{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30ffb843-0f7d-4e46-9b7b-e2674259e811",
   "metadata": {},
   "source": [
    "## COMPLETED Franklin notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0676f004-b5f7-479e-a4ad-b09500d4854d",
   "metadata": {},
   "source": [
    "## Overview\n",
    "In this notebook, you will create visualizations of tropical cyclone data, using Matplotlib and Cartopy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64e44801-c39d-4f8b-82e1-7bf8f5afd91d",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d676e223-6d1d-40b1-8bfc-834f2e3a9b33",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Task</p>\n",
    "    Import the necessary libraries in the cell below.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6973635f-2098-46b6-9f90-1dcb86b5da84",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e2d8bb-cc34-4c72-9512-717514e47b33",
   "metadata": {},
   "source": [
    "### Part 1: Minimum central sea-level pressure and maximum wind speed of Hurricane Franklin (2023)\n",
    "Search for Franklin's central sea-level pressure and maximum wind speed, for at least eight distinct times since the storm was named.\n",
    "Use Matplotlib and create a single Figure with two subplots (i.e., *Axes*), one on top of the other:\n",
    "1. On the top subplot, plot date and time on the x-axis, and central sea-level pressure in hPa on the y-axis.\n",
    "1. On the bottom subplot, as in the top, but plot maximum sustained wind speed in mph on the y-axis.\n",
    "1. Save your figure as a PNG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5e7a87-d44b-4492-8f51-002aae4e4296",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Task</p>\n",
    "    Write your code in the cell below.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbe919a-b896-4763-827f-757c48e78874",
   "metadata": {},
   "source": [
    "Manually create the following lists, corresponding to 2100 UTC data for the period 8/21 through 8/28:\n",
    "1. Latitude (deg)\n",
    "1. Longitude (deg)\n",
    "1. Central SLP (hPa)\n",
    "1. Maximum wind speed (kts)\n",
    "1. Date and time (format: YYYY-MM-DD-HH; time zone: UTC)\n",
    "Source: [NHC archived advisories for Franklin](https://www.nhc.noaa.gov/archive/2023/FRANKLIN.shtml?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "066d6668-5b0c-4f93-82e5-44c3165826cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "lat = [14.3, 15.8, 19.8, 22.4, 21.9, 23.8, 25.9, 28.5]\n",
    "lon = [-70.1, -71.4, -70.6, -68.9, -67.0, -67.5, -70.0, -71.0]\n",
    "slp = [1002, 1002, 1004, 998, 1003, 982, 970, 937]\n",
    "wspd = [45, 35, 35, 50, 45, 75, 85, 125]\n",
    "dattim = ['2023-08-21-21', '2023-08-22-21', '2023-08-23-21', '2023-08-24-21', \n",
    "          '2023-08-25-21', '2023-08-26-21', '2023-08-27-21', '2023-08-28-21']\n",
    "          \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab38bda-f39f-46c1-b1ea-089e9aeaafa0",
   "metadata": {},
   "source": [
    "Create a `Figure` with two subplots (aka, `Axes`) and make two line graphs. On the top (bottom) `Axes`, plot SLP (max wind) versus date/time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "337023ac-2675-4007-8117-a235336f8013",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "ax1 = fig.add_subplot (2,1,1)\n",
    "ax1.plot(dattim, slp)\n",
    "ax2 = fig.add_subplot(2,1,2)\n",
    "ax2.plot(dattim, wspd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d5de92-956e-430c-9e10-edbe0afb4c73",
   "metadata": {},
   "source": [
    "A quick and easy way to make the plot better looking is to import and apply the [Seaborn](https://seaborn.pydata.org/) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9809f654-4951-4f37-8849-6767af2866b4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1cda1f5-7c49-435c-b78e-808fef04c555",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "ax1 = fig.add_subplot (2,1,1)\n",
    "ax1.plot(dattim, slp)\n",
    "ax2 = fig.add_subplot(2,1,2)\n",
    "ax2.plot(dattim, wspd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16eec3f-91ae-40c1-b014-6548a0ad51cd",
   "metadata": {},
   "source": [
    "Add a title and axis labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33f6e7e6-5dc6-4775-8080-7da1007bfc97",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "\n",
    "fig.suptitle('Franklin (2023) 2100 UTC Min. SLP, Max. Wind, 8/21-8/28', fontsize=16)\n",
    "\n",
    "ax1 = fig.add_subplot (2,1,1)\n",
    "ax1.plot(dattim, slp)\n",
    "ax1.set_xlabel('Date and time')\n",
    "ax1.set_ylabel('SLP (hPa)')\n",
    "\n",
    "ax2 = fig.add_subplot(2,1,2)\n",
    "ax2.plot(dattim, wspd)\n",
    "ax2.set_xlabel('Date and time')\n",
    "ax2.set_ylabel('Windspeed (kts)');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16254516-4bcc-47b0-af70-ca17e84b407b",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-info\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">A better visualization:</p>\n",
