Demonstration Workbook 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demonstration Workbook 2 \n",
    "\n",
    "## Outline of topics:\n",
    "\n",
    "1. Introducing Python packages/modules and third party libraries\n",
    "2. Using Python's file input output to read a `csv` file containing average US corn yields by year.\n",
    "3. Using Python's string methods to process data read in from text files, etc.\n",
    "\n",
    "\n",
    "## Some useful background material:\n",
    "\n",
    "The <a href=\"https://the-examples-book.com/book/introduction\" target=\"_blank\">Purdue Data Mine Examples Book</a> contains many useful chapters on data science. While they have not been directly designed for this class, they may be useful. You will not need to use scholar to perform the exercises of this class so don't worry about that part. Here is a direct link to the <a href=\"https://the-examples-book.com/book/python/introduction\" target=\"_blank\">Python chapter.</a>\n",
    "\n",
    "\n",
    "Additional useful links for Python include:\n",
    "\n",
    "<a href=\"https://docs.python.org/3/\" target=\"_blank\">Python 3.9.4 documentation</a>\n",
    "\n",
    "The <a href=\"https://pypi.org/\" target=\"_blank\">Python Package Index</a> (This contains many of the useful Python \"add-on\" packages such as the math package)\n",
    "\n",
    "The <a href=\"https://numpy.org/\" target=\"_blank\">Numpy Package</a> (This contains specialized array (vector and matrix) routines. Numpy stands for \"Numerical Python\")\n",
    "\n",
    "United States Department of Agriculture: <a href=\"https://quickstats.nass.usda.gov/\" target=\"_blank\">Quick Stats</a> (The USDA's National Ag Statistics Service -- go here and familiarize yourself with the available data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Discussion 1* : The math package\n",
    "Python provides many modules designed for specialized programming tasks. It has its own built in packages. math is one of Pythons builtin packages. you can find the complete list at [The Python Standard Library](https://docs.python.org/3/library/index.html) \n",
    "\n",
    "The math package contains trigonometric, exponential, logarithmic, hyperbolic, and special functions. It also contains a number of useful constants such as `pi` and `e`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the value of Pi from math library:  3.141592653589793\n",
      "2 raised to the power 3 =  8.0\n",
      "rounding up of 3.2 =  4\n",
      "rounding down of 3.9 =  3\n",
      "factorial of 5 =  120\n"
     ]
    }
   ],
   "source": [
    "import math # This will import math library\n",
    "P = math.pi #assigning the value of pi to P\n",
    "print(\"the value of Pi from math library: \",P)\n",
    "power_result = math.pow(2, 3)     # Exponentiation: 8.0\n",
    "print(\"2 raised to the power 3 = \",power_result)\n",
    "ceil_result = math.ceil(3.2)      # Ceiling function: 4.0\n",
    "print(\"rounding up of 3.2 = \",ceil_result)\n",
    "floor_result = math.floor(3.9)    # Floor function: 3.0\n",
    "print(\"rounding down of 3.9 = \",floor_result)\n",
    "factorial_result = math.factorial(5)  # Factorial: 120\n",
    "print(\"factorial of 5 = \",factorial_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use the math package to create something interesting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.0\n",
      "0.12566370614359174 0.12533323356430426\n",
      "0.25132741228718347 0.2486898871648548\n",
      "0.37699111843077515 0.3681245526846779\n",
      "0.5026548245743669 0.4817536741017153\n",
      "0.6283185307179586 0.5877852522924731\n",
      "0.7539822368615503 0.6845471059286886\n",
      "0.8796459430051421 0.7705132427757893\n",
      "1.0053096491487339 0.8443279255020151\n",
      "1.1309733552923256 0.9048270524660196\n",
      "1.2566370614359172 0.9510565162951535\n",
      "1.382300767579509 0.9822872507286886\n",
      "1.5079644737231006 0.9980267284282716\n",
      "1.6336281798666925 0.9980267284282716\n",
      "1.7592918860102842 0.9822872507286887\n",
      "1.8849555921538756 0.9510565162951536\n",
      "2.0106192982974678 0.9048270524660195\n",
      "2.1362830044410597 0.844327925502015\n",
      "2.261946710584651 0.7705132427757893\n",
      "2.3876104167282426 0.6845471059286888\n",
      "2.5132741228718345 0.5877852522924732\n",
      "2.6389378290154264 0.4817536741017152\n",
      "2.764601535159018 0.36812455268467814\n",
      "2.8902652413026093 0.24868988716485524\n",
      "3.015928947446201 0.12533323356430454\n",
      "3.141592653589793 1.2246467991473532e-16\n",
      "3.267256359733385 -0.12533323356430429\n",
      "3.3929200658769765 -0.24868988716485457\n",
      "3.5185837720205684 -0.3681245526846779\n",
      "3.6442474781641603 -0.4817536741017154\n",
      "3.7699111843077513 -0.5877852522924727\n",
      "3.895574890451343 -0.6845471059286884\n",
      "4.0212385965949355 -0.7705132427757894\n",
      "4.1469023027385274 -0.8443279255020153\n",
      "4.272566008882119 -0.9048270524660198\n",
      "4.39822971502571 -0.9510565162951535\n",
      "4.523893421169302 -0.9822872507286887\n",
      "4.649557127312894 -0.9980267284282716\n",
      "4.775220833456485 -0.9980267284282716\n",
      "4.900884539600077 -0.9822872507286887\n",
      "5.026548245743669 -0.9510565162951536\n",
      "5.15221195188726 -0.9048270524660199\n",
      "5.277875658030853 -0.844327925502015\n",
      "5.403539364174444 -0.7705132427757896\n",
      "5.529203070318036 -0.684547105928689\n",
      "5.654866776461628 -0.5877852522924734\n",
      "5.780530482605219 -0.4817536741017161\n",
      "5.906194188748811 -0.36812455268467786\n",
      "6.031857894892402 -0.24868988716485535\n",
      "6.157521601035994 -0.12533323356430465\n"
     ]
    }
   ],
   "source": [
    "# This short program will print a table with 50 rows where\n",
    "# each row contains an argument and the sine of the argument.\n",
    "# The sine function will trace on complete period of length\n",
    "# 2*pi.\n",
    "N = 50\n",
    "for k in range(N):\n",
    "    t = 2*P*k/N\n",
    "    print(t, math.sin(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table above is difficult to read because it is not well formatted. Python has tools to simplify formating with the print command. There is a nice explanation in the Data Mind notes mentioned in the introduction. One prefered method uses `f-strings`, which stands for **format string**. They are indicated by preceeding the string with `f` or `F`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of pi is approximately 3.141592653589793\n",
      "\n",
      "The value of pi is approximately 3.142.\n"
     ]
    }
   ],
   "source": [
    "# Compare two ways of printing pi\n",
    "print('The value of pi is approximately', P)\n",
    "print()\n",
    "print(f'The value of pi is approximately {math.pi:.3f}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a prettier (more readable) sinewave table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0.00     0.000\n",
      " 0.13     0.125\n",
      " 0.25     0.249\n",
      " 0.38     0.368\n",
      " 0.50     0.482\n",
      " 0.63     0.588\n",
      " 0.75     0.685\n",
      " 0.88     0.771\n",
      " 1.01     0.844\n",
      " 1.13     0.905\n",
      " 1.26     0.951\n",
      " 1.38     0.982\n",
      " 1.51     0.998\n",
      " 1.63     0.998\n",
      " 1.76     0.982\n",
      " 1.88     0.951\n",
      " 2.01     0.905\n",
      " 2.14     0.844\n",
      " 2.26     0.771\n",
      " 2.39     0.685\n",
      " 2.51     0.588\n",
      " 2.64     0.482\n",
      " 2.76     0.368\n",
      " 2.89     0.249\n",
      " 3.02     0.125\n",
      " 3.14     0.000\n",
      " 3.27    -0.125\n",
      " 3.39    -0.249\n",
      " 3.52    -0.368\n",
      " 3.64    -0.482\n",
      " 3.77    -0.588\n",
      " 3.90    -0.685\n",
      " 4.02    -0.771\n",
      " 4.15    -0.844\n",
      " 4.27    -0.905\n",
      " 4.40    -0.951\n",
      " 4.52    -0.982\n",
      " 4.65    -0.998\n",
      " 4.78    -0.998\n",
      " 4.90    -0.982\n",
      " 5.03    -0.951\n",
      " 5.15    -0.905\n",
      " 5.28    -0.844\n",
      " 5.40    -0.771\n",
      " 5.53    -0.685\n",
      " 5.65    -0.588\n",
      " 5.78    -0.482\n",
      " 5.91    -0.368\n",
      " 6.03    -0.249\n",
      " 6.16    -0.125\n"
     ]
    }
   ],
   "source": [
    "# Prettier sine wave table. Do you know why it is prettier?\n",
    "N = 50\n",
    "for k in range(N):\n",
    "    t = 2*P*k/N\n",
    "    print(f'{t: 1.2f}', '  ', f'{math.sin(t): .3f}')# 1.2f means 1 digit before decimal and 2 digits after decimal.  .3f means 3 digits after decimal\n",
    "\n",
    "#pretty data output is consistent and easy to read. It is also easier to spot errors in the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Discussion 2* : The NumPy and matplotlib Package  \n",
