jake_example_helene.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example usage of aggregated NHCI drifter data v0.\n",
    "\n",
    "Example code for loading and working with Spotter data from the NHCI\n",
    "aggregated drifter dataset in Python .pickle format. Since all datasets\n",
    "have the same variable names and structure, this example framework can\n",
    "also be used for DWSD and microSWIFT drifters.\n",
    "\n",
    "Contact Jake Davis (davisjr@uw.edu) with any questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I/O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIRECTORY = 'D:/crs/proj/2025_NOPP_comparison/helene_drifters/'\n",
    "DATA_FILENAME = 'hurricane_helene_drifter_data_v0.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\csherwood\\AppData\\Local\\Temp\\1\\ipykernel_55048\\3496847223.py:2: DeprecationWarning: numpy.core.numeric is deprecated and has been renamed to numpy._core.numeric. The numpy._core namespace contains private NumPy internals and its use is discouraged, as NumPy internals can change without warning in any release. In practice, most real-world usage of numpy.core is to access functionality in the public NumPy API. If that is the case, use the public NumPy API. If not, you are using NumPy internals. If you would still like to access an internal attribute, use numpy._core.numeric._frombuffer.\n",
      "  drifter_dict = pickle.load(handle)\n"
     ]
    }
   ],
   "source": [
    "with open(os.path.join(DATA_DIRECTORY, DATA_FILENAME), 'rb') as handle:\n",
    "    drifter_dict = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`drifter_dict` is a python dictionary keyed by drifter type (spotter,\n",
    "dwsd, or microswift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "spotter_dict = drifter_dict['spotter']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`spotter_dict` is a python dictionary of Pandas DataFrames, keyed by\n",
    "each drifter ID. The drifter ids can then be accessed as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "spotter_ids = list(spotter_dict.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data for a single ID is a Pandas DataFrame. For example, the last ID can be accessed as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPOT-31995C\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 288 entries, 2024-09-25 00:15:00+00:00 to 2024-09-28 23:55:00+00:00\n",
      "Data columns (total 20 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   drifter_type              288 non-null    object \n",
      " 1   latitude                  288 non-null    float64\n",
      " 2   longitude                 288 non-null    float64\n",
      " 3   significant_height        96 non-null     float64\n",
      " 4   peak_period               96 non-null     float64\n",
      " 5   peak_direction            96 non-null     float64\n",
      " 6   peak_directional_spread   96 non-null     float64\n",
      " 7   mean_period               96 non-null     float64\n",
      " 8   mean_direction            96 non-null     float64\n",
      " 9   mean_directional_spread   96 non-null     float64\n",
      " 10  frequency                 96 non-null     object \n",
      " 11  energy_density            96 non-null     object \n",
      " 12  a1                        96 non-null     object \n",
      " 13  b1                        96 non-null     object \n",
      " 14  a2                        96 non-null     object \n",
      " 15  b2                        96 non-null     object \n",
      " 16  wave_check_factor         0 non-null      float64\n",
      " 17  scalar_wave_check_factor  0 non-null      float64\n",
      " 18  sea_surface_temperature   288 non-null    float64\n",
      " 19  sea_level_pressure        288 non-null    float64\n",
      "dtypes: float64(13), object(7)\n",
      "memory usage: 47.2+ KB\n"
     ]
    }
   ],
   "source": [
    "spotter_id = spotter_ids[-1]\n",
    "print(spotter_id)\n",
    "\n",
    "spotter_df = spotter_dict[spotter_id]\n",
    "spotter_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables are queried using the column name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "2024-09-25 00:15:00+00:00    0.505\n",
       "2024-09-25 00:35:00+00:00      NaN\n",
       "2024-09-25 00:55:00+00:00      NaN\n",
       "2024-09-25 01:15:00+00:00    0.498\n",
       "2024-09-25 01:35:00+00:00      NaN\n",
       "                             ...  \n",
       "2024-09-28 22:35:00+00:00      NaN\n",
       "2024-09-28 22:55:00+00:00      NaN\n",
       "2024-09-28 23:15:00+00:00    0.889\n",
       "2024-09-28 23:35:00+00:00      NaN\n",
       "2024-09-28 23:55:00+00:00      NaN\n",
       "Name: significant_height, Length: 288, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This works:\n",
    "spotter_df['significant_height']\n",
    "\n",
    "# But use of .loc is preferred (where `:` indicates all rows)\n",
    "spotter_df.loc[:, 'significant_height']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(The NaNs are expected--see the **important note** below!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame is indexed by time. The array of datetimes can be\n",
    "accessed using the index property; this also enables slicing the\n",
    "dataset over two datetimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drifter_type</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>significant_height</th>\n",
       "      <th>peak_period</th>\n",
       "      <th>peak_direction</th>\n",
       "      <th>peak_directional_spread</th>\n",
       "      <th>mean_period</th>\n",
       "      <th>mean_direction</th>\n",
       "      <th>mean_directional_spread</th>\n",
       "      <th>frequency</th>\n",
       "      <th>energy_density</th>\n",
       "      <th>a1</th>\n",
       "      <th>b1</th>\n",
       "      <th>a2</th>\n",
       "      <th>b2</th>\n",
       "      <th>wave_check_factor</th>\n",
       "      <th>scalar_wave_check_factor</th>\n",
       "      <th>sea_surface_temperature</th>\n",
       "      <th>sea_level_pressure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-09-26 12:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.522080</td>\n",
       "      <td>-83.764680</td>\n",
       "      <td>4.222</td>\n",
       "      <td>10.240</td>\n",
       "      <td>168.227</td>\n",
       "      <td>18.371086</td>\n",
       "      <td>7.940206</td>\n",
       "      <td>160.857278</td>\n",
       "      <td>28.368958</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.432095232, 0.7801896960000001, 0.648167424,...</td>\n",
       "      <td>[-0.026393, -0.053763, -0.11828, -0.107527, 0....</td>\n",
       "      <td>[0.160313, 0.226784, 0.304008, 0.352884, 0.510...</td>\n",
       "      <td>[-0.11828, -0.082111, -0.004888, -0.209189, -0...</td>\n",
       "      <td>[-0.371456, -0.4565, -0.437928, -0.334311, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.20</td>\n",
       "      <td>999.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-26 12:35:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.532733</td>\n",
       "      <td>-83.773100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.20</td>\n",
       "      <td>998.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-26 12:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.543283</td>\n",
       "      <td>-83.782350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.18</td>\n",
       "      <td>998.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-26 13:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.553800</td>\n",
       "      <td>-83.790920</td>\n",
       "      <td>4.314</td>\n",
       "      <td>10.240</td>\n",
       "      <td>165.892</td>\n",
       "      <td>18.753253</td>\n",
       "      <td>7.988163</td>\n",
       "      <td>161.280795</td>\n",
       "      <td>27.695164</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.0059965439999999995, 0.018014208000000004, ...</td>\n",
       "      <td>[-0.212121, -0.225806, -0.31085, -0.217009, 0....</td>\n",
       "      <td>[0.44477, 0.391984, 0.457478, 0.423265, 0.5835...</td>\n",
       "      <td>[-0.302053, -0.26393, -0.235582, -0.228739, -0...</td>\n",
       "      <td>[-0.338221, -0.28739, -0.347019, -0.329423, -0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.18</td>\n",
       "      <td>998.9</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-26 13:35:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.564650</td>\n",
       "      <td>-83.800500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.18</td>\n",
       "      <td>998.5</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-27 10:35:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.426900</td>\n",
       "      <td>-84.056317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.74</td>\n",
       "      <td>1000.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-27 10:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.430933</td>\n",
       "      <td>-84.052100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.74</td>\n",
       "      <td>1001.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-27 11:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.435020</td>\n",
       "      <td>-84.047030</td>\n",
       "      <td>3.067</td>\n",
       "      <td>8.533</td>\n",
       "      <td>284.587</td>\n",
       "      <td>39.311193</td>\n",
       "      <td>6.807981</td>\n",
       "      <td>272.901173</td>\n",
       "      <td>42.859691</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.008003584, 0.018006016, 0.02400256, 0.02200...</td>\n",
