diff --git a/talks/matlab_vs_python/fitting/fit_model.ipynb b/talks/matlab_vs_python/fitting/fit_model.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Short example of Model fitting\n",
+    "\n",
+    "Here we fit a MRI-style model to some simulated data.\n",
+    "\n",
+    "The model is: $\\textrm{Signal} = M_0\\exp\\left[-R_2\\textrm{TE}\\right]\\left(1-\\exp\\left[-R_1\\textrm{TI}\\right]\\right)$ \n",
+    "\n",
+    "The parameters that we will be fitting are $(M_0,R_1,R_2)$. \n",
+    "\n",
+    "Basic imports:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import minimize\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section:\n",
+    "\n",
+    "- defining a numpy array\n",
+    "- double list comprehension\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TEs = np.array([10,40,50,60,80]) # TE values in ms\n",
+    "TRs = np.array([.8,1,1.5,2])     # TR in seconds (I know this is bad)\n",
+    "\n",
+    "# All combinations of TEs/TRs\n",
+    "comb    = np.array([(x,y) for x in TEs for y in TRs])\n",
+    "TEs,TRs = comb[:,0],comb[:,1]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Now we define our forward model\n",
+    "\n",
+    "In this section:\n",
+    "\n",
+    "- inline function definition\n",
+    "- random number generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x121b13978>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# function for our model\n",
+    "def forward(p):\n",
+    "    M0,R1,R2 = p\n",
+    "    return M0*np.exp(-R2*TEs)*(1-np.exp(-R1*TRs))\n",
+    "\n",
+    "# simulate data using model \n",
+    "true_p    = [100,1/.8,1/50]   # M0, R1=1/T1,R2=1/T2\n",
+    "data      = forward(true_p)\n",
+    "snr       = 50\n",
+    "noise_std = true_p[0]/snr\n",
+    "noise     = np.random.randn(data.size)*noise_std\n",
+    "data      = data + noise\n",
+    "\n",
+    "# quick plot of the data\n",
+    "plt.figure()\n",
+    "plt.plot(data)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we have the data and our forward model we are almost ready to begin fitting.\n",
+    "\n",
+    "We need a cost function to optimise. We will use mean squared error. \n",
+    "\n",
+    "In this section:\n",
+    "\n",
+    "- '**' operation\n",
+    "- np.mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cost function is mean square error divided by 2\n",
+    "def cf(p):\n",
+    "    pred = forward(p)\n",
+    "    return np.mean((pred-data)**2)/2.0\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can set up our optimisation.\n",
+    "\n",
+    "In this section:\n",
+    "\n",
+    "- scipy minimize\n",
+    "- dictionary\n",
+    "- keyword arguments\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get ready to minimize\n",
+    "p0 = [200,1/1,1/70]  # some random initial guess\n",
+    "method = 'powell' # pick a method. scipy has loads!\n",
+    "\n",
+    "\n",
+    "kw_args = {'x0':p0,'method':method}\n",
+    "result  = minimize(cf,**kw_args)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the data with the model prediction.\n",
+    "\n",
+    "In this section\n",
+    "\n",
+    "- printing\n",
+    "- text formatting\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fitted = [1.02562035e+02 1.16194491e+00 1.98071179e-02]\n",
+      "true   = [100, 1.25, 0.02]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(data,'o')\n",
+    "plt.plot(forward(result.x))\n",
+    "print('fitted = {}'.format(result.x))\n",
+    "print('true   = {}'.format(true_p))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional: use gradients and hessians to help with the optimisation\n",
+    "\n",
+    "In this example the forward model is simple enough that calculating the derivatives of the cost function is relatively easy to do analytically. Below is an example of how you could define these and use them in the fitting\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# gradient of the forward model\n",
+    "def forward_deriv(p):\n",
+    "    M0,R1,R2 = p\n",
+    "    E1,E2    = np.exp(-R1*TRs),np.exp(-R2*TEs)\n",
+    "    dE1      = -TRs*E1\n",
+    "    dE2      = -TEs*E2\n",
+    "    \n",
+    "    # f = M0*E2*(1-E1)\n",
+    "    dfdM0 = E2*(1-E1)\n",
+    "    dfdR1 = M0*E2*(-dE1)\n",
+    "    dfdR2 = M0*dE2*(1-E1)\n",
+    "    return np.array([dfdM0,dfdR1,dfdR2])\n",
+    "\n",
+    "# hessian of the forward model\n",
+    "def forward_deriv2(p):\n",
+    "    M0,R1,R2 = p\n",
+    "    E1,E2    = np.exp(-R1*TRs),np.exp(-R2*TEs)\n",
+    "    dE1      = -TRs*E1\n",
+    "    dE2      = -TEs*E2\n",
+    "    ddE1     = (TRs**2)*E1\n",
+    "    ddE2     = (TEs**2)*E2\n",
+    "    \n",
+    "    dfdM0dM0 = np.zeros(E1.shape)\n",
+    "    dfdM0dR1 = E2*(-dE1)\n",
+    "    dfdM0dR2 = dE2*(1-E1)\n",
+    "\n",
+    "    dfdR1dM0 = E2*(-dE1)\n",
+    "    dfdR1dR1 = M0*E2*(-ddE1)\n",
+    "    dfdR1dR2 = M0*(dE2)*(-dE1)\n",
+    " \n",
+    "    dfdR2dM0 = dE2*(1-E1)\n",
+    "    dfdR2dR1 = M0*dE2*(-dE1)\n",
+    "    dfdR2dR2 = M0*ddE2*(1-E1)\n",
+    "\n",
+    "    return np.array([[dfdM0dM0,dfdM0dR1,dfdM0dR2],\n",
+    "                     [dfdR1dM0,dfdR1dR1,dfdR1dR2],\n",
+    "                     [dfdR2dM0,dfdR2dR1,dfdR2dR2]])\n",
+    "\n",
+    "\n",
+    "# cost function is mean square error divided by 2\n",
+    "def cf(p):\n",
+    "    pred = forward(p)\n",
+    "    return np.mean((pred-data)**2)/2.0\n",
+    "\n",
+    "def cf_grad(p):\n",
+    "    pred  = forward(p)\n",
+    "    deriv = forward_deriv(p)\n",
+    "    return np.mean( deriv * (pred-data)[None,:],axis=1)\n",
+    "\n",
+    "def cf_hess(p):\n",
+    "    pred   = forward(p)\n",
+    "    deriv  = forward_deriv(p)\n",
+    "    deriv2 = forward_deriv2(p)\n",
+    "    \n",
+    "    H = np.zeros((len(p),len(p)))\n",
+    "    for i in range(H.shape[0]):\n",
+    "        for j in range(H.shape[1]):\n",
+    "            H[i,j] = np.mean(deriv2[i,j]*(pred-data) + deriv[i]*deriv[j])\n",
+    "    return H\n",
+    "    \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fitted = [1.02576306e+02 1.16153921e+00 1.98044006e-02]\n",
+      "true   = [100, 1.25, 0.02]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# get ready to minimize\n",
+    "p0 = [200,1/1,1/70] # some random guess\n",
+    "method = 'trust-ncg'\n",
+    "\n",
+    "kw_args = {'x0':p0,'method':method,'jac':cf_grad,'hess':cf_hess}\n",
+    "\n",
+    "result = minimize(cf,**kw_args)\n",
+    "\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(data,'o')\n",
+    "plt.plot(forward(result.x))\n",
+    "print('fitted = {}'.format(result.x))\n",
+    "print('true   = {}'.format(true_p))\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/talks/matlab_vs_python/fitting/fit_model.m b/talks/matlab_vs_python/fitting/fit_model.m
new file mode 100644
index 0000000000000000000000000000000000000000..dd42d37c1d79e2a56723e6a40f3840e5aa94879e
--- /dev/null
+++ b/talks/matlab_vs_python/fitting/fit_model.m
@@ -0,0 +1,49 @@
+%% Play with model fitting
+
+% experimental parameters
+TEs = [10 40 50 60 80];
+TRs = [.8 1 1.5 2];
+[TEs,TRs] = meshgrid(TEs,TRs);
+TEs = TEs(:)'; TRs = TRs(:)';
+
+% forward model
+forward = @(p)( p(1)*exp(-p(3)*TEs).*(1-exp(-p(2)*TRs)));
+
+% simulate data
+
+true_p    = [100,1/.8,1/50];
+data      = forward(true_p);
+snr       = 50;
+noise_std = 100/snr;
+noise     = randn(size(data))*noise_std;
+data      = data+noise;
+
+plot(data)
+
+%%
+% cost function is mean squared error (MSE)
+cf = @(x)( mean( (forward(x)-data).^2 ) );
+
+% initial guess
+p0 = [200,1/1,1/70];
+
+
+% using fminsearch (Nelder-Mead)
+p  = fminsearch(@(x) cf(x),p0);
+
+% plot result
+figure,hold on
+plot(data,'.')
+plot(forward(p))
+
+%% The below uses fminunc, which allows morre flexibility 
+% (like choosing the algorithm or providing gradients and Hessians)
+
+options  = optimoptions('fminunc','Display','off','Algorithm','quasi-newton'); 
+
+[x,fval] = fminunc(cf,p0,options);
+
+figure,hold on
+plot(data,'.')
+plot(forward(x))
+
diff --git a/talks/matlab_vs_python/rbf/.ipynb_checkpoints/fit_model-checkpoint.ipynb b/talks/matlab_vs_python/rbf/.ipynb_checkpoints/fit_model-checkpoint.ipynb
deleted file mode 100644
index 1e34a5150af45276a07ba2c6b3b90b509c4ba8df..0000000000000000000000000000000000000000
--- a/talks/matlab_vs_python/rbf/.ipynb_checkpoints/fit_model-checkpoint.ipynb
+++ /dev/null
@@ -1,157 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x121ca4278>]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Fit a model to some data\n",
-    "# Model is:\n",
-    "#    prediction = M0 * exp(-TE/T2)*(1-exp(-TR/T1))\n",
-    "#    where M0,T1,T2 are unknown parameters and TE/TR are experimental parameters\n",
-    "\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "\n",
-    "\n",
-    "TEs = np.array([10,40,60,80]) # TE values in ms\n",
-    "TRs = np.array([.5,1,1.5,2])  # TR in seconds\n",
-    "\n",
-    "# All combinations of TEs/TRs\n",
-    "combinations = np.array([(x,y) for x in TEs for y in TRs])\n",
-    "TEs,TRs = combinations[:,0],combinations[:,1]\n",
-    "\n",
-    "# function for our model\n",
-    "def forward(p):\n",
-    "    M0,T1,T2 = p\n",
-    "    return M0*np.exp(-TEs/T2)*(1-np.exp(-TRs/T1))\n",
-    "\n",
-    "# simulate data using model \n",
-    "true_p = [100,.8,50]\n",
-    "data   = forward(true_p)\n",
-    "data   = data + np.random.randn(data.size)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Now for the fitting\n",
-    "# we need a cost function:\n",
-    "\n",
-    "def cf(p):\n",
-    "    pred = forward(p)\n",
-    "    return np.mean((pred-data)**2)/2\n",
-    " \n",
-    "# always a good idea to calculate gradient\n",
-    "def forward_deriv(p):\n",
-    "    M0,T1,T2 = p\n",
-    "    E1,E2 = np.exp(-TEs/T2),np.exp(-TRs/T1)\n",
-    "    \n",
-    "    dfdM0 = E2*(1-E1)\n",
-    "    dfdT1 = M0*E2*(-E1/T1**2)\n",
-    "    dfdT2 = M0*(E2/T2**2)*(1-E1)\n",
-    "    return np.array([dfdM0,dfdT1,dfdT2])\n",
-    "    \n",
-    "def gradient(p):\n",
-    "    pred  = forward(p)\n",
-    "    deriv = forward_deriv(p)\n",
-    "    return np.mean( deriv * (pred-data)[None,:],axis=1)\n",
-    "\n",
-    "# get ready to minimize\n",
-    "p0 = [200,70,1000] # some random guess\n",
-    "method = 'TNC'\n",
