diff --git a/notebooks/2DinvDC.ipynb b/notebooks/2DinvDC.ipynb
new file mode 100644
index 00000000..d096edb3
--- /dev/null
+++ b/notebooks/2DinvDC.ipynb
@@ -0,0 +1,545 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from SimPEG import *\n",
+    "import simpegDCIP as DC\n",
+    "from numpy.polynomial import polynomial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: pylab import has clobbered these variables: ['linalg', 'inv']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cs = 0.5\n",
+    "mesh = Mesh.TensorMesh([np.ones(100)*cs, np.ones(50)*cs], \"CN\")\n",
+    "x = mesh.vectorCCx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "actx = (x>-15.)&(x<15.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "order = 3\n",
+    "Vobs = polynomial.polyvander(x, order)\n",
+    "dobs = 0.05*x-4\n",
+    "H = np.dot(Vobs.T, Vobs)\n",
+    "g = np.dot(Vobs.T, dobs)\n",
+    "mest = np.linalg.solve(H, g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dpred = Vobs.dot(mest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "V = polynomial.polyvander(x, order)\n",
+    "m1D = Mesh.TensorMesh([V.shape[1]+2])\n",
+    "weight = (1./(V**2).sum(axis=0))**0.5\n",
+    "weight = weight / weight.max()\n",
+    "weightmap = Maps.Weighting(m1D, weights=np.r_[1., 1., weight])\n",
+    "m0_poly = np.r_[np.log(1e-3), np.log(1e-3), np.r_[-3., np.zeros(V.shape[1]-1)] / weight]\n",
+    "mtrue_poly = np.r_[np.log(1e-3), np.log(1e0), mest / weight]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "polymap = Maps.PolyMap(mesh, V.shape[1]-1, logSigma=True, normal='Y')\n",
+    "mappingfwd = polymap*weightmap\n",
+    "mapping = Maps.ExpMap(mesh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sigma = mappingfwd*mtrue_poly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "xr = np.linspace(-15, 15, 20)\n",
+    "xz_A = Utils.ndgrid(xr, np.r_[-0.25])\n",
+    "xz_B = Utils.ndgrid(np.ones_like(xr)*22, np.r_[-0.25])\n",
+    "xz_M = Utils.ndgrid(xr, np.r_[-0.25])\n",
+    "xz_N = Utils.ndgrid(np.ones_like(xr)*-22, np.r_[-0.25])\n",
+    "\n",
+    "ntx = xz_A.shape[0]\n",
+    "txList = []\n",
+    "for i in range(ntx):\n",
+    "    offset = abs(xz_A[i,0]-xz_M[:,0])\n",
+    "    actrx = offset > 5.\n",
+    "    rx = DC.RxDipole(xz_M[actrx,:], xz_N[actrx,:])\n",
+    "    src = DC.SrcDipole([rx], xz_A[i,:], xz_B[i,:])\n",
+    "    txList.append(src)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10c7a9210>]"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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wXNtmVhV9ep97bhZGxB5gT/b5F5I2A9OAjuFeyLBMjlnZkMyQpPcWXYyZGTTCPWXpp2yq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\n5HA3M6uh/w9x5JF4EBHHlQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ab76b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dat = mesh.plotImage(np.log10(sigma), clim=(-2, 0))\n",
+    "plt.colorbar(dat[0])\n",
+    "plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
+    "plot(xz_B[:,0], xz_B[:,1], 'y.')\n",
+    "plot(xz_N[:,0], xz_N[:,1], 'r.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10c039b50>]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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YJsecbEhmSNL7iy7GzAwa4Z6y9FM21d55wBN5bft25d7jjN97gVkR8Wo2/rRG0rkR8Yt+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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bfe44d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dat = mesh.plotImage(np.log10(mappingfwd*m0), clim=(-2, 0))\n",
+    "plt.colorbar(dat[0])\n",
+    "plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
+    "plot(xz_B[:,0], xz_B[:,1], 'y.')\n",
+    "plot(xz_N[:,0], xz_N[:,1], 'r.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from pymatsolver import MumpsSolver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10d8c2d90>]"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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phKJH9wyMKILpl/CI6IECY5NOJJ2cdqZCEQqGktrbvSbpsJ19SV6JJzv72vER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\nuA2o7hsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10cd0a810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mtrue = np.log(sigma)\n",
+    "m0 = np.log(np.ones(mesh.nC)*1e-3)\n",
+    "survey = DC.SurveyDC(txList)\n",
+    "problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
+    "problem.pair(survey)\n",
+    "problem.Solver = MumpsSolver\n",
+    "dtrue  = survey.dpred(mtrue)\n",
+    "d0  = survey.dpred(m0)\n",
+    "plot(dtrue)\n",
+    "plot(d0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.004688525906217933"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(dtrue).min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  1.,   2.,   3.,   4.,   5.,   4.,   6.,   7.,   8.,   9.,   8.,\n",
+       "         10.,  17.,  17.,  18.,  33.,  41.,  30.,  32.,  17.]),\n",
+       " array([-2.32896368, -2.11250236, -1.89604103, -1.67957971, -1.46311839,\n",
+       "        -1.24665706, -1.03019574, -0.81373442, -0.59727309, -0.38081177,\n",
+       "        -0.16435044,  0.05211088,  0.2685722 ,  0.48503353,  0.70149485,\n",
+       "         0.91795617,  1.1344175 ,  1.35087882,  1.56734014,  1.78380147,\n",
+       "         2.00026279]),\n",
+       " <a list of 20 Patch objects>)"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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G2yZV9bMkfwV8mZWPCl5tcEvS5tjwmbckaX46vT0+yfuT3JlkV5IvJ9na5fE2W5K/T3LP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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10a2299d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist(np.log10(abs(dtrue)), bins = 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "noise = 0.05*abs(dtrue)*np.random.randn(dtrue.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
+      "                    ***Done using same solver as the problem***\n",
+      "============================ Inexact Gauss Newton ============================\n",
+      "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
+      "-----------------------------------------------------------------------------\n",
+      "   0  1.00e+00  1.40e+08  0.00e+00  1.40e+08    8.32e+06      0              \n",
+      "   1  1.00e+00  1.88e+07  6.18e-03  1.88e+07    1.13e+06      0              \n",
+      "   2  1.00e+00  2.52e+06  2.45e-02  2.52e+06    1.55e+05      0   Skip BFGS  \n",
+      "   3  1.25e-01  3.50e+05  5.43e-02  3.50e+05    2.18e+04      0   Skip BFGS  \n",
+      "   4  1.25e-01  6.62e+04  9.32e-02  6.62e+04    3.26e+03      0   Skip BFGS  \n",
+      "   5  1.25e-01  1.56e+04  7.26e+00  1.56e+04    4.80e+02      1   Skip BFGS  \n",
+      "   6  1.56e-02  4.47e+03  1.84e+01  4.47e+03    5.41e+02      1              \n",
+      "   7  1.56e-02  2.73e+03  4.08e+01  2.73e+03    2.91e+03      0   Skip BFGS  \n",
+      "   8  1.56e-02  5.74e+02  3.75e+01  5.75e+02    2.97e+02      0              \n",
+      "   9  1.95e-03  3.37e+02  3.70e+01  3.37e+02    2.87e+02      0   Skip BFGS  \n",
+      "  10  1.95e-03  2.47e+02  3.98e+01  2.47e+02    8.70e+01      0              \n",
+      "  11  1.95e-03  1.99e+02  3.89e+01  1.99e+02    1.22e+02      0              \n",
+      "  12  2.44e-04  1.60e+02  4.09e+01  1.60e+02    1.45e+02      0              \n",
+      "  13  2.44e-04  1.38e+02  4.20e+01  1.38e+02    8.79e+01      0   Skip BFGS  \n",
+      "------------------------- STOP! -------------------------\n",
+      "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.3961e+07\n",
+      "1 : |xc-x_last| = 1.1216e+00 <= tolX*(1+|x0|) = 4.8945e+01\n",
+      "0 : |proj(x-g)-x|    = 8.7946e+01 <= tolG          = 1.0000e-01\n",
+      "0 : |proj(x-g)-x|    = 8.7946e+01 <= 1e3*eps       = 1.0000e-02\n",
+      "0 : maxIter   =      30    <= iter          =     14\n",
+      "------------------------- DONE! -------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "survey.dobs = dtrue +noise\n",
+    "dmis = DataMisfit.l2_DataMisfit(survey)\n",
+    "dmis.Wd = 1./(0.05*abs(dtrue)+ 0.1)\n",
+    "reg = Regularization.Tikhonov(mesh)\n",
+    "opt = Optimization.InexactGaussNewton(maxIter=30,maxIterLS=20)\n",
+    "opt.remember('xc')\n",
+    "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
+    "betaSched = Directives.BetaSchedule(coolingFactor=8, coolingRate=3)\n",
+    "targetmis = Directives.TargetMisfit()\n",
+    "savemodel = Directives.SaveModelEveryIteration()\n",
+    "inv = Inversion.BaseInversion(invProb, directiveList=[betaSched,targetmis])\n",
+    "reg.alpha_s = 1e-5\n",
+    "reg.mref = m0\n",
+    "mopt = inv.run(m0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "XC = opt.recall('xc')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact, IntSlider\n",
+    "def viewInv(iteration):\n",
+    "    fig  = plt.figure(num=0,figsize = (10,5))\n",
+    "    ax = plt.subplot(111)\n",
+    "    dat = mesh.plotImage(np.log10(mapping*XC[iteration]), grid=True, ax=ax, clim=(-3, 0), gridOpts={'alpha':0.2}, pcolorOpts={'cmap':cm.RdPu})\n",
+    "#     ax.set_xlim(mesh.vectorNx.min(), mesh.vectorNx.max())\n",
+    "#     ax.set_ylim(mesh.vectorNy.min(), mesh.vectorNy.max())\n",
+    "    ax.plot(mesh.vectorCCx, dpred, 'r--')\n",
+    "    ax.plot(xz_A[:,0], xz_A[:,1], 'k.')\n",
+    "    ax.plot(xz_B[:,0], xz_B[:,1], 'r.')\n",
+    "    ax.plot(xz_N[:,0], xz_N[:,1], 'b.')    \n",
+    "#     plt.colorbar(dat[0], ax=ax)\n",
+    "#     ax.set_ylim (-15, 0.)\n",
+    "#     ax.set_xlim (-15, 15.)\n",
+    "    plt.show()\n",
+    "    return True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GvZiz5V0v5mwM6wMAAESIRgwAACAQGjEAAIBAaMQAAAACSXZYn1WTAAAgBaya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\nAAAgEBoxAACAQGjEAAAAAvn/WBe8gjsi3HIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11286c350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "interact(viewInv, iteration = IntSlider(min=0, max=opt.iter-1,step=1, value=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10d8cdb50>]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jDMKlpY62cAPc+/we/MnFBKmje2hunsGuPQZLVto/wT2fUGKQh6Hh7GI+4mCX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\n8P+lWV/BXY4nkQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d8cd110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(survey.dobs)\n",
+    "plt.plot(invProb.dpred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/DCexample.ipynb b/notebooks/DCexample.ipynb
index d1f46570..1ef758cb 100644
--- a/notebooks/DCexample.ipynb
+++ b/notebooks/DCexample.ipynb
@@ -1,437 +1,482 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:9354750ce3568fae04f3b8199a2bd9f439e665cb37041f567e2cebfbe9d7a3bb"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from SimPEG import *\n",
-      "import simpegDCIP as DC\n",
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "Vendor:  Continuum Analytics, Inc.\n",
-        "Package: mkl\n",
-        "Message: trial mode expires in 29 days\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cs = 25.\n",
-      "hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
-      "hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
-      "hz = [(cs,7, -1.3),(cs,20)]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "blk1 = Utils.ModelBuilder.getIndecesBlock(np.r_[-50, 75, -50], np.r_[75, -50, -150], mesh.gridCC)\n",
-      "sighalf = 1e-3\n",
-      "sigma = np.ones(mesh.nC)*sighalf\n",
-      "sigma[blk1] = 1e-1\n",
-      "sigmahomo = np.ones(mesh.nC)*sighalf"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mesh.plotSlice(sigma, normal='X', grid=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "(<matplotlib.collections.QuadMesh at 0x10d902650>,\n",
-        " <matplotlib.lines.Line2D at 0x10d902bd0>)"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<matplotlib.figure.Figure at 0x10d864050>"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "xtemp = np.linspace(-150, 150, 21)\n",
-      "ytemp = np.linspace(-150, 150, 21)\n",
-      "xyz_rxP = Utils.ndgrid(xtemp-10., ytemp, np.r_[0.])\n",
-      "xyz_rxN = Utils.ndgrid(xtemp+10., ytemp, np.r_[0.])\n",
-      "xyz_rxM = Utils.ndgrid(xtemp, ytemp, np.r_[0.])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax = plt.subplots(1,1, figsize = (5,5))\n",
-      "mesh.plotSlice(sigma, grid=True, ax = ax)\n",
-      "ax.plot(xyz_rxP[:,0],xyz_rxP[:,1], 'w.')\n",
