feat: ML 02

author: Martial Simon <msimon_fr@hotmail.com> 2026-02-20 12:03:22 +0100
committer: Martial Simon <msimon_fr@hotmail.com> 2026-02-20 12:03:22 +0100
commit: f066cebf3b972bbaefd59c26b75848c764c4fd34 (patch)
tree: 9cda673bd2dffae8ac247b1fb96b8c86a3b5e0de /ML
parent: 3751c4fe3fa670d1710286a61bce797e50a9fde8 (diff)
14 files changed, 37030 insertions, 0 deletions
diff --git a/ML/02_e2e/02_end_to_end_machine_learning_project-wo-correction.ipynb b/ML/02_e2e/02_end_to_end_machine_learning_project-wo-correction.ipynb
new file mode 100644
index 0000000..3f83d20
--- /dev/null
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@@ -0,0 +1,16174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**MACH 02 – End-to-end Machine Learning project**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This project requires Python 3.7 or above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "assert sys.version_info >= (3, 7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It also requires Scikit-Learn ≥ 1.0.1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from packaging import version\n",
+    "import sklearn\n",
+    "\n",
+    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Get the Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Welcome to Machine Learning Housing Corp.! Your task is to predict median house values in Californian districts, given a number of features from these districts.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download the Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "import tarfile\n",
+    "import urllib.request\n",
+    "\n",
+    "def load_housing_data():\n",
+    "    tarball_path = Path(\"datasets/housing.tgz\")\n",
+    "    if not tarball_path.is_file():\n",
+    "        Path(\"datasets\").mkdir(parents=True, exist_ok=True)\n",
+    "        #url = \"https://github.com/ageron/data/raw/main/housing.tgz\"\n",
+    "        url = \"https://figshare.com/ndownloader/files/5976036\"\n",
+    "        urllib.request.urlretrieve(url, tarball_path)\n",
+    "        with tarfile.open(tarball_path) as housing_tarball:\n",
+    "            housing_tarball.extractall(path=\"datasets\")\n",
+    "    return pd.read_csv(Path(\"datasets/housing/housing.csv\"))\n",
+    "\n",
+    "housing = pd.read_csv(\"datasets/housing.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Take a Quick Look at the Data Structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>452600.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7099.0</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>1138.0</td>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>358500.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1467.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>352100.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1274.0</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>341300.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1627.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>342200.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0    -122.23     37.88                41.0        880.0           129.0   \n",
+       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
+       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
+       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
+       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
+       "\n",
+       "   population  households  median_income  median_house_value ocean_proximity  \n",
+       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
+       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
+       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
+       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
+       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 20640 entries, 0 to 20639\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           20640 non-null  float64\n",
+      " 1   latitude            20640 non-null  float64\n",
+      " 2   housing_median_age  20640 non-null  float64\n",
+      " 3   total_rooms         20640 non-null  float64\n",
+      " 4   total_bedrooms      20433 non-null  float64\n",
+      " 5   population          20640 non-null  float64\n",
+      " 6   households          20640 non-null  float64\n",
+      " 7   median_income       20640 non-null  float64\n",
+      " 8   median_house_value  20640 non-null  float64\n",
+      " 9   ocean_proximity     20640 non-null  str    \n",
+      "dtypes: float64(9), str(1)\n",
+      "memory usage: 1.6 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "housing.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ocean_proximity\n",
+       "<1H OCEAN     9136\n",
+       "INLAND        6551\n",
+       "NEAR OCEAN    2658\n",
+       "NEAR BAY      2290\n",
+       "ISLAND           5\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing[\"ocean_proximity\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20433.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-119.569704</td>\n",
+       "      <td>35.631861</td>\n",
+       "      <td>28.639486</td>\n",
+       "      <td>2635.763081</td>\n",
+       "      <td>537.870553</td>\n",
+       "      <td>1425.476744</td>\n",
+       "      <td>499.539680</td>\n",
+       "      <td>3.870671</td>\n",
+       "      <td>206855.816909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.003532</td>\n",
+       "      <td>2.135952</td>\n",
+       "      <td>12.585558</td>\n",
+       "      <td>2181.615252</td>\n",
+       "      <td>421.385070</td>\n",
+       "      <td>1132.462122</td>\n",
+       "      <td>382.329753</td>\n",
+       "      <td>1.899822</td>\n",
+       "      <td>115395.615874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-124.350000</td>\n",
+       "      <td>32.540000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.499900</td>\n",
+       "      <td>14999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-121.800000</td>\n",
+       "      <td>33.930000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>1447.750000</td>\n",
+       "      <td>296.000000</td>\n",
+       "      <td>787.000000</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>2.563400</td>\n",
+       "      <td>119600.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-118.490000</td>\n",
+       "      <td>34.260000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>2127.000000</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>1166.000000</td>\n",
+       "      <td>409.000000</td>\n",
+       "      <td>3.534800</td>\n",
+       "      <td>179700.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>-118.010000</td>\n",
+       "      <td>37.710000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>3148.000000</td>\n",
+       "      <td>647.000000</td>\n",
+       "      <td>1725.000000</td>\n",
+       "      <td>605.000000</td>\n",
+       "      <td>4.743250</td>\n",
+       "      <td>264725.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>-114.310000</td>\n",
+       "      <td>41.950000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>39320.000000</td>\n",
+       "      <td>6445.000000</td>\n",
+       "      <td>35682.000000</td>\n",
+       "      <td>6082.000000</td>\n",
+       "      <td>15.000100</td>\n",
+       "      <td>500001.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
+       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
+       "std        2.003532      2.135952           12.585558   2181.615252   \n",
+       "min     -124.350000     32.540000            1.000000      2.000000   \n",
+       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
+       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
+       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
+       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+       "\n",
+       "       total_bedrooms    population    households  median_income  \\\n",
+       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
+       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
+       "std        421.385070   1132.462122    382.329753       1.899822   \n",
+       "min          1.000000      3.000000      1.000000       0.499900   \n",
+       "25%        296.000000    787.000000    280.000000       2.563400   \n",
+       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
+       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
+       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+       "\n",
+       "       median_house_value  \n",
+       "count        20640.000000  \n",
+       "mean        206855.816909  \n",
+       "std         115395.615874  \n",
+       "min          14999.000000  \n",
+       "25%         119600.000000  \n",
+       "50%         179700.000000  \n",
+       "75%         264725.000000  \n",
+       "max         500001.000000  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell is not shown either in the course. It creates the `images/end_to_end_project` folder (if it doesn't already exist), and it defines the `save_fig()` function which is used through this notebook to save the figures in high-res for the book. **you can jump this part**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extra code – code to save the figures as high-res PNGs \n",
+    "\n",
+    "IMAGES_PATH = Path() / \"images\" / \"end_to_end_project\"\n",
+    "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
+    "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
+    "    if tight_layout:\n",
+    "        plt.tight_layout()\n",
+    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# extra code – the next 5 lines define the default font sizes\n",
+    "plt.rc('font', size=14)\n",
+    "plt.rc('axes', labelsize=14, titlesize=14)\n",
+    "plt.rc('legend', fontsize=14)\n",
+    "plt.rc('xtick', labelsize=10)\n",
+    "plt.rc('ytick', labelsize=10)\n",
+    "\n",
+    "housing.hist(bins=50, figsize=(12, 8))\n",
+    "save_fig(\"attribute_histogram_plots\")  # extra code\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a Test Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def shuffle_and_split_data(data, test_ratio):\n",
+    "    shuffled_indices = np.random.permutation(len(data))\n",
+    "    test_set_size = int(len(data) * test_ratio)\n",
+    "    test_indices = shuffled_indices[:test_set_size]\n",
+    "    train_indices = shuffled_indices[test_set_size:]\n",
+    "    return data.iloc[train_indices], data.iloc[test_indices]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16512"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_set, test_set = shuffle_and_split_data(housing, 0.2)\n",
+    "len(train_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4128"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(test_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To ensure that this notebook's outputs remain the same every time we run it, we need to set the random seed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: another source of randomness is the order of Python sets: it is based on Python's `hash()` function, which is randomly \"salted\" when Python starts up (this started in Python 3.3, to prevent some denial-of-service attacks). To remove this randomness, the solution is to set the `PYTHONHASHSEED` environment variable to `\"0\"` _before_ Python even starts up. Nothing will happen if you do it after that. Luckily, if you're running this notebook on Colab, the variable is already set for you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from zlib import crc32\n",
+    "\n",
+    "def is_id_in_test_set(identifier, test_ratio):\n",
+    "    return crc32(np.int64(identifier)) < test_ratio * 2**32\n",
+    "\n",
+    "def split_data_with_id_hash(data, test_ratio, id_column):\n",
+    "    ids = data[id_column]\n",
+    "    in_test_set = ids.apply(lambda id_: is_id_in_test_set(id_, test_ratio))\n",
+    "    return data.loc[~in_test_set], data.loc[in_test_set]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_with_id = housing.reset_index()  # adds an `index` column\n",
+    "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"index\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_with_id[\"id\"] = housing[\"longitude\"] * 1000 + housing[\"latitude\"]\n",
+    "train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, \"id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(44)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set[\"total_bedrooms\"].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To find the probability that a random sample of 1,000 people contains less than 48.5% female or more than 53.5% female when the population's female ratio is 51.1%, we use the [binomial distribution](https://en.wikipedia.org/wiki/Binomial_distribution). The `cdf()` method of the binomial distribution gives us the probability that the number of females will be equal or less than the given value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.10736798530930253\n"
+     ]
+    }
+   ],
+   "source": [
+    "# extra code – shows how to compute the 10.7% proba of getting a bad sample\n",
+    "\n",
+    "from scipy.stats import binom\n",
+    "\n",
+    "sample_size = 1000\n",
+    "ratio_female = 0.511\n",
+    "proba_too_small = binom(sample_size, ratio_female).cdf(485 - 1)\n",
+    "proba_too_large = 1 - binom(sample_size, ratio_female).cdf(535)\n",
+    "print(proba_too_small + proba_too_large)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you prefer simulations over maths, here's how you could get roughly the same result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.1071)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extra code – shows another way to estimate the probability of bad sample\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "samples = (np.random.rand(100_000, sample_size) < ratio_female).sum(axis=1)\n",
+    "((samples < 485) | (samples > 535)).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing[\"income_cat\"] = pd.cut(housing[\"median_income\"],\n",
+    "                               bins=[0., 1.5, 3.0, 4.5, 6., np.inf],\n",
+    "                               labels=[1, 2, 3, 4, 5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing[\"income_cat\"].value_counts().sort_index().plot.bar(rot=0, grid=True)\n",
+    "plt.xlabel(\"Income category\")\n",
+    "plt.ylabel(\"Number of districts\")\n",
+    "save_fig(\"housing_income_cat_bar_plot\")  # extra code\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import StratifiedShuffleSplit\n",
+    "\n",
+    "splitter = StratifiedShuffleSplit(n_splits=10, test_size=0.2, random_state=42)\n",
+    "strat_splits = []\n",
+    "for train_index, test_index in splitter.split(housing, housing[\"income_cat\"]):\n",
+    "    strat_train_set_n = housing.iloc[train_index]\n",
+    "    strat_test_set_n = housing.iloc[test_index]\n",
+    "    strat_splits.append([strat_train_set_n, strat_test_set_n])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "strat_train_set, strat_test_set = strat_splits[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's much shorter to get a single stratified split:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "strat_train_set, strat_test_set = train_test_split(\n",
+    "    housing, test_size=0.2, stratify=housing[\"income_cat\"], random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "income_cat\n",
+       "3    0.350533\n",
+       "2    0.318798\n",
+       "4    0.176357\n",
+       "5    0.114341\n",
+       "1    0.039971\n",
+       "Name: count, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "strat_test_set[\"income_cat\"].value_counts() / len(strat_test_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>Overall %</th>\n",
+       "      <th>Stratified %</th>\n",
+       "      <th>Random %</th>\n",
+       "      <th>Strat. Error %</th>\n",
+       "      <th>Rand. Error %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Income Category</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>1</th>\n",
+       "      <td>3.98</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>4.24</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>6.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>31.88</td>\n",
+       "      <td>31.88</td>\n",
+       "      <td>30.74</td>\n",
+       "      <td>-0.02</td>\n",
+       "      <td>-3.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35.06</td>\n",
+       "      <td>35.05</td>\n",
+       "      <td>34.52</td>\n",
+       "      <td>-0.01</td>\n",
+       "      <td>-1.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.63</td>\n",
+       "      <td>17.64</td>\n",
+       "      <td>18.41</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>4.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>11.44</td>\n",
+       "      <td>11.43</td>\n",
+       "      <td>12.09</td>\n",
+       "      <td>-0.08</td>\n",
+       "      <td>5.63</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Overall %  Stratified %  Random %  Strat. Error %  \\\n",
+       "Income Category                                                      \n",
+       "1                     3.98          4.00      4.24            0.36   \n",
+       "2                    31.88         31.88     30.74           -0.02   \n",
+       "3                    35.06         35.05     34.52           -0.01   \n",
+       "4                    17.63         17.64     18.41            0.03   \n",
+       "5                    11.44         11.43     12.09           -0.08   \n",
+       "\n",
+       "                 Rand. Error %  \n",
+       "Income Category                 \n",
+       "1                         6.45  \n",
+       "2                        -3.59  \n",
+       "3                        -1.53  \n",
+       "4                         4.42  \n",
+       "5                         5.63  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extra code – computes the data for a Figure \n",
+    "\n",
+    "def income_cat_proportions(data):\n",
+    "    return data[\"income_cat\"].value_counts() / len(data)\n",
+    "\n",
+    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
+    "\n",
+    "compare_props = pd.DataFrame({\n",
+    "    \"Overall %\": income_cat_proportions(housing),\n",
+    "    \"Stratified %\": income_cat_proportions(strat_test_set),\n",
+    "    \"Random %\": income_cat_proportions(test_set),\n",
+    "}).sort_index()\n",
+    "compare_props.index.name = \"Income Category\"\n",
+    "compare_props[\"Strat. Error %\"] = (compare_props[\"Stratified %\"] /\n",
+    "                                   compare_props[\"Overall %\"] - 1)\n",
+    "compare_props[\"Rand. Error %\"] = (compare_props[\"Random %\"] /\n",
+    "                                  compare_props[\"Overall %\"] - 1)\n",
+    "(compare_props * 100).round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for set_ in (strat_train_set, strat_test_set):\n",
+    "    set_.drop(\"income_cat\", axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Discover and Visualize the Data to Gain Insights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing = strat_train_set.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing Geographical Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True)\n",
+    "save_fig(\"bad_visualization_plot\")  # extra code\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True, alpha=0.2)\n",
+    "save_fig(\"better_visualization_plot\")  # extra code\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", grid=True,\n",
+    "             s=housing[\"population\"] / 100, label=\"population\",\n",
+    "             c=\"median_house_value\", cmap=\"jet\", colorbar=True,\n",
+    "             legend=True, sharex=False, figsize=(10, 7))\n",
+    "save_fig(\"housing_prices_scatterplot\")  # extra code\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The argument `sharex=False` fixes a display bug: without it, the x-axis values and label are not displayed (see: https://github.com/pandas-dev/pandas/issues/10611)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next cell generates the first figure in the chapter (this code is not in the book). It's just a beautified version of the previous figure, with an image of California added in the background, nicer label names and no grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# extra code – this cell generates a nice figure\n",
+    "\n",
+    "# Download the California image\n",
+    "filename = \"california.png\"\n",
+    "\n",
+    "\n",
+    "housing_renamed = housing.rename(columns={\n",
+    "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
+    "    \"population\": \"Population\",\n",
+    "    \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n",
+    "housing_renamed.plot(\n",
+    "             kind=\"scatter\", x=\"Longitude\", y=\"Latitude\",\n",
+    "             s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
+    "             c=\"Median house value (ᴜsᴅ)\", cmap=\"jet\", colorbar=True,\n",
+    "             legend=True, sharex=False, figsize=(10, 7))\n",
+    "\n",
+    "california_img = plt.imread(filename)\n",
+    "axis = -124.55, -113.95, 32.45, 42.05\n",
+    "plt.axis(axis)\n",
+    "plt.imshow(california_img, extent=axis)\n",
+    "\n",
+    "save_fig(\"california_housing_prices_plot\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Looking for Correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_matrix = housing.corr(numeric_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#!pip install seaborn\n",
+    "import seaborn as sns\n",
+    "plt.figure(figsize=(10,7)) # Ajuste la taille du graphique\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "median_house_value    1.000000\n",
+       "median_income         0.688380\n",
+       "total_rooms           0.137455\n",
+       "housing_median_age    0.102175\n",
+       "households            0.071426\n",
+       "total_bedrooms        0.054635\n",
+       "population           -0.020153\n",
+       "longitude            -0.050859\n",
+       "latitude             -0.139584\n",
+       "Name: median_house_value, dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pandas.plotting import scatter_matrix\n",
+    "\n",
+    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
+    "              \"housing_median_age\"]\n",
+    "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
+    "save_fig(\"scatter_matrix_plot\")  # extra code\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
+    "             alpha=0.1, grid=True)\n",
+    "save_fig(\"income_vs_house_value_scatterplot\")  # extra code\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experimenting with Attribute Combinations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing[\"rooms_per_house\"] = housing[\"total_rooms\"] / housing[\"households\"]\n",
+    "housing[\"bedrooms_ratio\"] = housing[\"total_bedrooms\"] / housing[\"total_rooms\"]\n",
+    "housing[\"people_per_house\"] = housing[\"population\"] / housing[\"households\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "median_house_value    1.000000\n",
+       "median_income         0.688380\n",
+       "rooms_per_house       0.143663\n",
+       "total_rooms           0.137455\n",
+       "housing_median_age    0.102175\n",
+       "households            0.071426\n",
+       "total_bedrooms        0.054635\n",
+       "population           -0.020153\n",
+       "people_per_house     -0.038224\n",
+       "longitude            -0.050859\n",
+       "latitude             -0.139584\n",
+       "bedrooms_ratio       -0.256397\n",
+       "Name: median_house_value, dtype: float64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix = housing.corr(numeric_only=True)\n",
+    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prepare the Data for Machine Learning Algorithms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's revert to the original training set and separate the target (note that `strat_train_set.drop()` creates a copy of `strat_train_set` without the column, it doesn't actually modify `strat_train_set` itself, unless you pass `inplace=True`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
+    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Cleaning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the book 3 options are listed to handle the NaN values:\n",
+    "\n",
+    "```python\n",
+    "housing.dropna(subset=[\"total_bedrooms\"], inplace=True)    # option 1\n",
+    "\n",
+    "housing.drop(\"total_bedrooms\", axis=1)       # option 2\n",
+    "\n",
+    "median = housing[\"total_bedrooms\"].median()  # option 3\n",
+    "housing[\"total_bedrooms\"].fillna(median, inplace=True)\n",
+    "```\n",
+    "\n",
+    "For each option, we'll create a copy of `housing` and work on that copy to avoid breaking `housing`. We'll also show the output of each option, but filtering on the rows that originally contained a NaN value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>14452</th>\n",
+       "      <td>-120.67</td>\n",
+       "      <td>40.50</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5343.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2503.0</td>\n",
+       "      <td>902.0</td>\n",
+       "      <td>3.5962</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18217</th>\n",
+       "      <td>-117.96</td>\n",
+       "      <td>34.03</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2093.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1755.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>3.4115</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11889</th>\n",
+       "      <td>-118.05</td>\n",
+       "      <td>34.04</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>1348.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1098.0</td>\n",
+       "      <td>257.0</td>\n",
+       "      <td>4.2917</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20325</th>\n",
+       "      <td>-118.88</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>4260.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1701.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>5.1033</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14360</th>\n",
+       "      <td>-117.87</td>\n",
+       "      <td>33.62</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1266.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>375.0</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>9.8020</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "14452    -120.67     40.50                15.0       5343.0             NaN   \n",
+       "18217    -117.96     34.03                35.0       2093.0             NaN   \n",
+       "11889    -118.05     34.04                33.0       1348.0             NaN   \n",
+       "20325    -118.88     34.17                15.0       4260.0             NaN   \n",
+       "14360    -117.87     33.62                 8.0       1266.0             NaN   \n",
+       "\n",
+       "       population  households  median_income ocean_proximity  \n",
+       "14452      2503.0       902.0         3.5962          INLAND  \n",
+       "18217      1755.0       403.0         3.4115       <1H OCEAN  \n",
+       "11889      1098.0       257.0         4.2917       <1H OCEAN  \n",
+       "20325      1701.0       669.0         5.1033       <1H OCEAN  \n",
+       "14360       375.0       183.0         9.8020       <1H OCEAN  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "null_rows_idx = housing.isnull().any(axis=1)\n",
+    "housing.loc[null_rows_idx].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_option1 = housing.copy()\n",
+    "\n",
+    "housing_option1.dropna(subset=[\"total_bedrooms\"], inplace=True)  # option 1\n",
+    "\n",
+    "housing_option1.loc[null_rows_idx].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>14452</th>\n",
+       "      <td>-120.67</td>\n",
+       "      <td>40.50</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5343.0</td>\n",
+       "      <td>2503.0</td>\n",
+       "      <td>902.0</td>\n",
+       "      <td>3.5962</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18217</th>\n",
+       "      <td>-117.96</td>\n",
+       "      <td>34.03</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2093.0</td>\n",
+       "      <td>1755.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>3.4115</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11889</th>\n",
+       "      <td>-118.05</td>\n",
+       "      <td>34.04</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>1348.0</td>\n",
+       "      <td>1098.0</td>\n",
+       "      <td>257.0</td>\n",
+       "      <td>4.2917</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20325</th>\n",
+       "      <td>-118.88</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>4260.0</td>\n",
+       "      <td>1701.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>5.1033</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14360</th>\n",
+       "      <td>-117.87</td>\n",
+       "      <td>33.62</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1266.0</td>\n",
+       "      <td>375.0</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>9.8020</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
+       "14452    -120.67     40.50                15.0       5343.0      2503.0   \n",
+       "18217    -117.96     34.03                35.0       2093.0      1755.0   \n",
+       "11889    -118.05     34.04                33.0       1348.0      1098.0   \n",
+       "20325    -118.88     34.17                15.0       4260.0      1701.0   \n",
+       "14360    -117.87     33.62                 8.0       1266.0       375.0   \n",
+       "\n",
+       "       households  median_income ocean_proximity  \n",
+       "14452       902.0         3.5962          INLAND  \n",
+       "18217       403.0         3.4115       <1H OCEAN  \n",
+       "11889       257.0         4.2917       <1H OCEAN  \n",
+       "20325       669.0         5.1033       <1H OCEAN  \n",
+       "14360       183.0         9.8020       <1H OCEAN  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_option2 = housing.copy()\n",
+    "\n",
+    "housing_option2.drop(\"total_bedrooms\", axis=1, inplace=True)  # option 2\n",
+    "\n",
+    "housing_option2.loc[null_rows_idx].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_11969/3218827148.py:4: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+      "Such inplace method never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).\n",
+      "\n",
+      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' instead, to perform the operation inplace on the original object, or try to avoid an inplace operation using 'df[col] = df[col].method(value)'.\n",
+      "\n",
+      "See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html\n",
+      "  housing_option3[\"total_bedrooms\"].fillna(median, inplace=True)  # option 3\n"
+     ]
+    },
+    {
+     "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>14452</th>\n",
+       "      <td>-120.67</td>\n",
+       "      <td>40.50</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5343.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2503.0</td>\n",
+       "      <td>902.0</td>\n",
+       "      <td>3.5962</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18217</th>\n",
+       "      <td>-117.96</td>\n",
+       "      <td>34.03</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2093.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1755.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>3.4115</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11889</th>\n",
+       "      <td>-118.05</td>\n",
+       "      <td>34.04</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>1348.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1098.0</td>\n",
+       "      <td>257.0</td>\n",
+       "      <td>4.2917</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20325</th>\n",
+       "      <td>-118.88</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>4260.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1701.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>5.1033</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14360</th>\n",
+       "      <td>-117.87</td>\n",
+       "      <td>33.62</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1266.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>375.0</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>9.8020</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "14452    -120.67     40.50                15.0       5343.0             NaN   \n",
+       "18217    -117.96     34.03                35.0       2093.0             NaN   \n",
+       "11889    -118.05     34.04                33.0       1348.0             NaN   \n",
+       "20325    -118.88     34.17                15.0       4260.0             NaN   \n",
+       "14360    -117.87     33.62                 8.0       1266.0             NaN   \n",
+       "\n",
+       "       population  households  median_income ocean_proximity  \n",
+       "14452      2503.0       902.0         3.5962          INLAND  \n",
+       "18217      1755.0       403.0         3.4115       <1H OCEAN  \n",
+       "11889      1098.0       257.0         4.2917       <1H OCEAN  \n",
+       "20325      1701.0       669.0         5.1033       <1H OCEAN  \n",
+       "14360       375.0       183.0         9.8020       <1H OCEAN  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_option3 = housing.copy()\n",
+    "\n",
+    "median = housing[\"total_bedrooms\"].median()\n",
+    "housing_option3[\"total_bedrooms\"].fillna(median, inplace=True)  # option 3\n",
+    "\n",
+    "housing_option3.loc[null_rows_idx].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.impute import SimpleImputer\n",
+    "\n",
+    "imputer = SimpleImputer(strategy=\"median\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Separating out the numerical attributes to use the `\"median\"` strategy (as it cannot be calculated on text attributes like `ocean_proximity`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_num = housing.select_dtypes(include=[np.number])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
+      ],
+      "text/plain": [
+       "SimpleImputer(strategy='median')"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imputer.fit(housing_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-118.51  ,   34.26  ,   29.    , 2125.    ,  434.    , 1167.    ,\n",
+       "        408.    ,    3.5385])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imputer.statistics_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check that this is the same as manually computing the median of each attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-118.51  ,   34.26  ,   29.    , 2125.    ,  434.    , 1167.    ,\n",
+       "        408.    ,    3.5385])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_num.median().values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Transform the training set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = imputer.transform(housing_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
+       "       'total_bedrooms', 'population', 'households', 'median_income'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imputer.feature_names_in_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
+    "                          index=housing_num.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>14452</th>\n",
+       "      <td>-120.67</td>\n",
+       "      <td>40.50</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5343.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>2503.0</td>\n",
+       "      <td>902.0</td>\n",
+       "      <td>3.5962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18217</th>\n",
+       "      <td>-117.96</td>\n",
+       "      <td>34.03</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2093.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>1755.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>3.4115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11889</th>\n",
+       "      <td>-118.05</td>\n",
+       "      <td>34.04</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>1348.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>1098.0</td>\n",
+       "      <td>257.0</td>\n",
+       "      <td>4.2917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20325</th>\n",
+       "      <td>-118.88</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>4260.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>1701.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>5.1033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14360</th>\n",
+       "      <td>-117.87</td>\n",
+       "      <td>33.62</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1266.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>375.0</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>9.8020</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
+       "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
+       "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
+       "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
+       "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
+       "\n",
+       "       population  households  median_income  \n",
+       "14452      2503.0       902.0         3.5962  \n",
+       "18217      1755.0       403.0         3.4115  \n",
+       "11889      1098.0       257.0         4.2917  \n",
+       "20325      1701.0       669.0         5.1033  \n",
+       "14360       375.0       183.0         9.8020  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_tr.loc[null_rows_idx].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'median'"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imputer.strategy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
+    "                          index=housing_num.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>14452</th>\n",
+       "      <td>-120.67</td>\n",
+       "      <td>40.50</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5343.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>2503.0</td>\n",
+       "      <td>902.0</td>\n",
+       "      <td>3.5962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18217</th>\n",
+       "      <td>-117.96</td>\n",
+       "      <td>34.03</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2093.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>1755.0</td>\n",
+       "      <td>403.0</td>\n",
+       "      <td>3.4115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11889</th>\n",
+       "      <td>-118.05</td>\n",
+       "      <td>34.04</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>1348.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>1098.0</td>\n",
+       "      <td>257.0</td>\n",
+       "      <td>4.2917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20325</th>\n",
+       "      <td>-118.88</td>\n",
+       "      <td>34.17</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>4260.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>1701.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>5.1033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14360</th>\n",
+       "      <td>-117.87</td>\n",
+       "      <td>33.62</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1266.0</td>\n",
+       "      <td>434.0</td>\n",
+       "      <td>375.0</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>9.8020</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "14452    -120.67     40.50                15.0       5343.0           434.0   \n",
+       "18217    -117.96     34.03                35.0       2093.0           434.0   \n",
+       "11889    -118.05     34.04                33.0       1348.0           434.0   \n",
+       "20325    -118.88     34.17                15.0       4260.0           434.0   \n",
+       "14360    -117.87     33.62                 8.0       1266.0           434.0   \n",
+       "\n",
+       "       population  households  median_income  \n",
+       "14452      2503.0       902.0         3.5962  \n",
+       "18217      1755.0       403.0         3.4115  \n",
+       "11889      1098.0       257.0         4.2917  \n",
+       "20325      1701.0       669.0         5.1033  \n",
+       "14360       375.0       183.0         9.8020  "
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_tr.loc[null_rows_idx].head()  # not shown in the book"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#from sklearn import set_config\n",
+    "#\n",
+    "# set_config(pandas_in_out=True)  # not available yet – see SLEP014"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's drop some outliers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import IsolationForest\n",
+    "\n",
+    "isolation_forest = IsolationForest(random_state=42)\n",
+    "outlier_pred = isolation_forest.fit_predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1,  1,  1, ...,  1,  1,  1], shape=(16512,))"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outlier_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you wanted to drop outliers, you would run the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#housing = housing.iloc[outlier_pred == 1]\n",
+    "#housing_labels = housing_labels.iloc[outlier_pred == 1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handling Text and Categorical Attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's preprocess the categorical input feature, `ocean_proximity`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "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>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13096</th>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14973</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3785</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14689</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20507</th>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1286</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18078</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4396</th>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ocean_proximity\n",
+       "13096        NEAR BAY\n",
+       "14973       <1H OCEAN\n",
+       "3785           INLAND\n",
+       "14689          INLAND\n",
+       "20507      NEAR OCEAN\n",
+       "1286           INLAND\n",
+       "18078       <1H OCEAN\n",
+       "4396         NEAR BAY"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_cat = housing[[\"ocean_proximity\"]]\n",
+    "housing_cat.head(8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "\n",
+    "ordinal_encoder = OrdinalEncoder()\n",
+    "housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[3.],\n",
+       "       [0.],\n",
+       "       [1.],\n",
+       "       [1.],\n",
+       "       [4.],\n",
+       "       [1.],\n",
+       "       [0.],\n",
+       "       [3.]])"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_cat_encoded[:8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
+       "       dtype=object)]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordinal_encoder.categories_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "cat_encoder = OneHotEncoder()\n",
+    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Compressed Sparse Row sparse matrix of dtype 'float64'\n",
+       "\twith 16512 stored elements and shape (16512, 5)>"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_cat_1hot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, the `OneHotEncoder` class returns a sparse array, but we can convert it to a dense array if needed by calling the `toarray()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 1., 0.],\n",
+       "       [1., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       ...,\n",
+       "       [0., 0., 0., 0., 1.],\n",
+       "       [1., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 1.]], shape=(16512, 5))"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_cat_1hot.toarray()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can set `sparse_output=False` when creating the `OneHotEncoder` (note: the `sparse` hyperparameter was renamned to `sparse_output` in Scikit-Learn 1.2):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 1., 0.],\n",
+       "       [1., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       ...,\n",
+       "       [0., 0., 0., 0., 1.],\n",
+       "       [1., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 1.]], shape=(16512, 5))"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# cat_encoder = OneHotEncoder(sparse=False)\n",
+    "# housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
+    "# housing_cat_1hot\n",
+    "\n",
+    "cat_encoder = OneHotEncoder(sparse_output=False)\n",
+    "housing_cat_1hot = cat_encoder.fit_transform(housing_cat)\n",
+    "housing_cat_1hot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
+       "       dtype=object)]"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_encoder.categories_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "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>ocean_proximity_INLAND</th>\n",
+       "      <th>ocean_proximity_NEAR BAY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ocean_proximity_INLAND  ocean_proximity_NEAR BAY\n",
+       "0                    True                     False\n",
+       "1                   False                      True"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test = pd.DataFrame({\"ocean_proximity\": [\"INLAND\", \"NEAR BAY\"]})\n",
+    "pd.get_dummies(df_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 0., 1., 0.]])"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_encoder.transform(df_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "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>ocean_proximity_&lt;2H OCEAN</th>\n",
+       "      <th>ocean_proximity_ISLAND</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ocean_proximity_<2H OCEAN  ocean_proximity_ISLAND\n",
+       "0                       True                   False\n",
+       "1                      False                    True"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test_unknown = pd.DataFrame({\"ocean_proximity\": [\"<2H OCEAN\", \"ISLAND\"]})\n",
+    "pd.get_dummies(df_test_unknown)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0.]])"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_encoder.handle_unknown = \"ignore\"\n",
+    "cat_encoder.transform(df_test_unknown)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['ocean_proximity'], dtype=object)"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_encoder.feature_names_in_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['ocean_proximity_<1H OCEAN', 'ocean_proximity_INLAND',\n",
+       "       'ocean_proximity_ISLAND', 'ocean_proximity_NEAR BAY',\n",
+       "       'ocean_proximity_NEAR OCEAN'], dtype=object)"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_encoder.get_feature_names_out()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown),\n",
+    "                         columns=cat_encoder.get_feature_names_out(),\n",
+    "                         index=df_test_unknown.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "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>ocean_proximity_&lt;1H OCEAN</th>\n",
+       "      <th>ocean_proximity_INLAND</th>\n",
+       "      <th>ocean_proximity_ISLAND</th>\n",
+       "      <th>ocean_proximity_NEAR BAY</th>\n",
+       "      <th>ocean_proximity_NEAR OCEAN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ocean_proximity_<1H OCEAN  ocean_proximity_INLAND  ocean_proximity_ISLAND  \\\n",
+       "0                        0.0                     0.0                     0.0   \n",
+       "1                        0.0                     0.0                     1.0   \n",
+       "\n",
+       "   ocean_proximity_NEAR BAY  ocean_proximity_NEAR OCEAN  \n",
+       "0                       0.0                         0.0  \n",
+       "1                       0.0                         0.0  "
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "min_max_scaler = MinMaxScaler(feature_range=(-1, 1))\n",
+    "housing_num_min_max_scaled = min_max_scaler.fit_transform(housing_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "std_scaler = StandardScaler()\n",
+    "housing_num_std_scaled = std_scaler.fit_transform(housing_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# extra code – this cell generates Figure 2–17\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True)\n",
+    "housing[\"population\"].hist(ax=axs[0], bins=50)\n",
+    "housing[\"population\"].apply(np.log).hist(ax=axs[1], bins=50)\n",
+    "axs[0].set_xlabel(\"Population\")\n",
+    "axs[1].set_xlabel(\"Log of population\")\n",
+    "axs[0].set_ylabel(\"Number of districts\")\n",
+    "save_fig(\"long_tail_plot\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if we replace each value with its percentile?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# extra code – just shows that we get a uniform distribution\n",
+    "percentiles = [np.percentile(housing[\"median_income\"], p)\n",
+    "               for p in range(1, 100)]\n",
+    "flattened_median_income = pd.cut(housing[\"median_income\"],\n",
+    "                                 bins=[-np.inf] + percentiles + [np.inf],\n",
+    "                                 labels=range(1, 100 + 1))\n",
+    "flattened_median_income.hist(bins=50)\n",
+    "plt.xlabel(\"Median income percentile\")\n",
+    "plt.ylabel(\"Number of districts\")\n",
+    "plt.show()\n",
+    "# Note: incomes below the 1st percentile are labeled 1, and incomes above the\n",
+    "# 99th percentile are labeled 100. This is why the distribution below ranges\n",
+    "# from 1 to 100 (not 0 to 100)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics.pairwise import rbf_kernel\n",
+    "\n",
+    "age_simil_35 = rbf_kernel(housing[[\"housing_median_age\"]], [[35]], gamma=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# extra code – this cell generates Figure 2–18\n",
+    "\n",
+    "ages = np.linspace(housing[\"housing_median_age\"].min(),\n",
+    "                   housing[\"housing_median_age\"].max(),\n",
+    "                   500).reshape(-1, 1)\n",
+    "gamma1 = 0.1\n",
+    "gamma2 = 0.03\n",
+    "rbf1 = rbf_kernel(ages, [[35]], gamma=gamma1)\n",
+    "rbf2 = rbf_kernel(ages, [[35]], gamma=gamma2)\n",
+    "\n",
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "ax1.set_xlabel(\"Housing median age\")\n",
+    "ax1.set_ylabel(\"Number of districts\")\n",
+    "ax1.hist(housing[\"housing_median_age\"], bins=50)\n",
+    "\n",
+    "ax2 = ax1.twinx()  # create a twin axis that shares the same x-axis\n",
+    "color = \"blue\"\n",
+    "ax2.plot(ages, rbf1, color=color, label=\"gamma = 0.10\")\n",
+    "ax2.plot(ages, rbf2, color=color, label=\"gamma = 0.03\", linestyle=\"--\")\n",
+    "ax2.tick_params(axis='y', labelcolor=color)\n",
+    "ax2.set_ylabel(\"Age similarity\", color=color)\n",
+    "\n",
+    "plt.legend(loc=\"upper left\")\n",
+    "save_fig(\"age_similarity_plot\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "target_scaler = StandardScaler()\n",
+    "scaled_labels = target_scaler.fit_transform(housing_labels.to_frame())\n",
+    "\n",
+    "model = LinearRegression()\n",
+    "model.fit(housing[[\"median_income\"]], scaled_labels)\n",
+    "some_new_data = housing[[\"median_income\"]].iloc[:5]  # pretend this is new data\n",
+    "\n",
+    "scaled_predictions = model.predict(some_new_data)\n",
+    "predictions = target_scaler.inverse_transform(scaled_predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[131997.15275877],\n",
+       "       [299359.35844434],\n",
+       "       [146023.37185694],\n",
+       "       [138840.33653057],\n",
+       "       [192016.61557639]])"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import TransformedTargetRegressor\n",
+    "\n",
+    "model = TransformedTargetRegressor(LinearRegression(),\n",
+    "                                   transformer=StandardScaler())\n",
+    "model.fit(housing[[\"median_income\"]], housing_labels)\n",
+    "predictions = model.predict(some_new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([131997.15275877, 299359.35844434, 146023.37185694, 138840.33653057,\n",
+       "       192016.61557639])"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Custom Transformers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create simple transformers:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import FunctionTransformer\n",
+    "\n",
+    "log_transformer = FunctionTransformer(np.log, inverse_func=np.exp)\n",
+    "log_pop = log_transformer.transform(housing[[\"population\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rbf_transformer = FunctionTransformer(rbf_kernel,\n",
+    "                                      kw_args=dict(Y=[[35.]], gamma=0.1))\n",
+    "age_simil_35 = rbf_transformer.transform(housing[[\"housing_median_age\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2.81118530e-13],\n",
+       "       [8.20849986e-02],\n",
+       "       [6.70320046e-01],\n",
+       "       ...,\n",
+       "       [9.55316054e-22],\n",
+       "       [6.70320046e-01],\n",
+       "       [3.03539138e-04]], shape=(16512, 1))"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_simil_35"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_coords = 37.7749, -122.41\n",
+    "sf_transformer = FunctionTransformer(rbf_kernel,\n",
+    "                                     kw_args=dict(Y=[sf_coords], gamma=0.1))\n",
+    "sf_simil = sf_transformer.transform(housing[[\"latitude\", \"longitude\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.999927  ],\n",
+       "       [0.05258419],\n",
+       "       [0.94864161],\n",
+       "       ...,\n",
+       "       [0.00388525],\n",
+       "       [0.05038518],\n",
+       "       [0.99868067]], shape=(16512, 1))"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sf_simil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.5 ],\n",
+       "       [0.75]])"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ratio_transformer = FunctionTransformer(lambda X: X[:, [0]] / X[:, [1]])\n",
+    "ratio_transformer.transform(np.array([[1., 2.], [3., 4.]]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "from sklearn.utils.validation import check_array, check_is_fitted\n",
+    "\n",
+    "class StandardScalerClone(BaseEstimator, TransformerMixin):\n",
+    "    def __init__(self, with_mean=True):  # no *args or **kwargs!\n",
+    "        self.with_mean = with_mean\n",
+    "\n",
+    "    def fit(self, X, y=None):  # y is required even though we don't use it\n",
+    "        X = check_array(X)  # checks that X is an array with finite float values\n",
+    "        self.mean_ = X.mean(axis=0)\n",
+    "        self.scale_ = X.std(axis=0)\n",
+    "        self.n_features_in_ = X.shape[1]  # every estimator stores this in fit()\n",
+    "        return self  # always return self!\n",
+    "\n",
+    "    def transform(self, X):\n",
+    "        check_is_fitted(self)  # looks for learned attributes (with trailing _)\n",
+    "        X = check_array(X)\n",
+    "        assert self.n_features_in_ == X.shape[1]\n",
+    "        if self.with_mean:\n",
+    "            X = X - self.mean_\n",
+    "        return X / self.scale_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
+    "    def __init__(self, n_clusters=10, gamma=1.0, random_state=None):\n",
+    "        self.n_clusters = n_clusters\n",
+    "        self.gamma = gamma\n",
+    "        self.random_state = random_state\n",
+    "\n",
+    "    def fit(self, X, y=None, sample_weight=None):\n",
+    "        self.kmeans_ = KMeans(self.n_clusters, random_state=self.random_state)\n",
+    "        self.kmeans_.fit(X, sample_weight=sample_weight)\n",
+    "        return self  # always return self!\n",
+    "\n",
+    "    def transform(self, X):\n",
+    "        return rbf_kernel(X, self.kmeans_.cluster_centers_, gamma=self.gamma)\n",
+    "    \n",
+    "    def get_feature_names_out(self, names=None):\n",
+    "        return [f\"Cluster {i} similarity\" for i in range(self.n_clusters)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n",
+    "similarities = cluster_simil.fit_transform(housing[[\"latitude\", \"longitude\"]],\n",
+    "                                           sample_weight=housing_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.  , 0.98, 0.  , 0.  , 0.  , 0.  , 0.13, 0.55, 0.  , 0.56],\n",
+       "       [0.64, 0.  , 0.11, 0.04, 0.  , 0.  , 0.  , 0.  , 0.99, 0.  ],\n",
+       "       [0.  , 0.65, 0.  , 0.  , 0.01, 0.  , 0.49, 0.59, 0.  , 0.28]])"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "similarities[:3].round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# extra code – this cell generates Figure 2–19\n",
+    "\n",
+    "housing_renamed = housing.rename(columns={\n",
+    "    \"latitude\": \"Latitude\", \"longitude\": \"Longitude\",\n",
+    "    \"population\": \"Population\",\n",
+    "    \"median_house_value\": \"Median house value (ᴜsᴅ)\"})\n",
+    "housing_renamed[\"Max cluster similarity\"] = similarities.max(axis=1)\n",
+    "\n",
+    "housing_renamed.plot(kind=\"scatter\", x=\"Longitude\", y=\"Latitude\", grid=True,\n",
+    "                     s=housing_renamed[\"Population\"] / 100, label=\"Population\",\n",
+    "                     c=\"Max cluster similarity\",\n",
+    "                     cmap=\"jet\", colorbar=True,\n",
+    "                     legend=True, sharex=False, figsize=(10, 7))\n",
+    "plt.plot(cluster_simil.kmeans_.cluster_centers_[:, 1],\n",
+    "         cluster_simil.kmeans_.cluster_centers_[:, 0],\n",
+    "         linestyle=\"\", color=\"black\", marker=\"X\", markersize=20,\n",
+    "         label=\"Cluster centers\")\n",
+    "plt.legend(loc=\"upper right\")\n",
+    "save_fig(\"district_cluster_plot\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transformation Pipelines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's build a pipeline to preprocess the numerical attributes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "\n",
+    "num_pipeline = Pipeline([\n",
+    "    (\"impute\", SimpleImputer(strategy=\"median\")),\n",
+    "    (\"standardize\", StandardScaler()),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('steps',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
+       "            steps\n",
+       "            <span class=\"param-doc-description\">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;simpleimputer&#x27;, ...), (&#x27;standardscaler&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transform_input',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
+       "            transform_input\n",
+       "            <span class=\"param-doc-description\">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
+       "            memory\n",
+       "            <span class=\"param-doc-description\">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \" data-param-prefix=\"standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-2');</script></body>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler())])"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn import set_config\n",
+    "\n",
+    "set_config(display='diagram')\n",
+    "\n",
+    "num_pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.42,  1.01,  1.86,  0.31,  1.37,  0.14,  1.39, -0.94],\n",
+       "       [ 0.6 , -0.7 ,  0.91, -0.31, -0.44, -0.69, -0.37,  1.17]])"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_num_prepared = num_pipeline.fit_transform(housing_num)\n",
+    "housing_num_prepared[:2].round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def monkey_patch_get_signature_names_out():\n",
+    "    \"\"\"Monkey patch some classes which did not handle get_feature_names_out()\n",
+    "       correctly in Scikit-Learn 1.0.*.\"\"\"\n",
+    "    from inspect import Signature, signature, Parameter\n",
+    "    import pandas as pd\n",
+    "    from sklearn.impute import SimpleImputer\n",
+    "    from sklearn.pipeline import make_pipeline, Pipeline\n",
+    "    from sklearn.preprocessing import FunctionTransformer, StandardScaler\n",
+    "\n",
+    "    default_get_feature_names_out = StandardScaler.get_feature_names_out\n",
+    "\n",
+    "    if not hasattr(SimpleImputer, \"get_feature_names_out\"):\n",
+    "      print(\"Monkey-patching SimpleImputer.get_feature_names_out()\")\n",
+    "      SimpleImputer.get_feature_names_out = default_get_feature_names_out\n",
+    "\n",
+    "    if not hasattr(FunctionTransformer, \"get_feature_names_out\"):\n",
+    "        print(\"Monkey-patching FunctionTransformer.get_feature_names_out()\")\n",
+    "        orig_init = FunctionTransformer.__init__\n",
+    "        orig_sig = signature(orig_init)\n",
+    "\n",
+    "        def __init__(*args, feature_names_out=None, **kwargs):\n",
+    "            orig_sig.bind(*args, **kwargs)\n",
+    "            orig_init(*args, **kwargs)\n",
+    "            args[0].feature_names_out = feature_names_out\n",
+    "\n",
+    "        __init__.__signature__ = Signature(\n",
+    "            list(signature(orig_init).parameters.values()) + [\n",
+    "                Parameter(\"feature_names_out\", Parameter.KEYWORD_ONLY)])\n",
+    "\n",
+    "        def get_feature_names_out(self, names=None):\n",
+    "            if callable(self.feature_names_out):\n",
+    "                return self.feature_names_out(self, names)\n",
+    "            assert self.feature_names_out == \"one-to-one\"\n",
+    "            return default_get_feature_names_out(self, names)\n",
+    "\n",
+    "        FunctionTransformer.__init__ = __init__\n",
+    "        FunctionTransformer.get_feature_names_out = get_feature_names_out\n",
+    "\n",
+    "monkey_patch_get_signature_names_out()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_housing_num_prepared = pd.DataFrame(\n",
+    "    housing_num_prepared, columns=num_pipeline.get_feature_names_out(),\n",
+    "    index=housing_num.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "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>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13096</th>\n",
+       "      <td>-1.423037</td>\n",
+       "      <td>1.013606</td>\n",
+       "      <td>1.861119</td>\n",
+       "      <td>0.311912</td>\n",
+       "      <td>1.368167</td>\n",
+       "      <td>0.137460</td>\n",
+       "      <td>1.394812</td>\n",
+       "      <td>-0.936491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14973</th>\n",
+       "      <td>0.596394</td>\n",
+       "      <td>-0.702103</td>\n",
+       "      <td>0.907630</td>\n",
+       "      <td>-0.308620</td>\n",
+       "      <td>-0.435925</td>\n",
+       "      <td>-0.693771</td>\n",
+       "      <td>-0.373485</td>\n",
+       "      <td>1.171942</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "13096  -1.423037  1.013606            1.861119     0.311912        1.368167   \n",
+       "14973   0.596394 -0.702103            0.907630    -0.308620       -0.435925   \n",
+       "\n",
+       "       population  households  median_income  \n",
+       "13096    0.137460    1.394812      -0.936491  \n",
+       "14973   -0.693771   -0.373485       1.171942  "
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_housing_num_prepared.head(2)  # extra code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       " ('standardscaler', StandardScaler())]"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_pipeline.steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-3');</script></body>"
+      ],
+      "text/plain": [
+       "StandardScaler()"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_pipeline[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('steps',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
+       "            steps\n",
+       "            <span class=\"param-doc-description\">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;simpleimputer&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transform_input',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
+       "            transform_input\n",
+       "            <span class=\"param-doc-description\">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
+       "            memory\n",
+       "            <span class=\"param-doc-description\">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-4');</script></body>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median'))])"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_pipeline[:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-5');</script></body>"
+      ],
+      "text/plain": [
+       "SimpleImputer(strategy='median')"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_pipeline.named_steps[\"simpleimputer\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('steps',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
+       "            steps\n",
+       "            <span class=\"param-doc-description\">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;simpleimputer&#x27;, ...), (&#x27;standardscaler&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transform_input',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
+       "            transform_input\n",
+       "            <span class=\"param-doc-description\">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
+       "            memory\n",
+       "            <span class=\"param-doc-description\">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-6');</script></body>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler())])"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_pipeline.set_params(simpleimputer__strategy=\"median\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "\n",
+    "num_attribs = [\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\",\n",
+    "               \"total_bedrooms\", \"population\", \"households\", \"median_income\"]\n",
+    "cat_attribs = [\"ocean_proximity\"]\n",
+    "\n",
+    "cat_pipeline = make_pipeline(\n",
+    "    SimpleImputer(strategy=\"most_frequent\"),\n",
+    "    OneHotEncoder(handle_unknown=\"ignore\"))\n",
+    "\n",
+    "preprocessing = ColumnTransformer([\n",
+    "    (\"num\", num_pipeline, num_attribs),\n",
+    "    (\"cat\", cat_pipeline, cat_attribs),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import make_column_selector, make_column_transformer\n",
+    "\n",
+    "preprocessing = make_column_transformer(\n",
+    "    (num_pipeline, make_column_selector(dtype_include=np.number)),\n",
+    "    (cat_pipeline, make_column_selector(dtype_include=object)),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_prepared = preprocessing.fit_transform(housing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "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>pipeline-1__longitude</th>\n",
+       "      <th>pipeline-1__latitude</th>\n",
+       "      <th>pipeline-1__housing_median_age</th>\n",
+       "      <th>pipeline-1__total_rooms</th>\n",
