Chicago_copy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chicago: \"we makin' cars\"\n",
    "Implement the Formula SAE contectualized design problem from Zurita et al"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TO DO: CHECK convexity \n",
    "convergence of agents in space\n",
    "slices in 2d\n",
    "have them only go, downhill they all get to the same solution?\n",
    "sample space in different places, Hessian, compare "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import chi\n",
    "# from opteval import benchmark_func as bf\n",
    "import matplotlib\n",
    "\n",
    "#for server:\n",
    "# matplotlib.use('Agg')\n",
    "\n",
    "#for retina display:\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import copy\n",
    "import time as timer\n",
    "import threading\n",
    "\n",
    "from multiprocessing import Pool\n",
    "from playsound import playsound\n",
    "# from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cp(x): #make a copy instead of reference\n",
    "    return copy.deepcopy(x)\n",
    "\n",
    "def isNumber(s):\n",
    "    try:\n",
    "        float(s)\n",
    "        return True\n",
    "    except ValueError:\n",
    "        return False\n",
    "\n",
    "def isNaN(num):\n",
    "    return num != num\n",
    "\n",
    "def mean(x):\n",
    "    return np.mean(x)\n",
    "\n",
    "def norm(x):\n",
    "    return float(np.linalg.norm(x))\n",
    "\n",
    "def dist(x,y):\n",
    "    return np.linalg.norm(np.array(x)-np.array(y))\n",
    "\n",
    "def bounds(x,low,high):\n",
    "    if x > high:\n",
    "        return high\n",
    "    if x < low:\n",
    "        return low\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeAiScore():\n",
    "    ai = np.random.normal(97,17)\n",
    "    ai = bounds(ai, 40,150)\n",
    "    return ai\n",
    "\n",
    "def makeIqScore(): #this IQ ranges from 0 to 1, bc it is basically efficiency\n",
    "    iq = np.random.normal(0.5,0.2)\n",
    "    iq = bounds(iq, 0.1,1.0)\n",
    "    return iq\n",
    "\n",
    "def pickWorseScore(betterScore,worseScore,temperature):\n",
    "    if temperature <=1E-6: #never pick worse answers, and avoid devide by 0\n",
    "        return False\n",
    "    if np.random.uniform(0,1) < np.exp((betterScore-worseScore)/temperature):  #\n",
    "        return True \n",
    "    return False\n",
    "\n",
    "def calculateDecay(steps,T0=1.0,Tf=0.01):\n",
    "    if T0<=Tf or T0<=0: \n",
    "        return 0\n",
    "    return (Tf / float(T0) ) ** (1/steps)\n",
    "  \n",
    "def calculateAgentDecay(agent, steps):\n",
    "    E_N = normalizedE(agent.kai.E)\n",
    "    E_transformed = np.exp((E_N*-1)+3.3)\n",
    "    startEndRatio = bounds(1/E_transformed, 1E-10,1)\n",
    "    T0 = agent.temp\n",
    "    TF = T0 * startEndRatio\n",
    "    return calculateDecay(steps,T0,TF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chaching():\n",
    "    playsound(\"/Users/samlapp/SAE_ABM/missionComplete.wav\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## constants and params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {},
   "outputs": [],
   "source": [
    "complexSharing = True #if False, shareBasic() is used, eg strictly-greedy one-dimension jump \n",
    "commBonus = 10 #increasing the communication bonus makes successful communication more likely\n",
    "commRange = 180\n",
    "pCommTime = None\n",
    "selfBias = 0 #increasing self bias will make agents choose their solutions more over others\n",
    "startRange = 10\n",
    "nDims = 56\n",
    "\n",
    "SO_STRENGTH = 10\n",
    "RG_STRENGTH = 10\n",
    "TEAM_MEETING_COST = 1 #1 turn\n",
    "\n",
    "VERBOSE = False\n",
    "showViz = False\n",
    "\n",
    "AVG_SPEED = 7.0E-3\n",
    "SD_SPEED = 7.0E-4\n",
    "MIN_SPEED = 1.0E-4\n",
    "AVG_TEMP = 1\n",
    "SD_TEMP = 0.5\n",
    "\n",
    "steps = 500 #0 #100\n",
    "nAgents = 33\n",
    "constructor = carDesigner\n",
    "pComm = 0.2\n",
    "pCommTime = None\n",
    "meetingTimes = steps #have one meeting at the end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Table of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>name</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "      <th>kind</th>\n",
       "      <th>minV</th>\n",
       "      <th>maxV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>rear wing height</td>\n",
       "      <td>hrw</td>\n",
       "      <td>rw</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>rear wing length</td>\n",
       "      <td>lrw</td>\n",
       "      <td>rw</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>rear wing width</td>\n",
       "      <td>wrw</td>\n",
       "      <td>rw</td>\n",
       "      <td>1</td>\n",
       "      <td>300</td>\n",
       "      <td>r_track-2*rear_wheel_r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>rear wing angle of attack</td>\n",
       "      <td>arw</td>\n",
       "      <td>rw</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7853981634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>rear wing y position</td>\n",
       "      <td>yrw</td>\n",
       "      <td>rw</td>\n",
       "      <td>1</td>\n",
       "      <td>.500 + (h_rw)/2</td>\n",
       "      <td>1.200 -  (h_rw)/2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                       name variable team  kind             minV  \\\n",
       "0           0           rear wing height      hrw   rw     1            0.025   \n",
       "1           1           rear wing length      lrw   rw     1             0.05   \n",
       "2           2            rear wing width      wrw   rw     1              300   \n",
       "3           3  rear wing angle of attack      arw   rw     1                0   \n",
       "4           5       rear wing y position      yrw   rw     1  .500 + (h_rw)/2   \n",
       "\n",
       "                     maxV  \n",
       "0                     0.7  \n",
       "1                    0.25  \n",
       "2  r_track-2*rear_wheel_r  \n",
       "3            0.7853981634  \n",
       "4       1.200 -  (h_rw)/2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#FIRST TIME:\n",
    "paramsDF = pd.read_csv(\"./SAE/paramDB.csv\")\n",
    "paramsDF.columns = ['paramID','name','variable','team','kind','minV','maxV','used']\n",
    "# paramsDF.at[3,\"maxV\"] = np.pi/4\n",
    "# paramsDF.at[10,\"maxV\"] = np.pi/4\n",
    "paramsDF.at[17,\"maxV\"] = np.pi /4\n",
    "paramsDF.used = pd.to_numeric(paramsDF.used)\n",
    "paramsDF = paramsDF.drop(columns=[\"paramID\"],axis=1)\n",
    "#remove unused variables... print(len(paramsDF))\n",
    "paramsDF = paramsDF.loc[paramsDF.used > 0 ]\n",
    "paramsDF.to_csv(\"./SAE/paramDBreduced.csv\")\n",
    "paramsDF = pd.read_csv(\"./SAE/paramDBreduced.csv\")\n",
    "print(len(paramsDF))\n",
    "paramsDF = paramsDF.drop([\"used\"],axis=1)\n",
    "paramsDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>name</th>\n",
       "      <th>variable</th>\n",
       "      <th>team</th>\n",
       "      <th>kind</th>\n",
       "      <th>minV</th>\n",
       "      <th>maxV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>side wings angle of attack</td>\n",
       "      <td>asw</td>\n",
       "      <td>sw</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7853981633974483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                        name variable team  kind minV  \\\n",
       "15          17  side wings angle of attack      asw   sw     1    0   \n",
       "\n",
       "                  maxV  \n",
       "15  0.7853981633974483  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paramsDF.loc[paramsDF.variable == \"asw\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#logical vector for parameters with fixed min/max (TRUE), or min/max as f(p) (FALSE)\n",
    "hasNumericBounds = [True if isNumber(row.minV) and isNumber(row.maxV) else False for i, row in paramsDF.iterrows()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Material</th>\n",
       "      <th>Code</th>\n",
       "      <th>q</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Glass-filled epoxy (35%)</td>\n",
       "      <td>GFE</td>\n",
       "      <td>1890</td>\n",
       "      <td>1900000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Glass-filled polyester (35%)</td>\n",
       "      <td>GFP</td>\n",
       "      <td>1989</td>\n",
       "      <td>2000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Glass-filled nylon (35%)</td>\n",
       "      <td>GFN</td>\n",
       "      <td>1607</td>\n",
       "      <td>1600000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>S-glass epoxy (45%)</td>\n",
       "      <td>SGE</td>\n",
       "      <td>1781</td>\n",
       "      <td>1800000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Carbon epoxy (61%)</td>\n",
       "      <td>CE</td>\n",
       "      <td>1627</td>\n",
       "      <td>1600000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                      Material Code     q           E\n",
       "0           0      Glass-filled epoxy (35%)  GFE  1890  1900000000\n",
       "1           1  Glass-filled polyester (35%)  GFP  1989  2000000000\n",
       "2           2      Glass-filled nylon (35%)  GFN  1607  1600000000\n",
       "3           3           S-glass epoxy (45%)  SGE  1781  1800000000\n",
       "4           4            Carbon epoxy (61%)   CE  1627  1600000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# materialsDF = pd.read_csv(\"/Users/samlapp/Documents/THRED Lab/SAE/materials.csv\")\n",
    "# materialsDF.q = [int(1 + np.random.uniform(0.98,1.02)*materialsDF.iloc[i]['q']) for i in range(len(materialsDF))]\n",
    "# materialsDF.to_csv(\"/Users/samlapp/Documents/THRED Lab/SAE/materialsTweaked.csv\")\n",
    "materialsDF = pd.read_csv(\"./SAE/materialsTweaked.csv\")\n",
    "materialsDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>radius</th>\n",
       "      <th>mass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T1</td>\n",
       "      <td>0.22860</td>\n",
       "      <td>3.636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T2</td>\n",
       "      <td>0.22987</td>\n",
       "      <td>4.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T3</td>\n",
       "      <td>0.23241</td>\n",
       "      <td>4.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T4</td>\n",
       "      <td>0.24638</td>\n",
       "      <td>4.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T5</td>\n",
       "      <td>0.24765</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>T6</td>\n",
       "      <td>0.26670</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>T7</td>\n",
       "      <td>0.26670</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T8</td>\n",
       "      <td>0.26162</td>\n",
       "      <td>5.455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID   radius   mass\n",
       "0  T1  0.22860  3.636\n",
       "1  T2  0.22987  4.091\n",
       "2  T3  0.23241  4.545\n",
       "3  T4  0.24638  4.545\n",
       "4  T5  0.24765  5.000\n",
       "5  T6  0.26670  5.000\n",
       "6  T7  0.26670  5.000\n",
       "7  T8  0.26162  5.455"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tiresDF = pd.read_csv(\"./SAE/tires.csv\")\n",
    "tiresDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ind</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>le</th>\n",
       "      <th>we</th>\n",
       "      <th>he</th>\n",
       "      <th>me</th>\n",
       "      <th>Phi_e</th>\n",
       "      <th>T_e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>M1</td>\n",
       "      <td>GX200</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.346</td>\n",
       "      <td>16.1</td>\n",
       "      <td>4157</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>M2</td>\n",
       "      <td>GX240</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.422</td>\n",
       "      <td>27.5</td>\n",
       "      <td>5980</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>M3</td>\n",
       "      <td>GX270</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.422</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6208</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>M4</td>\n",
       "      <td>GX340</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.449</td>\n",
       "      <td>31.5</td>\n",
       "      <td>7866</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>M5</td>\n",
       "      <td>GXV340</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.382</td>\n",
       "      <td>0.406</td>\n",
       "      <td>32.3</td>\n",
       "      <td>6677</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ind  id    name     le     we     he    me  Phi_e   T_e\n",
       "0    0  M1   GX200  0.321  0.376  0.346  16.1   4157  12.4\n",
       "1    1  M2   GX240  0.380  0.429  0.422  27.5   5980  18.3\n",
       "2    2  M3   GX270  0.380  0.429  0.422  25.0   6208  19.1\n",
       "3    3  M4   GX340  0.407  0.485  0.449  31.5   7866  26.4\n",
       "4    4  M5  GXV340  0.433  0.382  0.406  32.3   6677  21.6"
      ]
     },
     "execution_count": 1019,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# motorsDF = pd.read_csv(\"/Users/samlapp/Documents/THRED Lab/SAE/motors.csv\")\n",
    "# # first time: we want to make motors with the same power slightly different:\n",
    "# motorsDF.Power = [int(1 + np.random.uniform(0.98,1.02)*motorsDF.iloc[i]['Power']) for i in range(len(motorsDF))]\n",
    "# motorsDF.to_csv(\"/Users/samlapp/Documents/THRED Lab/SAE/motorsTweaked.csv\")\n",
    "enginesDF = pd.read_csv(\"./SAE/motorsTweaked.csv\")\n",
    "print(\"unique\" if len(enginesDF)-len(np.unique(enginesDF.Power)) == 0 else \"not uniuqe\")\n",
    "enginesDF.columns = [\"ind\",\"id\",\"name\",\"le\",\"we\",\"he\",\"me\",\"Phi_e\",\"T_e\"]\n",
    "enginesDF.at[0,'T_e'] = 12.4\n",
    "enginesDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>krsp</th>\n",
       "      <th>crsp</th>\n",
       "      <th>mrsp</th>\n",
       "      <th>kfsp</th>\n",
       "      <th>cfsp</th>\n",
       "      <th>mfsp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S1</td>\n",
       "      <td>3982</td>\n",
       "      <td>600</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3982</td>\n",
       "      <td>600</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S2</td>\n",
       "      <td>21189</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21189</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S3</td>\n",
       "      <td>20784</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20784</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S4</td>\n",
       "      <td>21231</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.3</td>\n",
       "      <td>21231</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S5</td>\n",
       "      <td>20650</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.3</td>\n",
       "      <td>20650</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   krsp  crsp  mrsp   kfsp  cfsp  mfsp\n",
       "0  S1   3982   600   0.3   3982   600   0.3\n",
       "1  S2  21189  2000   0.3  21189  2000   0.3\n",
       "2  S3  20784  2000   0.3  20784  2000   0.3\n",
       "3  S4  21231  2000   0.3  21231  2000   0.3\n",
       "4  S5  20650  2000   0.3  20650  2000   0.3"
      ]
     },
     "execution_count": 1020,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# susDF = pd.read_csv(\"/Users/samlapp/Documents/THRED Lab/SAE/suspension.csv\")\n",
    "# susDF.krsp = [int(np.random.uniform(0.98,1.02)*susDF.iloc[i]['krsp']) for i in range(len(susDF))]\n",
    "# susDF.kfsp = susDF.krsp\n",
    "# susDF.to_csv(\"/Users/samlapp/Documents/THRED Lab/SAE/suspensionTweaked.csv\")\n",
    "susDF = pd.read_csv(\"./SAE/suspensionTweaked.csv\")\n",
    "print(\"unique\" if len(susDF)-len(np.unique(susDF.krsp)) == 0 else \"not uniuqe\")\n",
    "susDF = susDF.drop(columns=[susDF.columns[0]])\n",
    "susDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1021,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brakeID</th>\n",
       "      <th>qbrk</th>\n",
       "      <th>lbrk</th>\n",
       "      <th>hbrk</th>\n",
       "      <th>wbrk</th>\n",
       "      <th>tbrk</th>\n",
       "      <th>rbrk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.025447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0175</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.024598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0615</td>\n",
       "      <td>0.0175</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.027990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0380</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.030169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.031523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  brakeID  qbrk  lbrk    hbrk    wbrk   tbrk      rbrk\n",
       "0      B1   0.4  0.10  0.0425  0.0200  0.350  0.025447\n",
       "1      B2   0.4  0.14  0.0490  0.0175  0.350  0.024598\n",
       "2      B3   0.4  0.14  0.0615  0.0175  0.350  0.027990\n",
       "3      B4   0.5  0.10  0.0380  0.0160  0.350  0.030169\n",
       "4      B5   0.5  0.10  0.0425  0.0200  0.355  0.031523"
      ]
     },
     "execution_count": 1021,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# brakesDF = pd.read_csv(\"/Users/samlapp/Documents/THRED Lab/SAE/brakes.csv\")\n",
    "# brakesDF.columns = [a.strip() for a in brakesDF.columns]\n",
    "# brakesDF.rbrk = [np.random.uniform(0.98,1.02)*brakesDF.iloc[i]['rbrk'] for i in range(len(brakesDF))]\n",
    "# brakesDF.to_csv(\"/Users/samlapp/Documents/THRED Lab/SAE/brakesTweaked.csv\")\n",
    "brakesDF = pd.read_csv(\"./SAE/brakesTweaked.csv\")\n",
    "print(\"unique\" if len(brakesDF)-len(np.unique(brakesDF['rbrk'])) == 0 else \"not uniuqe\")\n",
    "brakesDF = brakesDF.drop(columns=[brakesDF.columns[0]])\n",
    "brakesDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paramsDF.variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1023,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Params:\n",
    "    def __init__(self,v = paramsDF):\n",
    "        self.vars = v.variable\n",
    "        self.team = v.team\n",
    "        for i, row in v.iterrows():\n",
    "            setattr(self, row.variable.strip(),-1)\n",
    "p = Params()\n",
    "for v in p.vars:\n",
    "    value = np.random.uniform()\n",
    "    setattr(p,v,value)\n",
    "paramsDF.loc[paramsDF.variable==\"hrw\"][\"team\"][0]\n",
    "\n",
    "teams = np.unique(paramsDF.team)\n",
    "teamDimensions = [[row.team == t for i, row in paramsDF.iterrows()] for t in teams]\n",
    "teamDictionary = {}\n",
    "for i in range(len(teams)):\n",
    "    teamDictionary[teams[i]] = teamDimensions[i]\n",
    "paramList = np.array(paramsDF.variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1024,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert parameter vector to Parameter object\n",
    "pNames = paramsDF.variable\n",
    "blankParameterObject = Params()\n",
    "def asParameters(pList):\n",
    "    p = cp(blankParameterObject)\n",
    "    for i in range(len(pList)):\n",
    "        setattr(p,pNames[i],pList[i])\n",
    "    return p\n",
    "\n",
    "numberParameters = len(paramsDF)\n",
    "def asVector(params):\n",
    "    r = np.zeros(numberParameters)\n",
    "    for i in range(numberParameters):\n",
    "        pName = pNames[i]\n",
    "        r[i] = getattr(params,pName)\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1025,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = startParams()\n",
    "v = asVector(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1026,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "810 µs ± 19.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "581 µs ± 6.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit asParameters(v)\n",
    "%timeit asVector(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective Subfunctions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### constants\n",
    "\n",
    "The car’s top velocity vcar is 26.8 m/s (60 mph).\n",
    "\n",
    "The car’s engine speed x_e is 3600 rpm. \n",
    "\n",
    "The density of air q_air during the race is 1.225 kg/m3. \n",
    "\n",
    "The track radio of curvature r_track is 9 m. \n",
    "\n",
    "The pressure applied to the brakes Pbrk is 1x10^7 Pa\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1027,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scale parameters to go between unit cube (approximately) and SI units\n",
    "paramMaxValues = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "metadata": {},
   "outputs": [],
   "source": [
    "v_car = 26.8 #m/s (60 mph)\n",
    "w_e = 3600 * 60 * 2 *np.pi #rpm  to radians/sec \n",
    "rho_air = 1.225 #kg/m3.\n",
    "r_track = 9 #m\n",
    "P_brk = 10**7 #Pascals\n",
    "C_dc = 0.04 #drag coefficient of cabin\n",
    "gravity = 9.81 #m/s^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mass (minimize)\n",
    "def mrw(p):\n",
    "    return p.lrw * p.wrw *p.hrw * p.qrw\n",
    "def mfw(p):\n",
    "    return p.lfw * p.wfw *p.hfw * p.qfw\n",
    "def msw(p):\n",
    "    return p.lsw * p.wsw *p.hsw * p.qsw\n",
    "def mia(p):\n",
    "    return p.lia * p.wia *p.hia * p.qia\n",
    "def mc(p):\n",
    "    return 2*(p.hc*p.lc*p.tc + p.hc*p.wc*p.tc + p.lc*p.hc*p.tc)*p.qc\n",
    "def mbrk(p):\n",
    "    #CHRIS missing parameters: how is mbrk calculated? assuming lrw*rho\n",
    "    return p.lbrk * p.wbrk * p.hbrk * p.qbrk\n",
    "def mass(p): #total mass, minimize\n",
    "    mass = mrw(p) + mfw(p) + 2 * msw(p) + 2*p.mrt + 2*p.mft + p.me + mc(p) + mia(p) + 4*mbrk(p) + 2*p.mrsp + 2*p.mfsp\n",
    "    return mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "metadata": {},
   "outputs": [],
   "source": [
    "#center of gravity height, minimize\n",
    "def cGy(p): \n",
    "    t1 =  (mrw(p)*p.yrw + mfw(p)*p.yfw+ p.me*p.ye + mc(p)*p.yc + mia(p)*p.yia) / mass(p)\n",
    "    t2 = 2* (msw(p)*p.ysw + p.mrt*p.rrt + p.mft*p.rft + mbrk(p)*p.rft + p.mrsp*p.yrsp + p.mfsp*p.yfsp) / mass(p)\n",
    "    \n",
    "    return t1 + t2  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drag (minimize) and downforce (maximize)\n",
    "def AR(w,alpha,l): #aspect ratio of a wing\n",
    "    return w* np.cos(alpha) / l\n",
    "    \n",
    "def C_lift(AR,alpha): #lift coefficient of a wing\n",
    "    return 2*np.pi* (AR / (AR + 2)) * alpha\n",
    "\n",
    "def C_drag(C_lift, AR): #drag coefficient of wing\n",
    "    return C_lift**2 / (np.pi * AR)\n",
    "\n",
    "def F_down_wing(w,h,l,alpha,rho_air,v_car): #total downward force of wing\n",
    "    wingAR = AR(w,alpha,l)\n",
    "    C_l = C_lift(wingAR, alpha)\n",
