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*.obs
*.sus
+*.sublime-project
+*.sublime-workspace
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from ipywidgets import interact, IntSlider\n",
+ "%matplotlib inline\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import FormatStrFormatter\n",
+ "matplotlib.rcParams['font.size'] = 14"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "# DC resistivity example: Gradient array with single source"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Purpose\n",
+ "\n",
+ "- Explore the basic setup and physics of a direct current (DC) resistivity survey within the context of kimberlite exploration. \n",
+ "\n",
+ "- Run DC forward modelling and inversion using the SimPEG-Static package. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "Physical behavior of DC resistivity survey is governed by steady-state maxwell's equation:\n",
+ "\n",
+ "$$\\vec{j} = \\sigma \\vec{e}$$\n",
+ "\n",
+ "$$\\vec{e} = -\\nabla \\phi$$\n",
+ "\n",
+ "$$\\nabla \\cdot \\vec{j} = -\\vec{j}_s = I_0 (\\delta(\\vec{r}-\\vec{r}_+)-\\delta(\\vec{r}-\\vec{r}_-))$$\n",
+ "\n",
+ "$$\\vec{j} \\cdot \\hat{n} \\ \\Big|_{\\partial \\Omega} = 0$$ \n",
+ "\n",
+ "- $\\vec{j}$: Current density (A/m $^2$)\n",
+ "\n",
+ "- $\\vec{e}$: Electric field (V/m)\n",
+ "\n",
+ "- $I_0$: Current (A)\n",
+ "\n",
+ "- $\\delta$: Volumetric delta function (m $^{-3}$)\n",
+ "\n",
+ "Consider a simple gradient array having a pair of A (+) and B (+) current electrodes as the transmitter (Tx) with multiple M (+) and N (-) potential electrode pairs for receivers (Rx). Using a current source (large battery or generator), we setup a significant potential difference between the A and B electrodes which allows electrial currents to flow from the A to B electrode. The presence of conductive or resistive structures within the earth distorts current flow and leads to the accumulation of charges on interfaces. The potential differences measured at each M and N electrode pair will be reflective on those chrage accumulations and current distortions. Forward modelling is the process by which we caluclate the potentials (or associated fields) that would result from a given conductivity model. Conversely, with the inverse problem we reconstruct the model from the observed data. \n",
+ "\n",
+ "Using a DC resitivity survey for diamond or mineral exploration is possible anytime that there is a suiteably large contrast in conductivity between the target and host/background rock. In this example, the kimberlite doposits (which incorporate diamonds) are more conductive than the granitic background rock."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![](\"DCIP_kimberlites.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Forward problem\n",
+ "\n",
+ "### Setup mesh, load model, design survey, and forward model data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from SimPEG import Mesh, Utils, Maps, Survey\n",
+ "import numpy as np\n",
+ "from SimPEG.EM.Static import DC, IP, Utils as StaticUtils\n",
+ "from pymatsolver import PardisoSolver\n",
+ "\n",
+ "### Mesh\n",
+ "\n",
+ "# Core cell sizes in x, y, and z\n",
+ "csx, csy, csz = 25., 25., 25.\n",
+ "# Number of core cells in each directiPon s\n",
+ "ncx, ncy, ncz = 48, 48, 20\n",
+ "# Number of padding cells to add in each direction\n",
+ "npad = 7\n",
+ "# Vectors of cell lengthts in each direction\n",
+ "hx = [(csx,npad, -1.3),(csx,ncx),(csx,npad, 1.3)]\n",
+ "hy = [(csy,npad, -1.3),(csy,ncy),(csy,npad, 1.3)]\n",
+ "hz = [(csz,npad, -1.3),(csz,ncz), (csz/2.,6)]\n",
+ "# Create mesh\n",
+ "mesh = Mesh.TensorMesh([hx, hy, hz],x0=\"CCN\")\n",
+ "# Map mesh coordinates from local to UTM coordiantes\n",
+ "xc = 300+5.57e5\n",
+ "yc = 600+7.133e6\n",
+ "zc = 425.\n",
+ "mesh._x0 = mesh.x0 + np.r_[xc, yc, zc]\n",
+ "\n",
+ "# Load synthetic conductivity model matching the designated mesh\n",
+ "sigma = mesh.readModelUBC(\"VTKout_DC.dat\")\n",
+ "# Identify air cells in the model\n",
+ "airind = sigma == 1e-8\n",
+ "\n",
+ "# Generate background model (constant conductiivty below topography)\n",
+ "sigma0 = np.ones_like(sigma)*1e-4\n",
+ "sigma0[airind] = 1e-8\n",
+ "\n",
+ "### Survey\n",
+ "\n",
+ "# Obtain topographic surface from 3D conductivity model\n",
+ "mesh2D, topoCC = StaticUtils.gettopoCC(mesh, airind)\n",
+ "\n",
+ "# Setup gradient array survey\n",
+ "\n",
+ "# Define the source electrode locations\n",
+ "# Here we use a single dipole source (A-B electrode) in the x-direction)\n",
+ "Aloc1_x = np.r_[-600., 0, 0.] + np.r_[xc, yc, zc]\n",
+ "Bloc1_x = np.r_[600., 0, 0.] + np.r_[xc, yc, zc]\n",
+ "\n",
+ "# Rx locations (M-N electrodes, x-direction)\n",
+ "# Find x and y cell centres in the interior portion of the mesh\n",
+ "x = mesh.vectorCCx[np.logical_and(mesh.vectorCCx>-300.+ xc, mesh.vectorCCx<300.+ xc)]\n",
+ "y = mesh.vectorCCy[np.logical_and(mesh.vectorCCy>-300.+ yc, mesh.vectorCCy<300.+ yc)]\n",
+ "# Grid selected cell centres to get M and N Rx electrode locations\n",
+ "Mx = Utils.ndgrid(x[:-1], y, np.r_[-12.5/2.])\n",
+ "Nx = Utils.ndgrid(x[1:], y, np.r_[-12.5/2.])\n",
+ "# Get cell ind for electrode locations to extract electrode elevation from topoCC\n",
+ "inds_Mx = Utils.closestPoints(mesh2D, Mx[:,:2])\n",
+ "inds_Nx = Utils.closestPoints(mesh2D, Nx[:,:2])\n",
+ "# Draped M and N electrode x,y,z locations\n",
+ "Mx_dr = np.c_[Mx[:,0], Mx[:,1], topoCC[inds_Mx]]\n",
+ "Nx_dr = np.c_[Nx[:,0], Nx[:,1], topoCC[inds_Nx]]\n",
+ "\n",
+ "# Create Src and Rx classes for DC problem\n",
+ "rx_x = DC.Rx.Dipole(Mx_dr, Nx_dr)\n",
+ "src1 = DC.Src.Dipole([rx_x], Aloc1_x, Bloc1_x)\n",
+ "\n",
+ "# Form survey object using Srcs and Rxs that we have generated\n",
+ "survey = DC.Survey([src1])\n",
+ "\n",
+ "### Problem\n",
+ "\n",
+ "# Define problem and set solver\n",
+ "problem = DC.Problem3D_CC(mesh, sigmaMap=Maps.IdentityMap(mesh))\n",
+ "\n",
+ "problem.Solver = PardisoSolver\n",
+ "# Pair problem and survey\n",
+ "problem.pair(survey)\n",
+ "\n",
+ "# Forward model fields due to the reference model and true model\n",
+ "f0 = problem.fields(sigma0)\n",
+ "f = problem.fields(sigma)\n",
+ "\n",
+ "# Get observed data\n",
+ "dobs = survey.dpred(sigma, f=f)\n",
+ "\n",
+ "# Compute secondary potential\n",
+ "phi_sec = f[src1, \"phi\"] - f0[src1, \"phi\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Scripts for visualizations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def viz(mesh, sigma, ind, airind, normal=\"Z\", ax=None, label=\"Conductivity (S/m)\", scale=\"log\", clim=(-4, -1), xc=0, yc=0,zc=0., cb=True, pltshow=True):\n",
+ " if normal == \"Z\":\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 5))\n",
+ " ax = plt.subplot(111)\n",
+ " else:\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 2.5))\n",
+ " ax = plt.subplot(111)\n",
+ "\n",
+ " temp = sigma.copy()\n",
+ "\n",
+ " if scale == \"log\":\n",
+ " temp = np.log10(temp)\n",
+ "\n",
+ " temp[airind] = np.nan\n",
+ "\n",
+ " dat = mesh.plotSlice(temp, ind=ind, clim=clim, normal=normal, grid=False, pcolorOpts={\"cmap\":\"viridis\"}, ax=ax)\n",
+ " if normal == \"Z\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Northing (m)\")\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+yc, 500.+yc\n",
+ " ax.set_title((\"Elevation at %.1f m\")%(mesh.vectorCCz[ind]))\n",
+ " elif normal == \"Y\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Elevation (m)\")\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+zc, 0.+zc\n",
+ " ax.set_title((\"Northing at %.1f m\")%(mesh.vectorCCy[ind]))\n",
+ "\n",
+ " ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ " ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ " ax.set_xlim(xmin, xmax)\n",
+ " ax.set_ylim(ymin, ymax)\n",
+ " # plt.tight_layout()\n",
+ "\n",
+ " if scale == \"log\":\n",
+ " cbformat = \"$10^{%1.1f}$\"\n",
+ " elif scale == \"linear\":\n",
+ " cbformat = \"%.1e\"\n",
+ "\n",
+ " if clim is None:\n",
+ " vmin, vmax = dat[0].get_clim()\n",
+ " else:\n",
+ " vmin, vmax = clim[0], clim[1]\n",
+ "\n",
+ " if cb:\n",
+ " cb = plt.colorbar(dat[0], format=cbformat, ticks=np.linspace(vmin, vmax, 3))\n",
+ " cb.set_label(label)\n",
+ " if pltshow:\n",
+ " plt.show()\n",
+ " return ax, dat\n",
+ "\n",
+ "def vizEJ(mesh, sigma, ind, f, src, airind, normal=\"Z\", ftype=\"E\", clim=None, xc=0, yc=0,zc=0., ax=None, cb=True, pltshow=True):\n",
+ " if normal == \"Z\":\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 5))\n",
+ " ax = plt.subplot(111)\n",
+ " else:\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 2.5))\n",
+ " ax = plt.subplot(111)\n",
+ "\n",
+ " temp = sigma.copy()\n",
+ " temp[airind] = np.nan\n",
+ "\n",
+ " if ftype == \"E\":\n",
+ " dat=mesh.plotSlice(f[src,'e'], vType=\"F\", view=\"vec\", ind=ind, normal=normal, grid=False, streamOpts={'color':'w'}, pcolorOpts={\"cmap\":\"viridis\"}, ax=ax)\n",
+ " cb_label = \"Electric fields (V/m)\"\n",
+ " elif ftype == \"phi\":\n",
+ " dat=mesh.plotSlice(f[src,'phi'], ind=ind, normal=normal, pcolorOpts={\"cmap\":\"viridis\"}, ax=ax)\n",
+ " cb_label = \"Potential (V)\"\n",
+ " elif ftype == \"charg\":\n",
+ " dat=mesh.plotSlice(f[src,'charge'], ind=ind, normal=normal, pcolorOpts={\"cmap\":\"viridis\"}, ax=ax)\n",
+ " cb_label = \"Electric charges (C)\"\n",
+ " elif ftype == \"J\":\n",
+ " dat=mesh.plotSlice(f[src,'j'], vType=\"F\", view=\"vec\", ind=ind, normal=normal, grid=False, streamOpts={'color':'w'}, pcolorOpts={\"cmap\":\"viridis\"}, ax=ax)\n",
+ " cb_label = \"Electric currents (V/m)\"\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ "\n",
+ " cbformat = \"%.1e\"\n",
+ "\n",
+ " if clim is None:\n",
+ " vmin, vmax = dat[0].get_clim()\n",
+ " else:\n",
+ " vmin, vmax = clim[0], clim[1]\n",
+ "\n",
+ " if cb:\n",
+ " cb = plt.colorbar(dat[0], format=cbformat, ticks=np.linspace(vmin, vmax, 3))\n",
+ " cb.set_label(cb_label)\n",
+ "\n",
+ " if normal == \"Z\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Northing (m)\")\n",
+ " xmin, xmax = -700+xc, 700+xc\n",
+ " ymin, ymax = -700+yc, 700.+yc\n",
+ " ax.set_title((\"Elevation at %.1f m\")%(mesh.vectorCCz[ind]))\n",
+ " elif normal == \"Y\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Elevation (m)\")\n",
+ " xmin, xmax = -700+xc, 700+xc\n",
+ " ymin, ymax = -700+zc, 0.+zc\n",
+ " ax.set_title((\"Northing at %.1f m\")%(mesh.vectorCCy[ind]))\n",
+ "\n",
+ "\n",
+ "# ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ "# ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ " ax.set_xlim(xmin, xmax)\n",
+ " ax.set_ylim(ymin, ymax)\n",
+ " if pltshow:\n",
+ " plt.show()\n",
+ " return ax, dat\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualize synthetic conductivity model: $\\sigma (x, y, z)$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "50da36072f89409c9a8d843db9809e17"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ">"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interact(lambda indz: viz(mesh, sigma, indz, airind=airind, normal=\"Z\", xc=xc,yc=yc,zc=zc), indz=IntSlider(min=0, max=mesh.nCz-1,step=1, value=28))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9b5394a164814014a883c1ce2b9f8c12"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ">"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interact(lambda indy: viz(mesh, sigma, indy, airind=airind, normal=\"Y\", xc=xc,yc=yc,zc=zc), indy=IntSlider(min=0, max=mesh.nCy-1,step=1, value=30))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### View electrode geometry for Gradient arrray\n",
+ "This simple survey is comprised of a single Tx electrode pair (A and B electrodes) with 552 associated Rx electrode pairs (M and N). \n",
+ "\n",
+ "- A and B electrode are separeted by 1200m in the E-W direction.\n",
+ "- M and N electrodes have 50m sepatation distances and are also oriented in the E-W direction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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4g3D8duw3ffpvv/2GUI9QBDkHwVnjDCMjI5ibm8POzg4VUyuirWtb9Pfuj+PH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\nZlaDO0wzM7Ma3GGamZnV8A/a6Ij47f6xMAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,1, figsize=(5*1.3,5*1.3))\n",
+ "plt.xlabel(\"Easting (m)\"); plt.ylabel(\"Northing (m)\")\n",
+ "ax.plot(Aloc1_x[0], Aloc1_x[1], 'ro', ms=10)\n",
+ "ax.plot(Bloc1_x[0], Bloc1_x[1], 'bo', ms=10)\n",
+ "ax.scatter(Mx[:,0], Mx[:,1],s=20, edgecolors=\"k\", facecolors=\"none\")\n",
+ "ax.scatter(Nx[:,0], Nx[:,1],s=5, edgecolors=\"none\", facecolors=\"g\")\n",
+ "ax.legend((\"A\", \"B\", \"M\", \"N\"), fontsize=12)\n",
+ "xmin, xmax = -700+xc, 700+xc\n",
+ "ymin, ymax = -700+yc, 700+yc\n",
+ "ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ "ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ "ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ "ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ "ax.axis('equal')\n",
+ "ax.set_xlim(xmin, xmax)\n",
+ "ax.set_ylim(ymin, ymax)\n",
+ "ax.set_xlabel(\"Easting (m)\")\n",
+ "ax.set_ylabel(\"Northing (m)\")\n",
+ "ax.set_title(\"Gradient array (DC-IP survey)\")\n",
+ "ax.grid(True)\n",
+ "plt.tight_layout()\n",
+ "# fig.savefig(\"./images/GradientArray\", dpi=400)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot computed currents, charges, and secondary potentials\n",
+ "\n",
+ "Using a finite volume discretization it is possible to numerically model the potential at any point on the mesh. With the relations below it is also possible to compute and visualize current density, charge distributions, and secondary potentials. These plots can be used to gain a better phyiscal understanding of how currents channel in conductive structures and accuulated charges on the interfaces produce secondary potentials. Since structures with varying conductivity from the background alter the total potentials we can use these measurements to reconstruct a conductivity model. \n",
+ "\n",
+ "- Current density: $\\mathbf{j} = \\sigma \\mathbf{e} = -\\sigma \\nabla \\phi$\n",
+ "\n",
+ "\n",
+ "- Charge: $\\int_V \\epsilon_0 \\nabla \\cdot \\mathbf{e} \\ dV = \\int_V\\rho_v dV$ \n",
+ " - where $\\rho_v$: Volumetric charge density (C/m $^3$), $\\epsilon_0$: Dielectric constant in free-space (F/m)\n",
+ "\n",
+ "\n",
+ "- Secondary potential: $\\phi_s = \\phi-\\phi_0$\n",
+ " - where $\\phi = F[\\sigma]$ , $\\phi_0 = F[\\sigma_{halfspace}]$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def dataPanelPlot(indz,indy):\n",
+ " fig = plt.figure(figsize=(11, 8))\n",