+       "      <th>pipeline-1__total_bedrooms</th>\n",
+       "      <th>pipeline-1__population</th>\n",
+       "      <th>pipeline-1__households</th>\n",
+       "      <th>pipeline-1__median_income</th>\n",
+       "      <th>pipeline-2__ocean_proximity_&lt;1H OCEAN</th>\n",
+       "      <th>pipeline-2__ocean_proximity_INLAND</th>\n",
+       "      <th>pipeline-2__ocean_proximity_ISLAND</th>\n",
+       "      <th>pipeline-2__ocean_proximity_NEAR BAY</th>\n",
+       "      <th>pipeline-2__ocean_proximity_NEAR OCEAN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13096</th>\n",
+       "      <td>-1.423037</td>\n",
+       "      <td>1.013606</td>\n",
+       "      <td>1.861119</td>\n",
+       "      <td>0.311912</td>\n",
+       "      <td>1.368167</td>\n",
+       "      <td>0.137460</td>\n",
+       "      <td>1.394812</td>\n",
+       "      <td>-0.936491</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14973</th>\n",
+       "      <td>0.596394</td>\n",
+       "      <td>-0.702103</td>\n",
+       "      <td>0.907630</td>\n",
+       "      <td>-0.308620</td>\n",
+       "      <td>-0.435925</td>\n",
+       "      <td>-0.693771</td>\n",
+       "      <td>-0.373485</td>\n",
+       "      <td>1.171942</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       pipeline-1__longitude  pipeline-1__latitude  \\\n",
+       "13096              -1.423037              1.013606   \n",
+       "14973               0.596394             -0.702103   \n",
+       "\n",
+       "       pipeline-1__housing_median_age  pipeline-1__total_rooms  \\\n",
+       "13096                        1.861119                 0.311912   \n",
+       "14973                        0.907630                -0.308620   \n",
+       "\n",
+       "       pipeline-1__total_bedrooms  pipeline-1__population  \\\n",
+       "13096                    1.368167                0.137460   \n",
+       "14973                   -0.435925               -0.693771   \n",
+       "\n",
+       "       pipeline-1__households  pipeline-1__median_income  \\\n",
+       "13096                1.394812                  -0.936491   \n",
+       "14973               -0.373485                   1.171942   \n",
+       "\n",
+       "       pipeline-2__ocean_proximity_<1H OCEAN  \\\n",
+       "13096                                    0.0   \n",
+       "14973                                    1.0   \n",
+       "\n",
+       "       pipeline-2__ocean_proximity_INLAND  pipeline-2__ocean_proximity_ISLAND  \\\n",
+       "13096                                 0.0                                 0.0   \n",
+       "14973                                 0.0                                 0.0   \n",
+       "\n",
+       "       pipeline-2__ocean_proximity_NEAR BAY  \\\n",
+       "13096                                   1.0   \n",
+       "14973                                   0.0   \n",
+       "\n",
+       "       pipeline-2__ocean_proximity_NEAR OCEAN  \n",
+       "13096                                     0.0  \n",
+       "14973                                     0.0  "
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extra code – shows that we can get a DataFrame out if we want\n",
+    "housing_prepared_fr = pd.DataFrame(\n",
+    "    housing_prepared,\n",
+    "    columns=preprocessing.get_feature_names_out(),\n",
+    "    index=housing.index)\n",
+    "housing_prepared_fr.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def column_ratio(X):\n",
+    "    return X[:, [0]] / X[:, [1]]\n",
+    "\n",
+    "def ratio_name(function_transformer, feature_names_in):\n",
+    "    return [\"ratio\"]  # feature names out\n",
+    "\n",
+    "def ratio_pipeline():\n",
+    "    return make_pipeline(\n",
+    "        SimpleImputer(strategy=\"median\"),\n",
+    "        FunctionTransformer(column_ratio, feature_names_out=ratio_name),\n",
+    "        StandardScaler())\n",
+    "\n",
+    "log_pipeline = make_pipeline(\n",
+    "    SimpleImputer(strategy=\"median\"),\n",
+    "    FunctionTransformer(np.log, feature_names_out=\"one-to-one\"),\n",
+    "    StandardScaler())\n",
+    "cluster_simil = ClusterSimilarity(n_clusters=10, gamma=1., random_state=42)\n",
+    "default_num_pipeline = make_pipeline(SimpleImputer(strategy=\"median\"),\n",
+    "                                     StandardScaler())\n",
+    "preprocessing = ColumnTransformer([\n",
+    "        (\"bedrooms\", ratio_pipeline(), [\"total_bedrooms\", \"total_rooms\"]),\n",
+    "        (\"rooms_per_house\", ratio_pipeline(), [\"total_rooms\", \"households\"]),\n",
+    "        (\"people_per_house\", ratio_pipeline(), [\"population\", \"households\"]),\n",
+    "        (\"log\", log_pipeline, [\"total_bedrooms\", \"total_rooms\", \"population\",\n",
+    "                               \"households\", \"median_income\"]),\n",
+    "        (\"geo\", cluster_simil, [\"latitude\", \"longitude\"]),\n",
+    "        (\"cat\", cat_pipeline, make_column_selector(dtype_include=object)),\n",
+    "    ],\n",
+    "    remainder=default_num_pipeline)  # one column remaining: housing_median_age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(16512, 24)"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_prepared = preprocessing.fit_transform(housing)\n",
+    "housing_prepared.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['bedrooms__ratio', 'rooms_per_house__ratio',\n",
+       "       'people_per_house__ratio', 'log__total_bedrooms',\n",
+       "       'log__total_rooms', 'log__population', 'log__households',\n",
+       "       'log__median_income', 'geo__Cluster 0 similarity',\n",
+       "       'geo__Cluster 1 similarity', 'geo__Cluster 2 similarity',\n",
+       "       'geo__Cluster 3 similarity', 'geo__Cluster 4 similarity',\n",
+       "       'geo__Cluster 5 similarity', 'geo__Cluster 6 similarity',\n",
+       "       'geo__Cluster 7 similarity', 'geo__Cluster 8 similarity',\n",
+       "       'geo__Cluster 9 similarity', 'cat__ocean_proximity_<1H OCEAN',\n",
+       "       'cat__ocean_proximity_INLAND', 'cat__ocean_proximity_ISLAND',\n",
+       "       'cat__ocean_proximity_NEAR BAY', 'cat__ocean_proximity_NEAR OCEAN',\n",
+       "       'remainder__housing_median_age'], dtype=object)"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preprocessing.get_feature_names_out()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Select and Train a Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and Evaluating on the Training Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-7 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-7 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-7 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                             (&#x27;standardscaler&#x27;,\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;functiontransformer&#x27;,\n",
+       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x31a...\n",
+       "                                                   &#x27;households&#x27;,\n",
+       "                                                   &#x27;median_income&#x27;]),\n",
+       "                                                 (&#x27;geo&#x27;,\n",
+       "                                                  ClusterSimilarity(random_state=42),\n",
+       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x31aabb8c0&gt;)])),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('steps',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
+       "            steps\n",
+       "            <span class=\"param-doc-description\">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;columntransformer&#x27;, ...), (&#x27;linearregression&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transform_input',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
+       "            transform_input\n",
+       "            <span class=\"param-doc-description\">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
+       "            memory\n",
+       "            <span class=\"param-doc-description\">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>columntransformer: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformers',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples\">\n",
+       "            transformers\n",
+       "            <span class=\"param-doc-description\">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;bedrooms&#x27;, ...), (&#x27;rooms_per_house&#x27;, ...), ...]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('remainder',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27\">\n",
+       "            remainder\n",
+       "            <span class=\"param-doc-description\">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">Pipeline(step...ardScaler())])</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3\">\n",
+       "            sparse_threshold\n",
+       "            <span class=\"param-doc-description\">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformer_weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone\">\n",
+       "            transformer_weights\n",
+       "            <span class=\"param-doc-description\">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue\">\n",
+       "            verbose_feature_names_out\n",
+       "            <span class=\"param-doc-description\">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out=\"{transformer_name}__{feature_name}\"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``\"{feature_name}__{transformer_name}\"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse\">\n",
+       "            force_int_remainder_cols\n",
+       "            <span class=\"param-doc-description\">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>bedrooms</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>column_ratio</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=func,-callable%2C%20default%3DNone\">\n",
+       "            func\n",
+       "            <span class=\"param-doc-description\">func: callable, default=None<br><br>The callable to use for the transformation. This will be passed<br>the same arguments as transform, with args and kwargs forwarded.<br>If func is None, then func will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function col...t 0x31979eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inverse_func,-callable%2C%20default%3DNone\">\n",
+       "            inverse_func\n",
+       "            <span class=\"param-doc-description\">inverse_func: callable, default=None<br><br>The callable to use for the inverse transformation. This will be<br>passed the same arguments as inverse transform, with args and<br>kwargs forwarded. If inverse_func is None, then inverse_func<br>will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=validate,-bool%2C%20default%3DFalse\">\n",
+       "            validate\n",
+       "            <span class=\"param-doc-description\">validate: bool, default=False<br><br>Indicate that the input X array should be checked before calling<br>``func``. The possibilities are:<br><br>- If False, there is no input validation.<br>- If True, then X will be converted to a 2-dimensional NumPy array or<br>  sparse matrix. If the conversion is not possible an exception is<br>  raised.<br><br>.. versionchanged:: 0.22<br>   The default of ``validate`` changed from True to False.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=accept_sparse,-bool%2C%20default%3DFalse\">\n",
+       "            accept_sparse\n",
+       "            <span class=\"param-doc-description\">accept_sparse: bool, default=False<br><br>Indicate that func accepts a sparse matrix as input. If validate is<br>False, this has no effect. Otherwise, if accept_sparse is false,<br>sparse matrix inputs will cause an exception to be raised.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=check_inverse,-bool%2C%20default%3DTrue\">\n",
+       "            check_inverse\n",
+       "            <span class=\"param-doc-description\">check_inverse: bool, default=True<br><br>Whether to check that or ``func`` followed by ``inverse_func`` leads to<br>the original inputs. It can be used for a sanity check, raising a<br>warning when the condition is not fulfilled.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=feature_names_out,-callable%2C%20%27one-to-one%27%20or%20None%2C%20default%3DNone\">\n",
+       "            feature_names_out\n",
+       "            <span class=\"param-doc-description\">feature_names_out: callable, 'one-to-one' or None, default=None<br><br>Determines the list of feature names that will be returned by the<br>`get_feature_names_out` method. If it is 'one-to-one', then the output<br>feature names will be equal to the input feature names. If it is a<br>callable, then it must take two positional arguments: this<br>`FunctionTransformer` (`self`) and an array-like of input feature names<br>(`input_features`). It must return an array-like of output feature<br>names. The `get_feature_names_out` method is only defined if<br>`feature_names_out` is not None.<br><br>See ``get_feature_names_out`` for more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function rat...t 0x31aa6eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=kw_args,-dict%2C%20default%3DNone\">\n",
+       "            kw_args\n",
+       "            <span class=\"param-doc-description\">kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inv_kw_args,-dict%2C%20default%3DNone\">\n",
+       "            inv_kw_args\n",
+       "            <span class=\"param-doc-description\">inv_kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to inverse_func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rooms_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>column_ratio</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=func,-callable%2C%20default%3DNone\">\n",
+       "            func\n",
+       "            <span class=\"param-doc-description\">func: callable, default=None<br><br>The callable to use for the transformation. This will be passed<br>the same arguments as transform, with args and kwargs forwarded.<br>If func is None, then func will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function col...t 0x31979eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inverse_func,-callable%2C%20default%3DNone\">\n",
+       "            inverse_func\n",
+       "            <span class=\"param-doc-description\">inverse_func: callable, default=None<br><br>The callable to use for the inverse transformation. This will be<br>passed the same arguments as inverse transform, with args and<br>kwargs forwarded. If inverse_func is None, then inverse_func<br>will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=validate,-bool%2C%20default%3DFalse\">\n",
+       "            validate\n",
+       "            <span class=\"param-doc-description\">validate: bool, default=False<br><br>Indicate that the input X array should be checked before calling<br>``func``. The possibilities are:<br><br>- If False, there is no input validation.<br>- If True, then X will be converted to a 2-dimensional NumPy array or<br>  sparse matrix. If the conversion is not possible an exception is<br>  raised.<br><br>.. versionchanged:: 0.22<br>   The default of ``validate`` changed from True to False.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=accept_sparse,-bool%2C%20default%3DFalse\">\n",
+       "            accept_sparse\n",
+       "            <span class=\"param-doc-description\">accept_sparse: bool, default=False<br><br>Indicate that func accepts a sparse matrix as input. If validate is<br>False, this has no effect. Otherwise, if accept_sparse is false,<br>sparse matrix inputs will cause an exception to be raised.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=check_inverse,-bool%2C%20default%3DTrue\">\n",
+       "            check_inverse\n",
+       "            <span class=\"param-doc-description\">check_inverse: bool, default=True<br><br>Whether to check that or ``func`` followed by ``inverse_func`` leads to<br>the original inputs. It can be used for a sanity check, raising a<br>warning when the condition is not fulfilled.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=feature_names_out,-callable%2C%20%27one-to-one%27%20or%20None%2C%20default%3DNone\">\n",
+       "            feature_names_out\n",
+       "            <span class=\"param-doc-description\">feature_names_out: callable, 'one-to-one' or None, default=None<br><br>Determines the list of feature names that will be returned by the<br>`get_feature_names_out` method. If it is 'one-to-one', then the output<br>feature names will be equal to the input feature names. If it is a<br>callable, then it must take two positional arguments: this<br>`FunctionTransformer` (`self`) and an array-like of input feature names<br>(`input_features`). It must return an array-like of output feature<br>names. The `get_feature_names_out` method is only defined if<br>`feature_names_out` is not None.<br><br>See ``get_feature_names_out`` for more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function rat...t 0x31aa6eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=kw_args,-dict%2C%20default%3DNone\">\n",
+       "            kw_args\n",
+       "            <span class=\"param-doc-description\">kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inv_kw_args,-dict%2C%20default%3DNone\">\n",
+       "            inv_kw_args\n",
+       "            <span class=\"param-doc-description\">inv_kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to inverse_func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>people_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>column_ratio</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=func,-callable%2C%20default%3DNone\">\n",
+       "            func\n",
+       "            <span class=\"param-doc-description\">func: callable, default=None<br><br>The callable to use for the transformation. This will be passed<br>the same arguments as transform, with args and kwargs forwarded.<br>If func is None, then func will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function col...t 0x31979eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inverse_func,-callable%2C%20default%3DNone\">\n",
+       "            inverse_func\n",
+       "            <span class=\"param-doc-description\">inverse_func: callable, default=None<br><br>The callable to use for the inverse transformation. This will be<br>passed the same arguments as inverse transform, with args and<br>kwargs forwarded. If inverse_func is None, then inverse_func<br>will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=validate,-bool%2C%20default%3DFalse\">\n",
+       "            validate\n",
+       "            <span class=\"param-doc-description\">validate: bool, default=False<br><br>Indicate that the input X array should be checked before calling<br>``func``. The possibilities are:<br><br>- If False, there is no input validation.<br>- If True, then X will be converted to a 2-dimensional NumPy array or<br>  sparse matrix. If the conversion is not possible an exception is<br>  raised.<br><br>.. versionchanged:: 0.22<br>   The default of ``validate`` changed from True to False.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=accept_sparse,-bool%2C%20default%3DFalse\">\n",
+       "            accept_sparse\n",
+       "            <span class=\"param-doc-description\">accept_sparse: bool, default=False<br><br>Indicate that func accepts a sparse matrix as input. If validate is<br>False, this has no effect. Otherwise, if accept_sparse is false,<br>sparse matrix inputs will cause an exception to be raised.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=check_inverse,-bool%2C%20default%3DTrue\">\n",
+       "            check_inverse\n",
+       "            <span class=\"param-doc-description\">check_inverse: bool, default=True<br><br>Whether to check that or ``func`` followed by ``inverse_func`` leads to<br>the original inputs. It can be used for a sanity check, raising a<br>warning when the condition is not fulfilled.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=feature_names_out,-callable%2C%20%27one-to-one%27%20or%20None%2C%20default%3DNone\">\n",
+       "            feature_names_out\n",
+       "            <span class=\"param-doc-description\">feature_names_out: callable, 'one-to-one' or None, default=None<br><br>Determines the list of feature names that will be returned by the<br>`get_feature_names_out` method. If it is 'one-to-one', then the output<br>feature names will be equal to the input feature names. If it is a<br>callable, then it must take two positional arguments: this<br>`FunctionTransformer` (`self`) and an array-like of input feature names<br>(`input_features`). It must return an array-like of output feature<br>names. The `get_feature_names_out` method is only defined if<br>`feature_names_out` is not None.<br><br>See ``get_feature_names_out`` for more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function rat...t 0x31aa6eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=kw_args,-dict%2C%20default%3DNone\">\n",
+       "            kw_args\n",
+       "            <span class=\"param-doc-description\">kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inv_kw_args,-dict%2C%20default%3DNone\">\n",
+       "            inv_kw_args\n",
+       "            <span class=\"param-doc-description\">inv_kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to inverse_func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=func,-callable%2C%20default%3DNone\">\n",
+       "            func\n",
+       "            <span class=\"param-doc-description\">func: callable, default=None<br><br>The callable to use for the transformation. This will be passed<br>the same arguments as transform, with args and kwargs forwarded.<br>If func is None, then func will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;ufunc &#x27;log&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inverse_func,-callable%2C%20default%3DNone\">\n",
+       "            inverse_func\n",
+       "            <span class=\"param-doc-description\">inverse_func: callable, default=None<br><br>The callable to use for the inverse transformation. This will be<br>passed the same arguments as inverse transform, with args and<br>kwargs forwarded. If inverse_func is None, then inverse_func<br>will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=validate,-bool%2C%20default%3DFalse\">\n",
+       "            validate\n",
+       "            <span class=\"param-doc-description\">validate: bool, default=False<br><br>Indicate that the input X array should be checked before calling<br>``func``. The possibilities are:<br><br>- If False, there is no input validation.<br>- If True, then X will be converted to a 2-dimensional NumPy array or<br>  sparse matrix. If the conversion is not possible an exception is<br>  raised.<br><br>.. versionchanged:: 0.22<br>   The default of ``validate`` changed from True to False.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=accept_sparse,-bool%2C%20default%3DFalse\">\n",
+       "            accept_sparse\n",
+       "            <span class=\"param-doc-description\">accept_sparse: bool, default=False<br><br>Indicate that func accepts a sparse matrix as input. If validate is<br>False, this has no effect. Otherwise, if accept_sparse is false,<br>sparse matrix inputs will cause an exception to be raised.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=check_inverse,-bool%2C%20default%3DTrue\">\n",
+       "            check_inverse\n",
+       "            <span class=\"param-doc-description\">check_inverse: bool, default=True<br><br>Whether to check that or ``func`` followed by ``inverse_func`` leads to<br>the original inputs. It can be used for a sanity check, raising a<br>warning when the condition is not fulfilled.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=feature_names_out,-callable%2C%20%27one-to-one%27%20or%20None%2C%20default%3DNone\">\n",
+       "            feature_names_out\n",
+       "            <span class=\"param-doc-description\">feature_names_out: callable, 'one-to-one' or None, default=None<br><br>Determines the list of feature names that will be returned by the<br>`get_feature_names_out` method. If it is 'one-to-one', then the output<br>feature names will be equal to the input feature names. If it is a<br>callable, then it must take two positional arguments: this<br>`FunctionTransformer` (`self`) and an array-like of input feature names<br>(`input_features`). It must return an array-like of output feature<br>names. The `get_feature_names_out` method is only defined if<br>`feature_names_out` is not None.<br><br>See ``get_feature_names_out`` for more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;one-to-one&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=kw_args,-dict%2C%20default%3DNone\">\n",
+       "            kw_args\n",
+       "            <span class=\"param-doc-description\">kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inv_kw_args,-dict%2C%20default%3DNone\">\n",
+       "            inv_kw_args\n",
+       "            <span class=\"param-doc-description\">inv_kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to inverse_func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>geo</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__geo__\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ClusterSimilarity</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__geo__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_clusters',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_clusters</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">gamma</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state</td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x31aabb8c0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;most_frequent&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__onehotencoder__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27\">\n",
+       "            categories\n",
+       "            <span class=\"param-doc-description\">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('drop',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            drop\n",
+       "            <span class=\"param-doc-description\">drop: {'first', 'if_binary'} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- 'first' : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- 'if_binary' : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop='if_binary'` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_output',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue\">\n",
+       "            sparse_output\n",
+       "            <span class=\"param-doc-description\">sparse_output: bool, default=True<br><br>When ``True``, it returns a :class:`scipy.sparse.csr_matrix`,<br>i.e. a sparse matrix in \"Compressed Sparse Row\" (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dtype',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64\">\n",
+       "            dtype\n",
+       "            <span class=\"param-doc-description\">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;class &#x27;numpy.float64&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('handle_unknown',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27\">\n",
+       "            handle_unknown\n",
+       "            <span class=\"param-doc-description\">handle_unknown: {'error', 'ignore', 'infrequent_if_exist', 'warn'},                      default='error'<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- 'error' : Raise an error if an unknown category is present during transform.<br>- 'ignore' : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- 'infrequent_if_exist' : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `'infrequent'` if it exists. If the<br>  `'infrequent'` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown='ignore'`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide <encoder_infrequent_categories>`.<br>- 'warn' : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown=\"infrequent_if_exist\"`.<br><br>.. versionchanged:: 1.1<br>    `'infrequent_if_exist'` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `\"warn\"` was added in 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;ignore&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_frequency',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            min_frequency\n",
+       "            <span class=\"param-doc-description\">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone\">\n",
+       "            max_categories\n",
+       "            <span class=\"param-doc-description\">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_name_combiner',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22\">\n",
+       "            feature_name_combiner\n",
+       "            <span class=\"param-doc-description\">feature_name_combiner: \"concat\" or callable, default=\"concat\"<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`\"concat\"` concatenates encoded feature name and category with<br>`feature + \"_\" + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"linearregression__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_intercept',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
+       "            fit_intercept\n",
+       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy_X',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
+       "            copy_X\n",
+       "            <span class=\"param-doc-description\">copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('tol',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
+       "            tol\n",
+       "            <span class=\"param-doc-description\">tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies a different convergence criterion for the `lsqr` solver.<br>`tol` is set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. This parameter has no effect when fitting<br>on dense data.<br><br>.. versionadded:: 1.7</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1e-06</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets > 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('positive',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
+       "            positive\n",
+       "            <span class=\"param-doc-description\">positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-7');</script></body>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+       "                                                              SimpleImputer(strategy='median')),\n",
+       "                                                             ('standardscaler',\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[('bedrooms',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='median')),\n",
+       "                                                                  ('functiontransformer',\n",
+       "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x31a...\n",
+       "                                                   'households',\n",
+       "                                                   'median_income']),\n",
+       "                                                 ('geo',\n",
+       "                                                  ClusterSimilarity(random_state=42),\n",
+       "                                                  ['latitude', 'longitude']),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+       "                                                                  ('onehotencoder',\n",
+       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
+       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x31aabb8c0>)])),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "lin_reg = make_pipeline(preprocessing, LinearRegression())\n",
+    "lin_reg.fit(housing, housing_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try the full preprocessing pipeline on a few training instances:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([246000., 372700., 135700.,  91400., 330900.])"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_predictions = lin_reg.predict(housing)\n",
+    "housing_predictions[:5].round(-2)  # -2 = rounded to the nearest hundred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare against the actual values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([458300., 483800., 101700.,  96100., 361800.])"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_labels.iloc[:5].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-46.3%, -23.0%, 33.4%, -4.9%, -8.5%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# extra code – computes the error ratios discussed in the book\n",
+    "error_ratios = housing_predictions[:5].round(-2) / housing_labels.iloc[:5].values - 1\n",
+    "print(\", \".join([f\"{100 * ratio:.1f}%\" for ratio in error_ratios]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "68972.88910758459"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import root_mean_squared_error\n",
+    "\n",
+    "lin_rmse = root_mean_squared_error(housing_labels, housing_predictions)\n",
+    "lin_rmse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-8 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-8 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-8 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                             (&#x27;standardscaler&#x27;,\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;functiontransformer&#x27;,\n",
+       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x31a...\n",
+       "                                                 (&#x27;geo&#x27;,\n",
+       "                                                  ClusterSimilarity(random_state=42),\n",
+       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x31aabb8c0&gt;)])),\n",
+       "                (&#x27;decisiontreeregressor&#x27;,\n",
+       "                 DecisionTreeRegressor(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('steps',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
+       "            steps\n",
+       "            <span class=\"param-doc-description\">steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators <combining_estimators>` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;columntransformer&#x27;, ...), (&#x27;decisiontreeregressor&#x27;, ...)]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transform_input',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
+       "            transform_input\n",
+       "            <span class=\"param-doc-description\">transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
+       "            memory\n",
+       "            <span class=\"param-doc-description\">memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>columntransformer: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for columntransformer: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformers',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformers,-list%20of%20tuples\">\n",
+       "            transformers\n",
+       "            <span class=\"param-doc-description\">transformers: list of tuples<br><br>List of (name, transformer, columns) tuples specifying the<br>transformer objects to be applied to subsets of the data.<br><br>name : str<br>    Like in Pipeline and FeatureUnion, this allows the transformer and<br>    its parameters to be set using ``set_params`` and searched in grid<br>    search.<br>transformer : {'drop', 'passthrough'} or estimator<br>    Estimator must support :term:`fit` and :term:`transform`.<br>    Special-cased strings 'drop' and 'passthrough' are accepted as<br>    well, to indicate to drop the columns or to pass them through<br>    untransformed, respectively.<br>columns :  str, array-like of str, int, array-like of int,                 array-like of bool, slice or callable<br>    Indexes the data on its second axis. Integers are interpreted as<br>    positional columns, while strings can reference DataFrame columns<br>    by name.  A scalar string or int should be used where<br>    ``transformer`` expects X to be a 1d array-like (vector),<br>    otherwise a 2d array will be passed to the transformer.<br>    A callable is passed the input data `X` and can return any of the<br>    above. To select multiple columns by name or dtype, you can use<br>    :obj:`make_column_selector`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">[(&#x27;bedrooms&#x27;, ...), (&#x27;rooms_per_house&#x27;, ...), ...]</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('remainder',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=remainder,-%7B%27drop%27%2C%20%27passthrough%27%7D%20or%20estimator%2C%20default%3D%27drop%27\">\n",
+       "            remainder\n",
+       "            <span class=\"param-doc-description\">remainder: {'drop', 'passthrough'} or estimator, default='drop'<br><br>By default, only the specified columns in `transformers` are<br>transformed and combined in the output, and the non-specified<br>columns are dropped. (default of ``'drop'``).<br>By specifying ``remainder='passthrough'``, all remaining columns that<br>were not specified in `transformers`, but present in the data passed<br>to `fit` will be automatically passed through. This subset of columns<br>is concatenated with the output of the transformers. For dataframes,<br>extra columns not seen during `fit` will be excluded from the output<br>of `transform`.<br>By setting ``remainder`` to be an estimator, the remaining<br>non-specified columns will use the ``remainder`` estimator. The<br>estimator must support :term:`fit` and :term:`transform`.<br>Note that using this feature requires that the DataFrame columns<br>input at :term:`fit` and :term:`transform` have identical order.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">Pipeline(step...ardScaler())])</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=sparse_threshold,-float%2C%20default%3D0.3\">\n",
+       "            sparse_threshold\n",
+       "            <span class=\"param-doc-description\">sparse_threshold: float, default=0.3<br><br>If the output of the different transformers contains sparse matrices,<br>these will be stacked as a sparse matrix if the overall density is<br>lower than this value. Use ``sparse_threshold=0`` to always return<br>dense.  When the transformed output consists of all dense data, the<br>stacked result will be dense, and this keyword will be ignored.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.3</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Number of jobs to run in parallel.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary <n_jobs>`<br>for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('transformer_weights',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=transformer_weights,-dict%2C%20default%3DNone\">\n",
+       "            transformer_weights\n",
+       "            <span class=\"param-doc-description\">transformer_weights: dict, default=None<br><br>Multiplicative weights for features per transformer. The output of the<br>transformer is multiplied by these weights. Keys are transformer names,<br>values the weights.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\">verbose: bool, default=False<br><br>If True, the time elapsed while fitting each transformer will be<br>printed as it is completed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=verbose_feature_names_out,-bool%2C%20str%20or%20Callable%5B%5Bstr%2C%20str%5D%2C%20str%5D%2C%20default%3DTrue\">\n",
+       "            verbose_feature_names_out\n",
+       "            <span class=\"param-doc-description\">verbose_feature_names_out: bool, str or Callable[[str, str], str], default=True<br><br>- If True, :meth:`ColumnTransformer.get_feature_names_out` will prefix<br>  all feature names with the name of the transformer that generated that<br>  feature. It is equivalent to setting<br>  `verbose_feature_names_out=\"{transformer_name}__{feature_name}\"`.<br>- If False, :meth:`ColumnTransformer.get_feature_names_out` will not<br>  prefix any feature names and will error if feature names are not<br>  unique.<br>- If ``Callable[[str, str], str]``,<br>  :meth:`ColumnTransformer.get_feature_names_out` will rename all the features<br>  using the name of the transformer. The first argument of the callable is the<br>  transformer name and the second argument is the feature name. The returned<br>  string will be the new feature name.<br>- If ``str``, it must be a string ready for formatting. The given string will<br>  be formatted using two field names: ``transformer_name`` and ``feature_name``.<br>  e.g. ``\"{feature_name}__{transformer_name}\"``. See :meth:`str.format` method<br>  from the standard library for more info.<br><br>.. versionadded:: 1.0<br><br>.. versionchanged:: 1.6<br>    `verbose_feature_names_out` can be a callable or a string to be formatted.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.compose.ColumnTransformer.html#:~:text=force_int_remainder_cols,-bool%2C%20default%3DFalse\">\n",
+       "            force_int_remainder_cols\n",
+       "            <span class=\"param-doc-description\">force_int_remainder_cols: bool, default=False<br><br>This parameter has no effect.<br><br>.. note::<br>    If you do not access the list of columns for the remainder columns<br>    in the `transformers_` fitted attribute, you do not need to set<br>    this parameter.<br><br>.. versionadded:: 1.5<br><br>.. versionchanged:: 1.7<br>   The default value for `force_int_remainder_cols` will change from<br>   `True` to `False` in version 1.7.<br><br>.. deprecated:: 1.7<br>   `force_int_remainder_cols` is deprecated and will be removed in 1.9.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>bedrooms</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>column_ratio</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=func,-callable%2C%20default%3DNone\">\n",
+       "            func\n",
+       "            <span class=\"param-doc-description\">func: callable, default=None<br><br>The callable to use for the transformation. This will be passed<br>the same arguments as transform, with args and kwargs forwarded.<br>If func is None, then func will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function col...t 0x31979eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inverse_func,-callable%2C%20default%3DNone\">\n",
+       "            inverse_func\n",
+       "            <span class=\"param-doc-description\">inverse_func: callable, default=None<br><br>The callable to use for the inverse transformation. This will be<br>passed the same arguments as inverse transform, with args and<br>kwargs forwarded. If inverse_func is None, then inverse_func<br>will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=validate,-bool%2C%20default%3DFalse\">\n",
+       "            validate\n",
+       "            <span class=\"param-doc-description\">validate: bool, default=False<br><br>Indicate that the input X array should be checked before calling<br>``func``. The possibilities are:<br><br>- If False, there is no input validation.<br>- If True, then X will be converted to a 2-dimensional NumPy array or<br>  sparse matrix. If the conversion is not possible an exception is<br>  raised.<br><br>.. versionchanged:: 0.22<br>   The default of ``validate`` changed from True to False.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=accept_sparse,-bool%2C%20default%3DFalse\">\n",
+       "            accept_sparse\n",
+       "            <span class=\"param-doc-description\">accept_sparse: bool, default=False<br><br>Indicate that func accepts a sparse matrix as input. If validate is<br>False, this has no effect. Otherwise, if accept_sparse is false,<br>sparse matrix inputs will cause an exception to be raised.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=check_inverse,-bool%2C%20default%3DTrue\">\n",
+       "            check_inverse\n",
+       "            <span class=\"param-doc-description\">check_inverse: bool, default=True<br><br>Whether to check that or ``func`` followed by ``inverse_func`` leads to<br>the original inputs. It can be used for a sanity check, raising a<br>warning when the condition is not fulfilled.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=feature_names_out,-callable%2C%20%27one-to-one%27%20or%20None%2C%20default%3DNone\">\n",
+       "            feature_names_out\n",
+       "            <span class=\"param-doc-description\">feature_names_out: callable, 'one-to-one' or None, default=None<br><br>Determines the list of feature names that will be returned by the<br>`get_feature_names_out` method. If it is 'one-to-one', then the output<br>feature names will be equal to the input feature names. If it is a<br>callable, then it must take two positional arguments: this<br>`FunctionTransformer` (`self`) and an array-like of input feature names<br>(`input_features`). It must return an array-like of output feature<br>names. The `get_feature_names_out` method is only defined if<br>`feature_names_out` is not None.<br><br>See ``get_feature_names_out`` for more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function rat...t 0x31aa6eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=kw_args,-dict%2C%20default%3DNone\">\n",
+       "            kw_args\n",
+       "            <span class=\"param-doc-description\">kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inv_kw_args,-dict%2C%20default%3DNone\">\n",
+       "            inv_kw_args\n",
+       "            <span class=\"param-doc-description\">inv_kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to inverse_func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__bedrooms__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>rooms_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>column_ratio</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=func,-callable%2C%20default%3DNone\">\n",
+       "            func\n",
+       "            <span class=\"param-doc-description\">func: callable, default=None<br><br>The callable to use for the transformation. This will be passed<br>the same arguments as transform, with args and kwargs forwarded.<br>If func is None, then func will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function col...t 0x31979eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inverse_func,-callable%2C%20default%3DNone\">\n",
+       "            inverse_func\n",
+       "            <span class=\"param-doc-description\">inverse_func: callable, default=None<br><br>The callable to use for the inverse transformation. This will be<br>passed the same arguments as inverse transform, with args and<br>kwargs forwarded. If inverse_func is None, then inverse_func<br>will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=validate,-bool%2C%20default%3DFalse\">\n",
+       "            validate\n",
+       "            <span class=\"param-doc-description\">validate: bool, default=False<br><br>Indicate that the input X array should be checked before calling<br>``func``. The possibilities are:<br><br>- If False, there is no input validation.<br>- If True, then X will be converted to a 2-dimensional NumPy array or<br>  sparse matrix. If the conversion is not possible an exception is<br>  raised.<br><br>.. versionchanged:: 0.22<br>   The default of ``validate`` changed from True to False.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=accept_sparse,-bool%2C%20default%3DFalse\">\n",
+       "            accept_sparse\n",
+       "            <span class=\"param-doc-description\">accept_sparse: bool, default=False<br><br>Indicate that func accepts a sparse matrix as input. If validate is<br>False, this has no effect. Otherwise, if accept_sparse is false,<br>sparse matrix inputs will cause an exception to be raised.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=check_inverse,-bool%2C%20default%3DTrue\">\n",
+       "            check_inverse\n",
+       "            <span class=\"param-doc-description\">check_inverse: bool, default=True<br><br>Whether to check that or ``func`` followed by ``inverse_func`` leads to<br>the original inputs. It can be used for a sanity check, raising a<br>warning when the condition is not fulfilled.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=feature_names_out,-callable%2C%20%27one-to-one%27%20or%20None%2C%20default%3DNone\">\n",
+       "            feature_names_out\n",
+       "            <span class=\"param-doc-description\">feature_names_out: callable, 'one-to-one' or None, default=None<br><br>Determines the list of feature names that will be returned by the<br>`get_feature_names_out` method. If it is 'one-to-one', then the output<br>feature names will be equal to the input feature names. If it is a<br>callable, then it must take two positional arguments: this<br>`FunctionTransformer` (`self`) and an array-like of input feature names<br>(`input_features`). It must return an array-like of output feature<br>names. The `get_feature_names_out` method is only defined if<br>`feature_names_out` is not None.<br><br>See ``get_feature_names_out`` for more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function rat...t 0x31aa6eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=kw_args,-dict%2C%20default%3DNone\">\n",
+       "            kw_args\n",
+       "            <span class=\"param-doc-description\">kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inv_kw_args,-dict%2C%20default%3DNone\">\n",
+       "            inv_kw_args\n",
+       "            <span class=\"param-doc-description\">inv_kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to inverse_func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__rooms_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>people_per_house</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>column_ratio</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=func,-callable%2C%20default%3DNone\">\n",
+       "            func\n",
+       "            <span class=\"param-doc-description\">func: callable, default=None<br><br>The callable to use for the transformation. This will be passed<br>the same arguments as transform, with args and kwargs forwarded.<br>If func is None, then func will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function col...t 0x31979eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inverse_func,-callable%2C%20default%3DNone\">\n",
+       "            inverse_func\n",
+       "            <span class=\"param-doc-description\">inverse_func: callable, default=None<br><br>The callable to use for the inverse transformation. This will be<br>passed the same arguments as inverse transform, with args and<br>kwargs forwarded. If inverse_func is None, then inverse_func<br>will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=validate,-bool%2C%20default%3DFalse\">\n",
+       "            validate\n",
+       "            <span class=\"param-doc-description\">validate: bool, default=False<br><br>Indicate that the input X array should be checked before calling<br>``func``. The possibilities are:<br><br>- If False, there is no input validation.<br>- If True, then X will be converted to a 2-dimensional NumPy array or<br>  sparse matrix. If the conversion is not possible an exception is<br>  raised.<br><br>.. versionchanged:: 0.22<br>   The default of ``validate`` changed from True to False.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=accept_sparse,-bool%2C%20default%3DFalse\">\n",
+       "            accept_sparse\n",
+       "            <span class=\"param-doc-description\">accept_sparse: bool, default=False<br><br>Indicate that func accepts a sparse matrix as input. If validate is<br>False, this has no effect. Otherwise, if accept_sparse is false,<br>sparse matrix inputs will cause an exception to be raised.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=check_inverse,-bool%2C%20default%3DTrue\">\n",
+       "            check_inverse\n",
+       "            <span class=\"param-doc-description\">check_inverse: bool, default=True<br><br>Whether to check that or ``func`` followed by ``inverse_func`` leads to<br>the original inputs. It can be used for a sanity check, raising a<br>warning when the condition is not fulfilled.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=feature_names_out,-callable%2C%20%27one-to-one%27%20or%20None%2C%20default%3DNone\">\n",
+       "            feature_names_out\n",
+       "            <span class=\"param-doc-description\">feature_names_out: callable, 'one-to-one' or None, default=None<br><br>Determines the list of feature names that will be returned by the<br>`get_feature_names_out` method. If it is 'one-to-one', then the output<br>feature names will be equal to the input feature names. If it is a<br>callable, then it must take two positional arguments: this<br>`FunctionTransformer` (`self`) and an array-like of input feature names<br>(`input_features`). It must return an array-like of output feature<br>names. The `get_feature_names_out` method is only defined if<br>`feature_names_out` is not None.<br><br>See ``get_feature_names_out`` for more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;function rat...t 0x31aa6eb60&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=kw_args,-dict%2C%20default%3DNone\">\n",
+       "            kw_args\n",
+       "            <span class=\"param-doc-description\">kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inv_kw_args,-dict%2C%20default%3DNone\">\n",
+       "            inv_kw_args\n",
+       "            <span class=\"param-doc-description\">inv_kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to inverse_func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__people_per_house__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>log</div><div class=\"caption\">FunctionTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html\">?<span>Documentation for FunctionTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__functiontransformer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=func,-callable%2C%20default%3DNone\">\n",
+       "            func\n",
+       "            <span class=\"param-doc-description\">func: callable, default=None<br><br>The callable to use for the transformation. This will be passed<br>the same arguments as transform, with args and kwargs forwarded.<br>If func is None, then func will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;ufunc &#x27;log&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inverse_func',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inverse_func,-callable%2C%20default%3DNone\">\n",
+       "            inverse_func\n",
+       "            <span class=\"param-doc-description\">inverse_func: callable, default=None<br><br>The callable to use for the inverse transformation. This will be<br>passed the same arguments as inverse transform, with args and<br>kwargs forwarded. If inverse_func is None, then inverse_func<br>will be the identity function.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('validate',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=validate,-bool%2C%20default%3DFalse\">\n",
+       "            validate\n",
+       "            <span class=\"param-doc-description\">validate: bool, default=False<br><br>Indicate that the input X array should be checked before calling<br>``func``. The possibilities are:<br><br>- If False, there is no input validation.<br>- If True, then X will be converted to a 2-dimensional NumPy array or<br>  sparse matrix. If the conversion is not possible an exception is<br>  raised.<br><br>.. versionchanged:: 0.22<br>   The default of ``validate`` changed from True to False.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('accept_sparse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=accept_sparse,-bool%2C%20default%3DFalse\">\n",
+       "            accept_sparse\n",
+       "            <span class=\"param-doc-description\">accept_sparse: bool, default=False<br><br>Indicate that func accepts a sparse matrix as input. If validate is<br>False, this has no effect. Otherwise, if accept_sparse is false,<br>sparse matrix inputs will cause an exception to be raised.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('check_inverse',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=check_inverse,-bool%2C%20default%3DTrue\">\n",
+       "            check_inverse\n",
+       "            <span class=\"param-doc-description\">check_inverse: bool, default=True<br><br>Whether to check that or ``func`` followed by ``inverse_func`` leads to<br>the original inputs. It can be used for a sanity check, raising a<br>warning when the condition is not fulfilled.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_names_out',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=feature_names_out,-callable%2C%20%27one-to-one%27%20or%20None%2C%20default%3DNone\">\n",
+       "            feature_names_out\n",
+       "            <span class=\"param-doc-description\">feature_names_out: callable, 'one-to-one' or None, default=None<br><br>Determines the list of feature names that will be returned by the<br>`get_feature_names_out` method. If it is 'one-to-one', then the output<br>feature names will be equal to the input feature names. If it is a<br>callable, then it must take two positional arguments: this<br>`FunctionTransformer` (`self`) and an array-like of input feature names<br>(`input_features`). It must return an array-like of output feature<br>names. The `get_feature_names_out` method is only defined if<br>`feature_names_out` is not None.<br><br>See ``get_feature_names_out`` for more details.<br><br>.. versionadded:: 1.1</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;one-to-one&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=kw_args,-dict%2C%20default%3DNone\">\n",
+       "            kw_args\n",
+       "            <span class=\"param-doc-description\">kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inv_kw_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.FunctionTransformer.html#:~:text=inv_kw_args,-dict%2C%20default%3DNone\">\n",
+       "            inv_kw_args\n",
+       "            <span class=\"param-doc-description\">inv_kw_args: dict, default=None<br><br>Dictionary of additional keyword arguments to pass to inverse_func.<br><br>.. versionadded:: 0.18</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__log__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>geo</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__geo__\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>ClusterSimilarity</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__geo__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_clusters',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_clusters</td>\n",
+       "            <td class=\"value\">10</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('gamma',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">gamma</td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state</td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x31aabb8c0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;most_frequent&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__cat__onehotencoder__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=categories,-%27auto%27%20or%20a%20list%20of%20array-like%2C%20default%3D%27auto%27\">\n",
+       "            categories\n",
+       "            <span class=\"param-doc-description\">categories: 'auto' or a list of array-like, default='auto'<br><br>Categories (unique values) per feature:<br><br>- 'auto' : Determine categories automatically from the training data.<br>- list : ``categories[i]`` holds the categories expected in the ith<br>  column. The passed categories should not mix strings and numeric<br>  values within a single feature, and should be sorted in case of<br>  numeric values.<br><br>The used categories can be found in the ``categories_`` attribute.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('drop',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=drop,-%7B%27first%27%2C%20%27if_binary%27%7D%20or%20an%20array-like%20of%20shape%20%28n_features%2C%29%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
+       "            drop\n",
+       "            <span class=\"param-doc-description\">drop: {'first', 'if_binary'} or an array-like of shape (n_features,),             default=None<br><br>Specifies a methodology to use to drop one of the categories per<br>feature. This is useful in situations where perfectly collinear<br>features cause problems, such as when feeding the resulting data<br>into an unregularized linear regression model.<br><br>However, dropping one category breaks the symmetry of the original<br>representation and can therefore induce a bias in downstream models,<br>for instance for penalized linear classification or regression models.<br><br>- None : retain all features (the default).<br>- 'first' : drop the first category in each feature. If only one<br>  category is present, the feature will be dropped entirely.<br>- 'if_binary' : drop the first category in each feature with two<br>  categories. Features with 1 or more than 2 categories are<br>  left intact.<br>- array : ``drop[i]`` is the category in feature ``X[:, i]`` that<br>  should be dropped.<br><br>When `max_categories` or `min_frequency` is configured to group<br>infrequent categories, the dropping behavior is handled after the<br>grouping.<br><br>.. versionadded:: 0.21<br>   The parameter `drop` was added in 0.21.<br><br>.. versionchanged:: 0.23<br>   The option `drop='if_binary'` was added in 0.23.<br><br>.. versionchanged:: 1.1<br>    Support for dropping infrequent categories.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('sparse_output',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=sparse_output,-bool%2C%20default%3DTrue\">\n",
+       "            sparse_output\n",
+       "            <span class=\"param-doc-description\">sparse_output: bool, default=True<br><br>When ``True``, it returns a :class:`scipy.sparse.csr_matrix`,<br>i.e. a sparse matrix in \"Compressed Sparse Row\" (CSR) format.<br><br>.. versionadded:: 1.2<br>   `sparse` was renamed to `sparse_output`</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('dtype',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=dtype,-number%20type%2C%20default%3Dnp.float64\">\n",
+       "            dtype\n",
+       "            <span class=\"param-doc-description\">dtype: number type, default=np.float64<br><br>Desired dtype of output.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&lt;class &#x27;numpy.float64&#x27;&gt;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('handle_unknown',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=handle_unknown,-%7B%27error%27%2C%20%27ignore%27%2C%20%27infrequent_if_exist%27%2C%20%27warn%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27error%27\">\n",
+       "            handle_unknown\n",
+       "            <span class=\"param-doc-description\">handle_unknown: {'error', 'ignore', 'infrequent_if_exist', 'warn'},                      default='error'<br><br>Specifies the way unknown categories are handled during :meth:`transform`.<br><br>- 'error' : Raise an error if an unknown category is present during transform.<br>- 'ignore' : When an unknown category is encountered during<br>  transform, the resulting one-hot encoded columns for this feature<br>  will be all zeros. In the inverse transform, an unknown category<br>  will be denoted as None.<br>- 'infrequent_if_exist' : When an unknown category is encountered<br>  during transform, the resulting one-hot encoded columns for this<br>  feature will map to the infrequent category if it exists. The<br>  infrequent category will be mapped to the last position in the<br>  encoding. During inverse transform, an unknown category will be<br>  mapped to the category denoted `'infrequent'` if it exists. If the<br>  `'infrequent'` category does not exist, then :meth:`transform` and<br>  :meth:`inverse_transform` will handle an unknown category as with<br>  `handle_unknown='ignore'`. Infrequent categories exist based on<br>  `min_frequency` and `max_categories`. Read more in the<br>  :ref:`User Guide <encoder_infrequent_categories>`.<br>- 'warn' : When an unknown category is encountered during transform<br>  a warning is issued, and the encoding then proceeds as described for<br>  `handle_unknown=\"infrequent_if_exist\"`.<br><br>.. versionchanged:: 1.1<br>    `'infrequent_if_exist'` was added to automatically handle unknown<br>    categories and infrequent categories.<br><br>.. versionadded:: 1.6<br>   The option `\"warn\"` was added in 1.6.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;ignore&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_frequency',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=min_frequency,-int%20or%20float%2C%20default%3DNone\">\n",
+       "            min_frequency\n",
+       "            <span class=\"param-doc-description\">min_frequency: int or float, default=None<br><br>Specifies the minimum frequency below which a category will be<br>considered infrequent.<br><br>- If `int`, categories with a smaller cardinality will be considered<br>  infrequent.<br><br>- If `float`, categories with a smaller cardinality than<br>  `min_frequency * n_samples`  will be considered infrequent.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_categories',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=max_categories,-int%2C%20default%3DNone\">\n",
+       "            max_categories\n",
+       "            <span class=\"param-doc-description\">max_categories: int, default=None<br><br>Specifies an upper limit to the number of output features for each input<br>feature when considering infrequent categories. If there are infrequent<br>categories, `max_categories` includes the category representing the<br>infrequent categories along with the frequent categories. If `None`,<br>there is no limit to the number of output features.<br><br>.. versionadded:: 1.1<br>    Read more in the :ref:`User Guide <encoder_infrequent_categories>`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('feature_name_combiner',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.OneHotEncoder.html#:~:text=feature_name_combiner,-%22concat%22%20or%20callable%2C%20default%3D%22concat%22\">\n",
+       "            feature_name_combiner\n",
+       "            <span class=\"param-doc-description\">feature_name_combiner: \"concat\" or callable, default=\"concat\"<br><br>Callable with signature `def callable(input_feature, category)` that returns a<br>string. This is used to create feature names to be returned by<br>:meth:`get_feature_names_out`.<br><br>`\"concat\"` concatenates encoded feature name and category with<br>`feature + \"_\" + str(category)`.E.g. feature X with values 1, 6, 7 create<br>feature names `X_1, X_6, X_7`.<br><br>.. versionadded:: 1.3</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;concat&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__simpleimputer__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('missing_values',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=missing_values,-int%2C%20float%2C%20str%2C%20np.nan%2C%20None%20or%20pandas.NA%2C%20default%3Dnp.nan\">\n",