-      "ax.plot(xyz_rxN[:,0],xyz_rxN[:,1], 'r.', ms = 3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x10449ead0>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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MdvNk67tCamabJf2OpGFJX3b3z3R5l1ChV199VVLNv9n09NNv/H2mpzN+s+kNsWOpWGqx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\ngiikABBEIQWAoP8Phk4K25ESlbsAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10449e510>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "rx = DC.RxDipole(xyz_rxP, xyz_rxN)\n",
-      "tx = DC.SrcDipole([rx], [-200, 0, -12.5],[+200, 0, -12.5])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "survey = DC.SurveyDC([tx])\n",
-      "problem = DC.ProblemDC_CC(mesh)\n",
-      "problem.pair(survey)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try:\n",
-      "    from pymatsolver import MumpsSolver\n",
-      "    problem.Solver = MumpsSolver\n",
-      "except Exception, e:\n",
-      "    problem.Solver = SolverLU"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "data = survey.dpred(sigmahomo)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "u1 = problem.fields(sigma)\n",
-      "u2 = problem.fields(sigmahomo)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Msig1 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigma)))\n",
-      "Msig2 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigmahomo)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "j1 = Msig1*mesh.cellGrad*u1[tx, 'phi_sol']\n",
-      "j2 = Msig2*mesh.cellGrad*u2[tx, 'phi_sol']"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "us = u1-u2\n",
-      "js = j1-j2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "TypeError",
-       "evalue": "unsupported operand type(s) for -: 'FieldsDC_CC' and 'FieldsDC_CC'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-17-fd20083b1fc4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mu1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mu2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mjs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mj1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mj2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'FieldsDC_CC' and 'FieldsDC_CC'"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mesh.plotSlice(mesh.aveF2CCV*j1, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
-      "xlim(-300, 300)\n",
-      "ylim(-300, 0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "(-300, 0)"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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WhWvyqlOnOBcudKNLl1L4+gYwbtxpkpN1rEebg6OjCdu3V2X+fFc+/vg+Q4aE\nkpioXwyBJszNBWbPltzFt2yBevXg6lXN/YqYwq6x0KgidJkPz3IJEZsisH8ybP0Tjl/Xf552epi2\ndNFIQNKgPl8EC78AOz3PFU+fzqJ+/UQGDzZl+fIiWhW0ionR77n6JwoSIE8bTQdgqyiKmaIohiLl\n+a6dV+dOnS7z5Iluvu4dOxrj729Bu3ZJnDol392xZjnJNXjg9zrdFgAPjPI0a5lYWWFqrX53/ZAM\nPDSck5RD80v88gMYtR5+HQ92lhqbyyIoCBo3hoULYdeuYvj7F8XRsXAfvf3746lc+Trp6Spu3mxE\n3776VUl88CCRceNO4+q6jtmzL+Ln50ZYWD82bfKjWTNXrc+WKlQoyoIFTXF1/fvtYWlpTKNGJfjq\nq6qsWtWUgIA2fP/9FZo23c2tW9qnDs9N374uBAU1JCQklSpVrnPyZMG5C+eFQgF9+lhw+HBxXF0l\nDXXlSnnmJxcX+HMWxKdAi2lvnokYKSUhcyxISiqqCQeUmKkx/RojeWDJTU30KvfJ1PgiLY4yX1Fz\n7jbs/lNy6NGX0NBs6tdPxM1NwaFDVtjZvR1nkbdTXUh7vhQEoQ9wERgtimI8UAI4+0qbCKBAa6h2\n727C4sVFaN06iStX5O/SzEzg5wlS8sYneqx3D4zyPGivPWwYJpaWHJ88Od++YWRQVU1NCxMENuFK\nD8IJQb2mtikQypWAZQOg/zLIKKDwgS1b4LffIvnmG1tu3HBh1qwEfvwxsVByNAEkJan45pv77N79\njJUrK/HppyUZMuQmt27JjxPIjUolsn9/KPv3h2Jvb0avXmWZO7c+xYqZs3btTSIi4oiPTycuLu2V\nf9NISEhHoRBo1640EyfWpXJlB4yNlSiVAnZ2ZtSs6YyHhzVBQdFcvPiMY8ceMWbMKZKS9NspglST\nZNWqyhQtakyrVhe5ejUR3euRyMPPz4x58+xJTs5m3Lg4/vrLWXbfmjWlc5MfjsP8X/Nus3wQpGXC\nVzJKsANMwJG7pOebMsUNE2zc3MjW4WGMQEUT1Ad0rMOBOcRzkrxjDiaugWs/QceG8OsprafwGklJ\n0L59MvPnF+HsWWvatUvi3r3CdZ54J4JEEISjgFMelyYDK4DpOT/PAL4HPstnqAILse3Tx4TvvitC\nixZJ3Lihnao/81MIegA7/tBvDjYoeHrtzQMZhZGRxgc8lEw6kv+BcAYi24inN7ZMQ3MyvynbYcdo\n+HkE9FxNAtZWAAAgAElEQVSouSaLXJKTRcaPj8PfP5kffrCne/cizJyZwIEDBVB7NR8uX06kbt0z\nDB7sxh9/1GHt2kfMmBFCaqp+Jp3Y2DSWLQti2bIgvL2L0qRJCerXd8HOzgxbW9PX/rWyMkGpVCCK\nIkKO+qJSZePiYsnhw6EsWnSdmzdjycoquAVvaalk3Dgvata04dixaBYuDEWlKtxCdlWrmjBvnh1u\nbkZMmBDHr79q5xDQpQssXw4DB8KefLb5YztCrdLQaLI87cYeJS2wZKaa4mhemBB9V+ZhaC7CycIZ\nI4wh3xwWB0ilDUXyFSQv0qHfXNg5Bf4Mghg9ExRkZ8Po0ancumXKokVFWLAgjRMnCi+g9B9dIVEQ\nBA9gryiK3oIgTAAQRXFOzrVDwBRRFM/l6iPCR/D/NOk1gJpq79Onjx1jxjjRtesD7t7N/UFXV9u3\ncWNpp+3jk8t9UYd06zunw04b2PH49d9PKwMqEaarOWMtuQHO/QguajLzliwm7Xrce0oeIwD8kX97\nU1M4fAiuXoORI3NdFPWrWPiSVq0sWLCgOAkJKr7++jknTuT34tF1z/N6rZbixeH7741p0EDB8OGZ\n7N2b35tI1/vlbV5UKKB6dcmLrUkTKW4iJQXKl4eICMj/FaSJN18OSiV89pmSqVONOXJExddfZxER\nkXudy6hJLvN+AO7uxsycWYxmzSyYPv05a9bE59I0NWtAkydbMGiQOR06xHP1ahYIb1ZT69QJliyG\nevUh3+oJvq//OL4nlC0Jn83P/95ju4OTH4y+rXGabzIH7m+FNuPgXj7ZjcqUhMCl4NL5lU3Zn2+2\nmz8fXF2hh1rvyctaTa9JE0u2bfNk8uQo/P1fjab+FXg1i8Yv70+FREEQXtWBOwEvj9F+A3oIgmAi\nCIInUAY4n/coXYHBOV/qhcjgwQ7MmOFCp04heQgR9Vhawrp1UjCeJh94OZR2gft5BCgbCZClQd5H\nRkuHdUXUaNgRz+H4FfhEZvR5ejp06AhNfWHcOHl9tOXQoRR8fB7w449xrF7tzLFjbtSpU3jVC58+\nhd69M/nss0zmzzdm1y5jypUrfDtydraU/qN1aym+5u5dsLCA+ILNr0jr1gquXTOle3clbdum069f\nZh5CpOCwt1cwf74jly55EBKSQdmyIaxcmVuIqMfSUmDBAkvatzelTp1YSYjkQc2asGql9EzKLcGj\nVMAX7WFZPiayl5QtCXd1t3hy75EkLPIjOAKiE6BuJfXjfPONtCnt2lX3ueQmMDCZxo3vMWFCcebM\nKfHKeV4lpHflyy/d+ccJEmCuIAhBgiBcA5oAXwGIongL2IEkQg8CX4h6qlNDhjgwcWJxmja9R0iI\n9h5e33wDO3bA/v36zOJvSrtAcB4bcqUMQSKK8OAxlC6hvt2yX2GYFnUNEhKgVWsY8jn0KYQU1iC9\nZLdsSaRChRC2bk1kx46S7N1bkipVCs/D6/jxbHx80jlyJJvAQFP27DGhUaO3sxz++ktyha1VS7P7\nq1yqVBE4etSE7783Zvz4TJo1y+DKlcITIDY2CqZNc+DevVJkZGRTufJDpk6NJjlZO9Oct7cRFy/a\nY2OjoHHjWJ48ybt/uXIwby58+pkUCS+XdvUhIhquBKtvV84V7umRZSY4Qr0gAQgIhM4a3HzT0qBf\nP/jhB3DUrsqDWoKD06lb9y5161qwa5cXRYoU7LP+jxMkoij2EUXRRxTFKqIodhTFv20ooijOFkWx\ntCiK5UVRPKzPfYYOLca4ccVp2jSYBw+0FyI1akgv1nnz9JnF3zgXlbIJJ+WxGZOjkQBcuCMtCHUE\nXoNsEZpqEX/0+LEkTObOkXbUhUVWFqxdG0/ZsiEcPpzCgQOubN/uQvnyheNmm5EBq1er8PBIY/9+\nFWvWGHP2rClduihQyi8xoRMvNRR9KVMG/P2NOXTIlF9+UeHtnc7+/YV3sGplpeDrrx0IDi6Fi4sx\nNWuGMnlyNE+eaG9/HzDAnN9/t2PGjBQGDkzMN/eduzscOQwbfoZ9+7S7x7COsExGwGM5V7hb2ILk\npGZBApKb/Jo1MH687vPJi9hYFX5+90lMVBEYWAZn54LT/P9xguRtMHx4MUaNcsTXN5iHD7UXIgqF\nlE9q3DgpOlcTfrXAVMO7sExJuJ+Pui5XkDx6DpU8NLfTVisByRTTsRNsWA+183S6LjjS00WWLYuj\nTJkQLl58wYIFxdmyxYlKlQpHoKSlSQKlQoV0vvsuk5Ejjbh3z4ihQxUU0T/jit4Uz3VUULQoDBmi\n4PRpJX/+acbdu9mULZvGypUqVIUUEmJhITBhgh3375eiTBlj6tcPZcCAx4SGan+2Y2kpsGmTNV9+\naU6jRrFs3px/3JSzMxw7CvPmw4YN2t2ngjtUdIddGmo22VpKnpePtbNsv4YcQRIUIlkOqpTWPN7M\nmdCunZQRuSDJyBDp3z+MgIB4zp5tjo9PwQTY/ucEyciRjowY4UjTpsGEhekWsPjFF5KL3caNmtsW\ns4XtU6U8QOooXVJ6GPPCSCFPkNwMhYoemtttOgqNfcBdS9X53Dno1186MK5atfAd/lJTRebPj6VH\nj0iuXk3n2DEXdu1ypmrVwjF5ZWfDnj3ZNGyYQe/eKpo2FQgNNWLaNAVVqxZ+ZH5ubGxgxw4lERFG\n2NtD164Ce/YouX/fiIYNBWbOzKZkyTTmzlWRVEghIebmAqNH2xIS4oGPjymNG4fRt+9j7t/XzTnA\nx0cyZaWkiNSpE8vdu/lLvqJF4egRWOsPP/6o/b2GtIfV+yBTg7JUtiTc09X3IAc5ggTkayXp6TB0\nKCxbJjlmFDTfffeU0aOvsmVLXVq3zsuBVjveS0HywQd5vyFHjXJk6FAHfH3vER6umxBxdpbqcHz+\nubz2XXxh/1nJvU8dZdQIkvhMSJFhObgVBpXcNbdLSYOlATC+l+a2uTlwQNLGDh2ypVq1t+M9npyc\nzbx5cZQqFcqpUy/Yt68Ev/3mTK1ahXeGcuaMSJcuKurXz+LFC9i82YiYGCMOHFAycaKCBg2EQik1\n/JIWLQRCQozo0EGQzr8eGDFggIJdu7Jxdc3i449VHDwoFloMjrm5wFdf2fLHHy7UqWNGs2aR9Or1\nhLt3dVs3AAMHmnPsmB3TpqUweHAS6hI4WFsLHDoIv+2FOXO0v5dzUama4PLfNLct5QJ/6Ob5+3/C\nnkoOLyYakijsOA61K8gb8+hRafOmJnxML3btiuDTT8+zdm1tBg700mus91KQjBtX/s2qaSOK0aCB\nBb6+wTx6pHuQ16JFUnTu3bvy2vdoBtt+19xOnWnL0QRMZHxSdx+Bp7OU80sTP+6Bbk0k+7O2BATA\n558ncfCgLdWrv71QpNRUkcWL4ylVKpSDB1PZtcuZQ4dK0KBB4Xl53b8Pc+ZkU6lSFmXKZLF6dTYO\nDrB4sYKYGCMCA5XMmKHAz0/QKXW+QgFubuDrK/DppwKzZilITzfiyBEjihYVMDERyMqC4cNVtGyp\nYuNGscAO6POiSBGBMWNsefDAg/r1zRgw4Bnduj3h5k3dBYiLixFbt1rTq5cpDRvGsnWr+hRA5uaw\nb58tZ8/BpEm63XPSx7D9hFRVUBM1ysBzPb3nVCrJ+uCpIe7y4l0o5ybPvAUwapSUp658ef3mlx/n\nz8fSqNFxxo7V7wb/1Mh2vWjX7hSiWOr/Pw8e7MCIEY40aXKPyEjdhUjLlpILYr9+8tq7FJPOLI5c\n0Nw2SwV3w8kz6YsgSAfkmsjIlHZGZUtKZi51xCTCir2S59mAAZrHzs2vv6YjinDggC1t28Zz6VLh\nVs97lfR0kRUrElizJoE+fazZsKE4Z86ksX59Ir//XniBjdHR8OuvIr/+Kn0YlpZQr55Ao0YCkyYp\nSE+HDz6QigilpPz976vfp6ZCVBRUqAClSklCJDoaHjxQEBICDx6I7N6djYWFQOPG0m6oSBGwtxco\nwPjbN7CwEBg61JZRo2wJDHxBixaR3Lihu/AASUgOGWLHlCkOLFuWRt++iWRoGNLEBAICbHnwQMXw\n4brd19URejaFCv3ltffxgoW7yB1ypDUhkZLn5V0NWVa2HoNezeGajKj8x49h+nSp6FfTpurbWlsr\n+OYbZyZOjNRKUw0JSaZ+fRm7XTW8l4IkM/Nvr5V+/eyZPNkJX997emkipqaSvXLoUNSq5K/SramU\n7iBDxm3b1oVB+QRMKZD/CrkZKh0wahIkAN/vgGB/+K4UaJm5HoA9eyRhsn+/LR9+GM/Fi29PmABk\nZsLatYmsX5/IRx9ZsHSpI4mJ2cyaFcvevQVTMVAdyclw9KjI0aN/fzpKpQILC+nlb2FBnt+rVFJd\n8pAQCA19+Ty9eVagUECTJgL9+gmEhhaOELGyUjBsmA0jR9py/PgLPvggklu39BMgAN7epqxe7UxG\nhkjjxmHcuaM5cZuFhUBAgA03bmQxblwyoqjb4cDXvWH1fvlaho9XTq0hPTPw3o+Uzjo1sfV32D8X\nJgjyMkYsXw59+8Inn6g/l01OzqZSJTPmzXNh1CiZgTY5REfr4WnAeypIXtKjhx2zZpXQ2cX3VSZM\nkFKjH9bC6bhHM5j8k+Z2jnaQlgGJ+bz7BEG+ILkVJmlBOwM1t41PhiU/wJRvoU9fmTfIxW+/pZOd\nLbJ/vx0ffhjHhQtvV5iA9GLesSOFnTtT6NzZkmnT7Jk5syizZ8eyc6d+NSp0mUtiovSlL9nZcOKE\nyIkTBS9EbGwUDB5szZgxdhw5koqvbyS3b+svQMzMBL791oHPPrNl8uTnrF0bL+tlaWsrcOCALbdu\nqRg3LlnyPtMhTtTLCzo3grIyY54c7SRTcKR21a3z5H6kvAP3Gw+k2usNGsApGXm1srOlM9l9+6Sv\n/DxFs7OhV69QLl4sz4ULqWzdWvilAV7yXp6RAHTqZMvChSXx87vPvXv6SVt3d6mCmzaHfl4lwL04\nnJARPFXKRQomzA8BeaYtkFwMi9vJawuwZIlkstPHBrtvXwaffprAvn121Knz7vYmogi//JJM9eqP\nmDAhmi+/tOX2bVP691dirF8m+fcGFxeYN8+IkBAP7OyUNGwYQe/eTwtEiDRrVoTr173w9DTGx+cB\na9bIEyLFiysIDLTjr78yGTAgUS8X5m+/kdzb42R6sXl7SnnyCoL7EVBKQ0DwS7Yeg5495Y996ZIU\nWzJhgvp28fEqOnV6wJIlJalSpRDcvfLhvRQkbdo4s2KFK23a3OfmTf0rvi1cKB2yP8onj05edP8A\ndgUia1GUKiHZV/NDG9PWjVBooT4rzGskJcH3C2HqFPl98mL//gx69Ihn925bOnUq3Jojcjh4MJWG\nDSMYNCiTXr2UBAebMmiQ8h8RF/IuqFhRwN/fmKAgM5RKgWrVwpk4MYZ79/TPLlyqlDELFjiydm0J\nRox4Ss+eUTx9Kk8auLsrOHXKjh070hkzRj8vgnLlpLiLRbvk9/HxKjhBEhIlz7QFknmra1e0qhmy\nYIEUBF29unoBcf36C778MoKAAC/s7Ao5sjaH91KQrFtXm/btQ7h6Vf+D1xYtpNw332tZZ6RTI9gk\n0wxWykV6CPNDG9PWvQhwtJWCrOSybJmUfNLbW36fvDhxIpO2beP54QcrRo36Z7yxAwOzadEig27d\nMqhYUSAszIx584xwd387dRreNY0aKdi714Tffzfl/n2R0qXTGD06k0eP9DdBengYs2aNM2fPehAX\np6JSpRAOHJAvDCpUUPLnn/YsXpzKrFn6n2l9PRkWLc7fRJwXPl5wXftaVnny8DG4OUq1UjQR+ljy\nCGzeXP74CQmSF9uyZZrr32zfHsfu3fFs3er5VuKf3ktB0qnTKc6fzzuLrJeXCd27y7P9GBsL/PCD\nlPk2v/QNeVGliuQKeE5mJlEvZ/UaiTamrexsuHofqpWR1x4kT6JvvoU538nvkx9XrmRRr14sffua\nsXy5VaGnGpHL+fMiI0dmUatWOoIAFy+asnu3CU2bvn9LQKGATp0UnDljytq1xuzdq8LTM43Zs7Nk\nZWLQhKurEStXOnHxogeRkZmUKRPCrFkxpKTIP8upUcOI48ftmDQpmR9/1H/DV60aVPaWclRpQ0V3\nyRxcEGRkwuMYcHszaXGebNig2RMrN+vXS+WX+/TRnE15/PhIjI0FZsyQaW/Tg/dvFQF//RWT5+/N\nzQUCArwoWlTe223EiGKEhGiflPHjj2HzUfk1PDRpJFHpkKaF3fhyMNQoK789wM8/S3mbtNkh5UdE\nRDYNG8bh4aFk715brKz03/0XVKxIaKjI2LFZuLuncfCgiqVLjbl+3ZSBA//9Zq/KlQXmzjUiPNyM\nrl2VzJ+fSfny6axerZLtaaiOEiWULFtWnCtXPImJUVG27AOmTIkmPl47b4Z27UxZutSKwYOT2LSp\nACYGfL9AcpFN0UIbMTGGsq4QVEAaCUibOC+Z7+3du6UYEW2eO1GEYcMe8d13LtjYqH+PqVTQvftD\nOnWyoW1b9RVW9eW9FCT5sXq1O9euvWD5cs0uGs7Oxowf7/RmHQ4NKBTSIdrmo/L7aDojcTYFcy12\n9pfuSUFW2pCZCRMmwoL5BZMKJClJpF27eMLCVJw6ZUfJkroPam+vZMUKR4KC3OjXzxoTE/0FU2qq\nlF+rcuV0RozIpG1b5f/NXt7e/x6zl6MjjByp5PJlUw4cMEGlghYt0unVK5OAgOwC8VhzclKyeLED\n16+7k5IiUr78AyZPfk5srHan4kZGsGCBJUuXWjFyZBK//aafE8xL2raV8pGtXatdv6qlJHNUWsFM\nA5BSxcsVJM+eSZmgO3TQ7h4XL6ayd28CU6dqrjoZHZ1Fnz6hrFvnTtmyhXd2+Z8RJMOGFaNyZTM+\n/1xeTeYpU5xYvTqa+2qKSeVFkybw/DncDpPX3twUnsVLKnF+ZIuac3W9yiUdNBKQItaTkyV/9YJA\npYIhQ5LYsCGNM2fsdY6Cj41V4eMTzujR0fToYUloqAeTJ9thb18wj+/x49l07JhB7drpxMbCnj0m\nhIYasWyZgpYtBUzfve/Aa5iZQbduAvv2Kbl714wqVRSMGZOJh0c6kyZlcft2wbgLe3ub4O9fnHPn\nXMnIEKlYMYzx458RHa29W5Wrq4KTJ+0oW9aIatViOH++YNzElUopvfy48fIcW16ldgU4f6dApvF/\nQp+AhxapqzZvliwY2jJpUiS9etlRubJmTf3ixRdMnhxFQIAXFhaF88r/TwiSBg0s+PprJzp3fsCL\nF5oXWYMGFnzwgRWzZqnxyc2Hjz+GTZvkt/dwAlNj9WawbLQTJHfCpVxD1hby+7xk9BiYOaNgE8Ut\nXJjK8OFJLFlixdix9jprPEePptKqVRQtWkTi5WXM/fse/PhjMUqXLhjf3ocPRebMycLLK502bbII\nD4evv1bw9KkRAQFKPv1UeCMTb0FSogQsXKigRK4drYkJ1KolMHSogkWLFERGGvHZZwq2bs3GxSWN\n/v0zOX68YLQPgJYti3DkiAuHDrkQHJxBtWrhjBsXI9sTKzdt2lhy/rw9AQHpdOgQT1xcwcXFfPYZ\nPHmiW02gOuXln2PK5eFjzWlSXuXXX6V4kmLFtLtPTIyKqVMfs3SphroROfz0Uwxnz6awZo2bdjeS\nyT+61K4uSKV21wHSindyMuXChfoMHHidQ4c0m7QUCrh4sQFz5oSwY8cTwEX2vU1NBaKiKuDtfY+o\nKHl+gK1bGzF8uAmtW+c4Bwhv2jLXrIEzZzSo7uLrB5anTpkweXIWgYGa3i5vZorcts2J69fTmTVL\n3cms9iUh3d1NWLvWEwsLJf37B3Pnjn6HrMWLGzNsmDODBztx+nQiCxZEcvp07gACXasD/f05FC0q\n0KqVKe3ameLnZ0JwsIqdO18QGSkSFqYiLEzF48cvX+TaCzVra4GvvzZh6NAiGBnBl18mkZkpUquW\nMbVqGVOxohF372Zx4UImp09ncuxYBlFRLz9XXSMfn732k6mpwMcfF2PUKBeyskQWLoxk27ZoMjJ0\nfz8YGQnMnOlGz57F6NnzPn/9pa17r/oDZUtLgXv3PGjbNoorV161T8lbe3fvmtK5cwY3b+b8jYKO\nuyfx78+gXj0lixaZUbeunMOaUAB+/tmV8+dTWbZMjVniNSQ7uEIBP/9chd9+e5rzrlKPmZmCU6fq\nsnFjFEuWhMIbNez761xq972ObDcyEti+vSo//fRIlhABGDTIjfj4TFkfTG4+/NCaK1deEBUlX233\n8BAIDVX/sleptD+3OHQom4oVBQJlRLjnZuLEaC5ccGPNmkSdd6F5ERaWQYsWNxk82Ik///Rm3rxI\nvv8+Uued9NOnmXzzTTizZ0fQp08xli3zQhAEli59zJYtz3nxomC26DExIps3p7F5cxpGRtCokTFV\nqxrToYMJ7u4K3N2V2NsriIhQERYmEhaWnfOlIjxchVIpYG7+8ktKjPjy+88+M8XFRfpwjY0FRFFk\nyhQLjh/P4MKFTDZuTOPq1UxeFFIKMQcHI774wpkhQ5y4fDmZESMe8PvvCXqP6+JiwrZt5UhKUlG9\n+lVi5L4jtWDsWDuOHUvNJUTkYWcHTk5CgZkBX/LwYTaentq9izdtimP6dCctBIlEdjasXBnO5s1V\n2LfvOamp6tdqWlo2H310hXPn6nHpUgKnTuUWJLrzXmskU6aUpmZNG9q3vyTLg8re3phbtxrRvPl5\nbtx4uXuSr5EsXuzMhQupbN6cAHjI6jN3rimxsSJz5+ZEFuehkaxYAVevSunb8yWXRtKtm5JevZR0\n7KgpYjnv3PULFjhgbAwjRuQngHUtUi8JWQ8PU9auLU2RIkr69Qvm7l3935SCAM2b2zJ8uDN16ljh\n7/+U5cszCQ/XxR6vnZeLqSm4uSlxdzfB3V2Jh4cCd3cFqakiXl5KXrwQSU0VefECXrwQc76gQwdj\nXF0VmJkJmJkJJCdnM2hQksYMuX+jm0ZSu3YqQ4c6U768OdeupbBoURS3b+v/GZiaCgwd6kzz5jYE\nBiYyb15kztrTZc+av0ZSsqSSq1fdqVYtPI+YGM0aiZ+fggkTjPjgg1fWRwFoJAApKVY4OibJ8CAL\nBaRznoiICjRqFML9+3IyDLzumbNlSxWCg1OZMkVDPeEc/Pwc8Pf3platPTx+/OpnbtBI3qBLFyd6\n9SpBnTp/yXbDnTmzLNu3P35FiMjHykpBv372TJ36THPjV/DwUHD5svro4uxstI7HOHVKxY8/GkvB\njDrsFWbMiOHyZTe2bEnm3LmCcdF8ldDQdJo3f107WbhQd+0EpL/z6NF4jh6Np1QpM4YOdebyZTcC\nA1/www/xBAYWXmbg9HQIDlYRHJzFS2Eph2++kebk5ycwbZoFdeuaFNjhvqmpwJo1jsydG8eNGxmY\nmQn07GnFF1/YYGcnsmLFE7766iGxsfoffAsC9OjhwKxZ7gQFpTBqVKjepkt1LFpUjDlzYnUOrKxT\nR8G5c4WThC0sLBsPDwU3b8obX6WCbdvi6d3bjqlTtdcSxo27y9WrDfD3jyAsTPP/+ZEj0axYEc7W\nrU1o3vwwWXKq5mngvTxsL13aiuXLK9Gz51Xi4+U9aNWqWdOpU3HZUj037dtbc/JkCvHx2pmCPDwU\nGjO76mLaioqCxESR8uV1c2VNSBCZNCmGn35y1CqNgzaIIqxc+YTata/RurUtGzaUwc+vYEp/hoSk\nMWrUQ9zdH3LsWOr/3Yc/+cQKK6t/3mN/5EgG9erFUalSNAEB+vujGhvDgQMl6NHDismT7Zk/34FH\njzz56CNLvv02hjJlLvP991EFIkR8fW04f74KI0eWoF+/YDp2vFOoQqR9ewt8fExZulR3E5yzM5w+\nXTiC5Pp1FW5u2q27jRvjaN1ah4I2QEREGosXhzJ/fjnZfWbPDuHevQTmzKmh0z1z889bUQXArl1N\n+fbbYC5flq/2L11aka+/vpev4PH1tVCblqB7d1u2b9e+Oo6cM5Jnz7SLrH/Jn39m07Ch7h/x9u3J\nPHqUxdixWmSB1IGX2smePTEsXOjJuXM+fPhhwdwzJUWqXVKxYhijRkVTv74ZYWEerFtXnIYNC68g\nlq7cuqUiMVG/HaKREezdW4L69c0wMhLo1k3Kl1OnziM+/DCKgwdTddJSc1Oxojl791Zg7drSLFgQ\nSd26QZw8qX/a41q1LHB3z7v8pJWVgmXLijFo0DPS03X7I5RK6NnTiLNn8153deuiV5LPmBjw9NRu\n3V2+nEaxYkb4+Oj2TC5Y8JBatWzw9dUc8Q7SJm7ChMt06eJB+/byPL/U8V4Kklu34lm5Ul68CMBH\nHxVHqRTw98/7vKBu3SL4+7vmqxXY2ipp3NiC337TbhGZm0vmh6dP1S8IKyvpcFBbTp3KplEj/T7i\nIUOeMWqUHWXKFG76XFGEXbti8fa+wrx5kcyY4cbly1Xo3LmoxrxCcjl2LJUhQ55TrlwYQUHprFzp\nyL177kyYYEeJEv+QXC560qiRGcnJpfDzK4KZmfTZp6WJHD2ayoMH+idoBHB1NWH16lKcOFGZ339P\noEKFy2zfHl2AwqkcZcvmfV4xa1ZRDh9O1ctMWb26wKNHItF5HP8JAhz/XT9B8uhRNq6u2q+7HTvi\n6dbNRqd7pqVlM2bMXZYsqYBSKW/BxMam0717IKtX18fDQ4vkfHnwXgqSQYP+kt3WzEzBokUVGDXq\ndr4LYfRoBxYtep5vwFPHjtb8/nsySUnaqcpubgqePNHcJz0dnezm+mokAOHhWcycGcuqVbq60WqH\nlAo+hmrVrjFlyiMmTHAhKKgqPXo4FFjyuefPVSxaFE/lyuH07v0EDw9jrl93Z+/eEnTqZPGvSznv\n7W3C7NlFefjQg5UrHQkNzWL16gR+/TWZGzfSycgQqVxZ/wLzTZvasGtXOS5dqkpkZDrlyl1m8eIo\nvVyEX8Xd3YRDh8rx1VdhHD36ptmqbl0zPvrIknHj9Cse0rSpkhMn8l53Li4QHy9lPsiNkRE8kJGX\n68SwG24AACAASURBVNEjUUdBkkC3bupNu4IAlpZ5b3p++eUJMTGZDBokX8M4d+45c+ZcZ/v2JlrN\nNTfvpSBJTpZv9x050oNz5xI4cyZvs5Snpwm+vpb4++cfU6GrWatkSYGICM2LMD1dimbWlrt3RSwt\nBVzkO57lydKl8VhaKujfv3Dz9eRm795YatcOYsyYUIYNc+bWrer07OmAuXnBPbbnz6fz+efPcHV9\nyI4dSYwYYcv5826sXWtNr15mODn9M5eIp6eSSZMsuH7djb17SyAI0KFDFJUqhVO+fBiff/6cTp0e\n4+0djp3dAxYu1K0oubW1Muf/vho//ODJsWMJeHldYtq0CK3PA9VRrJgRR46UZ968x2zd+qYbrLEx\nrF7tyFdfPScuTr+zjaZNFZw4kffcS6mpFmpmBg4OmscPD8/W+owE4PLlFygUULVq/ot96NCiLFlS\nMd/rw4ffYvBgV6yt5R9sLl58i8jIvJPcyuWfuUreEg4OJowe7cnEiXfzbTNypAM//RRLSkreD2/R\nokrq1SvCvn3a24ZLllQQEVF4GglI5q2GDfUz22Rnw4ABT5kzpyiOjurH8vAo+Hwihw/H07DhdYYM\nCaFlS1siImqyYkUpatTQTx1/ldRUkY0bk/D1jaRjx0guX86kSxdTbt0qyo0bRVmyxIr27U2xsXn7\nebiUSvDxMWLAAHNWr7bi1Ck7/vrLjhIlFHz++TM8PUOZODGGoCD9i1O9pHLlIqxYUYrQ0Jo0aGDF\n4MEheHtfZeXKJyQnF5wAAbCyUnLwYHm2bYth2bK8vZbGjLEjPDyLHTv0q1liZAT16yvyDdTVJEjk\nJL/U1bQFsHOneq1k69Z4OnUqjqNj3hrmjRvJXLqUyPjxXlrdt3//01q1z81/WpBMmVKaLVuiuH8/\nb2lsZ2dM7962LF2avyrdsaM1hw4lkZqqvXrv6ipfI9FVkBw4kIWvr/4fc1BQBmvWJDJpUv6HNeXK\nmXHuXCUaN9bN+0QTJ04k0K/ffXx8rhIRkc6OHeW4erUqX37pjL19wbmWhYWp+PHHF3TunICDw3P6\n9UsgKkrFsGHmPHrkwLlz9syebUnz5sZ4eSmxsChY4eLmpqDr/9g77/gm6v+PPy9pmu6W7kkHbSkt\npVBG2RsFlCEqIEsRcSAKrp8giiJfFUQFUVw4UNnIkFH2aNllQwsUOqC7dO+Z3O+PUCzQJJe0ONDX\n45FH0vQud03vc6/3fL0fV7JggRVRUc0oKHBi9WpbundXcOZMLdOmleDhkcvUqSUcPlzZJLkJAHNz\nGSNGOBAd3Zrt24NJT68iOPg0TzxxhYMHm2B2cANQKgU2bQogJqaUd99tWLm0VSsFY8daM2WKYaX1\nDaFjRxmJiaJWOf0WLSBRy6ArqUSSni7i5iYYFYrVhLe050ny8lSsWZPJCy9olzqZPfsqzz3nhYeH\n9DBGUVHjjJD7to9EHwICLBg92o2goGit2zz3nBdbthSTmak9VPboo7YsXWpcc56np4yzZ/Vbd40h\nkuhokdmzm8ZeeP/9fI4e9WTKFFu++uruvzk+vpLRoxP47bcAxo9PZOfOxndIN4T09Go++CCNDz9M\no3dvWyZNcuH995uzY0cB33+fzb59RU12c1Wr4eTJWk6erGX+/HKUSujSRUHfvqaMHGlG376muLnJ\nUatFsrLUZGeLZGWJZGer6/2sRi4Ha2sBa2sBKyvh1us/HiLW1gI+PnIEAY4fr+H48RrmzCnj5Mma\nRldyaYMgQI8eNkyY4MyIEQ5s317AokWZbN6c3yT9Bbogl8PKlf7k5NQydeq1BrexsBBYu9aNTz8t\nNLKx9HZowlraowAt/GDzloZ/Z24ujUiqq6GgQMTFRSAz07Dv8MyZCkRRMwXx9OmGCwoWLbpGVFQE\n8+YlUVV199+Snl7Jd9+lMmeOP888E2vQ8Y3Fv5ZI5s1ryYIFyeTlNVzJolAITJ3qzaBB2qu/bGxk\ndOtmyciR0ivE6sPTU2DrVokeiZG50itXRGpqNKNWL15s3I2hqkrk8cezOHrUk5iYG5w8eXfr7v79\nxQwbdoWNGwN5/vlkNm1qgklKWiCKGi9l//4imjUzYcwYRz75xIeiIhWHDhWzalUucXFNe8yqKjhw\noIYDB26/bqysBFxcZLi6muLqWvdaRocOMtRqOS4uMkpKxNseN26oKSkRKS2FkpIaSkpEEhNVkgow\nGovAQHPGj3di3DgniotV/PzzDd5++zpZWU1T2aUPpqYCCxZ4YWIiMHp0gtZG1K++cubUqSp++qlp\nPKI+fWR8/rl2QmqK0BZoEu7Nm8vIzDQ8DLhsWT79+llpJZL4+DJOnixi7Fh3rZWm8+YlceVKT1q3\ntjKqwdpQ/CuJpHv3ZrRvb8vYsee0bvPYY67ExZVy4YL2K2fQIGsOHiyjtNS4hS81R1JZabxHAhrd\nrUGD5Fy82HiLLjGxhhdeyGHtWn/Cw2MbTLgePVrKoEGXiYxsiYWFjJUr74HQ0h0oKKhlyZIslizJ\nIjTUgvHjndm+PZjCQli9uoRVq0pITm4a6fKGUFoqUlqqIjHR2GPc+xu4vb2MUaOsmTDBGm9vOStX\n5jBs2CXOn29cotXw8zBh48YAcnJqGT06gZqahg2ciRNt6NBBSadOqU1yXAsLjVcYHa19zfn5NQ2R\nxMTU4mxkoePOnaX8+qsXCxbkaN1m4cJrLFrUSiuRFBfX8uGHicyb15KHHz5l3IkYgL8kRyIIwuOC\nIMQJgqASBCH8jt/NFAThqiAIlwVBeKDe++0FQbhw83efN+b406b5MGvWFSortV9Q//d/fixYoCVY\nehPDhtny++/GW0pSq7ZKSgyftVAfO3aoGDiw6f7V69eXsmVLIT/9pD2hd+ZMOf36Xebjj5vzzDMG\namQ3EhculPN//3cNb++TvPDCDdzdTTh2zItjx7yYNs0ON7f7o2dECpyd5TzzjA3btrkTGelB9+7m\nzJmTj5fXCV5//dqfTiKBgWYcOxbCkSOlPP74Va3Cmm3aWDB/vgOPPZapM/84bJilZPmg/v1llJaK\nFGtZsnZ2cPIkDfaXgIZIpIpniqImTGkMTp2qwNparnMQ1d69eajVIgMGaC8j+/rrFIKCrCQ3KTYG\nf1Wy/QLwCHBbgkIQhGBgFBAMDAS+EoRb7WhfA5NEUQwAAgRBGGjMgR95xAU/P3NWrtQ+27ZnT3vM\nzGTs3avdklYoBAYOtDK4CbEO5uaQkiKSm6ufSCorwdWAYTl3Yv9+NRERMiyNmE+iDW+8kYK7uymv\nvKL9xC5erKBXr4vMmuXBtGmN+AOMhCjC4cOVTJ2ag7t7MrNn5xEWpiQ21pt9+zx48klrfH3vP6fc\n29uE6dPtiI72JD7em379LPj552L6909j7Ngsduwob5RhYix69bImOjqY+fMzmDkzVWsey9pazrp1\n/kyfnsvly9o9tcGDLfjoIwn1uDcxZIicLVu0/+HBwWCjo8LdzEzTYyIFmZlq3NyMK8IQRdi8uZhh\nw3SX2y9ceI1XXvHR+vuaGpG33opnwYKgJmvq1Ya/hEhEUbwsiuKVBn41DFglimKNKIrXgAQgQhAE\nN8BaFMWYm9v9Agw39LhyucCHHwYyc+YVncnYl1/2ZvHi6zq36d3bksuXq8jONi6U4eoqYGcn7b9b\nUqLpbtcFex1GR2kpnDihbpLqrTpUV4uMHHmVN990p0sX7WW4iYlV9Ox5kYEDbfnyS+8m7QExBCoV\n7NpVztNPZ+PunszixYWEhys5csSLK1e8+fJLJ4YOtfxb6nBJQUiIKW+/bc/p017ExHgREmLKvHn5\nuLom88QTWaxdW0pp6V+n9P3UU46sWRPAmDEJ/PCD9pANwPff+7JvXzErV945W+YPaMb2OvH669ob\nhetDEODhh+Vs2aI9ChESAhd1DLqyspIeGcjMFI0mEoDffy/SSyQrV2ZiaiojKEi7hbhuXRYJCeU8\n/vi9NeT+bqvGndt1zdPQ6Ljf+X46hui738TEiR5kZFSxa5f2cl5vb3N697bnl190DFFHU/a7aZPx\nYS1XV2ld7aCfSGQySEkx0+lx7NihZuDApg3pXL9ezeTJSaxe7Y+zs3bLPjW1mtGjE7CxMeHMmbZ0\n7Nh0/R/GoKpKZNOmMqZNy8XNLZnHHsvk2rUapk61Iz3dl6goT2bNsqRjR5Mm66ZvSigU0L69CVOm\nmPPzzy6sX+/Ktm3uODjImD49F3f3ZCZPvkFkZLnRelRNBUGADz5ozttve9Cr10X27dO9ZqZMcSYg\nwIzp03XPqn72WVvS0mqJjJQWmuvYUSA3VyQpSfv3ERyMzuIMGxu0hsXuhIZIjL949u8vIyTETOe6\nqq5Wc+RIAS++6K11G1GEpUtT+d//AjExuXduyT3z6wVB2A00RINviaKopcCuqbABqCtzCgZCMDeX\n8+67LXjkkcPomuHw4ou+LFuWTFmZ9mojQYChQwPp3z8G0DZ0QLs1BeDqak92tjNwx9Bo8W6NodIS\nBVZWLUFsuJRPrYLLlwMJCUkkJqbhCo3ISHM++MD77uNhrGaRJuSwZUs+LVqo2bbNn379YigubthD\nKyqCCRNyePxxJ7ZsCWXJkmQ+/DABleqvn4dz/rzm8cknml6Knj1teOABd3780QpXVxP27y/n+vVa\n4uOruXy5msuXa3TMLdevr2JpKfDss9b07WvGkCF1vRHaQzi+viZERJjderRpoyQxsYbjxyuJjs7h\n6NFiLl409P9o7DwZ6XB1VfL22wGEhVnSufNhcnN19yoMH+7CpEn2PProCaqqKtC2Tm1tTZg9uzkD\nBpwBGrre7+43GTq0OVu2CIB2ggoJDmXnjht3zRepg421E8XFShDvTHDf/d1nZlri5haAJoqvDdrv\nQ9XVsGuXIw8/rOLHH+80av/Y79tvL3L+/IO89dZJSkoaXnv79hWTnNycSZMc+fbb+pUEcYD2ZmxD\ncM+IRBTFAUbslg7UF4rxROOJpHP7tBpP7pzuchuGAreb8C+/HMDRo3mcPKl9AVlYyJk40ZeOHXfr\nPMmwMGtOniwiPl7KOM2G4epqKrnUsqREjbW1bm8iNrac0FBLrUQSF1dBSIgF7dpZcuaM9PMODLQg\nP7+G3Fzt57po0TX8/S3YurU9Awee1Dmpbd26TA4fLuDHH8M4fLgb48ef4epV47/HpkZFhZqdOwvZ\nuVNjiLi6yunUyYygIFO6djVn4kQbWrUyRRS5SSrVxMfXcPlyNWlpNZSXm1BVJVJZqXnUvVarwclJ\nxmuv2TJlijVyucYgadHCBCcnOU5Opjg5yXF0lN/8WU51tciwYZbU1sLx45UcO1bJzJl5nDpVWS9M\n1fgmvaaGQiEwbZofb77pz5dfJtO//1GqqnRb5wMGOPLttyEMHHiS5GTdpDhrlg+bN+dy4YL0stah\nQ+159lndQlnBwWbExWk/to2NnOJiaVGEzMxqXF0bJ9r2++83GDXKVWtlFkB6egV79mTz5JO+fPml\n9hEYM2eeZ8uWHvz667V667PlzQd4eVmSmmq8ff93yDTW97c2AysFQfgMTegqAIgRRVEUBKFYEIQI\nIAYYDyyWeoBmzUx57bWWdOu2V+d248f7cOhQDteu6b6xDR3qQkJC425+Li4KsrKkdZOWlqqwstK9\nEC9cKCM01ELnNmvX5jJypKNBRDJpkjseHkrGjdPdkPHSSxf56adQNm4MZ8iQU1RXa19wGRmVDBx4\nnClTfDhypDvvvHOZb77RHcr4q5CVpWLz5jI2b779O3NyktOypYKgIFOCgkzp0cMWpVLA09Pk1rRD\npVK49Vqt1ox+FkWRuvoRURTZudOFnBw1OTk15OaqycmpJTu7ltjYKtLSann//XzS0u5d2XJT48EH\nnfj889YkJJTRpcuheutE+/XbrVszVqwIY/jwU5w5ozt25OdnzsSJ7rRufUzyOfn4KHF2VhAToz1K\nYGsrx8ZGTmqqdoPJxkZOXp60/0VOTg12diaYmAhGN3ZGRt7g669DsLCQ6zTOvvjiKt9/35ElS65q\nzeuePl3AwYM5vPxyIPPm3Z0I8vGxJrURVdZ/CZEIgvAIGiJwBLYJgnBGFMVBoiheFARhLXARzZi5\nKeIfs4CnAMsAcyBSFMUdUo83Y0YQ69encfWqbgvm5ZcDePHF03o/76GHnHjzzca5hK6uppJv6CqV\nJrltbi5QUdHwlXLhQjmDB+vWml+7No8NG4KYOVP6TXvOnCQuXOjMwIEO7NihvYpNFGHSpFhWr27L\n6tVhjBx5Vu8C+uqra+zdm8uvv7Zj4EBn3nzzEvHx9755qimQk6MiJ0fFoUN3NhY0bIVqNJ6UfPRR\nM9q2NcXCQkZenhp//zrH+s9pBLxX8POz4LPPQggOtmL69DgiI6V5SuHhNmzY0I4xY85y5Ij+kqh5\n8/xZuDCF7Gzpkh79+9uybl2uzumbwcEWXLyou0nExkZGcrK0bLsoasjExUVBerpx8iOFhbX8/ns2\n/frZs2WL9gKFQ4dyqahQMWCAK7t2ZWnd7u23L3DkSD++/TaRgoLbz+ngQe37ScFfVbW1URRFL1EU\nzUVRdBVFcVC9330oiqK/KIpBoijurPf+KVEUQ2/+7mWpx3J3N2fECC/ef1+3Rd2njxOxsUUcOKB7\nAbi4mBIQYMmhQ43r2HZ1le6RAGRm1ugMb8XGltO6tW6P5Ny5MmpqRIPEDsvL1Tz33GW+/joIS0vd\n4TWVSmTMmLOYmcn56adQSSWH8fGldO16iMjIG0RFdWXLlk707u0g+fz+KaithejoKrp1y2Lw4BvE\nxVWTn3/vO9jvNSws5Pzvf0HExPTg6NECWreOkkwiwcFWbNvWgcmTY9mzR3/Tau/edtjbK1i40DAl\niUmTXNm6Vfd6DQmRQiRyioul103v31+Es3PjwltnzxYzeLD+zsYvvrjK1Kn+OrdJSChl/fo0ZswI\natQ5NYS/YU1K02L27BDWrk0hM1P3RfL88/5ERekuSwQYNMiJPXtytVrbrVpZERKi/0atyZFIJ5La\nWhE7O+038oyMakxMBL0Xria8ZdiNes+efKKjC5k7t4XebWtqRB599DReXuZ89VWIpM+vrRX57rvr\n+Pjs4fffs/jqq1BOnerJ2LEe97TS5K9CVFQlrVtnEBGR+VefitFwdVXy2mt+REV1xdfXnLCwKObP\nT9AZ0qyPFi0s2LmzI6++eonNm/UTj7W1nJ9+CuHTT69rbWJsCP7+Zvj4KNmzR7e34+2t5MgR3d6w\nITkSACcnBS4ujSOSbdtyeOgh/Q29K1em0LmzA76+upvF3n8/jkmT/PDwaHhwmLG4r4nEz8+SRx/1\n5JNPdIehnJ2VDBjgyvLl+kM+Dz3kzLZt2gln8mRPBg/W/4/PzKwmO1t6OCMvrxZHR92RyAsXyiXn\nSQzFq69e4YknXOjQQf9MkooKNUOGnCI83Ib33w+QXEJbWanm++9TCAk5wDvvXObpp5uTnNyPN95o\nga3t3yGd17QoLPxneSRKpYyRI93Ztq0TFy/2JjjYmsmTzzF27BnS0yVqh6AhkcjI9rz/fgKrVkkj\n088+C2T37ny2bzdMbmfcOCdWr87V2//Rt68tV6/qnmddVFRLQYH0fFVOTg2Ojo0jkvh4TRShdWvd\nxmllpYrFi68werR2VWCAzMxK5s+/xIwZrRp1XnfiviaS2bND+OKLq3fFA+/ExIm+rF+fRnGx7hu7\nQiHQv78D27drJ5IOHWw5eVK/6u2AAXbk5kq/KHNzVTg46L6ZHjxYTIsWuqWjY2PLqahQG9zLkZdX\nw+uvX+X771tJ8hJKSmp54IETtG9vw44dHXFwkL6gRFGTaOzX7yhDh56gTRsbkpL6sWBBMJ062f0t\nezvuZ3Tt2oxvv21DRsYAJk1qzooV6Xh67mHSpHOcPWtYL9VDDzlx+HBn3nsvgaVLpWV3Bw92oF8/\ne159taEeZt0YN86J5ct1Rxo0814sOX1ad09K27YWBg3zys2tbTSRAERG5kgKb61cmcL06YGYmupe\nID/8kMzo0c3x9W26cQ/37ZIMCrJm0CA3Fi3SffEJAkye3OKO+uqG0b17M65cKePGjYaJSTPdzIbT\np3UvLqVSQKEQDBoQlJdXi4OD7hxFQkIlvXrpn/m8dm0uo0YZ7pWsWJFFZmYVr72m2+qpQ1FRLUOH\nnubUqSJOnepGx46Gz6M+c6aI8ePP0KZNFBkZFfzwQxjZ2Q+yalU4Tz3lhZtb0w/S+g/QsqUlb78d\nwJUrffj++zCSkspp0yaKBx88xsqV6TqriBqCTAZz5gTw9dchPPLIacmeiL29gu++a8XEiRcNHqjV\nubM11dUip07pDlm1amVBenq13lHZTk4m5OQYYvzV6I0iSIGGSPRHOZKSyjh/vpBHHtHdq52fX82S\nJVd55512jT63Oty3RPLee6359NN4vV5G//4uFBXV6OwvqcNDDzmzdat26yYoyIqsrCqKinRfbA4O\nCvLzDSvplBLaiokpoVMn/Z7GihU5DBpkZ1T+4fnnL9OrVzPatJFmzahUIjNnXmH69Ets3dqeZ5/V\n3oWrC+nplSxcmExoaBTt2kWxe3cOgwY5Exvbm3PnevHxx63o189RrzX2H+5GnQH00ku+rF3bnszM\nAaxZ0x5XVyVjx54hOPgA8+cnGBS+qg97ewWRkR3o0aMZHToc0TrWuiEsWdKStWtvEBVleHGLFG8E\noH17K06e1E02gqBRLTZk3TaVR3LgQB7h4TaSwrtLlyYxebL+XObChVd4+GEvAgKaZnz2fbnq2rSx\np0cPJ50NOnV4/nl/Sd4IQNeudmzbpj0xKDWsZW9vIrkevQ65ubV6Q1vx8RU4Oyv0Tgu8erWSvLxa\nHn5Yd7lwQ7h+vZKff85kw4Zw7OykW1ubNmXTvfsxXnrJhx9/DMPMzPhLLy2tkh9/TGXUqFM4O+/i\nuefOU1amYu7cluTkPMC337Zh9uxAHn7YBXd3Iwe53McwNRXo1s2eGTP8iYyMIC9vICtXhtO6tTWb\nN2cTEXGItm2jmTo1lhMnjJv1Xof27W05daon586VMGDACa3efEMYOdKFtm2teeutBIOPq1AIjBzp\nyMqV+omkQwcrvV5Ls2aaiq1aA5ZtU3kkFRVqDh7M16n0W4dNm9IJDbWlRQvdBmVRUQ2LFsXx3nvh\nOreTivuSSN5/vz3z5l3S6367uZnRp48zK1fqT7J7eZnh72+ps2GqQwcbSUTi4GCidaCWNmhCW7ov\nSs00v1JJXsnSpdlMnuxi0DnUYc2abDZvzmb58jCDVEWvXi0nIuIQSqWMI0e64+uruzBAClQqkWPH\nCpgz5wpdux7Gx2cvv/+ehVIpY8oUH86cCSMrqyPbtwfzwQfNefRRB3x9/z3hMDc3UwYMsGP6dHe+\n/96fPXtCyMuLYNGiEJydlXz33XUCAvYRHHyA5547z/LlaaSkGCubczueeaY527dH8NprF3nzzXiD\n5HBcXU1ZvDiQCRPidI570IaBA5tx+XI5167pTqADtG9vqdcjMTSsBXVEIs0jEQR05v42b74hqdCl\nulrNL79c45ln9M9sX7w4jn79PAgJMdygvBP3XykMEBZmz6hRMXq3GzHCk++/T6K0VP8FMmCAI3v2\n5OpUBG7f3pbffsvW+1kODgqDPZK8PP3JdoCYmFI6dbJmxw7dluS6dXksXOhL8+ZmpKQYHrL4v/+L\nZ8