From 0c3207b2a08d2e7ba0c195c7fe5e8a7f44fd4d45 Mon Sep 17 00:00:00 2001
From: mcarfagno <m.carfagno@outlook.com>
Date: Tue, 21 Apr 2020 16:33:00 +0100
Subject: [PATCH] WIP: V2 implementation notebooks

---
 .../MPC_cvxpy-checkpoint.ipynb                |   64 +-
 notebook/MPC_cvxpy.ipynb                      |  162 ++-
 notebook/MPC_cvxpy_v2.ipynb                   | 1083 +++++++++++++++++
 notebook/diff_eq.ipynb                        |  102 --
 notebook/equations.ipynb                      |  344 ++++++
 5 files changed, 1595 insertions(+), 160 deletions(-)
 create mode 100644 notebook/MPC_cvxpy_v2.ipynb
 delete mode 100644 notebook/diff_eq.ipynb
 create mode 100644 notebook/equations.ipynb

diff --git a/notebook/.ipynb_checkpoints/MPC_cvxpy-checkpoint.ipynb b/notebook/.ipynb_checkpoints/MPC_cvxpy-checkpoint.ipynb
index 0ccbd42..a84d822 100644
--- a/notebook/.ipynb_checkpoints/MPC_cvxpy-checkpoint.ipynb
+++ b/notebook/.ipynb_checkpoints/MPC_cvxpy-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -329,15 +329,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 7.06 ms, sys: 0 ns, total: 7.06 ms\n",
-      "Wall time: 6.23 ms\n"
+      "CPU times: user 6.68 ms, sys: 142 µs, total: 6.82 ms\n",
+      "Wall time: 10.8 ms\n"
      ]
     }
    ],
@@ -367,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -426,15 +426,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2.19 ms, sys: 343 µs, total: 2.54 ms\n",
-      "Wall time: 1.6 ms\n"
+      "CPU times: user 2.25 ms, sys: 121 µs, total: 2.37 ms\n",
+      "Wall time: 1.49 ms\n"
      ]
     }
    ],
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
@@ -529,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,6 +570,7 @@
     "    \n",
     "    #heading error w.r.t path frame\n",
     "    psi = path[2,target_idx] - state[2]\n",
+    "    #psi = -path[2,target_idx] + state[2]\n",
     "        \n",
     "    # the cross-track error is given by the scalar projection of the car->wp vector onto the faxle versor\n",
     "    #cte = np.dot([dx[target_idx], dy[target_idx]],front_axle_vect)\n",
@@ -618,7 +619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -656,7 +657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -699,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -737,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -865,15 +866,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 383 ms, sys: 12.2 ms, total: 395 ms\n",
-      "Wall time: 388 ms\n"
+      "CPU times: user 367 ms, sys: 110 µs, total: 367 ms\n",
+      "Wall time: 363 ms\n"
      ]
     }
    ],
@@ -959,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -1030,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1149,21 +1150,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/marcello/.local/lib/python3.6/site-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in true_divide\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CVXPY Optimization Time: Avrg: 0.1656s Max: 0.2457s Min: 0.1539s\n"
+      "CVXPY Optimization Time: Avrg: 0.1679s Max: 0.2200s Min: 0.1538s\n"
      ]
     }
    ],
    "source": [
     "\n",
-    "track = compute_path_from_wp([0,10],\n",
-    "                             [0,0])\n",
+    "track = compute_path_from_wp([0,3,4,6],\n",
+    "                             [0,0,2,1])\n",
     "\n",
     "sim_duration =50\n",
     "opt_time=[]\n",
@@ -1268,12 +1276,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
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Ie3cG7cQTAF1HAgMAAABAr2GMkQYPC05iobRYSk7x7zN9tmSM7LoPAr8PgG4hgQEAAACgdxkyLEgzMLxS8kBJkklJk06bJLtutazjBHwvAF1HAgMAAABAr2LSh0mFQThKtbREJmnQ8X1mzvGfdrJ3R2D3AdAtJDAAAAAA9C6Dh0n1dZK3ILDrHiv2z9hoYCZPl+LiGeYJRAgSGAAAAAB6FTMk8Eep2ppqqbqqeQIjJlbm7PNlN2fLVlcGbC8A3UMCAwAAAEDvEoyjVEuL/V9PaCGRJDNzrlRbI7s5O3B7AegWEhgAAAAAepfkFCkmNrCDPI/5ExgmuXkCQ6PHS0MyaSMBIgAJDAAAAAC9ijFGSh8meyQ3cIs2VmB8JYFhjJE5d560d6fsl58Hbj8AXUYCAwAAAECvY8afKe3eLhugQZ62KYExsOVesy6TEj1yXn02IHsB6B4SGAAAAAB6HXPRNyVJ9q+vBmbB0hLJ5ZISk1ruFRcvc+m3pJ1bZffuDMx+ALqMBAYAAACAXsekpsucc4HsR3+VLTvW8wVLvZJnoIwrqvX9Zs2XklPkvPJHWWt7vh+ALiOBAQAAAKBXMpd+y39CyOo3e7yWPVbSYv5Fs71iY2Xmf0fK2SXt3NLj/QB0HQkMAAAAAL2SyRgpTfqa7Oo3Zasre7ZYaXG7CQxJMudfJKWmy3nlWaowgDAggQEAAACg13Jd9m2pslx27Xs9W6i0WCap5QDPExl3tMw3rpUOfiFtWdez/QB0GQkMAAAAAL2WGT1eGn+m7Puvyfp83VrDOvVSWYmUnNLxftNnSUOHy3ntOf99AEKGBAYAAACAXs319W9LJV7Z9R90b4HyMslxWj1C9auMK0quBddLeX+X/WRt9/YD0C0RlcBwHEf/8i//ooceeijcoQAAAADoLU6bJI0cI/vuX7pXFVFaLEkyHczAaDJ5hjRytOzrf5Kt617VB4Cui6gExttvv63MzMxwhwEAAACgFzHG+Ksw8g+3OpvClh2Tratre4GGBEZHQzyb9nO55FrwXanwqGz2qu6EDKAb3OEOoFFRUZG2bNmihQsX6s03e34MEgAAAIB+ZPJ0aUimnLdekqkslw4dlD10QDp8UCorlTl3nszNd7Z6qz3WkMBI6mQFhiSdMUUaNVZ25Ruy518iY0wAHgJAeyKmAuPpp5/WDTfcwL/4AAAAALrMuKJkLl0o5X4p+8dfy2atlGprZCZ9TcocJbtvT9s3d7ECQ/JXfZg5l0t5f5d2b+9h9AA6IyIqMDZv3qzk5GSNHj1aO3fubPO6lStXauXKlZKkhx56SGlpaaEKsYnb7Q7LvgguPte+i8+2b+Jz7Zv4XPsuPluEijl3nkx6hpSSJqUMlnH5/77WeeVZ2Xf/LOvzyURHt7yxtFgaECcTO6Br+519vuyfn5Kz+k1FnT4pEI8AoB0RkcDYs2ePNm3apK1bt6q2tlZVVVV6/PHHdeedzUu85s2bp3nz5jX9XFhYGOpQlZaWFpZ9EVx8rn0Xn23fxOfaN/G59l3h+mwzMjJCvifCyxgjnTKh5RuZI/2njBzNlYaf3PL90uKutY807hcdLXPhpbJvvSSbnyeTPqwbUQPorIhIYFx33XW67rrrJEk7d+7UG2+80SJ5AQAAAADdYTJHyUqyuQdkWklg2GPFnTpCtdW1L7xU9p0/y37wtszVt/YwUgDtiZgZGAAAAAAQFEMypCi3dPhA6++Xlsgkp3RraTMwVWbqubJZ78tWV/UgSAAdibgExoQJE3TPPfeEOwwAAAAAfYRxR0tDM2UPHWz9glJvlwZ4tlh/zuVSVaXsug+6vQaAjkVcAgMAAAAAAs1kjJQOtazAsDXVUnWVlNS9FhJJ0ujx0knjZFe/Kes4PYgSQHtIYAAAAADo+zJHSUX5stWVzV8/VuL/2s0WEqnhSNW5l0tHcjlSFQgiEhgAAAAA+jyTOcr/zVfbSEq9/ve7OcSzaf2p50lJA+WseqNH6wBoGwkMAAAAAH1fQwLDHv5qAqOhAqMbx6ieqPFIVX26Sfbo4R6tBaB1JDAAAAAA9H2p6VJMbIs5GPZYsf+bgT1LYEiSueBSKcot+8FbPV6rM6y1srn7Q7IXEAlIYAAAAADo84zLJWWMbFmBUVIsGZeUmNTzPQam+I9UzV4lW1PT4/U6tGubnJ/9SLaV4aRAX0QCAwAAAEC/YDJHtTyJ5FixlJQs44oKzB4XXOw/UnXruoCs1x6b15CM8RYGfS8gEpDAAAAAANA/ZI6SjpXIlpU2vWRLi6XknrePNBk3QUobIpu9KnBrtqUwX5Jky48Ffy8gApDAAAAAANAvmMyR/m9OrMIoLe7xAM9me7hcMjPmSJ/9TbYoP2DrtsYWHvV/U0ECA/0DCQwAAAAA/UNGw0kkJx6leqy4x0eofpWZOUeyVnbd6oCu20JjgqS8LLj7ABGCBAYAAACA/iF5kJTgkQ77KzCs40jHSqTklIBuY9KGSOPPlM1eLWttQNduZK2VmiowSGCgfyCBAQAAAKBfMMZImaOOn9pRfkxynIC2kDTtde48qeCI9PnOgK8tSaosl6qr/N+X0UKC/oEEBgAAAIB+w2SOlA4d8FcwHCv2vxbgFhJJMlNmSAPigjfMs/D4fA1LBQb6CRIYAAAAAPqPjFH+ygVvoVRa4n8twC0kkmRiB8hMO092U5ZsY6VEIDW2j6SkMQMD/QYJDAAAAAD9hsn0D/LU4QOypV7/90GowJAkM3OuVFMtuyU74GvbooYExqix/lYYoB8ggQEAAACg/2g4StUeOnC8AiMIMzAkSWNPk9KHyWYFoY2k8KgUlyAzeBhDPNFvkMAAAAAA0G+Y+ERpUJp06IB/BkZsnMyAuODsZYy/CmPvDtmCIwFd2xbmS2npUqJH8tXK1tQEdH0gEpHAAAAAANC/ZI5sqMAoDlr7SCMzY7ZkjGz26sAuXHhUSh0iJSb5f6aNBP0ACQwAAAAA/YrJGCXl5coWFwavfaRxr5TB0mlnya5bLes4AVnTWisV5cukDZFJ8PhfrCCBgb6PBAYAAACA/iVzlFTnk77MkUkObgJDahjmWZQv7fk0MAuWH5Nqa463kEicRIJ+gQQGAAAAgH6l6SSSOp8UigTG5OlSYpKc918LzIINR6ia1PSmFhJLCwn6AXe4AwAAAAD6im3btumpp56S4ziaO3euFixY0Ox9n8+n5cuXa9++ffJ4PFq8eLHS09MlSa+88opWr14tl8ulW265RZMmTWp3zfz8fC1dulRlZWUaPXq0Fi1aJLfbrcLCQj3xxBOqqKiQ4zi67rrrNGXKlND+IiLdsOGSMZK1oUlgxMTKzL1c9rU/yeZ+KTP8pB6tZwvz/d+kDTlegcFJJOgHqMAAAAAAAsBxHK1YsUL33nuvHnvsMWVlZSk3N7fZNatXr1ZCQoKWLVum+fPn67nnnpMk5ebmKjs7W48++qjuu+8+rVixQo7jtLvms88+q/nz52vZsmVKSEjQ6tX+IZEvv/yyZsyYoV/+8pdavHixVqxYEdpfRC9gYmKlwcP8P4QggSFJZvZ8KXaA7Ht/6fliDRUYSkuX4mkhQf9BAgMAAAAIgJycHA0dOlRDhgyR2+3WzJkztXHjxmbXbNq0SbNmzZIkTZ8+XTt27JC1Vhs3btTMmTMVHR2t9PR0DR06VDk5OW2uaa3Vzp07NX36dEnSrFmzmvYyxqiyslKSVFlZqUGDQvMH9F4nc6QkyQR5iGcjk+CROf8S2Q1rZYvye7ZY0VEp0SMzIF7G7ZbiEqjAQNjZslLZ6sqg7kELCQAAABAAXq9XqampTT+npqbq888/b/OaqKgoxcfHq6ysTF6vV+PGjWu6LiUlRV6vt2mdr65ZVlam+Ph4RUVFtbj+qquu0s9//nO9++67qqmp0f33399qvCtXrtTKlSslSQ899JDS0tK69Lxut7vL90SS8nGnq2Lreg086WRFf+U5gvVs9VffrMIP3lTsR+8p6R/u6vY6xceK5QzJVGpDjIVJyYr21Si5g5h7+2fWnr76bL3luay1KvnNL1RfcESpjz0jE9VxqqE7z0YCAwAAAOhDsrKyNGvWLF1xxRXau3evli1bpkceeUQuV/Pi63nz5mnevHlNPxcWFnZpn7S0tC7fE0nsuAnSmFNVEhMv85XnCN6zRcl8bZaq3n9NNXO/KeNJ6tYq9Xm5Uuaophjr4xJUX1QgXwcx9/bPrD199dl6y3M5H78vu2WdzDXfV1FxSafuaXy2jIyMTu9DCwkAAAAQACkpKSoqKmr6uaioSCkpKW1eU19fr8rKSnk8nhb3er1epaSktLmmx+NRZWWl6uvrm10v+edszJgxQ5J0yimnyOfzqayM9oKvMieNU9Q9v5SJHRDafS9dKNXWyn7wZrfut9ZKRQUyqUOOv5iYxAwMhI0tKpB98XfS+DNlZn89qHuRwAAAAAACYMyYMcrLy1N+fr7q6uqUnZ2tadOmNbtm6tSpWrNmjSRp/fr1mjBhgowxmjZtmrKzs+Xz+ZSfn6+8vDyNHTu2zTWNMZowYYLWr18vSVqzZk3TXmlpadqxY4ck/3BQn8+npKTu/U0/As8MGyGddY7s6rdka6q7vkBpseSr9Q/wbFwz0SNxjCrCwFor55nHJSu5blok4wpuioEWEgAAACAAoqKi9L3vfU8PPvigHMfR7NmzNWLECL344osaM2aMpk2bpjlz5mj58uVatGiREhMTtXjxYknSiBEjNGPGDC1ZskQul0u33nprU8tHa2tK0vXXX6+lS5fqhRde0Mknn6w5c+ZIkm688UY9+eSTeuuttyRJt99+u4wxYfiNoC2uS78l57/+Vfbj92XmXtG1mxsGgJq0EyowEjwM8URY2A/fkXZvl7nhdpnBQ4O+HwkMAAAAIECmTJmiKVOmNHvt6quvbvo+JiZGS5YsafXehQsXauHChZ1aU5KGDBmiX/ziFy1eHz58uB544IGuho4QMmNPk8adLvvXV2UvvMx/kkgn2cYjVFOPV2AoMUmqrpKt88m4owMcLdA6W3BE9s9PS6dPkrngkpDsSQsJAAAAAISY69JvSd4C2Y0fde3GpgTGiTMwPP6vzMFAiFjHkfP045LL5W8dCVGVFwkMAAAAAAi1M6dJmaNk331Z1nE6f19RvuRJlomNPf5aQsOME9pIECJ29ZvS3h0yV/+DTMrgkO1LAgMAAAAAQswYI3PZt6XDB6Wt6zt9ny08Kp04/0INQzwlKjAQErbgiOwrf5Amni0zc25I9yaBAQAAAABhYM4+TxqSKefNFzpfhVGU33yAp+SfgSFxEglCwnnhfyUTJdcNoR8QTAIDAAAAAMLAuKJk5n9Hyv1S2r6hw+utUy8VFTQ7QlWS/xQSSbaCBAaCy27fIP1to8w3rpEZlBry/UlgAAAAAECYmHMukNKH+aswrG3/4pJiqb6u+QBPiSGeCAlbWyPn+f+Rho2QmdPF438DhAQGAAAAAISJiYqS+fp3pIP7pL9tav/ihhNITGrzCgwTEyvFxNJCgqCy77wsFeXLdf1tXTr6N5BIYAAAAABAGJmvXSilDZHzxvPtVmHYonz/N1+dgSH5qzCowECQ2PzDsu++LHPOhTLjzwxbHCQwAAAAACCMjNst8/WrpAM50o4tbV/YUIGh1FaOrUzwyHKMKoLAWivn+f+V3G6Zq24OaywkMAAAAAAgzMyM2VLK4ParMIqOSgNTZKJjWr6XmCSRwEAwbP9E2rFZ5hvXyQwM/eDOE5HAAAAAAIAwM+5ofxXG/r3S7m2tXmML86WvzL9ouj8xSSpjBgYCy9bUyHnhd1LmKJnZ88MdDgkMAAAAAIgEZuZcaVCanDfaOJGk8KhMa/MvJP9RqlRgIMDs6jf8gzuv+39hG9x5IhIYAAAAABABTHS0zGXfknJ2y65f0+w9W18vFRe2PEK1UWKSVFku69QHP1D0G3b/Xv+xqaecEe5QJJHAAAAAAICIYc67WDrlDNmn/1t22/rjbxQXSo4jpbXeQqJEj2StVFERmkDRP3gLWx8aGyYkMAAAAAAgQpjoaLkW/Zs0aqycJ38pu2ur/42GI1TbbSGRpArmYCCAvAUyKSQwAAAAAACtMAPi5frRT6Shw+U88aDs3qPUroYAACAASURBVJ2yTUeotjXEsyGBUc4cDASGra2RykqlQWnhDqUJCQwAAAAAiDAmwSPXXT+TBg2Ws+w/ZLeul4xLSmnjD5OJSf6v5VRgIECKi/xfqcAAAAAAALTHJA2Sa8kD/vaQ7RukQSky7ujWL25oIbGcRIJA8RZIkgwzMAAAAAAAHTEpaf4kxsAUadiIti9sqsAggYHAsA0JjEiqwAj/Qa4AAABAGC1fvrxT17ndbt12221BjgZoyaQPk+snj7d/0YA4KcpNCwkCp6hAMkYamBruSJqQwAAAAEC/lp2drSuvvLLD6958800SGAgb01hh0db7xviPUqWFBIHiLZCSBslEt9G2FAYkMAAAANCvpaam6qqrrurwuqysrBBEA/RAgkeWCgwEiPUWtD00NkyYgQEAAIB+bdmyZZ26bunSpUGOBOihxCQqMBA43kKZCJp/IZHAAAAAANp09OhR5efnhzsMoHMSPVIZFRjoOWutv4Ukgk4gkUhgAAAAAE2WLl2qPXv2SJI++OADLVmyRP/0T/+k1atXhzkyoGMmgRkYCJDyY5KvNqJOIJFIYAAAAABNduzYoTFjxkjyD+28//779Z//+Z969dVXwxwZ0AkNLSTW2nBHgnbY6qpwh9CxhiNUaSEBAAAAIlRdXZ3cbre8Xq/Ky8t16qmnasSIESotLQ13aEDHEj1Sfb1UVRnuSNAGu2eHnB9dK3vgi3CH0r4ifwIj0iowOIUEAAAAaHDSSSfplVdeUUFBgaZMmSJJ8nq9iouLC3NkQCckNBy1WlEmxSeENxa0ynn9OclxpOICadSYcIfTJuuNzAQGFRgAAABAg9tuu00HDx5UbW2trrnmGknS3r17dd5554U5MqBjJrEhgVHOHIxIZPd8Ku3d6f++tjbM0XTAWyDFxPireiIIFRgAAADo91atWqXJkydr6NCh+tGPftTsvenTp2v69OlhigzogsY/bJYH/iQSe+ignP/5pVzfvF5myoyAr98fOG+84E8K1NZKPl+4w2mX9RZIKYNljAl3KM2QwAAAAEC/98UXX+jll19WQkKCpkyZosmTJ2v8+PER9x/vQLsS/AkMW3FMgfwn1+7/XM5//9Q/IHT/XhIY3WD37pD2fCpz2bdk33lZ8tWEO6T2eQsjrn1EIoEBAAAA6Pvf/74k6eDBg9qyZYuef/55HT58WBMmTNCUKVM0adIkJSUlhTlKoANNFRiBayGxez6Vs+znkidJso5UxkDb7nDefFFKGigz5wp/AiPiW0gKZc6cGu4oWiCBAQAAADQYOXKkRo4cqQULFqiyslLbtm3T1q1b9eyzz2rw4MG66qqrNGnSpHCHCbQuPkEyroC1kNi/bZTz2/+S0obIddd/yHn8P2SD0J7S19mcXdLu7TJX3dJUJSNf5CYwrM8nlXqpwAAAAAB6i/j4eM2cOVMzZ86UJOXk5IQ5IqB9xhUlJST4TyHpIeeTD2WfWioNP1muH/1UxpPkr8IggdFlzhsvSJ5kmQsvk9xuyZiITmCopMj/lQQGAAAAENl2796t/fv3q7q6utnrCxcuDFNEQBckJPW4hcT54G3Z55+Uxk2Q64f/JhMXL8l/yok98EUgouw37BefSbu2yXz7ZpnYAf4Xo2MiO4HRcISqSUkLcyAtkcAAAAAAGvz+97/XunXrdOqppyomJqbpdYZ5otdI9HS7zcM6juxf/iD73l+ks86R6/t3y8TEnrB2klQeeTMw7JFDqo+KzH9HnTdfkBKT/NUXjaJjInoGhi3yJzCowAAAAAAi2EcffaRHHnlEKSkp4Q4F6J7EJKnxD6BdYH0+2aeWym78SGbWZTLXfF8mKqr5RZ5kqbJCtq5Oxh0Zf5S0x0rk/Oc/q3z6hdJ1t4U7nGbs/r3Sji0yC2+UGRB3/I1eUoGhQanhjaMVkfFPHQAAABAB0tLSFB0dHe4wgG4zCR7Zg/u6dI+tKJfz6welvTtlvnWTzCULW686Smw4iaeiTEoeFIBoe86++qxUVSGnxBvuUFpw3npJSvDIzP568zdiekECw5PcvPomQpDAAAAAABrcdtttevLJJ3XuuecqOTm52Xunn356mKICuiAxSarofAuJLcqX898/kwryZP7hn+T62oVtXms8SbKSf5BnBCQw7MEvZD9+3/99ZXmYo2nO5uVK2zfIXH6NzID45m9Gx8hGcguJtyAi20ckEhgAAABAk3379mnr1q3avXt3sxkYkvSb3/wmTFEBXZDokWprZWtqZGLb/xt0u3ennCf/S6rzybX4P2TGn9HB2g0VGGXhn4NhrZXzwv/6Yxp+kpwIiOlEduVrkju6ZfWF5G8hqYvcBIa8hdKw4eGOolUkMAAAAIAGzz//vP71X/9VEydODHcoQPckePxfK45Jsa3/Lbq1VvaDt2RfWiGlDpHrjntlMkZ2vLanoSopAo5StZuypM93yXz3DmnfHtmjhxQpYzztsRLZ7NUyM2bLJA1seUFM5A7xtNZK3gKZCZPDHUqrSGAAAAAADWJjY2kVQa9mEhvbPMpabQOwtTWyz/5adt0H0sSz5br1Lpn4xM4t3lCBYcuOhTVZYGtqZP/8lDTiZJnz5snm5cpWVkROAmPN21KdT+aiBa1fEB0jVVaENqjOqiyXaqojtoXEFe4AAAAAgEhx9dVX6+mnn1ZJSYkcx2n2P6BXSGyowGilSqI+P0/Of90ju+4DmSuuleuO+zqfvJCOV3eEuQLD/vUVyVsg1zX/KOOKkuLiZaurZOvqwhqX1JAg+uBtaeLZMm21YUTyKSQNJ9iYCE1gUIEBAAAANGicc/H++++3eO/FF18MdThA1yU0VElUlDWrSLDbN6romcf98y5++G8yZ53T5aWN2y3FJ4R1Bob1Fsi++2eZaefJnNIwsyM+wf+1uvL4nI4wses+kMqPyXXxlW1eYyJ5iGfjEaokMAAAAIDItnz58nCHAPRMUwVGmSTJVlXKvvg72ayVco8aI+cf/llmaGYP1k8OawWG/fPTkpXMt28+/mJcQwKjKrwJDOs4su+/Jo0aK50yoe0LI/gYVduYwEhNC28gbSCBAQAAADQYPDgy/9YR6LQT2jzs7u1ynn5cKi6SuezbSrnlhyoq7WHywZMkG6YEhs3ZJbvxI//RpKnpTa+buHj/3I+qMM+V+NtG6eghmX/8ZxnTzkSO6FjJVxO6uLrCWyC5o/2JqgjEDAwAAAD0ay+88EKnrnvppZeCHAnQc8bt9s+EWPuenEfvl6Jj5PrXh+RaeKNMdEzHC3QkMUkqC30Cw1or56XfSwNTZC5d2PzNxhaSMA/GdN5/VUoZLDP13PYvjI6WfL7QBNVV3kIpJU3GFZmpAiowAAAA0K+9/fbbmjNnjv/4wHa88847+s53vhOiqIAe8AyU8g/LzL1C5sobZWJjA7a0SUySPZATsPU6y27Kkvbvlbn5TpnYAc3fPLGFJEzs/s+lvTtlvnOrTFRU+xdHx0q1tbLWtl+pEQbWWxCx8y8kEhgAAADo52pqarRo0aIOr4uOjg5BNEDPuW65UzIumTGnBn5xj38GRij/8G19Ptm/PCMNP0lmxuyWF8TF+6+rCt9Rqvb9V6W4BJnzL+r44pgYyTpSfZ2/XSOSeAtlTjsr3FG0iQQGAAAA+jVOF0FfY8aeHrzFE5OkujqpuqopcRBs9oO3pMKjct31M/+xqV8V5goMW1QguzlL5qJvygzoxO+kMRnq80VUAsPW1Ukl3oiuwIjMxhYAAAAAQOTxNJzyEaJBnraiTPatF6UzpsicPrn1ixoTKWGagWHXvuc/GWX2/M7dEN3Q0hNpgzxLvf7KkJTIPIFEIoEBAAAAAOgk03hMaVlpSPazb70kVVXJ9e1b2o7J7ZZiB4TlFBJb55P96D1p4rRmJ6O0K6ZhmGpthB2lWuQ/QtWkRm4FBi0kAAAAQIBs27ZNTz31lBzH0dy5c7VgwYJm7/t8Pi1fvlz79u2Tx+PR4sWLlZ7u/0PPK6+8otWrV8vlcumWW27RpEmT2l0zPz9fS5cuVVlZmUaPHq1FixbJ7fb/5312drb+7//+T8YYjRo1Sj/60Y9C+FtAn+ZpOF4zBBUYtuCI7Oq3ZM6dK5M5qt1rXfGJsmFoIbFb10tlpXLNuqzzNzWeBuOLrASG9foTGLSQAAAAAH2c4zhasWKF7r33Xj322GPKyspSbm5us2tWr16thIQELVu2TPPnz9dzzz0nScrNzVV2drYeffRR3XfffVqxYoUcx2l3zWeffVbz58/XsmXLlJCQoNWrV0uS8vLy9Oqrr+qBBx7Qo48+qptvvjmkvwf0cQ0VGDYER6nav/xBioqS+eZ1HV5rEhJlw1GBseZtafBQqa32llaYCE1gqDGBMYgEBgAAANArVFdXq6ioSNXV1V26LycnR0OHDtWQIUPkdrs1c+ZMbdy4sdk1mzZt0qxZsyRJ06dP144dO2St1caNGzVz5kxFR0crPT1dQ4cOVU5OTptrWmu1c+dOTZ8+XZI0a9aspr1WrVqlSy65RImJiZKk5OTkHv5GgBMkhmYGhv3iM9lNH8tccqXMwNQOr3fFJ4R8BoY9dMB/dOqFl8q4uvBH6+gIbSHxFkiJnoAeuxtotJAAAACg3zt48KBWrlypLVu2qKCgoOn19PR0TZo0SRdddJFGjhzZ7hper1epqcf/oJWamqrPP/+8zWuioqIUHx+vsrIyeb1ejRs3rum6lJQUeb3epnW+umZZWZni4+MVFRXV4vrDhw9Lku6//345jqOrrrqqqR3lRCtXrtTKlSslSQ899JDS0ro2uM/tdnf5nt6irz5bIJ7LWqt8d7Ti6mvlCdLvyFqr4kefkwalKvXaf5CrE6edlCR65D5WqtQQfm7H/vKMqqJjlHbFd+RKGtjp+2oHD1axpKT4OMV2EG8o/1ksLi+Vkz4sZL/D7jwbCQwAAAD0a0uXLlVubq5mzpypRYsWKTMzU3FxcaqqqtKhQ4e0a9cuPf744xo+fLgWL14c7nA75DiO8vLy9JOf/ERer1c/+clP9Ktf/UoJCQnNrps3b57mzZvX9HNhYWGX9klLS+vyPb1FX322gD1XokdV+UdVE6Tfkd2+Qc7u7TI33C5vRaVU0fFsC3dcguoO/T1kn5utrpTzwdsy086Vt7ZO6sK+trJKknSssECmg/tC+c9i/ZHD0uChIduv8dkyMjI6fQ8JDAAAAPRr559/vqZOndri9cTERI0fP17jx4/XlVdeqc2bN7e7TkpKioqKipp+LioqUkpKSqvXpKamqr6+XpWVlfJ4PC3u9Xq9Tfe2tqbH41FlZaXq6+sVFRXV7PqUlBSNGzdObrdb6enpGjZsmPLy8jR27Niu/3KA1iQmywaphcQ69XJe+aOUniFz7ryOb2jgSkiUKsuDElNr7PoPpeoqmQu7MLyzUQTPwDDjzwx3FO1iBgYAAAD6tROTF19t+WiUk5PTapLjRGPGjFFeXp7y8/NVV1en7OxsTZs2rcVea9askSStX79eEyZMkDFG06ZNU3Z2tnw+n/Lz85sSDm2taYzRhAkTtH79eknSmjVrmvY655xztHPnTknSsWPHlJeXpyFDhnTrdwO0ypMUtBkYdv2H0qEDMgtu8B+P2kkmPlEK0Skk1lrZD9+RRo6WRo/v+gINx6jaCJqBYYsK/L+/wZH9/xVUYAAAAAANfv7zn+uZZ55p8fqDDz6op556qt17o6Ki9L3vfU8PPvigHMfR7NmzNWLECL344osaM2aMpk2bpjlz5mj58uVatGiREhMTm1pSRowYoRkzZmjJkiVyuVy69dZb5WoYCtjampJ0/fXXa+nSpXrhhRd08skna86cOZKks846S9u3b9ddd90ll8ulG264QR6PJ5C/JvRzJjFJ9kBOwNe1Pp/s63+SRo2VmTqzazHFJ0h1PlmfTyY6OuCxNfPFbin3S5kbfyhjTNfvdzdWYNQENq4esJs/liSZiWeHOZL2kcAAAABAv+c4jqSGv1lt+F+jo0ePNg3L7MiUKVM0ZcqUZq9dffXVTd/HxMRoyZIlrd67cOFCLVy4sFNrStKQIUP0i1/8osXrxhjddNNNuummmzoVM9BlicGpwLAfviMV5ct14w+7dqqHGlpIJKmqQoru/EDN7rAfvCPFJcicc0H3FohpTGD4AhdUD9kNH/kTR+mdn0cRDiQwAAAA0O9de+21Td9fc801zd5zuVy68sorQx0SELk8yVJlhWxdXZfaPNpjqypl33pJOu0smdNbnprTERPfkMCorJC6cCJIV9ljJbJbsmQuvEwmdkD3FoluOKY0QlpI7NHD0oEcmatuCXcoHSKBAQAAgH5v+fLlstbqpz/9qX72s581vW6MUVJSkmIa/8YUgL8CQ5IqyqTkQQFZ0v71Van8mFwLb+zW/ccrMII7B8NmrZTq6ro3vLNRY4tLhLSQ2I0fSZLMtPPCHEnHSGAAAACg3xs8eLAk6de//nWYIwEin/EkyUpSWWlAEhj2WLHs+6/KTD1X5qRx3YspvuGY4KqKHsfTFmut7MfvS6dMkBk2vNvrGJdLcrsjpoXEbvxIGnu6TMrgcIfSIU4hAQAAQL/2zDPPqKSkpN1rSkpKWh3uCfRLjRUYAZqDYd/6P8lXK7Pghm6vYRIaBtUGswIjZ7eUnyczs/PHu7YpOjYijlG1hw5Ihw92f55HiFGBAQAAgH4tIyNDP/7xjzV8+HCddtppysjIUFxcnKqqqpSXl6ddu3bp8OHDrQ7YBPolT7IkyZYdUzfO4GjGFhyR/fBdmfMukhma2e11XA0VGLayvMcxtcVmrZRiB3T5hJRWxcRIteFvIbEbPpKMKzDPFAIkMAAAANCvXXTRRZo9e7Y2bdqkrVu3auPGjaqsrFRCQoJGjhypiy66SFOnTu30SSRAnxfACgz7xguSyyVz+TUdX9wOE+QZGLamWnZTlsy0c2UGxPV8QXd02CswrLWyG9dKp02UCeLg00AigQEAAIB+z+12a/r06Zo+fXq4QwEiX2O7Rg8TGDbv77Lr18jMu0JmUGqP1jID4v3fBCuBsTlLqqkKTPuIJMXEyoa7heRAjlRwRObrV4U3ji5gBgYAAADQ4Omnn1ZOTk64wwAimnG7pfgE/xDPHrCv/UmKiZW57Ns9jykqSoqLD9oQT5u1SkofJo07PTALRseE/RhVu2GtFOWWmTwjrHF0BRUYAAAAQANrrR5++GHFxsbqvPPO03nnnaeMjIxwhwVEnsTkHlVg2INfyG7Okpn/HZmGmRo9FhcvVQY+gWELjkh7d8gsuEHGBGjCRkxMWFtIrOPIbvxYOmPK8fabXoAEBgAAANDglltu0U033aQdO3bo448/1n333af09HSdf/75uvzyy8MdHhA5PEmyPUhgOK8+J8Unyly8IHAxxSXIBqGFxGavkoyRmTE7cItGh3mIZ85uqaRI5ts3hy+GbqCFBAAAADiBy+XSxIkTdfvtt+uRRx6Rx+PRH//4x3CHBUSWxCSprHsJDPvFZ9Knm2QuXSgTH8C//Y9LCHgLiXUc2ezV0mmTZFIGB27h6DBXYGz8SIqJkTnrnLDF0B1UYAAAAAAnqK6u1oYNG5SVlaVdu3bp9NNP1x133BHusICIYhKTZA90b16M88ofpaSBMnMCXNUUFy+VegO75md/k7wFMt+6KaDLmugY2TDNwLD19f72nYnnBOZElRAigQEAAAA0ePTRR7V161aNHj1a5557ru644w4lJSWFOywg8nj8MzCstV2aC2F3b5f2fCpzzT/KxA4IaEgmLkH2SG5A17RZq6T4BJnJAT6hKJwVGHv+JpWVypx9fnj27wESGAAAAECDMWPG6MYbb1RaWlq4QwEiW2KSVFcnVVf5Kx86wVrrr75ISZO54NLAxxQf2FNIbGW57NZ1MufOlYmOCdi6ksI6xNNuzpZi46Qzp4Zl/55gBgYAAADQ4Jvf/CbJC6AzPA2VSV0Z5Pm3jdL+vTKXXyMTHR34mOISpKpKWWsDspzd+LHkq5WZOS8g6zUTpgoM6ziy2zfInDEl8EmZECCBAQAAAADoEpPYkMAoK+3U9dZx5Lz2nJQ+TGbGnOAEFZcg1dcH7HQPm7VSyhgpnTQ2IOs1ExMjhWMGxoEcqbRYmtS7hnc2IoEBAAAAAOgaT7L/a2crMLaul/6+X+aKa2TcQZpk0NjKEoA2EnvkkL9aZObcLs346DR3jFRfJ+vUB37tdthtn0gul8yZ00K6b6CQwAAAAAAAdE1DBYbtxFGq1nHkvP4naehwmXMuCF5M8Qn+r1WVPV7KblgrGRO8eGMa2jd8vuCs3wa7fYM0boJMgiek+wYKCQwAAAAAQNd0YQaG3ZwlHT4o841rZVxRQQvJxDUkMCp7VoFhrfUnME45Q2ZQagAia0V0rP9rCNtIbMER6dABmV7aPiJFyCkkhYWFeuKJJ1RSUiJjjObNm6evf/3r4Q4LAAAAANCa2DjJ7e5wBoZ16mVff17KGCkz9dzgxhSoFpKD+6Sjh2QuXtDzmNrSOMQ0hIM87fZPJEnmrK+FbM9Ai4gERlRUlL773e9q9OjRqqqq0j333KOJEydq+PDh4Q4NAAAAAPAVxhgpMbnDCgy7Ya10JFeu2+6RcQW5AaChAsNWVaonUyvshg+lKLfM1JmBias1TS0kIUxgbNsgZY6SGTw0ZHsGWkS0kAwaNEijR4+WJMXFxSkzM1NerzfMUQEAAAAA2pSYJNtOAsPW18u+8YI0/GRp8vTgx9M0A6P7FRjWcWQ3fCSdMSWocyJMYwuJLzAnpnTEVpRJn++UOav3to9IEVKBcaL8/Hzt379fY8e2PKpm5cqVWrlypSTpoYceCssZ3W63m7PB+yA+176Lz7Zv4nPtm/hc+y4+W6CP8iS1W4Fh138g5efJdcd9wa++kI63kPRkBsbnu6SSIpmrbglMTG1prMAI0QwM++kmyXFkJvXe9hEpwhIY1dXVeuSRR3TzzTcrPj6+xfvz5s3TvHnzmn4uLCwMZXiSpLS0tLDsi+Dic+27+Gz7Jj7XvonPte8K12ebkZER8j2B/sQkJskeyGn1PVtX56++GDVWCtXf+scOkFyuHp1CYjd8KMXEBr9Swd04AyNEp5Bs2yAlD/J/Hr1YRLSQSFJdXZ0eeeQRnX/++fra13p3VggAAAAA+jxP2zMwbPZKqShfrm9e55+XEQLGGGlAfLdbSGydT3Zztsyk6TKxAwIc3VfEhK6FxPp8sju2yEw8OzSVMEEUEdFba/Xb3/5WmZmZuvzyy8MdDgAAAACgI4lJUmWFbF1ds5etzyf71kvSyadIZ0wNbUzxCd2vwNi5TaookznngsDG1JroELaQ7PlUqqnq9e0jUoS0kOzZs0dr167VyJEjdffdd0uSrr32Wk2ZMiXMkQEAAAAAWuVJ8n+tKPO3JzSwH78veQvlunFRyKovmsTFy3ZzBobdsFZK8EgTJgU4qFY0JDCsr7ZHJ6Z0ht3+ib/i49SJQd4p+CIigXHqqafqpZdeCncYAAAAAIBOMolJspJUVtqUwLC+Wtm3/08ac6p0eggSAV8Vl9CtFhJbUy27bb3M9FkyjfMpgilEx6haa/3Hp06YLNPYttKLRUQLCQAAAACgl/Ek+7+eMAfDfvRXqaRIrm9eH/rqC6nbLSR2+waptkbmnAuDEFQrokOTwNDBff5TVc7q/e0jEgkMAAAAAEB3JPpbSGyZP4Fha2tk3/mzNO70sLUrmLj4bh2jajeslQam+mMPhRDNwLDbPpGMS2bitKDuEyokMAAAAAAAXdc4A6OhAsOufU8q8cr1jdCdPNJCXNcrMGxFmbRji8w554fulI4QVWDY7Z9IY06VaayW6eVIYAAAAAAAui7e4/9aVuqvvnj3ZemUM2TCOSwyLl6qrpR1nE7fYresk+rrQtc+Ism43ZLLFdQEhi3xSn/fLzPx7KDtEWokMAAAAAAAXWbcbik+USo/Jvvhu1JpsVzfuC68QcUnSNZKNdWdvsVuypIGD5VGjg5iYK2Ijg1qC4ndtU2SZCZMDtoeoUYCAwAAAADQPYlJst4Cf/XFqRNlxp8R3njiEvxfOzkHw1aUS3v+JjNlZujbXqKjpbogtpDs2uoftDr8pODtEWIkMAAAAAAA3eNJkv62UTpWEv7qCzUM8ZQ6fZSq3f6JVF8vM3VmEKNqQ0xM0CowrOPI7tomc/qk0M31CIG+8yQAAAAAgNBKTPK3bJx2lkyoTvBoT3xDBUYnB3naLeuklDTppHFBDKoN0bHBm4GR+6VUViqd3nfaRyQSGAAAAACAbmo83SISqi8kHW8h6UQFhq2ulHZulZk8IzynpkRHy9bWBGVpu2urJMmcPiko64eLO9wBAAAAAAB6J3PeRdLQ4TJjTwt3KH4NLSS2skIdpSTsp5ulOp/MlDC0j0j+o1R9vqAsbXdtkzJHyQxMCcr64UICAwAAAADQLWbMqTJjTg13GMfFdaGFZHO2lDRQGhum+GNiJV/gKzBsTY30+U6ZOZcHfO1wo4UEAAAAANA3xHeuhcTW1sju2CwzabqMKyoEgbUiOkhDPD/fIdXVyfSx+RcSCQwAAAAAQB9homMkt7vjCoydW6WaapmpM0ITWGuio6W6wLeQ2J3b/MmRSBiqGmAkMAAAAAAAfUdcglTZQQXGlmwpPlE65cwQBdXS/2fv3qOjqu/9/7/2zGSSmUkyyWSANIAKEVtBFCGIYi0IETnlrJZSv7Ra6/Fyvi3VhVK7WsR61C6k0irCErDH03K81H5bTvurtse2v9NGDrZH5FsUEAUVIxa0oCH3hNzmsr9/7MmEmACZZGb2ZPJ8rJW193xmz573JxNcycvPxcjJlVKwiKd5YI80aYoMd27S7203AgwAAAAAQPbw+E47hcQMh2S+tkvGtFkyXDYuC+l2J30bVbOhTjp6RMaU7Np9pBsBBgAAAAAge3i8Mk83heStfVL7Cft2H+mWb4GvIQAAIABJREFUk5P0XUjMA3slKSvXv5AIMAAAAAAA2cR7hhEYu1+W8jzS5IvSWFQ/clKwC8mBPZK/WBp7dnLvmyEIMAAAAAAA2cPjPeUaGGYkInPPThlTK6wFP+3ktnYhMU0zKbczo1GZB/bKmDxNhmEk5Z6ZhgADAAAAAJA1DI/v1LuQvLNfam2WMcPm6SOStVOIlLydSN5/T2ptlrJ0+ohEgAEAAAAAyCanCTDM3TuskQ8XzEhzUf3oDjCStJCneWCPJMmwe2pMChFgAAAAAACyh8crdbbLjER6NZvRqMw9O6Up02Xk5tlU3Em6A4yuJAUY+/dI4yfIKCxOyv0yEQEGAAAAACB7eL3WseNjozCOvCs11suYdmn6a+qPO3kjMMzODqn6zazdfaQbAQYAAAAAIHt48q3jxxbyNF/bJRkOGVMrbCiqH8mcQnLwDSkSljGFAAMAAAAAgGHB8MRGYHxsHQxz31+l8k/KKCi0oaq+jCQGGOZb+yRXjnTu+UO+VyYjwAAAAAAAZI94gNEzAsOsPy4dOSTjoktsKqof7uStgWEe3C9NPM/+rWFTzGV3AQAAAEC22Lt3r5544glFo1HNnz9fixcv7vV8KBTSpk2bdOjQIRUUFGjFihUaPXq0JOnZZ5/Vtm3b5HA4dNNNN2natGmnvWdNTY02bNiglpYWTZw4UcuXL5fL1fPr/c6dO/XII4/owQcfVHl5eZq+A0AG8MamkJwcYOzbJUmZFWDk5FrHUOeQbmN2tElH3pXx2f+VhKIyGyMwAAAAgCSIRqPasmWL7r77bq1fv14vvfSSPvjgg17XbNu2TT6fTxs3btSiRYv0s5/9TJL0wQcfaMeOHXrkkUf03e9+V1u2bFE0Gj3tPZ955hktWrRIGzdulM/n07Zt2+Lv097erj/84Q+aNGlS+r4BQKaIjcAw23qmkJiv7ZJGlUql4+yqqq+cHOsYCg3tPtVvStGojElThl5ThiPAAAAAAJKgurpapaWlGjNmjFwul2bPnq1du3b1uuaVV17R3LlzJUmXXnqp3njjDZmmqV27dmn27NnKycnR6NGjVVpaqurq6lPe0zRN7d+/X5deau2mMHfu3F7vtXXrVn3+859XTvcfSMBI4vFZx9gIDLOzQ3prn4yLLpFhGDYW9jGxERjmEKeQmAf3S06nVP6pZFSV0ZhCAgAAACRBfX29SkpK4o9LSkr0zjvvnPIap9Mpr9erlpYW1dfX9xotEQgEVF9fH7/Px+/Z0tIir9crp9PZ5/pDhw6ptrZW06dP129/+9tT1ltVVaWqqipJ0tq1axUMBhPqr8vlSvg1w0W29i1b+yX17ptZVKQaSV5Dyg8G1fF/X1RTOKSiz1wldwb1PxzuVJ2kgly3PKeoayCfWf17b0vln1JgbAaNLhmAwfw8EmAAAAAAWSIajerpp5/WrbfeesZrKysrVVlZGX9cW1ub0HsFg8GEXzNcZGvfsrVfUj99c+eqre64OmprFf1LleTxqWnUWBkZ1H+ztVWS1NJQrxOnqOtMn5nZ2anoO2/KuOrzw+6z7e5bWVnZgF9DgAEAAAAkQSAQUF1dXfxxXV2dAoFAv9eUlJQoEomora1NBQUFfV5bX18ff21/9ywoKFBbW5sikYicTmf8+o6ODr3//vv63ve+J0lqbGzUD3/4Q33nO99hIU+MLB6f1N4mMxqVue8VGRdMl+HKsD9/k7GI56G3pEhYxnkXJKemDMcaGAAAAEASlJeX69ixY6qpqVE4HNaOHTtUUVHR65oZM2Zo+/btkqxdQqZMmSLDMFRRUaEdO3YoFAqppqZGx44d07nnnnvKexqGoSlTpmjnzp2SpO3bt6uiokJer1dbtmzR5s2btXnzZk2aNInwAiOTxyuzrVV676DU0iRl0u4j3ZKwjap5cL9kOKRzz09SUZktwyIoAAAAYHhyOp26+eabtWbNGkWjUV155ZUaP368tm7dqvLyclVUVGjevHnatGmTli9frvz8fK1YsUKSNH78eF122WW688475XA4dMstt8jhsP5fY3/3lKSvfOUr2rBhg37xi19owoQJmjdvnm19BzKONzYCY98uyeGQccEMuyvqy9W9C8lQAow3pLMmyojtvJLtCDAAAACAJJk+fbqmT5/eq+1LX/pS/NztduvOO+/s97VLlizRkiVLBnRPSRozZowefPDB09Zz//33D6BqIAt5vFLbCZmv/VWaNEWGL9/uivowDEPKcQ86wDBDIenQ2zLmfjbJlWUuppAAAAAAALKK4fFJH34g/f2wjAtn2l3OqeW4Bz+F5L2DUjgk45NTkltTBiPAAAAAAABkF49Xam+TJBmZuP5FN/cQRmAcfMM6OXdyEgvKbAQYAAAAAIDs4vVZx9JxMsYMfJvOtBvKFJJ39ktjz5aRX5jkojIXAQYAAAAAILt4rADDuCiDp49IUo5b5iACDDMclt59a8Rsn9qNAAMAAAAAkF1iIzCMCzN4+og0+DUwDldLnR0yzhs5619I7EICAAAAAMgyxvTZUigknXu+3aWc3iCnkJjv7LdORliAwQgMAAAAAEBWMfzFcixYLMOR4X/yDnIRT/Pgfql0rIzC4hQUlbky/NMEAAAAACBLDWIKiRmNSNUHRtz6FxIBBgAAAAAAtjDcuYmPwHj/b9YWsZNG1vQRiQADAAAAAAB7uHISDjDMg29IEiMwAAAAAABAmgxiDQzz4H5pVKmMQDBFRWUuAgwAAAAAAOyQk5vQGhimaVrrX5w7OYVFZS4CDAAAAAAA7JCTI4UTGIFx/JjU2iyd+6nU1ZTBCDAAAAAAALCD2y1FIjIjkQFdbh56W5JkTPxkKqvKWAQYAAAAAADYISfXOoY6B3b9obel3Dyp7KzU1ZTBCDAAAAAAALCD220dB7gOhnnooHTOJBkOZwqLylwEGAAAAAAA2MGVYx1DoTNeanZ2Sh+8N2Knj0gEGAAAAAAA2MOdwBSSI+9KkQgBBgAAAAAASC8jZ+BTSLoX8BQBBgAAAAAASKvuACM0kADjLWlUqYzCohQXlbkIMAAAAAAAsIN7YAGGaZrSu2/LmDByR19IBBgAAAAAANhjoCMwGmqlpvoRPX1EIsAAAAAAAMAeA91GNbb+xUhewFMiwAAAAAAAwB4uK8AwzzSF5NDb1par489JQ1GZiwADAAAAAAA7DHQNjENvS2eXy3DlpKGozEWAAQAAAACAHXJyreNpppCY4ZB0+N0RP31EIsAAAAAAAMAeObERFeHTjMB4/29SOCSj/FNpKSmTEWAAAAAAAGCHASziacYW8NQI30JVIsAAAAAAAMAWhsMpOV1SqPPUFx16SyoqkREIpq+wDEWAAQAAAACAXdzuM4/AYP0LSQQYAAAAAADYx5UjhUL9PhVprJdqP2IBzxgCDAAAAAAA7OLOPeUUktDB/ZJEgBFDgAEAAAAAgF1yTj2FJHRwv+R0SmeXp7mozESAAQAAAACAXXJyZIb7n0ISevsNadwEGe7cNBeVmQgwAAAAAACwiztX6uo7hcSMRhSufpPpIychwAAAAAAAwC45binUzxSSo0dkdrSzA8lJCDAAAAAAALBLjrvfXUjM996RJBkTzkt3RRmLAAMAAAAAALvkuPudQqLD1TK8+dLoT6S/pgxFgAEAAAAAgE0Md/9TSMwjh+SaeJ4Mw7ChqsxEgAEAAAAAgF36WQPDDIel999TzkSmj5yMAAMAAAAAALv0t4jnh+9L4ZBc5Z+yp6YMRYABAAAAAIBdctxS18dGYBw+ZD3FCIxeCDAAAAAAALCL2y2FQzKj0Z62w9VSrkfOsrPsqysDEWAAAAAAAGCXHLd1DPdspWoeeVcaP0GGgz/ZT8Z3AwAAAAAAu3QHGLF1MMxoRHr/PRlnl9tYVGYiwAAAAAAAwC7uWIDRvQ7Gh3+XujqlswgwPo4AAwAAAAAAu+TkWsdQpyTJPPyuJMk4+1y7KspYBBgAAAAAANjEyMmxTkKxNTCOvGuNyigda19RGYoAAwAAAAAAu3SPwIhNITEPV0vjJshwOm0sKjMRYAAAAAAAYJfuNTBCndZWqkdYwPNUCDAAAAAAALDLyVNIao5Jne0s4HkKBBgAAAAAANjlpEU8zcPVkljA81RcdhcAAAAAZIu9e/fqiSeeUDQa1fz587V48eJez4dCIW3atEmHDh1SQUGBVqxYodGjR0uSnn32WW3btk0Oh0M33XSTpk2bdtp71tTUaMOGDWppadHEiRO1fPlyuVwuPf/883rhhRfkdDpVWFiob3zjGxo1alR6vxEABi42hcTs6pKOHJJcOdInxttcVGZiBAYAAACQBNFoVFu2bNHdd9+t9evX66WXXtIHH3zQ65pt27bJ5/Np48aNWrRokX72s59Jkj744APt2LFDjzzyiL773e9qy5Ytikajp73nM888o0WLFmnjxo3y+Xzatm2bJOmcc87R2rVr9fDDD+vSSy/VM888k95vBIDE5HSvgdEVW8DzHBkuxhr0hwADAAAASILq6mqVlpZqzJgxcrlcmj17tnbt2tXrmldeeUVz586VJF166aV64403ZJqmdu3apdmzZysnJ0ejR49WaWmpqqurT3lP0zS1f/9+XXrppZKkuXPnxt/rggsuUG6uNSR90qRJqq+vT983AUDiugOM2AgMg/UvTolYBwAAAEiC+vp6lZSUxB+XlJTonXfeOeU1TqdTXq9XLS0tqq+v16RJk+LXBQKBePDQ3z1bWlrk9XrljG2zePL1J9u2bVt8KsrHVVVVqaqqSpK0du1aBYPBhPrrcrkSfs1wka19y9Z+ScO7b1FPno5Lyms4rvb2E8qfcqG8sb4M536dyWD6RoABAAAAZKE///nPOnTokO6///5+n6+srFRlZWX8cW1tbUL3DwaDCb9muMjWvmVrv6Th3TczHJIktb++W5J0oqRUbbG+DOd+nUl338rKygb8GqaQAAAAAEkQCARUV1cXf1xXV6dAIHDKayKRiNra2lRQUNDntfX19QoEAqe8Z0FBgdra2hSJRHpd323fvn169tln9Z3vfEc53Vs0AshMTpdkGNLfD1vnZWfbXVHGIsAAAAAAkqC8vFzHjh1TTU2NwuGwduzYoYqKil7XzJgxQ9u3b5ck7dy5U1OmTJFhGKqoqNCOHTsUCoVUU1OjY8eO6dxzzz3lPQ3D0JQpU7Rz505J0vbt2+Pv9d577+nHP/6xvvOd78jv96f1ewAgcYZhWOtgmFFp7FkyCB1PiSkkAAAAQBI4nU7dfPPNWrNmjaLRqK688kqNHz9eW7duVXl5uSoqKjRv3jxt2rRJy5cvV35+vlasWCFJGj9+vC677DLdeeedcjgcuuWWW+RwWP+vsb97StJXvvIVbdiwQb/4xS80YcIEzZs3T5K1O0lHR4ceeeQRSdYw7ZUrV9rwHQEwYG631NXJAp5nYJimadpdxGAdPXo07e+ZzXOQRjI+1+zFZ5ud+FyzE59r9rLrs01kXjUS/906m//NZmvfsrVf0vDvW+Q7N0sNtTK+skyOuZ+Ntw/3fp0Oa2AAAAAAADDcxKaNMALj9AgwAAAAAACwU45bcjikcefYXUlGI8AAAAAAAMBO7lzpE+NluHPtriSjsYgnAAAAAAA2ciz6kjUCA6dFgAEAAAAAgI2Mi2baXcKwQMQDAAAAAAAyHgEGAAAAAADIeAQYAAAAAAAg4xFgAAAAAACAjEeAAQAAAAAAMh4BBgAAAAAAyHgEGAAAAAAAIOMRYAAAAAAAgIxHgAEAAAAAADJexgQYe/fu1R133KHly5frueees7ucU1q92tlv+7p1BSlrT+W9aaeddtppP317JtVCO+3Dtf1Uvz8BAJAIwzRN0+4iotGo7rjjDt1zzz0qKSnRqlWrdMcdd2jcuHGnfd3Ro0fTVGGPsWPL9Pe/933fVLbb8Z4jrT0YDCo3150x9dCevPZgMKja2tqMqYf25LR3f67peF+7+zqS2js7u3p9rnbXQ3vq21OtrKws7e85nCX6u/XH/1ucTbK1b9naLyl7+5at/ZJ6+pbIf6szYgRGdXW1SktLNWbMGLlcLs2ePVu7du2yuywAAAAAAJApzAzw8ssvmz/60Y/ij1988UXzJz/5SZ/r/vSnP5krV640V65caZqmaXZ2dqbl6557wqZk9vm64opIStvteE/aaaeddtpP/ZVpNdKejvao+ZkrukyXETLdjk4zz9Fuep0nzHxXi3n1FQ1mUU6DWeKuM0e5j5tjcmvM0rwPzcWf+bs5zvN382zv++Y53sPmRN975rm+Q+aX5xw0P1nwjnl+wdvmlMI3zamFB8wL/W+YN855zZxe9JpZUbzHvKR4tzkr8Ip5WWCXuWzuy+YVwZfNMbkfZcD3IXnt99wTTtvvcACA7JIRU0h27typvXv3atmyZZKkP//5z3rnnXd0yy23nPZ1TCGhPVntTCHJ3namkGRnO1NIztxuRqNSNKJzJ4xR9cEPpGhEikSkWPtllwT18kvH4o8ViUhmVP/4DwE9/9sPrfZYmyJR3XiDX0/+e60UjVj3jkSkaETf+mah1j1Ub90jGo1/rVnt03dXNZ7UZj3/2Cavbv1Gc69rFY3q/zyTp5tuDKmzva1X++9+59ZnF/ZukxnVS3/J0eWz209qM6VoVG/sc+iCKV0fa4/oyGGnzhrXFb9OpvV8Q72hYn/YenzSc+EuUy5nrM1mxj8t1/gv/VNG/Fwluz3VmEKSWnfddZfWrl1rdxkpka19y9Z+Sdnbt2ztlzS4vrlSVEtCAoGA6urq4o/r6uoUCARsrAgAMFKYpilFwlI4LH9Ok8zmBikcsdoiYZ2X3ybzyDEpHLb+aI9dO3/0uzJ318jsbosdv3pWvqJVdT3Xxo4rP+lV9JeNscc9z224yK3ov7XKjF9rBQM/v8SlyA/aesKFWPu2zxiK3N0Zf9wdSrxxVVSR28I97bE/vA8ulKK39+33S1dK0Xv6tv/2cin6g77t/14hRR/r2/7whZL5077td39KMp89qcEwJIdDN53jlLndOo9/GQ7NG52jrtccMqV4mxwOlfvcUm20V5scDrkcDkmG5MrpdZ9jHR5dEAxJDkOG0dO+a49PZ8/vkByG5HDG7mXouScLdPM/tMcfd9f5o42Fuv2OE73f1zD0/Qf9uvueE7HrDMlwSg5Dq+4u1oM/aLauNYz4cfkdJdq4qVFyOGTE26WbbgnqiScbe10rh6EvfXmUtv5Hfa82jf7EAH6SAQDIfhkRYJSXl+vYsWOqqalRIBDQjh07dPvt/fy2lQHuuSfSb/udd7akrD2V96addtppT3W7aZr69oo6me1tUjgkhULWMRzSg7d+KPPdRiscCIfix8dvcSr6Umv8uu72//iqoej/16aWHJeirS3x9j99yVDkR+2xx7F7RcLatSSqyP2dPUFC7LmD/xhR5LZQT3vM61dJ0W/1rr/qM1J0dd++PlEhRX/Ut33NBZK59WONhqGvn+uSud0hOV2S0xn/mneOW+YRw3rsiLW7XDp7vNs6d+f2XO9wKtKSJ6M82nNtrL16T55mXBLpaXc4JYdDf37Jq8/MDVnvG2uTw6H//08+LfxsSHI6ZTgcseec+vVzPi35X50nXWsdn/l5ga7/akfs3j3tP95SoP/99Y7egYTDqUc3Fer2FW3xP/4Nh7Xs1r+uK9C3vtX35+Tn6wr04IO5fRYq+8O6Ak3u5/qX1xVodj/t+0MFWnhb3/b3mwvkuLlve9OHBXJ8uW975L0CORb3bfe8WSDH1X3bR19TIMdn+rZP/HKBHLP6tk+9vkDGRX3bZ/1TgYxPZd/vAqf6/QkAgERkxBQSSdq9e7eeeuopRaNRXXnllVqyZMkZX2PHFJJsXgV2JONzzV58tieNMAiFpFCn1NUlhbp6jvGvkMyPPVaoM3bs6nU0w7Hz7kDi5PNwV09I0X1MJpdLRo5bptMpOXMkl8v64zwnxzq6XD1tLqvN6NXW3e7seXzysTtgiD02TjpX93t2v7bXsft+fZ8zHGwhORD8e81edn22TCFJraqqKlVWVtpdRkpka9+ytV9S9vYtW/slDa5vGRNgDAYBBpKFzzV7ZfJna0ajsSChs/dXZ0f83OzV3h0+dLdZrzW77xHqsq4JdfUNKszo4As1HJLbbQUErtgxx22FAB87N7rPu587+djPudF97uoOIHKknJ7goef6k8IIw8jozxWDx+eavQgwAADJkBFTSAAgU5mmaY0g6OiQOtutcCH+1S6zs9Nq7zipvavDChI6O6wAIt5+ckDRYQULiTIc1pQCt9s65rh7P84vlBFvd1vHnFwrCHDHzmPtRo77Y9fl9Fzb/VyO2xqBAAAAANiMAANA1jHDYamjTepoV6i1UeaHR6X2dpkd7fH2Xl+dsec6O05q64g/p2gCoxdcLsmdJ+XmSbm5sfNcqaBQcufJyM21nnPnWYFDbm4sgLDaDffJj0867/6KjUAAAAAARhoCDAAZw4xGrdCg/YT11XbCCh7aT1jBQ3vsuY42qb3NWhQyfn33eVuvNRfqT/eGOW4pz2N95eZZR1++VDJKRp5Hyj3puVyPlGcFD0ZeXk9bbm7saLUxWgEAMBLs3btXTzzxhKLRqObPn6/FixfbXdKgPfbYY9q9e7f8fr/WrVsnSWptbdX69et1/PhxjRo1St/85jeVn59vc6WJqa2t1ebNm9XY2CjDMFRZWanPfvazw75vXV1duu+++xQOhxWJRHTppZdq6dKlqqmp0YYNG9TS0qKJEydq+fLlcrmG55+70WhUd911lwKBgO66666s6Nttt92mvLw8ORwOOZ1OrV27dlA/i8Or1wAymmmaVojQdkJqa419nZDZ1iqdaLWChhOtsfDhpJCirTuAaItv/XhKDofk8Uker5TnlbxeqahERul4yeOx2vI8sec9Khw9Ri1d4VhQ4e0JLPI8hA0AAAxCNBrVli1bdM8996ikpESrVq1SRUWFxo0bZ3dpgzJ37lwtXLhQmzdvjrc999xzmjp1qhYvXqznnntOzz33nK6//nobq0yc0+nUV7/6VU2cOFHt7e266667dOGFF2r79u3Dum85OTm67777lJeXp3A4rHvvvVfTpk3T888/r0WLFunyyy/Xv/3bv2nbtm1asGCB3eUOyu9//3uNHTtW7e3tkqRnnnkmK/p23333qbCwMP54MP/OCDAA9GGGQ1bQ0NYqnWiRTrTKjB37tLWdsB7HworTTrcwjFjo4LNCCK9PKhktY/xJjz1eyeOTcdJ5/JjnldzuhKZQ5AWDamVRQAAAkqa6ulqlpaUaM2aMJGn27NnatWvXsA0wJk+erJqaml5tu3bt0v333y9JmjNnju6///5h9Ue+JBUXF6u4uFiS5PF4NHbsWNXX1w/7vhmGoby8PElSJBJRJBKRYRjav3+/7rjjDklWKPXLX/5yWP6RX1dXp927d2vJkiV6/vnnZZpm1vTt4wbzs0iAAWQ5s7NTam2Of5mtzVJri3Si+9giM3ZUa7N17Gg/9Q0NQ/LmW1MtfAWS1ycjOCZ+Lm++1RY7dj+WL1/K88pwONLXeQAAkHT19fUqKSmJPy4pKdE777xjY0XJ19TUFP/jv6ioSE1NTTZXNDQ1NTV67733dO6552ZF36LRqFauXKkPP/xQV199tcaMGSOv1ytnbHRtIBBQff1pJxJnrCeffFLXX399fPRFS0tL1vRtzZo1kqSrrrpKlZWVg/pZJMAAhhHTNK1RDi2NUkuz1NIos6VZamnqCShamnsFFurqPPUNvT4reMgvlAr8Mj4xLva4QPIVSr58Gb6CnrCCEAIAAIwwhmEM6wW0Ozo6tG7dOt14443yer29nhuufXM4HHrooYd04sQJPfzwwzp69KjdJSXFq6++Kr/fr4kTJ2r//v12l5NUq1evViAQUFNTkx544IE+21wP9GdxwAHGk08+qblz5+qcc85JuFgAp2aGQlYg0dQoNTfKbG6wAonYl9nSJDU39YQUkXD/N/J4rSAiv1DyF8sYe7ZU4I+1Fcgo8FvBRH5hLJzwyXCwBgQAAEhMIBBQXV1d/HFdXZ0CgYCNFSWf3+9XQ0ODiouL1dDQ0Gve/nASDoe1bt06XXHFFZo1a5ak7OmbJPl8Pk2ZMkUHDx5UW1ubIpGInE6n6uvrh+XP5Ntvv61XXnlFe/bsUVdXl9rb2/Xkk09mRd+6a/b7/Zo5c6aqq6sH9bM44AAjGo1qzZo1Kiws1BVXXKErrrii19AxAD3MSKQnlGiql9nUIDU3SrGj2WKFFWputEZU9CfPYwUQBX5rV4wJk6zwodAvFRTJKCiUCoqs7TnzC2W4ctLbSQAAMCKVl5fr2LFjqqmpUSAQ0I4dO3T77bfbXVZSVVRU6MUXX9TixYv14osvaubMmXaXlDDTNPWv//qvGjt2rP7xH/8x3j7c+9bc3Cyn0ymfz6euri7t27dPn//85zVlyhTt3LlTl19+ubZv366Kigq7S03Yddddp+uuu06StH//fv3nf/6nbr/9dj3yyCPDum8dHR0yTVMej0cdHR3at2+frrnmmkH9LBqmeaYl/3tEo1Ht2bNHf/nLX7R7925NmjRJn/nMZzRr1qz4QirpZMdQoWAwqFoWBMw6A/1czVCX1FgvNdVLjbFgoqleamqUGTuqqd4aKdHfPy2vTyoskgqLZBQUxc9VWCTDX9zzuMAvw52bgp6OPPybzU58rtmJzzV72fXZfnyIMpJn9+7deuqppxSNRnXllVdqyZIldpc0aBs2bNCBAwfU0tIiv9+vpUuXaubMmVq/fr1qa2uH5VajkvTWW2/p3nvv1VlnnRUfmn/ttddq0qRJw7pvhw8f1ubNmxWNRmWapi677DJdc801+uijj7Rhwwa1trZqwoQJWr58uXJyhu//4OsOMO66665h37ePPvpIDz/8sCRr4dVPf/rTWrJkiVpaWhL+WUwowDjZ+++JoGVwAAAgAElEQVS/r0cffVRHjhyR2+3W5ZdfrqVLl6Z1OAsBBpKlpLhYte9VSw11VjDRWB8LKup6zhvrrQUuP87ptEZC+IulooCMwiLJH5D8RTL8Aas9Fk4YOe70d26E499sduJzzU58rtmLAAMAkAwJLeLZ1tamnTt36i9/+YsOHz6sWbNm6ZZbblEwGNTzzz+v73//+/FkBcgUZigkNdZJDbUyG+pi53XWeUOt1FivmqYGKRrp/UKHQyq0QgmNKpUxabIVTBQFZBRZR/kDkq+ARS0BAAAAIMUGHGCsW7dOr732ms4//3xdddVVmjlzZq9hKzfccINuvPHGVNQInJIZDluBRH2tzIZaqb42FlT0nKuln+148jxScdAKI86/SN6ycWrL9ciItamoRCooZJFLAAAAAMgQAw4wJk2apFtuuUVFRUX9Pu9wOPTjH/84aYUBpmlKrS1S/XGprkZm/XGp/njsWGu1Nzf2XWvC45MCQak4KOOcc62gojgoo7hEKi6RikpkeHpvIZUfDKqDYcsAAAAAkLEGHGB87nOfO+M1ubksOoiBM8Nha4RE/XGZdTVS3ckBReyrq6v3i9xuKTBKCoySccGMnqAiMMoKJwJBGXne/t8QAAAAADBsJbQGBpAIs6vTCiXqamTW1/Sc1x2X6mukhnrJjPZ+kb/YCifGniNdONM6jwUWCoyS8gviqygDAAAAAEYOAgwMmtnRHgslPrJCibqPpLru0RQ11vSOkzkc1nSOktEyPjlVKhltBRQlo2PnQXbpAAAAAAD0iwADp2S2nbBGStTWxAOK+FSPuo+s9SlO5nJJgdFScLSMiy6xQomSUTJibSoKsCgmAAAAAGBQCDBGKGuBzGZrpERdbN2JuhoroKitsYKLthO9X+R2WwFFySgZZ59rhRIlo3tGUBQWsZ0oAAAAACAlCDCylBkOSQ2x7UVj4YS1WGZs/Yn+FsjM9VihRGCUjEnnSyVjZJSMkkrGSCWjpAI/608AAAAAAGxBgDEMmaGQ1FgnNdTKjIUU8fMG67zP+hOSVFhkLYQ59mwZ3QtklsQWxywZLXnzCSgAAAAAABmJACNDmKYptZ+QmhqkpgaZTQ1WCNHUIDXHHne3tTT1vYHHF9tStETGWROtxTKLS07awSMow802twAAAACA4YkAIwnMaEQKhaVISAqHpHBYCoWsQKKtVeaJVulEq9TWczS7z0+0Sm0tUnOT9dqPc7kkf8AaPTGqVEb5p6TiEqk4KKO4RCoeJRUHZOR5099xAAAAAADShABjgMyjRxTdvEbHo1FFOzukSLgnrIhGB36jHLfkzZd8+daxZJSM8edYAUVhseQvluG3jioslrw+pnUAAACMMI899ph2794tv9+vdevWSZLWr1+vo0ePSpLa2trk9Xr10EMP9Xntbbfdpry8PDkcDjmdTq1duzattQNAqhBgDFSuR8Y558md71NnOGqNjHDlnHTM6dNmeLyxsKJA8vmsNSaYxgEAAIAzmDt3rhYuXKjNmzfH2775zW/Gz59++ml5vacegXvfffepsLAwpTUCQLoRYAyQUTJKxv/+lvzBoGpra+0uBwAAAFls8uTJqqmp6fc50zT18ssv6957703qe3aP7hioYBb/XpytfcvWfknZ27ds7ZfU07eysrIBv4YAAwAAABhG3nzzTfn9fn3iE5845TVr1qyRJF111VWqrKzs95qqqipVVVVJktauXatgMJhQHS6XK+HXDBfZ2rds7ZeUvX3L1n5Jg+sbAQYAAAAwjLz00ku6/PLLT/n86tWrFQgE1NTUpAceeEBlZWWaPHlyn+sqKyt7hRuJ/l/ekfB/hrNNtvZLyt6+ZWu/pMGNwHCksB4AAAAASRSJRPTXv/5Vs2fPPuU1gUBAkuT3+zVz5kxVV1enqzwASCkCDAAAAGCYeP3111VWVqaSkpJ+n+/o6FB7e3v8fN++fTrrrLPSWSIApAxTSAAAAIAMs2HDBh04cEAtLS1atmyZli5dqnnz5vU7faS+vl6PP/64Vq1apaamJj388MOSrNEan/70pzVt2jQ7ugAASUeAAQAAAGSYFStW9Nt+22239WkLBAJatWqVJGnMmDF66KGHUlobANiFKSQAAAAAACDjEWAAAAAAAICMR4ABAAAAAAAyHgEGAAAAAADIeAQYAAAAAAAg4xFgAAAAAEiIebhaJ377C5mmaXcpAEYQAgwAAAAACTHfel2tTzwqdbbbXQqAEYQAAwAAAEBiPF7r2NZmbx0ARhQCDAAAAACJ8fisYzsBBoD0IcAAAAAAkBDD47FOOggwAKQPAQYAAACAxMRHYJywtw4AIwoBBgAAAIDExNbAMJlCAiCNCDAAAAAAJIYRGABsQIABAAAAIDHda2AwAgNAGhFgAAAAAEhMrkdyOAgwAKQVAQYAAACAhBiGIcPjI8AAkFYEGAAAAAASZnh9rIEBIK0IMAAAAAAkzOHLZxcSAGlFgAEAAAAgYUwhAZBuBBgAAAAAEmZNISHAAJA+BBgAAAAAEubw5bMGBoC0IsAAAAAAkDBGYABINwIMAAAAAAkzPD6pgwADQPoQYAAAAABImMOXL4XDMkNddpcCYIQgwAAAAACQMMPrs05YBwNAmhBgAAAAAEiY4c23TtqYRgIgPQgwAAAAACTM0T0Cg3UwAKQJAQYAAACAhPVMISHAAJAeBBgAAAAAEhafQsIaGADShAADAAAAQMIcPivAMBmBASBNCDAAAAAAJMzwsAsJgPRy2V0AAAAAgN4ee+wx7d69W36/X+vWrZMk/cd//IdeeOEFFRYWSpKuvfZaTZ8+vc9r9+7dqyeeeELRaFTz58/X4sWLU1Kj4fVaJ+3tKbk/AHwcAQYAAACQYebOnauFCxdq8+bNvdoXLVqkz33uc6d8XTQa1ZYtW3TPPfeopKREq1atUkVFhcaNG5f0Gg2nS8rNYwQGgLRhCgkAAACQYSZPnqz8/PyEX1ddXa3S0lKNGTNGLpdLs2fP1q5du1JQYYzHyy4kANKGERgAAADAMPFf//Vf+vOf/6yJEyfqhhtu6BNy1NfXq6SkJP64pKRE77zzTr/3qqqqUlVVlSRp7dq1CgaDCdXicrnk9BXIFQmrKMHXZjqXy5Xw92M4yNZ+Sdnbt2ztlzS4vhFgAAAAAMPAggULdM0110iStm7dqqefflq33nrroO9XWVmpysrK+OPa2tqEXh8MBhVx5yrS1JjwazNdMBjMuj5J2dsvKXv7lq39knr6VlZWNuDXMIUEAAAAGAaKiorkcDjkcDg0f/58vfvuu32uCQQCqquriz+uq6tTIBBIXVEeH2tgAEgbAgwAAABgGGhoaIif//Wvf9X48eP7XFNeXq5jx46ppqZG4XBYO3bsUEVFRcpqMlgDA0AaMYUEAAAAyDAbNmzQgQMH1NLSomXLlmnp0qXav3+//va3v8kwDI0aNUpf+9rXJFnrXjz++ONatWqVnE6nbr75Zq1Zs0bRaFRXXnllv0FH0hBgAEgjAgwAAAAgw6xYsaJP27x58/q9NhAIaNWqVfHH06dP1/Tp01NWWy8er9RBgAEgPZhCAgAAAGBwPD6ps0NmJGJ3JQBGAAIMAAAAAIPj8VpHRmEASAMCDAAAAACD4/FZxzZ2IgGQegQYAAAAAAbF8Hisk452ewsBMCIQYAAAAAAYnO4RGO2MwACQegQYAAAAAAanew0MtlIFkAYEGAAAAAAGJzYCw2QEBoA0IMAAAAAAMDjda2AwAgNAGhBgAAAAABic+BoYBBgAUo8AAwAAAMCgGDluyeUiwACQFgQYAAAAAAbP42MXEgBpQYABAAAAYPDyPIzAAJAWBBgAAAAABs/jk0mAASANCDAAAAAADJ7HywgMAGlBgAEAAABg8FgDA0CaEGAAAAAAGDTDwxoYANKDAAMAAADA4Hl8UgcBBoDUI8AAAAAAMHger9TeLtM07a4EQJYjwAAAAAAweB6fZEalzna7KwGQ5QgwAAAAAAyex2Md25hGAiC1CDAAAAAADJ7HZx1ZBwNAihFgAAAAABg0w+O1TtiJBECKEWAAAAAAGLzuERjtJ+ytA0DWI8AAAAAAMHixERgmIzAApBgBBgAAAIDBy+ueQsIIDACpRYABAAAAYPC83QEG26gCSC0CDAAAAACDl+uRDAcjMACkHAEGAAAAgEEzDEPyeNiFBEDKEWAAAAAAGJo8LyMwAKQcAQYAAACAofF4ZbIGBoAUc9ldAAAAAIDeHnvsMe3evVt+v1/r1q2TJP30pz/Vq6++KpfLpTFjxujWW2+Vz+fr89rbbrtNeXl5cjgccjqdWrt2beoL9vgYgQEg5QgwAAAAgAwzd+5cLVy4UJs3b463XXjhhbruuuvkdDr1zDPP6Nlnn9X111/f7+vvu+8+FRYWpqtcyeOVmhrS934ARiSmkAAAAAAZZvLkycrPz+/VdtFFF8npdEqSzjvvPNXX19tRWr8MD2tgAEg9RmAAAAAAw8y2bds0e/bsUz6/Zs0aSdJVV12lysrKfq+pqqpSVVWVJGnt2rUKBoMJ1eByueKvaS4uUcdbHQnfI1Od3Ldskq39krK3b9naL2lwfSPAAAAAAIaRX//613I6nbriiiv6fX716tUKBAJqamrSAw88