1-Generating-Random-Numbers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# multidimensional arrays\n",
    "import numpy as np\n",
    "\n",
    "# inline plots\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# nicer figures\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['axes.titlesize'] = 16\n",
    "mpl.rcParams['axes.labelsize'] = 14\n",
    "mpl.rcParams['legend.fontsize'] = 12\n",
    "mpl.rcParams['xtick.labelsize'] = 12\n",
    "mpl.rcParams['ytick.labelsize'] = 12\n",
    "mpl.rcParams['axes.spines.right'] = False\n",
    "mpl.rcParams['axes.spines.top'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating uniformly distributed random numbers\n",
    "We will use `numpy` to generate random numbers. To generate numbers uniformly distributed between 0 and 1, we can use the function `np.random.uniform()`. If you execute the cell below more than once, you will see how the output changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7840317064701763\n"
     ]
    }
   ],
   "source": [
    "x = np.random.uniform() #se non inserisco nulla dentro la parentesi mi genera un solo numero\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate many random numbers at once, use the `size` keyword"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.14609531 0.77606629 0.0944927  0.80407444 0.4266986  0.16068978\n",
      " 0.22067894 0.92436409 0.94484418 0.91736785]\n"
     ]
    }
   ],
   "source": [
    "x_1d = np.random.uniform(size=10) #se non metto la keyword size non mi genera quessto numero di numeri ma mi genera altro\n",
    "print(x_1d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also generate a whole matrix of random uniform numbers at once!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.38382128 0.45105057 0.16503545]\n",
      " [0.46229301 0.04113109 0.69215016]\n",
      " [0.69514597 0.85839342 0.20473779]\n",
      " [0.08706844 0.05849166 0.65225015]]\n"
     ]
    }
   ],
   "source": [
    "x_2d = np.random.uniform(size=(4, 3)) #(righe,colonne)\n",
    "print(x_2d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that `x_1d` and `x_2d` are a vector and a matrix. In python, elements of vectors and matrices are accessed with the `[]` synthax. Notice that indexes start at 0. For instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([4, 7, -3.1, 2, 5, 6]) #questo è il modo in cui si crea un array in py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0] #accedere agli array-> sintassi delle qadre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1] #se scrivo la variabile e basta fa una cosa simile a print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also select a subset of a vector with the `[start:stop]` synthax. For example, to get the 2nd and 3rd elements of our array, we could do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.1,  2. ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[2:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matrices work the same way, but we use two indices instead of one. The first index corresponds to **rows** and the second index corresponds to **columns**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([\n",
    "    [1, 2, 3],\n",
    "    [4, 5, 6],\n",
    "    [7, 8, 9]\n",
    "]) #una matrice è un array di array, infatti metto una quadra e dentro ho altre quadre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 6])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[0:2, 2] #la differenza tra scrivere e no print sta nel fatto che se non lo uso mi scrive anche che tipo di oggetto sto guardando\n",
    "#indice 2 significa che stampa solo i due valori. Anche le operazioni sono un po' più sempliificate\n",
    "#in questo caso sto selezionando gli elementi con indice i t.c. 0<=i<2 della seconda colonna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you come from C, you might be used to operate on arrays iterating over them. In python, we can operate with arrays without iterating over them! For instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([\n",
    "    [1, 2, 3],\n",
    "    [4, 5, 6],\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4],\n",
       "       [5, 6, 7]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A + 1 #sommo +1 a tutti gli elementi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  4,  6],\n",
       "       [ 8, 10, 12]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * A #lo stesso ragionamento vale per ogni operazione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,   8,  27],\n",
       "       [ 64, 125, 216]], dtype=int32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A ** 3 #elevamento a potenza"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also make arithmetic operations between arrays. Python will \"guess\" what to do as long as the shapes of the last index(es) match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([\n",
    "    [1, 2, 3],\n",
    "    [4, 5, 6],\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n"
     ]
    }
   ],
   "source": [
    "v = np.array([0, 1, 2])\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Create a vector containing the numbers 9,...3, 2, 1. Then:\n",
    "\n",
    "+ Select the first element (9)\n",
    "+ Select the last element (1)\n",
    "+ Select the 2nd, 3rd and fourth elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "1\n",
      "[8 7 6]\n"
     ]
    }
   ],
   "source": [
    "# write your answer here\n",
    "v = np.array([9, 8, 7, 6, 5, 4, 3, 2, 1])\n",
    "#v=np.array(range[9,1,-1])\n",
    "# fuzione range-> prende tutti i valori nell'intervallo che gli indico io (indico con gli estremi) e con il passo che scelgo io (l'ultima cosa che inserisco nella parentesi quadra, se lo step è negativo torno indietro)\n",
    "print(v[0]) #il primo elemento è sempre indicato con l'indice zero\n",
    "print(v[-1]) #l'elemento con indice -1 è l'ultimo della fila (sempre)\n",
    "print(v[1:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "1. Create a random matrix A of 3 rows and 6 columns\n",
    "2. Add 1 to rows 0 and 2\n",
    "3. Substract 5 from columns 1 and 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.49640073 0.3915711  0.87815524 0.90489953 0.5981795  0.34220171]\n",
      " [0.45678157 0.89379965 0.77667668 0.79512119 0.59200589 0.29085041]\n",
      " [0.31694075 0.32156899 0.95087929 0.17168145 0.55812554 0.41360137]]\n",
      "[[1.49640073 1.3915711  1.87815524 1.90489953 1.5981795  1.34220171]\n",
      " [0.45678157 0.89379965 0.77667668 0.79512119 0.59200589 0.29085041]\n",
      " [1.31694075 1.32156899 1.95087929 1.17168145 1.55812554 1.41360137]]\n",
      "[[2.49640073 1.3915711  2.87815524 1.90489953 1.5981795  1.34220171]\n",
      " [1.45678157 0.89379965 1.77667668 0.79512119 0.59200589 0.29085041]\n",
      " [2.31694075 1.32156899 2.95087929 1.17168145 1.55812554 1.41360137]]\n"
     ]
    }
   ],
   "source": [
    "# write your code here\n",
    "#1\n",
    "A = np.random.uniform(size=(3,6))\n",
    "print(A)\n",
    "#2\n",
    "A[0,:]=A[0,:]+1 #se [non specifico niente oltre le due dimensioni della matrice sto dicendo che lo step è 1]\n",
    "A[2,:]=A[2,:]+1\n",
    "print(A)\n",
    "#3\n",
    "A[:,0]=A[:,0]+1\n",
    "A[:,2]=A[:,2]+1\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "How would you generate 1000 random numbers uniformely distributed between 5 and 20?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11.45814416  9.99947039 13.27213509 19.45433889  6.44058013 14.48735291\n",
      " 17.0843198  17.94422693  8.28992844 15.28492027]\n"
     ]
    }
   ],
   "source": [
    "# write your code here\n",
    "data = np.random.uniform(size=1000)*15+5 #è analogo a fare tutto diviso in due passaggi:\n",
    "#è come dire questo è il vettore, poi prendo il vettore e aggiungo 15 \n",
    "#poi prendo il vettore a cui ho aggiunto 15 e lo moltiplico per 5\n",
    "print(data[0:10]) #15=20-5 e poi moltiplico per 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the density of a sample\n",
    "As a simple sanity check, we want to plot the density of the data we have generated. We will use the function `plt.hist()` to plot the estimated pdf of our randomly generated data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4)) #definisco due variabili:\n",
    "#fig è la figura in se\n",
    "#ax è il contenuto della figura\n",
    "#in questo caso creo un solo subplot (quindi 1,1 -occupa tutto il quadrato- non devo mettere tot subplots in fila o in colonna)\n",
    "bins = np.linspace(0, 25, num=26)#(punto di partenza, punto di arrivo, quanti bin voglio)\n",
    "#disegno davvero l'istogramma\n",
    "ax.hist(\n",
    "    data, #inserisco i dati\n",
    "    bins=bins, #ampiezza della colonna per l'istogramma\n",
    "    density=True #solo per normalizzare\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure our estimation ressembles the actual probability density function of a uniform random variable, we will create a function that computes the pdf of a uniform random variable between $a$ and $b$ at an arbitrary point $x$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "What is the probability density function (pdf) of a uniform random variable between $a$ and $b$?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Write a function that gives the density of a uniform random variable between $a$ and $b$ at an arbitrary point $x$. Notice that you can define optional arguments by including them in the definition and giving them a default value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def uniform_pdf(x, a=0, b=1):\n",