       "      <td>[0.333333, 0.258065, 0.314761, 0.531769, 0.413...</td>\n",
       "      <td>[0.002933, -0.006843, 0.086999, 0.089932, 0.33...</td>\n",
       "      <td>[0.407625, 0.307918, 0.211144, 0.294233, 0.251...</td>\n",
       "      <td>[-0.043988, 0.009775, -0.018573, 0.073314, 0.2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.74</td>\n",
       "      <td>1001.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-27 11:35:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.438950</td>\n",
       "      <td>-84.041883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.72</td>\n",
       "      <td>1002.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-27 11:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.442883</td>\n",
       "      <td>-84.036633</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.70</td>\n",
       "      <td>1003.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          drifter_type   latitude  longitude  \\\n",
       "time                                                           \n",
       "2024-09-26 12:15:00+00:00      spotter  27.522080 -83.764680   \n",
       "2024-09-26 12:35:00+00:00      spotter  27.532733 -83.773100   \n",
       "2024-09-26 12:55:00+00:00      spotter  27.543283 -83.782350   \n",
       "2024-09-26 13:15:00+00:00      spotter  27.553800 -83.790920   \n",
       "2024-09-26 13:35:00+00:00      spotter  27.564650 -83.800500   \n",
       "...                                ...        ...        ...   \n",
       "2024-09-27 10:35:00+00:00      spotter  28.426900 -84.056317   \n",
       "2024-09-27 10:55:00+00:00      spotter  28.430933 -84.052100   \n",
       "2024-09-27 11:15:00+00:00      spotter  28.435020 -84.047030   \n",
       "2024-09-27 11:35:00+00:00      spotter  28.438950 -84.041883   \n",
       "2024-09-27 11:55:00+00:00      spotter  28.442883 -84.036633   \n",
       "\n",
       "                           significant_height  peak_period  peak_direction  \\\n",
       "time                                                                         \n",
       "2024-09-26 12:15:00+00:00               4.222       10.240         168.227   \n",
       "2024-09-26 12:35:00+00:00                 NaN          NaN             NaN   \n",
       "2024-09-26 12:55:00+00:00                 NaN          NaN             NaN   \n",
       "2024-09-26 13:15:00+00:00               4.314       10.240         165.892   \n",
       "2024-09-26 13:35:00+00:00                 NaN          NaN             NaN   \n",
       "...                                       ...          ...             ...   \n",
       "2024-09-27 10:35:00+00:00                 NaN          NaN             NaN   \n",
       "2024-09-27 10:55:00+00:00                 NaN          NaN             NaN   \n",
       "2024-09-27 11:15:00+00:00               3.067        8.533         284.587   \n",
       "2024-09-27 11:35:00+00:00                 NaN          NaN             NaN   \n",
       "2024-09-27 11:55:00+00:00                 NaN          NaN             NaN   \n",
       "\n",
       "                           peak_directional_spread  mean_period  \\\n",
       "time                                                              \n",
       "2024-09-26 12:15:00+00:00                18.371086     7.940206   \n",
       "2024-09-26 12:35:00+00:00                      NaN          NaN   \n",
       "2024-09-26 12:55:00+00:00                      NaN          NaN   \n",
       "2024-09-26 13:15:00+00:00                18.753253     7.988163   \n",
       "2024-09-26 13:35:00+00:00                      NaN          NaN   \n",
       "...                                            ...          ...   \n",
       "2024-09-27 10:35:00+00:00                      NaN          NaN   \n",
       "2024-09-27 10:55:00+00:00                      NaN          NaN   \n",
       "2024-09-27 11:15:00+00:00                39.311193     6.807981   \n",
       "2024-09-27 11:35:00+00:00                      NaN          NaN   \n",
       "2024-09-27 11:55:00+00:00                      NaN          NaN   \n",
       "\n",
       "                           mean_direction  mean_directional_spread  \\\n",
       "time                                                                 \n",
       "2024-09-26 12:15:00+00:00      160.857278                28.368958   \n",
       "2024-09-26 12:35:00+00:00             NaN                      NaN   \n",
       "2024-09-26 12:55:00+00:00             NaN                      NaN   \n",
       "2024-09-26 13:15:00+00:00      161.280795                27.695164   \n",
       "2024-09-26 13:35:00+00:00             NaN                      NaN   \n",
       "...                                   ...                      ...   \n",
       "2024-09-27 10:35:00+00:00             NaN                      NaN   \n",
       "2024-09-27 10:55:00+00:00             NaN                      NaN   \n",
       "2024-09-27 11:15:00+00:00      272.901173                42.859691   \n",
       "2024-09-27 11:35:00+00:00             NaN                      NaN   \n",
       "2024-09-27 11:55:00+00:00             NaN                      NaN   \n",
       "\n",
       "                                                                   frequency  \\\n",
       "time                                                                           \n",
       "2024-09-26 12:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-26 12:35:00+00:00                                                NaN   \n",
       "2024-09-26 12:55:00+00:00                                                NaN   \n",
       "2024-09-26 13:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-26 13:35:00+00:00                                                NaN   \n",
       "...                                                                      ...   \n",
       "2024-09-27 10:35:00+00:00                                                NaN   \n",
       "2024-09-27 10:55:00+00:00                                                NaN   \n",
       "2024-09-27 11:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-27 11:35:00+00:00                                                NaN   \n",
       "2024-09-27 11:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                              energy_density  \\\n",
       "time                                                                           \n",
       "2024-09-26 12:15:00+00:00  [0.432095232, 0.7801896960000001, 0.648167424,...   \n",
       "2024-09-26 12:35:00+00:00                                                NaN   \n",
       "2024-09-26 12:55:00+00:00                                                NaN   \n",
       "2024-09-26 13:15:00+00:00  [0.0059965439999999995, 0.018014208000000004, ...   \n",
       "2024-09-26 13:35:00+00:00                                                NaN   \n",
       "...                                                                      ...   \n",
       "2024-09-27 10:35:00+00:00                                                NaN   \n",
       "2024-09-27 10:55:00+00:00                                                NaN   \n",
       "2024-09-27 11:15:00+00:00  [0.008003584, 0.018006016, 0.02400256, 0.02200...   \n",
       "2024-09-27 11:35:00+00:00                                                NaN   \n",
       "2024-09-27 11:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                                          a1  \\\n",
       "time                                                                           \n",
       "2024-09-26 12:15:00+00:00  [-0.026393, -0.053763, -0.11828, -0.107527, 0....   \n",
       "2024-09-26 12:35:00+00:00                                                NaN   \n",
       "2024-09-26 12:55:00+00:00                                                NaN   \n",
       "2024-09-26 13:15:00+00:00  [-0.212121, -0.225806, -0.31085, -0.217009, 0....   \n",
       "2024-09-26 13:35:00+00:00                                                NaN   \n",
       "...                                                                      ...   \n",
       "2024-09-27 10:35:00+00:00                                                NaN   \n",
       "2024-09-27 10:55:00+00:00                                                NaN   \n",
       "2024-09-27 11:15:00+00:00  [0.333333, 0.258065, 0.314761, 0.531769, 0.413...   \n",
       "2024-09-27 11:35:00+00:00                                                NaN   \n",
       "2024-09-27 11:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                                          b1  \\\n",
       "time                                                                           \n",
       "2024-09-26 12:15:00+00:00  [0.160313, 0.226784, 0.304008, 0.352884, 0.510...   \n",
       "2024-09-26 12:35:00+00:00                                                NaN   \n",
       "2024-09-26 12:55:00+00:00                                                NaN   \n",
       "2024-09-26 13:15:00+00:00  [0.44477, 0.391984, 0.457478, 0.423265, 0.5835...   \n",
       "2024-09-26 13:35:00+00:00                                                NaN   \n",
       "...                                                                      ...   \n",
       "2024-09-27 10:35:00+00:00                                                NaN   \n",
       "2024-09-27 10:55:00+00:00                                                NaN   \n",
       "2024-09-27 11:15:00+00:00  [0.002933, -0.006843, 0.086999, 0.089932, 0.33...   \n",
       "2024-09-27 11:35:00+00:00                                                NaN   \n",
