)**2 + + count = progress(dd,count,ndata) + + + wr = np.sqrt(wr)/V + wr = mkvc(wr) + wr = np.sqrt(wr/(np.max(wr))) + + return wr \ No newline at end of file From 201fe6bf4638f07139dfde16e5aedade60a6535c Mon Sep 17 00:00:00 2001 From: D Fournier Date: Tue, 29 Dec 2015 16:11:16 -0800 Subject: [PATCH 12/31] Implement active cell from topo and topocheck function --- simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py | 36 +++--- simpegPF/Dev/Intgrl_MAG_Inv_Driver.py | 33 ++++-- simpegPF/Magnetics.py | 157 +++++++++++++++++++++----- 3 files changed, 172 insertions(+), 54 deletions(-) diff --git a/simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py b/simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py index 448e6b83..064f737f 100644 --- a/simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py +++ b/simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py @@ -1,14 +1,17 @@ import os -home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' +home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\ownCloud\Research\Modelling\Synthetic\Block_Gaussian_topo' -inpfile = 'MAG3Cfwr.inp' +inpfile = 'PYMAG3C_fwr.inp' + +dsep = '\\' os.chdir(home_dir) #%% -from SimPEG import np, Utils +from SimPEG import np, sp, Utils, mkvc, Maps import simpegPF as PF +import pylab as plt ## New scripts to be added to basecode #from fwr_MAG_data import fwr_MAG_data @@ -23,27 +26,34 @@ mesh = Utils.meshutils.readUBCTensorMesh(mshfile) # Load model file model = Utils.meshutils.readUBCTensorModel(modfile,mesh) + +# Load in topofile or create flat surface +if topofile == 'null': + + actv = np.ones(mesh.nC) +else: + topo = np.genfromtxt(topofile,skip_header=1) + actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') + + +Utils.writeUBCTensorModel('nullcell.dat',mesh,actv) + # Load in observation file [B,M,dobs] = PF.BaseMag.readUBCmagObs(obsfile) rxLoc = dobs[:,0:3] +#rxLoc[:,2] += 5 # Temporary change for test ndata = rxLoc.shape[0] +#%% Run forward modeling # Compute forward model using integral equation -d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,'tmi') +d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi') # Form data object with coordinates and write to file -data = np.c_[rxLoc , d , np.zeros((ndata,1))] +wd = 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' +PF.Magnetics.writeUBCobs(home_dir + dsep + 'FWR_data.dat',B,M,rxLoc,d,wd) diff --git a/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py b/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py index b1dfa137..7d186f7c 100644 --- a/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py +++ b/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py @@ -1,8 +1,9 @@ import os -home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' +#home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' +home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Block_Gaussian_topo' -inpfile = 'MAG3D_inv.inp' +inpfile = 'PYMAG3D_inv.inp' dsep = '\\' os.chdir(home_dir) @@ -27,18 +28,23 @@ mesh = Utils.meshutils.readUBCTensorMesh(mshfile) [B,M,dobs] = PF.BaseMag.readUBCmagObs(obsfile) rxLoc = dobs[:,0:3] +d = dobs[:,3] +wd = dobs[:,4] + ndata = rxLoc.shape[0] # Load in topofile or create flat surface -#============================================================================== -# if topofile == 'null': -# -# Nx,Ny = np.meshgrid(mesh.vectorNx,mesh.vectorNy) -# Nz = np.ones(Nx.shape) * mesh.vectorNz[-1] -# -# topo = np.c_[mkvc(Nx),mkvc(Ny),mkvc(Nz)] -#============================================================================== +if topofile == 'null': + + Nx,Ny = np.meshgrid(mesh.vectorNx,mesh.vectorNy) + Nz = np.ones(Nx.shape) * mesh.vectorNz[-1] + + topo = np.c_[mkvc(Nx),mkvc(Ny),mkvc(Nz)] + +else: + topofile = np.genfromtxt(topofile,delimiter=' \n',dtype=np.str,skip_header=0) + # Work with flat topogrphy for now nullcell = np.ones(mesh.nC) @@ -63,4 +69,9 @@ F = PF.Magnetics.Intrgl_Fwr_Op(mesh,B,M_xyz,rxLoc,'tmi') # Get distance weighting function wr = PF.Magnetics.get_dist_wgt(mesh,rxLoc,3.,np.min(mesh.hx)/4) -Utils.writeUBCTensorModel(home_dir+dsep+'wr.dat',mesh,wr) \ No newline at end of file +Utils.writeUBCTensorModel(home_dir+dsep+'wr.dat',mesh,wr) + +# Write out the predicted +pred = F.dot(mstart) +PF.Magnetics.writeUBCobs(home_dir + dsep + 'Pred.dat',B,M,rxLoc,pred,wd) + diff --git a/simpegPF/Magnetics.py b/simpegPF/Magnetics.py index 213e10b9..20db93df 100644 --- a/simpegPF/Magnetics.py +++ b/simpegPF/Magnetics.py @@ -409,7 +409,7 @@ if __name__ == '__main__': # plt.show() -def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,flag): +def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,flag): """ Forward model magnetic data using integral equation @@ -419,6 +419,7 @@ def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,flag): M = Magnetization matrix [Minc, Mdecl] rxLox = Observation location informat [obsx, obsy, obsz] model = Model associated with mesh + actv = Active cells from topo (from getActiveTopo) flag = Data type "tmi" | "xyz" OUTPUT: @@ -429,11 +430,21 @@ def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,flag): @author: dominiquef """ + inds = np.nonzero(actv)[0] + P = sp.csr_matrix((np.ones(inds.size),(inds, range(inds.size))), shape=(mesh.nC, len(inds))) + xn = mesh.vectorNx; yn = mesh.vectorNy; zn = mesh.vectorNz; - mcell = (len(xn)-1) * (len(yn)-1) * (len(zn)-1) + yn2,xn2,zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:]) + yn1,xn1,zn1 = np.meshgrid(yn[0:-1], xn[0:-1], zn[0:-1]) + + Yn = P.T*np.c_[mkvc(yn1), mkvc(yn2)] + Xn = P.T*np.c_[mkvc(xn1), mkvc(xn2)] + Zn = P.T*np.c_[mkvc(zn1), mkvc(zn2)] + + mcell = len(inds) ndata = rxLoc.shape[0] @@ -474,14 +485,14 @@ def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,flag): for ii in range(ndata): - tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) + tx, ty, tz = get_T_mat(Xn,Yn,Zn,rxLoc[ii,:]) Gxyz = np.vstack((tx,ty,tz))*Mxyz if flag=='xyz': - d[ii::ndata] = mkvc(Gxyz.dot(model)) + d[ii::ndata] = mkvc(Gxyz.dot(P.T*model)) elif flag=='tmi': - d[ii] = Ptmi.dot(Gxyz.dot(model)) + d[ii] = Ptmi.dot(Gxyz.dot(P.T*model)) # Display progress count = progress(ii,count,ndata) @@ -602,10 +613,15 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): return F -def get_T_mat(xn,yn,zn,rxLoc): +def get_T_mat(Xn,Yn,Zn,rxLoc): """ Load in the active nodes of a tensor mesh and computes the magnetic tensor for a given observation location rxLoc[obsx, obsy, obsz] + + INPUT: + Xn, Yn, Zn: Node location matrix for the lower and upper most corners of + all cells in the mesh shape[nC,2] + M OUTPUT: Tx = [Txx Txy Txz] Ty = [Tyx Tyy Tyz] @@ -621,37 +637,21 @@ def get_T_mat(xn,yn,zn,rxLoc): @author: dominiquef """ - ncx = len(xn)-1 - ncy = len(yn)-1 - ncz = len(zn)-1 - - mcell = ncx*ncy*ncz + mcell = Xn.shape[0] # 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; + dz2 = rxLoc[2] - Zn[:,0]; + dz1 = rxLoc[2] - Zn[:,1]; - dy2 = yn2 - rxLoc[1]; - dy1 = yn1 - rxLoc[1]; + dy2 = Yn[:,1] - rxLoc[1]; + dy1 = Yn[:,0] - rxLoc[1]; - dx2 = xn2 - rxLoc[0]; - dx1 = xn1 - rxLoc[0]; + dx2 = Xn[:,1] - rxLoc[0]; + dx1 = Xn[:,0] - rxLoc[0]; R1 = ( dy2**2 + dx2**2 ); R2 = ( dy2**2 + dx1**2 ); @@ -848,4 +848,101 @@ def get_dist_wgt(mesh,rxLoc,R,R0): wr = mkvc(wr) wr = np.sqrt(wr/(np.max(wr))) - return wr \ No newline at end of file + return wr + +def writeUBCobs(filename,B,M,rxLoc,d,wd): + """ + writeUBCobs(filename,B,M,rxLoc,d,wd) + + Function writing an observation file in UBC-MAG3D format. + + INPUT + filename : Name of out file including directory + B : Inducing field parameters [Inc, Decl, Intensity] + M : Magnetization orientation [Inc, Decl, dtype] + rxLoc : Observation locations [obsx, obsy, obsz] + d : Data vector + wd : Uncertainty vector + + OUTPUT + Obsfile + + Created on Dec, 27th 2015 + + @author: dominiquef + """ + + data = np.c_[rxLoc , d , wd] + + with file(filename,'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' %len(d) ) + np.savetxt(fid, data, fmt='%e',delimiter=' ',newline='\n') + + + print "Observation file saved to: " + filename + +def getActiveTopo(mesh,topo,flag): + """ + getActiveTopo(mesh,topo) + + Function creates an active cell model from topography + + INPUT + mesh : Mesh in SimPEG format + topo : Scatter points defining topography [x,y,z] + + OUTPUT + actv : Active cell model + + Created on Dec, 27th 2015 + + @author: dominiquef + """ + import scipy.interpolate as interpolation + + if (flag=='N'): + Zn = np.zeros((mesh.nNx,mesh.nNy)) + # wght = np.zeros((mesh.nNx,mesh.nNy)) + cx = mesh.vectorNx + cy = mesh.vectorNy + + F = interpolation.NearestNDInterpolator(topo[:,0:2],topo[:,2]) + [Y,X] = np.meshgrid(cy,cx) + + Zn = F(X,Y) +#============================================================================== +# for ii in range(topo.shape[0]): +# dx = topo[ii,0] - cx + 1e-8 +# dy = topo[ii,1] - cy + 1e-8 +# +# [Wx,Wy] = np.meshgrid(dx**2.,dy**2.) +# +# W = np.sqrt(Wx + Wy)**-1. +# +# wght = wght + W +# Zn = Zn + topo[ii,2]*W +# +# Zn = Zn/wght +#============================================================================== + + actv = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz)) + + if (flag=='N'): + Nz = mesh.vectorNz[1:] + + + for jj in range(mesh.nCy): + + for ii in range(mesh.nCx): + + temp = [kk for kk in range(len(Nz)) if np.all(Zn[ii:(ii+2),jj:(jj+2)] > Nz[kk]) ] + actv[ii,jj,temp] = 1 + + + actv = mkvc(actv) + + return actv + + \ No newline at end of file From 958c4d151aea19e49388a756b79c05c8a4c4f7cd Mon Sep 17 00:00:00 2001 From: D Fournier Date: Mon, 4 Jan 2016 12:29:28 -0800 Subject: [PATCH 13/31] Test interpolation methods. Attempt at using FFT to resample grid. Need to can an FFT method for 2 grid ( pad, taper, resample, extract) --- .../Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py | 172 ++++++++++ .../Dev/Intgrl_MAG_Fwr_InterpData_Test.py | 301 ++++++++++++++++++ simpegPF/Magnetics.py | 37 +-- 3 files changed, 492 insertions(+), 18 deletions(-) create mode 100644 simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py create mode 100644 simpegPF/Dev/Intgrl_MAG_Fwr_InterpData_Test.py diff --git a/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py b/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py new file mode 100644 index 00000000..62d52c2e --- /dev/null +++ b/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py @@ -0,0 +1,172 @@ +import os + +home_dir = 'C:\\LC\\Private\\dominiquef\\Projects\\4414_Minsim\\Modeling\\MAG' + +os.chdir(home_dir) + +#%% +from SimPEG import * +import matplotlib.pyplot as plt +import simpegPF as PF +import scipy.interpolate as interpolation + +#from fwr_MAG_data import fwr_MAG_data + +plt.close('all') + +topofile = 'Gaussian.topo' + +zoffset = 2 +#%% Create survey +# Load in topofile or create flat surface +if not topofile: + + actv = np.ones(mesh.nC) + +else: + topo = np.genfromtxt(topofile,skip_header=1) + + +B = np.array(([90.,0.,50000.])) + +M = np.array(([90.,0.,315.])) + +# Sphere radius +R = 25. + +# # Or create juste a plane grid +xr = np.linspace(-99., 99., 40) +yr = np.linspace(-49., 49., 20) +X, Y = np.meshgrid(xr, yr) + +d_iter = 1 +lrl = np.zeros(d_iter) +sclx = 100. +dx = 5 +#%% Loop through decreasing meshes and measure the residual +# Create mesh using simpeg and write out in GIF format + +for ii in range(d_iter): + + + nc = int(sclx/dx) + + hxind = [(dx, 2*nc)] + hyind = [(dx, nc)] + hzind = [(dx, nc)] + + mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN') + + actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') + + # Drape observations on topo + offset + if not topofile: + Z = np.ones((xr.size, yr.size)) * 2.5 + + else: + F = interpolation.NearestNDInterpolator(topo[:,0:2],topo[:,2]) + Z = F(X,Y) + zoffset + + rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] + + ndata = rxLoc.shape[0] + + xn = mesh.vectorNx + yn = mesh.vectorNy + zn = mesh.vectorNz + + mcell = mesh.nC + + print 'Mesh size: ' + str(mcell) + + #%% Create model + chibkg = 0. + chiblk = 0.01 + model = np.ones(mcell)*chibkg + + # Do a three sphere problem for more frequencies + sph_ind = PF.MagAnalytics.spheremodel(mesh, 0., 0., -sclx/3, R) + model[sph_ind] = 0.5*chiblk + + sph_ind = PF.MagAnalytics.spheremodel(mesh, -sclx/2., 0., -sclx/3., R/3.) + model[sph_ind] = 4.*chiblk + + sph_ind = PF.MagAnalytics.spheremodel(mesh, sclx/2., 0., -sclx/2.5, R/2.5) + model[sph_ind] = 2.5*chiblk + + Utils.writeUBCTensorMesh('Mesh.msh',mesh) + Utils.writeUBCTensorModel('Model.sus',mesh,model) + actv = np.ones(mesh.nC) + #%% Forward mode ldata + d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi') + #fwr_tmi = d[0:ndata] + #fwr_y = d[ndata:2*ndata] + #fwr_z = d[2*ndata:] + + #%% Get the analystical answer and compute the residual + #bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') + Bd = (450.-float(B[1]))%360. + Bi = B[0]; # Convert dip to horizontal to cartesian + + Bx = np.cos(np.deg2rad(Bi)) * np.cos(np.deg2rad(Bd)) * B[2] + By = np.cos(np.deg2rad(Bi)) * np.sin(np.deg2rad(Bd)) * B[2] + Bz = np.sin(np.deg2rad(Bi)) * B[2] + + Bo = np.c_[Bx, By, Bz] + + Ptmi = mkvc(np.r_[np.cos(np.deg2rad(Bi))*np.cos(np.deg2rad(Bd)),np.cos(np.deg2rad(Bi))*np.sin(np.deg2rad(Bd)),np.sin(np.deg2rad(Bi))],2).T; + + bxa,bya,bza = PF.MagAnalytics.MagSphereFreeSpace(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0., 0., -sclx/3, 0.5*chiblk, Bo) + bxb,byb,bzb = PF.MagAnalytics.MagSphereFreeSpace(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R/3., -sclx/2., 0., -sclx/3.,4.