{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <span style=\"color:purple\">Cartopy 2: NYS Mesonet Data</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this notebook, we'll use Cartopy, Matplotlib, Datetime, and Pandas to visualize data from the <a href=\"http://www.nysmesonet.org/\">New York State Mesonet</a>, headquartered right here at UAlbany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from cartopy import crs as ccrs\n",
    "from cartopy import feature as cfeature\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a regional map, centered over NYS, and add in some geographic features.\n",
    "#### <span style=\"color: red\"><b>Be patient</b></span>: this may take a minute or so to plot, depending on the resolution of the Natural Earth shapefile features you are adding! \n",
    "\n",
    "<img src = \"NaturalEarthScales.png\" width=800 height=600 />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a img=\"images/NaturalEarthScales.png\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the figure\n",
    "#### For a quick look, let's just choose the coarsest (110,000,000:1) Natural Earth shapefiles set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Set the domain for defining the plot region.\n",
    "latN = 45.2\n",
    "latS = 40.2\n",
    "lonW = -80.0\n",
    "lonE = -71.5\n",
    "cLat = (latN + latS)/2\n",
    "cLon = (lonW + lonE )/2\n",
    "\n",
    "\n",
    "proj = ccrs.LambertConformal(central_longitude=cLon, central_latitude=cLat)\n",
    "\n",
    "res = '110m' # Coarsest and quickest to display; other options are '10m' (slowest) and '50m'.\n",
    "\n",
    "fig = plt.figure(figsize=(11,8.5),dpi=125)\n",
    "ax = plt.subplot(1,1,1,projection=proj)\n",
    "ax.set_extent ([lonW,lonE,latS,latN])\n",
    "ax.add_feature(cfeature.COASTLINE.with_scale(res))\n",
    "ax.add_feature (cfeature.STATES.with_scale(res));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot some data on the map. We'll use Pandas to read in the file containing the most recent NYS Mesonet obs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('http://www.atmos.albany.edu/products/nysm/nysm_latest.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View the first and last five lines of this `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine the column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create objects pointing to some columns of interest. In Pandas, we can refer to columns by using a \".\" in addtion to \"[]\" in most circumstances, though not if a column name starts with a number, nor if there are spaces in the column name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stid = df.station\n",
    "lat = df.lat\n",
    "lon = df.lon\n",
    "tmp2 = df['temp_2m [degC]'] # Use brackets due to the presence of a space in the column name\n",
    "tmp9 = df['temp_9m [degC]']\n",
    "time = df.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will plot one of the variables on a map later on in this notebook. In case we want to go back later and pick a different variable to plot (e.g. 9 m temperature), let's define a generic object name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param = tmp2 # replace with tmp9, e.g., if you want to plot 9 m temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "param"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the time object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The times are the same for all stations, so let's just pull out one of them, and then create some formatted datetime strings out of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeString = time[0]\n",
    "timeString"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The `timeString` above could make for a perfectly fine part of an informative title for a map, but let's create a string of the form \"Month Day, Year, HourMin UTC\" (e.g., *Mar 30, 2021, 0120 UTC* )\n",
    "First, make a `datetime` object from the string, using `strptime` with a format that matches that of the string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeObj = datetime.strptime(timeString,\"%Y-%m-%d %H:%M:%S\")\n",
    "timeObj"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, make a `string` object from the `datetime` object, using `strftime` with a format that matches what we want to use in the map's title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titleString = datetime.strftime(timeObj,\"%B %d %Y, %H%M UTC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "titleString"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a scatterplot to show the locations of each NYS Mesonet site using Matplotlib's `scatter` method. This method accepts an entire array of lon-lat values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax.set_title ('New York State Mesonet Site Locations')\n",
    "ax.scatter(lon,lat,s=9,c='r',edgecolor='black',alpha=0.75,transform=ccrs.PlateCarree())\n",
    "# Plot the figure, now with the sites plotted\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <span style=\"color: red\"> Did you notice the `transform` argument? </span> Since we are plotting on a Lambert Conformal-projected map, which uses a Cartesian x-y coordinate system where each point is equally separated in meters, we need to convert, or *transform*, the lat-lon coordinates into their equivalent coordinates in our chosen projection. We use the `transform` argument, and assign its value to the coordinate system that our lat-lon array is derived from."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next, plot the site IDs, using Matplotlib's `text` method. This method only accepts a single value for its x and y coordinates, so we need to loop over all the values in the arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that in a Python `list`, one can use the `enumerate` function to set a numerical value to be used as a counter. We can use the same technique on a Pandas `series` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for count, site in enumerate(stid):\n",
    "    print (count, site)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll repeat the enumeration below; instead of printing out the counter value and its associated list element name, we'll pass them in directly to Matplotlib's `ax.text` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for count, site in enumerate(stid):\n",
    "    ax.text(lon[count],lat[count],site,horizontalalignment='right',transform=ccrs.PlateCarree(),fontsize=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's attempt to plot the site locations again, but this time we'll **omit** the `transform` argument in `ax.scatter` and `ax.text`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(11,8.5),dpi=125)\n",
    "ax = plt.subplot(1,1,1,projection=proj)\n",
    "ax.set_extent ([lonW,lonE,latS,latN])\n",
    "ax.add_feature(cfeature.COASTLINE.with_scale(res))\n",
    "ax.add_feature (cfeature.STATES.with_scale(res))\n",
    "ax.set_title ('New York State Mesonet Site Locations')\n",
    "ax.scatter(lon,lat,s=9,c='r',edgecolor='black',alpha=0.75)\n",
    "for count, site in enumerate(stid):\n",
    "    ax.text(lon[count],lat[count],site,horizontalalignment='right',fontsize=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What do you think happened here?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Exercise</p>\n",
    "    Create a new figure which plots the current 2m temperature at the NYSM sites. Include the current date/time to the title.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# %load '/spare11/atm350/common/mar07/mar07.py'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We could plot additional variables, and in so doing create a standard surface station plot, but we would find it challenging to align all the variables around the points corresponding to the NYS Mesonet sites. Fortunately, Metpy's `StationPlot` method takes care of this, and we will be using it in upcoming notebooks!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sneak preview: [Latest NYSM Surface Map](http://www.atmos.albany.edu/facstaff/ktyle/nysm/sfcmap.png)"
   ]
  }
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