    "You can find the list of python packages at <a href=\"https://pypi.org\" target=\"_blank\">The Python Package Index</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All data manipulated by a computer is represented in binary. In otherwords, via one method or another, all data -- temperature sensor readings, hourly barometric pressure from your Davis weather station, an audio file, images from your Bushnell game camera, a yield map -- are represented as arrays of numbers.\n",
    "\n",
    "**NumPy (Numerical Python)** provides an efficient interface to store and compute on dense data buffers. NumPy arrays are much more efficient than Python's built-in list data type.\n",
    "\n",
    "See: <a href=\"http://www.numpy.org\" target=\"_blank\">The Numpy Package</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the numpy package. This command allows us to refer to numpy\n",
    "# commands using the alias \"np\".\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]),\n",
       " array([ 3,  1,  6,  5,  4, 11, -1,  1,  2,  6]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array from a list of numbers.Manually create a numpy array with 10 elements\n",
    "x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "y = np.array([3, 1, 6, 5, 4, 11, -1, 1, 2, 6])\n",
    "x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "         0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "         1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "         1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "         2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "         2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "         3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "         3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "         4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "         4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "         5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "         5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "         6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "         6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "         7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "         7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "         8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "         8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "         9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "         9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ]),\n",
       " 0.0,\n",
       " 10.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr = np.linspace(0, 10, 100)# 100 points between 0 and 10. Generating it automatically.\n",
    "xr #print the array.but it is not printing the array. Note: this output method works only if its the last line of the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the min and max values of array xr  0.0 10.0\n"
     ]
    }
   ],
   "source": [
    "print(\"the min and max values of array xr \", xr.min(), xr.max()) #print the min and max values of array xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the 50 th percentile of the array xr is :  5.0\n"
     ]
    }
   ],
   "source": [
    "print(\"the 50 th percentile of the array xr is : \",np.percentile(xr,50))#print the 50 th percentile of the array xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr2d = xr.reshape(10,10)#reshape the array xr to a 10x10 array\n",
    "xr2d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d=np.array([[1,2,3],[4,5,6],[7,8,9]])#create a 2d array\n",
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d.shape#check the shape of the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4 7]\n",
      " [2 5 8]\n",
      " [3 6 9]]\n"
     ]
    }
   ],
   "source": [
    "arr2dT=arr2d.T #transpose of arr2d\n",
    "print(arr2dT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  8, 21],\n",
       "       [ 8, 25, 48],\n",
       "       [21, 48, 81]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2Dmul=arr2d*arr2dT #element wise multiplication\n",
    "arr2Dmul"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**matplotlib**\n",
    "This is the most popular graphing library in Python.   \n",
    "You can refer to [matplotlib documentation](https://matplotlib.org/stable/users/index) for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25076190810>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pzUzbtm0BoNS7+9qu49vcfffduPvuu/Hrr7+ymSEiIqICyqxmysjIwOHDhwt2KSVyN4MHD8aOHTt4ickgWrdujR07dvASE5EBKDEBODc3F9OnT0eDBg1K7GJqY7VacfHiRfj6+hbcwZiIiIiMLT8/H9euXUPt2rUd3sLB8M1MVlYWJk+ejOvXr2PmzJll/lZ68eJFPP300y6ujoiIiLSwevXqMjf/vBFDNzPp6emIiIhA9erVMWPGjHK367btTXH69GnUqFHDVSW6TPfu3REXFye7DF0wm5qYTV1mzsds6klLS8Ptt99e5h3f7SG1mbHd9TU3NxeA2CXVy8sLlSpVQmpqKl5//XU0aNAAb775ZomdU4uzXVqqUaOGKZuZFi1amDIXwGyqYjZ1mTkfs6nLmSkiUpuZrVu3IioqquDjXr16AQBmz56NhIQE/Pnnnzh79myRTaTuuuuuIl/jLm50gz6VMZuamE1dZs7HbO5JajPTo0ePMncXbd26dZFb3Lu7sm5IZwbMpiZmU5eZ8zGbe1Jmaba7q8j9cVTDbGpiNnWZOR+zuSc2M4pISEiQXYJumE1NzKYuM+djNvck9d5MWsrIyMBjjz2GK1eumHqCFBERVUxWVhZycnJkl+HWKleuDB8fn1I/l5aWhpo1a+Lbb79FtWrVHHp9Qy/NJiIickZWVhbuuOMOpKSkyC7FrQUEBODEiRNlNjTOYjNDRESmlZOTg5SUFNPuQaYC2z4yOTk5bGbcXUREBN59913ZZeiC2dTEbOoyc76yspl1DzISOGdGESkpKQgICJBdhi6YTU3Mpi4z5yuezTYfw6w/G1Rwo2OgxZwZrmZShFn/4QGYTVXMpi4z5zNzNiobmxkiIiJSGpsZRZjx5mI2zKYmZlOXmfOZORuVjc2MIlJTU2WXoBtmUxOzqcvM+cycrbCpU6dW+MaMJ0+ehIeHBxYuXKhZHR4eHpg6dapmr+coNjOKeOaZZ2SXoBtmUxOzqcvM+cycjcrGZoaIiIiUxmZGEVlZWbJL0A2zqYnZ1GXmfGbOVp6PP/4Y7du3R61ateDv74927dph48aNpT43JycH4eHhuOWWW1C1alU89thjOHnyZInnLViwACEhIfDx8cHNN9+MF198EZcuXdI5iWPYzCgiMjJSdgm6YTY1MZu6zJzPzNnKc/LkSQwdOhRr1qxBdHQ07r33Xjz22GOIjY0t8dx3330Xf/zxB5YsWYL58+dj//796NatG3Jzcwue88Ybb+DVV1/FI488gm+++QYffPABYmNj0bNnT+Tl5bkyml24aZ4isrKydNsGWjZmUxOzqcvM+Ypnq8imefn5QHq6vvVVrw5UcN5uqaZOnYpp06YhP7/kj3Cr1Qqr1YpevXrB19cXMTExAETDc8cdd6Bly5b4/fff4ekpxjN2796Nhx56CAsXLsSLL76IkydPokmTJpgyZQomT55c8Lq253399dd44oknAIgJwFOmTCl3ErArNs3j7QwUYdZ/eABmUxWzqcvM+ZzJlp4O1KypYTGluHIF0OP37f3792PKlCnYt28fzp8/X9DktGjRosRz+/TpU9DIAMCDDz6I2267DXv37sWLL76IrVu3wmq1YsCAAbh+/XrB8+6//35Ur14dO3fuLGhmjILNDBEREcSoyZUr+r+H1k6fPo0uXbogKCgIH330ERo0aIBKlSph0qRJOHz4cInn161bt9THkpOTAQD//PMPAKBp06alvt/Fixc1rF4bbGYUER0dbdolh8ymJmZTl5nzOZPNw0OfURO9xcbG4sqVK1i9ejVuu+22gsczMzNLff65c+dKfax169YAgNq1awMAtmzZgptuuqnEc22fNxI2M4rw9/eXXYJumE1NzKYuM+czc7ay2JoWb2/vgseOHj2K3bt3F2lubNauXYupU6cWmTNz5swZtG/fHgDQtWtXeHp64tSpU+jatasLEjiPzYwiunfvLrsE3TCbmphNXWbOZ+ZsZXnkkUdQqVIlDBo0COPGjcPff/+NKVOmoEGDBrBarSWen56ejieeeAIjRozA+fPnERERgWbNmmHQoEEAgCZNmmDixIkYOXIkjhw5gk6dOsHHxwenT5/G1q1bMXToUISGhro6ZrnYzBARFZORATi4qILI5YKDg/HFF19g8uTJ6N27N5o0aYL33nsPsbGx+O6770o8PyIiAseOHcPgwYORkZGB0NBQfPzxx0VGdmbOnImWLVti/vz5mD9/Pjw8PHD77bejS5cuaNasmQvT2YdLsxWRkpJi2lvbM5uazJrt8GGgbdsUXLwYgMqVZVejD7MeO6BktooszSZ9uGJpNjfNU8TcuXNll6AbZlOTWbN9+y1w9epcHD0quxL9mPXYAebORmXjyAwRUSEPPwzs2AF8+SVg0gU/boUjM/JxZIaIyIXS0oAffgA6dwYOHZJdDRHZi80MEdH/97//AU2bAo8+CiQmyq6GiOzFZkYRFotFdgm6YTY1mTHbpk1Az57AX39ZTD0yY8ZjZ2PmbFQ2NjOKCAkJkV2CbphNTWbLlp8PbN4M9OoFdOkSgj/+ALKzZVelD7Mdu8LMnI3Kxn1mFNGuXTvZJeiG2dRktmwHDwKpqUCHDkDlyu3g6wscPQq0aiW7Mu2Z7dgVZuZsVDY2M0REEKMyXboAVaqIj4OCxCRgMzYz7igtLU12CW7LFd97NjOKSEpKQmBgoOwydMFsajJbtk2bgP79xf8nJSUhODjQtPNmzHbsCiuerXLlyggICMDtt98usSoKCAhAZR13oWQzo4iYmBjT/uPDbGoyU7YrV4A9e4Dly8XHMTExCA4OxO7dcuvSi5mOXXHFs/n4+ODEiRPIycmRWJU2Zs+ejbFjx8ouwyGVK1eGj4+Pbq/PTfOIyO2tXQtMmVJ0b5nYWGDMGCApSVpZRG6Bm+YREWnAtoqpsOBg4Ngx865oIjITNjNE5NZsS7J79iz6+G23AVWrAkeOyKmLiOzHZkYRUVFRskvQDbOpySzZEhKA9HTgoYf+fSwqKgoeHv+uaDIbsxy70jCbe2Izo4iwsDDZJeiG2dRklmybNgGPPAIUXmhhyxYcbM5mxizHrjTM5p7YzCjCrCsPAGZTlVmylTZfxpbNrM2MWY5daZjNPbGZISK3dfkysHdvyfkyNmZtZojMhs2MIuLj42WXoBtmU5MZsm3dKubF3HZb0cdt2YKDgePHgawsCcXpyAzHrizM5p7YzCgiISFBdgm6YTY1mSGb7S7Zxdmy1a8P+PmZb0WTGY5dWZjNPXHTPCJyS1YrUK8eEB0NdOpU9vMeeAAYORJ49lnX1UbkTrhpHhGRg379Fbh2TTQr5eG8GSLjYzNDRG5p82aga1fA27v855l1rxkiM5HazGzfvh2vvfYaHn30UYSGhiIvL6/I548fP47Ro0ejZ8+e6NOnD5YuXYr8fFNcFauwiIgI2SXohtnUpHq2subLAEWzmXFkRvVjVx5mc09S58z89NNPSE9PR3Z2Nj744ANs27YNXl5eAIDMzEw899xz6NGjBwYNGoTk5GRMnDgRTz/9NPr27Vvitcw+ZyYlJQUBAQGyy9AFs6lJ5WwXLwJ16wKnTol5M8UVzpacDNx+O5CRAfj6urhQnah87G6E2dSj/JyZtm3bokuXLqhXyr8mO3fuhNVqxZAhQ1ClShU0btwYzzzzDNavX+/6Qg3AjCewDbOpSeVsW7YAd95ZeiMDFM1Wrx5Qo4a57p6t8rG7EWZzT4adM3P8+HE0bdq0YKQGELsfnj17FhkZGRIrIyLVlXZjybJ4eIhLTYmJ+tZERI4zbDOTkZEBPz+/Io9Vr14dgLgE5W7i4uJkl6AbZlOTqtmsViA2tuQtDAorns1s82ZUPXb2YDb3ZNhmplq1arh69WqRx9LT0wEAVatWLfPr+vbti/DwcISHh2PDhg0IDw9HVqHtO6Ojo4ucECkpKSUmVVksliI7LSYlJZW4W2lUVBSSCo07x8fHw2KxFHlOREQEUlJSCj6Oi4tDdHR0wcdZWVl215eammro+gqraH2pqamGrg9w/Ps3b948Q9fnzPfPdk4atT6b4vXt3w+kp0fhppvKri81NbVIfcHBwI4d6p1/ZdVnO3ZGrc/GkfpSU1MNXR/g+Pdv06ZNhq6vIt+/uLg4jBgxAm3btkVkZCScZYhN83777TeMHTu2yATg2NhYWCwWrF27tuCxdevW4auvvsIXX3xR4jXMPgGYiLQxfTrw++/A6tX2f83WrcDLLwPHjulXF5G7Un4CcF5eHnJycpCbmwsAyMnJQU5ODqxWKzp27AhPT08sWbIE2dnZOHHiBFavXs1boBORU8pbkl2W4GDgzz8BN7zCTaQEqc3M1q1b0b17d0yYMAEA0KtXL3Tv3h0HDhxA1apV8f777+PAgQMICwvDuHHj0LNnz1KXZbuDLLPd6a4QZlOTitkuXAB+/hno0aP85xXPduutQM2a5lnRpOKxsxezuSepzUyPHj2wY8eOEn9at24NAGjSpAnmzZuH2NhYfPXVVxg8eDA8PDxkliyNFtcUjYrZ1KRitrg44K67RHNSnuLZbCuazDIJWMVjZy9mc0+GmDOjBbPPmcnKyoKPj4/sMnTBbGpSMdvAgUCjRsCMGeU/r7RsI0YAtWoB776rX32uouKxsxezqUf5OTNkPzOewDbMpibVsuXliSXZ9syXKS2bmUZmVDt2FcFs7onNDBG5hZ9/FnvM3H+/Y19vpmaGyGzYzCii8Pp9s2E2NamWbdMmoFs3oFKlGz+3tGxBQcCJE+ZY0aTasasIZnNPbGYU4e/vL7sE3TCbmlTLtnlz+bv+FlZatoAAwN8fOHxY07KkUO3YVQSzuSdOACYi0/vnH7GC6exZcbdsR3XoAAwbBgwapF1tRO6OE4CJiOwQFwfcc49zjQzAeTNERsVmRhGF74FhNsymJpWyVXTX37KymaWZUenYVRSzuSc2M4qYO3eu7BJ0w2xqUiVbXp4YmbF3vgxQdrbgYCAxUaPCJFLl2DmC2dwT58wQkant2QP07g2cOwf8/3vWOuzcOTER+OpVwMFL+0RUDOfMEBHdwObNQPfuzjcyAHDLLWIXYDOsaCIyEzYzRGRqjtwluyxmu0cTkVmwmVGExWKRXYJumE1NKmRLSQF++02MzFREednM0MyocOwcxWzuic2MIkJCQmSXoBtmU5MK2WJjgXvvBerUqdjXlZfNDM2MCsfOUczmnjgBmIhM65lnxG0IpkzR7jV37ABeeAE4eVK71yRyZ5wATERUhuvXgS1btJsvYxMcDPz1l1jRRETGwGZGEUlJSbJL0A2zqcno2eLjAW9vcZmposrLdsstwM03q72iyejHzhnM5p7YzCgiJiZGdgm6YTY1GT3bpk1i4q+nA//K3Sib6vNmjH7snMFs7olzZojIlO6+G5gwAejfX/vXfuUVsWneBx9o/9pE7oZzZoiISnH2LHDwINCtmz6vr/rIDJHZsJkhItOJjQXatgVq19bn9dnMEBkLmxlFREVFyS5BN8ymJiNnc3bX3xtlCw4GTp0C0tMdfw+ZjHzsnMVs7onNjCLCwsJkl6AbZlOTUbPl5gJbt1bsLtnF3ShbnTrij6ormox67LTAbO6JzYwiAgMDZZegG2ZTk1Gz7dkD+PiICcCOsiebypeajHrstMBs7onNDBGZyubN4hKTI0uyK0LlZobIbNjMKCI+Pl52CbphNjUZNZsWd8m2J1tQkLrNjFGPnRaYzT2xmVFEQkKC7BJ0w2xqMmK2M2eAxETnl2Tbk03lkRkjHjutMJt74qZ5RGQaCxcCS5cCu3bp/14XLohJwFeuAPwnh8hx3DSPiKiQTZucW8VUETffLO7TlJjomvcjorKxmSEiU8jJAbZt0/4u2eVR+VITkZmwmVFERESE7BJ0w2xqMlq23bvF/ZJat3b+tezNFhys5siM0Y6dlpjNPXHOjCJSUlIQEBAguwxdMJuajJZtwgQxj2XxYudfy95sn34KrF8vbp+gEqMdOy0xm3o4Z8aNmPEEtmE2NRktmxZLsm3szabq8myjHTstMZt7YjNDRMo7dQpISgK6dnXt+wYHi+XgV6649n2JqCg2M4qIi4uTXYJumE1NRsq2eTPwwAOAv782r2dvttq1gbp11Zs3Y6RjpzVmc09sZhSRmpoquwTdMJuajJRt82Ztl2RXJJuKK5qMdOy0xmzuiROAiUhp2dlihGT3biAkxPXv/9prgJcXMHu269+byAw4AZiI3N6uXUDNmsBdd8l5f1WXZxOZCZsZRWRlZckuQTfMpiajZLOtYvLw0O41K5JNxctMRjl2emA298RmRhGRkZGyS9ANs6nJKNm0ni8DVCxbUBCQnAyoNJ3BKMdOD8zmnjhnRhFZWVnw8fGRXYYumE1NRsh28iTQrBlw8aK2N3usaLZbbwXWrRMrqlRghGOnF2ZTD+fMuBEznsA2zKYmI2TbvBl46CHt71pd0WyqXWoywrHTC7O5JzYzRKQsLXf9dYZqzQyR2bCZUUR0dLTsEnTDbGqSnS0rC9i+Xfv5MkDFs6nWzMg+dnpiNvfEZkYR/lptbWpAzKYm2dl27gRq1RKNhNYqmk21Zkb2sdMTs7knw08AvnTpEubPn49ff/0VOTk5aNiwIYYNG4bWrVsXeZ7ZJwATUVFjxwKZmYDFIrsSsZLpppuAS5fEf4nIfm4xAXjOnDk4f/48Fi9ejJiYGHTq1AmRkZFIS0uTXRoRSWSU+TKAuCdUvXrcPI9IFsM3M8nJyejYsSP8/f3h5eWFxx9/HNeuXcOZM2dkl+ZSKSkpskvQDbOpSWa248eBEyeALl30eX1HsgUFqXOpieelmsyczVmGb2b69++P3bt34+LFi7h+/TrWr1+PevXqoUmTJrJLc6m5c+fKLkE3zKYmmdk2bwY6dACqV9fn9R3JptK8GZ6XajJzNmcZfs5MSkoKZs+ejZ9++gmenp6oUaMGpk+fjlatWhV5HufMELmPRx8FHn4YGDdOdiX/+uwzIDoa2LZNdiVEajH9nBmr1Ypx48ahVq1aiImJwZYtWzBu3DhERETg2LFjpX5N3759ER4ejvDwcGzYsAHh4eFF7mcRHR2NuLi4go9TUlIQERFR5DUsFgvi4+MLPk5KSkJUVFSR50RFRSEpKang4/j4eFiKzUSMiIgoMiwYFxdXZGldVlYW62N9rK+C9X3zTRy2bxfzZYxUX3Aw8PPPxv/+sT7WZ4T64uLiMGLECLRt21aT2zQYemTmypUreOKJJ7BgwQI0a9as4PHhw4cjNDQU/fv3L3iMIzNE7iE2FnjpJTFnRsubSzrLtqLp4kWxZJyI7GP6kZmaNWuiYcOGWL9+PTIyMmC1WrF3716cPHkSzZs3l12eSxXvis2E2dQkK5sed8kuzpFs/v5A/fpqrGjieakmM2dzlqGbGQCYMWMGrly5goEDB+Lxxx+HxWLBqFGj0KZNG9mluVRISIjsEnTDbGqSlU2Pu2QX52g2VSYB87xUk5mzOcvQl5kqgpeZiMzvjz+AO+8Um9M5OBqtq7Fjgbw8YN482ZUQqcP0l5mIiArbvBno1MmYjQygzsgMkdmwmVFE4ZnlZsNsapKRzVW7/jqaTZVmhuelmsyczVlsZhQRExMjuwTdMJuaXJ0tMxP47jv958sAjmcLCgLOnRMrmoyM56WazJzNWZwzQ0RK2LgRGDVK3MrASEuyi7v9duCLL4COHWVXQqQGzpkhIrdhW8Vk5EYGEJeaVFieTWQmbGaIyPDy80UzY5S7ZJdHlXkzRGbCZkYRxbebNhNmU5Mrsx09CiQnA6Ghrnk/Z7KpcPdsnpdqMnM2Z7GZUURYWJjsEnTDbGpyZbbNm4HOnYGqVV3zfs5kU2FkhuelmsyczVlsZhQRGBgouwTdMJuaXJlt0ybXrGKycSZbUBDwzz/AhQsaFqQxnpdqMnM2Z7GZISJDy8gAvv9ejfkyAFCjhljRZPTRGSIzYTOjiMK3bDcbZlOTq7Jt3y6ag2bNXPJ2AJzPZvRLTTwv1WTmbM5iM6OIhIQE2SXohtnU5KpsrrixZHHOZjN6M8PzUk1mzuYsbppHRIaVnw/ccQfwySfqXGYCgCVLgOXLgR07ZFdCZHzcNI+ITC0pSdweoHNn2ZVUjArLs4nMhM0MERnWpk1ibxlfX9mVVExQEHD+vPhDRPpjM6OIiIgI2SXohtnU5IpsMubLAM5nq14daNDAuKMzPC/VZOZszuKcGUWkpKQgICBAdhm6YDY16Z0tPR2oXRs4fBho0kS3tymVFtl69QIefRR49VWNitIQz0s1mTUb58y4ETOewDbMpia9s23fLib/urqRAbTJZuQVTTwv1WTmbM5iM0NEhrRpk1ormIozcjNDZDZsZhQRFxcnuwTdMJua9Mxmu0u2jPkygDbZgoOBxEQNitEBz0s1mTmbs9jMKCI1NVV2CbphNjXpme3QIeDiRaBjR93eolxaZGvZUtyf6Z9/nK9Hazwv1WTmbM7iBGAiMpwPPgB27gQ2bJBdiXMaNRIb6IWGyq6EyLg4AZiITEn1+TI2nDdD5BpsZhSRlZUluwTdMJua9MqWlgbs2iW3mdEqm1GbGZ6XajJzNmexmVFEZGSk7BJ0w2xq0ivbtm1A06ZiWbYsWmUzajPD81JNZs7mLM6ZUURWVhZ8fHxkl6ELZlOTXtmGDQNq1AD+8x/NX9puWmX7+Wege3cxEdjDQ4PCNMLzUk1mzcY5M27EjCewDbOpSY9stiXZsufLaJWtZUvg0iXjrWjieakmM2dzFpsZIjKMgweB1FSgQwfZlWijWjVxucyIl5qIzITNjCKio6Nll6AbZlOTHtk2bQK6dAGqVNH8pStEy2xBQcZrZnheqsnM2ZzFZkYR/v7+skvQDbOpSY9sMnf9LUzLbEacBMzzUk1mzuYsTgAmIkNITQVuvhn480+gQQPZ1Whn+XLgs8+AH36QXQmRMXECMBGZxrZtQGCguRoZ4N+RmXxT/NpIZExsZhSRkpIiuwTdMJuatM5mpF1/tczWsqUYdTLSqcDzUk1mzuYsNjOKmDt3ruwSdMNsatIym+y7ZBenZbaqVY23oonnpZrMnM1ZnDNDRNL9+qu4Q/bFi0DlyrKr0V7v3sAjjwCvvSa7EiLj4ZwZIjKFzZuBrl3N2cgAxlyeTWQmbGaISDojzZfRgxGXZxOZCZsZRVgsFtkl6IbZ1KRVtsuXgfh4YzUzWh83o61o4nmpJjNncxabGUWEhITILkE3zKYmrbJt3Souw9x2myYvpwmtj1tgIHDlCvD335q+rMN4XqrJzNmcxQnARCTV4MFAQADw3nuyK9FX06bAJ5+IuUFE9C9OACYipVmtQGyssS4x6YXzZoj0w2ZGEUlJSbJL0A2zqUmLbL/+Cly7BjzwgAYFaUiP4xYcDCQmav6yDuF5qSYzZ3MWmxlFxMTEyC5BN8ymJi2y2ZZke3trUJCG9DhuRlqezfNSTWbO5izOmSEiaR54ABg6FBgyRHYl+vv1VyA0VKze8vCQXQ2RcXDODBEp6+JF4KefgB49ZFfiGoGBQHo6cPas7EqIzKeS7ALscejQISxatAhJSUnw9PREo0aNMG/ePHh6shcjUtWWLUCrVkC9erIrcQ1fX6BxY3GpqX592dUQmYvhu4FDhw5h4sSJ6N69O7766ivExMTglVdegYebjdNGRUXJLkE3zKYmZ7Nt3mzcVUx6HTejrGjieakmM2dzluFHZiwWC3r16oXu3bsXPBYUFCSxIjnCwsJkl6AbZlOTM9lsS7LXrdOwIA3pddyM0szwvFSTmbM5y9AjM1lZWTh06BA8PT3x8ssvIywsDMOHD8f3338vuzSXCwwMlF2CbphNTc5k278fyMkB2rfXsCAN6XXcjNLM8LxUk1mzZWY6/xqGbmbS09NhtVqxZcsWjB49Gl999RUGDhyIGTNm4JAR/kUgp82bJ+ZNrFwJ5OXJroZcZdMmoFs3oJLhx4a1Zdtrxij3aCIygueec/41DN3M+Pr6AgC6d++OwMBAeHl5oWPHjmjdujV27dpV6tf07dsX4eHhCA8Px4YNGxAeHo6srKyCz0dHRyMuLq7g45SUFERERBR5DYvFgvj4+IKPk5KSSlyrjIqKKrKBUXx8fImbgEVERCAlJaXg47i4OERHRxd8nJWVZXd9hesxYn2F2VtfYmISZs8GmjSJx7hx8ahf34Ivvvi3qZFdnxbfv/79+xu6Pme+f7YaHalv82YgI8O4xzc+Pl6X71+LFkB6ugUbNsg9vrbvj8rnX1n1xcfHG7o+wPHv34wZMwxdX0W+f3FxcRgxYgSCgtriu+8i4SzD7zMzYMAAdOrUCcOHDy947PXXX0fTpk0xYsSIgsfMvs+MxWIpktcMYmOBF14AJk2yYNiwEVi+HJgxA6hcGZg0CejXT/3f3M143GwczXb+vLgXU3Ky+K8R6XncmjcHPvoIKDQN0OV4XqrJjNl69ACCgtIwe7Zz+8wYvplZu3YtVq5ciffffx+NGzfG3r17MW3aNMyZM6fIRGCzNzNm9OSTYti90C8byM0FVqwQj1WqBLz1FvDss+o3NfSvL74APvxQzJtxR08+CXToAISHy66ESK69e8Xl5gMH0tC4sXPNjOF/RPTp0wfZ2dmIjIzE1atXcdttt2Hy5MluuaLJTM6eBTZuBGbPLvq4t7fYDfa554DPPwemTQPefls0NQMGsKkxg02bjLsk2xWMMgmYSLapU4FRo4DatZ1/LSV+NAwYMAADBgyQXQZpaPFioEsXoFGj0j/v7S0uQQ0cKH6Tf/vtf5uagQPZ1KgqLw+IiwO++UZ2JfIEBwPbtsmugkiuPXvEyMzKldq8nqEnANO/ik/iUlleHvDZZ4Dt0m952by9gcGDgaQkYPJk4J13xCTKJUvEJSmjM9NxK86RbPv2iT1m7r9fh4I0pOdxM8KKJp6XajJTtqlTgdde02ZUBlBgzoy9zD5nJiUlBQFGnS1ZQZs2AcOGAX/9JUZYKpLt+nVg1SoxSnP9uhipee4549112cZMx604R7JNmQIcPSqOoZHpedyys4GqVYGTJ4Hbb9flLW6I56WazJJt925xqfnkSaBWLd5o0q2Y4QS2sViAF1/891JRRbJVqiSal8REYPp0ICpKjNQsXGjMkRozHbfiHMmmynwZPY9blSpA06Zy583wvFSTWbJNnQqMHi0aGa2wmSGXOnNG7DEydKhzr1Opkpg7k5goRmlmzRJLXj/7TOwsS8Zz7hzwyy/uc5fs8nASMLmrXbuAn34Cxo7V9nXZzCii8EZIKlu0SCzFa9Dg38ecyeblJVY5HTok5tN8+KFoahYsMEZTY5bjVpqKZouLA+65B7jlFp0K0pDex012M8PzUk1myDZlivajMgCbGWWkpqbKLsFp16+Ly0HF93zSIpuXl9iP5vffgffeA+bMAZo1E5e0ZDY1ZjhuZalots2bgV699KlFa3ofN9nNDM9LNamebedO4OeftR+VATgBmFzo22+Bl18GTpzQf2l1Xh6wdq2YV5OeDkRGiqXeVaro+75UuuvXxYjMpk1Au3ayq5Hv4EHggQeAtDTAw0N2NUSu8fDDYsPIadOKPs4JwKSU4hN/9eTlBTzzjPihMWsW8PHHYtLlJ5+I1STkWj/9BHh6AvfdJ7sSY2jeHLh2DTh1SnYlRK7x/fdizpweozIAmxllFL5Zl4pOnxZzJkqb+KtnNk9P4OmngQMHxHya//5XNDXz5wOu+JaqftzKU5FsmzaJexF5eelYkIb0Pm5VqojLoImJur5NmXheqknlbFOnAmPGAP7++rw+mxlFREY6f1dRmRYuFKtYbrut5Odckc3TE+jbF0hIELdQsFhEU/Pxx/o2Naoft/JUJJtK82UA1xy3oCB582Z4XqpJ1WzffQf8+qtoZvTCOTOKyMrKgo+Pj+wyHHL9OtCwoVhh9OijJT8vI5vVCqxfL67dXrgAvPGG2MhP6zJUPm43Ym+2lBSgfn2xNPvmm11QmAZccdwmTxYjlkuW6Po2peJ5qSZVs3XuDISGipVMpeGcGTei4glss3GjmCdT1v4iMrJ5egL/93/it4WPPxZLxhs3BubNE3MZtKLycbsRe7PFxoq5Mqo0MoBrjpvMFU08L9WkYrYdO8SIuJ6jMgCbGXIBi0XMlTHifAlPT+DJJ8XEtP/+V/yW3LgxMHeutk2NO1Nl119Xs92jyWqVXQmRPvLzxVyZ8HCgZk1934vNjCKio6Nll+CQkyfFHYJffLHs5xghm6cn8MQToqn59FNg2TLR1MyeDWRmOv66RsimF3uyXb8ObNmi1nwZwDXHrXlzsbJOxoomdz8vVaVath07xOKL117T/73YzCjCX68p4DpbtEjMk6lXr+znGCmbhwcQFgbs3y9GlD7/XDQ1H37oWFNjpGxasyfb3r1A5cpAmzb616MlVxy3ypXFiiYZl5rc/bxUlUrZ8vPFHJlx4/QflQE4AZh0lJsrJv4uWqTuZYb8fDHnZ+pUMVlzwgTgpZcAB+eouZ3ISHE/ruXLZVdiTH37ivlEEybIroRIW//7nzi/T54EbvQjmROAydC+/Vb89tmtm+xKHOfhATz2GLBvH7B4MfDll2KkZtYsICNDdnXGt3mzuo2sKwQFydtrhkgvhUdlXDW2wGZGESkpKbJLqDCLRSx3vtHEXxWyeXiIy2U//SQmCa9eDdxxB/DBB+U3NSpkc9SNsp09K3ZgVrGZddVxk7WiyZ3PS5Wpku1//wMOHwZGjXLde7KZUcTcuXNll1AhJ06IyV9Dhtz4uSpl8/AQk1l//FFMEl63DmjUCIiKAq5eLfl8lbJV1I2yxcYCbdsCtWu7qCANueq4yVrR5M7npcpUyCZjVAaowJyZ3bt344EHHoCHQe+KxjkzxhIZCRw5In7Ym1l+vrhNw7RpwB9/AOPHA6++ClSvLrsy+fr0AVq3Bt56S3YlxpWTI+ZfHT0qRvqIVLd1K9Cvn5grY++/gy6dM/PBBx+gX79+WL58OS5cuODQm5F7yM0V80tGjJBdif48PMRmgHv2ACtXAt98I34ovfuuuFu3u8rNFf+ocb5M+SpXFku0ZW2eR6Ql26jM+PGu/4XO7mZmzZo1GDp0KPbv34/+/ftj0qRJ+Omnn/SsjRT1zTfit81HHpFdiet4eIi5Ibt3A6tWicnPjRoBM2cCaWmyq3O9PXsAX1/g7rtlV2J8MncCJtLSli1ilHHkSNe/t93NjLe3N7p27Yq5c+di4cKFCAgIwDvvvINnn30WK1euRJo7/ovtQhaLRXYJdrNN/PW08+xSKduNeHgAXbsCu3YB0dHA4sUWNGoEvPOO+Zqa8o7b5s1ixMrec8BoXHlOymhmzPR3rjhmk8O22+/rr8u5zO7QPzU1a9ZErVq14Ofnh9zcXOzatQv9+vXDtm3btK6P/r+QkBDZJdjl+HHg+++BwYPt/xpVslWEh4cYmVqxIgRr1ojJsI0aATNmAFeuyK5OG+Udt02b1Nv1tzBXnpO2ScCuZMa/czbMJkdcHHDsmJgzKEOFNs3bv38/NmzYgL179+LOO+9E79698dBDD8HLywvx8fGIiorC119/rWe9ZeIEYGN44w3R0KxZI7sSY8nPF6u7pk4Vy5XHjgVGj3bNzpiuduaMaNzOnwduukl2NcaXmCg2zktPV3cki9xbfj7Qvr24ea8jG0BqMQG4kr1PHDBgANLT09GtWzd89tlnaNCgQZHPt2vXDpUrV3aoCDKHnByxB8sXX8iuxHg8PICHHwZCQ4HvvhOrn2bPFneSHT0aUGiX8hvavFn8w8ZGxj7NmokJ0ydPig0ZiVQTGyt+iX3lFXk12P17wIABA7BmzRqMHDmyRCNjo9pNsFSSlJQku4QbWr9eXCt9+OGKfZ0K2RxVPJuHx78Nzddfi/82aiRGbFJTXV+fM8o6bmbY9deV56S3t+tXNLnT3zkzMWI22wqmCRMAPz95ddjdzPTq1QtVqlTRsxYqR0xMjOwSbshiAYYPr/hQuQrZHFVets6dxaWnmBhg505xH6spU4DLl11XnzNKy5aTI5ZkqzxfBnD9OenqScDu+ndOdUbMtnmzGFWUOSoD8EaTpJE//gDuvFPcjPGWW2RXo6adO8Xlp59/FpeexowBatWSXVXF7NgBDBgAJCeLUSiyz/Tp4u/QihWyKyGyX36+2OX7mWfE3jKO4o0myTA++wx48kk2Ms7o2FHc0+Tbb8U+LY0aid1zL12SXZn9Nm0SS7LZyFQM95ohFW3aBJw6Bbz8suxK2MyQBrKzxcTf4cNlV2IOHToA27aJfyh+/FE0NW++CVy8KLuyG9u8Wf1LTDIEB4sb8+Xlya6EyD62fWUmTBCbpMrGZkYRUVFRskso09dfi5UroaGOfb2RsznLmWwPPSTmn8TGiktPjRqJe14Z5W4ixbOdOiXux2WGnZ9dfU42aSIamZMnXfN+/DunJiNl+/Zb44zKAGxmlBEWFia7hDLZJv46emnByNmcpUW2Bx4QG1Jt2QL88ou491NEhPympni2zZtFrWZYZu7qc9LbG2jRwnWXmvh3Tk1GyWYblZk4EahaVXY1ApsZRQQGBsouoVRHjgB791Zsx9/ijJpNC1pma99ejNJs3Qr89psYqXnjDbE5nQzFs23apP6SbBsZ56Qr583w75yajJJtwwYxyf+ll2RX8i82M+SUBQvEro833yy7EvfRrp0YBfnf/4ADB8RIzYQJwD//yKspO1vUw/kyjuMkYFKBbVTmjTeMMyoDsJlRRnx8vOwSSsjKApYuBUaMcO51jJhNK3pmu/9+MRqyfbv4Idi4sbjJm6uamsLZfvhBXF5q1co17603GeekK5sZ/p1TkxGyffMN8Pffzv+7rzU2M4pISEiQXUIJ69aJpdgdOzr3OkbMphVXZGvbFti4UezxcviwGKkZPx44d07f9y2czbbrr1mWZMs4J4ODgaQk16xo4t85NcnOVnhUxtdXaiklcNM8clinTsATT4ibJpJx/Pyz2ITtf/8T17Rffx0ICND3PVu2BN55R1xyJMdcvy6WuP7+u7hfE5HRrF8vdvo9flzbZoab5pE0hw+LPVAGDZJdCRV3771iKHjnTuDYMXH5KTwcSEnR5/1OnBDvY4Yl2TJVqiRWNCUmyq6EqCSr1bijMgCbGXLQggVAnz5A7dqyK6GytGkj7vu0axfw55+iqRkzRlzv1tLmzWJPHA6IOo+TgMmoYmLEfDyjbo7KZkYRERERsksocO0asGyZdhPAjJRNa0bIds89Ynh4927gr7/EBm2jRwNnzzr3urZsZtz1V9Zxc1UzY4TzUi/Mpj3bqExEBODjI6WEG2Izo4jRo0fLLqHA2rViDsZDD2nzekbKpjUjZbv7brFb85494oagTZsCr70m9otwxOjRo5GVJebmmGV/GRtZx81VzYyRzkutMZv21q8Xm3QOGybl7e3CZkYRAXrP4KwAi0WMymi1csVI2bRmxGytWwNffSU2O0xOFk3NqFEVb2oCAgKwc6fYYyg4WJdSpZF13Fy1osmI56VWmE1bKozKAGxmqIIOHQL27+fEXzMICRHL63/8UUwObtoUGDkSOHPG/tew7fprliXZsjVpIv57/LjcOohsvvoKuHQJGDpUdiXlU6qZmTRpEkJDQ7F//37ZpbhcXFyc7BIAiIm/Tz8tbiypFaNk04MK2e66C1izBvjpJzHBr1kzsfzy9Onyvy4uLs6U82UAecfNywsIDNT/UpMK56WjmE07ViswbZrxR2UAhZqZuLg4ZGVlyS5DmtTUVNkl4No1YPly7Xd+NEI2vaiUrVUrYPVqYN8+4OJFoHlzcUfcU6dKf/6RI6k4cQJ4+GHX1ukKMo9bUJD+y7NVOi8ritm0s24dkJpq/FEZQJFm5vz581i8eDHGjx8vuxRpnnnmGdklYPVqoH59ccNDLRkhm15UzHbnnUB0tNh87/JlsffJSy+JlVCFeXo+g44dgerV5dSpJ5nHzRWTgFU8L+3FbNqwjcpERgJVqrjsbR1m+GYmPz8f77//PgYOHIi6devKLsetaT3xl4wtOBj48ksxR+rKFdHUjBjxb1NjprtkGwn3miEjWLsWSEsDhgyRXYl9DN/MxMTEID8/H48//rjsUqSSfYnt4EHgt9+A557T/rVlZ9OTGbIFBQGrVgG//gqkp4umZtgwYPv2LFPOlwHkHjfbiqbr1/V7DzOcl2VhNufl5ak1KgMYvJlJTk7GihUrKnR5qW/fvggPD0d4eDg2bNiA8PDwIidAdHR0kUlUKSkpJTYislgsRe5OmpSUhKioqCLPiYqKQlJSUsHH8fHxsFgsRZ4TERGBlEJ7yMfFxSE6Orrg46ysLLvri4yMlFqfxQI88wwQF6f99y8yMlL3758z9QGOf/8efPBBQ9dXke9fy5bAypVARIQFp0/Hw98/EoGBxqnPRovvX2RkpLT6GjcGrNYobN2q3/fP9u+JSuefvfVFRkYauj7A8e/fU0895ZL6nnsuCpcuJeGFFypWX0W+f3FxcRgxYgTatm1b5Oebowx9o8nY2Fj85z//QdWqVQseS0tLQ7Vq1dC5c+ciTY7ZbzSZlZUFH0nTyTMygHr1gLg4oF077V9fZja9MZuaZGe7+25g0iT9btwpO5+emM05eXliMcDo0dov9iiLFjearKRxTZrq3Lkz2rRpU+Sxp59+GuHh4bj33nslVSWHzL+c0dFAo0bA/ffr8/pm/YcHYDZVyc5mmzejVzMjO5+emM05a9aIX2BtozKqMHQz4+PjU+rBq1mzpilHX4xqwQJO/CVypeBg4MAB2VWQu7HNlXnzTaByZdnVVIyh58yUZseOHSVGa9xB4WuRrpSQICb/Dhig33vIyuYKzKYm2dmCgvRd0SQ7n56YzZnXF/uJDR6s69voQrlmxl35+/tLeV+LBejXD6hZU7/3kJXNFZhNTbKzBQcDR47ot6JJdj49MZtj8vKA6dPVHJUBDD4BuCLMPgFYhqtXxcTfbduAtm1lV0PkPvLyAD8/sRw+MFB2NeQOvvgCeOst0US7upnRYgIwR2aoTF9+KW58d999sishci9eXmIpPDfPI1dQfVQGYDOjjMLr+V3FVTv+ysjmKsymJiNk03MnYCPk0wuzVdyqVUBuLvD887q8vEuwmVHE3LlzXfp+v/wCHD4MPPus/u/l6myuxGxqMkI2PZsZI+TTC7NVzPXrwNtvi0tM3t6av7zLcM4MlWrECCA/XyzLJiLX27ABiIgAfv9ddiVkZitWAFOniltoyGpmTL9pHsmRni62rf/uO9mVELmvoCDg6FEx/K/yb8xkXNevi7kyqo/KALzMRKVYtUrcTNANt/MhMow77gAqVQKOHZNdCZnVypWA1QoMHCi7EuexmVFE8Zt86ftewPDhLns7l2ZzNWZTkxGyeXrqt6LJCPn0wmz2sc2VmTRJ/VEZgM2MMkJCQlzyPj//LIa2+/d3ydsBcF02GZhNTUbJptckYKPk0wOz2eeLL8S8SDOMygCcAEzFDBsm9rj49FPZlRBRVBSwfz+werXsSshMrl8XmzFOmmSM5djcNI80lZYm5su46rbvRFQ+PZdnk/tasULsH6bnPfdcjc2MIpKSknR/jy++ECso7r5b97cqwhXZZGE2NRklW3CwuOybk6Pt6xolnx6YrXy5ucCMGWJUppKJ1jOzmVFETEyMrq+fn//vjr+upnc2mZhNTUbJ1rCh2F5e6xVNRsmnB2Yr34oVYiqBKzZEdSXOmSEAwE8/AV27AmfPAg5esiQiHdx7LzBxItC3r+xKSHW5uWLbjenTjTXxl3NmSDMWizi52cgQGQvnzZBWli8Xy7D79ZNdifZMdMWMHHXlirhD9p49sishouKCg4F9+2RXQarLyRFzZWbMMNdcGRuOzCgiKipKt9f+/HOgVStA1vYMemaTjdnUZKRseozMGCmf1pitdMuXA1WqmHNUBmAzo4ywsDBdXlfmxF8bvbIZAbOpyUjZgoOBP/7QdkWTkfJpjdlKso3KTJ4sJv+aEZsZRQQGBuryuvHxwKlTwDPP6PLydtErmxEwm5qMlK1BA/Eb9dGj2r2mkfJpjdlKWrYM8PWV+++83tjMuDmLBXjuOaBqVdmVEFFp9LxHE5mfO4zKAGxmlBEfH6/5a16+LLZJl73jrx7ZjILZ1GS0bMHBQGKidq9ntHxaYrailiwRq1SfflqHggyEzYwiEhISNH/Nzz8Xu/3eeafmL10hemQzCmZTk9GyaT0J2Gj5tMRs/8rJAd55x/yjMoAJN81btuwKBg3ipnk3kp8vVjBNmAAMGiS7GiIqz6ZNwLhxwOHDsishlXz6KfDRR8CBA8ZuZrhpXileeglYs0Z2Fca3Zw+QnMxdRYlUYFvRlJ0tuxJSRXY2MHOme4zKACZsZpYvBwYPFnNBqGwWixiR8fWVXQkR3UiDBuLvqpYrmsjcFi8GatRwn19YTdfMdOsmRmZeeAGIjpZdjXYiIiI0e61Ll8T3SPbEXxstsxkNs6nJaNk8PMQd7bWaN2O0fFpitn9HZaZMEavh3IHp5szYbjS5ebPoSBctMsfa+pSUFAQEBGjyWnPmAOvWAT/8oMnLOU3LbEbDbGoyYrYhQ4D69YG333b+tYyYTyvMBvz3v8AnnwAJCWo0M5wzU46ePYG1a4EXXwRWrZJdjfO0+suZnw8sWGCcURlAu2xGxGxqMmK2oCDtlmcbMZ9W3D1bVpb7jcoAJm5mAKBHDzECMWwYsHKl7GqMYdcu4Nw5oE8f2ZUQUUXw7tlkj0WLgFq1gP/7P9mVuJapmxkA6N4d+OorYPhw4IsvZFfjuLi4OE1ex2IBnn8e8PHR5OU0oVU2I2I2NRkxW3AwcOyYNiuajJhPK+6czV1HZQDAhDcCL6lbN+Drr4EnnxSXWQYOlF1RxaWmpjr9Ghcviktvv/3m9EtpSotsRsVsajJitttvF7cdOXIEuOsu517LiPm04s7ZFi4Ebr5Z/KxzN6adAFyabduAJ54QGwmp2NA468MPgZgY4PvvZVdCRI5o1w4YPRro3192JWQ0WVlAkyZikzzVLjFxAnAFPfKI+GH+8stiPxp3YsSJv0RUMZw3Q2VZsACoU0f8wu6O3KqZAYAuXURD8+qr4rboqsjKynLq67//HrhwAXjqKY0K0pCz2YyM2dRk1GxaNTNGzacFd8x27Rrw3nvuOVfGxi1jP/ywaGhGjgSWLpVdjX0iIyOd+nqLReyMXKWKNvVoydlsRsZsajJqNq2WZxs1nxbcMduCBUDduu47KgO42ZyZ4nbsAHr3BubNEzsGG1lWVhZ8HFyCdP68mDx44ADQvLnGhWnAmWxGx2xqMmq206eBRo2AjAznViQaNZ8W3C3btWtA48ZikzxVmxnOmXFSaCiwYQPw2mviPhZG5sxfzmXLgAceMGYjAziXzeiYTU1GzXbbbYCfn1jR5Ayj5tOCu2WzWIBbbwXCwiQUZCBu3cwAQOfOwLffihUCixbJrkZ7nPhLZB5a36OJ1JaZKebKTJ0qzg135vbNDAB06gRs3AiMHSvW6RtRtIN3zdyxA7h82djDj45mUwGzqcnI2bSYBGzkfM5yp2wWi7hf1+OPSyrIQNxi0zx7dOwoGppHHxWjGcOGya6oKH9/f4e+zmIR84GMOPHXxtFsKmA2NRk5W3Cw83tFGTmfs9wlW2YmEBUFfPYZR2UAN58AXJoffhANzaxZ4hYIKvvnHzHx99AhoGlT2dUQkRa2bBFbS/zxh+xKSKYPPxT3HNy3T/1mRosJwByZKaZDB2DTJqBXLzFCo/JckyVLRB42MkTmERQEHD8uVrH4+squhmTIyBCjMosWqd/IaIVzZkrx0EPA5s3A66+LWx8YQUpKSoWeb7WqM/G3otlUwmxqMnK2+vWB6tWdW9Fk5HzOcodsn3wCNGggriKQwGamDA8+CMTGAhMnihNHtrlz51bo+du3A1evqrFcr6LZVMJsajJyNg8P5ycBGzmfs8yeLSMDeP99rmAqztBzZhYsWID4+HicO3cOPj4+aN26NUaMGIFbbrmlxHO1mjNT3N69QI8e4rbqr76q2cvqrm9fcXnp3XdlV0JEWhs2TNyHZ+ZM2ZWQq33wAbBmDfDjj+ZpZkw/Z8bDwwMTJ05E48aNkZ2djdmzZyMyMhILXbh+un17MULTo4f4WIWGJiUF+OYb4PBh2ZUQkR6Cg8W2C+RebKMyy5aZp5HRiqEvMw0bNgwtWrSAt7c3/Pz80L9/fxw/fhzp6ekuraN9eyAuDnjzTeDjj1361g5ZskTsndO4sexKiEgPvHu2e5o/H2jSBOjZU3YlxmPoZqa4ffv2oW7duqhevbrL37tdO9HQvPUW8NFHLn97WCwWu55ntYp9B1SY+GtjbzYVMZuajJ4tOBj480+x14gjjJ7PGWbNdvUqMH26hXNlyqBMM7N//34sX74c4eHh0mq4/37R0EyaBLh6jllISIhdz9u6VSzZ7N1b54I0ZG82FTGbmoye7dZbgRo1gKQkx77e6PmcYdZs8+YBDRuGoHt32ZUYkxLNzN69ezFlyhRERkaibdu25T63b9++CA8PR3h4ODZs2IDw8HBkZWUVfD46OhpxcXEFH6ekpCAiIqLIa1gsFsTHxxd8nJSUhKioKACiodmyBXjjjShERPz7L0l8fHyJ3wgiIiKKLBOMi4srsh11VlaW3fW1a9fOrvosFmDIEODDD6OQlOS6+gorrz6bqKh/62vXrp2h6wMc//6tXr3a0PU58/2znZNGrc/GkfratWtn6Po8PAA/vwjs2eNYfbZjp/L5V1Z97dq1M3R9QMW+fz/+KPY9mz49Dn36/FUwKmOU+mwq+v2Li4vDiBEj0LZtW0RGRsJZhl7NBABbt27F3LlzMXny5HIbGb1WM5Vl3z6gWzdg8mRxTycj+PtvoFEjsf9Eo0ayqyEiPQ0fDtSuzRWLZhUfD0ybJnalHzkSGDdOrGAzIy1WMxl6ZObrr7/GvHnzMHPmzBuOyLjaffeJSzrTp4ttpfWWZMd48uLFwMMPq9fI2JNNVcymJhWyOTMJWIV8jlI9m207kK5dgdatgZMnxZ2x69RRP5ueDN3MzJs3D9euXcPEiRPRs2fPgj8HDhyQXRoA4N57gW3bgBkzgP/8R9/3iomJKffzeXnqTfy1uVE2lTGbmlTI5kwzo0I+R6mabc8eoHt3MeJ/zz3AiRNi1O3mm/99jqrZXMHwl5ns5erLTIXt3y+66MhIYPx4l751gc2bgRdfBE6dAioZevcgItLC33+LWxtcvQpUrSq7GnLU7t3iclJ8PDBqFBAeLi4fuhPTX2ZSRZs2YoRm5kyxoZEMFotoZtjIELmHgADA35+bY6pq1y7gkUfEnjFt24qRmHfecb9GRitsZjRyzz3A//4n7mRabGK47pKTxZ2+hw517fsSkTxa3KOJXO+HH4AuXcRNIh94QMyJmTGDTYyz2Mxo6O67RUPz/vtiwpaWii+dK2zRInGZq2FDbd/TVcrLpjpmU5Mq2YKCgMTEin+dKvkcYdRs338vFmg89hjw0EOiiZk+HahVy/7XMGo2I+BFCY21bi0amkceAfLzgWJL/B0WVsbtr/PygIUL1bjNQlnKymYGzKYmVbIFB4tVlRWlSj5HGC3bd9+JOTG//gqMGQOsWwfcdJNjr2W0bEbCkRkd2Bqa//xHu7vaBgYGlvp4bKy4hUGvXtq8jwxlZTMDZlOTKtkcvcykSj5HGCXbd98BnTsDTzwh/nvyJDB1quONDGCcbEbEkRmdhISIhqZLF9FsvPWWPu9jsYi5Mpz4S+R+goPFxNGMDMDBRSCkofx8cTfzadOAAwfEhqrr14uJ2qQvjszoKCQE2L5d3Mfp7bede63C20/bnD4t7hWl+sTf0rKZBbOpSZVsdeuKORcVXdGkSj5HyMiWny9+ee3UCejTR0wzOHlS7BCvZSNj5uPmLDYzOrvrLtHQzJsnJns5KiEhocRjixaJTZZuu82JAg2gtGxmwWxqUiWboyuaVMnnCFdmy88X23J07Aj07Sv+PT55UtyMuGZN7d/PzMfNWdw0z0V+/13MZH/1VWDKFOdf7/p1cduCTz8Vs+OJyD29/DJQvbq8Pa7cka2JmTpV3Ll83Dhx/yQD/uhRghab5nGmhYvceacYoXn4YfEXYepU515v0ybA01NsuERE7isoSCwEIP3l54vVY1OnAkePiiYmNlY0kyQXLzO50J13islhn3wiRmfynRgTs0389fLSrj4iUk9wsGN7zZD98vPF/MQHHgCefRbo3VtMvI6IYCNjFGxmXCw4WDQ0n35asYYmotCGNX/9JX47ePFFnYp0sQitNuMxIGZTk0rZgoPFPI2rV+3/GpXyVZSW2fLzxX3v2rcHBg4Uy6xPngTeeENOE2Pm4+YszpmRJDFRXHIaNkxMDPbwKP/5KSkpCAgIACBmyB84IJb8mUHhbGbDbGpSKVt+PlCnjvihe9999n2NSvkqSotstiZm2jTgzz+B118HXnkF8PPTqEgHmfW48UaTCgsKEiM0n30mZr7faITGdgJfvy5WMY0Y4YIiXcSMfzltmE1NKmVzZEWTSvkqypls+fnAxo3A/fcDgweLFUonTwITJshvZABzHzdncQKwRC1bil0iQ0PFX6IZM248QvPtt4C3N9Ctm0tKJCIF8IaTzrE1MdOmAadOieblpZe4EaFKODIjWWCgGKFZvBh4882yR2ji4uIAiIm/w4aZa+KvLZsZMZuaVMtW0WZGtXwVUZFs+fnAhg3i8tzQoUD//mJi77hxxmxkzHzcnMVmxgACA8UIzdKlYnZ8aQ1NamoqTp4Uy7tfeMHFBeosNTVVdgm6YTY1qZYtKKhizYxq+SrCnmz5+cA33wD33it+ORwwQMyNCQ8HqlbVv0ZHmfm4OYsTgA3kyBFxyem554D33it5yenNN8W25V99Jac+IjKmf/4RtzZIS+NS4fLk5wMxMWLRxd9/AxMnAsOHG7uBcQfcNM9kWrQoOocmKurfhiY3V1yKWrpUZoVEZES33ALcfLP4ZadtW9nVGI/V+m8Tc+7cv02Mr6/sykgrvMxkMM2bi4Zm5UoxCc12yWnduiz4+gJdu0otTxdZWVmyS9ANs6lJxWwVmTejYj57Fc5mtYqR7HvuEbeSGTIEOH4cGD1azUbGzMfNWWxmDKhZM9HQrFol9jfIzwfeeCMSw4aJWxiYTWRkpOwSdMNsalIxW0WaGRXz2SsyMhJWK7BuHXD33cCoUWJy759/iv9XsYmxMfNxcxbnzBjYsWPiklNoKPDll1k4dcoHZtxmICsrCz4+PrLL0AWzqUnFbP/9r1iZs3nzjZ+rYj57WK3i38p33/XB5ctiQcWLLwJmiWrW48Y5MybXtKkYoencGQgLM2cjA8CUfzltmE1NKmYLDhYLB+yhYr4bOXcOePRR4Nw5n4ImpkoV2VVpy4zHTStsZgyuSRPg11/Nta8MEWkvKAg4fVqsaDLJ4LTdUlLE7WHuuQfYvdt8TQzdmAlnYJjPzTcDW7ZEyy5DN9HRzKYiZjOWOnXEH3vuoK1ivrL8/be4FH/vvcCyZcD69ebJVpyZjpvW2Mwowt/fX3YJumE2NTGb8QQH29fMqJqvOFsj07YtsGSJGME2S7bSmDmbszgBmIjIJEaOFJdY/vMf2ZXoz9bItGsnbr7LS/Hq4l2ziYiogLvccPLsWbEwon17NjIksJlRREpKiuwSdMNsamI247G3mVE1HwAkJ4tG5sEHgYULSzYyKme7ETNncxabGUXMnTtXdgm6YTY1MZvxBAcDZ84AV66U/zxV8yUni0tLHTqU3sgA6mazh5mzOYtzZoiITCQgAPj6a3EJxkzOnBGNTKdOwIIF5twN3V1xzgwRERURFGS+eTNnzohLS507s5Gh0vGUICIyEXuXZ6vi9GnRxDz8MGCxsJGh0vG0UITFYpFdgm6YTU3MZkz2TAJWJd+pU6KR6dIF+PRT+xoZVbI5wszZnMVmRhEhISGyS9ANs6mJ2YzJnmZGhXy2RqZrV+CTT+wfkVEhm6PMnM1ZnABMRGQily4BtWsDly8Dqm4Y+9dfYrJvjx7Axx/z0pLZcQIwEREVUauWWNGk6ryZv/4SIzI9egDz57ORIfvwNFFEUlKS7BJ0w2xqYjbjutGlJqPmO3lSNDK9eolGxsOj4q9h1GxaMHM2Z7GZUURMTIzsEnTDbGpiNuO60fJsI+azNTKPPiouLTnSyADGzKYVM2dzFufMEBGZjMUCrFsHbNkiuxL7nDghGpnevYF58xxvZEhNnDNDREQlqHTDyT//FI3ME0+wkSHHsZkhIjKZ4GBxZ+nUVNmVlM/WyDz5JDBnDhsZchybGUVERUXJLkE3zKYmZjOum24Cbr217NEZI+Q7flw0Mk89BcyerV0jY4RsejFzNmexmVFEWFiY7BJ0w2xqYjZjK+9Sk+x8tkamTx/gww+1HZGRnU1PZs7mLDYziggMDJRdgm6YTU3MZmzlNTMy8x07Ju58/fTTwH/+o/2lJTMcu7KYOZuzKsku4Eby8/OxdOlSbNy4ERkZGWjevDnGjBmDO+64Q3ZpRESGFRQErF4tu4qi/vhD7Ozbrx/wwQecI0PaMfzITHR0NDZv3oz3338f69evx5133okJEybg2rVrsktzqfj4eNkl6IbZ1MRsxlbeyIyMfH/8IS4tPfusvo2MGY5dWcyczVmGb2ZiYmLw9NNPo3HjxqhSpQqGDBmC3Nxc/PDDD7JLc6mEhATZJeiG2dTEbMYWHAykpIh7NRXn6nxHj4pGZuBAICpK3xEZMxy7spg5m7MMvWne1atX8fjjj+Pjjz9GcHBwweOvv/46GjVqhFdffbXgMW6aR0RUVP36QHQ08NBD8mo4ckRcWho0CHj3XV5aopJMv2leZmYmAMDPz6/I435+fgWfIyKi0snePM/WyDz/PBsZ0pehm5mqVasCECM0hV29erXgc8X17dsX4eHhCA8Px4YNGxAeHo6srKyCz0dHRyMuLq7g45SUFERERBR5DYvFUuTaZFJSUon1/VFRUUVu+hUfHw+LxVLkOREREUhJSSn4OC4uDtHR0QUfZ2VlsT7Wx/pYn271XbgQjoQEOfUNHx6BDh1SMHgwMHMmsGWLet8/1qdffXFxcRgxYgTatm2LyMhIOMvQl5kAoH///ujTpw+eeuopAEBeXh769OmDl19+Gd26dSt4ntkvM0VERODdd9+VXYYumE1NzGZ8CxcCq1YB//tf0cf1zpeUJEZkXnwRePtt147ImOXYlcas2Ux/mQkQmwStXr0aJ06cQHZ2NpYsWYJKlSqhQ4cOsktzqdGjR8suQTfMpiZmM76y7p6tZ77Dh8Vk36FDXd/IAOY5dqUxczZnGX6fmWeeeQaZmZkYN24cMjMz0aJFC0RFRcHX11d2aS4VEBAguwTdMJuamM34goKAc+eAixeB2rX/fVyvfImJwMMPA8OHA9OmyZkjY5ZjVxozZ3OW4ZsZDw8PDBkyBEOGDJFdChGRUvz9xYqmxERA78HsQ4dEI/PSS8DUqZzsS65l+MtMJBSe1GU2zKYmZlNDaSuatM536JCYI/Pyy/JGZGzMdOyKM3M2Z7GZUURqaqrsEnTDbGpiNjWU1sxome/330Uj8+qrYkRGNjMdu+LMnM1Zhl/NZC+zr2YiInLEokXAF18A27dr/9q//y4uLY0cCUyerP3rk3twi9VMRETkOL02zjt4UIzIjBrFRobkYzOjiMIbD5kNs6mJ2dQQFAT88w9w4cK/jzmb78AB0ciMHg1MmuRkgRoz07ErzszZnMVmRhFa7JBoVMymJmZTQ40awG23FR2dcSZfQoK4tDR2LPDWWxoUqDEzHbvizJzNWZwzo4isrCz4+PjILkMXzKYmZlNHjx5AWJhYbQQ4ni8hAejSBQgPB4z6c9Vsx64ws2bjnBk3YsYT2IbZ1MRs6ig+b8aRfL/9JkZkxo83biMDmO/YFWbmbM5iM0NEZHLOTgL+9VcxIjNhAvDGG9rVRaQVNjOKKHw3UrNhNjUxmzqKNzMVyffLL6KRmThR/DE6sx27wsyczVlsZhTh7+8vuwTdMJuamE0dQUHA+fPiD2B/vl9+AR55BIiIEKMyKjDbsSvMzNmcxQnARERuoGFDYNkycUdre+zfD3TtKubHjB+va2nk5jgBmIiI7BIUZP+8mZ9/FiMyb77JRobUwGZGESkpKbJL0A2zqYnZ1FJ43kx5+fbtEyMykycD48a5qDgNmfHY2Zg5m7PYzChi7ty5skvQDbOpidnUEhwMJCaK/y8r308//dvIjB3rwuI0ZMZjZ2PmbM7inBkiIjfw00/Ao4/+Owm4tM936ybufD1mjCsrI3fHOTNERGSXoCBxf6Z//in5uR9/FI3MtGlsZEhNbGaIiNyAn59Y0VR8EnB8PNC9O/D22+LGkUQqYjOjCIvFIrsE3TCbmphNPbZJwLZ8e/eKRmbGDGDUKMnFacSsxw4wdzZnsZlRREhIiOwSdMNsamI29diWZ4eEhGDPHnEDypkzgZEjZVemHbMeO8Dc2ZzFZkYR7dq1k12CbphNTcymHtvIjNXaDj17Au++C7z6quyqtGXWYweYO5uzKskugIiIXCM4WNyioGdP4L33gJdfll0RkTY4MqOIpKQk2SXohtnUxGzqCQoCPDyAsWOTTNvImPXYAebO5iw2M4qIiYmRXYJumE1NzKaeatXE8mxfX3PmA8x77ABzZ3MWN80jIiIiabhpHhEREbk9NjNERESkNDYzioiKipJdgm6YTU3Mpi4z52M298RmRhFhYWGyS9ANs6mJ2dRl5nzM5p44AZiIiIik4QRgIiIicntsZhQRHx8vuwTdMJuamE1dZs7HbO6JzYwiEhISZJegG2ZTE7Opy8z5mM09cc4MERERScM5M0REROT22MwQERGR0tjMKCIiIkJ2CbphNjUxm7rMnI/Z3BPnzCgiJSUFAQEBssvQBbOpidnUZeZ8zKYezplxI2Y8gW2YTU3Mpi4z52M298RmhoiIiJTGZkYRcXFxskvQDbOpidnUZeZ8zOae2MwoIjU1VXYJumE2NTGbusycj9ncEycAExERkTScAExERERuj82MIrKysmSXoBtmUxOzqcvM+ZjNPRm2mYmLi8PIkSPRu3dvhIWFYcyYMTh48KDssqSJjIyUXYJumE1NzKYuM+djNvdUSXYBZcnMzMSgQYNw5513onLlyli/fj0mTpyIZcuWoU6dOrLLc7nQ0FDZJeiG2dTEbOoycz5mc0+GHZl58skn0bZtW1StWhWVKlVCnz594OnpiaSkJNmlSbFjxw7ZJeiG2dTEbOoycz5mc0+GbWaKS0xMxLVr19CkSRPZpRAREZGBuPwy03vvvVfuxj8hISGYM2dOkcf++ecfTJ8+Hc8++yzq1atX6tfl54sV5mlpaZrVaiSHDh1iNgUxm5rMnA0wdz5mU48tk+3nuCNcvs/MtWvXkJOTU+bnvby84OfnV/BxcnIyxo8fj06dOuGll14q8+vOnz+Pp59+WtNaiYiIyDVWr17t8JxYQ2+ad/z4cUyYMAFhYWEYNGhQuc+1Wq24ePEifH194eHh4aIKiYiIyBn5+fm4du0aateuDU9Px2a/GHY10++//47IyEgMGjQIffr0ueHzPT093XKVExERkeoKX5FxhGFHZsaOHYuEhARUqVKlyOMDBgzAwIEDJVVFRERERmPYZoaIiIjIHsoszSYiIiIqjWHnzNhr+/btWL9+PY4fP47MzExs27YNXl5essvSxIIFCxAfH49z587Bx8cHrVu3xogRI3DLLbfILs1py5Ytw5YtW3DlyhV4eXmhefPmGDFiBJo2bSq7NM1NmjQJu3btwqxZs9CmTRvZ5Thl6dKlWLFiBSpXrlzw2AMPPIBJkyZJrEpbhw4dwqJFi5CUlARPT080atQI8+bNc3hiohEMHjwY586dK/g4Pz8f2dnZmD59Ojp06CCxMm1cunQJ8+fPx6+//oqcnBw0bNgQw4YNQ+vWrWWX5rT09PSCnwVXr15FcHAwXnvtNTRo0EB2aRV2o5/Xx48fx7x583D06FFUq1YNjz32GJ5//nm7FvUo38z4+fkhLCwM2dnZ+OCDD2SXoykPDw9MnDgRjRs3RnZ2NmbPno3IyEgsXLhQdmlOCw0Nxf/93/+hevXqyM3Nxddff40JEyZgzZo1pmlGAXGPMbPdHC4oKAgfffSR7DJ0cejQIUycOBGjRo3CzJkz4e3tjSNHjii/QnLp0qVFPl63bh2WL1+O+++/X05BGpszZw5SU1OxePFiVK9eHevWrUNkZCS+/PJL1KhRQ3Z5TnnvvfeQm5uLhQsXwtfXFwsWLMD48eOxbNky+Pr6yi6vQsr7eZ2ZmYkJEyagR48eeP/995GcnIyJEyeiWrVq6Nu37w1fW91fNf6/tm3bokuXLmVupqeyYcOGoUWLFvD29oafnx/69++P48ePIz09XXZpTmvQoAGqV68OQPyW6OnpicuXL5sim8358+exePFijB8/XnYpZCeLxYJevXqhe/fu8PHxgZeXF4KCgpRvZor75ptv0KtXryIjbCpLTk5Gx44d4e/vDy8vLzz++OO4du0azpw5I7s0p1y7dg3x8fEYPHgwatasicqVK2P48OG4ePEidu3aJbu8Civv5/XOnTthtVoxZMgQVKlSBY0bN8YzzzyD9evX2/Xayo/MuJN9+/ahbt26BU2A6vbu3Yt33nkHGRkZ8PDwQJ8+feDv7y+7LE3k5+fj/fffx8CBA1G3bl3Z5Wjq2LFjeOKJJ+Dj44Pg4GAMHToUt956q+yynJaVlYVDhw4hKCgIL7/8Ms6ePYu6detiwIAB6NSpk+zyNPPLL7/gzJkz6N27t+xSNNO/f39s3LgRoaGhqFmzJtavX4969eqZ4vY3+fn5RXbGtf3/H3/8ga5du8oqS3PHjx9H06ZNi4zMBwYG4uzZs8jIyEC1atXK/Xo2M4rYv38/li9fjmnTpskuRTPt27fHt99+i7S0NMTFxZlqn6CYmBjk5+fj8ccfl12Kpjp16oQePXqgbt26uHDhAiwWC8aPH18wBK6y9PR0WK1WbNmyBTNnzkSzZs2we/duvP3227j55psRHBwsu0RNxMTE4L777jNFA2pz5513YuvWrQU3JK5RowamT59eYmsP1fj6+qJNmzZYsmQJIiMj4evri88++wz5+fnIzMyUXZ6mMjIySuw1Y/vFPTMz84bNjPKXmdzB3r17MWXKFERGRqJt27ayy9FcjRo18NRTT2HWrFk4duyY7HKclpycjBUrVpjy8tIdd9yBgIAAeHh4oE6dOpgwYQLOnz+P33//XXZpTrM1Y927d0dgYCC8vLzQsWNHtG7dWskh/dJcuHABu3fvRlhYmOxSNGO1WjFu3DjUqlULMTEx2LJlC8aNG4eIiAhT/HsSGRmJ2rVrY8SIERg4cCD8/PzQoEED1KxZU3ZpmqpWrRquXr1a5DHbtIOqVave8Os5MmNwW7duxdy5czF58mRTNjI2+fn5uH79OpKTk5Vf0XTw4EGkpaVhxIgRRR6fMmUKOnfubKomx8PDAx4eHk7dIM4o/Pz8UK9ePdPNjyns22+/xS233GKaib+A+IF39uxZTJ06tWCy70MPPYR69eph3759yv97ctNNNyEiIqLg48uXLyM6Ohr33HOPxKq016RJE2zbtg15eXkFl5qOHDmCevXq3XBUBjDByExeXh5ycnKQm5sLAMjJyUFOTg6sVqvkypz39ddfY968eZg5c6bpGpm1a9fi0qVLAIDU1FTMnj0blSpVwp133im5Mud17twZK1euxMKFCwv+AEB4eDiGDx8uuTrn7NixA1euXAEglsN+8MEHuOmmm0xx3ADgySefRGxsLI4dOwar1Yrdu3cjISHBFMuX8/LysHHjRjz++ONKLzMvrmbNmmjYsCHWr1+PjIwMWK1W7N27FydPnkTz5s1ll+e0U6dO4fLlywDEqO8777yDu+++W8ltHsr7ed2xY0d4enpiyZIlyM7OxokTJ7B69Wq7RxGV3wE4NjYWUVFRJR6fPXu28nsMhIaGwsvLC97e3kUej4qKwl133SWpKm1ERETgyJEjuHbtGqpWrYrAwEAMGjQILVq0kF2aLkJDQ02xz8ybb76JQ4cOISsrC9WrV8ddd92FIUOGoH79+rJL08wXX3yBmJgYXL16FbfddhsGDRqEhx56SHZZTvv+++/xzjvvYM2aNaa7RHHmzBl8+umnOHToEHJyclCnTh089dRTppiztmnTJixZsgTp6emoUaMGHn74YQwZMkTJlWg3+nl9/PhxzJ07F0ePHkXVqlXRu3dvu/eZUb6ZISIiIvdmnrFGIiIicktsZoiIiEhpbGaIiIhIaWxmiIiISGlsZoiIiEhpbGaIiIhIaWxmiIiISGlsZoiIiEhpbGaIiIhIaWxmiIiISGlsZoiIiEhpbGaIyJAuXbqEvn37Yu3atQWPrVq1Cv369Su4czcREQBUkl0AEVFpatWqhSlTpuD1119HYGAgcnNzsXz5csyePdt0d30mIufwrtlEZGhfffUVVq1ahby8PAwePBi9e/eWXRIRGQwvMxGRofXs2RPZ2dnw9vbGo48+KrscIjIgNjNEZGhRUVFo1qwZqlWrhgULFsguh4gMiHNmiMiw1qxZg8OHD2PBggVIS0vDyy+/jODgYHTs2FF2aURkIByZISJDOnjwIBYvXoypU6eiZs2auP322/H666/j/fffx+nTp2WXR0QGwgnAREREpDSOzBAREZHS2MwQERGR0tjMEBERkdLYzBAREZHS2MwQERGR0tjMEBERkdLYzBAREZHS2MwQERGR0tjMEBERkdL+H1vuHE20d88IAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import matplotlib and define an alias \"plt\".\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt #importing pyplot from matplotlib   #pyplot is a collection of command style functions that make matplotlib work like MATLAB.\n",