+eTsye7c+cOdItxvJyFWPHnuGll3zZvj2Czz9P4ocfUiWXjepDQUENkZE36vUy2OPubkp4uCXh\n4VaMH+/EwoW+WFnJ+f33PKqrRZKSKm8+qkhKqjSoMqcxaNNGQUCAgvXrGzcTRCbT9De1bGlOSIg5\nrVtb3nquqVETG1tOXFw5MTEl/PRTNmfPllFWpl24tLHw9jZn2jRfHnjAme7dD3PlShmG3GqUShmr\nVrXlgw+SOXHCOGHUJ55w5Ouv9TfZ1SXaNc3B2r1XJycTg4pjwLDQVmRkBz79NFlrP83u3XkcPBjB\njBn6Cw6WLk0iKqoPs2fHUlOjfV2VltawYMF53nsvnMcf1636oQ/3JZF88MHZBmcZ34np0wMZM0aa\n1MKDDzoSGandTZbLBcLCrPVKPECdR2LYRZmdXSPJMjt+vIRnntHvaVRWqlmxIoenn3bnvfd0D/Bq\nCLW1IqNGneHEia6cPFmksyS6IXzxRTL79+cyb14r3norgAULElm6NIWKiqYflJGRUU1GRvVtTWmO\njiYEB1sQHGyBn58ZHTta4ednRosWZqhUkJRUS1JSDUlJNVy5Uk1enpr8fBX5+WoKCjTPlZXGSV+0\nb2/KvHnN6NXLjPT0Wp1EoiEJOV5eJnh6KvD0NLn5uu7ZCzc3U+Liyikr+4M01qzJJS6unJycP69j\n3t/fkpkz/Rk2zJVFi5KIiDhIWZnh/88ffgglJ6eaL7/UrjGlCz4+SgYMsOOZZ/QbOMHB5qSkVOlN\n5Ds6Gu6R5OXVSO7Av3y5jLAwG61EkpRUTmWlmpAQK+LidN9jrlwpIT6+hCFD3NmwQfd3+NVXF3n1\n1daEhdlzTvvAWL24L4nkl1+uArobc3r0cKKyUs2JE/qT7DIZ9OvnwCuvaB9WEBhoyfbtuVoVOOvD\nmNBWTk4tHTvqDwXFxJTy3Xe6O1zrsHRpNtu3t2Lu3GSjVHizs6sZOfIsv/8eTteux0hMNMyyjo0t\n4eGHYwgPt2XWrABmzvRn4cIkvvrqmsEVOoYiN7eW6OhioqPvXpT29ib4+bnRooUCPz8F/v6mDB1q\nir29HHt72a1ntRry8+sIRkVBgfqWzLhKpRFqVKm4+Sxiby/jkUcskMsF5HKQyQQcHeVs2OCElZWA\npaWAlZUMKyvZrdenT1fh768gLa2WtLRaUlM1z6dPV5GWVkNqajYZGdVUV/91KsohIda89ZY/AwY4\n8cUXyfj776Ow0DgCe/ddf/z9Lejd+ziiaNzYgylT3Fi27Abl5foNr65dbTl4UL/xZ0xoq6hIK2cV\n4wAAIABJREFURefO0opSzp0rpm9f3Y3Cu3bl8sADjsTFaR/KV4fvvkti5EhPvURSUaFi9uzTvPxy\nCJMmSTrVBnFfEomUMMnEib789FOypM/r0MGWjIwqMjK0x1vbtrVGrZa2mEURg8NJGRk1eHjoTxxr\nbipqfHyUeuPDsbHlpKZWMmiQA1u3GhfqOHaskPfeS2DDhnZ06XLM4LJQgNOni3j00ZOEhFgza1YA\nSUn9+OKLZBYvTtZbAXcvkJ9fS35+FSdP6v7+LCwEmjW7nVzMzBSYmFCPLDSvZTKN+m9+vhonJ/mt\nEGlZmcgvv5RRWlpDWZmasjI1paXizWc15eWiznAq6M8B3Cu0a2fL228H0LVrMxYuTOK55843ygAY\nM8adp57yICLi6E3v23AiMTeXMXGiM506STOve/a00dv1Dppw25Urhq3Zigo1CoUgSbjx/PkSndMO\nQUMkkyd7sXCh/mOvX5/KokVt8fQ0Jy1Nd85rxYoE5s5tr/9DdeC+JBJ9sLIyYfhwD2bMkHaxPfCA\no85hWABt2lhz4YLuGSR18PEx49w5w9SDS0vV1NaK2NrKKSrSvVjXrs2jUycrSYnG775LZ/JkD6OJ\nBDSzoSMibPnoo0BeffWy0TNG4uJKGDPmNIGBlsyY4U9iooZQtm7N5uzZ4r/F7JL6KC8XKS+vJf22\ngQa6Y+Jz5hTh62vCRx81Y8QICwoL1WzaVM4/RbTR0lJOjx4OTJ3qQ1iYDQsWJDJu3JlGhyS7d2/G\nwoVB9O0bY1Bl150YM8aVo0dLSE6WRrA9e9owe7Z+7a7gYDNiYw0vQiguVmFjI9dbNhwXV4q/vyUK\nhUBNTcPX+b59efz8cxuUSpne0H1VlZrffktj/HgfPvpIx9hHNF3xn36qa26KftyXVVv68PjjXkRH\n53DjhrSLTQqRhIZac/68NCKxsZFTUmL4wktPr8HDQ3/yLimpkgEDpFVirF2bjUIh4O2tuyNeHyZP\njiUgwJLly9s0Wh/rypUynn76HB06RFNermLZsrbk5T1IZGQEb77pT5cuzVAo/rkaXMnJtYwenUPr\n1um89JJhkh9/NhwdTRk+3JVPPw0mJqYHWVkP8PLLPvz+exYtWuxj8eLkRpNIixYWrFvXjvHjzxMX\n1zj156lTvfjiC2nNjr6+SmQygcRE/Z6Gp6cpaWmGk31xsQpbW/2eVVWVmuTkclq10l5xWVRUS2xs\nCd26SWsm/umnZCZO9JW07Tff6CYbffhXEokhYS1raznm5nKio3XnUtq0kU4k1taGqYjWIT29WhKR\n7N9fRJ8+0hqNysvVnDtXymuvGdcoWIeaGpFHHjmNlZUJa9a0bZIb/bVrFSxYkEhoaBT+/vv47rvr\nuLoq+fLLUPLyBrJ7d2feeSeAnj0dUCr/eZfylSu17N6t/Sbm5ta0o5GloHlzc8aO9eCbb9oQF9eb\nhIS+PPusN7m51bz6ahyOjjsZPDiGpUtTmqTSzs7OhK1b2/Pee1f1Gmv60L27HebmMkmhKoCePW2J\njtZfrg/g6akgLc1wT6moqFayTty5cyWEhenOqezalcuAAdLmr584kU9NjZquXfUTT1lZ40LI92lo\nqwZto279/W0IDLRi27Z4QH+opFcvFwoLy6ms1H6R29mZYmtrwvXr9WMc2hN41tYBlJRk0fB0O+1E\nkZHhgLt7EaDbFY+LA2vrVnh5FZOaqs8dr+Hzz8uIixvI3LmnyMmRGnO/+8KrqoIRI/ayZk131q9v\nzeOPH5RUPScFubkmbNpUyKZNmgozW1sF3bo50quXE/Pnt6SqSkXLltakp1eQkVFBRkblzWcFGRlV\npKdXk5FRRW5ujZ6cg/a/797ij+P17m3BwoUuhIQoMTW9czTynZBmJVtYyHB3V+LuboqbmxJ3dwvc\n3c3veJiRklLOxYvFHDyYw7ffxnPuXOEdub+m+17c3c357bce/PzzVb799mIDWxh2e5o6NZAvv4xH\nFPWX4AP07OlBdHQ6oH97T88Q0tKSaDgnpf1/UFQUgI1NPtBQVePt3+W5c9mEhZnx66/ajdbdu5NZ\nvDicmTOlGa3LlsXz1FMeHDnSkOHcdOHU+5RItGP8eH+WL0+QPLWsXz9X9u7VfaGFhtoRG1so8QYF\nNjYKvdItDSE9vVKy/POBA7n06ePEL7/oj/9mZVWybl0aL70UwOzZDc+Fl4qaGjUjRx5kxYpubNrU\ni0ceiaaysukrsIqKaoiMzCQyUhPGkMkEnJ2VeHj8cWP08DCnc2cb3N1NcXdX4uGh5Ny5UsLDrSgt\nVVFSotLyXEtmpikKhYzKSvWtcblVVXWjc+u/p6amRlOZpVaLqFTym6//qNyqey2KInK57GYCXnPO\nf7yWMXSoFePG2dKihSnm5hoP68EHLW9NWlQqZbdNXxRFcHS0xMpKjrW1/Lbn+q8tLTXnlJWlIdPM\nzGoyMgrIyKjgzJlCMjPryLdCUtVhUyA01I6tW3uzZMkVPv64IRIxDN7eFtjaKvj552RuH7qqHT17\nOvLZZ/pLhJVKGTY2JgYYWX+guLgGW1tpvSRnz+YzerSPzm2OH88lN7cSBwcleXn6z2f58gRiY0cw\nbdrRe1JaX4d/FZEIAowd68/w4bpnstdH//6uTJx4VOc2bdrYceGCdCkJa2sTSkqMIZIKWrWSVk64\nf3+OZCIBWLDgMseO9efjjy9LatDUhdpakTFjDrNsWRe2bu3N0KEHjKrmMgRqtUhWViVZWZWcOlVf\nl+n2nhoTE+Gum661tcldN2NQ4uAgx8zMBKVSuOtmXvdeTY1Is2byetVZghaiELh6tRp/f9N6pcF/\nEI0giAQGKu8axfvKK/b1Zr/fTmr5+SqKimpJT6+6iwjvJMi7e5CkWe33ApqRDd146aWTrF3bNCOW\nZ80K5tSpgptEqP/G7e5uRrNmCi5e1F/66+FhTnp6pWRDsT6KimqwsZFGJJcuFfPgg246t6mtFamp\nUdO7txvr11/T+5mZmeUcPXqDESN8WbHCcKkZqfhXEUnPnm6UltYQGytNAM7FRWPZnj6te/vQUDvO\nn5dOJBqPxPCbdXJyGS1bSieSGTNaSv7sxMRS9u7NZvJkPxYuNFyu+06oVCJPPnmU77+PYPv2vjz0\n0P5GE1RToLZWpKBAylwJ4+RjjF9StZiYwJgxtnzwgRO2tjKsreU8/HCqHn2nv44QjMHTT7fggw/C\nGDEimsOHDWti1QYfH0tGjPAkMDBS8j6dO9vz228ZkshBSgmtNuTkVGFuLi3XlZpajo2NAltbBUVF\n2g3Nffsy6dvXXRKRACxbdpXnngu6p0Tyz8tQNgLjx/vz66/Sv8y+fd2Jirqhtz/Ey8uC8+elq5Ma\n75FU0ru3tIqN+PhSTE1lBulZzZ9/mVdfbdlkCrpqtcikScc4ejSHvXv70bWrfinsfzNqa+GXX4rw\n9k7g2Wez2Ly5ROec8X8a5s4NY+bMEHr12t1kJAIab+TrrxPIz5eeDB861I1z56Qm2rUTSd++TnrX\ni7W1dOPi8uViWrXSPW5h374M+vZ1l/yZW7akoFKJeHnpbtJuDP41RGJmJmfECB9WrpSm9AvQr587\ne/fq1+uJiHDkyhVpyS+ZTCAhodSoUE9SUhl+ftIvhv37c+jVS/rckTNnCoiLK2Ls2MZVcNWHKMKM\nGWeZP/8i69Z1Z9Gi9lha/qscYYOhVsPq1cUMG5Z2XxCJqamM5cu70q+fC1267JS8VqTA19eS4cM9\n+Owz6V60IMCgQS5ERupf26ApgT53ruGIw4YNnbGw0O5xVFSoJHskoAlvtWqlu+Ly3Lk8nJzMcHOT\nZiRWVam4fr2UJ57QLy9vLP41RDJkSHNOnswlM1O6jIcUInFwUCKTwY0b0rpelUoZAQGGTSesQ0lJ\nLeXlKlxcpKnXbtuWyUMP6Y653omPPrrEkCHuTT4rfcOGVFq33oadnSnnzw+mXz9pJYz/4Z+NPn1c\nWLOmOzKZQN++e8nNbdpO/FmzgvnqqwS9U1Dro337ZuTkVHP9urR7QViYLfn5d0cQBEHT3KwrTF1R\nocLMTDqRXLxYpNcjEUU4cCCTPn2kr+3lyxMYO1aadJIx+NcQyfjxAfz6q/75JHXw87PG1FTG5cu6\nk3FBQTZ6t6kPpVJmlCx2HRITy2jRQppXsmvXDQYMcDYoVBUVlYOVlYmk4TiGoqCgmqeeOsqLL57k\nhx86s3RphOSKlv/wz0JYWDO2b+/Dd99FsGrVdcaMOdzk1Xt+fpYMG+bBwoXxBu330EOukr0RAH9/\nKxIS7m6UtLY2oby8Vmfo23CPRD+RgOHhrUOHsrC1VRAaai95H0PwryASR0czevRwYcOGa5L36dLF\nmeXL9edTgoJsiI+XTiRmZnKqqoxfUElJZXqH1tQhN7eaCxeKJOdV6vD66+d4992Qe3aT37Ejg9DQ\nrVRXq4mNfZghQ3SPBr0fIQjw6KNW7NnjgaXlP7dL/074+lqxfHlXtm/vw+bNaQQHb22yyqw7MWtW\nMEuWXDVYIHLwYOlhLdCoGick3C1pZGur0Hts44hEfzPx/v2ZBhGJKMKKFYmMG3dvvJJ/BZGMGuXH\ntm2pBnVvPvCAJ1ev6ieIli0N80jMzOSN9kgMyZNs3pzJ0KGGhbfOny9ky5YM3nqrlaGnJxklJbW8\n+OIJxo49zEcfteX333vy/PMBuLo2Tqrln4Dhwy25etWbn35yoUcPc+zs/vwO9qaGo6OSRYvac+LE\nQOLjiwkI2MzXX1/VOQ+jMWjZ0hofH0sWLTKswtDZWUlgoBWHD0uTprG0lGNnpyA9/e5ku77qKtDo\nWBlCJImJpXh4WOgNh126VIiZmRwfH+lh8hUrEhgzpgUyWdMbLv+KrGenTk4sW2bYBdezpysffnhW\n73ZBQTb88IP0BL4mtGW8R5KYWEq/fs6St9+yJYtdu7oxdaphwwbeeecCsbED+frrRK5dM0xg0hBE\nR98gLCySoUM9efRRLz78MIy4uCI2bEhl48bUe3psXZDLNdLhLi5ynJxMcHCQY2kpYGkpw9KyTupd\nduu9up8zM1W0aKFAEEAQhJvP3OonadvW7LZeEZVKZOVKV4qKam/1iNQ1Ota9Li7WSNXn5qrIza0l\nL0/zOj9fheretufohCBAYKANI0d68/LLgaxYcY1WrbYY1bhnKJYsac/mzRkGeyMPPujC3r05WoUR\n70SLFlYkJZU1WCasIRLdxqmhHolKJbJ5cxr+/lbExuquKtu9O51evdy4dk1ayP7ixUJyciro2dOV\nAwek6ZFJxX1PJD4+Vgwc6MnTT0dL3qd5cyssLEyIjy8CdHeSGxPaagyRJCWV8eyz0j2Sy5dLqKxU\n07atLWfPSit3BE23+6JFV5g3rw2jR+tuyGwsVCqRjRs1xGFqKqNvX1dGjPBixowQ0tLK2bAhlQ0b\nMrh0ybhpeXWQycDRUYGrqymurqY0a2aCt7cZLi6an11c6h4K7OwU5OeryM6uJTu7ltxcFRUVIqWl\n6lty77m5qluv62Tfy8oEBEHTda55aJoO634eNMiCoUOtCA01vdXQ+M03RZSU1NxqdDQzk9V7LWBv\nLyMw0BRHRzkODvJbz82aySkp8Sc3t4bc3BrOni1FFCE1tYqUlEpSU6tITa0iPb1K8o1TF5yclERE\nuNCpkwMREY507GhPYWENP/+cRKdOO0lObpzgolSMGuWFo6OSL7+UnvOsQ1OFtQDs7PR7JOXlKoPG\nUUNdQY6NXiI5fvwG3bu78vPP0r+H5cs14a3/iMRAjBrlx/r11wySIO/Rw5XoaP0Xm4mJDAsLExIT\npS8gTY7EeHf/6tVSg6UONm/OZMgQN4OIBODTT+OJjx9M584OHDv256jUVler2bEjgx07MnjhhRi6\nd3dixAgvdu3qxenTBbi7m1NWpqleKy+vpaxM81xerrr1fmFhNV5eFri6muHqaourqylubqY4Oioo\nKKglK6uarKxqLl0qp7paTUZGNadPl5KdXX3rkZtrb2Tpre4ldfp0FR98UEDbtkrmz3egf38L9u8v\nJyvLcCteJgM7uzwcHBQ4OmoePj5meHkpCQtzxMtLiZeXEldXU/Lyam4Ry4ULpSiVzlRWqqmsVN18\nqKmoUNX7WYVaLRIaakdEhAMREfY0a2bKiRN5HD+exxdfxBMTkye5WrGpYG1twieftGXkyCMGjxVQ\nKmW0bGnNtGnnJe/j72/F1asNr28poa2aGrXkzvY6JCaWSsqDHjqUzUsvhRj02atWJbJ58wCUSjlV\nVf9pbUnGqFF+vPLKcYP26dHDhYMH9ROJn58lFRUqg+LAjQ1tZWVVER5uh5OTUnIIYcuWTD75JJS5\nc/UJAN6OigoVs2ZdYOHCdnTpskfv9kFBNjzxhA/vvit9oeqCSiUSFXWDqKgbTJ9+Dh8fSxwclFhY\nyLG0NMHCQo6FhQmWln88awoERMrLVRw5kkdmZukt4rhxo0ayxtq9xtmzVTz4YAYODjLy8owzLDQT\nGmvJz6/l6lXtnddyuYCrq+ktYrG0lOPqWouZmQx7e1PMzeWYmdU9ZJiZaRSvr10ro7ZWZMeOTObM\niSU+vgRR/GvVCd57rzW7dmVx9Kjhhs3AgS4UFtZIHh8BEBBgxYkTDTcbm5gIenti1GrR4JxEQkIJ\nYWH6x0DExhbg6mqOk5MZOTnSCD0zs5ySkhoGDfJk06am63T/S4hEEITHgfeAIKCjKIqnb77vA1wC\n6u54R0VRnHLzd+2BZYAZECmK4jR9xwkMtMXFxVwSKdRHz55ufPON/puun581SUmGufMmJrJGS4XE\nxhYTEmLNgQPSFsShQ3nk5FTRvLk5KSmGST0sX36Nxx7z5IknmrNqlW7druzsSgYMcMXT04Jnnz3e\npIOoRFEjEZOcbGjOxFipkz8HxpKIIVCpRNLTNSGuY8fq3v1nSasAtG5ty7hx3oSE7DBq/9GjPVm9\n2rA58F5e5ixbdq3B3/n4WNzKdWmDSiUilxtGJImJpYwY4aV3O7Va5MiRG3Tr5sKmTdIr49asSWLk\nSL8mJZK/qmrrAvAI0FDiIkEUxXY3H1Pqvf81MEkUxQAgQBCEgfoOMmqUH+vWJUsegQvc7Bg15/x5\n/bPc/fysDCYSuZxGS5DExRUTEiJt3ghoLuaUlApGj9Z/cd4JUYT33otj0aJ2epWHCwqq6d9/Lx4e\n5qxb1+MfOSPkP/x98dVX7Zk9O9aopkYLCzmDBrmyfn26/o3roXNney5daniNu7iY6fVujCGShIQS\nWrSQpql36FAW3bsb1ty7YcM1Bg3yxNy86fyIv2Sli6J4WRRFyWVUgiC4AdaiKMbcfOsXYLi+/UaP\n9mP16iSDzq1bNxcOH86WRD7GEIkgCAYRW0OIiyumdWvpRAKwfHkK48cbTiSgkU758sur/PBDR73b\nlperGDo0ipoaNdu29cHK6r6Pnv6HPwETJvigVMpYutSw9VyHIUPcOHIkj7w86R3wzZtr8nHaNLyc\nnZVkZzc9kaSklOHubo5Cof/2fOhQNt27G+Z15+RUcuJELoMHNzdoP13Qe6aCIOwTBOGhO977rsnO\n4G74CoJwRhCEA4IgdL/5ngdQ3ydNv/meVrRu3QwLCxOOHWtoeJR2dOvmwr59GZK29fOzIjHRMN0g\nmQyj5KjrQxPaMoxIDh/Ow8rKhDZt9HfNNoSPPrqEvb2S55/X3/FeXa3miScOk5BQwr59/XFwkCbp\n8m+Bq6ucRYscCQ01/atP5U+HXC7wxhvBODlJvyY8PMwZPbo5L7xwymgjzJiwVmioLRcuaK8UdHFR\n3hOPpLZWJC2tHB8f/dWZMTE5hIRo7nWGYM2aJEaNarrmRCkeiS/wpiAI79Z7T69pKgjCbkEQLjTw\nGKJjtwzASxTFdsCrwEpBEKT5eLdhK/b2O5k8+f8Aw+QTunRx4fRpaYm8v84jKTHYI9F0tqYybpxx\nXkltrciECceYOzdUUkWJWi3y/PMx7N6dycGDA/D0lK5CfL/CxkbG/PkOJCb6MGWKHe3b3//Nl/XR\nvLkl+/f354EHXCUnoOVygVWrunDoUI7ecQ7aYGuroE8fJzZtkmYg1iE01IYLF7RXOrq4KMnO1p3k\nVqkMT7aD9PBWVZWKbdtSad/eMPWKjRuvYW6eiEIRCWy9+TAeUmisEOgLLBYEYQswXsoHi6I4wNCT\nEUWxGqi++fq0IAiJQAAaD8Sz3qaeN9/Tgn58993TjB27B82IS2llbgqFjLZt7Tl5MlPSPn5+liQl\nSf98AJnM7mblizZPRv+/JCcHamvVuLnVkJlZlzzXn8BfsSKW3bsHMWPGQaPI7PLlEv73v9MsW9ae\nXr22SfqMWbOOkZ9fxr59fXniiX2cOmVY4cMfMLZAwTCP9A8clrSVk5MFfn52uLpa4uxsdXPglRyl\n0uS258ceC8bVVUPAJiYyampUPPZYMY6O1ygqqqS4uJbi4qqbj2qKiirJzCyVUBFomEHReBgXqnzs\nsUCWLOnKJ59c4JNPzkv2yufM6UBFRRUffRSjf2MtGD48gH370iku1nct3P63hYZasHNnOtrGZmtC\nW7mAdvFHtdoMuVyN9vXeME6cyMbDQw40VBxz+/0mLa2Y7t2dOHgwVfLn5+fXIJd3Z8SI51mzps7Y\nlj7P5U5IuipEzZ1viiAITwEHAf21adJxi64FQXAECkRRVAmC4IeGRJJEUSwUBKFYEIQIIAYNmS3W\n9oEhIQ6Ymck5dcqwmQdhYQ4kJBRRWqqfGBwdzaiuVhs8MlfjkRi0S4OIjS2gdetm9YhEPy5dKiQr\nq4Levd0kh+/uxOLFcQwb5s1rr4WyYIG0Mt9PP73A5cuFbN36IKtXJzB7doxR81j+CigUMnx97WjR\nohl+fs3w87Or92hGVVUtSUmFnDiRiUIho6qqhqoqFVVVtVRVqSgoqKCqSkVsrCbv5uJiiUz2R9e7\ni4slgYEO2NiYYWOjxMbGFBsbJba2SuzszMjIKOHChRxiY3OIjb1BbGwOiYkFTVoRdy9hYWHC4sXd\n6dnTnYce2snJk7mS9x0wwIOnngogPHxTo8LBXbu6SNLNuxOhoc345JOGR08rlXIsLOQUFurOudTW\nqg1SHK9DWVktgYF2kraNibnBE08EGHyMtWuvMGpUy3pEYjykEMk3dS9EUVwmCMIF4MXGHFQQhEfQ\nEIEjsE0QhDOiKA4CegFzBEGoAdTAc6Io1g0CmIKm/NccTfmv1hrARx8NYMMGw5NynTu7cOyYtLJI\nb28r9u83vDtUkyNp/E3g2LEcwsKasXu3YYSwfHkC48b5G00koggTJ0Zz4sQwtm9PlTxtctu2VEJC\n1vPxxx24eHE006cfZv164xKn9xKmpjIiIlzp3bsNvXt74+JiiampnKSkApKSCklKKuTw4VSSkgpJ\nTi6kqOjOGHnDntOiRZq62/BwN+bM6c3gwQHs3ZvMZ5/VqQbcvRQVChmBgQ60bu1E69ZOTJjQhtat\nnXBzsyI+Po8LF25w/Hg+J07c4OTJ7Ebn3poa7do5smrVAI4ezSI8fJMkA60Obm4W/PxzL8aM2c+N\nG8ZNJwTw9bXmkUe8efllw9QZFAoZ/v7WXLrUcGjLyUkpqRlTJpPh7m54WDclpZSwMGlKvceP32DR\nom4GH2PTpgQmTWqNpaWCsrLGGXZ6iUQUxW/v+PkU8HRjDiqK4kZgYwPvrwfWa9nnFBAq5fNHjAjg\nxRcPGXxenTu7sGePtIScj49Vg9IHHTo44uJizrZtDbuZajUGLShtiIsrZOhQw/Mdq1cn8sYboZib\ny7V2yD//fCtyciq0jvK8fr2UF144zK+/9qZfv0jy86WVY+bnV/HMMwfo3t2Nb77pydNPBzF16kGS\nk5tu0JGhUCrlRES40Lu3J716udOpkwsXLxZw4EASn3xyjEOHUikpkV7pow+nT2cyZMgqvL1tGyCh\n21FToyYuLoe4uBzWrPnjfUtLBa1aOdK6tRPe3s78+GM/nJ3N2bEjhcjIa+zalUJBwb3Xu9IGhULG\nSy+1ZsaMcKZNO8yqVVeRMke9DjKZwIoVvfnmm0s6pTxMTATmzu3Au++eorq6YTd/8uSW/PprgsGK\n24GBNpw5k6+1edjFxZzoaP1Gp4mJYJT3eP16Kc2bSxNkvH69BJlMwNPTkrQ06X1WhYVVlJfXMHCg\nD+vXGy43Ux/3ZaG/i4sFR48a3nBliEfi5WXV4D+tRw9X+vXTLu8sCGBt3fhqnZMn8+jQwbAEG0BW\nVgVHj95g9Gjt1VdxcQV89llnnWJz69dfY+fONDZu7G9wX8yhQ5m0a7eOqKgMYmIeZcaMdpJKHZsK\noaEO/N//hfPTT/3JzX2GBQu6Y2lpwiefnMHd/UciItby5pv72L49sUlJpD6uXy+isNA4eZGyshpO\nnsxk2bLzzJkTQ2joSjp2XMvRo5mMHduSa9ee4uDBR5k5swNhYYZfI8YiMNCOjz/uQmrqePz8bOjU\naf1NEjEM77zTDrVa5H//0y2aOnVqCG3b2mslEYVCxsSJgXz7rWGKDgAdOzpy7Zr2QhofHytJA6vk\ncsEoNYWUFOlEAprwVkSE4c23GzcmMHx446u37ksi2bgxweBksqOjGQ4OZsTHNzxS8054elqSmno3\nkdjZmeqMm9bUqFEoGi/jfOVKEQ4OSqNKa5cujeell4K1/v7gwSyOHr3Bm2+G6fycmTNPcONGJd9/\n38Pgc6ipUfPxx2fp0GE93bq5cvbs44wZE4C9fdOXCltZKRg2zI9vv+1DaupENm16CE9PK1atiq9H\nHEfYvv36PyZ3cydSUkr45ptYhg7dirPz98ydewIXF3N++20QaWkTWbKkFxMmBOHtbUQRpA6YmckZ\nNy6QqKhhREUNQ60W6d59E1OnHuLaNcM9zQED3HnyyQDGjj2gcw07O5vz1lthTJ9+TOs2w4