    "    return 0.5 * alpha * h * w * rho_air * (v_car**2) * C_l\n",
    "\n",
    "def F_drag_wing(w,h,l,alpha,rho_air,v_car): #total drag force on a wing\n",
    "    wingAR = AR(w,alpha,l)\n",
    "#     print(wingAR)\n",
    "    C_l = C_lift(wingAR, alpha)\n",
    "#     print(C_l)\n",
    "    C_d = C_drag(C_l,wingAR)\n",
    "#     print(C_d)\n",
    "    return F_drag(w,h,rho_air,v_car,C_d)\n",
    "    \n",
    "def F_drag(w,h,rho_air,v_car,C_d):\n",
    "    return 0.5*w*h*rho_air*v_car**2*C_d\n",
    "\n",
    "def F_drag_total(p): #total drag on vehicle\n",
    "    cabinDrag = F_drag(p.wc,p.hc,rho_air,v_car,C_dc)\n",
    "    rearWingDrag = F_drag_wing(p.wrw,p.hrw,p.lrw,p.arw,rho_air,v_car)\n",
    "    frontWingDrag = F_drag_wing(p.wfw,p.hfw,p.lfw,p.afw,rho_air,v_car)\n",
    "    sideWingDrag = F_drag_wing(p.wsw,p.hsw,p.lsw,p.asw,rho_air,v_car)\n",
    "    return rearWingDrag + frontWingDrag + 2* sideWingDrag + cabinDrag\n",
    "\n",
    "def F_down_total(p): #total downforce\n",
    "    downForceRearWing = F_down_wing(p.wrw,p.hrw,p.lrw,p.arw,rho_air,v_car)\n",
    "    downForceFrontWing = F_down_wing(p.wfw,p.hfw,p.lfw,p.afw,rho_air,v_car)\n",
    "    downForceSideWing = F_down_wing(p.wsw,p.hsw,p.lsw,p.asw,rho_air,v_car)\n",
    "    return downForceRearWing + downForceFrontWing + 2*downForceSideWing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1032,
   "metadata": {},
   "outputs": [],
   "source": [
    "#acceleration (maximize)\n",
    "def rollingResistance(p,tirePressure,v_car):\n",
    "    C = .005 + 1/tirePressure * (.01 + .0095 * (v_car**2))\n",
    "    return C * mass(p) * gravity\n",
    "\n",
    "def acceleration(p):\n",
    "    mTotal = mass(p)\n",
    "    tirePressure = p.Prt #CHRIS should it be front or rear tire pressure?\n",
    "    total_resistance = F_drag_total(p) + rollingResistance(p, tirePressure,v_car)\n",
    "    \n",
    "    w_wheels = v_car / p.rrt #rotational speed of rear tires\n",
    "    \n",
    "    efficiency = total_resistance * v_car / p.Phi_e\n",
    "    \n",
    "    torque = p.T_e\n",
    "    \n",
    "    #converted units of w_e from rpm to rad/s !!!\n",
    "    F_wheels = torque * efficiency * w_e /(p.rrt * w_wheels) \n",
    "    \n",
    "    return (F_wheels - total_resistance) / mTotal\n",
    "# acceleration(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1033,
   "metadata": {},
   "outputs": [],
   "source": [
    "#crash force (minimize)\n",
    "def crashForce(p):\n",
    "    return np.sqrt(mass(p) * v_car**2 * p.wia * p.hia * p.Eia / (2*p.lia))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "metadata": {},
   "outputs": [],
   "source": [
    "#impact attenuator volume (minimize)\n",
    "def iaVolume(p):\n",
    "    return p.lia*p.wia*p.hia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1035,
   "metadata": {},
   "outputs": [],
   "source": [
    "#corner velocity (maximize)\n",
    "y_suspension = 0.05 # m\n",
    "dydt_suspension = 0.025 #m/s \n",
    "def suspensionForce(k,c):\n",
    "    return k*y_suspension + c*dydt_suspension\n",
    "\n",
    "def cornerVelocity(p):\n",
    "    F_fsp = suspensionForce(p.kfsp,p.cfsp)\n",
    "    F_rsp = suspensionForce(p.krsp,p.crsp)\n",
    "    downforce = F_down_total(p)\n",
    "    mTotal = mass(p)\n",
    "    \n",
    "    #CHRIS again, rear tire pressure?\n",
    "    C = rollingResistance(p,p.Prt,v_car)\n",
    "    forces = downforce+mTotal*gravity-2*F_fsp-2*F_rsp\n",
    "    if forces < 0: \n",
    "        return 0\n",
    "    return np.sqrt( forces * C * r_track / mTotal )\n",
    "# cornerVelocity(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1036,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.049\n",
      "0.0175\n",
      "28183\n",
      "2500\n",
      "rear\n",
      "3982\n",
      "600\n",
      "66.2462778701892\n",
      "0.22987\n",
      "0.7656600250205535\n",
      "now random\n",
      "0.049\n",
      "0.0175\n",
      "20650\n",
      "2000\n",
      "rear\n",
      "20650\n",
      "2000\n",
      "716.7670551704774\n",
      "0.2286\n",
      "0.758\n"
     ]
    }
   ],
   "source": [
    "p1 = pc\n",
    "print(p1.hbrk)\n",
    "print(p1.wbrk)\n",
    "print(p1.kfsp)\n",
    "print(p1.cfsp)\n",
    "print('rear')\n",
    "print(p1.krsp)\n",
    "print(p1.crsp)\n",
    "print(mass(p1))\n",
    "print(p1.rrt)\n",
    "print(p1.Prt)\n",
    "print('now random')\n",
    "p1 = startParams()\n",
    "print(p1.hbrk)\n",
    "print(p1.wbrk)\n",
    "print(p1.kfsp)\n",
    "print(p1.cfsp)\n",
    "print('rear')\n",
    "print(p1.krsp)\n",
    "print(p1.crsp)\n",
    "print(mass(p1))\n",
    "print(p1.rrt)\n",
    "print(p1.Prt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1037,
   "metadata": {},
   "outputs": [],
   "source": [
    "#breaking distance (minimize)\n",
    "def breakingDistance(p):\n",
    "    mTotal = mass(p)\n",
    "    C = rollingResistance(p,p.Prt,v_car)\n",
    "    \n",
    "    #CHRIS need c_brk break friction coef, and A_brk (rectangle or circle?)\n",
    "    #breaking torque\n",
    "    A_brk = p.hbrk * p.wbrk\n",
    "    c_brk = .37 #?   most standard brake pads is usually in the range of 0.35 to 0.42\n",
    "    Tbrk = 2 * c_brk * P_brk * A_brk * p.rbrk\n",
    "    \n",
    "    #y forces:\n",
    "    F_fsp = suspensionForce(p.kfsp,p.cfsp)\n",
    "    F_rsp = suspensionForce(p.krsp,p.crsp)\n",
    "    Fy = mTotal*gravity + F_down_total(p) - 2 * F_rsp - 2*F_fsp\n",
    "    if Fy<=0: Fy = 1E-10\n",
    "    \n",
    "    #breaking accelleration\n",
    "    #CHRIS front and rear tire radius are same? (rrt and rft)\n",
    "    a_brk = Fy * C / mTotal + 4*Tbrk*C/(p.rrt*mTotal)\n",
    "    \n",
    "    #breaking distance\n",
    "    return v_car**2 / (2*a_brk)\n",
    "# breakingDistance(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "metadata": {},
   "outputs": [],
   "source": [
    "#suspension acceleration (minimize)\n",
    "def suspensionAcceleration(p):\n",
    "    Ffsp = suspensionForce(p.kfsp,p.cfsp)\n",
    "    Frsp = suspensionForce(p.krsp,p.crsp)\n",
    "    mTotal = mass(p)\n",
    "    Fd = F_down_total(p)\n",
    "    return (2*Ffsp - 2*Frsp - mTotal*gravity - Fd)/mTotal\n",
    "# suspensionAcceleration(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1039,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pitch moment (minimize)\n",
    "def pitchMoment(p):\n",
    "    Ffsp = suspensionForce(p.kfsp,p.cfsp)\n",
    "    Frsp = suspensionForce(p.krsp,p.crsp)\n",
    "    \n",
    "    downForceRearWing = F_down_wing(p.wrw,p.hrw,p.lrw,p.arw,rho_air,v_car)\n",
    "    downForceFrontWing = F_down_wing(p.wfw,p.hfw,p.lfw,p.afw,rho_air,v_car)\n",
    "    downForceSideWing = F_down_wing(p.wsw,p.hsw,p.lsw,p.asw,rho_air,v_car)\n",
    "    #CHRIS assuming lcg is lc? and lf is ?\n",
    "    lcg = p.lc\n",
    "    lf = 0.5\n",
    "    return 2*Ffsp*lf + 2*Frsp*lf + downForceRearWing*(lcg - p.lrw) - downForceFrontWing*(lcg-p.lfw) - 2*downForceSideWing*(lcg-p.lsw)  \n",
    "# pitchMoment(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Objective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1040,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Global objective: linear sum of objective subfunctions\n",
    "#sub-objectives to maximize will be mirrored *-1 to become minimizing\n",
    "\n",
    "subObjectives = [mass,cGy,F_drag_total,F_down_total,acceleration,crashForce,iaVolume,cornerVelocity,breakingDistance,suspensionAcceleration,pitchMoment]  \n",
    "alwaysMinimize = [1,1,1,-1,-1,1,1,-1,1,1,1] #1 for minimizing, -1 for maximizing\n",
    "weightsNull = np.ones(len(subObjectives)) / len(subObjectives)\n",
    "weights1 = np.array([14,1,20,30,10,1,1,10,10,2,1])/100\n",
    "weights2 = np.array([25,1,15,20,15,1,1,15,5,1,1])/100\n",
    "weights3 = np.array([14,1,20,15,25,1,1,10,10,2,1])/100\n",
    "\n",
    "weightsCustom = np.array([14,1,20,30,11,1,1,10,10,2,0])/100 #pitch moment is zero bc incorrect eqn\n",
    "\n",
    "def objectiveDetailedNonNormalized(p,weights):\n",
    "    score = 0\n",
    "    subscores = []\n",
    "    for i in range(len(subObjectives)):\n",
    "        obj = subObjectives[i]\n",
    "        subscore = obj(p)\n",
    "        subscores.append(subscore)\n",
    "        score += weights[i]*alwaysMinimize[i]*subscore\n",
    "    return score,subscores\n",
    "\n",
    "# subscoreMean = np.zeros(len(subObjectives))\n",
    "# subscoreSd = np.ones(len(subObjectives))\n",
    "\n",
    "def objective(p,weights):\n",
    "    score = 0\n",
    "    for i in range(len(subObjectives)):\n",
    "        obj = subObjectives[i]\n",
    "        subscore= obj(p)\n",
    "        normalizedSubscore = (subscore - subscoreMean[i]) / subscoreSd[i]\n",
    "        score += weights[i]*alwaysMinimize[i]*normalizedSubscore\n",
    "    return score\n",
    "\n",
    "def objectiveDetailed(p,weights):\n",
    "    score = 0\n",
    "    subscores = []\n",
    "    for i in range(len(subObjectives)):\n",
    "        obj = subObjectives[i]\n",
    "        subscore= obj(p)\n",
    "        normalizedSubscore = (subscore - subscoreMean[i]) / subscoreSd[i]\n",
    "        subscores.append(normalizedSubscore)\n",
    "        score += weights[i]*alwaysMinimize[i]*normalizedSubscore\n",
    "    return score, subscores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## constraints not done\n",
    "I didnt actually do the constraint functions yet just bounds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1041,
   "metadata": {},
   "outputs": [],
   "source": [
    "#a list with all the min-max functions (!) which can be called to return max and min value as f(p)\n",
    "minMaxParam = [None for i in range(len(paramsDF))]\n",
    "def wrw(p):\n",
    "    minV = 0.300\n",
    "    maxV = r_track - 2 * p.rrt\n",
    "    \n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"wrw\"].index[0]] = wrw\n",
    "\n",
    "# def xrw(p):\n",
    "#     minV = p.lrw / 2\n",
    "#     maxV = .250 - minV\n",
    "#     return minV, maxV\n",
    "# minMaxParam[paramsDF.loc[paramsDF.variable==\"xrw\"].index[0]] = xrw\n",
    "\n",
    "def yrw(p):\n",
    "    minV = .5 + p.hrw / 2\n",
    "    maxV = 1.2 - p.hrw / 2\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"yrw\"].index[0]] = yrw\n",
    "wheelSpace = .1 #?? don't have an equation for this rn, min is .075\n",
    "\n",
    "aConst = wheelSpace\n",
    "def lfw(p):\n",
    "    minV = .05 \n",
    "    maxV = .7 - aConst\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"lfw\"].index[0]] = lfw\n",
    "\n",
    "f_track = 3 # bounds: 3, 2.25 m \n",
    "def wfw(p):\n",
    "    minV = .3\n",
    "    maxV = f_track\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"wfw\"].index[0]] = wfw\n",
    "\n",
    "# def xfw(p):\n",
    "#     minV = p.lrw + p.rrt + p.lc + p.lia + p.lfw/2\n",
    "#     maxV = .25 + p.rrt + p.lc + p.lia + p.lfw/2\n",
    "#     return minV, maxV\n",
    "# minMaxParam[paramsDF.loc[paramsDF.variable==\"xfw\"].index[0]] = xfw\n",
    "\n",
    "xConst = .030 #ground clearance 19 to 50 mm\n",
    "def yfw(p):\n",
    "    minV = xConst + p.hfw / 2\n",
    "    maxV = .25 - p.hfw/2\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"yfw\"].index[0]] = yfw\n",
    "\n",
    "# def xsw(p):\n",
    "#     minV = p.lrw + 2*p.rrt + aConst + p.lsw / 2\n",
    "#     maxV = .250 + 2*p.rrt + aConst + p.lsw / 2\n",
    "#     return minV, maxV\n",
    "# minMaxParam[paramsDF.loc[paramsDF.variable==\"xsw\"].index[0]] = xsw\n",
    "\n",
    "def ysw(p):\n",
    "    minV = xConst + p.hsw/2\n",
    "    maxV = .250 - p.hsw/2\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"ysw\"].index[0]] = ysw\n",
    "\n",
    "# def xrt(p):\n",
    "#     minV = p.lrw + p.rrt\n",
    "#     maxV = .250 + p.rrt \n",
    "#     return minV, maxV\n",
    "# minMaxParam[paramsDF.loc[paramsDF.variable==\"xrt\"].index[0]] = xrt\n",
    "\n",
    "# def xft(p):\n",
    "#     minV = p.lrw + p.rrt + p.lc\n",
    "#     maxV = .250 + p.rrt  + p.lc\n",
    "#     return minV, maxV\n",
    "# minMaxParam[paramsDF.loc[paramsDF.variable==\"xft\"].index[0]] = xft\n",
    "\n",
    "# def xe(p):\n",
    "#     minV = p.lrw + p.rrt - p.le / 2\n",
    "#     maxV = p.lrw + aConst + p.rrt - p.le / 2\n",
    "#     return minV, maxV\n",
    "# minMaxParam[paramsDF.loc[paramsDF.variable==\"xe\"].index[0]] = xe\n",
    "\n",
    "def ye(p):\n",
    "    minV = xConst + p.he / 2\n",
    "    maxV = .5 - p.he / 2\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"ye\"].index[0]] = ye\n",
    "\n",
    "def hc(p):\n",
    "    minV = .500\n",
    "    maxV = 1.200 - xConst\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"hc\"].index[0]] = hc\n",
    "\n",
    "# def xc(p):\n",
    "#     minV = p.lrw + p.rrt + p.lc / 2\n",
    "#     maxV = .250 + p.rrt + p.lc / 2\n",
    "#     return minV, maxV\n",
    "# minMaxParam[paramsDF.loc[paramsDF.variable==\"xc\"].index[0]] = xc\n",
    "\n",
    "def yc(p):\n",
    "    minV = xConst + p.hc / 2\n",
    "    maxV = 1.200 - p.hc / 2\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"yc\"].index[0]] = yc\n",
    "\n",
    "def lia(p):\n",
    "    minV = .2\n",
    "    maxV = .7  - p.lfw # what is l_fr?\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"lia\"].index[0]] = lia\n",
    "\n",
    "# def xia(p):\n",
    "#     minV = p.lrw + p.rrt + p.lc + p.lia / 2\n",
    "#     maxV = .250 + p.rrt + p.lc + p.lia/ 2\n",
    "#     return minV, maxV\n",
    "# minMaxParam[paramsDF.loc[paramsDF.variable==\"xia\"].index[0]] = xia\n",
    "\n",
    "def yia(p):\n",
    "    minV = xConst + p.hia / 2\n",
    "    maxV = 1.200 - p.hia / 2\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"yia\"].index[0]] = yia\n",
    "\n",
    "def yrsp(p):\n",
    "    minV = p.rrt\n",
    "    maxV = p.rrt * 2\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"yrsp\"].index[0]] = yrsp\n",
    "\n",
    "def yfsp(p):\n",
    "    minV = p.rft\n",
    "    maxV = p.rft * 2\n",
    "    return minV, maxV\n",
    "minMaxParam[paramsDF.loc[paramsDF.variable==\"yfsp\"].index[0]] = yfsp\n",
    "\n",
    "#test:\n",
    "# for f in minMaxParam:\n",
    "#     if f is not None:\n",
    "#         print(f(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1042,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAttr(obj):\n",
    "    return [a for a in dir(obj) if not a.startswith('__')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1043,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findMaterialByDensity(rho):\n",
    "    differences = abs(np.array(materialsDF.q) - rho)\n",
    "    material = materialsDF.iloc[np.argmin(differences)]\n",
    "    return material.Code, material.q, material.E\n",
    "def findTireByRadius(radius):\n",
    "    differences = abs(np.array(tiresDF.radius) - radius)\n",
    "    tire = tiresDF.iloc[np.argmin(differences)]\n",
    "    return tire.ID, tire.radius, tire.mass\n",
    "\n",
    "def findEngineByPower(power):\n",
    "    differences = abs(np.array(enginesDF.Phi_e) - power)\n",
    "    engine = enginesDF.loc[np.argmin(differences)]\n",
    "    return engine\n",
    "\n",
    "def findSuspensionByK(k):\n",
    "    differences = abs(np.array(susDF.krsp) - k)\n",
    "    sus = susDF.loc[np.argmin(differences)]\n",
    "    return sus\n",
    "\n",
    "def findBrakesByR(r): #what is the driving variable for brakes??? r?\n",
    "    differences = abs(np.array(brakesDF.rbrk) - r)\n",
    "    brakes = brakesDF.loc[np.argmin(differences)]\n",
    "\n",
    "    return brakes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "metadata": {},
   "outputs": [],
   "source": [
    "def constrain(p,dimsToConstrain=np.ones(len(paramsDF))):\n",
    "#     p_attribute = getAttr(p)\n",
    "    paramIndices = [i for i in range(len(dimsToConstrain)) if dimsToConstrain[i] ==1]\n",
    "    for i in paramIndices: #range(len(paramsDF)): # we need to do the equations bounds last\n",
    "#         if not dimsToConstrain[i]: #we don't need to check this dimension, it didn't change\n",
    "#             continue\n",
    "        param = paramsDF.loc[i]\n",
    "        variable = param.variable\n",
    "        value = getattr(p,variable)\n",
    "        if param.kind == 1: #continuous param with min and max\n",
    "            if hasNumericBounds[i]:\n",
    "                newValue = bounds(value,float(param[\"minV\"]),float(param[\"maxV\"]))\n",
    "                setattr(p,variable,newValue)\n",
    "            #do the equation ones after setting all other parameters\n",
    "                \n",
    "        elif param.kind == 2: #choose a material based on density\n",
    "            materialID,density,modulusE = findMaterialByDensity(value)\n",
    "            setattr(p,variable,density)\n",
    "            #find other variables that are driven by this one:\n",
    "            for i, otherParam in paramsDF[paramsDF.team == variable].iterrows():#dimension is driven by this one\n",
    "                setattr(p,otherParam.variable,modulusE)    \n",
    "        elif param.kind == 3: #choose tires\n",
    "            tireID,radius,weight = findTireByRadius(value)\n",
    "            setattr(p,variable,radius)\n",
    "            #find other variables that are driven by this one:\n",
    "            for i, otherParam in paramsDF[paramsDF.team == variable].iterrows():\n",
    "                setattr(p,otherParam.variable,weight)\n",
    "        elif param.kind == 4: #choose motor\n",
    "            tableRow= findEngineByPower(value) #Phi_e,l_e,w_e,h_e,T_e,m_e\n",
    "            setattr(p,variable,tableRow[variable])\n",
    "            #find other variables that are driven by this one:\n",
    "            for i, otherParam in paramsDF[paramsDF.team == variable].iterrows():\n",
    "                setattr(p,otherParam.variable,tableRow[otherParam.variable])\n",
    "        elif param.kind == 5: #choose brakes\n",
    "            tableRow = findBrakesByR(value) # r is driving variable\n",
    "\n",
    "            setattr(p,variable,tableRow[variable]) #df columns need to be same as variable names\n",
    "            #find other variables that are driven by this one:\n",
    "            for i, otherParam in paramsDF[paramsDF.team == variable].iterrows():\n",
    "            #their \"team\" is THIS VAR: dimension is driven by this\n",
    "                setattr(p,otherParam.variable,tableRow[otherParam.variable])\n",
    "\n",
    "        elif param.kind == 6: #choose suspension\n",
    "            tableRow = findSuspensionByK(value) #kfsp, cfsp, mfsp\n",
    "            setattr(p,variable,tableRow[variable])\n",
    "            #find other variables that are driven by this one:\n",
    "            for i, otherParam in paramsDF[paramsDF.team == variable].iterrows():#their \"team\" is THIS VAR: dimension is driven by this\n",
    "                setattr(p,otherParam.variable,tableRow[otherParam.variable])\n",
    "    \n",
    "    #now we can do the ones that depend on other variables\n",
    "    for i in paramIndices: \n",
    "        param = paramsDF.loc[i]\n",
    "        variable = param.variable\n",
    "        value = getattr(p,variable)\n",
    "        if param.kind == 1 and not hasNumericBounds[i]:\n",
    "            f = minMaxParam[i] #list of minMax functions for each variable\n",
    "            minV, maxV = f(p) \n",
    "            newValue = bounds(value,minV,maxV)\n",
    "            setattr(p,variable,newValue)\n",
    "    return p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## create the scaling vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1045,
   "metadata": {},
   "outputs": [],
   "source": [
    "pVmax = asParameters([1e15 for i in range(len(paramsDF))])\n",
    "maxVals = constrain(pVmax)\n",
    "scalingVector = asVector(maxVals) #this acts as a scaling vector to map SI unit values to ~unit cube\n",
    "# paramsDF.iloc[11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1046,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a2f8afd68>]"
      ]
     },
     "execution_count": 1046,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pVmin = asParameters([-1E10 for i in range(len(paramsDF))])\n",
    "minVals = constrain(pVmin)\n",
    "# scalingVector1 = asVector(maxVals) #this acts as a scaling vector to map SI unit values to ~unit cube\n",
    "# paramsDF.iloc[11]\n",
    "n = range(len(asVector(minVals)))\n",
    "plt.plot(n,asVector(maxVals)/scalingVector)\n",
    "plt.plot(n,asVector(minVals)/scalingVector)\n",
    "# plt.plot(n,asVector(maxVals)/scalingVector)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1047,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2809879900294584"
      ]
     },
     "execution_count": 1047,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mins = asVector(minVals)/scalingVector\n",
    "mins[28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1048,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7700000000.0"
      ]
     },
     "execution_count": 1048,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(scalingVector)\n",
    "scalingVector[41]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1049,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0               34\n",
       "name          engine torque\n",
       "variable                T_e\n",
       "team                  Phi_e\n",
       "kind                     -4\n",
       "minV                    NaN\n",
       "maxV                    NaN\n",
       "Name: 28, dtype: object"
      ]
     },
     "execution_count": 1049,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paramsDF.loc[28]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## create realistic starting values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "metadata": {},
   "outputs": [],
   "source": [
    "def startParams(returnParamObject=True):\n",
    "    nParams = len(paramsDF)\n",
    "    pV = np.random.uniform(0,1,nParams) * scalingVector\n",
    "    p = constrain(asParameters(pV))\n",
    "#     print(asVector(p))\n",
    "    return p if returnParamObject else asVector(p)\n",
    "# objective(p,weightsCustom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1051,
   "metadata": {},
   "outputs": [],
   "source": [
    "# p = startParams()\n",
    "# constrained = asVector(p)\n",
    "# # constrained/ scalingVector\n",
    "# for i, row in paramsDF.iterrows():\n",
    "#     print(row[1] + \" : \\t\"+str(constrained[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run random possible start values through objectives to get distribution of outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1052,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subscores = []\n",
    "# for i in range(3000):\n",
    "#     p = startParams()\n",