    "   Rather than placing SLP and wind speed in their own subplots, let's have them share a single subplot. We will need to add a legend, and also have a separate y-axis for each variable.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de5c6722-9e16-4993-8435-f565c82a7d83",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "\n",
    "fig.suptitle('Franklin (2023) 2100 UTC Min. SLP, Max. Wind, 8/21-8/28', fontsize=16)\n",
    "\n",
    "ax1 = fig.add_subplot (1,1,1)\n",
    "\n",
    "ax1_color='blue'\n",
    "ax1.plot(dattim, slp, label='SLP', color=ax1_color)\n",
    "ax1.set_xlabel('Date and time')\n",
    "ax1.set_ylabel('SLP (hPa)', color=ax1_color)\n",
    "ax1.tick_params(axis='y', labelcolor=ax1_color)\n",
    "\n",
    "ax2_color='red'\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "ax2.plot(dattim, wspd, label='Windspeed', color=ax2_color)\n",
    "ax2.set_ylabel('Windspeed (kts)', color=ax2_color)  # we already handled the x-label with ax1\n",
    "ax2.tick_params(axis='y', labelcolor=ax2_color);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c75a146-bfa7-4d8d-954f-f60efda81ef5",
   "metadata": {},
   "source": [
    "There are still a few things we can improve:\n",
    "1. Re-cast the x-axis from strings to `Datetime` objects, using a handy **Pandas** method\n",
    "1. Re-format the x-axis labels, now that they are `Datetime` objects\n",
    "1. Distinguish the y-axis gridlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a859c5a9-b475-4cb1-a910-f8708dcd26ec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib.dates import DateFormatter, AutoDateLocator,HourLocator,DayLocator,MonthLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cfb1559-2bb5-4ee1-a0b9-a9714e78b6e1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dattim_dt = pd.to_datetime(dattim,format=\"%Y-%m-%d-%H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3205a231-ec24-4314-8c8a-28656c164da3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "\n",
    "fig.suptitle('Franklin (2023) 2100 UTC Min. SLP, Max. Wind, 8/21-8/28', fontsize=16)\n",
    "\n",
    "ax1 = fig.add_subplot (1,1,1)\n",
    "\n",
    "\n",
    "ax1_color='blue'\n",
    "ax1.set_ylim(900,1020)\n",
    "ax1.plot(dattim_dt, slp, label='SLP', color=ax1_color)\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('SLP (hPa)', color=ax1_color)\n",
    "ax1.tick_params(axis='y', labelcolor=ax1_color)\n",
    "ax1.grid(color=ax1_color, axis='y', linestyle='dashed', linewidth=0.5)\n",
    "ax1.xaxis.set_major_locator(DayLocator(interval=1))\n",
    "dateFmt = DateFormatter('%b %d')\n",
    "ax1.xaxis.set_major_formatter(dateFmt)\n",
    "\n",
    "\n",
    "ax2_color='red'\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "ax2.set_ylim(0, 160)\n",
    "ax2.plot(dattim_dt, wspd, label='Windspeed', color=ax2_color)\n",
    "ax2.set_ylabel('Windspeed (kts)', color=ax2_color)  # we already handled the x-label with ax1\n",
    "ax2.tick_params(axis='y', labelcolor=ax2_color)\n",
    "ax2.grid(color=ax2_color, axis='y', linestyle='dotted', linewidth=0.3)\n",
    "\n",
    "# ask matplotlib for the plotted objects and their labels\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2, loc=0);\n",
    "#ax1.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e035bd2-c1c8-48b6-a4a4-a9536174b0d6",
   "metadata": {},
   "source": [
    "Save the figure as a PNG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b68f4c7-894f-4c8b-ac07-e9f1d717c038",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig.savefig('Franklin2023_TimeSeries_SLP_WSPD.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "269a60cc-d749-4231-8fdd-727e6dfd3c48",
   "metadata": {},
   "source": [
    "### Part 2: Plot the center of Franklin on a map\n",
    "1. Use Matplotlib and Cartopy to plot the locations of Franklin for the same time period you used in Part 1.\n",
    "1. Save your figure as a PNG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c77a32-4fdd-466e-9a8c-35deb4510e56",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-warning\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Task</p>\n",
    "    Write your code in the cell below.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15114cb8-235e-4447-a3b7-a1fc9c02ea35",
   "metadata": {},
   "source": [
    "Define an object pointing to the Cartopy coordinate reference system in which the dataset is based. Since the data is in lat-lon coordinates, we'll use `PlateCarree`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec2f7400-09bc-49b0-b48b-1c0a0a7ec450",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "projData = ccrs.PlateCarree()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8247c51-0637-405d-9a66-854a3173d7a7",
   "metadata": {},
   "source": [