       "2024-09-27 11:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                                          a2  \\\n",
       "time                                                                           \n",
       "2024-09-26 12:15:00+00:00  [-0.11828, -0.082111, -0.004888, -0.209189, -0...   \n",
       "2024-09-26 12:35:00+00:00                                                NaN   \n",
       "2024-09-26 12:55:00+00:00                                                NaN   \n",
       "2024-09-26 13:15:00+00:00  [-0.302053, -0.26393, -0.235582, -0.228739, -0...   \n",
       "2024-09-26 13:35:00+00:00                                                NaN   \n",
       "...                                                                      ...   \n",
       "2024-09-27 10:35:00+00:00                                                NaN   \n",
       "2024-09-27 10:55:00+00:00                                                NaN   \n",
       "2024-09-27 11:15:00+00:00  [0.407625, 0.307918, 0.211144, 0.294233, 0.251...   \n",
       "2024-09-27 11:35:00+00:00                                                NaN   \n",
       "2024-09-27 11:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                                          b2  \\\n",
       "time                                                                           \n",
       "2024-09-26 12:15:00+00:00  [-0.371456, -0.4565, -0.437928, -0.334311, -0....   \n",
       "2024-09-26 12:35:00+00:00                                                NaN   \n",
       "2024-09-26 12:55:00+00:00                                                NaN   \n",
       "2024-09-26 13:15:00+00:00  [-0.338221, -0.28739, -0.347019, -0.329423, -0...   \n",
       "2024-09-26 13:35:00+00:00                                                NaN   \n",
       "...                                                                      ...   \n",
       "2024-09-27 10:35:00+00:00                                                NaN   \n",
       "2024-09-27 10:55:00+00:00                                                NaN   \n",
       "2024-09-27 11:15:00+00:00  [-0.043988, 0.009775, -0.018573, 0.073314, 0.2...   \n",
       "2024-09-27 11:35:00+00:00                                                NaN   \n",
       "2024-09-27 11:55:00+00:00                                                NaN   \n",
       "\n",
       "                           wave_check_factor  scalar_wave_check_factor  \\\n",
       "time                                                                     \n",
       "2024-09-26 12:15:00+00:00                NaN                       NaN   \n",
       "2024-09-26 12:35:00+00:00                NaN                       NaN   \n",
       "2024-09-26 12:55:00+00:00                NaN                       NaN   \n",
       "2024-09-26 13:15:00+00:00                NaN                       NaN   \n",
       "2024-09-26 13:35:00+00:00                NaN                       NaN   \n",
       "...                                      ...                       ...   \n",
       "2024-09-27 10:35:00+00:00                NaN                       NaN   \n",
       "2024-09-27 10:55:00+00:00                NaN                       NaN   \n",
       "2024-09-27 11:15:00+00:00                NaN                       NaN   \n",
       "2024-09-27 11:35:00+00:00                NaN                       NaN   \n",
       "2024-09-27 11:55:00+00:00                NaN                       NaN   \n",
       "\n",
       "                           sea_surface_temperature  sea_level_pressure  \n",
       "time                                                                    \n",
       "2024-09-26 12:15:00+00:00                    30.20               999.1  \n",
       "2024-09-26 12:35:00+00:00                    30.20               998.8  \n",
       "2024-09-26 12:55:00+00:00                    30.18               998.8  \n",
       "2024-09-26 13:15:00+00:00                    30.18               998.9  \n",
       "2024-09-26 13:35:00+00:00                    30.18               998.5  \n",
       "...                                            ...                 ...  \n",
       "2024-09-27 10:35:00+00:00                    29.74              1000.6  \n",
       "2024-09-27 10:55:00+00:00                    29.74              1001.1  \n",
       "2024-09-27 11:15:00+00:00                    29.74              1001.6  \n",
       "2024-09-27 11:35:00+00:00                    29.72              1002.4  \n",
       "2024-09-27 11:55:00+00:00                    29.70              1003.1  \n",
       "\n",
       "[72 rows x 20 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spotter_df.index # array of datetimes\n",
    "\n",
    "start_date = pd.Timestamp('2024-09-26 12:00', tz='UTC')\n",
    "end_date = pd.Timestamp('2024-09-27 12:00', tz='UTC')\n",
    "spotter_df.loc[start_date:end_date, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: plot significant wave height over the entire dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SPOT-31995C significant wave height')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "ax.scatter(\n",
    "    spotter_df.index,\n",
    "    spotter_df.loc[:, 'significant_height'],\n",
    "    label=spotter_id,\n",
    ")\n",
    "ax.set_ylabel('significant wave height (m)')\n",
    "ax.legend()\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "ax.set_title(f'{spotter_id} significant wave height')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: plot significant wave height over the specified datetimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SPOT-31995C significant wave height from \\n 26 Sep 12Z to 27 Sep 12Z')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "ax.scatter(\n",
    "    spotter_df.loc[start_date:end_date].index,\n",
    "    spotter_df.loc[start_date:end_date, 'significant_height'],\n",
    "    label = spotter_id,\n",
    ")\n",
    "ax.set_ylabel('significant wave height (m)')\n",
    "ax.legend()\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "date_format = '%d %b %HZ'\n",
    "ax.set_title(\n",
    "    f'{spotter_id} significant wave height from \\n '\n",
    "    f'{start_date.strftime(date_format)} to {end_date.strftime(date_format)}'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: plot energy as a function of frequency\n",
    "Plot the spectra over the specified datetimes (a loop is the easiest way to do this)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SPOT-31995C energy density spectra from \\n 26 Sep 12Z to 27 Sep 12Z')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "for E,f in zip(spotter_df.loc[start_date:end_date, 'energy_density'],\n",
    "               spotter_df.loc[start_date:end_date, 'frequency']):\n",
    "    ax.plot(f, E, color='k')\n",
    "\n",
    "ax.set_ylabel('energy density (m^2/Hz)')\n",
    "ax.set_xlabel('frequency (Hz)')\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "date_format = '%d %b %HZ'\n",
    "ax.set_title(\n",
    "    f'{spotter_id} energy density spectra from \\n '\n",
    "    f'{start_date.strftime(date_format)} to {end_date.strftime(date_format)}'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other tips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A DataFrame of all drifters with the same type can be created directly from the dictionary.  The result is a DataFrame that is multi-indexed by time and ID\n",
    "(see https://pandas.pydata.org/docs/user_guide/advanced.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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></th>\n",
       "      <th>drifter_type</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>significant_height</th>\n",
       "      <th>peak_period</th>\n",
       "      <th>peak_direction</th>\n",
       "      <th>peak_directional_spread</th>\n",
       "      <th>mean_period</th>\n",
       "      <th>mean_direction</th>\n",
       "      <th>mean_directional_spread</th>\n",
       "      <th>frequency</th>\n",
       "      <th>energy_density</th>\n",
       "      <th>a1</th>\n",
       "      <th>b1</th>\n",
       "      <th>a2</th>\n",
       "      <th>b2</th>\n",
       "      <th>wave_check_factor</th>\n",
       "      <th>scalar_wave_check_factor</th>\n",
       "      <th>sea_surface_temperature</th>\n",
       "      <th>sea_level_pressure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th>time</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",
       "      <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 rowspan=\"5\" valign=\"top\">SPOT-31990C</th>\n",
       "      <th>2024-09-25 03:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>29.979700</td>\n",
       "      <td>-86.140330</td>\n",
       "      <td>0.857</td>\n",
       "      <td>4.096</td>\n",
       "      <td>127.181</td>\n",
       "      <td>25.709238</td>\n",
       "      <td>3.946361</td>\n",
       "      <td>132.317078</td>\n",
       "      <td>29.802414</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.0032511999999999997, 0.0035010560000000002,...</td>\n",
       "      <td>[-0.048876, -0.014663, -0.040078, -0.058651, -...</td>\n",
       "      <td>[0.220919, 0.136852, 0.031281, 0.040078, 0.087...</td>\n",
       "      <td>[-0.189638, -0.176931, 0.00782, 0.048876, -0.1...</td>\n",
       "      <td>[-0.209189, -0.237537, -0.349951, -0.343108, -...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25 04:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>29.985570</td>\n",
       "      <td>-86.146700</td>\n",
       "      <td>0.785</td>\n",
       "      <td>4.096</td>\n",
       "      <td>128.601</td>\n",
