final_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IN THE NAME OF GOD:)\n",
    "\n",
    "\n",
    "# PARHAM YAZDEKHASTI 9638033\n",
    "# ALI ZINDARI 9629183\n",
    "\n",
    "# WE DIDN'T USE ANY FUZZY LOGIC , PACKAGES INSTEAD WE IMPLEMENTED EVERY THING FROM SCRATCH !!!!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "import math\n",
    "import cv2 as cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementation of triangle membership function and it's features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "class triangle:\n",
    "    def __init__(self,ps,pm,pe):\n",
    "        self.ps = ps\n",
    "        self.pm = pm\n",
    "        self.pe = pe\n",
    "\n",
    "        self.a1 = (pm[1] - ps[1]) / (pm[0] - ps[0])\n",
    "        self.a2 = (pe[1] - pm[1]) / (pe[0] - pm[0])\n",
    "\n",
    "        self.b1 = pm[1] - self.a1* pm[0]\n",
    "        self.b2 = pm[1] - self.a2* pm[0]\n",
    "        #print(self.a1, self.a2,self.b1,self.b2)\n",
    "\n",
    "    def evaluate(self, x):\n",
    "        if x < self.ps[0] or x > self.pe[0]:\n",
    "            return 0\n",
    "\n",
    "        if x< self.pm[0]:\n",
    "            return self.a1 * x + self.b1\n",
    "        else:\n",
    "             return self.a2 * x + self.b2\n",
    "\n",
    "    def to_array(self):\n",
    "        return [self.ps[0], self.pm[0], self.pe[0]]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A class for Fuzzy rules and it's features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class rule:\n",
    "    def __init__(self, I1, I2, O):\n",
    "        self.i1 = I1\n",
    "        self.i2 = I2\n",
    "        self.o = O\n",
    "        self.degree = 0\n",
    "        \n",
    "        self.names_fi = {\n",
    "            -65: \"s3\",\n",
    "            0:'s2',\n",
    "            52.5:'s1',\n",
    "            90:'ce',\n",
    "            127.5:'e1',\n",
    "            180:'b2',\n",
    "            245:'b3',\n",
    "        }\n",
    "        \n",
    "        self.names_x = {\n",
    "            1.5:'s2',\n",
    "            7:'s1',\n",
    "            10:'ce',\n",
    "            13:'b1',\n",
    "            18.5:'b2'\n",
    "        }\n",
    "        \n",
    "        self.names_theta = {\n",
    "            -40:'s3',\n",
    "            -20:'s2',\n",
    "            -7:'s1',\n",
    "            0:'ce',\n",
    "            7:'b1',\n",
    "            20:'b2',\n",
    "            40:'b3'\n",
    "        }\n",
    "    \n",
    "    def cal_degree(self, fi, x, theta, uni_fi, uni_x, uni_theta):\n",
    "        m1 = findmax(fi,uni_fi)[1]\n",
    "        m2 = findmax(x, uni_x)[1]\n",
    "        m3 = findmax(theta,uni_theta)[1]\n",
    "        self.degree = m1*m2*m3\n",
    "        return self.degree\n",
    "\n",
    "    def eval(self, x, fi):\n",
    "        score = self.i1.evaluate(x) * self.i2.evaluate(fi)\n",
    "        mid = self.o.pm[0]\n",
    "        return mid, score\n",
    "    \n",
    "    def show_me(self):\n",
    "        print('if x is '+self.names_x[self.i1.pm[0]]+ ' and fi is '+self.names_fi[self.i2.pm[0]]+\n",
    "              ' then theta is '+self.names_theta[self.o.pm[0]])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Here is some useful functions consist of :\n",
    "# - find best match for triangle membership function\n",
    "# - Removing ruls that have conflict to each other\n",
    "# - Searching for equality of two rules\n",
    "# - Evaluates the final output using all of the rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findmax(inp,triangles):\n",
    "    res = np.zeros((len(triangles)))\n",
    "    for i in range(len(triangles)):\n",
    "        res[i] = triangles[i].evaluate(inp)\n",
    "    return(triangles[np.argmax(res)], res[np.argmax(res)])   \n",
    "\n",
    "\n",
    "def remove_rules(rules):\n",
    "    list_which_should_be_delete = []\n",
    "    \n",
    "\n",
    "    for i in range(len(rules)):\n",
    "        for j in range(len(rules)):\n",
    "            if ( i==j):\n",
    "                continue\n",
    "            \n",
    "            if is_eq(rules[i].i1, rules[j].i1) and is_eq(rules[i].i2, rules[j].i2) and (not is_eq(rules[i].o,rules[j].o)):          \n",
    "                # print([i,j])\n",
    "                # print(rules[i].i1.to_array(), rules[j].i1.to_array())\n",
    "                # print(rules[i].i2.to_array(), rules[j].i2.to_array())\n",
    "                # print(rules[i].o.to_array(), rules[j].o.to_array())\n",
    "                # print('_________________________________________')\n",
    "\n",
    "                if rules[i].degree > rules[j].degree:\n",
    "                    list_which_should_be_delete.append(i)\n",
    "\n",
    "                else :\n",
    "                    list_which_should_be_delete.append(j)\n",
    "    rules = rules.copy()\n",
    "    list_which_should_be_delete = set(list_which_should_be_delete) \n",
    "    list_which_should_be_delete = sorted(list_which_should_be_delete, reverse=True)\n",
    "\n",
    "\n",
    "    for x in list_which_should_be_delete:\n",
    "        rules.pop(x)\n",
    "    return rules\n",
    "\n",
    "\n",
    " \n",
    "def is_eq(t1,t2):\n",
    "    return t1.ps == t2.ps and t1.pm == t2.pm and t1.pe == t2.pe \n",
    "\n",
    "def new_rules(rules):\n",
    "    return remove_rules(rules.copy())\n",
    "\n",
    "\n",
    "def eval_rulse( x, fi, rules):\n",
    "    up =0.0\n",
    "    down = 0.0\n",
    "    for i in range(len(rules)):\n",
    "        temp = rules[i].eval(x,fi)\n",
    "        # print(temp)\n",
    "        # print(rules[i].i1.to_array(),rules[i].i2.to_array())\n",
    "        # print()\n",
    "        up += temp[1]*temp[0]\n",
    "        down += temp[1]\n",
    "\n",
    "    return up/down"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# t1 = triangle([0,0],[1,1],[2,0])\n",
    "# t2 = triangle([0,0],[1,1],[2,0])\n",
    "\n",
    "# is_eq (t1,t2)\n",
    "\n",
    "\n",
    "# sorted([1,2,3],reverse=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Declaring three linguistic variables : Fuzzy x,Fuzzy fi,Fuzzy theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#unis:\n",
    "\n",
    "fuzzy_fi = []\n",
    "inps = np.array([[-115,-15],[-45,45],[15,90],[80,100],[90,165],[135,225],[195,295]])\n",
    "for i in range(len(inps)):\n",
    "    fuzzy_fi.append(triangle([inps[i,0],0], [(inps[i,0]+inps[i,1])/2,1], [inps[i,1],0]))\n",
    "\n",
    "fuzzy_x = []\n",
    "inps = np.array([[-4,7],[4,10],[9,11],[10,16],[13,24]])\n",
    "for i in range(len(inps)):\n",
    "    fuzzy_x.append(triangle([inps[i,0],0], [(inps[i,0]+inps[i,1])/2,1], [inps[i,1],0]))\n",
    "\n",
    "fuzzy_theta = []\n",
    "inps = np.array([[-60,-20],[-33,-7],[-14,0],[-4,4],[0,14],[7,33],[20,60]])\n",
    "for i in range(len(inps)):\n",
    "    fuzzy_theta.append(triangle([inps[i,0],0], [(inps[i,0]+inps[i,1])/2,1], [inps[i,1],0]))    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initializing Rules and removing the rules which have conflict to each other\n",
    "# This is for first part of assignment that we should print all of the rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LIST OF THE RULES :\n",
      "if x is s2 and fi is s2 then theta is s2\n",
      "if x is s2 and fi is s2 then theta is s2\n",
      "if x is s2 and fi is s2 then theta is s2\n",
      "if x is s2 and fi is s2 then theta is s2\n",
      "if x is s2 and fi is s1 then theta is s2\n",
      "if x is s1 and fi is s1 then theta is s2\n",
      "if x is s1 and fi is s1 then theta is s2\n",
      "if x is s1 and fi is s1 then theta is s2\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is ce and fi is s1 then theta is s1\n",
      "if x is ce and fi is s1 then theta is s1\n",
      "if x is ce and fi is s1 then theta is s1\n",
      "if x is ce and fi is ce then theta is s1\n",
      "if x is ce and fi is ce then theta is s1\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is s2 and fi is ce then theta is b2\n",
      "if x is s2 and fi is s1 then theta is b2\n",
      "if x is s2 and fi is s1 then theta is b2\n",
      "if x is s2 and fi is s1 then theta is b2\n",
      "if x is s2 and fi is s1 then theta is b1\n",
      "if x is s2 and fi is s1 then theta is b1\n",
      "if x is s2 and fi is s1 then theta is b1\n",
      "if x is s2 and fi is s1 then theta is b1\n",
      "if x is s2 and fi is s1 then theta is ce\n",
      "if x is s1 and fi is s1 then theta is ce\n",
      "if x is s1 and fi is s1 then theta is ce\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is s2 and fi is s3 then theta is s3\n",
      "if x is s2 and fi is s3 then theta is s3\n",
      "if x is s2 and fi is s3 then theta is s3\n",
      "if x is s2 and fi is s3 then theta is s2\n",
      "if x is s2 and fi is s3 then theta is s2\n",
      "if x is s2 and fi is s2 then theta is s2\n",
      "if x is s2 and fi is s2 then theta is s2\n",
      "if x is s1 and fi is s2 then theta is s2\n",
      "if x is s1 and fi is s2 then theta is s2\n",
      "if x is s1 and fi is s2 then theta is s2\n",
      "if x is s1 and fi is s1 then theta is s2\n",
      "if x is s1 and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is ce then theta is s2\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is s1 and fi is s2 then theta is s3\n",
      "if x is s1 and fi is s2 then theta is s3\n",
      "if x is s1 and fi is s1 then theta is s3\n",
      "if x is s1 and fi is s1 then theta is s3\n",
      "if x is ce and fi is s1 then theta is s3\n",
      "if x is ce and fi is ce then theta is s1\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is s1 and fi is ce then theta is b2\n",
      "if x is s1 and fi is ce then theta is b1\n",
      "if x is s1 and fi is s1 then theta is b1\n",
      "if x is s1 and fi is s1 then theta is b1\n",
      "if x is s1 and fi is s1 then theta is b1\n",
      "if x is s1 and fi is s1 then theta is ce\n",
      "if x is s1 and fi is s1 then theta is ce\n",
      "if x is s1 and fi is s1 then theta is ce\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is ce and fi is s1 then theta is s1\n",
      "if x is ce and fi is s1 then theta is s1\n",
      "if x is ce and fi is s1 then theta is s1\n",
      "if x is ce and fi is ce then theta is s1\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is s1 and fi is b2 then theta is b3\n",
      "if x is s1 and fi is b2 then theta is b3\n",
      "if x is s1 and fi is e1 then theta is b3\n",
      "if x is s2 and fi is e1 then theta is b3\n",
