{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import subprocess\n",
    "import sys\n",
    "\n",
    "COLAB = \"google.colab\" in sys.modules\n",
    "\n",
    "\n",
    "def _install(package):\n",
    "    if COLAB:\n",
    "        ans = input(f\"Install { package }? [y/n]:\")\n",
    "        if ans.lower() in [\"y\", \"yes\"]:\n",
    "            subprocess.check_call(\n",
    "                [sys.executable, \"-m\", \"pip\", \"install\", \"--quiet\", package]\n",
    "            )\n",
    "            print(f\"{ package } installed!\")\n",
    "\n",
    "\n",
    "def _colab_install_missing_deps(deps):\n",
    "    import importlib\n",
    "\n",
    "    for dep in deps:\n",
    "        if importlib.util.find_spec(dep) is None:\n",
    "            if dep == \"iris\":\n",
    "                dep = \"scitools-iris\"\n",
    "            _install(dep)\n",
    "\n",
    "\n",
    "deps = [\"ipyleaflet\"]\n",
    "_colab_install_missing_deps(deps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Po0C-dJCrtWM"
   },
   "source": [
    "# IOOS GTS Statistics\n",
    "\n",
    "Created: 2020-10-10\n",
    "\n",
    "Updated: 2023-06-26\n",
    "\n",
    "The Global Telecommunication System (GTS) is a coordinated effort for rapid distribution of observations.\n",
    "The GTS monthly reports show the number of messages released to GTS for each station.\n",
    "The reports contain the following fields:\n",
    "\n",
    "- location ID: Identifier that station messages are released under to the GTS;\n",
    "- region: Designated IOOS Regional Association (only for IOOS regional report);\n",
    "- sponsor: Organization that owns and maintains the station;\n",
    "- Met: Total number of met messages released to the GTS\n",
    "- Wave: Total number of wave messages released to the GTS\n",
    "\n",
    "In this notebook we will explore the statistics of the messages IOOS is releasing to GTS.\n",
    "\n",
    "## Using this notebook\n",
    "\n",
    "1. Pick the appropriate date range of interest.\n",
    "1. Edit the variables `start_date` and `end_date` in the cell below to reflect your time period of interest (use `YYYY-MM-DD` format).\n",
    "1. Run all the cells in the notebook.\n",
    "\n",
    "The first step is to pick the appropriate date range of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "em-Mxt5MrtWS"
   },
   "outputs": [],
   "source": [
    "start_date = \"2021-07-01\"\n",
    "end_date = \"2021-10-30\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wgtBvHNmrtWU"
   },
   "source": [
    "Now we download the data. We will use the NDBC ioosstats server that [hosts the CSV files](https://www.ndbc.noaa.gov/ioosstats/) with the ingest data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "kJjt1UkZrtWV"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>met</th>\n",
       "      <th>wave</th>\n",
       "      <th>time (UTC)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>695.000000</td>\n",
       "      <td>695.000000</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5492.500719</td>\n",
       "      <td>1207.418705</td>\n",
       "      <td>2021-08-16 07:35:49.640287744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-07-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-08-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2576.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021-09-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8789.000000</td>\n",
       "      <td>1409.000000</td>\n",
       "      <td>2021-09-16 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17814.000000</td>\n",
       "      <td>17814.000000</td>\n",
       "      <td>2021-10-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5841.635417</td>\n",
       "      <td>2841.529514</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                met          wave                     time (UTC)\n",
       "count    695.000000    695.000000                            695\n",
       "mean    5492.500719   1207.418705  2021-08-16 07:35:49.640287744\n",
       "min        0.000000      0.000000            2021-07-01 00:00:00\n",