    "fig = plt.figure() # Create a figure object\n",
    "plt.style.use('classic')# Use the classic style for plotting\n",
    "plt.plot(x, y, label=\"label\")# Plot y versus x with label.\n",
    "plt.title(\"Matplotlib Example Plot\")# Add a title\n",
    "plt.xlabel(\"x\")# Add x-axis label\n",
    "plt.ylabel(\"y\")# Add y-axis label\n",
    "plt.grid()# Add a grid to the plot background.\n",
    "plt.legend()# Add legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2037fd6a9d0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 100)# 100 points between 0 and 10\n",
    "fig2 = plt.figure()\n",
    "plt.style.use('ggplot')# Use the ggplot style for plotting\n",
    "plt.plot(x, np.sin(x))# Plot y = sin(x). sin() is a numpy method.\n",
    "plt.plot(x, np.cos(x))# Plot y = cos(x). cos() is a numpy method.\n",
    "\n",
    "# Could you add the figure elements such as title, x-axis label, y-axis label and legend to the figure?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Historical average wheat yield data for the USA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011,\n",
       "       2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000,\n",
       "       1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989,\n",
       "       1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979, 1978,\n",
       "       1977, 1976, 1975, 1974, 1973, 1972, 1971, 1970, 1969, 1968, 1967,\n",
       "       1966, 1965, 1964, 1963, 1962, 1961, 1960, 1959, 1958, 1957, 1956,\n",
       "       1955, 1954, 1953, 1952, 1951, 1950, 1949, 1948, 1947, 1946, 1945,\n",
       "       1944, 1943, 1942, 1941, 1940, 1939, 1938, 1937, 1936, 1935, 1934,\n",
       "       1933, 1932, 1931, 1930, 1929, 1928, 1927, 1926, 1925, 1924, 1923,\n",
       "       1922, 1921, 1920, 1919, 1918, 1917, 1916, 1915, 1914, 1913, 1912,\n",
       "       1911, 1910, 1909, 1908, 1907, 1906, 1905, 1904, 1903, 1902, 1901,\n",
       "       1900, 1899, 1898, 1897, 1896, 1895, 1894, 1893, 1892, 1891, 1890,\n",
       "       1889, 1888, 1887, 1886, 1885, 1884, 1883, 1882, 1881, 1880, 1879,\n",
       "       1878, 1877, 1876, 1875, 1874, 1873, 1872, 1871, 1870, 1869, 1868,\n",
       "       1867, 1866])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The years for which we have average wheat yield data. Manually create a numpy array with years from 2021 to 1866\n",
    "dates = np.array([2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000,1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979, 1978, 1977, 1976, 1975, 1974, 1973, 1972, 1971, 1970, 1969, 1968, 1967,1966, 1965, 1964, 1963, 1962, 1961, 1960, 1959, 1958, 1957, 1956, 1955, 1954, 1953, 1952, 1951, 1950, 1949, 1948, 1947, 1946, 1945,1944, 1943, 1942, 1941, 1940, 1939, 1938, 1937, 1936, 1935, 1934, 1933, 1932, 1931, 1930, 1929, 1928, 1927, 1926, 1925, 1924, 1923,1922, 1921, 1920, 1919, 1918, 1917, 1916, 1915, 1914, 1913, 1912,1911, 1910, 1909, 1908, 1907, 1906, 1905, 1904, 1903, 1902, 1901,1900, 1899, 1898, 1897, 1896, 1895, 1894, 1893, 1892, 1891, 1890,1889, 1888, 1887, 1886, 1885, 1884, 1883, 1882, 1881, 1880, 1879,1878, 1877, 1876, 1875, 1874, 1873, 1872, 1871, 1870, 1869, 1868, 1867, 1866])\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([44.5, 49.7, 51.7, 47.6, 46.4, 52.7, 43.6, 43.7, 47.1, 46.2, 43.6,\n",
       "       46.1, 44.3, 44.8, 40.2, 38.6, 42. , 43.2, 44.2, 35. , 40.2, 42. ,\n",
       "       42.7, 43.2, 39.5, 36.3, 35.8, 37.6, 38.2, 39.3, 34.3, 39.5, 32.7,\n",
       "       34.1, 37.7, 34.4, 37.5, 38.8, 39.4, 35.5, 34.5, 33.5, 34.2, 31.4,\n",
       "       30.7, 30.3, 30.6, 27.3, 31.6, 32.7, 33.9, 31. , 30.6, 28.4, 25.8,\n",
       "       26.3, 26.5, 25.8, 25.2, 25. , 23.9, 26.1, 21.6, 27.5, 21.8, 20.2,\n",
       "       19.8, 18.1, 17.3, 18.4, 16. , 16.5, 14.5, 17.9, 18.2, 17.2, 17. ,\n",
       "       17.7, 16.4, 19.5, 16.8, 15.3, 14.1, 13.3, 13.6, 12.8, 12.2, 12.1,\n",
       "       11.2, 13.1, 16.3, 14.2, 13. , 15.4, 14.7, 14.7, 12.8, 16. , 13.3,\n",
       "       13.8, 12.7, 13.5, 12.9, 14.8, 13.2, 11.9, 16.7, 16.1, 14.4, 15.1,\n",
       "       12.4, 13.7, 15.5, 14.3, 14.2, 16. , 15.2, 12.9, 13.7, 14.9, 15. ,\n",
       "       12.2, 12.5, 15.2, 14. , 12.8, 13.9, 13.5, 12.4, 14.2, 16.5, 12.2,\n",
       "       14. , 12.1, 13.3, 14.1, 11.4, 14.8, 12.3, 15.1, 11. , 13.2, 13. ,\n",
       "       13.5, 14.1, 10.9, 11.1, 13. , 12.9, 11.8, 12.2, 12.1, 13.7, 12.9,\n",
       "       12.6, 11. ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The average wheat yield data in the order to line up with the corresponding years entered manually.\n",
    "yields = np.array([44.5, 49.7, 51.7, 47.6, 46.4, 52.7, 43.6, 43.7, 47.1, 46.2, 43.6, 46.1, 44.3, 44.8, 40.2, 38.6, 42. , 43.2, 44.2, 35. , 40.2, 42. , 42.7, 43.2, 39.5, 36.3, 35.8, 37.6, 38.2, 39.3, 34.3, 39.5, 32.7, 34.1, 37.7, 34.4, 37.5, 38.8, 39.4, 35.5, 34.5, 33.5, 34.2, 31.4, 30.7, 30.3, 30.6, 27.3, 31.6, 32.7, 33.9, 31. , 30.6, 28.4, 25.8, 26.3, 26.5, 25.8, 25.2, 25. , 23.9, 26.1, 21.6, 27.5, 21.8, 20.2, 19.8, 18.1, 17.3, 18.4, 16. , 16.5, 14.5, 17.9, 18.2, 17.2, 17. , 17.7, 16.4, 19.5, 16.8, 15.3, 14.1, 13.3, 13.6, 12.8, 12.2, 12.1, 11.2, 13.1, 16.3, 14.2, 13. , 15.4, 14.7, 14.7, 12.8, 16. , 13.3, 13.8, 12.7, 13.5, 12.9, 14.8, 13.2, 11.9, 16.7, 16.1, 14.4, 15.1, 12.4, 13.7, 15.5, 14.3, 14.2, 16. , 15.2, 12.9, 13.7, 14.9, 15. , 12.2, 12.5, 15.2, 14. , 12.8, 13.9, 13.5, 12.4, 14.2, 16.5, 12.2, 14. , 12.1, 13.3, 14.1, 11.4, 14.8, 12.3, 15.1, 11. , 13.2, 13. , 13.5, 14.1, 10.9, 11.1, 13. , 12.9, 11.8, 12.2, 12.1, 13.7, 12.9, 12.6, 11. ])\n",
    "yields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x250764d0190>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the corn yield trend\n",
    "fig = plt.figure()\n",
    "plt.style.use('classic')\n",
    "plt.plot(dates, yields, label=\"Wheat Yield\")\n",
    "plt.title(\"United States Average Wheat Yield by Year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Yield in bushels per acre\")\n",
    "plt.grid()\n",
    "plt.legend( loc='upper left')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Discussion 3* : files for Input and Output \n",
    "To this point we have only used the `print()` command for program output. Python also provides an `input()` function for user input of single variables, etc.However, in order to handle large amounts of data we need a way to handle files for reading and writing.  \n",
    "  *All of the data in this lab will be located in a `Data` subdirectory relative to the home directory where python is running.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a simple example of file I/O we will open a file containing a poem and we will write some code to reverse the order of the characters and write it back to disk.  \n",
    "`Limericks` are five line poems intended to amuse the reader. The rhyming scheme is such that the 1st, 2nd, and 5th lines rhyme with each other and the 3rd and 4th lines rhyme separately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The `input()` command.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello Sneha! Welcome to data input example in Python Programming\n"
     ]
    }
   ],
   "source": [
    "name=input(\"Enter your name: \")\n",
    "print(\"Hello \"+name+\"! Welcome to data input example in Python Programming\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"The text file PLimerick located in the Data subdirectory contains a limerick. Define a string variable \n",
    "to hold the file name (and the path to it) by entering the text: `Data/Limerick`\n",
    "into the text box that appears when you execute the input() command below.\"\"\"\n",
    "\n",
    "my_file_name = input('Enter a filename that you want to open: ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Data/Limerick'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check your filename ...\n",
    "print(type(my_file_name))\n",
    "my_file_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The `open()` command**  \n",
    "When opening a file there are several modes that can be set and one must be careful because it is possible and easy to accidently overwrite an existing file. The modes of most interest are: \n",
    "1. `'r'` ... read-only\n",
    "2. `'w'` ... write-only\n",
    "3. `'a'` ... write-only but leave old data intact and append new data at file end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data/Limerick \n",
      "\n",
      "Wish I Were a Fly on the Wall\n",
      "by Robert D. Cowan\n",
      "\n",
      "There once was a fly on the wall\n",
      "I wonder why didn't it fall\n",
      "Because its feet stuck\n",
      "Or was it just luck\n",
      "Or does gravity miss things so small?"