      "if x is s2 and fi is e1 then theta is b2\n",
      "if x is s2 and fi is e1 then theta is b2\n",
      "if x is s2 and fi is ce then theta is b2\n",
      "if x is s2 and fi is s1 then theta is b2\n",
      "if x is s2 and fi is s1 then theta is b2\n",
      "if x is s2 and fi is s1 then theta is b1\n",
      "if x is s2 and fi is s1 then theta is b1\n",
      "if x is s1 and fi is s1 then theta is b1\n",
      "if x is s1 and fi is s1 then theta is ce\n",
      "if x is s1 and fi is s1 then theta is ce\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is s1 and fi is s1 then theta is s1\n",
      "if x is s1 and fi is s1 then theta is s2\n",
      "if x is s1 and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is s1 and fi is s3 then theta is s3\n",
      "if x is s1 and fi is s3 then theta is s3\n",
      "if x is s1 and fi is s3 then theta is s3\n",
      "if x is s1 and fi is s3 then theta is s3\n",
      "if x is s1 and fi is s2 then theta is s3\n",
      "if x is ce and fi is s2 then theta is s3\n",
      "if x is b1 and fi is s2 then theta is s3\n",
      "if x is b1 and fi is s1 then theta is s3\n",
      "if x is b1 and fi is s1 then theta is s3\n",
      "if x is b1 and fi is s1 then theta is s3\n",
      "if x is b1 and fi is s1 then theta is s3\n",
      "if x is b1 and fi is ce then theta is s3\n",
      "if x is b1 and fi is e1 then theta is s3\n",
      "if x is b1 and fi is e1 then theta is ce\n",
      "if x is b1 and fi is e1 then theta is b2\n",
      "if x is b1 and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b1\n",
      "if x is ce and fi is e1 then theta is b1\n",
      "if x is ce and fi is ce then theta is b1\n",
      "if x is ce and fi is ce then theta is b1\n",
      "if x is b1 and fi is ce then theta is ce\n",
      "if x is b1 and fi is s2 then theta is s3\n",
      "if x is b1 and fi is s2 then theta is s3\n",
      "if x is b1 and fi is s1 then theta is s3\n",
      "if x is b2 and fi is s1 then theta is s3\n",
      "if x is b2 and fi is s1 then theta is s2\n",
      "if x is b2 and fi is s1 then theta is s2\n",
      "if x is b2 and fi is ce then theta is s2\n",
      "if x is b2 and fi is e1 then theta is s2\n",
      "if x is b2 and fi is e1 then theta is s2\n",
      "if x is b2 and fi is e1 then theta is s1\n",
      "if x is b2 and fi is e1 then theta is s1\n",
      "if x is b1 and fi is e1 then theta is s1\n",
      "if x is b1 and fi is e1 then theta is ce\n",
      "if x is b1 and fi is e1 then theta is ce\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is b1 and fi is ce then theta is s2\n",
      "if x is b1 and fi is ce then theta is s1\n",
      "if x is b1 and fi is e1 then theta is s1\n",
      "if x is b1 and fi is e1 then theta is s1\n",
      "if x is b1 and fi is e1 then theta is s1\n",
      "if x is b1 and fi is e1 then theta is ce\n",
      "if x is b1 and fi is e1 then theta is ce\n",
      "if x is b1 and fi is e1 then theta is ce\n",
      "if x is ce and fi is e1 then theta is b1\n",
      "if x is ce and fi is e1 then theta is b1\n",
      "if x is ce and fi is e1 then theta is b1\n",
      "if x is s2 and fi is e1 then theta is b1\n",
      "if x is ce and fi is ce then theta is b1\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is b1 and fi is b2 then theta is b3\n",
      "if x is b1 and fi is b2 then theta is b3\n",
      "if x is b1 and fi is e1 then theta is b3\n",
      "if x is b1 and fi is e1 then theta is b3\n",
      "if x is ce and fi is e1 then theta is b3\n",
      "if x is ce and fi is ce then theta is b1\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is b1 and fi is b3 then theta is b3\n",
      "if x is b1 and fi is b3 then theta is b3\n",
      "if x is b1 and fi is b3 then theta is b3\n",
      "if x is b1 and fi is b3 then theta is b3\n",
      "if x is b1 and fi is b2 then theta is b3\n",
      "if x is ce and fi is b2 then theta is b3\n",
      "if x is s1 and fi is b2 then theta is b3\n",
      "if x is s1 and fi is e1 then theta is b3\n",
      "if x is s1 and fi is e1 then theta is b3\n",
      "if x is s1 and fi is e1 then theta is b3\n",
      "if x is s1 and fi is e1 then theta is b3\n",
      "if x is s1 and fi is ce then theta is b3\n",
      "if x is s1 and fi is s1 then theta is b3\n",
      "if x is s1 and fi is s1 then theta is ce\n",
      "if x is s1 and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s2\n",
      "if x is ce and fi is s1 then theta is s1\n",
      "if x is ce and fi is s1 then theta is s1\n",
      "if x is ce and fi is s1 then theta is s1\n",
      "if x is ce and fi is ce then theta is s1\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is b2 and fi is ce then theta is s2\n",
      "if x is b2 and fi is e1 then theta is s2\n",
      "if x is b2 and fi is e1 then theta is s2\n",
      "if x is b2 and fi is e1 then theta is s2\n",
      "if x is b2 and fi is e1 then theta is s1\n",
      "if x is b2 and fi is e1 then theta is s1\n",
      "if x is b2 and fi is e1 then theta is s1\n",
      "if x is b2 and fi is e1 then theta is s1\n",
      "if x is b2 and fi is e1 then theta is ce\n",
      "if x is b1 and fi is e1 then theta is ce\n",
      "if x is b1 and fi is e1 then theta is ce\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is b2 and fi is b2 then theta is b2\n",
      "if x is b2 and fi is b2 then theta is b2\n",
      "if x is b2 and fi is b2 then theta is b2\n",
      "if x is b2 and fi is b2 then theta is b2\n",
      "if x is b2 and fi is e1 then theta is b2\n",
      "if x is b1 and fi is e1 then theta is b2\n",
      "if x is b1 and fi is e1 then theta is b2\n",
      "if x is b1 and fi is e1 then theta is b2\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is b1 and fi is e1 then theta is b1\n",
      "if x is ce and fi is ce then theta is b1\n",
      "if x is ce and fi is ce then theta is b1\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is ce and fi is ce then theta is ce\n",
      "if x is b2 and fi is b3 then theta is b3\n",
      "if x is b2 and fi is b3 then theta is b3\n",
      "if x is b2 and fi is b3 then theta is b3\n",
      "if x is b2 and fi is b3 then theta is b2\n",
      "if x is b2 and fi is b3 then theta is b2\n",
      "if x is b2 and fi is b2 then theta is b2\n",
      "if x is b2 and fi is b2 then theta is b2\n",
      "if x is b1 and fi is b2 then theta is b2\n",
      "if x is b1 and fi is b2 then theta is b2\n",
      "if x is b1 and fi is b2 then theta is b2\n",
      "if x is b1 and fi is e1 then theta is b2\n",
      "if x is b1 and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is e1 then theta is b2\n",
      "if x is ce and fi is ce then theta is b2\n",
      "if x is ce and fi is ce then theta is b2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<__main__.rule at 0x7f03584bac10>,\n",
       " <__main__.rule at 0x7f03584ba3d0>,\n",
       " <__main__.rule at 0x7f03584bacd0>,\n",
       " <__main__.rule at 0x7f03584bae50>,\n",
       " <__main__.rule at 0x7f0358126d00>,\n",
       " <__main__.rule at 0x7f033e477910>,\n",
       " <__main__.rule at 0x7f033e477460>,\n",
       " <__main__.rule at 0x7f033decdbe0>,\n",
       " <__main__.rule at 0x7f033decdd30>,\n",
       " <__main__.rule at 0x7f033decdf40>,\n",
       " <__main__.rule at 0x7f034e3056a0>,\n",
       " <__main__.rule at 0x7f0358126220>,\n",
       " <__main__.rule at 0x7f033e477820>,\n",
       " <__main__.rule at 0x7f0358126670>,\n",
       " <__main__.rule at 0x7f0357d6dca0>,\n",
       " <__main__.rule at 0x7f03584f0070>,\n",
       " <__main__.rule at 0x7f033e02f940>,\n",
       " <__main__.rule at 0x7f0358126e50>,\n",
       " <__main__.rule at 0x7f033decdb20>,\n",
       " <__main__.rule at 0x7f033decdcd0>,\n",
       " <__main__.rule at 0x7f033decdd00>,\n",
       " <__main__.rule at 0x7f033decde50>,\n",
       " <__main__.rule at 0x7f034e206100>,\n",
       " <__main__.rule at 0x7f033e078a60>,\n",
       " <__main__.rule at 0x7f033e078880>,\n",
       " <__main__.rule at 0x7f033e078e50>,\n",
       " <__main__.rule at 0x7f033e4922b0>,\n",
       " <__main__.rule at 0x7f033e492400>,\n",
       " <__main__.rule at 0x7f033e078f40>,\n",
       " <__main__.rule at 0x7f033e4928e0>,\n",
       " <__main__.rule at 0x7f035826be20>,\n",
       " <__main__.rule at 0x7f035826bca0>,\n",
       " <__main__.rule at 0x7f035826b370>,\n",
       " <__main__.rule at 0x7f035826b610>,\n",
       " <__main__.rule at 0x7f035826be80>,\n",
       " <__main__.rule at 0x7f035826b4f0>,\n",
       " <__main__.rule at 0x7f034e3536a0>,\n",
       " <__main__.rule at 0x7f034e353430>,\n",
       " <__main__.rule at 0x7f034e353670>,\n",
       " <__main__.rule at 0x7f034e353460>,\n",
       " <__main__.rule at 0x7f034e353a30>,\n",
       " <__main__.rule at 0x7f033e4659d0>,\n",
       " <__main__.rule at 0x7f033e465610>]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('LIST OF THE RULES :')\n",
    "table = np.load('tabel1.npy')[:,:3]\n",
    "for i in range(14-1):\n",
    "    temp_talbe = np.load('tabel'+str(i+2)+'.npy')\n",
    "    table = np.concatenate([table,temp_talbe],axis= 0)\n",
    "\n",
    "rules = []\n",
    "for i in range (table.shape[0]):\n",
    "    i1 = findmax(table[i,0], fuzzy_x)[0]\n",
    "    i2 = findmax(table[i,1], fuzzy_fi)[0]\n",
    "    o = findmax(table[i,2], fuzzy_theta)[0]\n",
    "    temp = rule(i1,i2,o)\n",
    "    temp.show_me()\n",
    "    temp.cal_degree(table[i,0],table[i,1],table[i,2],fuzzy_fi,fuzzy_x, fuzzy_theta)\n",
    "    rules.append(copy.copy(temp) )\n",
    "\n",
    "remove_rules(rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# print(rules[7].i1.to_array())\n",
    "# print(rules[7].i2.to_array())\n",
    "# print(rules[7].o.to_array())\n",
    "# rules[7].o.pm[0]\n",
    "# rules[7].eval(1,-90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# eval_rulse(10,90,rules)\n",
    "# pos = [10,30]\n",
    "# theta = 60\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the Fuzzy control system and Simulating the parking of track at desired location starting from 12 DIFFERRENT positions .\n",
    "# It's obvious in the plot that at all of the starting positions we achieved the goal you can see the final phi too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, -90] -40.00000000000001\n",
      "[7.000610202775657, -71.25146125761518] -40.0\n",
      "[7.247359129178354, -52.50292251523035] -40.0\n",
      "[7.714087263997482, -33.75438377284553] -35.00155879627233\n",
      "[8.35131361487365, -15.005845030460709] -30.00116909720426\n",
      "[9.14271672923739, 1.6614528244338977] -30.76841659796349\n",
