diff --git a/Ovingsforelesninger/OF1/tasks.ipynb b/Ovingsforelesninger/OF1/tasks.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Øvingsforelesning 1 - TDT4110"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Læremål**:\n",
+    "\n",
+    "* Bli introdusert til hva programmering er\n",
+    "\n",
+    "* Grunnleggende syntaks\n",
+    "\n",
+    "* Utføre enkle utregninger i Python\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Hva tilsvarer 80 grader celsius i Fahrenheit?**\n",
+    "\n",
+    "$F = \\frac{9}{5} \\cdot Celsius + 32$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Skriv koden din her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Oppgave 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Regn ut følgende i Python ved hjelp av kode**\n",
+    "\n",
+    "$4 \\cdot -2-2\\left(2+-5\\right)$\n",
+    "\n",
+    "$-2--2-2$\n",
+    "\n",
+    "$5-2^{-1\\cdot-1}$\n",
+    "\n",
+    "$-(1 \\cdot 1 \\cdot 2 \\cdot 3 \\cdot 5 \\cdot -8)$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Skriv regnestykke 1 her"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Skriv regnestykke 2 her"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Skriv regnestykke 3 her"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Skriv regnestykke 4 her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Er 1000 000 000 større enn $2^{30}$?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Skriv koden din her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 4\n",
+    "\n",
+    "Hva blir resten når $2^7$ deles på $42$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Skriv kode her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 5: Input\n",
+    "\n",
+    "**Lag et program som gjør følgende:**\n",
+    "1. Tar inn to tall fra bruker\n",
+    "2. Multipliserer dem \n",
+    "3. Skriver ut `(tall 1) * (tall 2) = (resultat)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Skriv kode her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 6: Innebygde funksjoner \n",
+    "\n",
+    "Skriv et program som spør brukeren om to tall og printer absoluttverdien av differansen\n",
+    "<br>\n",
+    "\n",
+    "Hint: Bruk den innebygde funksjonen **abs()**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Skriv kode her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 7: Feilretting og variabeltyper\n",
+    "\n",
+    "Følgende program skal regne ut og printe summen av prisen på tre varer. Prisene er lagret som forskjellige variabeltyper. Undersøk feilmeldingene og rett koden slik at den skriver ut riktig totalsum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "banan_pris = 10.50\n",
+    "melk_pris = '23.50'\n",
+    "havregryn_pris = 15\n",
+    "\n",
+    "total_sum = banan_pris + melk_pris + havregryn_pris\n",
+    "\n",
+    "print('Totalsummen for varene er ' + total_sum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 8: Bruk av variabler\n",
+    "\n",
+    "Regn ut volumet av en kjegle med radius 3 og høyde 7.\n",
+    "\n",
+    "$V = \\frac{\\pi r^2 h}{3}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Skriv kode her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 9: Variabler og input \n",