+       "            missing_values\n",
+       "            <span class=\"param-doc-description\">missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan<br><br>The placeholder for the missing values. All occurrences of<br>`missing_values` will be imputed. For pandas' dataframes with<br>nullable integer dtypes with missing values, `missing_values`<br>can be set to either `np.nan` or `pd.NA`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">nan</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('strategy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=strategy,-str%20or%20Callable%2C%20default%3D%27mean%27\">\n",
+       "            strategy\n",
+       "            <span class=\"param-doc-description\">strategy: str or Callable, default='mean'<br><br>The imputation strategy.<br><br>- If \"mean\", then replace missing values using the mean along<br>  each column. Can only be used with numeric data.<br>- If \"median\", then replace missing values using the median along<br>  each column. Can only be used with numeric data.<br>- If \"most_frequent\", then replace missing using the most frequent<br>  value along each column. Can be used with strings or numeric data.<br>  If there is more than one such value, only the smallest is returned.<br>- If \"constant\", then replace missing values with fill_value. Can be<br>  used with strings or numeric data.<br>- If an instance of Callable, then replace missing values using the<br>  scalar statistic returned by running the callable over a dense 1d<br>  array containing non-missing values of each column.<br><br>.. versionadded:: 0.20<br>   strategy=\"constant\" for fixed value imputation.<br><br>.. versionadded:: 1.5<br>   strategy=callable for custom value imputation.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fill_value',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=fill_value,-str%20or%20numerical%20value%2C%20default%3DNone\">\n",
+       "            fill_value\n",
+       "            <span class=\"param-doc-description\">fill_value: str or numerical value, default=None<br><br>When strategy == \"constant\", `fill_value` is used to replace all<br>occurrences of missing_values. For string or object data types,<br>`fill_value` must be a string.<br>If `None`, `fill_value` will be 0 when imputing numerical<br>data and \"missing_value\" for strings or object data types.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If True, a copy of X will be created. If False, imputation will<br>be done in-place whenever possible. Note that, in the following cases,<br>a new copy will always be made, even if `copy=False`:<br><br>- If `X` is not an array of floating values;<br>- If `X` is encoded as a CSR matrix;<br>- If `add_indicator=True`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('add_indicator',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=add_indicator,-bool%2C%20default%3DFalse\">\n",
+       "            add_indicator\n",
+       "            <span class=\"param-doc-description\">add_indicator: bool, default=False<br><br>If True, a :class:`MissingIndicator` transform will stack onto output<br>of the imputer's transform. This allows a predictive estimator<br>to account for missingness despite imputation. If a feature has no<br>missing values at fit/train time, the feature won't appear on<br>the missing indicator even if there are missing values at<br>transform/test time.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.impute.SimpleImputer.html#:~:text=keep_empty_features,-bool%2C%20default%3DFalse\">\n",
+       "            keep_empty_features\n",
+       "            <span class=\"param-doc-description\">keep_empty_features: bool, default=False<br><br>If True, features that consist exclusively of missing values when<br>`fit` is called are returned in results when `transform` is called.<br>The imputed value is always `0` except when `strategy=\"constant\"`<br>in which case `fill_value` will be used instead.<br><br>.. versionadded:: 1.2</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"columntransformer__remainder__standardscaler__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('copy',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
+       "            copy\n",
+       "            <span class=\"param-doc-description\">copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_mean',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
+       "            with_mean\n",
+       "            <span class=\"param-doc-description\">with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('with_std',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
+       "            with_std\n",
+       "            <span class=\"param-doc-description\">with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">True</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"decisiontreeregressor__\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('criterion',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=criterion,-%7B%22squared_error%22%2C%20%22friedman_mse%22%2C%20%22absolute_error%22%2C%20%20%20%20%20%20%20%20%20%20%20%20%20%22poisson%22%7D%2C%20default%3D%22squared_error%22\">\n",
+       "            criterion\n",
+       "            <span class=\"param-doc-description\">criterion: {\"squared_error\", \"friedman_mse\", \"absolute_error\",             \"poisson\"}, default=\"squared_error\"<br><br>The function to measure the quality of a split. Supported criteria<br>are \"squared_error\" for the mean squared error, which is equal to<br>variance reduction as feature selection criterion and minimizes the L2<br>loss using the mean of each terminal node, \"friedman_mse\", which uses<br>mean squared error with Friedman's improvement score for potential<br>splits, \"absolute_error\" for the mean absolute error, which minimizes<br>the L1 loss using the median of each terminal node, and \"poisson\" which<br>uses reduction in the half mean Poisson deviance to find splits.<br><br>.. versionadded:: 0.18<br>   Mean Absolute Error (MAE) criterion.<br><br>.. versionadded:: 0.24<br>    Poisson deviance criterion.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;squared_error&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('splitter',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=splitter,-%7B%22best%22%2C%20%22random%22%7D%2C%20default%3D%22best%22\">\n",
+       "            splitter\n",
+       "            <span class=\"param-doc-description\">splitter: {\"best\", \"random\"}, default=\"best\"<br><br>The strategy used to choose the split at each node. Supported<br>strategies are \"best\" to choose the best split and \"random\" to choose<br>the best random split.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">&#x27;best&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_depth',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
+       "            max_depth\n",
+       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.<br><br>For an example of how ``max_depth`` influences the model, see<br>:ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_split',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
+       "            min_samples_split\n",
+       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">2</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
+       "            min_samples_leaf\n",
+       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
+       "            min_weight_fraction_leaf\n",
+       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=max_features,-int%2C%20float%20or%20%7B%22sqrt%22%2C%20%22log2%22%7D%2C%20default%3DNone\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\">max_features: int, float or {\"sqrt\", \"log2\"}, default=None<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls the randomness of the estimator. The features are always<br>randomly permuted at each split, even if ``splitter`` is set to<br>``\"best\"``. When ``max_features < n_features``, the algorithm will<br>select ``max_features`` at random at each split before finding the best<br>split among them. But the best found split may vary across different<br>runs, even if ``max_features=n_features``. That is the case, if the<br>improvement of the criterion is identical for several splits and one<br>split has to be selected at random. To obtain a deterministic behaviour<br>during fitting, ``random_state`` has to be fixed to an integer.<br>See :term:`Glossary <random_state>` for details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
+       "            max_leaf_nodes\n",
+       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow a tree with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
+       "            min_impurity_decrease\n",
+       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
+       "            ccp_alpha\n",
+       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.tree.DecisionTreeRegressor.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
+       "            monotonic_cst\n",
+       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multioutput regressions (i.e. when `n_outputs_ > 1`),<br>  - regressions trained on data with missing values.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
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+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
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+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
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+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
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+       "            return \"light\";\n",
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+       "\n",
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+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
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+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
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+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-8');</script></body>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('columntransformer',\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+       "                                                              SimpleImputer(strategy='median')),\n",
+       "                                                             ('standardscaler',\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[('bedrooms',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='median')),\n",
+       "                                                                  ('functiontransformer',\n",
+       "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x31a...\n",
+       "                                                 ('geo',\n",
+       "                                                  ClusterSimilarity(random_state=42),\n",
+       "                                                  ['latitude', 'longitude']),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+       "                                                                  ('onehotencoder',\n",
+       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
+       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x31aabb8c0>)])),\n",
+       "                ('decisiontreeregressor',\n",
+       "                 DecisionTreeRegressor(random_state=42))])"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "\n",
+    "tree_reg = make_pipeline(preprocessing, DecisionTreeRegressor(random_state=42))\n",
+    "tree_reg.fit(housing, housing_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_predictions = tree_reg.predict(housing)\n",
+    "tree_rmse = root_mean_squared_error(housing_labels, housing_predictions)\n",
+    "tree_rmse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Better Evaluation Using Cross-Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "tree_rmses = -cross_val_score(tree_reg, housing, housing_labels,\n",
+    "                              scoring=\"neg_root_mean_squared_error\", cv=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count       10.000000\n",
+       "mean     67013.360949\n",
+       "std       1460.198570\n",
+       "min      64289.376198\n",
+       "25%      66776.146282\n",
+       "50%      67086.216281\n",
+       "75%      68140.275029\n",
+       "max      68659.294290\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(tree_rmses).describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count       10.000000\n",
+       "mean     70003.404818\n",
+       "std       4182.188328\n",
+       "min      65504.765753\n",
+       "25%      68172.065831\n",
+       "50%      68743.995249\n",
+       "75%      70344.943988\n",
+       "max      81037.863741\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 212,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extra code – computes the error stats for the linear model\n",
+    "lin_rmses = -cross_val_score(lin_reg, housing, housing_labels,\n",
+    "                              scoring=\"neg_root_mean_squared_error\", cv=10)\n",
+    "pd.Series(lin_rmses).describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning:** the following cell may take a few minutes to run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "forest_reg = make_pipeline(preprocessing,\n",
+    "                           RandomForestRegressor(random_state=42))\n",
+    "forest_rmses = -cross_val_score(forest_reg, housing, housing_labels,\n",
+    "                                scoring=\"neg_root_mean_squared_error\", cv=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count       10.000000\n",
+       "mean     47124.604437\n",
+       "std       1069.311372\n",
+       "min      45292.329302\n",
+       "25%      46712.106520\n",
+       "50%      47172.209883\n",
+       "75%      47561.377695\n",
+       "max      49354.705514\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(forest_rmses).describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compare this RMSE measured using cross-validation (the \"validation error\") with the RMSE measured on the training set (the \"training error\"):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "got an unexpected keyword argument 'squared'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[215]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m forest_reg.fit(housing, housing_labels)\n\u001b[32m      2\u001b[39m housing_predictions = forest_reg.predict(housing)\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m forest_rmse = \u001b[43mmean_squared_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhousing_labels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhousing_predictions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m      4\u001b[39m \u001b[43m                                 \u001b[49m\u001b[43msquared\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m      5\u001b[39m forest_rmse\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/lib/python3.12/site-packages/sklearn/utils/_param_validation.py:196\u001b[39m, in \u001b[36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    193\u001b[39m func_sig = signature(func)\n\u001b[32m    195\u001b[39m \u001b[38;5;66;03m# Map *args/**kwargs to the function signature\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m196\u001b[39m params = \u001b[43mfunc_sig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbind\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    197\u001b[39m params.apply_defaults()\n\u001b[32m    199\u001b[39m \u001b[38;5;66;03m# ignore self/cls and positional/keyword markers\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/lib/python3.12/inspect.py:3280\u001b[39m, in \u001b[36mSignature.bind\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m   3275\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbind\u001b[39m(\u001b[38;5;28mself\u001b[39m, /, *args, **kwargs):\n\u001b[32m   3276\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Get a BoundArguments object, that maps the passed `args`\u001b[39;00m\n\u001b[32m   3277\u001b[39m \u001b[33;03m    and `kwargs` to the function's signature.  Raises `TypeError`\u001b[39;00m\n\u001b[32m   3278\u001b[39m \u001b[33;03m    if the passed arguments can not be bound.\u001b[39;00m\n\u001b[32m   3279\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3280\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_bind\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/miniforge3/lib/python3.12/inspect.py:3269\u001b[39m, in \u001b[36mSignature._bind\u001b[39m\u001b[34m(self, args, kwargs, partial)\u001b[39m\n\u001b[32m   3259\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m   3260\u001b[39m             \u001b[33m'\u001b[39m\u001b[33mgot some positional-only arguments passed as \u001b[39m\u001b[33m'\u001b[39m\n\u001b[32m   3261\u001b[39m             \u001b[33m'\u001b[39m\u001b[33mkeyword arguments: \u001b[39m\u001b[38;5;132;01m{arg!r}\u001b[39;00m\u001b[33m'\u001b[39m.format(\n\u001b[32m   (...)\u001b[39m\u001b[32m   3266\u001b[39m             ),\n\u001b[32m   3267\u001b[39m         )\n\u001b[32m   3268\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3269\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m   3270\u001b[39m             \u001b[33m'\u001b[39m\u001b[33mgot an unexpected keyword argument \u001b[39m\u001b[38;5;132;01m{arg!r}\u001b[39;00m\u001b[33m'\u001b[39m.format(\n\u001b[32m   3271\u001b[39m                 arg=\u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(kwargs))))\n\u001b[32m   3273\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._bound_arguments_cls(\u001b[38;5;28mself\u001b[39m, arguments)\n",
+      "\u001b[31mTypeError\u001b[39m: got an unexpected keyword argument 'squared'"
+     ]
+    }
+   ],
+   "source": [
+    "# forest_reg.fit(housing, housing_labels)\n",
+    "# housing_predictions = forest_reg.predict(housing)\n",
+    "# forest_rmse = mean_squared_error(housing_labels, housing_predictions,\n",
+    "#                                  squared=False)\n",
+    "# forest_rmse\n",
+    "\n",
+    "forest_reg.fit(housing, housing_labels)\n",
+    "housing_predictions = forest_reg.predict(housing)\n",
+    "forest_rmse = root_mean_squared_error(housing_labels, housing_predictions)\n",
+    "forest_rmse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The training error is much lower than the validation error, which usually means that the model has overfit the training set. Another possible explanation may be that there's a mismatch between the training data and the validation data, but it's not the case here, since both came from the same dataset that we shuffled and split in two parts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-Tune Your Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Grid Search"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning:** the following cell may take a few minutes to run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "             estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                                        ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                    (&#x27;standardscaler&#x27;,\n",
+       "                                                                                     StandardScaler())]),\n",
+       "                                                          transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
+       "                                                                                          FunctionTransformer(feature_names_out=&lt;f...\n",
+       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;)])),\n",
+       "                                       (&#x27;random_forest&#x27;,\n",
+       "                                        RandomForestRegressor(random_state=42))]),\n",
+       "             param_grid=[{&#x27;preprocessing__geo__n_clusters&#x27;: [5, 8, 10],\n",
+       "                          &#x27;random_forest__max_features&#x27;: [4, 6, 8]},\n",
+       "                         {&#x27;preprocessing__geo__n_clusters&#x27;: [10, 15],\n",
+       "                          &#x27;random_forest__max_features&#x27;: [6, 8, 10]}],\n",
+       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
+       "             estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                                        ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                     SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                    (&#x27;standardscaler&#x27;,\n",
+       "                                                                                     StandardScaler())]),\n",
+       "                                                          transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                                         Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                          SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                         (&#x27;functiontransformer&#x27;,\n",
+       "                                                                                          FunctionTransformer(feature_names_out=&lt;f...\n",
+       "                                                                         &lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;)])),\n",
+       "                                       (&#x27;random_forest&#x27;,\n",
+       "                                        RandomForestRegressor(random_state=42))]),\n",
+       "             param_grid=[{&#x27;preprocessing__geo__n_clusters&#x27;: [5, 8, 10],\n",
+       "                          &#x27;random_forest__max_features&#x27;: [4, 6, 8]},\n",
+       "                         {&#x27;preprocessing__geo__n_clusters&#x27;: [10, 15],\n",
+       "                          &#x27;random_forest__max_features&#x27;: [6, 8, 10]}],\n",
+       "             scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                             (&#x27;standardscaler&#x27;,\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;functiontransformer&#x27;,\n",
+       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b89...\n",
+       "                                                   &#x27;median_income&#x27;]),\n",
+       "                                                 (&#x27;geo&#x27;,\n",
+       "                                                  ClusterSimilarity(random_state=42),\n",
+       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;)])),\n",
+       "                (&#x27;random_forest&#x27;, RandomForestRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                            (&#x27;standardscaler&#x27;,\n",
+       "                                             StandardScaler())]),\n",
+       "                  transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                 (&#x27;functiontransformer&#x27;,\n",
+       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                                                                      func=&lt;function column_ratio at 0...\n",
+       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
+       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
+       "                                (&#x27;geo&#x27;, ClusterSimilarity(random_state=42),\n",
+       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                 (&#x27;onehotencoder&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                    func=&lt;function column_ratio at 0x1627b9300&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-75\" type=\"checkbox\" ><label for=\"sk-estimator-id-75\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                    func=&lt;function column_ratio at 0x1627b9300&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-76\" type=\"checkbox\" ><label for=\"sk-estimator-id-76\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-77\" type=\"checkbox\" ><label for=\"sk-estimator-id-77\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-78\" type=\"checkbox\" ><label for=\"sk-estimator-id-78\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-79\" type=\"checkbox\" ><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                    func=&lt;function column_ratio at 0x1627b9300&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=3,\n",
+       "             estimator=Pipeline(steps=[('preprocessing',\n",
+       "                                        ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+       "                                                                                     SimpleImputer(strategy='median')),\n",
+       "                                                                                    ('standardscaler',\n",
+       "                                                                                     StandardScaler())]),\n",
+       "                                                          transformers=[('bedrooms',\n",
+       "                                                                         Pipeline(steps=[('simpleimputer',\n",
+       "                                                                                          SimpleImputer(strategy='median')),\n",
+       "                                                                                         ('functiontransformer',\n",
+       "                                                                                          FunctionTransformer(feature_names_out=<f...\n",
+       "                                                                         <sklearn.compose._column_transformer.make_column_selector object at 0x16259df10>)])),\n",
+       "                                       ('random_forest',\n",
+       "                                        RandomForestRegressor(random_state=42))]),\n",
+       "             param_grid=[{'preprocessing__geo__n_clusters': [5, 8, 10],\n",
+       "                          'random_forest__max_features': [4, 6, 8]},\n",
+       "                         {'preprocessing__geo__n_clusters': [10, 15],\n",
+       "                          'random_forest__max_features': [6, 8, 10]}],\n",
+       "             scoring='neg_root_mean_squared_error')"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "full_pipeline = Pipeline([\n",
+    "    (\"preprocessing\", preprocessing),\n",
+    "    (\"random_forest\", RandomForestRegressor(random_state=42)),\n",
+    "])\n",
+    "param_grid = [\n",
+    "    {'preprocessing__geo__n_clusters': [5, 8, 10],\n",
+    "     'random_forest__max_features': [4, 6, 8]},\n",
+    "    {'preprocessing__geo__n_clusters': [10, 15],\n",
+    "     'random_forest__max_features': [6, 8, 10]},\n",
+    "]\n",
+    "grid_search = GridSearchCV(full_pipeline, param_grid, cv=3,\n",
+    "                           scoring='neg_root_mean_squared_error')\n",
+    "grid_search.fit(housing, housing_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the full list of hyperparameters available for tuning by looking at `full_pipeline.get_params().keys()`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['memory', 'steps', 'verbose', 'preprocessing', 'random_forest', 'preprocessing__n_jobs', 'preprocessing__remainder__memory', 'preprocessing__remainder__steps', 'preprocessing__remainder__verbose', 'preprocessing__remainder__simpleimputer', 'preprocessing__remainder__standardscaler', 'preprocessing__remainder__simpleimputer__add_indicator', 'preprocessing__remainder__simpleimputer__copy', 'preprocessing__remainder__simpleimputer__fill_value', 'preprocessing__remainder__simpleimputer__keep_empty_features', 'preprocessing__remainder__simpleimputer__missing_values', 'preprocessing__remainder__simpleimputer__strategy', 'preprocessing__remainder__standardscaler__copy', 'preprocessing__remainder__standardscaler__with_mean', 'preprocessing__remainder__standardscaler__with_std', 'preprocessing__remainder', 'preprocessing__sparse_threshold', 'preprocessing__transformer_weights', 'preprocessing__transformers', 'preprocessing__verbose', 'preprocessing__verbose_feature_names_out', 'prepr...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# extra code – shows part of the output of get_params().keys()\n",
+    "print(str(full_pipeline.get_params().keys())[:1000] + \"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The best hyperparameter combination found:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'preprocessing__geo__n_clusters': 15, 'random_forest__max_features': 6}"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-10 {color: black;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                             (&#x27;standardscaler&#x27;,\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;functiontransformer&#x27;,\n",
+       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b89...\n",
+       "                                                  ClusterSimilarity(n_clusters=15,\n",
+       "                                                                    random_state=42),\n",
+       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x1623b0e50&gt;)])),\n",
+       "                (&#x27;random_forest&#x27;,\n",
+       "                 RandomForestRegressor(max_features=6, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                             (&#x27;standardscaler&#x27;,\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;functiontransformer&#x27;,\n",
+       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b89...\n",
+       "                                                  ClusterSimilarity(n_clusters=15,\n",
+       "                                                                    random_state=42),\n",
+       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x1623b0e50&gt;)])),\n",
+       "                (&#x27;random_forest&#x27;,\n",
+       "                 RandomForestRegressor(max_features=6, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                            (&#x27;standardscaler&#x27;,\n",
+       "                                             StandardScaler())]),\n",
+       "                  transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                 (&#x27;functiontransformer&#x27;,\n",
+       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                                                                      func=&lt;function column_ratio at 0...\n",
+       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
+       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
+       "                                (&#x27;geo&#x27;,\n",
+       "                                 ClusterSimilarity(n_clusters=15,\n",
+       "                                                   random_state=42),\n",
+       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                 (&#x27;onehotencoder&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x1623b0e50&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-97\" type=\"checkbox\" ><label for=\"sk-estimator-id-97\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-98\" type=\"checkbox\" ><label for=\"sk-estimator-id-98\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                    func=&lt;function column_ratio at 0x1627b9300&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-99\" type=\"checkbox\" ><label for=\"sk-estimator-id-99\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-100\" type=\"checkbox\" ><label for=\"sk-estimator-id-100\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-101\" type=\"checkbox\" ><label for=\"sk-estimator-id-101\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-102\" type=\"checkbox\" ><label for=\"sk-estimator-id-102\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                    func=&lt;function column_ratio at 0x1627b9300&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-103\" type=\"checkbox\" ><label for=\"sk-estimator-id-103\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-104\" type=\"checkbox\" ><label for=\"sk-estimator-id-104\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-105\" type=\"checkbox\" ><label for=\"sk-estimator-id-105\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-106\" type=\"checkbox\" ><label for=\"sk-estimator-id-106\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                    func=&lt;function column_ratio at 0x1627b9300&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-107\" type=\"checkbox\" ><label for=\"sk-estimator-id-107\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-108\" type=\"checkbox\" ><label for=\"sk-estimator-id-108\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-109\" type=\"checkbox\" ><label for=\"sk-estimator-id-109\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-110\" type=\"checkbox\" ><label for=\"sk-estimator-id-110\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-111\" type=\"checkbox\" ><label for=\"sk-estimator-id-111\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-112\" type=\"checkbox\" ><label for=\"sk-estimator-id-112\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-113\" type=\"checkbox\" ><label for=\"sk-estimator-id-113\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(n_clusters=15, random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-114\" type=\"checkbox\" ><label for=\"sk-estimator-id-114\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x1623b0e50&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-115\" type=\"checkbox\" ><label for=\"sk-estimator-id-115\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-116\" type=\"checkbox\" ><label for=\"sk-estimator-id-116\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-117\" type=\"checkbox\" ><label for=\"sk-estimator-id-117\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-118\" type=\"checkbox\" ><label for=\"sk-estimator-id-118\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-119\" type=\"checkbox\" ><label for=\"sk-estimator-id-119\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-120\" type=\"checkbox\" ><label for=\"sk-estimator-id-120\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=6, random_state=42)</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('preprocessing',\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+       "                                                              SimpleImputer(strategy='median')),\n",
+       "                                                             ('standardscaler',\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[('bedrooms',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='median')),\n",
+       "                                                                  ('functiontransformer',\n",
+       "                                                                   FunctionTransformer(feature_names_out=<function ratio_name at 0x1627b89...\n",
+       "                                                  ClusterSimilarity(n_clusters=15,\n",
+       "                                                                    random_state=42),\n",
+       "                                                  ['latitude', 'longitude']),\n",
+       "                                                 ('cat',\n",
+       "                                                  Pipeline(steps=[('simpleimputer',\n",
+       "                                                                   SimpleImputer(strategy='most_frequent')),\n",
+       "                                                                  ('onehotencoder',\n",
+       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
+       "                                                  <sklearn.compose._column_transformer.make_column_selector object at 0x1623b0e50>)])),\n",
+       "                ('random_forest',\n",
+       "                 RandomForestRegressor(max_features=6, random_state=42))])"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search.best_estimator_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the score of each hyperparameter combination tested during the grid search:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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>n_clusters</th>\n",
+       "      <th>max_features</th>\n",
+       "      <th>split0</th>\n",
+       "      <th>split1</th>\n",
+       "      <th>split2</th>\n",
+       "      <th>mean_test_rmse</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>15</td>\n",
+       "      <td>6</td>\n",
+       "      <td>43536</td>\n",
+       "      <td>43753</td>\n",
+       "      <td>44569</td>\n",
+       "      <td>43953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>15</td>\n",
+       "      <td>8</td>\n",
+       "      <td>44084</td>\n",
+       "      <td>44205</td>\n",
+       "      <td>44863</td>\n",
+       "      <td>44384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>44368</td>\n",
+       "      <td>44496</td>\n",
+       "      <td>45200</td>\n",
+       "      <td>44688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>44251</td>\n",
+       "      <td>44628</td>\n",
+       "      <td>45857</td>\n",
+       "      <td>44912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>44251</td>\n",
+       "      <td>44628</td>\n",
+       "      <td>45857</td>\n",
+       "      <td>44912</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   n_clusters max_features  split0  split1  split2  mean_test_rmse\n",
+       "12         15            6   43536   43753   44569           43953\n",
+       "13         15            8   44084   44205   44863           44384\n",
+       "14         15           10   44368   44496   45200           44688\n",
+       "7          10            6   44251   44628   45857           44912\n",
+       "9          10            6   44251   44628   45857           44912"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_res = pd.DataFrame(grid_search.cv_results_)\n",
+    "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n",
+    "\n",
+    "# extra code – these few lines of code just make the DataFrame look nicer\n",
+    "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n",
+    "                 \"param_random_forest__max_features\", \"split0_test_score\",\n",
+    "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
+    "score_cols = [\"split0\", \"split1\", \"split2\", \"mean_test_rmse\"]\n",
+    "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
+    "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
+    "\n",
+    "cv_res.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Randomized Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.experimental import enable_halving_search_cv\n",
+    "from sklearn.model_selection import HalvingRandomSearchCV"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try 30 (`n_iter` × `cv`) random combinations of hyperparameters:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning:** the following cell may take a few minutes to run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n",
+      "/Applications/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
+      "  super()._check_params_vs_input(X, default_n_init=10)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-11 {color: black;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3,\n",
+       "                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                                              ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                           SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                          (&#x27;standardscaler&#x27;,\n",
+       "                                                                                           StandardScaler())]),\n",
+       "                                                                transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
+       "                                                                                                FunctionTransformer(feature_names_...\n",
+       "                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;)])),\n",
+       "                                             (&#x27;random_forest&#x27;,\n",
+       "                                              RandomForestRegressor(random_state=42))]),\n",
+       "                   param_distributions={&#x27;preprocessing__geo__n_clusters&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x1624fd410&gt;,\n",
+       "                                        &#x27;random_forest__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x162433f90&gt;},\n",
+       "                   random_state=42, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-121\" type=\"checkbox\" ><label for=\"sk-estimator-id-121\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3,\n",
+       "                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                                              ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                           SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                          (&#x27;standardscaler&#x27;,\n",
+       "                                                                                           StandardScaler())]),\n",
+       "                                                                transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                                               (&#x27;functiontransformer&#x27;,\n",
+       "                                                                                                FunctionTransformer(feature_names_...\n",
+       "                                                                               &lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;)])),\n",
+       "                                             (&#x27;random_forest&#x27;,\n",
+       "                                              RandomForestRegressor(random_state=42))]),\n",
+       "                   param_distributions={&#x27;preprocessing__geo__n_clusters&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x1624fd410&gt;,\n",
+       "                                        &#x27;random_forest__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x162433f90&gt;},\n",
+       "                   random_state=42, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-122\" type=\"checkbox\" ><label for=\"sk-estimator-id-122\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                 ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                              SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                             (&#x27;standardscaler&#x27;,\n",
+       "                                                              StandardScaler())]),\n",
+       "                                   transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                                  (&#x27;functiontransformer&#x27;,\n",
+       "                                                                   FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b89...\n",
+       "                                                   &#x27;median_income&#x27;]),\n",
+       "                                                 (&#x27;geo&#x27;,\n",
+       "                                                  ClusterSimilarity(random_state=42),\n",
+       "                                                  [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                                  (&#x27;onehotencoder&#x27;,\n",
+       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                                  &lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;)])),\n",
+       "                (&#x27;random_forest&#x27;, RandomForestRegressor(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-123\" type=\"checkbox\" ><label for=\"sk-estimator-id-123\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessing: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                             SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                            (&#x27;standardscaler&#x27;,\n",
+       "                                             StandardScaler())]),\n",
+       "                  transformers=[(&#x27;bedrooms&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                                 (&#x27;functiontransformer&#x27;,\n",
+       "                                                  FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                                                                      func=&lt;function column_ratio at 0...\n",
+       "                                 [&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;,\n",
+       "                                  &#x27;households&#x27;, &#x27;median_income&#x27;]),\n",
+       "                                (&#x27;geo&#x27;, ClusterSimilarity(random_state=42),\n",
+       "                                 [&#x27;latitude&#x27;, &#x27;longitude&#x27;]),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
+       "                                                 (&#x27;onehotencoder&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
+       "                                 &lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-124\" type=\"checkbox\" ><label for=\"sk-estimator-id-124\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">bedrooms</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-125\" type=\"checkbox\" ><label for=\"sk-estimator-id-125\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-126\" type=\"checkbox\" ><label for=\"sk-estimator-id-126\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                    func=&lt;function column_ratio at 0x1627b9300&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-127\" type=\"checkbox\" ><label for=\"sk-estimator-id-127\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-128\" type=\"checkbox\" ><label for=\"sk-estimator-id-128\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">rooms_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_rooms&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-129\" type=\"checkbox\" ><label for=\"sk-estimator-id-129\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-130\" type=\"checkbox\" ><label for=\"sk-estimator-id-130\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                    func=&lt;function column_ratio at 0x1627b9300&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-131\" type=\"checkbox\" ><label for=\"sk-estimator-id-131\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-132\" type=\"checkbox\" ><label for=\"sk-estimator-id-132\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">people_per_house</label><div class=\"sk-toggleable__content\"><pre>[&#x27;population&#x27;, &#x27;households&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-133\" type=\"checkbox\" ><label for=\"sk-estimator-id-133\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-134\" type=\"checkbox\" ><label for=\"sk-estimator-id-134\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&lt;function ratio_name at 0x1627b8900&gt;,\n",
+       "                    func=&lt;function column_ratio at 0x1627b9300&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-135\" type=\"checkbox\" ><label for=\"sk-estimator-id-135\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-136\" type=\"checkbox\" ><label for=\"sk-estimator-id-136\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">log</label><div class=\"sk-toggleable__content\"><pre>[&#x27;total_bedrooms&#x27;, &#x27;total_rooms&#x27;, &#x27;population&#x27;, &#x27;households&#x27;, &#x27;median_income&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-137\" type=\"checkbox\" ><label for=\"sk-estimator-id-137\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-138\" type=\"checkbox\" ><label for=\"sk-estimator-id-138\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(feature_names_out=&#x27;one-to-one&#x27;, func=&lt;ufunc &#x27;log&#x27;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-139\" type=\"checkbox\" ><label for=\"sk-estimator-id-139\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-140\" type=\"checkbox\" ><label for=\"sk-estimator-id-140\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">geo</label><div class=\"sk-toggleable__content\"><pre>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-141\" type=\"checkbox\" ><label for=\"sk-estimator-id-141\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ClusterSimilarity</label><div class=\"sk-toggleable__content\"><pre>ClusterSimilarity(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-142\" type=\"checkbox\" ><label for=\"sk-estimator-id-142\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x16259df10&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-143\" type=\"checkbox\" ><label for=\"sk-estimator-id-143\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-144\" type=\"checkbox\" ><label for=\"sk-estimator-id-144\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-145\" type=\"checkbox\" ><label for=\"sk-estimator-id-145\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;housing_median_age&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-146\" type=\"checkbox\" ><label for=\"sk-estimator-id-146\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-147\" type=\"checkbox\" ><label for=\"sk-estimator-id-147\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-148\" type=\"checkbox\" ><label for=\"sk-estimator-id-148\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomizedSearchCV(cv=3,\n",
+       "                   estimator=Pipeline(steps=[('preprocessing',\n",
+       "                                              ColumnTransformer(remainder=Pipeline(steps=[('simpleimputer',\n",
+       "                                                                                           SimpleImputer(strategy='median')),\n",
+       "                                                                                          ('standardscaler',\n",
+       "                                                                                           StandardScaler())]),\n",
+       "                                                                transformers=[('bedrooms',\n",
+       "                                                                               Pipeline(steps=[('simpleimputer',\n",
+       "                                                                                                SimpleImputer(strategy='median')),\n",
+       "                                                                                               ('functiontransformer',\n",
+       "                                                                                                FunctionTransformer(feature_names_...\n",
+       "                                                                               <sklearn.compose._column_transformer.make_column_selector object at 0x16259df10>)])),\n",
+       "                                             ('random_forest',\n",
+       "                                              RandomForestRegressor(random_state=42))]),\n",
+       "                   param_distributions={'preprocessing__geo__n_clusters': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x1624fd410>,\n",
+       "                                        'random_forest__max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x162433f90>},\n",
+       "                   random_state=42, scoring='neg_root_mean_squared_error')"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "from scipy.stats import randint\n",
+    "\n",
+    "param_distribs = {'preprocessing__geo__n_clusters': randint(low=3, high=50),\n",
+    "                  'random_forest__max_features': randint(low=2, high=20)}\n",
+    "\n",
+    "rnd_search = RandomizedSearchCV(\n",
+    "    full_pipeline, param_distributions=param_distribs, n_iter=10, cv=3,\n",
+    "    scoring='neg_root_mean_squared_error', random_state=42)\n",
+    "\n",
+    "rnd_search.fit(housing, housing_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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>n_clusters</th>\n",
+       "      <th>max_features</th>\n",
+       "      <th>split0</th>\n",
+       "      <th>split1</th>\n",
+       "      <th>split2</th>\n",
+       "      <th>mean_test_rmse</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>45</td>\n",
+       "      <td>9</td>\n",
+       "      <td>41115</td>\n",
+       "      <td>42151</td>\n",
+       "      <td>42695</td>\n",
+       "      <td>41987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>32</td>\n",
+       "      <td>7</td>\n",
+       "      <td>41604</td>\n",
+       "      <td>42200</td>\n",
+       "      <td>43219</td>\n",
+       "      <td>42341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>41</td>\n",
+       "      <td>16</td>\n",
+       "      <td>42106</td>\n",
+       "      <td>42743</td>\n",
+       "      <td>43443</td>\n",
+       "      <td>42764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>42</td>\n",
+       "      <td>4</td>\n",
+       "      <td>41812</td>\n",
+       "      <td>42925</td>\n",
+       "      <td>43557</td>\n",
+       "      <td>42765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23</td>\n",
+       "      <td>8</td>\n",
+       "      <td>42421</td>\n",
+       "      <td>43094</td>\n",
+       "      <td>43856</td>\n",
+       "      <td>43124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  n_clusters max_features  split0  split1  split2  mean_test_rmse\n",
+       "1         45            9   41115   42151   42695           41987\n",
+       "8         32            7   41604   42200   43219           42341\n",
+       "0         41           16   42106   42743   43443           42764\n",
+       "5         42            4   41812   42925   43557           42765\n",
+       "2         23            8   42421   43094   43856           43124"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extra code – displays the random search results\n",
+    "cv_res = pd.DataFrame(rnd_search.cv_results_)\n",
+    "cv_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n",
+    "cv_res = cv_res[[\"param_preprocessing__geo__n_clusters\",\n",
+    "                 \"param_random_forest__max_features\", \"split0_test_score\",\n",
+    "                 \"split1_test_score\", \"split2_test_score\", \"mean_test_score\"]]\n",
+    "cv_res.columns = [\"n_clusters\", \"max_features\"] + score_cols\n",
+    "cv_res[score_cols] = -cv_res[score_cols].round().astype(np.int64)\n",
+    "cv_res.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Bonus section: how to choose the sampling distribution for a hyperparameter**\n",
+    "\n",
+    "* `scipy.stats.randint(a, b+1)`: for hyperparameters with _discrete_ values that range from a to b, and all values in that range seem equally likely.\n",
+    "* `scipy.stats.uniform(a, b)`: this is very similar, but for _continuous_ hyperparameters.\n",
+    "* `scipy.stats.geom(1 / scale)`: for discrete values, when you want to sample roughly in a given scale. E.g., with scale=1000 most samples will be in this ballpark, but ~10% of all samples will be <100 and ~10% will be >2300.\n",
+    "* `scipy.stats.expon(scale)`: this is the continuous equivalent of `geom`. Just set `scale` to the most likely value.\n",
+    "* `scipy.stats.reciprocal(a, b)`: when you have almost no idea what the optimal hyperparameter value's scale is. If you set a=0.01 and b=100, then you're just as likely to sample a value between 0.01 and 0.1 as a value between 10 and 100.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are plots of the probability mass functions (for discrete variables), and probability density functions (for continuous variables) for `randint()`, `uniform()`, `geom()` and `expon()`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lSyq//J69+pyXl4cJEybAxMQEiYmJ6Nmzp1LfrKwsjcbLycmBi4sLzp49C1NTUwBAaGgoevXqhf/+9794/fXX0aRJE8XVgsOHD+PWrVtKFfHU1FSkpKQgICAALVq0qHS8+Ph4nDt3Dt26dVNqd3FxQU5ODpo3b67UfvLkSfj4+GDJkiVqTwpiY2ORnJyMTp06AQA+//xz9OjRAzExMfjqq69gZ2cH4Gky27RpE7p3747Tp08r7tWfO3cuevToodFn9awDBw4gLi4Offv2BfB09oGPjw/i4uJw9uxZ9OnTBz169EBISAi+/fZb9OjRQ+10uDNnzgAAevXqpbItIyMDANChQwe1MXTo0AGXLl3CjRs38OKLL1b5PVQkMTERf/75J/r06YMuXbro7Li1Tb7IkfwzJiIiVfX9vKOqDh06hO3btyMgIEDRNmHCBPzvf//Dvn37MGbMmAr3nThxIm7dulVh3v/4449x7do1zJkzB0uXLlXab8iQIQgKCkJ6errKbW6//fYbUlJS1N7KmJOTg379+mHbtm2KKfs9evTA7Nmz4eHhgUmTJmHFihWKCwvvv/8+1qxZgwMHDmDkyJGK45w5cwZt2rRBq1atVMbIyMhA48aN1d4GIT9HkZ+z6JK8yFTZxQlXV1cAT+MXe80jgDMEiEhHTE1NFUnyWc8mvc2bN+Phw4f48MMPVZIyALRt21bj8T777DOl8Tp37ozJkyfj/v372L9/v6L93XffhSAIiqvHcvI/1N9+++3njvXOO++oFAMAwMrKSqUYAAADBw5Ely5dcPz4cbXHmzFjhqIYAAANGjTA2LFjIQgCkpOTFe3R0dEAoLJwX5s2bao1Q2DcuHGKYgAAGBsbIygoCABw4cIFjY8jP4GysbFR2SaVSgFAaXres+Sr58v76YomCbguaNKkCSwsLDQ+SSUiMlT1+byjqvr3769UDACePm0HqFp+/7fi4mLExMSgRYsWKovdDh48GIMHD0ZGRobaIvZHH31U6bpGX331ldL9++PGjQMAlJaW4rPPPlOaZTh27FgAwKVLl5Riu3fvntpzEeDpeUZtn4vcvHkTJ0+ehIODA1599dUK+8lj1pdcz4IAEWlt9OjRePToEbp27YpZs2bh4MGDuH//vkq/8+fPA1A/fasqTE1N1U4P8/LyAvC0Ci/n7++Ptm3bIjIyUnHffVFREbZt24Z27dph0KBBzx3P3d29wm1xcXEYMWIEbG1tYWpqqrj37vLly/jrr7/U7qPuyrr8pOTZz02e+OTv61nq2p5H03GfR371pVmzZlWOoSYUFhbixx9/ROPGjVVOiJ5Hfq/jsy/5gocDBw5U2VYbz6du3rw58vPza3wcIqK6qr6fd1SVrvL7v6Wnp+PJkydwd3dHw4YNVbbLbyt49v3LVXbu1LRpU8W6BXK2trYAnl69//fTi+TbsrOzFW36di4CPL11Ub6IcmULA8svJulLrtfLgsDq1avh7OwMCwsLuLq6IjExscK+OTk5GDduHDp16gQjIyOEhISo9JEvsvXv19Ch//cMVPlCHc++6tpKm0Ri+fjjj7F+/Xq0bt0aX3/9NYYNG4aWLVti+PDhuHnzpqKfPCm1adNGq/FatGih9hetvOL6bMXX2NgYU6ZMQVZWFo4cOQIA2LVrF+7fv48pU6Zo9AzZiqrPP/74IwYNGoRffvkF/fr1Q0hICBYsWICFCxfC0dERxcXFavdTV7GWV8mfvT9OKpXCyMhI7SOQKoqpMpqO+zwNGjQAALWLIMrHqKjqXlhYWGEs1RUTE4PHjx8jICBA7TOIKzNixAildRIWLlyoWFMhKChIZZu2jyjSxJMnT9SeeNVHzPdEVB31/byjqnSV3/9NnrMrOueQ/+5Ul/MrO0+pLF751Xt120pKShRtlZ2LyMeozXOR8vJyREVFwcjISDE7oyLymPUl1+vdGgI7duxASEgIVq9ejb59++KHH36An58frl69qnbaiUwmQ8uWLTFv3rwKV+zcs2eP0ol5QUEBunfvrrIi5b+n+BobG+voXRHVbxKJBFOnTsXUqVNRUFCAxMRExMTEYOfOncjIyMDly5dhbGyMpk2bAnha4X32vrqqKigoQHl5uUpyvnv3LgDVX/BTp07FkiVLsGHDBvj7+2PDhg0wMTFRrHiryftTJywsDBYWFkhOTla5Z3779u0avpuKWVlZoby8HPn5+Sr3SMrfqxjksfz9998q2553X15GRgaMjIzQrl07ncWjze0CI0aMUHlEYVRUFPbv34+JEyfWSgHgWeXl5ZBKpXV6HQRNMd8TUXXV1/MO+fFLS0tVtul6ersm5H+cV3TOIW9X90d8TRQ+ntW0aVOYmpqqPRcBnp6PJCUlITc3V6Xo+7z1jqrjyJEjyMrKwuDBg5/7CGh5zBUtOljb9G6GwNdff40pU6Zg6tSpcHFxQUREBOzt7bFmzRq1/Z2cnPDtt99iwoQJFVZ5mjdvjtatWytesbGxaNiwocoJgomJiVK/532TZDIZCgsLlV5Ehk7+DPgdO3Zg0KBBSEtLUzwqRj597NixY1qNUVJSgrNnz6q0y68u/nvBvbZt28LPzw8HDx7E6dOnkZCQAH9/f8XifdV1/fp1uLi4qCSUv/76S+1jB6uqe/fuAKD2qmllV1K1Jf/jqKKrCvL1FNT90d+nTx+Ym5sjNjYWgiAobcvJycHly5fRu3dvWFhY6CTWy5cv48KFC+jSpYva6Zx1TUZGBsrLy9WuWVHf1JV8z1xPpN/q03mHfPr7s1Pj5VJSUqoZffV17twZFhYWuHDhAh4/fqyyXb5Kf3UWOtaFrl274tatW0ozB+S8vb0BqP/eHz16VKmPLlTl4sQff/wBAHqT6/WqIFBcXIzk5GSV+3x8fX11uuLyxo0bMWbMGJX7UzIyMmBnZwdnZ2eMGTMGN27cqPQ44eHhsLKyUrzs7e11FiNRXXL06FGVanZJSYmiAiqf1hUUFITGjRtjxYoVau83U5cAKzJ//nylBJCeno5NmzbByspK6TF6cu+++y5KSkowevRoCIKgtKiPVCpFeno6cnJyNB4fABwdHXHt2jWlynlRURHee+89tdX9qpI/lmjx4sV49OiRoj07Oxvffvut1sevSLNmzSCRSCpc7MbLywtGRkaKezOfZWlpiYCAANy4cQNr165VtAuCgDlz5qC8vFxlQaVbt25BIpFU6+qNPAFPmTKlyvvqo3PnzgHQ7UmKPqpL+Z65nkj/1NfzDvmTZqKiopTad+3aVSOPyHseMzMzjB07Fvn5+QgPD1fadvz4cfz888944YUXlBYsrk3e3t4oKirC5cuXVbZNmjQJJiYm+Pzzz5VmV1y5cgXR0dFo3769ynoO8nWF/v35P8+9e/fw008/wdraGq+//vpz++tbrterWwby8/NRVlamcs+JjY0NcnNzdTLG+fPn8fvvv6s8d7R3796Ijo5Gx44dcffuXSxZsgSenp64cuVKhY8GmTNnDkJDQxVfFxYW8kSBDFJAQAAaNmyIfv36wdHRESUlJYiNjcXVq1cREBCgmDrVqlUrREdHY8yYMXB3d8frr7+OTp06IT8/H+fOnYOTk5PK84PVsbW1xf3799GjRw8MHToUUqkUMTExKCoqwvr169GkSROVffz9/WFvb487d+6gTZs28PPzU2zbu3cvJk2ahKCgoColgWnTpmHatGno2bMnRo0ahdLSUsWV8e7duyuthlsdAwYMwKRJkxAZGYlu3bph5MiRkMlk2LFjB/r06YODBw9qdfyKNG7cGC+//DISEhIwadIkdOjQAUZGRhg3bhwcHBzQrFkz9O/fH4mJiZDJZDA3N1fa/4svvsDJkyfxwQcf4Pjx4+jYsSMSExNx+vRpDB48WPFkAzn5okvPrjasieLiYmzZsgVmZmYIDAzU7k3XgGenhspP+p5tW758ucr6ELGxsTA2NsZrr71WGyGKpi7le+Z6Iv1TX887RowYAWdnZ0RFReHOnTvo2bMn0tLS8Msvv8Df3x+HDx/W+rOrqmXLliE+Ph5LlizBmTNn0Lt3b9y6dQu7du1Cw4YNERkZWekCejVpxIgRiIiIwPHjx1UWVuzYsSPCwsLw6aef4qWXXsKoUaPw6NEjxMTEoKSkBOvXr1c576ju+Uh0dDRKSkowYcIEmJmZVdpXEAScOHECLi4u6NixY5XGqSl6VRCQ+/c9J4Ig6Ow+lI0bN6Jr164qK18++0ParVs3eHh4oH379ti8ebPSicCzzM3NVU6EidS59cXQ53eqw8LDw3HkyBGcP38eP/30Exo1aoQXXngBP/zwg8rCKiNHjsS5c+cQHh6O+Ph4HDhwANbW1ujRo4fGj+IxMzNDbGwsPvnkE2zevBlSqRTdunXD/PnzK6zMGhkZYfz48QgPD8ekSZN0cs/wBx98AFNTU3z//fdYv349mjZtiqFDh2Lp0qUYPXq01scHnj6mqGPHjli/fj1WrlyJtm3bIjQ0FKNHj66xggAA/O9//8PMmTOxb98+SKVSCIKAPn36KE6y3n33XYwdOxaHDh3CG2+8obSvra0tzp07h08//RSHDh3CwYMH4eDggEWLFuGTTz5ROXH4/fffAaDS5ySrs2/fPhQUFGD06NFqF14U2+bNmyttCwsLU4r78ePH2LdvH4YNG6b17Sx1RV3I98z1VFfV53OP+nre0aBBA5w4cQIzZ87EL7/8grNnz6JPnz5ISEjAwYMHRSkItGzZEufOncNnn32G/fv3IzExUTErYuHChejatWutxyTn7e2Nzp07Y8uWLfj4449Vts+bNw9OTk6IiIjAmjVrYGZmBk9PTyxevBgvv/yySv/ff/8dTZo0UVqIVhNVuV0gISEBmZmZiIiIqNIYNUrQIzKZTDA2Nhb27Nmj1D59+nShf//+z93f29tbmDFjRoXbHz16JFhaWgoREREaxePj4yMEBwdr1FcQBEEqlQoABKlUqvE+VD88efJEuHr1qvDkyROxQ6n3HB0dBUdHx2rt6+fnJ0gkEuHmzZs6jckQyWQywdHRURgyZIjWx/rwww+Fhg0bCvfu3dNBZHXXxo0bBQBCfHy8Rv01/b2jj7mpLud7ffw8yfDwvKP28LxDv/3www8CAOHs2bNaHUcqlQpGRkbCRx99pKPI1AsMDBSaNWsm/PPPPxr1r41cr1drCJiZmcHV1RWxsbFK7bGxsfD09NT6+Dt37oRMJsP48eOf21cmkyEtLU3x3EsiqtuuXLmCI0eOYMiQIVqtNExPmZmZYenSpThy5IjiXrjqSkxMxNtvv62XV/lrS2lpKZYuXYrXX38d/fv3FzucGsd8T0T1Hc87aseUKVPg4uKCRYsWaXWc06dPw9TUtMKZ4bpw7do1bNu2DfPnz1c8AUMf6N0tA6GhoQgMDISbmxs8PDywbt06ZGZmIjg4GMDTe/mys7MRHR2t2Ee+SMjDhw9x7949pKamwszMDC+++KLSsTdu3IgRI0aovUdw1qxZGDZsGBwcHJCXl4clS5agsLBQ5V5XIqpbtm3bhj/++EPxO2P+/PkiR1R/jB07FllZWcjPz9fqONoWFOqDrKwsjB8/Xi/XQqgpzPdEVB/xvKN2GRsbIzIyEkeOHMHDhw/RuHHjah3Hz88PRUVFOo5OWVZWFhYuXIgPPvigRsepKr0rCAQEBKCgoACLFy9GTk4OunbtisOHD8PR0RHA04WZMjMzlfbp2bOn4t/JycnYtm0bHB0dcevWLUX7n3/+iVOnTlX42JGsrCzFKpotW7ZEnz59cPbsWcW4RFQ3rVu3DomJiXB0dMTGjRvh4eEhdkj1hkQiUXvPHlWdk5MTwsLCxA6jVjHfE1F9xPOO2te7d2/07t1b7DCea8CAARgwYIDYYaiQCMK/HhRN1VZYWAgrKytIpVJYWlqKHQ7VoqKiIty8eRPOzs46e746EVFlNP29w9ykW/w8SR/wvIPIMNRGrterNQSIiIiIiIiIqHawIEBERERERERkgFgQINIh3oFDRLWFv2+IiL8HiOq32vgZZ0GASAdMTJ6uz1laWipyJERkKEpKSgA8XWGZiAyL/Ode/nuAiOon+d8W8r81agILAkQ6YGxsDGNjYxQWFoodChEZAEEQIJVKYW5uDlNTU7HDIaJaZmpqCnNzc0ilUs4SIKrHCgsLFX9n1BS9e+wgUV0kkUjQqlUr5OTkwNzcHI0aNYJEIhE7LCKqZwRBQElJCaRSKR4+fIg2bdqIHRIRicTa2hrZ2dnIysqClZUVTE1Nee5BVE8IgoBHjx6hsLAQtra2NfqzzYIAkY5YWVnhyZMnyM/Px71798QOh4jqMXNzc7Rp04aPvSMyYPKf//z8fGRnZ4scDRHpmkQiQdOmTWFlZVWj47AgQKQjEokEtra2aNWqFe/pI6IaY2xszNsEiAjA06KApaUlSkpKUFZWJnY4RKRDpqamtbJOEAsCRDpW0/f5EBERET3L1NSUhUIiqhYuKkhERERERERkgFgQICIiIiIiIjJALAgQERERERERGSAWBIiIiIiIiIgMEAsCRERERERERAaIBQEiIiIiIiIiA8SCABEREREREZEBYkGAiIiIiIiIyACxIEBERERERERkgFgQICIiIiIiIjJAelkQWL16NZydnWFhYQFXV1ckJiZW2DcnJwfjxo1Dp06dYGRkhJCQEJU+UVFRkEgkKq+ioqJqj0tERETaYb4nIiISl94VBHbs2IGQkBDMmzcPKSkp8PLygp+fHzIzM9X2l8lkaNmyJebNm4fu3btXeFxLS0vk5OQovSwsLKo9LhEREVUf8z0REZH4JIIgCGIH8azevXujV69eWLNmjaLNxcUFI0aMQHh4eKX7DhgwAD169EBERIRSe1RUFEJCQnD//v0aGVeusLAQVlZWkEqlsLS01GgfIiKimqSvuamu5nt9/TyJiMhwaZOb9GqGQHFxMZKTk+Hr66vU7uvrizNnzmh17IcPH8LR0RFt27bFa6+9hpSUFK3HlclkKCwsVHoRERFR5epSvmeuJyKi+kyvCgL5+fkoKyuDjY2NUruNjQ1yc3OrfdzOnTsjKioKBw4cQExMDCwsLNC3b19kZGRoNW54eDisrKwUL3t7+2rHSEREZCjqUr5nriciovpMrwoCchKJROlrQRBU2qqiT58+GD9+PLp37w4vLy/s3LkTHTt2xPfff6/VuHPmzIFUKlW87ty5U+0YiYiIDE1dyPfM9UREVJ+ZiB3As6ytrWFsbKxSpc/Ly1Op5mvDyMgIL7/8suKKQXXHNTc3h7m5uc7iIiIiMgR1Kd8z1xMRUX2mVzMEzMzM4OrqitjYWKX22NhYeHp66mwcQRCQmpoKW1vbWh2XiIiImO+JiIj0hV7NEACA0NBQBAYGws3NDR4eHli3bh0yMzMRHBwM4OnUvezsbERHRyv2SU1NBfB0IaF79+4hNTUVZmZmePHFFwEAixYtQp8+fdChQwcUFhbiu+++Q2pqKlatWqXxuERERKQ7zPdERETi07uCQEBAAAoKCrB48WLk5OSga9euOHz4MBwdHQEAOTk5Ks8K7tmzp+LfycnJ2LZtGxwdHXHr1i0AwP379/HOO+8gNzcXVlZW6NmzJxISEuDu7q7xuERERKQ7zPdERETikwiCIIgdRH3BZxMTEZG+YW7SLX6eRESkb7TJTXq1hgARERERERER1Q4WBIiIiIiIiIgMkN6tIUBERERENcNp9iGxQyAi0lu3vhgqdgi1jjMEiIiIiIiIiAwQCwJEREREREREBogFASIiIiIiIiIDxIIAERERERERkQHSqiDw7bff4p9//tFVLERERERERERUS7QqCMycORN2dnb4z3/+g7i4OB2FREREREREREQ1TauCwMqVK+Hi4oKYmBi88sor6NChA5YtW4bc3FxdxUdERERERERENUCrgsD777+PX3/9FSkpKQgODkZBQQHmzJkDR0dHvPHGG/j5558hCIKuYiUiIiIiIiIiHdHJooLdu3fHqlWrkJOTg+joaHh4eGD//v147bXX4OjoiLCwMGRmZupiKCIiIiIiIiLSAZ0+ZcDc3Bzjx49HXFwcEhMTYWtri6ysLCxevBjt2rXDsGHDcP78eV0OSURERERERETVoPPHDh45cgSjRo3CoEGD8Ndff8He3h5z5syBr68vDh8+DE9PT0RHR+t6WCIiIiIiIiKqAhNdHCQzMxObNm1CZGQksrKyYGRkBD8/P7z77rvw9/eHRCIBAFy5cgXDhg3D4sWLMWHCBF0MTURERERERETVoFVBYPfu3Vi/fj2OHz+O8vJytG3bFgsWLMDUqVPRpk0blf5dunRBYGAgli5dqs2wRERERERERKQlrQoCb731FoyNjZVmAxgZVX4XQufOndGvXz9thiUiIiIiIiIiLWlVEJDPBmjbtq3G+4wdOxZjx47VZlgiIiIiIiIi0pJWiwoOGjQI5eXllfbJyspCQkKCNsMQERERERERkY5pVRAYOHAgoqKiKu2zdetWDBw4UJthiIiIiIiIiEjHtCoICILw3D7l5eWKpwxoavXq1XB2doaFhQVcXV2RmJhYYd+cnByMGzcOnTp1gpGREUJCQlT6rF+/Hl5eXmjWrBmaNWsGHx8fnD9/XqlPWFgYJBKJ0qt169ZVipuIiIg0x3xPREQkLq0KAprIyMiAlZWVxv137NiBkJAQzJs3DykpKfDy8oKfnx8yMzPV9pfJZGjZsiXmzZuH7t27q+0TFxeHsWPH4uTJk0hKSoKDgwN8fX2RnZ2t1K9Lly7IyclRvC5fvqz5GyUiIiKNMd8TERGJTyJocpn/GZMnT1b8OyoqCj169ECPHj1U+pWVlSnWDxgyZAh++uknjY7fu3dv9OrVC2vWrFG0ubi4YMSIEQgPD6903wEDBqBHjx6IiIiotF9ZWRmaNWuGlStXYsKECQCeXjHYt28fUlNTNYpTncLCQlhZWUEqlcLS0rLaxyEiItIVfc1NdTXf6+vnqSmn2YfEDoGISG/d+mKo2CFUiza5qcpPGXh2zQCJRILU1NQKk6pEIsHLL7+Mb775RqNjFxcXIzk5GbNnz1Zq9/X1xZkzZ6oaaoUeP36MkpISNG/eXKk9IyMDdnZ2MDc3R+/evbF06VK0a9euwuPIZDLIZDLF14WFhTqLkYiIqL6qS/meuZ6IiOqzKhcEbt68CeDp+gHt2rVDSEgIZsyYodLP2NgYzZo1Q6NGjTQ+dn5+PsrKymBjY6PUbmNjg9zc3KqGWqHZs2ejTZs28PHxUbT17t0b0dHR6NixI+7evYslS5bA09MTV65cQYsWLdQeJzw8HIsWLdJZXNWhr5V+TaprjF33GLs4GLs46nvs9Vldyvf6kOuJiIhqSpULAo6Ojop/R0ZGokePHkptuvDvRQgFQajywoQV+fLLLxETE4O4uDhYWFgo2v38/BT/7tatGzw8PNC+fXts3rwZoaGhao81Z84cpW2FhYWwt7fXSZxERET1XV3I98z1RERUn1W5IPCsoKAgXcUBALC2toaxsbHK1YG8vDyVqwjVsXz5cixduhTHjx/HSy+9VGnfRo0aoVu3bsjIyKiwj7m5OczNzbWOi4iIyJDUpXzPXE9ERPVZlQoCCQkJAAB3d3dYWFgovtZE//79n9vHzMwMrq6uiI2NxciRIxXtsbGxGD58eFVCVfHVV19hyZIlOHr0KNzc3J7bXyaTIS0tDV5eXlqNS0RERMqY74mIiPRDlQoCAwYMgEQiQVpaGjp27Kj4WhNlZWUa9QsNDUVgYCDc3Nzg4eGBdevWITMzE8HBwQCeTt3Lzs5GdHS0Yh/5ooYPHz7EvXv3kJqaCjMzM7z44osAnk4bnD9/PrZt2wYnJyfFFYnGjRujcePGAIBZs2Zh2LBhcHBwQF5eHpYsWYLCwkKdz4IgIiIi5nsiIiJ9UKWCwIIFCyCRSGBtba30tS4FBASgoKAAixcvRk5ODrp27YrDhw8r1inIyclReUZxz549Ff9OTk7Gtm3b4OjoiFu3bgEAVq9ejeLiYowaNUppv4ULFyIsLAwAkJWVhbFjxyI/Px8tW7ZEnz59cPbsWZ2vj0BERETM90RERPqgSgUBeTKt6Gtdef/99/H++++r3fbsYw/lBEGo9HjyE4XKbN++XZPQiIiISEeY74mIiMRlJHYARERERERERFT7WBAgIiIiIiIiMkBVumVg0KBB1RpEIpHgxIkT1dqXiIiIiIiIiHSvSgWBuLi4ag2i64UHiYiIiIiIiEg7VSoIlJeX11QcRERERERERFSLuIYAERERERERkQFiQYCIiIiIiIjIAFXploGEhAQAgLu7OywsLBRfa6J///5Vi4yIiIiIiIiIakyVCgIDBgyARCJBWloaOnbsqPhaE2VlZdUKkIiIiIiIiIh0r0oFgQULFkAikcDa2lrpayIiIqo/Fi9ejAEDBnB2HxERUT1XpYJAWFhYpV8TERFR3RcWFoawsDClgsCyZcvw5ZdfoqCgQMTIiIiISJe4qCARERE9V1FREe7fvy92GERERKRDVZohUJkzZ84gNTUVUqkUVlZW6NGjBzw9PXV1eCIiIiIiIiLSIa0LAgkJCXj77bdx7do1AIAgCIp1BTp06ID169fDy8tL22GIiIiIiIiISIe0KggkJSXB19cXJSUl8Pf3h5eXF2xsbHD37l0kJCTg559/hq+vL06ePIk+ffroKmYiIiIiIiIi0pJWBYG5c+dCIpEgLi5OZRbAxx9/jPj4eAwePBhz587FL7/8olWgREREVHuysrJw/vx5pa8B4MKFCxAEQe0+7u7utRIbERER6YZWBYELFy4gICCgwlsCvL29ERAQgN27d2szDBEREdWyjRs3YuPGjUptgiBUOuOvrKyspsMiIiIiHdKqIGBhYYE2bdpU2qdNmzawsLDQZhgiIiKqRUFBQWKHQERERLVAq4LAK6+88txbAX755Rf4+PhoMwwRERHVosjISLFDICIiolpgpM3OK1aswF9//YVJkyYhOztbaVt2djYmTpyI3NxcLF++vErHXb16NZydnWFhYQFXV1ckJiZW2DcnJwfjxo1Dp06dYGRkhJCQELX9du/ejRdffBHm5uZ48cUXsXfvXq3GJSIiIu0w3xMREYmrSgWBQYMGKb0mTJiA5s2bIzo6Gs7OzujQoQP69u2LDh06wNnZGf/73//QrFmzKk093LFjB0JCQjBv3jykpKTAy8sLfn5+yMzMVNtfJpOhZcuWmDdvHrp37662T1JSEgICAhAYGIhLly4hMDAQo0ePxrlz56o9LhERkaEoLCxEXFwcdu3ahV27diE+Ph4PHjzQ6pjM90REROKTCBUtFayGkVH1JhRIJBKNFxrq3bs3evXqhTVr1ijaXFxcMGLECISHh1e674ABA9CjRw9EREQotQcEBKCwsBA///yzom3IkCFo1qwZYmJitB5XrrCwEFZWVpBKpbC0tNRoH205zT5UK+NU1a0vhj63D2PXPcYuDsYujvoeu65ok5t+//13zJ49G0ePHkV5ebnSNmNjY/j7+2Pp0qV48cUXqxxXXc33YuR6XdLXnwkiIn1Qm/lZl7TJTVX6C7+8vLxaL02LAcXFxUhOToavr69Su6+vL86cOVOVUJUkJSWpHHPw4MGKY1Z3XJlMhsLCQqUXERFRfRAfHw9PT08cPnwY5ubm6NevH0aPHo233noL/fr1g6mpKQ4cOABPT0+cPn26SseuS/meuZ6IiOozrdYQ0LX8/HyUlZXBxsZGqd3Gxga5ubnVPm5ubm6lx6zuuOHh4bCyslK87O3tqx0jERGRvnj8+DHGjx+PJ0+eYNGiRbh79y7i4+MRExOD7du3Iz4+Hnl5eVi4cCEePnyI8ePHo6ioSOPj16V8z1xPRET1mV4VBOQkEonS14IgqLTVxDGrOu6cOXMglUoVrzt37mgVIxERkT748ccfkZ2djc8//xzz589Ho0aNVPo0atQICxcuxNKlS5GZmYkff/yxyuPUhXzPXE9ERPWZVo8dlMvKysLJkyfx119/QSaTqWyXSCSYP3/+c49jbW0NY2NjlSp9Xl6eSjW/Klq3bl3pMas7rrm5OczNzasdFxERkT46dOgQrK2tMXPmzOf2DQkJwVdffYWDBw8iMDBQo+PXpXzPXE9ERPWZ1gWBjz76CN9++63SOgHPVtrl/9akIGBmZgZXV1fExsZi5MiRivbY2FgMHz682jF6eHggNjZW6cTm2LFj8PT0rNFxiYiI6qJLly6hf//+MDU1fW5fMzMzeHt7IzU1VePjM98TERHpB60KAuvXr8eKFSvw6quvIjg4GG+++SYmTpyIwYMHIyEhARs2bMDw4cPxwQcfaHzM0NBQBAYGws3NDR4eHli3bh0yMzMRHBwM4OnUvezsbERHRyv2kZ+EPHz4EPfu3UNqairMzMwUqx7PmDED/fv3x7JlyzB8+HDs378fx48fx6lTpzQel4iIyFDk5eXByclJ4/7Ozs44ceJElcZgviciIhKfVgWBdevWwcnJCT///LPikYROTk4ICAhAQEAARo8ejVdffRWjR4/W+JgBAQEoKCjA4sWLkZOTg65du+Lw4cNwdHQEAOTk5Kg8K7hnz56KfycnJ2Pbtm1wdHTErVu3AACenp7Yvn07Pv30U8yfPx/t27fHjh070Lt3b43HJSIiMhQPHjyo0mOLGjdujIcPH1ZpDOZ7IiIi8WlVEEhPT0dgYKCiGAAApaWlin97e3tj6NChWL58OUaNGqXxcd9//328//77ardFRUWptAmC8Nxjjho16rkxVDYuERGRoSgvL6+VfZjviYiIxKX1GgJNmzZV/LtRo0YoKChQ2t6pUyccP35c22GIiIioFv3+++/YuXOnxn2JiIio7tGqINCmTRtkZWUpvm7fvj3OnTun1Of3339X+7giIiIi0l+7d+/G7t27Neqri8cFEhERUe3TqiDQt29fJCYmKr4ePnw4lixZguDgYAwbNgynTp3Czz//jDfffFPrQImIiKh2LFy4UOwQiIiIqBZoVRAIDAzEX3/9hdu3b8PR0REfffQRDh48iHXr1mH9+vUQBAFOTk746quvdBUvERER1TAWBIiIiAyD0fO7VGzAgAH4+eefFSvzNm7cGGfPnsXOnTuxdOlSbNu2DZcvX+bKvURERHXM2bNn8corr8DS0hKWlpbw8fHB+fPnxQ6LiIiIdEjrRQX/zdTUtEpPFCAiIiL9cvnyZQwcOBAymUzR9ssvv2DgwIE4f/48unTpImJ0REREpCtazRB4VmlpKa5evYqkpCRcvXpV6fGDREREVHd88cUXkMlkmDdvHnJzc3H37l3MnTsXT548wbJly8QOj4iIiHRE64LAvXv38Pbbb6Np06bo1q0b+vXrh27duqFp06Z45513cO/ePV3ESURERLUkMTER/fr1w2effYZWrVqhZcuWWLJkCfr27Yv4+HixwyMiIiId0eqWgezsbPTt2xeZmZlo2bIl+vfvDxsbG9y9exfJycnYsGEDYmNjcerUKbRp00ZXMRMREVENunv3LsaMGaPS3qdPH1y4cEGEiIiIiKgmaDVD4OOPP0ZmZiYWLVqE27dv4/Dhw4iMjMThw4dx+/ZthIWF4fbt2/jkk090FS8RERHVsJKSEjRu3FilvXHjxigpKREhIiIiIqoJWs0QOHLkCIYMGYL58+erbLOwsMCCBQtw5swZ/Pzzz9oMQ0REREREREQ6plVBoLi4GL169aq0j6urK06fPq3NMERERFTLtmzZgrNnzyq1Xbt2DQDg7++v0l8ikeDQoUO1EhsRERHphlYFAVdXV6Snp1faJz09Ha6urtoMQ0RERLXs2rVrigLAvx05ckSlTSKR1HRIREREpGNaFQQ+++wzvPrqq4iKisLEiRNVtm/atAmHDx9GbGysNsMQERFRLbp586bYIRAREVEtqFJBYPHixSptAwcOxJQpU/Dll1+ib9++aNWqFfLy8nD69Gn88ccf8PX1xcmTJ9GvXz+dBU1EREQ1x9HRUewQiIiIqBZUqSAQFhZW4bb09HS1tw8cPXoUx44dU7vwIBERERERERGJo0oFgZMnT9ZUHERERERERERUi6pUEPD29q6pOIiIiIiIiIioFhmJHQARERERERER1T6tnjIgd+bMGURFRSE1NRVSqRSWlpbo2bMnJkyYwMUEiYiIiIiIiPSQ1jMEZs2aBS8vL2zYsAEXL17E9evXkZycjA0bNsDb2xuhoaFVPubq1avh7OwMCwsLuLq6IjExsdL+8fHxcHV1hYWFBdq1a4e1a9cqbR8wYAAkEonKa+jQoYo+YWFhKttbt25d5diJiIhIM8z3RERE4tKqIBAdHY2vv/4anTp1QkxMDHJyclBaWorc3Fxs374dnTt3xrfffovo6GiNj7ljxw6EhIRg3rx5SElJgZeXF/z8/JCZmam2/82bN+Hv7w8vLy+kpKRg7ty5mD59Onbv3q3os2fPHuTk5Chev//+O4yNjfHWW28pHatLly5K/S5fvly9D4aIiIgqxXxPREQkPq1uGVizZg3s7e1x7tw5NGnSRNHeqlUrjB49GkOGDEG3bt2wevVqTJgwQaNjfv3115gyZQqmTp0KAIiIiMDRo0exZs0ahIeHq/Rfu3YtHBwcEBERAQBwcXHBxYsXsXz5crz55psAgObNmyvts337djRs2FDlBMHExIRXCYiIiGoB8z0REZH4tJoh8Pvvv+PNN99UKgY8y9LSEm+88QauXLmi0fGKi4uRnJwMX19fpXZfX1+cOXNG7T5JSUkq/QcPHoyLFy+ipKRE7T4bN27EmDFj0KhRI6X2jIwM2NnZwdnZGWPGjMGNGzcqjVcmk6GwsFDpRURERJWrS/meuZ6IiOozrdcQEASh0u0SiUTjY+Xn56OsrAw2NjZK7TY2NsjNzVW7T25urtr+paWlyM/PV+l//vx5/P7774orEnK9e/dGdHQ0jh49ivXr1yM3Nxeenp4oKCioMN7w8HBYWVkpXvb29pq+VSIiIoNVl/I9cz0REdVnWhUEunbtit27d+Phw4dqtz948AC7d+9Gly5dqnTcfxcRBEGotLCgrr+6duDp1YKuXbvC3d1dqd3Pzw9vvvkmunXrBh8fHxw6dAgAsHnz5grHnTNnDqRSqeJ1586dyt8YERERKdSFfM9cT0RE9ZlWBYHg4GBkZWXBw8MDu3fvVlTo8/PzsWvXLnh6eiIrKwvvvfeeRseztraGsbGxytWBvLw8lasCcq1bt1bb38TEBC1atFBqf/z4MbZv365ytUCdRo0aoVu3bsjIyKiwj7m5OSwtLZVeREREVLm6lO+Z64mIqD7TqiAQFBSEGTNm4MqVKxg9ejRsbGxgamoKGxsbBAQE4MqVK/jvf/+LoKAgjY5nZmYGV1dXxMbGKrXHxsbC09NT7T4eHh4q/Y8dOwY3NzeYmpoqte/cuRMymQzjx49/biwymQxpaWmwtbXVKHYiIiLSDPM9ERGRftB6DYFvvvkGCQkJmDhxInr06AEnJyf06NEDkyZNQnx8PL799tsqHS80NBQbNmzApk2bkJaWhpkzZyIzMxPBwcEAnk7de/aJBcHBwbh9+zZCQ0ORlpaGTZs2YePGjZg1a5bKsTdu3IgRI0aoXEkAgFmzZiE+Ph43b97EuXPnMGrUKBQWFmpczCAiIiLNMd8TERGJT6vHDiYkJMDS0hL9+vVDv379dBJQQEAACgoKsHjxYuTk5KBr1644fPgwHB0dAQA5OTlKzyh2dnbG4cOHMXPmTKxatQp2dnb47rvvFI8gkvvzzz9x6tQpHDt2TO24WVlZGDt2LPLz89GyZUv06dMHZ8+eVYxLREREusN8T0REJD6tCgIDBw5EcHAwVq1apat4AADvv/8+3n//fbXboqKiVNq8vb3x66+/VnrMjh07VvpEhO3bt1cpRiIiItIO8z0REZG4tLploFWrVjAzM9NVLERERERERERUS7QqCAwePBjx8fGVVuKJiIiIiIiISP9oVRBYunQpCgoK8M477+Dvv//WVUxEREREREREVMO0WkNg/PjxaNq0KTZt2oQtW7bA2dkZNjY2kEgkSv0kEglOnDihVaBEREREREREpDtaFQTi4uIU/5bJZEhPT0d6erpKv38XCIiIiIiIiIhIXFoVBMrLy3UVBxERERERERHVomqtIXD27Fm88sorsLS0hJWVFXx8fHD+/Hldx0ZERERERERENaTKMwQuX76MQYMGoaioSNH2yy+/YODAgTh//jy6dOmi0wCJiIiIiIiISPeqPEPgiy++QFFREebNm4fc3FzcvXsXc+fOxZMnT7Bs2bKaiJGIiIiIiIiIdKzKMwQSExPRr18/fPbZZ4q2JUuWID4+HvHx8ToNjoiIiIiIiIhqRpVnCNy9exd9+vRRae/Tpw/u3r2rk6CIiIiIiIiIqGZVuSBQUlKCxo0bq7Q3btwYJSUlOgmKiIiIiIiIiGpWtZ4yQERERERERER1W5XXEACALVu24OzZs0pt165dAwD4+/ur9JdIJDh06FB1hiIiIiIiIiKiGlCtgsC1a9cUBYB/O3LkiEqbRCKpzjBEREREREREVEOqXBC4efNmTcRBRERERERERLWoygUBR0fHmoiDiIiIiIiIiGoRFxUkIiIiIiIiMkAsCBAREREREREZIBYEiIiIiIiIiAyQXhYEVq9eDWdnZ1hYWMDV1RWJiYmV9o+Pj4erqyssLCzQrl07rF27Vml7VFQUJBKJyquoqEircYmIiKj6mO+JiIjEpXcFgR07diAkJATz5s1DSkoKvLy84Ofnh8zMTLX9b968CX9/f3h5eSElJQVz587F9OnTsXv3bqV+lpaWyMnJUXpZWFhUe1wiIiKqPuZ7IiIi8eldQeDrr7/GlClTMHXqVLi4uCAiIgL29vZYs2aN2v5r166Fg4MDIiIi4OLigqlTp2Ly5MlYvny5Uj+JRILWrVsrvbQZFwBkMhkKCwuVXkRERPR8dSXfM9cTEVF9plcFgeLiYiQnJ8PX11ep3dfXF2fOnFG7T1JSkkr/wYMH4+LFiygpKVG0PXz4EI6Ojmjbti1ee+01pKSkaDUuAISHh8PKykrxsre31/i9EhERGaq6lO+Z64mIqD7Tq4JAfn4+ysrKYGNjo9RuY2OD3Nxctfvk5uaq7V9aWor8/HwAQOfOnREVFYUDBw4gJiYGFhYW6Nu3LzIyMqo9LgDMmTMHUqlU8bpz506V3zMREZGhqUv5nrmeiIjqMxOxA1BHIpEofS0Igkrb8/o/296nTx/06dNHsb1v377o1asXvv/+e3z33XfVHtfc3Bzm5ubPeTdERESkTl3I98z1RERUn+nVDAFra2sYGxurVOnz8vJUqvlyrVu3VtvfxMQELVq0ULuPkZERXn75ZcUVg+qMS0RERNXDfE9ERKQf9KogYGZmBldXV8TGxiq1x8bGwtPTU+0+Hh4eKv2PHTsGNzc3mJqaqt1HEASkpqbC1ta22uMSERFR9TDfExER6Qe9u2UgNDQUgYGBcHNzg4eHB9atW4fMzEwEBwcDeHovX3Z2NqKjowEAwcHBWLlyJUJDQ/H2228jKSkJGzduRExMjOKYixYtQp8+fdChQwcUFhbiu+++Q2pqKlatWqXxuERERKQ7zPdERETi07uCQEBAAAoKCrB48WLk5OSga9euOHz4MBwdHQEAOTk5Ss8KdnZ2xuHDhzFz5kysWrUKdnZ2+O677/Dmm28q+ty/fx/vvPMOcnNzYWVlhZ49eyIhIQHu7u4aj0tERES6w3xPREQkPokgX5GHtFZYWAgrKytIpVJYWlrWyphOsw/VyjhVdeuLoc/tw9h1j7GLg7GLo77Hriti5Kb6rK5/nvr6M0FEpA9qMz/rkja5Sa/WECAiIiIiIiKi2sGCABEREREREZEBYkGAiIiIiIiIyACxIEBERERERERkgFgQICIiIiIiIjJALAgQERERERERGSAWBIiIiIiIiIgMEAsCRERERERERAaIBQEiIiIiIiIiA8SCABEREREREZEBYkGAiIiIiIiIyACxIEBERERERERkgFgQICIiIiIiIjJALAgQERERERERGSAWBIiIiIiIiIgMEAsCRERERERERAaIBQEiIiIiIiIiA8SCABEREREREZEBYkGAiIiIiIiIyADpZUFg9erVcHZ2hoWFBVxdXZGYmFhp//j4eLi6usLCwgLt2rXD2rVrlbavX78eXl5eaNasGZo1awYfHx+cP39eqU9YWBgkEonSq3Xr1jp/b0RERPQU8z0REZG49K4gsGPHDoSEhGDevHlISUmBl5cX/Pz8kJmZqbb/zZs34e/vDy8vL6SkpGDu3LmYPn06du/eregTFxeHsWPH4uTJk0hKSoKDgwN8fX2RnZ2tdKwuXbogJydH8bp8+XKNvlciIiJDxXxPREQkPhOxA/i3r7/+GlOmTMHUqVMBABERETh69CjWrFmD8PBwlf5r166Fg4MDIiIiAAAuLi64ePEili9fjjfffBMAsHXrVqV91q9fj127duHEiROYMGGCot3ExIRXCYiIiGoB8z0REZH49GqGQHFxMZKTk+Hr66vU7uvrizNnzqjdJykpSaX/4MGDcfHiRZSUlKjd5/HjxygpKUHz5s2V2jMyMmBnZwdnZ2eMGTMGN27cqDRemUyGwsJCpRcRERFVri7le+Z6IiKqz/SqIJCfn4+ysjLY2NgotdvY2CA3N1ftPrm5uWr7l5aWIj8/X+0+s2fPRps2beDj46No6927N6Kjo3H06FGsX78eubm58PT0REFBQYXxhoeHw8rKSvGyt7fX9K0SEREZrLqU75nriYioPtOrgoCcRCJR+loQBJW25/VX1w4AX375JWJiYrBnzx5YWFgo2v38/PDmm2+iW7du8PHxwaFDhwAAmzdvrnDcOXPmQCqVKl537tx5/psjIiIiAHUj3zPXExFRfaZXawhYW1vD2NhY5epAXl6eylUBudatW6vtb2JighYtWii1L1++HEuXLsXx48fx0ksvVRpLo0aN0K1bN2RkZFTYx9zcHObm5pUeh4iIiJTVpXzPXE9ERPWZXs0QMDMzg6urK2JjY5XaY2Nj4enpqXYfDw8Plf7Hjh2Dm5sbTE1NFW1fffUVPvvsMxw5cgRubm7PjUUmkyEtLQ22trbVeCdERERUEeZ7IiIi/aBXBQEACA0NxYYNG7Bp0yakpaVh5syZyMzMRHBwMICnU/eeXSk4ODgYt2/fRmhoKNLS0rBp0yZs3LgRs2bNUvT58ssv8emnn2LTpk1wcnJCbm4ucnNz8fDhQ0WfWbNmIT4+Hjdv3sS5c+cwatQoFBYWIigoqPbePBERkYFgviciIhKfXt0yAAABAQEoKCjA4sWLkZOTg65du+Lw4cNwdHQEAOTk5Cg9o9jZ2RmHDx/GzJkzsWrVKtjZ2eG7775TPIIIAFavXo3i4mKMGjVKaayFCxciLCwMAJCVlYWxY8ciPz8fLVu2RJ8+fXD27FnFuERERKQ7zPdERETi07uCAAC8//77eP/999Vui4qKUmnz9vbGr7/+WuHxbt269dwxt2/frml4REREpAPM90REROLSu1sGiIiIiIiIiKjmsSBAREREREREZIBYECAiIiIiIiIyQCwIEBERERERERkgFgSIiIiIiIiIDBALAkREREREREQGiAUBIiIiIiIiIgPEggARERERERGRAWJBgIiIiIiIiMgAsSBAREREREREZIBYECAiIiIiIiIyQCwIEBERERERERkgFgSIiIiIiIiIDBALAkREREREREQGiAUBIiIiIiIiIgPEggARERERERGRAWJBgIiIiIiIiMgAsSBAREREREREZIBYECAiIiIiIiIyQCwIEBERERERERkgvSwIrF69Gs7OzrCwsICrqysSExMr7R8fHw9XV1dYWFigXbt2WLt2rUqf3bt348UXX4S5uTlefPFF7N27V+txiYiIqPqY74mIiMSldwWBHTt2ICQkBPPmzUNKSgq8vLzg5+eHzMxMtf1v3rwJf39/eHl5ISUlBXPnzsX06dOxe/duRZ+kpCQEBAQgMDAQly5dQmBgIEaPHo1z585Ve1wiIiKqPuZ7IiIi8eldQeDrr7/GlClTMHXqVLi4uCAiIgL29vZYs2aN2v5r166Fg4MDIiIi4OLigqlTp2Ly5MlYvny5ok9ERAReffVVzJkzB507d8acOXPwyiuvICIiotrjEhERUfUx3xMREYnPROwAnlVcXIzk5GTMnj1bqd3X1xdnzpxRu09SUhJ8fX2V2gYPHoyNGzeipKQEpqamSEpKwsyZM1X6yE8QqjMuAMhkMshkMsXXUqkUAFBYWFj5G9WhctnjWhurKjT5DBi77jF2cTB2cdT32HU9liAItTbm89SlfK8PuV6X9PVngohIH9TV3+3a5Hq9Kgjk5+ejrKwMNjY2Su02NjbIzc1Vu09ubq7a/qWlpcjPz4etrW2FfeTHrM64ABAeHo5FixaptNvb21f8Jg2EVYTYEVQfYxcHYxcHYxeHGLEXFBTAysqq9gdWoy7le+Z6IiLDUZfPLYDq5Xq9KgjISSQSpa8FQVBpe17/f7drcsyqjjtnzhyEhoYqvr5//z4cHR2RmZmpNyddmiosLIS9vT3u3LkDS0tLscOpEsYuDsYuDsYujrocu1QqhYODA5o3by52KCrqQr5nrtcPjF0cjF0cjF0cdTl2bXK9XhUErK2tYWxsrFKlz8vLU6nmy7Vu3VptfxMTE7Ro0aLSPvJjVmdcADA3N4e5ublKu5WVVZ37TyRnaWnJ2EXA2MXB2MXB2MVhZKQ/ywbVpXzPXK9fGLs4GLs4GLs46nLs1cn1+nN2AMDMzAyurq6IjY1Vao+NjYWnp6fafTw8PFT6Hzt2DG5ubjA1Na20j/yY1RmXiIiIqof5noiISD/o1QwBAAgNDUVgYCDc3Nzg4eGBdevWITMzE8HBwQCeTt3Lzs5GdHQ0ACA4OBgrV65EaGgo3n77bSQlJWHjxo2IiYlRHHPGjBno378/li1bhuHDh2P//v04fvw4Tp06pfG4REREpDvM90RERHpA0EOrVq0SHB0dBTMzM6FXr15CfHy8YltQUJDg7e2t1D8uLk7o2bOnYGZmJjg5OQlr1qxROeaPP/4odOrUSTA1NRU6d+4s7N69u0rjaqKoqEhYuHChUFRUVKX99AFjFwdjFwdjFwdjF4c+x14X870+f57Pw9jFwdjFwdjFwdjFoU3sEkHQo+cQEREREREREVGt0Ks1BIiIiIiIiIiodrAgQERERERERGSAWBAgIiIiIiIiMkAsCBAREREREREZIBYEasjnn38OT09PNGzYEE2bNhU7nOdavXo1nJ2dYWFhAVdXVyQmJood0nMlJCRg2LBhsLOzg0Qiwb59+8QOSWPh4eF4+eWX0aRJE7Rq1QojRozAH3/8IXZYGlmzZg1eeuklWFpawtLSEh4eHvj555/FDqvKwsPDIZFIEBISInYoGgkLC4NEIlF6tW7dWuywNJadnY3x48ejRYsWaNiwIXr06IHk5GSxw3ouJycnlc9dIpHggw8+EDu0SpWWluLTTz+Fs7MzGjRogHbt2mHx4sUoLy8XO7R6py7le+b62sVcrx/qUr5nrhdHXc31gG7yPQsCNaS4uBhvvfUW3nvvPbFDea4dO3YgJCQE8+bNQ0pKCry8vODn54fMzEyxQ6vUo0eP0L17d6xcuVLsUKosPj4eH3zwAc6ePYvY2FiUlpbC19cXjx49Eju052rbti2++OILXLx4ERcvXsSgQYMwfPhwXLlyRezQNHbhwgWsW7cOL730ktihVEmXLl2Qk5OjeF2+fFnskDTyzz//oG/fvjA1NcXPP/+Mq1evYsWKFXr/xxPw9P/Ks595bGwsAOCtt94SObLKLVu2DGvXrsXKlSuRlpaGL7/8El999RW+//57sUOrd+pKvmeur33M9eKri/meub721dVcD+go3+v8IYikJDIyUrCyshI7jEq5u7sLwcHBSm2dO3cWZs+eLVJEVQdA2Lt3r9hhVFteXp4AoErPwtYnzZo1EzZs2CB2GBp58OCB0KFDByE2Nlbw9vYWZsyYIXZIGlm4cKHQvXt3scOolk8++UTo16+f2GHoxIwZM4T27dsL5eXlYodSqaFDhwqTJ09WanvjjTeE8ePHixRR/afv+Z65XnzM9bWrLuZ75nr9UFdyvSDoJt9zhoCBKy4uRnJyMnx9fZXafX19cebMGZGiMjxSqRQA0Lx5c5EjqZqysjJs374djx49goeHh9jhaOSDDz7A0KFD4ePjI3YoVZaRkQE7Ozs4OztjzJgxuHHjhtghaeTAgQNwc3PDW2+9hVatWqFnz55Yv3692GFVWXFxMbZs2YLJkydDIpGIHU6l+vXrhxMnTuDPP/8EAFy6dAmnTp2Cv7+/yJGRGJjr9QNzfe2qq/meuV5cdSnXA7rJ9yY1FRzVDfn5+SgrK4ONjY1Su42NDXJzc0WKyrAIgoDQ0FD069cPXbt2FTscjVy+fBkeHh4oKipC48aNsXfvXrz44otih/Vc27dvx6+//ooLFy6IHUqV9e7dG9HR0ejYsSPu3r2LJUuWwNPTE1euXEGLFi3EDq9SN27cwJo1axAaGoq5c+fi/PnzmD59OszNzTFhwgSxw9PYvn37cP/+fUycOFHsUJ7rk08+gVQqRefOnWFsbIyysjJ8/vnnGDt2rNihkQiY68XHXF+76mq+Z64XX13K9YBu8j1nCFSBuoU+/v26ePGi2GFWy78rYIIg1ImqWH3w3//+F7/99htiYmLEDkVjnTp1QmpqKs6ePYv33nsPQUFBuHr1qthhVerOnTuYMWMGtmzZAgsLC7HDqTI/Pz+8+eab6NatG3x8fHDo0CEAwObNm0WO7PnKy8vRq1cvLF26FD179sS7776Lt99+G2vWrBE7tCrZuHEj/Pz8YGdnJ3Yoz7Vjxw5s2bIF27Ztw6+//orNmzdj+fLldeL/iz6or/meuV48zPW1py7ne+Z68dWlXA/oJt9zhkAV/Pe//8WYMWMq7ePk5FQ7weiItbU1jI2NVa4Q5OXlqVxJIN2bNm0aDhw4gISEBLRt21bscDRmZmaGF154AQDg5uaGCxcu4Ntvv8UPP/wgcmQVS05ORl5eHlxdXRVtZWVlSEhIwMqVKyGTyWBsbCxihFXTqFEjdOvWDRkZGWKH8ly2trYqV5VcXFywe/dukSKqutu3b+P48ePYs2eP2KFo5KOPPsLs2bMVOatbt264ffs2wsPDERQUJHJ0+q++5XvmenEx19eu+pTvmetrV13L9YBu8j0LAlVgbW0Na2trscPQKTMzM7i6uiI2NhYjR45UtMfGxmL48OEiRla/CYKAadOmYe/evYiLi4Ozs7PYIWlFEATIZDKxw6jUK6+8orJS76RJk9C5c2d88skndebkQE4mkyEtLQ1eXl5ih/Jcffv2VXnU1p9//glHR0eRIqq6yMhItGrVCkOHDhU7FI08fvwYRkbKkwCNjY352EEN1bd8z1wvDuZ6cdSnfM9cX7vqWq4HdJPvWRCoIZmZmfj777+RmZmJsrIypKamAgBeeOEFNG7cWNzg/iU0NBSBgYFwc3ODh4cH1q1bh8zMTAQHB4sdWqUePnyIa9euKb6+efMmUlNT0bx5czg4OIgY2fN98MEH2LZtG/bv348mTZoortpYWVmhQYMGIkdXublz58LPzw/29vZ48OABtm/fjri4OBw5ckTs0CrVpEkTlfs2GzVqhBYtWtSJ+zlnzZqFYcOGwcHBAXl5eViyZAkKCwvrxNXemTNnwtPTE0uXLsXo0aNx/vx5rFu3DuvWrRM7NI2Ul5cjMjISQUFBMDGpG2lz2LBh+Pzzz+Hg4IAuXbogJSUFX3/9NSZPnix2aPVOXcn3zPW1j7leHHU53zPXi6cu5npAR/leV488IGVBQUECAJXXyZMnxQ5NrVWrVgmOjo6CmZmZ0KtXrzrxSJyTJ0+q/YyDgoLEDu251MUNQIiMjBQ7tOeaPHmy4v9Ky5YthVdeeUU4duyY2GFVS115DJEgCEJAQIBga2srmJqaCnZ2dsIbb7whXLlyReywNPbTTz8JXbt2FczNzYXOnTsL69atEzskjR09elQAIPzxxx9ih6KxwsJCYcaMGYKDg4NgYWEhtGvXTpg3b54gk8nEDq3eqUv5nrm+djHX64+6ku+Z68VTF3O9IOgm30sEQRCqW5EgIiIiIiIiorqJTxkgIiIiIiIiMkAsCBAREREREREZIBYEiIiIiIiIiAwQCwJEREREREREBogFASIiIiIiIiIDxIIAERERERERkQFiQYCIiIiIiIjIALEgQERERERERGSAWBAgIiIiIiIiMkAsCBAREREREREZIBYEiIiIiIiIiAwQCwJEREREREREBogFASIiIiIiIiIDxIIAERERERERkQFiQYCIiIiIiIjIALEgQERERERERGSAWBAgIiIiIiIiMkAsCBAREREREREZIBYEiIiIiIiIiAwQCwJEREREREREBogFASIiIiIiIiIDxIIAERERERERkQFiQYCIiIiIiIjIANXbgkBCQgKGDRsGOzs7SCQS7Nu377n7xMfHw9XVFRYWFmjXrh3Wrl1b84ESERFRtTDXExERaafeFgQePXqE7t27Y+XKlRr1v3nzJvz9/eHl5YWUlBTMnTsX06dPx+7du2s4UiIiIqoO5noiIiLtSARBEMQOoqZJJBLs3bsXI0aMqLDPJ598ggMHDiAtLU3RFhwcjEuXLiEpKakWoiQiIqLqYq4nIiKqOhOxA9AXSUlJ8PX1VWobPHgwNm7ciJKSEpiamqrsI5PJIJPJFF+Xl5fj77//RosWLSCRSGo8ZiIioucRBAEPHjyAnZ0djIzq7cRAjTDXExFRfaRNrmdB4P/Lzc2FjY2NUpuNjQ1KS0uRn58PW1tblX3Cw8OxaNGi2gqRiIio2u7cuYO2bduKHYaomOuJiKg+q06uZ0HgGf+u9MvvpqjoCsCcOXMQGhqq+FoqlcLBwQF37tyBpaVlzQVKRESkocLCQtjb26NJkyZih6IXmOuJiKi+0SbXsyDw/7Vu3Rq5ublKbXl5eTAxMUGLFi3U7mNubg5zc3OVdktLS54kEBGRXuH0dt3m+oflpmjLXE9ERHqkOrnesG8mfIaHhwdiY2OV2o4dOwY3Nze19xQSERFR3aLLXH/h5t+6DI2IiEgU9bYg8PDhQ6SmpiI1NRXA00cNpaamIjMzE8DTKYATJkxQ9A8ODsbt27cRGhqKtLQ0bNq0CRs3bsSsWbPECJ+IiIieQ8xcf+EWCwJERFT31dtbBi5evIiBAwcqvpbf/xcUFISoqCjk5OQoThgAwNnZGYcPH8bMmTOxatUq2NnZ4bvvvsObb75Z67ETERHR84mZ6y/cZkGAiIjqPokgX02HtFZYWAgrKytIpVKuIUBERHqBuUm35J+nfchOnFkwFG2bNRQ7JCIiMnDa5Pp6e8sAERERUU1Kul4gdghERERaqbe3DBBR7SopKUFZWZnYYRDVe8bGxlzsVk8kXS/AW272YodBeoy5kYiqy9TUFMbGxjU+DgsCRKSVwsJC5OfnQyaTiR0KkcEwNzeHtbU1bwEQWdKNAgiCwEc6kgrmRiLSlkQigZWVFVq3bl2jeYYFASKqtsLCQmRnZ6Nx48awtraGqakpT4yJapAgCCgpKYFUKkV2djYAsCggohxpEW4XPIaTdSOxQyE9wtxIRNoSBAGPHj3CvXv30KBBAzRt2rTGxmJBgIiqLT8/H40bN0bbtm15skNUSxo0aIAmTZogKysL+fn5LAiI7PT1fBYESAlzIxHpQoMGDSCTyZCXlwcrK6sa+33CRQWJqFpKSkogk8lq9BcUEaknn0Yok8lQUlIidjgG7QwXFqRnMDcSkS5ZWlqirKysRtciYUGAiKpF/ouJi5sRiUP+s8cFy8SVdL0A5eV8gjM9xdxIRLpkYvJ0Qn9paWmNjcGCABFphVdAiMTBnz398PejYqTnPhA7DNIz/PkkIl2ojd8lLAgQERERaeHM9XyxQyAiIqoWFgSIiIiItHDqGgsCRERUN7EgQERERKSF8zf/RnFpudhhEBERVRkLAkREeiAuLg4SiQRhYWFih1KvlZeXo3v37vD39xc7FIWgoCA4OjqiqKhI7FComh4XlyH1zn2xwyCqd5gbxbNr1y5IJBKcO3dO7FCUREVFQSKRICoqSuxQqiQyMhLGxsa4fPmy2KGoMBE7ACKqv5xmHxI7hArd+mKo2CGQCKKiovDbb79h/fr1KttkMhmWLVuGLVu2IDMzE82aNcPQoUOxZMkStG7dWuMxBgwYgPj4eLXbBg8ejCNHjii1zZ8/H1u3bsU333yDOXPmVO0Nkd44lXEP7s7NxQ6D6gDmRtJ3JSUlmDNnDvz9/dG7d2+xw9FLqamp2LlzJ5KTk/Hrr78iPz8f3t7eiIuLU9s/MDAQn3/+OWbNmoWjR4/WbrDPwYIAEZEecHd3R1paGqytrcUOpd4qKyvDokWL4O3tDXd3d6Vt5eXlGD58OI4ePYrevXvjjTfewPXr1xEZGYnY2FicO3euSkUBAFi4cKFK2wsvvKC2bcSIEVi2bBmmT5+ORo0aVe2NkV5IvJaPUN9OYodBVK8wN4ojKioK165dU1s8p6f27duH8PBwmJmZoWPHjsjPr3wtGRMTE4SEhGDatGk4deoU+vXrV0uRPh8LAkREeqBhw4bo3Lmz2GHUa4cPH0ZmZiYWLFigsm3z5s04evQoxowZg23btike8xMZGYnJkyfjk08+webNm6s0XlWmuI4fPx67d+9GTEwMpk6dWqVxSD9cunMf0iclsGrA588T6QpzozjWrl0LBwcHeHt7ix2K3nrrrbfw+uuvo1u3bigoKICtre1z9xkzZgxmzpyJtWvX6lVBgGsIEBFV0+7du+Ht7Y1WrVrBwsIC9vb2GDJkCPbt26fU77fffsP48ePRtm1bmJubw9bWFkOGDMFPP/2k6FPRfZJOTk5wcnLCP//8g7fffhs2NjZo0KAB3N3dceDAAaW+CxcuhEQiwY8//qg23tWrV0MikeCbb76p8D3l5+fjnXfeQatWrdCwYUO8/PLL2Lt3b6X37P32228YM2YMbG1tYWZmBkdHR0ybNg0FBQVqxzh48CAGDhwIKysrNGjQAD169EBERATKysqU+t26dQsSiQQTJ05EWloaXnvtNTRt2hTNmjXD2LFjFdX4c+fO4dVXX4WlpSWaNWuGt99+G48ePVIZV/4e3nzzTZVt8qsgX3zxhdIzfydNmgQXFxfs2LEDDx7U3LPm/f390ahRI0RGRtbYGFSzygUgiY8fJKqXuVEQBGzatAl9+/aFpaUlGjZsCDc3N2zatEmp35YtWyCRSDBs2DCVY8hz0MiRI1XaoqKisHfvXrz88sto2LAhWrdujffeew///POP2njOnDmDoUOHonnz5rCwsEDnzp0RFhaGx48fq/SVSCQYMGAA7t27h8mTJ6NVq1Zo0KAB+vTpo3Z6++XLl/Hrr7/izTffVMqHcpp+fwHNvsdSqRTLli2Dt7c37OzsYGZmBjs7O0yYMAHXr19X+/4rcvPmTUydOhUODg6K8SZOnIjbt29X6Tia6NKlC3r16gVTU82LwNbW1hg4cCB27dqFhw8f6jym6mJBgIioGtasWYNRo0YhIyMDI0eORGhoKHx8fHDnzh2lpLh37164u7tj586d6N27Nz788EMMHToU2dnZ2Lhxo0ZjFRcXw8fHB6dPn0ZQUBACAwORnp6OESNGYOvWrYp+U6dOhbGxcYVT/DZs2AAzMzNMmDBB7faHDx/C29sb69evR6dOnTBjxgy4uLhg7Nix2LNnj9p9Dhw4AHd3d/z0008YMGAAQkJC0K1bN6xcuRIeHh4qJzPffvsthg0bht9++w3jxo3DBx98gCdPnmDmzJkYPXo0BEFQGePmzZvw9PSETCbD1KlT0b17d2zfvh0jRozA6dOnMXDgQDRs2BDvvPMO2rdvjw0bNiAkJETpGIIgIC4uDp07d0bTpk2VthUVFeHcuXPo1KkTHB0dVcb39fWFTCbD2bNn1X4GFdm+fTvCw8Px3XffISkpqdK+ZmZmcHV1xfnz59UWM6huSMxgQYAMW33MjYIgYPz48ZgyZQry8/Mxbtw4TJ06FY8ePcKUKVMwa9YsRd/x48fjP//5Dw4ePIiVK1cq2q9du4Zp06bBzs4OGzZsUBlj165dGDNmjCL3tmvXDmvXrsXAgQPx5MkTpb7yP8jj4uIwYsQIhISEoHHjxli0aBF8fHwgk8lUjn///n307dsXv/32G/7zn//gjTfewMWLFzF48GD8/vvvSn1PnDgBAOjTp4/KcTT9/gKaf4/T0tKwYMECNGjQACNHjkRISAjc3Nywbds2uLu7a/zH/Llz59CzZ09s3rwZbm5umDFjBry8vLB161a4u7vjxo0bGh2npnl4eEAmk+H06dNih6LAWwaIiKpBfgJx6dIltGzZUmmb/Mp4Xl4eJkyYABMTEyQmJqJnz55K/bKysjQaKycnBy4uLjh79qyiEh0aGopevXrhv//9L15//XU0adJEUaU/fPgwbt26BScnJ8UxUlNTkZKSgoCAALRo0ULtOMuWLcPVq1fxwQcfKJ3ITJ48GQMHDlTpX1BQgMDAQLRs2RKnT5+Gg4ODYltMTAzGjRuHBQsW4PvvvwcA3LhxA7NmzUKrVq1w8eJF2NvbAwCWLl0KX19f7NmzB1u3bsX48eOVxklISEBERARmzJgB4OnJ2WuvvYbDhw/jtddeQ0xMDIYPHw7g6UJIbm5u2Lx5M5YsWQIbGxsAT084/v77b/j5+am8j+vXr6O8vBwdOnRQ+7nI2zMyMvDqq6+q7aPO2LFjlb5++eWXsWPHDjg7O6vt7+rqioSEBJw/f17t5036jwUBMnT1MTdu2LAB27Ztw5QpU7B27VqYmDz986m4uBijRo3CihUrMHbsWLi6ugJ4+kdzUlISPvroI3h7e8PFxQXjxo3Do0ePsH//frXjHDp0CMePH8crr7yiaJs8eTIiIyOxfPlyzJ8/HwDw4MEDRYEjKSkJL730EoD/K1ps27YNX331FT799FOl41+6dAnvv/8+vv/+exgZPb0ePGjQIEydOhUrV67E2rVrFX3PnDkDAOjVq5faz+J531+gat9jFxcX5OTkoHlz5UVZT548CR8fHyxZsuS5axmUlJRgzJgxKC8vx8WLF9G9e3fFtlOnTmHAgAGYMWOG0syEqKgo3Lp1q9LjPmvEiBHo0aOHxv0rIv9/cubMGQwePFjr4+kCZwgQEVWTqamp2qli8mS/efNmPHz4EB9++KFKMgSAtm3bajzWZ599pjRW586dMXnyZNy/fx/79+9XtL/77ruKqY3PkifTt99+u8IxtmzZAnNzc5XF8AYMGKA2aUVHR6OwsBDh4eFKxQDg6R/DvXr1wvbt2xVtW7duRWlpKT788ENFMQB4enX8iy++AAC1tyS0a9cO06ZNU3wtkUgwZswYAEDPnj0VxQDg6fdk1KhRKCkpQVpamqJdfvIhLxA8SyqVAgCsrKxUtgGApaWlUr/nGTFiBH7++Wfk5OTg0aNHSE1NxYQJE3DhwgX4+PiondL5bGyangyT/sn8+zFu5XOGBxm2+pYbV65ciUaNGmHlypWKYgDwNHd9/vnnAJ4WweWaNGmCbdu2obS0FGPHjsXHH3+MCxcu4KOPPsKgQYPUjvHqq68qFQMAYMmSJTA1NVVav2bfvn24f/8+Jk+erCgGAE/z4hdffAETExO1ebRRo0ZYtmyZohgAPH3krYmJCS5cuKDUt7J8CTz/+wtU7XtsZWWlUgwAgIEDB6JLly44fvy42jiedfDgQdy6dQsff/yxUjEAAPr164fhw4fj8OHDKCwsVLRHRUVh0aJFGr9SU1OfG4cm9DHXc4YAEVE1jB49GrNnz0bXrl0xZswYDBgwAP369VOajn7+/HkAT6eca8PU1FTt1D0vLy+sWrUKqampiqvq/v7+aNu2LSIjIxEWFgYjIyMUFRVh27ZtaNeuXYUnI4WFhbh16xa6dOmiUvUHAE9PT5XH5Min0J89exbXrl1T2aeoqAj5+fnIz8+HtbU1UlJSADwtMPxbnz590KBBA7UJt3v37konMQAUi/eoq9bLt2VnZyva5FcumjVrptJf1/59u0L37t2xefNmlJaWYtu2bYiMjMQHH3ygsp/8hOh5KxWTfkvIuAcnaz4pggxTfcuNjx8/xuXLl2FnZ6coXD+rpKQEAJCenq7U3rt3b4SFheHTTz/FlStX4ObmhiVLllT4Xry8vFTa7Ozs0L59e6Snp+PBgwdo0qRJpXnU3t4e7du3xx9//KHoL9ehQwc0btxYqb+JiQlsbGxw//59pfaCggIYGxsr7S+nyfcXqPr3OC4uDhERETh37hzy8/NRWlqq2GZmZvbc/eXnI+np6WoX9M3NzUV5eTn+/PNPuLm5KcYUgz7mehYEiIiq4eOPP0aLFi2wdu1afP3111ixYgVMTEzg7++PiIgIODs7K5JsmzZttBqrRYsWKn8QA/9XZX72yrWxsTGmTJmCRYsW4ciRI/D398euXbtw//59fPTRR2oXCAKgqJqrKwY8O9az/v77bwDAqlWrKo3/0aNHsLa2VoxR0VWHVq1aKf0RLye/Qv8s+VWayrbJT9QAoEGDBgCgci8m8H8zAyqaASCPu6IZBJqaMmUKtm3bhtOnT6stCMhja9iwoVbjkLgS/ryHCR5OYodBJIr6lhv/+ecfCIKA7OxsLFq0qMJY1K39MnLkSMyfPx+CIOCdd96pdPG5Vq1aqW23sbFBeno6CgsL0aRJk+fm0datW+OPP/5Q9JerKH+ZmJioLOjboEEDlJWVoaSkRCVmTb6/AKr0Pf7xxx8REBCAxo0bY/DgwXByckLDhg0Viy1qsoaA/Hzk2bUj1NGHNXr0MdezIEBEVA0SiQRTp07F1KlTUVBQgMTERMTExGDnzp3IyMjA5cuXFRXz7OxspXsWq6qgoADl5eUqJz53794FoJrop06diiVLlmDDhg3w9/fHhg0bYGJigokTJ1Y4hvwP63v37qndLh9L3T6XL19G165dn/s+5P3v3r2rdvG+vLw8tX/g64K80CE/aXhW+/btYWRkhIyMDLX7ytsrWmNAU/LnaFd0y4A8toqKMlQ3nLlegOLScpiZ8K5MMjz1NTe6urri4sWLGsdWXFyM8ePHw8jICE2aNMHcuXPx2muvVfhoury8PLXt8vcij+PZPKpJ/+p4Nl/+u/CgyffX2Ni4St/jsLAwWFhYIDk5WSXPPnvbYWXk7/enn37Ca6+9ptE+Yq0hoI+5ngUBIiIttWjRAiNGjMCIESOQn5+PX375BdeuXYO7uzt27dqFY8eOoW/fvtU+fklJCc6ePQtPT0+l9sTERACq0+bbtm0LPz8/HDx4EKdPn0ZCQgKGDRsGOzu7CsewtLSEk5MTrl27hnv37qkkKvkiQ8/q3bs39uzZg6SkJI0KAj179sTevXsRFxcHd3d3pW3nz5/HkydP4OHh8dzjVEeXLl0q/KPfwsIC7u7uOHv2LG7fvq1SrDh27BjMzc3Ru3dvrWI4d+4cAFR4cvTHH38AALp166bVOCSux8VlSL79Dzzaq1+gjMhQ1Ifc2KRJE7i4uCAtLQ33799XmRpfkdmzZyMlJQVhYWHo3r07Ro4ciQkTJuDYsWNqZyPIY37WX3/9hevXr6N9+/aKq/3y+/Hj4uIwevRopf7Z2dm4fv062rVrp3a6v6a6deuG48ePIyMjo8KZCEDF399OnTpV6Xt8/fp1dOnSRaUYIH//mpDn56SkpCoVBOLj4zXqCzzN3booCOhjrmf5moioGo4ePap0jxvw9OREXvlt0KABgoKC0LhxY6xYsULtvfHqpsdXZP78+UpT4NPT07Fp0yZYWVkpLaon9+6776KkpETxKL9nF0ySSqVIT09HTk6O0j7/+c9/IJPJVKZFxsXFqawfAACTJk1CkyZNMG/ePFy5ckVl++PHj5Ue1Tdu3DiYmJjg66+/xl9//aVoLykpwezZswGg0is12mjatCleeuklXLx4Ue2jDd955x0AT0/int0eGRmJtLQ0BAQEqFxxSU9PV7lv9MaNGyqfK/D0KQfz5s0DAMWCiP927tw52Nraaj0TgcQX96f6q31E9V19zI3Tp0/H48eP8fbbb6udcn7z5k2lK83Hjh1DREQEPD098emnn2LEiBF45513cPz4caxYsULt+4iNjVU87k/u008/RUlJCYKCghRtw4cPh5WVFSIjI5XyriAImDNnDkpKSrTOo97e3gD+bx2AZ2ny/QVQpe+xo6Mjrl27pjTroaioCO+9957KWBUZPnw4HBwc8PXXXyMhIUFle0lJCU6dOqXUFhcXB0EQNH7p6vxEfnFA/jnrA84QICKqhoCAADRs2BD9+vWDo6MjSkpKEBsbi6tXryIgIECx6n50dDTGjBkDd3d3vP766+jUqRPy8/Nx7tw5ODk5qTy3Vx1bW1vcv38fPXr0wNChQyGVShETE4OioiKsX79e7ZUAf39/2Nvb486dO2jTpo3S4/b27t2LSZMmISgoSGk14k8++QS7d+/GqlWr8Ntvv6Ffv37IysrCzp07MWzYMPz0009KUzNbtmyJmJgYvPXWW+jevTuGDBmCzp07o6ioCLdv30Z8fDw8PT1x5MgRAE+n5i9btgwffvghXnrpJYwePRqNGjXCwYMHkZ6ejuHDh6s8clCXRowYgbCwMFy4cEFlhsKECROwY8cObN++HTdv3sSAAQNw48YN7N69G/b29li2bJnK8VxcXABAqYCQkJCAt99+GwMHDlRc1cnIyMChQ4dQUlKCBQsWqF0E6/r167h58ybee+89Hb9rEkP8H/cwx89F7DCIal19zI3vvvsuzp49i82bN+P06dPw8fGBnZ0d7t69i/T0dJw7dw7btm2Dk5MT7t27h6CgIDRp0gRbt26FsbExAOCbb75BfHw85s2bh0GDBqk80m/o0KHw9/fHW2+9BXt7e8THxyMpKQndu3fHrFmzFP0sLS2xfv16jB07Fr1790ZAQABatmyJEydO4OLFi3B3d8dHH31UlW+ZildeeQVNmjTB8ePHERoaqrRN0+9vq1atNP4eT5s2DdOmTUPPnj0xatQolJaWIjY2FoIgoHv37rh06dJzYzY3N8euXbvg5+cHb29vvPLKK4qZi5mZmUhMTESLFi1UivjaSE9PVyw0KV8XID09XVE4sLa2xvLly5X2EQQBJ06cgIuLCzp27KizWLQmkM5IpVIBgCCVSsUOhajGPXnyRLh69arw5MkTsUMRxerVq4XXX39dcHR0FCwsLIQWLVoIvXv3Fn744QehpKREqW9KSoowevRowcbGRjA1NRVsbW0FPz8/4eDBg4o+J0+eFAAICxcuVNrX0dFRcHR0FAoKCoSpU6cKrVq1EszNzQU3Nzdh//79lcY4Z84cAYDw6aefKrVHRkYKAISgoCCVffLy8oQpU6YI1tbWgoWFheDq6irs2bNHWL58uQBA2Lt3r8o+6enpwpQpUwRHR0fBzMxMaNasmdCtWzdh+vTpwvnz51X679+/X/D29haaNGkimJubC926dRNWrFih8rndvHmzwjgr+ryefX+RkZFK7VlZWYKxsbEwbdo0lX0EQRCKioqERYsWCS+88IJgZmYm2NjYCJMnTxb++usvtf0BCP9Oo5cuXRICAwMFFxcXwcrKSjAxMRFsbGyE119/XTh69Kja4wiCIISFhQkAhNTU1Ar7/JumP4PMTbol/zztQ3YKjp8crPCVc98wfzcaOubG+pkbBUEQduzYIfj4+AjNmjUTTE1NhTZt2ggDBgwQVqxYIdy7d08QBEF47bXXBADCli1bVPb/9ddfBTMzM6FTp07Cw4cPlcaMjIwU9uzZI7i6ugoWFhZCq1athHfffVcoKChQG0tCQoLg5+cnNG3aVDAzMxM6duwozJ8/X3HcZwEQvL291R5H/jn+27vvviuYmJgId+/eVWqvyvdXEDT7HpeXlwtr164VunTpIlhYWAitW7cWpkyZIty9e1fw9vZWybMV5XhBeJrnZ8yYIXTo0EEwNzcXLC0tBRcXF2Hq1KnCiRMn1H4G1SX/v1nRS93nGhcXJwAQIiIiNB6nNnK9RBDUzJ2kaiksLISVlRWkUmmNLYxFpC+Kiopw8+ZNODs7w8LCQuxw6i35/eZVWfhGzt/fH0eOHMGNGze0WrgJAMaPH4+tW7fi6tWriivjddG4ceNw7Ngx3L59G40a6cej4UpLS9GxY0c4OTnhl19+0Xg/TX8GmZt0S/552ofshJF5xatEL3uzGwJedqjFyEgfMDfWDn3JjdqKiorCpEmTEBkZWWO3zFVHWloaunXrhs8//xyffPKJ2OHUGxMmTMDBgwdx48YNjdejqI1czzUEiIjqoStXruDIkSMYMmRIlU541N3/Hh8fj+3bt6NTp051uhgAAJ9//jkePnz43Ecl1qb//e9/uHXrFr766iuxQyEdOpmu/okdRCSe6uZGQ+Pi4oLJkydjxYoVevGovvrg2rVr2LZtG+bPn69xMaC2cA0BIqJ6ZNu2bfjjjz8QHR0N4OmCS1Xh7++PBg0aoEePHmjUqBGuXr2KI0eOwNjYGN9//31NhFyrnJ2dsXnzZuTn54sdioJEIsH69evh6uoqdiikQ6ev5aOkrBymxrz2QiQ2bXOjIfrss89gZ2eHW7duoUuXLmKHU+dlZWVh4cKF+OCDD8QORQULAkRE9ci6deuQmJgIR0dHbNy4scqP8QsKCsLWrVuxfft2PHjwAE2bNsWwYcMwZ84crR+7py8CAgLEDkGJPk0TJd15ICtF8u1/0KcdHz9IJDZtc6MhsrGxQVhYmNhh1BsDBgzAgAEDxA5DLRYEiIj0WFXvj4yLi9NqvJCQEISEhGh1DCJ66mR6HgsCRDWgtnNjTZk4cSKLwiQ6zmMjIiIiqgG/pOeJHQIREVGlWBAgIiIiqgEZeQ9x5+/HYodBRERUIRYEiEgrfHIpkTj4s1c3cJaAYeLPJxHpQm38LmFBgIiqxdjYGABQUlIiciREhkn+syf/WST9xIKAYWFuJCJdKi0tBQCYmNTc0n8sCBBRtZiamsLc3BxSqZRXQohqmSAIkEqlMDc3h6mpqdjhUCWSrhfgkaxU7DColjA3EpEuFRYWwtjYuEaL/3zKABFVm7W1NbKzs5GVlQUrKyuYmppCIpGIHRZRvSUIAkpKSiCVSvHw4UO0adNG7JDoOYrLynHqWj4Gd2ktdihUS5gbiUhbgiDg0aNHKCwshK2tbY3+DmFBgIiqzdLSEgCQn5+P7OxskaMhMhzm5uZo06aN4meQ9NuJtLssCBgQ5kYi0gWJRIKmTZvCysqqRsep1wWB1atX46uvvkJOTg66dOmCiIgIeHl5Vdh/69at+PLLL5GRkQErKysMGTIEy5cvR4sWfIYwUUUsLS1haWmJkpISlJWViR0OUb1nbGzM2wSeURdy/S/peSgvF2BkxKvEhoK5kYi0ZWpqWivrBNXbgsCOHTsQEhKC1atXo2/fvvjhhx/g5+eHq1evwsHBQaX/qVOnMGHCBHzzzTcYNmwYsrOzERwcjKlTp2Lv3r0ivAOiusXU1JR/pBBRraoruT7/YTFS7tyHq2OzGhuD9BNzIxHpu3q7qODXX3+NKVOmYOrUqXBxcUFERATs7e2xZs0atf3Pnj0LJycnTJ8+Hc7OzujXrx/effddXLx4sZYjJyIiIk3UpVx/PO1ujY9BRERUVfWyIFBcXIzk5GT4+voqtfv6+uLMmTNq9/H09ERWVhYOHz4MQRBw9+5d7Nq1C0OHDq1wHJlMhsLCQqUXERER1by6lutjr7IgQERE+qdeFgTy8/NRVlYGGxsbpXYbGxvk5uaq3cfT0xNbt25FQEAAzMzM0Lp1azRt2hTff/99heOEh4fDyspK8bK3t9fp+yAiIiL16lquv5b3EDfzH1VrXyIioppSLwsCcv9+PIMgCBU+suHq1auYPn06FixYgOTkZBw5cgQ3b95EcHBwhcefM2cOpFKp4nXnzh2dxk9ERESVq0u5Pvaq+kIFERGRWOrlooLW1tYwNjZWuUKQl5enciVBLjw8HH379sVHH30EAHjppZfQqFEjeHl5YcmSJbC1tVXZx9zcHObm5rp/A0RERFSpupjrj125i3f6t9fJsYiIiHShXs4QMDMzg6urK2JjY5XaY2Nj4enpqXafx48fw8hI+eOQP+ZBEISaCZSIiIiqpS7m+uTMf3DvgazGxyEiItJUvSwIAEBoaCg2bNiATZs2IS0tDTNnzkRmZqZiWuCcOXMwYcIERf9hw4Zhz549WLNmDW7cuIHTp09j+vTpcHd3h52dnVhvg4iIiCpQ13K9IPBpA0REpF/q5S0DABAQEICCggIsXrwYOTk56Nq1Kw4fPgxHR0cAQE5ODjIzMxX9J06ciAcPHmDlypX48MMP0bRpUwwaNAjLli0T6y0QERFRJepirj96JRdj3R1qbTwiIqLKSATOh9eZwsJCWFlZQSqVwtLSUuxwiIiImJt0TP552ofshJF5wyrvb2ZshOT5PmhiYVoD0RERkSHSJtfX21sGiIiIiPRNcVk5Tv5xT+wwiIiIALAgQERERFSrjvyeI3YIREREAFgQICIiIqpVJ9Pv4UlxmdhhEBERsSBAREREVJuelJQh/k/eNkBEROJjQYCIiIiolvG2ASIi0gcsCBARERHVsuNpeZCV8rYBIiISFwsCRERERLXsoawUCX/mix0GEREZOBYEiIiIiERw+DJvGyAiInGxIEBEREQkguNX76KohLcNEBGReFgQICIiIhLBA1kpEjN42wAREYmHBQEiIiIikRz87S+xQyAiIgPGggARERGRSHjbABERiYkFASIiIiKRPCouw8n0PLHDICIiA8WCABEREZGIDlzibQNERCQOFgSIiIiIRPRLeh4eFJWIHQYRERkgFgSIiIiIRCQrLUfs1btih0FERAaIBQEiIiIike1P5W0DRERU+1gQICIiIhLZqWv5yH8oEzsMIiIyMCwIEBEREYmsrFzAod9yxA6DiIgMjF4UBL799lv8888/YodBREREJJr9qdlih0BERAZGLwoCM2fOhJ2dHf7zn/8gLi5O7HCIiIiIat2vmfeRWfBY7DCIiMiA6EVBYOXKlXBxcUFMTAxeeeUVdOjQAcuWLUNubq7YoRERERHVmn2cJUBERLVILwoC77//Pn799VekpKQgODgYBQUFmDNnDhwdHfHGG2/g559/hiAIYodJREREVKP2pmTznIeIiGqNXhQE5Lp3745Vq1YhJycH0dHR8PDwwP79+/Haa6/B0dERYWFhyMzMFDtMIiIiohpxM/8RUu7cFzsMIiIyEHpVEJAzNzfH+PHjERcXh8TERNja2iIrKwuLFy9Gu3btMGzYMJw/f17sMImIiIh0bu+vvG2AiIhqh14WBADgyJEjGDVqFAYNGoS//voL9vb2mDNnDnx9fXH48GF4enoiOjpa7DCJiIiIdOrApb8gKy0TOwwiIjIAJmIH8KzMzExs2rQJkZGRyMrKgpGREfz8/PDuu+/C398fEokEAHDlyhUMGzYMixcvxoQJE0SOmoiIiEh3pE9K8EtaHvy62YodChER1XN6MUNg9+7dGDJkCNq1a4fFixdDEAQsWLAAt27dwoEDBzB06FBFMQAAunTpgsDAQNy+fVvEqImIiIhqxq7kLLFDICIiA6AXMwTeeustGBsbK80GMDKqvFbRuXNn9OvXr5YiJCIiIqo9cX/eQ96DIrRqYiF2KEREVI/pxQyBBQsW4ObNm/jpp5/w2muvPbcYAABjx47FyZMnayE6IiIiotpVVi5gXwoXFyQiopqlFwWBQYMGoby8vNI+WVlZSEhIqKWIiIiIiMT148UsCIIgdhhERFSP6UVBYODAgYiKiqq0z9atWzFw4MDaCYiIiIhIZBl5D5F6577YYRARUT2mFwUBTarf5eXlSgsLEhEREdV3Oy/eETsEIiKqx/SiIKCJjIwMWFlZiR0GERERUa05kPoXHslKxQ6DiIjqKdGeMjB58mSlr/ft24dbt26p9CsrK1OsHzBkyJBaio6IiIhIfI+Ky3DotxyMftle7FCIiKgeEq0g8OyaARKJBKmpqUhNTVXbVyKR4OWXX8Y333xTO8ERERER6YntFzJZECAiohohWkHg5s2bAJ6uH9CuXTuEhIRgxowZKv2MjY3RrFkzNGrUqLZDJCIiIhLdr5n38UfuA3Rq3UTsUIiIqJ4RrSDg6Oio+HdkZCR69Oih1EZERERET8Wcz0TY613EDoOIiOoZvVhUMCgoCN27d9f5cVevXg1nZ2dYWFjA1dUViYmJlfaXyWSYN28eHB0dYW5ujvbt22PTpk06j4uIiIh0w1By/Z5fs1BUUiZ2GEREVM+IMkMgISEBAODu7g4LCwvF15ro37+/Rv127NiBkJAQrF69Gn379sUPP/wAPz8/XL16FQ4ODmr3GT16NO7evYuNGzfihRdeQF5eHkpLubIvERGRPjKkXF9YVIpDv+XgTde2YodCRET1iEQQBKG2BzUyMoJEIkFaWho6duyo+FoTZWWaVcd79+6NXr16Yc2aNYo2FxcXjBgxAuHh4Sr9jxw5gjFjxuDGjRto3ry5Zm/kXwoLC2FlZQWpVApLS8tqHYOIiEiX6nNuEjPX24fshJF5w2rHXh09HZpi7/t9a3VMIiLSf9rkelFmCCxYsAASiQTW1tZKX+tKcXExkpOTMXv2bKV2X19fnDlzRu0+Bw4cgJubG7788kv873//Q6NGjfD666/js88+Q4MGDdTuI5PJIJPJFF8XFhbq7D0QERFRxQwx16dk3sfVvwrxol39KuwQEZF4RCkIhIWFVfq1tvLz81FWVgYbGxuldhsbG+Tm5qrd58aNGzh16hQsLCywd+9e5Ofn4/3338fff/9d4b2F4eHhWLRokU5jJyIioucz1Fy/5dxtLB3ZTewwiIiontCLRQVryr9nHQiCUOFMhPLyckgkEmzduhXu7u7w9/fH119/jaioKDx58kTtPnPmzIFUKlW87ty5o/P3QERERBUztFy/LyUbhUUlosZARET1R70sCFhbW8PY2FjlCkFeXp7KlQQ5W1tbtGnTBlZWVoo2FxcXCIKArKwstfuYm5vD0tJS6UVEREQ1z1Bz/ePiMuxJVh8rERFRVYlyy8CgQYOqtZ9EIsGJEyee28/MzAyurq6IjY3FyJEjFe2xsbEYPny42n369u2LH3/8EQ8fPkTjxo0BAH/++SeMjIzQti1X9CUiItInhpzro8/exgQPJxgZ6W79JSIiMkyiFATi4uKqtV9VFh4MDQ1FYGAg3Nzc4OHhgXXr1iEzMxPBwcEAnk4BzM7ORnR0NABg3Lhx+OyzzzBp0iQsWrQI+fn5+OijjzB58uQKFxoiIiIi8Rhqrr9x7xFOXctH/44txQ6FiIjqOFEKAuXl5TU+RkBAAAoKCrB48WLk5OSga9euOHz4MBwdHQEAOTk5yMzMVPRv3LgxYmNjMW3aNLi5uaFFixYYPXo0lixZUuOxEhERUdUZcq6PTrrFggAREWlNIgiCIHYQ9UV9ftYzERHVTcxNuiX/PO1DdsLIvKFocUgkQNysAXBs0Ui0GIiISD9ok+vr5aKCRERERPWZIACbz9wWOwwiIqrjRLllICEhAQDg7u4OCwsLxdea6N+/f02FRURERFRn7Lx4BzNf7YAmFqZih0JERHWUKAWBAQMGQCKRIC0tDR07dlR8rYmysrIajo6IiIhI/z2UleLHi1mY3M9Z7FCIiKiOEqUgsGDBAkgkElhbWyt9TURERPXH4sWLMWDAAM7uq0FRZ24hyNMJxnwEIRERVQMXFdQhLtxERET6RszcZGRkhLCwMCxYsEDRtmzZMnz55ZcoKCio1Vh0RV8WFXzW2vGuGNK1tdhhEBGRSLioIBEREdUJRUVFuH//vthh1CsbEm+IHQIREdVRotwyUJkzZ84gNTUVUqkUVlZW6NGjBzw9PcUOi4iIiEgvXbz9D37N/Ae9HJqJHQoREdUxelMQSEhIwNtvv41r164BAARBUKwr0KFDB6xfvx5eXl5ihkhERESkl9Yn3MCa8a5ih0FERHWMXhQEkpKS4Ovri5KSEvj7+8PLyws2Nja4e/cuEhIS8PPPP8PX1xcnT55Enz59xA6XiIiISK8cuZKLW/mP4GTdSOxQiIioDtGLgsDcuXMhkUgQFxenMgvg448/Rnx8PAYPHoy5c+fil19+ESlKIiIiqqqsrCycP39e6WsAuHDhAipa19jd3b1WYqtPBAFYl3gDS0d2EzsUIiKqQ/TiKQONGzfGqFGjEBUVVWGfoKAg7N69Gw8fPqy9wKqITxkgIiJ9I/ZTBtQ9VvjZ2wLVKSsrq8mwtKKPTxmQMzMxwqlPBqJVEwuxQyEiolqkTa7XixkCFhYWaNOmTaV92rRpAwsLJjgiIqK6IigoSOwQDEpxaTkiT9/CJ0M6ix0KERHVEXpREHjllVeeeyvAL7/8Ah8fn1qKiIiIiLQVGRkpdggGZ0vSbQR7t4dVA1OxQyEiojrASOwAAGDFihX466+/MGnSJGRnZytty87OxsSJE5Gbm4vly5eLFCERERGR/nsgK8X/km6JHQYREdURoswQGDRokEpb8+bNER0dja1bt8LR0RGtWrVCXl4ebt++jbKyMrz00ksICgrCiRMnRIiYiIiItFVYWIhff/0V+fn5AICWLVuiV69eaNKkiciR1S+bTt/C5H7OaGimFxNBiYhIj4mSKeLi4ircVlpaiuvXr+P69etK7ZcuXap0ASIiIiLST7///jtmz56No0ePory8XGmbsbEx/P39sXTpUrz44osiRVi//P3/2rvvsKjO9H3g9xRmqAMiRTE0S9QAMQpREf1iC2iM0Y2JZt0objSGrDEqPzfRNDVFTDRGzUbXnrg2bDHuYiNRsGIBMVgwahSUUC2Ahf7+/kBGx0GlDWeYuT/XNRfwnnfO3Gcw+8w+nHPe28VYeyQNY3q0lDoKEREZOUkaAg9/GCAiIiLTFBcXh4EDB+LWrVuwtraGv78/3NzcIIRARkYGjh8/jm3btiE2NhbR0dEICgqSOrJJWLzvD7zR1ROWFgqpoxARkRHjuWQmzmtKtNQR6uzyrAFSRyAiolq4c+cO3njjDdy9exczZsxAREQEbGxsdObcvn0bc+bMweeff4433ngDZ8+e5apC9SCnoAhRx64grJuX1FGIiMiIGcVNBYmIiMj0bNy4Eenp6fjyyy/xySef6DUDAMDGxgbTpk3DzJkzkZaWho0bN0qQ1DQtir2IotIyqWMQEZERM6ozBK5evYq9e/fizz//RFFRkd52mUyGTz75RIJkREREVFPR0dFwcnLCpEmTnjh34sSJmD17Nv73v/9hxIgRDZDO9GXmFyLq2BWMDPSSOgoRERkpo2kI/POf/8T8+fNRVna/ky2E0N5IsPJ7NgSIiIgah5MnT+L//u//YGFh8cS5KpUKwcHBSEpKMnwwM/L93gsYGuDOewkQEVGVjOKSgaVLl+Kbb75Br169sGnTJgghEBYWhnXr1iE8PBxKpRKvvvoq9uzZI3VUIiIiqqbs7Gx4eXlVe763tzeys7MNF8gMZeUXYe2RNKljEBGRkTKKhsCSJUvg5eWFHTt24C9/+QsAwMvLC8OGDcP333+P3bt3Y+vWrcjJyZE4KREREVVXQUEBNBpNtefb2tri1q1bBkxknhbGXsSd4lKpYxARkREyioZASkoK+vXrB7n8fpzS0vuFKzg4GAMGDMCcOXOkiEdERES1UJtlhrk0cf3LvVWEHw+lSh2DiIiMkNHcQ8DBwUH7vY2NDa5du6azvW3btvjll18aOBURERHVxalTp7Bhw4ZqzyXD+HfcRfytqwc0lk++nwMREZkPo2gItGjRAlevXtX+3KpVKxw5ckRnzqlTp6pcroiIiIiM1+bNm7F58+ZqzX3wZsJUv/LulmDpvj/w/0LaSh2FiIiMiFE0BIKCgrB//37tz4MGDcIXX3yB8PBwDBw4EAcOHMCOHTswZMgQCVMSERFRTUybNk3qCPSA5QcuYWSgF5zt1FJHISIiI2EUDYERI0bgzz//RGpqKjw9PfHPf/4T//vf/7BkyRIsXboUQgh4eXlh9uzZUkclIiKiamJDwLjcKS7Dd3vO47NBvlJHISIiI2EUNxXs2bMnduzYAU9PTwAVdxmOj4/Hhg0bMHPmTKxduxbJycna7URERNQ4xMfHo0+fPtBoNNBoNOjbty+OHj0qdSyztfZIGi7l3pY6BhERGQmjOEOgKhYWFnj11VeljkFERES1lJycjF69eqGoqEg7tmfPHvTq1QtHjx6Fj4+PhOnMU2m5wJxd5/D93zpJHYWIiIyAUZwh8KDS0lKcOXMGhw8fxpkzZ3SWHyQiIqLGY9asWSgqKsJHH32EzMxMZGVl4cMPP8Tdu3fx1VdfSR3PbEUnZyAx7YbUMYiIyAgYTUMgJycHb731FhwcHODn54fu3bvDz88PDg4OGDt2LHJycqSOSERERDWwf/9+dO/eHZ9//jlcXFzg7OyML774AkFBQYiLi5M6nln7MvoshBBSxyAiIokZRUMgPT0dzz//PJYvXw4bGxuEhoZi5MiRCA0NhY2NDZYtW4bOnTsjPT1d6qhERERUTVlZWejataveeNeuXZGVlSVBIqqUkHoDO05lSh2DiIgkZhQNgffffx9paWmYMWMGUlNTsX37dqxcuRLbt29Hamoqpk+fjtTUVHzwwQdSRyUiIqJqKikpga2trd64ra0tSkpKJEhED4rccRaFJWVSxyAiIgkZRUNg586d6NevHz755BNYWlrqbLO0tMSnn36KkJAQ7NixQ6KERERERKblyvW7WHHwktQxiIhIQkaxykBxcTE6dXr83W79/f1x8ODBBkpERERE9WH16tWIj4/XGbtw4QIA4MUXX9SbL5PJEB0d3SDZCPh+zwW82ukpuGgsnzyZiIhMjlE0BPz9/ZGSkvLYOSkpKfD392+gRERERFQfLly4oG0APGznzp16YzKZzNCR6AG3i8vw1c5z+GZoB6mjEBGRBIyiIfD555/jhRdewA8//IBRo0bpbV+xYgW2b9+OmJiYhg9HREREtXLpEk9Hbww2J17F8C4e8PdsInUUIiJqYJI0BD777DO9sV69emH06NH4+uuvERQUBBcXF2RnZ+PgwYM4d+4cQkJCsHfvXnTv3r3ar7Nw4ULMnj0bGRkZ8PHxwbx589CjR48nPu/gwYMIDg6Gr68vkpKSanJoREREdI+np6fBX4O1vn5M23YKP4/rDoWcZ2gQEZkTmZBgEVq5vHb3MpTJZCgrq97dcKOiojBixAgsXLgQQUFBWLx4MZYtW4YzZ87Aw8Pjkc/Ly8tDp06d0Lp1a2RlZdXoQ0J+fj7s7e2Rl5cHjUZT7ecZkteUxn8d5uVZA6SOQETUaBljbaovUtZ694kbIFdb18NRGI/PB/tiRFfDN3GIiKh+1aXWS9IQiIuLq/Vzg4ODqzWvS5cu6NSpExYtWqQda9++PQYPHozIyMhHPu/1119HmzZtoFAosHXrVjYEjAAbAkREtWeMtam+SFnrTbEhYG9lgT3/LxhNbdVSRyEiohqoS62X5JKB6v6f+toqLi5GQkICpkyZojMeEhKCQ4cOPfJ5K1euxMWLF7F69Wp88cUXT3ydoqIiFBUVaX/Oz8+vfWgiIiKqNtb6+pd3twSRO1Iw5zXeYJCIyFzU7tx9I5ebm4uysjK4urrqjLu6uiIzM7PK55w/fx5TpkzBmjVroFRWr08SGRkJe3t77cPd3b3O2YmIiOjJWOsNY1PCVcT/cU3qGERE1ECMqiFw6NAhjB07Fp07d0bbtm3x/PPPY+zYsThw4ECt9vfw0kVCiCqXMyorK8Pw4cMxY8YMPP3009Xe/9SpU5GXl6d9XLlypVY5iYiIqHZY6+vfRz8lo6i0evdsIiKixs0olh0EgMmTJ+Pbb79F5S0N5HI5ysvLkZCQgOXLl2PChAmYO3dutfbl5OQEhUKh9xeC7Oxsvb8kAEBBQQGOHz+OEydO4N133wUAlJeXQwgBpVKJ3bt3o3fv3nrPU6vVUKt5nR0REVFDY603nIs5t7Eo9iIm9q1+44SIiBonozhDYNWqVZg7dy7atm2LdevWISMjA6WlpcjMzMT69evRrl07zJ8/H6tWrarW/lQqFfz9/RETE6MzHhMTg27duunN12g0SE5ORlJSkvYRHh6Otm3bIikpCV26dKmX4yQiIqL6wVpvWN/vvYDzWQVSxyAiIgMzijMEFi1aBHd3dxw5cgR2dnbacRcXFwwdOhT9+vWDn58fFi5ciJEjR1ZrnxERERgxYgQCAgIQGBiIJUuWIC0tDeHh4QAqTgFMT0/HqlWrIJfL4evrq/N8FxcXWFpa6o0TERGRcWCtN5ySMoEPNv+GjeHdoJDrX4JBRESmwSgaAqdOncJbb72l0wx4kEajwSuvvIJly5ZVe5/Dhg3DtWvX8NlnnyEjIwO+vr7Yvn07PD0r1tfNyMhAWlpaveQnIiKihsdab1iJaTex6vBl/D3IW+ooRERkIDJRedG+hOzs7DBmzBh8++23j5wTERGBpUuXoqDAeE9fM8a1nr2mREsdoc4uzxogdQQiokbLGGtTY1b5frpP3AC52lrqOAZnZaHAron/B4+mpn+sRESNVV1qvVHcQ8DX1xebN2/GrVu3qtxeUFCAzZs3w8fHp4GTEREREZmvuyVl+Oemkygvl/zvR0REZABG0RAIDw/H1atXERgYiM2bNyM3NxdAxRrDmzZtQrdu3XD16lW88847EiclIiIiMi9HLl3Hj4cvSx2DiIgMwCjuIRAWFoakpCTMnz8fQ4cOBXB/2UGgYk3h8ePHIywsTMqYRERERGbpq50pCH7aGS2dbaWOQkRE9cgoGgIA8O2332LIkCFYuXIlkpKSkJ+fD41Gg44dOyIsLAw9evSQOiIRERGRWSosKUfEhpPYFB4IpcIoTjAlIqJ6YBQNgX379kGj0aB79+7o3r271HGIiIiI6CFJV27i+70XMaFvG6mjEBFRPTGKFm+vXr2wdOlSqWMQERER0WMs2HMeJ9JuSB2DiIjqiVE0BFxcXKBSqaSOQURERESPUVYuMGF9Em4VlUodhYiI6oFRNARCQ0MRFxcHIbikDREREZExS7t+B59uPSV1DCIiqgdG0RCYOXMmrl27hrFjx+L69etSxyEiIiKix9hyIh1bEq9KHYOIiOrIKG4q+MYbb8DBwQErVqzA6tWr4e3tDVdXV8hkMp15MpkMv/76q0QpiYiIiKjSx1tPoYO7A1pxKUIiokbLKBoCsbGx2u+LioqQkpKClJQUvXkPNwiIiIiISBp3isswbk0ito4LgqWFQuo4RERUC0ZxyUB5eXm1HmVlZVJHJSIiIqJ7UjILMH3baaljEBFRLUnaEIiPj0efPn2g0Whgb2+Pvn374ujRo1JGIiIiIqIaWH/sCjYevyJ1DCIiqgXJLhlITk5G7969UVhYqB3bs2cPevXqhaNHj8LHx0eqaERERERUAx9vPYVn3DTwcbOXOgoREdWAZGcIzJo1C4WFhfjoo4+QmZmJrKwsfPjhh7h79y6++uorqWIRERERUQ0VlZYjfHUCbt4pljoKERHVgGQNgf3796N79+74/PPP4eLiAmdnZ3zxxRcICgpCXFycVLGIiIiIqBauXL+L8etOoLSsXOooRERUTZI1BLKystC1a1e98a5duyIrK0uCRERERERUF/vP5+LrXeekjkFERNUkWUOgpKQEtrb669ba2tqipKREgkREREREVFdL9v2BzQlXpY5BRETVYBTLDhIRERGR6Zi6JRkJqdeljkFERE8g2SoDALB69WrEx8frjF24cAEA8OKLL+rNl8lkiI6ObpBsRERERFQ7xWXlePs/CfjpH0Fwd7SWOg4RET2CpA2BCxcuaBsAD9u5c6femEwmM3QkIiIiIqoHubeK8eYPx7DpnW6wt7KQOg4REVVBsobApUuXpHppIiIiImoA57Nv4R9rErByVGeolLxSlYjI2EjWEPD09JTqpYmIiIiogRy8cA1TNv+Gb4Z24NmeRERGhq1aIiIiIjKoLSfSuRwhEZERYkOAiIiIiAxuUexFrDjAS0aJiIwJGwJERERE1CA++98Z/HTiqtQxiIjoHjYEiIiIiKjBTN74G2LOZEkdg4iIwIYAERERETWgsnKBcWsTceB8rtRRiIjMHhsCRERERNSgikvL8daq4zh66brUUYiIzJpkyw4SGYrXlGipI9TJ5VkDpI5ARERkcHdLyvD3lUexanRn+Hs6Sh2HiMgs8QwBIiIiIpLE7eIyhK04hoTUG1JHISIyS2wIEBEREZFkbhWVYuTyIzh2mZcPEBE1NDYEiIiIiEhSt4vLMHL5URy6wBsNEhE1JDYEiIiIiEhyd0vK8PcfjmFvSrbUUYiIzAYbAkRERERkFIrurT6w7eSfUkchIjILbAgQERERkdEoLReYsP4E/hOfKnUUIiKTx4YAERERERkVIYBPtp7C3N3nIISQOg4RkcliQ4CIiIiIjNKCPRfwwebfUFJWLnUUIiKTZNINgYULF8Lb2xuWlpbw9/fH/v37Hzl3y5YteOGFF+Ds7AyNRoPAwEDs2rWrAdMSERFRTbHWm74Nx6/izR+OIb+wROooREQmx2QbAlFRUZg4cSI++ugjnDhxAj169ED//v2RlpZW5fx9+/bhhRdewPbt25GQkIBevXph4MCBOHHiRAMnJyIioupgrTcf+8/n4rVFh3H1xh2poxARmRSZMNELs7p06YJOnTph0aJF2rH27dtj8ODBiIyMrNY+fHx8MGzYMHz66afVmp+fnw97e3vk5eVBo9HUKnd985oSLXWEOrs8a0CN5jf2Y67p8RIRPY4x1qb6ImWtd5+4AXK1da1yU+052aqweIQ//D0dpY5CRGQ06lLrTfIMgeLiYiQkJCAkJERnPCQkBIcOHarWPsrLy1FQUABHx0cXnKKiIuTn5+s8iIiIyPBY681T7q1i/HXJEWw4fkXqKEREJsEkGwK5ubkoKyuDq6urzrirqysyMzOrtY9vvvkGt2/fxtChQx85JzIyEvb29tqHu7t7nXITERFR9bDWm6/isnK8v+k3TPv5FG82SERURybZEKgkk8l0fhZC6I1VZd26dZg+fTqioqLg4uLyyHlTp05FXl6e9nHlCrvVREREDYm13nz9eDgVw5fGIzu/UOooRESNllLqAIbg5OQEhUKh9xeC7Oxsvb8kPCwqKgqjR4/Gxo0b0bdv38fOVavVUKvVdc5LRERENcNaTwBw7PINvLjgAP41vCO6tmwqdRwiokbHJM8QUKlU8Pf3R0xMjM54TEwMunXr9sjnrVu3DqNGjcLatWsxYABv7EZERGSsWOupUu6tIgxfGo/v915AeblJ3iubiMhgTPIMAQCIiIjAiBEjEBAQgMDAQCxZsgRpaWkIDw8HUHEKYHp6OlatWgWg4gPCyJEjMX/+fHTt2lX7FwcrKyvY29tLdhxERERUNdZ6qlQugNm7ziH+j2v4ZmgHuNhZSh2JiKhRMMkzBABg2LBhmDdvHj777DM899xz2LdvH7Zv3w5PT08AQEZGhs46xYsXL0ZpaSnGjRuH5s2bax8TJkyQ6hCIiIjoMVjr6WH7z+fixfn7sTclW+ooRESNgkwIwXOr6okxrvXsNSVa6gh1dnlWzU7pbOzHXNPjJSJ6HGOsTY1Z5fvpPnED5GprqePQY4zo6okPX2wPK5VC6ihERAZVl1pvsmcIEBEREZH5+k98KgYs2I8TaTekjkJEZLTYECAiIiIik/RH7m0MWXQIX+1MQWFJmdRxiIiMDhsCRERERGSyygWwKPYiBizYj4TU61LHISIyKmwIEBEREZHJu5hzG6/++zA+/fkUCgpLpI5DRGQU2BAgIiIiIrMgBLDqcCr6zo1D9G8Z4L21icjcsSFARERERGYlK78I49YmYuSKo/gj55bUcYiIJMOGABERERGZpf3ncxE6bx8id5zFraJSqeMQETU4NgSIiIiIyGyVlAksjvsDPWfHIupYGsrKeRkBEZkPNgSIiIiIyOzl3irCB5uTMWDBfuz7PUfqOEREDYINASIiIiKie1IyCzByxVH8bVk8frt6U+o4REQGxYYAEREREdFDDl64hpf/dRDh/0nAucwCqeMQERmEUuoARFQ3XlOipY5QJ5dnDZA6AhER0SPtPJ2JXWcyMcCvOSb0aYM2rnZSRyIiqjc8Q4CIiIiI6DGEAP73WwZC5u3DuDWJOP1nntSRiIjqBc8QICIiIiKqBiGA6OQMRCdnoFdbZ4QHt0Jnb0fIZDKpoxER1QobAkRERERENbT3XA72nsvBc+4OeKtHS4T6uEKp4Mm3RNS4sCFARERERFRLSVduYtzaRDzVxAphgV4Y+rw77K0spI5FRFQtbGMSEREREdXR1Rt38eX2s+g681dM3ZKMsxn5UkciInoiniFARERERFRP7paUYd3RNKw7moZOHg4Y3sUTA/yaw0qlkDoaEZEeNgSIiIiIiAwgMe0mEtNuYsZ/T2PQc24YGuAOvxb2vAkhERkNNgSIiIiIiAyooLAUq+PTsDo+DW1d7fBKpxYY3LEFXDWWUkcjIjPHhgARERERUQM5l1WAyB0p+GpnCgJbNcWgDi0Q6tuMNyIkIkmwIUBERERE1MDKBXDwwjUcvHANH289hf972gkDnm2OPu1dobFkc4CIGgYbAkREREREEiouK8cvZ7Pxy9lsqBRydG/jhFAfV/Rt74qmtmqp4xGRCWNDgIiIiIjISBSXlWNPSjb2pGRDLkuGv2cT9Gnvij7tXNDaxZY3JCSiesWGABERERGRESoXwLHLN3Ds8g3M2pECd0cr9Grrgp5tndG1ZVNYq/hRnojqhv8rQkRERETUCFy5fherDqdi1eFUqBRyBHg1Qfc2Tuje2gk+bvZQyHn2ABHVDBsCRNToeE2JljpCnVyeNUDqCERE1MgVl5Xj0MVrOHTxGr7GOWgslejSsikCWzZF15ZN0a6ZHeRsEBDRE7AhQERERETUyOUXliLmTBZizmQBAOytLBDg2QTPezsiwLMJ/J6yh1qpkDglERkbNgSIiIiIiExM3t0S/JqSjV9TsgEAKoUcvi006OTRBM95OOA5dwe0cLDiTQqJzBwbAkREREREJq64rByJaTeRmHZTO+Zkq8KzTznAr4U9nn3KHn4t7OGisZQuJBE1ODYEiIiIiIjMUO6tYu0Sh5WcbNXwbaFB++YVj2ea28GrqQ2UCrmESYnIUNgQICIiIiIiAEDurSLEnstB7Lkc7ZhKKUdrZ1u0a2aHNq52eNrVFm1c7PBUEyveuJCokWNDgIiIiIiIHqm4tBxnMvJxJiNfZ9zSQo6WTrZo5WKLlk42aOlsg5ZOtvBysoadpYVEaYmoJtgQICIiIiKiGissqbpRAFTcn8CzqQ08m1rD07Hiq7ujNdwdreBsq+bNDImMBBsCRERGzmtKtNQR6uTyrAFSRyAiogaWe6sYubeKkZB6Q2+bpYUcLRys8FQTa7RoYoUWDhUPNwcrNLe3RDN7S1jwngVEDYINASIiIiIiajCFJeW4mHMbF3NuV7ldJgOcbdVoZm8JV40lmmkstd+72KnhbKeGi50aTaxVvIcBUR2xIUBEREREREZDCCC7oAjZBUUA8h45TymXwclWDSc7FZxt1XCyVaOprRpOtio0tVXB0UaNpjYqON57WFooGu4giBoJNgSIiIiIiKjRKS0XyMwvRGZ+YbXmW1ko4GijQhMbCzhYqeBgbQEHaws0sVbB3soCGisLONz7an/vobGygI1KwXsekMky6YbAwoULMXv2bGRkZMDHxwfz5s1Djx49Hjk/Li4OEREROH36NNzc3PD+++8jPDy8ARMTEVFjv2cCwPsmNCTWeiKqrrslZUi/eRfpN+/W6HkKuQy2aiXsLJXQWFrAzlJ572EBW7UStpbKiq9qJWzUStiqFbBRK2GtqhizVilgraoYUyvlbC6QUTHZhkBUVBQmTpyIhQsXIigoCIsXL0b//v1x5swZeHh46M2/dOkSXnzxRbz11ltYvXo1Dh48iH/84x9wdnbGkCFDJDgCIiIiehzWeiJqCGXlAnl3S5B3twRAzZoJD5PLAGvV/SaBpUXFVyuVAlYWyntf5bCyqNhmaVGxzVIph9pCAUsLOSyVFeNqCznUSgXUSjks732vUsqhVsrvfVVAwXss0BOYbENg7ty5GD16NMaMGQMAmDdvHnbt2oVFixYhMjJSb/6///1veHh4YN68eQCA9u3b4/jx45gzZw4/JBARkUEZ8qyI8qI7Btu31FjriaixKRfAraJS3CoqbZDXU8hlUCnkUFvIYaGQV3x/r2Fgoaj8KtNus1DIYaGUw0Iuu/e9DEp5xTylXAalomKbUnH/eQq5DBYKGRTyijGlvGJMKZdBoZDBovJnhez+uPz+vMoxeeVX2f2fFXIZFDKZdp5cBp5hUc9MsiFQXFyMhIQETJkyRWc8JCQEhw4dqvI5hw8fRkhIiM5YaGgoli9fjpKSElhYWOg9p6ioCEVFRdqf8/IqbnqSn6+/FqtUTOGDYE3fz8Z+zDzeJzO3Y+bxNj7GdMyV+xZCGOw1pCB1rQ9ws4SFlVVdD4OISALl9x4AygBRBhSj4tEYKOSAQnavYSCraBLI7/0sl91vHCjkMsjubVfIZJDd2145XyaTQVH5XJkMMu1+Kl6nYl+ADPe3ySr3i4rGhExW8X1lo6JyvOLn+69TMX5/H7i3T+1+AMjvvRYeGJfL7r0+KsZw72e5dqBiv3dvFwCoXa03yYZAbm4uysrK4OrqqjPu6uqKzMzMKp+TmZlZ5fzS0lLk5uaiefPmes+JjIzEjBkz9Mbd3d3rkJ4eZj9P6gQNi8dr+sztmM3teAHjPOZr167B3t5e6hj1Rupa/9P7L9chPRERUf2rTa03yYZApYdPJxFCPPYUk6rmVzVeaerUqYiIiND+fPPmTXh6eiItLc2kPnQ9Tn5+Ptzd3XHlyhVoNBqp4xgcj9f0mdsx83hNX15eHjw8PODo6Ch1FINgrTc8c/vvxtyOFzC/Y+bxmj5zO+a61HqTbAg4OTlBoVDo/YUgOztb7y8DlZo1a1blfKVSiaZNm1b5HLVaDbVarTdub29vFv/wHqTRaMzqmHm8ps/cjpnHa/rkledAmgjW+oZnbv/dmNvxAuZ3zDxe02dux1ybWm9anw7uUalU8Pf3R0xMjM54TEwMunXrVuVzAgMD9ebv3r0bAQEBVV5TSERERNJhrSciIqo7k2wIAEBERASWLVuGFStW4OzZs5g0aRLS0tK0aw1PnToVI0eO1M4PDw9HamoqIiIicPbsWaxYsQLLly/H5MmTpToEIiIiegzWeiIioroxyUsGAGDYsGG4du0aPvvsM2RkZMDX1xfbt2+Hp6cnACAjIwNpaWna+d7e3ti+fTsmTZqE77//Hm5ubliwYEGNliFSq9WYNm1alacWmipzO2Yer+kzt2Pm8Zo+Uz5m1vqGYW7HbG7HC5jfMfN4TZ+5HXNdjlcmTG0dIiIiIiIiIiJ6IpO9ZICIiIiIiIiIHo0NASIiIiIiIiIzxIYAERERERERkRliQ4CIiIiIiIjIDLEhUI8WLlwIb29vWFpawt/fH/v375c6ksHs27cPAwcOhJubG2QyGbZu3Sp1JIOJjIzE888/Dzs7O7i4uGDw4ME4d+6c1LEMatGiRXj22Weh0Wig0WgQGBiIHTt2SB2rwURGRkImk2HixIlSRzGY6dOnQyaT6TyaNWsmdSyDSk9PxxtvvIGmTZvC2toazz33HBISEqSOZTBeXl56v2OZTIZx48ZJHa1RY603XeZW71nrWetNEWt9zWs9GwL1JCoqChMnTsRHH32EEydOoEePHujfv7/Ockem5Pbt2+jQoQP+9a9/SR3F4OLi4jBu3DjEx8cjJiYGpaWlCAkJwe3bt6WOZjBPPfUUZs2ahePHj+P48ePo3bs3Bg0ahNOnT0sdzeCOHTuGJUuW4Nlnn5U6isH5+PggIyND+0hOTpY6ksHcuHEDQUFBsLCwwI4dO3DmzBl88803cHBwkDqawRw7dkzn9xsTEwMAeO211yRO1nix1ps2c6v3rPWs9aaGtb6WtV5QvejcubMIDw/XGWvXrp2YMmWKRIkaDgDx008/SR2jwWRnZwsAIi4uTuooDapJkyZi2bJlUscwqIKCAtGmTRsRExMjgoODxYQJE6SOZDDTpk0THTp0kDpGg/nggw9E9+7dpY4hqQkTJohWrVqJ8vJyqaM0Wqz1P0kdo0GZY71nrTctrPXmpza1nmcI1IPi4mIkJCQgJCREZzwkJASHDh2SKBUZSl5eHgDA0dFR4iQNo6ysDOvXr8ft27cRGBgodRyDGjduHAYMGIC+fftKHaVBnD9/Hm5ubvD29sbrr7+OP/74Q+pIBrNt2zYEBATgtddeg4uLCzp27IilS5dKHavBFBcXY/Xq1XjzzTchk8mkjtMosdabH3Oq96z1pou1nrX+SdgQqAe5ubkoKyuDq6urzrirqysyMzMlSkWGIIRAREQEunfvDl9fX6njGFRycjJsbW2hVqsRHh6On376Cc8884zUsQxm/fr1SExMRGRkpNRRGkSXLl2watUq7Nq1C0uXLkVmZia6deuGa9euSR3NIP744w8sWrQIbdq0wa5duxAeHo733nsPq1atkjpag9i6dStu3ryJUaNGSR2l0WKtNy/mUu9Z600baz1rfXUoDRPHPD3ciRFC8C8xJubdd9/Fb7/9hgMHDkgdxeDatm2LpKQk3Lx5E5s3b0ZYWBji4uJM8oPClStXMGHCBOzevRuWlpZSx2kQ/fv3137v5+eHwMBAtGrVCj/++CMiIiIkTGYY5eXlCAgIwMyZMwEAHTt2xOnTp7Fo0SKMHDlS4nSGt3z5cvTv3x9ubm5SR2n0WOvNg7nUe9Z608Zaz1pfHTxDoB44OTlBoVDo/YUgOztb7y8J1HiNHz8e27Ztw969e/HUU09JHcfgVCoVWrdujYCAAERGRqJDhw6YP3++1LEMIiEhAdnZ2fD394dSqYRSqURcXBwWLFgApVKJsrIyqSManI2NDfz8/HD+/HmpoxhE8+bN9T7gtm/f3mRvBveg1NRU/PLLLxgzZozUURo11nrzYU71nrWetd6UsNbXrtazIVAPVCoV/P39tXd1rBQTE4Nu3bpJlIrqixAC7777LrZs2YI9e/bA29tb6kiSEEKgqKhI6hgG0adPHyQnJyMpKUn7CAgIwN/+9jckJSVBoVBIHdHgioqKcPbsWTRv3lzqKAYRFBSkt3zY77//Dk9PT4kSNZyVK1fCxcUFAwYMkDpKo8Zab/pY71nrTR1rvemqS63nJQP1JCIiAiNGjEBAQAACAwOxZMkSpKWlITw8XOpoBnHr1i1cuHBB+/OlS5eQlJQER0dHeHh4SJis/o0bNw5r167Fzz//DDs7O+1fh+zt7WFlZSVxOsP48MMP0b9/f7i7u6OgoADr169HbGwsdu7cKXU0g7Czs9O7RtTGxgZNmzY12WtHJ0+ejIEDB8LDwwPZ2dn44osvkJ+fj7CwMKmjGcSkSZPQrVs3zJw5E0OHDsXRo0exZMkSLFmyROpoBlVeXo6VK1ciLCwMSiVLfl2x1pturQfMr96z1rPWmxrW+lrWegOsdmC2vv/+e+Hp6SlUKpXo1KmTSS9Ts3fvXgFA7xEWFiZ1tHpX1XECECtXrpQ6msG8+eab2n/Lzs7Ook+fPmL37t1Sx2pQpr4U0bBhw0Tz5s2FhYWFcHNzE6+88oo4ffq01LEM6r///a/w9fUVarVatGvXTixZskTqSAa3a9cuAUCcO3dO6igmg7XeNGu9EOZX71nrWetNEWt9zcmEEKL2/QgiIiIiIiIiaox4DwEiIiIiIiIiM8SGABEREREREZEZYkOAiIiIiIiIyAyxIUBERERERERkhtgQICIiIiIiIjJDbAgQERERERERmSE2BIiIiIiIiIjMEBsCRERERERERGaIDQEiIhMWGxsLmUyG6dOnSx2FiIiIDIC1nuqCDQEyC5cvX4ZMJkO/fv0eOSc+Ph4ymQyjRo1quGAkqVGjRkEmk+Hy5ctSRyEiojpiraeqsNYTPR4bAkRERERERERmiA0BIiIiIiIiIjPEhgDRYwQHB8PCwgIZGRlVbh86dChkMhlOnDgBQPcarn379iE4OBi2trZwdHTE8OHDcfXq1Sr3k52djUmTJqF169ZQq9VwcnLCkCFDcOrUKb25Xl5e8PLyws2bN/Hee+/B3d0dSqUSP/zwAwCgZ8+ekMlkKCwsxPvvvw93d3dYWlrCz88PK1as0NtfXl4evvrqKwQHB8PNzQ0qlQpubm4YOXIkLl68qDd/+vTpkMlkiI2NxY8//gh/f39YW1ujZ8+edd7fypUr4efnBysrK3h7e2PBggUAACEE5s+fj3bt2sHS0hJPP/00/vOf/1T5XhYXF2Pu3Lno1KkTbGxsYGdnhx49emDbtm167+OPP/4IAPD29oZMJoNMJtMeR6VLly5hzJgx8PDwgFqtRvPmzTFq1CikpqbqvXbl89PT0zFq1Cg0a9YMcrkcsbGxVWYFgN69e0MulyMtLa3K7W+99RZkMhn279+vPb7vvvsOoaGhcHd3h1qthouLC1555RXtv8PqqOpYK1X+G3tYdd9bIqLGhLWetZ61/j7WevOjlDoAkTF7++23sW/fPqxcuRIffvihzrbc3Fz8/PPP8Pf3R8eOHXW2xcfHIzIyEgMGDMB7772HxMRErFu3DgcOHMCxY8fg6uqqnXvx4kVtYQkJCcHgwYORnZ2NzZs3Y9euXfj111/RpUsXnf0XFRWhd+/eKCgowMCBA6FSqXT2CQCvvfYafvvtN7z22msoKSnBhg0bMHr0aGRlZWHq1KnaeWfPnsWnn36KXr164S9/+QtsbGyQkpKCtWvXIjo6GomJifD09NR7b2bPno29e/fi5ZdfxgsvvAClUlmn/c2bNw+xsbEYNGgQevfujc2bN2PChAmwtrbGyZMnsXHjRrz00kvo3bs31q9fj5EjR8Lb2xvdu3fXeV/69euH2NhYdOzYEaNHj0ZJSQmio6MxaNAgfPfdd3j33XcBABMnTsQPP/yAkydPYsKECXBwcAAAneJ45MgRhIaG4vbt2xg4cCBat26Ny5cvY82aNdixYwcOHz6Mli1b6hzHtWvXEBgYCEdHRwwbNgzFxcXQaDR6x1tpxIgR2Lt3L9asWaPze6k8nk2bNsHLy0t7nNevX8fEiRPRo0cPvPjii2jSpAn++OMPbNu2DTt27MC+ffvw/PPPP/L1aqsm7y0RUWPCWs9az1p/PwtrvRkSRGbg0qVLAoBo1aqVmDZtWpWP0aNHCwAiLCxM+7zCwkLRtGlT0apVK1FeXq6zz7lz5woAYtGiRdqxvXv3CgACgFi2bJnO/BkzZggA4s0339QZ79atm1AqlWL37t064+fOnRN2dnbCz89PZ9zT01MAECEhIeLOnTt6xxocHCwAiGeeeUbk5+drxzMyMkTz5s2FUqkUFy9e1I7fvHlTXLt2TW8/e/bsEXK5XIwZM0ZnfNq0aQKAsLGxEb/99pve82q7P0dHR51caWlpQqVSCXt7e/H000+L7Oxs7bYjR44IAOLll1/W2deHH34oAIjp06fr/L7y8/NFQECAUKlUIj09XTseFhYmAIhLly7p5S0uLhZeXl7Czs5OJCUl6Wzbv3+/UCgU4qWXXtIZr/zd//3vfxelpaV6+6xKfn6+sLKyEs8884zetk2bNgkA4uOPP9aOFRYWiqtXr+rNPXXqlLC1tRV9+/bVGa/8Nzlt2jS9rMHBwVVm8vT0FJ6enjpjNX1viYgaGms9az1r/TS9rKz19CRsCJBZqPyQUJ3Hgx8ShBAiIiJCABC//vqrzriPj4+wtrYWeXl52rHK/0Fu27at3oeKO3fuCGdnZ2FlZSWKioqEEEIkJiYKAGL06NFV5q587eTkZO1Y5YeEkydPVvmcyg8Ja9as0ds2e/ZsAUB8/vnnj36zHuDn5ye8vLx0xiqL+qRJk6q1j+rub/r06Xrze/fuLQCIH3/8UW9by5YtdQpZWVmZaNKkiWjdurXeey+EENu2bRMAxHfffacde9yHhC1btjz2vXrllVeEXC7X+f0DECqVSuTk5FT5nEcZNmyYACASExN1xgcPHiwAiJSUlGrtZ+DAgUKlUoni4mLtWH18SKjNe0tE1NBY6yuw1rPWP5iVtZ6ehJcMkFkJDQ3Fzp07q9wWHx+PwMBAvfGxY8di7ty5WLZsGXr37q2de/r0aYwaNarKU8SCgoIgk8l0xqysrODv74+dO3fi999/h6+vL+Lj4wEAmZmZVa4dm5KSov3q6+urHa+8TvBxevTo8cixpKQknfHY2FjMmzcPR44cQW5uLkpLS7XbVCpVlfvv3LnzI1+7Nvt7+FRMAGjevDkA4Lnnnqty25EjR7Q/nzt3Djdu3ICbmxtmzJihNz8nJwfA/ff0SSp/NykpKVX+bjIzM1FeXo7ff/8dAQEB2nFvb284OTlV6zUqjRgxAlFRUVi9erX2fbh+/Tq2b9+O559/Hm3bttWZn5SUhK+//hoHDhxAZmYmSkpKdLbn5uZq37v6UN/vLRGRIbHWs9az1tcca735YkOA6Anatm2L4OBgbNmyBdevX4ejoyOWLVsGoOImMFVxcXGpcrzy2r+8vDwAFYUAAKKjoxEdHf3IDLdv39bb/8MfQqqT4eHXB4CNGzdi2LBhsLW1RWhoKLy8vGBtbQ2ZTIYffvihyhvqPLivh9V2f1V92Kq8VvFR2x788FH5Xp4+fRqnT5+u8jUA/ffyUSr3t2bNmsfOe3h/j3pfHic0NBQuLi5Yt24dZs+eDblcjg0bNqC4uBgjRozQmXvo0CHth9WQkBC0adMGtra2kMlk2Lp1K06ePImioqIaZ3ic+n5viYiMDWs9a/3jsNbfx1pvetgQIKqGt99+G3FxcVi9ejXefPNNREVF4ZlnnkG3bt2qnJ+dnV3leFZWFgDA3t4ewP3iV9ObtDzpA0JlBnd398e+PlBx519LS0skJCSgTZs2OvPXr19f4wy13V9dVb6XQ4YMwaZNm+ptf//973/x0ksvVft51fndPEypVOL111/HggULsGfPHvTt2xerV6/Wjj/oyy+/RFFREQ4cOICgoCCdbfHx8Th58mS1cz74IetBeXl5Ov9G6vu9JSIyRqz11c/AWs9aT6aDyw4SVcOQIUPg5OSEZcuWISoqCrdu3cKYMWMeOf/gwYMQQuiM3b17FwkJCbCyssLTTz8NANo7Ch8+fLjeM1cuXVPV2IOn5V28eBHt27fXK+h//vlnlUsHPUl976+62rdvD41Gg+PHj+udVvcoCoUCAFBWVqa3zZC/m6q88cYbAIDVq1fj0qVLOHToEEJDQ+Hs7Kwz7+LFi3B0dNT7gHDnzh0kJiZW+/WaNGmC9PR0vfHLly/j5s2bOmO1eW+JiBob1vrqY62vHdZ6MkZsCBBVg0qlQlhYGJKTk/Hpp59CpVJh5MiRj5x/7tw5vXWAZ8+ejZycHPz1r3/VXlvXuXNndOnSBevWrUNUVJTefsrLyxEXF1erzF9++SUKCgq0P2dlZWHu3LlQKpUYPny4dtzT0xMXLlzQ/kUBAAoLC/HOO+88sqv8OPW9v+pSKpV45513kJqaismTJ1dZzE6dOqXzFx1HR0cAqHLN6EGDBsHDwwNz587Fvn379LaXlJTgwIED9Za/8vrBLVu2YOnSpRBC6J1CCFS8vzdu3NA5na+srAyTJ0/WXt9XHQEBAbh8+bLOusnFxcWIiIjQm1ub95aIqLFhra8+1vraYa0nY8RLBoiqaezYsfjmm2/w559/YtiwYWjatOkj54aEhOAf//gHoqOj0a5dOyQmJmLXrl1wd3fHzJkzdeauW7cOvXr1wuuvv4558+bB398flpaWSEtLw+HDh5GTk4PCwsIa523ZsiV8fX0xZMgQ7drE2dnZ+PLLL3XW0x0/fjzGjx+Pjh074tVXX0VpaSliYmIghECHDh2qfVqaofZXEzNmzEBiYiIWLFiA6OhoBAcHw9nZGenp6UhOTsbJkydx+PBh7TWXvXv3xpw5c/D222/jtddeg42NDTw8PDB8+HCo1Wps2rQJ/fv3R3BwMPr06aO92VNaWhr279+Ppk2b1uvNdUaMGIGPP/4Yc+bMgUajwcsvv6w3Z/z48di9eze6d++OoUOHwtLSErGxsUhPT0fPnj11iv7jTJo0Cbt378aAAQPw17/+FdbW1oiJiYGDg0OVNymq6XtLRNQYsdZXD2t97bHWk9GRboEDooZTuRRRaGjoI+ccPny4yqWIHhQYGCgAiF9++aXK7Q8u+xIXFyd69OghrK2thYODg3j99ddFWlpalc+7fv26+Pjjj4Wvr6+wsrIStra2ok2bNmL48OFiy5YtOnOrWjf2QZVLEd25c0dMnjxZtGjRQqhUKuHj46O3XrIQQpSXl4t///vfwsfHR1haWopmzZqJ0aNHi6ysLO2+HlS5dNDevXurfP363N/jlgqqal9CCFFaWioWL14sgoKChEajEWq1Wnh4eIh+/fqJRYsWiVu3bunM//rrr0WbNm2EhYVFlcvzXL16VUyYMEG0adNGqNVqodFoRPv27cWYMWP0lqeq6vk1cfnyZSGTybTrGz/Kpk2bRKdOnYS1tbVwcnISQ4cOFRcvXqzy/XrUUkRCCBEVFSX8/PyESqUSzZo1E+PHjxcFBQWP/DdW0/eWiKghsdaz1rPWT9PbD2s9PYlMiIcufiKiKhUWFqJFixZwcHDAhQsXqryhTGxsLHr16oVp06ZVuXxNQ+jZsyfi4uL0rmskIiKix2OtJyJzw3sIEFXTihUrcP36dbz99tu1urssERERGTfWeiIyN7yHANETzJo1Czk5OVi8eDFcXFwQHh4udSQiIiKqR6z1RGSu2BAgeoKpU6dCpVKhQ4cOWLBggXadViIiIjINrPVEZK54DwEiIiIiIiIiM8R7CBARERERERGZITYEiIiIiIiIiMwQGwJEREREREREZogNASIiIiIiIiIzxIYAERERERERkRliQ4CIiIiIiIjIDLEhQERERERERGSG2BAgIiIiIiIiMkP/Hyiysw62xFDpAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# extra code – plots a few distributions you can use in randomized search\n",
+    "\n",
+    "from scipy.stats import randint, uniform, geom, expon\n",
+    "\n",
+    "xs1 = np.arange(0, 7 + 1)\n",
+    "randint_distrib = randint(0, 7 + 1).pmf(xs1)\n",
+    "\n",
+    "xs2 = np.linspace(0, 7, 500)\n",
+    "uniform_distrib = uniform(0, 7).pdf(xs2)\n",
+    "\n",
+    "xs3 = np.arange(0, 7 + 1)\n",
+    "geom_distrib = geom(0.5).pmf(xs3)\n",
+    "\n",
+    "xs4 = np.linspace(0, 7, 500)\n",
+    "expon_distrib = expon(scale=1).pdf(xs4)\n",
+    "\n",
+    "plt.figure(figsize=(12, 7))\n",
+    "\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.bar(xs1, randint_distrib, label=\"scipy.randint(0, 7 + 1)\")\n",
+    "plt.ylabel(\"Probability\")\n",
+    "plt.legend()\n",
+    "plt.axis([-1, 8, 0, 0.2])\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.fill_between(xs2, uniform_distrib, label=\"scipy.uniform(0, 7)\")\n",
+    "plt.ylabel(\"PDF\")\n",
+    "plt.legend()\n",
+    "plt.axis([-1, 8, 0, 0.2])\n",
+    "\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.bar(xs3, geom_distrib, label=\"scipy.geom(0.5)\")\n",
+    "plt.xlabel(\"Hyperparameter value\")\n",
+    "plt.ylabel(\"Probability\")\n",
+    "plt.legend()\n",
+    "plt.axis([0, 7, 0, 1])\n",
+    "\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.fill_between(xs4, expon_distrib, label=\"scipy.expon(scale=1)\")\n",
+    "plt.xlabel(\"Hyperparameter value\")\n",
+    "plt.ylabel(\"PDF\")\n",
+    "plt.legend()\n",
+    "plt.axis([0, 7, 0, 1])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the PDF for `expon()` and `reciprocal()` (left column), as well as the PDF of log(X) (right column). The right column shows the distribution of hyperparameter _scales_. You can see that `expon()` favors hyperparameters with roughly the desired scale, with a longer tail towards the smaller scales. But `reciprocal()` does not favor any scale, they are all equally likely:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# extra code – shows the difference between expon and reciprocal\n",
+    "\n",
+    "from scipy.stats import reciprocal\n",
+    "\n",
+    "xs1 = np.linspace(0, 7, 500)\n",
+    "expon_distrib = expon(scale=1).pdf(xs1)\n",
+    "\n",
+    "log_xs2 = np.linspace(-5, 3, 500)\n",
+    "log_expon_distrib = np.exp(log_xs2 - np.exp(log_xs2))\n",
+    "\n",
+    "xs3 = np.linspace(0.001, 1000, 500)\n",
+    "reciprocal_distrib = reciprocal(0.001, 1000).pdf(xs3)\n",
+    "\n",
+    "log_xs4 = np.linspace(np.log(0.001), np.log(1000), 500)\n",
+    "log_reciprocal_distrib = uniform(np.log(0.001), np.log(1000)).pdf(log_xs4)\n",
+    "\n",
+    "plt.figure(figsize=(12, 7))\n",
+    "\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.fill_between(xs1, expon_distrib,\n",
+    "                 label=\"scipy.expon(scale=1)\")\n",
+    "plt.ylabel(\"PDF\")\n",
+    "plt.legend()\n",
+    "plt.axis([0, 7, 0, 1])\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.fill_between(log_xs2, log_expon_distrib,\n",
+    "                 label=\"log(X) with X ~ expon\")\n",
+    "plt.legend()\n",
+    "plt.axis([-5, 3, 0, 1])\n",
+    "\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.fill_between(xs3, reciprocal_distrib,\n",
+    "                 label=\"scipy.reciprocal(0.001, 1000)\")\n",
+    "plt.xlabel(\"Hyperparameter value\")\n",
+    "plt.ylabel(\"PDF\")\n",
+    "plt.legend()\n",
+    "plt.axis([0.001, 1000, 0, 0.005])\n",
+    "\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.fill_between(log_xs4, log_reciprocal_distrib,\n",
+    "                 label=\"log(X) with X ~ reciprocal\")\n",
+    "plt.xlabel(\"Log of hyperparameter value\")\n",
+    "plt.legend()\n",
+    "plt.axis([-8, 1, 0, 0.2])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analyze the Best Models and Their Errors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.06, 0.06, 0.05, 0.01, 0.01, 0.01, 0.01, 0.19, 0.01, 0.01, 0.02,\n",
+       "       0.04, 0.01, 0.  , 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.  , 0.  ,\n",
+       "       0.01, 0.  , 0.01, 0.02, 0.02, 0.01, 0.01, 0.01, 0.03, 0.01, 0.01,\n",
+       "       0.01, 0.01, 0.01, 0.01, 0.  , 0.01, 0.01, 0.02, 0.01, 0.01, 0.01,\n",
+       "       0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.  , 0.08,\n",
+       "       0.  , 0.  , 0.  , 0.01])"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_model = rnd_search.best_estimator_  # includes preprocessing\n",
+    "feature_importances = final_model[\"random_forest\"].feature_importances_\n",
+    "feature_importances.round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0.1898423270105783, 'log__median_income'),\n",
+       " (0.07709175866873944, 'cat__ocean_proximity_INLAND'),\n",
+       " (0.06455488601956336, 'bedrooms__ratio'),\n",
+       " (0.056936146643377976, 'rooms_per_house__ratio'),\n",
+       " (0.0490294770805355, 'people_per_house__ratio'),\n",
+       " (0.03807069074492323, 'geo__Cluster 3 similarity'),\n",
+       " (0.025643913400094476, 'geo__Cluster 22 similarity'),\n",
+       " (0.02179127543243723, 'geo__Cluster 17 similarity'),\n",
+       " (0.021575251507503695, 'geo__Cluster 6 similarity'),\n",
+       " (0.017868654556924362, 'geo__Cluster 2 similarity'),\n",
+       " (0.017431400050755975, 'geo__Cluster 32 similarity'),\n",
+       " (0.015981159400591683, 'geo__Cluster 18 similarity'),\n",
+       " (0.01488846425739688, 'geo__Cluster 40 similarity'),\n",
+       " (0.014488389218107143, 'geo__Cluster 43 similarity'),\n",
+       " (0.014252940099964142, 'geo__Cluster 7 similarity'),\n",
+       " (0.014038173319370725, 'geo__Cluster 21 similarity'),\n",
+       " (0.013846025114732157, 'geo__Cluster 38 similarity'),\n",
+       " (0.01362570996472274, 'geo__Cluster 34 similarity'),\n",
+       " (0.013547297167034428, 'geo__Cluster 41 similarity'),\n",
+       " (0.012900089026066918, 'geo__Cluster 24 similarity'),\n",
+       " (0.012620908145579916, 'geo__Cluster 10 similarity'),\n",
+       " (0.011621275372313349, 'remainder__housing_median_age'),\n",
+       " (0.01159076532124518, 'geo__Cluster 42 similarity'),\n",
+       " (0.011475471902949968, 'geo__Cluster 30 similarity'),\n",
+       " (0.011145862167873194, 'geo__Cluster 31 similarity'),\n",
+       " (0.011019562821385253, 'geo__Cluster 16 similarity'),\n",
+       " (0.010872453237288457, 'geo__Cluster 1 similarity'),\n",
+       " (0.010683824851366689, 'geo__Cluster 25 similarity'),\n",
+       " (0.01060046411739516, 'geo__Cluster 26 similarity'),\n",
+       " (0.010233275929521545, 'geo__Cluster 20 similarity'),\n",
+       " (0.01000904987677275, 'geo__Cluster 35 similarity'),\n",
+       " (0.009948648710662759, 'geo__Cluster 14 similarity'),\n",
+       " (0.009237024861905358, 'geo__Cluster 39 similarity'),\n",
+       " (0.009095509036677479, 'geo__Cluster 37 similarity'),\n",
+       " (0.009039692793107918, 'geo__Cluster 0 similarity'),\n",
+       " (0.008824238591690455, 'geo__Cluster 8 similarity'),\n",
+       " (0.0088028519810115, 'geo__Cluster 9 similarity'),\n",
+       " (0.00865735444375151, 'geo__Cluster 36 similarity'),\n",
+       " (0.008468774649648216, 'geo__Cluster 28 similarity'),\n",
+       " (0.008132158692323862, 'geo__Cluster 44 similarity'),\n",
+       " (0.00806872019824202, 'geo__Cluster 4 similarity'),\n",
+       " (0.007900076467114424, 'geo__Cluster 11 similarity'),\n",
+       " (0.007654000251882609, 'log__total_rooms'),\n",
+       " (0.007117524274343567, 'log__population'),\n",
+       " (0.006828512079872657, 'log__total_bedrooms'),\n",
+       " (0.006436845508698041, 'log__households'),\n",
+       " (0.00606171524521791, 'geo__Cluster 23 similarity'),\n",
+       " (0.005534308195396936, 'geo__Cluster 19 similarity'),\n",
+       " (0.00549408919549185, 'geo__Cluster 27 similarity'),\n",
+       " (0.0052089915629445595, 'geo__Cluster 33 similarity'),\n",
+       " (0.004814425966149223, 'geo__Cluster 15 similarity'),\n",
+       " (0.004210239717026306, 'geo__Cluster 12 similarity'),\n",
+       " (0.004038656871498407, 'geo__Cluster 13 similarity'),\n",
+       " (0.00373031587904682, 'geo__Cluster 29 similarity'),\n",
+       " (0.003249851526492918, 'cat__ocean_proximity_<1H OCEAN'),\n",
+       " (0.001973755321832911, 'geo__Cluster 5 similarity'),\n",
+       " (0.0018976773551793616, 'cat__ocean_proximity_NEAR OCEAN'),\n",
+       " (0.0002358514810131377, 'cat__ocean_proximity_NEAR BAY'),\n",
+       " (6.124671466559031e-05, 'cat__ocean_proximity_ISLAND')]"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(zip(feature_importances,\n",
+    "           final_model[\"preprocessing\"].get_feature_names_out()),\n",
+    "           reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate Your System on the Test Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "41549.20158097943\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_test = strat_test_set.drop(\"median_house_value\", axis=1)\n",
+    "y_test = strat_test_set[\"median_house_value\"].copy()\n",
+    "\n",
+    "final_predictions = final_model.predict(X_test)\n",
+    "\n",
+    "final_rmse = mean_squared_error(y_test, final_predictions, squared=False)\n",
+    "print(final_rmse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute a 95% confidence interval for the test RMSE:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([39395.35475927, 43596.76969025])"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy import stats\n",
+    "\n",
+    "confidence = 0.95\n",
+    "squared_errors = (final_predictions - y_test) ** 2\n",
+    "np.sqrt(stats.t.interval(confidence, len(squared_errors) - 1,\n",
+    "                         loc=squared_errors.mean(),\n",
+    "                         scale=stats.sem(squared_errors)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could compute the interval manually like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39395.35475926931, 43596.76969025394)"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extra code – shows how to compute a confidence interval for the RMSE\n",
+    "m = len(squared_errors)\n",
+    "mean = squared_errors.mean()\n",
+    "tscore = stats.t.ppf((1 + confidence) / 2, df=m - 1)\n",
+    "tmargin = tscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
+    "np.sqrt(mean - tmargin), np.sqrt(mean + tmargin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, we could use a z-score rather than a t-score. Since the test set is not too small, it won't make a big difference:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39396.00369767951, 43596.18328117898)"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# extra code – computes a confidence interval again using a z-score\n",
+    "zscore = stats.norm.ppf((1 + confidence) / 2)\n",
+    "zmargin = zscore * squared_errors.std(ddof=1) / np.sqrt(m)\n",
+    "np.sqrt(mean - zmargin), np.sqrt(mean + zmargin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model persistence using joblib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the final model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['my_california_housing_model.pkl']"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "joblib.dump(final_model, \"my_california_housing_model.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you can deploy this model to production. For example, the following code could be a script that would run in production:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "# extra code – excluded for conciseness\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "from sklearn.metrics.pairwise import rbf_kernel\n",
+    "\n",
+    "def column_ratio(X):\n",
+    "    return X[:, [0]] / X[:, [1]]\n",
+    "\n",
+    "#class ClusterSimilarity(BaseEstimator, TransformerMixin):\n",
+    "#    [...]\n",
+    "\n",
+    "final_model_reloaded = joblib.load(\"my_california_housing_model.pkl\")\n",
+    "\n",
+    "new_data = housing.iloc[:5]  # pretend these are new districts\n",
+    "predictions = final_model_reloaded.predict(new_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([439808.14, 455211.06, 109492.  ,  98208.  , 340021.04])"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You could use pickle instead, but joblib is more efficient."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise \n",
+    "## You can do the exercice here to take benefit from the code above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1:\n",
+    " _Try a Support Vector Machine regressor (`sklearn.svm.SVR`) with various hyperparameters, such as `kernel=\"linear\"` (with various values for the `C` hyperparameter) or `kernel=\"rbf\"` (with various values for the `C` and `gamma` hyperparameters). Note that SVMs don't scale well to large datasets, so you should probably train your model on just the first 5,000 instances of the training set and use only 3-fold cross-validation, or else it will take hours. Don't worry about what the hyperparameters mean for now (see the SVM notebook if you're interested). How does the best `SVR` predictor perform?_\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise2:\n",
+    " _Try replacing the `GridSearchCV` with a `RandomizedSearchCV`._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise3:\n",
+    " _Try adding a `SelectFromModel` transformer in the preparation pipeline to select only the most important attributes._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise4:\n",
+    " _Try creating a custom transformer that trains a k-Nearest Neighbors regressor (`sklearn.neighbors.KNeighborsRegressor`) in its `fit()` method, and outputs the model's predictions in its `transform()` method. Then add this feature to the preprocessing pipeline, using latitude and longitude as the inputs to this transformer. This will add a feature in the model that corresponds to the housing median price of the nearest districts._\n",
+    "\n",
+    "Rather than restrict ourselves to k-Nearest Neighbors regressors, let's create a transformer that accepts any regressor. For this, we can extend the `MetaEstimatorMixin` and have a required `estimator` argument in the constructor. The `fit()` method must work on a clone of this estimator, and it must also save `feature_names_in_`. The `MetaEstimatorMixin` will ensure that `estimator` is listed as a required parameters, and it will update `get_params()` and `set_params()` to make the estimator's hyperparameters available for tuning. Lastly, we create a `get_feature_names_out()` method: the output column name is the ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise5:\n",
+    " _Automatically explore some preparation options using `RandomSearchCV`._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise6: \n",
+    "_Try to implement the `StandardScalerClone` class again from scratch, then add support for the `inverse_transform()` method: executing `scaler.inverse_transform(scaler.fit_transform(X))` should return an array very close to `X`. Then add support for feature names: set `feature_names_in_` in the `fit()` method if the input is a DataFrame. This attribute should be a NumPy array of column names. Lastly, implement the `get_feature_names_out()` method: it should have one optional `input_features=None` argument. If passed, the method should check that its length matches `n_features_in_`, and it should match `feature_names_in_` if it is defined, then `input_features` should be returned. If `input_features` is `None`, then the method should return `feature_names_in_` if it is defined or `np.array([\"x0\", \"x1\", ...])` with length `n_features_in_` otherwise._"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  },
+  "nav_menu": {
+   "height": "279px",
+   "width": "309px"
+  },
+  "toc": {
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": "block",
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/ML/02_e2e/End to end ML project.md b/ML/02_e2e/End to end ML project.md
new file mode 100644
index 0000000..cda9b45
--- /dev/null
+++ b/ML/02_e2e/End to end ML project.md
@@ -0,0 +1,77 @@
+# Approche d'un projet ML
+1. Cadrer le problème et le besoin
+2. Obtenir des données
+3. Explorer et visualiser les données pour se l'approprier
+4. Préparer les données pour les algos ML
+5. Choisir et entrainer le modele
+6. Ajuster le modèle (hyperparams)
+7. Présenter la solution
+8. Déployer
+
+## Cadrage du problème
+1. Objectifs en termes business
+2. Comment sera utilisée la solution
+3. Solutions existantes
+4. Choisir supervisé/non-supervisé, en/hors ligne...
+5. Choisir les métriques de performance (RMSE, MAE : normes)
+6. Aligner les métriques sur l'objectif business
+7. Perf min pour objectif business ?
+8. Réut expérience + outils
+9. Expertise ?
+10. Résolution manuelle ?
+
+## Récupération de données
+1. Automatiser autant que possible pour avoir facilement des données récentes
+2. Documenter les sources
+3. Pb d'espace
+4. Pb de droit sur les données
+5. Pb autorisations d'accès aux données
+6. Créer un workspace avec suffisamment de place
+7. Récupérer les données
+8. Convertir les données sous un format exploitable
+9. Anonymiser le jeu de données
+10. Vérifier la taille et le type des données
+11. Créer un jeu de test et ne pas y toucher
+
+## Appropriation du jeu de données
+- Attention valeurs capées
+- Normalisation potentiellement
+- Correlations (Pearson)
+
+## Jeu de test
+- Eviter l'overfitting
+- Instances choisies aléatoirement
+- Sampling stratifié pour être représentatif
+
+# Modèle
+- Paramètre = coefs de droite si regression linéaire
+- Hyperparamètres = Params qui vont permettre d'apprendre le modèle (ex : strategy simpleimputer)
+
+## Pour les données textuelles ou catégoriques
+- Encodeurs ordinaux
+
+## Scaling et transformation
+
+- Pb de données qui n'ont pas le même intervalle d'existence
+- Scaling MinMax ou Centrée réduite
+- Revenir aux données d'origine `inverse_transform()`
+
+## Choix et entrainement du modèle
+- Entrainer plusieurs petits modèles et comparer leurs perfs
+- Identifier les erreurs récurrentes des modèles et chercher les données manquantes pour pallier
+- Sélectionner les modèles les plus performants
+
+## Fine tuning
+- Grid Search : bidouiller les hyperparams à la main => Voir GridSearchCV
+- Randomized Search :  RandomizedSearchCv = n itérations avec des HP différents, mieux que GridSearch pour bcp de hyperparams
+
+## Ensemble de modèles
+- Possiblement bonnes perfs avec plusieurs modèles (même mauvais)
+- Analyser les meilleurs modèles, peut permettre de retirer les attributs moins utiles
+- Attention aux valeurs capées
+- `Sklearn.feature_selection.SelectFromModel`
+
+## Evaluation du modèle
+- Jeu de données de test
+- Enregistrement du modèle:w
+- 
+\ No newline at end of file
diff --git a/ML/02_e2e/MACH_2.md b/ML/02_e2e/MACH_2.md
new file mode 100644
index 0000000..3e2240b
--- /dev/null
+++ b/ML/02_e2e/MACH_2.md
@@ -0,0 +1,9 @@
+MACH: End-to-end ML pipeline
+==
+1. Try a Support Vector Machine regressor (`sklearn.svm.SV`) with various hyperparameters, such as `kernel="linear` (with various values for the C hyperparameter) or `kernel="rbf"` (with various values for the C and gamma hyperparameters). Note that SVMs don't scale well to large datasets, so you should probably train your model on just the first 5,000 instances of the training set and use only 3-fold cross-validation, or else it will take hours. Don't worry about what the hyperparameters mean for now (we will study it further in the course). How does the best SVR predictor perform?