oLKyMk2ePLnPdZWVlb3Cjdra2oTqCAaD8ddEDYfMttaE75GpTu5bNsnWfknZ27ds7ZfU07eysrIBv4YpJAAAAMAwsX37dr366qu6/fbbZRhGv9cEAgFJkt/v18yZM1VdXZ36wjxeKRyWGepK/XsBGLEIMAAAAIBhYO/evfrNb36jlStXKjc3t99rOjo61B7bzrSjo0P79u3TWWedlfriPF7ryDoYAFLI9ikkA90OCgAAABgpNmzYoAMHDqilpUXLli3T0qVL9eyzzyocDmv16tWSpEmTJulrX/ua6uvr9fjjj2vVqlVqamrSww8/LEmKRCL69Kc/rWnTpqW+4O4Ao61NKixO/fsBGJFsDzAS2Q4KAAAAGAlWrFjRp23evHn9XhsIBLRq1SpJ0pgxY/TQQw+ltLb+GB6fTEnqaEv7ewMYOWyfQpLJ20EBAAAAGID4FBICDACpY3uAcbJt27alZ4gbAAAAgOTJYw0MAKmXlikkq1evVmNjY5/2L3/5y5o5c6akM28HJQ19r+pkyOZ9eEcyPtfsxWebnfhcsxOfa/bisx0BYiMwzPY29b83CgAMXVoCjH/5l3857fPd20Hde++9p9wOShr6XtXJkM378I5kfK7Zi882O/G5Zic+1+xl12dbVlaW9vccsbyxRfiZQgIghWyfQjKQ7aAAAAAAZLA81sAAkHq270KyZcuWfreDAgAAADA8GE6n5M5lDQwAKWV7gLFx40a7SwAAAAAwVB4fIzAApJTtU0gAAAAAZAGPlwADQEoRYAAAAAAYOo9XJgEGgBQiwAAAAAAwdB4fa2AASCkCDAAAAABD5/EwhQRAShFgAAAAABgyw+OTOggwAKQOAQYAAACAoWMRTwApRoABAAAAYOg8PqmzQ2YkYnclALIUAQYAAACAofN4rCPTSACkCAEGAAAAgKHz+KxjGzuRAEgNAgwAAAAAQ2Z4vNZJR7u9hQDIWgQYAAAAAIauewRGOyMwAKQGAQYAAACAocuLjcBgJxIAKUKAAQAAAGDoYlNITEZgAEgRAgwAAAAAQ+ftHoHBGhgAUoMAAwAAAMDQsQYGgBQjwAAAAAAwZEaOW8rNk1qa7S4FQJYiwAAAAACQHIVFUnOD3VUAyFIEGAAAAACSw18ss4kAA0BqEGAAAAAASI7CYqm50e4qAGQpAgwAAAAASWH4iyRGYABIEQIMAAAAAMlRWCy1tcoMheyuBEAWIsAAAAAAkBz+YuvINBIAKUCAAQAAACApjMLuAINpJACSjwADAAAAQHL4i6wj62AASAGX3QUAAAAA6O2xxx7T7t275ff7tW7dOklSa2ur1q9fr+PHj2vUqFH65je/qfz8/D6v3b59u379619LkpYsWaK5c+emr/DYCAyzuUFG+t4VwAjBCAwAAAAgw8ydO1d33313r7bnnntOU6dO1aOPPqqpU6fqueee6/O61tZW/epXv9L3v/99ff/739evfvUrtba2pqtsqdBvHZtYAwNA8hFgAAAAABlm8uTJfUZX7Nq1S3PmzJEkzZkzR7t27erzur179+rCCy9Ufn6+8vPzdeGFF2rv3r1pqVmSDFeOlF/AGhgAUoIpJAAAAMAw0NTUpOJia4pGUVGRmpqa+lxTX1+vkpKS+ONAIKD6+vp+71dVVaWqqipJ0tq1axUMBhOqx+Vy9fua2uKgXO1tKkrwfpnkVH0b7rK1X1L29i1b+yUNrm8EGAAAAMAwYxiGDGNoq0xUVlaqsrIy/ri2tjah1weDwX5fE8kvVKT2o4Tvl0lO1bfhLlv7JWVv37K1X1JP38rKygb8GgIMAAAAIAneeOONAV3ncDg0efLkhO/v9/vV0NCg4uJiNTQ0qLCwsM81gUBABw4ciD+ur68f1HsNhVFYJPPdt9L6ngBGBgIMAAAAIAlWr16tUaNGyTTN017X3Nysn/70pwnfv6KiQi+++KIWL16sF198UTNnzuxzzbRp0/Tzn/88vnDna6+9puuuuy7h9xoSf7HU3CDTNIc8SgQATkaAAQAAACRBbm6uNm3adMbrbrrppjNes2HDBh04cEAtLS1atmyZli5dqsWLF2v9+vXatm1bfBtVSXr33Xf1pz/9ScuWLVN+fr6++MUvatWqVZKka665pt+tVlOqsFjq6pI62iWPN73vDSCrEWAAAAAASfDtb397QNd961vfOuM1K1as6Lf93nvv7dNWXl6u8vLy+ON58+Zp3rx5A6olJfxF1rGpgQADQFKxjSoAAACQBFOnTh3QdRdccEGKK7GXUWjtlMJWqgCSjREYAAAAQJL9z//8j8455xyNGzdOR48e1eOPPy6Hw6F//ud/1tixY+0uL7X8VoBhNjWKFTAAJBMjMAAAAIAk27p1a3ztiaefflrl5eU6//zz9ZOf/MTmytKAERgAUoQAAwAAAEiy5uZmFRUVqaurS2+//bauvfZaXXPNNfrb3/5md2mp58uXnE5rDQwASCKmkAAAAABJVlhYqA8//FBHjhxReXm5cnJy1NnZaXdZaWE4HFJBESMwACQdAQYAAACQZF/84he1cuVKORyO+Hanr7/+us4++2ybK0sTf7HMpka7qwCQZQgwAAAAgCTp7OxUbm6u5s6dq8suu0ySlJubK0maNGnSKbdHzTqFRVJTvd1VAMgyBBgAAABAktx6662aMGGCLr74Ys2YMUOlpaXx5/x+v42VpZfhL5Z55JDdZQDIMgQYAAAAQJI8/vjjevPNN7Vnzx794Ac/UDQa1bRp03TxxRfrggsukMs1Qn79LiyWWhplRiMyHE67qwGQJUbIf0EBAACA1HO5XJo6daqmTp2qG264QR999JH27NmjP/zhD9q4caPOO+88XXzxxbrkkktUVFRkd7mp4y+SolGptcWaTgIASUCAAQAAAKTImDFjtHDhQi1cuFBdXV16/fXXtWfPHjmdTs2fP9/u8lLG8BfLlKydSAgwACQJAQYAAACQZNFotE+by+XSjBkzNGPGDBsqSrPCYuvY1CiNs7cUANmDAAMAAABIsmuvvbbfdqfTqeLiYs2aNUtLly5VXl5emitLE7816sJsapBhcykAsgcBBgAAAJBkN910k3bt2qXFixerpKREtbW1+u1vf6vp06errKxMv/zlL/Xkk09q2bJldpeaGt0jMJob7K0DQFYhwAAAAACS7He/+51+8IMfyOv1SpLKyspUXl6uu+66Sxs3btRZZ52llStX2lxl6hh5Hik3z5pCAgBJ4rC7AAAAACDbtLW1qbOzs1dbZ2en2traJElFRUXq6uqyo7T0KSxiBAaApGIEBgAAAJBkc+bM0QMPPKB/+Id/UDAYVF1dnX7/+99rzpw5kqTXXntNZWVlNleZYv5imU0EGACShwADAAAASLLrr79epaWl2rFjhxoaGlRUVKSrr75alZWVkqQpU6boe9/7ns1VplhhsXTsfburAJBFCDAAAACAJHM4HFqwYIEWLFjQ7/NutzvNFaWf4S+S+dY+u8sAkEUIMAAAAIAkM01TL7zwgnbs2KHm5mY9/PDDOnDggBobGzV79my7y0uPwmKprVVmKCQjJ8fuagBkARbxBAAAAJJs69at+u///m/Nnz9ftbW1kqSSkhL95je/sbmyNPJ3b6XKTiQAkoMAAwAAAEiyF198UStXrtTll18uwzAkSaNHj1ZNTY3NlaWPUdgdYLCQJ4DkIMAAAAAAkiwajSovL69XW0dHR5+2rOYvso7sRAIgSQgwAAAAgCS7+OKL9fTTTysUCkmy1sTYunWrZsyYYXNlaRQbgWEyAgNAkhBgAAAAAEl2ww03qKGhQTfeeKPa2tp0ww036Pjx4/rKV75id2npU+i3jk2sgQEgOdiFBAAAAEgyr9erb3/722psbFRtba2CwaCKiorsLiutDFeOlF/AGhgAkoYAAwAAAEiCaDTap62wsFCFhYW9nnc4RtAg6MJimayBASBJCDAAAACAJLj22msHdN3WrVtTXEkG8RezjSqApCHAAAAAAJJg06ZN8fPdu3dr586d+sIXvqBgMKja2lr95je/0axZs2ysMP2MwiKZ775ldxkAsgQBBgAAAJAEo0aNip8///zzWrt2rXw+nySprKxMEydO1KpVq7RgwQK7Skw/f7HU3CDTNGUYht3VABjmCDAAAACAJGtra1NnZ2c8wJCkrq4utbW1Dem+R48e1fr16+OPa2pqtHTpUi1atCjetn//fv3whz/U6NGjJUmzZs3SNddcM6T3HbTCYqmrS+polzxee2oAkDUIMAAAAIAkmzNnjlavXq1FixappKREdXV1+sMf/qA5c+YM6b5lZWV66KGHJFmLgn7961/XJZdc0ue6888/X3fdddeQ3isp/LGdV5oaCDAADBkBBgAAAJBk119/vUpLS7Vjxw41NDSoqKhIV199tSorK5P2Hq+//rpKS0t7TV3JNEZhsUzJ2kq1dKzd5QAY5ggwAAAAgCRzOBxasGBBSte7eOmll3T55Zf3+9zBgwf17W9/W8XFxfrqV7+q8ePHp6yO0/IXS5LMpkaxAgaAoSLAAAAAAJLgtdde00UXXXTG6/bt26cLL7xwSO8VDof16quv6rrrruvz3IQJE/TYY48pLy9Pu3fv1kMPPaRHH320z3VVVVWqqqqSJK1du1bBYDChGlwu1xlfE3W7dFxSfqRL3gTvb6eB9G04ytZ+Sdnbt2ztlzS4vhFgAAAAAEnwyCOP6KmnnjrjdevXr9cTTzwxpPfas2ePJkyYoKKioj7Peb09a01Mnz5dW7ZsUXNzswoLC3tdV1lZ2WtKS21tbUI1dG8PezpmNCo5nWo9+oHaEry/nQbSt+EoW/slZW/fsrVfUk/fysrKBvwaAgwAAAAgCTo6OvSNb3zjjNeFw+Ehv9fppo80NjbK7/fLMAxVV1crGo2qoKBgyO85GIbDIRUUWWtgAMAQEWAAAAAASXDfffcN6DrDGNpqEB0dHdq3b5++9rWvxdv++Mc/SpIWLFignTt36o9//KOcTqfcbrdWrFgx5PccEn+xzKZG+94fQNYgwAAAAACSYPLkyWl5n7y8PP37v/97r7aTFwtduHChFi5cmJZaBqSwSGqqt7sKAFnAYXcBAAAAALKX4S+WmphCAmDoCDAAAAAApE5xidTcKDMcsrsSAMMcAQYAAACA1AmWSqYp1R23uxIAwxwBBgAAAICUMYJjrJPaj+wtBMCwxyKeAAAAQJJEIhG98sor2r17tw4fPqwTJ07I5/Pp7LPP1sUXX6yZM2fK6XTaXWZ6jSqVJJnHP5SNe6EAyAIEGAAAAEAS/PGPf9Szzz6rcePG6fzzz9eMGTOUl5enjo4OffDBB3rhhRf01FNP6Qtf+EKvXUOynr9YcuVItR/aXQmAYY4AAwAAAP+vvXuPjrI89D3+eyb3C7lMJhcSEkkQhKCACAUtIpeIbqq71mVtrbXVqkfFpcWu7ZZlW+06VEGFBRtF0aMH3T3d7mpPL4fqqZa6vFQ2PVCgUBABRQUh9wu5JzPznD8mRiKoSRjyzEy+n7Vmzcw7M1m/x3dcvPnleZ8XYVBZWally5YpKyvrhNe+8pWvSJIaGhq0YcOGoY7mlPF4JF+eLKeQADhFFBgAAABAGHzve9/70vdkZ2f3630xx1cg1VBgADg1LOIJAAAAhNkNN9xw0u033XTTECeJDMaXzykkAE4ZBQYAAAAQZoFA4IRtfr9fwWDQQZoIkJsvtbXKtra4TgIginEKCQAAABAm9913n4wx6u7u1v3339/ntbq6Oo0bN85RMreMr0BWCl1KNS3ddRwAUYoCAwAAAAiTefPmSZIOHDiguXPn9m43xigzM1Nnn322q2hu+fJD97WV0hlj3GYBELUoMAAAAIAwmTNnjiRp7NixKioqchsmkvQUGLa2SsZxFADRizUwAAAAgDDYunVr7+MvKi+Of99wYVLTpPQRUg0LeQIYPGZgAAAAAGHw9ttv6/nnn9esWbNUXl6uwsJCpaSkqL29XUePHtWePXv01ltv6YwzztC0adNcxx16OfmytVxKFcDgUWAAAAAAYfDDH/5QH330kf70pz/pscceU3V1de9rBQUFOvfcc7V48WIVFxc7TOmOyS2Q/eh91zEARDEKDAAAACBMSkpKdOONN0qSOjs71draqrS0NCUlJTlOFgFy86Xtm2WDARlPnOs0AKIQa2AAAAAAYfbhhx8qKSlJXq+X8uITvnwp4Jca610nARClmIEBAAAAhNny5cvV2dmp8ePHq7y8XOXl5SotLZUxw/caHMZXICtJNVWSN9d1HABRiAIDAAAACLMnnnhCVVVVeuedd7Rnzx698nh2lOQAAB5XSURBVMoram5u1vjx47VkyRLX8dzILZAk2dpKmbPOdhwGQDSiwAAAAABOg/z8fAUCAfn9fvn9fu3YsUNNTU2uY7mT7ZOMR+JKJAAGiQIDAAAACLNVq1Zp37598nq9Ki8v16xZs3TzzTcrJSXFdTRnTHy85PVJNZWuowCIUiziCQAAAITZwYMH5fF4dMYZZ+iMM87Q6NGjh3V50Su3QJYZGAAGiRkYAAAAQJitWbNGDQ0NvWtg/P73v1dXV5cmTJigW2+91XU8Z4wvX3bXVtcxAEQpZmAAAAAAp0F2drYKCwtVUFCg3NxcNTY2avv27a5jueXLl5oaZDs7XScBEIWYgQEAAACE2UMPPaS9e/cqJSVF5eXlOu+883Tddddp5MiRrqO51XMlEtVWSUUlbrMAiDoUGAAAAECYzZgxQzfccIPy8vJcR4koxpcvK1FgABgUCgwAAAAgzObMmeM6QmTqmYFhaytlHEcBEH1YAwMAAADA0EjPkJKSuZQqgEGhwAAAAAAwJIwxki+fS6kCGBQKDAAAAABDJ7cgtAYGAAwQa2AAAAAAUeT2229XcnKyPB6P4uLitHz58j6vW2u1fv16bd++XUlJSVq0aJHKysocpT2R8eXL7tkha21oRgYA9BMFBgAAABBl7r//fmVkZJz0te3bt6uyslJr1qzR/v379fTTT+vBBx8c4oRfwFcgdXVKzU1SRpbrNACiCKeQAAAAADFk69atmj17towxGjdunFpbW9XQ0OA6Vi+Tmx96wEKeAAaIGRgAAABAlHnggQckSRdffLEqKir6vFZfXy+fz9f7PCcnR/X19crOzu7zvo0bN2rjxo2SpOXLl/f5TH/Ex8cP+DOS5B87XnWS0jvblDKIzw+FwY4t0sXquKTYHVusjksa3NgoMAAAAIAosnTpUnm9XjU1NennP/+5CgsLVV5ePuCfU1FR0af8qK2tHdDnfT7fgD8jSdaTKElqPnhAreVTB/z5oTDYsUW6WB2XFLtji9VxSZ+OrbCwsN+f4RQSAAAAIIp4vV5JUmZmpqZPn64DBw6c8Prxv/DU1dX1fiYSmKQkKdPLKSQABowCAwAAAIgSHR0dam9v7328c+dOlZSU9HnPtGnT9Oabb8paq3379ik1NfWE00ec8+XJcilVAAMUMaeQbNiwQb/4xS/09NNPf+6KygAAAMBw1tTUpBUrVkiSAoGAZs2apSlTpujVV1+VJC1YsEDnnnuutm3bpjvvvFOJiYlatGiRy8gnZXILZPfvcR0DQJSJiAKjtrZWO3fujNnFSQAAAIBwyM/P1yOPPHLC9gULFvQ+NsbopptuGspYA+crkP76pqy/WyY+wXUaAFEiIk4hee6553TttdfKGOM6CgAAAIDTzZcv2aBUX+M6CYAo4nwGxpYtW+T1ejV69Ogvfe+pXuopHGL5MjbDGfs1drFvYxP7NTaxX2MX+xafZXLzZSWptkrK6/8VCAAMb0NSYCxdulSNjY0nbP/2t7+t3/72t/rJT37Sr59zqpd6CodYvozNcMZ+jV3s29jEfo1N7NfY5WrfDuTSfBhiuSMlSbbqqEz5uY7DAIgWQ1Jg/PSnPz3p9o8++kjV1dW6++67JYUu8XTPPfdo2bJlysrKGopoAAAAAIZalldKTZMOf+A6CYAo4vQUkpKSEj399NO9z2+//XYtW7aMq5AAAAAAMcwYIxWXyR4+6DoKgCgSEYt4AgAAABheTHGpdPgD2WDAdRQAUSKiCoy1a9cy+wIAAAAYDkaVSl2dUvVR10kARImIKjAAAAAADA+muFSSZA9xGgmA/qHAAAAAADD0CouluHiJAgNAP1FgAAAAABhyJj5BGjmKGRgA+o0CAwAAAIATpriUGRgA+o0CAwAAAIAbxWVSU73ssUbXSQBEAQoMAAAAAE6YUaNDDw4zCwPAl6PAAAAAAOAGVyIBMAAUGAAAAACcMOkZktfHOhgA+oUCAwAAAIA7o0qZgQGgXygwAAAAADhjikulysOy3V2uowCIcBQYAAAAAJwxxWVSMCgd+ch1FAARjgIDAAAAgDvFoyVJ9qP33eYAEPEoMAAAAAC44yuQklJYyBPAl6LAAAAAAOCM8Xik4tGyhykwAHwxCgwAAAAATplRpdKhg7LBoOsoACIYBQYAAAAAt4pLpY52qa7adRIAEYwCAwAAAIBTprgs9IB1MAB8AQoMAAAAAG4VlUjGI0uBAeALUGAAAAAAcMokJkkFRbKHuJQqgM9HgQEAAADAOVNcyikkAL4QBQYAAAAA90aVSvU1sq0trpMAiFAUGAAAAACcM8WloQeHmYUB4OQoMAAAAAC4VxIqMFgHA8DniXcdAAAAAED/1NbWau3atWpsbJQxRhUVFVq4cGGf9+zevVsPP/yw8vLyJEkzZszQVVdd5SLugJiMbCkjSzr0gesoACIUBQYAAAAQJeLi4nTdddeprKxM7e3tWrJkiSZNmqRRo0b1ed+ECRO0ZMkSRylPQXGp7EfvuU4BIEJxCgkAAAAQJbKzs1VWViZJSklJUVFRkerr6x2nCh9z5gTp4w9lW465jgIgAlFgAAAAAFGourpaBw8e1JlnnnnCa/v27dPdd9+tBx98UIcOHXKQbnDMhCmStdK7u1xHARCBOIUEAAAAiDIdHR1auXKlrr/+eqWmpvZ5rbS0VI8//riSk5O1bds2PfLII1qzZs0JP2Pjxo3auHGjJGn58uXy+XwDyhAfHz/gz3wZmz1TNSmpSnr/XWVc8vWw/uyBOB1jiwSxOi4pdscWq+OSBjc2CgwAAAAgivj9fq1cuVIXXnihZsyYccLrxxcaU6dO1TPPPKNjx44pIyOjz/sqKipUUVHR+7y2tnZAOXw+34A/0x923Nlq375ZXafhZ/fX6Rqba7E6Lil2xxar45I+HVthYWG/P8MpJAAAAECUsNZq3bp1Kioq0mWXXXbS9zQ2NspaK0k6cOCAgsGgRowYMZQxT4mZMEWqqZStqXQdBUCEYQYGAAAAECXeffddvfnmmyopKdHdd98tSbrmmmt6/0K7YMECbd68Wa+++qri4uKUmJioxYsXyxjjMvaAmPLJspLsO3+XyS1wHQdABKHAAAAAAKLE+PHj9cILL3zhey699FJdeumlQ5ToNCgYJWV5pXf+Ls2+xHUaABGEU0gAAAAARAxjjMyEybJ7/y4bDLqOAyCCUGAAAAAAiCwTpkgtzdLhg66TAIggFBgAAAAAIoqZMElSaB0MAPgEBQYAAACAiGKycqSRxbJ7KDAAfIoCAwAAAEDEMeVTpAO7Zbu7XEcBECEoMAAAAABEHDNhitTVJb2313UUABGCAgMAAABA5DlrouTxsA4GgF4UGAAAAAAijklOlcrOosAA0IsCAwAAAEBEMhMmSx8ckG1tcR0FQASgwAAAAAAQkcyEKZINSu/udB0FQASgwAAAAAAQmUrHSUkpnEYCQBIFBgAAAIAIZeLjpXETZfdQYACgwAAAAAAQwUz5ZKn6iGxdtesoAByjwAAAAAAQsczEqZIku/2/HCcB4BoFBgAAAICIZUYWS6PHyv5lo6y1ruMAcIgCAwAAAEBEM1+tkD7+UProPddRADhEgQEAAAAgopmvXCglJMr+ZaPrKAAcosAAAAAAENFMarrM1PNl//qGbFen6zgAHKHAAAAAABDxzFcrpPZW2e2bXUcB4AgFBgAAAIDId9Y5ki9f9m1OIwGGKwoMAAAAABHPeDwyX50vvfN32doq13EAOECBAQAAACAqmPPnS8bIbvqz6ygAHKDAAAAAABAVTE6uNGGK7Nt/lg0GXMcBMMQoMAAAAABEDTPrYqm+Rtq703UUAEOMAgMAAABA1DBTZkhpI2T/wmKewHBDgQEAAAAgapiEBJkZF8lu3yzb2uw6DoAhRIEBAAAAIKqYr1ZI/m7Zv77hOgqAIUSBAQAAACCqmJIyqWSM7Gsvyfr9ruMAGCIUGAAAAACijuefr5GqPpZ96xXXUQAMEQoMAAAAANFn0nRp/CTZ//Mfsm0trtMAGAIUGAAAAACijjFGnm/+QGptkX3pRddxAAyBeNcBAAAAAPTfjh07tH79egWDQc2fP19XXHFFn9e7u7v12GOP6f3339eIESO0ePFi5eXlOUp7epmSMpkL5su+tkH2oktl8ka6jgTgNGIGBgAAABAlgsGgnnnmGd17771atWqV3n77bR0+fLjPe1577TWlpaXp0Ucf1de+9jX98pe/dJR2aJgrrpXi4hX8zXOuowA4zSgwAAAAgChx4MABFRQUKD8/X/Hx8brgggu0ZcuWPu/ZunWr5syZI0maOXOm/vGPf8ha6yDt0DBZOTKXXCn9bZPs/j2u4wA4jTiFBAAAAIgS9fX1ysnJ6X2ek5Oj/fv3f+574uLilJqaqubmZmVkZPR538aNG7Vx40ZJ0vLly+Xz+QaUJT4+fsCfOV3sNTeq9u0/yfOb5+R96H/IeE7t77SRNLZwitVxSbE7tlgdlzS4sVFgAAAAAMNQRUWFKioqep/X1tYO6PM+n2/Anzmd7D9fK//61ap5+TfyzJxzSj8r0sYWLrE6Lil2x3a6x2UDAam7U+rulrq7Pr33d5/w2Pq7P93uP+7W7ZcC3ZLfL2VkybPwmwMaW2FhYb/zUmAAAAAAUcLr9aqurq73eV1dnbxe70nfk5OTo0AgoLa2No0YMWKoow45M3OO7J83yP7m32XPnSmTlOw6EjBoNhiQOjsVqK+RrToqdXZIXR1SZ6fU2SHb1Rl63tUZ2tbVFXrcc7Pdn9nW3RV63v2ZWzB46mGNR0qIl+ISpOJSqZ8FxmBQYAAAAABRYsyYMTp69Kiqq6vl9Xq1adMm3XnnnX3ec9555+n111/XuHHjtHnzZk2cOFHGGEeJh47xeOT51k0KrrhXwf+5Wp5b/vWUTyUBBsJ2d0vtrVJ7m9TRJrW1Sh3tsu1tUmd7z/b20Gsd7bKdHT3P20MFxSf3nR2hckFSv+deeDxSYtKnt4TEnseJUkqalJktk5DYsz1Riu+5/2RbQkLvvUnoeT0hvuc+IXSL73lPfMKnt4R4GU/caftv+lkUGAAAAECUiIuL0w9+8AM98MADCgaDmjt3roqLi/WrX/1KY8aM0bRp0zRv3jw99thjuuOOO5Senq7Fixe7jj1kzLiJMlddL/vietnf/4fMN77rOhKiiA0GQyVDa7PU2iK1Nsu2tYSKiLaWnltraFt7T0HR3hba3t4WOp3iy8TFSUkpUnLPLSk5dJ+ZHZo19Mm2pNB9eo5PLd3dMonJPduTpD6Pe25x8cOiqKTAAAAAAKLI1KlTNXXq1D7bvvWtb/U+TkxM1I9+9KOhjhUxzMVXSEcPy778goIFRfKcP9d1JDgQKiNapeYmqfmY1HJMtrkp9Ly1OfS8pbnncc99W4v0RVfsiU+Q0tJDMxpS06T0ETK5BaHnKamhW2qalJwq88nz5FQpJSV0n5wiJSQOqGhI9fnUFoNrewwWBQYAAACAmGGMka69VbamUvbfH5XNzZc5s9x1LISBDQaklmNSU6PU1CB7rEE61ig1Naqpq12B2mrpWE9J0dIkBQIn/0GJSVJ6hpQ+QkobIZOTJ6WNCD1PTZfS0mXSeh6npktpaVJqukxi0tAOGCegwAAAAAAQU0x8gjy3LVHwwX9R8PFl8ty7QsaX7zoWPoe1NlRMNNRJTfWyDXVSY53UWC/b1CA1NUiN9VJz48kXnUxMUnd2TqhsyMmVGX2mNCKz92bSM3oeZ0jpGRQRUYwCAwAAAEDMMWkj5Lnjpwouu1vBR5fKs+Th0LR+DDnb1iLV10h1tbL1NVJDjVRfK9tQGyotGmpDl+A8njGhWRKZXinLKzNqdM/jbJmMbCkzS8rIkjKyZZJTYvYyquiLAgMAAABATDIFo+S55R4F/+1nCq6+X55b7pHx+lzHiinW2tD6EXXVUm2VbG21VNdzX18TurW39f1QXLyU5ZW8PpnScdLU86Vsn0xWTmh7Vk5oUct4fl1FX3wjAAAAAMQsUz5Fnv/2rwqu/zcFf36XPDf/i8yEya5jRRXr90v11VJ1pWxtpVRTKVtTKdVUSbWVoct/Hi81TfLmSbkFMmedEzqtw5sr5eRJ2T4pI4tL3GJQKDAAAAAAxDRz3gXyFJYo+MQyBVfdL/P178j801X8En0c6/eHZlFUfSxbfUSqOipbc1SqPhrafvzaEwmJki9f8uXLjJsYuvflhwoKX55Marq7gSCmUWAAAAAAiHlm5Ch57l0h+4vHZX/3v2Tf2yvPjXeFrjYxTFhrZRvrpMqPZSs/lqqOyFaF7lVb2bekSEmT8kbKjB4rTZ8depxbIOUVhNadoPyBAxQYAAAAAIYFk5wi3fQj6cwJsr96WsH//kOZr31L5vx5MgkJruOFje3qlKqP9BQVh3sLi5qqI7Idx61HkZgo5RXKFJdK074q5RfK5BVK+UVS+ojQJWmBCEKBAQAAAGDYMMbIzF0oO/pMBX+5TvYXa2U3PC9z8ddlZl8aKjmigLU2dHnRqo9ljx4O3feUFaqrlqz99M05eVJBkZLP/po6MnNk8gulgiIpK4eZFIgqFBgAAAAAhh1TOk6eH6+U3tmh4Mu/ln1xvexLL8rMu0yBy78peSJjRoZtb5Oqj8hWHQmd6lH5cc9pHx/3XTwzMUkqKJIpO0u6YH7occGo0AyLpCRJUobPpy4uNYooRoEBAAAAYFgyxkjl5yqu/FzZ999V8P/+b9k//Kdq//CfoTUfxk+WmTBJOmuSzIiM05LBBgJSY51UWy1bVyXV9lyOtPpIaAHN5qa+H/DmhsqJ8+eF7vOLpIJRUjazKRD7KDAAAAAADHum7CzF3X6vbNURpb3/jpr/9l+y/+8N2Tf/KBkjjSwOXW3Dmyt5fZI3VybbJyUnS544KS4udO/xSDYYmh3Rc7Md7VJ7m3SsUWqql21qCJ3+0VQfug8EjgtipExvqECZMiN0nzdSyiuUckf2zqYAhiMKDAAAAADoYfILlTpxktrOnx+aHfHBftm9O2UP7pPqa2Tf2yu1NkuS7Jf8rJNKHxEqKDKzZXrWoQhdhjQvdGnS7NyYWlAUCCcKDAAAAAA4CRMXJ40ZLzNmfJ/ttrNDqq+VGmqkrk4pEJQNBkIzKYIBSSa0GGhKqpSc0nNLlUZkyMRTTgCDRYEBAAAAAANgkpKlkaNCt0+2OcwDDBes8gIAAAAAACIeBQYAAAAAAIh4FBgAAAAAACDiUWAAAAAAAICIR4EBAAAAAAAiHgUGAAAAAACIeBQYAAAAAAAg4lFgAAAAAACAiEeBAQAAAAAAIh4FBgAAAAAAiHgUGAAAAAAAIOJRYAAAAAAAgIhnrLXWdQgAAAAAAIAvwgyMAVqyZInrCDgN2K+xi30bm9ivsYn9GrvYt7EplvdrrI4tVsclxe7YYnVc0uDGRoEBAAAAAAAiHgUGAAAAAACIeHE/+9nPfuY6RLQpKytzHQGnAfs1drFvYxP7NTaxX2MX+zY2xfJ+jdWxxeq4pNgdW6yOSxr42FjEEwAAAAAARDxOIQEAAAAAABGPAgMAAAAAAES8eNcBosWOHTu0fv16BYNBzZ8/X1dccYXrSAiD2tparV27Vo2NjTLGqKKiQgsXLnQdC2ESDAa1ZMkSeb3emL4E1XDT2tqqdevW6dChQzLG6LbbbtO4ceNcx8Ip+sMf/qDXXntNxhgVFxdr0aJFSkxMdB0Lg/D4449r27ZtyszM1MqVKyVJLS0tWrVqlWpqapSbm6u77rpL6enpjpPiVMTSsXGsfmc/7zg32sfW1dWl+++/X36/X4FAQDNnztTVV1+t6upqrV69Ws3NzSorK9Mdd9yh+Pjo/HX3s8ewsTC222+/XcnJyfJ4PIqLi9Py5csH9V1kEc9+CAaDevDBB/XjH/9Y3/jGN7R+/XqVl5crIyPDdTScos7OTo0bN07XXHONZs+erSeffFLnnHMO+zZGvPTSS/L7/fL7/Zo1a5brOAiTp556Suecc44WLVqkiooKpaam8otulKuvr9dTTz2lFStWaOHChdq0aZP8fr9Gjx7tOhoGIS0tTXPnztWWLVt0ySWXSJJeeOEFFRcX66677lJDQ4N27typSZMmOU6KwYq1Y+NY/c5+3nHuH//4x6gem8fj0axZs7Rw4ULNnz9fzz//vIqLi/XrX/9ac+fO1S233KJdu3apoaFBY8aMcR13UD57DPvkk09G/dhefvllLV26VJdffrkqKiokDe7/M04h6YcDBw6ooKBA+fn5io+P1wUXXKAtW7a4joUwyM7O7l35NiUlRUVFRaqvr3ecCuFQV1enbdu2af78+a6jIIza2tr0zjvvaN68eZKk+Ph4paWlOU6FcAgGg+rq6lIgEFBXV5eys7NdR8IglZeXn/AXtC1btuiiiy6SJF100UUcR0W5WDs2jtXv7Ocd50b72IwxSk5OliQFAgEFAgEZY7R7927NnDlTkjRnzpyoG9cnPnsMa62NmbF91mC+i9E178SR+vp65eTk9D7PycnR/v37HSbC6VBdXa2DBw/qzDPPdB0FYfDss8/qu9/9rtrb211HQRhVV1crIyNDjz/+uD788EOVlZXp+uuv7z2QQXTyer26/PLLddtttykxMVGTJ0/W5MmTXcdCGDU1NfWWUllZWWpqanKcCKdiOBwbx9p39vjj3FgYWzAY1D333KPKykpdcsklys/PV2pqquLi4iSF/l2J1j9KfvYYtrm5OWbG9sADD0iSLr74YlVUVAzqu8gMDEBSR0eHVq5cqeuvv16pqamu4+AU/e1vf1NmZmZMXzN7uAoEAjp48KAWLFighx9+WElJSfrd737nOhZOUUtLi7Zs2aK1a9fqySefVEdHh958803XsXCaGGNkjHEdA+i3aP/OftFxbrSOzePx6JFHHtG6dev03nvv6ciRI64jhUUsH8MuXbpUDz30kO6991698sor2rNnT5/X+/tdZAZGP3i9XtXV1fU+r6urk9frdZgI4eT3+7Vy5UpdeOGFmjFjhus4CIN3331XW7du1fbt29XV1aX29natWbNGd955p+toOEU5OTnKycnR2LFjJUkzZ86kwIgBu3btUl5eXu/58zNmzNC+ffs0e/Zsx8kQLpmZmWpoaFB2drYaGhqidq0EhAyHY+NY+c6e7Dg3VsYmhdYvmThxovbt26e2tjYFAgHFxcWpvr4+Kr+TJzuGffbZZ2NibJ9kzszM1PTp03XgwIFBfReZgdEPY8aM0dGjR1VdXS2/369NmzZp2rRprmMhDKy1WrdunYqKinTZZZe5joMw+c53vqN169Zp7dq1Wrx4sc4++2zKixiRlZWlnJyc3r+07Nq1S6NGjXKcCqfK5/Np//796uzslLVWu3btUlFRketYCKNp06bpjTfekCS98cYbmj59uuNEOBXD4dg4Fr6zn3ecG+1jO3bsmFpbWyWFrkiyc+dOFRUVaeLEidq8ebMk6fXXX4/K7+TnHcNG+9g6Ojp6T4np6OjQzp07VVJSMqjvorHW2tOaNkZs27ZNzz33nILBoObOnasrr7zSdSSEwd69e3XfffeppKSkd8rSNddco6lTpzpOhnDZvXu3NmzYwGVUY8gHH3ygdevWye/3Ky8vT4sWLYqqy7/h5F544QVt2rRJcXFxGj16tG699VYlJCS4joVBWL16tfbs2aPm5mZlZmbq6quv1vTp07Vq1SrV1tZG5WUbcaJYOjaO1e/s5x3njh07NqrH9uGHH2rt2rUKBoOy1ur888/XVVddpaqqKq1evVotLS0qLS3VHXfcEdX/jhx/DBvtY6uqqtKKFSskhU4HnjVrlq688ko1NzcP+LtIgQEAAAAAACIep5AAAAAAAICIR4EBAAAAAAAiHgUGAAAAAACIeBQYAAAAAAAg4lFgAAAAAACAiEeBAQAAAAAAIh4FBgAAAAAAiHgUGAAAAAAAIOJRYABAFKusrNQNN9yg999/X5JUX1+vG2+8Ubt373acDAAAAAgvCgwAiGIFBQW69tpr9eijj6qzs1NPPPGELrroIk2cONF1NAAAACCsjLXWug4BADg1Dz30kKqrq2WM0bJly5SQkOA6EgAAABBWzMAAgBgwf/58HTp0SJdeeinlBQAAAGISBQYARLmOjg4999xzmjdvnl588UW1tLS4jgQAAACEHQUGAES59evXq6ysTLfeequmTp2qp556ynUkAAAAIOwoMAAgim3ZskU7duzQzTffLEn6/ve/r4MHD+qtt95ynAwAAAAILxbxBAAAAAAAEY8ZGAAAAAAAIOJRYAAAAAAAgIhHgQEAAAAAACIeBQYAAAAAAIh4FBgAAAAAACDiUWAAAAAAAICIR4EBAAAAAAAiHgUGAAAAAACIeP8fDEi1hoY7NQAAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 1080x720 with 3 Axes>"
       ]
diff --git a/notebook/MPC_cvxpy.ipynb b/notebook/MPC_cvxpy.ipynb
index 0ccbd42..b262387 100644
--- a/notebook/MPC_cvxpy.ipynb
+++ b/notebook/MPC_cvxpy.ipynb
@@ -2,7 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -329,15 +336,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 7.06 ms, sys: 0 ns, total: 7.06 ms\n",
-      "Wall time: 6.23 ms\n"
+      "CPU times: user 2.58 ms, sys: 3.15 ms, total: 5.73 ms\n",
+      "Wall time: 5.54 ms\n"
      ]
     }
    ],
@@ -367,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -426,15 +433,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2.19 ms, sys: 343 µs, total: 2.54 ms\n",
-      "Wall time: 1.6 ms\n"
+      "CPU times: user 3.07 ms, sys: 1.38 ms, total: 4.44 ms\n",
+      "Wall time: 2.86 ms\n"
      ]
     }
    ],
@@ -468,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -529,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,6 +577,7 @@
     "    \n",
     "    #heading error w.r.t path frame\n",
     "    psi = path[2,target_idx] - state[2]\n",
+    "    #psi = -path[2,target_idx] + state[2]\n",
     "        \n",
     "    # the cross-track error is given by the scalar projection of the car->wp vector onto the faxle versor\n",
     "    #cte = np.dot([dx[target_idx], dy[target_idx]],front_axle_vect)\n",
@@ -618,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -656,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -699,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -737,7 +745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -865,15 +873,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 383 ms, sys: 12.2 ms, total: 395 ms\n",
-      "Wall time: 388 ms\n"
+      "CPU times: user 270 ms, sys: 109 µs, total: 270 ms\n",
+      "Wall time: 274 ms\n"
      ]
     }
    ],
@@ -900,7 +908,7 @@
     "x0[3]=psi\n",
     "x0[4]=cte\n",
     "\n",
-    "# Prediction\n",
+    "# Linearized Model Prediction\n",
     "x_bar=np.zeros((N,T+1))\n",
     "x_bar[:,0]=x0\n",
     "\n",
@@ -950,6 +958,93 @@
     "solution = prob.solve(solver=cp.ECOS, verbose=False)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 411 ms, sys: 2.64 ms, total: 413 ms\n",
+      "Wall time: 404 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "track = compute_path_from_wp([0,10],[0,0])\n",
+    "\n",
+    "MAX_SPEED = 2.5\n",
+    "MIN_SPEED = 0.5\n",
+    "MAX_STEER_SPEED = 1.57/2\n",
+    "\n",
+    "#starting guess\n",
+    "u_bar = np.zeros((M,T))\n",
+    "u_bar[0,:]=0.5*(MAX_SPEED+MIN_SPEED)\n",
+    "u_bar[1,:]=0.01\n",
+    "\n",
+    "# Starting Condition\n",
+    "x0 = np.zeros(N)\n",
+    "x0[0] = 0\n",
+    "x0[1] = 1\n",
+    "x0[2] = np.radians(-0)\n",
+    "_,psi,cte = calc_err(x0,track)\n",
+    "x0[3]=psi\n",
+    "x0[4]=cte\n",
+    "\n",
+    "# Linearized Model Aprrox. Prediction\n",
+    "# x_bar=np.zeros((N,T+1))\n",
+    "# x_bar[:,0]=x0\n",
+    "\n",
+    "# for t in range (1,T+1):\n",
+    "#     xt=x_bar[:,t].reshape(5,1)\n",
+    "#     ut=u_bar[:,t-1].reshape(2,1)\n",
+    "    \n",
+    "#     A,B,C=get_linear_model(xt,ut)\n",
+    "    \n",
+    "#     xt_plus_one = np.squeeze(np.dot(A,xt)+np.dot(B,ut)+C)\n",
+    "    \n",
+    "#     _,psi,cte = calc_err(xt_plus_one,track)\n",
+    "#     xt_plus_one[3]=psi\n",
+    "#     xt_plus_one[4]=cte\n",
+    "#     x_bar[:,t]= xt_plus_one\n",
+    "\n",
+    "\n",
+    "#CVXPY Linear MPC problem statement\n",
+    "cost = 0\n",
+    "constr = []\n",
+    "\n",
+    "for t in range(T):\n",
+    "    \n",
+    "    # Cost function\n",
+    "    cost += 10*cp.sum_squares( x[3, t]) # psi\n",
+    "    cost += 10*cp.sum_squares( x[4, t]) # cte\n",
+    "        \n",
+    "    # Actuation effort\n",
+    "    cost += cp.quad_form( u[:, t],10*np.eye(M))\n",
+    "    \n",
+    "    # Actuation rate of change\n",
+    "    if t < (T - 1):\n",
+    "        cost += cp.quad_form(u[:, t + 1] - u[:, t], 5*np.eye(M))\n",
+    "    \n",
+    "    #constrains -> approx linear model at xo\n",
+    "    A,B,C=get_linear_model(x0,u_bar[:,t])\n",
+    "    constr += [x[:,t+1] == A*x[:,t] + B*u[:,t] + C.flatten()]\n",
+    "    \n",
+    "# sums problem objectives and concatenates constraints.\n",
+    "constr += [x[:,0] == x0]\n",
+    "constr += [u[0, :] <= MAX_SPEED]\n",
+    "constr += [u[0, :] >= MIN_SPEED]\n",
+    "constr += [cp.abs(u[1, :]) <= MAX_STEER_SPEED]\n",
+    "\n",
+    "\n",
+    "prob = cp.Problem(cp.Minimize(cost), constr)\n",
+    "solution = prob.solve(solver=cp.ECOS, verbose=False)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -959,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -971,7 +1066,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x720 with 4 Axes>"
       ]
@@ -1030,7 +1125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1149,21 +1244,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/marcello/.local/lib/python3.6/site-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in true_divide\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CVXPY Optimization Time: Avrg: 0.1656s Max: 0.2457s Min: 0.1539s\n"
+      "CVXPY Optimization Time: Avrg: 0.1777s Max: 0.2998s Min: 0.1523s\n"
      ]
     }
    ],
    "source": [
     "\n",
-    "track = compute_path_from_wp([0,10],\n",
-    "                             [0,0])\n",
+    "track = compute_path_from_wp([0,3,4,6],\n",
+    "                             [0,0,2,1])\n",
     "\n",
     "sim_duration =50\n",
     "opt_time=[]\n",
@@ -1268,12 +1370,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x720 with 3 Axes>"
       ]
diff --git a/notebook/MPC_cvxpy_v2.ipynb b/notebook/MPC_cvxpy_v2.ipynb
new file mode 100644
index 0000000..818b4e0
--- /dev/null
+++ b/notebook/MPC_cvxpy_v2.ipynb
@@ -0,0 +1,1083 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.integrate import odeint\n",
+    "from scipy.interpolate import interp1d\n",
+    "import cvxpy as cp\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use(\"ggplot\")\n",
+    "\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### kinematics model equations\n",
+    "\n",
+    "The variables of the model are:\n",
+    "\n",
+    "* $x$ coordinate of the robot\n",
+    "* $y$ coordinate of the robot\n",
+    "* $\\theta$ heading of the robot\n",
+    "* $\\psi$ heading error = $\\psi = \\theta_{ref} - \\theta$\n",
+    "* $cte$ crosstrack error = lateral distance of the robot from the track w.r.t robot frame, cte is a function of the track and x\n",
+    "\n",
+    "The inputs of the model are:\n",
+    "\n",
+    "* $v$ linear velocity of the robot\n",
+    "* $w$ angular velocity of the robot\n",
+    "\n",
+    "These are the differential equations f(x,u) of the model:\n",
+    "\n",
+    "* $\\dot{x} = v\\cos{\\theta}$ \n",
+    "* $\\dot{y} = v\\sin{\\theta}$\n",
+    "* $\\dot{\\theta} = w$\n",
+    "* $\\dot{\\psi} = -w$\n",
+    "* $\\dot{cte} = v\\sin{\\psi}$\n",
+    "\n",
+    "The **Continuous** State Space Model is\n",
+    "\n",
+    "$ {\\dot{x}} = Ax + Bu $\n",
+    "\n",
+    "with:\n",
+    "\n",
+    "$ A =\n",
+    "\\quad\n",
+    "\\begin{bmatrix}\n",
+    "\\frac{\\partial f(x,u)}{\\partial x} & \\frac{\\partial f(x,u)}{\\partial y} & \\frac{\\partial f(x,u)}{\\partial \\theta} & \\frac{\\partial f(x,u)}{\\partial \\psi} & \\frac{\\partial f(x,u)}{\\partial cte} \\\\\n",