+ " Imin, Imax = 2.8e-8,2.7e-5\n",
+ " Qmin, Qmax = -18e-11, 3.0e-10\n",
+ " Vsmin, Vsmax = -2., 2.\n",
+ " dI, dQ, dV = (Imax-Imin)*0.2, (Qmax-Qmin)*0.2, (Vsmax-Vsmin)*0.2\n",
+ " xmin, xmax = -700+xc, 700+xc\n",
+ " ymin, ymax = -700+yc, 700+yc\n",
+ " zmin, zmax = -700+zc, 0+zc\n",
+ "# indz = 28\n",
+ "# indy = 31\n",
+ " matplotlib.rcParams['font.size'] = 10\n",
+ " # Axis label\n",
+ " x = np.linspace(xmin+200, xmax-200,3)\n",
+ "\n",
+ " # Currents z-section\n",
+ " ax1 = plt.subplot(1,1,1)\n",
+ " pos = ax1.get_position()\n",
+ " ax1.set_position([pos.x0-0.1, pos.y0+0.3, pos.width*0.5, pos.height*0.5])\n",
+ " ax1,dat1 = vizEJ(mesh, sigma, indz, f=f, airind=airind, src=src1, normal=\"Z\", ftype=\"J\", xc=xc, yc=yc, zc=zc, clim=(Imin, Imax), ax=ax1, cb=False, pltshow=False)\n",
+ " ax1.plot(Aloc1_x[0], Aloc1_x[1], 'ro', ms=8)\n",
+ " ax1.plot(Bloc1_x[0], Bloc1_x[1], 'bo', ms=8)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax1.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ " ax1.xaxis.set_visible(False)\n",
+ " ax1.set_xlim(xmin, xmax)\n",
+ " ax1.set_ylim(ymin, ymax)\n",
+ " ax1.set_ylabel('Northing (m)')\n",
+ "\n",
+ " # Cuurents y-section\n",
+ " pos = ax1.get_position()\n",
+ " ax2 = fig.add_axes([pos.x0+0.0525, pos.y0 - 0.36, pos.width*0.725, pos.height])\n",
+ " ax2, dat2 = vizEJ(mesh, sigma, indy, f=f, airind=airind, src=src1, normal=\"Y\", ftype=\"J\", xc=xc, yc=yc, zc=zc, clim=(Imin, Imax), ax=ax2, cb=False, pltshow=False)\n",
+ " ax2.plot(Aloc1_x[0], Aloc1_x[2], 'ro', ms=8)\n",
+ " ax2.plot(Bloc1_x[0], Bloc1_x[2], 'bo', ms=8)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax2.set_title((\"Slice at y=%.1f m\")%(mesh.vectorCCy[indy]))\n",
+ " ax2.set_xlim(xmin, xmax)\n",
+ " ax2.set_ylim(zmin, zmax)\n",
+ " ax2.set_xticks(map(int, x))\n",
+ " ax2.set_xticklabels(map(str, map(int, x)),size=12)\n",
+ " ax2.set_xlabel('Easting (m)')\n",
+ " ax2.set_ylabel('Elev. (m)')\n",
+ "\n",
+ " # Currents colorbar\n",
+ " cbI = plt.colorbar(dat1[0],orientation=\"horizontal\", format=\"%1.1e\", ticks=np.linspace(Imin+dI, Imax-dI, 3))\n",
+ " cbI_label = \"Electric currents (V/m)\"\n",
+ " cbI.set_label(cbI_label)\n",
+ "\n",
+ " # Charges z-section\n",
+ " ax3 = fig.add_axes([pos.x0+0.3, pos.y0, pos.width, pos.height])\n",
+ " ax3,dat3 = vizEJ(mesh, sigma, indz, f=f, airind=airind, src=src1, normal=\"Z\", ftype=\"charg\", xc=xc, yc=yc, zc=zc, clim=(Qmin, Qmax), ax=ax3, cb=False, pltshow=False)\n",
+ " ax3.plot(Aloc1_x[0], Aloc1_x[1], 'ro', ms=8)\n",
+ " ax3.plot(Bloc1_x[0], Bloc1_x[1], 'bo', ms=8)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax3.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ " ax3.xaxis.set_visible(False)\n",
+ " ax3.yaxis.set_visible(False)\n",
+ " ax3.set_xlim(xmin, xmax)\n",
+ " ax3.set_ylim(ymin, ymax)\n",
+ "\n",
+ " # Charges y-section\n",
+ " ax4 = fig.add_axes([pos.x0+0.3525, pos.y0 - 0.36, pos.width*0.725, pos.height])\n",
+ " ax4, dat4 = vizEJ(mesh, sigma, indy, f=f, airind=airind, src=src1, normal=\"Y\", ftype=\"charg\", xc=xc, yc=yc, zc=zc, clim=(Qmin, Qmax), ax=ax4, cb=False, pltshow=False)\n",
+ " ax4.plot(Aloc1_x[0], Aloc1_x[2], 'ro', ms=8)\n",
+ " ax4.plot(Bloc1_x[0], Bloc1_x[2], 'bo', ms=8)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax4.set_title((\"Slice at y=%.1f m\")%(mesh.vectorCCy[indy]))\n",
+ " ax4.yaxis.set_visible(False)\n",
+ " ax4.set_xlim(xmin, xmax)\n",
+ " ax4.set_ylim(zmin, zmax)\n",
+ " ax4.set_xticks(map(int, x))\n",
+ " ax4.set_xticklabels(map(str, map(int, x)),size=12)\n",
+ " ax4.set_xlabel('Easting (m)')\n",
+ "\n",
+ " # Charges colorbar\n",
+ " cbQ = plt.colorbar(dat4[0],orientation=\"horizontal\", format=\"%1.1e\", ticks=np.linspace(Qmin+dQ, Qmax-dQ, 3))\n",
+ " cbQ_label = \"Electric charges (C)\"\n",
+ " cbQ.set_label(cbQ_label)\n",
+ "\n",
+ " # Secondary potential z-section\n",
+ " ax5 = fig.add_axes([pos.x0+0.6, pos.y0, pos.width, pos.height])\n",
+ " ax5,dat5 = viz(mesh, phi_sec, indz, airind, normal=\"Z\", xc=xc, yc=yc, zc=zc, scale=\"linear\", clim=(Vsmin, Vsmax), ax=ax5, cb=False, pltshow=False)\n",
+ " ax5.plot(Aloc1_x[0], Aloc1_x[1], 'ro', ms=8)\n",
+ " ax5.plot(Bloc1_x[0], Bloc1_x[1], 'bo', ms=8)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax5.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ " ax5.xaxis.set_visible(False)\n",
+ " ax5.yaxis.set_visible(False)\n",
+ " ax5.set_xlim(xmin, xmax)\n",
+ " ax5.set_ylim(ymin, ymax)\n",
+ "\n",
+ " # Secondary potential y-section\n",
+ " ax6 = fig.add_axes([pos.x0+0.6525, pos.y0 - 0.36, pos.width*0.725, pos.height])\n",
+ " ax6, dat6 = viz(mesh, phi_sec, indy, airind, normal=\"Y\", xc=xc, yc=yc, zc=zc, scale=\"linear\", clim=(Vsmin, Vsmax), ax=ax6, cb=False, pltshow=False)\n",
+ " ax6.plot(Aloc1_x[0], Aloc1_x[2], 'ro', ms=8)\n",
+ " ax6.plot(Bloc1_x[0], Bloc1_x[2], 'bo', ms=8)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax6.set_title((\"Slice at y=%.1f m\")%(mesh.vectorCCy[indy]))\n",
+ " ax6.yaxis.set_visible(False)\n",
+ " ax6.set_xlim(xmin, xmax)\n",
+ " ax6.set_ylim(zmin, zmax)\n",
+ " ax6.set_xticks(map(int, x))\n",
+ " ax6.set_xticklabels(map(str, map(int, x)),size=12)\n",
+ " ax6.set_xlabel('Easting (m)')\n",
+ "\n",
+ " # Secondary potential colorbar\n",
+ " cbVs = plt.colorbar(dat6[0],orientation=\"horizontal\", format=\"%1.1e\", ticks=np.linspace(Vsmin+dV, Vsmax-dV, 3))\n",
+ " cbVs_label = \"Potential (V)\"\n",
+ " cbVs.set_label(cbVs_label)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4f43bf7adf6a42a5ae23612422259fd9"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ">"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interact(lambda indz,indy: dataPanelPlot(indz,indy), indz=IntSlider(min=0, max=mesh.nCz-1,step=1, value=28), indy=IntSlider(min=0, max=mesh.nCy-1,step=1, value=31))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Compute DC data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Evaluate potential at Rx locations\n",
+ "dataObs = Survey.Data(survey, v=dobs)\n",
+ "Xx = 0.5*(Mx[:,0]+Nx[:,0]).reshape((23, 24), order=\"F\")\n",
+ "Yx = Mx[:,1].reshape((23, 24), order=\"F\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualize DC data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def vizdata(data, src, rx, rxcomponent=\"X\", clim=None, title=None, figsave=None, ax=None, cb=True,cbOrient=\"vertical\"):\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(6.8, 5.5))\n",
+ " ax = plt.subplot(111)\n",
+ "\n",
+ " temp = data[src, rx]\n",
+ " if rxcomponent==\"X\":\n",
+ " X = Xx.copy()\n",
+ " Y = Yx.copy()\n",
+ " else:\n",
+ " X = Xy.copy()\n",
+ " Y = Yy.copy()\n",
+ " temp = temp.reshape(X.shape, order=\"F\")\n",
+ " if clim is not None:\n",
+ " vmin, vmax = clim[0], clim[1]\n",
+ " dat = ax.contourf(X, Y, temp, 20, clim=clim, vmin=vmin, vmax=vmax, cmap=\"plasma\")\n",
+ " ax.contour(X, Y, temp, 10, colors=\"k\")\n",
+ " else:\n",
+ " vmin, vmax = temp.min(), temp.max()\n",
+ " dat = ax.contourf(X, Y, temp, 20, cmap=\"plasma\")\n",
+ " ax.contour(X, Y, temp, 10, colors=\"k\")\n",
+ " \n",
+ " if title is not None:\n",
+ " ax.set_title(title)\n",
+ " xmin, xmax = X.min(), X.max()\n",
+ " ymin, ymax = Y.min(), Y.max() \n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Northing (m)\")\n",
+ " ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ " ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ " ax.set_xlim(xmin, xmax)\n",
+ " ax.set_ylim(ymin, ymax) \n",
+ " \n",
+ " if cb:\n",
+ " cb = plt.colorbar(dat, orientation=cbOrient, ticks=np.linspace(vmin, vmax, 3), format=\"%.1e\")\n",
+ " cb.set_label(\"Potential (V)\")\n",
+ " \n",
+ " if figsave is not None:\n",
+ " fig.savefig(figsave, dpi=200)\n",
+ " return ax\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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HmXq/BWdCjwXA56PUeQLOhbvRrWsNzkXr2LAy9wCdCZ7HFW7bOgmbxj+izKHAg+5z7HKf\n80PAmRHlTnTb2+W25zrcJXOSeD1/4LYjtHXgLHPwBM6argVxHnsozhqH691zN+HMuPnVeI9L9bw4\naVzPuudpwLnrXuc+/gcRZW9y2xUgbMkI4GicL3ZtODclfocT/Pda0sI222yzLRMbToD4E/d60+5+\n9r2Ps3ZrXUTZC93P9k73M+9+YHxEmajXm2jXAJx1z//qfobucv9/erTPP5zr5hr3s30RTiB4D32X\n0fHgBIqhG8I7ca6xx4WVmYJzY7HVPddcd/+s8M/niHrjXiNjtCXqeSLKvOcem9SPv5kCdyQoE3Up\nuWT+hm65T7v/Jjrd1/KCaM/RLft5YJ7776cZWAL8nN5L+82N9vxts21f2UQ11RnYjTHGGGOMGbpE\nZD7QraqRqdTGmH5yx5fPAl5U1YtilDkH+AXODbCfqOofB6NtNqbWGGOMMcbsddyZlI/EyTAyxgzc\nr4GrYh0UER/OUL9TgBk4qfPVg9EwC2qNMcYYY8xew12f/HM46bzbibKCgjGm/1T1ZZxhe7F8Aliq\nqpvUmbH7GdIwkWkyLKg1xhhjjDF7k4twVkgoAi5V1fYst8eYjBKRE0XkcRHZJCIqIldHKXO9iKwV\nkU4RWejOzJ1uo3AmRgvZhDMpaMZZUGuMMcYYY/YaqvoDVfWo6lS3Z8mYvV0pzgRi38CZjK4XEbkE\n+D+cGcpnAG8Cz0QsD7lYRJZE2Ub1ox2RyzBC/9etTokt6WOMMcYYY4wxOUpVn8ZZPgx32a1I/wHc\no6p3ub9/TUTOxFnH+ma3julpaEpkz+xonJm7M86C2iypqanRCRMmZLsZxhgT18KFC3eq6vBsnPu0\nM4q1vj6Q0mMXv9v9nKqemeYmmSjypUALKcl2M4zJqMCUgoyfw7uqK+Pn2Je10Ji169nsk0u0viG1\n69nC97uW4iz9FHKnqt6Z7ONFJB84AmcZqHBzgJkpNSq2ecDBIjIaZymxs4AfpfkcUVlQmyUTJkxg\nwYIF2W6GMcbEJSIfZ+vc9fUB5r6V2lCcyoK1NWlujomhkBKOllOz3QxjMqrp11Myfo6Ks1dl/Bz7\nshf0oaxdz3Y2BHjnuTEpPTZv5JpOVT1yAKevAbzAtoj924DT+lORiLwAHAaUiMhG4GJVfUtEFqvq\ndFX1i8h/Ai/jDHP9qarWD6DtSRu0MbUiss4duBy5PeUeT2aA849EZIWItIlIo4i8KCIzI8rcJSJr\nRKRDRHaIyGMickBEmf1F5FER2SkiLSLyttsFH17mKBF5wT3PLvdcn4goM1tE3nLr2Omea/+0vWjG\nGGOMMcYYM3CRY1slyr74FaiepqrDVbVYVceo6lvu/ulhZR5X1f1VdXJ/epQHajAnijoKGBm2HY7z\nQj7gHo87wNm1EvgqcAhwPLAWeFZE6sLKLACuBg7AmUJagBdEJC+szJNAIXAqzmDp14HHRGQSgIiU\nAs8Cm3G65Y8FtgDPiUiZW2Y/4DHgNbeO03Bm2Xu6X6+KMcYYY4wxZi+mBDSY0pYGO4EAMCJify19\ne29z1qAFtaq6Q1W3hjbgbKAZeNA9/rSq3qKqDwFR/4Kq+ndVfVFVP1LVpTiDnsuA8LsDf1DV11R1\nnaq+C9yKM730RAARqQGmAD9R1fdUdTVwE04q9gy3mmlANfB9VV2uqsuB7wKVwFS3zBFAHnCzqq5W\n1cXAj4FJ7jmMMcYYY4wx+zgFgmhK24DPrdoNLAROjzh0Os4syHuFrCzpIyICXAv8PdW1w9xBz1/C\nCYwXxyhTAlwDrAfWubvrgeXAlSJSKiJet54W4A23zEpgB3CtiBSISAHwRbeepW6ZBUAP8AUR8bo9\nuJ8D5qvqzlSe00Dt2rUrG6ftl+bmZt55551sN2OftWbNmmw3wbhUFdXkL1Z+vz+DrTHGGGNMJgVT\n/C8ZbkwzXUSm48R349zfQ0v2/BK4WkS+ICIHiMj/4XT63ZGRJ5sF2Zoo6nRgP+CP/X2giJwD3A8U\n46QEn66q2yLKXA/8FCjBCVBPVdUuAFVVETkdeAQnIA4CDcBZqrrFLdMiIrNw0otvdqtd556rwy2z\nzq3nQeC3OP+AFuHM8hWr7V/CCaAZN25crGJR/WnE3XGPz21/mftb7uPmgt9S4mRIp0V9V3rvezyp\nt7OcNzmYc5jBRXh7ZYXnnh2enmw3IWlN+jFvBH/AOO9nqfGke7K73jZLW9rqGqXRZ1VVDfKe/5tM\nlk8x2XNu1DLDg+n99/WK/hRFuVhu7nNsWEH8C0/4e2mFvsXT/I7z+CnlvUZPRBdQP/dyLSdwKUfJ\nJ/vf8DA/DV4yoMcbY4wxpn8UJdCPG9kpOBJncqaQ/3K3vwBXq+o/RWQYTgbrSJwhn2eratYmz0q3\nrPTU4vR6zndTdvvrZZx045k4414fEJGREWX+gZNKfBLwIfCgiBTD7l7i3+H02J4AfAJ4CPiXO/00\nIlIE/Bl4CzgGOA4nYH3M7f1FREYAfwL+ijNeeBZOb+8DIhL1dVXVO1X1SFU9cvjw/s0oXlkRv0N7\nQt5++PGzLDC0Z1Q+gy9wKCezhCd4lu/SqOuz3aR9gmqQD4J3k0cplXJYxs8XKxBNLyVANxonNSfd\nNx26UTppHXA9+RQSoIdOmpMq38p2AvRQRPpuWBljjDFm8GQy/VhV56qqRNmuDivzO1WdoKoFqnqE\nqr6aqeeaDYMe1IpILXAecFeistGoaps7hvVtVb0WNwU4okyTqq5y/1gXAfsDF7qHTwHOBS5T1TdU\n9V1VvR5ow0lVBrgcmARco6rzVfVtd9844AK3zFeBNlX9tqoucs91BU4gnZFusHiB7XjfeGq8Nazw\nvJXWcybqfeqvfClitnyJC7iBNpp4kltZok8STM9A+EGX7p7ATPlYX6KJtYz2XohP9o71JBVnvTcP\n3rjl0hnY5lFId8x57JJXRDkAXUkGtc1sBaCayPt3Jl1E5BZ35v3fJCiXkVnv3aEut7t1trmrAYyJ\nKPN/IrJARDpFZN1Az2mMMWZwKBBAU9pMcrLRU3s10IWTQpwOHiDeitjibqEyxe7PyCgqyJ7Xo5jQ\nmO7exzWiTOQqyqHfs3GzgCMLjmJ59zLatGWwT99vk+UIruFnTGQGC7iXl7iNFt2R7WbtlTq1kZX6\nEDUcRJUcle3m9Eu8Hl91354yiG+3PIro7rX+efLCbxAVuz2unST3Xm1mCwBVfSYuNOkgIsfgZBC9\nn6BcJme9/xXOzdfLcLKIyoEn3XkfQjw4qWR/TcP5jDHGmL3GoAZfburvF4D7VXtHXokGOItIuYj8\nt4gcLSLjROQIEfkzMAZ3WSARmSwiN7rHxrlr2D6IE0Q/6Z7qLZwxtHeLyGHumrU/w5kdOVTmeZwv\nFL9zB1MfBNyNE7S+5JZ5CjhcRL4vIlNE5HC3zAacGcYyIl5v7ZGFRxEkOORTkEOKpZzz+A/O4its\nZx1PcBOrdG6/Js8xiS3Tewnip873GZy34ODIdApyqKdWEvTUQvp6a31p7qlNNv24ma0UUkpRGsfL\nG4eIVOAMWbkWaExQPKlZ70XkXBFZ6PaorhWR29zJDeO14VrgBlV93p25/0rgUJzAGQBV/Zqq3o4z\nrMYYY0wOydbsx/uKwe5RnIWznE601OMjccatLsK58/1f7v//0D3uBw7CmeBpFfAEMAw4UVVDd9e7\n3HM8A6wG/okzzvVYdxkh3JmJz8RZF/clnFmMTwTOd79IoKorcFKUD8EJgl/HCZ7PUtWNbpmXcFKS\nz3Pb+RxOKvSZqpq+WXL6YU8Kcnpn5053CnI4EeFgOYmr+Ql1TOAN7uQN/pcObcrYOfcl2/V9tug8\nRnjOpFBqs92cfkkUFGenp7aQHjoHfOMlXwrxkZ90T20H26yXNnPuBB5yP9MTSTjrvYjMxgmSf4Nz\nzfo8zjCY/4lTbyhYnhPaoaobcGbqz+ysbsYYYzJOgYBqSptJzqDOfqyqL+OkAkc7NjfWMfd4O3vG\ns8Yqs4E4sw+HlVsAzE5Q5nmcHtt4Ze4nfWnUSausaGdXU3Gf/aEU5Dntz9FW0JLWWZAzrUJquUS/\nywKe5jXuZzM3cYx+gXFyRLablrMC2sWS4F8pYSR1nsilyfYGoaA2cU8tOL21Ax0DnUcRitJDF/kU\n7t6fyo2fYsrpJLmbN41sYSwH9Psc+7gaEQlPW7lTVe8MLyAiXwQm4/SKJpTkrPffAX6mqqEp69eI\nyI3A30XkBo1+R2QETiZQ5HJw29xjxhhjclxuzh6TO7K1pI/JkCMLj+LZ9mdYFljAUb6Ts92cfhHx\ncBTnMEEP4yl+w0v8gkP0ZA7lCvKkKNvNi2l4MG9ILu2zSh+jgx3s7/0mnhxfOimaPenHyffUDjSw\nzXMD2W46ewW1qXCC2sQ9tX7tpoV6qmySqP7aqapHxjooIlNxek9PcBemTyhi1vv7gDKcbKIHROQU\nVQ3i9Lp+wg1kQzw4GUgjROQa4JawYwfGOyVY7pnJrPrrLBkgGcPOTm8WnNm3qE36lHHZWtLHZMh4\n33iGe4fnVApypOEyliu5jaM5jyXM5Wm+Q6tNItUvLbqRj/RZxsjxlHkGPDFrylIdV5vM40Lpx4lm\nP06nPJybKz1pGlfrTyKobXFnPragNu2OBWqAJSLiFxE/zuz117u/R5uAMJlZ7z04w2emh22H4gy9\n2YGz0H34sc3AVsDrtidcLU5vrTHGmFymEEhxM8mxntocFS8F+YiCI3MyBTmcV3ycyGXsp9N5hJ8z\nhx9xmn6HcqnLdtOGPGdN2nvwUcQw73nZbk7GpDqmdiC9teE9tQNVTDn1bExYrnl3ULvvZaFKQMjb\nlbEsg0dxxsiGuxtnzob/AaL13iYz6/27wDRVXR3jvA3utpuILMQZq3s6cK+7bwxwAGDdQ8YYY0wC\n1lO7FwrNgrw0R2ZBjmesHMAl3EoPXczhRzTp5mw3acjboK/SyCpGeS/AJ6XZbk7G9Gf240ippouH\nemoTzYDcqe38JfgjtgVjB61FlNGexOzH+3JQm0mquktVl4RvOOuVN7i/q4j8WEReDHtYMrPe/xC4\nXER+KCIHi8g0EblIRH4apy1NOGnNPxOR00RkBvA3nCWGXgiVc2f4nw6MAvJDKwbEm1nZGGNM9oXW\nCU1lM8mxoDaHxVreJ5SCvDKHU5DD1cl+XMJ3CRLgOX5Eo27ISjtyQZc2s0IfoJqpDJNjs90cILlU\n4lFa0mtLjttTK6l9jKUS2CbbU7sy+B7Lu5fzjP++Xvt7r1Vbjp9uejR+Xc1spZgKCqRvZobJuJHA\npNAvycx6r6rPAZ8ETgbmudtNwPoE5/om8DDOrP1vAK3Auaoa3jP8R/e833TbFloxYNRAnqQxxphM\nEwIpbiY5ln68FwpPQd6av4ERnrHZbtKADZdxXKrf45/8N89zG2forVTKmGw3a8hZo0/hp5MRvksG\ndU3aRDKzZq0TzPZoe5x50+PrbyqyL8mgttVdksob5yO2mAoAOmnaHSxHrYtNVFvMMihUdVbE71dH\nKZNw1ntVnUPY8jxJnrsT+Jq7JdU+Y4wxuUGBoI2PzSjrqd1LnVp8GmWeMv7q/wlN2pD4ATlgmIzm\nMr6H4GUOt1kqcoSg+tmkb1DHDIpk759UqJA6ihjOFp03oHr602O7p6c2fvpxN10ASJxou5xhALRS\nH7OMX7vZxkeMYnLU49nKnjDGGGNM/1hPbWZZUJvjYqUgV3mr+EblN2nXdv4W+H90avRy/ZXtL9FV\nMpJLuBWA57mNZt2a1fYMJdtZTDctlHqPznZTBoWIh0rPUexkKZ2DdONmz5ja+D21fnUC5WCc0TDl\nDAegrc/SpHvsZA1BAoxhWswywwqCuzdjjDHGDD2KBbWZZkHtXmxc3ji+UvFVtvi38E/9BX71Z7tJ\naTFMRvMZbiWAn+e5jRbdnu0mDQkbgq9RQCXlckC2mzJoqj1HA8omfWtA9STbW+tLsqfWT4/7M/by\np2W7e2pjB7XbWAHAKKb2ORYtiLUA1xhjjBmagiopbSY5FtTuBWL11gIcVHAQnyu/muXdy3lSfo/q\n3pHQP1zGcjHfoYdOXuA2WjV2YLAv8GsHO1lChecIRAZv3dZsK5RaqpjMBn2NoEautJJ+HvGQR0HS\nQS3e2D0HZ6rSAAAgAElEQVS6PsmjhKq4PbWNrKSGsRSlMIu1BbfGGGOM2VdYULsPmFl0HOeXXMDb\nnW8xx//AgOsbKl+U62QCF/MdOmnjBW6jbS8ZO5yKHSwhiJ9KOTTbTRl0Vd5ZtLGFNfrEgOpJtrc2\nj8LE6cduUNutsXtqAcqpoYsdUY8FNcAmPoybepwM6701xhhjssvSjzPPgtp9xCdLzuGkolm8HHiM\nt/0vJH5AjhghE7mIm2mnmRe4jXZtzHaTsmKbLiKPEkplUuLCe5kqzxGMlpl8qI/SoB9m/Hz5FNGT\ndFAbP1CuoIbmGD21jaynh84BB7XhLLA1xhhjBp8iBPCktJnk