From ec7b073679bdd490505ffc176d90d7f278b7bedc Mon Sep 17 00:00:00 2001
From: Morten Hjorth-Jensen <morten.hjorth-jensen@fys.uio.no>
Date: Wed, 24 Oct 2018 07:50:56 +0200
Subject: [PATCH] Added dimensionality red methods

---
 .../$N=$100, $\\sigma=$1pred-linear.pdf"      |  Bin 0 -> 15275 bytes
 .../$N=$100, $\\sigma=$1train-linear.pdf"     |  Bin 0 -> 17057 bytes
 .../CH2 Marsland Gaussian-checkpoint.ipynb    |  144 +
 .../ML for Physicists-checkpoint.ipynb        | 3111 +++++++++++++++++
 .../.ipynb_checkpoints/Blobs-checkpoint.ipynb |  256 ++
 .../Boston Housing-checkpoint.ipynb           |  125 +
 .../Make Moons-checkpoint.ipynb               |  102 +
 .../Functions in mglearn-checkpoint.ipynb     |  107 +
 doc/src/DimRed/DimRed.do.txt                  |  206 ++
 doc/src/DimRed/clean.sh                       |    3 +
 doc/src/DimRed/make.sh                        |   95 +
 .../.ipynb_checkpoints/mlp-checkpoint.ipynb   | 1311 +++++++
 doc/web/course.do.txt                         |    5 +-
 13 files changed, 5463 insertions(+), 2 deletions(-)
 create mode 100644 "doc/Programs/JupyterFiles/Examples/$N=$100, $\\sigma=$1pred-linear.pdf"
 create mode 100644 "doc/Programs/JupyterFiles/Examples/$N=$100, $\\sigma=$1train-linear.pdf"
 create mode 100644 doc/Programs/JupyterFiles/Examples/.ipynb_checkpoints/CH2 Marsland Gaussian-checkpoint.ipynb
 create mode 100644 doc/Programs/JupyterFiles/Examples/.ipynb_checkpoints/ML for Physicists-checkpoint.ipynb
 create mode 100644 doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Blobs-checkpoint.ipynb
 create mode 100644 doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Boston Housing-checkpoint.ipynb
 create mode 100644 doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Make Moons-checkpoint.ipynb
 create mode 100644 doc/Programs/JupyterFiles/Examples/My Own Examples/.ipynb_checkpoints/Functions in mglearn-checkpoint.ipynb
 create mode 100644 doc/src/DimRed/DimRed.do.txt
 create mode 100755 doc/src/DimRed/clean.sh
 create mode 100755 doc/src/DimRed/make.sh
 create mode 100644 doc/src/NeuralNet/notes/.ipynb_checkpoints/mlp-checkpoint.ipynb

diff --git "a/doc/Programs/JupyterFiles/Examples/$N=$100, $\\sigma=$1pred-linear.pdf" "b/doc/Programs/JupyterFiles/Examples/$N=$100, $\\sigma=$1pred-linear.pdf"
new file mode 100644
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diff --git "a/doc/Programs/JupyterFiles/Examples/$N=$100, $\\sigma=$1train-linear.pdf" "b/doc/Programs/JupyterFiles/Examples/$N=$100, $\\sigma=$1train-linear.pdf"
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HcmV?d00001

diff --git a/doc/Programs/JupyterFiles/Examples/.ipynb_checkpoints/CH2 Marsland Gaussian-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/.ipynb_checkpoints/CH2 Marsland Gaussian-checkpoint.ipynb
new file mode 100644
index 000000000..43341cfe6
--- /dev/null
+++ b/doc/Programs/JupyterFiles/Examples/.ipynb_checkpoints/CH2 Marsland Gaussian-checkpoint.ipynb	
@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qL9UAzAKwEzX7nX4EoI2a89hjjzzDhw+nPn36mNa/nolLrTH24LG0vAW4XC7T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\nSSEV2tHzkPR6vXTjjTdaHv6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SwEoAwwCUAZhKRFskjs0GkA0A6enpl5aUlOgel2GMwi/I\ndQW6rmj7mz+0Y7PZ4HK56ol+cAt+KLRu3RoJCQmSdhQWFiI3NxelpaVIT09HXl4eizpTD8OEXQix\nGkDrEL/KBZAH4GMA9wK4DMAKAOeTQqfssTONlbKyMni9XjRr1qxJ5M8zDQu1wh6rdAARXSUzSA6A\nt84J+WYhhA+AG8ARLcYyTGMhPj5ed/ydYSJFuCUF3gHwawAQQnQC4ABQfxaIYRiGiRiKHrsCiwAs\nEkLsAlABYJJSGIZhGIYxl7CEnYgqAIw3yBaGYRjGALi6I8MwTJTBws4wDBNlsLAzjAoKCwuRmZkJ\nm82GzMxMFBYWWm0Sw0gS7uQpw0Q9dVeGlpSUIDs7GwB4ERHTIGGPnWEUyM3NrbXcHwC8Xi9yc3Mt\nsohh5GFhZxgFpOrJSH3OMFbDws4wCqSnp2v6nGGshoWdYRTIy8uD0+ms9ZnT6UReXp5FFjGMPCzs\nDKPAuHHjkJ+fj4yMDAghkJGRwSV1mQYNb43HMAzTSFBb3ZE9doZhmCiDhZ1hGCbKYGFnGIaJMljY\nGYZhogwWdoZhmCiDhZ1hGCbKYGFnGIaJMljYGYZhooywhF0IsUII8eW5ViyE+NIowximKcH13hkj\nCXfP05v9/xZCzAVwImyLGKaJwfXeGaMxpKSAEEIAKAXwayL6Rul4LinAML+QmZmJkpKSep9nZGSg\nuLg48gYxDZZIlxTIAvCTGlFnGKY2XO+dMRpFYRdCrBZC7ArRrgs6bCyAfyr0ky2E2CqE2HrkyJFw\n7WaYqIHrvTNGoyjsRHQVEXUP0VYCgBAiFsBoACsU+sknoj5E1CctLc0Y6xkmCuB674zRGBGKuQrA\nXiI6ZEBfDNPk4HrvjNGEPXkqhHgVwOdEtFDtOTx5yjAMox21k6dhpTsCABHdEm4fDMMwjHHwylOG\nYZgog4WdYRgmymBhZxiGiTJY2BmGYaIMQ0oKaB5UiCMA6q+hDo0bgMdEc6yEr61xwtfWOImGa8sg\nIsWFQJYIuxaEEFvVpPc0RvjaGid8bY2TaL62unAohmEYJspgYWcYhokyGoOw51ttgInwtTVO+Noa\nJ9F8bbVo8DF2hmEYRhuNwWNnGIZhNNCohF0IMVUIQUIIt9W2GIUQ4m9CiL1CiJ1CiLeFEM2ttilc\nhBDDhBBfCyH2CyEettoeoxBCtBdCrBVC7BFCfCWEuNdqm4xGCBEjhPhCCPF/VttiJEKI5kKIN879\nv7ZHCNHfapvMpNEIuxCiPYChqNmCL5pYBaA7EfUAsA/AIxbbExZCiBgALwAYDqArgLFCiK7WWmUY\nVQD+TEQXAegH4K4oujY/9wLYY7URJjAPwH+IqAuASxCd1xig0Qg7gKcBPAggqiYFiOgjIqo69+Pn\nANpZaY8BXA5gPxEdIKIKAMsBXKdwTqOAiH4gou3n/n0KNeLQ1lqrjEMI0Q7ANQBettoWIxFCpAAY\nBOAVACCiCiL62VqrzKVRCLsQ4rcAvieiHVbbYjJ/APCB1UaESVsAB4N+PoQoEj8/QohMAL0AbLLW\nEkN5BjXOk89qQwzmfABHACw+F2Z6WQiRaLVRZhJ2PXajEEKsBtA6xK9yAUwD8JvIWmQcctcWtMVg\nLmpe9QsjaZsJiBCfRdVblhAiCcCbAP5ERCettscIhBDXAjhMRNuEEL+y2h6DiQXQG8AfiWiTEGIe\ngIcBPGqtWebRYISdiK4K9bkQ4mIAHQDsEEIANaGK7UKIy4noxwiaqBupa/MjhJgE4FoAQ6jx558e\nAtA+6Od2AP5nkS2GI4Swo0bUC4noLavtMZABAH4rhBgBIB5AihCigIjGW2yXERwCcIiI/G9Xb6BG\n2KOWRpfHLoQoBtCHiBp7MR8ANRkkAP4BYDARHbHannA5t7n5PgBDAHwPYAuA3xPRV5YaZgCixrNY\nAuAYEf3JanvM4pzHPpWIrrXaFqMQQhQBuI2IvhZCzASQSEQPWGyWaTQYj70J8zyAOACrzr2RfE5E\nd1prkn6IqEoIcTeADwHEAFgUDaJ+jgEAJgD4rxDiy3OfTSOif1toE6OOPwIoFEI4ABwAcKvF9phK\no/PYGYZhGHkaRVYMwzAMox4WdoZhmCiDhZ1hGCbKYGFnGIaJMljYGYZhogwWdoZhmCiDhZ1hGCbK\nYGFnGIaJMv4fQCACuy40EsoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11438b630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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YDWAb6rNjspXOZ2E3jtWvx2o8cCmP0Ewh9AuU04Js5qEm3i6HVfF0tZw+fZoe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\nYZgo4/8DIxNAsjirPbcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1141e4ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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TAOUUrQQwb968qInYQruEKqV0F3NPt1iN0OPHj0dbWxuyqSce5ReuCUw55fnnnwfweUlA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\nlYqswwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11426ca90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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swh158+alS5cuTlmr0YYvTRYqIM2aNYuDBw8CULJkSVq0aOFyROGrV69eRERY\nu4olS5awceNGlyNSbtBkoQKOMcbrCLZHjx7kzJnTxYjCW+nSpWnevLlTHj9+vIvRKLdoslABZ9my\nZfz0008AxMTE0LVrV5cjUp43uvVZF+FJk4UKOJ7VZR9++GEKFy7sYjQKoE6dOtx2222A9awLz2eg\nq/CQbrIQkRgRaSki40Rktoi8JyLPi8hN2RWgCi87d+5k7ty5Tll7lw0MIuJVySA+Pp6LFy+6GJHK\nbmkmCxEZAiwDagOrgDeAWUAiMEJEvhSRqtkRpAof8fHxTl3+++67j5tvvtnliFSy1q1bU6xYMQD2\n7dvHnDlzXI5IZaf0zixWG2NuM8b0McbMMMYsMcYsMMaMNsY8CLQHorIpThUGTp8+zdSpU52ynlUE\nlqioKK/uVsaMGaPPuggjaSYLY8xnYF2KSjlORAobYw4ZY9akt3ARaSAi20Rku4j0T2X8PSLyk4gk\nikjLFOOSRGSd/Tff97ekgtV7773HiRMnALjxxhtp1KiRyxGplLp160Z0dDQAq1evZuXKlS5HpLKL\nLze4V4vIHckFEWkBLM9oJhGJBOKBhkAVoK2IVEkx2S6gIzAjlUWcNcZUs/+a+BCnCmKXLl3yqpL5\n1FNPOXX7VeAoWrSoVxfx2kgvfPjya2wHvC4io0TkA6AL4MtjymoC240xO4wxF4APgaaeExhj/jDG\n/AxohzNh7osvvmDr1q0A5M+fn06dOrkckUqL5+XBjz/+mN27d7sYjcouGSYLY8wGYBjQHbgX6GWM\n2ePDsksCnt+iPfYwX8WIyBoRWSkizVKbQES62tOsOXz4cCYWrQKNZ3XZzp07ky9fPhejUempWrUq\n9957LwBJSUnEx8e7HJHKDhkmCxF5C3gaqAp0Av4nIj19WHZqz73MzN2wG4wxcVhnNmNF5Ma/LcyY\nKcaYOGNMXJEiRTKxaBVItmzZwqJFiwCrimavXr1cjkhlxLMa7ZQpU5zno6vQ5ctlqI3AvcaY340x\ni4E7gBo+zLcHuN6jXArY52tgxph99v8dwDdAdV/nVcHF815F06ZNufHGvx0XqADTqFEj53M6fvw4\n77//vssRKX/z5TLUGONRP84Yc9IY85gPy14NlBeRsiISBbQBfKrVJCIFRSTafl0YqANs9mVeFVyO\nHj3q9VAd7V02OERGRvLUU0855XHjxumzLkJceo3y/iciD4rI33pwE5FyIjJURDqnNb8xJhHoBSwG\ntgCzjDGb7Pma2Mu5XUT2AK2AN0Rkkz17ZWCNiKwHlgIjjDGaLELQ1KlTOXv2LADVqlXjnnvucTki\n5auOHTs695a2bt3Kl19+6XJaO8fWAAAbj0lEQVREyp8krUY1IlIMeBZoDhwHDgMxQBngN2CCMWZe\n9oSZsbi4OLNmTbrNPlSAuXjxImXLlmXv3r0ATJs2jQ4dOrgclcqMZ555xqk+26BBA304UhASkbX2\n/eH0p8uoBaaIPAn8gJUozgK/GGPOZEmUWUiTRfCZOXMm7dq1A+C6665j586dToMvFRx27NhBbGys\n05J7y5YtVKpUyeWoVGb4mix8ucF9HTAbeAYohpUwlLoqxhjGjBnjlHv06KGJIgiVK1eOpk0vN5/S\nZ12ELl9ucA8EygNvYbW2/lVEhqdWlVUpX61cuZLVq1cDVp9D3bt3dzkidaU8G+m9++67HDt2zMVo\nlL/41J+CXRvqgP2XCBQE5ojIq36MTYUwz24i2rdvT9GiRV2MRl2NunXrcuuttwJw5swZ3nzzTZcj\nUv7gS6O8p0RkLfAqVpfltxhjngBuA/TByCrTdu3axccff+yUtbpscEv5rIsJEyaQmJjoYkTKH3w5\nsygMNDfGPGCMmW2MuQhgjLkENPZrdCokTZgwgaSkJADq1atH1ar6WJRg16ZNG+fscPfu3XzyyScu\nR6Symi/3LF40xuxMY9yWrA9JhbK//vrL65kVelYRGmJiYrzuO2lvtKFH+4BW2crzmRWxsbH6zIoQ\n8sQTT5Azp9WGd/ny5U4FBhUaNFmobHPp0iWvI87evXvrMytCSLFixWjbtq1T9uxJWAU//aWqbPPZ\nZ5/x66+/AlCgQAE6duzobkAqy3lWo/3oo4/Yt8/nvkNVgNNkobLNqFGjnNddunQhb968Lkaj/KFG\njRrcfffdACQmJjJx4kSXI1JZRZOFyharVq3i+++/ByBHjhxeR6AqtHhWWpg8ebLTUaQKbposVLbw\nPKto164dpUqVcjEa5U9NmzalTJkygNUF/YwZM9wNSGUJTRbK77Zv387cuXOd8nPPPediNMrfIiMj\nefLJJ53y2LFjyajDUhX4NFkovxs9erSzs2jQoAG33HKLyxEpf+vcuTN58uQBYOPGjXz99dcuR6Su\nliYL5VeHDx/mnXfeccp9+/Z1MRqVXa655ho6derklF977TUXo1FZQZOF8qv4+HjOnTsHWDVl7r33\nXpcjUtnFsx3NokWLWLduncsRqauhyUL5zZkzZ5gwYYJT7tu3LyLiYkQqO8XGxtKyZUunPHLkSBej\nUVdLk4Xym2nTpnH06FEAypQp47XjUOGhX79+zutZs2bx22+/uRiNuhqaLJRfJCUlMXr0aKf8zDPP\nkCNHDhcjUm6oUaMG999/P2B196L3LoKXJgvlFx9//LFzFFmwYEE6d+7sckTKLf3793dev/POOxw4\ncMDFaNSV0mShspwxhuHDhzvlnj17atceYewf//gHNWvWBOD8+fPafXmQ0mShstxnn33G+vXrAcid\nO7d27RHmRMTr7GLSpEmcPHnSxYjUldBkobKUMYZhw4Y55e7du1O4cGEXI1KBoGnTplSsWBGAU6dO\nMWnSJJcjUpmlyUJlqaVLl7Jy5UoAoqKi6NOnj8sRqUAQERHhVTNq7Nix2sFgkNFkobKU51lFp06d\nKFGihIvRqEDSvn17SpYsCcDBgweZNm2auwGpTNFkobLMypUrnT6AIiMjvY4klUp5pjlixAguXLjg\nYkQqM/yaLESkgYhsE5HtItI/lfH3iMhPIpIoIi1TjOsgIr/afx38GafKGp5nFe3ataNs2bIuRqMC\nUdeuXSlSpAgAu3bt0rOLIOK3ZCEikUA80BCoArQVkSopJtsFdARmpJj3WmAwUAuoCQwWkYL+ilVd\nvfXr17NgwQLAqv3ywgsvuByRCkR58uTx6qJ++PDhenYRJPx5ZlET2G6M2WGMuQB8CDT1nMAY84cx\n5mfgUop5HwC+NMYcM8YcB74EGvgxVnWVXn75Zed18+bNqVy5sovRqEDWo0cPp4bczp07ee+991yO\nSPnCn8miJLDbo7zHHpZl84pIVxFZIyJrDh8+fMWBqquzfv165syZ45QHDBjgYjQq0OXNm9fr7GLY\nsGFcvHjRxYiUL/yZLFLrXtTXx2X5NK8xZooxJs4YE5d8HVRlvyFDhjivH3roIapXr+5eMCoo9OzZ\nk0KFCgHwxx9/6NlFEPBnstgDXO9RLgXsy4Z5VTZau3Ytn376qVP2TBxKpUXPLoKPP5PFaqC8iJQV\nkSigDTDfx3kXA/eLSEH7xvb99jAVYDyTQ6tWrahatap7waig0rNnT6699loAfv/9d6ZPn+5yRCo9\nfksWxphEoBfWTn4LMMsYs0lEhopIEwARuV1E9gCtgDdEZJM97zHgJayEsxoYag9TAeTHH3/0qgE1\nePBglyNSwSRfvnxe7S5efvllPbsIYGKMr7cRAltcXJxZs2aN22GElYYNG7Jo0SIA2rZty4wZMzKY\nQylvp06domzZshw7Zh0LTpo0ie7du7scVXgRkbXGmLiMptMW3OqKrFixwkkUERERvPjiiy5HpIJR\n/vz5vXqkHTp0KGfOnHExIpUWTRYq04wxXo3u2rdvT6VKlVyMSAWzXr16OX2I7d+/n9dff93liFRq\nNFmoTFu0aBHffvstADly5GDQoEEuR6SCWa5cubzud40YMYLjx4+7GJFKjSYLlSlJSUleHQR27dqV\n8uXLuxiRCgWdOnUiNjYWgBMnTvDqq6+6HJFKSZOFypQZM2awYcMGwOrnR88qVFbImTOnV5cx48aN\nY//+/S5GpFLSZKF8du7cOQYOHOiU+/TpQ7FixVyMSIWSVq1aUa1aNQDOnj3LSy+95HJEypMmC+Wz\niRMnsmvXLgCKFCmiT8FTWSoiIoLhw4c75alTp7Jt2zYXI1KeNFkon5w4ccLreRWDBg0if/78Lkak\nQlGDBg2oW7cuAImJiV5dgih3abJQPnnppZechlNly5alW7duLkekQpGIMHr0aESsvkQXLFjAkiVL\nXI5KgSYL5YNt27Yxfvx4p/zKK68QFRXlYkQqlNWoUYMOHS4/HPPZZ58lKSnJxYgUaLJQPujTpw+J\niYkA3HXXXbRu3drliFSoGzZsGHny5AFgw4YNvP322y5HpDRZqHQtWrSIzz77DLAuEYwbN865RKCU\nv5QoUcKrPc/AgQM5deqUixEpTRYqTRcvXuSZZ55xyp07d6ZGjRouRqTCSZ8+fShVqhQAhw4d8qpg\nobKfJguVpkmTJrF161bA6k5af6wqO+XOnZtXXnnFKY8ePZotW7a4GFF402ShUnXw4EGv/noGDRrE\ndddd52JEKhy1a9eOOnXqAFZV2p49exIqj1UINposVKr69OnDiRMnAIiNjeWpp55yOSIVjiIiIpg4\ncSKRkZEALF26lJkzZ7ocVXjSZKH+5quvvuKDDz5wyhMnTiQ6OtrFiFQ4q1q1qtfBSp8+fTh58qSL\nEYUnTRbKy7lz53jiiSecctu2bfnnP//pYkRKWc96T37mxYEDB/RhWy7QZKG8jBw5kl9//RWAAgUK\nMHr0aJcjUsp6ot6YMWOc8oQJE/jpp59cjCj8aLJQjm3btnl15DZ8+HDtVVYFjFatWlG/fn0ALl26\nROfOnbl48aLLUYUPTRYKsB5q1KlTJy5cuADA7bffrv0/qYAiIkyaNIlcuXIBsH79ekaOHOlyVOFD\nk4UCrIfNrFixArAelTplyhSnBopSgSI2NtbrIUlDhw5l06ZNLkYUPjRZKH755RcGDBjglAcOHOg8\nhEapQNO7d29q1aoFWL0MdO7cWTsazAaaLMJcUlISnTt35ty5cwDceuutvPDCCy5HpVTaIiMjeeut\nt5yej3/88UetiJENNFmEuXHjxrFs2TLAuvw0bdo07X5cBbybbrrJ6/nvAwcOZN26dS5GFPo0WYSx\nhIQE+vfv75QHDBigl59U0OjXrx9xcXEAXLhwgXbt2nH27FmXowpdmizC1OnTp2nXrp1T9fC2227j\n3//+t8tRKeW7nDlzMn36dHLnzg3Ali1beP75512OKnT5NVmISAMR2SYi20Wkfyrjo0XkI3v8KhEp\nYw8vIyJnRWSd/TfZn3GGo2effdbpUTZPnjzMmDFDLz+poFOxYsW/Ndb7/PPPXYwodPktWYhIJBAP\nNASqAG1FpEqKyR4DjhtjYoExgGel6d+MMdXsv+7+ijMczZ07lylTpjjl119/nQoVKrgYkVJXrkuX\nLjRt2tQpd+rUiX379rkYUWjy55lFTWC7MWaHMeYC8CHQNMU0TYF37ddzgPtEH8PmVzt27OCxxx5z\nyq1bt6Zjx47uBaTUVRIRpk6d6vQ2cOjQIf7v//5PW3dnMX8mi5LAbo/yHntYqtMYYxKBk0Ahe1xZ\nEUkQkW9F5G4/xhk2zpw5Q/PmzZ2ux2+44QYmT56sj0lVQa9IkSLMmDGDiAhrl/bDDz9oFfAs5s9k\nkdoeKOVTS9KaZj9wgzGmOvAsMENE8v9tBSJdRWSNiKw5fPjwVQccyowxPPHEE6xfvx6AqKgoZs+e\nTcGCBV2OTKmsce+993q17v7vf//L3LlzXYwotPgzWewBrvcolwJSXkh0phGRHEAB4Jgx5rwx5iiA\nMWYt8Bvwt4vqxpgpxpg4Y0xckSJF/PAWQsfkyZN57733nPL48eOpWbOmixEplfX69etH48aNnXKn\nTp3Ytm2bixGFDn8mi9VAeREpKyJRQBtgfopp5gMd7Nctga+NMUZEitg3yBGRckB5YIcfYw1pP/zw\nA71793bKnTp1omvXri5GpJR/RERE8N5771G2bFkATp06RePGjTl69KjLkQU/vyUL+x5EL2AxsAWY\nZYzZJCJDRaSJPdlbQCER2Y51uSm5eu09wM8ish7rxnd3Y8wxf8UayrZv306zZs2cm301atQgPj5e\n71OokFWwYEE+/vhjp3fa7du307JlS6dHZXVlJFQefh4XF2fWrFnjdhgB5ejRo9SuXdt5mFHRokVZ\ntWoVZcqUcTcwpbLB3LlzadGihVN+7LHHmDp1qh4opSAia40xcRlNpy24Q9T58+dp3ry5kyhiYmKY\nP3++JgoVNpo3b+71MK+33nqLUaNGuRhRcNNkEYKSkpLo2LEj3333nTPs/fffd7p1Vipc9O/fn0cf\nfdQp9+vXj7ffftvFiIKXJosQY4yhe/fufPjhh86wESNG0LJlSxejUsodIsKUKVOoW7euM6xLly58\n8sknLkYVnDRZhBBjDH379uXNN990hvXo0UM7V1NhLTo6mnnz5lG9enXAen53mzZt+Prrr12OLLho\nsgghQ4cO5b///a9TfuSRR3j99df1hp4KewUKFGDRokWUL18esLo0b9KkCd9++63LkQUPTRYhwBjD\nwIEDGTJkiDPsoYce4u2333a6P1Aq3BUtWpQvv/ySkiWtXodOnz5Nw4YN+eqrr1yOLDjoniTIGWN4\n9tlnGTZsmDPs/vvvZ+bMmeTIkcPFyJQKPKVLl+arr76iRIkSAJw9e5bGjRuzaNEilyMLfJosglhS\nUhJPPPEEY8eOdYY1atSIefPmER0d7WJkSgWuihUr8u2333L99VZvROfOnaNp06bMnj3b5cgCmyaL\nIHX69GlatGjBG2+84Qxr2bIlc+fOJSYmxsXIlAp8sbGxfPvtt5QuXRqw7mG0bt2a0aNHEyoNlbOa\nJosgtH//furWrcu8efOcYY888ggzZ87Up90p5aOyZcvy3XffUbFiRWdYnz59ePrpp0lKSnIxssCk\nySLIrF+/nlq1arF27VpnWN++fZk2bZreo1Aqk2644QaWLVtGnTp1nGHjx4+nadOmznNflEWTRRCZ\nNm0ad9xxB7t3W8+UioyMZPLkybz66qta60mpK1SoUCGWLFni1XD1s88+Iy4ujp9//tnFyAKL7mGC\nwLlz5+jSpQudOnXi3LlzAOTLl48FCxbQrVs3l6NTKvjFxMTw0UcfeTVg/e2337jjjjv44IMPXIws\ncGiyCHAbNmzgjjvu8GqVXblyZVatWkWDBg1cjEyp0BIREcHIkSOZNWsWefLkAayqtQ8//DDt27cP\n+8tSmiwCVFJSEiNHjuS2225zHoUK0K5dO3788UcqV67sYnRKha5WrVrx448/UqHC5Ydzzpgxg6pV\nq7J06VIXI3OXJosAtHHjRu655x769+/vPLQoJiaG+Ph4pk+fTt68eV2OUKnQVqVKFVavXk3Hjh2d\nYbt376ZevXp069aN48ePuxecSzRZBJA///yTPn36UK1aNZYvX+4Mv/3220lISKBHjx7az5NS2SR/\n/vy88847zJkzh2uvvdYZPmXKFCpVqsT06dPDqk2GJosAkJSUxLvvvkulSpUYPXq0U8c7R44cvPTS\nSyxfvpxKlSq5HKVS4alFixZs3LiRBx980Bl26NAhHnnkEe666y6vA7tQpsnCRcYY5s2bR9WqVenY\nsSP79u1zxtWtW5d169YxcOBAbT+hlMuKFy/OvHnz+OSTTyhVqpQzfPny5dSpU4cWLVqwdetWFyP0\nP00WLkhKSmLOnDnUrFmTZs2asXnzZmdcsWLF+OCDD1i6dCk33XSTi1EqpTyJiPN77dOnDzlz5nTG\nzZ07lypVqtC6dWsSEhJcjNJ/NFlkozNnzjB58mQqVqxIq1atWLNmjTMub968DBkyhF9++YV27drp\nvQmlAlS+fPl47bXX2Lp1K23atHGGG2OYPXs2NWrUoGHDhnzxxRdcunTJxUizloTKDZq4uDjjufMN\nJOvWrWPq1KlMnz6dU6dOeY2Ljo6mR48evPDCCxQpUsSlCJVSV2r16tUMHjyYhQsX/m1cuXLl6Nat\nG506dQrY37eIrDXGxGU4nSYL/9i5cydz5sxh5syZXv04Jbvmmmvo2bMnTz75JNddd50LESqlslJC\nQgIjRoxg9uzZf6sllTNnTh544AHatGlD06ZNA6r6uyaLbGaMYfPmzSxcuJA5c+awatWqVKcrX748\nPXr04LHHHiNfvnzZHKVSyt9+/fVXJk6cyLRp01Jt9Z0rVy4aNWpE48aNadCggesHi5osssH+/fv5\n/vvvWbx4MYsXL2bv3r2pThcdHU2LFi3o0qULdevW1fsRSoWBs2fPMmvWLN544w1WrFiR5nS33XYb\nDRo04J577qF27drZfhCpySKLnTlzhk2bNrFq1SqWL1/OihUr+OOPP9KcPkeOHNSvX5/WrVvTrFkz\nChYs6LfYlFKBbceOHXz00UfMnDmTDRs2pDldREQE1atX56677iIuLo7q1atTsWJFv1af12RxhU6e\nPMlvv/3Gb7/9xqZNm9iwYQMbNmxg+/btGbbWLFCgAPXr1+df//oXTZs2pVChQlcdj1IqtGzevJkF\nCxbw+eef88MPP2T4oKWYmBhuueUWbr31VipWrEiFChWoUKEC5cqVy5KHnWmySMEYw8mTJ9m/fz8H\nDhxg//79zutdu3axY8cOduzYwbFjx3xeZ65cuYiLi+Pee+/lgQceoGbNmtqATinls5MnT7JkyRK+\n+eYbvv/+e37++WefuxCJiIigTJkylC5dmlKlSnn9lSxZkiJFilC4cGFy586d7nICIlmISANgHBAJ\nvGmMGZFifDTwHnAbcBT4P2PMH/a4F4DHgCTgKWPM4vTWdf3115sOHTpw4sQJjh8/zokTJ5y/48eP\nc/z4cedZEFciIiKC8uXLU716dWrXrs2dd97Jrbfe6tUwRymlrsaJEydYsWIFK1asICEhgYSEhDTv\nhfoqJiaGwoULU6hQIed/gQIFyJ8/P/nz52fw4MHuJgsRiQR+Af4J7AFWA22NMZs9pukBVDXGdBeR\nNsBDxpj/E5EqwEygJlACWAJUMMakeb4mIlnyRqKjoylXrhw33ngjFSpU4JZbbqFq1apUrlyZXLly\nZcUqlFLKZ4cPH2bdunVs3ryZX375xfnbtWtXVq3C9WRRGxhijHnALr8AYIx5xWOaxfY0K0QkB3AA\nKAL095zWc7p01pfhG8mdOzfFixenePHiFCtWzPlfsmRJypUrR7ly5ShevLg+olQpFfDOnj3Ljh07\n2Lt3L3v27PH627dvH0eOHOHIkSOcP38+o0X5lCz8eYG9JLDbo7wHqJXWNMaYRBE5CRSyh69MMW/J\nlCsQka5AV7v4F7AtvYDOnDnj3Lz2o8LAEX+uIIjotrhMt8Vlui0uC4RtUdqXifyZLFJrTJDy6D+t\naXyZF2PMFGBK5kPzHxFZ40uWDge6LS7TbXGZbovLgmlb+PN6yx7geo9yKWBfWtPYl6EKAMd8nFcp\npVQ28WeyWA2UF5GyIhIFtAHmp5hmPtDBft0S+NpYN1HmA21EJFpEygLlgR/9GKtSSql0+O0ylH0P\nohewGKvq7NvGmE0iMhRYY4yZD7wFvC8i27HOKNrY824SkVnAZiAR6JleTagAE1CXxVym2+Iy3RaX\n6ba4LGi2Rcg0ylNKKeU/WkdUKaVUhjRZKKWUypAmCz8SkedExIhIYbdjcYuIjBKRrSLys4h8IiLX\nuB1TdhKRBiKyTUS2i0h/t+Nxi4hcLyJLRWSLiGwSkd5ux+Q2EYkUkQQRWeB2LL7QZOEnInI9Vlcn\nWdYmP0h9CdxsjKmK1f3LCy7Hk23sLm/igYZAFaCt3ZVNOEoE+hhjKgN3AD3DeFsk6w1scTsIX2my\n8J8xwPOk0pgwnBhjvjDGJNrFlVhtZsJFTWC7MWaHMeYC8CHQ1OWYXGGM2W+M+cl+/SfWTvJvvTKE\nCxEpBTQC3nQ7Fl9psvADEWkC7DXGrHc7lgDTGfj7U+1DV2pd3oTtDjKZiJQBqgOpP3s4PIzFOpi8\n5HYgvtKHL1whEVkCFEtl1ADg38D92RuRe9LbFsaYefY0A7AuRXyQnbG5zKdua8KJiOQFPgaeNsac\ncjseN4hIY+CQMWatiPzD7Xh8pcniChlj6qc2XERuAcoC6+1nbZcCfhKRmsaYA9kYYrZJa1skE5EO\nQGPgPhNeDXu02xoPIpITK1F8YIyZ63Y8LqoDNBGRfwExQH4RmW6MedjluNKljfL8TET+AOKMMW73\nLOkK+wFYo4G6xpjDbseTnez+zn4B7gP2YnWB084Ys8nVwFwg1pHTu8AxY8zTbscTKOwzi+eMMY3d\njiUjes9C+dsEIB/wpYisE5HJbgeUXewb+8ld3mwBZoVjorDVAR4B6tnfg3X2kbUKEnpmoZRSKkN6\nZqGUUipDmiyUUkplSJOFUkqpDGmyUEoplSFNFkoppTKkyUIppVSGNFkopZTKkCYLpfxERG63n+MR\nIyJ57Oc43Ox2XEpdCW2Up5QficjLWP3/5AL2GGNecTkkpa6IJgul/EhEorD6hDoH3GmMSXI5JKWu\niF6GUsq/rgXyYvWPFeNyLEpdMT2zUMqPRGQ+1hPyygLFjTG9XA5JqSuiz7NQyk9E5FEg0Rgzw34e\n93IRqWeM+drt2JTKLD2zUEoplSG9Z6GUUipDmiyUUkplSJOFUkqpDGmyUEoplSFNFkoppTKkyUIp\npVSGNFkopZTK0P8DQMIgBKGOq28AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11018c0f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "\n",
+    "x = np.arange(-5,5,0.01)\n",
+    "s = 1\n",
+    "mu = 0\n",
+    "y = 1/(np.sqrt(2*np.pi)*s) * np.exp(-0.5*(x-mu)**2/s**2)\n",
+    "pl.plot(x,y,'k')\n",
+    "\n",
+    "pl.close('all')\n",
+    "mu = np.array([2,-3])\n",
+    "s = np.array([1,1])\n",
+    "#s = array([0.5,2])\n",
+    "x = np.random.normal(mu,scale=s,size = (500,2))\n",
+    "pl.plot(x[:,0],x[:,1],'ko')\n",
+    "#axis(array([0,3,-8,4]))\n",
+    "pl.axis('equal')\n",
+    "\n",
+    "theta = np.arange(0,2.1*np.pi,np.pi/20)\n",
+    "\n",
+    "pl.plot(mu[0]+2*np.cos(theta),mu[1]+2*np.sin(theta),'k-')\n",
+    "pl.plot(mu[0]+3*np.cos(theta),mu[1]+3*np.sin(theta),'k-')\n",
+    "\n",
+    "\n",
+    "pl.figure()\n",
+    "\n",
+    "mu = np.array([2,-3])\n",
+    "s = np.array([0.5,2])\n",
+    "x = np.random.normal(mu,scale=s,size = (500,2))\n",
+    "phi = 2*np.pi/3\n",
+    "pl.plot(x[:,0]*np.cos(phi)+x[:,1]*np.sin(phi),x[:,0]*(-np.sin(phi)) + x[:,1]*np.cos(phi),'ko')\n",
+    "pl.axis('equal')\n",
+    "\n",
+    "theta = np.arange(0,2.1*np.pi,np.pi/20)\n",
+    "pl.plot((mu[0]+3*s[0]*np.cos(theta))*np.cos(phi)+(mu[1]+3*s[1]*np.sin(theta))*np.sin(phi), (mu[0]+3*s[0]*np.cos(theta))*np.sin(-phi)+(mu[1]+3*s[1]*np.sin(theta))*np.cos(phi), 'k-')\n",
+    "\n",
+    "pl.figure()\n",
+    "mu = np.array([2,-3])\n",
+    "s = np.array([0.5,2])\n",
+    "x = np.random.normal(mu,scale=s,size = (500,2))\n",
+    "pl.plot(x[:,0],x[:,1],'ko')\n",
+    "pl.axis('equal')\n",
+    "\n",
+    "theta = np.arange(0,2.1*np.pi,np.pi/20)\n",
+    "pl.plot(mu[0]+3*s[0]*np.cos(theta),mu[1]+3*s[1]*np.sin(theta), 'k-')\n",
+    "\n",
+    "pl.show()\n",
+    "\n",
+    "import pylab as pl\n",
+    "import numpy as np\n",
+    "\n",
+    "gaussian = lambda x: 1/(np.sqrt(2*np.pi)*1.5)*np.exp(-(x-0)**2/(2*(1.5**2)))\n",
+    "x = np.arange(-5,5,0.01)\n",
+    "y = gaussian(x)\n",
+    "pl.ion()\n",
+    "pl.plot(x,y,'k',linewidth=3)\n",
+    "pl.xlabel('x')\n",
+    "pl.ylabel('y(x)')\n",
+    "pl.axis([-5,5,0,0.3])\n",
+    "pl.title('Gaussian Function (mean 0, standard deviation 1.5)')\n",
+    "pl.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/Programs/JupyterFiles/Examples/.ipynb_checkpoints/ML for Physicists-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/.ipynb_checkpoints/ML for Physicists-checkpoint.ipynb
new file mode 100644
index 000000000..3858eec43
--- /dev/null
+++ b/doc/Programs/JupyterFiles/Examples/.ipynb_checkpoints/ML for Physicists-checkpoint.ipynb	
@@ -0,0 +1,3111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Notebook 1: Why Is ML Difficult?\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/base.py:509: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
+      "  linalg.lstsq(X, y)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-6, 12)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print (\"Notebook 1: Why Is ML Difficult?\")\n",
+    "#This is Python Notebook to walk through polynomial regression examples\n",
+    "#We will use this to think about regression\n",
+    "\n",
+    "\n",
+    "\n",
+    "import numpy as np\n",
+    "import sklearn as sk\n",
+    "from sklearn import datasets, linear_model\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "from matplotlib import pyplot as plt\n",
+    "#import ml_style as sty  # optional style file, can be commented\n",
+    "#mpl.rcParams.update(sty.style) # optional style file, can be commented\n",
+    "\n",
+    "%matplotlib notebook\n",
+    "\n",
+    "# The Training Data\n",
+    "\n",
+    "N_train=100\n",
+    "\n",
+    "sigma_train=1;\n",
+    "\n",
+    "# Train on integers\n",
+    "x=np.linspace(0.05,0.95,N_train)\n",
+    "# Draw random noise\n",
+    "s = sigma_train*np.random.randn(N_train)\n",
+    "\n",
+    "#linear\n",
+    "y=2*x+s\n",
+    "\n",
+    "#Tenth Order\n",
+    "#y=2*x-10*x**5+15*x**10+s\n",
+    "\n",
+    "p1=plt.plot(x,y, \"o\",ms=15, label='Training')\n",
+    "\n",
+    "#Linear Regression\n",
+    "# Create linear regression object\n",
+    "clf = linear_model.LinearRegression()\n",
+    "\n",
+    "# Train the model using the training sets\n",
+    "clf.fit(x[:, np.newaxis], y)\n",
+    "# The coefficients\n",
+    "\n",
+    "xplot=np.linspace(0.02,0.98,200)\n",
+    "linear_plot=plt.plot(xplot, clf.predict(xplot[:, np.newaxis]),label='Linear')\n",
+    "\n",
+    "#Polynomial Regression\n",
+    "\n",
+    "\n",
+    "poly3 = PolynomialFeatures(degree=3)\n",
+    "X = poly3.fit_transform(x[:,np.newaxis])\n",
+    "clf3 = linear_model.LinearRegression()\n",
+    "clf3.fit(X,y)\n",
+    "\n",
+    "\n",
+    "Xplot=poly3.fit_transform(xplot[:,np.newaxis])\n",
+    "poly3_plot=plt.plot(xplot, clf3.predict(Xplot), label='Poly 3')\n",
+    "\n",
+    "\n",
+    "\n",
+    "#poly5 = PolynomialFeatures(degree=5)\n",
+    "#X = poly5.fit_transform(x[:,np.newaxis])\n",
+    "#clf5 = linear_model.LinearRegression()\n",
+    "#clf5.fit(X,y)\n",
+    "\n",
+    "#Xplot=poly5.fit_transform(xplot[:,np.newaxis])\n",
+    "#plt.plot(xplot, clf5.predict(Xplot), 'r--',linewidth=1)\n",
+    "\n",
+    "poly10 = PolynomialFeatures(degree=10)\n",
+    "X = poly10.fit_transform(x[:,np.newaxis])\n",
+    "clf10 = linear_model.LinearRegression()\n",
+    "clf10.fit(X,y)\n",
+    "\n",
+    "Xplot=poly10.fit_transform(xplot[:,np.newaxis])\n",
+    "poly10_plot=plt.plot(xplot, clf10.predict(Xplot), label='Poly 10')\n",
+    "\n",
+    "axes = plt.gca()\n",
+    "axes.set_ylim([-7,7])\n",
+    "\n",
+    "handles, labels=axes.get_legend_handles_labels()\n",
+    "plt.legend(handles,labels, loc='lower center')\n",
+    "plt.xlabel(\"$x$\")\n",
+    "plt.ylabel(\"$y$\")\n",
+    "Title=\"$N=$\"+str(N_train)+\", $\\sigma=$\"+str(sigma_train)\n",
+    "plt.title(Title+\" (train)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "#Linear Filename\n",
+    "filename_train=Title+\"train-linear.pdf\"\n",
+    "#Tenth Order Filename\n",
+    "#filename_train=Title+\"train-o10.pdf\"\n",
+    "\n",
+    "plt.savefig(filename_train)\n",
+    "\n",
+    "# Generate Test Data\n",
+    "\n",
+    "#Number of test data\n",
+    "N_test=20\n",
+    "\n",
+    "sigma_test=sigma_train\n",
+    "\n",
+    "max_x=1.2\n",
+    "x_test=max_x*np.random.random(N_test)\n",
+    "# Draw random noise\n",
+    "s_test = sigma_test*np.random.randn(N_test)\n",
+    "\n",
+    "#Linear\n",
+    "y_test=2*x_test+s_test\n",
+    "#Tenth order\n",
+    "#y_test=2*x_test-10*x_test**5+15*x_test**10+s_test\n",
+    "\n",
+    "#Make design matrices for prediction\n",
+    "x_plot=np.linspace(0,max_x, 200)\n",
+    "X3 = poly3.fit_transform(x_plot[:,np.newaxis])\n",
+    "X10 = poly10.fit_transform(x_plot[:,np.newaxis])\n",
+    "\n",
+    "%matplotlib notebook\n",
+    "\n",
+    "fig = plt.figure() \n",
+    "p1=plt.plot(x_test,y_test.transpose(), 'o', ms=12, label='data')\n",
+    "p2=plt.plot(x_plot,clf.predict(x_plot[:,np.newaxis]), label='linear')\n",
+    "p3=plt.plot(x_plot,clf3.predict(X3), label='3rd order')\n",
+    "p10=plt.plot(x_plot,clf10.predict(X10), label='10th order')\n",
+    "\n",
+    "\n",
+    "plt.legend(loc=2)\n",
+    "plt.xlabel('$x$')\n",
+    "plt.ylabel('$y$')\n",
+    "plt.legend(loc='best')\n",
+    "plt.title(Title+\" (pred.)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "#Linear Filename\n",
+    "filename_test=Title+\"pred-linear.pdf\"\n",
+    "#Tenth Order Filename\n",
+    "#filename_test=Title+\"pred-o10.pdf\"\n",
+    "plt.savefig(filename_test)\n",
+    "plt.ylim((-6,12))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Notebook 2: Gradient Descent\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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OfY1wP+TaJYyykiJc7SBp8NwVXRBg14nUrssIgwOgpEAKyPRoEIkMQn61RZrq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IEuvEnGIY2M6dHxYazJcRszQM1iCEk6YH+68BqzNeGtQQoXgY0mbSSjGdI/vqEh5XE3OH8fOprw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\n/MQnPsE999zD3Llzueeee+js7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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10329bfd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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f+tSn+PDDD/n444+5fv06v/u7v7vv4//qr/4qf/u3f8vo6ChXrlw5wDPfiueC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\nmlU7Ki8kcgmjLf42nLaQMHVBFpYZ8P4Ia8O2wnCJ0VLm3PgW7/q0VUW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ybmvO64rGJ9R+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y9D+cKjyLT8C01ILJIbfroALU7GliXp7wczahHxedD0nLikTeJVIvHGngOBIDk6MtECffHFF4/o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\nt771La677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LkCybpHhwxZlUUWx8Yrr7zCV77yFUzT5NZbb+X+++8f6iVlMUTIukcHBVlC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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x105319278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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1uPNbnpIk2SHpRjGMhjC+cYtEztuZRNGNPPSLTyMVJll6AYkXDkM7GfTL/+HV\nTcZ94foKPfdKhcXpUBLynCBNx+LBCKPqqV0XCmsiy3JLRNeOLJPj3JKdrgLiFHQppVeTdzjLK5wm\nKeMxnCyPoN/5CMfKcB3wKnnQ0LbA3+5RVbg+xuMxKi/keDxGlUjsj8fKh9j3ffXyQFgure8HqMKb\n/d6IZJgRhhDHGaNx7oMfX/96gnhd98A3f/obVzTTcas6kZBfIfUb0MROB9MfeMMA3XOIBoG4PyOT\nIxUn620foHRXnA4lIc8JRQELd6nNUEHIBTEY0I5eMNPIDaM4esY2BTAsgwOlwXFkBVNZzTJB6Lt3\nWtdtGqeM45yo/eAYYd+6gablQSrHgWo1Lx/k0UPbTrEsMC2wLA3TzKjVaqhsr2qtphCHFSyvSutX\nGacZcZQRxfm1jsKMLB4wup+QZTAeQRjmJJym3xxuW6vCUbb7JPGKCTAFJU7y7Ek1gRdwq+juOo1y\n3LaPFB3JQM74fBZUcRQoJW+nRUnIc0KRBWA2a8rl0hQToLJQYbCdm0JxAge1Gy0rf3FUM/JCfhdS\npFVujAO4LgRnKKshks2JZVmWuz7CMA/mDfrAk+ka0I4LwQd/faidNgwN3QTb0tDufUk2iNA0Jv68\n0X3GD6LDzLWDP/3eV+hBxHiUkcSQJBlpAslffECWZieLrwEPDq+FLykKZwiMHUAv8Ml6dYNgLLgl\nhG2KAnfSICxHR9dSNC2/B8dh1xzCWYlZ0wi2y+am3wYlIc8JlUvr1K5cYnBzugynLnRQKGhphx6r\n3/4oygnHMDUcKyNJ4KBmUJE7Q+wCkWZounrbIn+11awRbKoDlhIhnzX+pgFkR/K8A4IJrIx4V00A\nDa1L76nipHY61Ooag/5UfnHhGM9aIF/an+5ZJEN1db6iUqNSFxnD0kkEffJxWGZuDYcRREFKaqjH\naBEqZyQgn5OztECwtaNY3qb+0uVnjq3E6WYzJU4Bb22Zwc17LP7wTWonHr5MIMNCpVsBeRkGJPt+\nyyDMX7JKRa4HBM9QYAifZenlB7D1gspy33UJze9shwU4gxUMcmajbZ4tGCCpJQypSBT50CuVPMga\n7atlDvTN4gdDiDmA7BqxFxoT/+2uLbH802tEwzF2q6HeqMQESgt5TnBXl9Ati51ffAKA99oLJH5A\neOuB2FUapuqTH6JI/eA0XOLdIws6ivM/0wS3bufWTZhH+Q9RwMiGoRErWKVQgldAUFbVIu4p/NVn\nJdA5E3zRdrMXPSpG3mNveqeSOu5ZSAVC1q3JEZomuB6kmX4YcJ0e3NnqWEgzO2u/uWnlwhrV5y6w\n/dcf4//8g/1lpQ/5NCgJeU6zECMDAAAgAElEQVTQNA13fZnRvbx4/Pj6LQDqr78IrrrQOMiEXKS/\nNxTBOchfLr8fckDVB/IzodXeIXSBNJJTCU8V+9MF0nCtfK6swJkyeL8DRj6Ly8JaqB3pF09AzJCT\nphEFY4A8kUS9Px3LBtfJ4wvjMblPnhTPFcZtHaVjT+9QHoNEyGajxvJP3mXrrz5i9ODJxG+VC2vy\nDksconRZzBEqK6D/2Q3CJzuYl9dpvf3y1O/STLMo4Ce9zCetnTCEfn8/GKVBo6VTa+hT1plkrUlk\n8iyILouCr4zdrsj7cwVG+R5hteTxaan8xZMs2rMScnrCT1ypaTTaOpatE+3f75PWsCZcP9MqeP0L\nxnCSkOvPrbP4wzfJkpStv/jV9AqaVgb1TonSQp4jJGlP3B8SP9ykc+cxdsXC1iNGo5xAJb+v2CqJ\nYmvSqphEo+lwexqn9Dv5y6xpUKtr6IZGkFjouppQ5Kg9hS+sJvgfCwm+ICJpOoJVaOriVMKoOIDa\narWaHqga0BbkT2hii9JnEOgZXALS/gwzLzrkuRloGaNB/gcZzdWCHQofDMOc/aNgWUczrmoVUsOh\n/81j+OYxy++/q9zGXVlELyzCUeIAJSHPEZ4wLUs6R4XJw1FECOimRq0CEgMIiWbPhOkIhHyMDLOM\nfZVBBgR4SzrNRYMshfEwJQrzdYtcFoUZfsJHpihIWAipYlxhdbMi8leTa6E2uIBBpfPSDXmmc9r9\n6QZU60Yua0sy+nsJfZUarYCP40gaX1HgTgoe5m6wIDYYDhOOlwiVOlGXSSGnR0nIc4RkIWfDMZpj\nTzQZTeOMwQAa5+pUsgGaaTLsHnugM1lbWkREpi0VPJenp1mm0d05Yg6vouEuNUiHQ8bDhNCfPl5x\nTY3ZLeRCgp+1QtEZIcn/ngVRqWCqS14eHEu5jZHrg9uXaoSdPsNeSr+T3xuvWhCYrXqAOoUvDtUn\nVRRYPPncuZ6G5VlopklvcwSKGUMk9IIsA3qnR0nIc0RR4MJaaBI+ni6/aSQ+vWEGRLgVHduCwSAl\nTfJIuYqQi9QPuqUm5Dg4vck9HmWM7+UZV5aTp+u6ng46RKnNaGdUrHkWeKPIL34mcp1zUE8rqPlw\nJheDoF45Dq/p4lhxfm39jGEvYdxPGA+nM96k4GsO9cXVTU30Yxda1XEGGtQaOlkKw36KPw5ZWJe3\nCXd7yuVlQO/0KAl5jvCEAkMAVquuJOTjulJ/lOKTW0n1pgYZBArrVHzBAMMUNM++TMhmuwE76iJI\nuqHhD1P84QEDx+gGmDYsrNlkWUY4ThkN0qOpdsGLfpYkFLFU8XdgIcs/yj/JLovJjdyqjls10A0N\nwwTL1Rh3fU4q0iRXkdGsQU9NepmwjeWZBP1nyGxObmOBV9PwRzDoTt4sucWgJhbSKtOmT4+SkOeI\nIkvArKqj9Cr/XppAv5vRWPGoWmBpIf1OcmgtFwX8JPO0yELWUjlF1lBYZWkCwShj0Dl60TVDo9Yy\nsT0d29FpLlsEoxR/lExsrhvFkj71AGf3IZ8FWkGt4SKc/MDYro5bM7ArOl7DIA6y3MIcJocftoU1\nm0jxsYUCWZkh38OT6ovDbRxDJuRjh9c0qLfyoimjQURvTz026eo4i22C7V3lb1JspcQ0SkKeI7xz\nK2JkyLDVUeYokNnJcjJ693NVsW5Ac9EgiWE8lOf+cucLMB2NOJjNH2w2qrmW6gRO+kazJGPQiaED\ni+cculs5Ceimhlc3sF0D3dTQsowwSIkiA7/nH5JZUcCvKJX4LBB9vjNkfzh1B9tJMW0d09GxPZ0o\nSBkPM8JxTOin1BZMBruzR2elbLwiCz4R/MSGEFOAPHDnVnTcqs6wF9Pby4umVBdM8QMuWeL2QlMk\n5NJCPj1KQp4jDNum9f679O88ILk3KYyXeqdFCoI83OYYQaQJh4E3t6LRvNhitNk95krYR2HATycO\npsm8KNhmmELdWwwkv+Xx6X0aZwz3Yob7pdPdqr4/5v0ed1Udu2JiVwwWL7porkfqj0mTjCTKiIM0\n9+0qjNd5uyx0S8OpGViOjmnr6KaG7lXJ/GFu9a/ahOMUfxAT9AOC/e+U1zAY96avhV6kNTZtUFSL\n0C2NVFBFFGmX41DyIU8Tsmlr1FerZP44d5ON0hO/y8wvKTasRnVqmXduhdqVDbwyqHdqlIQ8Z0S7\nXZJ7T2i/8xpZktD5OG9wWlRJyHa1mZQM/igj2x4RDFPqbQPLMejvRURBVhhsy180BSEXaZ4FaVQm\nSMeehZOkEgxTgmGIrtl0N0NguqiS1zRz1YmtYVoahpWTpWFqaI6HFgeg738H9v/HqtosbnhHlneW\n83lmuVhaQGPZJo5TkjgjCVPiKL92wSAhGBw/t3w8S5dcuk/VaW2itK2otoiQ62I6OmE0+7WNx2q3\nxEFRKd3QaC6apEBvK2Lv/gCv7aJ6Hor0yXEo+LePFdCqbpyjcnGd7b/6mPGjTaoXCyKBJSZQEvKc\nYa4uwGew9+F1APTL69RaTVKhkwKA7RqEiqBbkeVq1WyCfkB/76g2Z2PJxKqYmLamfHF0ITOrqEqY\nZJXFBa4WrVmDh2q/tLS/01i7SZiRhBmTtYLV13XR8di5qyrg4NNYteltTW9XqA0uGJd0n/RmDTbV\ngS5p6m/aBqE085AGoaktZE3Pg3rtVYveTsTe5iRpR2P1tSuSSIa+kKRjGNSubOAuL7D91x8zvJuX\nELAXWpgVT9xfiUmUqdNzhr402TcsvfOY3kdfgqFjvfa8kpCkamuJMD0EMN3pqWhvO2a0OyaJM5qr\nNgvnHWzv6BYbgrWbCFYPyFaeNK0G0IRaGwB6Vf1ynkmHPGcUqiwKkAkb6mcIwp0sEjRxHOEamY52\nSNa6qdFed2ifdzBMjThI2HsaTcknNQNigVwl6Ibav9187QpG1WVw8y7bf/nrCV1mqUGeDaWFPG8s\nqssM9r/6hrg/xG071JY9Onf7h/5cqVaxJOiHgw4g00iyfOp9fHrdWLaxmlX0RF1jueg4RTBcnUTx\nUhcF6HRLsP4cB1VHk/xAFipXBlCY7ixCWt8wQNkmFLRGXRxDKrgYiixuyao2CupLSFa145k0ztdJ\nRwN6WyF7j49mJ5qrDibbNYegK6hrBOK3PR1/sJ9+r0P7+TbRcET3+k0MwQqulO6KmVAS8pyRLagJ\nOe4PwXPw9wL8vQDD0Vi4Ume0FaEJ1dFCv0BNIZH4aJrUelshbIUsXfaoLJhUGhaRn9LbCsiSZ7gf\nCgS4piURsrhJQWEk+Vz1uvyYarqmJipHrrCH56FydRiKWcfhcbQia1dKJikgZCmdueCNPH6Jaks2\nbt0gHCWkacbuLbXCIRU0hpZnioQsnY/l6MSRRuNSndHWkN2bR+4YqZde5WIpeZsFpctizjBWFuTf\nWvXDfydBxu7NPn7Px1qp0Lw03SI9DjIxqCbxZCR0oQCgWme0G7N9Z0z3SYBuaLSfa7Kw4eG11UxQ\nWC9CiMYXWshSkPCMleUkrZpWEJgS1SGFFdgEAi1wMRSWsCwoo6mCVTFw6gaLV5dwagaD7ZDtb8b0\nnoaYtWmFwwEk6930ZOZXKSkqSxWqF9ugpeze6OB3Jp8zMSmk1CDPhNJCniOSJMFaXRR/12uVaSrI\nIOoHdO+NqK552A2X3p3uoa7UrloEvWlrLhHajWRphlnRiUfTL7x2oqpbEmbsfdPdP46B2zSpLtqQ\nwWA3JuiGhcE2aXotFabJxyAQ8pkLD6kXJ0X9saRdnaU3nqWLpFcESTd8wMemZ1JftjBsHb8XMdgM\n6T8NGe1OV7DTXfnahSN14C4rKCx0MGPSNGhsNEFP6d7uU1nQiVXJLKZBsKNupVW6LGZDSchzxHg8\npnZxLX+rVAUnhOSQZP8FGD4ZM3wyxnB1Fl9ZxN8colmmkpCLug/bFZNY8SIWqTbMisNoa4TfPZqa\nVxZsLE9n8UoVvxMx3Akns+4EK7TI2hVdFkXZhwVMqenqtBG9oHKOaPWfxUIussSLLOQThOw2TCqL\nNnbNpL7u0H8SsHdv0k0SJ+od6kK6PEAiuL2kIlT5DnUWX1li+LRP985RTQ3pNrgri/iPNpW/lUG9\n2VAS8hzh+z5erYa12CLamvbpaQqRPkA4nnxpEj9l54u8WeTqtQs4LYfO7b2Jl7jIKlMpMKC4QLxZ\nmX6pR7shds2gcy+Xj5meTm2tjuWmeYReKiJUSK7qxYUui6LdSS6LwmpAwr6KLGTh0hWRoTS9MByN\n2oqLXTNJMofh4w5+N8bvxSxfrdF/rPbtSlrjonFLLiz9pDJEg9blJkbNY/fzJ4w729OnI8xinIWW\nTMilhTwTSkKeI3zfx3VdWu+/Q+fPfkW0NelXy4QybWFfDhglYcTOlzsYnsHiq8tEnSG9RyNlxt0B\nDEdQYBSoKXRhm9R0Yb/8TTxO6XxzZDEtvFDFbVlUFm0MzyUNA8Z7UXGiiVSXorDAxez7K4JY83je\nFnIGTtOisuRg1qqkoyF+NyIYRHTvH2ikT6SlV+qoCutrBqTi/RMsZ1sjFTL4Dk7Ha9tUzzUZPOnT\n+aaLtxJL5aJFeaRZm1ZYGBWXhWtvlEG9GVES8hxxQMhZEBLt9ai9+yrBoy2i/SpvmdDcLokyzIpJ\nrCgsf0ASyThh5/N8P9X1GpXVBvHIn3AxHEAiCWn6CnJ2VlrQkE8zdPxOtB/gOYqyV1ZsGhc97KqJ\nZudEHfQiRtvBGa3qIrfAWUTKs5ejlJQjuqGj6eAtObhNG6NahXCUz3p0jaAbEXQjjpOs2zSJJZNb\nkN1ZVYtQ0Tg2H5ygiqiYBIoehqajY9Ucmi+06d7aY7x3VIXQqtmMN9WKiVh4fvRjrjirWaf91st0\nPv2a3he3MH8D2m/9/wklIc8Rvu/TbDbzaVqcMPjV56BreG++hDEKGAuZUQB2w1ESsoo8ho8HjLdH\npElK68oCppHQvTcgGh/omoXsuiIZnTD1LiJxrVZhysIjt6RHmwfneux3DQxbp3m5ilnz0LWELMtI\ngoRolBB0QqKhsiK/PIZ58rFgIRuOjukZtF6oY7oGmqGRaQ7xYIBuaww3YbQZMNoMOH6+i1fr6v1V\nHVB8SEF2C5ieKRKyFBuwKwZB56jIU2ujiuZ47N3aZdwL6N6aVkaYrkwJgVDUStM0nKU2zdeusPvh\n52z+6S8BWHj3NXFfJdQoCXmO8H0fz/Oobpw7WphmjD/5GgDj1eepX1mif3PaP2dVbI5bmYebC5Zj\nGqWYVZPOzdxXrZka7ZcW0fUMTVe/OEqy24eYIl1QR1nM4hOCT2R5hlr3zhDVuUJ+Hk7NwqybmK6B\nYRnYdYvld9YhCcjS3PWTJRlplKI5LvFgTJZkZOn+X5Jh2AbekotmaGh6fn6arqGZNk49tzh1U9v/\nXQPTw7ICjNdbpGFK7CeEo5iwH5MECWE/ovvNtCuhuVERp/iZpf5gaQUBNcmXXqSRlrL+DMeg9UIb\nw9Hp3e+ze2vAgaWeCP5osTqcDqHi+amsVTHrNaLB6JCID3+7dG5q/RLFKAl5jhiPx7iuS23jvPL3\n5Ms79ElpbNSwaha7X3YOX0BT8vsW1DG26zbx/kuSxRl7X+eBwJV3z7PwiomWhvTuj4hGOWMkYYpm\nQjZDRchkXCDpEszTojHj1IHpbhjA/tgy/E4InaPZxPKbbbY+UQeN7FZE2JnOoKteSBhvqzLrhlTX\nPYaPp+tcLLzcZPdL9dgQLFfNqyK1TpKSXYyCamqJEKwt2ua4b1k3NZobVYyKg+a4bH3wULlNOBBm\na8I9dZsu/t7R9Wy+0MBwDHa/2MO+/4jUnw5EVi+VAb1ZURLyHJEkCaZp0njugnqFNMVeqNC7m1sp\n7qJD/UKNzs0u+rFqWcchRdYh9xGqxxGyu6/S0AyN9ostDMegfz/vNhF0T99BIo1TNEsjUyQLSHWK\nsyRDMzUylXVvFgQWLZNE1d1VK8jUO0tQT4yNFVihhoPKqteKak8IWvEiZUYqJIxIrbkgd1ksvrYM\nWUz33pC9fUt4+V1ZchZ1hVR0yR/d9Ah6AQuvtIkGId1bR51LxNZNpYU8M0pCnhN2dnbw/fwhbzx/\nSVzPbLiEu7lF5e8E+DsBhmdgNR2q52oMH01Oi8O+7Hc2hGyr4xZTlmTs3dgX7Wuw9OYyjSxP+ujd\nGx76U4sSMyzPIoymx1GYNOKaxAMF8Rc1O7F0ElWBNrcgDVrSGxsF25gWTDVOAs1TfxQBMoHFCzs3\nS+RaRMhSwsiJoGtl1aW64hFHMNzy6d+fbg8m6arNmkk8UM9iVO4Pu+lQu9wiGozY+Wxazulv7ij3\nVVrIs6Mk5O8A3soiOBYo2qIblWmiSMYJwd6Y4aMBrZfbGKbO7hd7ZElK1A/FAjrSi51Ift8MonFC\n9+s8mGM3HeoXa6BlYoEjAMMzQGUEFZC44RgzE7Kk0y6CWCqygCjFRqZFkmKpROkZCFlKjwbZZ68Z\nOu2XW5iex/Bxj9GTIaOnuQEgBWQlOHVHJOTjgd/mlRZWzWTn+g5RL8TfnnZL2A2HsKfWTZcW8uwo\nCXlOSJJkYvpsLLdJHkz7PWU1Q05enS9zsnQXXOrPNejfHZKlKcHutEUnWWdF9SyOa5TDbsDOfoGZ\n1ivL1C7V8BYckiChf39ItE+opiPUuSiqWSEFh4pqY0iZf0VZ1WdyWcyeGSLppAsLCAkaYLE+CUeq\nFt029mMNJmE/RrNc9n79AJhMUTYqJolSnSOP2azKr32awtLby/h7Pt2bR8eSxmy3PZGQSwt5dpSE\nPCecrJ3gvv0iievg37w/uaLwAp90Tfi7Pv6uD4bG6u+cJ+qG7H2+PWGpSckKUYGbQyLKNEkZ3Bsw\nuLfvMtGgfrmOu+Bi2AbhICTYDU5sU0Su0nFm36ZQ9naW8lhnKJshKRkK06Ol5B3FNmbFpH6xjlWz\nCPshvTt9OjeOAozLP1ZXEbTqtkjIkoWuCiA3XlzEadt0b3Xp354OUoq99GrTMz7v/Crt157DXV1S\nblNCRknIc0IYTpKgaWhkD++z8t7LdPtjgi/vArKMLeyonKdAkhHu+ex9to2z6NF4ocnosc/wfkd8\nSeJRLAbVRAt9fOKlzqB/p0//Tp/26+sEuwHeskdlvYZu64SdoJAoRVVAoeP5DKLiM2Xqzd6zThmg\nfMY2iWQh6xq1i3XcZQ9Nsxhvdhk8GNC92ycVXBZSlqdVsfEFlYekIT/oHOO0PRovtRk/GdC7sYPu\nGKTCR0RSzhxv3VR7/iL1S8v0f/0JyaZ3poDrbztKQp4TfN+fKGij75fhHHz2JQZQubKGt7DMeE8t\n34pHEWbVPJSxHceBmyHYGbO1kxN346VFnNU2VmNI1JuOmDsNJ7ewT0B6ReJhgVW933VkvDVmvHX0\n4WhcaVG73MBdcNEMk2gYMHo4IOwGaEIluKKaFZIfu7Aj9Sytog9whspyqaCYKELixxiuQfViA6dV\ngywk7AVkZAzu9xncn9QoWzWbQCJk4YMgBXZB7brSLQNraYGFt132Pn3I1l8/OvzNabuMnwhZesLz\nodkGzVdfoLJQo/fRJ/Sf5jPCykW19LNEMUpCnhN830fTNLIsQ9M09JNlOB8+YfzwCfbzKzR+tMbW\nR1tTU1qn5SgJWWXV9r7egTTDJmThvTXSzGT3syeHAT2zZoGCkCUqiiRdKnIQKk11Bnc6DO5MRvzc\nZY/KagW7boNmEA0C/K0R/taosGaFWMN4zt2lz9KtujC1G7AaDpX1Klajhq5HpHHC6OmY0cM+vRt7\nwFFW3PKPLyv3YXoWQg8PsZiUVLcEju6ppmu0X1vDaRiMbm2Rjsfs/vrB1Pp2wxEJOTg5g9tX7Hh1\ng9Ent+jfnfzYVy6VhHwWlIQ8J/i+j2VZh1pkltWF6sO727j1jLWLDtraCluf7xHsE6dVd1FmsAlc\nEHZ99Cil/9kTALyKSe2tC0QhpEGEKktMslDTMEGzdTKF7EoK6EhqDn9rTNjX2fnwycRyo2Ji1myW\n311Btx2yLCUNE8J+SLAzloNd806dPkNQz3QMvNUqdsPB9Ew0wyYLR1hVE3fRJdzz6d8IgFwWZjW8\naRLbh2TxF1m7kstAVFjoUD2/gPeDCv43jwlvPz7skSIVxzcr6vKwmqER7gd/Dcdg5Z0V7PGIeHOb\n2FaP2ROSo0oUoyTkOcH3fRzHIQzDfUJuq1dMUsxWhWRvDDefsFDVsV5bo/MwwqypdbBScCbs+Bwv\nPZCOY3of55bPyu9uUFu8hD+GveuPD1s7FZXttOsOwY5Cnytl5BWlVSsSJpJRTNBJ8L9Ru23cyzU4\nX8Ws2RgVC9020AwDs26z+juXyZKINE5Jo4QkyP+sZpU0jEiTjCxOyZKUNMk7rRiuiWbqeQEgQ0Mz\ndTTDxGnbuEsVdNvAsPX9dRzsts7aT85DmpGFCYkfEw8j4u6QcOQTDn3CE4lvCz+6Qrg3PRPRC1Kd\nJX+wLqhZQOHj38dx2Z9maLRfXaO67KIN+uxef0L/3vQ2sbAvqSu5s1SFBJZfbcCTXdK7Tw5LIIUd\ntf+6UhLymVAS8pyQZRmO4xAdVEdrN9AskyyafvjNupMTMkCUEt14ShWov3kZQ19n88OnE2oK6WVM\noxSzZRMr3A1alhF8cQ8NWGwb2G+fJ8Im8mVJnFmxlIQsBcESoRsFyPrgrKiTh+4Q7mwR7kzOElpv\nnmN4/ZFyE9MzMAZDJuhPA9fO8Iw4n13E+3/7/gBNc4i7AQmTbVWd9zbofahINTY0cZaSCf0QdUdt\nbYL8USzKxhN9uIbF8rVLuHWT5P5j0s3HRJtgnxMMAlA+L6D2x7dfXWb5pTrDD++S3py8LxkZwdZ0\nfQ8oXRZnRUnIc0CapmiahmVZh2oLXdexN84R3Jw2UaROwGYS0+o8ofVWBb+5wOZnu4yfDomGBd2n\nG67yBcuOWbVZmBB8nRNNdWOBxk/WiN0avQc9eje2DqfQhieQiHD4xI/zbDDVFFwqsykoD6AgDVkv\naD5qCI+wLhNihrA/W53dp9smKMpYAqL7QyzSwxkJ+ViSTfVik9bzSzh6hF3X6fz1nal+3boiAekA\noSIIDJCRXzOzarF2bY16NkTf2sZMbYYK2Z/Z9Ei7Cn28bZUuizOiJOQ5IAgCHMfBsiyiKCKOY0zT\n5OKP1wlfXufeL24Qbx5LOTUEje6BC6A3wu2NuNiG7PUL9EYawwcdpS/VqjnKQJBUryEZBGibe+g8\npQW0X6mgra0y9jNiRWYhyJ2LIX95Y5XuWXBzFLlMpODhmVQWBdlwkk9aOkpOlOprI41NF3yrUJAw\nIqU6123aV1epLrjouztk23twt0cGZK+o0/R1oV1YpqEsxgRg1S2u/MEL2E/vo20eC/oJz6vR8Die\ngm5UK6z/7hu0sk3ctRXlNiWKURLyHHBQdtO2bcIwPPQnZ60W1ev/D1cv6my+/BLjB2MGt++ToLZe\nomE4cUO0FLT7D2kB535/kZ5R5/EnO4weHqkaNMHvKCUyxD0f89gmWX9E1v8GB1h77zJL3hJRtcWw\nG7J3Y4dgeyinDQOmqyZkKT5WaCGfpQHqGWRv0v6kPYkJK0X7KuxzJ/je98/FrFi0X16jtuziRkPM\nYMDg4QPYnv5opKGwL2HMVtMjG4yP/bfL+rV12s4Ykx7+N9M1MaTLr9drwC72Qou1H12lPryNvvsx\n1sbzpQb5jCgJeQ44KLtp2zaj0eiQoKP2EjagZylrgztkLRj9wYsEaaK0t6KOL94QS09pb92jtQ7h\nm+fYHVo8+eDRfhWyaUhR+SxK0Oo2maqKnKGjbe1gb+1gA+0lSK4uES81sCsW/fsdhvcmy1MagvtF\nMjeTIEakKpHFZUJOJf92ka9aCKqJhesLXAmSjK+wgNCJe+MuV6k/16ax3qD9z53D3HqKNn4A+94u\nfV2t2AG5rrH0odJrFdB9Ft86z+p5k8rOY7T+fehDaE+3YgI5wcWomFz+l16m1nmMtv3x4XJzvXRX\nnBUlIc8BB62bDnzIvu9TqVTot9pUj62nAdWtu7TOL7DyL1xibydh79ePDgkn6oxxGxqagoC0igt0\n0QBn6ynrwNpbNvHVOjv6BfY+fjRBDsmoIHhXc4lUL7IiD9nY2aFSN2kOnrLehvRclai9wjg1GeyO\nicYx3J/elRQITINYNGplC7mgZKc0nS4oliSNTaL9QkIWZg/SNkbFor7RYvUH61TsDHuwi9nrQDTG\n8GoMnzye3ldBtbtkqFYuq06x9uIKK2+ucvliitF7Ak+Pra9BLCgmTnayqb20Qvv5Ol5Vx//0ztT6\n1rmL4nhLFKMk5DnA930WFxexbZsoig4t5O1GS7l+vDfA9casGLD401X6qcf2rx6RDAOMVpN0V1Ek\nXeGa0MKQpulTc3a48HtthpVFdr7p07n+hGgQizdXrzgoNcrC+qkfHobB9PEIZ3wHB2gB7usbBKsL\nhNUWfmox6gb0H6rr40LuQ84c0FQHExugnkWHXDBlnjFTr6g6m+Rf13QN71yDxqUW1QUPz8qwgx5m\nb5c0eAzb02MQPzyCPxgg7qv9wWmcj7lyaZGl15doaAOsbgejFjLsTd97vVEBxXKAuBeCodN+5zzt\nBR2r14XuDqmpVnKY60I98BLPREnIc8CBhWyaJmEYEgQBzWaTeEFdXCUbhdBqQBBhjEa0GNF4q8ao\neomoP8ZXEbIUCNwPkhmjEY3RiEYd4t9fYegtMnpYYfDltFxMc9QWl+QPTUeBpEsAw8Do9/D6PTyg\nDbAE5kurXFy4QGhWCBKd8SBktD1mcK+Dnkbqmg0ShxaXeyv4TdidSMiz+4M1XaNxdYXqahWv4eDY\nGnYyxlttEacPgfFBrkgO2wJBxigWtLeE19TQSAbTFrJzfoHm5RarKxs4e9vQOxagE75getVD9ZHW\n6y7tl1epZjXMUX+iDGsyUCe+WOdKl8VZURLyHHBAyLquH1rIruuSuR6tP/jn2fmnv8DoT1qNWs0j\nO6Zq0KOIWucp7huXCBAJiJ4AACAASURBVC9W6T316X9yH/YtsCwT0pdHwRQlmf0ezX6PpfUq6cXz\njJwm3QdDep89IIsTNEEjKxFCMgoQZ+2m+hHSsgy7s4UN1A4WNoE3QF9ZI8IgtlyiVCcMU4JhhNn0\nqF5eIuwM844W+0RcmG59piankqh48j+NqoOzUKN6sYW3WsOp2diOjq1nmEmIGQwwGhbpwx2gN0FW\n+npTPV7XAcGqVWnW852p771R92CQa4OrL56j+UKbij7G7u2R6V3ivenejdKpayc61ngvrlF/bgFX\nCwjuP1Vuk+xN6pI1x6X69g+wLz+nPkiJZ6Ik5DkgDEPsfQ3rQWLIAUFXmin2q3W244vojzvEj/YT\nDyo12FFM7TUDp7/FcgUWfu8iw6xC55MHCO3ZSIe+aL3qlQpap0d9NKRehfT9ZYbVZWLDJbyzRdKf\n9BmKCog4gYq64L7kEJbIHUAnwenucjIcaV58jXP9MWzoZFqNxK2Q2B5ZvUF6bZUUSNKMNM1rSyRx\nilGpUln1IM314Ow3OnVWaug/eu6ouamhoWkamm6iWxmGqWEYGrqhoWugo2G1K5xbegE9DjD8EXoS\nASHaRZPk9sO8dd4JN2tWma3NvTQ7AcgExUSmuMaaZeJd3aDxtkU17mCNenDMYk1HQtq24PPOXBfN\ntWm8c4VaLcIc9GGwK2ecug7ZTl4vWa83qP7gNdx0Cz1+gLFSFqY/K0pC/paIougwMeT4MtM0sSyL\ntL2GcfcLVs1dsosawxdeJ7i/iWaoJ8hZmh5avIbv08Cn/kqVeL1NUNEYX797aDUDJN0hhi2YiSeC\nQXroUw/vY71xlZV3qgS1DYahxeDuHuMbD0iDAkvUcyYs+sPlkn+1qDqaVCv5mK2vZSnmeIA5HoCV\noXenW9YDaK3LZN2nYMDxL5N+foW0r24tRIRaVrzyMll3Oq07K+i1l0VC8FTyihT4g9OxOoPuYHZk\nn1um9uI6lXqGN9pBW7aIFIlHAGlfaLyquIf28+t4zy3RcgP0aPegMXUOS6hv0ahgjBOqr13Gifro\nwZ18+cIamimfY4lilIT8LbG9vX2ULn0MB5l7QX2RAxtKI6MW7lBbNUgvtvEdnfDrBxMvb6aQq2lp\nhhMMqDZC0p89h695DL5+SnR/i8yPoFEHRddfzbHVpJ9l6FmG29/CBRbXIL50kXHzHPHGIv7tx0SP\nJ/WouueQdKbTZDPBhytOvwHNNGcr4FagspB8yJKLpxBiZkjBvqTzlGouS/5gIB1P3kOjVce5chH7\nwjILrQx73AEeH7p6M4EscWwYqFOa0/1qgnqrTvWNS1QqGabfJ/EyEkXfRFXswjy3iH1ljVr9EZo/\n6RbRlkrr+NugJORviT/5kz/hwoWjqLKmaYd1kS3LYlxpo5rUmsTU2ynJ7z5PMAT/+n2yICIdx0oX\nRNofgquhhz4VfCobNuHLrzEaaqS7XdJHCj+flNCg8Mma4Yi6/xTbCuCqRfTGS4yMFuNuSHDnUf6S\nKyDG4aJI/k0IUIoOzgIf8sxBPU0r8CFLGRAFhesFC1n01Qo+dwDNdqhc28BZrFA1Rlj+HjpdIq9J\n9qgzvYFwHbWKCzsKQjZNrAvL1F9dwx3voNGDA3e2lKWZ7ZO+pmFfvYCz7GIGA6joJIqT1JdKhcW3\nQUnI3xK9Xo+//Mu/5A//8A+BvIaFtW+52LbNsLZAvb2CeaIwfTb00XQwIp+KDe61c4RUCe5NB2IA\nCCKyZh0tOLKi7HEHWwftRxeJBqv4OwHBF3fIDiwtYaotWq/jMbj7H5NwQJMBTRt4ySS+fIXw1XME\nw5R4q0d45z7ZaCxqepGm8oBmGPOzkGfN1NN1kJJGREIuaL4qnad0jIOPpGlibVzAXFvAblg4eoDZ\n2ULL9iDam3CpZLHgWxY+Rpp3LMFD17Fe3MBZb2LRR+90YTx9PbOxEGjMEtx3r2J7CUY4hGCwPybF\nPak10S+/qNxPidOhJORvie3tbW7evHn434ZhHBKyZVn0rBrecwb+hVfINgeY+x0Vst6ArOUcvlJ6\nEuHSwblgka28QvSoR/LNo0mSqLgQKBIBDAsn3MKpQ/ajdcbmIv7mkBhbmRWXhUKVtjgmtaroCpIx\nDKgHT6ibwDqk68uE9gJ+dRWr2SDZ6RA9eEy2H/XPAtlCliw7kRCliCZwJgtZguRmkPTRmib6yic0\nxbaNeX4Nc3kRfXmB2vkmbrSLniXADkSQuhX5/KX7JeD/a+/dY+Q66/v/1/M85zaXnd2dvfmyDrHj\nODWOiTGhgSbfqKmoSFMKbVKVRLQQOVXDVVFTqRKqftUvaqGRvqKEUvghtRRVoU2EaBUkQqCiuRSs\nJoiQqgRMCCEG3+31endnduZcn+f3x+zMzuzO2DvrWe+OfV7SxpmzM+d85uyc93zO5/lcEjuLumY7\nzmSRjJhDJT4kZ2vH6PDlpkutGRNyYgRr+xjCRMhz07DEBNO03iCGx1HbtyHlSdTmNGRxMaSCfJFE\nUUSl0rqAYi3cltq2TTWM0ZkRMuYMbIFwyzXIGR99+Bhks7DktcIYpJNgjWn05u1EVZvolaMwPw9O\nHmiTo9xUYSdMQjY6TXYY4q3jxNveTHTWJ/7ZYaivvFc7zaUAXK+t12eWxFEl4IXTWJtGsYMpyAFX\njRGoHQQiTxwITKWKnp4hOXUGU21a9e8Uk+1YmXK+kEWXnG8qasc4QwcRt21obpZkWaiJcWRxGDk6\nirdtAldUceJpJAaYJhwZw27TLwK7fQk80P5LmCUFM0ohd1yNvWkQNZzFOfIq6CULlG77jBBjWVAp\n1+zftR27KFBxCZhFdxj1SLmMmBhBXTWK1LMIFtZCCmkO8sWQCvJFcscdd3DkSGvtcHMMOQxDTGEL\nVGoXoUMZhkDfeDWoLOb1I8tvb61aJZ2Mq7h2FXvvELG4Gp0oRLuU0A5CInVClikYAT0yTiBGCGYT\nkuNtBKGO076Kr6MoJa23025SwqVUy3goKkRRwM5xQpUnJEccW+B6mEIePTePnpnBzMzURLdji7rz\neMjdJiKfL8TRUZCb/ndgAFEcRg4MIAr5WiN8S+OIKraeWxDeeaKBIawzy8ckdTy07XT29f32qpgY\nG7HnepzRDBk5jTQRMEVkte8Ah9WhIGh0DPWGq3HsMlKXqXefN8oCf3ksWmyZQHkKlZRBN8W2nRwi\n0746NWVlpIJ8kdxwww0ETR5MfaYe0CillpuvxZz6IaJJcGRcRWwahvwWdGChf3Fq0VsWravn0mgc\nM43ZdjVi9FqieQvz+nGYrXnLJu5wKYeLcUGJJmPOkCmAGbSI828imrdJTp3DHPklYkFYjd0pT3Zl\ngtyMsV1EVLPBScq1LyMB4fiv4MoSjAEU0GaISOXwc5uRVxXRcS1GacIE44e1EEvgQxhiAh98f9Fr\n7LYwpNlDtm3wPITrgesiisOQ2YPwXIRjIWyJsiHOFshttXH0PKoxK2OeOGOh5s7VPMOlWt7hvHQO\n47TPmNBQi+0vYDZtRWydwB4A15bY544Bc63H73RD0dSIyrgeYvs21JDEzjpw9Ojy17k5GjlwykJc\nvQVVUGArONVm6sv4r3R6dykrJBXki6RYLFIqlUiSBKUUSZI0BNm27Vpv5C3XoMs7KZ3xyZePIRZi\noiYBaSKUEyGuHULrbejjZzGatpkWJklQpoKbBf3GHDHbiM9G4Edtny/8KrS5zoUx2FaIm52B7ZBs\nvxo/GSaajUk0KNpM5+gUXz3P4p2xHIjaDFpdEjaQQuPqEmTGcSsnajbbQAYYrC1e1cYLeQs/Ne3Q\nOASD27CucTCm5psaIcEI4vw46ioLgUAsPFtiMJaNSqooEyKXKJBf8PDOnaY1ERcq+QKZueWhInOe\nJvgi6ZSf3KGxkbLairUujKAnR1EjWTynhM08UMuvjuJC+2N0+DJIjEJs246cyOOqaaSovc9IbGq/\n1mB5mFwesW0ryguQRGASjBpYfA4gxnYiChnE+M729qSsmFSQL5LBwUEqlQphGJLJZEiSpJF54DhO\nzWPOTyJFhcFx0OOTEAo4cRyaKrMkGilnYdIizhYwAzacOIpojp82iZ8UAodzOKMQecMYsxc9VUYe\n/2VD8KnOoy277V26tj1kVPPIFSE5dQqKEGzahXrDrxAFA8SzIeLkSdT0qY5el4g7LziZDl5f5xd0\n6lNsMIgWwZKAJCSxwDVNArqwCz8zildeniqWiCzKtM8oEJ3eZMfxTefJvuh0XjrFw2XtUtS5AvHm\nbVjFLLbno1xBfuYMLXXZdXuD9sUfYsl0k2RsC2J8FDVg4c0eZjHXrf5Gln9AzOgmGBnFGkqQS76g\njLFrdxoTWxBZF0kIJoRsuqB3saSCfJFIKTHGEEURmUwGrXWjF289lizcIbQ7ggjOIknAAf2GTYCL\nmTfLLiyJjywK9PB2dGBjjp9Ezs/UFuParP1Y0TwyG8AWSDa/gTAcxJyeQZ38BThZiJZfuKZDH2WQ\n2Mxju/MwDow7RFxHVUwQDucR09NYZ44veoBh0Pk+vJMgd+x7fL5YsardUix9H12HLC4uhtyyWZ4n\np3gFgmyEIBmeQI+MkQwPk9miyIhZauJbE+BIbWq/GwQibB9b1mGELm6GTZtwshUcUQHOEYn2ZdD1\nkJexPdjyBkQeLBGQWBJZbT0nRlmQyyJ2XI00MY38POlALl3Qu1hSQe4BUsrGLD0hRKNyL27KHxWT\nezh35KcMyTLCLy/cLldhyyZIIJmeQ5VrOcjCL2PsPFLESC9Gby+Q6K2YmSomCFpi0QAiCTEyi9Ah\nSkRk3CnYBvHkNYRqAnHuLNaZXyL0oj2mk5i0GxNFBT0Q4nnTMAZ61yR+UiCq2uhyiD0/gzV7etEz\nr++q07y7Trp3nq5uRkrE+Rb3Vsp5BHnpeV08eGebOu4rWp4ZEQ8UCQpDxG96C3ZW49olMiIC5pjP\nFnHPLg+L6E5hETuLWDK8Kx7egh4dxxI+DtPAkiq6NuEjAO24sOt6LLuCFEHT8xe/VLQ7gBgZQTgg\n/BixUFFqLAcGr0JkLERmrL2tKSsmFeQe4DgOpVKJ4eFhlFINQQ6CYNFLzl7N8MjPgAHQQ5hyCTE/\nC8pFUMUay5OMDGPKPnL6GMbyEEntApJCIFUZRiDJ7yHxE8TZU6jyYq8GbXmoJfmqlvDRQwIna0i2\nbCcMB9AzFezTv0B3GCPVMWyQNIdLNFlrBgYW3o6XxZirCeI8oW9hKjGyVEIoq10IuzNtPOBFAyza\nN6BY+zzkjk/vUHij7QzhwDBJYRCRc7A8jefM44oIpSRedfmCWMcvow7HSKwMUs4RjVyFKQ7hZnw8\n6WMoIcrt+xrLcPFOSbsDJCNbkVkLO/JRUZvKvqBKMjAOg0Moy0eIWuiIsIpxMpjBIsIyCFEB743t\n7U/pilSQe8Dw8DBTU1OMj4+TyWSoLqyKB0GwWFSQvZay9T/k7RD8EnIwhxkYwCChWkUASkZQUOiB\n7WiVh7lTyKj1ttRYFk6mCpPDRPEWkvkYa+oXJDKDahNnNAt/YiViMu45mAA9sYVQbcIfzKHOncIu\nLebSLfVyG9uTzqEJLR0s7ZO158ja1IR6Asr5LYidDkHkEgcK7SeIio9xPWw3hwqWtG88T75xu45n\nq8F0HiDVOe2ujYusbY/YyxFvuRadzSA8C8s1uHaAZQm8YI6li4MAQnda7OvQhW2JICdegWh4K3pg\nEG9LjqyMgcVYeaKyWG2HDyhEUiEc3Yko5LGteZRIMCbBLAlpGWmRFDYhibBUBATU//jGzsPYAEIl\nSGEwTg6cAiJ3ffv3ldIVV6QgHzlyhPe///2cPHkSKSV/8id/wgMPPLDq/Y2MjHD69Gl27NiB53mN\nQhHf9xuZF0pZfOelHHe8YzfGfR0TlsAvI+wMZHNE1RirOo0wMVIk6KyHzIyRxA6mXEaVTy5cEos+\np21VsQdBF7YSqU2E8zmsmaNIff7KLolGZQRZpwrDBaJkDN93oFSGWLcLUyPjoG3GBtQEGd0mmwKJ\nrSJsFdWSIxZaBM9mhvGu2kysFWHsEscWSQixVljhKCIMUVGADKuoYB4ZVGifd8IqGyJ32JXRaDtD\n4uZInAyJ4yJsl6o3QDy0H+WAZSU4dogrIwJ7hEL1CND6xRKKXPsDALKDIHf6Ioy0QBevRheGcLIa\nz6rgEFG1FFZpeTaFlq1/PYMkHthCUhjBNedwRdxir5YeckHAY3cYBkex3AgpXNT8mYV9CBKviPQc\nkArhz4JbACeLcLaCfT2ozu85ZeVckYJsWRaf+tSn2L9/P6VSibe85S385m/+Jm984+puu4rFIlNT\nU43G9HWCIMC2baIoQilVuzjUHuzCDfz00JNcvTmP0hVAYOfAZCaIY5Dz07VhpDZYdgjDDknhGmLf\ntL2rlsIgMxZuVqFHtuP7HpRKOLO/RHcYf9QcT7ZVgJ0LIAeBVSRkhKhiMKU5vNIJrLiCjH3o0KlM\niw4fow5iWc9msGSC5VTAAbLgqwKeiKh5Y/UUt2ESA4EpYJKIxEi0Fo2fCI9gpFDrg1x/qwYS5eAX\n8o1CECEW/iMkUmqkMLVeIlIjlcESMSinlivdQoRRLoNBu7ac7d+fPs9in9Qdqu7ioPHaan4L5IeQ\nORvpZMgHR1nqbXfy9BOtMAjC3Bb0YBE3G+GohBAbWV4u4In0iAtXIXMuth1Sr5HWWiKFTZIdQdoK\nSya1tEIp0blxQrGZbP4ttcW8lJ5xRQry5s2b2bx5MwADAwPs3r2bY8eOrVqQx8bGOHv2bIsgG2Pw\nfR/XdRv9kZVShGGIbdv89OeQGXgrOXeKrDuFkgEiqmLJKsYeAuORGA8Z1gabKhWhchBZI0SmgKnM\nY1dPL6Zq6QQUSKnJZCuQVeixa9DxML7v4cwfa/HOhA7bhiCkCbFdXWulPOSgzVUEkUdYERBHuPOn\nccPWdDLTQZA7RmQ7prd1mG4sQFkGTy0Xs1mnwGA8vXy7NcRgvHyRLBA53KRDa0phd0jv6zj+tO3W\njufDgExa714ilSXIjRE5g5gtm8i6PgMSauVyMfNJhwb4ZulDQZjZTJwdJRnN4FkRzeltxrTeYUTO\nCCY7hLFd3Pgkzc0qEpXFOHnwBJYALRSxM0iCQxgUKPlbyOcLqRivAVekIDdz+PBhXnrpJW666aZV\n72N0dLQhyAMDA42CkCAIGoIshMCyrMaCnzGGrVu3IuU2qtUqP/nZi+z5lU3oaAoTVbGieYSbw3h5\nwtBgBWeRJkCaoNaQ3nFICm8gCixUZbq26Lbkrl5KjZMxOAUbrbdT8R30fBVv/mRtwbDNX18lPjQt\n+EkhyDgBGQcCdxBXWETxNqpVi6QaoSoltLBoP0C+k4fcodTb6M5rdB0Wt0SH3hSdJlifr5dFJ7s6\nbe8syO2PESuPwM4TZ4cQGRcvq/GcGAcIAoPbJj9amA6d3nRCImz87GZMboBsJsFTCVq72O16kRhB\nrPJEmQmsjMC2IiAmTGpxKIMickaRroNtJQg/QoscuDmENcjUlEeSjDA5uY0C8OEPf5jPf/7zHc5L\nymq5ogW5XC5z11138cgjj1AodKh6WgFjY2O8+uqrDQ/ZcRzCMGwIdBiGGGNaBFkIwdTUFFJKKpUK\nc3OC6dmrga3kvOPY9jRgkCLAzUQYr0gUWRBWkCZECFAyQWUSyOQI4iw+41jBFJZe9AClrgISKQ25\nbABZiRndQjX0KMUSu3oONzzbEB1JQoLVVCK8iMYGfGwrwR5Iaot35JiVmwiTIaq+JPETZFDBDmZJ\nOizSiQ7ZFJ22147dqRF9h8q3Tts7HuF8aW/dDUWNtcS3CvjuMNrLIT0bzwNhKwZMfcFtSUaMaR/3\nX7oeEKoCQWaUxPLIjLvkpWnZ19JzmAiHyJkgkR5ugVpopuUdSMLMNmxH40qDNoZI5BCeJmaIanWC\nRGeZmjqBELN4Xu2r96c//WmHc5JyMVyxghxFEXfddRfve9/7uPPOOy9qX2NjY8zMzBAEAZ7nLTYV\nMqbhIWutG70ttNa4rsvp07W4ZLVaJYoiTp48ubBHF5hgaDhguJhgiRAZ+9h2GeE4hGqCJAix4hLW\nwpA3x6qAW0DmR/DDCWI/xgmnsEwFY7It4VwhIOv6JLk8aniIKC5SrSjwK3j+GYxxUKJNvLHDwppB\n4NoJrl0X6Vr896wepZIME4aQhAkiDLCiSsf91NpRdrdIp7sU5I4Dp+lcqdfpiyLSgpIzRmTn0I6H\ndCxcRxDZeQbVVNNdQ+1cVrTTVsO1EW2/AGvvQ1Nyt6K9PG5W4DkJDuCHLDQyWkISkGDj2+MI1yPr\nxlgSgiBBLFTRJNhEcgjLs3F0BSkSIuMRihyJcCjPSc5O2WgtqRepzM/PN5yJD37wgxw6dIgbb7wR\nqN0hfvOb32xrf0p3XJGCbIzhvvvuY/fu3Tz44IMXvb/R0VHm5uYa8eG6IMNixzcpZUOcgyCgUChw\n/fW1VKHjx49TKpW47rrr2u4/0WUS+xixmcZEPkIoMgMSowuE0Qg6jHDiKbRxkaKK58TggNEjBNFm\nQq3wknPYpjUbIMZGkWBbGrugoWADW5iNhgiiABNUycTTOKaeeneeUuE2SAG5TEyuoUwO4FDWebQa\nwQ8lYQRJpDFRAnGIRYJMAmwdYBsfyyyEeDocW0lJu9Cz7BTKOG/IoibICYpIekTCA9ulTBY/M4Sw\nFcpW2DZkHIPQBQqqeW5f7fVh3GFCeIfLLcFGLsR7Y2zm1TDSG0A6Ck9UyCjD0uC2WlIUkuDgqyLk\nFNIdXPCcF0XeMlUCOQJOFsdNyEhItEUc5THSA1nAkhN4skjOg4nxVhvPnj3L8ePHuf766/nud7/L\n7bffzj//8z8zMTHR8XymdM8VKcgHDx7k0UcfZe/evezbtw+AT37yk9xxxx2r2l+xWGRurpYDLITA\ncRyq1SqO42DbNpVKBWMMnuc1BLk+pRpoxJo7oWQexXVoo0nkCRJ9jihJUCLAceYQrkDrcYJwEG3m\nsc0MUmiEFHhuhJEDOLakGowT+QlWPItnZjsKhLQtClmfmqe7hUoga83WEoEyAR7lVj+2Y8Vxh1gx\nGqUgl9FNYq2ADAkKJRZfFyc1gavGHqHeVJs4rQGt0dpghEPVySIMGAwYUzuu8vAtt2aFqGdaCIR0\nmDPjCCkRUiClQCqwLYExGs/R2FbtwmiYFmUoqHoYqKkxe3d940naZEaEeMzJIiozhu1Z5NyEoYWn\nxRpUmywZY8AiJCBLoIaxPJusm5BFIKJK427IGIhEgdDk8QaqeJbGmITI5AijLGDh2BkssQklz9OP\nmVrxU3NXw4mJCU6fPp0Kco+5IgX5lltu6Tx6aBWMjIzg+35L281KpYLruo0wRZIkDAwMEATBMgEO\nw3BFMWwpJFJsxZZbiZMqgT5HaOaQ2scRFWxHIFWWUGeJQoNK5skwCwsr7Bk3IeMCDBLGw1R9l0R7\neGamJba4dDRQ1tVkXZiOi2QzFlE8wbwviIIYFVeIOzSU6BQCkOJ8sWKJanJ5LQWW0sSWomAt3Z9g\nOh6gaC/f37l4kGFr+d94PvHIqeb9NOXKdYghd17U6y5OHWvJLEUilUM5DhlXkHENbuhRELMsdfUj\nbbXcF2gjCMQgvsyTGYjIuLqp950gShwcUcHXeUIxgLIVlgKpbZAR8/EAiXGBPK4cxLXyHSxdjuu6\nywT51KlT7N27d8X7SLkwV6Qg95qBgQGUUg2v17ZtqtVqSzw5jmMmJiYol8vLBHmpx7wSLJXBUhmM\n2UyUlAnMObSpQCyxZBXPC4A8QZKnGtmYxMKTJaSoiY5jaSzPI+dJtM5R8gVxEGLr8nlyCmq/sS0Y\nyhvIK2CA6XCAwB6mWjVEYYSIA1xT6dw4f0FA2qGNhDaC3Wl+XLd02o82dKzhW9qmc3F7pyYXME8W\nnxyJ5WHZNrmMxEQuQ+5c6xOp5fy2i8gkRlHVGXxZQNgu2YzCVZCEFhnV3IMbAp2jkuRxVB7bra1C\nJFpRjXMYo4jMILYYJG8NNsr5u6GeT19nfHy8sQaS0jtSQe4BQggKhULLLL0gCBgcHGx8kMMwJJvN\nNkIW2Wy28foLhSwudGzHGsBhAK015WieSlwFqigR4KkKjq3QokDZDBBHGkmVvCphLYQGpBTks0DW\nAYpMVzzO6iF0HOBRISfna8UnopPHW8tb9hxBPU4MA5R8m3lp8H1NEsWQhNgmwCHANnHbupFOBQ+9\nEuROnG//UrQX3tgIZpICofAwykFaFllPksQO+XyBpf5nucMggea7tUriUTE5lJUlsGzGBqrLKicF\nAm0k5ThPZDJIZWNJgTASqTSVKEdsXAwetvDIqQxOx8EDK2PpXMG6h5zSW1JB7hGDg4OoheGd9Xhb\nc8iinllRF+ShocVRN2EYrlqQm5FSUnBrHX8SranEFebjCsb4IGrNhhwnAvJUTY5KqAgCgy3nyVp+\nQyCVEmQ9i9rHI0c1GaUaJETGYCKbvJrHkovirDoItWUZMq4gn1HUXECXhTQMwkThh4YwMkSRxiRx\no3rQSRwcEeHIGEfW5wld9Ok5735qXwQ1zzzWksDYhNpCSIdKopihiFAWylI4tiTjSuxIMOwtL1bx\n5zuI+5I7Bm0ElThDhRyxyeM4Nm5WNIQ8ClrdZj+2CZMs1UiRuDmkJXCoe8IeWksiM4QtMwzaWZxO\n3fYugvpEnImJCX70ox/1fP9XOqkg94hisdjog1wXYdd1UUoRxzFCiIa3vNQjrlfy9RIlJQNOngHy\nJDphJgyoxjHVOAACXOXj2jGJyROaAapBQqJDbFnFkjHNymUpwUDWYj6ysawCVW3w44Q4jpC6Nkki\nZ6JlnmRNtDt0REOSz5iFlTNFvVHGrO8x4C6GLMLEEESaed8mkqNordHGYLTGGIPBohp6tckVC7OU\nBGCwqQROU/l27bdSWszGgwghFxb5JFKCEha2pXFsgW2Jptkk4GmDo5YruYw7Talevs0YiIzktD9E\nLDyUcnAthfIkpmPzZAAAHQ1JREFUBDCQWV4UooRgJsgRJh5SOFjKAgG2FRFrTZDUqjnBxZGKYcfC\nvUhP+HzUP9eO46QhizUiFeQecebMmUZuZr1Sr7mvhW3bKKXQWjfylZvpNGq+FyipGPFqIRKtNaUo\npBJH6DgkNgJBhGdFOFYt0yHUMO9bJNrHlj4528dVCbZMAAshBRnHAqeWixBpwYx2CaKIJIkQBDgy\nIG8HKG2QbarmtKnJ5lKWhg6UEmSVoqpthrPLns7UvEsxu7wy7VzVYTizPA2i5FsMttnux5KM1T4X\nWHUIWXS6MwCY9jP4iUtiHJS0cW2HWBny3vJjqIUYdawl5dAh0B6W9Ii1Ju9oHKsW465GNolxEAik\nsMkoi4JjY6vu0hFXS31hz3GcNGSxRqSC3CMOHTrErbfeCtDo8FYX3Xq4ok49Xxk6FzCsFVJKBl2P\nQdfDGMOsH1FONKXIABGCEFdFgIVj1cIflQTmwojE+AgMnhWRsRZjwLY0hAlknHr8uNb5KwDOVhTa\nRGgdIkWEJSIyVoiQEm9Z1gSdQwo9Ok2d+2t0Oq5BtQlrJ1rgxxbl0CXSDomxkdLBVQ6R0eQdU2tF\n2oSSrWLsx4owsfETSVhxsKSNEBJHLfS9ECGzgY02LsbY2FIyYAmGPXtVC3MXSz0UNzAwwOjoKGfO\nnGd6ecqqSAW5R5RKiz1ohRCNUun646VZFHWPuFfx49UghGAo41CPZifa5VwQU441fhyjjUaKBFdp\nXMvGEjaVyCHQioqfkOgAg48jQyyhyNh62e26QZF1WrJ6SYCSL6nGhiiJSEyMMRGCCINkPopwlMZR\nGtdKcJTp2GWz6wlOHbbrhdS9OBEEiSSIJaFWCBRhItBYCCyUtLCkjW1ZaBFh2WLZRWTadHSLEoEf\n21SiDAYXJTyshRFXtqxgSUGYQDUWJEYgUHhWhqytKDoK17o0XvD5cF23peCpeSJOtxw4cICvf/3r\njI+P8/LLL/fKxL4nFeQecfvtt7fkaTYjpWyIs5QS3dTjYTUpb2uFkpLRjMPowmM/TjgXxJTChEqs\nwSQkWmNbAldJHCsL1OIIsyGUY4h1beClEhEZO+no2WpqdxJqye32uaogY1towDfgR6ADzWw1ohzX\nSomNqQ+S1RgDM4FuyhWu/SuQzPoetdixWNgsEEJy1jcLFXsSKSRCKIwRzMUKqz64VIKSECWanNcm\nDc8YrDahmETXClnmAkWsa+OzlXSxlIUUGkctlC9rQyVKiLVZ6MSmUFKSsyVF12bA2XiX5tJcZFhc\n5OuWe++9l49+9KO8//3v75V5lwUb76/ep+zdu5cTJ04AkCQJUkqSJMGyLIQQDeGxLGuZIK+Xh3wh\nPEux2VJsXug9/uOfvIJTKKKVzYyvFzxosCRIBFIpXCtD3RuODJT8iLnQoE0CJkaIBEdphKl5hLZs\nXQRrd2lLKUHatbj1EmYqCYOZ5Z3z56oJhcxyr7IcxAx4y2/3K6HGahMFaPeFEieGIKl5vWEiSYwE\nFFLaSKnwLIVSUP+uSbTBjxISowmbKu8cKcgpwfGf/ZT/c9OvtnnnGwvHcZidXWxpOjg4yOzsbEvG\n0Eq59dZbOXz4cA+tuzxIBblHjI2N8eMf/xhYnBTSnD1Rj/lJKVvif+sZsuiWJAoZdwSjxZpCG2Mo\nhTHTfsxcGDPtR+gFj0kJgavEggelFlKwau/TAHNhTIBayJpI0FrXKvuMoRQmWBJsJbAX/u1YtNEp\nlNFtiMNAGBtibYgSiBJDpGv7P1OpedNCSKRUKKmIE03GVihrMY9EG0Mca8qJIUg0sdYNv92zJAXb\nYkvOouhajc+AMYYj1eVTwTci7ar1Tp8+vSpBTmlPKsg9ot7xDVonhWQymZbbumZvuf7cjRKyuBC1\nUVSLtgshKLg2BbfVQ50PY876EXNhQkVrZishxiwM7ABsJZEY/AgsKWpx2YVb+fkgwrUsEmq3/74G\nYqgEEXOxwCxMBjELKW5Gw7Qf11zZhcQNQe1nuqoX0uGaAxmGs1Vd+3sIsWCTIE4kXrMHLgAFQZw0\nPHNjDIk2BFGCNpog1oSJXrCkhqckGVsy6lkMexZDrn3eW/q1zK7pNc0xZFgsDtm1a9ey577jHe9o\n6l64yCc+8Qne8573rKmd/UwqyD1iZGSk0WDI9/2Wjm9a6/MK8vDw8KU3eBXUQzAXIudY5NqEF6px\nUhPqIKYSJsxHMbFeaKG5oEsCKAUxSkosKZBCoGRNYrVmwUutNc4HCMIE114emvDDGKfNyKkwSvAW\nnm+MQZva8bUBP0rQ2pCYmqecaIOShlIQgxBoY2ohGiHIWJK8rSjkHIZdiwHX6itxXQ1LGwydLxf5\n29/+9qUy67IiFeQeURdkY0wjLlwX5CRJGultSxdB+ilkEcfxskW4bshYism8YllNMbXzUo0TzvkR\nlUhTjROC2BDomieqjaEUmlrGrln0SKUxJEG0LBQhjCHx40ZIo/F8DDOhbGyoj9pzZc19t6XEVYKC\no8goScZWDLqKgmOtS6rZRsKyrJbMijQXufekgtwjisUi5XKZJElaZunVaR7dFMdxI02u3vi7OW1u\noxIEAb7vNyoS14ICC22Z7XrgYWUiWPN2DYlhIdyhefl/f8ib992AErW9dC+oCz2Fo5j5qH0GTS/Q\nWvfF3x9abR0bG+Oll15a1X7uuecenn32WaamppicnOShhx7ivvvu66WpfUkqyD0il8tRrVYJw5Ag\nCMhkMkRRhDEGKWXL6KYwDHnllVcAmJ2d5fDhw33hfc3Pz/Paa6/1za15VClz+GevrrcZF8T3fX7y\nk5/0xXlttvWv/uqvOHLkCF//+teB7iaHPPbYY2tpZt9yxQtyrxLUhRBIKRuz9EZGRiiVSkRR1NK6\n0BjDm9/8ZjKZWmrYs88+y6/+6sZPeYKarTfeeGNfCAcs2rvROXjwIPv27WtUb25k/vu//5u9e/fi\neR5f/epXefDBB/n3f//39TbrsmHju2VrzL333tuzeWB177feXjOKosbg07ogb+S845XQL2LcT9Qb\nUPUDzWsj4+PjaQy5x1zxgnzrrbdSLBZ7sq9cLsfMzExjuGlz+KL+Ia6HMKA1+yLlyqXe+6QfaM5F\nzmazVKvVC7wipRuueEHuJcViseExOI7TEGTP89AL7SKb6XdvOaU3WJbVN4K8NPUtpbekgtxDisUi\nZ8+ebQw3jeO4RXTjOG6JE/ZTytul7kp3JdFvIYtmQfY8L/WSe0gqyD1kZGSE6elpPM9rdHyrx5CB\nRjpcnX6q0tNa90UmSD/SbyGLpdV6aaP63pFeYT1kdHSU+fn5ZaLrui6WZVGtVtv+rh9YWjad0jv6\nOWSRLuz1litekO+55x7e/va388orrzA5OckXv/jFVe9rbGysxSMWQjS8Ytu2qVQqLR5xP4UsVlo2\nndI9/RyySD3k3nLFX2G9TFAfHx/ntddeawhyffp0syDncrnG8+vTF/qBiy2bTulMv4csUg+5d1zx\nHnIvGRsbA2h4vY7jNHpXOI7T1zHkNGSxdvRTyGLpl0c3IYsDBw4wPj7O9ddfv+x3999/PwcPHuTI\nkSPcdttt7N69mz179vCZz3ymZ7b3A6kg95BisYhlWQ0P2bKsRnZC3VtOQxYpS+mnkMVSuglZnK8I\n64UXXuBtb3sblmXxqU99ikOHDvH888/zuc99rtFn/EogFeQeMjIyguu6DdFtHlFUF+TmadP95CGn\nIYu1o588ZKAxDQe6C1l0KsI6dOgQu3btQinF5s2b2b9/PwADAwPs3r2bY8eO9c74DU4qyD2kWCzy\n/e9/v/FYStkQsXqhSLNHrLXuG5FLQxZrRz/FkGF5+fTFLuo99dRT3H777cu2Hz58mJdeeombbrrp\novbfT6T3oD0kk8nw1FNPEcdxI35cz921bbuvBHgpcRynIYs1ot9CFvVMi0wmw9DQUGNSzmqnhHzr\nW9/iS1/6Usu2crnMXXfdxSOPPEKhUOjtG9jApFdYD6lUKpTLZcIwJJvNtjSjt227pdqt30IAqYe8\ndvRbyKI5F7leAAWrmxJSqVSYmZlhy5YtjW1RFHHXXXfxvve9jzvvvLM3RvcJaciix4yOjjY+rM3T\npZdWuYVh2DfxY0gFeS3p55AFXJyH/8wzz3Dbbbc1HhtjuO+++9i9ezcPPvjgRdvab6SC3EOy2Sxb\ntmxhenoaOP/opn6q0oM0y2It6deQRZ2xsTGmpqYu+Lp2RVhL48cHDx7k0Ucf5emnn2bfvn3s27eP\nb3zjG2vyPjYi6RXWY4aHhzl9+jS7d+8mjuOGIGutSZKEX/ziFwDMzc3h+37j8UZnenqaKIpaPKON\nThiGfXF+tdZUKpW+sBWgVCpRKpUajbL27dvHqVOn2LRp03lf164Ia//+/Xz6059uPL7llluu6EZW\nqSD3mJGREc6cOQPUYmF1zyeKIkZHRxu3pmEYtqQPbXTqXy79Ym+dfrG3/oXdDwghiKKIJEm49957\n+fnPf85Xv/rVxuJbN6OcfvCDH6ylqX1HKsg9pt6CE2jM1INaiGJ8fJzt27cD8Oqrr5LJZJicnFw3\nW7thZmaGq666isHBwfU2ZcX88pe/ZMeOHettxoroJ1t936dUKrFjxw7+67/+i7/9279lfHycP/zD\nP1xv0/qeNIbcY8bGxhqCDIsjj9qVTfdbDDld1EuB9h3f0gZDvSEV5B4zOjrK9PR0y3gmY8yyopB+\nKpuGdFEvZREpZUucN20w1DtSQe4xExMTTE9PN9La2k0Ogf4qm4b+y5vuR/p1MSvtidw7UkFe4Jvf\n/CbXXXcdO3fu5OGHH171fkZHR5mbm2v0rbBtmyiKlglymoec0oxSqiVvfaNjWVZjwTrtidw7UkGm\nJjYf+chHeOqpp/jxj3/MY489tuoOUyMjI42Utnof5DAMiaKo5Za/uay6H+g3e/uNfstFbo4jj46O\nNjKLUi6OvrzChBAdf+69996u9/e9732PnTt3smPHDhzH4e677+ZrX/vaqmwrFostguw4DlEUNeyG\n/r01TVk7+rFary7I/Vb6vZHpy1WaRx99dNm2J598kscff5yJiYmu93fs2DG2bdvWeDw5OckLL7yw\nKtuKxSKlUolqtUo+nydJEnzfb6nSSxv1pCyl30Rtafl0vadF8+c8pXv6UhWW5ju++OKL3H///bzt\nbW/joYce6np/7TzW1X6wXNclSRKq1Sqjo6PEcdz3KW8pa08/hyyARte34eHhdbSq/+nLkEUzR44c\n4Xd+53cYGxvja1/7WksD+JUyOTnJkSNHGo+PHj3a0n2qW4QQjQnTjuMsmzbdbwt6aYhl7elHD/li\ncpF7tYh+udHXglwqlXjXu97F/Pw8Tz75JOPj46vaz1vf+lZeffVVXn/9dcIw5PHHH+fd7373qu1S\nSrUMN+13DznNQV57+jGGvNphpxeziF6tVpmcnOSqq65q+UIA+OM//mOUUjz++OMrfyMbjL4V5CRJ\neO9738uPfvQjvvKVr7Bnz55V78uyLP7+7/+ed77znezevZs/+IM/uKj9DQ4O4vs+tm23TJ6u02+C\nnOYgrz39HrLoRpAvZhE9k8nw0EMPceTIET7/+c83tn/84x/ni1/8Ip/97Ge5++67u3szG4i+FeQH\nHniAp556is9+9rO8853vvOj93XHHHfz0pz/ltdde4y/+4i8ual/Dw8PEcdyYNh1FUd9X6aWCvLb0\no4e82pBFu0X0bubm3XvvvezZs4e/+Zu/oVwu88gjj/Dwww/z0EMP8eEPf3jlb2ID0peC/Mgjj/C5\nz32OBx54gA996EPrbc4y6ot5QKMwpJ+r9FJBXnuaCy36gfqMyDrdeMgXu4iulOLhhx/mzJkz/O7v\n/i4PPvggH/vYx/jLv/zLFe9jo9J3gcGXX36ZP/uzP2PTpk3s37+fL3/5yy2/v+aaa3j729++TtbV\n2LRp07IczX4PWaQx5LVFKdVXvaaXCmg35dO9WER/17vexf79+/nP//xP7r77bj7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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ef835f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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/Ufz3P4o67Pa9VPrpj4luep/YxvfRPz6OcHvwL19NYPU6fKULhqz8Rfr4cSI7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\nGx/uU3BtlnIur9xvsZRz+XCvz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8otnd7JO2ob/dcmjHYFhKUz4NsuCMCrvSR0pL8M/sOP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AWidKPbQVTy0m8ra1LUA0ZsKcQWxElAf5nAaouTqhZqdk/zAj6hxKf4B7qrQFB+kPuwmBqrnQ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MW5qSBnJwP4YhlkUaCzysaxxuLk5GY6s+v3ZdKf4PxsiKHFCNm8itOo5aFWN33Vdip2qK1M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\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4NttLFbbbFa61K1xbrk6kLaMk4Z/f2eft3S4vrdX5i69uZlGoONH86/vHvH/Q57mlCj9/\naxXXtugFEd/Z6XI0CrnWLPHWZjMrBD+OEu6cDHgyCCjZihfbVa6a/1GYJDzs+eyPQhwlthbTJr3j\nSMBYz9S2XCo5rJTdGR5esaC9eJBZ1B1vbkQ00THjuI+fDEnMeOZZFTyrirdAvSdArFcAYjFiU5EC\nsXNScdonB2EnZM9xZux8QV9CHRvwZYycs9q1CpThqWYg7OKF6CVyfwDhfg7E0nHNqkoELI3428tP\nRXIXANbNx8/wQEBQUdVoNwzoSu2PVkxW4lnB13gSdPkGiBUX+so2mZhVsbIoLRvw1f7MFvuQAq8B\njA9zsDU+MnPuEcRT1ASvZTJYK1n2SnjjbfCezWN0Xvu8gLKPkeTTkfnVt7XWf/e8933lK1/R3/ve\n9xb+XSexpBjHxwLWRkfo0SEMDwUA+dNovWS+kKbZWqhSC8rt/Isqt54JCJzVtE4k5ZiBNLONjYo0\nbcqW6FoBrU/8/BnfSDrqCzgb74NvgFqQRm4AyzNcteWJ9Cfe0lNZeUxcuwjUgj0IduVnubDhp63k\nA4y78gyqnQjCPeGkhU+EywFm1bpqImmrYK9c7nNpbVbb+xDv5gO9auVRNOt8YJWdiz4CzvYQsJea\n264hVhtnRcAkHRQlhyRa7n9LNXDUmvE+m/9/mwZnripRdZYWWmkkWpuUZoilFG2vPNf6QGudpTNd\nS3G9XpqbKkuB2fVGiSu1ycnwfnfMe8cDXlupzaQxe37Eb356xAvLVb60kYtHfvfuEZ8cD/lbX96e\n6dODkyH/5w8f8dff3J6wvPjfv32P93bz9I5rK7bbFbbbVbaXKlxbqrBcXZxK01rzKz96zKfHQ77x\n3Ao/e3M5e20YJ/zWRwc87o750laTr21L+n6n5/OHj09RCt7abHCjUIlgfxjw48M+sdY816xwy5jv\naq3ZG4U87I2JNWxWBcwWgdYwijkah/TDBAtYKjsslybBWOop1gt8fEPcrzoSFfPsyUlca02YjBkn\nA0KjtHNUiZJdxbOqCwp5JxNK4hSI2aqZKYkXKyTTKHIH4YalC8cyOWfzbNsZ0uLbKVdUdwqRMGUW\nUKsGhLW5iMdYpmAvGmfHHfFoBDmvswzOesZzVZdZnGEWzxnXq1PYdwq8L8uMh6sm27AqVV2ecjyW\nYELXEOw7hhpzmv8cFecnx9BhVs1mjt32M89LAriGkkYMDNk/KG6GShQXhTyWzItlMzeVVwrHSz+x\n+XxRuygo+2mrL5//SZxXWbakMivLErVG4gvZdeMARkcZSNOjIwFq/imc3gf/VHLf0/116waoCWBT\npXbh5yUoL6Oci6+isv4qq8Bpe2HymtFIkH8K1Pxjw2F7f27IVWfqUAPYyunNuIpyLlevSzl1qNeh\nfivvTxKaaNo++AakDR9B973JfjuNKaBmtgtWOVDKNjyzVai9IueMxwKe/CcSSQsew+jDwptK6BSg\neevic2OfH/pXygHvqmykkbQnYnIbHYrS04hetd3KOWnuFsqez/UyJ5bVr70M7svGF+gJJHsQfYyY\nzToyAWQgbcFArRSZBF/fRtYxewhI20GKqK8CV0XVNfP2XBwgqc0jIn1MoD/FooZjbcwFZ7Ocs1NO\nw10qdpOK3Zj5Li2laJcqVI1K89gfUXFc2l55ImqmlGK57FJxLB72fe71xlyvlydUhEopnmuWiRLN\ng55Pw7UnDGavN0o8Gfh8cDJko+pNgLpGyeFqo8T90xFvrNeza69WPd4/6M8tPJ56iE17le33ZKD/\n+nPL/InnVlhvzI8ALmofHvb59HjIL7ywxlev5WnTIE74jQ/22ev7/PytFV5clVTrvdMR393p0i47\nfGO7nakitdZ81BnxyemIhmfz5lqDuptWIoi52x3TD2Oans1zzfJEWnccJeyNAoZRgq1gtSypY3sK\njI3jiF7oEyYJllI03RJV150xDY6SQEp6xQOTnhTT4kWVI7SOjZXLKYlOndhTINbGUrP3knkjYk+R\ncsNSkJOS8zeQhckZHC4dTIp1khNyPphlfMBuCs3AWjkXhElk6tT4LB4JvzRKo3SQc7KWwbltgNjK\nhakRskg8mfKTPDaeYoVm1yX9WH0h5+S6l+MEQ8rzOi5sBaPUaZ4XxlbJa4mSsQjCnqGCjZD8UyK9\nyXZNc7pmtnsCEAAAIABJREFULK6UkPm9psxxzVsT8x1e+5m45nnfEggG0o+x6U9tE7X8wvlvfor2\nb5P68uItDtDBANyni4wo24P6lmxMAjYo5J/TL8A/hXEHnX0pAt502J85t3brU9y2leyYyvJTAKMK\n1K/JNn2tJCwQE0/yY78D3U9nyInaqRZuWnPjpoDNvhiYVJYLlSuyTfclNcsNTMg4OIbTdyd9YZSD\nTjlrJcNbK69faCWn7DLYNyY91pJxzpdIt+H7MHhbXmCV0d6G1F7zNsBbO7dYurKqULotG2kKIjW6\nPZgwutV2S1Kd7hXDSTtj9WXVwXoBMZoNITnMU52hsd5QdcNDWzcTxJxBRVlIdGzN8GhSBece8ATx\nQdsGVudGzyzlYdlbOHqDWB8TJvsEycXAWcmuMog6jOIufjyk5i7hzUlppirN1Hg2TGJWStUZ/lHF\nsbnRKPOg53O/P+ZavTThZ6aU4larwuCoz0edEV9crWdRHaUUX1ip8fs7p9w5HvLFKZuKm60yj3o+\nu/0gc/lfMnyr41E4C8pK8+tfVjwbBrBU9dhsXi7dEieaf/XxIas1j7eutrPf+1HMr3+wz+Ew4N9/\nfpVbywLuPzoe8oO9HutVl29stzNfMj9O+NFBj6NxxHa9xKvLNWxLSi896vs8GQY4lihYVwqFxGOt\nsxJYtoKNiku7NOkVlqae+4YvZpsIZ3UqRZnoxJTwGmReYp5VoWTV5laJkFJGpwaIDZCxyDGlw1pG\nfDIPiIXIPX1k9ukCuQk8h6Qm64sjwzoxAOxgiueJLFjsjYL3YPPcCDPJYNLoOjoiV0E6wv0qvyzR\ne2O4ehFgJPza01nwFZ1Cwe5HUn1bxmKobWx/mpeK9Ewamxs+VXocdidfbNdkYV+5Cs1XJyyWcJ+O\nb6a1FmeD8eEk6MrAV5cZo1a3IYGF+rYAL69lvDqbmZ/os4IurRPwuybdKaI9PT7J5vxsvp8GhNf+\n9L8DZROtu0Pyq/+FpB2ry1A1xP7seCU/dp6mvqMCtypbIwcfM+AtCc0X2hGeWJHfNngCh+/ORNy0\nUzUAbQWqa6jqGlTXoLouadPLWmhUDOdsTtNJJOBoZHL9aY69excO/3jytW5dQFplDSqbIneubl4Y\nRCrLzQDWxHm1FnfkDKgdSZSt9wF0fpS/zlvOuWpGZKDOKTIv1y1D6Yps2TUTM8jtmdTnPozv5393\nlgxA25CImrN85gpPWSXwrsiWfaZOnu4cfwTjO4hP25q8zt0Ce3nx96lcsLdky+T1hocW3zMloRxR\ndDnXZQKZex6bHKCFSOTsEfAu4oF2FdiaS25WysJRq9hqeQqcVXGszbngzFI2DXeFMKnRj07ohQd4\nVpWa054p+ixGsmU8y+bYH7E/6rNUqlBxJieTkm1xs1Hift/nQc9nu17KIkAgPmYvtKu8ezTgk9MR\nL7ZzflLDc7jVqvDJ6Ygr9dJERYDNegnPVtzrjjJQtlyVvx+Nggln/LQfUmppvlfZ8WCx8eyi9v1H\nHTqjkL/2xtXMg2wcxvyzD/Y5HgX8uefXuLlURWvNe4cD3jkccLVe4utXW1kU63gc8scHPcJE8/pK\njW2Tqk0LigdGNHG9MVkyqRvG7A9DIq1pew7rFXciMibkfanUEGuNa1kseRUq9qR5rKgnB0Y9KTYW\nVbtNya7OfOeJ9gWEJackiG5L4YkC2GoZ89apZyJLzReVkgAuOTds6WyCfjI0z8+BgLEUNFlLYL9Y\nSEWeDWSENrFvOGAGhGXcLEObKN0mLwl3sehQRrwPds2YtCdjVNGY224JHaNyOyffX7KCCqS0E0M3\n8Q9z8FW0lFCuZDEq29BamaKePANfNxoaGs6R4XYV9hOWFkqAVWkJGjcn6TilJfBaz1SGMOtPEXSZ\nuVmPjgo/TwvwAMvNqUtLt4TUX26ZzJPJjJWa8y/4GbTPJyirraHe+OswPEYPhTemT98WpIuetI9z\nq1BdgdoqqrYm762tQnr8FOnGtAkoWpEt/V3h71pr8TzLRAhH6JEhHQ524eBdCVWnzfbQKUDLANu6\nbN7lxQjKcqCyLttUE6XoceHBMQ/P0TsQ/1H+OreZATQqG3JcWb/wAyN+ccYVuZpLiDM16HhPNn9f\nzHF7d/LX2DUBaJVNqFyD6tWLRdSUJYRSbxVqX5BzJb6Aswyk3YOhuZZy0N4VKF+D0vVz1Z7ymYwx\nbuVVGcjDfQh3BKQNfwj80KRT0yjaFso+IzWpGqaw7+2cdBw/hPiBgDTVBOcG2FcXT07KBa6B3kYm\nt0fAp8A90BssTm1eHpy5Vpm2u8ko7jKKu4TBiKrTojTHC67suKxbNsf+kGN/RD2JabqTxH3XtrjZ\nKPOgPxari3ppom5m3bW53ihxvydeW1sFftntVoUnA593j/p840o7Ax62UlxvlrnbkUoBnm1Rdmxa\nZYed7pgvbU2aJSulqHk2/SlQNgpl4jwaXg6UDYOY3793xK3lKreM19gojPm1O3ucjkN+8QUpmaS1\n5o/3xX/sZqvMV7eamdHr3e6YD0+GVByLr2w1aXoOcSLq1INRKDUs2xWahbSuHyfsDiVVWbYttquT\n9iNSLilgEAYkaFHMeh6lCTCWmLqpfRJiSU9adUp2babOZErWj/QBiZbsgRgZbwpRf149Sa0R8LWL\nRHnTxWsD8Qtb7NMn74/MM7IvIMxcF1URMY29LrSAc5SWEgnrQbBjnt9dBCgpAUnetgFgq1Lm7YIA\nSSehjAkpAAv2cnd75YigqP5aAXxdXs0n0adOPoamW1Gg5TSFxN56bdL+yLm8ojO/biLRreEujPYm\ngdeEEbuVU2iaz+XZmdKyAV2fjWhNJyEMj2C4D8N99PDAAK4FoMtryJzdvAbrb+ZODWVDR/oJuB9c\npn0+QZlXw3rxF2d+rZNIvoThsYCf4bEBbkfQP0DvvSupz+KbSk0D2NbBgDVVX4P6BlTOtg84ryml\ncvEAN+V3xf7qRCJsw30YHKCH+zA8gN4j2P+R/D1tThld24T6Fqp+xaRfr4D3dA+Xsj0BWtVJCzl5\n0Lsw3JNttCv73T8gN8+10OWV/P21q1C/inIuTlyVaKTxm2nkYWAdjyeFBeM9OPxD4A8AhS5vQnUb\nqtegsn3hayqrJKCrXCjrFHfzAdN/CKcPgN8Hu4EuX4fSNShtX6AagS3pBU/S4ToZGSPbJzLQB/fk\n984qeDehdONskq+yZVKx14UPEz+G6AGEb0P4romwXTeTzpzvXqUKzVXE0PIRMvmdndq8LDhTShkg\nVqUfnTCIOvhqSM1ZmvGfciyLtXKNTjCmHwaEccxSuTLBVXIsxfV6mfu9MQ/7Ptcbk6nMzapHN4gN\nv8yhbgxgbUvx2kqd7+51+fh0xEsFH7EbrTIfn4x41PO5ZSJj11oV7hz0iRI9ozasew4DfzJ9OQ7l\nOTy5ZKTs9+8dEUQJP/e8RLIHQcQ/u7NPL4j45ovrbLcqJFrz3Z0u97tjXliq8qUN+R+HccLbR332\nhiGbVY/XVmu4lsUwjPmoM2IUJ1yteVyt5/y2RGsOTWkrS8Fm1aXtFVKZSSJgLApM3UqHuitgLG0p\nGBvFPSPsKFO123PVk7my98C46jsGiC0tVvbOqIkt5F41ZcfOAlHah3jH8DJTxbQtUTDnhqT9Vf1c\newohzO/mQCwxIMZqiBeYewXcjbOpCBP/B8MxSwVJmc2PmWmcNpSv59H5cyLz86+RSNRrvFtYyB4U\nqCFKeF315/JsQ2n9mdWNOhoJ+EoB2HBPjotRL68lgGv5C4YSY2gxpaXPJNoFiHhvfCzz42DfzJVm\nvhwVxGcgPLPKCjSuwfobOYWoKvt/E/ZYz9J+6j5lT9POU18ualpr8HsCgAYHYDY5PhS0XQRClguN\nDahvohqb0NhA1WX/tGDown1Nb8KB3Hh6uC/Rtf4Tib6lza1lAE2lQK2+hXIvp+w5tz86lpVQ+lCm\nD6h/nL+otCz5/9pVqG1D7cpn8gDo2JfaoMOHMHooxylA9FYEoBmgps4oY3XudaKu1Pr0H0oJKR0h\nq70tAWjl62ZAvfj3nqc6H4F/X9RZAM4GlG6Cd+NcjlvWko6As/gREIGqCjhzrnFuAXUdIlUDHiPl\nZUrAVeDKwpSOTLoCziA04GwL25pNK4tib8gg6qBJKNt1qnZr7uQzCAM6wRhbKZZLFTx7cuCOEs39\nnthNXG+UJqI8UaL58WEfpeD1lfoEqPrxYZ+dvs+fvNLKIkdaa37jkyOqrsXP3VgG4H5nxG9+uM9f\nfGmda63J/9s//vFjTkchf+dnbma/+wf/9B2iRGMrxX/7l79wIZL/0SDgf/nuPd680uLPv7RBP4j4\ntff3GIQxf+HFda4YAcMfPOrwZBDw+lqdV1ZkhX7qR/zwoMc4SnhpqcpNw2PbH4Xc644z7lirlH/G\nfhizOwqJEk3Ls1mveBOpzF4Y0AtlAq/YLg3Pm/CSE5J/v6CyLVNxmnMrO2gdEiWHRFrsHSQqtmZ8\nxBaR9Y+R++8QmUCb5L57Z0zcOhDlcvzYpCUR4GVvgLW2mHc5cXktfmApCIsOSPltuJuGbnAFZc9G\nkReeM+qB/zjfUmCnPCMyMgDMfTqPR0kDPpaxbvQYRk/IzL+VKxZJpY0CAFt7JgCkdSxpx+GTyTE+\nKDgVOJUJakuaObkoH/lC/YgDGOzBYBfdf2LmvD1jG1VM9ZYl22UySaq6BjVz/BnPfRP9i8bQ3wen\njKrPZqDOap8LS4ynbU8Lys5rOokFdfcP0P096O+ie+am6B9M3hRuFRqbEmGrLktUrbZquGwrpqD5\nTwa0ab8nnLX+jty4vR0Ba0UfllJbwFptw/iyrRlvtpXPbPUCZiU12IHBI3HrHzwqPMhKUqe1KyaN\navhvpcuvFCeumUSyYhw+FOXn6FG+YnSaAtAqW1DeEuPbp+BIiBpoV0Da+IHxMkMK55a3ZSAvbV+I\n9zZx3vgU/HuyJV1EQbQlhdndq2erOfOTSLQgfiBiATDigG2Jop1nB8AhEj3rIMHya8D2wslxGpzZ\nqo1rXZ2rJkt0wjDq4CcDLGxqzhKePQsYgzjm2B8Sa03bK1NzJ8F7mCTc7/nEWnOzUc78uQB6QcR7\nx0OWSg4vFqJiQZzwe487VBybr281s+fvnYM+7x4O+KXnV6m6NmGc8A9/8JDXNhr8yevLE9f9rTt7\nfHjQ5+/9KRF5hHHCf/Nr77JUcTkZhfzXf+4llqrnLzT+8Y8f8/BkxC9//SaWUvw/7+0yjgSQbTbK\nhEnC7z7ocDwKeWuzwW3zOXb6Pm8f9vFsizfX6iyVXRKt+eR0xNE4ouXZ3G7llQrCRFKV/TChZKmZ\nGpZ+HHHij4l1QsV2aHrlCbGFOPQPGEVdEuKFZbbEUmVInBwSG+K8pVo41ioWc8a6zFF/z2w+whHb\nQBYCZ9znemwqZewKTwxtFiBXZbPO5/PoeCjRsHBXVNrajI32cgbCcNYuyAeLDUd1X6Jg/k5u+GqV\nobQtnFZvS+wnLrtoi7pif+SbbfRE1JdyAeHpVq4I8b68YaquPKXKscgzTtOOwz0BY2maT9kz3GKq\nG0Zd+WxzmmRi+saqyviMjo/FCWGwJynHNOqlLJm3apsCtmrrOf/6JxAUkXnlVLJtoxP0qCPH4w56\ncCg4YCxzm3rhz2G9+Tcudf7PhSXGU7fBMfE7v42qr6BqS1BbgcoCSfUlmrLsnGu28erE33QSy43T\ny4Ga7u2ijz+Bx9+DJJpMizrlDKCp6ooAteJx5enKLAGoUgNKDVh+MUuHaq0lFdovgLX+DnQ+lUhT\n/m50eVkAWnUNZfZUBLRdNrKlnAq0bsuW9iXsQ/9RDtROP4HDH+Zvslx0JX3YN6G6JSuuC4sKHBMZ\n2zafPZHBLAVpw4cFiw6FLq2ZQc0AtdLqufeKUjaUrsrW+jo67sP4oQFp92H4gVzbaZlBeRtKV89X\ndtotqL6BrnxRomb+PQjuw+A7cj57Waw53G0jo5+XnrTB2ZYtGRje2UMIfwChB85NSefMi54pRS4M\n6AN3zfYI9HWEdzZN2M/TmlGyT6T3ieM+nnUVe0qAYCmLurtMKakxiI7pRYeUdcNEzfLP4tk265U6\nx/6QTiATZhGYuZbF9XqJez3hmN1slrEL5P7teomHfZ+OH2Uu9Z5t8dJSlbePBhyMQtYNeLrWLPPu\n4YDdQcCtttSC3GqUeXQ6W+ey5tkMw5g40diWpBEBGgaU+VEy857p9t5ul48PB/zpWyvUPIffv39M\nz4/4K69K2SRtUpbHo5CvX21xzUTCHvd9fnzYZ7nk8OZ6g5KpWflhZ0THj7hWL3GlJn5oWmu6Qczu\nKEBrZqojaK05DXwGUYCtFCulKmWnmKacBmMedWd5xhxYrCxOiJIjYz5sYatVAWPzTFT1EAFh+8AQ\nIWwsAc+zSA0sQpeOAWJ7YHz0UBVwbhke5dlly0SFvScgLNw1Cx4kcuVeMc/UFso6e4zJUpH+bqHi\nyCGZMtAqSwSs/rqMDZeInGsdSwpytCv8Wd8Q8YupQKcB5U1YesOAsM2n5Jr1JdI1OpjkfPknTKT6\nnJosmDd+RhbP1S1R4z8D3ytPNRrLqaExSE99QuMpGoBdFvFb8zrqytcEhNW3ZH76rNKfOoFx13DP\nDLd7eCSc9OGRUJ38HjAVpFI2VNoyd29+EeobEiFbujH3Op9F+1yCMj3skPze/zr5S8uB2hKqtgJ1\ns68to+rLUFtBNdaguiC8foGmLFt4ZvUN1NYbk/1JFR7D9Is+mvjC9fG9LOWYf+XKcNjW5Uuu53tq\n65cWICilcm+2tS9MgrWgLw/n8EAekOGh5OL3foAOJytb6fJyZheiapuiPq1tXgqsKbcOSy/Llp43\nGhtj3P2co3DyLhzkEU/ttQtAzYC18vmrQqWsPIy/LAsRHfZl4Bs9gfEOdN+Hzh+nHUSXNwSkVa4I\nUDunOoGy6+KXVnvFrG6PJcU5fiwAbfAuoKS2ZtmANG9zITFYxALisK2rX5KJIHxkvNfegdHbYNXQ\npeehdHtxBM2qgfUKOC9Leie6C9GHEH1kuGfPGRfyeeCuDrxuJsG7wCfAQ9A3EcXmLOfMtTexdYsg\nfkiQ3MfWp3OjZq5VouVuMohOGMc94iSk7q5MmIlaBiwc+aO5wMyzLa7WSjzo+zwZBFyt5QatWzWP\n/VHAg96YlpdHaq7US3zUGXG3O8pAWdOzKdmK/WGQ8cq2m2W+86gzU5w8tckYhhGNkktsHPtjA87K\n7tmT1YcHfX7t/V2utSt89doS/SDivf0eL63WsjqW7x0OeNTzeXO9ngGyHQPIVsoOb603xe5Caz48\nGXIaxDzXLLNhPk+ipSJCN4ip2BZXapMebYGJjkU6oea4NAsecWmaeRh1SYiwlUfDWcJVk2R8rSMD\nwI+ABEUF19rGVnPI7lojUdcHSJoSoI1wFxeUGEuBWPRAor6pb5i1DPYrkp5UjYVATHhh+zkIS2kB\nOGJ/U35BUpMXqMoxkYoMHudE+bTkW/11IeZ76xfyPZSPlxjF+xOJ6o92hSebZluskoxXrdehtJZ5\nfV021Smem/sFSokZW4uEe8sTnld9G1bfmOB9XdaiKbtuYtKdg10zrxzmwGt8PEUDcqQ6TWVVAgmp\nOC61inIuX8Nzpj86EXDVP0AP9nPwVQRdepIril2SgE51BdW+DpUlyXhV2tmxCOw+W2f/89rnEpSp\ntVs4f/N/Rg+OYHCC7h/B4Bg9OIb+EfrgLvre9yGaRuQu1FcFoDXWUGajsYpqrEPt6ZyHBRQY5cby\nrRnrDAAd+cYqIwVth1maVD/6LgSDSYxebs8CtcaWpEwvAdhEbGAia+3ZvulwKADNbHqwC/1dOPqg\noAxV6MoK1AtCg5pJjdoXW8Uppzzjt5aLCnYnt86H5CtTF129IsVfGzegfh3lnp/iU24d3BcyEYFc\n60RA2mhHBsqTH8CxUZo6TXT9OTFErN08c3AUkYIxqa2/YVKdewLS/MfQ+yH0fmBUnVt5JM2dH/kS\ngGb8hyqvmVX/Y/A/hdGPYPQjtHsFSi+Ad3U+0FMqFwckA4jvmglvR6IMznMm4jDvvS3gTdAnCDj7\nEHhgwNnGDDizVIWS/QKR3idK9kzUbBvbmlUz1t1lnNhjEJ1wGuzRcFcnqgEopVgpVTg2wEwD9QIw\nq7k2axWXg1HIiR+xbEoXWUpEAR+djiaiYpZS3GyWTT3IkHZJlIJrVY+Dgnryahqd6o55YTW/n1ID\n2b4f0yi5WRml0OyL5rbT7dOjAf/knR22GmX+6hev4toWf/jwBA182XiUPeqOeedwwM1WmRdN1YBi\nhOzLKSBLNB90hnSDmFvNcvb5gjjh0cDHjzVrZXfCk0xsMHz6oYmOlauU7Zx3FiQjRvEpsY6wlUvD\nXp3xF9M6kWL2