      "[10.008500865134168, 16.140025698589824] -27.775222497822966\n",
      "[10.83404388710232, 30.960691028194912] -24.7010048935603\n",
      "[11.592985908953235, 44.434994553395484] -40.0\n",
      "[12.242011833277838, 56.49562076544816] -40.0\n",
      "[12.665313813458615, 75.24415950783298] -40.0\n",
      "[12.860976955087212, 93.9926982502178] -1.9639148082499513\n",
      "[12.808257690991075, 112.74123699260262] 7.305555555555552\n",
      "[12.422834262334778, 113.72305031991581] 7.3055555555555545\n",
      "[12.024694728172165, 110.07769578467236] 7.305555555555553\n",
      "[11.685092160246038, 106.43234124942889] 7.305555555555555\n",
      "[11.405399393865608, 102.78698671418543] 7.305555555555554\n",
      "[11.186747080391262, 99.14163217894196] 6.399850816764574\n",
      "[11.03001911660332, 95.4962776436985] 1.1828747381465068\n",
      "[10.93567018711701, 92.30134155621394] -2.784692016920854\n",
      "[10.896339417467683, 91.70993566622789] -2.3699194741068665\n",
      "[10.867345188335864, 93.1018709130867] -0.3603902975290075\n",
      "[10.814101886268986, 94.28657746311217] 1.2832910554248598\n",
      "[10.740190177180033, 94.46677172162121] 1.7918685993183472\n",
      "[10.663161892446743, 93.82516638996145] 1.5326683583230165\n",
      "[10.597310257406116, 92.92934152103238] 1.1456149680868988\n",
      "[10.547045033484203, 92.16307582100792] 0.8809117870830769\n",
      "[10.510123477241775, 91.59029693401094] 0.720660264182722\n",
      "[10.483184399164788, 91.1498540421046] 0.6123796904137283\n",
      "[10.46392487415523, 90.78953102854328] 0.5273704870567014\n",
      "[10.450949375382756, 90.48334555110618] 0.45361466651663607\n",
      "[10.443314404853776, 90.21966309719153] 0.3861085602742758\n",
      "[10.440278930653, 89.992857539174] 0.322201784294069\n",
      "[10.441199832689454, 89.79980435380922] 0.25092046842095894\n",
      "[10.445488380424335, 89.63870409784069] 0.1807057058506682\n",
      "[10.452587185881976, 89.51324416410158] 0.11480668204800643\n",
      "[10.461874518150017, 89.42289142340655] 0.05494428787595391\n",
      "[10.472737947975638, 89.36548811116289] 0.002051454256146212\n",
      "[10.48460269832541, 89.33801597037964] -0.04346404104386186\n",
      "[10.49694665523721, 89.33699024325173] -0.08154100714405059\n",
      "[1, 0] -20.0\n",
      "[1.939753130473184, 9.846708616080011] -20.0\n",
      "[2.865676580094336, 19.693417232160023] -10.44035813481139\n",
      "[3.7505180303843977, 29.540125848240034] 0.8557640310082233\n",
      "[4.606266087104564, 34.73861610913645] -2.4452234520332725\n",
      "[5.428109785779458, 34.310746013253095] -6.300959228175686\n",
      "[6.253521973777816, 35.53307964033044] -7.812992819139841\n",
      "[7.062572694712997, 38.67879777881313] -8.21451086552061\n",
      "[7.836206752016301, 42.5762128644376] -7.631224840661504\n",
      "[8.565289464949283, 46.67291240825027] -7.333333333333337\n",
      "[9.24567853205964, 50.48006314854963] -10.418741246806622\n",
      "[9.877167057951267, 54.13922151680243] -14.83099177335262\n",
      "[10.453717550552748, 59.32703795847804] -16.903411442954347\n",
      "[10.947312992499503, 66.68023205290616] -29.856118870644995\n",
      "[11.326608176807145, 75.0395867689629] -39.99999999999999\n",
      "[11.5510877486964, 89.45324604935027] -17.189763818771286\n",
      "[11.559006373141912, 108.2017847917351] 7.305555555555551\n",
      "[11.261449805494129, 116.69952484360405] 7.305555555555563\n",
      "[10.816697207591487, 113.0541703083606] 9.581667040032906\n",
      "[10.429184869063691, 109.40881577311713] 13.624295904885038\n",
      "[10.10241028659778, 104.63474218260757] 16.408547233972087\n",
      "[9.85772715887737, 97.87086171380689] 7.07286684819623\n",
      "[9.72718105219147, 89.75103754288553] 1.1550964393469807\n",
      "[9.73228129936073, 86.22133997595066] -0.6763630404896529\n",
      "[9.798931243388562, 85.64382106926304] -1.1477206478494018\n",
      "[9.875637913791822, 85.98199670462687] -0.9239098573781089\n",
      "[9.946452524830423, 86.55582827352538] -0.5670392631587844\n",
      "[10.007285015512148, 87.01776820225254] -0.32319118157063537\n",
      "[10.060077579320648, 87.30128436617318] -0.22158604928303757\n",
      "[10.107932455454607, 87.46287931490137] -0.1899965350217457\n",
      "[10.152971864909523, 87.57367213261361] -0.1881504918433484\n",
      "[10.196080533329491, 87.66867026967822] -0.1979525178226304\n",
      "[10.237533507643747, 87.76274538891913] -0.2121014307926338\n",
      "[10.277346735191115, 87.86172150030134] -0.2278531241035503\n",
      "[10.315434666595507, 87.96777203421904] -0.24419167590986127\n",
      "[10.351673858060417, 88.08169837128264] -0.2606546441440067\n",
      "[10.385926878543835, 88.2037939322965] -0.27687257336278914\n",
      "[10.418051162257187, 88.33412091755363] -0.2923957045315266\n",
      "[10.447903040356127, 88.47255680055734] -0.306631454913231\n",
      "[10.475340964228911, 88.61875417736968] -0.31882765892262044\n",
      "[1, 90] 20.0\n",
      "[1.000748350519291, 80.15329138391999] 9.479107840967217\n",
      "[1.1621147493317405, 70.30658276783998] 9.384615384615383\n",
      "[1.49508281466917, 65.58325573998566] 9.384615384615385\n",
      "[1.9034476566278953, 60.90669395969772] 9.384615384615387\n",
      "[2.3836443223811683, 56.23013217940978] 9.384615384615381\n",
      "[2.932478731104814, 51.55357039912184] 9.384615384615385\n",
      "[3.54630025049425, 46.8770086188339] 9.384615384615389\n",
      "[4.221025979345919, 42.20044683854596] 4.030615039118403\n",
      "[4.952167905422748, 37.52388505825802] -3.617763266392241\n",
      "[5.7435094473497506, 35.50982332153155] -6.81386366975435\n",
      "[6.556086970143284, 37.31780416635572] -7.900646734011864\n",
      "[7.345959277362979, 40.71871377335361] -7.8909693105271845\n",
      "[8.096926363038193, 44.65964648017035] -7.372675072420728\n",
      "[8.801767778425967, 48.59577496758775] -7.333333333333332\n",
      "[9.457992406726168, 52.27448263129422] -12.301311123376978\n",
      "[10.065234658713935, 55.933640999547016] -15.516558010986701\n",
      "[10.612940139937928, 62.04879165071455] -18.129639597367134\n",
      "[11.065060756384131, 69.73569604433065] -40.0\n",
      "[11.394784725626996, 78.68648319021437] -40.0\n",
      "[11.54563310690772, 97.4350219325992] 4.282287410729183\n",
      "[11.447131293760396, 116.18356067498402] 7.305555555555551\n",
      "[11.008033673453507, 114.04391104690987] 7.305555555555555\n",
      "[10.604815438565144, 110.39855651166641] 11.906415168030932\n",
      "[10.260001915289704, 106.75320197642294] 15.126543477445217\n",
      "[9.97883246262783, 100.83218280400871] 17.238772726622116\n",
      "[9.798250417311014, 93.33502308792498] 2.3514514410177543\n",
      "[9.743474277663953, 84.81361486724177] -1.86343828558845\n",
      "[9.83454087935415, 83.63813646033684] -3.246736511020342\n",
      "[9.946024849649941, 84.56973252864744] -2.1637477966682406\n",
      "[10.04125102893512, 86.19244972198902] -0.8692456351887895\n",
      "[10.108369510116924, 87.27413093311633] -0.2906546044410529\n",
      "[10.156692719215972, 87.7087412588222] -0.12468983168710468\n",
      "[10.197446967577454, 87.85406809403226] -0.10784850597298037\n",
      "[10.235668481479719, 87.91641297300458] -0.13365045565922276\n",
      "[10.272803220615033, 87.97033720213292] -0.1671933833526874\n",
      "[10.308997881405933, 88.03716238455732] -0.19871347734503786\n",
      "[10.344027502904, 88.12075898734389] -0.2263858490838459\n",
      "[10.37759963366344, 88.22011557677938] -0.25049581933938325\n",
      "[10.409439424810627, 88.33330828065168] -0.2715717349528157\n",
      "[7, 180] 40.0\n",
      "[6.23372907937324, 161.25146125761518] 39.412654823154526\n",
      "[5.5084689939931835, 142.50292251523035] 35.07312515039465\n",
      "[4.895923495886386, 123.99286216667888] 30.763235497848477\n",
      "[4.438998946775595, 107.29497317348694] 26.897185002527415\n",
      "[4.184277928452317, 92.47661069807603] 20.907332699466878\n",
      "[4.146465503302329, 79.4030406444233] 6.161165014454021\n",
      "[4.318913802468531, 69.12460750820006] 3.4276372552046004\n",
      "[4.673762513594573, 66.04847639200268] -0.21301694219280876\n",
      "[5.079533649912267, 64.33542384744581] -2.7100358029735334\n",
      "[5.513145571154265, 64.44193213470344] -4.41883741214774\n",
      "[5.944602929645312, 65.7965714366964] -5.587308066113496\n",
      "[6.353892461127152, 68.00434849685789] -6.402222626053949\n",
      "[6.727206257131014, 70.79468312989681] -6.981568972515276\n",
      "[7.054698908274176, 73.99079956991346] -7.333333333333333\n",
      "[7.329072342470185, 77.47510584683607] -7.333333333333338\n",
      "[7.544827721075227, 81.13426421508886] -6.0934912358391315\n",
      "[7.698391358703324, 84.79342258334167] -0.24649400543909608\n",
      "[7.789368983853198, 87.83586195389357] 9.055830582772046\n",
      "[7.82790759388528, 87.95910867176465] 9.598038382361498\n",
      "[7.863844966585013, 83.44534003084753] -2.825120131897659\n",
      "[7.977122909965994, 78.66316692427084] -7.333333333333337\n",
      "[8.174142298991864, 80.07529807549014] -7.256799775031109\n",
      "[8.345779890664422, 83.73445644374294] -2.3232962692732304\n",
      "[8.454773673159536, 87.35558082034227] 7.106033591593124\n",
      "[8.50164468148003, 88.51699041967736] 12.303546578041368\n",
      "[8.528103529337775, 84.97080474608562] 0.14354030903390055\n",
      "[8.614487215799395, 78.85455576404958] -7.333333333333335\n",
      "[8.808471332231274, 78.7827856657818] -7.333333333333333\n",
      "[9.002088956291447, 82.4419440340346] -4.3982568921189396\n",
      "[9.13326206595866, 86.1011024022874] 0.55305435540578\n",
      "[9.201815930889385, 88.29861203900244] 2.261083110949637\n",
      "[9.23228588990109, 88.02208807866415] 1.7689139994739607\n",
      "[9.267551010553802, 86.89176640337712] 0.6163437471009472\n",
      "[9.322514815219842, 86.00741468228885] -0.35241038852224144\n",
      "[9.392897257644385, 85.69924726187915] -0.7682154052579535\n",
      "[9.468643770466993, 85.87545162372233] -0.7137023399752327\n",
      "[9.541319879029887, 86.25955070355327] -0.4880701514065229\n",
      "[9.607313144137706, 86.61639495921456] -0.29927915942373207\n",
      "[9.667096741004697, 86.86042782364149] -0.1949206537133712\n",
      "[12.71, 100.98] 7.3055555555555625\n",
      "[12.52194837143185, 97.33464546475653] 4.142195042964584\n",
      "[12.396166622143289, 93.68929092951306] -2.7048328091668594\n",