    "#     _,ss = objectiveDetailedNonNormalized(p,weightsNull)\n",
    "#     subscores.append(ss)\n",
    "# s = np.array(subscores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1185.7112677593059\n",
      "0.48445089770786354\n",
      "78.429039619868\n",
      "859.4706062109291\n",
      "8344168.146002732\n",
      "21263753.781849384\n",
      "0.014857308817886128\n",
      "2319.316013769643\n",
      "0.0005975240460277152\n",
      "-10.642333944146998\n",
      "2742.6436839845387\n"
     ]
    }
   ],
   "source": [
    "# x = s[:,7]\n",
    "# s[:,7 ] = [3000 if isNaN(x[i]) else x[i] for i in range(len(x)) ]\n",
    "# for i in range(len(s[0,:])):\n",
    "#     print(np.mean(s[:,i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### capture the mean and standard deviations of subscores\n",
    "so that we can normalize them assuming Normal dist.\n",
    "Now, the objective function will fairly weight sub-objectives using custom weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1053,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (<ipython-input-1053-b354ed20484a>, line 11)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1053-b354ed20484a>\"\u001b[0;36m, line \u001b[0;32m11\u001b[0m\n\u001b[0;31m    plt.hist((np.array(s[:,i]) - subscoreMean[i])/subscoreSd[i])\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "# # FIRST TIME, need to run this if we don't have the file with saved values\n",
    "# subscoreMean = []\n",
    "# subscoreSd = []\n",
    "# for i in range(len(subscores[0])):\n",
    "#     subscoreMean.append(np.mean(s[:,i]))\n",
    "#     subscoreSd.append(np.std(s[:,i]))\n",
    "\n",
    "# subscoreStatsDF = pd.DataFrame(columns=[\"subscoreMean\",\"subscoreSD\"],data=np.transpose([subscoreMean,subscoreSd]))\n",
    "# subscoreStatsDF.to_csv(\"./SAE/subscoreStatsDF.csv\")\n",
    "\n",
    "#     plt.hist((np.array(s[:,i]) - subscoreMean[i])/subscoreSd[i])\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscoreStatsDF = pd.read_csv(\"./SAE/subscoreStatsDF.csv\")\n",
    "subscoreMean = list(subscoreStatsDF.subscoreMean)\n",
    "subscoreSd = list(subscoreStatsDF.subscoreSD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Virtual Population with represntative KAI scores \n",
    "based on KAI score and subscore dataset provided by Dr. J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaiDF_DATASET = pd.read_csv(\"./KAI/KAI_DATA_2018_07_09.csv\")\n",
    "kaiDF_DATASET.columns = [\"KAI\",\"SO\",\"E\",\"RG\"]\n",
    "def makeKAI(n=1,asDF=True):\n",
    "    pop = np.random.multivariate_normal(kaiDF_DATASET.mean(),kaiDF_DATASET.cov(),n)\n",
    "    if asDF:\n",
    "        popDF = pd.DataFrame(pop) \n",
    "        popDF.columns = kaiDF_DATASET.columns\n",
    "        return popDF if n>1 else popDF.loc[0]\n",
    "    else:\n",
    "        return pop if n>1 else pop[0]\n",
    "    \n",
    "# def makeSubscores(kai,n=1,asDF=True):\n",
    "#     pop = np.random.multivariate_normal(kaiDF_DATASET.mean(),kaiDF_DATASET.cov(),n)\n",
    "kaiPopulation = makeKAI(100000)\n",
    "kaiPopulation=kaiPopulation.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findAiScore(kai):\n",
    "    kai = int(kai)\n",
    "    a = kaiPopulation.loc[kaiPopulation['KAI'] == kai]\n",
    "    ind = np.random.choice(a.index)\n",
    "    me = kaiPopulation.loc[ind]\n",
    "    return KAIScore(me) #this is a KAIScore object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalizedAI(ai):\n",
    "    return (ai - kaiDF_DATASET.mean().KAI)/kaiDF_DATASET.std().KAI\n",
    "def normalizedRG(rg):\n",
    "    return (rg - kaiDF_DATASET.mean().RG)/kaiDF_DATASET.std().RG\n",
    "def normalizedE(E):\n",
    "    return (E - kaiDF_DATASET.mean().E)/kaiDF_DATASET.std().E\n",
    "def normalizedSO(SO):\n",
    "    return (SO - kaiDF_DATASET.mean().SO)/kaiDF_DATASET.std().SO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.251845955448993"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaiDF_DATASET.mean().E\n",
    "kaiDF_DATASET.std().E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dotNorm(a,b): #return normalized dot product (how parallel 2 vectors are, -1 to 1)\n",
    "    if norm(a) <= 0 or norm(b)<= 0:\n",
    "#         print(\"uh oh, vector was length zero\")\n",
    "        return 0\n",
    "    a = np.array(a)\n",
    "    b = np.array(b)\n",
    "    dotAB = np.sum(a*b)\n",
    "    normDotAB = dotAB / (norm(a)*norm(b))\n",
    "    return normDotAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotCategoricalMeans(x,y):\n",
    "    categories = np.unique(x)\n",
    "    means = []\n",
    "    sds = []\n",
    "    for c in categories:\n",
    "        yc = [y[i] for i in range(len(y)) if x[i] == c]\n",
    "        means.append(np.mean(yc))\n",
    "        sds.append(np.std(yc))\n",
    "    plt.errorbar(categories,means,yerr=sds,marker='o',ls='none')\n",
    "    \n",
    "    return means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#speed distributions:\n",
    "dfConstant=1.9\n",
    "def travelDistance(speed): #how far do we go? chi distribution, but at least go 0.1 * speed\n",
    "    r = np.max([chi.rvs(dfConstant),0.1]) \n",
    "    return r * speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def memoryWeightsPrimacy(n):\n",
    "    if n==1:\n",
    "        return np.array([1])\n",
    "    weights = np.arange(n-1,-1,-1)**3*0.4 + np.arange(0,n,1)**3\n",
    "    weights = weights / np.sum(weights)\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aiColor(ai): #red for innovators, blue for adaptors\n",
    "    ai01 = bounds((ai - 40)/ 120,0,1)\n",
    "    red = ai01\n",
    "    blue = 1 - ai01\n",
    "    return (red,0,blue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeParamString():\n",
    "    s= \"\"\n",
    "    s+= \"steps: \"+ str(steps) + \" \\n\"\n",
    "    s+= \"self-bias: \" +str(selfBias)+ \" \\n\"\n",
    "    s+= \"num agents: \" +str(nAgents)+ \" \\n\"\n",
    "    s+= \"rg strength: \" +str(RG_STRENGTH)+ \" \\n\"\n",
    "    s+= \"so strength: \" +str(SO_STRENGTH)+ \" \\n\"\n",
    "    s+= \"repeats: \" +str(reps)+ \" \\n\"\n",
    "    s+= \"avg speed: \" +str(AVG_SPEED) + \" \\n\"\n",
    "    s+= \"sd speed: \" + str(SD_SPEED)+ \" \\n\"\n",
    "    s+= \"min speed: \" +str(MIN_SPEED)+ \" \\n\"\n",
    "    s+= \"avg temp: \"+ str(AVG_TEMP)+ \" \\n\"\n",
    "    s+= \"sd temp: \" +str(SD_TEMP)+ \" \\n\"\n",
    "    return s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent and Team Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent:\n",
    "    def __init__(self, id=-1):\n",
    "        self.id = id\n",
    "        self.score = np.inf\n",
    "        self.params = startParams()\n",
    "        self.r = asVector(self.params)\n",
    "        self.rNorm = self.r / scalingVector\n",
    "        self.nmoves = 0\n",
    "        self.kai = KAIScore()\n",
    "        self.speed = bounds(AVG_SPEED + normalizedAI(self.kai.KAI) * SD_SPEED, MIN_SPEED ,np.inf)\n",
    "        self.temp = bounds(AVG_TEMP + normalizedE(self.kai.E) * SD_TEMP, 0 ,np.inf)\n",
    "        self.iq = 1 #makeIqScore()\n",
    "        self.memory = [Solution(self.r,self.score,self.id,type(self))]\n",
    "        self.team = -1\n",
    "        self.decay = calculateAgentDecay(self,100)\n",
    "        self.startTemp = self.temp\n",
    "        self.startSpeed = self.speed\n",
    "        \n",
    "    def move(self,soBias=False,groupConformityBias=False,teamPosition=None):\n",
    "        if np.random.uniform()>self.iq: #I'm just thinking this turn\n",
    "            return False \n",
    "        \n",
    "#         print(\"my dimensions:\" +str(self.myDims))\n",
    "        #pick a new direction\n",
    "        d = np.random.uniform(-1,1,nDims)\n",
    "        d = d * self.myDims #project onto the dimensions I can move\n",
    "        dn = np.linalg.norm(d)\n",
    "        if dn==0: print(\"divide by zero (dn)\")\n",
    "        #distance moved should be poisson distribution, rn its just my speed\n",
    "        distance = travelDistance(self.speed) * nDims\n",
    "        d = d / dn * distance\n",
    "#         print('considering moving '+str(d) + ' from '+str(self.r))\n",
    "        candidateSolution = asParameters((self.rNorm + d)*scalingVector)\n",
    "        candidateSolution = constrain(candidateSolution,self.myDims)\n",
    "        \n",
    "        acceptsNewSolution = self.evaluate(candidateSolution,soBias,groupConformityBias,teamPosition=teamPosition)\n",
    "        if acceptsNewSolution: \n",
    "            self.moveTo(asVector(candidateSolution))\n",
    "            return True\n",
    "        self.score = self.f()\n",
    "        return False\n",
    "\n",
    "    def moveTo(self, r):\n",
    "        self.r = r\n",
    "        self.rNorm = self.r / scalingVector\n",
    "        self.params = asParameters(self.r)\n",
    "        self.score = self.f()\n",
    "        self.memory.append(Solution(self.r,self.score,self.id,type(self)))\n",
    "        self.nmoves += 1\n",
    "        \n",
    "    def startAt(self,position):\n",
    "        self.r = position\n",
    "        self.rNorm = self.r / scalingVector\n",
    "        self.params = asParameters(self.r)\n",
    "        self.memory = [Solution(r=self.r,score=self.f(),owner_id=self.id,agent_class=type(self))]\n",
    "\n",
    "    def wantsToTalk(self,pComm):\n",
    "        if(np.random.uniform() < pComm):\n",
    "            return True\n",
    "        return False\n",
    "    \n",
    "    def getBestScore(self):\n",
    "        bestScore = self.score\n",
    "        for s in self.memory:\n",
    "            if s.score < bestScore:\n",
    "                bestScore = s.score\n",
    "        return bestScore\n",
    "    \n",
    "    def getBestSolution(self):\n",
    "        bestSolution = self.memory[0]\n",
    "        for m in self.memory:\n",
    "            if m.score < bestSolution.score:\n",
    "                bestSolution = m\n",
    "        return bestSolution\n",
    "    \n",
    "    def soBias(self,currentPosition,candidatePosition): #influences preference for new solutions, f(A-I)\n",
    "        #positions should be given as NORMALIZED positions on unit cube! \n",
    "        soNorm = normalizedSO(self.kai.SO) #normalized score for Sufficiency of Originality\n",
    "        memSize = len(self.memory) \n",
    "        if memSize < 2: return 0 #we don't have enough places be sticking around them\n",
    "        \n",
    "        candidateDirection = candidatePosition - currentPosition #in unit cube space\n",
    "        \n",
    "        memDirection = 0 # what is the direction of past solns from current soln?\n",
    "        weights = memoryWeightsPrimacy(memSize) #weights based on temporal order, Recency and Primacy Bias\n",
    "        for i in range(memSize-1): #don't include current soln\n",
    "            past_soln = self.memory[i]\n",
    "            pairwiseDiff = past_soln.rNorm - currentPosition\n",
    "            memDirection += pairwiseDiff * weights[i]\n",
    "        #now we see if the new solution is in the direction of the memories or away from the memories\n",
    "        paradigmRelatedness = dotNorm(memDirection, candidateDirection)\n",
    "        raw_PR_score = soNorm * (paradigmRelatedness + 0) #shifting the x intercept #biasOfSO(PR,soNorm)\n",
    "        sufficiency_of_originality = raw_PR_score*SO_STRENGTH #the agent should have a memory of their path & interactions\n",
    "            \n",
    "        return sufficiency_of_originality\n",
    "    \n",
    "    def groupConformityBias(self,teamPosition,currentPosition,candidatePosition): #influences preference for new solutions, f(A-I)\n",
    "        rgNorm = normalizedRG(self.kai.RG) #normalized score for Rule/Group Conformity\n",
    "        candidateDirection = candidatePosition - currentPosition \n",
    "        \n",
    "        #all teammates have equal weight\n",
    "        teamDirection = teamPosition - currentPosition\n",
    "\n",
    "        #now we see if the new solution is in the direction of the team or away from the team\n",
    "        groupConformity = dotNorm(teamDirection, candidateDirection)\n",
    "        nominalGC = 0 #can change intercept with -0 (using dot product of direction,so is perpendicular the null case?)\n",
    "        groupConformityBias = (groupConformity-nominalGC)*rgNorm*RG_STRENGTH \n",
    "        return groupConformityBias\n",
    "    \n",
    "    def evaluate(self,candidateSolution,soBias=False,groupConformityBias=False,teamPosition=None): #implements simulated annealing greediness\n",
    "        candidateSolutionNorm = asVector(candidateSolution) / scalingVector\n",
    "        candidateScore = self.fr(candidateSolution)\n",
    "        if soBias:\n",
    "            candidateScore += self.soBias(self.rNorm,candidateSolutionNorm)\n",
    "        if groupConformityBias:\n",
    "            gcB = self.groupConformityBias(teamPosition,self.rNorm,candidateSolutionNorm)\n",
    "            candidateScore += gcB\n",
    "        #if better solution, accept\n",
    "        if candidateScore < self.score:\n",
    "            return True\n",
    "        #accept worse solution with some probability, according to exp((old-new )/temp)\n",
    "        elif pickWorseScore(self.score,candidateScore,self.temp):\n",
    "            self.score = candidateScore #(its worse, but we go there anyways)\n",
    "            return True              \n",
    "        return False\n",
    "    \n",
    "    \n",
    "#Solutions are objects\n",
    "class Solution():\n",
    "    def __init__(self, r,  score, owner_id=None, agent_class=None):\n",
    "        self.r = cp(r)\n",
    "        self.rNorm = self.r / scalingVector\n",
    "        self.score = cp(score)\n",
    "        self.owner_id = cp(owner_id)\n",
    "        self.agent_class = cp(agent_class)\n",
    "        \n",
    "#KAI scores are objects \n",
    "class KAIScore():\n",
    "    def __init__(self,subscores=None):\n",
    "        if subscores is None:\n",
    "            subscores = makeKAI(1,True)\n",
    "        self.KAI = subscores.KAI\n",
    "        self.SO = subscores.SO\n",
    "        self.E = subscores.E\n",
    "        self.RG = subscores.RG\n",
    "    \n",
    "#subclasses (Types) of Agents\n",
    "class carDesigner(Agent):\n",
    "    def __init__(self, id=-1):\n",
    "        Agent.__init__(self,id)\n",
    "        self.myDims = [1 for t in paramsDF.team ] #owns all dimensions by default\n",
    "#         self.params = startParams() ##these already set in class Agent()\n",
    "#         self.r = asVector(params)\n",
    "#         self.rNorm = self.r / scalingVector\n",
    "         \n",
    "    def f(self):\n",
    "        return objective(self.params,weightsCustom)\n",
    "    def fr(self,params):\n",
    "        return objective(params,weightsCustom)\n",
    "    \n",
    "class cabinPerson(Agent):\n",
    "    def __init__(self, id=-1):\n",
    "        Agent.__init__(self,id)\n",
    "        self.myDims = [( 1 if t == \"c\" else 0 ) for t in paramsDF.team ]\n",
    "#         self.r = np.random.uniform(-1*startRange,startRange,nDims)\n",
    "        self.params = startParams() # asParameters(self.r)\n",
    "        \n",
    "    def f(self):\n",
    "        return objective(self.params,weightsCustom)\n",
    "    def fr(self,params):\n",
    "        return objective(params,weightsCustom)\n",
    "    \n",
    "# class Steinway(Agent): #tuneable roughness\n",
    "#     def __init__(self, id=-1):\n",
    "#         Agent.__init__(self,id)\n",
    "#         self.myDims = np.ones(nDims)\n",
    "#         self.r = np.random.uniform(-1*startRange,startRange,nDims)\n",
    "        \n",
    "#     def f(self):\n",
    "#         return testy(self.r,ROUGHNESS)\n",
    "#     def fr(self,r):\n",
    "#         return testy(r,ROUGHNESS)\n",
    "\n",
    "# class LewisMultiDim(Agent): #objective is beauty\n",
    "#     def __init__(self,id=-1):\n",
    "#         Agent.__init__(self,id)\n",
    "#         self.myDims = np.ones(nDims) #logical vector, LewD controls/varies x0 AND x1\n",
    "        \n",
    "#     def f(self):\n",
    "#         return ellipsoid(self.r)\n",
    "#     def fr(self,r):\n",
    "#         return ellipsoid(r)\n",
    "\n",
    "def tryToShare(a1,a2):\n",
    "    deltaAi = abs(a1.kai.KAI - a2.kai.KAI) #harder to communicate above 20, easy below 10\n",
    "    successful =  tryComm(deltaAi)\n",
    "    if successful: #in share(), agents might adopt a better solution depending on their temperature\n",
    "        share(a1,a2) if complexSharing else shareBasic(a1,a2)\n",
    "        return True\n",
    "    return False\n",
    "  \n",
    "def shareBasic(a1,a2): #agents always go to better solution\n",
    "    if a1.score < a2.score:\n",
    "        a2.moveTo(a1.r)\n",
    "    elif a2.score < a1.score:\n",
    "        a1.moveTo(a2.r)\n",
    "    return True\n",
    "\n",
    "def share(a1,a2): #agent chooses whether to accept new solution or not, holistic NOTTTTTT dimension by dimension\n",
    "    copyOfA1 = cp(a1)\n",
    "    considerSharedSoln(a1,a2)\n",
    "    considerSharedSoln(a2,copyOfA1) #so they could theoretically swap positions...\n",
    "    return True\n",
    "\n",
    "def considerSharedSoln(me,sharer): #,dim): #will only move (jump) in the dimensions that sharer controls\n",
    "#         candidateSoln = me.r #other dimensions won't change\n",
    "#         candidateSoln[dim] = sharer.r[dim] #tells a1 where to go in This [i] dimension only\n",
    "        candidateSolution = sharer.params\n",
    "        candidateScore = me.fr(candidateSolution)\n",
    "        myScore = me.score - selfBias #improve my score by selfBias\n",
    "        #Quality Bias Reduction? would go here\n",
    "        if(candidateScore<myScore):\n",
    "            if not pickWorseScore(candidateScore,myScore,me.temp): #sometimes choose better, not always\n",
    "                me.moveTo(asVector(candidateSolution))  #(but never take a worse score from a teammate)\n",
    "#                 me.speed = me.startSpeed\n",
    "#                 me.temp = me.startTemp # !! CHRIS trying somethign new here: restart temp at teammate's soln\n",
    "constructor = carDesigner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# commBonus = 0\n",
    "# commRange = 200\n",
    "def tryComm(deltaAi):\n",
    "    c = np.random.uniform(commBonus,commBonus+commRange) #increasing commBonus makes sharing easier \n",
    "    return (deltaAi < c )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Team(): #a group of agents working on the same dimension and objective function\n",
    "    def __init__(self, nAgents, agentConstructor, dimensions = np.ones(nDims), specializations = None, temp=None,speed=None,aiScore=None,aiRange=None,startPositions=None):\n",
    "        self.agents = []\n",
    "        self.dimensions = dimensions\n",
    "        if (aiScore is not None) and (aiRange is not None):\n",
    "            minScore = np.max([40, aiScore-aiRange/2.0])\n",
    "            maxScore = np.min([150,aiScore+aiRange/2.0])\n",
    "            aiScores = np.linspace(minScore,maxScore,nAgents)\n",
    "            np.random.shuffle(aiScores) #randomly assign these to agents, not in order...\n",
    "            #or we could try putting them in subteams according to a rule:\n",
    "            \n",
    "        for i in range(nAgents):\n",
    "            a = agentConstructor(id = i)\n",
    "            if startPositions is not None:\n",
    "                a.startAt(startPositions[i])\n",
    "            if (aiScore is not None) and (aiRange is not None):\n",
    "                aiScore = aiScores[i]\n",
    "                a.kai = findAiScore(aiScore)\n",
    "                a.speed = bounds(AVG_SPEED + normalizedAI(a.kai.KAI) * SD_SPEED, MIN_SPEED ,np.inf)\n",
    "                a.temp = bounds(AVG_TEMP + normalizedE(a.kai.E) * SD_TEMP, 0 ,np.inf)\n",
    "            if speed is not None:\n",
    "                a.speed = speed\n",
    "            if temp is not None:\n",
    "                a.temp = temp\n",
    "            a.startSpeed = a.speed\n",
    "            a.startTemp = a.temp\n",
    "            a.myDims = dimensions #default: all dimensions owned by every agent\n",
    "            self.agents.append(a)\n",
    "        self.nAgents = nAgents\n",
    "        aiScores = [a.kai.KAI for a in self.agents]\n",
    "        self.dAI = np.max(aiScores)- np.min(aiScores)\n",
    "        self.nMeetings = 0\n",
    "        self.shareHistory = []\n",
    "        self.nTeamMeetings = 0\n",
    "        self.subTeamMeetings = 0\n",
    "        \n",
    "        self.scoreHistory = []\n",
    "        \n",
    "        #if there are subteams owning certain dimensions, each subteams dimensions are listed in a matrix\n",
    "        self.specializations = specializations\n",
    "        \n",
    "#         #we should give them same position in the dimensions they don't control\n",
    "#         a0 = self.agents[0]\n",
    "#         for i in range(len(a0.myDims)):\n",
    "#             if not a0.myDims[i]:  #this isn't our dimension to control\n",
    "#                 for a in self.agents: \n",
    "#                     a.r[i] = a0.r[i] \n",
    "\n",
    "    def getSharedPosition(self): #this is in the normalized space\n",
    "        positions = np.array([a.rNorm for a in self.agents])\n",
    "        return [np.mean(positions[:,i]) for i in range(len(positions[0]))]\n",
    "    \n",
    "    def getSubTeamPosition(self,team): #this is in the normalized space\n",
    "        positions = np.array([a.rNorm for a in self.agents if a.team == team])\n",
    "        return [np.mean(positions[:,i]) for i in range(len(positions[0]))]\n",
    "    \n",
    "    def getBestScore(self):\n",
    "        return np.min([a.getBestScore() for a in self.agents])\n",
    "    \n",
    "    def getBestSolution(self):\n",
    "        allSolns = [a.getBestSolution() for a in self.agents]\n",
    "        allScores = [s.score for s in allSolns]\n",
    "        return allSolns[np.argmin(allScores)]\n",
    "    \n",
    "    def getBestTeamSolution(self,team=-1): #returns a Solution object \n",
    "        bestIndividualSolns = [a.getBestSolution() for a in self.agents if a.team == team ]\n",
    "        bestScoreLocation = np.argmin([s.score for s in bestIndividualSolns])\n",
    "        return bestIndividualSolns[bestScoreLocation]\n",
    "    \n",
    "    def haveMeetings(self,talkers):\n",
    "        nMeetings = 0\n",
    "        for i in np.arange(0,len(talkers)-1,2):\n",
    "            #if you don't have a partner, you don't talk to anyone?\n",