yT6QYKs2jrWKWmhlcYCAsR6SnnwOAfQLxigdCicyuo9UuLDB3gRrQ+7ds0p/xQMR\nzQQPjFeYRmwq1qHypoAwZ+XcMVzHgwIfbCoV6V2B+lVJRzoXmw9k4XtsFoG7ORk/5YBZnol+vWWi\n9ZvG5f4yac5E0o7D3VyENdw1nN60woorPK+lV3LOV2XNeIs9pdIyiWRuMPwu3d8V6sxgf5bOU1mD\n1g3YfEsyMCb7QumzqTSjI1+u299H9404zuwZTHHOlCWgqrqCWr4N218tZKpSitFPV2W5qH0uQRmA\nqi1J6nJBE1L/QEBQ/wjdO4Degdkfou99Dz2aKrdkOQLQ6quo1ga0tlCtTVRrE5obKOfpSevKKUFj\nCxpb80FbMDA3297Ejad33xEfNNJHz0TYmldRzSuF/dbTebK5VXmQWjfSs8u1kliia/0nwq3rP5Hj\nw/cKqlCFrq1L2Ll5A1rXobF94aiaUiov+tp+Kf9fJKFZ/RmuQ/+hqD+f/J78vbwqIK1+Q4BaefXc\nh0uuZaoKtIxNho5N9G4Hhvege8d4pyl05YoAtPotSSGc5WOm7AmvNKklumNA2iPo/qG80G6hqy9C\n9QWUs1iQIN5rYl6r4x74n8jW/12x2SjdhvLzkgqd16waWK9J9Cx+JNGz8I8hfM/YatyEeWkctQS6\njUQ77gJ3yMHZ+kTUQimFqzawVZMgfkCQ3MPW87lm4vLu0gsPOQ33qDvLlAr2IMoQ/o/9EacmYlYE\nZislh1GUsGcsIFLi/3LZoT60edj3WS7UbbzWKPNxZ8Td0zFfWhcQt1b1eNTzGYQxNddmteZRsi0e\ndUcToKxuDGRTW4w0UhbFsvcWgLL7J0P+77d3WKuV+GtvXKXkWHT9kDsHfV5Zq9MsOXTGId/Z6bJc\ndvnKpvBzHvXHvH04YNlEyBwDyO6cDOmFMc+3Kpn3Wi+I2Rn6KODalJdbEMec+CMinVB1XFqF6Fis\nIwbhCaEeYyuHurMyo6aUIuLHhMkuEGGpJq61iTUvBa5jRD35ECl1UQFeQgD8HNCfRcXuC2GfWCxe\n3C+ahcLihZ1OAmPWfFc4YgD2ClReMyBs7VybCh2PhOSfArEoLaPmyTP7NKnIsMf/x96bxkiS5ud9\nvzfyvisrj8q6j+7qu3t6untmdmZnl8trCdm0xcOUBQikaEMQbVnQB/OLZUMWDMMfDNkWBNuyYUAy\nBEGwBEm2KJO0KHK5y92ZPebo6ZmePqu67qqsyqPyrjwjXn/4R+VRV1f1QWjXDiAR2Z2ZEVGZEfE+\n7/M8/+dPbUXuG7XVXrsh5RIGbOgN8WR5R20P2NkGfzGUr0N1zV6v93mGlVQ3+lMQv9nzfXlePC1A\na0s8XZVNe2Ke7rJg9N3v8cVESUlcQ9kTc/yJV2aul+PIQXkLXdmCchpdSYu5vnkg5NblF+JiaAom\n7vRUp0BCGK9XFLkxcHyWBbU8KIeocK9h+bEFZc9blFLgDYI3iIodrf/qdlPYqnIWXc3ZoC0D5SzW\n0kfQqPRvEYKxHkiLpFCRUdSQDdhOyRgde7zuAAzPooZnDx/n/gyhnEaXt+SELW+ht++DNnsMmz8O\n4bEeWIuMy/pFWj8ZDtH2Aym51+4fS9/MSYoMNmD3CTr98f4H0cExAWqRaQhPC7t2hgtEGS67gnO8\n9x1YbfGnVVehsiptprJ35UWnHx20mbTQFATGT+XDUMphZ6ClYPiWXYG0BdVlqC1D7gN5OLxo/4yU\nmwfmUK6Tq7SU4QbfjDzA9qOtwd4CVD6GysfSZcB3AXznUEcY4bvbcoTAf1M8aO00NBeg8QgaD9HO\nJHjOg2fq6PJ95RR/mWNaCgI6yyJrdhalObpz1q5e6xswlAJioIeRgoBl4CGwCnpOXut7v7BmF+jo\nnRNZM5fhIeIeodrOU+3kMXUbn6NnHD4IzAyl8Nshs0opxvxulisNNqotZsMSkqpsVuzLfK3rOwM7\nWiPsYanU6IKwhB0am91rEYj4MJRiPOxls9xAa909juB+q6XmAVBmWXicxpGVlxvFOv/si02GfC7+\n/ZsTXYnz080SCnhzLEKzY/HBRhGXQ/H+hATDrlcafJmvEfO6uJ0M4TAUHRuQVdsm8xEfMZ8LbUdd\n5BodPA7FRKDXJ7S/stKwv8P9cN79iso9UyaffscQXkfwEBizdIm2tY2mKRW2jmkc6ogCFt1Gqnc3\nkFyxEHAVkSiPAB1dVmwFdAVhxSZsv+PxHjEJf94Spri1AZgSV+G7AZ5ZlOM0Rv8q1JehviTRNGgB\nTO5R8F8WEOZ6PqvW215DvKq1FXm07HbNDh8EZsA/LR4wz+m3OfD31nM2ALNBWH2/QtQulopdtyva\nR+2cyJcIeN0HYOU1dHkdymtQ3uh525QhLFcwJTlewZStjCRfWZyENtsy2S+nobwFlS15XtmG/sB1\nT1iIjNE3RDEKJiVvNJhEuc9WZHX6Y+tANYcubaPLO1DaluelbShnwOpgvPGLON779dey/59YUHaa\nRbk8EJ1ARSeOfF03q+jitv2jpOVHKW1jPfshNPtbLClh2KITqOi4bM9eK8/LzyCU0wORSYhMDuac\nWR2VH5viAAAgAElEQVShbkubfWBtE5152Fd4oIRFG5qGoSlU1F6/4AmtDGfPc9Z/LI0ilFfRpVW5\nyHfuoTe/Ly8aLnRoAiLTqPAUDM1JFehZuxeEZ+TBvnEzJwCtsiZgrdgXBhuahsh5KUDwj57qRqmU\nw47XmAS+LmXptRWoLQlQs4NttScOgXMQvgDeseezdI4gBK5A4IqkbdcXYO8plL4HpQ8lE81/AbzT\nR1Y0yrEZ3f6c2qrLgNVYhNr3Ye9jtHsGvPMoZ+yoD0tfP0dCks/NFWEtWmlJPHdeEfbh4GdIIP00\nd4AV4D4QBn1poHpOWLMUDhU5kTVzKCdhV5Jap0DdLNPRLYLOns9sH5jlGnsUmnUUdAGGw1CMBzys\nVhps1SRcVuRRByN+F9t7LeI+V5c9mg75WC41WCnXuRoLEvE4cRmK7F6bGTsGYzziZamwR6nRYchm\no7rypd1qqZ8p8xzBkqXLDf7p55sE3U7+/M2Jrj+tWG/zNFfj+kgIv8vBd9YK1DsWPzM9jM/l6AKy\nuNfFrT5A9mi3xl7H4sKQj2GveNo2a01qHYuI20HK7+4Cw7Yl7FjbksrKiMfbzX3rWC2qnQKmbuEy\nvASc0UM9KU2rSttKo9lD4cFtzNjNwA/KlA2EFUsjwarDwBTS+PsISdMqgLkq0jmmADDXDQFkx1b4\napEkW0tS/KKbNpN1Djxz4Hw+G647ZQFijWdiJwAJeA7dklBo9/NZtd7xmBJFUVsRJqy+hQA7p9wf\nhm4IGPMkz86CmS0BX5VVG4Rt9Fiw/bZ6sesQnILAxAspIL2/ox+ArUF5fRCAGW4IT8DEe6jwJIQm\nBHy9ombdWlvCcpXW0aUNdHFNQFjVrqoFuupPaBQ1clXGq5Ct/rwu4KUtsToVt9CFLbDHd13ahko/\nOwg4PRAZkfF85rYQM8nX0rYb4MczEuP2+Un9o7//tyCSQIViqHBM1q8AAJ120Y0qupTuoejiJrqw\nCcU0mH1IPxBFDY2jhidgaAwVSqLCCQgmBBS+jmPTlg3WNtCFVbkQimt2yJ69+GMCzoamhVELpmT2\n8RIdDgaPQcvsr7SKLq9KMGt5vXczcIcheg41NCshf6Hxl6bAdbsqAK2yAqVFMb4COP0C0MJzfXLn\nWWezWio8a8tQXZJZM5ZUS4UuiHfNN3HqaqHuILT3FPYWxQem3OCbA9958Iw9X5bRWszOzUXJQMOU\n/DPfFYnXOGmw0B1hMdpPgKZ4elxXhJE48v0WIlktIS1spuVx4HvUWndZM3AeyZpprWlYVfY6RRzK\nScgVx9HH9EkIao22ZRH3+gfCTQvNjgAwr5OET2btHUvzea6K21Bci/VM//dzVbZqTb4xEcXjMPje\neoFKy+TfOhcHoNRo8398scX708NcG+n93X/ne8+4lAzyCxdHWC/s8T/9yTNcDkXU7+a3f/ZC933Z\napN/dHcdj9PgL9yaJOzt/Q1/tJhlpVjnL7wxzsN8jcVCnXfGwsxEfD1A5nNxKyGArG1ZPNrdo24D\nsqjXRaMj/rG2pQ8Fwe5nvRkohjzeAXasbpaomxUUhkSSHJAqLV2nbaXtxuAuXMYIDnWExKZriF/M\nvo5IAlN2gcjB88O0Q46fHWbFjmsRBmizbEfELNsVk4ak6HvmJDvshGugew011oQRa9s5Zq44eOfA\nN4dyHW9xObSt1i7srUL1GdTWbGZQiQ/MPyMgzDd25orAbjV6dV1a3NU2bAnYZsFCUwLAgpOnsmIc\nux+rbVtftoQBK60cDcDCUwLAwlPiC34Vfi+rYxvtd9CVHShvyrhT2uirtlQQSvVZbsZQ4VHJAX1F\n487AMZkd8ZpXc2JZKu8ICCtuQXFr0HPu9qEiowK+wimxL4VtVcx/Nv/fcctPdE7Z7amE/vC3vgoH\ny1fdPgFnoWEIx1HBYVQ4Lv8XScjD+3qQ9/6iLQsqGXRhE13YEKC2v24fKMH3D6HCIxBOosIjPe9a\neAR8L58Jc+jYmmUorqELa1C0wVplm4Hv0R+HSP9FMy7rl5itdfdvmeJTKC6hi0tQeCaVOvuLNyYX\nbGhCZmyhCfCeLfdnYH+tssRxlBahvChl4iAl2MEpAYPBaQhOnDkAUZsNqC5C+YkwadoUeSQwBYE5\nCM6h3KcdECzxoNWfyuCi22KU9s1B4JKdLv4clsBqCThrPBI2zBEG72XwzB3LvskHOz1Zk45Ina6L\noI75vXULWEQG6jBw5cjoDUvXaZlraBo4VRKnkTr0N7StBpV2HoUi7E4OMDmmtsjV97C0JuELdLO1\ntF15WGqZnAt7uzJert5msVTnXMTbBWuVVocPtkpcivqZjfh4lKvxRbbKL19I4HYYaK35R59vkgx4\n+OZ8r3/s3/vRChGfi3/vxjgr+Rr/y/eWAJiM+virP9WbIf/jzzbIVJv8xp2pLtMGsFNt8n893Obm\naJgL8SB/uLLLhWE/b46EKDU7/CBdItYHyExL87BQY68tYbFDHidN02K10kChmAj22iRJ1EWDWqeN\nx+Eg6ul1RTB1m0pbpGGP4cd/oB+p1m3a1jam3gUcOI0kTnXEBEW3ENk6jRgXxoDJo88J3RR5srMM\ntMQr1i0qOYYVs1oSA9N8Zge5Yocpz9phyieY/a1mL+C5sS7nOkh1pM8GYif4NXvfhRYJsrYGe2sy\nydqvunRFIHjOliWnzlQRqdu1Xm5jbVOed3MbDSkiCM3KBDE4+UL3Vak03Pf7ptFVO6dyL9NjeAyn\n3N/21Ynw5EsDMJmYlno+r36/115ukF1yBWBoEhWZhKEJVGTKHkdeYdCsZQrbVc7ImFvOdO1HupqF\nWpFBjKBkrB0aFaIkOiZEydAY+M7Wd/pFlp9oUHbnzh398Q9/gK7koLKLLufQlTy6souu5GRdzkG1\nMHiiAHj8As7CyR5QiyS7a6nAfPU/jtYa9gr2MWflBCrvQHlH1tWDrSK8PbAWTgmCj6REGn1FyB1s\nv1p1W2Y3lXTXr0YlDVZfzzB/7MAMxwZrrhcrqe7uv1kWX1plA13ZgPKm3Fy6LUp8MqMbmhNWLTLz\nQoya5LjlZLZaWbV9G/v7UeJfG5oXRi0weSYPnLZacnPflzn3gx9dQ1IoEJiFwMmDTe84OzLY1Jeg\nsSSgyRmVOA7fhRP9Z/J5Swa8+kNJMVce8F4A78WTM5p0E9pPRdrEAOd56cN5HKDTGeAJ8v1dBDVy\n5LG0rU1MvYuhIriNqUODQsdqUW5nUMpBxJUcABFtyyRbr+E0HCS8vUqpjqVZLNUJuR2M2z0wtdZ8\nma/R0Zo34sGuxPe9zSJuh+KdVIStSpPvbRT52ekocbua8VvPcmyU6/zGzYnu9v/JvQ3qbZPffGua\n1d09/u53nwEwnwjyl74qns9ivc3/+oNl3p+N8f5sT/qttTr88wfbOBT86rVR7u5U2K62+MXzcRxK\n8f10ibZl8bWxIVwOA1NrHu+KqX9fsmxbFiuVJtoOzt0HnpbW7DbrNM0OAaebiLvXP7Rp1qh1CoAi\n6Izi7iukEBN/nrYl3iqniuM0kofBujYRmXINqX4eA2aOroi0ytBZsrtLWBJe7Dx3bOsv8YltCxBr\nrQOm9Jb0nAP3zImhydpqSzZgfVEAGaZ9Xk/anTYmxCJwwtJluvfW+0CYDeicIenL65+CwCS4TjcR\nFC9YFspLws5XN6BV7L3BG+95Y4PjYqM48+SvLffH0qrIj/sG/K7vat98b3dy2e/qEjh9f+JD+9y3\nxZTTg2b7Shra9d4bHR5JAwhJVFR/vBPeVzNGdRWpcj/okudUDwBBpex0hWSvYC+UgFAMFYzLay9R\nrPeyy092eCygnC5UdBSio8e+R5iZooC2cg5dyqBL2e7aWn8oFZr9i8MlAG0ohRoeRUXtx7BQmsr5\nYlq7Ukpy0wLDkLpw6HXdafWBtW0o7dh0axq9dm9QEnX7UdHxnm8tOi7/Dp0uoXrguJweGJoWz1n/\n8VimfWFuosubveKCAb8aAtais6jhGVR0FqIzUrRw2v17wuC5AvErvWKCTlOCbyub6Mo6lFZh6V/Z\nreYVOpgSkBaZFX+aP3G66sv9jgKJW/Z+9iucVoVR2/wObH4bHB50eA4i8xCZR3lPrrJRhhtC5+UB\n6FZBJM7aEhTvS/SGcqCD5ySXKDh3Qn6ZE3yz4JtFW1+Tgaj2CErfh9IP0b65rlH56HgNAzyz4jHr\nZASc1e9D/QHaMwveyyjnEQye8oD7Oliz0H4EnSfiO3NdAsfk4YFWJUGHkCKAh6B3gfkBEKeUgcuY\nwNBe2tYWTXMRj2N2oCjBabgJuRKU21nK7SxhV7LrMXMZwgSJ+b/JkKdn5B/2Osk3OsQ8Fl6ngVKK\niZCHJ4U6mbr0iwQY8btZKklD8rBdWVlumcRtzDIa8rCQr1FudojY8mPI4yRblcDl/taY/Z6yL7bE\nxH9jtGc671iaP1jI0jItfvlKio6l2Sg3uRjz43YYPN6tUW2b3EmGcO0Hw/ZVWe57yNYrTSxLM9UH\nyDqWRb6xR0dbAx0QtLak56hVkwBYV2yAcbR0i7a1jqWrGCoov8fBuAqtEeZzCUnejwPnQPkPv8/K\nikRpZQFDzg3n3LGyt3SwWJKHtdfziXnPgeN4X6lURq/bQGxZJieGX7yZvvPgTj4/+sJqQWURKk8E\niJk2oHCGZbK0D8Sek1HYOyYtE7nysg3Elnt5YJ6oHfnzFQFh/rOrC13/V2nfm7sClc0e8PCEBdwN\nf63X/zjwctKfbhShsIYuroqKUtoQ5qs/XsI7JP6uqXfF9xUekyQB34urGAPHYHZsdWmrT15Mo4ub\nB4rtEEYrlECNzKPmvyqgK5xEhZOSR+p49ZBGt5vo4g66sI0ubqML2xhTV3Fc/uor3xf8GIOy0yzK\ncAhKDsVg/OKR79GN2gBQ0+UMuphBF3ewNh9Dc69/gyKHRlM2UOsDbdHUS3nElNNtg6zxQ6/JxVq0\nT9hN2BU5VK/dQz/+Tu+NDpfQsX0FByoxA6Gzm1Hlu0vJTGj89oFjyQpIK23acugKevOTHlALJFHD\nMwLWojMC1M7AqAlQnIWh2T6g1hBwVlxCF5dh+y5640N50RVED82ihubkc+GpU1UJKadX2LGheZj4\nOWkZVV6C0gIUF6TCE9CeYbtoYB7Cc8+92Sp3FIZvw/BtmXXWN2RwKD+EylNw+NGRqxC5jvIe3z9N\nGe5egUA7L+Bs76kMVI4Q2n8JApeOZAkkS01672mzDPVH3WgN7RoF7xVJOD94XhhB8LwFZl4iNNr3\nhBFxXZEcqYGd+EC/iRQBrAIl0FdB9QZopRROlUDhoWWt0jAX8DhmMPoGfJfhIeSKU2lnqbSzhF29\nyYXP6SJgmtQ6LTwOR9c7FfO4KDQ7ZBttJu1Q1iG3k5DLwWa1SdLnwlCKpN/NMzvLbDTgxqGg3Owx\nwCn7s+lKswvKgh4ntZaJZemB78djFxFYluaLdIm5WKDrI9Na892VPJlai2+eTxDzu/l0u4xScCHq\np9Bos1xuMBH0kPC70VqzWKxTsnPI4j5pp7RebdKyNJNBDz4bBLbMDvlGHY0eyB4TM38eU3fwOcID\n1ay9iIst+zueOMY3VkDk6CpSTXkF1AEPmDbtKsol2y/mkbgV5zRH5ZGJPLkiQKyTBZSk6vvvgHvi\n2AlJT8ZfsGX8li3jXwD/eXA/v2BHWy3xhJUfy1p3wBGA4HkbhE2i3Md73AaPR0tkzj4IKy9Dx57E\nu4ckxic8B+FZlOd0VoWBbTeLPQC2XyDVNfx7pXJ95ue6VezKe7rjPnp/9n3b9hjvg7D9tkGAREkM\nTcn9Pjxq52KOvrQa0j2GdgOdX4PCpniwi2kBYOUdsPpAoC8iEuPsWyhbXlThpJAOrpe30Rw6Lq1l\nfO2Crh2sYhpdECBGrTD4AbcPfKH/H5S9rkV5AyhvAEZmDr0mkmMZXUijC9tYhbQ8301jPf4+1Acj\nM9TQCCo+iUpMYsQn5Xls/KULEJQyIGh3J5i4NniMzartW9vs+dh2nqIXP+y9ye1HxWdQ8VlUYhYV\nnxHw9gI5LnIsdmeDsTd7x9GqygVfWEbvrqDzz2D9ox5QC6Z6QC0+L+zcWWIynF6IXYTYRRR9s8p9\nf1pxGZ29v/9mdPQcKi4MHIHDfqaj9+GD4aswfNW+aeYFnJUWIXcPMh8BBjo0CZELELuO8h5R7di/\nTcMp3pTADHrkp4VBK92H3U9h92PpLBC5DuErKOfx54lyxWDofXTkK1JhtvfYjtf4BO2ZFHnTO33k\ngKccYQi+g/bfhOZTqD+ByrckN813Q3w8B78fRwyM96WCrvMIWj8Uicp1ZbD3oDKAOdBR4BHwKRKd\nMciuOYwwHnWelrlM03yG25gcaNHkNrwEnTGqnTyVdo6Qq8d+RtweWnalodMwcBkOHIYi5nGRbbSp\nd0x8ToewZUEPjwp77Oy1GA14iLgdeBwGO3stxoMeQm7nACiL+lx4HAbb1SaXEgJug24nGqi1zYEK\n432m7NlujWrL5JtjPe/SF9sVnuZq3BmPMDfsp9GxWC7WmbZ9bx/vlPE6DC4N+9Fa86zUYLfZYTrk\nIWmDtM1ak7ppMR5wE7AB4F6nTaFZx6EUcW+g20S8YdaodXZRGIRdiYH2SMKObWDpis2OTWIclCB1\nDengkAe8wBUOZtGJX2xZPGP7fjHXm+AY46g8Mt3ZhfqDQXnSf0tiLIyjz23xdu3YQOwZWHXxZnpn\nBYh5jgdx3W0cB8SGbkD4khTgnDb6ol2TKu7igjBh+z5Ud0Qmb/t+MM/ZWSLdLEko9+4T2H0qbfFA\nvsvQBIy9LQAsMi29Hl84c0yL4T6/CIWVXqFXp9HbX3gMNXINorZKMjT5CrPGtKg++VV0bgWdX0Pn\nVwV87S+GU2KlohOo2bfF1zU0ioqOoTyvqeKy00LvbqHzm+j8JlZ+w36+Aa1+v7eCcAwjmsI4f1vG\n9WhK1LNoym7p+Pr8Zz+WnrJbE3H9/f/yt1CJCYzkBEZiHJWYQEXiKOPFTuQXWXSjKui6kEbnNrHy\n6+jcOjq/CWafHysc74G0eB9g872+ogPdbqILG5BbkQ4HuWX07lqv4sThQsWmu0CN+AxqePKVau66\nWZabwu4KurAMhdVeBajTC7HzqMRFVOKigLWXzHrTrQoUV9CFBcg9FO8FgDcq8mj8CgxffDFzrdUR\nqbO0II+aMBD4x6R8PXb9TLNl3dkT5qx0X9K/MUT+fI68ObiNsoCz2mOp3jT8ELwBgasnm6W1afvO\nHoBZBGcC/LdRrsQxHzBtM/dToAPOC+CcP1R5KdVqj5F8s2Hg8iE/ktZtmuYKmj2cRgqnGmRx98GG\n2/AT7Avz7FgW2XoNQykSvgCGUlhavGUeh8F0qPebPtqtUWtbvJkI4jAUD/JVNqtNfnZymI/SJfL1\nNr94vve3/j9PM5Qabf78DWGpF7JV/vn9Lf7inSm01vydby8C8DMXEvzClRT/7ItN0uUGf+W9Ockb\nK9b5/acZZqN+fv68VM/tN0H/M3MxNmtNVsoN3h4JM+x1slJusFNvMxH0MGH3wtwvXkj5XUQ9roH8\nMbfhYNgrhn6tNXtmiYZZwak8hFyxrg9P2LECbWsTAJcxikMdkAi7Jv4tZE4+zaF+p9rsteuiI5W5\nznOHM+26P3tWJPL2pp0FNvt8edJqynlb+9JO1HeAdxr88+CdOrk4hX0gtmQDscUeEAtfhNAl8J8B\niDXywooXHonfFA2uUJcFExD2AgGw7ToUFgSE5Z/07keugLQbip6DyIxUnr9M7pjZgt1ldH5RgFhu\nsdvWD4dHANfQNESl2p7w+Evfa7v7ttkvnV+F/Kr9fK3Pe6YEfMWmZLyJTaGGJ0W9eU1jta4VBXTl\nNtBd4LWJLu4w4NsOJzDi46hh+xFNoaIjL2VTOmn5iTb6354d1x/8J/8OVnZzEOG6PALQ4uMYSQFq\nRmICNTyCCg3/qQE2bZmiQWcFpFm5fbC2Ae2+5uChGEZyBpWclnViWiTR1xWVYZlQ3JLZyz5Qy61A\ny5ZoDQcMT2Ikz6NGzqMS54RRe4U6va4X0LmnkH2Czj6Vkm2QFiGxcwLS4hclSPdliwjqu5B/iM49\nkpui2RAgMbTPol2G4NgLzUh1swi7X0L+C6myAjEJx67D8DWU5/kVYN1tNTICzkoPxHzs8EvXgaEb\nKM8xQKn/89qSarTqF9JBQHkEnAWvn9ggXT73DPbugW6Aewb8t443XesWtO9L9IGKgPvNQdZMNooM\n+IuAAwFmsQNvsWhb65i6iENFcRmDA+hep0zdLOF1hAg4e2xao9Mh39zD73Qx5Jb2QLuNtgCcgJuQ\nnTFWaXV4sLvXBT3ZeotPdircTobI1lp8mavxqxeT3Q4An22V+NFGkb/45gQ+l4N0ucE/+GSNX7k+\nRtjj5G//8QIAf+ZKitvTUf7u95d4ZyrKN84lKNbb/J8P04TcTn7pSgqXw6BtWvzusxwJn5sriQA/\n2i4zFfJwZTjQ7eM5GnAzZUun2/U2xWaHhNdF3A6LLTQb1M02PqeLqP23Wtqi2snTthp4jSB+Z89M\nrXWblrUu7BgBXI7JQe+YthAD/76JfxyJNOkDzVqDlRbZWu+dGJMicRQ7Nhjbto33l+2CkhMmBO08\nVL+USmPdAXdKJHrv7HMLYbQ2bab5gc2IteVaCV0URsw/eaprWVj2rR4Q24/O8aekRVH0snjCzpzC\n34TSSg+EldcALVEU0XOo2EUYvigg7GVYsPou7C6hc4vo/IJMdvc9YMGUKBGx86jYeZEhX1HvRt2s\novPr6OxS90HRDuYFST8YtoFXfBpi0zLRf12S414ZvbuFlV1DZ1fR2TWs7Brs9UmyTreoVbFxVGwC\nw16r4TGU+9Uf10nLTzQou3Pnjv7kk0/QWqNLeXR2A8t+6MymrPPpQZ3a4UJFE6hoEiM6Ioh4uO/5\nUPy1oOP+RWtLfGu5dfsEWkXvrKJz64OVjqEYKjqKEU3JybPvXRsee+WATajmDDq7Yl9oz9CZZ4NA\nbWhMLq6+B+EXp9cH9t+sQO4pOvsUnXsiN5j9isjQiHjShudQsXmZ8b1MRVFxGZ17CPmHYqAFyTAb\nmpMbZuyyXTZ+xptxY7cH0PbS8p/+UWnIPnQJAqcDfr1B57540LDANyEtW8KXTjWb1q0d6bvZWLEj\nOq5B8AbKcbw0oXXbboIuHjp818B39Xi2ztyC1hdAR0JBnVNHbLSKFAHUgDkk36r3vfbnmRkqjNuY\nGfBDiXm9SsAZxdvnmSu3GlTarW5yvdaapbL0zJwL91oLPSnsUW51uJ0UQPFHawXGgm4iLiff3yzx\nzdlhorYfLF1p8DuPdviF+QSzUT+VZof/+cMlvnkhyXjEy//wLQFlv3RjDKfL4FsLWX7rKzNE/W7+\nxcNtio02v3J1lLDHidaaH26VWC83+ZnpKI8Ke5ha8/7YEMVmh8VSnRG/i5mQAK1cvU220WbY4yRp\nx2rsNus0zA5hl4egy20DMpNyO4up24e+E9Oq0LJWActmxw5kXek9+7eocKyJ3yoL4Lby4gl0XT3s\nIbR/G9qbcr50suIr9F2R4OJj2iVp3RFpsvYQWtuAQxixwDWU++RJh8Q/rEH5kRj2zcaLAbHGrsTi\nlJ6JP6yzh9xjZrpA7HkFPYe22apCYRFdWITCohQnaUsmfpEZYeWHL8LQzAvdt8TrlBWVYXcZXViB\n4jq0bV+b4ZLJa3xeAFjsPMpzcreR0+2zgC5uHLDFbEJ/W8JAFBWf69phVGzaLjR7dbKekBsZkRyL\n231m+x0Jde8nZNxeVGJaVKjElK1ITUg01p9yQ/Hjlv9PgLKTFm120LvbWJlN27CXwbLXurCDLu/a\ns3p7UQoVHhaAFk1ixMdQ8TGhN2Oj8tpr0pG12RF6NbcmfjXbt6YLW1A70J8zkrTlzwlB/vvPfS93\nMQ4cj7aguC0AbXe9+6CS7b3J6ZGihNg0KjGHSs4JPf2yEmS7DvkF9P5NqLACddt74XDbAO08Km7f\nhF4w8Vk3itIeqvAMCguS+wMidcYuo2KXxb92Rp+Frueg8FB8KRV7puwKCjiLvwGhmdMNIp09AWfF\nzyXU0vBA5BoM3UR5T8GetfMCzurPAEM8Z8GbKOfx54k2a7D3qUibRhACd8A1cfR5rxvQuivtmxzT\n4Lp22GekTUTOzCDxChcOyV8dK0vb2sKh4riMsQFgVm5n6egWEVcSp+Hu/v9OvSoypleCYqttk3Xb\n3B+zgVax2eFxYa8bM/HRdpm2ZXFlOMC/Xt7lvfEIk3Zbpo5l8fc/XeeNVJh3JqNYWvPffWeBd6aG\nuZ4K89/+4RMA/tytCTbKDTZLdf7KV+fI7bX4Z1+meXcqyhspYQyXi3U+Spe5ngiQCLj5ZKfCjXiQ\nVMDN59kqTkNx3Q64rbQ6bNRahN0OxvwCvkqtBtV2i4jbQ9CegPUDspAzjtuORRFgm6VjpSWR3zGD\n0Z8lpjUS+LuA5I1dAnXg3NFtCRA2lwGnXW07c+h3kqiVNQFjZkH6q/qugufcsVJjrzjlibCszgj4\nr4D/0onZX8IGbQgQKz+R/DDDLWb98OVTyfsCLjZg94E8mrZtwhXudfqIXDjT9S12jEWxR+wuSmNu\nEHA0NNurCI+eezGLRL1gg69l9O6y3PtatqfNcNph31PS2SU6I36wF5ykwr7Hag1y+96vFbnPt/pi\nL/or/e0QdBWfPbHv9JmOQWuoldC7m1j5zZ7na3dLTPb9ZIXTbXu7Rmx/14iQF4kpCL9aQNg9PstC\nl3fR+TTW7jY6n0bn0xgXbuF66+fPtK2f+EiM5y3K4ezKl0ctutNCF3NSabHbA2vWbgZr9THm598F\nqy8Dxe3tA2pjIpHGR1Hx8Zf2simHE5WchuT0odd0c09mB/bJKlLoBtbq/cFE4sAQRnwCFbNBmu1d\nI3h2Q6pSBkTHJFyv/1hadfGp9QE1vfIJ+vG35Q2GA4anMJJzqMQ5VGIOhifPJH8qlw9SN1CpG8Px\nKPkAACAASURBVL397uWlcCC3IL6JJ7+Pfmz/NqExG6DNyzp4SlO/dwjG3kGNvSP7qOch/0ikzp27\ndosohY7MoOKXhUWLTD8XUClfHHxfh7Gv26bhpwLQ8vch+wl4ouj4LUi8eaIHTTn9EHsHPfy2MAXF\nz6F4Dwqfon3jMHTzRPZMuWIw/PPozltQuScsRe0h2j8PwTePTDpXjgCEvo5up6H2MVS+A65xdODO\n4X6Dygvur0DnsfTStErgviO+tt4GQV9BjORrQEv+3TegOo0EWrfp6CxKu3HZoEEpRcgVo9japtrJ\nE3FJ8KX8v4diq0HD7OBzSmulgNMg12gz5HbiMBQRtwOnIUzUsNdFxONkuVTHa5v199soyTEYxHxu\ndroxGIqg20m52cbl6Ku+dBrsVBuMhAQsPdyp4DQUl+yG5tVWh7s7FRJ+F5diAe5mKrgNRSrgJl1r\n0bI054ckXb9hWmzWWngdBqM2IKu1W1TbLQJOVx8gs44BZKYtAZfsDLjJQaCi20iOXBYYQmTkA4DN\n3BCpkqYNrI/yAJqStl//EqyKyNWB98S8f8S1oK12jxVr215J3zmRKN1HVPt296OhkbaB2GPoVCRe\nJXjOBmLnnssUa23JRKhgA7FWSVir8DlIvSdg7AyJ+RIXsSBM2O4i7Nkyp8MjAGz0DkTPy33hrCn/\nrZoNwJYEgO0uQ2O/Ubohvq/xW3bc0CxETt8t5Mj97RV7xvvcKjq/Iqn2uk96jE1jXPh6D4BFx19Z\nJqa2TBnHMisyfvWBr4FYKodTVKH4BMaFt1GxcYzhMekx/QJj2amOrVnHym0J4NrdxrKBl5XfRu9u\nQ6cvjkop1FAClTo8Vr+q5SeWKXvZRZi2HfmxcptYuTQ6t4WV20Tntwdzw5wuVGwMIzWFMTKNkZrG\nGJlGJcdfW1idtsyeFLoP1PIbIoX2x3gEhjBG5lCpOYyUrNXQ6YDLqY5Da2nemulJnzq71JM/9wsK\nkgLSBKid3oR75D47TfFU5BfRuQXIP+tR+u4gxOdRqRuo0TdQ/rNJEmB770or6PwjyD3qeUOcfmHP\nYpelcOAMJerabAmDlv1U5BOUGIgTtyB65VQRHkezZ1dt9uz4aA35bBWq92DvkXh5fOcEnB0jH0lv\n0cdQ/0IYL98V8F07WqIy09D6DGmRc1t6bB7a4AbC2ISB6wODv9aalrWKpUu4jemBqsy21aDczg4Y\n/49iyxqmxXK5wbDHyYidUbZcrpPZa3M7GSJfb3M3W+ErqTA/2Czhcxr89HTv3PjeSp6nuRr/we1J\nDKX4h5+u4VCKX7k+xt/8vYcA/PrbU/zu4x2+NhvjzmSUf3hvg/PDfr4xF8fSmm+tFKi0OvzCXAyN\nBNeeH/IxFfLyebbKkNfJhSE/HUuzUmmgNcyEPbgMg4bZId/Yw+NwEvP4uh6ycjtzBCBr0zSX0dRx\nGqMSOTIgVxYRubIFzHJQOsYqQfsL6VGpopJPd6AVUtdvWP/C7hAxLLK2+2i5UFtN8TRWv7BZsSE7\nU+zkwGPdzEHpSwFj7ZKcQ8E5G4idf27Yqtam5HntfinXV7sqYC5yHoavQfSSVFWfYhHP7TP0zj0p\nFKrn5AWnV3yo0XkYPg+hs4VLg3395xbRmYfonQfCgu17sYIpAV7Ds5L1ODT1ktljVanA334q3uH8\nCuz1hdoG413JUaryZ16p9Kj3yliZFQFgmVV5nl0bJBHCcfF3DY+J32vYfh5JvFAqwHOPybLQxQxW\nZh29sy52p8w6OrOBLuUG3+wNCPEynMKIjaJiKVRsFCOWQkWTLzym/0TLl7emRvUHf+tvYKQmcaQm\ncYxMoiKvT148uIjWnRsEa5k1rJ01AWz7YX/KQMVHbaAmgE2NTGEkJ1GeV5P9cujYtIbqrlSeZNew\ndpbQ20tyUXRDCAOokVmMLlA7JxfGK7oYtNbSqcAGaF1T6H6bKW8INXoZNX4VY/yqNIV/id9OZsjb\nNkBbRGceSdsPEKp/7CZq9A2RPl/E1N+qitSZewT5R9JqBAXReZktj9w8owxSgOxdyH0GzYLMvGM3\nIH5LWq88r6WS1pJIXrwnPhttgm8Mht6EyJUTpR1t7kH1vlS96Zb0CIy8d6ysqa092PtMMqcMP/jv\noDxHzBKtKrQ+Bl0B52XpCHAoEyuLgAUPcGPA16S1Rct8hkUdt2MOR19/xb1OibpZHvBS7fd+3PeW\nAWzVmgPtl/YN/3NhL2G3g29vFLk87KfSNHmUq/Hvzsfx2u2LnmSrfHs5z5+7Nsqw383vfJlmu9Lg\nL70zw3/+L78E4FffHOePn+X4tRtj1E2LD1YL/MqVFMmgh/uZKg/zNd4djzAV9vIgX2W90uSnJ6Os\nVRrsNjq8EQ/icSjWqk3qHYvpkAef00HbNMk2ajiU0VdZejQgs3SDlrmExrQBbB+DqS16mXE+JHOs\n//UWtB/bXRvcdvbc4WBg3c5C7SPpCOFMgO+63YfyiMpLqwW1+8LG6pZEWQRvPJ8Vq63A7kfSSxYl\nsTHhyxC68Ny2RtrqQPmZsGGFR+IPM1ySGzZ8FYYunjo5X1sd2H0qQCzzhYA6wwWxS6jheYjOv5Ax\nX2tLKs93Hkrgdm5BUviVQ+5DI1ekoCk6faaw7cP70dJ/efsJ1vYT9PYTKNh+WcMh99Z+ABabfmWt\nBrXZQWfX0JkV8UdnVrB2VgdzvfwRjOQ0KjmDSs7I8/jk6ytmazWwtldlLLaBl5XZQOc2od0HCr0B\njOSkjMX7CQ6xUQFh/ldnBepffqJB2ZujMf2tf/uNQVrR6xeAlprEGJnsez6B4X+9/S77F91uycmw\nvYq1IyeHtS0nSH/hgRpOYYzNYYyf6z5U9Owm81MfV6cls5btJfTOkqwzK73Zi9MtVaBjFzDGL6Im\nLkn7qVc1e7IsKG2hdxaxth6iNx9ImwyQsMCxKwLSxq5KXs1LgTQNlS301ufo9OeQX5AByx1Cjd6A\n0TdQqWsv3q6pmobM5+j0x9ISSjkhcRU1+hbEr57aV6e1JbP83KcywFht8NodB+I3Ue7w87fR2ROm\noXhP2DNXBOJfhci1EweSHrNxT/4jeAtCb5zgD8rYA3UB3HMQeOtwtZzuQPtzqc40UlKdeZBZ0yXA\nzpPjulRxdr+PDk1zEU0Hj2O+Wz0o8RBZ2n3+sqPYsrZl8azUIOhydKMm7uWqeB0Gl4cD/PH6LjGv\ni+mQjz9YznM7FeJ8VM6BQr3NP7m/xTdmY1xKBPnjhSx3N4v89k+d5z/7HQFlP395hE82i/zVr87y\ne0+yOB2KX706SmavxbdXC8xEvLwzFqFlWnxno0Aq4GE27OXB7h7jATcTQQ/be22KrQ5jfjcRj1Oi\nPho1FBD3So/PnmTZGgBkYuhfAQw8jtmBAF50HQG8ZSDFwe4KmNvQuge0wDFr9zc9IFVadRuAPxMD\nf+C2tEA6Eoy1BdhX74HVkDiL0FsnGvcFTD2A3Y+hmZMIi+HbwvSekNHX/XxtEzKfSEGN2RCmOHpJ\ngFhk/lRsMyCti/KPBIhl70OnLhOjxDXUyE2IXTkzUyX3nLQwYZlHkHkEbVstiEygkldRI5chfvGl\nqsq12Rb2ywZgevtpz4DvDqBSF1Cpi6jRi2IheUXgR2stZvutBaytp1hbC+jtpcGIpcRUL0kgOY2R\nmEEFXzzw9sTjsSyRGreWsLaWsdLLWOkldK5PkjUMAVoJG3glJyVCKzkJwdfTTvGk5ScalN25c0d/\n/NGPsPI7mNvrWNvrmNvrmDvrWNsbWPnt3g8DqPAwjrFpHGMz9kOe/6mya2ZHmDUbpFnbK1hbS+jM\nRo/B8gYGQJoxfh5jZOq1VYVqyxRdf3sJa/uZALX0Yo/RCkQxJi5ijF/EmLgkjNorvMipZNCbD7A2\nH6C3HvRmWIEoauwKxthV1PhVadL+MiCtVUNv34f05+jtL6BVkxlrfF4kzrGbp/aiHfobymsCzrY/\nlWwgp0+Ys9G3IHr+9FlJnQbs3hcGrboGGBC9CKn3IXREuOtRx1J9BrkPoLENrigkviqhtCeBs05F\nWjg1lqSJeeQ98B4zEGvLbtl0385Eex/lSh58k5jG2w+ECXO/dURsxh7wBdLS58qA+dzSTZrmAgoH\nHsd8FyRa2qTY2sZQRtdfdhRblq23yTXaTIc8+J0O1isNNmstbiWC3M9VqbVNvjY+xL9ayuPtkzC1\n1vzvd9c5Pxzg67MxPl4v8K2FLH/t/XP8V78v8uWd2WEy1Sa/dH2M//vxDt+YjTE37OcPlvIYSvHN\n2WGchuJupkK23ua90TCr1SYtU3MzHqTU6rBTbxPzOEn63d2m66a2SPgkGNbSFhW7wKEfkHWsXdrW\nhm3on+2FwXZbJD1FzPwXQfX9Jrojv4W5KqyZ+00wBuNaDknV3svgv36kVC1g7CFU7woY80xB+C2U\n+3j5XHf2oPAZFD6VyBdPAobfhvDl5/qktNmE/OeQ+VhiLJRTZMnYdYg832fWO4Ym5B6iM59B9gGY\nTblWkzdQyZvCjJ2xSEl3mpB5iE5/jk5/0ctg9MdQI1cheQWVvIzynj4e5/A+WiJDbn6J3nqEzj7r\nWWfCI6jURYzRi6jURfGCvar4i1oRywZgemsRK73QC0t3ulGpcxhj8xhj86iRWZEeX4PsKMdSFtC1\nJcBLANhKr/pSKfF6j85ijM5hjM2IKhUbfe2JCmdZfixAmVLqvwb+LBKckwF+U2u99bzPPc9TpltN\nzOxWD6xtr2FurWJtraDrPVOh8gUxkqMY8TEciVGM+ChGYhRHYgwjnkK5Xw/FOnisDaz0CtbmM6yt\nZ/Z6qXfCOZx27toExsikLYFOY4xMvpacFW2ZwqhtPMbafILefCxVMACGU6jnkRmMkVmRQEdmX0nl\np9DwafTWQwFpmw96M8BgDDV6CZW6hDF66aU8aVpbkH9m30jvQcnOSQuO2D6065C4dOpZd3e7liky\nSPpjyHwuN31PBJJvyOx76Nypb1q6nhVwlv1YZvH+MUi9KwG1zzU7awnTzH4PmhlwD0P8fRkATwB2\nurEBpQ+gU5CBdujrx0ua7SxUP5DAWv9N8F49vG0zD61PgI7tM0sd2EgLAWYVBJj1GpqbukbLfIah\n/LiNc91t7/vLPEaAoGv4SLbMstPy3Q7FdMhLvWPyea7GTMhLtd1hoVjn56aiPMnv8fCAhPm7j3eo\nd0x+7doYjzMV/sWXaX7zrSn+th2JMTcSJBZwMxRws15s8OtvjnN3u8JKqcHPzgwT87lYqzR4kK9x\nKeon5HayWKpzLuIl6HKyUmkQdBlM2E3Uc409WpZJ3OvH43DaFaeZIwBZjra1iaGCdnSIfR5pCwFj\naSBif4999wSrCq2PQFcl/NV56VCFrEiVP5QQYdcYBO6gHIdBhNYdqaSs3BWPmWdCmDFP6tB7u59p\n7EjnivJDAYeBcxB7C/wnTzKEQV4WeX/3AVgt8I1A8i1hkE/rEWtVIXsfvfM57D4RJtoVFCA2clMC\nXM9gnteWCYVldOYROvMYck9lm04vjFxFpa6jklcg8GL+rO5EdWcRvbOAziyis8tSgagM8eTaAEyl\nLqL8L89CacuUSv+dJayd5a4HjEpe3qAMVGJKwNeoDcISU6+8x6Qcx44da2XLjvbzAc+XP4Qxdg5j\ndEaUptFZjNT0a7MDdY9Pa3RpV3BFNo2RHMN1/trzP9i3/LiAsrDWumw//2vAFa31f/S8z72o0V8o\n2Dzm1oo80mtYuTRmNo2VSw9qzkphDCcxRiZwJCdkPTIu68TYawVs2jLFp7Zpg7TMupgSs5s9CVQp\nVHSkV1SwX2QwMoXyvpp2Gd3jqRWxNp8ISNtexsosQ7XnG1BDKZQ9azJGzwuj9pKAUWsNxU2bRXuE\nTj+GPXufbn/vxjR6EZU8/8LmS72XR2/dE5kz80husg63ALNRqQJVwZNN9Ie2abZkMNj+VAoF9geD\n0Tuo8XdRocP9TY/dTu4e7PxAGiE7/ZB8G5JvPzecVm7wT4U5a2bBExdwFrp4gtfHlGDPykfyH+G3\nIXD9aGO3bkP1BxKf4Z6F4LuHvWy6Ac2PRLJ0vXE4z0ybwOcIMLtFf89MYYbWcaoRXH2ArtYp0DCr\nDLlHcSjnkWxZvtEmU28zF/bicRh8lq3gdzoY8ji5m6nw7mgEtOYPlne5kwpxzpYwf7Re4PPtMv/h\n7Smy1WY3QPbvfbgMQDzi5dZEhI1Kk8mIj/dnhvmdhSyzER93RsM0TYvvbhYJux28PRLmwe4eHUvz\nRjzAerVJw7Q4F/bhMBTlVpNKu0nU48NvH3e1vUvTqhF0xvDYuXKmVaJlrdhZbn3Vv7oNfAkUkVT+\n2UFvmJmxQfHRxRdat0WqbDyxG32/fWT8idam3dLrU4mmcI8JM+YZrMweeH9lAXY/kVgL5ZQol+E7\nKE/8yM90P7uXESCW/1wqJx0eGL4OyTsQOJ3vVDfLYi3YuSdRN9oC7zAkr6OSb5xtcqQtKK6LJJl9\nDNknvVZFkUlhwUZviCT5AlFAulEV4JVZFCCWWew14Ha6JQdsZB41flXuc+6Xu6/rVgOd3bewLNsg\nbKUnQRpOqdxPTmOMzGKMXXgl9/KBY6iVRS3aB1/ZDdvztTVYPOcPYeyHv6emMcYFgKnw8V0iXvrY\nmg3MXBorsyW4ILOFlZWHmU0P5KJ5fuaXCfzGf3qm7f9YgLL+RSn114EprfV//Lz3vo7qy/08EjOb\nxsqmsTKbmDsbmDsbknVW7csLUwojmrCB2gRGchzH6BSOsRmMxOjro3H3JdDtNfGrba+id9awdtYH\nTmg1lJDZQ2oax/h5jMl5aUP1Co9L10pYmWV0+hlWegFraxHKdtaXMiSSY2weNXpe1smXyzDr9lNL\nP0ZvP8ZKP4HCfjcAh/gnRi+iUpdk7T07e6fNFmQeo7e/ELmzapfAvwSLpjtN8a9s3xUjse5Ic+GJ\n9yB1+1R5RiKTLgk4KzwGFAxfgZF3nyttyvf2GLIfQCsPniQkviaVbceBs04Fit+F5hq4EjD0DZT7\n8IAqeVL3of45OOMQ+gbK8B3cmLA1Vg6cV8F17sDrLWD/Wr5Nf4PrlrmGqQu4jXM4DPGFWtqk0Erj\nMfxdtixTr6HRjPiCKKXoWJqFUr0rEz4r1Sk0OlwY8vJhuswb8SCjATe/9yxP2O3g61MSD7K0u8e/\nXszyy1dSBN0O/scPlvi5+QT/9NMNlIKhkIf3Z2N8ma3y9ZlhXE4Hd3cq/PzMMMM+F19kq2zVmrw/\nNoRScD9fYzrkYcjjZKXS7Cb2N80OucYePqeLYXuG3zCr1DqFgS4Glt6jaS6i8OJx9EnhuoGA2TqS\nPdbHVmkNnWfQeWjLlW8PxpQAurUu/kBrD7wXwf/mIalSfttFKP8IzAq4RyD0NnjGj5G2TSh+Abnv\nS5yFKwLRWzD0xsmZZO2aeMRyn9mdMQzpMRl/UyonTxOY3CjAzj0BYsUlQIM/KTaCkZtSLXnaGIxm\nBb15V2wO2cdidQAIpVCJy6jkZUheQnme7/c8vO099NYD9MZ9rM0ve4Z8lEiPI+dRyfMYI/Ni0H8J\nNko3avYkekmKvXaW0bt96fveoK12zEh1/sisALJX1X7J7KAz6yI5bi2J7Li1PMh6OZwiOyYnxWif\nnBQglpyEQPj1xF+YHQFZaVHQrPSaKGk7G+jS7uCbvT5RzRJj3bWRtJ/HRs5MzPzYgDKl1H8D/AZQ\nAn5aa519zke4feOG/uTTT1Cu1xM3cdRi1SpYXZC2gbm9gZnZxNrZGARsLjeOUdu/Nm572MZnMZKv\nUXM3hYI+DNZWe+yf2ys+tYl5jIl5HBPzqJEplOMVArVqESu9KD6EtO1D2CvLiw6X3ADGLmBMXcWY\nvo7yn/3GNrC/RkX8FjZQ05klO2xQyQxz5jbGzO0Xru7U1R102gZoB1m0sZuoybfPFF6rW1VIf4ze\n+L4ETzrcAszG34XI7OmYgMYuZH4EmU/BrEvngJGvQPyNEwcwkYQeQvZDaBfAOyrgLHD0fruDculD\n8Q4F34DQnSP3oZurUP1QjNehn0Y5D8SQaNMOmk1Lz0znpUFWR1eAu0AQeJP9nppamzTNBTQmXsfF\nrr/sIFu2336pP3B1vdqk3jGZj/jI1tsslRtcG/bzva0S80M+zg/5ubdTYaGwxy/NJ3A5DCrNDv/o\n803enx7majLIf/8ni9waH+IPHmzjMBThoJt3Z2I8ylX5tWujfLItrMYvzMXYbbT50XaZuYiPi1E/\nS6U6uXqbW8kQWzWbJYv4UECmLoGgSV8QQ6muLOtSXkKuuC3DNm1AZtiAzP7edQ0BZB2kUKIvb06b\ndqHFBhijdqFFb2DXVl3y51qr0iw88O6R/U51pwjF70nLLldcGFPP1PHnSeUxZL8LrQL4xiH2FckV\nO8nL2NiF7Q/kPNYdkegTb0LsBsr1/GtK7+VsIPYZlFflP4OjkLyJGnkTgqcvFtKNEnrzU/TGJwLE\ntCW+sOQVSAoQU76zh6XqTkuiKTa+RG/cFz+Y1hK8PXpJKtBHzktc0EuwYCKtZdDrj7A25KEzdowP\nSNjqvtVkZBZjZM5Oun8F+WNao8u7PdCVtkHYzlqv/7PDKQrO6KxIjqlpqXqMjrzS8ad/saolUcL2\nQVd6TfznmQ0w+wruQkM4RqekODC5D8BGMRJjqNCrLQb4NwaUKaX+CCkHOrj8F1rr3+l7318HvFrr\nv3nMdv4y8JcBbkT8t3//p67hSI7iHJ+2H1O47LURe3VVg6dZrFpFfvytFczN5e7a2s303uR0C5s2\nPoNjbFbWU/PiXXttnQJM9M4a5sYC1v5j81mPhnW55SKxgZoxMY8xOvPK/ALdm8XWQpdN6xUSKAFp\nMzfkMXkF5XlJer7TkviNjfvo1bsSwwEQSmLM3EbN3JYb4Qv8fSJJPu6BtOqOpGyP3cSYeR9Grp9B\nFtFQWpWA2u1PwGxBICXgbOxtlPv5LJ82WyLzbP9Aevc5/eK5Sb1/YlWp1pZUa+Y+lFyowAykvoly\nH53npq0GlH4gEpYjDEM/hfIeDmTWnV2ofBusphQAeA5KlVqyscxVO6j0xgFglgEeILeKHmizdJ2m\nuWD7qWbZbznUz5YB5Bo1WqbJiD+IQxndtPyJgAeXobiXqzIb9vIwXyPmdXEjESS71+KPVwvdGAut\nNf/gsw1mhnx8Yy7O//bDZRIBDz98lsflNAj6XdycGGKr0uCXrqT4w5UCt0ZCnIv6+HCrhGlpvjY+\nhAbuZivEvC7GAm5hyXwu4l4XhWadvU676yMzrTaldgZDGYRdIxjKOFCFer6X0q/LiA9PIZEifeeJ\nbkDzY9AFcF6UpvH9ba1a61D9vgAg3/Uj22iJ9PiZ+MaUA8LvQOD4QhFdXYbsn0hhiScBia+fyMAC\n6L1t2PquhCkrBfGbkHoP5T/em9b9bHsPtj5Cb/0QKjZLHp4Uo/7ITVRg5OQN9G+rXugDYk8ALYz4\nxFuoibckK+yshT+WBfkVrI37AsTSj0TBUIZYLCauoyauyYTxZVQDsyMM2MY+CHvcs5O4fVKcNXEZ\nNXEZY/TcK4vAANB7Fcy1x1ir8jDXn0K1l4GmInGMsX3D/RzG2CwqebYA8TMdT7Mh4+36MzobzzDX\nn2FuLKErfblsTheO5DjG6JSkMoxO40hNYYxOYQReT/zFUcu/MaDstItSahr4Pa31c91zty7O6+/8\njd+ms7VGZ3OVzuYautnTe5XXh3NsCuf4VBe0uabncE7M/Kmya7pew0yvClDbtH1smytSHbp/rIEQ\njukLOKcvyHrmAkZy4rU1T9eWKZEdG4sC1tYXsDYWe4Gzbi/G9CUcs9cwZq/imLmM8r54js6h/Zsd\nYdFWvpDHxuPejWtsHmPmDYyZ61Lt+ZLBu7q6i7X6qQC0jfuyH7cPNXUTY/o2aurNF7phid9tDb36\nAXr1h1J16Qmjpt9DzXwVFZk8/bY6Ddi2OwiUVmQwTN5Ajb8nQbXPKWgQpmJFwFnhobBvo1+Tge6E\nrCatTSjcE4ZDdyD2LsS+cqzxWTc3ofAnYJYg+Kb4ig4O6lZdugB0cuB7A3zXD4S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tH3VcbacB/2UB/OUA/2cP++PssAZllV/MyhWp01VNa8kOwyacfUuHOgXQG1oa693LL0LIzqZoya\nM5jVzegVtae2A/GX5/D6vsTruYU31KFcS4aptBO159Brz6GVVD9zaKCUPnJ2CK/nU7zez5RRwLDQ\nwufQ66+gVV88MYMmPVdloPV9iJzqACQivxat6Tqi+sqxz6SlE0GOfIQc/DXENiG3Dq3hu5D3ZFG/\neuyOOjBOfgKagah6G8pfe3I/5uY4jP4EoksqV6rk+pFjKeW2+0TFJSRXQvH3DrFr0tuB5V+D8xAy\n30Qk1Txy+3acMXMg4529HDPpqjGmjELwNQVOQDFG3AFCIM4BEHOHkdgE9AaEEOy460S8DdLNAgzN\nVIJ/3yM/mMzgepR0S2diU4n+N22X8Y0or5dmEtC1RDRGVUaIcwVp/LxvHseXlKUF+WDoIZapkxoy\nSUsOEM4MsRR1eLkonZ6VHcpSAhQmW4xuqN9DVVqQiOuwakfJCSbh+BvE/G01tkTH9hXLF9Br0RL6\nuX5gjoSpQcr4yHJJhcLqefveuw1Yfw/wIe3NA2yjdDdh6edxQPw6IvRIATwg13vUuBIBeW9AxmMc\nmNszMPYL2J6C5GIl4k97PFMlt+YUU/uwG4wglL2GKHvtqf2wcnNe6SvHP4tLAUoRtV9HlF469ohS\nLk/iD36q5ATbq2Alo9VdQ6t/HZFTcaznSDyX5+BP9OAP38UfvotcVccEkV+JXn8Vrf4KWs6zM/f+\n+jLewF38oQ68kU7kalw6kpyGXndeAbG682hpJweQh17LbqzSaJ/af4/14U0OJ3optaw8jLo2jPo2\nzLq2F8KCSc/DnZnAGR3AGR1UIGxiBH9lzxWqpaYrAFbTiFmrrvXM0wspf9zix2I4k+NqujY1jjM1\nqYDY9AT+xr5O613iJlyLFa4ldO4lgg1PLR86sPwrKHvGxdvaVB/K7gg08YFNgLNnndUyMpVmrbwy\nrl2rJBCufaFxGKDGtO70+N58PT5r99dXE/fRc/L3qN26JqzaM+gZz/8DP7AdrqscniM9uEPduMPd\n+A/jegfDUqaBOFAzwk2nEschYxG80S68wfv4gw9U6wBAKAW9pi0O0s4ick4e/ArxHdhkD37/TbyB\nm6pPTjeUU6r+CnrNSycuUZdby/hDn+P3f6zcXaF0tKbrCqAdU8ci3Zhizvp/pTLPsqoUOCtsfTo4\n23mIHPgxPOxSkQN1fwi5zY99nPRsJeBe/FKVRFf/2WNZEbnWCXO/hmA+lP7Jofwq6Tuw/Cuw5xRj\nlnSQfZPuKqz/CsxCVWS+u03+JsQ+BS1LMWYJfdnuGLMNRCaev4btTyRE8nsp/ymkmJlsOzZrdpS8\nUDLTWw6agMUdm5x4D+Xns+s0ZCVRkaYA4SeTq+w4Ht8M53BjcoWehU1eq8zmR52zBC2dtCSL5JBF\nSXoAAZSlhZjeinEuN4Wo5zOzbVOcbJFmGSxFtnGlT14wiVVnjoAWIsXMTmyzqRVjaPH3Vc6jEvvL\nQMRBlDsKTvdhUf8BQHYdYez9rqS7HgdkDuS8g7AO6sJUjtj7qvs0VALF30GYR1Qu+S7Mfgwzn4CZ\npET8OW2Pr1FydpAjv4KpT1UeX8Wbih17StirXBnF7/kpzHeC0FWwa/WbkF1zrN+v3FlTv63Bz1Sg\nq6YjStvQ6l5BlJ87UU1b4rff8yle/02IbqmTs/JmtOrzaNUX0DKO36154LldR+U77o4kd/dbKRno\nVc3o4Wa0cLOqKXrORHppx3BHenD623EH4zKUXf1yMEnphSsblPu+qgEt63SZJ2nHFOiKAzB7dAB3\nbGhP/2WYyuwW132ZZVUvPEYKVCSGMzGGPT6KPT6CPT6CMz6KM7svRB4lb7JKKzBLyjBLyzFLyjHL\nyjELS557avZ7DcrOnz0r7z148JWuU3oe7vxsQqtmT+5q18bx11YS99PS0rHCtQSq6+LXtZiVYbRT\ndDQe2rZdQ8HYI2LI6QmlRwD0vCKsOEAz65qwwvXPNQI8avHXlnCHe3CHunCGu/HGB/bOyApKMarP\nYNQ0YzZdRM95Nqv5/kVuruINPcAbfKBo/3gej8jMR2/9GkbrK8ow8CwATfrImQEFlKLMAAAgAElE\nQVS8/ptqJ72+qMwIFc1odVfQ668gkk8g6JcSOd2J3/lr5OQDxV5VX0Fv+RYi9+maMVBn8HL8c2T/\nL2FnCTLK0Bq+A8Xnnx4lsNSHHPgRbM8rvVndHyFSHh9JIld6YezH6vtT8Q7knDuaTdkchpmfgpEC\nZX92aASmgNkvwZ4/GphFB1TQ6aMi9YS+rAnM8O4bANwCkkCcRUqfqNeHJpIJ6BUAidyyTKsIT8JC\nZIs0K8CWDZuOR9T1CegadZlJfDG7hgCuFqnPsX95m47FLb5bncPUeoQPR5f5RnUO/9g+QyhgkJMa\nxDR10pMMSlOCuFKiC0FjVhJjcTNBVVoQL240SDUDWLrNtrtGmpmHIUxi3gCgxVsJBHtGhjQUS6aB\nvxEHpbnxGqp4DllCj+cdBmTOCiz9AvAh5zsI8yCQlrElmP4x2KuqHinn5aO1YzvzMPJPKnA4uw0q\n3nlsJZKUPszcQA69q8bsJVfVqNx6yphybUKBsdkHYKUo4X7Vq8fLJnNt5Ngd/IFPkdMdIKUaT9Ze\nUxE1oeOfNEkpkbODeD2f4fV9rk7CrCBa7WX0hpeVW/IZjEAA/tKsEuf338EbaldTBd1Qovz6C2ok\nWfTsLQCJ1xDdwR3uViBsoF0J8j1XaYHLqtU+t0qBMK2g7FSDWP1oBGeoF2dkAHt0AGd0AHdqPHHM\nEckpihioqsMK12FW1an+6RdpYotGsEeH1WV8BGd8BHt8FHd+du9OhoFZWoFVURW/hJVcqbQMLfhi\nRrXwew7KmkJB+YPz5wiEwwTD1QTDYQLhaoLV1RjZ2V+548LbWMceG8YeHsQeGSQ2rEaiCYelpmGW\nVRCIU59WdZ1i1fJe8NlBNIoz0o890IU92IMz0L2X16LrmJW1WHVnEkDNKCo79bMmd3xAiUeHu3GH\nuxIRIVpROVbzZczmSxh1rc8t1JRSIpdmFEDrvY03cA88F5GRi976Ckbb8wA0iZwbxhu4id9/U9nd\nNR2t7jL6ubfRyh/PPB35fGuz+N2/VeyZE0UU1KG1fBNR+dKxzpSl76o6p753YWtelSo3vKPGPU94\nvPS9uN7slyr2oPQVRPhbj2U0pL2uDtAbo5DVDJV/cOQBWu7MwPQPAQ3K/hQRPAi4DwCzrOsHRmqq\nrPpjcGYh/Vt7QEPKeATEAgRe2et0lFPAMHAWRAaON4srHxLQG9CEhePH2HAWSTYyCOqpLOxsoQuB\nJiwWIorpdjyflpwUxtYj9K/uJNL9V6MO742t8FJhGhkBg3/smuW1iix+2j1HctAgLz1ESsBEMxQQ\nm9txKE8NkGIZTG/FKEyyyAgYLEd3iHkuecFktrxFQJBu5uPKRVx/Pl40nqpGtdwFPBKRH9JTgEza\n8fGtAgVKh/fe0YDMXoLlX6j3P+c7CPMgKy43B1WsiTCh+A8QyYddtlJ6qhZp+gPQg+qzzjrc0pC4\n/+oIsv+HsDkNGWFE/Z8g0koee38AuT6D3/MTmLkLZhKi9m0FyMynHwhldAu/5z38rt+o3tqUbLTa\nV9BqryEyT9Zf6T+cxOv5FL/nM+TavAJL1RfQm15Bq77wbI7sWARvuCMBxBJa2KwC9IaLaiRZ0/bc\n+ix/Z0vtTwfacfrb8SYGlCFL05Uhq65NjSNrW9CSTrdxxlteJNbbgd3Xgd3XiTM6mABgWlaOAl9V\nCnyZ4Vr0/GebWBx7ezbWiQ32YQ8NEBvqIzbYjzM5tpeLaVlKcrQLvCrDWBVVSit+SvKakyy/16Cs\nrbJS/uLf/SXRkWGiw8P4m5uJ2/T0dILhagXYqhVgC1ZXY+R+dQJB2NWDTRMbHogDtQHs4UHcub1q\nTz0rm0BjM8GGZgKNzQTqm9BTX0yX5e7irSxhD3RjD3YroDbUi4zsWp3TCDS2Emi5QKDlAkZ59ame\nWSmXzwRO123sztu4A+0qTdkKYjacw2y5jNlyCT33+JUoj11XZAu3+yZe+6d4A3cPA7Sy+md6bVJK\n5MMJvM4P8To+gOgWIqsI/dzb6C1vIEInKCCP7eD3f4Tf/VvYWIDkbLQzX0drfBMRfPrzSOkjp+8g\ne38BG9OQko/W8qdQdDSrlXicvYkc/iVMfwGBdETTXzw2QkNKH+Y+UzEIZhpU/+mR4aAytgSTPwA/\nCiV/eChoVgGzd8FeOAzM/CisvQuapYBZIr3ehujHKs0+8Iq6foQtU2n4/egiE0svRUrJurMASNLN\nAjbsGFuuTYaZxOS2jSFgPeZxIT+VmOfz0dQqlelB6jKTkVLys6GHFKQEeKkwjf/r3hQNuSl8NrJE\nSsgkPyOJzCQTR0oaspKY33Foy0lmfsfBlZJwWpCY77Ec3SHNDBA0iAPETAJagKjXjyZSFat3wLzQ\nBiLOEtld4I0dMDocBGQHo0SkvQBL8fcu5zsIY49tUtEtX6jeymCB+lzMw/sXGXkIIz9S2rHMJgXI\nzMdUKUVXkYM/hfl7EMhA1H0f8p/yfducQ/b8DDl1G4wAovYbyklpPV2rKTcW8Tt/qRyUbkyNJ1u/\nrUq/j5nWD+CvLeD3fIbX+6nq0xUaWkULWtM1FTAdPDmAkbEIXs8t3Acf4/XfSezL9OrWBBv2rFKK\nxDp8T40jO27idN/BmxgC6avooqoGjLo2zPo2jPAZROj0IjGk5+FMDCsA1tuJ3deB91BVMwkrgFl3\nhkBDK1Z9M2Z1A3rm6WWiHdoWKfEWFxLAKzbUhz3Yf0D/reflE6iuJ1Bbj1XTQCBcg1FYjND//1NQ\n/nsNyvZryqSUuEsPiQ6PEB0eIjYyQnRE/dtb3xPq6ZmZhOrqCdXXE6pvIFhfR7Cy6iuNwgCVuRIb\nGcIe6ifa102srwtnYixxu1leSbCxmUBDM8HGZqxw7YsNl/U83Okx7P5u7IFuYt33ExZkLTUdq/k8\ngZbzBFouKur5NJm0WASn7wFO122czlsJTZpWUIrZrACaWdf23GPWgwDtHniOAmgt1zDOvvrsAM2J\n4fffwL33a+TMgNKgNH4N49zbiKLa44fJ+j5y4j5+169Vz55hIWquobd+C5H5ZPYB4sBp9gF+94+V\nay2vCe3sXzy1JUCuTyC7/x810iy+iqj7Q4Rx9Jhdbk3B8A9UVVPZtyH/qEqlTZj6AcSWoegdRPpB\noCd9O86YLcaB2d7YVtpzsPk7CNQiUi7tPch7CPZN1fFotcbvPAmMsMuW2d4MnlwioNehiSBRb4tt\nd5U0MxdPKuYqIxBictMhoAkWIw4v5aeiCcHdhQ02bY/XSjIQQnBjZo2HOw7frc7hp30LaAL65jZI\nS7LISQ+Rm2wiBeSELDQhqEwLMrkVIz9kkhlQ6f0SSX4ohS13JTFKdfxpPLkW38bAvtewF/OBt6DE\n/XoVWEpEvAfI3DhDtg+QxWaVZk8LxQHZHuCSXgzmfgmbg5DeBAVvHzJ5SOnD/E2VV6eZUPGdeHH9\nEWNqz4GJD5Cj7wESKt5EVFx/Yk2Y3FpE9v4MOXEDdEuxYrVvIwJPB0D+4jB++7vI0VsqEqPma+it\n7yCyj87SO3L9Tgy/7wvcB7/dc02W1KM3voLe8DIi5RmyyewYXv+XuPc/xuu9DU4MkZ6tTvaaLqNV\nnTmRlu2oxd9ax+n6UgGxrtvI7Q2VxVV9BqPhrGLDwk2nG/Idi2L3dxHreaCA2EB34mRdy8pVAKyx\nBau+BbOq7oWOIL2NdWK9XUR7u4j1dhLt78Ffi2umhcAsLSdQ04BVq0BYoLr+xPEUz7tI3z/xMeNf\nDCh73CKlxF1ZITYyTGRwkOjAAJGBfqKDg0hbuReFaRKsriFYvwvW6gmGq9EzM79SVs3b3CDW33Pg\ni+itxi3KloVV26DYtKYWgo3N6gzgBW6f+3Aeu+sesc67xDrvJs6QtMxsAs0XFEhrPo9e+HzBiPsX\nKSX+whRO520F0vofKJepFcBsOI917hpm21W09Of78cnINm73jUMAzTj7OsbF62iFFc/0vP7CGN79\n3+B1fwx2FJFXiX7+bfSmV05WxbQ8idf1G+Tgp4rdq3kZ/cIfITKezh5K31Mutp6fgBtFhN9ENH3v\niYyE9Bw1zhz/AIKZiDN/icg6OnpDulEY+SGs9UPueaj47iF3pvSiMP0j2JlSnZlZFx/ZxicAs+17\nEO1VJdqBfQdfpxfcYbAuqlgI6QE3gRQQbUjpEPX60UUall6OlD6r9hymFiDFyGZuZ5Mkw2QpCgLi\ndUjJhAydue0Y7Q+3eCk/jeyQycjqDnfnN3m7KpvOuQ0Gl7Z4uTSDTyZXyUgJkpWk4i4cCeWpAWxf\nEvN8qtNDbLs2G3aMrECIgK6xas8S1FMI6RYxbxhD5GHqhSBXUUXjeUCj0ozJaJwVDELgGgg9Dsji\nDtVDgGxauVv1FAXI9nWKSntVfQaxZch7HbIuHgbQ0RUY/TFsjkFGHVR+D2EdwaJJCYudyMEfQ2QZ\n8toQdd9HhB7PjMjtJWTfz1Xgq9AR1W8i6r6FCD55CiClj5x4gN/+C+RcH1ghtMbraM3fRKQc/3fv\nr8yq32LnhxDZRGQXo7e8gdZ47ZnE+tK18QbuK0as+4YKu07JwGi9hnH2NRXR8xwTBen7eFMjOJ03\ncTpuqvgh6SNSM9T0oPUKZtPF063Es2PYQ33EutQ+3u7vUvtbTcMsD2M1tMYvLeh5pxd59Ojix2LY\nY8PEejrjx74unKlxdaMQWJVhAg3NBGobCNQ2YIVr0ZJOjxF82iI9D3tmhujgIJH+PiIDA0T7+8j4\nzncp/I//w4me6188KHvcIl2X2NgYkYF+Iv39Cqj19eGu7In19fT0Pb1adXX8OvyVjUCllLgLc/tA\nWhexgV5kTFnutZRUAnWNWDX16sta14BZUv5CqFopJd7CDLHOe8Q67xDruoe/sqS2IzUdq6EFq+ks\ngTPnMMN1p5abJmNRnIF2nM5bOA++wF9WwFCvqMNsvqS0aOHG51rfAYDWfwd8D1FYiXH2NYyzr6Pl\nnLybU8Z2VLzGvd8gF8fADKI3vIzedh1RcoIqpsgGfse7SkPj2YjaV9Av/DEi7Rgp/7FNZM9PVEtA\nIAXR8m8Q5VefPGJaG1Ws2c4SVH1Dac2OEoJLX+mOZj+GtCqo+beH2DXl3vu5YmlyX0HkXH3kdjs+\nynwIuX+IsHLjz+3B+m+V+zL9nb2wU+lD7DOQEQi+FR9j7jJNF0Ck4nhzuHIxwZZtu6tEvS0yrSJW\nYjE838f2TaKux3LUpSEzifSAgedLPpxaJT/ZoiUnhW3b49145ZLv+Xw8tsz3Gwv4+cAC6UkBUkMG\npSkBtlyfpqwkprdt8uIs2fzOJpZukBNMIuJusOOtk2EW4MrJvRJ1AG6jisbPx1/Lbv/nw7h+Li3+\nXsSz3g4Bsjn1/ulpcUCWtO+2JZj4L+o9K/4DRMrhCAu50gOjP1L/Kf/2400cO0vI/h/AUq9qiqj7\nY0R23aH77a17E9nzU+Tox6pDsuo1RP07T80Yk04Uf+BT/M5fqfqylGy0lm+jNbyOsI53AE6wYh2/\nQ072PJfmE0DubOL1fYnbdUPtG2IRSErFaPkaRttraNWtz7W/9RZncXru4Pbew+m/n9Da6hV1mK1X\nsFqvoFc8G4N/6LVIifdwAXugMz4R6cIZGVDjViEwK2sT0xCrsQ0t+XS1aLuLt7ZKbKCX2FA/9vAA\nseFBBcDiTTx6VnaceGhRsp76phe2LYe2bXuL2NgYsdExomOj6t9jo8QmJhIkDkIQqKggVN9A+te/\nTsY33j7ROv4VlJ1g2R2BRvoHiI2OEB0dVWPQ4WG8fVklelraPq3aHmAz8l48WJOuiz0ySLSvG3uw\nT32xR/axfqGQcnzGzygCtY1YlVWnLmiUUuLOTGD3PMDu78Lu68CdmYxvQxJWfQuBM+ewms5i1Tae\nStKylBJvclidSXbdxh1WKdcilILRdAGr+RJm80toWU8HLI9dx+Yq7oNPcNs/VinagFZWj3H2NfS2\nV9EyTpaZk3B2PXhPObvsKKKwBuPK99HqLh/b+i531vAf/By/5z2QPlr9G2jnv49IebqGQ65N4t/7\nz7AyArn1aOf+CpH2eMZNujF1EJ69DVl1iOa/fmzgp1xqVwf2UAHU/3uEeXDnqUaqv1TF1nmvI7Iv\nHbzdi8LiD5QWKu+PEzoy6W3A2i/ByFb6qV1g6K2A/TkYjWBWK/aIG0AhiFqkdIl6vegiC0svSQj+\nU4wsYp7BphNDJ8Ca7bEWc6lOD5ETUr+NzodbLOzYvFmmapfeHV4iI2BQn53EP3XP8VJJBg/mN0hP\ntkgOGJSkBHClpCQlyFLUoSY9RNSzWbdj5AaTsXSdNXsegSDVzCDm9WNo+ZhaAcgJYJTd0SuwL/7i\nDBiKOZTb9yHaA6lvIKx9Kf72Eiz9TI0sc7/3CCBbgYn/V/2n/C8QgYPfEWX0eA/mP1e5YzV/fmQh\nvZQS5r5E9v0QAFH9bWUKecx3Vkpf9VJ2/VAZVyqvIRq+g0h68ndUxnbUiUf3byG2hcitQmv5NiJ8\n+VgnWwkDTvv7KmMwtoPILERvfUvpO1NPxqpLO4bXfQP37vt4A/fVPiY1C/3MFfTmq6qf9xn3p9J1\ncYe7cNpvYHfcxJ9TvZEiMxez8bzS1J65eOL9zJHr8n2c8SHFgPV1Yvd3JTLBhBVQeWD1zVh1zQTO\nnEVLPf3qwV0Rfqy/R10Geg/oqY38QqzqOqxwDYHqOgKNzRj5L46R2138WIzY6CiRoUGiQ0NEB9W1\nM38wn9QqKSFYVUWgsopAZaU61tfUoj8HS/evoOwUFgXWlhIatV2g9qheTUtNJVhTQ1JDI6GGBkKN\nTQTD4ReuV5Ougz0+qhwog/3qRzDUh4wo16ewLAL1Zwg2txFsPkuwue2FhN56K0tKi9B9n1jPA9yJ\nEXWDFcCqO0PgzDkCTWcx65rRgs+vg/C3N9UZZtdt7K7byFW1w9FLqhSL1nJZOTqfMfPHX13Ea/8E\n9/5H+NNDIARa1Rk14my9dmItirQjeN2f4N36KXJ1ThkDLn8fvfm1Y+tP5NYK/v2f4Pd9oETKjW+h\nnfveU7POEgfMzh+qkWbdN9UB80laoJmbyL4fKIdcy3+FyDwcQAqoiqahvwcrHer/+tABXkUm/Bw2\n+6Hg64jMcwdvj04pxif5DCLj2r6/j8D2DUhqQ4T2VTnFboK/vo8t6wFWgasgNGxvCk+uEdQbAJ1V\nexZTC2KKNJZjESzNYinqsxFzKU0NUJSs3oPFHZt7i5ucz0slL8niy9l1pjdjfLc6h7+5P0VBapCl\niEN6skXI0slLskizdAxNQ6DGmAuRLXShkRtKxvVt1p0FkvQMDLGNKxeUOxSBMimkg2hRr8lfUyyg\nlq/Gs0IgnYew8VsIhBEpV/beF2cNln6iXnvO9xDGHmCW9mqcIfPigOyRSAxnG4b+C2yOq4qk8m8d\nHQzs7CB7/wEW7kNmNeLMXz2xo1KuTuDf/8+wMgo5dQr4pz9Fy+jaykl5/