d7c+lS\nIVeuGKYxB9CpkyPHj2vPsXp7W5GSot/6NzGRGeWRZGSU4+xsJnlS6fHj2UYRye+/JzJ2bsVHAAAg\nAElEQVR4sG+jDbn7kkg2bDDcCmrb1oHIyBTJcWYvr4bdSDs7pQQiafzXLopw+rRxXsmuXWlYWSno\n0kW7HP0bbxxn6tRgnVaRKMKECQcIDLRl9ux2Bp8HaNzyIUO2M23aYUaN8icpaSzHj49g7txO9Ojh\nhomJcd9Vy5bNeOWVtuzePZyMjKd58cVQLl0qoF+/jbRo8QsvvxzNrl2p/1ji0IWqKhW7dqUwffpB\nAgJ+pVevDVy4kMfgwT4cO/Y4yclP8tNP/XnyySB8fIyr+mrTxoHFi7uTljaBMWMCWLToPF5evzJj\nxjESEgy/cYMmKb5iRR+eeGIf2dm68yIffNCBX35JuKnQ3TCeey5IktRRQ4iIcCImRnthQPPmlly/\nrr/0X+ORGF5do1KJZGVV4OEhTen7+PEbdOokfbxEHTIzy4iPz6d3b+PaAupwX7YdHziQBhhm2Xbt\n6maQBaXLI9EVn24qIgE4cSKXDh0cbpYoSocowpIlF5k6NZijRxsuiUxNLWPx4jgWLOjEqFH7tH5W\nRYWKYcN2c+zYUBITS1ixwjj9nj170tizJw2FQkaXLi48+GBzPvusKwEBthw4kMGuXdfZuTOFxMQi\n5HIBd3dLmje3rvewuvXaw8OK3NwKDhxI54svzjF8eFqjk4n/ZCQmFpGYWMQ332gqkFq2bEbv3h4M\nHOjNvHldqapSERWVwaFDGchkdeXLGhFHzbNJvZ9NcHIyJyTEnh9+uER4+DpSUhovH9+unQMbN/Zn\n3LgDOj0BgPbtHXnoIS+CgtZp3SYgwIbWre3ZuPGawediZiYnKMiWM2e0yyR5e1tJzJEY55HAH+Et\nKYR14sQNwsMdkcsNz8ls3JjAI4/4s3u3UacJ3KdEYowFEB7uyK+/Sh9+pd0j0R3aqq0Vjbay78SJ\nE7mMG2fcbPVly67y7rvhuLqak5XVsPW3YMF5Ll58jF693IiK0p70zM6u4OGHd7Fv32CuXy/h0CHj\nBQFratRER2cSHZ3JrFnHcXQ0o39/Tx54wINZszoQG5tP794eZGeXk5JSQkpKKSkpJcTF5bN9+/Vb\nPxcW/nXJZoBmzcyIiPCkSxdPFAoZJiZyLl/O5fLlXOLjc8nLu/07d3S0YOjQAJ58sg0nTmTw+ut7\n79m5xccXEB9fwLffaoglMNCO3r09cHOzwN3diqoqFZWVmvLl8vJaCgqqbr6noqpK5PLlQk6dyml0\nY20dWrWyY9u2B3nuuUPs2qXbKBIEWLy4C7NmndRZev/ss0EsW3bFqAmN7drZc/Fioc4EffPmlpII\n1FiPBAxLuBcVVbNjRypBQXbExRnWuLlxYwJRUSOZMkX/ttpwXxKJMQgPd+KVV6Q1ocnlAi4u5mRk\nGJsjaRoiOXkyj0WLOhm1b1FRNWvWJDF5chBz555pcJuKChVvvBHD5593pn37TTotnbi4AsaNO8C6\ndf3o0WMrCQmNG0JVh9zcSlavTmD1ak2Iwt5eSXFxjdGL815AJhMICXGiSxcPunRxp3NnT9zdrTlx\nIp1jx9KJi8vBy8uWHj2aM3lyOEFBjtTWqklIyMPZ2QoLCwX29uZUVdViba0kLs6w/qfG4sqVQq5c\nkZYbbOpbhp+fNbt2DeL1149LUrAdO9YfhUKmc+KpUilj7Fh/unffYtQ5RUQ46fWKvL0tuX5dSo7E\nuKotgMTEEtzddQul1kdtrZr27Z0NJpKEhELy8xs3V+Y/IgGcnMyxtlZILkN1dTUnN7eywWqMPytH\nAnDtWimZmRW4uZmTmWl4YnPJkovs2DGQjz46q7Wy5LffknnxxWAmTw7im28u6fy83bvTef3146xb\n14/x4w9I7jExBFJLjf8MdOvmyXPPtWPYsJZkZJRw7Fg6R4+msnDhMWJjb+hJFlsSEuLEK6905oEH\nWmBiIsPUVElVVS2xsX8ukfxV8PCwYM+ewbz//mlWrtQ/rtrKSsG8eR159NE9OnOZEyYEsH9/BklJ\nxpWVd+rkyI4d2j0jzSwcE0kzQFQq0aiiA4CCgiq8vaVXbp05k0u7do788ovhDYb/lf82Adq1c+T0\naekdt66u5uzd23BDn8Yj+XNyJKCp7jBU/bMOsbEFXL1azCOP+OjcburUw0ye3BIvL/2JvxUrEpk3\n7xx79w6md283o87rn4DXXovgt98eZdeuZHx9l9Cq1bdMnLiV7747xfnz+tWjb9woY//+awwduhoP\nj8/YtSuJiopaTExkvPVWVyZPboepqfRZ3/80ODmZsWfPYL788iJLl0q78b33Xjt++eWqTm9BLhd4\n880wvvpKt9GjC506OepNtEup2AKwtTXFwcE4TbWsrHLc3KQ3M545k0vbtg5GHWvr1sY1B/9HJGjy\nI6dPS7cCPTwssbNrOJmfkFCsM3ZbXa2SlDyTiqiobHr2NH5408cfn2PixECd28TFFbJ2bRK//NJb\nkgCdZnDOPlav7svo0X5Gn9vfETY2Stavf5SRI4OJiPiJ5ctjyc83vvMaIC+vgkGDVjNt2k6Ki6t5\n/vkdDBsWSHLyi7z+eucm6Tv6O8HPz5rFi7uwenUSn312QdI+Awd68thjvsyfr1uW5/HHfcnIKOfw\nYePydI6OZiQmluisBvPxseLECWmGp4WFCeXlxunEZWZW4OoqPbSlIRLj+oZOnWrcoLP/iARNfsQQ\nj8TZ2VxreWKPHq46K4QqKlS0aSNN+kAKoqOz6NXLsME29bFjRxpeXpY8+KCnzu0WLLiATAavvtpa\n0udGRWXSr18k8+d34vXXJQkS/O3Rpo0zJ08+TWZmKT16/EJKStPkgeqwdOlZ7O0/ZevWqzz88BoG\nDVpNeLgrSUkvMmtWN6ys/vmE8vDDzTl6dCgHD2YxZ85pSfs4Oprxww89ePLJaIqKdDeIzpzZlo8+\n0t3IqAu9e7tQXa3WGToLCrKVHGJtDJEY6pHk5VVSUlKDr6/hvUKNldf5j0gw3CNxcTFvUP+nrnlI\nVydrWVktlpbSU1N2dqY6m5LOnMmneXNLoxv5RBHmzj3Lu+/q7gNRq0UmTIjijTfaSCbCuLgCunbd\nzIQJAXz+eRej5LT/LnjyyTbs2TOGd9+NZurUnVRXGya5YQzOn7/BmDGbiIj4CTs7M86cmUTHjtpV\nE/7OkMkE/ve/DixZ0pVhw3YbFHr6/vse/Pprgs7KQYCHHvJCrRbZvt2wuSP10aePG/v361aobtnS\nlvh4aUZE4zyScoM8kv9n77zDorjaNv4bem8WiqKAghUbChZQbFhi7yWxRhM1YolJNCYxiRqjfsZo\niomxxdh7byh2sSEWVFSaNAFFkSKd+f5YMUS3zOxCkjd6X9dcu+zOmRl2d85znnbfAFevPqJx40pa\nnU8XvPaGxNLSkMjIDO7dk95EZW+v3COR8qPJyirAwkI679CxY51o3Fh13LOoSCQk5CF+ftp7Jdu2\nxWBtbUTHjmq1wrh/P4upUy+wfr2/JHoIgMTEZ/j57cPT05bNm9tJHvdvgYmJAcuXd2X69Bb4+69j\n48abf/s1REen89FHx5g+/Tj79g1g+vSW/1NGuWJFEw4f7kzz5pVo2nSXLLG5d9+tRbVq5nz+eajG\nfRXeyDVdLhV/fweNZKweHlZqQ1+loYshycgowNBQT5ZoVUnC/e/Ga29IPD0rYGNjJKsmXhdDUlLX\nLjXhfvt2OnXqqFc1PHlSt/BWcbHI7NlhzJrVROO+69dHcvNmOvPmaZbdLcHTp/l07nyIvLwiVq9u\n94/80LVB8+ZVWL++J5aWRjRrtppbt6SHP8sD27dH0LTpKrp0qUFQ0BCcnMqW7qQ84ONjT2hoPy5e\nfEhAwCFJlU4lcHe34ptvmjJkyAmN/SB+fg5UrmzCtm0xWl+rvb0pjo6mXLumvtpQ4ZFIMyTm5gY8\ne6a99yrXK3ljSP4heHraER6uuoNVGVQZElNTaasPOV7JrVtPqVtXvS7ByZPJtG6tW4XUli0xVKhg\nTLt2mkMn48adpW9fFzp0UO/BlEZ+fjFvv32Cw4fjOHDgLX74wRdr639nzN/GxphffunC9u192bLl\nNoMH7yIrq3zIG+UiPj6Dtm3Xcfz4fa5cGUWPHq7/9CWpxPjx9dizpwsTJ55h5szLshZrBgYC69b5\n8+WXV4iI0NzjMmNGQ+bPv65Tk6S/vwOnTqmvuLO0NMTa2pDERGkaI2ZmBjqxKkRFZWBvL73q6+rV\nR5JpVcoS/+E+EmmVNPXrW3PjRrKa/V81DApltCevjDEzMyUnp0DjubOy8rGwKOTJE2X7/fUruX07\nlREjaqs95uXLCXh4dMTKqoiMDO0mvOJimDPnIrNmNSQ4WH1N/5MnOYwYEcRXX/lw40YSKSnShXvW\nrLnK7t0RfPNNK27dGsgnn5xm3TrtSzU1Q15YYejQuixc6M+OHRHUrfurRqr3VyHtlnJ0tMDc3JDI\nSPm9Ngpm3DMcOxbD+vW96NSpCh9+eJzcXDn/q7a3vuZJsXXrqnz2mQ+FhcW0bLmRqKh05NDIA0yb\n5kVaWg4//6w5Id+qlSP6+iJ//HEdhYyRdvD3r8jx4/Gou9c8PCy4d+8polh6H9Wfu5mZwLNnOWqP\nqQ7Z2fnY2xu8NF71d3D/fg41a1rpNBdogzceiWdFbtyQF7KwtzdTmmw3MzOU5JEoEu7Sbqzbt59Q\np456j6SgoJgNG+7QqpVuXsmmTXdxcDDD31+zpxEcnMCBA7Hs3dtNVgwX4MmTXMaNO0avXnuYPLkJ\nJ04MoF497erfywru7rYcPTqQDz9sRq9eO/ngg8NaGBFpGDmyAVFR41m2rItOxwkJSaRRozXY2pqw\nbt1bjBrl+Y/2nrRr58yJEwNYuTKADRsi6NFj93MjIg9vv12LMWPq8c47RzTuKwiwaJEvv/9+WyWd\nvFS0bVuFEyfUU7TUqmUjmSEcFL91XbrGHz/OldWHIopw69bjv/1+eu0NSf36FQgPT5O8v76+wO3b\nj5X+OBQ5Es0rNoVHIs2QREZm4OxsgbGx+gkiMvIp3bq5SDqmKhQVicycGcL48dLKdefNC+XWrces\nX99Jq+TvpUvJeHtvYMuWOxw/3p8FC1pLNrBlBWNjfWbNasW5c0PZty+KZs3WcvGifOVLKXB0tCA4\neChLl3bC1NSQGjV0V6zOyMhnyJC9/PDDFfr1q0VMzHvMmNFcZZ9TeSAgoDpnzgzk55/bs2LFDWrX\nXs2aNTe1orFp06YKixb50r37PtLSNE/AAwa4o6cnsHGjdJ48ZXB0NKdiRROuX1c/F7i4WHL7tnQv\n0tOzgkpxLClIS5NnSABu3nxEvXp/b57ktTYkjo7mFBWJpKZKD83Y2hpTq5at0rprQ0M9EhI0Nxte\nu/YIExNpq/jCwmKiozPw8FCfcD948D5dulSXdEx12L49ChcXK4YOrSVp/zFjgrG2NmLhQvlSn6AI\n0/z88zXq119L5cqmbN78FosX++PnV6XcKpP09AT8/Kry/fft2L+/Hw0bVqJx49/5/vvLOmuhCwI4\nOVnSqlVV3nnHky++8GP16m7cuvUeSUmT8Pev/qIfxNnZivbtXTAz0914njwZT9eu2+jUaQseHrZE\nRY3l++/bUb26djTxUtC1qyvnzw9m8WJ/fvzxKnXr/s66dbe1/gxr17Zl8+bODB58mFu3NOctjY31\nmTevBdOmndHYB9G3bw26d1edT+rQoSobN97TeBwvr0qyDImNjTFPnmjvkagyJIIAP/zQTqlKa3h4\nGvXr/70eyX84R6IZ9etXJDxcXljL1tZEJU28sbE+traaVw92diZUrCh9laGo3LLlxg3VN9fNm4/R\n19ejdm1bIiK057gSRQgMPMXWrV3YtStaY6KwoKCYPn0OcO5cP6KinvLzz9I6lV9GauozRow4jIeH\nDQMH1mbp0rY4Opqza1cUO3bc4/jxeK2YXEugry/g5+dM//616NPHneTkbLZtu8uECUHcuSOv2OJl\nuLhYM3RoferUqUifPrV4+jSPmJj0F7rup0/Hs3HjLRwdzWnUyJ533vHE1NQQU1MD5szxp379SoSH\nP+TUqThOnYrj7NkErdUTw8MfMXLkQZycLAgM9CI0dDhBQbH83/9dIjRUfX+EJlSrZkmTJvZ4eVWm\nUaNKVKtmxezZ59m+XfMErAmVK5uyf393PvnkLMHB0vpAJk5swLVraZw6pZyuqDRmzmzG9OnnVL7f\nvbsrBw4ol8cujUaNKjJjxgVJ1weKhacuTNRpabm4u7+6iBRFGDjQg9mzz7+yEL55M40uXVy0Pqc2\neK0NiTb5ETs7Y5WGRCpltNy454ULKVStqpm8rcQr0cWQAJw/n8zx4wl8+mlTZs4M0bh/enoeb721\nl7Nn+xEbm8mBA7Fan/vu3XRmzz7P7NnncXOzpnfvmsya1YKNG99i//5oduyI5PDhWHJyNOeiDAz0\n8PevTr9+tejd2524uEy2bbuDn99GrZLcpWFra0L//nV45x1PatWyY/Pm2/z8cyhjxx5QG978/fcb\nTJ16lObNq9C1a03mzDmDIAh4ezvRunU1Jk1qxoYNvdiz5y4hIYmsXx+uVa4mKSmL6dNPMnduCKNH\nN2DTpu7k5BSQmppDXFzGC7p9xfNM4uMz//KZurlZ4+VlT5MmlZ9v9hQUFBEamsqVK6nMn3+Zs2cT\ndTYgoKh23LOnG+vW3eH336UJUVWoYMLHH3vRqtU2jfv6+NhjaWlIUFCc0vcNDfXo2LEaH3ygnv3b\nysqIypVNZQl3qVt4SoHCI1Fe/hsXl4mzs+UrhiQ8/BH16/+9oa3X2pBUqWIhm2NG8cNQvlo0MNCT\nZEjS0nKxs5NuSKKiMhg+vBbffae+2ergwftMmODJ4sWqKSI++6wZv/4azsOH6qtIPvnkHNevD2bl\nyptER2vu4o2JyaB37/3s3dudgIBdXL2qe89FdPRTFi0KZdGiUJycLOjZswYTJjRk5kwfHB3NEQSe\nb4LSx7CwVCwsDNm27Q4+PuuIjdVOua8ERkb6vPVWTd55x5O2batz6FAU3357jsOHo2XlA0RRkSgP\nCfkzsVvijYDid9S2bXVGjWrI3Ln+7NlzlxUrrnL6tOYV88vIzMzn++8v8/33l/HwsHsu/mVFtWo2\n+Po64exci2rVrHB2tiQrK5+QkAe0bl2FjIz850YjhSVLwrhyJZXkZGlEhXKgpyewbl0Ad++mM2uW\n9JX+F194s2nTXe7d05z4fv99T379NVyl0fP1deLOnSdKC2hKo0GDCoSHP5ZcYiwIinJhTbQu6pCW\nlqNy0Rkfn0m1apavzGGJiVmYmOhToYKJpDxTWeC1NiTe3g7s3q2Zvro0bG2NVfLsSFVDe/w4T5ZH\ncuXKQ5Ys0ZyDOHYsnj/+6Ii5uaHKkJSzswVjx9Zj7tzLao/14EE2ixaFsWiRH71775d0nRcupDBu\n3HFWr+5A374HJBkgqUhKymLZsmssW3YNU1MD7OxMEEUQRVHlY05OIc+e6XYjGRoqvJoOHVwZPboh\nV6+msG5dOCNG7CUjo3yqugoLiwkKiiEoKIYKFUx5++36LFvWBQMDPVauvMrvv98gNVX+pH737mPu\n3i0J471661eqZEqFCiY8epTLo0e6EVFKxVdfeWNnZ8zgwYckj3F3t2bwYA/q1FmncV9bW2N69XJj\n2rQzKvfp1s2F/ftjNR6rYcMKXLsmfYFkbW1MVlaBTr0t6pLtcXEKQ6IMFy4kU7u2HWfPag77lQVe\n62R77dp2suPjtraqk2dSQ1tyPZLY2EzMzAyoXFl9h2tWVgEXL6bQrp1qAsYffrjOuHGekjrrFy++\niqdnBTp0kK7nvH17FIsXX+XUqb40bFg+7nVOTiGJiVkkJWXx4EE2ycnZpKQ8IzX1GQ8f5vDoUQ5p\nablaU1NYWhrRv38d1q/vSUrKZL78sjVRUU9o1GgFHTpsYM2a6+VmRF5GWloOS5Zcon795YwYsZda\ntSoQEfEe27f3pUuXGpialt1a8OHDHCIinvwtRkRfX2D58nb4+VWhd+8Dskp3f/yxDdOnn5O02h4x\nog779sWo3bdbNxf27YvVeKxGjSpy9ar0Ck/FXKHb7yQtLYfkZOXFQHFxGTg7KzckCQlZ1K379yXc\nX1tDYmdngqGhnqxmupJxqn4citCWFI9EfknflSuPaNJEMxnbwYP36dSpmsr3w8PTuHPnCX37apbo\nzcsrYurU0yxZ0lqWPPDatRFMmnSKI0d60rr1/wbJoL29OWPGNGT//r4kJIxjxIgGnDgRR926y2nV\n6neWLw8jIUE7gaKywvnzibz77n6qV/+Rgwej6N+/DikpH3D06EA+/tibxo3tlVbxlBVq1rShXTvp\niwpVMDHRZ+vWLlSvbslbb+2VlYweM6Ye1tbGrFmjuYlVEOD99+uzbFm4yn3c3W0wNzckLEwzaatc\nj0RRsaWbIXn6tIAWLZTTHyk8EuVVeXfvPsHDQ/fycql4bUNbtWrZcueO/ISrra2xUoldUNA6SE22\ny2XrvXLlIU2aVOTQIeUJwxLs3BnN8eO9CQw8pdKlXrLkGtOne7Fpk2ZVtD17YggIqMYnn3gxd+4l\nyde7fXsUT57ksXVrF8aMCWbPHu05kEpDEOCtt9xeJInlVsQIgqKfo0YNG9zcbKhWzZJOnVypW7ci\nBw9Gs2ZNOIMG7SUzs+zzAWWFzMx8Vqy4yooVVwkMPI6/vzMdO7qwYUM37OxMOHYsjiNHYggKiiUx\nUTvtmwoVTPD2dsTHxwEfH0e8vR3IzMzn0KFYgoPl52pKYG1txO7d3UhMzGLgwEOyKvGqVrVg7twW\n+PvvkBQuatu2Krm5RZw7p7ovSGpYS19foG5d9ZWTL8PGxkiW4VGGnJxCDA31lOZfS3IkynD37hNa\ntfr7FnGvsSGRH9YCRXJQVaJaao5EmyajK1ce0b+/Zi8iOvopjx/n0qqVI6dPK4+P7tsXy/ff+9Gs\nmT2XLmkuNpg37zJXrgziwIFYSSu3EgQHJ9Clyx727etOhQomrF6tOxWKubkh77/f4HnC2BI9PYG4\nuAzi47NeGJe4uAySkrIxMtKnRg3LF0ajRg0bXF2tyczMJyoqnaiodCIi0vjqq3OcOBGnU3nxP4Ws\nrHz27Yti3z5Frs/Z2ZKOHV3o1MmVhQv9efgwh2vXUsnLKyInp/D5VkBurljqb8Xm4GBO06b2+Pg4\nULGiKZcuJXPhQjI//3yVESOSZXvvL8PR0ZxDh3pw/HgCU6acll3xtXx5O5YsuSqpxwSgd283fvpJ\nvRBWo0YV2bpVc560Vi1rjh1LJCtLOm9WlSoWZaKGmpVVgKWl4SvejbocyRuPpAzw7bd+LF0aRlKS\n6tWYth6Jg4OZSi0KURQleSSPHuUSHy9vpXjlykPmzfORtO/WrZEMGOCu0pAUF4v8+OMNAgMb8M47\nQRqPl5iYzZQpp/njj454eW0mL096p+6VKw9p02YHhw/3oFIlUxYskF6ZowxZWQV067brxd9WVkY4\nO1u+MCzVqlkSEOCCpaUhJiYGREc/ISoqnZMn44mKUvR26EKi929HfHwmq1bdYNWqGwgC1K9fidq1\n7TAzU/St/LkZYWVl9pfX4uMzOXIkltmzz3PnzuMyKe0tgbu7DYcP92D58pt8+61mSviXMWxYbRwc\nzJg/X5oYloeHDQMGuDN9uurydXt7M3r0cOO9945rPF7z5g6yuavs7c1U5jfkIDMzH0tLo1cMSUlu\nUJm3EhWVjouLFfr6gs5NtlLwnzQkH3zQiNmzz6vdp1YtOzZskL9CtrAwVLkqEUUwMtK8AklNfUbH\njs6yvuTIyKfY2RljZ6e6aqwEW7dGcvJkHyZNUh3eWrXqFtHRw3BwkPZj37DhLj17ujF3bgu1FTDK\ncO9eOq1abefw4R7Y25swbdrJMpukMjLyuXkzjZs3VSVBtUu4/xcginDjxkNu3FDmRf59t76Xlz17\n9/bis8/Os2rVLdnjHR3NWbiwFQEBuyWXWX/yiRc//nhd7aKhX7+a7N0bI4nCpEULB0JC5DV0Ku4t\n3UOkmZkKj+RlFBeLODiYU6mSKQ8e/PU8eXlFJCVl4eJirRXfmVz8I8l2QRAWCoJwWxCEa4Ig7BAE\nwbrUezMEQbgnCEKEIAgBpV73EgThxvP3lqg7/v37GRpXnTVqWGvlkShKa5VPTkVFIvr6mj/SoiKR\ntLRcKleWLqMpirB1a7QkrYF799JJSXmGr69qEsf09DzWrImQTIUCMH78CQYNcqdNG+n08SV48CCb\n1q13YGFhSHBwf1xcyo+64w3+HRAEmDSpMfPn+/H++8e1MiIAy5b58+uv4ZLzDVWrWtCrlxs//qg+\nrDVokLtkjq4WLewJCZHXcyZ1kaYJJR6JMiQnZ+PgoJw2/u7d9L8tvPVPVW0dAeqJotgQuAvMABAE\noS4wEKgLdAZ+FoQXdSjLgNGiKLoD7oIgdFZ1cE1NhoIANWrYEBkp31Kbm6v2SBSGRFrZTFJStiw9\nZoDU1Bz8/KQl0LZsUYS31OGnn64zfbqXZF2QtLRcxo49zpo1HZSukDQhPT2PceOOsXdvNBcvDmHM\nmP+GlvsbvAo3N2tOnBhA377uvPfeUa2LLfr1q4GrqxVz5kgv9Pjww8asWnVLbcWUs7MFtWvbcvSo\n5sIBa2sjqle3lJVoB3BwMNc5rwQKr1vV/Zac/Ax7e+XziCJPop45vKzwjxgSURSDRFEs8VEvACWN\nDz2BjaIoFoiiGAtEAj6CIDgClqIoXny+31qgl6rjazIkjo4WZGTkSaLZeBkWFqqb/YqKiiUbkgcP\nnuHkJE+A5syZB/j6SlNC3Lr1Hn371lBLfBgV9ZS9e2OYNKmR5Gs4cCCWw4fj+P771pLHlEZxsch3\n34XSps0Wxozx5ODBPlSpopn+5Q3+NyAIMG5cQy5cGMKuXZH4+2/VOrRSt64dP//clqFDj0juM6lQ\nwYRhw2rz3Xeq2R0ABg50Z8eOKEkFFt7elQkNfSibzbjsPJIClR5JSopqj+T69cObIesAACAASURB\nVFRJ3H9lgX9DH8ko4MDz505Aaca2BKCKktcTn7+uFFeuqNeEdnOzJiZGu65rc3PVimfFxaJkxtqk\npGzZhiQkJBlv78qSejoiI5/y4MEzjR7M3LmXmTixgSy1wg8/PIOvrxPdu7tIHvMybt9+TIsWGzlz\nJpErV97mnXfqaH2sN/h3oFo1S44c6cvw4XXx9d3E4sVXtO7qtrQ0ZMeOrnz00RlZMg+BgQ3ZujXy\nlZzByxg0yENS+Ttolx+Bsku237uXrvKeT05+hoODco8kKekZzZvrplEkFeVmSARBCHqe03h5615q\nn5lAviiKG8ry3OfPrwEOP98iX3nfzc2a6GjteJfKKrT14EE2jo7yDEl6ej6xsZk0bCitY3Xlypt0\n6KC6yx0UXsm+fbEEBjaUfB3Z2QUMGnSQFSvaU7euneRxL6OoSGTu3AsEBGxn2rSm7NzZQ1be6A3+\nPRg9uj6XLw/l6NE4WrXapFX+sTTWrOlIcHCCZBJHUEQLxo3zZMEC9VVh7u42ODmZc/KkehGrEmiT\nHzEw0MPGxqhMuK5sbIyxtVXed5acnI29vfJ5JDb2qYZcZCR/zpOHdbrGcivdEEWxo7r3BUEYAXQF\n2pd6OREo3TpbFYUnksif4a+S11X+CgoK2qLuX5NnSP5qNExNDcjOzkFZNVBRUSH6+q+OUYakpAya\nNKmsZF/1Y8+cScDXtwKhoeobEwF27brL9evD+Oab82rDeHPmhBASMoilS0MlM82GhT1g6tST7Nnz\nFt7eG3RSgbt27SHNmm3giy+as3NnD3bujGT58usvlVu+vtVX5QNtP8+/3le+vlUYPrwuTZpUpm3b\nrWqq56SXXH/ySTOcnMwYPHgfIL3UfOTI+hw9ep/oaPUezKBBNdiy5Q7FxS+X8756jYIAPj6VGT78\nAHLkch0cLLh5M03JOeQjNzcfY2Pl15eS8gwfH+Xh7tjYDKpXt0IQUFEl6fJ8K4FmRUpV+KeqtjoD\nHwE9RVEsPQPtAQYJgmAkCIIr4A5cFEUxGcgQBMHnefL9HWDXKweWCFdXa6KjtYvbmpsbqtTGzs8v\nkqybnZSUhZOT/NzAmTOJtGolrWoqMTGLCxce0KeP+qR7VFQ6+/fHEBjYWNa1