+    "\n",
+    "1. Endre kjegleprogrammet fra oppgave 6 til å ta inn radius og høyde fra brukeren\n",
+    "2. Verdiene skal være av desimaltall (float) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Skriv kode her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 10: Sammenligning\n",
+    "\n",
+    "Ta inn to tall fra brukeren og skriv ut True om det første tallet er større enn det andre, False dersom ikke"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Skriv kode her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 11 (Ekstraoppgave): Lag en funksjon\n",
+    "\n",
+    "Lag en funksjon som kan regne om fra Celsius til Fahrenheit\n",
+    "\n",
+    "$F = \\frac{9}{5} \\cdot Celsius + 32$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Skriv koden din her"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Oppgave 12 (Ekstraoppgave): Plotting\n",
+    "\n",
+    "Plot Celsius på x-aksen og Fahrenheit på y-aksen, fra -100 til 100 Celsius (hint: np.linspace). \n",
+    "\n",
+    "Bonus: Legg til labels på aksene og en \"Legend\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9pTstHI3q3/ZylmzGGFNfT75q1SquvfbaXy2/6667+Pvf/84tt9xCZmYmBQUFREVF0bdvX5577jkiIiKqx+bn55Oens4HH3yA3W5n4MCBzJgxA39//1rncDqdBAUFUVhYSGCgmreISHOUsecEjyzMJK+oDB+vH69WuuOiaE0rNWK1ff+u1zLTWKjMiIg0X1Uuw6yVu3llxU5cBuLC/Xl1eCIdIwKsjia/o7bv3zquJiIiTVZeUSmPLszimz0/fobZoN5tmHRzN/y89fbXlGhviohIk7R613EefSuT48Xl+Hl78NdbunNbYhurY0k9UJkREZEmpbLKxSsrdjHr890YA50jA5g1LJG48NqfaynuRWVGRESajJzCUkYuzOTbffkADL2kLRNv7IqPl4fFyaQ+qcyIiEiTsGpHHqMXbSC/pJwW3h5MHtiTm+L1MR7NgcqMiIi4tYoqFy9/upPXvtgDQLeoQGYNSyQmtIXFyaShqMyIiIjbOlxwipELMll34AcA/pjcjieu76JppWZGZUZERNzSZ1tzeeztDRScrCDA4cnfbu/J9T1aWx1LLKAyIyIibqW80sXUZdv55+p9APRsE8SsoYm0beVncTKxisqMiIi4jYP5J0lfkMmGgwUA3HN5DOP7d8bb025tMLGUyoyIiLiFZZuPMvbtjRSVVhLo48lLg+Lp2y3S6ljSCKjMiIhIo1ZWWcULH23jzYwDACS0DWbm0ATatNS0kvxIZUZERBqtAydKSJ+fyabDhQD8+apYHkvthJeHppXkP1RmRESkUfpw4xHG/3sTxWWVtPTz4uU74rmuc4TVsaQRUpkREZFGpbSiiuc+3Mq8tdkAXNy+JTOGJtA6yNfiZNJYqcyIiEijsfdYMWnzM9l21InNBg9dcyGjUjriqWkl+Q0qMyIi0ii8l3WYJ97ZREl5Fa1aeDN9cC+u6hhmdSxxAyozIiJiqVPlVUz6YAsLvzsIwKWxIbwyJIGIQB+Lk4m7UJkRERHL7M4rIm1eJjtyi7DZYOR1HRjZpwMedpvV0cSNqMyIiIgl3l53iKfe3cypiirCAhy8MrgXl8WFWh1L3JDKjIiINKiT5ZX85d3NvLP+MABXxIUyfXAvwgIcFicTd6UyIyIiDWZ7jpO0eevZc6wEuw1G/6EjD14Tp2klOS8qMyIiUu+MMbz13UEmvr+FskoXEYEOZgxJICm2ldXRpAlQmRERkXpVXFbJE+9s4v0NRwC4umMY0+6Ip5W/ppWkbqjMiIhIvdlypJD0+ZnsO16Ch93GY3078eerYrFrWknqkMqMiIjUOWMM/1pzgOc+2kZ5pYvWQT7MHJrARe1DrI4mTZDKjIiI1ClnaQXj/72RpZtyAOjTOZyXBsXTsoW3xcmkqVKZERGROrPxUAHp8zPJzj+Jp93G+P6dufeKGGw2TStJ/VGZERGR82aMYc7X+5n88TYqqgwXBPsya1gCCW1bWh1NmgGVGREROS+FJysY+/YGPt2aC0BqtwimDownyM/L4mTSXKjMiIjIOVuf/QMPz8/kcMEpvD3sPHF9Z+66rL2mlaRBqcyIiMhZc7kM/1y9l6nLdlDpMrQN8WP2sER6tAmyOpo0QyozIiJyVn4oKWfM4g2s3J4HwICerZl8Ww8CfTStJNZQmRERkVr7bn8+IxdkcrSwFG9PO0/f0JXhSW01rSSWUpkREZHf5XIZ/v7FHqYt30mVyxAb2oJZwxLpGhVodTQRlRkREfltx4vLGL1oA1/uPAbALb2i+OutPfB36C1EGgd7fT75l19+yY033khUVBQ2m4133323xnpjDE8//TStW7fG19eXlJQUdu3aVWNMfn4+w4cPJzAwkODgYO69916Ki4vrM7aIiPx/a/ae4PpXvuLLncfw8bLzt4E9mD64l4qMNCr1WmZKSkqIj49n9uzZp10/depUZsyYwWuvvcbatWtp0aIFqamplJaWVo8ZPnw4W7ZsYfny5Xz44Yd8+eWX3H///fUZW0Sk2atyGWas2MWw/7OGvKIy4sL9eS/tCgZfrPNjpPGxGWNMg/wgm40lS5Zwyy23AD8elYmKimLMmDE89thjABQWFhIREcHcuXMZMmQI27Zto2vXrnz33XdcdNFFACxbtozrr7+eQ4cOERUVVauf7XQ6CQoKorCwkMBAze+KiPyWvKJSRr2Vxde7TwAwMLENz93SDT9vHY2RhlXb9+96PTLzW/bt20dOTg4pKSnVy4KCgkhKSiIjIwOAjIwMgoODq4sMQEpKCna7nbVr157xucvKynA6nTVuIiLy+77efZzrX1nN17tP4OvlwUuD4nn5jngVGWnULPvbmZPz47epRkRE1FgeERFRvS4nJ4fw8PAa6z09PQkJCakeczqTJ09m0qRJdZxYRKTpqqxyMWPFLmZ+vhtjoFNEALOHJxAXHmB1NJHfZdmRmfo0YcIECgsLq28HDx60OpKISKOV6yxl2D/XMmPlj0Vm6CXRvJd+uYqMuA3LjsxERkYCkJubS+vWrauX5+bm0qtXr+oxeXl5NR5XWVlJfn5+9eNPx+Fw4HA46j60iEgTs2pHHqMXbSC/pJwW3h68cFsPbu51gdWxRM6KZUdmYmJiiIyMZMWKFdXLnE4na9euJTk5GYDk5GQKCgpYt25d9ZiVK1ficrlISkpq8MwiIk1FZZWLvy3bzog535FfUk6X1oF88PAVKjLilur1yExxcTG7d++uvr9v3z6ysrIICQmhbdu2PProo/z1r3+lQ4cOxMTE8NRTTxEVFVV9xVOXLl3o168f9913H6+99hoVFRWkp6czZMiQWl/JJCIiNR0pOMXIBZl8f+AHAO68tB1PDuiCj5eHxclEzk29lpnvv/+ea6+9tvr+6NGjAbjrrruYO3cujz/+OCUlJdx///0UFBRwxRVXsGzZMnx8fKofM2/ePNLT0+nTpw92u52BAwcyY8aM+owtItJkrdiWy5jFGyg4WUGAw5MpA3syoGfr33+gSCPWYJ8zYyV9zoyINHfllS6mLtvOP1fvA6DHBUHMGpZAu1YtLE4mcma1ff/WBweIiDRxB/NP8vCCTLIOFgBw9+XtGd+/Mw5PTStJ06AyIyLShH2yJYexizfgLK0k0MeTFwfFk9rtzFeDirgjlRkRkSaorLKKyUu3M/eb/QD0ig5m5tAEokP8rA0mUg9UZkREmpgDJ0pIn5/JpsOFANx/VSxjUzvh5dEkPydVRGVGRKQp+WjjUcb/eyNFZZUE+3kx7Y54rusc8fsPFHFjKjMiIk1AaUUVf/1oK/9akw3ARe1aMmNoAlHBvhYnE6l/KjMiIm5u77Fi0uZnsu2oE4CHrrmQ0X/oiKemlaSZUJkREXFj72Ud5ol3NlFSXkWrFt5MG9yLqzuGWR1LpEGpzIiIuKFT5VVM+mALC787CEBSTAgzhiYQEejzO48UaXpUZkRE3MzuvCLS5mWyI7cImw0evq4DI6+L07SSNFsqMyIibuTtdYd46t3NnKqoItTfwStDenF5XKjVsUQspTIjIuIGTpZX8tS7W/j3+kMAXB7XiumDexEeoGklEZUZEZFGbkdOEQ/NW8eeYyXYbfBoSkfSro3Dw26zOppIo6AyIyLSSBljeOu7g0x8fwtllS4iAh28MiSBS2NbWR1NpFFRmRERaYSKyyp5cskm3ss6AsBVHcOYfkc8rfwdFicTaXxUZkREGpktRwp5eH4me4+X4GG3MaZvRx646kLsmlYSOS2VGRGRRsIYw7/WZvPch1spr3TROsiHmUMTuKh9iNXRRBo1lRkRkUbAWVrBhHc28dHGowD06RzOS4PiadnC2+JkIo2fyoyIiMU2HSokbf56svNP4mm3Mb5/Z+69IgabTdNKIrWhMiMiYhFjDG9+s58Xlm6nvMrFBcG+zBqWQELbllZHE3ErKjMiIhYoPFnB4//ewCdbcgHo2zWCF2+PJ8jPy+JkIu5HZUZEpIFlZv9A+vxMDhecwsvDxhPXd2HEZe01rSRyjlRmREQaiDGGf361j78t206ly9A2xI9ZwxLo2SbY6mgibk1lRkSkAfxQUs5jizewYnseAAN6tGbywB4E+mhaSeR8qcyIiNSz7/fn8/CCTI4WluLtaeepG7ryX0ltNa0kUkdUZkRE6onLZXjtyz28/OlOqlyGmNAWzBqWQLeoIKujiTQpKjMiIvXgRHEZoxdt4IudxwC4uVcUz9/aA3+HXnZF6pp+q0RE6tjavScYuTCTXGcZDk87z97cjTsuita0kkg9UZkREakjVS7Dq5/vZvpnO3EZuDCsBa8O702nyACro4k0aSozIiJ14FhRGaPeymL17uMADExsw3O3dMPPWy+zIvVNv2UiIufp693HeWRhFseLy/D18uC5W7pze+82VscSaTZUZkREzlGVy/DKil3MXLkLY6BTRACzhiXQIULTSiINSWVGROQc5DpLeWRhJmv25gMw5OJoJt7YDV9vD4uTiTQ/KjMiImfpi53HGP1WFidKymnh7cELt/Xg5l4XWB1LpNlSmRERqaXKKhfTlu/k1VV7AOjSOpDZwxKIDfO3OJlI82a3OsAzzzyDzWarcevcuXP1+tLSUtLS0mjVqhX+/v4MHDiQ3NxcCxOLSHN0pOAUQ/6xprrIDE9qy5KHLlOREWkEGsWRmW7duvHZZ59V3/f0/E+sUaNG8dFHH7F48WKCgoJIT0/ntttu4+uvv7Yiqog0Qyu35zJ60QYKTlbg7/BkysAe3NAzyupYIvL/NYoy4+npSWRk5K+WFxYW8vrrrzN//nyuu+46AObMmUOXLl1Ys2YNl156aUNHFZFmpKLKxYuf7OAfX+4FoMcFQcwalkC7Vi0sTiYiP2f5NBPArl27iIqKIjY2luHDh5OdnQ3AunXrqKioICUlpXps586dadu2LRkZGVbFFZFm4GD+SQa9llFdZEZc1p63H0xWkRFphCw/MpOUlMTcuXPp1KkTR48eZdKkSVx55ZVs3ryZnJwcvL29CQ4OrvGYiIgIcnJyzvicZWVllJWVVd93Op31FV9EmqBPtuQwdvEGnKWVBPp4MvX2ePp1//XRYxFpHCwvM/3796/+c8+ePUlKSqJdu3YsWrQIX1/fc3rOyZMnM2nSpLqKKCLNRFllFVM+3s6cr/cDEB8dzKyhCUSH+FkbTER+U6OYZvq54OBgOnbsyO7du4mMjKS8vJyCgoIaY3Jzc097js1PJkyYQGFhYfXt4MGD9ZxaRNxd9omT3P73jOoic9+VMSz+c7KKjIgbaHRlpri4mD179tC6dWt69+6Nl5cXK1asqF6/Y8cOsrOzSU5OPuNzOBwOAgMDa9xERM5k6aajDJjxFZsOFxLs58U//3gRTw7oirdno3uJFJHTsHya6bHHHuPGG2+kXbt2HDlyhIkTJ+Lh4cHQoUMJCgri3nvvZfTo0YSEhBAYGMjDDz9McnKyrmQSkfNWWlHFXz/ayr/W/HjRQe92LZkxNIELgs9tiltErGF5mTl06BBDhw7lxIkThIWFccUVV7BmzRrCwsIAmD59Ona7nYEDB1JWVkZqaiqvvvqqxalFxN3tO15C2rz1bD364wUCD1x9IWP6dsTLQ0djRNyNzRhjrA5R35xOJ0FBQRQWFmrKSUR4L+swT7yziZLyKkJaeDPtjniu6RRudSwR+YXavn9bfmRGRKShlFZU8cz7W1j43Y8XBVwSE8KMIQlEBvlYnExEzofKjIg0C7vzikibl8mO3CJsNnj42jhG9umAp6aVRNyeyoyINHn/XneIv7y7mVMVVYT6O/jvwb24okOo1bFEpI6ozIhIk3WyvJKn39vC2+sOAXDZha347yG9CA/QtJJIU6IyIyJN0o6cItLmr2d3XjF2Gzya0pG0a+PwsNusjiYidUxlRkSaFGMMi74/yMT3t1Ba4SI8wMGMoQlcGtvK6mgiUk9UZkSkySguq+QvSzbxbtYRAK7qGMa0O+IJ9XdYnExE6pPKjIg0CVuPOEmfv569x0vwsNsY07cjD1x1IXZNK4k0eSozIuLWjDHMW5vNsx9upbzSResgH2YOTeCi9iFWRxORBqIyIyJuq6i0gvHvbOKjjUcB6NM5nJcGxdOyhbfFyUSkIanMiIhb2nSokPQF6zlw4iSedhvj+nXmT1fGYLNpWkmkuVGZERG3YozhzW/288LS7ZRXubgg2JeZwxJIbNvS6mgiYhGVGRFxG4WnKhj39kaWbckBoG/XCF68PZ4gPy+Lk4mIlVRmRMQtZB0sIH3+eg79cAovDxtPXN+FEZe117SSiKjMiEjjZozh9dX7mPLxdipdhugQX2YPS6Rnm2Cro4lII6EyIyKNVsHJch5bvIHPtuUBcH2PSKYM7Emgj6aVROQ/VGZEpFFadyCfh+dncqSwFG9PO0/d0JX/SmqraSUR+RWVGRFpVFwuwz++2suLn+ygymWICW3BrGEJdIsKsjqaiDRSKjMi0micKC5jzOINrNpxDICb4qN44bYe+Dv0UiUiZ6ZXCBFpFNbuPcHIhZnkOstweNqZdFM3Bl8crWklEfldKjMiYqkql+HVz3cz/bOduAxcGNaC2cMT6RwZaHU0EXETKjMiYpljRWWMeiuL1buPA3Bb4gU8d3N3WmhaSUTOgl4xRMQS3+w+zsiFWRwvLsPXy4Nnb+7GoIuirY4lIm5IZUZEGlSVy/DKil3MXLkLY6BjhD+zhyXSISLA6mgi4qZUZkSkweQ6S3lkYSZr9uYDMPiiaJ65qRu+3h4WJxMRd6YyIyIN4sudxxj1VhYnSsrx8/bg+Vu7c2tCG6tjiUgToDIjIvWqssrFtOU7eXXVHgC6tA5k9rAEYsP8LU4mIk2FyoyI1JujhacYuSCT7/b/AMDwpLY8dUNXfLw0rSQidUdlRkTqxcrtuYxZtIEfTlbg7/Bk8m09uDE+yupYItIEqcyISJ2qqHLx4ic7+MeXewHofkEgs4Ym0j60hcXJRKSpUpkRkTpz6IeTpM/PJOtgAQAjLmvPhOs74/DUtJKI1B+VGRGpE59syWHs4g04SysJ8PHkxdt70q97a6tjiUgzoDIjIuelvNLF5I+3Mefr/QDERwcza2gC0SF+1gYTkWZDZUZEzln2iZOkL1jPxkOFAPzpihge79cZb0+7xclEpDlRmRGRc7J001HGvb2RorJKgny9eHlQPCldI6yOJSLNkMqMiJyV0ooqnv9oG/+75gAAvdu1ZMbQBC4I9rU4mYg0V25zLHj27Nm0b98eHx8fkpKS+Pbbb62OJNLs7Dtewm2vflNdZB64+kIW3n+pioyIWMotysxbb73F6NGjmThxIuvXryc+Pp7U1FTy8vKsjibSbLy/4Qg3zPiKrUedhLTwZu7dFzO+f2e8PNziZUREmjCbMcZYHeL3JCUlcfHFFzNr1iwAXC4X0dHRPPzww4wfP/53H+90OgkKCqKwsJDAwMD6jivSpJRWVDHpg60s+DYbgEtiQpgxJIHIIB+Lk4lIU1fb9+9Gf85MeXk569atY8KECdXL7HY7KSkpZGRknPYxZWVllJWVVd93Op31nlOkKdqdV0z6/PVszynCZoP0a+N4pE8HPHU0RkQakUb/inT8+HGqqqqIiKh5lURERAQ5OTmnfczkyZMJCgqqvkVHRzdEVJEmZUnmIW6atZrtOUWE+nvzv/ckMaZvJxUZEWl0muSr0oQJEygsLKy+HTx40OpIIm7jZHklYxdvYNRbGzhZXkVybCuWjrySKzqEWh1NROS0Gv00U2hoKB4eHuTm5tZYnpubS2Rk5Gkf43A4cDgcDRFPpEnZmVtE2rz17Morxm6DkX068PB1HfCw26yOJiJyRo3+yIy3tze9e/dmxYoV1ctcLhcrVqwgOTnZwmQiTYcxhkXfHeSmWavZlVdMeICDeX+6lEdTOqrIiEij1+iPzACMHj2au+66i4suuohLLrmE//7v/6akpIS7777b6mgibq+krJK/vLuZJZmHAbiyQyjTB/ci1F9HN0XEPbhFmRk8eDDHjh3j6aefJicnh169erFs2bJfnRQsImdn6xEn6fPXs/d4CR52G6P/0JEHr74Qu47GiIgbcYvPmTlf+pwZkZqMMcz/NptJH2ylvNJFZKAPM4clcHH7EKujiYhUazKfMyMidauotIIJ72ziw41HAbi2Uxgv39GLkBbeFicTETk3KjMizcjmw4WkzV/PgRMn8bTbeLxfJ/50RaymlUTEranMiDQDxhj+b8YBnv9oG+VVLi4I9mXmsAQS27a0OpqIyHlTmRFp4gpPVTDu7Y0s2/LjJ2b/oWsEL97ek2A/TSuJSNOgMiPShGUdLCB9/noO/XAKLw8bE/p34e7L22OzaVpJRJoOlRmRJsgYw+ur9/G3ZdupqDJEh/gya2gi8dHBVkcTEalzKjMiTUzByXIeW7yBz7blAXB9j0imDOxJoI+XxclEROqHyoxIE7LuQD4Pz8/kSGEp3h52nrqhC/91aTtNK4lIk6YyI9IEuFyGf3y1lxc/2UGVy9C+