6cQ3URUXkRrfQdRUHfM\nfLINvJ5P8Np/h1wcV1rOhqsYrdcRZU0nc0L7Pv5YD+7d93HbP4HojpIznHsdvfnlZ9abQlwb1nkL\np/0GTveXyJ0tJdCvP4vVdlWdSOY/vwRESok7O0ms444CYp138XeZt4Ji5bCvb1Gi/IqaU9eD+dtb\nxAbiAGygh2h/D+7MXjWSUVRCoL6JQH0TwfomrOq6F95BLT0Pe2rqIPgaHiI2MZFg5oRhEghXEayp\nVYbAcJhAZRVWaSmadfq1Tb/XoKylrFx++L//J5LrawmWlZyqQ/A4S0KvNjwUB2zDRAYHiPb14+86\nGU2TYG0tocZGQg3xS13dqYCSJ26b7+NMTyqA1tdNtKud2GBvolLKLK9MALRg81nM0uerCDlq8dbX\nsHvbiXXfx+55gDM2qGzKhoFV00jw/MsEL72CUR4+lR2SNzOWMAu4g51xp2U21qU3CVz5Onr58UX3\njy7+4jRu+8e49z9CLkyCpqmQ2nNvYLS9eiImUPoe/sAt3Js/Rs4NQ3ImxkvfQT/39rFbA+TGIt69\nHyMHPlFW/jNvo5397lPdmjK6gez8R+TEF5CUg3buLxGFbU9YzzSy4/+E6Aqi5g+g/I3H5JRNwMDf\nKsar/q8RSY90XUpP5WNtjUDhtw/1Zcq1L2C7U/VkBsvjj5Gw9RnYk5D+NsKIA4xDbNky0AmcAZGL\nLyPEvEEMrRBTy2PLWcb2I6SZhSxEtgnoFg8jPjHXw9I1GrPUe+5LyQdTq+SHLFpyU5hYj3BrdoPr\nFVl8NLrEw22bkGUoUGZqpAdNSlMsNhyf3JBJsgErsQhZgRBB3WDDeYgnHdLNAhx/ACGCBPQqkMPA\nFCr+IkWxfbFPAAmBV0FYSOkqphBPjXA19f1SSf0/VYL/nO8hzP2RGGtxQOZA2Z8fKhOXO4sw+Ldg\nb6oKrZyjP3e5Mojs+luwNxDV70DFW0eOsCEO3rp/jBz+QNUhtf4ZouwpI3LfQw5+infnh7C1jCht\nQb/058fO6fNXZvFu/EjpNj0XUVitWLGmV07cuuE/nMG9+zvcu79DrsyDFVQasYvX0cLH76w98Pp2\n90XtN3A6bihGX/qItCw1kmy7itl44VRckt7SAtEECLuDt7QIqODwQOtFAi0XCbReQM9+9unB4xZn\nfpZo+z0iHfeIdtzDmRxP3GYUFCkAVtdIoL6RQF3TiwdgjkN0ZISdnm4i3d3s9HQTHR5GRpXcByGw\nSkoJ1tYSrKkhVKOuA+XlX6nR7/calNVbQfl/5FcAoCUlkVxbTVJ9LcnxS1J9LVb2V+vGAIXOY5MT\nRHp7ifT1xa9791g1TSNYFVZArbGRpJYWQo1NLwSV71/8WDQB0KJdD4h2teNvbqhNysgi2NxKsPks\noeazBOqbTv2L6m9vYfd1EOu+T6zjDs5wH6ACbYOXrhG89AqBpnOncgYnozs4nbeJ3XwPp/MWeC5a\nQRmBK9exrlxHzztZplHieaVEzo3jPvgI98HHyOU5SMnAvPxNjJe/g5aRe6Ln8ie68G78CH+sHQJJ\n6Ofexrj4nWOPXOTaHN7df0IOfQFmEK3lWyoqIPDkg5N82I9/729hcxaKz6Od/UtE6Oi2BelEkD1/\nB4sdkNeKaPq3R2aayZ156P+/wXeh7q8QqQcdcqqP84eqZ7H4DxBp9fveCxcWf6S0U3l/htBD8cfE\nYP1dQIeMbyNE/Du5ny1DQwn+00AosBfzhpFSVS/ZfoQtd5k0M5elqIMpdB5GIaQL5nccWrKTSTIV\nm9q5tMXCts0bpZnYns/Ph5dozUthcTNK7+IWSUGTrGST9KCBqeuUp6pRaFVakA07gid98kMpOH6U\nTXeJJCMDS0hsfwxLK0cXQZSWLA9Eg9KROffBmwXrKuhq1JcwO6S+leizlN6OAmReBHK+m9Dgqc9o\nXQEyLwrlf344I25tCIb/Xrkra/8SkXK4Hkj6jopIGf8AknIRLX+NSDva5SilVP2UHf8AsU3VS3nm\nD59sJpESOXYH78t/gNUZRF412qU/Rys5Xk2NPz+Ke+Of8Ptvqjyu1jeVViz/+OXkgHJhP/gE9+77\nSqIgBFrNWcyL19GbX36mDDHp+7hDXdhffojTfuOg9rX1ClbrVfSKuucmDvytTWIdXxLruEO04w7e\nrJKMaGkZynzVepFA60X0wtOtSZJS4kxNEO24R6T9LtH2e4koCi0llWDLOZUWEAdiL9oFKV2X6Nho\nAnxFunuI9PclZDxaaipJjY3KvFdTS7CmlkA4/Fxjx9Nafq9B2flz5+RH/+lv2OkfZHvfxV3Zqxoy\nc3MUSKurIbmhjuQzjSTVhNG+4ggMKSXO7CyRPgXUdnp7iPT14S6qMxthmoSamkhuO0tSWxvJZ89i\n5p7+2c2BbfJ9nMnxOEB7QLSzHWc6rnEIhQi1XSB04TKhC5exqo5XeXKSxVtZInrnM6K3PyXacQfs\nGCI5heD5qwRfeoXg+atoKc/vNPK3NrDvfox9832ViQbo4SYCl9/CuvTmM1c/SSnxhx7gfPYzvJ5b\nIEBvfhnz2vfQqk4mKvbnRnBv/Ri/74Zi4ZpfR7/8fbTs44FHuTKFd+eHyNHbEEhWB7uGN594EJC+\nixz4DbL3Z2r0c/6vESUXj76vlDDxoeo0DGYhWv/rI0NCZXQF+v8GnE0l/s94pOvSt2HyHyEyB6V/\nhEjZl1PmLMPiP0GwDLLeTrx/0llQ7sNAFSIlbhY4xJYNA9PAFRABPH8d2x/H0soRIo1Ve4agnkrU\ntXB9n5WYRlbAYHwjSl7IpDJdHYgfRmzuLmxyLi+V/CSLX40skWLq5AYNPhlfISVkkp0SICuuQytI\ntnB9SUmKyVJ0h3QrSLLx/7H33kFy3HeW5yczy7t2QMM7wrX3Dg2gQQAEQBL0RqK8l0ZjpJ2duYvY\nu72LuNjYvbjY0c7s7EojaSRRhqIoDUnQE96j0d4bmIb3aLQp7zLzd3/8Ct0A0QAaQIMzo51vREVH\nVGVWZ2ZVZb583/d9z4o/eRkBpFtnkjDPYIoQDi0PhaPAVaBaTljqZyDZCZYcsC5L7e/VVNty3BZE\nmHEZnaSPQtZTKPYbgseTQakhM6Iw/xUU582DKeJKA5z+EFzZsOxLKPZbW90idBnR/UtpAjt3Jcqy\nF27b2haBi5htv4bBI5CxCLX8KygZCydc9nqZF3oxG15HXB2A9Nlo1a9Ik+RJ/EbMs73o9W9hnmgF\nmxOt/AksVc+geCb/uxWmiXGkGb1pO0bvYdCTKDPmY6ncgKV8/T3dTN1Y+oVTJA5vJ3F4pwRiNjvW\n/EqsJbXYilagTkFuo37lojxHNu0n3tMGhiF1u/llOIolEzblXpKmSeLUALGOFqIdkgkzhocA0DIy\ncRSX4yytwFFcLq8ND7FLJYQgceYMka4uIr0pBqy/HzMlz1FdLpz5+bjy8nEWFODKL8A2f2pTC6ay\n/qhB2USaMiEEycFrYwDtOmCLHBvAjMvcLcVmxb18GZ7CPDwF+bgLcnHnLNI4FZsAACAASURBVEdz\nPtyW4kSVHLxKpLOTcHs74Y52or29Y2jfNmeOBGgpoOZctvyh06z6yBCxrg6ibY1Emw+PUdJaZtYY\nQHNV1GDJfvB4pBvLjEWJdzQRa9pPrOmAzPDUNOz5pTiq63BUrcYy8+5eXXcrY+gKicadJA7vwDh3\nAlQNa34FthUbsJWtRnHc352UOXwZ/eD7JBs/hkgQdfYjWFY/i6VsHYpt8t8rc+QSRsO7GF27QE+i\nLq/BsvIl1FlL7r4yIAZPYdT/GnGxTwqm676Jmj2xFmxsneBlzMafwMgplIWrUUpvnaQcW3bkBKLr\nF5CMoBR+GWVG6a3LJEMSmEUHYfFLKFlFN79uxODs76RVw7yXUdzjJrQi1An+ekhfg+Ie9zcTkQ6I\ndoNnNYp9oXzyJrYsiWSgZqcE/4K4cQQFK3bLEgKJq5gYqGQSTMYJJzU8VgtR3WQolqRsuheLqmAK\nOYU53WmleLqXlksBzgRirJ6Txtt9l/G5bGR4bExzWnFoKhZNJctuwaLqxAydmS4vCTNEWB/Fa5mG\nVbWmBg+ysKlpyDD1eaAsuSFGKRNsK6SOTOjSQBcT0p9GUaw3GO5ehawnUBzj7JVIhuDs66CHUoBs\nfIBDCAPOfAxXDku7iyWfvTUwXgiZ7nBsi8yrzP88SvbNn9fYsnpc+o0d2wYWB0rhSzIW6Q7mrWLw\nFEbjG4hzHeDORKt8GWX5mrvqPIUQmCda0evfQpzrA5cPS+XTaBVP3pPBq4iG0Bu3kTz4rmS13WlY\nylK2N3Pv70bTHL5KvOH6OWTghnPIRmxlq+77HDK2zaZJcqCfWNN+oo370U8PAGCZtwhH1Woclaux\nLS+YUk2YEILk+bNEWxqINh8m2t481kWxZM/EUVqBs7gcR0k51nlT61P5yTKjUSK9PYTb24l0dBDu\naMcYkUSL6nTiyM3FlS/Bl7OgAPvChZ86ANNDIcJ9R7Gk+XAvX3r3FW6o/+VA2e1KGAbRU2cI9fQR\n7u0n1NNHqLsP/XpLUdNwLlqAa+liXEuXyL/LluB6ZBGqY2qzIe9UZiJBtL9PfhlTQG2MTXM4pI9a\nXj6uwkJcBYXYFy16qF9I/cplIq2pH2pLw5hvmnXuAhxFpZK2Li7DOnfqopmEYZA43ivvDhv3o5+T\nprqWBYtTJ6VV2JYVPLD1h37+pLzLbdiJOXQFbA5sZaux1W7Cml9+XzYbIhFDb9uNfuBdGTjs8mKp\n2oh1xVOo2ZMHlSI8it78AUbrRxALo+auxLL2S6gZd7fnEEIgjh/CqP81RAOohZtQqz+HYr09OBSm\nLi+4/R+AJxu19nu3FWqLeADR8VPwn0ZZ9jzKwvW3LqPH4NhvIHhG6peml3/i9Yhst+kBWPjlMUG6\nECIFQC7LNmbKCFUIU7JHhh/Sn0NRHeNsmTUfLItBHAEuc50tS5qD6OZF7Noy4oZOxBjFoU5jNJFE\nNyygqGQ5rPQMhVngtTMrJfjvvhbiUjjO+nmZXAzFqb/gZ+38DN7uvUS6x06ay0qmw0pWyqdsnseK\nPxHFY7Xhs9oYSVxCU6wp09prJFPboDIAjAI1gAXie0HEUzFKjtTn3gyxI9L+wjpTHo/h7RA7dWsC\nghGDM69Bwi/zRl3j3y8hDDj+Boz0wcyVMP/xW8CT0GOInl/D1S7IypPedPaJPcTE5W7M1l9CZAhl\n4WqUopdvuyykNI9Nb8i2ut2NWvY8asGmuw60CNOQlhb1b0srGd80LDXPSyuZe4g9Ms4PoB96H71t\nNyRiqIvysa5+Hq2w9r4mJ82RQRIt+0i07JV6VSHQHsnFvmIjtqp1D+ybaIwOE+9oJNbeSLy9AXNk\nCFQVW14Jjuo6nFWrscyevGHu3WqMCetqJ9bVRqyzDf2qbLlaZs7GWV6Ns6QCR0k5lpmzHxoIM8Jh\nYkePjnWOon19xE4MjGmf7QsX4iopxV1SgqukBMcjix+qYe1E2xcZOEnk+An5ODZA5PgJYmfPgRDM\n+vLnWfKf/+97es9/A2V3KCEE8QsXCXX3EerpSx3wAaKnz45NZqBpuBYvki3Q3OXykZeDbebDzau8\ncRuTly4R7mgn0tkp9Wl9fWODBKrHgys/H2dB4RhQs856OCPFQggSJ49LgNbZSqyrHdM/CoCWNU3+\nkMurcVWswDJj6pg0/eI5ok37iTUfJNHbDoYhNRQVK3FWrsJeVvNA+W5jepCGHSSadiPCQZS0THnC\nrd2EZf7kWKqb3lMIzJPdJA++h9F1EExD6lZWPo1WUDtpQCniEfSGLRiN74JhoJVtwrLqsyjuu4tm\nRTyC2fSGtBrwTkdb823UeROzIGPrDB7FbPgR6HHUqm+jzCmbeDkjiej+FVztgEUbUZY8fasxqZmE\no69B4AQsfQUl82btkEgG4dSrMsR80VdQFC313iG48gZYp0vt1PU2pu4H//vgyEVxp0BebD8gwLEG\nRBQ51TgflMUpe4xeLMoMFCUTf/IKTi2DkbiJEBbihsLiNCc9Q2F0U1A8zY2iKGMtzPJsLz6bhXeP\nD1I43cP+k9fI8jnwOiykO6xku6wkDMFct4Y/GSfb6cYQ0bEUAavqIKYfB0wc2iKkdcc8UBaDfg6S\n7WCrAG126nhcgsBOsC9H8VTJ5yIDMLIDfDUo3nFWUphJ2QaOXUqxjQvHXxOmNIS91g7zn0SZtfLW\nzy8yiGj/KUSuoCx7DuavnXiAQ48jOn8nPce8s1ArvoYybWJT4evfC7P9XczWLaCqqIVPygGUu2kc\nk3EZWdawBTF6RRq9rngBrWANijY5ECUScfSOfej172OeOQJWu2TFVj2LNvfef8PmyCCJpt0kmvei\nD/QAoM1ZhK1yLbaax9Bm3qrLm2wJIUgO9BM9vJd4az3Jk0cB6floL6nCUbEKe0Utmm9qpuTNeJz4\nkZ5xENbdgRmSsYRa1nQcxWU4SytxVa7AMmfqzMBv2oZEgtjRo4Q7O1LXsl7ip09LXSVgycwcG4hz\nl5biKi7BknF/0pJ7LSMaI3L0uOyqHR9IgbAB4ufHA8wVqxXnIwtxLV2Ce/lS2WkrKsA2/d5a1P8G\nyu6jzHiC6KnTRI4NyDZo/1HCR47e9AFZ0tJw594A1HKW41q+5FMREgrDIH7yJJHuLiI9PUR6uokd\nOYrQZdCyJStL9tYLi3ClwNrD+HILIUiePUWss41oezPR1sYx3YF13kKcFdWy5VlaOWWTN2YoSKz9\nMLGmg8Ra66XZoqZhLyjDUbkKR1Udlln33+YUyQTJrgbih7aS7DwMho42bzG22k3Yazbcl0bEDAyj\nN36MXv8hYnQQZfocrBu+IFubkwVnwWH0A29gdOyQF5sVL6BVPTOp1qh56QjGnh+D/xJKzlq02i/d\n8SIpoiOYh/5etjPzn5fWGbcxkBV9b8CFeqlDyv3srWyMkZCtzPAFKf5Pu/niKILHpet8VjVK9trx\n58P9MLoX0mpRPMU3LH9QTmNmPI+iOse9vexrQfWC6EHaY6wAxUJcH0BgYNeWMZy4gF11409oKGiE\nkgrL0l0MRhOc8MfGjGRNIdh1doSZbhuF0zxsPTmE06Li1hTOhBL4nBa8NgvTnFYsqoLHaqKbBtkO\nNwFdstpp1hkIksSNfizqTKxKAhmgXgU4Ib4bsKayPJXxgQbFAmmbURRLysPtDenWP/2FsWMrjXu3\nQOh4amAi94bPRMDZj+HyIZizDmXuBCzm0BFE5y9AAaXoG7c1ghX+CxKgBy5KC5X851C02zNd5qUj\nGPt+KkX8S2rRVnzxrj56wjQwuvei7/stBIdQZi/FUvsS6rKqSWdamoPnSdZ/gN60HSJBlOx5WGuf\nwlK5AcV1b5pUYehyQGjf+/L3L0y0eUuwVT6KrWIN2uyF9/R+n3zvRF8n0cN7iB3ei3HtCqgattwi\nHGUrsJdWY108RUaxpkn8aB+RxoNEmw4T6++GpLw+WBcuxnm9y1FU+lCSYa6TCZHOTsJdHWOEwnVp\njiU7G1dBoRx0SwGxT8PnEyBxbUh2y/qOEE49IgMnxwLMVbsd55JHPtE1W4xzwfwpYen+DZRNYen+\nAOGjx8dAWrj/KJEjxzDCkrVCUXA+shBvcSGekkK8JUV4cnM+lfanmUgQO3KESE+3fHT3ED95Yuwu\nxLFkKe6qSjyVVXgqq7BkTv10jBCCxKkBqUtoaSDa3iJz01QV+/I8CdDKq3EUlqLaH/yYCMMgcaRb\nDgs0H0Q/exIAy9yFEqBV12HLvb+xdpD+QonG3cTrt2Gc6AVFldqR2k3Yylff85SWvADVk9z+GubF\nkyhZs7Bu+DyWiscm3So1r51H3/NrzGON4MnAUvd5tOL1d9fo6AnMljcxO94HZxpa3TdQF00s6gcJ\npkTrr6R1xpxy1Mpvolhv3V8hBGLgPTi1QwZSF3751sglPQp9/wjxEcj9BornZtAsLm2F0Q5p4+C+\nwQ5jeCvEzkH2iyjW1FSiEYDR98CRg+KuQMYSbQfLMrDmgPADbcBSUOaim4Op9mEOwaQfEMR0N4YA\nf0IlJ92JAFqvBkmzW1iWLm+qOgaDXIsmWTcvg44rQU6ORtm4KIudZ4ZJc1pwWy2k2y2k2zQM4rgt\nNtxWZSx43KF5Un5pI9i1HFQ6kSioYlzcb6sGbUbK+mM/JM6nrD9S+zq8C6IDqf2/ob17eZs8XjMe\nQ8m8+dwuLuyRofEzVsCCzbeyl+frEf1vgGsGSul3UFy33mTIKcn9iI7fgsWBWv1tlBm3n5AU8TBm\nw+uYfTslI7v6G6gLbtUafrKMUx3oO3+JuHpKgrG1X560674wDIzeepKHPsA81ianMQtXYl35tHTZ\nv8eLuzF4ifj+D4kf+BAxeg3Fl4l99RPYVz+JNvP+24YiESfW0USsYS+xhn3SN8xmx1FajWPFWhyV\nq6aMDdOHrhFpOkS08RCR5sOyi6Eo8txbViXlJoWlD8ejMhIh2tsjQVhnJ5HOTvRrKaNau11qwIqL\n5aOoGNvMqdUkT1TCMIiePkO4VwKvUN8Rwr39JK4Oji1jnz0Ld14O7vxcPPk5uHOWS3uthxhg/kcN\nygrnLxS7fvYrfEW5OGY+3EnF25UwTWLnLowBtVBXL8HObpKpD16xWnHnLMNbUoS3pBBPcSGuJY98\nKqn1RjhEtLeXcEcH4ZZmwq1tY23PG0Gau6ISa9bdneHvtYSeJNbXMwbSYr0p7zCbHUdRKe5Vj+Je\nvX7KWp365fPEmg8RazpAvKcVdB0tezautU/gWrcZy+z7bzcYl88SP7SdRP02OWXlcGIrX4N99ZNY\nlpfcs1Gl0dsgwdn54yiZM7E+9jl5Zz9JvYt5rp/krlcRF46iTJsrL2hL7z7NJgZPou/5MQydQVm8\nAm3V11BcE7OYUpu2HdH1e9m6Wvl9FM/EvzNxeifi2DuQlYNS/K1bJvdEIgB9PwUjDnnfRnHeYOdg\nJuDUL8FMwiPfQNFS+iojAld/D5obpr843t4MHYL4Gch4DkV1ScG/CIN9fSo/shVIANWYJIkbR7Cq\ns4mbVmJGECEySRgm/oTGkjQHVlXlTCDG5UiC0ukebJp6UxZmNGlw6IKf0hle+obCpDstuK0aHpuF\nbKdGzIiT5XCRNP1jweOCBHHjKBZlOlbVhxT4LwVmQXwX4AT7KsmSxQYgfBhcpSjOVAB57KzMBvWW\nofjGUxDE4EG4dhCyalCyH735GF9phNPvQVYJLH7xJqZJCIE48RGc/BiyclGKvzHhMIdIRhGtv0Sc\na5SB9tXfQnFMfBEXQiBONmIcfBWifhmJVPnyHbWLAObgWfRdv8Q80YqSlo1l3ZdRc1dNGozprbtI\n7nwdMXhBGryu2Iyl+nHUtHs7hwk9SbL9ELF976H3ypt7a2E19jVPYy2uvW9WxIyEiLXUEzu8R7L5\n0QiKy42jchXOFeuk3GIKPMpEMkmsu4NI40EijYdIDMgWqJaZhbOyFlf1SlyVKx6KPYUZjxPpaCfY\n0ECosYFId/eY5Me2YAHu4pIUACv6VAbUABKD1wi2dxFs6yDQ3kmos3uMMFEsFlxLF+POz8GTl4s7\nPwd3bg7WjKkHqHerP2pQtlRziL9N3Vnbs6fhK8rFV5RLWlEuvqI83I/884zFCiFIXL5CsKObYGcX\nwY5uQl09GMEQAJrbhaeoYIxR85WXYp957wG597xdySSRvl5CTU2Em5sJt7Z+qiDNjISJdrYSbWkg\n0lhP8rR0/Lfn5OOuW497zXpsCyZnIDmZ/xVrOkBk94fEO5vANLHlFuFatxnnqg33bbUh9WddJA5t\nJdG8FxENo81fimPTZ7BVr78nEbEQAqOvUYKzs0dR0qdLcFZ9d0H09fXNow3oe36NGL6IMi8P6/qv\nos65fS4hyDaK2fEuZsvb0mJg5VdQlt7+oiiu9GE2/BAEqCu+e1vWRFw4jOh9HdIWoJR9F8V6c4tU\nxK5B70+lV1b+t1Fs42BQRC/B6d+AbznMvkFHFj0lGTNPKUpaTWr7gzD6LjiWo7grQT8LyQ4Z4K1m\ngBgEeoB8ULKJ6UdR0ECZTUgfQiWdiC4IJDQWep04LSpR3aDzWph5HjtzPHZ0U7Dr3DDzPA4Wpzl5\n5/gg2S4rgaRBpstKut2CRVXJdkLc0JnhdDGavIRD8+C2ZBA3TmOKIA4tF4XTSLuOWjDOy3arbQVo\n01P78sFNcVLCTEowqmiQ/fJ49NRIB1zeCmkF0oD3hs9LDHXBwB8gfZm0IrmBORWmgeh7XcZmzVmB\nkvvKhMyqGD6F2fAPELkm29Y5m29vGhu6hnHgVcTpFpi2EMuj37mr+asIjaDv/51swducWFa9jFbx\n1KR+M8LQ0Vt2ktzxOmLoEuqcxVg3fEEK9+8xscO4fI74/g+IH/wYERhBzczGtnoz9rrNaFn3dx4W\n8RjRhn1E9m4l3tEoJ6fTM3FUr8FZuxZ7YcWUABP9ymXCh/ZKRqy1SXYiNAuOwhIJwqpXYlvy4H5o\nnyxhGET7+gg2HCbU0EC4rRURj0vNdUEBnuoa3GXluIoKsaQ/fB2YGU8Q6usn2NY5BsLi52R8k2Kx\n4M5djre0GE9RPp78XFxLl6DaH64P6GTrjxqUlZeWie3/8yf4O/sIdB8h0NVHsH8AkZrc0DwufAU5\n+ApzSSvOJa0oD2/e0odu0jpRCdMkevI0wY4ugh1dhDp7CPX1IxKyz++YPw9fVRm+ynLSKstxLnnk\n4edoJpNE+voINzdJoHYjSFu+HO/KVXhXrsJdXv5Qjlni7GnC+3cR3r+beF8XIJMGJEB7DPvyvCk5\nBsbQVSJ7txLZ9YGc5LTacNaswbVuM/bS6vsOMxeJOPHD24lv+wPGxdMo6Vk41r2Afd2zqJ7Ja+iE\nEBhHWkhu+w3mmX6UtGlY138WS82Tk0oKEIaO0bED/cAbEB5FzanFuuHrKL47ey+J4fMYe3+MuHIc\nZVEV2trvoNgnHpgQoatSZxa4IF3bl26aWBh+pRPR9Sq4p6OU/dktLIsIX4C+n4M9DfK+hWK5IbPx\nWj0M7ofZT6GkjQM/MbIXIv3SwT7lzyVChyF+EtKfR1GtENsG2gKwFaZa9o2AFSgjaV5BF1ewqsvx\nJwexKF5CSZVgQmOOx4E3ZRzbNxwmppuUTvegKAqtVwIEEgaPzk1n26lh/HEdp01lmstGhsOKpgi8\nNhObquG0JIkaAdKtM1GUBHFjIKUlm86YsS25ENsFqgdstQjE+ERp2tNjweti9CCEu2/e3+BxmYjg\nXiT93ZQbQNfoMTnp6pkv0xTU8Yu/0KOIzp/D0BGUxU/CI0/c2tIcY0T/AI401JrvokybeMxfmCZm\n7zbMxjdACNTKl1GLnrwjMBLJOEbju+iH3wI9iVb+JJZVn0Fx3X56c3z7k+jNO0ju/B1i+DLq3KVY\nN30RLX/FvbHTuk6iZS/xve+hH2mXNhYltZIVK6y6ryg2IQSJvk4iuz8genAnIhJGmzYD56r1OGrW\nYsspnJKOSOLMScL7dxPet5P4EZnHa5k1B1f1KlzVtTjLqqY8sFsIQfzECUKNDQQbGgg3N2EEpEWG\nY+lSPDUr8NTU4KmsQvM8/LDw+KXLBJrbCLR1EmzvINTTN3bttM2aia+sGG9pMd6yEjwFef8s9laT\nrT9qUDaRpsyIJwgdGcDf1Uegs59Adz/+7n6MUGpa0WbFW5BDWlEu3rxlePOW4sv/53H+N+MJwn1H\nCLS04W9uJdDcRvKaFMpbMtLxlZdKNq24EE9R/kPfxusgLdTUSKj+EOHWNoSeRHU6cVdJLZqnqgpn\nTu6UjyXrg1fkiWf/LqIdLWAYWLJn4lr1KK4Vq3GWVqI67t1p+8a6PvEU2f0hkX3bEEE/akYWrjWb\ncNZtwrok975AoBCCZE8TsW2/R+9pBpsde+3jODa8iDZn8m7jQgjMY20ktv0G81SvjGVZ91ksKzaj\n2O6uwROJKEZD6uKnqljWfVVGN90x8saUYdBNb4ArA23TX6JmTzypJvQYZtM/woVWlAUrUSq+NnFu\n4tBRaZlh9aBUfv+WzEThPwlHfynDsHO/MfYeQpjSJiN+VbYxrWmpbUzA1X8ChLTJUK1yQnP0nZTB\nahXEm8EcAsdGUFQQ54HjQCkmNuLGcSzKXIJ6DE2xE0raCCc1sp120u3y/w/FkhwfjbI83UmGw8r5\nYIzuoTC1s9I4MRLh+EgUt11jmtNKusOKywKaqpNmtZMUQ9IGwzaduHECU8RwaDkojDIWAaX7Qe8D\n20rQshCRLoh23uS9JhJXYPBtcOejpNfJ56KXpDmsfbp061fHgboInoUjvwDHNMj95k0tSZEIIVr/\nJ4QuouR9DmXOeH7m2DLJqPSou9QBs0tRK7+BYrsNMB+5gLH7R4irAyjzitHqvoniu71sRAgTs3sv\nyT2/gdAwas4K2WbPnH3bdcbW1RPSX2zXG4iRq6jzcyQYy52c4ez1MkN+4nvfJ7brbcTIIOr02djX\nPIV91RP3HfqdPH+a6P7tRPZuxbh0DsXhxFG7Dve6zdgKyx+YpRJ6klhPJ5HDBwgf2kvytNTK2nML\ncdetw123Duv8RVPu2J84d45Qs+yiBA/Xow9KCY5tzpxxEFZdg3Xagxvi3qmMcJhQTz+hrh6Cnd0E\nWtvHhuxUu112mcqKx4CYfdbD16d9spL+IMG+o1gz0vHm3Ntk778qUKYoyl8D/xWYLoS4drflJyv0\nF6ZJ5NQ5/B29jHb04G/vIdB9lOTI6Ngy9uxpePOX4c1dijdvKd685XhzFmPx3FuW2oOUEILY6TP4\nmyRAC7S2Ez1xakysb587G2+xHCDwVZTiKch/qEMERjhMuLmJwMGDhOoPyfFlQHW7cZdX4KmsxF1R\niSs/f0pBmuEfJVy/TwK05gZELIpis+OsqMG9dgPulY8+8DSnSCaJtRwksutDYq2HpP5s1lxc6zbj\n3vAsWtZ9OnyfP0l8+x+I1+8APYElvxLHhhexFq2Y9MlaCIE50CnB2YkulPTp2J7+JlrpxLYFnyxz\n9Ar6Rz/CPNWBuqgE61N/flfWzLwygLHj7yAyirbmW6jL19xm20xE33uIvndgZiHqij+f0Pld+M9I\nQGBPQ6n697fEMomhbhh4A2atRpn/+PjziVE4+XNwL0SZ9+L48/GLcO1d8FWjeKVNhwgdgsQ5yHgJ\nxbwGiSawloNlDggDOATMQLAsZeDqJWY4MYRBRHcR0VXSbXamp9z5TSFoHwzhsWosz3CRMEx2nRth\ncZoTt0Xl4Hk/XodGptNGmt1Cug1MdLLsVsLGNTyWLKyqclPuJqIfuAaiFhJ7pR+ZfSXCjMHI22Cb\ng+Jdkzq2hgRkRhRmvIKi2uRzp16VWrxFX7uZWYxdg96fgOaA/O+gWMfBlNDjiJa/l4Cs5Fso08aN\neMeWiQxhHvxbOV1Z/DmUJY9NzH4KE7N7K2bD62B1oK38KsrSlXf8LpoXjpLc/o+Ii8dRZi3FuuHr\nqPNu3YZb/lcygd74Mcldv0eMDqIuyMW66UtoORX3BEL08yeJ73iT+OHtkIhjySvHsemzWAur7ws0\n6VcvEz2wnej+bSRPHpMC+sJynOs246xd98AaMX3omtSGHT5AtPmwtKvQLDhLyiUQW71uSg27r7vk\nh5qbCDU3E25uInnlCiCtKTzVNRKE1azAPu/+tbh3KzMWJ9R3HYD1EOrqJTJwYmwS0jZrJr7yEnyV\nZfgqynDnLv9U03jMRILQ8VMEe48R6DtGsPcYwb5jRM9JkLjgW1+g8Ad/pD5liqLMA34G5ADlUwnK\nJiohBPErgwT7jqceRwn0HSd0ZAAjEh1bzr14IWkl+aSV5JFWko+vKA9bxsMNVr2x9GCIUE8voc4e\ngl29hDq7pXEdMpnAW1SAt7wUX3kpvorSe/ZMuZdKDl5N/YCbCTU3y+lOUrmj5RX46urwrnkU+9wH\nd96/XmY8TqyzlUj9fsIHdsu8Nc2Cs6Iaz9qNuFetRXtADYMZChA9vJfo3o+Jd7WAquGoWo378eex\nl9bc10ncDIwQ3/c+sV1bEKPXUGfMxfHYi9hXPYHinDzQNwY6SbzzY8wLA6iL8rE9/6do827vE3W9\nhBAY7dvQd74qWzUbv4laeGdQJ6IBjO1/h7jYKzM0V3zxtm0d8+Q+ROsvIWsx6qp/NyG7IoaPIVp/\nJDVm5X9+i9+UOPUuXG2CnK/dZJUhrjXA4F6Y+xKK98bnP4TEFZj5BRTVPu7r5VmFYlsI8T2Amgr1\nVkD0Iu0xVhI3zyBEFIOZxIwwMd2LblpImiqLfY6x43ImGONyeFzw33DJj24Kqmf6ePvYIOlO6VHm\ns1vIsAuEMPHZDGJmkAzbHHTzIoYYTkUqqUhvskwQsyB+AKwlYJmPCLfJbMv0p1G0FCMYaIJgq4yY\nci5K7XOqpTv3RRTveEtRJEMSkBkxyP8TFMe4BlSYBqL9JzDULwHZBA79YuQ05sG/k350tX92e51g\nNICx64eIcx0oC8rQHv0Oiuv24mgRj6Dv+TVG68fgycC69suohY/ewqxn9gAAIABJREFU1d5CJOLo\nDR+R3P17hH9Iftc3fQl1WdmkwZgwDZKdh4lt/yf0/jaw2rDXbsK+4SUsc+9dq2r4R8eAWKI/Ja9Y\nXoBr9Qacqzbc943b9UqcPU1o18dEDu4lfrQPkJ5hrhWr5aOiZsrakkII4qdOSalKCohdZ8IsWdPw\nVFXhrpSaYvsjD086Ezt/gUBTK/7mVoLtXUSOHh+TG1mnZeEtLhjXWxfmY8t+sGM82RJCELt0lUBn\nH8G+YwRS4Ct07OTY9ilWK55li/DmLcOXvxxv3jLSSvLuecjwXxMoexP4T8C7QMXDBmW3K2GaRE6f\nJ9h/jEDPUQJd/fg7+4ievTC2jGvhvBRIKyCtOI+0krxPtf2ZuDZEoKWdYGs7gdZ2gp3d49q0BfPx\nVV4HaWW4lk1tJtqNlbx2jXBLC6HmJoL1h0ickbmZ9kcewbe6Dm/dminVowkhiB/pJbxnO6G9O9Av\nnpdJDKWVuB/dgLtuHZbMBwOl+sVzhLe/Q2Tn+5j+EbTs2bg3PYdrw9No9+FRNqZl2fmWNKB0uHBs\neAnH46+guic3bCBMA71pO4kPfwFhP5aqTVif/Bqq7+7fOXPkEsn3/x5xrg91WTXWJ/8UxX2Hi6qh\nYx5+DbP7Y5Q5BWgbvo/ivI3L+/kWzMYfg2cGat1fTxhoLi61yEzFGaUoRV+7eSLQSEDPjySwKPyL\nscEAIQw4+YsbpjElEycSgzD4JnjLUXxV0h5i9G3QMlB868YF/7Ya0LJBXAV6gRKSIoluXgIWEDGC\nxHUvFtXGUAxmu22k2STTe13wP99rZ7bbzil/lCMjEdbMSef9gWtkua2k2624bRrpdgOrqmFRAyio\n+KxZKUYuDZs2/4aBgwJIXgLjLDg2ySilkS1gm4/iXZXat6uSJXMuRclcn3puWLKGnqUoc5+7+bj1\n/wyiV1MWI+NMhhAmouc3cKkZJe/zKHNrb/1MLrZLQb/di7rqL1HSJr6JMq8cx9j+txDxo676Kmre\nxEza9TJOdZD88IfgH0SregrLmi+g2O4sOZA3D3tIvP8zyYwtLpRgbMnkJ5rNSIjEgY+I7XwLc/Ai\namY29vUvYF/z1D1pO69X4ngf4Q/+QOTADkgmsCxYjKtuE866DQ8c9Za8fInQ7q2Edn5M4lg/KAqO\ngmJcK+pwrajDtmTZlAEi3e8neGA/gX17CTU0og/JS6pl+nQ8VVV4KipxV1VhXzi1rdDrJUyTyLEB\n2flpaiHQ3Eb8ogwx17we6UhQXChBWEnhp2bIDhAfHGK0rQd/Wzej7bJ7Fr8ybpXhnD8nBb6WSZlT\n/jI8SxZOybXsXwUoUxTlGWC9EOL7iqKc5p8RlN2uEkPD+Dv78Xf04u/sxd/eS+T0ubHXnfNmS0at\ntICM6lIyyovQXA+mgZpsmfEEoZ7eVMuzg0DLuDZN83nxlZeSvmoFGXUrcS2f+mDx6xU/fZrAgf0E\n9u8j3NSESCZRXS68tSvx1tXhW12HdcbUTJkKIUgcP0Jozw7Ce3eQPHcaVBVHURmetRtwr3kMy7T7\nt0kRyQTRhr2Et24h0dUCmoajqg734y9gL6m6vxbIyX5iH/+ORPMeFKcHx+OfxbHx5UkzZyIaJrnj\ntyT3bwGLDeuGz2Nd8/ykomuMpvfR974GdifWJ/4ULedWfdGNZR7Zh7H/H8GVjuXxv0aZtnDi977a\nj3nov4PNjVr3v6F4b22xiNO7ZLbignWoy1+4+bXwRej98fjU4NjU5QU4/RqkF6HMemJ8+aFtED8H\nM76AojnHGaeMF1EUu7SbUFxgXwlCR7YwZ2Ewh4QxgKrMJqTHMAwPmmpnNK6gKLDIO86W9QyFMYSg\nKMtNRDfZf2GU3EwXGnDMHyXDbsVpVfHZDDwWDZ1hXFoaVjVJ0jyPTVuChgtpg2GCqID4TtCmga0C\nEW6FWP8YSyaELoPYzQTM+KxkAYWQ+aCxK/DIt8Zak0IYcOx1GD0Ky76AknGzcaw4tgXO7JYJC49s\nuvVzPbkX0foryFiAuvLfoTgnCCcXArNnG2b9r8GdiWXTv7/jZKWIhdF3vYrRsQMlczbWp76HOi/3\ntstfL+PcMRJbfoR5qhd1zhJsz34HbWnJXdcbW//yWWI73iJ+6GOIRbEsLcSx4WWs5avveXhHJBNE\nD+4k9OE/kTzag+Jw4lr7JO4nXsS66N6yDT9Z+vA1eSO5cyux7nZA6sM8G57As3YjlulTN3kfP3uW\nwN49BPbsJtSS0uZmZeFZUTsGxGwLFjw0x/5QVw/+5rYUCGsfizG0ZU/HV1VBWnU5vspy3DnLPhVb\nKIDE8KiULrV142/vYbS9h9h5CQ5RFDzLF5NeWkBaaQFpJXl485Zj9T284YV/MaBMUZSdwERN8f8T\n+D+AjUII/91AmaIo3wa+DTB//vzyMyl25p+jEiN+Al19+Dv78Hf04e/oITxwWm6nxYKvKJfMmjIy\nasrIrC7FMevh215Aioo9c45Aa7scImhoJjogxaK27Omk19WSUbeK9FUrHlq704hE5OTOvn0E9u8n\neVn+CBw5ufjq6vDVrcFVXDwlP8zrprXhPTsI7d1B8tSAvAMtLMX75LN41m1Cdd2/NjB54QyRbSn2\nLOhHmzkH98bncD32NFrGvVuH6GcHiG75Ocn2gyieNBxPfg7H+hcmbUZrDp4n8e5PMHobUKbNlhez\nSUyjmYNnSb73d4jLJ1ALHsW68VsoztuffMwrAxjbfgDxENra76IuuZV1gVQr7MAPQIBa91coGQtv\nfl0IxNE34ew+lOUvoSx49ObXLx2Csx/BwmdQZtzgyXVlDww3wrzPonhS7bzksLSL8BSjpNUi9FEZ\nveSqQHHmjjv821aBlgmiGwggqCFm9KIqmYR0gWm6AAc21c7FSIK5bhveFFt2JZLgVCBGQaYbj01j\n/4VRHJpKbqaLlitBMhxW3FYFl9XAZxUkRIA06wx08zQgsGvLUBhEsnS5YCL1brYqhJJ2K0vmr4dQ\nJ2RtHgsbF6NdcOkjmLkJJaN07Dhy+j3Z8l34NMqMmpuP46mdiOPvwPw18jjfaJkhBOLIh4ieN2FG\nAWrtX0ysBUzGMPb+BDFQL9uV6//sthO5AMZAK8mPfgihEbSa57CsfuWuGZVmYJjkR6+iN20Ddxq2\nzV/DUrVp0hOQxoVTRN95lUTzHtAs2KrX49j4MpaFd7aCmaj0wctEtm4hvG0Lpn8Ey5z5uDd/Bte6\nzQ/UOjSCAcL7dhHa+RHRtpQlzyNL8Dz2JJ71j2OdMzU6LWEYRLq7COzeTWDvXmIDxwE5Ielbuw7f\n2nW4CgsfSrdEGAah7j5G9h9i9GA9wbZOzHgcAOfiRdJJoKocX1WFNGT9FFgwU9cJ9hxluKGNkYY2\nRtu6byJP3IsXklZaQHpZAWllhaQV5X6qunH4FwTKbvuPFaUQ2AWkbPGZC1wEqoQQl++07mLNJX5Y\n/iSZ5flkVRSQWV5AWv6ST1UI+MlKDI8y0tTBSGMbw43tjLZ2YUZjADgXzCGzOgXSasrw5i791O4W\n4hcvMbK/ntEDhxg5UI+eGnJw5+eSUbeS9NW1pFWWP5TBASEEsYHjKYC2j3C7zK+0TJtO2sYNpG96\nHHdZ+ZQdi8Tpk4T37iC48yOSp0+iOJ141m7C+9QLOArvzej1pv1IJojW7yG89W0SPW2ydVq7Ds8L\nX8K25O7MwCdLP9kvwVl3I4ovE+dTX8T+6DOTmrQE0I80k3jnx4grZ1GXlWF/7ruosxbeeR8MHePQ\nm+iH/gCuNKxP/QXa4onzLQFEZBRj239DXD6KWvosatUrE57gRfAy5v7/Cokw6srvoWTfLOoWwpTW\nDFe7pHHpjJKbXuPor2R4ecGfojglwylMXYrczSQ88vVxU9nhXRA9IbVlmhsx+iEASvpmyY7FdoCa\nBfYqEFeAPqCUmCHbE2HDCcJO0nQww+nhRCCGpigs9NpRFAXdFLRdDTLNaeWRNCdHh8OcCsQozfZy\nZDhMptOKzwYW1cRrjWOIJD5rGglzAKs6G4syDcmSCaAKkq1gXAPHRkSkPcWSPYOi+RDxS3DtHXDl\noWSkBP96BE7+FGzTYMEN7OHFfXBuO8yqQ5l/MwsmLjQgel+DmWUohV/9hHGsiej8PeL4NpR5NShV\n35x4anb4PPq2H4D/EmrVZ1FLn729T1k0RHLHzzC796BMmyfZsTl31joKPYl+4B0S214DPYG17nms\nG74waabYuHJegrGGHWB34HjsJRyPvYiafo+msUKQ6G4l9MEfiDXuB2FKDenmz2Avrrz/BJB4jPCB\nPYR2fkSk8RAkk1hmz8Xz2BN4HnsC+yMPxrhdLyMSIXT4MIE9uwns24s+NASahqeiEt+6dfgeXfvQ\nxPmxCxcZPVDPyD4JxPRRyYS5C/JIr6nEVyWZMNu0qfe5nKj0YIiRli6GD7cy0tjGSHPHmNOCY85M\nMiqLUyCskLTifKzpd7dhedj1Lx6UfbLupX2ZN3Ou+B/56xlu7SXpl+Gqqt1GRnEOmRUFZJTmkV64\njLT8JVg/ZTR8vcxEAn/XEUYa2hhulOj9eu/a4vOQUVFC5soKMqpKSCstfKi06fUSpkmop4/R/YcY\n2X+IQEu7bDXa7aTVVJK+upb02mrc+bkP5Q7LCAQIHNiPf8d2Avv3I2IxLFnTSNuwgbTHNuCuqJiS\n3r0QgnhvJ4EPthDatRURjWCdvxDv5ufxbnrqgdqbyXOnCW/bQmTHu4hIGHtRBZ4XvywHA+4R9CWP\ndxF9++fo/W0oGdNxPv1l7HWbJ2+qeegDElt/BfEIltqnsT3xlbtm/pmXBiRrdu0cWukmLI99/bY5\nmsLQMQ++itm3E2VeidSZ2W+dNhPREcz9fwOhK9Lrak75J94nIScCgxdQKv4CJX28JSYSAej+H2Dz\nQf53x20yohelqewNbUyhB+DK78Cdh5K+GhHtg0grpD2DYkmD5BHQj4H9UdnK5CAwh6TpRhfXiJvT\nMYVCTHcxy+VlNKFzOZJkvseOO+VZNjAaZSSepCzbSyCu03A5wCy3jUDCIMtpJc0m0FQTuxbAprqw\nqWEMMYJDy0fhGhII5gGZKQ+1+QjL0hRLtgDFuzJlEvsHQED2Z8asLsSF9yHQL4GoPRWvdK0TTvwB\nsopg8cs3g67BHmlBkrEUpexPbvYpMw1Ey88RZ+rldGXJ5ycEWuZAvcxHtTrQNnwPdc7tY5WMY00k\nP/oRRPxotS9iWfXZu35X9d5GEu/+A2LwAlpeDbZnv4OaPTl9ljF0hdi7vyR+8GOwWHCsfwHHk59H\n9d6bG7sZCRHZ8zHhD/8J/dwpFG8a7o3P4n7iRSwz7m7TMVFd17kGP3qH0I6PMENBtGnZeNZvwvPY\nk9hz8qeEIdJHRwnu38/oju0EDx5AxOOoXi++1avxrV2Hd9VqLGlTP4CWHBkh0NTGaH0DI/sPjXdc\nZmTLm/nUDf2noakWpkn45Fn87T1jxIe/q19OaioKvoLlZNSUk5kiPZzz7u8zfdj1Rw3KrmvKhGkS\nPHGW4ZYehlt7U3970EORsWXdi+aSXriMjJJcMkrzyCjJwb1g6oNY71ZCCCKnz0tU39jG8OE2gn3H\n5IuKgjdnCekVRaRXFJNRUYwndwnqFHuCfbKMcBh/YwsjKZAWPS6nKi3paaTVVpOxqpb0VTU4Fk69\nFsEIh+XJZttWAvv3IWIxVI8H3+o6fI+uxbt6NZb0B4/CMCMRQnu2Efxgi9R1qCrO8mq8jz+Nu279\nfY+0m5EQ4a1bCL37O8zhQSwLl+J9/os4V2+4ZwfvZF8r0bd/hj7Qgzp9Fs7nvo5txYZJtXVEyE9i\n66/Q6z8Etw/7y9/HUrTqzuvoCfR9v8VoeBclez7Wl/4Dasas2+9r306MA7+AjDlYNv8HFPetJ2KR\nCGEe+FsYPYO65n9HmbbsE68HEY0/ADOJsuI/3DS1KUb64dhrMHcjypxxS46xNubCr6A4U2aqI3sh\ncgxmfQUwYeRNcBahuIpBJGQe5piZbCcQRRc5JM0z6CKbhGkS073MSoHXAX8Uh6Yy3yuBqT+u0z8S\nYUmakyyHhd3nRtBNgdumkemwkmY3sWsCRfHj1jIRnLxB4N8CGEAVGBcg2Qa2VYj4BYh2QPqzkiUL\ntkGgEaY9g2KfI/crck56kmXVomSnfMrio9D138E9W06q3sByieAFRNMPZJZl5fdv9ikTAtH6KuLU\n/lSw/DO3/H6FEJgd72M2/BZlZg7axu9P+LkCiGQcfdcvMVo/QsleKNmxWYtv+30BMIcvk9jyI4ye\nwyjZ87A9910subfPXL1p3dEhoh/8hvje9wCwr30W5+Yv3jMzZgxdJfTu7whv3YKIhrEuzsH91Gdw\nrd6AYr8/k9Hk5UuEdnxIcOt7JM+cQrHZcNetx/vUCzjL7k9z+smKnzlDYM9u/Ht2E25rS3k3ZpO+\nYSO+desfiql3ctSPv76B0fpG/A3NRI7KdqjqcJBWUzkGxFzLljz0a2dieJSR5k5Gmzvk37Yu9BT5\normcpFcWk1ldRuaKctIrSz4VQuOTJUzznj/rf3Wg7F7qTkJ/YZqETp1ntPsY/p5jjHYfY7TrKMFj\npxEpDxRruo+MkpybgFpa7uJPvf2ZGB5ltK2b0ZZORlu6GGnpJDks24uayynp14oiMiqKSa8oxjnn\n4ZrlxS9dYbS+gdGDhxk91EDikuwi22fPIn1lDemrVpC+sgbbjKnNGzWjUYINhwns2UNgzx45LaRp\nuMvLSVu7jrTHHsM258HtNhJnTxPa/gHBbR+gX7qA4nTirnsM7+NP4yy/Px8jkUwS2beV0JbX0M+e\nRM3KxvPMK7gffx7VNfmThRCCZHcj0bd+inHmOOrsBbhe/hOsJXf2hbpexoUBEm/8N8zzx7FUPIbt\nhT+7o24MwDjZTnLL3wAC63N/fcd2pnmuS+rMHF4sT/9HlLQJhP2JEOau/wSJCOr6/+uWvEwROCeB\nWdZylNI/uVn7dPQ3EDgJxX+JYpOtBmHE4cSPwZ6NsuBzqf9xFQbfgvQ1KO48hH8biCRK+lPyjeLN\nYA5LM1nOAScwKSdunMIki5ghiOk+Zji9WFSVa9Ekg7Eki7wOHBYVkfIsc1pUcjPd9FwLcS4UJ81m\nIcNpwWczcFoMBCF8Vh+6eRqbugBN0YAWrgeik2gBYwhh3wCBD0Cxo6Rtkoa4l18D2wyUaZvlPgkT\nTv8K9Ags/lbKp0xIoBo4AUXfR7GPT7iKuB/R+DcgBEr1X9+SnmD2vIXofx8l92nUghf5ZAlhyknb\nzg9RlqxAW/dnt9iWjL3X4FmS7/wN4uoZtOpnsTz6pTuyY0JPkNzzJskdr4OiYN34RaxrXpgU+2uG\nAsQ+fp3YDpkAYF/1BI5nv3rPEUjJc6cIvf0bIns/BtPEuWoDnmdewbrs/tgrMxwitHcHwW0fEGtv\nBiFwFJXJG7u1G9G8D9YaE4ZBpKsT/549BHbvHrMccixbhm/tOtLWrsNZUDCl3QszmSTY3iV1YQcO\nEezoBtNEdbnwVZSSVl1JWk0l3uLChxpTZOo6of4BRpraGWnuZKSpfUyfjariK1guyYqyQtLLCvHk\nPHyy4pMVvXKNkbZeRtr7GW7rY7itl0Vfepai/+d79/Q+/8uCstuVHo4w2nOckY5+Rtr7GOk4wmjX\nUYyU7ku1WUnLX0pGaS4ZJblkVRaSUZqH9inmZgkhiJw6x0hzB6MtXYy2dBLo7sdM2V44580ma1UV\nWXXVZK2uxjV/zkPdluip04webGD00GH89Y1jOgLn0sUSpK1cQXptNRbf/eVJTvh/TZNId7fUTezZ\nTey4vGNzl1eQ8cwzpG96HM33gCdB0yTW3U5w6/uE92zHDAWxzJlH+stfwPvkc/c1HCCEIN5aT3DL\nayS6WlBcbtybnsfzzCto0yZ/URGmSbJ1H5G3f4556QzWohpcX/g+2oy7g1Jh6CS3/5bkztdRfFnY\nX/krtOXld1zHHLlM8s3/F3H1DJZHv4hW++LtszAHT6J/8F9AtUhglnnrNongZQnMnGmo6/7jreax\nZ/cijryJsvxFlAVrx5+PXYOuv4dpxSiP3GAeO9wMV3aNif6FEKl8SBtK9gs3tDCfRrGkg3FRAiLb\nClBtQBuCPGLGZSCdiKES071Md3ixaRqGKTjuj+Kzacx2S03f2WCMi+EEZdM9+BN6SuRvId1uwWsz\ncFniCBJ4LCqGuIpDK0BhALgM1AIaxLaCNhuhzgf/h+CuRnEsG2fJpr+AYpPfCzHUAFf3wpznUHw5\nqf3ugeO/g/lPoMwaZz6FkUA0/x2ELqNU/SWK72YNkXl8B6LjtyiL6lDKv3YrQ2boGHv+AXH8IGrh\n46grv3Jb/ZjeuRN960/A5sT69PfQltz5emKc7iP+u79BXD2HVrQK23PfRc24+w2ciEaIbf8Dsa1v\nIGIRbNXrcT73dbSZ96aPih/pIvTmr4k17kOx2XFteAbPc1/AMvPez5NC14m2NBDc+h7hA3sQ8RjW\nufPxPP403o1PYZ39YDeJRiRCqL4e/57dBPftRR8eBotF6sPWrsW3dt2U+j5eNygf2Se7Iv7DjRih\nMKgq3pJC0levJKNuJd7SoodKTiSGhqXmOgXARtt6xsLDbdMyyagqIaOqlPTKYtJLCz51E/fIuUsM\nt/Ux0tbLcHs/I229RC9eHVvGs2QBmaW5zP/ME8x/6fE7vNut9W+gbBJlGgbB46cZae+XYK3jCCPt\nfcQHhwEJ1DJK85hWU0xWTQnTaoo/9danEU8Q6OpntKWTofoWhg40jrFproVzyVotAVpWXQ3O2Q9v\nylOYJuHefkYPSSbN39iCGYuhWCykr6wh68mNZG1cP+VCz/jZs4x+9BEj771D/PRpFJsN39p1ZDzz\nDL5Vqx847NeMxwkf2E3grdeJdXeguj14n3qBtBc/d98n3sRAP6EtrxE9uBMUBWfdJrzPf/GeRuyF\nrhPf9RaRLb8APYnjyc/j3PzFSbVdjLNHif/2/0NcPYdl1TPYnvrmHSc8RSJG8qMfYvbuR81ZgfWp\n702oHQMQQ2fRP/jPYBoSmE1gmSGu9mHu/wHMyJP2CzeGZAshdVDX+lCq/wrFN3/8tbMfw6VDUPBd\nFHeqrWfqUvyuOWHhV1EUBRHsgMBhyH5FPj/yNtgXoXhWgDBSgGgOWIuAA8AsYoaJwELEsBPXPWTa\nPThS7M3lSIKRuM7/396Zh0lVXXv73TV09Tx3AzKIiiDzJMiMMiMgCIhGicYkmuQmNya5+TKZL97c\nmNHcJN+NMbkZMEZFBUFAkHmeu5lnmZGxu6u6u6q75jpnfX+cohuwoRl6QNjv8/A81HDqrF2n65zf\nWXvt32qTkYjTVt2k/M40F02SE1j+SRlZiU7SXTZSnAbJDj925SDBVobChst+F5YFRy6oDhcIw15I\n6DSEPoasyYANit4CZ351lizsgWPTIPUeaP6oNb5YCHb9AZyp1ncR73dpFe5Pg+KdNZrDmic3I5v+\nAnd0szouXDL9LdEQxuL/Rk7uwvbAE9i6T6jZyd+IElvyD4xtC7G17oLzkW+j0i5fOySxCNHFbxJd\nPsPqQvHYN3G0733Z91fvJ0Z4xRyC8/6JVHhxdh9A0sQv42h55anRiz5DhPCW9VTM+heRvdtRqemk\njnmMlHGPY8+4dnPpWEkR3tnvUrHgA4xSD7b0DFKHjCJt1DhcHbvc0LlfTBN/YQGlc+fgXbwEMxjA\nnp5O2sBBlhAbMPCGbzgvJFrupXz9RquGeO2GqsbdrpbNyRpkibCMfn1wZtafKXqs0o9n/RY8qzfi\nXr0R3+4DQNyloPN9ZPXuTlavrmT26kZy64ZZqXmesKcM98YduDfuwFOwy7r2e6xrq7LZSG9/D1k9\nOpDdo0N8Rq09CRnXn4DQouw6ERGCZ4rxFOzCvWkHnk078RTursqoJTbJvUikZd/fqUEXE4hpUrH/\nEJ41m3Gv3UzpukKi8QxW8t13khvPouUM7H3NjsPXghmO4Nu2g7IVq3F/tMTqNmCzkd6rB7mjR5Az\nahiJzeuu4FJECO7eTem8uZQv/AijrAx7VhaZox8m+5HxJHXufMM/6NC+3XhnvkXliiUgJikDHiJj\nylQSu/a8rs+OFZ2hcu50AkvnIaEgif2GkD71azhbtr7qzzDL3ATee43IpqXYcpqS/NQ3cXYfUGs8\nEgkTWTCN2JrZqLzmuJ78HvbWl293IyIYBfOILf8nKqc5zsd+dNlehVJ+ltiHP4NICPuYH2Br+unV\nd5Yv1j9RbYZh6z71ktgqkY2/ApsD1ff7KIclGCUWgp2/A1c2dHy+KoMj3j1wZj40H49Kb48YATj3\nL0jthsrog1RuhvBhyJqIsiVBZBsYRZA4Eqv/ZDRe5B8gYKQRMZLJcKaRHG/6HjFMjvhC5CQ6yE+y\nntvtrkSALrmp7CqpICqQ4VIkOQwSHT6S7MkoTsWbj5vAAaA7kAHhNUAUSXjQ6tPpyEGlP4RUbAff\npqosmYhYdWThkos9yY7F7S86fhWVWn1jYB6aB8eWoNo+imo99OLvtGgv5trfWZ0WBn0XZb84wy+h\nCowFv0RKjmIf/BVs7R+iJqSyjMisXyOn9mPv8yiOhz5/xdpG88xRwm//GvPMURwPjCJhwldRibWf\nD6Mf7yTw5u8wTh3F0b4HyZOfx3FPx1q3q4ozFiO4ZgkVs/9F7MQR7LlNSH30KZKHj7+uOtHQvt14\nZ7xJ5cqlICbJ/QeTNmo8KX0Hom6wfit84gSlc+dQNm8u0TNnsKWkkDlqNJljxpLas+cN31heSKSo\nGPfi5XgWLaV8w2YwDOypKWT260NmXIgltm5Vb+LHjEQoK9yJe5Ulwsq37EJiMWsh3gPdyR3cl5wB\nvcjo1qlBm4eLaeLddxj3hu24N+6gZMM2Kg4eByyBmNmlHdk9O5LdvT1ZPTqS2bktjjr2G9WirA4x\no1HK9xzCs2kH7k07cW/aUX1AbTYyOrclt2838gfeT/6gXiSb/wlNAAAgAElEQVS3aLhGqWKa+HYf\nwLO2AM/aTXg2bKkqikxtezd5wwaSN2wgOQN6Y6+nfpkiQuDAQdwLl+D+aElVkWhq187kjh5Ozujh\nJN999Q26a91fNIpv3VrK5s3Dt3IFEongat2arEfGkzVu3A3Xn1l3y+/hmzsD0+cloW17Mh+bSurQ\nUdd1gjYrvFR++B6Vc95GwmGSh40l/cnnsedcvWiOHthO4M3fY5w+dk1TmsahHYTfeQUpd+Mc+jjO\nkVeuCTKO7ST6wStgmjjHfwf7vTWfQ6SihNiHL4O/DPvD38fW/NMXVHPHO5YdQ/fPY2tziYgoO4wU\n/j9o2hPV+Zlq+wf3DjgyE+4cg2pq+aOJmHGLjBjc82WUsiOejyDihqZTwayE8rmQ1AmV3N0SZJHN\nkNAbbEHgOFGzLTFxEzCyiBpJpDjTSbvAV+tUZRh/zODejCRsSnHWH+ZERZguOSmYAoe8ATJckGiP\n4nL4SbE7EYpw2e/Fxn4gBvQGs9jat7MrYiZAxTJIHQTOO+JZsjxUrlX/JqVboWgpNHsYldkl/r1+\nAvv+Ck37ou4cU/19nbe+aN7PajJ+YT1e2XHMVb+ClFxsD/4QlXCxKJJAuXWsvOewD/8WtrtqPqbm\n6Y+JvP8rCFValikdB9X4PrBWd0ZXzCS66A1UchoJU76No9OVjYnBKuIPzPgzkQ2LseU0IfnJb+Ls\nMfDqXfxDIQKLZ1M5ZzqGuwjHnfeQNvFpkgaNuOY+vBKL4V+zHO/Mt+osS34ew+ejfNFCSufOIbDd\nWmCU1q8fWeMnkDFkKLakurvgh06ewr1wKZ5FS/Ft2Q4iJN11JzmjR5A97CHSunWutylJMU18u/bj\nXr0R96qNlG7carUrtNnI6NaR3Af7kju4L9l9ejSoCIt4K/Bs3klJXIR5Nu0g6qsEwJWXTW7fbuT1\n605u3+5k39+pzgVYTWhRVs+EPWXxbNpOKwW6aQexCj8AKa2bkz+oF/mD7idvQE/S7m1dby2PLkUM\nA+/OfXjWFeBeuQHPugLMcARbUiI5A3qTP3wQeUMHkNKmdb3dLQWOHsOzaBmehUup2GH1jktuey85\no4eTO3o4KR3uq7N9Gz4f5UsWUzZvLv7430TK/feTM3kKGSNHYnNdvxA1Q0EqlyygfMZbRI8fwZ6d\nQ/qjj5M+YQqO6zCPNcpLqZgxDf/CWWCzkzrucdImPY0t7eqmDyQWI7TsfYJzXq+e0hz7+Vr9zSTk\nJzLnL8Q2L8J2x924nvo+tjsu79RulhdZdWZFx3EMfhJ7/8k11h2Jv8y62PuKsI/8D2x3dr/4dTEt\n1/9zu7EN+A6q6cV2C3JkIXJkAarjVFTzPvFtBD7+F1Qchy7frCpwl4rDcOp9aDoCldUDCR6F0sVV\nxqtSsQqiRVa2DHvcjiIfnHcDOzDkbiJmOUEjnZiZgk2lkHtBRicQMzhREaZJkpPsRCdRw2RrSSV3\npCSQ5XJwxBci0yUkOyLYbQFSHQam+Em0t0KxBbgHaAmRdSAhcA1F/Jsg8glkPQaVu+JZskdRCU2r\nG7Ant4CWU6xpS9OAPX+y2k91eaG6zVTpIavJe1YbVI9/u3g6uOIc5sqfgz3BquG7pOWVVLqJzXsZ\n/KXYR/8fbC0613jMY9uXEFv0v6j0HGs1bpPL30SZJacJT/8N5vF92LsOxDX5BVQtbY2qpipn/x2J\nhEkc/TmSxn3+qo2TJRYjsHQevnf/hlnqJqFjd9ImPY3r/qtbCHMhhs+Lb/5sfO9PJ1Z8DkfzlmRM\nfor0MddXT3phjBUbNlA2dw7e5cusm8V72pA9YQJZY8fVaVeT4OGjcSG2hMrdVv/MlA73xc+xI+p1\nlWToXDHuFRsoXrqGkpXrq8ppUu9rQ+7gvuQ+2Iec/r0b1BsscOocJeu3UbymkOI1W/DuPWQthLHZ\nyOh0L7n9uleJsNR76i9TeCW0KGtgTMOgfOcBitduoWTNForXFBJ2lwHWas+c3l3I6d2Z3Ae6kvNA\nVxLzGqZnphEI4llXQPHStZQsW4v/yHEAEu9oQs6gPtZ056A+9bZoIHzmLO5FS/EsXIq3YCuYJomt\nWpAzyhJoaT261ZlgjZw+RdmH8ymd8wGRT05gT08nc+xYsidOIql9h+s3jxUhWLgR74w3CWxaB04n\nacMfJnPql0hode0ZwNi50/im/5XgqoWo5FTSpjxL6tgpV20ee9GUZn5zUp75Ls6Otf7Wie3ZSGTG\n75FgJQmPfh1H34cvX9QfDVt1ZntWY+8yBMeYT9coAUjQZxX/l57EPv6lT01lSjRoiYaAB9uwn160\nIlPERLa8Cr7jqAH/iXLFV12Gy6yi/4x7UG2nxt8bn+qLlkObfwPEmsJ0tURlD0OixeBbXFVQT2Qn\nGKfANQLUeoRmhIwQETMNU1IJxBLJTUzGdUFbnuO+EDGRqkblB8oCBKIGd6UncrIyTFaiSZIjhE1F\nSLKXY1fpJNgiwCmgn5Wxi6wDZ2fE3grKZkJCK0jpY8XqzK3Okp36ACqPWpk/Z7wx+fkOB22nVrVR\nkqgfWf8yOJKtGrwLFk5IxI+5/L8s494hL6LSLrY1kUA5sTkvQdCL/eEfYGt236ePn4hlj7J+Jra7\nu+Gc8F1U0uXrZmLbVxF+73dgs+Oa9O/Ye1y52T1A7MRB/NN+jXHiII5OvUiZ+i3sTVtdcZsL4wtt\nWo3vjVeJnT5BQvsupD/zDVwdu9e+8SWEjxzEN+sdKhbPR8IhEnv0JnPKVJL7DrohA+vAvr2Uzp6F\nd9EiYqWl2DMyyBwzluwJE0jq2KlOBIAZieDdWEDp8lWULl9tlYsAaT26xstFhpPU+uq+02sl4inF\ns64Q9+pNeNZupvJja3WoKz+XvGEDyX2oH7mD+9RrucyFRCsq8RTuxlOwG8/mnXgKdlUV4ztSk8nt\n14O8AT3I69udnN5dGsUyoya0KGtkRATfx0dxr9+Gp2A37s078e4+WGXLkXJXC/L69yB/4P3kDbyf\n9PvubhD17j/6Ce6V63Gv3YxnzWYibmtRQ3LrFuQM6kPe0AHkDRmA8wYKGi9HxO2hdOkK3IuWUb52\nAxKN4szPI3fkMHJGDyejT686SbOLaVJZUGCdKJcuQcJhEtvdR/bEiWSNG4cj89oLgKvGcOKoVQg8\n/wMkEiZ1yCgyn3nuuly7o8cO4X3jVcJbN2DPa0r6579G0uBRVy1So/u24n/jt5hFp0joP4rkz32j\n1kbMUllO+O1fYxzYgr3nUFyPvXDZbIWIYKx9j9jad7C1749z/LdrtE6QUCWxWT+CWBjHpF+iUi++\n4ZDKYsxlL0FKviUeLqh3kkAJsv5n0Lwftg5PVD9/ejWcWmI13k63snpScRBOzYYWE1FpbZGyFRA8\nDs2+ACir7ZJKQGWMuqDYvj/YjiC4CBkQM1MxSSMQS8KhbOQmJlf97nyRGKf9EVqkuEhLsOMJRjnk\nDZKf5MQfM8lyGSQ5/TiUkGArwWlrjkMdBRJAdYPIdjDOWg7+MTf4lkLag2AKeBZA9ihU0l1IqMia\njs3tj8obaI0tFoKd/w3Jd6DaP1v1PZh73oKzBag+30OlVU+pVWch91i+cHkXtxqSsJ/Y3J9aU5bj\nflxj3Z+IEFv6D4zCD7F3G45j9NcuWz8msQiRuX8ltm4uttYdcD39Yq0rKyUcIjhnGqHFM1CpGSRP\nfYGEXrWLuPOED+zCN+1/iOzfiaPlXaQ/83USew+6pvOkmCaBdSspn/k2oe2FqAQXqSPGkDHpSVz3\nXnt7pvMYFRWULZhP6cyZBPfvQ7lcpD/4EFljxpA2aHCd+IiZkQjl6zbinr8Iz5LlxLw+bC4XmQP6\nkDXkQXKGP4SrWd2XykS9FZRuqBZh54vz7SnJZPftSe7gPuQO7kt6l/oxGb8Q0zDw7j6Ie/POuADb\njXffYYjrltQ2d5L7QBdyencht283srp3aHDLjKtFi7KbkJg/QOm2fXg278S9aScl67YSKrK8cl15\n2eQN6BkXaT3J6ta+3v+4RKR60cCaTZSuKyBa7kM5HGT37UH+yAfJH/kgqW3rXjDGfBWUrliNZ9FS\nSleswQwGcWRkkD38IXJHjyBzYL86qUEwfD7KPlpA6exZBPfsQTmdpA8ZSvbESaT163fdd8ixMg/e\nd/+Fd/a7SDBAyuBhZD3zPK621952KbSzAN/rfyR65ADOu9uR/uy/k9jtgdo3xCroD857g9DC6ajk\nNFK++AMSuve/8jamSXTZO0QX/Qtbs9a4vvyzK15gY5vnEls2Dds9PXFO+n6NPQ6l9CSx2T9GZd6B\nfcJPP9UsXU5vw9zwP6i7H8LW85mLXjP3z4BT61D9XkSlxG0izCjs/AM4kuMrEG1Wbdnh1yCxCarl\nY0jwCJQugdzxKNcdSHAPBLZD5gSUzWWtwnS0A4cBWFOXhriISRoOlY03EiLHlVS1ClNEOOwN4bIr\nWqUlYoqwtbgCEUh22shKNEl0VOCyCXblwWVvhY0dWFOXzeLGtS0goWu8+fgByJ4CZash9Ak0e8aq\nhzs1G/wnoM3XqltInVwCZ1Zb7abOrzz1fIxs/SPcNRzbveMv/s72zEb2z0N1n4qtzbCLv+tYBGP+\nz5GiQ9hHfw9bq083+xbTIPbRaxg7l2HvNQ7H8C9d9jdulhYRfuNlzE8O4Bg8iYRxX6618Xd07xb8\n/3wFs+QMrkFjSXr837ClXN2NXuzMJ3jf+BOhDSuwZeWQ/uTzJA9/5JqajYtp4l+znLLX/0zkyCEc\nTZqRPvEJ0sdOxJ5xfabUIkJgx3Y8M2fiXbwIMxgksd195Dz2GFljx9XJykkzEqF8/SbcHy6sEmL2\ntFRyRgwl9+GRZA7si70O69EgXgS/fQ9FH62gePk6vDv2Wp5liS6rOH9QH3IGPUBmj/qrTTuPGY1S\num0fxasLKF5dSMm6rdW1YDmZ5DzQlZy4CMvp1RlXzvXfYDc0WpR9BhARKg6foGRNIcVrt1KydguV\nR620tCM1mdy+3ckbaAm1nAe64qjnQkkzFqO8cAdFi1dTvHgVFXutjgPJrVuQP+JB8kcOJmfgA3W+\nYMAIhihfsx73oiWULl1p3REmJ5M9ZBA5o4aTPXQwjtQbT0EHP/6Y0g9mUzZvLkZ5Oc6mTckaP4Hs\nRyfianV9qX/DW4535lt435+OWVlBcr/BZH3hKyR2qLl253KIaRJcswTfm69hFJ/F1aMPGc++gLN1\nm6vaPnbyMP6//hzj5GFcQyaQ/PjXa7XPiB0oJPzGz8HhJPGL/4n9rsuvfottX0Lso9dQrTqS8PiP\nUQmfvjCYxwoxFv0W1XagZUh6yUXe3PUe8vFC1ANfwdaquihcwhXIup9CTjts3Z6rfv580f/dk1F5\n8ebcxavBswnafA3siXD2dUjtgsroixh+KJ9d7fAfWg3KCQnNgaOEjGZWmyUzjayEZhQFK7EpRV5i\nSlWs581k705PxGW3ccQbpCQYJSPBRrrr/MpLA4WXRHsuioNYDv7FEN0NrkFgy0TK54FKgrTBcO4N\nSG6Lyhxcc5Ys6LZaTWV3RLWZYj1nRJANvwClrA4IF2YXTxZgbnoN1Xog6v4vXlz0b8SsnqUntllt\nk2poJi9GjOi8P2DuW4u9/xQcg5+8rCCL7S8g/NavwDRwfe67OLoMvOzfCIBZ6SXwzqtE1i/C1qQF\nKV/4Hs72VzfVaJSXUvHu3/Evmo1yJpA68fOkTnjqmlZTXirGnC1bk/XsV0gdMuqaFwKcJ1ZeRtm8\neZS+/z6hw4ewJSeTOWYMOZOnWKauN3jDWiXE5i/Cs3jZxUJs7CiyBvavcwNXIxjCvWojRQtXULRo\nJeFzJWCzkdW7m7VCctADZPXqVm+Lw6riCEfwFO6ieHUhxasLcW/YTizuW5Z+393kD+5F/qBe5Pbp\nRspdDWuZUddoUfYZJXC6iJJ1W+O1aYWU77EKFm1OJzl9utJs5ACajRxAdo+O9Z46Dp48Q/GS1RQt\nXo179UbMYAh7chK5g/tUZdHqusuAGY3i3VhgFbEuXka0xI0tOZm8saNo8vgk0nv1qJOToG/VSkpn\nz6Zi3VowTdIGDCTv2S+S2ufae1gCGBU+fLPeoXzGm5g+L0m9+5H1zFdI6np5l/yakGiEygUzqXhv\nGhLwk/roU6Q98WVsibXfHUs0QnDW3wgtehdbsztJ/epPcNx55WbRZtEnhP7+f5GyEhKmfAtn7xGX\nH+Oe1UTn/QHV4j4SnvhJjcLM2DILs3AGtr5TsXcbd3F8Zgxz9W+g7AS2YS+h0qstN+ToIuTwfFSv\nb6OyLJ8qERP2/gUiFZbTvz3BKpA/8hfIHYDKG4C4PwTDj2piTX2KbykYlVa2LLYXYsetBuVqLxGz\nGTExCBrpZCe0IGjEKAsHyXIlkRzPlsVM4bA3SKbLQdPkBHyRGPtKA2S5bKQlRHE5Kkm2h1CYJNqj\ngA+kD0TWAAoSByNGJZR/AMk9QexQtgJyJ6BczT6VJRMRODAN/Gegy7dQCVY2yTw0F44tRfX8d1RO\n9TSblJ/AXPFzyGyFbfD3L5pOFjExVryGHFyLbeCXsHf69LGUWITo7FcwDxXgeOhpHP0+7fhvHSuT\n6JK3iC5+01oc8oWfYMu7ct1pZPs6/NN+jQQqSBz9JEmPPHNVdZJmKIR/7nQqZv0LCYdIGTmBtM89\nh/0aFtOIaeJfu4KyaX8mcuRgtRgbOvq6suEigr9gM573Z+JdsgSJRknu0pXsyZPJHP0w9pQbs0G6\nWIgtJ+b11rsQCxWVULx4FUUfraBk5QbMYAhHWgp5wwbSZPQQ8ocPIqGeM09GKIx70464CCvAvXEH\nRigMQEantuQP7kWTwb3IG9SLpCa59RpLQ6NF2S1CpMxbtark3PJNlG3bC1jTnU2H96PZyIE0G9Gf\npKZ59RqHEQzhXrOZ4sWrKF6ymuAnlhFhetcO3DFhFM0mjCLlnjvrdJ9iGPi2bKd41lxK5i3A8AdI\nurs1TR6fRJPJE0jIv/ExR4uKKJ09C/f06cQ8bpLadyDv2WfJHDnquvyDzIAf7wfvUf7OG5jlpSR2\n70XWF75i9cW7BrFn+Mrx/fNVAkvnYs9vRubXvk/i/Veelqwa094tVP7tZaTCS9Lk50kc+fgVBbz4\nfYTeeBnz0HacDz2Gc+yXLltbZOxbS3TO71DN21nC7BKTWRHBWPJ75FiBVVx+ydSZBMswl/4EXGnY\nhr6EcsRXGMbCyPqfQmIOqvd3qi0yKo7Dvr9Bi6Go5kOs5z55F8Kl0Oar4N8L3nVWY29nDhI+CpXr\nIX0kyiZxe4qeYD9IzMwnKgYBI5OshBYoFMVBPwI0SarOlp3xh6mIGLTJTMIGbC+pJC1BkZoQJsHu\nJ9nuixf5HwfyQZpAeK1lVutoHZ+63A+ZE6BsFcTKoclTEC6OZ8ksQQkgJdvh6PvQejyqSe/4mE8h\nm34DzXpj61Tt8SZhH+ayn4KYlqi9oL2SiGBueBNz1wJsvadg71lDe6VIiOj7v8A8thPHyOdx3D/m\nU++x9hMkPP0VjF1rcfQeQcKkb15RXEk4RODdVwmvnIu91b2kPPcjHC1rz/CKCIHl8/G9+WfM0hIS\n+z5I+tNfx9mida3bVn1GHYsxw19J6fvv437nnarFQlnjHiF78mMktbv+GrTz+A8cpOi9WRTNnkes\ntKxehZiIULHvIEULV1L00QrKt+wErM4wTUYPocnDQ8gZ0KvO+2heGoNv/xHOLl7H2cVrKV5daIkw\npcjq1r4qE5Y3sCeJuQ2z+K2x0KLsFiVY5Obc0vWcXbyOc0vWEyr2AJDVrX1VFi23fw/s9fxDqzxw\nmKLFqzj34TLKC3cA9SvQjEAA9/xFnHt3Fr7CrWC3kz1kME0fn0TWkEE3XOtghsOUzf+QktenET56\nFGezO8h7+mmyJ0/GnnLtU6dmKIhv3vuUv/06hqeExM7dyP7KCyR1q32V5IWE926n/E+/JHbyGEn9\nh5Hx3Hew59QuRs1KL/7Xf0N06xocHXqS+tyL2LIuv50YMcs2Y91c7O1743r6R5c1ATX2ryf6wW9R\nzduS8MRLnxZm0RCxD/4vVLhxTPo5KvNiE1op2ou55reoVn1QvZ+vFmCnNiD7pqO6fhnVpFrMycHp\n4D1U1RdTfPvh9FxoOQWS74Bzb0JSG1TWQ4hEofR9cLVGpdwfd/dvA043pqQRNhUBI5PMhDuwKyfB\nWJTScJDMhERS4maywZjB8QvsMc76wwRiUVKcARLsAZLsXpy2zHiRfyeInrYakCeOsLJ7ZbMhoQUk\n97BiS+uBSu+NnJwFgU+qs2RRv+Xcn5gLHZ6rqpuTzb+FUBmq/49RTusYWFnGV6D0KLaHfoTKvnjV\nr7HtA8zN72LrPDreOumS9krhAJH3foacOoBjzDdwdL3YN67q76asmPA/foJ55hgJjzyPY/DEK95M\nxE4cpPIv/4V59oRlczHxyyhn7eee6MljlP/pl0T2bsfZrjMZX3wBV4eutW5XNZ46FmPRoiJK3noT\nz4z3MCsqSOnRk5wpj5MxYgS2xBsrG4lVVFIybwFF786iYsculNNJzogh5E8aT9agAXUuxLzb93B2\nziLOzllE4PgpADJ6dKbpw5YQS+vYrl6nASPlPs4t2xAXYusInDwLQHq7u2g6cgBNh/Ujf+D9JDSg\nZcbNwNWKMqvtyWfsX8+ePUUjYhqGeLbtlT2/+IssHTxVpjs6yNu0lS3f/kWDxhH45LQc+eM0WTtk\ninyY1lbmZ3eUiLei3vbnP3JUjv7yt7KpxwBZ06KdnHn7vTr7bNMwxLtyhRx6eqrsaN9OTr/ymxv6\nPCMUkvJZ78ixCUPk6Oj+Yvj91x5TJCK+96bJqYn9pexPv7z67UxTgqvmiee5YeJ/59Wr2iaybp5U\nfmekRLcsv+L7YvvXS/CXEyX28eaa9+0tksi0L0lsx/waXzf2zpHY/O+IGfJWb2PExFj3shiHPrz4\ns4JuMbe8LGbZgfj7omIe+rOYpdutx2WrxSyZL6ZpWo8r1ovpW21tHNooEtknYu4Ww9gjgeh+KQuf\nlKgRsd5rmlIcqJSyUPCifR7zBeWcPywiIuGYIcd9lXIuUCLe8EkJRg+IYZ4UMTeIGBGRUIFIOB5L\npFhMz0wxox4xQ2fEPPuGmJFSMY2ImEdfF7N4bfW4yg6KWfgzMf3nqp+rLBJj1Y/EPFN48XdQdkJi\nH3xVjOPrP/1dR4ISefsFiS77o5imUeP3HTtYYB2vvWtrfP084Y9el8ofPCLRfTUf10vx/voFKX1h\ngkT2Ftb+5gsoefFrcvqJIVK5eI6YRs0xX4nQwf1yuH8nOfHEWPEt/lDMWOyaP+NCDn/xWdnRsb0c\n+9YL4t+164Y+61I+/vYPZE2LdrJl6Fg59bd/StjtqdPPv5DSwp3WOTirg2x69Ety/PX3JHi2qN72\nVxMLuj4ib9NWZqT3kDUTvyGH/vddqTx+qkFjuBkBtshV6JvPZKZMKVUCnGik3ecC7kbad2Oix317\nocd9e6HHfXuhx93w3CkitU5zfCZFWWOilNoiV5OCvMXQ47690OO+vdDjvr3Q4755aZjePxqNRqPR\naDSaK6JFmUaj0Wg0Gs1NgBZl185fGzuARkKP+/ZCj/v2Qo/79kKP+yZF15RpNBqNRqPR3AToTJlG\no9FoNBrNTYAWZTeAUuq7SilRSt1a/SAug1LqZ0qpXUqpHUqpJUqpO2rf6rOPUuoVpdSB+Ng/UEpd\nX0fjzxhKqceUUnuVUqZS6qZesXSjKKVGKaU+VkodVkr9oLHjaSiUUtOUUsVKqT2NHUtDopRqqZRa\nqZTaH/8bf6GxY2oIlFKJSqkCpdTO+Lh/2tgxNRRKKbtSartSan5jx3IltCi7TpRSLYHhwCeNHUsD\n8oqIdBGRbsB84CeNHVADsRToJCJdgIPADxs5noZiDzARWNPYgdQnSik78CdgNNAB+JxSqkPjRtVg\n/BMY1dhBNAIx4D9EpD3QB/j6bXLMw8AQEekKdANGKaX6NHJMDcULwP7GDqI2tCi7fn4PfA+4bYry\nRMR3wcMUbpOxi8gSEYnFH24CWjRmPA2FiOwXkY8bO44GoDdwWESOikgEeBcY38gxNQgisgYobew4\nGhoROSsi2+L/r8C6WF+54/otQNxcvjL+0Bn/d8ufx5VSLYAxwN8bO5ba0KLsOlBKPQKcFpGdjR1L\nQ6OU+rlS6iTwFLdPpuxCvggsbOwgNHVKc+DkBY9PcRtcoDUWSqnWQHdgc+NG0jDEp/F2AMXAUhG5\nHcb9B6wkitnYgdSGo7EDuFlRSi0Dmtbw0ovAj4ARDRtRw3ClcYvIXBF5EXhRKfVD4BvASw0aYD1R\n27jj73kRa9rj7YaMrT65mnHfBtTUnfmWzx5oQCmVCswCvnXJTMAti4gYQLd4bewHSqlOInLL1hQq\npcYCxSKyVSn1YGPHUxtalF0GERlW0/NKqc7AXcBOpRRYU1nblFK9ReRcA4ZYL1xu3DUwHVjALSLK\nahu3UuoZYCwwVG4hH5lrON63MqeAlhc8bgGcaaRYNA2EUsqJJcjeFpHZjR1PQyMi5UqpVVg1hbes\nKAP6A48opR4GEoF0pdRbIjK1keOqET19eY2IyG4RyReR1iLSGuuE3uNWEGS1oZS694KHjwAHGiuW\nhkQpNQr4PvCIiAQaOx5NnVMI3KuUuksplQA8Acxr5Jg09Yiy7qj/AewXkd81djwNhVIq7/zqcaVU\nEjCMW/w8LiI/FJEW8ev1E8CKm1WQgRZlmmvjV0qpPUqpXVjTt7fFMnLgVSANWBq3A/lLYwfUECil\nHlVKnQL6AguUUosbO6b6IL6I4xvAYqyC7xkisrdxo2oYlFLvABuBdkqpU0qpLzV2TA1Ef+DzwJD4\nb3pHPJNyq9MMWBk/hxdi1ZTd1BYRtxva0V+j0Wg0Go3mJkBnyjQajUaj0WhuArQo02g0Go1Go7kJ\n0KJMo9FoNBqN5iZAizKNRqPRaDSamwAtyjQajUaj0cwNy40AAAEbSURBVGhuArQo02g0Go1Go7kJ\n0KJMo9FoNBqN5iZAizKNRnPbo5RaqZQaHv//y0qp/2nsmDQaze2H7n2p0Wg0Vg/X/1JK5QPdsdqI\naTQaTYOiHf01Go0GUEqtBlKBB0WkorHj0Wg0tx96+lKj0dz2KKU6Y/UFDGtBptFoGgstyjQazW2N\nUqoZ8DYwHvArpUY2ckgajeY2RYsyjUZz26KUSgZmA/8hIvuBnwH/2ahBaTSa2xZdU6bRaDQajUZz\nE6AzZRqNRqPRaDQ3AVqUaTQajUaj0dwEaFGm0Wg0Go1GcxOgRZlGo9FoNBrNTYAWZRqNRqPRaDQ3\nAVqUaTQajUaj0dwEaFGm0Wg0Go1GcxOgRZlGo9FoNBrNTcD/B5SWS3FedgbCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d650080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:1537: UserWarning: Log scale: values of z <= 0 have been masked\n",