       "      <td>25.708424</td>\n",
       "      <td>3.925176</td>\n",
       "      <td>132.143635</td>\n",
       "      <td>30.090075</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.005251072, 0.008001536, 0.004250624, 0.0015...</td>\n",
       "      <td>[-0.129032, 0.00782, -0.013685, -0.148583, -0....</td>\n",
       "      <td>[-0.061584, -0.062561, -0.034213, 0.012708, 0....</td>\n",
       "      <td>[0.025415, 0.024438, 0.016618, 0.031281, -0.07...</td>\n",
       "      <td>[-0.105572, -0.085044, -0.207234, -0.27175, -0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25 05:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>29.991220</td>\n",
       "      <td>-86.150470</td>\n",
       "      <td>0.733</td>\n",
       "      <td>3.938</td>\n",
       "      <td>128.931</td>\n",
       "      <td>25.334135</td>\n",
       "      <td>3.903908</td>\n",
       "      <td>134.135331</td>\n",
       "      <td>30.051221</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.0035010560000000002, 0.008001536, 0.0055009...</td>\n",
       "      <td>[0.02737, 0.077224, 0.091887, 0.021505, -0.093...</td>\n",
       "      <td>[-0.079179, -0.073314, -0.045943, -0.016618, 0...</td>\n",
       "      <td>[0.236559, 0.211144, 0.079179, 0.008798, -0.08...</td>\n",
       "      <td>[-0.059629, -0.078201, -0.171065, -0.146628, -...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25 06:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>29.996520</td>\n",
       "      <td>-86.151730</td>\n",
       "      <td>0.673</td>\n",
       "      <td>5.389</td>\n",
       "      <td>138.272</td>\n",
       "      <td>21.039632</td>\n",
       "      <td>3.945869</td>\n",
       "      <td>135.077235</td>\n",
       "      <td>30.725773</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.000499712, 0.001000448, 0.002000896, 0.0015...</td>\n",
       "      <td>[-0.073314, -0.049853, -0.050831, -0.087977, -...</td>\n",
       "      <td>[0.006843, 0.018573, 0.053763, 0.028348, 0.014...</td>\n",
       "      <td>[-0.028348, 0.125122, 0.116325, -0.02737, -0.2...</td>\n",
       "      <td>[-0.40958, -0.419355, -0.337243, -0.252199, -0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25 07:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>30.000320</td>\n",
       "      <td>-86.154180</td>\n",
       "      <td>0.638</td>\n",
       "      <td>5.389</td>\n",
       "      <td>135.779</td>\n",
       "      <td>23.589515</td>\n",
       "      <td>3.899171</td>\n",
       "      <td>136.419793</td>\n",
       "      <td>31.637561</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.001000448, 0.000750592, 0.00150016, 0.00175...</td>\n",
       "      <td>[-0.052786, -0.092864, -0.062561, 0.033236, -0...</td>\n",
       "      <td>[0.079179, 0.076246, 0.02737, 0.024438, 0.0263...</td>\n",
       "      <td>[0.081134, 0.047898, 0.043011, 0.026393, 0.011...</td>\n",
       "      <td>[-0.15738, -0.11828, -0.202346, -0.305963, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">SPOT-31995C</th>\n",
       "      <th>2024-09-28 22:35:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.582067</td>\n",
       "      <td>-83.772683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.96</td>\n",
       "      <td>1010.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28 22:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.581650</td>\n",
       "      <td>-83.769600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.90</td>\n",
       "      <td>1010.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28 23:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.581550</td>\n",
       "      <td>-83.766530</td>\n",
       "      <td>0.889</td>\n",
       "      <td>6.400</td>\n",
       "      <td>272.606</td>\n",
       "      <td>30.304299</td>\n",
       "      <td>4.959117</td>\n",
       "      <td>260.441967</td>\n",
       "      <td>41.812865</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.002500608, 0.004000768, 0.00550092799999999...</td>\n",
       "      <td>[0.036168, 0.049853, 0.088954, 0.055718, 0.026...</td>\n",
       "      <td>[0.076246, 0.071359, 0.02737, -0.005865, -0.01...</td>\n",
       "      <td>[0.01955, 0.159335, 0.232649, 0.034213, 0.0019...</td>\n",
       "      <td>[-0.199413, -0.356794, -0.15347, 0.102639, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.86</td>\n",
       "      <td>1010.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28 23:35:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.581700</td>\n",
       "      <td>-83.763300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.84</td>\n",
       "      <td>1010.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28 23:55:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.581767</td>\n",
       "      <td>-83.759683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.82</td>\n",
       "      <td>1010.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3494 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      drifter_type   latitude  longitude  \\\n",
       "id          time                                                           \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00      spotter  29.979700 -86.140330   \n",
       "            2024-09-25 04:55:00+00:00      spotter  29.985570 -86.146700   \n",
       "            2024-09-25 05:55:00+00:00      spotter  29.991220 -86.150470   \n",
       "            2024-09-25 06:55:00+00:00      spotter  29.996520 -86.151730   \n",
       "            2024-09-25 07:55:00+00:00      spotter  30.000320 -86.154180   \n",
       "...                                            ...        ...        ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00      spotter  28.582067 -83.772683   \n",
       "            2024-09-28 22:55:00+00:00      spotter  28.581650 -83.769600   \n",
       "            2024-09-28 23:15:00+00:00      spotter  28.581550 -83.766530   \n",
       "            2024-09-28 23:35:00+00:00      spotter  28.581700 -83.763300   \n",
       "            2024-09-28 23:55:00+00:00      spotter  28.581767 -83.759683   \n",
       "\n",
       "                                       significant_height  peak_period  \\\n",
       "id          time                                                         \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00               0.857        4.096   \n",
       "            2024-09-25 04:55:00+00:00               0.785        4.096   \n",
       "            2024-09-25 05:55:00+00:00               0.733        3.938   \n",
       "            2024-09-25 06:55:00+00:00               0.673        5.389   \n",
       "            2024-09-25 07:55:00+00:00               0.638        5.389   \n",
       "...                                                   ...          ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                 NaN          NaN   \n",
       "            2024-09-28 22:55:00+00:00                 NaN          NaN   \n",
       "            2024-09-28 23:15:00+00:00               0.889        6.400   \n",
       "            2024-09-28 23:35:00+00:00                 NaN          NaN   \n",
       "            2024-09-28 23:55:00+00:00                 NaN          NaN   \n",
       "\n",
       "                                       peak_direction  \\\n",
       "id          time                                        \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00         127.181   \n",
       "            2024-09-25 04:55:00+00:00         128.601   \n",
       "            2024-09-25 05:55:00+00:00         128.931   \n",
       "            2024-09-25 06:55:00+00:00         138.272   \n",
       "            2024-09-25 07:55:00+00:00         135.779   \n",
       "...                                               ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00             NaN   \n",
       "            2024-09-28 22:55:00+00:00             NaN   \n",
       "            2024-09-28 23:15:00+00:00         272.606   \n",
       "            2024-09-28 23:35:00+00:00             NaN   \n",
       "            2024-09-28 23:55:00+00:00             NaN   \n",
       "\n",
       "                                       peak_directional_spread  mean_period  \\\n",
       "id          time                                                              \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00                25.709238     3.946361   \n",
       "            2024-09-25 04:55:00+00:00                25.708424     3.925176   \n",
       "            2024-09-25 05:55:00+00:00                25.334135     3.903908   \n",
       "            2024-09-25 06:55:00+00:00                21.039632     3.945869   \n",
       "            2024-09-25 07:55:00+00:00                23.589515     3.899171   \n",
       "...                                                        ...          ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                      NaN          NaN   \n",
       "            2024-09-28 22:55:00+00:00                      NaN          NaN   \n",
       "            2024-09-28 23:15:00+00:00                30.304299     4.959117   \n",
       "            2024-09-28 23:35:00+00:00                      NaN          NaN   \n",
       "            2024-09-28 23:55:00+00:00                      NaN          NaN   \n",
       "\n",
       "                                       mean_direction  \\\n",
       "id          time                                        \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00      132.317078   \n",