     ]
    }
   ],
   "source": [
    "\"\"\"Open the file for read-only. Then print it line by line\n",
    "and close the file. To see why the **end=''** is added to the\n",
    "print command, remove it, run, and see what happens.\n",
    "Notice that the open command looks in the current working \n",
    "directory for the file. If you get an error, make sure that you have the correct path to the file.\"\"\"\n",
    "\n",
    "print(my_file_name, '\\n')\n",
    "temp_file = open(my_file_name, \"r\")#open the file for read-only\n",
    "for line_str in temp_file:#print it line by line\n",
    "    print(line_str, end='')#end='' is added to the print command, remove it, run, and see what happens.\n",
    "temp_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Input and Output from a Python Program**  \n",
    "\n",
    "Until now the available ways to communicate with a program have been via what is termed `standard input` and `standard output`. For standard input the input device is the **keyboard** and for standard output the output device is the **terminal window** or in case of jupyter the **interactive window**. Our examples so far have made use of the `print()` command and the `input()` command.\n",
    "\n",
    "Standard input and output are really only useful for simple communications using very short messages. In the typical case in analyzing large data we need to use `file input` and `file output`.\n",
    "\n",
    "The `open()` command above creates a sort of pipe between the operating system of the computer, which can access and modify files stored on disk, and the python program.\n",
    "\n",
    "When it does this the command creates a **file object**. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a name for a new file to put the reversed poem in.\n",
    "my_new_file_name = my_file_name + 'Backwards'\n",
    "\n",
    "# This creates the new file and, if aleady exists, erases any \n",
    "# old content and replaces it with the first part of the poem\n",
    "fout = open(my_new_file_name, \"w\")\n",
    "fout.close()\n",
    "\n",
    "# Get the data out of the original file and put it into a long\n",
    "# string variable called \"longline\" which we initialize as an\n",
    "# empty string.\n",
    "longline = ''\n",
    "fin = open(my_file_name, \"r\")\n",
    "\n",
    "for line in fin:\n",
    "    longline = longline + line\n",
    "\n",
    "fin.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ag Data Analysis I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Discussion 4*: Data Wrangling Methods—String manipulation I\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the string variable longline in two ways.\n",
    "\n",
    "First print as a single string\n",
    "Then use the print command to see how Python formats it for display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Wish I Were a Fly on the Wall\\nby Robert D. Cowan\\n\\nThere once was a fly on the wall\\nI wonder why didn't it fall\\nBecause its feet stuck\\nOr was it just luck\\nOr does gravity miss things so small?\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As a string\n",
    "longline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wish I Were a Fly on the Wall\n",
      "by Robert D. Cowan\n",
      "\n",
      "There once was a fly on the wall\n",
      "I wonder why didn't it fall\n",
      "Because its feet stuck\n",
      "Or was it just luck\n",
      "Or does gravity miss things so small?\n"
     ]
    }
   ],
   "source": [
    "# Via print command\n",
    "print(longline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write a function that takes an input string of arbitrary length and returns the string in reverse order ...**\n",
    "\n",
    "Test it on the string `bananas` and then run it on longline to create a string variable backwardslongline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hint:** A string variable can be iterated through one character at a time just as we did in the Lect 1 with the for loop ... `for x in 'banana:'` Your function will take an input string and return an output string. Before the loop begins the output string will be initialized as the empty string. Then, using the for loop, characters will be read from the input string and pre-pended to the output string, reversing the input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Skeleton\n",
    "\n",
    "def anameforyourfunction(instring):\n",
    "    initialize output string\n",
    "    for loop ...\n",
    "    pre-pend to output string\n",
    "    return the output string\n",
    "\n",
    "# Test it on 'bananas' ...\n",
    "\n",
    "anameforyourfunction('bananas')\n",
    "\n",
    "# Reverse longline variable ...\n",
    "\n",
    "backwardslongline = anameforyourfunction(longline)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hint on Line Reverser**\n",
    "\n",
    "The function is to take an input string of arbitrary length as its input and return as output the string in reverse order.\n",
    "\n",
    "For example: \n",
    "\n",
    "1. If the input string is `bananas` then the output should be `sananab`.\n",
    "2. If the input string is `But here's my number, so call me, maybe` then the output should be `ebyam ,em llac os ,rebmun ym s'ereh tuB`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some useful ideas ...\n",
    "instring = 'But here\\'s my number, so call me, maybe'; # notice the backslash!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instring is an array of characters and we can address them with the notation ...\n",
    "instring[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can find the length of instring using the command\n",
    "len(instring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0;\n",
    "while i < len(instring):\n",
    "    print(instring[i])\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What if I wanted to go in reverse?\n",
    "\n",
    "j = len(instring) - 1\n",
    "while j >= 0:\n",
    "    print(instring[j])\n",
    "    j = j - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "def mystringreverse(instring):\n",
    "    outstring = ''\n",
    "    for x in instring:\n",
    "        outstring = x + outstring\n",
    "    return outstring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sananab'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test it on 'bananas' ...\n",
    "\n",
    "mystringreverse('bananas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reverse longline variable ...\n",
    "\n",
    "backwardslongline = mystringreverse(longline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"?llams os sgniht ssim ytivarg seod rO\\nkcul tsuj ti saw rO\\nkcuts teef sti esuaceB\\nllaf ti t'ndid yhw rednow I\\nllaw eht no ylf a saw ecno erehT\\n\\nnawoC .D treboR yb\\nllaW eht no ylF a ereW I hsiW\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See what it looks like ...\n",
    "\n",
    "backwardslongline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now print to the new file\n",
    "fout = open(my_new_file_name, \"a\")\n",
    "print(backwardslongline, file=fout)\n",
    "\n",
    "fout.close() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Discussion 5*: Get USDA Corn yield data from file\n",
    " **Example:** Lets process the raw string data to numerical data and put them into informative columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "Data was obtained from the United States Department of Agriculture: <a href=\"https://quickstats.nass.usda.gov/\" target=\"_blank\">Quick Stats</a> (The USDA's National Ag Statistics Service -- we will make more use of this in the lab later)\n",
    "\n",
    "The choices made on the Quick Stats page are shown below ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img align=\"left\" src='Figs/USDA-NASS-screen-shot.png' width=\"700\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A file is found in the `Data` subdirectory which was obtained by ...**\n",
    "1. Pressing the `Get Data` button on NASS webpage\n",
    "2. Saving it as a `csv` file\n",
    "3. Opening the `csv` file with Microsoft Excel to delete superfluous columns and saving the file as `US-National-CornYields.csv`\n",
    "\n",
    "Our **goal** now is to read this file and use the data to make a plot of average corn yield vs. year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To that end let's read in the corn yields data file, print the first few lines, and examine the data, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year,Value\n",
      "2021,176.3\n",
      "2020,172\n",
      "2019,167.5\n",
      "2018,176.4\n",
      "2017,176.6\n",
      "2016,174.6\n",
      "2015,168.4\n",
      "2014,171\n",
      "2013,158.1\n",
      "2012,123.1\n",
      "2011,146.8\n",
      "2010,152.6\n",
      "2009,164.4\n",
      "2008,153.3\n",
      "2007,150.7\n",
      "2006,149.1\n",
      "2005,147.9\n",
      "2004,160.3\n",
      "2003,142.2\n"
     ]
    }
   ],
   "source": [
    "# Put the file name into a str variable for convenience\n",
    "file_name = 'Data/US-National-CornYields.csv' # This is a relative path to the file. It is relative to the current working directory.\n",
    "\n",
    "# Open the file for reading only and print the first few \n",
    "# lines.\n",
    "\n",
    "fin = open(file_name, \"r\")#open the file for reading only\n",
    "\n",
    "for i in range(20):\n",
    "    line = fin.readline()\n",
    "    print(line, end='')\n",
    "\n",
    "fin.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example 1**: **What is here ... ?**\n",
    "\n",
    "The first line of the file contains the header information. Now that we know it, we are not in much need of it. \n",
    "\n",
    "The variable `line` in the cell above has taken in turn the first 20 lines of the file `'Data/US-National-CornYields.csv'`. When the loop ends at the 20th step the variable `line` should still contain the last line read. Let's take a closer look at it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003,142.2\n",
      "\n",
      "<class 'str'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2003,142.2\\n'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the last line of the 20 lines read in the previous cell from the file\n",
    "print(line)\n",
    "print(type(line))\n",
    "# What is really in the line variable?\n",
    "line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Clearly the lines are just text strings ...\n",
    "\n",
    "The data fields are separated by a comma, the first field containing the year, and the second containing the U.S. average corn yield for that year. \n",
    "\n",
    "The line ends with a `\\n` new line character"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have learnt about string manipulation in previous workbook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some example string operations ...**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**United states wheat yield trends as a plotting example simpliefied ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2003,142.2'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remember the variable line which was set at the 20th line of the file\n",
    "# Remove the newline character, which was found at the end of each line in the\n",
    "# yield data file\n",
    "\n",
    "line.replace('\\n','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2003', '142.2']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the remaining line on the comma. Note that we can cascade these\n",
    "# string methods ...\n",
    "\n",
    "line.replace('\\n','').split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'> 2003\n",
      "<class 'float'> 142.2\n"
     ]
    }
   ],
   "source": [
    "# The elements in the list above are still text strings, which cannot be plotted\n",
    "# until they are converted to numbers (either floats or ints)\n",
    "\n",
    "templist = line.replace('\\n','').split(',')\n",
    "d = int(templist[0])\n",
    "y = float(templist[1])\n",
    "print(type(d),d)\n",
    "print(type(y),y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the years for which we have average corn yield data:  [2021. 2020. 2019. 2018. 2017. 2016. 2015. 2014. 2013. 2012. 2011. 2010.\n",
      " 2009. 2008. 2007. 2006. 2005. 2004. 2003. 2002. 2001. 2000. 1999. 1998.\n",
      " 1997. 1996. 1995. 1994. 1993. 1992. 1991. 1990. 1989. 1988. 1987. 1986.\n",
      " 1985. 1984. 1983. 1982. 1981. 1980. 1979. 1978. 1977. 1976. 1975. 1974.\n",
      " 1973. 1972. 1971. 1970. 1969. 1968. 1967. 1966. 1965. 1964. 1963. 1962.\n",
      " 1961. 1960. 1959. 1958. 1957. 1956. 1955. 1954. 1953. 1952. 1951. 1950.\n",
      " 1949. 1948. 1947. 1946. 1945. 1944. 1943. 1942. 1941. 1940. 1939. 1938.\n",
      " 1937. 1936. 1935. 1934. 1933. 1932. 1931. 1930. 1929. 1928. 1927. 1926.\n",
      " 1925. 1924. 1923. 1922. 1921. 1920. 1919. 1918. 1917. 1916. 1915. 1914.\n",
      " 1913. 1912. 1911. 1910. 1909. 1908. 1907. 1906. 1905. 1904. 1903. 1902.\n",
      " 1901. 1900. 1899. 1898. 1897. 1896. 1895. 1894. 1893. 1892. 1891. 1890.\n",
      " 1889. 1888. 1887. 1886. 1885. 1884. 1883. 1882. 1881. 1880. 1879. 1878.\n",
      " 1877. 1876. 1875. 1874. 1873. 1872. 1871. 1870. 1869. 1868. 1867. 1866.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Put the file name into a str variable for convenience\n",
    "file_name = 'Data/US-National-CornYields.csv'\n",
    "# Read the whole file using a for loop and create the data to be plotted\n",
    "fin = open(file_name, \"r\")\n",
    "fin.readline() #Read first line to move past header\n",
    "corndates = np.array([])\n",
    "cornyields = np.array([])\n",
    "\n",
    "for line in fin:\n",
    "    templist = line.replace('\\n','').split(',')\n",
    "    corndates = np.append(corndates, float(templist[0]))#append the year values to the array corndates\n",
    "    cornyields = np.append(cornyields, float(templist[1]))#append the yield values to the array cornyields\n",
    "\n",
    "fin.close()\n",
    "print(\"the years for which we have average corn yield data: \",corndates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the histroical corn yield:  64.3051282051282\n",
      "The standard deviation of the histroical corn yield:  49.26904394901762\n",
      "The minimum of the histroical corn yield:  18.2\n",
      "The maximum of the histroical corn yield:  176.6\n",
      "The variance of the histroical corn yield:  2427.43869165023\n"
     ]
    }
   ],
   "source": [
    "#Does this central tendency measure make sense for the data?\n",
    "print(\"The mean of the histroical corn yield: \",cornyields.mean())\n",
    "print(\"The standard deviation of the histroical corn yield: \",cornyields.std()) \n",
    "print(\"The minimum of the histroical corn yield: \",cornyields.min())\n",
    "print(\"The maximum of the histroical corn yield: \",cornyields.max())\n",
    "print(\"The variance of the histroical corn yield: \",cornyields.var())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to plot the graph similar to the plot in **Discussion 2**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `plot` not found.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the corn yield trend\n",
    "fig = plt.figure() #create a figure object\n",
    "plt.style.use('classic') #set the style of the plot\n",
    "plt.plot(corndates, cornyields,color=\"green\", label=\"Corn Yield\") #plot the data\n",
    "plt.title(\"United States Average Corn Yield by Year\")#set the title of the plot\n",
    "plt.xlabel(\"Year\")#set the x-axis label\n",
