{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This block contains comments as markdown code\n",
    "# **Chapter 1**  \n",
    "**ATMOS 5340: Environmental Programming and Statistics**  \n",
    "**John Horel **\n",
    "\n",
    "- Follow  these directions for doing these steps using a linux terminal window\n",
    "- are you in your atmos_5340/chapter1 directory?\n",
    "- check the directory you are in: pwd\n",
    "- If not in that directory, then type: cd \n",
    "- And then type: cd atmos_5340/chapter1\n",
    "- Type the following (Note the dot after the space): cp ~u0035056/atmos_5340_2022/chapter1/* . \n",
    "- Have you already copied the data directory? If not, you'll need to do that too. Review the instructions for that\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Python modules\n",
    "\n",
    "`numpy` provides routines to handle arrays and many calculations efficiently and is imported by convention as `np`. Numpy functions are very good at handling homogeneous data arrays (and similar in that respect to matlab functions).\n",
    "\n",
    "`pandas` is really good at handling tabular/array data that may have heterogeneous types (floating and text, for example). It is imported by convention as `pd`. \n",
    "\n",
    "There are a couple sets of panda library routines  (`Series`, and `DataFrame`) used so frequently that we'll import those directly too.\n",
    "\n",
    "`scipy` has a bunch of statistical functions and we'll import `stats` from `scipy`\n",
    "\n",
    "\n",
    "`pyplot` is a _submodule_ of matplotlib. It is typically imported as the alias `plt` to handle basic plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# these are python modules used in the program\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alta snowfall\n",
    "https://utahavalanchecenter.org/alta-monthly-snowfall\n",
    "\n",
    "\n",
    "Look in the `data` folder at the called `alta_snow.csv`\n",
    "\n",
    "Open the `alta_snow.csv` file see the column contents and the units.\n",
    "\n",
    "- The 0th column is the Year at Season End\n",
    "- The 1st-6th column are the total snowfall in each month from November to April (in inches)\n",
    "- The 7th column is the Nov-Apr total snowfall (inches)\n",
    "\n",
    "Begins in the 1946 season and ends in 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1946. 1947. 1948. 1949. 1950. 1951. 1952. 1953. 1954. 1955. 1956. 1957.\n",
      " 1958. 1959. 1960. 1961. 1962. 1963. 1964. 1965. 1966. 1967. 1968. 1969.\n",
      " 1970. 1971. 1972. 1973. 1974. 1975. 1976. 1977. 1978. 1979. 1980. 1981.\n",
      " 1982. 1983. 1984. 1985. 1986. 1987. 1988. 1989. 1990. 1991. 1992. 1993.\n",
      " 1994. 1995. 1996. 1997. 1998. 1999. 2000. 2001. 2002. 2003. 2004. 2005.\n",
      " 2006. 2007. 2008. 2009. 2010. 2011. 2012. 2013. 2014. 2015. 2016. 2017.\n",
      " 2018. 2019. 2020. 2021. 2022.]\n",
      "[1145.54   949.96  1394.46  1328.42  1211.58   886.46  1628.14  1043.94\n",
      "  972.82  1198.88  1168.4    980.44  1421.13   980.44  1004.57   828.04\n",
      " 1019.81  1018.54  1437.64  1455.42  1099.82  1381.76  1217.93  1437.894\n",
      " 1165.86  1223.01  1185.164 1261.11  1512.824 1536.7   1116.33   798.83\n",
      " 1332.23  1239.52  1305.56   993.14  1767.84  1617.98  1888.49  1160.78\n",
      " 1521.46   969.772 1042.162 1477.01  1137.92  1473.708 1003.3   1652.016\n",
      " 1245.362 1893.316 1427.48  1521.714 1460.246 1164.336 1132.84  1193.038\n",
      " 1441.958 1014.476 1449.832 1406.144 1609.09   904.24  1661.16  1468.12\n",
      " 1092.2   1404.62   836.93   971.55   908.05   679.45   998.22  1347.47\n",
      "  731.52  1206.5   1056.64   949.96   717.296]\n",
      "Min: 679.5 Max: 1893.3\n"
     ]
    }
   ],
   "source": [
    "#read the year of the Alta snowfall data\n",
    "year = np.genfromtxt('../data/alta_snow.csv', delimiter=',',usecols=0,skip_header = 1)\n",
    "print(year)\n",
    "#read the seasonal total and convert from inches to cm\n",
    "snow = 2.54  * np.genfromtxt('../data/alta_snow.csv', delimiter=',', usecols=7, skip_header=1)\n",
    "#print out the data after converting it to cm\n",
    "print(snow)\n",
    "#what are the min and max values?\n",
    "print(\"Min: %.1f Max: %.1f\" % (np.min(snow),np.max(snow)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Alta Snow (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>1145.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>1394.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>1328.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>1211.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>731.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1206.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1056.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>717.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Alta Snow (cm)\n",
       "1946          1145.5\n",
       "1947           950.0\n",
       "1948          1394.5\n",
       "1949          1328.4\n",
       "1950          1211.6\n",
       "...              ...\n",
       "2018           731.5\n",
       "2019          1206.5\n",
       "2020          1056.6\n",
       "2021           950.0\n",
       "2022           717.3\n",
       "\n",
       "[77 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use pandas module to organize the data in a convenient manner\n",
    "#by default the pandas display format shows up to 5 places to the right of the decimal point, limit it to 1\n",
    "#this next line is really obtuse, so don't stress over what it means\n",
    "pd.set_option('display.float_format', lambda x: '%.1f' % x)\n",
    "#define a dataframe, df, from the snow organized by year\n",
    "df = pd.DataFrame(snow, index=year.astype(int),columns=['Alta Snow (cm)'])\n",
    "#list out the content of the dataframe\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create bar plot time series of Alta seasonal snowfall\n",
    "#create a list for the times for tick marks on the x axis. This will stop at 2020 (not 2030)\n",
    "decade_ticks = np.arange(1950,2030,10)\n",
    "\n",
    "#create a fig of Alta snowfall time series\n",
    "fig,(ax1) = plt.subplots(1,1,figsize=(10,3))\n",
    "ax1.bar(year,snow,color='green')\n",
    "ax1.set(xlim=(1945,2023),ylim=(600,2000))\n",
    "ax1.set(xlabel=\"Year\",ylabel=\"Snowfall (cm)\")\n",
    "ax1.set(xticks=decade_ticks)\n",
    "ax1.set(title=\"Alta Snowfall: 1946-2022\")\n",
    "#add grids to the plot\n",
    "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n",
    "\n",
    "#save the figure to \n",
    "plt.savefig('alta_snowfall.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate a Gaussian type empirical distribution for figure 1.3\n",
    "from numpy.random import normal\n",
    "sample = normal(loc=0, scale=1, size=1000000)\n",
    "# plot the histogram\n",
    "fig,(ax1) = plt.subplots(1,1,figsize=(5,5))\n",
    "ax1.hist(sample, bins=31, color='cyan',edgecolor='black',linewidth=1,align='mid')\n",
    "ax1.set(xlim=(-3,3),ylim=(0,150000))\n",
    "ax1.set(xlabel=\"Magnitude\",ylabel=\"Count\")\n",
    "ax1.set(title=\"Figure 1.3\")\n",
    "#add grids to the plot\n",
    "ax1.grid(linestyle='--', color='grey', linewidth=.2)\n",
    "#save the figure to \n",
    "plt.savefig('figure_1.3.png')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}