rF9/m23b7rFtW3dZ\nFCrKkJ9fxGefnWXMmCM0alSJ6OjRLFzYWhJ9/hv8vdDXF+jf34Pz5wezalUAZ88m4eOzUY0RUUAK\nfUv79tWYNKkx/frtVdmvpQzW1sZ89pkPX32lWfagfftq/PGHtAqyOnUqcPJkvCzxO1Bo1GjL9fYy\n8vKKVArhqavayskpJD09D0dH9feQo6MFO3f21uka/6kcyQ+ABRAkCEKYIAg/A4iieAvYAtwCDgLj\nRfGFLR0PrADuAZGiKEqnC30J2oa2DA311P64i4pEycmtuLhMrRQAz55NwtdXOvXBypXhjB5dX+N+\nc+deIDCwMVZW0nMlAJ9+eobs7AKWLPGXNU4Vbt16zNtvH6Rx43Xo6QlcuzaMtWs706CBZp6xNyhf\nWFgYERjYmHv3RjFxYiPmzbtI7dprJMnqdu3qyrlzg9WGfp2dLVm3rgtDhx6UTe0yfXoz9uyJ0hhS\n8/d3xs7OhNBQ9XnUErRvX42HD+V729WrW3H/ftmwX+flFamU2k5Nfab2no2J0RTegocPn9Gpk4su\nl/iPVW25i6JYXRTFxs+38aXe+0YUxZqiKNYWRfFwqddDRVH0fP5eoC7nVyTb5RsSIyN9tVUeOTmF\nktlY4+MzadlSfiLs/v0MiopEatSQVta3Z08U9epVwM1NvVRvZGQ6+/dH8+678kpyi4tFhgw5gL+/\nM++/30DWWHWIj8/kww9P4ua2kvDwNA4c6Mfhw/3p0EF3OeE3kAcnJwvmzWtNTMxYWrVyYtCg/bRu\nvYXdu6M0JtMFAWbNasGvv3bgww9PqmzANTExYPv27ixaFMrx4/K4vJydLRkzxpNZszR7I2PHerJ8\nufr+ktJo186Z4GDNYeSXUb26ZZkZktzcQkxMlBuSx49z1eZaY2MzcHVVf+8XFhYTEaGbSui/oWrr\nb4WxsT5pabkqiRfVwchIX61HIseQPH6ci56egI2NPPJGgAMHYvD3V59EL0FBQTHr199m5Mh6Gved\nNescM2Z4yy4CyMzMp0eP3Xz5ZQv8/XVnhy2Np0/zWLDgEq6uv7Jhw20WL27H7t29mTPHj06dXFWW\nRf4voUuXGgwY8O+qWqtVy47RoxuweHFbbtwYiZmZId7efzBw4H4uXpRWwWRjY8zevb1o186Zpk3X\nc+6ccsoeAwM9Nm7syokTCfzf/6nXyVGGr79uybJl19RSIoGiNLhLFxfWrZPGaKGnJ9CmTVXZhg0U\nHklsbPl7JE+e5KmVpIiJeYqrq+bm32vXpHloqvDaGRJnZ0vMzAy0KkvUFNrKySmQpQ8RE6N5taAM\n584lERAgfWW+cmU4I0bU0xhKu38/k99+u8G8eb6yrykqKp0hQw6wcWNXatYs+yaogoJifv89HE/P\n1cybd57iYpHp031IShpPaOhwvv++HX37evxPVX117lyD8PCx7NrVnw8/lMajVh4wMNDD29uRqVOb\nsWNHL1JTP+DgwX60aeNMaGgKNWv+xqRJx2R58Q0aVOTy5aHcu5dO+/bbVDbmCQKsXt0JY2N9Zs6U\nR71Tcp7OnV1YsECzARo2rC579kRLZoxu3LgyiYlZWjUVlmVoKzdXdY4kN7eQoiIRMzPl70udY65f\nl07Gqgz/Sa4tdaha1ZKEBO2otY2M9MostAUlqwVrwsLkrQaCguJYtKgNenqCJIN482YaiYlZBARU\n59ChWLX7fvPNBSIiRuLt7SB55VmC4OB4pkw5QVBQX9q12yZp4hk3riGbNt1RKRamDOfPP+D8eQU1\nuJGRPl5e9vj5VWXECE9++60zDx8+4/TpBE6fTiAjI48nT/J48iT3xZaZqV0ljYGBHpaWRlhaGmFl\nZYyVlfGL56UfzcwMMDNTeEolCeYSggZBgOrVrQkIcEUQBAwNFStNJydLPvigKRkZeWRm5j/fSj/P\nJyenQBZhZgkMDfWwtjbG2toYGxsTrK2NsLU1oUGDyvj6VqFZM0eio9M5fTqBzZsjmDjxqNb08wBD\nh9Zh8eI2BAYeZ9OmO2r3/emn9jg7W9Klyw6t2Jfnz2/NnDkXJH2nY8c24N13pVcmKcJa2lHm/105\nEiiRpTDh2bNXv7OoqHS8vTU3Mevqkbx2hsTZ2UJrgaKyDG2BdLfzZSQlZZGcnE3jxpUla86vWhVO\n//4eGg1JVlYBM2eeYcmStrRsuVE2ueKmTXewtTUhOLifRmOiry9Qq5YtN28OY8qUk2zerH7SUYb8\n/CJCQpIICUliwYKLLxhv/fyqUrOmDfXqVcTW1uQvm4mJPk+f/mlgrl1Lxd3dFhMTA0xM9DE2Vjya\nmBhgbKz//HUDLl5MolYtOzIz819M+H8+/vlcETr986Yu+QxLHp8+zaNhw8rY21s8Z0PQQ09PoFat\nCn8xSKWNVnj4Q9q2rY6hoT55eQra99zckseiF89jYtKpUcMWG5s/jYaxsQFPn+aRnp5LenoeT5/m\nkZiYRXx8BgsXXiQkJEly2bc6GBvrs3Bhazp3dqFdu22Eh6vnxpo/34+mTe1p336bViwTHTpUo0YN\na0k5D1/fKhQXi7KkZ9u1q8Yvv1yTfV2gaM4sK0OSkZHH48eqS49LDImyBXJycjatW2uu8nzjkciE\nLh6JIrRVlh5JBrVqaUeqFhQUR0BAdcmGZP36CObO9cXZ2ZL4ePWGdO3aW0yY0IghQ+qwfr18huRl\nyxQ3nyZjUlQkMnnyCTZsiGDFigDefrsO48cf03h96qCe8VYBRbOYMba2JtjZmWBsbIAgKMIEeXlF\nzx/zyM0tfPFaXl5hmdJxz5x5AhcXa77+ug2DB9cjLi6DiRM1N4Xp6QkvjJupqSEmJiaYmho8/9sA\nPT2FuFp6eu5z45H3t9Dmd+vmxnfftWHfvmiaNdug0TB9+qk3Xbu60qbNFq08RD09gRkzvJkx44wk\nT2bsWE9+++2G5OMbGurRsqUjgwfvl31tlSqZkpSUVWblv0ZG+jg5KdcdgRLpbuUy3AkJWZIoiHSV\nV34NDYmFZGGcl2FgoKd2kpNrSGJjn9Kpk3ZVSEFB95k2rSnz5l3UvDOKbvTff7/JxImN+fjjU2r3\nFUUIDDzO1q3d2LUrUquJSKoxAbh4MRkvr3V89FFTrlx5m9mzz/Pjj1e1ptfQhMLCYh49ytFw85T/\n5Bsb+5Rhw/bw+ecnqVhRWm6nuPhPQSpFOFD3zmld4OFhy/ff++Pqas3Eicc5fDhW45iJExszcmR9\n/Pw2a93IOmFCI/Lzi9i+XTPNSYUKJjg6mkvuHQHw9nbg9u3HshU4QSFJrEnqQQ6Ki0W1PTglHoky\nZGbmI4oK3R65eipy8Nol2xUeiXYrXn19QW0yNyenUFYZXUyMovNUG5w8mUDTpvYqk2zK8MMPVxk5\nsh7m5pqZe8+ff8CJEwlMn95Mq+sDhTFZsOAywcH9NCb8CgqK+eabi7RqtYk+fdw5d24Q9etrpsz/\nL+D+/aeEhqqWg/03wtLSiIULW3PmzECCgu7ToMFaSUZk2LA6TJvmRYcO27TW6nB2tuSLL5ozefIJ\nSfuPG9eQ2NgMWXQlXbu6Svp/lEFb9VVVEEXUFsokJWWp7SVJTJTmleiC186QODtrH9rSBFFU/Igs\nLKRRrEdGPqF6dSvJ/FylkZ1dQGhoCm3aSC+3vX8/g+PH4yWVAgNMn36aXr1q4uamvXaIHGMCCurr\ntm23sGJFOMeO9ePTT32oWFG52/4Gfz8EAUaMqEdExAhsbU2oX38tixdfkRRe+uKL5nzyiTft2m3V\nKX/w00/tWLLkiiQ+L2NjfSZMaMR338lTZezWzY2DB2O1uj4PD1vu3tWNa6w0RFG9R5KXV6S2BDgh\nIZOqVVWHxsoCr50hqVpV+2Q7aKZ5SE/Pw9paWm9Ibm4Rjx7laFUCDLBrVyRNm9rLGrN4cSiTJzeR\n1FWfmJjFqlXhrFzZSRK9hSqUGJNVqwKoW1cz4aQowooVN2jY8A+srAy5e3ckv/wSgIeH9gSRb6A7\n/PyqcP78EMaO9aRnz928++4RSdQhRkb6rF3bma5dXWnbditRUdoRpgL06+eOm5s18+dL0ycZNqwu\nly+ncPu2dA+hWjVLHBzMZVctlkBbGW9VEMU/q/6UQaHp/sYj+dtgZKTHrVtpsnlzSiClgunp0zxZ\nTYYREY+pXVu7CTI4WLp3UYKQkAc8epRD9+5ukvZfsiQMQ0M9JkyQrlmiDMuWXeOXX65x/Hh/eveu\nKWlMcnI206efoVat1SQnZ3Pq1GB27+5D69Zl2/T4BqphY2PMBx804fr1kXzzTRt+/DGMVq02cfmy\ntCIPOzsTgoL6YmpqQNu2W5Xee4GBjXn7bc0NmTY2xixZ0pYxY4IkeUCCAB9+6MXChfKaHLt1c+PA\ngRitc3QKQ1J2oS1NOZKsrHy1UZA3hqSMYW9vTo0aNjrphatbGYA8jwQUuhx16mhnSG7ceIQoijRo\nIC+XsHjxFaZM8ZK0b3GxyMiRh5k1q4VkWhZV2Lz5Ll267GDxYn++/rqlZC/n4cMcvvzyLC4uv7J/\nfxTLl3fi0qVhDBpUR2eyyDdQjpYtq7BmTVdiYt6jZcsqBAYew89vA3/8cVvy/VOzpg0hIYM5dy6J\nAQP2KS3xffddT6ZMacLJkwlKjvBXzJ/vx65dkYSESMsnde9eg4yMfE6d0nzsl8ft2xcta0wJ9PQE\nrdVXVUEU1escZWUVqDUkUiu3dMFrdRfa25vpJH35b/NIAHbtiqJXL2kr/BJs334PNzfr5zT2mnHv\nXjpz515g1aoAnUJcoBAda9ZsPa1bV2X37p6ySCJzcwtZvvwadeqs4KuvzvLeew2JjBzDBx80eRP2\nKgPY2poQGOhFePgoVq3qwvXrD3F3/40hQ/Zy4oQ8vik/vyqcPj2QhQsvM2PGGaX3ztChdZg1qzkd\nOmzXWPLt51eFrl1dmTFDevf7tGlesilXzM0NadXKiSNH7ssaV4Lq1a1ITX1WZqW/IC20ZWGh+j66\nfz+j3BdcbwyJTJRljgR080hAkSeRa0gKC4v5+usQPvqoqeQxS5eGIQiK0k1d8fBhDh06bCM2NoOL\nF4fINqSiCPv2RdG27Sb69t2Nvb0ZR48OICbmPZYtC6BXL/f/BAfX3wE3NxuGD6/Pd9+1Izp6LM2a\nOTBu3BFq117Bd99d0qq/YOjQ2mzb1p233z7IihXKezd6967JwoV+BARs1yjFa2ZmwKRJjQkMPC65\nhNXHx5GqVS0llQeXRseO1Tl//oHW7AdlnR8Bxf2akqK6wk1TaOvJk1zq15cmhqctXqs+Ent7c7Vf\niCaIElwSuR7J7dtpOhmSs2eTqFLFQjYlw/r1EXz9dUsaNKjI9evqO5ChJMR1hJCQQRw4EKOz615Y\nWExg4HFGjKjHyZMDePfdI+zdKz+cEBqaTGhoMp9/foa6dSvQqZMr48Y1Yu3atwgLS+HQoRgOH44h\nLCxFp5DmfwGCAJ6eiq5/Pz9n/PyqIooip08nsHdvJHPmnNNJoMzW1oQlS/yxsjKmbdut3LqlXJ+k\nSxdXli1rT+fOOyQlwX/4oR1ZWQXs3Pmq2qkqjBhRl8WLr8huIu3WzU2r32EJqla14MKFsi3lNjU1\nUFuVpSnZnpqaU+4cdP9hQ/Kqa1m5sjEpKVlK35OCoqJC7t17rHZ8enoO1tYGks+RlpZFYWEx9vbG\nWhm54mLYuzeSnj1dWbpUeoljTk4hCxZcZNas5vTtK00jLCrqEbNnn2P16gDatNlYJg2Da9Zc4+bN\nFLZt60WDBhX47rtLWtFlANy6lcatW2ksXnwZU1MDWrd2pnNnV9at64adnQmHDkWTmppDVFQ6UVFP\niIpKJy4uo0w71nWFubkhkyZ5k5CQwdq10juxX4aBgR5VqlhQrZoVLVo40bq1My1bViE1VcFDduBA\nFDNmnNRCTkH5d9O9e02WLevItm13eP/9wzx7pryh09+/GmvWBNCjxw6uXtU84Q4ZUpdWrZzw8vpd\n5blfRqNGlenZswZTpx6TPAYUfWL29ibs3XtH1rjSaN7cgUuXkrUerwyGhlBQUKTymFlZOVhYqJ5z\nUlMznhuS8lMZ/Q8bkldhb29OXJz29evFxSL16qlPbMfFZcgKbQGcOBFPrVp2WntLu3bdY+rUZrIM\nCcAvv1zlo4+8adSoMlevSiNt+/HHKzRpYs+0ac1YsEBaV70mXLqUTOPGa/jpp46Eh49iwoQgDh2K\n0emYOTmFHD6s8EYAqlWzwsvLHg8PO7y87BkwoBY1athgb29OfHzmC8MSFZVOdHQ62dm5ZGbmkZGR\n/+IxKyu/3LrtTU0NmDixKTNn+mJqasDOnXeUGhJDQz1MTQ2xtDSkShUrnJ1tcXa2wtnZkmrVFI/O\nzlZUrGhKSko2J0/Gk5aWw6pVNxg16qDWFYuqYGtrwtKl7Wne3InBg/dy+rTqxHaLFk5s3tyd/v33\nSFq116xpy/fft6Njxy2y2BVmz/Zj3jz18tLK0KaNMw4OFjrRvzdoUIkVK6TrnUiBgYGeWuGwzMx8\ntc2dmZn5GBrqYWJiIFnBVS5eO0OiWC1oh7y8QrUsnKDoDWnWTF6oKiUlm0aNKnPqlHZMo0eP3n+x\n6pYTmsjNLeTbb8/z5Zet6NVrp6QxoggzZ57m0qVhhIWlEhQUq9U1v4zHj3MZPHgvAQEu/PRTR8LC\nUpg8OVijxoRUxMVlKF1EGBnp4+pqTY0aNi82Dw87atSw+gurr5WVMebmhuTkFL4gZ7x8+QFubjYU\nFhaX2sSX/i7myZNcLC2N0NMT/rIJgqLKp27dinh4VEAURfT1FWnLDh1cCQ8fi5mZIaamBpiZGWJm\nZogoijx7VsCJE3E4OVkQH68gX4yPz+TChQcvnj94kFXunlaPHjVZtiyArVsjaNhwjUovpGTfefNa\nM2TIPkm/cyMjfTZt6s6XX56VxUzbvLkTDRpUkuxll8agQXXYtEk+t1wJ9PUF6tatoJGsUi4MDdWz\njuflFWlkgXj48BmVKpnqxGOnDq+ZITHTKUeSl1eEkZF6Q5Ka+kx2PPLq1VRatpSmw64MubmF/P57\nOL16ubNqlbxwyPLl1/j4Yx+aNLHnyhVpvQFJSVkMGbKXTZu64+OzTicv72UcORKLp+dqPv20Odeu\njeDrr8/x009h5eYJ5OcXcefOYyV1/69OioIA5uZ/MvKamRlibKyPgYGeik14US1jYKBHcbH4YhNF\nXjx3cbGmf/86NGniQFGRiJGRPteupfDBB4oQUU5O4YvHV1em0lgUyhKlvZBBg/ao9UIAJk9uyocf\nNqNXr52EhkpbyC1Y0Ib79zP4+ecwWdc2d64fX399TpbeOygm69693WnS5HdZ40qjZk1bHjzIJiur\nbDmtFIZE9f+Tny91XjJ/Y0jKAg8f5mjN7wOKL0yTR5Kami3bkISFpTJhgm7VUMePxxEY6CXbkOTl\nFfHtt+f56itfunffLnncyZPxLFx4iW3beuLnt0ErnQxVyM0t5IsvzrB+/S2WLQtg+PD6vPfeEcmT\nUHlBFBUVMllZ+Tx4ULY0O4sXX6RSJTOmT2/JhAlePHqUw61bZbuy1RXGxvqMGdOQbt1qcPt2Gg0a\nrFYbPtLXF1i6tAO+vlVo2XKd5EmsR4+a9OzpTuPGa2RdX7t21XB2tuL338NljQPo0MGFO3ce6zTR\nNmhQSWc6dmUwNFQv8V1eC1w5eK3Kf319q5Cern1VSnl9YTdvPsLDww5DQ+2/jgMHomnQoJJWnDor\nVlynYcNKNGumWQCnNL777hKxsU9ZurSD7HNKwZ07j2nXbhNLloSyb18f5szxpVo17Xm//u14+PAZ\nH354FCenpUyaJF2AqbxhYmJAYKAXUVFj6djRhenTTzJlSrBaI2JpacTevX1xdbXG13eD5Am6WjVL\nli/vxJAhe2Uz786d25pZs86ozSeowqBBtdm0KUL2uNJo2LCyzgJRyqApRyJtgfvGkJQZbG1NePJE\ne3pnKV/Yw4fyS+1ycwuJjk7XmMjXdG3bt99l8GD52t95eUXMmHGK6dObyx47atQh/PyqMmqUp+yx\nUvHHHzepW3cVublFXLkynPXru9G4sTyOsf8lPH6cU+YejzYwNTVg8uSmREWNpW3banTvvoOePXdo\nLMxwdrbkzJkhxMQ8pXv37ZJ7MiwsjNi5sw+ffXaakBDpAlQA3brVwNzcUKsch7GxPt2712TbNvnC\naqVRfh6J+hxJfn6xxgVuQkKm7CIgOXhtDImRkSKWrS4hqAkFBcUYGuqrbUp89qyAoiJRbaepMoSF\npdKokbROc1VYt+4mb79dV6uxmzbdxt3dlt693WWNy8rKp0+fnXz7bRuaNCm/yf3Jk1zmzAnBze1X\nQkNT2L27N0ePDqRzZ9dyO+frClNTA6ZMURgQP7+qdO26jd69dxIWpjmH5uXlwLlzb7N6dTgTJgRJ\nTvjr6wts3tydy5eTZVc96ekJTJ3ajM8/P61Vr1CXLm6EhaXoFPaG8vNI9PQEMjJUL4Dz8go1GpLc\n3MJyZdF+bQyJwhvRXQRIypembcJdV0Ny5kwCNjYmeHpWkj1WoVZ4jEWL2mr0ul5GRMRjxo07wqJF\nbcu98SkjI5/vvruEm9tyVq++wbx5rblxYyQjRtTX+L28gXrY25szcWIToqLG0rJlFTp33krfvrsk\nTY4K1oMmfPVVKyZMCOL77+VRkyxd2gF9fT0mTAiSfd2jRnlSWFjM7t3SGxZLo29fD53DWnZ2Jty+\nnUZsrPbMxqpga2tCYaFqC1lUJL6oBFSF9HR5jdJy8doYErmlsapw4kQ8xsbqaxS0MSRhYSk6h2tE\nEdavv8XQodp5JcHBcYSFpTJ1qnwxq+3b7xIUFMuhQ/1l8WeVxv79fRk2TBqbcWFhMevX36Jx49+Z\nPDmYgQNrExPzHpMne+Hmphu55OsECwsj3nmnHocO9ef27dFUrWpJQMAW+vffLTlM4+xsSVDQQAYN\nqsPkycHs2SNvQp8ypSl+flUZMGC37PyGra0Jc+b48dFHJ2SNK4GNjTEdOriwdatuYS1vb0cMDfXK\nhT3B3NxQYyWYpsqt9PRcbG1Vd8fritfGkJSVR+LpWQlLS/UllxcvPqBCBXlf2tWrKTx7VihJJ0Qd\n1q+/xZAhdbQmV5w27TgfftgMJyf5bKHffHOes2cT2b27j2yvBhT9KZMnN2Xfvr6y2EqPHbtPly7b\n6Nx5K7a2ppw7N5SwsOF8/nlLnfJO/1UYGurRvXtNNm3qQULCOPr1q8WqVTeoUuVnPvnkpKw+iOHD\n6xMaOpygoFj8/DYQGSmPZ6pXL3emTm3GW29t00oKdvZsX7Ztu6N1SGno0LocPx6nUxEOgI+PU5lT\no5TA3NxQY0PmqVPxaot1FB7JG0OiM8rKkGRm5mskBNTTE2Svip88ycPNzVqS8JM63Lz5iLCwVHx9\nq2o1PibmKb/+epV581prNT4w8CjJydls3NhdtvLj1aupeHv/wfnzSYSFjeDddxvIGn/jxkNmzTqD\nk9PPTJx4DFtbE/bv78udO+8yb15r2VVp/yUIAvj6VmXZsgCSksbz0UfNOH48Dje3X+nZcwdbtkTI\n6gSvXNmMXbt6M2VKUzp02Mz8+Rdk9/o0a+bA8uWd6Nlzh1Zlt40aVaZfv1p8/rl0RuCXMWZMQ377\n7ZrW40vg4+PIhQvyCgSkQooh8fZ2fBPa+jtga2v8txmSuLgMrcpUQ0KSaNFC+8bEEgQFxTJ+vPZ9\nKfPmXaB9++r4+DjKHiuKMGzYfszMDPnll06yxxcWFjNnTgjt2m1i7NiGHDkyQLaufXGxyJkzCUyd\nGoyLy68MHryXoiKRtWvfIi7ufZYsaU+XLq7lns/5J2FubkjbttWYObMFFLkqhwAAIABJREFU+/f3\n5dGjicye3YrY2Kd4ea2ldeuN/PrrVa3CvX36eHDt2ghu3nyEt/cfWlUqubhYs2tXH0aNOii5EfZl\n/PhjBz777LTW97WXlwMWFoayKfKVwdvb8R/1SDThyZM3oa0ygZGRvhYEda9CiiGJj8/E2VlbQ+Kk\n7aW9wNq14XTqpP1EmZWVz4wZp1iypL1WIbKCgmL69t1F/foVtfZswsMf0aLFOo4ejeXSpWGMG9dI\n63DdlSspfPbZaerUWUlAwBaSkrIIDPQiIuJd4uLeZ+fO3nz2WQs6d3alUqV/j3ExNzdk2rTmVKmi\nuTeoWjUrBg2qw9Kl7bl8eRgpKROYPdsXa2vj5xouK2nbVuE5aMtE4ORkwdKl7fn22zb07r2LmTNP\ny+4gB4Vmx969fZg58xT79kVpdS1vv10XIyN92Q24pTFmTANWrryhc16jZk1bsrMLdK76UgULC2mG\nRN39kZ6eW64eyT/S2S4IwkKgG5APRAEjRVF8KgiCC3AbKCmhCBFFcfzzMV7AGsAEOCCK4iQ556xU\nyaxMqnqkGZIMnJ3lNwaGhCQyebI05UJ1yMjIZ/v2u4wa5cm3317Q6hjr1t0kIKA6773XiF9+uSp7\nfHZ2AW+9tY3Tp4fw8GEO330nTWO7NIqKRBYsuMiePZGsWtWFZs0cWbnyOmfPJso+VgkiIh4TEXGB\n+fMVn4urqzVNmzrg5eXAtGneNGliT2ZmPqGhDwgNfcDNmw9JSsoiKSmT5ORsrZrd5EJPT2D06IbM\nn98eCwtDYmLS2b49gsqVzXF1tcbFxQYXl5JHG5ycLLG3N+Ps2UTOnUtk48bbhIamaDXJK4OdnQnT\npzdn9GhPFi68SKNG6nm11MHNzYZjxwbyf/93kTVr5Hegg6LZcf58f3r33qk1dY65uSEDBtSmXr1V\nWo0vjfIMa4G0ZLsmY5ienqezKJ06/FMUKUeAT0RRLBYE4VtgBjD9+XuRoigqi8ssA0aLonhREIQD\ngiB0FkXxkNQTWlho/jKkoDxDW+Hhj3BysiiTfM4vv1xl69YeLFhwUaubTRRh7tzznD49hIMHo2Vp\nnZTg8eNcOnXayqFD/XnyJIfVq7WbOCIiHuPru4GhQ+vyxx9vERmZzqxZZ2Q3rSlDTMxTYmKe/qVq\nx83NhqZNK+Ll5cjgwfVwdVVM1pUrm5GWlvPCsJQ8JiZmkpSUSWGhSF5eIfn5ReTlKTbF88IXz/Pz\nizAxMcDCwuj5Zoi5+Z/Phw3zxMenCqamBhgbG1BYWMzSpQGsXduD7Ox8YmOfEhOTTmzsU65fT2XP\nnmhu3Xqk1fejCRYWRkye7MWkSV5s3XoHT8/VOpFourvbcvToQObODWH5cu3zEpMne7F/fxQXL2of\nSurfvxanTyeUSeOnwpCUT1gLFAuLrCzdQlvZ2QU4OFigpyeUC2/dP2JIRFEsXSx+Aeirbn9BEBwB\nS1EUS3jL1wK9AMmGxNzciMePdb/ZpBiSpKQsKlc200ht8DKKi0UuXUqmeXMnDh7UXlwHFIJPjx7l\nEBDgojUle0TEYxYuvMiKFZ3p2HGLVsdISMike/ftBAUNwNHRgm++Oa/VcYqLRf744yabNt1m2LD6\nbNjQnbt3HzNr1lnOny/b1WB0dDrR0Q/ZsuWvXdJ6egL29uY4OVni5GTx4tHHpwoFBUXUqGGLsbE+\nRkb6GBsblHqu/+J5WFgKDRpUJju74AVvl2JT/F23biVMTf+8LYuKRFatusa3355TEd4oe9JGY2N9\n3n+/EdOn+3DsWBzNm6/TqGKoCbVr2xEUNJAvvjjD6tXah6P8/Kry/vuNqV9/pU7X8+67DbT21l+G\nj48TW7boVj6sDg4O5pLYAdTJ8QLk5BRgamqgc75FGf4NpI2jgI2l/nYVBCEMeAp8JoriGaAKUJpi\nNPH5a5Kh8Eh0/wClGJKiIpGUlGycnCxkx6NDQhJp0UJ3QwIKr2TcuMY6aXssWnSJvn1r6VTdEhPz\nFF/fDRw61J/Klc2YMiVY67h0QUExK1deZ+3acIYPr8/Gjd25c+cxX35Z9gblZRQXizx4kMWDB1mE\nypN+kQV9fYG+fWsza5YftWtXJDMzv1xufmXnHTasPrNmteTatYcEBGzlxg3dKT/q16/I4cMD+Pjj\nE6xff0vr41hYGLFmTVfee++wTlRHnp4VefQop0zuMTMzQ2xtTbh8ufwIRaVEKKSot2ZnF5RJ4l4Z\nyi3ZLghCkCAIN5Rs3UvtMxPIF0Vxw/OXkgDn56GtqcAGQRDkJxuUoKw+wLi4DEmToCLhrk2epGwS\n7gCbNkXQqlUVra6jBEVFIiNHHmDuXD+djpOcnE2bNhtp3Niedeu66URQCQqDsmLFdTw8fmP79rts\n2tSdgwf74eure9XbP42iIpEtW25Tr95yWrRYw/r12oUEpcLR0YLp033YubM3w4bVY/DgvfTsuaNM\njEjDhpUJChrA1KnBOhkRgIUL/TlxIk7rBH0JpkxpxoULD8pEr6VlSyeSk7PKTTDKwsLoRVhUVzx7\nVoiZWfnIDpSbIRFFsaMoip5Ktr0AgiCMALoCQ0uNyRdF8cnz51dQJOLdUXggpRsjqj5/TQUOldru\nAAVYWBiQlZWDQmdCzvZXFBYWS+oRuXTpgVYT77lziVSsaKrzRAsK3q/1628xcqRuhIol8rW//dZZ\n474mJqqd3KdP8+jUaQPm5gbs2dMbMzOQ/338dSsoyOO33y7j7v4zO3dG8NVXLbl3711mzWqOm5u5\nDsf+d+DixSQSE9X1WGj3/xkaFtGrlxt79/bm5s2RuLpaMWfOadq2/YOQkPtaH7f01qJFZXbt6sWE\nCYfYvFk3CpJOnVzp0sWVKVOCdTqOvb05vXq58+uv8gtIlMHfvxonTmgnSCcFyhk5Xv2sL15MUvp6\n6S07Ox9zc6HUa3f461ypPf6R8l9BEDoDHwE9RVHMLfV6RUEQ9J8/d0NhRKJFUXwAZAiC4CMoAoHv\nAGok0NqX2tyAEo9E92R7WlqOpK71jIx8ateW31yYnp5HcTE0aya/h0MZfvjhCmPHNvxL3F0bLFx4\nkYoVTdUapbZtq3HmzBC1ob/c3EL69t1GUlIWx44Nxc6ubIjkCgqKWb48jPbtNzB48C7s7EwJCRnB\n2bPDef/9JuVaQ/+/hDp1KrJwYXvi4ycyeXIztm69TdWqP/DeeweeT0Zlg9GjG7FrV39Gj97Hjh26\n5Q9sbIxZsaIzo0Yd1Kr7vTTGj2/Mxo23y4QuCcDf37lM+lBUQWFIcjTu5+vrrJYhGBShrb96JG78\nda7UHv9UH8kPgAUQJAhCmCAIPz9/vQ1w7XmOZCvwniiKJVm+8cAK4B6Kyi5ZJtTCwqhMqrYePcqh\nYkXNvQb37j3B3d1Wq3McO3af9u2razX2ZURGPuHixQc6eyWFhcWMHHmQzz9vobIi7fjxOM6fT9JI\nkVJUJDJ69D5OnLjPmTPDcHGx1unaXsblyw+YNOkIVaos5ZtvzuLvX52YmAns2NGP3r1rvXbkjk5O\nlowc2YBz54Zz9OgQCgqK8PVdi7//OtauvaETI/bLMDDQ48cfOzFtmg9+fmsJDr6v8zGXLu3Azp13\nCQ7WbcI2MTHg/fcbsmRJ2SS4zMwMadiwcplUD6qCVOkLIyN9jeGvZ88UOZLywD9VtaWUq1wUxe2A\nUpk+URRDAa1nw9zcwjK5YaR6JPfuPWHixCZanSM4+D6ffOLD7NnntBr/MhYsuMj69d349derOsWF\nb9x4yPffh7JpU3fatNmodAUUGHiM9eu7sWFDdwYM2K32fDNmHOfevcecOzeC0aP3cfCgbrHvl1FY\nWMz+/ZHs3x+JlZUx/frVJjCwGVOn+pCSks3p03GcPh3PtWsp5a5v/neiUiUz/P2r066dC23bVqdi\nRVNWr77O3LlnOXQoqtz+10qVzNi6tQ+Zmfn4+KxRS30uFb17u+Pj40jjxtpL4Jbg7bfrcuHCA+7e\nfVlWWTu0bOlEWFiKLGqZEkgtw5XqkRgZ6auV4wVlHknZ4bXpbLe3N9fo+knB3+GRnD6dQNOmDjqH\no0pw/rwizt63by2dj/XDD6E8fJjD/Pn+St8vLhYZNmw/FhbSKFJWrbpG//7b+fXXrnz7bdsXGudl\njYyMPFatukbbtuvo3387O3ZEULt2Rdau7UFa2lQOHRrEzJmtaN26mto8z98JJydLGjbUzAhtY2NC\nz54eLFkSwPXrY7h7dxzvvOPJnTtpDBy4k0qVFvPRR8fYvz+y3IxI48YOXLw4ktOn4+nZc2uZGBFn\nZ0vGjm3I8OEHdF4ECoKCZfi77+TR26uDLvmR8eMbs2hRW4372dmZagzDGRgoNN01FQHFx2eUSe5V\n6TWUy1H/hTA01Oz6SYFUjyQtLQdRhIoVTXn0SPOKojSyswu4ejWVVq2qcPSo7qEBUHglX37Zii1b\ndEt6iiKMGHGA0NBhnDmTwI4dd1/Zp6CgmD59dnHs2EC++aY1n356Su0xz55NoHHjFaxd24MTJ95m\n0KCdJCRor52tCcnJ2WzYcJMNG24CUKGCKa1aOdO6tTMLF7anbt2KXL2awsGDUSQkZBAdnU5MTDpJ\nSZnlQhP+MkxNDZgxoyUff9yS69dT8PZeDYCVlTHu7nZ4eNi9eNTTE+jWrSbnziUQHHyf0aP3c+VK\n2VQkScWgQXVZujSA8eMPsW2b5t+Xs7MlzZs7qaVuNzbWZ/v2XmzeHFEmZd2dOrmSn19UpvkMf39n\nvvhCO8LI2rXtuHNHs2dkbKxPfLz6FgKFN6J5kWxpaSRbcE8qXhtDIiWGKAV5eUUUFBRLyrmUeCVy\nDQkotEHatateZoZk//4ovv22De3bV+fYMd2O+eRJLgMG7GH//r5cu5aqtFlNQZGy/TlFyjMWL1a/\nEkxLy6Fbt8189FELLl0axejR+zlwQDuhIrlIS8thz5677NmjMIrm5oY0b16FOnUq0qGDK25uNri5\n2WJjY8z9+0+fNyymEx395IWRefw4h2fPFOFTuaWgxsb6WFkZY2trwrhxXowc2QATE0VDo4eHHadO\nvYOHhx3m5kbcu/eYu3cV2+HD0YSHpzJ8+J4y8bblwthYn2nTmjNqVEM6dNjA9euaqdyrV7ciOHgQ\nP/xwRe1+P/zQgdjYpyxaJJ9aRxl69/Zg4cKLmneUCDMzA8zNDbXOj9SpU0GSEJerqzWJieq776XO\nbQUFRW88El1hZKRHfn7Z3GyK8JapRkMSGakwJNr82IKD7zN/fhttL/EViKKi8urjj711NiQAly8n\n89VX59i2rSctWqxXOnmmpeUQELCFM2cUxmTdOvV9BKIICxaEcPZsPBs29GLjxpt89tnJv4XfqjSy\nsws4diyWY8di//K6qakBLi42zw2Lwri0aVMdPT2BRo3sMTMzwMzMEGNjA3JyCnj2rOCFcSnZcnOL\nsLc3w9LSGCsrI6ysjCkqEsnIyKOgoIgqVawoLhZfUIKLInz++Unu3n38r9BxL0Hz5lVYtaoboaEP\n8PZeTVqa5sVSCc/WokWX+PFH1YZk9OgG+PpWxdv7jzK51latqtCxY3Wt1BdVoV276qSl5WqVHwGo\nXbsCERFpGvdzdLQgNFQ9O7KhoZ5EQ6KQCi8PvEaGRHMySirCwlKoVMlMo6zmvXtPqFlTuzxJSEgS\ntrYmWFsb8/Sp7vFmgA0bbjFnjh+NGlXm6lXdtaV//jkMX9+qLF3anrFjDyvdJyEhk44dt7B7dx8s\nLY1YtkzzCvPs2QSaNFnJ2rU9OHBgIOPHH5ItmFQeyMkp5PbtR9y+rV74SRDA1NQQMzPDF8alZCsq\nKiY7u4CMjDwyM/PJzMz/yyRgZKTPoEF1+ewzX6pWVfQhnTxZfuWlcmFqasCcOf4MHlyPwMDDkkJZ\noGDIVYQ6z6vt4fDycmDevNb4+W0okypLgK++8mXOnJAyXZB07erGgQP/3955h0VxbQH8d0EWUIgK\nioAFLBQNNoyFRMWuWFARNPYWa2KJ3Zi8GEtseUkUY9SIRo2KCooNxYaF2LsUUewRu1ijIjLvjwXD\nM+zubEEU5/d9+zHM3t2Zwwxz7jn3FMMy49UTCJUs962jYwGdEwj5Fkl6jlkk781iu6nWSEAdcSGn\ng19s7B3KlzesUVVq6kuSklJo0sRV73PTxIsX6Ywf/yfDh+vfSlcTfftGUadOCbp21dwi99y5FJo3\nD2PYsOp8801tWd+b6eoKDY1n//4eTJjga7Lgg5xGktShlnfu/M2VKw85c+Yux47dICbmKvv3X+PU\nqVtcuvSAu3ef/uueTE19yZIlp3F3/5WmTVcwYcLeXJLi39StW4pTp/rg6FiASpV+k61EPDzsiY7+\nlAkT9mlVIvb21oSHt6Z//62y1g/kUKdOCVxdP2DJkjiTfF8mzZuXMbjEiqenPYmJ92Sttzk5FdBZ\nnt7S0lyWWzEtLT3HglneG0ViqjUSUBdllNOKNj7+DhUrFjX4OBs3nqdly7J6fSY6+lO8vTVH+ixb\nFk/9+qWoVs003QIfP04lMHAdn31WiVq1NJd2uXRJXW8rIMCTn39uLKuktSSpo7qqVFmAm1th4uL6\n0apVtpHjeZK9e6/y00+m8+sbio2Nitmzm7J8eRuGD99B587ruHPnb1mfrVChCDt2dGbcuD2EhJzS\nOM7MTLBiRStCQ89kG8BhKOPHf2Jya6RCBXskCRISdLumssPT044zZ+QpSjkWiY2NSlbPmpxcI3lv\nFMnLl+kmW5C8du2xLIvk7NkUSpWyNTicdNOmC/j5ldGrj3tk5AW++EJz/sqzZ2lMmrSfSZPkWQZy\niIu7w5QpB1i7to3WkOebN59Qr95SvL0dWbLEX/bs6Nq1R3TsGEGfPpuYPr0BGza0p3Rp/VoZKxhG\n48alOX26D9bWFnh5zX8VkCAHL6+ibN/emVGjdui0CEaNqgHAuHHZR/ipVOaMHVtLrwdh3bolKVXq\nA5YuNbU1UtZgtxaoLRI5SsjKKh9WVvm4f1+7a1tusnVOrpG8F4pECLC0NJ1bRK5FkpaWzrlzKQa7\nt65ceUhy8mO9Wt4uWHCKNm3cKFJEc+mRkJBTuLvbUbduSYPOKzu2bLnI11/HsHlzoNYug+p6Wyso\nWNCKtWsD9XJX7dhxiUqVfiMm5iqHDvXkP/+p89bkfOQ1qld3ZsuWT/nii4/o338zvXtv5P59+WVF\nmjQpw6JFrRg8eMurMGtNDBlSje7dvejQYb3GsOUpU+pSo4aTXpPBTGvE1KHQfn6ljVIk9vZWxMXp\nLojp6KjbrQX6KhLFInlrkKtIAE6fvkPFikUMPtbGjedp1aqc7PF37z5l7dqzfPZZJY1jXrxI59tv\nY5g8uY7B55UdISGnWLYsgY0b22nNoH36NI2AgDBSUp4RFdWRggXltwB98SKdadP24+0dQsWKRYmN\n7Yu/v1uOdn97n6hcuRjr1gURHt6OtWvPEhgYTlSUfg/NL7+swaJFLRk6dCthYdrrbHXqVIHhw6vT\ntOlqjaXS/fzKEBjoQa9em2Wfg69vSUqUsOWPP0xrjdjaqvjoI0eiow0PgGjUyJX4eN2uLScn3W4t\nkN8iIyXlqckCGF5HUSQGcO3aI1k+SYDY2NtvfJ0kOPgYAwZUxdxc89N1+fIEChe2ws+vjMHnlh3f\nfhtDfPwdQkNbaT1+Wlo63buv5/DhZBYtaoW7u51ex7l69SFBQWvo3z+SMWM+Ji6uHz17Vn7v6miZ\nigoVirB6dQCRkR3Yvv0ibm5zmDfvmF4WgKWlOYsWtaRbt0rUqvU7f/75l9bxTZuW5scf6+PnF6ax\nb4+Tkw0hIc3o3HmjXl1DP/usEhMn7jO5NdKokQv79ycbnGlfsKAlDg75ZUUhFilizeHDuvucyLVI\nbG0tKVQoZ4qXKorEAPS1SLy8DLdIDh26jqNjAVxc5LfuPXHiFleuPMTfX7Mlk54u8fXXe5k0qY7J\nZ/N9+kRhaWlOcHAjreMkCYYPV5fuiInpRlBQeb2PtX37JT7+eDGff76F9u3Lc+HC54wYUUtn8zEF\nNW5udvzxR2t27uzMwYPJlCs3h+DgIzx/rl9giqOjDdHRXbCxUfHJJ4t1ZmPXqOHE0qUtCAiIIC4u\n+3BqMzPB0qUt+PXXE8TEaFdKWWnTxo3KlR2M7n+SHZ98UpzQ0ATdAzVQubIDp0/fllVnq1y5wrIa\nVslVJEKQI212QVEkBnHv3jOsrMxl+fdPnzbOIklPl4iMvECLFvpZJbNnH2PQoGpax0REnOPly3ST\n1ODKSlpaOoGB6/DxcWb06Jo6x4eEnKBJkxVMmVKfn39ubJAfNzr6Mn5+obRosZKqVYtx4cLnfP99\nPRwd5Sn8943atUsyfXpD/vyzG/HxdyhX7ld++OGAQQl21ao5cehQTzZvPk/79mt0ztY9POxYt64t\nPXtuZt8+zW2Fxoypibm5YPLk/bLPRaUyZ8aMegwbttPkD00LCzN69KjItm2XDP6OKlUcOH5cXg6X\ni8sHXLqku8OqXEViZiZyrMSPokgM5MKF+xrLqWflypWHXL/+BDs7w03KsLBEqlXTXbwvK+HhZ3F3\nL8yHH2q3hkaOjGbAgCpay74bwqNHqTRvHk7Nmk4MGaJdoQGcOHGTatVCcHEpyJ493ShZUr4FlpWT\nJ2/SufM6qldfiI2Niri4vsyb15wPPzRcmecVSpb8gHHjPuHcuQHMnevH1asPcHP7le+//9Ng33mH\nDhXYvLkDQ4ZsZeLEGJ0PquLFbdiyJYgxY/awaZPmas8+Ps4MGuRNly4b9VIIgwZ5k5Bw12SlhbJS\nv34pEhPv6SxZog19koFdXQty+bL2pGfIVCS6XW1CCFkWjiEoisRA/vrrMaVLy+uj8eTJC6PyNnbs\nuExAgLvWSKzXSUtLZ9q0g/To4aV13O7df/HwYSrDhpkuSTGT69cfM3ToTgYN8n4V3qmNBw+e07Zt\nGOHhZzh0qCdNmxq+fnPp0gMGD96Kh8dckpMfsWxZa2Jj+zJ+fN33SqlYWeWjY8cP2bq1I8eP98bZ\n2ZaOHSPw8ppPcPARg6sm2NqqmDevOR06VKBRo+WsXau7eZWDQ35mzWrEnDnHWbxYc/vgwoUtWbq0\nBX36ROn10C5SxJrRo2syYsQu2Z/Rh8BAD53BA7qoWtWB48e1lzzJxNW1oCyLRKUyl5XXo7i2cpXs\n21ZevJhCmTK2Gt/P+jp8+BrVqzvIGpvd6+nTp2zZcp6AgLJ6fW7ZslP06lWREiWstI778ssohg37\nSOc4Q15Xrtylbt2l9OpVkf/8p5asv/gPPxygffs1LFjQgvHj6+qVR/M6d+78zXff7aVq1QV89pm6\nvP2mTR1ISOjHhAm+VKrkYPB3v60Ioa6FNXeuH9euDaZbt4osWHCC4sVn8fnnWzhy5LpR39+woSun\nTvVBCOjefb2srOpSpayJienE0aPJzJjxJ5rul3z5XrJ6tT+LFp1k48YzGsdl95ow4WOWLYvl7Nmb\nen1OzsvcPI02bcoRHh5n8HdYWLzE3d2O2Njrssa7uHzA5ct3s3nv/3F2tlFcW2+KpCTTlFzI5OLF\n+7KT4g4fTuajj4xrnbtyZQLt2+u3GH3v3lNCQk4wcqSP1nGXLj3gl1+O8sMP2hfHDSU5+RG+vksJ\nCvJk8uR6sj6zd+9VqlVbiLu7HdHRXYx+4EsSHDhwjREjduDqOptu3TZgZWVOREQQiYn9mTy5Ht7e\npsn2zw3Kly/CwIHVWLUqgJs3hzJiRE0uX35ApUq/4ecXyqpVCXovoL+OjY2KOXOasXBhS/r120zf\nvpE8eqT7Aebpac/evd2YNesw33//p9axs2c35enTNKZM0a+pm5dXUdq188yxkjK+vi5cvPhAY3SZ\nHCpUKMqFCymyqkMXLGiJubmQ1dTK3l53zxJQTzAU15YRSBKULVvYqJnt61y4kKKHIrludA/2zZuT\n8PZ2xMGhgF6f+/HHQ3Tp4qXzc9Om7aNmTWfq1TNNi9/XUWe1/4GfX1n++195CuvWrSd07hzBkiWn\n2batEzNmNDRZq9DDh5MZNWonZcr8QqdO6zAzE3z3XV3u3RtGVFRHJk2qh7+/O05Ob26xvmBBy1eF\nGnXh7m5Hv37ehIa25caNIWza1IFq1ZxYv/4s3t4hBAauYcqUfVy7Zpq+LvXquXDqVB9UKnMqVvyN\nrVvl5ZZUrerIzp1d+Prr3cyerb2VwJAh1fHxKU7HjhF6u2B+/LEREyfG6BUirA9BQeUJCzM8WgvU\nyj4yUl4XUBcXeesjkNn8SrfCEUJeV0ZDeG/SglNTX6JSmevdK0ITFy8+oEwZeYrk4sX7WFnlw9HR\nhhs3DFuoe/78JZs2JREY6MmcOfJ7Tt+48ZjQ0HiGDq3OV1/t0jju6dM0hg/fTnBwE6pWDcmR0u13\n7z6lQYNlREV15JdfmvHFF1t0mtqSpI7qWr/+LDNmNCQ+vh+DB29l3TrT1WM6evQ6R4+q3T0ODgWo\nXt2J6tWd6d/fm5CQFjx//pJDh5I5fDiZw4evc+TIdb2yvHWRP78FX35Zg6+++oQ//7xKkyYrXr3n\n4FCAMmUKUbZsYcqUKYSdnTXt25cnLS2d6OjLbNlyntGjd8p+6OhLgQIWTJ3agDZt3OnbN1Kvdsh1\n6pQkLKwdfftG6rxezZuXY9QoH3x8ftd74d/Pryw2NirmztXe48RQzMwEbdu68/HHxrX7rV/fRZYb\nEMDVtZDO6uKZ2NlZySrjf//+M5Pet1lRFImB6GORABw5orZKNmw4Z/AxV65MYMSImnopElD3+Dh6\ntBfTpx/QeiOtWZNI//7eDBxYjVmzTNNQ6HXu339Go0bLiIz8lP/+txGjRu2UpbRu3/6bHj02UK+e\nC7/+2oyePSszeHCUUa6G7Lh168mrPu+ZlC5d6JVy+eab2uTLZ4anpz03bjzmxo0n//p58+Y/28+e\npWFhYYaFhXnGT/W2SmVO4cKW9O5dhbZtPbGwMMPKKh8ffliUiIiC9kTgAAAViElEQVSgVz1P/v77\nBRcu3Of8eXUTrZiYqwQHH+bChX83EzM1rVq58Z//1CYu7g4VK/6m10PIz68sv//eio4dI9i585LW\nsV5eRfn995b4+6/W+3ra2qqYO9ePHj025Fjfmk8+KcH58/eN/pv7+BRn/vzjssYWK5afQ4fk9TFS\nu7Z0KxInJxuTPf9e571TJKYiJeUZQqj7Zcv5B1O7t5yNUiRbt15g8eJWODnZ6NXk6PLlB2zYcI4v\nvviISZO0twYdPHgre/Z0ZcWKOG7fllfhVV8ePUqlWbMVzJvXnG3bOhEUtEZ2Ndlduy5TufICRo6s\nxdGjvZk2bT8//3woR5tfXbyo7oK4apXatSGE2p1QrFgBHB1tcHT856eXV9FX24UKWVGokBUvXqi7\naqpfL0lNVf9esKAlJUt+gCT9U/4/LS2dxYtPverAKGcNwtT4+pZiypT65M9vwdChW/VuT9uhQ3lm\nzmxCq1ardD4MHRwKsGFDe4YM2caBA5pzSjQxdWoDtm69SHS09nBfW1t1BeOePfULJwbo3r0S4eHG\ntai2tVVRunQhTp6UF7FVpYojZ85o73sD6nuxYEF5zyArq3w8e2aaCuiv8x4pknSTl8/Yv/8arq4F\nOXFCjiJJZsAA3fkU2khNfcm6dWfx93dn3jz9zPipU/ezZ09XfvrpIE+eaI45P3PmLjNmHGTmzMZ0\n6rTOoPPMn9+CypUd2L9f84PhyZMXdOu2nokTfTl0qCdt2qyWbfanpr5k8uQ/CQ2N55dfmuLjU5zw\n8DOsXBn/RnqVS5LaTXf37lPi43X/s2vDxkZF//7efPXVx1hbW3Dv3jNZobQ5gbe3I99/X49y5ez4\n5pvdhIbG6RXlY2YmGD++LrVqFadRo+XExmovTGhlZc7atYEsWXKaFSv0r4lVu3ZJWrd2x8trvs6x\nQ4eqw8/1VSL581sQEODB11/v1vv8slKjhjPHj9+QPeFRJ23qdt8WLGjFo0fPZd33lpbmPH+eMxbJ\ne7HYDvD8eRoqlWnFffjwOeXLyyt/cuhQMpIkGV2O5I8/Yunbt6ren0tMvEt4+Bm6d9dczDGT4ODD\neHs7ERjoacgp4uZmR0REEA0auGodl54uMW7cLsaM2cn27Z30Pt758yk0axbK3LnH6NfPm8TEAXz2\nWZV3qt7W48ep/PDDAZycZjJkyFaDHqjG4u5ux8qVbVm/vj0REWcpX34uK1bop0Ts7KyJjOxA7dol\n6NJlnU4lolKZExYWSEzMVcaPz750vDYsLc1ZsKAFgwZF6ZyN29lZM3hwdcaP1z+iq21bD/bvv2bw\n2mYmtWoV1zqxeh0PD3sSE3WXmre3t+LmTd0VgkFdAT2nXFvvjSIxtWsL1I1t5CqSmzef4OFhj6en\n4XW3AKKjL2Fvb02VKvplugPMmnWY8ePr6Czc9uxZGt27ryc4uKnWkvCaOHnyJoGB4axY0YbatXWX\nql+1KoEmTVYwY0ZDJkzw1VvZbtt2EV/fpfTosYF27TxJShrIoEEfvTMdFUEdTDF//nGmT5dfDsRY\nSpSw5bffWhAT042jR2/g5jaHuXP1K9QI6sisI0d6cerULRo3Xs6tW9ofbPnymbFyZVueP09j3Lhd\nBuU2fPNNHWJjb8uy3kaP9mH16gQuXtR/jaN794osXqy5IZdcfHxKyHbdFShggb29NVeu6F5sd3Ao\noLNfSSZWVvmMDgHXhKJIjODMmTt4esrvNbJ37xXq1i1l1DElCRYtOknPnpX1/uyZM3eJiDjLmDHa\n80oADh5MZvHiU8yZ08yQ02Tv3qt06hRBeHg7atbU3DkxkxMnblKjxiLq1SvF2rVBBhVdjIm5ip9f\nKAEBYdSv78qFC58zapSPUsAxC0Koo4eWL2/D/PktuHXrCe7uc5k+fb9BdbZ69KhEVNSnjBq1g1Gj\ndup0sZibC5Yvb4O5uRmffrrWoLWtSpUc6Nu3Cl98EaVzrJOTDb17V2bSJO35K9lRooQt3t6OslxM\n06c30DpBq1XLWbYicXe3JykpRWYrXvnrpWrXlqJIjCI+/q7Ju4OpLRL5imTPnqvUqWN8M6nFi0/R\nqdOHBtXHGj9+L599VkVWvsK33+6hQoUieidCZrJjxyV69tzIunVBVK2qO9nv9u2/adhwGTduPGbf\nvh54eBjWEOzIkesEBITRuPFyKld24MKFzxk92kcvpZ/XKFo0PyNH1iIxcQAzZzZh376/6NQpgnHj\ndhkUEqpSmTN3rh+jR/vg6/uHrP7tZmaCJUv8sbFRERgYblDHUnNzQUhIS8aO3SXL3fT117VZuPAk\nycn659N06VKRsLAzOh++ZcoUomvXijx4kP3f0c3NjsePX8h+4Ht42MlyawE4OdnqpUgU15aRODkV\nMPnM9OzZu5QtW1hr342s7N17BV9f4ywSUGeinzx5E39/d70/m5z8iHnzjjN+fF2dY58/f0mPHhuZ\nNasJxYrplwiZSWRkEv37byEysgNeXrprXL14kU7//puZPDmGmJhuDBtW0+BE0tjY23TuvA4fn9+x\nsVGxfXtnjh3rzciRtQwuCvkuIQQ0alSaVasCSEzsj6dnEbp2XUelSr8xe/YRg3MKSpX6gD17ulKk\nSH5q1FhEQoK86KKFC1vi4FCAgIAwUlMNmxkPHVqD2NhbhISc0Dm2dOlCtG9fnmnTDHMXynVr+fu7\ns2HDOY0WhI9PcdaskR/15e5uT2KivEoc+lgkateWokiM4tGjVJMrkqdP07hx4wllymjuU56VpKQU\nzM3NcHWVV+xRGwsXGubeAnVeSatWblSooHu95vDhZEJCTjJ3rp9BxwKIiEhkyJCtREV1lG1lhIbG\nU7PmIvz93dizpytubvo1vspKUlIK33yzm1Klgvnyy22UK2fHsWO92b27K/37e2NvL78Y5rtAuXKF\n+eqrT0hKGsj06Q3YufMSrq6/0Lv3Rg4elJebkB1CwIAB1ThwoCdLlpwmMDBcVniyEDBvXnNcXAri\n77/K4Fmxj09xRo6sJTuCavz4OgQHH5GVrPc6NWo4Y2YmZC2Qt27tzvr1msP6GzUqLatHeyb6WSTy\nFclffz3SGrFpDLmiSIQQE4UQJ4UQJ4QQO4QQJbO8N1YIcU4IcUYI0STL/mpCiNMZ783U95iPH6di\nY2N6X3lCgv7rJHXqaLZKbGxUsqrTrlmTSM2azrJLamTlwYPnTJ26jylT6ssa/913e1/N7gxl1aoE\nxo6NZsaMBlSuLC9Q4MKF+9Sv/wcrV8azb193hg6tYVSZm/R0id27r9CvXyTOzjOZMeMAdeuW4vz5\ngWzc2IHOnb0MCi7IbezsrAkKKs/8+c25ePFzdu3qSoEC+Wjffi3e3iHMnXuMhw8Nq/KbSfnyRdi7\ntxudO39Iw4bLZCfFmpkJpk1rQPnyRWjZcqVB6zCgztdavrwNffpEyir7UrlyMezsrPnpp4MGHe/T\nTyswc6bupFw7O2uqVi3G9u0XNY6pX9+F6OhLso9dtGh+WT3dQT9F4u3tmLcUCTBdkqTKkiRVASKA\nbwGEEBWADkAFoBkwR4hXMTy/Ar0lSXID3IQQeq