2JI+OeJd/xvMb1rJdcM+H/V4ZUU7\nu5pir2MpIlxe9lkaA4081n035VLFgd4jMtXcQTVKpnCh3sRD/A8v8j+cprdSJBXZbtagCWqA7foe\ntXLYPpV6HG649yIa/atYHLyDEzw/Ik8ysYSQIz+Znlo3mA3NghxLOTWsYj6qwT7r7YbG00YLai04\nNcZEU3/dzGw3wRgTg42PzSwL/3NEQ3czz9e/y0edG1Kuwyterqv8MuN9E7i/53bWB1enXNdQ+1I9\nRqZyITfSxA5e5Md0aku2mzRoGllFD60UeA7KdlOyxitFjPN+nk528YHenfLY8WRSkJ2gNtGYWmdS\ntp4EPbXlDCeAnw6a+hzbxUrKqaFMhiVskzHGGGOGLks/zjwLanPE7JojKfTk80LbwFKHC6SAr1d9\ngwpvJX/t+Sk7g1vS1MLsGysH8mluoIFNLOOf2W7OoNmmi/Dgo1wOzHZTsqrEM4FRnnPZovPZoK9m\n8EzJjKl1xtImM6YWoDViXK2qspGVaU09NsYYY0y2CAH1pLSZ5NgrlSNKfUWcPuxwXm5YTJM/ei9k\nvFmQw5V7yvn3ym8C8JfAT2jVvr1EuWq8HMIMzuADXqZRN2a7ORmnqmzTRQzjALxSmO3mZF2d53SG\ncSDL9O+06uaU6kjUW+tLoqdWfMkFtRXuWrWRy/o0s5V2miyoNcYYY/YCCgTxpLSZ5NgrlUPOq51J\njwZ4oemNAddV56vj61XfoCmwi78Hfkq3xu95imaopSCHHMunyaeID7gv203JuDa20M42Cvfh1ONw\nIh5G+a7EQz6Lgr8nkCD9NxV5FCXsqQ2lHQcIEIhYHzr8fVPuBrWRy/rsGU/bd83hofq+M8YYY4zJ\nFgtqc8j4olqOKJ/CnOZX8cdYkzPZ3lqAiXmT+FLFdXzsX8eT3JGuZmZdkZRxDBfwEYvYqsuz3ZyM\n2qaLAKj0HJLllgwd+VLJOO+VNLOelZraElbxemvzkuipDddD7N7afCmkkFJaItKPt7KcIsqoZlTS\n59kbSQ/kbw+mtBljjDFDiY2pzSwLanPMBbXHUd/TzDuti9NS3/TCGZxUNItFXYsIav+/CA7VXqPD\nmU0+RazhtWw3JaN26BLKGEO+VGe7KUNKpedQxstprNU5bNP0vFdC8immh86k3y/eBJPMj2QSW3kP\ndetr03o+5h2mcBQidjEzxhhjcp2qjanNNHulcsxRFfszqmAYc1peSlud4/PG00MPjbojceEc4ZN8\nJjGDTbybUrDeX8ODeRk/R6SAdtPIKoo8Uwf93Lmg2vspyhjL+8E/0pnG9YvzcZYL6qItdplAOQCF\nUkie5Met7yBOpJmdbMXJKviAx1GCHMP5fcoO1ZtIxhhjjIkviKS0meRYUJtjPOLhU8OPYWnrxzGX\n9+lPCjLAKO9oALalOLHSsILgkPyyPYVP0E4z21mZ7aZkRCOrCNJDuVhQG41H8hjru4YAXSwO3rm7\nJ3SgCtygtjNOUFsspQCUecoS1jeZo8iniI28Rqvu5ENe4hBOpkJq09LekBZt4kXPPfT0pH+csTHG\nGGNic5b08aS0meTYK5WDnOV98nhxgMv7hIz0jQRgu24aUD1DLbDdj8Pwksd6FmS7KRmxU5cheCmV\nKdluypBVJCMZ4/0M9SxjjT7Vr8fGGlebn0xQixPMJhPU5kk+0ziWD3mHpTyAIFF7aQdiW3Ajd/Tc\nyivtc3n//ffTWrcxxhhjErH040yzVyoHOcv7HMFLDe/FXN6nP4o9xVR5qtgWHPgSOEMpsM2XIsZz\nMJtYgKpmuzlpV6/LqGQ/W8ongWEyk5FyNB/qwzTq6gHXtyeobY1ZJtRT6wkURW9TxPvkIE6ghy6W\n8zqHcirlUpPwMclaFfiAO3q+jx8/366+kSOOOCKleowxxhiTGlvSJ/PslcpR59UeS4/6ebH5zajH\n+52C7BtNvSd6OnN/DaXAdgpH0cQOGvg4201Jqx5tYxdrKfTsn+2mDHkiQp33EgoZxqLg7+nR2D2s\nyUgq/RgnqJUkP2JHM40KavGSxzGcN6D2hZvnf5l7en5KtXcYt1TfyoS8/dJWtzHGGGPMUGFBbY4a\nX1THjLLJPN/8KoEYy/v0xyjfKLb6t6RtUqWhEthO5kgE2etSkOtZAShlMi3bTckJXilivPcaOmnk\nA717QD330Xpq67t6f5QWi5N2LElO8CAinMl1nMPXKI0yk3V/309BDfJMz3087L+LafkHcFPVzQzz\nDutXHcYYY4xJn4BKSptJTvy1JsyQdn7tsXx/zd+Y3/o+x5TN6HO8sqKdXU3FSdU1yjeKbrpp1B0M\nk7q0tC/0RTzyC/9gKpZyRutUNrOAGVyUtXak205dhpd8SsR63pJV4tmPUXoum4KPUsMrjJNZKdWT\nzOzHXrxA8kEtwDg5KKX2ROrRbh7lNywMLOCkollcXvZZvOK0p78ZHMaY7Kq/bma2m2Bcqfwthv0h\nejad2fcoYpM+ZZi9ujmivLKZ8srmXvuOrjyAEflVPJeG5X1GeUcBqc+AHE+2e22ncBQ7WE+Lbstq\nO9KpXpdRzVQ8Yvel+qPOczo1HMRS/Qetujlh+WiTRfkkHx/5CWY/dgLfGhkZs0wm3hct2sSfA//F\nu10L+UzpJVxRdqUFtMYYY8wQEFRPSptJjr1SOcwrHj5VeywftK5lXVf0mYuT/SI70ucEtQOdATmW\nbAa2kzkKIOMpyIO1Vm2nNtLKZvI9kwflfHsTEQ+jfFciwFp9PuV6CimJO1HUBM80rsj7d870XZry\nOUKSfe+EZjje5N/E9RVf5YyS2Yg4PcUW0BpjjDHZY0v6ZJ69Ujkmsrd2ds2RFHjyeKbxlQHVG5oB\neXswM0EtZC+wrZRahjOezXvJuNqdugyAcs8BWW5JbsqTCmplOlt1PsEUx6M7QW38CacO9n6CPMlP\nqf7+ipzheEbh4buPWUBrjDHGZJeS2nhaG1ObPAtqc1y5r5hTq2fweut8WgLRv2Qn+6W2zjeCXd7M\nBbWQvcB2CkexiQ/p0KasnD+d6llGHqUUMTrbTclZRZ7pdNNCAytSenxBEkFtOiTzfpm/e4bj6l4z\nHFdWtFtAa4wxxgwRtqRPZtkrtRc4r/ZYuoI9vNY8f0D1jPWNZX3PenYGt6apZdFlI7CdzBGAspkP\nBv3c6VavyxnGNETs7ZuqCjkIH0WsDc5J6fEeSumIk36crIG+F3YGt/Cw/49MzZ/GTVW3JJzheKUu\nxO/3D+icxhhjjDFDjX0rzkGRKcgTi0cyoaiO+R0LYz4mmR6b2cWzyZM8nuKuAS15kozBDmyHMw4f\nedSzdlDPm27tuoMO6snzTMp2U3KaR/Kp88xmO4vZqUvjlo02WVQR5bQTv9d/RWARfu372GQl8x55\nO/AiHjx8vvxaijxFu/dHe78vbV/Fd1bdzR/+8IeU22SMMcaY/lOFgHpS2kxy7JXaS5xYdQhLWtfR\n6G9OXDiGSm8Vny69iOXdy1kUfD2NrYtuMANbj3gZzjha+HjQzpkJDboSgDLPlCy3JPfVek6hiOEs\nC97b77G1hVTQQXPMdZ23BzdxT8/PeNn/WMK6or0PknlvdGsX7wZfYUbB4VR6K+OWDWqQvzc8xPC8\nCq655pqEdQ8V4he82/NT2owxxpihQwimuJnkWFCboyJ7a0+oOgRFmdf63oDqPanoJCblTeZp/99o\n09QD5GQNZmBby35s5+OM9kJnegbkelaSRwmFxF4qxiTHI3mM9J5PCxvZoK/GLRvZW1tEJYrSQfT3\nyC7dCcA7wTl0a1fCtqTyPpjjf4B2bePU4lN77Y/WS/t6ywJWtW/imjGzKS5Obu1qY4wxxqSHYj21\nmWav1F5ifGEtYwuHM78j9gy/yaQge8TDVeVX0aEdvCB/TWcTYxqswLaWCXTRRis7BuV8mdCgK6hm\nqo2nTZNKmUE1U/lQ/0WPJj+pUhEVALTFSEFu0kYAWrWVhYH4AXOkZN4PKwPv8XrgGU4uOpkp+fvH\nLdsV7Oa+hseYUjyaU6un96stxhhjjEkPW9Ins+yV2kuICCdWHcL7LR/R5G8ZUF2jfWM4s+Qs3up8\nk1WBwZlYaTAC2zomANDAuoyfKxM6tYF2tpPnmZjtpvTLZmnr9zZYRIRa3wV008pqfSLpx+0JandF\nPd5MAwDjfeN5U58kkER687CCYFLvgxZt4qHA7xntG83FZZf0OhbtxtWTjS+zo7uJ68Z+Eo/dDDHG\nGGMGnSIENbXNJMe+4eSwyBTkE6sOIZggBTnZJT7OKTmXOm8djwf/RI92D6idycp0YFvDOAQP9Tka\n1Nbn0HjagQaogxnclsh4xsjxrNXnaNNtST0mFNTGmiyqWRspkzLOLjmHHYEdLA0ObGbykKAGeUx/\nR0ewnS9VXEd+gnVwd/mbebTxOWZWHshhZbl1M8QYY4zZm1hPbWb5st0Akz77FY1gdEEN8zsXcjrH\nD6iuPMnjivKr+EXjz3iRhzkz79I0tTK+YQVB6rsy8wbOk3yG6Shac3SyqAZW4qOIIsZkuylRZSII\nDa9zlJakvf6Qau8n2eKfx/LgPznS+/WE5QsTph83UOmtZEbBDOq8dbyhj3GIHo3IwO64vhl4jiX+\nD7i87LOM9iX+d/Boy2N0q58vjjlrQOc1xhjTf/XXzcz4OYb94c2Mn8OYXGDhf44L760VEU6oOpjF\nzWtoCcReQzPZ3toD8g9gZuFxvBZ4ii3B9QNua7Iy2WNbywS252xP7Qqq2H9IjacdzJThTJ4rTyqo\n88xmGwvZqcujlgmfLCqPInzkxUw/bvfUU+mpwiMeziiezcf+j/kouGxAbdwcXMez/vuYXjCdk4tO\n6XM88n29oWsLT++YxznDj2ZM4XCgb3aHMcYYYzJPgaB6UtpMcuyVyhFP7HyTby+5L2E5JwU5yLzW\n9IyF/UzZJRRJEU/onTGXL8kltexHK410aPw1RgciEzMgd2kTbWwhfwiMp83G2NfBaEOd51SKqGF5\n8F40wb91EaGEypjpx43BXVS5y+zMLDqOMk85b0ni5X1i6dYuHgjcTomnhM+VX5NUj+99ux6iyFvA\nlSNPS/m8xhhjjEkHIZDiZpJjQW2OCKryyta1LG/eErfc5OJRjMivZkGcWZD7o9RTyqVll/FRz0e8\nHXg+LXUmI1O9tbk6WVQDHwJQKpOz2o5sBrKxpCu49Ug+I7zn08z6hEv8ABRTETX92K9+WoLNVHqq\nACeV/9SiU1nSvSTljIcXPPewLbCVL5R/kTJPWZ/jkb2077etYF7TSi4feTIVeU7atvXSGmOMMdlh\nPbWZZ69Ujjh33AEUen08smlRn2ORKcgnVh3MopbVtAZipxknm4IMcHThMRyUfxBzAv+kSev71/AB\nyERgW8t4gJybLKpeV+ClgBIZn7U2DMWANlw62lclh1PFFD7Uh+nRjrhlfVRE7altwVnOp9JTuXvf\nrOKTKZAC5nn631u7JDCPVzteYXbxmRxQcGDC8gEN8veGfzEiv4rzazM/nssYY4wxiVlPbWZZUJsj\nyvMLOHvs/szZuoRWf1fcsidUHYJfAyxofT8t5xYRrii7kqAGeVLvGtQ05HQHtoVSSgXDc6+nVldQ\nxWREvIN+7mynGvfHQNspItR5L6SLJpboXwhoT8yyRTF6apvdNWorvVW795V6Sjmh6ETmdc7jg8C8\npNuzObiOh/13McE3gfNLL4haJvIG1Wst8/moYwvXjjmTfI+TCm+9tMYYY0z2qIr11GaYvVI55MIJ\nB9MR8DN3+8q45aaWjGF4XgWLu/r26obrT2/tcF8tF5VdzAfd7/NS4JGkH5cO6Q5s65hII2vTWmcm\ntet2WthIgWfqoJ87V4LZcAMNwks84xnlOZfN+hZvBW+jIyw7IXyyqCIqaaeZgPp7Pb5NnXWiyyPS\nhM8qPpvRvjH8o+dXPKi/pDXBuO7VgaXc2fNDCiSfL1V8GZ8knqw+oEEebXyGycWjOKnq0ITljTHG\nGDM4AupJaTPJsVcqhxxWPYKxJRU8v6PvOrSRKcjHVB7AguZVdAXTt8bsyUWncGzhTF7w/4tlgYVp\nq3ewjWQyTWynU3Oj92qrOq91lWfGoJ0zl3pnYxnIcxjpPZuJ3utoYwuvBb/HTl3ap0wJ1YD2mQG5\nA+ecJdJ7CaIKbwXfqb6VT5deyHtdi/lV9w0sDryBqvap+/3A29zT8xOqPcO4ufo71Ppqo7Yz8sbU\nO62L2di1k8tGnLx7MinrpTXGGGPM3s6C2hwiIpwzbirv7NjA9s6WuGVPqjqEzmA3b7SkL/gUEa4s\nv4rxvvE84P8t24Ob0lb3YBrJJAB2sibLLUnOFl1AOeMpkJqMn2tvCGYjpfqcqjzTmeq7iQIqeCf4\nM9YG5/Q6XoyTXtxCQ6/97eosp1XiKe1Tp1e8nF3ySb437PvU+mq5v+e3/JOf7U5ZBnjD/xz39dzO\nfnn7cWP1TVR7q5Nqr6ry2K5nGFs4nOOqDgJ6B7SqyivN79LZ2ZlUfcYYY4xJDwWCSEqbSY4FtTnm\nnLHTUGDOtr49R+EOLZvIxKIRPNv8YtSeoJD+pCAD5Es+11f+G3nkcW/gl3Rq/x6fqnSmINcxEUHY\nkcGgNl3L+nRqI7tYTakns6mke2MwGymV51godUz23cBwDmaFPtBrjG0xwwBojQhqO7QVQSiUwpj1\njvKN5qaqW7i49DMs7VrKr7pvYIH/FZ7tuZ8n/H/hsILpfLPqPynxlMSsI/K9u6h9GWs6tnDJiJPw\nRlnL+I36NXx73rPcc889yTx1Y4wxxqSNWPpxhtkrlWMmlFVxSFUdc7b3nQQqMgX5grrj+ahjK0s6\nPkxrG4Z5h/HlyuvZHtjGI/obAhpIa/2Zli+FDGMMzXyU7aYktFXfBaAyg6nHe3swG6m/wa1XCij3\nziRID01h/2Zi9dR20EaxFOOJEliG84iH2SVn8oNhP2S0bwwP+f/A3MDjnFh0El+puJ58yY/52Gg3\nox5veoba/EpOrXb+rYR/HviDAW5fPYcJpZV8/vOfT/ykhwq/h+DO4pQ2Y4wxZqhwlvSRlDaTHAtq\nc9C546axsmknq1u3xy13SvVhVPpKeK7lhbS3YWr+VC4tu4z3uhfzmOReYDuSSWxlTdxe7KFgqy6g\nhJEUyciM1L+vBbTh+vPcQ+sD1+uK3ZNFFVCKl7w+PbXt2ha3hzVSna+OG6q+zZVlV3FZ2eVcWXYV\n3hizXFdWtEcNaJe1r2ZJ6zourjsBn6fvYx/cuJB1rbu44dATyM+PHSwbY4wxJjMCeFLaTHLslcpB\nZ47ZH68Iz22Jn4Kc78njnOHH8E7TCrZ0xw6A+5uCHHJK8alcWHoR8zrf4TFuz3hgm84U5BFMpoMW\nWol/YyCburWFBlZQ5jksI/XvywFtSLK9tj4ppYyxNOiemcdFhFKq+qYf00qxJB/UgtNre1LxLE4t\nPm33BE+R4r1Pn2h+mkpfCWfWHAX07qXd1d3On9e+xszacZw0Yr9+tcsYY4wxA6ek1ktrPbXJS7xG\nhBlyhhUWc1zdeOZs/4CvTJ6FJ+xLcHllM827ynf/fm7tMfxz61ye3vUK19ZenPa2nFVyNiD8q/VB\nKLid8/RrMXuZhpLQZFE7WEMZdVluTXTbdBFKMCOzHltA21vo9RilsYPRQs8kGoJvElQ/4IyZLqOa\nFhp7levxtPSrpzYZ8QLajzo3ML/5Qz4/ejaF3r69sHd+9Cpt/m5uPOzEmAGzMSbz6q+bme0mZM3P\nb7wj203Iim/95MsZP8fNH/UdjpZuP55oS8SlQ9D6EjPKXt0cUVLZ2uv3c8ZNY2tHK4sa18d9XHVe\nGbOqD+Pl5rdoC8T+Ypxqby3AWSVncWHpxczrmjcoPbbpUMNYfOQN6RmQt+oCiqihiLFprdcC2tji\nvTZlMoUA3TSxbve+PKppob5XubZ+ph8nkui9+WTL0xR7C/jU8GOB3r20a1q388jGRXxmv0OYXD4s\nbW0yxhhjjBlKLKjNIeGB7SkjJ1Lsy+PZrUsSPu7TdcfRGezmxaa3Mta2s0rO4iI3sH00BwJbr/io\nY2KviX+Gkh7tYCdLKfMcltbeNQtoE4uVklwqUwBnXG1IMVW00thrbHZbsK3PGrWpShTQburexmuN\nSzhv+ExKfL1nW1ZVfvPRHErz8vnqgccAfW+OGWOMMSbzVCGgktJmkmNBbY4q8uVx+ujJvLR9OV0B\nf69j4T01AJOLR3No6X481/xyRoPNM93Adn4GA9v0jqudxDbWuumkQ8tOPiCIn0pJ33haC2j7J/L1\nypMyShlFQ9hkUcVUE6CHTpxgMahB2rWdEs/AZ99NJnvi6Zanyff4uKDuOKD3e/+Nnat5c/t6rj/g\naKoKiiygNcYYY7LIxtRmlgW1OSb8i+m5Y6fR6u/m9Z2rEj7uwrrj2da9i3mtscdeDCQFOeTMkrO4\nuPQzzO+axyP8ekj32I5kMn66aWRjtpvSx05dio8iSmViWuqzgDY1kb22RZ7JNLCKoPvvuiRiWZ8u\nOlCUYikd0HmTeS+u6VzPCw2LOKvmKKryep+vJxjg9jXPs19pFZdOsrFQxhhjTDY5E0V5UtpMcuyV\nymGfqB1DTWExLzd8kLDs0ZUHMLKgmmea07+8T6TZJWdycelnWNA1n+f9D2b8fKnaM1nU6iy3pLce\nbWeLzqeGg5E0TLplAe3AhV7DUplEgE7a2OL8znAAGt3fFScN2U9PyudKFNCqKnN2vc6tG35Jta+M\nS0acBPTupX1u61LWte7iW4ceT57Ha720xhhjTJYFkJQ2kxwLanOYVzycPHIir2/9uE8KcrSy59fO\nZFnbx6zu/DhmuXT01oIT2J5YdBJzA4+zLLAwLXWmWwW1FFPBdj7MdlN6+Uifpoc2qnynD7guC2jT\nq8ANYtvZCUAV4/CSx2acbIliKWWMbwxLu+IvtxVNrDVow7UFOvjNzru4c/v9HFY2kd8e+DVq8it6\nlVFVHtz0DpPLh9kSPsYYY8wQoFj6caZZUJuDwntdTh01iY5ADwsa1/UqEzmuFuDMmqMo9hYwp/X5\nuPWnK7C9rOxyxvvG86D/d9QHt6WlTkjfuFoRYTRTqR9CQW2n7mKtPscoOZpiGdisxxbQpl++VAPQ\noTsA8EoedezHZlZR3+V8nE7SGazuWUVHsCOt517d+TE3bfwxrzUu4drRZ3LblKt3px2Hv98X7VrP\niqYdXDl5OiJivbTGGGNM1ln6cabZK5Xjjh4+hmJfHq/uSByYFXsLOKvmKF5pfJ/6nl0Zb1ue5PGV\nyq8ieLg/+Et6tDvj5+yv0Uylie20a2PiwoNgtT5OkABV3rMHVI8FtJnhoxwPeXSELeNTxRS2sRa/\nOinHU73TCRBgefeypOuNdyNJVXmqcS63bvglfg3wi6lf4tKRs/BI9I/vh7a8TWV+IeeMm5b0+Y0x\nxhhjcpkFtTku3+vj+LrxvF7/IcGwZUViOa92JqrKs02vxi2Xrt7aGm8NX6z4Ihv9G3lG7uq19MlQ\nMJr9AdKeghyaHbc/2nQb63UuY+UkCqU25XNbQJs5IkIRNbTrzt1/41qmEKCH7e76teNlCkVSxAfd\nice6J9ISaON/d/yeu3c8xJHl+3PHgV/n4LIJvcqE99Juam/kpc1r+MzEQyj0+nqVU1V+uOAV3nnn\nnQG3yxhjjDH9E0RS2kxyLKjNUb3WrB01iZ2d7Sxr3tyrTLQU5JEF1cysPJAXml6nKxi/5zRdge0h\nBYdyTsm5vNn5BvMDc9NSZ7rUsR8+8obEuNoP9WE8eKn0npFyHRbQZl4Rw+hwx9QCDMdZv3az+2/I\nKz4OzD+QJV0fJHUTJ9b7bGXHR9y44cfMa1rJV8aeww8nX0W5L/76tw9sWIBXPFw20ZnxOPxz4q0t\nW/jze6tZsWJFrIcbY4wxJgNsndrMs6B2L3DCiAl4RXh1R+KlfQAurDuBlkA7rzTPS1g2XYHtuSWf\n4qD8g3jcfzebguvSUmc6eMXHCCbTkOWgtkk/ZrO+zXDPKeRJReIHmOzxVPYKaouligqG9xpXOyFw\nOI3BRjb6+79cVFCDPNrwPN/d8Cs8ePjVtC/z6brjEYl/YWv1d/HElvc4c8wUaov6Lin091XvU12Y\nzyWXXNLvNhljjDFmYGxMbWb5EhcxQ11lfiFH1ozmtfoVXD95VsLyB5WOZ0rxaJ5pfpHTKmbGHJu3\nu/6KdnY1FQ+ojR7x8IWKL/Hdnd/hdXmIS/jWgOpLp1FMYQFP4ddufJKflTasDD5IHiXUeVKf8dh6\naQdHPtV004Jfu4A8AKqZwib29IBO9U7H6/fycseLXJV3dcy6Im8aNflbuGPnPcxvXskJVYfwH+M/\nTamvKObjw7Mxntz8Hm3+bq6cMgPo3Uu7qbWVZ9ds5vojp1JYWNifp5tV6vfg3zmwNX+NyZT662Zm\nuwlZ8fMb78h2E3LG3vJa3fzR+/0q/+OJtj56JGedWut1zSQL/3NYZAryRy0NrG9v6FUmWgqyiHBh\n3fFs6NzBe+2Dl4pY5inj+KITeK9rMU3akPgBg2QkkwkSoJH1WTl/vS5nBx9Q65mNTwZ288BkXoEM\nA+g1WVQtU2ilgWZ1enDLpYqjvafxesfrbPVvSareDzvW8u0NP2Zxyxq+Pu58vjvx8qQD2oAGeWjT\nfCr//HUAACAASURBVKYPG8nBVXV9yt7rphx/7tDJSbXFGGOMMellY2ozy4LaHLE5uIU5H8deX/aU\nkRMBkpoFGeDEqkOozivj2Zb4y/uEpCsN+aSikwgSZH7g5bTUlw4jmQTATtakpb7+TBKlqqwIPkgh\nVdR6Tkr5nNZLO3jyCQW1O3bv2zOuds8QgFN855MneTzS+nDUesLfUz3q59fb/kyeePn1Addzbu0x\nMdONyyub+9ysen3Haja0NXHV5L69tF2BAPd/uILTJ45kXEX8MbnGGGOMST9bpzbzLKjNEb+et4xv\nvfYKXYFA1OOjSsqZWlHDGw19e16j9dbmeXycVzuThc2r2NCVXE9SOgLb4b5aDso/mIXBlwho9OeS\njHStVQtQSjUlVLEjTUFtf2zjXXaxhjrv2XiylPpskhO6cZAf6qnV+t03MKoZh498NvPh7nG1pVLB\n8Z5PsrBrIWt71sate27T22ztbuRr489ncvGomOWivZcBHtzyNiOKSjl11KQ+x55eu5b6ji6unT4l\n8ZM0xhhjjMlBFtTmiPP2H0dzVw+vbuw98UzvFOSJLK7fQmN3cr12n6z5BPni46ldyfeapiOwnVU0\ni8ZgIyuCiwZcVzqICCOZROMgB7WqQVYGH6KEkQyTY1Oux3ppB1ce5QjeXpNFecTHSCbtngE55ETf\nJymTMv7V+lCvmZB79dIGe3h41zMcUDKOo8r3j3neWAHthy3bmL9jI5+dNB2fp+9H+t9WfsDkqjJO\nGNc3LdkYY4wxg8Mmisose6VyxAnj6qguzOeZjbHTi08dNYkgyus7V/c5Fu0LcUVeCacNO5xXm+fR\n6I/+hTmagQa2hxYcRqWnkvfkxQHVk04jmEQjW+jW9KRZJ2ODvkorm6nznoOIN6U6LKAdfCKePsv6\nAFQyhW2sw6970s8LpIhZ3k+zons5a3qi3zR5vWUhO7qb+Nyo06OmHEdLNw73r61vUuT1ceF+BwG9\nb3Qtb2jg3a0NXH3YZDwJZk82xhhjTIakmHps6cfJs6A2R+R5PZwxaTQvrt1CTzB66u20iuHUFBYz\nrz5+qmO4i0eciF+DPNzwbLqampBXvEwvmMGHPSsJavrSiAfC604ELoM0IL9Lm1mhD1DN/lTKjEE5\np0mfPEro0Y5e+8qoJUiAdpp2pyADTPU4s0DWB3oHwSEfdW2g2FPA4eX9n8RpW2czT65fyQUTDqIi\nv++sxgu2bQPgzMmj+123McYYY9JDsYmiMs2C2hwye9Iomrp6mL91a9TjIsIxw8eycNfaXqmOIdF6\ne8YU1nBWzZE83/QG23qif+nOhEl5k+jUTrZr/9fxzIQOmvHiw8fAljtJdpKo5XoffjoZ4bs04fqj\nsVgvbfZ4yCNI7791Ac7SMx209tofWiaqm+6odTXqNkYUVMfspY3n/vXzUZSrpxwO9O6lBViycyfV\nhfmMKbNZtY0xxphssp7azLKgNoecNH4EBV4PL6zvvfRM+BfZY2rHUd/VwZq2HZEPj+mKUafiQXik\n+bGkHzPQFORJeU6v1MfBVQlKDo4OWiiiLOUAsz926lI26ZvUec6gSEamVIcFtNnlFy+BPkFtGQCd\ntPTa73PXsu3R6Dc8tnQ1MLKgqs/+RAFtU3c7j27+/+ydd3ib5bn/P7dkyUOWvJ3EdpadRciEJGwo\nUHYHpe2vdJ1CaemgtOd00UEH9LQ9p4VO6KKD0QEFCuUALRBG2COL7OkkzvLeU5Z0//54pUS21ivZ\nJnHyfK7rvSLrfd7nfRTLtu7ne9/fezUXV82i0uOLO2ZTeyPzJxS9Je9rg8FgMBgM8THux2OPCWrH\nER5XFmdNmcDT+3bHVWIBTi2fDMAbLbvjno/3QbnUXcDlE87g6Za11A0cGLX1JqPMWYZXvEddUDvW\nBNXPhtBd5DGBSY6Lx/x+hrHBQRahYcprRKntZ+iGgytJUKuq1PvbmJhdnNb9gxri5q0P4Q8G+cTs\nJUCsSjsQDLKluZP55bEBs8FgMBgMhrcWE9SOLSaoHWdcVFPBno4edrS3xz0/Kc/LtPxC1nTFmkUl\n4wMTzyHPmc3drfcmDJiHMxK1VkSocc9gv2zNeI7RbOvTOwpBrZ3U4536KD00UOn8QMYtfIxKe+Rx\n4Iqj1EbSj+0rte3BLgZCgzFBbSqV9jc7V/Biwx6+tfhcZhWUxh2zva2NwVCIhRPSC5gNBoPBYDAY\nxhsmqB1nXDDd6mH5VIoU5Neb9hMIxe8DG+8Dsy8rj09WXcLarlqe7nxlFFecmBpXDQ3BBnrUvvPy\nWNFHF44xVmq79QA79FEq5XR8jhMymsMEtEcHkkZNrQMnghAgNqhtDNexT3IfDjxTBbTLGzZx9+5X\n+MD0+bxv+jwgVqUF2NDSAsACo9QaDAaDwXBEUYz7cSJEpFpErhaRm0TkRyLyFRE5X0TSMroxQe04\nY5I3j0UTinhmf2KH49PKJ9MXHGRDZ3qpxJeULmWht5p7mv9BayC+Ejyckai1Na4a4Oioq+2ji5wx\nDGpVlfWhu8gihxLnuzOawwS0Rw8OiQ1qneLCRXaMUisiuHAxqIGYeRoGrcDTbvrxtq4G/nvToywu\nmcTXFp2TdOyG5mZ82S6mFnhszW0wGAwGg2HsMO7HQxGRD4vI68AO4AfAO4DTgauBx4EGEfmViEy1\nM1/WmK3UMGZcWF3Jj1/ZQHNfH6W5uTHnl5ZV4UBY2bqbRYWT487hK+yks32ouYxDHPzX1Cu4duPP\nuLP1r/xX2WfG1GBmqmsaTpzUhbYz13nymN0nFSEN0k/PIaOfTEiVerxPX6SVLUx1fhiXxDf1MYwf\notOPmxyDlIWsFONcvPQTq5q6xBU3/bjxUFBrqanJVNoOfy9fW38/PncOPz31MtwOq7dxPJUWYGN7\nI/PKjEmUwWBIzi03/OZIL8FgOPZRjgvV1S4isgYIAHcC71XVvcPOZwOnAVcCK0Xks6p6f7I5jVI7\nDrmopgIFntm7N+75AncOc4vKWdWxM+25K3NKuaryQl5u38Qr3WtsXZOpWpst2UzOmswBR+Z1taOB\nZeyjY6bUhjTANn2QQmZQIqdnNIdRaY8u4qUfA+SQnzioDY+P/nlpHGyh2OUl2+FKer9AKMS3Nz9I\nU38PPz/1Mspykquvg6EQm5raWTCh0M7LMRgMBoPBMIYY9+MYblTVpap6+/CAFkBVB1T1OVX9NHAC\nsDvVhCaoHSfkFh/+oHxiWSGV3jyePVA7ZEy0YnNa+WTWtdbTNdifcM5EqtAVE85gVl4Vf2z6O13B\n+CrQaFHjmsHuwV0E46RmvlVE0kVHotQmY7++TD9tlDkvQST9HzkT0B59RPrUqg41K8shPyb9GBIr\ntS3awKRw6nEylfb2Hc/watNevr34POYXT0y5vp3t7QwEQ+Pe+VgDTvxtnowOg8FgMBiOJkxQexhV\nfSyNsc2q+kaqcSaoHUdEAlsR4cLqCp7bU09/IH4weFHVTAIa4sF9q9K+j1OcfGnae+kK9vLX9r/b\nuiZTtbbGXYMfPwe1LvXgMaIPK5gYC6VWNcROfRwfU/FKZuZQhqMPR9jROERg2PP5MUZRQLimNjao\nrR9oPRTUJuJfBzfw17rX+UjNIt4zbe6Qc4lSjw+bRBnnY4PBYDAYjjTGKCo1IlIsInNEZG70Yfd6\nE9SOUy6qqaAvEOSVgwfjnj+hsJyzJkzl3r2v0xf0xx0DidWh6rxJXDnxbTzVspo1PZtGZc3xqHHN\nAKAuQ7Oo0Wjr0ztCpTZZPW09q+jhIKXOCzKqbTQq7dGJHApqhzsgew6lH7cMHP71miUuBnXoz2FA\ngzT5O5joThx4butq4IebH2NpWRVfXnDmkHOJAlqwTKLyXFnUFOXbe0EGg8FgMBgMRwARWSwia4Em\nYCOwPnxsCP9rCxPUjlNOryrH48pK2trnE7OX0ubv49mGzGpWPzTpPCa4C3mq+2lb4zNRa4sdxRQ4\nCtgXqk09eIzYzTocOMknfr/PEc0dWk4eZRTK4rSvNQHt0YsDy6RpeK9aF3n46YsZnyVOggxtsdUR\n6CSEUupObBx2b90bZDuz+Mkpl+AKG0OlorG3l3/W7uDUylKcDvMr3mAwGAyGowFVyeg4DvgjsB84\nDzgRmBs+Tgj/awvziWecEUlBzs5ycu60iTy7bw+qGnfsyaUVVOb5eLplXUb3cjuyWFIwmw3duwnq\nyBXReIgIlVlVtDiOTPpxv/awiReYy1lky+iqWr3aSCtbKHScllEtreHoJfITJ8Os9gVBif15DKnG\njB0IK7e5juyE99ncvZeTSyspzs4b8nwilVZV+fbrz9M7GODmt6W/kWIwGAwGg2FsMC19EjIT+IKq\nrlDVLaq6NfqwO4n5pD2OubC6goPdfWwM188NR0S4ZPIsXmmso82fmeq3IH86vcEBdg/sszU+E7W2\nMquSg4GDhMYocE7GBlYwyADTuSCj65OlHu/TFwGhxHFK2vMalfZo53BYa4cQIRzDft0OhKz3TiLn\n487BPmq72lhowxgqwj937uTfO/fztTPmM7M4VgGONpwzGAwGg8Hw1qBqjKKS8CKWKjsiTFA7jjl/\n+iQEWF6XWOW8dPIsgqo807gl4ZhkrqsLvNUAbOrLrObVDpVZlfjx06qNY3aPeKiGWMOTVDCLEpk+\n6nPv0xcpYx5uMWY9xwuJlFpFcQxT6yNKbaKgdlOnVS9vx+0YrLTj7776CksmlfCpk2als2yDwWAw\nGAxjjEk/Tsg1wGdF5Asicr6InB192J3EBLXjkIjaUpqXw5KKEp49sHvI+ejUxFm+Umq8xSxvsl1n\nPYRSt4+K7BK2DSYOikdKRVYlAA2xbarGlN2so516ZnBhRtcnU2lb2EwfLeQ7l2W6PMNRjRW4Dk8p\ntpTbOOnHhGLG+kPJg9oNHfsRYH7RhCHPx0s9VlW+9foK+gNBfn7Rsri1tEalNRgMBoPhSGHcj5Mw\nE1gE/BR4Cngu6njW7iQmqB3nXFhdyZsNbdT3xE9XFREunTyb1S0HqO/vyOgeC7zTWd+1y3Zdbbop\nyBXOCgDq1V6K82ixmifIo4CpjH7guVdfIIs8CmVh2tea1OPxQOL047hKrWpM+rFfk6cfb+6pY6av\nBI/LnXI1D+/cyRM7D/C1M+Yxw6QdGwwGg8Fw1GGU2oT8FngamA+UA2VRR7ndSUxQO865sNoKCJ/e\nm1jlvGSylYr4VP3mjO6xwFtNd7CfuoEDGV2fihxHDqXOUhpCb51S26b11LKWmZyPU7LSvj6ZSjuo\nPdTrSoocS3BI/IDFML6Jb80GkuBXajyl9nD6cWzQGlJlXWs9C4onpVxLY28vN4XTjq81accGg8Fg\nMBjGF1XAd1R1o6o2q2pL9GF3EhPUjlMiysvsEh9TCjw8eyBxS5yp+YXMK5rA082ZpSAvzE+/rjZd\ntbbSWUWzvHVB7VqewoGD2Zw/6nMf0NcIMUiJ4/T0rzUq7TghUfpx+OwwR3KNaxRlBbU5cZTavb2t\ndA4OsLBkaD3t8NRjVeXG10zascFgMBgMRzOKMYpKwlPAySOdxAS14xwR4cLqCl6sa6QvEDj0/PAP\nv5dOnsXm9ib29Nje8DhEeXYhE91FY1pXW5lVSUOwnoAGUg8eIYM6wHqeYyZLyZOitK9PptIC7NMX\n8FJFHlMyXaLhqCe+Vns4yB16PoTGtHVKZhS1scPKiliQwiTqoR07eLLWpB0bDAaDwXBUo5YDcibH\nccC/gVtF5L9F5AMickX0YXcSE9SOEwbzOll3MH5N7IXVFfQHg7y4f3/C6y+pmoUAT9ZvzOj+873T\nWd+9K622O+motZVZlQQJ0qwHM1leWmzmJQbooToDg6hUAW2X7qOdWgocpyKS3u6aUWnHI/GMomJD\nXtUQjhijqMM1tcMdyDd07ic/y02197Bz9vCNqua+Pm567RWWVpi0Y4PBYDAYjnZMn9qE/AqoBL4B\n/A14IOq43+4kJqgdJ3zqz1t4/59fJxQ6/HE5osKcVlWG1+3i6SStfcpz81laVsXypg0xqZGQvK0P\nWHW1nYFe9vnrM3wFyXmrHJBVldU8QRlTKGf2qM+/V19AcFLsMK7HxzKawChKokZEE0Jj6m0jRlHu\nOErtpu69zC+egCPJxsh9W7fSMTDILW9fatKODQaDwWA4ilGMUVQiVNWR5HDanccEteOEdy4s5WCH\nn9f3tsWcczudnDttIs/sryMUFbDGpiDPZnd3O1u7GtK+/8Jwv9qNY9SvdmLWRBw4qA+NrQPyfrbS\nxB5mcmHaSmoqlTakAfbry5SzCJd4R7JMw1FPpKZ2OBGlNl5N7TCjqJAfl2ThHJaW3Bf0s62jOalJ\nVEiV+3Zs4YzJ5cwpLYg5bwJag8FgMBiOJkxLn+GIyK0icpYMr8/KEBPUjhPOnFEIwLM7m+OeX1ZR\nSmNPP7Udidv2XFA5gyxx8Exj+rWxE91FlLkL2OxPL33ZbgqyS1yUOyfQMMZtfdbyJNl4mE56Jk6p\nAlqAel2Fn86MetOa1OPxhvVHJkRw2LPWhuIAQ9/3VpA73P14IK5J1PNN2wmqsjCqnnb4BtVTe/ZQ\n19HDR+ZXZ/wKxgOhoIO+1vyMDoPBYDAYjiZMTW0MeVjpxg0icqeIXC4iuZlOZoLaccJdrxzEIXD+\nzLKYc/5gkLvW7WBaQT6TvYkVwkJ3DjN8JdT2Ja69TYSIcFbhPF7v2EpHoCvt6+1Q4iyh15G+kZVd\nBrSX7bzBdE7HJTmjOndQB9mq9+OligI5cVTnNhx95EkVAO3sHPJ8FYsAWMOTQ573OPLp1qE/Nx3B\nbnxZniHP7ett48db/8WJReWcNsEyGhse0PYMDnLz668wu8THO2ZWxawtmUq7s8VsnhgMBoPBYDjy\nqOpnVLUKuAzYD/w30Cwij4jIx0UkNuhJQsqgVkQcInKeiNwkIneJyN9F5HYRuUZEJmf2Mgzp8pUL\np/CPjy1j2eTDbr0RNeIPa7azvbWLby07jWxn8tTzGl8xO7taM1rDJWXLCGiQ5zpfy+j6VPgcPjpD\nyWt7R8JWXiXAIDWcldZ1dlTaPbqcXpqY6HwPIrbT/wGj0o5H8qUGBy6adMOQ54tlCjNYwir+xYAe\nVmuLHIW0BYeWDnRrG8Wuw4pif3CQb2y8H0H46SmX4XbEfx/dumoV+7t6ufWCpbhT/LxH8/LuVk7+\n2XPcd999tq8xGAwGg8EwOpia2vio6uuq+k1VnQcsBFYAVwH7RORFEfmyiFSmmidhUCsiuSLyTWAv\n8BhwIZAP+IHpwHeAXSLyuIicOuJXZEiKdno5b0bshkVDdx+3vLqRt0+fxPlTUreQqfEVc6C3i96A\nP+01TMudwFzPFJ7rfimu2dRIKXAU0BnqGJO5ATbyPMVUUEqN7WvsBLR+7WK7PkIZC/A55o5kiYZx\ngkPcFDOb5nBQG/0+mcPlDNAzRK3NDZbRHmofMkdboJsi1+HMit/uXMHWjmb+Z+lFVHqs9jzDVdp1\nTU3cuWkTVy2sYWlFacy6Eqm0Pf4An314DVNLcrjsssvSfLWG8YyInCAiN4vIChHZIyKNIrJRRO4R\nkQ+JSPaRXqPBYDAc61ipxCaoTYWq7lDVW1X1bKAK+CNwJvDBVNcmU2q3A4uAawGfqp6mqu9V1Y+o\n6qWqOgWoAV4A7hORT474lRhsE1Fpv/fCm/iDIW5ccmbcccM/FNeEW4Ts6Y1N803lgAyWWru3v4nN\nfTtTjk0Xn8NHgAD92G8F1DJgL4O+XRvYxxamcVbaBlGp2KYPE6SfsqzL077WqLTjlxzHbLo5QJ8O\n/VkqlWqms4iVPMaA9gPgo4jOUOeQPsytg10Uu7yHfu5WNG/hvEnVnDNpOhD7sxsIhfjGK89TlpfN\nN89ckNZab16xgV0t/fzuI3PIzzf1pscDInKSiCwH1gBnAC8Dt2C1TLgLy+3s+8ABEbnBBLcGg8Ew\nthijKHuIyBQRmQs0q+ofVfVyVb0l1XXJIoKLVfX9qvqYqsaVq1R1j6r+EJgJPJfRyg1pEwloX9vf\nxP2b9/DJefOZ5vPZurbGVwLAru74hlOpOKdoAXmObF7oe972NXbNogoclotrtyY2u8qUjbwACNWc\nYfsaOypttx6gTp9hsryNXEnsVhsPE9COb3yOEwBoVss8baha+2766OLVgdcBKJASFKUjZL23B0J+\neoL9h5Tahv5O9vd2sqzMqpEdHtAC/GnjRjY0tfP9c0/Cl+2OOZ9IpX2+tplfr9jP595WxVkzCzN9\nuYbxx0PAw8AkVT1fVb+uqr9U1d+r6o9U9T9UdTrwDuBk4EtHdLUGg8FwjGOMooYiIh8Qkc8Me+7X\nwC5gPbDBTtpxhIRBreqwYrEkqKpfVcem14shLsFQiG88s5qK/FyuW7jQ9nWTPQVkiYNdPZkFtblO\nN+eWLOL5tvX0BO0rqnbwOazAvIvRDWpVlY08zxTmki+xKZvxsBPQAmwO3YeTbAqdF41kiYZxSA4V\nZFNAE7G/KsuZSTYe9rEVgAKxauEjdbUdQcs0KlJTu7bd6s+8pCz+7+69XV38ZPUqLqyuiGsOlYiu\ngQDX/fNNZpTnctO7ptu+znBMMFNVb1PV2D5wUajqK6r6/7BUXIPBYDCMESb9OIbrgVDkCxF5O/Ap\n4NvA+7Hi1G/ZnSzL7kARcQPzgHKGBcOq+rjdeQwjI6LS3rO+lg1N7dx+3nnkuWLbgiQiy+FgureI\nOn99xmu4tHQpjzW9xgtdK7m48OyM5xmOzzk2Su1+ttJBI/O5YlTnbdaNNLKWSsd7cIk9pTyCUWnH\nPyKCR+bQrBtQDSHioMkxSFnIhYiDSp3F/kNBrZX23x5qAypoC1jv8WKX9b5Z01ZHfpabWQWlMSqt\nqvLd119ARPjheSfFTZ9PpNJ+59l17G3t55kvLibPnZ6BmWF8o6p+EbkGuFdVU/7CUdX0jRaOIVo+\nlV6bN4PheOLjKz6e3gV/Sv8eM69emf5F4wjlmA9QM2E2EO0++27gSVX9PoCI9AO32Z3MVkGiiFwA\n1AErgceBR6OO/7N7M0PmRPdebOkb4IcvrufMyeVcOm1a2nPV+IrZ2ZmZAzLAzLxKanIn8WzXC6Nq\n6nRIqdX2FCPTYyPP4yKbqdjrH2tHpVUNsSn0N3Ippdxx7kiXaBin+BxzGaSHDvbEnCtkJq0coEe7\n8IWD2tawUtseGKrUvtm5h8Ulk/AVxWY/PLprF0/vPsjXzphHlc8Tcz4RT29v4vcvHuQ/z5/MqdUF\nab82wzHBbcBBEbnDGDoaDAaD4SgjH4gOSE4Hnon6eiMw0e5kdvvU3o4VwE7HapSbG3Xk2b2ZYXT4\n4Yvr6PIP8u2lZ2ZkelTjLWZfTwf9QXsptsMRES4tW8bOvoPUDuzNaI54eMSDE+eoKrWD6mcrrzKT\nZbZ609pNO96rL9DFXiY6L8ch9pVyMCrtsYRP5gAcckGGw++hcmYDsCe0jVw8uHCFlVpoD1rmUEVZ\nXtr8PdR2tXLa5Fh3846BAW5+7RUWTijiE4tmxl1DPJW2o3+Qz/3zTeZMzOPb75h26Pms0sQ9bA3H\nJJOwjKFOBl4WkQ0i8p8iNuswDAaDwTBqaIbHMcw+4EQAEfEB84GXos6XALY/uNgNaicBPwgbQ/Wr\n6kD0YfdmhpGztr6VP6+v5aq5c5lVVJT6AuI4IPtKUKCuN1atteOADHBe8SKyHS6Wd7yUejD2zKIc\n4sDr8I5qTe1OVjJAL1Vp9qZNxqD2sU0fpJAZFMlJaV1rAtpjC5f48DElpl8tQCnVOHCyZXA7IkKR\ns+hQTW17oBNBmFwa4s32fQAsmxi7GXnLmy/T2jfArRcsxemw++sabnx6HfWdA/z+P+aQ4zJpx8cr\nqtoerqs9CViC1fvvW1i9//4uIsYMwGAwGN4KTEufeNwP/EJEPg78HjgIvBp1fgmwxe5kdj8lPYol\nCRuOICFVvv7MKkrzcvjCSekFU9HU+KxUyEzNogDys3I5u2g+L3WtZCA0eqVYPkfBqCq1G3kBL8VM\nJHX/WLsq7Ua9hwE6meh876i3BzKMP3Idc2hjOwHtO/Rck2OQLMlmAtMO1dUWOoqGKLUFWR6yHA7W\ntNWR7XAyv3SoePZ6fT33rK/l2pNmMb88/gZWPJX231sbuOuVer50wRSWTD1c621U2uMbVV2tqtcB\nFcDHgWLgMRHZfUQXZjAYDMcLRqodzveAV4BbsVTaj6hqMOr8B4HH7E5m1yjq08BfRORkYAMw5NO/\nqt5t94aGzHlg825W17dy69ln43PHtvSwy5T8Qpwi7OpuGtF6Lig5iadaVrOqZwOnezMPsqPxOXx0\nkXm9bzTd2s4u3mQe78Ahyfdv7Aa0B0KvsV9fYpLjUjyOaWmtx6i0xyY+OYEGnqSFLUxg8ZBzRcxi\nK8sJ6CB5wdJDbshtgc5D9bTru/awoGQS2c7DiupgKMSNr7zAZF8eXzl9nu21tPb6+cIj65hX4eGb\nl0w79LwJaA0RVHVARNYAa4GTSKNeyWAwGAyZc4yrrmmjqn3AfyQ5n5Zpjd2g9iLgfOBSoJeh+wYK\nmKD2LaC2zfpgOtnrHdE8boeTKZ7ChEqtr7CTzvbUbr4LvNV4nXms6dk0qkHt/sC+UZlrCy+hhKjh\nzFGZr1PrWKd/oJAaJjkuHZU5DeMfj0wDrJ7FE2RoUFtIFUEG6aKdPMmnJ2RtbPSF+vE4rRrvvT0d\nXDZ59pDrtra2sq21k19evAyPK/6v6eEqbSikfPaR1TR3D/KPT88n22Vt5JiA1gAgIvnAlVgq7SnA\nVuCHwF1Hcl0Gg8FwvHAs95w9GrCbfnwLlouiV1XzVdUbdaTXy8SQMdcvncNkn4evvbSCvkBgRHNV\n+4qp6888/RjAKQ7meaeybWDHiOaJxufw0RXqGhVX5Y28wESqKZTkfT3tqLQD2snK0M/JIpfJWZ9A\nJL06RaPSHrs4sIzCQsT+TApy6N/I4+jnwTJeG57GvqfLckc+sazQ9jp++VItj29o4X+vqGHxAb8E\n7AAAIABJREFUlJFtfBmOHUTkLBH5E1at0s+AbcA5qjpXVX+sqo1HdoUGg8Fw7KOYmtpoRGS5iKRU\nnUSkUES+KSLXpxprV6ktBH5jp9edYezwuF389MKlvO+B57h11SpuPOWUjOea7i1ixcFdBEJBshyZ\nG8nMy5/GK+2baQ90UpiVfH+jsKCX9o7kZtleh5cAAQboIyeFsXbLQOI9mUbdQyO7OYWPJZ3DDiEN\nsDp0GwN0MMv5RdxiP9AwHPuIOBEchEjPTTwUTnhxIISGbeLs6bQM26YV5Me9drhK++qeVr771Bau\nWFzGZ86pPPT8eFdpQ0EnfR3x/w8MqRGRbUANsAr4MvBXVe06sqsyGAwGg4E/A38TkT7gEay2sQeB\nfqAImAucCVwMPAx8NdWEdpXaB4G3Z7Bgwyhz1pQJXLWwhj9s2MDKhgbb1w13QK72FhPQEPv74veE\nteuCPC9/GgBb+mptryUZkV613Wrv/onYxAs4cDKd05KOs6PSbtQ/08pWpjg/knYdLRiV9njAQRbB\nOEGtDqnUGKrORuJYhxCTmbCns5OyvBw87tTtopp7Brj6/lVMK8nl1x+efUj1jRfQtveMLMPDMO74\nN7BIVZep6m9NQGswGAxHCAVUMjuOQVT1TqAa+C4wG/g18CyWcdRjwNVALbBYVT+kqilrE+0GtbXA\n90XkLyJyg4h8MfpI/6UYRsK3zlpIlS+Pr774HP0ZpiFP91puqrt7Wka0lpl5lbgliy19O0c0TwRv\nOKjtIfOgNqRBNvEi1SwmR0aWHb8n9DR1+iwTHBdS4liW9vUmoD0+cOCKm358GInzlRXIisgh1TbC\n3t52phemVmlDIeXah1fR3D3IX6+ZS0GulXwTL6BdubOH6uvX8e9//zvl6zEcG6jq51V1/ZFeh8Fg\nMBismtpMjmMVVR1U1b+q6jtVtQhLoa0AclR1vqp+WVW32p3PblD7caALq63Pp4Hro47PpfUKDCMm\n3+3iJxcuo7a9m1tXrcpojupIUNs7srpalyOLOZ7JbPdvH9E8EXwOqxZwJG199rCBHtqZnMIgKpVK\n26yb2ah/oZyFVDrenfF6DMc+VlCbWvU/rNzKoUfx0o93t3cxLUFQG80tK3awfHMbt75/JgsnJ66j\n7ewN8uHbdlDgcbBsWfqbM4bxx1jUKxkMBoNhBJiWPklR1Q5VrVfV9Oq5wtiqqVXV6ZlMbhg7zp4y\ngY8tqOH36zZw8bRpnDxhQlrX57uyKc/xJFVq7bogz/NO496DK+gLDZDryE5rHcPxjkL68UaeJwcP\nVcPaq6RDrzayOnQbHiZQkfUxJEVLoHgYlfb4wUFWyqBWhj2OBLgOkSFpyv2BAAe6++LW00artKv2\ntfODZ7Zx5dJyrjlj0qHnh6u0qsp19+xkV/0gz/1kGsXFxWm8MsM4ZtTrlQwGg8GQKceu6dPRgl2j\nKMNRyLfPXsjTuw5yw0srePRdV5CTld63s9pbzN7+kRtfzsufRohn2d6/mwV5s5OOTWUWle+wPsh3\nZ5h+7Nd+tvMGNZyNU1LXI8YjoH2sDP0cUKZmfQqn5GY0j+H4IVX6ceTPWCR4He5+HK3U7g07HydT\nalWVG5/aQGm+i19eOStpHe1dz7Xwt2c6ufnqMs6cn9x8zXDsoKp3ishfgPdjNbC/BiiInAY2AU9g\n1SvZTu8yvLXccsNvjvQSDMcYH1/x8SO9hOOX40h1PRIklJ9E5EYR8diZRETOEJF3jt6yDHaw0pCX\nsqOti5+sXp1yfIxZlK+Y2q62EbfPmeuZiiCjUlfrEhe5kktPhkptHRsI4GcayZ2hE6Ueq4ZYG/od\n3RxgmvMacqQ8o3UYlfb4womLkKaqbx+q1UZ+7qz048Nn9iQIaqNV2se3NPDSzg6+ddk0vDmJ62g3\n7+vj83fu4dxFeXz9g6X2X5DhmGC065UMBoPBkCFqWvqMNclyKmcAdSLyOxF5h4hMjJwQkRwROUlE\nPi8irwN3A21jvVhDLOdMnchH51fz+w0bWJWGGzJYZlE9AT/N/pG1/fBk5TA9dyI7/NtGNE8En8OX\ncU3tLtbhIptyZmV0/TZ9iAZWU+V4Hz7HCRnNYQLa44/E6cfxN4yslOPDj6PH7Q6380lkFDUYDPGd\n5ZuYPSGPq0+fFHcMQJ8/xIdv30FetoN7vl6J02n+MB7vjLReyWAwGAyGo5WEQa2qXgW8DevT1j3A\nfhEJhOtzerDqc/4D+D1wgqq+OOarNcTlO2cvoiI/lxteWpGWG3K116qt292T2CwqndY+m3rqCGrQ\n9v0T4XV48WclD2oT9ajdxVqmcGLS1ONEKu2B0Gvs0EeYLOdQ5nib3eUaDDhwxW3pc5jYgDK6jjY6\n/biusxNftouiHPeh56JV2rtW7mVbQx/fv7yaLKf1cxBPpf3q/btYVzvAnTdUUFGaWSq+wWAwGAyG\nUcIYRcVFRH4mIvNGOk9S9xtVXa+qnwJKgJOB92L1DboIKFfVJar6O1X1j3QhhszxZh9OQ/6pjTTk\nCNWj1NYHLLOo/pCf3QP7RzyX1+GjK5R++nGb1tNBI6UsSPvaDt3Dm/p7iphFqfN9h2oU08WotMc2\nib6/g+KwYRQlQx5HG0VFt/TZ29vG9ML8Q+/B6IC2ayDA/zy3lTNnFHDZ/BIgfkD70Gtt/PqRNr74\n/mIuPeWwK3Kw3PyqNhgMBoPhyCAZHsc8S4E3ReR1EblWJLN+nLYsXVU1pKprVfWfqnqvqi5X1ZH1\ngjGMKudMnchH5ldzx4YNbG+zlwleluMhP8vNnlEIak/MnwrAtv5dI57LIx66Q+mnRO9iLQCVSYLa\neCptQPtYFfoFbvKZnHUNDsnMP80EtMcvDpxxjaKi1diEj1UP9aELqbKlpSOhSdQdr+6msWuQ/7mi\nBhGJG9A2dgxy7R27WDI7hx9ck54rusFgMBgMhjHCKLVxUdUzsBz5nwW+AxwQkbtF5Jx05km/T4nh\nqOUbZy4g2+ngd5vt9a4VESo9Pur7k6uidlKQy1wFZImT1sF2W/dORp/2kSfpu7Ru4zVKqMJ3uPzb\n3nX6MH20MNX5cVyZbQ4ZjnNCBHHEMZMPYCmjLtz000uO5ADQRxc+Zx6qSvNALyU51vv91YMHOdjd\nx0XVlbH3CCl3rdnDObMKWTI18fv0Gw/upqs3xF03VOJ2Hd7hNSqtwWAwGAxHEBPUJkRVt6rqDcBk\n4EogH3hSRLaLyNdEJGU/QhPUHkOU5GbzsYU1/GNLHXWd9tJ3J+V5aRwcuceXiFCUlU97MPV9Cwt6\nk57vCLVT4CxM6/492s5etlDFsoRj4qu0/ezV56iQU8l31KR1z2iMSnt8k6VBnLhjng/QD4CbHPq0\nB49YCmzbYBdFLi/dgQF6A4NMyrVShB/ctQlftotLZ1hBbXTq8Qu7WtjV3J/UHOqlLV3c+UQHX3x/\nCSdMHVnPaIPBYDAYDKOEAiqZHccXLsCH1YLOCdQBH8UyL/5QsgtNUHuM8ZmT5+B0CL9dvz7u+eFt\nfSblejnY2zUq9y5yeekeBRPsjlAHOYGitK7ZzkpAmZokqI3HAX2FAP14HWekdd2QOUxAe9wTwo8j\nblA7gAMnWZJFwNmFx2Gps22Bbopc+YeyJKaVuegYGOCx7ft475yp5LpiVd971u+iKC+LyxdZrXmG\npx4Hgsr1d+1mcnkW3/pI2ZBzRqU9/hCR9SKyzs5xpNdqMBgMxwOqmR3HAyKyRER+BRwEfgS8CsxU\n1fNV9UTgm8BPk82RWfGg4ahlYn4uV544nXs3buPzixYxwZO81fCkPC+dgwN0BwbIzxqZslPsyqfJ\nn1krngiqSkewgznO9ILabbxGERMpYnLc8/FUWlVljz6Llyo8Up3Reg0GgCB+nMQ6DA/Sjxsr5bhX\neyhxltAX6mcgNEixy0vDgBXUTvJ4eKS2lv5gkA/Omw4MVWlbev08vLaJT5xRQY7LGXcNt/+7kXW1\nAzx4UxWe3MP7lSagPW554EgvwGAwGAyGVIjIemA28ARwFfCYakw7lb9igtrjj+uWzOEv62u5Y8MG\nbjzllKRjJ+VZaY+N/Z3k55clHZuKIpeXbb0jcz/u1378+PFKQcIxw9v59GkXdWxkHu9Iy7W4nVo6\n2cMUx5XG7dgwIoIM4pT4Sq0rHNT2hHqYnDWFjqCVGVFRkEVDv7UJVOnxcNv6N5hZ7GVBeeyGzt/f\n3I8/oFx9hpV6PFylPdDq57sP7OeSZflcfoY35vrxSijgoKc9vmmWITmqetORXoPBYDAYojhOVNcM\n+DvwR1VNGESoahMpMoxtBbUi8sdE9wD6gR3Afap6wM58hrFlWmE+75kzhb9s2cwXFi/G6479sB0h\nEtTW93dSnSSo9RV20tme3ESp2OWlfbCboIZwSmaZ7R0hy2jKK/ZranewEiWUMPU4UV/aOn0GJ9kU\nO9JLWY5gAlpDBCv9OFapDdCPCysDokd78Dg8tAUsdbbY7aG2rYkscVCam8vu9m7mlRfFbLCoKnev\n2cOSqV7mV8YP8L769934B5VfXD9xyPVGpTUYDAaD4Sjh+KuPtYtAbP2iiOQCX1HVm+1MYlepLQPO\nAkLAhvBz88KLWAVcAdwsImep6lqbcxrGkHfNmswDm/ewo72dxeXlCcdFDGrq+0eWNgyWUhtC6Qp2\nU5iVmYtwR7g/rRf7Qe02XqeAMkqYbvsav3ZzQF+j2HEqTslNe50moDVEE2QwrlFUJP04oAEGdACP\neA4ptSXZHhr7O5mQawWqezt7uXRmFTA09XjV/g42Hujhtg/Oinvvp9d3cu+znXznY2XUVCTewDIc\n34jI1cAHgSkw9M2qqqb+4i3glht+c6SXYDAYjiBilNpEfAf4DTDcSTYvfM5WUGtXTnsJ+BdQpapn\nq+rZQBXwOPAkMBV4DLjV5nyGMWZ6oRWs7k7hglyW6yFLHDSkaOtjh2KX9eHcjgNyItJVavu1h92s\no4plcVOIE6m0+/UlQgxS5jg747UaDBFC+OMGtYIfF9n0YW2ClHiyaI9SapsCbUzK81Lf28tgKMTU\nglgl9u6VdeS5Hfy/k63NqejUY38gxBfu3k1NhYsbriwZcp1RaQ0RROQrWH+fVwHTgIexNqiLgUSZ\nWAaDwWAYLTJt53N8BMJC/Fe6GGi1O4ndoPYLwM2qeiiCDj/+PvBfquoH/hdYZPfGhrFlaoEHAXal\nCGqd4mBCbv7oKLVZViAd+dCeCe1Bax12g9qdrCZEMC3X44hBVCE15ElV2ms0Kq0hGtUQIQJx048H\n6cdFDn1qBaJeh4f2YCcOHBS4cqnv7WZibj51XZZ6O7XAM0Sl7R4I8MD6A7z3pHJ8ubGJNT99tIEt\ndX5+8bmJ5LiNmb0hIZ8ErlXVrwODwG2q+i6sQHfqEV2ZwWAwHBdk2M7nGE5ZFpEuEenECmhrRaQz\n6ujBMo76u9357H4KygfiNUecGD4H0IkxnjpqyM5yUuXLY0+coDamrU+el6bAyFvxFLvCQW0w8xZB\nnaEOssgil/iuzcNNorbxGvkUU0Zsj9lEKm0Lm+nhIIXOMzNep8EQIYT1Pouffjxg9agNK7X5zjza\nA10Uuqz3d0NfN5PyvOw9FNQOVWof3nCQ7oHgod600Srt/lY///2PA1xxlpdLThlqDhVPpe3pDXHB\nZw/w4osvZvpSDeOXKuD18OM+rB6AAH8D3ntEVmQwGAzHG0apHc7ngM9jKbXfBK6POj4BnKmq19md\nzG4Q+hDwBxH5KvAG1n/xMqw+Qv8Ij1kGbLN7Y8PYM73Qy57u1MHqpDwvq5pH5loMUBRJPx6BUtsR\naqfAUWDLjdivfeziTWZxHpKGMVWdPoMLD0VyUtrrMyqtYTgaDmrj9an1h42i+tR633gcebQHOynK\nyqfV30NAQ0wpcVPX2YlThEpvHtElJXe/uZvZE/I4rTq2Rv2O5U30+5UfXTthyPOJ0o5vvbON517o\nxemM3xLIcExTD5RiNbHfA5wGrAVmcKx/ZDIYDAbDUYmq3gUgIruAl1U1vhplE7uRwKexJOA/AzuB\n2vDjfwOfDY/ZjJXiZBgDcou7h6Ql2mF6YT6721NfMynXS0Of5VqcDF9h8mA115lNjsM9svTjUAcF\nzsTtfKKpZQ1BBuOmHidSafu1nXpdTZHjVBxxWrAYDOkSwgoiExlFucihF+vnMN/poVvbKXJ5qQ/X\nsVd6PNR1dVHpy8NXdjig3Vjfyau1nVx9+qSYTZ7BQIg7n2/ioiUeqm2YQ+1vCHDLne28791eTjvt\ntIxfq2Hc8gzwrvDjPwA/EZFngfs4vDFtMBgMhrHEKLWHEJHiqC/XA14RKY532J3TllIbrp/9tIh8\nCajBkol3qGpP1BjjenyUUeHNo33AT38gQE5W4m91aY6HoCodg30Uu+On/dohEAoS1BCODNv5qCr7\nA/uoZj7YEJP2sJFsPJQz2/Y9GnQ1SpBSx+lpr8+otIZ4DKoVnLplaApwSAMM0EeRM48utQzQCpz5\nNPjbqM6bRF1PCwBTfD62djQzs+iwGjsQCPKZf66hKC+LD59iKbHRqce/faqJfU0Bfv2fQ6tCEqm0\nt/2lnYEB5bvfKIl73nDMcy3hTWxV/Y2ItAFnAA8Cvz2SCzMYDIbjhnEeoIrIZOAeoBwIAN9T1fvj\njNuNVZYaAtpU9dw40zWJyCRVbQSaif+/EzGQspVillYNbDiIXZfONYYjhy/bMq7pGhxMGtR6XZbS\n0x0YGFFQu7e/iUENMC07ffMlgIZgA52hTqZnzbE1/iA7mERNWkF0o75JLmXkxC0RNxjSp4+DAORT\ncei5spCLHloAxSdFHNDdeMRDkCDtgR5mFfnY3t1IjjOLstxctrV0csXCw2nE33l2PWv3dvPAp+ZR\n5nUPCWhbuwPc9OB+zj/Jw6WnxO9bG01Pb4g/PNTJuy/LZ/o0k51wPKKqIawPF5Gv78NSaQ0Gg8Hw\nVqAcC6ZPAeA/VXWtiEwEVonI49EiZxSnq2qydNHzOOxsfB6jEPLbigZEJEdEbhCRJ0VkrYisiz7s\n3kxEdouIxjkeC58/W0QeEZH94eevijPH90Rki4j0iEibiDwtIqcPG3OHiOwUkT4RaRKRf4rICXHm\nukhEXhGRXhFpF5Gnh52fIiL/F75Xs4j8QmRozqqInCMiq0SkX0RqReTTdv8/xhqfOxzU+pO39vC6\nsgHoCQyM6H47+g4AMD3DoHabf6t1vSN+UBttEuXXfpqpoyANg6ig+mlmE/mOE23V7EZjVFpDIvq1\nHgdZ5FE25Pk+Dren6nY2UOosZb+/EYCpnhJ29h1gpq+EjS0tKLCkynL8/r9N9fxqxX6uP7eKdywo\nHRLQAnzvgQN09IT4yWcmDHkfJ1Jp//xoF23tIa67tmi0XrJhHCAiJ0nYbCD8OOFxpNdqMBgMxwOi\nmR1HC6p6MJKZq6r1WAqr7fTgYXOtUNVA+PFz4a/jHnbntCtx/Qr4GrAbq7/dg8MOuyzFclGOHCdh\nReYRu+Z8rN55X8ByaIzHVuA6YD5wJrAL+LeIRLulrASuAk4ALsKSr5eLyKGeGyJyOXAvloy+GMs4\n449R551YvXe9wFlYTevfR1QvXhGZjtWr9+XwHD8EfikiY+YmmU5drTc7vaC2a7A/6bjO9lizmmh2\n9h7ALVlUuMttrzGabYPb8Dl8lElFyrEN1KIoZcywPX8Lmwnhp0DmZ7Q+gyEeGmrEw0QcMjQ7phfL\npM0nRTQHmyh1lrHfXw/A1NxitnY0M6+8kLVNTQCcVFVIXVsv1z30JidP8fL9y6tj7rV5Xx+/eqKR\nT15WxPzqnJRrC4WUX97bweKF2Zy6NPV4wzHFSixzqMjjN8L/Dj/eOCKrMxgMhuONMayptSkMflZE\ndoWFuFUiclamL0VETgacqro3zmkFVojIGyLyYRtzrRGRL4rIiNIo7aYfXw68X1WXj+RmqtoU/bWI\nXIOVc31/+PzjWEEiInJngjn+PGyOLwLXYPXIfSI8JrpGaLeI3Ai8CVQDW8MB6y+Ar6rqHVFjN0c9\nvhA4EZga+YaF3Z9/LyLfVNVOLAOtA6p6feR6ETkF+DLpBftjgs9uUOs+nH48Enb2HmR67kSckjz1\nvb0jL+Y5VWWbfyuzXLNsqagH2AFAaRylNhGN+iZO3Hhlpu1rDIZUdLOfApl+6OuykPVzFwlqvRTQ\nEmzhVN989vsbcEkWDoeDDn8/c4uLebmxjumF+fiys7jyb68RUuWea+biznLEqLQ3/H0PnlwHN181\nVBVOxPJXetm63c/vb5+YdnaCYdwzHWiKemwwGAyGY5eIMHh3+BiCiHwA+DmWwe+L4X//JSJzVbUu\nPGYt8WPDC1X1QNRcxeF7JDIIPkNVD4SD1OUisk5V1ydZ+7+w2vv8r4g8hyU4/iNF+nIMdpXaXiBe\nJJ4xYn3Cugb4c9iIKpM53FgGGJ1Y7QnijfEAV2O1MtgdfvpkYDIwICKrRaQ+nFq9OOrS04DNw3Yg\nngCyw9dHxjw57JZPAEuiVeEjhe2gNstSarsDyZXaZKgqO3sPMtk1JaPrW0IttIZaqQieaGv8QbZT\nyARyJLl6HL2+Rl1LKSfiSPNbY1KPDYkIqZ9emofU00bopQ1BCBIiQIByVwmNof1U5ZRS223FGieU\nlLCmvpWlU338+LkdvLark19/eDbVpbkxAe2/13bw+GvdfOsjZZQVDv2bkyj1+Of3dzCh3Ml73+WN\ne95w7KKqe1Q1ssevQF34uSEH4966xGAwGAyq+riqfkNVHyDKQyGKLwJ3quodqro5LMgdBD4TNcci\nVZ0X54gOaLOxsnb/R1VfTrCWA+F/D2KJlSfHGxc1/huqWg2cC2zHyoptEJG/icildv8P7Aa1PwK+\nKOk0A03NBVi7x79P90IReYeIdAP9wH8BF6hqw7Axnw2P6QYuAc5X1YgUGcnr+x7wA+AyYB+WVB75\ndDoRGDInVu54MHwu0ZgGrF2O0mHPIyLXishKEVnZ1NQ0/PSoE1FguwaTt33yuiNBbeZKbdNgB13B\n3oxNoiL1tNWOmNJnYGg9LVgmUcVppB53s58+Wsh2zs1ofQZDPPppABSvVMac66OdPArpxHI5LneV\nUNffxOScMrZ1Wb82Ct1u6nv6WFJVyMOb93P+nCLee1J5TEA7GAjxlb/sYUalm+vfY698ZUutnyef\n7uXaqwtxu41Ke5yzC4iR90WkJHzOYDAYDGPMCGpqSyPxQ/i4Nq37WiLgycQKcU8CttuBhAXJO4Fn\nVPWeBGM8IlY7CBHJxzKB2mhnflV9UVU/i1Wi+gFgDvB/dtdnN/34Aqy60otFZBMwJEpS1XfFvSo5\nnwTeyLAV0LNY6cal4Xn+LiKnhXcEIvwFeArrP+bLwP0ickZYFY5ESN8P72gQfoO8Hfgo8L/h84l2\nsDXBY7Dqd+Neq6q/A34HsGTJkox3x3OLu+lrTe16GlFqO1MotZ4sNwJ0jUCp3dE7cpMoj3gol9TX\nd2kL3bRxYpygNpFJVKO+CUCBzEtrXUalNSSjX60a2XhKbYB28imiTZsBKM7yUT/QysUVc9je3chk\nTwHb2y0zqZmlHrbU9/LBpRNi5gH43fJmNtf5efh7k3G7hgaoiVTa2//agdstfPyj9vo+H40Egw66\nOzJ3ZE+GiHwduAKYDQwArwJfV9UNNq+fCawGRFVT/0JOPV82cAuWf0Mu8DTwWVXdFzXm51iteOYB\n9ao6ze70xP97lo+1OXzM0fKp9Nu2GQwGw5iSuftxs6ouGcGdS7Ha4sQT4t6exjxnYAWb68LeRAAf\nVdX1IrJWVRcBE4CHwiVPTuAOVbXt3SBW26APAR/GKgN90e61doPaZuAhu5OmQkTKgXdjGT6lTdg6\nekf4eFVEtgOfwFJeI2M6gA5gu4i8CrQB78XK044Ev5uixgfC80TyZ+uxvnnRDH9T1HNYtY0Q6d3U\nkslrG028buvb250iqHWIkO9yj0ip3dl7AEGYkh2rWNlh2+A2Zrpn2WrPc4DtQLr1tGvxMQW3FGa0\nPoMhHn16EMFBHlYwGqmnBeimDR8ltKrleBwMhQihTPOUsrxxAyeWFbG2qQm300HfoJUpdGq1L0al\nbe0OcNMDVgufd55mL3Zq6why9yOdfOAKL+VlaXVuO554G5YJ4htYQd/NWLU/c1W1NdmF4V3ve4Hn\ngXNGaT0/w/q7+EGsvx8/AR4VkZNVNRge4wDuwjJKvDDVhCLyi/BDBX4oItGlPk5gGQlKdwwGg8Ew\niqRh+jTGq4gm0YZn/ItVXyRBlm84oEVVa4GF6SxKRIqA92MFsmdgmQL/BatEtc7uPLY+7ajq1eks\nzgZXYe2M3ztK8zmwal0TIeEjMmZV+P6zCe8AhFOrawibTQGvADeKSFXUTvkF4etWRY2J7FQQNWal\nqibP+X0LcDoceFxZKWtqAfJd2SNq6bOz9yCV2SXkOpJ9G+KbRLUH22gMNrJULrD1jjzIDpy4KGaa\nrbX5tZtWtjPRcZGt8QaDXfq1njwm4IxTp91DG9OdNbRqEwWOAhoC1j7XhGwfe3s6eN/sGl5t3MuJ\nZYWsOdCB0wGnnhT7t+J7DxygvSfIrZ+eEGP2lEil/dNDnfT2KZ81bXwSoqpDfiGIyEexNkLPIHW6\n0/9i9WxfQZygVkTeCXwXa5f5IPBX4CZVjfsNE5ECLI+Jq1X1qaj17MHaRY+YIF4fPvdlbAS1WMEv\nWH//TgCi7+/HUppvsTGPwWAwGEbKkQtqh5dPRignVr09EkTaA90H/Jeqrs5kkrd8Cz+cj/0J4F5V\n7Rp2Lh8O5ZQ6gCkisghoVdU6EfEBX8X6wHEQq0boOqCKcFsgEZmBpcgux3J+rMJqRzQAPAqgqp0i\n8hvgJhHZh2Ug9TmgCIi4Kz+JlQN+t4h8CSgBfowlo3eGx/wG+JyI/Az4LdaHoauwdtrHlHRSkFPV\n1AJ4s9x0jTCorXZPy+jabYPbAJieoJ52OAfZQTnTcIq9t2+Trgc07VY+JvXYkIqANpC7la8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1SKC2MeF8v1GEipntY9HE1zpShqlUmUIhlDeh15VGERNk7yAm5auNH+Fg7LPZwK1fPWohs55DtO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\n+nFsJqvM1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fqY/t74Ef+ANDzCHCK54ZRHN262M4kfIfZgGGA5MWeA5ZWGCM40UVupmBzO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\nnewmKKXUpEhZT62I7BARE2L7l2//KhG5W0SafK9fGqKO74rIJhHpF5FOE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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(11, 5.5))\n",
+ "xmin, xmax = -300+xc, 300+xc\n",
+ "ymin, ymax = -300+yc, 300+yc\n",
+ "indz = 28\n",
+ "\n",
+ "matplotlib.rcParams['font.size'] = 14\n",
+ "# Axis label\n",
+ "x = np.linspace(xmin+100, xmax-100,3)\n",
+ "\n",
+ "# DC observed data\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "pos = ax1.get_position()\n",
+ "ax1.set_position([pos.x0, pos.y0, pos.width*0.5, pos.height])\n",
+ "ax1 = vizdata(dataObs, src1, rx_x, ax=ax1, rxcomponent=\"X\", title=\"Observed DC Data\", clim=(4.5e-3, 4.8e-1))\n",
+ "plt.gca().set_aspect('equal')\n",
+ "# ax1.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ "# ax1.xaxis.set_visible(False)\n",
+ "ax1.set_xlim(xmin, xmax)\n",
+ "ax1.set_ylim(ymin, ymax) \n",
+ "ax1.set_xticks(map(int, x))\n",
+ "ax1.set_xticklabels(map(str, map(int, x)),size=14)\n",
+ "ax1.set_xlabel('Easting (m)')\n",
+ "ax1.set_ylabel('Northing (m)')\n",
+ "ax1.set\n",
+ "\n",
+ "# True conductivity model\n",
+ "ax2 = fig.add_axes([pos.x0+0.55, pos.y0, pos.width, pos.height])\n",
+ "ax2,dat2 = viz(mesh, sigma, indz, airind, normal=\"Z\", xc=xc, yc=yc, zc=zc, ax=ax2, pltshow=False)\n",
+ "plt.gca().set_aspect('equal')\n",
+ "ax2.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ "ax2.yaxis.set_visible(False)\n",
+ "ax2.set_xlim(xmin, xmax)\n",
+ "ax2.set_ylim(ymin, ymax)\n",
+ "ax2.set_xticks(map(int, x))\n",
+ "ax2.set_xticklabels(map(str, map(int, x)),size=14)\n",
+ "ax2.set_xlabel('Easting (m)')\n",
+ "ax2.set_title('Conductivity Model')"
+ ]
+ }
+ ],
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+ "anaconda-cloud": {},
+ "kernelspec": {
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+ "language": "python",
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diff --git a/docs/case-studies/DCIP/TKC_DC_inv.ipynb b/docs/case-studies/DCIP/TKC_DC_inv.ipynb
new file mode 100644
index 0000000..156634e
--- /dev/null
+++ b/docs/case-studies/DCIP/TKC_DC_inv.ipynb
@@ -0,0 +1,950 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from ipywidgets import interact, IntSlider\n",
+ "import numpy as np\n",
+ "%matplotlib inline\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import FormatStrFormatter\n",
+ "matplotlib.rcParams['font.size'] = 14"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Inverse problem"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Run inverse problem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SimPEG.InvProblem will set Regularization.mref to m0.\n",
+ "\n",
+ " SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
+ " ***Done using same Solver and solverOpts as the problem***\n",
+ "SimPEG.SaveOutputEveryIteration will save your inversion progress as: '###-InversionModel-2017-07-27-11-46.txt'\n",
+ "SimPEG.SaveModelEveryIteration will save your models as: '###-InversionModel-2017-07-27-11-46.npy'\n",
+ "model has any nan: 0\n",
+ "============================ Inexact Gauss Newton ============================\n",
+ " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
+ "-----------------------------------------------------------------------------\n",
+ "x0 has any nan: 0\n",
+ " 0 6.73e+02 2.26e+06 0.00e+00 2.26e+06 9.60e+04 0 \n",
+ " 1 6.73e+02 2.36e+05 7.23e+00 2.41e+05 1.26e+04 0 \n",
+ " 2 1.35e+02 2.84e+04 2.11e+01 3.12e+04 1.90e+03 0 Skip BFGS \n",
+ " 3 1.35e+02 2.80e+03 3.94e+01 8.10e+03 3.23e+02 0 Skip BFGS \n",
+ " 4 2.69e+01 9.18e+02 4.40e+01 2.10e+03 1.67e+02 0 Skip BFGS \n",
+ " 5 2.69e+01 3.35e+02 5.23e+01 1.74e+03 7.28e+01 0 Skip BFGS \n",
+ "------------------------- STOP! -------------------------\n",
+ "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.2605e+05\n",
+ "1 : |xc-x_last| = 2.5903e+01 <= tolX*(1+|x0|) = 3.2629e+02\n",
+ "0 : |proj(x-g)-x| = 7.2823e+01 <= tolG = 1.0000e-01\n",
+ "0 : |proj(x-g)-x| = 7.2823e+01 <= 1e3*eps = 1.0000e-02\n",
+ "0 : maxIter = 20 <= iter = 6\n",
+ "------------------------- DONE! -------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "from SimPEG import Mesh, Utils, Maps, Survey\n",
+ "from SimPEG import DataMisfit, Regularization, Optimization, Directives, InvProblem, Inversion\n",
+ "from SimPEG.EM.Static import DC, IP, Utils as StaticUtils\n",
+ "from pymatsolver import PardisoSolver\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Setup tensor mesh\n",
+ "\n",
+ "# Core cell sizes in x, y, and z\n",
+ "csx, csy, csz = 25., 25., 25.\n",
+ "# Number of core cells in each direction\n",
+ "ncx, ncy, ncz = 48, 48, 20\n",
+ "# Number of padding cells to add in each direction\n",
+ "npad = 7\n",
+ "# Vectors of cell lengthts in each direction\n",
+ "hx = [(csx,npad, -1.3),(csx,ncx),(csx,npad, 1.3)]\n",
+ "hy = [(csy,npad, -1.3),(csy,ncy),(csy,npad, 1.3)]\n",
+ "hz = [(csz,npad, -1.3),(csz,ncz), (csz/2.,6)]\n",
+ "# Create mesh\n",
+ "mesh = Mesh.TensorMesh([hx, hy, hz],x0=\"CCN\")\n",
+ "# Map mesh coordinates from local to UTM coordiantes\n",
+ "xc = 300+5.57e5\n",
+ "yc = 600+7.133e6\n",
+ "zc = 425.\n",
+ "mesh._x0 = mesh.x0 + np.r_[xc, yc, zc]\n",
+ "\n",
+ "# Load synthetic conductivity model matching the designated mesh\n",
+ "sigma = mesh.readModelUBC(\"VTKout_DC.dat\")\n",
+ "# Identify air cells in the model\n",
+ "airind = sigma == 1e-8\n",
+ "\n",
+ "# Obtain topographic surface from 3D conductivity model\n",
+ "mesh2D, topoCC = StaticUtils.gettopoCC(mesh, airind)\n",
+ "\n",
+ "\n",
+ "# Setup gradient array survey\n",
+ "\n",
+ "# Define the source electrode locations\n",
+ "# Here we use a single dipole source (A-B electrode) in the x-direction)\n",
+ "Aloc1_x = np.r_[-600., 0, 0.] + np.r_[xc, yc, zc]\n",
+ "Bloc1_x = np.r_[600., 0, 0.] + np.r_[xc, yc, zc]\n",
+ "\n",
+ "# Rx locations (M-N electrodes, x-direction)\n",
+ "# Find x and y cell centres in the interior portion of the mesh\n",
+ "x = mesh.vectorCCx[np.logical_and(mesh.vectorCCx>-300.+ xc, mesh.vectorCCx<300.+ xc)]\n",
+ "y = mesh.vectorCCy[np.logical_and(mesh.vectorCCy>-300.+ yc, mesh.vectorCCy<300.+ yc)]\n",
+ "# Grid selected cell centres to get M and N Rx electrode locations\n",
+ "Mx = Utils.ndgrid(x[:-1], y, np.r_[-12.5/2.])\n",
+ "Nx = Utils.ndgrid(x[1:], y, np.r_[-12.5/2.])\n",
+ "# Get cell ind for electrode locations to extract electrode elevation from topoCC\n",
+ "inds_Mx = Utils.closestPoints(mesh2D, Mx[:,:2])\n",
+ "inds_Nx = Utils.closestPoints(mesh2D, Nx[:,:2])\n",
+ "# Draped M and N electrode x,y,z locations\n",
+ "Mx_dr = np.c_[Mx[:,0], Mx[:,1], topoCC[inds_Mx]]\n",
+ "Nx_dr = np.c_[Nx[:,0], Nx[:,1], topoCC[inds_Nx]]\n",
+ "\n",
+ "# Create Src and Rx classes for DC problem\n",
+ "rx_x = DC.Rx.Dipole(Mx_dr, Nx_dr)\n",
+ "src1 = DC.Src.Dipole([rx_x], Aloc1_x, Bloc1_x)\n",
+ "\n",
+ "# Setup mappings\n",
+ "# Inversion model is log conductivity in the subsurface. This can be realized as a following mapping:\n",
+ "expmap = Maps.ExpMap(mesh) # from log conductivity to conductivity\n",
+ "actmap = Maps.InjectActiveCells(mesh, ~airind, np.log(1e-8)) # from subsurface cells to full3D cells\n",
+ "mapping = expmap*actmap\n",
+ "\n",
+ "\n",
+ "# Create inital and reference model (1e-4 S/m)\n",
+ "m0 = np.ones_like(sigma)[~airind]*np.log(1e-4)\n",
+ "\n",
+ "# Form survey object using Srcs and Rxs that we have generated\n",
+ "survey = DC.Survey([src1])\n",
+ "# Define problem and set solver\n",
+ "problem = DC.Problem3D_CC(mesh, sigmaMap=mapping)\n",
+ "problem.Solver = PardisoSolver\n",
+ "# Pair problem and survey\n",
+ "problem.pair(survey)\n",
+ "\n",
+ "# Define true model based on mapping\n",
+ "mtrue = np.log(sigma)[~airind]\n",
+ "\n",
+ "# Forward model fields due to the reference model and true model\n",
+ "f0 = problem.fields(m0)\n",
+ "f = problem.fields(mtrue)\n",
+ "\n",
+ "# Get observed data\n",
+ "dobs = survey.dpred(mtrue, f=f)\n",
+ "\n",
+ "# Compute secondary potential\n",
+ "phi_sec = f[src1, \"phi\"] - f0[src1, \"phi\"]\n",
+ "\n",
+ "\n",
+ "# Inversion\n",
+ "\n",
+ "# Depth weighting\n",
+ "depth = 1./(abs(mesh.gridCC[:,2]-zc))**1.5\n",
+ "depth = depth/depth.max()\n",
+ "\n",
+ "# Setup inversion object\n",
+ "regmap = Maps.IdentityMap(nP=m0.size)\n",
+ "# Assign uncertainties\n",
+ "std = 0.05\n",
+ "eps = 1e-3\n",
+ "survey.std = std\n",
+ "survey.eps = eps\n",
+ "survey.dobs = dobs\n",
+ "# Define datamisfit portion of objective function\n",
+ "dmisfit = DataMisfit.l2_DataMisfit(survey)\n",
+ "# Define regulatization (model objective function)\n",
+ "reg = Regularization.Simple(mesh, mapping=regmap, indActive=~airind, cell_weights=depth[~airind])\n",
+ "opt = Optimization.InexactGaussNewton(maxIter = 20)\n",
+ "invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)\n",
+ "# Define inversion parameters\n",
+ "beta = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)\n",
+ "betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0)\n",
+ "save = Directives.SaveOutputEveryIteration()\n",
+ "savemodel = Directives.SaveModelEveryIteration()\n",
+ "target = Directives.TargetMisfit()\n",
+ "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaest, save, target, savemodel])\n",
+ "reg.alpha_s = 1e-1\n",
+ "reg.alpha_x = 1.\n",
+ "reg.alpha_y = 1.\n",
+ "reg.alpha_z = 1.\n",
+ "problem.counter = opt.counter = Utils.Counter()\n",
+ "opt.LSshorten = 0.5\n",
+ "opt.remember('xc')\n",
+ "\n",
+ "# Run inversion\n",
+ "mopt = inv.run(m0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Apply mapping to model to get and save recovered conductivity\n",
+ "sigopt = mapping*mopt\n",
+ "# np.save(\"sigest_singlesrc_withdweights\", sigopt)\n",
+ "\n",
+ "# Calculate dpred\n",