    "    if x < a:\n",
    "        return 0\n",
    "    elif x > b:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1/abs(b-a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can add the expected pdf into the plot. The function `np.linspace(start, stop, num)` generates `num` equally spaced numbers between `start` and `stop`, both included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667 0.06666667\n",
      " 0.06666667 0.06666667 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "x = np.linspace(start=0, stop=25, num=1000)\n",
    "pdf_x = np.array([\n",
    "    uniform_pdf(t, a=5, b=20) #apro le quadre per l'array, chiamo la funzione appena definita\n",
    "    #definisco questa come funzione della variabile t (posso anche s enon l'ho ancora definita)\n",
    "    for t in x #faccio il ciclo for finchè i valori di t non hanno occupato tutto x\n",
    "])\n",
    "\n",
    "print(pdf_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2004921a370>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD7CAYAAABt0P8jAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAarElEQVR4nO3dcZBV53nf8e9PrAeRha0BbbYdpyuKBhxDEuR6M82ExPKU2MjuODAhfyhgAh0SYlF5JrSdiqRQCXCjRtNhpqkxHVowgsSaOGPkYjuD/0hxJ6h1xptRNjObylu58iLHBi0Ib9iVBDJ6+sc9d311ubt7dnmXZd/z+8zcmct7nj33fffc8/Due97zHkUEZmZWHffMdgXMzOzOcuI3M6sYJ34zs4px4jczqxgnfjOzimmb7QqU8fDDD8fZs2dnuxpmZnONWhXOiR7/5cuXZ7sKZmbZmBOJ38zM0nHiNzOrGCd+M7OKceI3M6sYJ34zs4px4jczqxgnfjOzinHiNzOrGCd+M7OKmRNLNpjNZcv2fLVl+Xf+wz+7wzUxq3GP38ysYpz4zcwqxonfzKxinPjNzCrGid/MrGJKJX5JSyQ9J2lU0qCkzRPE7pZ0UdKwpOOS5jdsWybpTyVdLWI+I8kzi8zM7qCyPf7DwA2gC9gCHJG0ujlI0npgD7AOWAYsB/Y3hHwWeBX4B8CDwEPArulV3czMpmPSxC+pHdgE7IuIkYg4D5wBtrYI3wYci4j+iLgKHAS2N2z/R8AXIuLNiLgInAVu+Q/EzMxmTpke/0rgZkQMNJT10Tphry62NcZ1SVpa/Ps/AY9I+jFJ7wE+Si3530LSTkm9knqHhoZKVNPMzMooM76+EBhuKhsGFpWIrb9fBFwB/ifwm8DfAfOAZ4AvtfrQiDgKHAXo6emJEvU0sxJ8J7GV6fGPAB1NZR3AtRKx9ffXJN0DfA04DbQD9wGLgd+fSoXNzOz2lEn8A0CbpBUNZWuA/hax/cW2xrhLEXEFWAL8Q+AzEXG9KPsc8LFp1dzMzKZl0sQfEaPUeukHJLVLWgtsAE61CD8J7JC0StJiYC9wotjPZeBl4FFJbZLeTe1icF+L/ZiZ2QwpO51zF7CA2lTMZ4FHI6JfUrekEUndABFxFngaOAcMFq8nGvbzK8DDwBDwEvBDYHeKhpiZWTmlbp6KiNeAjS3KL1C7oNtYdgg4NM5+/gr40BTraFOQ6sKdLwCa5ctLNpiZVYwTv5lZxTjxm5lVjBO/mVnFOPGbmVWME7+ZWcU48ZuZVYwTv5lZxTjxm5lVjB97aHeNmb5beLz9j8d3KU9sqr/P8fj3fOe5x29mVjFO/GZmFePEb2ZWMU78ZmYVU+rirqQlwDHgI8Bl4Hci4vPjxO4GHqe2fv8Xqa3df73YNtIUvgD4bER8anrVtzvNyzWbzX1le/yHgRtAF7AFOCJpdXOQpPXAHmAdsAxYDuyvb4+IhfVXsa83gD+5nQaYmdnUTJr4JbUDm4B9ETESEeeBM8DWFuHbgGMR0R8RV4GDwPZxdv2r1J7o9efTqbiZmU1PmR7/SuBmRAw0lPUBt/T4i7K+prguSUtbxG4DTkZEtPpQSTsl9UrqHRoaKlFNMzMro0ziXwgMN5UNA4tKxNbfvyO2eEbvQ8Az431oRByNiJ6I6Ons7CxRTTMzK6PMxd0RoKOprAO4ViK2/r459teB8xHxcplK2u3zRdl0/Lu0ua5Mj38AaJO0oqFsDdDfIra/2NYYdykirjTF/ToT9PbNzGzmTJr4I2IUOA0ckNQuaS2wATjVIvwksEPSKkmLgb3AicYAST8PvAfP5jEzmxVlp3Puojbn/lXgWWpz8/sldUsaKcbsiYizwNPAOWCweD3RtK9twOmIaDVUZGZmM6zUDVwR8RqwsUX5BWoXdBvLDgGHJtjXb02timZmlpKXZc7MfaNXWTk0WDp+8yN/1bL856f6wX92b+v9/9dv3FL2+d/8udaf+Z3WdRlv31M17v7HM87nTrWeM92uqUpVnyn/PsdT9nM7OqCnB6Q0n1thGmca/V2lp6cnent7Z7sad7+332aoYymdoz+Y7ZqYzYxTp+ATn5jtWswlLf+XdI8/J2+9NZb0n7//Z+7oR6994L6W5c9/+/JtxU4UP1Xj7X88qeo50+2aqlT1mervczylPvell+DCBXjllSSfWXVO/Dkp/nq7Pq+NLY/83h396PHmsG9pMed9KrETxU/VePsfT6p6znS7pipVfab6+xxPqc/93d+Fp54a+47b7fGyzGZmFeMef07GekO++DUbUj2D1ncGt1C/oOsefxLu8ZuZVYwTf4bC090sN/5OJ+XEnxP/GWy583c8CSd+M7OK8cXdnBS9ofDF3SRSXayd6+6K34Mv7iblHr+ZWcU48eek3uN3h99y4x5/Uk78ZmYV48SfE4/xW67c40+q1MVdSUuAY8BHgMvA70TE58eJ3Q08Tu3BLV+k9tCW6w3bH6H2cJZu4CKwPSL+/HYaYWa3uisuytpdqWyP/zBwA+gCtgBHJK1uDpK0HtgDrAOWAcuB/Q3bPwz8PvDPgUXAB4H/N/3q2zuMjfG7x2+ZcY8/qUkTv6R2YBOwLyJGIuI8cAbY2iJ8G3AsIvoj4ipwENjesH0/cCAivhERb0fE30bE3952K8zMrLQyPf6VwM2IGGgo6wNu6fEXZX1NcV2SlkqaB/QAnZJekvRdSZ+RtKDVh0raKalXUu/Q0FC51lTd2Bi/WWbc40+qTOJfCAw3lQ1TG6qZLLb+fhG1YaJ3Ab8K/CLwIPB+YG+rD42IoxHRExE9nZ2dJappZmZllLm4OwJ0NJV1ANdKxNbfXwPeLt7/54j4PoCkQ9QS/78tW2GbQMVm9VRt+eKqtfcd3ONPqkyPfwBok7SioWwN0N8itr/Y1hh3KSKuFGP+38UjEWZms2rSxB8Ro8Bp4ICkdklrgQ3AqRbhJ4EdklZJWkytN3+iYfvngE9J+vFi+28DX7m9JtgtPKvHcuMef1Jlp3PuojYv/1XgWWpz8/sldUsakdQNEBFngaeBc8Bg8XqiYT8HgW9S+yvi/wAvAP8+RUMMnxRmVkqpG7gi4jVgY4vyC9Qu6DaWHQIOjbOft6j9J7JrqhW18pz+LVvu3CThZZlz4pNiTpnpZ/TO1n5Sfe47Llp7+DIpr9WTId+5a9ly5yYJJ/6c+KSwXLkzk5QTf4aqMo/fzKbHiT8n7vFbrjydMylf3LUkZvLCoJcXrqbG477rf3+LfwMcPvcS/2L2qpQN9/hz4mWZLXPyZOUknPjNzCrGiT8nXpbZMlX/K1b+cifhxG9mVjG+uJuTOTLGP9MXa30xOD/1Kcoe40/DPX4zs4px4s9JxR7EYtUR9a+05/En4cRvZlYxTvw5coffMvOjMX5LoVTil7RE0nOSRiUNSto8QexuSRclDUs6Lml+w7avS3qzeHjLiKRvpWiEFfxnsJmVULbHfxi4AXQBW4AjklY3B0laD+wB1gHLgOXA/qawxyJiYfF673QrbuPzGL/lSu7cJDFp4pfUDmwC9kXESEScB84AW1uEbwOORUR/8XD1g8D2hPW1ifiksEy5M5NWmR7/SuBmRAw0lPUBt/T4i7K+prguSUsbyp6SdFnS85I+NN6HStopqVdS79DQUIlqWt3dPo/fbLo8jz+NMol/ITDcVDYMLCoRW39fj32c2vDPe4CjwJclPdDqQyPiaET0RERPZ2dniWqae/yWq3BfJqkyiX8E6Ggq6wCulYitv78GEBF/ERHXIuJ6RDwDPA98bGpVtsk4/VuuPMafRpnEPwC0SVrRULYG6G8R219sa4y7FBFXxtl34Bla6fiksEx5jD+tSRN/RIwCp4EDktolrQU2AKdahJ8EdkhaJWkxsBc4ASDp3ZLWS7pXUpukLcAHga8laosVfJKY2UTKTufcBSwAXgWeBR6NiH5J3cV8/G6AiDgLPA2cAwaL1xPFPt4FfBoYAi4DnwI2RoTn8qfiHr/lSr6BK6VSq3NGxGvAxhblF6hd0G0sOwQcahE7BPzstGppZmbJeMmGnMyRZZnNpmpsyQb/VZuEE7+ZWcU48efEyzJbpurz+H0DVxpO/GZmFePEnyHf5Wi58V+xaTnx58QXvsysBCf+LLl3ZHnyrJ40nPhz4pPCMuUpymk58WfIJ4nlyrN60nDiz4l7/JYpf7PTcuLPkE8Sy5X85U7CiT8n7vFbpjx8mZYTf4Z8kliuPMafhhN/Ttzjt0z5Bq60nPgz5JPEcuV5/GmUSvySlkh6TtKopEFJmyeI3S3poqRhScclzW8Rs0LSm5L+8HYqb018UliuPHyZVNke/2HgBtAFbAGOSFrdHCRpPbAHWAcsA5YD+8fZ3zenUV8rwWv1WK48xp/GpIlfUjuwCdgXESMRcR44A2xtEb4NOBYR/RFxFTgIbG/a3yPAD4A/u72q2y3c47dM+ZudVpke/0rgZkQMNJT1Abf0+Iuyvqa4LklLASR1AAeAfzXZh0raKalXUu/Q0FCJalqdx/jNbCJlEv9CYLipbBhYVCK2/r4ee5DaXwSvTPahEXE0Inoioqezs7NENc09fstVfYqyb+BKo8zD1keAjqayDuBaidj6+2uSHgR+CXj/FOtoZmYJlUn8A0CbpBUR8X+LsjVAf4vY/mLbFxriLkXEFUlbqV3wvaDa/94LgXmSVkXEP76NNlgT38BlufnR8KW7/ClMOtQTEaPAaeCApHZJa4ENwKkW4SeBHZJWSVoM7AVOFNuOAg8ADxav/wJ8FVh/e02wMR7qMbMSyk7n3AUsAF4FngUejYh+Sd2SRiR1A0TEWeBp4BwwWLyeKLa9HhEX6y9qw0JvRoSv3CbnHr/lyTdwpVFmqIeIeA3Y2KL8ArUhm8ayQ8ChEvt8slQNrTyfFJYpf7PT8pINGfJJYrny37JpOPHnxD1+y5QnLKTlxJ8hnySWK4/xp+HEnxOfFJYp342elhN/hnySWK68SFsaTvw5cY/fMuUVZ9Ny4s+QTxLLldfqScOJPyfu8Vu23JtJyYk/Qx7jt1x5jD8NJ/6cuMdvmfIU5bSc+DPkk8Ry5R5/Gk78OXGP3zLl4cu0nPjNbO5w5yYJJ34zu+t5inJapRK/pCWSnpM0KmlQ0uYJYndLuihpWNJxSfMbtv2hpO9L+jtJA5J+I0UjrFD0hjzGb2YTKdvjPwzcALqALcARSaubgyStB/YA66g9ZnE5sL8h5ClgWUR0AL8MfFrSB6ZdezOrFHdp0pg08UtqBzYB+yJiJCLOA2eArS3CtwHHIqI/Iq4CB4Ht9Y1F+fX6P4vXA7fXBBvj8U/LlC/uplWmx78SuBkRAw1lfcAtPf6irK8prkvS0nqBpM9Keh14Efg+8KdTrrVNyCeJ5crLMqdRJvEvBIabyoaBRSVi6+/HYiNiV/HvX6T2EPfrtCBpp6ReSb1DQ34sbyk+KSxTvm6VVpnEPwJ0NJV1ANdKxNbfvyM2Im4WQ0Y/ATza6kMj4mhE9ERET2dnZ4lqWp1PEsuVb+BKo0ziHwDaJK1oKFsD9LeI7S+2NcZdiogr4+y7DY/xp+Mev2XK3+y0Jk38ETFKbUjmgKR2SWuBDcCpFuEngR2SVklaDOwFTgBI+nFJj0haKGleMQPo14D/kagtVvBJYrnyssxplJ3OuQtYALwKPAs8GhH9kroljUjqBoiIs8DTwDlgsHg9UewjqA3rfBe4CvxH4Lcj4r+nakzlucdvmfLwZVptZYIi4jVgY4vyC9Qu6DaWHQIOtYgdAh6aVi1tSnySWK48xp+Gl2zIiXv8li13ZlJy4s+Q5/FbrjyPPw0n/pz4pLBM+ZudlhN/hrySoeXKX+00nPjN7K7nCQtpOfHnZGyoxyeJZcrDmUk48ZuZVYwTf07qD2Jxj98yU/9Oex5/Gk78ZmYV48Sfk7FHL85yPcwS88XdtJz4zWzO8A1caTjx58QnhWXK3+y0nPgz5Iu7lit/s9Nw4s+Je/yWKY/xp+XEnyGfJJYrT+dMo1Til7RE0nOSRiUNSto8QexuSRclDUs6Lml+UT5f0rHi569JekHSR1M1xHCP37Ll4cu0yvb4DwM3gC5gC3BE0urmoOJxinuAdcAyYDmwv9jcBrxC7WEsfw/YB3xB0rLpV99a8UliufKsnjQmTfyS2oFNwL6IGImI88AZYGuL8G3AsYjoj4irwEFgO9Se3RsRT0bEdyLi7Yj4CvAy8IFEbTGfFJYr92WSKtPjXwncjIiBhrI+4JYef1HW1xTXJWlpc6CkrmLf/a0+VNJOSb2SeoeGhkpU0+p8A5flyl/tNMok/oXAcFPZMLCoRGz9/TtiJb0L+CPgmYh4sdWHRsTRiOiJiJ7Ozs4S1TT3+C1XHr5Mq0ziHwE6mso6gGslYuvvx2Il3QOconbN4LHSNbXSfJJYrjzGn0aZxD8AtEla0VC2htZDNP3Ftsa4SxFxBUCSgGPULhJvioi3plVrM6sUT1FOa9LEHxGjwGnggKR2SWuBDdR67c1OAjskrZK0GNgLnGjYfgR4H/DxiHjjditvTeq9IZ8kli33+FMoO51zF7AAeBV4Fng0IvoldUsakdQNEBFngaeBc8Bg8XoCQNL9wG8BDwIXi58bkbQlZYPMzGxibWWCIuI1YGOL8gvULug2lh0CDrWIHcQX5WfW2INYzPIy9iAWj/En4SUbzMwqxok/J2MPYvEfVpaX+r0p/man4cRvZlYxTvw58cPWLVMe40/Lid/MrGKc+HPih61bpnzdKi0nfjObM/wgljSc+HPi8U/LlL/ZaTnxZ8gXdy1X8v8ASTjx58Q9fsuWOzMpOfFnyD1+y5XH+NNw4s+Je/yWKc/qScuJP0M+SSxXvoErDSd+M7vrOd2n5cSfE/eGzKyEUolf0hJJz0kalTQoafMEsbslXZQ0LOm4pPkN2x6T1CvpuqQTCepvZmZTVLbHf5jaw9G7gC3AEUmrm4MkrQf2AOuAZcByYH9DyPeATwPHp19lG5eXZbZM1b/THuNPY9LEL6kd2ATsi4iRiDgPnAG2tgjfBhyLiP6IuAocBLbXN0bE6Yj4EnAlQd3NzGwayvT4VwI3I2KgoawPuKXHX5T1NcV1SVo61YpJ2lkMC/UODQ1N9cerycsyW6bGlmX2Zd4kyiT+hcBwU9kwsKhEbP19q9gJRcTRiOiJiJ7Ozs6p/riZmY2jTOIfATqayjqAayVi6+9bxVpqXpbZMjU2xu8efxJlEv8A0CZpRUPZGqC/RWx/sa0x7lJEeEzfzOwuMWnij4hR4DRwQFK7pLXABuBUi/CTwA5JqyQtBvYCJ+obJbVJuheYB8yTdK+ktgTtMPAYv2Wr3s/36pxplJ3OuQtYALwKPAs8GhH9kroljUjqBoiIs8DTwDlgsHg90bCfvcAb1KZ8fqJ4vzdFQ8zMrJxSve2IeA3Y2KL8ArULuo1lh4BD4+znSeDJKdbRyvI8fsuUx/jT8pINZmYV48Sfk7ExfrPc+K/YlJz4zWzO8JINaTjxm9ldz/empOXEnxNf3LXM+ZudhhO/mVnFOPHnZGz80/0iy8vYTYke40/Cid/MrGKc+HPiMX7LlG/gSsuJ38ysYpz4c+IbuCxTXqQtLSd+M7OKceLPicf4LVMe40/Lid/MrGKc+HPiB7FYpsYetu55/EmUSvySlkh6TtKopEFJmyeI3S3poqRhScclzZ/OfszMbGaUfezhYeAG0AU8CHxVUl9EvOO5u5LWU3u61j8Fvgc8B+wvykrvJ5kXXoC3356RXd+Vvv1twLN6LD/1Hv+PvfUm/OVfznJt7rDubujsTLrLSRO/pHZgE/BTETECnJd0BtjKjxJ63TbgWD2RSzoI/BGwZ4r7SeMXfgFef31Gdn03e1sewbO81C/uvm/oO9DTM7uVudOOHIFPfjLpLhWTjJlJej/wvyJiQUPZvwYeioiPN8X2Ab8XEX9c/Ps+YAi4D+guu59i205gZ/HP9wLfmnrzoPjsy9P82bnKba4Gtzl/t9veyxHxcHNhmaGehcBwU9kwsKhEbP39oinuh4g4ChwtUb8JSeqNiEp1EdzmanCb8zdT7S0zJjACdDSVdQDXSsTW31+b4n7MzGyGlEn8A0CbpBUNZWuAVhdk+4ttjXGXIuLKFPdjZmYzZNLEHxGjwGnggKR2SWuBDcCpFuEngR2SVklaDOwFTkxjPynd9nDRHOQ2V4PbnL8Zae+kF3ehNv8eOA58GLgC7ImIz0vqBv4GWBURF4rYfwk8DiwAvgh8MiKuT7Sf5K0yM7NxlUr8ZmaWD0/4NjOrGCd+M7OKyTbxV3FdIElfl/SmpJHiNd2b3u5Kkh6T1CvpuqQTTdvWSXpR0uuSzkm6f5aqmdR4bZa0TFI0HOsRSftmsarJSJov6Vhx3l6T9IKkjzZsz+5YT9TmmTjWZdfqmYvu7LpAd4/HIuK/zXYlZsj3gE8D66lNHgDG7hA/DfwG8GXgIPDHwM/NQh1Ta9nmBu+OiB/e2SrNuDbgFeAh4ALwMeALkn6a2v1AOR7ridpcl+xYZ5n4Z2VdIJtxEXEaQFIP8BMNm34F6I+IPym2PwlclvSTEfHiHa9oQhO0OVvF1O8nG4q+Iull4APAUjI81pO0OfmqdLkO9awEbkbEQENZH7B6lupzJz0l6bKk5yV9aLYrc4espnZ8gbGT6NtU43gPSvqupM8Vf/lkR1IXtXO6n4oc66Y21yU71rkm/imtC5SRx4HlwHuo3fjxZUkPzG6V7ogqHu/LwM8C91PrFS6ithJuViS9i1q7nil69Nkf6xZtTn6sc038lVwXKCL+IiKuRcT1iHgGeJ7aWGHuKne8I2IkInoj4ocRcQl4DPiIpObfw5wl6R5qd/bfoNY+yPxYt2rzTBzrXBO/1wWqCajEcxjfsUZUcY3nAap1vOt3YmZxvCUJOEZtcsamiHir2JTtsZ6gzc1u+1hnmfhncV2gWSPp3ZLWS7pXUpukLcAHga/Ndt1SKdp1LzAPmFdvK7Unvf2UpE3F9n8H/PVcvthXN16bJf0TSe+VdI+kpcAfAF+PiOZhkLnqCPA+4OMR8UZDebbHmnHaPCPHOiKyfAFLgC8Bo9SmR22e7TrNcHs7gW9S+5P3B8A3gA/Pdr0St/FJar2dxteTxbZfAl4E3gC+Diyb7frOZJuBXwNeLr7f36e2QOLfn+36Jmrz/UU736Q2tFN/bcn1WE/U5pk41l6rx8ysYrIc6jEzs/E58ZuZVYwTv5lZxTjxm5lVjBO/mVnFOPGbmVWME7+ZWcU48ZuZVcz/B259uPBz9u39AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate a figure with a single subplot, of some size\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
