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   "source": [
    "<center><img src=\"https://matplotlib.org/_static/logo2.png\" width=600 alt=\"Matplotlib Logo\"></img></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LAB EXERCISE: Plot temperature data from the NYS Mesonet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Overview\n",
    "You will replicate the workflow in a portion of the 01_MatplotlibIntro notebook, but use recent NYSM temperature data.\n",
    "\n",
    "1. Create a basic line plot.\n",
    "1. Add labels and grid lines to the plot.\n",
    "1. Plot multiple time series of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import the matplotlib library's `pyplot` interface; this interface is the simplest way to create new Matplotlib figures. To shorten this long name, we import it as `plt` to keep things short but clear. We also import the `pandas` library, using its standard alias of `pd`. Finally, we import the `datetime` library, which allows for efficient operations on time-based variables and datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">TASK 1:</p>\n",
    "    In the code cell below, add a line that imports the  matplotlib library's `pyplot` interface with its standard alias.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Read in the most recent hour's worth of NYSM observations using `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "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": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot some temperature data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of \"hard-coding\" lists of variables and hours, Pandas creates \"list-like\" objects, called `Series`. First, let's specify a couple of NYSM sites, and then retrieve time and temperature data from the data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "site1 = 'RUSH'\n",
    "site2 = 'BEAC'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in 2 meter temperature for these sites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "site1_t2m = df.query('station == @site1')['temp_2m [degC]']\n",
    "site2_t2m = df.query('station == @site2')['temp_2m [degC]']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "site1_t2m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = site1_t2m.index\n",
    "temps = site1_t2m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">TASK 2:</p>\n",
    "    Choose your own two NYSM sites and repeat the execution of the above four code cells.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line plots\n",
    "\n",
    "Let's create a `Figure` whose dimensions, if printed out on hardcopy, would be 10 inches wide and 6 inches long (assuming a landscape orientation). We then create an `Axes`, consisting of a single subplot, on the `Figure`. After that, we call `plot`, with `hours` as the data along the x-axis (independent values) and `temps` as the data along the y-axis (the dependent values)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">TASK 3:</p>\n",
    "    Insert a code cell and use Matplotlib to create a time series plot of 2m temperature versus time for the first NYSM site you chose.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding axes labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, add x- and y-axis labels to our `Axes` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">TASK 4:</p>\n",
    "    Insert a code cell and add x- and y-axis labels to your `Axes` object you just created. Also, add a meaningful title with an appropriately-readable font size.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">TASK 5:</p>\n",
    "    Insert a code cell and add x- and y-axis labels to your `Axes` object you just created. Also, add a meaningful title with an appropriately-readable font size.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding labels and a grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we call `plot` more than once to plot multiple series of temperature on the same plot; when plotting we pass `label` to `plot` to facilitate automatic creation of legend labels. This is added with the `legend` call. We also add gridlines to the plot using the `grid()` call."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">TASK 6:</p>\n",
    "    Insert a code cell and plot 2 meter temperatures for both NYSM sites on the same plot. Include a legend and gridlines.\n",
    "</div>"
   ]
  }
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