*chiblk, Bo) + bxc,byc,bzc = PF.MagAnalytics.MagSphereFreeSpace(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R/2.5, sclx/2., 0., -sclx/2.5,2.5*chiblk, Bo) + + bx = bxa + bxb + bxc + by = bya + byb + byc + bz = bza + bzb + bzc + + b_tmi = mkvc(Ptmi.dot(np.c_[bx,by,bz].T)) + + r_tmi = d - b_tmi + #r_By = fwr_y - bya + #r_Bz = fwr_z - bza + + lrl[ii] = sum( r_tmi**2 ) **0.5 + + +#%% Write predicted to file + +PF.Magnetics.writeUBCobs('Obsloc.loc',B,M,rxLoc,d,np.ones(len(d))) + +#%% Plot results +print 'Residual between analytical sphere and integral forward' +for ii in range(d_iter): + nc = 3**(ii+1) + + print "||r||= " + str(lrl[ii]) + "\t dx= " + str(1./nc) + +#%% Plot fields +plt.figure(1) +#ax = plt.subplot(221) +plt.imshow(np.reshape(b_tmi,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar(fraction=0.04) +plt.contour(X,Y, np.reshape(b_tmi,X.shape),10) +plt.scatter(X,Y, c=np.reshape(b_tmi,X.shape), s=20) +ax.set_title('Analytical') + +#%% Plot the forward solution from integral +plt.figure(2) +#ax = plt.subplot(222) +plt.imshow(np.reshape(d,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') +plt.colorbar(fraction=0.04) +plt.contour(X,Y, np.reshape(d,X.shape),10) +plt.scatter(X,Y, c=np.reshape(d,X.shape), s=20) +ax.set_title('Numerical') + +#%% Plot residual data +plt.figure(3) +#ax = plt.subplot(212) +plt.imshow(np.reshape(r_tmi,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar(fraction=0.04) +plt.contour(X,Y, np.reshape(r_tmi,X.shape),10) +plt.scatter(X,Y, c=np.reshape(r_tmi,X.shape), s=20) +ax.set_title('Sphere Ana Bx') \ No newline at end of file diff --git a/simpegPF/Dev/Intgrl_MAG_Fwr_InterpData_Test.py b/simpegPF/Dev/Intgrl_MAG_Fwr_InterpData_Test.py new file mode 100644 index 00000000..3a9525ca --- /dev/null +++ b/simpegPF/Dev/Intgrl_MAG_Fwr_InterpData_Test.py @@ -0,0 +1,301 @@ +import os + +home_dir = 'C:\\LC\\Private\\dominiquef\\Projects\\4414_Minsim\\Modeling\\MAG' + +os.chdir(home_dir) + +#%% +from SimPEG import * +import matplotlib.pyplot as plt +import simpegPF as PF +import scipy.interpolate as interpolation +import scipy.signal as sn +import time + +#from fwr_MAG_data import fwr_MAG_data + +plt.close('all') + +topofile = 'Gaussian.topo' + +zoffset = 5 +#%% Create survey +# Load in topofile or create flat surface +if not topofile: + + actv = np.ones(mesh.nC) + +else: + topo = np.genfromtxt(topofile,skip_header=1) + + +B = np.array(([90.,0.,50000.])) + +M = np.array(([90.,0.,315.])) + +# Sphere radius +R = 25. + +sclx = 100. +dx = 5. +#%% Loop through decreasing meshes and measure the residual +# Create mesh using simpeg and write out in GIF format + +# # Or create juste a plane grid +xr = np.linspace(-102.5, 97.5, 41) +yr = np.linspace(-52.5, 47.5, 21) +X, Y = np.meshgrid(xr, yr) + +nc = int(sclx/dx) + +hxind = [(dx, 2*nc)] +hyind = [(dx, nc)] +hzind = [(dx, nc)] + +mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN') + +actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') + +# Drape observations on topo + offset +if not topofile: + Z = np.ones((xr.size, yr.size)) * 2.5 + +else: + F = interpolation.interp2d(topo[:,0],topo[:,1],topo[:,2]) + #F = interpolation.NearestNDInterpolator(topo[:,0:2],topo[:,2]) + Z = F(xr,yr) + zoffset + +rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] + +ndata = rxLoc.shape[0] + +xn = mesh.vectorNx +yn = mesh.vectorNy +zn = mesh.vectorNz + +mcell = mesh.nC + +print 'Mesh size: ' + str(mcell) + +#%% Create model +chibkg = 0.000 +chiblk = 0.01 +model = np.ones(mcell)*chibkg + +# Do a three sphere problem for more frequencies +sph_ind = PF.MagAnalytics.spheremodel(mesh, 0., 0., -sclx/3, R) +model[sph_ind] = 0.5*chiblk + +sph_ind = PF.MagAnalytics.spheremodel(mesh, -sclx/2., 0., -sclx/3., R/3.) +model[sph_ind] = 4.*chiblk + +sph_ind = PF.MagAnalytics.spheremodel(mesh, sclx/2., 0., -sclx/2.5, R/2.5) +model[sph_ind] = 2.5*chiblk + +Utils.writeUBCTensorMesh('Mesh.msh',mesh) +Utils.writeUBCTensorModel('Model.sus',mesh,model) +actv = np.ones(mesh.nC) +#%% Forward mode ldata +d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi') +#fwr_tmi = d[0:ndata] +#fwr_y = d[ndata:2*ndata] +#fwr_z = d[2*ndata:] + + +#%% Compute data on a line +xx = np.linspace(-101., 99., 200) +yy = np.zeros(len(xx)) +zz = F(xx,0.) + zoffset + +rxLoc = np.c_[xx,yy,zz] + +d_line = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi') + +d_iter = 4 + +l1_r = np.zeros((d_iter,5)) +l2_r = np.zeros((d_iter,5)) +linf_r = np.zeros((d_iter,5)) +timer = np.zeros((d_iter,5)) + +#%% Try different interpolation schemes +for ii in range(d_iter): + + indx = ii+1 + + d2d = np.reshape(d, (len(yr),len(xr))) + d2d = mkvc(d2d[::indx,::indx].T) + + xsub = xr[::indx] + ysub = yr[::indx] + + # Nearest Neighbourg + start_time = time.time() + F = interpolation.NearestNDInterpolator(np.c_[mkvc(Y[::indx,::indx].T),mkvc(X[::indx,::indx].T)],d2d) + d_i2d_nnb = mkvc( F(0.,xx) ) + l1_r[ii,0] = np.sum( np.abs(d_line - d_i2d_nnb) )**0.5 + l2_r[ii,0] = np.sum( (d_line - d_i2d_nnb)**2. ) + linf_r[ii,0] = np.max( np.abs(d_line - d_i2d_nnb) ) + timer[ii,0] = (time.time() - start_time) + + # Linear interpolation + start_time = time.time() + F = interpolation.interp2d(ysub,xsub,d2d) + d_i2d_lin = mkvc( F(0.,xx) ) + l1_r[ii,1] = np.sum( np.abs(d_line - d_i2d_lin) )**0.5 + l2_r[ii,1] = np.sum( (d_line - d_i2d_lin)**2. ) + linf_r[ii,1] = np.max( np.abs(d_line - d_i2d_lin) ) + timer[ii,1] = (time.time() - start_time) + + + # Cubic interpolation + start_time = time.time() + F = interpolation.interp2d(ysub,xsub,d2d,kind='cubic') + d_i2d_cub = mkvc( F(0.,xx) ) + l1_r[ii,2] = np.sum( np.abs(d_line - d_i2d_cub) )**0.5 + l2_r[ii,2] = np.sum( (d_line - d_i2d_cub)**2. ) + linf_r[ii,2] = np.max( np.abs(d_line - d_i2d_cub) ) + timer[ii,2] = (time.time() - start_time) + + + # Quintic interpolation + start_time = time.time() + F = interpolation.interp2d(ysub,xsub,d2d,kind='quintic') + d_i2d_qui = mkvc( F(0.,xx) ) + l1_r[ii,3] = np.sum( np.abs(d_line - d_i2d_qui) )**0.5 + l2_r[ii,3] = np.sum( (d_line - d_i2d_qui)**2. ) + linf_r[ii,3] = np.max( np.abs(d_line - d_i2d_qui) ) + timer[ii,3] = (time.time() - start_time) + + + + +#%% FFT interpolation + +d2d = np.reshape(d, (len(yr),len(xr))) + +# Add padding values by reflection (2**n) +lenx = np.floor( np.log2( 2*len(xr) ) ) +npadx = int(np.floor( ( 2**lenx - len(xr) ) /2. )) + +#Create hemming taper +if np.mod(npadx*2+len(xr),2) != 0: + oddx = 1 + +else: + oddx = 0 + +tap0 = sn.hamming(npadx*2) +tapl = sp.spdiags(tap0[0:npadx],0,npadx,npadx) +tapr = sp.spdiags(tap0[npadx:],0,npadx,npadx+oddx) + +# Mirror the 2d data over the half lenght and apply 0-taper +d2dpad = np.hstack([np.fliplr(d2d[:,0:npadx]) * tapl, d2d, np.fliplr(d2d[:,-npadx:]) * tapr]) + +# Repeat along the second dimension +leny = np.floor( np.log2( 2*len(yr) ) ) +npady = int(np.floor( ( 2**leny - len(yr) ) /2. )) + +#Create hemming taper +if np.mod(npady*2+len(yr),2) != 0: + oddy = 1 + +else: + oddy = 0 + +tap0 = sn.hamming(npady*2) +tapu = sp.spdiags(tap0[0:npady],0,npady,npady) +tapd = sp.spdiags(tap0[npady:],0,npady+oddy,npady) + +d2dpad = np.vstack([tapu*np.flipud(d2dpad[0:npady,:]), d2dpad, tapd*np.flipud(d2dpad[-npady:,:])]) + +# Compute FFT +FFTd2d = np.fft.fft2(d2dpad) + +# Compute IFFT at a given location +# Do an FFT shift +#FFTshift = np.fft.fftshift(FFTd2d) +FFTd2d = np.hstack([FFTd2d[:,0:31],np.zeros((32,64)),FFTd2d[:,31:]]) +FFTd2d = np.vstack([FFTd2d[0:15,:],np.zeros((32,128)),FFTd2d[15:,:]]) +# Pad with zeros +#temp = np.zeros((FFTd2d.shape[0]*2,FFTd2d.shape[1]*2)) + + +# Compute inverse FFT +IFFTd2d = np.fft.ifft2(FFTd2d)*FFTd2d.size/d2dpad.size + + + +# Extract core +#d2d_out = np.real(IFFTd2d[npady*2:-(npady*2+oddy+1),npadx*2:-(npadx*2+oddx+1)]) +d2d_out = np.real(IFFTd2d[:-1,:-1]) +d2d_out = d2d_out[npady*2:-(npady*2+oddy),npadx*2:-(npadx*2+oddx)] + +# Create new distance vector +XX = np.linspace(np.min(xr),np.max(xr),d2d_out.shape[1]) +YY = np.linspace(np.min(yr),np.max(yr),d2d_out.shape[0]) + +start_time = time.time() +F = interpolation.interp2d(XX,YY,d2d_out) +d_i2d_fft = mkvc( F(xx,0.) ) +l1_r[ii,4] = np.sum( np.abs(d_line - d_i2d_nnb) )**0.5 +l2_r[ii,4] = np.sum( (d_line - d_i2d_nnb)**2. ) +linf_r[ii,4] = np.max( np.abs(d_line - d_i2d_nnb) ) +timer[ii,4] = (time.time() - start_time) + +print("--- FFT completed in %s seconds ---" % (time.time() - start_time)) + +plt.figure() +ax = plt.subplot() +plt.imshow(d2d_out, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') +plt.colorbar(fraction=0.04) + + +#%% Write predicted to file + +#PF.Magnetics.writeUBCobs('Obsloc.loc',B,M,rxLoc,d,np.ones(len(d))) + +#%% Plot results +#============================================================================== +# print 'Residual between analytical sphere and integral forward' +# for ii in range(d_iter): +# nc = 3**(ii+1) +# +# print "||r||= " + str(lrl[ii]) + "\t dx= " + str(1./nc) +#============================================================================== + + +#%% Plot the forward solution from integral +plt.figure() +ax = plt.subplot() +plt.imshow(d2d, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') +plt.colorbar(fraction=0.04) + +plt.figure() +plt.contour(X,Y, np.reshape(d,X.shape),10) +plt.scatter(X,Y, c=np.reshape(d,X.shape), s=10) +plt.scatter(xx,yy, c='k', s=20, marker='o') +ax.set_title('Numerical') + + +#%% + +plt.figure() +ax = plt.subplot() +plt.plot(xx,d_line,c='r', linewidth=3) +plt.plot(xx,d_i2d_lin,c='b') +plt.plot(xx,d_i2d_cub,c='g') +plt.plot(xx,d_i2d_qui,c='m') +plt.plot(xx,d_i2d_nnb,c='k') +plt.plot(xx,d_i2d_fft,c='c',marker='o') +plt.plot(xr[::indx],np.zeros(len(xr[::indx])),c='r',linewidth=0.,marker='o') +ax.set_title('Analytical') + +#%% Write result to file +with file('l2_residual.dat','w') as fid: + fid.write('NearestN \t Linear \t Cubic \t Quintic \t FFT\n') + np.savetxt(fid, l2_r, fmt='%e',delimiter=' ',newline='\n') + +with file('l1_residual.dat','w') as fid: + fid.write('NearestN \t Linear \t Cubic \t Quintic \t FFT\n') + np.savetxt(fid, l1_r, fmt='%e',delimiter=' ',newline='\n') \ No newline at end of file diff --git a/simpegPF/Magnetics.py b/simpegPF/Magnetics.py index 20db93df..b3ed6cab 100644 --- a/simpegPF/Magnetics.py +++ b/simpegPF/Magnetics.py @@ -637,6 +637,7 @@ def get_T_mat(Xn,Yn,Zn,rxLoc): @author: dominiquef """ + mcell = Xn.shape[0] # Pre-allocate space for 1D array @@ -644,29 +645,29 @@ def get_T_mat(Xn,Yn,Zn,rxLoc): Ty = np.zeros((1,3*mcell)) Tz = np.zeros((1,3*mcell)) - dz2 = rxLoc[2] - Zn[:,0]; - dz1 = rxLoc[2] - Zn[:,1]; + dz2 = rxLoc[2] - Zn[:,0] + dz1 = rxLoc[2] - Zn[:,1] - dy2 = Yn[:,1] - rxLoc[1]; - dy1 = Yn[:,0] - rxLoc[1]; + dy2 = Yn[:,1] - rxLoc[1] + dy1 = Yn[:,0] - rxLoc[1] - dx2 = Xn[:,1] - rxLoc[0]; - dx1 = Xn[:,0] - rxLoc[0]; + dx2 = Xn[:,1] - rxLoc[0] + dx1 = Xn[:,0] - rxLoc[0] - R1 = ( dy2**2 + dx2**2 ); - R2 = ( dy2**2 + dx1**2 ); - R3 = ( dy1**2 + dx2**2 ); - R4 = ( dy1**2 + dx1**2 ); + 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 ); + 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 ) From 27900498c65e3d0374e30d4c8cad06130a8a1a25 Mon Sep 17 00:00:00 2001 From: D Fournier Date: Mon, 11 Jan 2016 16:40:28 -0800 Subject: [PATCH 14/31] Add surface to mesh test Fix forward function call --- .../Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py | 59 ++++--- .../Dev/Intgrl_MAG_Fwr_InterpData_Test.py | 158 +++++++----------- simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py | 4 +- simpegPF/Dev/interpFFT.py | 85 ++++++++++ 4 files changed, 184 insertions(+), 122 deletions(-) create mode 100644 simpegPF/Dev/interpFFT.py diff --git a/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py b/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py index 62d52c2e..115b5e4a 100644 --- a/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py +++ b/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py @@ -9,6 +9,7 @@ from SimPEG import * import matplotlib.pyplot as plt import simpegPF as PF import scipy.interpolate as interpolation +import time #from fwr_MAG_data import fwr_MAG_data @@ -39,24 +40,33 @@ xr = np.linspace(-99., 99., 40) yr = np.linspace(-49., 49., 20) X, Y = np.meshgrid(xr, yr) -d_iter = 1 -lrl = np.zeros(d_iter) + + sclx = 100. -dx = 5 +dx = np.asarray([15., 10., 5., 2.5]) + +d_iter = len(dx) +l1_r = np.zeros(d_iter) +l2_r = np.zeros(d_iter) +linf_r = np.zeros(d_iter) +timer = np.zeros(d_iter) +mcell = np.zeros(d_iter) #%% Loop through decreasing meshes and measure the residual # Create mesh using simpeg and write out in GIF format for ii in range(d_iter): - nc = int(sclx/dx) + nc = int(sclx/dx[ii]) - hxind = [(dx, 2*nc)] - hyind = [(dx, nc)] - hzind = [(dx, nc)] + hxind = [(dx[ii], 2*nc)] + hyind = [(dx[ii], nc)] + hzind = [(dx[ii], nc)] mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN') + mcell[ii] = mesh.nC + actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') # Drape observations on topo + offset @@ -74,15 +84,13 @@ for ii in range(d_iter): xn = mesh.vectorNx yn = mesh.vectorNy zn = mesh.vectorNz - - mcell = mesh.nC - - print 'Mesh size: ' + str(mcell) + + print 'Mesh size: ' + str(mcell[ii]) #%% Create model chibkg = 0. chiblk = 0.01 - model = np.ones(mcell)*chibkg + model = np.ones(mcell[ii])*chibkg # Do a three sphere problem for more frequencies sph_ind = PF.MagAnalytics.spheremodel(mesh, 0., 0., -sclx/3, R) @@ -98,7 +106,13 @@ for ii in range(d_iter): Utils.writeUBCTensorModel('Model.sus',mesh,model) actv = np.ones(mesh.nC) #%% Forward mode ldata + + start_time = time.time() + d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi') + + timer[ii] = (time.time() - start_time) + #fwr_tmi = d[0:ndata] #fwr_y = d[ndata:2*ndata] #fwr_z = d[2*ndata:] @@ -130,8 +144,9 @@ for ii in range(d_iter): #r_By = fwr_y - bya #r_Bz = fwr_z - bza - lrl[ii] = sum( r_tmi**2 ) **0.5 - + l2_r[ii] = np.sum( r_tmi**2 ) **0.5 + l1_r[ii] = np.sum( np.abs( r_tmi ) ) + linf_r[ii] = np.max( np.abs( r_tmi ) ) #%% Write predicted to file @@ -139,34 +154,34 @@ PF.Magnetics.writeUBCobs('Obsloc.loc',B,M,rxLoc,d,np.ones(len(d))) #%% Plot results print 'Residual between analytical sphere and integral forward' +print "dx \t nc \t l1 \t l2 \t linf \t Runtime" for ii in range(d_iter): - nc = 3**(ii+1) - print "||r||= " + str(lrl[ii]) + "\t dx= " + str(1./nc) + print str(dx[ii]) + "\t" + str(mcell[ii]) + "\t" + str(l1_r[ii]) + "\t" + str(l2_r[ii]) + "\t" + str(linf_r[ii]) + "\t" + str(timer[ii]) #%% Plot fields plt.figure(1) -#ax = plt.subplot(221) +ax = plt.subplot() plt.imshow(np.reshape(b_tmi,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) +plt.colorbar(fraction=0.02) plt.contour(X,Y, np.reshape(b_tmi,X.shape),10) plt.scatter(X,Y, c=np.reshape(b_tmi,X.shape), s=20) ax.set_title('Analytical') #%% Plot the forward solution from integral plt.figure(2) -#ax = plt.subplot(222) +ax = plt.subplot() plt.imshow(np.reshape(d,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') -plt.colorbar(fraction=0.04) +plt.colorbar(fraction=0.02) plt.contour(X,Y, np.reshape(d,X.shape),10) plt.scatter(X,Y, c=np.reshape(d,X.shape), s=20) ax.set_title('Numerical') #%% Plot residual data plt.figure(3) -#ax = plt.subplot(212) +ax = plt.subplot() plt.imshow(np.reshape(r_tmi,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) +plt.colorbar(fraction=0.02) plt.contour(X,Y, np.reshape(r_tmi,X.shape),10) plt.scatter(X,Y, c=np.reshape(r_tmi,X.shape), s=20) ax.set_title('Sphere Ana Bx') \ No newline at end of file diff --git a/simpegPF/Dev/Intgrl_MAG_Fwr_InterpData_Test.py b/simpegPF/Dev/Intgrl_MAG_Fwr_InterpData_Test.py index 3a9525ca..6c747f4a 100644 --- a/simpegPF/Dev/Intgrl_MAG_Fwr_InterpData_Test.py +++ b/simpegPF/Dev/Intgrl_MAG_Fwr_InterpData_Test.py @@ -1,19 +1,18 @@ -import os - -home_dir = 'C:\\LC\\Private\\dominiquef\\Projects\\4414_Minsim\\Modeling\\MAG' - -os.chdir(home_dir) - #%% from SimPEG import * import matplotlib.pyplot as plt import simpegPF as PF import scipy.interpolate as interpolation -import scipy.signal as sn import time - +from interpFFT import interpFFT #from fwr_MAG_data import fwr_MAG_data +import os + +home_dir = 'C:\\LC\\Private\\dominiquef\\Projects\\4414_Minsim\\Modeling\\MAG' + +os.chdir(home_dir) + plt.close('all') topofile = 'Gaussian.topo' @@ -78,7 +77,7 @@ mcell = mesh.nC print 'Mesh size: ' + str(mcell) #%% Create model -chibkg = 0.000 +chibkg = 0.0001 chiblk = 0.01 model = np.ones(mcell)*chibkg @@ -92,9 +91,12 @@ model[sph_ind] = 4.