       "25%        0.000000      0.000000            2021-08-01 00:00:00\n",
       "50%     2576.000000      0.000000            2021-09-01 00:00:00\n",
       "75%     8789.000000   1409.000000            2021-09-16 00:00:00\n",
       "max    17814.000000  17814.000000            2021-10-01 00:00:00\n",
       "std     5841.635417   2841.529514                            NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# example https://www.ndbc.noaa.gov/ioosstats/rpts/2021_03_ioos_regional.csv\n",
    "\n",
    "start = dt.datetime.strptime(start_date, \"%Y-%m-%d\")\n",
    "end = dt.datetime.strptime(end_date, \"%Y-%m-%d\")\n",
    "\n",
    "# build an array for days between dates\n",
    "date_array = (start + dt.timedelta(days=x) for x in range(0, (end - start).days))\n",
    "\n",
    "# get a unique list of year-months for url build\n",
    "months = []\n",
    "for date_object in date_array:\n",
    "    months.append(date_object.strftime(\"%Y-%m\"))\n",
    "months = sorted(set(months))\n",
    "\n",
    "df = pd.DataFrame(columns=[\"locationID\", \"region\", \"sponsor\", \"met\", \"wave\"])\n",
    "for month in months:\n",
    "    url = f\"https://www.ndbc.noaa.gov/ioosstats/rpts/{month.replace(\"-\", \"_\")}_ioos_regional.csv\"\n",
    "    df1 = pd.read_csv(url, dtype={\"met\": float, \"wave\": float})\n",
    "    df1[\"time (UTC)\"] = pd.to_datetime(month)\n",
    "    df = pd.concat([df, df1])\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "N3jFAjCvrtWV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DatetimeArray>\n",
       "['2021-07-01 00:00:00', '2021-08-01 00:00:00', '2021-09-01 00:00:00',\n",
       " '2021-10-01 00:00:00']\n",
       "Length: 4, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"locationID\"] = df[\"locationID\"].str.lower()\n",
    "\n",
    "df[\"time (UTC)\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WnuNU5eHrtWW"
   },
   "source": [
    "The table has all the ingest data. We can now explore it grouping the data by IOOS Regional Association (RA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "ZdGeaMPfrtWW",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# observations')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x375 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = df[[\"met\", \"wave\", \"region\"]].groupby(\"region\")\n",
    "\n",
    "ax = groups.sum().plot(kind=\"bar\", figsize=(11, 3.75))\n",
    "ax.yaxis.get_major_formatter().set_scientific(False)\n",
    "ax.set_ylabel(\"# observations\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5oP7aFrErtWX"
   },
   "source": [
    "Let us check the monthly sum of data released both for individual met and wave and the totals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "keIBv28trtWX"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df[\"time (UTC)\"] = pd.to_datetime(df[\"time (UTC)\"])\n",
    "\n",
    "# Remove time-zone info for easier plotting, it is all UTC.\n",
    "df[\"time (UTC)\"] = df[\"time (UTC)\"].dt.tz_localize(None)\n",
    "\n",
    "groups = df.groupby(pd.Grouper(key=\"time (UTC)\", freq=\"M\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9_iEHmiTrtWY"
   },
   "source": [
    "We can create a table of observations per month,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "JxcIjc05rtWY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Monthly totals:\n",
      "                  met      wave      total\n",
      "time (UTC)                                \n",
      "2021-07     1000690.0  227112.0  1227802.0\n",
      "2021-08      967746.0  226282.0  1194028.0\n",
      "2021-09      923172.0  205020.0  1128192.0\n",
      "2021-10      925680.0  180742.0  1106422.0\n",
      "\n",
      "Sum for time period 2021-07 to 2021-10: 4656444.0\n"
     ]
    }
   ],
   "source": [
    "s = groups[[\"met\", \"wave\"]].sum()  # reducing the columns so the summary is digestable\n",
    "totals = s.assign(total=s[\"met\"] + s[\"wave\"])\n",
    "totals.index = totals.index.to_period(\"M\")\n",
    "\n",
    "print(f\"Monthly totals:\\n{totals}\\n\")\n",
    "\n",
    "print(\n",