    "\n",
    "\n",
    "# plot empirical estimation\n",
    "bins = np.linspace(0, 25, num=2 * 26)\n",
    "ax.hist(\n",
    "    data,\n",
    "    bins=bins,\n",
    "    density=True\n",
    ");\n",
    "\n",
    "# plot theoretical prediction\n",
    "ax.plot( #NB questo è un plot non è istogramma\n",
    "    x, pdf_x, #variabile x, variabile y \n",
    "    color=\"red\",\n",
    "    lw=2 #mi dice quanto è larga\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding labels and legend\n",
    "Axis labels and legends are the most important parts of a plot. Without them, we don't know what is what! So, let us add labels, a legend and a title to our plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAE2CAYAAACa6BByAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAA0W0lEQVR4nO3de5wcVZ338c+XBAEDWQjEKEiIIFEB5RYRxTUoavDC5VnwWSAiuCgadF3Yx9UoIDcVZVlcXRHFDQQQUFRALhoVBF0QXIMYdLxEkISbgUkIIRMggfB7/jjVoej0THfN9ExPVX/fr1e/0n3qdNWpqU7/+lzqHEUEZmZmVk4bdLoAZmZmNngO5GZmZiXmQG5mZlZiDuRmZmYl5kBuZmZWYg7kZmZmJeZAbmZmVmIO5GZmZiXmQF4CkqKFxyJJp2bPx3a6zEXUyl2X9k+S/iJpjaTHOlS0QZN0s6Sbc6/XO8cW9nGwpH8t+J7nHafdn4mByjSYc2xTmeZI6pO03veZpLdl539gXfp/Sbp2mMozNTvmOwbIc4KkuxqV2awof4jK4fV1jyXAj+vS/k/HSjd0/006BwAkbQ2cD/wSeAvw1g6Vq52ed44tOhgoFMgHeZwiDqb/Mg33sfuzG7AgIp5tsG337N87awmSdgA+BJw2TOXZI/v31wPk+TrwIuCoYSqDdZFS1dy6VUTcnn8taTWwtEH6QSNasDaJiAeAB3JJOwJjgIsi4pah7l/SRhGxeqj7GYoG59hWtXMc7uMMpBPHzloadib9iGhkd2BZRNyfSzueFPjnD1Ox9gQWRcTS/jJExJOSLgY+Dlw4TOWwLuEaeTW9TNL1WXPjYkmfqW/Ck7SrpGskLZf0pKRbJf19sx1LmitpUYP0hk3JknZsoSzrmmQlzQVq+7kx28fcXN79Jd2WlXmFpKslvaLBcXeR9GNJfcAVddtemW1bJek+Se/Pth8p6U9ZWW/Kam5NSTose99qST2S1msdadDkPVXSVZIekfRUVo7v1prAs3M+Ctgm333S6jk2KOarsnN6QtLfJJ2evw6tXNeBytTfsZtdr7rzafpZaWAnYCNyNe46u/P82vhGwHuByxqc69skXSfpweya3C/pbEkbNilDvT2BX0s6VtIfss/ZbZL2qMv3bWAnSW8ouH+z53Egr6argJ+RmkGvJjUhrmvCy75QfglMAD4IHAIsA26QtOdIlqWBM4CPZc8/QmqqPSMr9/7A9UAf8I/ALGAX4BZJ29Tt5wfAz4EDgS/Vbftutp+DgTuACyR9PtvfbOD9wCto8GVfT9Jbs3x/Af4B+Hfgy9n7B3IdsE12zBnZcVfz3P/JM4AfAr30330y0DnWuxq4gXTOlwEnA59p8p56rZRpnYLXC4p/ViA1q0ODQC5pHKl1J79tb2Bz4H8a7GtX4EbS/4kZwDnAh4F/a1KGersD+wAHkGrcRwOTgGuzMtX8Fngc2L/g/s2eLyL8KNkDWAR8q0H6qUAA769L/x3wk9zrG4E/Ai/IpY3J0q5ucuy5pGbD+vSbgZuLliWfN/f6rdl7963LN58UMMfm0l4GPA2cU3fcfxng7/O+XNoWwDOkHzLjc+kfy/Ju1+TvcSvwB2CDXNrrsveu9/fInm+VbT+whb/1AwOcR7/n2CDv7Lp83wRWApsXvK4Ny9TPsZter6KflQbH/BKwhtxnObdtn2y/h+fSPgk82yh/3XvHkLoefwBcP1DeuvftkB1zHqBc+v5Z+tvr8v9Ps3P0w49mD9fIq+n6ute/ByYDSNoEmE6qlT4raWzWnCtSje1NI1WWIrKazB7AdyLimVp6RNxLCqbT695y1QC7+1Hu/cuBR4DbI+LxXJ4/Zf9uO0CZxgCvBb4XuYFWEfEr0o+t/iwD/gp8QdIHJe04QN6BDHSO9a6oe/1tYFNSDbntBnG9YHCfld2AnohY02DbegPdgK2Bx+vzZ/8Pjs6awJeSftw9TWrt6GtShrxai9aJEZHvZvhd9u/Euvy9WZnMBs2BvJoerXu9Gtg4ez6BVNs4mfRFlX98FNiihX7JdpWliC1IPzb+1mDbEtJ55TXKV7O87vWaftJg4LJuBWwIPNxgW6M0YF21/G2kGuuZwEJJf5U0a4BjNTLQOTYrT+11oybudih6vWBwn5VdeS5I1nstqel6YS5t42y/9S4D/ovUAnEUqQl+X1It+vdNypC3B3BPRNxRl/6S7N/6wYBPApsU2L/Zejxqvfs8RmpaPBe4uFGGaHwbT81TwAsapG9JqmkOl+WkL9UXN9j24gbHHon7mZeSfgBNarBtErC4vzdGxF+B90kSKRh9FPiapEUR8aP+3le/mwJlnURqBci/Bngw+7fd17Xo9Sosq/VvQboO9ds2JDVn/6zu87wse08+727Ae4CZEXFZLv1Q0o+R/gbSNbIn8FCD9H8EVgC31aVPaFR+syJcI+8yEbGK1C+3K/CbiJhf/2iyi8XAJElb1RKy0d3NBncNSVbuO4D3ZE3atWNvB7yBNOhrREXEWtK9wofWjQB/HTClxX1ERPyW5+7Nzjd1r6Z9tbX/W/f6MFKTca222ep1balMI3S9niSdQ6MBmh8n3af91br0PwEbSnppLq3WfP/nXDnHAZ/NXv6mQJn2AHZQbgKe7FgfBv6zQRfAy/LHNRsMB/Lu9K+kL78fZ7dOTZd0iKTPSfpCk/d+l1TTulTSDEkzSQOCRqJWcTJpFPJ1kg6QdDjwU1JN5z9G4PiNnAK8Erha0rskHU3qj17S3xskvSa7FezDkt4qaQbwDVK/7M9yWf8ATJA0S9JrJb16COX8oKRPZ7dYnQ18ADg7Ih7Ltrd6XYuUaVivV1bT/hrwJkmXSTpc0jGSvgd8HvhKRNxY97ZfZP/ulUu7k9SV8u/Z3+dI4HZSC8UjEbGuhi1pSnar3Kn15ZE0hVTDXg3MlbSfpKNIP5wXZGXK598cmJork9mgOJB3oYj4Dan/cBnwFeAnpFumXk2TL5WIuBs4lNS3ejXwCdIPg4UDvK0tImIe8C7S7UNXkGbH+iPwxvyX7UiKiBuAmaSa65WkW5WOZ+Ba1hLgPtLf7RrgctKAp3fX9a3+N2lQ2ueB/wWGMqXoQaR++WtI91F/luy2vuw8Wr2uLZdphK7Xp0i3Ke4CzCGNYn8RqZn8XxqUaVFW7gNyafeTruF2pL/PR0jnfzfrN6vXbh9r9EOt1jLwblKrxbXAF0ifixkNauPvIv2AKDJo0Ww9ev7ASjOzastaTb4MvCQinij43mOBz5FuSyz03gb7+hFphsYjh7IfM9fIzazbXEIa5HfcIN47HfhSG4L4bsCbGb753q2LuEZuZl1H0t7AHhHxtQ4df39gi4i4vBPHt2pxIDczMysxN62bmZmVmAO5mZlZiTmQm5mZlZgDuZmZWYk5kJuZmZWYA7lZm0jaN5u+s/7x2BD2OTXbxzvaWNTavg+V9H1JiyU9KenPks6UtFmDvNtK+p6kFZIel3SlpMLL0ZpZ+3n1M7P2+xhpMZWaZ/rL2II9sn9/PWCuwfk4aarYT5OW19wdOBV4s6Q31FYNk/RC0hzwq0lLfAZpitebJL0mWyDFzDrEgdys/f4YEbe3aV97AosiYjgWpTkgInpzr38u6VHgItJa3LUFXD4IbA+8IpuTHUl3AX8BPgScMwxlM7MWuWnduk62wtV1kh6U9JSk+yWdna1hPdrsCfxa0rGS/iBplaTbJO3R9J1N1AXxmlrNf5tc2oHA7bUgnr33XuBW0mIsZtZBrpFbN9oVuJG0BOYqUvP1GcCjwOclCRjT/9vXiWxN8nqXZut6Pwb8GJgdEfcNsqy7A0+QVtP6OGn1rS8C10qaGhGr2lDevOnZv3/Mpe1MWtK0Xg/wnhaOa2bDyIHcuk5EnF17LmkMqWa5L7BPljwduKmFXf08e19NbZ3tnwOPk4Lwp4HbJO0eEY8UKaekHUhLgP4KODCy+ZQlrQR+lJX3J0Mob/3xtgFOB26IiPm5TROA5Q3e8iiwRQvHNbNh5EBuXUXSWNJ63B8CdgS2zG2+Ivv3DtJ67c2szL+IiDt5/vrVP5f0C9L61x8DTipY3Nr61ifG8xdF+F3278ShlDdP0qakWvczwPsbZGm0KINaOKaZDTMHcus2lwHvAL5KGnm9FNiYVKP9fZanD/htC/tquuJQRPxG0kJaC7T19gDuiYg76tJfkv37QPbvkMoraWPgGtKAtukR8UBdluWkWnm9LWhcUzezEeTBbtY1sjWg3wN8KCI+FRHXR8SvSDVb8VxtejrwdAuPG1s9NC0E/Qb2BB5qkP6PpGb824Za3myA3/eBvYB3RsTv6vOQ+sJ3bpC+E/CH1k/HzIaDa+TWTWoTmPy5liBpHKlmDvCb7N8hN1Xn9j8NmMpzzfZF7AE8JWlsRDyT7e+lwIeB/4iINUMpr6QNgEuB/YB3DXDL3DXA2ZK2j4i/Zu+dQuqjn13slMys3bweuXUNSdsCd5MGt50JvBj4BGkk+LiImDTE/V8K3Ev6QfAYabDbp0ijzveo3QueBcF7gdMi4tR+9lXLcy/wS+BC4KWkCVseBN6SC+SDLe95pB8FnwOuq9v8QK2JPfuxswB4ktTPH6RR/psBr4mIvqGUw8yGxk3r1jUi4n5gJrAdqZb5EVIgv5vnD1IbrN+T7rm+kHTb2fHAlcDr6iZ0GZf9u2SAfdUGur2bdOvZtcAXsv3NGGoQz9SmfT2R1Eyff3yglimbue0twELgElIt/l7SjwkHcbMOc43cbIRJOpZUC94uIp7odHnMrNxcIzcbedOBLzmIm1k7uEZuZmZWYq6Rm5mZlZgDuZmZWYl19X3k+++/f8ybN6/TxTAzKxtPzzuKdHWNfOnS4Vji2czMbOR0dSA3MzMrOwdyMzOzEnMgNzMzKzEHcjMzsxJzIDczMysxB3IzM7MScyA3MzMrMQdyMzOzEnMgNzMzK7GunqLVrMymzL6+YfqiL7xrhEtiZp3kGrmZmVmJOZCbmZmVmAO5mZlZiTmQm5mZlVhHA7mkCZKukrRK0mJJRwyQ9wRJSyStkHSBpI1y26ZI+qGk5Vmer0ryQD4zM6u8TtfIzwXWAJOAmcB5knauzyRpBjAb2A+YAmwPnJbL8jXgEeAlwG7AdOC4YSy3mZnZqNCxQC5pHHAIcHJE9EXELcA1wJENsh8FzImInohYDpwBHJ3b/jLgioh4KiKWAPOA9X4QmJmZVU0na+RTgbURsTCXtoDGAXjnbFs+3yRJW2avvwwcJumFkrYB3kEK5mZmZpXWyUC+KbCiLm0FsFkLeWvPa3l/Tgr2jwMPAPOBqxsdVNKxkuZLmt/b2zu4kpuZmY0SnRwQ1geMr0sbD6xsIW/t+UpJGwA/Br4BvIEU9C8Avgh8on5HEXE+cD7AtGnTYgjlN7MczzRn1hmdrJEvBMZK2jGXtivQ0yBvT7Ytn+/hiFgGTAC2Bb4aEauztAuBdw5Psc3MzEaPjgXyiFgFXAmcLmmcpH2Ag4BLGmS/GDhG0k6StgBOAuZm+1kK3AvMkjRW0uakwXELGuzHzMysUjp9+9lxwCakW8cuB2ZFRI+kyZL6JE0GiIh5wFnATcDi7HFKbj//AOwP9AJ3A88AJ4zYWZiZmXVIRydNiYhHgYMbpN9H6uvOp50DnNPPfn4L7Nv2ApqZmY1ynv3MWtKugUweEGVm1l6dblo3MzOzIXAgNzMzKzEHcjMzG/UknSrpW50uRyOSZkr6SRv2E5JeXvR9DuRmZtZx2Z1Ktcezkp7MvZ7Z6fLVZKttRn6FzYi4NCLe3qkyOZCbmVnHRcSmtQdwH3BALu3SkSpHGZfAdiA3M7OyeIGkiyWtlNQjaVptg6StJX1fUq+keyV9LLdtI0n/Kemh7PGfkjbKtu0r6QFJn5S0BLhQ0gaSZku6R9IySVdImpDt7hfZv49lrQWvl3S0pFtyx9tZ0k8lPSrpYUmfztL3knSbpMck/U3SVyW9YKh/FAdyMzMriwOBbwObk5a9/ipAtubGtaQZPbcB9gOOlzQje9+JwN7AbqQpvvcizRBa82LSdN/bAccCHyPNcTId2BpYDpyb5X1T9u/mWWvBbfkCStoMuIG0AufWwMuBG7PNa0mTlW0FvD4r53GD+kvkOJCbmXUzaTCPKPxoj1si4ocRsZY0nXdtDY7XAhMj4vSIWBMRfwW+CRyWbZ8JnB4Rj0REL3AacGRuv88Cp2TrdTwJfAg4MSIeiIjVwKnAoS02u78bWBIR/xERT0XEyoj4FUBE3BERt0fEMxGxiLTY1/TB/zmS0vUFmJlZ11qSe/4EsHEWXLcDtpb0WG77GOB/sudbk6b2rlmcpdX0RsRTudfbAVdJejaXthaY1EIZtwXuabRB0lTSDKXTgBeSYvAdLexzQA7kVnnDPZtcf/vvj2exG1jRv2d//HduUQyqsqx2F2OI7gfujYgd+9n+ECk411bXnJyl1dT/Ee4H/ikibq3fkaTtWijL4f1sOw+4Ezg8IlZKOh44tMn+mnLTupmZld3/Ao9nA9Y2kTRG0i6SXpttvxw4SdJESVsBnwEGuif968DnakE7e99B2bZeUlP89v289zrgxZKOzwbZbSbpddm2zYDHgT5JrwRmDfaE8xzIzcys1LI+8wNIg9nuBZYC/w38XZbls8B84C7gd8BvsrT+fJk0mO4nklYCtwOvy471BPA54NZs9PnedWVZCbwtK88S4C/Am7PNHweOAFaS+vC/M9hzznPTupmZjSoRMaVB2ql1rxeRa+KPiIfop0k76//+WPao33Yz8NK6tGdJfdn9rbj5GVKtvuZ2YG5u++9JI9Lr3/cL4JV1yZ/JbR9Ul4Vr5GZmZiXW0Rp5doP9HODtpKaQT0XEZf3kPQH4JLAJ8H1gVnZbAJL66rJvAnwtIv55uMpu7eXlTc3MBqfTNfJzgTWkIf0zgfMk7VyfKbupfzapqWIKaZDBabXtdVP7TQKeBL477KU3MzPrsI4FcknjgEOAkyOiLyJuIQ0uOLJB9qOAORHRExHLgTOAo/vZ9aHAIzx3/6CZmVlldbJGPhVYGxELc2kLgPVq5Fnagrp8kyRt2SDvUcDFEY1vjpR0rKT5kub39vYOsuhmZmajQycD+abAirq0FaT77JrlrT1/Xl5Jk0nT3V3U30Ej4vyImBYR0yZOnFi40GZmZqNJJwe79QHj69LGk+6va5a39rw+7/tIc/He25YSWlMepNY+/lua2WB0ska+EBgrKT+l3q48N4VeXg/PTY5fy/dwRCyry/c+BqiNm5mZVU3HAnlErAKuBE6XNE7SPsBBpBVt6l0MHCNpJ0lbkJafm5vPIOkNpOXrPFrdzMz6la0j3t8Uq0j6uqSTh3iMfSU9MJR9tKrTM7sdB1xAGmW+jHRveE/W1/0HYKeIuC8i5kk6C7iJ5+4jP6VuX0cBV2bT45mZWYsGsVBNoZVWFn3hXS3NWCZpEekW4rW55LkR8dEix2smu1V5oO0fbufxhltHA3lEPEpavL0+/T7SALd8Wr/T5WXbP9Tu8pmZ2Yg7ICJu6NTBJY3J5m4vjU7XyK0ktlq1nKm9i5tnzBxx2G8bpr+h6IFv3Ljx/r95+3ppl31w7wY54Q2LGpelv30X1e/++9PPcYuWc7jPq6h2lafw37M/rR53/HiYNg002lbmtBpJRwMfJK1y9n7gUeC9pNuYzwA2Av4tIi7K8s8FngJ2APYmLZLyvohYnG0PYMeIuDvL+yRpmdPpwEGS3gs8EBEnZfkPIk1Ctj1p9bOPZC3F7wc+QZqrvRf4YkR8Y1j/GA04kFtzzz7Ljy78Zyauemzkj93P2kAN5/EtkneA/EX1u/+Cxy1azuE+r6LaVZ7Cf892HPeSS+C9723XkW14vI60otmWpKD6beBa4OWkAPx9Sd+PiNqU3TOBdwG/As4CLgXe2M++jwDeCbwbeAHpRwIAkvYijdM6FLgReAnP3fr8SPaevwJvAn4k6dcR8Zs2nG/LHMituaefXhfEb93uNSN66H122Kph+q33LB1S3oHyF9Xf/vvTrnIO93kV1a7yFP179qel4959N9x3H9x/f1uOaW1xtaRncq//DXgauDciLgSQ9B3gROD0bM2Nn0haQwrqv83ed3222hiSTgRWSNo2Ihpd7B9ExK3Z86f0/NaZY4ALIuKn2esHaxsiIj+44OeSfgL8PakFYMQ4kFtz2SR5q8eMZeZhnx/RQ/d3D/XMBoNziuQdKH9R/e2/P+0q53CfV1HtKk/Rv2d/Wjrupz8NZ5657jNuo8LB9X3kWdP6w7mkJwEioj4tP7ZqXcCOiD5JjwJb59Mb5W1gW+CHjTZIegdp4PVU0l1gLyStdz6iOr1oipmZ2XDYtvZE0qbABOChfvIO9EvuflJf+/NI2oh0B9XZwKSI2JwU8Ed8sIVr5NbcutqKBwN1wiBuDSq0n66eOa7WhOoaeRW9U9IbSQPkzgB+1U+zejNzSE3315Fuga71kT9IGmTXCzyT1c7fDvy+HYUvwjVyMzMbTa7NJmypPa4a5H4uIzV7PwrsSRr8VlhE1EbKf4m0zsfPge2yOUs+BlwBLCcNmLtmkGUdEtfIrWXh23OsavyZBgbVKjMsf7iImDLA5rm5fHfXlyEiXlqXf2l/E7tEhHLPj26w/ei611cB6/2giIhzgXP7OcbNpNvShp1r5Nacmx2t6vwZtxJzIDczMysxN61bc1ltJTzYrS3aNXit7EbF38GD3SqpUXN5lblGbmZmVmIO5NZcrUbuCrlVjWvkVgEO5GZmZiXmQG7NuY/cqso1cquAjg52kzSBNGvO24GlwKciouHiR5JOAD4JbEKaFm9WNll+bfthpJv/JwNLgKMj4n+G9wzMus+oGKRmZut0ukZ+LrAGmESadec8STvXZ5I0A5gN7AdMIa0Je1pu+9uAL5Jm39mMtJzcX4e57N1jXR+5a+RWMa6RWwUUDuSSNpL0Mkk7SZo42ANLGgccApwcEX0RcQtpersjG2Q/CpgTET0RsZw0b+7Rue2nkZazuz0ino2IByPiwQb7MTMzq5SWArmkzSTNkvQL0lyzd5Mmhl8i6X5J35T02oLHngqsjYiFubQFwHo18ixtQV2+SZK2lDQGmAZMlHS3pAckfVXSJv2cy7GS5kua39vbW7DIXWpdH7lZxbhGbhXQNJBnfdOLgH8CfgocBOxGCsSvJ/VLjwV+KmmepB1bPPampB8FeStITePN8taeb0Zqlt8QOJS0oPtuwO7ASY0OGhHnR8S0iJg2ceKgGxTMzMxGhVYGu70BmB4R/S3N9r/ABZI+DBwDTAf+0sJ++4DxdWnjgZUt5K09Xwk8mz3/r4j4G4Ckc0iB/MQWymHNdNmo9W5b7rPbzvd5XCO3CmgayCPiPbXnkiZGRMP26GwE+dcKHHshMFbSjhFRC/y7Aj0N8vZk267I5Xs4IpZl5XoAt/yamVkXKjrY7ZeStm/HgSNiFXAlcLqkcZL2ITXbX9Ig+8XAMdkAuy1Ite25ue0XAv8s6UXZ9uOB69pRTsvxqHWrGtfIrQKKBvIfkoL5HvlESW+SdOsgjn8c6b7wR4DLSfeG90ianC0oPxkgIuYBZwE3AYuzxym5/ZwB/JpUy/8jcCfwuUGUxxrxl5yZ2ahVaEKYiPgXSfcDN0l6DykAfwF4G881exfZ36PAwQ3S7yMNcMunnQOc089+nib9KDiuaBmsdQ7nVln+sWolVnhmt4g4O7vl6zpAwNXAayKiUd+2VYG/5EqlXTOvjbb9tOu4zxvE5+4iq4BCTeuStpX0DeB0UlP2auB6B/Hu4JndrLL8Y9VKrGiN/C/AXcC7I+Knkt4CfF/SNhHhPumq8pecVZV/nFoFFA3k742I79VeRMTPJO0LXJ8Fc/dRV1i33EduZlYmhZrW80E8l7YA2AfYt01lstHGNXKrKt9+ZhXQtEYu6WURce9AeSJisaR9JAl4aUTc37YSWlcbzoFSXo6zO+Wv+3G3/ZlPAOfedDcf6VyRzIaklRr5bZLmSHp9fxmySVgOA/5AmtTFqsTLmFrFyTdXWom10kf+SuDTpH7wtcAdwN+Ap4AtgJ2AV5HmXD8+In48TGU1MzOzOq3UyM8ETgW2AT4M/AnYHHgZ8AxwEbB7ROzjIF5RXsbUKqrWyiR/uK3EWqmRHwOcEhGPSDoCOCoi+oa5XGZmZtaCVgL5/cDewDWk6VQ3IS0rat2iJH3kwz14zYPjqqd2S6X7yK3MWmla/wJp0pffZK8/kC2SUr+WuJmZmY2wpoE8Ir4J7AJ8mzS3+lHADcBySX+VdKWkz0g6cHiLah2zro98dNfIzYqK2kfa95FbibU0IUxE/DkiziJN0fpGYDNgL9JSoQ+SVj+7eLgKaWZmZo0VXcb0FbmXd2QP6xaukFvFPNdHblZehaZobTdJEyRdJWmVpMXZqPj+8p4gaYmkFZIukLRRbtvNkp6S1Jc9/jwyZ9Al3OxoZjZqdTSQA+cCa4BJwEzgPEk712eSNAOYDewHTAG2B06ry/bRiNg0e7wCazv3kVtVyT9WrcQ6FsgljQMOAU6OiL6IuIV0i9uRDbIfBcyJiJ6IWA6cARw9YoXtdv6Ss4ryj1Orgk7WyKcCayNiYS5tAbBejTxLW1CXb5KkLXNpZ0paKunWbGnVhiQdK2m+pPm9vb2DL30XGu33kZsNlu8jtzLrZCDfFFhRl7aCNCK+Wd7a81reT5Ka27cBzgeulbRDo4NGxPkRMS0ipk2cOHGwZe8urpFbRYV/m1oFtC2QS5osqcj++oD6SWXGAytbyFt7vhIgIn4VESsjYnVEXATcCryzQFmsBQ7nVlXuI7cya2eNfBGwQNKbWsy/EBgracdc2q5AT4O8Pdm2fL6HI2JZP/sOfEdJ+/hLzirKfeRWBe0M5P8EXAn8eyuZI2JVlv90SeMk7UNay/ySBtkvBo6RtFO29vlJwFwASZtLmiFpY0ljJc0E3gR4JbY285eemdnoU2hCmIFExNzs6SkF3nYccAHwCLAMmBURPZImA38AdoqI+yJinqSzgJtIi7Z8P3ecDYHPktZNX0taZvXgiPC95O3iGrlVlTwhjJVf2wL5YETEo6QV1erT7yMNcMunnQOc0yBvL/DaYSqimZnZqFaoaV3S1ZLeXXBQm5VdSZYxNStq3RStbnWyEisakFcB3wEekPT5uoFqZmZmNsIKBfKImAm8hDSz2luBP0v6haT3SdpkOApoo4CXMbWKqt1H7glhrMwKN5FHxOMRcV5E7AW8mrQC2jeAJZK+IelV7S6kmZmZNTbovm5JW5NuF3s38AzwPWBb4C5JH29P8Ww08SxYVjVuZbIqKDrYbUNJh0r6IbCYNOL8LOAlEXFMRLyTtIrZSW0vqXWOBwKZmY1aRW8/+xvplsvLgNkRcVeDPD8Flg+1YDYaufZi1eRR61ZmRQP5l4H/iIgn8omSBGybTd6yHHhZuwpoo4C/5KyifEulVUHRPvJTqZuoJTMBuHfIpbFRzV96VlUetW5lVjSQ9/dNvinw1BDLYqOVa+RWUf5kWxW01LQu6SvZ0wA+LynftD4G2Av4bXuLZqONv/SsquQPt5VYq33kr87+FfAqYE1u2xrgN8DZbSyXjSaukVtFubvIqqClQB4RbwaQdCHwLxHx+LCWykYlf+lZVbmP3Mqs0Kj1iHj/cBXERjHXyK2iPCGMVUHTwW6SrpE0Pve830fRg0uaIOkqSaskLZZ0xAB5T5C0RNIKSRdI2qhBnh0lPSXpW0XLYs35S8+qyveRW5m1Mmp9Gc+Nc1rW5FHUuaQ+9kmkGeHOk7RzfSZJM4DZwH7AFGB74LR+9vfrQZTDBuIvOasqdxdZBTRtWs83p7ezaV3SOOAQYJeI6ANuyWr1R5KCdt5RwJyI6MneewZwaT6fpMOAx4BfAi9vVzntOZ5r3arKfeRWZkXnWt9J0ityr98m6VuSPiVpTMFjTwXWRsTCXNoCYL0aeZa2oC7fJElbZuUYD5wO/L+CZbBWuEZuFeVPtlVB0Qlh5gC7A0h6KfAD0qxuHwE+W3BfmwIr6tJWAJu1kLf2vJb3DFKN/f5mB5V0rKT5kub39vYWLHJ3cx+5mdnoUzSQv4p0zzjAe4BfZSueHQkcXnBffcD4urTxwMoW8taer5S0G/BW4EutHDQizo+IaRExbeLEicVK3K1cI7eKqt1S6QlhrMyKLpoyhucmg9kP+GH2/B7SgLUiFgJjJe0YEX/J0nYFehrk7cm2XZHL93BELJN0JGkA3H1p7RY2BcZI2iki9ihYJjMzs1IpWiP/PTBL0t+TAvm8LH0bYGmRHUXEKuBK4HRJ4yTtAxwEXNIg+8XAMVkf/Rak9c7nZtvOB3YAdsseXweuB2YUKY815wlhrGqe6y5yldzKq2gg/yTwQeBm4PKI+F2WfiDwv4M4/nHAJsAjwOXArIjokTRZUp+kyQARMQ84C7gJWJw9Tsm2PRERS2oPUjP8UxHhDvB2cdO6mdmoVXRmt19ImgiMz9Ydr/kG8EQ/bxtof48CBzdIv4+65VIj4hzgnBb2eWrRclirXCO3avKEMFZmRfvIiYi1wPK6tEXtKpCNQv6Ss4ryJ9uqoHAgl/SPpP7xF1HXNB8RB7apXDYK+UvPqsptTVZmhQK5pH8Hjif1VT+Ev9u7g2vkVlEewGlVULRG/j7g8Ij43nAUxkY3f+lZVbmP3Mqs6Kj1DYDfDkM5bDTzl5xVlGcrtCooGsjPB947HAWx0c9felZVXjTFyqxo0/rmwBGS3gbcBTyd3xgRH2tTuWw0cY3cKsor+lkVFA3kO/Fc0/or67b5277i/KVnVeW51q3Mik4I8+bhKoiNYq6RW2X516mVX9E+cuti7iO3qnIfuZVZ4UAu6R2Srpf0R0nbZmkfkLRf+4tno4Jr5FZRvqXSqqBQIJc0k7SU6ELS0qEbZpvGAJ9oa8ls1PGXnlWVa+RWZkVr5J8APhgRJwDP5NJvJy0halXkGrlVlLuLrAqKBvIdgdsapPcB44deHDOzDvCPVSuxooH8IWBqg/Q3AfcMvThmZiPHt1RaFQxmZrevSNone72tpKOAs4Dzih5c0gRJV0laJWmxpCMGyHuCpCWSVki6QNJGuW3fkvQ3SY9LWijpA0XLYgPIaivuIzczG30KBfKIOAu4EvgpMI60CtrXga9HxLmDOP65wBpgEjATOE/SzvWZJM0AZpOWT50CbA+clstyJjAlIsYDBwKflbTnIMpjZl3IP1GtzArffhYRJwJbAXsBewMTI+LkovuRNA44BDg5Ivoi4hbgGuDIBtmPAuZERE9ELAfOAI7OlaknIlbXXmaPHYqWyfrh/kOrKA92sypoOZBL2kTSKZLuAh4h1ca/CRwvaZNBHHsqsDYiFubSFgDr1ciztAV1+SZJ2jJXvq9JegL4E/A34IeDKJMNwF96VlVextTKrKVALmks8DPg08C9wH+RmsUXA58BbsjyFLEpsKIubQWwWQt5a8/X5Y2I47LXf09q/l9NA5KOlTRf0vze3t6CRe5S/pKzivK4D6uCVmvkxwIvB/aIiIMi4lMRMTsiDgT2INWujy147Ea3rI0HVraQt/b8eXkjYm3WRP9SYFajg0bE+RExLSKmTZw4sWCRu5u/9KyqPCGMlVmrgfxQ4HMR0VO/ISJ+TxpsdmjBYy8ExkraMZe2K7DeMbK0XevyPRwRy/rZ91jcR94+rpFbRfmTbVXQaiDfmdS03p8bgF2KHDgiVpGawE+XNC67pe0g4JIG2S8GjpG0k6QtgJOAuQCSXiTpMEmbShqTjXA/vEl5bRD8pWdV5WVMrcxaDeRbAAN1KPcCmw/i+McBm5AGz10OzIqIHkmTJfVJmgwQEfNI96rfROqXXwycku0jSM3oDwDLgbOB4yPiB4MojzXiGrlVlLuLrApaHaA2hufPrV7v2SxPIRHxKHBwg/T7SAPc8mnnAOc0yNsLTC96bCvOX3pWVe4jtzJrNZAL+JakhiPBgY36SbcqcI3cKss/Tq38Wg3kF7WQ5+KhFMRGP99HblXl+8itzFoK5BHx/uEuiI1i/pKzivIn26qg8BSt1r28UpRVlT/aVmYO5GbWtTyA06rAgdyaW9e07i89qyh3H1mJOZCbmZmVmAO5NZfVVjxq3aqm9pn2feRWZg7kZmZmJeZAbs3VauSukFvFeLCbVYEDuZl1PU8IY2XmQG7N+UvOKsqfbKsCB3JrmQe7WVX5k21l5kBuzblGbhXlPnKrAgdya5m/9KyqfPuZlVlHA7mkCZKukrRK0mJJRwyQ9wRJSyStkHSBpI2y9I0kzcnev1LSnZLeMXJn0QVcI7eKcneRVUGna+TnAmuAScBM4DxJO9dnkjQDmA3sB0wBtgdOyzaPBe4HpgN/B5wMXCFpyjCXvev4S8+qyqPWrcw6FsgljQMOAU6OiL6IuAW4BjiyQfajgDkR0RMRy4EzgKMBImJVRJwaEYsi4tmIuA64F9hzRE6kG/hLzqrKv02tAjpZI58KrI2Ihbm0BcB6NfIsbUFdvkmStqzPKGlStu+eRgeVdKyk+ZLm9/b2Drrw3cgTwlhV+aNtZdbJQL4psKIubQWwWQt5a8+fl1fShsClwEUR8adGB42I8yNiWkRMmzhx4qAK3nVcI7eKcneRVUEnA3kfML4ubTywsoW8tefr8kraALiE1Of+0fYV02r8pWdV5T5yK7NOBvKFwFhJO+bSdqVxk3hPti2f7+GIWAYgScAc0qC5QyLi6eEpsplViW+ptCroWCCPiFXAlcDpksZJ2gc4iFSrrncxcIyknSRtAZwEzM1tPw94FXBARDw5vCXvQrXair/0rLJcI7fy6vTtZ8cBmwCPAJcDsyKiR9JkSX2SJgNExDzgLOAmYHH2OAVA0nbAh4DdgCXZ+/okzRzxszEzMxthYzt58Ih4FDi4Qfp9pAFu+bRzgHMa5F2MB50Or9oyph0uhlm71cZ9uI/cyqzTNXIzMzMbAgdya65WI3cfuVVMbW4Ef7KtzBzIzczMSsyB3Jpb10fueotVi/vIrQocyM3MzErMgdyaW9dH3uFymLWZx31YFTiQm1nXk2+utBJzILfm3H9oFeVPtlWBA7m1zIPdrKrkiG4l5kBuzblGbpXlH6dWfg7k1jLXyK2q3EduZeZAbs25Rm4V5VHrVgUO5NYyf+lZVXlCGCszB3Iz61oO31YFDuTWnGsrZmajVkcDuaQJkq6StErSYklHDJD3BElLJK2QdIGkjXLbPippvqTVkuaOSOHNzMxGgU7XyM8F1gCTgJnAeZJ2rs8kaQYwG9gPmAJsD5yWy/IQ8FnggmEub3fyMqZWUbXPtPvIrcw6FsgljQMOAU6OiL6IuAW4BjiyQfajgDkR0RMRy4EzgKNrGyPiyoi4Glg27AU3MzMbRTpZI58KrI2Ihbm0BcB6NfIsbUFdvkmStix6UEnHZs3w83t7e4u+vTt5GVOrqHXLmHrYm5VYJwP5psCKurQVwGYt5K09b5R3QBFxfkRMi4hpEydOLPp2MzOzUaWTgbwPGF+XNh5Y2ULe2vNGea3dvIypVdS6PnLXyK3EOhnIFwJjJe2YS9sV6GmQtyfbls/3cES4T9zMzLpaxwJ5RKwCrgROlzRO0j7AQcAlDbJfDBwjaSdJWwAnAXNrGyWNlbQxMAYYI2ljSWOH/SS6hfvIraJq9XCvfmZl1unbz44DNgEeAS4HZkVEj6TJkvokTQaIiHnAWcBNwOLscUpuPycBT5JuUXtv9vykETsLMzOzDulorTUiHgUObpB+H2mAWz7tHOCcfvZzKnBq2wtoie8jt4pyH7lVQadr5GZmZjYEDuTW3Lo+crOqcSuTlZ8DuZl1PU/RamXmQG5mXctzI1gVOJBbcx7sZhXnT7aVmQO5mZlZiTmQW3Pr+g9db7FqWTfJkfvIrcQcyM3MzErMgdyacx+5VZQnhLEqcCA3MzMrMQdya84TwlhFedEUqwIHcjMzsxJzILfm3EduFeU+cqsCB3IzM7MS62gglzRB0lWSVklaLOmIAfKeIGmJpBWSLpC00WD2Y4Owro/cNXKrltpn2nOtW5l1ukZ+LrAGmATMBM6TtHN9JkkzgNnAfsAUYHvgtKL7MTMzq5qxnTqwpHHAIcAuEdEH3CLpGuBIUtDOOwqYExE92XvPAC4FZhfcT3vceSc8++yw7HpUuucewKPWrXpqNfIXPv0U3HFHh0szwiZPhokTO10Ka4OOBXJgKrA2Ihbm0hYA0xvk3Rn4QV2+SZK2BCYX2E97vPGN8MQTw7b70epZdboBx6y9aoPdXtW7CKZN62xhRtp558GHP9zpUlgbdDKQbwqsqEtbAWzWQt7a880K7gdJxwLHZi/7JP25QJnztgKWDvK95fTnW7bii+/urnPuxuvcRee8mHUrCHTNOWe2YtaspcyaNdj3z4uI/dtZIBu8TgbyPmB8Xdp4YGULeWvPVxbcDxFxPnB+0cLWkzQ/IrrqJ7zPuTv4nKuv28636jrZVroQGCtpx1zarkBPg7w92bZ8vocjYlnB/ZiZmVVKxwJ5RKwCrgROlzRO0j7AQcAlDbJfDBwjaSdJWwAnAXMHsR8zM7NK6fTopeOATYBHgMuBWRHRI2mypD5JkwEiYh5wFnATqVtrMXBKs/0Mc9mH3DxfQj7n7uBzrr5uO99KU3giBDMzs9LqdI3czMzMhsCB3MzMrMQcyAvqxnndJd0s6als3MJQ7r0flSR9VNJ8Saslza3btp+kP0l6QtJNkrbrUDHbqr9zljRFUuSudZ+kkztY1LaRtJGkOdn/25WS7pT0jtz2yl3rgc65yte623TyPvKyys/rvhtwvaQFIzC4rtM+GhH/3elCDJOHgM8CM0iDJgGQtBXpjogPANcCZwDfAfbuQBnbreE552weEc+MbJGG3VjgftKsj/cB7wSukPRq0nwUVbzWA51zTRWvdVdxIC+gI/O627CLiCsBJE0DXprb9A9AT0R8N9t+KrBU0isj4k8jXtA2GuCcKyu7VfXUXNJ1ku4F9gS2pILXusk5d9nk8tXlpvVi+psfvhtWWjtT0lJJt0rat9OFGSE7k64vsO5L8R6643ovlvSApAuzlonKkTSJ9H+6hy651nXnXFP5a111DuTFFJrXvUI+SVo6dhvS/afXStqhs0UaEd14vZcCrwW2I9XaNiOtNFgpkjYknddFWY278te6wTl3xbXuBg7kxRSa170qIuJXEbEyIlZHxEXAraS+tqrruusdEX0RMT8inomIh4GPAm+XVP93KC1JG5BmflxDOj+o+LVudM7dcK27hQN5MZ7XPQnWLRpVac+b4z8bI7ED3XW9azNGVeJ6SxIwhzRY9ZCIeDrbVNlrPcA516vUte4mDuQFdOO87pI2lzRD0saSxkqaCbwJ+HGny9Yu2XltDIwBxtTOFbgK2EXSIdn2zwB3lXnwU01/5yzpdZJeIWkDSVsCXwFujoj6ZueyOg94FXBARDyZS6/staafc+6Ca909IsKPAg9gAnA1sIp0O8cRnS7TMJ/vRODXpCbGx4Dbgbd1ulxtPsdTSbWR/OPUbNtbgT8BTwI3A1M6Xd7hPGfgcODe7PP9N9KCRS/udHnbdM7bZef5FKkpvfaYWdVrPdA5V/lad9vDc62bmZmVmJvWzczMSsyB3MzMrMQcyM3MzErMgdzMzKzEHMjNzMxKzIHczMysxBzIzczMSsyB3MzMrMQcyM3MzErMgdxslJD0HkmrJW2XS/uypHuydaTNzNbjKVrNRolslapfA3dGxAclfRz4BLBPRPyls6Uzs9FqbKcLYGZJRISkTwPXS7oHOBF4i4O4mQ3ENXKzUUbSL4G9SMtO/qjT5TGz0c195GajiKS3ALsCAh7ucHHMrARcIzcbJSTtCvwc+FfgXcCmETGjs6Uys9HOgdxsFMhGqv8S+EZEnC5pF+AuUh/5zR0tnJmNag7kZh0maQJwK/CLiPhQLv07wOSIeH3HCmdmo54DuZmZWYl5sJuZmVmJOZCbmZmVmAO5mZlZiTmQm5mZlZgDuZmZWYk5kJuZmZWYA7mZmVmJOZCbmZmVmAO5mZlZif1/zX0gNXYCHfIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate a figure with a single subplot, of some size\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4))\n",