*chiblk sph_ind = PF.MagAnalytics.spheremodel(mesh, sclx/2., 0., -sclx/2.5, R/2.5) model[sph_ind] = 2.5*chiblk +# Zero out +model[actv==0] = -100 Utils.writeUBCTensorMesh('Mesh.msh',mesh) Utils.writeUBCTensorModel('Model.sus',mesh,model) -actv = np.ones(mesh.nC) +Utils.writeUBCTensorModel('nullcell.dat',mesh,actv) +#actv = np.ones(mesh.nC) #%% Forward mode ldata d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi') #fwr_tmi = d[0:ndata] @@ -103,7 +105,7 @@ d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi') #%% Compute data on a line -xx = np.linspace(-101., 99., 200) +xx = np.linspace(xr.min(), xr.max(), 200) yy = np.zeros(len(xx)) zz = F(xx,0.) + zoffset @@ -118,20 +120,21 @@ l2_r = np.zeros((d_iter,5)) linf_r = np.zeros((d_iter,5)) timer = np.zeros((d_iter,5)) +d2d = np.reshape(d, (len(yr),len(xr))) #%% Try different interpolation schemes for ii in range(d_iter): indx = ii+1 - d2d = np.reshape(d, (len(yr),len(xr))) - d2d = mkvc(d2d[::indx,::indx].T) + + dsub = d2d[::indx,::indx] xsub = xr[::indx] ysub = yr[::indx] # Nearest Neighbourg start_time = time.time() - F = interpolation.NearestNDInterpolator(np.c_[mkvc(Y[::indx,::indx].T),mkvc(X[::indx,::indx].T)],d2d) + F = interpolation.NearestNDInterpolator(np.c_[mkvc(Y[::indx,::indx].T),mkvc(X[::indx,::indx].T)],mkvc(dsub.T)) d_i2d_nnb = mkvc( F(0.,xx) ) l1_r[ii,0] = np.sum( np.abs(d_line - d_i2d_nnb) )**0.5 l2_r[ii,0] = np.sum( (d_line - d_i2d_nnb)**2. ) @@ -140,7 +143,7 @@ for ii in range(d_iter): # Linear interpolation start_time = time.time() - F = interpolation.interp2d(ysub,xsub,d2d) + F = interpolation.interp2d(ysub,xsub,mkvc(dsub.T)) d_i2d_lin = mkvc( F(0.,xx) ) l1_r[ii,1] = np.sum( np.abs(d_line - d_i2d_lin) )**0.5 l2_r[ii,1] = np.sum( (d_line - d_i2d_lin)**2. ) @@ -150,7 +153,7 @@ for ii in range(d_iter): # Cubic interpolation start_time = time.time() - F = interpolation.interp2d(ysub,xsub,d2d,kind='cubic') + F = interpolation.interp2d(ysub,xsub,mkvc(dsub.T),kind='cubic') d_i2d_cub = mkvc( F(0.,xx) ) l1_r[ii,2] = np.sum( np.abs(d_line - d_i2d_cub) )**0.5 l2_r[ii,2] = np.sum( (d_line - d_i2d_cub)**2. ) @@ -160,95 +163,48 @@ for ii in range(d_iter): # Quintic interpolation start_time = time.time() - F = interpolation.interp2d(ysub,xsub,d2d,kind='quintic') + F = interpolation.interp2d(ysub,xsub,mkvc(dsub.T),kind='quintic') d_i2d_qui = mkvc( F(0.,xx) ) l1_r[ii,3] = np.sum( np.abs(d_line - d_i2d_qui) )**0.5 l2_r[ii,3] = np.sum( (d_line - d_i2d_qui)**2. ) linf_r[ii,3] = np.max( np.abs(d_line - d_i2d_qui) ) timer[ii,3] = (time.time() - start_time) + # CloughTocher interpolation + start_time = time.time() + F = interpolation.CloughTocher2DInterpolator(np.c_[mkvc(Y[::indx,::indx].T),mkvc(X[::indx,::indx].T)],mkvc(dsub.T)) + d_i2d_CTI = mkvc( F(0.,xx) ) + l1_r[ii,4] = np.sum( np.abs(d_line - d_i2d_CTI) )**0.5 + l2_r[ii,4] = np.sum( (d_line - d_i2d_CTI)**2. ) + linf_r[ii,4] = np.max( np.abs(d_line - d_i2d_CTI) ) + timer[ii,4] = (time.time() - start_time) + - -#%% FFT interpolation - -d2d = np.reshape(d, (len(yr),len(xr))) - -# Add padding values by reflection (2**n) -lenx = np.floor( np.log2( 2*len(xr) ) ) -npadx = int(np.floor( ( 2**lenx - len(xr) ) /2. )) - -#Create hemming taper -if np.mod(npadx*2+len(xr),2) != 0: - oddx = 1 - -else: - oddx = 0 - -tap0 = sn.hamming(npadx*2) -tapl = sp.spdiags(tap0[0:npadx],0,npadx,npadx) -tapr = sp.spdiags(tap0[npadx:],0,npadx,npadx+oddx) - -# Mirror the 2d data over the half lenght and apply 0-taper -d2dpad = np.hstack([np.fliplr(d2d[:,0:npadx]) * tapl, d2d, np.fliplr(d2d[:,-npadx:]) * tapr]) - -# Repeat along the second dimension -leny = np.floor( np.log2( 2*len(yr) ) ) -npady = int(np.floor( ( 2**leny - len(yr) ) /2. )) - -#Create hemming taper -if np.mod(npady*2+len(yr),2) != 0: - oddy = 1 - -else: - oddy = 0 - -tap0 = sn.hamming(npady*2) -tapu = sp.spdiags(tap0[0:npady],0,npady,npady) -tapd = sp.spdiags(tap0[npady:],0,npady+oddy,npady) - -d2dpad = np.vstack([tapu*np.flipud(d2dpad[0:npady,:]), d2dpad, tapd*np.flipud(d2dpad[-npady:,:])]) - -# Compute FFT -FFTd2d = np.fft.fft2(d2dpad) - -# Compute IFFT at a given location -# Do an FFT shift -#FFTshift = np.fft.fftshift(FFTd2d) -FFTd2d = np.hstack([FFTd2d[:,0:31],np.zeros((32,64)),FFTd2d[:,31:]]) -FFTd2d = np.vstack([FFTd2d[0:15,:],np.zeros((32,128)),FFTd2d[15:,:]]) -# Pad with zeros -#temp = np.zeros((FFTd2d.shape[0]*2,FFTd2d.shape[1]*2)) - - -# Compute inverse FFT -IFFTd2d = np.fft.ifft2(FFTd2d)*FFTd2d.size/d2dpad.size - - - -# Extract core -#d2d_out = np.real(IFFTd2d[npady*2:-(npady*2+oddy+1),npadx*2:-(npadx*2+oddx+1)]) -d2d_out = np.real(IFFTd2d[:-1,:-1]) -d2d_out = d2d_out[npady*2:-(npady*2+oddy),npadx*2:-(npadx*2+oddx)] - -# Create new distance vector -XX = np.linspace(np.min(xr),np.max(xr),d2d_out.shape[1]) -YY = np.linspace(np.min(yr),np.max(yr),d2d_out.shape[0]) - -start_time = time.time() -F = interpolation.interp2d(XX,YY,d2d_out) -d_i2d_fft = mkvc( F(xx,0.) ) -l1_r[ii,4] = np.sum( np.abs(d_line - d_i2d_nnb) )**0.5 -l2_r[ii,4] = np.sum( (d_line - d_i2d_nnb)**2. ) -linf_r[ii,4] = np.max( np.abs(d_line - d_i2d_nnb) ) -timer[ii,4] = (time.time() - start_time) - -print("--- FFT completed in %s seconds ---" % (time.time() - start_time)) - -plt.figure() -ax = plt.subplot() -plt.imshow(d2d_out, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') -plt.colorbar(fraction=0.04) +#============================================================================== +# #%% FFT interpolation +# d2d_out = interpFFT(xsub,ysub,dsub) +# +# # Create new distance vector +# XX = np.linspace(np.min(xsub),np.max(xsub),d2d_out.shape[1]) +# YY = np.linspace(np.min(ysub),np.max(ysub),d2d_out.shape[0]) +# +# start_time = time.time() +# F = interpolation.interp2d(XX,YY,d2d_out) +# d_i2d_fft = mkvc( F(xx,0.) ) +# l1_r[ii,4] = np.sum( np.abs(d_line - d_i2d_nnb) )**0.5 +# l2_r[ii,4] = np.sum( (d_line - d_i2d_nnb)**2. ) +# linf_r[ii,4] = np.max( np.abs(d_line - d_i2d_nnb) ) +# timer[ii,4] = (time.time() - start_time) +# +# print("--- FFT completed in %s seconds ---" % (time.time() - start_time)) +# +# +# plt.figure() +# ax = plt.subplot() +# plt.imshow(d2d_out, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') +# plt.colorbar(fraction=0.04) +#============================================================================== #%% Write predicted to file @@ -287,8 +243,12 @@ plt.plot(xx,d_i2d_lin,c='b') plt.plot(xx,d_i2d_cub,c='g') plt.plot(xx,d_i2d_qui,c='m') plt.plot(xx,d_i2d_nnb,c='k') -plt.plot(xx,d_i2d_fft,c='c',marker='o') -plt.plot(xr[::indx],np.zeros(len(xr[::indx])),c='r',linewidth=0.,marker='o') +plt.plot(xx,d_i2d_CTI,c='c') + +# Plot interpolation from true value on line +F = interpolation.interp1d(xx,d_line) +dtrue = F(xr[::indx]) +plt.plot(xr[::indx],dtrue,c='r',linewidth=0.,marker='o') ax.set_title('Analytical') #%% Write result to file diff --git a/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py b/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py index ddea3760..fbf48ae7 100644 --- a/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py +++ b/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py @@ -61,8 +61,10 @@ for ii in range(d_iter): model = np.ones(mcell)*chibkg model[sph_ind] = chiblk + actv = np.ones(mcell) + #%% Forward mode ldata - d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,'xyz') + d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'xyz') fwr_x = d[0:ndata] fwr_y = d[ndata:2*ndata] fwr_z = d[2*ndata:] diff --git a/simpegPF/Dev/interpFFT.py b/simpegPF/Dev/interpFFT.py new file mode 100644 index 00000000..76269f96 --- /dev/null +++ b/simpegPF/Dev/interpFFT.py @@ -0,0 +1,85 @@ +def interpFFT(x,y,m): + """ + Load in a 2D grid and resample + + OUTPUT: + m_out + + + """ + + from SimPEG import np, sp + import scipy.signal as sn + + + # Add padding values by reflection (2**n) + lenx = np.round( np.log2( 2*len(x) ) ) + npadx = int(np.floor( ( 2**lenx - len(x) ) /2. )) + + #Create hemming taper + if np.mod(npadx*2+len(x),2) != 0: + oddx = 1 + + else: + oddx = 0 + + tap0 = sn.hamming(npadx*2) + tapl = sp.spdiags(tap0[0:npadx],0,npadx,npadx) + tapr = sp.spdiags(tap0[npadx:],0,npadx,npadx+oddx) + + # Mirror the 2d data over the half lenght and apply 0-taper + mpad = np.hstack([np.fliplr(m[:,0:npadx]) * tapl, m, np.fliplr(m[:,-npadx:]) * tapr]) + + # Repeat along the second dimension + leny = np.round( np.log2( 2*len(y) ) ) + npady = int(np.floor( ( 2**leny - len(y) ) /2. )) + + #Create hemming taper + if np.mod(npady*2+len(y),2) != 0: + oddy = 1 + + else: + oddy = 0 + + tap0 = sn.hamming(npady*2) + tapu = sp.spdiags(tap0[0:npady],0,npady,npady) + tapd = sp.spdiags(tap0[npady:],0,npady+oddy,npady) + + mpad = np.vstack([tapu*np.flipud(mpad[0:npady,:]), mpad, tapd*np.flipud(mpad[-npady:,:])]) + + # Compute FFT + FFTm = np.fft.fft2(mpad) + + # Do an FFT shift + FFTshift = np.fft.fftshift(FFTm) + + # Pad high frequencies with zeros to increase the sampling rate + py = int(FFTm.shape[0]/2) + px = int(FFTm.shape[1]/2) + + FFTshift = np.hstack([np.zeros((FFTshift.shape[0],px)),FFTshift,np.zeros((FFTshift.shape[0],px))]) + FFTshift = np.vstack([np.zeros((py,FFTshift.shape[1])),FFTshift,np.zeros((py,FFTshift.shape[1]))]) + + # Inverse shift + FFTm = np.fft.ifftshift(FFTshift) + + + # Compute inverse FFT + IFFTm = np.fft.ifft2(FFTm)*FFTm.size/mpad.size + + + m_out = np.real(IFFTm) + # Extract core + #m_out = np.real(IFFTm[npady*2:-(npady*2+oddy+1),npadx*2:-(npadx*2+oddx+1)]) + + + + m_out = m_out[npady*2:-(npady+oddy)*2,npadx*2:-(npadx+oddx)*2] + + if np.mod(m.shape[0],2) != 0: + m_out = m_out[:-1,:] + + if np.mod(m.shape[1],2) != 0: + m_out = m_out[:,:-1] + + return m_out From dbcd57bc4e5d0d6258a10b760966b58419e0a9d2 Mon Sep 17 00:00:00 2001 From: Rowan Cockett Date: Mon, 11 Jan 2016 18:35:19 -0800 Subject: [PATCH 15/31] Minor clean ups. --- simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py | 64 ++-- simpegPF/Magnetics.py | 357 +++++++++++----------- 2 files changed, 205 insertions(+), 216 deletions(-) diff --git a/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py b/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py index fbf48ae7..4aed811b 100644 --- a/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py +++ b/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py @@ -1,8 +1,8 @@ import os -home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' +# home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' -os.chdir(home_dir) +# os.chdir(home_dir) #%% from SimPEG import * @@ -16,12 +16,12 @@ plt.close('all') #%% Create survey B = np.array(([-45.,315.,50000.])) - + M = np.array(([-45.,315.])) - + # Sphere radius R = 0.25 - + # # Or create juste a plane grid xr = np.linspace(-2., 2., 5) yr = np.linspace(-2., 2., 5) @@ -37,67 +37,67 @@ lrl = np.zeros(d_iter) # Create mesh using simpeg and write out in GIF format for ii in range(d_iter): - + nc = 3**(ii+1) - + 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 - + print 'Mesh size: ' + str(mcell) - + sph_ind = PF.MagAnalytics.spheremodel(mesh, 0, 0, 0, R) - + chibkg = 0. chiblk = 0.01 model = np.ones(mcell)*chibkg model[sph_ind] = chiblk - + actv = np.ones(mcell) - + #%% Forward mode ldata d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'xyz') fwr_x = d[0:ndata] fwr_y = d[ndata:2*ndata] fwr_z = d[2*ndata:] - + #%% Get the analystical answer and compute the residual bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') Bd = (450.