{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NYSM_TimeSeries.ipynb\n",
    "### This notebook generates time series of recent meteorological data for various [New York State Mesonet (NYSM)](https://nysmesonet.org) sites."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\"><b>TRY THIS: </b>Edit the next <i>Markdown</i> cell with your first and last name.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Author: firstName lastName"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Pandas DataFrame to read in the file containing the past hour's worth of NYSM data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First define the format and then define the lambda function\n",
    "timeFormat = \"%Y-%m-%d %H:%M:%S UTC\"\n",
    "# This function will iterate over each string in a 1-d array\n",
    "# and use Pandas' implementation of strptime to convert the string into a datetime object.\n",
    "parseTime = lambda x: datetime.strptime(x, timeFormat)\n",
    "df = pd.read_csv('/data1/nysm/latest.csv',parse_dates=['time'], date_parser=parseTime).set_index('time')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect the `Dataframe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify a particular NYSM site ID, and then create a `Dataframe` specific to that site."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\"><b>TRY THIS: </b>Edit the next cell with your own choice of NYSM site.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the station ID to any one of the 126 NYSM stations.\n",
    "stnID = 'VOOR'\n",
    "dfStn = df.query(' station == @stnID ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Inspect this `Dataframe`, now consisting only of the station that was specified in the above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dfStn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Output the names of all the variables (i.e., *columns*) in the `Dataframe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dfStn.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a nested set of `for` loops that plots time series traces for several variables at several sites."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\"><b>TRY THIS: </b>Edit the next two cells with your own choice of NYSM sites and variables.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set your own list of variarbles here\n",
    "varList = ['temp_2m [degC]', 'temp_9m [degC]', 'relative_humidity [percent]']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set your own list of sites here\n",
    "sites = ['SCHO','QUEE','CHAZ']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Invoke the `seaborn` library to make our time series plots more readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\"><b>NOTE: </b> To reset the plots to their default look, <i>uncomment</i> and run the following cell. Note how the <code>seaborn</code>-enabled plots look nicer.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#sns.reset_orig()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a nested `for` loop over the sites and variables, and make time series plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for site in sites:\n",
    "    for var in varList:\n",
    "        dfStn = df.query(' station == @site ')\n",
    "        titleStr = \"Past hour time series of \" + var + \" for site \" + site\n",
    "        dfStn[var].plot(title=titleStr)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\"><b>Before you leave ... </b>:\n",
    "<ol>\n",
    "    <li>Save the notebook (<i>File-->Save Notebook</i>)</li>\n",
    "    <li>Shutdown the notebook (<i>File-->Close and Shutdown Notebook</i>)</li>\n",
    "</ol>\n",
    "</div>"
   ]
  }
 ],
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