       "            2024-09-25 04:55:00+00:00      132.143635   \n",
       "            2024-09-25 05:55:00+00:00      134.135331   \n",
       "            2024-09-25 06:55:00+00:00      135.077235   \n",
       "            2024-09-25 07:55:00+00:00      136.419793   \n",
       "...                                               ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00             NaN   \n",
       "            2024-09-28 22:55:00+00:00             NaN   \n",
       "            2024-09-28 23:15:00+00:00      260.441967   \n",
       "            2024-09-28 23:35:00+00:00             NaN   \n",
       "            2024-09-28 23:55:00+00:00             NaN   \n",
       "\n",
       "                                       mean_directional_spread  \\\n",
       "id          time                                                 \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00                29.802414   \n",
       "            2024-09-25 04:55:00+00:00                30.090075   \n",
       "            2024-09-25 05:55:00+00:00                30.051221   \n",
       "            2024-09-25 06:55:00+00:00                30.725773   \n",
       "            2024-09-25 07:55:00+00:00                31.637561   \n",
       "...                                                        ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                      NaN   \n",
       "            2024-09-28 22:55:00+00:00                      NaN   \n",
       "            2024-09-28 23:15:00+00:00                41.812865   \n",
       "            2024-09-28 23:35:00+00:00                      NaN   \n",
       "            2024-09-28 23:55:00+00:00                      NaN   \n",
       "\n",
       "                                                                               frequency  \\\n",
       "id          time                                                                           \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "            2024-09-25 04:55:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "            2024-09-25 05:55:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "            2024-09-25 06:55:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "            2024-09-25 07:55:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "...                                                                                  ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                                                NaN   \n",
       "            2024-09-28 22:55:00+00:00                                                NaN   \n",
       "            2024-09-28 23:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "            2024-09-28 23:35:00+00:00                                                NaN   \n",
       "            2024-09-28 23:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                                          energy_density  \\\n",
       "id          time                                                                           \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00  [0.0032511999999999997, 0.0035010560000000002,...   \n",
       "            2024-09-25 04:55:00+00:00  [0.005251072, 0.008001536, 0.004250624, 0.0015...   \n",
       "            2024-09-25 05:55:00+00:00  [0.0035010560000000002, 0.008001536, 0.0055009...   \n",
       "            2024-09-25 06:55:00+00:00  [0.000499712, 0.001000448, 0.002000896, 0.0015...   \n",
       "            2024-09-25 07:55:00+00:00  [0.001000448, 0.000750592, 0.00150016, 0.00175...   \n",
       "...                                                                                  ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                                                NaN   \n",
       "            2024-09-28 22:55:00+00:00                                                NaN   \n",
       "            2024-09-28 23:15:00+00:00  [0.002500608, 0.004000768, 0.00550092799999999...   \n",
       "            2024-09-28 23:35:00+00:00                                                NaN   \n",
       "            2024-09-28 23:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                                                      a1  \\\n",
       "id          time                                                                           \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00  [-0.048876, -0.014663, -0.040078, -0.058651, -...   \n",
       "            2024-09-25 04:55:00+00:00  [-0.129032, 0.00782, -0.013685, -0.148583, -0....   \n",
       "            2024-09-25 05:55:00+00:00  [0.02737, 0.077224, 0.091887, 0.021505, -0.093...   \n",
       "            2024-09-25 06:55:00+00:00  [-0.073314, -0.049853, -0.050831, -0.087977, -...   \n",
       "            2024-09-25 07:55:00+00:00  [-0.052786, -0.092864, -0.062561, 0.033236, -0...   \n",
       "...                                                                                  ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                                                NaN   \n",
       "            2024-09-28 22:55:00+00:00                                                NaN   \n",
       "            2024-09-28 23:15:00+00:00  [0.036168, 0.049853, 0.088954, 0.055718, 0.026...   \n",
       "            2024-09-28 23:35:00+00:00                                                NaN   \n",
       "            2024-09-28 23:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                                                      b1  \\\n",
       "id          time                                                                           \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00  [0.220919, 0.136852, 0.031281, 0.040078, 0.087...   \n",
       "            2024-09-25 04:55:00+00:00  [-0.061584, -0.062561, -0.034213, 0.012708, 0....   \n",
       "            2024-09-25 05:55:00+00:00  [-0.079179, -0.073314, -0.045943, -0.016618, 0...   \n",
       "            2024-09-25 06:55:00+00:00  [0.006843, 0.018573, 0.053763, 0.028348, 0.014...   \n",
       "            2024-09-25 07:55:00+00:00  [0.079179, 0.076246, 0.02737, 0.024438, 0.0263...   \n",
       "...                                                                                  ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                                                NaN   \n",
       "            2024-09-28 22:55:00+00:00                                                NaN   \n",
       "            2024-09-28 23:15:00+00:00  [0.076246, 0.071359, 0.02737, -0.005865, -0.01...   \n",
       "            2024-09-28 23:35:00+00:00                                                NaN   \n",
       "            2024-09-28 23:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                                                      a2  \\\n",
       "id          time                                                                           \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00  [-0.189638, -0.176931, 0.00782, 0.048876, -0.1...   \n",
       "            2024-09-25 04:55:00+00:00  [0.025415, 0.024438, 0.016618, 0.031281, -0.07...   \n",
       "            2024-09-25 05:55:00+00:00  [0.236559, 0.211144, 0.079179, 0.008798, -0.08...   \n",
       "            2024-09-25 06:55:00+00:00  [-0.028348, 0.125122, 0.116325, -0.02737, -0.2...   \n",
       "            2024-09-25 07:55:00+00:00  [0.081134, 0.047898, 0.043011, 0.026393, 0.011...   \n",
       "...                                                                                  ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                                                NaN   \n",
       "            2024-09-28 22:55:00+00:00                                                NaN   \n",
       "            2024-09-28 23:15:00+00:00  [0.01955, 0.159335, 0.232649, 0.034213, 0.0019...   \n",
       "            2024-09-28 23:35:00+00:00                                                NaN   \n",
       "            2024-09-28 23:55:00+00:00                                                NaN   \n",
       "\n",
       "                                                                                      b2  \\\n",
       "id          time                                                                           \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00  [-0.209189, -0.237537, -0.349951, -0.343108, -...   \n",
       "            2024-09-25 04:55:00+00:00  [-0.105572, -0.085044, -0.207234, -0.27175, -0...   \n",
       "            2024-09-25 05:55:00+00:00  [-0.059629, -0.078201, -0.171065, -0.146628, -...   \n",
       "            2024-09-25 06:55:00+00:00  [-0.40958, -0.419355, -0.337243, -0.252199, -0...   \n",
       "            2024-09-25 07:55:00+00:00  [-0.15738, -0.11828, -0.202346, -0.305963, -0....   \n",
       "...                                                                                  ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                                                NaN   \n",
       "            2024-09-28 22:55:00+00:00                                                NaN   \n",
       "            2024-09-28 23:15:00+00:00  [-0.199413, -0.356794, -0.15347, 0.102639, -0....   \n",
       "            2024-09-28 23:35:00+00:00                                                NaN   \n",
       "            2024-09-28 23:55:00+00:00                                                NaN   \n",
       "\n",
       "                                       wave_check_factor  \\\n",
       "id          time                                           \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00                NaN   \n",