lR+zhiXS/YIgq6OJiNQ7lRkRN3eiuIwxizewascxAG6Mj+KFW7sToGklEWkmVGZE3NjavScYuTCTXGcZDk87z9zUjSEXR2taSUSaFZUZETfkchleXbWbact34jIQG9aC2cMS6dJa3z0mIs2PyoyImzlWVMboRVl8tes4ALclXMBzt3SnhUO/ziLSPOnVT8SNfLPnOI8szOJYURk+Xnaeu7k7gy6KtjqWiIilVGZE3ECVyzBjxS5mrNyFMdAh3J9XhyfSISLA6mgiIpZTmRFp5PKcpYxcmMmavfkA3HFRGybd1B1fbw+Lk4mINA4qMyKN2Jc7jzHqrSxOlJTj5+3B87d259aENlbHEhFpVFRmRBqhyioX0z/byaur9mAMdI4MYNawROLC/a2OJiLS6KjMiDQyRwtP8ciCLL7d/+O00rCktjx9Q1d8vDStJCJyOiozIo3I59vzGL0oix9OVuDv8OSF23pwU3yU1bFERBo1lRmRRqCiysVLn+zgf77cC0C3qEBmD0ukfWgLi5OJiDR+KjMiFjtccIqH569nfXYBAHclt2PC9V00rSQiUksqMyIWWr41l8cWb6DwVAUBPp5MHdiT/j1aWx1LRMStqMyIWKC80sWUj7fzxtf7AIhvE8SsYYlEh/hZnExExP2ozIg0sIP5J0mfv54NhwoBuPeKGMb164y3p93iZCIi7kllRqQBLdt8lLFvb6SotJIgXy9eGhTPH7pGWB1LRMStqcyINIDSiiomL93GmxkHAEhsG8yMoQm0aalpJRGR86UyI1LP9h8vIW3+erYccQLw56tjeaxvJ7w8NK0kIlIXVGZE6tEHG44w4Z1NFJdV0tLPi2l39OLazuFWxxIRaVJUZkTqQWlFFZM+2MqCb7MBuKR9CK8M7UXrIF+Lk4mIND0qMyJ1bM+xYtLmrWd7ThE2G6RfG8cjfTrgqWklEZF6oTIjUoeWZB7iySWbOVleRai/N9MH9+LKDmFWxxIRadJUZkTqwMnySia+t4XF6w4BkBzbileG9CI80MfiZCIiTZ/KjMh52plbRNq89ezKK8Zmg0f6dODh6zrgYbdZHU1EpFlQmRE5R8YYFq87xNPvbaa0wkVYgINXhvTisgtDrY4mItKsqMyInIOSskr+8u5mlmQeBuDKDqFMH9yLUH+HxclERJofSy+vaN++PTabrcZtypQpNcZs3LiRK6+8Eh8fH6Kjo5k6dapFaUV+tO2okxtnrWZJ5mHsNnisb0fevPsSFRkREYtYfmTm2Wef5b777qu+HxAQUP1np9NJ3759SUlJ4bXXXmPTpk3cc889BAcHc//991sRV5oxYwwLvj3IMx9sobzSRWSgDzOGJnBJTIjV0UREmjXLy0xAQACRkZGnXTdv3jzKy8t544038Pb2plu3bmRlZTFt2jSVGWlQRaUVPLFkMx9sOALANZ3CmHZHL0JaeFucTERELP8UrylTptCqVSsSEhJ48cUXqaysrF6XkZHBVVddhbf3f94wUlNT2bFjBz/88MMZn7OsrAyn01njJnKuNh8u5MaZq/lgwxE87DYm9O/MG3ddrCIjItJIWHpkZuTIkSQmJhISEsI333zDhAkTOHr0KNOmTQMgJyeHmJiYGo+JiIioXteyZcvTPu/kyZOZNGlS/YaXJs8Yw/+uOcBfP9xGeZWLC4J9mTE0gd7tTv/3TkRErFHnR2bGjx//q5N6f3nbvn07AKNHj+aaa66hZ8+ePPDAA7z88svMnDmTsrKy88owYcIECgsLq28HDx6si02TZqTwVAUPzVvP0+9tobzKRUqXCD4aeYWKjIhII1TnR2bGjBnDiBEjfnNMbGzsaZcnJSVRWVnJ/v376dSpE5GRkeTm5tYY89P9M51nA+BwOHA4dGWJnJsNBwtIX7Ceg/mn8PKwMa5fZ+69IgabTR+CJyLSGNV5mQkLCyMs7Ny+iyYrKwu73U54eDgAycnJPPnkk1RUVODl5QXA8uXL6dSp0xmnmETOlTGGN77ez5SPt1FRZWjT0pdZwxLpFR1sdTQREfkNlp0zk5GRwdq1a7n22msJCAggIyODUaNG8V//9V/VRWXYsGFMmjSJe++9l3HjxrF582ZeeeUVpk+fblVsaaIKTpbz2OKNfLbtxyN//bpF8rfbexLk62VxMhER+T2WlRmHw8HChQt55plnKCsrIyYmhlGjRjF69OjqMUFBQXz66aekpaXRu3dvQkNDefrpp3VZttSpdQd+YOSCTA4XnMLbw85fbujCnZe207SSiIibsBljjNUh6pvT6SQoKIjCwkICAwOtjiONhMtl+MdXe3nxkx1UuQztWvkxe1gi3S8IsjqaiIhQ+/dvyz80T8QK+SXljF6UxaodxwC4oWdrJt/WgwAfTSuJiLgblRlpdr7dl8/IBZnkOEvx9rTzzI3dGHpJtKaVRETclMqMNBsul+HVVbuZtnwnLgOxYS2YPSyRLq019Sgi4s5UZqRZOF5cxqi3svhq13EAbk24gL/e0p0WDv0KiIi4O72SS5P3zZ7jPLIwi2NFZfh42Xn25u4M6t1G00oiIk2Eyow0WVUuw8yVu5ixYhcuAx3C/Zk9PJGOEQFWRxMRkTqkMiNNUl5RKY8uzOKbPScAuOOiNky6qTu+3h4WJxMRkbqmMiNNzupdx3n0rUyOF5fj5+3B87d259aENlbHEhGReqIyI01GZZWLV1bsYtbnuzEGOkcGMGtYInHh/lZHExGReqQyI01CTmEpIxdm8u2+fACGJbXl6Ru64uOlaSURkaZOZUbc3uc78hizaAP5JeX4OzyZfFsPboyPsjqWiIg0EJUZcVsVVS5e+nQH//PFXgC6RQUye1gi7UNbWJxMREQaksqMuKXDBacYuSCTdQd+AOCu5HZMuL6LppVERJohlRlxO8u35vLY4g0UnqogwMeTqQN70r9Ha6tjiYiIRVRmxG2UV7r427LtvL56HwDxbYKYOTSRtq38LE4mIiJWUpkRt3Aw/yTpCzLZcLAAgHsuj2F8/854e9qtDSYiIpZTmZFGb9nmo4x9eyNFpZUE+njy0qB4+naLtDqWiIg0Eioz0miVVVbxwkfbeDPjAAAJbYOZOTSBNi01rSQiIv+hMiON0v7jJaQvWM/mw04A/nxVLI+ldsLLQ9NKIiJSk8qMNDofbjzC+H9voriskpZ+Xrx8RzzXdY6wOpaIiDRSKjPSaJRWVPHsh