+2. Try replacing the `GridSearchCV` with a `RandomizedSearchCV`.
+3. Try adding a `SelectFromModel` transformer in the preparation pipeline to select only the most important attributes.
+4. Let's create a new pipeline that runs the previously defined preparation pipeline, and adds a SelectFromModel transformer based on a RandomForestRegressor before the final regressor:
+5. Try creating a custom transformer that trains a k-Nearest Neighbors regressor (`sklearn.neighbors.KNeighborsRegressor`) in its `fit()` method, and outputs the model's predictions in its `transform()` method. Then add this feature to the preprocessing pipeline, using latitude and longitude as the inputs to this transformer. This will add a feature in the model that corresponds to the housing median price of the nearest districts.
+6. Automatically explore some preparation options using `RandomSearchCV`.
+7. Try to implement the `StandardScalerClone` class again from scratch, then add support for the `inverse_transform()` method: executing `scaler.inverse_transform(scaler.fit_transform(X))` should return an array very close to `X`. Then add support for feature names: set `feature_names_in_` in the `fit()` method if the input is a DataFrame. This attribute should be a NumPy array of column names. Lastly, implement the `get_feature_names_out()` method: it should have one optional `input_features=None` argument. If passed, the method should check that its length matches `n_features_in_`, and it should match `feature_names_in_` if it is defined, then `input_features` should be returned. If `input_features` is `None`, then the method should return `feature_names_in_` if it is defined or `np.array(["x0", "x1", ...])` with length `n_features_in_` otherwise.
+\ No newline at end of file
diff --git a/ML/02_e2e/california.png b/ML/02_e2e/california.png
new file mode 100644
index 0000000..0103e3b
--- /dev/null
+++ b/ML/02_e2e/california.png
diff --git a/ML/02_e2e/datasets/housing.csv b/ML/02_e2e/datasets/housing.csv
new file mode 100644
index 0000000..be59d57
--- /dev/null
+++ b/ML/02_e2e/datasets/housing.csv
@@ -0,0 +1,20641 @@
+longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income,median_house_value,ocean_proximity
+-122.23,37.88,41.0,880.0,129.0,322.0,126.0,8.3252,452600.0,NEAR BAY
+-122.22,37.86,21.0,7099.0,1106.0,2401.0,1138.0,8.3014,358500.0,NEAR BAY
+-122.24,37.85,52.0,1467.0,190.0,496.0,177.0,7.2574,352100.0,NEAR BAY
+-122.25,37.85,52.0,1274.0,235.0,558.0,219.0,5.6431,341300.0,NEAR BAY
+-122.25,37.85,52.0,1627.0,280.0,565.0,259.0,3.8462,342200.0,NEAR BAY
+-122.25,37.85,52.0,919.0,213.0,413.0,193.0,4.0368,269700.0,NEAR BAY
+-122.25,37.84,52.0,2535.0,489.0,1094.0,514.0,3.6591,299200.0,NEAR BAY
+-122.25,37.84,52.0,3104.0,687.0,1157.0,647.0,3.12,241400.0,NEAR BAY
+-122.26,37.84,42.0,2555.0,665.0,1206.0,595.0,2.0804,226700.0,NEAR BAY
+-122.25,37.84,52.0,3549.0,707.0,1551.0,714.0,3.6912,261100.0,NEAR BAY
+-122.26,37.85,52.0,2202.0,434.0,910.0,402.0,3.2031,281500.0,NEAR BAY
+-122.26,37.85,52.0,3503.0,752.0,1504.0,734.0,3.2705,241800.0,NEAR BAY
+-122.26,37.85,52.0,2491.0,474.0,1098.0,468.0,3.075,213500.0,NEAR BAY
+-122.26,37.84,52.0,696.0,191.0,345.0,174.0,2.6736,191300.0,NEAR BAY
+-122.26,37.85,52.0,2643.0,626.0,1212.0,620.0,1.9167,159200.0,NEAR BAY
+-122.26,37.85,50.0,1120.0,283.0,697.0,264.0,2.125,140000.0,NEAR BAY
+-122.27,37.85,52.0,1966.0,347.0,793.0,331.0,2.775,152500.0,NEAR BAY
+-122.27,37.85,52.0,1228.0,293.0,648.0,303.0,2.1202,155500.0,NEAR BAY
+-122.26,37.84,50.0,2239.0,455.0,990.0,419.0,1.9911,158700.0,NEAR BAY
+-122.27,37.84,52.0,1503.0,298.0,690.0,275.0,2.6033,162900.0,NEAR BAY
+-122.27,37.85,40.0,751.0,184.0,409.0,166.0,1.3578,147500.0,NEAR BAY
+-122.27,37.85,42.0,1639.0,367.0,929.0,366.0,1.7135,159800.0,NEAR BAY
+-122.27,37.84,52.0,2436.0,541.0,1015.0,478.0,1.725,113900.0,NEAR BAY
+-122.27,37.84,52.0,1688.0,337.0,853.0,325.0,2.1806,99700.0,NEAR BAY
+-122.27,37.84,52.0,2224.0,437.0,1006.0,422.0,2.6,132600.0,NEAR BAY
+-122.28,37.85,41.0,535.0,123.0,317.0,119.0,2.4038,107500.0,NEAR BAY
+-122.28,37.85,49.0,1130.0,244.0,607.0,239.0,2.4597,93800.0,NEAR BAY
+-122.28,37.85,52.0,1898.0,421.0,1102.0,397.0,1.808,105500.0,NEAR BAY
+-122.28,37.84,50.0,2082.0,492.0,1131.0,473.0,1.6424,108900.0,NEAR BAY
+-122.28,37.84,52.0,729.0,160.0,395.0,155.0,1.6875,132000.0,NEAR BAY
+-122.28,37.84,49.0,1916.0,447.0,863.0,378.0,1.9274,122300.0,NEAR BAY
+-122.28,37.84,52.0,2153.0,481.0,1168.0,441.0,1.9615,115200.0,NEAR BAY
+-122.27,37.84,48.0,1922.0,409.0,1026.0,335.0,1.7969,110400.0,NEAR BAY
+-122.27,37.83,49.0,1655.0,366.0,754.0,329.0,1.375,104900.0,NEAR BAY
+-122.27,37.83,51.0,2665.0,574.0,1258.0,536.0,2.7303,109700.0,NEAR BAY
+-122.27,37.83,49.0,1215.0,282.0,570.0,264.0,1.4861,97200.0,NEAR BAY
+-122.27,37.83,48.0,1798.0,432.0,987.0,374.0,1.0972,104500.0,NEAR BAY
+-122.28,37.83,52.0,1511.0,390.0,901.0,403.0,1.4103,103900.0,NEAR BAY
+-122.26,37.83,52.0,1470.0,330.0,689.0,309.0,3.48,191400.0,NEAR BAY
+-122.26,37.83,52.0,2432.0,715.0,1377.0,696.0,2.5898,176000.0,NEAR BAY
+-122.26,37.83,52.0,1665.0,419.0,946.0,395.0,2.0978,155400.0,NEAR BAY
+-122.26,37.83,51.0,936.0,311.0,517.0,249.0,1.2852,150000.0,NEAR BAY
+-122.26,37.84,49.0,713.0,202.0,462.0,189.0,1.025,118800.0,NEAR BAY
+-122.26,37.84,52.0,950.0,202.0,467.0,198.0,3.9643,188800.0,NEAR BAY
+-122.26,37.83,52.0,1443.0,311.0,660.0,292.0,3.0125,184400.0,NEAR BAY
+-122.26,37.83,52.0,1656.0,420.0,718.0,382.0,2.6768,182300.0,NEAR BAY
+-122.26,37.83,50.0,1125.0,322.0,616.0,304.0,2.026,142500.0,NEAR BAY
+-122.27,37.82,43.0,1007.0,312.0,558.0,253.0,1.7348,137500.0,NEAR BAY
+-122.26,37.82,40.0,624.0,195.0,423.0,160.0,0.9506,187500.0,NEAR BAY
+-122.27,37.82,40.0,946.0,375.0,700.0,352.0,1.775,112500.0,NEAR BAY
+-122.27,37.82,21.0,896.0,453.0,735.0,438.0,0.9218,171900.0,NEAR BAY
+-122.27,37.82,43.0,1868.0,456.0,1061.0,407.0,1.5045,93800.0,NEAR BAY
+-122.27,37.82,41.0,3221.0,853.0,1959.0,720.0,1.1108,97500.0,NEAR BAY
+-122.27,37.82,52.0,1630.0,456.0,1162.0,400.0,1.2475,104200.0,NEAR BAY
+-122.28,37.82,52.0,1170.0,235.0,701.0,233.0,1.6098,87500.0,NEAR BAY
+-122.28,37.82,52.0,945.0,243.0,576.0,220.0,1.4113,83100.0,NEAR BAY
+-122.28,37.82,52.0,1238.0,288.0,622.0,259.0,1.5057,87500.0,NEAR BAY
+-122.28,37.82,52.0,1489.0,335.0,728.0,244.0,0.8172,85300.0,NEAR BAY
+-122.28,37.82,52.0,1387.0,341.0,1074.0,304.0,1.2171,80300.0,NEAR BAY
+-122.29,37.82,2.0,158.0,43.0,94.0,57.0,2.5625,60000.0,NEAR BAY
+-122.29,37.83,52.0,1121.0,211.0,554.0,187.0,3.3929,75700.0,NEAR BAY
+-122.29,37.82,49.0,135.0,29.0,86.0,23.0,6.1183,75000.0,NEAR BAY
+-122.29,37.81,50.0,760.0,190.0,377.0,122.0,0.9011,86100.0,NEAR BAY
+-122.3,37.81,52.0,1224.0,237.0,521.0,159.0,1.191,76100.0,NEAR BAY
+-122.3,37.81,48.0,828.0,182.0,392.0,133.0,2.5938,73500.0,NEAR BAY
+-122.3,37.81,52.0,1010.0,209.0,604.0,187.0,1.1667,78400.0,NEAR BAY
+-122.3,37.81,48.0,1455.0,354.0,788.0,332.0,0.8056,84400.0,NEAR BAY
+-122.29,37.8,52.0,1027.0,244.0,492.0,147.0,2.6094,81300.0,NEAR BAY
+-122.3,37.81,52.0,572.0,109.0,274.0,82.0,1.8516,85000.0,NEAR BAY
+-122.29,37.81,46.0,2801.0,644.0,1823.0,611.0,0.9802,129200.0,NEAR BAY
+-122.29,37.81,26.0,768.0,152.0,392.0,127.0,1.7719,82500.0,NEAR BAY
+-122.29,37.81,46.0,935.0,297.0,582.0,277.0,0.7286,95200.0,NEAR BAY
+-122.29,37.81,49.0,844.0,204.0,560.0,152.0,1.75,75000.0,NEAR BAY
+-122.29,37.81,46.0,12.0,4.0,18.0,7.0,0.4999,67500.0,NEAR BAY
+-122.29,37.81,20.0,835.0,161.0,290.0,133.0,2.483,137500.0,NEAR BAY
+-122.28,37.81,17.0,1237.0,462.0,762.0,439.0,0.9241,177500.0,NEAR BAY
+-122.28,37.81,36.0,2914.0,562.0,1236.0,509.0,2.4464,102100.0,NEAR BAY
+-122.28,37.81,19.0,1207.0,243.0,721.0,207.0,1.1111,108300.0,NEAR BAY
+-122.29,37.81,23.0,1745.0,374.0,1054.0,325.0,0.8026,112500.0,NEAR BAY
+-122.28,37.8,38.0,684.0,176.0,344.0,155.0,2.0114,131300.0,NEAR BAY
+-122.28,37.81,17.0,924.0,289.0,609.0,289.0,1.5,162500.0,NEAR BAY
+-122.27,37.81,52.0,210.0,56.0,183.0,56.0,1.1667,112500.0,NEAR BAY
+-122.28,37.81,52.0,340.0,97.0,200.0,87.0,1.5208,112500.0,NEAR BAY
+-122.28,37.81,52.0,386.0,164.0,346.0,155.0,0.8075,137500.0,NEAR BAY
+-122.28,37.81,35.0,948.0,184.0,467.0,169.0,1.8088,118800.0,NEAR BAY
+-122.28,37.81,52.0,773.0,143.0,377.0,115.0,2.4083,98200.0,NEAR BAY
+-122.27,37.81,40.0,880.0,451.0,582.0,380.0,0.977,118800.0,NEAR BAY
+-122.27,37.81,10.0,875.0,348.0,546.0,330.0,0.76,162500.0,NEAR BAY
+-122.27,37.8,10.0,105.0,42.0,125.0,39.0,0.9722,137500.0,NEAR BAY
+-122.27,37.8,52.0,249.0,78.0,396.0,85.0,1.2434,500001.0,NEAR BAY
+-122.27,37.8,16.0,994.0,392.0,800.0,362.0,2.0938,162500.0,NEAR BAY
+-122.28,37.8,52.0,215.0,87.0,904.0,88.0,0.8668,137500.0,NEAR BAY
+-122.28,37.8,52.0,96.0,31.0,191.0,34.0,0.75,162500.0,NEAR BAY
+-122.27,37.79,27.0,1055.0,347.0,718.0,302.0,2.6354,187500.0,NEAR BAY
+-122.27,37.8,39.0,1715.0,623.0,1327.0,467.0,1.8477,179200.0,NEAR BAY
+-122.26,37.8,36.0,5329.0,2477.0,3469.0,2323.0,2.0096,130000.0,NEAR BAY
+-122.26,37.82,31.0,4596.0,1331.0,2048.0,1180.0,2.8345,183800.0,NEAR BAY
+-122.26,37.81,29.0,335.0,107.0,202.0,91.0,2.0062,125000.0,NEAR BAY
+-122.26,37.82,22.0,3682.0,1270.0,2024.0,1250.0,1.2185,170000.0,NEAR BAY
+-122.26,37.82,37.0,3633.0,1085.0,1838.0,980.0,2.6104,193100.0,NEAR BAY
+-117.04,32.8,11.0,1802.0,440.0,630.0,428.0,2.0337,146700.0,<1H OCEAN
+-121.29,38.89,10.0,653.0,120.0,407.0,146.0,3.3889,110800.0,INLAND
+-118.15,33.77,39.0,2428.0,634.0,1312.0,612.0,2.7212,266300.0,NEAR OCEAN
+-118.47,33.99,24.0,1438.0,454.0,665.0,416.0,2.975,500001.0,<1H OCEAN
+-121.77,37.33,9.0,3160.0,468.0,1675.0,470.0,7.5443,348400.0,<1H OCEAN
+-118.5,33.97,29.0,2737.0,808.0,1157.0,696.0,5.128,500001.0,<1H OCEAN
+-122.96,38.26,20.0,1982.0,358.0,308.0,132.0,3.1429,240900.0,NEAR OCEAN
+-118.39,34.08,27.0,6605.0,1710.0,2665.0,1520.0,3.8088,500001.0,<1H OCEAN
+-117.91,34.07,36.0,1390.0,270.0,887.0,266.0,5.0897,189000.0,<1H OCEAN
+-117.85,33.85,17.0,4678.0,1065.0,2427.0,1020.0,4.2276,254100.0,<1H OCEAN
+-119.92,38.91,15.0,3831.0,625.0,984.0,328.0,5.0718,162500.0,INLAND
+-122.49,37.67,29.0,3795.0,675.0,2494.0,696.0,5.2848,260300.0,NEAR OCEAN
+-117.71,33.65,16.0,3774.0,456.0,1587.0,430.0,8.6088,307400.0,<1H OCEAN
+-118.36,33.91,36.0,2064.0,474.0,1366.0,421.0,4.1,243100.0,<1H OCEAN
+-117.27,33.04,27.0,1839.0,392.0,1302.0,404.0,3.55,214600.0,NEAR OCEAN
+-121.96,37.3,23.0,4040.0,843.0,2181.0,843.0,4.0403,303400.0,<1H OCEAN
+-122.16,37.74,46.0,1029.0,181.0,567.0,211.0,3.4844,129500.0,NEAR BAY
+-117.47,34.12,6.0,10565.0,1767.0,5690.0,1555.0,4.1797,141000.0,INLAND
+-117.72,34.0,15.0,4363.0,690.0,2410.0,666.0,5.4824,179700.0,INLAND
+-118.31,33.75,34.0,2338.0,393.0,1031.0,373.0,6.287,396400.0,NEAR OCEAN
+-118.19,33.9,32.0,2762.0,652.0,2677.0,632.0,2.5719,105600.0,<1H OCEAN
+-117.05,32.57,13.0,2880.0,576.0,2450.0,567.0,3.1696,138000.0,NEAR OCEAN
+-122.17,37.71,38.0,890.0,200.0,481.0,198.0,3.244,179800.0,NEAR BAY
+-122.28,37.49,29.0,4148.0,635.0,1638.0,627.0,6.912,457200.0,NEAR OCEAN
+-117.91,33.75,8.0,2346.0,679.0,3842.0,674.0,3.0635,160000.0,<1H OCEAN
+-119.25,34.26,30.0,2948.0,827.0,1635.0,750.0,2.67,214900.0,NEAR OCEAN
+-118.12,33.86,34.0,2116.0,427.0,972.0,396.0,4.8516,213600.0,<1H OCEAN
+-120.04,36.96,23.0,2126.0,506.0,2091.0,491.0,1.3713,51800.0,INLAND
+-118.38,33.86,15.0,1778.0,311.0,908.0,330.0,7.674,329300.0,<1H OCEAN
+-122.35,37.95,45.0,2142.0,431.0,1318.0,431.0,3.0737,111600.0,NEAR BAY
+-118.23,33.9,45.0,1285.0,238.0,840.0,211.0,3.4107,112500.0,<1H OCEAN
+-117.57,33.91,22.0,2620.0,396.0,1324.0,362.0,5.3735,214600.0,INLAND
+-122.34,37.55,44.0,2465.0,328.0,843.0,324.0,6.9533,500001.0,NEAR OCEAN
+-118.46,34.05,21.0,3639.0,1002.0,1489.0,983.0,4.6197,387500.0,<1H OCEAN
+-122.19,37.78,52.0,2198.0,397.0,984.0,369.0,3.22,156500.0,NEAR BAY
+-117.99,33.88,15.0,2298.0,567.0,1261.0,527.0,4.2422,159400.0,<1H OCEAN
+-119.05,36.06,23.0,2344.0,407.0,1184.0,406.0,3.1625,70600.0,INLAND
+-119.19,34.15,31.0,4175.0,1004.0,3310.0,954.0,3.1989,185400.0,NEAR OCEAN
+-121.62,36.67,45.0,1827.0,408.0,1507.0,410.0,2.8942,129000.0,<1H OCEAN
+-116.77,33.92,19.0,2307.0,525.0,1266.0,437.0,1.6875,63000.0,INLAND
+-119.1,35.79,19.0,1809.0,477.0,2051.0,416.0,1.8144,49800.0,INLAND
+-119.06,34.37,32.0,3885.0,759.0,2504.0,736.0,3.6453,201700.0,<1H OCEAN
+-121.86,39.75,18.0,1651.0,309.0,856.0,293.0,3.5046,118300.0,INLAND
+-118.02,34.13,34.0,1966.0,319.0,980.0,297.0,7.7307,429000.0,INLAND
+-118.05,34.06,25.0,1022.0,291.0,1570.0,297.0,3.023,142000.0,<1H OCEAN
+-118.09,33.98,37.0,1226.0,255.0,1068.0,271.0,3.1607,172200.0,<1H OCEAN
+-122.51,38.06,24.0,9493.0,1935.0,5162.0,1880.0,3.0742,118800.0,NEAR BAY
+-117.94,34.06,32.0,3418.0,662.0,2003.0,622.0,4.0333,210200.0,<1H OCEAN
+-118.37,34.02,44.0,1944.0,458.0,981.0,377.0,2.6154,193200.0,<1H OCEAN
+-118.26,33.85,24.0,9071.0,1335.0,4558.0,1327.0,5.542,197500.0,<1H OCEAN
+-121.28,38.68,16.0,3467.0,615.0,1478.0,601.0,3.75,147300.0,INLAND
+-118.11,33.91,29.0,889.0,166.0,597.0,163.0,4.9609,186700.0,<1H OCEAN
+-117.2,33.38,14.0,5392.0,821.0,2350.0,810.0,5.0507,291500.0,<1H OCEAN
+-118.2,34.11,52.0,1901.0,525.0,1856.0,480.0,3.0,156400.0,<1H OCEAN
+-118.38,34.05,40.0,2352.0,598.0,1133.0,563.0,3.238,287500.0,<1H OCEAN
+-117.18,32.84,30.0,2290.0,523.0,1272.0,472.0,3.5606,165100.0,NEAR OCEAN
+-119.7,34.4,25.0,1858.0,493.0,865.0,460.0,3.0938,312500.0,NEAR OCEAN
+-118.51,34.3,24.0,6145.0,868.0,2710.0,875.0,7.5078,344000.0,<1H OCEAN
+-116.39,33.69,10.0,11659.0,2007.0,2186.0,1083.0,6.9833,238800.0,INLAND
+-117.28,32.83,34.0,2392.0,653.0,933.0,619.0,3.7306,500000.0,NEAR OCEAN
+-122.45,40.85,20.0,2701.0,573.0,892.0,358.0,2.7736,107800.0,INLAND
+-117.87,33.72,39.0,3167.0,669.0,2789.0,619.0,3.5902,165900.0,<1H OCEAN
+-119.32,36.31,21.0,2309.0,424.0,1047.0,453.0,2.9886,87500.0,INLAND
+-122.27,37.54,15.0,2126.0,310.0,905.0,306.0,8.9083,500001.0,NEAR BAY
+-122.71,38.5,15.0,5645.0,830.0,2324.0,769.0,6.6104,330900.0,<1H OCEAN
+-119.77,36.77,29.0,2554.0,705.0,2669.0,655.0,1.2176,61900.0,INLAND
+-121.44,38.64,18.0,1756.0,442.0,837.0,320.0,1.125,70500.0,INLAND
+-117.16,32.8,37.0,422.0,79.0,211.0,80.0,3.0625,159700.0,NEAR OCEAN
+-119.78,36.65,27.0,1226.0,240.0,706.0,211.0,2.77,68400.0,INLAND
+-122.16,39.78,32.0,1288.0,221.0,562.0,203.0,2.325,69600.0,INLAND
+-118.19,33.86,38.0,2009.0,524.0,1449.0,451.0,2.7045,155400.0,<1H OCEAN
+-117.09,32.72,33.0,1096.0,240.0,716.0,224.0,1.6944,111800.0,NEAR OCEAN
+-118.29,33.92,23.0,2503.0,532.0,1735.0,505.0,2.7368,162800.0,<1H OCEAN
+-122.29,37.55,27.0,3789.0,874.0,2243.0,866.0,4.39,270100.0,NEAR OCEAN
+-120.76,37.61,30.0,816.0,159.0,531.0,147.0,3.2604,87900.0,INLAND
+-118.58,34.22,35.0,2560.0,441.0,1428.0,468.0,5.6345,228200.0,<1H OCEAN
+-117.67,33.47,22.0,2728.0,616.0,1081.0,566.0,1.6393,500001.0,<1H OCEAN
+-119.16,34.2,35.0,2183.0,636.0,3504.0,623.0,1.9704,160300.0,NEAR OCEAN
+-117.29,33.18,15.0,3780.0,792.0,1632.0,721.0,2.7644,111400.0,NEAR OCEAN
+-119.57,36.71,10.0,1657.0,359.0,958.0,380.0,2.6458,84800.0,INLAND
+-118.15,34.19,48.0,1854.0,360.0,1126.0,382.0,3.2216,161600.0,<1H OCEAN
+-118.1,34.01,29.0,2077.0,564.0,2087.0,543.0,2.66,189200.0,<1H OCEAN
+-118.27,34.04,13.0,1784.0,,2158.0,682.0,1.7038,118100.0,<1H OCEAN
+-117.25,33.08,13.0,3651.0,465.0,1311.0,435.0,7.5402,340300.0,NEAR OCEAN
+-118.14,34.06,39.0,2390.0,444.0,1246.0,422.0,3.7857,245700.0,<1H OCEAN
+-118.21,34.04,47.0,1325.0,393.0,1557.0,352.0,2.8,148400.0,<1H OCEAN
+-120.66,35.13,41.0,2666.0,751.0,940.0,507.0,1.9653,236100.0,<1H OCEAN
+-117.92,33.75,8.0,2325.0,598.0,1511.0,565.0,3.3629,137500.0,<1H OCEAN
+-120.15,39.36,9.0,2254.0,400.0,694.0,243.0,5.6856,138100.0,INLAND
+-118.08,33.89,28.0,1035.0,275.0,1545.0,269.0,3.0357,123400.0,<1H OCEAN
+-122.07,37.91,28.0,1731.0,295.0,810.0,295.0,5.0391,259800.0,NEAR BAY
+-117.18,34.05,29.0,3436.0,731.0,1323.0,676.0,2.4943,122300.0,INLAND
+-117.93,33.82,28.0,2444.0,555.0,1848.0,567.0,3.0179,198800.0,<1H OCEAN
+-117.17,32.79,43.0,1269.0,297.0,946.0,285.0,2.1447,133300.0,NEAR OCEAN
+-119.55,36.69,21.0,1551.0,423.0,1519.0,406.0,1.7132,55900.0,INLAND
+-122.02,37.94,23.0,3516.0,661.0,1465.0,623.0,4.2569,213100.0,NEAR BAY
+-122.14,37.44,52.0,3117.0,468.0,1114.0,421.0,6.6756,500001.0,NEAR BAY
+-121.92,37.24,26.0,6777.0,1051.0,3319.0,1061.0,6.3663,279400.0,<1H OCEAN
+-121.82,38.01,42.0,1017.0,253.0,798.0,266.0,2.1719,99100.0,INLAND
+-116.94,32.8,28.0,3042.0,729.0,1964.0,703.0,2.4141,137500.0,<1H OCEAN
+-118.19,33.86,36.0,2013.0,546.0,1659.0,522.0,3.1215,153600.0,<1H OCEAN
+-121.31,37.96,52.0,2654.0,468.0,1157.0,494.0,3.226,107600.0,INLAND
+-117.98,33.81,28.0,3528.0,816.0,2304.0,764.0,2.582,181800.0,<1H OCEAN
+-117.97,34.05,36.0,931.0,160.0,746.0,201.0,3.1667,158000.0,<1H OCEAN
+-117.56,33.94,6.0,575.0,73.0,318.0,88.0,7.0215,257100.0,INLAND
+-121.88,37.44,14.0,2073.0,343.0,1107.0,330.0,6.7093,311200.0,<1H OCEAN
+-122.14,37.41,35.0,2419.0,426.0,949.0,433.0,6.4588,437100.0,NEAR BAY
+-122.28,37.86,52.0,1659.0,367.0,788.0,346.0,2.8214,164300.0,NEAR BAY
+-118.0,33.91,19.0,5166.0,770.0,2374.0,753.0,5.979,285200.0,<1H OCEAN
+-122.73,38.26,35.0,3941.0,645.0,1668.0,620.0,4.385,317700.0,<1H OCEAN
+-118.34,34.02,48.0,1614.0,320.0,684.0,318.0,4.2218,181000.0,<1H OCEAN
+-121.77,36.92,9.0,4934.0,1112.0,3198.0,977.0,3.5,194800.0,<1H OCEAN
+-122.04,37.39,5.0,8745.0,2211.0,3959.0,2019.0,4.7685,280100.0,<1H OCEAN
+-118.27,34.09,52.0,2327.0,555.0,1048.0,491.0,3.7847,252300.0,<1H OCEAN
+-118.46,34.26,33.0,1358.0,247.0,738.0,235.0,5.0947,210300.0,<1H OCEAN
+-122.2,37.75,36.0,606.0,132.0,531.0,133.0,1.5809,70000.0,NEAR BAY
+-122.54,38.36,40.0,2725.0,531.0,1167.0,458.0,3.7969,202800.0,<1H OCEAN
+-121.98,38.37,21.0,3027.0,675.0,2018.0,642.0,2.8438,111500.0,INLAND
+-122.22,37.78,44.0,2968.0,710.0,2269.0,610.0,2.3906,111700.0,NEAR BAY
+-122.26,37.55,17.0,4576.0,814.0,1941.0,807.0,5.9572,443800.0,NEAR BAY
+-117.76,34.07,51.0,1538.0,394.0,1173.0,388.0,2.3156,109800.0,INLAND
+-122.59,38.44,14.0,1665.0,390.0,505.0,348.0,3.183,201200.0,<1H OCEAN
+-117.76,34.06,30.0,1700.0,504.0,1719.0,459.0,2.227,91900.0,INLAND
+-117.92,33.73,17.0,1692.0,293.0,934.0,280.0,4.4728,205800.0,<1H OCEAN
+-117.83,33.88,18.0,2112.0,340.0,1048.0,315.0,6.9308,231700.0,<1H OCEAN
+-121.96,38.33,3.0,7985.0,1257.0,3664.0,1215.0,4.976,158300.0,INLAND
+-122.0,38.35,24.0,745.0,116.0,300.0,115.0,3.6176,158500.0,INLAND
+-121.37,38.58,37.0,2839.0,390.0,1006.0,400.0,7.3343,280400.0,INLAND
+-118.25,34.01,28.0,481.0,136.0,596.0,128.0,1.2396,90300.0,<1H OCEAN
+-116.98,32.75,25.0,4137.0,662.0,1905.0,630.0,4.375,214000.0,<1H OCEAN
+-122.44,37.78,52.0,1118.0,279.0,514.0,284.0,2.4196,346200.0,NEAR BAY
+-122.58,38.58,32.0,2723.0,637.0,1549.0,556.0,2.3942,183100.0,INLAND
+-122.3,38.32,20.0,2063.0,486.0,1045.0,460.0,2.5035,153200.0,NEAR BAY
+-121.39,38.59,33.0,2091.0,468.0,1053.0,470.0,2.2264,108100.0,INLAND
+-118.45,34.04,19.0,3330.0,1010.0,1837.0,915.0,3.0173,393800.0,<1H OCEAN
+-120.24,37.21,31.0,2447.0,465.0,1313.0,352.0,3.3929,93800.0,INLAND
+-116.91,32.82,14.0,1978.0,424.0,1085.0,387.0,3.8073,170100.0,<1H OCEAN
+-121.77,37.68,41.0,1501.0,299.0,629.0,288.0,4.6806,209400.0,INLAND
+-117.87,33.78,30.0,2022.0,522.0,1196.0,463.0,3.7454,186000.0,<1H OCEAN
+-118.13,33.76,52.0,2216.0,526.0,940.0,530.0,4.5469,381000.0,NEAR OCEAN
+-118.25,33.93,42.0,819.0,233.0,899.0,228.0,1.1346,85400.0,<1H OCEAN
+-117.05,32.63,31.0,4911.0,861.0,2334.0,843.0,4.1958,160100.0,NEAR OCEAN
+-119.29,36.34,28.0,1440.0,431.0,2178.0,440.0,1.2634,50600.0,INLAND
+-121.88,37.33,45.0,1192.0,371.0,1084.0,345.0,2.8594,205900.0,<1H OCEAN
+-118.28,34.02,52.0,281.0,103.0,470.0,96.0,1.9375,38800.0,<1H OCEAN
+-118.36,34.05,48.0,1835.0,380.0,956.0,370.0,3.2813,243600.0,<1H OCEAN
+-118.31,34.08,49.0,2549.0,630.0,1539.0,594.0,2.6218,350900.0,<1H OCEAN
+-118.01,34.12,32.0,1937.0,332.0,922.0,340.0,3.94,278400.0,INLAND
+-121.3,38.74,41.0,4374.0,1039.0,2387.0,959.0,2.3611,87900.0,INLAND
+-118.38,34.18,27.0,4834.0,1527.0,3847.0,1432.0,2.1449,165300.0,<1H OCEAN
+-117.1,32.68,42.0,2013.0,568.0,1920.0,557.0,2.0724,107600.0,NEAR OCEAN
+-118.37,33.85,29.0,3662.0,586.0,1626.0,611.0,6.3974,410000.0,<1H OCEAN
+-118.38,34.06,29.0,3946.0,1008.0,1676.0,876.0,2.7824,450000.0,<1H OCEAN
+-122.01,37.94,26.0,1619.0,224.0,706.0,220.0,6.0704,268000.0,NEAR BAY
+-121.39,38.55,18.0,1734.0,467.0,783.0,447.0,1.9044,154300.0,INLAND
+-118.23,34.21,29.0,2584.0,608.0,1217.0,568.0,3.3287,273400.0,<1H OCEAN
+-117.93,33.85,25.0,1026.0,288.0,1646.0,283.0,4.2019,163900.0,<1H OCEAN
+-119.85,36.8,14.0,1876.0,324.0,1031.0,311.0,3.6563,88800.0,INLAND
+-118.21,34.04,36.0,1825.0,479.0,2097.0,480.0,2.1862,135300.0,<1H OCEAN
+-118.43,34.22,34.0,1588.0,360.0,1080.0,340.0,3.66,184600.0,<1H OCEAN
+-117.63,33.47,4.0,2320.0,405.0,1408.0,477.0,6.3369,256000.0,NEAR OCEAN
+-118.0,33.76,12.0,1250.0,331.0,1047.0,334.0,3.0625,208800.0,<1H OCEAN
+-119.49,34.39,17.0,4617.0,982.0,2303.0,923.0,3.9224,230600.0,NEAR OCEAN
+-122.91,39.03,14.0,2374.0,557.0,723.0,427.0,1.3532,95800.0,INLAND
+-121.98,37.36,34.0,1735.0,318.0,1019.0,301.0,4.5625,242700.0,<1H OCEAN
+-117.88,33.76,37.0,2988.0,677.0,2354.0,666.0,3.4345,235500.0,<1H OCEAN
+-116.97,32.8,15.0,3927.0,1018.0,2204.0,977.0,2.4367,111400.0,<1H OCEAN
+-121.93,36.63,28.0,3983.0,852.0,1582.0,778.0,3.5147,313900.0,NEAR OCEAN
+-117.99,34.13,37.0,1568.0,371.0,1618.0,350.0,2.9605,129400.0,INLAND
+-117.06,32.8,17.0,2247.0,340.0,973.0,318.0,5.5,222000.0,<1H OCEAN
+-118.16,34.03,41.0,1377.0,293.0,1142.0,272.0,3.1724,141600.0,<1H OCEAN
+-117.33,34.06,42.0,530.0,123.0,390.0,124.0,1.0469,67000.0,INLAND
+-121.27,38.11,15.0,2039.0,384.0,1178.0,375.0,3.8672,120100.0,INLAND
+-118.24,34.2,41.0,2067.0,452.0,1282.0,455.0,5.5756,309900.0,<1H OCEAN
+-122.15,37.74,52.0,2898.0,557.0,1338.0,550.0,3.851,183500.0,NEAR BAY
+-122.07,37.72,26.0,3204.0,477.0,1411.0,484.0,5.4834,295200.0,NEAR BAY
+-118.19,33.79,43.0,1823.0,600.0,2339.0,560.0,1.6792,130600.0,NEAR OCEAN
+-120.98,37.65,40.0,422.0,63.0,158.0,63.0,7.3841,172200.0,INLAND
+-122.26,38.15,26.0,3699.0,671.0,2388.0,699.0,4.0515,121900.0,NEAR BAY
+-118.39,34.08,28.0,833.0,230.0,349.0,210.0,3.067,375000.0,<1H OCEAN
+-121.04,39.08,8.0,2870.0,526.0,1307.0,451.0,3.463,201700.0,INLAND
+-117.61,34.34,18.0,5210.0,912.0,1301.0,464.0,4.8623,176900.0,INLAND
+-120.44,34.94,24.0,2481.0,476.0,1101.0,474.0,3.1576,147200.0,<1H OCEAN
+-121.91,37.23,22.0,2614.0,453.0,1240.0,462.0,6.0712,271800.0,<1H OCEAN
+-121.84,37.34,27.0,2512.0,526.0,3033.0,526.0,4.25,162900.0,<1H OCEAN
+-118.34,34.06,52.0,1311.0,310.0,707.0,290.0,3.4812,432800.0,<1H OCEAN
+-120.8,35.33,20.0,2200.0,393.0,996.0,365.0,3.587,330000.0,NEAR OCEAN
+-118.13,34.08,40.0,1931.0,449.0,1367.0,446.0,2.575,228400.0,<1H OCEAN
+-119.33,36.22,9.0,3748.0,644.0,1955.0,620.0,4.2011,108100.0,INLAND
+-118.71,34.29,24.0,2983.0,406.0,1203.0,381.0,6.3236,302000.0,<1H OCEAN
+-117.36,33.99,42.0,1178.0,261.0,804.0,283.0,2.9688,92900.0,INLAND
+-118.24,33.99,44.0,448.0,116.0,504.0,96.0,1.875,98600.0,<1H OCEAN
+-117.9,33.88,28.0,2696.0,346.0,947.0,356.0,9.0055,375400.0,<1H OCEAN
+-117.76,34.11,22.0,4935.0,954.0,2874.0,938.0,3.9825,180500.0,INLAND
+-118.17,33.74,36.0,2006.0,453.0,807.0,426.0,3.7838,500001.0,NEAR OCEAN
+-122.32,37.56,26.0,2339.0,704.0,1283.0,654.0,3.162,415000.0,NEAR OCEAN
+-118.47,34.01,44.0,2175.0,475.0,1019.0,448.0,4.793,470800.0,<1H OCEAN
+-120.98,37.68,27.0,4006.0,762.0,1806.0,718.0,3.1848,112800.0,INLAND
+-120.43,34.95,43.0,2020.0,344.0,692.0,310.0,3.6815,181800.0,<1H OCEAN
+-117.92,33.63,24.0,1562.0,441.0,696.0,347.0,3.5161,236400.0,<1H OCEAN
+-121.36,38.6,36.0,1275.0,227.0,530.0,245.0,3.875,133600.0,INLAND
+-118.06,34.03,36.0,21.0,7.0,21.0,9.0,2.375,175000.0,<1H OCEAN
+-119.16,36.31,7.0,2946.0,664.0,1608.0,622.0,1.6829,80200.0,INLAND
+-121.32,38.03,25.0,2474.0,513.0,1947.0,524.0,2.5742,98400.0,INLAND
+-116.5,33.81,26.0,5032.0,1229.0,3086.0,1183.0,2.5399,94800.0,INLAND
+-118.42,34.32,30.0,3027.0,604.0,1970.0,590.0,4.3409,156000.0,<1H OCEAN
+-118.36,34.1,52.0,1096.0,247.0,423.0,230.0,3.0179,500001.0,<1H OCEAN
+-122.7,39.0,18.0,793.0,148.0,186.0,59.0,2.3125,162500.0,INLAND
+-120.04,36.93,11.0,3606.0,699.0,2074.0,644.0,2.6941,63300.0,INLAND
+-118.31,33.88,21.0,1490.0,430.0,686.0,400.0,2.3812,237500.0,<1H OCEAN
+-122.32,38.35,20.0,3494.0,549.0,1673.0,541.0,5.5718,185200.0,INLAND
+-121.52,38.53,31.0,3089.0,585.0,1366.0,561.0,4.2885,160300.0,INLAND
+-121.2,38.67,26.0,1546.0,287.0,773.0,299.0,2.9803,115400.0,INLAND
+-122.47,37.78,52.0,2388.0,507.0,1078.0,494.0,3.5221,443300.0,NEAR BAY
+-121.1,38.33,14.0,1357.0,247.0,695.0,224.0,4.1974,157800.0,INLAND
+-117.97,34.1,26.0,1399.0,277.0,1285.0,276.0,4.0,160100.0,INLAND
+-121.26,38.27,20.0,1314.0,229.0,712.0,219.0,4.4125,144600.0,INLAND
+-122.3,41.32,13.0,2300.0,513.0,1151.0,488.0,2.1571,81500.0,INLAND
+-121.95,36.61,31.0,1736.0,250.0,497.0,170.0,6.3835,407800.0,NEAR OCEAN
+-119.69,36.81,15.0,2892.0,496.0,1634.0,501.0,4.4934,88000.0,INLAND
+-118.35,33.96,21.0,2714.0,881.0,1549.0,853.0,1.2094,157500.0,<1H OCEAN
+-121.71,36.78,19.0,2371.0,324.0,944.0,332.0,5.9175,240200.0,<1H OCEAN
+-117.9,34.13,32.0,1640.0,391.0,1312.0,358.0,2.6292,136100.0,<1H OCEAN
+-122.45,37.71,52.0,1658.0,322.0,1086.0,326.0,3.8583,261600.0,NEAR OCEAN
+-118.41,34.19,39.0,1169.0,242.0,612.0,247.0,4.1429,200000.0,<1H OCEAN
+-122.07,37.66,28.0,2280.0,610.0,1255.0,587.0,2.6719,161200.0,NEAR BAY
+-121.89,37.31,52.0,1994.0,404.0,1014.0,389.0,4.3882,223600.0,<1H OCEAN
+-117.65,34.04,15.0,3393.0,,2039.0,611.0,3.9336,151000.0,INLAND
+-118.41,34.17,35.0,2027.0,428.0,879.0,402.0,4.692,330900.0,<1H OCEAN
+-121.24,38.79,15.0,2615.0,485.0,1063.0,428.0,3.7904,173200.0,INLAND
+-116.99,33.75,18.0,9601.0,2401.0,4002.0,2106.0,1.4366,77000.0,INLAND
+-119.73,36.62,35.0,2080.0,365.0,1026.0,333.0,3.5781,92800.0,INLAND
+-122.24,37.76,52.0,2646.0,581.0,1128.0,522.0,3.0718,266700.0,NEAR BAY
+-117.89,33.62,24.0,1016.0,238.0,465.0,236.0,3.0625,93800.0,<1H OCEAN
+-122.47,37.76,52.0,2465.0,489.0,1170.0,498.0,4.0793,306700.0,NEAR BAY
+-117.13,32.74,52.0,1512.0,321.0,651.0,321.0,3.6852,185300.0,NEAR OCEAN
+-121.91,37.93,13.0,1610.0,198.0,703.0,217.0,8.7059,329400.0,INLAND
+-117.16,32.91,5.0,1619.0,272.0,1063.0,296.0,6.0891,214600.0,<1H OCEAN
+-118.38,34.07,21.0,3653.0,956.0,1510.0,890.0,3.5573,500001.0,<1H OCEAN
+-116.86,33.05,17.0,9044.0,1689.0,5030.0,1596.0,3.6348,164500.0,<1H OCEAN
+-121.3,38.72,15.0,2514.0,482.0,1166.0,503.0,2.2813,131900.0,INLAND
+-116.94,33.77,14.0,7240.0,1410.0,2708.0,1240.0,2.4145,137600.0,INLAND
+-116.58,32.69,19.0,4085.0,876.0,2133.0,718.0,2.919,116500.0,INLAND
+-122.01,37.56,6.0,3028.0,778.0,1531.0,736.0,4.4259,158000.0,NEAR BAY
+-117.1,32.76,31.0,987.0,267.0,619.0,250.0,2.9286,151800.0,NEAR OCEAN
+-118.01,34.1,35.0,2120.0,412.0,1375.0,405.0,3.4609,166300.0,INLAND
+-117.69,33.47,23.0,3499.0,722.0,1480.0,634.0,3.86,300000.0,<1H OCEAN
+-121.42,38.64,44.0,1728.0,367.0,1042.0,349.0,1.6033,58500.0,INLAND
+-122.18,37.74,35.0,504.0,126.0,323.0,109.0,1.8438,90500.0,NEAR BAY
+-116.49,33.8,13.0,8789.0,1875.0,1274.0,688.0,3.7396,148900.0,INLAND
+-117.9,33.72,36.0,443.0,117.0,577.0,115.0,3.6875,137500.0,<1H OCEAN
+-118.11,33.98,36.0,446.0,108.0,410.0,117.0,3.3942,147200.0,<1H OCEAN
+-122.4,37.72,41.0,1975.0,440.0,1528.0,424.0,3.8625,218300.0,NEAR BAY
+-120.4,34.86,11.0,1633.0,348.0,504.0,327.0,2.0508,275000.0,<1H OCEAN
+-122.45,37.72,52.0,982.0,197.0,653.0,171.0,4.2167,231900.0,NEAR BAY
+-115.56,32.79,20.0,2372.0,835.0,2283.0,767.0,1.1707,62500.0,INLAND
+-118.16,34.11,48.0,1091.0,236.0,632.0,234.0,3.7235,263600.0,<1H OCEAN
+-116.98,32.73,16.0,952.0,143.0,530.0,143.0,5.0864,175300.0,<1H OCEAN
+-121.87,37.42,19.0,12128.0,2112.0,6810.0,2040.0,6.4419,264500.0,<1H OCEAN
+-121.24,37.95,36.0,361.0,63.0,169.0,62.0,3.7734,63800.0,INLAND
+-121.66,39.15,22.0,2144.0,376.0,1200.0,370.0,3.4426,102400.0,INLAND
+-121.41,38.52,25.0,3087.0,720.0,2529.0,708.0,1.8689,66800.0,INLAND
+-116.98,33.26,12.0,5898.0,1002.0,3129.0,945.0,4.7647,254100.0,<1H OCEAN
+-118.16,33.85,36.0,2668.0,473.0,1315.0,478.0,4.0714,215600.0,<1H OCEAN
+-117.2,34.04,24.0,1587.0,222.0,676.0,234.0,6.0715,173400.0,INLAND
+-118.03,33.86,19.0,1795.0,328.0,1014.0,322.0,4.535,289300.0,<1H OCEAN
+-122.5,37.75,44.0,1819.0,,1137.0,354.0,3.4919,271800.0,NEAR OCEAN
+-122.64,38.23,52.0,1075.0,249.0,519.0,210.0,3.0769,230900.0,<1H OCEAN
+-121.0,37.25,31.0,1923.0,341.0,806.0,349.0,3.1738,97600.0,INLAND
+-121.3,39.0,16.0,3155.0,541.0,1630.0,540.0,4.0282,126400.0,INLAND
+-121.29,38.15,23.0,4183.0,633.0,1886.0,628.0,4.8787,175300.0,INLAND
+-121.7,36.48,19.0,2150.0,479.0,1052.0,428.0,3.5039,288400.0,<1H OCEAN
+-120.24,38.01,11.0,1214.0,228.0,633.0,199.0,3.125,148600.0,INLAND
+-122.54,37.98,52.0,1758.0,316.0,607.0,264.0,5.5083,371900.0,NEAR BAY
+-122.48,37.78,44.0,3371.0,794.0,1738.0,753.0,3.1653,335300.0,NEAR BAY
+-117.24,34.15,23.0,3847.0,608.0,1621.0,630.0,4.6111,128400.0,INLAND
+-121.31,38.97,16.0,1210.0,228.0,726.0,222.0,2.7083,82100.0,INLAND
+-118.05,34.11,42.0,3677.0,627.0,1779.0,622.0,5.1509,426500.0,INLAND
+-118.21,34.08,26.0,2574.0,807.0,3163.0,802.0,1.9495,173200.0,<1H OCEAN
+-118.31,33.93,43.0,1834.0,292.0,997.0,295.0,4.9464,150300.0,<1H OCEAN
+-118.01,33.96,36.0,1805.0,288.0,882.0,308.0,5.3054,273500.0,<1H OCEAN
+-122.33,37.57,27.0,3085.0,876.0,1453.0,896.0,3.4333,290000.0,NEAR OCEAN
+-117.37,33.19,18.0,975.0,382.0,650.0,286.0,1.9562,192500.0,NEAR OCEAN
+-117.36,34.0,19.0,4592.0,895.0,2769.0,838.0,3.3622,105100.0,INLAND
+-118.21,34.12,52.0,1590.0,360.0,1127.0,321.0,3.4625,173900.0,<1H OCEAN
+-118.23,33.9,34.0,2462.0,553.0,2334.0,502.0,1.641,96800.0,<1H OCEAN
+-120.42,34.87,18.0,2505.0,376.0,1162.0,382.0,4.8359,195700.0,<1H OCEAN
+-120.94,39.05,8.0,3758.0,717.0,1744.0,661.0,3.1972,151500.0,INLAND
+-122.01,37.58,17.0,4313.0,717.0,2629.0,721.0,5.7579,231800.0,NEAR BAY
+-122.44,37.73,52.0,2381.0,492.0,1485.0,447.0,4.3898,270000.0,NEAR BAY
+-117.06,32.55,5.0,3223.0,940.0,3284.0,854.0,1.4384,108800.0,NEAR OCEAN
+-118.2,33.77,52.0,1375.0,457.0,1089.0,317.0,2.2344,200000.0,NEAR OCEAN
+-117.66,34.09,26.0,1151.0,200.0,593.0,188.0,3.6667,166300.0,INLAND
+-118.13,34.02,36.0,984.0,275.0,1024.0,284.0,2.125,153500.0,<1H OCEAN
+-122.69,38.35,16.0,1689.0,254.0,921.0,270.0,4.4444,191800.0,<1H OCEAN
+-121.36,38.01,16.0,2178.0,667.0,1192.0,579.0,2.3339,87100.0,INLAND
+-118.39,33.84,16.0,2472.0,572.0,965.0,529.0,5.137,348600.0,<1H OCEAN
+-117.18,32.82,25.0,1756.0,301.0,722.0,312.0,4.5625,162300.0,NEAR OCEAN
+-121.26,37.93,33.0,2109.0,531.0,2248.0,588.0,1.4583,53000.0,INLAND
+-122.05,37.38,24.0,2424.0,501.0,1367.0,507.0,4.072,364200.0,<1H OCEAN
+-117.7,35.64,8.0,2683.0,416.0,1154.0,399.0,5.8625,109400.0,INLAND
+-119.53,34.4,14.0,1671.0,383.0,1079.0,365.0,3.1389,248700.0,<1H OCEAN
+-120.5,37.37,18.0,8606.0,1678.0,5303.0,1644.0,2.4012,79700.0,INLAND
+-121.76,37.67,6.0,3023.0,518.0,1225.0,468.0,6.3705,350000.0,INLAND
+-118.37,34.07,52.0,2195.0,435.0,884.0,432.0,5.24,486400.0,<1H OCEAN
+-118.37,34.22,17.0,1787.0,463.0,1671.0,448.0,3.5521,151500.0,<1H OCEAN
+-117.23,33.19,22.0,2554.0,447.0,1147.0,422.0,3.6346,192500.0,<1H OCEAN
+-122.49,37.76,49.0,1637.0,304.0,729.0,281.0,4.3281,323100.0,NEAR BAY
+-118.36,33.99,35.0,3702.0,648.0,1449.0,614.0,5.3194,313700.0,<1H OCEAN
+-117.87,33.64,26.0,3521.0,455.0,1336.0,451.0,10.2849,500001.0,<1H OCEAN
+-119.64,36.33,41.0,3095.0,766.0,1852.0,721.0,1.4524,51700.0,INLAND
+-122.08,40.09,19.0,2611.0,503.0,1185.0,483.0,2.3657,94000.0,INLAND
+-121.51,38.54,34.0,2815.0,479.0,1075.0,471.0,3.9792,164800.0,INLAND
+-118.14,34.04,43.0,1949.0,464.0,1216.0,457.0,3.3214,209300.0,<1H OCEAN
+-122.26,38.13,28.0,3072.0,790.0,1375.0,705.0,1.6368,91200.0,NEAR BAY
+-120.9,37.66,19.0,3377.0,669.0,2426.0,663.0,2.9783,82500.0,INLAND
+-117.8,33.74,30.0,3569.0,551.0,1540.0,537.0,5.2998,247200.0,<1H OCEAN
+-118.31,34.06,34.0,2470.0,1197.0,2326.0,1055.0,1.9038,325000.0,<1H OCEAN
+-118.24,33.96,37.0,1602.0,388.0,1553.0,342.0,2.0655,93400.0,<1H OCEAN
+-123.32,40.43,15.0,661.0,146.0,131.0,57.0,0.4999,56700.0,INLAND
+-118.13,33.91,36.0,1967.0,316.0,910.0,306.0,4.4948,190600.0,<1H OCEAN
+-121.96,37.27,22.0,6114.0,1211.0,2983.0,1163.0,5.2533,269100.0,<1H OCEAN
+-121.3,38.75,36.0,3903.0,885.0,2313.0,804.0,2.655,86300.0,INLAND
+-122.41,37.72,32.0,1650.0,316.0,904.0,295.0,4.0583,236200.0,NEAR BAY
+-118.33,34.19,45.0,1505.0,347.0,799.0,319.0,3.138,217000.0,<1H OCEAN
+-122.01,37.18,37.0,3852.0,652.0,1534.0,567.0,5.8596,318700.0,<1H OCEAN
+-122.44,37.76,52.0,1968.0,472.0,784.0,430.0,3.3702,370000.0,NEAR BAY
+-118.37,33.92,40.0,928.0,187.0,521.0,185.0,5.5255,242700.0,<1H OCEAN
+-117.12,32.76,28.0,1605.0,501.0,936.0,460.0,2.5991,147500.0,NEAR OCEAN
+-117.74,34.08,35.0,1613.0,298.0,911.0,293.0,3.4398,134300.0,INLAND
+-118.98,37.64,17.0,3769.0,908.0,1160.0,453.0,3.05,188900.0,INLAND
+-119.65,37.09,17.0,1280.0,254.0,707.0,267.0,3.55,106300.0,INLAND
+-118.19,34.08,38.0,1241.0,298.0,1055.0,263.0,2.3409,115500.0,<1H OCEAN
+-117.18,33.16,15.0,5923.0,1206.0,3943.0,1006.0,3.1793,159900.0,<1H OCEAN
+-118.11,33.88,19.0,3203.0,708.0,1761.0,667.0,4.0911,239700.0,<1H OCEAN
+-118.32,34.09,32.0,563.0,191.0,626.0,185.0,2.0341,250000.0,<1H OCEAN
+-117.98,34.53,13.0,2815.0,535.0,1492.0,491.0,4.0945,135700.0,INLAND
+-120.48,37.34,8.0,6146.0,1017.0,2821.0,987.0,4.67,127600.0,INLAND
+-117.62,34.09,26.0,3271.0,595.0,2259.0,566.0,4.0139,132000.0,INLAND
+-118.12,33.97,35.0,708.0,145.0,471.0,153.0,3.2,197400.0,<1H OCEAN
+-117.25,33.21,13.0,1203.0,292.0,1035.0,293.0,2.6339,117000.0,<1H OCEAN
+-121.62,39.15,23.0,1984.0,528.0,1043.0,452.0,1.9375,65300.0,INLAND
+-122.25,37.81,52.0,1758.0,460.0,686.0,422.0,3.1691,259400.0,NEAR BAY
+-122.42,37.8,52.0,2657.0,772.0,1014.0,685.0,4.038,500001.0,NEAR BAY
+-118.48,34.47,36.0,84.0,12.0,29.0,17.0,3.375,187500.0,<1H OCEAN
+-120.35,37.86,25.0,287.0,57.0,118.0,50.0,2.3056,162500.0,INLAND
+-121.91,37.28,29.0,5650.0,817.0,2098.0,813.0,6.4285,337300.0,<1H OCEAN
+-122.23,37.82,52.0,1611.0,203.0,556.0,179.0,8.7477,500001.0,NEAR BAY
+-122.41,37.66,37.0,694.0,188.0,658.0,225.0,4.6103,237500.0,NEAR OCEAN
+-118.43,34.18,31.0,2417.0,510.0,1102.0,507.0,3.8906,282200.0,<1H OCEAN
+-118.19,33.78,31.0,1648.0,484.0,898.0,457.0,1.5844,162500.0,NEAR OCEAN
+-119.64,37.31,15.0,2654.0,530.0,1267.0,489.0,2.8393,104400.0,INLAND
+-117.32,33.15,15.0,13245.0,2212.0,5495.0,2060.0,5.4904,262100.0,NEAR OCEAN
+-122.33,37.39,52.0,573.0,102.0,232.0,92.0,6.2263,500001.0,NEAR OCEAN
+-117.93,33.62,37.0,2204.0,428.0,807.0,410.0,7.0516,500001.0,<1H OCEAN
+-121.92,37.3,35.0,1335.0,296.0,635.0,296.0,3.6053,345800.0,<1H OCEAN
+-116.75,32.85,17.0,4863.0,845.0,2266.0,769.0,4.2321,217400.0,<1H OCEAN
+-121.2,37.97,39.0,440.0,83.0,270.0,97.0,6.0582,157700.0,INLAND
+-122.01,36.91,19.0,691.0,191.0,324.0,167.0,3.1312,388500.0,NEAR OCEAN
+-118.13,33.79,29.0,2937.0,524.0,1132.0,528.0,4.6133,500001.0,NEAR OCEAN
+-121.54,39.5,38.0,1438.0,310.0,779.0,275.0,1.3289,39400.0,INLAND
+-117.21,34.14,16.0,1613.0,245.0,811.0,267.0,5.2591,140700.0,INLAND
+-117.15,34.06,25.0,3670.0,644.0,1815.0,634.0,4.0658,127400.0,INLAND
+-117.29,33.2,16.0,2150.0,461.0,1428.0,407.0,2.4754,157300.0,NEAR OCEAN
+-121.98,38.34,18.0,3876.0,916.0,2386.0,867.0,2.5938,129500.0,INLAND
+-118.39,34.08,52.0,1244.0,304.0,444.0,282.0,3.5114,430800.0,<1H OCEAN
+-120.67,35.29,44.0,2236.0,411.0,1036.0,437.0,3.0833,219300.0,NEAR OCEAN
+-118.09,33.92,33.0,879.0,181.0,547.0,169.0,5.3146,168600.0,<1H OCEAN
+-117.88,33.83,22.0,3522.0,543.0,1706.0,524.0,6.4685,241200.0,<1H OCEAN
+-117.98,34.05,35.0,2342.0,426.0,2176.0,416.0,3.7454,156900.0,<1H OCEAN
+-121.49,38.5,30.0,1715.0,271.0,842.0,263.0,3.0313,87900.0,INLAND
+-121.85,36.59,42.0,891.0,203.0,525.0,212.0,3.3156,186300.0,<1H OCEAN
+-118.2,33.81,46.0,1388.0,254.0,742.0,241.0,4.6458,212100.0,NEAR OCEAN
+-117.88,34.14,32.0,1764.0,365.0,924.0,329.0,3.875,186700.0,INLAND
+-118.38,34.21,42.0,715.0,145.0,730.0,158.0,3.8,169500.0,<1H OCEAN
+-117.8,34.1,17.0,5153.0,1164.0,2949.0,1083.0,3.5603,174600.0,INLAND
+-122.06,37.88,34.0,4781.0,703.0,1879.0,714.0,6.5378,340900.0,NEAR BAY
+-117.99,34.14,30.0,2346.0,,1988.0,474.0,2.5625,153000.0,INLAND
+-117.58,33.6,5.0,5348.0,659.0,1862.0,555.0,11.0567,495400.0,<1H OCEAN
+-118.3,34.05,46.0,1386.0,457.0,1845.0,485.0,2.1414,157700.0,<1H OCEAN
+-118.32,34.07,52.0,2980.0,366.0,967.0,359.0,11.2185,500001.0,<1H OCEAN
+-118.13,33.94,34.0,522.0,138.0,373.0,139.0,3.5481,265000.0,<1H OCEAN
+-121.2,37.6,30.0,2110.0,406.0,1301.0,345.0,2.3173,86500.0,INLAND
+-122.09,37.39,30.0,1722.0,490.0,1057.0,517.0,3.725,261300.0,NEAR BAY
+-122.2,37.47,37.0,1053.0,266.0,939.0,267.0,3.1989,320800.0,NEAR BAY
+-122.14,37.88,34.0,6986.0,1096.0,2865.0,1124.0,6.2275,394400.0,NEAR BAY
+-118.3,34.19,52.0,1704.0,277.0,746.0,262.0,4.7986,326100.0,<1H OCEAN
+-118.2,34.13,50.0,2929.0,588.0,1931.0,574.0,3.3438,173600.0,<1H OCEAN
+-121.04,37.68,28.0,1909.0,398.0,1140.0,380.0,2.3783,81400.0,INLAND
+-122.79,38.5,18.0,4839.0,918.0,2755.0,841.0,3.75,248300.0,<1H OCEAN
+-117.97,34.07,22.0,1438.0,364.0,1325.0,335.0,2.7802,162500.0,<1H OCEAN
+-122.41,37.8,52.0,1394.0,395.0,1700.0,400.0,2.75,168800.0,NEAR BAY
+-121.44,38.61,41.0,1404.0,313.0,765.0,330.0,1.8792,63300.0,INLAND
+-122.37,37.95,32.0,1298.0,363.0,716.0,268.0,0.9797,76400.0,NEAR BAY
+-120.86,37.69,5.0,6660.0,1217.0,3012.0,1087.0,3.0809,143600.0,INLAND
+-114.59,34.83,41.0,812.0,,375.0,158.0,1.7083,48500.0,INLAND
+-118.5,34.03,32.0,6365.0,1784.0,2767.0,1698.0,3.6451,383300.0,<1H OCEAN
+-117.93,34.03,35.0,2160.0,399.0,1694.0,403.0,3.8581,163100.0,<1H OCEAN
+-117.98,33.75,37.0,1264.0,274.0,783.0,273.0,3.3438,199600.0,<1H OCEAN
+-122.31,37.56,40.0,1351.0,330.0,701.0,297.0,3.32,292900.0,NEAR OCEAN
+-118.37,33.88,21.0,966.0,172.0,417.0,158.0,5.5335,342600.0,<1H OCEAN
+-122.6,38.0,21.0,2198.0,462.0,1100.0,449.0,4.1098,246600.0,<1H OCEAN
+-121.9,39.59,20.0,1465.0,278.0,745.0,250.0,3.0625,93800.0,INLAND
+-121.1,35.6,20.0,3389.0,704.0,1309.0,520.0,3.2112,204500.0,NEAR OCEAN
+-118.08,33.92,38.0,1335.0,,1011.0,269.0,3.6908,157500.0,<1H OCEAN
+-121.46,38.65,14.0,3167.0,551.0,1787.0,533.0,3.8125,92600.0,INLAND
+-118.27,33.98,39.0,2062.0,588.0,1933.0,570.0,1.3801,97000.0,<1H OCEAN
+-118.6,34.16,37.0,3441.0,584.0,1283.0,544.0,4.1656,313100.0,<1H OCEAN
+-121.91,37.33,52.0,2562.0,410.0,973.0,398.0,4.8854,330600.0,<1H OCEAN
+-117.6,33.45,4.0,2369.0,566.0,996.0,435.0,5.4031,243800.0,NEAR OCEAN
+-117.69,35.65,5.0,1131.0,276.0,520.0,232.0,4.0167,87500.0,INLAND
+-121.86,37.38,15.0,2052.0,405.0,1380.0,409.0,5.8686,181100.0,<1H OCEAN
+-121.32,38.01,36.0,391.0,74.0,171.0,79.0,2.7045,102800.0,INLAND
+-117.17,32.83,24.0,3541.0,530.0,1591.0,530.0,5.3538,212500.0,NEAR OCEAN
+-120.25,37.93,13.0,493.0,76.0,196.0,68.0,3.375,134100.0,INLAND
+-118.41,34.07,52.0,3562.0,394.0,1163.0,361.0,15.0001,500001.0,<1H OCEAN
+-122.41,37.66,37.0,2155.0,446.0,1255.0,428.0,3.8438,250700.0,NEAR OCEAN
+-120.45,34.96,26.0,1949.0,396.0,1575.0,377.0,2.875,121400.0,<1H OCEAN
+-117.08,32.63,28.0,2080.0,427.0,1266.0,434.0,2.2788,146300.0,NEAR OCEAN
+-118.3,33.95,27.0,1774.0,444.0,1622.0,402.0,2.2031,96900.0,<1H OCEAN
+-119.34,36.3,13.0,2394.0,458.0,1177.0,389.0,2.6875,74400.0,INLAND
+-117.05,32.57,22.0,2857.0,516.0,2412.0,496.0,4.7337,127600.0,NEAR OCEAN
+-118.77,34.26,26.0,3038.0,468.0,1825.0,468.0,5.6385,196900.0,<1H OCEAN
+-117.92,34.59,7.0,681.0,125.0,485.0,104.0,2.7396,125600.0,INLAND
+-117.21,32.87,12.0,1428.0,303.0,528.0,269.0,4.1429,254400.0,NEAR OCEAN
+-121.27,37.82,26.0,1170.0,238.0,830.0,216.0,2.6458,127500.0,INLAND
+-122.28,37.89,52.0,2616.0,473.0,1085.0,487.0,4.125,270900.0,NEAR BAY
+-120.65,35.27,52.0,2254.0,642.0,1237.0,590.0,2.6208,227100.0,NEAR OCEAN
+-118.39,33.71,18.0,1193.0,233.0,475.0,228.0,7.5594,500001.0,NEAR OCEAN
+-119.18,34.28,17.0,4526.0,717.0,2088.0,655.0,5.6885,268200.0,NEAR OCEAN
+-122.1,38.24,13.0,7367.0,1042.0,3193.0,983.0,5.3102,195000.0,NEAR BAY
+-117.9,34.1,35.0,2739.0,475.0,1481.0,483.0,4.5655,176600.0,<1H OCEAN
+-118.18,34.04,36.0,1807.0,630.0,2118.0,669.0,1.55,129000.0,<1H OCEAN
+-117.15,32.8,23.0,2395.0,476.0,2284.0,488.0,3.7292,146300.0,NEAR OCEAN
+-117.94,33.66,16.0,2095.0,450.0,963.0,411.0,5.5,224100.0,<1H OCEAN
+-118.19,34.87,2.0,2103.0,389.0,923.0,338.0,5.0553,111100.0,INLAND
+-118.17,33.98,31.0,1236.0,329.0,1486.0,337.0,3.0938,155400.0,<1H OCEAN
+-118.33,33.98,28.0,3889.0,1199.0,3121.0,1046.0,1.8806,113900.0,<1H OCEAN
+-118.13,33.95,37.0,1709.0,333.0,778.0,344.0,3.9036,326600.0,<1H OCEAN
+-117.25,33.22,19.0,2167.0,443.0,1654.0,435.0,3.5,135800.0,<1H OCEAN
+-119.67,34.42,37.0,1673.0,444.0,1477.0,446.0,2.0643,246700.0,<1H OCEAN
+-121.83,38.45,32.0,2139.0,440.0,1154.0,411.0,3.2672,107500.0,INLAND
+-118.25,33.95,41.0,1576.0,339.0,1252.0,302.0,1.9798,98100.0,<1H OCEAN
+-118.04,34.1,38.0,2317.0,451.0,1155.0,426.0,4.1488,235300.0,INLAND
+-121.8,37.27,17.0,3912.0,737.0,2693.0,746.0,5.0772,221500.0,<1H OCEAN
+-117.22,33.81,4.0,9911.0,1946.0,5145.0,1661.0,3.4237,113700.0,INLAND
+-122.21,37.38,28.0,4518.0,578.0,1489.0,559.0,11.3176,500001.0,NEAR OCEAN
+-122.76,38.35,30.0,2260.0,374.0,958.0,359.0,5.0323,222400.0,<1H OCEAN
+-121.39,39.61,22.0,2828.0,610.0,986.0,391.0,2.8871,94700.0,INLAND
+-118.33,34.07,52.0,1482.0,171.0,531.0,161.0,15.0001,500001.0,<1H OCEAN
+-118.45,34.04,23.0,3771.0,1321.0,2031.0,1241.0,2.7679,277500.0,<1H OCEAN
+-124.21,40.75,32.0,1218.0,331.0,620.0,268.0,1.6528,58100.0,NEAR OCEAN
+-120.93,38.26,13.0,2084.0,449.0,834.0,305.0,3.2937,114200.0,INLAND
+-119.78,36.76,50.0,1343.0,322.0,1063.0,342.0,1.75,49800.0,INLAND
+-118.16,34.06,27.0,1675.0,274.0,785.0,275.0,5.828,301100.0,<1H OCEAN
+-118.56,34.03,34.0,2095.0,343.0,662.0,299.0,8.2934,500001.0,<1H OCEAN
+-122.46,37.73,52.0,2857.0,469.0,1431.0,496.0,5.2088,344200.0,NEAR BAY
+-120.71,37.39,11.0,1479.0,341.0,1476.0,327.0,3.2721,73800.0,INLAND
+-117.9,34.06,35.0,1313.0,194.0,599.0,209.0,7.5,287200.0,<1H OCEAN
+-122.09,37.65,35.0,1184.0,200.0,572.0,194.0,4.7143,193800.0,NEAR BAY
+-121.44,38.54,47.0,2518.0,501.0,1308.0,471.0,2.5389,75700.0,INLAND
+-120.38,37.99,36.0,2864.0,603.0,1155.0,565.0,2.3571,113400.0,INLAND
+-121.79,37.32,6.0,2850.0,561.0,2160.0,581.0,5.5336,241900.0,<1H OCEAN
+-119.94,37.04,14.0,1636.0,253.0,766.0,225.0,3.125,88500.0,INLAND
+-117.93,34.04,30.0,1336.0,239.0,905.0,253.0,4.8854,178100.0,<1H OCEAN
+-118.37,33.96,26.0,138.0,23.0,100.0,20.0,4.875,175000.0,<1H OCEAN
+-116.94,32.89,24.0,2541.0,381.0,1078.0,372.0,5.2542,227800.0,<1H OCEAN
+-118.1,34.08,37.0,2894.0,659.0,1977.0,636.0,2.543,208100.0,<1H OCEAN
+-122.49,37.89,23.0,1650.0,403.0,541.0,336.0,6.0238,500001.0,NEAR BAY
+-119.07,35.36,19.0,5254.0,894.0,2155.0,831.0,4.6705,110700.0,INLAND
+-118.04,34.06,30.0,2019.0,551.0,2481.0,484.0,3.1875,154200.0,<1H OCEAN
+-122.01,37.03,21.0,5904.0,956.0,2616.0,916.0,5.9039,355300.0,NEAR OCEAN
+-117.09,32.63,27.0,2920.0,770.0,1935.0,746.0,2.4148,67500.0,NEAR OCEAN
+-118.11,33.91,19.0,3056.0,759.0,1561.0,740.0,3.1369,196900.0,<1H OCEAN
+-118.04,34.07,39.0,2451.0,649.0,2536.0,648.0,2.3098,173100.0,<1H OCEAN
+-117.32,34.14,32.0,1691.0,353.0,1457.0,329.0,1.8438,66600.0,INLAND
+-122.24,40.18,39.0,2191.0,493.0,1307.0,499.0,1.6483,60800.0,INLAND
+-121.27,37.96,43.0,1624.0,448.0,1805.0,440.0,1.425,61300.0,INLAND