    "\n",
    "\n",
    "# plot empirical estimation\n",
    "bins = np.linspace(0, 25, num=2 * 26)\n",
    "ax.hist(\n",
    "    data,\n",
    "    bins=bins,\n",
    "    density=True,\n",
    "    label=\"Empirical\"\n",
    ");\n",
    "\n",
    "# plot theoretical prediction\n",
    "ax.plot(\n",
    "    x, pdf_x,\n",
    "    color=\"red\",\n",
    "    lw=2,\n",
    "    label=\"Theoretical\"\n",
    ")\n",
    "\n",
    "# add labels for axes\n",
    "ax.set_xlabel(r\"$x$\")\n",
    "ax.set_ylabel(r\"Density, $f(x)$\")\n",
    "\n",
    "# add legend\n",
    "ax.legend(loc=(0.95, 0.7), frameon=False)\n",
    "\n",
    "# add title\n",
    "ax.set_title(f\"The uniform distribution $U(a, b)$\\n$a$=5, $b$=20\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Verify that, if the sample size increases, our empirical estimation gets closer to the theoretical prediction. Do so for the following cases:\n",
    "+ $a=0, b=1$\n",
    "+ $a=-3, b=-1.2$\n",
    "+ $a=1800, b=1990$\n",
    "\n",
    "**Hint** Instead of copy-pasting the code to generate the figure many times, you could put the code inside a function `plot_comparison(N, a, b)`. The function will have 3 arguments: the sample size $N$ and the bounds $a, b$ of the uniform random variable. You can set a title that depends on the values of $a, b, N$ as follows\n",
    "\n",
    "```python\n",
    "ax.set_title(f\"The uniform distribution $U(a, b)$\\n$a$={a}, $b$={b}, $N$={N}\");\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_comparison(N, a, b): #non assegno subito i valori alle variabili ma scrivo un codice generico e poi posso assegnare dei valory alle variabili\n",
    "    # generate the random data\n",
    "    #NB con def plot_comparison(N,a,b)\n",
    "    \n",
    "    data = np.random.uniform(size=N)*abs(b-a)+a #faccio sì che i numeri vengano estratti in questo intervallo\n",
    "    \n",
    "    # compute the theoretical distribution\n",
    "    dx = (b - a) / 10\n",
    "    x = np.linspace(a - dx, b + dx, num=1000)\n",
    "    pdf_x = np.array([uniform_pdf(t,a,b)\n",
    "                     for t in x\n",
    "                     ])\n",
    "    \n",
    "    # generate a figure with a single subplot, of some size\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(6, 4)) #la figura posso generarla esattamente come prima\n",
    "    \n",
    "    # plot empirical estimation\n",
    "    ax.hist(data, #da dove prendo i numeri\n",
    "            bins = np.linspace(a,b,100),\n",
    "            density = True,\n",
    "            label = 'Empirical'\n",
    "           );\n",
    "\n",
    "    # plot theoretical prediction\n",
    "    ax.plot(x, pdf_x, color = \"red\", label = 'Thoretical')\n",
    "\n",
    "    # add labels for axes\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    \n",
    "    # add legend\n",
    "    ax.legend(loc=(0.95, 0.7), frameon=False)\n",
    "    \n",
    "    # add title\n",
    "    ax.set_title(\"the uniform distribution $U(a,b)$\\n$a$={a}, $b$={b}, $N$={N}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAE1CAYAAACm3+q8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAr3klEQVR4nO3debwcVZn/8c+XBAgmREBiFCHEBRRQ2eIyIgOKCogs80NHJKyjoCAqLuNEhSEGRHRQ3AAHFTMqKDiyBxgUBAkiEpGIQYkCYV8SlpAECBKe3x9VTSqdql7u7T637+3v+/Xq17196lTVU6fr3qfPqU0RgZmZmaWzxlAHYGZm1m+cfM3MzBJz8jUzM0vMydfMzCwxJ18zM7PEnHzNzMwSc/I1MzNLzMnXzMwsMSffHiBpH0mfKimfLikkjR6KuAaiFnNJ+b9J+pukZyQ9PgShDZikqyVdXXhfuo1NllH6GTeZZ5X1dGN/aLbvdWo9bcb0A0lLJa32/0nSO/M22KtQ9m1JF3cpls3z9e3epN4nJf2pLGazMt5ResM+QFv/mHvY94F/KhZI2gg4A/gt8HbgHUMQVyetto0t2If2P+OBrKdd+1AeV4p1V9kGmBsRz5VM2zb/+UcASa8EPgx8sUuxbJf/vLFJve8CLwYO7lIcNsIMmx6VDQ8RcS9wb13xZsAo4H8iYvZg1yFp7YhYPtjlDFTFNnZMbfu6vZ5Ghmrdea9+K7LkX2Zb4JGIuCd/fzRZop7TpZC2BxZExKJGlSLiKUk/Aj4D/LBLsdgI4p7vEJM0k+zb8svy4a2QtKCu2sslzcqH4u6S9J/1w1uStpZ0kaTHJD0l6TpJO7ay/pL1VQ61StqsUSwlQ6UzgdpyrsyXMTOftpuk6/N4F0u6QNKr6+Korfe1kv5P0lLg3Lppr8mnLZN0t6RD8+kHSvprHuuv815Ss/bYL59nuaR5kv6lpM5qQ7L58OT5kh6W9HQex88ljW70GbeyfSVhbpFvz5OSHpA0o2R/aPq5thJX3bztfF4N95MGtgTWJu/ZltiWlb3etYEDgLNLtvOdki6RdF/+edwj6WRJa7YQQ9H2wI2SDpd0a76PXS9pu5K6PwO2lPSWNtdhfcg936F3PDABeANQO45V36s7n+zb9CnAnmRDbPfkZeT/CK4l+6d0GPAk8BHgV5LeEhF/6GC8DWMpcTzwB+BbwEeBm4CFknYDZgFXAe8HxgEzgNmStomI++qWcyHwA+ArQP1w5M+B7wEnA0cCZ0raDNgZmAasCXyT7J/0m6o2TNI78jqzgE+TfS7fzOe/rWq+3CXA48ARwCLgZcC7yb7gtvIZN9q+ehcAZwJfBnYFjs3nmd5kvnqtxAVkiZf2Pq9295OabfKfqyVfSWPJRlEuyoveDKxHtu/X2xq4EjgNWEY2fHw88ChwYpMYirYl+3tah6xXO5bsM7pY0uYRsaxQ92bgCWA3skMsZtUiwq8hfgEzgXtLyqcDARxaV34LcEXh/ZXAX4C1CmWj8rILWlj3gpLyq4GrBxDL9Gy3WqXOO/J5dy6UzQH+BowulL0c+Afw9ZL1fqJB+xxUKFsfeBZ4BBhfKP94XnfTBm1xHXArsEah7E35fKu1ReH9hnmdvQbxGVduX0ndaXX1vgcsAdYbwOfaMK5BfF4N95MG7XQK8AyFfbkwbYd82R/I3/8H2ZeO1erWzTeKrKNxITCrWQyF+V6Zr+9yQIXy3fLyd5XMc20r2+mXXx52Hh5m1b3/MzAJQNI6wE5kvb/n8mHO0YCAXwH/nCqWVuU9mO2AcyLi2Vp5RNxJlgB3Kpnt/AaLvKywjMeAh4HfRcQThTp/zX9uUhHTKLIe4P9G4USfiLgBWNBoe8gS/R3ASZIOy3vd7Wq0ffXOrXv/M7Ke6GsHsN6mBvh5DXQ/2QaYFxHPlExb5WQrYCPgifq6+d/AIfnw8CKyL2P/IOvdL20hhprt859fiIjiEPwt+c8JJfMszOMya8jJd3h4tO79cmBM/vsGZN/sjyX7B1N8HQWs3+Kxtk7E0qr1yb4cPFAy7UGybapXVrfmsbr3z1SUQXWsG5INLz9UMq2s7Hn5P+Z3kvUOvwzMl3SHpCMazVen0fY1i6f2/mVtLKMdA/m8BrqfbM3K5FbvDWTDuvPz92MoHyY/G/g2WS//YLLh6Z3Jeqt/biGGmu2A22P1wzYvzX+WnZD2FNkQtVlDPuY7/D1ONvR2KvCjsgpRfslGzdPAWiXlLyLr0XXDY2T/CF9SMu0lFevt9jWni8i+sEwsmTYRuKvRzBFxB3CQJJElkKOA0yQtiIjLGs1bW0QbsU4k62kX3wMUj7t28nMdyOfVtryHvT7ZZ1E/bU2y4d6rCvvzI3n9Yr1tgPcBUyPi7EL5e8m+QFSdyFVme+D+kvL3A4uB60umbVAWv1k993x7w3IG+G05shM+riX7h39TRMypfzVZxF3AREkb1grys4JfXT3L4OQx/wF4Xz7cW1vvpsBbgGu6te4GMa0gu5bzvVr17O03AZPbWE5ExM2svHa2NhQ84M+4xL/Wvd+PbDi12Ktr9XNtGlfCz+spsu3YvmTaZ8iuo/1OoeyvwJqSNi6U1Ya2nz9BLk/qJ+Rvb2ojnu2AV6pwU5N8XR8BvlExNP5ymp+cZ+bk2yNuBTaQdISkN0h6XZvzf4rsH9b/5ZfK7CRpX0lfknRSk3l/TtarOUvSrpKmkp2Y0u1v78eSnbl6iaQ9JX0A+CVZj+JrXV53leOA1wAXSNpD0iFkx1cfbDSTpNfnl/58RNI7JO0K/DfZscar8mqD/YyLDpP0+fxympOBDwEnR8TjhTqtfq6txtX1zyvv0Z4G/LOksyV9QNIHJf0v2RnK34qIKwuz/Cb/+cZC2R/JDjH8V94+BwK/IxsFeDginu/JSpqcXxY1vT4WSZPJerHLgZmSdpF0MNkX3bmUnDEtaT1g80JcZpWcfHvD98lOmjkR+D3Q1q3yIuImsuNhj5Bd0nMF2SUyr6PJP4KI+DvwXrLjhRcAnyVL5vMbzDZoEXE5sAfZpSLnkt0h6C/AW4v/IFOKiF8BU8l6h+cB/052E4dmPZkHgbvJ2u0i4KdkJ928p3C8cFCfcZ29yY4xX0R2nesJZJfRFLel1c+1pbgSfl6fI7sk7bVkl16dQtbjnRoRn6iLaUEe856FsnvIPsNNydrno2Tb/ndWH3Iem/8s+3JV632/h2xk4GLgJLL9YteKXu8eZIm/nZPnrE9p1ZP4zMyGj3x04pvASyPiyTbnPRz4EtnlZ23NW7G8y4BFEXHgYJdlI597vmY2nP2Y7ESzIwcw707AKR1KvNsAb6N795i2EcY9XzMb1iS9GdguIk4bwhh2A9aPiJ8OVQw2vDj5mpmZJeZhZzMzs8ScfM3MzBJz8jUzM0vMydfMzCwxJ19LQtK6+V2LFkpakd9BqN1lrCfpOUmPSrpY0kubz9V0mZvndznafbDLarKeyu3XygfQN7zXepe2/7B83Uskjamb9pJ82qcHu57CMhu1wyH5+h6XVH/P5tH1d6PqRnuYpeLka6l8CPgA8A2y6yvbeYpPzRJgR+DzwO5kdy4arO3ynzd2YFmN9Or2b0N2C8VxZM9dLqp/hF8ntNIOLyR7Vm8z3WgPsyT8VCNLZQtgcUR8aaALyB9+cB1wXX53oi07ENf2ZA+d7/a9rHt1+7chu0XjeGAf4JLCtFryvbkD66lppR2uAD4m6RsRUXlf7S61h1kS7vka+Q3oL5F0n6SnJd0j6eT8MW6dshYVDzKX9CpJP5Z0p6SnlD0L9/T6occ6TwBrdyCu7YEbJR0u6VZJy5Q9hH27pnO2p3L7C7bIH9DwpKQHJM1Q9bOYB739kgS8niy5XgDsWbe+bYC7I6L+2byD0Uo71J5A9IU2ltup/cEsCfd8DbLHEV5J9kSZZWRDsceTPRD9xPyf9Kjq2Z8XeW+kXRuRPZj8aLJnx76CbCjxUuCfBrC8dmwLPEl28/zPkN1s/yvAxZI2j4hlCba/5gLgTODLwK5kTxJ6Dpg+iGU2shnZcPMfyRLwcWSPCJydT9+WwpBzwnZ4gOzRgUdLOjkiGj5L2Ww4cvI1IuLk2u/Kntd6HbAzsENevBPw6xYWdU0+X5n1yRJ72fp/Q+HpS5J+S/YUmmslbRsRZcccnyRL2gOm7Pm26wE3AHtFfrs3SUuAy8i2/wq6vP0F34uI2iMgr5A0Hvh0Pvz6eF3dQW8/Wc8W4OaI+KOku8iGnmdLWhd4JfCTQv1U7QDZF6APk30h+LcW6neiPcyScfLtc/kZtgeQ/aPbDHhRYfK5+c8/kD2ysJklJcsfk8+7C/CLihjWIut1HkT2KLjiWbevpvyEnxuA/5S0J3DlAG+OX3ts3Bdi1fus3pL/nJD/7Or2F5xb9/5nZCcovZaVvdGaTmz/tsA/gHn5+4vIku9nyEZDxKrHe1O1AxHxqKSvAcdJ+gpwe5NZOtEeZsk4+drZZGeKfofsWNsisuT3a+DPeZ2ltHbSzSo3Cs8fLv5Y/vYG4OMV830Z+BgwA/gt2T/vjcmenTqmYp4TyZLnRfm6Xp4/37Ud2wG3F565W1O7ZOXe/Ge3t7/moYr3Lyup24nt3wb4S+HZtBeQnej0OsrPdE7VDjWnsHK/mNqkbifawywZn3DVx/LHoL0P+HBEfC4iZkXEDWQ9PrHyH+9OZD2kZq8r61axhOwY4tHAFLKH05fZD/hRRJwQEVdFxI3A403CP4jsIerTgbcCA3mg+/YV870fWAxcn7/v9vbXTKx4f19J3U5s/zasmlx/Q5Yk9yFLvo9GxN2F6anaAYCIWEr2xex9rBwir9KJ9jBLxj3f/jYp/3lbrUDSWFaebXpT/nNAw435STfXA9dLeg+wN3BMyXwvIPunXXRok3XtBcyPiME8P3U74GlJoyPiWQBJGwMfAb5W6BF2e/tr/hU4qfB+P7Le5p9L6g5q+yVNBF5CoScbEc9KmkWWfOuHnCFdOxSdBnyKlftklU7sD2bJOPn2tz8CzwD/JenLZP+MP0t2OcjDEXE/QEQsAeYMcl33A6+pmHY5cLCkW8hOtPp/ZD2lRsYDpdeA5ndNuhP4YkRMb1Bng7zeTEk/JBvqng7MJRvGBJJsf81h+aU+N5Kd7fwhYHrJyVYwyO2n+hreC8nOAXgW+GZxQsJ2KK5zuaQZwBlNqla2h1kv8rBzH4uIe8iOpW1Kdqzso2TJ9+909q5GkB0HVMW0j+Xr/xJwDrAu2V2QGhF1xxYLxuY/G/0zrp1s9R6yy4wuJut1ngfsWuj1dkqj7a/ZG3gnWVscQNbbO76i7mC3f5v858115ZcDT5N9Ma+f1gmttEO9HwJ/a1KnUXuY9RytepKnWXdI+j6wd0RMaFq5teX9DlgSEe8smXY4WSLftFfOeu337a/pdDsUllvZHma9yD1fS+VuYENJ76q/gX87JK0taVuyy2+qbr6wE3BKjyWeft/+mo60Q02L7WHWc9zztSQkbUJ2FuxmeVHbl4LUXbJyP/DuiJjbqRi7qd+3v6YT7VBY1noM8/aw/uXka0kVTnT6c7vHVfO7b21NdhnQnRHxXOcj7K5+3/6awbRDYRkjpj2s/zj5mpmZJTaiLjXabbfd4vLLLx/qMMzMhpt2z0C3QRpRJ1wtWtTtR7KamZkN3ohKvmZmZsOBk6+ZmVliTr5mZmaJOfmamZkl5uRrZmaWmJOvmZlZYk6+ZmZmiTn5mpmZJebka2ZmlpiTr1mbJk+bxeRps4Y6jLYN17jNRiInXzMzs8ScfM3MzBJz8jUzM0vMydfMzCwxJ18zM7PEkiZfSUdJmiNpuaSZLc5zlaSQNLrL4ZmZmSWROqHdD5wA7Aqs06yypKmkj9HMzKyrkvZ8I+K8iLgAeKRZXUkvBI4DPtvtuMzMzFLq5WO+JwKnAw82qiTp8Hwoe87ChQvTRGZmZjYIPZl8JU0BdgC+3axuRJwREVMiYsqECRO6H5yZmdkg9VzylbQGcBrwiYh4dqjjMTMz67SeS77AeGAKcI6kB4Eb8/J7Je04dGGZmZl1RtIzifPLhUYDo4BRksYAz9b1cBcDGxXebwL8Htge8EFdMzMb9lL3fI8BngKmAQfkvx8jaZKkpZImRebB2ouVCfehiHgmcbxmZmYdl7TnGxHTgekVk8dVzLMAUHciMjMzS68Xj/mamZmNaE6+ZmZmiTn5mpnZkJA0XdJPhnD9l0k6eJDLOETS7Hbn832TzcysKyQtLbx9AbAcWJG//3DiWKYDr4qIA2plEbF7yhiK3PM1M7OuiIhxtRdwN7BnoeysTq5L0qhOLq/bnHzNzGworSXpR5KWSJqX314YAElbSLpa