       "            2024-09-25 04:55:00+00:00                NaN   \n",
       "            2024-09-25 05:55:00+00:00                NaN   \n",
       "            2024-09-25 06:55:00+00:00                NaN   \n",
       "            2024-09-25 07:55:00+00:00                NaN   \n",
       "...                                                  ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                NaN   \n",
       "            2024-09-28 22:55:00+00:00                NaN   \n",
       "            2024-09-28 23:15:00+00:00                NaN   \n",
       "            2024-09-28 23:35:00+00:00                NaN   \n",
       "            2024-09-28 23:55:00+00:00                NaN   \n",
       "\n",
       "                                       scalar_wave_check_factor  \\\n",
       "id          time                                                  \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00                       NaN   \n",
       "            2024-09-25 04:55:00+00:00                       NaN   \n",
       "            2024-09-25 05:55:00+00:00                       NaN   \n",
       "            2024-09-25 06:55:00+00:00                       NaN   \n",
       "            2024-09-25 07:55:00+00:00                       NaN   \n",
       "...                                                         ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                       NaN   \n",
       "            2024-09-28 22:55:00+00:00                       NaN   \n",
       "            2024-09-28 23:15:00+00:00                       NaN   \n",
       "            2024-09-28 23:35:00+00:00                       NaN   \n",
       "            2024-09-28 23:55:00+00:00                       NaN   \n",
       "\n",
       "                                       sea_surface_temperature  \\\n",
       "id          time                                                 \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00                      NaN   \n",
       "            2024-09-25 04:55:00+00:00                      NaN   \n",
       "            2024-09-25 05:55:00+00:00                      NaN   \n",
       "            2024-09-25 06:55:00+00:00                      NaN   \n",
       "            2024-09-25 07:55:00+00:00                      NaN   \n",
       "...                                                        ...   \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00                    29.96   \n",
       "            2024-09-28 22:55:00+00:00                    29.90   \n",
       "            2024-09-28 23:15:00+00:00                    29.86   \n",
       "            2024-09-28 23:35:00+00:00                    29.84   \n",
       "            2024-09-28 23:55:00+00:00                    29.82   \n",
       "\n",
       "                                       sea_level_pressure  \n",
       "id          time                                           \n",
       "SPOT-31990C 2024-09-25 03:55:00+00:00                 NaN  \n",
       "            2024-09-25 04:55:00+00:00                 NaN  \n",
       "            2024-09-25 05:55:00+00:00                 NaN  \n",
       "            2024-09-25 06:55:00+00:00                 NaN  \n",
       "            2024-09-25 07:55:00+00:00                 NaN  \n",
       "...                                                   ...  \n",
       "SPOT-31995C 2024-09-28 22:35:00+00:00              1010.4  \n",
       "            2024-09-28 22:55:00+00:00              1010.4  \n",
       "            2024-09-28 23:15:00+00:00              1010.5  \n",
       "            2024-09-28 23:35:00+00:00              1010.5  \n",
       "            2024-09-28 23:55:00+00:00              1010.4  \n",
       "\n",
       "[3494 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_spotter_df = pd.concat(spotter_dict, names=['id', 'time'])\n",
    "all_spotter_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important note:** some Spotter datasets contain barometer and\n",
    "temperature measurements interspersed at a different time interval\n",
    "than the wave measurements. This means rows corresponding to times\n",
    "where there are pressure and temperature measurements, but not wave\n",
    "measurements, will contain NaNs for wave properties. This is rarely a\n",
    "problem, except for when a lineplot or the extraction of all\n",
    "spectral data as a single matrix is desired. In these cases, the rows\n",
    "containing NaNs can be filtered out using the notnull() method (and\n",
    "the wave data can be assigned to a new variable, if neccessary)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drifter_type</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>significant_height</th>\n",
       "      <th>peak_period</th>\n",
       "      <th>peak_direction</th>\n",
       "      <th>peak_directional_spread</th>\n",
       "      <th>mean_period</th>\n",
       "      <th>mean_direction</th>\n",
       "      <th>mean_directional_spread</th>\n",
       "      <th>frequency</th>\n",
       "      <th>energy_density</th>\n",
       "      <th>a1</th>\n",
       "      <th>b1</th>\n",
       "      <th>a2</th>\n",
       "      <th>b2</th>\n",
       "      <th>wave_check_factor</th>\n",
       "      <th>scalar_wave_check_factor</th>\n",
       "      <th>sea_surface_temperature</th>\n",
       "      <th>sea_level_pressure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-09-25 00:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.08635</td>\n",
       "      <td>-83.20085</td>\n",
       "      <td>0.505</td>\n",
       "      <td>4.876</td>\n",
       "      <td>147.606</td>\n",
       "      <td>18.658772</td>\n",
       "      <td>3.944217</td>\n",
       "      <td>145.500180</td>\n",
       "      <td>30.768574</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.001250304, 0.000750592, 0.000499712, 0.0012...</td>\n",
       "      <td>[0.125122, -0.086021, -0.13001, -0.096774, -0....</td>\n",
       "      <td>[-0.113392, -0.066471, -0.068426, -0.018573, 0...</td>\n",
       "      <td>[0.134897, 0.111437, 0.071359, 0.15738, 0.1427...</td>\n",
       "      <td>[-0.363636, -0.269795, -0.266862, -0.251222, -...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.72</td>\n",
       "      <td>1010.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25 01:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.08742</td>\n",
       "      <td>-83.20338</td>\n",
       "      <td>0.498</td>\n",
       "      <td>4.655</td>\n",
       "      <td>145.664</td>\n",
       "      <td>20.245129</td>\n",
       "      <td>3.935978</td>\n",
       "      <td>146.014464</td>\n",
       "      <td>31.012555</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.00150016, 0.0027504639999999998, 0.00400076...</td>\n",
       "      <td>[-0.047898, 0.002933, -0.038123, -0.02346, -0....</td>\n",
       "      <td>[0.14565, 0.048876, -0.034213, 0.022483, 0.013...</td>\n",
       "      <td>[0.097752, 0.097752, 0.043988, 0.115347, 0.159...</td>\n",
       "      <td>[-0.57087, -0.486804, -0.383187, -0.268817, -0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.68</td>\n",
       "      <td>1011.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25 02:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.08902</td>\n",
       "      <td>-83.20953</td>\n",
       "      <td>0.520</td>\n",
       "      <td>4.876</td>\n",
       "      <td>147.754</td>\n",
       "      <td>20.469224</td>\n",
       "      <td>3.760899</td>\n",
       "      <td>140.673575</td>\n",
       "      <td>37.788053</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.003750912, 0.007251968, 0.00625152000000000...</td>\n",
       "      <td>[-0.029325, 0.016618, 0.034213, -0.038123, -0....</td>\n",
       "      <td>[0.031281, -0.017595, -0.111437, -0.01173, -0....</td>\n",
       "      <td>[-0.096774, -0.139785, -0.280547, -0.218964, 0...</td>\n",
       "      <td>[-0.148583, -0.163245, -0.221896, -0.233627, -...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.62</td>\n",
       "      <td>1011.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25 03:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.09300</td>\n",
       "      <td>-83.22107</td>\n",
       "      <td>0.572</td>\n",
       "      <td>4.876</td>\n",
       "      <td>150.067</td>\n",
       "      <td>22.530018</td>\n",
       "      <td>3.474382</td>\n",
       "      <td>126.781689</td>\n",
       "      <td>41.143762</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.004250624, 0.00925184, 0.007002112000000000...</td>\n",
       "      <td>[-0.02737, 0.059629, 0.100684, -0.01564, -0.10...</td>\n",
       "      <td>[-0.138807, -0.033236, 0.025415, 0.079179, -0....</td>\n",
       "      <td>[-0.326491, -0.202346, -0.175953, -0.268817, -...</td>\n",
       "      <td>[0.021505, -0.041056, -0.139785, -0.031281, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.60</td>\n",
       "      <td>1011.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-25 04:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>27.09860</td>\n",
       "      <td>-83.23700</td>\n",
       "      <td>0.803</td>\n",
       "      <td>3.793</td>\n",
       "      <td>106.113</td>\n",
       "      <td>29.220406</td>\n",
       "      <td>3.622508</td>\n",
       "      <td>112.180572</td>\n",
       "      <td>36.998687</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.004250624, 0.00925184, 0.011502592, 0.00550...</td>\n",
       "      <td>[-0.050831, -0.097752, -0.119257, -0.058651, -...</td>\n",