+      "  warnings.warn('Log scale: values of z <= 0 have been masked')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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u5yo9BuDOD70Pd71/McYmonj4S1+BVWNzbaZurhU3Vv2Ga4TDMFJJ7QZccYMO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1UpKSWhbOLLBQJZRYGUrkBKFNFEdnvjpqQD/sVI6GgBVkLoWF/6jBu1nQ1V9+6XJ7/876BwHGGu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\nt+ArWQz/w1VX5V6qy97//pL3SlEAFVWKoijKLrDCy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ZUx5pUAvkJEn1r0tSwKY8xbjDFf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\nSlEURVEUZR9QUaUoiqIoirIP/P8xJxyoVP0nnQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d74d780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RONKaoJfHQV+vi7iu0aLFcDui+FWBpBGPOgpBNTAMxHC7C1PG7IJ/oSBjNZA8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v67AtLAq6gtLwhIm7yGOy3Pen680savKu7JVXZjdOy6K6zJludTLODMTuDOjJXBmgC2sUp3AEBV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wZimfJadA/ei+buO9Heeiseq/Wa7ctUnaKFHv0i6lc7uecjd8Db3w5AaDRJ27ERjh3z0dbu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\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NaVBkAlp5YJYAWjgksMbkDyQF1NQyfWVGCgAtCyMDlfMgE4+5D6CVmm0kDKSGhEmTc591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rAJl9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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10a3476a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print (\"Notebook 2: Gradient Descent\")\n",
+    "#This cell sets up basic plotting functions awe\n",
+    "#we will use to visualize the gradient descent routines.\n",
+    "\n",
+    "#Make plots inline\n",
+    "%matplotlib inline\n",
+    "\n",
+    "#Make 3D plots\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import cm\n",
+    "#from matplotlib import animation\n",
+    "#from IPython.display import HTML\n",
+    "from matplotlib.colors import LogNorm\n",
+    "#from itertools import zip_longest\n",
+    "\n",
+    "#Import Numpy\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "#Define function for plotting \n",
+    "\n",
+    "def plot_surface(x,y,z,azim=-60,elev=40, dist=10, cmap=\"RdYlBu_r\"):\n",
+    "\n",
+    "    fig = plt.figure()\n",
+    "    ax = fig.add_subplot(111, projection='3d')\n",
+    "    plot_args = {'rstride': 1, 'cstride': 1, 'cmap':cmap,\n",
+    "             'linewidth': 20, 'antialiased': True,\n",
+    "             'vmin': -2, 'vmax': 2}\n",
+    "    ax.plot_surface(x, y, z, **plot_args)\n",
+    "    ax.view_init(azim=azim, elev=elev)\n",
+    "    ax.dist=dist\n",
+    "    ax.set_xlim(-1, 1)\n",
+    "    ax.set_ylim(-1, 1)\n",
+    "    ax.set_zlim(-2, 2)\n",
+    "    plt.xticks([-1, -0.5, 0, 0.5, 1],\n",
+    "               [r\"$-1$\", r\"$-1/2$\", r\"$0$\", r\"$1/2$\", r\"$1$\"])\n",
+    "    plt.yticks([-1, -0.5, 0, 0.5, 1],\n",
+    "               [r\"$-1$\", r\"$-1/2$\", r\"$0$\", r\"$1/2$\", r\"$1$\"])\n",
+    "    ax.set_zticks([-2, -1, 0, 1, 2])\n",
+    "    ax.set_zticklabels([r\"$-2$\", r\"$-1$\", r\"$0$\", r\"$1$\", r\"$2$\"])\n",
+    "    ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) \n",
+    "    ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) \n",
+    "    ax.w_zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n",
+    "    ax.set_xlabel(r\"$x$\", fontsize=18)\n",
+    "    ax.set_ylabel(r\"$y$\", fontsize=18)\n",
+    "    ax.set_zlabel(r\"z\", fontsize=18)\n",
+    "    return fig,ax;\n",
+    "\n",
+    "def overlay_trajectory_quiver(ax,obj_func,trajectory, color='k'):\n",
+    "    xs=trajectory[:,0]\n",
+    "    ys=trajectory[:,1]\n",
+    "    zs=obj_func(xs,ys)\n",
+    "    ax.quiver(xs[:-1], ys[:-1], zs[:-1], xs[1:]-xs[:-1], ys[1:]-ys[:-1],zs[1:]-zs[:-1],color=color,arrow_length_ratio=0.3)\n",
+    "    \n",
+    "    return ax;\n",
+    "\n",
+    "def overlay_trajectory(ax,obj_func,trajectory,label,color='k'):\n",
+    "    xs=trajectory[:,0]\n",
+    "    ys=trajectory[:,1]\n",
+    "    zs=obj_func(xs,ys)\n",
+    "    ax.plot(xs,ys,zs, color, label=label)\n",
+    "    \n",
+    "    return ax;\n",
+    "\n",
+    "    \n",
+    "def overlay_trajectory_contour_M(ax,trajectory, label,color='k',lw=2):\n",
+    "    xs=trajectory[:,0]\n",
+    "    ys=trajectory[:,1]\n",
+    "    ax.plot(xs,ys, color, label=label,lw=lw)\n",
+    "    ax.plot(xs[-1],ys[-1],color+'>', markersize=14)\n",
+    "    return ax;\n",
+    "\n",
+    "def overlay_trajectory_contour(ax,trajectory, label,color='k',lw=2):\n",
+    "    xs=trajectory[:,0]\n",
+    "    ys=trajectory[:,1]\n",
+    "    ax.plot(xs,ys, color, label=label,lw=lw)\n",
+    "    return ax;\n",
+    "#DEFINE SURFACES WE WILL WORK WITH\n",
+    "\n",
+    "#Define monkey saddle and gradient\n",
+    "def monkey_saddle(x,y):\n",
+    "    return x**3 - 3*x*y**2\n",
+    "\n",
+    "def grad_monkey_saddle(params):\n",
+    "    x=params[0]\n",
+    "    y=params[1]\n",
+    "    grad_x= 3*x**2-3*y**2\n",
+    "    grad_y= -6*x*y\n",
+    "    return [grad_x,grad_y]\n",
+    "#Define saddle surface\n",
+    "\n",
+    "def saddle_surface(x,y,a=1,b=1):\n",
+    "    return a*x**2-b*y**2\n",
+    "def grad_saddle_surface(params,a=1,b=1):\n",
+    "    x=params[0]\n",
+    "    y=params[1]\n",
+    "    grad_x= a*x\n",
+    "    grad_y= -1*b*y\n",
+    "    return [grad_x,grad_y]\n",
+    "\n",
+    "\n",
+    "# Define minima_surface\n",
+    "\n",
+    "def minima_surface(x,y,a=1,b=1):\n",
+    "    return a*x**2+b*y**2-1\n",
+    "\n",
+    "def grad_minima_surface(params,a=1,b=1):\n",
+    "    x=params[0]\n",
+    "    y=params[1]\n",
+    "    grad_x= 2*a*x\n",
+    "    grad_y= 2*b*y\n",
+    "    return [grad_x,grad_y]\n",
+    "\n",
+    "\n",
+    "def beales_function(x,y):\n",
+    "    f=np.square(1.5-x+x*y)+np.square(2.25-x+x*y*y)+np.square(2.625-x+x*y**3)\n",
+    "    return f\n",
+    "\n",
+    "def grad_beales_function(params):\n",
+    "    x=params[0]\n",
+    "    y=params[1]\n",
+    "    grad_x=2*(1.5-x+x*y)*(-1+y)+2*(2.25-x+x*y**2)*(-1+y**2)+2*(2.625-x+x*y**3)*(-1+y**3)\n",
+    "    grad_y=2*(1.5-x+x*y)*x+4*(2.25-x+x*y**2)*x*y+6*(2.625-x+x*y**3)*x*y**2\n",
+    "    return [grad_x,grad_y]\n",
+    "\n",
+    "def contour_beales_function():\n",
+    "    #plot beales function\n",
+    "    x, y = np.meshgrid(np.arange(-4.5, 4.5, 0.2), np.arange(-4.5, 4.5, 0.2))\n",
+    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "    z=beales_function(x,y)\n",
+    "    ax.contour(x, y, z, levels=np.logspace(0, 5, 35), norm=LogNorm(), cmap=\"RdYlBu_r\")\n",
+    "    ax.plot(3,0.5, 'r*', markersize=18)\n",
+    "\n",
+    "    ax.set_xlabel('$x$')\n",
+    "    ax.set_ylabel('$y$')\n",
+    "\n",
+    "    ax.set_xlim((-4.5, 4.5))\n",
+    "    ax.set_ylim((-4.5, 4.5))\n",
+    "    \n",
+    "    return fig,ax\n",
+    "    \n",
+    "#Make plots of surfaces\n",
+    "\n",
+    "x, y = np.mgrid[-1:1:31j, -1:1:31j]\n",
+    "fig1,ax1=plot_surface(x,y,monkey_saddle(x,y))\n",
+    "fig2,ax2=plot_surface(x,y,saddle_surface(x,y))\n",
+    "fig3,ax3=plot_surface(x,y,minima_surface(x,y,5),0)\n",
+    "\n",
+    "#Contour plot of Beale's Function\n",
+    "\n",
+    "fig4,ax4 =contour_beales_function()\n",
+    "plt.show()\n",
+    "\n",
+    "#This writes a simple gradient descent, gradient descent+ momentum,\n",
+    "#nesterov. \n",
+    "\n",
+    "#Mean-gradient based methods\n",
+    "def gd(grad, init, n_epochs=1000, eta=10**-4, noise_strength=0):\n",
+    "    #This is a simple optimizer\n",
+    "    params=np.array(init)\n",
+    "    param_traj=np.zeros([n_epochs+1,2])\n",
+    "    param_traj[0,]=init\n",
+    "    v=0;\n",
+    "    for j in range(n_epochs):\n",
+    "        noise=noise_strength*np.random.randn(params.size)\n",
+    "        v=eta*(np.array(grad(params))+noise)\n",
+    "        params=params-v\n",
+    "        param_traj[j+1,]=params\n",
+    "    return param_traj\n",
+    "\n",
+    "\n",
+    "def gd_with_mom(grad, init, n_epochs=5000, eta=10**-4, gamma=0.9,noise_strength=0):\n",
+    "    params=np.array(init)\n",
+    "    param_traj=np.zeros([n_epochs+1,2])\n",
+    "    param_traj[0,]=init\n",
+    "    v=0\n",
+    "    for j in range(n_epochs):\n",
+    "        noise=noise_strength*np.random.randn(params.size)\n",
+    "        v=gamma*v+eta*(np.array(grad(params))+noise)\n",
+    "        params=params-v\n",
+    "        param_traj[j+1,]=params\n",
+    "    return param_traj\n",
+    "\n",
+    "def NAG(grad, init, n_epochs=5000, eta=10**-4, gamma=0.9,noise_strength=0):\n",
+    "    params=np.array(init)\n",
+    "    param_traj=np.zeros([n_epochs+1,2])\n",
+    "    param_traj[0,]=init\n",
+    "    v=0\n",
+    "    for j in range(n_epochs):\n",
+    "        noise=noise_strength*np.random.randn(params.size)\n",
+    "        params_nesterov=params-gamma*v\n",
+    "        v=gamma*v+eta*(np.array(grad(params_nesterov))+noise)\n",
+    "        params=params-v\n",
+    "        param_traj[j+1,]=params\n",
+    "    return param_traj\n",
+    "\n",
+    "# Investigate effect of learning rate in GD\n",
+    "\n",
+    "x, y = np.meshgrid(np.arange(-4.5, 4.5, 0.2), np.arange(-4.5, 4.5, 0.2))\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "z=minima_surface(x,y,1,10)\n",
+    "ax.contour(x, y, z, levels=np.logspace(0, 5, 35), norm=LogNorm(), cmap=\"RdYlBu_r\")\n",
+    "ax.plot(0,0, 'r*', markersize=18)\n",
+    "\n",
+    "#initial point\n",
+    "init1=[-2,4]\n",
+    "init2=[-1.7,4]\n",
+    "init3=[-1.5,4]\n",
+    "init4=[-3,4.5]\n",
+    "eta1=0.1\n",
+    "eta2=0.5\n",
+    "eta3=1\n",
+    "eta4=1.01\n",
+    "gd_1=gd(grad_minima_surface,init1, n_epochs=100, eta=eta1)\n",
+    "gd_2=gd(grad_minima_surface,init2, n_epochs=100, eta=eta2)\n",
+    "gd_3=gd(grad_minima_surface,init3, n_epochs=100, eta=eta3)\n",
+    "gd_4=gd(grad_minima_surface,init4, n_epochs=10, eta=eta4)\n",
+    "#print(gd_1)\n",
+    "overlay_trajectory_contour(ax,gd_1,'$\\eta=$%s'% eta1,'g--*', lw=0.5)\n",
+    "overlay_trajectory_contour(ax,gd_2,'$\\eta=$%s'% eta2,'b-<', lw=0.5)\n",
+    "overlay_trajectory_contour(ax,gd_3,'$\\eta=$%s'% eta3,'->', lw=0.5)\n",
+    "overlay_trajectory_contour(ax,gd_4,'$\\eta=$%s'% eta4,'c-o', lw=0.5)\n",
+    "plt.legend(loc=2)\n",
+    "plt.show()\n",
+    "fig.savefig(\"GD3regimes.pdf\", bbox_inches='tight')\n",
+    "\n",
+    "#Methods that exploit first and second moments of gradient: RMS-PROP and ADAMS\n",
+    "\n",
+    "\n",
+    "def rms_prop(grad, init, n_epochs=5000, eta=10**-3, beta=0.9,epsilon=10**-8,noise_strength=0):\n",
+    "    params=np.array(init)\n",
+    "    param_traj=np.zeros([n_epochs+1,2])\n",
+    "    param_traj[0,]=init#Import relevant packages\n",
+    "    grad_sq=0;\n",
+    "    for j in range(n_epochs):\n",
+    "        noise=noise_strength*np.random.randn(params.size)\n",
+    "        g=np.array(grad(params))+noise\n",
+    "        grad_sq=beta*grad_sq+(1-beta)*g*g\n",
+    "        v=eta*np.divide(g,np.sqrt(grad_sq+epsilon))\n",
+    "        params= params-v\n",
+    "        param_traj[j+1,]=params\n",
+    "    return param_traj\n",
+    "                        \n",
+    "                        \n",
+    "def adams(grad, init, n_epochs=5000, eta=10**-4, gamma=0.9, beta=0.99,epsilon=10**-8,noise_strength=0):\n",
+    "    params=np.array(init)\n",
+    "    param_traj=np.zeros([n_epochs+1,2])\n",
+    "    param_traj[0,]=init\n",
+    "    v=0;\n",
+    "    grad_sq=0;\n",
+    "    for j in range(n_epochs):\n",
+    "        noise=noise_strength*np.random.randn(params.size)\n",
+    "        g=np.array(grad(params))+noise\n",
+    "        v=gamma*v+(1-gamma)*g\n",
+    "        grad_sq=beta*grad_sq+(1-beta)*g*g\n",
+    "        v_hat=v/(1-gamma)\n",
+    "        grad_sq_hat=grad_sq/(1-beta)\n",
+    "        params=params-eta*np.divide(v_hat,np.sqrt(grad_sq_hat+epsilon))\n",
+    "        param_traj[j+1,]=params\n",
+    "    return param_traj\n",
+    "\n",
+    "#Make static plot of the results\n",
+    "Nsteps=10**4\n",
+    "lr_l=10**-3\n",
+    "lr_s=10**-6\n",
+    "init1=np.array([4,3])\n",
+    "fig1, ax1=contour_beales_function()\n",
+    "gd_trajectory1=gd(grad_beales_function,init1,Nsteps, eta=lr_s, noise_strength=0)\n",
+    "gdm_trajectory1=gd_with_mom(grad_beales_function,init1,Nsteps,eta=lr_s, gamma=0.9,noise_strength=0)\n",
+    "NAG_trajectory1=NAG(grad_beales_function,init1,Nsteps,eta=lr_s, gamma=0.9,noise_strength=0)\n",
+    "rms_prop_trajectory1=rms_prop(grad_beales_function,init1,Nsteps,eta=lr_l, beta=0.9,epsilon=10**-8,noise_strength=0)\n",
+    "adam_trajectory1=adams(grad_beales_function,init1,Nsteps,eta=lr_l, gamma=0.9, beta=0.99,epsilon=10**-8,noise_strength=0)\n",
+    "overlay_trajectory_contour_M(ax1,gd_trajectory1, 'GD','k')\n",
+    "overlay_trajectory_contour_M(ax1,gd_trajectory1, 'GDM','m')\n",
+    "overlay_trajectory_contour_M(ax1,NAG_trajectory1, 'NAG','c--')\n",
+    "overlay_trajectory_contour_M(ax1,rms_prop_trajectory1,'RMS', 'b-.')\n",
+    "overlay_trajectory_contour_M(ax1,adam_trajectory1,'ADAMS', 'r')\n",
+    "plt.legend(loc=2)\n",
+    "#init2=np.array([1.5,1.5])\n",
+    "#gd_trajectory2=gd(grad_beales_function,init2,Nsteps, eta=10**-6, noise_strength=0)\n",
+    "#gdm_trajectory2=gd_with_mom(grad_beales_function,init2,Nsteps,eta=10**-6, gamma=0.9,noise_strength=0)\n",
+    "#NAG_trajectory2=NAG(grad_beales_function,init2,Nsteps,eta=10**-6, gamma=0.9,noise_strength=0)\n",
+    "#rms_prop_trajectory2=rms_prop(grad_beales_function,init2,Nsteps,eta=10**-3, beta=0.9,epsilon=10**-8,noise_strength=0)\n",
+    "#adam_trajectory2=adams(grad_beales_function,init2,Nsteps,eta=10**-3, gamma=0.9, beta=0.99,epsilon=10**-8,noise_strength=0)\n",
+    "#overlay_trajectory_contour_M(ax1,gdm_trajectory2, 'GDM','m')\n",
+    "#overlay_trajectory_contour_M(ax1,NAG_trajectory2, 'NAG','c--')\n",
+    "#overlay_trajectory_contour_M(ax1,rms_prop_trajectory2,'RMS', 'b-.')\n",
+    "#overlay_trajectory_contour_M(ax1,adam_trajectory2,'ADAMS', 'r')\n",
+    "init3=np.array([-1,4])\n",
+    "gd_trajectory3=gd(grad_beales_function,init3,10**5, eta=lr_s, noise_strength=0)\n",
+    "gdm_trajectory3=gd_with_mom(grad_beales_function,init3,10**5,eta=lr_s, gamma=0.9,noise_strength=0)\n",
+    "NAG_trajectory3=NAG(grad_beales_function,init3,Nsteps,eta=lr_s, gamma=0.9,noise_strength=0)\n",
+    "rms_prop_trajectory3=rms_prop(grad_beales_function,init3,Nsteps,eta=lr_l, beta=0.9,epsilon=10**-8,noise_strength=0)\n",
+    "adam_trajectory3=adams(grad_beales_function,init3,Nsteps,eta=lr_l, gamma=0.9, beta=0.99,epsilon=10**-8,noise_strength=0)\n",
+    "overlay_trajectory_contour_M(ax1,gd_trajectory3, 'GD','k')\n",
+    "overlay_trajectory_contour_M(ax1,gdm_trajectory3, 'GDM','m')\n",
+    "overlay_trajectory_contour_M(ax1,NAG_trajectory3, 'NAG','c--')\n",
+    "overlay_trajectory_contour_M(ax1,rms_prop_trajectory3,'RMS', 'b-.')\n",
+    "overlay_trajectory_contour_M(ax1,adam_trajectory3,'ADAMS', 'r')\n",
+    "\n",
+    "init4=np.array([-2,-4])\n",
+    "gd_trajectory4=gd(grad_beales_function,init4,Nsteps, eta=lr_s, noise_strength=0)\n",
+    "gdm_trajectory4=gd_with_mom(grad_beales_function,init4,Nsteps,eta=lr_s, gamma=0.9,noise_strength=0)\n",
+    "NAG_trajectory4=NAG(grad_beales_function,init4,Nsteps,eta=lr_s, gamma=0.9,noise_strength=0)\n",
+    "rms_prop_trajectory4=rms_prop(grad_beales_function,init4,Nsteps,eta=lr_l, beta=0.9,epsilon=10**-8,noise_strength=0)\n",
+    "adam_trajectory4=adams(grad_beales_function,init4,Nsteps,eta=lr_l, gamma=0.9, beta=0.99,epsilon=10**-8,noise_strength=0)\n",
+    "overlay_trajectory_contour_M(ax1,gd_trajectory4, 'GD','k')\n",
+    "overlay_trajectory_contour_M(ax1,gdm_trajectory4, 'GDM','m')\n",
+    "overlay_trajectory_contour_M(ax1,NAG_trajectory4, 'NAG','c--')\n",
+    "overlay_trajectory_contour_M(ax1,rms_prop_trajectory4,'RMS', 'b-.')\n",
+    "overlay_trajectory_contour_M(ax1,adam_trajectory4,'ADAMS', 'r')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Section 3: Linear Regression\n",
+      "Automatically created module for IPython interactive environment\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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wMUR34NZ/fc6osX1JzfFmyX9WkvrP7rD5Hy5ZiTuCwc0GY/Iy8Wnyp3Xqr4Mb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\nhNTUizGbkzp1fT1JbxWUA/FrktV1jXxdVc/zReV4NclvMxL5XWYSNn1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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109757710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Dginrf5VPdn5AYnIiCUkJ5mdyAg/d9xAPVH2A01dOM2rVqAzHShcuzbCQYbSq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\nIYSFBAUhhBAWEhSEEEJY/D+EbrGrAi6xjgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a0f933470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print (\"Section 3: Linear Regression\")\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "#This code is modified from plot_cv_diabetes.py in the skit-learn documentation\n",
+    "#and plot_ridge_path.py\n",
+    "\n",
+    "\n",
+    "\n",
+    "from __future__ import print_function\n",
+    "print(__doc__)\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn\n",
+    "\n",
+    "from sklearn import datasets, linear_model\n",
+    "\n",
+    "#Load Training Data set with 200 examples\n",
+    "\n",
+    "number_examples=200\n",
+    "diabetes = datasets.load_diabetes()\n",
+    "X = diabetes.data[:number_examples]\n",
+    "y = diabetes.target[:number_examples]\n",
+    "\n",
+    "#Set up Lasso and Ridge Regression models\n",
+    "ridge=linear_model.Ridge()\n",
+    "lasso = linear_model.Lasso()\n",
+    "\n",
+    "\n",
+    "#Chooose paths\n",
+    "alphas = np.logspace(-2, 2, 10)\n",
+    "\n",
+    "# To see how well we learn, we partition the dataset into a training set with 150 \n",
+    "# as well as a test set with 50 examples. We record their errors respectively.\n",
+    "\n",
+    "n_samples = 150\n",
+    "n_samples_train = 100\n",
+    "X_train, X_test = X[:n_samples_train], X[n_samples_train:]\n",
+    "y_train, y_test = y[:n_samples_train], y[n_samples_train:]\n",
+    "train_errors_ridge = list()\n",
+    "test_errors_ridge = list()\n",
+    "\n",
+    "train_errors_lasso = list()\n",
+    "test_errors_lasso = list()\n",
+    "\n",
+    "\n",
+    "\n",
+    "#Initialize coeffficients for ridge regression and Lasso\n",
+    "\n",
+    "coefs_ridge = []\n",
+    "coefs_lasso=[]\n",
+    "for a in alphas:\n",
+    "    ridge.set_params(alpha=a)\n",
+    "    ridge.fit(X_train, y_train)\n",
+    "    coefs_ridge.append(ridge.coef_)\n",
+    "    \n",
+    "    # Use the coefficient of determination R^2 as the performance of prediction.\n",
+    "    train_errors_ridge.append(ridge.score(X_train, y_train))\n",
+    "    test_errors_ridge.append(ridge.score(X_test, y_test))\n",
+    "    \n",
+    "    lasso.set_params(alpha=a)\n",
+    "    lasso.fit(X_train, y_train)\n",
+    "    coefs_lasso.append(lasso.coef_)\n",
+    "    train_errors_lasso.append(lasso.score(X_train, y_train))\n",
+    "    test_errors_lasso.append(lasso.score(X_test, y_test))\n",
+    "    \n",
+    "###############################################################################\n",
+    "# Display results\n",
+    "\n",
+    "# First see how the 10 features we learned scale as we change the regularization parameter\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.semilogx(alphas, np.abs(coefs_ridge))\n",
+    "axes = plt.gca()\n",
+    "#ax.set_xscale('log')\n",
+    "#ax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\n",
+    "plt.xlabel(r'$\\lambda$',fontsize=18)\n",
+    "plt.ylabel('$|w_i|$',fontsize=18)\n",
+    "plt.title('Ridge')\n",
+    "#plt.savefig(\"Ridge_sparsity_scale.pdf.pdf\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.semilogx(alphas, np.abs(coefs_lasso))\n",
+    "axes = plt.gca()\n",
+    "#ax.set_xscale('log')\n",
+    "#ax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\n",
+    "plt.xlabel(r'$\\lambda$',fontsize=18)\n",
+    "#plt.ylabel('$|\\mathbf{w}|$',fontsize=18)\n",
+    "plt.title('LASSO')\n",
+    "#plt.savefig(\"LASSO_sparsity_scale.pdf\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Plot our performance on both the training and test data\n",
+    "plt.semilogx(alphas, train_errors_ridge, 'b',label='Train (Ridge)')\n",
+    "plt.semilogx(alphas, test_errors_ridge, '--b',label='Test (Ridge)')\n",
+    "plt.semilogx(alphas, train_errors_lasso, 'g',label='Train (LASSO)')\n",
+    "plt.semilogx(alphas, test_errors_lasso, '--g',label='Test (LASSO)')\n",
+    "#plt.vlines(alpha_optim, plt.ylim()[0], np.max(test_errors), color='k',\n",
+    "#           linewidth=3, label='Optimum on test')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.ylim([0, 1.0])\n",
+    "plt.xlabel(r'$\\lambda$',fontsize=18)\n",
+    "plt.ylabel('Performance')\n",
+    "#plt.savefig(\"Ridge_LASSO_sparsity_performance.pdf\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Section 4: Hamiltonian\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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FfYGC94XY98zmzexm2IY3tGWqWc5Fki8mTsyDvzxvDyHcnuPsMf4cwC8HBnAZfgrgC0he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\n2ba3kzmQezPw+RmeHkIIicGos5PXQVwI4SvAV3LbzOxBYD+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+CvwR8DPgXOCPgZ+Y2StC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\nYBAFdgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a11d2a278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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KoaOfc3bs9nOOd1jXZ1NeQ/1lyDlDcUEpYPY5J+W1zXbnwClTzun1iiyVK+cM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\nJnEiIiIiIiI5okmciIiIiIhIjvw/fP1kh8IaHEMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a11d3e2e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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BnDMcF5QCSs85Ke9ttjsHToVyTp9XZKlSOWfYKexF6mZIBeXnsEk7dsbHb1oB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\nJyIiIiIikiGaxImIiIiIiGTI/wNKT5P+Qg8PbQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1e346a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Ds59ztu/yc463W9dnU95DfWXIOYNxQSlg5jkn5b3NNmfHKVPO6fGKLJUr5ww6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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1e38d898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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d57c7VbwaRna7sQxk4+e3d/ix1fFubW/3Q+tG4qpuY7VHu7Ft9U6lqq1bo6b+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\nrtn6WNQ22L7ajW3yKoylVatz2qs2xz9ArF23zn+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sDWPOypXxJmx6V9jetCyUlTEHYMuW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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1e2f9588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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DGf/bZlP1UNT2eG98jK1o3O3Ov5u2qK2t9wF/vdrb4/WqjecHaMo4z+ftL+ChXU1R25ru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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1fa70198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ec41vu+66uO/Nn/eT+d8S5Mus7o3TSHc2nenaTj4QP9ZJwZba1dsc9l2/xk/m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\nOogTERERERFJkf8Hm4LcasybnAQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a20017e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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juXDDI3FhKtlkS39c5oXJeAkBwFRzvA3acont5QUoJEIGvLCHjtZWt27DuPN6\nXkINUGifff5M+TkNi9fUFBRmB1pMNvvn1dNPx2UdS/3jxDv+nCwmwA8xOf41/o667fb4mNq0Ka7X\nNLjDff5kZxx61JTzX2uyGB+TA6N+Mk5fc3zOjyRCi/bujcuc3AwAik5Ay4oVcVlDwQ9RmSjE7W0Z\n3+m/mCMZMPOK+L0mGecxWlkYTarPybU6S04E17jd3qhfeWQ0Xre2Rr/uVC5+/2jwdo6IyEGkK3Ei\nIiIiIiJ1QnPiRERERERE6owGcSIiIiIiInVEgzgREREREZE6oWATERERERGROmMHuwEH2IIaxE3u\nMXYMzk7BGh31664rPhCV3bZ1vVv39M6HorKO1avdule/L07581IoAS65JC7zFvvY1hPc53c7YWTJ\n5LThOAmsuztOmgPcqLdUMl7TeJwqN9nop8p1NTspdlu2REVDHevc5y9zwsyGG9e4dQ8/PC678Cw/\nfXTHeLwMLzUTYLw53mZtw4/FFRML8I7HtlY/CbNrW3yMugcIsNQJsHtk0N8Pa8bvq3i5LYXZ+3dJ\nUKLcTFO2hInc7MTH4UG/bk8hPk6251a5dfsK8Xnc4CUSAse/Mt75XgolwOmb4nPw+i/EN5Bc/C7/\nhM8X4+Raxp2DD8g76YPt7X4G41RzfKy2kYhCdTt1/xwaL8SpiG6f1eqfK03E6zvR6vfnnuPbne0F\nTBSTWZRxG1qd7ZBIovQUnN3TlHjnbnjQ6XN6eyt+re2DifRRtle83LyXnisiUmMGLDnYjTjAFtQg\nTkREREREZL40J05ERERERKRO6CsGRERERERE6owGcSIiIiIiInVC6ZQHWX6qQM/4I7PKeroTCRUD\ncdMTuQF+qsfAgF/XCQU55xy/qh9iUlnwAMBFF8VlY/iT7luG44nlbaPOZHPgkcG+qGzN6N1uXW8l\n8oXExPR77omKhl59elTWsdQPIBlxwgD6ik6oCDC1Ig6MmCj4YQI9xK+3fcCv29cb75/J5vi18sN+\nwExzexw4MTLq798274B0ji+AtpyzzTv9cAsa1/rljqn2ssCHxOsvVg0N0Ng4u6ynNXH8D8fbzgvg\nAWBpY1w2HIcmAW7HtWmTX9UPMYmP6bvv8Y/JDRvioJB8c+KY2LYtKmrpdtYLGBmNAzHaRvv95TY7\nISaJoI82JwRlsjvu3/Ljfp8zlov7gZbBRJ+zMu4Hpoi3F0CT00eOFP3ztSnnhAmVH3QATpBMUuo8\nXrmy4uV6fU7O6d8yTl+WWO5kY2oZIiK1o9spRURERERE6sxiG8QttvUVEREREZFDTEOFj0qZ2XvN\n7HEzK5jZ/Wb2ujnqbjSz4DyfphvDAAAgAElEQVT874CqgorWxcy6zexvzexHZjZRalSvU6/RzD5h\nZj83s92l+qdUu9EicuhTvyMi1aZ+ReTQNH07ZbUGcWZ2HvA3wNXAscDdwDfNLPElzS84GnjpjMfP\n5rUi81DpuqwEfht4Gvj+HPX+G/Bu4M+As4CfA982s2P2p5Eisiip3xGRalO/InKIqvKVuMuAG0II\nnw8h/HsI4f1k/cB79vG8J0MIgzMee+e9IhWqdE7cXSGEDgAzexcQpVmY2auBC4B3hhD+/1LZ94CH\ngY8AZ+/zVZYsgdbWihq0s31dVLZ+3J+wTiGeSH/XQDw5HqDTyVFZNeyHgjy29YSorNLgAYDLPhjX\nvfRStyqN3mT+RADJmsbJuHC4163rTvwv+iEBkxudEBMvAMT8w2rzg3HZxo1xmABA/gd3RWVNJ5/s\n1qUQFyVyEuD22+PXSi3Xa9fgjqhscNz/o0xjb1xedNoK0NwcByi0FSbcuiMFJ0TCCXcBaCgP8Ckk\nGrAw1b7fKRZpGH5ydpkXOgFMdMbnYMf4k05NIBcvY3vBDy3qdPIhmpzjDODid8UhTV6IyYkn+H3O\nHXfGdU8+OfGW1uufmx4vq4RGP5RqqjE+fhsSx7oXYuJmeiRCNka9fqDTX6+WAWebe6FYCXv2JH7h\nBMTQ2xuXJcJKvGNhpNnvc1pb41CRVF5Kvhhv85ZG/7gZGY2X25bz6+ZH/b5oATkwn2dE5ICqZjql\nmeWB44BPlv3qNuDEfTx9s5kdBjwCXBVC+G6VmhWpaEAaQvB769nOBvYAN894XhG4CfiN0gqJiFRE\n/Y6IVJv6FZFDl5lV9ADazWzzjMfFZYtqB5YAQ2XlQ0AiNv+Fq3RvBs4FHgX+tZa3YVcznfJo4PEQ\nQvmf9x4G8mS3MDxcxdcTEVG/IyLVpn5FpN6YVf41Snv2DIcQNlRQM5S/ilOWVQzhUbKB27Qflebb\nfhCIby+rgmqmU7aR3WNebmTG7yNmdvH0SHjXU09VsTkisgjMu99RnyMi+7D/n2d27apZ40QkIZer\n7LFvw8Be4qtuRxFfnZvLvcAr5lF/Xqo5iEuNTm2uJ4UQrg8hbAghbFhx5JFVbI6ILALz7nfU54jI\nPuz/55kVK2rTMhHxTV+Jq8IgLoQwCdwPnFb2q9PIUiordQzZbZY1Uc3bKUcAb7b18hm/FxGpJvU7\nIlJt6ldE6k1DQzKYLPLss5XUuhb4kpndB/wQuAToAj4LYGY3AoQQLiz9fCnQzy9uu347cA7ZHLma\nqOYg7mHgTWbWVHYf+RpgEnAiuiqQGDG3t8dlU4nUMc8pjYk+eOvWqOixdj+Ipn00LrvoorjMS6EE\nuPaT8fzq9/yBX/czf+qkQPb3u3VZ7Xyv4OCgW7Wl1UkrTJwEeed9a0cxTtzrdPYN+MmfqbDE/Alx\n8ufYuL9tWpz0t3XeNgDGeuOEzZZRZ9smtsGO8fgumjUrnTRQYKIYJ062DDzi1vXS6saKcYof+KfE\njlH37h6a22eXF53UxDq3//1O+QZNHJRN3qZLpek6O6kvkfrKcByhONnppw/mi/GxtmFDfJx5KZQA\np26M+5yv3OTXveAMp72pqEMvnnLU6SCBhmZnGYk42Xx7vB0nnfMq78ZjwmHOtZREVWiOkyiTfY5T\n1nGEn7A5tmxN/PyikyyceK8ba42PhbZcIrl21EmuLTj9W+r1UvvXWePUtmlu9fuiOlObzzMiUjvz\nmRNXgRDCzWZ2JHAF2fe9bQHODCE8UapS3jnnydIsXwbsJutH3hBC+EbVGlWmmrdT3gosBd46XWBm\nOeA84LYQwvNVfC0REVC/IyLVp35FpB5Vb04cACGE60IIvSGEw0IIx4UQ7prxu40hhI0zfr4mhLAy\nhLAshNAWQnhdLQdwMI8rcWb2ltJ/jyv9+5tmtgvYFUL4XgjhQTO7GfiUmS0FHieL2nw58LZqNlpE\nFgf1OyJSbepXRA5BVb4SVw/ms7b/s+zn60r/fg/YWPr/7wEfBa4CWoF/A84IITywH20UkcVL/Y6I\nVJv6FZFDjQZxaSGEOVOZSnV2A5eVHiIi+0X9johUm/oVkUOQBnEH1/NTS9lemB2U0dfsTAAHBgbi\nsr5uP2DCM7THn3zdsXZtVPaS3f4y2opPRmVjhaOisksv9Z/vhZh85tNx8ADAhy6PA0SuuSIxQ98J\nZphYvd6t6h3v+eHERHhn0nsPO6KykXE/lMELo0lkH9CSG47LUokETijCA9u86AFYubKy56c6Ai/H\nYqwQBy0AtDTH+3KkMw45AGjrjwNPWryDHLiNOJzl9A1+aMZE4+zjvKGas2APAVMNuWgbNeX8fmTn\ncLyfu/D7p8nG+Pgr5Pw+J9celzfl/H6A8fjczjfHx+rJJ/s72gsxueB8/7Vuuz1u18kn+83yttlE\na9xngZ8Z1JAK1HDK84X4fE315/ub8p4MQemP+6ehw/sqb0PBCWzB70c8E/ihR22NceDJZLO/H/JF\nJxwlEerjFXe1++fJ1DzWQ0SkaswqT6c8RCyoQZyIiIiIiMi86EqciIiIiIhIHdEgTkREREREpI5o\nECciIiIiIlJHNIgTERERERGpMxrEHTzFIuzaNbusr9lPy+rLxbGGUzk/FdHTYXGyJMBkY5wueURq\nKw3HbWsZ3x6VNXb7qWWf+dM4BdJLoQS45uNxgtyHLvcTGK+5PE50a9qa+Gqb1aujosn2RJpZ/2Nx\n3d5VUVmbl3oGUIxT5caa4+0NuOltEzl/fZtOOCEqW++kPQLcvSVOhzzmmHgdUh7cHJedcrKf7vfI\n1jgJcM1qv+5YLm5X82o/yfLVu+KysUTyYcvw7PTQhmLlCa6LQbEIw2VBg93dfrpel5eUm0jC8tL8\nWnKJ88IxWfTTB/NeiuO2bXGZc14CXHBGnGLqpVACnL4pPlZv/p9+6uV5v+X0OeN+9OyU08dONvtt\nyA/G6bdT3XE/31FIbNvBuA2p1Mym0cGorKGz01+uE7XbMRz3/QBDxP3/ihXx/s3j9w2Do/E27+n0\nz+OR8Xi5bTk/QdWN3kwcz81OgK+X1grp1EoRkZpqaFA6pYiIiIiISN3Q7ZQiIiIiIiJ1RIM4ERER\nERGROqJBnIiIiIiISJ3RIO7gaWqCY48tKyw6k6/BTw5IaNgWB3LsaPQn/veMxxP/t/T7k+5f9jJn\ngn0xntyeLyQmlvf3R0XXXOGvrxdi4oWdAFx4UdzeG69yZqYnDAz45X3OJH9nFVi1svKJpS3bHvJ/\n4QQHpOar7hiIJ/O39/qhICcOx0EJ5JzwgnF/e51ycmtUNoUf9rCmO97vk0U/nMXTkAhrWLYsXl8v\n2wJgfbwZZYZcLj7UGhIBE5ON8b7L44c4tIzGx9lIsx+81Ebc5wyM+sEm7e3xud3SPY+J3E4wyskn\n+1W9EJPz3upvm298K27vmSf4fbQXrvP0035IRkd3d1RWHn4F0LE88VbWGp+vTYV4ewNuBzNZ9M/t\nohOy1JTYDx1O0NMU8fZKvZazCZjC315tjU6f0ei/p3j9VqrPwWnvIssPEJGFTsEmIiIiIiIidWQR\n3k7p/+lPRERERESkHkwP4ip5VLxIe6+ZPW5mBTO738xeN0fdc83sNjPbZWbPmtm9ZnZ2WZ2LzCw4\njxd1CVGDOBERERERqW9VHMSZ2XnA3wBXA8cCdwPfNLPUl1K/HrgDeEOp/jeAf3QGfhPAS2c+QgiV\nzxGbYXFddxQRERERkUNL9W+nvAy4IYTw+dLP7zezM4D3AH9SXjmE8IGyor8wszcA5wDfn101DFaj\ngboSJyIiIiIi9auKt1OaWR44Drit7Fe3ASfOo1VHAE+XlS0zsyfMbMDM/sXMyiMdK7agrsTZ8wXy\n/WVJkl40F7gpa6OjiQW3x0mUPeNOSiHw0EB8lXT9Wj+BzjsQHnmqLypb05h4/urVcVkidfOay+P1\n9VIoAW68IU6QO/sc/+rvrR9/JCrr6+1163opZ17V7f3+3wba2+OEs5a1a926Q7viZXQ4qXYAnZ1x\nUls+56fouclFzn6cbPa3bX5LnKZZWLnOrds0PBw/vzNxyjU6aYTj/rGQc+quX+2nyk0we79P5fxU\nu8WqYaoYpRVO5hL7vrxvAli50q072Rmfb21O8i3AzkL8en3NT7p1p5qPispGnCTL5kSor/eLppx/\nXp33W3Gf46VQApx5Rny+feUmfztesClet47lcYok+CmMHSvi1xob94/rYjEub8vF6wUw4SVOJvqR\nvHca+4t136u8FMjxgr9t27z3qtT7oifxxlhojPdPk9NW8D/ztCTe18YK6mNE5CCYXzplu5ltnvHz\n9SGE62f+HlgCDJU9bwjYVMkLmNkfAN3Al2YUPwq8E/g3sgHeB4AfmtmrQwg/q7Tx0xbUIE5ERERE\nRGTeKr+dcjiEsKGCeqHsZ3PKImb2ZuATwPkhhCdeWFgIPwJ+NKPe3cCDwPuBP6ygPbNoECciIiIi\nIvWrunPihoG9QPkXCR9FfHWurBn2ZrKrbxeGEG6dq24IYW/piuArXkwjNSdORERERETqVxXnxIUQ\nJoH7gdPKfnUaWUplogn228CXgYtCCF/dd5PNgHXAz/fZKIeuxImIiIiISP2qfjrltcCXzOw+4IfA\nJUAX8Nns5exGgBDChaWfzye7AvdB4C4zm76KNxlCGCnV+XPgHuBnQAvZLZTryBIv521hDeL27o0n\nYSdCNu7ojwNEUnkc7e1OYSJAxMspeKzfn6jtzS1fM+oM0IcTDRuME0YnVq93qzZtfSAqu/Gqcbeu\nF2Jy6y3+BP3rv7AmKru42Q998UIR8gMDUVlfIqyE/v6oaCIX70eAjue2x4XP+4dr3pnIOpKLAyAA\ncMrbRuPAiXyiIxjpjkNM2hLBECOt8brtedZvlhfWsGPcD4bowQnIcEJUAJq2bp31c8NYKv1nESsL\nc8jj78/tuTggqXMe3+ySinvoao1DLkYK/vHbRnyctI32xxUby+8AKXFCLiZau9yqTeNx3TNP8FfY\nCzG54Hy/z3loS7xu61r9bd7ghRk5fXdLajK7U9cLMAFoGnfCZAqpIKL49aa8cCKg4IQJlYfpZIv0\nnz/ZHPfneScYBWDECUfJ5fzltjj91mRq2xSd1xv1339aEu+tIiI1ZTafYJN9CiHcbGZHAleQfZ/b\nFuDMGXPcyjvnS8jGVZ8qPaZ9D9hY+n8rcD3ZbZrPAD8BTgkh3Pdi2riwBnEiIiIiIiLzUf0rcYQQ\nrgOuS/xu41w/J57zR8AfVaNtoEGciIiIiIjUsxoM4ha6xbW2IiIiIiJyaNEgTkREREREpI5oECci\nIiIiIlJHNIg7yA4/nKkNr51V1NDvpBQCp54Qp695yVwALQUnday11a3rhfy95CVuVT/NbPXqqGgs\n56cMtrTGKV7J489ZbsqtH38kKvNSKAEuflecIHfdZ+M0NIDzz4/LhhvjdVuVSE6b6o3TGofjcEsA\nOrvjuqnQMy+MqG1LnOYJ+PGjhWJUNNXq77M2Ly1vHp55xi9fvjz+ysaeRuf4AkZwEjZX+sdzdEAt\nWzZn+xabsCTHZOvs7Zkf9bd7X28cc7tz0P+qza5OL5nRT80aejbut/budau66ZJeamwqKbGhOT7W\nU2FeU43xceamRQIXbIq3mZdCCbBubbxtHtnqZ3e6p6uT89nipScCU81x2mIqu2zSSa5N9jlOP50f\n9o+bJu+9ZjxOdmxy9mOyEYm6rc7KOS8FwFjB2Y6MuXW9RM+m1sSbVeoFRURqqcrplPVgYQ3iRERE\nRERE5kNX4kREREREROqIBnEiIiIiIiJ1RIM4ERERERGROqNB3MHzzDPwrW/NLjuz05nID4y1x8EX\ne/YkFjw0GBVNrl7nVo3jUiA/PuLWnWyOJ8LnC/HE8Jai/3xvAmZ+eKf/Wu1dUdlAIhSkr7c3Kru4\neYdb1wsxee8lXigDnPuWOMThppvieiPjfqiCN900NZf/Bz+IyzZs8Ot6drSvd8t7mp11c8IiGvC3\ngddBjI374RZthXhfDhbj/QhQjPMmyCdSFdqcwJMdA36IRHlATMgf5tZbrHbvhi1bZpetb/WDGSac\n8z05h3ow7nOmOv19f4RTln4fck4YJ0iiIREu5Nb1Dj5gsjkO93n6aT+ApGN5HN6xrtUPQfFCTNas\n9s+3W/8lPrfOOiuuN5boc1qc8zh1vjq7jFUr/XZNFOJljDvBKABtOWcZ7XFIjtsJAGPEoSKJbpOG\n8fj9Z3w8fj4ksr1y/gHd5LwHDu3xw5+WLfPLRURqSlfiRERERERE6khDg9IpRURERERE6oauxImI\niIiIiNQZDeJERERERETqhK7EiYiIiIiI1BEN4g6ulzTv5cyTy9K1Bv0crpZCnNDHMj8djP7+qGhL\n0U+nPOaYuGxnwU/b6mp1UsfuuScqmtx4uvv8PE5qZSKhLN//WFTW1+llaQKNzjZLxECef35c5qVQ\nAnz9q/H6/vcb4rpvf7vfrLyT0tb0YLy9AI4++sSoLJVk2dC/PSrr7o3TSwF/+3oTYVPRn04HUWz0\nUwe9Bq9pTCSVDjuJiIkJuiNOCl5Pzk81pTg7gs5CInVzkWrKF1nfXdaX5LzYPmgqOPuuMZHENxgn\nno43+8eJt5sT3QDjhTjZsc1JV53s9o//fLvT5af6nME40baju9utO0Xcroain065cmVc5qVQApx9\nVny8PvBgXHf9Mf5xPVmM67YMx/0FQEu3158m0hob49fL5fx1mCIub5jHB40W57W8ZQJMNcdJlF25\nypNKUx+Aplrj47xj2HkPBmh2kjdFRGpNwSYiIiIiIiJ1ZpFdifP/nCciIiIiIlIPpm+nrORR8SLt\nvWb2uJkVzOx+M3vdPuq/vlSvYGbbzeyS/V6vOexzEGdmbzGzr5nZE2a228weNbOPmdkRZfWWm9kX\nzGzYzJ4zs9vN7FW1a7qIHKrU74hILahvETlEVXkQZ2bnAX8DXA0cC9wNfNPMehL1Xw58o1TvWOBj\nwN+a2ZursHauSq7EfRDYC3wYOAP4DPAe4Dtm1gBgZgbcWvr9+4E3A0uB75qZP4lCRCRN/Y6I1IL6\nFpFDUfWvxF0G3BBC+HwI4d9DCO8Hfk7WX3guAXaGEN5fqv954ItkfU5NVLImvxVC2DXj5++Z2QhZ\nwzYCdwBnAycDp4YQvgtgZj8CHgc+BPxhRa3xJiWmwjsKhaiopf8ht+rIyWdHZesb/cneQ7uaorJU\nE9iyJX6tDXGISduwHzqxoxgHHfQQhwkATPauisqcvBYAenvjsnwiqGPYCWa46SZ/uV6IyTsviifd\nv/Nd/t8GPvaxuLyj1Q+R8Nx5p19+6to4QOTHP/brvvKVcQDD7hAHhaxIvE033HN3VPZgwQ+sOPnk\nOGQgP+4Hm0x1x3/Y2bXLqQgsdwJeHhrw29Bcdpo8v6du7qA+IP3OVEOOiebZ+7+pOJaoHWsa9c/t\nse41UVkqmMfbzytWJF5vPA6T8EJMUu9Rk8X4+M8XnIAL5ndMdqxwgkWcPhqg4ISgnHWWv9xKQ0xu\nu90/rjdudAoTE98niPv+/q1+u9asjtuQ6o+9IJcJZz+k9ll+MD7GHhz0z/f1a50wmcR+oD0OIBkZ\n9bdjmxNSs33cDxLrXfi5JgfuM42IHDjzS6dsN7PNM36+PoRw/S8WZXngOOCTZc+7DYiT9zK/Wvr9\nTN8G3mFmS0MIeyptXKX2+YmurLObNv0R+WWlf88mG31+d8bzngH+GXjj/jZSRBYX9TsiUgvqW0QO\nTSFkicSVPIDhEMKGGY/ryxbXDiwBhsrKh4DUpZ3ORP1caXlV92L/LP/60r//Xvr3aCC+LAUPAz1m\nlvgbtIhIxdTviEgtqG8RqXMhZN+YU8ljPost+9mcsn3V98qrYt6DODN7GfAR4PYQwvSlyDbgaaf6\n9L1jy+dY3sVmttnMNu8aHp5vc0RkEahmvzOzzxkeTtwfKCKLQq36ll2pe49FpCaqPIgbJps7W37V\n7Sjiq23TBhP1i8BTla9J5eY1iCv99emfSg36vZm/wh9lmlM2Swjh+unLmSuce/RFZHGrdr8zs89p\nb09MPhORQ14t+5YVqYmtIlIT1RzEhRAmgfuB08p+dRpZ+qTnR8Amp/7mWsyHg3l82beZNZKlNfUB\nrw8hzEzKGCH7y1W56b9WeX/Rik1NRZOwd4zG4RAAPeNOUIc3gxxoc8IHhvb4E8OfcsbKHRaHCQAM\ndayL6+IEVyQmWnY6Y9aRcTe5lLZiHMSyaqU/QX97fzw271u71q27qhAvd2Q8nuAP8Pa3x2VeiMl/\n/4ITcgB86PK47jXnjLp1O3rjdq3Y6LeLYhyOcvwr/XCKkWJ8PHUsi+s+8KB/3K3fsCEqO/XO8nms\n0zbGRYmQgYY774jbldhnO4fjQIFUp3T44WWvUze5Jpla9zsNFmjKzQ5tGCn4+77NO7cTIRktxbju\nyKjX1ES7nPMSYLI13vduWE7igMg76SpDe/x2dTht6Fju92Vj43FQR0ty28TLHUv0OZWGmJy+ye9z\nvvGtuO6Zq/1z0Gvu6tVuVSYK8XJX9TqhIsBEId42TQXn+HAPZWhz9tn65sf81yrGAVhNqYn+g4MV\nvRbAyHh8TqQyqVI5KgvNAflMIyIH1DxvldyXa4Evmdl9wA/J0ie7gM8CmNmNACGEC0v1Pwu8z8w+\nBXwOOAm4CPidqrZqhooGcWa2FPga8FpgUwjhp2VVHgbiWEZYA+wIIfjxZyIiCep3RKQW1LeIHHqm\nr8RVb3nhZjM7ErgCeCnZPNkzQwhPlKr0lNV/3MzOBP6a7GsIdgJ/GEL4WvVaNVslX/bdAPw98OvA\nG0MI9zjVbgVeZmavn/G8FuC3Sr8TEamY+h0RqQX1LSKHpumb+Sp5VCqEcF0IoTeEcFgI4bgQwl0z\nfrcxhLCxrP73QgjrS/VfHkL4bNVW0FHJlbhPA28FPgo8Z2YnzPjdQOkWhFvJ7gX9spn9MdmtBn9C\ndv/4NdVtsogsAup3RKQW1LeIHIKqfSWuHlQyQ+Y3S//+F7JObebjXQAhhCngLOA7wHXAP5Kluvxa\nCOE/qtxmETn0qd8RkVpQ3yJyiKrBVwwsaPu8EhdC6K1kQSGEEeCdpYeIyIumfkdEakF9i8ihaTFe\nias4nfJg6cnFyZIAI51rorI2/EQ37wbYFb1+1Y7lTsJY0U/sWuYcLCPFONBq84P+a3U63/me/JaF\nYuXzqNvbnaS3/n637lRvX1TmZ8pBPhcnwH3sY/HFXC+FEuCaj8fPv+yDJ7p13+dsm75Of//e/WC8\nvo2NcSIcwPrO+HjaUYyTSvekwmC9m6lTMW3jzj7zdjow0Rq3oanRT9zrGo2T7bqa/e9YnDhidlrd\nkiVutUUrYEwy+1hxUyiByeb43PbOiZQ2/LpTzg0REwU/rbGJuH8ay8XtGk10F4c5oenJJPRBJzk2\ncawXi875lph4MNUcJx22JLbNZDHeNhs3xvW8FEqAM8/w0i3jPg9gg7NqqVN71Nk0o/h9Tld7vM+m\nWuN9VojDIjPtqR45Vp60mvHf5qc64z4n9QHIS0d2+zdggjhBVUSk1jSIExERERERqSPOt5Qd8jSI\nExERERGRuqYrcSIiIiIiInVCt1OKiIiIiIjUEQ3iDrZiEYbLAhp6e92qOWdO9WTODwN4+vB4InvH\n8JNu3R2FeFJ2z+hWt+5w87qorK/wSFS2cWMcwgL+vbvehHmAsea4XS3bHnLrtqxdG5VN5PzJ/MMD\ncVmzn+NC04Pxd6J2ODP/rznHXwkvxOTaT/qBBl+/JQ4q6DvGn/l/Yi4O9djR/lq37gOD8WT+9b1x\nkEWPbXOfP7Q7Xu6KDf5rDTjbtqfoH3dNXkjAPG7u3tnqH2NdA4/N+rlhcpHdML4PFqbIl4U2TDTG\noRMARWfTFXN+oMagc6j2tY+5dcdxgj7G/UAnLwCnZfCxuGLnqriM9Lld6Ws1FfzQl7Zc/M45kYvX\nC/zgpLFxfzu2DG93FhAv4czV/nHthZicvsnvc+79cdyG41/lhyl1OW9AQ8EP9Bh6Og486TgiXm5X\n0Q8nGhnvicqae/3963UjbTl/2zQ474F+NAuQiz8qjOT89W0r+se5iEgtaRAnIiIiIiJSRzSIExER\nERERqSMhKJ1SRERERESkbuhKnIiIiIiISB3RIE5ERERERKSOaBB3sC1ZEsenJeIad++JE+R27/YX\n27HCSSMr+DFtPYNO4qOThgbgBDMy5aQE5n9wl/v8/AknRGUtTtIiAAVnV7W3u1WHdsUpax3POSlv\nQGd3nN72gx/4TTj66Dhd0tPR6ye6va8zLvNSKAHOPSfeZx+63E/YvOaq7qisO3Fk93THy50sxsdS\nPhHj17HMSV4b9m/CzjnpbdvH/US3Pu/lnEQ4ALrj9V2yy6+66Hq0+dq7N+pjmpzzGoDmuB+Ywj9+\n3VDdQSc6EGhJpAdWamplnFTYMrDDr9wcHzspTaNOxGaiL/SSKJvG/STWSee88NI8AVq6405jgjiF\nONEsNjj70kuhBDj+NXHfcPc9fuLxiRvic3NF4nR1T8Gc02DnvAZow3n/SmywXHOcKDq0208J7Vjm\nFKYmlDj9YWOqaymozxGRA0+DOBERERERkTqz2AZx/p8kRURERERE6sDUVHYzQSWPF8MyV5rZTjPb\nbWZ3mtnR+3jOu83s+2Y2YmajZvZdMzu5rM6VZhbKHon7U2bTIE5EREREROrW9O2UlTxepA8B/xl4\nP/Aa4EngO2Z2xBzP2QjcDPw6cDzwKPBtM3tFWb1HgZfOeLyqkgbpdkoREREREalbtZwTZ2YGXAp8\nPITwtVLZO8gGchcAn/PbFN5Wtpz3AOcAZwA/m/GrYgihoqtvMy2sQVwux1T77Env99zjVz3mmLis\nCT9QA+fS6de/5U9YP/ectVHZjV/2L1heeNZIVDZRiEMymk4+OSoDGBuPl9uSCNTwggNSk/k7ipNx\n4fP+rvYuK2/Y4C/Xa085p5EAACAASURBVNqdd8ZlKzb627avM94/fcf4x6wXYnLNx50J/sB7/iAf\nlX30o25Vmpvjbb51a1xv3drV/gJuuSUqGtl4rlu13QlVePppf7Huxk3s4J2D8Tp0tSaO/efKlmGW\naMAitWRJlFA09Kx//HbknPMqFx97AA2FeH88NByHTgCsWxsf18nwjfa4DVM4bUiEZHh9TqLLoaEz\nDhWZLPrtasp54VGV9zmrVvrnNsTnQL9zvq5OnK5e+NTxr/LPFS/E5MQT/HZ95aZ4m19wvl83l4u3\n2fb+uKyv2zm+APr7oyIvzAagxen7m5v9Y9TbP5OtfvCS1291HJHoc1JvTCIiNTaPQVy7mW2e8fP1\nIYTr56j/cqATuG26IISw28zuAk4kMYhz5Mne2Mp71T4z+z/AJHAv8OEQgp9IOMPCGsSJiIiIiIjM\nwzyvxA2HEBKXLFzTf9UcKisfAl42j+VcBYwDt84ouxe4CNgKHAVcAdxtZkeHEJ6aa2EaxImIiIiI\nSN2aDjapBjN7G7Ovrr2h9G8or+qUpZb5AeD3gU0hhBe+ryqE8M2yevcA24F3ANfOtUwN4kRERERE\npG5VeU7crWRXyKYdVvq3E/iPGeVHEV+di5QGcFcBvxlCuG+uuiGEcTN7GCgPP4loECciIiIiInWt\nWoO4EMKzwLPTP5eCTQaB04Afl8oagdcBfzzXsszsMuAjwJkhhB/s67VLy10NfHdfdTWIExERERGR\nulXLdMoQQjCzTwH/xcy2Ao+RzV0bB74yXc/M/hW4L4TwJ6Wf/xj4KPB24DEzm55btzuE8EypzieB\nfwZ2kF3Z+1PgcOCL+2rXwhrEhUBDWbrWia3b/LqDTgJWe7tb9eZvxsmOp53mL9ZL/rtwwyNu3R3j\na6KyHuLESi8dE6Blm7Nu4+Nu3aYTTohff8BP0evsjNPI8onEsPkEiTX0x0E5p651ou2KTiQccPeD\nTvpbbtite81Vcbqel0IJ8JlPx6lwX7nJT9E7//y4zNsGE4VECt/GjVFZ27bElfHe3qioY4V/jE4R\nb5tUZ7Rkt1M4OupXLj8ncgvrlD/YgjUwmZu97TvsSb9yMT7WU1+0+cDWeH+uXOnXnXKWcvwrnH4E\nmCg66beFMaemL+4JgX7/HPT606KTkguQ9w6rVJ/j1E2eb43xub1mdVyWer53WnTl/D72xA1xw7wU\nSvCTKB/b5rdhVW+cGOn10RNF/7WanP3QMLDDreu+ByYSVL39U/A3DUuXOoWpDkrplCJyENRyEFdy\nDbAM+DSwnOx2y9NLV+2m/TKzb7f8A2Ap2XfFzfRFsjATgG7gH4B2YBdwD3BCCOGJfTVIn+hERERE\nRKRu1XoQF0IIwJWlR6pO71w/J57jXF6ojAZxIiIiIiJSt0KoXjplvdAgTkRERERE6tYBuJ1ywdEg\nTkRERERE6pYGcQfZnqKxc3j2JOyuVj8kg87OqGholz+x/LyXx8ETDw281q3b7OR0+IVuE9g+EAcP\nJLJKWLd6dVT2wDY/OGB9fxyu0t4bB6sA5HPxpPuR3FFu3bYtD0RlO9rXu3W7e/uish//OK53/Cv9\noIXGxniC/Y52fz90O0fmRz/qVnVDTLzgAYA/uzKue+ml/nJdzrX6uwr+Oqx11qFtqx+S0+AEjhQ6\nV7l1V6yIy6bocuvu2jX75z1TS9x6i5X35aCJGAgmnPCZVAe6vjsOR3ls0D8HvSyKuBdJGynGfcae\nPX7djiMmorKhw+PzGqBjOA4yaupOhFY4b5xTjX7wUn443jbjif4pl4vP1/7+uJ4XHgIw6uzNoeC/\n1gpnZ6b6ES/EZNVKv+4dd8ZtOPpo5/Wd8xqAYtyw7cUet2q7sx9aRv2QHK8va2z3+5F8MT5uphr9\n96rF9iFKRBYGDeJERERERETqiAZxIiIiIiIidUTBJiIiIiIiInVEV+JERERERETqiAZxIiIiIiIi\ndUSDuINs6ZIpulpnp2CNFf20rJZinEbW4cWLAZPL4/TAxn6/DX2tTpLXFr/y04fFCWF9vU5C2e23\nu88f6z09Klu50m/X3VviJMoTh3f4lRudBLlE+pv3gj3Nfsqad3a88pVx8pqXlgewvnNnVPbAoL9/\ne7rjNjQ3++mj5zvfde+lUAJ85Mp4udd9Nq57ySXu012n5O52y79xz4lR2cqVfqLoqvb4uHMCKwH/\nnu+m8TjxD6Bj6eyFLLVF1sPtw5Il0FJ2vE8kzpWmopO6mvPTGsca42UkQm5pa4yT/xj1b+xvao2P\n36acs0+3bfPbtSw+/lKpiEPEqZUdTkoh4PYNhZyf89nkJA63OYm6AFPE56bXR04U/Nfqao/fJ4ae\n9ut6b/5eOib4aZheCiXAqRvjdXtoS7zcZcvcp9PidAR9xMmhAI8MxPussdHPOu1r91OEPV4ya9O4\n//z8YvsUJSILggZxIiIiIiIidUaDOBERERERkTrhfe/roU6DOBERERERqVu6nVJERERERKSOaBB3\nkE0WG9gxPHsCdU9rYvK1d8l0fNytmncm0q9cGU/UBmCzEwiwdq1btWPwkahscnkcHJA/+WT3+S2j\ncdBHah2OOWZVXJjrdOt6iRhto05gC0DBOeJHR/26TmDK7hAHOHQs8/fZDiekZn2v367JYjwZf+vW\nipvFpZf6db0Qk/deEgcPXPupVIhKvA5da/3EijOc4l27/HY9Mhiv75p2P6zE66WmOv2AmIbhxDIE\ngL17YWx89r5uIdHneEkziXs3Wprjuo2N/jHFsHO+tbf7db3+wTsBenv9dnnhLAX/bWDFiriPnHIC\nLgAaCnHgSVMh0ed465BY3wZnm08U4wCR1GtNtcbnVccRiXAWJ6Rme7+/zzo74zYcfbS/WC/EZN3a\nuM+598f+a73qVfE2b+yNA0wAVjrdeepDzc7ROICqqzEObAHIF5x9lkrqWWyfokRkwVhs3c+CGsSJ\niIiIiIjMx2K8Epf407CIiIiIiMjCNz2Iq+TxYljmSjPbaWa7zexOM0vcg/HCcy4ys+A8/O8nmqeK\nBnFm9htmdoeZDZrZ82Y2YGb/w8zWlNX7JTP7qpk9Y2ZjZvZ1M4u/TE1EZB/U74hItalfETk0TadT\nVvJ4kT4E/Gfg/cBrgCeB75jZEft43gTw0pmPEEJVcjQrvZ2yDbgfuA7YBfQAlwP3mNmrQghPmFkT\ncAfwPPAOIABXAd81s3UhhOeq0WARWTTU74hItalfETlE1ep2SjMz4FLg4yGEr5XK3kE2kLsA+Nwc\nTw8hhMFatKuiQVwI4R+Af5hZZmb3AVuBtwD/FXg30Af8SghhW6nOQ8DPgN8Hrt3X6+SZpIcdswuL\nicnT3qTq1N4bGIiKGlau9Ot6ISapoI/u7qgoP+yElaR4gQReeEJKIgRlsjmezJ9PLNeb+N9APOke\ncLfjingT8MCD8YR5gD174rIec4JkgLyzf9etXe3WnShUflfwJZfEZV6IyWWX+tvgQ5fHdS+/3F/f\nRufvLM8/77drzejdUdlY94lu3ZbRHVFZcp+Vb8eG+rmD+kD0O0tCkZZiWShG6hz0ztdU3zA8HBV5\nAUuAH+qROLddXr+XWgenfJI4pAMg7xxTk0X/+BkvOOEbjX4IStN8+m6Ht2ojxP0YQMF52+wqxvsG\ncPvzvm4/6MMLV1mxwl/ssmVxmRdicvxr/HP4rh/EdY85xn8t7xBNbdou4veqncN+QFJX6zzeqxb4\npJQD9XlGRA6sec6JazezzTN+vj6EcP0c9V8OdAK3/eL1wm4zuws4kbkHccvM7AlgCfAg8KchhJ9U\n3NI57M8nuqdK/05/ND8buGe6wwMIITwO/BB44368jojINPU7IlJt6ldEDgEhTFX0AIZDCBtmPOYa\nwEE2gAMYKisfmvE7z6PAO8n6jd8hy9b/oZm9Yv5rF5vXIM7MlphZvvTinwMGgZtKvz4a2OI87WEg\nzt0XEamA+h0RqTb1KyKHmgDsrfAxNzN7m5mNTz+ApTNeZFZVp+wXLQrhRyGEL4YQHgwhfB84D/jf\nZPPq9tt8v2LgXuC40v+3AaeGEKa/iKoNeNp5zgiwPLVAM7sYuBig52Uvm2dzRGQRqGq/M6vPcW6h\nE5FFobafZ3qUgSJyYAXAvwX+RbiVrI+Ydljp307gP2aUH0V8dS4phLC3dBvngb8SB/wucALZJL4x\nslSW3pntc55jcy0whHD99OXMFW3+vAYRWdSq2u/M6nOOPLKa7RSR+lHbzzOpSZIiUkNTFT7mFkJ4\nNoSwbfoBPEJ2tf606Tqlrwl4HRCHGiSUAlLWAT+v9DlzmdcgLoTw7yGEe0sTg3+d/9ve/QfZVdZ3\nHH9/l5vNzbJsN3cWdrus6xLXGJcYAkMlxTREHCGlFh1Ey+BYsaMOdvAH/miROkopLWIdzeg4YvzR\njraUOsUf1FJE0WhrCIoYURBiGmPYLhuyXZbNulk2l336x7mBu/f5nuRu2M3ec+/nNXMH9tnnnvs8\n95zz3Zx7z/kcaCVJdYLkUyvvKGw5/idaIiJHpbojIvNNdUWk3szf6ZTRkkMIwGbgGjO7xMxWA/8I\nTAC3HO5nZneb2Y1lP3+4dFuTFWa2FvgCyUHczccyw0pzPZ3yGSGEMTPbBRyOeXyQ5DzySgMkR7BH\nNU0ze5l9CkJv8XG37849cTrYSifkDQAvFS7tRhFO4tZMl5/Y5QXTtXbESYXNw3GaIMDeifhvRFqA\n3Y774rYN6/3Ozb94IGob7Vnj9i0Una+e55Bs17Q9/gDirLPP9p/vvOf7Dr7U7dq5bDxu/PrX3b4t\nGzdW9VppLrssXr9eCiXARz8Sf4JzwSa/7+bNcdtAq78tsCpO3mzDeQ/A3fCmu/xTd3KVCYEZSqf0\nzHvdyeXinS4l3mrf/vi9W77cP3ugeQ6ndHjpkDknNRaq36xbUmrOeHv1p3gNj8XzTTv7tDARv950\na8prOZMYx094bcvH+1vzcJyqWPASLwE64lTF0Ql/XAXv09k9e9y+LV6iaNGvm21O3XzJS+LkTi+F\nEmDD+nhct9zq9920KW4r5FLqiPPHpjuXst0OO4medXQq8kL8e0ZEjrfDB3EL5qPAMuDTJB/o3Atc\nEEI4UNbnBcw+3bId2EJyGuaTwE+BDSGEH83HgI75X3Rm1gmsIrlAD5LzR9eZ2YqyPn3Ay0q/ExF5\nTlR3RGS+qa6I1IuF+SYOkm/jQgjXhRB+N4SQDyGcF0L4RUWfvhDCFWU/Xx1CeH4IYWkI4ZQQwoUh\nhHuOaQCOqr6JM7OvAfcDD5CcO74SuBooktxTBeBzwFXAN8zsgySHxH9DckR6pPsniIhEVHdEZL6p\nrojUqwX/Jq7mVHs65Xbg9cB7gWaSQrYVuDGEsAcghPBbMzsf+ATwZZILgO8G3h1CmMOda0VEANUd\nEZl/qisidSnw7K0eG0NVB3EhhJuAm6rotxd47XMdlIiI6o6IzDfVFZF6pW/iFlXz9AS9gxVBGWvX\nun1Xtk5GbdM5PwzAyyloGYsvjgcYzcchF2kXhhfa44vpxyfiywx3pVxIP9AfX0Q+PhWHHIB/cftM\nyiWNU/1xiEkh7YJ1hzcHgKLz3uyYitvO33qXv2DnQvqTz/aDTRiJww9GN17idi3siq8P/cGUv9wN\nuTiIpXt1vB6vucYPWvBCTO6604+r/dB1cd/rr4qDFgDIx+07B+PwA4DWjnj9du/yr7Ufap99X9pD\njfUh1dEVi1FQzExKqEjncmcfSglBmakMlAGanEAOgGY3JMPtSotXsZ3gjNGUUJFCLq6bk/jbWW9X\nPN8ZJ4QFcEMumqfi1wLACSFJiSVxa9yO4bjmnNW6M2UJzsv3rfR/MTwcv36/37dpMA5y2V303/MV\n7I7a8n0roraUP3VuiMnll/k15447474XrfeX6wUkTbb7IV4HlsZz60xZv+NFf3sSEVl4OogTERER\nERHJCH0TJyIiIiIikjFHv5F3PdFBnIiIiIiIZJi+iRMREREREcmQAFSf/1APdBAnIiIiIiIZpm/i\nFtfSpdDXN7vtvvvcrkP9G6K27mKcGAawZypO1lrZ3+X2nYoDyhjCTyrs3nV/1NbmJM319fmpZZPF\nOOmtrdU/n/ehh+PUsYEePzWzZWQkahttj9PQ0hSm/BQ9L1Vu/XrvvdnoP38ivr3O4KDfNZc7JWrr\niMMtE5XbDLA6Zcu+Y/u5UdsmJxovH4djArB5c9zmpVACXH9dvC7f8754XgA33BC3PfGEP4aVrfH6\nmewbcHpC964HZv28pHjQX2ijyuWi1NSmPXGaIMC+E+N9qHOZv6E4wX+0d/nJf85u4e1qydh2xDWH\n/v74tdr9mjU6FicHFvJ+yuDoRPV93edP+SmF7U5Aa9OEX8tmWuN5nLU6/qR1suinSLY4qbze+w2Q\na43XT1sx5VNdp853pCSKPjQYbzf9Tl8noBaATZviNi+FEuCiTXHNueNOf1tYuzZuTyuxnU/HNWc6\n52/PbcP+/iMisvB0TZyIiIiIiEhG6Js4ERERERGRjNFBnIiIiIiISEYo2ERERERERCRDArombhEd\nYglDzL5YOr/av3i6e8IJMSn6V5Y716DDlB9I0D22J25MSxlYtSpuy8VvaTElJKNt8KGobbTLD6gY\nWBVvmNNF/4L15q54DIcO+GN48sm4bbjov+cD+dH4tSbitrT3lq44TKa3+LjbdfdEHACSFvTReXK8\nggsPx+8tQH9//P7u3x/3e+op/7UGWuPt7vqr/EQCL8Tk4x/zC8zffSQOKrj20p1u32174hCHc3v8\nUJ/oPV+yxO8nz5js8kOAOotO+IaXYAIUupxtYsTv2+YlWoylpGQ4IT5e3UsphW5o0bQT6AFQyDnz\nzafUQud9yOX8YBMvWGRiIiU8KucEqTj1pcWpu6VRRC2FnF+f9h2Mx9DaGodPJYt1QqnGnFoI5POF\nqM1bP6nrzFkPF633+3ohJl7YCcC27XHNWbfOX+4DI/E2ssb5ewD426iIyHGh0ylFREREREQyovGC\nTfycYhERERERkUw4fBBXzWPuLHGdmQ2Z2UEz22pmpx/lOVvNLDiPB8v6XJHSJ+XGM8/SN3EiIiIi\nIpJxC3pN3F8A7wWuAB4BPgR828xeFEJIuWiJS4Dy8++XAj8HvlLRbxJ4QXlDCCHl2qRn6SBORERE\nREQybOHSKc3MgHcDHwkh3FZqexPwOHA58Fl3RCHMunjYzN4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NIfwL8AqgFbim9OtG2jYb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\nOogTERERERHJkP8HNRFhWgfpGDkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1ffccc18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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GWXS2BfgJjKOJBMZWnATERMqgl0TppVCCn0S5a3dc293tpyrWlF7opCXmEsmd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\nnTddAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1e1bb5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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bkKRZmNlLgTcBb3X3/6/U9h3gQeDDwLlTvsrChdDeXlGH9nSemLStH4pDMhhu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\niCdxZmZmZmZmFeJJnJmZmZmZWYX8H/uoYib+P8K/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1f88f550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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oCmzahrebtxR+QjiLfv3AwwPWrjU7iWhGUvg1sedXP0+XN7pQbis3O4oQFyQ2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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a10174dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print (\"Section 4: Hamiltonian\")\n",
+    "import numpy as np\n",
+    "import scipy.sparse as sp\n",
+    "np.random.seed(12)\n",
+    "\n",
+    "\n",
+    "import warnings\n",
+    "#Comment this to turn on warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "### define Ising model aprams\n",
+    "# system size\n",
+    "L=40\n",
+    "\n",
+    "# create 10000 random Ising states\n",
+    "states=np.random.choice([-1, 1], size=(10000,L))\n",
+    "\n",
+    "def ising_energies(states,L):\n",
+    "    \"\"\"\n",
+    "    This function calculates the energies of the states in the nn Ising Hamiltonian\n",
+    "    \"\"\"\n",
+    "    J=np.zeros((L,L),)\n",
+    "    for i in range(L):\n",
+    "        J[i,(i+1)%L]-=1.0\n",
+    "    # compute energies\n",
+    "    E = np.einsum('...i,ij,...j->...',states,J,states)\n",
+    "\n",
+    "    return E\n",
+    "# calculate Ising energies\n",
+    "energies=ising_energies(states,L)\n",
+    "\n",
+    "# reshape Ising states into RL samples: S_iS_j --> X_p\n",
+    "states=np.einsum('...i,...j->...ij', states, states)\n",
+    "shape=states.shape\n",
+    "states=states.reshape((shape[0],shape[1]*shape[2]))\n",
+    "# build final data set\n",
+    "Data=[states,energies]\n",
+    "\n",
+    "# define number of samples\n",
+    "n_samples=400\n",
+    "# define train and test data sets\n",
+    "X_train=Data[0][:n_samples]\n",
+    "Y_train=Data[1][:n_samples] #+ np.random.normal(0,4.0,size=X_train.shape[0])\n",
+    "X_test=Data[0][n_samples:3*n_samples//2]\n",
+    "Y_test=Data[1][n_samples:3*n_samples//2] #+ np.random.normal(0,4.0,size=X_test.shape[0])\n",
+    "\n",
+    "from sklearn import linear_model\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+    "import seaborn\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# set up Lasso and Ridge Regression models\n",
+    "leastsq=linear_model.LinearRegression()\n",
+    "ridge=linear_model.Ridge()\n",
+    "lasso = linear_model.Lasso()\n",
+    "\n",
+    "# define error lists\n",
+    "train_errors_leastsq = []\n",
+    "test_errors_leastsq = []\n",
+    "\n",
+    "train_errors_ridge = []\n",
+    "test_errors_ridge = []\n",
+    "\n",
+    "train_errors_lasso = []\n",
+    "test_errors_lasso = []\n",
+    "\n",
+    "# set refularisations trength values\n",
+    "lmbdas = np.logspace(-4, 5, 10)\n",
+    "\n",
+    "#Initialize coeffficients for ridge regression and Lasso\n",
+    "coefs_leastsq = []\n",
+    "coefs_ridge = []\n",
+    "coefs_lasso=[]\n",
+    "\n",
+    "for lmbda in lmbdas:\n",
+    "    \n",
+    "    ### ordinary least squares\n",
+    "    leastsq.fit(X_train, Y_train) # fit model \n",
+    "    coefs_leastsq.append(leastsq.coef_) # store weights\n",
+    "    # use the coefficient of determination R^2 as the performance of prediction.\n",
+    "    train_errors_leastsq.append(leastsq.score(X_train, Y_train))\n",
+    "    test_errors_leastsq.append(leastsq.score(X_test,Y_test))\n",
+    "    \n",
+    "    ### apply Ridge regression\n",
+    "    ridge.set_params(alpha=lmbda) # set regularisation parameter\n",
+    "    ridge.fit(X_train, Y_train) # fit model \n",
+    "    coefs_ridge.append(ridge.coef_) # store weights\n",
+    "    # use the coefficient of determination R^2 as the performance of prediction.\n",
+    "    train_errors_ridge.append(ridge.score(X_train, Y_train))\n",
+    "    test_errors_ridge.append(ridge.score(X_test,Y_test))\n",
+    "    \n",
+    "    ### apply Ridge regression\n",
+    "    lasso.set_params(alpha=lmbda) # set regularisation parameter\n",
+    "    lasso.fit(X_train, Y_train) # fit model\n",
+    "    coefs_lasso.append(lasso.coef_) # store weights\n",
+    "    # use the coefficient of determination R^2 as the performance of prediction.\n",
+    "    train_errors_lasso.append(lasso.score(X_train, Y_train))\n",
+    "    test_errors_lasso.append(lasso.score(X_test,Y_test))\n",
+    "\n",
+    "    ### plot Ising interaction J\n",
+    "    J_leastsq=np.array(leastsq.coef_).reshape((L,L))\n",
+    "    J_ridge=np.array(ridge.coef_).reshape((L,L))\n",
+    "    J_lasso=np.array(lasso.coef_).reshape((L,L))\n",
+    "\n",
+    "    cmap_args=dict(vmin=-1., vmax=1., cmap='seismic')\n",
+    "\n",
+    "    fig, axarr = plt.subplots(nrows=1, ncols=3)\n",
+    "    \n",
+    "    axarr[0].imshow(J_leastsq,**cmap_args)\n",
+    "    axarr[0].set_title('$\\\\mathrm{OLS}$',fontsize=16)\n",
+    "    axarr[0].tick_params(labelsize=16)\n",
+    "    \n",
+    "    axarr[1].imshow(J_ridge,**cmap_args)\n",
+    "    axarr[1].set_title('$\\\\mathrm{Ridge},\\ \\\\lambda=%.4f$' %(lmbda),fontsize=16)\n",
+    "    axarr[1].tick_params(labelsize=16)\n",
+    "    \n",
+    "    im=axarr[2].imshow(J_lasso,**cmap_args)\n",
+    "    axarr[2].set_title('$\\\\mathrm{LASSO},\\ \\\\lambda=%.4f$' %(lmbda),fontsize=16)\n",
+    "    axarr[2].tick_params(labelsize=16)\n",
+    "    \n",
+    "    divider = make_axes_locatable(axarr[2])\n",
+    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
+    "    cbar=fig.colorbar(im, cax=cax)\n",
+    "    \n",
+    "    cbar.ax.set_yticklabels(np.arange(-1.0, 1.0+0.25, 0.25),fontsize=14)\n",
+    "    cbar.set_label('$J_{i,j}$',labelpad=-40, y=1.12,fontsize=16,rotation=0)\n",
+    "    \n",
+    "    fig.subplots_adjust(right=2.0)\n",
+    "    \n",
+    "    plt.show()\n",
+    "    \n",
+    "# Plot our performance on both the training and test data\n",
+    "plt.semilogx(lmbdas, train_errors_leastsq, 'b',label='Train (OLS)')\n",
+    "plt.semilogx(lmbdas, test_errors_leastsq,'--b',label='Test (OLS)')\n",
+    "plt.semilogx(lmbdas, train_errors_ridge,'r',label='Train (Ridge)',linewidth=1)\n",
+    "plt.semilogx(lmbdas, test_errors_ridge,'--r',label='Test (Ridge)',linewidth=1)\n",
+    "plt.semilogx(lmbdas, train_errors_lasso, 'g',label='Train (LASSO)')\n",
+    "plt.semilogx(lmbdas, test_errors_lasso, '--g',label='Test (LASSO)')\n",
+    "\n",
+    "fig = plt.gcf()\n",
+    "fig.set_size_inches(10.0, 6.0)\n",
+    "\n",
+    "#plt.vlines(alpha_optim, plt.ylim()[0], np.max(test_errors), color='k',\n",
+    "#           linewidth=3, label='Optimum on test')\n",
+    "plt.legend(loc='lower left',fontsize=16)\n",
+    "plt.ylim([-0.01, 1.01])\n",
+    "plt.xlim([min(lmbdas), max(lmbdas)])\n",
+    "plt.xlabel(r'$\\lambda$',fontsize=16)\n",
+    "plt.ylabel('Performance',fontsize=16)\n",
+    "plt.tick_params(labelsize=16)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "notebook 6: Logistic Regression, Ising\n"
+     ]
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/Users/dylansmith/Dropbox/MachineLearningReview/Datasets/isingMC/Ising2DFM_reSample_L40_T=All.pkl'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-fd8d2f3c3b85>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;31m# load data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0mfile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Ising2DFM_reSample_L40_T=All.pkl\"\u001b[0m \u001b[0;31m# this file contains 16*10000 samples taken in T=np.arange(0.25,4.0001,0.25)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_to_data\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pickle reads the file and returns the Python object (1D array, compressed bits)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munpackbits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1600\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Decompress array and reshape for convenience\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/dylansmith/Dropbox/MachineLearningReview/Datasets/isingMC/Ising2DFM_reSample_L40_T=All.pkl'"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"notebook 6: Logistic Regression, Ising\")\n",
+    "import numpy as np\n",
+    "\n",
+    "import warnings\n",
+    "#Comment this to turn on warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "np.random.seed() # shuffle random seed generator\n",
+    "\n",
+    "# Ising model parameters\n",
+    "L=40 # linear system size\n",
+    "J=-1.0 # Ising interaction\n",
+    "T=np.linspace(0.25,4.0,16) # set of temperatures\n",
+    "T_c=2.26 # Onsager critical temperature in the TD limit\n",
+    "\n",
+    "##### prepare training and test data sets\n",
+    "\n",
+    "import pickle,os\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "###### define ML parameters\n",
+    "num_classes=2\n",
+    "train_to_test_ratio=0.5 # training samples\n",
+    "\n",
+    "# path to data directory\n",
+    "path_to_data=os.path.expanduser('~')+'/Dropbox/MachineLearningReview/Datasets/isingMC/'\n",
+    "\n",
+    "# load data\n",
+    "file_name = \"Ising2DFM_reSample_L40_T=All.pkl\" # this file contains 16*10000 samples taken in T=np.arange(0.25,4.0001,0.25)\n",
+    "data = pickle.load(open(path_to_data+file_name,'rb')) # pickle reads the file and returns the Python object (1D array, compressed bits)\n",
+    "data = np.unpackbits(data).reshape(-1, 1600) # Decompress array and reshape for convenience\n",
+    "data=data.astype('int')\n",
+    "data[np.where(data==0)]=-1 # map 0 state to -1 (Ising variable can take values +/-1)\n",
+    "\n",
+    "file_name = \"Ising2DFM_reSample_L40_T=All_labels.pkl\" # this file contains 16*10000 samples taken in T=np.arange(0.25,4.0001,0.25)\n",
+    "labels = pickle.load(open(path_to_data+file_name,'rb')) # pickle reads the file and returns the Python object (here just a 1D array with the binary labels)\n",
+    "\n",
+    "# divide data into ordered, critical and disordered\n",
+    "X_ordered=data[:70000,:]\n",
+    "Y_ordered=labels[:70000]\n",
+    "\n",
+    "X_critical=data[70000:100000,:]\n",
+    "Y_critical=labels[70000:100000]\n",
+    "\n",
+    "X_disordered=data[100000:,:]\n",
+    "Y_disordered=labels[100000:]\n",
+    "\n",
+    "del data,labels\n",
+    "\n",
+    "# define training and test data sets\n",
+    "X=np.concatenate((X_ordered,X_disordered))\n",
+    "Y=np.concatenate((Y_ordered,Y_disordered))\n",
+    "\n",
+    "# pick random data points from ordered and disordered states \n",
+    "# to create the training and test sets\n",
+    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,train_size=train_to_test_ratio)\n",
+    "\n",
+    "# full data set\n",
+    "X=np.concatenate((X_critical,X))\n",
+    "Y=np.concatenate((Y_critical,Y))\n",
+    "\n",
+    "print('X_train shape:', X_train.shape)\n",
+    "print('Y_train shape:', Y_train.shape)\n",
+    "print()\n",
+    "print(X_train.shape[0], 'train samples')\n",
+    "print(X_critical.shape[0], 'critical samples')\n",
+    "print(X_test.shape[0], 'test samples')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "notebook 5: Logistic Regression, SUSY\n",
+      "WARNING:tensorflow:From <ipython-input-9-57e47b7861fc>:61: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "\n",
+      "Future major versions of TensorFlow will allow gradients to flow\n",
+      "into the labels input on backprop by default.\n",
+      "\n",
+      "See tf.nn.softmax_cross_entropy_with_logits_v2.\n",
+      "\n",
+      "Accuracy for alpha 1.0E-10 : 0.770\n",
+      "Accuracy for alpha 1.3E-09 : 0.770\n",
+      "Accuracy for alpha 1.6E-08 : 0.770\n",
+      "Accuracy for alpha 2.0E-07 : 0.770\n",
+      "Accuracy for alpha 2.5E-06 : 0.770\n",
+      "Accuracy for alpha 3.2E-05 : 0.770\n",
+      "Accuracy for alpha 4.0E-04 : 0.769\n",
+      "Accuracy for alpha 5.0E-03 : 0.764\n",
+      "Accuracy for alpha 6.3E-02 : 0.748\n",
+      "Accuracy for alpha 7.9E-01 : 0.659\n",
+      "Accuracy for alpha 1.0E+01 : 0.543\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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HPdRu8pFOOhBPa72q7s//1p0wvv6kNaVUY5PYyoA+Ho97A3t9HPOh1roW2KWU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\nAJV0oVgYM5Tk0x+/H2fHG+77vm44D1zX+b42xnW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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1119e8a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for alpha 1.0E-05 : 0.770\n",
+      "Accuracy for alpha 1.0E-10 : 0.790\n",
+      "Accuracy for alpha 1.3E-09 : 0.790\n",
+      "Accuracy for alpha 1.6E-08 : 0.790\n",
+      "Accuracy for alpha 2.0E-07 : 0.790\n",
+      "Accuracy for alpha 2.5E-06 : 0.790\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"notebook 5: Logistic Regression, SUSY\")\n",
+    "# Importing the SUSY Data set\n",
+    "import sys, os\n",
+    "import pandas as pd\n",
+    "\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "#Commnet this to turn on warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "\n",
+    "seed=12\n",
+    "np.random.seed(seed)\n",
+    "import tensorflow as tf\n",
+    "# suppress tflow compilation warnings\n",
+    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
+    "\n",
+    "tf.set_random_seed(seed)\n",
+    "\n",
+    "columns=[\"signal\", \"lepton 1 pT\", \"lepton 1 eta\", \"lepton 1 phi\", \"lepton 2 pT\", \"lepton 2 eta\", \n",
+    "         \"lepton 2 phi\", \"missing energy magnitude\", \"missing energy phi\", \"MET_rel\", \n",
+    "         \"axial MET\", \"M_R\", \"M_TR_2\", \"R\", \"MT2\", \"S_R\", \"M_Delta_R\", \"dPhi_r_b\", \"cos(theta_r1)\"]\n",
+    "\n",
+    "#Load 1,500,000 rows as train data, 50,000 as test data\n",
+    "filename=\"/Users/dylansmith/Downloads/SUSY.csv\"\n",
+    "df_train=pd.read_csv(filename,names=columns,nrows=1500000,engine='python')\n",
+    "df_test=pd.read_csv(filename,names=columns,nrows=50000, skiprows=1500000,engine='python')\n",
+    "\n",
+    "%matplotlib inline\n",
+    "#import ml_style as style\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "#mpl.rcParams.update(style.style)\n",
+    "\n",
+    "def getTrainData(nVar):\n",
+    "    ExamplesTrain = df_train.iloc[:,1:nVar+1].as_matrix()\n",
+    "    #now the signal\n",
+    "    ResultsTrain = df_train.iloc[:,0:1].as_matrix()\n",
+    "    return (ExamplesTrain,ResultsTrain)\n",
+    "\n",
+    "def getTestData(nVar):\n",
+    "    ExamplesTest = df_test.iloc[:,1:nVar+1].as_matrix()\n",
+    "    #now the signal\n",
+    "    ResultsTest = df_test.iloc[:,0:1].as_matrix()\n",
+    "    return (ExamplesTest,ResultsTest)\n",
+    "\n",
+    "#let's define this as a function so we can call it easily\n",
+    "def runTensorFlowRegression(nVar,alpha):\n",
+    "\n",
+    "    #make data array placeholder for just first 8 simple features\n",
+    "    x = tf.placeholder(tf.float32,[None,nVar])\n",
+    "    #make weights and bias\n",
+    "    W = tf.Variable(tf.zeros([nVar,2])) #we will make y 'onehot' 0 bit is bkg, 1 bit is signal\n",
+    "    b = tf.Variable(tf.zeros([2]))\n",
+    "\n",
+    "    #make 'answer variable'\n",
+    "    y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
+    "    #placeholder for correct answer\n",
+    "    y_ = tf.placeholder(tf.float32, [None, 2])\n",
+    "    #cross entropy\n",
+    "    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y,labels=y_)+alpha*tf.nn.l2_loss(W))\n",
+    "\n",
+    "    #define training step\n",
+    "    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
+    "    #initialize variables \n",
+    "    init = tf.global_variables_initializer()\n",
+    "    #setup session\n",
+    "    sess = tf.Session()\n",
+    "    sess.run(init)\n",
+    "\n",
+    "    #ok now everything is setup for tensorflow, but we need the data in a useful form\n",
+    "    #first let's get the variables\n",
+    "    Var_train,Sig_train_bit1 = getTrainData(nVar)\n",
+    "    #now the signal\n",
+    "    Sig_train_bit0 = Sig_train_bit1.copy()\n",
+    "    Sig_train_bit0 = 1 - Sig_train_bit0\n",
+    "    Sig_train = np.column_stack((Sig_train_bit0,Sig_train_bit1))\n",
+    "\n",
+    "    #now run with batches of 100 data points\n",
+    "    for i in range(0,15000):\n",
+    "        start = i*100\n",
+    "        end = (i+1)*100-1\n",
+    "        batch_x = Var_train[start:end]\n",
+    "        batch_y = Sig_train[start:end]\n",
+    "        sess.run(train_step, feed_dict={x: batch_x, y_: batch_y})\n",
+    "    \n",
+    "    \n",
+    "    #now test accuracy\n",
+    "    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
+    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
+    "    #setup test data\n",
+    "    Var_test = df_test.iloc[:,1:nVar+1].as_matrix()\n",
+    "    #now the signal\n",
+    "    Sig_test_bit1 = df_test.iloc[:,0:1].as_matrix()\n",
+    "    Sig_test_bit0 = Sig_test_bit1.copy()\n",
+    "    Sig_test_bit0 = 1 - Sig_test_bit0\n",
+    "    Sig_test = np.column_stack((Sig_test_bit0,Sig_test_bit1))\n",
+    "    print(\"Accuracy for alpha %.1E : %.3f\" %(alpha,sess.run(accuracy, feed_dict={x: Var_test, y_: Sig_test})))\n",
+    "    \n",
+    "    #get the weights\n",
+    "    weights = W.eval(session=sess)\n",
+    "    #get probabilities assigned (i.e. evaluate y on test data)\n",
+    "    probs = y.eval(feed_dict = {x: Var_test}, session = sess)\n",
+    "    #print probs\n",
+    "    #now let's get the signal efficiency and background rejection on the test data\n",
+    "    Acceptance = []\n",
+    "    Rejection = []\n",
+    "    theshes = np.arange(0,1,0.01)\n",
+    "    for thresh in theshes:\n",
+    "        it=0\n",
+    "        nBkg = 0.0\n",
+    "        nBkgCor = 0.0\n",
+    "        nSig = 0.0\n",
+    "        nSigCor = 0.0\n",
+    "        for prob in probs:\n",
+    "            if prob[0] > thresh:\n",
+    "                sig=False\n",
+    "            else:\n",
+    "                sig=True\n",
+    "            if Sig_test_bit1[it][0]: #actual signal\n",
+    "                nSig+=1\n",
+    "                if sig:\n",
+    "                    nSigCor+=1\n",
+    "            else: #actual background\n",
+    "                nBkg+=1\n",
+    "                if not sig:\n",
+    "                    nBkgCor+=1\n",
+    "                \n",
+    "            it+=1\n",
+    "        #now append signal efficiency and bakcground rejection\n",
+    "        Acceptance.append(nSigCor/nSig)\n",
+    "        Rejection.append(nBkgCor/nBkg)\n",
+    "\n",
+    "    return (probs,Acceptance,Rejection)\n",
+    "\n",
+    "alphas = np.logspace(-10,1,11)\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "it=0\n",
+    "for alpha in alphas:\n",
+    "    c1 = 1.*( float(it) % 3.)/3.0\n",
+    "    c2 = 1.*( float(it) % 9.)/9.0\n",
+    "    c3 = 1.*( float(it) % 27.)/27.0\n",
+    "    probsSimple,accep,rej = runTensorFlowRegression(8,alpha)\n",
+    "    ax.scatter(accep,rej,c=[c1,c2,c3],label='Alpha: %.1E' %alpha)\n",
+    "    it+=1\n",
+    "    \n",
+    "ax.set_xlabel('signal efficiency')\n",
+    "ax.set_ylabel('background rejection')\n",
+    "plt.legend(loc='lower left', fontsize = 'small');\n",
+    "plt.show()\n",
+    "\n",
+    "#now let's investigate how mixed the events are\n",
+    "probsSimple,accep,rej = runTensorFlowRegression(8,.00001)\n",
+    "Signal = df_test.iloc[:,0:1]\n",
+    "#print probsSimple[:,1]\n",
+    "\n",
+    "df_test_acc = pd.DataFrame({'PROB':probsSimple[:,1]})\n",
+    "df_test_acc['SIG']=Signal\n",
+    "df_test_acc_sig = df_test_acc.query('SIG==1')\n",
+    "df_test_acc_bkg = df_test_acc.query('SIG==0')\n",
+    "df_test_acc_sig.plot(kind='hist',y='PROB',color='blue',alpha=0.5,bins=np.linspace(0,1,10),label='Signal')\n",
+    "df_test_acc_bkg.plot(kind='hist',y='PROB',color='red',label='Background')\n",
+    "\n",
+    "alphas = np.logspace(-10,1,11)\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "it=0\n",