    "            #this needs to be adjusted\n",
    "            a1 = talkers[i]\n",
    "            a2 = talkers[i+1]\n",
    "            didShare = tryToShare(a1,a2)\n",
    "            if didShare: \n",
    "#                 print(str(a1.id) + ' and '+str(a2.id)+' shared!')\n",
    "                nMeetings +=1\n",
    "        self.nMeetings += nMeetings\n",
    "        self.shareHistory.append(nMeetings)\n",
    "        return nMeetings\n",
    "    \n",
    "    def haveTeamMeeting(self):\n",
    "        #they all go to the best position of the group\n",
    "        bestSolution = self.agents[0].getBestSolution()\n",
    "        for a in self.agents:\n",
    "            agentBest = a.getBestSolution()\n",
    "            if agentBest.score < bestSolution.score:\n",
    "                bestSolution = agentBest\n",
    "        #now move all agents to this position\n",
    "        for a in self.agents:\n",
    "            a.moveTo(bestSolution.r)\n",
    "            \n",
    "        return bestSolution\n",
    "    \n",
    "    def haveSubTeamMeeting(self,team,gap=False):\n",
    "        #they all go to the best position of their specialized team\n",
    "        teamAgents = [a for a in self.agents if a.team == team]\n",
    "        if gap: #the meeting might fail if the cognitive style gap is large\n",
    "            #take the KAI differences of the team into account\n",
    "            kaiScores = [a.kai.KAI for a in teamAgents]\n",
    "            deltaAi = max(kaiScores) - min(kaiScores) #hard to communicate above 20, easy below 10\n",
    "            successful =  tryComm(deltaAi)\n",
    "            if not successful: #in share(), agents might adopt a better solution depending on their temperature\n",
    "                return None  #our cognitive style gap caused the meeting to fail (only if complexSharing=True)\n",
    "        #ok, phew, we have a successful meeting despite any cognitive gap:\n",
    "        bestSolution = teamAgents[0].getBestSolution()\n",
    "        for a in teamAgents:\n",
    "            agentBest = a.getBestSolution()\n",
    "            if agentBest.score < bestSolution.score:\n",
    "                bestSolution = agentBest\n",
    "        #now move all agents to this position\n",
    "        for a in teamAgents:\n",
    "            a.moveTo(bestSolution.r)\n",
    "            \n",
    "        return bestSolution\n",
    "    \n",
    "    def haveInterTeamMeeting(self): #when you have teams of teams\n",
    "        consensusPosition = np.zeros(nDims)\n",
    "        #get the best solution from each specialized subteam, and extract their specialized dimensions \n",
    "        for team in range(len(self.specializations)):\n",
    "            bestTeamSoln = self.getBestTeamSolution(team)\n",
    "            specializedInput = bestTeamSoln.r * self.specializations[team]\n",
    "            consensusPosition += specializedInput\n",
    "        consensusPositionP = constrain(asParameters(consensusPosition))\n",
    "        consensusPosition = asVector(consensusPositionP)\n",
    "        consensusScore = self.agents[0].fr(consensusPositionP)\n",
    "        #now move all agents to this position\n",
    "        for a in self.agents:\n",
    "            a.moveTo(consensusPosition)\n",
    "        \n",
    "        self.nTeamMeetings += 1\n",
    "        return [consensusScore, consensusPosition]\n",
    "    \n",
    "    \n",
    "    def step(self,pComm,showViz=False,soBias=False,groupConformityBias=False):\n",
    "        #what happens during a turn for the team? \n",
    "        #each agents can problem solve or interact (for now, inside team)\n",
    "        talkers = []\n",
    "        for a in self.agents:\n",
    "            if a.wantsToTalk(pComm):\n",
    "                talkers.append(a)\n",
    "            else: #CHANGE: #CHRIS I'm going to make getSharedPosition look only at your subteam members\n",
    "                teamPosition = self.getSubTeamPosition(a.team) if groupConformityBias else None #position is on unit cube\n",
    "                didMove = a.move(soBias=soBias,groupConformityBias = groupConformityBias, teamPosition=teamPosition)\n",
    "        if len(talkers)%2>0: #odd number, have last one explore instead of share\n",
    "            a = talkers.pop()\n",
    "            didMove = a.move(soBias=soBias,groupConformityBias = groupConformityBias, teamPosition=teamPosition)\n",
    "        nMeetings = self.haveMeetings(talkers)\n",
    "#         print(\"number of successful meetings: \"+str(nMeetings))\n",
    "        \n",
    "#         if showViz:\n",
    "#             self.plotPositions()\n",
    "        \n",
    "        self.updateTempSpeed()\n",
    "            \n",
    "        return nMeetings\n",
    "    \n",
    "    def updateTempSpeed(self):\n",
    "        for a in self.agents:\n",
    "            a.temp *= a.decay\n",
    "            a.speed *= a.decay\n",
    "            \n",
    "        \n",
    "    def plotPositions(self):\n",
    "        xs = [a.r[0] for a in self.agents]\n",
    "        ys = [a.r[1] for a in self.agents]\n",
    "        cs = [aiColor(a.kai.KAI) for a in self.agents]\n",
    "        plt.scatter(xs,ys, c=cs)\n",
    "#         teamPosition = self.getSharedPosition()\n",
    "#         plt.scatter(teamPosition[0],teamPosition[1],c='orange')\n",
    "        \n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune Comm Bonus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IdA6oP5FyoUfz2owf0pvftDksFpu7eiv9R0zk1SlLcdfEZBEREQlGUIVBXSDfpwV5/ELhuyOLyD5qVErimYtSuW/A0SQnRP7J7s7O5W/v/8DVr2SwcXtmyBmKiIhIWRBUYbAZaFjIPQ2B7QH1J1KumBkXdTuSD2/oxVGH7d1g/OPZv9B3WDpfL1wfYnYiIiJSFgRVGEwFBpjZ4fldjE5KHhC9T0SK6KjDq/DBDT25pPuRsdjqLbu44Lkp/Ou/c8nKyQ0xOxERESnNgioMRgBVgHQzO93MkgHMLNnMzgAmAJWB4QH1J1JupSTGc88ZR/PcxZ2pUTERAHd4/IuFnPPU1yxbvyPkDEVERKQ0CqQwcPePgfuAZsB7wHYzW0tk6NC7QFPgPncfH0R/IgIntzmM8Tf1pmfzWrHYjOWb6Dc8nfenrwwxMxERESmNAtvgzN3vAvoAY4ENQLXoeQzwW3e/O6i+RCTisKopvPL7rtzatxUJcZFND7btzuamN2bwhzdmsG13dsgZioiISGmREGRj0ScHHwfZpojsX1yccc1xzejetBaDR01naXQo0bvTV5KxbCPDzu9Ihzw7KYuIiIjkJ7AnBiISrvYNqzNmcBpndqofiy1dv4Ozn5zME18uIDdXex6IiIhIwQIpDMzsJDMbGV19KL/rR0SvHx9EfyKSv8rJCTxybgeGnd+BKsmRB4LZuc4/x//Ehc9/w+rNu0LOUEREREqqoJ4Y3Aj0cPd8NzmLxrtH7xORg+yMDvUZOySNjo32DiGavHA9fYdN4JPZv4SYmYiIiJRUQRUGnYDJhdwzEegcUH8iUoiGNSvy5tXdufHE5lhkXjIbd2Rx5cvfccf7P7ArKyfcBEVERKRECaowqAvk+7Qgj1+i94nIIZIYH8ctpxzF61d2o161lFj8lSlLOeOxSfy0emuI2YmIiEhJElRhsBloWMg9DYnsayAih1i3prUYNySNPm33bk7+0y9bOe2xibz89RLcNTFZRESkvAuqMJgKDDCzw/O7GJ2UPCB6n4iEoHrFJJ68sBMPDmxHSmLkn35mdi53fvAjV76cwYbtmSFnKCIiImEKqjAYAVQB0s3sdDNLBjCzZDM7A5gAVAaGB9SfiBSBmXFB10Z8dEMvWh1eJRb/dM4v9B02gckL1oWYnYiIiIQpkMIgurHZfUAz4D1gu5mtJTJ06F2gKXCfu48Poj8RKZ4Wh1Xh/et7clnPxrHYL1t287vnv+Hv4+aSlZMbXnIiIiISisA2OHP3u4A+wFhgA1Ateh4D/Nbd7w6qLxEpvpTEeO46rS0vXHostSolAeAOT321kLOfnMySdZoSJCIiUp4EuvOxu3/s7qe5e113T4qeT3f3T4LsR0SCc0KruowbkkZai9qx2MwVmzl1eDrvTlsRYmYiIiJyKAVaGIhI6VS3agovXdaFv/ZrTWJ8ZNOD7Zk5/OHNmdw0ajpbd2WFnKGIiIgcbCoMRASAuDjjyt5NeffanjSpXSkWf3/GKvoNT2faso0hZiciIiIHWyCFgZnlmlnOARzZQfQnIgdPuwbVGH1jL85JbRCLLd+wk3Oe+prHPp9PTq72PBARESmLEgJqZwKQ36eF6kBLoAIwE9gUUH8ichBVSk7gX+e0p3fLOtz+3iy27somJ9f598fzmLhgHY+e14F61SqEnaaIiIgEKKjlSo939xPyOToChwMjgWTgzCD6E5FD47T2RzB2cBqpR9aIxaYs2kCfoemM/2F1iJmJiIhI0A76HAN33wpcBWQDDxzs/kQkWA1rVuSNq7ox+KQWxEXmJbN5ZxbXvJrB7e/NYmdmTrgJioiISCAOyeRjd88FvgAGHIr+RCRYCfFx/OE3LRl1VXeOqJYSi7/2zTJOf2wic37eEmJ2IiIiEoRDuSpRClCj0LtEpMTq0qQm44b0pl+7w2Ox+Wu2ccbjk3hx0mLcNTFZRESktDokhYGZtQLOARYE1F4DMxtpZqvMbLeZLTGzoWb2qwoPMxtoZp+b2SYz22Vmc8zsTjNLKfzdIuVTtYqJPH5BJ/5+ZjsqJMYDkJmdy90fzebyl75j/bbdIWcoIiIiRRHIqkRmNnI/7TcEegLxwC0B9NUMmAzUBT4A5gJdgCFAHzPr6e7rD6Cd+4C/AduAd4D1QC/gHuAUM/uNu+8sbr4iZZGZcX6XRnRuXJPBr09ndnQo0edz19BnWDqPnNuetBZ1Qs5SREREfo2gliu9tJDrc4F/ufsLAfT1BJGiYLC7j9gTNLNHgJuJTHC+Zn8NmFlH4K9Elk9NdfdF0bgBw4EbgL8AdweQr0iZ1bxuZd67vgf/HP8Tz09cDMDarbu56PmpXN27KbecchRJCdpHUUREpDQI6i92kwKOI4Gq7t4miKLAzJoCpwBLgMf3uXwXsB24yMwqsX8DAQOe21MUAHhkgPTtRPZkuNbM4oubs0hZl5wQzx392/DiZcdSu3JSLP70hEWc9eRkFq/bHmJ2IiIicqCC2sdgaQHHcnffFkQfUSdGzx9HVzrKm8NWYBJQEehWSDt7Zk4u2vdCtJ11RJ5KtCtWtiLlyPFH1WXckN70brl3CNGslZs5dXg6b323XBOTRURESriD/ozfzGocwDf4B+qo6HleAdfnR88tC2lnXfTcZN8LZlYFqB192aqwhMwsI7/jQN4rUtbUqZLMi5cey99ObU1ifGTTgx2ZOfzp7e8ZPGoGm3dmhZyhiIiIFCSQwsDMTjKzf+ZdFcjM6prZV0Q+hG+IzgEormrR8+YCru+JVy+kndHR8xVm1nifa/cTGWYEWl5V5FeLizOuSGvKe9f1pGmdvd8JfDRzFf2GpZOxdEOI2YmIiEhBgnpicCNwprtvzBP7N5BGZInS9cAQMzs3oP4KsucD/X7HLLj7ZOBpIh/8vzezF8zsYTObQmTi8Y/RWwvd0tXdU/M7iEy4Fim3jq5fjdE39uK8zg1jsZWbdnLu01MY/tl8cnI1tEhERKQkCaowaA9M3PPCzCoAZwOfuPtRRIYALaeQ1YIOwJ4nAtUKuF51n/sK5O7XAJcDs4Fzo7llAr8FZkVvW1PkTEWEikkJ/OPsY3j8gk5UTYksgpaT6zzyyTwGPTOFlZu0IrCIiEhJEVRhUBdYled1VyI7Hb8IsQm9o9k7R6CofoqeC5pD0CJ6LmgOwv9w95Hu3s3dK0WP3u7+KdA9esu3xchVRKJOPaYe427qzbGN947Om7pkA32HTmDcrJ9DzExERET2CKow2A1UyPM6jchwngl5YluAmsXs54vo+RQz+5/co5OGewI7gSlF7cDMTiGyzOpX7r6yqO2IyP+qX70Cr1/ZjZtPbklcdNDfll3ZXPufadz27vfsyMwON0EREZFyLqjCYDF7lxIFOAuYv88H64bsXQ2oSNx9IfAx0Bi4fp/L9wCVgJfdPbZwupm1MrP/t0KQmVXNJ9YMeIbI3IJbi5OriPx/CfFxDDm5BW9e3Z361fd+l/D61OWcNmIiP64qdBSgiIiIHCRBFQYvAe3M7BszSyey/v9r+9zTib1DgYrjOiJj/4eb2ftm9pCZfU5k1+N5RHY0zmtO9NjX82Y21cyeNLMHzexN4AegAXCFuxf5qYOI7F/nxjUZOySN/sfUi8UWrt3OwMcn81z6InI1MVlEROSQC6oweBIYBXQmMpxnNPCPPRfNrAvQGviyuB1Fnxp0JjJ/oStwC9AMGA50d/f1B9jUaCCLyMTjPwI9gHeATu7+YnHzFJH9q1YhkRGDOvKvs4+hYlJkk/HMnFzuHzOHy178lrVbd4ecoYiISPliQe5GGh2e49HJxnnjtYH6wBJ3LxdjBcwso1OnTp0yMjLCTkWkxFu0dhtDRs1g1sq9vx5qV07i4XM7cFyenZRFRETk/0tNTWXatGnTokvmF1mgOx+7+5Z9i4JofJ27zywvRYGI/DpN61TmnWt7cFXvprHYum2ZXDJyKvePns3u7EK3FBEREZFiCrQwEBEpqqSEOG7v15qXf9+FOlWSY/HnJi5m4OOTWbBmW4jZiYiIlH0qDESkROndsg7jhqRxYqu6sdjsn7dw2oiJjJq6jCCHP4qIiMheKgxEpMSpXTmZ5y/pzN2ntSEpIfJramdWDre+O4vrX5vG5h1ZIWcoIiJS9qgwEJESycy4tGcTPri+J83rVo7Fx85aTb/h6Xy7ZEOI2YmIiJQ9KgxEpERrXa8qH93Qiwu6NorFVm7ayXlPf82jn8wjOyc3xOxERETKDhUGIlLiVUiK58GB7Xjqwk5Uq5AIQK7DsM/mc/4zU1ixcUfIGYqIiJR+KgxEpNToc3Q9xt+URtcmNWOx75ZupO+wdEZ/vyrEzEREREq/hKAaMrNE4AygC1ADiM/nNnf3y4PqU0TKn3rVKvDald148ssFPPrpfHJyna27srnhtelMmLeWu05rS6XkwH61iYiIlBuB/PU0syOAT4BWgO3nVgdUGIhIscTHGTec2ILuzWozZNR0VmzcCcCb363guyUbGT6oI0fXrxZyliIiIqVLUEOJHgZaA6OAE4EWQJN8jqYFNSAi8mulHlmDsUPSOL39EbHYonXbGfjEJJ6dsIjcXO15ICIicqCCet5+CjDB3X8XUHsiIgekakoiw87vQO+Wdbjrgx/YnplDVo7zwNg5TJi/lofPbU/dKilhpykiIlLiBfXEIAX4JqC2RER+FTPj7NQGjBmcxjEN9g4hSp+/jr5D0/li7poQsxMRESkdgioMfgCODKgtEZEiaVy7Em9f04NrjmuGRWc7rd+eyWUvfss9H/3IrqyccBMUEREpwYIqDP4FnG5mbQJqT0SkSJIS4ri1bytevbwrdaskx+IvTFrCwCcms2DN1hCzExERKbmCKgzWAB8Bk83sHjM73cx653cE1J+IyH71bF6b8Tf15uTWdWOxOT9vof+Iibz2zTLcNTFZREQkr6AmH39JZClSA+6I/lyQ/PY3EBEJXM1KSTx7cWdembKU+8fMITM7l11Zudz+3iwmzFvL389qR/WKSWGnKSIiUiIEVRjcy/6LARGRUJgZF3dvTJcmNRn8+nTm/bINgPE/rmbmik08el4HujWtFXKWIiIi4QukMHD3u4NoR0TkYGl1eFU+vKEXD4yZwytTlgLw8+ZdDHp2Cjec0JwhJ7UgIT6o0ZUiIiKlj/4Kiki5kZIYz30DjuaZi1KpXjERAHcY8fkCzn36a5Zv2BFyhiIiIuEJvDAws15mdqOZ3WFmg82sV9B9iIgUxyltD2f8kN50zzOEaNqyTfQbls4HM1aGmJmIiEh4AisMzKyTmc0GvgKGAvcAjwJfmdlsM+scVF8iIsV1eLUUXr2iK3/ucxQJcZFND7buzmbIqBnc8uZMtu3ODjlDERGRQyuQwsDMmgOfA62AScB9wLXR88Ro/BMzaxFEfyIiQYiPM647vjlvX9uDRjUrxuLvTFtB/+HpfL9iU4jZiYiIHFpBPTG4A6gMnOfuvd39bnd/Ono+DjgXqAL8LaD+REQC06FhdcYM7sXAjvVjsSXrd3DmE5N56quF5OZq0TURESn7gioMTgbed/e38rvo7m8DH0TvExEpcaqkJPLoeR149Lz2VE6OLNiWnev8fdxcLh45lTVbdoWcoYiIyMEVVGFQG5hbyD1zo/eJiJRYAzs2YMzgXrRvWD0Wm7hgHX2GpfPZnF9CzExEROTgCqowWAu0KeSeVsC6gPoTETlojqxVibev6c51xzfDIvOS2bA9k8tf+o67P/yRXVk54SYoIiJyEARVGHwOnG5m5+d30czOAs4APg2oPxGRgyoxPo4/92nFf67oymFVk2PxFycvYcDjk5j3y9YQsxMREQleUIXBvcB24D9mlm5m95rZtWZ2j5l9BbwJbAPuD6g/EZFDokez2owf0pvftDksFpu7eiunjZjIK1OW4q6JySIiUjYEUhi4+wIiE4vnAT2JrD70GJHVitKi8VPcfX4Q/YmIHEo1KiXxzEWp3D/gaJITIr82d2fncsf7P3D1Kxls3J4ZcoYiIiLFlxBUQ+7+LdDazHoAnYBqwGZgurtPCqofEZEwmBkXdjuSLk1qMvj16cxdHRlK9PHsX5i5YgKPnteBHs20voKIiJRege18vIe7T3b3x9z9gehZRYGIlBnMgB/oAAAgAElEQVQtD6vC+9f35JLuR8Ziv2zZze+e+4Z/jp9LVk5uiNmJiIgUXeCFgYhIWZeSGM89ZxzNcxd3pkbFRADc4YkvF3LOU1+zbP2OkDMUERH59Yo0lMjM7gQceNzdN0RfHwh39/uK0qeISElzcpvDGH9Tb/7w5gwmLVgPwIzlm+g3PJ37BrRlYMcGIWcoIiJy4KwoK2qYWS6RwqC1u8+Lvj4Q7u7xv7rDUsjMMjp16tQpIyMj7FRE5CDLzXWeTV/Ev/77E9m5e3+nDuxYn3vPaEuVlMQQsxMRkbIuNTWVadOmTXP31OK0U9TJxydEz8v2eS0iUu7ExRlXH9eM7s1qMfj16SyJDiV6b/pKMpZuZPigjnTIs5OyiIhISVSkwsDdv9rfaxGR8uiYBtUZPTiNuz74kXemrQBg2YYdnP3kZG7+TUuuOa4Z8XEWcpYiIiL5C2TysZldbGbHFHLP0WZ2cRD9iYiUVJWTE3j43PYMO78DVZIj371k5zr/+u9PXPjcN6zevCvkDEVERPIX1KpELwIDCrnnDOCFgPoTESnRzuhQn7FD0ujUaO8Qoq8XrafvsAl8/OPqEDMTERHJ36FcrjSeyIRlEZFyoWHNirx5dXcGn9icPSOINu7I4qpXMrjj/R/YlZUTboIiIiJ5HMrCoCWw8RD2JyISuoT4OP5wylG8fmU36lVLicVfmbKU0x+byNzVW0LMTkREZK+irkqEmY3cJzTAzBrnc2s80AhIA8YUtT8RkdKsa9NajBuSxq3vzGJ8dCjRvF+2cfpjk/hrv9Zc3P1IzDQxWUREwlPkwgC4NM/PDnSIHvlx4Bvg5mL0JyJSqlWvmMSTF3bi9anLuXf0j+zKyiUzO5e7PvyR9Plr+efZ7alZKSnsNEVEpJwqzlCiJtGjKWDA0DyxvEcjoKq793D3RcVLV0SkdDMzLujaiNE39qJ1vaqx+Kdz1tBn6AQmLVgXYnYiIlKeFbkwcPel0WMJcA/wfp5Y3mOFu28PLGMRkTKged0qvHddDy7r2TgWW7N1Nxc+/w1/HzeXzOwD3VBeREQkGIFMPnb3e9x9QhBtiYiUFymJ8dx1WlteuPRYakWHELnDU18t5OynJrNknb5TERGRQyfwVYnMLN7MDjOzRvkdQfcnIlLandCqLuNuSiOtRe1Y7PsVmzl1eDrvZKzAXSs9i4jIwRdYYWBm7cxsDLAVWAUszufQHAMRkXzUrZLCS5d14a/9WpMYH1mdaHtmDre8NZOb3pjBll1ZIWcoIiJlXSCFgZm1AiYDvYFPiExG/j768/ro6y+BV4LoT0SkLIqLM67s3ZR3r+1Jk9qVYvEPZqzi1OHpTFumrWBEROTgCeqJwR1AItDD3c+Ixt5z9z5EViZ6AWgD3BlQfyIiZVa7BtUYfWMvzu3cIBZbvmEn5zz1NY99Pp+cXA0tEhGR4AVVGBwPjHb3WXliBhBdkehqIrse3xdQfyIiZVql5AT+eXZ7HrugI1VSIlvO5OQ6//54Hr97bgo/b94ZcoYiIlLWBFUY1Abm53mdDVTc88Lds4EvgFMC6k9EpFzof8wRjBuSRucja8RiUxZtoM/QdMb/sDrEzEREpKwJqjDYAFTO83odkY3N8soEqgXUn4hIudGgRkVGXdWNm05uQVxkXjKbd2ZxzasZ3P7eLHZm5oSboIiIlAlBFQYLgcZ5XmcAvzGzugBmVgk4g8jKRCIi8islxMdx08kteePq7tSvXiEWf+2bZZz22ERmr9oSYnYiIlIWBFUYfAycEC0AAJ4CagLTzewtYBZwJPBcQP2JiJRLxzauydjBaZzarl4stmDNNgY8MYkXJi3WngciIlJkQRUGzwKXAxUA3H0McFP09VlAXeAfwPCA+hMRKbeqVUzksQs68o+z2lEhMR6AzOxc7vloNpe/9B3rt+0OOUMRESmNAikM3P1nd3/D3dfliQ0H6gD1gCrufru75wbRn4hIeWdmnHdsI0YP7kXbI6rG4p/PXUOfYelMmLc2xOxERKQ0Cmzn4/y4e467/+J6ti0iclA0q1OZd6/rwRW9msRia7fu5uKRU3lw7Bwys/V9jIiIHJigdj5uZmYXm1mtAq7Xjl5vGkR/IiKyV3JCPH/r34aXft+F2pWTY/FnJizirCcns2jtthCzExGR0iKoJwa3Ag8DBS2LsRn4N/CngPoTEZF9HNeyDuOGpHH8UXVisVkrN9N/xETe/G65JiaLiMh+Bbnz8afunpXfxWj8E+DEIDozswZmNtLMVpnZbjNbYmZDzaxG4e/+n3Z6mdkH0ffvMrNlZjbWzPoEkaeIyKFWp0oyIy85ljv6tyEpPvIrfkdmDn9++3tufH06m3fm+2taREQksMKgPrCkkHuWAUcUtyMza0Zkn4TLgKnAo8AiYAjwdUHDmfJp51ogHTgpen4U+Ao4DhhnZn8tbq4iImGIizMu79WE967vQbM6lWLx0d//TL9h6WQs3RBidiIiUlIFVRhkAlULuacKEMRz7CeILH862N0HuPut7n4ikQ/2RwEPFNaAmSUCDwG7gFR3v8jdb3P3i4DOwG7gr2aWvL92RERKsrZHVOOjG3sxqEvDWGzlpp2c+/QUhn06n5xcDS0SEZG9gioMfgBOjX7g/n/MLAnoD8wuTifRycunEHk68fg+l+8CtgMX5dlorSA1gWrAPHf/Ke8Fd58DzCOyB0Pl4uQrIhK2ikkJPHTmMTzxu05UTUkAICfXefTTeQx6ZgorN+0MOUMRESkpgioMXgUaAW+a2eF5L0Rfvwk0BF4uZj975ih8vO+eCO6+FZgEVAS6FdLOGmAt0NLMWuyTb0ugBTDD3dcXM18RkRKhX7t6jL+pN10a14zFpi7ZQN+hExg76+cQMxMRkZIiqMLgGeAz4AxggZlNNrO3zGwysAA4PXr9qWL2c1T0PK+A6/Oj55b7ayS6r8L1RP77M8zsJTN7yMxeJjJ/4UfgnANJyMwy8juAVgfyfhGRQ+WI6hV4/apu/OE3LYmPMwC27Mrmuv9M47Z3v2dHZnbIGYqISJiC2vk4F+gH/B3IIvKN/VnRcybwIHBqADsfV4ueNxdwfU+8emENuftbRJ5AbAIuJrLk6kVEhiO9QGRCs4hImRIfZww+qQVvXt2N+tUrxOKvT13OaSMm8uOqgn69iohIWRfYzsfunuXutwO1gKOBXtFzbXf/W0FLmQbM9qRT6I1mFwKfElmRqDWRIUitiTzZeAwYdSAduntqfgcwtyj/ASIih0LqkTUZOySN/sfUi8UWrt3OwMcn81z6InI1MVlEpNwJrDDYw91z3X22u0+Onov7lCCvPV9lVSvgetV97stXdB7BSCJDhi5y97nuvtPd5xJ5apABnGNmxxc/ZRGRkqlahURGDOrIv84+hopJ8QBk5uRy/5g5XPbit6zdujvkDEVE5FAKvDA4yPasIFTQHII9E4kLmoOwxylAIvBVPpOYc4EJ0ZepRUlSRKS0MDPO6dyQ0Tf2ol39vd+5fDVvLX2HTeDLn9aEmJ2IiBxKCUV5k5l9TmS4ziXuviL6+kC4u59UlD6jvoieTzGzuLwf6s2sCtAT2AlMKaSdPfsT1Cng+p54ZlETFREpTZrWqcw71/bg4Y9/4ukJkSlW67ZlcukL33J5ryb8uc9RJCfEh5yliIgcTEUqDIDjiRQGFfO8PhDFGrTq7gvN7GMi3/hfD4zIc/keoBLwtLtv3xM0s1bR9+Yd858ePZ9tZv929+/z3N8BODua64EWPCIipV5SQhy39WtNrxa1+cObM2NDiZ6fuJgpi9YzfFBHmtXR9i4iImVVkYYSuXucu8e7+7w8rw/kCOLrpuuI7EMw3Mzejy4z+jlwM5EhRH/d5/450SNv/lOJrDxUAfjWzEaZ2T/M7A3gGyAFGObuPwaQr4hIqZLWog7jhqRxYqu6sdiPq7bQf/hERk1dRmTFZxERKWtK2xwD3H0h0Bl4EegK3AI0A4YD3X/FpmSXA5cBXwO/jbbzG2AiMMjdbw42cxGR0qN25WSev6Qzd5/WhqSEyJ+KnVk53PruLK5/bRqbdxyKheZERORQKupQolC5+3IiH+oP5F4rIO5EiosXA0tMRKQMMTMu7dmErk1rcePr01mwZhsAY2etZsayTQw9vyNdmtQspBURESktAi0MzKwukW/zawD5Dhty95eD7FNERA6u1vWq8tENvbhvzGxe+2YZAKs27+L8Z77mhhNbMPjE5iTEl7oH0CIiso9ACgMzSwSeIrKDcEF/HYzIhF4VBiIipUyFpHgeHNiO3i1q85d3ZrF5Zxa5DsM/m8+kBesYel4HGtasWHhDIiJSYgX1xOA+IkN7FgL/AZYD2QG1LSIiJUSfo+vRvmF1bho1g28WbwAgY+lG+g1P58GB7Tit/REhZygiIkUVVGFwAZEVgTq6+86A2hQRkRKoXrUKvHZlN576aiGPfDKPnFxn665sbnx9OhPmreXu09tSKblUTmETESnXghoUWhcYq6JARKR8iI8zrj+hOW9d052GNSvE4m9lrKD/iInMWrE5xOxERKQogioMlgFVA2pLRERKiU6NajBmcBpndNg7hGjxuu2c+eQknpmwkNxc7XkgIlJaBFUYvAj0NbNqAbUnIiKlRNWURIae14GHz2lPpaTIgnRZOc6DY+dyyQtTWbN1V8gZiojIgQiqMPg7kY3BPjWzE8xMTw9ERMoRM+Os1AaMGZxG+wZ7vyNKn7+OvkPT+XzuLyFmJyIiByKowiALOBtIBT4FNppZTj6HVioSESnDGteuxFvX9OCa45ph0e0l12/P5PcvfsfdH/7IrqyccBMUEZECBbVsRDqRPQpERKScS0qI49a+rUhrUZub35jBmq27AXhx8hKmLFrPiEEdaXFYlZCzFBGRfQVSGLj78UG0IyIiZUfP5rUZf1Nv/vz2TD6dswaAuau3ctpjE7mzf1sGdWmI7XmsICIiodMe9iIictDUrJTEsxd35t4z2pKUEPmTsysrl9vfm8W1r05j047MkDMUEZE9VBiIiMhBZWZc3L0xH97Qk5aHVY7Fx/+4mr7D0pmyaH2I2YmIyB6BDCUyszsP8FZ39/uC6FNEREqXVodX5cMbevHAmDm8MmUpAD9v3sWgZ6dwwwnNGXxSCxLj9X2ViEhYgpp8fPd+ru2ZlGzRn1UYiIiUUymJ8dw34GjSWtTmz+98z6YdWbjDiM8XMHHBOoaf35GGNSuGnaaISLkU1FczJxRwDAQeArYDbwAnBtSfiIiUYqe0PZzxQ3rTo1mtWGz6sk30G5bOBzNWhpiZiEj5FdSqRF/t5/IHZvYGMBUYFUR/IiJS+h1eLYVXLu/K0xMW8sjH88jOdbbuzmbIqBlMmLeOe85oS+XkoB5si4hIYQ7JYE53nwV8ANx+KPoTEZHSIT7OuO745rx9bQ8a5RlC9M60FfQfns7M5ZtCzE5EpHw5lLO8lgFHH8L+RESklOjQsDpjBvfizI71Y7El63dw1pOTeeqrheTmag9NEZGD7VAWBl2BnYewPxERKUWqpCTyyHkdGHpeh9gQouxc5+/j5nLRyG/4ZcuukDMUESnbAikMzKxRAUdTMzvOzF4FegGfBNGfiIiUXQM61mfs4DQ6NKwei01asJ4+Qyfw6exfQsxMRKRsC+qJwRJgcT7HfOBz4AJgAfDHgPoTEZEyrFGtirx1TXeuP6EZZpHYxh1ZXPHyd9z1wQ/sysoJN0ERkTIoqOUeXmbvfgV55QIbiaxI9IG77w6oPxERKeMS4+P4029b0bN5bf7wxkxWR4cSvfT1UqYs2sCICzrS8rAqIWcpIlJ2BLVc6aVBtCMiIrKvHs1qM25IGn9553s+jg4l+umXrZw2YiJ/69+GC7s2wvY8VhARkSLT3vMiIlLi1aiUxNMXpXL/gKNJToj86dqdncsd7//A1a9ksHF7ZsgZioiUfioMRESkVDAzLux2JB/d2ItWh+8dQvTx7F/oM2wCkxeuCzE7EZHSL7DCwMwamNnDZvaZmf1kZovyORYG1Z+IiJRPLQ+rwvvX9+TSHo1jsV+27OZ3z33DP8fPJSsnN7zkRERKsaCWKz0emAfcDKQBFQHL59ATChERKbaUxHjuPr0tz1/SmZqVkgBwhye+XMjZT33N0vXbQ85QRKT0CeqD+j+BeOBiIMXdG7p7k/yOgPoTERHhpNaHMX5IGr2a147FZi7fxKnDJ/Le9BUhZiYiUvoEVRi0A15391fdXc9wRUTkkKlbNYWXf9+F2/q2IiEusjrRtt3Z3PzGTG5+YwZbd2WFnKGISOkQVGGwEdgQUFsiIiK/SlyccfVxzXj3uh40rlUxFn9v+kpOHT6R6cs2hpidiEjpEFRhMBo4LqC2REREiuSYBtUZPTiNs1MbxGLLNuzgnKe+5vEvFpCTm99enCIiAsEVBrcD1czscTOrFFCbIiIiv1rl5AT+fU57hg/qSJXkyD6e2bnOv/77Exc+9w2rN+8KOUMRkZIpkMLA3dcBfYDzgdVmlmFmn+dzfBZEfyIiIoU5vf0RjB2SRqdG1WOxrxetp8+wCXz84+oQMxMRKZkSgmjEzNoCXwA1oqGOBdyqZ7giInLINKxZkTev7s7wz+bz2BcLyHXYtCOLq17J4MJujfjbqW1ISYwPO00RkRIhqKFEjwC1gDuBI4FEd4/L59BvXxEROaQS4uP4wylH8fqV3ahXLSUWf3XKMk4bMZG5q7eEmJ2ISMkRVGHQHXjX3e939+XunhNQuyIiIoHo2rQW44ak0aft4bHY/DXbOP2xSbw0eQnueqgtIuVbUIVBJrAkoLZEREQOiuoVk3jywk48dGY7UhIjfwIzs3O568MfueKl71i/bXfIGYqIhCeowuBLoEtAbYmIiBw0ZsagLo0YfWMv2tSrGot/NncNfYelM3H+uhCzExEJT1CFwZ+BNmZ2q5lZQG2KiIgcNM3rVuG963vw+55NYrE1W3dz0chveGjcHDKzc0PMTkTk0AtkVSLgb8APwAPAlWY2A9icz33u7pcH1KeIiEixJCfEc+dpbUhrUZs/vjWT9dszcYenv1rE1wvXM+z8jjSpre15RKR8sCAmW5nZgX6t4uVlZSIzy+jUqVOnjIyMsFMREZEDsGbrLm55cybpeYYSVUyK594zjuasTvXRA3ERKalSU1OZNm3aNHdPLU47QT0xaFL4LSIiIiVX3SopvHRZF0ZOWsw/xs8lK8fZkZnDH9+ayYR5a7l/4NFUTUkMO00RkYMmkMLA3ZcG0Y6IiEiY4uKMK9Ka0q1pLQa/Pp1F67YD8OHMVUxbtpFh53ck9cgahbQiIlI6BTX5WEREpMw4un41PrqxF+d2bhCLrdi4k3Of/poRn80nJ1d7HohI2RPUUCIAzCwOqA80APJ93uruE4LsU0RE5GColJzAP89uT++Wdbjt3Vls3ZVNTq7z8CfzmLhgHY+e14EjqlcIO00RkcAEVhiY2Z+APwK1C7m1XEw+FhGRsqH/MUfQoWF1bho1g++WbgTgm8Ub6DssnX+c1Y4+R9cLOUMRkWAEUhiY2d3AncB64CVgJZAdRNsiIiJha1CjIqOu6sZjXyxg+GfzyXXYvDOLa16dxqAujbizfxsqJOl7LxEp3YJ6YnA5sAhIdff89i8QEREp1RLi47jp5Jb0bF6bm0bNYOWmnQC8PnUZUxevZ8SgTrQ5omohrYiIlFxBTT6uBXyookBERMq6YxvXZOzgNE5tt3cI0cK12xnw+CRGTlxMEPsDiYiEIajCYAGg9dtERKRcqFYxkccu6Mg/zmpHhcTIEKLMnFzuHT2by178lnXbdoecoYjIrxdUYfAE0N/MDg+oPRERkRLNzDjv2EaMHtyLtnmGEH3501r6DE1nwry1IWYnIvLrBVIYuPtTwCvAJDO7xMzamVmj/I4g+hMRESkpmtWpzLvX9eDKtCax2Lptu7l45FQeGDObzOzcELMTETlwQW5wNpPIcKKRwAxgcT7HogD7ExERKRGSE+L566lteOn3XahdOTkWfzZ9MWc+OYmFa7eFmJ2IyIEJarnSK4CniSxR+iWwCi1XKiIi5cxxLesw/qY0/vjWTL78KTKU6IeVW+g/fCL3nN6Wczo3wMxCzlJEJH9BLVd6C7AG6OHuiwNqU0REpNSpXTmZFy49lhcmLeHv4+aSmZPLzqwc/vzO93w1fy0PDmxHtQqJYacpIvL/BDWUqDHwtooCERGRyMTk3/dqwnvX96BZnUqx+Jjvf6bfsHS+W7IhxOxERPIXVGGwEtDXHyIiInm0PaIaH93Yi0FdGsZiKzft5Nynv2bop/PIztHEZBEpOYIqDF4G+plZlYDaExERKRMqJiXw0JnH8MTvOlE1JTKCN9dh6KfzGfTslNgOyiIiYQuqMHgQmAp8ambHH+wCwcwamNlIM1tlZrvNbImZDTWzA9pkLZqjH8DRsPDWRERECtevXT3G39SbLk1qxmLfLtlI36ETGDvr5xAzExGJCGry8Z4tHg34DCho1QV392L1aWbNgMlAXeADYC7QBRgC9DGznu6+vpBmlgD3FHCtHXAm8KO7Ly9OriIiInkdUb0Cr1/Zjce/WMCwz+aTk+ts2ZXNdf+ZxnmdG3LX6W2omBTUn2YRkV8nqN8+6YAH1FZhniBSFAx29xF7gmb2CHAz8ABwzf4acPclwN35XTOz16M/PhNAriIiIv8jPs4YfFILejavxeDXZ8SGEr3x3XK+XbqB4ed35Oj61ULOUkTKI3M/VJ/ni8/MmgILiXzj38zdc/NcqwL8TOSpRV13316E9msRmUidC9R3943FyDWjU6dOnTIyMorahIiIlHGbd2bxt/d/4KOZq2KxxHjjL31a8fueTYiL054HIlK41NRUpk2bNs3dU4vTTpA7Hx8KJ0bPH+ctCgDcfSswCagIdCti+5cCycBbxSkKREREDkS1CokMP78D/zr7GComxQOQlePcP2YOl734LWu37i6kBRGR4AReGJhZopm1M7M0MzvGzIJcxvSo6HleAdfnR88ti9j+FdHz00V8v4iIyK9iZpzTuSFjBqfRLs8Qoq/mraXvsAl8+dOaELMTkfIksMLAzKqa2VPAJmAG8CUwHdhkZk+ZWfUAutnzG3NzAdf3xH91X2Z2HNCKyKTjyb/ifRn5HdG2REREDkiT2pV459oeXN27aSy2blsml77wLfd+NJvd2TkhZici5UEghYGZVSUyjOcqIJvIZOQ3o+esaHxi9L6Dac9gzKJMnLgqetbTAhERCUVSQhy39WvNK5d3oU6V5Fh85KTFDHx8MgvWbAsxOxEp64J6YnAb0BZ4EjjS3Y9390HufjxwJPA40CZ6X3HseSJQ0HINVfe574CYWU3gLGAn8Mqvea+7p+Z3EFlGVURE5FdLa1GH8UPSOLFV3Vhs9s9bOG3EREZNXUZpWjhEREqPoAqDM4Ep7n69u2/Ke8HdN7v7jcDXRD58F8dP0XNBcwhaRM8FzUEoyCVEJh2/uW/+IiIiYahVOZnnL+nM3ae1ISkh8ud6Z1YOt747i+tfm8bmHVkhZygiZU1QhUEjInMK9ucroLg7CX8RPZ9iZv+Te3S50p5EvvWf8ivb/b/27ju+qvr+4/jrkxAIG9kqU5YIyoiyg9A6wIVbUFGss4pAq9Vaq3W0P6ttLUPrVty4KjjAPQjLEcAtINvBnrKTfH5/nJMQ0yxuTrgZ7+fjcR6H+z3fe76f5ORe7uee77g03GvtAhERKTPMjBF9WzPlqr60bVwrp3zqF6sYPG46Hy/dEMfoRKSiiSox2E6w6FhhGoX1Yubui4G3gFbAVXkO3wrUBJ7IvYaBmR1qZgUOBDazVKAj8OW+DDoWERHZXzoeWIdXR/bjvJ4tcsp+3LyToQ/O5u63F5KRmVXIs0VEiieqxOAT4Cwza5ffQTNrA5wd1iupK4E1wHgzm2xmd5jZewSrHi8EbsxT/5twK0j2oGPdLRARkTKretVE/nba4dx/fgr1agQzgWc5jH93Eec8OIeVG0r03ZuISGSJwT+AWsAnZna7mf3KzDqa2UAzu5UgIagF/LOkDYV3DY4EJgI9gWuANsB4oLe7ry/uuczsAOBMYhh0LCIiEg+DOjdl2uhUeh1SP6csfflGThif9osVlEVE9pVFNbOBmV0OjAPyLmhmBFOWjnH3+yJprBwws/Tu3bt3T09Pj3coIiJSAWVmOfd/uJi7315IZtbe/8vPTGnGrad0oma1KnGMTkT2p5SUFObOnTs3nBkzZpG9a7j7A2Y2DRgOdCOYUnQzwSJnT7n78qjaEhERqewSE4yrBrald5sGjJ40j5UbdgDwYvr3pC/fyLihXTmiWRRri4pIZRHZyscA7r7C3f/m7me6+7Hh/m9KCkREREpH9xYH8PqoVIZ0PSinbOm6bZxx3ywe+HAxWVla80BEiifSxEBERET2vzrJSYwb2o27z+5CzaqJAOzJdO6Y9i0XPvYxa7bsjHOEIlIeRJIYmNkVZrbYzA4q4PjB4fGLo2hPRERE/tfp3ZsxdXQqXZrv7UKUtmgdg8al8d63q+MYmYiUB1HdMTgX+Mnd850Owd1/AL4Hzo+oPREREclHywY1efGK3vx2QBvMgrIN23bzm4mfcssrX7FzT2Z8AxSRMiuqxKAD8FkRdT4HClxoTERERKKRlJjA9YMO5emLe9KkTrWc8omzlnHqvTNZtHprHKMTkbIqqsSgLrCpiDpbgAMiak9ERESK0KdtQ6aN7s8xHZvklH27aisn3zODpz9aTlRTlotIxRBVYvATcEQRdY4A1kbUnoiIiBRD/ZpVeeiCFG4f0olqVYL/9nfuyeLGl7/kiqfS2bhtd5wjFJGyIqrE4H1gkJn1y++gmaUCg4F3I2pPREREisnMGN67Fa+M7Ef7JrVyyt/8ajWDx6Uxe/H6OEYnImVFVInBncBu4B0zu9vMjjOzTuH+38DbwK6wnoiIiMRBh0xq2gYAACAASURBVKa1eWVkP4b3aplTtmrLTs59eA7/fHMBezKz4hidiMRbJImBuy8Azib48D8GmEYw2HgaMBrYCZzl7t9E0Z6IiIjEJjkpkdtP7cxDFxzJATWSAHCHe97/jrMfmM2K9dvjHKGIxEtkC5y5++vAIcAfgJcIug29BFwLtHH3qVG1JSIiIiVz7GFNmDa6P33aNMgpm7diEyeMT2PK/B/iGJmIxEuVKE/m7uuBf0V5ThERESkdTesm8+TFPXlg+mLufmshGVnOz7syGD1pPh8uXMttQzpTq1qkHxVEpAyL7I6BiIiIlD+JCcaVA9ry4m/70LJBjZzy/879