0uP5tL0K02ZKOl3SpZKWAW+TtJGkX0haKOlOSR/P6+4GfB54f35Z69y8/GpJHyos8zBJf8ljuVXSdnn5NEm3F8r/ZbAb7eRrZmZDaS/gZ8B6wEXAdwAkrQlcDFwBvBj4GHCWpFcX5t0f+BKwLvDbvP5c4GXALsDRknaNiMvJHtZzTt7r3ro+CEnvI7sU9iCyOy3uxcon8N0O7Ai8EPgi8BNJLx3MRvuYr5nZSHL00XDzze3Nc801V7e5lpuJOLrNearMjohLAST9GKgt981k9384KSKeA66SdAnwAVbeL+LCiLgun/d1wISImJFPu0PS94D9gP9rIY4PAV+NiNotjf9emxARPy/UO0fS54A3Ahe2s6FFTr5mZjaUio+NfRIYk9+KeCPgnjzx1txF1qutuafw+6bARpIeL5SNAq5tMY5NyHq4q5F0EPApYHJeNA7YsMXllnLyNTMbSb7xjYHMtXNng+iI+4FNJK1RSMCTgPmFOlH4/R7gzojYrGJ5UVFenP+V9YWSNgW+RzaMfX1ErJB0M4O886KTr1kLJk+bNdQhrKIWz4KT9hjiSMy65gZgGfBZSV8je8b7nsAbKur/HnhC0n8A3wKeAbYA1smHkh8C3lmXzIu+D3w9v2b3JrJE/A9gLFniXggg6VDgtYPdOJ9wZWZmPSd/kM5ewO7AIrLnvB8UEX+tqL+CLDlvA9yZz/N9spOkAGrHbR+RdFPJ/D8nO3nrbGAJcAGwQUTcCnwNuJ4sgb8OuG6w2+eer5mZdV1ETC4pm173fgGF4dyImAfsVLG8Q0rK7ic7Iaus/iPAW+vKdq57/13guyXzfgH4QsVyZwIzy6Y14p6vmZlZYk6+ZmZmiTn5mpmZJebka2ZmlpiTr5mZWWJJk6+koyTNkbRc0swG9Q6W9AdJT0i6V9JX8zuemJmZDXupe773AycAZzap9wKy+3tuCLyJ7M4in+lqZGZmZokk7U1GxHkA+SOjNm5Q7/TC2/sknQW8rcvhmZmZJTFcjvn+MzCvbIKkw/Oh7DkLFy5MHJaZmfWC/Dm9r2gw/buSjh3kOnaWdO9gllHT88dR8/toTiF73NNqIuIM4AyAKVOmNLtxtpnZiDeAe5G39b9zwUl7tPxQAUkLgInAikLxzIg4qp11NhMR45pM/0gn1zdYPZ18Je0DnAS8IyIWDXE4ZmY2MHtGxK+GauWSRuX3fu4ZPTvsLGk3ssc47RkRtwx1PGZm1jmSDpF0naRTJD0u6Q5Jb8nL75H0sKSDC/Vn5kPHv5S0RNI1+eP+atND0qsKdU+XdKmkZcDb8rITCvX3lnRzflXN7XnOQdKhkv6Sr+MOSR/uxvanvtRotKQxZA84HiWp9tDk+npvB84C9o2I36eM0czMknkT8CfgRWRPE/oZ2SMDXwUcAHxHUnE4eSpwPNmVMDeT5Ykq+5M9pWhdYHZxgqQ3Aj8C/h1Yj+y8ogX55IeB9wDjgUOBUyRtN7DNq5a653sM8BQwjaxhnwKOkTQpP1g+Ka93LNljoC7Ny5dKuixxrGZm1hkX5L3b2uuwvPzOiPhhPiR8DrAJMCMilkfEFWTP5H1VYTmzIuI3EbGc7ClD/yRpk4p1XhgR10XEcxHxdN20DwJnRsQv8+n31R5VGBGzIuL2yFwDXAHs2JlmWCn1pUbTgekVk8cV6vmyIjOzkWOf+mO+kg4hez5uzVMAEVFfVuz53lP7JSKWSnoU2KhYXla3xCbApWUTJO0OHAdsTtZBfQHQ8UOfPXvM18zMrM7zvdx8OHoDsps3lWl0Bvc9wCvrCyWtDfwCOBmYGBHrkSXpls/ubpWTr5mZDRfvlvRWSWuRHfu9ISIa9XCr/AA4VNIuktaQ9DJJrwHWAtYGFgLP5r3gd3Us+gInXzMz67aLC+fvLJV0/gCXczbZkPCjwPZkJ2C1LT+R91DgFGAxcA2waUQsAT4OnAs8RnbS1kUDjLWhnr7O18zM2rfgpD3anaXjw6o1ETG5weSZhXp/r48jIupvQ7yo6mYZEaHC74eUTD+k7v35wGpfAiLiVODUinVcTYNbI7fDPV8zM7PEnHzNzMwS87CzmZn1vLKh5OHMyXeYqt04fQDHdnpC8cbvg9mGTi2n2bJHusG242D2x25+hma9ysPOZmZmiTn5mpmZJebka2ZmlpiTr5mZWWJOvmZmZok5+ZqZmSXm5GtmZpaYk6+ZmVliTr5mZmaJOfmamZklljT5SjpK0hxJyyXNbFL3k5IelLRY0pmS1k4UppmZWVel7vneD5wAnNmokqRdgWnALsBk4BXAF7sdnJmZWQpJk29EnBcRFwCPNKl6MPCDiJgXEY8BxwOHdDk8MzOzJHr1mO9WwNzC+7nAREkvGqJ4zMzMOqZXk+84YHHhfe33desrSjo8P448Z+HChUmCMzMzG4xeTb5LgfGF97Xfl9RXjIgzImJKREyZMGFCkuDMzMwGo1eT7zxg68L7rYGHIqLZsWIzM7Oel/pSo9GSxgCjgFGSxkgaXVL1R8AHJW0paX3gGGBmwlDNzMy6JnXP9xjgKbLLiA7Ifz9G0iRJSyVNAoiIy4GvAr8G7spfxyWO1czMrCvKep1dExHTgekVk8fV1f068PUuh2RmZpZcrx7zNTMzG7GcfM3MzBJz8jUzM0vMydfMzCwxJ18zM7PEkp7tbOUmT5sFwIKT9hjiSIaPWpu1Uy9F+w52fSN9X+jU9nVzOSP9M7De4J6vmZlZYk6+ZmZmiTn5mpmZJebka2ZmlpiTr5mZWWJOvmZmZok5+ZqZmSXm5GtmZpaYk6+ZmVliTr5mZmaJOfmamZkl5uRrZmaWmJOvmZlZYkmTr6QNJJ0vaZmkuyTtX1FPkk6QdJ+kxZKulrRVyljNzMy6JXXP91TgGWAiMBU4vSKpvg/4N2BHYAPgeuDHqYI0MzPrpmTJV9JYYF/g2IhYGhGzgYuAA0uqvxyYHRF3RMQK4CfAlqliNTMz66aUPd/NgRURMb9QNhco6/n+DHiVpM0lrQkcDFxetlBJh0uaI2nOwoULOx60mZlZp41OuK5xwOK6ssXAuiV1HwCuBW4DVgD3AG8vW2hEnAGcATBlypToVLBmZmbd0nLPV9IFkt4jaaC95aXA+Lqy8cCSkrrHAW8ANgHGAF8ErpL0ggGu28zMrGe0k0iXAecA90o6UdJmba5rPjC6br6tgXkldbcGzomIeyPi2YiYCayPj/uamdkI0HLyjYipwEuB44F3ALdJ+o2kgySt08L8y4DzgBmSxkraAdib8rOYbwTeJ2mipDUkHQisCfy91XjNzMx6VVtDyBHxREScHhFvBF4H/AH4b+BBSf8taYsmizgSWAd4GPgpcEREzJM0SdJSSZPyel8hOxnrZuBx4JPAvhHxeDvxmpmZ9aIBnXAlaSOyXut7gGeB/yU7PvsnSZ+LiJPL5ouIR4F9SsrvJjshq/b+aeCj+cvMzGxEaeeEqzUlvVfSpcBdZEn0q8BLI+KDEfFushtnHNOVSM3MzEaIdnq+DwACzgamRcSfSur8EnisE4GZmZmNVO0k308CP8+HhEtFxGNkd6eyQZo8bdbzvy84aY+25inWb7acsnkGolPLGa6K7Vxf1mtt2+q+VbZNxXmqpndC2bLb2f5e2B97IQbrXS0n34jwvZXNzMw6wI8UNDMzS8zJ18zMLDEnXzMzs8ScfM3MzBJz8jUzM0vMydfMzCwxJ18zM7PEnHzNzMwSc/I1MzNLzMnXzMwsMSdfMzOzxJx8zczMEnPyNTMzS8zJ18zMLLGkyVfSBpLOl7RM0l2S9m9Q9xWSLpG0RNIiSV9NGauZmVm3pO75ngo8A0wEpgKnS9qqvpKktYBfAlcBLwE2Bn6SME4zM7OuSZZ8JY0F9gWOjYilETEbuAg4sKT6IcD9EfH1iFgWEU9HxJ9SxWpmZtZNKXu+mwMrImJ+oWwusFrPF3gzsEDSZfmQ89WSXle2UEmHS5ojac7ChQu7ELaZmVlnpUy+44DFdWWLgXVL6m4M7Ad8C9gImAVcmA9HryIizoiIKRExZcKECR0O2czMrPNSJt+lwPi6svHAkpK6TwGzI+KyiHgGOBl4EbBFd0M0MzPrvpTJdz4wWtJmhbKtgXkldf8ERJKozMzMEkuWfCNiGXAeMEPSWEk7AHsDPy6p/hPgzZLeIWkUcDSwCPhLqnjNzMy6ZXTi9R0JnAk8DDwCHBER8yRNAm4FtoyIuyPiNkkHAN8FXgzcBOyVD0EPX7fdBtdeu1rx++fmJ3J//4HVy+rKW5pnkMsZiHaX0yyutuZvsJzBrKd0HRXra1i3jfW2/bkOZNklsZW1U+U2NZreJJ5W99Fmy262/a22T6fatuGyr1sPdthhUMuykUcRI2d0d8qUKTFnzpyhDqPa7rvD5ZcPdRRmltKGG0LvX4mhoQ6g36Tu+fa3p5+GN74RfvGLVYrffOKVDWf73ed3Wa2sNk9xWrPltKvReqvWPZB42llPo2UPZDmNtPO5NKrbznobtWOzslaXXRZbO/tOo3maxdNsP2k3nqr1tbutg23bqhiOvu5s9rvz+kEtx0YmJ9+UImDMGNh441WKHxy/YeP56uqvMk9hWtPltKvReqvWPZB42lhPw2UPYDmNtPO5NKzbxnobtmOTspaXXRJbW/tOo3maxNN0P2k3nor1tb2tg2zbqhiWrbVO9ndvVscPVkgpAuTRHbN+EeDka6WcfFNy8jXrKyE5+VopJ9+UnHzN+krg5GvlnHxTcvI16yvu+VoVJ9+UnHzN+oqP+VoVJ9+UnHzN+ot7vlbByTclJ1+zvuJjvlbFyTclJ1+zvhLCyddKOfmm5ORr1lfc87UqTr4pOfma9RWf7WxVnHxTcvI16yvu+VoVJ9+UnHzN+oovNbIqTr4pOfma9ZXw37tVcPJNycnXrM/kf+/u/VodJ9+UnHzN+krU/tydfK2Ok29KTr5mfSXc87UKSZOvpA0knS9pmaS7JO3fwjxXSQpJo1PE2FVOvmZ95fljvk6+Vid1QjsVeAaYCGwDzJI0NyLmlVWWNJX0MXaPk69ZX3k+5Tr5Wp1kPV9JY4F9gWMjYmlEzAYuAg6sqP9C4Djgs6li7DonX7O+4p6vVUk57Lw5sCIi5hfK5gJbVdQ/ETgdeLDRQiUdLmmOpDkLFy7sTKTd4uRr1ld8zNeqpEy+44DFdWWLgXXrK0qaAuwAfLvZQiPijIiYEhFTJkyY0JFAu8bJ16yvuOdrVVIm36XA+Lqy8cCSYoGkNYDTgE9ExLOJYkvDydesPzn5Wp2UyXc+MFrSZoWyrYH6k63GA1OAcyQ9CNyYl98racfuh9lFTr5mfcU9X6uS7EziiFgm6TxghqQPkZ3tvDfwlrqqi4GNCu83AX4PbA/0+EHdJpx8zfqKj/laldSX8RwJnAk8DDwCHBER8yRNAm4FtoyIuymcZCVpTP7rQ8N+GDqCi//0AB+bNqut2SY3qN9oWieVracb6x7MeprVK5u+4KQ9VptWK+uG2nqaraNT29wNnVhn1TLaXXY7n1u7f0fNlt1KrL7DlVVJmnwj4lFgn5Lyu8lOyCqbZwHP3yB1mIvwjdbN+shztSN7Tr5Wx7eXTCli5TCUmY147vlaFSfflCJW/jGa2YjnY75Wxck3Jfd8zfqLz3a2Ck6+KfmYr1lfcc/Xqjj5phSB/wTN+oeP+VoVJ9+UfJ2vWV9xz9eqOPmm5GO+Zn3Fd7iyKk6+KTn5mvUVP8/Xqjj5puRLjcz6inu+VsXJNyX3fM36io/5WhUn35R8qZFZf3HP1yo4+abkS43M+oqP+VoVJ9+U3PM16ys+5mtVnHxTimCkPKDJzJrzMV+r4uSbks92NusrHna2Kk6+KflsZ7O+4mFnq+Lkm5KP+Zr1FQ87WxUn35Tc8zXrK36wglVJmnwlbSDpfEnLJN0laf+KegdL+oOkJyTdK+mrkkanjLUrfMzXrM+452vlUvd8TwWeASYCU4HTJW1VUu8FwNHAhsCbgF2AzySKsXvc8zXrKz7ma1WS9SYljQX2BV4bEUuB2ZIuAg4EphXrRsTphbf3SToLeFuqWLvGx3zN+oqP+VqVlD3fzYEVETG/UDYXKOv51vtnYF5XokrJf4BmfcXHfK1KyuQ7DlhcV7YYWLfRTJIOBaYAJ1dMP1zSHElzFi5c2JFAu8bDzmZ9xT1fq5Iy+S4FxteVjQeWVM0gaR/gJGD3iFhUVicizoiIKRExZcKECZ2KtTs87GzWV3zM16qkPIN4PjBa0mYR8be8bGsqhpMl7QZ8D9gjIm5JFGN3DbMHK0yeNqtn5+mUoVx3uzF0I9Zubn/qtu3FbfEdrqxKsp5vRCwDzgNmSBoraQdgb+DH9XUlvR04C9g3In6fKsauc8/XrK+452tVUl9qdCSwDvAw8FPgiIiYJ2mSpKWSJuX1jgVeCFyaly+VdFniWDvPx3zN+oyTr5VLeuOKiHgU2Kek/G6yE7Jq74f/ZUVl3PM16ysedrYqvr1kSsPsmK+ZDY6Hna2Kk29KEeCer1nf8KVGVsXJNyUf8zXrK+75WhUn35T8YAWzvuJjvlbFyTcl93zN+op7vlbFyTcln+1s1ld8zNeqOPmm5J6vWX/xgxWsgpNvSr7UyKyvuOdrVZx8U/Kws1lf8TFfq+Lkm5yTr1m/cM/Xqjj5ppL/8flSI7P+ET7maxWcfFOpJV/3fM36hnu+VsXJN5Xne75Ovmb9wsd8rYqTbyrP93zNrF/4DldWxck3Ffd8zfqQe75Wzsk3FR/zNes7/rJtVZx8U3HP16zveNjZqjj5puI/PrO+4xOurErS5CtpA0nnS1om6S5J+zeo+0lJD0paLOlMSWunjLXj3PM16zu+1MiqpO75ngo8A0wEpgKnS9qqvpKkXYFpwC7AZOAVwBfThdkFPuZr1nfc87UqyZKvpLHAvsCxEbE0ImYDFwEHllQ/GPhBRMyLiMeA44FDUsXaFb7DlVnf8TFfqzI64bo2B1ZExPxC2Vxgp5K6WwEX1tWbKOlFEfFIxyM7/3w46KCOL3YV+R/fCvkwu1m/eG6N/O/9Xe+C0V3+d/vJT8KMGd1dh3WMItE3Mkk7Aj+PiJcUyg4DpkbEznV1bwc+GhGX5+/XJBuufnlELKirezhweP721cBt3dqGDtkQWDTUQfQgt0s1t005t0u1dttmUUTs1q1gbHUpe75LgfF1ZeOBJS3Urf2+Wt2IOAM4oxMBpiBpTkRMGeo4eo3bpZrbppzbpZrbpvelHAOdD4yWtFmhbGtgXkndefm0Yr2HujLkbGZmlliy5BsRy4DzgBmSxkraAdgb+HFJ9R8BH5S0paT1gWOAmaliNTMz66bUZ/8cCawDPAz8FDgiIuZJmiRpqaRJAPmx3q8Cvwbuyl/HJY61W4bNEHlibpdqbptybpdqbpsel+yEKzMzM8v4uhczM7PEnHzNzMwSc/LtMkk/kfSApCckzZf0oSb1R9Y9rUtIWlvSD/L7ey+R9EdJuzeof4ikFfl5AbXXzukiTqPddsnnGfH7S42koyTNkbRc0swmdftin4H22iWv3zf7TC9z8u2+LwOTI2I8sBdwgqTtyyqOyHtalxsN3EN2d7MXAscC50qa3GCe6yNiXOF1dffDTK6tdumj/aXmfuAE4MwW6/fDPgNttEsf7jM9y8m3y/L7Uy+vvc1fr6yoPvLuaV0iIpZFxPSIWBARz0XEJcCdQOmXkn4xgHbpi/2lJiLOi4gLAF/vX9Bmu/TVPtPLnHwTkHSapCeBvwIPAJdWVN2K7D7WNc/f07rLIQ4pSRPJ7v1ddsOVmm0lLcqH7o+VlPLubEOihXbpy/2lDX23z7TA+0yPcPJNICKOBNYFdiS70cjyiqrjgMWF97Xf1+1edEMrv2/3WcD/RMRfK6r9Bngt8GKyJ2N9APj3NBEOjRbbpe/2lzb03T7TIu8zPcLJdxAkXS0pKl6zi3UjYkX+GMWNgSMqFtnyPa17WavtImkNsjucPQMcVbW8iLgjIu7Mh2JvAWYA7+36hnRYp9uFEbK/QHt/S63ot32mDSNmnxnuPAwzCPVPY2rRaKqP+dbuaX1u/n5Y3tO6lXaRJOAHwETg3RHxj3ZWAQy7JyN3oV1GxP4CA/5bamsVjNB9pk0jZp8Z7tzz7SJJL5a0n6RxkkblZxp+ALiqYpZ+uqf16cAWwJ4R8VSjipJ2z49/Iuk1ZGcBX9honmGs5Xahv/YXJI2WNAYYBYySNKbqOG4/7TPttAt9ts/0tIjwq0svYAJwDfA48ARwC3BYYfoksmGgSYWyTwEP5fV/CKw91NvRhXbZlKwn8nS+/bXX1LJ2AU7O22QZcAfZEOKaQ70dQ90u/bK/FLZ1OiuvGKi9pvfzPtNuu/TbPtPLL9/b2czMLDEPO5uZmSXm5GtmZpaYk6+ZmVliTr5mZmaJOfmamZkl5uRrZmaWmJOvmZlZYk6+ZmZmiTn5mpmZJebka9YlkiZIekDSfxbKXi/paUnD7gk7ZtY5vr2kWRflD9O4GNgJuBmYA/w+Ig4dyrjMbGg5+Zp1maRvAHuRPWRjR2CbiFg6pEGZ2ZBy8jXrMklrA3OBzYC3RMQNQxySmQ0xH/M1677JwCZkj3p7xdCGYma9wD1fsy6StCZwPfA34AayZ6++PiLuHsq4zGxoOfmadZGkk4D9gdcDi4HLgHWAt0XEc0MZm5kNHQ87m3WJpJ2ATwMHRcTjkX3TPQTYAviPoYzNzIaWe75mZmaJuedrZmaWmJOvmZlZYk6+ZmZmiTn5mpmZJebka2ZmlpiTr5mZWWJOvmZmZok5+ZqZmSXm5GtmZpbY/weAijBwUUQWDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAE1CAYAAACm3+q8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAArhklEQVR4nO3debgcVZ3/8feHhM1ABCRGEQKo4AJIgLiMiKCigMgyP2RUkG0UFFwG1FFUGCOiooOiMwIzIMiIgOAM+zaoCBpEJCqIcYkCCWFPgIQkbEP4/v441aToVPXtvrf73L7pz+t5+rm3T52qOlVd1d8+p06dUkRgZmZm+awy2gUwMzMbNA6+ZmZmmTn4mpmZZebga2ZmlpmDr5mZWWYOvmZmZpk5+JqZmWXm4GtmZpaZg28fkLS3pE9UpE+XFJLGj0a5hqNR5or0f5T0V0lPSVo4CkUbNknXSbqu9L5yG4dYRuVnPMQ8z1lPL46HoY69bq2nwzKdIWmJpBW+nyS9vdgHe5bS/l3SZT0qy+bF+nYbIt9Rkn5fVWazKj5Q+sPeQEdfzH3su8DflRMkbQCcBvwSeCuw8yiUq5tW2MY27E3nn/Fw1tOpvakuV45115kK3BoRz1RM26b4+zsASS8DPgR8sUdl2bb4e/MQ+f4DeCFwUI/KYSuZMVOjsrEhIu4G7m5K3gwYB/xXRMwY6TokrR4RT450OcNVs41d09i+Xq+nldFad1Gr34IU/KtsAzwUEfOK90eSAvXMHhVpO2BORCxolSkiHpf0feBTwPd6VBZbibjmO8oknUX6tfySonkrJM1pyrappCuKpri5kv6luXlL0taSLpX0iKTHJd0gaYd21l+xvtqmVkmbtSpLRVPpWUBjOT8tlnFWMW1XSTcW5V0k6WJJr2gqR2O9W0r6X0lLgAuapr2ymLZU0l2SDimmHyDpz0VZf1bUkobaH+8t5nlS0ixJf1+RZ4Um2aJ58iJJD0p6oijHjySNb/UZt7N9FcV8VbE9j0m6T9JxFcfDkJ9rO+VqmreTz6vlcdLCq4HVKWq2FbZhea13deD9wLkV2/l2SZdLuqf4POZJOlHSqm2UoWw74GZJh0n6Y3GM3Shp24q8PwReLemNHa7DBpBrvqPvS8Ak4LVA4zpWc63uItKv6ZOAPUhNbPOKNIovgl+QvpQOBR4DPgz8RNIbI+I3XSxvy7JU+BLwG+DfgI8AvwXmS9oVuAK4FngPsBZwHDBD0tSIuKdpOZcAZwBfA5qbI38EnA6cCBwBnClpM2An4GhgVeDbpC/p19dtmKSdizxXAJ8kfS7fLub/S918hcuBhcDhwALgJcA7ST9w2/mMW21fs4uBM4GvArsAxxbzTB9ivmbtlAtIgZfOPq9Oj5OGqcXfFYKvpAmkVpRLi6Q3AOuQjv1mWwM/BU4BlpKaj78EPAx8ZYgylG1DOp/WJNVqJ5A+o8skbR4RS0t5bwEeBXYlXWIxqxcRfo3yCzgLuLsifToQwCFN6bcB15Te/xT4E7BaKW1ckXZxG+ueU5F+HXDdMMoyPR1Wz8mzczHvTqW0mcBfgfGltE2B/wO+WbHef2qxfw4spa0LPA08BEwspX+8yLtxi31xA/BHYJVS2uuL+VbYF6X36xd59hzBZ1y7fRV5j27KdzqwGFhnGJ9ry3KN4PNqeZy02E8nAU9ROpZL07Yvlv2+4v1nSD86VsjbNN84UkXjEuCKocpQmu9lxfquBlRK37VIf0fFPL9oZzv98svNzmPDFU3v/wBMAZC0JrAjqfb3TNHMOR4Q8BPgzbnK0q6iBrMtcH5EPN1Ij4g7SQFwx4rZLmqxyKtKy3gEeBD4VUQ8Wsrz5+LvRjVlGkeqAf53lDr6RMRNwJxW20MK9HcAJ0g6tKh1d6rV9jW7oOn9D0k10S2Hsd4hDfPzGu5xMhWYFRFPVUx7TmcrYAPg0ea8xTlwcNE8vID0Y+z/SLX7JW2UoWG74u/nI6LcBH9b8XdSxTzzi3KZteTgOzY83PT+SWCN4v/1SL/sjyV9wZRfHwXWbfNaWzfK0q51ST8O7quYdj9pm5pV5W14pOn9UzVpUF/W9UnNyw9UTKtKe1bxxfx2Uu3wq8BsSXdIOrzVfE1abd9Q5Wm8f0kHy+jEcD6v4R4nW7M8uDV7LalZd3bxfg2qm8nPBf6dVMs/iNQ8vROptvqHNsrQsC1we6x42ebFxd+qDmmPk5qozVryNd+xbyGp6e1k4PtVGaL6lo2GJ4DVKtJfQKrR9cIjpC/CF1VMe1HNent9z+kC0g+WyRXTJgNzW80cEXcAB0oSKYB8FDhF0pyIuKrVvI1FdFDWyaSadvk9QPm6azc/1+F8Xh0ratjrkj6L5mmrkpp7ry0dzw8V+cv5pgL7AvtHxLml9HeTfkDUdeSqsh1wb0X6e4BFwI0V09arKr9ZM9d8+8OTDPPXcqQOH78gfeH/NiJmNr+GWMRcYLKk9RsJRa/gV9TPMjJFmX8D7Fs09zbWuzHwRuD6Xq27RZmWke7lfLee23v79cAmHSwnIuIWlt8722gKHvZnXOEfmt6/l9ScWq7Vtfu5DlmujJ/X46Tt2K5i2qdI99F+p5T2Z2BVSRuW0hpN2892kCuC+vHF2992UJ5tgZepNKhJsa4PA9+qaRrflKE755k5+PaJPwLrSTpc0mslbdXh/J8gfWH9b3GrzI6S9pH0ZUknDDHvj0i1mnMk7SJpf1LHlF7/ej+W1HP1ckl7SHof8GNSjeIbPV53nS8ArwQulrS7pINJ11fvbzWTpNcUt/58WNLOknYB/pN0rfHaIttIP+OyQyV9rrid5kTgg8CJEbGwlKfdz7XdcvX88ypqtKcAb5Z0rqT3SfqApP8m9VD+t4j4aWmWnxd/X1dK+x3pEsO/FvvnAOBXpFaAByPi2ZqspE2K26KmN5dF0iakWuyTwFmS3ibpINIP3Vup6DEtaR1g81K5zGo5+PaH75I6zXwF+DXQ0VB5EfFb0vWwh0i39FxDukVmK4b4IoiIvwHvJl0vvBj4NCmYz24x24hFxNXA7qRbRS4gjRD0J+BN5S/InCLiJ8D+pNrhhcA/kwZxGKomcz9wF2m/XQqcR+p0867S9cIRfcZN9iJdY76UdJ/r8aTbaMrb0u7n2la5Mn5enyXdkrYl6dark0g13v0j4p+ayjSnKPMepbR5pM9wY9L++Qhp2//Gik3OE4q/VT+uGrXvd5FaBi4DTiAdF7vU1Hp3JwX+TjrP2YDSczvxmZmNHUXrxLeBF0fEYx3OexjwZdLtZx3NW7O8q4AFEXHASJdlKz/XfM1sLDub1NHsiGHMuyNwUpcC71TgLfRujGlbybjma2ZjmqQ3ANtGxCmjWIZdgXUj4rzRKoONLQ6+ZmZmmbnZ2czMLDMHXzMzs8wcfM3MzDJz8DUzM8vMwdeykLR2MWrRfEnLihGEOl3GOpKekfSwpMskvXjouYZc5ubFKEe7jXRZQ6yndvu1/AH0Lcda79H2H1qse7GkNZqmvaiY9smRrqe0zFb74eBifQslNY/ZPL55NKpe7A+zXBx8LZcPAu8DvkW6v7KTp/g0LAZ2AD4H7EYauWikti3+3tyFZbXSr9s/lTSE4lqk5y6XNT/Crxva2Q/PJz2rdyi92B9mWfipRpbLq4BFEfHl4S6gePjBDcANxehEr+5CubYjPXS+12NZ9+v2TyUN0TgR2Bu4vDStEXxv6cJ6GtrZD9cAH5P0rYioHVe7R/vDLAvXfI1iAPrLJd0j6QlJ8ySdWDzGrVtWo+ZB5pJeLulsSXdKelzpWbinNjc9NnkUWL0L5doOuFnSYZL+KGmp0kPYtx1yzs7Ubn/Jq4oHNDwm6T5Jx6n+Wcwj3n5JAl5DCq4XA3s0rW8qcFdEND+bdyTa2Q+NJxB9voPldut4MMvCNV+D9DjCn5KeKLOU1BT7JdID0b9SfEmPq5/9WVHURjq1AenB5EeSnh37UlJT4pXA3w1jeZ3YBniMNHj+p0iD7X8NuEzS5hGxNMP2N1wMnAl8FdiF9CShZ4DpI1hmK5uRmpt/RwrAXyA9InBGMX0bSk3OGffDfaRHBx4p6cSIaPksZbOxyMHXiIgTG/8rPa/1BmAnYPsieUfgZ20s6vpivirrkgJ71fp/TunpS5J+SXoKzS8kbRMRVdccHyMF7WFTer7tOsBNwJ5RDPcmaTFwFWn7r6HH219yekQ0HgF5jaSJwCeL5teFTXlHvP2kmi3ALRHxO0lzSU3PMyStDbwM+EEpf679AOkH0IdIPwj+sY383dgfZtk4+A64ooft+0lfdJsBLyhNvqD4+xvSIwuHsrhi+WsU874N+J+aMqxGqnUeSHoUXLnX7Suo7vBzE/AvkvYAfjrMwfEbj437fDx3nNXbir+Tir893f6SC5re/5DUQWlLltdGG7qx/dsA/wfMKt5fSgq+nyK1hojnXu/NtR+IiIclfQP4gqSvAbcPMUs39odZNg6+di6pp+h3SNfaFpCC38+APxR5ltBep5vnDBRePFz8keLtTcDHa+b7KvAx4Djgl6Qv7w1Jz05do2aer5CC56XFujYtnu/aiW2B20vP3G1o3LJyd/G319vf8EDN+5dU5O3G9k8F/lR6Nu3FpI5OW1Hd0znXfmg4ieXHxf5D5O3G/jDLxh2uBljxGLR9gQ9FxGcj4oqIuIlU4xPLv3h3JNWQhnr9tGkVi0nXEI8EppEeTl/lvcD3I+L4iLg2Im4GFg5R/ANJD1GfDrwJGM4D3berme89wCLgxuJ9r7e/YXLN+3sq8nZj+6fy3OD6c1KQ3JsUfB+OiLtK03PtBwAiYgnph9m+LG8ir9ON/WGWjWu+g21K8fcvjQRJE1je2/S3xd9hNTcWnW5uBG6U9C5gL+CYivmeR/rSLjtkiHXtCcyOiJE8P3Vb4AlJ4yPiaQBJGwIfBr5RqhH2evsb/gE4ofT+vaTa5h8q8o5o+yVNBl5EqSYbEU9LuoIUfJubnCHffig7BfgEy4/JOt04HsyycfAdbL8DngL+VdJXSV/GnybdDvJgRNwLEBGLgZkjXNe9wCtrpl0NHCTpNlJHq/9Hqim1MhGovAe0GDXpTuCLETG9RZ71inxnSfoeqal7OnArqRkTyLL9DYcWt/rcTOrt/EFgekVnKxjh9lN/D+8lpD4ATwPfLk/IuB/K63xS0nHAaUNkrd0fZv3Izc4DLCLmka6lbUy6VvYRUvD9G90d1QjSdUDVTPtYsf4vA+cDa5NGQWpFNF1bLJlQ/G31ZdzobPUu0m1Gl5FqnRcCu5Rqvd3Savsb9gLeTtoX7yfV9r5Uk3ek2z+1+HtLU/rVwBOkH+bN07qhnf3Q7HvAX4fI02p/mPUdPbeTp1lvSPousFdETBoyc3vL+xWwOCLeXjHtMFIg37hfer0O+vY3dHs/lJZbuz/M+pFrvpbLXcD6kt7RPIB/JyStLmkb0u03dYMv7Aic1GeBZ9C3v6Er+6Ghzf1h1ndc87UsJG1E6gW7WZHU8a0gTbes3Au8MyJu7VYZe2nQt7+hG/uhtKx1GOP7wwaXg69lVero9IdOr6sWo29tTboN6M6IeKb7JeytQd/+hpHsh9IyVpr9YYMna/CV9FHgYGAr4LyIOLgm30GkG/E3Iw2Yfi7wucbtIGZmZmNZ7luN7iX14NyF1MO0zvNIN+Q3Bny4lDTk3Qkt5mHXXXeNq6++uisFNTMbIJ32QLcRyhp8I+JCAEnTSPdU1uU7tfT2HknnAG8ZavkLFvT6kaxmZmYjN1Z6O7+Z5YO/m5mZjWl9H3wlHUIaD/bEmumHSZopaeb8+fPzFs7MzGwY+jr4StqbdJ13t4iobFOOiNMiYlpETJs0qav37ZuZmfVE347tLGlX4HRg94i4baj8ZmZmY0XW4Fs8uH08MA4YV4xw83TzLUSS3gqcA/x9RPw6ZxnNzMx6LXez8zHA48DRpIHjHweOkTRF0hJJjUfcHQs8H7iySF8i6arMZTUzM+uJlWqEq2nTpsXMmSN94pmZ2cDxfb6Z9XWHKzMzs5WRg6+ZmVlmfdvb2Xpnk6OvePb/OSfsPoolMTMbTK75mpmZZebga2ZmlpmDr5mZWWYOvmZmZpk5+JqZmWXm4GtmZpaZg6+ZmVlmDr5mZmaZOfiamZll5uBrZmaWmYOvmZlZZg6+ZmZmmTn4mpmZZebga2ZmlpmDr5mZWWYOvmZmZpk5+JqZmWXm4GtmZqNC0nRJPxjF9V8l6aARLuNgSTM6nW/8SFZqZmZWR9KS0tvnAU8Cy4r3H8pclunAyyPi/Y20iNgtZxnKXPM1M7OeiIi1Gi/gLmCPUto53VyXpHHdXF6vOfiamdloWk3S9yUtljRL0rTGBEmvknSdpIXFtD1L086SdKqkKyUtBd4iaQNJ/yNpvqQ7JX28yLsr8DngPZKWSLq1SL9O0gdLyzxU0p+KsvxR0rZF+tGSbi+l//1IN9rB18zMRtOewA+BdYBLge8ASFoVuAy4Bngh8DHgHEmvKM27H/BlYG3gl0X+W4GXAG8DjpS0S0RcDXwFOL+odW/dXAhJ+wLTgQOBiUW5Hiom3w7sADwf+CLwA0kvHslG+5qvmdnK5Mgj4ZZbOpvn+uuv63AttxBxZIfz1JkREVcCSDobaCz3DcBawAkR8QxwraTLgfeRgiTAJRFxQzHvVsCkiDiumHaHpNOB9wL/20Y5Pgh8PSJuLt7/rTEhIn5Uyne+pM8CrwMu6WRDy7LWfCV9VNJMSU9KOmuIvEdJul/SIklnSlo9UzHNzCyf+0v/PwasIWk8sAEwrwi8DXNJtdqGeaX/NwY2KJqoF0paSGpqntxmOTYi1XBXIOlASbeUlrslsH6by62Uu+Z7L3A8sAuwZl0mSbsARwNvLea5iFTVPzpDGc3Mxq5vfWs4c+3U3UJ0xb3ARpJWKQXgKcDsUp4o/T8PuDMiNqtZXtSkl+d/WXOipI2B00nN2DdGxDJJtwAaehPqZa35RsSFEXExy9vR6xwEnBERsyLiEeBLwME9Lp6ZmfWPm4ClwKclrSppJ2AP0vXhKr8GHpX0GUlrShonaUtJry2mPwBsIqku7n0X+JSk7ZS8vAi8E0iBez6ApENINd8R6dcOV1uQLpo33ApMlvSCUSqPmZllFBFPkTo97QYsAE4BDoyIP9fkX0YKzlOBO4t5vkvqJAXQuG77kKTfVsz/I1LnrXOBxcDFwHoR8UfgG8CNpAC+FXDDSLdPEUPVxLtP0vHAhhFxcM3024GPFD3UGr3engI2jYg5TXkPAw4DmDJlynZz587tYclXDpscfcWz/885YfdRLImZ9YkRNaFa5/q15ruE1NW7ofH/4uaMEXFaREyLiGmTJk3KUjgzM7OR6NfgOwso34e1NfBARAx1rdjMzKzv5b7VaLykNYBxwDhJjS7lzb4PfEDSqyWtCxwDnJWxqGZmZj2Tu+Z7DPA46Zah9xf/HyNpSjHk1xSA4lrv14Gfke7rmgt8IXNZzczMeiLrfb4RMZ3lI5M0W6sp7zeBb/a4SGZmZtn16zVfMzOzlZaDr5mZWWYOvmZmZpk5+JqZ2ZhXdNp9aYvp/yHp2BGuYydJd49kGQ1+pKCZ2UqmPIpdmzoa6nDOCbu3PSKWpDmkJwstKyWfFREf7WSdQ4mItYaY/uFurm+kHHzNzCp4GNau2iMifjJaK5c0rhj7uW+42dnMzLKTdLCkGySdVDwn9w5JbyzS50l6UNJBpfxnFU3HP5a0WNL1xVOHGtND0stLeU+VdKWkpcBbirTjS/n3Kp7R+6ik2yXtWqQfIulPxTrukPShXmy/g6+ZmY2W1wO/B15AeprQD4HXAi8nDcT0HUnl5uT9SY+YXR+4BTinxbL3Iz2laG1gRnmCpNeRRlL8Z2Ad4M3AnGLyg8C7SM8UOAQ4SdK2w9u8eg6+ZmbWaxcXtdvG69Ai/c6I+F7RJHw+sBFwXEQ8GRHXkJ5m9/LScq6IiJ9HxJPA54G/k7RRzToviYgbIuKZiHiiadoHgDMj4sfF9HsajyqMiCsi4vZIrgeuAXbozm5YzsHXzMx6be+IWKf0Or1If6CU53GAiGhOK9d85zX+iYglwMPABjXrnFeTDinI3141QdJukn4l6WFJC4F3kmraXeXga2ZmY8WztdyiOXo94N6avK16cM8DXtacKGl14H+AE4HJEbEOcCU9eN6xg6+ZmY0V75T0Jkmrka793hQRrWq4dc4ADpH0NkmrSHqJpFcCqwGrA/OBpyXtBryja6UvcfA1M7Neu6wYBKPxumiYyzmX9IS7h4HtSB2wOhYRv6boTAUsAq4HNo6IxcDHgQuAR0idti4dZllb8n2+ZmYrmWHcl9z1ZtWGiNikxeSzSvn+1lyOiNiwKf+CusEyIkKl/w+umH5w0/uLgBV+BETEycDJNeu4Dmgu07C45mtmZpaZa75j3FgYhadfytgoR7/uJzMbHA6+ZmbW96qakscyNzubmZll5uBrZmaWmYOvmZlZZg6+ZmZmmTn4mpmZZebga2ZmlpmDr5mZWWYOvmZmZpl5kA0bFf0y6lW7RlLesbatDWO13GZjQdaar6T1JF0kaamkuZL2q8knScdLukfSIknXSdoiZ1nNzMx6JXez88nAU8Bk0qOgTq0JqvsC/wjsQHpY8o3A2bkKaWZm1kvZgq+kCcA+wLERsSQiZpCek3hARfZNgRkRcUdELAN+ALw6V1nNzMx6KWfNd3NgWUTMLqXdClTVfH8IvFzS5pJWBQ4Crq5aqKTDJM2UNHP+/PldL7SZmVm35exwtRawqCltEbB2Rd77gF8AfwGWAfOAt1YtNCJOA04DmDZtWnSrsGZmZr2Ss+a7BJjYlDYRWFyR9wvAa4GNgDWALwLXSnpeT0toZmaWQc7gOxsYL2mzUtrWwKyKvFsD50fE3RHxdEScBayLr/uamdlKIFvwjYilwIXAcZImSNoe2IvqXsw3A/tKmixpFUkHAKsCf8tVXjMzs17JPcjGEcCZwIPAQ8DhETFL0hTgj8CrI+Iu4GvAC4FbgAmkoLtPRCzMXF4zM7Ouyxp8I+JhYO+K9LtIHbIa758APlK8rE95BCRrpXF8DOfY8LFlKzuP7WxmZpaZg6+ZmVlmDr5mZmaZOfiamZll5uBrZmaWmYOvmZlZZg6+ZmZmmTn4mpmZZebga2Zmllnu4SXNuqo8ElJDJyMijWQUplzGQhnNrDOu+ZqZmWXm4GtmZpaZg6+ZmVlmDr5mZmaZOfiamZll5uBrZmaWmYOvmZlZZg6+ZmZmmXmQDetLIxk8ozzvcAamqFr3ymSk+6fbRlKeftsWs3a55mtmZpaZg6+ZmVlmDr5mZmaZOfiamZll5uBrZmaWmYOvmZlZZg6+ZmZmmbUdfCVdLOldkoYdsCWtJ+kiSUslzZW0X4u8L5V0uaTFkhZI+vpw12tmZtZPOgmkS4HzgbslfUXSZsNY38nAU8BkYH/gVElbNGeStBrwY+Ba4EXAhsAPhrE+MzOzvtP2CFcRsb+kiaSgeQhwtKQZwHeBH0XE463mlzQB2AfYMiKWADMkXQocABzdlP1g4N6I+GYp7fftlnWsaYzSUx6hZzRH/RnNEZ7aXfdYHYVqqJG7Ot3+us93JCOEDWfdQ5WnbhmjrdNy93o5Njg6akKOiEcj4tSIeB2wFfAb4D+B+yX9p6RXtZh9c2BZRMwupd0KrFDzBd4AzJF0VdHkfJ2kraoWKukwSTMlzZw/f34nm2NmZjYqhnX9VtIGwF7Au4Cngf8GNgJ+L+lTNbOtBSxqSlsErF2Rd0PgvcC/ARsAVwCXFM3RzxERp0XEtIiYNmnSpOFsjpmZWVaddLhaVdK7JV0JzAX2Br4OvDgiPhAR7yQ1SR9Ts4glwMSmtInA4oq8jwMzIuKqiHgKOBF4AdCqZm1mZjYmdPJUo/sAAecCR0dE1TXYHwOP1Mw/GxgvabOI+GuRtjUwqyLv74HtOyibmZnZmNFJs/NRwEsi4mM1gZeIeCQiNq2ZthS4EDhO0gRJ25Oars+uyP4D4A2SdpY0DjgSWAD8qYPympmZ9aW2g29EnB0RT4xwfUcAawIPAucBh0fELElTJC2RNKVY11+A9wP/QapJ7wXsWTRBm5mZjWmdNDuPWEQ8TLpW3Jx+F6lDVjntQlJN2czMbKWiiBjtMnTNtGnTYubMmaNdjHrXXguXXbZC8hkz7gTgA2/adIW05vS6ecv5hpq3k+nt6lYZW6273XxDzdNuWjvLbEcny+t0++vKUrWcdo+PqmWMZD92cox1q4ztareMQy275ecxdSocdFDHZctMo12AQePgm9POO8PPfgZrPaeSz6NPPA3AxDXGr5DWnN6sKt9Q83YyvV3dKmOrdbebb6h52k1rZ5nt6GR5nW5/XVmqltPu8VG1jJHsx06OsW6VsV3tlnGoZdcu5/HH4XnPg4ULOy5bZg6+mWVtdh54y5bBm94E11/f0Qg/jVFzqkauek2baWWdTO9Uu+urK0Ordbebbzg6WXa3R3HKtf1V+75q2UONjvWaitGcWqW1W4a66