+    "for alpha in alphas:\n",
+    "    c1 = 1.*( float(it) % 3.)/3.0\n",
+    "    c2 = 1.*( float(it) % 9.)/9.0\n",
+    "    c3 = 1.*( float(it) % 27.)/27.0\n",
+    "    probsSimple,accep,rej = runTensorFlowRegression(18,alpha)\n",
+    "    ax.scatter(accep,rej,c=[c1,c2,c3],label='Alpha: %.1E' %alpha)\n",
+    "    it+=1\n",
+    "    \n",
+    "ax.set_xlabel('signal efficiency')\n",
+    "ax.set_ylabel('background rejection')\n",
+    "plt.legend(loc='lower left', fontsize = 'small');\n",
+    "plt.show()\n",
+    "\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "\n",
+    "def runSciKitRegressionL2(nVar,alpha):\n",
+    "    X_train, y_train = getTrainData(nVar)\n",
+    "    X_test, y_test = getTestData(nVar)\n",
+    "    clf = SGDClassifier(loss=\"log\", penalty=\"l2\",alpha=alpha,max_iter=5,tol=None)\n",
+    "    clf.fit(X_train,y_train.ravel())\n",
+    "    predictions = clf.predict(X_test)\n",
+    "    print('Accuracy on test data with alpha %.2E : %.3f' %(alpha,clf.score(X_test,y_test)) )\n",
+    "    probs = clf.predict_proba(X_test)\n",
+    "    #print probs\n",
+    "    #get signal acceptance and background rejection\n",
+    "    thresholds = np.arange(0,1,.01)\n",
+    "    Acceptance = []\n",
+    "    BkgRejection = []\n",
+    "    for thresh in thresholds:\n",
+    "        it=0\n",
+    "        nPredSig=0.0\n",
+    "        nPredBkg=0.0\n",
+    "        nTotSig=0.0\n",
+    "        nTotBkg=0.0\n",
+    "        for prob in probs:\n",
+    "            if prob[1]>thresh:\n",
+    "                predSig=True\n",
+    "            else:\n",
+    "                predSig=False\n",
+    "            if y_test[it][0]:\n",
+    "                Sig = True\n",
+    "            else:\n",
+    "                Sig = False\n",
+    "            if Sig:\n",
+    "                if predSig:\n",
+    "                    nPredSig+=1\n",
+    "                nTotSig+=1\n",
+    "            else:\n",
+    "                if not predSig:\n",
+    "                    nPredBkg+=1\n",
+    "                nTotBkg+=1\n",
+    "            it+=1\n",
+    "        Acceptance.append(nPredSig/nTotSig)\n",
+    "        BkgRejection.append(nPredBkg/nTotBkg)\n",
+    "    return (probs,Acceptance,BkgRejection)\n",
+    "\n",
+    "\n",
+    "def runSciKitRegressionL1(nVar,alpha):\n",
+    "    X_train, y_train = getTrainData(nVar)\n",
+    "    X_test, y_test = getTestData(nVar)\n",
+    "    clf = SGDClassifier(loss=\"log\", penalty=\"l1\",alpha=alpha,max_iter=5,tol=None)\n",
+    "    clf.fit(X_train,y_train.ravel())\n",
+    "    predictions = clf.predict(X_test)\n",
+    "    print('Accuracy on test data with alpha %.2E : %.3f' %(alpha,clf.score(X_test,y_test)) )\n",
+    "    probs = clf.predict_proba(X_test)\n",
+    "    #print probs\n",
+    "    #get signal acceptance and background rejection\n",
+    "    thresholds = np.arange(0,1,.01)\n",
+    "    Acceptance = []\n",
+    "    BkgRejection = []\n",
+    "    for thresh in thresholds:\n",
+    "        it=0\n",
+    "        nPredSig=0.0\n",
+    "        nPredBkg=0.0\n",
+    "        nTotSig=0.0\n",
+    "        nTotBkg=0.0\n",
+    "        for prob in probs:\n",
+    "            if prob[1]>thresh:\n",
+    "                predSig=True\n",
+    "            else:\n",
+    "                predSig=False\n",
+    "            if y_test[it][0]:\n",
+    "                Sig = True\n",
+    "            else:\n",
+    "                Sig = False\n",
+    "            if Sig:\n",
+    "                if predSig:\n",
+    "                    nPredSig+=1\n",
+    "                nTotSig+=1\n",
+    "            else:\n",
+    "                if not predSig:\n",
+    "                    nPredBkg+=1\n",
+    "                nTotBkg+=1\n",
+    "            it+=1\n",
+    "        Acceptance.append(nPredSig/nTotSig)\n",
+    "        BkgRejection.append(nPredBkg/nTotBkg)\n",
+    "    return (probs,Acceptance,BkgRejection)\n",
+    "alphas = np.logspace(-10,1,11)\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "it=0\n",
+    "for alpha in alphas:\n",
+    "    c1 = 1.*( float(it) % 3.)/3.0\n",
+    "    c2 = 1.*( float(it) % 9.)/9.0\n",
+    "    c3 = 1.*( float(it) % 27.)/27.0\n",
+    "    probs,accept,rej = runSciKitRegressionL1(8,alpha)\n",
+    "    ax.scatter(accept,rej,c=[c1,c2,c3],label='Alpha: %.1E' %alpha)\n",
+    "    it+=1\n",
+    "\n",
+    "ax.set_xlabel('signal efficiency')\n",
+    "ax.set_ylabel('background rejection')\n",
+    "plt.legend(loc='lower left', fontsize = 'small');\n",
+    "plt.show()\n",
+    "\n",
+    "#now let's investigate how mixed the events are\n",
+    "probsSimple,accep,rej = runSciKitRegressionL1(8,.5)\n",
+    "Signal = df_test.iloc[:,0:1]\n",
+    "#print probsSimple[:,1]\n",
+    "\n",
+    "df_test_acc = pd.DataFrame({'PROB':probsSimple[:,1]})\n",
+    "df_test_acc['SIG']=Signal\n",
+    "df_test_acc_sig = df_test_acc.query('SIG==1')\n",
+    "df_test_acc_bkg = df_test_acc.query('SIG==0')\n",
+    "df_test_acc_sig.plot(kind='hist',y='PROB',color='blue',alpha=0.5,bins=np.linspace(0,1,10),label='Signal')\n",
+    "df_test_acc_bkg.plot(kind='hist',y='PROB',color='red',label='Background')\n",
+    "\n",
+    "alphas = np.logspace(-10,1,11)\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "it=0\n",
+    "for alpha in alphas:\n",
+    "    c1 = 1.*( float(it) % 3.)/3.0\n",
+    "    c2 = 1.*( float(it) % 9.)/9.0\n",
+    "    c3 = 1.*( float(it) % 27.)/27.0\n",
+    "    probs,accept,rej = runSciKitRegressionL1(18,alpha)\n",
+    "    ax.scatter(accept,rej,c=[c1,c2,c3],label='Alpha: %.1E' %alpha)\n",
+    "    it+=1\n",
+    "\n",
+    "ax.set_xlabel('signal efficiency')\n",
+    "ax.set_ylabel('background rejection')\n",
+    "plt.legend(loc='lower left', fontsize = 'small');\n",
+    "plt.show()\n",
+    "\n",
+    "alphas = np.logspace(-10,1,11)\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "it=0\n",
+    "for alpha in alphas:\n",
+    "    c1 = 1.*( float(it) % 3.)/3.0\n",
+    "    c2 = 1.*( float(it) % 9.)/9.0\n",
+    "    c3 = 1.*( float(it) % 27.)/27.0\n",
+    "    probs,accept,rej = runSciKitRegressionL2(8,alpha)\n",
+    "    ax.scatter(accept,rej,c=[c1,c2,c3],label='Alpha: %.1E' %alpha)\n",
+    "    it+=1\n",
+    "\n",
+    "ax.set_xlabel('signal efficiency')\n",
+    "ax.set_ylabel('background rejection')\n",
+    "plt.legend(loc='lower left', fontsize = 'small');\n",
+    "plt.show()\n",
+    "\n",
+    "alphas = np.logspace(-10,1,11)\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "it=0\n",
+    "for alpha in alphas:\n",
+    "    c1 = 1.*( float(it) % 3.)/3.0\n",
+    "    c2 = 1.*( float(it) % 9.)/9.0\n",
+    "    c3 = 1.*( float(it) % 27.)/27.0\n",
+    "    probs,accept,rej = runSciKitRegressionL2(18,alpha)\n",
+    "    ax.scatter(accept,rej,c=[c1,c2,c3],label='Alpha: %.1E' %alpha)\n",
+    "    it+=1\n",
+    "\n",
+    "ax.set_xlabel('signal efficiency')\n",
+    "ax.set_ylabel('background rejection')\n",
+    "plt.legend(loc='lower left', fontsize = 'small');\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Blobs-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Blobs-checkpoint.ipynb
new file mode 100644
index 000000000..2acd80223
--- /dev/null
+++ b/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Blobs-checkpoint.ipynb	
@@ -0,0 +1,256 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 2)\n",
+      "(3,)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------Uncertainty Estimates-------\n",
+      "(25, 2)\n",
+      "(25,)\n",
+      "[ True False False False  True  True False  True  True  True False  True\n",
+      "  True False  True False False False  True  True  True  True  True False\n",
+      " False]\n",
+      "['red' 'blue' 'blue' 'blue' 'red' 'red' 'blue' 'red' 'red' 'red' 'blue'\n",
+      " 'red' 'red' 'blue' 'red' 'blue' 'blue' 'blue' 'red' 'red' 'red' 'red'\n",
+      " 'red' 'blue' 'blue']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1300x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-------Predicting Probabilities---------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1300x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import mglearn\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import make_blobs\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.svm import LinearSVC\n",
+    "X,y= make_blobs(random_state=42)\n",
+    "\n",
+    "linear_svm=LinearSVC().fit(X,y)\n",
+    "print(linear_svm.coef_.shape)\n",
+    "print(linear_svm.intercept_.shape)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=60, cmap=mglearn.cm3)\n",
+    "line = np.linspace(-15, 15)\n",
+    "for coef, intercept in zip(linear_svm.coef_, linear_svm.intercept_):\n",
+    "    plt.plot(line, -(line * coef[0] + intercept) / coef[1])\n",
+    "    plt.ylim(-10, 15)\n",
+    "    plt.xlim(-10, 8)\n",
+    "    \n",
+    "mglearn.plots.plot_2d_classification(linear_svm, X, fill=True, alpha=.7)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=y, s=60)\n",
+    "line = np.linspace(-15, 15)\n",
+    "for coef, intercept in zip(linear_svm.coef_, linear_svm.intercept_):\n",
+    "    plt.plot(line, -(line * coef[0] + intercept) / coef[1])\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "print (\"----------Uncertainty Estimates-------\")\n",
+    "# create and split a synthetic dataset\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.datasets import make_blobs, make_circles\n",
+    "# X, y = make_blobs(centers=2, random_state=59)\n",
+    "X, y = make_circles(noise=0.25, factor=0.5, random_state=1)\n",
+    "# we rename the classes \"blue\" and \"red\" for illustration purposes:\n",
+    "y_named = np.array([\"blue\", \"red\"])[y]\n",
+    "# we can call train test split with arbitrary many arrays\n",
+    "# all will be split in a consistent manner\n",
+    "X_train, X_test, y_train_named, y_test_named, y_train, y_test = \\\n",
+    "    train_test_split(X, y_named, y, random_state=0)\n",
+    "# build the gradient boosting model model\n",
+    "gbrt = GradientBoostingClassifier(random_state=0)\n",
+    "gbrt.fit(X_train, y_train_named)\n",
+    "\n",
+    "print(X_test.shape)\n",
+    "print(gbrt.decision_function(X_test).shape)\n",
+    "# show the first few entries of decision_function\n",
+    "gbrt.decision_function(X_test)[:6]\n",
+    "print(gbrt.decision_function(X_test) > 0)\n",
+    "print(gbrt.predict(X_test))\n",
+    "# make the boolean True/False into 0 and 1\n",
+    "greater_zero = (gbrt.decision_function(X_test) > 0).astype(int)\n",
+    "# use 0 and 1 as indices into classes_\n",
+    "pred = gbrt.classes_[greater_zero]\n",
+    "#pred is the same as the output of gbrt.predict\n",
+    "np.all(pred == gbrt.predict(X_test))\n",
+    "decision_function = gbrt.decision_function(X_test)\n",
+    "np.min(decision_function), np.max(decision_function)\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n",
+    "mglearn.tools.plot_2d_separator(gbrt, X, ax=axes[0], alpha=.4, fill=True, cm=mglearn.cm2)\n",
+    "scores_image = mglearn.tools.plot_2d_scores(gbrt, X, ax=axes[1], alpha=.4, cm='bwr')\n",
+    "for ax in axes:\n",
+    "    # plot training and test points\n",
+    "    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=mglearn.cm2, s=60, marker='^')\n",
+    "    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=mglearn.cm2, s=60)\n",
+    "plt.colorbar(scores_image, ax=axes.tolist())\n",
+    "plt.show()\n",
+    "\n",
+    "print (\"-------Predicting Probabilities---------\")\n",
+    "gbrt.predict_proba(X_test).shape\n",
+    "np.set_printoptions(suppress=True, precision=3)\n",
+    "# show the first few entries of predict_proba\n",
+    "gbrt.predict_proba(X_test[:6])\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n",
+    "mglearn.tools.plot_2d_separator(gbrt, X, ax=axes[0], alpha=.4,\n",
+    "                                fill=True, cm=mglearn.cm2)\n",
+    "scores_image = mglearn.tools.plot_2d_scores(gbrt, X, ax=axes[1], alpha=.4,\n",
+    "                                            cm='bwr', function='predict_proba')\n",
+    "for ax in axes:\n",
+    "    # plot training and test points\n",
+    "    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=mglearn.cm2, s=60, marker='^')\n",
+    "    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=mglearn.cm2, s=60)\n",
+    "plt.colorbar(scores_image, ax=axes.tolist())\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---------Scaling training and test data same------\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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pwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhXF1mAws3Yzu9vM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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109de6a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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pwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhXF1mAws3Yzu9vM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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c1a685ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print (\"---------Scaling training and test data same------\")\n",
+    "from sklearn.datasets import make_blobs\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "# make synthetic data\n",
+    "X, _ = make_blobs(n_samples=50, centers=5, random_state=4, cluster_std=2)\n",
+    "# split it into training and test set\n",
+    "X_train, X_test = train_test_split(X, random_state=5, test_size=.1)\n",
+    "# plot the training and test set\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(13, 4))\n",
+    "axes[0].scatter(X_train[:, 0], X_train[:, 1],\n",
+    "                c='b', label=\"training set\", s=60)\n",
+    "axes[0].scatter(X_test[:, 0], X_test[:, 1], marker='^',\n",
+    "                c='r', label=\"test set\", s=60)\n",
+    "axes[0].legend(loc='upper left')\n",
+    "axes[0].set_title(\"original data\")\n",
+    "# scale the data using MinMaxScaler\n",
+    "scaler = MinMaxScaler()\n",
+    "scaler.fit(X_train)\n",
+    "X_train_scaled = scaler.transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "# visualize the properly scaled data\n",
+    "axes[1].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],\n",
+    "                c='b', label=\"training set\", s=60)\n",
+    "axes[1].scatter(X_test_scaled[:, 0], X_test_scaled[:, 1], marker='^',\n",
+    "                c='r', label=\"test set\", s=60)\n",
+    "axes[1].set_title(\"scaled data\")\n",
+    "# rescale the test set separately, so that test set min is 0 and test set max is 1\n",
+    "# DO NOT DO THIS! For illustration purposes only\n",
+    "test_scaler = MinMaxScaler()\n",
+    "test_scaler.fit(X_test)\n",
+    "X_test_scaled_badly = test_scaler.transform(X_test)\n",
+    "# visualize wrongly scaled data\n",
+    "axes[2].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],\n",
+    "                c='b', label=\"training set\", s=60)\n",
+    "axes[2].scatter(X_test_scaled_badly[:, 0], X_test_scaled_badly[:, 1], marker='^',\n",
+    "                c='r', label=\"test set\", s=60)\n",
+    "axes[2].set_title(\"improperly scaled data\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Boston Housing-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Boston Housing-checkpoint.ipynb
new file mode 100644
index 000000000..7c04e4dc2
--- /dev/null
+++ b/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Boston Housing-checkpoint.ipynb	
@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(506, 104)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training set score: 0.771865\n",
+      "test set score: 0.725968\n",
+      "training set score: 0.771686\n",
+      "test set score: 0.722415\n",
+      "training set score: 0.771863\n",
+      "test set score: 0.725626\n",
+      "--------------------\n",
+      "training set score: 0.771865\n",
+      "test set score: 0.725916\n",
+      "number of features used: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "#!pip install mglearn\n",
+    "import mglearn\n",
+    "import sklearn\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import IPython\n",
+    "\n",
+    "from sklearn.datasets import load_boston\n",
+    "boston = load_boston()\n",
+    "X, y = mglearn.datasets.load_extended_boston()\n",
+    "print(X.shape)\n",
+    "mglearn.plots.plot_knn_classification(n_neighbors=3)\n",
+    "plt.show()\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X, y=mglearn.datasets.make_forge()\n",
+    "\n",
+    "X_train, X_test, y_train, y_test=train_test_split(X, y, random_state=0)\n",
+    "\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "clf=KNeighborsClassifier(n_neighbors=3)\n",
+    "clf.fit(X_train, y_train)\n",
+    "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski')\n",
+    "clf.predict(X_test)\n",
+    "clf.score(X_test, y_test)\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "lr=LinearRegression().fit(X_train, y_train)\n",
+    "\n",
+    "print(\"training set score: %f\" % lr.score(X_train, y_train))\n",
+    "print(\"test set score: %f\" % lr.score(X_test, y_test))\n",
+    "\n",
+    "from sklearn.linear_model import Ridge\n",
+    "ridge = Ridge().fit(X_train, y_train)\n",
+    "print(\"training set score: %f\" % ridge.score(X_train, y_train))\n",
+    "print(\"test set score: %f\" % ridge.score(X_test, y_test))\n",
+    "\n",
+    "ridge01 = Ridge(alpha=0.1).fit(X_train, y_train)\n",
+    "print(\"training set score: %f\" % ridge01.score(X_train, y_train))\n",
+    "print(\"test set score: %f\" % ridge01.score(X_test, y_test))\n",
+    "\n",
+    "print (\"--------------------\")\n",
+    "\n",
+    "from sklearn.linear_model import Lasso\n",
+    "lasso00001 = Lasso(alpha=0.0001).fit(X_train, y_train)\n",
+    "print(\"training set score: %f\" % lasso00001.score(X_train, y_train))\n",
+    "print(\"test set score: %f\" % lasso00001.score(X_test, y_test))\n",
+    "print(\"number of features used: %d\" % np.sum(lasso00001.coef_ != 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Make Moons-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Make Moons-checkpoint.ipynb
new file mode 100644
index 000000000..baccc39c2
--- /dev/null
+++ b/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/.ipynb_checkpoints/Make Moons-checkpoint.ipynb	
@@ -0,0 +1,102 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Tw2DOQMqYIpjvWIBVq2fDaj0a7a4SEVEUMCeIiMgXZgRRbGMRR4Uqtq9FYlY+4gcP8/r9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\nJ3Dx5IXezydu/TXe/0cJcyKAdW0tkk4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XH/qVopV3IqJI+8ejj+PauY95zYkNBwUcePKp\niBz3qqu3wDx1Sff//edEEeKHjsCq1bNRHMZoAOYEEVFgUnJiyrFLcExU7nTYMyMA5gTJQ1Vz4sQM\nQQAEAXpBwCW6uB7/Bgk6pAgCMgR9r++F+y9W2tZXv4p7s73PbH5vlh66D1+NSL87HA2ISx3Qvf/G\nvW8gKbugVzXdJX7wMMRn5eOpMv+rIvjjeT9pOPePRvI+WCKiSFLLChgtzeeCzgljZh4qd6wNeZ/M\nCSKiwNSQE54ZATAnSB4ciaOgaxNMuP2Sy6PdDU2prz+F3x94Gwsf8v6nuXC0Di8/X4lf/tdCpKam\ny75/q/UoKravRXX1FkAU8c3KaTBdezOSr78dzQd39RiZ4018ZgHWvTIHK4sKg963Z9XcJZTqeaTv\ngyUiihRfx0qXcK44SuGeE2IIOZGYlY+q0nkh3ebBnCAiCkxqTlx15VTApNz5BPQGHFv2E+iMqTBd\nM5Y5QbJhEYciqr7+FNb9/REU3veM1yJM5baVmJ6tDzCzuR6VZStx55Tfydq32tqdWLV6NhKz8mGe\nugTpXfenNlnKceKl2ehoaexVTfcUl5KOxubGkPYv5YqB1HtZI30fLBGRXAIN3Q5mBQy5j3ty5USL\nPbRVs5gTpHWOdifWHP0cYocoS3sdYgfsEGGCDoIg76SNbFu+tpu7Ph5zTYMQ1/m1F7/8LKi2zyAO\nEIEj587ikv9ehqsKHkG8qXdOHNn5T0zN9J8Td2fGYf0HryJpwsO9+hIMz37bDn+Iz99Y1mu+m2Bz\nwtFsw6uffIgGQY8Usd3r7TPHBQMQ11msKeuai7WmYq3X5+56zhPWbMXwvBmS5g11tXVdhh53Z4rd\njw34M4mReVoj3bZOD+iMKWYp7bGIQxFVuW0ljn21x2cR5thXH6P8SDP+HGBm86uv2Ctrv/zNHm8e\nOx2JV+bi5PoFcNaf6pxR3gdngxXJpuSg9y/nlWU574MlIoo096Hb3ooK0VoBQ86cSEgM/ljMnKBY\nUN/uxPF2+SdYbEAHIE9diG0r1bYgAAZDjy99K7YH13bX41v2boTz5Kc4sHcrksY/1LsN62H85Rs7\n/lLlv0vmSz/r7JePvgSjAR1oO1uHE28s8zrfTbA5oUtMwRf9TACAUxL2fwZxOHnuLGyWHVgw03tO\nLBitw9+f24H6EVO75xc94uNpO5t6tuXtsYH4alsOMdm2E4hLHXCFlHZYxJGZKIpeP6fOUTi7P9iI\nndOMuHnda5hQ8Mteo3Fm/Warz8dv3PBbVH/wCnJHTZV9FI6v2eNd4gcPgyH9O2jatw3mm31fqWyt\nKUPhqFFB71/OK8v+7oPlVVYi8uYYgLsUXO0vEFfbJscZ7Nj1Dt4tMmLMS+/g8NV3w+jxZvHSnz6F\nn/pop6biBXxrqcDgrHxcmjcDdx3s2e8z6Ox4KFddv6x8AQmZeWHnRLOlDKlX3+h3/81evsacoFiQ\nqo/DYH0cR+Kw7ZDbPt90FtbaSrxXZMSNL1XiOz+cin5J/Xtsc0nhX3y2/e3bf0FD7VtIyfwxBo9/\nWNZ+f7X3TSQHmO/GkP5dNO17C+ab7/fZZrOlDFdceyOu07UFlbs1H5biJ17mFXXJSNbhviw9dn5Y\nimvGz/Dbtmdb7o8NNBon2iNatNq2Tg9sqD91REp7LOLIzNHR0f35wVY7zni8SVPDwS9abR+vXInC\nLB2uy9DjnkwRK//xewwe/7Ckts83ncWh9//RdcD+B84Mu7XXATucfn+4+3UMCHB/atrY6bC+9r9I\n/N4PvR6cW48fQqulHLNKFnl9vL9bBOS6siznfbBEFPtauz+Lw26JF8eVvELV9P5GTM++cIvPhn9v\n9HqV1RvXVcP3ioy44aXeVwuPtAMwAOi6wPKtGNxogFMHdwacx0BKTjTuL4epcBm+7ei9f2fTWTS/\n9SSSbp0LfbIZJ92+x5ygWGDUx2H6kKui3Q3SsI0bfouf5RhwXYYe92cDnx56S/LFXdfcmzuLjLh5\nnfxzbD76l3d6rUblKW1sUVdO/MhnTrTXlGNbySIMHejo9X1/5xM3bPgEf/ki8Aik0VcewqvDe7ft\nvo9ra3dggUdOLBitwyvPb0fl9InMCYW84miwSdmORRyZGXW67jeI7fD9JrEB7QoOcVRf287/z96d\nx0dV3/sff39nyU4gaEBEpAouCESoAhUUlQq4VK3YoqIGxau12tufiqWicqulytWKta1VS2+poKKo\nFetWBUVEUYIbBHCtCyKyBAkJ2Wcy5/dHMpiEmckkObOcmdfz8fARyJyc840h5z3n892qdql8wzLd\n3Dwk7+YxLj14/zIFRkze+yY70rmr3npYlzS/sZ9aZGnx6keUNy66N/bRtNvfZieqULIOGizL36Cd\n/5yt7sMmKHPoRHnyC+WvLFP9+pdUX7pUj1/1s7Dbi0eaIvDGrbdH/b1EYud6CQBSn1dSU0r59QNP\n5Jt7rHuotlSWa92GV1rlxML7X9aYMefuMxonlGCv4fA+bl1aZO3tLWzZ7kCjtNkY9ZMlTwffAW3q\nYE7kHj1BuUXf5UR16UuqXrdUx5/1S/Xptb8k3z5fv/6dRare+oFq1yxS3g+v1n4tXiMnkApYE4dz\nd+XcwU7dB5tzYuZoo8Pub79zN3ju3a/cH7JD2a5211bvVmEUOaHGBlUtmS1fEc8T6ByKODbbe9Mx\nUg/5dWSbN8WJHqaVqHOvfyfMkLx32h/OV7tnl17Z2Lk39tG2+5vsbtHNT83truEX3q6Rm57Vw49e\nrz3Ve9Qtt5suGj1a186+JewNNx6ruyd6xxYAzhN8e3awpMeOCt8rFw/XLnxUI4e1zon/Otoj89Gj\n7b5ZbNtrGIvewhfzosuJ7t3y9e6tv9EfXlq2b07cFsyJ0L2rgze8quXF2TrhoeXKHjVFnryOr7EW\nCTmBRKvw+7XFv28Bs6uSsQOTc9t/7mCnbsucmFrkjqpzN5oO5a6225WdH1VO5OdFyAmeJxAFijgx\ndHq3Rt1+SH37ByZQe7uA2HWNrgzJu3bhYzq0k2/so3X1Ccdp8fql8o6dGvaY6tKXlDvoRGUV9NG9\nJ17UoW3E47ELSCJ3bAGQmuKREcHrdGWKTzzWeLlwdPs5EVwXbUDv3rq3uPM5MbXI0uKSx6QfXm5H\n0/e5BjmBRDnIG9DITJ+sQPvHRiOZOzDT5dy1e3Zp7XN3a9iZ06MaNdmRc7fUtlM3KJrO3UBAemfN\no7o0TIdyNDsuRdNu7+DjVVa6VD1ODJ8TNaX25ATPE+mNIk6aa28XELuu0dkhefGau3/dxAlaOOsW\neQeMDDs/tWrdUvW++K4Onzteu4AkascWAKkrHhkRvE4q5ESkddEiafs9NPUQv6KGUT+RlNnFln+H\nnECiFXgtPXpkYkf9wV7XLnxMr2/9SANs7FwNd52WnbpB0XTubi0v1+APlsd8jZfP9h+nYbNuUf3A\n8DlRU7pU1/7ulg6fm+cJtEQRJ405YUhevOZkDujdW49f9TNNvi/8/NQjz/ilqgr6KLiCRLTitQtI\ne/NgW/aoA0B74pERLa+TCjkRaR2DSELlxCVFbr1Q8rg0+uIutz2InABgJ3KitUg5UVX6kqrWLdVP\nfvoL23KC54n0FXqcFFLW1vJynTpntrbt3t1mSF7TTcBuHRmSF6qtC15fqRmjQ3/tjNEuLXh9pbbt\n3m1LW08bNkxrZ9+i8/OrVf3o9fr67kmqfvR6nZ9frbWzb1HPAcd0+Jzhvge72x6Nlj3qABBOMCdu\neXJxzDNCSq2cOG3YsA6fM9z3cPMYl8o2LicnACQdciK8UDmx65FfyfL7dMDFd2nA4d/v8Dl5nkBb\njMRJM8FfvP954jE9uaZEG6/M0NY9Aa3bVq/3172WVEPyEjEnM+L81O37fqo9ybK6e9uekqknjtOv\nFy2M+ToXAJznzmef1ur/fKxVn3yiT36RI0maWiSNffAVXXLiOBX172/r9VIqJzohUk5MJScAJCFy\nIrK2OXHjF5latCc4Nbbj66XyPIG2KOKkkZa/eCctWKVLh2eqTzeXrn2xTmu3NeqwnibuQ/Iicfqc\nzGRa3b3tQmiXz7tP/9n+DYuSAWgleN868zC3umWavW8YF6zzy+2y9F/z7tOa2+6w9ZrkRPicuHkM\nOQEguZAT8cXzBEKhiJNGgr94B+QZBayAfj3ara17AlqwrkGvFOfqhwur9elK+0fjdFZXbtjJIFlW\nd297859aJP31na+1alpuTOcvA3CeO599Wj850q0nP2jQxqvyJGlvTiwvztXx//hapZs22d7L2lnk\nhD3ICQDRIifii5xAKBRx0kTLX7w7VzXosuHevaNwph6d0bSl6dEZWrU5QDXVJslS+W9781+wzq/L\nhntjuj0hAOcJ5sRPjrQ09eiMvfeMO1c17M2JacO8MellTVfkBAAnISfij5xAKBRx0kTwF0+SFqxr\nqpwHq+bBKvqMMRkafF9VUo3GabkCejK0pyOSofLftmq+z888jkMwASS373pXa/fpXQ3+/cYTMjXw\nz8nVy5qKOTG2NEdfyaOD5dfKopqYtoGcABAtciL+eJ5AKOxOlQZarmgerJT36db6z1LzIopHZ+iw\nAsu2Fcdb7obVGayA3jVtq+ahfuax3E0AgDM0VO3SgtdXSvKH7F1tec8I9rLahZxILHICQDSCzxPk\nRPohJ5IPRZw00PIX7+1vGnVPSYPMrZV64N0GzRjTepGsGWMytGGHX298/JFt1+7sTbPlQszx3j4v\nFbTdjjBYNd/nZ56A7QkBJJftJU9oapFHH5QF9mZEuJy48YRMfbBli233DHIiccgJANEKPk+QE+mF\nnEhOFHFSXNtfvDem5cr6Tb6uGZWhK4/J2GeRrD7dXPrZsTk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SseUkbNnn2m00hNBQWPv14nPfgrFXzzSfwbFyeNGdl68eq/qSEzR35P5t7P+zJzVNH7EL1xwa22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\ntt2FKTpj8gjdOedcfbK+gz5Z30F3v95P3e4eqe8LU+u/QAAvLO4m74kD7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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11091f438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy on test set: 0.880000\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import mglearn\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.datasets import make_moons\n",
+    "import numpy as np\n",
+    "X, y = make_moons(n_samples=100, noise=0.25, random_state=3)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)\n",
+    "forest = RandomForestClassifier(n_estimators=5, random_state=2)\n",
+    "forest.fit(X_train, y_train)\n",
+    "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
+    "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+    "            min_samples_leaf=1, min_samples_split=2,\n",
+    "            min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n",
+    "            oob_score=False, random_state=2, verbose=0, warm_start=False)\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(20, 10))\n",
+    "for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):\n",
+    "    ax.set_title(\"tree %d\" % i)\n",
+    "    mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)\n",
+    "mglearn.plots.plot_2d_separator(forest, X_train, fill=True, ax=axes[-1, -1], alpha=.4)\n",
+    "axes[-1, -1].set_title(\"random forest\")\n",
+    "plt.scatter(X_train[:, 0], X_train[:, 1], c=np.array(['r', 'b'])[y_train], s=60)\n",
+    "plt.show()\n",
+    "\n",
+    "forest = RandomForestClassifier(n_estimators=100, random_state=0)\n",
+    "forest.fit(X_train, y_train)\n",
+    "print(\"accuracy on test set: %f\" % forest.score(X_test, y_test))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neural_network import MLPClassifier\n",
+    "mlp = MLPClassifier(hidden_layer_sizes=[100], activation='relu', random_state=0, learning_rate='constant')\n",
+    "mlp.fit(X_train, y_train)\n",
+    "mglearn.plots.plot_2d_separator(mlp, X_train, fill=True, alpha=.3)\n",
+    "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=60, cmap=mglearn.cm2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/Programs/JupyterFiles/Examples/My Own Examples/.ipynb_checkpoints/Functions in mglearn-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/My Own Examples/.ipynb_checkpoints/Functions in mglearn-checkpoint.ipynb
new file mode 100644
index 000000000..882beca5e
--- /dev/null
+++ b/doc/Programs/JupyterFiles/Examples/My Own Examples/.ipynb_checkpoints/Functions in mglearn-checkpoint.ipynb	
@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mglearn\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from scipy import signal\n",
+    "from sklearn.datasets import load_boston\n",
+    "from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures\n",
+    "from mglearn.make_blobs import make_blobs\n",
+    "\n",
+    "#DATA_PATH = os.path.join(os.path.dirname(__file__), \"data\")\n",
+    "\n",
+    "\n",
+    "def make_forge():\n",
+    "    # a carefully hand-designed dataset lol\n",
+    "    X, y = make_blobs(centers=2, random_state=4, n_samples=30)\n",
+    "    y[np.array([7, 27])] = 0\n",
+    "    mask = np.ones(len(X), dtype=np.bool)\n",
+    "    mask[np.array([0, 1, 5, 26])] = 0\n",
+    "    X, y = X[mask], y[mask]\n",
+    "    return X, y\n",
+    "\n",
+    "\n",
+    "def make_wave(n_samples=100):\n",
+    "    rnd = np.random.RandomState(42)\n",
+    "    x = rnd.uniform(-3, 3, size=n_samples)\n",
+    "    y_no_noise = (np.sin(4 * x) + x)\n",
+    "    y = (y_no_noise + rnd.normal(size=len(x))) / 2\n",
+    "    return x.reshape(-1, 1), y\n",
+    "\n",
+    "\n",
+    "def load_extended_boston():\n",
+    "    boston = load_boston()\n",
+    "    X = boston.data\n",
+    "\n",
+    "    X = MinMaxScaler().fit_transform(boston.data)\n",
+    "    X = PolynomialFeatures(degree=2, include_bias=False).fit_transform(X)\n",
+    "    return X, boston.target\n",
+    "\n",
+    "\n",
+    "def load_citibike():\n",
+    "    data_mine = pd.read_csv(os.path.join(DATA_PATH, \"citibike.csv\"))\n",
+    "    data_mine['one'] = 1\n",
+    "    data_mine['starttime'] = pd.to_datetime(data_mine.starttime)\n",
+    "    data_starttime = data_mine.set_index(\"starttime\")\n",
+    "    data_resampled = data_starttime.resample(\"3h\").sum().fillna(0)\n",
+    "    return data_resampled.one\n",
+    "\n",
+    "\n",
+    "def make_signals():\n",
+    "    # fix a random state seed\n",
+    "    rng = np.random.RandomState(42)\n",
+    "    n_samples = 2000\n",
+    "    time = np.linspace(0, 8, n_samples)\n",
+    "    # create three signals\n",
+    "    s1 = np.sin(2 * time)  # Signal 1 : sinusoidal signal\n",
+    "    s2 = np.sign(np.sin(3 * time))  # Signal 2 : square signal\n",
+    "    s3 = signal.sawtooth(2 * np.pi * time)  # Signal 3: saw tooth signal\n",
+    "\n",
+    "    # concatenate the signals, add noise\n",
+    "    S = np.c_[s1, s2, s3]\n",
+    "    S += 0.2 * rng.normal(size=S.shape)\n",
+    "\n",
+    "    S /= S.std(axis=0)  # Standardize data\n",
+    "    S -= S.min()\n",
+    "    return S\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/src/DimRed/DimRed.do.txt b/doc/src/DimRed/DimRed.do.txt
new file mode 100644
index 000000000..297acc510
--- /dev/null
+++ b/doc/src/DimRed/DimRed.do.txt
@@ -0,0 +1,206 @@
+TITLE: Data Analysis and Machine Learning: Dimensionality Reduction
+AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
+DATE: today
+
+
+!split
+===== Reducing the number of degrees of freedom, overarching view  =====
+!bblock
+
+Many Machine Learning problems involve thousands or even millions of features for each training
+instance. Not only does this make training extremely slow, it can also make it much harder to find a good
+solution, as we will see. This problem is often referred to as the curse of dimensionality.
+Fortunately, in real-world problems, it is often possible to reduce the number of features considerably,
+turning an intractable problem into a tractable one.
+
+and we will go through three of the most popular dimensionality
+reduction techniques: PCA, Kernel PCA, and LLE.
+
+!eblock
+
+
+
+!split
+===== Principal Component Analysis =====
+!bblock
+Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm.
+First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it.
+
+The following Python code uses NumPy’s svd() function to obtain all the principal components of the
+training set, then extracts the first two PCs:
+X_centered = X - X.mean(axis=0)
+U, s, V = np.linalg.svd(X_centered)
+c1 = V.T[:, 0]
+c2 = V.T[:, 1]
+
+
+PCA assumes that the dataset is centered around the origin. As we will see, Scikit-Learn’s PCA classes take care of centering
+the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t
+forget to center the data first.
+
+Once you have identified all the principal components, you can reduce the dimensionality of the dataset
+down to d dimensions by projecting it onto the hyperplane defined by the first d principal components.
+Selecting this hyperplane ensures that the projection will preserve as much variance as possible. For
+example, in Figure 8-2 the 3D dataset is projected down to the 2D plane defined by the first two principal
+components, preserving a large part of the dataset’s variance. As a result, the 2D projection looks very
+much like the original 3D dataset.
+
+W2 = V.T[:, :2]
+X2D = X_centered.dot(W2)
+
+Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The
+following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note
+that it automatically takes care of centering the data):
+from sklearn.decomposition import PCA
+pca = PCA(n_components = 2)
+X2D = pca.fit_transform(X)
+After fitting the PCA transformer to the dataset, you can access the principal components using the
+components_ variable (note that it contains the PCs as horizontal vectors, so, for example, the first
+principal component is equal to pca.components_.T[:, 0]).
+
+Another very useful piece of information is the explained variance ratio of each principal component,
+available via the explained_variance_ratio_ variable. It indicates the proportion of the dataset’s
+variance that lies along the axis of each principal component. For example, let’s look at the explained
+variance ratios of the first two components of the 3D dataset represented in Figure 8-2:
+>>> print(pca.explained_variance_ratio_)
+array([ 0.84248607, 0.14631839])
+This tells you that 84.2% of the dataset’s variance lies along the first axis, and 14.6% lies along the
+second axis. This leaves less than 1.2% for the third axis, so it is reasonable to assume that it probably
+carries little information.
+
+
+Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to
+choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%).
+Unless, of course, you are reducing dimensionality for data visualization — in that case you will
+generally want to reduce the dimensionality down to 2 or 3.
+The following code computes PCA without reducing dimensionality, then computes the minimum number
+of dimensions required to preserve 95% of the training set’s variance:
+pca = PCA()
+pca.fit(X)
+cumsum = np.cumsum(pca.explained_variance_ratio_)
+d = np.argmax(cumsum >= 0.95) + 1
+You could then set n_components=d and run PCA again. However, there is a much better option: instead
+of specifying the number of principal components you want to preserve, you can set n_components to be
+a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve:
+pca = PCA(n_components=0.95)
+X_reduced = pca.fit_transform(X)
+
+
+
+Obviously after dimensionality reduction, the training set takes up much less space. For example, try
+applying PCA to the MNIST dataset while preserving 95% of its variance. You should find that each
+instance will have just over 150 features, instead of the original 784 features. So while most of the
+variance is preserved, the dataset is now less than 20% of its original size! This is a reasonable
+compression ratio, and you can see how this can speed up a classification algorithm (such as an SVM
+classifier) tremendously.
