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   "cell_type": "markdown",
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    "<center><img src=\"https://matplotlib.org/stable/_images/sphx_glr_logos2_003_2_00x.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 `times` 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": [
    "### 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 5:</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>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try to plot another variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">TASK 6:</p>Next, insert code cells to import data from two <i>different</i> stations, using a <i>different</i> variable (be sure to enter the exact text corresponding to the variable you are interested in importing, at the top of each column in the dataframe).  Plot the data using different <a href=\"https://matplotlib.org/stable/gallery/color/named_colors.html\">colors</a> and/or <a href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html\">line types</a>.\n",
    "    </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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