-    "\n",
-    "arguments = {'x0':p0,'method':method,'jac':gradient}\n",
-    "\n",
-    "result = minimize(cf,**arguments)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x121f34eb8>]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(data)\n",
-    "plt.plot(forward(result.x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/talks/matlab_vs_python/rbf/.ipynb_checkpoints/play_rbf-checkpoint.ipynb b/talks/matlab_vs_python/rbf/.ipynb_checkpoints/play_rbf-checkpoint.ipynb
deleted file mode 100644
index d431889a2f49cfcad804fb059622f71c3b1e270c..0000000000000000000000000000000000000000
--- a/talks/matlab_vs_python/rbf/.ipynb_checkpoints/play_rbf-checkpoint.ipynb
+++ /dev/null
@@ -1,97 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#!/usr/env/python\n",
-    "\n",
-    "# Python version of RBF fitting\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Generate noisy sine wave\n",
-    "x = np.linspace(0,10,100)\n",
-    "y = np.sin(3*x) + np.random.randn(x.size)*.5\n",
-    " \n",
-    "# Define RBF atom\n",
-    "# two options:\n",
-    "#   inline function definition (not available in matlab)\n",
-    "#   lambda : like @ in matlab\n",
-    "\n",
-    "sig = 2\n",
-    "# option 1\n",
-    "# def rbf(x,c):\n",
-    "#     return np.exp(-(x-c)**2/sig**2)\n",
-    "# option 2\n",
-    "rbf = lambda x,c : np.exp(-(x-c)**2/sig**2)\n",
-    "\n",
-    "# create design matrix\n",
-    "# (use list comprehension to show off)\n",
-    "xi     = np.linspace(0,10,15)\n",
-    "desmat = [rbf(x,c) for c in xi] \n",
-    "desmat = np.asarray(desmat).T\n",
-    "\n",
-    "# invert model\n",
-    "beta   = np.linalg.pinv(desmat)@y.T\n",
-    "\n",
-    "# plot fit\n",
-    "plt.figure()\n",
-    "plt.plot(x,y,'.')\n",
-    "plt.plot(x,desmat,'k') \n",
-    "plt.plot(x,desmat@beta)\n",
-    "\n",
-    "\n",
-    "# make it pretty\n",
-    "plt.grid()\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('y')\n",
-    "plt.title('RBF fitting')\n",
-    "plt.savefig('/Users/saad/Desktop/RBF.pdf')\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/talks/matlab_vs_python/rbf/fit_model.ipynb b/talks/matlab_vs_python/rbf/fit_model.ipynb
deleted file mode 100644
index 40754f988437b67e5287e075e2be2ff9aea6e0fc..0000000000000000000000000000000000000000
--- a/talks/matlab_vs_python/rbf/fit_model.ipynb
+++ /dev/null
@@ -1,232 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x121ca4278>]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Fit a model to some data\n",
-    "# Model is:\n",
-    "#    prediction = M0 * exp(-TE/T2)*(1-exp(-TR/T1))\n",
-    "#    where M0,T1,T2 are unknown parameters and TE/TR are experimental parameters\n",
-    "\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "\n",
-    "\n",
-    "TEs = np.array([10,40,60,80]) # TE values in ms\n",
-    "TRs = np.array([.5,1,1.5,2])  # TR in seconds\n",
-    "\n",
-    "# All combinations of TEs/TRs\n",
-    "combinations = np.array([(x,y) for x in TEs for y in TRs])\n",
-    "TEs,TRs = combinations[:,0],combinations[:,1]\n",
-    "\n",
-    "# function for our model\n",
-    "def forward(p):\n",
-    "    M0,T1,T2 = p\n",
-    "    return M0*np.exp(-TEs/T2)*(1-np.exp(-TRs/T1))\n",
-    "\n",
-    "# simulate data using model \n",
-    "true_p = [100,.8,50]\n",
-    "data   = forward(true_p)\n",
-    "data   = data + np.random.randn(data.size)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/scipy/optimize/_minimize.py:506: RuntimeWarning: Method Nelder-Mead does not use gradient information (jac).\n",
-      "  RuntimeWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Now for the fitting\n",
-    "# we need a cost function:\n",
-    "\n",
-    "def cf(p):\n",
-    "    pred = forward(p)\n",
-    "    return np.mean((pred-data)**2)/2\n",
-    " \n",
-    "# always a good idea to calculate gradient\n",
-    "def forward_deriv(p):\n",
-    "    M0,T1,T2 = p\n",
-    "    E1,E2    = np.exp(-TEs/T2),np.exp(-TRs/T1)\n",
-    "    \n",
-    "    dfdM0 = E2*(1-E1)\n",
-    "    dfdT1 = M0*E2*(-E1/T1**2)\n",
-    "    dfdT2 = M0*(E2/T2**2)*(1-E1)\n",
-    "    return np.array([dfdM0,dfdT1,dfdT2])\n",
-    "\n",
-    "def forward_deriv2(p):\n",
-    "    M0,T1,T2 = p\n",
-    "    E1,E2    = np.exp(-TEs/T2),np.exp(-TRs/T1)\n",
-    "    \n",
-    "    dfdM0dM0 = 0\n",
-    "    dfdM0dT1 = -E2\n",
-    "    dfdM0dT2 = (1-E1)\n",
-    "\n",
-    "    dfdT1dM0 = E2*(-E1/T1**2)\n",
-    "    dfdT1dT1 = M0*E2*(-E1/T1**4)\n",
-    "    dfdT1dT2 = M0*(E2/T2**4)*(1-E1)\n",
-    " \n",
-    "    dfdT2dM0 = (E2/T2**2)*(1-E1)\n",
-    "    dfdT2dT1 = M0*(E2/T2**2)*(1-E1/T1**2)\n",
-    "    dfdT2dT2 = M0*(E2/T2**4)*(1-E1)\n",
-    "\n",
-    "    return np.array([dfdM0dM0,dfdM0dT1,dfdM0dT2],[dfdT1dM0,dfdT1dT1,dfdT1dT2],[dfdT2dM0,dfdT2dT1,dfdT2dT2])\n",
-    "\n",
-    "def gradient(p):\n",
-    "    pred  = forward(p)\n",
-    "    deriv = forward_deriv(p)\n",
-    "    return np.mean( deriv * (pred-data)[None,:],axis=1)\n",
-    "\n",
-    "def hess(p):\n",
-    "    pred   = forward(p)\n",
-    "    deriv  = forward_deriv(p)\n",
-    "    d2Fdp2 = forward_deriv2(p)\n",
-    "    \n",
-    "    deriv*deriv\n",
-    "\n",
-    "\n",
-    "# get ready to minimize\n",
-    "p0 = [200,1,70] # some random guess\n",
-    "method = 'Nelder-Mead'\n",
-    "\n",
-    "arguments = {'x0':p0,'method':method,'jac':gradient}\n",
-    "\n",
-    "result = minimize(cf,**arguments)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x123074ef0>]"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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V5L/FGPOUMSbXGJMbHx9/Zmk7YOfad4mmBhnqw9e97KDAuEwipYGS0hKroyilLNChAm6MKXJ/LQPeAMYCpSKSBOD+6hVnlNSue4VaE8rwyf6xdOyphPdxrQteVbzH4iRKKSu0W8BFJFxEIo/fBs4DtgJvA3PdT5sLvNVVITuqqamRAceW80WvcwgLi7A6Tpfr5S7gTeX7LU6ilLJCQAeekwi84T4ZJgB4yRjzgYh8DrwiIvOAA4DlDddbVr1NLnUE5/j/0TdAQFyG60alrguuVE/UbgE3xuwDRp5k+xFgWleE6qzmTa9RQxiDz/aahpiuFRpNnYQTXKvrgivVE/nNmZjVdXUMq17F3tjJBASHWh2n21QGJxOlveBK9Uh+U8A3LXuDKKknMvd7VkfpVg3hKSQ4Smlq1V5wpXoavyngbFtIDeFkjb3I6iTdytmrr7sXXNcFV6qn8YsCXnKkkpH1n3AgYSoSEGx1nG4VGJdJqDRTWqxroijV0/hFAd+0fCFR0kDvcVdZHaXbRfZxrQteo73gSvU4flHAA3e+RbVE0ifnPKujdLvoZPe64BUF1gZRSnU7ny/ge4rKGNu0luLkc8EeaHWcbhfgXhdctBdcqR7H5wv41hULiZBGEvOutjqKNYIjqLb1Ikh7wZXqcXy6gBtjiNi7iGpbL6IHT7U6jmUqg5Lopb3gSvU4Pl3AN+w9TF7r51SknQ/2jqwK4J8aw1NJcJTQ3Oq0OopSqhv5dAHfufp1wqWJpLN76PSJmzO6LylSQXFlndVRlFLdyGcLeHOrk7iC96i2xxDab2L7L/BjQb0zCRIHZUUFVkdRSnUjny3gH28r4DtmHdWZF4LNbnUcS0UmunrBq0v2WpxEKdWdfLaA71+7kFBpps+Enj19AhCd0h/QdcGV6ml8soDXNrWSevhDqgPiCMiYYHUcywXGprtuVB60NohSqlv5ZAFfsnEvE9lAY/+ZPX76BIDAEI7aYgmt015wpXoSn+y9K/p0ISHSQvD4K62O4jWqdF1wpXocnzsCL6tppF/5YmqCEpC08VbH8RqN4SkkOEu1F1ypHsTnCvgH63YzybYJx6BZYPO5+F3GRKeTxBFKjtVaHaXTCirqKKtutDqGUj7D56ZQKvLfIFhaCT7rCqujeJWguAwC9jgpL9pH3/gcq+Octj1lNcx4dDXNrU6GJEUxaWA8kwfEMzo9hkC7/qJW6mR8qoDvK69lRNVSasL6EJl6ltVxvEpkkmtZ2erivZDjWwXc4TTc+epmwoPs/GRaf1buKufplft4fPleIoMDOLtfbyYPjGfSwHiSenXweqeOFtixCEJ6QT+vuva2Uh7T4QIuInYgHygyxswUkUzgZSAOWAdcZ4xp7pqYLu/n7+Qm22Zaht0MIl05lM+JcfeCNx/xvV7wp1ftY+OhSh69ahSzRiZz65R+VDe28MmeClbsKmf5znI+2FYCwMDEyC+LeW56LEEB3zg6b6iE9fPh0yehughCY+Hne3W6Tfml0zkC/wmwA4hy3/8j8FdjzMsi8gQwD3jcw/m+ZIyhesMbBImDoFE968LFHREUm4YDm8+tC76nrIaHFu/igqF9uHhE0pfbo0ICuWBYEhcMS8IYw67SWlbsKmP5znKe+3g/T67cR3iQnQn9ejNpQDzT+tSTtON5WP8CtNRBxndgwPmQ/xyU74DEodZ9k0p1kQ4VcBFJBS4C/he4Q0QEmAocPw1yPnAvXVjANx6qZHzDSmojUohIGd1Vw/gueyBHbb0J8aFe8FaHk5+5p07unzMMaeNdlYgwsE8kA/tEcvPEbGqbWlmz9wjLd5ZRsWMVsbvfIMH2Oa1iY3vcdFrH/hdDx3yH4JpCVwEvWK0FXPmljh6BPwz8Aoh0348DKo0xre77hUDKyV4oIjcDNwP07du300E//HwHP7NtxTniRzp90oaqkCR6NRRbHaPDnl61n02HKvnbVaOIj+z4xagjAmC6+YTpRx6D5s9xhPViS5/v81zzdD44aKP5jTpC31lMXnYc/whPJWT/Shj3wy78TpSyRrsFXERmAmXGmHUiMvl0BzDGPAU8BZCbm2tOOyHQ4nDSvPVtAsUBI7/bmV30CI3hqSTUraHF4fT6zo3dpTX8dfEuLhzWh5knTJ2cUmM1bHgB1j4BVQchJhNm/Bn7yKvICY7gUaC+uZW1+4645s23lvBha39mHfgYcTp1Hlz5nY4cgZ8NzBKRGUAIrjnwR4BoEQlwH4WnAkVdFXL1ngomt66mPiqNsCTf6rDoVtHpJJa9R9GRKtISYqxO06ZWh5M7X91EeLCd+2a3PXXypcpD8OkTsG4+NNdA3wlw4QMw4IJvLaUQFhTA1EGJTB2UyPCUXqxYOJDZZhmUbYM+w7vwu1Kq+7V7SGKMuccYk2qMyQCuBJYaY64BlgGXuZ82F3irq0J+9Pk2Jti3ETTyMp0+OYWg3hnYxFBR5N3Lyj61ah+bCqu4b/awU0+dFK6DV2+AR0bC2sddH0retBRufB8GXdTuOjh52XGsdQ5x3SlY7cHvQCnvcCZ94HcBL4vI74ANwLOeifRtc0LWE4AThl/aVUP4hSh3L3hNyR4g19owbdhVWsPDi3e3PXXidMDO92DNY3BwDQT3grxbXXPYvVJPa6zUmDACYvtS1pxEwv5VMP6/PPRdKOUdTquAG2OWA8vdt/cBYz0f6dvOqlsBsdn6FrgdsSmuAt5cUWBtkDYcnzqJCAn4dteJMbD+X7D6r3BsP0T3hQsegFHXQnBk2zttx4TsOFZvGcQlOg+u/JBvnIk5489QW6LTJ+0IikmlhQDES9cFf3LlPjYXVvH3q0fRO+KEqZPWZnj3dtjwb0jJhXPvhUEzPXKh6rzsOJatG8ylLIPSrZA04oz3qZS38I0CHj/A9Uedms1OhT3eK9cF31lSwyMf7WbG8D7MHJH81QMNx+CV62H/Sph0F0y+x6O/qPOy4viDc7DrTsEqLeDKr+j7ST9THZxErybv6gVvdTj5+WuuqZP7Zg/76oGj++HZ8+DAGpjzBEz5pcffZSVEhRCRkE5JQLJ+kKn8jhZwP9MUkUais4RWh/esC3586uT+2cO+mjo59Bk8cy7UlsH1b0LOVV02fl5WHKtaBmEKVrs+JFXKT2gB9zfRfekt1ZQeOWp1EsA1dfLwR7u4aHgSFx3vOtn6Ojw/E0Ki4AdLIOOcLs0wITuO1S2DkKZqKNnSpWMp1Z20gPuZoN6ZAFQU7rE4yVddJ1Ehgdw3e6ir02Tln+G1GyFlNMz7CHr36/Ic47K0H1z5Jy3gfuarXnDrT+Z5cuU+thRVcf+cYcSFCLz1Y1h6Pwz/Hlz/FoTHdUuO2PAgYpMyKA5IcX2QqZSf0ALuZ+JSj/eCW7su+JdTJyOSmNEvBP59KWz8N0y6Gy59GgI6vniVJ0zIjmNF8yDMgY91Hlz5DS3gfiY4OplGgrBXWdcL3nLC1MnvJoa7Ok0OroVLnoQpnm0T7Ki8rDg+aR2MNNVA8aZuH1+prqAF3N+IcMSeQEhdl60t1q4nV+xlS1EVf/9OMzEvXQh15a4pk5FXWpZpbFYsn5rj/eA6D678gxZwP1QVkkx002FLxv6ipJpHluzmNxk7yFt1g+ualD9YAhlnW5LnuKiQQPqkZFBkT9UCrvyGFnA/5OoFL+32XvAWh5M7X9nI7cFvc0PJ/a5Okx8sgbjsbs3Rlrzs3qxsHuiaB3e0tv8CpbycFnB/FN2XaKmjrKK8W4d9aukXfL/8QX7kXADDL3dNm4TFdmuGU5mQHcfHjiFIcy2U6Dy48n1awP1QcLyrF/xI4e5uG3NnwUHGrJrHZfaVrvVMLn2q2ztN2pObEcM6cfeD79d2QuX7tID7oV59XFMWtSX7umW8lvK9hP3rQkbbdlE74x8w+W6vXDkyLCiA1LQMDtnTdB5c+QUt4H4oLs21cmPLkW7oBT+4ltYnpxLhqGTDpPlEjL2m68c8A3nZvVnRPBBz8BOdB1c+Twu4HwqJiqeOEGxVB7p2oC2v4Zw/i9KWEP6e9QTjpszs2vE8IC8rjjWOIUhzHRRvtDqOUmdEC7g/EqHCnti1veC7F8Pr89gu/ZgX8AA/vuz8rhvLg0b1jWa9bajrjp5Wr3ycFnA/VR2SQkxXrQve0gDv/oyjoRl8t/bn/OLSPGLCg7pmLA8LCbSTmZ7BAVtf/SBT+Twt4H6qKTKVRGcZjq7oBV/1EFQe4P9VX8v5IzM4f2gfz4/RhSZkx7G8eSDm4BpwtFgdR6lO0wLupyQ6nQhpoLzcw0fhR/ZiPn6YFcGT2RGSw29nDfXs/rtBXnZv1jqHIC31cHiD1XGU6rR2C7iIhIjIZyKySUS2ichv3dszReRTEdkjIv8nIr7xHrqHCI7PAjzcC24MvHcnLQRxZ9X3uHfWUJ+ZOjnRiNRebA5wX9pN58GVD+vIEXgTMNUYMxLIAS4QkfHAH4G/GmP6AceAeV0XU52uqCRXAa8t9eC64NvfhL1LebDlMkYMGsjM41fY8TGBdhv9MzPYb0vXfnDl09ot4Mal1n030P3HAFOB19zb5wNzuiSh6pSE473gFQWe2WFTDeaDeygIzOYVOZ/75wxDvPBknY7Ky4pz9YMfWAOtzVbHUapTOjQHLiJ2EdkIlAGLgb1ApTHm+JkQhUBKG6+9WUTyRSS/vLx71+boyUIiY6kmHHvVIc/scPkDSE0xt9dez50XDCE5OtQz+7XIhOzerHEOQVobdB5c+awOFXBjjMMYkwOkAmOBQR0dwBjzlDEm1xiTGx8f38mYqjPKA/oQWl945jsq3YZZ+zgLmYat71iuGZd+5vu02JDkKHYEHZ8HX2ltGKU66bS6UIwxlcAyIA+IFpEA90OpgHVXEFAnVROSfOa94MbAuz+jzhbOAy1X8sfvDsdm892pk+PsNmFgViZ7JEPnwZXP6kgXSryIRLtvhwLTgR24Cvll7qfNBd7qqpCqc5oiUkl0luI8k17wTQvg4Brua7yCa6eOol9CpOcCWmxCdhyrWgbiPLhW58GVT+rIEXgSsExENgOfA4uNMe8AdwF3iMgeIA54tutiqs6QmAxCpIWK0k5OozQcw/mfX7FFBrIp7iJumeQdF2bwlOPz4LbWRihaZ3UcpU5bQHtPMMZsBkadZPs+XPPhykuFxGfADlcveEJy39PfwZL7of4odzXfwR9uyCEowL/O+xqQGMHukBE4nYKtYDWk51kdSanT4l8/kepropP7AVBX1ole8KJ1mPznmN86nXF5kxndN8bD6awnIgzpl8EeScfoB5knV1sOR7tnXXl1+to9Ale+Kz7V1Qveerq94E4Hznfu4JhEsyD8Ot44b6Dnw3mJvKw4Vm0fTL+Dy5DWJq+7ilC3MwbKdsCu92HnB1D4OQSEwB3bveryeMpFj8D9WGhEFEeJwlZ98PReuO6f2Io38tuma/jlpeMID/bf3/MTsuNY6xyMzdGD58Fbm2HvUnjvF/DICHg8D5bcB45mGPdDaG2Ara9bnVKdhP/+ZCoAKgL6EFZ3Gh9i1pbjWHwfnzmHYB9xGZMHJnRdOC+Q2TucgvAcnC2Cbf8qSJ9gdaTuUXcEdv/HdaS9Zyk017iOtLMmwzl3wIALIMq9VELBx7DxRRh7k5WJ1UloAfdzNSHJJNbt7PDznYt/hbO5jj8H3MzTF/veSoOnS0QY1i+dXTsyGFiwCuEuqyN1DWOgfOcJUyOfgXFCRCIMuxQGXgiZkyAo7NuvzbkaPrzHNbWSMLj7s6s2aQH3c82RaSTUrMLZ2ootoJ1/7oKPsW1awOOts7juu+cR64MrDXZGXnYcq7cMYsChpUhLIwSGWB2p06rqWyipbsRuEwJMK2ElnxFWsJjQfR9id19iz5k4HOfZdyADZ2BLyUFs9lPvdPj3YPGvYONLcN793fBdqI7SAu7nJCadoGIHFaUH6J1yij5uRwsti26n3PRmU+YP+FFOcveFtFheVhz3OofwA8f7UJQPGedYHem0ldU08tSKfbz16XYmONZxrn09k2ybiZJ6mkwgK51DWOKcyhLHaIoPxMEB4KMS4AMCbILdJgTaba7C774fYBPsdiGrdwT/7Dcd2+ZXYNpvwK5lw1vov4SfC/1yXfA9pyzgZu3jBAqsMVQAABX6SURBVB7Zye/Nnfz60rN8eqXB05UWG8bhXqNwNthc8+A+VMBLqxt5YsVeXvl0H5fzH1YELSTMXktTcBwlieezOWEyxbHjabSF0s/hJMNpcDgNrce/Opxf3f7yq9P9mOFoXTNLvihjw+QLGbPrfdi3DPpPt/rbVm5awP1c1PFe8FOtC15VhGPp71nuGMWY868hNeYk86B+bkR2Oju2ZjCkYBXCPVbHaVdxVQNPLN/Lgs8PMcFsZGnEAhKbDkDGFJh8N8GpY0m32TjTZcccTsOUPy/nT/vS+b/QGNc0ihZwr6EF3M8lpLqOuluOFLT5nKZ378I4Wnk14Tb+MSGzm5J5l7zsOD7eOJjBhR959Tx4UWUDjy/fwyufF5JmDvNW3GsMrvkEwjLhkgWuDyM9+O7JbhO+PyGD+97ZTvnoWcR/8TI0VEJotMfGUJ2nfeB+Ljw8gjJisVe3sS74no8I3rWIxx1z+Nnl52H3g5UGOyMvO861LoqjydWh4WUOHa3nnoVbmPzgMt79fCfPJL/NRyF3MbhpM5z7W7j1Uxg0w6PF+7jLz0ojMjiAf9blgaMJti30+Biqc/QIvAeoCOhD+MnWBW9ppP7NOyhx9sF2zk8ZkOg/Kw2ersSoEI7EjsJZa3Oti5I50epIABw8Us9jy/bw+vpC7GL4Y9ZmZh95Bnt5BeRcC9N+DZGJXZohIjiAK85K48lPWrk9aSCBGxdA7o1dOqbqGD0C7wFqQ5OJaS751vbmlQ8TVnuAJyL+i1umaX/viH592W4ycO63fl2Ugoo6fv7qJqb8ZTlvbCzi7qGVbEl5gEsPPYA9NgtuWgpzHuvy4n3c3AkZGAOrwqa73qFUePBi2arT9Ai8B2iOTCO+egmmtRkJcPd2H92HbfVfeMcxniuuuJ7ggHZ6gXuACdm9WZ0/hCGF/4Hm+pOf1NLF9pXX8vdle3hr42ECbMKPx4RwS/O/CN35BkQmw6XPwPDLumSq5FTSYsM4f2gf/nfPCKaIDdm0wHX0ryylR+A9gET3xS6GIyX7XRuMoWrhHTQ6bewYcTdj0nWRIoDxWe51UZzNrkWcutGeshp++vIGzn1oBe9tKeamcX1Y95113L7jakL3vg8TfwG35cOI73V78T5u3jmZ7G2M4HDcBNj0MjgdluRQX9Ej8B4gNCELtsPRwj30Th1Iy/Z36FW4jEcCvs8tF/tOz3NXiw0Pojo+F0eVDXvBKsia1OVj7i6t4dGle3hn82FCAuzcdE4mtyZuIWrlnVBdCEPmwPT7IMb665COSY9hZGovnqnJ4zdNq2H/SsieYnWsHk2PwHuA6OT+ANSX7oXmOhrf/hk7nGkMv/TnRIYEWpzOu+T068tWk4lz/6ouH2vDwWPMeHQVS3eUcsukbNZ8P457Su8g6p2bITQGvv8uXD7fK4o3uNaNufGcTF6qGkpLYJTrcnvKUlrAe4CE1CwcRmg9eoCj7/+OyKZSPkj/OVOHplodzevkZcexxjHEtbRsc32XjVPd2MJtCzaQEBnCyluHcVfzY0S/MB0qdsHMh+GHK7zyjNAZw5OIiYpiZdA5sP1taKy2OlKPpgW8B4gIC6VUepNY9jFRG57kLSZz3RVXWh3LK43NjGWtcwg2Zwsc+rRLxjDG8MuFWyiuauDlERuIey7PdYbj+B/Bbesh9wZob4EpiwTabVw/IZ2/Hx3nWid8u17L3EpawHuIIwF9SGvYQa0JwXbe/fSO6OFXnmlDr9BAGvvk4sAGBau7ZIxX8g/xzuZiXu6/jLTP7oPUs+C/1sAFv/eJMxyvHtuXLwIGUhaU5vrFoyyjBbyHqAl1rS74Ruw8ZuYNtziNdxvZP43NzmwcXdAPvqeshnvf3s6v+qzhrANPw6hr4drXIX6Ax8fqKtFhQXx3TCovNJwNBz/Ra2ZaqN0CLiJpIrJMRLaLyDYR+Yl7e6yILBaR3e6v/nfVWz9SnHYR/2fO5dxrftGjVhrsjAnZvVnjHIwUrYfmOo/tt7HFwY9f2sCMwHxurHoM+p8PMx+xrC3wTNxwdiavtpyNQVwthcoSHTkCbwV+ZowZAowHbhWRIcDdwBJjTH9gifu+8lIzL72WKT97kbTePfd0+Y7KTY/hMzMUm2mFg2s9tt8/vLeDqNLP+BOPIClj4HvP++za2tnxEQwZNJhPGY5z4wJwOq2O1CO1W8CNMcXGmPXu2zXADiAFmA3Mdz9tPjCnq0KqMxccYCchyjtX2PM24cEBtCafRSt2j82D/2dbCWvXruJfYX/FHpMOV/2fJWd6etK8czJZ0HwOtqqDcOBjq+P0SKc1By4iGcAo4FMg0RhT7H6oBOieRRmU6gaj+6eyyZlFqwf6wYurGnjotSW8GPIgwWGRcN1CCI/zQEprTciOoyB+CnWEYjbph5lW6HABF5EI4HXgp8aYrzV/GmMMYNp43c0iki8i+eXl5WcUVqnuMv748rKHN0BTbaf343Aa/uellTzm/B0xgS3Ita9DdF8PJrWOiHDNOYNZ1DoO59Y3z+jvSXVOhwq4iATiKt4vGmOOLwZcKiJJ7seTgLKTvdYY85QxJtcYkxsfH++JzEp1udF9Y8gX9zz4oc7Pgz+xeCs/Kv5vMuwV2K9eAIlDPZjSerNykvkoeCr21nrYscjqOF7LdYzreR3pQhHgWWCHMeahEx56G5jrvj0X0I5+5TdCAu2QMo4WAqCT0yif7S1j0OrbGGXbg/2yZ7zyzMozFRJoZ+i4CzjgTKD+8xesjuM16ppa+WRPBX9bspsbnvuUC377EmVVDR4fpyMfgZ8NXAdsEZGN7m2/BB4AXhGRebiucX25x9MpZaEx/VPYeDiLUftWnfaqb5V1TZS8eAuz7BtoPP9BQobM7pKM3uDavAxeWjWJnxS9CpUH/WaKqKOMMRw62sC6g0dZf6CS/fv3EFqxmWGyjxGyj2sDCoihiuKjo6DXEI+O3e7/S2PMaqCtRtVpHk2jlBeZ0C+Oj5cNYUzJImiqgeCOtWAaY/j4mTuY5VxCac5tJObd3MVJrRUfGUzdoMtg96s05L9I6Lnef1HoM9HY4mBLURXrDxxj1759tB5aT3rTLobb9nGbbT8JcgwCwYgNZ+9B2FMuhuQckhI83+fhm02oSnWDEanRPGYbhs286eoH7+DV2D979U9cdOzffJF0CYNm39/FKb3DnCkTWPPFEAave5HQaXf75MlJbSmuamDdgWPs2HuA2oJ8oo5uYSj7mGnbR4ocAcAECs3R/Qjsex4kj4bkUUif4di7uFVUC7hSbQi027D3HUdLYQCBBas6VMCLPl7AWdv+wIaw8Yyc97RfFbJTGZIcxeNxM8ir/DOtB9YQkDHB6kidVnisnmWb9nB0z2cEFG8kvXkXI2QfM23uLjo71EVkEJA6CfqOcRXrpBEEd/AdmidpAVfqFHL7p7D+YD9G711BYDv1u2n3SuIX/5gtMoDUm17GFtCz1lofPPUa6l7/G+XLniXjBt8r4LU1lax64ykS977CdfLVNT9rIlJw9BmLI+ss7CmjIWkk4V6y6JgWcKVOIS87jmWLBzO25C3X2tchUSd/YslWzIIrOehMpP7yF4mP7XlLA00clsl/3j6b7xx8D9Ncj/jCmabG4DyUT8Hix0k89C4X0khJcDqVo35OdP/xkDyayDDvveSgFnClTmFoci8eDhiO8AYcXAMDzv/2kyoP0vj8HI45glk8+h/8aFj/7g/qBWw2wZZzFeH5S9m3+hWypn7f6khtqz8Km/+P+rX/JKxyJ31MMGtCJ5I27RYG5E7zmakvLeBKnYLdJgSnj6f5QABBBau+XcDrjtAyfw7NjfX8IfZB/nLxRGuCeolzps+hKP+XNOa/AN5WwJ1OKFgJ6/+F2b4IcTazy5nFB0G3MPz8G5mRO8DnVurUAq5UO87qn8yG/f0YtXclQSc+0FyHeelyzLGD3Gb+h99ddwmB9p69xH5YcBDrUy4mr/CfHD64l+S+2VZHgqoi14UnNrwAlQdosEfxautUXjdTmDJpKj+ZmE1okHdeAak9WsCVaseEfnF88MEQxpa+CY1VENILHC3w6g2YovXc1vwTvnvF5aTF+sCcbzfoP/0m7M8/xxcfPk3yTQ9YE8LRArs+hPX/gj2LwTgpjRvHo/ZLeK0uhwtzMnjiwkEk9Qq1Jp+HaAFXqh0DEiL5S9AIxLkQDrjnwRf9FHZ/yP+0zKPX6EuYNTLZ6pheIzFjCHtDh5NR+Da1jfcTEdKN3ThH9rqK9saXoK4MIvpweNgt/PrQKD4qCicnLZoFc4cwuq9/fMisBVypdthsQlhmHk17AwnavxIp/Aw2/punbZfzaewsFs3yrwWqPCFwzDX0XX03i5a8z8UXzerawZrrYcfbrsJ94GMQOww4n/L+V3DfzhQWfV5Gn6gQHr5iELNGJmOz+dY896loAVeqA3L7J7F+V3/GrXseaaljecRFPHjsEt78wWjCgvTH6Jv6nnM1Tat/Q+uGF3FceDH2riqa+1fBwpugphhiMmHar6kffDmPravj6Tf3Y5MKfnpuf26emOWX/07+9x0p1QUmZMfxlnMIeS3bORA/mXmHruRXFw9mSHIbfeE9XUgvKtKmM/XgUpZuPcj0Eeme3b/TAaseguW/h9gsuP4tnOnf4fUNh/nTkzspr2liTk4yv7hgEMnRvj3PfSpawJXqgKze4XwQdhGxgRH85fBUpgxOZu6EDKtjebU+E2/E/uI7bFn6MtNH3OW5HdeWuY669y2H4ZfDzIf4vLiF+/6xhi1FVeSkRfPkdWP8Zp77VLSAK9UBIsKQ7Ezu3RhMn6gQHrxshM/1DHc3e/ZkaoMTGHnkfbYW3cKwlF5nvtP9q+D1ea5uoFl/Y33cTJ56ZRcfbCtxz3Pn+N0896n07KZVpU7DlEEJBNiEh6/MISY8qP0X9HQ2O4GjrmKSbROvLM8/s305HbDiT/CvWZjgKJZPfJk5a7K59PE1fLy3gp9M68/SOycxZ1RKjyneoEfgSnXYrJHJTBoQT3SYFu+OCh5zLax9hNAdr1NafTaJUSGnv5MTpkx2Jc7glmPXsO+9ejLi4LezhvLdMalEBPfMUtYzv2ulOkFEtHifrvgBNCWO5pLilfzrk/38/ILBp/f6/StpfXUepqGS3zp/yL8PTGRCdm9+OSeTqYMSetTR9snoFIpSqksF517LINsh1n+6goZmR4deYxytHFj4G5zzZ1NQG8Alzb+jafg1vPf/JvLSTeM5d0hijy/eoEfgSqmuNuxSnO/fzXnNS3ljwwyuHtf2NTMbWxx88Okm0pf9lFGOTbwnE9l/9v38c8Jg4iODuzG0b9ACrpTqWqExyOCLuHT7Eq5YvYurxqZ9q4OnrLqRf689wM6173K/4xF6ST2fj7iPaRffSnCglqm26N+MUqrLycir6bXtDdKPrGbFrhFMHpgAwNaiKp77eD/vbirkFlnI4wELaeyVRdDV73JWn2EWp/Z+WsCVUl0veyomIpGr61bzzOoLaWp18uzq/Xy2/yh9g2p4P/YpsmrWwYgrCbvoLxAcYXVin9Duh5gi8pyIlInI1hO2xYrIYhHZ7f7q/6c8KaU6zx6AjLicc1jPtt37+OEL6yg61sBj42tYHvkrshq2w+zH4JIntHifho50oTwPXPCNbXcDS4wx/YEl7vtKKdW2kVdjNw7uy9rOP64eycqxn3LRxluwhUbDTUth1LU+cykzb9HuFIoxZqWIZHxj82xgsvv2fGA54MHFDpRSfidxCCTlMLNlMWzYCPtXwsirYMaf9ai7kzrbB55ojCl23y4BEtt6oojcLCL5IpJfXl7eyeGUUn4h52oo3wGHPofZ/9ApkzN0xh9iGmOMiJhTPP4U8BRAbm5um89TSvUAOVdD5UHIucZ1RK7OSGcLeKmIJBljikUkCSjzZCillJ8KjoTz/9fqFH6js1MobwNz3bfnAm95Jo5SSqmO6kgb4QJgDTBQRApFZB7wADBdRHYD57rvK6WU6kYd6UK5qo2Hpnk4i1JKqdOgqxEqpZSP0gKulFI+Sgu4Ukr5KC3gSinlo7SAK6WUjxJjuu/kSBEpBw508uW9gQoPxukK3p7R2/OB92f09nygGT3B2/KlG2Piv7mxWwv4mRCRfGNMrtU5TsXbM3p7PvD+jN6eDzSjJ3h7vuN0CkUppXyUFnCllPJRvlTAn7I6QAd4e0Zvzwfen9Hb84Fm9ARvzwf40By4Ukqpr/OlI3CllFIn0AKulFI+yicKuIhcICI7RWSPiHjVBZRFJE1ElonIdhHZJiI/sTpTW0TELiIbROQdq7N8k4hEi8hrIvKFiOwQkTyrM32TiNzu/jfeKiILRCTECzI9JyJlIrL1hG2xIrJYRHa7v8Z4Wb4H3f/Om0XkDRGJtipfWxlPeOxnImJEpLcV2drj9QVcROzAY8CFwBDgKhHxpmsxtQI/M8YMAcYDt3pZvhP9BNhhdYg2PAJ8YIwZBIzEy3KKSArw/4BcY8wwwA5caW0qAJ4HLvjGtruBJcaY/sAS932rPM+38y0GhhljRgC7gHu6O9Q3PM+3MyIiacB5wMHuDtRRXl/AgbHAHmPMPmNMM/AyMNviTF8yxhQbY9a7b9fgKjwp1qb6NhFJBS4CnrE6yzeJSC9gIvAsgDGm2RhTaW2qkwoAQkUkAAgDDlucB2PMSuDoNzbPBua7b88H5nRrqBOcLJ8x5j/GmFb33bVAarcH+3qek/0dAvwV+AXgtZ0evlDAU4BDJ9wvxAsLJICIZACjgE+tTXJSD+P6z+i0OshJZALlwD/dUzzPiEi41aFOZIwpAv6M62isGKgyxvzH2lRtSjTGFLtvlwCJVoZpx43A+1aH+CYRmQ0UGWM2WZ3lVHyhgPsEEYkAXgd+aoyptjrPiURkJlBmjFlndZY2BACjgceNMaOAOqx92/8t7nnk2bh+2SQD4SJyrbWp2mdcfcJeeQQpIv+NawryRauznEhEwoBfAr+2Okt7fKGAFwFpJ9xPdW/zGiISiKt4v2iMWWh1npM4G5glIgW4pqCmisi/rY30NYVAoTHm+DuX13AVdG9yLrDfGFNujGkBFgITLM7UllIRSQJwfy2zOM+3iMj3gZnANcb7TkbJxvWLepP7ZyYVWC8ifSxNdRK+UMA/B/qLSKaIBOH64OhtizN9SUQE19ztDmPMQ1bnORljzD3GmFRjTAauv7+lxhivOXo0xpQAh0RkoHvTNGC7hZFO5iAwXkTC3P/m0/CyD1pP8DYw1317LvCWhVm+RUQuwDWdN8sYU291nm8yxmwxxiQYYzLcPzOFwGj3/1Ov4vUF3P1hx4+BD3H9wLxijNlmbaqvORu4DtdR7Ub3nxlWh/JBtwEvishmIAf4vcV5vsb97uA1YD2wBdfPjuWnW4vIAmANMFBECkVkHvAAMF1EduN65/CAl+X7OxAJLHb/vDxhVb5TZPQJeiq9Ukr5KK8/AldKKXVyWsCVUspHaQFXSikfpQVcKaV8lBZwpZTyUVrAlVLKR2kBV0opH/X/AWKLgN1WmxwUAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(data)\n",
-    "plt.plot(forward(result.x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3,)"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(forward_deriv(p0),axis=1).shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[100, 0.8, 50]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "true_p"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/talks/matlab_vs_python/rbf/play_rbf.ipynb b/talks/matlab_vs_python/rbf/play_rbf.ipynb
index d431889a2f49cfcad804fb059622f71c3b1e270c..793afa4853e4940997f5b53764efd96d86f09303 100644
--- a/talks/matlab_vs_python/rbf/play_rbf.ipynb
+++ b/talks/matlab_vs_python/rbf/play_rbf.ipynb
@@ -1,61 +1,119 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": 2,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "#!/usr/env/python\n",
+    "## Matlab v Python : introduction\n",
     "\n",
-    "# Python version of RBF fitting\n",
+    "This is a very small piece of code that fits RBFs to some data\n",
     "\n",
+    "In this section\n",
+    "\n",
+    "- numpy\n",
+    "- matlab-like functions like np.linspace\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "\n",
     "# Generate noisy sine wave\n",
     "x = np.linspace(0,10,100)\n",
-    "y = np.sin(3*x) + np.random.randn(x.size)*.5\n",