+It is also possible to decompress the reduced dataset back to 784 dimensions by applying the inverse
+transformation of the PCA projection. Of course this won’t give you back the original data, since the
+projection lost a bit of information (within the 5% variance that was dropped), but it will likely be quite
+close to the original data. The mean squared distance between the original data and the reconstructed data
+(compressed and then decompressed) is called the reconstruction error. For example, the following code
+compresses the MNIST dataset down to 154 dimensions, then uses the inverse_transform() method to
+decompress it back to 784 dimensions. Figure 8-9 shows a few digits from the original training set (on the
+left), and the corresponding digits after compression and decompression. You can see that there is a slight
+image quality loss, but the digits are still mostly intact.
+pca = PCA(n_components = 154)
+X_mnist_reduced = pca.fit_transform(X_mnist)
+X_mnist_recovered = pca.inverse_transform(X_mnist_reduced)
+Figure
+
+
+
+Incremental PCA
+One problem with the preceding implementation of PCA is that it requires the whole training set to fit in
+memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have
+been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch
+at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new
+instances arrive).
+The following code splits the MNIST dataset into 100 mini-batches (using NumPy’s array_split()
+function) and feeds them to Scikit-Learn’s IncrementalPCA class5 to reduce the dimensionality of the
+MNIST dataset down to 154 dimensions (just like before). Note that you must call the partial_fit()
+method with each mini-batch rather than the fit() method with the whole training set:
+from sklearn.decomposition import IncrementalPCA
+n_batches = 100
+inc_pca = IncrementalPCA(n_components=154)
+for X_batch in np.array_split(X_mnist, n_batches):
+inc_pca.partial_fit(X_batch)
+X_mnist_reduced = inc_pca.transform(X_mnist)
+
+
+
+Alternatively, you can use NumPy’s memmap class, which allows you to manipulate a large array stored in
+a binary file on disk as if it were entirely in memory; the class loads only the data it needs in memory,
+when it needs it. Since the IncrementalPCA class uses only a small part of the array at any given time,
+the memory usage remains under control. This makes it possible to call the usual fit() method, as you
+can see in the following code:
+X_mm = np.memmap(filename, dtype="float32", mode="readonly", shape=(m, n))
+batch_size = m // n_batches
+inc_pca = IncrementalPCA(n_components=154, batch_size=batch_size)
+inc_pca.fit(X_mm)
+
+
+Randomized PCA
+Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic
+algorithm that quickly finds an approximation of the first d principal components. Its computational
+complexity is O(m × d2) + O(d3), instead of O(m × n2) + O(n3), so it is dramatically faster than the
+previous algorithms when d is much smaller than n.
+rnd_pca = PCA(n_components=154, svd_solver="randomized")
+X_reduced = rnd_pca.fit_transform(X_mnist)
+
+
+!eblock
+
+
+!split
+===== Kernel PCA =====
+!bblock
+
+Kernel PCA
+The kernel trick is a mathematical technique that implicitly maps instances into a
+very high-dimensional space (called the feature space), enabling nonlinear classification and regression
+with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature
+space corresponds to a complex nonlinear decision boundary in the original space.
+It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear
+projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at
+preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a
+twisted manifold.
+For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an
+from sklearn.decomposition import KernelPCA
+rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.04)
+X_reduced = rbf_pca.fit_transform(X)
+Figure 8-
+
+!eblock 
+
+
+!split
+===== LLE =====
+
+Locally Linear Embedding (LLE)8 is another very powerful nonlinear dimensionality reduction
+(NLDR) technique. It is a Manifold Learning technique that does not rely on projections like the previous
+algorithms. In a nutshell, LLE works by first measuring how each training instance linearly relates to its
+closest neighbors (c.n.), and then looking for a low-dimensional representation of the training set where
+these local relationships are best preserved (more details shortly). This makes it particularly good at
+unrolling twisted manifolds, especially when there is not too much noise.
+
+
+
+!split
+=====  Other techniques =====
+
+
+There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.
+Here are some of the most popular:
+Multidimensional Scaling (MDS) reduces dimensionality while trying to preserve the distances
+between the instances (see Figure 8-13).
+Isomap creates a graph by connecting each instance to its nearest neighbors, then reduces
+dimensionality while trying to preserve the geodesic distances9 between the instances.
+t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep
+similar instances close and dissimilar instances apart. It is mostly used for visualization, in
+particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST
+images in 2D).
+Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it
+learns the most discriminative axes between the classes, and these axes can then be used to define a
+hyperplane onto which to project the data. The benefit is that the projection will keep classes as far
+apart as possible, so LDA is a good technique to reduce dimensionality before running another
+classification algorithm such as an SVM classifier
diff --git a/doc/src/DimRed/clean.sh b/doc/src/DimRed/clean.sh
new file mode 100755
index 000000000..2e5da2c72
--- /dev/null
+++ b/doc/src/DimRed/clean.sh
@@ -0,0 +1,3 @@
+#!/bin/sh
+doconce clean
+rm -rf *.pdf *.tex ipynb*.tar.gz *.html ._*.html *~ reveal.js Trash README.txt
diff --git a/doc/src/DimRed/make.sh b/doc/src/DimRed/make.sh
new file mode 100755
index 000000000..d908b90b9
--- /dev/null
+++ b/doc/src/DimRed/make.sh
@@ -0,0 +1,95 @@
+#!/bin/sh
+set -x
+
+function system {
+  "$@"
+  if [ $? -ne 0 ]; then
+    echo "make.sh: unsuccessful command $@"
+    echo "abort!"
+    exit 1
+  fi
+}
+
+if [ $# -eq 0 ]; then
+echo 'bash make.sh slides1|slides2'
+exit 1
+fi
+
+name=$1
+rm -f *.tar.gz
+
+opt="--encoding=utf-8"
+# Note: Makefile examples contain constructions like ${PROG} which
+# looks like Mako constructions, but they are not. Use --no_mako
+# to turn off Mako processing.
+opt="--no_mako"
+
+rm -f *.aux
+
+
+html=${name}-reveal
+system doconce format html $name --pygments_html_style=perldoc --keep_pygments_html_bg --html_links_in_new_window --html_output=$html $opt
+system doconce slides_html $html reveal --html_slide_theme=beige
+
+# Plain HTML documents
+
+html=${name}-solarized
+system doconce format html $name --pygments_html_style=perldoc --html_style=solarized3 --html_links_in_new_window --html_output=$html $opt
+system doconce split_html $html.html --method=space10
+
+html=${name}
+system doconce format html $name --pygments_html_style=default --html_style=bloodish --html_links_in_new_window --html_output=$html $opt
+system doconce split_html $html.html --method=space10
+
+# Bootstrap style
+html=${name}-bs
+system doconce format html $name --html_style=bootstrap --pygments_html_style=default --html_admon=bootstrap_panel --html_output=$html $opt
+system doconce split_html $html.html --method=split --pagination --nav_button=bottom
+
+# IPython notebook
+system doconce format ipynb $name $opt
+
+
+# Ordinary plain LaTeX document
+rm -f *.aux  # important after beamer
+system doconce format pdflatex $name --minted_latex_style=trac --latex_admon=paragraph $opt
+system doconce ptex2tex $name envir=minted
+# Add special packages
+doconce subst "% Add user's preamble" "\g<1>\n\\usepackage{simplewick}" $name.tex
+doconce replace 'section{' 'section*{' $name.tex
+pdflatex -shell-escape $name
+pdflatex -shell-escape $name
+mv -f $name.pdf ${name}-minted.pdf
+cp $name.tex ${name}-plain-minted.tex
+
+
+
+# Publish
+dest=../../pub
+if [ ! -d $dest/$name ]; then
+mkdir $dest/$name
+mkdir $dest/$name/pdf
+mkdir $dest/$name/html
+mkdir $dest/$name/ipynb
+fi
+cp ${name}*.pdf $dest/$name/pdf
+cp -r ${name}*.html ._${name}*.html reveal.js $dest/$name/html
+
+# Figures: cannot just copy link, need to physically copy the files
+if [ -d fig-${name} ]; then
+if [ ! -d $dest/$name/html/fig-$name ]; then
+mkdir $dest/$name/html/fig-$name
+fi
+cp -r fig-${name}/* $dest/$name/html/fig-$name
+fi
+
+cp ${name}.ipynb $dest/$name/ipynb
+ipynb_tarfile=ipynb-${name}-src.tar.gz
+if [ ! -f ${ipynb_tarfile} ]; then
+cat > README.txt <<EOF
+This IPython notebook ${name}.ipynb does not require any additional
+programs.
+EOF
+tar czf ${ipynb_tarfile} README.txt
+fi
+cp ${ipynb_tarfile} $dest/$name/ipynb
diff --git a/doc/src/NeuralNet/notes/.ipynb_checkpoints/mlp-checkpoint.ipynb b/doc/src/NeuralNet/notes/.ipynb_checkpoints/mlp-checkpoint.ipynb
new file mode 100644
index 000000000..0856797c0
--- /dev/null
+++ b/doc/src/NeuralNet/notes/.ipynb_checkpoints/mlp-checkpoint.ipynb
@@ -0,0 +1,1311 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Building neural networks in numpy and scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Neural networks\n",
+    "\n",
+    "Artificial neural networks are computational systems that can learn to perform tasks by considering examples,\n",
+    "generally without being programmed with any task-specific rules. It is supposed to mimic a biological system, wherein neurons interact by sending signals in the form of mathematical functions between layers. All layers can contain an arbitrary number of neurons, and each connection is represented by a weight variable.  \n",
+    "  \n",
+    "In this tutorial we will build a feed-forward neural network, where information moves in only in direction:\n",
+    "forward through the layers. Each neuron or *node* is represented by a circle, while arrows display the connections\n",
+    "between the nodes and indicate the direction of information flow. Each node in a layer is connected to all nodes in the subsequent layer, which makes this a so-called *fully-connected* feed-forward neural network."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![FFNN](https://upload.wikimedia.org/wikipedia/commons/4/46/Colored_neural_network.svg)  \n",
+    "Via [Wikipedia](https://en.wikipedia.org/wiki/File:Colored_neural_network.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prerequisites\n",
+    "\n",
+    "To follow this tutorial we require an installation of Python with the numerical package **numpy**, either:  \n",
+    "1) Python 2.7.x  \n",
+    "2) Python 3.5.x or greater  \n",
+    "  \n",
+    "With a version of Numpy 1.0.x or greater.  \n",
+    "We will also use the packages **matplotlib**, **scikit-learn**, **Tensorflow** and **Keras**, though these are not strictly necessary.  \n",
+    "To open and run this notebook you also need an installation of **IPython** and **Jupyter Notebook**.  \n",
+    "  \n",
+    "# Anaconda\n",
+    "Anaconda is a free and open source Python and R distribution, that aims to simplify package management and deployment. Anaconda comes with more than 1000 data packages, as well as the Conda package and package and virtual environment manager. Anaconda is available on Linux, OS X and Windows systems, and contains nearly all prerequisite software, it comes highly recommended.  \n",
+    "If Anaconda is installed you can install Tensorflow and Keras using:  \n",
+    "  \n",
+    "```conda install tensorflow```  \n",
+    "```conda install keras```  \n",
+    "  \n",
+    "(You may run into minor problems with conflicting package versions).  \n",
+    "  \n",
+    "# Pip package manager\n",
+    "If you do not wish to install Anaconda you may download Python from [here](https://www.python.org/downloads/),\n",
+    "or you can use package managers like **brew**, **apt**, **pacman**,...  \n",
+    "Python distributions come with their own package manager, **pip**, and once you have Python installed\n",
+    "you can run the following command:  \n",
+    "  \n",
+    "```pip install numpy matplotlib scikit-learn ipython jupyter```  \n",
+    "  \n",
+    "To install Tensorflow follow the instructions [here](https://www.tensorflow.org/install/).  \n",
+    "After you have installed tensorflow you can install keras:  \n",
+    "  \n",
+    "```pip install keras```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Python](https://imgs.xkcd.com/comics/python_environment.png)  \n",
+    "Via [xkcd](https://xkcd.com/1987/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Workflow\n",
+    "  \n",
+    "One can identify a set of key steps when using neural networks to solve supervised learning problems:  \n",
+    "  \n",
+    "**\n",
+    "1) Collect and pre-process data  \n",
+    "2) Define model and architecture  \n",
+    "3) Choose cost function and optimizer  \n",
+    "4) Train the model  \n",
+    "5) Evaluate model performance on test data  \n",
+    "6) Adjust hyperparameters (if necessary, network architecture)\n",
+    "**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1) Collect and pre-process data\n",
+    "  \n",
+    "In this tutorial we will be using the MNIST dataset, which is readily available through the **scikit-learn**\n",
+    "package. You may also find it for example [here](http://yann.lecun.com/exdb/mnist/).  \n",
+    "The **MNIST** (Modified National Institute of Standards and Technology) database is a large database\n",
+    "of handwritten digits that is commonly used for training various image processing systems.  \n",
+    "The MNIST dataset consists of 70 000 images of size 28x28 pixels, each labeled from 0 to 9.  \n",
+    "  \n",
+    "To feed data into a feed-forward neural network we need to represent the inputs as a feature matrix $X = [n_{inputs}, \n",
+    "n_{features}]$.  \n",
+    "Each row represents an **input**, in this case a handwritten digit, and each column represents a **feature**, in this case a pixel.  \n",
+    "The correct answers, also known as **labels** or **targets** are represented as a 1D array of integers $Y = [n_{inputs}] = [5, 3, 1, 8,...]$.  \n",
+    "  \n",
+    "Say I wanted to build a neural network using supervised learning to predict Body-Mass Index (BMI) from\n",
+    "measurements of height (in m)  \n",
+    "and weight (in kg). If I had measurements of 5 people the feature matrix could be for example:  \n",
+    "  \n",
+    "$$ X = \\begin{bmatrix}\n",
+    "1.85 & 81\\\\\n",
+    "1.71 & 65\\\\\n",
+    "1.95 & 103\\\\\n",
+    "1.55 & 42\\\\\n",
+    "1.63 & 56\n",
+    "\\end{bmatrix} ,$$  \n",
+    "  \n",
+    "and the targets would be:  \n",
+    "  \n",
+    "$$ Y = (23.7, 22.2, 27.1, 17.5, 21.1) $$  \n",
+    "  \n",
+    "Since each input image is a 2D matrix, we need to flatten the image (i.e. \"unravel\" the 2D matrix into a 1D array)  \n",
+    "to turn the data into a feature matrix. This means we lose all spatial information in the image, such as locality and translational invariance ([explanation](https://stats.stackexchange.com/questions/208936/what-is-translation-invariance-in-computer-vision-and-convolutional-neural-netwo)).  \n",
+    "More complicated architectures such as Convolutional Neural Networks can take advantage\n",
+    "of such information, and are most commonly applied when analyzing images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)\n",
+      "labels = (n_inputs) = (1797,)\n",
+      "X = (n_inputs, n_features) = (1797, 64)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAACPCAYAAADnRe/OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAACjlJREFUeJzt3V+InflZB/Dv081KrX8SF0W03U1qS4X1YnMjKlYygYJXksBSENRNIhW8MruoCN5kViro1SbihV7txBWsqJCAil7oJqKrqLCTy0KpKWuh2EJm7IL4p7xenAkdQ7b+npM5M+ec+XzgwMzynPf8znne981333lnnpqmKQAAfGPvO+oFAACsAqEJAGCA0AQAMEBoAgAYIDQBAAwQmgAABhy70FRVd6rqU4f9XA6eXq4X/Vwferk+9PL/WtnQVFX3q+oTR72O91Izn66qL1bV7t7O8wNHva5ltAK9/J2qenff4z+r6qtHva5lpZ/rYwV6+ZNV9dm9c+y/VdXNqvr2o17XMtLLg7GyoWkFfDLJzyb5sSTPJPn7JG8c6YqYyzRNPz9N07c+fCT5gyR/dNTrYj76uVb+LsmPTtN0Msn3JTmR5NNHuyTmtBK9XLvQVFXfUVV/WlVfrqoHe19/6JGyj1TVP+4l2ttV9cy+5/9wVb1VVTtVda+qNuZcyoeT/O00TZ+fpulrSX4/yfNzbutYWqJe7l/TtyR5McnNJ93WcaOf62NZejlN0zvTNH1l33/6WpKPzrOt40ove9YuNGX2nl5PcjrJc0n+I8lvP1LzUmZXgb43yf8k+a0kqaoPJvmzzNLtM0l+KcmfVNV3PfoiVfXc3k7y3Hus4zNJPlpVH6uqp5NcSvIXT/jejptl6eV+Lyb5cpK/mecNHXP6uT6WppdV9fGq2k3y1cz6ef3J3tqxo5cd0zSt5CPJ/SSfGKg7m+TBvu/vJPmNfd8/n+S/kjyV5FeSvPHI8/8yyaV9z/3U4Pq+KcmNJFNmO9m/JPnwUX9uy/hY9l4+so2/SrJ51J/ZMj/0c30eK9bLDybZTPKxo/7clvGhlwfzWLsrTVX1gar63ar6QlX9e2b/B3mqqp7aV/bOvq+/kOTpJN+ZWdL+5F4a3qmqnSQfT/I9cyzlWpIfTPJskvcneTXJX1fVB+bY1rG0RL18uJ5nk5xL8nvzbuM408/1sWy9TJJpmr6Y2dX8zzzJdo4bvew5cdQLWIBfTPL9SX5omqYvVdXZJG8nqX01z+77+rkk/53kK5ntGG9M0/RzB7COF5L84TRN/7r3/VZVXc8spf/zAWz/OFiWXj70UpK3pmn6/AFu8zjRz/WxbL186ESSjyxgu+tMLxtW/UrT01X1/n2PE0m+LbOfye7s3ax27THP++mqen7vqs+vJfnj6es3a/9EVf14VT21t82Nx9wUN+KfMkvg311V76uqn8ksnX9urne6/pa5lw+9lGTrCZ5/nOjn+ljaXlbVT+3dK1NVdTrJr2f2I1ceTy+f0KqHpj/PrNkPH5uZ3Tj2zZml4H/I42++fiOzk+WXMvvR2S8ks7v3k1xI8quZ3Rz6TpJfzmM+p73mvlvvfVPbbya5l2Q7yU6SV5K8OE3TTv9tHgvL3MtU1Y8k+VD8avoo/Vwfy9zL55O8leTdzH5l/bNJFnHVY13o5ROqvZuuAAD4Blb9ShMAwKEQmgAABghNAAADhCYAgAFCEwDAgEX9ccuF/kre1tZWq35zc7NVf+rUqVZ9kly/3huRs7Gx0X6Npvr/S4YstJd37txp1Xd7f+vWrVZ9kuzu7rbq33zzzVb9HL1fiV7evn27VX/16tUFreTruvvXmTNnFrKOfQ6ql0mzn/fv329tvHtO6x6b3eMsSU6ePNmq397ebtXP0f8jOTZ3dnp/uWbRveyuJzmU3nQN9dKVJgCAAUITAMAAoQkAYIDQBAAwQGgCABggNAEADBCaAAAGCE0AAAOEJgCAAUITAMCARY1RaemOOrhy5Uqr/sKFC636ecaoXLx4sVU/z5+dX0cvv/xyq777uV2+fLlVnyQ3btxo1c+zv6yC7tiN7jFwGLpjdLr74ypZ9Hu7efNmq747fijpH5vrep7tvq/ucdA9lucZV7XocWiL4koTAMAAoQkAYIDQBAAwQGgCABggNAEADBCaAAAGCE0AAAOEJgCAAUITAMAAoQkAYIDQBAAwoKZpWsR2WxvtzkTqzsTqzsXZ2Nho1Sf9+WPzzOppqgPazkJ2kIe6vex+znfv3m3VJ8mlS5da9Ycw32olenn9+vVW/dmzZ1v158+fb9Unyblz51r13TmYczioXiYL7ueizTMLb3t7u1W/Qv1c6V7O829m9/jvnl/mMNRLV5oAAAYITQAAA4QmAIABQhMAwAChCQBggNAEADBAaAIAGCA0AQAMEJoAAAYITQAAA4QmAIABJ456AUly5syZVn13Xtnm5marfp55ZW+//Xb7OfTntnV7f+3atVZ90p9v111Td39fFZcvX27Vd4/LeXSP5e6aDuM9rKvu7LEk2draatV3zy/dY39VdM9RFy9eXMxC9jmEWXIL4UoTAMAAoQkAYIDQBAAwQGgCABggNAEADBCaAAAGCE0AAAOEJgCAAUITAMAAoQkAYIDQBAAwoKZpWsR2F7LRh7ozi+7du9eqv3TpUqs+6c9EOgR1QNtp9fL27dutjR/GjKNF6863m2Ne2ZH0cnt7u7XxjY2NVv3u7m6rfh7dY7nbmznmCB5UL5MFn2eXUffz7p5f5piHdiTHZtcyHsuvv/56q74723IOQ710pQkAYIDQBAAwQGgCABggNAEADBCaAAAGCE0AAAOEJgCAAUITAMAAoQkAYIDQBAAwQGgCABggNAEADDgWA3u7Tp061X5Od01zDIbsOpJBknfu3Glt/NatW6367uDJ+/fvt+rneY159pemlejl+fPnW/VdFy5caD+nu38dAgN7n0B3kGxXd5/PER2bOzs7B/Syj9c9p83Tl+65eZ5zeZOBvQAAB0VoAgAYIDQBAAwQmgAABghNAAADhCYAgAFCEwDAAKEJAGCA0AQAMEBoAgAYIDQBAAw4cdQLmMei5+Jsbm626pP+mrqvcQjzzQ5E97Pe3d1t1W9tbbXqL1682KpPVuezXrRuL69evdqqv3HjRqv+ypUrrXqezO3bt1v1p0+fbtV3ZzzO85x5zuWr4O7du636a9eutepfffXVVv3ly5db9Un/eO7O21vUedyVJgCAAUITAMAAoQkAYIDQBAAwQGgCABggNAEADBCaAAAGCE0AAAOEJgCAAUITAMAAoQkAYMBKzp575ZVXWvXd+WPduTtJcuHChVa9+WYzDx48aNV3Z9XNMxOJw/HCCy+06rvHGE/mtddea9V356GdPHmyVZ/0j+d1Pf7PnTvXqu/Okez2vjsXLunPqlyWfzNdaQIAGCA0AQAMEJoAAAYITQAAA4QmAIABQhMAwAChCQBggNAEADBAaAIAGCA0AQAMEJoAAAbUNE1HvQYAgKXnShMAwAChCQBggNAEADBAaAIAGCA0AQAMEJoAAAYITQAAA4QmAIABQhMAwAChCQBggNAEADBAaAIAGCA0AQAMEJoAAAYITQAAA4QmAIABQhMAwAChCQBggNAEADBAaAIAGCA0AQAMEJoAAAYITQAAA/4XpwVNrPHJOwIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x720 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import necessary packages\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "\n",
+    "#ensure the same random numbers appear every time\n",
+    "np.random.seed(0)\n",
+    "\n",
+    "# display images in notebook\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = (10,10)\n",
+    "\n",
+    "\n",
+    "# download MNIST dataset\n",
+    "digits = datasets.load_digits()\n",
+    "\n",
+    "# define inputs and labels\n",
+    "inputs = digits.images\n",
+    "labels = digits.target\n",
+    "\n",
+    "print(\"inputs = (n_inputs, pixel_width, pixel_height) = \" + str(inputs.shape))\n",
+    "print(\"labels = (n_inputs) = \" + str(labels.shape))\n",
+    "\n",
+    "\n",
+    "# flatten the image\n",
+    "# the value -1 means dimension is inferred from the remaining dimensions: 8x8 = 64\n",
+    "n_inputs = len(inputs)\n",
+    "inputs = inputs.reshape(n_inputs, -1)\n",
+    "print(\"X = (n_inputs, n_features) = \" + str(inputs.shape))\n",
+    "\n",
+    "\n",
+    "# choose some random images to display\n",
+    "indices = np.arange(n_inputs)\n",
+    "random_indices = np.random.choice(indices, size=5)\n",
+    "\n",
+    "for i, image in enumerate(digits.images[random_indices]):\n",
+    "    plt.subplot(1, 5, i+1)\n",
+    "    plt.axis('off')\n",
+    "    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
+    "    plt.title(\"Label: %d\" % digits.target[random_indices[i]])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train and test datasets\n",
+    "\n",
+    "Performing analysis before partitioning the dataset is a major error, that can lead to incorrect conclusions  \n",
+    "(see \"Bias-Variance Tradeoff\", for example [here](https://ml.berkeley.edu/blog/2017/07/13/tutorial-4/)).  \n",
+    "  \n",
+    "We will reserve $80 \\%$ of our dataset for training and $20 \\%$ for testing.  \n",
+    "  \n",
+    "It is important that the train and test datasets are drawn randomly from our dataset, to ensure\n",
+    "no bias in the sampling.  \n",
+    "Say you are taking measurements of weather data to predict the weather in the coming 5 days.\n",
+    "You don't want to train your model on measurements taken from the hours 00.00 to 12.00, and then test it on data\n",
+    "collected from 12.00 to 24.00."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# one-liner from scikit-learn library\n",
+    "train_size = 0.8\n",
+    "test_size = 1 - train_size\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,\n",
+    "                                                    test_size=test_size)\n",
+    "\n",
+    "# equivalently in numpy\n",
+    "def train_test_split_numpy(inputs, labels, train_size, test_size):\n",
+    "    n_inputs = len(inputs)\n",
+    "    inputs_shuffled = inputs.copy()\n",
+    "    labels_shuffled = labels.copy()\n",
+    "    \n",
+    "    np.random.shuffle(inputs_shuffled)\n",
+    "    np.random.shuffle(labels_shuffled)\n",
+    "    \n",
+    "    train_end = int(n_inputs*train_size)\n",
+    "    X_train, X_test = inputs_shuffled[:train_end], inputs_shuffled[train_end:]\n",
+    "    Y_train, Y_test = labels_shuffled[:train_end], labels_shuffled[train_end:]\n",
+    "    \n",
+    "    return X_train, X_test, Y_train, Y_test\n",
+    "\n",
+    "#X_train, X_test, Y_train, Y_test = train_test_split_numpy(inputs, labels, train_size, test_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2) Define model and architecture\n",
+    "  \n",
+    "Our simple feed-forward neural network will consist of an **input** layer, a single **hidden** layer and an **output** layer. The activation $y$ of each neuron is a weighted sum of inputs, passed through an activation function:  \n",
+    "  \n",
+    "$$ z = \\sum_{i=1}^n w_i a_i ,$$\n",
+    "  \n",
+    "$$ y = f(z) ,$$\n",
+    "  \n",
+    "where $f$ is the activation function, $a_i$ represents input from neuron $i$ in the preceding layer\n",
+    "and $w_i$ is the weight to neuron $i$.  \n",
+    "The activation of the neurons in the input layer is just the features (e.g. a pixel value).  \n",
+    "  \n",
+    "The simplest activation function for a binary classifier (e.g. two classes, 0 or 1, cat or not cat)\n",
+    "is the **Heaviside** function:\n",
+    "  \n",
+    "$$ f(z) = \n",
+    "\\begin{cases}\n",
+    "1,  &  z > 0\\\\\n",
+    "0,  & \\text{otherwise}\n",
+    "\\end{cases}\n",
+    "$$\n",
+    "  \n",
+    "A feed-forward neural network with this activation is known as a **perceptron**.  \n",
+    "This activation can be generalized to $k$ classes (using e.g. the *one-against-all* strategy), \n",
+    "and we call these architectures **multiclass perceptrons**.  \n",
+    "  \n",
+    "However, it is now common to use the terms Single Layer Perceptron (SLP) (1 hidden layer) and  \n",
+    "Multilayer Perceptron (MLP) (2 or more hidden layers) to refer to feed-forward neural networks with any activation function.  \n",
+    "  \n",
+    "Typical choices for activation functions include the sigmoid function, hyperbolic tangent, and Rectified Linear Unit (ReLU).  \n",
+    "We will be using the sigmoid function $\\sigma(x)$:  \n",
+    "  \n",
+    "$$ f(x) = \\sigma(x) = \\frac{1}{1 + e^{-x}} ,$$\n",
+    "  \n",
+    "which is inspired by probability theory (see logistic regression) and was most commonly used until about 2011.\n",
+    "  \n",
+    "# Layers\n",
+    "  \n",
+    "**Input**:  \n",
+    "Since each input image has 8x8 = 64 pixels or features, we have an input layer of 64 neurons.  \n",
+    "  \n",
+    "**Hidden layer**:  \n",
+    "We will use 50 neurons in the hidden layer receiving input from the neurons in the input layer.  \n",
+    "Since each neuron in the hidden layer is connected to the 64 inputs we have 64x50 = 3200 weights to the hidden layer.  \n",
+    "  \n",
+    "**Output**:  \n",
+    "If we were building a binary classifier, it would be sufficient with a single neuron in the output layer,\n",
+    "which could output 0 or 1 according to the Heaviside function. This would be an example of a **hard** classifier, meaning it outputs the class of the input directly. However, if we are dealing with noisy data it is often beneficial to use a **soft** classifier, which outputs the probability of being in class 0 or 1.  \n",
+    "  \n",
+    "For a soft binary classifier, we could use a single neuron and interpret the output as either being the probability of being in class 0 or the probability of being in class 1. Alternatively we could use 2 neurons, and interpret each neuron as the probability of being in each class.  \n",
+    "  \n",
+    "Since we are doing multiclass classification, with 10 categories, it is natural to use 10 neurons in the output layer. We number the neurons $j = 0,1,...,9$. The activation of each output neuron $j$ will be according to the **softmax** function:  \n",
+    "  \n",
+    "$$ P(\\text{class $j$} \\mid \\text{input $\\boldsymbol{a}$}) = \\frac{e^{\\boldsymbol{a}^T \\boldsymbol{w}_j}}\n",
+    "{\\sum_{k=0}^{9} e^{\\boldsymbol{a}^T \\boldsymbol{w}_k}} ,$$  \n",
+    "  \n",
+    "i.e. each neuron $j$ outputs the probability of being in class $j$ given an input from the hidden layer $\\boldsymbol{a}$, with $\\boldsymbol{w}_j$ the weights of neuron $j$ to the inputs.  \n",
+    "The denominator is a normalization factor to ensure the outputs sum up to 1.  \n",
+    "The exponent is just the weighted sum of inputs as before:  \n",
+    "  \n",
+    "$$ z_j = \\sum_{i=1}^n w_ {ij} a_i = \\boldsymbol{a}^T \\boldsymbol{w}_j .$$  \n",
+    "  \n",
+    "Since each neuron in the output layer is connected to the 50 inputs from the hidden layer we have 50x10 = 500\n",
+    "weights to the output layer.\n",
+    "  \n",
+    "# Weights and biases\n",
+    "  \n",
+    "Typically weights are initialized with small values distributed around zero, drawn from a uniform\n",
+    "or normal distribution. Setting all weights to zero means all neurons give the same output, making the network useless.  \n",
+    "  \n",
+    "Adding a bias value to the weighted sum of inputs allows the neural network to represent a greater range\n",
+    "of values. Without it, any input with the value 0 will be mapped to zero (before being passed through the activation). The bias unit has an output of 1, and a weight to each neuron $j$, $b_j$:  \n",
+    "  \n",
+    "$$ z_j = \\sum_{i=1}^n w_ {ij} a_i + 1\\cdot b_j = \\boldsymbol{a}^T \\boldsymbol{w}_j + b_j .$$  \n",
+    "  \n",
+    "The bias weights $\\boldsymbol{b}$ are often initialized to zero, but a small value like $0.01$ ensures all neurons have some output which can be backpropagated in the first training cycle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Bias](http://ufldl.stanford.edu/tutorial/images/Network331.png)  \n",
+    "Via [Stanford UFLDL](http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# building our neural network\n",
+    "\n",
+    "n_inputs, n_features = X_train.shape\n",
+    "n_hidden_neurons = 50\n",
+    "n_categories = 10\n",
+    "\n",
+    "# we make the weights normally distributed using numpy.random.randn\n",
+    "\n",
+    "# weights and bias in the hidden layer\n",
+    "hidden_weights = np.random.randn(n_features, n_hidden_neurons)\n",
+    "hidden_bias = np.zeros(n_hidden_neurons) + 0.01\n",
+    "\n",
+    "# weights and bias in the output layer\n",
+    "output_weights = np.random.randn(n_hidden_neurons, n_categories)\n",
+    "output_bias = np.zeros(n_categories) + 0.01"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feed-forward pass\n",
+    "\n",
+    "For each input image we calculate a weighted sum of input features (pixel values) to each neuron $j$ in the hidden layer:  \n",
+    "  \n",
+    "$$ z_{j}^{hidden} = \\sum_{i=1}^{n_{features}} w_{ij}^{hidden} x_i + b_{j}^{hidden} = \\boldsymbol{x}^T \\boldsymbol{w}_{j}^{hidden} + b_{j}^{hidden} ,$$\n",
+    "  \n",
+    "this is then passed through our activation function  \n",
+    "  \n",
+    "$$ a_{j}^{hidden} = f(z_{j}^{hidden}) .$$  \n",
+    "  \n",
+    "We calculate a weighted sum of inputs (activations in the hidden layer) to each neuron $j$ in the output layer:  \n",
+    "  \n",
+    "$$ z_{j}^{output} = \\sum_{i=1}^{n_{hidden}} w_{ij}^{output} a_{i}^{hidden} + b_{j}^{output} = (\\boldsymbol{a}^{hidden})^T \\boldsymbol{w}_{j}^{output} + b_{j}^{output} .$$  \n",
+    "  \n",
+    "Finally we calculate the output of neuron $j$ in the output layer using the softmax function:  \n",
+    "  \n",
+    "$$ a_{j}^{output} = \\frac{\\exp{(z_j^{output})}}\n",
+    "{\\sum_{k=1}^{n_{categories}} \\exp{(z_k^{output})}} .$$  \n",
+    "   \n",
+    "# Matrix multiplication\n",
+    "  \n",
+    "Since our data has the dimensions $X = (n_{inputs}, n_{features})$ and our weights to the hidden\n",
+    "layer have the dimensions  \n",
+    "$W_{hidden} = (n_{features}, n_{hidden})$,\n",
+    "we can easily feed the network all our training data in one go by taking the matrix product  \n",
+    "  \n",
+    "$$ X W^{hidden} = (n_{inputs}, n_{hidden}),$$ \n",
+    "  \n",
+    "and obtain a matrix that holds the weighted sum of inputs to the hidden layer\n",
+    "for each input image.  \n",
+    "We also add the bias to obtain a matrix of weighted sums $Z^{hidden}$:  \n",
+    "  \n",
+    "$$ Z^{hidden} = X W^{hidden} + B^{hidden} ,$$\n",
+    "  \n",
+    "meaning the same bias (1D array) is added to each input image.  \n",
+    "This is then passed through the activation  \n",
+    "  \n",
+    "$$ A^{hidden} = f(X W^{hidden} + B^{hidden}) .$$  \n",
+    "  \n",
+    "This is fed to the output layer:  \n",
+    "  \n",
+    "$$ Z^{output} = A^{hidden} W^{output} + B^{output} .$$\n",
+    "  \n",
+    "Finally we receive our output values for each image and each category by passing it through the softmax function:  \n",
+    "  \n",
+    "$$ output = softmax (Z^{output}) = (n_{inputs}, n_{categories}) .$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "probabilities = (n_inputs, n_categories) = (1437, 10)\n",
+      "probability that image 0 is in category 0,1,2,...,9 = \n",
+      "[5.41511965e-04 2.17174962e-03 8.84355903e-03 1.44970586e-03\n",
+      " 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03\n",
+      " 9.84443254e-01 3.11507992e-04]\n",
+      "probabilities sum up to: 1.0\n",
+      "\n",
+      "predictions = (n_inputs) = (1437,)\n",
+      "prediction for image 0: 8\n",
+      "correct label for image 0: 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# setup the feed-forward pass\n",
+    "\n",
+    "def sigmoid(x):\n",
+    "    return 1/(1 + np.exp(-x))\n",
+    "\n",
+    "def feed_forward(X):\n",
+    "    # weighted sum of inputs to the hidden layer\n",
+    "    z1 = np.matmul(X, hidden_weights) + hidden_bias\n",
+    "    # activation in the hidden layer\n",
+    "    a1 = sigmoid(z1)\n",
+    "    \n",
+    "    # weighted sum of inputs to the output layer\n",
+    "    z2 = np.matmul(a1, output_weights) + output_bias\n",
+    "    # softmax output\n",
+    "    # axis 0 holds each input and axis 1 the probabilities of each category\n",
+    "    exp_term = np.exp(z2)\n",
+    "    probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n",
+    "    \n",
+    "    return probabilities\n",
+    "\n",
+    "probabilities = feed_forward(X_train)\n",
+    "print(\"probabilities = (n_inputs, n_categories) = \" + str(probabilities.shape))\n",
+    "print(\"probability that image 0 is in category 0,1,2,...,9 = \\n\" + str(probabilities[0]))\n",
+    "print(\"probabilities sum up to: \" + str(probabilities[0].sum()))\n",
+    "print()\n",
+    "\n",
+    "# we obtain a prediction by taking the class with the highest likelihood\n",
+    "def predict(X):\n",
+    "    probabilities = feed_forward(X)\n",
+    "    return np.argmax(probabilities, axis=1)\n",
+    "\n",
+    "predictions = predict(X_train)\n",
+    "print(\"predictions = (n_inputs) = \" + str(predictions.shape))\n",
+    "print(\"prediction for image 0: \" + str(predictions[0]))\n",
+    "print(\"correct label for image 0: \" + str(Y_train[0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3) Choose cost function and optimizer (needs more work)\n",
+    "  \n",
+    "To measure how well our neural network is doing we need to introduce a cost function.  \n",
+    "We will call the function that gives the error of a single sample output the **loss** function, and the function\n",
+    "that gives the total error of our network across all samples the **cost** function.\n",
+    "A typical choice for multiclass classification is the **cross-entropy** loss, also known as the negative log likelihood.  \n",
+    "In multiclass classification it is common to treat each integer label as a so called **one-hot** vector:  \n",
+    "  \n",
+    "$$ y = 5 \\quad \\rightarrow \\quad \\boldsymbol{y} = (0, 0, 0, 0, 0, 1, 0, 0, 0, 0) ,$$  \n",
+    "\n",
+    "  \n",
+    "$$ y = 1 \\quad \\rightarrow \\quad \\boldsymbol{y} = (0, 1, 0, 0, 0, 0, 0, 0, 0, 0) ,$$  \n",
+    "  \n",
+    "  \n",
+    "i.e. a binary bit string of length $K$, where $K = 10$ is the number of classes.  \n",
+    "If $\\boldsymbol{x}_i$ is the $i$-th input (image), $y_{ik}$ refers to the $k$-th component of the $i$-th\n",
+    "output vector $\\boldsymbol{y}_i$. The probability of $\\boldsymbol{x}_i$ being in class $k$ is given by the softmax function:  \n",
+    "  \n",
+    "$$ P(y_{ik} = 1 \\mid \\boldsymbol{x}_i, \\boldsymbol{\\theta}) = \\frac{e^{(\\boldsymbol{a}_i^{hidden})^T \\boldsymbol{w}_k}}\n",
+    "{\\sum_{k'=0}^{K-1} e^{(\\boldsymbol{a}_i^{hidden})^T \\boldsymbol{w}_{k'}}} ,$$\n",
+    "  \n",
+    "where $\\boldsymbol{a}_i^{hidden}$ is the activation in the hidden layer from input $\\boldsymbol{x}_i$.\n",
+    "The vector $\\boldsymbol{\\theta}$ represents the weights and biases of our network.  \n",
+    "The probability of not being in class $k$ is just $1 - P(y_{ik} = 1 \\mid \\boldsymbol{x}_i)$.  \n",
+    "  \n",
+    "For Maximum Likelihood Estimation (MLE) we choose the label with the largest probability.  \n",
+    "Denote the output label $\\hat{y}$ and the correct label $y$, for example $\\hat{y} = 5$ and $y = 8$. The likelihood that input $\\boldsymbol{x}$\n",
+    "gives an output $\\hat{y} = k'$ is then\n",
+    "  \n",
+    "$$ P(\\hat{y} = k' \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = \\prod_{k=0}^{K-1} [P(y_{k} = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta})]^{y_{k}} \n",
+    "\\times [1 - P(y_{k} = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta})]^{1-y_{k}} ,$$  \n",
+    "  \n",
+    "where $y_k$ is the $k$-th component of the one-hot vector of (correct) labels.  \n",
+    "A perfect classifier should give a $100 \\%$ probability of the correct label, so the product\n",
+    "should just be 1 if $y = k$ and 0 otherwise. If the network is not a perfect classifier, the likelihood should be a number between 0 and 1.  \n",
+    "  \n",
+    "If we take the log of this we can turn the product into a sum, which is often simpler to compute:  \n",
+    "  \n",
+    "$$ \\log P(\\hat{y} = k' \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) = \\sum_{k=0}^{K-1} y_{k} \\log P(y_{k} = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) \n",
+    "+ (1-y_{k})\\log (1 - P(y_{k} = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}))$$  \n",
+    "  \n",
+    "For a perfect classifier this should just be $\\log 1 = 0$. Otherwise we get a negative number.  \n",
+    "Since it is easier to think in terms of minimizing a positive number, we take our loss function\n",
+    "to be the negative log-likelihood:  \n",
+    "  \n",
+    "$$ \\mathcal{L}(\\boldsymbol{\\theta}) = - \\log P(\\hat{y} = k' \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) $$  \n",
+    "  \n",
+    "We then take the average of the loss function over all input samples to define the cost function:  \n",
+    "$$ \\begin{split} \\mathcal{C}(\\boldsymbol{\\theta}) &= \\frac{1}{N} \\sum_{i=1}^N \\mathcal{L}(\\boldsymbol{w}) \\\\\n",
+    " &= -\\frac{1}{N}\\sum_{i=1}^N \\sum_{k=0}^{K-1} y_{k} \\log P(y_{k} = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta}) \n",
+    "+ (1-y_{k})\\log (1 - P(y_{k} = 1 \\mid \\boldsymbol{x}, \\boldsymbol{\\theta})) \\end{split} .$$\n",
+    "  \n",
+    "# Optimizing the cost function\n",
+    "  \n",
+    "The network is trained by finding the weights and biases that minimize the cost function. One of the most widely used classes of methods is **gradient descent** and its generalizations. The idea behind gradient descent\n",
+    "is simply to adjust the weights in the direction where the gradient of the cost function is large and negative. This ensures we flow toward a **local** minimum of the cost function.  \n",
+    "Each parameter $\\theta$ is iteratively adjusted according to the rule  \n",
+    "  \n",
+    "$$ \\theta_{i+1} = \\theta_i - \\eta \\nabla \\mathcal{C}(\\theta) ,$$\n",
+    "\n",
+    "where $\\eta$ is known as the **learning rate**, which controls how big a step we take towards the minimum.  \n",
+    "This update can be repeated for any number of iterations, or until we are satisfied with the result.  \n",
+    "  \n",
+    "A simple and effective improvement is a variant called **Stochastic Gradient Descent** (SGD).  \n",
+    "Instead of calculating the gradient on the whole dataset, we calculate an approximation of the gradient\n",
+    "on a subset of the data called a **minibatch**.  \n",
+    "If there are $N$ data points and we have a minibatch size of $M$, the total number of batches\n",
+    "is $n/M$.  \n",
+    "We denote each minibatch $B_k$, with $k = 1, 2,...,n/M$. The gradient then becomes:  \n",
+    "  \n",
+    "$$ \\nabla \\mathcal{C}(\\theta) = \\frac{1}{N} \\sum_{i=1}^N \\nabla \\mathcal{L}(\\theta) \\quad \\rightarrow \\quad\n",
+    "\\frac{1}{M} \\sum_{i \\in B_k} \\nabla \\mathcal{L}(\\theta) ,$$\n",
+    "  \n",
+    "i.e. instead of averaging the loss over the entire dataset, we average over a minibatch.  \n",
+    "This has two important benefits:  \n",
+    "1) Introducing stochasticity decreases the chance that the algorithm becomes stuck in a local minima.  \n",
+    "2) It significantly speeds up the calculation, since we do not have to use the entire dataset to calculate the gradient.  \n",
+    "  \n",
+    "# Regularization\n",
+    "  \n",
+    "It is common to add an extra term to the cost function, proportional to the size of the weights.  \n",
+    "This is equivalent to constraining the size of the weights, so that they do not grow out of control.  \n",
+    "Constraining the size of the weights means that the weights cannot grow arbitrarily large to fit the training data, and in this way reduces overfitting.  \n",
+    "  \n",
+    "We will measure the size of the weights using the so called **L2-norm**, meaning our cost function becomes:  \n",
+    "  \n",
+    "$$ \\nabla \\mathcal{C}(\\theta) = \\frac{1}{N} \\sum_{i=1}^N \\nabla \\mathcal{L}(\\theta) \\quad \\rightarrow \\quad\n",
+    "\\frac{1}{N} \\sum_{i=1}^N \\nabla \\mathcal{L}(\\theta) + \\lambda \\lvert \\lvert \\boldsymbol{w}_2^2 \\rvert \\rvert \n",
+    "= \\frac{1}{N} \\sum_{i=1}^N \\nabla \\mathcal{L}(\\theta) + \\lambda \\sum_{ij} w_{ij}^2,$$  \n",
+    "  \n",
+    "i.e. we sum up all the weights squared. The factor $\\lambda$ is known as a regularization parameter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4) Train the model\n",
+    "  \n",
+    "In order to train the model, we need to calculate the derivative of the cost function with respect\n",
+    "to every bias and weight in the network. Using an approximation to the derivative (e.g. using the [finite difference method](https://en.wikipedia.org/wiki/Finite_difference_method)) is much too costly.  \n",
+    "In total our network has $(64 + 1) \\times 50 = 3250$ weights in the hidden layer and $(50 + 1) \\times 10 = 510$ weights to the output layer ($ + 1$ for the bias), and the gradient must be calculated for every parameter.  \n",
+    "  \n",
+    "The **backpropagation** algorithm is a clever use of the chain rule that allows us to calculate gradient efficently. Here we will simply state the backpropagation equations that we will use for our network, and then a derivation is given at the end of this tutorial.  \n",
+    "  \n",
+    "The error $\\delta_i^o$ at each output neuron $i$ is just the difference between the output probability $\\hat{y}_i$ and the correct label $y_i$ (0 or 1 using one-hot vectors):  \n",
+    "  \n",
+    "$$ \\delta_i^o = \\hat{y}_i - y_i .$$  \n",
+    "  \n",
+    "The gradient of the cost function with respect to each output weight $w_{i,j}^o$ is then  \n",
+    "  \n",
+    "$$ \\frac{\\partial \\mathcal{C}}{\\partial w_{i,j}^o} = \\delta_i^o a_j^h ,$$\n",
+    "  \n",
+    "where $a_j^h$ is the activation at the $j$-th neuron in the hidden layer.  \n",
+    "The gradient with respect to each output bias $b_i^o$ is  \n",
+    "  \n",
+    "$$ \\frac{\\partial \\mathcal{C}}{\\partial b_i^o} = \\delta_i^o .$$  \n",
+    "  \n",
+    "The error at each hidden layer neuron $\\delta_i^h$ is given as  \n",
+    "  \n",
+    "$$ \\delta_i^h = \\sum_{k=0}^{K-1} \\delta_k^o w_{ki}^o f'(z_i^h) ,$$\n",
+    "  \n",
+    "where $K$ is the number of output neurons or categories and $f'(z_i^h)$ is the derivative of the activation function:  \n",
+    "  \n",
+    "$$ f'(z_i^h) = \\sigma '(z_i^h) = \\sigma(z_i^h)(1 - \\sigma(z_i^h)) = a_i^h (1 - a_i^h) ,$$\n",
+    "  \n",
+    "since our activation function is the sigmoid/logistic function.  \n",
+    "The gradient with respect to each hidden layer weight is:  \n",
+    "  \n",
+    "$$ \\frac{\\partial \\mathcal{C}}{\\partial w_{i,j}^h} = \\delta_i^h x_j ,$$  \n",
+    "  \n",
+    "and the gradient with respect to the hidden bias  \n",
+    "  \n",
+    "$$ \\frac{\\partial \\mathcal{C}}{\\partial b_i^h} = \\delta_i^h .$$  \n",
+    "  \n",
+    "The regularization terms using the L2-norm are just  \n",
+    "  \n",
+    "$$ \\frac{\\partial }{\\partial w_{ij}} (\\lambda \\sum_{ij} w_{ij}^2) = 2 \\lambda w_{ij} = \\hat{\\lambda} w_{ij} ,$$\n",
+    "  \n",
+    "for the weights in both the output and hidden layers.  \n",
+    "  \n",
+    "# Matrix  multiplication\n",
+    "  \n",
+    "Text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Old accuracy on training data: 0.1440501043841336\n",
+      "New accuracy on training data: 0.10368823938761308\n"
+     ]
+    }
+   ],
+   "source": [
+    "# to categorical turns our integer vector into a onehot representation\n",
+    "from keras.utils import to_categorical\n",
+    "# calculate the accuracy score of our model\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "Y_train_onehot, Y_test_onehot = to_categorical(Y_train), to_categorical(Y_test)\n",
+    "\n",
+    "# equivalently in numpy\n",
+    "def to_categorical_numpy(integer_vector):\n",
+    "    n_inputs = len(integer_vector)\n",
+    "    n_categories = np.max(integer_vector) + 1\n",
+    "    onehot_vector = np.zeros((n_inputs, n_categories))\n",
+    "    onehot_vector[range(n_inputs), integer_vector] = 1\n",
+    "    \n",
+    "    return onehot_vector\n",
+    "\n",
+    "#Y_train_onehot, Y_test_onehot = to_categorical_numpy(Y_train), to_categorical_numpy(Y_test)\n",
+    "\n",
+    "def feed_forward_train(X):\n",
+    "    # weighted sum of inputs to the hidden layer\n",
+    "    z1 = np.matmul(X, hidden_weights) + hidden_bias\n",
+    "    # activation in the hidden layer\n",
+    "    a1 = sigmoid(z1)\n",
+    "    \n",
+    "    # weighted sum of inputs to the output layer\n",
+    "    z2 = np.matmul(a1, output_weights) + output_bias\n",
+    "    # softmax output\n",
+    "    # axis 0 holds each input and axis 1 the probabilities of each category\n",
+    "    exp_term = np.exp(z2)\n",
+    "    probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n",
+    "    \n",
+    "    return a1, probabilities\n",
+    "\n",
+    "def backpropagation(X, Y):\n",
+    "    a1, probabilities = feed_forward_train(X)\n",
+    "    \n",
+    "    # error in the output layer\n",
+    "    error_output = probabilities - Y\n",
+    "    # error in the hidden layer\n",
+    "    error_hidden = np.matmul(error_output, output_weights.T) * a1 * (1 - a1)\n",
+    "    \n",
+    "    # gradients for the output layer\n",
+    "    output_weights_gradient = np.matmul(a1.T, error_output)\n",
+    "    output_bias_gradient = np.sum(error_output, axis=0)\n",
+    "    \n",
+    "    # gradient for the hidden layer\n",
+    "    hidden_weights_gradient = np.matmul(X.T, error_hidden)\n",
+    "    hidden_bias_gradient = np.sum(error_hidden, axis=0)\n",
+    "\n",
+    "    return output_weights_gradient, output_bias_gradient, hidden_weights_gradient, hidden_bias_gradient\n",
+    "\n",
+    "print(\"Old accuracy on training data: \" + str(accuracy_score(predict(X_train), Y_train)))\n",
+    "\n",
+    "eta = 0.01\n",
+    "lmbd = 0.01\n",
+    "for i in range(1000):\n",
+    "    dWo, dBo, dWh, dBh = backpropagation(X_train, Y_train_onehot)\n",
+    "    \n",
+    "    dWo += lmbd * output_weights\n",
+    "    dWh += lmbd * hidden_weights\n",
+    "    \n",
+    "    output_weights -= eta * dWo\n",
+    "    output_bias -= eta * dBo\n",
+    "    hidden_weights -= eta * dWh\n",
+    "    hidden_bias -= eta * dBh\n",
+    "\n",
+    "print(\"New accuracy on training data: \" + str(accuracy_score(predict(X_train), Y_train)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Full object-oriented implementation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NeuralNetwork:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        X_data,\n",
+    "        Y_data,\n",
+    "        n_hidden_neurons=50,\n",
+    "        n_categories=10,\n",
+    "        epochs=10,\n",
+    "        batch_size=100,\n",
+    "        eta=0.1,\n",
+    "        lmbd=0.0,\n",
+    "\n",
+    "    ):\n",
+    "        self.X_data_full = X_data\n",
+    "        self.Y_data_full = Y_data\n",
+    "\n",
+    "        self.n_inputs = X_data.shape[0]\n",
+    "        self.n_features = X_data.shape[1]\n",
+    "        self.n_hidden_neurons = n_hidden_neurons\n",
+    "        self.n_categories = n_categories\n",
+    "\n",
+    "        self.epochs = epochs\n",
+    "        self.batch_size = batch_size\n",
+    "        self.iterations = self.n_inputs // self.batch_size\n",
+    "        self.eta = eta\n",
+    "        self.lmbd = lmbd\n",
+    "\n",
+    "        self.create_biases_and_weights()\n",
+    "\n",
+    "    def create_biases_and_weights(self):\n",
+    "        self.hidden_weights = np.random.randn(self.n_features, self.n_hidden_neurons)\n",
+    "        self.hidden_bias = np.zeros(self.n_hidden_neurons) + 0.01\n",
+    "\n",
+    "        self.output_weights = np.random.randn(self.n_hidden_neurons, self.n_categories)\n",
+    "        self.output_bias = np.zeros(self.n_categories) + 0.01\n",
+    "\n",
+    "    def feed_forward(self):\n",
+    "        self.z1 = np.matmul(self.X_data, self.hidden_weights) + self.hidden_bias\n",
+    "        self.a1 = sigmoid(self.z1)\n",
+    "\n",
+    "        self.z2 = np.matmul(self.a1, self.output_weights) + self.output_bias\n",
+    "\n",
+    "        exp_term = np.exp(self.z2)\n",
+    "        self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n",
+    "\n",
+    "    def feed_forward_out(self, X):\n",
+    "        z1 = np.matmul(X, self.hidden_weights) + self.hidden_bias\n",
+    "        a1 = sigmoid(z1)\n",
+    "\n",
+    "        z2 = np.matmul(a1, self.output_weights) + self.output_bias\n",
+    "        \n",
+    "        exp_term = np.exp(z2)\n",
+    "        probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n",
+    "        return probabilities\n",
+    "\n",
+    "    def backpropagation(self):\n",
+    "        error_output = self.probabilities - self.Y_data\n",
+    "        error_hidden = np.matmul(error_output, self.output_weights.T) * self.a1 * (1 - self.a1)\n",
+    "\n",
+    "        self.output_weights_gradient = np.matmul(self.a1.T, error_output)\n",
+    "        self.output_bias_gradient = np.sum(error_output, axis=0)\n",
+    "\n",
+    "        self.hidden_weights_gradient = np.matmul(self.X_data.T, error_hidden)\n",
+    "        self.hidden_bias_gradient = np.sum(error_hidden, axis=0)\n",
+    "\n",
+    "        if self.lmbd > 0.0:\n",
+    "            self.output_weights_gradient += self.lmbd * self.output_weights\n",
+    "            self.hidden_weights_gradient += self.lmbd * self.hidden_weights\n",
+    "\n",
+    "        self.output_weights -= self.eta * self.output_weights_gradient\n",
+    "        self.output_bias -= self.eta * self.output_bias_gradient\n",
+    "        self.hidden_weights -= self.eta * self.hidden_weights_gradient\n",
+    "        self.hidden_bias -= self.eta * self.hidden_bias_gradient\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        probabilities = self.feed_forward_out(X)\n",
+    "        return np.argmax(probabilities, axis=1)\n",
+    "\n",
+    "    def predict_probabilities(self, X):\n",
+    "        probabilities = self.feed_forward_out(X)\n",
+    "        return probabilities\n",
+    "\n",
+    "    def train(self):\n",
+    "        data_indices = np.arange(self.n_inputs)\n",
+    "\n",
+    "        for i in range(self.epochs):\n",
+    "            for j in range(self.iterations):\n",
+    "                chosen_datapoints = np.random.choice(\n",
+    "                    data_indices, size=self.batch_size, replace=False\n",
+    "                )\n",
+    "\n",
+    "                self.X_data = self.X_data_full[chosen_datapoints]\n",
+    "                self.Y_data = self.Y_data_full[chosen_datapoints]\n",
+    "\n",
+    "                self.feed_forward()\n",
+    "                self.backpropagation()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5) Evaluate model performance on test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score on test set:  0.08888888888888889\n"
+     ]
+    }
+   ],
+   "source": [
+    "eta = 0.1\n",
+    "lmbd = 0.1\n",
+    "epochs = 10\n",
+    "batch_size = 100\n",
+    "\n",
+    "dnn = NeuralNetwork(X_train, Y_train_onehot, eta=eta, lmbd=lmbd, epochs=epochs, batch_size=batch_size,\n",
+    "                    n_hidden_neurons=n_hidden_neurons, n_categories=n_categories)\n",
+    "dnn.train()\n",
+    "test_predict = dnn.predict(X_test)\n",
+    "print(\"Accuracy score on test set: \", accuracy_score(Y_test, test_predict))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 6) Adjust hyperparameters (if necessary, network architecture)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eta_vals = np.logspace(-5, 0, 6)\n",
+    "lmbd_vals = np.logspace(-5, 0, 6)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.17777777777777778\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.08888888888888889\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.125\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.05277777777777778\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.10277777777777777\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.06388888888888888\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.06944444444444445\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.16111111111111112\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.16944444444444445\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.16666666666666666\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.14722222222222223\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.13333333333333333\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.6083333333333333\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.6027777777777777\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.6222222222222222\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.6361111111111111\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.575\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.5972222222222222\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.8861111111111111\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.8916666666666667\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9138888888888889\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8861111111111111\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.6944444444444444\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.11388888888888889\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.08888888888888889\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.125\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.125\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/andreas/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:44: RuntimeWarning: invalid value encountered in true_divide\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.11388888888888889\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "DNN_numpy = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
+    "\n",
+    "for i, eta in enumerate(eta_vals):\n",
+    "    for j, lmbd in enumerate(lmbd_vals):\n",
+    "        dnn = NeuralNetwork(X_train, Y_train_onehot, eta=eta, lmbd=lmbd, epochs=epochs, batch_size=batch_size,\n",
+    "                            n_hidden_neurons=n_hidden_neurons, n_categories=n_categories)\n",
+    "        dnn.train()\n",
+    "        \n",
+    "        DNN_numpy[i][j] = dnn\n",
+    "        \n",
+    "        test_predict = dnn.predict(X_test)\n",
+    "        \n",
+    "        print(\"Learning rate  = \", eta)\n",
+    "        print(\"Lambda = \", lmbd)\n",
+    "        print(\"Accuracy score on test set: \", accuracy_score(Y_test, test_predict))\n",
+    "        print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# scikit-learn implementation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/andreas/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  % self.max_iter, ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.15833333333333333\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.15555555555555556\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.2638888888888889\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.24722222222222223\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.26666666666666666\n",
+      "\n",
+      "Learning rate  =  1e-05\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.225\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.8888888888888888\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9055555555555556\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8888888888888888\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.8972222222222223\n",
+      "\n",
+      "Learning rate  =  0.0001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.8805555555555555\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9888888888888889\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.001\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.975\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.9916666666666667\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.9861111111111112\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.9805555555555555\n",
+      "\n",
+      "Learning rate  =  0.01\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.975\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.8722222222222222\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.9083333333333333\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.8583333333333333\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.8555555555555555\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.7944444444444444\n",
+      "\n",
+      "Learning rate  =  0.1\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.9055555555555556\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1e-05\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.0001\n",
+      "Accuracy score on test set:  0.08611111111111111\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.10555555555555556\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.01\n",
+      "Accuracy score on test set:  0.45555555555555555\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  0.1\n",
+      "Accuracy score on test set:  0.23055555555555557\n",
+      "\n",
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.07777777777777778\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neural_network import MLPClassifier\n",
+    "\n",
+    "DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
+    "\n",
+    "for i, eta in enumerate(eta_vals):\n",
+    "    for j, lmbd in enumerate(lmbd_vals):\n",
+    "        dnn = MLPClassifier(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',\n",
+    "                            alpha=lmbd, learning_rate_init=eta, max_iter=100)\n",
+    "        dnn.fit(X_train, Y_train)\n",
+    "        \n",
+    "        DNN_scikit[i][j] = dnn\n",
+    "        \n",
+    "        print(\"Learning rate  = \", eta)\n",
+    "        print(\"Lambda = \", lmbd)\n",
+    "        print(\"Accuracy score on test set: \", dnn.score(X_test, Y_test))\n",
+    "        print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Appendix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deriving the cost function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Deriving the backpropagation equations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# References"
+   ]
+  }
+ ],
+ "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": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc/web/course.do.txt b/doc/web/course.do.txt
index 711a07061..e2aec2115 100644
--- a/doc/web/course.do.txt
+++ b/doc/web/course.do.txt
@@ -10,17 +10,18 @@ AUTHOR: "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/" at Depart
 
 <%
 pub_url = 'https://compphysics.github.io/MachineLearning/doc/pub'
-published = ['Intro2Course', 'Introduction', 'How2ReadData', 'Linalg', 'Statistics', 'Splines', 'Regression', 'LogReg', 'NeuralNet', 'Bayesian', 'DecisionTrees', 'svm', 'BM',]
+published = ['Intro2Course', 'Introduction', 'How2ReadData', 'Linalg', 'Statistics', 'Splines', 'Regression', 'LogReg', 'NeuralNet', 'DimRed', 'Bayesian', 'DecisionTrees', 'svm', 'BM',]
 chapters = {
  'Intro2Course': 'Basic introduction to the course with schedule etc',
  'Introduction': 'Introduction to Data Analysis and Machine Learning',
  'How2ReadData': 'Getting started with Machine Learning',
  'Linalg': 'Review of central linear algebra elements',
  'Statistics': 'Monte Carlo methods and elements of probability theory',
- 'Splines': 'Splines and Gradient methods',
+ 'Splines': 'Gradient methods',
  'Regression': 'Regression Methods',
  'LogReg': 'Logistic Regression',
  'NeuralNet': 'Neural Networks',
+ 'DimRed': 'Reduction of dimensionality',
  'Bayesian': 'Elements of Bayesian theory',
  'DecisionTrees': 'Decision trees, from simple to random ones',
  'svm': 'Support Vector Machines',