-float(B[1]))%360. - Bi = B[0]; # Convert dip to horizontal to cartesian - - Bx = np.cos(np.deg2rad(Bi)) * np.cos(np.deg2rad(Bd)) * B[2] - By = np.cos(np.deg2rad(Bi)) * np.sin(np.deg2rad(Bd)) * B[2] - Bz = np.sin(np.deg2rad(Bi)) * B[2] - + Bi = B[0]; # Convert dip to horizontal to cartesian + + Bx = np.cos(np.deg2rad(Bi)) * np.cos(np.deg2rad(Bd)) * B[2] + By = np.cos(np.deg2rad(Bi)) * np.sin(np.deg2rad(Bd)) * B[2] + Bz = np.sin(np.deg2rad(Bi)) * B[2] + Bo = np.c_[Bx, By, Bz] - + bxa,bya,bza = PF.MagAnalytics.MagSphereFreeSpace(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, Bo) #bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') - + r_Bx = fwr_x - bxa r_By = fwr_y - bya r_Bz = fwr_z - bza - - lrl[ii] = sum( r_Bx**2 + r_By**2 + r_Bz**2 ) **0.5 - - + lrl[ii] = sum( r_Bx**2 + r_By**2 + r_Bz**2 ) **0.5 + + + #%% Plot results -print 'Residual between analytical sphere and integral forward' +print 'Residual between analytical sphere and integral forward' for ii in range(d_iter): nc = 3**(ii+1) print "||r||= " + str(lrl[ii]) + "\t dx= " + str(1./nc) - + #%% Plot fields plt.figure(1) @@ -168,4 +168,6 @@ plt.imshow(np.reshape(r_Bz,X.shape).T, interpolation="bicubic", extent=[xr.min() plt.colorbar(fraction=0.04) plt.contour(X,Y, np.reshape(r_Bz,X.shape).T,10) plt.scatter(X,Y, c=np.reshape(r_Bz,X.shape).T, s=20) -ax.set_title('Sphere Ana Bz') \ No newline at end of file +ax.set_title('Sphere Ana Bz') + +plt.show() diff --git a/simpegPF/Magnetics.py b/simpegPF/Magnetics.py index b3ed6cab..6017a36d 100644 --- a/simpegPF/Magnetics.py +++ b/simpegPF/Magnetics.py @@ -59,7 +59,7 @@ class MagneticsDiffSecondary(Problem.BaseProblem): .. math :: \mathbf{rhs} = \Div(\MfMui)^{-1}\mathbf{M}^f_{\mu_0^{-1}}\mathbf{B}_0 - \Div\mathbf{B}_0+\diag(v)\mathbf{D} \mathbf{P}_{out}^T \mathbf{B}_{sBC} - + """ B0 = self.getB0() Dface = self.mesh.faceDiv @@ -68,13 +68,13 @@ class MagneticsDiffSecondary(Problem.BaseProblem): mu = self.mapping*m chi = mu/mu_0-1 - + #temporary fix Bbc, Bbc_const = CongruousMagBC(self.mesh, self.survey.B0, chi) self.Bbc = Bbc self.Bbc_const = Bbc_const # return self._Div*self.MfMuI*self.MfMu0*B0 - self._Div*B0 + Mc*Dface*self._Pout.T*Bbc - return self._Div*self.MfMuI*self.MfMu0*B0 - self._Div*B0 + return self._Div*self.MfMuI*self.MfMu0*B0 - self._Div*B0 def getA(self, m): """ @@ -410,13 +410,13 @@ if __name__ == '__main__': def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,flag): - """ + """ Forward model magnetic data using integral equation - + INPUT: - xn, yn, zn = Mesh nodes location + mesh = SimPEG.TensorMesh B = Inducing field parameter [Binc, Bdecl, B0] - M = Magnetization matrix [Minc, Mdecl] + M = Magnetization matrix [Minc, Mdecl] -90:90, 0:360 rxLox = Observation location informat [obsx, obsy, obsz] model = Model associated with mesh actv = Active cells from topo (from getActiveTopo) @@ -428,124 +428,125 @@ def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,flag): Created on Oct 7, 2015 @author: dominiquef - """ - + """ + inds = np.nonzero(actv)[0] P = sp.csr_matrix((np.ones(inds.size),(inds, range(inds.size))), shape=(mesh.nC, len(inds))) xn = mesh.vectorNx; yn = mesh.vectorNy; zn = mesh.vectorNz; - + yn2,xn2,zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:]) yn1,xn1,zn1 = np.meshgrid(yn[0:-1], xn[0:-1], zn[0:-1]) - + Yn = P.T*np.c_[mkvc(yn1), mkvc(yn2)] Xn = P.T*np.c_[mkvc(xn1), mkvc(xn2)] Zn = P.T*np.c_[mkvc(zn1), mkvc(zn2)] - + mcell = len(inds) - - ndata = rxLoc.shape[0] - + + 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 + + # 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; - + + if flag=='tmi': + 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) - + elif flag=='xyz': d = np.zeros(int(3*ndata)) - + # 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. count = -1 for ii in range(ndata): - - tx, ty, tz = get_T_mat(Xn,Yn,Zn,rxLoc[ii,:]) + + tx, ty, tz = get_T_mat(Xn,Yn,Zn,rxLoc[ii,:]) Gxyz = np.vstack((tx,ty,tz))*Mxyz - + + # Remove non-active cells if flag=='xyz': d[ii::ndata] = mkvc(Gxyz.dot(P.T*model)) - + elif flag=='tmi': d[ii] = Ptmi.dot(Gxyz.dot(P.T*model)) - + # Display progress count = progress(ii,count,ndata) - - + + print "Done 100% ...forward modeling completed!!\n" - + return d def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): - """ + """ Magnetic forward operator in integral form - + INPUT: mesh = Mesh in SimPEG format B = Inducing field parameter [Binc, Bdecl, B0] - M = Magnetization information + M = Magnetization information [OPTIONS] 1- [Minc, Mdecl] : Assumes uniform magnetization orientation 2- [mx1,mx2,..., my1,...,mz1] : cell-based defined magnetization direction 3- diag(M): Block diagonal matrix with [Mx, My, Mz] along the diagonal - + rxLox = Observation location informat [obsx, obsy, obsz] - + flag = 'tmi' | 'xyz' | 'full' [OPTIONS] 1- tmi : Magnetization direction used and data are projected onto the inducing field direction F.shape([ndata, nc]) - + 2- xyz : Magnetization direction used and data are given in 3-components F.shape([3*ndata, nc]) - + 3- full: Full tensor matrix stored with shape([3*ndata, 3*nc]) - + OUTPUT: F = Linear forward modeling operation Created on Dec, 20th 2015 @author: dominiquef - """ - + """ + xn = mesh.vectorNx; yn = mesh.vectorNy; zn = mesh.vectorNz; - - ndata = rxLoc.shape[0] - - - # Convert Bdecination from north to cartesian + + ndata = rxLoc.shape[0] + + + # Convert Bdecination from north to cartesian D = (450.-float(B[1]))%360. - - + + # Pre-allocate space and create magnetization matrix if required if (flag=='tmi') | (flag == 'xyz'): # If assumes uniform magnetization direction @@ -553,16 +554,16 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): print 'Magnetization vector must be 3*Ncells' return - - + + Mx = Utils.sdiag(M[:,0]*B[2]) My = Utils.sdiag(M[:,1]*B[2]) Mz = Utils.sdiag(M[:,2]*B[2]) - - Mxyz = sp.vstack((Mx,My,Mz)) - - + Mxyz = sp.vstack((Mx,My,Mz)) + + + if flag == 'tmi': F = np.zeros((ndata, mesh.nC)) @@ -570,56 +571,56 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): 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; - + elif flag == 'xyz': F = np.zeros((int(3*ndata), mesh.nC)) - - elif flag == 'full': + + elif flag == 'full': F = np.zeros((int(3*ndata), int(3*mesh.nC))) - + else: print """Flag must be either 'tmi' | 'xyz' | 'full', please revised""" return - - + + # Loop through all observations and create forward operator (ndata-by-mcell) print "Begin calculation of forward operator: " + flag - + # Add counter to dsiplay progress. Good for large problems count = -1; for ii in range(ndata): - - tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) - + + tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) + if flag=='tmi': F[ii,:] = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz - + elif flag == 'xyz': F[ii,:] = tx*Mxyz F[ii+ndata,:] = ty*Mxyz F[ii+2*ndata,:] = tz*Mxyz - - elif flag == 'full': + + elif flag == 'full': F[ii,:] = tx F[ii+ndata,:] = ty F[ii+2*ndata,:] = tz - - + + # Display progress count = progress(ii,count,ndata) - - + + print "Done 100% ...forward modeling completed!!\n" - + return F def get_T_mat(Xn,Yn,Zn,rxLoc): - """ - Load in the active nodes of a tensor mesh and computes the magnetic tensor + """ + Load in the active nodes of a tensor mesh and computes the magnetic tensor for a given observation location rxLoc[obsx, obsy, obsz] - + INPUT: - Xn, Yn, Zn: Node location matrix for the lower and upper most corners of + Xn, Yn, Zn: Node location matrix for the lower and upper most corners of all cells in the mesh shape[nC,2] M OUTPUT: @@ -631,35 +632,35 @@ def get_T_mat(Xn,Yn,Zn,rxLoc): 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. - + Created on Oct, 20th 2015 @author: dominiquef - """ - - + """ + + mcell = Xn.shape[0] - + # Pre-allocate space for 1D array Tx = np.zeros((1,3*mcell)) Ty = np.zeros((1,3*mcell)) Tz = np.zeros((1,3*mcell)) - + dz2 = rxLoc[2] - Zn[:,0] dz1 = rxLoc[2] - Zn[:,1] - + dy2 = Yn[:,1] - rxLoc[1] dy1 = Yn[:,0] - rxLoc[1] - + dx2 = Xn[:,1] - rxLoc[0] dx1 = Xn[:,0] - 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 ) @@ -668,9 +669,9 @@ def get_T_mat(Xn,Yn,Zn,rxLoc): 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 ) ) +\ @@ -679,13 +680,13 @@ def get_T_mat(Xn,Yn,Zn,rxLoc): - 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 ) ) +\ @@ -694,57 +695,57 @@ def get_T_mat(Xn,Yn,Zn,rxLoc): - 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); - - + Tz = Tz/(4*np.pi); + + return Tx,Ty,Tz def progress(iter,prog,final): - """ + """ progress(iter,prog,final) - + Function measuring the progress of a process and print to screen the %. Useful to estimate the remaining runtime of a large problem. - + Created on Dec, 20th 2015 @author: dominiquef """ arg = np.floor(float(iter)/float(final)*10.); - + if arg > prog: - - strg = "Done " + str(arg*10) + " %" + + strg = "Done " + str(arg*10) + " %" print strg prog = arg; @@ -753,73 +754,73 @@ def progress(iter,prog,final): def dipazm_2_xyz(dip,azm_N): """ dipazm_2_xyz(dip,azm_N) - + Function converting degree angles for dip and azimuth from north to a 3-components in cartesian coordinates. - + INPUT dip : Value or vector of dip from horizontal in DEGREE azm_N : Value or vector of azimuth from north in DEGREE - + OUTPUT M : [n-by-3] Array of xyz components of a unit vector in cartesian - + Created on Dec, 20th 2015 @author: dominiquef """ mcell = len(azm_N) - + M = np.zeros((mcell,3)) - + # Modify azimuth from North to Cartesian-X azm_X = (450.- azm_N) % 360. - + D = np.deg2rad(dip) I = np.deg2rad(azm_X) - + M[:,0] = np.cos(D) * np.cos(I) ; M[:,1] = np.cos(D) * np.sin(I) ; M[:,2] = np.sin(D) ; - + return M def get_dist_wgt(mesh,rxLoc,R,R0): """ get_dist_wgt(xn,yn,zn,rxLoc,R,R0) - + Function creating a distance weighting function required for the magnetic inverse problem. - + INPUT xn, yn, zn : Node location rxLoc : Observation locations [obsx, obsy, obsz] R : Decay factor (mag=3, grav =2) R0 : Small factor added (default=dx/4) - + OUTPUT wr : [mcell] Vector of distance weighting - + Created on Dec, 20th 2015 @author: dominiquef """ - + p = 1/np.sqrt(3); - + # Create cell center location Ym,Xm,Zm = np.meshgrid(mesh.vectorCCy, mesh.vectorCCx, mesh.vectorCCz) - hY,hX,hZ = np.meshgrid(mesh.hy, mesh.hx, mesh.hz) - + hY,hX,hZ = np.meshgrid(mesh.hy, mesh.hx, mesh.hz) + V = np.reshape(mesh.vol,hY.shape) wr = np.zeros(hY.shape) - + ndata = rxLoc.shape[0] count = -1; print "Begin calculation of distance weighting for R= " + str(R) - + for dd in range(ndata): - + nx1 = (Xm - hX * p - rxLoc[dd,0])**2 nx2 = (Xm + hX * p - rxLoc[dd,0])**2 @@ -837,26 +838,26 @@ def get_dist_wgt(mesh,rxLoc,R,R0): R6 = np.sqrt(nx1 + ny2 + nz2) R7 = np.sqrt(nx2 + ny2 + nz1) R8 = np.sqrt(nx2 + ny2 + nz2) - + temp = (R1 + R0)**-R + (R2 + R0)**-R + (R3 + R0)**-R + (R4 + R0)**-R + (R5 + R0)**-R + (R6 + R0)**-R + (R7 + R0)**-R + (R8 + R0)**-R wr = wr + (V*temp/8.)**2 - + count = progress(dd,count,ndata) - - + + wr = np.sqrt(wr)/V wr = mkvc(wr) wr = np.sqrt(wr/(np.max(wr))) - + return wr - + def writeUBCobs(filename,B,M,rxLoc,d,wd): """ writeUBCobs(filename,B,M,rxLoc,d,wd) - + Function writing an observation file in UBC-MAG3D format. - + INPUT filename : Name of out file including directory B : Inducing field parameters [Inc, Decl, Intensity] @@ -864,43 +865,43 @@ def writeUBCobs(filename,B,M,rxLoc,d,wd): rxLoc : Observation locations [obsx, obsy, obsz] d : Data vector wd : Uncertainty vector - + OUTPUT - Obsfile - + Obsfile + Created on Dec, 27th 2015 @author: dominiquef """ - + data = np.c_[rxLoc , d , wd] with file(filename,'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' %len(d) ) + fid.write('%6.2f %6.2f %6.2f\n' %(M[0], M[1], 1) ) + fid.write('%i\n' %len(d) ) np.savetxt(fid, data, fmt='%e',delimiter=' ',newline='\n') - - + + print "Observation file saved to: " + filename - + def getActiveTopo(mesh,topo,flag): """ getActiveTopo(mesh,topo) - + Function creates an active cell model from topography - + INPUT mesh : Mesh in SimPEG format topo : Scatter points defining topography [x,y,z] - + OUTPUT - actv : Active cell model - + actv : Active cell model + Created on Dec, 27th 2015 @author: dominiquef - """ + """ import scipy.interpolate as interpolation if (flag=='N'): @@ -908,42 +909,28 @@ def getActiveTopo(mesh,topo,flag): # wght = np.zeros((mesh.nNx,mesh.nNy)) cx = mesh.vectorNx cy = mesh.vectorNy - + F = interpolation.NearestNDInterpolator(topo[:,0:2],topo[:,2]) [Y,X] = np.meshgrid(cy,cx) - + Zn = F(X,Y) -#============================================================================== -# for ii in range(topo.shape[0]): -# dx = topo[ii,0] - cx + 1e-8 -# dy = topo[ii,1] - cy + 1e-8 -# -# [Wx,Wy] = np.meshgrid(dx**2.,dy**2.) -# -# W = np.sqrt(Wx + Wy)**-1. -# -# wght = wght + W -# Zn = Zn + topo[ii,2]*W -# -# Zn = Zn/wght -#============================================================================== - + actv = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz)) - + if (flag=='N'): Nz = mesh.vectorNz[1:] - - - for jj in range(mesh.nCy): - + + + for jj in range(mesh.nCy): + for ii in range(mesh.nCx): - + temp = [kk for kk in range(len(Nz)) if np.all(Zn[ii:(ii+2),jj:(jj+2)] > Nz[kk]) ] actv[ii,jj,temp] = 1 - - + + actv = mkvc(actv) - + return actv - - \ No newline at end of file + + From c0445e5db59194edcbe65a31e58b37a90638d295 Mon Sep 17 00:00:00 2001 From: D Fournier Date: Fri, 15 Jan 2016 00:01:24 -0800 Subject: [PATCH 16/31] Modify Inv Integral function Start example in a notebook --- simpegPF/Dev/Intgrl_MAG_Inv_Driver.py | 14 +- simpegPF/Magnetics.py | 48 +- ...SimPEG Tutorial - MAG Linear Problem.ipynb | 1290 +++++++++++++++++ 3 files changed, 1331 insertions(+), 21 deletions(-) create mode 100644 simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb diff --git a/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py b/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py index 7d186f7c..39778559 100644 --- a/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py +++ b/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py @@ -43,29 +43,31 @@ if topofile == 'null': topo = np.c_[mkvc(Nx),mkvc(Ny),mkvc(Nz)] else: - topofile = np.genfromtxt(topofile,delimiter=' \n',dtype=np.str,skip_header=0) + topo = np.genfromtxt(topofile,skip_header=1) # Work with flat topogrphy for now -nullcell = np.ones(mesh.nC) +actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') + +nC = int(sum(actv)) # Load model file if isinstance(mstart, float): - mstart = np.ones(mesh.nC) * mstart + mstart = np.ones(nC) * mstart else: mstart = Utils.meshutils.readUBCTensorModel(mstart,mesh) - + mstart = mstart[actv==1] # Get magnetization vector for MOF if magfile=='DEFAULT': - M_xyz = PF.Magnetics.dipazm_2_xyz(np.ones(mesh.nC) * M[0], np.ones(mesh.nC) * M[1]) + M_xyz = PF.Magnetics.dipazm_2_xyz(np.ones(nC) * M[0], np.ones(nC) * M[1]) else: M_xyz = np.genfromtxt(magfile,delimiter=' \n',dtype=np.str,comments='!') # Create forward operator -F = PF.Magnetics.Intrgl_Fwr_Op(mesh,B,M_xyz,rxLoc,'tmi') +F = PF.Magnetics.Intrgl_Fwr_Op(mesh,B,M_xyz,rxLoc,actv,'tmi') # Get distance weighting function wr = PF.Magnetics.get_dist_wgt(mesh,rxLoc,3.,np.min(mesh.hx)/4) diff --git a/simpegPF/Magnetics.py b/simpegPF/Magnetics.py index b3ed6cab..0a167674 100644 --- a/simpegPF/Magnetics.py +++ b/simpegPF/Magnetics.py @@ -502,7 +502,7 @@ def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,flag): return d -def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): +def Intrgl_Fwr_Op(mesh,B,M,rxLoc,actv,flag): """ Magnetic forward operator in integral form @@ -534,13 +534,30 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): @author: dominiquef """ + # Find non-zero cells + inds = np.nonzero(actv)[0] + # Create active cell projector + P = sp.csr_matrix((np.ones(inds.size),(inds, range(inds.size))), + shape=(mesh.nC, len(inds))) + + mcell = len(inds) + + # Create vectors of nodal location (lower and upper coners for each cell) xn = mesh.vectorNx; yn = mesh.vectorNy; zn = mesh.vectorNz; + + yn2,xn2,zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:]) + yn1,xn1,zn1 = np.meshgrid(yn[0:-1], xn[0:-1], zn[0:-1]) + + Yn = P.T*np.c_[mkvc(yn1), mkvc(yn2)] + Xn = P.T*np.c_[mkvc(xn1), mkvc(xn2)] + Zn = P.T*np.c_[mkvc(zn1), mkvc(zn2)] ndata = rxLoc.shape[0] + # Convert Bdecination from north to cartesian D = (450.