{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![CDS 411 logo](../../img/cds-411-logo.png)\n",
    "\n",
    "# Class 18: Monte Carlo simulations I\n",
    "\n",
    "---\n",
    "\n",
    "![CC BY-SA 4.0 license](../../img/cc-by-sa.png)\n",
    "\n",
    "This notebook is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_style": "center",
    "slideshow": {
     "cell_style": "split",
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "\n",
    "np.random.seed(14234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview\n",
    "\n",
    "Random numbers are essential for computer simulations of real-life events, such as weather or nuclear reactions. To pick the next weather or nuclear event, the computer generates a sequence of numbers, called **random numbers** or **pseudorandom numbers**.\n",
    "\n",
    "*   As discussed in *Module 9.2: Simulations* in your textbook, an algorithm actually produces the numbers. \n",
    "\n",
    "*   They are not really random, but appear to be random. \n",
    "\n",
    "*   A uniform random number generator produces numbers in a **uniform distribution** with each number having an equal likelihood of being anywhere within a specified range.\n",
    "\n",
    "For example, suppose we wish to generate a sequence of uniformly distributed, four-digit random integers.  The algorithm used to accomplish this should, in the long run, produce approximately as many numbers between, say, 1000 and 2000 as it does between 8000 and 9000.\n",
    "\n",
    "> ***Definition***\n",
    ">\n",
    "> **Pseudorandom numbers** (also called **random numbers**) are a sequence of numbers that an algorithm produces but which appear to be generated randomly.  The sequence of random numbers is **uniformly distributed** if each random number has an equal likelihood of being anywhere within a specified range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random numbers using the `numpy` module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uniform distribution of floats\n",
    "\n",
    "The `numpy` module has an [ensemble of random number generator routines](https://docs.scipy.org/doc/numpy/reference/routines.random.html) that are quite useful.\n",
    "\n",
    "*   The uniform random number generator is provided by the function `np.random.uniform()`.\n",
    "\n",
    "The simplest way to use it is to give it a single argument, specifying how many random numbers you want using the `size` parameter. That way, each call to `np.random.uniform` returns a vector of uniformly distributed pseudorandom floating point numbers between 0 and 1.  Evaluate the following commands several times to observe the generation of different random numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.31758847])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.68168571, 0.76585959, 0.68450908, 0.57855728, 0.773761  ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.1990609 , 0.62125217, 0.69300428, 0.51733964, 0.76369778,\n",
       "       0.63150697, 0.20096115, 0.52575685, 0.92221297, 0.21902962])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose, however, we need our random floating point numbers to be in the range from 2.0 up to 5.0. We can specify `low` and `high` parameters to `np.random.uniform` to get a different range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.99451505, 2.36326583, 4.30423531, 4.55577852, 3.44023328,\n",
       "       2.08717776, 2.54533052, 3.10344858, 4.57575228, 4.13391795])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(low=2, high=5, size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `size` parameter will also accept a tuple, which we can use to generate a two-dimensional random matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.97849711, 9.15434153, 2.94958953],\n",
       "       [9.88098085, 4.32639396, 9.41562831],\n",
       "       [9.02787087, 2.97336346, 9.75391349],\n",
       "       [3.05626464, 2.745779  , 4.01571054]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(low=0, high=10, size=(4, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uniform distribution of integers\n",
    "\n",
    "To obtain an integer random number, we have a couple of different options available to us.\n",
    "One method is to use the `np.floor()` function in conjunction with `np.random.uniform()`. \n",
    "`np.floor()` returns the integer immediately below the floating-point number it is passed as an argument;\n",
    "for instance `np.floor(6.8)` returns the answer 6.\n",
    "Thus, the following command returns a random integer from the set {1, 2, ..., 25}:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.floor(np.random.uniform(low=1, high=26, size=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we had to make the max 26 instead of 25, since all floating-point numbers in the range 25.0 to 26.0 will get \"floored\" to the integer value 25.\n",
    "\n",
    "Combining these operations, the following commands assign to `matrix_sqr` a \\\\(4 \\times 4\\\\) array of integers from the set {11, 12, ..., 19} and to `matrix_rect` a \\\\(2 \\times 3\\\\) array of integers from the same set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13. 17. 19. 18.]\n",
      " [12. 19. 13. 14.]\n",
      " [14. 12. 12. 19.]\n",
      " [17. 17. 17. 14.]]\n"
     ]
    }
   ],
   "source": [
    "matrix_sqr = np.floor(np.random.uniform(low=11, high=20, size=(4, 4)))\n",
    "print(matrix_sqr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[19. 13. 15.]\n",
      " [18. 15. 11.]]\n"
     ]
    }
   ],
   "source": [
    "matrix_rect = np.floor(np.random.uniform(low=11, high=20, size=(2, 3)))\n",