      "[12.332813859217424, 91.61954556631056] -9.128006749669975\n",
      "[12.305392116325118, 92.9715855480357] -4.634401386901674\n",
      "[12.255018377437844, 97.52110095882392] 4.400897696560825\n",
      "[12.12540727811201, 99.83640777233471] 7.1400048113005\n",
      "[11.95594271711906, 97.63758065176498] 4.559198190375387\n",
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      "[16.985324234681805, 122.97522244025161] -9.384615384615383\n",
      "[16.449226092066198, 127.65178422053955] -9.384615384615383\n",
      "[15.847415282136502, 132.32834600082748] -5.815054208225956\n",
      "[15.183894815544123, 137.0049077811154] 1.8998928930334456\n",
      "[14.45706559166794, 139.90869262125864] 5.423024996214958\n",
      "[13.69326397749057, 138.9588766149453] 6.87660114050733\n",
      "[12.943202233154704, 136.25039913758917] 7.441821826735111\n",
      "[12.226852861632743, 132.81828877800083] 7.305555555555555\n",
      "[11.553751800456972, 129.10522458569343] 7.305555555555556\n",
      "[10.929000031125575, 125.45987005044996] 8.219417822029632\n",
      "[10.354469310262095, 121.8145155152065] 14.304918837429325\n",
      "[9.833614719906139, 117.71538143729664] 17.65213752829275\n",
      "[9.383834218678237, 110.61881051266482] 20.455662274231166\n",
      "[9.049122629335326, 101.89796635976687] 32.43711839271678\n",
      "[8.856767316786152, 91.83419947270013] 23.936925589944277\n",
      "[8.830434369500416, 76.27711498284236] -7.333333333333333\n",
      "[9.047876997043362, 64.57227718860823] -8.03821830978718\n",
      "[9.47425231215685, 68.23143555686102] -12.424552837066983\n",
      "[9.842025779564347, 72.24065450030525] -14.660854844314363\n",
      "[10.140503551662956, 78.41634606888566] -15.696268110822393\n",
      "[10.335428319799753, 85.68659693158232] -1.7047732568165355\n",
      "[10.408566265641376, 93.46083991868191] 1.8217062221115665\n",
      "[10.349051725811268, 94.31313231080087] 2.4103740719329076\n",
      "[10.27471414845356, 93.40239418886107] 1.9446083443210214\n",
      "[10.21624278380127, 92.1974735292174] 1.452402688713569\n",
      "[10.17873581125359, 91.22530922621704] 1.141705518669203\n",
      "[10.158165355881769, 90.49916615559572] 0.9432177618740917\n",
      "[10.150255657938853, 89.92834170153557] 0.7924021294339777\n",
      "[10.152301745510997, 89.45674878077035] 0.6250820117916098\n",
      "[10.162573625893412, 89.06055717921791] 0.4636561591803837\n",
      "[10.179756143463273, 88.74802081856613] 0.3167918863573592\n",
      "[10.202389878045773, 88.51619463473953] 0.18885274101819802\n",
      "[10.229066792448265, 88.35779929622927] 0.08136558021568627\n",
      "[10.258505984377058, 88.26337305382471] -0.006413555071812834\n",
      "[10.289591763450886, 88.22269027396197] -0.07679153475425683\n",
      "[15, 60] -34.72527472527473\n",
      "[15.411411806133646, 76.54899531532979] -32.362682543816994\n",
      "[15.603177584973247, 93.09799063065958] -10.977901529765694\n",
      "[15.558214684536136, 108.62245380632922] -1.5875773311435895\n",
      "[15.245615872655975, 114.08618478498501] 1.1366303187868971\n",
      "[14.838583431762629, 114.87989734440096] 3.338361253662628\n",
      "[14.418870751702453, 114.31161011447676] 4.798585558096617\n",
      "[14.008790034303189, 112.64313724810663] 5.79117678390045\n",
      "[13.626064621959317, 110.24594691093417] 6.48138490457505\n",
      "[13.282688991114213, 107.35405485379448] 6.970420040431147\n",
      "[12.987218950430398, 104.11854495702116] 7.30555555555555\n",
      "[12.745978027901048, 100.63978214136179] 7.305555555555552\n",
      "[12.563709693142322, 96.99442760611834] 3.6525650525270357\n",
      "[12.443769382234583, 93.34907307087488] -3.5866076172220036\n",
      "[12.386291751938641, 91.5237175842465] -9.499706448776363\n",
      "[12.360560973352742, 93.31614368075836] -3.674998741605588\n",
      "[12.304321130727235, 98.04966387647384] 5.0997512789553525\n",
      "[12.16543466770525, 99.88621901821277] 7.190749196350538\n",
      "[11.995288778109236, 97.3388671838524] 4.148126289371337\n",
      "[11.869402526057932, 93.750571108891] -2.5518609925782636\n",
      "[11.804986226221684, 91.67786594057691] -8.905745645817365\n",
      "[11.776545164443315, 92.95348034078324] -4.6866251599242235\n",
      "[11.726452066578126, 97.39289897418637] 4.223733291084038\n",
      "[11.599060927293761, 99.73425290711873] 7.034999165543837\n",
      "[11.431308498583425, 97.62381987144678] 4.540626837186586\n",
      "[11.30048757451006, 94.11294846699116] -1.6813175377906084\n",
      "[11.229817589131606, 91.84441624497445] -8.286897845799512\n",
      "[11.198457741367667, 92.68498461419277] -5.483675819161922\n",
      "[11.152912344457938, 96.81759587917718] 3.3886493487433773\n",
      "[11.03559290774174, 99.55629575500575] 6.849007499282559\n",
      "[10.870733491332775, 97.86271131456462] 5.498431936088774\n",
      "[10.735760533723417, 94.44432351225464] 1.788028035993965\n",
      "[10.659455738133124, 91.69827098847666] 0.24492759619215684\n",
      "[10.63064468488118, 90.80436569905035] -0.039392558312021944\n",
      "[10.617409806146156, 90.68190218040675] -0.02961305780308266\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.606310958739236, 90.70159845840014] 0.027802265844307294\n",
      "[10.594868541436373, 90.71640498680777] 0.0820623078451553\n",
      "[10.583167849614696, 90.70250385429435] 0.12339692174618819\n",
      "[10.571709649432671, 90.66147271088232] 0.15115660000554834\n",
      "[10.560967183786115, 90.59977428574535] 0.1669751531186697\n",
      "[14, 45] -38.12499999999999\n",
      "[14.556625388741182, 62.98139923922558] -36.521664930681666\n",
      "[14.91447685415054, 80.96279847845116] -31.12353864959093\n",
      "[15.041306769183398, 98.27509269180676] 0.5821067656622794\n",
      "[14.918814518111212, 113.25336203165158] 2.982427692211294\n",
      "[14.524957928032736, 112.96231240032124] 4.481854133657134\n",
      "[14.136279490996108, 111.47160318937966] 5.5161367726931285\n",
      "[13.772272840830158, 109.23238902724573] 6.238755748040602\n",
      "[13.445304737309806, 106.47751475415292] 6.751545256651096\n",
      "[13.164242003391376, 103.3627586582095] 7.117299554417912\n",
      "[12.935611267596169, 99.99284443587777] 7.298380432674466\n",
      "[12.764286496455913, 96.441058343566] 2.803786069383434\n",
      "[12.653854531407298, 92.79926950444413] -5.1391498754246205\n",
      "[12.60589504993323, 91.39779573979318] -10.00081646180812\n",
      "[12.582404508274651, 93.9647879059011] -2.0303811658821482\n",
      "[12.515127521065367, 98.94613615669716] 6.180062060070902\n",
      "[12.360583532450574, 99.96116753303528] 7.266544830463897\n",
      "[12.189473254630279, 96.87562899851031] 3.475995917325162\n",
      "[12.071563432349869, 93.24966146367395] -3.8551615104796073\n",
      "[12.015802984330051, 91.51246247747984] -9.543903122444043\n",
      "[11.990275870500833, 93.43895318703166] -3.3481458157875705\n",
      "[11.93193414363123, 98.19434243018254] 5.282645004074907\n",
      "[11.790504532957888, 99.86770133524335] 7.171919316624629\n",
      "[11.620724871148854, 97.22918412958238] 3.992897823507436\n",
      "[11.496716702272671, 93.65024749603975] -2.8032016578604604\n",
      "[11.434030293156294, 91.65500971348388] -8.99250044936403\n",
      "[11.405993137912457, 93.05619153451839] -4.392816349538129\n",
      "[11.354144588971316, 97.53859013180603] 4.424826432328981\n",
      "[11.224188780481105, 99.73338549855671] 7.034102107751488\n",
      "[11.056495692699242, 97.52262061396996] 4.402979157091448\n",
      "[10.927411522718804, 94.01219520160384] -0.1203980382082297\n",
      "[10.858476373964319, 91.81232965347237] -1.472373341005906\n",
      "[10.827662563122665, 91.87252863938302] -0.9631959813057871\n",
      "[10.795809571992827, 92.6086545990736] -0.05122983814214407\n",
      "[10.751120572107089, 93.09023559375665] 0.626077213150992\n",
      "[10.698034442667504, 93.11585051027052] 0.9082324570564525\n",
      "[10.644505282081496, 92.80281657116177] 0.9194997261997869\n",
      "[10.596432607260933, 92.34871459188894] 0.8282561895341569\n",
      "[10.556272886646807, 91.8889795149728] 0.7248895776599966\n",
      "[10.524125912243532, 91.47486222692922] 0.6341885287855346\n",
      "91.11242468269586\n"
     ]
    }
   ],
   "source": [
    "angels = []\n",
    "reses = []\n",
    "pos = [7,-90]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())\n",
    "angels.append(pos[1])  \n",
    "######################################################################\n",
    "pos = [1,0]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())\n",
    "angels.append(pos[1])  \n",
    "########################################################################\n",
    "pos = [1,90]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())\n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [7,180]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())\n",
    "angels.append(pos[1]) \n",
    "########################################################################    \n",
    "pos = [12.71,100.98]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())  \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "########################################################################\n",
    "pos = [15.20,152.24]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())  \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [1.22,-75.2]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy()) \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [7.48,57.91]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())  \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [4.16,48.21]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())  \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [17,90.84]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())   \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [18,80.]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())   \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [15,60]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())   \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [14,45]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())   \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "# plt.figure(figsize=[10,10])\n",
    "# to_show = cv.resize(to_show,(1200,1000))\n",
    "# plt.imshow(to_show,'gray')\n",