    "    f\"Sum for time period {totals.index.min()} to {totals.index.max()}: {totals['total'].sum()}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mkEpqlRLrtWZ"
   },
   "source": [
    "and visualize it in a bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "WEz1GN6zrtWZ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# observations')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x375 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 3.75))\n",
    "\n",
    "s.plot(ax=ax, kind=\"bar\")\n",
    "ax.set_xticklabels(\n",
    "    labels=s.index.to_series().dt.strftime(\"%Y-%b\"),\n",
    "    rotation=70,\n",
    "    rotation_mode=\"anchor\",\n",
    "    ha=\"right\",\n",
    ")\n",
    "ax.yaxis.get_major_formatter().set_scientific(False)\n",
    "ax.set_ylabel(\"# observations\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pt3yasWfrtWa"
   },
   "source": [
    "Those plots are interesting to understand the RAs role in the GTS ingest and how much data is being released over time. It would be nice to see those per buoy on a map.\n",
    "\n",
    "For that we need to get the position of the NDBC buoys. Let's get a table of all the buoys and match with what we have in the GTS data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "lTcddYcJrtWa",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>elev</th>\n",
       "      <th>name</th>\n",
       "      <th>owner</th>\n",
       "      <th>pgm</th>\n",
       "      <th>type</th>\n",
       "      <th>met</th>\n",
       "      <th>currents</th>\n",
       "      <th>waterquality</th>\n",
       "      <th>dart</th>\n",
       "      <th>seq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0y2w3</th>\n",
       "      <td>44.794</td>\n",
       "      <td>-87.313</td>\n",
       "      <td>179</td>\n",
       "      <td>Sturgeon Bay CG Station, WI</td>\n",
       "      <td>U.S.C.G. Marine Reporting Stations</td>\n",
       "      <td>IOOS Partners</td>\n",
       "      <td>fixed</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001</th>\n",
       "      <td>12.000</td>\n",
       "      <td>-23.000</td>\n",
       "      <td>0</td>\n",
       "      <td>NE Extension</td>\n",
       "      <td>Prediction and Research Moored Array in the At...</td>\n",
       "      <td>International Partners</td>\n",
       "      <td>buoy</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13002</th>\n",
       "      <td>21.000</td>\n",
       "      <td>-23.000</td>\n",
       "      <td>0</td>\n",
       "      <td>NE Extension</td>\n",
       "      <td>Prediction and Research Moored Array in the At...</td>\n",
       "      <td>International Partners</td>\n",
       "      <td>buoy</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13008</th>\n",
       "      <td>15.000</td>\n",
       "      <td>-38.000</td>\n",
       "      <td>0</td>\n",
       "      <td>Reggae</td>\n",
       "      <td>Prediction and Research Moored Array in the At...</td>\n",
       "      <td>International Partners</td>\n",
       "      <td>buoy</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13009</th>\n",
       "      <td>8.000</td>\n",
       "      <td>-38.000</td>\n",
       "      <td>0</td>\n",
       "      <td>Lambada</td>\n",
       "      <td>Prediction and Research Moored Array in the At...</td>\n",
       "      <td>International Partners</td>\n",
       "      <td>buoy</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lat     lon elev                         name  \\\n",
       "id                                                        \n",
       "0y2w3  44.794 -87.313  179  Sturgeon Bay CG Station, WI   \n",
       "13001  12.000 -23.000    0                 NE Extension   \n",
       "13002  21.000 -23.000    0                 NE Extension   \n",
       "13008  15.000 -38.000    0                       Reggae   \n",
       "13009   8.000 -38.000    0                      Lambada   \n",
       "\n",
       "                                                   owner  \\\n",
       "id                                                         \n",
       "0y2w3                 U.S.C.G. Marine Reporting Stations   \n",
       "13001  Prediction and Research Moored Array in the At...   \n",