       "      <td>[-0.000978, 0.010753, 0.059629, 0.042033, 0.07...</td>\n",
       "      <td>[0.016618, -0.114369, -0.140762, -0.02346, 0.1...</td>\n",
       "      <td>[0.119257, 0.044966, -0.094819, -0.021505, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.58</td>\n",
       "      <td>1010.9</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28 19:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.59162</td>\n",
       "      <td>-83.79280</td>\n",
       "      <td>1.049</td>\n",
       "      <td>6.400</td>\n",
       "      <td>271.236</td>\n",
       "      <td>34.810301</td>\n",
       "      <td>5.209647</td>\n",
       "      <td>256.696614</td>\n",
       "      <td>45.744951</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.001501184, 0.003500032, 0.004501504, 0.0025...</td>\n",
       "      <td>[0.017595, 0.098729, 0.013685, -0.063539, 0.04...</td>\n",
       "      <td>[-0.101662, -0.054741, 0.020528, 0.033236, 0.0...</td>\n",
       "      <td>[-0.180841, -0.107527, -0.010753, 0.035191, -0...</td>\n",
       "      <td>[-0.26393, -0.178886, -0.222874, -0.101662, -0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.06</td>\n",
       "      <td>1010.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28 20:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.58825</td>\n",
       "      <td>-83.78887</td>\n",
       "      <td>0.986</td>\n",
       "      <td>6.827</td>\n",
       "      <td>270.193</td>\n",
       "      <td>29.105699</td>\n",
       "      <td>5.114493</td>\n",
       "      <td>257.787222</td>\n",
       "      <td>43.817195</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.002500608, 0.004000768, 0.00325119999999999...</td>\n",
       "      <td>[-0.013685, -0.013685, 0.030303, 0.097752, 0.0...</td>\n",
       "      <td>[0.089932, 0.107527, 0.040078, 0.075269, 0.000...</td>\n",
       "      <td>[0.16911, 0.160313, 0.055718, -0.101662, 0.059...</td>\n",
       "      <td>[-0.350929, -0.341153, -0.229717, -0.018573, -...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.10</td>\n",
       "      <td>1010.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28 21:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.58533</td>\n",
       "      <td>-83.78332</td>\n",
       "      <td>0.961</td>\n",
       "      <td>6.400</td>\n",
       "      <td>270.260</td>\n",
       "      <td>30.293825</td>\n",
       "      <td>5.050125</td>\n",
       "      <td>258.942647</td>\n",
       "      <td>42.480857</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.00150016, 0.002500608, 0.004250624, 0.00300...</td>\n",
       "      <td>[-0.119257, -0.136852, -0.037146, 0.115347, 0....</td>\n",
       "      <td>[-0.009775, -0.013685, 0.024438, 0.02346, 0.01...</td>\n",
       "      <td>[-0.14174, -0.071359, -0.031281, 0.266862, 0.3...</td>\n",
       "      <td>[-0.224829, -0.259042, -0.114369, 0.01173, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.08</td>\n",
       "      <td>1010.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28 22:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.58277</td>\n",
       "      <td>-83.77577</td>\n",
       "      <td>0.942</td>\n",
       "      <td>6.400</td>\n",
       "      <td>274.494</td>\n",
       "      <td>30.219788</td>\n",
       "      <td>5.099244</td>\n",
       "      <td>261.444866</td>\n",
       "      <td>42.100628</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.00150016, 0.0032511999999999997, 0.00600166...</td>\n",
       "      <td>[0.026393, -0.001955, -0.086999, 0.00391, 0.03...</td>\n",
       "      <td>[0.052786, 0.012708, -0.00391, -0.049853, -0.0...</td>\n",
       "      <td>[-0.074291, 0.001955, 0.041056, 0.00782, 0.053...</td>\n",
       "      <td>[-0.188661, -0.086021, -0.043011, -0.000978, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.02</td>\n",
       "      <td>1010.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28 23:15:00+00:00</th>\n",
       "      <td>spotter</td>\n",
       "      <td>28.58155</td>\n",
       "      <td>-83.76653</td>\n",
       "      <td>0.889</td>\n",
       "      <td>6.400</td>\n",
       "      <td>272.606</td>\n",
       "      <td>30.304299</td>\n",
       "      <td>4.959117</td>\n",
       "      <td>260.441967</td>\n",
       "      <td>41.812865</td>\n",
       "      <td>[0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...</td>\n",
       "      <td>[0.002500608, 0.004000768, 0.00550092799999999...</td>\n",
       "      <td>[0.036168, 0.049853, 0.088954, 0.055718, 0.026...</td>\n",
       "      <td>[0.076246, 0.071359, 0.02737, -0.005865, -0.01...</td>\n",
       "      <td>[0.01955, 0.159335, 0.232649, 0.034213, 0.0019...</td>\n",
       "      <td>[-0.199413, -0.356794, -0.15347, 0.102639, -0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.86</td>\n",
       "      <td>1010.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          drifter_type  latitude  longitude  \\\n",
       "time                                                          \n",
       "2024-09-25 00:15:00+00:00      spotter  27.08635  -83.20085   \n",
       "2024-09-25 01:15:00+00:00      spotter  27.08742  -83.20338   \n",
       "2024-09-25 02:15:00+00:00      spotter  27.08902  -83.20953   \n",
       "2024-09-25 03:15:00+00:00      spotter  27.09300  -83.22107   \n",
       "2024-09-25 04:15:00+00:00      spotter  27.09860  -83.23700   \n",
       "...                                ...       ...        ...   \n",
       "2024-09-28 19:15:00+00:00      spotter  28.59162  -83.79280   \n",
       "2024-09-28 20:15:00+00:00      spotter  28.58825  -83.78887   \n",
       "2024-09-28 21:15:00+00:00      spotter  28.58533  -83.78332   \n",
       "2024-09-28 22:15:00+00:00      spotter  28.58277  -83.77577   \n",
       "2024-09-28 23:15:00+00:00      spotter  28.58155  -83.76653   \n",
       "\n",
       "                           significant_height  peak_period  peak_direction  \\\n",
       "time                                                                         \n",
       "2024-09-25 00:15:00+00:00               0.505        4.876         147.606   \n",
       "2024-09-25 01:15:00+00:00               0.498        4.655         145.664   \n",
       "2024-09-25 02:15:00+00:00               0.520        4.876         147.754   \n",
       "2024-09-25 03:15:00+00:00               0.572        4.876         150.067   \n",
       "2024-09-25 04:15:00+00:00               0.803        3.793         106.113   \n",
       "...                                       ...          ...             ...   \n",
       "2024-09-28 19:15:00+00:00               1.049        6.400         271.236   \n",
       "2024-09-28 20:15:00+00:00               0.986        6.827         270.193   \n",
       "2024-09-28 21:15:00+00:00               0.961        6.400         270.260   \n",
       "2024-09-28 22:15:00+00:00               0.942        6.400         274.494   \n",
       "2024-09-28 23:15:00+00:00               0.889        6.400         272.606   \n",
       "\n",
       "                           peak_directional_spread  mean_period  \\\n",
       "time                                                              \n",
       "2024-09-25 00:15:00+00:00                18.658772     3.944217   \n",
       "2024-09-25 01:15:00+00:00                20.245129     3.935978   \n",
       "2024-09-25 02:15:00+00:00                20.469224     3.760899   \n",
       "2024-09-25 03:15:00+00:00                22.530018     3.474382   \n",
       "2024-09-25 04:15:00+00:00                29.220406     3.622508   \n",
       "...                                            ...          ...   \n",
       "2024-09-28 19:15:00+00:00                34.810301     5.209647   \n",
       "2024-09-28 20:15:00+00:00                29.105699     5.114493   \n",
       "2024-09-28 21:15:00+00:00                30.293825     5.050125   \n",
       "2024-09-28 22:15:00+00:00                30.219788     5.099244   \n",
       "2024-09-28 23:15:00+00:00                30.304299     4.959117   \n",
       "\n",
       "                           mean_direction  mean_directional_spread  \\\n",
       "time                                                                 \n",
       "2024-09-25 00:15:00+00:00      145.500180                30.768574   \n",
       "2024-09-25 01:15:00+00:00      146.014464                31.012555   \n",
       "2024-09-25 02:15:00+00:00      140.673575                37.788053   \n",
       "2024-09-25 03:15:00+00:00      126.781689                41.143762   \n",
       "2024-09-25 04:15:00+00:00      112.180572                36.998687   \n",
       "...                                   ...                      ...   \n",
       "2024-09-28 19:15:00+00:00      256.696614                45.744951   \n",
       "2024-09-28 20:15:00+00:00      257.787222                43.817195   \n",
       "2024-09-28 21:15:00+00:00      258.942647                42.480857   \n",
       "2024-09-28 22:15:00+00:00      261.444866                42.100628   \n",
       "2024-09-28 23:15:00+00:00      260.441967                41.812865   \n",
       "\n",
       "                                                                   frequency  \\\n",
       "time                                                                           \n",
       "2024-09-25 00:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-25 01:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-25 02:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-25 03:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-25 04:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "...                                                                      ...   \n",