-    " \n",
+    "y = np.sin(3*x) + np.random.randn(x.size)*.5\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "We define our Radial basis functions as $\\textrm{RBF}(x) = \\exp\\left[-\\frac{(x-c)^2}{\\sigma^2}\\right]$ where $c$ is the centre and $\\sigma$ the extent.  By having multiple such functions with different centres we can fit an arbitrary function.\n",
+    "\n",
+    "In this section\n",
+    "\n",
+    "- defining a function (two options)\n",
+    "- list comprehension\n",
+    "- power with double *\n",
+    "- numpy array from list\n",
+    "- transpose\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Define RBF atom\n",
     "# two options:\n",
     "#   inline function definition (not available in matlab)\n",
     "#   lambda : like @ in matlab\n",
     "\n",
-    "sig = 2\n",
+    "sig = 2 # extent of an atom\n",
     "# option 1\n",
     "# def rbf(x,c):\n",
     "#     return np.exp(-(x-c)**2/sig**2)\n",
     "# option 2\n",
     "rbf = lambda x,c : np.exp(-(x-c)**2/sig**2)\n",
     "\n",
-    "# create design matrix\n",
-    "# (use list comprehension to show off)\n",
-    "xi     = np.linspace(0,10,15)\n",
-    "desmat = [rbf(x,c) for c in xi] \n",
-    "desmat = np.asarray(desmat).T\n",
+    "\n",
+    "# create a design matrix\n",
+    "# (using list comprehension to show off)\n",
+    "ci     = np.linspace(0,10,20)   # centres of the atoms\n",
+    "desmat = [rbf(x,c) for c in ci] # desmat contains a list of atoms\n",
+    "\n",
+    "# from list to numpy array\n",
+    "desmat = np.asarray(desmat).T\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can fit to the sine wave with pseudoinverse. \n",
+    "\n",
+    "In this section:\n",
+    "\n",
+    "- pinv\n",
+    "- basic plotting and prettifying\n",
+    "- saving figure to file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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maoLjVGXiZNqOE11b4zvnJon5fWSzWdJKlq98DypPdFAlBH98ysrz3jDHehrRt7SjXPgQmec+hf5Nf042myUej5NIJEgkEqTTaTKZDNlsFkVREOvpHQwGA0ajEaPRSGVlJVVVVVRVVZHL5YjFYsTjcVKpFOl0mnQ6TTabVdsUnU6HwWCQ36HqKysrURSlSJ9KpZiZmWFsbIxcLgeATqdDr9djMBgwmUyYTCYqKyuprq4ml80izn2KnOMOfMkaUlNTZDIZcrlckV6n09HS+ipqJx/CvzBHlbk+r1+vfyKRIJlMkkqlyGazZDIZef9CCHQ6HRUVFRiNRkwmE9XV1fL+4/G4bMNMJiPvP/+YgBBC3rvBYChqP4BYLEYgEGB2dla2Xy6XQ1EU2X7q/RuNRtl2Uu/3Uuu7Qujor+Cfnpb6je2n1+s3tR+6CrD2kJk5x/LcHMlkkmw2Szab3dR+6vNXUVFBVVUV1dXVKIpS8vkp1X6F+ng8Lp+RwvbbqC9sv8LnR71/IQSZy1/DBCwM/jRxt1vev+nO/03Xd99P4Cu/jf/4b6LX66moqCjS63Q6otFo0e9/q/ZT37/Kykpqamrk7y+ZTJJIJEilUvL9yWaz8t51Oh3z8/PMzc3JOqj6wvZT9YXtB6DX6+X7V1FRIZ8/nU63qf3UNlTfPyGE1Bc+f5WVlej1euLxOLFYTL5/qVSq6PlT21/9jsL20+v1Re9PMpmU95/L5ZiZmbnp/SK89DfctQGzBf+fW//ZJkMhhPgA+VkHDoeD06dP77owny/f8Z89e5ZcLkc2myWRSBCLxTgcfZzXAR954BkyVVOsZkyQ0CF0RvR6PdWJFS5dCsiXrY8sK5OzfPVykvt1PbSc/Qx/9ZwJdAb5i6+pqZEPhE6nQwgh9eqDn0wmZceeTCbR6/VSX11dLV9oVQ/5h1XVp1IpotFoSX1VVRVGo5FUKsX58+dL6gvLT6VSNAs/v8Qkp6t/iKmHHqKioqKo/oX65oid16djPPHpP2Ys20EqlUIIgclkoqqqCn/GxGxMT6/VSLfVuOX9J5NJ4vE4Op2uqOM2Go2yUyl3/+qLpepVg7m0tERFRYVsv/XnqKRebT+dTschg5sfQeERd5bAyqPbtn+hXgjBj+uMtAXP8uCDD2IymWTHWKjP5XJkMhl532rHBhQZPrX9Vb3afqpeLT8YDHLmzJkifWVlJSaTacv2S6fTsv5q+e/VPUCNromvPXoRk8lU1H7G6gNYXQ9zJvMKsrkcqVSqqGNVy1frUKjfeP/q71/VJ5NJFEWRz4+qL1X/XC5HKBTiwQcfLGq/cvq5CEyHFHrqdHRadLL91PvfTq/X60s+P5lMRrZfPB5HCCENV3V19Zb6XC5Xsv3L6YUQZDKZF9T3bcdL3VDsGEVRPg58HODYsWPKqVOnXtD3nD59mle+8pWsrq4SCAQIhUKk02n6zz9MItLMPa/8YXK5HEajkTvuTHJ9Lcepwz286sCN/E7ZbJZAIEAgECAcDpObjVN74Q/5sTttBJtOkM1m5Uipvr4em82G2WwuqQ+FQnLUYzAY5MirUN/Q0IDZbJYvSjabJRgM4vf7Zf1VvTryLtRfuXKFN7/5zVKfy+UIBAKsrq7K8tXRWvPsgzAOtuM/is3aRV1dHQ0NDVgslk36wMpxsl/8DPc1rGIfercc6WWzWa6vxPnwgxNkDUaeCNXy+be8lmNdDUX1L7z/dDot9el0GpPJREVFhbz/jeUHg8Gi+qfTadl+2WyWK1eucOLECerr67FardTV1RXp19bWZPup7a/T5TuQ7vGPkl0w0XXvD3PYasdqtVJfX1+y/HA4LEd9Op0ORVGomvZTN/VpTt45Qq2tlYaGhpL6wvtXjbxOpyOTyciRbl1dHTabrWT9A4EAa2trpFIpzpw5w/HjxxFCkE6n5UxD/f3V1dXJzl5RFNbW1lhdXSUYDMr20+v16NNhOk7/DfPd7+TEgRPU1dXJ+9fpdHA2Bd/4Vd7+imGWscn6p1Ip9Hq97MTV2aLFYpH3X6p8tf7pdLro/tTRvsViwWazldQ/9NBDDA8Py5G32plms1k5WzabzczG9PzJF8dIZ+GJqI7PvPFu+q16VldXeWp0hmueFQZaKzngtMrOWG1/s9ks66929oqiEA6H8fv9sv3U+1ef70K91WrFarWW1K+trZFMJuXzr7Zf4f0X6k+fPs0L7fu24qVuKLxAe8H/nes/uyVEIhF8Ph/PP/88uVyOyspKWltbqa+ro/qJ69D/Gu69916y2SyhUAjH2hojwSDZxDJXroSoqqqSv+RcLofJZKK5uZn6/vfC+N8ykroA9/4q2WyWcDhMMBiUHbo6vVUUhVAoJEcuLS0t1NXVUV1dXfSihMNhAoHAJn0ulyMSicjyW1tbt9V7vV6uXLkiy99Y/7q6OmpqavL6Bz6NUuug+85TBNfWZIdYWP9IJEI2m8VkMpHsPEXj/FM03XuP3HuSy+U48+AlcsY5sokI8USEBx5+ivpTQwghCIfD0pg2NTVhtVo31T8SicjOdHV1FYPBQG1tbVH9VX19ff2N+hfcf0NDg6x/oV6tv6qvq6ujtrb2xoh1/A/INt9BQ2OzNOjl9Ha7nfr6+mJ9YwSmPk0zKyxG6wgGgxgMBlnHSCQiOyObzYbVai3Wr9+jagzK6Q0Gg9THYjHuvfdeqS9sv2AwiF6vl4OVQn1jY6O8f51OB89/EZQc1rvfRa6qcZNeb+ynC1g792Uigz8pO3GpXy8/Go1KrcvlknrVNaoadrUTLhwIqPVX71/Vq20UjUZJp9NEo1HZiZYqXzVG/3Vmioh3BoxVpHWCBx5+ih+9s4WJxSgf/JaLrLEG0zx87u5hjnZaZR3VZ8ftdqPT6eRgTS1fr9fL+pvN5qLyVX0gEMDj8TA7Oyv1qltKr9fLQYzFYpF6gGg0uknf0NBAPB6/yb1inpe6ofg68CtCiC8AdwNrt2p9IpfLce3aNSKRCDabDbvdnvcpQ37xLrokd+Cqv0Cr1Sr9gmNjY/j9fvR6PV1dXQwODtLQ0HCjgEM/lk/9EQ+gr8qPwOrr66V+YmKC5eVldDqd1NtspQ+50el01NXVUVdXRy6XY3Z2lomJCZaWltDpdHR2djI0NLQjvaIoTE5OsrCwIPUdHR0MDQ1ht9s3i2efQTiPU1dfT119PR0dHczMzDA5OcmZq9NML8e4745B3nbqOI2NjaB7J0w9mD/cZz2UVKfT8ZrDXXziWR+pdBYRX8VZlebMmTMIIejs7GRwcDCvL1N/i8WCxWKhvb0dr9fLxMQEly9fRghBe3s7g4ODOByOsvqamho6Ozvp6Ogoq59LVPC1a35O9tRw1LyeGjybQfiuYDj2Xjo6Omhvb2d+fp6JiQmuXMkvUDudTgYHB2lubi5Zvmg+BIBDWabp0NtZWFgo0re1tTE4OEhLS+lcYUIIef9OpxOfz1ekb21tlXq1c1X/Vv9tNpsxm804nU4WFxcZHx/n6tV8wEVLSwtDQ0NFesnkQ1DTSHXfK6nW6Whvb5flq3p7dTuNwYs4D//5Zv16+bW1tdTW1tLe3s7i4mKRvrm5mcHBQdra2srqC+u/tLQk9Yqi0NLSwuDgIL29vXR2dm5ZfltbG69LV/L5p66R8M2i14GzykZjYyNPr1lQasOgQCaLPKlRCEFNTQ01NTW0tbWxvLzMxMSEXOdzOBwMDAzQ3t5e1LkXll+oX1lZYXx8XOobGxsZGhoqqweK9NFolJWVFVZXV/F6vUVrLTeLPTUUQojPA6cAuxBiDvh9wAigKMpHgW8CbwKuAzHg525VXXQ6Hf39/QQCATo6NqQJn30m//d6xJNKIBBgYWGBeDyO0+nkwIEDZDIZOUoKhUK0tLRgMpngjnflU3pceQCO/zwAwWCQhYUFYrEYra2tDA8PS7eL2+0mHA7T3NxMZWXpyJq1tTXm5+eJxWLy5VAUhUAggNvtluWX04dCIbxeL4FAgKGhIYaGhqTbyOPxEAqFaG1tvaGPLEPABcfeW6SPxWL4s1X863QV6bSRR749TWVVFfcMd9Dadh9VhkoYf7Boz8HRTisfe+cID58bZ9DWzuEOmzR8wWCQmZkZWb66iLyRcDjM/Pw8kUiExsZG+vr6UBSFYDDI3NwckUiElpaWGwZ/A5FIBK/XSyQSwW6309fXJ3+v33lulA8+NE2uqp7Kquob+0RWJvNpOFrvIBKJMD8/Tzgcxmaz0dvbK/Ver5dwOExbW9vm8s0tUNVAeu4CLus1wuEwVquVnp4eWX/1vlpbW+UibKn6LywsEAqFsFqtdHd3Azeeq2g0uqU+Go2ysLDA2toaVquVrq4uIP9cqfqWlhZqa2vzgmwarj0MI28FnY5YLMb8/Dxra2vU1dXR0dGBTqcj9sQrsF//ItdHL9Lc0VfkVi2kUG+xWLjvvvsQQhAMBllcXJT134m+trZ2k352dpZwOFxWH4/HmZ+fx6qE+LMfP8FUSOHOjgY6zbC8vEyLkkGfTSD0lSVPaozH4ywsLBAIBKipqeGee+5Br9eztrbGysoK8XhcegQ2cs4T4PHxebqrUzirc0X6YDDIysqK7BdK6QtRjYY6aLjZRgL2PurpXdtcV4AXLdVm4fS0iJmnobIO7INA/gWdm5sjGo1SWVlJV1cXDQ0N8heUTqdZXFxkeXmZ1dVVbDYbrS0HMDYOw6XPExl+p9SbTKZN+kwmg8/nY3l5Gb/fj91up7W1FaPRuKn8Unp1lLi0tMTq6uomfTQalR1pRUUFzc3NHDp0qEi/tLTE0tISgUAAu91OS0sLFXPPAhC3H2J2cpJwOExFRQWdnZ08FVyFmjX0CuiULJ54BQfDYUaDQUYaD2PyPCljsWOxGHNzc9Qmw/z4Xa00Nzdjs9lk22ezWVn/0dFRGhoaaGtro6KiIl9+PC47AaPRSHt7O3a7vUi/tLTE4uIiwWAQq9WK0+ks0s/NzWE2m0vqnU4n37z+DMl4jFw0Qq6qlsfH5/OGYuEiAJ60lZWJibL65eVlFhcXGRsbw2q1FhnceCKBYulBmT1Hoj+B0+mksbGxqP4rKyv4fD7Gx8epr6+nra1N6hOJBF6vV7qcnE4ndrtd+rjb29tZXl6W+rq6OlKplHycN+rb2tpobGyU+lwuJ/UTExPU1dXhdDqpnH8Gkmuke17HzNSUdDlt0mfehe7a5zHOPsVkIkddXR1tbW3S4CeTSTlAKaVvb2+X9z85OYnFYikyuMlkkvn5eVZXV9Hr9bS2ttLY2IjBYJB6v9/PxYsXmZyclDMPVZ9KpfB6vVLf0tLCkSNNUq8oCisrK1RU+Pj9VzUwuZrhB0+MyL1CqVSK+fl5/H5/PrqvpYWmps16n8/H9evXqa2txel0SoN95toi7/6bh0hE1jAa9Hz0/a/l/iMDm+pfTl8OvV5fdlD1/fJSdz29NJi/AG3HSKbTzM3NEQwGMRqNdHZ2YrPZNllwo9GI0+nE4XCwsLDAysoKgUCAzs43YD37YVznH0Gp7yyrV19+h8MhDYZqcFKpFGtra1uWr778avmqvqGhQc54jEYjHR0d2O12/H5/0XcYDAZaW1tpamrC5/NJg9Mz/TAWnYGxtUr0pjjt7e00NjYihODePj1/f3qKdCa/0PaGEyMcbDPj8/lYMw/guPY5Zq6PkRYVsoMq1BeivvxNTU3SYAQCAWw2m1zoV/WFHXShXn15FxcXpcFoaGiQM6ZEYnMHXaj/wePDfOrCKom1VUQyRGNmGbfbjXX8MWr1lQR0DbS1tNHU1FRS39zcTGNjY1H5Vmu+o1ldXaW9tptG/39ycGQYncG4Se9wOLDb7dLgjY6OSn0gEECn09Ha2orD4dhUvk6nw+Fw0NjYyNLSEj6fD7fbjcvlknohhGxjtYPeSj86OkrvtS9g0VVwNd6IkgmV13feAxW1dKYmqGz7UalXF1zV562lpQWHw1Gy/KamJux2O8vLyywsLDA2NkZDQwM6nU7qm5ubaW5uLqlvbGyku7tbjrJVvRCC1dX8Po9yeiEEjY2N2O12HI5lDi0skIn6mJ7OL4ivrq6iKArNzc04HA7ZwZfSr6ysMD8/Lw2+wWDgq49cJBFZQ1TVozNbmYpW8IaC7xBCYLfbsYTMxIUAACAASURBVNlsrKyssLCwwPj4+KYBz4uJZih2gBKaJ2YdYeLqVYQQtLWV7iA2onbGjY2NXLhwgceXLfwQ0BS5SuMr3rwjvdoZXrx4kccff5yKigoOHz5Mf3//tvrCzvjSpUs88cQTGI1GDh8+zMDAwI70amd66dIlktcew2/qoMpcz8DAQNELVu6cbKfTSfrIGxGTn2Hy9L/jNw9z8OBBhoaGNr2gpcpva2vDbrfz/PPP8+STT6LX6zlw4ADDw8Pb6lWDY7PZuHz5Mk899RR6vZ6RkRG6u7vLrmGo9/O5D9yX90s7zVSEvTz11FO8fvZZEvUDHDp8x47Lt9vtXLlyRa7BjIyMYBt5FWLqi4igG+z9ZfUtLS00NjZKPcDwcL4NN3ZQG9HpdDQ3N2O32zl79ixnzpxBURSpV2eYO9GPXr2K/vq3ma8aRDFWb603VEDPKcT179L81o9gt9sZGxvjueeeI5vNMjg4yMGDB7ft8FSDZbfbGR0d5dlnnyWbzTIwMMDhw4d3pR8bG5P6vr4+jhw5kncJb4EQgqamJmw2GxMTEzz33HNkMhn6+/t3rG9sbKShoYHJyUnOnTtHOp1muMlBtaOLnDCUdGlt1NtsNnw+H4uLi6ytrUkDeytcTOXQDMU2hAMrmGN+grlqadG3e8GK9OEwHo8Hk8lE552vJbX4Dxhnn8Lj8ezou8LhMDMzM1RUVHDo0CEZ8eN2u2lvb99WH4lE8Hg8GAwGDh48KCM+XC4XHR0d2+qj0Sgejwe9ULCnZljpfhuxWEzqC1/WUudkR6NR5lI2BoEDlihLg0MkEomS+nLlz8zMoNPpOHDgAJB3PbhcLtrb27d9WWOxGB6PByEEBw4cQFEUUqkUCwsLJJPJLfVHO62MNFXi8XiIKgoHRoawTnlZaX4T3qkpOjs7ty0/Ho/j8XhQFIWRkREURSGdTjOXttIJTF85w0OKKDKuhSQSCdxuN9lsluHhYRmeObVefrn1p0K9x+PZpJ+enqajo2NbV0Uikci3/5qH2vQKwZH3kMvlZPll9f2vg/Fv8G9f/U9qra10mAVDQ0Nyj8DU1BRdXV3blp9MJpmZmSGVSjE4OLjp/sutP6mkUik8Hg/JZLJIPz09vWP9zMwM8Xi8ZPnbuYNSqRSzs7PEYjEGBgak/k9ra5hNVfPqAx3bni2vzh5tNhtzc3N4vV78fj+dnZ031o9uMZqhKEM2m837wWevcgiwdQxTub5YuFP93NwcKysrmEwm+vv7uRbIMlV3N70rj+NezcdIt7e3l4xOymazeL1elpeXMZlM9PX1ySgln8+Hz+cjFArtSF9RUVGkX1xcZGFhgatXr+J0OkvWP5fL4fV65aa0QUsSXS5F411vQbE7mZ+fZ3R0VPqXS+nn5+dZXFzEaKwha+2hJTNL8+HDLC0tMT8/z9WrV+XsbGu9kZ6eHqzWfGji8vIyXq9Xll9Ov7CwgM/nw2g00t3dTUNDg9RfuHCB0dFR6T7ZODpTFEWWr9fr8/rMEmQTVPXcw0Istq1eLV/q16PgVlZW8HqytAs93/rWf/KXWDEZ9UWJFdXf88LCgoykU3/PKysrzM3NMTo6SktLC83NzSXLX1xcZH5+XrrC7rjjDgD8fj9zc3OMjY3tSK/T6eg25lPUOO9+G9WVHczOzjI2NkZzc3PJ6KhLlcc5ArhPf4Z/jr2KT/zSD3L/kfzMaXV1tUivZkLYWL76nADSTSqEkLvqx8fHaWpqorW1teTsOBAIyEiqQjenqh8bG8PhcJTVLy8vMzc3l79vp1P+noPBYFH5bW1t2+pVV7Cqr5idpSeVokkXIZer23Z2D2Aymejt7WVtbU1GSjY2NtLW1rbt7Pb7RTMUJVhbW8Pj8ZDJZHDW5F+AysadGwlVn06n5YN4YXaNd3/iDG/MdfBh4xr61BqVDU7cbvf6+kWnHN2HQiE8Hg+pVGrTg6z6dhsaGnC73bjdblZXV+ns7JSj83A4jNvtJpVKbXqQVd+u1WrF4/HIGOxUKlWkV0dh6ouof+6f8vr2EzgsDurr65mZmWFmZkbWXx1dqzOeZDJ540Gevgcmv4Ugv3PearUyMzPD7OwswWCwrN5ut+N0Oot2rqp7I1R9IBCgq6tL6qPRKG63m0QiUVbf1dWF2Wxmbm5O6tXReaHeZrPhdDrzbp5L3wbAMvhKDlj7mJmZYW5ujtXVVbq7u6U+FovhdruJx+M0NDTQ3t5e5Cay2+3U1dWx/F9tDERdpFbnoL5Jhl/uRF9fX8/s7Czz8/MEg8Gi0Xk8HsftdhOLxbBarXR0dBAI3EhcqW7QU/Xq/auj64369vZ2jI99HUUY+NiYkeN9giMHDjA7O8vCwoIsX9UnEgm+dHYOU6qJ+6o8/KupnYk1wf3r5asbJOfm5mTUUFdXlxydq7OoaDQqo6kKZ55Wq1XqVXdMoT6ZTOJ2u1laWuLgwYNF70ah3uv1yvWjrq4uOTpPJpN4PB7C4TAWi2WTXt0XoQ6k1PJVfSqVklGLFouFjo6Oopmnui9E1avll4vO2khdXR0HDhzYVP5O9S8EzVAUkM1mZaRBVVUVfX19VLvzIwLMpWPiC1H3NKysrFBVVUVvb++NSIf1rLJPKAfACNFrj3LkPX/E0tISXq9Xju5jsRjLy8tUVlYyNDRUdmprMpkYHByUenV0HY/H5SxkcHCw7NTUZDIxMDDA8vIy58+fl/pEIsHS0tJm/ewzUNcOllap7+/vLxrdtrW1kUqlWFxc3Kx3HoeLn4XVabD1ylmO3+9ndnZW6tPpND6fj4qKCgYGBso+/DvR9/f3Y7FYSuoNBgN9fX1Fo9vW1lb5DKjXi0IT5y+CoQrsAxh1enp7ewkEAszMzMjZhTqTKKkvwGg0YnDexYHEo4hcGiXopbdmmIWFBanv7e2lvr6+bP27u7ulwVdnB4CchaizsO30MzMzjI+Py5G9Ogvp6elhOgQPPeHhh8efIZxr5S8edlNxeiY/+1mfJXk8HsbHx+XMYn5+npGmaq5M9vMa02UqcsZNfniDwSCj9TweDxMTE3JheX5+HiFE0SxsI3q9ns7OTnn/av2NRiNer1cOiPr7y6//dHR0YLVacbvdTExM4HA4MJlMzM3Nyf08JfcSbdCr9Xc4HFRUVOD15vcEb6XXre9BUcufnJzccnZSTq8OGFW9mi/rZqMZinUymQxjY2Osra3R3NxMa2trfjod9uU/YC69+UlF9fsnk0kcDsemzUJqVtnVjJVJxcmBxHkAufN3dHSU7373u1RVVXHkyJEtN9sUourHxsZ45JFHMJlMHD58mM7Ozh3pGxsb5aakLfVzz+U7+w3Y7XYsFgvj4+M8+uijcrG8q6urWN9+4sb32Hrlj9X0JRMTE1J/8OBBuru7dzSdttlsWCwWJiYmeOyxx9Dr9Rw8eJCenp4d6dX0J5OTkzz++ONysby3t3ezfuEiNB/KZ0tdR931Ozk5yZNPPinXQnp7e7ddbLb13gXTX+X3fuwENqNCdHaUp2bhwIED9PX1basH5K7na9eu8dRTTwEwMjJCf3//rvRTU1NFi90DAwNc8oZ59yfOkMrkeEfFFa7mDpJTkKcZHu20ytHt9evXefrpp6X+HfcfYoHXY3vmSf7jXV0cLuOHt1gsjIyMMD09zZkzZ8jlcrL8nawFqnqXyyUXq4eGhhgcHCQYDG6rN5vNjIyM4Ha7OXv2LOl0msHBQYaGhnYUXVSoP3fuHOPeVYIVjbzt1cfLGolCamtrGRkZkbODUChEd3f3tmsnKjU1NQwPD0u9x+Mhl8vt6N3fDXudZvwlg5ruoKOjo7iTDy+AzgDVpSMT1BHkxMQEiqIwODiI0+nc5LNVo4J+7fWD1I3cT93SWUjnk4ytrq7KHZlWq5VQKEQ0Gt1RvdUNdtlsFrvdTkNDA+FweMd6QKb8UMsPh8NEIpEbHwjNw9ps2ZP9gsEgmUymqPxwOFz8ocYhqDDD7LMl9el0Wuqj0Whx+dug6m02GzabjVgstrn8bfSpVEqGJJbSn3OtkJ67yJJ5eJNezUeklh+Px3dWvuMgAD/RukyvzSQzAiQSCUKhnR/4FAqFpJvOZrORTCZZW1vblV51cxXq1Vlwg7KGQwQZU7o3n2ZYoFfvX9V3HMynDDmsc21ZfjgcJhaLYbfbsdvtMgR8p6jPu1p+Op3ekZFQiUQiRCIRGhoasNvtMoR8t/qlhIG/e2qRTz85xU/9w3eLzqnZCnV20N/fTzabZXx8nMXFxR2Xr+oHBgY2pfq4WWgzigJaW1uZnJws/mFkEWodUKLxU6kULpdLPmQdHR1bjmJlVNDEG2Dsk6RdT+HCKfMO3XHHHTKiZ3JykpaWltJpFNZJp9O4XC65s/fIkSOyTpOTk8UzozJ6t9vN8vIyR44c4fDhw6TTaaanp7l27dqNmdF8fpMZbUdL6kOhEPX19VLvcrm4fv06TU1NN4ymTg/OozB3w1BkMhncbrfc2avq3W631G81Fc9kMng8HoLBIBaLhYMHD5LNZnG5XExNTdHY2IjT6dyVPpfLMT09zdTUFHa7nfb2di7MrvG7//JVHtLH+fDVKt7hCXC000o2m8Xj8RAIBDCbzTKqyuVyMT09LQce5crP2gbQA2vXn6X64LulXt3zEAqFttZns8zMzLC6uipHpgAul0v+XrZ6JtX0MX6/n9raWhkVpa59OQ06jDqFA4oHgLvvPUV91aCM0Cp0taojW51Oh8vlyvv4zTV0CR1i4VL+nPkS5av66upqTpw4gV6vx+124/F4WFtbo7Ozs+zMKJfLMTc3x/Ly8ib9zMwMXq9X5qwqp1dH4lVVVZw4cQKDwYDb7WZ2dpZQKERXV1dZvaIocp2jqqqKSF0nhrYsqeASieAyX3/sHId/4tU7jpJUZ0cej4e5uTlZ/k71ZrO5bNqe7xfNUGxHeKHk+oSaZgPY0pdaks77UIQe/9kHiA7/fFFES3V1NcPDw3KhMBQK0dPTs2kavLa2htvtJpfLFekNBoPU+3w+wuEw3d3dm8I4Q6EQLpeLXC5Hc3OzTD+h6tWFwnA4TL/vcv5BaRoq0qthm+peEVU/NDQkX8BIJEJPT0++fOcJePwvIRkhnMp3qJlMhvb2dhm5VKhXy+/p6dkUBhoOh0vqjUZjkT4SidDd3b0pDDMejzM2NkY6nZabG1WGhoaYn5/H5/MRiUR4bFZhKDcFeriY6cI57WfIXsH0+lkUbW1tRXmdBgcH5VpDNBqlp6dnU/nRaJTp2QAj+kpsSoDq9dBJgIGBAalX22+jKyIWizE9PU0qlaK1tbUocmlgYACfz8f8/DzRaFSm9iilTyaTmwYk/f39LC4uIubn+aNXWWmZC8Is3H/qtdxf3SD1LpeLRCKxaUBSWH6LuRPD7NlNHU08Hmd6erqkXi3f6/UyNjZGd3f3prW2RCLB9PQ08Xh8k6u3v7+fpaUlzp07x+joKN3d3ZvWugr1GwckfX19LC8vy7WvcnqXy0UsFpP6eM0aH3tyFl1DCyIZZqDBKPXl1so2oq5NraysFJW/U/2tQjMU2xH2QUOP/G/hKKampqZkJ7wViqIwt7yGtX6IupXzWEf+apNeTexnNpvlQmlXVxf19fVFo5jq6uqiaJuNeovFIhc61YW/jaOgnp6eTdN8NTGgxWLJj0ynnqXO3IbeZJZhoz6fj8rKSvr7+zd1gupU2Gw24/F4GB0dpbOzk4b2E6DkWLn8HTyio6xeCIHT6cRsNuN2uxkbG6OjowObzSbDRufn56msrMwHHGzoRFW9xWLB5XIxPj4uNy6q+pmZGdrb2xkaGiqpb2trk/qmbIC63AQJxciMvp2+2gwTExP5sOHBwU0BB+quZ7PZjMvlYmxsTIZnArITNBqNYOulJumDglnfRv34+LgMz9yoHxgY2NSJqpFxqn5iYkLuRgZYWlpibm4Og8FQMmBAXQjOpziZpm1mkmxtK/p1I6F2ouX0gNTHLw5S430On88njanaCer1+rIBBw6HA7PZzPT0NBMTE0XGUNWr+dlK6ZuamuRsauPs3O/3y7055QIOGhsbqa2tZXp6WurnU5U841pluEGHNbeGTqcrCjjYuOl0pKkSl8vFtWvXtp3db8Rut8vyi2b3L+Imu0I0Q7Ed4QWZDHCrUcxOSCaTTE9PE4vFsHS9kpqLH0fk4kBpQ9PQ0EBNTY10hdTV1cmDTIrcOmWwWq1SPz09LXP+xOPxbd0ykF/oHBkZIXfaTaTKydK1a/IgFtUts52+uroal8uFy+Vi1WSlD0heewzbPf9jS7cK5MMA1YVKNQw4m81Kf/R2+sKFTo/HI/WxWAyLxbLt7m6z2czw8DBVVW7M40uspDr5wxON2HVR6uutdHZ2bqtXFzrVMGI1RbbVuq4f6wff89vq1TBgyPvE6+vrt3TLwI2FUtW9ODExIfV1dXVbulUgv1A6MjJC9mEX4ZouFicmZBr4neqrhl6FbuZbLE49TzAYRKfTybDR7dwq6ux6ZmZGhgHr9XqZ6K+7u3tLfWVlpdSrs3O9Xk8oFNqRvqqqSs7Ov3d+gt97cJJMTqDPJvirnzzJW19xx6aZ/sZNp0NDQ3J2r85ud5qCQ418VGf36uxyL1J4aIvZW5FJQjwA5hZWV1elq6Kvr2/bTnojgUCAsbExkskkvb291B1+M0LJ5aOAtsBkMjE0NITJZOLZZ59lbGwMp9NJe3v7jspXR72VlZVS39raum0nK/V6HabwDJmGPs6ePSs3ee00qkoNc62pqeG5y9cImVpoiLs2R0WVwWg00t/fj9ls5uzZs1y5cgWHw7FrvcVi4fz581y5coWmpiZaWlp2FBVlNBrp7+ujMTVDqrKZnN+D3W7fcVSVGiZrtVq5cOECly5dwmaz3dDb+iDgyWdm3UZ/8eJFLl68SENDw46iqiAfxtnb24vFYpF6q9W646gqfTZBxZobWg5z6dIlLly4sCu9ru1OAFpY4vLly1y4cIG6ujr6+/t35HtXNys2NTVx9epVzp8/j8Vi2bFeTdvvcDi4cuUKZ8+epba2dlf6zs5OPPEKogtuEgvXyIoKFrDuqMNW9T09PcTjcUZHR3e1UK/O7nt6ekgkEoyOju5qof1moc0otmI9NHYlZcTjclFbW7urEQFsdlXJEUHVnYDIJxzsf11ZvaIozK2frdzX14cQgoWFBSoqKsrGyG/Ue71eEomE1Pt8Pkwm047WVZTVaUQ2Raiynb62fBruxcVFKisrd6Zfd1VFo1F6e3tJhUeoXHoev9+/o4U3NaosHA7LNODqPpOdhB+qrqZQKERvb6/cmb3Tl01RFJamnseRXMPQe5ju7m78fj/V1dVlz8vYiM/nIxgMyrWC1dVVqqur864kWy8o2byxsPeV1KubwlR9IBCgurp6y1xVG/XhcFjuzFbTcO9IvzQGKPiNbXS1dkl9oStpS5oPoSDIzDxHZ++7EULINOaldoSXYnl5meXlZZn+PxQKsbCwsGWgRyErKyssLy/LMHA1PftO9X6/n67qDFUNDjLZHHolRW9NalfnPqiHb01PT3P9+vVdeyQK9VNTUzvyKNxMNEOxBUn/DCYgkDHt2scIxa6mTQ+GyQz2AfCe35VejUqanp7e1n2USqWYnp4mGo3KB0uNSlKjtcql8FD1yxcfpg2o7LiDo3ccJZvNMj09LaOttnI/FUaFqa4qJflK9N/9LrMTlwi39mw5s1HvtVCvRjWpO2e3mtkURoWprqpcLofL5WJxcVHmmyo3M8hkMrhcLpTrT+MA2u56Pa1dd0pXklr+dnr1vIjOzk4Z1aSmSe+ydqMHWJ3aZCgKo8JUPYDb7ZZp4rdyPxVGddXU1HD0aD5qTY2qCYfDW7qPstksq1e+RyOgb7uDuw7ehRBCRhSpgRKXvOFNySBVvce7TGuNE0vUxV133YVOp8Pj8cjzNrZyPxVGdVksFrq7u9HpdNKVpLpyyunV3/Xq6qp0Nen1ehkoogZKbKVXo8KO97fwH/9rmGc9QborEzQZEkxOTu5q4KhuQn2hriTVuzA3N8fS0pIMlHgxXFGaoSjD6uoqaxNn6QZaBu6ktq1tV3r18B8hRPkduq13wvTpLfVA0YKZ6kpSF6TVh2XjgnhhVFbhDl3VFVQY1VN4VoGKGlXV5J9EQdBy8NWwfmZ1YVSOWv7GBfXCqKyiqLDW/KjWaQzg8fvLRgUVRmUVRnWpC5jqgnYsFiupV6Oistls0Q5ZVX/58mUCgYAsf+OCdqF+wJCffehbDoHBwJrRxrfdK3T4ZmT5G/WRSITp6WkymUxRVBjko2rUBelJJcswgP868INFepfLRTqd3qTv7e2VC9JjY2P09PRsWlCPRqMyKqu9vZ1wOCwNWk9PT5G+VFSRGhXlWHieXIWFriOvlAvu6udnZ2f50sNn+OAjS2R0JioMOpmvqjAqq6XlCLVLF2DdIKlRROqO+FJRRfF4nKmpKZLJ5KaoMjVdhRroUSoqSE3GWFdXtykqTE2mt5U+kUgwNTVFIpEoWgi/uy8fULC6uioDRbq6urY9XEhFdSUVBnp0d3fvWK+ewKgGehQGutxKtDWKDeRyOTweDy6Xi+psftNUbXNpl0A5/czMDNPT03IxrexD0HYXRHz5DW3rKIrC7Oys1I+MjGx6CNSont7eXpLJJGNjY3KhU9VPTU1hMpkYHh7e5KJSo3r6+vpIp9NyoVfVz83Ncf369fyZ0fgR1i6oqC7St7a20t/fTzqdlsfAqnqv18v169cxGo0MDw8Xu6jWjwG1p+fp7+8nk8kwPj5epJ+fn+fatWtSv9FFpUb1DAwMyA1KKysrRfrJyUn0en3ZI10bGhoYGBggl8sxPj7O8vKy1C8sLBTpayKe/F6aGjvnPAHe/Ykz/NNZP3/42CpXvWuMj4+ztLQkv1s9cEen0zE0NFTSReVwOBgcHCRjtJAxmonNXd6kF0KU1Tc1NTE4mD9Ia2JiomiDlnq0KORDdUslTWxqamJoKH9G+eTkJD6fT15bWlpifHwcRVFoSHnRtR4pisoC5HGdl+dDRJdmSYVX5Y7t5eVlqR8YGKCq5ySEvPkTEtex2+0y1bx6FG/+nLK8q0k9FnRgYKCki8tmszE0NITBYODatWvMz89LvXq0qKov5WKy2WwMDw9jNBo36f1+P2NjYzKleClPQkNDg9Rfv34d38RzKBc/D1/7ZfjkW+DCZyCzeQCmYrVaGR4exmQycf36debm5mT5O6G+vl7qp6ammJ2d3ZV+t+z1UahvAD4C6IFPKIry5xuu/yzwfwDv+o/+TlGUT9yq+qgplevq6mhubqZpNQc6I1TtbI/EbqKiznkCuBYaeQfk3U+W1iJX0058kGpUkuqKslgspNNpGRu+nb6uro7h4WEuXbqEy+XC7/dLvXRrfW8SmjbvRobi9Atutxu/3y+jisq6xWrsYG4F32Us91qKopoK9TuJqlKjktQNWoVRTTuJilKjglwul3Rx5HI5YrFY8QbKpavQlN/Mpu5WzimQ1ZlY1Ns4ajHIqCQ1jftONmDW1NQwPDJC5rFOMosTcnd/NBq9ERW1jV6NalKTG0J+NlFfX09XV9eWejWqyOPx4PV65SKrjIrqaEf/tTE4VvoE4urqat526gSfP79IKrpKLhunRWlkZmatOCqqJT+LZOES9N8v9WpUkeqKUqOiIpGIdDVttWCu6lVX1NraWlFUlBpiXg41qkh1RW3U7ySqaqirhfiXf4WaqW8AoFTWI2rseYPxyJ/Bff8PHH9/yQ27pVxRuwm33+iKKucduBnsmaEQQuiBvwdeB8wBzwkhvq4oyuiGj/67oii/cqvro45s1VGExWKBZ335zXY7iK5Rp6JbxWarqKNSkRG8vULH8tjTmBz3MjMzA7BlMriNqK6oq1ev8txzz2Eymbjvvvt2ttC4rm9vb6eqqkrq77333nyCuUwq7xIZfFNZvRrLPz4+ztmzZzGZTJw8eZK2rVx1zYfAd1nq+/v75cEuRqORe+65Z2v9hvL7+vq4du0a586dw2AwcPLkSdrb23ekNxgM+RTw63q9Xs/dd99949z0XBaWJ+D4+4AbObvSmRxGg477+h30dVqZmpri7Nmz+c+cPCnXE3ZSvqH1AIbrj3LxYn4H/IkTJ0pukiuFGtWk5jp6Ifqenh7cbjfPPJM/G/7YsWP5DZjL6+eDr6caKcWJHjtf/M23882nn8fonya97KK+85jcwAlAy+H83wsXiwwF5F0x6trBM888Qzab5ejRo2WT+W1EjWpS9el0mrvuuov+/n6Zonw7vbrO8/TTTxfpt12PdD+B7iu/SE1onvjRX8JjOU7C0k1XVzf1K2fh8Q/BQ7+Vj2i7t3QXttEVpbqydvr+b3RFzczM3JJcT3s5ozgBXFcUZRpACPEF4G3ARkPxomAwGOjo6GBlZQWLxcI5T4AmzzTWCjtbHQ2yMQ3CTha3boxKK5hUnJiuP8l85498X1FVyWRSPtwLCwsYjcYdRRXlcjmWlpawWCwMDAzIKCGj0Yg9uwS5jBxNl9N7vV5isZgsXz3DomxUUPMhuP5wPteVwSQXZtWopsXFxa31BaiurnA4TH9/v4xqMplMJV0upfTz8/MyKgqQZ4g4HI58tttMQrbBxk1Vd3XUy9FwT09+Y6bf78dkMu3IWCuKQriiCUvMR09HKxgqCQQCVFVV7Vjv8/nyR9Wul7+6ukplZeWOo4o26oPBYH5X9doEAop25JfCWZnizQNmstmDUu/1em+4bCrr8ptW188b38ji4iIrKyt0dXUB+fWpIv02LC0tbYpq8nq9O3bFqOebd3Z2IoSQ+i2jks78I3zrf0JDN/z8t6lyHqN73SMwNT1NU9MQbT/zDXT//pPwyJ/A8FvA2lW2DmpU007Tz2xE9S6oGxFvNntpKNqA2YL/zwGlss79qBDiVcAk8P8qijJb4jM3hYaGBgwGgxzxf13MMSZaEA5KswAAIABJREFUsa3n9tnIVmkQtkIdlSbjMZ7POfhh4ziVDgetu9zAV5gGQXV1qZE2O8n1E4/HcblcBIPBIr3qyskGn8UBZTuKjWkMnE6n1KtRQR0dHZvdB82HQMmS9F5iOm4pSoOQy+WkPhQKbRnVU+iqU18sVb+TXD1qXq1oNCpdXeoalZprpydxOR+V5LhhLNVNValUisnJyeKoLkWRrhw1qqic+0KNCjPm6rAAd3XWg2NE6tVMouX0hVFdqqsLkBvU1PLLDTw25gpTO0rVlWOcOUMjQENvWb36nBXqC9PHyKgcx0FYvFqkL4zqUjcQ6nS6bdPPFOrVqC7V1aXT6aQrZ2ZmZstTDLfTq+VvOkXwuU/At34Hht4CP/wxMOWHkhtdQeFwmJ4f+BMq//nV8OCvw7u/tGmtpxDVFbVd+plyVFRU3LIzKcStXADZsmAh3gG8QVGU963//6eBuwvdTEIIGxBRFCUphPgF4J2Kory2zPd9APgAgMPhOPqFL3zhBdUrEolwerGCL19Lc8H0fr6evZfnuz/AW3qLX7bV1VVWVlbkucY7TQusct61wgXXIj9efY63Jr7Mmbs/RqJqZ+4iyEdFLS8vy9PLCqNe1Iy0fr8fo9FIc3PzpoctGAyyvLyMEAKz2bwppn51dZWu6c9wR+R7fOf4pzDV1JXVNzc3b4qaUdvHYDDQ3Nxc1D5VsQXufvYXeaz+x5isPbmlvlz7rq2tsbS0VLb8rdoHkBFbcCNdxMb7W1pa4lj429wZfpjHX/nv5PQ3OpxQKCQXsJuamjZFzWzXPuFwmMXFRRRFob82xqmJD3LlwO+w0njPju4vEong8/lQFEWmmt+qfYCi79hOHwqFGBz/WzoSozx850c3tU+hvrGxcZOrZGP7HPZ/g07PF9fbsYJoNMrCwgK5XE4eRFWqfSC/cL6xftFoFJ/PRzablVmPN9ZvenqaqqqqkvcXi8VYWFgoyrq8k/ZtXniYoYm/ZcV2nKsHfhtFV9qIF+rv5gKHvZ9ldPjXWHK8uuTnN6K2T7n2LUckEnnBx6O+5jWvOacoyrFS1/ZyRuEFCh3JTm4sWgOgKIq/4L+fAP6i3JcpivJx4OMAx44dU06dOvWCKnX69GnedegI33E/Rr2I4tc18K77j8sZhToKM5vNHDlyZNs0ChtRR2Fms5kfeuUddJlOwj9/mZPtFXBw+zoX6g8dOrTliLnwpDh1xqOOotRsp11dXTz55JOUaq/MZ/+DtK8de7NT6tWMqf+XvTePbiy967w/V5slb1pt2Zb3pfalq6sqvSZdSTorZJ2QhMwEmJdMYF62YYaBsAxwOAwJnGF5DwRCSIBAICQEAknTZO2uJJ3uqq6uffFS3vdNkrVZu+77x/XzlCRLtuyybFXG33N8ZFv3e5/nSvc+v+e3Z/OL7XizNS6Rh5JOp5kYHyN92cLxRoVH3/MjRXe82YXnhMYjYuv9fj+HDh3a0FSXrXEJjUVVVdlG8ty5c5vyk599hnjGQ9eBI5I/NTWF1+vlwIEDG+54s4MbhMaTze/r69P4JOCjv86xpip49bkcvtDYBB+QFVd7enoK73jXkK1x3