1uZvzYbgIvbt2TG0ARaB/lanExERBozlRlpFPYcKyZt3nq25xRhs8FD11zIqJSOeGpaSUREfofKjFju3czDPLFkEyfLq2jVwpvpg3txVccwq2OJiIibUJkRy5wqr+KZ97fw1vcHAbg0NoQZQxIID/SxOJmIiLgTlRmxxK7cItLmr2dnbjE2G4y8rgMj+3TAw26zOpqIiLgZlRlpcIu/P8hT722mtMJFWICDVwb34rK4UKtjiYiIm1KZkQZTUlbJU+9t5p31hwG4skMo0+7oRViAw+JkIiLizlRmpEFsO+okff569hwrwW6D0X/oyEPXxGHXtJKIiJwnlRmpV8YYFnx7kEkfbKGs0kVEoIMZQxJIim1ldTQREWkiVGak3hSVVvDEks18sOEIANd0CuPlQfG08te0koiI1B2VGakXmw8Xkj5/PftPnMTDbmNsaifuvzJW00oiIlLnVGakThlj+NeaAzz34TbKq1xEBfkwc1gCvduFWB1NRESaKJUZqTPO0grG/3sjSzflAJDSJZyXBsUT7OdtcTIREWnKVGakTmw4WED6gvUczD+Fl4eNcf06c+8VMdhsmlYSEZH6pTIj58UYw5yv9zP5421UVBnatPRl1rBEekUHWx1NRESaiXr7SuLnn3+eyy67DD8/P4KDg087Jjs7mwEDBuDn50d4eDhjx46lsrKyxphVq1aRmJiIw+EgLi6OuXPn1ldkOUsFJ8v58/+u49kPt1JRZejXLZKPRl6pIiMiIg2q3spMeXk5gwYN4sEHHzzt+qqqKgYMGEB5eTnffPMNb775JnPnzuXpp5+uHrNv3z4GDBjAtddeS1ZWFo8++ih/+tOf+OSTT+orttTS+uwfGDBjNZ9uzcXbw86km7rx9/9KJMjXy+poIiLSzNiMMaY+f8DcuXN59NFHKSgoqLH8448/5oYbbuDIkSNEREQA8NprrzFu3DiOHTuGt7c348aN46OPPmLz5s3VjxsyZAgFBQUsW7as1hmcTidBQUEUFhYSGBhYJ9vVXLlchn+u3svUZTuodBnatfJj9rBEul8QZHU0ERFpYmr7/l1vR2Z+T0ZGBj169KguMgCpqak4nU62bNlSPSYlJaXG41JTU8nIyGjQrPKj/JJy/vR/v+eFpdupdBlu6NmaDx++QkVGREQsZdkJwDk5OTWKDFB9Pycn5zfHOJ1OTp06ha+v72mfu6ysjLKysur7TqezLqM3S9/tz2fkgkyOFpbi7WnnmRu7MfSSaF2tJCIiljurIzPjx4/HZrP95m379u31lbXWJk+eTFBQUPUtOjra6khuy+UyzP58N0P+sYajhaXEhrbgvbTLGZbUVkVGREQahbM6MjNmzBhGjBjxm2NiY2Nr9VyRkZF8++23NZbl5uZWr/vpvz8t+/mYwMDAMx6VAZgwYQKjR4+uvu90OlVozsHx4jJGvZXFV7uOA3BrwgX89ZbutHDoin4REWk8zupdKSwsjLCwsDr5wcnJyTz//PPk5eURHh4OwPLlywkMDKRr167VY5YuXVrjccuXLyc5Ofk3n9vhcOBw6MsMz8eavScYuSCTvKIyfLzsPHtTdwZd1EZHY0REpNGpt39iZ2dnk5+fT3Z2NlVVVWRlZQEQFxeHv78/ffv2pWvXrtx5551MnTqVnJwc/vKXv5CWllZdRB544AFmzZrF448/zj333MPKlStZtGgRH330UX3FbvaqXIZZK3fzyoqduAzEhfvz6vBEOkYEWB1NRETktOrt0uwRI0bw5ptv/mr5559/zjXXXAPAgQMHePDBB1m1ahUtWrTgrrvuYsqUKXh6/qdjrVq1ilGjRrF161batGnDU0899btTXb+kS7NrJ6+olEcXZvHNnhMADOrdhkk3d8PPW9NKIiLS8Gr7/l3vnzPTGKjM/L7Vu47z6FuZHC8ux9fLg+dv7c5tiW2sjiUiIs1Ybd+/9U/uZq6yysUrK3Yx6/PdGAOdIwOYNSyRuHB/q6OJiIjUispMM5ZTWMrIhZl8uy8fgKGXtGXijV3x8fKwOJmIiEjtqcw0U6t25DF60QbyS8pp4e3B5IE9uSk+yupYIiIiZ01lppmpqHLx8qc7ee2LPQB0bR3I7OGJxIS2sDiZiIjIuVGZaUYOF5xi5IJM1h34AYA/Jrfjieu7aFpJRETcmspMM/HZ1lwee3sDBScrCHB48rfbe3J9j9ZWxxIRETlvKjNNXHmli6nLtvPP1fsA6NkmiFlDE2nbys/iZCIiInVDZaYJO5h/kvQFmWw4WADAPZfHMK5/JxyemlYSEZGmQ2WmiVq2OYexb2+gqLSSQB9PXhoUT99ukVbHEhERqXMqM01MWWUVk5duZ+43+wFIaBvMzKEJtGmpaSUREWmaVGaakAMnSkifn8mmw4UA/PmqWB5L7YSXh93iZCIiIvVHZaaJ+HDjEcb/exPFZZW09PPi5Tviua5zhNWxRERE6p3KjJsrrajiuQ+3Mm9tNgAXt2/JjKEJtA7ytTiZiIhIw1CZcWN7jhWTNm8923OKsNngoWsuZFRKRzw1rSQiIs2IyoybejfzME8s2cTJ8ipatfBm+uBeXNUxzOpYIiIiDU5lxs2cKq/imfe38Nb3BwG4NDaEV4YkEBHoY3EyERERa6jMuJFduUWkzV/PztxibDYYeV0HRvbpgIfdZnU0ERERy6jMuInF3x/k6fe2cKqiirAAB68M7sVlcaFWxxIREbGcykwjV1JWyVPvbead9YcBuCIulOmDexEW4LA4mYiISOOgMtOIbc9xkjZvPXuOlWC3weg/dOTBa+I0rSQiIvIzKjONkDGGt747yMT3t1BW6SIi0MGMIQkkxbayOpqIiEijozLTyBSXVfLEO5t4f8MRAK7uGMa0O+Jp5a9pJRERkdNRmWlEthwpJH1+JvuOl+BhtzE2tRP3XxmLXdNKIiIiZ6Qy0wgYY/jX2mye+3Ar5ZUuooJ8mDksgd7tQqyOJiIi0uipzFjMWVrBhH9v4qNNRwFI6RLOi7fH07KFt8XJRERE3IPKjIU2HiogfX4m2fkn8fKwMa5fZ+69IgabTdNKIiIitaUyYwFjDHO/2c8LS7dRUWVo09KXWcMS6RUdbHU0ERERt6My08AKT1Yw9u0NfLo1F4DUbhFMvT2eIF8vi5OJiIi4J5WZBpSZ/QPp8zM5XHAKbw87Tw7owh+T22laSURE5DyozDQAl8vw+up9/G3