    "print(matrix_rect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, `numpy` provides the `np.random.randint()` function, which automatically draws integers from a uniform distribution without the need for `np.floor()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13 15 13 19]\n",
      " [19 17 18 16]\n",
      " [18 12 18 12]\n",
      " [17 11 14 18]]\n"
     ]
    }
   ],
   "source": [
    "matrix_sqr_alt = np.random.randint(low=11, high=20, size=(4, 4))\n",
    "print(matrix_sqr_alt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11 17 15]\n",
      " [12 17 15]]\n"
     ]
    }
   ],
   "source": [
    "matrix_rect_alt = np.random.randint(low=11, high=20, size=(2, 3))\n",
    "print(matrix_rect_alt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reproducibility with a random seed\n",
    "\n",
    "*   A random number generator starts with a number, which we call a seed because all subsequent random numbers sprout from it. \n",
    "\n",
    "*   The generator uses the seed in a computation to produce a pseudorandom number.\n",
    "\n",
    "*   The algorithm employs that value as the seed in the computation of the next random number, and so on.\n",
    "\n",
    "*   Typically, we seed the random number generator once at the beginning of a program."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, we seed the random number generator with 14234 as follows:\n",
    "\n",
    "```r\n",
    "np.random.seed(14234)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   If the random number generator always starts with the same seed, it always produces the same sequence of numbers.\n",
    "\n",
    "*   A program using this generator performs the same steps with each execution.\n",
    "\n",
    "*   The ability to reproduce detected errors is useful when debugging a program.\n",
    "\n",
    "*   However, this replication is not desirable when you start running simulations with different inputs. For example, if we have a computer simulation of weather, we do not want the program always to start with a thunderstorm.\n",
    "\n",
    "*   By default, if the seed is not set, the `numpy` module initializes it to something different every time you run your program."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modulus function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An algorithm for a random number generator often employs the modulus operator, `%` in Python, which gives the remainder of a first integer argument divided by a second integer argument.  To return the remainder of the division of $m\\times{}n$, we employ a command of the following form:\n",
    "\n",
    "```python\n",
    "m % n\n",
    "```\n",
    "\n",
    "You may notice that this call looks exactly like it does in C, C++, and Java. Thus, the following statement returns 3, the remainder of 23 divided by 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "23 % 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical measures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `np.mean` returns the arithmetic mean, or average, of the elements in an array and has the following format:\n",
    "\n",
    "```python\n",
    "np.mean(vector)\n",
    "```\n",
    "\n",
    "Similarly, `np.std` returns the standard deviation of the elements in an array.  The following segment creates an array of 10 floating point numbers in the range 6 to 12 and returns the mean and standard deviation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_for_stats = np.random.uniform(low=6, high=12, size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.560442666306956\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(array_for_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9799999489244051\n"
     ]
    }
   ],
   "source": [
    "print(np.std(array_for_stats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A histogram of a data set is a bar chart where the base of each bar is an interval of data values and the height of this bar is the number of data values in that interval. For example, the histogram of the array `array_to_plot = np.array([1, 15, 20, 1, 3, 11, 6, 5, 10, 13, 20, 14, 24])` is given by the code below. As we've been doing all semester, we continue to use `pandas` to make our life easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_to_plot = np.array([1, 15, 20, 1, 3, 11, 6, 5, 10, 13, 20, 14, 24])\n",
    "df = pd.DataFrame(data=array_to_plot, columns=[\"my numbers\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that the data frame was set up correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>my numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    my numbers\n",
       "0            1\n",
       "1           15\n",
       "2           20\n",
       "3            1\n",
       "4            3\n",
       "5           11\n",
       "6            6\n",
       "7            5\n",
       "8           10\n",
       "9           13\n",
       "10          20\n",
       "11          14\n",
       "12          24"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now that we have our data frame, how do we create a histogram? First, we need to create a figure object and an axes object that stores subfigures, then we access the `\"my numbers\"` column and run the method `hist()`, making use of a couple of other parameters,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=120)\n",
    "df[[\"my numbers\"]].hist(bins=5, range=(0,25), ax=ax, linewidth=0.5, edgecolor='w');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `bins` parameter specifies the number of bins (or buckets) used to create the histogram.\n",
    "The `range` parameter corrects the numerical range over which we should create the `bins`.\n",