b17l7e85S0oiiITQDfjZ5Y+TizaI/s1i2oAgt/d3V0wNDp7/PHxccLhMNV1D6NMfJ4nD7uZSTtYXFykq6trwx1zoerMojHSwsICnZ2dG/Zu+MY3vkFHR4fUmIR2LXbsHR0dG/KzNS6bzUbn6nX05/8Eel6H6/2f4ynjxv3KpcYVOEksfp3DE5/hyNt+BqpLC44REYmiunJ2F8xiOH/+fMFn+X6xl4LiEtCnKEoXmoB4P/CB7AMURWlWVXVu7c+3A/27MbHTHXY++75O+CK86zVn6FwTEj6fj8nJSVRV3bB7VTGIbmiZTOZebHwqAfoqmL0Cx969IX9lZUU2JsmPrS8EUTpaRHWI/r0GgyGn4moxGLxD6D0ncDqdkq8oCnq9fl3F1UKorq6WdtP5+XnZ+ctgMJBuOIw9NgUb+GNEVI4wBQjnpMFgyOlBXAwiKkZUshUhoHq9HpfLJZvdb8Q3R6eINhxmcXGRubk5OX4ppkYRHi0WJqFBCC3pHr9KC7/1jazjZ1fCzZ5/KQmgwhQyOzvLK6+8wuXLl7fE1/lGsfS9kebmZubm5mQIsU6n27DXdPb4opLscqiBRmDs4rOstL+xpG5uIudH9A4XGppOpyvJhi8CJcS9s7y8vGX+gQMHWFhYYGnoEnz7p0g1n8Lwvr+DTYRE9viLi4uMhX6Ow9/+L0Qv/hWW1/6PTbmC39vbK3NeRHHNcudMFMKe5VGoqpoCfhr4GpoA+IKqqrcVRfktRVHevnbYzyqKcltRlOvAzwI/tlvzO163CkBnZzepVEpmI49443x70cREpPRm5sJvMDo6islk4vDhw/cWeYMJmo5pbTaLIJ1OMz4+zsjIyHr+JtDr9VrP47V476mpKZxO5+aO3mQU/GMo7qOSPzIywuTkJE6ns+TyEaKSrMlkkny73Y6x7TTM34JNWjfqdDpaW1tlvPjExAQ2m21TIZHN93g8mM1mRkZGGB8fx2q14nA4NucnIii+Mcwdp2X5BMEv1R8lclZEJdDx8XHq6+vX85294B3ZkC8y6uvq6kp29Iqcl5qaGsmvra3dnB8LQGQRxdVLc3OzrKQ7OjpKTU3NpkIie/ympiaqWo6SQU98+hoWi6Xklp+Cb7PZmJiYYGRkBLPZXLKjV1EU3G635A8PD1NVVbUlR7Hb5eDInd8HRUf/kV9gYm6JdDpdEhc001vno28jZu0ldfNLmv9vi/zDhw/LZ0iU999N7GkehaqqzwLP5v3v17N+/2Xgl3d7XoCWCAcEqWX8zh1SqRTLmRp+5flRkullPvHCpMxC3QiBQICJiQlSqdS6DFGJlofh+j9oi2bezSv6PqRSqS05zLP5ExMTJJNJHn/8cZLJJMvLy8TjcTo6OorHbC8PgZphtbaDkTt3SCaTPPbYY5Ifi8Xo7OzcNOY7FAoxMTFBIpGQfJ/Ph0VpoCkRgpXxnDLu+cg2nz322GOkUin8fj+JRILOzs6iZo9C/EceeYR0Os3KygpTU1PEYrGN+UsDgMp0QnO4P/LII2QyGVZWVhgYGKCzs3NTR2MkEmF8fJxYLMbZs1orWb/fz8DAAB0dHffMHo5uGPraOv7q6irj4+NEo1HOnj2LqqqsrKzIMMrNfGOCH4lEePrpp1EURRao3JC/JrTide2MDgywurrKmTNnUBRFFsjs7Ows2tNdIBqNMj4+zurqKklrBx2WKANrvUBEhvRGiMVicv6nTp1Cr9fj8/lkRvL98DfLs5A4/1F0c1fIvOevsNtOsrCwIAMlSnUeWywW1FM/RNX5jzE6PbQt/qFDh2RFBFE+Zrf6VOyX8CiCdGAWPTC6tEqVzUpvby8vXZwlmVbX9Q0uhFQqxdTUlLYoWiwF+yZItJyCS3+h5Sw0HJD86elpvF7v5vxC80+nmZ6eZnl5WZowxEO9vLwsVVlhd89HZmEAHTAWrkJXrePgwYPyofR6vbKpSktLC42NjeuEVzqdZmZmRoaqZvN9Ph9LwQ6agJWBF7A+1rUpP7vvgc/nyxm/kHaR3b1MmDAE3+/3c/XqVVkJt5DwzmQyrAx8DwcQrevK6XsgTIj9/f0b8oUt3WQy5eTWrKysMDk5ycDAAE1NTZrwd/ZC5G+1nbzZSiaTYW5uToYqZ/PF5kPwm5qa1u2OBX9hYUGa6kT4bzbf7XbT3Ny8jq96h1GAET8k7Ymc3B6n08n4+LjkF9IuRNju3NyczNWoGjlO1dwN+vr6mJiYYHBwsKgJSlVVaTISFWSFw9nlcpXEFwJB5GoIfkNDA+Pj4wwNDdHQ0IDH4ymemDj2XS0f4tQH0R17N61ooagl87OgHHkHnP8oh9S73FWaGRoawuVy0draWhp/TTu0Wq2Mj49z9+5d6TsqhX8/2BcUBeD1ekmP3cSlM+LuOETT2i4+P9kqu29wNsRClk6nS9MCWrIyVxsOyIVI8AstBBvB7/czNTVFKpWSC1E23+VyUV9fz+TkJFNTU0xNTRGNRuXueGVlhfjASzSiYO06RUt7Vw7f6XTKpkgiI7ijoyOHPzU1RSKRKLiQOBwO6h55K+p39ETHLjJnP5uzuw4EAkxOTpJIJAouBA6HQ9b6mZmZkeMLvtCiEolEwQfZbrfT1dWFzablQAi+EKTBYJDJyUkapq+R0ZvpPfs0eqMphy9qFW3EF32s8xcCm80mayXNzc3h9/vprvFgAfCOEKrvY3JyklgsVpAv+nRMT09LfvbuPBwOMzExQSwWw+l00tramtO4yGq1cvToUaanp5mfn5d8IUjD4TCrAy/RgEJ161FaO3NLitfX13P06FHpO1lZWcnhRyIRJiYmiEaj0olsMBig4TDc+TL1FiNHjhyRgjwQCMhGWRvy1yD6dAj+ysoK7e3tUpCurq4yMTHB0tISDz30kDSdCoiM/Pzx1yXJppPwzM9ruRJv+d11fLEREONv6jtoOATOPqpGvsqRD/6k3AiI8Uv1PYi2s2IjEAwGaWtrK6ma9HaxLyiyEIvFmJqaoq6ujt6EH2rdNLe0yPfzk63ytYl4PC7j/2tqanIWzw3h7AOdgdTcTcYsDxEMBqmuri7JtJE//tTUFIFAgOrq6g21ELHL9Xq9XL16lf7+fux2O6lUimAwSO/qDKq1ldbOwjH0YpcrhGJ/fz82m410Ok0wGJSqcjHThLG6HlwHcCZnWUomGRgYyOHna0GFxu/p6ZG7czH/bH62FpMPscvN3t2LDoKBQACz2Ywzs4jOfRiM6x3uBoNB7lIFX3QQXFlZWacFbcYfDSgcBRb6X2TarlJVVVW0e5vgd3Z2Sv7g4KBcaFZWVjCZTBvy9Xq91nXQ4WBiYoKhoSGsVis6nQ6/309PaBK1vpXO3sI5NHq9nvb2dux2ew5fURQ5/roKBY2HABWWh9A3n5T8yclJ7t69mzO++H6LLZ7C9yXGHx4epr6+Hr1eL/ktLS0yibAYX1z/8PAwdrud1tbWe1FwVz4D3rvw/s+BqWYdv7W1VY4/MjKCzWaT/riCUBQ48g544Q/RRf14PB7sdrv0P1qtVtrb20vucyH4ExMTsjFZMlm4r8n9Yr8o4BrS6bRsLNTe3o5VF0VX37LuuNMddn7qtb05QkKYGW7fvk0kEqG9vZ2DBw+WvMhndAaS1i7Co5eIRCKyRWfJ/DUzw507dwiFQrS2thZs8VkITqeTzs5O0uk0L774IlevXqWuro765CI61+ZlFBwOh9YFLZ3mpZde4urVq3LHs5n9mqZjmHxDWhe9TIaXXnqJK1euyGipTfncy0hVFIULFy5w5coVLBYLR44cKSmeXPB1Oh0XL17k8uXLMlrJ4B2CxqMb8kW9LL1ez4ULF2QZkyNHjpRkfxbaQbpeS5ZbGLiIyWTiyJEjJdmfRac+o9HIpUuXuHTpEiaTiaNHj5bEF7tzk8nEK6+8wsWLFzEYDFhTS+gaNv/+Bb+qqkry9Xr9umKYlyf8/P3Y2ve5OLCObzabuXz5MhcuXECn0xUshlkIYndvsVi4cuUKL730EkDJn7+4V1taWggEAty+fVuLTosF4fzHoOMJOPiWTfkej4dgMMjt27dl/kRBHHm71n9k8N+Ae5F9ra2thEIhyc9sEuQhUF1dzaFDh2SFYBEVudPY1yjWoNfr6ezsxOv1ahFFoXloOLgpz+/3Mz09TSKRwOFw0NraWlLnrHy+x9JKXXCQo0ePbom/srIiS3is2xGVgEAgwNTUFHa7nRMnTpBOpwkFg2SW7qKeeN+mN4jg63Q6jh8/Lova3b17V9aQKorGI3DzH7l78xUUxSj5Iu6/tbV1U2EXDAZl5cyjR7VFPRqNMjQ0RFtb26b8UCjE1NQUmUyGY8eOoaqqVmL65sv0hRc2LV8h+Ol0WvLj8bgcfzNhFw6HmZqaIqnqSZpdtFanGFvL+N4SP5nk8OHDKIpCIpFgcHCQtra2TYVlOByW9++RI1r2eTKRILM8TProKTa7kyKRCFNTU8TjcQ4f1opHplIpOf+6ujpZ6SCTSvAekx7v8FWaT74P0MxEIrBAVLNNJpM5/I0g+NFoNIc/ODgoEyo3g6hG7HA4mJ6eZnp6Gt35v6EhsgQ//PkNs6kFv6mpCYfDwdTUFDMzMywvL9PW1rbenNV0AmwdcOfL8PCPSL7b7cZut+fwW1tbSxKWiqLIpMWZmZnvuxIeFQe73X7PFhr1QXXxWkmRSETWKLJYLBuaOYrxRZkHi8VCwHEMx+zzXJ2Y51Tv5gXtVldXZfMZi8WyoZlhMz4g+aqq4p3oR5+K8OXhBPoXb/LGs4fWCa/V1VVZZsJsNufwl5eXmZmZ4c6dO7hcLlpaWtbxo9Eo/rSdFsAcHKXloR/AarVK/uzsLP39/UX5sViM6elpAoEAVVVV0kyhqiper5eZmRn6+/txOp14PJ51/EQiwfDwMIFAAJPJlNOzw+v1snJNqwY6rzpwJBLrhG8sFmNmZkaaWfL5MzMzDAwM4HA48Hg86/jxeJzp6ekcvrGxD4cShK4upqenpTnL4/Gsiy6Lx+PSP2MymXKctT6fj+npaQYHByU/H4lEgpmZGXw+H0ajkc6snh8r00PokxFmUrUkR0ZkeHLJ/DUf1dDQEDabje8Ox9ZqmxkYV5sxzt7O4QszWjZ/enpamrNaW1vXRaclEglmZ2fxer0YDIacvCaxeZmenpYbjlK0c3EfBWcGqX32c/haXos3Wk9rlv9uI5hMJo2/tnkR5rCc8RVF0youfAKiK2CxFeWPjIxQV1dX0oZJ8Leblb0Z9gVFMcTDsoZLNmKxmHRgGo1GOjo6cDqdJYesZvNFIcKJiJ4/vl7Fn+nhY5/5Er/4oQ8WjabKXiAE3+VylTx+PB5ndnZWPqDt7e0Eg0EpZBRFwbswiQv4wmQtL9w5z//2+3jtqT7cbjfpdDrnAW1ra6OhoUGOryiKLKkwPz/P4uIiPp8Pt9u9jm82tdACdFpWUdZ2XoLvcDiYm5uT/MbGRpqamnL4IvEvO+pKURRcLpccf2FhAb/fT2NjI263W5rpxsfHpaO8sbFxnbPeXhsHYAkXs2t9tpuammS/iuXlZWknLsS32Wzy+sX4+XwRxeJ2uzW+vRPGvovD4ZB84SxtaGjQKvpCTvJZoWAHwRdJeqIcSTKZlEUjN+Lb0lpvj5r2k0yumVNcLpcMyhDXJfhut3uds76+vl4mGTakVlAiy+jMNobxcG51mFu3bgHIqK1CfJEkmT2+Xq+X46uqKqO28p399fX1DA0NEYlE5Ialubm5JG27/sqfoqoZMq/9Ncl3Op20tLSUxl8rv7+0tMTs7Cx37tzB4XDQ0tKiCdzD74AX/1grjHn8PTncyxN+Lox6eaSrmfaGFHNzc/T39+fy9wD7gqIQ0imtvLLpntobj8eZm5vD6/Wi0+lyH/ASIPg+n2/dA/aFW8PcSXlAD92ZyYJht4lEQo5f7AHdCPn87Ac0X8jMjNzkIDBuOYTOaGd4JU3P5CQ3btxAp9PhcDhkNFWx8Q0GA62trTQ0NDAzM8Pk5CTXr19Hr9dLfpP7BHzbirKYWzBYPCyPdjs5dvQos7OzTE9Pc/PmTXQ6HXa7XS5wxcqX6PV6PB4PDQ0Nki8WJ7GQHjt2rChftzQAZisHz5xjdm6OmZkZbt++jaIo2O12uUAVMxPmjz8zM8OtW7fk/Avy7Z1aPk0qjs5QRUtLi+SLBUfMf7PxdTodzc3NuFwu5ubmuHjxIs8//zyqquaMn7/wXZ7wE/jei7wOcPa9ivpaj4yuEc2ABH+jhVOM39DQgNM5x2+m01wYmMRR1YTF/zKOOjMtHb0b8puamuT85+fnZTMku90uhXwxvqIoOBwOjh07JgWjMCs3NTUVN+9GvHDtcyin/hOuvjPYUinm5+dZWlrC5/PR0NCA2+3eVGAIc5DT6czZMLhcLpoaj2EyVsP0pRxBIUx0iVRGdgt86NixHL7T6SxZ4O0k9gVFISTC2mtVLbFYTJZhFrZEt9tdsh8hmw8UvFEf7XbyJwY3EbWKw/opjmaF3WYLmGL8jRCPx5mfn5cd5ErhHzEtkVANLODCYFA509VAOh0kHo/LhbXU+laKomglO9JpEomEFCx6vR5Fp9OqsmZVFc1/WP76Rx7GY9b48Xg8l1+iFqXX68lkMsRiMSnYNxXwi/1aOOdayZJsvqqqGAyGkjOjDQaD9F0IfsH52zsBFVamcvpnZ/NBC7wodYMgSq4I34uqqmQymYLji8/+59VrPKk3cHOllpN16/lbuX7QPv+DTXXYFBc6XzuKX+WbL17kGE4e7ds8w19cazKZJJ1Ok8lkSr7/hNbrdrulwFhaWtIW7Kam9Qvulc9AOg6P/ITkC621JH6BuQutU2iSy8vLHHEcxDT9Sk40UXZTrOw8LcEXAsvr9eJ0OrWM913SMPYFRSGsCYrFQJSp27dlbZitLNCiuqXo2rUR/3SHnc9+6HHCX+zlnbVB6jvsRCIRaTYR/FJ2MtnjC74wx2x2Y4ud/AeCY6SsHfynplp66qHZnMTpbOXs2bMkk0m5Q56fn8flctHY2LjuvKurq3J8gNbWe/y5uTkZg95b007N6L+jqCooinxYUok4qRU///JcgveebcPj8XD69GnS6bTkZ4+f/8BEo1EWFhakgG1paeHhhx+W/CtXrnDz5s3CfFVFXbxDuOMN3F3TQpqbmzl16pTsXyF22aKcSf74sViMhYUFvF6vNJE89NBD0vQkdok5fNGvwD/Oi94avnVliO56lUNNdTn8+fn5HH5jY+M6G34sFmNxcRGv1yu1gDe84Q3S9LSwsJAzvtlslp99p2GOcbWRZy/eQbdSRyaTweVycfz48Q352YjH4ywuLrK8vCz5x44dY/C2A8b+mJuXvsNvfzfMH//Ya3jDmYMFfRCiT0Umk8HhcHD06FH0en3Ogi20q818CCaTiY6ODpqamrTaU2sLtsPhoLGxUfMBpFNw6dPQ+ep1XR0Fv7m5OYcvtKvNfAhGo5H29nY5fqCmh4bxLzM6PERTi+aD2ChPy2g0SoGXP35+deZyYF9QZCGTyRAIBBjpv0EPEE1r6nNjY2NJO5hMJiPLW0ciEVn8raGhYVMBc7rDjtr7MOrAswwMDEh+U1MTjY2NJQkoEcMv2iLq9fqSNaDLE34+8BcvEYuEeEPNdcyuTt5+2CYFlOCL+HwhCMSCYbNpx6ZSKTm+TqejsbExR5CI+PpoNKppWoZmauNBJm6+iKPrBEedBgjMkomuYjDoefWJbo4dO5gjiHp6eiR/aWmJxcVF7HY7LpeLTCbD0tISwWCwqIDt6emRcfOCL+avOfPv0B1bYcXUXFDA9vT0SE1xeXmZpaWlHP7i4iLBYFAK6HxB0t3dLQWJ4FutVtzVTuqAay9/mw88f4dkWsVcZ+OzP/0murrulaDv6uqipaUlZ3yr1UpjY6Ns3BQIBFAURS7kIughny/KuVutVo65jOhSUbp0U4ylHHTXZeRClL2QC74QhKLZl/AVLS0tsbKyIs0/2fyXQk6OqAYO1sdR1Tq+d2ecFkuKuro66evZiA/Q2dkpx19eXsbr9Uq+yOUohqqqKrng5/M9wcvUBKfhLR8ryjeZTHLBF4LQ5/NRV1cny4FvNL7gp44/jW70iyRmbtAfCFFbW0t3YyOf/fFHuDjmK5inlc1vbm6WDZv8fj81NTXy+y8H9gXFGtLptIxhPm7XIhHaeo+ga1mfS5GPWCwmb7hUKoXZbKatrQ2Xy1WSDyMWi2mmoZQVT9SLGl6irf1gyfx4PC53GKlUiqqqKtra2nA6nSWZKBKJBF+9eJvI3ChqOklH/TJXjU9x9sSJovzq6mq5YExPTzM8PMzLL7+MXq+noaGBvr6+dU7KbFgsFrq6ukhmXg83/wjvne/w0h0tzPYjTzWxqNh445nDvKq7cIVewfd4PMzMzDA8PMylS5ekM7yvr29DH0pVVRWdnZ14PB6mp6dlK1NFUTig16rctpx8PXrREjUPZrO5KL+hoYGeHq08d7ENhtlspqOjg5aWFmZnZxkeHuby0hL/QTHiHb9F2vR2DDUOMBi4MhPmsQPr5y/4c3NzspUraJn3vb29BaPF8vkej4fZ2VlGRkZYXFzkQ+1Bur1edH2v53VvfGJDfnt7uxx/eHiYK1euAJozv7e3t2C02SM9bsa/3cJB/SwWh5t3vf4M7qo4w8PDXLt2DVVVcTqd9PT0bOiDEC18m5ubpbAcGRnROjO6XJtWdhb8lpYWlpeXWVxcJPPSJ0hY3CzXncRVINItn9/a2irHX1xcZHR0VI7vdDo3NAsZ2l8FwIGaEEutrSwtLWlFQ41G3nnAicu1uYbi8XhoamrC6/VqFWrXij6eO3duS/XgSsG+oFiDWODa2tro9mi7F525eLipKE7n9XqJRCIoioLVaqWhoaGkMNV8PkCz+yjchMOONGxS3TWdTuPz+fD5fITD4Zzx6+rqNr1R0um0HH9sbIwOzwFMZgueKoUqJU3TgYc3FDKC7/P5ZL1/EfOt0+lYWFggHo/jcDgK7vJEcT5/tIZeoEFdItn9OlkGWqfTYVUDrKwYNuR7vV7ZL0DErOt0OhYXF4nH47LcSL7ATafTeL1efD4fwWBQRsqoqopzQlvwJuO12Px+mTFcaPxsvvjcdTody8vLJJNJef2FajEJvug3UFdXR2K6hT5DBH0iTCaTQldr5VWd63eWQvv1er2SX1tbK8f3er1y/EKx+Nn8YDBIXV0dNTU1HHTPYTifwmBtYXx8XEZwFZp/Ib6qqjnjO51OrFarvJdOd9jxdZ+kae4yf/bWQ7gIsrgYoLa2VppPROHBVCol579R0ERTUxNut5tAIMDS0pIsnCf6fmzEF1p3o7qE4r3G8qmfYW5hibmFJerq6nA4HNjt9s2496pDAAAgAElEQVT5jY0Eg8Gc8Wtra7UIukJ8exdUWdHNXcd95j/nzF+YFgXfZrNtGLQhtHZRw2unhQTsC4ocNDc3Mzg4CPGg9g9TbnhsMpkkEAjg9/sJhUKoqipLHjscjk3NO6lUSlscs/gWi+UeP94G30RzpHafK8pfWVkhGAzK8T0eD06ns6Txxfyz+S6Xi7e/6UkOH3+ImVe+Ajehs+9EUf7KygqBQABV1UpNiMY2Yge2uroqF2G/349er5chj2KByeanaltoMazQ9ojWCTcajbK8vIzf72dlZUXy6+rqZImN/PEdDofcwUWjUTm+4GcLgkAgwOjoKDabjaqqKpqbm3N2gKnpvyJtdhJKm/CNjqLT6eT8VVUlGAwSCATIZDKYTCYZnSP4QkP0er3SRyX4gJy/4OeMP3yY1pUp/vRDr+WFW+McaarBsDLJ2FhQCqJsvuhg6HQ6pXlGjO/z+RgbG0On08mQ4vzxjUYjbrf7Hn/4mwDUtp9gbq1xkk6nw2q1yjyXUChEIBAgnU5jNBppbGzE5XLJ8ePxuLz+fD6AztpG/dgzWKOzRKhbx08kElJDHx8fl3yxGSi0aCuKgs1mw2azSf6NGzckv76+HrvdXpx/6VNgMON6w3+n3lAr5z8xMcHk5KQcv5jQERs1q9VKIpGQHSYFP3t8g8GgVYlueUjrQ7MGwU8mk+vGX8cvAFE+vxzYFxSFkBX1tLq6SjAYZGVlRe78q6qqcLvdOByOTZ1ogh8IBAiHwzl80VBdwtCgJfllRQFFo1H5YAu+yWSisbERh8OxqRNL8IPBIOFwGFVV1/GXlpYwGo1aL+j5KNxE64+wxs+efyF+Pqqrq6murqa1tZXFxUUZGivmL2riyEJm14/D0r2eVBaLhba2NlrXVPKJiYl1/NbWVlmrJx9C+Ho8HpaWlphcC+0VfKGhZCepZcPgG4Lmoxw/fpylpSWmpqa4efOmTE60Wq14PJ6cJLFsCOEtzBqTk5M5fJGEVZBv70Q3/gJvfeQob330WFG+x+MpmsMjxvd4PCwvLzM1NcX4+Dhf/epXAWQSV0G+bwwAV9+rcNU34/V6mZyc5M6dOwQCAcnfaHwhvFtaWmSzr/7+flZWVgA4kDJwFpWuuiS2w8fX8U0mk+RHIhG54RCBGXV1dVLwFyoTL/hdXV0cOnRI8oXfo7a2Vi7KZrMZUnG49U9w9F1Q7cAEspinGF9s0Arxs8O5T3fY5eahqamJSCQi5y78RjU1NdhsNhzuExhf/gQkYzmNkITwb2pqYnV1Vc5ffP61tbVy47GVWnD3g31BkYdEIkHIO0cdcHt4kphei9qprq6mpaUFm8224ZcTi8UIh8OEQiGCwSCpVErym5ubsdlsxRd3RSHtOkRm9ibTay0YRZGvkvhou7lQKCR/BN9isRQWTnlILw6iGGuZWIoQGruxZb4YPxwOEwwGSSaT6HQ6enp65E4omUySSCQYHR3VNDJLK/Uj3yIVi5BRDDmfn+D39vbKMM1UKiXbZM7NzVFXV0ddXR21tbVkMhnJD4VCJBIJFEWhp6dHhoQmk0lmZmYYGxtjdnaW+vp6yVczaYyLA4R638HErVskEglAc0Dnz398fJy5uTnq6+upra2VWbHi+kOhkAxp7erqkhpfPj97/rq6VgyJMFND11hJGOT4hfgTExPMz8/n8IGc6xfjNzU1cfr0aVRVlaHGExMTcv6Cr/eOotNXMemLEZq8JfnZbThTqdQ6fm1trdR4xNjhcJhYLAagmXS7uzWzotcKY7By9yLTaWcOP98vUFNTQ01NDW1tbUQiEblgT01NAZpQyL7+fL9AIX4gEJClOkwmE43+V3DHgyQOvWNdyZLN+HeXovzKM3dJG8xUWWr43E++OscJLfitra1EIhGpkU9PTxNOOunJJJm5+nWquh+ntrZ2neDL3nStrq7K6xfdKo1G47rvvxzYFxRryGQy3Lp1i7GxMY5aZ6kDauxumhyN1NfXFzTrJJNJVldXWV1dJRKJEIlEpGAwGo3U19fLn0L8VColuaurq4TDYZr1jTiX/51QMEBdvVXunjbiC27++OIG2ozv8/kYGRkhHA7TOXkdQ3ULwVBIKw64Nv9Cjr3s8cX1C8FiMBg25EejUXw+n6ZxxGo5nknxzX/4BEGLVu6grq4Ol8sl6+UUCn/1+/0sLCwwODjI6uoq0WgURVFy+KJeTj5/dnZWRo5k82uSXt6WjDCb0swsxfixWEyOf/fuXcLhsBzfbDZTW1srS5zb7faCJTh8Ph8LCwsMDw/LRbUlNMergfk7FzB1P74pf3FxMYcPyPFdLhcejwebzUYoFOLQoUOb8p+YepF6o4vhkVFZPsVutxcMf/X7/SwuLjIyMkIoFJJ5FhaLhZqamuL8dDfqNw006vxkqqtZWVlheVnLBjcajXJxrampwWKxSAEt/ufxeEgkElLTFb6SfL54HorxA4EAoVAI08WvkDRZuRl2YLxxQx4nFumNxn9m4Abx1TCZTJB0AL74DQXba49syG9paSGRSBB2GOHyb6LOXGFC75HPjfDXiHMIvjhfS0sLyWRSXn8oFJJh4KOjozz11FP7zuxyQafTyVBIt3kGdAatxLKikEql5IMUi8WIRqNEo9Gckr5ms1k6FOvq6nIe7K3wLZ1n0I99iRPtNhlXv9Xx83cmgh+Px4lGo8RiMa3j2Bp/aWmJWCym8WPz0HaWkydP5vAjkYgcO58vxhe7mrq6unXjC744RzZf13QMhuCANYnX0wVoTrpEIiH7bSuKIn8ymQyZTEYKRWG7Bc1WLBLaEomE7Lmg0+nkw6OqKlNTUzIyJtsRb51f1CbdeJhkMlmUn06nZTtKYQYR7wmHstBcFhYW1tm1U6nUOr6qqlhCCkyDUxfCn0qVzBfF88T163Q6Uln80dFRWeol//PL5jumA8TrNeGcSqVkvkt2gp5I2hN8oZGI90B7nkS5leXlZSwWi9aD3GzGbDZT7eihJjIlmylFo1HC4bDc8AgzFWhaQz5f+NbEd1iIPz09zfXr1yXfbDbL16qqKhoaGmiwVsPCS6SOvof2zm4ikQjhcLjg+Pl8l8vFDz55is8NxIlHo+jVBI8faicajebwjUajnL/FYqGqqgqz2Yyj8zhUu2hVFnEdPVr0+rP52dfvdDql6VJYMsbHx7//nNmKorwZ+P8APfApVVU/lvd+FfA3wGnAC7xPVdXxcs3HYrGQTqeJBpaoNlQzNDBAPB7P6U+r0+kwm83U19dTXV1NVVUVRqMRVVVJJBIkEgmWlpaIx+MkEol1fLHjFfZFs9mMyWQik8mQSCRI2rXSznPXn2PF/VhRfl1dHdXV1ev5Wa1Oi40vFnXBX1paorOzk8RqEF1ohlDV21keHSUej5fEF+MLk8jy8rIcuyS+vhf1BSMeY4C6zk4SiYRU04PBIJFIRF6PoiiYTCaqqqqkUKqursZkMslFEjQTojDBFOIvLCwQCATW8W0zCwCoDYdIJ9JFxxcF2MR9UGj8SCQiTTDxeFwKRqPRiNlspqamRkYLCT51mlmzJrnEcjpdlJ9//fkaRzKZJBKJSN/U5OSkPEbwa2pqqK+vp6amhqqqKtRMBnN0nnjLo1gsFulfy77+fL4QcuIaTCYTRqNRmvjEpkacS3w+3UY3lunrjPX3U1VVJbmiPEc+X/jKsvMEDAZDDjefPzc3J002hfh6vR7XwndoTa7i87wOVFVGDgpBL7jF+NVVVfzuG5u4ORfmkR43Z3u0nCfBz75+EcCSze+1HsA0fpHl5WVMJlPO+IlEImdjVYhfVVWV8xmUK1N7zwSFoih64OPAG4Bp4JKiKF9WVTW78M+PA35VVXsVRXk/8LvA+8o1p4GBAaamplg1L2DSmQmFQhgMBoxGI3q9HoPBIHdK4XAYn89HMpmUZQ3ETg2Qx+f/FOIDkqsk4HHgysvfxXuwg2Nt9oLjCydZPj97fMEzmUzodLqC/FQqJZueVAXHeRiV2UQ1vvl5OeeNxk+lUjnXXuj6S+Efq/YQH7/EoHVQagRGo1Fmzur1+hyu2EF6vV4pIMX4gm8wGOQDJBY10BZxsVsWwi2dTqMoCq9beYEqnZUXLt2QC5HZbKa6ulpWp00mk7I0yuTkJMlkklQqJQWFKNthMBjk7lMIE0AuusX4zbo6VkavcTt0ZB0/e/yFhYVNxxe7YZPJpJXPX7t+saGZmZmR2lF1JkxfKsrgUoLRrJ241WqlurpamoHS6bTUaFKpFF6vl4WFhZzQZqGBGY1GTCaT/CwFJ2Htwjb3AuEVL35VJ+efzxfXIEypQpMR5xF+QPH9ZZ9jampKFtLT6/WSL7ipVIrq0a8Sr3IyGLXD4GDB8fP52eOHQiEa9Cle06SirC5w+/ZSwfkLU2w+f6WmG8/cBSaG+8nozUWvvxBfmJ+ytVvhu9lpKOXK5Nt0YEV5DPhNVVXftPb3LwOoqvrRrGO+tnbMS4qiGIB5oEHdZNJnzpxRX3nllS3P6eGHHyYQCPDnr1+lszbJe55rZW1OYj751yBvzGzTSD62wg8l0vzba/p5bqaKD33HweGWeuqqjDnHbzZ+tolAvIr/ZfMFz+v10tjYyKM2L7/ac5v/MXiakWh9Dj8QTeJbTeKsqcJWbcwZN3v8fBONeBXmkOzxhTlDp9PxE65LHKha5iOLby/IL3T9QniI82bfFuJBzBZgYh6iDEZLSwuKosjzZDIZ/lfj1wmmzfzB4hM5/Ox5ZH9+2eNnQyxI2ZuI7O8tmy/GF2P8WvN3SKo6fmfmsZzxi/ENBoN09Iv/Z/PS6bS8XsHPHluM321c4ufr/50/Dz3N7aQn53PLnoP4PfueyjZpZXPyueLvx+pm+dmWa/zS+KuZSdly3svnZ//kfw/Zc8j/rkSJj0JcVVWpMaT5wtkrPDPv5s8nOteNJcbP5+U/z9nfvRg/f+7Z1y9eFUXhiYYwf3Bmmp+42MF1f3XBa84fO38NyD53IpGQxSO3CkVRLquqeqbQe5tqFIqi/AzwWVVV/dsavTg8QLb4mwYeKXaMqqopRVECgBNYLjDPDwMfBnC73Zw/f37LExLdofSpDP6IJp2L2fuKfVkC4sEv9F4hgSFeIwmV/qMKh6xx0qtBJqfC1JkKnyP/Rs0XAvnHFRoPtJs6FApx7pAWPvq9/nlWM8vymFgaJkMqGRV0CrRb9dQY1o+fPWb++PmLSv48rqfTPNodZWFikEjamFO8L5+T/9lmL3zib2HTzxei4pjsBSSVSmmLJhlajCFurWrJjsKEUkhAF3r4s38vds3iGLGwidfs/y+nazlQtSz9PNkLsfg7e0EBZAnxQnsoRdFKYWQvpNmLsfi9zzoL9XBjOsh0LCXnJM4pdq35wi/79/zFtdDirqoqhupVaAHjyih353L9G/mCudj5Cv2dfbxoiyuQL+ze1RXFpFP57PUow8vDRb/LUv5X7H4o9j8xl9VFFc5AY2qG0VH9Ok6xPXGheYB2v29n7dsUhaRX3pfw28Aw8AXgzaxpIff7A7wHzS8h/v4g8Cd5x9wCWrP+HgFcm5379OnT6nbx/PPPq+pfvF5VP/OObZ/jfvDKuE/9m//1XjX0643qwV/7N/WVcV/Zx3z++ee1X/71p1X1d7vXvf8nz91Vuz7yjNrxS8+o3R95Rv2T5+7u/CSGvqGqv1GvqmPf3flzF4C85mws3NHmcP3zuzKHonjuf6vqb1hVNRnfsVMWvN51B310bdzYjo1bFImoqv6mTbvWMmHTa/7bd6vqHx5T1UymbHMoCR/rUNUv/9yOnKqk77kIgFfUImvqpoWEVFX9NaAP+DTwY8BdRVF+R1GUnvuUUTNAdiu31rX/FTxmzfRkRXNqlxdFmhbtBk532HnNE09Sq8T4wvs7ijYwKgu8IzLRLhuiqqVeYV1Vyx1D0zHtNSvZcNchxnZv3Ce77BDlxgPlsTcXhW8M6j1g2IXS1UazVsZiaWDzY8uBeAhGvw1H3rFpq9NyI1zbxfTwDS5P7LTRZudQUtedNWkzv/aTAuzAFxVF+b37GPsS0KcoSpeiKCbg/cCX8475MvCja7+/B3hubS7lRSKc07Rot9Fx8BQAJ6rmdndg7wg418v/0x12/u5Dj/Lf33iQv/vQo+URXrVuLSt9/ubOn7tULNwCnRGcfXsy/OUJPx9/fpjB+Jog9o/t7gT8Y/dKne8GGg7B0uDujZeNkechk4QDby7bEOL73EgAXJ7w842FWgwro/zHT12oWGFRio/i54AfQfMLfAr4n6qqJhVF0QF3gV/czsCq5nP4aeBraOGxf6mq6m1FUX4LTQX6MpoW87eKogwDPjRhUn7EQ3umUQDgOqi9Lg1B79NbouaXEygZ8TCE58HRXfDt0x328mo3igLuY9pivVdYuA0NB8Gwu93DILdh0+cNK3xHD/jHd3cS/nHoe8PujddwEO5+HdJJ0JfW52XHMPQ1MFuhLd8tujMo1K2u0PNzYdTLaqaZdxm+gykVKdjdshJQSnisA3i3qqoT2f9UVTWjKMoP3s/gqqo+Czyb979fz/o9BvzQ/YyxjUmtaRR7KChqXGBxwPLWdlul3pwF4RvVXgtoFLsG9zF45dOQSYOutA5uO4qF29D55O6PS253s9lUPSmDCYNvFzWKRATCC5o5aLfQcEjb1fvGoOHA5sfvFDIZTUD1vL5sAqpYt7p8PNrt5K+f07Kyew2L5THr7gBK8VH8Rr6QyHqvv9D/H2ToMknIpPZWo1AUbbe1RbW80M1ZMrxa1EchH8VOYkN1vOkYpGKaCWy3seqD4Mye+Sey/UAGg4FkfRusFHzsygOhveyq6Ulozrvsp5i7CpHFspqdSvXrne6w85P/4U0A/J/XVVekNgH7JTzWQZ+Oar/soY8CANcB6P/KligbtVLcFL61xbmI6WknsKnGIxbphVu7u8MEWNRiz+8qHXz9+eGtm+7uE8IPJMyGlhd6dtf0JLQXxy5qFK4DgLL7foqhr2vjbtGsuxXkf58b3UtHjz4E/6LQzS77JLeAfUGRByko9lKjAE0tv/IZiCxrpqgSsJWbcx28o1DXDKaabU54c2yqjjccAkWvCYpj7y7bPApiLeLp//n3VWZSg1s33e0AcvxAtzth8oJmCt2NqBypUeyioDBVg61t9zWKu1+DtldBTXnNPCX79Yxm7XMQWn0FoqSop/+bYEitar/spY8C7u2ot/gQne6w81Ov7d36AucbAUd5/RObquOGKm2XuRchsgu3iBptzKTqt2e622nYu7QGWtFdioLxj0GVFSy7bPpoOLSrguJ6/yDMXmWm4TW7NmZJcPaB9+5ez6Io9gVFHipKo4ActbyUcLttwzsCzvKZnaDEMNumYzC/B5FPC7dJuo5gMujLmy9SKoSvYLdCZH1j4Ojc/ZyCxsOwPKRFPpUZlyf8fP5znwbg/73kqqxQVFef9gzuQvT/drBvespDxfgo6j2aVrMmKO4romkTGJJhWF0uuyMbSlDH3Ufh5j9qO+nd2t1m0rDYT/3pH+Pv3rxN091OQwgK3xh4Tpd/PP84NB0v/zj5cB+DdEIzuzQeLutQF0a9PKVeYVZ1cCvVWlmhqM5eLdoyNA/1zXs9m3XY1yjyUDEahaJou4y1ENn7imjaBJbomhOtzKankuBeW6x20/zkH4fkKriPbt90t9Owd2ivu+HQzqRhZXJ3I54EZABD+b/vRzvreUJ3i/OZUxgN+soKRRWbtAr1U+wLijwYUkJQ7LFGAWv22yGgvGU0LNFZ7Ze9zKEQ2MWFQ0Ik+e116Y5smGqgpnF3BEVgWstn2M2IpzVcibhIKwbmhrZe7XkjXJ7w88xIIse8dFq5S60SxXbiLbseqLAppKCoTD/FvukpD/dMT3usUYDm2L3+OYgF7i+iaRNoGoWyuxEvxVDXtPulPBZug6K75xeqFNg7d0dQ7EXEE2vm1L+6wj8rHpZuvMTsGf+O3NfCTBtPZnhm/MI9oTDyHCh63vq292pZ2ZWEeg8YLHuTQ1QC9jWKPFSUoBAJScvaLqNcZpHq1RmwtmphensNRdF29ruqUdzWdnRGy+6NWQrsneDfhaQ74TDfZdOTMKf2q20cYHLHzKnivCp5ZtqR56D1bOUJCQCdTtPolytTo9gXFHnQp6OaZNdXgLIlI5/KGz5oic5VhtlJoOmElgCXTu3OePM3ofHI7oy1FTi6IDgNqUR5x/GNgt6kbRZ2EcKcOqS206z4eKJlZ5YjcV4dWWbaVR/MXoWe1+3IGGWBs7diTU/7giIPhlR07x3ZAvZO0FeVV1CoquajqARHtkDLKa2Ux9IuVIiJ+rVSGS0PlX+srcLeCWqm/OXGfaNg69j1+lrCnHrk1BMAPGSa3tHzvrvPeM/sNHoeUCtbULj6NA2y3BuDbWBfUORBn45WhtkJtAfX1VfeEgerPoypSGVpFC1amXVmr5Z/LDGGGLOSIHMpxss7jm+8rKVbNsLpDjvveNMbtT920Nx4usPOD/aY7plpR57TTE6V+D0LOHtBTe9+1eASsC8o8qBPV5BGAZr5abGMGoUIx6skjcLepWUJ76agaD5Z/rG2it0QFKpK2jvC9VXH3iWg1TZCTUP5SsyrqtZ/ouupyjApF4Pog1KBIbL7giIPmkZRAaGxAo2HIDCp9YsoA8aGbgBwK95QlvNvCzodtJzcPUHh6N790hWloLZJMz2WMTv7+sAQ+tQq/zxu2tvGOe6j5cvIXx7SfD2VbHaCe1p9Bfop9gVFHirKRwHQsJatWgbz0+UJP1/7zgukVB0//I+zlVXSoOWUtnCk4lumbqnUyey1yjVH6HRa4l0ZNYrhQS0MeUJ17219K/cxzRdXjgCGkee010oXFBabFhq+m31ISsS+oMhDRfkooKyRTxdGvXSpM0yoblZTur0tgpePllNaEtha+e9SIWLof//rg5vvkMNLmqO4UgUFlD2X4uFa7fOZwr239a3ca71IRAOtHUTg1tfwWzq4HKzf8XPvOGwdu9uHpETsC4o8VJyPwtG1Fvm08xFAj3Y76dPNMKx69r4IXj626dDeUqkT6ch+eJuT3AWIXIoyFYvr0i2gKnre8/on9jZbWWbk72yi5ZXReYxTL/KV8IGK7kktYWvXyqlUGPZEUCiK4lAU5RuKotxdey14dyqKklYU5draz5d3Y26V5qO4PBVk2dxBYGLnM5VPe2ro0i1gsLVVXkkDW4fmN9iioNhSqZPZq4ACzSfub67lRLnLjftGUayt/NfXH97b77/hIOgMO55oOXXtOaqVOOfTJ/e+dHwpsHdogiKT2euZ5GCvNIqPAN9SVbUP+Nba34UQVVX1obWft5d9VpkMhnSsYjQKYUb5XtBFePrWzu+GfKMoapoWT8eeC4l1fgVF0bSKLQqKkkqZC8xe1cqkVEJdr2Iod7lx39iehcbmoEy9SB7JXCWhGrikHqk8rbkQbO1aNd3w/F7PJAd7JSjeAXxm7ffPAO/co3nkIhnRXivERyHMKEOZVjzKMpeHdlglXatMG6lp29nzbhFF/Qotp2CxH5JReVwpTuqSSp2oKsxeqWz/BJQ/RNY3WhmCAsoS+dS0+D1iLY/wk288WXlacyHYOrXXCjM/7VVQsVtVVdEgdh5wFznOrCjKK0AK+Jiqqv9S7ISKonwY+DCA2+3m/PnzW56UKe7lcWBwYpa55Nb5O42qlTQGBYZVDwDuxZc4f37ntIqO8a/RBSxlbNv6vHYKz4wkiCe12jyJZIbPffMSoR4TLp+RY5kUl//9b7ia7uX3LsVIZsCog188a6bXvv1M4pR/CsIL3F2tZWYPr30z6NIxXgOMXn6eyeXt74bD4fC679iQDPFkbIVhv8p0BXwGrau19AanefFrXyJRdf8Leso3CYu3Wer+UY4q04TGpjm/hwFFw/40A740hxz6oveuZXWeR4D+F7/Kwmhsy2MU+p53AmUTFIqifBNoKvDWr2b/oaqqqihKMU9dh6qqM4qidAPPKYpyU1XVguUVVVX9JPBJgDNnzqjnzp3b+qSXhuAlOHjsNAdPbIO/wzgHnHrYT/8tC1z6Q95xpBZOndu5Af7ps2Btw2J1sa3Pa4dQ1+XnmfELJFMZjAYdP/z0WW3nF+iF2x/jdJOOC5EOUuogKpBWIW7r4Ny57TdauvnF3wWg76n30df2qh26kjLhqptuq0r3fXxH58+fX/8dz1yG70Hv2afpPbT9c+8Ypmpg5K94vE0HR85tmX55wp9TXXngc78GQM+bfoKePS4hf3nCz//5lmg8li6u3SRj8PJPcbi5msNPndvyOAW/5x1A2QSFqqpPF3tPUZQFRVGaVVWdUxSlGVgsco6ZtddRRVHOA6eA8tXhTYS01wqyWZ/usHO67Sm4UqWZYXYSS4OaXXiPUbSEer1Hy9idvcqjp96DyaCTwmQ7tubshUS3OEQGPdcSrVRwzJMGZ295snVFvH6lmJ6aT2oRflMvw5F3bIlaqANkm+8K1DVXRMHHQtF4BQWF0awlWlZYiOxemZ6+DPwo8LG113/NP2AtEmpVVdW4oigu4Ang98o6K5H9XCHObAmdXlvQdzLpLpPRShp3Prlz57wPFGyRmuXQPv3O++vHkb2QGHQKn9IPMYiHD/z1df7uQ5bKtl07e2Hg33b+vCJnYS862xWCwQSeh2Hywpap+QvxxZEFTvivwfF37X4f8AIQ0XglbXTsHbtTXn4L2CtB8THgC4qi/DgwAbwXQFGUM8BPqqr6IeAw8OeKomTQnO4fU1V1a9lXW0ViTVBUiDM7B42HtvUAFUVgClJRTQCVpzrIzqDlYRj+pmzetN0FPWchSWc4ZhjlG+kzJNMb7O4qBc5eraf5TvcR941pWluF9OG4POEH9QCn5v4eXTK6pXnlL8Svq53Wil32FjVs7Cq21HjM1g5TL68zpe0l9kRQqKrqBV5f4P+vAB9a+/1FYHe7vUuNonJMTxINh2lCX9EAABmVSURBVODmP0I8tDPzWx5aO+9BCFdeWWOJjse1UtuTF+DAm7Z9muyF5IBuFocS5pra92CETMo2mSPQembnzusbrYyuhtzT+J7MuPiUMcnglW9z8JE3l8zPX4gPjf4ZKjqU7nPlmvKWUfJGx9aBeuuf+ZFPfY9oSpGmtL0UFvuZ2dkQPopK1ChkKY+hnTmfMGO5Du7M+cqFtldpTXXGv3tfp8nOr/jzV2vhtkee+IE9fwBLgqtMVUV9o3vSJ7sQhMb3SlrzmY1ffa70el1ryAmLHv4mwfoDlVnscTPY2lHUNI7UcmkVBnYB+4IiG5XqowBoFMUBd8ihvTyoFSCrqfDdtNGita8cf+G+TyUWkvbAK8SqXHzwLecqX0iAlqWu6He2TWY8DJHFinFkC40vpNQxorZgmn25tHpdhRBegpkr+BwVniNTDPYOALoMy6VVGNgF7AuKbCTCqOjAWL3XM1kPeycYzDuXubo0VPnahEDnkzB3HWKB+z9XJgPjL7BiO14RTs6SYDBpi8dOahT+yop4ytb4/M6HOaUMoaqZ7e2mB74CqCy7Hi3LXMsOmyYofuuputIqDOwC9gVFNuJh0npzZS4gOr1WYXPuxv2fS1U1jaJh70NjS0LnqzU/xcRL93+uxTsQ9WmC4kGCs0/zUewURMRThZie4J7G5zr8GmxKhAO62e3tpu/8Kzh7idR0lGei5Ya1FRQdnXrv5hUGdgn7giIbiRBpfWVEgBRE80mYv3H/BcMiaxE0D4pG0XpWi6+/Tz8FIM/htz9ogmItl2KnisXJzoaVoVFko/OU1jfil44Ft76bjnhh7LtaHkYlbvhKgd6oRaNVUIjsvqDIRjxMylDBgqLlIa2S6P0WiBO9LR4UjcJo3jE/BWPfBXsncXPj/Z9rN+Hs0cKZQ7M7c76lQahvrcwIP2cvVDt5XfXYpkJiXf2vgWe0vtNbTNirOFRYX4p9QZGNRLjyNQrQ7PX3g7VigDKS6kFA16s1bSq6sv1zZNIw8YJmynrQsNORT0sDWmh0JUJRoO2RTfOGChaTvPMvWshvUwWXji8FFdaXYl9QZCNe4YKi4TDojPcvKBYHtBDges/OzGs30PnkWj6F5qfYUrtTgfmbmkO86zVlmmQZIXIpdiLyKZPRghkqeaPQ/hj4RjY0v+RnY18dHIHRbz/YZicBewcEZ7fVCrgc2BcU2ah0jcJgAvcRmLt2f+eZvQrNDz1YD5PnzJqf4oWttTvNhvBxPIgaRV0zGGt2xqEdmNTMWJWqUQAcWWs/c/Mfix6S36TqaeWyZnY6WhldC+4Ltg5AhcD0Xs8E2BcUuYiHKttHAZr5ae769ltjphLazrrloZ2dV7lhNGvJd+Pf3Vq702yMfVfbmdc3l3eu5YCiaH4K7w5oFEsPgOnR3qlpFTc+X/Rez29S1bnwdc1k0/yA3duFYGvXXivET7EvKLJR6RoFaIIi6tdqNW0HS/2QjmvF1x40dL4a5m7wZAultzsVSKc0s9WDqE0I7FQV2QclmOHEe7VSMxuYWmU2doMKo+fhyDsfLE25GNaS7iol8mlfUGSj0n0UcG+3tF0/xcwV7bXSO7sVwuG3ASonA8+X3u5UYOQ5LWKs7w1ln+ZOQ/hjZg2tmoPzfu3WS4NaKetKL29x9F1a+ZYbX9j82IufgEwKTv5w+ee1G6hr1vyRFeLQ3hcU2fjIJOOd79/rWWwM91GtnMN2BcXsVVImKx+/lt75HtzlhvuIlnR44/OltTvNxvXPgcUBvQ+WoMj2x/zB1Yzm0PftQHh0JfsnBCx26Huj5qdIp4ofF12BC5+AQz+o3SPfD9DptcS7fdNTBcJoJqOv2utZbAyjRbMtb1NQrI5f4mK8nd//xtD26ujsNU68F6Yvbc2pGwvA4LNw7N1aQMADhGx/zHBqrWPw/ZifVFXTKCrZP5GNE+/TalKNnS9+zMufhHgAnvrFXZvWrqCC+lLsC4oHEcKhvVUko5h9g1xLd1dMVcot49h7AAVufrF0zp0vQyr2QJolsiN7pvVr4cwFBEXJ4cLBGa3vyoOgUYBWWt5sLW5+igXhpY/DgbfcyzP6fkEFJd3tC4oHEc0nIbwAofmt8eZvoSNNv9JTMVUptwyrR8up2CAaZh2u/wM4esBzurxzKwNyyqN/6HVQ07gul2JL4cLSkf2AaBSGKs1X0f8VCM6tf//SX0BsBZ76n7s/t3LD3gGRpXtVrfcQ+4IiC5cn/Dwzkqh8c4zYOc1uMZ9iVnNkf/iHf6hiqlJuCyfepyVjCcf8RliZ1LKxT76/aDTMtpL3dhE5/hj3US1DPQtbChd+EEJj8/HIT4Kig795O9cH7t77rkIL8OKfaH6nB3ATsCnWqsiW6tAu5/q1J4JCUZQfUhTltqIombX2p8WOe7OiKIOKogwrivKRcs5J7Mr+6W6y8m33TccAZeuJd7NXoaaRE4cPV0xVym3hyNu15Lsbn9/8WGGyOPHegm9vO3lvr9B8Ehb7tXyYNeQnnm2oJS4NQLWr8vuQZKPxMHzgC2T8k1R97t18+uuv8KVP/w6pPz4DiQi89lf2eoblgehlXoL56fKEn7//9O/TM/Y3ZbmP90qjuAW8G/hOsQMURdEDHwfeAhwBflhRlLKFNIhdmcoDYLuvqtN2lhPf2xpv5oqWP/Ggx5mbrXDwzXDrnyCdLH6cqmpmp/bH7z10edh28t5eofkkZJI5DazyE8823AA8SI7sbHQ+wVeO/D5dzPEd08/x27pPMm/pg//64oOZE1QKbKXnUlwY9fK4eo236V8qy328J4JCVdV+VVUHNznsVcCwqqqjqqomgH8AylYSUuzKdDwgtvue12n9GUq1X8ZDWvLSg5g/UQinfgRWl+GFPyx+zM0vapnMDxV3Ym9pN14JKFIYspRw4cvjPmJzd1i0dJZxguVD65kf4GczP8+s6uJXMj/Bwru+CK7eHR+nYkyRNS6tiVoJGsWj3U7cugDLqrUs97FhR8+2s/AA2enH08AjxQ5WFOXDwIcB3G4358+f3/KAv/CwievzUU42mQiNXef8fYarlxO2SAMPZZLc/Mqf4nW9atPjrSu3OIXKDa8BX95nEw6Ht/V57S0MHG58DY3Pf5QrATuh+r6cd83RBc688t+I1B/m2ooHdYNr/oWHTQz40hxy6Cv+e0fN8KTewsIrz3I32F4y7cZsmM9//Vm+Zwrxe7f0tHzpW/Ta9WWcaHlw9uFH+aTvrPZdjd/g/HjxY7dzXw/70/zepRjJDBh18ItnzXv6OZ01uojevcwt8/lNjz1uCTCjOvmFQzu/fpVNUCiK8k2gqcBbv6qq6r/u9Hiqqn4S+CTAmTNn1HPnzm35HOeA8+fPsx3uriP1GNz5KMctC1DKfF+8CcCJN/0o1DbkvPXAXHM+HjkJf/o4pyc/CR/+NpjWWtimU/DXbwWDAet//jxP2dd3Osu+5nO7N+OdwfjDeNLLzHed5MKol0e7nZv6m5759NfpYgaAuxkPTlsH587t/G683Di3hWO3c1/ffn6YlDqICqRViO/15zR7hJrAdGnXcWmVeP0hPvSu1+/4NMomKFRVffo+TzEDtGX93br2v32AFjbY9RoY/lZpx49/Tys0lickHmhY7PDOP4W/fSd849fh9f9LK/nwwh/B1EV496fu1cz5fkLzSTKX/pIPfupFYimt7tVmvolDDj22Ca3p0bi+jf9W6Sa2PYIwRSZTmcowRdo6tGdXVTf2LWbSsOol4bKVZRqVbHq6BPQpitKFJiDeD3xgb6dUYeh9Goa+qmUpO3uKHxddgZFvwdn/sntz2y30vFYLn7z4CS2mXuDE++DED+3dvMqJ5pPo0jFa09MMqa3SebmRoOi163lXX5DVSQd/9GNvfnAj3soMERhQqqZWdtg7IRHSCoFWO4ofF1kGNUPCVJ757omgUBTlXcAfAw3AvymKck1V1TcpitICfEpV1beqqppSFOWnga8BeuAvVVW9vRfzrVj0rqmYw9/aWFAM/jukE1ri0vcj3vBbWkezqF+7TqMFTn1wr2dVPqw5tE8aJhhJtpa883V6L0PvE5zu3GDB2QenO+x7LyAEZBXZsY0FRXgBgITp+0ijUFX1S8CXCvx/Fnhr1t/PAs/u4tQeLDi6tbaPw9+ERz5c/Ljb/wzWNmgtmrLyYMNQBaf+417PYvfg7AODhV84GKPTdbCkna8p7tWiZ161wX2yj8pDdojsRkmF4UWAsmkU+5nZDzp6n9Y6txUrPR31ayW2j36f1OnfB+gN0HQMd3iw5MRJa2CtdEf7Y2We3D52FEKj2CxEtswaxb6geNDR+zQkV2Uv6XUY+DfIpOh3PF0ZseH72Bk0n9RKeWQyJR1uDfSDwQLNJ8o8sX3sKKrqtPL4myXdfT+anvaxg+h8Uov0GfwqdJ9b//6tfyZe28a7/nWVRGqwpAiZfVQuLk/4uTDq5QdMfXTGg5rteiP/1BqsgTua6VFv3IVZ7izENVeEc3kvYP//27v3ICmqK47j38MuAu4S3uzyEo0a5KUi4LsMqBUJWgEtNSbRmJSGmPKVVKpSGv/gD000lZRlSk1KghqrREmCEkhiKfJYtWJAQAnPBJU3oqwIyGIAlz35o3vXZdltx2V6errn96miZqZ3ZvrccZzTffvec3OoIru/Fo7rSkNZ51hCUKJIu06Vwcpvy56AsTcfOVP1k49gQw2rB17PoV1+RJmKkvwfLuUa61Idqm9gfnkDs8sIzio+L1EcrKOybiOMSt9ghuZtLtmDnO6Dg3Xuo9R9AJV9YwtBXU9ZcNkvoWNnmHv7kV0R6+aCH6bLqGvSVaYij4qmHEMeNK9Lta5+AIetPLd1SbYvw2hI5fWJ1NXiikOPwbB3a3Q3Y91OqKyKLQSdUWRB1+ogWcy5FZY/CWNvgrfnw4J7odepDBt1ITN67cns6XtbXRNZOxptPhmM8uM40HMoFVsWH/W8oz6PLUtwDBs0NoGoj02cE+BS06XVfXAw7HvfjmA9ltbUfQB941sGVokiK878TrC28MtTYedaWDod+g6Ha54Es+IaG55HUcmgtaPRNH8GLSeDVWy8AmoeCBaw6hpUy2n189jyL/ZXDKayc7eEW/DFxTUBLlUHEc1HPkUlii+Pjy0EdT1lwPLNu3m05l1WnXUv+OEgSYy9GX6wID1LXrZTVNdE6irD5uCIKrHDJgMerP4Wavl5LHl3J2xbxt5uQ494nzR1yeVSGfeLSlWXVvcTg9vdm1r/+6cHgnXhYyzPozOKlGt+ZPRweQfmXP44Q/p0DsqQl4CoromiK8eQb31PC9aWWPNXODsoz9Ly8xjXvRYO7WNvt6E0Houm6mg6JkVX0ylK90GAtT1Edn8w2Y7KKvg4nhCUKFKu5ZHR/P8NYcjJ6asK2l6flwyy2uXWZNhkeOVXwbKgXauO+jyGvf8XgCPOKLLWJdceqTqIKO8EXfu1PUS2rja4VaKQtqTqyCgmmU8GUYZPhlceCEa4hWcVTZ+HO7w4A3qezMFOn3VL6DsTSNX3pvcpwTK2rQkn2wXDY/fGsnslipRL1ZGR5F/fodB7yBHdT03WvwQ7VrBgyFQ27mloWstB35kUqj4d3vhDsNZKWYuf7aZEUYUShbQpVUdGkn/Drzyi+wkAd/bPu49d3pcfrTwZ7ACjztrd9D3RdyZlqkfC4YOw653g2lRzYUFAKvoA62PZvUY9iaTd8MbRT3M/27b+JSp2reKR+skc8nLqGyjukT0SrXpkcNtihvbyzbtZtX49n3bqGWt5FiUKkbRr7H567UFYNSuYwVtzPwe7nsALHS6izKC8AyV7LSITen8lqOn2/sqmTY2j197bupmNB46Pdaizup5EsmDy72DuHfDcTbDwPti9kU6THuWpnheyeMMuOu3ZrK6mNCvrGBwQNDujaBy91qvjXmq9Gys27GJ4TCsJ6IxCJAsGjoFbXoMrHwvWT+49BE7/ZtNktVN6lCUdoRyrqpFBonAHPhu91sf2sIsesZ4xKlGIZEWHMjjjOrjjLfjhq6ksKS4RqkfCJx82jXIaPbgHM246h/5lH3P2yKGxnjEmkijM7BozW2NmDWbW5vqcZrbJzFaZ2QozW1bIGEVSq6w8qCYs2dLKBe3R/TrSseEA1f1PiHXXSZ1RrAauAl7N4bnj3f1Md8/ogs8iIjmoHhHcNrug3TQ0NsYS45DQxWx3XwdgWsNZRCQ3nbsdvYhR02S7+AoCQvGPenJgnpk58Ji7T2vriWY2BZgCUFVVRU1NTbt2WFdX1+7XppXanH2l1l7IZpuHl/ejYsMS3gjb1WfnPxkOLF23hf1ba+Jrs7vH8g+YT9DF1PLfpGbPqQHGRLzHgPC2L/Bv4KJc9j169Ghvr0WLFrX7tYW0bNNH/sjCt33Zpo+O+b3S0uZ8KrU2l1p73TPa5kX3u0/t5n6wLni8+DH3qV9yr6sN/nwMbQaWeRu/qbGdUbj7pXl4j+3h7U4zmw2cTW7XNTJNZaJFSlT1SMDhg7UwaGzQ9WRl0KVnrLst2uGxZlZhZl0b7wNfIzgjKXmpWnTlGKVpgR2R2DWNfFoJB/cFa6ZX9oUO8f6UJ3KNwsyuBB4G+gD/MLMV7n6ZmfUHprv7RKAKmB1e8C4HnnH3F5OIt9jkWiY6NWsCt0FnTiItdBsUXNR+/eFg2eND+2DktbHvNqlRT7OB2a1sfw+YGN7fAJxR4NBSIZcy0Vn4kdUCOyItmMEJ58G7i2DEVcGSxwNGx77bYh/1JG34vDLRWfiR1QI7Iq24+gloqA/OLApEiSKjsvAjqwV2RFpxXEXBd6lEkVFZ+ZHVAjsiyVOiyDD9yIpkQ9IDU5QoRESKWDEMTCnaeRQiIlIc86aUKEREiljjwJQyI7GBKep6EhEpYsUwMEWJQkSkyCU9MEVdTyIiEkmJQkREIilRiIhIJCUKEZEUSaL0vi5mi4ikRFKT73RGISKSEklNvlOiEBFJiaQm36nrSUQkJZKafKdEISKSIklMvkuk68nMfm1m/zGzlWY228y6t/G8CWb2XzN7x8zuKnScIiKS3DWKl4ER7n46sB64u+UTzKwMeBT4OjAM+JaZDStolCIikkyicPd57l4fPlwMDGzlaWcD77j7Bnc/BMwEJhUqRhERCZi7JxuA2d+AP7n70y22Xw1McPebw8c3AOe4+21tvM8UYApAVVXV6JkzZ7Yrnrq6OiorK9v12rRSm7Ov1NoLavMXNX78+OXuPqa1v8V2MdvM5gPVrfzpHnefEz7nHqAemHGs+3P3acA0gDFjxvi4cePa9T41NTW097VppTZnX6m1F9TmfIotUbj7pVF/N7PvAVcAl3jrpzXbgUHNHg8Mt4mISAEl0vVkZhOAB4GvunttG88pJ7jQfQlBglgKfNvd1+Tw/rXA5naG1xv4sJ2vTSu1OftKrb2gNn9Rg929T2t/SCpRvAN0Ahrnny9291vMrD8w3d0nhs+bCDwElAFPuPsvChDbsrb66bJKbc6+UmsvqM35lMiEO3c/pY3t7wETmz1+AXihUHGJiMjRVOtJREQiKVEcbVrSASRAbc6+UmsvqM15k/g8ChERKW46oxARkUhKFCIiEkmJIlRqlWrNbJCZLTKztWa2xszuTDqmQjGzMjN7y8z+nnQshWBm3c1sVlixeZ2ZnZd0THEzs5+E3+vVZvasmXVOOqZ8M7MnzGynma1utq2nmb1sZm+Ht3mpR65EQclWqq0Hfuruw4BzgVtLoM2N7gTWJR1EAf0WeNHdTwPOIONtN7MBwB3AGHcfQTAP67pko4rFH4EJLbbdBSxw91OBBeHjY6ZEESi5SrXuvsPd3wzv7yP48RiQbFTxM7OBwOXA9KRjKQQz6wZcBDwO4O6H3H1PslEVRDnQJazwcDzwXsLx5J27vwp81GLzJOCp8P5TwOR87EuJIjAA2Nrs8TZK4EezkZmdCIwCliQbSUE8BPwMaEg6kAI5CagFngy726abWUXSQcXJ3bcDvwG2ADuAve4+L9moCqbK3XeE998HqvLxpkoUJc7MKoHngB+7+8dJxxMnM7sC2Onuy5OOpYDKgbOA37v7KGA/eeqOKFZhv/wkgiTZH6gws+uTjarwwmKreZn/oEQRKMlKtWbWkSBJzHD355OOpwAuAL5hZpsIuhcvNrOno1+SetuAbe7eeLY4iyBxZNmlwEZ3r3X3T4HngfMTjqlQPjCzfgDh7c58vKkSRWApcKqZnWRmxxFc+JqbcEyxMjMj6Lde5+4PJh1PIbj73e4+0N1PJPhvvNDdM32k6e7vA1vNbEi46RJgbYIhFcIW4FwzOz78nl9Cxi/gNzMXuDG8fyMwJx9vmkhRwGLj7vVmdhvwEp9Vqv3ccuYpdwFwA7DKzFaE234eFmKUbLkdmBEeBG0Avp9wPLFy9yVmNgt4k2B031tksJyHmT0LjAN6m9k2YCrwAPBnM7uJYKmFa/OyL5XwEBGRKOp6EhGRSEoUIiISSYlCREQiKVGIiEgkJQoREYmkRCEiIpGUKEREJJIShUjMzGysma00s85mVhGukzAi6bhEcqUJdyIFYGb3AZ2BLgS1l+5POCSRnClRiBRAWD5jKXAAON/dDycckkjO1PUkUhi9gEqgK8GZhUhq6IxCpADMbC5BafOTgH7uflvCIYnkTNVjRWJmZt8FPnX3Z8L12V83s4vdfWHSsYnkQmcUIiISSdcoREQkkhKFiIhEUqIQEZFIShQiIhJJiUJERCIpUYiISCQlChERifR/39+AkRnN1DEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
     "\n",
     "# invert model\n",
     "beta   = np.linalg.pinv(desmat)@y.T\n",
     "\n",
-    "# plot fit\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# plot data, RBFs, and fitted model\n",
     "plt.figure()\n",
     "plt.plot(x,y,'.')\n",
-    "plt.plot(x,desmat,'k') \n",
+    "plt.plot(x,desmat,'k',alpha=.2) \n",
     "plt.plot(x,desmat@beta)\n",
     "\n",
-    "\n",
     "# make it pretty\n",
     "plt.grid()\n",
     "plt.xlabel('x')\n",
@@ -65,6 +123,13 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The end."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/talks/matlab_vs_python/rbf/play_rbf.m b/talks/matlab_vs_python/rbf/play_rbf.m
new file mode 100644
index 0000000000000000000000000000000000000000..41d7caf7b6d93444ca9109bd2534d644bb711f39
--- /dev/null
+++ b/talks/matlab_vs_python/rbf/play_rbf.m
@@ -0,0 +1,42 @@
+%% Fitting Radial Basis Functions to some data
+
+% Generate a noisy sine wave and plot it
+x = linspace(0,10,100);
+y = sin(3*x) + randn(size(x))*.5;
+    
+figure,
+plot(x,y)
+
+%% Fit RBF
+
+% This defines a RBF atom function
+sig = 2;
+rbf = @(x,c)(exp(-(x-c).^2/sig^2));
+
+% design matrix for fitting
+xi     = linspace(0,10,20);
+desmat = zeros(length(x),length(xi));
+for i=1:length(xi)
+    % each column is an RBF centered around xi
+    desmat(:,i) = rbf(x,xi(i));
+end
+% fit model
+beta = desmat\y';
+
+% plot fit
+figure(1),hold on
+plot(x,y,'.','markersize',10)
+h = plot(x,desmat,'k'); for i =1:20;h(i).Color=[0,0,0,0.2];end
+plot(x,desmat*beta,'-','linewidth',2)
+% make it a little prettier
+grid on
+xlabel('x')
+ylabel('y')
+set(gca,'fontsize',16)
+title('RBF fitting')
+
+%% save figure
+print -depsc ~/Desktop/RBF.eps 
+close(1)
+!open  ~/Desktop/RBF.eps
+