-float(B[1]))%360. @@ -549,9 +566,9 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): # Pre-allocate space and create magnetization matrix if required if (flag=='tmi') | (flag == 'xyz'): # If assumes uniform magnetization direction - if M.shape != (mesh.nC,3): + if M.shape != (mcell,3): - print 'Magnetization vector must be 3*Ncells' + print 'Magnetization vector must be Nc x 3' return @@ -564,7 +581,7 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): if flag == 'tmi': - F = np.zeros((ndata, mesh.nC)) + F = np.zeros((ndata, mcell)) # Projection matrix Ptmi = mkvc(np.r_[np.cos(np.deg2rad(B[0]))*np.cos(np.deg2rad(D)), @@ -572,10 +589,10 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): np.sin(np.deg2rad(B[0]))],2).T; elif flag == 'xyz': - F = np.zeros((int(3*ndata), mesh.nC)) + F = np.zeros((int(3*ndata), mcell)) elif flag == 'full': - F = np.zeros((int(3*ndata), int(3*mesh.nC))) + F = np.zeros((int(3*ndata), int(3*mcell))) else: print """Flag must be either 'tmi' | 'xyz' | 'full', please revised""" @@ -589,7 +606,7 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): count = -1; for ii in range(ndata): - tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) + tx, ty, tz = get_T_mat(Xn,Yn,Zn,rxLoc[ii,:]) if flag=='tmi': F[ii,:] = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz @@ -609,7 +626,7 @@ def Intrgl_Fwr_Op(mesh,B,M,rxLoc,flag): count = progress(ii,count,ndata) - print "Done 100% ...forward modeling completed!!\n" + print "Done 100% ...forward operator completed!!\n" return F @@ -637,6 +654,7 @@ def get_T_mat(Xn,Yn,Zn,rxLoc): @author: dominiquef """ + eps = 1e-10 # add a small value to the locations to avoid /0 mcell = Xn.shape[0] @@ -644,15 +662,15 @@ def get_T_mat(Xn,Yn,Zn,rxLoc): Tx = np.zeros((1,3*mcell)) Ty = np.zeros((1,3*mcell)) Tz = np.zeros((1,3*mcell)) - - dz2 = rxLoc[2] - Zn[:,0] - dz1 = rxLoc[2] - Zn[:,1] + + dz2 = rxLoc[2] - Zn[:,0] + eps + dz1 = rxLoc[2] - Zn[:,1] + eps - dy2 = Yn[:,1] - rxLoc[1] - dy1 = Yn[:,0] - rxLoc[1] + dy2 = Yn[:,1] - rxLoc[1] + eps + dy1 = Yn[:,0] - rxLoc[1] + eps - dx2 = Xn[:,1] - rxLoc[0] - dx1 = Xn[:,0] - rxLoc[0] + dx2 = Xn[:,1] - rxLoc[0] + eps + dx1 = Xn[:,0] - rxLoc[0] + eps R1 = ( dy2**2 + dx2**2 ) R2 = ( dy2**2 + dx1**2 ) diff --git a/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb b/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb new file mode 100644 index 00000000..7d6ab4e0 --- /dev/null +++ b/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb @@ -0,0 +1,1290 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Objective:** \n", + "\n", + "In this tutorial we will create a simple magnetic problem from scratch using the SimPEG framework.\n", + "\n", + "We are using the integral form of the magnetostatic problem. In the absence of free-currents or changing magnetic field, magnetic material can give rise to a secondary magnetic field according to:\n", + "\n", + "$$\\vec b = \\frac{\\mu_0}{4\\pi} \\int_{V} \\vec M \\cdot \\nabla \\nabla \\left(\\frac{1}{r}\\right) \\; dV $$\n", + "\n", + "Where $\\mu_0$ is the magnetic permealitity of free-space, $\\vec M$ is the magnetization per unit volume and $r$ defines the distance between the observed field $\\vec b$ and the magnetized object. Assuming a purely induced response, the strenght of magnetization can be written as:\n", + "\n", + "$$ \\vec M = \\mu_0 \\kappa \\vec H_0 $$\n", + "\n", + "where $\\vec H$ is an external inducing magnetic field, and $\\kappa$ the magnetic susceptibility of matter.\n", + "As derived by Sharma 1966, the integral can be evaluated for rectangular prisms such that:\n", + "\n", + "$$ \\vec b(P) = \\mathbf{T} \\cdot \\vec H_0 \\; \\kappa $$\n", + "\n", + "Where the tensor matrix $\\bf{T}$ relates the three components of magnetization $\\vec M$ to the components of the field $\\vec b$:\n", + "\n", + "$$\\mathbf{T} =\n", + "\t \\begin{pmatrix}\n", + " \t\tT_{xx} & T_{xy} & T_{xz} \\\\\n", + "\t\tT_{yx} & T_{yy} & T_{yz} \\\\\n", + "\t\tT_{zx} & T_{zy} & T_{zz} \n", + "\t\\end{pmatrix} $$\n", + " \n", + "In general, we discretize the earth into a collection of cells, each contributing to the magnetic data such that:\n", + "\n", + "$$\\vec b(P) = \\sum_{j=1}^{nc} \\mathbf{T}_j \\cdot \\vec H_0 \\; \\kappa_j$$\n", + "\n", + "giving rise to a linear problem.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using matplotlib backend: nbAgg\n", + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dominiquef.MIRAGEOSCIENCE\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n", + " \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n" + ] + } + ], + "source": [ + "%matplotlib notebook\n", + "%pylab" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Efficiency Warning: Interpolation will be slow, use setup.py!\n", + "\n", + " python setup.py build_ext --inplace\n", + " \n" + ] + } + ], + "source": [ + "from SimPEG import *\n", + "import simpegPF as PF" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "# First we need to define the direction of the inducing field\n", + "# As a simple case, we pick a vertical inducing field of magnitude 50,000nT. \n", + "# From old convention, field orientation is given as an azimuth from North \n", + "# (positive clockwise) and dip from the horizontal (positive downward).\n", + "H0 = np.array(([90.,0.,50000.]))\n", + "\n", + "# Assume all induced so the magnetization M is also in the same direction\n", + "M = np.array([90,0])\n", + "\n", + "# Create a mesh\n", + "hxind = [(5, 20)]\n", + "hyind = [(5, 20)]\n", + "hzind = [(5, 10)]\n", + "\n", + "mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')\n", + "\n", + "# Assume flat topo for now, so all cells are active\n", + "nC = mesh.nC \n", + "actv = np.ones(nC)\n", + "\n", + "\n", + "# Create and array of observation points\n", + "xr = np.linspace(-20., 20., 20)\n", + "yr = np.linspace(-20., 20., 20)\n", + "X, Y = np.meshgrid(xr, yr)\n", + "Z = np.ones(X.size)*(mesh.vectorNz[-1]+1.) # Let just put the observation flat\n", + "\n", + "rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have all our spatial components, we can create our linear system. For a single location and single component of the data, the system would looks like this:\n", + "\n", + "$$ b_x =\n", + "\t\\begin{bmatrix}\n", + "\tT_{xx}^1 &... &T_{xx}^{nc} & T_{xy}^1 & ... & T_{xy}^{nc} & T_{xz}^1 & ... & T_{xz}^{nc}\\\\\n", + "\t \\end{bmatrix}\n", + "\t \\begin{bmatrix}\n", + "\t\t\\mathbf{M}_x \\\\ \\mathbf{M}_y \\\\ \\mathbf{M}_z\n", + "\t\\end{bmatrix} \\\\ $$\n", + "\n", + "where each of $T_{xx},\\;T_{xy},\\;T_{xz}$ are [nc x 1] long. For the $y$ and $z$ component, we need the two other rows of the tensor $\\mathbf{T}$.\n", + "In our simple induced case, the magnetization direction $\\mathbf{M_x,\\;M_y\\;,Mz}$ are known and assumed to be constant everywhere, so we can reduce the size of the system such that: \n", + "\n", + "$$ \\vec{\\mathbf{d}}_{\\text{pred}} = (\\mathbf{T\\cdot M})\\; \\kappa$$\n", + "\n", + "\n", + "\n", + "In most geophysical surveys, we are not collecting all three components, but rather the magnitude of the field, or $Total\\;Magnetic\\;Intensity$ (TMI) data.\n", + "Because the inducing field is really large, we will assume that the anomalous fields are parallel to $H_0$:\n", + "\n", + "$$ d^{TMI} = \\hat H_0 \\cdot \\vec d$$\n", + "\n", + "We then end up with a much smaller system:\n", + "\n", + "$$ d^{TMI} = \\mathbf{F\\; \\kappa}$$\n", + "\n", + "where $\\mathbf{F} \\in \\mathbb{R}^{nd \\times nc}$ is our $forward$ operator." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Begin calculation of forward operator: tmi\n", + "Done 0.0 %\n", + "Done 10.0 %\n", + "Done 20.0 %\n", + "Done 30.0 %\n", + "Done 40.0 %\n", + "Done 50.0 %\n", + "Done 60.0 %\n", + "Done 70.0 %\n", + "Done 80.0 %\n", + "Done 90.0 %\n", + "Done 100% ...forward operator completed!!\n", + "\n" + ] + } + ], + "source": [ + "# First, convert the magnetization direction to Cartesian\n", + "mi = np.ones(mesh.nC) * M[0]\n", + "md = np.ones(mesh.nC) * M[1]\n", + "M_xyz = PF.Magnetics.dipazm_2_xyz( mi , md ) # Ouputs an nc x 3 array\n", + "\n", + "# Create the forward model operator\n", + "F = PF.Magnetics.Intrgl_Fwr_Op(mesh,H0,M_xyz,rxLoc,actv,'tmi')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('

');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " fig.waiting = false;\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " this.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width);\n", + " canvas.attr('height', height);\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Close figure', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate a distance weighting\n", + "wr = PF.Magnetics.get_dist_wgt(mesh,rxLoc,3.,np.min(mesh.hx)/4)\n", + "wrMap = PF.BaseMag.WeightMap(mesh, wr**-1)\n", + "\n", + "plt.figure()\n", + "ax = subplot()\n", + "mesh.plotSlice(wr, ax = ax, normal = 'Y', ind=10)\n", + "title('Distance weighting')\n", + "xlabel('x');ylabel('z')\n", + "plt.gca().set_aspect('equal', adjustable='box')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, "metadata": { "collapsed": false }, @@ -993,7 +1779,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 37, "metadata": { "collapsed": false }, @@ -1034,7 +1820,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 44, "metadata": { "collapsed": false }, @@ -1046,40 +1832,18 @@ "survey.pair(prob)\n", "#survey.makeSyntheticData(data, std=0.01)\n", "survey.dobs=data\n", - "survey.std = np.ones(len(data))*0.01\n", + "survey.std = np.ones(len(data))*1e-1\n", "survey.mtrue = model\n", "\n", - "reg = Regularization.Tikhonov(mesh)\n", + "reg = Regularization.Tikhonov(mesh, mapping = wrMap)\n", "dmis = DataMisfit.l2_DataMisfit(survey)\n", "opt = Optimization.ProjectedGNCG(maxIter=35,lower=0.,upper=1.)\n", - "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n", + "invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = 1e-2)\n", "beta = Directives.BetaSchedule()\n", - "betaest = Directives.BetaEstimate_ByEig()\n", + "#betaest = Directives.BetaEstimate_ByEig()\n", "target = Directives.TargetMisfit()\n", - "inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest, target])\n", - "m0 = np.zeros_like(survey.mtrue)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Directives.BetaSchedule()" + "inv = Inversion.BaseInversion(invProb, directiveList=[beta, target])\n", + "m0 = np.ones_like(survey.mtrue)*1e-4" ] }, { @@ -1091,7 +1855,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 45, "metadata": { "collapsed": false }, @@ -1100,14 +1864,21 @@ "name": "stdout", "output_type": "stream", "text": [ + "SimPEG.InvProblem will set Regularization.mref to m0.\n", "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", " ***Done using same solver as the problem***\n", "=============================== Projected GNCG ===============================\n", " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", "-----------------------------------------------------------------------------\n", - " 0 3.73e+12 1.37e+06 0.00e+00 1.37e+06 4.24e+01 0 \n", + "SimPEG.l2_DataMisfit is creating default weightings for Wd.\n", + " 0 1.00e-02 3.06e+05 1.89e-04 3.06e+05 3.98e+01 0 \n", + " 1 1.00e-02 5.42e+04 1.86e-04 5.42e+04 3.93e+01 0 \n", + " 2 1.00e-02 2.72e+04 1.78e-04 2.72e+04 3.89e+01 0 Skip BFGS \n", + " 3 1.25e-03 1.98e+04 1.42e-04 1.98e+04 3.81e+01 0 Skip BFGS \n", + " 4 1.25e-03 1.97e+04 1.63e-04 1.97e+04 3.80e+01 3 \n", + " 5 1.25e-03 1.86e+04 5.30e-05 1.86e+04 3.72e+01 0 \n", "------------------------------------------------------------------\n", - "0 : ft = 1.7528e+14 <= alp*descent = 1.6470e+06\n", + "0 : ft = 1.9170e+04 <= alp*descent = 1.8567e+04\n", "1 : maxIterLS = 10 <= iterLS = 10\n", "------------------------- End Linesearch -------------------------\n", "The linesearch got broken. Boo.\n" @@ -1120,22 +1891,782 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "min(mrec)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " fig.waiting = false;\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " this.