Zdipdhnat/Jg1NJEebYKsjiYiIuL2VGbq2Q8l5YxZvIGV2/MAGNCzNZNv60Ggj6aVRERE6oLKTD36fn8+Dy/I5GhhKd6edp6+oSvDk9pqWklERKQOqczUA5fL8NqXe3j5051UuQwxoS2YNSyBblGaVhIREalrKjN17HhxGaMXbeDLnccAuLlXFM/f2gN/h/5Xi4iI1Ae9w9ahNXtPMHJBJnlFZfh42Zl0UzfuuCha00oiIiL1SGWmDlS5DLNW7uaVFTtxGYgL92f2sEQ6RQZYHU1ERKTJs9fXEz///PNcdtll+Pn5ERwcfNoxNpvtV7eFCxfWGLNq1SoSExNxOBzExcUxd+7c+op8TvKKSvnjG2uZ/tmPReb23m14P/1yFRkREZEGUm9HZsrLyxk0aBDJycm8/vrrZxw3Z84c+vXrV33/58Vn3759DBgwgAceeIB58+axYsUK/vSnP9G6dWtSU1PrK3qtfb37OI8szOJ4cRm+Xh789ZbuDOzdxupYIiIizUq9lZlJkyYB/O6RlODgYCIjI0+77rXXXiMmJoaXX34ZgC5durB69WqmT59ueZk5VV5VXWQ6RwYwa1giceH+lmYSERFpjuptmqm20tLSCA0N5ZJLLuGNN97AGFO9LiMjg5SUlBrjU1NTycjI+M3nLCsrw+l01rjVNV9vD16+I56hl7Tl3bTLVWREREQsYukJwM8++yzXXXcdfn5+fPrppzz00EMUFxczcuRIAHJycoiIiKjxmIiICJxOJ6dOncLX1/e0zzt58uTqI0P16eqOYVzdMazef46IiIic2VkdmRk/fvxpT9r9+W379u21fr6nnnqKyy+/nISEBMaNG8fjjz/Oiy++eNYb8UsTJkygsLCw+nbw4MHzfk4RERFpnM7qyMyYMWMYMWLEb46JjY095zBJSUk899xzlJWV4XA4iIyMJDc3t8aY3NxcAgMDz3hUBsDhcOBwOM45h4iIiLiPsyozYWFhhIXV37RKVlYWLVu2rC4iycnJLF26tMaY5cuXk5ycXG8ZRERExL3U2zkz2dnZ5Ofnk52dTVVVFVlZWQDExcXh7+/PBx98QG5uLpdeeik+Pj4sX76cF154gccee6z6OR544AFmzZrF448/zj333MPKlStZtGgRH330UX3FFhERETdjMz+/fKgOjRgxgjfffPNXyz///HOuueYali1bxoQJE9i9ezfGGOLi4njwwQe57777sNv/cyrPqlWrGDVqFFu3bqVNmzY89dRTvzvV9UtOp5OgoCAKCwsJDAw8300TERGRBlDb9+96KzONicqMiIiI+6nt+7flnzMjIiIicj5UZkRERMStqcyIiIiIW1OZEREREbemMiMiIiJuTWVGRERE3JrKjIiIiLg1S781u6H89FE6TqfT4iQiIiJSWz+9b//eR+I1izJTVFQEQHR0tMVJRERE5GwVFRURFBR0xvXN4hOAXS4XR44cISAgAJvNVmfP63Q6iY6O5uDBg032k4Wb+jZq+9xfU99GbZ/7a+rbWJ/bZ4yhqKiIqKioGl919EvN4siM3W6nTZs29fb8gYGBTfIv6M819W3U9rm/pr6N2j7319S3sb6277eOyPxEJwCLiIiIW1OZEREREbemMnMeHA4HEydOxOFwWB2l3jT1bdT2ub+mvo3aPvfX1LexMWxfszgBWERERJouHZkRERERt6YyIyIiIm5NZUZERETcmsqMiIiIuDWVmVp6/vnnueyyy/Dz8yM4OPi0Y7KzsxkwYAB+fn6Eh4czduxYKisra4xZtWoViYmJOBwO4uLimDt3bv2HPwerVq3CZrOd9vbdd98BsH///tOuX7NmjcXpa6d9+/a/yj5lypQaYzZu3MiVV16Jj48P0dHRTJ061aK0Z2f//v3ce++9xMTE4Ovry4UXXsjEiRMpLy+vMcad9x/A7Nmzad++PT4+PiQlJfHtt99aHemcTJ48mYsvvpiAgADCw8O55ZZb2LFjR40x11xzza/21QMPPGBR4rP3zDPP/Cp/586dq9eXlpaSlpZGq1at8Pf3Z+DAgeTm5lqY+Oyc7vXEZrORlpYGuOf++/LLL7nxxhuJiorCZrPx7rvv1lhvjOHpp5+mdevW+Pr6kpKSwq5du2qMyc/PZ/jw4QQGBhIcHMy9995LcXFx3Yc1UitPP/20mTZtmhk9erQJCgr61frKykrTvXt3k5KSYjIzM83SpUtNaGiomTBhQvWYvXv3Gj8/PzN69GizdetWM3PmTOPh4WGWLVvWgFtSO2VlZebo0aM1bn/6059MTEyMcblcxhhj9u3bZwDz2Wef1RhXXl5ucfraadeunXn22WdrZC8uLq5eX1hYaCIiIszw4cPN5s2bzYIFC4yvr6/5n//5HwtT187HH39sRowYYT755BOzZ88e895775nw8HAzZsyY6jHuvv8WLlxovL29zRtvvGG2bNli7rvvPhMcHGxyc3OtjnbWUlNTzZw5c8zmzZtNVlaWuf76603btm1r/H28+uqrzX333VdjXxUWFlqY+uxMnDjRdOvWrUb+Y8eOVa9/4IEHTHR0tFmxYoX5/vvvzaWXXmouu+wyCxOfnby8vBrbtnz5cgOYzz//3Bjjnvtv6dKl5sknnzTvvPOOAcySJUtqrJ8yZYoJCgoy7777rtmwYYO56aabTExMjDl16lT1mH79+pn4+HizZs0a89VXX5m4uDgzdOjQOs+qMnOW5syZc9oys3TpUmO3201OTk71sr///e8mMDDQlJWVGWOMefzxx023bt1qPG7w4MEmNTW1XjPXhfLychMWFmaeffbZ6mU/vRlmZmZaF+w8tGvXzkyfPv2M61999VXTsmXL6v1njDHjxo0znTp1aoB0dW/q1KkmJiam+r67779LLrnEpKWlVd+vqqoyUVFRZvLkyRamqht5eXkGMF988UX1squvvto88sgj1oU6TxMnTjTx8fGnXVdQUGC8vLzM4sWLq5dt27bNACYjI6OBEtatRx55xFx44YXV//hz9/33yzLjcrlMZGSkefHFF6uXFRQUGIfDYRYsWGCMMWbr1q0GMN999131mI8//tjYbDZz+PDhOs2naaY6kpGRQY8ePYiIiKhelpqaitPpZMuWLdVjUlJSajwuNTWVjIyMBs16Lt5//31OnDjB3Xff/at1N910E+Hh4VxxxRW8//77FqQ7d1OmTKFVq1YkJCTw4osv