+    "\\end{bmatrix}\n",
+    "\\quad\n",
+    "=\n",
+    "\\quad\n",
+    "\\begin{bmatrix}\n",
+    "0 & 0 & -vsin(\\theta) & 0 & 0 \\\\\n",
+    "0 & 0 & vcos(\\theta) & 0 & 0 \\\\\n",
+    "0 & 0 & 0 & 0 & 0 \\\\\n",
+    "0 & 0 & 0 & 0 & 0 \\\\\n",
+    "0 & 0 & 0 & vcos(\\psi) & 0 \n",
+    "\\end{bmatrix}\n",
+    "\\quad $\n",
+    "\n",
+    "\n",
+    "$ B = \\quad\n",
+    "\\begin{bmatrix}\n",
+    "\\frac{\\partial f(x,u)}{\\partial v} & \\frac{\\partial f(x,u)}{\\partial w} \\\\\n",
+    "\\end{bmatrix}\n",
+    "\\quad\n",
+    "=\n",
+    "\\quad\n",
+    "\\begin{bmatrix}\n",
+    "\\cos{\\theta_t} & 0 \\\\\n",
+    "\\sin{\\theta_t} & 0 \\\\\n",
+    "0 & 1 \\\\\n",
+    "0 & -1 \\\\\n",
+    "\\sin{(\\psi_t)} & 0  \n",
+    "\\end{bmatrix}\n",
+    "\\quad $\n",
+    "\n",
+    "discretize with forward Euler Integration for time step dt:\n",
+    "\n",
+    "* ${x_{t+1}} = x_{t} + v_t\\cos{\\theta}*dt$\n",
+    "* ${y_{t+1}} = y_{t} + v_t\\sin{\\theta}*dt$\n",
+    "* ${\\theta_{t+1}} = \\theta_{t} + w_t*dt$\n",
+    "* ${\\psi_{t+1}} = \\psi_{t} - w_t*dt$\n",
+    "* ${cte_{t+1}} = cte_{t} + v_t\\sin{\\psi}*dt$\n",
+    "\n",
+    "The **Discrete** State Space Model is then:\n",
+    "\n",
+    "${x_{t+1}} = Ax_t + Bu_t $\n",
+    "\n",
+    "with:\n",
+    "\n",
+    "$\n",
+    "A = \\quad\n",
+    "\\begin{bmatrix}\n",
+    "1 & 0 & -v\\sin{\\theta}dt & 0 & 0 \\\\\n",
+    "0 & 1 & v\\cos{\\theta}dt & 0 & 0 \\\\\n",
+    "0 & 0 & 1 & 0 & 0 \\\\\n",
+    "0 & 0 & 0 & 1 & 0 \\\\\n",
+    "0 & 0 & 0 & vcos{(\\psi)}dt & 1 \n",
+    "\\end{bmatrix}\n",
+    "\\quad\n",
+    "$\n",
+    "\n",
+    "$\n",
+    "B = \\quad\n",
+    "\\begin{bmatrix}\n",
+    "\\cos{\\theta_t}dt & 0 \\\\\n",
+    "\\sin{\\theta_t}dt & 0 \\\\\n",
+    "0 & dt \\\\\n",
+    "0 & -dt \\\\\n",
+    "\\sin{\\psi_t}dt & 0  \n",
+    "\\end{bmatrix}\n",
+    "\\quad\n",
+    "$\n",
+    "\n",
+    "This State Space Model is **non-linear** (A,B are time changing), to linearize it the **Taylor's series expansion** is used around $\\bar{x}$ and $\\bar{u}$:\n",
+    "\n",
+    "$ \\dot{x} = f(x,u) \\approx f(\\bar{x},\\bar{u}) +  A(x-\\bar{x}) + B(u-\\bar{u})$\n",
+    "\n",
+    "So:\n",
+    "\n",
+    "$ x_{t+1} = x_t + (f(\\bar{x},\\bar{u}) +  A(x_t-\\bar{x}) + B(u_t-\\bar{u}) )dt $\n",
+    "\n",
+    "$ x_{t+1} = (I+dtA)x_t + dtBu_t +dt(f(\\bar{x},\\bar{u}) - A\\bar{x} - B\\bar{u}))$\n",
+    "\n",
+    "The Discrete linearized kinematics model is\n",
+    "\n",
+    "$ x_{t+1} = A'x_t + B' u_t + C' $\n",
+    "\n",
+    "with:\n",
+    "\n",
+    "$ A' = I+dtA $\n",
+    "\n",
+    "$ B' = dtB $\n",
+    "\n",
+    "$ C' = dt(f(\\bar{x},\\bar{u}) - A\\bar{x} - B\\bar{u}) $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----------------\n",
+    "[About Taylor Series Expansion](https://courses.engr.illinois.edu/ece486/fa2017/documents/lecture_notes/state_space_p2.pdf):\n",
+    "\n",
+    "In order to linearize general nonlinear systems, we will use the Taylor Series expansion of functions.\n",
+    "\n",
+    "Typically it is possible to assume that the system is operating about some nominal\n",
+    "state solution $\\bar{x}$ (possibly requires a nominal input $\\bar{u}$) called **equilibrium point**.\n",
+    "\n",
+    "Recall that the Taylor Series expansion of f(x) around the\n",
+    "point $\\bar{x}$ is given by:\n",
+    "\n",
+    "$f(x)=f(\\bar{x}) + \\frac{df(x)}{dx}|_{x=\\bar{x}}(x-\\bar{x})$ + higher order terms...\n",
+    "\n",
+    "For x sufficiently close to $\\bar{x}$, these higher order terms will be very close to zero, and so we can drop them.\n",
+    "\n",
+    "The extension to functions of multiple states and inputs is very similar to the above procedure.Suppose the evolution of state x\n",
+    "is given by:\n",
+    "\n",
+    "$\\dot{x} = f(x1, x2, . . . , xn, u1, u2, . . . , um) = Ax+Bu$\n",
+    "\n",
+    "Where:\n",
+    "\n",
+    "$ A =\n",
+    "\\quad\n",
+    "\\begin{bmatrix}\n",
+    "\\frac{\\partial f(x,u)}{\\partial x1} & ... & \\frac{\\partial f(x,u)}{\\partial xn} \\\\\n",
+    "\\end{bmatrix}\n",
+    "\\quad\n",
+    "$ and $ B = \\quad\n",
+    "\\begin{bmatrix}\n",
+    "\\frac{\\partial f(x,u)}{\\partial u1} & ... & \\frac{\\partial f(x,u)}{\\partial um} \\\\\n",
+    "\\end{bmatrix}\n",
+    "\\quad $\n",
+    "\n",
+    "Then:\n",
+    "\n",
+    "$f(x,u)=f(\\bar{x},\\bar{u}) + \\frac{df(x,u)}{dx}|_{x=\\bar{x}}(x-\\bar{x}) + \\frac{df(x,u)}{du}|_{u=\\bar{u}}(u-\\bar{u}) = f(\\bar{x},\\bar{u}) + A_{x=\\bar{x}}(x-\\bar{x}) + B_{u=\\bar{u}}(u-\\bar{u})$\n",
+    "\n",
+    "-----------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinematics Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Control problem statement.\n",
+    "\n",
+    "N = 5 #number of state variables\n",
+    "M = 2 #number of control variables\n",
+    "T = 20 #Prediction Horizon\n",
+    "dt = 0.25 #discretization step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_linear_model(x_bar,u_bar):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    x = x_bar[0]\n",
+    "    y = x_bar[1]\n",
+    "    theta = x_bar[2]\n",
+    "    psi = x_bar[3]\n",
+    "    cte = x_bar[4]\n",
+    "    \n",
+    "    v = u_bar[0]\n",
+    "    w = u_bar[1]\n",
+    "    \n",
+    "    A = np.zeros((N,N))\n",
+    "    A[0,2]=-v*np.sin(theta)\n",
+    "    A[1,2]=v*np.cos(theta)\n",
+    "    A[4,3]=v*np.cos(psi)\n",
+    "    A_lin=np.eye(N)+dt*A\n",
+    "    \n",
+    "    B = np.zeros((N,M))\n",
+    "    B[0,0]=np.cos(theta)\n",
+    "    B[1,0]=np.sin(theta)\n",
+    "    B[2,1]=1\n",
+    "    B[3,1]=-1\n",
+    "    B[4,0]=np.sin(psi)\n",
+    "    B_lin=dt*B\n",
+    "    \n",
+    "    f_xu=np.array([v*np.cos(theta),v*np.sin(theta),w,-w,v*np.sin(-psi)]).reshape(N,1)\n",
+    "    C_lin = dt*(f_xu - np.dot(A,x_bar.reshape(N,1)) - np.dot(B,u_bar.reshape(M,1)))\n",
+    "    \n",
+    "    return A_lin,B_lin,C_lin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motion Prediction: using scipy intergration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define process model\n",
+    "def kinematics_model(x,t,u):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "\n",
+    "    dxdt = u[0]*np.cos(x[2])\n",
+    "    dydt = u[0]*np.sin(x[2])\n",
+    "    dthetadt = u[1]\n",
+    "    dpsidt = -u[1]\n",
+    "    dctedt = u[0]*np.sin(-x[3])\n",
+    "\n",
+    "    dqdt = [dxdt,\n",
+    "            dydt,\n",
+    "            dthetadt,\n",
+    "            dpsidt,\n",
+    "            dctedt]\n",
+    "\n",
+    "    return dqdt\n",
+    "\n",
+    "def predict(x0,u):\n",
+    "    \"\"\"\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    x_bar = np.zeros((N,T+1))\n",
+    "    \n",
+    "    x_bar[:,0] = x0\n",
+    "    \n",
+    "    # solve ODE\n",
+    "    for t in range(1,T+1):\n",
+    "\n",
+    "        tspan = [0,dt]\n",
+    "        x_next = odeint(kinematics_model,\n",
+    "                         x0,\n",
+    "                         tspan,\n",
+    "                         args=(u[:,t-1],))\n",
+    "\n",
+    "        x0 = x_next[1]\n",
+    "        x_bar[:,t]=x_next[1]\n",
+    "        \n",
+    "    return x_bar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Validate the model, here the status w.r.t a straight line with constant heading 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.22 ms, sys: 0 ns, total: 7.22 ms\n",
+      "Wall time: 5.83 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "u_bar = np.zeros((M,T))\n",
+    "u_bar[0,:] = 1 #m/s\n",
+    "u_bar[1,:] = np.radians(-10) #rad/s\n",
+    "\n",
+    "x0 = np.zeros(N)\n",
+    "x0[0] = 0\n",
+    "x0[1] = 1\n",
+    "x0[2] = np.radians(0)\n",
+    "x0[3] = 0\n",
+    "x0[4] = 1\n",
+    "\n",
+    "x_bar=predict(x0,u_bar)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the model prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot trajectory\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.plot(x_bar[0,:],x_bar[1,:])\n",
+    "plt.plot(np.linspace(0,10,T+1),np.zeros(T+1),\"b-\")\n",
+    "plt.axis('equal')\n",
+    "plt.ylabel('y')\n",
+    "plt.xlabel('x')\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.plot(np.degrees(x_bar[2,:]))\n",
+    "plt.ylabel('theta(t) [deg]')\n",
+    "#plt.xlabel('time')\n",
+    "\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.plot(np.degrees(x_bar[3,:]))\n",
+    "plt.ylabel('psi(t) [deg]')\n",
+    "#plt.xlabel('time')\n",
+    "\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.plot(x_bar[4,:])\n",
+    "plt.ylabel('cte(t)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the results seems valid:\n",
+    "* the cte is correct\n",
+    "* theta error is correct"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motion Prediction: using the state space model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.58 ms, sys: 0 ns, total: 2.58 ms\n",
+      "Wall time: 1.89 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "u_bar = np.zeros((M,T))\n",
+    "u_bar[0,:] = 1 #m/s\n",
+    "u_bar[1,:] = np.radians(-10) #rad/s\n",
+    "\n",
+    "x0 = np.zeros(N)\n",
+    "x0[0] = 0\n",
+    "x0[1] = 1\n",
+    "x0[2] = np.radians(0)\n",
+    "x0[3] = 0\n",
+    "x0[4] = 1\n",
+    "\n",
+    "x_bar=np.zeros((N,T+1))\n",
+    "x_bar[:,0]=x0\n",
+    "\n",
+    "for t in range (1,T+1):\n",
+    "    xt=x_bar[:,t-1].reshape(5,1)\n",
+    "    ut=u_bar[:,t-1].reshape(2,1)\n",
+    "    \n",
+    "    A,B,C=get_linear_model(xt,ut)\n",
+    "    \n",
+    "    xt_plus_one = np.dot(A,xt)+np.dot(B,ut)+C\n",
+    "    \n",
+    "    x_bar[:,t]= np.squeeze(xt_plus_one)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot trajectory\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.plot(x_bar[0,:],x_bar[1,:])\n",
+    "plt.plot(np.linspace(0,10,T+1),np.zeros(T+1),\"b-\")\n",
+    "plt.axis('equal')\n",
+    "plt.ylabel('y')\n",
+    "plt.xlabel('x')\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.plot(x_bar[2,:])\n",
+    "plt.ylabel('theta(t)')\n",
+    "#plt.xlabel('time')\n",
+    "\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.plot(x_bar[3,:])\n",
+    "plt.ylabel('psi(t)')\n",
+    "#plt.xlabel('time')\n",
+    "\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.plot(x_bar[4,:])\n",
+    "plt.ylabel('cte(t)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are the same as expected"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "------------------\n",
+    "\n",
+    "the kinematics model predictits psi and cte for constant heading references. So, for non-constant paths appropriate functions have to be developed.\n",
+    "\n",
+    "-----------------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PRELIMINARIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_path_from_wp(start_xp, start_yp, step = 0.1):\n",
+    "    final_xp=[]\n",
+    "    final_yp=[]\n",
+    "    delta = step #[m]\n",
+    "\n",
+    "    for idx in range(len(start_xp)-1):\n",
+    "        section_len = np.sum(np.sqrt(np.power(np.diff(start_xp[idx:idx+2]),2)+np.power(np.diff(start_yp[idx:idx+2]),2)))\n",
+    "\n",
+    "        interp_range = np.linspace(0,1,np.floor(section_len/delta).astype(int))\n",
+    "        \n",
+    "        fx=interp1d(np.linspace(0,1,2),start_xp[idx:idx+2],kind=1)\n",
+    "        fy=interp1d(np.linspace(0,1,2),start_yp[idx:idx+2],kind=1)\n",
+    "        \n",
+    "        final_xp=np.append(final_xp,fx(interp_range))\n",
+    "        final_yp=np.append(final_yp,fy(interp_range))\n",
+    "\n",
+    "    return np.vstack((final_xp,final_yp))\n",
+    "\n",
+    "def get_nn_idx(state,path):\n",
+    "\n",
+    "    dx = state[0]-path[0,:]\n",
+    "    dy = state[1]-path[1,:]\n",
+    "    dist = np.sqrt(dx**2 + dy**2)\n",
+    "    nn_idx = np.argmin(dist)\n",
+    "\n",
+    "    try:\n",
+    "        v = [path[0,nn_idx+1] - path[0,nn_idx],\n",
+    "             path[1,nn_idx+1] - path[1,nn_idx]]   \n",
+    "        v /= np.linalg.norm(v)\n",
+    "\n",