0CP378IkcWXRMS7siO3AL1Oom2BfcqVYqxbFlr\nLd+grnH07FkaK1fG062b7nDe7Jgz5yiVKjnIiqpKTX1J587rmD27qVHrDEuWnGbp0li2bu1I48al\nsxnx7/pNkgTBwUeoVet3AgI82L27K+XKybMAtfHiRTobN56jU6cIihefxbJlsXz8cXHOnh3AuXMD\nWLLEn/79valcuZhs16VutNensrFRybKOVCpz6td34fvv63H4cC8uXvycbt0qEht7m+bNV1KixCzG\njdv9quyLMVhYmPHNN7XZs6cry5bFUafOEi2urP+Xz9o6H6tXB+DmZkfz5qFGPcR++60569adlZ3Q\nGxzchPXrzxmU0FmokBU9elTKZt3n39evefOy7Nx5WaOVpe5yiKzWuqCOaKtdu6QsdyHop0g++EBl\n9IRCE7miSCRJyjqlsAEy/2qtgRWSJL2QJOkSkATUFEI4AbaSJB3KGLcEaKPPMR89ep4jFsnRozf0\nalqltkg0P7z37fsLB4cCWlw5/8x85s8/RoMGLgbN0p8/f8lXX+2iT58qssJt4+JuM2ZMNGFh7cif\n3/DCiatXJ9CuXThLl/pnk9OieVZ3/nwKvr5LCQtLYP/+HgweXN1kLYKfPHnBihVxfP55FHZ2/6V1\n69Xs2XOF6tWdWbGiDffuDWf79k5MnOiLn19ZChc2tElZ9vJZWeVj5MhaXL8+hODgpq/2C6GOHKpX\nz4U+faoyfXoDFi1qyZ07XzJlSn3S0iSGDdtGkSI/0qrVKmbNOiz7ASSHWrWKc+xYb6pXd6Zq1QX8\n+utRHZFE/8hXtGh+du7swpMnL+jQYa1RGfr9+nlTtmxhRo3aKWt8p04fkj+/hax1lOzo0aMSkZHn\nswlj/vf1U7u1NEd1NWjgqpc14uFhz5UrD2Vbbk5OBWQrEltbyxxTJLkWaC+EmAx0BZ4CmV2PnIED\nWYb9BRRHXYQ/a9Pmaxn7ZZNTFsm5c/cYNcqHMWOiZY0/ffoWf//9guLFbbM10dPTJdasOUO7dp5M\nnaq9lPaJE7coUEBFy5ZuWm9mTYSGxjFo0Ef06lVF1j/dwoUnqVOnJPPm+dG163q9j5dJTMxVfH3/\nIDKyAy4uBWWX/pYkmDnzMJs2JfHjj40JCvLku+9itLoV9EWSID7+DvHxd1iwQP03KVzYilq1ivPx\nxyUYPrwm+fKZ4eVVlOvXH5Oc/Jjk5EfZbl+//pjU1JeYmQlUKnPS082xtbXOqLVlhp2dNZ06edGn\nj7pDpbW1BTVqOBMe3g43NzvKli3MgwfPOXfuHklJKZw7d4/t2y/x5Zfbc6z4Hqgto8mT6xEUVJ4h\nQ7ayerV+VW/d3e2IjPyUZcti+fZb/ZMNs1KxogMTJ9aldu0lshbobWxUTJvWgKCgNQZVuhUCBg6s\nRvfuuu9vS0tzGjUqzYABWzSOqV/fhagoeZWSQR3aLLeMSr58glu3/ub2bd0JifnymaFSmRvsWtT5\n/TnyrYAQYhuQXcznV5IkbZAkaRwwTggxBvgZ6JlT5wLqNRJ9OgzKJTb2Nh4edqhU5rJudEmCs2fv\n0bhxaX7/PfuIkLCwM/zwQ0OdigTUSYZDhlQ3SJFIEgwYsIWoqE+JiEiUtSg5cOAWDhzoQd++VWUX\noMuOxMS7+PgsZuPG9ri4FKRfv0jSZN7jSUkptG69inbtPJk9uynXrj1i7Nho2UXu9CUl5RmbN5//\nv8q3dnbWODvb4Oxs++qnh4c99eu7vNpnbm6Gk5MNkiSRmvqSCRMeMXx4f168SCc19SUffKB6lXuQ\n6cGVJIkVK+JISkohKSnF4Ba4hmBpaU7fvlXx93fn6tWHfPjhPL3LsteuXZKwsADGjt3FokUnjTqf\n/PktCA1tw4gROzh7Vl4U09dff8KOHZcMqtsF0LhxGR4/TpW1yO7rW4rTp29prRNXr56L7EkmqBWJ\n3FJBxYt/QKFCVrLKo9jaqnj0KGesEQCRU41OZJ+AEKWASEmSvDKUCpIkTc14bwvq9ZPLQLQkSeUz\n9ncEfCVJ6p/N9+WuQAoKCgrvKJIkGeQwzhXXlhDCTZKkzFWz1kDm1HY9sFwI8SNq15UbcEiSJEkI\n8VAIURM4hNolNiu77zb0D6GgoKCgYBi5tUYyRQjhAbwEzgMDACRJihdCrALigTRgoPSPyTQQ+B2w\nRm3BaHZMKigoKCi8MXLdtaWgoKCg8G7zzma250ZS45tECDFDCJGQIeMaIUTBLO/lBfmChBBxQoiX\nQgjv19575+V7HSFEswx5zgkhRuf2+eiLEGKhEOKmEOJ0ln12QohtQoizQoitQohCWd7L9hq+rQgh\nSgohojPuyVghxOCM/XlCRiGElRDiYMbzMl4IMSVjv2nkkyTpnXyhzivJ3B4ELMjYrgCcACwAV9S5\nKJmW1yGgRsZ2JNAst+XQIl9jwCxjeyowNY/J5wm4A9GAd5b9eUK+12Q1z5DDNUOuE0D53D4vPWWo\nA1QFTmfZNx0YlbE9Wsc9apbbMuiQzxGokrFtAyQC5fOYjPkzfuZDnWZR21TyvbMWiZQLSY1vEkmS\ntkmSlFke9SBQImM7r8h3RpKk7GKW84R8r1EDSJIk6ZIkSS+AUNRyvjNIkrQXeD092x9YnLG9mH+u\nR3bXsAZvMZIk3ZAk6UTG9mMgAXXAT16SMTNOWYV6cpOCieR7ZxUJqJMahRBXgB7AlIzdzvx/8mJm\nUuPr+/VOasxFeqGegUPelC8reVG+4sDVLL9nyvSuU0ySpMzsuZtAZhE1TdfwnUAI4Yra+jpIHpJR\nCGEmhDiBWo5oSZLiMJF8b3ULubctqdHU6JIvY8w4IFWSpOVv9ORMgBz53hPyfESLJEmSjhyud+Jv\nIISwAcKBIZIkPRJZ6vC86zJmeDiqZKy3Rgkh6r/2vsHyvdWKRJKkxjKHLuefGfs1IGsxqxKotek1\n/nEPZe43LP3VROiSTwjRA2gONMyyO8/Ip4F3Rj49eF2mkvz/bO9d5aYQwlGSpBsZrsfMlOzsruFb\nf62EEBaolchSSZIiMnbnKRkBJEl6IITYBFTDRPK9s64tIYRbll9fT2r8VAihEkKU5p+kxhvAQyFE\nTaGeZnRFXXn4rUSoqxuPBFpLkpS1TkWekO81siaR5kX5jqCuWO0qhFChrnBteLGyt4f1QPeM7e78\ncz2yvYa5cH6yybinQoB4SZJ+zvJWnpBRCFEkMyJLCGGNOpjnOKaSL7cjCYyIQAgDTqOOLAgHHLK8\n9xXqxaEzQNMs+6tlfCYJmJXbMuiQ7xzq0jDHM15z8ph8bVGvGzwFbgCb85J82cjrhzoSKAkYm9vn\nY8D5rwCSgdSM69YTsAO2A2eBrUAhXdfwbX2hjmBKz3ieZP7PNcsrMgIVgWMZ8p0CRmbsN4l8SkKi\ngoKCgoJRvLOuLQUFBQWFtwNFkSgoKCgoGIWiSBQUFBQUjEJRJAoKCgoKRqEoEgUFBQUFo1AUiYKC\ngoKCUSiKREFBQUHBKBRFoqCgoKBgFIoiUVDIYYQQ1TMalFkKIQpkNE6qkNvnpaBgKpTMdgWFN4AQ\nYiJgBVgDVyVJmpbLp6SgYDIURaKg8AbIqCx7BHVtMR9J+cdTyEMori0FhTdDEaAA6m6e1rl8LgoK\nJkWxSBQU3gBCiPWo++aUAZwkSRqUy6ekoGAy3urGVgoKeQEhRDfguSRJoUIIM2CfEKKeJEm7cvnU\nFBRMgmKRKCgoKCgYhbJGoqCgoKBgFIoiUVBQUFAwCkWRKCgoKCgYhaJIFBQUFBSMQlEkCgoKCgpG\noSgSBQUFBQWjUBSJgoKCgoJRKIpEQUFBQcEo/gcPi6BQggHsAAAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10de3b550>"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mesh.plotSlice(mesh.aveF2CCV*js, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
-      "xlim(-300, 300)\n",
-      "ylim(-300, 0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = np.random.randn(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print (a.reshape([1,-1])).repeat(3, axis = 0)\n",
-      "print (a.reshape([1,-1])).repeat(3, axis = 0).sum(axis=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def DChalf(txlocP, txlocN, rxloc, sigma, I=1.):\n",
-      "    rp = (txlocP.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)\n",
-      "    rn = (txlocN.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)\n",
-      "    rP = np.sqrt(((rxloc-rp)**2).sum(axis=1))\n",
-      "    rN = np.sqrt(((rxloc-rn)**2).sum(axis=1))\n",
-      "    return I/(sigma*2.*np.pi)*(1/rP-1/rN)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "data_analP = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxP, sighalf)\n",
-      "data_analN = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxN, sighalf)\n",
-      "data_anal = data_analP-data_analN"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Data_anal = data_anal.reshape((21, 21), order = 'F')\n",
-      "Data = data.reshape((21, 21), order = 'F')\n",
-      "X = xyz_rxM[:,0].reshape((21, 21), order = 'F')\n",
-      "Y = xyz_rxM[:,1].reshape((21, 21), order = 'F')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
-      "vmin = np.r_[data, data_anal].min()\n",
-      "vmax = np.r_[data, data_anal].max()\n",
-      "dat0 = ax[0].contourf(X, Y, Data, 60, vmin = vmin, vmax = vmax)\n",
-      "dat1 = ax[1].contourf(X, Y, Data_anal, 60, vmin = vmin, vmax = vmax)\n",
-      "cb0 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[0])\n",
-      "cb1 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[1])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: pylab import has clobbered these variables: ['linalg']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n"
+     ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "from SimPEG import *\n",
+    "import simpegDCIP as DC\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cs = 25.\n",
+    "hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
+    "hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
+    "hz = [(cs,7, -1.3),(cs,20)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "blk1 = Utils.ModelBuilder.getIndicesBlock(np.r_[-50, 75, -50], np.r_[75, -50, -150], mesh.gridCC)\n",
+    "sighalf = 1e-3\n",
+    "sigma = np.ones(mesh.nC)*sighalf\n",
+    "sigma[blk1] = 1e-1\n",
+    "sigmahomo = np.ones(mesh.nC)*sighalf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<matplotlib.collections.QuadMesh at 0x1077619d0>,\n",
+       " <matplotlib.lines.Line2D at 0x107761e90>)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vUtumfF3O2PBjbXJt8qVtZ9m3i3H6nqNp7npewpIkFbGASJKKWEAkSUUsIJKkIhYQSVIR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\nQCRJRSwgkqQiFhBJUpH/D+59VatNuYwRAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107575a50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mesh.plotSlice(sigma, normal='X', grid=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "xtemp = np.linspace(-150, 150, 21)\n",
+    "ytemp = np.linspace(-150, 150, 21)\n",
+    "xyz_rxP = Utils.ndgrid(xtemp-10., ytemp, np.r_[0.])\n",
+    "xyz_rxN = Utils.ndgrid(xtemp+10., ytemp, np.r_[0.])\n",
+    "xyz_rxM = Utils.ndgrid(xtemp, ytemp, np.r_[0.])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10820a490>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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mWyX9jqRhSV919891eUmo0Ouvvy6p5t9sevrpt/4+09MZv9n0ltjxVCy12ZSZ9zS/2bSM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\nEPT/Ac5ECZ93vwuPAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10820a390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,1, figsize = (5,5))\n",
+    "mesh.plotSlice(sigma, grid=True, ax = ax)\n",
+    "ax.plot(xyz_rxP[:,0],xyz_rxP[:,1], 'w.')\n",
+    "ax.plot(xyz_rxN[:,0],xyz_rxN[:,1], 'r.', ms = 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rx = DC.RxDipole(xyz_rxP, xyz_rxN)\n",
+    "tx = DC.SrcDipole([rx], [-200, 0, -12.5],[+200, 0, -12.5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "survey = DC.SurveyDC([tx])\n",
+    "problem = DC.ProblemDC_CC(mesh)\n",
+    "problem.pair(survey)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    from pymatsolver import MumpsSolver\n",
+    "    problem.Solver = MumpsSolver\n",
+    "except Exception, e:\n",
+    "    problem.Solver = SolverLU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.15 s, sys: 155 ms, total: 1.3 s\n",
+      "Wall time: 941 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "data = survey.dpred(sigmahomo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "u1 = problem.fields(sigma)\n",
+    "u2 = problem.fields(sigmahomo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Msig1 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigma)))\n",
+    "Msig2 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigmahomo)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "j1 = Msig1*mesh.cellGrad*u1[tx, 'phi_sol']\n",
+    "j2 = Msig2*mesh.cellGrad*u2[tx, 'phi_sol']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# us = u1-u2\n",
+    "# js = j1-j2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-300, 0)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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WhWvyqlOnOBcudKNLl1L4+gYwbtxpkpN1rEebg6OjCdu3V2X+fFc+/vg+Q4aE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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x108233510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mesh.plotSlice(mesh.aveF2CCV*j1, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
+    "xlim(-300, 300)\n",
+    "ylim(-300, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'js' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-23-575f23801c4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maveF2CCV\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mjs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvType\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CCv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mview\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'vec'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstreamOpts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"density\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"color\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'js' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "mesh.plotSlice(mesh.aveF2CCV*js, vType='CCv', normal='Y', view='vec', streamOpts={\"density\":3, \"color\":'w'})\n",
+    "xlim(-300, 300)\n",
+    "ylim(-300, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = np.random.randn(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print (a.reshape([1,-1])).repeat(3, axis = 0)\n",
+    "print (a.reshape([1,-1])).repeat(3, axis = 0).sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def DChalf(txlocP, txlocN, rxloc, sigma, I=1.):\n",
+    "    rp = (txlocP.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)\n",
+    "    rn = (txlocN.reshape([1,-1])).repeat(rxloc.shape[0], axis = 0)\n",
+    "    rP = np.sqrt(((rxloc-rp)**2).sum(axis=1))\n",
+    "    rN = np.sqrt(((rxloc-rn)**2).sum(axis=1))\n",
+    "    return I/(sigma*2.*np.pi)*(1/rP-1/rN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data_analP = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxP, sighalf)\n",
+    "data_analN = DChalf(np.r_[-200, 0, 0.],np.r_[+200, 0, 0.], xyz_rxN, sighalf)\n",
+    "data_anal = data_analP-data_analN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Data_anal = data_anal.reshape((21, 21), order = 'F')\n",
+    "Data = data.reshape((21, 21), order = 'F')\n",
+    "X = xyz_rxM[:,0].reshape((21, 21), order = 'F')\n",
+    "Y = xyz_rxM[:,1].reshape((21, 21), order = 'F')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
+    "vmin = np.r_[data, data_anal].min()\n",
+    "vmax = np.r_[data, data_anal].max()\n",
+    "dat0 = ax[0].contourf(X, Y, Data, 60, vmin = vmin, vmax = vmax)\n",
+    "dat1 = ax[1].contourf(X, Y, Data_anal, 60, vmin = vmin, vmax = vmax)\n",
+    "cb0 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[0])\n",
+    "cb1 = plt.colorbar(dat1, orientation = 'horizontal', ax = ax[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}