    "print(pos[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mMsBId3eS3qDt3O7e0917Tjxh1zw2B8Aah8rhoV0WAyyYY2LmbTPF2nVVdXKSDF+vn0+XAJiRHAZYPVnMwsw0GuQGXpfk7CS/NXx97Vx6BMCs5DDA6o06i42qu7XNdGatql6W5K+SfH1VXVlVT83kB/LhVfWhJN8zvAZgAeQwwOrJYpZtpjNr3f3EDZoeNse+ALABOQywerKYZZvLACMAAADMl2INAABghDYzwAgALN2sN8tvNC/ATidHtw5n1gAAAEZIsQYAADBCijUAAIARUqwBAACMkAFGAABgh9toIJH1Bh4x6MjyOLMGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACNkNEgAtjyjmAGwHTmzBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEZhpgpKrOT/L9Sa7v7gcM056T5KeS3DDM9qzufsMiOrkVuakdmCc5DLB6OzGL1zt+dZy7PLOeWbsgySPWmf6C7t49PLbNDyXACF0QOQywahdEFrNEMxVr3f32JJ9acF8A2IAcBlg9WcyybfaetadX1SVVdX5V3XO9GarqnKraW1V7b7hx/yY3B8Aah83hRBYDLJhjYhZiM8Xai5LcN8nuJNck+d31Zuruc7t7T3fvOfGEXZvYHABrzJTDiSwGWCDHxCzMTAOMrKe7rzvwvKr+MMnr59IjAGYihwFWbydm8ayDjmw0L7M76jNrVXXy1MsfTHLp5rsDwKzkMMDqyWIWadah+1+W5CFJ7lVVVyZ5dpKHVNXuJJ3kiiRPW0wXAZDDAKsni1m2mYq17n7iOpPPm3NfANiAHAZYPVnMsm12NEgAAAAWQLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABihmf7PGgCM2Vmn7F53+sVX71tqPwB2qo3ydr18ls2zc2YNAABghBRrAAAAI6RYAwAAGCHFGgAAwAgZYGST3NQOAAAsgjNrAAAAI6RYAwAAGCHFGgAAwAgp1gAAAEZopmKtqk6vqrdW1fur6rKqesYw/fiqenNVfWj4es/FdhdgZ5LDAKsni1m2Wc+s3Zbk57v7/km+Ncm/qqr7J3lmkrd09/2SvGV4DcD8yWGA1ZPFLNVMxVp3X9Pdfz08vznJ5UlOTfKYJBcOs12Y5LEL6CPAjieHAVZPFrNsR3zPWlWdkeRBSd6Z5KTuvmZoujbJSevMf05V7a2qvTfcuH8zfQUgR57DwzKyGGCOHBOzDEdUrFXV3ZO8KsnPdfdnp9u6u5P02mW6+9zu3tPde048YdemOguw0x1NDg9tshhgThwTsywzF2tVdWwmP5Qv6e5XD5Ovq6qTh/aTk1w//y4CkMhhgDGQxSzTMbPMVFWV5Lwkl3f386eaXpfk7CS/NXx97dx7CIAcnnLWKbsPmnbx1fuW3g9g55HFLNtMxVqSByd5cpL3VdW+YdqzMvmBvKiqnprk40keP/ceApDIYYAxkMUs1UzFWne/I0lt0Pyw+XUHgPXIYYDVk8Us2xGPBgkAAMDiKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACM06z/FBgAAWNdZp+xed/rFV+9baj+2G2fWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMELHHG6Gqjo9yYuTnJSkk5zb3f+pqp6T5KeS3DDM+qzufsOiOgqwk8ligNWSw19y1im7D5p28dX7lt6PneCwxVqS25L8fHf/dVXdI8l7qurNQ9sLuvt5i+seAANZDLBacpilO2yx1t3XJLlmeH5zVV2e5NRFdwyAL5HFAKslh1mFI7pnrarOSPKgJO8cJj29qi6pqvOr6p4bLHNOVe2tqr033Lh/c70FQBYDrJgcZllmLtaq6u5JXpXk57r7s0lelOS+SXZn8inD7663XHef2917unvPiSfs2nyPAXYwWQywWnKYZZqpWKuqYzP5oXxJd786Sbr7uu7e391fTPKHSc5cXDcBkMUAqyWHWbbDFmtVVUnOS3J5dz9/avrJU7P9YJJL5989ABJZDLBqcphVmGU0yAcneXKS91XVvmHas5I8sap2ZzJ06RVJnraA/gEwIYsBVksOs3SzjAb5jiS1TtO2/v8RAGMiiwFWSw6zCkc0GiQAAADLoVgDAAAYoVnuWQOA0bj46n0HTTvrlN0zzwsAW4UzawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEZqpWKuqu1TVu6rqvVV1WVX96jD9q6vqnVX14ap6RVXdabHdBdiZ5DDA6slilu2YGee7NclDu/uWqjo2yTuq6o1J/k2SF3T3y6vqD5I8NcmLFtRXgJ1MDgOsnixOcvHV+w6adtYpu2eel9nNdGatJ24ZXh47PDrJQ5O8cph+YZLHzruDAMhhgDGQxSzbzPesVdWuqtqX5Pokb07ykSQ3dfdtwyxXJjl17j0EIIkcBhgDWcwyzVysdff+7t6d5LQkZyb5hlmWq6pzqmpvVe294cb9R9dLAI46hxNZDDAvjolZpiMeDbK7b0ry1iTfluS4qjpw39tpSa5aZ/5zu3tPd+858YRdm+krADnyHB6WkcUAc+SYmGWYdTTIE6vquOH5lyd5eJLLM/kB/eFhtrOTvHYBfQTY8eQwwOrJYpZt1tEgT05yYVXtyqTAu6i7X19V70/y8qr6tSR/k+S8BfUTYKeTwwCrJ4tZqpmKte6+JMmD1pn+0Uyu1QVggeQwwOrJYpbtiO9ZAwAAYPEUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACM0U7FWVXepqndV1Xur6rKq+tVh+gVV9bGq2jc8di+0twA7lBwGWD1ZzLIdM+N8tyZ5aHffUlXHJnlHVb1xaPvF7n7lYroHwEAOA6yeLGapZirWuruT3DK8PHZ49KI6BcAdyWGA1ZPFLNvM96xV1a6q2pfk+iRv7u53Dk2/XlWXVNULqurO6yx3TlXtraq9N9y4fz69BtiBjjaHh2VlMcAcOCZmmWYu1rp7f3fvTnJakjOr6gFJfjnJNyT55iTHJ/mldZY7t7v3dPeeE0/YNZ9eA+xAR5vDw7KyGGAOHBOzTEc8GmR335TkrUke0d3X9MStSf4oyZlz7h8Aa8hhgNWTxSzDrKNBnlhVxw3PvzzJw5N8oKpOHqZVkscmuXQx3QTY2eQwwOrJYpZt1tEgT05yYVXtyqTAu6i7X19V/6uqTkxSSfYl+ReL6SbAjieHAVZPFrNUs44GeUmSB60z/aFz7xEAB5HDAKsni1m2I75nDQAAgMVTrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIxQdffyNlZ1Q5KPDy/vleSTS9v4cmz3fbpPd5+4ys4AmyeLtyRZDNuIHN6SVpLDSy3W7rDhqr3dvWclG18Q+wRsNdvxd9w+AVvJdvz9tk/z4zJIAACAEVKsAQAAjNAqi7VzV7jtRbFPwFazHX/H7ROwlWzH32/7NCcru2cNAACAjbkMEgAAYIQUawAAACO0kmKtqh5RVX9bVR+uqmeuog+bVVXnV9X1VXXp1LTjq+rNVfWh4es9V9nHI1VVp1fVW6vq/VV1WVU9Y5i+pfcLONh2yOFk+2WxHIadZTtk8XbL4WRcWbz0Yq2qdiV5YZJHJrl/kidW1f2X3Y85uCDJI9ZMe2aSt3T3/ZK8ZXi9ldyW5Oe7+/5JvjXJvxq+N1t9v4Ap2yiHk+2XxXIYdohtlMUXZHvlcDKiLF7FmbUzk3y4uz/a3V9I8vIkj1lBPzalu9+e5FNrJj8myYXD8wuTPHaZfdqs7r6mu/96eH5zksuTnJotvl/AQbZFDifbL4vlMOwo2yKLt1sOJ+PK4lUUa6cm+cTU6yuHadvBSd19zfD82iQnrbIzm1FVZyR5UJJ3ZhvtF5Bke+dwsk0ySw7Dtreds3jbZNaqs9gAIwvSk/+JsCX/L0JV3T3Jq5L8XHd/drptK+8XsPNs1cySw8B2sZUzawxZvIpi7aokp0+9Pm2Yth1cV1UnJ8nw9foV9+eIVdWxmfxQvqS7Xz1M3vL7BdzBds7hZItnlhyGHWM7Z/GWz6yxZPEqirV3J7lfVX11Vd0pyROSvG4F/ViE1yU5e3h+dpLXrrAvR6yqKsl5SS7v7udPNW3p/QIOsp1zONnCmSWHYUfZzlm8pTNrTFlckzN4y1VVj0rye0l2JTm/u3996Z3YpKp6WZKHJLlXkuuSPDvJnyS5KMlXJfl4ksd399obLkerqr49yV8keV+SLw6Tn5XJNbpbdr+Ag22HHE62XxbLYdhZtkMWb7ccTsaVxSsp1gAAADg0A4wAAACMkGINAABghBRrAAAAI6RYAwAAGCHFGgAAwAgp1gAAAEZIsQYAADBCijUAAIARUqwBAACMkGINAABghBRrAAAAI3TIYq2qbpl6fLGqPj/1+klV9Zyq+m/rLNdV9bXD8+dU1T+sWddNG2zvjGHZ6XnfO7T9WFW9Y2reK6rq+qq629S0n6yqt61ZZ1XVR6vq/ets721V9ZOHfouSqnrIsP/T/frTqf076D1Ysz/HDK9Pq6pXVdUnq+ozVXVpVf3Y4bZ/iH79TFV9rKo+W1V7q+rbp9reuKa/X6iq9x1iXQ+rqg9U1d9V1Vur6j5TbY+vqv89tL1tnWV3V9V7hvb3VNXuqbbvHtb3maq64mj3FXYyWXz7fFsxi4+rqguH9+j6qnrOYdb1k1X14WHf3lRVp0y1/evhPfxsVV1dVS84sE9D++6q+othn66sql9Zs+67VtXvT+332492n2GnkcO3z7cVc/jOVfUHVXVdVX2qqv60qk7dYD3fsWbfbhn6/bih/Q/WtN1aVTdPbee8qvp4Vd1cVfuq6pFr1v/4qrp8aH9/VT32cPt2yGKtu+9+4JHk/yZ59NS0lxz2nfuSV0yvq7uPO8z8x03N+8BDzLcryTMOs67vTHLvJF9TVd98BH1e6+o1+/Doo1jHHyf5RJL7JDkhyZOTXHc0namqb0nyW0l+OMlXJjkvyWuqaleSdPcj13z//neS/77Buu6V5NVJfiXJ8Un2JnnF1CyfSvJ7w/bWLnunJK9N8t+S3DPJhUleO0xPks8lOT/JLx7NfgKyeI0tlcVJXpDkrknOSHJmkidX1Y9vsK6HJPmNJI/JJIs/luRlU7O8Lsn/r7u/IskDkjwwyc9Otb80yduHZb8ryU9X1Q9MtZ87tP3j4eu/Popdhh1JDt/BVsvhZyT5tiTflOSUJJ9O8l/WW1d3/8Wa7/X3J7klyZuG9n+xpv1l+dLx9THDPn3X0I9/n+Siqjpj6OepmRwv/5skX5HJsfFLq+reh9q/rX4Z5H9M8gtVddwh5jk7k2LiDcPzVfrmJBd09+e6+7bu/pvufuNRruuMJJd193u6u5O8OMm9MvklvIPhh+Q7hnnW80PDuv57d/99kuckeWBVfUOSdPf/7O6Lkly9zrIPyeSH8/e6+9bu/s9JKslDh2Xf1d1/nOSjR7mfwPjJ4o2z+NFJfqe7/667r8jkIOInNljX9yf57919WXd/Icn/k+Q7q+q+SdLdH+num4Z5K8kXk3ztmr68pLv3d/dHkrwjyTcmyZDnP5DknO6+YZjnPUe5z8D4yOGNc/irk1zc3dcNx7mvyJCNMzg7ySu7+3NrG4YzmY/L5ERFhn15Tndf0d1f7O7XZ/Kh2z8ZFjktyU3d/cae+B+ZnNS476E6sNWLtb1J3pbkF9ZrrKq7ZlJlv2R4PGHqjM8q/J8kL6yqJ1TVVx1u5qq6afo07hpvTLKrqr5l+OTgJ5LsS3LtOvM+JclfDAcK6/nGJO898GL4gfxIZvtB/sYklwy/HAdcMuOywPYgiw+dxbXm+QMOtbl1nt8+f1X986r6bJJPZnJm7f+bmv/3kjylqo6tqq/P5JPk/zm0nZnk40l+dbjs6H0HLusBtgU5vHEOn5fkwVV1yvA+PGlY5nDbvFsm79mFG8zyuCQ3ZHJFw3rLn5Tk65JcNkzam+TyqvqBqto1XAJ5aybHzRuaR7H2+OENvP0xwzxvPcw6Pzk177o/dFP+Q5KfqaoT12n7oUzehD9L8j+SHJvk+w6zvo2csmYfHn8U6/hnSf4ik8sNPzZcy7rhaejuPq6737FB881JXpXJJ6e3Jnl2Jp+Y9jrzPiXJBYfo192TfGbNtM8kucchlpnHssD8yOLZLTOL35TkmVV1j5rct/ITmVwWuZ43ZfI9+qaq+vJM3tOenr+7XzpcBvl1Sf4gd7xs6PWZHFh8PskHkpzX3e8e2k7LpOj7TCaXAT09yYVV9Y832m/giMnh2S0zhz+UyeWJVyX5bCaXgj93hj7+UCYfjP35Bu1nJ3nxesfeVXVsJkXxhd39gWEf9mdy1u+lQz9fmuRp6521mzaPYu2i4Q28/THDPN99mHXea2re5x1qxu6+NJM/UM9cp/nsYdu3Dac9X5WjP+179Zp9uOhIV9Ddn+7uZ3b3NyY5KZOq/0+qqg695LqemuTHMzmDdackP5rk9TV1M3qSDJ9C/KMkrzzEum7J5NrZaV+RyQ//4WxmWWB+ZPGMlpzFP5tJ8fShTC4/elmSKzfo1//M5CDjVUmuGB43rzd/d38ok09rfz9Jqur4TIq95ya5S5LTk5xVVT89LPL5JP+Q5Ne6+wvd/edJ3prke49in4H1yeEZLTmHX5jkzpncG3e3TMZpmOWSy0MVY1+Vya1AB91iVFVflsk9eV/I5IOxA9O/J8nvDMvdKZN72/5rTQ3Mt56tfhnkAc9O8lNJbh/ZpapOy+S+qR+tqmur6tpMPnF8VE0G1Fip7v5kkudl8gnn8Uexit1JXt/dHxyui31TkmuS/NM1852d5NXdfcsh1nVZJpfTJLn9tO9986XTtodyWZJvWvPL9U0zLgtsL7J4TRZ396e6+0nd/Y+Gg5IvS/KuQ/Tnhd19v+4+KZODqWOSXLrB7MfkS/c6fE2S/d394uFg7MokL0/yqKF9vcts1rsSA9ja5PDBx8S7M7k/7lPdfWsmg4uceah9r6rTs0ExNnhykr/s7juMyTAcD5+XSQH6uO7+hzX9fHt37x36+e4k70zyPYfauW1RrHX3hzO5WXB6VKwnJ/lgkq/P5M3ZncllI1cmeeLUfMdU1V2mHscuqp9V9dtV9YCqOqaq7pHkXyb5cHffeBSre3eS76uqr6mJh2eyf7f/UR8uo3l8Dn0JZJK8JskDqupxVXWXTE6jX3LgtO1wXe1dMjkw+LI179PbkuxP8rM1GbL0wCcI/2tY9suGZY+dvKy71GqvkQYWRBYfnMVVdd+qOmHI0UcmOSfJr23Qr7sM/arhU9tzk/yn7v700P6TNYwaVlX3T/LLSd4yLP7ByeT650Pu/qMkP5IvFWlvz2QEu18e9vvBSb47ycVHsc/ASMnhdY+J353J/bxfOezTT2dydvCTh1jnk5P8754M1rSejW4xelEml1k+urs/v04/v+PAmbSqelAmAwAu/J61WfxIHfw/Cw45TOVReG4mpzYPODvJ73f3tdOPTK7xnz7t+6JMLg858PijOfdr2l0zKYxuymR0xPtkMjrXuob36Ts2aH5xJp+avi2T62//cybXvX5gap7HDts66Hroqrqsqp6UJN19QyY3Sf56JsOZfkuSJ0zN/uRM3psXZfJD9fkkfzgs+4VhO08ZtvUTSR47TE8mw8R+PpORh75qeP5nG+0zsFCyeGKZWfxPkrwvk8sZfzPJk7r79isPprM4k8sXX5rJ5eXvSvJXmdzPccCDk7yvqj6XSaa+IcmzkqS7P5vJ/RX/OpMc35fJgcqvDe3/kMm/BHhUJvet/WGSp6z5mwEsnhyeWGYO/0KSv8/kcvQbMsnBH5xa9xur6llr1vmUbDCwSFV9Wyb3Af/3NdPvk+RpmRTD1059fw8cb/95JiOuv7Im/5vtVUl+o7sPeVxc61yGCQAAwIpti8sgAQAAthvFGgAAwAgp1gAAAEZIsQYAADBCmy7WquoRVfW3VfXhqlrvn/ABsEByGGD1ZDGLsKnRIKtqVyb/t+HhmfyvhncneWJ3v3+9+e91/K4+4/SF/cuGo/LBS+667vSv+6a/W3JPxu89l9z6ye4+cdX9AL7kSHM4GWcWL9N6ub+VMl8Ww/hsh2PirWTVOb7MHD5mk8ufmck/sPtoklTVyzP5Py7r/mCecfqxedfFp29yk/N11im7151+8cX7ltqPrWDXyR/++Kr7ABzkiHI4GWcWL9N6ub+VMl8Wwyht+WPirWTVOb7MHN7sZZCnJvnE1Osrh2m3q6pzqmpvVe294cb9m9wcAGscNocTWQywYI6JWYiFDzDS3ed2957u3nPiCbsWvTkA1iGLAVZLDnM0NnsZ5FVJps/hnjZMG6V1T5levW/p/QCYoy2VwwDblCxekJ1+/L7ZM2vvTnK/qvrqqrpTkicked3muwXAjOQwwOrJYhZiU2fWuvu2qnp6kouT7EpyfndfNpeeAXBYchhg9WQxi7LZyyDT3W9I8oY59AWAoyCHAVZPFrMICx9gBAAAgCOnWAMAABihTV8GOVY7feQYAADYKtY7dk8cvzuzBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEtvwAI25GBACArcNAgLNzZg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACG35AUYAAIBxMpjI5jizBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjtOnRIKvqiiQ3J9mf5Lbu3rPZdQIwOzkMsHqymEWY19D9393dn5zTugA4cnIYYPVkMXPlMkgAAIARmkex1kn+rKreU1XnrG2sqnOqam9V7b3hxv1z2BwAaxwyhxNZDLAEjomZu3lcBvnt3X1VVd07yZur6gPd/fYDjd19bpJzk2TPA+/Sc9geAHd0yBxOZDHAEjgmZu42Xax191XD1+ur6jVJzkzy9kMvBcC8yGGA1dvpWXzWKbvXnX7x1fuW2o/tZlOXQVbV3arqHgeeJ/neJJfOo2MAHJ4cBlg9WcyibPbM2klJXlNVB9b10u5+06Z7BcCs5DDA6sliFmJTxVp3fzTJA+fUFwCOkBwGWD1ZzKIYuh8AAGCE5vVPsQFgdNa74d3N7gCbI1uXx5k1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjNAxq+7AZl189b51p591yu6Z5wUAAA7mmHq1nFkDAAAYIcUaAADACCnWAAAARkixBgAAMEIzFWtVdX5VXV9Vl05NO76q3lxVHxq+3nNx3QTY2eQwwOrJYpZt1jNrFyR5xJppz0zylu6+X5K3DK8BWIwLIocBVu2CyGKWaKZirbvfnuRTayY/JsmFw/MLkzx2ft0CYJocBlg9WcyybeaetZO6+5rh+bVJTlpvpqo6p6r2VtXeG27cv4nNAbDGTDmcyGKABXJMzMLMZYCR7u4kvUHbud29p7v3nHjCrnlsDoA1DpXDQ7ssBlgwx8TM22aKteuq6uQkGb5eP58uATAjOQywerKYhdlMsfa6JGcPz89O8trNdweAIyCHAVZPFrMwsw7d/7Ikf5Xk66vqyqp6apLfSvLwqvpQku8ZXgOwAHIYYPVkMct2zCwzdfcTN2h62Bz7AsAG5DDA6slilm0uA4wAAAAwX4o1AACAEZrpMsit6OKr9x007axTds80HwAAwKo5swYAADBCijUAAIARUqwBAACMkGINAABghBRrAAAAI6RYAwAAGCHFGgAAwAgp1gAAAEZIsQYAADBCijUAAIAROmbVHVimi6/ed9C0s07ZPfO8AACwk8x6/OzYeTGcWQMAABghxRoAAMAIKdYAAABGSLEGAAAwQjMVa1V1flVdX1WXTk17TlVdVVX7hsejFtdNgJ1NDgOsnixm2WY9s3ZBkkesM/0F3b17eLxhft0CYI0LIocBVu2CyGKWaKZirbvfnuRTC+4LABuQwwCrJ4tZts3es/b0qrpkOCV8z/VmqKpzqmpvVe294cb9m9wcAGscNocTWQywYI6JWYjNFGsvSnLfJLuTXJPkd9ebqbvP7e493b3nxBN2bWJzAKwxUw4nshhggRwTszBHXax193Xdvb+7v5jkD5OcOb9uAXA4chhg9WQxi3TUxVpVnTz18geTXLrRvADMnxwGWD1ZzCIdM8tMVfWyJA9Jcq+qujLJs5M8pKp2J+kkVyR52mK6CIAcBlg9WcyyzVSsdfcT15l83pz7AsAG5DDA6slilm2zo0ECAACwAIo1AACAEZrpMkgAAIAkufjqfQdNO+uU3TPPy+ycWQMAABghxRoAAMAIKdYAAABGSLEGAAAwQgYYAWDLc2M7ANuRM2sAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjNFOxVlWnV9Vbq+r9VXVZVT1jmH58Vb25qj40fL3nYrsLsDPJYYDVk8Us26xn1m5L8vPdff8k35rkX1XV/ZM8M8lbuvt+Sd4yvAZg/uQwwOrJYpZqpmKtu6/p7r8ent+c5PIkpyZ5TJILh9kuTPLYBfQRYMeTwwCrJ4tZtiO+Z62qzkjyoCTvTHJSd18zNF2b5KT5dQ2A9chhgNWTxSzDERVrVXX3JK9K8nPd/dnptu7uJL3OMudU1d6q2nvDjfs31VmAne5ocnhYThYDzIljYpZl5mKtqo7N5IfyJd396mHydVV18tB+cpLr1y7X3ed2957u3nPiCbvm0WeAHelocziRxQDz4piYZZp1NMhKcl6Sy7v7+VNNr0ty9vD87CSvnW/3AEjkMMAYyGKW7ZgZ53twkicneV9V7RumPSvJbyW5qKqemuTjSR4/9x4CkMhhgDGQxSzVTMVad78jSW3Q/LD5dQeA9chhgNWTxSzbEY8GCQAAwOIp1gAAAEZo1nvWtq2Lr9637vSzTtk987wAAADz5swaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjdNhirapOr6q3VtX7q+qyqnrGMP05VXVVVe0bHo9afHcBdiZZDLBacphVOGaGeW5L8vPd/ddVdY8k76mqNw9tL+ju5y2uewAMZDHAaslhlu6wxVp3X5PkmuH5zVV1eZJTF90xAL5EFgOslhxmFY7onrWqOiPJg5K8c5j09Kq6pKrOr6p7brDMOVW1t6r23nDj/s31FgBZDLBicphlmblYq6q7J3lVkp/r7s8meVGS+ybZncmnDL+73nLdfW537+nuPSeesGvzPQbYwWQxwGrJYZZppmKtqo7N5IfyJd396iTp7uu6e393fzHJHyY5c3HdBEAWA6yWHGbZZhkNspKcl+Ty7n7+1PSTp2b7wSSXzr97ACSyGGDV5DCrMMtokA9O8uQk76uqfcO0ZyV5YlXtTtJJrkjytAX0D4AJWQywWnKYpZtlNMh3JKl1mt4w/+4AsB5ZDLBacphVOKLRIAEAAFgOxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQsesugNjdfHV+w6adtYpu2eaDwAAYLOcWQMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEZqpWKuqu1TVu6rqvVV1WVX96jD9q6vqnVX14ap6RVXdabHdBdiZ5DDA6slilm3WM2u3Jnlodz8wye4kj6iqb03y20le0N1fm+TTSZ66kF4CIIcBVk8Ws1QzFWs9ccvw8tjh0UkemuSVw/QLkzx23h0EQA4DjIEsZtlmvmetqnZV1b4k1yd5c5KPJLmpu28bZrkyyanrLHdOVe2tqr033Lh/Dl0G2JmONoeHZWUxwBw4JmaZZi7Wunt/d+9OclqSM5N8w4zLndvde7p7z4kn7Dq6XgJw1Dk8LCuLAebAMTHLdMSjQXb3TUnemuTbkhxXVccMTacluWp+XQNgPXIYYPVkMcsw62iQJ1bVccPzL0/y8CSXZ/ID+sPDbGcnee0C+giw48lhgNWTxSzbMYefJUlycpILq2pXJgXeRd39+qp6f5KXV9WvJfmbJOctqJ8AO50cBlg9WcxSzVSsdfclSR60zvSPZnKtLgALJIcBVk8Ws2xHfM8aAAAAi6dYAwAAGCHFGgAAwAgp1gAAAEZIsQYAADBCijUAAIARUqwBAACMkGINAABghBRrAAAAI6RYAwAAGCHFGgAAwAgp1gAAAEZIsQYAADBCijUAAIARUqwBAACMkGINAABghBRrAAAAIzRTsVZVd6mqd1XVe6vqsqr61WH6BVX1saraNzx2L7S3ADuUHAZYPVnMsh0z43y3Jnlod99SVccmeUdVvXFo+8XufuViugfAQA4DrJ4sZqlmKta6u5PcMrw8dnj0ojoFwB3JYYDVk8Us28z3rFXVrqral+T6JG/u7ncOTb9eVZdU1Quq6s7rLHdOVe2tqr033Lh/Pr0G2IGONoeHZWUxwBw4JmaZZi7Wunt/d+9OclqSM6vqAUl+Ock3JPnmJMcn+aV1lju3u/d0954TT9g1n14D7EBHm8PDsrIYYA4cE7NMRzwaZHfflOStSR7R3df0xK1J/ijJmXPuHwBryGGA1ZPFLMOso0GeWFXHDc+/PMnDk3ygqk4eplWSxya5dDHdBNjZ5DDA6slilm3W0SBPTnJhVe3KpMC7qLtfX1X/q6pOTFJJ9iX5F4vpJsCOJ4cBVk8Ws1SzjgZ5SZIHrTP9oXPvEQAHkcMAqyeLWbYjvmcNAACAxVOsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjFB19/I2VnVDko8PL++V5JNL2/hybPd9uk93n7jKzgCbJ4u3JFkM24gc3pJWksNLLdbusOGqvd29ZyUbXxD7BGw12/F33D4BW8l2/P22T/PjMkgAAIARUqwBAACM0CqLtXNXuO1FsU/AVrMdf8ftE7CVbMffb/s0Jyu7Zw0AAICNuQwSAABghBRrAAAAI7SSYq2qHlFVf1tVH66qZ66iD5tVVedX1fVVdenUtOOr6s1V9aHh6z1X2ccjVVWnV9Vbq+r9VXVZVT1jmL6l9ws42HbI4WT7ZbEchp1lO2TxdsvhZFxZvPRirap2JXlhkkcmuX+SJ1bV/Zfdjzm4IMkj1kx7ZpK3dPf9krxleL2V3Jbk57v7/km+Ncm/Gr43W32/gCnbKIeT7ZfFchh2iG2UxRdke+VwMqIsXsWZtTOTfLi7P9rdX0jy8iSPWUE/NqW7357kU2smPybJhcPzC5M8dpl92qzuvqa7/3p4fnOSy5Ocmi2+X8BBtkUOJ9svi+Uw7CjbIou3Ww4n48riVRRrpyb5xNTrK4dp28FJ3X3N8PzaJCetsjObUVVnJHlQkndmG+0XkGR753CyTTJLDsO2t52zeNtk1qqz2AAjC9KT/4mwJf8vQlXdPcmrkvxcd392um0r7xew82zVzJLDwHaxlTNrDFm8imLtqiSnT70+bZi2HVxXVScnyfD1+hX354hV1bGZ/FC+pLtfPUze8vsF3MF2zuFki2eWHIYdYztn8ZbPrLFk8SqKtXcnuV9VfXVV3SnJE5K8bgX9WITXJTl7eH52kteusC9HrKoqyXlJLu/u5081ben9Ag6ynXM42cKZJYdhR9nOWbylM2tMWVyTM3jLVVWPSvJ7SXYlOb+7f33pndikqnpZkockuVeS65I8O8mfJLkoyVcl+XiSx3f32hsuR6uqvj3JXyR5X5IvDpOflck1ult2v4CDbYccTrZfFsth2Fm2QxZvtxxOxpXFKynWAAAAODQDjAAAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYoUMWa1V1y9Tji1X1+anXT6qq51TVf1tnua6qrx2eP6eq/mHNum7aYHtnDMtOz/veoe3HquodU/NeUVXXV9Xdpqb9ZFW9bc06q6o+WlXvX2d7b6uqnzz0W5RU1UOG/Z/u159O7d9B78Ga/TlmeH1aVb2qqj5ZVZ+pqkur6scOt/1D9OtnqupjVfXZqtpbVd8+1XZcVV04vEfXV9VzZlznfxj6/D1T0+5cVecP27m2qv7NmmUeX1WXV9XNVfX+qnrsVNuPVdX+Ne/dQ452n2EnksW3zze6LB72699V1f8dMvLlVfUVU+2HzM816zq7qt4zzHtlVf3OgT4P7cdX1Wuq6nNV9fGq+udTbSdX1euq6uphX89Ys+7L1rxvtx1474AjJ5dvn2+75/ITqupvhz5dX5Nj6+l1/eOq+l9D+4er6gfXLH/Xqvr9qf16+5HuzyGLte6++4FHkv+b5NFT015yBNt5xfS6uvu4w8x/3NS8DzzEfLuSPOMw6/rOJPdO8jVV9c1H0Oe1rl6zD48+inX8cZJPJLlPkhOSPDnJdUfTmar6liS/leSHk3xlkvOSvKaqdg2zvCDJXZOckeTMJE+uqh8/zDrvm+SfJblmTdNzktxv6Pd3J/m3VfWIYZlTk/y3JP8myVck+cUkL62qe08t/1dr3ru3Hc0+w04li+9gVFmc5CnD8g9OckqSL0/yX6ban5MN8nMdd03yc0nuleRbkjwsyS9Mtb8wyReSnJTkSUleVFXfOLR9McmbkjxuvRV39zdO/QzdI5P9/+9HsJ/AFLl8B9s5l/8yyYO7+yuTfE2SY5L8WpIMheZrk7w+yfFJzkny36rq66aWP3do+8fD1399pDuz1S+D/I9JfqGqjjvEPGdn8ka+YXi+St+c5ILu/lx339bdf9PdbzzKdZ2R5LLufk93d5IXZ/IH/kCR9Ogkv9Pdf9fdV2RSzP3EYdb5wiS/lMnBwLSzk/w/3f3p7r48yR8m+bGh7bQkN3X3G3vifyT5XJL7HuV+AVvPTs7iRyc5r7s/0d23JPntJD9SVXcd2g+Vn3fQ3S/q7r/o7i9091VJXpLJwUaGT8gfl+RXuvuW7n5HktdlckCS7r6uu38/ybtn6PN3ZvL34lVHt8vAFiCX55PLn+juT05N2p/ka4fn35BJMfiC7t7f3f8rk+LuyUlSVd+Q5AeSnNPdNwzzvOdId2arF2t7k7wtd/zk8XbDN+WHM/mD95IkT6iqOy2tdwf7P0leOJxS/arDzVxVN9XUpY1rvDHJrqr6luFs2k8k2Zfk2ulVrHn+gENs658lubW737Bm+j2TnJzkvVOT35vkwKe5e5NcXlU/UFW7anIJ5K1JLpma/0HD6d8PVtWv1NRlPcC2sJOzODk4a++c5H4z5OfhfGeSy4bnX5fktu7+4FGua9rZSV7V3Z87imWBrUEu3/H5UedyVX17VX0myc2ZfGj2e4fZ7oHj7TOTfDzJrw7Hwe+rqnWvfjiUeRRrjx/esNsfM8zz1sOs85NT8677QzblPyT5mao6cZ22H8qkcPizJP8jybFJvu8w69vIKWv24fFHsY5/luQvkvxKko9V1b5DnXbu7uOGT0/Xc3Mmn4q+I5N9fHYmlXsP7W9K8syqukdNro3+iUwusTlIVd0jyW9k/dPldx++fmZq2mcyuYwm3b0/k7N6Lx368dIkT5s6CHh7Jj+0987kB/yJmVwqCcyXLJ7dPLP4TUl+sib3X3xlJlcnJJO8PWR+HkpV/USSPUmeN0y6e5LPrpltpnWtWe+BA7QLjmQ54KjI5dmNNpe7+x3DZZCnZXLG8oqh6W+TXJ/kF6vq2Kr63iTflS8db5+WyTHwZzI5A/f0JBdW1T/eaFvrmUexdtHwht3+mGGe7z7MOu81Ne/zDjVjd1+aybWiz1yn+exh27d1999nUtwc7Wneq9fsw0VHuoLhdOszu/sbM7nnYF+SP6mqOvSS63pqkh/P5JOAOyX50SSvr6pThvafTfL5JB/K5BT3y5JcucG6npPkj4fLJde6Zfj6FVPTviKTYjE1GYjkd5I8ZOjHdyX5r1W1O0m6+6Pd/bHu/mJ3vy/JczM5UADmSxbPaM5ZfH4m+fq2TM6CHTjQujKHyc+NDFco/GaSR05dfnPLmvXMtK51/FCSTyX58yNcDjhycnlGY8/loY9XZVIIvnx4/Q9JHptJkXttkp9PclG+dLz9+ST/kOTXhsvb/3zoy/ceyc5s9csgD3h2kp9KcuqBCVV1WpKHJvnRmoz0cm0mRcKjqupeq+nmlwx/gJ+XSaV9/FGsYneS13f3B4dC6E2ZDAzyT4f1f6q7n9Td/2j4wf+yJO/aYF0PS/KzU+/T6Ukuqqpf6u5PD+udvon1gfnSpTm7k7y9u/cO/Xh3kncm+Z6sr3PHU9PA9rHjsnjIvWd39xndfVom2XhVkqtmyM+D1OQm9z/MZLCC9001fTDJMVV1v1nXtYGzk7x46ioMYHuTy5vM5TWOydS4DN19SXd/V3ef0N1nZTIIyYHj7UvWWf6Is3dbFGvd/eEkr8jkbNIBT87kj9vXZ1JQ7M7kmv8rM7kU74BjquouU49jF9XPqvrtqnpAVR0zXHr4L5N8uLtvPIrVvTvJ91XV19TEwzPZv0uHbd23qk6oyX1kj8xkhJpf22BdD8vkNO3u4XF1kqdlMuBIMrnM8d9X1T1rcrPkT+VLl9C8O8l3HDiTVlUPSvIdGX5Aq+qRVXXS8PwbMjm9/dqj2F9g5HZiFtdkOP37Djl8/yTPT/Lc7v7iMMuh8nPtuh6ayb0jj+vuO3y4Nlxa/uokz62qu1XVg5M8JpMR1A4sf5dM7stIkjsPr6fXf1omI59deKT7CWxNcnnTufykGu6hq6r7JPn1JG+Zav+m4b2563BZ6slT63p7JiOF/vKwXw/OJIMvPpL9WVax9iN1x/+/cEvdcWj3eXhukrtNvT47ye9397XTjyR/kDue5n1RJqcpDzz+aM79mnbXJK9JclOSj2YyZOgPbDTz8D59xwbNL87kNOzbMrmP4T9ncq/YB4b2f5LkfZmc1v3NJE/q7ts/NajJ/9x5UpJ0941r3qP9ST7dkxF0ksmnMh/J5CbJP0/yH4czeRlO6T4nySur6sB9dL/R3X82LPuwJJdU1ecyGW3o1ZncHwcsnyyemGcW3yuTbPtcJgM/nd/d5061b5ifVfVVw7oP3Ez/K5n8K5Y3TH1/pkdD++lMhqC+PpNLfP7ldK5n8r4dyO0PDK+nPTmTf6XykY32FVg6uTwx1ly+f5L/PRzH/mUm96n91NS6npzJmbrrMznmfXh335rcfpnkY5I8KpP71v4wyVOmjtVnUq6EAAAAGJ9tcRkkAADAdqNYAwAAGCHFGgAAwAhtulirqkdU1d9W1Yerar3/4wDAAslhgNWTxSzCpgYYqapdmQz9+fBMhvt8d5Indvf715v/Xsfv6jNOv+Oonx+85K7rzZqv+6a/O+p+sRjvueTWT3b3iavuB/AlR5rDyfpZvGr+FsxOFsP4zOOYeLPk6PIsM4eP2eTyZ2byPxA+miRV9fJMhqhc9wfzjNOPzbsuPv0O0846Zfe6K7744n2b7BrztuvkD3981X0ADnJEOZysn8Wr5m/B7GQxjNKmj4k3S44uzzJzeLOXQZ6a5BNTr6/M1H9IT5KqOqeq9lbV3htu3L/JzQGwxmFzOJHFAAvmmJiFWPgAI919bnfv6e49J56wa9GbA2AdshhgteQwR2OzxdpVSabP4Z42TANgOeQwwOrJYhZis/esvTvJ/arqqzP5gXxCkn++6V4BMKttkcMXX71v3enr3YOx0bwAK7Qtspjx2VSx1t23VdXTk1ycZFeS87v7srn0DIDDksMAqyeLWZTNnllLd78hyRvm0BcAjoIcBlg9WcwiLHyAEQAAAI6cYg0AAGCEFGsAAAAjtOl71gAAgNUyqu725MwaAADACCnWAAAARkixBgAAMEKKNQAAgBFa+QAjboYEAIDFWO/42XH21uHMGgAAwAgp1gAAAEZIsQYAADBCijUAAIARWvkAIxtxMyQA/hYAsJM5swYAADBCijUAAIARUqwBAACMkGINAABghDY9wEhVXZHk5iT7k9zW3Xs2u04AZieHAVZPFrMI8xoN8ru7+5NzWteGZh0VbKN5AbaxpeQwAIe0JbLYMfXW4TJIAACAEZpHsdZJ/qyq3lNV56xtrKpzqmpvVe294cb9c9gcAGscMocTWQywBI6Jmbt5XAb57d19VVXdO8mbq+oD3f32A43dfW6Sc5NkzwPv0nPYHgB3dMgcTmQxwBI4JmbuNn1mrbuvGr5en+Q1Sc7c7DoBmJ0cBlg9WcwibOrMWlXdLcmXdffNw/PvTfLcufRsRhvd9LjeTZJukAS2mzHkMMBOtx2y2DH1OG32MsiTkrymqg6s66Xd/aZN9wqAWclhgNWTxSzEpoq17v5okgfOqS8AHCE5DLB6sphFMXQ/AADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYoU39U+wxu/jqfQdNO+uU3TPNBwAAOKZeNWfWAAAARkixBgAAMEKKNQAAgBFSrAEAAIzQth1gZD2z3iC50bwAALDTOaZeHmfWAAAARkixBgAAMEKKNQAAgBFSrAEAAIzQTAOMVNX5Sb4/yfXd/YBh2vFJXpHkjCRXJHl8d396Md1cnI1uevSf2YEx2c45DLBVyGKWbdYzaxckecSaac9M8pbuvl+StwyvAViMCyKHAVbtgshilmimYq27357kU2smPybJhcPzC5M8dn7dAmCaHAZYPVnMsm3mnrWTuvua4fm1SU5ab6aqOqeq9lbV3htu3L+JzQGwxkw5nMhigAVyTMzCzGWAke7uJL1B27ndvae795x4wq55bA6ANQ6Vw0O7LAZYMMfEzNtmirXrqurkJBm+Xj+fLgEwIzkMsHqymIWZaTTIDbwuydlJfmv4+tq59Ggk1hv50QiRwMhs6xwG2CJkMQsz05m1qnpZkr9K8vVVdWVVPTWTH8iHV9WHknzP8BqABZDDAKsni1m2mc6sdfcTN2h62Bz7AsAG5DDA6slilm0uA4wAAAAwX4o1AACAEdrMACMAAAAbDrpngL7NcWYNAABghBRrAAAAI6RYAwAAGCHFGgAAwAgZYOQIrHcz5Ho3TW40LwAAwKycWQMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAETIaJAAAsClGSF8MZ9YAAABGSLEGAAAwQoo1AACAEZqpWKuq86vq+qq6dGrac6rqqqraNzwetbhuAuxschhg9WQxyzbrACMXJPl/k7x4zfQXdPfz5tqjLWajmybXu8nSDZbAJlwQOQywahdEFrNEM51Z6+63J/nUgvsCwAbkMMDqyWKWbbP3rD29qi4ZTgnfc70ZquqcqtpbVXtvuHH/JjcHwBqHzeFEFgMsmGNiFmIzxdqLktw3ye4k1yT53fVm6u5zu3tPd+858YRdm9gcAGvMlMOJLAZYIMfELMxRF2vdfV137+/uLyb5wyRnzq9bAByOHAZYPVnMIh11sVZVJ0+9/MEkl240LwDzJ4cBVk8Ws0gzjQZZVS9L8pAk96qqK5M8O8lDqmp3kk5yRZKnLaaLAMhhgNWTxSzbTMVadz9xncnnzbkvAGxADgOsnixm2TY7GiQAAAALoFgDAAAYIcUaAADACM10zxoAjMXFV+87aNpZp+yeeV4A2CqcWQMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjNAxs8xUVacneXGSk5J0knO7+z9V1fFJXpHkjCRXJHl8d396MV0dp7NO2b3u9Iuv3rfUfgDbmxwGWD1ZzLLNembttiQ/3933T/KtSf5VVd0/yTOTvKW775fkLcNrAOZPDgOsnixmqWYq1rr7mu7+6+H5zUkuT3JqksckuXCY7cIkj11AHwF2PDkMsHqymGU74nvWquqMJA9K8s4kJ3X3NUPTtZmcEl47/zlVtbeq9t5w4/7N9BWAHHkOD8vIYoA5ckzMMhxRsVZVd0/yqiQ/192fnW7r7s7k2t2smX5ud+/p7j0nnrBrU50F2OmOJoeHNlkMMCeOiVmWmYu1qjo2kx/Kl3T3q4fJ11XVyUP7yUmun38XAUjkMMAYyGKWaaZiraoqyXlJLu/u5081vS7J2cPzs5O8dr7dAyCRwwBjIItZtpmG7k/y4CRPTvK+qto3THtWkt9KclFVPTXJx5M8fu49BCCRwwBjIItZqpmKte5+R5LaoPlh8+sOAOuRwwCrJ4tZtiMeDRIAAIDFU6wBAACMkGINAABghBRrAAAAI6RYAwAAGCHFGgAAwAgp1gAAAEZIsQYAADBCijUAAIARUqwBAACMkGINAABghBRrAAAAI6RYAwAAGCHFGgAAwAgp1gAAAEZIsQYAADBCijUAAIAROmyxVlWnV9Vbq+r9VXVZVT1jmP6cqrqqqvYNj0ctvrsAO5MsBlgtOcwqHDPDPLcl+fnu/uuqukeS91TVm4e2F3T38xbXPQAGshhgteQwS3fYYq27r0lyzfD85qq6PMmpi+4YAF8iiwFWSw6zCkd0z1pVnZHkQUneOUx6elVdUlXnV9U9N1jmnKraW1V7b7hx/+Z6C4AsBlgxOcyyzFysVdXdk7wqyc9192eTvCjJfZPszuRTht9db7nuPre793T3nhNP2LX5HgPsYLIYYLXkMMs0U7FWVcdm8kP5ku5+dZJ093Xdvb+7v5jkD5OcubhuAiCLAVZLDrNss4wGWUnOS3J5dz9/avrJU7P9YJJL5989ABJZDLBqcphVmGU0yAcneXKS91XVvmHas5I8sap2J+kkVyR52gL6B8CELAZYLTnM0s0yGuQ7ktQ6TW+Yf3cAWI8sBlgtOcwqHNFokAAAACyHYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEZirWquouVfWuqnpvVV1WVb86TP/qqnpnVX24ql5RVXdabHcBdiY5DLB6sphlm/XM2q1JHtrdD0yyO8kjqupbk/x2khd099cm+XSSpy6klwDIYYDVk8Us1UzFWk/cMrw8dnh0kocmeeUw/cIkj513BwGQwwBjIItZtpnvWauqXVW1L8n1Sd6c5CNJburu24ZZrkxy6jrLnVNVe6tq7w037p9DlwF2pqPN4WFZWQwwB46JWaaZi7Xu3t/du5OcluTMJN8w43Lndvee7t5z4gm7jq6XABx1Dg/LymKAOXBMzDId8WiQ3X1Tkrcm+bYkx1XVMUPTaUmuml/XAFiPHAZYPVnMMsw6GuSJVXXc8PzLkzw8yeWZ/ID+8DDb2Uleu4A+Aux4chhg9WQxy3bM4WdJkpyc5MKq2pVJgXdRd7++qt6f5OVV9WtJ/ibJeQvqJ8BOJ4cBVk8Ws1QzFWvdfUmSB60z/aOZXKsLwALJYYDVk8Us2xHfswYAAMDiKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIzVSsVdVdqupdVfXeqrqsqn51mH5BVX2sqvYNj90L7S3ADiWHAVZPFrNsx8w4361JHtrdt1TVsUneUVVvHNp+sbtfuZjuATCQwwCrJ4tZqpmKte7uJLcML48dHr2oTgFwR3IYYPVkMcs28z1rVbWrqvYluT7Jm7v7nUPTr1fVJVX1gqq68yI6CYAcBhgDWcwyzVysdff+7t6d5LQkZ1bVA5L8cpJvSPLNSY5P8ktrl6uqc6pqb1XtveHG/fPpNcAOdLQ5nMhigHlxTMwyHfFokN19U5K3JnlEd1/TE7cm+aMkZ64z/7ndvae795x4wq5NdxhgpzvSHB6WkcUAc+SYmGWYdTTIE6vquOH5lyd5eJIPVNXJw7RK8tgkly6mmwA7mxwGWD1ZzLLNOhrkyUkurKpdmRR4F3X366vqf1XViUkqyb4k/2Ix3QTY8eQwwOrJYpZq1tEgL0nyoHWmP3TuPQLgIHIYYPVkMct2xPesAQAAsHiKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIxQdffyNlZ1Q5KPDy/vleSTS9v4cmz3fbpPd5+4ys4AmyeLtyRZDNuIHN6SVpLDSy3W7rDhqr3dvWclG18Q+wRsNdvxd9w+AVvJdvz9tk/z4zJIAACAEVKsAQAAjNAqi7VzV7jtRbFPwFazHX/H7ROwlWzH32/7NCcru2cNAACAjbkMEgAAYIRWUqxV1SOq6m+r6sNV9cxV9GGzqur8qrq+qi6dmnZ8Vb25qj40fL3nKvt4pKrq9Kp6a1W9v6ouq6pnDNO39H4BB9sOOZxsvyyWw7CzbIcs3m45nIwri5derFXVriQvTPLIJPdP8sSquv+y+zEHFyR5xJppz0zylu6+X5K3DK+3ktuS/Hx33z/Jtyb5V8P3ZqvvFzBlG+Vwsv2yWA7DDrGNsviCbK8cTkaUxas4s3Zmkg9390e7+wtJXp7kMSvox6Z099uTfGrN5MckuXB4fmGSxy6zT5vV3dd0918Pz29OcnmSU7PF9ws4yLbI4WT7ZbEchh1lW2TxdsvhZFxZvIpi7dQkn5h6feUwbTs4qbuvGZ5fm+SkVXZmM6rqjCQPSvLObKP9ApJs7xxOtklmyWHY9rZzFm+bzFp1FhtgZEF6Mszmlhxqs6runuRVSX6uuz873baV9wvYebZqZslhYLvYypk1hixeRbF2VZLTp16fNkzbDq6rqpOTZPh6/Yr7c8Sq6thMfihf0t2vHiZv+f0C7mA753CyxTNLDsOOsZ2zeMtn1liyeBXF2ruT3K+qvrqq7pTkCUlet4J+LMLrkpw9PD87yWtX2JcjVlWV5Lwkl3f386eatvR+AQfZzjmcbOHMksOwo2znLN7SmTWmLF7JP8Wuqkcl+b0ku5Kc392/vvRObFJVvSzJQ5LcK8l1SZ6d5E+SXJTkq5J8PMnju3vtDZejVVXfnuQvkrwvyReHyc/K5BrdLbtfwMG2Qw4n2y+L5TDsLNshi7dbDifjyuKVFGsAAAAcmgFGAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIzQIYu1qrpl6vHFqvr81OsnVdVzquq/rbNcV9XXDs+fU1X/sGZdN22wvTOGZafnfe/Q9mNV9Y6pea+oquur6m5T036yqt62Zp1VVR+tqvevs723VdVPHvotSqrqIcP+T/frT6f276D3YM3+HDO8Pq2qXlVVn6yqz1TVpVX1Y4fb/gbrrqr6d1X1f6vqs1X18qr6iqn2O1fV+UPbtVX1b2Zc71um+zxM211VfzH0+cqq+pWptvtX1d6q+vTw+J9Vdf+p9n89vP+fraqrq+oF0+sGDk8W3z7f6LJ4WN/PVNXHhpzbW1XfPtV256r6g6q6rqo+VVV/WlWnbrCer6uq11bVDcO8F1fV10+1n11V7xm2c2VV/c6arH5bVf391Hvzt1Nth/ybARyaHL59vtHl8OHyrY7gmHh4b/ev2b+HDG33rqqX1eR49jNV9ZdV9S0brOf8Nd/7O1fVeVX18aq6uar2VdUjZ9m/QxZr3X33A48k/zfJo6emvWSWDQxeMb2u7j7uMPMfNzXvAw8x364kzzjMur4zyb2TfE1VffMR9Hmtq9fsw6OPYh1/nOQTSe6T5IQkT05y3VH25ynD8g9OckqSL0/yX6ban5PkfsO2vjvJv62qRxxqhVX1pCTHrtP00iRvT3J8ku9K8tNV9QND29VJfnhou1eS1yV5+dSyr0vy/+vur0jygCQPTPKzs+4kIIvXGFUWD3+ofyuTHPzKJOcleU1V7RpmeUaSb0vyTZlk9adzx6yedlwmmfn1SU5K8q4kr51qv2uSn8ska78lycOS/MKadTx96r35+qnph/ubARyCHL6DUeVw5n9M/Fdr9u9tw/S7J3l3kn+SyXHvhUn+R1XdfXrhmnxgd9816zwmk/39rkz+Vvz7JBdV1RmH27mtfhnkf0zyC1V13CHmOTuTP3ZvGJ6v0jcnuaC7P9fdt3X333T3G49yXY9Ocl53f6K7b0ny20l+pKruOrSfneT/6e5Pd/flSf4wyY9ttLKq+sokz07yb9dpPiPJS7p7f3d/JMk7knxjknT3Td19RXd3kkqyP8nXHliwuz/S3Tcd2EySL063A9vCTs7iM5Jc1t3vGXLwxZkUU/ce2r86ycXdfV13/32SV2TIz7W6+13dfV53f6q7/yHJC5J8fVWdMLS/qLv/oru/0N1XJXlJJgcnszjc3wxga9vJOTzXY+KNdPdHu/v53X3NcEx8bpI7ZfIBW5JkOHP4X5L8zJplP9fdzxmOmb/Y3a9P8rFMCr9D2urF2t4kb8vBnywmSYZv0g9n8gftJUmeUFV3WlrvDvZ/krywqp5QVV91uJmr6qaaupxmvVnWPL9zkvtV1T2TnJzkvVPt780GBwiD30jyoiTXrtP2e0meUlXH1uSSnG9L8j/X9jXJ32fyA/oba9r+eVV9NsknMzmz9v8doh/A1rOTs/iNSXZV1bcMZ9N+Ism+fClLz0vy4Ko6ZXgfnjQsM4vvTHJtd994iPbL1kz7zeGyor88cOnO9K6seX7nTD5tBra+nZzDyXyPiR805OgHq+pXaoPbd6pqdybF2oenJv/rJG/v7ksOsf5U1UlJvi4HZ/hB5lGsPX54A29/zDDPWw+zzk9OzbvuD92U/5DkZ6rqxHXafijJrUn+LMn/yOQSv+87zPo2csqafXj8UazjnyX5iyS/kuRjw/WqG56G7u7juvsdGzS/KclPDtcAf2WSXxqm3zWT07RJ8pmp+T+T5B7rraiq9mTy6exGl8S8PpNf8M8n+UAmn168e21fMzmt+/Qkf7Om7aXDZZBfl+QPcvSnuYGNyeLZzTOLb07yqkyuOLg1kysUzhnOsiXJhzK59OWqJJ9N8o+TPPdwHayq05K8MMm691ZU1U8k2ZPkeVOTfynJ1yQ5Ncm5Sf60qg5cinOovxnAfMjh2Y3ymDiT234ekMnVEY9L8sQkv7h2pprcE/fHSX61uz8zTDs9ydMy+T5sqKqOzaRgvrC7P3CoeZP5FGsXDW/g7Y8Z5vnuw6zzXlPzPu9QM3b3pZkUE89cp/nsYdu3DZefvCpHf9r36jX7cNGRrmA4/frM7v7GTO5H2JfkT6qqDr3kus5P8rJMPkW5LMmBX/Yrk9wyPJ++efwrMjmouIOq+rIkv5/kGd192zrtx2fyS/DcJHdJcnqSs6rqp9fZv89lUoy9uKruvU77h4a+/v5MewgcCVk8ozln8VOT/Hgmn9LeKcmPJnl9VZ0ytL8wk094T0hytySvzmHOrA0HWn+W5Pe7+2XrtD82yW8meWR3f3Jqv97Z3Td3963dfWGSv0zyqKH5UH8zgPmQwzMa4zHx0K+PdvfHhksV35fJ8e8PT89TVV+e5E+T/J/u/s2ppt9L8twDxdt6huPuP07yhUxOcBzWVr8M8oBnJ/mpTD5NTHL7p5IPTfKjNRn55dpM3uxHVdW9VtPNLxn+wD4vkxshjz+K5b/Y3c/u7jO6+7RMfjivSnJVd386yTWZXHJ4wAOz/qnWr8jk09lXDO/RgTNmV1bVd2TyKe3+7n7x8At+ZSYDiDxqnXUlk5+pu2bqe7HGMTn4pktge9hxWZxkd5LXd/cHh1x+Uyb5+0+n2i/oyX1ot2ZyBcOZG+37cMnOnyV5XXf/+jrtj8jkfotHDwcSh3LgXuJD/s04st0FRm7H5fAcj4nXXX2mLrGsqjsn+ZNMCsGnrZn3YUn+49R7nCR/VVX/fFi2Mrk0/qQkj+vJvcmHtS2Kte7+cCY3bU+PMvjkJB/M5Ka/3cPj6zJ5c584Nd8xVXWXqcd6oyHORVX9dlU9oKqOqap7JPmXST7cG9+PcKh1HV9V962J+yd5fibV/BeHWV6c5N9X1T2r6hsy+cW9YJ1VfSaTX47dw+NAEfZPkrwzk/ewanLf2ZdV1T9K8iNJLhn68fCqelBV7RpOCT8/k9HOLh/af/LAWbahn7+c5C1Hur/A+O3ELM7kA67vq6qvGfL44Zns36VT7U+pqq8c9umnM/lU+pNrVzRk6MVJ/rK7D/pkvKoemsmlM4/r7netaTuuqs4a3rtjajK673dmcmXELH8zgG1gJ+bwHI+JU1WPrMn9ZBnm/ZUMo/IO78crM7kt6Ox18vPrMikEdw+PZDL4yWuG5y/K5FL4R3f352fdv2UVaz9Sd/x/Bbesd5ncJj03k0tMDjg7k0tIrp1+ZHKZ3vRp3xdl8qYfePzRnPs17a6ZfMNuSvLRTIYQ/YGNZh7ep+/YoPlemYzm87lMLqk5vyej0hzw7CQfSfLxJH+e5D8On/imqr5qWPdX9cT0+3PDsPx1PRlx7LOZXOf8rzMpwvZlchDya8N8x2Vy6vkzw/bum+QRwyn2ZHIv3Puq6nNDf9+Q5FmHfJeARZHFE/PM4hdncrXB2zK5J+0/J3laf+k+hF/IZPClD2WSr49K8oNT635jVR3IxB/MZIS0H1/zPTpw8/2vZHJv8Bum2g5cUnlsJrl8QyaDOf1Mksd29weH9sP9zQCWQw5PjO6YeJj3YUkumTpufXW+NHDeP03y/Um+N8lNU9+/70iS7r5+zfubJJ/s7s9X1X0yORO3O8m1U8s+6XBvVPXt90ADAAAwFtviMkgAAIDtRrEGAAAwQoo1AACAEVKsAQAAjNAxm11BTf7ny39KsivJf+3u39po3nsdv6vPOH1ho4Ay5YOX3PWgaV/3TX+3qXW+55JbP9ndJ25qJcDcHUkOJ7J41Tabz7IYxskx8fIs4jj3SCwzhzdVrFXVriQvTPLwTP5Xw7ur6nXd/f715j/j9GPzrotP38wmmdFZp+w+aNrFF+/b1Dp3nfzhj29qBcDcHWkOJ7J41Tabz7IYxscx8XIt4jj3SCwzhzd7GeSZmfwDu4929xcy+V8zj9l8twCYkRwGWD1ZzEJstlg7Ncknpl5fOUy7XVWdU1V7q2rvDTfu3+TmAFjjsDmcyGKABXNMzEIsfICR7j63u/d0954TT9i16M0BsA5ZDLBacpijsdkBRq5KMn3B7WnDNJZkvWt2k+Tiq/cttR/AyshhgNWTxSzEZs+svTvJ/arqq6vqTkmekOR1m+8WADOSwwCrJ4tZiE2dWevu26rq6UkuzmSY0vO7+7K59AyAw5LDAKsni1mUTf+fte5+Q5I3zKEvABwFOQywerKYRVj4ACMAAAAcOcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCENv1/1gCA9Z11yu6Dpl189b6l9wOArcmZNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBE6ZrMrqKorktycZH+S27p7z2bXCcDs5DDA6sliFmHTxdrgu7v7k3NaFxs465TdB027+Op9S+8HMEpyeIXWy+dERsMOJIvnbKcf/7oMEgAAYITmUax1kj+rqvdU1TlrG6vqnKraW1V7b7hx/xw2B8Aah8zhRBYDLIFjYuZuHpdBfnt3X1VV907y5qr6QHe//UBjd5+b5Nwk2fPAu/QctgfAHR0yhxNZDLAEjomZu02fWevuq4av1yd5TZIzN7tOAGYnhwFWTxazCJsq1qrqblV1jwPPk3xvkkvn0TEADk8OA6yeLGZRNnsZ5ElJXlNVB9b10u5+06Z7BcCs5DDA6sliFmJTxVp3fzTJA+fUFwCOkBwGWD1ZzKIYuh8AAGCEFGsAAAAjNI+h+1mAnf7f2gHGSj4DsCzOrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACBkNcsXWG1UsMbIYAAA7h2Pi9TmzBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEDDACABtY74b3nX6zOwDL48waAADACCnWAAAARkixBgAAMEKKNQAAgBGaaYCRqjo/yfcnub67HzBMOz7JK5KckeSKJI/v7k8vppvbgxvVgaMlhxdrvXxOZDRwR7J4PhwTz27WM2sXJHnEmmnPTPKW7r5fkrcMrwFYjAsihwFW7YLIYpZopmKtu9+e5FNrJj8myYXD8wuTPHZ+3QJgmhwGWD1ZzLJt5p61k7r7muH5tUlOWm+mqjqnqvZW1d4bbty/ic0BsMZMOZzIYoAFckzMwsxlgJHu7iS9Qdu53b2nu/eceMKueWwOgDUOlcNDuywGWDDHxMzbZoq166rq5CQZvl4/ny4BMCM5DLB6spiFmWk0yA28LsnZSX5r+PraufQIgFnJ4aNgFDJgzmQxCzPTmbWqelmSv0ry9VV1ZVU9NZMfyIdX1YeSfM/wGoAFkMMAqyeLWbaZzqx19xM3aHrYHPsCwAbkMMDqyWKWbS4DjAAAADBfijUAAIAR2swAIxyCG9gBAIDNcGYNAABghBRrAAAAI6RYAwAAGCHFGgAAwAgZYASAbctgTwBsZc6sAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIGQ1yk9YbaSwx2hjAMsliALYjZ9YAAABGSLEGAAAwQoo1AACAEZqpWKuq86vq+qq6dGrac6rqqqraNzwetbhuAuxschhg9WQxyzbrmbULkjxinekv6O7dw+MN8+sWAGtcEDkMsGoXRBazRDMVa9399iSfWnBfANiAHAZYPVnMsm32nrWnV9Ulwynhe86lRwAcCTkMsHqymIXYTLH2oiT3TbI7yTVJfne9marqnKraW1V7b7hx/yY2B8AaM+VwIosBFsgxMQtz1MVad1/X3fu7+4tJ/jDJmRvMd2537+nuPSeesOtoNwfAGrPm8DCvLAZYAMfELNIxR7tgVZ3c3dcML38wyaWHmh+A+dqpOXzWKbsPmnbx1fuW3g+AZOdmMcsxU7FWVS9L8pAk96qqK5M8O8lDqmp3kk5yRZKnLaaLAMhhgNWTxSzbTMVadz9xncnnzbkvAGxADgOsnixm2TY7GiQAAAALoFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEZvqn2AAAAEfqrFN2HzTt4qv3Lb0fW5UzawAAACOkWAMAABghxRoAAMAIKdYAAABGyAAjAIyWG9MB2MmcWQMAABghxRoAAMAIKdYAAABGSLEGAAAwQjMVa1V1elW9tareX1WXVdUzhunHV9Wbq+pDw9d7Lra7ADuTHAZYPVnMss16Zu22JD/f3fdP8q1J/lVV3T/JM5O8pbvvl+Qtw2sA5k8OA6yeLGapZirWuvua7v7r4fnNSS5PcmqSxyS5cJjtwiSPXUAfAXY8OQywerKYZTvie9aq6owkD0ryziQndfc1Q9O1SU5aZ/5zqmpvVe294cb9m+krADnyHB6WkcUAc+SYmGU4omKtqu6e5FVJfq67Pzvd1t2dpNcu093ndvee7t5z4gm7NtVZgJ3uaHJ4aJPFAHPimJhlmblYq6pjM/mhfEl3v3qYfF1VnTy0n5zk+vl3EYBEDgOMgSxmmY6ZZaaqqiTnJbm8u58/1fS6JGcn+a3h62vn3kMAdmwOX3z1voOmnXXK7pnmA5i3nZrFrM5MxVqSByd5cpL3VdW+YdqzMvmBvKiqnprk40keP/ceApDIYYAxkMUs1UzFWne/I0lt0Pyw+XUHgPXIYYDVk8Us2xGPBgkAAMDiKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjNAxq+4AAByJi6/ed9C0s07ZPfO8ALBVOLMGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQOW6xV1elV9daqen9VXVZVzximP6eqrqqqfcPjUYvvLsDOJIsBVksOswqzjAZ5W5Kf7+6/rqp7JHlPVb15aHtBdz9vcd0DYCCLAVZLDrN0hy3WuvuaJNcMz2+uqsuTnLrojgHwJbIYYLXkMKtwRPesVdUZSR6U5J3DpKdX1SVVdX5V3XODZc6pqr1VtfeGG/dvrrcAyGKAFZPDLMvMxVpV3T3Jq5L8XHd/NsmLktw3ye5MPmX43fWW6+5zu3tPd+858YRdm+8xwA4miwFWSw6zTDMVa1V1bCY/lC/p7lcnSXdf1937u/uLSf4wyZmL6yYAshhgteQwyzbLaJCV5Lwkl3f386emnzw12w8muXT+3QMgkcUAqyaHWYVZRoN8cJInJ3lfVe0bpj0ryROraneSTnJFkqctoH8ATMhigNWSwyzdLKNBviNJrdP0hvl3B4D1yGKA1ZLDrMIRjQYJAADAcijWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACCnWAAAARkixBgAAMEKKNQAAgBFSrAEAAIyQYg0AAGCEFGsAAAAjpFgDAAAYIcUaAADACM1UrFXVXarqXVX13qq6rKp+dZj+1VX1zqr6cFW9oqrutNjuAuxMchhg9WQxy3bMjPPdmuSh3X1LVR2b5B1V9cYk/ybJC7r75VX1B0memuRFC+orwE4mhwFWTxZv4KxTdq87/eKr9y21H9vNTGfWeuKW4eWxw6OTPDTJK4fpFyZ57Lw7CIAcBhgDWcyyzXzPWlXtqqp9Sa5P8uYkH0lyU3ffNsxyZZJT11nunKraW1V7b7hx/xy6DLAzHW0OD8vKYoA5cEzMMs1crHX3/u7eneS0JGcm+YYZlzu3u/d0954TT9h1dL0E4KhzeFhWFgPMgWNilumIR4Ps7puSvDXJtyU5rqoO3Pd2WpKr5tc1ANYjhwFWTxazDLOOBnliVR03PP/yJA9PcnkmP6A/PMx2dpLXLqCPADueHAZYPVnMss06GuTJSS6sql2ZFHgXdffrq+r9SV5eVb+W5G+SnLegfgLsdHIYYPVkMUs1U7HW3ZckedA60z+aybW6ACyQHAZYPVnMsh3xPWsAAAAsnmINAABghBRrAAAAI6RYAwAAGCHFGgAAwAgp1gAAAEZIsQYAADBCijUAAIARUqwBAACMkGINAABghBRrAAAAI6RYAwAAGCHFGgAAwAgp1gAAAEZIsQYAADBCijUAAIARUqwBAACMkGINAABghGYq1qrqLlX1rqp6b1VdVlW/Oky/oKo+VlX7hsfuhfYWYIeSwwCrJ4tZtmNmnO/WJA/t7luq6tgk76iqNw5tv9jdr1xM9wAYyGGA1ZPFLNVMxVp3d5JbhpfHDo9eVKcAuCM5DLB6sphlm/metaraVVX7klyf5M3d/c6h6der6pKqekFV3Xmd5c6pqr1VtfeGG/fPp9cAO9DR5vCwrCwGmAPHxCzTzMVad+/v7t1JTktyZlU9IMkvJ/mGJN+c5Pgkv7TOcud2957u3nPiCbvm02uAHehoc3hYVhYDzIFjYpbpiEeD7O6bkrw1ySO6+5qeuDXJHyU5c879A2ANOQywerKYZZh1NMgTq+q44fmXJ3l4kg9U1cnDtEry2CSXLqabADubHAZYPVnMss06GuTJSS6sql2ZFHgXdffrq+p/VdWJSSrJviT/YjHdBNjx5DDA6slilmrW0SAvSfKgdaY/dO49AuAgchhg9WQxy3bE96wBAACweIo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEVKsAQAAjJBiDQAAYIQUawAAACOkWAMAABghxRoAAMAIKdYAAABGSLEGAAAwQoo1AACAEaruXt7Gqm5I8vHh5b2SfHJpG1+O7b5P9+nuE1fZGWDzZPGWJIthG5HDW9JKcnipxdodNly1t7v3rGTjC2KfgK1mO/6O2ydgK9mOv9/2aX5cBgkAADBCijUAAIARWmWxdu4Kt70o9gnYarbj77h9AraS7fj7bZ/mZGX3rAEAALAxl0ECAP//9u7fRaorjMP480VMlcYgLMEosbDZLk1IkcLSpNlUopV/gIWCjdhYpQ1pLLNoIREhIbENImglgo1RkYggKv4oUiSdiG+KvcVkFwLLZOeee+b5NHPvmeYcdnngZX5JkhrksCZJkiRJDRplWEtyJMmjJI+TnB1jD/NKsp7kTZLfZ9Y+SvJbkj+Gxz1j7nG7kuxPciPJgyT3k5wa1id9Lklb9dBh6K/FdlhaLj20uLcOQ1stXviwlmQXcAH4ClgFjidZXfQ+/gcXgSOb1s4C16vqEHB9uJ+Sd8CZqloFvgBODn+bqZ9L0oyOOgz9tdgOS0uioxZfpK8OQ0MtHuOVtc+Bx1X1pKreAleAtRH2MZequgn8uWl5Dbg0XF8CvlnknuZVVS+r6u5w/TfwENjHxM8laYsuOgz9tdgOS0ulixb31mFoq8VjDGv7gGcz98+HtR6sVNXL4foVsDLmZuaR5FPgM+A2HZ1LEtB3h6GTZtlhqXs9t7ibZo3dYr9gZIfUxm8iTPJ3EZJ8CPwEnK6qv2afm/K5JC2fqTbLDkvqxZSb1UKLxxjWXgD7Z+4/GdZ68DrJxwDD45uR97NtSXaz8U95uap+HpYnfy5J/9Jzh2HizbLD0tLoucWTb1YrLR5jWLsDHEpyMMkHwDHg2gj72AnXgBPD9Qng1xH3sm1JAvwAPKyq72aemvS5JG3Rc4dhws2yw9JS6bnFk25WSy3Oxit4i5Xka+B7YBewXlXfLnwTc0ryI3AY2Au8Bs4DvwBXgQPAU+BoVW3+wGWzknwJ3ALuAe+H5XNsvEd3sueStFUPHYb+WmyHpeXSQ4t76zC01eJRhjVJkiRJ0n/zC0YkSZIkqUEOa5IkSZLUIIc1SZIkSWqQw5okSZIkNchhTZIkSZIa5LAmSZIkSQ1yWJMkSZKkBv0DIiZX7JZzxX0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x1296 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "images = [res1,res2,res3,res4]\n",