+    "        d = [path[0,nn_idx] - state[0],\n",
+    "             path[1,nn_idx] - state[1]]\n",
+    "\n",
+    "        if np.dot(d,v) > 0:\n",
+    "            target_idx = nn_idx\n",
+    "        else:\n",
+    "            target_idx = nn_idx+1\n",
+    "\n",
+    "    except IndexError as e:\n",
+    "        target_idx = nn_idx\n",
+    "\n",
+    "    return target_idx\n",
+    "\n",
+    "def road_curve(state,track):\n",
+    "    \n",
+    "    #given vehicle pos find lookahead waypoints\n",
+    "    nn_idx=get_nn_idx(state,track)\n",
+    "    LOOKAHED=6\n",
+    "    lk_wp=track[:,nn_idx:nn_idx+LOOKAHED]\n",
+    "\n",
+    "    #trasform lookahead waypoints to vehicle ref frame\n",
+    "    dx = lk_wp[0,:] - state[0]\n",
+    "    dy = lk_wp[1,:] - state[1]\n",
+    "\n",
+    "    wp_vehicle_frame = np.vstack(( dx * np.cos(-state[2]) - dy * np.sin(-state[2]),\n",
+    "                                   dy * np.cos(-state[2]) + dx * np.sin(-state[2]) ))\n",
+    "\n",
+    "    #fit poly\n",
+    "    return np.polyfit(wp_vehicle_frame[0,:], wp_vehicle_frame[1,:], 3, rcond=None, full=False, w=None, cov=False)\n",
+    "\n",
+    "def f(x,coeff):\n",
+    "    return coeff[0]*x**3+coeff[1]*x**2+coeff[2]*x**1+coeff[3]*x**0\n",
+    "\n",
+    "def df(x,coeff):\n",
+    "    return 3*coeff[0]*x**2+2*coeff[1]*x+coeff[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "test it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MPC Problem formulation\n",
+    "\n",
+    "**Model Predictive Control** refers to the control approach of **numerically** solving a optimization problem at each time step. \n",
+    "\n",
+    "The controller generates a control signal over a fixed lenght T (Horizon) at each time step."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![mpc](img/mpc_block_diagram.png)\n",
+    "\n",
+    "![mpc](img/mpc_t.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Linear MPC Formulation\n",
+    "\n",
+    "Linear MPC makes use of the **LTI** (Linear time invariant) discrete state space model, wich represents a motion model used for Prediction.\n",
+    "\n",
+    "$x_{t+1} = Ax_t + Bu_t$\n",
+    "\n",
+    "The LTI formulation means that **future states** are linearly related to the current state and actuator signal. Hence, the MPC seeks to find a **control policy** U over a finite lenght horizon.\n",
+    "\n",
+    "$U={u_{t|t}, u_{t+1|t}, ...,u_{t+T|t}}$\n",
+    "\n",
+    "The objective function used minimize (drive the state to 0) is:\n",
+    "\n",
+    "$\n",
+    "\\begin{equation}\n",
+    "\\begin{aligned}\n",
+    "\\min_{} \\quad &  \\sum^{t+T-1}_{j=t} x^T_{j|t}Qx_{j|t} + u^T_{j|t}Ru_{j|t}\\\\\n",
+    "\\textrm{s.t.} \\quad &  x(0) = x0\\\\\n",
+    "  & x_{j+1|t} = Ax_{j|t}+Bu_{j|t}) \\quad  \\textrm{for} \\quad t<j<t+T-1    \\\\\n",
+    "\\end{aligned}\n",
+    "\\end{equation}\n",
+    "$\n",
+    "\n",
+    "Other linear constrains may be applied,for instance on the control variable:\n",
+    "\n",
+    "$ U_{MIN} < u_{j|t} < U_{MAX}    \\quad  \\textrm{for} \\quad t<j<t+T-1 $\n",
+    "\n",
+    "The objective fuction accounts for quadratic error on deviation from 0 of the state and the control inputs sequences. Q and R are the **weight matrices** and are used to tune the response.\n",
+    "\n",
+    "Because the goal is tracking a **reference signal** such as a trajectory, the objective function is rewritten as:\n",
+    "\n",
+    "$\n",
+    "\\begin{equation}\n",
+    "\\begin{aligned}\n",
+    "\\min_{} \\quad &  \\sum^{t+T-1}_{j=t} \\delta x^T_{j|t}Q\\delta x_{j|t} + u^T_{j|t}Ru_{j|t}\n",
+    "\\end{aligned}\n",
+    "\\end{equation}\n",
+    "$\n",
+    "\n",
+    "where the error w.r.t desired state is accounted for:\n",
+    "\n",
+    "$ \\delta x = x_{j,t,ref} - x_{j,t} $\n",
+    "\n",
+    "#### Non-Linear MPC Formulation\n",
+    "\n",
+    "In general cases, the objective function is any non-differentiable non-linear function of states and inputs over a finite horizon T. In this case the constrains include nonlinear dynamics of motion.\n",
+    "\n",
+    "$\n",
+    "\\begin{equation}\n",
+    "\\begin{aligned}\n",
+    "\\min_{} \\quad &  \\sum^{t+T}_{j=t} C(x_{j|t},{j|t})\\\\\n",
+    "\\textrm{s.t.} \\quad &  x(0) = x0\\\\\n",
+    "  & x_{j+1|t} = f(x_{j|t},u_{j|t}) \\quad  \\textrm{for} \\quad t<j<t+T-1    \\\\\n",
+    "\\end{aligned}\n",
+    "\\end{equation}\n",
+    "$\n",
+    "\n",
+    "Other nonlinear constrains may be applied:\n",
+    "\n",
+    "$ g(x_{j|t},{j|t})<0 \\quad  \\textrm{for} \\quad t<j<t+T-1 $"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "track = compute_path_from_wp([0,10],[0,0])\n",
+    "\n",
+    "# Constrains\n",
+    "MAX_SPEED = 2.5\n",
+    "MIN_SPEED = 0.5\n",
+    "MAX_STEER_SPEED = 1.57/2\n",
+    "\n",
+    "# Starting Condition\n",
+    "x0 = np.zeros(N)\n",
+    "x0[0] = 0\n",
+    "x0[1] = 1\n",
+    "x0[2] = np.radians(10)\n",
+    "\n",
+    "K=road_curve(x0,track)\n",
+    "\n",
+    "x0[3]=-np.arctan(K[2])\n",
+    "x0[4]=K[3]\n",
+    "\n",
+    "#starting guess\n",
+    "u_bar = np.zeros((M,T))\n",
+    "u_bar[0,:]=0.5*(MAX_SPEED+MIN_SPEED)\n",
+    "u_bar[1,:]=0.0\n",
+    "\n",
+    "# Linearized Model for Prediction##############\n",
+    "x_bar=np.zeros((N,T+1))\n",
+    "x_bar[:,0]=x0\n",
+    "\n",
+    "for t in range (1,T+1):\n",
+    "    xt=x_bar[:,t].reshape(5,1)\n",
+    "    ut=u_bar[:,t-1].reshape(2,1)\n",
+    "    \n",
+    "    A,B,C=get_linear_model(xt,ut)\n",
+    "    \n",
+    "    xt_plus_one = np.squeeze(np.dot(A,xt)+np.dot(B,ut)+C)\n",
+    "    \n",
+    "    xt_plus_one[3]=-np.arctan(df(x_bar[0,t],K))\n",
+    "    xt_plus_one[4]=f(x_bar[0,t],K)\n",
+    "    x_bar[:,t]= xt_plus_one\n",
+    "#################################################\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------------------\n",
+      "           OSQP v0.6.0  -  Operator Splitting QP Solver\n",
+      "              (c) Bartolomeo Stellato,  Goran Banjac\n",
+      "        University of Oxford  -  Stanford University 2019\n",
+      "-----------------------------------------------------------------\n",
+      "problem:  variables n = 283, constraints m = 323\n",
+      "          nnz(P) + nnz(A) = 859\n",
+      "settings: linear system solver = qdldl,\n",
+      "          eps_abs = 1.0e-05, eps_rel = 1.0e-05,\n",
+      "          eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,\n",
+      "          rho = 1.00e-01 (adaptive),\n",
+      "          sigma = 1.00e-06, alpha = 1.60, max_iter = 10000\n",
+      "          check_termination: on (interval 25),\n",
+      "          scaling: on, scaled_termination: off\n",
+      "          warm start: on, polish: on, time_limit: off\n",
+      "\n",
+      "iter   objective    pri res    dua res    rho        time\n",
+      "   1   0.0000e+00   1.02e+00   1.06e+02   1.00e-01   3.10e-04s\n",
+      " 200   2.6661e+03   1.40e-02   3.46e-02   1.86e+01   1.82e-03s\n",
+      " 400   2.6955e+03   5.00e-04   1.18e-03   1.86e+01   3.67e-03s\n",
+      " 575   2.6965e+03   3.06e-05   7.23e-05   1.86e+01   4.96e-03s\n",
+      "\n",
+      "status:               solved\n",
+      "solution polish:      unsuccessful\n",
+      "number of iterations: 575\n",
+      "optimal objective:    2696.5396\n",
+      "run time:             5.74e-03s\n",
+      "optimal rho estimate: 4.38e+01\n",
+      "\n",
+      "CPU times: user 228 ms, sys: 204 µs, total: 228 ms\n",
+      "Wall time: 221 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "x = cp.Variable((N, T+1))\n",
+    "u = cp.Variable((M, T))\n",
+    "\n",
+    "#CVXPY Linear MPC problem statement\n",
+    "cost = 0\n",
+    "constr = []\n",
+    "\n",
+    "for t in range(T):\n",
+    "    \n",
+    "    # Cost function\n",
+    "    cost += 10*cp.sum_squares( x[3, t]) # psi\n",
+    "    cost += 500*cp.sum_squares( x[4, t]) # cte\n",
+    "        \n",
+    "    # Actuation effort\n",
+    "    cost += cp.quad_form( u[:, t],10*np.eye(M))\n",
+    "    \n",
+    "    # Actuation rate of change\n",
+    "    if t < (T - 1):\n",
+    "        cost += cp.quad_form(u[:, t + 1] - u[:, t], 5*np.eye(M))\n",
+    "    \n",
+    "    # KINEMATICS constrains\n",
+    "    A,B,C=get_linear_model(x_bar[:,t],u_bar[:,t])\n",
+    "    constr += [x[:,t+1] == A*x[:,t] + B*u[:,t] + C.flatten()]\n",
+    "    \n",
+    "# sums problem objectives and concatenates constraints.\n",
+    "constr += [x[:,0] == x0]\n",
+    "constr += [u[0, :] <= MAX_SPEED]\n",
+    "constr += [u[0, :] >= MIN_SPEED]\n",
+    "constr += [cp.abs(u[1, :]) <= MAX_STEER_SPEED]\n",
+    "\n",
+    "prob = cp.Problem(cp.Minimize(cost), constr)\n",
+    "solution = prob.solve(solver=cp.OSQP, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print Results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_mpc=np.array(x.value[0, :]).flatten()\n",
+    "y_mpc=np.array(x.value[1, :]).flatten()\n",
+    "theta_mpc=np.array(x.value[2, :]).flatten()\n",
+    "psi_mpc=np.array(x.value[3, :]).flatten()\n",
+    "cte_mpc=np.array(x.value[4, :]).flatten()\n",
+    "v_mpc=np.array(u.value[0, :]).flatten()\n",
+    "w_mpc=np.array(u.value[1, :]).flatten()\n",
+    "\n",
+    "#simulate robot state trajectory for optimized U\n",
+    "x_traj=predict(x0, np.vstack((v_mpc,w_mpc)))\n",
+    "\n",
+    "#plt.figure(figsize=(15,10))\n",
+    "#plot trajectory\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.plot(track[0,:],track[1,:],\"b*\")\n",
+    "plt.plot(x_traj[0,:],x_traj[1,:])\n",
+    "plt.axis(\"equal\")\n",
+    "plt.ylabel('y')\n",
+    "plt.xlabel('x')\n",
+    "\n",
+    "#plot v(t)\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.plot(v_mpc)\n",
+    "plt.ylabel('v(t)')\n",
+    "#plt.xlabel('time')\n",
+    "\n",
+    "#plot w(t)\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.plot(w_mpc)\n",
+    "plt.ylabel('w(t)')\n",
+    "#plt.xlabel('time')\n",
+    "\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.plot(cte_mpc)\n",
+    "plt.ylabel('cte(t)')\n",
+    "plt.legend(loc='best')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "full track demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CVXPY Optimization Time: Avrg: 0.1988s Max: 0.2729s Min: 0.1579s\n"
+     ]
+    }
+   ],
+   "source": [
+    "track = compute_path_from_wp([0,3,4,6],\n",
+    "                             [0,0,2,1])\n",
+    "\n",
+    "sim_duration = 20\n",
+    "opt_time=[]\n",
+    "\n",
+    "x_sim = np.zeros((N,sim_duration))\n",
+    "u_sim = np.zeros((M,sim_duration-1))\n",
+    "\n",
+    "MAX_SPEED = 1.25\n",
+    "MIN_SPEED = 0.75\n",
+    "MAX_STEER_SPEED = 1.57/2\n",
+    "\n",
+    "# Starting Condition\n",
+    "x0 = np.zeros(N)\n",
+    "x0[0] = 0\n",
+    "x0[1] = -0.25\n",
+    "x0[2] = np.radians(-0)\n",
+    "\n",
+    "K=road_curve(x0,track)\n",
+    "\n",
+    "x0[3]=-np.arctan(K[2])\n",
+    "x0[4]=K[3]\n",
+    "\n",
+    "x_sim[:,0]=x0\n",
+    "                 \n",
+    "#starting guess\n",
+    "u_bar = np.zeros((M,T))\n",
+    "u_bar[0,:]=0.5*(MAX_SPEED+MIN_SPEED)\n",
+    "u_bar[1,:]=0.00\n",
+    "\n",
+    "for sim_time in range(sim_duration-1):\n",
+    "    \n",
+    "    iter_start=time.time()\n",
+    "    \n",
+    "    K=road_curve(x_sim[:,sim_time],track)\n",
+    "    \n",
+    "    # dynamics\n",
+    "    x_bar=np.zeros((N,T+1))\n",
+    "    x_bar[:,0]=x_sim[:,sim_time]\n",
+    "    for t in range (1,T+1):\n",
+    "        xt=x_bar[:,t].reshape(5,1)\n",
+    "        ut=u_bar[:,t-1].reshape(2,1)\n",
+    "\n",
+    "        A,B,C=get_linear_model(xt,ut)\n",
+    "\n",
+    "        xt_plus_one = np.squeeze(np.dot(A,xt)+np.dot(B,ut)+C)\n",
+    "\n",
+    "        xt_plus_one[3]=-np.arctan(df(x_bar[0,t],K))\n",
+    "        xt_plus_one[4]=f(x_bar[0,t],K)\n",
+    "        x_bar[:,t]= xt_plus_one\n",
+    "    \n",
+    "    #CVXPY Linear MPC problem statement\n",
+    "    cost = 0\n",
+    "    constr = []\n",
+    "    x = cp.Variable((N, T+1))\n",
+    "    u = cp.Variable((M, T))\n",