       "2024-09-28 19:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-28 20:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-28 21:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-28 22:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "2024-09-28 23:15:00+00:00  [0.0293, 0.03906, 0.04883, 0.05859, 0.06836, 0...   \n",
       "\n",
       "                                                              energy_density  \\\n",
       "time                                                                           \n",
       "2024-09-25 00:15:00+00:00  [0.001250304, 0.000750592, 0.000499712, 0.0012...   \n",
       "2024-09-25 01:15:00+00:00  [0.00150016, 0.0027504639999999998, 0.00400076...   \n",
       "2024-09-25 02:15:00+00:00  [0.003750912, 0.007251968, 0.00625152000000000...   \n",
       "2024-09-25 03:15:00+00:00  [0.004250624, 0.00925184, 0.007002112000000000...   \n",
       "2024-09-25 04:15:00+00:00  [0.004250624, 0.00925184, 0.011502592, 0.00550...   \n",
       "...                                                                      ...   \n",
       "2024-09-28 19:15:00+00:00  [0.001501184, 0.003500032, 0.004501504, 0.0025...   \n",
       "2024-09-28 20:15:00+00:00  [0.002500608, 0.004000768, 0.00325119999999999...   \n",
       "2024-09-28 21:15:00+00:00  [0.00150016, 0.002500608, 0.004250624, 0.00300...   \n",
       "2024-09-28 22:15:00+00:00  [0.00150016, 0.0032511999999999997, 0.00600166...   \n",
       "2024-09-28 23:15:00+00:00  [0.002500608, 0.004000768, 0.00550092799999999...   \n",
       "\n",
       "                                                                          a1  \\\n",
       "time                                                                           \n",
       "2024-09-25 00:15:00+00:00  [0.125122, -0.086021, -0.13001, -0.096774, -0....   \n",
       "2024-09-25 01:15:00+00:00  [-0.047898, 0.002933, -0.038123, -0.02346, -0....   \n",
       "2024-09-25 02:15:00+00:00  [-0.029325, 0.016618, 0.034213, -0.038123, -0....   \n",
       "2024-09-25 03:15:00+00:00  [-0.02737, 0.059629, 0.100684, -0.01564, -0.10...   \n",
       "2024-09-25 04:15:00+00:00  [-0.050831, -0.097752, -0.119257, -0.058651, -...   \n",
       "...                                                                      ...   \n",
       "2024-09-28 19:15:00+00:00  [0.017595, 0.098729, 0.013685, -0.063539, 0.04...   \n",
       "2024-09-28 20:15:00+00:00  [-0.013685, -0.013685, 0.030303, 0.097752, 0.0...   \n",
       "2024-09-28 21:15:00+00:00  [-0.119257, -0.136852, -0.037146, 0.115347, 0....   \n",
       "2024-09-28 22:15:00+00:00  [0.026393, -0.001955, -0.086999, 0.00391, 0.03...   \n",
       "2024-09-28 23:15:00+00:00  [0.036168, 0.049853, 0.088954, 0.055718, 0.026...   \n",
       "\n",
       "                                                                          b1  \\\n",
       "time                                                                           \n",
       "2024-09-25 00:15:00+00:00  [-0.113392, -0.066471, -0.068426, -0.018573, 0...   \n",
       "2024-09-25 01:15:00+00:00  [0.14565, 0.048876, -0.034213, 0.022483, 0.013...   \n",
       "2024-09-25 02:15:00+00:00  [0.031281, -0.017595, -0.111437, -0.01173, -0....   \n",
       "2024-09-25 03:15:00+00:00  [-0.138807, -0.033236, 0.025415, 0.079179, -0....   \n",
       "2024-09-25 04:15:00+00:00  [-0.000978, 0.010753, 0.059629, 0.042033, 0.07...   \n",
       "...                                                                      ...   \n",
       "2024-09-28 19:15:00+00:00  [-0.101662, -0.054741, 0.020528, 0.033236, 0.0...   \n",
       "2024-09-28 20:15:00+00:00  [0.089932, 0.107527, 0.040078, 0.075269, 0.000...   \n",
       "2024-09-28 21:15:00+00:00  [-0.009775, -0.013685, 0.024438, 0.02346, 0.01...   \n",
       "2024-09-28 22:15:00+00:00  [0.052786, 0.012708, -0.00391, -0.049853, -0.0...   \n",
       "2024-09-28 23:15:00+00:00  [0.076246, 0.071359, 0.02737, -0.005865, -0.01...   \n",
       "\n",
       "                                                                          a2  \\\n",
       "time                                                                           \n",
       "2024-09-25 00:15:00+00:00  [0.134897, 0.111437, 0.071359, 0.15738, 0.1427...   \n",
       "2024-09-25 01:15:00+00:00  [0.097752, 0.097752, 0.043988, 0.115347, 0.159...   \n",
       "2024-09-25 02:15:00+00:00  [-0.096774, -0.139785, -0.280547, -0.218964, 0...   \n",
       "2024-09-25 03:15:00+00:00  [-0.326491, -0.202346, -0.175953, -0.268817, -...   \n",
       "2024-09-25 04:15:00+00:00  [0.016618, -0.114369, -0.140762, -0.02346, 0.1...   \n",
       "...                                                                      ...   \n",
       "2024-09-28 19:15:00+00:00  [-0.180841, -0.107527, -0.010753, 0.035191, -0...   \n",
       "2024-09-28 20:15:00+00:00  [0.16911, 0.160313, 0.055718, -0.101662, 0.059...   \n",
       "2024-09-28 21:15:00+00:00  [-0.14174, -0.071359, -0.031281, 0.266862, 0.3...   \n",
       "2024-09-28 22:15:00+00:00  [-0.074291, 0.001955, 0.041056, 0.00782, 0.053...   \n",
       "2024-09-28 23:15:00+00:00  [0.01955, 0.159335, 0.232649, 0.034213, 0.0019...   \n",
       "\n",
       "                                                                          b2  \\\n",
       "time                                                                           \n",
       "2024-09-25 00:15:00+00:00  [-0.363636, -0.269795, -0.266862, -0.251222, -...   \n",
       "2024-09-25 01:15:00+00:00  [-0.57087, -0.486804, -0.383187, -0.268817, -0...   \n",
       "2024-09-25 02:15:00+00:00  [-0.148583, -0.163245, -0.221896, -0.233627, -...   \n",
       "2024-09-25 03:15:00+00:00  [0.021505, -0.041056, -0.139785, -0.031281, 0....   \n",
       "2024-09-25 04:15:00+00:00  [0.119257, 0.044966, -0.094819, -0.021505, -0....   \n",
       "...                                                                      ...   \n",
       "2024-09-28 19:15:00+00:00  [-0.26393, -0.178886, -0.222874, -0.101662, -0...   \n",
       "2024-09-28 20:15:00+00:00  [-0.350929, -0.341153, -0.229717, -0.018573, -...   \n",
       "2024-09-28 21:15:00+00:00  [-0.224829, -0.259042, -0.114369, 0.01173, -0....   \n",
       "2024-09-28 22:15:00+00:00  [-0.188661, -0.086021, -0.043011, -0.000978, 0...   \n",
       "2024-09-28 23:15:00+00:00  [-0.199413, -0.356794, -0.15347, 0.102639, -0....   \n",
       "\n",
       "                           wave_check_factor  scalar_wave_check_factor  \\\n",
       "time                                                                     \n",
       "2024-09-25 00:15:00+00:00                NaN                       NaN   \n",
       "2024-09-25 01:15:00+00:00                NaN                       NaN   \n",
       "2024-09-25 02:15:00+00:00                NaN                       NaN   \n",
       "2024-09-25 03:15:00+00:00                NaN                       NaN   \n",
       "2024-09-25 04:15:00+00:00                NaN                       NaN   \n",
       "...                                      ...                       ...   \n",
       "2024-09-28 19:15:00+00:00                NaN                       NaN   \n",
       "2024-09-28 20:15:00+00:00                NaN                       NaN   \n",
       "2024-09-28 21:15:00+00:00                NaN                       NaN   \n",
       "2024-09-28 22:15:00+00:00                NaN                       NaN   \n",
       "2024-09-28 23:15:00+00:00                NaN                       NaN   \n",
       "\n",
       "                           sea_surface_temperature  sea_level_pressure  \n",
       "time                                                                    \n",
       "2024-09-25 00:15:00+00:00                    30.72              1010.9  \n",
       "2024-09-25 01:15:00+00:00                    30.68              1011.3  \n",
       "2024-09-25 02:15:00+00:00                    30.62              1011.5  \n",
       "2024-09-25 03:15:00+00:00                    30.60              1011.3  \n",
       "2024-09-25 04:15:00+00:00                    30.58              1010.9  \n",
       "...                                            ...                 ...  \n",
       "2024-09-28 19:15:00+00:00                    30.06              1010.5  \n",
       "2024-09-28 20:15:00+00:00                    30.10              1010.5  \n",
       "2024-09-28 21:15:00+00:00                    30.08              1010.3  \n",
       "2024-09-28 22:15:00+00:00                    30.02              1010.3  \n",
       "2024-09-28 23:15:00+00:00                    29.86              1010.5  \n",
       "\n",
       "[96 rows x 20 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_waves = spotter_df['energy_density'].notnull()\n",
    "spotter_df[only_waves]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: plot significant wave height for the first five Spotters using plot (which cannot handle NaNs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "\n",
    "for spotter_id in spotter_ids[0:5]:\n",
    "    spotter_df = spotter_dict[spotter_id]\n",
    "    only_waves = spotter_df.loc[:, 'energy_density'].notnull()\n",
    "    ax.plot(\n",
    "        spotter_df.loc[only_waves].index,\n",
    "        spotter_df.loc[only_waves, 'significant_height'],\n",
    "        label=spotter_id,\n",
    "    )\n",
    "\n",
    "ax.set_ylabel('significant wave height (m)')\n",
    "ax.legend()\n",
    "ax.tick_params(axis='x', rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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