gRPHp/HZyqJmIxeRikKJgYiIiNC1eT1eH5XK6d0Pzilbvn47Z9w3i/s+WExWltY8EKnolBiIiIgIALWqVeHus7sybmjXnC5EGVnOnW98y/BHP2L1lp1xjlBESpMSAxEREfmFIV0PZuqoVLo2r5dTNvO79QwaO513vl4dx8hEpDQpMRAREZH/0aJBDV64ojcjB7bFLCjbuH0PlzzxKTdP+ZKdezLjG6CIRE6JgYiIiOQrKTGBa4/vwDOX9KJpneSc8idmL2fIPTNZuHprHKMTkahFkhiYWX8za1FEneZm1j+K9kRERGT/6d2mAdNGp3LcYU1yyhas3srJE2bw5OxluGtgskhFENUdg/eBEUXUuSCsJyIiIuXMATWr8sDwFP52WmeSk4KPD7sysrhpyldc9mQ6G7btjnOEIlJSUSUGVsw6+kpBRESknDIzzuvZkldH9uPQprVzyt/+ejWDx01n1nfr4hidiJTU/hxj0AJQZ0QREZFyrl2T2ky+qi8j+rTKKVu9ZRfnPfIRd77xLXsys+IXnIjELOZ1zs3s5jxFA8zyvXGQSJAUDAVmxNqeiIiIlB3JSYncckon+rdvyLUvfM6Gbbtxh/s+WMysxesZP7QrLRvUjHeYIrIPLNYBQ2aW++sAp+juRD8Ap7p7ekwNljNmlt69e/fu6emV4scVEZFKbM2Wnfz++c+YkasrUc2qidx+amdO794sjpGJVA4pKSnMnTt3rrunlOQ8Md8xAAaGewPeAyYCj+dTLxNYDyxwd91bFBERqWAa10nmid/04KG0JfzjzQVkZDnbdmfy++c/Y/rCtdx+amdqJyfFO0wRKULMiYG7f5j9bzN7HJicu0xEREQqj4QE4/Kj29C7TQNGT5rP0nXbAJg8/0fSV2xk3NBudG9xQJyjFJHCRDL42N0vcvdXojiXiIiIlF9HNKvHa1f346yUvV2IVm7YwVn3z+be978jM0sTFIqUVVr5WERERCJVs1oV/nFWF8YP60btakHnhMws5x9vLuC8h+fw0+YdcY5QRPITWWJgZgea2b1m9p2Z7TCzzHy2jKjaExERkbLtlC4HMXV0Kt1b1Mspm7NkA4PHpfHmV6viGJmI5CeSxMDMDgY+BS4HtgHVgBXAIoLBxwZ8BqRF0Z6IiIiUD83r1+D5y3sz6tftSAjnL9y0fQ+XP5nOjS9/wY7dmfENUERyRHXH4GagKTDI3buEZY+5+6HAIcCbQHXg9IjaExERkXKiSmICvz+2Pc9e2osD6ybnlD/90QpOuWcG3/y0JY7RiUi2qBKD44E33P2dvAfc/XvgLILE4NaI2hMREZFypuchDZg2OpVBnZrmlC1a8zND7p3JxJlLiXVtJRGJRlSJQVPgq1yPMwkSAQDc/WfgbWBIRO2JiIhIOVSvRlXuO787d5x+OMlJwceQ3RlZ3PLq11zy+Kes/3lXnCMUqbyiSgy2AFVzPd4IHJynzmagUUTtiYiISDllZgzr0YLXru5HxwPr5JS/++0aBo9LY8aidYU8W0RKS1SJwXKgea7HnwG/MrMaAGaWABwHfB9ReyIiIlLOtW1cm8lX9eE3fVvnlK3ZuovzH/mIO6Z+w+6MrDhGJ1L5RJUYvAsMNLPs9c4fBw4CZpnZP4CZQCfguYjaExERkQqgWpVEbj75MB4bcRQNau7tfPDA9CWcef+snBWURaT0RZUYPALcCTQEcPengHFAZ+AaoCdBUvC3iNoTERGRCmTgoY2ZNiaV1HYNc8o+/34zJ45P44VPV2pgssh+EEli4O6L3P1Od/8pV9nvgAOB3sCB7n6uu++Moj0RERGpeBrXTubxi3rw5xM7kpQYLHqwfXcmf3jxc0ZNms+WnXviHKFIxRbZysf5cfe17v6Ru68uzXZERESkYkhIMC5JPYSXr+zLIY1q5pS/+tmPnDAujfTlG+MYnUjFVqqJgYiIiEgsOh9cl9eu7sc5R+6d2+T7jTs4+4HZTHh3EZlZ6lokErUqUZ3IzOoDvwF6AAcAiflUc3f/dVRtioiISMVVo2oV7jzzCPq3b8QN//2cLTszyMxy/vX2QtK+W8fYc7pyUL3qRZ9IRIolksTAzA4FPiBYp8AKqar0XkRERPbJiUccSNcW9RgzaR6fLAu6En28dAODx6Vx5xmHM6jzgXGOUKRiiKor0T+BxgQzEx0CJLl7Qj5bfncRRERERAp1cL3qPHtpL353THsSwq8gN+/YwxVPzeWG/37O9t0Z8Q1QpAKIKjFIBV539z+5+zJ3z4zovCIiIiIAVElMYPQx7Xj+8t4cnKsL0bMfr+TkCTP46sfNcYxOpPyLKjEw4OuIziUiIiJSoCNb1Wfq6FROPGJvF6LFa7dx2r2zeGTGUq15IBKjqBKDdKBDROcSERERKVTd6kncM6wbd51xBNWTgp7KuzOzuP21r7lo4ies3borzhGKlD9RJQa3ASeY2YCIziciIiJSKDPj7KOa89qofnQ+uE5O+QcL1jJ4XBofLlwbx+hEyp+YZiUyswvyKZ4CvGVmzxLcQdiU33Pd/YlY2hQRERHJT5tGtXjpt33455sLeChtKQDrft7FhY9+zCX9WvOHQR2oVkXzn4gUxWLph2dmWfzv1KN5pynN77hXlpmJzCy9e/fu3dPT0+MdioiISKUxfeFafv/8Z6z7eW9Xok4H1WH8sG60aVQrjpGJlJ6UlBTmzp07191TSnKeWNcxuKgkjYqIiIiUhv7tG/HGmFT+8MJnvL8g6Er01Y9bOGn8DG455TDOPrI5ZoUtuSRSecWUGLj741EHIiIiIhKFhrWq8eiIo5g4axl3TP2W3ZlZ7NiTyfUvfcH0Rev4v9MOp271pHiHKVLmRDL42Mz6m1mLIuo0N7P+UbQnIiIiUhgz46K+rZl8VV/aNt7bhej1z3/ihHFpfLJsQxyjEymbopqV6H1gRBF1LgjriYiIiOwXhx1Uh1dH9uPcnnu/v/xh0w7OeWA2Y99ZSEZmVhyjEylbolzgrDh1tOKIiIiI7FfVqybyf6cdzv3nd8/pQpTlMPadRQx7aA7fb9we5whFyoaoEoPiaAFs3Y/tiYiIiOQY1PlApo1OpWfr+jllnyzbyOBxabz2+Y9xjEykbIh1ViLM7OY8RQMKGOWfSJAUDAVmxNqeiIiISEkdVK86z1zai/+8/x1j311EZpazdWcGI5+Zx/SFa7nllE7UqBrzxyORcq0kf/m35Pq3AwPCrSA/AH8sQXsiIiIiJZaYYFz963b0aduQ0ZPm8f3GHQA8/+n3fLpsI+OHdaPzwXXjHKXI/leSrkQDw+1XBOMHJuYqy731BzoBLd1dq32JiIhImZDS8gCmjk7llC4H5ZQtWbeN0/4zk4fTlpCVpaGRUrnEfMfA3T/M/reZPQ5Mzl0mIiIiUtbVSU5i3NCu9G/fiJunfMn23ZnsyXT++vo3TF+0jn+edQSNayfHO0yR/SKSwcfufpG7vxLFuYrDzJqZ2aNm9qOZ7TKzZWY21swOiOFch5vZE2a2MjzXGjP70MwuKI3YRUREpGwxM85Macbro1I5otneLkTTF67lhHFpvL9gTRyjE9l/9uesRJEwszZAOnAR8DHwb2AJMBqYbWYN9uFcI4B5wKlAGvAv4EWCrlEnRBq4iIiIlGmtG9bkxSv6cPnRh+SUrft5Nxc99gm3vfo1uzIy4xidSOkrj8Pu/wM0Bka5+4TsQjO7G/gd8DfgiqJOYma9gIeBL4FB7r4qz3GtlS4iIlLJVK2SwA2DO5LathG/f34+a7buAuDRmUuZvWQ9E4Z1pW3j2nGOUqR0lKs7BmZ2CHAcsAy4N8/hvwDbgOFmVrMYp7uLYCrV8/MmBQDuvqdk0YqIiEh51a9dQ6aNTuXXhzbOKfvmpy2cNGEGz368AncNTJaKp1wlBgQzIAG85e6/WMPc3bcCM4EaQK/CTmJmzYBU4FPgKzMbaGbXmtk1ZvZrMytvvxcRERGJWINa1Xj4wiO59ZROVK0SfDTYuSeLG/77BVc+PZfN2/UdolQs5a0rUYdwv7CA44sI7ii0B94t5DxH5ar/Hv+7/sIXZna6u39XVEBmVtAUrIcW9VwREREp28yMC/u0okfr+ox6dh6L1vwMwLQvV/HZyk2MHdqNHrlWUhYpz8rbN+PZUwVsLuB4dnm9Is6TfV/wbKAjcHp47rbAk8DhwOtmVjX2UEVERKSi6HhgHV69uh/n92qRU/bj5p0MfXA2d7+1gIzMrEKeLVI+RJIYmNkUMxtsZhbF+UoSSrgvquNfYq79Je7+srtvcffFwIUEXYzaA2cU1aC7p+S3Ad/G+DOIiIhIGZSclMhfTz2cB4anUK9GMEdJlsP4977jnAfnsHLD9jhHKFIyUd0xOBl4DVhqZjeaWdOIzptX9h2BgtYpr5OnXkE2hvtdwNTcBzwYTTQlfNhjXwMUERGRiu34Tk2ZNjqVXofs7UKUvnwjJ4xL49XPfoxjZCIlE1VicCTwCFAfuB1YYWYvmtmxEZ0/24Jw376A4+3CfUFjEPKeZ2veQcyh7MSh+j7EJiIiIpXEgXWr8/QlvfjD8R1ITAg6LGzdlcHVz87j2hc+Y9uujDhHKLLvolr5eK67XwYcCPwW+IKg3/4bZrbYzK4zs0YRNPV+uD8u78xBZlYb6AvsAOYUcZ7PgXVAQzNrks/xzuF+WeyhioiISEWWmGBcNbAtL17Rmxb1a+SUv5j+PSdNmMEX3xfVgUGkbIl08LG7b3P3B8I+9kcR3EVoBNwBrDSzSWY2oATnXwy8BbQCrspz+FagJvCEu2/LLjSzQ83sFzMEuXsG8ED48K7cSYaZHQ6MADIIVkEWERERKVC3Fgfw+qh+nNr1oJyypeu2cfp9M3lw+mKysrTmgZQPVtoLdJhZH+A54OCwyIGvgZvcfXIM52sDzCKYWWgK8A3QExhI0IWoj7uvz1XfAdzd8pynBsGUpr2AecAHBEnMGQRdiK5x97v3Nb5c50/v3r179/T0gmYzFRERkYrm5Xnf8+eXv2Tb7sycstR2DfnXWV1oXCc5jpFJRZaSksLcuXPnhl/Ox6xUpis1s2Qzu9DMZgFpBEnBZ8AYgulA2wIvmdnV+3ru8K7BkcBEgoTgGqANMB7onTspKOI824FfE9xpqEFwB+IUgqTjhJIkBSIiIlI5ndatGVNHp9Kl+d6Z09MWrWPQuDTe+3Z1HCMTKVqkdwzMrBNwGTCcYOag3QTdce5z91m56jUn6BKU7O6tIwugDNEdAxERkcprT2YWd7+9kPs/XEzuj1oj+rTij4MPJTkpseAni+yjMnXHwMzON7M0gkG9VxPM6nMD0Nzdh+dOCgDcfSVB96LmUbQvIiIiUpYkJSZw/aBDefrinjSpUy2nfOKsZZx670wWrt4ax+hE8hdVV6IngN4EawKcBLR197vcfV0hz1lA0M1IREREpELq07Yh00b355iOeydB/HbVVk6eMIOn5iyntMd6iuyLqBKDO4E27n6yu0/1YvyVu/uz7j4wovZFREREyqT6Navy0AUp3D6kE9WqBB+9dmVk8efJX3L5k+ls3LY7zhGKBKJKDO5j76Jg+TKz2mbWIqL2RERERMoNM2N471a8MrIfHZrUzil/6+vVDB6XxuzFxZo7RaRURZUYLAVGF1FnVFhPREREpFLq0LQ2U0b25YLeLXPKVm3ZybkPz+Efb37LnsysOEYnlV1UiYGFm4iIiIgUIjkpkduGdOahC47kgBpJALjDve8v5qz7Z7Ni/fY4RyiVVamsY1CAJsC2ImuJiIiIVALHHtaEN8b0p2/bBjll81du4oTxaUye90McI5PKqkqsTzSzC/IUdc2nDCARaEGwtsEXsbYnIiIiUtE0qZPMk7/pyQPTl/CvtxaQkeX8vCuDMc/NZ/rCtdx2amdqVYv545rIPinJX9pEIHv2IQeGhFte2V2MthOsMiwiIiIioYQE47cD2tCnTQNGTZrH8rAr0X/n/UD6io2MG9qNrrlWUhYpLSVJDC4K9wY8CkwGpuRTLxNYD8x2900laE9ERESkwurSvB6vj0rl5ilf8t+5QVei5eu3c+Z9s/j9ce25on8bEhI0pFNKT8yJgbs/nv1vM7sQmOzuT0QSlYiIiEglVKtaFe4+uytHt2/En1/+kq27MsjIcu56YwEzFq3j3+d0pUmd5HiHKRVUJIOP3X2gkgIRERGRaAzpejBTR6fSrcXeLkSzFq9n0NjpvP316jhGJhXZ/pyVSERERESKqXn9Gjx/eW9GDmyLhT2INm7fw6VPfMpNk79k557M+AYoFU5MXYnMbAnBgONj3H1p+Lg43N3bxNKmiIiISGWTlJjAtcd3oF+7hoyZNJ9VW3YC8OSc5Xy8dAPjh3WjQ9PaRZxFpHhivWOQkOe5Cexd5KywTXcoRERERPZRr0Ma8MaYVI7v1CSnbMHqrZx8zwyemL0Mdy/4ySLFFNMdA3dvVdhjEREREYlWvRpVuf/8FJ79eCW3vfYVO/dksTsji5unfMX0hWu568wu1K9ZNd5hSjmmb/BFREREygkz49yeLXh1ZD8OzdWF6J1v1jBo7HRmfrcujtFJeafEQERERKScadekNpOv6suIPq1yytZs3cX5j3zE36d9y57MrPgFJ+VWrIOPL4i1QU1rKiIiIlJyyUmJ3HJKJ45u34hrX/iM9dt24w73f7iY2YvXMW5oN1o1rBnvMKUcsVgGq5hZFsGsRPv0NIJZiRL3ucFyyMzSu3fv3j09PT3eoYiIiEgFt2bLTq554TPSFu3tSlSzaiK3DenM6d0PxkwrJldkKSkpzJ07d667p5TkPLGufHxRSRoVERERkeg0rpPM4xf14OEZS/jHmwvYk+ls253JNS98xvRFa/nrqZ2pnZwU7zCljIt1VqLHow5ERERERGKXkGBc1r8NvQ9pyKhJ81i6bhsAU+b/yNwVGxk3tBvdWxwQ5yilLNPgYxEREZEK5PBmdXnt6n6cldIsp2zlhh2cdf9s7nlvEZlZWvNA8qfEQERERKSCqVmtCv84qwsThnWjdrWgg0hmlvPPtxZy3sNz+GnzjjhHKGVRTImBmS0xs8Vm1jrX4+Jsi6MNX0REREQKcnKXg5g6OpWUlnu7EM1ZsoFBY9N448tVcYxMyqJY7xgk5HluAsGsQ0VtukMhIiIish81r1+D5y7rxahftyMhnJxo8449XPFUOn96+Qt27M6Mb4BSZsQ6+LhVYY9FREREpOyokpjA749tT7+2DRkzaR4/bt4JwDMfreCTpRsYP6wbHQ+sE+coJd70Db6IiIhIJdGjdX2mje7P4M5Nc8oWrfmZIffOZOLMpcSyvpVUHKWSGJhZHTNrbmZKPUVERETKkLo1kvjPed35++mHUz0pWHd2d0YWt7z6NRc//inrf94V5wglXiJLDMysqpndGA4w3ggsAzaGg5RvNLOqUbUlIiIiIrEzM4b2aMGrV/fjsFxdiN77dg2DxqWRtmhtHKOTeIkkMTCz2sAM4DagJbAC+DjctwzL08ysVhTtiYiIiEjJtW1ci5ev6sPF/VrnlK3duovhj3zMHVO/YXdGVhyjk/0tqjsGtwJHAi8D7dy9tbv3dvfWQDtgMnBUWE9EREREyohqVRK56aTDmHjRUTSstbeDxwPTl3DGfbNyVlCWii+qxOAsYL67n+nuS3MfCB+fCXwGnB1ReyIiIiISoQEdGjNtdH+Obt8op+yLHzZz4vg0Xvh0pQYmVwJRJQYNgTcLOujBX9KbQIOI2hMRERGRiDWqXY3HRhzFn0/sSFJisOjB9t2Z/OHFzxk1aT6bd+yJc4RSmqJKDJYB9YqoUzesJyIiIiJlVEKCcUnqIbx8ZV8OaVQzp/zVz37khHFppC/fEMfopDRFlRg8BJxtZs3yO2hmLYBzwnoiIiIiUsZ1Prgur13dj6FHNc8p+2HTDs5+YA7j311EZpa6FlU0MSUGZtYi9wb8F0gD5prZn8ysn5m1C/c3Ap8CHxIMThYRERGRcqBG1Sr8/Ywj+M953amTXAWAzCzn7rcXMuzBOfywaUecI5QoVYnxecuA/NJEA24voHwIcHIJ2hQRERGRODjh8APp0rweYybN45NlGwH4eNkGBo+dzp1nHMHgww+Mc4QShVg/pD9B/omBiIiIiFRAB9erzrOX9uLe9xcz7t2FZDls2ZnBb5+ey7AezbnppMOoUVXf/5ZnMV09dx8RcRwiIiIiUsZVSUxg9DHt6Nu2AaMnzc/pSvTsxyv5eOkGxg/rRqeD6sY5SolVVIOPRURERKSSOLJVfaaOTuWkI/Z2IVq8dhun3TuLR2Ys1ZoH5ZQSAxERERHZZ3WrJzFhWDfuOvMIalRNBGB3Zha3v/Y1F038hHU/74pzhLKvIu0IZmZHAccDBwPV8qni7n5xlG2KiIiISHyYGWcf2ZwjWx7AqEnz+PKHLQB8sGAtg8am8a+zu/xiJWUp2yJJDMzMgInA+QQzEHm4z+a5ypUYiIiIiFQghzSqxX9/25d/vPktD6UtBWDdz7u48NGPuaRfa/4wqAPVqiTGOUopSlRdiUYCw4EngSMJkoCxQB/gT8BWYBJwSETtiYiIiEgZUrVKAjeeeBhP/KYHDWvt7Tjy8IylnP6fWSxe+3Mco5PiiCoxuBBY4O4j3H1uWLbJ3ee4+9+BgcAZwK8iak9EREREyqD+7RvxxphUBnbY24Xoqx+3cNL4GTz/yUoNTC7DokoMOgDv5SnL6abk7vOA14ArI2pPRERERMqohrWq8eiIo/jLyYdRNTH4uLljTybXvfQ5I5+dx+Yde+IcoeQnqsTAgM25Hm8D6uepswg4NKL2RERERKQMMzMu6tuayVf1pW3jWjnlr3/+EyeMS+PTZRviGJ3kJ6rE4AeCmYiyLQFS8tRpR5AwiIiIiEglcdhBdXh1ZD/O7dkip+yHTTs4+4HZjH1nIRmZWXGMTnKLKjH4mF8mAtOAHmZ2k5l1MrOrgCHAnIjaExEREZFyonrVRP7vtMO5//wU6lZPAiDLYew7ixj20By+37g9zhEKRJcYvAQkmlnr8PFdwHLgVuBzYAKwCfhjRO2JiIiISDkzqHNT3hiTSs/We3ucf7JsI4PHpfH65z/FMTKBiBIDd5/s7h3dfWn4eAPQDbgOeBC4ATjc3b+Noj0RERERKZ8OrFudZy7txbXHtScxIVj2auvODK56Zi7Xv/g523dnxDnCyivSlY9zc/fNwD9L6/wiIiIiUj4lJhgjf9WO3m0aMnrSPL7fuAOA5z5dySfLNjB+WDc6H1w3zlFWPlF1JRIRERER2ScpLQ9g6uhUhnQ9KKdsybptnPafmTyctoSsLK15sD9FmhiY2Xlm9q6ZbTCzjHD/rpmdF2U7IiIiIlIx1ElOYuw5XfnXWV2oWTURgD2Zzl9f/4YREz9h7dZdcY6w8ogkMTCzJDObAjxBsMpxLWBtuB8IPGFmU8wsKYr2RERERKTiMDPOSGnG66NS6dJsbxei6QvXMnjcdN5fsCaO0VUeUd0xuAE4GfiIIBFIdvcDgWTgVwTTmZ4EXB9ReyIiIiJSwbRqWJMXrujDFUe3wYJxyaz7eTcXPfYJt736NbsyMuMbYAUXVWJwAfAdMMDdP3T3LAB3z3L3D4ABBIuejYioPRERERGpgKpWSeCPgw/lqYt70rh2tZzyR2cu5dR7Z/Hdmq1xjK5iiyoxaAZMcffd+R10913AFH65OrKIiIiISL76tm3IG2P6c0zHxjll3/y0hZMmzOCZj1bgroHJUYsqMfgRKGr8QFJYT0RERESkSPVrVuWhC47ktiGdqFol+Ni6c08Wf3r5C3771Fw2bc/3O2mJUVSJwTPAmWZWJ7+DZlYPOBN4OorGzKyZmT1qZj+a2S4zW2ZmY83sgH04xwdm5oVsyVHEKiIiIiKxMzMu6N2KV0b2pX2TWjnlb3y1isHj0pizZH0co6tYolrg7DagM/Cxmd0GTAdWA02Ao4GbCAYg317ShsysDTALaEzQPelboAcwGhhkZn3dfV/+Qm4toFzL7omIiIiUEYc2rcMrI/vx19e/5qk5KwD4afNOhj00h5ED2zLq1+1IStQSXSURU2JgZllAfh27DHiygPJ2wI5Y28zlPwRJwSh3n5ArpruB3wF/A64o7snc/ZYSxiMiIiIi+0FyUiJ/PfVw+rdrxHUvfc6m7XtwhwnvfceM79Yxfmg3mtevEe8wy61YP6RPJ//EoFSZ2SHAccAy4N48h/8CXAYMN7Nr3H3bfg5PRERERPaD4zo15Yhm9fjdc/OZHXYlmrdiEyeMS+Ovp3VmSFfNdxOLmBIDdx8QcRzF9atw/1b2lKjZ3H2rmc0kSBx6Ae8W54Rmdg7QGtgNfAO8F86iJCIiIiJlVNO6yTx1SU8emL6Yu99aSEaWs3VXBqMnzWf6wnXcOqQTtapF1Wu+cihvv60O4X5hAccXESQG7SlmYgBMyvN4jZld5e4vFufJZpZewKFDi9m+iIiIiMQgMcG4ckBb+rRpyKhn57Fiw3YAXpr7PenLNzB+WDeOaFYvzlGWH5GP0DCzJDM73MxSzewIMytqGtN9kb1G9uYCjmeXF+cvYArBas3NgOoEH+TvCJ/7nJkNLkGcIiIiIrKfdG1ej9dH9eO0ERQ+9wAAGCZJREFUbnu7EC1bv53T/zOL+z9cTFaW1jwojsgSAzOrY2b3A5uA+cAHwDxgk5ndH05ZWtrCxbOLHv/g7v9299fc/Qd33+nuC9z9T8A1BL+X/ytOg+6ekt9GMFuSiIiIiOwHtZOT+Pc5Xfn3OV1yuhBlZDl/n/YtFzz6MWu27IxzhGVfJIlBuH7BTILBvxlAGvB8uN8Tls8oaJ2DfZB9R6BuAcfr5KkXi4cJfoauZla7BOcRERERkf3stG7NeH1UP7o03/ud9Izv1jFoXBrvfL06jpGVfVHdMbgB6ATcB7R09wHuPiwcpNySYAahw8J6JbEg3Lcv4Hi7cF/QGIQiuftOYGv4sGas5xERERGR+GjZoCYvXtGbKwe0wcL+JBu27eaSJz7lL1O+ZOeezPgGWEZFlRicDsxx96vcfVPuA+6+2d2vBmYDZ5SwnffD/XFm9ovYw2/3+xKslTAn1gbMrANwAEFysC7W84iIiIhI/CQlJnDdoEN5+uKeNKlTLaf88dnLOfXemSxcvbWQZ1dOUSUGLQjGFBTmQ6B5SRpx98XAW0Ar4Ko8h28l+Ib/idxrGJjZoWb2ixmCzOwQM/ufCW7NrCHwWPhwkrtr9WMRERGRcqxP24a8Mbo/xx7WJKfs21VbOXnCDJ6csxx3DUzOFtV0pdsJViMuTKOwXkldCcwCxpvZrwnWHugJDCToQnRjnvrfhHvLVdYfeNjMPgQWAxsIkpsTCMYvfApcF0GsIiIiIhJnB9SsyoPDU3jqoxX89bWv2ZWRxa6MLG6a/CVpC9dy5xlHcEDNqvEOM+6iumPwCXCWmbXL76CZtQHODuuVSHjX4EhgIkFCcA3QBhgP9Hb39cU4TTrwFEEyc0Z4jkHAF8AooG/eLlEiIiIiUn6ZGcN7teSVkf3o0GTv/DJvfb2awePSmLVYPcgtitsn4Tf3bxH0y59AMBbgJ6ApMAC4muCb+OPd/Z0SN1gOmFl69+7du6enF7T+mYiIiIjEw849mdwx9Rsen708p8wMrhzQhjHHtCcpMfKlvkpVSkoKc+fOnRtOmR+zSH5qd3+XoItPMvAn4G3gS+Ad4CaCvv8jK0tSICIiIiJlV3JSIrcO6czDFxzJATWCtXjd4d73F3PW/bNZsT6K3u/lT2TpkLs/QDCN6M3Ay8B74f4moL273xdVWyIiIiIiJXXMYU14Y0x/+rZtkFM2f+UmThifxuR5P8QxsviIavAxAO6+AvhblOcUERERESktTeok8+RvevJg2hL++eYCMrKcn3dlMOa5+UxfuJZbh3SidnJSvMPcL6Ja+TjTzJ6J4lwiIiIiIvtTQoJxxdFteOm3fWjZoEZO+X/n/cCJ42cwf2XlmJMmqq5EW4HlRdYSERERESmjujSvx+ujUjm9+97lrlZs2M6Z983iPx98R1ZWxV7zIKrEYB5wWETnEhERERGJi1rVqnD32V0ZN7QrtasFve4zspy73ljA+Y98xKrNO+McYemJKjG4EzjBzI6N6HwiIiIiInEzpOvBTB2dSrcW9XLKZi1ez+Bx03nrq1VxjKz0RDX4uDHwBjDNzCYTLGS2Cvif+y3u/kREbYqIiIiIlJrm9Wvw/OW9Gf/uIu55/zvcYeP2PVz2ZDrDe7XkxhM7kpyUGO8wIxNVYjCRIAkw4PRwg18mBhY+VmIgIiIiIuVCUmIC1xzXgb5tG/K75+bzU9iV6Mk5y/lo6XomDOtOh6a1izhL+RBVYnBRROcRERERESlzeh3SgGmjU/njS1/wRtiVaOHqnzn5nhn8+cSODO/VEjOLc5QlE0li4O6PR3EeEREREZGyql6Nqtx3fncmfbKSW1/9ip17stidkcXNU75i+sK13HVmF+rXrBrvMGMW2crHIiIiIiIVnZkxrEcLXru6Hx0PrJNT/s43axg0djozv1sXx+hKJtLEwMxqmdlwM7vbzB4J98PNrFaU7YiIiIiIxFPbxrV5+co+XNS3VU7Zmq27OP+Rj/hgwZr4BVYCUY0xwMzOAu4H6hEMNM7mwFgzu9zdX4yqPRERERGReEpOSuQvJ3eif7tGXPvCZ6zftpsjmtWjb9uG8Q4tJpHcMQjXL3gWqE0w69BFwOBw/2RY/qyZHRNFeyIiIiIiZcXAQxszbUwqgzo1ZfzQriQlls/e+lHdMbgZ2AWkuvvcPMceN7N7gOlhvXcialNEREREpExoXDuZ+4enxDuMEokqnekGPJdPUgCAu38KPA90j6g9ERERERGJUFSJwS7gpyLq/BjWExERERGRMiaqxCAN6FdEnb4E3YlERERERKSMiSoxuB443Mz+bmY1cx8ws5pmdhfQGfhjRO2JiIiIiEiEohp8fD3wOfAH4DIzmwusBpoQjCuoS3C34Po8S0W7u18cUQwiIiIiIhKjqBKDEbn+XQ/4VT51jg633BxQYiAiIiIiEmdRJQatIzqPiIiIiIjEQSSJgbsvj+I8IiIiIiISH+VzWTYREREREYmUEgMREREREVFiICIiIiIiSgxERERERAQlBiIiIiIighIDERERERFBiYGIiIiIiKDEQEREREREAHP3eMdQIZnZ+urVq9fv2LFjvEMRERERkQrsm2++YceOHRvcvUFJzqPEoJSY2VKgDrAsDs0fGu6/jUPbsn/oGlcOus6Vg65z5aDrXPHF8xq3Ara4e+uSnESJQQVkZukA7p4S71ikdOgaVw66zpWDrnPloOtc8VWEa6wxBiIiIiIiosRARERERESUGIiIiIiICEoMREREREQEJQYiIiIiIoJmJRIREREREXTHQEREREREUGIgIiIiIiIoMRAREREREZQYiIiIiIgISgxERERERAQlBiIiIiIighIDERERERFBiUGFYmbNzOxRM/vRzHaZ2TIzG2tmB8Q7NikeM2tgZpeY2ctm9p2Z7TCzzWY2w8wuNrN8X7Nm1sfMpprZBjPbbmafm9kYM0vc3z+DxMbMhpuZh9slBdQ5ycw+CP8mfjazj8zswv0dq+w7M0s1s5fM7Kfw/fknM3vLzE7Ip65ez+WMmZ0YXs/vw/ftJWb2gpn1LqC+rnEZZGZnmtkEM0szsy3h+/FTRTxnn69lWX4v1wJnFYSZtQFmAY2BKcC3QA9gILAA6Ovu6+MXoRSHmV0B3Af8BLwPrACaAKcDdYGXgLM81wvXzIaE5TuB54ANwMlAB+BFdz9rf/4Msu/MrDnwBZAI1AIudfeH89QZCUwA1hNc593AmUAz4F/ufu1+DVqKzcz+DNwOrANeI3h9NwS6Ae+7+3W56ur1XM6Y2Z3AdQSvzckE17ktcApQBbjA3Z/KVV/XuIwys/lAF+Bn4HvgUOBpdz+/gPr7fC3L/Hu5u2urABvwJuDA1XnK7w7L7493jNqKdR1/RfCmkpCnvClBkuDAGbnK6wBrgF3AkbnKkwkSRQeGxvvn0lboNTfgHWAx8I/wml2Sp04rgv941gOtcpUfAHwXPqd3vH8Wbfle37PC6/M2UDuf40m5/q3XcznbwvfmTGAV0DjPsYHhNVuia1w+tvCatQvflweE1+OpAuru87UsD+/l6kpUAZjZIcBxwDLg3jyH/wJsA4abWc39HJrsI3d/z91fdfesPOWrgPvDhwNyHToTaARMcvdPc9XfCfw5fPjb0otYIjCKICG8iOC1mp/fANWAe9x9WXahu28E/i98eEUpxigxCLv+3QlsB851961567j7nlwP9Xouf1oSdMv+yN3X5D7g7u8DWwmuaTZd4zLM3d9390UeflovQizXssy/lysxqBh+Fe7fyucD5VZgJlAD6LW/A5NIZX+AyMhVln3t38in/nSCDyR9zKxaaQYmsTGzjsDfgXHuPr2QqoVd52l56kjZ0QdoDUwFNob90K83s9EF9D3X67n8WUTQFaSHmTXMfcDM+gO1Ce4IZtM1rjhiuZZl/r1ciUHF0CHcLyzg+KJw334/xCKlwMyqABeED3O/oRR47d09A1hK0Mf1kFINUPZZeE2fJOgi9qciqhd2nX8iuNPQzMxqRBqklNRR4X41MJdgfMHfgbHALDP70Mxyf5us13M54+4bgOsJxoJ9bWYPmtkdZvY88BZBF7LLcz1F17jiiOValvn3ciUGFUPdcL+5gOPZ5fX2QyxSOv4OdAamuvubucp17cuvmwkGn45w9x1F1C3uda5bwHGJj8bh/gqgOnAMwTfInQnGhfUHXshVX6/ncsjdxxJMEFEFuBT4I8HYkpXAxDxdjHSNK45YrmWZfy9XYlA5WLjXFFTlkJmNAq4hmGlq+L4+Pdzr2pchZtaD4C7Bv9x9dhSnDPe6zmVL9nSFBpzp7u+6+8/u/hVwGsGsJ0cXNKVlPnSdyyAzuw54EZgItAFqAinAEuBpM7trX04X7nWNy79YrmXcr78Sg4qhqAyzTp56Uk6Y2VXAOOBrYGB42zo3XftyJlcXooXATcV8WnGv85YShCbR2xjul7j7Z7kPhHeJsu/+9Qj3ej2XM2Y2gGCA+Svu/nt3X+Lu2919LkHy9wNwTThJCOgaVySxXMsy/16uxKBiWBDuCxpD0C7cFzQGQcogMxsD3AN8SZAUrMqnWoHXPvwA2ppgsPKS0opT9lktguvVEdiZa1EzJ5hFDOChsGxs+Liw63wgwTeU37v79lKOXfZN9nXbVMDx7MShep76ej2XHyeF+/fzHghfjx8TfNbqFhbrGlccsVzLMv9ersSgYsh+Qzou78q4ZlYb6AvsAObs78AkNmZ2PfBvYD5BUrCmgKrvhftB+RzrTzAb1Sx33xV9lBKjXcAjBWzzwjozwsfZ3YwKu86D89SRsmM6wQeDdmZWNZ/jncP9snCv13P5kz3jTKMCjmeX7w73usYVRyzXsuy/l8dzEQVt0W1ogbMKsxF0L3HgU6B+EXXrAGvRYjkVYgNuIf8FzlpTxhfF0VbgNX0qvD5/zVN+LJBFcDehXlim13M524Czw+uyCjg4z7HB4TXeATTQNS5fG8Vb4GyfrmV5eC+3MCAp58ysDcEfYmNgCvAN0JNgFb+FQB93Xx+/CKU4zOxCggFsmQRLpufXz3SZu0/M9ZxTCQa+7QQmESzJfgrhkuzA2a4XerlgZrcQdCe61N0fznPsamA8wX8ozxF8A3km0IxgEPO1+zdaKQ4za0ywlkxbII2ga0lLgv7nTrDw2Qu56uv1XI6Ed+nfJJhxaivwMkGS0JGgm5EBY9x9XK7n6BqXUeG1OTV82BQ4nqArUFpYti73e20s17LMv5fHOyPTFt0GNAceA34i+ENbTjBwtdBvnbWVnY293xgXtn2Qz/P6Ei6iRPDt1BfA74DEeP9M2mK6/pcUcPxk4EOCDyDbgE+AC+Mdt7Yir2t9gru3S8P35vUEX+D0KqC+Xs/laAOSgDEE3XW3EHQfW0OwbsVxusblZyvG/8HLoriWZfm9XHcMREREREREg49FRERERESJgYiIiIiIoMRARERERERQYiAiIiIiIigxEBERERERlBiIiIiIiAhKDEREREREBCUGIiIiIiKCEgMREREREUGJgYiIiIiIoMRARERERERQYiAist+Y2TIzW5anbISZuZmNyKf+cWY2y8w2hnUm5zp2pJm9bWbrwmPzS/8nqHjC390H8Y5DRKQsUGIgIlIGmVkrYArQGngMuBWYFB6rA7wO9AjLbgXuj0ecUjgzG1BY8mFmJ5rZNjPbYWZDCqjzUHiO7WZWr5C2bgnr3RJN9CJS2VSJdwAiIpXcy8Ac4Kc85ccAycA17v5MnmM9gMbAje7+f6UfYoXWEdgej4bDu0QPAVuBU9x9Rj51agNDAQeqA+cD9+zHMEWkEtEdAxGROHL3ze7+rbtvznPooHD/Yz5PK+yY7IPwd79if7drZn8kuBO0CkjNLykInQvUAsYCu4FL90+EIlIZKTEQEYmQBUaa2VdmttPMfjCze8ysbgH1fzHGILvrCUH3IID3w+OeXRd4PDz2WO5juc5Zw8xuMLP5YTeVn81stpkNy6f97K4ut5hZDzN73cw2hGWtctVrFv4cS8xsl5mtN7NXzOyofM6Z3aVlgJmdaWYfh91gNpjZJDM7uIDfRX0z+5uZfRnW32xmn5nZ382sZj517zCzb8JuOJvN7F0zO67gq5Nvm//TzSfW+IvZnpnZOOAO4Gugj7t/VchTLgWygH8DrwFHmFnPWNsXESmMuhKJiERrLDCKoGvQg8AeYAjQE6hK8K1vYZYRJAUDgKMJkoBl4bH54bGu4TmnhGXZxwj7oL8HdAPmAo8SfAl0PPCMmXVy9z/n025v4AZgRvichtmxmll34C2gPvAm8N/w+KnADDM7zd2n5nPOK4FTgFeAD8PfwTlAFzPr6u67siuaWWvgfaAlkA7cF8bdHvgdwRiKbWHdlsAHQCsgDXgDqAmcBLxhZpe7+0P5xLOvih1/cZhZVYLrORSYBZzs7hsKqd8NSAHedveVZjYROB24DPho338cEZEiuLs2bdq0aYtgA/oQ9AX/DqifqzwZmB0eW5bnOSPC8hF5ym8Jywfk006+zwmPTQyPXZenPJngA3QW0DVX+YCwvgOX53O+KuHPsxM4Os+xg4AfCJKgavnEvgU4PM9zngmPnZ2nfGZYfkM+MTQEknM9/iD8OYbmqVePIEHaATQp5jVz4IMCfvfFjr+Q82f/fj8F3g7//QpQvRjPvT+sPyzXtVgF/AzUyad+dty3xPu1oE2btvK5qSuRiEh0Lgr3f/Nc3wS7+06Cb+NLlZk1IBic+qm735X7WBjD9YAR9FvPa767P5BP+YlAG2CCu3+Y55w/AncBTYFf5/Pc8e7+RZ6y7G/ye+SKO4UgqZoP3Jn3JO6+LowfM+tCcCflJXeflKfeJuAvBEnQGfnEs6+KFX8xpRAMKF8AnO7uOwqrHHadGgZsJhigjrtnAE8T3B3J7xqKiJSIuhKJiESne7j/MJ9jaUBGKbd/FJAIFDRlZVK475jPsY8LOGfvcN+ygHO2y3XOvN2JPs2n/spwf0Cusl7h/k13zyogjrzx1C0gnka54imp4sZfHAuAakAHYIKZXenuXkj9oUAd4IHspCj0GPB7gu5EmqJWRCKlxEBEJDrZA4xX5z3g7plmtr6U228Q7o8Kt4LUyqdsVRHnPKuItvM756Z8yrKTo8RcZdlz8/9QRBu54zk23PYlnn1V3PiLYxUwHHgXuAJINrOLC0mELgv3E3MXuvuXZpYOpJjZke6eX/IiIhITdSUSEYlO9pSjTfIeMLNE9n6oLe32/+3uVsg2MJ/nFvTtdfY5hxRxzlsLeH5xZH8AL85sP9nxjC4inosKPUscuPtKoD/BbEQjgKfN7H++oDOzI9jbVWl2rpmnPJyVKiU8dlne54qIlIQSAxGR6MwN90fncyyV0r9L+zHBoNzUCM85J9xHec6C2jjezIr6f2l/xFNq3H0Vwd/HfILuQi+EsxXllv2B/wPgkQK2HcAwM4vizoiICKDEQEQkShPD/Y1mVj+70MySCeatL1XuvoZgcOqRZnZTAd9GtwmnBi2uKcBi4CozOyG/CmbW28xqxBQ04O7pBNN3diUYIJ33/A3C3yFh15k04HQz+00B8RxuZo1jjae0ufs6YCDBlKOnApOzfz4zqw6cB2QC57n7JfltwEsE3aX+Z20KEZFYaYyBiEhE3H2mmU0Arga+NLMX2buOwUaCaT1L20iCAcG3AcPNbAbBmIeDCAbkHkXwYXJpcU7m7nvM7HSC9QteN7NZBN92bweah+c7BDgwLIvV+QTfkP+fmZ0R/tvCn+U44FD2rudwLsFaDY+Y2SiCD9ibgGbAEUBngkHKa0oQT6ly901mdizwOjCY4Hd7CsFYjnrAq+GsTwV5mOB3dhl7Z0oSESkRJQYiItEaDSwErgIuB9YTTDf5J+Cz0m7c3beY2dEEHxjPJZi2M5kgOVhEsFjY2/t4zs/DaUJ/T7CI2EUEXZZ+AuYRTBG6roRxLw0XUruO4Fv0kQRrJywD/kWuD/nu/n04xenVBD/feQSDgVcR9N+fAOSdZrTMcfetZjaI4K7MMQTJV/YKzw8X8dwPzWwhwd2hbu4+r3SjFZHKwAqfLU1ERERERCoDjTEQERERERElBiIiIiIiosRARERERERQYiAiIiIiIigxEBERERERlBiIiIiIiAhKDEREREREBCUGIiIiIiKCEgMREREREUGJgYiIiIiIoMRARERERERQYiAiIiIiIigxEBERERERlBiIiIiIiAhKDEREREREBCUGIiIiIiKCEgMREREREQH+H9PvHzcIGyAgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 387
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "deltaAis = np.linspace(0,100,100)\n",
    "# pSuccess = []\n",
    "# for deltaAi in deltaAis: #hard to communicate above 20, easy below 10\n",
    "#     successful = []\n",
    "#     for i in range(1000):\n",
    "#         successful.append(tryComm(deltaAi))\n",
    "#     pSuccess.append(np.mean(successful))\n",
    "theoreticalP = [min(1 - (d-10)/170,1) for d in deltaAis]\n",
    "plt.plot(deltaAis,theoreticalP)\n",
    "# plt.plot(deltaAis,pSuccess)\n",
    "plt.xlabel(\"difference in KAI\")\n",
    "plt.ylabel(\"probability that communication succeeds\")\n",
    "plt.savefig(\"./figs/successfullP.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nAgents = 22\n",
    "# steps = 1\n",
    "# teamDims, agentTeams = saeTeams(nAgents)\n",
    "# team = teamWorkSpecialized(nAgents,constructor,teamDims,agentTeams,showViz=showViz,speed=None,pComm=pComm,steps=steps,groupConformityBias=True,soBias=True,aiScore=aiScore,aiRange=aiRange)\n",
    "\n",
    "# print(team.agents[0].score)\n",
    "# print(team.agents[11].score)\n",
    "# print(team.getBestScore())\n",
    "# team.haveSubTeamMeeting(0)\n",
    "# print(team.agents[0].score)\n",
    "# print(team.agents[11].score)\n",
    "# print(team.getBestScore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Individual Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def work(AgentConstructor,steps=100,ai=None,temp=None, speed=None, showViz=False, soBias=False, groupConformityBias = False, color = 'red',startPosition = None,teamPosition=None):    \n",
    "#     a = AgentConstructor()\n",
    "#     if ai is not None:\n",
    "#         a.kai = findAiScore(ai)\n",
    "#         self.speed = bounds(AVG_SPEED + normalizedAI(self.kai.KAI) * SD_SPEED, MIN_SPEED ,np.inf)\n",
    "#         self.temp = bounds(AVG_TEMP + normalizedE(self.kai.E) * SD_TEMP, 0 ,np.inf)\n",
    "#     if startPosition is not None:\n",
    "#         a.startAt(startPosition)\n",
    "#     if temp is not None:\n",
    "#         a.temp = temp\n",
    "#     if speed is not None:\n",
    "#         a.speed = speed\n",
    "    \n",
    "#     a.decay = calculateAgentDecay(a, steps)\n",
    "\n",
    "#     scores = []\n",
    "#     shareSuccess = []          \n",
    "\n",
    "#     for i in range(steps):\n",
    "#         didMove = a.move(soBias=soBias,groupConformityBias = groupConformityBias,teamPosition = teamPosition)\n",
    "#         if didMove:\n",
    "#             scores.append(copy.deepcopy(a.score))\n",
    "#             if(showViz and a.nmoves>0):\n",
    "# #                     plt.scatter(a.rNorm[0],a.rNorm[1],c=color)\n",
    "#                 plt.scatter(a.rNorm[0],a.score,c=color)\n",
    "#         a.speed *= a.decay\n",
    "#         a.temp *= a.decay\n",
    "\n",
    "#     return a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {},
   "outputs": [],
   "source": [
    "# meetingTimes = 20\n",
    "def teamWork(teamSize,agentConstructor, pComm, steps=100, soBias=False,groupConformityBias=False, speed=None, temp=None, showViz=False,aiScore=None,aiRange=None,startPositions=None):\n",
    "    meetingTotals = []\n",
    "    squad = Team(teamSize,agentConstructor,temp=temp,speed=speed,aiScore=aiScore,aiRange=aiRange,startPositions=startPositions)\n",
    "    for a in squad.agents:\n",
    "        a.decay = calculateAgentDecay(a,steps)\n",
    "    \n",
    "    meetingTotal = 0\n",
    "    i = 0\n",
    "    while i < steps:\n",
    "        meetingTotal += squad.step(pComm,showViz,soBias,groupConformityBias) \n",
    "#         if showViz: \n",
    "#             rGroup = squad.getSharedPosition()\n",
    "#             plt.scatter(rGroup[0],rGroup[1],marker='o',s=100,c='black')\n",
    "        \n",
    "        if (i+1)%meetingTimes == 0: \n",
    "            squad.haveTeamMeeting()\n",
    "            squad.nTeamMeetings +=1\n",
    "            i += TEAM_MEETING_COST\n",
    "            if(showViz): \n",
    "                plt.show()\n",