    "plt.ylabel(\"Yield in bushels per acre\")#set the y-axis label\n",
    "plt.grid()\n",
    "#Could you add the legend to the plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### *Discussion 6* : Ag Data Analysis Methods — Graphical Comparison\n",
    " \n",
    " \n",
    " Plot and compare Corn and Wheat yield from Quickstat\n",
    "\n",
    "Copy the corn data from the downloaded csv file and plot them, however might be interesting to compare them . Copying wheat yield data **d,y** cells from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdates = np.array([2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000,1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979, 1978, 1977, 1976, 1975, 1974, 1973, 1972, 1971, 1970, 1969, 1968, 1967,1966, 1965, 1964, 1963, 1962, 1961, 1960, 1959, 1958, 1957, 1956, 1955, 1954, 1953, 1952, 1951, 1950, 1949, 1948, 1947, 1946, 1945,1944, 1943, 1942, 1941, 1940, 1939, 1938, 1937, 1936, 1935, 1934, 1933, 1932, 1931, 1930, 1929, 1928, 1927, 1926, 1925, 1924, 1923,1922, 1921, 1920, 1919, 1918, 1917, 1916, 1915, 1914, 1913, 1912,1911, 1910, 1909, 1908, 1907, 1906, 1905, 1904, 1903, 1902, 1901,1900, 1899, 1898, 1897, 1896, 1895, 1894, 1893, 1892, 1891, 1890,1889, 1888, 1887, 1886, 1885, 1884, 1883, 1882, 1881, 1880, 1879,1878, 1877, 1876, 1875, 1874, 1873, 1872, 1871, 1870, 1869, 1868, 1867, 1866])\n",
    "wyields = np.array([44.5, 49.7, 51.7, 47.6, 46.4, 52.7, 43.6, 43.7, 47.1, 46.2, 43.6, 46.1, 44.3, 44.8, 40.2, 38.6, 42. , 43.2, 44.2, 35. , 40.2, 42. , 42.7, 43.2, 39.5, 36.3, 35.8, 37.6, 38.2, 39.3, 34.3, 39.5, 32.7, 34.1, 37.7, 34.4, 37.5, 38.8, 39.4, 35.5, 34.5, 33.5, 34.2, 31.4, 30.7, 30.3, 30.6, 27.3, 31.6, 32.7, 33.9, 31. , 30.6, 28.4, 25.8, 26.3, 26.5, 25.8, 25.2, 25. , 23.9, 26.1, 21.6, 27.5, 21.8, 20.2, 19.8, 18.1, 17.3, 18.4, 16. , 16.5, 14.5, 17.9, 18.2, 17.2, 17. , 17.7, 16.4, 19.5, 16.8, 15.3, 14.1, 13.3, 13.6, 12.8, 12.2, 12.1, 11.2, 13.1, 16.3, 14.2, 13. , 15.4, 14.7, 14.7, 12.8, 16. , 13.3, 13.8, 12.7, 13.5, 12.9, 14.8, 13.2, 11.9, 16.7, 16.1, 14.4, 15.1, 12.4, 13.7, 15.5, 14.3, 14.2, 16. , 15.2, 12.9, 13.7, 14.9, 15. , 12.2, 12.5, 15.2, 14. , 12.8, 13.9, 13.5, 12.4, 14.2, 16.5, 12.2, 14. , 12.1, 13.3, 14.1, 11.4, 14.8, 12.3, 15.1, 11. , 13.2, 13. , 13.5, 14.1, 10.9, 11.1, 13. , 12.9, 11.8, 12.2, 12.1, 13.7, 12.9, 12.6, 11. ])        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the histroical wheat yield:  23.51346153846154\n",
      "The standard deviation of the histroical wheat yield:  12.106303118509741\n",
      "The minimum of the histroical wheat yield:  10.9\n",
      "The maximum of the histroical wheat yield:  52.7\n",
      "The variance of the histroical wheat yield:  146.5625751972387\n"
     ]
    }
   ],
   "source": [
    "#central tendency measures for wheat yield\n",
    "print(\"The mean of the histroical wheat yield: \",wyields.mean())\n",
    "print(\"The standard deviation of the histroical wheat yield: \",wyields.std()) \n",
    "print(\"The minimum of the histroical wheat yield: \",wyields.min())\n",
    "print(\"The maximum of the histroical wheat yield: \",wyields.max())\n",
    "print(\"The variance of the histroical wheat yield: \",wyields.var())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x250767673d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the wheat and corn yield trend on the same axes\n",
    "ax = plt.subplots()\n",
    "plt.style.use('classic')\n",
    "ax=plt.plot(corndates, cornyields,color=\"green\", label=\"Corn Yield\") #corn yield data\n",
    "plt.title(\"US Average Corn (blue) and Wheat (green) Yields by Year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Yield in bushels per acre\")\n",
    "plt.grid()\n",
    "ax1=plt.plot(wdates, wyields,color=\"blue\", label=\"Wheat yield\")#wheat yield data\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### *Discussion 7*: Data Wrangling Methods —String Manipulation II \n",
    "\n",
    "\n",
    "Get Agricultural land use data \n",
    "\n",
    "\n",
    "We are interested in the land asset in US states. For example,we want to look at the value of land per acre in Arkansas, Colorado and Kansas.  \n",
    "\n",
    "The choices on the USDA NASS quickstat webpage that result in the `csv` below are:\n",
    "\n",
    "<img align=\"left\" src='Figs/USDA-NASS-screen-shot--landvalues.png' width=\"700\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Program\",\"Year\",\"Period\",\"Week Ending\",\"Geo Level\",\"State\",\"State ANSI\",\"Ag District\",\"Ag District Code\",\"County\",\"County ANSI\",\"Zip Code\",\"Region\",\"watershed_code\",\"Watershed\",\"Commodity\",\"Data Item\",\"Domain\",\"Domain Category\",\"Value\",\"CV (%)\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"ARKANSAS\",\"05\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"2,130\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"CALIFORNIA\",\"06\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"5,900\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"COLORADO\",\"08\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"1,400\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"FLORIDA\",\"12\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"6,320\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"GEORGIA\",\"13\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"3,130\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"IDAHO\",\"16\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"1,890\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"KANSAS\",\"20\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"2,250\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"LOUISIANA\",\"22\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"3,020\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"MISSISSIPPI\",\"28\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"2,830\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"MISSOURI\",\"29\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"3,700\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"MONTANA\",\"30\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"835\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"NEBRASKA\",\"31\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"3,990\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"NEW MEXICO\",\"35\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"485\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"OKLAHOMA\",\"40\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"1,790\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"OREGON\",\"41\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"2,340\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"SOUTH DAKOTA\",\"46\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"3,360\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"TEXAS\",\"48\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"2,090\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"UTAH\",\"49\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"1,550\",\"\"\n",
      "\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"WASHINGTON\",\"53\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"1,310\",\"\"\n"
     ]
    }
   ],
   "source": [
    "# Put the file name into a str variable for convenience.\n",
    "file_name = 'Data/US-LandValue-by-State-and-Year.csv' # This is a relative path to the file. It is relative to the current working directory.\n",
    "\n",
    "# Open the file for reading only and print the first few \n",
    "# lines.\n",
    "\n",
    "fin = open(file_name, \"r\")\n",
    "\n",
    "for i in range(20):\n",
    "    line = fin.readline()\n",
    "    print(line, end='')\n",
    "\n",
    "fin.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The situation here is clearly more complicated than what we've seen before ...**\n",
    "\n",
    "Let's look at the header and a representative data line separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fin = open(file_name, \"r\")\n",
    "\n",
    "headerline = fin.readline()\n",
    "dataline = fin.readline()\n",
    "\n",
    "fin.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('\"Program\",\"Year\",\"Period\",\"Week Ending\",\"Geo Level\",\"State\",\"State ANSI\",\"Ag District\",\"Ag District Code\",\"County\",\"County ANSI\",\"Zip Code\",\"Region\",\"watershed_code\",\"Watershed\",\"Commodity\",\"Data Item\",\"Domain\",\"Domain Category\",\"Value\",\"CV (%)\"\\n',\n",
       " '\"SURVEY\",\"2021\",\"YEAR\",\"\",\"STATE\",\"ARKANSAS\",\"05\",\"\",\"\",\"\",\"\",\"\",\"\",\"00000000\",\"\",\"AG LAND\",\"AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE\",\"TOTAL\",\"NOT SPECIFIED\",\"2,130\",\"\"\\n')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headerline, dataline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Looking at these we see ...`\n",
    "\n",
    "* There are many columns that are superfluous for our application. We want to look at the trends over time of the values of ag land and attempt to correlate with corn productivity.\n",
    "\n",
    "* To this end the columns of interest are `Year`, `State`, and `Value` since we already know the context\n",
    "\n",
    "* We also note that comma can appear in the data both in the text string under the \"Data Item\" column and in the \"Value\" column. So we don't want to simply split on comma as it will create false columns. One thing we could do is to split on `\",\"` and use the fact that they've put these superfluous double quotes on the lines.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Let's try more string.split method with `dataline` ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\"SURVEY',\n",
       " '2021',\n",
       " 'YEAR',\n",
       " '',\n",
       " 'STATE',\n",
       " 'ARKANSAS',\n",
       " '05',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '',\n",
       " '00000000',\n",
       " '',\n",
       " 'AG LAND',\n",
       " 'AG LAND, CROPLAND, NON-IRRIGATED - ASSET VALUE, MEASURED IN $ / ACRE',\n",
       " 'TOTAL',\n",
       " 'NOT SPECIFIED',\n",
       " '2,130',\n",
       " '\"\\n']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Experimenting with split method for finding the data we want ...\n",
    "\n",
    "dataline.split('\",\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2021'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The year field is in positon 1 of the list ...\n",
    "\n",
    "dataline.split('\",\"')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The state field is in positon 5 of the list ...\n",
    "\n",
    "dataline.split('\",\"')[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The value field is in positon 19 of the list ...\n",
    "\n",
    "dataline.split('\",\"')[19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can we convert this number to a float?\n",
    "\n",
    "float(dataline.split('\",\"')[19])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can but the float() function does not like the comma in the string. So we first\n",
    "# remove it and then call float ...\n",
    "\n",
    "dataline.split('\",\"')[19].replace(',','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now call float() and it works!!\n",
    "\n",
    "float(dataline.split('\",\"')[19].replace(',',''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Discussion 8*: Ag Data Analysis Methods — Graphical Comparison\n",
    "\n",
    "Let's get land values data out for some states of interest ... COLORADO, ARKANSAS, KANSAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put the file name into a str variable for convenience\n",
    "file_name = 'Data/US-LandValue-by-State-and-Year.csv'\n",
    "\n",
    "# Open the file for reading only and print the first few \n",
    "# lines.\n",
    "def landValueByState(state1,state2, state3):\n",
    "    fin = open(file_name, \"r\")\n",
    "    fin.readline() #Read first line to move past header\n",
    "    #declare empty arrays for year and land value for each state. This is needed to reset the arrays each time the cell is run. \n",
    "    #Python will append the data to the arrays each time the cell is run if the arrays are not reset.\n",
    "    d_st1 = np.array([])\n",
    "    v_st1 = np.array([])\n",
    "    d_st2 = np.array([])\n",
    "    v_st2 = np.array([])\n",
    "    d_st3 = np.array([])\n",
    "    v_st3 = np.array([])\n",
    "\n",
    "    for line in fin:\n",
    "        templist = line.split('\",\"')\n",
    "        state = templist[5]\n",
    "        if state == state1:\n",
    "            d_st1 = np.append(d_st1, float(templist[1])) #read the year\n",
    "            v_st1 = np.append(v_st1, float(templist[19].replace(',','')))#read the land value in dollars per acre for arkansas\n",
    "        elif state == state2:\n",
    "            d_st2 = np.append(d_st2, float(templist[1]))#read the year\n",
    "            v_st2 = np.append(v_st2, float(templist[19].replace(',','')))#read the land value in dollars per acre for colorado\n",
    "        elif state == state3:\n",
    "            d_st3 = np.append(d_st3, float(templist[1]))#read the year\n",
    "            v_st3 = np.append(v_st3, float(templist[19].replace(',','')))#read the land value in dollars per acre for kansas\n",
    "            \n",
    "    fin.close()\n",
    "    return d_st1, v_st1, d_st2, v_st2, d_st3, v_st3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_ark,v_ark,d_col,v_col,d_kan,v_kan = landValueByState(\"ARKANSAS\",\"COLORADO\",\"KANSAS\") #call the function landValueByState and assign the returned values to the variables d_ark,v_ark,d_col,v_col,d_kan,v_kan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2021., 2020., 2019., 2018., 2017., 2016., 2015., 2014., 2013.,\n",
       "        2012., 2011., 2010., 2009., 2008., 2007., 2006., 2005., 2004.,\n",
       "        2003., 2002., 2001., 2000., 1999., 1998., 1997.]),\n",
       " array([2250., 1970., 2050., 1960., 1910., 1970., 2110., 2160., 1840.,\n",
       "        1590., 1290., 1060.,  981.,  980.,  880.,  820.,  770.,  650.,\n",
       "         645.,  640.,  635.,  630.,  625.,  620.,  615.]))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_ark,v_kan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(880.0, 2130.0, 1532.8, 405.1816382809073)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_ark.min(),v_ark.max(),v_ark.mean(),v_ark.std()#get some stats on the land value in dollars per acre for arkansas.\n",
    "#Can you get the output in a more readable format using print statements?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the minimum value of non-irrigated land in Arkansas is 880.0 dollars per acre, Colorado is 380.0 dollars per acre and kansas 615.0 dollars per acre.\n"
     ]
    }
   ],
   "source": [
    "print(f\"the minimum value of non-irrigated land in Arkansas is {v_ark.min()} Dollars per acre, Colorado is {v_col.min()} Dollars per acre and kansas {v_kan.min()} Dollars per acre.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2507688a090>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the land value trends on the same axes\n",
    "fig = plt.figure()\n",
    "plt.style.use('classic')\n",
    "plt.plot(d_ark, v_ark, '*', label=\"Arkansas\")\n",
    "plt.title(\"US Average Non-irrigated Land Values by Year\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Dollars per acre\")\n",
    "plt.grid()\n",
    "plt.plot(d_col, v_col, '.', label=\"Colorado\")\n",
    "plt.plot(d_kan, v_kan, 'o', label=\"Kansas\")\n",
    "plt.legend(loc=\"upper left\")#add a legend to the plot and place it in the upper left corner"
   ]
  }
 ],
 "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}