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width);\n", + " canvas.attr('height', height);\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Close figure', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" ], "text/plain": [ "" @@ -2675,42 +3429,41 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 72, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Here is the recovered susceptibility model\n", - "plt.figure()\n", - "ax = subplot(211)\n", - "mesh.plotSlice(mrec, ax = ax, normal = 'Y', ind=midx)\n", - "title('Recovered model.')\n", - "xlabel('x');ylabel('y')\n", - "plt.gca().set_aspect('equal', adjustable='box')\n", - "\n", "# Plot predicted data and residual\n", + "plt.figure()\n", "pred = F.dot(mrec) #: this is matrix multiplication!!\n", "\n", - "subplot(234)\n", + "subplot(221)\n", "imshow(data.reshape(X.shape))\n", - "title('Predicted data.')\n", + "title('Observed data.')\n", "plt.gca().set_aspect('equal', adjustable='box')\n", "colorbar()\n", "\n", - "subplot(235)\n", + "subplot(222)\n", "imshow(pred.reshape(X.shape))\n", "title('Predicted data.')\n", "plt.gca().set_aspect('equal', adjustable='box')\n", "colorbar()\n", "\n", - "subplot(236)\n", + "subplot(223)\n", "imshow(data.reshape(X.shape) - pred.reshape(X.shape))\n", "title('Residual data.')\n", "plt.gca().set_aspect('equal', adjustable='box')\n", + "colorbar()\n", + "\n", + "subplot(224)\n", + "imshow( (data.reshape(X.shape) - pred.reshape(X.shape)) / wd.reshape(X.shape) )\n", + "title('Normalized Residual')\n", + "plt.gca().set_aspect('equal', adjustable='box')\n", "colorbar()" ] }, @@ -2718,100 +3471,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Hopefully you now have an idea of how to create a Problem class in SimPEG, and how this can be used with the other tools available." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "class MagProblem(object):\n", - " \n", - " def __init__(self, mesh, **kwargs):\n", - " self.mesh = mesh\n", - " Utils.setKwargs(self, **kwargs)\n", - " \n", - " @property\n", - " def dtype(self):\n", - " return getattr(self, '_dtype', 'tmi')\n", - " @dtype.setter\n", - " def dtype(self, val):\n", - " assert type(val) is str, 'dtype must be a string'\n", - " assert val in ['tmi', 'xyz'], 'dtype must be either \"tmi\" or \"xyz\"'\n", - " self._dtype = val\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "p = MagProblem(M, dtype='xyz')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "p.dtype" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "p.dtype" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "d.height = 4\n", - "print d.height" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "M = Mesh.TensorMesh([5,5])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "M._cellGrad" + "Good job! \n", + "Hopefully we covered all the important points regarding the inversion of magnetic field data using the integral formulation.\n", + "\n", + "Make sure you visit the notebook for the compact norms regularization.\n", + "\n", + "Cheers!" ] }, { From 000e227ea4b60e2eb90c53f7d73f37c95fbbd8e8 Mon Sep 17 00:00:00 2001 From: D Fournier Date: Sun, 6 Mar 2016 16:56:39 -0800 Subject: [PATCH 31/31] Minor change --- ...SimPEG Tutorial - MAG Linear Problem.ipynb | 60 ++++++++++--------- 1 file changed, 31 insertions(+), 29 deletions(-) diff --git a/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb b/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb index 1b640c16..0abe8ef6 100644 --- a/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb +++ b/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -53,15 +53,19 @@ "output_type": "stream", "text": [ "Using matplotlib backend: nbAgg\n", - "Populating the interactive namespace from numpy and matplotlib\n" + "Populating the interactive namespace from numpy and matplotlib\n", + "Efficiency Warning: Interpolation will be slow, use setup.py!\n", + "\n", + " python setup.py build_ext --inplace\n", + " \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "WARNING: pylab import has clobbered these variables: ['linalg', 'beta', 'inv']\n", - "`%matplotlib` prevents importing * from pylab and numpy\n" + "C:\\Users\\dominiquef.MIRAGEOSCIENCE\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n", + " \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n" ] } ], @@ -74,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true @@ -164,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -201,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -990,7 +994,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -1734,7 +1738,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1746,10 +1750,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 138, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1798,7 +1802,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -1847,7 +1851,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -1861,17 +1865,15 @@ "=============================== Projected GNCG ===============================\n", " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", "-----------------------------------------------------------------------------\n", - " 0 1.00e+04 6.75e+04 3.37e-04 6.75e+04 8.30e+01 0 \n", - " 1 5.00e+03 2.77e+03 3.19e-02 2.93e+03 6.90e+01 0 \n", - " 2 2.50e+03 4.03e+02 5.53e-02 5.41e+02 7.04e+01 0 \n", - " 3 1.25e+03 2.15e+02 5.75e-02 2.87e+02 6.99e+01 0 Skip BFGS \n", - " 4 6.25e+02 2.00e+02 5.82e-02 2.37e+02 7.31e+01 0 Skip BFGS \n", + " 0 1.00e+04 6.72e+04 3.37e-04 6.72e+04 8.30e+01 0 \n", + " 1 5.00e+03 2.72e+03 3.17e-02 2.88e+03 6.90e+01 0 \n", + " 2 2.50e+03 3.73e+02 5.49e-02 5.10e+02 7.03e+01 0 \n", "------------------------- STOP! -------------------------\n", - "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 6.7516e+03\n", - "1 : |xc-x_last| = 3.1475e-03 <= tolX*(1+|x0|) = 1.0111e-01\n", - "0 : |proj(x-g)-x| = 7.3120e+01 <= tolG = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 7.3120e+01 <= 1e3*eps = 1.0000e-02\n", - "0 : maxIter = 10 <= iter = 5\n", + "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 6.7163e+03\n", + "1 : |xc-x_last| = 6.3549e-03 <= tolX*(1+|x0|) = 1.0111e-01\n", + "0 : |proj(x-g)-x| = 7.0322e+01 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 7.0322e+01 <= 1e3*eps = 1.0000e-02\n", + "0 : maxIter = 10 <= iter = 3\n", "------------------------- DONE! -------------------------\n" ] } @@ -1889,7 +1891,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -2633,7 +2635,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -2673,7 +2675,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -3417,7 +3419,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -3429,10 +3431,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 144, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }

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\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\nuXNWq4nhw3MxfLid77/XqF3bZMIEHY/Hz9ChV4qXcePs5M5tVcXUdYXly20ULAhvv63z/fcq48fb\niI6+PiFz9mxm0Vavnmw3IZFIJLeCpCSVVaush9t33/VToICgTBmTpCSF2bO9bNrkpmlTnTVrggJl\n9mwHc+bYado0nLZtDd5+Wyc8PIzZs+/g3Xdz0aaNn2eeceN2u/F6vQQCAQzDyBBAbrf7b9fgeeKJ\nJ1i2bFmmZVOnTqVYsWIcPHiQIkWKMG3aNADOnTvHlClTWL16NVOnTmXgwIF/65g3E9k89AZxuay2\nEU4nrFyp8swzOgMGGKxZo7J4sZrRMgJg0aIAlSoJwsLg5EmF+++3kZamcOiQnyJF4PHHbdxxh8b0\n6WFXHCcuTqVw4SuTmps3N9m0yZqNVaGCyd69V9ewGzZIjSuRSCQ3m7JlDQ4cyH66eLlywVYPzz/v\nJzbWmnL+/vtO6tTReeGFAA8+6GLvXpUJE6x7//btNqZOdZCQYGPXLhvLlnmYPt1JVJSd8PBwDMNA\n13U0TWPOnDls3LiRmjVr/m3B06BBA44cOZJpWUJCAiNGjCAkJISePXvy5ptvArBlyxZatmxJsWLF\nKFasGEII0tLSyHV5rsUtRgqeG8TlgrfftrFxo8qHH+p07Gh5UHr0MImOhnXrVOrWNSlWzMuQIS7O\nnlVISlJYs0ale3eDb7/VcDisBOmDBxV27AijaVOrcdyvv6p07Wrw1VfZ/2gubzPxZ7FTqJDg9GmZ\nxCyRSCT/NFmJHUURWRaDXbPGzsSJzoyitCtWuNE0BadTsHWrjY8+8tKsmc7atXZ69LAeiG02wRtv\nhDBnjoNnngmgKAp2uz0jfNW8eXOEEKxcuZK1a9dSsWJFmjVrRrNmzWjUqNHf7q21detWypUrB0C5\ncuVISEgALMFTvnz5jPViYmJISEigadOmf+s4NwP5uH8VrqdQU1ycyvff2xg0yCA21hI7mgbDhtl4\n/nk7332n8e23GiNHprF1a4CuXYOJxLNm2ciVK4SGDW306aOwY4eVePbLLyqPPmpSv75JZCRERwse\nf/yvJSDXqmUSFSVo2lSGsCQSieRWERISTD8oVEjgdAqio4P3ZZdLsGuXLUPsVKtm0K9fGG3auAgE\nFCZP9vHwwzq5csHSpXZq19Y5diyNbdvcGfv+6KMrp8EXK1aMXr16MWLECLp3785nn31G/vz5efvt\ntylYsCC7du36W+fzVyZ2327FDqXguUHsdkGlSiZLl6oUL+6kUiUHuXKF8N13NjZtClCnjkBRFFJT\nFbp2dbB+vUrDhibTp2vEx/upWVMjIcHOl18GXY7Vqpk8+qjB+vUq06bZKFxY8OOPKl9/rZGW5mfg\nwKyTli9nyxaV335TWb1afsUSiURyq/D7g4O+qkIgoJCUpLJvXzplyxp8/bWXEiVMSpY06dMnQFoa\n/P578L5dvrw1Hb1btzAuXFBYuNDLHXfA6dMqixfbsdsF06dnXYMHrByeXLlyUaNGDYYNG8aaNWs4\ne/Ysd9999986nxo1arBv3z4A9u3bR40aNQCoVasWe/fuzVjvt99+y3jvdkGOhjdIly4mAwYYLF2q\nMWOGzv791p80EIAGDZw8/7ydceNs3HdfPvLnF6xZo1GunMDtNrHZ0vB4FDp3Njh4MDhdcelSG8WK\nBfN1fv5ZJSlJYcYMyyO0aZNK9erWE0JsrEnv3nL6uUQikdxO1Kx55X35xIngkNuokYsDB2y0a+ei\na1eNnTvdTJjgZ8AAqwzJJS9QtWrhFCiQi6VL7Tz6qFV0cNkyG927hzJzpo+mTQ1cruy9Llk1Dg0P\nD//bM7dq1arFp59+itfr5dNPP6V27dr/O9+aLF++nGPHjrF27VpUVb2t8ndA5vDcMC4XpKXBq6/a\n+PxzGytWBGjYUCAE/PqrwkMPOTh82FL4R48afPKJyunTJtu2KYwdm4eRIw369DFQFBg7VmfOHJVz\n5xRy5RKZVD6Q4a3ZujW4PD5eYdUqqVslEonkdiIhwUZ4uMDtzjqsc3mNnrlzHSQmKuzaZePQIYWd\nO9NZv97OkiV2Tp5UcLkErVoZfP65g8cft3J4mjfXCQkRXLyoEBqa/efwer2EXyoQ9xd55JFHWLdu\nHefPn6do0aK89tpr9OvXj27duhETE0PVqlUZN24cAAUKFKBfv340adIEp9PJ9OnT/9YxbypCki2m\naQqfz3dVq1vXECBE48aGOHo0uDwpySe6dtVFxYqG2L3bL/bvPytmzfKKBx7wCasOs2UffRQQKSk+\n4Xb7xB13mAKE6NlTF9HRprDbTTFpUkA8/bSWaZvrsYgIU4wfr4lChcy/vK00adKkSct5q1RJFyBE\n8eJGxjKXyxQVK+qZ1qtVK/j65Zd9IiUlVaSmpooJE7wib15DjB/vFc8+6xcFC1r76dXLL1JTU7O0\nTz75RHzwwQe3eji9LZCugRtk40brT3juHEyaZGP9eoVfflGoX9+BzQbx8RplywqiogQVK6ZlFJe6\nRK9eDqKiQggPD+HiRetJID5eYdkyjdq1BQMGONixQ+Xxxw2KFBGsXBlg9uysKy9fTnq6wuDBdsqU\nETl/0hKJRCL5y1SubIW5jhxRmT/fg90uWL3aw549wRldpUqZ7NwZHCeGD7eaRb/1lpOpU52sXeuh\nb1+NXr0CGZX9H300+zHB7Xb/bQ/P/zek4LlBxozRee45ncmTdWw2iI11UqOGk99+U2nUyCQ9Hfx+\nP6tXO2jVKi8dOsD48Tr9++v4fFbH9D9z8KBK9epO1q+3vp5Nm1Q+/9zGjBkadjsMHWrnqaeuL28n\nLk5+xRKJRHI78PnnwRYQVjkShTfecNK0qc7p02kkJ6fRp0+AQCAYBmvXLoyoqFy88UYIy5Z5uOsu\nwf79Ki1buujdO0DJktaM3Ozw+XxS8PwPORpeheuZUudyCfx+hSpVBImJCmXKmCxaFGD6dI0ff1Sp\nUMFJVFQkjz6ah2ee0XjxRZOICKvp53vv2ejWzcHChRo+n5+vv9aoWjX7aeStWzu57z4nx48rfPyx\n9UTwzjvBGVtXS1yTSCQSyT9Ls2bZz6ht395KJF60yEHjxjonTqhs2GBj/Hgnc+d6ePbZAM8/72f3\nbuteny+fSd26LqpUCadGjXB69tTo10/D47FySbPD4/H87cKD/9+QScs3iMtlJSc3auSgTBnBhg0a\nkZFgGDr16rk5ffoOEhLsdO7s58svHUycqJKYaAmp4sUF8fEB7rrL2peuWz2wKlUyqV1bMH26daGH\nhQm83qzF14svBr9Cj0ehY0eDhQut7e68U3Dq1O1VB0EikUj+K6xcac8oNqgoguhoQWJiZj9D8eIm\nM2c6GTlSzXjtdiscOqTw449OmjXT+eILLy4XzJljp0+fMEJCBJMmOfjiCwdnzqi43dl/Bo/HIz08\n/0N6eK7Btbw8U6bYiI9XiYqCSZN0IiMhEAiwYoWX5s3z0rixQlpagClT3GzZ4mbSpKDiP3JEISYm\nhEqVHEycaOPRR63iUQULCrZvV3j2WZ2GDU2KFBF062ZQs+a1iwheEjsARYoIYmJk4UGJRCK5VVyq\nrFy8uKBSJSsVISRE0KmTlXfz0EMaf/wRHIpVFYYMCeHHH63xYM4cS+wsXWpj+PAQFi3ykJiYzi+/\nuElJsbbJly97735W09L/q0jBc4M8/LAlKMLCBKVLO7nvPpW77grn/vvzMnWqzujRBjablW8/c6aD\np5+20727QbNmJvv2+XngAYP9+1UGDw56apYvt7FggcYHH9iJi1P/l/ysUqqU4Px5P1OmaERGXjt8\nlZCgZtQFkkgkEsmt4/BhlVWrLBHTvr3OsGEBSpc2yZ1bULSoyapVbmbN8nL33UYmL9CCBXYmT3bw\nzDOhzJvnpVEjgwsXoFMnF82aWQIqMjL748qQVhAZ0rpBqlQxue8+kwULAhw86KFKlTyYpoKqWjOs\nWrQwadjQZN68XPzxh42fftI4cwZee81OoUIQFQVlypjMmqXzySe2jNycokWDhQd/+826+PPlU8ib\n11o+bpzOyy/bKVfORFWv7KMlkUgkkltDixZWEdojR7K+L3/zjYNvvrHEz9ChoWzd6iYmxuTkScGY\nMU5efNHPkiV2ChYU9Opl1d1xOATff+/g998Nxo0LoXlznaFDrfWuFoiQgieIHCVvEJcLPB7BunVu\n2rW7g6efNkhN9eN2B1i0SMMwoHt3B4sXO4mMFKxcqXL6tMKePQqNGztISYENGzSqVBG8+KIV7qpS\nxaRUqStDUdu2Bb+ul1+2tKrbrbB3r0p0tOXxuVq2vkQikUhuPsuX2zPETlYVly/HZhM0buwiJiac\n8uUjuOcekxEjAtx5p8DrhZIlTXbuTGfJEg9JSQq9eoVx6JDVOmj8+BDS0q6eduH1ev92o9D/b0jB\ncw2ulsMjhAD8bNlio1u3vIwbZzJhgoHTCYoCu3crLFqkMnWqxpEjF+jf388vvyg8/riDCxcUdu1S\nuecegeN/fd/WrLG+jiZNTAoUsJZVr27y2WfZ11g4ftz6fElJ1r8tWgSF0kcfadSvL3N4JBKJ5FaR\nkGB57WvVMihVyhI/TZoEczkLFbKqMZ8+bd3/4+JslCwZwerVdjZvtrNsmYdSpQR33AHr1tkYP97H\n8eNpPPmkxocfWtPczavc5qWHJ4gUPH8T0zRJT09n2zbr9fnzCuvXK6xcqZCaCs89Z2f0aDuLF2s8\n8YRJVJRCy5YB8ubN7IEZPdpO7twhhIaG0L+/pXy++spGs2YmX3+tsW2byqhRdt5+W6dIEcHLL+tZ\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 From 291cce9d5227b2e83564ea44dc80b8c729aa7fe0 Mon Sep 17 00:00:00 2001 From: D Fournier Date: Fri, 13 Nov 2015 12:49:19 -0800 Subject: [PATCH 07/31] Finish forward modeling in integral Start test function. --- simpegPF/Dev/MAG3D_fwr_Driver.py | 5 +++-- simpegPF/Dev/MAG3D_fwr_Test.py | 16 +++++++--------- simpegPF/Dev/fwr_MAG_obs.py | 7 ++++++- simpegPF/Magnetics.py | 4 ++++ 4 files changed, 20 insertions(+), 12 deletions(-) diff --git a/simpegPF/Dev/MAG3D_fwr_Driver.py b/simpegPF/Dev/MAG3D_fwr_Driver.py index 7e1ff802..f982563c 100644 --- a/simpegPF/Dev/MAG3D_fwr_Driver.py +++ b/simpegPF/Dev/MAG3D_fwr_Driver.py @@ -1,6 +1,6 @@ import os -home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\Research\SimPeg' +home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' inpfile = 'MAG3Cfwr.inp' @@ -28,9 +28,10 @@ model = Utils.meshutils.readUBCTensorModel(modfile,mesh) [B,M,dobs] = BaseMag.readUBCmagObs(obsfile) rxLoc = dobs[:,0:3] +ndata = rxLoc.shape[0] # Compute forward model using integral equation -d = fwr_MAG_obs(xn,yn,zn,B,M,rxLoc,model) +d = fwr_MAG_obs(mesh,B,M,rxLoc,model) # Form data object with coordinates and write to file data = np.c_[rxLoc , d , np.zeros((ndata,1))] diff --git a/simpegPF/Dev/MAG3D_fwr_Test.py b/simpegPF/Dev/MAG3D_fwr_Test.py index 03665e86..aa97e332 100644 --- a/simpegPF/Dev/MAG3D_fwr_Test.py +++ b/simpegPF/Dev/MAG3D_fwr_Test.py @@ -1,6 +1,6 @@ import os -home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\Research\SimPeg' +home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' os.chdir(home_dir) @@ -15,12 +15,10 @@ from fwr_MAG_obs import fwr_MAG_obs #%% Create survey -B[0] = 90. -B[1] = 00. -B[2] = 50000. +B = np.array(([90.,0.,50000.])) -M[0] = 90. -M[1] = 0. +M = np.array(([90.,0.])) + # # Or create juste a plane grid xr = np.linspace(-1./2., 1./2., 10) @@ -55,13 +53,13 @@ model = np.ones(mcell)*chibkg model[sph_ind] = chiblk #%% Forward mode ldata -d = fwr_MAG_obs(xn,yn,zn,B,M,rxLoc,model) +d = fwr_MAG_obs(mesh,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 +lrl = sum( r_Bz**2 ) **0.5 print "Residual between analytical and integral= " + str(lrl) #%% Plot fields @@ -95,6 +93,6 @@ 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.imshow(np.reshape(r_Bz,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/Dev/fwr_MAG_obs.py b/simpegPF/Dev/fwr_MAG_obs.py index 6b65ffe5..623ea89a 100644 --- a/simpegPF/Dev/fwr_MAG_obs.py +++ b/simpegPF/Dev/fwr_MAG_obs.py @@ -1,4 +1,4 @@ -def fwr_MAG_obs(xn,yn,zn,B,M,rxLoc,model): +def fwr_MAG_obs(mesh,B,M,rxLoc,model): """ Forward model magnetic data using integral equation @@ -21,6 +21,11 @@ def fwr_MAG_obs(xn,yn,zn,B,M,rxLoc,model): from SimPEG import np, Utils, sp, mkvc from get_T_mat import get_T_mat + + xn = mesh.vectorNx; + yn = mesh.vectorNy; + zn = mesh.vectorNz; + mcell = (len(xn)-1) * (len(yn)-1) * (len(zn)-1) ndata = rxLoc.shape[0] diff --git a/simpegPF/Magnetics.py b/simpegPF/Magnetics.py index 9d8589e9..dc96baa8 100644 --- a/simpegPF/Magnetics.py +++ b/simpegPF/Magnetics.py @@ -3,6 +3,10 @@ import BaseMag from scipy.constants import mu_0 from MagAnalytics import spheremodel, CongruousMagBC +class MagneticIntegral(Problem.BaseProblem): + """ + approach using IE + """ class MagneticsDiffSecondary(Problem.BaseProblem): From e9f02e1d77e255729b1fdae0d8e4853a51d12807 Mon Sep 17 00:00:00 2001 From: D Fournier Date: Fri, 27 Nov 2015 16:08:21 -0800 Subject: [PATCH 08/31] Finish test with analytical answer. O(h^2) error pass Add function MagSphereFreeSpace in MagAnalytic. Same results as MagSphereAnaFunA but with z-observation flipped. --- simpegPF/Dev/MAG3D_fwr_Test.py | 179 +++++++++++++++++++++++---------- simpegPF/Dev/fwr_MAG_obs.py | 7 +- simpegPF/MagAnalytics.py | 47 +++++++++ 3 files changed, 175 insertions(+), 58 deletions(-) diff --git a/simpegPF/Dev/MAG3D_fwr_Test.py b/simpegPF/Dev/MAG3D_fwr_Test.py index aa97e332..1d30dbb6 100644 --- a/simpegPF/Dev/MAG3D_fwr_Test.py +++ b/simpegPF/Dev/MAG3D_fwr_Test.py @@ -13,86 +13,155 @@ import matplotlib from fwr_MAG_obs import fwr_MAG_obs +plt.close('all') + #%% Create survey -B = np.array(([90.,0.,50000.])) +B = np.array(([-45.,315.,50000.])) -M = np.array(([90.,0.])) +M = np.array(([-45.,315.])) +# Sphere radius +R = 0.25 # # Or create juste a plane grid -xr = np.linspace(-1./2., 1./2., 10) -yr = np.linspace(-1./2., 1./2., 10) +xr = np.linspace(-2., 2., 5) +yr = np.linspace(-2., 2., 5) X, Y = np.meshgrid(xr, yr) -Z = np.ones((xr.size, yr.size))*.75 +Z = np.ones((xr.size, yr.size)) * 2.5 rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)] ndata = rxLoc.shape[0] -#%% +d_iter = 4 +lrl = np.zeros(d_iter) +#%% Loop through decreasing meshes and measure the residual # Create mesh using simpeg and write out in GIF format -nc = 30. -hxind = [(1./nc, nc)] -hyind = [(1./nc, nc)] -hzind = [(1./nc, nc)] +for ii in range(d_iter): + + nc = 3**(ii+1) + + 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, R) + + chibkg = 0. + chiblk = 0.01 + model = np.ones(mcell)*chibkg + model[sph_ind] = chiblk + + #%% Forward mode ldata + d = fwr_MAG_obs(mesh,B,M,rxLoc,model) + fwr_x = mkvc(d[0,:]) + fwr_y = mkvc(d[1,:]) + fwr_z = mkvc(d[2,:]) + + #%% Get the analystical answer and compute the residual + bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') + Bd = (450.-float(B[1]))%360. + Bi = B[0]; # Convert dip to horizontal to cartesian + + Bx = np.cos(np.deg2rad(Bi)) * np.cos(np.deg2rad(Bd)) * B[2] + By = np.cos(np.deg2rad(Bi)) * np.sin(np.deg2rad(Bd)) * B[2] + Bz = np.sin(np.deg2rad(Bi)) * B[2] + + Bo = np.c_[Bx, By, Bz] + + bxa,bya,bza = PF.MagAnalytics.MagSphereFreeSpace(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, Bo) + #bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') + + r_Bx = fwr_x - bxa + r_By = fwr_y - bya + r_Bz = fwr_z - bza + + lrl[ii] = sum( r_Bx**2 + r_By**2 + r_Bz**2 ) **0.5 + -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(mesh,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( r_Bz**2 ) **0.5 - -print "Residual between analytical and integral= " + str(lrl) + +#%% Plot results +for ii in range(d_iter): + nc = 3**(ii+1) + print "Residual= " + str(lrl[ii]) + "\t dx= " + str(1./nc) + #%% 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) +plt.imshow(np.reshape(bxa,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar() +plt.contour(X,Y, np.reshape(bxa,X.shape).T,10) +plt.scatter(X,Y, c=np.reshape(bxa,X.shape).T, s=20) 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) +plt.imshow(np.reshape(bya,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar() +plt.contour(X,Y, np.reshape(bya,X.shape).T,10) +plt.scatter(X,Y, c=np.reshape(bya,X.shape).T, s=20) 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) +plt.imshow(np.reshape(bza,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar() +plt.contour(X,Y, np.reshape(bza,X.shape).T,10) +plt.scatter(X,Y, c=np.reshape(bza,X.shape).T, s=20) ax.set_title('Sphere Ana Bz') -#%% Plot foward data + +#%% Plot the forward solution from integral + 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) +ax = plt.subplot(131) +plt.imshow(np.reshape(fwr_x,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') +plt.colorbar() +plt.contour(X,Y, np.reshape(fwr_x,X.shape).T,10) +plt.scatter(X,Y, c=np.reshape(fwr_x,X.shape).T, s=20) +ax.set_title('Sphere Ana Bx') -#%% Compare fields +ax = plt.subplot(132) +plt.imshow(np.reshape(fwr_y,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar() +plt.contour(X,Y, np.reshape(fwr_y,X.shape).T,10) +plt.scatter(X,Y, c=np.reshape(fwr_y,X.shape).T, s=20) +ax.set_title('Sphere Ana By') -plt.subplot(122) -plt.imshow(np.reshape(r_Bz,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 +ax = plt.subplot(133) +plt.imshow(np.reshape(fwr_z,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar() +plt.contour(X,Y, np.reshape(fwr_z,X.shape).T,10) +plt.scatter(X,Y, c=np.reshape(fwr_z,X.shape).T, s=20) +ax.set_title('Sphere Ana Bz') + + +#%% Plot foward data +plt.figure(3) +ax = plt.subplot(131) +plt.imshow(np.reshape(r_Bx,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar() +plt.contour(X,Y, np.reshape(r_Bx,X.shape).T,10) +plt.scatter(X,Y, c=np.reshape(r_Bx,X.shape).T, s=20) +ax.set_title('Sphere Ana Bx') + +ax = plt.subplot(132) +plt.imshow(np.reshape(r_By,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar() +plt.contour(X,Y, np.reshape(r_By,X.shape).T,10) +plt.scatter(X,Y, c=np.reshape(r_By,X.shape).T, s=20) +ax.set_title('Sphere Ana By') + +ax = plt.subplot(133) +plt.imshow(np.reshape(r_Bz,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') +plt.colorbar() +plt.contour(X,Y, np.reshape(r_Bz,X.shape).T,10) +plt.scatter(X,Y, c=np.reshape(r_Bz,X.shape).T, s=20) +ax.set_title('Sphere Ana Bz') \ No newline at end of file diff --git a/simpegPF/Dev/fwr_MAG_obs.py b/simpegPF/Dev/fwr_MAG_obs.py index 623ea89a..72fb8eff 100644 --- a/simpegPF/Dev/fwr_MAG_obs.py +++ b/simpegPF/Dev/fwr_MAG_obs.py @@ -53,7 +53,7 @@ def fwr_MAG_obs(mesh,B,M,rxLoc,model): 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)) + d = np.zeros((3,ndata)) # Loop through all observations and create forward operator (ndata-by-mcell) print "Begin forward modeling " +str(int(ndata)) + " data points..." @@ -64,10 +64,11 @@ def fwr_MAG_obs(mesh,B,M,rxLoc,model): tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) - G = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz + # G = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz + Gxyz = np.vstack((tx,ty,tz))*Mxyz #%% # Forward operator - d[ii,0] = G.dot(model) + d[:,ii] = Gxyz.dot(model) d_iter = np.floor(float(ii)/float(ndata)*10.); diff --git a/simpegPF/MagAnalytics.py b/simpegPF/MagAnalytics.py index 34034bae..bea7d508 100644 --- a/simpegPF/MagAnalytics.py +++ b/simpegPF/MagAnalytics.py @@ -192,6 +192,53 @@ def IDTtoxyz(Inc, Dec, Btot): return np.r_[Bx, By, Bz] +def MagSphereFreeSpace(x, y, z, R, xc, yc, zc, chi, Bo): + """ + Computing boundary condition using Congrous sphere method. + This is designed for secondary field formulation. + >> Input + mesh: Mesh class + Bo: np.array([Box, Boy, Boz]): Primary magnetic flux + Chi: susceptibility at cell volume + + .. math:: + + \\vec{B}(r) = \\frac{\mu_0}{4\pi}\\frac{m}{\| \\vec{r}-\\vec{r}_0\|^3}[3\hat{m}\cdot\hat{r}-\hat{m}] + + """ + if (~np.size(x)==np.size(y)==np.size(z)): + print "Specify same size of x, y, z" + return + + x = Utils.mkvc(x) + y = Utils.mkvc(y) + z = Utils.mkvc(z) + + nobs = len(x) + + Bot = np.sqrt(sum(Bo**2)) + + mx = np.ones([nobs]) * Bo[0,0] * R**3 / 3. * chi + my = np.ones([nobs]) * Bo[0,1] * R**3 / 3. * chi + mz = np.ones([nobs]) * Bo[0,2] * R**3 / 3. * chi + + M = np.c_[mx, my, mz] + + rx = (x - xc) + ry = (y - yc) + rz = (zc - z) + + rvec = np.c_[rx, ry, rz] + r = np.sqrt((rx)**2+(ry)**2+(rz)**2 ) + + B = -Utils.sdiag(1./r**3)*M + Utils.sdiag((3 * np.sum(M*rvec,axis=1))/r**5)*rvec + + Bx = B[:,0] + By = B[:,1] + Bz = B[:,2] + + return Bx, By, Bz + if __name__ == '__main__': hxind = [(0,25,1.3),(21, 12.5),(0,25,1.3)] From d6a2490a7b72dd233560ebe0273f5c009cb14d55 Mon Sep 17 00:00:00 2001 From: D Fournier Date: Mon, 21 Dec 2015 08:42:50 -0800 Subject: [PATCH 09/31] Create function to generate linear forward operator (F) Clean project --- simpegPF/Dev/FWR_data.dat | 1254 +++++------ simpegPF/Dev/G.mat | Bin 321119 -> 0 bytes simpegPF/Dev/MAG3D_fwr_Driver.py | 4 +- simpegPF/Dev/Obs_RAW_REM_GRID_TMI.obs | 345 --- simpegPF/Dev/Obs_loc.dat | 4 +- simpegPF/Dev/Obs_loc_3C.obs | 1878 ----------------- simpegPF/Dev/Obs_loc_TMI.obs | 628 ------ simpegPF/Dev/Obs_loc_lBl.obs | 628 ------ .../fwr_MAG_data.py} | 21 +- simpegPF/Dev/functions/get_MAG_F.py | 132 ++ simpegPF/Dev/{ => functions}/get_T_mat.py | 0 simpegPF/Dev/{ => functions}/get_UBC_mesh.py | 0 simpegPF/Dev/{ => functions}/read_MAG_obs.py | 0 .../Dev/{ => functions}/read_MAGfwr_inp.py | 0 simpegPF/Dev/magfor3d.log | 6 +- simpegPF/Dev/magfor3d.mag | 1254 +++++------ 16 files changed, 1408 insertions(+), 4746 deletions(-) delete mode 100644 simpegPF/Dev/G.mat delete mode 100644 simpegPF/Dev/Obs_RAW_REM_GRID_TMI.obs delete mode 100644 simpegPF/Dev/Obs_loc_3C.obs delete mode 100644 simpegPF/Dev/Obs_loc_TMI.obs delete mode 100644 simpegPF/Dev/Obs_loc_lBl.obs rename simpegPF/Dev/{fwr_MAG_obs.py => functions/fwr_MAG_data.py} (87%) create mode 100644 simpegPF/Dev/functions/get_MAG_F.py rename simpegPF/Dev/{ => functions}/get_T_mat.py (100%) rename simpegPF/Dev/{ => functions}/get_UBC_mesh.py (100%) rename simpegPF/Dev/{ => functions}/read_MAG_obs.py (100%) rename simpegPF/Dev/{ => functions}/read_MAGfwr_inp.py (100%) diff --git a/simpegPF/Dev/FWR_data.dat b/simpegPF/Dev/FWR_data.dat index dc88c9d4..40994f4a 100644 --- a/simpegPF/Dev/FWR_data.dat +++ b/simpegPF/Dev/FWR_data.dat @@ -1,628 +1,628 @@ - 45.00 135.00 50000.00 - 45.00 135.00 1.00 + 45.00 315.00 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 0.000000e+00 --5.000000e-01 -2.500000e-01 5.500000e-01 1.641466e+02 0.000000e+00 --5.000000e-01 -2.083330e-01 5.500000e-01 2.623794e+02 0.000000e+00 --5.000000e-01 -1.666670e-01 5.500000e-01 3.700392e+02 0.000000e+00 --5.000000e-01 -1.250000e-01 5.500000e-01 4.836628e+02 0.000000e+00 --5.000000e-01 -8.333330e-02 5.500000e-01 5.987970e+02 0.000000e+00 --5.000000e-01 -4.166670e-02 5.500000e-01 7.103443e+02 0.000000e+00 --5.000000e-01 0.000000e+00 5.500000e-01 8.130400e+02 0.000000e+00 --5.000000e-01 4.166670e-02 5.500000e-01 9.020055e+02 0.000000e+00 --5.000000e-01 8.333330e-02 5.500000e-01 9.732772e+02 0.000000e+00 --5.000000e-01 1.250000e-01 5.500000e-01 1.024210e+03 0.000000e+00 --5.000000e-01 1.666670e-01 5.500000e-01 1.053680e+03 0.000000e+00 --5.000000e-01 2.083330e-01 5.500000e-01 1.062073e+03 0.000000e+00 --5.000000e-01 2.500000e-01 5.500000e-01 1.051072e+03 0.000000e+00 --5.000000e-01 2.916670e-01 5.500000e-01 1.023319e+03 0.000000e+00 --5.000000e-01 3.333330e-01 5.500000e-01 9.820190e+02 0.000000e+00 --5.000000e-01 3.750000e-01 5.500000e-01 9.305613e+02 0.000000e+00 --5.000000e-01 4.166670e-01 5.500000e-01 8.722184e+02 0.000000e+00 --5.000000e-01 4.583330e-01 5.500000e-01 8.099267e+02 0.000000e+00 --5.000000e-01 5.000000e-01 5.500000e-01 7.461582e+02 0.000000e+00 --4.583330e-01 -5.000000e-01 5.500000e-01 -1.925379e+02 0.000000e+00 --4.583330e-01 -4.583330e-01 5.500000e-01 -1.662941e+02 0.000000e+00 --4.583330e-01 -4.166670e-01 5.500000e-01 -1.284713e+02 0.000000e+00 --4.583330e-01 -3.750000e-01 5.500000e-01 -7.722713e+01 0.000000e+00 --4.583330e-01 -3.333330e-01 5.500000e-01 -1.103644e+01 0.000000e+00 --4.583330e-01 -2.916670e-01 5.500000e-01 7.102407e+01 0.000000e+00 --4.583330e-01 -2.500000e-01 5.500000e-01 1.689640e+02 0.000000e+00 --4.583330e-01 -2.083330e-01 5.500000e-01 2.815633e+02 0.000000e+00 --4.583330e-01 -1.666670e-01 5.500000e-01 4.061587e+02 0.000000e+00 --4.583330e-01 -1.250000e-01 5.500000e-01 5.386001e+02 0.000000e+00 --4.583330e-01 -8.333330e-02 5.500000e-01 6.734053e+02 0.000000e+00 --4.583330e-01 -4.166670e-02 5.500000e-01 8.041995e+02 0.000000e+00 --4.583330e-01 0.000000e+00 5.500000e-01 9.243473e+02 0.000000e+00 --4.583330e-01 4.166670e-02 5.500000e-01 1.027700e+03 0.000000e+00 --4.583330e-01 8.333330e-02 5.500000e-01 1.109314e+03 0.000000e+00 --4.583330e-01 1.250000e-01 5.500000e-01 1.165985e+03 0.000000e+00 --4.583330e-01 1.666670e-01 5.500000e-01 1.196510e+03 0.000000e+00 --4.583330e-01 2.083330e-01 5.500000e-01 1.201637e+03 0.000000e+00 --4.583330e-01 2.500000e-01 5.500000e-01 1.183750e+03 0.000000e+00 --4.583330e-01 2.916670e-01 5.500000e-01 1.146384e+03 0.000000e+00 --4.583330e-01 3.333330e-01 5.500000e-01 1.093695e+03 0.000000e+00 --4.583330e-01 3.750000e-01 5.500000e-01 1.029956e+03 0.000000e+00 --4.583330e-01 4.166670e-01 5.500000e-01 9.591841e+02 0.000000e+00 --4.583330e-01 4.583330e-01 5.500000e-01 8.848866e+02 0.000000e+00 --4.583330e-01 5.000000e-01 5.500000e-01 8.099267e+02 0.000000e+00 --4.166670e-01 -5.000000e-01 