    "        i += 1\n",
    "    if showViz: plt.show()\n",
    "    meetingTotals.append(meetingTotal)\n",
    "    \n",
    "    return squad\n",
    "\n",
    "# meetingTimes = 20\n",
    "def teamWorkSpecialized(teamSize,agentConstructor,teamSpecializations,agentTeams, pComm, steps=100, soBias=False,groupConformityBias=False, speed=None, temp=None, showViz=False,aiScore=None,aiRange=None,startPositions=None):\n",
    "    np.random.seed()\n",
    "    meetingTotals = []\n",
    "    squad = Team(teamSize,agentConstructor,temp=temp,speed=speed,aiScore=aiScore,aiRange=aiRange,startPositions=startPositions,specializations = teamSpecializations)\n",
    "    for i in range(len(squad.agents)):\n",
    "        a = squad.agents[i]\n",
    "        aTeam = agentTeams[i]\n",
    "        a.team = aTeam\n",
    "        a.myDims = teamSpecializations[aTeam]\n",
    "        a.decay = calculateAgentDecay(a,steps)\n",
    "    \n",
    "    meetingTotal = 0\n",
    "    i = 0 #not for loop bc we need to increment custom ammounts inside loop\n",
    "    while i < steps:\n",
    "#                 pCi = pComm #*(i/steps) #we can make them wait until later to communicate\n",
    "        meetingTotal += squad.step(0,showViz,soBias,groupConformityBias)  #putting 0 for pComm here!\n",
    "        score = squad.getBestScore()\n",
    "        squad.scoreHistory.append(score)\n",
    "        if showViz: \n",
    "            plt.scatter(i,score,marker='o',s=100,c='black')\n",
    "        \n",
    "        if (i+1)%meetingTimes == 0: \n",
    "            squad.haveInterTeamMeeting()\n",
    "            squad.nTeamMeetings +=1\n",
    "            i += TEAM_MEETING_COST\n",
    "#             if showViz: \n",
    "#                 plt.show()\n",
    "\n",
    "        #use pComm for subTeam meetings instead of pair meetings\n",
    "        for team in range(len(squad.specializations)): \n",
    "            if pComm > np.random.uniform(): #on any step there is some chance of having a meeting\n",
    "                squad.haveSubTeamMeeting(team,gap=complexSharing)\n",
    "                squad.subTeamMeetings += 1\n",
    "        i += 1\n",
    "    if showViz: plt.show()\n",
    "    meetingTotals.append(meetingTotal)\n",
    "    \n",
    "    return squad\n",
    "\n",
    "def teamWorkSharing(teamSize,agentConstructor,teamSpecializations,agentTeams, pComm, steps=100, soBias=False,groupConformityBias=False, speed=None, temp=None, showViz=False,aiScore=None,aiRange=None,startPositions=None, curatedTeams=False,pCommTime = pCommTime):\n",
    "    np.random.seed()\n",
    "    squad = Team(teamSize,agentConstructor,temp=temp,speed=speed,aiScore=aiScore,aiRange=aiRange,startPositions=startPositions,specializations = teamSpecializations)\n",
    "    \n",
    "    for i in range(len(squad.agents)):\n",
    "        a = squad.agents[i]\n",
    "        aTeam = agentTeams[i]\n",
    "        a.team = aTeam\n",
    "        a.myDims = teamSpecializations[aTeam]\n",
    "        a.decay = calculateAgentDecay(a,steps)\n",
    "    if curatedTeams:\n",
    "        for team in range(len(teamSpecializations)):\n",
    "            teamAgents=[a for a in squad.agents if a.team == team]\n",
    "            for i in range(len(teamAgents)):\n",
    "                myKai = aiScore - aiRange/2.0 + aiRange*(float(i)/(len(teamAgents)-1)) \n",
    "                a= teamAgents[i]\n",
    "                a.kai = findAiScore(myKai)\n",
    "                           \n",
    "    i = 0 #not for loop bc we need to increment custom ammounts inside loop\n",
    "    while i < steps:\n",
    "        pCommT = pComm\n",
    "        if pCommTime is not None:\n",
    "            timeFraction = float(i)/steps\n",
    "            pCommT = pComm * pCommTime[int(timeFraction*len(pCommTime))]\n",
    "        squad.nMeetings += squad.step(pCommT,showViz,soBias,groupConformityBias) \n",
    "        score = squad.getBestScore()\n",
    "        squad.scoreHistory.append(score)\n",
    "        if showViz: \n",
    "            plt.scatter(i,score,marker='o',s=100,c='black')\n",
    "        \n",
    "        if (i+1)%meetingTimes == 0 and float(i)/steps > startHavingMeetings:  #start having meetings later on?\n",
    "            squad.haveInterTeamMeeting()\n",
    "            squad.nTeamMeetings +=1\n",
    "            i += TEAM_MEETING_COST\n",
    "#             if showViz: \n",
    "#                 plt.show()\n",
    "        i += 1\n",
    "    if showViz: plt.show()\n",
    "    \n",
    "    return squad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define the team specializations and assign agents to teams\n",
    "def specializedTeams(nAgents,nDims,nTeams):\n",
    "    teamDimensions = np.array([[1 if t%nTeams == dim%nTeams else 0 for dim in range(nDims)] for t in range(nTeams)])\n",
    "    agentTeams = np.array([a%nTeams for a in range(nAgents)])\n",
    "    return teamDimensions, agentTeams\n",
    "\n",
    "teams = ['brk', 'c', 'e', 'ft', 'fw', 'ia','fsp','rsp', 'rt', 'rw', 'sw']\n",
    "teamsDict = { i:teams[i] for i in range(10)}\n",
    "paramTeams = paramsDF.team\n",
    "nTeams = len(teams)\n",
    "teamDimensions_CONST = [[ 1 if paramTeam == thisTeam else 0 for paramTeam in paramTeams] for thisTeam in teams]\n",
    "    \n",
    "def saeTeams(nAgents):\n",
    "    for i in range(nAgents):\n",
    "        agentTeams = np.array([a%nTeams for a in range(nAgents)])\n",
    "    return agentTeams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def showScoreHistory(agent):\n",
    "    mem = agent.memory\n",
    "    for i in range(len(mem)):\n",
    "        m = mem[i]\n",
    "        plt.scatter(i,m.score,c='b')\n",
    "    plt.show()\n",
    "def showPath(agents):\n",
    "    for agent in agents:\n",
    "        mem = agent.memory\n",
    "        dims = [i for i in range(len(agent.myDims)) if agent.myDims[i]>0]\n",
    "        x = []\n",
    "        y = []\n",
    "        for i in range(len(mem)):\n",
    "            m = mem[i]\n",
    "            x.append(m.r[dims[0]])\n",
    "            y.append(m.r[dims[1]])\n",
    "        plt.scatter(x,y,c=aiColor(agent.kai.KAI))\n",
    "        plt.xlabel(paramsDF.name[dims[0]])\n",
    "        plt.ylabel(paramsDF.name[dims[1]])\n",
    "        plt.scatter(agent.r[dims[0]],agent.r[dims[1]],marker='x',s=100,c=aiColor(agent.kai.KAI))\n",
    "    plt.show()\n",
    "# a0 = team.agents[9]\n",
    "# showScoreHistory(a0)\n",
    "# a0.myDims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set standard Parameters\n",
    "def resetDefaultParameters():\n",
    "    global complexSharing\n",
    "    complexSharing = True #if False, shareBasic() is used, eg strictly-greedy one-dimension jump \n",
    "    global commBonus\n",
    "    commBonus = 10 #increasing the communication bonus makes successful communication more likely\n",
    "    global commRange\n",
    "    commRange = 180\n",
    "    global pCommTime\n",
    "    pCommTime = None\n",
    "    global selfBias\n",
    "    selfBias = 0 #increasing self bias will make agents choose their solutions more over others\n",
    "    global startRange\n",
    "    startRange = 10\n",
    "    global nDims\n",
    "    nDims = 56\n",
    "\n",
    "    global SO_STRENGTH\n",
    "    SO_STRENGTH = 10\n",
    "    global RG_STRENGTH\n",
    "    RG_STRENGTH = 10\n",
    "    global TEAM_MEETING_COST\n",
    "    TEAM_MEETING_COST = 1 #1 turn\n",
    "\n",
    "    global VERBOSE\n",
    "    VERBOSE = False\n",
    "    global showViz\n",
    "    showViz = False\n",
    "\n",
    "    global AVG_SPEED\n",
    "    AVG_SPEED = 7.0E-3\n",
    "    global SD_SPEED\n",
    "    SD_SPEED = 7.0E-4\n",
    "    global MIN_SPEED\n",
    "    MIN_SPEED = 1.0E-4\n",
    "    global AVG_TEMP\n",
    "    AVG_TEMP = 1\n",
    "    global SD_TEMP\n",
    "    SD_TEMP = 0.8\n",
    "    \n",
    "    global steps\n",
    "    steps = 500 #0 #100\n",
    "    global nAgents\n",
    "    nAgents = 33\n",
    "    global carDesigner\n",
    "    constructor = carDesigner\n",
    "#     global teamDims\n",
    "#     teamDims = [[ 1 if paramTeam == thisTeam else 0 for paramTeam in paramTeams] for thisTeam in teams]\n",
    "#     global agentTeams\n",
    "#     agentTeams = saeTeams(33)#[[ 1 if paramTeam == thisTeam else 0 for paramTeam in paramTeams] for thisTeam in teams]\n",
    "\n",
    "    global pComm\n",
    "    pComm = 0.2\n",
    "    global meetingTimes\n",
    "    meetingTimes = steps #have one meeting at the end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# visualize the solution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# team0 = teamWorkSharing(nAgents,constructor,teamDims,agentTeams,showViz=showViz,speed=None,pComm=pComm,steps=steps,groupConformityBias=True,soBias=True,aiScore=aiScore,aiRange=aiRange)\n",
    "s0 = team0.getBestSolution()\n",
    "carDesign = asParameters(s0.r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'carDesign' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-98-997ebdf3b7b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cabin len, m: \"\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcarDesign\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"front wing angle, deg: \"\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcarDesign\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafw\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m180\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'carDesign' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"cabin len, m: \" +str(carDesign.lc))\n",
    "print(\"front wing angle, deg: \" +str(carDesign.afw*180/np.pi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = startParams()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random start point:\n",
      "cabin len, m: 1.525\n",
      "front wing angle, deg: 22.96603615023486\n"
     ]
    }
   ],
   "source": [
    "print(\"random start point:\")\n",
    "print(\"cabin len, m: \" +str(p.lc))\n",
    "print(\"front wing angle, deg: \" +str(p.afw*180/np.pi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run SAE team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "resetDefaultParameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for k in range(reps):\n",
    "# #     if k%10 == 0:\n",
    "# #         startPositions = np.random.uniform(-1*startRange,startRange,[teamSize,nDims])\n",
    "#     for i in range(len(aiRanges)):\n",
    "#         aiRange = aiRanges[i]\n",
    "#         for j in range(len(aiScoresMeans)):\n",
    "#             aiScore = aiScoresMeans[j]\n",
    "#             team = teamWorkSpecialized(nAgents,constructor,teamDims,agentTeams,showViz=showViz,speed=None,pComm=pComm,steps=steps,groupConformityBias=True,soBias=True,aiScore=aiScore,aiRange=aiRange)\n",
    "#             score = team.getBestScore()\n",
    "#             scores.append(score)\n",
    "#             aiScores = [a.kai.KAI for a in team.agents]\n",
    "#             aiTeamMeans.append(np.mean(aiScores))\n",
    "#             aiTeamRange.append(np.max(aiScores) - np.min(aiScores))\n",
    "#             nMeetings.append(team.nMeetings)\n",
    "#             teamMeetings.append(team.nTeamMeetings)\n",
    "#             scoreMatrix[i,j] += score\n",
    "#             plt.clf()\n",
    "#             plt.plot(range(len(team.scoreHistory)),team.scoreHistory)\n",
    "#             plt.ylim([-3,0])\n",
    "#             plt.xlim([0,100])\n",
    "#             plt.savefig(\"./figs/teamScores_mu:\"+str(aiScore)+\"_sd:\"+str(aiRange)+\"_\"+str(timer.time())+\".pdf\")\n",
    "# #             print(team.nMeetings)\n",
    "# scoreMatrix = np.array(scoreMatrix) / reps #the average score\n",
    "# print(\"time to complete: \"+str(timer.time() - t))\n",
    "# print(\"ai ranges: \"+str(aiRanges))\n",
    "# print(\"ai means: \"+str(aiScoresMeans))\n",
    "# print(\"reps:\" +str(reps))\n",
    "# np.savetxt(\"./results/scoreMatrix_+\"+str(timer.time())+\".csv\", scoreMatrix, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use MultiProcessing for parallel computing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "metadata": {},
   "outputs": [],
   "source": [
    "curatedTeams = False\n",
    "def teamWorkProcess(processID):\n",
    "    np.random.seed()\n",
    "    team = teamWorkSharing(nAgents,constructor,teamDimensions_CONST,agentTeams,showViz=showViz,speed=None,pComm=pComm,steps=steps,groupConformityBias=True,soBias=True,aiScore=aiScore,aiRange=aiRange,curatedTeams=curatedTeams,pCommTime=pCommTime)\n",
    "    return team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !jupyter nbconvert --to script Chicago.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Solving Styles and paramter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first: how much is their style affecting their behavior?\n",
    "resetDefaultParameters()\n",
    "t0 = timer.time()\n",
    "allTeamObjects = []\n",
    "\n",
    "reps = 1\n",
    "nAgents = 33\n",
    "agentTeams = saeTeams(nAgents)\n",
    "\n",
    "steps = 50\n",
    "aiScoresMeans = [100]\n",
    "aiRanges = [90]\n",
    "pComms = [0.2]\n",
    "meetingTimes = 10 #team meating every how many steps?\n",
    "for i in range(reps):\n",
    "    for pComm in pComms:\n",
    "        for aiScore in aiScoresMeans:\n",
    "            for aiRange in aiRanges:\n",
    "                team = teamWorkSharing(nAgents,constructor,teamDimensions_CONST,agentTeams,showViz=showViz,speed=None,pComm=pComm,steps=steps,groupConformityBias=True,soBias=True,aiScore=aiScore,aiRange=aiRange)\n",
    "                allTeamObjects.append(team)\n",
    "print(\"time to complete: \"+str(timer.time()-t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team0 = allTeamObjects[0]\n",
    "team0.nTeamMeetings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed team\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 400
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "team = allTeamObjects[0]\n",
    "t = 9\n",
    "a0 = team.agents[t]\n",
    "a1 = team.agents[t+11]\n",
    "a2 = team.agents[t+22]\n",
    "\n",
    "print(\"mixed team\")\n",
    "showPath([a0,a1,a2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = timer.time()\n",
    "resetDefaultParameters()\n",
    "\n",
    "pComm = 0.2\n",
    "pComms = [0.2]# np.linspace(0,.3,20)\n",
    "\n",
    "VERBOSE = False\n",
    "showViz = False\n",
    "\n",
    "reps = 4 # 10 #$40 #5\n",
    "\n",
    "aiScoresMeans = [100]#np.linspace(80,120,3)\n",
    "aiRanges = [0] # np.linspace(0,30,2)\n",
    "\n",
    "meetingTimes = steps #have one meeting at the end\n",
    "TEAM_MEETING_COST = 1\n",
    "\n",
    "\n",
    "scoreMatrix = np.zeros([len(aiRanges),len(aiScoresMeans)])\n",
    "\n",
    "exitFlag = 0\n",
    "\n",
    "allTeamObjects = []\n",
    "\n",
    "for pComm in pComms:   \n",
    "    if __name__ == '__main__':\n",
    "        pool = multiprocessing.Pool(processes = 4)\n",
    "        allTeams = pool.map(teamWorkProcess, range(reps))#[pool.apply_async(teamWorkProcess, (i,)) for i in range(reps)]\n",
    "        print('moving on')\n",
    "        for team in allTeams:\n",
    "            allTeamObjects.append(team)\n",
    "# allTeams = [t for tl in allTeamObjects for t in tl]\n",
    "print(\"time to complete: \"+str(timer.time()-t0))\n",
    "allTeams = allTeamObjects\n",
    "chaching()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharing: optimal rate is 0.22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = timer.time()\n",
    "resetDefaultParameters()\n",
    "curatedTeams = False\n",
    "\n",
    "pComms = [0.2]# np.linspace(0,.3,20)\n",
    "\n",
    "steps = 50\n",
    "reps = 4 # 10 #$40 #5\n",
    "\n",
    "aiScoresMeans = [100]#np.linspace(80,120,3)\n",
    "aiRanges = [0] # np.linspace(0,30,2)\n",
    "\n",
    "meetingTimes = steps #have one meeting at the end\n",
    "\n",
    "allTeamObjects = []\n",
    "for pComm in pComms:   \n",
    "    if __name__ == '__main__':\n",
    "        pool = multiprocessing.Pool(processes = 4)\n",
    "        allTeams = pool.map(teamWorkProcess, range(reps))\n",
    "        print('moving on')\n",
    "        for team in allTeams:\n",
    "            allTeamObjects.append(team)\n",
    "# allTeams = [t for tl in allTeamObjects for t in tl]\n",
    "print(\"time to complete: \"+str(timer.time()-t0))\n",
    "allTeams = allTeamObjects\n",
    "chaching()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1c72b160>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 390
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "allScores = [t.getBestScore() for t in allTeamObjects]\n",
    "nShares = [t.nMeetings/nAgents for t in allTeamObjects]\n",
    "plt.scatter(nShares,allScores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "allScores = [t.getBestScore() for t in allTeamObjects]\n",
    "scoresForPComm = []\n",
    "for i in range(int(len(allScores)/4)):\n",
    "    theseScores = allScores[0+i*4:4+i*4]\n",
    "    scoresForPComm.append(np.mean(theseScores))\n",
    "plt.scatter(pComms,scoresForPComm)\n",
    "plt.title(\"avg score vs pComm\")\n",
    "plt.savefig(\"./figs/avgScore_Vs_pComm.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-2.039567859600141,\n",
       " -2.3723489379415725,\n",
       " -2.5074387728312346,\n",
       " -2.5356173009237106,\n",
       " -2.6708729308720294,\n",
       " -2.6598657473999867,\n",
       " -2.3124368793668864]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoresForPComm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bigTeamSet= bigTeamSet[0:20]\n",
    "# for t in allTeamObjects:\n",
    "#     bigTeamSet.append(t)\n",
    "# len(bigTeamSet)\n",
    "# bigTeamSet = [i for l in bigTeamSet for i in l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 395
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([t.nMeetings / 33 for t in bigTeamSet])\n",
    "yr = np.array([t.getBestScore() for t in bigTeamSet])\n",
    "# xcat = [round(i*2,-1)/2 for i in x]\n",
    "plt.scatter(x,yr)\n",
    "p = np.polyfit(x,yr,2)\n",
    "z = np.poly1d(p)\n",
    "x1 = np.linspace(0,max(x),100)\n",
    "plt.plot(x1,z(x1),color='red')\n",
    "plt.title('Performance vs Number of pair-meetings')\n",
    "plt.xlabel(\"avg n pair-shares per agent\")\n",
    "plt.ylabel(\"overall best score\")\n",
    "t = str(timer.time())\n",
    "np.savetxt(\"./savedParams/\"+t+\".txt\",[makeParamString()], fmt='%s')\n",
    "plt.savefig(\"./figs/pairwiseSharing_noReset_\"+t+\".pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best n shares per agent:\n",
      "mean:43.4431558731124\n",
      "std: 9.50356733199722\n"
     ]
    }
   ],
   "source": [
    "#bootstrapping for mean and sd of minimum point\n",
    "bestNshares = []\n",
    "\n",
    "allInd = range(len(bigTeamSet))\n",
    "for i in range(1000):\n",
    "    ind = np.random.choice(allInd,14)\n",
    "    teams = [bigTeamSet[j] for j in ind]\n",
    "    x = [t.nMeetings/33 for t in teams]\n",
    "    y = [t.getBestScore() for t in teams]\n",
    "    x1 = np.linspace(min(x),max(x),300)\n",
    "    p = np.polyfit(x,y,2)\n",
    "    z = np.poly1d(p)\n",
    "    predicted = z(x1)\n",
    "    bestNshares.append(x1[np.argmin(predicted)])\n",
    "    \n",
    "print(\"best n shares per agent:\")\n",
    "print(\"mean:\" +str(np.mean(bestNshares)))\n",
    "print(\"std: \"+str(np.std(bestNshares)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharing: when is the exact time that sharing is best?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = timer.time()\n",
    "resetDefaultParameters()\n",
    "curatedTeams = False\n",
    "\n",
    "# pComms = [0.2]# np.linspace(0,.3,20)\n",
    "pComm = 0.2\n",
    "\n",
    "steps = 500#500\n",
    "reps = 16 #16\n",
    "\n",
    "aiScore = 100\n",
    "aiRange = 0\n",
    "# meetingTimes = steps #have one meeting at the end\n",
    "\n",
    "teamsCommTiming = []\n",
    "nTimeDivisions = 10\n",
    "\n",
    "for i in range(nTimeDivisions):\n",
    "    pCommTime = np.zeros(nTimeDivisions)\n",
    "    pCommTime[i] = 1\n",
    "    allTeamObjects = []\n",
    "    if __name__ == '__main__':\n",
    "        pool = multiprocessing.Pool(processes = 4)\n",
    "        allTeams = pool.map(teamWorkProcess, range(reps))\n",
    "        for team in allTeams:\n",
    "            allTeamObjects.append(team)\n",
    "    teamsCommTiming.append(allTeamObjects)\n",
    "chaching()\n",
    "print(\"time to complete: \"+str(timer.time()-t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {},
   "outputs": [],
   "source": [
    "# teamsCommTiming1and2 = cp(allTeamsCommTiming)\n",
    "times2 = []\n",
    "for i in range(nTimeDivisions):\n",
    "    for j in range(24):\n",
    "        times2.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 24)"
      ]
     },
     "execution_count": 700,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allTeamsCommTiming = []\n",
    "for row in range(nTimeDivisions):\n",
    "    allTeamsCommTiming.append(np.concatenate([teamsCommTiming[row],teamsCommTiming1and2[row]]))\n",
    "np.shape(allTeamsCommTiming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scoresAtTiming = []\n",
    "scores1 = []\n",
    "times1=[]\n",
    "scoresAtTimes = []\n",
    "for i in range(nTimeDivisions):\n",
    "    theseTeams = allTeamsCommTiming[i]\n",
    "    scoresAtTiming.append(np.mean([t.getBestScore() for t in theseTeams]))\n",
    "    timeScores = []\n",
    "    for t in theseTeams:\n",
    "        scores1.append(t.getBestScore())\n",
    "        times1.append(np.argmax(t.shareHistory))\n",
    "        timeScores.append(t.getBestScore())\n",
    "    scoresAtTimes.append(timeScores)\n",
    "plt.scatter(np.array(times2)/10,scores1, c=[.8,.8,1])    \n",
    "#     t = teamsCommTiming[i][0]\n",
    "#     print(t.shareHistory)\n",
    "meanScores = plotCategoricalMeans(np.array(times2)/10,scores1)\n",
    "plt.plot(np.arange(10)/10,meanScores)\n",
    "plt.savefig(\"./timingOfSharing_vs_performance_24reps.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 908,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = allTeamsCommTiming[0][0]\n",
    "GOOD_SOLN = t.getBestSolution()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "significance: p= 0.6721980600680151\n"
     ]
    }
   ],
   "source": [
    "stat, p = scipy.stats.ttest_ind(scoresAtTimes[2],scoresAtTimes[8])\n",
    "print(\"significance: p= \"+str(p))\n",
    "#anova test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Sharing: does it change outcome? no"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## timing of team meetings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 845,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time to complete: 120.49429607391357\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-1187:\n",