+ "dpred = survey.dpred(np.log(sigopt[~airind]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot true and recovered conductivity models to assess how well anomalous structures are recovered."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def compareTrueRecoveredMod(indz,indy):\n",
+ " fig = plt.figure(figsize=(11, 8))\n",
+ " vmin, vmax = -4, -1\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+yc, 500+yc\n",
+ " zmin, zmax = -500+zc, 0+zc\n",
+ "# indz = 28\n",
+ "# indy = 31\n",
+ " # Axis label\n",
+ " x = np.linspace(xmin+200, xmax-200,3)\n",
+ " matplotlib.rcParams['font.size'] = 10\n",
+ "\n",
+ " # True Conductivity Model z-section\n",
+ " ax1 = plt.subplot(1,1,1)\n",
+ " pos = ax1.get_position()\n",
+ " ax1.set_position([pos.x0-0.1, pos.y0+0.3, pos.width*0.5, pos.height*0.5])\n",
+ " ax1,dat1 = viz(mesh, sigma, indz, airind, normal=\"Z\", xc=xc,yc=yc,zc=zc, ax=ax1, cb=False, clim=(vmin, vmax), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax1.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ " ax1.xaxis.set_visible(False)\n",
+ " ax1.set_xlim(xmin, xmax)\n",
+ " ax1.set_ylim(ymin, ymax) \n",
+ " ax1.set_ylabel('Northing (m)')\n",
+ "\n",
+ " # True Conductivity Model y-section\n",
+ " pos = ax1.get_position()\n",
+ " ax2 = fig.add_axes([pos.x0+0.0525, pos.y0 - 0.36, pos.width*0.725, pos.height])\n",
+ " ax2, dat2 = viz(mesh, sigma, indy, airind, normal=\"Y\", xc=xc,yc=yc,zc=zc, ax=ax2, cb=False, clim=(vmin, vmax), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax2.set_title((\"Slice at y=%.1f m\")%(mesh.vectorCCy[indy]))\n",
+ " ax2.set_xlim(xmin, xmax)\n",
+ " ax2.set_ylim(zmin, zmax) \n",
+ " ax2.set_xticks(map(int, x))\n",
+ " ax2.set_xticklabels(map(str, map(int, x)),size=12)\n",
+ " ax2.set_xlabel('Easting (m)')\n",
+ " ax2.set_ylabel('Elev. (m)')\n",
+ "\n",
+ " # DC Recovered Model z-section\n",
+ " ax3 = fig.add_axes([pos.x0+0.3, pos.y0, pos.width, pos.height])\n",
+ " ax3,dat3 = ax = viz(mesh, sigopt, indz, airind, normal=\"Z\", xc=xc,yc=yc,zc=zc, ax=ax3, cb=False, clim=(vmin, vmax), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax3.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ " ax3.xaxis.set_visible(False)\n",
+ " ax3.yaxis.set_visible(False)\n",
+ " ax3.set_xlim(xmin, xmax)\n",
+ " ax3.set_ylim(ymin, ymax)\n",
+ "\n",
+ " # DC Recovered Model y-section\n",
+ " ax4 = fig.add_axes([pos.x0+0.3525, pos.y0 - 0.36, pos.width*0.725, pos.height])\n",
+ " ax4, dat4 = viz(mesh, sigopt, indy, airind, normal=\"Y\", xc=xc,yc=yc,zc=zc, ax=ax4, cb=False, clim=(vmin, vmax), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax4.set_title((\"Slice at y=%.1f m\")%(mesh.vectorCCy[indy]))\n",
+ " ax4.yaxis.set_visible(False)\n",
+ " ax4.set_xlim(xmin, xmax)\n",
+ " ax4.set_ylim(zmin, zmax) \n",
+ " ax4.set_xticks(map(int, x))\n",
+ " ax4.set_xticklabels(map(str, map(int, x)),size=12)\n",
+ " ax4.set_xlabel('Easting (m)')\n",
+ "\n",
+ " pos = ax4.get_position()\n",
+ " cbarax = fig.add_axes([pos.x0-0.15 , pos.y0-0.01 , pos.width, pos.height*0.05]) ## the parameters are the specified position you set\n",
+ " cb = fig.colorbar(dat4[0],cax=cbarax, orientation=\"horizontal\", ax = ax4, ticks=np.linspace(vmin,vmax, 4), format=\"1.e%.f\")\n",
+ " cb.set_label(\"Conductivity (S/m)\",size=12)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def viz(mesh, sigma, ind, airind, normal=\"Z\", ax=None, label=\"Conductivity (S/m)\", scale=\"log\", clim=(-4, -1), xc=0, yc=0,zc=0., cb=True, pltshow=True):\n",
+ " if normal == \"Z\":\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 5))\n",
+ " ax = plt.subplot(111)\n",
+ " else:\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 2.5))\n",
+ " ax = plt.subplot(111)\n",
+ "\n",
+ " temp = sigma.copy()\n",
+ "\n",
+ " if scale == \"log\":\n",
+ " temp = np.log10(temp)\n",
+ "\n",
+ " temp[airind] = np.nan\n",
+ "\n",
+ " dat = mesh.plotSlice(temp, ind=ind, clim=clim, normal=normal, grid=False, pcolorOpts={\"cmap\":\"viridis\"}, ax=ax)\n",
+ " if normal == \"Z\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Northing (m)\")\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+yc, 500.+yc\n",
+ " ax.set_title((\"Elevation at %.1f m\")%(mesh.vectorCCz[ind]))\n",
+ " elif normal == \"Y\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Elevation (m)\")\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+zc, 0.+zc\n",
+ " ax.set_title((\"Northing at %.1f m\")%(mesh.vectorCCy[ind]))\n",
+ "\n",
+ " ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ " ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ " ax.set_xlim(xmin, xmax)\n",
+ " ax.set_ylim(ymin, ymax)\n",
+ " # plt.tight_layout()\n",
+ "\n",
+ " if scale == \"log\":\n",
+ " cbformat = \"$10^{%1.1f}$\"\n",
+ " elif scale == \"linear\":\n",
+ " cbformat = \"%.1e\"\n",
+ "\n",
+ " if clim is None:\n",
+ " vmin, vmax = dat[0].get_clim()\n",
+ " else:\n",
+ " vmin, vmax = clim[0], clim[1]\n",
+ "\n",
+ " if cb:\n",
+ " cb = plt.colorbar(dat[0], format=cbformat, ticks=np.linspace(vmin, vmax, 3))\n",
+ " cb.set_label(label)\n",
+ " if pltshow:\n",
+ " plt.show()\n",
+ " return ax, dat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1020e8a092b040f28e2ffdbff97a2c65"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ">"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interact(lambda indz,indy: compareTrueRecoveredMod(indz,indy), indz=IntSlider(min=0, max=mesh.nCz-1,step=1, value=28), indy=IntSlider(min=0, max=mesh.nCy-1,step=1, value=31))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot observed and predicted data to evaluate how well the recovered model fits the observed data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "Xx = 0.5*(Mx[:,0]+Nx[:,0]).reshape((23, 24), order=\"F\")\n",
+ "Yx = Mx[:,1].reshape((23, 24), order=\"F\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def vizdata(data, src, rx, rxcomponent=\"X\", clim=None, title=None, figsave=None, ax=None, cb=True,cbOrient=\"vertical\"):\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(6.8, 5.5))\n",
+ " ax = plt.subplot(111)\n",
+ "\n",
+ " temp = data[src, rx]\n",
+ " if rxcomponent==\"X\":\n",
+ " X = Xx.copy()\n",
+ " Y = Yx.copy()\n",
+ " else:\n",
+ " X = Xy.copy()\n",
+ " Y = Yy.copy()\n",
+ " temp = temp.reshape(X.shape, order=\"F\")\n",
+ " if clim is not None:\n",
+ " vmin, vmax = clim[0], clim[1]\n",
+ " dat = ax.contourf(X, Y, temp, 20, clim=clim, vmin=vmin, vmax=vmax, cmap=\"plasma\")\n",
+ " ax.contour(X, Y, temp, 10, colors=\"k\")\n",
+ " else:\n",
+ " vmin, vmax = temp.min(), temp.max()\n",
+ " dat = ax.contourf(X, Y, temp, 20, cmap=\"plasma\")\n",
+ " ax.contour(X, Y, temp, 10, colors=\"k\")\n",
+ " \n",
+ " if title is not None:\n",
+ " ax.set_title(title)\n",
+ " xmin, xmax = X.min(), X.max()\n",
+ " ymin, ymax = Y.min(), Y.max() \n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Northing (m)\")\n",
+ " ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ " ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ " ax.set_xlim(xmin, xmax)\n",
+ " ax.set_ylim(ymin, ymax) \n",
+ " \n",
+ " if cb:\n",
+ " cb = plt.colorbar(dat, orientation=cbOrient, ticks=np.linspace(vmin, vmax, 3), format=\"%.1e\")\n",
+ " cb.set_label(\"Potential (V)\")\n",
+ " \n",
+ " if figsave is not None:\n",
+ " fig.savefig(figsave, dpi=200)\n",
+ " return ax\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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EwPYK7Y3iEXqTZ/FQxKwkiyQUwjNqKAwKWwuMMKJNxgBHFh3JB8pvYV3/Oh6R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EiNS6Si9fPEEuapxuWO1IyEw7zld/fw/MAY/3P1dt551pqZ19Lzcjie5JFjfq5cIJt96+PjuByK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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(11, 5.5))\n",
+ "xmin, xmax = -300+xc, 300+xc\n",
+ "ymin, ymax = -300+yc, 300+yc\n",
+ "indz = 28\n",
+ "dataObs = Survey.Data(survey, v=survey.dobs)\n",
+ "preddata = Survey.Data(survey, v=dpred)\n",
+ "# Axis label\n",
+ "x = np.linspace(xmin+100, xmax-100,3)\n",
+ "\n",
+ "# DC observed data\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "pos = ax1.get_position()\n",
+ "ax1.set_position([pos.x0-0.4, pos.y0, pos.width, pos.height])\n",
+ "ax1 = vizdata(dataObs, src1, rx_x, ax=ax1, rxcomponent=\"X\", title=\"Observed DC Data\", clim=(4.5e-3, 4.8e-1), cb=False)\n",
+ "plt.gca().set_aspect('equal')\n",
+ "ax1.set_xlim(xmin, xmax)\n",
+ "ax1.set_ylim(ymin, ymax) \n",
+ "ax1.set_xticks(map(int, x))\n",
+ "ax1.set_xticklabels(map(str, map(int, x)),size=14)\n",
+ "ax1.set_xlabel('Easting (m)')\n",
+ "ax1.set_ylabel('Northing (m)')\n",
+ "\n",
+ "# DC Predicted data\n",
+ "ax2 = fig.add_axes([pos.x0, pos.y0, pos.width, pos.height])\n",
+ "ax2 = vizdata(preddata, src1, rx_x, ax=ax2, rxcomponent=\"X\", title=\"Predicted DC Data\", clim=(4.5e-3, 4.8e-1))\n",
+ "plt.gca().set_aspect('equal')\n",
+ "ax2.yaxis.set_visible(False)\n",
+ "ax2.set_xlim(xmin, xmax)\n",
+ "ax2.set_ylim(ymin, ymax)\n",
+ "ax2.set_xticks(map(int, x))\n",
+ "ax2.set_xticklabels(map(str, map(int, x)),size=14)\n",
+ "ax2.set_xlabel('Easting (m)')"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [default]",
+ "language": "python",
+ "name": "python2"
+ },
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diff --git a/docs/case-studies/DCIP/TKC_IP_fwd.ipynb b/docs/case-studies/DCIP/TKC_IP_fwd.ipynb
new file mode 100644
index 0000000..6f3327b
--- /dev/null
+++ b/docs/case-studies/DCIP/TKC_IP_fwd.ipynb
@@ -0,0 +1,556 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from ipywidgets import interact, IntSlider\n",
+ "import sys\n",
+ "%matplotlib inline\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import FormatStrFormatter\n",
+ "matplotlib.rcParams['font.size'] = 14"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# IP example: Gradient array with single source"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Purpose\n",
+ "\n",
+ "- Explore the basic setup and physics of an induced polarization (IP) survey within the context of kimberlite exploration. \n",
+ "\n",
+ "- Understand how the conductivity model, derived from the DC inversion, is used to solve the linearized IP problem. \n",
+ "\n",
+ "- Forward model and invert IP data using the SimPEG-Static package."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Linearization of induced polarization (IP) responses"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$ d^{IP} = F[\\sigma(1-\\eta)] - F[\\sigma] $$\n",
+ "$$\\simeq -\\frac{\\partial F[\\sigma]}{\\partial log(\\sigma)} $$\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- $F[\\cdot]$: Static Maxwell's operator (taking $\\sigma(x, y, z)$)\n",
+ "\n",
+ "- $\\eta$: Chargeability (ms or mV/V)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Forward problem\n",
+ "### Setup mesh, load model, design survey, and forward model data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from SimPEG import Mesh, Utils, Maps, Survey\n",
+ "import numpy as np\n",
+ "from SimPEG.EM.Static import DC, IP, Utils as StaticUtils\n",
+ "from pymatsolver import PardisoSolver\n",
+ "import pickle\n",
+ "\n",
+ "\n",
+ "########################\n",
+ "# General Problem Setup\n",
+ "########################\n",
+ "\n",
+ "# Setup tensor mesh\n",
+ "\n",
+ "# Core cell sizes in x, y, and z\n",
+ "csx, csy, csz = 25., 25., 25.\n",
+ "# Number of core cells in each direction\n",
+ "ncx, ncy, ncz = 48, 48, 20\n",
+ "# Number of padding cells to add in each direction\n",
+ "npad = 7\n",
+ "# Vectors of cell lengthts in each direction\n",
+ "hx = [(csx,npad, -1.3),(csx,ncx),(csx,npad, 1.3)]\n",
+ "hy = [(csy,npad, -1.3),(csy,ncy),(csy,npad, 1.3)]\n",
+ "hz = [(csz,npad, -1.3),(csz,ncz), (csz/2.,6)]\n",
+ "# Create mesh\n",
+ "mesh = Mesh.TensorMesh([hx, hy, hz],x0=\"CCN\")\n",
+ "# Map mesh coordinates from local to UTM coordiantes\n",
+ "xc = 300+5.57e5\n",
+ "yc = 600+7.133e6\n",
+ "zc = 425.\n",
+ "mesh._x0 = mesh.x0 + np.r_[xc, yc, zc]\n",
+ "\n",
+ "# Load synthetic conductivity model matching the designated mesh\n",
+ "sigma = mesh.readModelUBC(\"VTKout_DC.dat\")\n",
+ "# Identify air cells in the model\n",
+ "airind = sigma == 1e-8\n",
+ "\n",
+ "# Obtain topographic surface from 3D conductivity model\n",
+ "mesh2D, topoCC = StaticUtils.gettopoCC(mesh, airind)\n",
+ "\n",
+ "\n",
+ "# Setup gradient array survey\n",
+ "\n",
+ "# Define the source electrode locations\n",
+ "# Here we use a single dipole source (A-B electrode) in the x-direction)\n",
+ "Aloc1_x = np.r_[-600., 0, 0.] + np.r_[xc, yc, zc]\n",
+ "Bloc1_x = np.r_[600., 0, 0.] + np.r_[xc, yc, zc]\n",
+ "\n",
+ "# Rx locations (M-N electrodes, x-direction)\n",
+ "# Find x and y cell centres in the interior portion of the mesh\n",
+ "x = mesh.vectorCCx[np.logical_and(mesh.vectorCCx>-300.+ xc, mesh.vectorCCx<300.+ xc)]\n",
+ "y = mesh.vectorCCy[np.logical_and(mesh.vectorCCy>-300.+ yc, mesh.vectorCCy<300.+ yc)]\n",
+ "# Grid selected cell centres to get M and N Rx electrode locations\n",
+ "Mx = Utils.ndgrid(x[:-1], y, np.r_[-12.5/2.])\n",
+ "Nx = Utils.ndgrid(x[1:], y, np.r_[-12.5/2.])\n",
+ "# Get cell ind for electrode locations to extract electrode elevation from topoCC\n",
+ "inds_Mx = Utils.closestPoints(mesh2D, Mx[:,:2])\n",
+ "inds_Nx = Utils.closestPoints(mesh2D, Nx[:,:2])\n",
+ "# Draped M and N electrode x,y,z locations\n",
+ "Mx_dr = np.c_[Mx[:,0], Mx[:,1], topoCC[inds_Mx]]\n",
+ "Nx_dr = np.c_[Nx[:,0], Nx[:,1], topoCC[inds_Nx]]\n",
+ "\n",
+ "# Create Src and Rx classes for DC problem\n",
+ "rx_x = DC.Rx.Dipole(Mx_dr, Nx_dr)\n",
+ "src1 = DC.Src.Dipole([rx_x], Aloc1_x, Bloc1_x)\n",
+ "\n",
+ "\n",
+ "########################\n",
+ "# IP Problem\n",
+ "########################\n",
+ "\n",
+ "\n",
+ "# Load synthetic chargeability model matching the designated mesh\n",
+ "eta = mesh.readModelUBC(\"VTKout_eta.dat\")\n",
+ "\n",
+ "# Generate true IP data using true conductivity model\n",
+ "actmapIP = Maps.InjectActiveCells(mesh, ~airind, 0.)\n",
+ "problemIP = IP.Problem3D_CC(mesh, rho=1./sigma, etaMap=actmapIP)\n",
+ "problemIP.Solver = PardisoSolver\n",
+ "surveyIP = IP.Survey([src1])\n",
+ "problemIP.pair(surveyIP)\n",
+ "dataIP = surveyIP.dpred(eta[~airind])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Visualize Conductivity and Chargeability Models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def compareTrueModels(indz,indy):\n",
+ " fig = plt.figure(figsize=(11, 8))\n",
+ " sig_min, sig_max = -4, -1\n",
+ " eta_min, eta_max = 0, 1.6\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+yc, 500+yc\n",
+ " zmin, zmax = -500+zc, 0+zc\n",