1pgWzMjI4KqrrsLb27t6WWpqKjt27OCHH36wIu55KSwsJCQk5FfL3XH/lZeXs27duhq/T3a7nZSUFLf4ffo9hYWFAL/aX/PmzSM0NJTu3bszYcIETp48aUW8c7Zr1y6ioqKIjY1l+PDhZGdnA7Bu3ToqKipq7M/OnTvTtm1bt9yf5eXl/Otf/+Kee+6p8eXG7r7/fm7fvn3k5OTU2GdBQUEkJSVV77OMjAyCg4O56KKLqsekpKRgt9tZu3ZtneZpFl802RBycnJqFBmg+n5OTs5vjnE6nZw6dQpfX9+GCXsOXn/9dVJTU2t8Yae/vz8vv/wyl19+OXa7nX//+9/ccsstvPvuu9x0000Wpq2dkSNHkpiYSEhICN988w0TJkzg6NGjTJs2Dfhxf8XExNR4zM/3acuWLRs887navXs3M2fO5KWXXqpe5s777/jx41RVVZ3292n79u0WpaobLpeLRx99lMsvv5zu3btXLx82bBjt2rUjKiqKjRs3Mm7cOHbs2ME777xjYdraS0pKYu7cuXTq1ImjR48yadIkrrzySjZv3kxOTg7e3t6/Oh8xIiKi+vXTnbz77rsUFBQwYsSI6mXuvv9+6af9crrfwZ+/54WHh9dY7+npSUhISJ3v12ZdZsaPH8/f/va33xyzbdu2Giepubtz2eZDhw7xySefsGjRohrjQkNDGT16dPX9iy++mCNHjvDiiy9a9mZ4Ntv38+w9e/bE29ubP//5z0yePLnRfuz4uey/w4cP069fPwYNGsR9991Xvbwx7j+BtLQ0Nm/ezOrVq2ssv//++6v/3KNHD1q3bk2fPn3Ys2cPF154YUPHPGv9+/ev/nPPnj1JSkqiXbt2LFq0qFH/Q+5cvP766/Tv35+oqKjqZe6+/xq7Zl1mxowZU6M5n05sbGytnisyMvJXV1L8dCZ+ZGRk9X9/eXZ+bm4ugYGBDfbLfC7bPGfOHFq1alWrN7ikpCSWL19+PhHPy/ns06SkJCorK9m/fz+dOnU64/6C/+zThna223fkyBGuvfZaLrvsMv7xj3/87vNbvf9qKzQ0FA8Pj9PuH6v2TV1IT0/nww8/5Msvv6xxFPR0kpKSgB+Purnjm2FwcDAdO3Zk9+7d/OEPf6C8vJyCgoIaR2fccX8eOHCAzz777HePuLj7/vtpv+Tm5tK6devq5bm5ufTq1at6TF5eXo3HVVZWkp+fX+f7tVmXmbCwMMLCwurkuZKTk3n++efJy8urPqy2fPlyAgMD6dq1a/WYpUuX1njc8uXLSU5OrpMMtXG222yMYc6cOfzxj3/Ey8vrd8dnZWXV+Ivd0M5nn2ZlZWG326v3X3JyMk8++SQVFRXV2758+XI6depk2RTT2Wzf4cOHufbaa+nduzdz5szBbv/9U+Ss3n+15e3tTe/evVmxYgW33HIL8OP0zIoVK0hPT7c23DkwxvDwww+zZMkSVq1a9avpzdPJysoCcIv9dTrFxcXs2bOHO++8k969e+Pl5cWKFSsYOHAgADt27CA7O7tBXx/rwpw5cwgPD2fAgAG/Oc7d919MTAyRkZGsWLGiurw4nU7Wrl3Lgw8+CPz4GlpQUMC6devo3bs3ACtXrsTlclWXuTpTp6cTN2EHDhwwmZmZZtKkScbf399kZmaazMxMU1RUZIz5z6XZffv2NVlZWWbZsmUmLCzstJdmjx071mzbts3Mnj270V6a/ZPPPvvMAGbbtm2/Wjd37lwzf/58s23bNrNt2zbz/PPPG7vdbt544w0Lkp6db775xkyfPt1kZWWZPXv2mH/9618mLCzM/PGPf6weU1BQYCIiIsydd95pNm/ebBYuXGj8/Pzc4tLsQ4cOmbi4ONOnTx9z6NChGpeD/sSd958xP16a7XA4zNy5c83WrVvN/fffb4KDg2tcUeguHnzwQRMUFGRWrVpVY1+dPHnSGGPM7t27zbPPPmu+//57s2/fPvPee++Z2NhYc9VVV1mcvPbGjBljVq1aZfbt22e+/vprk5KSYkJDQ01eXp4x5sdLs9u2bWtWrlxpvv/+e5OcnGySk5MtTn12qqqqTNu2bc24ceNqLHfX/VdUVFT9XgeYadOmmczMTHPgwAFjzI+XZgcHB5v33nvPbNy40dx8882nvTQ7ISHBrF271qxevdp06NBBl2Zb6a677jLAr24/fYaAMcbs37/f9O/f3/j6+prQ0FAzZswYU1FRUeN5Pv/8c9OrVy/j7e1tYmNjzZw5cxp2Q87S0KFDz/hZD3PnzjVdunQxfn5+JjAw0FxyySU1Lq1szNatW2eSkpJMUFCQ8fHxMV26dDEvvPCCKS0trTFuw4YN5oorrjAOh8NccMEFZsqUKRYlPjtz5sw57d/Xn//7xZ33309mzpxp2rZta7y9vc0ll1xi1qxZY3Wkc3KmffXT60N2dra56qqrTEhIiHE4HCYuLs6MHTu20X9Oyc8NHjzYtG7d2nh7e5sLLrjADB482Ozevbt6/alTp8xDDz1kWrZsafz8/Mytt95ao3y7g08++cQAZseOHTWWu+v++/zzz0/79/Kuu+4yxvx4efZTTz1lIiIijMPhMH369PnVtp84ccIMHTrU+Pv7m8DAQHP33XdXHwSoSzZjjKnbYz0iIiIiDUefMyMiIiJuTWVGRERE3JrKjIiIiLg1lRkRERFxayozIiIi4tZUZkRERMStqcyIiIiIW1OZEREREbemMiMiIiJuTWVGRERE3JrKjIiIiLg1lRkRERFxa/8PQUNH5fPMN6wAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "# Skriv koden din her\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Fritt kodefelt (Du kan bruke  dette for å teste egen kode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Skriv koden din her"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.6 64-bit",
+   "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": "3.11.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}