5.500000e-01 -2.226649e+02 0.000000e+00 --4.166670e-01 -4.583330e-01 5.500000e-01 -1.969125e+02 0.000000e+00 --4.166670e-01 -4.166670e-01 5.500000e-01 -1.581213e+02 0.000000e+00 --4.166670e-01 -3.750000e-01 5.500000e-01 -1.039189e+02 0.000000e+00 --4.166670e-01 -3.333330e-01 5.500000e-01 -3.222871e+01 0.000000e+00 --4.166670e-01 -2.916670e-01 5.500000e-01 5.837249e+01 0.000000e+00 --4.166670e-01 -2.500000e-01 5.500000e-01 1.682414e+02 0.000000e+00 --4.166670e-01 -2.083330e-01 5.500000e-01 2.962328e+02 0.000000e+00 --4.166670e-01 -1.666670e-01 5.500000e-01 4.393757e+02 0.000000e+00 --4.166670e-01 -1.250000e-01 5.500000e-01 5.927629e+02 0.000000e+00 --4.166670e-01 -8.333330e-02 5.500000e-01 7.497117e+02 0.000000e+00 --4.166670e-01 -4.166670e-02 5.500000e-01 9.023120e+02 0.000000e+00 --4.166670e-01 0.000000e+00 5.500000e-01 1.042257e+03 0.000000e+00 --4.166670e-01 4.166670e-02 5.500000e-01 1.161841e+03 0.000000e+00 --4.166670e-01 8.333330e-02 5.500000e-01 1.254929e+03 0.000000e+00 --4.166670e-01 1.250000e-01 5.500000e-01 1.317665e+03 0.000000e+00 --4.166670e-01 1.666670e-01 5.500000e-01 1.348798e+03 0.000000e+00 --4.166670e-01 2.083330e-01 5.500000e-01 1.349577e+03 0.000000e+00 --4.166670e-01 2.500000e-01 5.500000e-01 1.323288e+03 0.000000e+00 --4.166670e-01 2.916670e-01 5.500000e-01 1.274584e+03 0.000000e+00 --4.166670e-01 3.333330e-01 5.500000e-01 1.208768e+03 0.000000e+00 --4.166670e-01 3.750000e-01 5.500000e-01 1.131147e+03 0.000000e+00 --4.166670e-01 4.166670e-01 5.500000e-01 1.046580e+03 0.000000e+00 --4.166670e-01 4.583330e-01 5.500000e-01 9.591841e+02 0.000000e+00 --4.166670e-01 5.000000e-01 5.500000e-01 8.722184e+02 0.000000e+00 --3.750000e-01 -5.000000e-01 5.500000e-01 -2.563686e+02 0.000000e+00 --3.750000e-01 -4.583330e-01 5.500000e-01 -2.320584e+02 0.000000e+00 --3.750000e-01 -4.166670e-01 5.500000e-01 -1.932949e+02 0.000000e+00 --3.750000e-01 -3.750000e-01 5.500000e-01 -1.370930e+02 0.000000e+00 --3.750000e-01 -3.333330e-01 5.500000e-01 -6.070809e+01 0.000000e+00 --3.750000e-01 -2.916670e-01 5.500000e-01 3.792517e+01 0.000000e+00 --3.750000e-01 -2.500000e-01 5.500000e-01 1.596604e+02 0.000000e+00 --3.750000e-01 -2.083330e-01 5.500000e-01 3.035530e+02 0.000000e+00 --3.750000e-01 -1.666670e-01 5.500000e-01 4.663861e+02 0.000000e+00 --3.750000e-01 -1.250000e-01 5.500000e-01 6.424575e+02 0.000000e+00 --3.750000e-01 -8.333330e-02 5.500000e-01 8.237273e+02 0.000000e+00 --3.750000e-01 -4.166670e-02 5.500000e-01 1.000488e+03 0.000000e+00 --3.750000e-01 0.000000e+00 5.500000e-01 1.162437e+03 0.000000e+00 --3.750000e-01 4.166670e-02 5.500000e-01 1.300004e+03 0.000000e+00 --3.750000e-01 8.333330e-02 5.500000e-01 1.405630e+03 0.000000e+00 --3.750000e-01 1.250000e-01 5.500000e-01 1.474709e+03 0.000000e+00 --3.750000e-01 1.666670e-01 5.500000e-01 1.505984e+03 0.000000e+00 --3.750000e-01 2.083330e-01 5.500000e-01 1.501366e+03 0.000000e+00 --3.750000e-01 2.500000e-01 5.500000e-01 1.465266e+03 0.000000e+00 --3.750000e-01 2.916670e-01 5.500000e-01 1.403687e+03 0.000000e+00 --3.750000e-01 3.333330e-01 5.500000e-01 1.323273e+03 0.000000e+00 --3.750000e-01 3.750000e-01 5.500000e-01 1.230503e+03 0.000000e+00 --3.750000e-01 4.166670e-01 5.500000e-01 1.131147e+03 0.000000e+00 --3.750000e-01 4.583330e-01 5.500000e-01 1.029956e+03 0.000000e+00 --3.750000e-01 5.000000e-01 5.500000e-01 9.305613e+02 0.000000e+00 --3.333330e-01 -5.000000e-01 5.500000e-01 -2.934733e+02 0.000000e+00 --3.333330e-01 -4.583330e-01 5.500000e-01 -2.717511e+02 0.000000e+00 --3.333330e-01 -4.166670e-01 5.500000e-01 -2.343043e+02 0.000000e+00 --3.333330e-01 -3.750000e-01 5.500000e-01 -1.774689e+02 0.000000e+00 --3.333330e-01 -3.333330e-01 5.500000e-01 -9.772515e+01 0.000000e+00 --3.333330e-01 -2.916670e-01 5.500000e-01 7.781858e+00 0.000000e+00 --3.333330e-01 -2.500000e-01 5.500000e-01 1.405745e+02 0.000000e+00 --3.333330e-01 -2.083330e-01 5.500000e-01 3.000746e+02 0.000000e+00 --3.333330e-01 -1.666670e-01 5.500000e-01 4.829339e+02 0.000000e+00 --3.333330e-01 -1.250000e-01 5.500000e-01 6.826713e+02 0.000000e+00 --3.333330e-01 -8.333330e-02 5.500000e-01 8.897772e+02 0.000000e+00 --3.333330e-01 -4.166670e-02 5.500000e-01 1.092510e+03 0.000000e+00 --3.333330e-01 0.000000e+00 5.500000e-01 1.278261e+03 0.000000e+00 --3.333330e-01 4.166670e-02 5.500000e-01 1.435280e+03 0.000000e+00 --3.333330e-01 8.333330e-02 5.500000e-01 1.554349e+03 0.000000e+00 --3.333330e-01 1.250000e-01 5.500000e-01 1.630013e+03 0.000000e+00 --3.333330e-01 1.666670e-01 5.500000e-01 1.661061e+03 0.000000e+00 --3.333330e-01 2.083330e-01 5.500000e-01 1.650226e+03 0.000000e+00 --3.333330e-01 2.500000e-01 5.500000e-01 1.603269e+03 0.000000e+00 --3.333330e-01 2.916670e-01 5.500000e-01 1.527757e+03 0.000000e+00 --3.333330e-01 3.333330e-01 5.500000e-01 1.431842e+03 0.000000e+00 --3.333330e-01 3.750000e-01 5.500000e-01 1.323273e+03 0.000000e+00 --3.333330e-01 4.166670e-01 5.500000e-01 1.208768e+03 0.000000e+00 --3.333330e-01 4.583330e-01 5.500000e-01 1.093695e+03 0.000000e+00 --3.333330e-01 5.000000e-01 5.500000e-01 9.820190e+02 0.000000e+00 --2.916670e-01 -5.000000e-01 5.500000e-01 -3.335430e+02 0.000000e+00 --2.916670e-01 -4.583330e-01 5.500000e-01 -3.157134e+02 0.000000e+00 --2.916670e-01 -4.166670e-01 5.500000e-01 -2.811460e+02 0.000000e+00 --2.916670e-01 -3.750000e-01 5.500000e-01 -2.254638e+02 0.000000e+00 --2.916670e-01 -3.333330e-01 5.500000e-01 -1.442910e+02 0.000000e+00 --2.916670e-01 -2.916670e-01 5.500000e-01 -3.385111e+01 0.000000e+00 --2.916670e-01 -2.500000e-01 5.500000e-01 1.082258e+02 0.000000e+00 --2.916670e-01 -2.083330e-01 5.500000e-01 2.819276e+02 0.000000e+00 --2.916670e-01 -1.666670e-01 5.500000e-01 4.839507e+02 0.000000e+00 --2.916670e-01 -1.250000e-01 5.500000e-01 7.071303e+02 0.000000e+00 --2.916670e-01 -8.333330e-02 5.500000e-01 9.404575e+02 0.000000e+00 --2.916670e-01 -4.166670e-02 5.500000e-01 1.169994e+03 0.000000e+00 --2.916670e-01 0.000000e+00 5.500000e-01 1.380567e+03 0.000000e+00 --2.916670e-01 4.166670e-02 5.500000e-01 1.557970e+03 0.000000e+00 --2.916670e-01 8.333330e-02 5.500000e-01 1.691116e+03 0.000000e+00 --2.916670e-01 1.250000e-01 5.500000e-01 1.773609e+03 0.000000e+00 --2.916670e-01 1.666670e-01 5.500000e-01 1.804321e+03 0.000000e+00 --2.916670e-01 2.083330e-01 5.500000e-01 1.786955e+03 0.000000e+00 --2.916670e-01 2.500000e-01 5.500000e-01 1.728801e+03 0.000000e+00 --2.916670e-01 2.916670e-01 5.500000e-01 1.639146e+03 0.000000e+00 --2.916670e-01 3.333330e-01 5.500000e-01 1.527757e+03 0.000000e+00 --2.916670e-01 3.750000e-01 5.500000e-01 1.403687e+03 0.000000e+00 --2.916670e-01 4.166670e-01 5.500000e-01 1.274584e+03 0.000000e+00 --2.916670e-01 4.583330e-01 5.500000e-01 1.146384e+03 0.000000e+00 --2.916670e-01 5.000000e-01 5.500000e-01 1.023319e+03 0.000000e+00 --2.500000e-01 -5.000000e-01 5.500000e-01 -3.758360e+02 0.000000e+00 --2.500000e-01 -4.583330e-01 5.500000e-01 -3.632921e+02 0.000000e+00 --2.500000e-01 -4.166670e-01 5.500000e-01 -3.333759e+02 0.000000e+00 --2.500000e-01 -3.750000e-01 5.500000e-01 -2.810098e+02 0.000000e+00 --2.500000e-01 -3.333330e-01 5.500000e-01 -2.009307e+02 0.000000e+00 --2.500000e-01 -2.916670e-01 5.500000e-01 -8.834796e+01 0.000000e+00 --2.500000e-01 -2.500000e-01 5.500000e-01 6.011465e+01 0.000000e+00 --2.500000e-01 -2.083330e-01 5.500000e-01 2.452270e+02 0.000000e+00 --2.500000e-01 -1.666670e-01 5.500000e-01 4.639656e+02 0.000000e+00 --2.500000e-01 -1.250000e-01 5.500000e-01 7.086786e+02 0.000000e+00 --2.500000e-01 -8.333330e-02 5.500000e-01 9.669552e+02 0.000000e+00 --2.500000e-01 -4.166670e-02 5.500000e-01 1.222630e+03 0.000000e+00 --2.500000e-01 0.000000e+00 5.500000e-01 1.457834e+03 0.000000e+00 --2.500000e-01 4.166670e-02 5.500000e-01 1.655720e+03 0.000000e+00 --2.500000e-01 8.333330e-02 5.500000e-01 1.803178e+03 0.000000e+00 --2.500000e-01 1.250000e-01 5.500000e-01 1.892790e+03 0.000000e+00 --2.500000e-01 1.666670e-01 5.500000e-01 1.923531e+03 0.000000e+00 --2.500000e-01 2.083330e-01 5.500000e-01 1.900149e+03 0.000000e+00 --2.500000e-01 2.500000e-01 5.500000e-01 1.831551e+03 0.000000e+00 --2.500000e-01 2.916670e-01 5.500000e-01 1.728801e+03 0.000000e+00 --2.500000e-01 3.333330e-01 5.500000e-01 1.603269e+03 0.000000e+00 --2.500000e-01 3.750000e-01 5.500000e-01 1.465266e+03 0.000000e+00 --2.500000e-01 4.166670e-01 5.500000e-01 1.323288e+03 0.000000e+00 --2.500000e-01 4.583330e-01 5.500000e-01 1.183750e+03 0.000000e+00 --2.500000e-01 5.000000e-01 5.500000e-01 1.051072e+03 0.000000e+00 --2.083330e-01 -5.000000e-01 5.500000e-01 -4.192739e+02 0.000000e+00 --2.083330e-01 -4.583330e-01 5.500000e-01 -4.133901e+02 0.000000e+00 --2.083330e-01 -4.166670e-01 5.500000e-01 -3.899878e+02 0.000000e+00 --2.083330e-01 -3.750000e-01 5.500000e-01 -3.433596e+02 0.000000e+00 --2.083330e-01 -3.333330e-01 5.500000e-01 -2.673914e+02 0.000000e+00 --2.083330e-01 -2.916670e-01 5.500000e-01 -1.562561e+02 0.000000e+00 --2.083330e-01 -2.500000e-01 5.500000e-01 -5.466449e+00 0.000000e+00 --2.083330e-01 -2.083330e-01 5.500000e-01 1.867276e+02 0.000000e+00 --2.083330e-01 -1.666670e-01 5.500000e-01 4.178600e+02 0.000000e+00 --2.083330e-01 -1.250000e-01 5.500000e-01 6.800979e+02 0.000000e+00 --2.083330e-01 -8.333330e-02 5.500000e-01 9.598952e+02 0.000000e+00 --2.083330e-01 -4.166670e-02 5.500000e-01 1.239032e+03 0.000000e+00 --2.083330e-01 0.000000e+00 5.500000e-01 1.497004e+03 0.000000e+00 --2.083330e-01 4.166670e-02 5.500000e-01 1.714316e+03 0.000000e+00 --2.083330e-01 8.333330e-02 5.500000e-01 1.875779e+03 0.000000e+00 --2.083330e-01 1.250000e-01 5.500000e-01 1.972898e+03 0.000000e+00 --2.083330e-01 1.666670e-01 5.500000e-01 2.004712e+03 0.000000e+00 --2.083330e-01 2.083330e-01 5.500000e-01 1.976976e+03 0.000000e+00 --2.083330e-01 2.500000e-01 5.500000e-01 1.900149e+03 0.000000e+00 --2.083330e-01 2.916670e-01 5.500000e-01 1.786955e+03 0.000000e+00 --2.083330e-01 3.333330e-01 5.500000e-01 1.650226e+03 0.000000e+00 --2.083330e-01 3.750000e-01 5.500000e-01 1.501366e+03 0.000000e+00 --2.083330e-01 4.166670e-01 5.500000e-01 1.349577e+03 0.000000e+00 --2.083330e-01 4.583330e-01 5.500000e-01 1.201637e+03 0.000000e+00 --2.083330e-01 5.000000e-01 5.500000e-01 1.062073e+03 0.000000e+00 --1.666670e-01 -5.000000e-01 5.500000e-01 -4.624569e+02 0.000000e+00 --1.666670e-01 -4.583330e-01 5.500000e-01 -4.644542e+02 0.000000e+00 --1.666670e-01 -4.166670e-01 5.500000e-01 -4.493508e+02 0.000000e+00 --1.666670e-01 -3.750000e-01 5.500000e-01 -4.109431e+02 0.000000e+00 --1.666670e-01 -3.333330e-01 5.500000e-01 -3.423827e+02 0.000000e+00 --1.666670e-01 -2.916670e-01 5.500000e-01 -2.368767e+02 0.000000e+00 --1.666670e-01 -2.500000e-01 5.500000e-01 -8.881283e+01 0.000000e+00 --1.666670e-01 -2.083330e-01 5.500000e-01 1.046762e+02 0.000000e+00 --1.666670e-01 -1.666670e-01 5.500000e-01 3.419595e+02 0.000000e+00 --1.666670e-01 -1.250000e-01 5.500000e-01 6.154219e+02 0.000000e+00 --1.666670e-01 -8.333330e-02 5.500000e-01 9.108369e+02 0.000000e+00 --1.666670e-01 -4.166670e-02 5.500000e-01 1.208360e+03 0.000000e+00 --1.666670e-01 0.000000e+00 5.500000e-01 1.485203e+03 0.000000e+00 --1.666670e-01 4.166670e-02 5.500000e-01 1.719448e+03 0.000000e+00 --1.666670e-01 8.333330e-02 5.500000e-01 1.893927e+03 0.000000e+00 --1.666670e-01 1.250000e-01 5.500000e-01 1.999050e+03 0.000000e+00 --1.666670e-01 1.666670e-01 5.500000e-01 2.033794e+03 0.000000e+00 --1.666670e-01 2.083330e-01 5.500000e-01 2.004712e+03 0.000000e+00 --1.666670e-01 2.500000e-01 5.500000e-01 1.923531e+03 0.000000e+00 --1.666670e-01 2.916670e-01 5.500000e-01 1.804321e+03 0.000000e+00 --1.666670e-01 3.333330e-01 5.500000e-01 1.661061e+03 0.000000e+00 --1.666670e-01 3.750000e-01 5.500000e-01 1.505984e+03 0.000000e+00 --1.666670e-01 4.166670e-01 5.500000e-01 1.348798e+03 0.000000e+00 --1.666670e-01 4.583330e-01 5.500000e-01 1.196510e+03 0.000000e+00 --1.666670e-01 5.000000e-01 5.500000e-01 1.053680e+03 0.000000e+00 --1.250000e-01 -5.000000e-01 5.500000e-01 -5.037297e+02 0.000000e+00 --1.250000e-01 -4.583330e-01 5.500000e-01 -5.145281e+02 0.000000e+00 --1.250000e-01 -4.166670e-01 5.500000e-01 -5.092308e+02 0.000000e+00 --1.250000e-01 -3.750000e-01 5.500000e-01 -4.813258e+02 0.000000e+00 --1.250000e-01 -3.333330e-01 5.500000e-01 -4.234266e+02 0.000000e+00 --1.250000e-01 -2.916670e-01 5.500000e-01 -3.279459e+02 0.000000e+00 --1.250000e-01 -2.500000e-01 5.500000e-01 -1.882470e+02 0.000000e+00 --1.250000e-01 -2.083330e-01 5.500000e-01 -3.133342e-01 0.000000e+00 --1.250000e-01 -1.666670e-01 5.500000e-01 2.352828e+02 0.000000e+00 --1.250000e-01 -1.250000e-01 5.500000e-01 5.115825e+02 0.000000e+00 --1.250000e-01 -8.333330e-02 5.500000e-01 8.143000e+02 0.000000e+00 --1.250000e-01 -4.166670e-02 5.500000e-01 1.122677e+03 0.000000e+00 --1.250000e-01 0.000000e+00 5.500000e-01 1.412318e+03 0.000000e+00 --1.250000e-01 4.166670e-02 5.500000e-01 1.659416e+03 0.000000e+00 --1.250000e-01 8.333330e-02 5.500000e-01 1.845121e+03 0.000000e+00 --1.250000e-01 1.250000e-01 5.500000e-01 1.958771e+03 0.000000e+00 --1.250000e-01 1.666670e-01 5.500000e-01 1.999050e+03 0.000000e+00 --1.250000e-01 2.083330e-01 5.500000e-01 1.972898e+03 0.000000e+00 --1.250000e-01 2.500000e-01 5.500000e-01 1.892790e+03 0.000000e+00 --1.250000e-01 2.916670e-01 5.500000e-01 1.773609e+03 0.000000e+00 --1.250000e-01 3.333330e-01 5.500000e-01 1.630013e+03 0.000000e+00 --1.250000e-01 3.750000e-01 5.500000e-01 1.474709e+03 0.000000e+00 --1.250000e-01 4.166670e-01 5.500000e-01 1.317665e+03 0.000000e+00 --1.250000e-01 4.583330e-01 5.500000e-01 1.165985e+03 0.000000e+00 --1.250000e-01 5.000000e-01 5.500000e-01 1.024210e+03 0.000000e+00 --8.333330e-02 -5.000000e-01 5.500000e-01 -5.412899e+02 0.000000e+00 --8.333330e-02 -4.583330e-01 5.500000e-01 -5.613715e+02 0.000000e+00 --8.333330e-02 -4.166670e-01 5.500000e-01 -5.669068e+02 0.000000e+00 --8.333330e-02 -3.750000e-01 5.500000e-01 -5.512986e+02 0.000000e+00 --8.333330e-02 -3.333330e-01 5.500000e-01 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