    "Define the bounds over which to plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8f04e4e-a1e4-482b-ad56-9d7b60031580",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mapBounds = [-90,-50,10,30]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f489b3df-b55f-41e5-a71a-ff205b00c959",
   "metadata": {},
   "source": [
    "Create the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c98e305-3dbc-4c5e-aba6-02e7749ef1b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "ax = fig.add_subplot (projection=projData)\n",
    "ax.set_extent(mapBounds, crs=projData)\n",
    "gl = ax.gridlines(\n",
    "    draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--'\n",
    ")\n",
    "ax.set_facecolor(cfeature.COLORS['water'])\n",
    "ax.add_feature(cfeature.LAND)\n",
    "ax.add_feature(cfeature.COASTLINE)\n",
    "ax.add_feature(cfeature.BORDERS, linestyle='--')\n",
    "ax.add_feature(cfeature.LAKES, alpha=0.5)\n",
    "ax.add_feature(cfeature.STATES)\n",
    "ax.add_feature(cfeature.RIVERS)\n",
    "ax.set_xlabel('Latitude')\n",
    "ax.set_title('Daily 2100 UTC locations of tropical cyclone Franklin, 8/21-8/28 2023');\n",
    "ax.plot(lon,lat);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad4d45a1-fd78-4bc1-837e-adc918c0e778",
   "metadata": {},
   "source": [
    "Let's improve the look of the figure by making the track line more conspicuous:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17d740e3-7638-4c22-b24a-25b754067580",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "ax = fig.add_subplot (projection=projData)\n",
    "ax.set_extent(mapBounds, crs=projData)\n",
    "gl = ax.gridlines(\n",
    "    draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--'\n",
    ")\n",
    "ax.set_facecolor(cfeature.COLORS['water'])\n",
    "ax.add_feature(cfeature.LAND)\n",
    "ax.add_feature(cfeature.COASTLINE)\n",
    "ax.add_feature(cfeature.BORDERS, linestyle='--')\n",
    "ax.add_feature(cfeature.LAKES, alpha=0.5)\n",
    "ax.add_feature(cfeature.STATES)\n",
    "ax.add_feature(cfeature.RIVERS)\n",
    "ax.set_xlabel('Latitude')\n",
    "ax.set_title('Daily 2100 UTC locations of tropical cyclone Franklin, 8/21-8/28 2023');\n",
    "ax.plot(lon,lat, color='darkgreen', marker='o', linestyle='dashed',\n",
    "     linewidth=2, markersize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca35096-6106-490a-b668-5bec18ea0e1e",
   "metadata": {},
   "source": [
    "We'll further improve the figure by using tropical cyclone symbols to mark the locations; the [tcmarkers](https://github.com/abrammer/tc_markers) package works nicely!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f9fa523-ccd1-4a64-a987-a7b9ef6f995f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tcmarkers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36ab8a03-8254-4b79-ac92-f5c712381a4d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "ax = fig.add_subplot (projection=projData)\n",
    "ax.set_extent(mapBounds, crs=projData)\n",
    "gl = ax.gridlines(\n",
    "    draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--'\n",
    ")\n",
    "ax.set_facecolor(cfeature.COLORS['water'])\n",
    "ax.add_feature(cfeature.LAND)\n",
    "ax.add_feature(cfeature.COASTLINE)\n",
    "ax.add_feature(cfeature.BORDERS, linestyle='--')\n",
    "ax.add_feature(cfeature.LAKES, alpha=0.5)\n",
    "ax.add_feature(cfeature.STATES)\n",
    "ax.add_feature(cfeature.RIVERS)\n",
    "ax.set_xlabel('Latitude')\n",
    "ax.set_title('Daily 2100 UTC locations of tropical cyclone Franklin, 8/21-8/28 2023');\n",
    "ax.plot(lon,lat, color='darkgreen')\n",
    "marker_kwargs = {'s': 40, 'color':'darkgreen', 'edgecolor':'darkgreen'}\n",
    "for idx, value in enumerate (wspd):\n",
    "    if (value < 34):\n",
    "        sym = tcmarkers.TD\n",
    "        if (lat[idx] < 0):\n",
    "            sym = tcmarkers.SH_TD\n",
    "    elif (value < 64): \n",
    "        sym = tcmarkers.TS\n",
    "        if (lat[idx] < 0):\n",
    "            sym = tcmarkers.SH_TS\n",
    "    else:\n",
    "        sym = tcmarkers.HU\n",
    "        if (lat[idx] < 0):\n",
    "            sym = tcmarkers.SH_HU\n",
    "    ax.scatter(lon[idx], lat[idx], marker=sym, **marker_kwargs);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9458f64-3586-4005-a717-ea542fb04ece",
   "metadata": {},
   "source": [
    "Save the figure as a PNG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93e8070d-a60e-45c9-98c9-8396aa48cfb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('Franklin2023_TrackMap.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63ac78e-8395-4eda-8b61-e1a5efd1d3fb",
   "metadata": {},
   "source": [
    "<div class=\"admonition alert alert-danger\">\n",
    "    <p class=\"admonition-title\" style=\"font-weight:bold\">REMINDER</p>\n",
    "    Remember to save, close and shutdown your notebook when you are not actively developing it!\n",
    "</div>\n"
   ]
  }
 ],
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