+-118.28,33.97,35.0,2305.0,634.0,1978.0,568.0,1.375,100000.0,<1H OCEAN
+-122.45,38.27,25.0,5024.0,881.0,1994.0,838.0,4.2237,262300.0,NEAR BAY
+-118.34,34.09,5.0,2665.0,954.0,1733.0,766.0,2.3568,204700.0,<1H OCEAN
+-122.44,37.72,48.0,2675.0,585.0,1773.0,540.0,3.9565,268500.0,NEAR BAY
+-117.81,33.81,19.0,3154.0,390.0,1404.0,384.0,8.9257,431800.0,<1H OCEAN
+-117.96,33.76,22.0,2520.0,556.0,2126.0,527.0,3.7734,193900.0,<1H OCEAN
+-122.14,38.16,4.0,3273.0,495.0,1497.0,454.0,5.3345,176100.0,NEAR BAY
+-118.25,33.91,36.0,1950.0,365.0,1125.0,374.0,3.1111,119300.0,<1H OCEAN
+-122.31,38.0,26.0,3735.0,641.0,1708.0,633.0,4.621,191100.0,NEAR BAY
+-117.71,34.1,52.0,567.0,152.0,2688.0,126.0,1.875,212500.0,INLAND
+-122.16,37.72,38.0,1007.0,245.0,618.0,239.0,2.875,144800.0,NEAR BAY
+-122.24,37.8,52.0,888.0,168.0,360.0,175.0,2.1944,211500.0,NEAR BAY
+-118.4,33.97,37.0,1364.0,248.0,494.0,242.0,4.6364,456300.0,<1H OCEAN
+-120.2,37.8,30.0,1189.0,255.0,446.0,165.0,3.4838,112500.0,INLAND
+-121.9,38.0,14.0,1930.0,363.0,990.0,322.0,4.1094,162200.0,INLAND
+-121.2,37.83,18.0,3415.0,580.0,1912.0,562.0,4.4423,161400.0,INLAND
+-117.13,32.73,43.0,2706.0,667.0,1531.0,614.0,2.1513,145000.0,NEAR OCEAN
+-118.0,34.03,25.0,6909.0,1154.0,3912.0,1121.0,5.257,226100.0,<1H OCEAN
+-117.11,32.69,34.0,1144.0,295.0,1271.0,302.0,2.09,91800.0,NEAR OCEAN
+-120.98,39.93,25.0,2220.0,511.0,912.0,449.0,1.8914,87800.0,INLAND
+-118.2,33.78,46.0,1889.0,651.0,1545.0,587.0,1.7064,175000.0,NEAR OCEAN
+-121.76,36.91,23.0,1276.0,437.0,1359.0,376.0,1.9609,155000.0,<1H OCEAN
+-121.87,37.67,10.0,4337.0,800.0,1813.0,743.0,5.5,247200.0,INLAND
+-118.16,33.79,25.0,3742.0,1180.0,3916.0,1063.0,2.4,153700.0,NEAR OCEAN
+-121.65,37.11,14.0,6006.0,914.0,2915.0,898.0,5.9356,321700.0,<1H OCEAN
+-120.84,38.63,12.0,1313.0,231.0,731.0,232.0,5.7373,208300.0,INLAND
+-121.96,38.34,15.0,2857.0,373.0,1325.0,359.0,6.0252,151700.0,INLAND
+-119.68,34.43,49.0,1785.0,386.0,1267.0,380.0,3.5208,251200.0,<1H OCEAN
+-121.25,37.96,26.0,2205.0,478.0,1730.0,472.0,2.4866,68300.0,INLAND
+-122.1,37.69,44.0,2341.0,500.0,1256.0,485.0,2.9507,157100.0,NEAR BAY
+-122.26,37.78,52.0,970.0,217.0,528.0,208.0,3.3438,201300.0,NEAR BAY
+-118.48,34.04,47.0,1956.0,277.0,724.0,277.0,8.9616,500001.0,<1H OCEAN
+-122.3,37.56,35.0,1873.0,351.0,945.0,333.0,5.5184,274800.0,NEAR OCEAN
+-121.05,39.13,10.0,3063.0,497.0,1168.0,507.0,4.4375,185100.0,INLAND
+-118.3,33.82,26.0,2080.0,560.0,2096.0,506.0,2.8106,184400.0,<1H OCEAN
+-118.33,34.06,52.0,1841.0,240.0,693.0,218.0,15.0001,500001.0,<1H OCEAN
+-117.15,34.22,10.0,1039.0,174.0,317.0,109.0,7.2371,171900.0,INLAND
+-122.75,38.5,16.0,4196.0,638.0,1713.0,615.0,5.449,252100.0,<1H OCEAN
+-118.16,33.9,28.0,2410.0,616.0,2399.0,594.0,2.7339,156700.0,<1H OCEAN
+-116.9,32.84,18.0,3612.0,737.0,1864.0,713.0,2.7069,153800.0,<1H OCEAN
+-122.43,37.8,52.0,2994.0,821.0,1240.0,779.0,3.3715,500000.0,NEAR BAY
+-117.97,33.66,14.0,6090.0,1338.0,1974.0,1248.0,2.8061,180300.0,<1H OCEAN
+-118.23,33.89,35.0,1255.0,344.0,1782.0,343.0,2.1949,95100.0,<1H OCEAN
+-121.89,37.31,47.0,2986.0,627.0,1399.0,613.0,3.7455,247400.0,<1H OCEAN
+-122.08,37.66,33.0,1547.0,372.0,1063.0,356.0,2.5625,154300.0,NEAR BAY
+-124.16,40.79,52.0,2148.0,421.0,975.0,430.0,2.2566,92700.0,NEAR OCEAN
+-120.11,39.21,18.0,2245.0,392.0,421.0,162.0,4.5795,158300.0,INLAND
+-117.03,32.67,15.0,5094.0,818.0,2118.0,758.0,5.3505,266600.0,NEAR OCEAN
+-122.06,37.77,12.0,14316.0,2045.0,5781.0,2007.0,7.2634,341600.0,NEAR BAY
+-122.24,37.83,41.0,2576.0,406.0,794.0,376.0,5.956,366100.0,NEAR BAY
+-118.23,34.1,41.0,1353.0,379.0,1536.0,416.0,2.1687,157000.0,<1H OCEAN
+-122.09,37.67,39.0,2069.0,500.0,1408.0,478.0,3.1115,153500.0,NEAR BAY
+-117.95,34.07,37.0,1375.0,260.0,951.0,272.0,3.2083,195200.0,<1H OCEAN
+-117.67,33.46,24.0,3571.0,722.0,1409.0,543.0,4.6518,277800.0,<1H OCEAN
+-117.14,32.75,35.0,1391.0,329.0,726.0,317.0,2.6818,159400.0,NEAR OCEAN
+-118.2,33.94,43.0,1934.0,511.0,1895.0,493.0,2.5029,159700.0,<1H OCEAN
+-117.13,32.72,32.0,2197.0,623.0,1784.0,599.0,1.901,120300.0,NEAR OCEAN
+-117.97,34.01,33.0,2006.0,381.0,1410.0,346.0,3.7083,165500.0,<1H OCEAN
+-118.37,34.22,11.0,2127.0,581.0,1989.0,530.0,2.9028,174100.0,<1H OCEAN
+-122.49,37.76,52.0,2245.0,425.0,1091.0,409.0,3.5909,331200.0,NEAR BAY
+-118.29,34.25,19.0,1988.0,594.0,1399.0,527.0,2.4727,175000.0,<1H OCEAN
+-122.82,38.38,27.0,2565.0,479.0,1227.0,467.0,4.5132,259900.0,<1H OCEAN
+-117.96,33.81,34.0,1941.0,356.0,1021.0,339.0,4.4663,183900.0,<1H OCEAN
+-119.08,35.42,10.0,4159.0,608.0,2089.0,591.0,5.5261,132000.0,INLAND
+-118.25,33.95,48.0,1766.0,424.0,1655.0,420.0,0.9751,95500.0,<1H OCEAN
+-117.03,32.77,34.0,1796.0,428.0,918.0,424.0,2.875,161200.0,<1H OCEAN
+-119.21,36.38,18.0,2158.0,413.0,1461.0,395.0,2.0216,58000.0,INLAND
+-118.22,34.13,52.0,791.0,174.0,501.0,162.0,3.3542,178100.0,<1H OCEAN
+-119.29,34.4,22.0,3891.0,657.0,1727.0,581.0,4.2656,241400.0,NEAR OCEAN
+-121.25,38.07,28.0,2103.0,422.0,1167.0,391.0,3.0592,152800.0,INLAND
+-118.39,34.01,25.0,1101.0,285.0,543.0,294.0,2.3571,340600.0,<1H OCEAN
+-121.9,37.66,18.0,7397.0,1137.0,3126.0,1115.0,6.4994,323000.0,INLAND
+-122.04,37.95,29.0,866.0,138.0,341.0,133.0,4.7188,197100.0,NEAR BAY
+-119.71,34.43,18.0,1170.0,372.0,681.0,346.0,2.1974,255000.0,<1H OCEAN
+-117.09,32.8,15.0,666.0,152.0,247.0,164.0,2.15,131300.0,NEAR OCEAN
+-118.36,34.15,34.0,3659.0,921.0,1338.0,835.0,3.6202,366100.0,<1H OCEAN
+-118.08,34.11,42.0,1969.0,353.0,927.0,354.0,5.5924,285300.0,<1H OCEAN
+-117.19,32.82,34.0,3850.0,608.0,1619.0,602.0,5.0465,208200.0,NEAR OCEAN
+-119.83,36.79,35.0,1872.0,363.0,1054.0,369.0,3.3272,65600.0,INLAND
+-122.34,37.57,52.0,2635.0,408.0,967.0,374.0,7.0422,500001.0,NEAR OCEAN
+-122.27,37.53,43.0,1145.0,230.0,586.0,254.0,3.5,267400.0,NEAR OCEAN
+-117.89,33.71,24.0,4365.0,804.0,2663.0,753.0,4.5814,233300.0,<1H OCEAN
+-117.27,33.06,7.0,3686.0,733.0,1612.0,672.0,3.197,367100.0,NEAR OCEAN
+-118.26,34.02,38.0,980.0,285.0,1308.0,310.0,1.5652,123100.0,<1H OCEAN
+-121.28,38.59,3.0,4188.0,1136.0,2081.0,995.0,3.0481,92500.0,INLAND
+-121.86,37.31,24.0,1939.0,652.0,1808.0,625.0,2.2259,112500.0,<1H OCEAN
+-120.97,37.69,15.0,4065.0,841.0,1986.0,680.0,3.072,114300.0,INLAND
+-117.01,32.66,11.0,9992.0,1368.0,4495.0,1316.0,6.9664,293900.0,NEAR OCEAN
+-122.08,37.99,19.0,4657.0,739.0,1914.0,732.0,5.0509,199900.0,NEAR BAY
+-122.25,37.49,44.0,4420.0,743.0,1790.0,735.0,6.142,394700.0,NEAR OCEAN
+-122.42,37.62,40.0,1545.0,264.0,756.0,282.0,4.4643,308100.0,NEAR OCEAN
+-117.03,32.61,23.0,1553.0,216.0,778.0,229.0,5.1538,171300.0,NEAR OCEAN
+-121.94,37.29,20.0,710.0,188.0,363.0,176.0,4.0962,214100.0,<1H OCEAN
+-122.31,38.3,25.0,3883.0,740.0,1641.0,676.0,3.9,187300.0,NEAR BAY
+-122.08,37.61,26.0,2261.0,443.0,1039.0,395.0,3.7931,203900.0,NEAR BAY
+-117.85,33.8,34.0,1593.0,283.0,872.0,255.0,3.825,216700.0,<1H OCEAN
+-117.05,33.13,17.0,2385.0,372.0,1118.0,369.0,4.2813,169900.0,<1H OCEAN
+-118.17,34.03,31.0,1014.0,252.0,1064.0,247.0,2.4167,125500.0,<1H OCEAN
+-120.48,34.7,26.0,3069.0,518.0,1524.0,539.0,4.3162,136400.0,NEAR OCEAN
+-118.56,34.17,35.0,2987.0,391.0,1244.0,387.0,7.1322,500001.0,<1H OCEAN
+-122.02,37.34,30.0,1036.0,151.0,467.0,156.0,6.448,360600.0,<1H OCEAN
+-119.73,36.83,14.0,3348.0,491.0,1584.0,493.0,5.0828,111400.0,INLAND
+-122.39,37.6,44.0,2304.0,384.0,986.0,379.0,4.652,387100.0,NEAR OCEAN
+-119.82,36.72,17.0,1276.0,242.0,927.0,238.0,2.6176,54100.0,INLAND
+-121.32,35.95,31.0,372.0,68.0,479.0,67.0,3.5547,200000.0,NEAR OCEAN
+-118.48,34.04,49.0,3780.0,741.0,1435.0,690.0,4.3158,500001.0,<1H OCEAN
+-122.03,38.26,25.0,4617.0,1046.0,2685.0,1011.0,2.9576,108500.0,INLAND
+-120.79,38.43,40.0,1391.0,246.0,546.0,214.0,3.9107,129800.0,INLAND
+-122.38,40.69,21.0,1774.0,370.0,875.0,354.0,1.7422,61500.0,INLAND
+-121.83,37.37,43.0,1461.0,284.0,800.0,258.0,3.2279,182400.0,<1H OCEAN
+-122.26,38.28,24.0,2831.0,502.0,1462.0,503.0,4.5,158300.0,NEAR BAY
+-121.99,36.98,25.0,2113.0,422.0,1365.0,439.0,4.6484,234600.0,NEAR OCEAN
+-121.7,36.67,37.0,641.0,129.0,458.0,142.0,3.3456,252600.0,<1H OCEAN
+-117.9,33.76,26.0,2678.0,702.0,3262.0,685.0,3.6953,176800.0,<1H OCEAN
+-119.98,38.94,25.0,1339.0,328.0,503.0,219.0,1.9018,109700.0,INLAND
+-122.02,37.95,25.0,1205.0,260.0,608.0,272.0,2.4519,208300.0,NEAR BAY
+-122.11,37.7,29.0,1298.0,306.0,835.0,338.0,2.3274,170400.0,NEAR BAY
+-118.36,33.98,46.0,1425.0,283.0,782.0,273.0,5.057,246300.0,<1H OCEAN
+-120.96,37.67,18.0,1442.0,229.0,538.0,220.0,4.2969,163200.0,INLAND
+-117.96,33.76,24.0,1328.0,290.0,1012.0,306.0,4.2813,189500.0,<1H OCEAN
+-118.45,34.31,9.0,1739.0,358.0,820.0,323.0,4.0556,182500.0,<1H OCEAN
+-118.16,33.97,23.0,1516.0,457.0,1977.0,435.0,2.3068,157800.0,<1H OCEAN
+-118.05,33.85,25.0,2856.0,388.0,1212.0,362.0,6.1737,313100.0,<1H OCEAN
+-122.09,37.6,36.0,385.0,94.0,295.0,92.0,2.9706,147900.0,NEAR BAY
+-117.82,33.64,18.0,1974.0,260.0,808.0,278.0,9.8589,500001.0,<1H OCEAN
+-121.46,38.57,52.0,893.0,159.0,367.0,160.0,3.2386,213200.0,INLAND
+-122.56,37.94,36.0,2023.0,242.0,653.0,241.0,10.6272,500001.0,NEAR BAY
+-121.03,40.35,52.0,5486.0,1044.0,1977.0,754.0,2.1833,49500.0,INLAND
+-120.58,35.0,37.0,523.0,119.0,374.0,95.0,1.4726,106300.0,<1H OCEAN
+-117.93,33.74,5.0,639.0,197.0,666.0,197.0,3.3017,87500.0,<1H OCEAN
+-118.25,34.01,36.0,879.0,262.0,1034.0,236.0,1.2857,99300.0,<1H OCEAN
+-121.36,38.04,4.0,2477.0,359.0,1234.0,377.0,5.5427,162100.0,INLAND
+-122.72,38.42,30.0,2099.0,406.0,1156.0,401.0,2.8036,152300.0,<1H OCEAN
+-118.25,33.96,42.0,1326.0,295.0,918.0,258.0,2.3864,98800.0,<1H OCEAN
+-118.13,33.76,46.0,2834.0,673.0,1175.0,670.0,4.7875,363800.0,NEAR OCEAN
+-118.09,34.16,45.0,2199.0,358.0,942.0,353.0,5.0393,321100.0,INLAND
+-118.55,34.26,21.0,4018.0,536.0,1508.0,529.0,8.203,445400.0,<1H OCEAN
+-117.13,34.39,29.0,2251.0,464.0,855.0,315.0,3.4183,104100.0,INLAND
+-122.19,37.48,35.0,7067.0,1646.0,5380.0,1597.0,4.1776,265300.0,NEAR BAY
+-121.92,37.96,14.0,5332.0,884.0,2093.0,839.0,5.2798,237400.0,INLAND
+-118.17,34.03,41.0,2099.0,530.0,2325.0,528.0,2.1979,140800.0,<1H OCEAN
+-122.69,38.51,18.0,3364.0,501.0,1442.0,506.0,6.6854,313000.0,<1H OCEAN
+-120.2,37.84,9.0,13670.0,2453.0,2811.0,1193.0,3.2589,137900.0,INLAND
+-118.32,34.09,34.0,1478.0,675.0,1976.0,653.0,2.0557,225000.0,<1H OCEAN
+-118.27,34.16,48.0,1301.0,253.0,637.0,260.0,4.3438,252700.0,<1H OCEAN
+-117.93,33.78,36.0,2169.0,359.0,1018.0,370.0,4.3906,231300.0,<1H OCEAN
+-118.37,34.06,52.0,2402.0,506.0,878.0,464.0,4.0217,500001.0,<1H OCEAN
+-118.28,33.82,30.0,3615.0,760.0,2813.0,752.0,5.3849,217700.0,<1H OCEAN
+-119.77,36.85,8.0,1519.0,234.0,711.0,248.0,5.9897,123600.0,INLAND
+-117.73,34.08,28.0,5173.0,1069.0,3502.0,954.0,3.8438,130800.0,INLAND
+-118.1,33.93,35.0,1622.0,302.0,845.0,284.0,4.5769,186100.0,<1H OCEAN
+-117.21,33.71,16.0,8476.0,1758.0,2711.0,1427.0,2.1848,97900.0,<1H OCEAN
+-118.36,33.92,26.0,3695.0,1144.0,2308.0,1009.0,2.6667,229300.0,<1H OCEAN
+-117.66,34.15,25.0,3430.0,485.0,1284.0,438.0,8.5282,360100.0,INLAND
+-118.29,33.73,30.0,2835.0,711.0,1669.0,652.0,3.3616,268500.0,NEAR OCEAN
+-117.11,32.95,11.0,4694.0,824.0,2223.0,783.0,4.9485,231800.0,<1H OCEAN
+-118.5,34.15,33.0,3104.0,387.0,1111.0,376.0,13.4196,500001.0,<1H OCEAN
+-121.34,38.66,16.0,3154.0,860.0,1837.0,793.0,1.9805,92900.0,INLAND
+-117.26,34.11,33.0,1210.0,288.0,850.0,238.0,1.2171,59300.0,INLAND
+-119.46,36.25,32.0,1702.0,348.0,1016.0,350.0,2.5,73600.0,INLAND
+-122.15,37.44,52.0,2063.0,320.0,774.0,309.0,7.2543,500001.0,NEAR BAY
+-116.8,33.52,3.0,830.0,145.0,272.0,104.0,3.8281,163500.0,INLAND
+-117.31,32.83,38.0,2367.0,480.0,891.0,428.0,4.1477,500001.0,NEAR OCEAN
+-121.85,39.73,52.0,444.0,80.0,1107.0,98.0,3.4191,137500.0,INLAND
+-121.86,37.23,24.0,4337.0,670.0,1936.0,652.0,5.8904,271400.0,<1H OCEAN
+-119.24,36.8,17.0,2052.0,405.0,975.0,340.0,2.6902,94400.0,INLAND
+-116.21,33.71,19.0,3114.0,787.0,3157.0,772.0,1.7083,82200.0,INLAND
+-117.87,33.89,17.0,1441.0,530.0,769.0,456.0,2.425,171700.0,<1H OCEAN
+-119.56,36.51,37.0,1018.0,213.0,663.0,204.0,1.6635,67000.0,INLAND
+-118.21,33.92,37.0,1705.0,403.0,1839.0,410.0,2.5833,132700.0,<1H OCEAN
+-122.04,36.97,40.0,1193.0,227.0,570.0,204.0,4.4659,237500.0,NEAR OCEAN
+-120.59,38.76,21.0,1728.0,417.0,731.0,334.0,1.7266,94700.0,INLAND
+-117.88,33.75,34.0,3004.0,673.0,5477.0,640.0,2.8342,187200.0,<1H OCEAN
+-120.03,38.89,15.0,3042.0,588.0,918.0,336.0,3.8333,118800.0,INLAND
+-117.66,34.04,10.0,3657.0,695.0,2079.0,663.0,4.2054,159900.0,INLAND
+-116.96,33.52,9.0,2802.0,471.0,1155.0,421.0,4.125,392100.0,INLAND
+-121.38,38.47,4.0,14418.0,2282.0,6578.0,2140.0,4.5604,145900.0,INLAND
+-118.5,34.02,28.0,5109.0,1482.0,2313.0,1451.0,3.3266,483300.0,<1H OCEAN
+-121.87,37.41,24.0,3308.0,548.0,1891.0,544.0,5.6683,248700.0,<1H OCEAN
+-115.41,32.99,29.0,1141.0,220.0,684.0,194.0,3.4038,107800.0,INLAND
+-118.16,34.7,33.0,2918.0,494.0,1365.0,478.0,4.8787,127700.0,INLAND
+-117.23,33.57,6.0,13724.0,2269.0,5860.0,1986.0,3.9617,183000.0,<1H OCEAN
+-118.38,33.79,32.0,10445.0,1620.0,4474.0,1576.0,7.7042,500001.0,NEAR OCEAN
+-121.98,37.54,17.0,5133.0,1375.0,3386.0,1339.0,3.1326,220800.0,<1H OCEAN
+-119.17,34.25,15.0,1329.0,282.0,1001.0,284.0,3.65,189300.0,NEAR OCEAN
+-123.24,39.81,25.0,1435.0,304.0,746.0,259.0,1.7788,57900.0,INLAND
+-118.11,34.16,52.0,2489.0,437.0,1101.0,438.0,4.2065,320300.0,INLAND
+-121.61,37.06,21.0,5322.0,908.0,3011.0,895.0,5.5936,386800.0,<1H OCEAN
+-117.7,35.6,16.0,2678.0,483.0,1473.0,487.0,3.858,70200.0,INLAND
+-119.8,36.76,52.0,2224.0,418.0,832.0,406.0,2.3952,78400.0,INLAND
+-117.37,33.18,19.0,1931.0,509.0,855.0,394.0,2.6979,266700.0,NEAR OCEAN
+-118.62,34.2,32.0,3233.0,553.0,1678.0,545.0,5.0025,234900.0,<1H OCEAN
+-119.27,34.26,23.0,3578.0,753.0,1455.0,649.0,4.1898,359100.0,NEAR OCEAN
+-122.74,38.45,17.0,3064.0,588.0,1704.0,590.0,3.9329,170900.0,<1H OCEAN
+-117.95,33.67,25.0,1799.0,233.0,810.0,265.0,8.289,372400.0,<1H OCEAN
+-121.91,37.25,31.0,1944.0,343.0,975.0,334.0,4.9205,240500.0,<1H OCEAN
+-121.9,36.6,45.0,2249.0,412.0,944.0,429.0,3.0625,260300.0,NEAR OCEAN
+-122.04,37.89,33.0,2423.0,322.0,998.0,346.0,7.5349,349100.0,NEAR BAY
+-117.98,33.7,17.0,1989.0,411.0,1401.0,453.0,4.1603,160500.0,<1H OCEAN
+-122.16,37.77,48.0,977.0,194.0,446.0,180.0,4.7708,156300.0,NEAR BAY
+-118.07,34.13,27.0,3787.0,913.0,1992.0,853.0,3.301,251200.0,INLAND
+-122.25,38.1,52.0,248.0,86.0,173.0,69.0,2.3,109400.0,NEAR BAY
+-121.98,36.99,14.0,6787.0,1454.0,3416.0,1357.0,3.5943,262400.0,NEAR OCEAN
+-118.28,33.99,37.0,1971.0,513.0,1673.0,464.0,1.4625,103000.0,<1H OCEAN
+-120.45,34.64,27.0,2696.0,622.0,1322.0,543.0,3.0352,135400.0,NEAR OCEAN
+-119.7,34.42,43.0,1802.0,557.0,1490.0,538.0,2.675,247900.0,<1H OCEAN
+-117.07,32.67,28.0,2758.0,623.0,2179.0,631.0,2.3814,112300.0,NEAR OCEAN
+-118.0,33.77,28.0,2401.0,503.0,1155.0,456.0,3.5139,211700.0,<1H OCEAN
+-117.39,33.69,5.0,6529.0,997.0,3464.0,1006.0,5.3275,168700.0,<1H OCEAN
+-121.9,37.3,52.0,1456.0,269.0,582.0,277.0,5.036,296600.0,<1H OCEAN
+-118.39,33.89,40.0,826.0,143.0,389.0,147.0,7.1845,438100.0,<1H OCEAN
+-122.16,37.75,38.0,2457.0,624.0,1516.0,482.0,1.5625,91700.0,NEAR BAY
+-122.5,37.88,28.0,5448.0,1089.0,2100.0,1023.0,4.7475,474600.0,NEAR BAY
+-117.26,33.26,9.0,4609.0,798.0,2582.0,746.0,4.3429,173900.0,<1H OCEAN
+-118.16,34.09,50.0,1568.0,302.0,1093.0,333.0,3.1442,162100.0,<1H OCEAN
+-122.44,37.79,52.0,1447.0,186.0,483.0,181.0,15.0001,500001.0,NEAR BAY
+-122.82,38.33,25.0,3067.0,569.0,1602.0,550.0,3.9917,244100.0,<1H OCEAN
+-122.74,38.47,16.0,1426.0,287.0,525.0,260.0,3.0714,161700.0,<1H OCEAN
+-118.07,33.87,28.0,2399.0,436.0,1613.0,429.0,3.6339,220100.0,<1H OCEAN
+-118.28,34.16,49.0,1393.0,290.0,605.0,282.0,2.9491,257400.0,<1H OCEAN
+-119.8,36.75,52.0,1788.0,449.0,1156.0,418.0,1.7298,58400.0,INLAND
+-117.27,34.05,34.0,1703.0,395.0,849.0,359.0,3.1607,138200.0,INLAND
+-117.17,32.69,45.0,3168.0,598.0,1341.0,562.0,4.5189,422200.0,NEAR OCEAN
+-117.9,33.92,27.0,698.0,116.0,391.0,126.0,5.9177,267600.0,<1H OCEAN
+-120.55,38.31,18.0,1411.0,312.0,592.0,230.0,1.625,94700.0,INLAND
+-121.98,37.53,28.0,2829.0,566.0,1610.0,540.0,4.6,223200.0,<1H OCEAN
+-120.99,37.67,28.0,1768.0,423.0,1066.0,392.0,1.8315,90500.0,INLAND
+-120.88,37.37,24.0,1294.0,222.0,684.0,221.0,2.6908,103100.0,INLAND
+-117.2,32.76,40.0,581.0,157.0,298.0,156.0,2.4,255000.0,NEAR OCEAN
+-117.06,32.75,34.0,1917.0,419.0,1181.0,426.0,3.0208,129200.0,NEAR OCEAN
+-118.39,34.23,18.0,3405.0,831.0,3001.0,795.0,3.0083,181900.0,<1H OCEAN
+-118.39,34.16,20.0,4084.0,1062.0,1637.0,987.0,3.2388,256300.0,<1H OCEAN
+-117.88,34.01,16.0,6756.0,1332.0,4429.0,1299.0,4.7589,178200.0,<1H OCEAN
+-121.06,37.67,31.0,906.0,146.0,383.0,129.0,3.4167,196900.0,INLAND
+-119.45,36.59,28.0,1274.0,215.0,572.0,202.0,3.825,84200.0,INLAND
+-120.16,39.04,18.0,2040.0,402.0,350.0,129.0,4.0313,126000.0,INLAND
+-117.07,32.61,22.0,5016.0,1331.0,3222.0,1196.0,2.1441,135500.0,NEAR OCEAN
+-118.34,34.09,37.0,1442.0,501.0,998.0,503.0,2.4432,200000.0,<1H OCEAN
+-121.85,37.36,15.0,3148.0,1116.0,3556.0,1037.0,3.0466,159600.0,<1H OCEAN
+-117.84,34.0,26.0,797.0,117.0,383.0,114.0,6.8758,253800.0,<1H OCEAN
+-117.89,33.94,20.0,3349.0,685.0,1822.0,675.0,4.7216,227000.0,<1H OCEAN
+-122.64,41.63,19.0,2722.0,479.0,1108.0,430.0,3.1062,100000.0,INLAND
+-119.69,36.43,29.0,1799.0,356.0,1278.0,387.0,1.7813,57900.0,INLAND
+-117.3,34.53,38.0,1643.0,489.0,1196.0,406.0,1.2275,64100.0,INLAND
+-118.0,33.93,35.0,802.0,153.0,445.0,150.0,5.0077,185000.0,<1H OCEAN
+-117.13,32.7,35.0,1179.0,344.0,1372.0,330.0,1.9509,70200.0,NEAR OCEAN
+-122.23,38.05,52.0,1736.0,358.0,638.0,297.0,2.5517,147100.0,NEAR BAY
+-118.16,33.78,33.0,2048.0,585.0,2074.0,597.0,2.0156,152700.0,NEAR OCEAN
+-118.07,34.51,14.0,2798.0,459.0,1236.0,404.0,4.8667,239900.0,INLAND
+-122.45,37.78,52.0,2686.0,606.0,1392.0,578.0,4.0,355300.0,NEAR BAY
+-117.27,34.49,7.0,2344.0,351.0,846.0,314.0,4.7361,174500.0,INLAND
+-121.76,36.92,20.0,2687.0,637.0,2154.0,610.0,2.6434,169700.0,<1H OCEAN
+-122.12,37.45,38.0,1276.0,314.0,955.0,287.0,2.0096,155700.0,NEAR BAY
+-122.19,39.5,23.0,462.0,97.0,261.0,90.0,2.1705,53000.0,INLAND
+-117.91,33.9,27.0,829.0,114.0,383.0,133.0,9.3125,293500.0,<1H OCEAN
+-117.19,32.83,30.0,3225.0,555.0,1601.0,532.0,4.3317,173300.0,NEAR OCEAN
+-118.19,33.78,35.0,1511.0,593.0,914.0,539.0,0.9318,187500.0,NEAR OCEAN
+-122.15,37.74,41.0,856.0,178.0,571.0,191.0,3.1458,130600.0,NEAR BAY
+-118.14,34.14,24.0,10239.0,2823.0,4210.0,2565.0,3.6997,225000.0,<1H OCEAN
+-121.38,36.85,13.0,4115.0,782.0,2903.0,747.0,3.7316,192400.0,INLAND
+-118.01,34.16,47.0,1745.0,270.0,753.0,275.0,5.532,318500.0,INLAND
+-122.49,38.32,17.0,3308.0,720.0,1587.0,632.0,3.2727,176000.0,<1H OCEAN
+-118.39,34.09,28.0,2347.0,608.0,785.0,548.0,4.4167,425000.0,<1H OCEAN
+-117.77,33.71,13.0,1939.0,247.0,928.0,244.0,8.1111,379800.0,<1H OCEAN
+-118.51,34.03,37.0,4072.0,905.0,1468.0,923.0,3.8571,500001.0,<1H OCEAN
+-121.96,39.3,39.0,701.0,130.0,271.0,89.0,2.1845,112500.0,INLAND
+-121.87,37.29,18.0,1892.0,568.0,974.0,553.0,2.3715,228000.0,<1H OCEAN
+-120.62,35.28,28.0,3952.0,592.0,1469.0,571.0,6.3144,328800.0,NEAR OCEAN
+-117.13,32.72,17.0,1285.0,423.0,1208.0,409.0,1.758,126600.0,NEAR OCEAN
+-117.68,33.6,24.0,1956.0,262.0,969.0,256.0,6.8154,265900.0,<1H OCEAN
+-121.25,37.94,30.0,1509.0,308.0,967.0,278.0,1.7798,65900.0,INLAND
+-118.19,34.04,40.0,1279.0,316.0,1438.0,329.0,2.1774,157600.0,<1H OCEAN
+-119.12,36.69,13.0,3963.0,812.0,1905.0,671.0,2.2278,90500.0,INLAND
+-117.91,33.64,37.0,1998.0,472.0,1030.0,436.0,3.9306,268400.0,<1H OCEAN
+-121.88,37.3,16.0,2692.0,749.0,1674.0,681.0,2.6763,191100.0,<1H OCEAN
+-117.65,33.59,8.0,2649.0,340.0,1238.0,354.0,8.0409,337900.0,<1H OCEAN
+-123.02,38.36,16.0,1496.0,298.0,778.0,284.0,3.8589,268800.0,NEAR OCEAN
+-122.18,37.45,43.0,2061.0,437.0,817.0,385.0,4.4688,460200.0,NEAR BAY
+-122.05,37.57,7.0,10648.0,1818.0,6075.0,1797.0,6.1047,278200.0,NEAR BAY
+-117.72,34.03,17.0,2902.0,476.0,1652.0,479.0,5.6029,161800.0,INLAND
+-117.85,34.12,30.0,4367.0,1033.0,2524.0,954.0,3.0448,192100.0,INLAND
+-121.67,38.54,13.0,6141.0,1019.0,2553.0,967.0,4.2432,326500.0,INLAND
+-117.19,34.08,22.0,2467.0,555.0,1567.0,494.0,2.6536,84700.0,INLAND
+-121.92,37.72,22.0,4638.0,716.0,2302.0,687.0,5.347,219500.0,<1H OCEAN
+-122.41,37.62,49.0,1464.0,302.0,636.0,259.0,4.25,284100.0,NEAR OCEAN
+-118.41,34.17,27.0,3277.0,648.0,1382.0,615.0,3.875,366100.0,<1H OCEAN
+-122.13,37.44,42.0,2390.0,462.0,1146.0,468.0,6.3111,397400.0,NEAR BAY
+-122.3,37.92,33.0,1615.0,271.0,710.0,285.0,4.0804,239000.0,NEAR BAY
+-118.36,33.88,28.0,1313.0,319.0,827.0,308.0,2.65,260800.0,<1H OCEAN
+-122.32,38.29,21.0,1607.0,356.0,834.0,352.0,2.3787,177900.0,NEAR BAY
+-118.33,33.86,36.0,854.0,160.0,473.0,150.0,6.3992,259600.0,<1H OCEAN
+-119.04,36.07,17.0,2623.0,659.0,1912.0,618.0,1.5893,52000.0,INLAND
+-118.38,34.04,35.0,2237.0,592.0,1794.0,543.0,2.2961,207700.0,<1H OCEAN
+-119.57,36.44,30.0,1860.0,337.0,1123.0,347.0,3.4926,94200.0,INLAND
+-118.1,33.96,40.0,1743.0,328.0,981.0,291.0,3.6667,173100.0,<1H OCEAN
+-115.55,32.79,22.0,565.0,162.0,692.0,141.0,1.2083,53600.0,INLAND
+-117.71,34.07,24.0,1948.0,362.0,1286.0,364.0,3.6,139300.0,INLAND
+-121.74,37.35,34.0,440.0,90.0,217.0,93.0,5.2327,500001.0,<1H OCEAN
+-122.2,37.78,39.0,1752.0,399.0,1071.0,376.0,3.1167,121600.0,NEAR BAY
+-118.42,34.19,33.0,3353.0,790.0,2318.0,775.0,2.2589,269700.0,<1H OCEAN
+-122.42,37.75,52.0,1855.0,611.0,1715.0,614.0,2.1289,250000.0,NEAR BAY
+-122.31,37.94,38.0,1794.0,349.0,810.0,335.0,3.8343,191400.0,NEAR BAY
+-122.26,37.53,4.0,5233.0,1109.0,1690.0,907.0,6.2007,311800.0,NEAR BAY
+-121.89,37.32,41.0,977.0,265.0,865.0,253.0,3.2317,184800.0,<1H OCEAN
+-122.19,37.76,52.0,2024.0,391.0,1030.0,350.0,2.4659,94700.0,NEAR BAY
+-118.13,34.16,36.0,2162.0,658.0,1337.0,590.0,2.2095,176700.0,<1H OCEAN
+-118.1,34.57,7.0,20377.0,4335.0,11973.0,3933.0,3.3086,138100.0,INLAND
+-122.26,37.45,17.0,2742.0,441.0,986.0,421.0,5.9285,496000.0,NEAR OCEAN
+-122.25,37.8,52.0,2087.0,510.0,1197.0,488.0,3.0149,218400.0,NEAR BAY
+-122.4,37.8,52.0,1860.0,575.0,679.0,448.0,4.775,500001.0,NEAR BAY
+-118.53,34.2,33.0,3270.0,818.0,2118.0,763.0,3.225,205300.0,<1H OCEAN
+-118.13,33.81,37.0,1013.0,199.0,493.0,183.0,4.7845,231400.0,<1H OCEAN
+-122.5,37.74,44.0,2082.0,470.0,1154.0,403.0,4.3611,268100.0,NEAR OCEAN
+-117.12,34.21,19.0,4641.0,994.0,1334.0,474.0,4.5972,123900.0,INLAND
+-120.1,39.33,9.0,2738.0,510.0,1193.0,412.0,4.3958,124800.0,INLAND
+-117.73,35.73,35.0,2916.0,594.0,1870.0,432.0,3.625,55000.0,INLAND
+-117.11,32.76,28.0,1457.0,397.0,672.0,342.0,1.9799,122700.0,NEAR OCEAN
+-122.02,37.31,34.0,2629.0,433.0,1301.0,431.0,6.083,341400.0,<1H OCEAN
+-121.5,38.56,46.0,2646.0,645.0,1684.0,616.0,1.128,123100.0,INLAND
+-118.3,33.93,35.0,1300.0,356.0,1216.0,326.0,1.2,99200.0,<1H OCEAN
+-117.21,33.93,4.0,10002.0,1468.0,5439.0,1397.0,5.0223,152600.0,INLAND
+-118.45,34.07,13.0,4284.0,1452.0,3806.0,1252.0,1.3125,350000.0,<1H OCEAN
+-117.23,32.73,44.0,1168.0,263.0,509.0,256.0,2.7273,269700.0,NEAR OCEAN
+-117.92,33.83,52.0,1514.0,301.0,855.0,293.0,3.6042,166400.0,<1H OCEAN
+-121.94,37.33,37.0,818.0,269.0,576.0,261.0,2.1902,250000.0,<1H OCEAN
+-120.13,39.06,22.0,2465.0,539.0,381.0,146.0,2.875,87500.0,INLAND
+-122.08,37.72,31.0,3866.0,531.0,1368.0,521.0,6.187,340400.0,NEAR BAY
+-119.44,36.59,28.0,1343.0,330.0,1331.0,305.0,1.516,56700.0,INLAND
+-119.29,34.28,38.0,2387.0,748.0,1537.0,741.0,2.3147,192500.0,NEAR OCEAN
+-117.3,32.85,28.0,2334.0,694.0,770.0,552.0,3.1324,500001.0,NEAR OCEAN
+-122.07,37.86,23.0,1025.0,205.0,263.0,191.0,3.12,155000.0,NEAR BAY
+-118.6,34.07,16.0,319.0,59.0,149.0,64.0,4.625,433300.0,<1H OCEAN
+-119.74,36.72,25.0,3972.0,842.0,2863.0,729.0,2.1304,58500.0,INLAND
+-118.01,34.11,36.0,1722.0,320.0,794.0,322.0,4.2132,212200.0,INLAND
+-118.49,34.03,31.0,3155.0,808.0,1208.0,745.0,3.6769,450000.0,<1H OCEAN
+-118.29,33.96,32.0,3508.0,917.0,2794.0,839.0,1.554,100000.0,<1H OCEAN
+-118.16,34.18,44.0,1870.0,389.0,1345.0,391.0,1.8932,136100.0,<1H OCEAN
+-122.01,37.3,25.0,4044.0,551.0,1699.0,533.0,8.0837,380600.0,<1H OCEAN
+-118.43,34.11,27.0,10806.0,1440.0,3511.0,1352.0,12.7296,500001.0,<1H OCEAN
+-118.12,33.82,42.0,1493.0,277.0,671.0,267.0,3.2794,224500.0,<1H OCEAN
+-121.33,38.7,15.0,2226.0,421.0,1004.0,417.0,2.7868,117800.0,INLAND
+-118.37,34.09,33.0,3180.0,865.0,1347.0,841.0,4.0651,500001.0,<1H OCEAN
+-121.01,37.61,5.0,3655.0,696.0,2316.0,647.0,3.4703,129300.0,INLAND
+-120.69,35.25,15.0,4210.0,899.0,1933.0,867.0,2.794,262500.0,NEAR OCEAN
+-118.09,33.84,27.0,1594.0,295.0,1061.0,320.0,4.7917,217700.0,<1H OCEAN
+-119.05,34.36,22.0,1815.0,506.0,2428.0,473.0,2.8417,162500.0,<1H OCEAN
+-120.85,37.49,39.0,2840.0,733.0,2606.0,737.0,1.9429,76400.0,INLAND
+-122.06,37.6,18.0,1726.0,276.0,1186.0,310.0,5.3226,231700.0,NEAR BAY
+-123.03,38.79,16.0,4047.0,769.0,1998.0,673.0,3.375,171900.0,<1H OCEAN
+-117.67,34.12,15.0,3162.0,495.0,1145.0,473.0,5.3525,191700.0,INLAND
+-121.42,38.64,42.0,1720.0,382.0,1069.0,362.0,1.8611,60500.0,INLAND
+-117.77,33.51,29.0,3590.0,772.0,1070.0,603.0,4.4464,500001.0,<1H OCEAN
+-120.75,37.44,27.0,2295.0,424.0,1252.0,350.0,3.6182,123200.0,INLAND
+-117.88,34.0,32.0,265.0,51.0,170.0,50.0,3.9375,187500.0,<1H OCEAN
+-121.3,38.51,19.0,822.0,134.0,457.0,133.0,4.15,157500.0,INLAND
+-118.15,34.05,31.0,3362.0,799.0,1939.0,754.0,3.5089,305800.0,<1H OCEAN
+-117.33,34.13,18.0,3009.0,740.0,2317.0,659.0,1.6375,72400.0,INLAND
+-117.23,33.16,2.0,4624.0,946.0,2091.0,808.0,3.6736,214500.0,NEAR OCEAN
+-119.31,36.3,20.0,1256.0,209.0,566.0,195.0,4.0221,86300.0,INLAND
+-121.79,37.31,22.0,2199.0,361.0,1270.0,386.0,5.1149,235700.0,<1H OCEAN
+-118.12,33.91,36.0,2053.0,386.0,1023.0,394.0,3.0,216600.0,<1H OCEAN
+-117.74,34.04,27.0,2215.0,440.0,1987.0,449.0,3.0429,129600.0,INLAND
+-122.36,37.94,27.0,844.0,249.0,583.0,265.0,0.9687,105800.0,NEAR BAY
+-117.76,35.63,12.0,2014.0,372.0,1027.0,356.0,3.9261,101300.0,INLAND
+-121.45,38.37,32.0,1441.0,261.0,629.0,249.0,4.4519,137500.0,INLAND
+-119.12,35.85,37.0,736.0,166.0,564.0,138.0,2.4167,58300.0,INLAND
+-118.32,33.94,36.0,1722.0,280.0,830.0,261.0,4.0536,189000.0,<1H OCEAN
+-118.32,34.04,39.0,2965.0,812.0,2638.0,794.0,2.532,172700.0,<1H OCEAN
+-118.26,34.24,35.0,1535.0,283.0,816.0,287.0,6.1873,312100.0,<1H OCEAN
+-117.47,33.98,8.0,12106.0,1913.0,5810.0,1717.0,4.9886,158100.0,INLAND
+-117.95,35.13,4.0,2630.0,502.0,1150.0,422.0,4.25,104400.0,INLAND
+-122.03,37.33,23.0,4221.0,671.0,1782.0,641.0,7.4863,412300.0,<1H OCEAN
+-117.82,33.88,18.0,1982.0,300.0,1027.0,324.0,7.0526,327500.0,<1H OCEAN
+-121.26,38.73,14.0,3323.0,499.0,1527.0,540.0,5.3451,172100.0,INLAND
+-118.07,33.99,35.0,1625.0,302.0,1134.0,288.0,4.5595,164900.0,<1H OCEAN
+-119.01,35.24,6.0,80.0,16.0,66.0,21.0,3.125,65000.0,INLAND
+-118.19,34.19,34.0,2061.0,260.0,825.0,254.0,15.0001,500001.0,<1H OCEAN
+-118.26,33.96,39.0,1255.0,323.0,902.0,327.0,1.5812,94000.0,<1H OCEAN
+-116.89,33.79,12.0,701.0,130.0,434.0,110.0,2.0577,56700.0,INLAND
+-121.74,39.15,20.0,2302.0,412.0,1205.0,399.0,2.8,71200.0,INLAND
+-122.02,37.31,35.0,2355.0,384.0,1248.0,378.0,5.9714,332500.0,<1H OCEAN
+-117.4,33.95,46.0,2189.0,423.0,866.0,389.0,3.1384,111500.0,INLAND
+-121.17,37.88,22.0,1283.0,256.0,3082.0,239.0,3.5365,111800.0,INLAND
+-122.39,37.79,52.0,94.0,24.0,113.0,27.0,4.6563,350000.0,NEAR BAY
+-121.24,37.94,5.0,2232.0,488.0,1857.0,435.0,2.8705,113600.0,INLAND
+-122.43,37.78,29.0,1310.0,364.0,1009.0,379.0,1.3844,177500.0,NEAR BAY
+-119.67,37.27,13.0,5087.0,981.0,2284.0,913.0,2.7413,123100.0,INLAND
+-117.88,33.84,31.0,3301.0,712.0,1532.0,682.0,3.7303,223800.0,<1H OCEAN
+-122.71,37.88,21.0,2845.0,552.0,599.0,250.0,4.3125,495800.0,NEAR OCEAN
+-117.95,34.11,29.0,1986.0,448.0,2013.0,432.0,3.1034,140800.0,INLAND
+-121.15,40.25,14.0,5156.0,880.0,616.0,281.0,3.3462,145200.0,INLAND
+-118.29,33.84,34.0,2617.0,558.0,1396.0,515.0,5.061,218000.0,<1H OCEAN
+-118.37,33.82,32.0,2815.0,607.0,1338.0,609.0,4.5687,381200.0,<1H OCEAN
+-117.05,33.13,20.0,7746.0,2035.0,5370.0,1838.0,2.3762,98500.0,<1H OCEAN
+-117.09,32.76,10.0,1922.0,577.0,1595.0,545.0,1.5208,118800.0,NEAR OCEAN
+-121.97,37.25,32.0,2892.0,496.0,1193.0,492.0,6.131,367800.0,<1H OCEAN
+-118.32,33.89,45.0,1928.0,453.0,1323.0,458.0,4.2813,210100.0,<1H OCEAN
+-118.41,34.28,32.0,2574.0,531.0,2609.0,472.0,3.7566,146700.0,<1H OCEAN
+-118.35,34.1,20.0,2745.0,782.0,1161.0,739.0,3.9044,436400.0,<1H OCEAN
+-117.93,33.88,52.0,2157.0,362.0,1001.0,373.0,5.1237,240000.0,<1H OCEAN
+-122.07,37.38,26.0,1272.0,306.0,562.0,284.0,4.5644,280200.0,NEAR BAY
+-122.45,37.74,46.0,6429.0,1093.0,2535.0,1109.0,5.0887,335100.0,NEAR BAY
+-118.02,34.62,38.0,248.0,55.0,261.0,53.0,2.1413,96900.0,INLAND
+-119.84,36.81,18.0,2789.0,378.0,937.0,364.0,7.7062,188300.0,INLAND
+-117.0,32.79,31.0,3256.0,726.0,1595.0,706.0,3.4773,155800.0,<1H OCEAN
+-122.12,37.64,40.0,432.0,102.0,264.0,77.0,3.8875,228100.0,NEAR BAY
+-121.91,37.69,18.0,2876.0,423.0,1395.0,427.0,6.3132,259200.0,<1H OCEAN
+-117.75,34.1,21.0,8069.0,2174.0,4369.0,2036.0,3.2756,156800.0,INLAND
+-122.38,40.58,34.0,1262.0,267.0,520.0,259.0,1.6983,72600.0,INLAND
+-117.31,33.25,13.0,3075.0,630.0,1843.0,674.0,2.8558,97100.0,<1H OCEAN
+-122.04,38.24,30.0,2081.0,456.0,1005.0,438.0,1.9954,92900.0,INLAND
+-117.1,32.75,11.0,2393.0,726.0,1905.0,711.0,1.3448,91300.0,NEAR OCEAN
+-120.8,38.51,23.0,1001.0,195.0,369.0,157.0,3.125,96400.0,INLAND
+-117.65,33.48,10.0,3484.0,582.0,1469.0,556.0,5.4188,402200.0,<1H OCEAN
+-121.89,36.59,18.0,2700.0,937.0,1042.0,744.0,3.1364,150000.0,NEAR OCEAN
+-118.44,33.88,35.0,2020.0,451.0,724.0,399.0,6.6494,500001.0,NEAR OCEAN
+-121.82,37.31,15.0,1504.0,294.0,1267.0,291.0,5.5145,219400.0,<1H OCEAN
+-118.56,34.18,39.0,1819.0,291.0,770.0,278.0,5.4088,457300.0,<1H OCEAN
+-118.37,34.18,33.0,1829.0,512.0,1345.0,500.0,3.1629,198900.0,<1H OCEAN
+-120.84,37.51,20.0,1901.0,313.0,1258.0,320.0,3.8958,126800.0,INLAND
+-122.31,37.5,22.0,14034.0,3020.0,6266.0,2952.0,4.3939,491200.0,NEAR OCEAN
+-118.13,34.15,24.0,1125.0,341.0,579.0,321.0,2.8125,141700.0,<1H OCEAN
+-122.02,37.6,32.0,1295.0,295.0,1097.0,328.0,3.2386,149600.0,NEAR BAY
+-118.31,34.08,27.0,1514.0,510.0,1603.0,518.0,2.8971,251100.0,<1H OCEAN
+-118.61,34.21,34.0,3494.0,557.0,1861.0,576.0,5.6407,251500.0,<1H OCEAN
+-118.53,34.25,20.0,6331.0,1537.0,2957.0,1509.0,3.3892,323100.0,<1H OCEAN
+-121.63,39.15,27.0,336.0,60.0,195.0,68.0,5.3946,71800.0,INLAND
+-118.06,34.06,28.0,1778.0,605.0,2184.0,574.0,1.9189,165900.0,<1H OCEAN
+-117.12,32.66,52.0,16.0,4.0,8.0,3.0,1.125,60000.0,NEAR OCEAN
+-117.06,32.56,5.0,2706.0,925.0,3148.0,855.0,1.7301,125000.0,NEAR OCEAN
+-118.53,34.37,8.0,3839.0,852.0,1342.0,593.0,3.9118,333700.0,<1H OCEAN
+-120.85,37.78,25.0,421.0,,303.0,106.0,2.2679,71300.0,INLAND
+-122.01,36.98,47.0,2403.0,517.0,1144.0,455.0,2.5954,229400.0,NEAR OCEAN
+-118.44,34.22,41.0,1030.0,214.0,664.0,223.0,3.8083,183800.0,<1H OCEAN
+-118.51,34.27,34.0,3787.0,771.0,1966.0,738.0,4.055,222500.0,<1H OCEAN
+-119.53,37.34,26.0,4047.0,702.0,571.0,199.0,2.3482,179500.0,INLAND
+-117.09,32.69,34.0,1469.0,267.0,1031.0,267.0,3.4583,112700.0,NEAR OCEAN
+-118.28,34.18,47.0,2243.0,339.0,911.0,319.0,7.4046,446800.0,<1H OCEAN
+-121.64,36.8,18.0,5915.0,1000.0,2975.0,975.0,4.5812,255200.0,<1H OCEAN
+-118.31,33.97,52.0,1595.0,325.0,823.0,302.0,3.2188,124200.0,<1H OCEAN
+-118.28,34.04,19.0,460.0,241.0,890.0,229.0,1.6089,162500.0,<1H OCEAN
+-121.87,37.22,17.0,2825.0,365.0,1052.0,345.0,8.0595,485000.0,<1H OCEAN
+-122.12,37.68,35.0,1958.0,484.0,1146.0,448.0,2.95,148900.0,NEAR BAY
+-122.15,37.46,30.0,4198.0,1244.0,2678.0,1147.0,3.6712,308600.0,NEAR BAY
+-118.04,34.16,38.0,1594.0,249.0,633.0,247.0,5.9582,350700.0,INLAND
+-117.18,32.68,29.0,1539.0,344.0,556.0,289.0,3.25,500001.0,NEAR OCEAN
+-118.46,34.0,39.0,4098.0,1100.0,2054.0,1053.0,2.918,345600.0,<1H OCEAN
+-117.67,34.03,20.0,8561.0,1411.0,4861.0,1450.0,4.7056,165500.0,INLAND
+-118.55,34.03,35.0,9075.0,1858.0,3646.0,1724.0,6.0307,500001.0,<1H OCEAN
+-121.29,38.63,24.0,2868.0,527.0,1284.0,487.0,3.3182,213000.0,INLAND
+-123.01,38.8,21.0,360.0,96.0,131.0,74.0,3.5156,133300.0,<1H OCEAN
+-120.48,39.78,11.0,513.0,104.0,204.0,86.0,2.375,100000.0,INLAND
+-122.39,37.73,43.0,4864.0,972.0,3134.0,959.0,4.3393,217300.0,NEAR BAY
+-118.27,34.27,27.0,5205.0,859.0,2363.0,888.0,6.1946,276100.0,<1H OCEAN
+-118.26,34.07,36.0,2306.0,813.0,2823.0,765.0,2.0214,170500.0,<1H OCEAN
+-122.01,38.25,11.0,6550.0,1149.0,3570.0,1123.0,3.8583,137900.0,INLAND
+-118.63,34.18,33.0,5252.0,760.0,2041.0,730.0,6.7977,389700.0,<1H OCEAN
+-117.28,34.15,38.0,1981.0,343.0,796.0,344.0,3.8125,97400.0,INLAND
+-118.03,33.79,32.0,3191.0,634.0,1718.0,611.0,4.1548,216600.0,<1H OCEAN
+-120.41,36.77,24.0,1335.0,312.0,1180.0,267.0,1.947,68900.0,INLAND
+-118.12,33.97,33.0,3099.0,839.0,2025.0,750.0,3.183,191100.0,<1H OCEAN
+-120.94,39.22,12.0,1321.0,268.0,661.0,232.0,4.0062,153800.0,INLAND
+-121.81,37.26,16.0,1911.0,327.0,1158.0,332.0,5.9359,249500.0,<1H OCEAN
+-120.67,35.5,15.0,2752.0,546.0,1422.0,545.0,3.2813,175000.0,<1H OCEAN
+-121.0,37.62,28.0,1153.0,420.0,1043.0,357.0,1.0801,75000.0,INLAND
+-118.09,33.7,13.0,4770.0,969.0,2261.0,972.0,5.883,295100.0,NEAR OCEAN
+-121.49,37.94,31.0,1860.0,394.0,1848.0,293.0,2.2891,162500.0,INLAND
+-122.47,37.69,34.0,1954.0,357.0,1130.0,367.0,4.6447,304500.0,NEAR OCEAN
+-122.7,38.45,26.0,2011.0,557.0,855.0,530.0,1.125,233300.0,<1H OCEAN
+-117.58,33.65,4.0,2000.0,422.0,833.0,386.0,5.7709,190300.0,<1H OCEAN
+-118.17,34.05,45.0,733.0,178.0,715.0,165.0,2.5962,124100.0,<1H OCEAN
+-118.24,34.16,52.0,2187.0,284.0,733.0,274.0,9.5823,406200.0,<1H OCEAN
+-122.1,37.97,18.0,4326.0,655.0,1753.0,646.0,5.6931,269600.0,NEAR BAY
+-118.41,34.03,24.0,3711.0,1192.0,1764.0,1147.0,3.1642,366700.0,<1H OCEAN
+-117.78,33.68,19.0,2500.0,331.0,1027.0,327.0,6.115,315600.0,<1H OCEAN
+-119.26,36.5,35.0,1689.0,371.0,1475.0,329.0,2.5719,74300.0,INLAND
+-119.83,36.73,21.0,1702.0,358.0,1347.0,316.0,2.4137,62100.0,INLAND
+-121.63,38.04,25.0,2019.0,411.0,888.0,326.0,3.2619,183800.0,INLAND
+-117.84,33.84,23.0,6157.0,1129.0,2817.0,1073.0,5.0629,232600.0,<1H OCEAN
+-117.21,33.12,4.0,3261.0,689.0,926.0,561.0,4.3672,258900.0,NEAR OCEAN
+-117.08,32.8,32.0,1587.0,268.0,635.0,249.0,3.375,178100.0,NEAR OCEAN
+-124.09,40.55,24.0,2978.0,553.0,1370.0,480.0,2.7644,97300.0,<1H OCEAN
+-122.53,37.65,20.0,4582.0,1124.0,2325.0,1040.0,4.0556,275000.0,NEAR OCEAN
+-121.97,37.54,31.0,1949.0,344.0,986.0,322.0,4.6349,196200.0,<1H OCEAN
+-122.31,37.52,35.0,1817.0,262.0,659.0,262.0,6.8336,457200.0,NEAR OCEAN
+-118.42,34.24,36.0,1181.0,220.0,775.0,218.0,4.7228,183800.0,<1H OCEAN
+-117.29,34.14,45.0,1598.0,314.0,771.0,319.0,2.5417,82900.0,INLAND
+-117.01,32.75,34.0,2105.0,340.0,973.0,357.0,4.3088,152500.0,<1H OCEAN
+-124.27,40.69,36.0,2349.0,528.0,1194.0,465.0,2.5179,79000.0,NEAR OCEAN
+-117.0,34.87,16.0,6862.0,1292.0,3562.0,1126.0,3.6091,87200.0,INLAND
+-118.43,34.08,46.0,778.0,90.0,238.0,93.0,15.0001,500001.0,<1H OCEAN
+-118.32,34.19,37.0,589.0,119.0,375.0,122.0,3.3897,222700.0,<1H OCEAN
+-117.3,34.39,11.0,3572.0,592.0,1876.0,507.0,3.6615,105100.0,INLAND
+-118.31,34.07,20.0,3264.0,1248.0,2919.0,1191.0,2.3674,500001.0,<1H OCEAN
+-118.36,34.09,28.0,1111.0,300.0,526.0,294.0,2.6136,383300.0,<1H OCEAN
+-118.13,34.15,28.0,1119.0,334.0,739.0,323.0,2.5903,125000.0,<1H OCEAN
+-120.15,39.8,19.0,785.0,151.0,366.0,140.0,3.0625,82500.0,INLAND
+-118.28,33.99,52.0,1283.0,342.0,1363.0,329.0,2.5848,101900.0,<1H OCEAN
+-118.33,34.0,47.0,825.0,187.0,416.0,173.0,2.3333,133300.0,<1H OCEAN
+-118.31,36.94,35.0,2563.0,530.0,861.0,371.0,2.325,80600.0,INLAND
+-118.1,34.14,26.0,6262.0,1645.0,3001.0,1505.0,3.6572,213200.0,<1H OCEAN
+-117.45,34.1,9.0,4288.0,1017.0,3156.0,900.0,2.7827,105800.0,INLAND
+-118.18,33.83,39.0,3622.0,745.0,1330.0,648.0,3.3125,425500.0,NEAR OCEAN
+-117.75,33.71,15.0,2849.0,537.0,878.0,520.0,3.2841,158300.0,<1H OCEAN
+-118.22,34.66,17.0,3810.0,662.0,1867.0,586.0,4.9,152400.0,INLAND
+-121.22,38.71,23.0,1843.0,273.0,818.0,276.0,4.4695,214700.0,INLAND
+-118.08,33.99,37.0,1419.0,310.0,1125.0,296.0,2.5,162000.0,<1H OCEAN
+-118.3,34.17,30.0,48.0,14.0,74.0,16.0,5.0056,162500.0,<1H OCEAN
+-116.92,32.77,16.0,2770.0,406.0,1269.0,429.0,6.6783,275000.0,<1H OCEAN
+-118.16,34.68,17.0,2994.0,832.0,1571.0,695.0,2.5902,85400.0,INLAND
+-117.25,33.65,10.0,1652.0,316.0,725.0,233.0,3.5125,155600.0,<1H OCEAN
+-118.07,33.98,41.0,1478.0,273.0,916.0,281.0,3.9688,169800.0,<1H OCEAN
+-118.35,34.0,46.0,3402.0,503.0,1389.0,504.0,5.3462,270400.0,<1H OCEAN
+-117.11,32.76,21.0,2226.0,600.0,1085.0,533.0,2.2604,126300.0,NEAR OCEAN
+-118.98,35.35,21.0,496.0,131.0,511.0,124.0,1.7614,33200.0,INLAND
+-124.23,40.54,52.0,2694.0,453.0,1152.0,435.0,3.0806,106700.0,NEAR OCEAN
+-118.1,33.78,35.0,4466.0,740.0,2134.0,743.0,5.7389,251800.0,<1H OCEAN
+-121.28,38.74,33.0,4384.0,778.0,1775.0,789.0,4.05,134700.0,INLAND
+-117.66,34.05,36.0,2341.0,520.0,2138.0,523.0,2.3347,104000.0,INLAND
+-118.52,34.18,34.0,2307.0,388.0,1168.0,427.0,4.2143,245400.0,<1H OCEAN
+-121.92,37.25,34.0,2231.0,360.0,1035.0,365.0,4.7917,243200.0,<1H OCEAN
+-122.08,37.63,35.0,517.0,108.0,391.0,107.0,4.0682,156900.0,NEAR BAY
+-122.27,37.91,47.0,1930.0,315.0,692.0,296.0,6.3669,315500.0,NEAR BAY
+-118.26,34.13,37.0,196.0,74.0,194.0,68.0,1.2188,218800.0,<1H OCEAN
+-117.35,34.01,23.0,3707.0,769.0,1938.0,658.0,2.725,95300.0,INLAND
+-117.08,32.75,16.0,1111.0,328.0,930.0,303.0,1.2347,128100.0,NEAR OCEAN
+-118.36,33.93,19.0,3103.0,918.0,2033.0,738.0,2.6961,212500.0,<1H OCEAN
+-117.27,32.85,34.0,2105.0,444.0,780.0,406.0,2.3187,488900.0,NEAR OCEAN
+-121.63,39.82,11.0,3974.0,727.0,1610.0,634.0,2.6147,107700.0,INLAND
+-117.3,34.15,38.0,740.0,163.0,332.0,138.0,2.4107,88000.0,INLAND
+-118.27,34.03,51.0,1280.0,422.0,1560.0,381.0,1.7115,125000.0,<1H OCEAN
+-121.95,37.39,24.0,5230.0,934.0,3795.0,970.0,5.4228,264100.0,<1H OCEAN
+-119.78,36.27,29.0,1871.0,315.0,1066.0,309.0,4.5714,100800.0,INLAND
+-118.05,33.9,41.0,550.0,129.0,642.0,125.0,1.875,119900.0,<1H OCEAN
+-121.3,38.64,20.0,5001.0,830.0,2330.0,830.0,4.0833,160000.0,INLAND
+-122.2,37.76,37.0,2680.0,736.0,1925.0,667.0,1.4097,84600.0,NEAR BAY
+-117.93,33.76,21.0,2884.0,662.0,2613.0,645.0,4.05,177900.0,<1H OCEAN
+-117.99,33.8,18.0,383.0,94.0,487.0,98.0,3.975,162500.0,<1H OCEAN
+-118.05,34.16,36.0,3908.0,732.0,1688.0,725.0,4.5625,376800.0,INLAND
+-121.42,38.62,33.0,3171.0,832.0,1591.0,695.0,2.0786,88600.0,INLAND
+-122.69,38.36,6.0,5496.0,1374.0,2502.0,1189.0,2.4827,177500.0,<1H OCEAN
+-122.3,37.9,41.0,2053.0,435.0,873.0,415.0,3.4091,223000.0,NEAR BAY
+-119.8,36.75,46.0,2625.0,593.0,1368.0,551.0,1.5273,59000.0,INLAND
+-117.73,33.53,3.0,6388.0,920.0,2129.0,819.0,7.8915,420600.0,<1H OCEAN
+-122.29,38.31,25.0,4927.0,1005.0,2756.0,998.0,2.7325,162900.0,NEAR BAY
+-118.23,34.21,38.0,1399.0,390.0,859.0,386.0,3.4148,234800.0,<1H OCEAN
+-118.04,34.12,30.0,2170.0,318.0,984.0,309.0,5.6916,500001.0,INLAND
+-117.81,33.64,4.0,1741.0,225.0,811.0,233.0,12.3411,500001.0,<1H OCEAN
+-118.21,34.65,17.0,4001.0,814.0,2313.0,756.0,3.0441,140100.0,INLAND
+-121.75,36.92,46.0,1362.0,321.0,1068.0,305.0,2.4615,177800.0,<1H OCEAN
+-121.64,39.11,18.0,3212.0,542.0,1817.0,508.0,3.3793,92900.0,INLAND
+-121.32,38.89,9.0,5927.0,1269.0,3369.0,1176.0,2.8194,111300.0,INLAND
+-117.75,34.05,37.0,378.0,92.0,503.0,103.0,2.1908,94600.0,INLAND
+-118.9,34.3,13.0,5591.0,1013.0,3188.0,971.0,5.5925,208600.0,<1H OCEAN
+-118.37,34.13,28.0,4287.0,627.0,1498.0,615.0,8.5677,500001.0,<1H OCEAN
+-122.43,37.81,39.0,3275.0,837.0,1137.0,725.0,3.7679,500001.0,NEAR BAY
+-118.47,33.99,37.0,2155.0,721.0,1082.0,637.0,3.4071,267500.0,<1H OCEAN
+-119.82,36.79,18.0,5822.0,1439.0,3415.0,1224.0,1.6854,64700.0,INLAND
+-120.01,38.89,24.0,1669.0,422.0,589.0,281.0,3.0089,100800.0,INLAND
+-117.89,33.99,22.0,3272.0,618.0,1784.0,591.0,4.0324,211300.0,<1H OCEAN
+-117.11,32.75,11.0,1607.0,478.0,1384.0,450.0,2.05,100000.0,NEAR OCEAN
+-122.18,37.81,30.0,292.0,38.0,126.0,52.0,6.3624,483300.0,NEAR BAY
+-118.24,33.92,42.0,328.0,100.0,605.0,87.0,2.4464,97400.0,<1H OCEAN
+-117.91,34.04,15.0,8749.0,1761.0,5278.0,1691.0,4.6324,168800.0,<1H OCEAN
+-118.33,34.03,33.0,2314.0,624.0,1714.0,582.0,1.7377,183900.0,<1H OCEAN
+-120.47,34.65,31.0,1438.0,320.0,816.0,270.0,2.4583,128100.0,NEAR OCEAN
+-117.15,32.74,43.0,2383.0,607.0,962.0,587.0,2.2578,263600.0,NEAR OCEAN
+-116.95,32.81,31.0,1277.0,293.0,698.0,237.0,3.1106,147700.0,<1H OCEAN
+-124.11,40.93,25.0,2392.0,474.0,1298.0,461.0,3.5076,73600.0,NEAR OCEAN
+-117.85,34.14,35.0,1582.0,248.0,654.0,221.0,4.9091,275000.0,INLAND
+-122.17,37.74,43.0,818.0,193.0,494.0,179.0,2.4776,101600.0,NEAR BAY
+-118.04,34.04,35.0,1734.0,363.0,1527.0,344.0,3.0,160600.0,<1H OCEAN
+-118.66,34.19,23.0,7544.0,1031.0,3221.0,1043.0,7.642,374900.0,<1H OCEAN
+-121.84,37.34,33.0,1019.0,191.0,938.0,215.0,4.0929,165000.0,<1H OCEAN
+-115.58,32.79,14.0,1687.0,507.0,762.0,451.0,1.6635,64400.0,INLAND
+-117.42,33.94,26.0,2420.0,532.0,1383.0,469.0,3.5403,113500.0,INLAND
+-117.99,33.79,21.0,2695.0,707.0,1888.0,683.0,3.2857,213300.0,<1H OCEAN
+-122.04,37.96,20.0,1143.0,346.0,578.0,298.0,2.2411,151800.0,NEAR BAY
+-119.79,36.78,41.0,2227.0,462.0,1129.0,415.0,2.319,59100.0,INLAND
+-118.48,34.25,36.0,1951.0,395.0,1040.0,375.0,5.1619,195300.0,<1H OCEAN
+-117.09,32.91,16.0,2005.0,266.0,827.0,270.0,7.0546,282200.0,<1H OCEAN
+-117.42,34.1,18.0,3977.0,809.0,2231.0,742.0,4.1399,115400.0,INLAND
+-117.98,33.76,29.0,1518.0,312.0,1086.0,317.0,4.32,196900.0,<1H OCEAN
+-118.11,33.95,34.0,1723.0,279.0,617.0,252.0,6.7501,400000.0,<1H OCEAN
+-121.82,37.37,42.0,2913.0,577.0,1873.0,580.0,3.7214,167900.0,<1H OCEAN
+-118.42,34.06,52.0,1881.0,334.0,640.0,321.0,6.871,500001.0,<1H OCEAN
+-117.89,33.76,36.0,2656.0,572.0,2370.0,571.0,3.8056,177200.0,<1H OCEAN
+-122.83,38.98,17.0,1383.0,347.0,719.0,296.0,1.6164,77800.0,INLAND
+-118.07,34.17,35.0,2142.0,373.0,986.0,374.0,5.7051,326000.0,INLAND
+-118.39,33.88,35.0,1267.0,216.0,521.0,191.0,6.0441,470000.0,<1H OCEAN
+-118.49,34.02,28.0,1394.0,582.0,716.0,543.0,1.5132,450000.0,<1H OCEAN
+-120.02,38.92,24.0,1194.0,246.0,414.0,151.0,3.2396,101900.0,INLAND
+-118.03,34.08,37.0,775.0,179.0,726.0,183.0,3.25,159200.0,INLAND
+-118.35,33.82,28.0,7591.0,1710.0,3420.0,1635.0,4.0708,328900.0,<1H OCEAN
+-117.89,33.61,44.0,2126.0,423.0,745.0,332.0,5.1923,500001.0,<1H OCEAN
+-118.15,33.85,36.0,1435.0,249.0,606.0,234.0,4.1439,212600.0,<1H OCEAN
+-117.29,33.97,4.0,18767.0,3032.0,8805.0,2723.0,4.6667,160600.0,INLAND
+-118.24,34.22,41.0,2476.0,506.0,1271.0,485.0,3.4531,263900.0,<1H OCEAN
+-118.18,34.1,10.0,1940.0,445.0,763.0,412.0,4.975,166700.0,<1H OCEAN
+-118.0,33.71,19.0,4808.0,1029.0,2422.0,971.0,4.0121,279700.0,<1H OCEAN
+-118.39,34.17,26.0,3345.0,818.0,1599.0,773.0,3.3516,241500.0,<1H OCEAN
+-121.98,38.34,13.0,3616.0,672.0,2022.0,652.0,4.0536,134800.0,INLAND
+-122.08,37.38,36.0,782.0,130.0,348.0,128.0,6.828,383900.0,NEAR BAY
+-117.84,33.66,5.0,665.0,171.0,384.0,171.0,4.5833,230400.0,<1H OCEAN
+-122.78,38.37,21.0,795.0,163.0,414.0,162.0,3.7991,175000.0,<1H OCEAN
+-119.8,36.29,7.0,479.0,84.0,327.0,103.0,5.1728,107500.0,INLAND
+-119.56,36.08,37.0,766.0,189.0,639.0,190.0,1.6607,42100.0,INLAND