       "13002  Prediction and Research Moored Array in the At...   \n",
       "13008  Prediction and Research Moored Array in the At...   \n",
       "13009  Prediction and Research Moored Array in the At...   \n",
       "\n",
       "                          pgm   type met currents waterquality dart  seq  \n",
       "id                                                                        \n",
       "0y2w3           IOOS Partners  fixed   n        n            n    n  NaN  \n",
       "13001  International Partners   buoy   y        n            n    n  NaN  \n",
       "13002  International Partners   buoy   y        n            n    n  NaN  \n",
       "13008  International Partners   buoy   y        n            n    n  NaN  \n",
       "13009  International Partners   buoy   n        n            n    n  NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xml.etree.ElementTree as et\n",
    "\n",
    "import pandas as pd\n",
    "import requests\n",
    "\n",
    "\n",
    "def make_ndbc_table():\n",
    "    url = \"https://www.ndbc.noaa.gov/activestations.xml\"\n",
    "    with requests.get(url) as r:\n",
    "        elems = et.fromstring(r.content)\n",
    "    df = pd.DataFrame([elem.attrib for elem in list(elems)])\n",
    "    df[\"id\"] = df[\"id\"].str.lower()\n",
    "    return df.set_index(\"id\")\n",
    "\n",
    "\n",
    "buoys = make_ndbc_table()\n",
    "buoys[\"lon\"] = buoys[\"lon\"].astype(float)\n",
    "buoys[\"lat\"] = buoys[\"lat\"].astype(float)\n",
    "\n",
    "buoys.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WxMZlZVdrtWa"
   },
   "source": [
    "For simplificty we will plot the total of observations per buoys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>locationID</th>\n",
       "      <th>region</th>\n",
       "      <th>sponsor</th>\n",
       "      <th>met</th>\n",
       "      <th>wave</th>\n",
       "      <th>time (UTC)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46108</td>\n",
       "      <td>AOOS</td>\n",
       "      <td>ALASKA OCEAN OBSERVING SYSTEM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>haxa2</td>\n",
       "      <td>AOOS</td>\n",
       "      <td>MARINE EXCHANGE OF ALASKA</td>\n",
       "      <td>8760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jmla2</td>\n",
       "      <td>AOOS</td>\n",
       "      <td>MARINE EXCHANGE OF ALASKA</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nkla2</td>\n",
       "      <td>AOOS</td>\n",
       "      <td>MARINE EXCHANGE OF ALASKA</td>\n",
       "      <td>8774.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gixa2</td>\n",
       "      <td>AOOS</td>\n",
       "      <td>MARINE EXCHANGE OF ALASKA</td>\n",
       "      <td>8534.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>ssbn7</td>\n",
       "      <td>SECOORA</td>\n",
       "      <td>COASTAL OCEAN RESEARCH AND MONITORING PROGRAM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2940.0</td>\n",
       "      <td>2021-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>41159</td>\n",
       "      <td>SECOORA</td>\n",
       "      <td>COASTAL OCEAN RESEARCH AND MONITORING PROGRAM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2716.0</td>\n",
       "      <td>2021-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>sipf1</td>\n",
       "      <td>SECOORA</td>\n",
       "      <td>FLORIDA INSTITUTE OF TECHNOLOGY</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2021-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>42098</td>\n",
       "      <td>SECOORA</td>\n",
       "      <td>GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2872.0</td>\n",
       "      <td>2021-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>44095</td>\n",
       "      <td>SECOORA</td>\n",
       "      <td>UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2916.0</td>\n",
       "      <td>2021-10-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>695 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    locationID   region                                          sponsor  \\\n",
       "0        46108     AOOS                    ALASKA OCEAN OBSERVING SYSTEM   \n",
       "1        haxa2     AOOS                        MARINE EXCHANGE OF ALASKA   \n",
       "2        jmla2     AOOS                        MARINE EXCHANGE OF ALASKA   \n",
       "3        nkla2     AOOS                        MARINE EXCHANGE OF ALASKA   \n",
       "4        gixa2     AOOS                        MARINE EXCHANGE OF ALASKA   \n",
       "..         ...      ...                                              ...   \n",
       "169      ssbn7  SECOORA    COASTAL OCEAN RESEARCH AND MONITORING PROGRAM   \n",
       "170      41159  SECOORA    COASTAL OCEAN RESEARCH AND MONITORING PROGRAM   \n",
       "171      sipf1  SECOORA                  FLORIDA INSTITUTE OF TECHNOLOGY   \n",
       "172      42098  SECOORA  GREATER TAMPA BAY MARINE ADVISORY COUNCIL PORTS   \n",
       "173      44095  SECOORA     UNIVERSITY OF NORTH CAROLINA COASTAL STUDIES   \n",
       "\n",
       "        met    wave time (UTC)  \n",
       "0       0.0     0.0 2021-07-01  \n",
       "1    8760.0     0.0 2021-07-01  \n",
       "2    8600.0     0.0 2021-07-01  \n",
       "3    8774.0     0.0 2021-07-01  \n",
       "4    8534.0     0.0 2021-07-01  \n",
       "..      ...     ...        ...  \n",
       "169     0.0  2940.0 2021-10-01  \n",
       "170     0.0  2716.0 2021-10-01  \n",
       "171     0.0     0.0 2021-10-01  \n",
       "172     0.0  2872.0 2021-10-01  \n",
       "173     0.0  2916.0 2021-10-01  \n",
       "\n",
       "[695 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "cDqw1MNzrtWa",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "groups = df[[\"locationID\", \"met\", \"wave\"]].groupby(\"locationID\")\n",
    "location_sum = groups.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "iZHCo1MartWa"
   },
   "outputs": [],
   "source": [
    "buoys = buoys.T\n",
    "\n",
    "extra_cols = pd.DataFrame({k: buoys.get(k) for k, row in location_sum.iterrows()}).T\n",
    "extra_cols = extra_cols[[\"lat\", \"lon\", \"type\", \"pgm\", \"name\"]]\n",
    "\n",
    "map_df = pd.concat([location_sum, extra_cols], axis=1)\n",
    "map_df = map_df.loc[map_df[\"met\"] + map_df[\"wave\"] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hEMYsV5LrtWb"
   },
   "source": [
    "And now we can overlay an HTML table with the buoy information and ingest data totals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "IMXl4O1ErtWb"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "094e65422fe54788b9879ae297563fd8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[35, -95], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_te…"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipyleaflet import AwesomeIcon, FullScreenControl, LegendControl, Map, Marker\n",
    "from ipywidgets import HTML\n",
    "\n",
    "m = Map(center=(35, -95), zoom=4)\n",
    "m.add_control(FullScreenControl())\n",
    "\n",
    "legend = LegendControl(\n",
    "    {\"wave\": \"#FF0000\", \"met\": \"#FFA500\", \"both\": \"#008000\"},\n",
    "    name=\"GTS\",\n",
    "    position=\"bottomright\",\n",
    ")\n",
    "m.add_control(legend)\n",
    "\n",
    "\n",
    "def make_popup(row):\n",
    "    classes = \"table table-striped table-hover table-condensed table-responsive\"\n",
    "    return pd.DataFrame(row[[\"met\", \"wave\", \"type\", \"name\", \"pgm\"]]).to_html(\n",
    "        classes=classes\n",
    "    )\n",
    "\n",
    "\n",
    "for k, row in map_df.iterrows():\n",
    "    if (row[\"met\"] + row[\"wave\"]) > 0:\n",
    "        location = row[\"lat\"], row[\"lon\"]\n",
    "        if row[\"met\"] == 0:\n",
    "            color = \"red\"\n",
    "        elif row[\"wave\"] == 0:\n",
    "            color = \"orange\"\n",
    "        else:\n",
    "            color = \"green\"\n",
    "        marker = Marker(\n",
    "            draggable=False,\n",
    "            icon=AwesomeIcon(name=\"life-ring\", marker_color=color),\n",
    "            location=location,\n",
    "        )\n",
    "        msg = HTML()\n",
    "        msg.value = make_popup(row)\n",
    "        marker.popup = msg\n",
    "        m.add_layer(marker)\n",
    "m"
   ]
  }
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