    "The `ax` parameter was the axes object we instantiated earlier, and specifying it here lets `pandas` know to use the `matplotlib` subplot environment within our `fig` object when creating the histogram."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the figure, the 13 data values are split into five intervals or categories: [0, 5), [5, 10), [10, 15), [15, 20), [20, 25). The interval notation, such as [10, 15), is the set of numbers between the endpoints 10 and 15, including the value with the square bracket (10) but not the value with the parenthesis (15).  Thus, for a number x in [10, 15), we have 10 ≤ x < 15.  Because four data values (10, 11, 13, 14) appear in this interval, the height of that bar is 4.\n",
    "\n",
    "> ***Definition***\n",
    ">\n",
    "> A **histogram** of a data set is a bar chart where the base of each bar is an interval of data values and the height of this bar is the number of data values in that interval."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we've been increasingly making use of `seaborn`, you may also use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=120)\n",
    "sns.distplot(a=df[\"my numbers\"], kde=False, bins=5, hist_kws={\"range\": (0, 25), \"alpha\": 1}, ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another example, we generate an array, `normal_array`, with 10,000 normally distributed random values.\n",
    "The commands to generate the table and produce a histogram with 30 categories are as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_array = np.random.normal(size=10000)\n",
    "df2 = pd.DataFrame(data=normal_array, columns=[\"x\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `seaborn`, we would write:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=120)\n",
    "sns.distplot(a=(df2[\"x\"],), bins=30, hist_kws={\"range\": (-5, 5), \"alpha\": 1.0},\n",
    "             kde=False, norm_hist=True, ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The keyword `norm_hist=True` normalizes the histogram so that the area within the bars sums to 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you use `kde=True` (which is the default), then you can also see the density plot for the histogram. If you use this, then the `norm_hist` keyword is no longer necessary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jglasbr2/.conda/envs/cds411-dev/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=120)\n",
    "sns.distplot(a=(df2[\"x\"],), bins=30, hist_kws={\"range\": (-5, 5)}, ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convenience methods for data frames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to making it easier to generate plots, `pandas` is quite nice to use for computing summary statistics.\n",
    "We can take `array_for_stats` from earlier in the notebook, put it in a data frame, and compute the mean and standard deviation just as easily,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x    8.560443\n",
      "dtype: float64\n",
      "x    2.087103\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_for_stats = pd.DataFrame(data=array_for_stats, columns=[\"x\"])\n",
    "\n",
    "print(df_for_stats.mean())\n",
    "print(df_for_stats.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, we can calculate several summary statistics with a single method!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.560443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.087103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.090413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.933153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.830375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.439608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.527838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               x\n",
       "count  10.000000\n",
       "mean    8.560443\n",
       "std     2.087103\n",
       "min     6.090413\n",
       "25%     6.933153\n",
       "50%     7.830375\n",
       "75%    10.439608\n",
       "max    11.527838"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_stats.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll find that `pandas` can be quite useful when analyzing our simulation results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Element of Chance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll consider the function $y(x)=-1.75x^2+x^3+1.4$ for this example.\n",
    "Numpy doesn't allow for symbolic mathematics, so we need to specify the function numerically.\n",
    "The vectorization features of `numpy` make this pretty easy to do.\n",
    "We specify our range of values for our independent variable \\\\(x\\\\) using the `np.linspace()` function and store it in the variable `x`, and then type out the above formula using `x` where necessary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cubic_function(x, a=1.4, b=0, c=-1.75, d=1.0):\n",
    "    return a + b * x + c * x**2 + d * x**3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We form our data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(start=0, stop=2.0, num=200)\n",
    "df = pd.DataFrame({\n",
    "    \"x\": x,\n",
    "    \"y\": cubic_function(x),\n",
    "    \"category\": \"function\",\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize the cubic function using `seaborn`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=120)\n",
    "sns.lineplot(x=\"x\", y=\"y\", data=df, ax=ax);\n",
    "ax.set_xlim([0, 2.0])\n",
    "ax.set_ylim([0, 2.5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's implement our dart throwing Monte Carlo simulation to calculate the area under the curve. The area under the curve is approximately equal to\n",
    "\n",
    "\\begin{equation}\n",
    "  \\text{area}\\approx{}\\left(\\text{area of enclosing rectangle}\\right)\\left(\\frac{\\text{number of darts below}}{\\text{number of darts}}\\right)\n",