+-122.4,37.73,45.0,3490.0,712.0,2337.0,781.0,3.4472,225400.0,NEAR BAY
+-119.81,36.78,36.0,1650.0,313.0,660.0,298.0,3.0,79700.0,INLAND
+-121.89,37.23,20.0,7754.0,976.0,3094.0,941.0,8.19,361600.0,<1H OCEAN
+-118.3,34.07,36.0,2657.0,738.0,2274.0,723.0,3.425,281700.0,<1H OCEAN
+-117.56,33.88,36.0,838.0,210.0,722.0,180.0,2.4861,96200.0,INLAND
+-122.31,37.57,45.0,1165.0,236.0,845.0,251.0,4.1875,267300.0,NEAR OCEAN
+-119.68,36.8,7.0,2855.0,518.0,1748.0,498.0,4.2066,88400.0,INLAND
+-118.08,34.04,20.0,5841.0,1146.0,3273.0,1131.0,4.7222,185100.0,<1H OCEAN
+-118.13,33.85,36.0,1885.0,391.0,1049.0,405.0,3.55,212800.0,<1H OCEAN
+-122.42,37.63,46.0,66.0,11.0,30.0,12.0,2.375,275000.0,NEAR OCEAN
+-117.02,32.74,30.0,4205.0,772.0,2012.0,734.0,3.5,144700.0,NEAR OCEAN
+-118.53,34.18,16.0,7194.0,1976.0,3687.0,1894.0,3.1887,189300.0,<1H OCEAN
+-118.11,33.82,36.0,1742.0,340.0,857.0,341.0,4.6875,218200.0,<1H OCEAN
+-121.67,39.37,27.0,2599.0,502.0,1241.0,502.0,1.9943,86300.0,INLAND
+-122.4,40.57,23.0,1321.0,259.0,749.0,222.0,1.655,90100.0,INLAND
+-119.81,36.92,14.0,4795.0,710.0,2047.0,640.0,4.665,121300.0,INLAND
+-119.35,36.2,31.0,1783.0,382.0,1266.0,358.0,2.2264,50800.0,INLAND
+-123.34,39.39,18.0,2821.0,628.0,1636.0,615.0,2.3333,84000.0,<1H OCEAN
+-119.62,36.35,10.0,3674.0,734.0,1864.0,718.0,2.6145,80300.0,INLAND
+-118.47,34.0,37.0,2586.0,765.0,1801.0,737.0,2.6042,305800.0,<1H OCEAN
+-120.28,38.03,13.0,2095.0,391.0,860.0,331.0,3.6838,145700.0,INLAND
+-122.92,39.05,38.0,3131.0,624.0,1591.0,568.0,2.5457,80700.0,INLAND
+-117.25,33.92,7.0,9812.0,1914.0,5595.0,1729.0,4.1482,124600.0,INLAND
+-118.0,33.73,26.0,2236.0,280.0,809.0,282.0,6.7395,342800.0,<1H OCEAN
+-118.59,34.23,14.0,4407.0,1209.0,2676.0,1128.0,3.4091,168800.0,<1H OCEAN
+-117.06,32.6,33.0,905.0,205.0,989.0,222.0,2.7014,108200.0,NEAR OCEAN
+-121.92,37.74,8.0,452.0,51.0,140.0,43.0,12.5915,432400.0,<1H OCEAN
+-120.12,38.12,37.0,3355.0,666.0,338.0,136.0,2.0625,88900.0,INLAND
+-118.21,33.81,43.0,905.0,199.0,764.0,204.0,3.3214,162200.0,NEAR OCEAN
+-117.94,33.88,35.0,1694.0,296.0,679.0,282.0,4.3333,239300.0,<1H OCEAN
+-118.25,33.93,36.0,2452.0,734.0,2664.0,667.0,0.9298,100000.0,<1H OCEAN
+-120.45,34.97,10.0,1897.0,354.0,1353.0,357.0,3.7679,131300.0,<1H OCEAN
+-118.05,33.94,34.0,495.0,120.0,527.0,130.0,1.9453,149000.0,<1H OCEAN
+-122.73,38.37,40.0,1389.0,309.0,841.0,288.0,3.1094,183300.0,<1H OCEAN
+-118.43,34.23,37.0,1737.0,369.0,1061.0,356.0,3.9615,173700.0,<1H OCEAN
+-118.39,34.04,44.0,1873.0,286.0,635.0,283.0,5.5951,461300.0,<1H OCEAN
+-118.37,33.87,13.0,2907.0,726.0,1573.0,694.0,3.5048,294000.0,<1H OCEAN
+-117.11,32.82,17.0,1787.0,330.0,1341.0,314.0,2.875,112500.0,NEAR OCEAN
+-122.38,40.61,14.0,4773.0,1133.0,2101.0,1072.0,1.7227,105000.0,INLAND
+-122.93,38.38,18.0,2562.0,500.0,1128.0,414.0,3.9336,262500.0,<1H OCEAN
+-118.17,33.85,37.0,3714.0,708.0,1956.0,694.0,4.2218,200500.0,<1H OCEAN
+-118.2,33.95,41.0,679.0,184.0,788.0,185.0,2.1406,165300.0,<1H OCEAN
+-118.47,34.27,33.0,1549.0,264.0,881.0,289.0,5.1408,222900.0,<1H OCEAN
+-118.13,33.89,36.0,599.0,125.0,361.0,139.0,5.0395,225800.0,<1H OCEAN
+-122.57,38.11,24.0,2863.0,734.0,1583.0,682.0,3.1981,215300.0,NEAR BAY
+-122.21,37.48,20.0,505.0,216.0,326.0,216.0,2.9286,237500.0,NEAR BAY
+-124.35,40.54,52.0,1820.0,300.0,806.0,270.0,3.0147,94600.0,NEAR OCEAN
+-116.88,33.05,11.0,7217.0,1583.0,4197.0,1502.0,2.8827,166700.0,<1H OCEAN
+-119.82,36.78,36.0,1370.0,289.0,812.0,282.0,2.6127,69600.0,INLAND
+-118.23,33.91,27.0,1694.0,393.0,1890.0,373.0,3.0341,89100.0,<1H OCEAN
+-118.44,34.04,16.0,18.0,6.0,3.0,4.0,0.536,350000.0,<1H OCEAN
+-118.24,33.94,37.0,869.0,241.0,1040.0,233.0,2.0,84200.0,<1H OCEAN
+-119.09,34.24,17.0,10214.0,1589.0,3409.0,1327.0,5.3806,452100.0,<1H OCEAN
+-117.69,34.05,10.0,1875.0,366.0,1055.0,363.0,4.3264,128900.0,INLAND
+-122.03,37.36,16.0,2697.0,803.0,1369.0,723.0,4.4699,367400.0,<1H OCEAN
+-117.97,33.66,22.0,3914.0,600.0,1871.0,607.0,5.8541,281500.0,<1H OCEAN
+-118.29,34.01,30.0,1385.0,518.0,1730.0,472.0,1.0539,142500.0,<1H OCEAN
+-121.96,37.34,18.0,4438.0,939.0,1901.0,895.0,5.3873,288300.0,<1H OCEAN
+-120.13,35.87,26.0,48.0,8.0,13.0,8.0,2.375,71300.0,INLAND
+-122.33,37.55,51.0,2565.0,332.0,870.0,309.0,9.3694,500001.0,NEAR OCEAN
+-121.28,38.76,47.0,2901.0,631.0,1276.0,578.0,2.1366,101900.0,INLAND
+-117.9,33.87,34.0,1411.0,292.0,1040.0,299.0,3.4338,195200.0,<1H OCEAN
+-122.44,37.78,31.0,1364.0,386.0,707.0,379.0,3.1607,293800.0,NEAR BAY
+-118.08,33.93,33.0,2263.0,511.0,1626.0,457.0,3.5556,172800.0,<1H OCEAN
+-118.29,33.96,39.0,1340.0,409.0,1463.0,367.0,1.5294,111400.0,<1H OCEAN
+-119.78,34.48,21.0,2377.0,322.0,1007.0,328.0,7.9248,500001.0,NEAR OCEAN
+-116.76,33.46,6.0,1251.0,268.0,544.0,216.0,3.0694,173400.0,INLAND
+-118.05,34.08,30.0,1572.0,427.0,1857.0,428.0,2.4914,159200.0,<1H OCEAN
+-121.94,37.35,18.0,1922.0,561.0,1096.0,545.0,2.3713,244000.0,<1H OCEAN
+-117.48,33.93,31.0,2191.0,459.0,1564.0,450.0,2.6776,122000.0,INLAND
+-118.44,34.25,33.0,1121.0,231.0,1038.0,236.0,4.8958,173700.0,<1H OCEAN
+-118.58,34.17,29.0,3393.0,574.0,1471.0,587.0,6.2064,334900.0,<1H OCEAN
+-122.1,38.02,28.0,4308.0,824.0,2086.0,776.0,3.6523,159700.0,NEAR BAY
+-117.09,32.67,31.0,2051.0,549.0,1581.0,538.0,2.052,108900.0,NEAR OCEAN
+-121.74,38.56,18.0,3960.0,1151.0,2248.0,1144.0,1.7257,179100.0,INLAND
+-121.97,37.51,25.0,3333.0,511.0,1671.0,504.0,5.4359,258300.0,<1H OCEAN
+-121.34,38.68,28.0,3379.0,552.0,1543.0,556.0,4.2743,124000.0,INLAND
+-122.08,37.4,25.0,1750.0,341.0,999.0,319.0,5.806,308700.0,NEAR BAY
+-118.25,33.97,38.0,1231.0,346.0,1217.0,354.0,1.8661,106600.0,<1H OCEAN
+-116.95,32.82,19.0,5308.0,1058.0,2852.0,1092.0,2.9161,135700.0,<1H OCEAN
+-122.16,37.17,35.0,6422.0,1380.0,2755.0,1064.0,5.0165,202300.0,NEAR OCEAN
+-122.03,37.99,35.0,3103.0,537.0,1614.0,566.0,4.9022,169300.0,NEAR BAY
+-120.49,37.3,35.0,1313.0,324.0,1350.0,343.0,1.75,57600.0,INLAND
+-122.26,37.86,35.0,5161.0,1744.0,3276.0,1742.0,1.6307,253600.0,NEAR BAY
+-122.34,37.94,47.0,2313.0,433.0,947.0,430.0,3.942,143300.0,NEAR BAY
+-117.09,32.81,7.0,6100.0,1185.0,2710.0,1040.0,5.5673,288200.0,NEAR OCEAN
+-118.8,34.41,45.0,1610.0,,1148.0,347.0,2.7,120400.0,<1H OCEAN
+-118.29,33.74,41.0,1382.0,361.0,905.0,344.0,2.75,238300.0,NEAR OCEAN
+-122.49,37.54,15.0,3456.0,545.0,1527.0,535.0,6.3256,368000.0,NEAR OCEAN
+-117.95,33.98,15.0,16042.0,2602.0,7732.0,2552.0,5.6716,330400.0,<1H OCEAN
+-118.11,33.81,36.0,1252.0,209.0,558.0,214.0,3.9722,235600.0,<1H OCEAN
+-118.09,33.74,44.0,1671.0,390.0,871.0,367.0,4.6369,422200.0,NEAR OCEAN
+-117.41,34.01,34.0,1231.0,216.0,841.0,199.0,2.6442,92000.0,INLAND
+-121.5,39.83,15.0,1896.0,408.0,893.0,334.0,1.6948,87500.0,INLAND
+-116.9,32.79,21.0,3770.0,491.0,1410.0,446.0,6.7685,294700.0,<1H OCEAN
+-121.87,39.75,22.0,1707.0,296.0,822.0,297.0,3.6625,126600.0,INLAND
+-118.6,34.08,40.0,866.0,181.0,399.0,176.0,6.91,380000.0,<1H OCEAN
+-120.98,37.79,20.0,2458.0,491.0,1227.0,481.0,2.7857,110900.0,INLAND
+-117.19,33.14,12.0,3652.0,923.0,1677.0,728.0,2.3267,92000.0,<1H OCEAN
+-117.24,32.78,44.0,2172.0,431.0,892.0,420.0,4.1742,342200.0,NEAR OCEAN
+-117.68,33.59,12.0,3473.0,466.0,1569.0,450.0,8.8636,314000.0,<1H OCEAN
+-121.31,37.96,52.0,1829.0,301.0,694.0,319.0,3.3466,92600.0,INLAND
+-121.82,37.35,24.0,2298.0,575.0,2409.0,569.0,3.4509,182400.0,<1H OCEAN
+-118.06,33.78,22.0,4048.0,562.0,1637.0,541.0,7.3463,355600.0,<1H OCEAN
+-121.83,38.0,8.0,2572.0,738.0,1384.0,684.0,1.7161,75800.0,INLAND
+-123.79,39.44,49.0,2290.0,482.0,1201.0,479.0,3.5,113300.0,<1H OCEAN
+-116.98,32.74,16.0,3361.0,537.0,1754.0,578.0,5.1098,162300.0,<1H OCEAN
+-121.9,36.61,29.0,3412.0,827.0,1574.0,759.0,2.9309,217100.0,NEAR OCEAN
+-121.62,39.75,20.0,1173.0,261.0,523.0,258.0,1.0625,92800.0,INLAND
+-117.69,34.11,16.0,2427.0,522.0,794.0,491.0,2.6929,119300.0,INLAND
+-121.46,38.56,52.0,907.0,180.0,479.0,177.0,2.2125,104000.0,INLAND
+-121.87,36.55,20.0,10053.0,1768.0,3083.0,1621.0,5.1506,387500.0,NEAR OCEAN
+-122.24,37.49,30.0,2956.0,590.0,1191.0,594.0,3.7463,427600.0,NEAR OCEAN
+-118.45,34.0,39.0,1909.0,359.0,867.0,345.0,4.7,334700.0,<1H OCEAN
+-117.93,33.73,19.0,4021.0,557.0,1872.0,545.0,6.7919,295600.0,<1H OCEAN
+-122.61,38.24,18.0,2933.0,481.0,1279.0,443.0,5.0849,188500.0,<1H OCEAN
+-118.38,33.84,25.0,5775.0,1149.0,2637.0,1117.0,5.4968,379800.0,<1H OCEAN
+-118.31,34.1,36.0,2288.0,1033.0,3030.0,890.0,1.5328,250000.0,<1H OCEAN
+-115.56,32.8,25.0,1311.0,375.0,1193.0,351.0,2.1979,63900.0,INLAND
+-121.27,38.13,35.0,2607.0,685.0,2016.0,618.0,1.75,82900.0,INLAND
+-121.26,37.96,40.0,535.0,105.0,335.0,102.0,2.5234,62800.0,INLAND
+-118.11,33.9,35.0,1323.0,269.0,1084.0,240.0,5.0753,178000.0,<1H OCEAN
+-121.45,38.65,5.0,2680.0,502.0,1885.0,498.0,2.6369,110000.0,INLAND
+-117.98,33.79,35.0,2356.0,478.0,1659.0,480.0,4.1115,179700.0,<1H OCEAN
+-120.25,38.04,22.0,4173.0,763.0,1086.0,444.0,2.5562,136200.0,INLAND
+-117.28,33.72,11.0,1161.0,235.0,640.0,210.0,2.1667,114600.0,<1H OCEAN
+-118.41,35.63,15.0,5907.0,1257.0,2310.0,1001.0,2.3125,96900.0,INLAND
+-121.41,38.57,16.0,4429.0,1124.0,1538.0,960.0,3.2443,190700.0,INLAND
+-118.18,34.09,40.0,2744.0,708.0,2747.0,674.0,2.6226,148800.0,<1H OCEAN
+-118.17,33.83,45.0,2019.0,363.0,880.0,339.0,4.1023,217300.0,NEAR OCEAN
+-118.35,34.17,44.0,2572.0,613.0,1280.0,570.0,3.5583,232000.0,<1H OCEAN
+-117.97,33.94,34.0,1632.0,263.0,690.0,268.0,5.5608,255800.0,<1H OCEAN
+-121.22,37.81,20.0,1811.0,352.0,1191.0,327.0,4.0125,121500.0,INLAND
+-118.19,34.65,33.0,1781.0,326.0,913.0,314.0,3.9963,126800.0,INLAND
+-117.41,34.11,12.0,6758.0,1550.0,3204.0,1279.0,2.5181,105500.0,INLAND
+-119.8,36.74,25.0,1717.0,542.0,1343.0,471.0,0.799,51800.0,INLAND
+-122.65,38.37,15.0,1848.0,280.0,786.0,282.0,5.7204,344100.0,<1H OCEAN
+-120.27,38.31,13.0,3297.0,662.0,267.0,97.0,3.075,108300.0,INLAND
+-121.25,36.32,12.0,4776.0,1082.0,4601.0,1066.0,2.9184,100500.0,<1H OCEAN
+-117.55,34.0,17.0,3583.0,700.0,1587.0,719.0,2.6979,75000.0,INLAND
+-119.95,38.95,21.0,2046.0,580.0,952.0,353.0,1.7245,92200.0,INLAND
+-118.31,33.9,28.0,1576.0,400.0,891.0,378.0,2.6312,171300.0,<1H OCEAN
+-121.97,37.31,25.0,5775.0,1225.0,3580.0,1138.0,3.9187,314900.0,<1H OCEAN
+-121.97,37.8,17.0,3279.0,418.0,1222.0,381.0,7.9168,356000.0,<1H OCEAN
+-121.87,37.33,37.0,3137.0,685.0,2048.0,651.0,3.0156,270300.0,<1H OCEAN
+-118.17,34.14,45.0,2257.0,285.0,759.0,305.0,11.7894,500001.0,<1H OCEAN
+-120.48,39.66,32.0,1516.0,289.0,304.0,131.0,1.8839,71000.0,INLAND
+-122.44,37.66,36.0,1447.0,276.0,799.0,275.0,4.7639,265600.0,NEAR OCEAN
+-119.81,34.44,23.0,3172.0,588.0,1467.0,559.0,4.6806,288900.0,NEAR OCEAN
+-119.71,34.41,18.0,1225.0,317.0,694.0,306.0,3.6823,255000.0,NEAR OCEAN
+-118.29,34.03,29.0,3544.0,1384.0,3323.0,1213.0,1.0219,258300.0,<1H OCEAN
+-122.47,37.75,45.0,2399.0,426.0,911.0,423.0,4.4312,361000.0,NEAR BAY
+-115.93,35.55,18.0,1321.0,272.0,754.0,226.0,3.4028,67500.0,INLAND
+-117.11,32.77,52.0,1506.0,233.0,478.0,240.0,4.3875,300000.0,NEAR OCEAN
+-118.34,34.08,50.0,3457.0,854.0,1584.0,841.0,3.1078,346700.0,<1H OCEAN
+-118.35,33.93,31.0,2746.0,697.0,1973.0,598.0,3.5139,192800.0,<1H OCEAN
+-122.66,38.48,16.0,2724.0,593.0,1124.0,586.0,2.825,186200.0,<1H OCEAN
+-118.98,35.37,36.0,1562.0,398.0,1223.0,329.0,0.9675,47100.0,INLAND
+-120.32,37.29,38.0,576.0,,478.0,112.0,2.3382,59600.0,INLAND
+-122.19,40.07,21.0,1548.0,290.0,744.0,265.0,1.9773,55000.0,INLAND
+-117.14,34.07,3.0,5542.0,828.0,2506.0,806.0,5.5875,162000.0,INLAND
+-117.66,33.64,17.0,3173.0,501.0,1555.0,520.0,6.7079,250800.0,<1H OCEAN
+-121.29,38.0,12.0,4038.0,1074.0,3440.0,942.0,1.9698,112300.0,INLAND
+-122.47,37.75,49.0,2747.0,472.0,1281.0,448.0,5.482,366300.0,NEAR BAY
+-117.77,33.7,4.0,2446.0,622.0,1315.0,560.0,3.7147,137500.0,<1H OCEAN
+-122.04,37.53,34.0,2316.0,478.0,1524.0,467.0,3.7364,190400.0,NEAR BAY
+-117.95,35.08,1.0,83.0,15.0,32.0,15.0,4.875,141700.0,INLAND
+-118.09,34.01,31.0,1108.0,238.0,1151.0,229.0,4.3333,149500.0,<1H OCEAN
+-119.0,35.36,40.0,850.0,227.0,764.0,186.0,0.9407,43600.0,INLAND
+-118.48,34.42,21.0,1375.0,259.0,728.0,258.0,5.0166,229000.0,<1H OCEAN
+-119.83,34.45,24.0,2168.0,373.0,934.0,366.0,5.4197,280900.0,NEAR OCEAN
+-122.01,36.99,28.0,1321.0,240.0,652.0,239.0,4.9808,263100.0,NEAR OCEAN
+-118.26,34.1,52.0,1310.0,263.0,689.0,208.0,4.0721,350000.0,<1H OCEAN
+-122.14,37.45,52.0,3841.0,537.0,1391.0,540.0,7.8647,500001.0,NEAR BAY
+-122.22,37.88,20.0,95.0,13.0,31.0,15.0,2.4444,475000.0,NEAR BAY
+-117.38,34.07,6.0,1156.0,191.0,910.0,234.0,4.9091,122400.0,INLAND
+-120.0,38.92,17.0,1106.0,207.0,466.0,180.0,3.3295,126600.0,INLAND
+-117.94,33.67,26.0,2552.0,314.0,925.0,323.0,8.1839,367000.0,<1H OCEAN
+-117.0,32.67,16.0,2238.0,307.0,1002.0,303.0,6.6143,264100.0,NEAR OCEAN
+-121.21,39.24,7.0,4194.0,673.0,1355.0,566.0,4.3702,226100.0,INLAND
+-121.94,37.75,16.0,5121.0,735.0,2464.0,761.0,6.6204,296100.0,<1H OCEAN
+-118.46,34.02,29.0,2329.0,833.0,1953.0,800.0,2.6639,233300.0,<1H OCEAN
+-120.34,38.25,17.0,5497.0,1056.0,997.0,408.0,2.9821,111500.0,INLAND
+-119.37,36.21,35.0,2228.0,476.0,1567.0,449.0,1.4455,54100.0,INLAND
+-120.04,38.86,16.0,2708.0,481.0,712.0,261.0,3.7891,117700.0,INLAND
+-121.06,38.25,13.0,651.0,102.0,301.0,104.0,3.6528,200000.0,INLAND
+-118.1,34.13,44.0,1917.0,265.0,754.0,257.0,12.4237,500001.0,<1H OCEAN
+-120.86,38.75,15.0,1533.0,300.0,674.0,287.0,2.5625,146100.0,INLAND
+-118.21,33.89,42.0,1254.0,225.0,929.0,235.0,4.3646,116200.0,<1H OCEAN
+-118.09,33.79,36.0,4210.0,657.0,1911.0,631.0,5.8491,247300.0,<1H OCEAN
+-117.61,34.02,15.0,1791.0,346.0,1219.0,328.0,3.8125,170300.0,INLAND
+-121.37,38.64,27.0,1672.0,299.0,757.0,282.0,3.6786,159700.0,INLAND
+-122.23,37.85,52.0,2800.0,411.0,1061.0,403.0,6.3434,373600.0,NEAR BAY
+-121.5,38.63,6.0,693.0,143.0,276.0,151.0,3.1944,117000.0,INLAND
+-117.1,32.76,52.0,2606.0,426.0,883.0,380.0,4.2813,270800.0,NEAR OCEAN
+-122.37,41.41,28.0,1729.0,419.0,929.0,370.0,1.27,53100.0,INLAND
+-117.19,34.06,37.0,1467.0,348.0,1316.0,339.0,1.448,72800.0,INLAND
+-122.06,37.6,17.0,5159.0,832.0,3174.0,817.0,5.8704,234400.0,NEAR BAY
+-118.22,33.92,43.0,1195.0,256.0,1251.0,262.0,3.4539,125000.0,<1H OCEAN
+-118.93,35.37,34.0,2412.0,446.0,1558.0,421.0,2.6903,62800.0,INLAND
+-121.86,37.37,15.0,8162.0,2124.0,8793.0,2086.0,3.3306,210300.0,<1H OCEAN
+-122.29,37.78,42.0,1241.0,309.0,821.0,300.0,1.9427,102200.0,NEAR BAY
+-117.98,33.77,7.0,2252.0,570.0,1576.0,550.0,3.6333,169800.0,<1H OCEAN
+-119.03,34.24,25.0,3655.0,545.0,1776.0,544.0,5.687,238100.0,<1H OCEAN
+-123.35,39.42,18.0,1619.0,346.0,904.0,295.0,2.1625,77200.0,<1H OCEAN
+-122.18,37.9,36.0,4760.0,610.0,1511.0,572.0,9.0064,500001.0,NEAR BAY
+-122.16,37.79,22.0,12842.0,2048.0,4985.0,1967.0,5.9849,371000.0,NEAR BAY
+-118.13,33.81,37.0,1228.0,237.0,572.0,242.0,4.325,223900.0,<1H OCEAN
+-118.18,34.07,28.0,2616.0,630.0,2218.0,621.0,2.6842,156000.0,<1H OCEAN
+-118.15,33.78,13.0,3056.0,861.0,1600.0,824.0,3.3003,207800.0,NEAR OCEAN
+-118.48,34.25,35.0,1865.0,335.0,1074.0,337.0,5.1068,223300.0,<1H OCEAN
+-121.18,39.25,9.0,3415.0,562.0,1208.0,479.0,4.3646,185900.0,INLAND
+-118.12,33.79,43.0,1471.0,301.0,767.0,311.0,4.3317,232400.0,<1H OCEAN
+-121.64,36.66,24.0,3174.0,506.0,1466.0,535.0,5.2285,248100.0,<1H OCEAN
+-118.17,33.91,39.0,1157.0,273.0,877.0,305.0,3.1087,171000.0,<1H OCEAN
+-117.73,34.07,33.0,1921.0,489.0,1430.0,467.0,2.3406,122600.0,INLAND
+-117.63,33.5,12.0,3619.0,536.0,1506.0,492.0,7.2013,353600.0,<1H OCEAN
+-117.95,33.81,24.0,2749.0,498.0,1367.0,460.0,4.025,240700.0,<1H OCEAN
+-118.3,33.99,47.0,2212.0,533.0,1903.0,554.0,1.9853,101100.0,<1H OCEAN
+-122.43,37.73,52.0,1985.0,401.0,1337.0,424.0,4.1071,240900.0,NEAR BAY
+-122.51,37.98,37.0,4801.0,699.0,1830.0,679.0,6.0762,487800.0,NEAR BAY
+-118.23,34.09,47.0,859.0,239.0,913.0,234.0,2.6442,136100.0,<1H OCEAN
+-117.93,33.73,27.0,3662.0,834.0,3009.0,743.0,3.9816,179500.0,<1H OCEAN
+-118.74,36.23,22.0,1033.0,232.0,442.0,136.0,2.6447,137500.0,INLAND
+-118.31,34.05,42.0,443.0,223.0,582.0,223.0,2.2937,350000.0,<1H OCEAN
+-118.25,33.94,43.0,1113.0,378.0,1305.0,334.0,1.1434,91300.0,<1H OCEAN
+-118.28,33.75,18.0,393.0,189.0,429.0,188.0,1.8393,187500.0,NEAR OCEAN
+-118.04,34.09,34.0,2597.0,461.0,1542.0,470.0,4.6211,248900.0,INLAND
+-119.0,35.36,39.0,896.0,217.0,805.0,197.0,1.25,42500.0,INLAND
+-117.96,33.81,34.0,1416.0,277.0,980.0,284.0,4.7772,182500.0,<1H OCEAN
+-118.16,33.84,36.0,2444.0,432.0,1199.0,424.0,4.1538,218800.0,<1H OCEAN
+-118.48,34.02,25.0,1583.0,460.0,983.0,422.0,2.7019,293800.0,<1H OCEAN
+-122.69,38.34,16.0,1683.0,341.0,880.0,327.0,3.2857,160200.0,<1H OCEAN
+-121.64,39.22,37.0,1189.0,248.0,627.0,219.0,3.8611,100000.0,INLAND
+-122.46,37.75,52.0,1207.0,152.0,465.0,162.0,10.7569,500001.0,NEAR BAY
+-117.31,33.94,7.0,11232.0,1791.0,4218.0,1644.0,5.2713,216500.0,INLAND
+-115.64,32.8,23.0,1228.0,235.0,569.0,235.0,3.1667,125000.0,INLAND
+-117.68,33.54,5.0,2840.0,403.0,1363.0,403.0,7.618,341400.0,<1H OCEAN
+-120.67,35.3,19.0,1540.0,715.0,1799.0,635.0,0.7025,500001.0,NEAR OCEAN
+-117.9,33.93,12.0,4325.0,1191.0,1897.0,1080.0,3.3173,247400.0,<1H OCEAN
+-117.21,34.11,27.0,1245.0,229.0,692.0,234.0,3.2176,89400.0,INLAND
+-118.13,33.86,45.0,1866.0,343.0,919.0,344.0,3.5833,200200.0,<1H OCEAN
+-118.31,33.79,35.0,2290.0,563.0,1374.0,530.0,3.2472,254700.0,<1H OCEAN
+-121.49,38.53,40.0,2966.0,536.0,1225.0,505.0,3.125,130600.0,INLAND
+-121.29,37.95,16.0,761.0,306.0,438.0,282.0,0.7714,87500.0,INLAND
+-122.75,38.41,17.0,3150.0,588.0,1857.0,610.0,3.9688,165000.0,<1H OCEAN
+-118.57,34.27,20.0,7384.0,845.0,2795.0,872.0,9.6047,500001.0,<1H OCEAN
+-118.3,33.91,30.0,1842.0,476.0,1491.0,420.0,3.0147,155100.0,<1H OCEAN
+-116.57,33.76,25.0,2616.0,547.0,581.0,343.0,3.1364,301600.0,INLAND
+-117.76,34.07,48.0,1157.0,247.0,677.0,218.0,2.8594,127200.0,INLAND
+-114.65,34.89,17.0,2556.0,587.0,1005.0,401.0,1.6991,69100.0,INLAND
+-117.26,33.04,16.0,3109.0,450.0,1433.0,453.0,6.6319,269600.0,NEAR OCEAN
+-117.87,33.69,4.0,2337.0,768.0,983.0,655.0,3.7174,275000.0,<1H OCEAN
+-123.58,39.66,15.0,1839.0,489.0,887.0,332.0,2.2429,100000.0,<1H OCEAN
+-118.33,34.11,37.0,2330.0,434.0,846.0,457.0,8.2335,430200.0,<1H OCEAN
+-120.38,40.98,27.0,777.0,185.0,318.0,115.0,1.6833,40000.0,INLAND
+-122.39,37.59,32.0,4497.0,,1846.0,715.0,6.1323,500001.0,NEAR OCEAN
+-122.32,37.55,50.0,2501.0,433.0,1050.0,410.0,4.6406,500001.0,NEAR OCEAN
+-118.09,34.04,18.0,5580.0,1369.0,3842.0,1276.0,3.6512,168500.0,<1H OCEAN
+-118.2,34.04,44.0,1582.0,544.0,1998.0,515.0,1.6888,125000.0,<1H OCEAN
+-118.43,33.87,41.0,847.0,173.0,344.0,170.0,6.822,500001.0,NEAR OCEAN
+-118.11,33.84,37.0,1588.0,272.0,692.0,245.0,4.8594,220300.0,<1H OCEAN
+-117.83,33.83,13.0,3759.0,489.0,1496.0,499.0,8.3818,377600.0,<1H OCEAN
+-117.24,32.81,33.0,1588.0,289.0,683.0,301.0,5.4103,332400.0,NEAR OCEAN
+-121.96,37.53,23.0,2215.0,475.0,1278.0,492.0,4.2955,218800.0,<1H OCEAN
+-118.44,34.5,5.0,1514.0,220.0,1355.0,215.0,8.1344,359000.0,INLAND
+-118.11,33.85,36.0,2418.0,389.0,1138.0,387.0,4.8393,216300.0,<1H OCEAN
+-118.12,34.71,46.0,40.0,10.0,14.0,7.0,1.125,225000.0,INLAND
+-121.33,38.65,24.0,3533.0,741.0,1496.0,723.0,2.8106,183200.0,INLAND
+-121.85,37.28,17.0,4208.0,954.0,1476.0,904.0,2.3971,87500.0,<1H OCEAN
+-122.36,40.66,17.0,2786.0,559.0,1528.0,517.0,2.0119,75800.0,INLAND
+-122.39,41.41,23.0,910.0,199.0,370.0,169.0,1.7448,80100.0,INLAND
+-117.76,33.72,15.0,941.0,266.0,366.0,248.0,4.3636,148400.0,<1H OCEAN
+-119.25,36.71,13.0,2813.0,579.0,1385.0,512.0,2.2202,100000.0,INLAND
+-119.79,36.84,22.0,1529.0,375.0,1543.0,395.0,1.7926,51700.0,INLAND
+-122.24,37.85,37.0,334.0,54.0,98.0,47.0,4.9643,335000.0,NEAR BAY
+-121.29,37.99,41.0,930.0,191.0,463.0,185.0,3.4141,90600.0,INLAND
+-117.87,33.75,26.0,411.0,114.0,448.0,95.0,1.7019,350000.0,<1H OCEAN
+-117.91,33.74,15.0,715.0,214.0,1394.0,244.0,3.3846,162500.0,<1H OCEAN
+-122.34,37.97,19.0,392.0,109.0,287.0,81.0,6.0426,110000.0,NEAR BAY
+-120.62,35.12,22.0,1240.0,294.0,768.0,288.0,2.655,160000.0,<1H OCEAN
+-122.09,37.37,34.0,2165.0,355.0,776.0,339.0,5.2971,442100.0,NEAR BAY
+-118.59,34.25,15.0,9716.0,2387.0,4992.0,2225.0,3.6231,193300.0,<1H OCEAN
+-122.03,37.62,32.0,2964.0,547.0,1472.0,527.0,4.2468,221200.0,NEAR BAY
+-118.03,34.06,31.0,1513.0,389.0,1396.0,364.0,2.4706,170600.0,<1H OCEAN
+-121.83,39.1,42.0,1282.0,198.0,451.0,159.0,3.2917,97900.0,INLAND
+-116.89,32.85,15.0,3560.0,593.0,1757.0,574.0,5.1185,185300.0,<1H OCEAN
+-119.06,34.38,33.0,1465.0,262.0,731.0,266.0,3.9464,230300.0,<1H OCEAN
+-118.22,33.94,40.0,930.0,258.0,1203.0,244.0,2.5938,115400.0,<1H OCEAN
+-118.31,33.97,48.0,1541.0,314.0,819.0,312.0,3.0917,136100.0,<1H OCEAN
+-122.51,38.0,17.0,2449.0,536.0,1157.0,543.0,3.9519,274200.0,NEAR BAY
+-121.99,37.35,16.0,3249.0,947.0,1637.0,841.0,4.5427,198400.0,<1H OCEAN
+-117.98,33.8,32.0,2161.0,432.0,1503.0,402.0,4.3036,191400.0,<1H OCEAN
+-118.09,33.96,36.0,1116.0,229.0,719.0,233.0,3.425,163200.0,<1H OCEAN
+-116.94,32.81,22.0,4266.0,1010.0,2766.0,985.0,2.8175,135200.0,<1H OCEAN
+-119.28,37.11,34.0,1901.0,394.0,171.0,73.0,3.0729,144600.0,INLAND
+-121.39,38.57,33.0,2648.0,357.0,863.0,359.0,8.4016,338700.0,INLAND
+-118.38,33.87,33.0,1993.0,371.0,918.0,361.0,6.9021,337600.0,<1H OCEAN
+-121.54,39.48,29.0,2896.0,596.0,1809.0,617.0,1.8047,53800.0,INLAND
+-118.57,34.2,33.0,1759.0,311.0,943.0,315.0,5.223,209200.0,<1H OCEAN
+-122.36,37.54,23.0,6184.0,747.0,2165.0,700.0,10.1675,500001.0,NEAR OCEAN
+-123.24,38.7,38.0,1460.0,311.0,569.0,176.0,2.7171,131300.0,NEAR OCEAN
+-117.9,34.01,27.0,1702.0,381.0,2091.0,352.0,3.7917,131100.0,<1H OCEAN
+-117.66,34.1,37.0,1971.0,345.0,939.0,358.0,3.4634,145300.0,INLAND
+-117.37,33.19,33.0,2205.0,453.0,1138.0,439.0,2.8819,208600.0,NEAR OCEAN
+-118.3,34.04,24.0,2092.0,585.0,1757.0,538.0,1.7109,175000.0,<1H OCEAN
+-118.33,33.79,29.0,4389.0,873.0,2069.0,901.0,4.1071,365600.0,<1H OCEAN
+-121.99,37.33,35.0,1802.0,291.0,841.0,315.0,4.8365,313900.0,<1H OCEAN
+-122.23,37.79,40.0,930.0,199.0,564.0,184.0,1.3281,113300.0,NEAR BAY
+-122.59,38.78,15.0,764.0,145.0,366.0,143.0,3.375,103100.0,INLAND
+-119.78,36.74,15.0,1461.0,415.0,924.0,356.0,2.5045,90300.0,INLAND
+-120.38,36.76,12.0,932.0,244.0,1043.0,243.0,1.4038,54300.0,INLAND
+-119.37,36.22,19.0,1673.0,318.0,1298.0,343.0,2.706,64800.0,INLAND
+-121.85,37.36,11.0,2109.0,592.0,2744.0,607.0,4.0452,205900.0,<1H OCEAN
+-118.32,34.18,44.0,1594.0,389.0,832.0,340.0,3.4,212100.0,<1H OCEAN
+-119.07,35.35,24.0,4119.0,865.0,1294.0,879.0,2.4123,86200.0,INLAND
+-117.67,34.02,16.0,3042.0,524.0,1516.0,475.0,4.8906,178500.0,INLAND
+-117.09,32.74,23.0,3130.0,779.0,2472.0,744.0,2.32,93200.0,NEAR OCEAN
+-121.42,38.53,36.0,1581.0,288.0,832.0,291.0,3.4083,71800.0,INLAND
+-118.33,34.15,39.0,493.0,168.0,259.0,138.0,2.3667,17500.0,<1H OCEAN
+-117.91,33.64,29.0,1652.0,310.0,832.0,326.0,4.8098,325400.0,<1H OCEAN
+-122.2,37.47,44.0,1927.0,332.0,846.0,362.0,4.2083,278200.0,NEAR BAY
+-121.0,37.69,18.0,3469.0,661.0,1452.0,628.0,3.4079,147500.0,INLAND
+-117.67,34.09,17.0,4418.0,1256.0,2417.0,1094.0,2.7266,101000.0,INLAND
+-117.3,33.06,31.0,2128.0,520.0,1049.0,485.0,4.027,290000.0,NEAR OCEAN
+-118.22,33.98,27.0,1095.0,340.0,1300.0,318.0,2.6548,123200.0,<1H OCEAN
+-116.95,33.74,20.0,2233.0,431.0,1024.0,399.0,2.4554,89400.0,INLAND
+-124.06,40.88,12.0,2087.0,424.0,1603.0,438.0,2.5667,139500.0,NEAR OCEAN
+-117.8,33.66,16.0,2542.0,498.0,1022.0,494.0,4.0,223400.0,<1H OCEAN
+-121.97,37.3,25.0,5463.0,1351.0,2758.0,1310.0,3.0079,277300.0,<1H OCEAN
+-122.06,37.39,22.0,1236.0,290.0,413.0,274.0,3.6875,40000.0,NEAR BAY
+-121.49,38.58,52.0,1000.0,324.0,456.0,250.0,1.4375,168800.0,INLAND
+-118.1,34.71,16.0,3914.0,819.0,1524.0,795.0,2.415,137500.0,INLAND
+-118.35,33.89,25.0,1769.0,440.0,1371.0,414.0,3.0833,232700.0,<1H OCEAN
+-117.04,32.68,11.0,1875.0,357.0,1014.0,386.0,4.375,115000.0,NEAR OCEAN
+-118.16,33.97,13.0,221.0,63.0,286.0,64.0,1.9063,175000.0,<1H OCEAN
+-122.25,37.47,38.0,645.0,124.0,265.0,103.0,5.4688,305000.0,NEAR OCEAN
+-118.08,33.93,39.0,1478.0,324.0,1127.0,320.0,3.525,158000.0,<1H OCEAN
+-118.51,34.2,35.0,1614.0,308.0,850.0,330.0,4.1806,209000.0,<1H OCEAN
+-118.4,33.96,44.0,1138.0,228.0,497.0,228.0,4.1852,303300.0,<1H OCEAN
+-117.67,34.1,28.0,1263.0,248.0,601.0,219.0,3.875,174000.0,INLAND
+-118.49,34.15,33.0,2829.0,360.0,1010.0,363.0,10.3587,500001.0,<1H OCEAN
+-120.83,35.33,14.0,4155.0,787.0,2112.0,755.0,4.4766,192700.0,NEAR OCEAN
+-118.42,37.36,18.0,2281.0,520.0,1425.0,465.0,1.7388,54400.0,INLAND
+-118.1,33.99,31.0,965.0,217.0,599.0,206.0,2.7202,190300.0,<1H OCEAN
+-121.99,37.34,26.0,3637.0,933.0,2249.0,905.0,3.9625,262900.0,<1H OCEAN
+-121.3,38.71,17.0,5434.0,1106.0,2755.0,1047.0,2.8226,99900.0,INLAND
+-122.25,37.86,48.0,2153.0,517.0,1656.0,459.0,3.0417,489600.0,NEAR BAY
+-122.69,38.32,16.0,3741.0,698.0,1938.0,658.0,4.6324,183600.0,<1H OCEAN
+-119.31,35.99,26.0,1460.0,316.0,880.0,286.0,1.3676,47800.0,INLAND
+-118.09,34.57,4.0,9761.0,1683.0,4970.0,1535.0,4.5266,142900.0,INLAND
+-122.05,37.37,27.0,2687.0,768.0,1362.0,725.0,3.4028,324200.0,<1H OCEAN
+-116.31,34.13,20.0,2352.0,556.0,1217.0,481.0,1.6063,55400.0,INLAND
+-118.13,33.89,29.0,2823.0,737.0,1723.0,678.0,2.7121,165500.0,<1H OCEAN
+-119.59,36.64,27.0,823.0,171.0,798.0,200.0,3.0521,113800.0,INLAND
+-117.51,34.16,2.0,718.0,98.0,119.0,50.0,4.1,315000.0,INLAND
+-122.19,39.53,34.0,2679.0,533.0,1287.0,505.0,2.165,58700.0,INLAND
+-117.69,34.06,29.0,873.0,226.0,649.0,198.0,2.7986,114400.0,INLAND
+-115.57,32.79,34.0,1152.0,208.0,621.0,208.0,3.6042,73600.0,INLAND
+-121.83,37.33,27.0,3127.0,610.0,3257.0,604.0,4.6333,173600.0,<1H OCEAN
+-117.28,33.02,21.0,2736.0,585.0,1251.0,576.0,4.2356,347700.0,NEAR OCEAN
+-122.0,37.4,17.0,5121.0,1017.0,1470.0,968.0,2.9706,81300.0,<1H OCEAN
+-121.94,37.31,30.0,4238.0,1010.0,1914.0,972.0,3.7632,307000.0,<1H OCEAN
+-118.38,33.81,20.0,1975.0,306.0,703.0,292.0,8.5491,410300.0,NEAR OCEAN
+-117.79,33.75,26.0,2955.0,377.0,1074.0,373.0,9.3845,500001.0,<1H OCEAN
+-118.12,33.94,31.0,2210.0,519.0,1047.0,472.0,3.3292,271300.0,<1H OCEAN
+-118.03,33.93,35.0,2470.0,416.0,1386.0,411.0,5.2736,179500.0,<1H OCEAN
+-120.33,38.26,13.0,2962.0,546.0,252.0,103.0,4.4063,155800.0,INLAND
+-121.98,37.28,28.0,3688.0,633.0,1877.0,620.0,5.7251,272600.0,<1H OCEAN
+-117.22,33.74,7.0,1810.0,386.0,931.0,355.0,2.5221,109200.0,<1H OCEAN
+-118.16,33.97,39.0,1444.0,447.0,1890.0,416.0,2.1181,176600.0,<1H OCEAN
+-117.96,33.75,25.0,1323.0,208.0,852.0,229.0,4.6167,237300.0,<1H OCEAN
+-122.15,37.42,44.0,3558.0,839.0,1779.0,832.0,3.9243,404800.0,NEAR BAY
+-118.11,33.81,37.0,1694.0,280.0,776.0,271.0,6.2187,257900.0,<1H OCEAN
+-122.01,38.27,17.0,9089.0,1542.0,4758.0,1520.0,4.0619,126600.0,INLAND
+-122.07,37.06,31.0,1634.0,370.0,939.0,332.0,3.8625,232300.0,NEAR OCEAN
+-118.3,33.8,27.0,2790.0,513.0,1498.0,519.0,5.3106,268300.0,<1H OCEAN
+-117.98,34.14,27.0,6341.0,1289.0,2899.0,1192.0,3.6336,235200.0,INLAND
+-118.01,33.81,25.0,1831.0,345.0,809.0,339.0,4.5179,177100.0,<1H OCEAN
+-118.14,34.17,52.0,2667.0,486.0,1681.0,504.0,4.0524,173100.0,<1H OCEAN
+-118.12,34.16,30.0,1762.0,416.0,940.0,398.0,2.8631,188600.0,<1H OCEAN
+-117.21,32.85,15.0,2593.0,521.0,901.0,456.0,4.2065,277800.0,NEAR OCEAN
+-119.78,36.75,43.0,2070.0,512.0,1925.0,444.0,1.4635,46600.0,INLAND
+-121.28,37.94,44.0,1406.0,357.0,1489.0,386.0,1.4688,56800.0,INLAND
+-118.37,34.2,33.0,1438.0,309.0,1378.0,306.0,2.8917,170400.0,<1H OCEAN
+-118.11,34.01,22.0,1141.0,332.0,1189.0,321.0,2.2042,162500.0,<1H OCEAN
+-117.12,32.96,16.0,3050.0,559.0,1444.0,512.0,5.2463,156300.0,<1H OCEAN
+-122.41,37.75,52.0,1892.0,415.0,1442.0,371.0,4.2891,230000.0,NEAR BAY
+-118.07,34.11,41.0,2869.0,563.0,1627.0,533.0,5.0736,270700.0,<1H OCEAN
+-122.13,37.66,19.0,862.0,167.0,407.0,183.0,4.3456,163000.0,NEAR BAY
+-118.37,34.05,48.0,1266.0,234.0,539.0,222.0,4.005,275000.0,<1H OCEAN
+-121.08,38.9,27.0,3436.0,755.0,1568.0,709.0,2.4273,138400.0,INLAND
+-117.9,34.12,35.0,957.0,194.0,804.0,221.0,3.3322,151400.0,<1H OCEAN
+-121.82,37.23,25.0,2349.0,394.0,1266.0,383.0,4.9688,233100.0,<1H OCEAN
+-119.73,36.31,20.0,2440.0,433.0,1579.0,400.0,2.8281,60200.0,INLAND
+-117.66,33.5,16.0,1956.0,346.0,862.0,326.0,4.4732,186300.0,<1H OCEAN
+-118.49,34.04,48.0,2381.0,345.0,859.0,306.0,8.0257,500001.0,<1H OCEAN
+-118.28,33.73,52.0,2085.0,588.0,1767.0,516.0,2.1935,243200.0,NEAR OCEAN
+-118.28,34.25,29.0,2559.0,,1886.0,769.0,2.6036,162100.0,<1H OCEAN
+-117.96,34.08,39.0,1076.0,338.0,1242.0,332.0,2.2679,151800.0,<1H OCEAN
+-119.01,35.37,44.0,593.0,136.0,364.0,121.0,1.4779,66000.0,INLAND
+-122.45,37.7,46.0,2193.0,499.0,1814.0,489.0,4.0125,230100.0,NEAR OCEAN
+-117.99,33.93,36.0,1287.0,233.0,779.0,229.0,4.8523,175800.0,<1H OCEAN
+-118.2,34.03,37.0,1583.0,392.0,1776.0,377.0,2.7266,140800.0,<1H OCEAN
+-121.44,38.62,37.0,1607.0,385.0,972.0,354.0,1.9107,64700.0,INLAND
+-118.38,34.02,31.0,1893.0,450.0,819.0,426.0,4.3077,140600.0,<1H OCEAN
+-117.65,34.07,35.0,2501.0,651.0,1182.0,591.0,1.4464,113200.0,INLAND
+-122.76,38.44,14.0,4376.0,797.0,1809.0,746.0,3.8244,180000.0,<1H OCEAN
+-121.92,36.61,27.0,1619.0,352.0,831.0,344.0,4.3,226400.0,NEAR OCEAN
+-121.97,37.36,34.0,884.0,153.0,534.0,154.0,6.0116,271200.0,<1H OCEAN
+-117.57,33.93,3.0,1240.0,151.0,519.0,146.0,7.5408,271900.0,INLAND
+-117.14,32.71,39.0,1647.0,478.0,2176.0,479.0,1.7642,82900.0,NEAR OCEAN
+-122.26,37.8,20.0,2373.0,779.0,1659.0,676.0,1.6929,115000.0,NEAR BAY
+-121.29,37.94,40.0,2827.0,655.0,2037.0,574.0,2.0303,63800.0,INLAND
+-117.82,33.89,21.0,3079.0,509.0,1431.0,480.0,4.0714,278900.0,<1H OCEAN
+-116.94,32.85,31.0,1293.0,232.0,599.0,228.0,4.7578,161000.0,<1H OCEAN
+-118.11,33.83,36.0,1462.0,233.0,664.0,220.0,5.1171,225300.0,<1H OCEAN
+-117.41,34.1,29.0,1362.0,251.0,776.0,253.0,3.1287,102000.0,INLAND
+-117.99,33.86,36.0,1138.0,228.0,725.0,219.0,3.4167,187200.0,<1H OCEAN
+-116.99,32.72,13.0,1330.0,216.0,719.0,215.0,3.8295,149600.0,<1H OCEAN
+-121.23,36.33,23.0,2095.0,536.0,1858.0,457.0,3.0543,92400.0,<1H OCEAN
+-118.12,34.17,52.0,2166.0,483.0,1308.0,467.0,3.0417,222600.0,INLAND
+-118.37,33.87,19.0,757.0,148.0,361.0,141.0,6.02,304200.0,<1H OCEAN
+-118.13,34.09,42.0,2562.0,781.0,1936.0,687.0,2.2214,219000.0,<1H OCEAN
+-122.36,37.94,45.0,907.0,188.0,479.0,161.0,3.0862,79000.0,NEAR BAY
+-117.63,34.1,15.0,4799.0,1209.0,2554.0,1057.0,2.6582,122800.0,INLAND
+-118.0,33.89,35.0,1011.0,183.0,578.0,171.0,3.9861,188700.0,<1H OCEAN
+-118.15,34.17,46.0,2553.0,558.0,1740.0,492.0,2.0278,127500.0,<1H OCEAN
+-118.33,33.96,42.0,1686.0,361.0,737.0,319.0,2.3,189200.0,<1H OCEAN
+-119.06,36.08,19.0,2554.0,443.0,1301.0,419.0,4.1856,72100.0,INLAND
+-118.02,34.57,4.0,10655.0,1706.0,5391.0,1529.0,5.083,151300.0,INLAND
+-117.08,32.75,15.0,1821.0,516.0,1385.0,439.0,2.5101,95300.0,NEAR OCEAN
+-122.03,37.86,25.0,3004.0,393.0,1145.0,376.0,7.2655,494000.0,NEAR BAY
+-121.52,39.51,30.0,3085.0,610.0,1688.0,575.0,2.334,72200.0,INLAND
+-118.57,34.22,17.0,3262.0,753.0,1879.0,708.0,4.1359,255200.0,<1H OCEAN
+-116.33,34.15,13.0,1808.0,411.0,735.0,320.0,1.5489,57400.0,INLAND
+-117.4,34.09,5.0,6190.0,993.0,3615.0,963.0,4.4034,133200.0,INLAND
+-117.83,33.83,23.0,2775.0,547.0,1226.0,510.0,3.6707,231400.0,<1H OCEAN
+-116.84,33.01,5.0,5673.0,855.0,2592.0,797.0,5.4155,199200.0,<1H OCEAN
+-118.36,33.95,26.0,3231.0,1089.0,3193.0,1020.0,2.6535,177200.0,<1H OCEAN
+-121.34,38.69,17.0,1968.0,364.0,996.0,331.0,3.7031,114300.0,INLAND
+-122.47,37.78,51.0,1485.0,386.0,880.0,385.0,2.7431,307100.0,NEAR BAY
+-122.29,37.97,20.0,3426.0,632.0,1512.0,580.0,4.4911,227400.0,NEAR BAY
+-118.34,33.98,40.0,2108.0,526.0,1922.0,544.0,3.163,137800.0,<1H OCEAN
+-121.96,37.25,19.0,1858.0,359.0,790.0,347.0,4.5156,339300.0,<1H OCEAN
+-117.82,33.85,21.0,2603.0,404.0,1350.0,390.0,6.057,235900.0,<1H OCEAN
+-118.04,34.13,22.0,3359.0,643.0,1227.0,588.0,4.645,276200.0,INLAND
+-117.05,33.03,16.0,87.0,20.0,32.0,21.0,4.3571,144600.0,<1H OCEAN
+-121.47,38.68,19.0,946.0,182.0,474.0,173.0,5.0155,97300.0,INLAND
+-116.81,33.9,17.0,2009.0,469.0,820.0,381.0,1.3286,81800.0,INLAND
+-122.41,37.75,9.0,1282.0,334.0,1176.0,305.0,2.6538,206300.0,NEAR BAY
+-117.69,33.48,25.0,3240.0,481.0,1462.0,497.0,6.1815,288500.0,<1H OCEAN
+-120.41,34.86,15.0,978.0,187.0,407.0,182.0,4.375,158000.0,<1H OCEAN
+-118.26,33.78,27.0,1672.0,491.0,1723.0,462.0,2.0458,174500.0,NEAR OCEAN
+-118.42,34.21,29.0,2893.0,543.0,1636.0,540.0,5.1586,237400.0,<1H OCEAN
+-118.31,33.72,26.0,2711.0,508.0,1372.0,459.0,4.1451,326700.0,NEAR OCEAN
+-117.25,33.08,14.0,7208.0,1012.0,3097.0,1014.0,6.617,285200.0,NEAR OCEAN
+-118.52,34.22,35.0,1620.0,272.0,1052.0,248.0,5.5209,203300.0,<1H OCEAN
+-118.26,34.02,37.0,1551.0,501.0,2173.0,474.0,2.1667,117700.0,<1H OCEAN
+-121.3,38.69,21.0,6575.0,1105.0,3358.0,1098.0,4.0739,115400.0,INLAND
+-117.91,33.63,30.0,2071.0,412.0,1081.0,412.0,4.9125,335700.0,<1H OCEAN
+-122.66,38.42,14.0,5315.0,1037.0,2228.0,950.0,4.023,208400.0,<1H OCEAN
+-116.48,33.96,11.0,1381.0,300.0,644.0,248.0,2.3382,89400.0,INLAND
+-120.54,40.29,17.0,3391.0,623.0,1529.0,571.0,3.4028,91000.0,INLAND
+-117.99,33.89,21.0,5195.0,1020.0,2539.0,988.0,4.5033,160500.0,<1H OCEAN
+-118.08,33.77,26.0,2461.0,562.0,971.0,544.0,2.1944,87500.0,<1H OCEAN
+-122.21,37.81,52.0,1389.0,212.0,510.0,224.0,5.2402,296400.0,NEAR BAY
+-122.34,37.95,38.0,1340.0,298.0,766.0,241.0,3.2833,111700.0,NEAR BAY
+-118.36,33.83,35.0,2828.0,487.0,1439.0,490.0,5.6013,350200.0,<1H OCEAN
+-117.92,33.65,25.0,1679.0,470.0,1314.0,473.0,4.1026,211500.0,<1H OCEAN
+-121.83,37.36,29.0,4045.0,885.0,3036.0,845.0,3.1982,171700.0,<1H OCEAN
+-122.2,37.82,37.0,1229.0,181.0,420.0,176.0,7.0175,366700.0,NEAR BAY
+-122.51,37.58,20.0,64.0,21.0,59.0,21.0,2.2375,450000.0,NEAR OCEAN
+-121.06,38.91,18.0,6501.0,1416.0,2954.0,1373.0,2.5373,143000.0,INLAND
+-121.45,38.55,19.0,3374.0,808.0,1412.0,753.0,1.4889,77600.0,INLAND
+-117.21,32.8,19.0,786.0,282.0,525.0,229.0,1.7273,137500.0,NEAR OCEAN
+-122.08,37.08,35.0,1541.0,297.0,791.0,277.0,4.425,204800.0,NEAR OCEAN
+-118.13,33.83,45.0,3087.0,574.0,1474.0,567.0,5.5196,227600.0,<1H OCEAN
+-117.44,33.96,29.0,124.0,22.0,50.0,18.0,12.5381,112500.0,INLAND
+-118.98,35.39,32.0,2620.0,682.0,2375.0,684.0,1.2618,46900.0,INLAND
+-117.28,34.11,39.0,1573.0,418.0,1258.0,359.0,1.4896,69500.0,INLAND
+-121.83,37.2,6.0,3694.0,574.0,1554.0,527.0,6.8333,348000.0,<1H OCEAN
+-117.76,33.87,16.0,3973.0,595.0,1971.0,575.0,6.4265,263700.0,<1H OCEAN
+-118.28,33.75,21.0,2442.0,719.0,1916.0,646.0,1.2276,208300.0,NEAR OCEAN
+-122.49,37.63,31.0,1256.0,328.0,785.0,297.0,3.2446,234600.0,NEAR OCEAN
+-120.6,34.91,44.0,711.0,140.0,384.0,116.0,2.1094,73800.0,NEAR OCEAN
+-117.46,33.94,35.0,1566.0,294.0,1056.0,279.0,3.5227,105400.0,INLAND
+-117.53,34.02,19.0,256.0,34.0,101.0,28.0,5.3269,375000.0,INLAND
+-117.21,32.86,26.0,1352.0,202.0,654.0,217.0,5.3693,260700.0,NEAR OCEAN
+-121.47,38.54,47.0,2085.0,464.0,1346.0,402.0,1.2679,56700.0,INLAND
+-118.43,34.22,36.0,1372.0,295.0,774.0,306.0,3.6618,187300.0,<1H OCEAN
+-121.86,36.63,37.0,338.0,109.0,231.0,100.0,2.5313,108300.0,<1H OCEAN
+-121.02,37.66,28.0,1437.0,400.0,806.0,338.0,1.6078,125000.0,INLAND
+-118.2,34.12,52.0,1580.0,426.0,1462.0,406.0,3.3326,167600.0,<1H OCEAN
+-118.32,34.19,37.0,1519.0,331.0,613.0,315.0,3.0179,272500.0,<1H OCEAN
+-120.99,39.13,14.0,770.0,116.0,285.0,116.0,3.6434,155400.0,INLAND
+-117.7,33.57,9.0,1204.0,355.0,469.0,293.0,3.6196,119900.0,<1H OCEAN
+-120.43,34.97,28.0,1433.0,270.0,1001.0,278.0,4.0125,130100.0,<1H OCEAN
+-118.32,34.0,50.0,3250.0,693.0,2300.0,675.0,1.95,112600.0,<1H OCEAN
+-118.4,34.15,31.0,3881.0,909.0,1535.0,846.0,3.0398,369100.0,<1H OCEAN
+-116.95,32.76,13.0,5543.0,857.0,2074.0,737.0,4.9528,266200.0,<1H OCEAN
+-117.93,33.65,34.0,2141.0,425.0,1559.0,429.0,4.2036,220100.0,<1H OCEAN
+-118.04,34.1,39.0,2302.0,412.0,1590.0,406.0,4.8017,273800.0,INLAND
+-119.02,35.41,41.0,2221.0,516.0,1106.0,473.0,1.97,51900.0,INLAND
+-117.78,33.71,16.0,2207.0,291.0,1081.0,308.0,7.3518,331200.0,<1H OCEAN
+-118.62,34.26,15.0,10860.0,1653.0,4178.0,1581.0,6.3249,262100.0,<1H OCEAN
+-122.03,37.32,15.0,5132.0,1059.0,2156.0,982.0,5.6511,404800.0,<1H OCEAN
+-121.87,37.23,19.0,7357.0,963.0,3018.0,981.0,6.9473,361400.0,<1H OCEAN
+-117.22,34.48,7.0,2449.0,447.0,1217.0,408.0,3.6646,109900.0,INLAND
+-118.22,33.96,35.0,1437.0,474.0,2113.0,484.0,2.6179,158800.0,<1H OCEAN
+-121.23,40.01,38.0,725.0,190.0,219.0,115.0,1.625,75000.0,INLAND
+-121.91,37.27,30.0,4412.0,862.0,2168.0,772.0,5.0062,232000.0,<1H OCEAN
+-120.43,34.69,33.0,2054.0,373.0,1067.0,358.0,3.6023,128300.0,NEAR OCEAN
+-121.73,36.9,23.0,2392.0,721.0,3074.0,718.0,2.5195,136900.0,<1H OCEAN
+-117.96,33.78,26.0,2136.0,557.0,1528.0,537.0,2.4931,236100.0,<1H OCEAN
+-117.61,34.04,8.0,4116.0,766.0,1785.0,745.0,3.1672,150200.0,INLAND
+-118.0,34.12,42.0,870.0,170.0,546.0,164.0,4.625,173800.0,INLAND
+-121.7,37.94,36.0,1710.0,320.0,861.0,300.0,2.8828,131100.0,INLAND
+-117.95,33.86,35.0,2375.0,439.0,1343.0,424.0,4.53,193500.0,<1H OCEAN
+-122.07,37.4,15.0,2940.0,910.0,943.0,711.0,4.359,192200.0,NEAR BAY
+-122.22,38.1,44.0,3013.0,563.0,1353.0,512.0,3.4559,111900.0,NEAR BAY
+-118.18,33.9,31.0,2536.0,603.0,2625.0,576.0,3.0909,150900.0,<1H OCEAN
+-117.9,33.91,33.0,4181.0,804.0,2049.0,834.0,4.3103,201600.0,<1H OCEAN
+-118.36,34.09,34.0,2832.0,883.0,1594.0,843.0,1.7558,312500.0,<1H OCEAN
+-122.24,37.82,52.0,1509.0,225.0,674.0,244.0,4.9306,313400.0,NEAR BAY
+-122.1,37.71,27.0,6740.0,1073.0,2723.0,1035.0,5.2131,252500.0,NEAR BAY
+-118.33,34.12,23.0,1894.0,416.0,769.0,392.0,6.0352,500001.0,<1H OCEAN
+-122.35,37.93,28.0,1995.0,488.0,1182.0,439.0,2.3352,84300.0,NEAR BAY
+-120.62,38.39,15.0,3750.0,691.0,1444.0,603.0,2.7399,134800.0,INLAND
+-122.0,37.36,19.0,2237.0,433.0,1158.0,426.0,6.7718,368300.0,<1H OCEAN
+-118.12,34.05,32.0,3775.0,786.0,2416.0,792.0,3.6625,247600.0,<1H OCEAN
+-118.13,33.78,31.0,3039.0,739.0,1199.0,697.0,3.7232,500001.0,NEAR OCEAN
+-117.26,34.17,30.0,1937.0,351.0,945.0,344.0,3.8906,123700.0,INLAND
+-122.4,40.71,22.0,2390.0,484.0,1131.0,452.0,2.1458,84700.0,INLAND
+-118.45,34.0,46.0,1777.0,362.0,896.0,334.0,4.45,348300.0,<1H OCEAN
+-121.28,37.98,52.0,941.0,184.0,414.0,171.0,2.1429,69900.0,INLAND
+-119.33,36.32,19.0,2778.0,431.0,1092.0,451.0,5.2561,121300.0,INLAND
+-120.39,36.78,11.0,1947.0,488.0,2104.0,486.0,1.7184,55200.0,INLAND
+-118.07,33.93,5.0,906.0,187.0,1453.0,158.0,4.125,171900.0,<1H OCEAN
+-121.35,38.68,20.0,7085.0,1222.0,3455.0,1229.0,4.3118,120000.0,INLAND
+-118.43,34.33,18.0,5891.0,920.0,2882.0,911.0,5.901,235600.0,<1H OCEAN
+-122.48,37.7,33.0,4167.0,1398.0,2923.0,1314.0,3.049,307000.0,NEAR OCEAN
+-122.07,38.26,15.0,1173.0,146.0,450.0,154.0,6.0487,197700.0,INLAND
+-118.36,33.79,34.0,5166.0,704.0,2071.0,668.0,8.3609,500001.0,NEAR OCEAN
+-118.26,33.77,36.0,886.0,253.0,809.0,219.0,2.4545,164200.0,NEAR OCEAN
+-117.77,33.53,32.0,3116.0,661.0,1105.0,543.0,5.1837,445600.0,<1H OCEAN
+-117.21,32.96,3.0,6251.0,988.0,2330.0,893.0,8.4355,467600.0,NEAR OCEAN
+-118.02,33.9,34.0,1636.0,358.0,977.0,357.0,3.5938,209900.0,<1H OCEAN
+-115.91,33.36,15.0,459.0,95.0,160.0,73.0,0.922,67500.0,INLAND
+-122.14,38.07,31.0,3401.0,616.0,1750.0,602.0,4.6761,143100.0,NEAR BAY
+-118.31,33.94,41.0,1353.0,286.0,751.0,250.0,2.7401,131700.0,<1H OCEAN
+-123.01,38.79,32.0,2697.0,529.0,1417.0,535.0,3.2546,134100.0,<1H OCEAN
+-120.9,35.69,14.0,5020.0,909.0,2105.0,796.0,3.8158,248700.0,<1H OCEAN
+-121.83,39.76,12.0,9831.0,1921.0,4644.0,1775.0,3.1142,112600.0,INLAND
+-123.39,38.99,28.0,1416.0,294.0,812.0,258.0,3.4063,109400.0,<1H OCEAN
+-117.11,32.62,27.0,1846.0,509.0,1078.0,482.0,2.1719,131500.0,NEAR OCEAN
+-120.58,38.77,21.0,1661.0,406.0,789.0,319.0,2.3583,108700.0,INLAND
+-118.03,33.97,39.0,2126.0,434.0,1103.0,433.0,3.2852,196200.0,<1H OCEAN
+-117.02,32.72,36.0,2030.0,369.0,1142.0,357.0,3.7763,126900.0,NEAR OCEAN
+-118.28,33.97,31.0,1068.0,271.0,1091.0,281.0,1.689,102600.0,<1H OCEAN
+-116.94,34.24,27.0,12342.0,2630.0,1300.0,566.0,1.998,153500.0,INLAND
+-118.24,33.96,44.0,1338.0,366.0,1765.0,388.0,1.7778,109900.0,<1H OCEAN
+-122.07,37.68,36.0,1815.0,426.0,1280.0,431.0,3.25,218100.0,NEAR BAY
+-117.91,33.91,34.0,1763.0,303.0,894.0,297.0,5.0096,221700.0,<1H OCEAN
+-117.55,33.89,25.0,2999.0,439.0,1396.0,458.0,5.6973,164800.0,INLAND
+-118.34,33.91,17.0,3724.0,1023.0,2536.0,971.0,3.2649,202100.0,<1H OCEAN
+-121.75,36.96,19.0,3461.0,634.0,2790.0,607.0,4.7569,190800.0,<1H OCEAN
+-122.26,37.88,52.0,2363.0,604.0,1558.0,573.0,2.944,338900.0,NEAR BAY
+-118.1,33.95,27.0,1666.0,365.0,995.0,354.0,4.5694,204300.0,<1H OCEAN
+-122.43,37.77,52.0,1567.0,482.0,654.0,425.0,2.6914,366700.0,NEAR BAY
+-119.76,36.75,35.0,2347.0,526.0,1676.0,481.0,1.6548,49400.0,INLAND
+-118.14,34.07,42.0,1036.0,199.0,656.0,215.0,4.1902,235000.0,<1H OCEAN
+-117.99,33.98,18.0,8399.0,1144.0,3727.0,1107.0,6.9695,360400.0,<1H OCEAN
+-118.42,33.92,25.0,3521.0,852.0,1524.0,764.0,3.8086,361300.0,<1H OCEAN
+-118.3,34.02,31.0,1933.0,478.0,1522.0,423.0,1.5781,119300.0,<1H OCEAN
+-120.46,38.09,16.0,3758.0,715.0,1777.0,615.0,3.0,122600.0,INLAND
+-118.29,34.06,42.0,3894.0,2293.0,6846.0,2156.0,1.5553,70000.0,<1H OCEAN
+-118.3,33.79,35.0,2793.0,686.0,2255.0,682.0,3.0057,235300.0,<1H OCEAN
+-117.84,33.6,21.0,4281.0,582.0,1443.0,576.0,9.0519,500001.0,<1H OCEAN
+-122.42,37.79,52.0,3457.0,1021.0,2286.0,994.0,2.565,225000.0,NEAR BAY
+-117.3,34.54,31.0,1174.0,360.0,1161.0,328.0,1.06,56500.0,INLAND
+-118.42,33.9,37.0,1576.0,345.0,662.0,340.0,5.308,500001.0,<1H OCEAN
+-118.25,34.0,29.0,1419.0,363.0,1696.0,317.0,2.2813,101300.0,<1H OCEAN
+-122.58,38.1,22.0,11872.0,2300.0,5600.0,2200.0,4.6463,276300.0,NEAR BAY
+-118.08,33.72,14.0,2021.0,396.0,696.0,367.0,7.1673,340700.0,NEAR OCEAN
+-118.32,34.02,50.0,1655.0,256.0,672.0,260.0,4.2554,194300.0,<1H OCEAN
+-117.15,32.92,16.0,2969.0,514.0,1594.0,465.0,4.5221,168300.0,<1H OCEAN
+-118.54,34.27,28.0,2309.0,300.0,931.0,302.0,6.7415,348200.0,<1H OCEAN
+-121.94,37.54,27.0,3715.0,526.0,1631.0,538.0,6.2179,305300.0,<1H OCEAN
+-121.37,38.66,9.0,3184.0,779.0,1929.0,769.0,2.3844,86000.0,INLAND
+-118.35,33.83,36.0,1102.0,193.0,522.0,172.0,6.1187,342000.0,<1H OCEAN
+-118.98,37.65,18.0,1795.0,416.0,483.0,208.0,4.5375,169800.0,INLAND
+-118.43,34.06,38.0,2982.0,664.0,1122.0,572.0,4.1908,500001.0,<1H OCEAN
+-122.4,37.62,44.0,1619.0,362.0,1064.0,335.0,4.0238,224200.0,NEAR OCEAN
+-118.39,33.85,24.0,4373.0,871.0,1830.0,824.0,5.7128,366200.0,<1H OCEAN
+-118.48,34.01,31.0,1829.0,458.0,719.0,392.0,4.4,353800.0,<1H OCEAN
+-118.71,34.28,27.0,2911.0,562.0,1773.0,580.0,4.6528,186600.0,<1H OCEAN
+-121.28,38.03,11.0,826.0,150.0,684.0,166.0,3.9265,107400.0,INLAND
+-117.31,33.1,15.0,2392.0,446.0,747.0,421.0,3.5341,500001.0,NEAR OCEAN
+-122.26,40.58,14.0,2539.0,466.0,1271.0,438.0,3.9762,138500.0,INLAND
+-117.61,33.43,24.0,2303.0,399.0,851.0,379.0,3.9875,346500.0,NEAR OCEAN
+-119.94,38.95,25.0,1789.0,536.0,1134.0,396.0,2.32,91300.0,INLAND
+-122.43,40.47,16.0,3552.0,704.0,1801.0,658.0,2.1496,97700.0,INLAND
+-121.99,38.36,35.0,2728.0,451.0,1290.0,452.0,3.2768,117600.0,INLAND
+-122.41,37.8,52.0,812.0,252.0,629.0,247.0,2.5875,500001.0,NEAR BAY
+-117.81,35.65,19.0,1124.0,290.0,598.0,261.0,1.8984,54300.0,INLAND
+-117.08,33.14,15.0,1497.0,250.0,827.0,239.0,4.3846,154200.0,<1H OCEAN
+-116.95,32.82,18.0,3038.0,592.0,1904.0,595.0,3.8024,144900.0,<1H OCEAN