a32dyefVdUoVFXlrlr2UJ957XKOOgrOOKPlvDaY/FSjnFaiVgYza5PPe6vg4JtTBMitO2YDw+e71XDwzc0no9ngkFzztUoOvjm55ms2WBx8rYaDb04+Cc0Gj897q+Dgm5NrvmaDxTVfq+Hgm5uDr9ng8PluNRx8c3LN12ywuOZrNRx8c3LwNRssDr5WwyNc5TTMk7CT0bByLGe0jIXy97KM/bb9/VaeKsMp40i2q3nez1x3O4c7+FoF13xzcs3XbKCET3er4eCbm4Ov2cAI3Oxs1Rx8c3LN12yghK/5Wg0H35x8EpoNFNd8rY6Db06u+ZoNHgdfq+Dgm5ODr9lAcdi1Og6+uTn4mg0MX/O1Og6+ObnmazZgfL5bNQffnPwL2GygPHufr899a+IRrnLqcc13JKP5zDlh924Xp+X6hkoba7o5KlKv5FjPUOvoh896ZT0GbWxxzTc3NzubDYxoNDu75mtNHHxz8jVfs4EScvC1ag6+OfkENBsoz57xPvetSdbgK2k9SRdJWipprqT92pjnWkkhaexfn3bN12wwOfhak9wB7WTgKWAyMBW4QtKtETGrKrOk/VmZOoU5+JoNlPD5bjWy1XwlTQD2AY6NiCURMQO4FDigJv/zgS8An85Vxix8MpoNDHe4sjo5m503B5ZFxOxS2q3AFjX5vwKcCtzfaqGSDpM0U9LM+fPnd6ekveKar9lAcYcrq5Mz+K4FLGpKWwSs3ZxR0jRge+Dfh1poRJwWEdMiYtqkSZO6UtCe8QloNph87luTnMF3CTCxKW0isLicIGkV4BTgnyLi6Uxly8M1XzMzI29nptnAeEmbRcRfi7StgebOVhOBacD5SoFqXJF+t6R9I+IXWUrbC6MQfD1yT/flHhWsH4zmcdRv+7uT8rjZ2epkC74RsVTShcBxkj5I6u28F/DGpqyLgA1K7zcCfg1sB/T5Rd02uOZrNjDc4crq5B5k4whgTeBB4Dzg8IiYJWmKpCWSpkRyf+PF8oD7QEQ8lbm83eUT0Gyg+MEKVifrPbQR8TCwd0X6XaQOWVXzzGFleS6Xr/maDRTXfK2Oh5fMycHXzMxw8M3LwddsoLjma3UcfHNz8DUbGL7ma3UcfHPyCWg2UFzztToOvjm52dlssPg+X6vh4JuTg6+ZmbEyPa5vrOhS8O3XkavK5eqXEYl6pV8/g+Hol21pVY5+KWNDO+Vxs7PVcc03J5+AZgPFHa6sjoNvTm52NhsorvlaHQffnBx8zcwMB9+8HHzNBoqfamR1HHxzc/A1GxjPhlwHX2vi4JuTT0CzgeKar9Vx8M3Jzc5mA8UdrqyOg29ODr5mZoYH2cgrgvNunsdn+2ywgF7pt0ERzLJzs7PVcM3XzKxH3OHK6jj45hSxvAOGma303OHK6jj45hQBOPiaDYrw+W41HHxzilg+1quZDQ7XfK2Jg29OEf4lbDZAfM3X6jj4mpn1iK/5Wh0H35zc4cpsoHiQDavj4JuTm53NBor7eFgdB9+c3OHKbDC55mtNPMKVAR6NatC331ob/vHhZmerlrXmK2k9SRdJWipprqT9avIdJOk3kh6VdLekr0sa+z8U3OxsNlDc4crq5G52Phl4CpgM7A+cKmmLinzPA44E1gdeD7wN+FSmMvaOO1yZDRT/2LY62WqTkiYA+wBbRsQSYIakS4EDgKPLeSPi1NLbeySdA7wlV1l7xr9+zQaTz31rkrPmuzmwLCJml9JuBapqvs3eDMyqmiDpMEkzJc2cP39+F4rZQ252Nhsoz3awdPC1JjmD71rAoqa0RcDarWaSdAgwDTixanpEnBYR0yJi2qRJk7pSUDOzbvB9vlYnZyemJcDEprSJwOK6GSTtDZwA7BwRC3pXtEx8zddsoLjDldXJWfOdDYyXtFkpbWvqm5N3BU4H9oiI2zKUr/ci8CloNjh8vludbME3IpYCFwLHSZogaXtgL+Ds5ryS3gqcA+wTEb/OVcaec83XbDC55mtNct9qdASwJvAgcB5weETMkjRF0hJJU4p8xwLPB64s0pdIuipzWbvPHa7MBoqbna1O1oErIuJhYO+K9LtIHbIa78f+bUVmZu5wZTU8tnNObnY2GygOuVbHwTcnd7gyGyhudrY6Dr45RYBrvmaDx8HXmjj45uQT0GygeJANq+Pgm5l7O5sNDjc7Wx0H35wilo/1amYrPYdcq+Pgm5Pv8zUbKK75Wh0H35x8q5HZYHLwtSYOvmZmPeIOV1bHwTcnNzubDRaf7lbDwTcnD7JhNlBc87U6Dr45+Zqv2WBy8LUmDr5mZj3i3s5Wx8E3O9d8zQaFm52tjoNvLsXJ50E2zAaHz3er4+CbSyP4uuZrNjBc87U6Dr65PFvzdfA1GzgOvtbEwTcXn3xmA8cdrqyOg29mPgXNBofPd6vj4JuLm53NBpBrvlbNwTcXd7gyGzhudrY6Dr65+OQzG1w+/62Jg28ubnY2GzjPhlwHX2vi4Gtm1iP+sW11HHxzcc3XbOB4kA2rkzX4SlpP0kWSlkqaK2m/FnmPknS/pEWSzpS0es6ydp07XJkNHHe4sjq5a74nA08Bk4H9gVMlbdGcSdIuwNHA24BNgJcCX8xXzB7wyWc2uHz+W5NswVfSBGAf4NiIWBIRM4BLgQMqsh8EnBERsyLiEeBLwMG5ytpLHmjdbHA45Fqd8RnXtTmwLCJml9JuBXasyLsFcElTvsmSXhARD3W9ZBddBAce2PXFPkfxy3eZfJndbFA8s0pxvr/jHTC+x1+3Rx0Fxx3X23VY1ygyNYdI2gH4UUS8qJR2KLB/ROzUlPd24CMRcXXxflVSc/WmETGnKe9hwGHF21cAf+nVNnTJ+sCC0S5EH/J+qed9U837pV6n+2ZBROzaq8LYinLWfJcAE5vSJgKL28jb+H+FvBFxGnBaNwqYg6SZETFttMvRb7xf6nnfVPN+qed90/9ytoHOBsZL2qyUtjUwqyLvrGJaOd8DPWlyNjMzyyxb8I2IpcCFwHGSJkjaHtgLOLsi+/eBD0h6taR1gWOAs3KV1czMrJdy9/45AlgTeBA4Dzg8ImZJmiJpiaQpAMW13q8DPwPmFq8vZC5rr4yZJvLMvF/qed9U836p533T57J1uDIzM7PE972YmZll5uBrZmaWmYNvj0n6gaT7JD0qabakDw6Rf+Ua07qCpNUlnVGM771Y0u8k7dYi/8GSlhX9AhqvnfKVOI9O90sxz0p/vDRI+qikmZKelHTWEHkH4piBzvZLkX9gjpl+5uDbe18FNomIicCewPGStqvKuFKOaV1tPDCPNLrZ84FjgQskbdJinhsjYq3S67reFzO7jvbLAB0vDfcCxwNntpl/EI4Z6GC/DOAx07ccfHusGJ/6ycbb4vWymuwr7ZjWZRGxNCKmR8SciHgmIi4H7gQqf5QMimHsl4E4Xhoi4sKIuBjw/f4lHe6XgTpm+pmDbwaSTpH0GPBn4D7gypqsW5DGsW54dkzrHhdxVEmaTBr7u2rAlYZtJC0omu6PlZRzdLZR0cZ+GcjjpQMDd8y0wcdMn3DwzSAijgDWBnYgDTTyZE3WtYBFpfeN/9fuXelGVzFu9znAf0XEn2uy/RzYEngh6clY7wP+OU8JR0eb+2XgjpcODNwx0yYfM33CwXcEJF0nKWpeM8p5I2JZ8RjFDYHDaxbZ9pjW/azd/SJpFdIIZ08BH61bXkTcERF3Fk2xtwHHAe/u+YZ0Wbf3CyvJ8QKdnUvtGLRjpgMrzTEz1rkZZgSan8bUpvHUX/NtjGl9QfF+TI5p3c5+kSTgDGAy8M6I+L9OVgGMuScj92C/rBTHCwz7XOpoFaykx0yHVppjZqxzzbeHJL1Q0nslrSVpXNHT8H3AtTWzDNKY1qcCrwL2iIjHW2WUtFtx/RNJryT1Ar6k1TxjWNv7hcE6XpA0XtIawDhgnKQ16q7jDtIx08l+YcCOmb4WEX716AVMAq4HFgKPArcBh5amTyE1A00ppX0CeKDI/z1g9dHejh7sl41JNZEniu1vvPav2i/AicU+WQrcQWpCXHW0t2O098ugHC+lbZ3O8jsGGq/pg3zMdLpfBu2Y6eeXx3Y2MzPLzM3OZmZmmTn4mpmZZebga2ZmlpmDr5mZWWYOvmZmZpk5+JqZmWXm4GtmZpaZg6+ZmVlmDr5mZmaZOfia9YikSZLuk/QvpbTXSHpC0ph7wo6ZdY+HlzTroeJhGpcBOwK3ADOBX0fEIaNZLjMbXQ6+Zj0m6VvAnqSHbOwATI2IJaNaKDMbVQ6+Zj0maXXgVmAz4I0RcdMoF8nMRpmv+Zr13ibARqRHvb10dItiZv3ANV+zHpK0KnAj8FfgJtKzV18TEXeNZrnMbHQ5+Jr1kKQTgP2A1wCLgKuANYG3RMQzo1k2Mxs9bnY26xFJOwKfBA6MiIWRfukeDLwK+Mxols3MRpdrvmZmZpm55mtmZpaZg6+ZmVlmDr5mZmaZOfiamZll5uBrZmaWmYOvmZlZZg6+ZmZmmTn4mpmZZebga2Zmltn/B29qKIsoopuiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all comparison in a loop\n",
    "for (a, b) in [(0, 1), (-3, -1.2), (1800, 1990)]:\n",
    "    for N in [100, 300, 1000, 3000, 10000]:\n",
    "        plot_comparison(N, a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating random numbers with non-uniform distributions\n",
    "In many cases, we want to draw random numbers from non-uniform distributions. For instance, we might want to generate Gaussian, exponential or Poisson distributed random numbers. How can we do this in plain python?\n",
    "\n",
    "### Inverse transform sampling\n",
    "The most general way of generating random numbers under a given distribution is known as **inverse transform sampling**. You can read about the method [here](link), but the key theoretical foundation of the method is very simple:\n",
    "\n",
    "Given a continous random variable $X$ with cumulative distribution function $F$, the random variable $F(X)$ has a uniform distribution between 0 and 1. \n",
    "\n",
    "$$\n",
    "F(X) \\sim U(0, 1)\n",
    "$$\n",
    "\n",
    "Think about it for a second. Here $F(X)$ is not a function, but a random variable, because we apply a function, $F$, to a random variable, $X$, getting a new random variable, $Y \\equiv F(X)$. The statement is then simply that $Y$ is a uniform random variable, no matter what form $F$ takes.\n",
    "\n",
    "As a consequence, one can see that $X = F^{-1}(U)$, where $U$ is a uniform random variable between 0 and 1. This gives as a very simple way of drawing random numbers from an arbitrary continous random variable $X$:\n",
    "\n",
    "1. Generate a uniform random number, $u$\n",
    "2. Compute $x = F^{-1}(u)$\n",
    "\n",
    "where $F$ is the cumulative distribution function of $X$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Consider the **exponential** distribution, with probability density function \n",
    "$$\n",
    "f(x) = \\lambda e^{-\\lambda x}\n",
    "$$\n",
    "and cumulative distribution function\n",
    "$$\n",
    "F(x) = 1 - e^{-\\lambda x}\n",
    "$$\n",
    "1. Write a function that generates exponentially-distributed random numbers, using the inverse transform sampling method. For simplicity, set $\\lambda = 1$.\n",
    "2. Generate 1000 exponentially-distributed random numbers with your function.\n",
    "3. Plot a histogram of your generated sample, together with the theoretical probability density function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inv_cdf_expo(y):\n",
    "    \"\"\"\n",
    "    The inverse of y = 1 - exp(-x)\n",
    "    \"\"\"\n",
    "    # exp(-x)=1-y -> -x = ln(1-y) -> x=ln(1/(1-y))\n",
    "    x = -np.log(1-y)\n",
    "    return x\n",
    "\n",
    "\n",
    "def inverse_sampling(size, inv_cdf):\n",
    "    # generate uniform data of given_size\n",
    "    u = np.random.uniform(size=size)\n",
    "    # inverse transform it\n",
    "    x = inv_cdf_expo(u)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 8, num=200) #linspace meno ovvio perchè va da zero a fin dove la definisco, in questo caso bisogna ricordare che la distribuzione esponenziale decade abbastanza velocemente\n",
    "pdf_x = np.exp(-x)\n",
    "#quella che sto costruendo con queste righe è la distribuzione teorica\n",
    "data = inverse_sampling(10**5, inv_cdf_expo)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 5))\n",
    "ax.hist(data, bins=np.linspace(0,8,num=200), density = 'True')\n",
    "ax.plot(x, pdf_x, color='red')\n",
    "ax.set_yscale(\"log\") #dato che sto plottando in scala logaritmica mi aspetto una retta\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In practical situations, do not reinvent the wheel!\n",
    "What we did so far is a good exercise to get comfortable with python, random number generation, histograms and probabilities.\n",
    "\n",
    "If you need to generate exponential random numbers as part of a larger project, however, it is better to use a function from a **well-tested**, **well-documented** library such as [numpy]() or [scipy](). For instance:\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "x = np.random.normal(location=0, scale=1, size=1000)\n",
    "```\n",
    "\n",
    "That is the beauty of **open-source** languages like python: there is a community of developers that contribute their code to standardized libraries in an organized fashion. These libraries are less likely than yours to contain errors or bugs, because they are used by many people and such bugs are reported and fixed very quickly. For instance numpy's `np.random` submodel contains functions to generate random numbers from many distributions, see [the documentation for numpy.random](https://docs.scipy.org/doc/numpy/reference/routines.random.html#distributions)\n",
    "\n",
    "However, as you can see **not all** distributions are included in numpy. If you need to work with a less well-known distribution, you might need to implement that yourself. In such case, knowing the **inverse sampling** method will come in handy!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}