    "w=10\n",
    "h=10\n",
    "fig=plt.figure(figsize=(18, 18))\n",
    "columns = 3\n",
    "rows = 4\n",
    "for i in range(0, columns*rows ):\n",
    "    img = reses[i]\n",
    "    fig.add_subplot(rows, columns, i+1,title='THE FINAL FI IS : '+str(np.round(angels[i],decimals=4)))\n",
    "  \n",
    "    plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is the third part of assignments that we removed lots of data to compare  the result of this part with the previous part "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<__main__.rule at 0x7f03584bac10>,\n",
       " <__main__.rule at 0x7f03584ba3d0>,\n",
       " <__main__.rule at 0x7f03584bacd0>,\n",
       " <__main__.rule at 0x7f03584bae50>,\n",
       " <__main__.rule at 0x7f0358126d00>,\n",
       " <__main__.rule at 0x7f033e477910>,\n",
       " <__main__.rule at 0x7f033e477460>,\n",
       " <__main__.rule at 0x7f033decdbe0>,\n",
       " <__main__.rule at 0x7f033decdd30>,\n",
       " <__main__.rule at 0x7f033decdf40>,\n",
       " <__main__.rule at 0x7f034e3056a0>,\n",
       " <__main__.rule at 0x7f0358126220>,\n",
       " <__main__.rule at 0x7f033e477820>,\n",
       " <__main__.rule at 0x7f0358126670>,\n",
       " <__main__.rule at 0x7f0357d6dca0>,\n",
       " <__main__.rule at 0x7f03584f0070>,\n",
       " <__main__.rule at 0x7f033e02f940>,\n",
       " <__main__.rule at 0x7f0358126e50>,\n",
       " <__main__.rule at 0x7f033decdb20>,\n",
       " <__main__.rule at 0x7f033decdcd0>,\n",
       " <__main__.rule at 0x7f033decdd00>,\n",
       " <__main__.rule at 0x7f033decde50>,\n",
       " <__main__.rule at 0x7f034e206100>,\n",
       " <__main__.rule at 0x7f033e078a60>,\n",
       " <__main__.rule at 0x7f033e078880>,\n",
       " <__main__.rule at 0x7f033e078e50>,\n",
       " <__main__.rule at 0x7f033e4922b0>,\n",
       " <__main__.rule at 0x7f033e492400>,\n",
       " <__main__.rule at 0x7f033e078f40>,\n",
       " <__main__.rule at 0x7f033e4928e0>,\n",
       " <__main__.rule at 0x7f035826be20>,\n",
       " <__main__.rule at 0x7f035826bca0>,\n",
       " <__main__.rule at 0x7f035826b370>,\n",
       " <__main__.rule at 0x7f035826b610>,\n",
       " <__main__.rule at 0x7f035826be80>,\n",
       " <__main__.rule at 0x7f035826b4f0>,\n",
       " <__main__.rule at 0x7f034e3536a0>,\n",
       " <__main__.rule at 0x7f034e353430>,\n",
       " <__main__.rule at 0x7f034e353670>,\n",
       " <__main__.rule at 0x7f034e353460>,\n",
       " <__main__.rule at 0x7f034e353a30>,\n",
       " <__main__.rule at 0x7f033e4659d0>,\n",
       " <__main__.rule at 0x7f033e465610>]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_rules = new_rules(rules)\n",
    "n_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, -90] -40.00000000000001\n",
      "[7.000610202775657, -71.25146125761518] -40.0\n",
      "[7.247359129178354, -52.50292251523035] -40.0\n",
      "[7.714087263997482, -33.75438377284553] -40.00000000000001\n",
      "[8.35131361487365, -15.005845030460709] -40.0\n",
      "[9.091481525486738, 3.7426937119241153] -40.0\n",
      "[9.856120811595753, 22.491232454308935] -40.0\n",
      "[10.564166913661525, 41.23977119669375] -40.0\n",
      "[11.140555096664405, 59.988309939078576] -40.0\n",
      "[11.52417856812388, 78.7368486814634] -40.0\n",
      "[11.674366820641621, 97.48538742384822] nan\n",
      "[11.575197386252032, 116.23392616623305] nan\n",
      "[1, 0] -20.0\n",
      "[1.939753130473184, 9.846708616080011] -20.0\n",
      "[2.865676580094336, 19.693417232160023] -20.0\n",
      "[3.7505180303843977, 29.540125848240034] -20.000000000000004\n",
      "[4.568234312205961, 39.386834464320046] -21.332828956691223\n",
      "[5.294757923491949, 49.23354308040006] nan\n",
      "[5.903347273193217, 59.71374575351557] nan\n",
      "[1, 90] 20.0\n",
      "[1.000748350519291, 80.15329138391999] 20.0\n",
      "[1.1621147493317405, 70.30658276783998] nan\n",
      "[1.4793497665637947, 60.459874151759976] nan\n",
      "[7, 180] 40.0\n",
      "[6.23372907937324, 161.25146125761518] 39.99999999999999\n",
      "[5.5084689939931835, 142.50292251523035] 40.00000000000001\n",
      "[4.901109449163688, 123.75438377284553] 40.0\n",
      "[4.476040719983037, 105.0058450304607] 40.0\n",
      "[4.2783272046987335, 86.25730628807588] 20.0\n",
      "[4.328929844401081, 67.50876754569106] nan\n",
      "[4.688943512113748, 57.66205892961105] nan\n",
      "[12.71, 100.98] nan\n",
      "[15.2, 152.24] 20.000000000000004\n",
      "[14.368996839360182, 142.39329138392] 20.0\n",
      "[13.625230421270343, 132.54658276784] nan\n",
      "[12.990591712319683, 122.69987415175999] nan\n",
      "[1.22, -75.2] nan\n",
      "[7.48, 57.91] nan\n",
      "[4.16, 48.21] nan\n",
      "[17, 90.84] -20.0\n",
      "[16.9869782514679, 100.68670861608001] nan\n",
      "[16.81353444409675, 110.53341723216002] nan\n",
      "[18, 80.0] nan\n",
      "[15, 60] -40.0\n",
      "[15.383488193000414, 78.74853874238482] -40.0\n",
      "[15.533523210016334, 97.49707748476965] -20.0\n",
      "[15.43419882860955, 116.24561622715447] nan\n",
      "[15.019488289685764, 126.09232484323448] nan\n",
      "[14, 45] -40.0\n",
      "[14.542051747724626, 63.748538742384824] -40.0\n",
      "[14.88137025162813, 82.49707748476965] -33.71768096002263\n",
      "[14.981982072257255, 101.24561622715447] nan\n",
      "[14.820470454609278, 117.36045458541649] nan\n",
      "nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-170-34a2a972cb60>:58: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return up/down\n"
     ]
    }
   ],
   "source": [
    "\n",
    "angels = []\n",
    "reses = []\n",
    "rules = n_rules\n",
    "pos = [7,-90]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())\n",
    "angels.append(pos[1])  \n",
    "######################################################################\n",
    "pos = [1,0]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())\n",
    "angels.append(pos[1])  \n",
    "########################################################################\n",
    "pos = [1,90]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())\n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [7,180]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())\n",
    "angels.append(pos[1]) \n",
    "########################################################################    \n",
    "pos = [12.71,100.98]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())  \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "########################################################################\n",
    "pos = [15.20,152.24]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())  \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [1.22,-75.2]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy()) \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [7.48,57.91]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())  \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [4.16,48.21]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())  \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [17,90.84]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())   \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [18,80.]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())   \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [15,60]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())   \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "pos = [14,45]\n",
    "theta = eval_rulse(pos[0],pos[1],rules)\n",
    "next_pos = [0,0]\n",
    "ground = np.ones([40,21])\n",
    "for i in range(40):\n",
    "    if  math.isnan( pos[0]):\n",
    "        break\n",
    "    ground[i,int(pos[0])] = 0\n",
    "    next_pos[0]= pos[0]+np.cos((pos[1]+theta)*(3.14/180))+np.sin(theta*(3.14/180))*np.sin(pos[1]*(3.14/180))\n",
    "    next_pos[1]= pos[1]- np.arcsin(2*np.sin(theta*(3.14/180))/4)*(180/3.14)\n",
    "    theta = eval_rulse(pos[0],pos[1],rules)\n",
    "    print(pos, theta)\n",
    "    pos = copy.copy(next_pos)\n",
    "\n",
    "        \n",
    "reses.append(ground.copy())   \n",
    "angels.append(pos[1]) \n",
    "########################################################################\n",
    "# plt.figure(figsize=[10,10])\n",
    "# to_show = cv.resize(to_show,(1200,1000))\n",
    "# plt.imshow(to_show,'gray')\n",
    "print(pos[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# As you can see the result is awful !!! but we expected that because now we have much more smaller dataset and we can see the truck couldn't finish its mission and stopped at the bigining of the time !!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "images = [res1,res2,res3,res4]\n",
    "w=10\n",
    "h=10\n",
    "fig=plt.figure(figsize=(18, 18))\n",
    "columns = 3\n",
    "rows = 4\n",
    "for i in range(0, columns*rows ):\n",
    "    img = reses[i]\n",
    "    fig.add_subplot(rows, columns, i+1,title='THE FINAL FI IS : '+str(np.round(angels[i],decimals=4)))\n",
    "  \n",
    "    plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CONCLUSION\n",
    "# So fuzzy logic is a nice approch for solving problems without much data but for this problem we can suggest something:\n",
    "# we are suffering from lack of data that the FI is negative and the algorithm does not work well there so it's good to generate some data with same distribution as past for negative fi and by doing this we can achieve a very high accurate non-linear fuzzy control system :)))))"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}