      "Process ForkPoolWorker-1188:\n",
      "Process ForkPoolWorker-1186:\n",
      "Process ForkPoolWorker-1184:\n",
      "Process ForkPoolWorker-1190:\n",
      "Process ForkPoolWorker-1195:\n",
      "Process ForkPoolWorker-1191:\n",
      "Process ForkPoolWorker-1194:\n",
      "Process ForkPoolWorker-1189:\n",
      "Process ForkPoolWorker-1193:\n",
      "Process ForkPoolWorker-1183:\n",
      "Process ForkPoolWorker-1185:\n",
      "Process ForkPoolWorker-1192:\n",
      "Process ForkPoolWorker-1180:\n",
      "Process ForkPoolWorker-1182:\n",
      "Process ForkPoolWorker-1181:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n",
      "    with self._rlock:\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "KeyboardInterrupt\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "  File \"/anaconda3/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "t0 = timer.time()\n",
    "resetDefaultParameters()\n",
    "curatedTeams = False\n",
    "complexSharing = True\n",
    "\n",
    "pComm = 0.2\n",
    "steps = 500#500\n",
    "reps = 4 #16\n",
    "\n",
    "aiScore = 100\n",
    "aiRange = 0\n",
    "# meetingTimes = steps #have one meeting at the end\n",
    "\n",
    "teamsCommTiming = []\n",
    "nTimeDivisions = 10\n",
    "\n",
    "meetingTimes = 20\n",
    "\n",
    "sections = 4\n",
    "for i in range(sections):#(10):#nTimeDivisions):\n",
    "    pCommTime = np.ones(nTimeDivisions)\n",
    "#     pCommTime[i] = 1\n",
    "    startHavingMeetings = float(i)/sections\n",
    "    allTeamObjects = []\n",
    "    if __name__ == '__main__':\n",
    "        pool = multiprocessing.Pool(processes = 4)\n",
    "        allTeams = pool.map(teamWorkProcess, range(reps))\n",
    "        for team in allTeams:\n",
    "            allTeamObjects.append(team)\n",
    "    teamsCommTiming.append(allTeamObjects)\n",
    "chaching()\n",
    "print(\"time to complete: \"+str(timer.time()-t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "metadata": {},
   "outputs": [],
   "source": [
    "times3 = []\n",
    "for i in range(sections):\n",
    "    for j in range(reps):\n",
    "        times3.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 860,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 388
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scoresAtTiming = []\n",
    "scores1 = []\n",
    "times1=[]\n",
    "scoresAtTimes = []\n",
    "for i in range(sections):\n",
    "    theseTeams = teamsCommTiming[i]\n",
    "    scoresAtTiming.append(np.mean([t.getBestScore() for t in theseTeams]))\n",
    "    timeScores = []\n",
    "    for t in theseTeams:\n",
    "        scores1.append(t.getBestScore())\n",
    "        timeScores.append(t.getBestScore())\n",
    "    scoresAtTimes.append(timeScores)\n",
    "plt.scatter(np.array(times3)/sections,scores1, c=[.8,.8,1])    \n",
    "#     t = teamsCommTiming[i][0]\n",
    "#     print(t.shareHistory)\n",
    "meanScores = plotCategoricalMeans(np.array(times3)/sections,scores1)\n",
    "# plt.plot(times3,meanScores)\n",
    "# plt.savefig(\"./timingOfSharing_vs_performance_24reps.pdf\")\n",
    "# plt.title(\"Start having teem meetings later?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "analysis of variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Curated teams have team composition mirroring superteam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 893,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "completed 1 of 1\n",
      "time to complete: 156.55819582939148\n"
     ]
    }
   ],
   "source": [
    "t0 = timer.time()\n",
    "resetDefaultParameters()\n",
    "curatedTeams = True\n",
    "\n",
    "pComm = 0.2\n",
    "aiScore = 100\n",
    "aiRanges = [0]# np.linspace(0,90,10)\n",
    "\n",
    "reps = 4 # 10 #$40 #5\n",
    "steps = 500\n",
    "testConvexity = []\n",
    "i = 0\n",
    "for aiRange in aiRanges:   \n",
    "    if __name__ == '__main__':\n",
    "        pool = multiprocessing.Pool(processes = 4)\n",
    "        allTeams = pool.map(teamWorkProcess, range(reps))\n",
    "        i+=1\n",
    "        print('completed %s of %s' % (i , len(aiRanges)))\n",
    "        for team in allTeams:\n",
    "            testConvexity.append(team)\n",
    "print(\"time to complete: \"+str(timer.time()-t0))\n",
    "chaching()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 886,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1159020464595883"
      ]
     },
     "execution_count": 886,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#random solutions distance:\n",
    "s1 = startParams()\n",
    "s1n = asVector(s1)/scalingVector\n",
    "s2 = startParams()\n",
    "s2n = asVector(s2)/scalingVector\n",
    "norm(s1n-s2n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 901,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-2.2265903820040576\n",
      "-2.402576959311419\n",
      "-2.234476180508415\n",
      "-2.384059832246231\n",
      "2.5638278010880637\n",
      "2.811557388030183\n",
      "2.3924582457330525\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-2.402576959311419"
      ]
     },
     "execution_count": 901,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = []\n",
    "for t in testConvexity:\n",
    "    sol = t.getBestSolution()\n",
    "    s.append(sol.rNorm)\n",
    "    print(sol.score)\n",
    "for i in range(len(s)-1):\n",
    "    print(norm(s[i]-s[i+1])  )\n",
    "objective(asParameters(s[1]*scalingVector),weightsCustom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 916,
   "metadata": {},
   "outputs": [],
   "source": [
    "solns = [t.getBestSolution() for group in allTeamsCommTiming for t in group]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 925,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.963519887595563\n",
      "2.384596865697219\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "allPairs = list(itertools.combinations(solns, 2))\n",
    "distances = []\n",
    "for p in allPairs:\n",
    "    distances.append(norm(p[0].rNorm - p[1].rNorm))\n",
    "print(np.max(distances))\n",
    "print(np.median(distances))\n",
    "print(np.min(distances))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 961,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Ideal wing angle of attack for 10 random start points')"
      ]
     },
     "execution_count": 961,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# p = startParams()\n",
    "angles = np.linspace(0,np.pi/4,100)\n",
    "for i in range(10):\n",
    "    p = startParams()\n",
    "    pv = asVector(p)\n",
    "    scores = []\n",
    "    for angle in angles:\n",
    "        pv[3] = angle\n",
    "        p1 = asParameters(pv)\n",
    "        scores.append(objective(p1,weightsCustom))\n",
    "    plt.scatter(angles, scores)\n",
    "plt.title(\"Ideal wing angle of attack for 10 random start points\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1072,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 388
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# p = startParams()\n",
    "# pressures = np.linspace(0.758,1.03,100)\n",
    "lia = np.linspace(0.05,.6,100)\n",
    "\n",
    "for i in range(10):\n",
    "    p = startParams()\n",
    "    pv = asVector(p)\n",
    "    scores = []\n",
    "    for t in lia:\n",
    "        pv[37] = t\n",
    "        p1 = asParameters(pv)\n",
    "        scores.append(objective(p1,weightsCustom))\n",
    "    plt.scatter(pressures, scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a3cb83ba8>]"
      ]
     },
     "execution_count": 1109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 388
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = []\n",
    "distances = []\n",
    "goodPosition = GOOD_SOLN.rNorm\n",
    "for s in solns:\n",
    "    rNorm = s.rNorm\n",
    "    dist = np.linalg.norm(rNorm - goodPosition)\n",
    "    if dist>10:\n",
    "        continue\n",
    "    scores.append(s.score)\n",
    "    distances.append(dist)\n",
    "plt.scatter(distances,scores)\n",
    "plt.xlim([0,4])\n",
    "plt.title(\"score vs norm distance to best known solution\")\n",
    "lm = np.polyfit(distances, scores,1)\n",
    "lf = np.poly1d(lm)\n",
    "x = np.linspace(0,4,100)\n",
    "plt.plot(x,lf(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: how many solution clusters are there? (local minima)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'avg dist to cluster center vs n clusters')"
      ]
     },
     "execution_count": 1103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 377
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "solnRs = [s.rNorm for s in solns]\n",
    "# kDistances = []\n",
    "\n",
    "# for k in range (1,50):\n",
    "#     # Create a kmeans model on our data, using k clusters.  random_state helps ensure that the algorithm returns the same results each time.\n",
    "#     kmeans_model = KMeans(n_clusters=k, random_state=1).fit(reducedSolns)\n",
    "\n",
    "#     # These are our fitted labels for clusters -- the first cluster has label 0, and the second has label 1.\n",
    "#     labels = kmeans_model.labels_\n",
    "#     # Sum of distances of samples to their closest cluster center\n",
    "#     kDistances.append( kmeans_model.inertia_)\n",
    "# print(np.argmin(kDistances))\n",
    "plt.plot(range(1,50),kDistances)\n",
    "plt.title('avg dist to cluster center vs n clusters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1094,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 373
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 388
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 388
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "reduced = pca.fit_transform(solnRs)\n",
    "# print(np.argmax(abs(pca.components_[0])))\n",
    "# print(np.max(abs(pca.components_[0])))\n",
    "\n",
    "\n",
    "#now we remove the one weird group related to engine torque. Then continue. \n",
    "\n",
    "# np.shape(reduced)\n",
    "# plt.scatter(reduced[:,0],reduced[:,1])\n",
    "group1 = [i for i in range(len(reduced)) if reduced[i,0]<=10]\n",
    "group2 = [i for i in range(len(reduced)) if reduced[i,0]>10]\n",
    "reducedSolns = [solnRs[i] for i in group1]\n",
    "\n",
    "for nC in range(1,25):\n",
    "    pca2 = PCA(n_components=nC)\n",
    "    reduced2 = pca2.fit_transform(reducedSolns)\n",
    "    plt.scatter(nC, sum(pca2.explained_variance_ratio_),c='b')\n",
    "plt.title('n components vs variance explained')\n",
    "plt.show()\n",
    "# for c in pca2.components_:\n",
    "#     print(np.argmax(c))\n",
    "#     print(max(c))\n",
    "    \n",
    "plt.scatter(reduced2[:,0],reduced2[:,1])\n",
    "plt.show()\n",
    "plt.scatter(reduced2[:,1],reduced2[:,2])\n",
    "plt.show()\n",
    "plt.scatter(reduced2[:,0],reduced2[:,2])\n",
    "plt.show()\n",
    "\n",
    "# print(solnRs[3])\n",
    "# print(solnRs[4])\n",
    "# print(solnRs[11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T_e'"
      ]
     },
     "execution_count": 1016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paramsDF.variable[28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1018,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20.67527758894176\n",
      "20.67527758894176\n",
      "0.4894629503738953\n",
      "0.5982324949014275\n"
     ]
    }
   ],
   "source": [
    "print(solnRs[15][28])\n",
    "print(solnRs[0][28])\n",
    "print(solnRs[11][28])\n",
    "print(solnRs[4][28])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = timer.time()\n",
    "resetDefaultParameters()\n",
    "curatedTeams = True\n",
    "\n",
    "pComms = [0.2]#np.linspace(0,.3,10)\n",
    "aiScore = 100\n",
    "aiRanges = np.linspace(0,90,10)\n",
    "\n",
    "reps = 4 # 10 #$40 #5\n",
    "steps = 100\n",
    "allTeamObjects_curatedTeams = []\n",
    "i = 0\n",
    "for aiRange in aiRanges:   \n",
    "    if __name__ == '__main__':\n",
    "        pool = multiprocessing.Pool(processes = 4)\n",
    "        allTeams = pool.map(teamWorkProcess, range(reps))\n",
    "        i+=1\n",
    "        print('completed %s of %s' % (i , len(aiRanges)))\n",
    "        for team in allTeams:\n",
    "            allTeamObjects_curatedTeams.append(team)\n",
    "print(\"time to complete: \"+str(timer.time()-t0))\n",
    "chaching()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "tMeans = []\n",
    "tSds = []\n",
    "tScores = [] \n",
    "tRanges = []\n",
    "tMeetings = []\n",
    "for t in allTeamObjects_curatedTeams:\n",
    "    kais = [a.kai.KAI for a in t.agents]\n",
    "    tMeans.append(np.mean(kais))\n",
    "    tRanges.append(np.max(kais)-np.min(kais))\n",
    "    tSds.append(np.std(kais))\n",
    "    tScores.append(t.getBestScore())\n",
    "    tMeetings.append(t.nMeetings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores vs stdevs\n",
      "[ 0.00320807 -1.96407987]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a24024da0>]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tRanges,tScores)\n",
    "p=np.polyfit(tRanges,tScores,1)\n",
    "print(\"scores vs stdevs\")\n",
    "print(p)\n",
    "f = np.poly1d(p)\n",
    "x = np.linspace(min(tRanges),max(tRanges),100)\n",
    "plt.plot(x,f(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tMeans = []\n",
    "tSds = []\n",
    "tScores = [] \n",
    "tRanges = []\n",
    "tMeetings = []\n",
    "for t in allTeamObjects_curatedTeams:\n",
    "    kais = [a.kai.KAI for a in t.agents]\n",
    "    tMeans.append(np.mean(kais))\n",
    "    tRanges.append(np.max(kais)-np.min(kais))\n",
    "    tSds.append(np.std(kais))\n",
    "    tScores.append(t.getBestScore())\n",
    "    tMeetings.append(t.nMeetings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = timer.time()\n",
    "resetDefaultParameters()\n",
    "reps = 16 # 10 #$40 #5\n",
    "\n",
    "aiScoresMeans = [None]#np.linspace(80,120,3)\n",
    "aiRanges = [None] # np.linspace(0,30,2)\n",
    "\n",
    "meetingTimes = steps #have one meeting at the end\n",
    "\n",
    "allTeamObjects = []\n",
    "\n",
    "for aiScore in aiScoresMeans:\n",
    "    for aiRange in aiRanges:\n",
    "        if __name__ == '__main__':\n",
    "            pool = multiprocessing.Pool(processes = 4)\n",
    "            allTeams = pool.map(teamWorkProcess, range(reps))#[pool.apply_async(teamWorkProcess, (i,)) for i in range(reps)]\n",
    "            print('moving on')\n",
    "            for team in allTeams:\n",
    "                allTeamObjects.append(team)\n",
    "print(\"time to complete: \"+str(timer.time()-t0))\n",
    "allTeams = allTeamObjects\n",
    "chaching()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tMeans = []\n",
    "tSds = []\n",
    "tScores = [] \n",
    "tRanges = []\n",
    "tMeetings = []\n",
    "for t in allTeamObjects:\n",
    "    kais = [a.kai.KAI for a in t.agents]\n",
    "    tMeans.append(np.mean(kais))\n",
    "    tRanges.append(np.max(kais)-np.min(kais))\n",
    "    tSds.append(np.std(kais))\n",
    "    tScores.append(t.getBestScore())\n",
    "    tMeetings.append(t.nMeetings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(tMeans,tScores)\n",
    "p=np.polyfit(tMeans,tScores,1)\n",
    "print(\"scores vs means\")\n",
    "print(p)\n",
    "f = np.poly1d(p)\n",
    "x = np.linspace(min(tMeans),max(tMeans),100)\n",
    "plt.plot(x,f(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(tSds,tScores)\n",
    "p=np.polyfit(tSds,tScores,1)\n",
    "print(\"scores vs stdevs\")\n",
    "print(p)\n",
    "f = np.poly1d(p)\n",
    "x = np.linspace(min(tSds),max(tSds),100)\n",
    "plt.plot(x,f(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specific Make-up Teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "moving on\n",
      "moving on\n",
      "moving on\n",
      "moving on\n",
      "moving on\n",
      "moving on\n",
      "moving on\n",
      "moving on\n",
      "time to complete: 907.2802548408508\n"
     ]
    }
   ],
   "source": [
    "t0 = timer.time()\n",
    "resetDefaultParameters()\n",
    "\n",
    "reps = 4\n",
    "aiScoresMeans = [100]#np.linspace(80,120,3)\n",
    "aiRanges = np.linspace(0,100,8)\n",
    "\n",
    "allTeamObjects = []\n",
    "for aiScore in aiScoresMeans:\n",
    "    for aiRange in aiRanges:\n",
    "        if __name__ == '__main__':\n",
    "            pool = multiprocessing.Pool(processes = 4)\n",
    "            allTeams = pool.map(teamWorkProcess, range(reps))#[pool.apply_async(teamWorkProcess, (i,)) for i in range(reps)]\n",
    "            print('moving on')\n",
    "            for team in allTeams:\n",
    "                allTeamObjects.append(team)\n",
    "print(\"time to complete: \"+str(timer.time()-t0))\n",
    "chaching()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getBestScoreAtTime(team,t):\n",
    "    # t is fraction of time in simulation. this is approximate\n",
    "    scores = []\n",
    "    for a in team.agents:\n",
    "        ml = len(a.memory)\n",
    "        memorySubset = a.memory[0:int(ml*t)]\n",
    "        scores.append(np.min([m.score for m in memorySubset]))\n",
    "    return min(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allTeamObjects1 = allTeamObjects\n",
    "# for t in allTeamObjects:\n",
    "#     allTeamObjects1.append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "tMeans = []\n",
    "tSds = []\n",
    "tScores = [] \n",
    "tRanges = []\n",
    "tMeetings = []\n",
    "bestScoresAtFraction = []\n",
    "so = []\n",
    "rg = []\n",
    "e = []\n",
    "for t in allTeamObjects:\n",
    "    if t.getBestScore()>-2: continue\n",
    "    kais = [a.kai.KAI for a in t.agents]\n",
    "    tMeans.append(np.mean(kais))\n",
    "    tRanges.append(np.max(kais)-np.min(kais))\n",
    "    \n",
    "    tSds.append(np.std(kais))\n",
    "    tScores.append(t.getBestScore())\n",
    "    tMeetings.append(t.nMeetings)\n",
    "    bestScoresAtFraction.append(getBestScoreAtTime(t,.25))\n",
    "    \n",
    "    sos = [a.kai.SO for a in t.agents]\n",
    "    so.append(np.mean(sos))\n",
    "    \n",
    "    es = [a.kai.E for a in t.agents]\n",
    "    e.append(np.mean(es))\n",
    "    \n",
    "    rgs = [a.kai.RG for a in t.agents]\n",
    "    rg.append(np.mean(rgs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a366e6358>"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3wGYVSnoAoBZMPFta2Jec8yaei3zgPzY2d1uZzJy9V3Pv3Zv33KyVjo/NVT3w745vC3XAXy2xPUHAL8HQ7FBJYV+Ssjar4VkCP4JFSQ8AhN3ls4XLeAq58GqkF/KeW1jS6+evuBqTPn9F5xaC23gLG0PmaqZgGU8hZxbOsJAXgSLwA0DYzU8EO64GTGYWAx0HFJK+lA50HOAFgR8Awu66x7vSXsfVgOVrNwMdBxSyfGM50HGAFwR+AAi7LR5rvr2OqwF1W70tQfM6DiikbnNdoOMAL6oe+I0xrcaYLxhjvmOMedsYc8MYs2CM+aYx5mMur3WfMeb/NcakjTGXjDHXjTE/Mcb8uTHms8aYD/j1fQCAb1p6gh1XA7wuvq32ol1ET3JnMtBxgBdVD/ySvijpS5Likr4l6Q8kTUr6DUnfMcYcdHGtX5H0TyX9VNKfrFzrFUlNkv69pP9kjOH2DoDasmOPs6mWG037nXERtTu+TfsK9LUvJNlcX1MdclAbEtsT6oi765jYGe+k+xECFYbA/21J7dbaX7PWpqy1T1pr+yX1SXpX0nPGmMYSr/U9Sduttf/QWjtorf1da21KzguBcUm9kvor/y0AgM96Djv99UthYlLP5/2dTwgc6mtVzBQ/T3L68B/sa/V3QtiwBtsGFSvxv8+YiSnVlvJ5RsDtqh74rbXPW2u/n+fxCTkhfbOkj5Z4rRvWrm2Ea619V84df0niLz6A2tPSKz18rHjoNzHp4eOR78EvOeU5z/Q/UDT0x4z0pf6PUM4D33Q1dunIg0eKhv6Yienog0fpwY/Ahb285d2Vz2W1VTDG3CHp4ytfvlHWjACgWtofk+7e5WyqdeHVtceb9jt39lt6g55Z1Tyyd5fu3X6Xjo/NKZ2nL3+yuV4H+1oJ+/Bdf2u/7qm7R8OzwzqzcGbN8c54p1JtKcI+qsJYa4ufVQXGmCZJfyXpPUn3WmuvuhjbIOmfSzKS/q6khyQlJH1d0qO2hG/aGFNoF43729vb75qedrfJBgBU1OWzTp/960tON56WnkjX7Jfi3MKSJjOLWr52U3VbN6k70UDNPqoiczWj9KW0lm8sq25znZI7k9Tsw5OOjg7NzMzMWGvdLRRZJZR3+I0xWyS9KGmLpMNuwv6KBklHbvnaSvqypN8tJewDQOjt2LPhA/5qu+PbCPgIhcT2BAEfoVKRwG+MeVNOJ5xSvWitfbTAte6QNCKpW9JLcoK6K9bav3QuZe6Q9MuSDkj6V5L2G2N+w1pbdD/2Qq+kVu78t7udEwAAAFANlbrD/yNJ11yc/5N8D64E9K9J+pSkl1Vi+U0h1tr3JL0l6ZgxZkHSH8sJ/v/c6zUBAACAWlKRwG+t7Sv3Giv98b8uJ+x/XdJjK4G9Uv5s5XNvBa8JAAAAhFooaviNMZvl3NH/pKQXJH02X3vNMv3yyueyOv4AAAAAtaTqffhXFuielBP2T6iEsG+M+ZAx5v7VG3IZY5LGmLvynF8n6djKl39amZkDAAAA4ReGO/xDcnrkL0r6saSnjFmzi8q4tXb8lq8PSPqKpK9K+swtjz8pqdcYMyGndv/nku6T9I8k3S1nJ95nKv4dAAAAACEVhsDfvPK5QdJT65w3XsK1/kjSzyTtlVOrf5ekq5Km5ZQM/XtrLSU9AAAA2DCqHvittb0exjwv6fk8j/+pKNkBAAAAfqHqNfwAAAAA/EPgBwAAACKMwA8AAABEGIEfAAAAiDACPwAAABBhBH4AAAAgwgj8AAAAQIQR+AEAAIAII/ADAAAAEUbgBwAAACKMwA8AAABEGIEfAAAAiDACPwAAABBhBH4AAAAgwgj8AAAAQIQR+AEAAIAII/ADAAAAEUbgBwAAACKMwA8AAABEGIEfAAAAiDACPwAAABBhBH4AAAAgwgj8AAAAQIQR+AEAAIAII/ADAAAAEUbgBwAAACKMwA8AAABEGIEfAAAAiDACPwAAABBhBH4AAAAgwgj8AAAAQIQR+AEAAIAII/ADAAAAEUbgBwAAACKMwA8AAABEGIEfAAAAiDACPwAAABBhBH4AAAAgwgj8AAAAQIQR+AEAAIAII/ADAAAAEUbgBwAAACKMwA8AAABEGIEfAAAAiDACPwAAABBhBH4AAAAgwgj8AAAAQIQR+AEAAIAII/ADAAAAEUbgBwAAACJsU7UnAACAV+cWljSZWdTytZuq27pJ3YkG7Y5vq/a0Qi1zNaP0pbSWbyyrbnOdkjuTSmxPVHta4Xb5rDQ/IV1fkrZsk1p6pB17qj0roGQEfgBAzZnMLOrY2JxeP39lzbF9zfU61Neq7kRDFWYWXlMXpzQ0O6Tphek1xzriHRpsG1RXY1cVZhZi8+PSxLPShcm1x5q6pZ7DUktvwJMC3KOkBwBQU146/ZYGTqTzhn1Jev38FQ2cSOvl028HPLPwGp0bVepUKm/Yl6TphWmlTqV0cu5kwDMLsZkXpJED+cO+5Dw+ckCaGQl2XoAHBH4AQM2YzCzqydEfKGvXPy9rpSdG39BkZjGYiYXY1MUpPf3a08ra7LrnZW1WR187qqmLUwHNLMTmx6VXDklFfmayWemVg875QIgR+AEANePY2FzRsJ+TtdLxsTl/J1QDhmaHiob9nKzNanh22OcZ1YCJZ4uH/RyblSae83c+QJkI/ACAmnBuYalgGU8h6fNXdG5hyacZhV/maqZgGU8hZxbOKHM149OMasDls4XLeAq58KozDggpFu0CwEZR451GvJbn5MZtxG4+6Utpz+M2bOee+Qnv40L23xMdmZBD4AeAqItIp5Hlazc9jft3f35eT7/ywzWPb4RuPss3lgMdFwnXPb4j5HWcD+jIhNWqXtJjjGk1xnzBGPMdY8zbxpgbxpgFY8w3jTEfq8D1Txhj7MoHL2sBbCwR6jRSt9XbPaof/807eR/fCN186jbXBTouErZ4fOfH67gKoyMT8ql64Jf0RUlfkhSX9C1JfyBpUtJvSPqOMeag1wsbYx6W9L9J2sC3KgBsWBHrNOLHnfiod/NJ7kwGOi4SWnqCHVdBdGRCIWEI/N+W1G6t/TVrbcpa+6S1tl9Sn6R3JT1njGl0e1FjzN+V9EeSXpLkbsUSAERBxDqN7I5v077m+opfN8rdfBLbE+qId7ga0xnv3Nh13jv2OKVubjTtD0X9Ph2ZUEjVA7+19nlr7ffzPD4haVzSZkkf9XDpP1z5/DveZwcANSqinUYO9bUqZip/3Sh38xlsG1TMlPZ/9zETU6ot5fOMakDPYanEn5lMTOr5vL/zKQEdmbCeqgf+It5d+exqpZYx5jOSflPSoLX2v1d6UgAQeuV0Ggmx7kSDnul/wJfQH9Wynq7GLh158EjR0B8zMR198CiLOSVnEfvDx4qHfhOTHj4eikXv5XRkQvSFNvAbY5rklPX8XNJ3XY47Julr1to/8Wl6ABBuEeg0Usgje3dp5PGkkgXKe+7dfqen63rtAlQL+lv7NfzQsDrjnXmPd8Y7NfzQsA60Hgh4ZiHW/pg0cNIp18mnab9zvH0g2HkVQEcmrCeUbTmNMVskvShpi6TD1tqrJY6LSfqqnEW6nhf7rlyr0Pti95dzXQAIRI13GimmO9Gg7kSDzi0sremvP5lZzNuGsxivXYBqRVdjl7oau+jN7kZLr/NRA3tY0JEJ66nIXzdjzJuSmlwMedFa+2iBa90haURSt5wFt192cd1/IalH0m+U+iIBACKphjuNuLE7vq1iG2hFuR//rRLbEwR8t3bsCV3AX42OTFhPpW5n/EjSNRfn/yTfgyth/2uSPiXpZUmPWmttKRc0xrRK+teSvmKt/ZaLueRlrc3b1mDlzn97udcHAF/lOo24Wbgbkk4j5cp183n9/JWSxySb6zfEzruIrlxHJjcLdzd8R6YNpCI1/NbaPmvt/S4+Dq++hjFmk6Q/lvSPJX1d0v9qrXVTUPlrckqAPnvLRlvWGGPl3PWXpLmVx36zzG8ZAMKvBjuNVIqbbj4xIx3sa/V3QkAA6MiEQkKxaNcYs1nSf5RzZ/8FSQPW2vdcXuZNSScKfFxaOecbK1+/WfakASDsWnprrtNIpZTazSdmpC/1f2TDlPMg2ujIhEKqvkJpZYHuqKSPywnjn7N2/V0jjDEfktQo6afW2ouSZK39r5J+u8D545J2Svpday0NZwFsHO2PSXfvcjbVuvDq2uNN+507+y29Qc/Md4/s3aV7t9+l42NzSucp70k21+tgXythH5HS39qve+ru0fDssM4snFlzvDPeqVRbirC/wVQ98EsakhP2FyX9WNJTxqy5JTNurR2/5esDkr4ipyPPZ/yfIgDUsJbemuk0UmnrdfOhZh9RRUcmrBaGwN+88rlB0lPrnDfu/1QAIMJqoNOIXyrZzQeoFXRkQk7VA7+1ttfDmOclPe/ncwAAAABREIpFuwAAAAD8QeAHAAAAIozADwAAAEQYgR8AAACIMAI/AAAAEGEEfgAAACDCCPwAAABAhBH4AQAAgAgj8AMAAAARRuAHAAAAIozADwAAAEQYgR8AAACIMAI/AAAAEGEEfgAAACDCCPwAAABAhBH4AQAAgAgj8AMAAAARRuAHAAAAIozADwAAAEQYgR8AAACIMAI/AAAAEGEEfgAAACDCCPwAAABAhBH4AQAAgAgj8AMAAAARRuAHAAAAIozADwAAAEQYgR8AAACIMAI/AAAAEGEEfgAAACDCCPwAAABAhBH4AQAAgAgj8AMAAAARRuAHAAAAIozADwAAAEQYgR8AAACIMAI/AAAAEGEEfgAAACDCCPwAAABAhBH4AQAAgAgj8AMAAAARRuAHAAAAIozADwAAAEQYgR8AAACIMAI/AAAAEGEEfgAAACDCCPwAAABAhBH4AQAAgAgj8AMAAAARRuAHAAAAIozADwAAAEQYgR8AAACIMAI/AAAAEGEEfgAAACDCNlV7AgDKdPmsND8hXV+StmyTWnqkHXuqPSsAEXduYUmTmUUtX7upuq2b1J1o0O74tmpPy5XM1YzSl9JavrGsus11Su5MKrE9Ue1pARVH4Adq1fy4NPGsdGFy7bGmbqnnsNTSG/CkAETdZGZRx8bm9Pr5K2uO7Wuu16G+VnUnGqows9JNXZzS0OyQphem1xzriHdosG1QXY1dVZgZ4A9KeoBaNPOCNHIgf9iXnMdHDkgzI8HOC0CkvXT6LQ2cSOcN+5L0+vkrGjiR1sun3w54ZqUbnRtV6lQqb9iXpOmFaaVOpXRy7mTAMwP8Q+AHas38uPTKIclm1z/PZqVXDjrnA0CZJjOLenL0B8ra9c/LWumJ0Tc0mVkMZmIuTF2c0tOvPa1skb+fWZvV0deOauriVEAzA/xF4AdqzcSzxcN+js1KE8/5Ox8AG8KxsbmiYT8na6XjY3P+TsiDodmhomE/J2uzGp4d9nlGQDCqHviNMa3GmC8YY75jjHnbGHPDGLNgjPmmMeZjLq/194wxdp2P/+DX9wEE4vLZwmU8hVx41RkHAB6dW1gqWMZTSPr8FZ1bWPJpRu5lrmYKlvEUcmbhjDJXMz7NCAhOGBbtflHSI5J+KOlbkq5I+lVJn5D0CWPMIWvtcZfXnJX0J3ke/4tyJgpU3fyE93F07gHgkdfynMnMYmg696QvpT2Po3MPal0YAv+3Jf2+tfb7tz5ojOmRdErSc8aYb1hrL7q45n+11h6t4ByBcLju8W6Z13EAIGn52s1Ax/lh+cZyoOOAMKl6SY+19vnVYX/l8QlJ45I2S/po0PMCQmmLxztlXscBgKS6rd7uD3od54e6zXWBjgPCJDz/Jeb37spnt7cI7jHGpCT9HUn/XdJr1to3KjozoBpaeoIdBwCS5776YerHn9yZDHQcECahDfzGmCZJfZJ+Lum7Loc/tPJx6/XGJf2WtfatEp+/0Mqe+13OBaicHXucTbXcLNxt2k/9PoCy7I5v077melcLd5PN9aGp35ekxPaEOuIdrhbudsY7qd9HJFS9pCcfY8wWSS9K2iLpqLX2aolDfy5nEXCHpO0rHz2S/oukXkljxpgPVnzCQJB6DkumxP90TUzq+by/8wGwIRzqa1XMlHZuzEgH+1r9nZAHg22DipX49zNmYkq1pXyeERCMigR+Y8ybRdphrv742jpiiHa3AAAgAElEQVTXukPSiKRuSS9J+nKp87DWXrbWPmWtnbHW/s3Kx3cl/UNJaUkJSb9d4rU68n1I+stS5wP4oqVXevhY8dBvYtLDx53zAaBM3YkGPdP/QNHQHzPSl/o/Eqpynpyuxi4defBI0dAfMzEdffCouhq7ApoZ4K9KlfT8SNI1F+f/JN+DK2H/a5I+JellSY9aa0vc5qMwa+1NY8y/k5SU9PclHSv3mkBVtT8m3b3L2VTrwqtrjzftd+7st/QGPTMAEfbI3l26d/tdOj42p3Se8p5kc70O9rWGMuzn9Lf26566ezQ8O6wzC2fWHO+MdyrVliLsI1IqEvittX3lXsMYs0nS1+WE/a9Lesxa+165173Ff1v5TEkPoqGl1/m4fNbps399yenG09JDzT4A33QnGtSdaNC5hSVNZha1fO2m6rZuUneiIVQ1++vpauxSV2OXMlczSl9Ka/nGsuo21ym5M0nNPiIpFIt2jTGb5dzR/6SkFyR91toS974uXe6l+nyFrwtU1449BHwAgdsd31YzAb+QxPYEAR8bQtUX7a4s0D0pJ+yfUAlh3xjzIWPM/caYxlWPJ1dePKw+/x9I+hcrXxZcPwAAAABETRju8A9J+rikRUk/lvSUMWtWBI1ba8dv+fqApK9I+qqkz9zy+O9L+rWVFpx/vfLYRyT9g5X//XvW2u9VcO4AAABAqIUh8DevfG6Q9NQ6542XcK0ROS8G9kr6R5I+IGlBTrnQ/22t/XPv0wQAAABqT9UDv7W218OY5yU9n+fxE3LKggAAAAAoBDX8AAAAAPxD4AcAAAAijMAPAAAARBiBHwAAAIgwAj8AAAAQYQR+AAAAIMII/AAAAECEEfgBAACACCPwAwAAABFG4AcAAAAijMAPAAAARBiBHwAAAIgwAj8AAAAQYQR+AAAAIMII/AAAAECEEfgBAACACCPwAwAAABFG4AcAAAAijMAPAAAARBiBHwAAAIgwAj8AAAAQYQR+AAAAIMII/AAAAECEEfgBAACACCPwAwAAABFG4AcAAAAijMAPAAAARBiBHwAAAIgwAj8AAAAQYQR+AAAAIMI2VXsCAAC4cvmsND8hXV+StmyTWnqkHXuqPSvfZK5mlL6U1vKNZdVtrlNyZ1KJ7YlqT0vnFpY0mVnU8rWbqtu6Sd2JBu2Ob6v2tADkQeAHANSG+XFp4lnpwuTaY03dUs9hqaU34En5Z+rilIZmhzS9ML3mWEe8Q4Ntg+pq7Ap8XpOZRR0bm9Pr56+sObavuV6H+lrVnWgIfF4ACqOkBwAQfjMvSCMH8od9yXl85IA0MxLsvHwyOjeq1KlU3rAvSdML00qdSunk3MlA5/XS6bc0cCKdN+xL0uvnr2jgRFovn3470HkBWB93+GvRBns7G8AGNz8uvXJIstn1z7NZ6ZWD0t33ebrTH5YSlamLU3r6taeVLfL9Zm1WR187qsa6xkDu9E9mFvXk6A+Uteufl7XSE6Nv6Je338mdftSMsJbOVQqBv5ZssLezAUCS83evWNjPsVlp4jlXfwvDVqIyNDtUNOznZG1Ww7PDgQT+Y2NzRcN+TtZKx8fmCPwIvbCWzlUaJT21YoO9nQ0Akpx3NAv93SvkwqvOuBKErUQlczVTsIynkDMLZ5S5mvFpRo5zC0sFf0aFpM9f0bmFJZ9mBJQvrKVzfiDw1wK3b2fPjwcxKwDw3/yEb+PclqhMZha9zcWF9KV0oONK5fV7D+JnBnjhtnRu6uJUQDPzB4G/Fnh5OxsAouC6xzvEJYzzUqLit+Uby4GOK/n6124GOg7wm5fSuVpG4A87n9/OBoBQ2+Jx0WyRcWEtUanbXBfouJKvv9Xbkj+v4wA/hbV0zk8E/rDz8e1sAAi9lh5fxoW1RCW5MxnouFJ5XXzLol2EUVhL5/xE4A87H9/OBoDQ27HH6ULmRtP+oq2Kw1qiktieUEe8w9WYznin7+0Dd8e3aV9zvasxyeZ6dt5FKIW1dM5PBP6w8+ntbACoGT2HJVPi/12ZmNTz+aKnhblEZbBtULESv9+YiSnVlvJ5Ro5Dfa2KmdLOjRnpYF+rvxMCPApr6ZyfKK4LO5/ezq5ZbDoGbDwtvdLDx4p3KzMx6eHjJfXgD3OJSldjl448eKRoB5GYienog0cD6xHenWjQM/0PFO1sFDPSl/o/EolynkKbMYVlkzZ4E9bSOT8R+MMu93a2m4W7JbydXXPYdAzY2Nofk+7e5XQhu/Dq2uNN+507+y29JV0uV6LiZuFukCUq/a39uqfuHg3PDuvMwpk1xzvjnUq1pQLfEOiRvbt07/a7dHxsTuk8P7tkc70OBrxRmR/W24zprmyrFt/u0Xs/v72MqhqbtMGbXOmcm4W7QZTO+clYW2JPMkiSjDHT7e3t7dPT7lZ3l2V+3NlUq5T2USYmDZyMVvideaH0O3vtA8HNC0B1VOidvsnMogZOpEtqzRkz0sjjyaqEuUJ3mastqne5R+dGC767Yq1kjGSt0bWL/br50723Hc+9u/HpvfcFNV14NHVxSqlTqZJac8ZMTMMPBbOj9WodHR2amZmZsda6W9yzCoHfpaoEfmnjht6N/mIHgK9eOv1WySUqhLjocxMCrTV6563H19zpr+aLQ7iz3ou7nFzp3IHWAwHO7H2VCvyU9NSKCr+dXTO8bDrW0uvnjBAiUb3DiOBslBIVlGbo9L8peTMmY6w2N4zpnbduD/y5Tdq8/JsJ67s5URXW0jk/EPhrSUuv87FRFq6Ws+lYFH8e+IXJzKKOjc3lrb+mjhZudSca1J1o4AXkRjY/rszEv9Z07NL7dTtFWCtt+uB5xTYvKHsjftux3CZtpf77WW/NQEe8Q4Ntg5EInWHU1dilrsauyL/YIvDXoh17NkagLWfTsY3w89mgipVgvH7+igZOpCnBgGu749sI+BvRSslsettd0t+pLynsS++fdscHM2sCv+TcmCjl31OxspLphWmlTqWqWlayESS2JyIV8FejDz/Ci03HsMpkZrFovbXkvKX+xOgbvu+KCqDGzY//Yn3ccsxbJDKxa3kfL2WTtqmLU0VryCUpa7M6+tpRTV2c8jRHgMCP8GLTMaxybGyupI4q0vt1tABQ0C3rxOqyJa4XW8Vmt+Z9vJRN2oZmh0peM5C1WQ3PDruaG5BD4Ed4sekYbnFuYclVz3Tp/TpaAFhj1Tqx5DvXnf9RYvfC3Gnv/Sx/GUixdUSZqxlXfeAl6czCGWWuZlyNASRq+BFmbDrmnxpc+O21PKfUOlogaCwSrrJV68QS776rjneuafrO/HfsVzNGuvmz5rz1+4U2abt1YehfXvlLT9NOX0pHutYc/iDwI9x6Drvrw9/z+dsfq8Fg66sa3rG4lHrYSo6DfzZ60KXLVEjkWe81+Dc/VWrrFmVL6tJjdGOxb83jMSMd7Gu97bH1uvC4tXxjuexrhFnUu+VUC4Ef4dbSKz18rPRNx1p6na9rONj6ptjmbRcmnRdXId28rZR62EqOQ+URdOkyFSp51nt1XbuuI4tX9HRDvRP6V7foXPk6t9Nuvk23vtT/kdv+HZeyuZMbdZvrKnKdsKE1qb+o4Uf4tT/m7KDbtD//8ab9zvFcSJ15wQmuhUqBcsF2ZsSf+YbRLZ0o1mWz0isHnfNDxmsQjHqArBUvnX5LAyfSBddh5ILuy6ffDnhmwaHLVMgUWO/Vv/wzDV+6rM53rq1t0WmMtt/cpXfeelw3f7r3tkPJ5nqNPJ687YVaqV143EjuTFbsWmExOjeq1KlUwXdAcq1JT86dDHhm0cGtL9SGlt7SNh1zG2zvvm9j3OmPwI7Fu+PbtK+53tXC3UJ1tAiW26D7y9vvjOQLNS9dpqL4cwiNddaJdV27rq5Ll5X5wAeUvnOLlmMx1WWzSm7/H5R4/E9LLktz04WnFJ3xzsiVt7htTdpY18idfg8I/KgtxTYdi0CwrbgI7Vh8qK9VAyfSJYWmfHW0qA6CbnldpnjR6qMi68QS776rxLvvOl+YmPSb/1JSaZu0eenCs56YiSnVlqrY9cLCS2tSAr97lPQgOsoJtlFWzo7FIdOdaNAz/Q8oVmQ9Xb46WlQH7VQd5XSZgo9aep11YqZIHFq9TqwE6UvpMiZ2u5iJ6eiDRyMXdGlNGpyqB35jTKsx5gvGmO8YY942xtwwxiwYY75pjPmYx2saY8xvGWPGjTFXjDHvGGPOG2NeNsbsrvT3gJCIULCtqIjtWPzI3l0aeTypZHN93uP56mhRPQRdB12mQsztOrESVaqbTme8U8MPDetA64GKXC9MvL4oquSLqY0iDCU9X5T0iKQfSvqWpCuSflXSJyR9whhzyFp7vNSLGWO2SvqGpP9F0l9J+rqkJUn3SPqfJO2WdK6S3wBCImLBtmIiuGNxd6JB3YmGDd/esRYQdB10mQq5lt7S1om54LWbzq/v+nXdX3//hmhJ6fVFUdRbk/ohDH9Jvi3p962137/1QWNMj6RTkp4zxnzDWnuxxOv9gZyw/4yk/9Pa2wvDjDEfqMCcEUYRDLYVEeEdi0upo0V1EXQddJmqEcXWibngtZvO7/yPvxPpkH8rry+Kotqa1E9VL+mx1j6/OuyvPD4haVzSZkkfLeVaxphfkTQo6bSkf7k67K9c992yJozwinCwLUuuE4Ub7FiMCiHoOnJdptygy1RtS2xPqCPe4WpMFLvwrMfri6Iotib1W9UDfxG5cF7qe7v/RM739FVJv2SMedQY86Qx5nPGmI3zX9BGRbAtrOdw8UVpOfl2LAY8Iui+71Bfa9EF5zl0mYqGwbZBxUr82xvVLjzr4UVRcEIb+I0xTZL6JP1c0ndLHJbbBeNDkn4kaUTS/yVpWNI5Y8z/Y4y5o8Tnn873Iel+V98IgkWwza+l17dOFEAxBF0HXaY2nq7GLh158EjR0B/VLjyl4EVRMEIZ+I0xWyS9KGmLpKPW2qslDt2x8vlfSToj6QFJ2+S8cPiRpP9d0u9VdrYIlZZegm0hPnWiAIoh6L6PLlMbT39rv4YfGlZnvDPv8Sh34SkFL4qCYawtcTeU9S5izJuSmlwMedFa+2iBa90h6Y8lfUrSS5L+iS1xksaY1+Xc5f9rSbutte/ccqxN0oykn0lqsNbecDHfW59jur29vX16unKbacAH8+POploXXl17rGm/c2e/pTfgSYVIBTtRVFVUvo8NYjKzqONjc0rn6cufbK7Xwb7WSIf91egytfFkrmaUvpTW8o3lQLrwBP185Zi6OKXh2WGdWTiz5lhnvFOpttSGDPsdHR2amZmZsda6q31apVJtEH4k6ZqL83+S78GVsP81OWH/ZUmPlhr2V+TeCfj2rWFfkqy1s8aY85J+RdIeSbMurota09Jb8RZrkVLBThRVMT/u7Kqcb6O1pm6ntKulN+BJoRSF/qCXf+up9tBlauNJbE8EErinLk5paHYo76ZWHfEODbYNhi48dzV2qauxq6ZepNSSitzhrwRjzCY5PfM/tfL5MWvtey6vcVzS/yHpy9baNcXZxpjTkjolPWitnfI4T+7wA9U084L0yiFpva3YcyVblCeFxkun39KToz9Qdp3/y8mV9FDOAng3Ojeqp197Wtl1/kbmymM2ahlRLanUHf5Q1PAbYzZL+o9ywv4Lkgbchv0VYyufP5znObZIyq0Ee9PDtQFU2/x48bAvOcdfOeicj6qbzCwWDfuSlLXSE6NvRG6XXSAoUxenioZ9ScrarI6+dlRTFz3d+0QNqnrgXwniJyV9UtIJSZ/N1z9/1ZgPGWPuN8Y0rjr0Z5LmJf3PxpiHVh37PTndeyastZcqM3sAgZp4tnjYz7FZZx0Hqu7Y2FzRsJ+TtdLxsTl/JwRE1NDsUNGwn5O1WQ3PDvs8I4RFGLYyHJL0cUmLkn4s6Slj1rRyGLfWjt/y9QFJX5HTb/8zuQettTeMMb8l6T9J+jNjzElJF+Qs5P37kv6bpM/58l0A8Nfls/lr9tdz4VVnXC2vV6hx5xaW9HqeRbrrSZ+/onMLS9S3Ay5krmby1uyv58zCGWWuZqiR3wDCEPibVz43SHpqnfPGS7mYtfZVY0ynpCOSPibpbkkLkv5Q0hettX/tfaoAqmZ+wvs4An/VeC3PmcwsEvgBF9KX0p7HEfijr+qB31rb62HM85KeX+f4DyU94nlSAMLn+lKw41ARy9dK3Si9MuOAjWr5xnKg41Bbql7DDwAl2eLxbq/XcaiIuq3e7it5HQdsVHWb6wIdh9pC4AdQG1p6gh2HivC6kdZG2oALqITkzmSg41BbuIUChA2bheW3Y4+zqZabhbtN+/392fG7Kmp3fJv2Nde7WribbK6nft8jr5sWhXXX39Xzui/+t1p49y/YlCmPxPaEOuIdrhbudsY7+fltEAR+ICzYPba4nsPSyIHSWnOamNSzZv+9yuB35cqhvlYNnEiX1JozZqSDfa3FT8RtvO6sOplZ1LGxubwvyPY11+tQX2tV3m1ZPa877spoc8OYNn3w/Jpzw7pzbDUMtg0qdSpVUmvOmIkp1ZYKYFYIA0p6gDCYecEJsoXuXl+YdI7PjAQ7r7Bp6ZUePuaE+fXkdtpt6a38HPhdudadaNAz/Q8otqbj8u1yO+1SzuPO6NyoUqdSBe/sTi9MK3UqpZNzJ297/KXTb2ngRLrguy+vn7+igRNpvXz67YrPeT2r5/WBD53WnbtOaNMHz8vmedFY6PvbiLoau3TkwSOKFfkbmdtplxdJGweBH6g2do91p/0xaeCkU66TT9N+53j7QOWfm9+VZ4/s3aWRx5NKNtfnPZ5srtfI40l9eu99Ac+stnndWTWsux+vntcdd2W0pXFUxjgPrN2mJzdPdo7N6W/t1/BDw+qMd+Y93hnv1PBDwzrQeiDgmaGaKOkBqs3L7rEtvX7OKPxaep2PoGvo+V2VpTvRoO5EQ2jrxWuRl51Vuxq7PO1+HMQ7L6vntblh7Bdhv5hbv7+NrquxS12NXZ7XdCB6CPxANbF7bHl27Anu58DvqmJ2x7cR8CvA686q/zkzG8rdj1fvyhzbvPCLMp5Cd/ZXY+fY2yW2J/hZQBIlPUB1lbN7LILF7woh43Vn1W/+lbd/k36X9ay+/h0fzEgqPezneP25AFFG4Aeqid1jawe/K4RM0Dur+r378errm9g1b9dh51hgDQI/UE3sHls7+F0hZILeWdXv3Y9XX99mt3q7DjvHAmsQ+IFqYvfY2sHvCiHjdYfUT/6qt3+Tfi/aXX39937m1J7na8W5HnaOBdYi8APVlNs91g2/d49FfvyuEDK5nVXd6Ix36tcTbdpXoD1qIUHsfpzblTkneyOumz9rdlXDz86xQH4EfqDaeg4X30gqx8/dY1EcvyuEzGDbYNFNlnJu3Vn1UF9r0Y3Q3h8X3O7Hq+d1Y7FP1pY2UXaOBQoj8APV1tJb/d1jUZqWXn5XCBWvO6uGdffj1fN67+cJXb/Y/4vQX6i8h51jgfXRhx8Ig/bHpLt3ORs1XXh17fGm/c7d4pbeoGeG1fhdIWT6W/t1T909Gp4d1pmFM2uOd8Y7lWpLrQnDj+zdpXu336XjY3NK5+nLn2yu18G+1sDCfqF5vfvTvcq+u12bG8a06YPn15xf6PsD8D5j3a6G2eCMMdPt7e3t09PuNjsBShb07rHwjt/VWhH7mdTaTqVe5xvW3Y9Xz+u++N9q4d2/KPn7C+v3BZSqo6NDMzMzM9Zadwt2VuEOPxA2Qe4ei/Lwu3rf/Lg08Wz+3Yibup31Dy29AU/Ku6mLUxqaHcq7k21HvEODbYOhvKPsdWfVsO5+nH9ebUXHTWYWdWxsLu+Owvua63WoCu9cANVEDT8AoDwzL0gjB/KHfcl5fOSANDMS7Lw8Gp0bVepUKm/Yl6TphWmlTqV0cu5kwDNDKV46/ZYGTqTzhn1Jev38FQ2cSOvl028HPDOgegj8AADv5selVw5JNrv+eTYrvXLQOT/Epi5O6enXnla2yPeTtVkdfe2opi5OBTQzlGIys6gnR3+gbJFq5ayVnhh9Q5OZxWAmBlQZgR8A4N3Es8XDfo7NOoudQ2xodqho2M/J2qyGZ4d9nhHcODY2VzTs52StdHxszt8JASFB4AcAeHP5bOEynkIuvOqMC6HM1UzBMp5CziycUeZqxqcZwY1zC0sFy3gKSZ+/onMLSz7NCAgPAj8AwJv5iWDH+Sx9KR3oOFSW1/IcynqwEdClBwAqLeDWlFVrPXjd451Rr+N8tnxjOdBxqKzlazcDHYfS1Fpr26gi8ANApQTcmrLqrQe3eHxR4XWcz+o21wU6DpVVt9VbpPE6Duur1da2UUVJDwBUQsCtKUPRerClJ9hxPkvuTAY6DpXl9cUt/fgrj9a24UPgB4ByBdyaMjStB3fscd65cKNpf2g3K0tsT6gj7m4zy854J+UJIbE7vk37mutdjUk214dyw7FaRmvbcCLwA0C5Am5NGarWgz2HJVPi/5WYmNTzef/mUgGDbYOKlfj9xExMqbaUzzOCG4f6WhUzpZ0bM9LBvlZ/J7QB0do2nAj8AFCOgFtThq71YEuv9PCx4qHfxKSHj1d0DYMfuhq7dOTBI0VDf8zEdPTBo9Qgh0x3okHP9D9QNPTHjPSl/o9QzlNhtLYNLwI/AJQj4NaUoWw92P6YNHDSKdfJp2m/c7x9wL85VFB/a7+GHxpWZ7wz7/HOeKeGHxrWgdYDAc8MpXhk7y6NPJ5UskB5T7K5XiOPJ/XpvfcFPLPoo7VteLE0HQDKEXBrytC2HmzpdT4Cbknql67GLnU1dvnbUjAiP6sw6k40qDvRUL2WtSWIYrtKWtuGF4EfAMoRcGvKy0vXPI0LrPXgjj2RCq2J7YnKh7CA27duZLvj20IT8HOi3K6S1rbhRUkPAJQjwNaUL51+S19Lv+Xp6ahVDomA27ciXKLerpLWtuFF4AeAcgTUmjLXitOW2J3nVrQeDImA27ciXDZCu0pa24YXgR8AyhVAa0o3rThvRevBEAm4fSvCZaO0q6S1bTgR+AEUd/msNDXkBJCpIc8tJSOrpdfX1pReWnFKkhGtB0Mj4PatCJdaaFeZuZrRi2df1PDssF48+6Ln56a1bTixaBdAYSwuLF37Y9Ldu5wXRRdeXXu8ab9zZ7+l1/WlvbbUfLSridaDYVFO+9YILYLeqMppV+l3uYsfi4j7W/t1T909Gp4d1pmFM2uOd8Y7lWpLEfYDROAHkN/MC+vXG+cWFz58vGb6q/uupdeX1pReW2ru2LbF83N6VmOtJgNr2xhw+1bcrtotMMParnJ0bnTddQW5RcRHHzzqet+JQFrbomQEfgBruV1cePd93Om/VYVbU3ptqRlYK06p5t4Nmsws6tjYXN5SqX3N9TrU11rZUqiA27fCEZYWmGFsV+l2EXFjXaOnn5UvrW3hGjX8ANZicWGoeA2egdXu11iryZdOv6WBE+mC6yJeP39FAyfSevn025V70pU2rKV2WfrFeV7bviJULTDD2K5yoywihoPAD+B2LC4Mnd3xbdrXXO9qTGCtOGus1WSuvWmxjkdZKz0x+obn9ROrnbP3Kp29X8aUdr4x0lR2j87Zeyvy/BtN2Fpghq1dZS0sIkZlEfgB3K6cxYXwzaG+VsVKDIuBtuKssXeD3LQ3zVrp+NhcRZ53MrOoYzf79Z4t7Zf4njU6fvNAxV5wbDRhvHsdpnaV5SwiRm0i8AO4HYsLQ6k70aBn+h8oGvpjJsBWnDX2bpCX9qbp81d0bqH8f9vL127qe9kP68mbv/2L0L+6vCf39XvW6Imb/0zfy37Y84LtjSysd6/D1K4yrIuI4R8CP4DbsbgwtB7Zu0sjjyeVLFDek2yu18jjyeBacdbYu0Fe75ZX4i57bgH1y+99TAPvPqmp7J415T25Mp6Bd5/UN97rvW0cShfmu9f9rf0afmhYnfHOvMc7450afmjYdUcct8K4iBj+4i8JgNt5XSTI4sJAdCca1J1oCK6d5Hpq7N0gr3fLK3GX/dZ3XL6X/bC+d+PDajV/re7YX6hO72hZd2oy+2HNrarZZ9M098J+9zoM7SrDuIgY/iLwA7jdjj1OG0U3pRpN+0Pdaz2Kdse3BR/wV6uxd4Oq2d40t/D61pKiOXuv5t4rvCg3sIXXEVMrd6+r2a4yt4jYTemTn4uI4T9KegCs1XNYKnFxmUzM2UEWG0+NvRtU7famoV14HTHcvS5NmBYRw38EfgBrtfRKDx8rHvpNzNlpt6XX/zkhfHLvBrlRxXeDqt3eNJQLryMobC0wwypMi4jhPwI/gPzaH5MGTjoBLZ+m/c7x9oFg54VwqbF3g6p9lz10C68jirvXpQnLImL4z9hSt/2DJMkYM93e3t4+Pe2u5RdQ0y6fdTqrXF9y6q9beqjZx/tmXii++Vbu3aAQvEB86fRbRTffyt1l9zN4h2LhdYSNzo0W3Xwrd/eaQKuqLiJGYR0dHZqZmZmx1rp722oVFu0CKG7HHgI+Cmt/TLp7l7Op1oVX1x5v2u/c2W/p9XceJb4wfWTvLt27/S4dH5tTOk9f/mRzvQ72tbouqXEb4EOx8DoA1QqS/a39uqfuHg3PDuvMwpk1xzvjnUq1pShVWVHNRcTwH4EfAFC+ll7noxrvBs2POzv+5uss1dTtlB219N72cCXbm05mFnVsbC7vpl77mut1yMOLhyiYujilodmhvJ1gOuIdGmwb9D1sh6EFJhAGlPS4REkPAIRIlcuJwlIeFDaU0wCVUamSHhbtAgBq0/x48bAvOcdfOeicX0GTmcWiYV+SslZ6YvSNiuzYWwumLk4VDfuSlLVZHX3tqKYuTgU0M2DjIvADAGrTxLPFw36OzTprDCro2Nhc0bCfk7XS8bG5ij5/WA3NDhUN+zlZm9Xw7LDPMwJA4AcA1J7LZ93tBi05C4ovn63I059bWMpbs7+e9PkrOrewVJHnD6vM1Yyr3Vsl6czCGWWuZq1TIrgAABSFSURBVHyaEQCJwA8AqEXzE8GOW8VreU7Uy3rSl9KBjgNQGgI/AKD2XPd4p9zruFWWr90MdFytWL6xHOg4AKWpeuA3xrQaY75gjPmOMeZtY8wNY8yCMeabxpiPubzW88YYW+RjzK/vBQAQkC0e+9d7HbdK3VZvXa29jqsVdZvrAh0HoDRh+MvzRUmPSPqhpG9JuiLpVyV9QtInjDGHrLXHS7zWn0h6s8CxAUktkv6srNkCAKqvpSfYcat47asf9X78yZ3JQMcBKE0YAv+3Jf2+tfb7tz5ojOmRdErSc8aYb1hrLxa7kLX2T+SE/tsYY+6WdFjSDUnPV2LSAIAq2rHH2VTLzcLdpv0V2wRsd3yb9jXXu1q4m2yuj/zOuontCXXEO1wt3O2Md7IJFuCzqpf0WGufXx32Vx6fkDQuabOkj5b5NAOS7pQ0aq2N9oopANgoeg47m2qVwsSkns9X9OkP9bUqZko7N2akg32tFX3+sBpsG1SsxN9LzMSUakv5PCMAVQ/8Rby78rncVU7/bOXzH5Z5HQBAWLT0Sg8fKx76czvttvRW9Om7Ew16pv+BoqE/t9Nu1Mt5croau3TkwSNFQ39up92uxq6AZgZsXGEo6cnLGNMkqU/SzyV9t4zrPCjpAUnnrLX/xcW4Qu9H3u91LgCACmt/TLp7l7Op1oVX1x5v2u/c2W/p9eXpH9m7S/duv0vHx+aUzlPek2yu18G+1g0T9nP6W/t1T909Gp4d1pmFM2uOd8Y7lWpLEfaBgIQy8Btjtkh6UdIWSYettVfLuNznVj7/UdkTAwCET0uv83H5rNNn//qS042npadiNfvr6U40qDvRoHMLS5rMLGr52k3Vbd2k7kRD5Gv219PV2KWuxi5lrmaUvpTW8o1l1W2uU3Jnkpp9IGAVCfzGmDclNbkY8qK19tEC17pD0oikbkkvSfpyGfP6kKRPy8NiXWttR4FrTktq9zonAIBPduwJJOAXsju+bUMH/EIS2xMEfKDKKnWH/0eSrrk4/yf5HlwJ+1+T9ClJL0t61Fpry5jXo5LukvQfWKwLAACAjagigd9a21fuNYwxmyR9XU7Y/7qkx6y175V52dxi3eEyrwMAAADUpFDU8BtjNsu5o/9JSS9I+qy1NlvmNZOS2uQs1h0ve5IAAABADap64F9ZoDsq6eOSTkj6XLGwv1Kb3yjpp+tsyJVbrEsrTgAAAGxYVQ/8kobkhP1FST+W9JQxa5oaj6+6S39A0lckfVXSZ1afbIz5JUmPyFms+9WKzxgAAACoEWEI/M0rnxskPbXOeeMurvlPJX1QLNYFAADABlf1wG+t7fUw5nmt02bTWvtvJf1bz5MCAAAAIqLIfuQAAAAAahmBHwAAAIiwqpf0AACi79zCkiYzi1q+dlN1WzepO9HArrQRlrmaUfpSWss3llW3uU7JnUn/dtu9fFaan5CuL0lbtkktPVXdcRkIIwI/AMA3k5lFHRub0+vnr6w5tq+5Xof6WtWdaKjCzOCHqYtTGpod0vTC9JpjHfEODbYNqquxqzJPNj8uTTwrXZhce6ypW+o5LLX0Vua5gBpHSQ8AwBcvnX5LAyfSecO+JL1+/ooGTqT18um3A54Z/DA6N6rUqVTesC9J0wvTSp1K6eTcyfKfbOYFaeRA/rAvOY+PHJBmRsp/LiACCPwAgIqbzCzqydEfKGvXPy9rpSdG39Bkhg7KtWzq4pSefu1pZdffN1NZm9XR145q6uKU9yebH5deOSQVeS7ZrPTKQed8YIOjpAcAUHHHxuaKhv2crJWOj82tKe2h7r86vNTfD80OFQ37OVmb1fDssPfSnolni4f9HJuVJp6jtCcsWG9RNQR+AEBFnVtYKljGU0j6/BWdW1jS/9/evQdLUtUHHP/+LsVjERZZkBUxsCy7CwsREl67BBJWMJCYQJlN1FQlaKxQxSYEUaOEVBLBJBWxLJRH0JAooKEoIlaiJgWJCAkBLECoAoUs4hZPRUBewvJakJM/zrlynZ37mL5998z0fj9VXb3bPadP96/nzvlNzzndyxZub7//Spr2v1/31LpJu/FM5tZHb2XdU+sGH8j72NrJu/FM5oEbcjkTy3ocb1GdXXokSa1q2j3nxnWP2++/ktn0v7/5kZsb1dmo3L3XNaqrcTnNnuMthoIJvySpVetffKVRubsefsZ+/xXMtv/9+g3rG9XbqNxLzzaqq3E5zY7jLYaGCb8kqVXbbdOst+gt9z05cL9/zV6T/vcTbbfVdo3qbVRu64ZjOJqW0+w0GW+hOWHCL0lqVdP+9Q8++fxArx/v96/mZtP/ftyKN65oVHejcouPbFRX43JqbjbjLdQ6E35JUquWLdyeQ/dcMFCZPXbatlFdduuZnTb63y/ZcQkHLTxooPIHLzy42ZN3d1meB3kOYo8jHLBbg+MthooJvySpdacevZSxmNlrxwIO2WOwLwjjmo4XUNZW//s1B6xhLGaWUozFGCcdcFKjeoF8R5cZ1kWMwZEfaV6XmnO8xVAx4Zckte7wJTvz8dVvmTbpHws4a/X+7Lfb/Eb1NB0voKyt/vcrd13JGYedMW3SPxZjnHnYmc3vwQ/59o3HnTt90h9jcNx53u6xFsdbDBU/KSVJc+Ldh+zOm3fclvOu+R4397nN5oo9F/D+ck/9pn3xvR//7LTZ/3710tW8abs3ceEdF3Lro7dutP7ghQdz0gEnzS7ZH3fge+D1u+dBng/csPH6PY7IV/YXr5p9XWrG8RZDxYRfkjRnDl+y808T+qmemjve73+QB3at2HOBT96dpfH+94MM3J2q//3KXVeycteVjZ7WO7DFq/Lk01uH0/h4i0EG7jreYs6Y8EuS5tyyhdtPm5yfevRSTvj8zTO6NedYwPuPXtrS3m3e1hywhpOuPmlGt+acaf/7JTsuaT/Bn8wuy00Sh9WRp+WHas3k1pyOt5hT9uGXJA2FQfv9252nHZu0/702L4tXOd5iSHiFX5I0NAbp96/2bNL+99q8ON5iKJjwS5KGykz7/atdm7T/vTYvi1c53qIyE35J0lCaSb9/tW+T9r/X5sXxFtXYh1+SJEnqMBN+SZIkqcNM+CVJkqQOM+GXJEmSOsyEX5IkSeowE35JkiSpw0z4JUmSpA4z4ZckSZI6zIRfkiRJ6jATfkmSJKnDTPglSZKkDjPhlyRJkjrMhF+SJEnqMBN+SZIkqcNM+CVJkqQOM+GXJEmSOsyEX5IkSeowE35JkiSpw0z4JUmSpA4z4ZckSZI6LFJKtfdhpETEE/PmzVuwfPny2rsiSZKkDlu7di0vvPDCkymlnWazHRP+AUXEfcB84P7Ku9JrnzK/u+pedIOxbI+xbI+xbI+xbI+xbI+xbE+XYrkIeCaltOdsNmLC3xERcRtASumg2vsy6oxle4xle4xle4xle4xle4xle4zlxuzDL0mSJHWYCb8kSZLUYSb8kiRJUoeZ8EuSJEkdZsIvSZIkdZh36ZEkSZI6zCv8kiRJUoeZ8EuSJEkdZsIvSZIkdZgJvyRJktRhJvySJElSh5nwS5IkSR1mwi9JkiR1mAl/ZRHx5oi4KCIejoiXIuL+iDgnInYccDtHRMRXS/kXI+LBiLgyIn5tmnLHR8RVEfGjUv9DEfG1iFg5uyPb9GrFMiK2iIjfi4jrI+KRiHg+Iu6JiIsjYr92jm7TajGWvxUR10bE0yWWayPioxGxzRRl9o2IL0XEY6XMdyPiYxExb/ZHtunViGVE7BYRp5S/7ftLvU9ExNURsbq9o9u0ar4ve8r/VUSkMr2t2dHUVTuWtj19tzNQLLvW9kTE70TE+eV4nil/X5c23NbA56Rrbc9GUkpOlSZgL+BRIAFfAc4Cri3/vxvYaYbb+aNSZj3wz8DHy/y5svwv+pQZA/6xrH8QuBD4O+DzwF3AybXjM0Kx/Jey7iHgM8AngCuBV4GXgKNqx6dSLP+mlHkWuAQ4G7i5LLsBmNenzIoS6w3AZSWW35pQZuva8RmFWJZ6EnAvcHF5H18GvFiWf6p2bEYlln3KH1jen8+WMm+rHZtRiiW2PW3Gsmttz+0TYrC2/PvSTXFO6Fjb0zcutXdgc56A/ypvplN6ln+qLP+HGWxjS+Bp4AVg7551y8kN/PO9b1bgI6WOLwJb9dtu7fiMQiyBQ8r27wS27SnzvrLu2trxqRDLXyyNzlPA4gnLAzi/bOfMnjJbAP9X1h0/YfkY8OWy/PTa8RmRWK4GjuyzreXAj0uZg2rHZxRi2VN+G3JS+s3y2TmqCX+1WGLb00os6Wbb81ZgaTnuVTRP+Ac6J3Sw7ekbl9o7sLlOwOLyJroPGOtZtz35CvNzwOum2c7Csp07Jln/7bJ+pwnL5pO/QT9EF7611o3lu8uyc/u8fkFZ953aMaoQy78u2/lkn3Xbl8btUWCLCcuPKmWum2K/7geidpyGPZbTbG/86uqf1o7RqMUS+DT5S/8y8lXYkUv4K/+N2/a0F8tOtT19jmEVDRL+Jueka23PZJN9+Os5qsy/nlJ6deKKlNKzwI3AtsB0/RkfA34ELIuIpRNXRMQy8rfl21NKT0xYdTywHXA5MFb6zZ0eESdHxAGNj6iemrG8a3wf+vTz+80y/8aMjmI4tBXLN5b5vb0rynYeB3YB3tKn7v/sU+Ze4B5gD/IH8CioGcupvFzmr8zw9cOgeiwj4q3AqcCfp5TuGWjvh0vNWNr29Nckll1re9rS5Jx0re3py4S/nr3LfLKG43tlvmyqjaT8FfRk8rm8LSK+EBEfj4gvAreRPxTe2VPskDJ/mdxP7gpyH9+/B26PiC9HxLaDHExl1WKZUrqTfNXv54G7I+KCiDgrIv6d3Cf1cuAvGxxTLa3EktxAAezZuyIitgd2Lv/dZw7qHhY1Y9lXRMwHfpt8xerr071+iFSNZUTsQL6ifz1w3jR1DLuasbTt6W/gWHaw7WlLk3PStbanLxP+enYo8x9Psn58+eun21BK6QryN9SngfcApwMnkH+2upiNrxrsUuanka9oryD/1LUCuJWcEHxmJgcxJGrGkpTSh4A1wBuAPwb+jHyF5Q7gCyml52Z6IEOgrVj+R5mfGBGLetb9LbmPJsDEOya0dh6HRM1YbiQiAvgcuevaZ1NKa6epd5jUjuX5wE7A+8qFgVFWM5a2Pf01el92rO1pS5Nz0rW2py8T/uE1/gc+beMSEb9P/unuevKgvG3L/BrylZPLe4psUeYvAMellG5JKa1PKd1C/sl1PXBCROw266MYDnMWy8jOAy4g98P8OXID9sulvqsi4uR2DmMozCiWKaVvku++sSPw7XKbuLMj4ibgT3jt5+iftF33CNnUsTyb/AvV9cCHGu/1cJqzWEa+jekJwGnl5/2um8v3pW1PHw3fl5tb29OWJu1IJ9oeE/56xr8x7jDJ+vk9r+ur9C2/iPyBcEJK6e6U0gsppbvJjdRtwDsjYtWEYk+V+U0ppUcmbi+l9EPyrcDGgINneCy11Yzle4FTgPNSSmellL5fGrAbgOPIDdtZEbFdkwOroJVYAqSU1gB/SL77wbvIV6I2AMcC3ykve2wu6h4SNWP5MyLik8AHgf8F3p5Semm6OodMlVhGxAJyInYt8NkmOz6Ear4vbXsm0SCWXWt72tLknHSt7enLhL+e75b5ZH3CxgeNTjc47Bjy7SSv6zNA5VVyAw9wUJ+6n55km+MfyqPysImasRwfHPXfvRsrDdrd5EFqe/euH1JtxRKAlNJFKaWVKaXXlelXUkrfAA4rL/nWXNU9BGrG8qci4tPAh8nv0V9PKa2f2e4PlVqx3J3cf/oo4NV47WFbiZxwAVxdln1goCOqZxj+xm17+hgwll1re9rS5Jx0re3py4S/nvE/0mMi4mfOQxmkczj5G/pN02xn6zJ/wyTrx5dvmLDsmjKf7El848vvn6buYVEzlk3KDLO2YjmpiDiGfMeD61JKP5iw6toy3+iJxhGxmPxh/AB9xlEMqZqxHP/J/wLgA8DVwG+klJ5vWldltWL5BHkAZL9pfCDfVeX/dzatexOr+b607RnQFLHsWtvTlibnpGttT3/T3bfTae4mBn84xD7APj3LDi2vfR7Yv2fdL5Df2K8C+/Wsu6GUO7Fn+Yll+TpmeF/vYZhqxZI8+CyRG/sdesqsKet+uLnFsiyf32fZXuTG/BVgZc+6qR5+cgUj+PCTirEM4J9KHVcC29SOxajGcor9uYQRvA9/7Vhi29PW33jn2p6eY1jFFPfhJ/8avw+wVwvnpHNtT78pykGpgojYi/zExl2Ar5JvU7aC/LS5e4BfShPu+V5+RialFD3buYj8ZL0NwL+Rv4kuAt4BbAWck1L6YE+ZvckfvDuTr1DdBewLvJ2c8B6bcl/AkVArlqV/5I3A/uT+lV8j/1x9ILkbwE+Ad6WU/rXtY54rLcbyCvKVqdvIP9UvIfct3ZLc2F/Sp+4V5KstW5KfcPggcDS5T++NwNFphPqf14plRJwBnEn+knoO/a/y3Z5S+spsj3FTqfm+nGR/LiF36/nVlLtdjIzKf+O2PbTyN97Ftucd5LYW8rMJjiVfVb++LHs8pfTh8tpF5IdrPZBSWtSznYHOSSnTqbanr9rfODb3iTyy/mLyN/EN5ATzXGBBn9cmyu3ie5YH8AfA/5A/KF4BniT/fPq709T9OeAHpe5HgMuA5bXjMkqxJPeT/ChwO/n2nS8DDwNfAg6tHZeKsXwv+YPyibKN7wOX0vPrSZ9y+5KvqjwOvET+gP4YMK92XEYllrx29Xmq6ZLasRmFWE6xL+MxHrkr/LVjiW1PK7GkY20P+SLFVJ9Z90947aLeZU3PyYQynWp7eiev8EuSJEkd5qBdSZIkqcNM+CVJkqQOM+GXJEmSOsyEX5IkSeowE35JkiSpw0z4JUmSpA4z4ZckSZI6zIRfkiRJ6jATfkmSJKnDTPglSZKkDjPhlyRJkjrMhF+SJEnqMBN+SZIkqcNM+CVJkqQOM+GXJEmSOsyEX5IkSeowE35JkiSpw/4fk3kVbSCXRe8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# any trends for subscores? not really\n",
    "plt.scatter(np.array(rg)/40,tScores)\n",
    "plt.scatter(np.array(e)/20,tScores)\n",
    "plt.scatter(np.array(so)/45,tScores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores vs means\n",
      "[-0.12731133 10.13473877]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a36805f98>]"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tMeans,tScores)\n",
    "p=np.polyfit(tMeans,tScores,1)\n",
    "print(\"scores vs means\")\n",
    "print(p)\n",
    "f = np.poly1d(p)\n",
    "x = np.linspace(min(tMeans),max(tMeans),100)\n",
    "plt.plot(x,f(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores vs stdevs\n",
      "[ 1.50189989e-03 -2.60862366e+00]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a367c9eb8>]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 382
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tRanges,tScores)\n",
    "p=np.polyfit(tRanges,tScores,1)\n",
    "print(\"scores vs stdevs\")\n",
    "print(p)\n",
    "f = np.poly1d(p)\n",
    "x = np.linspace(min(tRanges),max(tRanges),100)\n",
    "plt.plot(x,f(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores vs stdevs\n",
      "[ 0.00758675 -2.37335727]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'range of KAI on team')"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 402
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tRanges,bestScoresAtFraction)\n",
    "p=np.polyfit(tRanges,bestScoresAtFraction,1)\n",
    "print(\"scores vs stdevs\")\n",
    "print(p)\n",
    "f = np.poly1d(p)\n",
    "x = np.linspace(min(tRanges),max(tRanges),100)\n",
    "plt.plot(x,f(x))\n",
    "plt.ylabel(\"score\")\n",
    "plt.xlabel(\"range of KAI on team\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "diverseTeams= [ t for t in allTeamObjects if t.dAI > 0]\n",
    "uniformTeams = [ t for t in allTeamObjects if t.dAI == 0]\n",
    "colors = ['red' if t.dAI > 0 else 'blue' for t in allTeamObjects ]\n",
    "isDiverse = [True if t.dAI > 0 else False for t in allTeamObjects ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgDiverseScore = np.mean([t.getBestScore() for t in diverseTeams])\n",
    "avgUniformScore = np.mean([t.getBestScore() for t in uniformTeams])\n",
    "std1 = np.std([t.getBestScore() for t in diverseTeams])\n",
    "std2 = np.std([t.getBestScore() for t in uniformTeams])\n",
    "print(\"diverse score avg:\" + str(avgDiverseScore))\n",
    "print(\"uniform score avg: \" + str(avgUniformScore))\n",
    "print(\"stds:\" + str(std1) + \" and \"+str(std2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "diverseScores = [t.getBestScore() for t in diverseTeams]\n",
    "avgDiverseScore = np.mean(diverseScores)\n",
    "uniformScores = [t.getBestScore() for t in uniformTeams]\n",
    "avgUniformScore = np.mean(uniformScores)\n",
    "std1 = np.std([t.getBestScore() for t in diverseTeams])\n",
    "std2 = np.std([t.getBestScore() for t in uniformTeams])\n",
    "print(\"diverse score avg:\" + str(avgDiverseScore))\n",
    "print(\"uniform score avg: \" + str(avgUniformScore))\n",
    "print(\"stds:\" + str(std1) + \" and \"+str(std2))\n",
    "stat, p = scipy.stats.ttest_ind(diverseScores,uniformScores)\n",
    "print(\"significance: p= \"+str(p))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}