+ "# indz = 28\n",
+ "# indy = 31\n",
+ " # Axis label\n",
+ " x = np.linspace(xmin+200, xmax-200,3)\n",
+ " matplotlib.rcParams['font.size'] = 10\n",
+ "\n",
+ " # True Conductivity Model z-section\n",
+ " ax1 = plt.subplot(1,1,1)\n",
+ " pos = ax1.get_position()\n",
+ " ax1.set_position([pos.x0-0.1, pos.y0+0.3, pos.width*0.5, pos.height*0.5])\n",
+ " ax1,dat1 = viz(mesh, sigma, indz, airind, normal=\"Z\", xc=xc,yc=yc,zc=zc, ax=ax1, cb=False, clim=(sig_min, sig_max), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax1.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ " ax1.xaxis.set_visible(False)\n",
+ " ax1.set_xlim(xmin, xmax)\n",
+ " ax1.set_ylim(ymin, ymax) \n",
+ " ax1.set_ylabel('Northing (m)')\n",
+ "\n",
+ " # True Conductivity Model y-section\n",
+ " pos = ax1.get_position()\n",
+ " ax2 = fig.add_axes([pos.x0+0.0525, pos.y0 - 0.36, pos.width*0.725, pos.height])\n",
+ " ax2, dat2 = viz(mesh, sigma, indy, airind, normal=\"Y\", xc=xc,yc=yc,zc=zc, ax=ax2, cb=False, clim=(sig_min, sig_max), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax2.set_title((\"Slice at y=%.1f m\")%(mesh.vectorCCy[indy]))\n",
+ " ax2.set_xlim(xmin, xmax)\n",
+ " ax2.set_ylim(zmin, zmax) \n",
+ " ax2.set_xticks(map(int, x))\n",
+ " ax2.set_xticklabels(map(str, map(int, x)),size=12)\n",
+ " ax2.set_xlabel('Easting (m)')\n",
+ " ax2.set_ylabel('Elev. (m)')\n",
+ "\n",
+ " # True Chargeability Model z-section\n",
+ " ax3 = fig.add_axes([pos.x0+0.3, pos.y0, pos.width, pos.height])\n",
+ " ax3,dat3 = ax = viz(mesh, eta*1e3, indz, airind, normal=\"Z\", xc=xc,yc=yc,zc=zc, ax=ax3, cb=False, clim=(eta_min, eta_max), scale=\"linear\", pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax3.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ " ax3.xaxis.set_visible(False)\n",
+ " ax3.yaxis.set_visible(False)\n",
+ " ax3.set_xlim(xmin, xmax)\n",
+ " ax3.set_ylim(ymin, ymax)\n",
+ "\n",
+ " # True Chargeability Model y-section\n",
+ " ax4 = fig.add_axes([pos.x0+0.3525, pos.y0 - 0.36, pos.width*0.725, pos.height])\n",
+ " ax4, dat4 = viz(mesh, eta*1e3, indy, airind, normal=\"Y\", xc=xc,yc=yc,zc=zc, ax=ax4, cb=False, clim=(eta_min, eta_max), scale=\"linear\", pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax4.set_title((\"Slice at y=%.1f m\")%(mesh.vectorCCy[indy]))\n",
+ " ax4.yaxis.set_visible(False)\n",
+ " ax4.set_xlim(xmin, xmax)\n",
+ " ax4.set_ylim(zmin, zmax) \n",
+ " ax4.set_xticks(map(int, x))\n",
+ " ax4.set_xticklabels(map(str, map(int, x)),size=12)\n",
+ " ax4.set_xlabel('Easting (m)')\n",
+ "\n",
+ " pos = ax2.get_position()\n",
+ " cbarax1 = fig.add_axes([pos.x0+0.02 , pos.y0-0.01 , pos.width-0.05, pos.height*0.05]) ## the parameters are the specified position you set\n",
+ " cb1 = fig.colorbar(dat2[0],cax=cbarax1, orientation=\"horizontal\", ax = cbarax1, ticks=np.linspace(sig_min,sig_max, 4), format=\"1.e%.f\")\n",
+ " cb1.set_label(\"Conductivity (S/m)\",size=12)\n",
+ " \n",
+ " pos = ax4.get_position()\n",
+ " cbarax2 = fig.add_axes([pos.x0+0.02 , pos.y0-0.01 , pos.width-0.05, pos.height*0.05]) ## the parameters are the specified position you set\n",
+ " cb2 = fig.colorbar(dat4[0],cax=cbarax2, orientation=\"horizontal\", ax = cbarax2, ticks=np.linspace(eta_min,eta_max, 4), format=\"1.e%.f\")\n",
+ " cb2.set_label(\"Chargeability (ms)\",size=12)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def viz(mesh, sigma, ind, airind, normal=\"Z\", ax=None, label=\"Conductivity (S/m)\", scale=\"log\", clim=(-4, -1), xc=0, yc=0,zc=0., cb=True, pltshow=True):\n",
+ " if normal == \"Z\":\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 5))\n",
+ " ax = plt.subplot(111)\n",
+ " else:\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 2.5))\n",
+ " ax = plt.subplot(111)\n",
+ "\n",
+ " temp = sigma.copy()\n",
+ "\n",
+ " if scale == \"log\":\n",
+ " temp = np.log10(temp)\n",
+ "\n",
+ " temp[airind] = np.nan\n",
+ "\n",
+ " dat = mesh.plotSlice(temp, ind=ind, clim=clim, normal=normal, grid=False, pcolorOpts={\"cmap\":\"viridis\"}, ax=ax)\n",
+ " if normal == \"Z\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Northing (m)\")\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+yc, 500.+yc\n",
+ " ax.set_title((\"Elevation at %.1f m\")%(mesh.vectorCCz[ind]))\n",
+ " elif normal == \"Y\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Elevation (m)\")\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+zc, 0.+zc\n",
+ " ax.set_title((\"Northing at %.1f m\")%(mesh.vectorCCy[ind]))\n",
+ "\n",
+ " ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ " ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ " ax.set_xlim(xmin, xmax)\n",
+ " ax.set_ylim(ymin, ymax)\n",
+ " # plt.tight_layout()\n",
+ "\n",
+ " if scale == \"log\":\n",
+ " cbformat = \"$10^{%1.1f}$\"\n",
+ " elif scale == \"linear\":\n",
+ " cbformat = \"%.1e\"\n",
+ "\n",
+ " if clim is None:\n",
+ " vmin, vmax = dat[0].get_clim()\n",
+ " else:\n",
+ " vmin, vmax = clim[0], clim[1]\n",
+ "\n",
+ " if cb:\n",
+ " cb = plt.colorbar(dat[0], format=cbformat, ticks=np.linspace(vmin, vmax, 3))\n",
+ " cb.set_label(label)\n",
+ " if pltshow:\n",
+ " plt.show()\n",
+ " return ax, dat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1fa2f50a2e184e71b570c5e4da1c8343"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ">"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interact(lambda indz,indy: compareTrueModels(indz,indy), indz=IntSlider(min=0, max=mesh.nCz-1,step=1, value=28), indy=IntSlider(min=0, max=mesh.nCy-1,step=1, value=31))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### View electrode geometry for Gradient arrray\n",
+ "The survey geometry is shared with the DC problem. This simple survey is comprised of a single Tx electrode pair (A and B electrodes) with 552 associated Rx electrode pairs (M and N). \n",
+ "\n",
+ "- A and B electrode are separeted by 1200m in the E-W direction.\n",
+ "- M and N electrodes have 50m sepatation distances and are also oriented in the E-W direction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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74/7zpUfHaX87GzZsIDExkdmzZ+Pk5ETv3r3p0qULhw8f5tMFn3I77TaD+g1i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\nrkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1,1, figsize=(5*1.2,5))\n",
+ "plt.xlabel(\"Easting (m)\"); plt.ylabel(\"Northing (m)\")\n",
+ "ax.plot(Aloc1_x[0], Aloc1_x[1], 'bo', ms=5)\n",
+ "ax.plot(Bloc1_x[0], Bloc1_x[1], 'ro', ms=5)\n",
+ "ax.scatter(Mx[:,0], Mx[:,1],s=20, edgecolors=\"k\", facecolors=\"none\")\n",
+ "ax.scatter(Nx[:,0], Nx[:,1],s=5, edgecolors=\"none\", facecolors=\"g\")\n",
+ "ax.legend((\"A\", \"B\", \"M\", \"N\"), fontsize=12)\n",
+ "xmin, xmax = -700+xc, 700+xc\n",
+ "ymin, ymax = -700+yc, 700+yc\n",
+ "ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ "ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ "ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ "ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ "ax.axis('equal')\n",
+ "ax.set_xlim(xmin, xmax)\n",
+ "ax.set_ylim(ymin, ymax)\n",
+ "ax.set_xlabel(\"Easting (m)\")\n",
+ "ax.set_ylabel(\"Northing (m)\") \n",
+ "ax.set_title(\"Gradient array (DC-IP survey)\")\n",
+ "ax.grid(True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot IP data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def vizdata(data, src, rx, rxcomponent=\"X\", clim=None, title=None, figsave=None, ax=None, cb=True,cbOrient=\"vertical\"):\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(6.8, 5.5))\n",
+ " ax = plt.subplot(111)\n",
+ "\n",
+ " temp = data[src, rx]\n",
+ " if rxcomponent==\"X\":\n",
+ " X = Xx.copy()\n",
+ " Y = Yx.copy()\n",
+ " else:\n",
+ " X = Xy.copy()\n",
+ " Y = Yy.copy()\n",
+ " temp = temp.reshape(X.shape, order=\"F\")\n",
+ " if clim is not None:\n",
+ " vmin, vmax = clim[0], clim[1]\n",
+ " dat = ax.contourf(X, Y, temp, 20, clim=clim, vmin=vmin, vmax=vmax, cmap=\"plasma\")\n",
+ " ax.contour(X, Y, temp, 10, colors=\"k\")\n",
+ " else:\n",
+ " vmin, vmax = temp.min(), temp.max()\n",
+ " dat = ax.contourf(X, Y, temp, 20, cmap=\"plasma\")\n",
+ " ax.contour(X, Y, temp, 10, colors=\"k\")\n",
+ " \n",
+ " if title is not None:\n",
+ " ax.set_title(title)\n",
+ " xmin, xmax = X.min(), X.max()\n",
+ " ymin, ymax = Y.min(), Y.max() \n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Northing (m)\")\n",
+ " ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ " ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ " ax.set_xlim(xmin, xmax)\n",
+ " ax.set_ylim(ymin, ymax) \n",
+ " \n",
+ " if cb:\n",
+ " cb = plt.colorbar(dat, orientation=cbOrient, ticks=np.linspace(vmin, vmax, 3), format=\"%.1e\")\n",
+ " cb.set_label(\"Potential (V)\")\n",
+ " \n",
+ " if figsave is not None:\n",
+ " fig.savefig(figsave, dpi=200)\n",
+ " return ax"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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AZh+Hnm6Mea8rdSullAouA0FLPKUCR4Ncpbq5PXV1PLpqCc+sW4kxhusmjOC6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\nswfMrE1ImupHWjK1YcGiNitY1EaJRIGC3OZnAd69ry4EbgRyg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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Evaluate data at Rx locations\n",
+ "IPobsData = Survey.Data(surveyIP, v=dataIP)\n",
+ "Xx = 0.5*(Mx[:,0]+Nx[:,0]).reshape((23, 24), order=\"F\")\n",
+ "Yx = Mx[:,1].reshape((23, 24), order=\"F\")\n",
+ "\n",
+ "fig = plt.figure(figsize=(11, 5.5))\n",
+ "xmin, xmax = -300+xc, 300+xc\n",
+ "ymin, ymax = -300+yc, 300+yc\n",
+ "indz = 28\n",
+ "matplotlib.rcParams['font.size'] = 14\n",
+ "# Axis label\n",
+ "x = np.linspace(xmin+100, xmax-100,3)\n",
+ "\n",
+ "# IP observed data\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "pos = ax1.get_position()\n",
+ "ax1.set_position([pos.x0, pos.y0, pos.width*0.5, pos.height])\n",
+ "ax1 = vizdata(IPobsData, src1, rx_x, ax=ax1, rxcomponent=\"X\", title=\"Observed IP Data\",clim=(-2e-5,2e-5))\n",
+ "plt.gca().set_aspect('equal')\n",
+ "\n",
+ "ax1.set_xticks(map(int, x))\n",
+ "ax1.set_xticklabels(map(str, map(int, x)),size=14)\n",
+ "ax1.set_xlabel('Easting (m)')\n",
+ "ax1.set_ylabel('Northing (m)')\n",
+ "\n",
+ "# True Chargeability model\n",
+ "ax2 = fig.add_axes([pos.x0+0.55, pos.y0, pos.width, pos.height])\n",
+ "ax2,dat2 = viz(mesh, eta*1e3, indz, airind, normal=\"Z\", xc=xc, yc=yc, zc=zc, ax=ax2, label=\"Chargeability (ms)\", scale=\"linear\", clim=(0, 1.6), pltshow=False)\n",
+ "plt.gca().set_aspect('equal')\n",
+ "ax2.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ "ax2.yaxis.set_visible(False)\n",
+ "ax2.set_xlim(xmin, xmax)\n",
+ "ax2.set_ylim(ymin, ymax)\n",
+ "ax2.set_xticks(map(int, x))\n",
+ "ax2.set_xticklabels(map(str, map(int, x)),size=14)\n",
+ "ax2.set_xlabel('Easting (m)')\n",
+ "ax2.set_title('Chargeability Model')"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "kernelspec": {
+ "display_name": "Python [default]",
+ "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": "2.7.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/case-studies/DCIP/TKC_IP_inv.ipynb b/docs/case-studies/DCIP/TKC_IP_inv.ipynb
new file mode 100644
index 0000000..d021a47
--- /dev/null
+++ b/docs/case-studies/DCIP/TKC_IP_inv.ipynb
@@ -0,0 +1,551 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from ipywidgets import interact, IntSlider\n",
+ "import numpy as np\n",
+ "%matplotlib inline\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import FormatStrFormatter\n",
+ "matplotlib.rcParams['font.size'] = 14"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Inverse Problem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from SimPEG import Mesh, Utils, Maps, Survey\n",
+ "from SimPEG.EM.Static import DC, IP, Utils as StaticUtils\n",
+ "from SimPEG import DataMisfit, Regularization, Optimization, Directives, InvProblem, Inversion\n",
+ "from pymatsolver import PardisoSolver\n",
+ "\n",
+ "# Setup tensor mesh\n",
+ "\n",
+ "# Core cell sizes in x, y, and z\n",
+ "csx, csy, csz = 25., 25., 25.\n",
+ "# Number of core cells in each direction\n",
+ "ncx, ncy, ncz = 48, 48, 20\n",
+ "# Number of padding cells to add in each direction\n",
+ "npad = 7\n",
+ "# Vectors of cell lengthts in each direction\n",
+ "hx = [(csx,npad, -1.3),(csx,ncx),(csx,npad, 1.3)]\n",
+ "hy = [(csy,npad, -1.3),(csy,ncy),(csy,npad, 1.3)]\n",
+ "hz = [(csz,npad, -1.3),(csz,ncz), (csz/2.,6)]\n",
+ "# Create mesh\n",
+ "mesh = Mesh.TensorMesh([hx, hy, hz],x0=\"CCN\")\n",
+ "# Map mesh coordinates from local to UTM coordiantes\n",
+ "xc = 300+5.57e5\n",
+ "yc = 600+7.133e6\n",
+ "zc = 425.\n",
+ "mesh._x0 = mesh.x0 + np.r_[xc, yc, zc]\n",
+ "\n",
+ "# Load synthetic conductivity model matching the designated mesh\n",
+ "sigma = mesh.readModelUBC(\"VTKout_DC.dat\")\n",
+ "eta = mesh.readModelUBC(\"VTKout_eta.dat\")\n",
+ "# Identify air cells in the model\n",
+ "airind = sigma == 1e-8\n",
+ "\n",
+ "# Obtain topographic surface from 3D conductivity model\n",
+ "mesh2D, topoCC = StaticUtils.gettopoCC(mesh, airind)\n",
+ "\n",
+ "\n",
+ "# Setup gradient array survey\n",
+ "\n",
+ "# Define the source electrode locations\n",
+ "# Here we use a single dipole source (A-B electrode) in the x-direction)\n",
+ "Aloc1_x = np.r_[-600., 0, 0.] + np.r_[xc, yc, zc]\n",
+ "Bloc1_x = np.r_[600., 0, 0.] + np.r_[xc, yc, zc]\n",
+ "\n",
+ "# Rx locations (M-N electrodes, x-direction)\n",
+ "# Find x and y cell centres in the interior portion of the mesh\n",
+ "x = mesh.vectorCCx[np.logical_and(mesh.vectorCCx>-300.+ xc, mesh.vectorCCx<300.+ xc)]\n",
+ "y = mesh.vectorCCy[np.logical_and(mesh.vectorCCy>-300.+ yc, mesh.vectorCCy<300.+ yc)]\n",
+ "# Grid selected cell centres to get M and N Rx electrode locations\n",
+ "Mx = Utils.ndgrid(x[:-1], y, np.r_[-12.5/2.])\n",
+ "Nx = Utils.ndgrid(x[1:], y, np.r_[-12.5/2.])\n",
+ "# Get cell ind for electrode locations to extract electrode elevation from topoCC\n",
+ "inds_Mx = Utils.closestPoints(mesh2D, Mx[:,:2])\n",
+ "inds_Nx = Utils.closestPoints(mesh2D, Nx[:,:2])\n",
+ "# Draped M and N electrode x,y,z locations\n",
+ "Mx_dr = np.c_[Mx[:,0], Mx[:,1], topoCC[inds_Mx]]\n",
+ "Nx_dr = np.c_[Nx[:,0], Nx[:,1], topoCC[inds_Nx]]\n",
+ "\n",
+ "# Create Src and Rx classes for DC problem\n",
+ "rx_x = DC.Rx.Dipole(Mx_dr, Nx_dr)\n",
+ "src1 = DC.Src.Dipole([rx_x], Aloc1_x, Bloc1_x)\n",
+ "\n",
+ "# Use estimated conductivity model to compute sensitivity function\n",
+ "mapping = Maps.InjectActiveCells(mesh, ~airind, 0.)\n",
+ "problemIP = IP.Problem3D_CC(mesh, sigma=sigma, etaMap=mapping)\n",