    "\\end{equation}\n",
    "\n",
    "We implement a function to throw the darts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def throw_darts(number_of_darts, x_min, x_max, y_min, y_max):\n",
    "    return pd.DataFrame({\n",
    "        \"darts_x\": np.random.uniform(low=x_min, high=x_max, size=number_of_darts),\n",
    "        \"darts_y\": np.random.uniform(low=y_min, high=y_max, size=number_of_darts),\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next a function to test whether the darts are above the curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_darts(darts_df):\n",
    "    return darts_df[\"darts_y\"] < cubic_function(darts_df[\"darts_x\"].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then a function to estimate the area under the curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_area_under_curve(darts_df, x_min, x_max, y_min, y_max):\n",
    "    success_fraction = np.sum(darts_df[\"hit\"]) / len(darts_df)\n",
    "    width = x_max - x_min\n",
    "    height = y_max - y_min\n",
    "    area = width * height\n",
    "    area_under_curve = area * success_fraction\n",
    "    \n",
    "    return area_under_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run a test of our Monte Carlo integration. We will integrate in the range $0\\leq{}x\\leq{}2.0$ and $0\\leq{}y\\leq{}2.5$ using 1000 dart throws."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_min = 0\n",
    "x_max = 2.0\n",
    "y_min = 0\n",
    "y_max = 2.5\n",
    "number_of_darts = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run the methods and save whether or not the dart is a hit or a miss. First, we throw the darts and assign the resulting data frame to `darts_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "darts_df = throw_darts(number_of_darts, x_min, x_max, y_min, y_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create a column called *hit* detailing whether the dart is a hit or a miss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "darts_df[\"hit\"] = test_darts(darts_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first few rows in the data frame look as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>darts_x</th>\n",
       "      <th>darts_y</th>\n",
       "      <th>hit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.940828</td>\n",
       "      <td>1.605624</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.514035</td>\n",
       "      <td>2.034799</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.441216</td>\n",
       "      <td>0.909028</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.636250</td>\n",
       "      <td>1.990111</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.553756</td>\n",
       "      <td>0.702477</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    darts_x   darts_y    hit\n",
       "0  0.940828  1.605624  False\n",
       "1  0.514035  2.034799  False\n",
       "2  0.441216  0.909028   True\n",
       "3  1.636250  1.990111  False\n",
       "4  1.553756  0.702477   True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darts_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the area based on the hits and misses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated area under curve with 1000 darts = 2.1\n"
     ]
    }
   ],
   "source": [
    "area_under_cubic_function = estimate_area_under_curve(darts_df, x_min, x_max, y_min, y_max)\n",
    "print(\"Estimated area under curve with 1000 darts =\", area_under_cubic_function)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize what the Monte Carlo integration looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=120)\n",
    "sns.lineplot(x=\"x\", y=\"y\", data=df, color=\"black\", ax=ax)\n",
    "sns.scatterplot(x=\"darts_x\", y=\"darts_y\", hue=\"hit\", data=darts_df, ax=ax)\n",
    "ax.set_xlim([0, 2.0])\n",
    "ax.set_ylim([0, 2.5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exact integration\n",
    "\n",
    "The accuracy of Monte Carlo integration improves as we increase the number of \"darts\" thrown at the viewing window.\n",
    "We can track exactly how much improvement we get by comparing the estimated area with the exact integral of \\\\(f(x) = -1.75 x^{2} + x^{3} + 1.4\\\\).\n",
    "Computing the integral by hand is as follows:\n",
    "\n",
    "\\begin{equation}\n",
    "  \\int_{0}^{2}-1.75x^{2}+x^{3}+1.4dx=\\left.-1.75\\cdot\\dfrac{x^{3}}{3}+\\dfrac{x^{4}}{4}+1.4x\\right\\rvert_{0}^{2}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "  = -1.75 \\cdot \\dfrac{8}{3} + 4 + 1.4 \\cdot 2\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "  = - \\dfrac{7}{4} \\cdot \\dfrac{8}{3} + 4 + \\dfrac{14}{10} \\cdot 2\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "  = \\dfrac{32}{15}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "  \\approx 2.133333\n",
    "\\end{equation}\n",
    "\n",
    "If we wanted to compute the exact integral in Python, then we could use the precise integration methods available in the `scipy.integrate` module, [see here for an overview](https://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html). The general purpose integration function is `quad()`, [see the documentation for additional details](https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.quad.html). To use `quad()`, we run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exact area = 2.1333333333333333\n",
      "Upper bound on numerical error = 2.3684757858670007e-14\n"
     ]
    }
   ],
   "source": [
    "exact_area_numerical = scipy.integrate.quad(func=cubic_function, a=0, b=2)\n",
    "print(\"exact area =\", exact_area_numerical[0])\n",
    "print(\"Upper bound on numerical error =\", exact_area_numerical[1])"
   ]
  }
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