+    "    \n",
+    "    for t in range(T):\n",
+    "\n",
+    "        # Tracking\n",
+    "        cost += 50*cp.sum_squares( x[3, t]) # psi\n",
+    "        cost += 1000*cp.sum_squares( x[4, t]) # cte\n",
+    "\n",
+    "        # Actuation rate of change\n",
+    "        if t < (T - 1):\n",
+    "            cost += cp.quad_form(u[:, t + 1] - u[:, t], 10*np.eye(M))\n",
+    "        \n",
+    "        # Actuation effort\n",
+    "        cost += cp.quad_form( u[:, t],10*np.eye(M))\n",
+    "        \n",
+    "        # Constrains\n",
+    "        A,B,C=get_linear_model(x_bar[:,t],u_bar[:,t])\n",
+    "        constr += [x[:,t+1] == A*x[:,t] + B*u[:,t] + C.flatten()]\n",
+    "\n",
+    "    # sums problem objectives and concatenates constraints.\n",
+    "    constr += [x[:,0] == x_sim[:,sim_time]] # starting condition\n",
+    "    constr += [u[0, :] <= MAX_SPEED]\n",
+    "    constr += [u[0, :] >= MIN_SPEED]\n",
+    "    constr += [cp.abs(u[1, :]) <= MAX_STEER_SPEED]\n",
+    "    \n",
+    "    # Solve\n",
+    "    prob = cp.Problem(cp.Minimize(cost), constr)\n",
+    "    solution = prob.solve(solver=cp.OSQP, verbose=False)\n",
+    "    \n",
+    "    #retrieved optimized U and assign to u_bar to linearize in next step\n",
+    "    u_bar=np.vstack((np.array(u.value[0, :]).flatten(),\n",
+    "                    (np.array(u.value[1, :]).flatten())))\n",
+    "    \n",
+    "    u_sim[:,sim_time] = u_bar[:,0]\n",
+    "    \n",
+    "    # Measure elpased time to get results from cvxpy\n",
+    "    opt_time.append(time.time()-iter_start)\n",
+    "    \n",
+    "    # move simulation to t+1\n",
+    "    tspan = [0,dt]\n",
+    "    x_sim[:,sim_time+1] = odeint(kinematics_model,\n",
+    "                                 x_sim[:,sim_time],\n",
+    "                                 tspan,\n",
+    "                                 args=(u_bar[:,1],))[1]\n",
+    "    \n",
+    "print(\"CVXPY Optimization Time: Avrg: {:.4f}s Max: {:.4f}s Min: {:.4f}s\".format(np.mean(opt_time),\n",
+    "                                                                                np.max(opt_time),\n",
+    "                                                                                np.min(opt_time)))   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plot trajectory\n",
+    "grid = plt.GridSpec(2, 3)\n",
+    "\n",
+    "plt.figure(figsize=(15,10))\n",
+    "\n",
+    "plt.subplot(grid[0:2, 0:2])\n",
+    "plt.plot(track[0,:],track[1,:],\"b+\")\n",
+    "plt.plot(x_sim[0,:],x_sim[1,:])\n",
+    "plt.axis(\"equal\")\n",
+    "plt.ylabel('y')\n",
+    "plt.xlabel('x')\n",
+    "\n",
+    "plt.subplot(grid[0, 2])\n",
+    "plt.plot(u_sim[0,:])\n",
+    "plt.ylabel('v(t) [m/s]')\n",
+    "\n",
+    "plt.subplot(grid[1, 2])\n",
+    "plt.plot(np.degrees(u_sim[1,:]))\n",
+    "plt.ylabel('w(t) [deg/s]')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebook/diff_eq.ipynb b/notebook/diff_eq.ipynb
deleted file mode 100644
index a2ed180..0000000
--- a/notebook/diff_eq.ipynb
+++ /dev/null
@@ -1,102 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & - v \\sin{\\left(\\theta \\right)} & 0 & 0\\\\0 & 0 & v \\cos{\\left(\\theta \\right)} & 0 & 0\\\\0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & - v \\cos{\\left(\\psi \\right)} & 0\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[0, 0, -v*sin(theta),           0, 0],\n",
-       "[0, 0,  v*cos(theta),           0, 0],\n",
-       "[0, 0,             0,           0, 0],\n",
-       "[0, 0,             0,           0, 0],\n",
-       "[0, 0,             0, -v*cos(psi), 0]])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import sympy as sp\n",
-    "\n",
-    "x,y,theta,psi,cte,v,w = sp.symbols(\"x y theta psi cte v w\")\n",
-    "\n",
-    "gs = sp.Matrix([[ sp.cos(theta)*v],\n",
-    "             [ sp.sin(theta)*v],\n",
-    "             [w],\n",
-    "             [w],\n",
-    "             [ v*sp.sin(-psi)]])\n",
-    "\n",
-    "state = sp.Matrix([x,y,theta,psi,cte])\n",
-    "\n",
-    "gs.jacobian(state)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}\\cos{\\left(\\theta \\right)} & 0\\\\\\sin{\\left(\\theta \\right)} & 0\\\\0 & 1\\\\0 & 1\\\\- \\sin{\\left(\\psi \\right)} & 0\\end{matrix}\\right]$"
-      ],
-      "text/plain": [
-       "Matrix([\n",
-       "[cos(theta), 0],\n",
-       "[sin(theta), 0],\n",
-       "[         0, 1],\n",
-       "[         0, 1],\n",
-       "[ -sin(psi), 0]])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "state = sp.Matrix([v,w])\n",
-    "\n",
-    "gs.jacobian(state)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebook/equations.ipynb b/notebook/equations.ipynb
new file mode 100644
index 0000000..7eaebd5
--- /dev/null
+++ b/notebook/equations.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STATE SPACE MODEL MATRICES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}0 & 0 & - v \\sin{\\left(\\theta \\right)} & 0 & 0\\\\0 & 0 & v \\cos{\\left(\\theta \\right)} & 0 & 0\\\\0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & v \\cos{\\left(\\psi \\right)} & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[0, 0, -v*sin(theta),          0, 0],\n",
+       "[0, 0,  v*cos(theta),          0, 0],\n",
+       "[0, 0,             0,          0, 0],\n",
+       "[0, 0,             0,          0, 0],\n",
+       "[0, 0,             0, v*cos(psi), 0]])"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sympy as sp\n",
+    "\n",
+    "x,y,theta,psi,cte,v,w = sp.symbols(\"x y theta psi cte v w\")\n",
+    "\n",
+    "gs = sp.Matrix([[ sp.cos(theta)*v],\n",
+    "             [ sp.sin(theta)*v],\n",
+    "             [w],\n",
+    "             [-w],\n",
+    "             [ v*sp.sin(psi)]])\n",
+    "\n",
+    "state = sp.Matrix([x,y,theta,psi,cte])\n",
+    "\n",
+    "#A\n",
+    "gs.jacobian(state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}\\cos{\\left(\\theta \\right)} & 0\\\\\\sin{\\left(\\theta \\right)} & 0\\\\0 & 1\\\\0 & -1\\\\\\sin{\\left(\\psi \\right)} & 0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[cos(theta),  0],\n",
+       "[sin(theta),  0],\n",
+       "[         0,  1],\n",
+       "[         0, -1],\n",
+       "[  sin(psi),  0]])"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state = sp.Matrix([v,w])\n",
+    "\n",
+    "#B\n",
+    "gs.jacobian(state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PATH WAYPOINTS AS PARAMETRIZED CURVE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.interpolate import interp1d\n",
+    "\n",
+    "def compute_path_from_wp(start_xp, start_yp, step = 0.1):\n",
+    "    final_xp=[]\n",
+    "    final_yp=[]\n",
+    "    delta = step #[m]\n",
+    "\n",
+    "    for idx in range(len(start_xp)-1):\n",
+    "        section_len = np.sum(np.sqrt(np.power(np.diff(start_xp[idx:idx+2]),2)+np.power(np.diff(start_yp[idx:idx+2]),2)))\n",
+    "\n",
+    "        interp_range = np.linspace(0,1,np.floor(section_len/delta).astype(int))\n",
+    "        \n",
+    "        fx=interp1d(np.linspace(0,1,2),start_xp[idx:idx+2],kind=1)\n",
+    "        fy=interp1d(np.linspace(0,1,2),start_yp[idx:idx+2],kind=1)\n",
+    "        \n",
+    "        final_xp=np.append(final_xp,fx(interp_range))\n",
+    "        final_yp=np.append(final_yp,fy(interp_range))\n",
+    "\n",
+    "    return np.vstack((final_xp,final_yp))\n",
+    "\n",
+    "def get_nn_idx(state,path):\n",
+    "\n",
+    "    dx = state[0]-path[0,:]\n",
+    "    dy = state[1]-path[1,:]\n",
+    "    dist = np.sqrt(dx**2 + dy**2)\n",
+    "    nn_idx = np.argmin(dist)\n",
+    "\n",
+    "    try:\n",
+    "        v = [path[0,nn_idx+1] - path[0,nn_idx],\n",
+    "             path[1,nn_idx+1] - path[1,nn_idx]]   \n",
+    "        v /= np.linalg.norm(v)\n",
+    "\n",
+    "        d = [path[0,nn_idx] - state[0],\n",
+    "             path[1,nn_idx] - state[1]]\n",
+    "\n",
+    "        if np.dot(d,v) > 0:\n",
+    "            target_idx = nn_idx\n",
+    "        else:\n",
+    "            target_idx = nn_idx+1\n",
+    "\n",
+    "    except IndexError as e:\n",
+    "        target_idx = nn_idx\n",
+    "\n",
+    "    return target_idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define track\n",
+    "wp=np.array([0,5,6,10,11,15, 0,0,2,2,0,4]).reshape(2,-1)\n",
+    "track = compute_path_from_wp(wp[0,:],wp[1,:],step=0.5)\n",
+    "\n",
+    "#vehicle state\n",
+    "state=[3.5,0.5,np.radians(30)]\n",
+    "\n",
+    "#given vehicle pos find lookahead waypoints\n",
+    "nn_idx=get_nn_idx(state,track) #index ox closest wp\n",
+    "LOOKAHED=6\n",
+    "lk_wp=track[:,nn_idx:nn_idx+LOOKAHED]\n",
+    "\n",
+    "#trasform lookahead waypoints to vehicle ref frame\n",
+    "dx = lk_wp[0,:] - state[0]\n",
+    "dy = lk_wp[1,:] - state[1]\n",
+    "\n",
+    "wp_vehicle_frame = np.vstack(( dx * np.cos(-state[2]) - dy * np.sin(-state[2]),\n",
+    "                               dy * np.cos(-state[2]) + dx * np.sin(-state[2]) ))\n",
+    "\n",
+    "#fit poly\n",
+    "coeff=np.polyfit(wp_vehicle_frame[0,:], wp_vehicle_frame[1,:], 3, rcond=None, full=False, w=None, cov=False)\n",
+    "\n",
+    "def f(x,coeff):\n",
+    "    return coeff[0]*x**3+coeff[1]*x**2+coeff[2]*x**1+coeff[3]*x**0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.12829547,  0.96144073, -1.47313104, -0.47433648])"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coeff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use(\"ggplot\")\n",
+    "\n",
+    "x=np.arange(0,3,0.001) #interp range of curve \n",
+    "\n",
+    "# VEHICLE REF FRAME\n",
+    "plt.subplot(2,1,1)\n",
+    "plt.scatter(0,0)\n",
+    "plt.scatter(wp_vehicle_frame[0,:],wp_vehicle_frame[1,:])\n",
+    "plt.plot(x,[f(xs,coeff) for xs in x])\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "# MAP REF FRAME\n",
+    "plt.subplot(2,1,2)\n",
+    "plt.scatter(state[0],state[1])\n",
+    "plt.scatter(track[0,:],track[1,:])\n",
+    "plt.scatter(track[0,nn_idx:nn_idx+LOOKAHED],track[1,nn_idx:nn_idx+LOOKAHED])\n",
+    "plt.axis('equal');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# COMPUTE ERRORS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* **crosstrack error** cte -> desired y-position - y-position of vehicle: this is the bias term of the fitted polynomial (road curve)\n",
+    " \n",
+    "$\n",
+    "f = K_0 * x^3 + K_1 * x^2 + K_2 * x + K_3\n",
+    "$\n",
+    "\n",
+    "Then for the origin cte = K_3\n",
+    "  \n",
+    "* **heading error** epsi ->  desired heading - heading of vehicle : is the inclination of  tangent to the  fitted polynomial (road curve)\n",
+    "\n",
+    "The derivative of the fitted poly has the form\n",
+    "\n",
+    "$\n",
+    "f' = 3.0 * K_0 * x^2 + 2.0 * K_1 * x + K_2\n",
+    "$\n",
+    "\n",
+    "Then for the origin the equation of the tangent in the origin is $y=k2$   \n",
+    "\n",
+    "epsi = -atan(K_2)\n",
+    "\n",
+    "in general:\n",
+    "\n",
+    "$\n",
+    "y_{desired} = f(px) \\\\\n",
+    "heading_{desired} = -atan(f`(px))\n",
+    "$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.47433647872956336\n",
+      "55.83031014720696\n"
+     ]
+    }
+   ],
+   "source": [
+    "#for 0\n",
+    "\n",
+    "cte=coeff[3]\n",
+    "epsi=-np.arctan(coeff[2])\n",
+    "print(cte)\n",
+    "print(np.degrees(epsi))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ADD DELAY (for real time implementation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is necessary to take *actuation latency* into account: so instead of using the actual state as estimated, the delay factored in using the kinematic model\n",
+    "\n",
+    "* $x_{delay} = 0.0 + v * dt$\n",
+    "* $y_{delay} = 0.0$\n",
+    "* $psi_{delay} = 0.0 + w * dt$\n",
+    "* $cte_{delay} = cte + v * sin(epsi) * dt$\n",
+    "* $epsi_{delay} = epsi - w * dt$\n",
+    "\n",
+    "Note that the current position and heading is always 0; this is becouse the path is parametrized to vehicle reference frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}