+ "problemIP.Solver = PardisoSolver\n",
+ "surveyIP = IP.Survey([src1])\n",
+ "problemIP.pair(surveyIP)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Compute predicted data\n",
+ "IPdobs = surveyIP.dpred(eta[~airind])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "IPobsData = Survey.Data(surveyIP, v=IPdobs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SimPEG.InvProblem will set Regularization.mref to m0.\n",
+ "\n",
+ " SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
+ " ***Done using same Solver and solverOpts as the problem***\n",
+ "SimPEG.SaveOutputEveryIteration will save your inversion progress as: '###-InversionModel-2017-07-27-11-50.txt'\n",
+ "SimPEG.SaveModelEveryIteration will save your models as: '###-InversionModel-2017-07-27-11-50.npy'\n",
+ "model has any nan: 0\n",
+ "=============================== Projected GNCG ===============================\n",
+ " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
+ "-----------------------------------------------------------------------------\n",
+ "x0 has any nan: 0\n",
+ " 0 3.22e+09 2.33e+05 0.00e+00 2.33e+05 8.72e+07 0 \n",
+ " 1 3.22e+09 1.12e+04 7.77e-07 1.37e+04 7.55e+06 0 \n",
+ " 2 3.22e+09 6.34e+03 1.12e-06 9.96e+03 1.97e+06 0 Skip BFGS \n",
+ " 3 6.44e+08 5.57e+03 1.23e-06 6.37e+03 1.96e+06 0 \n",
+ " 4 6.44e+08 3.24e+03 2.65e-06 4.95e+03 1.50e+06 0 \n",
+ " 5 6.44e+08 2.94e+03 2.79e-06 4.74e+03 1.12e+06 0 \n",
+ " 6 1.29e+08 2.74e+03 2.97e-06 3.12e+03 7.79e+05 0 \n",
+ " 7 1.29e+08 2.03e+03 4.47e-06 2.61e+03 2.35e+06 0 \n",
+ " 8 1.29e+08 1.33e+03 6.86e-06 2.21e+03 2.26e+06 0 \n",
+ " 9 2.58e+07 1.11e+03 6.92e-06 1.29e+03 1.06e+06 0 \n",
+ " 10 2.58e+07 7.10e+02 9.99e-06 9.67e+02 1.42e+06 0 \n",
+ " 11 2.58e+07 5.85e+02 1.03e-05 8.50e+02 1.60e+06 0 \n",
+ " 12 5.15e+06 5.13e+02 1.08e-05 5.69e+02 6.67e+05 0 \n",
+ " 13 5.15e+06 4.74e+02 1.14e-05 5.33e+02 8.07e+05 0 \n",
+ " 14 5.15e+06 3.76e+02 1.35e-05 4.45e+02 1.17e+06 0 Skip BFGS \n",
+ " 15 1.03e+06 3.41e+02 1.33e-05 3.55e+02 8.47e+05 0 \n",
+ " 16 1.03e+06 2.89e+02 1.43e-05 3.04e+02 7.39e+05 0 Skip BFGS \n",
+ "------------------------- STOP! -------------------------\n",
+ "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 2.3327e+04\n",
+ "1 : |xc-x_last| = 2.2992e-04 <= tolX*(1+|x0|) = 1.0354e-01\n",
+ "0 : |proj(x-g)-x| = 7.3897e+05 <= tolG = 1.0000e-01\n",
+ "0 : |proj(x-g)-x| = 7.3897e+05 <= 1e3*eps = 1.0000e-02\n",
+ "0 : maxIter = 30 <= iter = 17\n",
+ "------------------------- DONE! -------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Setup IP inversion\n",
+ "\n",
+ "# Depth weighting\n",
+ "depth = 1./(abs(mesh.gridCC[:,2]-zc))**1.5\n",
+ "depth = depth/depth.max()\n",
+ "\n",
+ "# Assign uncertainties\n",
+ "std = 0.\n",
+ "eps = abs(IPdobs).max()*0.02\n",
+ "surveyIP.std = std\n",
+ "surveyIP.eps = eps\n",
+ "# Define initial and reference model\n",
+ "m0 = np.ones(mesh.nC)[~airind]*1e-4\n",
+ "\n",
+ "# Setup inversion object\n",
+ "regmap = Maps.IdentityMap(nP=m0.size)\n",
+ "# Set observed data for inversion object\n",
+ "surveyIP.dobs = IPdobs\n",
+ "# Define datamisfit portion of objective function\n",
+ "dmisfit = DataMisfit.l2_DataMisfit(surveyIP)\n",
+ "# Define regulatization (model objective function)\n",
+ "reg = Regularization.Simple(mesh, mapping=regmap, indActive=~airind, cell_weights=depth[~airind])\n",
+ "# reg.wght = weight\n",
+ "opt = Optimization.ProjectedGNCG(maxIter = 30)\n",
+ "opt.lower = 0.\n",
+ "invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)\n",
+ "# Define inversion parameters\n",
+ "beta = Directives.BetaSchedule(coolingFactor=5, coolingRate=3)\n",
+ "betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1.)\n",
+ "save = Directives.SaveOutputEveryIteration()\n",
+ "target = Directives.TargetMisfit()\n",
+ "\n",
+ "savemodel = Directives.SaveModelEveryIteration()\n",
+ "inv = Inversion.BaseInversion(invProb, directiveList=[betaest, beta, save, target, savemodel])\n",
+ "reg.alpha_s = 1.\n",
+ "reg.alpha_x = 1.\n",
+ "reg.alpha_y = 1.\n",
+ "reg.alpha_z = 1.\n",
+ "problemIP.counter = opt.counter = Utils.Counter()\n",
+ "opt.LSshorten = 0.5\n",
+ "opt.remember('xc')\n",
+ "\n",
+ "# Run IP inversion\n",
+ "mIPopt = inv.run(m0)\n",
+ "\n",
+ "# Apply mapping to model to get and save recovered conductivity\n",
+ "etaopt = mapping*mIPopt\n",
+ "\n",
+ "# Calculate dpred\n",
+ "IPdpred = invProb.dpred\n",
+ "\n",
+ "# Set observed and predicted data as data objects\n",
+ "IPpredData = Survey.Data(surveyIP, v=invProb.dpred)\n",
+ "IPobsData = Survey.Data(surveyIP, v=IPdobs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "### Plot true and recovered conductivity models to assess how well anomalous structures are recovered."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def compareTrueAndRecoveredModels(indz,indy):\n",
+ " fig = plt.figure(figsize=(11, 8))\n",
+ " vmin, vmax = 0., 1.6\n",
+ " xmin, xmax = -700+xc, 700+xc\n",
+ " ymin, ymax = -700+yc, 700+yc\n",
+ " zmin, zmax = -700+zc, 0+zc\n",
+ "# indz = 28\n",
+ "# indy = 31\n",
+ " # Axis label\n",
+ " x = np.linspace(xmin+200, xmax-200,3)\n",
+ "\n",
+ " # True Chargeability Model z-section\n",
+ " ax1 = plt.subplot(1,1,1)\n",
+ " pos = ax1.get_position()\n",
+ " ax1.set_position([pos.x0-0.1, pos.y0+0.3, pos.width*0.5, pos.height*0.5])\n",
+ " ax1,dat1 = viz(mesh, eta*1e3, indz, airind, normal=\"Z\", xc=xc,yc=yc,zc=zc, ax=ax1, scale=\"linear\", cb=False, clim=(vmin, vmax), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax1.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ " ax1.xaxis.set_visible(False)\n",
+ " ax1.set_xlim(xmin, xmax)\n",
+ " ax1.set_ylim(ymin, ymax) \n",
+ " ax1.set_ylabel('Northing (m)')\n",
+ "\n",
+ " # True Chargeability Model y-section\n",
+ " pos = ax1.get_position()\n",
+ " ax2 = fig.add_axes([pos.x0+0.0525, pos.y0 - 0.36, pos.width*0.725, pos.height])\n",
+ " ax2, dat2 = viz(mesh, eta*1e3, indy, airind, normal=\"Y\", xc=xc,yc=yc,zc=zc, ax=ax2, scale=\"linear\", cb=False, clim=(vmin, vmax), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax2.set_title((\"Slice at y=%.1f m\")%(mesh.vectorCCy[indy]))\n",
+ " ax2.set_xlim(xmin, xmax)\n",
+ " ax2.set_ylim(zmin, zmax) \n",
+ " ax2.set_xticks(map(int, x))\n",
+ " ax2.set_xticklabels(map(str, map(int, x)),size=12)\n",
+ " ax2.set_xlabel('Easting (m)')\n",
+ " ax2.set_ylabel('Elev. (m)')\n",
+ "\n",
+ " # IP Recovered Model z-section\n",
+ " ax3 = fig.add_axes([pos.x0+0.3, pos.y0, pos.width, pos.height])\n",
+ " ax3,dat3 = ax = viz(mesh, etaopt*1e3, indz, airind, normal=\"Z\", xc=xc,yc=yc,zc=zc, ax=ax3, scale=\"linear\", cb=False, clim=(vmin, vmax), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax3.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ " ax3.xaxis.set_visible(False)\n",
+ " ax3.yaxis.set_visible(False)\n",
+ " ax3.set_xlim(xmin, xmax)\n",
+ " ax3.set_ylim(ymin, ymax)\n",
+ "\n",
+ " # IP Recovered Model y-section\n",
+ " ax4 = fig.add_axes([pos.x0+0.3525, pos.y0 - 0.36, pos.width*0.725, pos.height])\n",
+ " ax4, dat4 = viz(mesh, etaopt*1e3, indy, airind, normal=\"Y\", xc=xc,yc=yc,zc=zc, ax=ax4, scale=\"linear\", cb=False, clim=(vmin, vmax), pltshow=False)\n",
+ " plt.gca().set_aspect('equal')\n",
+ " ax4.set_title((\"Slice at y=%.1f m\")%(mesh.vectorCCy[indy]))\n",
+ " ax4.yaxis.set_visible(False)\n",
+ " ax4.set_xlim(xmin, xmax)\n",
+ " ax4.set_ylim(zmin, zmax) \n",
+ " ax4.set_xticks(map(int, x))\n",
+ " ax4.set_xticklabels(map(str, map(int, x)),size=12)\n",
+ " ax4.set_xlabel('Easting (m)')\n",
+ "\n",
+ " pos = ax4.get_position()\n",
+ " cbarax = fig.add_axes([pos.x0+0.3 , pos.y0+0.095 , pos.width*0.1, pos.height*1.7]) ## the parameters are the specified position you set\n",
+ " cb = fig.colorbar(dat1[0],cax=cbarax, orientation=\"vertical\", ax = ax4, ticks=np.linspace(vmin,vmax, 4))\n",
+ "\n",
+ " cb.set_label(\"Chargeability (ms)\",size=12)\n",
+ " # fig.savefig('MAG_RecModel.png',dpi = 200)\n",
+ " plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def viz(mesh, sigma, ind, airind, normal=\"Z\", ax=None, label=\"Conductivity (S/m)\", scale=\"log\", clim=(-4, -1), xc=0, yc=0,zc=0., cb=True, pltshow=True):\n",
+ " if normal == \"Z\":\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 5))\n",
+ " ax = plt.subplot(111)\n",
+ " else:\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(5*1.2, 2.5))\n",
+ " ax = plt.subplot(111)\n",
+ "\n",
+ " temp = sigma.copy()\n",
+ "\n",
+ " if scale == \"log\":\n",
+ " temp = np.log10(temp)\n",
+ "\n",
+ " temp[airind] = np.nan\n",
+ "\n",
+ " dat = mesh.plotSlice(temp, ind=ind, clim=clim, normal=normal, grid=False, pcolorOpts={\"cmap\":\"viridis\"}, ax=ax)\n",
+ " if normal == \"Z\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Northing (m)\")\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+yc, 500.+yc\n",
+ " ax.set_title((\"Elevation at %.1f m\")%(mesh.vectorCCz[ind]))\n",
+ " elif normal == \"Y\":\n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Elevation (m)\")\n",
+ " xmin, xmax = -500+xc, 500+xc\n",
+ " ymin, ymax = -500+zc, 0.+zc\n",
+ " ax.set_title((\"Northing at %.1f m\")%(mesh.vectorCCy[ind]))\n",
+ "\n",
+ " ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ " ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ " ax.set_xlim(xmin, xmax)\n",
+ " ax.set_ylim(ymin, ymax)\n",
+ " # plt.tight_layout()\n",
+ "\n",
+ " if scale == \"log\":\n",
+ " cbformat = \"$10^{%1.1f}$\"\n",
+ " elif scale == \"linear\":\n",
+ " cbformat = \"%.1e\"\n",
+ "\n",
+ " if clim is None:\n",
+ " vmin, vmax = dat[0].get_clim()\n",
+ " else:\n",
+ " vmin, vmax = clim[0], clim[1]\n",
+ "\n",
+ " if cb:\n",
+ " cb = plt.colorbar(dat[0], format=cbformat, ticks=np.linspace(vmin, vmax, 3))\n",
+ " cb.set_label(label)\n",
+ " if pltshow:\n",
+ " plt.show()\n",
+ " return ax, dat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9f5d0695042744a1a60aa7acc90d1590"
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ ">"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interact(lambda indz,indy: compareTrueAndRecoveredModels(indz,indy), indz=IntSlider(min=0, max=mesh.nCz-1,step=1, value=28), indy=IntSlider(min=0, max=mesh.nCy-1,step=1, value=31))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot observed and predicted data to evaluate how well the recovered model fits the observed data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def vizdata(data, src, rx, rxcomponent=\"X\", clim=None, title=None, figsave=None, ax=None, cb=True,cbOrient=\"vertical\"):\n",
+ " if ax is None:\n",
+ " fig = plt.figure(figsize=(6.8, 5.5))\n",
+ " ax = plt.subplot(111)\n",
+ "\n",
+ " temp = data[src, rx]\n",
+ " if rxcomponent==\"X\":\n",
+ " X = Xx.copy()\n",
+ " Y = Yx.copy()\n",
+ " else:\n",
+ " X = Xy.copy()\n",
+ " Y = Yy.copy()\n",
+ " temp = temp.reshape(X.shape, order=\"F\")\n",
+ " if clim is not None:\n",
+ " vmin, vmax = clim[0], clim[1]\n",
+ " dat = ax.contourf(X, Y, temp, 20, clim=clim, vmin=vmin, vmax=vmax, cmap=\"plasma\")\n",
+ " ax.contour(X, Y, temp, 10, colors=\"k\")\n",
+ " else:\n",
+ " vmin, vmax = temp.min(), temp.max()\n",
+ " dat = ax.contourf(X, Y, temp, 20, cmap=\"plasma\")\n",
+ " ax.contour(X, Y, temp, 10, colors=\"k\")\n",
+ " \n",
+ " if title is not None:\n",
+ " ax.set_title(title)\n",
+ " xmin, xmax = X.min(), X.max()\n",
+ " ymin, ymax = Y.min(), Y.max() \n",
+ " ax.set_xlabel(\"Easting (m)\")\n",
+ " ax.set_ylabel(\"Northing (m)\")\n",
+ " ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+ " ax.set_xticks(np.linspace(xmin, xmax, 3))\n",
+ " ax.set_yticks(np.linspace(ymin, ymax, 3))\n",
+ " ax.set_xlim(xmin, xmax)\n",
+ " ax.set_ylim(ymin, ymax) \n",
+ " \n",
+ " if cb:\n",
+ " cb = plt.colorbar(dat, orientation=cbOrient, ticks=np.linspace(vmin, vmax, 3), format=\"%.1e\")\n",
+ " cb.set_label(\"Potential (V)\")\n",
+ " \n",
+ " if figsave is not None:\n",
+ " fig.savefig(figsave, dpi=200)\n",
+ " return ax"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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/bNnNj8UFTE3tcey8KyIOjKQp41OT+LHEN9s4AJTVVbOqJIdf9ZiEnx4003QQ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\nAxeVTZsKnJiD5JIqFDKBEPf250e0hR5+9gyLtOerzaWNfdEsyYBQexJ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nl+dQ19A1wmvb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uT9s7W0Z4IH+6YBx3r9zFn9cW87MF6U6df+n4CNb8bBQBOhXjUgJRO7GRtyMcaZOtNoVlbx/g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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize(11, 5.5))\n",
+ "xmin, xmax = -300+xc, 300+xc\n",
+ "ymin, ymax = -300+yc, 300+yc\n",
+ "indz = 28\n",
+ "\n",
+ "# Axis label\n",
+ "x = np.linspace(xmin+100, xmax-100,3)\n",
+ "Xx = 0.5*(Mx[:,0]+Nx[:,0]).reshape((23, 24), order=\"F\")\n",
+ "Yx = Mx[:,1].reshape((23, 24), order=\"F\")\n",
+ "\n",
+ "# DC observed data\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "pos = ax1.get_position()\n",
+ "ax1.set_position([pos.x0-0.4, pos.y0, pos.width, pos.height])\n",
+ "ax1 = vizdata(IPobsData, src1, rx_x, ax=ax1, rxcomponent=\"X\", title=\"Observed IP Data\", cb=False,clim=(-2e-5,2e-5))\n",
+ "plt.gca().set_aspect('equal')\n",
+ "# ax1.set_title((\"Slice at z=%.1f m\")%(mesh.vectorCCz[indz]))\n",
+ "# ax1.xaxis.set_visible(False)\n",
+ "ax1.set_xlim(xmin, xmax)\n",
+ "ax1.set_ylim(ymin, ymax) \n",
+ "ax1.set_xticks(map(int, x))\n",
+ "ax1.set_xticklabels(map(str, map(int, x)),size=14)\n",
+ "ax1.set_xlabel('Easting (m)')\n",
+ "ax1.set_ylabel('Northing (m)')\n",
+ "\n",
+ "# DC Predicted data\n",
+ "ax2 = fig.add_axes([pos.x0, pos.y0, pos.width, pos.height])\n",
+ "ax2 = vizdata(IPpredData, src1, rx_x, ax=ax2, rxcomponent=\"X\", title=\"Predicted IP Data\",clim=(-2e-5,2e-5))\n",
+ "plt.gca().set_aspect('equal')\n",
+ "ax2.yaxis.set_visible(False)\n",
+ "ax2.set_xlim(xmin, xmax)\n",
+ "ax2.set_ylim(ymin, ymax)\n",
+ "ax2.set_xticks(map(int, x))\n",
+ "ax2.set_xticklabels(map(str, map(int, x)),size=14)\n",
+ "ax2.set_xlabel('Easting (m)')"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
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diff --git a/docs/case-studies/DCIP/VTKout_DC.dat b/docs/case-studies/DCIP/VTKout_DC.dat
new file mode 100644
index 0000000..b12f66d
--- /dev/null
+++ b/docs/case-studies/DCIP/VTKout_DC.dat
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diff --git a/docs/case-studies/DCIP/VTKout_eta.dat b/docs/case-studies/DCIP/VTKout_eta.dat
new file mode 100644
index 0000000..c764b03
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