{
 "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",
    "- If you are viewing using a linux terminal window, are you in your atmos_5340/chapter1 directory?\n",
    "- If you are viewing using Jupyter notebook, then ignore the following\n",
    "- Don't type the ' ' quote marks\n",
    "- check! 'pwd'\n",
    "- If not, then 'cd'\n",
    "- 'cd atmos_5340/chapter1'\n",
    "- Do the following. Note the dot after the space\n",
    "- 'cp ~u0035056/atmos_5340_2021/chapter1/* . '\n",
    "- Have you already copied the data directory? If not, you'll need to do that too\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 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 949.96  1394.46  1328.42  1211.58   886.46  1628.14  1043.94   972.82\n",
      " 1198.88  1168.4    980.44  1421.13   980.44  1004.57   828.04  1019.81\n",
      " 1018.54  1437.64  1455.42  1099.82  1381.76  1217.93  1437.894 1165.86\n",
      " 1223.01  1185.164 1261.11  1512.824 1536.7   1116.33   798.83  1332.23\n",
      " 1493.52  1305.56   993.14  1767.84  1617.98  1888.49  1160.78  1521.46\n",
      "  969.772 1042.162 1477.01  1137.92  1473.708 1003.3   1652.016 1245.362\n",
      " 1893.316 1427.48  1521.714 1460.246 1164.336 1132.84  1193.038 1441.958\n",
      " 1014.476 1449.832 1406.144 1609.09   904.24  1661.16  1468.12  1092.2\n",
      " 1404.62   836.93   971.55   908.05   679.45   998.22  1347.47   731.52\n",
      " 1206.5   1056.64   949.96 ]\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=2)\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=2)\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>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>1951</th>\n",
       "      <td>886.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>1628.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>1043.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>972.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>1198.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>1168.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>980.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>1421.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>980.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>1004.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>1019.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>1018.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>1437.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>1455.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>1099.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>1381.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>1217.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>1437.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>1165.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>1223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>1185.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>1261.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>1512.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>1536.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>1116.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1003.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>1245.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1893.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1427.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1521.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1460.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>1164.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1132.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1442.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1014.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1449.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1406.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1609.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>904.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1661.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1468.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1092.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1404.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>836.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>971.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>908.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>679.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>998.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1347.5</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Alta Snow (cm)\n",
       "1947           950.0\n",
       "1948          1394.5\n",
       "1949          1328.4\n",
       "1950          1211.6\n",
       "1951           886.5\n",
       "1952          1628.1\n",
       "1953          1043.9\n",
       "1954           972.8\n",
       "1955          1198.9\n",
       "1956          1168.4\n",
       "1957           980.4\n",
       "1958          1421.1\n",
       "1959           980.4\n",
       "1960          1004.6\n",
       "1961           828.0\n",
       "1962          1019.8\n",
       "1963          1018.5\n",
       "1964          1437.6\n",
       "1965          1455.4\n",
       "1966          1099.8\n",
       "1967          1381.8\n",
       "1968          1217.9\n",
       "1969          1437.9\n",
       "1970          1165.9\n",
       "1971          1223.0\n",
       "1972          1185.2\n",
       "1973          1261.1\n",
       "1974          1512.8\n",
       "1975          1536.7\n",
       "1976          1116.3\n",
       "...              ...\n",
       "1992          1003.3\n",
       "1993          1652.0\n",
       "1994          1245.4\n",
       "1995          1893.3\n",
       "1996          1427.5\n",
       "1997          1521.7\n",
       "1998          1460.2\n",
       "1999          1164.3\n",
       "2000          1132.8\n",
       "2001          1193.0\n",
       "2002          1442.0\n",
       "2003          1014.5\n",
       "2004          1449.8\n",
       "2005          1406.1\n",
       "2006          1609.1\n",
       "2007           904.2\n",
       "2008          1661.2\n",
       "2009          1468.1\n",
       "2010          1092.2\n",
       "2011          1404.6\n",
       "2012           836.9\n",
       "2013           971.6\n",
       "2014           908.1\n",
       "2015           679.5\n",
       "2016           998.2\n",
       "2017          1347.5\n",
       "2018           731.5\n",
       "2019          1206.5\n",
       "2020          1056.6\n",
       "2021           950.0\n",
       "\n",
       "[75 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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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=(1946,2022),ylim=(600,2000))\n",
    "ax1.set(xlabel=\"Year\",ylabel=\"Snowfall (cm)\")\n",
    "ax1.set(xticks=decade_ticks)\n",
    "ax1.set(title=\"Alta Snowfall: 1946-2021\")\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 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}