+-119.81,36.78,35.0,1012.0,245.0,633.0,240.0,2.0324,55500.0,INLAND
+-122.26,38.0,5.0,6265.0,908.0,3326.0,872.0,6.2073,272900.0,NEAR BAY
+-118.34,33.88,42.0,725.0,183.0,493.0,172.0,3.2589,233300.0,<1H OCEAN
+-122.48,38.9,10.0,304.0,63.0,161.0,61.0,2.1964,112500.0,INLAND
+-122.18,37.76,37.0,1575.0,358.0,933.0,320.0,2.2917,107000.0,NEAR BAY
+-122.82,38.4,40.0,2406.0,423.0,1054.0,426.0,3.8846,215900.0,<1H OCEAN
+-122.91,39.07,21.0,2202.0,484.0,1000.0,381.0,2.4423,102300.0,INLAND
+-118.27,33.96,42.0,796.0,203.0,697.0,177.0,2.037,92600.0,<1H OCEAN
+-117.66,33.61,16.0,2022.0,254.0,789.0,270.0,8.4112,286900.0,<1H OCEAN
+-121.43,38.62,36.0,1765.0,438.0,1008.0,382.0,2.0639,73000.0,INLAND
+-118.17,33.84,29.0,4716.0,1372.0,2515.0,1272.0,2.726,208700.0,<1H OCEAN
+-120.09,39.92,19.0,2335.0,518.0,1028.0,383.0,1.7267,60700.0,INLAND
+-119.34,35.59,33.0,3240.0,654.0,1809.0,616.0,2.3934,71900.0,INLAND
+-117.27,33.21,5.0,5764.0,996.0,3161.0,1012.0,4.4531,177500.0,<1H OCEAN
+-122.05,37.97,16.0,60.0,10.0,65.0,19.0,6.1359,250000.0,NEAR BAY
+-118.06,33.9,37.0,1161.0,254.0,882.0,236.0,4.4167,158000.0,<1H OCEAN
+-117.4,34.0,31.0,1192.0,307.0,1013.0,283.0,2.0742,76200.0,INLAND
+-121.94,37.32,46.0,2451.0,472.0,1163.0,448.0,4.8519,225800.0,<1H OCEAN
+-118.12,33.83,45.0,1579.0,278.0,687.0,285.0,5.0424,225900.0,<1H OCEAN
+-118.11,33.91,22.0,1981.0,472.0,1231.0,457.0,4.0878,153700.0,<1H OCEAN
+-118.9,34.18,20.0,1288.0,179.0,539.0,181.0,5.8652,302600.0,<1H OCEAN
+-117.15,34.03,32.0,2832.0,393.0,1033.0,385.0,6.5648,237200.0,INLAND
+-117.41,34.0,38.0,2228.0,571.0,1697.0,530.0,1.9052,83400.0,INLAND
+-118.21,33.82,43.0,1005.0,199.0,723.0,191.0,4.3424,162500.0,NEAR OCEAN
+-117.77,33.88,8.0,4874.0,627.0,2070.0,619.0,8.1117,410200.0,<1H OCEAN
+-117.88,34.0,21.0,5459.0,1086.0,3394.0,1087.0,3.6308,192100.0,<1H OCEAN
+-117.98,33.83,17.0,3419.0,932.0,2460.0,766.0,3.2823,228500.0,<1H OCEAN
+-118.27,34.24,30.0,2180.0,369.0,1050.0,390.0,6.3688,277600.0,<1H OCEAN
+-121.96,37.13,26.0,50.0,5.0,17.0,4.0,15.0001,400000.0,<1H OCEAN
+-117.11,32.78,16.0,2220.0,512.0,930.0,527.0,3.6528,133200.0,NEAR OCEAN
+-117.75,33.95,13.0,984.0,127.0,364.0,119.0,7.5839,426900.0,<1H OCEAN
+-121.15,39.23,13.0,3883.0,763.0,1816.0,682.0,2.8102,144400.0,INLAND
+-122.47,37.51,15.0,4974.0,764.0,2222.0,774.0,6.7606,364300.0,NEAR OCEAN
+-121.95,37.33,31.0,1866.0,465.0,821.0,447.0,2.3547,275900.0,<1H OCEAN
+-118.61,34.24,17.0,5406.0,895.0,2337.0,882.0,6.0137,375900.0,<1H OCEAN
+-117.97,33.92,24.0,2017.0,416.0,900.0,436.0,3.0,251400.0,<1H OCEAN
+-121.87,37.34,39.0,2479.0,541.0,1990.0,506.0,2.4306,289100.0,<1H OCEAN
+-117.9,33.64,36.0,2017.0,357.0,850.0,348.0,5.0532,310900.0,<1H OCEAN
+-122.27,37.32,37.0,2607.0,534.0,1346.0,507.0,5.3951,277700.0,NEAR OCEAN
+-118.15,34.16,18.0,1711.0,383.0,1474.0,415.0,1.2891,181300.0,<1H OCEAN
+-121.07,38.66,22.0,1831.0,274.0,813.0,269.0,4.6394,173400.0,INLAND
+-118.18,34.01,42.0,1845.0,497.0,2191.0,492.0,2.3462,127300.0,<1H OCEAN
+-117.87,33.83,27.0,2287.0,,1140.0,351.0,5.6163,231000.0,<1H OCEAN
+-118.29,34.17,52.0,1732.0,305.0,875.0,311.0,4.325,292600.0,<1H OCEAN
+-122.02,37.28,25.0,3437.0,428.0,1198.0,411.0,9.3464,500001.0,<1H OCEAN
+-122.25,37.77,52.0,859.0,157.0,429.0,158.0,4.3098,197900.0,NEAR BAY
+-117.06,32.57,18.0,1384.0,311.0,1429.0,287.0,1.3362,95000.0,NEAR OCEAN
+-117.04,32.73,25.0,1375.0,267.0,1032.0,278.0,3.5492,125400.0,NEAR OCEAN
+-118.08,33.88,26.0,1507.0,270.0,931.0,275.0,5.1645,244900.0,<1H OCEAN
+-117.98,33.99,26.0,3343.0,455.0,1429.0,457.0,7.0778,334300.0,<1H OCEAN
+-117.97,33.81,30.0,2406.0,462.0,1753.0,456.0,4.485,180600.0,<1H OCEAN
+-119.92,36.77,18.0,1422.0,243.0,702.0,230.0,3.6204,119800.0,INLAND
+-118.25,34.23,37.0,1954.0,368.0,967.0,370.0,5.0862,261300.0,<1H OCEAN
+-118.08,33.82,30.0,2636.0,652.0,3412.0,649.0,2.8095,118300.0,<1H OCEAN
+-121.85,37.26,16.0,2312.0,303.0,1158.0,295.0,7.4323,311800.0,<1H OCEAN
+-122.86,39.05,20.0,1592.0,327.0,647.0,253.0,2.5326,136800.0,INLAND
+-121.94,37.83,11.0,2836.0,373.0,959.0,335.0,10.5815,500001.0,INLAND
+-117.89,33.85,18.0,2036.0,414.0,1292.0,380.0,3.875,273000.0,<1H OCEAN
+-119.39,36.64,38.0,949.0,190.0,578.0,187.0,2.3618,80000.0,INLAND
+-119.63,36.76,22.0,4126.0,614.0,1795.0,613.0,4.925,154700.0,INLAND
+-121.31,38.65,21.0,2759.0,409.0,1053.0,374.0,5.5,165700.0,INLAND
+-118.11,33.83,37.0,1249.0,202.0,517.0,189.0,4.4196,223100.0,<1H OCEAN
+-117.08,32.64,11.0,1651.0,533.0,947.0,515.0,1.6806,141700.0,NEAR OCEAN
+-118.12,34.17,52.0,1835.0,330.0,777.0,317.0,3.7159,315400.0,INLAND
+-118.94,35.4,14.0,5548.0,941.0,2815.0,935.0,4.2214,104600.0,INLAND
+-122.0,37.39,36.0,1236.0,229.0,880.0,247.0,5.791,239400.0,<1H OCEAN
+-117.61,33.67,3.0,4541.0,720.0,1600.0,583.0,6.8004,284900.0,<1H OCEAN
+-117.26,33.21,26.0,1906.0,408.0,1325.0,427.0,3.0197,136000.0,<1H OCEAN
+-122.49,37.73,48.0,1190.0,182.0,497.0,199.0,6.2642,438500.0,NEAR OCEAN
+-118.25,34.01,45.0,782.0,270.0,1030.0,235.0,1.0898,93400.0,<1H OCEAN
+-117.14,32.91,14.0,3014.0,710.0,2165.0,705.0,3.7837,160300.0,<1H OCEAN
+-121.98,37.14,37.0,74.0,19.0,63.0,17.0,9.5908,350000.0,<1H OCEAN
+-119.98,37.43,12.0,2776.0,592.0,1236.0,489.0,2.5551,105000.0,INLAND
+-119.29,34.29,33.0,3854.0,982.0,1835.0,894.0,3.5294,323900.0,NEAR OCEAN
+-118.12,34.04,35.0,1064.0,203.0,608.0,201.0,4.0938,246900.0,<1H OCEAN
+-122.01,37.93,25.0,2652.0,335.0,1062.0,334.0,7.5898,330200.0,NEAR BAY
+-119.39,34.32,19.0,3238.0,629.0,1195.0,443.0,4.8472,500001.0,NEAR OCEAN
+-114.94,34.55,20.0,350.0,95.0,119.0,58.0,1.625,50000.0,INLAND
+-122.05,38.0,16.0,1085.0,217.0,356.0,232.0,2.3462,75000.0,NEAR BAY
+-121.91,36.99,23.0,5675.0,964.0,2197.0,880.0,4.8693,322300.0,NEAR OCEAN
+-118.22,33.91,28.0,1847.0,500.0,2263.0,473.0,1.5161,103200.0,<1H OCEAN
+-118.07,33.99,41.0,1204.0,252.0,1002.0,248.0,3.0577,163300.0,<1H OCEAN
+-118.38,34.17,33.0,1588.0,454.0,739.0,392.0,2.8208,238500.0,<1H OCEAN
+-117.31,34.02,18.0,1634.0,274.0,899.0,285.0,5.2139,129300.0,INLAND
+-118.08,34.08,43.0,1716.0,402.0,1343.0,386.0,2.9688,211400.0,<1H OCEAN
+-121.37,38.56,18.0,2129.0,363.0,815.0,347.0,2.7679,118000.0,INLAND
+-118.37,34.19,19.0,2890.0,821.0,2203.0,705.0,2.6696,185100.0,<1H OCEAN
+-118.2,33.92,42.0,1411.0,314.0,1432.0,322.0,3.0871,138800.0,<1H OCEAN
+-121.97,37.76,8.0,3743.0,581.0,1633.0,567.0,6.7027,381900.0,<1H OCEAN
+-120.43,34.7,26.0,2353.0,389.0,1420.0,389.0,3.87,125800.0,NEAR OCEAN
+-122.07,37.94,43.0,1454.0,234.0,683.0,258.0,4.475,265700.0,NEAR BAY
+-115.53,34.91,12.0,807.0,199.0,246.0,102.0,2.5391,40000.0,INLAND
+-117.72,33.62,21.0,2322.0,518.0,662.0,457.0,3.1679,110000.0,<1H OCEAN
+-118.28,33.94,44.0,1631.0,338.0,1197.0,355.0,3.0788,100000.0,<1H OCEAN
+-121.34,38.67,35.0,643.0,117.0,331.0,134.0,3.0417,120700.0,INLAND
+-119.74,36.76,42.0,2093.0,470.0,1621.0,438.0,1.7994,58700.0,INLAND
+-118.31,34.02,41.0,1046.0,216.0,727.0,201.0,1.6667,116900.0,<1H OCEAN
+-122.24,37.72,5.0,18634.0,2885.0,7427.0,2718.0,7.611,350700.0,NEAR BAY
+-121.47,38.61,31.0,1072.0,,781.0,281.0,1.6563,65800.0,INLAND
+-122.49,37.79,52.0,3146.0,478.0,1143.0,455.0,6.1407,500001.0,NEAR BAY
+-118.98,35.41,36.0,1482.0,266.0,640.0,274.0,3.875,94500.0,INLAND
+-122.67,38.25,32.0,1333.0,235.0,660.0,206.0,4.0729,288500.0,<1H OCEAN
+-122.47,37.76,48.0,2064.0,484.0,1055.0,467.0,2.8711,329600.0,NEAR BAY
+-122.12,37.75,28.0,794.0,111.0,329.0,109.0,7.6923,329800.0,NEAR BAY
+-118.3,34.03,47.0,2241.0,559.0,1775.0,504.0,2.1571,147900.0,<1H OCEAN
+-121.96,37.43,18.0,2514.0,578.0,2205.0,545.0,3.3859,158000.0,<1H OCEAN
+-119.43,36.59,15.0,1371.0,306.0,1266.0,309.0,1.767,63300.0,INLAND
+-120.49,37.3,50.0,985.0,309.0,621.0,250.0,1.3125,60900.0,INLAND
+-122.13,37.45,41.0,3233.0,540.0,1251.0,506.0,6.6354,500001.0,NEAR BAY
+-122.51,37.87,21.0,3904.0,980.0,1949.0,919.0,2.862,258400.0,NEAR BAY
+-117.92,33.74,18.0,1639.0,491.0,2513.0,458.0,2.1838,159700.0,<1H OCEAN
+-121.98,37.25,19.0,755.0,93.0,267.0,99.0,15.0,500001.0,<1H OCEAN
+-118.47,35.64,17.0,2248.0,535.0,927.0,427.0,1.3023,68500.0,INLAND
+-118.2,33.77,22.0,1118.0,437.0,1190.0,399.0,1.9797,143800.0,NEAR OCEAN
+-117.7,34.05,24.0,2834.0,470.0,1815.0,471.0,4.7357,162500.0,INLAND
+-122.5,37.74,44.0,2527.0,518.0,1434.0,444.0,3.875,275700.0,NEAR OCEAN
+-118.49,34.31,25.0,1024.0,145.0,357.0,147.0,7.0598,356300.0,<1H OCEAN
+-121.35,38.65,20.0,2498.0,546.0,1185.0,506.0,3.2243,107900.0,INLAND
+-117.84,33.77,14.0,4412.0,952.0,1656.0,874.0,4.3292,206500.0,<1H OCEAN
+-118.07,33.8,17.0,2439.0,554.0,1161.0,532.0,3.6442,193100.0,<1H OCEAN
+-118.17,34.04,38.0,385.0,102.0,402.0,95.0,1.625,129700.0,<1H OCEAN
+-118.08,34.12,27.0,1685.0,341.0,757.0,317.0,4.2434,270500.0,<1H OCEAN
+-117.37,34.12,32.0,3190.0,568.0,1614.0,512.0,3.8398,118200.0,INLAND
+-117.97,33.86,34.0,2138.0,490.0,1682.0,463.0,3.6006,161700.0,<1H OCEAN
+-118.35,34.21,39.0,1470.0,312.0,1047.0,284.0,3.275,181400.0,<1H OCEAN
+-117.05,32.75,36.0,2024.0,,1030.0,390.0,3.8233,139800.0,NEAR OCEAN
+-121.92,36.57,42.0,3944.0,738.0,1374.0,598.0,4.174,394400.0,NEAR OCEAN
+-117.97,34.1,33.0,1558.0,316.0,1600.0,338.0,2.9712,143900.0,INLAND
+-121.27,37.88,43.0,968.0,249.0,664.0,240.0,1.6458,83600.0,INLAND
+-122.43,37.75,52.0,2459.0,507.0,1012.0,475.0,4.0568,387900.0,NEAR BAY
+-121.32,37.95,40.0,964.0,230.0,742.0,209.0,1.2625,43000.0,INLAND
+-118.32,34.1,43.0,1615.0,734.0,1460.0,644.0,1.4005,193800.0,<1H OCEAN
+-117.1,32.75,11.0,1976.0,548.0,1528.0,512.0,1.4886,89800.0,NEAR OCEAN
+-118.12,33.87,21.0,3764.0,1081.0,1919.0,977.0,2.5057,156300.0,<1H OCEAN
+-118.02,33.76,27.0,2905.0,587.0,1781.0,561.0,4.25,214800.0,<1H OCEAN
+-118.36,34.1,36.0,2963.0,838.0,1129.0,745.0,2.5588,500001.0,<1H OCEAN
+-117.99,34.08,35.0,1032.0,207.0,954.0,191.0,2.8906,134800.0,INLAND
+-118.21,33.97,35.0,1863.0,537.0,2274.0,510.0,2.1005,171300.0,<1H OCEAN
+-122.17,37.45,33.0,1828.0,396.0,766.0,378.0,4.4531,500001.0,NEAR BAY
+-121.43,38.53,36.0,2430.0,426.0,1199.0,437.0,3.1667,81900.0,INLAND
+-118.21,34.56,12.0,2472.0,408.0,1048.0,380.0,4.7097,262100.0,INLAND
+-121.01,37.25,16.0,2216.0,458.0,1135.0,424.0,2.7316,97500.0,INLAND
+-117.97,33.85,45.0,818.0,147.0,546.0,152.0,5.1057,170700.0,<1H OCEAN
+-117.78,33.69,16.0,4702.0,806.0,2529.0,814.0,5.1299,238900.0,<1H OCEAN
+-118.41,34.01,44.0,2010.0,394.0,961.0,365.0,4.5982,333500.0,<1H OCEAN
+-122.13,37.45,37.0,2295.0,332.0,933.0,332.0,6.7257,500001.0,NEAR BAY
+-116.88,32.81,35.0,2926.0,562.0,1590.0,506.0,4.2014,143200.0,<1H OCEAN
+-117.24,33.18,19.0,3337.0,565.0,1646.0,554.0,5.0195,200200.0,<1H OCEAN
+-118.06,34.11,39.0,2603.0,547.0,1196.0,487.0,3.0854,248700.0,INLAND
+-122.18,37.75,46.0,941.0,218.0,621.0,195.0,1.325,87100.0,NEAR BAY
+-121.62,36.68,43.0,2534.0,592.0,2448.0,603.0,2.4884,130500.0,<1H OCEAN
+-117.97,33.68,26.0,1616.0,292.0,700.0,241.0,5.5105,232100.0,<1H OCEAN
+-117.28,33.89,33.0,6982.0,1371.0,5650.0,1195.0,2.5379,152700.0,INLAND
+-122.01,37.92,16.0,2638.0,345.0,1055.0,334.0,8.1163,365800.0,NEAR BAY
+-120.91,38.98,13.0,7689.0,1415.0,3264.0,1198.0,3.653,146800.0,INLAND
+-121.21,37.8,33.0,1862.0,429.0,971.0,389.0,2.6053,99200.0,INLAND
+-117.97,33.63,25.0,2482.0,360.0,960.0,352.0,6.1572,344000.0,NEAR OCEAN
+-121.29,37.33,36.0,48.0,12.0,27.0,8.0,4.0,75000.0,INLAND
+-118.04,33.93,35.0,1805.0,387.0,1505.0,366.0,4.1667,151900.0,<1H OCEAN
+-117.12,33.61,2.0,2569.0,431.0,1232.0,388.0,4.3651,145600.0,<1H OCEAN
+-118.31,34.01,52.0,1793.0,350.0,1303.0,366.0,3.0759,123700.0,<1H OCEAN
+-117.94,33.86,33.0,1013.0,312.0,706.0,266.0,2.1432,197500.0,<1H OCEAN
+-118.21,33.96,48.0,284.0,104.0,422.0,119.0,1.2826,145500.0,<1H OCEAN
+-120.99,37.7,14.0,9849.0,1887.0,4356.0,1780.0,3.5877,160900.0,INLAND
+-118.21,34.06,52.0,470.0,115.0,434.0,123.0,2.095,109100.0,<1H OCEAN
+-117.09,32.63,33.0,620.0,161.0,420.0,164.0,1.8417,150000.0,NEAR OCEAN
+-124.0,40.22,16.0,2088.0,535.0,816.0,326.0,1.319,70700.0,<1H OCEAN
+-118.34,33.99,34.0,397.0,132.0,250.0,121.0,1.675,166700.0,<1H OCEAN
+-121.83,37.4,27.0,1145.0,150.0,492.0,160.0,5.716,348300.0,<1H OCEAN
+-121.94,37.24,35.0,1484.0,244.0,664.0,238.0,4.675,245300.0,<1H OCEAN
+-118.82,34.14,22.0,11668.0,1730.0,4054.0,1671.0,6.9935,385500.0,NEAR OCEAN
+-117.89,33.75,34.0,2753.0,654.0,3117.0,631.0,3.1713,170100.0,<1H OCEAN
+-122.54,37.96,44.0,1552.0,204.0,596.0,208.0,10.129,500001.0,NEAR BAY
+-118.32,33.8,29.0,4317.0,1037.0,2102.0,959.0,3.1275,286400.0,<1H OCEAN
+-122.01,37.37,25.0,2213.0,360.0,1066.0,390.0,7.2165,360900.0,<1H OCEAN
+-121.14,38.92,16.0,2069.0,312.0,889.0,299.0,4.6771,212000.0,INLAND
+-121.47,38.7,31.0,1007.0,181.0,563.0,185.0,3.625,91300.0,INLAND
+-119.29,34.3,24.0,7637.0,1705.0,4647.0,1623.0,3.5385,186800.0,NEAR OCEAN
+-121.31,37.99,15.0,3103.0,965.0,3061.0,861.0,1.3729,110300.0,INLAND
+-118.21,34.64,16.0,2573.0,427.0,1273.0,426.0,5.9508,181100.0,INLAND
+-118.27,34.68,19.0,552.0,129.0,314.0,106.0,3.2125,185400.0,INLAND
+-121.88,37.68,23.0,2234.0,270.0,854.0,286.0,7.333,337200.0,INLAND
+-119.98,38.93,25.0,1262.0,293.0,534.0,226.0,2.6607,90400.0,INLAND
+-119.84,36.75,34.0,1186.0,300.0,774.0,271.0,1.575,57100.0,INLAND
+-118.28,34.03,45.0,943.0,289.0,953.0,238.0,2.0673,151600.0,<1H OCEAN
+-121.98,37.97,26.0,2738.0,428.0,1316.0,430.0,5.2442,213200.0,INLAND
+-118.31,34.03,29.0,2438.0,867.0,2114.0,753.0,0.8351,150000.0,<1H OCEAN
+-117.88,33.9,15.0,1705.0,478.0,759.0,479.0,3.5333,114400.0,<1H OCEAN
+-122.1,37.68,31.0,1892.0,428.0,1162.0,389.0,3.125,167100.0,NEAR BAY
+-118.57,34.25,20.0,4679.0,609.0,1945.0,609.0,8.7471,419900.0,<1H OCEAN
+-121.84,38.02,46.0,66.0,22.0,37.0,21.0,0.536,87500.0,INLAND
+-119.11,35.42,52.0,154.0,,37.0,16.0,10.0263,200000.0,INLAND
+-122.43,37.79,52.0,3565.0,892.0,1377.0,852.0,3.8068,500001.0,NEAR BAY
+-118.22,33.89,36.0,873.0,240.0,1086.0,217.0,2.25,126600.0,<1H OCEAN
+-119.14,34.15,25.0,2202.0,390.0,1415.0,412.0,4.43,207700.0,NEAR OCEAN
+-119.77,36.75,44.0,1818.0,412.0,1680.0,418.0,1.7083,48300.0,INLAND
+-118.43,34.13,37.0,4400.0,695.0,1521.0,666.0,8.2954,500001.0,<1H OCEAN
+-118.51,34.2,34.0,2871.0,581.0,1350.0,535.0,3.7049,227500.0,<1H OCEAN
+-118.25,33.78,32.0,296.0,139.0,511.0,133.0,1.4444,182100.0,NEAR OCEAN
+-122.73,38.42,26.0,1446.0,296.0,884.0,295.0,4.3523,150000.0,<1H OCEAN
+-121.44,38.51,27.0,7212.0,1606.0,4828.0,1549.0,2.214,82400.0,INLAND
+-117.78,33.71,4.0,974.0,232.0,428.0,203.0,4.6141,195400.0,<1H OCEAN
+-118.08,33.91,30.0,3259.0,942.0,2744.0,895.0,2.8608,165600.0,<1H OCEAN
+-118.04,33.95,36.0,2722.0,515.0,1390.0,486.0,3.8214,178500.0,<1H OCEAN
+-122.03,37.27,32.0,4350.0,645.0,1551.0,609.0,7.8279,500001.0,<1H OCEAN
+-117.95,33.94,31.0,2237.0,431.0,1135.0,434.0,4.45,267900.0,<1H OCEAN
+-117.48,34.08,17.0,1834.0,390.0,1253.0,357.0,3.1028,106400.0,INLAND
+-117.35,34.09,14.0,5983.0,1224.0,3255.0,1150.0,2.5902,111500.0,INLAND
+-117.08,32.57,18.0,2203.0,544.0,1943.0,497.0,2.25,103200.0,NEAR OCEAN
+-118.41,33.85,16.0,6123.0,1989.0,2853.0,1789.0,4.425,336400.0,NEAR OCEAN
+-122.75,38.54,6.0,6719.0,1016.0,2699.0,997.0,5.4886,254200.0,<1H OCEAN
+-119.77,36.72,43.0,1763.0,389.0,1623.0,390.0,1.4427,47700.0,INLAND
+-122.3,38.27,4.0,1051.0,263.0,455.0,248.0,3.6389,130200.0,NEAR BAY
+-121.3,38.11,5.0,5979.0,1190.0,2679.0,1084.0,4.196,171700.0,INLAND
+-116.31,33.73,19.0,12467.0,2508.0,4086.0,1761.0,3.2846,131900.0,INLAND
+-121.96,37.1,20.0,922.0,155.0,361.0,135.0,6.3617,331500.0,<1H OCEAN
+-123.22,39.16,29.0,6121.0,1222.0,3595.0,1189.0,2.631,109600.0,<1H OCEAN
+-117.27,34.15,35.0,1490.0,253.0,705.0,253.0,3.3616,95300.0,INLAND
+-117.84,33.73,20.0,2572.0,732.0,1534.0,669.0,2.4211,175000.0,<1H OCEAN
+-121.25,38.68,13.0,503.0,70.0,267.0,77.0,6.1943,276100.0,INLAND
+-116.68,33.16,26.0,1820.0,374.0,1001.0,324.0,2.1797,156300.0,INLAND
+-122.01,37.39,16.0,3015.0,829.0,1769.0,807.0,4.0068,249500.0,<1H OCEAN
+-119.94,38.92,24.0,1258.0,216.0,235.0,96.0,4.6,136800.0,INLAND
+-118.08,34.1,32.0,2830.0,645.0,1500.0,527.0,3.0819,214600.0,<1H OCEAN
+-121.61,36.67,39.0,3260.0,821.0,3130.0,793.0,2.5224,119200.0,<1H OCEAN
+-118.34,34.05,39.0,975.0,292.0,723.0,285.0,2.2725,140600.0,<1H OCEAN
+-122.02,37.8,11.0,6200.0,907.0,2286.0,896.0,7.6518,359300.0,NEAR BAY
+-122.49,37.85,38.0,240.0,29.0,63.0,34.0,12.2547,500001.0,NEAR BAY
+-122.0,37.12,17.0,4413.0,672.0,1674.0,608.0,6.9772,383300.0,<1H OCEAN
+-121.99,37.36,33.0,2677.0,644.0,1469.0,633.0,3.2048,261800.0,<1H OCEAN
+-122.36,37.96,31.0,1157.0,276.0,956.0,232.0,1.5347,80400.0,NEAR BAY
+-118.33,34.05,45.0,1707.0,519.0,1446.0,466.0,2.1736,171300.0,<1H OCEAN
+-118.41,34.03,36.0,3053.0,635.0,1234.0,577.0,5.1637,500001.0,<1H OCEAN
+-119.32,36.21,25.0,2360.0,460.0,1424.0,436.0,2.3152,63100.0,INLAND
+-117.23,34.14,16.0,2577.0,521.0,956.0,472.0,2.5625,129400.0,INLAND
+-117.99,33.68,18.0,2024.0,462.0,1047.0,451.0,3.5848,186900.0,<1H OCEAN
+-117.74,34.1,29.0,2742.0,488.0,2477.0,532.0,3.5072,121900.0,INLAND
+-122.24,37.8,52.0,996.0,228.0,731.0,228.0,2.2697,127000.0,NEAR BAY
+-122.4,37.8,52.0,1642.0,570.0,1432.0,513.0,1.9063,300000.0,NEAR BAY
+-119.3,36.3,14.0,3023.0,469.0,1523.0,492.0,5.3602,118600.0,INLAND
+-122.43,37.73,52.0,1386.0,276.0,729.0,274.0,3.6694,275500.0,NEAR BAY
+-121.99,37.83,16.0,2939.0,380.0,1177.0,396.0,8.0839,372000.0,<1H OCEAN
+-118.37,33.97,26.0,6672.0,1729.0,3333.0,1557.0,2.9646,179800.0,<1H OCEAN
+-117.64,33.51,14.0,1343.0,175.0,650.0,184.0,7.2648,363200.0,<1H OCEAN
+-118.25,33.98,37.0,1503.0,392.0,1886.0,401.0,2.5637,125000.0,<1H OCEAN
+-121.9,37.35,42.0,2082.0,626.0,1396.0,610.0,3.25,185300.0,<1H OCEAN
+-121.32,38.67,10.0,3908.0,890.0,1898.0,798.0,2.8426,130600.0,INLAND
+-118.03,33.82,20.0,2662.0,464.0,1275.0,472.0,6.0162,318500.0,<1H OCEAN
+-118.32,33.94,36.0,1153.0,224.0,639.0,226.0,4.0,192000.0,<1H OCEAN
+-118.01,33.78,19.0,2648.0,478.0,1160.0,452.0,5.9357,207400.0,<1H OCEAN
+-117.16,32.75,34.0,1785.0,558.0,804.0,490.0,2.2687,200000.0,NEAR OCEAN
+-121.81,37.27,20.0,3244.0,520.0,1769.0,469.0,5.9214,224000.0,<1H OCEAN
+-118.34,33.98,47.0,2649.0,684.0,2374.0,607.0,2.3882,137700.0,<1H OCEAN
+-116.94,32.75,4.0,14934.0,2479.0,6945.0,2418.0,5.1221,229700.0,<1H OCEAN
+-117.08,32.78,21.0,2919.0,496.0,984.0,443.0,4.625,222800.0,NEAR OCEAN
+-117.15,32.9,12.0,1681.0,381.0,1050.0,362.0,4.2008,176100.0,<1H OCEAN
+-120.84,37.05,8.0,1944.0,283.0,814.0,276.0,5.3988,165500.0,INLAND
+-122.09,41.32,52.0,4019.0,824.0,1728.0,706.0,2.2462,62900.0,INLAND
+-118.43,34.16,34.0,2622.0,467.0,1233.0,476.0,4.0474,379700.0,<1H OCEAN
+-116.91,32.8,32.0,1943.0,287.0,1081.0,292.0,5.6846,208800.0,<1H OCEAN
+-122.4,37.73,50.0,1947.0,411.0,1170.0,384.0,3.4769,238700.0,NEAR BAY
+-122.02,37.87,14.0,3056.0,369.0,1209.0,377.0,8.4352,441400.0,NEAR BAY
+-117.27,33.03,16.0,2240.0,443.0,1104.0,416.0,3.5313,148700.0,NEAR OCEAN
+-122.36,37.96,30.0,950.0,317.0,1073.0,280.0,1.8664,107800.0,NEAR BAY
+-122.36,37.93,44.0,1891.0,449.0,1047.0,432.0,1.7727,86100.0,NEAR BAY
+-124.15,41.81,17.0,3276.0,628.0,3546.0,585.0,2.2868,103100.0,NEAR OCEAN
+-122.91,38.43,19.0,1968.0,350.0,852.0,308.0,4.6705,269800.0,<1H OCEAN
+-122.81,38.54,12.0,2289.0,611.0,919.0,540.0,1.1553,139300.0,<1H OCEAN
+-118.49,34.02,28.0,2545.0,752.0,1548.0,679.0,2.9125,475000.0,<1H OCEAN
+-121.73,36.5,27.0,3184.0,520.0,1121.0,493.0,5.6383,377000.0,<1H OCEAN
+-122.02,37.39,35.0,2297.0,497.0,1428.0,497.0,4.7431,239700.0,<1H OCEAN
+-117.76,34.71,15.0,2981.0,625.0,1694.0,540.0,2.9541,106700.0,INLAND
+-118.22,33.83,43.0,1426.0,272.0,871.0,276.0,3.7083,175200.0,<1H OCEAN
+-122.49,37.75,43.0,2044.0,393.0,979.0,378.0,3.9205,319100.0,NEAR OCEAN
+-122.43,37.74,52.0,3328.0,653.0,1260.0,614.0,4.7437,331000.0,NEAR BAY
+-121.81,37.25,25.0,4096.0,623.0,2128.0,618.0,6.2957,251800.0,<1H OCEAN
+-118.25,34.0,36.0,1176.0,309.0,1267.0,292.0,1.6382,105000.0,<1H OCEAN
+-117.43,33.55,8.0,446.0,62.0,188.0,68.0,9.4356,465600.0,<1H OCEAN
+-119.83,36.8,16.0,6101.0,1200.0,3407.0,1134.0,3.125,80800.0,INLAND
+-118.34,34.04,40.0,2064.0,662.0,2140.0,617.0,2.2254,127100.0,<1H OCEAN
+-118.32,34.1,30.0,2193.0,965.0,2197.0,836.0,1.8277,137500.0,<1H OCEAN
+-117.15,32.75,9.0,2818.0,821.0,851.0,555.0,2.6181,204200.0,NEAR OCEAN
+-118.39,33.89,38.0,1851.0,332.0,750.0,314.0,7.3356,422700.0,<1H OCEAN
+-117.72,34.1,46.0,2477.0,458.0,1034.0,455.0,5.5,289700.0,INLAND
+-122.59,38.43,20.0,2791.0,546.0,785.0,512.0,3.4561,216700.0,<1H OCEAN
+-118.46,34.02,45.0,3803.0,970.0,1690.0,871.0,3.0476,456200.0,<1H OCEAN
+-122.3,37.53,37.0,1338.0,215.0,535.0,221.0,5.4351,376600.0,NEAR OCEAN
+-118.32,33.84,42.0,1486.0,420.0,897.0,377.0,1.6228,376100.0,<1H OCEAN
+-119.27,34.45,15.0,1659.0,274.0,679.0,253.0,5.0,357900.0,<1H OCEAN
+-118.15,33.8,44.0,1886.0,399.0,1167.0,372.0,3.1042,219800.0,NEAR OCEAN
+-121.22,38.04,42.0,343.0,50.0,116.0,49.0,5.5376,212500.0,INLAND
+-121.52,38.51,23.0,6876.0,1456.0,2942.0,1386.0,3.0963,156900.0,INLAND
+-121.8,36.69,12.0,3877.0,914.0,2274.0,858.0,3.4239,194800.0,<1H OCEAN
+-118.46,34.23,19.0,9902.0,2814.0,7307.0,2660.0,2.585,145400.0,<1H OCEAN
+-117.9,33.74,24.0,2932.0,955.0,5516.0,911.0,2.7535,111000.0,<1H OCEAN
+-121.79,37.23,16.0,2240.0,300.0,1221.0,305.0,6.0198,289600.0,<1H OCEAN
+-122.24,37.47,41.0,1183.0,203.0,455.0,171.0,5.1071,314100.0,NEAR OCEAN
+-118.13,34.7,34.0,1943.0,500.0,1078.0,446.0,1.1296,93800.0,INLAND
+-117.03,33.13,15.0,7000.0,1185.0,3555.0,1118.0,4.7022,172800.0,<1H OCEAN
+-117.08,32.73,36.0,3331.0,643.0,1903.0,622.0,3.6974,122000.0,NEAR OCEAN
+-118.34,34.18,45.0,3566.0,701.0,1601.0,653.0,3.8668,232000.0,<1H OCEAN
+-122.5,37.87,17.0,4333.0,947.0,1650.0,919.0,6.3066,346100.0,NEAR BAY
+-117.07,32.63,37.0,2303.0,379.0,1026.0,357.0,3.455,156900.0,NEAR OCEAN
+-122.0,37.99,28.0,4035.0,641.0,1881.0,659.0,5.4607,192300.0,INLAND
+-121.23,38.71,18.0,4947.0,714.0,2227.0,675.0,4.8542,170500.0,INLAND
+-122.04,37.67,18.0,3000.0,419.0,1155.0,415.0,6.8233,332600.0,NEAR BAY
+-120.43,34.96,24.0,2739.0,414.0,1171.0,413.0,4.8155,162900.0,<1H OCEAN
+-117.78,34.07,18.0,3610.0,772.0,2899.0,765.0,3.9784,113500.0,INLAND
+-121.82,38.46,10.0,6331.0,1181.0,3419.0,1110.0,3.7083,154800.0,INLAND
+-122.26,37.88,52.0,2551.0,417.0,894.0,404.0,6.2425,391800.0,NEAR BAY
+-121.2,37.8,37.0,311.0,61.0,171.0,54.0,4.0972,101800.0,INLAND
+-118.54,34.17,34.0,2458.0,433.0,1034.0,373.0,5.6738,443600.0,<1H OCEAN
+-117.99,33.79,33.0,2064.0,324.0,1384.0,315.0,4.5263,169000.0,<1H OCEAN
+-118.33,33.86,38.0,914.0,176.0,519.0,174.0,6.0335,255400.0,<1H OCEAN
+-117.02,32.66,19.0,771.0,,376.0,108.0,6.6272,273600.0,NEAR OCEAN
+-122.7,38.36,11.0,5817.0,878.0,2538.0,876.0,4.221,227100.0,<1H OCEAN
+-117.92,33.88,32.0,1632.0,244.0,575.0,235.0,5.3986,318700.0,<1H OCEAN
+-119.2,34.25,18.0,3208.0,643.0,1973.0,614.0,3.8162,235000.0,NEAR OCEAN
+-118.1,33.74,32.0,2035.0,,934.0,512.0,4.2287,500001.0,NEAR OCEAN
+-117.87,33.86,28.0,2292.0,531.0,2197.0,509.0,3.4856,142800.0,<1H OCEAN
+-124.16,40.8,52.0,2167.0,480.0,908.0,451.0,1.6111,74700.0,NEAR OCEAN
+-121.05,37.62,37.0,1043.0,196.0,555.0,197.0,3.4125,125000.0,INLAND
+-122.04,37.67,29.0,1694.0,251.0,690.0,242.0,6.0501,254200.0,NEAR BAY
+-122.27,37.9,52.0,2079.0,273.0,684.0,275.0,7.9556,374400.0,NEAR BAY
+-121.96,37.84,29.0,7479.0,977.0,2744.0,943.0,7.5139,398200.0,INLAND
+-117.77,33.54,28.0,3404.0,497.0,1134.0,466.0,7.2217,500001.0,<1H OCEAN
+-119.82,36.83,14.0,2982.0,412.0,1408.0,423.0,5.3241,123000.0,INLAND
+-121.43,37.74,52.0,876.0,170.0,426.0,179.0,3.0865,119800.0,INLAND
+-120.37,36.13,10.0,2522.0,533.0,1335.0,493.0,3.2639,86400.0,INLAND
+-117.88,33.64,16.0,3615.0,570.0,1209.0,559.0,8.5574,392200.0,<1H OCEAN
+-117.96,33.68,26.0,1374.0,234.0,731.0,244.0,6.0905,224800.0,<1H OCEAN
+-120.0,38.92,26.0,529.0,116.0,191.0,83.0,3.5,103600.0,INLAND
+-117.64,33.64,11.0,2422.0,429.0,810.0,395.0,6.1935,293200.0,<1H OCEAN
+-118.0,33.76,29.0,1982.0,503.0,1426.0,502.0,3.0263,194200.0,<1H OCEAN
+-118.31,34.06,31.0,2827.0,1084.0,3107.0,993.0,2.0278,360000.0,<1H OCEAN
+-122.07,37.71,36.0,2879.0,480.0,1235.0,455.0,4.9801,241500.0,NEAR BAY
+-117.1,32.64,29.0,1578.0,460.0,1236.0,461.0,2.5658,134700.0,NEAR OCEAN
+-118.12,33.99,27.0,2316.0,559.0,2012.0,544.0,2.8155,176800.0,<1H OCEAN
+-122.03,37.37,30.0,1269.0,290.0,556.0,266.0,3.8125,325000.0,<1H OCEAN
+-118.29,33.94,38.0,2407.0,630.0,1774.0,562.0,1.5615,108600.0,<1H OCEAN
+-117.27,34.16,32.0,2894.0,427.0,1151.0,446.0,6.2236,159700.0,INLAND
+-118.3,33.97,50.0,2270.0,451.0,1000.0,412.0,2.1221,119400.0,<1H OCEAN
+-118.44,34.21,41.0,1440.0,325.0,1014.0,322.0,2.875,168600.0,<1H OCEAN
+-120.98,37.71,22.0,434.0,89.0,195.0,86.0,2.4211,268800.0,INLAND
+-122.25,40.6,16.0,2753.0,494.0,1414.0,459.0,3.8323,128300.0,INLAND
+-118.44,34.25,35.0,1583.0,324.0,1481.0,351.0,3.7,176000.0,<1H OCEAN
+-122.23,37.82,52.0,3242.0,366.0,1001.0,352.0,12.2138,500001.0,NEAR BAY
+-118.44,34.29,32.0,1260.0,382.0,1434.0,342.0,2.0286,122900.0,<1H OCEAN
+-117.73,33.61,16.0,590.0,130.0,178.0,121.0,4.8611,186800.0,<1H OCEAN
+-119.85,36.8,14.0,4177.0,914.0,2300.0,867.0,2.9565,73000.0,INLAND
+-117.17,34.51,15.0,5151.0,942.0,2896.0,897.0,3.4875,90800.0,INLAND
+-117.92,33.63,39.0,1469.0,226.0,553.0,225.0,7.8496,490800.0,<1H OCEAN
+-117.88,33.74,16.0,1444.0,446.0,2329.0,441.0,3.1691,159400.0,<1H OCEAN
+-117.95,33.92,32.0,1661.0,312.0,1201.0,302.0,4.0,178200.0,<1H OCEAN
+-119.55,36.51,46.0,55.0,11.0,26.0,5.0,4.125,67500.0,INLAND
+-120.92,37.6,12.0,4485.0,805.0,2445.0,832.0,3.7611,123100.0,INLAND
+-122.28,37.88,52.0,1193.0,200.0,506.0,207.0,4.1912,254500.0,NEAR BAY
+-116.01,33.41,20.0,1996.0,515.0,659.0,295.0,2.8684,62800.0,INLAND
+-121.78,37.69,35.0,2853.0,588.0,1761.0,572.0,4.3533,168400.0,INLAND
+-118.44,34.28,32.0,527.0,146.0,582.0,143.0,1.7708,138800.0,<1H OCEAN
+-119.53,36.55,34.0,2065.0,343.0,1041.0,313.0,3.2917,111500.0,INLAND
+-121.91,36.61,30.0,2755.0,597.0,1519.0,554.0,3.2952,234600.0,NEAR OCEAN
+-118.26,33.95,44.0,1513.0,369.0,1088.0,344.0,1.2969,94400.0,<1H OCEAN
+-117.85,33.84,26.0,2095.0,280.0,793.0,261.0,6.6719,271700.0,<1H OCEAN
+-117.88,33.66,26.0,6017.0,1244.0,2673.0,1135.0,3.5426,295400.0,<1H OCEAN
+-120.03,38.19,26.0,7005.0,1358.0,416.0,189.0,2.125,132500.0,INLAND
+-118.06,34.13,40.0,4307.0,918.0,1769.0,845.0,3.6341,391500.0,INLAND
+-118.32,34.08,52.0,1137.0,304.0,754.0,297.0,3.37,330300.0,<1H OCEAN
+-116.71,33.94,12.0,549.0,109.0,209.0,90.0,3.0208,66300.0,INLAND
+-117.12,32.58,34.0,2003.0,466.0,1226.0,443.0,3.0613,136700.0,NEAR OCEAN
+-118.04,33.98,28.0,1617.0,507.0,1158.0,486.0,1.9688,165600.0,<1H OCEAN
+-122.5,37.78,50.0,1922.0,427.0,1049.0,443.0,3.5833,348500.0,NEAR BAY
+-121.18,38.73,16.0,1584.0,264.0,613.0,226.0,6.0302,273100.0,INLAND
+-120.56,38.39,20.0,1326.0,307.0,563.0,237.0,2.6667,86600.0,INLAND
+-118.21,34.02,43.0,1811.0,513.0,2123.0,487.0,1.3615,133300.0,<1H OCEAN
+-117.78,33.81,23.0,1986.0,278.0,826.0,260.0,7.7752,380000.0,<1H OCEAN
+-122.02,36.97,29.0,2568.0,747.0,1743.0,659.0,1.9286,195300.0,NEAR OCEAN
+-121.42,36.86,41.0,440.0,106.0,389.0,94.0,2.6818,225000.0,INLAND
+-118.21,34.07,47.0,1346.0,383.0,1452.0,371.0,1.7292,191700.0,<1H OCEAN
+-118.0,33.82,24.0,3002.0,644.0,1495.0,634.0,3.1087,202800.0,<1H OCEAN
+-115.55,32.8,23.0,666.0,142.0,580.0,160.0,2.1136,61000.0,INLAND
+-118.49,34.22,30.0,1756.0,314.0,899.0,288.0,5.0325,238200.0,<1H OCEAN
+-121.96,37.35,37.0,1755.0,325.0,699.0,321.0,3.925,251300.0,<1H OCEAN
+-122.5,37.77,52.0,2739.0,569.0,1312.0,531.0,3.5833,322900.0,NEAR BAY
+-121.89,37.69,4.0,6159.0,1510.0,2649.0,1241.0,3.62,139300.0,<1H OCEAN
+-119.76,36.75,35.0,1607.0,383.0,1407.0,382.0,2.19,53400.0,INLAND
+-119.73,36.72,15.0,2246.0,456.0,1190.0,403.0,2.0294,70400.0,INLAND
+-122.34,37.97,19.0,2237.0,580.0,1438.0,551.0,2.3382,120700.0,NEAR BAY
+-122.27,41.23,40.0,1958.0,386.0,725.0,331.0,2.1898,65500.0,INLAND
+-117.93,33.77,36.0,3157.0,582.0,1842.0,561.0,4.5833,190700.0,<1H OCEAN
+-117.21,34.08,5.0,5749.0,1385.0,2382.0,1088.0,3.0587,143100.0,INLAND
+-120.85,37.77,10.0,423.0,110.0,295.0,94.0,1.3583,85200.0,INLAND
+-119.09,36.42,15.0,1517.0,361.0,1275.0,343.0,1.5875,55800.0,INLAND
+-118.72,34.28,17.0,3051.0,,1705.0,495.0,5.7376,218600.0,<1H OCEAN
+-119.5,36.62,34.0,1440.0,267.0,1018.0,265.0,2.2206,63400.0,INLAND
+-122.29,37.41,30.0,6373.0,854.0,2149.0,798.0,10.6868,500001.0,NEAR OCEAN
+-122.14,37.72,45.0,1397.0,253.0,555.0,248.0,2.983,202700.0,NEAR BAY
+-118.18,34.17,43.0,4269.0,591.0,1467.0,582.0,9.0702,500001.0,<1H OCEAN
+-121.91,36.62,40.0,1292.0,271.0,504.0,230.0,2.475,258300.0,<1H OCEAN
+-119.77,36.76,43.0,1623.0,294.0,781.0,272.0,1.869,56000.0,INLAND
+-118.93,35.44,13.0,1439.0,237.0,557.0,227.0,6.1563,204200.0,INLAND
+-121.92,36.63,40.0,1076.0,193.0,406.0,180.0,3.4943,311100.0,<1H OCEAN
+-118.2,34.21,42.0,1493.0,237.0,665.0,224.0,6.7571,443900.0,<1H OCEAN
+-119.76,36.83,22.0,2803.0,438.0,1234.0,457.0,4.5179,99600.0,INLAND
+-122.25,37.79,50.0,629.0,188.0,742.0,196.0,2.6458,125000.0,NEAR BAY
+-118.15,34.07,44.0,1626.0,383.0,1063.0,334.0,2.4348,220700.0,<1H OCEAN
+-121.38,38.4,15.0,4155.0,637.0,1722.0,616.0,4.8831,154400.0,INLAND
+-121.94,37.73,22.0,1980.0,291.0,861.0,290.0,6.2726,258200.0,<1H OCEAN
+-118.12,34.14,52.0,2337.0,352.0,981.0,328.0,5.8692,490400.0,<1H OCEAN
+-120.4,38.0,17.0,2098.0,370.0,912.0,354.0,2.6544,112600.0,INLAND
+-121.05,37.64,33.0,1438.0,237.0,569.0,208.0,3.3516,150000.0,INLAND
+-121.96,37.71,6.0,8072.0,1050.0,3386.0,1062.0,7.2494,336500.0,<1H OCEAN
+-117.88,33.84,25.0,1781.0,349.0,918.0,378.0,3.9286,262700.0,<1H OCEAN
+-117.95,33.99,15.0,3978.0,692.0,2418.0,665.0,5.0142,269900.0,<1H OCEAN
+-118.98,35.4,36.0,1864.0,331.0,1052.0,325.0,3.4205,76600.0,INLAND
+-117.15,32.92,16.0,1972.0,402.0,1377.0,413.0,4.4615,168300.0,<1H OCEAN
+-117.04,32.98,16.0,1332.0,196.0,640.0,193.0,6.0226,192900.0,<1H OCEAN
+-121.29,37.93,24.0,1438.0,351.0,1294.0,342.0,2.7829,61800.0,INLAND
+-117.02,32.7,22.0,2756.0,516.0,1849.0,486.0,4.1837,125400.0,NEAR OCEAN
+-117.94,33.91,18.0,8836.0,1527.0,3946.0,1451.0,5.6441,313000.0,<1H OCEAN
+-117.07,32.71,26.0,4151.0,823.0,2822.0,697.0,2.8372,123400.0,NEAR OCEAN
+-117.27,32.83,35.0,1420.0,193.0,469.0,177.0,8.0639,500001.0,NEAR OCEAN
+-122.5,37.74,42.0,1667.0,395.0,1041.0,387.0,3.9583,273700.0,NEAR OCEAN
+-119.86,34.42,23.0,1450.0,642.0,1258.0,607.0,1.179,225000.0,NEAR OCEAN
+-117.79,33.69,15.0,1875.0,316.0,890.0,316.0,6.5783,244800.0,<1H OCEAN
+-118.19,33.92,36.0,1356.0,314.0,1469.0,300.0,2.0785,139800.0,<1H OCEAN
+-118.23,34.09,41.0,438.0,201.0,690.0,161.0,2.0476,181300.0,<1H OCEAN
+-122.19,37.8,46.0,1938.0,341.0,768.0,332.0,4.2727,246900.0,NEAR BAY
+-118.24,34.23,42.0,1541.0,280.0,753.0,264.0,5.1028,292100.0,<1H OCEAN
+-118.0,33.85,34.0,1078.0,205.0,575.0,206.0,4.5083,188000.0,<1H OCEAN
+-117.64,34.09,34.0,2839.0,659.0,1822.0,631.0,3.05,121300.0,INLAND
+-121.7,37.93,10.0,3258.0,612.0,1779.0,558.0,4.6587,152500.0,INLAND
+-117.19,33.69,3.0,6484.0,1037.0,3295.0,1074.0,4.5881,136400.0,<1H OCEAN
+-117.68,34.03,16.0,2859.0,668.0,1946.0,591.0,3.0396,124300.0,INLAND
+-118.19,34.14,47.0,2525.0,523.0,1514.0,498.0,4.3359,209200.0,<1H OCEAN
+-117.26,32.96,36.0,1721.0,264.0,710.0,282.0,10.1768,500001.0,NEAR OCEAN
+-118.32,34.16,46.0,2345.0,453.0,1031.0,427.0,4.3173,278300.0,<1H OCEAN
+-118.23,33.95,43.0,1683.0,520.0,2190.0,494.0,2.2391,152800.0,<1H OCEAN
+-121.83,37.02,22.0,1903.0,350.0,760.0,322.0,2.9559,288400.0,<1H OCEAN
+-119.25,34.27,35.0,2532.0,407.0,1338.0,422.0,4.7727,219000.0,NEAR OCEAN
+-117.79,33.73,3.0,8240.0,1410.0,3318.0,1270.0,7.2074,291300.0,<1H OCEAN
+-117.54,34.12,4.0,17577.0,2819.0,7766.0,2473.0,5.1333,181800.0,INLAND
+-116.87,33.57,12.0,1153.0,265.0,446.0,195.0,3.038,128100.0,INLAND
+-121.99,37.35,18.0,1712.0,509.0,972.0,467.0,4.3971,238900.0,<1H OCEAN
+-122.47,37.73,52.0,2151.0,280.0,762.0,274.0,10.7309,500001.0,NEAR OCEAN
+-118.29,34.08,34.0,479.0,182.0,557.0,170.0,1.525,210000.0,<1H OCEAN
+-117.99,33.66,14.0,3155.0,653.0,951.0,575.0,3.0625,268800.0,<1H OCEAN
+-121.3,38.13,23.0,2864.0,504.0,1298.0,499.0,3.2303,131800.0,INLAND
+-117.4,33.9,32.0,1263.0,178.0,508.0,180.0,3.6667,314100.0,INLAND
+-118.33,33.69,41.0,2168.0,357.0,1171.0,374.0,4.7216,311900.0,NEAR OCEAN
+-121.77,36.53,18.0,2321.0,358.0,803.0,341.0,10.1854,426000.0,<1H OCEAN
+-122.25,38.1,52.0,1591.0,372.0,817.0,357.0,2.1411,97200.0,NEAR BAY
+-122.33,38.29,14.0,3541.0,499.0,1577.0,459.0,5.3351,269900.0,NEAR BAY
+-117.6,33.98,26.0,1225.0,199.0,717.0,204.0,2.7284,225000.0,INLAND
+-118.45,34.01,37.0,1328.0,250.0,626.0,228.0,5.8666,440100.0,<1H OCEAN
+-121.44,37.74,33.0,1875.0,363.0,970.0,381.0,3.5096,141700.0,INLAND
+-117.86,34.13,29.0,630.0,145.0,378.0,148.0,3.4107,170800.0,INLAND
+-121.09,39.23,35.0,2637.0,511.0,1181.0,480.0,2.7813,109200.0,INLAND
+-118.29,34.11,30.0,2774.0,570.0,1076.0,580.0,5.296,500001.0,<1H OCEAN
+-122.94,39.1,18.0,681.0,120.0,272.0,105.0,2.8906,140600.0,INLAND
+-117.9,33.63,32.0,3556.0,521.0,1381.0,537.0,6.1426,450700.0,<1H OCEAN
+-121.98,37.23,33.0,3585.0,935.0,1511.0,835.0,3.1176,396300.0,<1H OCEAN
+-118.18,33.82,43.0,284.0,65.0,167.0,68.0,4.25,207500.0,NEAR OCEAN
+-122.25,37.74,25.0,1914.0,365.0,897.0,390.0,4.4562,206200.0,NEAR BAY
+-117.97,34.17,35.0,5005.0,848.0,2112.0,813.0,4.9968,295000.0,INLAND
+-118.4,33.86,18.0,5152.0,1365.0,2286.0,1243.0,5.1677,380800.0,<1H OCEAN
+-121.9,38.02,12.0,1497.0,360.0,943.0,341.0,2.1417,122200.0,INLAND
+-118.26,34.08,52.0,984.0,276.0,994.0,260.0,2.3816,166700.0,<1H OCEAN
+-117.79,33.7,16.0,1416.0,249.0,636.0,244.0,5.1741,227700.0,<1H OCEAN
+-118.12,33.95,35.0,1604.0,280.0,802.0,280.0,5.752,291000.0,<1H OCEAN
+-121.68,39.29,29.0,1860.0,400.0,1137.0,365.0,1.5281,61600.0,INLAND
+-117.33,33.21,17.0,1246.0,300.0,424.0,288.0,2.2882,85800.0,NEAR OCEAN
+-118.13,34.19,43.0,1621.0,365.0,1015.0,329.0,2.92,242200.0,INLAND
+-118.04,33.95,36.0,1976.0,368.0,1236.0,355.0,4.615,174000.0,<1H OCEAN
+-117.95,33.84,34.0,1229.0,215.0,1035.0,218.0,3.5455,180000.0,<1H OCEAN
+-116.87,32.75,15.0,2053.0,321.0,993.0,309.0,5.5164,248900.0,<1H OCEAN
+-119.02,36.06,41.0,2279.0,538.0,1908.0,511.0,1.3952,43100.0,INLAND
+-118.21,33.8,44.0,1387.0,280.0,984.0,302.0,4.25,143100.0,NEAR OCEAN
+-115.98,33.32,8.0,240.0,46.0,63.0,24.0,1.4688,53800.0,INLAND
+-121.86,37.32,13.0,2519.0,581.0,2094.0,530.0,4.3621,173400.0,<1H OCEAN
+-122.71,38.42,23.0,1569.0,414.0,1031.0,368.0,1.6267,129200.0,<1H OCEAN
+-119.26,36.61,33.0,560.0,90.0,310.0,113.0,2.5417,118800.0,INLAND
+-121.88,37.35,52.0,1704.0,418.0,1336.0,411.0,2.8167,183500.0,<1H OCEAN
+-117.9,33.82,32.0,1187.0,302.0,1003.0,275.0,2.4931,166900.0,<1H OCEAN
+-120.61,35.1,14.0,2919.0,691.0,1896.0,577.0,2.4003,142100.0,<1H OCEAN
+-120.99,37.66,46.0,1750.0,347.0,754.0,356.0,2.9137,106000.0,INLAND
+-124.14,40.6,27.0,1148.0,206.0,521.0,219.0,4.025,128100.0,NEAR OCEAN
+-118.13,34.44,10.0,2726.0,465.0,1773.0,459.0,4.8295,319100.0,INLAND
+-124.14,40.67,23.0,580.0,117.0,320.0,109.0,4.2054,130600.0,NEAR OCEAN
+-122.31,37.53,39.0,1160.0,191.0,508.0,185.0,5.9539,379100.0,NEAR OCEAN
+-121.93,37.27,30.0,2862.0,544.0,1387.0,542.0,5.1104,278100.0,<1H OCEAN
+-117.96,34.04,35.0,1141.0,212.0,924.0,212.0,3.1591,148300.0,<1H OCEAN
+-121.4,38.53,38.0,152.0,30.0,65.0,35.0,0.9274,67500.0,INLAND
+-117.86,33.73,38.0,2284.0,511.0,2451.0,504.0,3.3125,159100.0,<1H OCEAN
+-122.41,37.75,52.0,1057.0,276.0,837.0,292.0,2.4531,229000.0,NEAR BAY
+-117.2,33.58,2.0,30450.0,5033.0,9419.0,3197.0,4.5936,174300.0,<1H OCEAN
+-119.09,36.42,17.0,877.0,219.0,966.0,218.0,2.0,52500.0,INLAND
+-117.9,34.07,36.0,1009.0,164.0,466.0,149.0,5.8519,249400.0,<1H OCEAN
+-122.7,38.44,45.0,883.0,202.0,401.0,194.0,3.2845,178300.0,<1H OCEAN
+-118.02,33.84,35.0,3473.0,563.0,2091.0,580.0,4.4821,214100.0,<1H OCEAN
+-122.78,38.44,14.0,4143.0,656.0,1569.0,629.0,3.9766,345300.0,<1H OCEAN
+-117.89,33.91,33.0,1264.0,224.0,527.0,227.0,3.7321,216500.0,<1H OCEAN
+-119.82,36.74,52.0,610.0,128.0,406.0,122.0,1.8967,43800.0,INLAND
+-119.45,36.58,18.0,1425.0,280.0,753.0,266.0,3.7813,87300.0,INLAND
+-117.07,32.72,18.0,1758.0,286.0,987.0,277.0,4.6875,141800.0,NEAR OCEAN
+-121.97,37.87,4.0,1029.0,126.0,416.0,122.0,13.4883,500001.0,INLAND
+-121.98,37.31,28.0,3840.0,629.0,1883.0,662.0,6.4095,335900.0,<1H OCEAN
+-122.44,37.78,52.0,2747.0,736.0,1309.0,653.0,2.943,341700.0,NEAR BAY
+-122.68,38.46,15.0,1811.0,406.0,718.0,403.0,2.3929,141300.0,<1H OCEAN
+-119.78,36.79,33.0,2260.0,440.0,966.0,413.0,2.9301,68300.0,INLAND
+-121.24,38.64,13.0,4491.0,689.0,1657.0,667.0,5.259,249400.0,INLAND
+-118.04,33.72,15.0,1836.0,490.0,942.0,477.0,4.0238,182500.0,<1H OCEAN
+-122.28,37.54,37.0,991.0,180.0,463.0,177.0,5.1701,294200.0,NEAR OCEAN
+-119.79,36.55,32.0,1393.0,276.0,999.0,245.0,2.0216,76800.0,INLAND
+-122.09,37.09,46.0,695.0,136.0,408.0,148.0,3.9408,222600.0,NEAR OCEAN
+-119.18,34.16,30.0,2053.0,368.0,1496.0,391.0,3.6546,186200.0,NEAR OCEAN
+-118.01,34.13,36.0,1332.0,217.0,648.0,203.0,4.7159,365900.0,INLAND
+-122.17,39.31,35.0,2791.0,552.0,1395.0,476.0,2.5625,62700.0,INLAND
+-118.37,34.23,32.0,1444.0,317.0,1177.0,311.0,3.6,164600.0,<1H OCEAN
+-117.06,32.57,25.0,1268.0,282.0,991.0,299.0,3.0284,123600.0,NEAR OCEAN
+-117.11,32.69,36.0,1421.0,367.0,1418.0,355.0,1.9425,93400.0,NEAR OCEAN
+-122.31,37.92,38.0,1250.0,236.0,631.0,279.0,3.724,220100.0,NEAR BAY
+-122.3,37.97,34.0,2854.0,528.0,1211.0,452.0,3.5353,164700.0,NEAR BAY
+-118.15,34.2,52.0,1786.0,306.0,1018.0,322.0,4.1518,182100.0,INLAND
+-121.98,37.28,27.0,3526.0,589.0,1725.0,553.0,5.7812,275000.0,<1H OCEAN
+-118.41,33.9,39.0,2311.0,404.0,1044.0,380.0,8.468,472100.0,<1H OCEAN
+-118.32,34.11,42.0,2462.0,543.0,857.0,482.0,4.0833,434400.0,<1H OCEAN
+-119.65,36.33,47.0,1059.0,268.0,693.0,241.0,1.3882,53800.0,INLAND
+-117.38,34.11,32.0,3179.0,662.0,1878.0,661.0,3.1375,101200.0,INLAND
+-120.45,34.71,21.0,1868.0,268.0,522.0,255.0,6.4678,249300.0,NEAR OCEAN
+-118.4,34.01,44.0,1494.0,262.0,618.0,266.0,5.4035,356300.0,<1H OCEAN
+-122.43,37.8,52.0,1380.0,322.0,553.0,288.0,4.0417,500001.0,NEAR BAY
+-120.83,37.58,30.0,1527.0,256.0,757.0,240.0,3.6629,171400.0,INLAND
+-118.18,33.97,30.0,2887.0,866.0,2806.0,830.0,2.2122,169400.0,<1H OCEAN
+-118.08,34.09,33.0,2557.0,578.0,1715.0,530.0,2.9196,208800.0,<1H OCEAN
+-120.14,36.0,33.0,1726.0,420.0,1371.0,388.0,2.0335,43900.0,INLAND
+-122.41,37.59,34.0,3931.0,622.0,1717.0,621.0,6.2946,450000.0,NEAR OCEAN
+-118.3,34.06,23.0,2512.0,1203.0,3720.0,1118.0,1.7896,322200.0,<1H OCEAN
+-124.09,40.88,31.0,1982.0,495.0,1052.0,467.0,1.5326,74100.0,NEAR OCEAN
+-118.3,33.87,31.0,1398.0,261.0,823.0,263.0,5.0641,234900.0,<1H OCEAN
+-118.14,33.9,39.0,1379.0,282.0,883.0,291.0,3.3375,180100.0,<1H OCEAN
+-121.81,37.28,17.0,2277.0,428.0,1887.0,422.0,5.7078,217000.0,<1H OCEAN
+-122.48,37.74,52.0,2285.0,435.0,1211.0,442.0,4.0208,323100.0,NEAR OCEAN
+-117.72,33.62,19.0,1144.0,268.0,365.0,279.0,2.8583,105800.0,<1H OCEAN
+-117.69,33.63,23.0,1444.0,260.0,792.0,253.0,4.9079,273800.0,<1H OCEAN
+-118.22,33.84,38.0,1928.0,429.0,1358.0,399.0,4.0687,160300.0,<1H OCEAN
+-120.59,38.45,13.0,2944.0,558.0,1091.0,466.0,3.0,119000.0,INLAND
+-121.62,39.76,14.0,2063.0,559.0,934.0,529.0,1.7788,85800.0,INLAND
+-122.41,40.58,35.0,2072.0,385.0,1029.0,375.0,2.8512,75600.0,INLAND
+-117.23,32.81,24.0,3271.0,508.0,1496.0,482.0,5.9359,422200.0,NEAR OCEAN
+-122.41,37.79,52.0,2302.0,938.0,1515.0,861.0,1.3668,55000.0,NEAR BAY
+-118.33,34.19,46.0,2115.0,463.0,1133.0,439.0,3.7344,222000.0,<1H OCEAN
+-119.73,36.75,39.0,1745.0,321.0,901.0,303.0,3.1719,67900.0,INLAND
+-118.34,34.07,52.0,3175.0,1057.0,1594.0,997.0,3.1766,225000.0,<1H OCEAN
+-118.31,33.9,38.0,1400.0,399.0,1131.0,405.0,3.5417,198400.0,<1H OCEAN
+-122.7,38.35,14.0,2313.0,,954.0,397.0,3.7813,146500.0,<1H OCEAN
+-117.3,34.05,7.0,4672.0,1121.0,2534.0,1046.0,3.4228,115700.0,INLAND
+-120.65,35.41,15.0,6725.0,1111.0,3139.0,1029.0,4.1875,261600.0,<1H OCEAN
+-117.27,33.77,16.0,2876.0,576.0,1859.0,545.0,2.0878,101300.0,<1H OCEAN
+-117.97,33.68,26.0,3653.0,568.0,1930.0,585.0,5.7301,260900.0,<1H OCEAN
+-117.9,34.12,33.0,1555.0,361.0,1571.0,386.0,4.0529,138200.0,<1H OCEAN
+-121.87,37.27,18.0,3561.0,560.0,1753.0,553.0,5.0292,269400.0,<1H OCEAN
+-121.51,38.53,34.0,1613.0,265.0,631.0,266.0,4.25,191900.0,INLAND
+-122.47,37.69,27.0,2447.0,720.0,2104.0,657.0,3.449,239100.0,NEAR OCEAN
+-120.97,37.68,9.0,1114.0,172.0,529.0,174.0,4.7159,163700.0,INLAND
+-118.34,34.17,49.0,3033.0,580.0,1284.0,561.0,4.1161,232500.0,<1H OCEAN
+-119.52,34.41,20.0,4489.0,800.0,2867.0,765.0,4.806,279700.0,<1H OCEAN
+-122.19,37.48,38.0,1300.0,269.0,608.0,292.0,4.5568,286900.0,NEAR BAY
+-118.2,34.04,47.0,1894.0,408.0,1629.0,379.0,3.7619,127600.0,<1H OCEAN
+-124.14,40.79,38.0,1552.0,290.0,873.0,291.0,2.4896,81000.0,NEAR OCEAN
+-118.52,34.21,34.0,1663.0,299.0,762.0,282.0,5.1265,211000.0,<1H OCEAN
+-119.79,36.81,35.0,1877.0,328.0,1155.0,353.0,3.069,69600.0,INLAND
+-119.27,34.27,52.0,459.0,112.0,276.0,107.0,2.375,198400.0,NEAR OCEAN
+-117.64,33.65,4.0,6842.0,1512.0,3256.0,1439.0,5.4132,216600.0,<1H OCEAN
+-117.23,33.16,4.0,3066.0,625.0,1164.0,582.0,4.0,228100.0,NEAR OCEAN
+-117.98,33.66,26.0,3527.0,547.0,1615.0,542.0,6.1624,279400.0,<1H OCEAN
+-122.16,37.47,33.0,3687.0,852.0,3091.0,852.0,2.6506,162600.0,NEAR BAY
+-122.07,37.39,30.0,1695.0,480.0,932.0,447.0,3.5045,352500.0,NEAR BAY
+-116.97,33.96,12.0,5876.0,1222.0,2992.0,1151.0,2.4322,112100.0,INLAND
+-122.03,37.36,28.0,2490.0,345.0,948.0,361.0,6.4913,411900.0,<1H OCEAN
+-117.8,33.9,22.0,3760.0,482.0,1485.0,461.0,7.8537,354900.0,<1H OCEAN
+-122.06,37.7,33.0,3906.0,790.0,1912.0,770.0,3.5187,209400.0,NEAR BAY
+-117.79,34.45,18.0,2986.0,597.0,1355.0,472.0,3.2765,165000.0,INLAND
+-117.09,32.75,28.0,1220.0,391.0,1286.0,396.0,1.2286,105000.0,NEAR OCEAN
+-121.98,37.8,17.0,3354.0,422.0,1457.0,425.0,7.6473,345800.0,<1H OCEAN
+-118.35,34.0,30.0,1879.0,226.0,740.0,266.0,6.431,492500.0,<1H OCEAN
+-118.56,34.2,35.0,1770.0,362.0,1083.0,355.0,5.0483,221000.0,<1H OCEAN
+-118.95,35.37,37.0,1475.0,327.0,946.0,295.0,1.6728,55400.0,INLAND
+-116.9,33.22,11.0,4132.0,773.0,2012.0,703.0,3.1906,234500.0,<1H OCEAN
+-122.18,37.47,37.0,2848.0,328.0,852.0,327.0,13.367,500001.0,NEAR BAY
+-122.27,37.45,41.0,830.0,136.0,353.0,153.0,6.3824,500001.0,NEAR OCEAN
+-118.14,34.68,31.0,2666.0,662.0,1337.0,602.0,2.4432,101100.0,INLAND
+-118.38,33.84,26.0,2869.0,567.0,1157.0,538.0,6.0382,355300.0,<1H OCEAN
+-118.35,33.9,25.0,3309.0,902.0,2299.0,837.0,3.0417,237000.0,<1H OCEAN
+-118.42,33.99,35.0,1724.0,419.0,1079.0,394.0,4.5521,263800.0,<1H OCEAN
+-116.31,33.66,7.0,4497.0,831.0,2248.0,713.0,3.6354,98000.0,INLAND
+-117.87,33.87,15.0,1898.0,476.0,1766.0,455.0,2.4929,158500.0,<1H OCEAN
+-118.35,33.86,24.0,2139.0,481.0,971.0,418.0,4.3859,271300.0,<1H OCEAN
+-122.44,37.72,52.0,2890.0,571.0,1769.0,541.0,3.8274,252000.0,NEAR BAY
+-122.08,37.38,36.0,1199.0,198.0,485.0,199.0,5.0796,373400.0,NEAR BAY
+-121.37,38.63,32.0,3658.0,797.0,1452.0,715.0,2.6623,120700.0,INLAND
+-117.28,33.18,14.0,676.0,118.0,384.0,126.0,6.2096,178100.0,NEAR OCEAN
+-117.09,32.61,23.0,1157.0,309.0,640.0,313.0,2.1548,118800.0,NEAR OCEAN
+-118.29,34.08,38.0,380.0,130.0,445.0,140.0,1.9286,137500.0,<1H OCEAN
+-123.73,40.48,25.0,2015.0,524.0,746.0,251.0,1.7153,77100.0,INLAND
+-122.39,37.73,46.0,1517.0,299.0,879.0,309.0,2.2222,195100.0,NEAR BAY
+-118.17,33.78,29.0,2920.0,962.0,3580.0,772.0,1.7393,140200.0,NEAR OCEAN
+-117.2,34.13,14.0,3998.0,711.0,1509.0,665.0,3.4138,126700.0,INLAND
+-118.46,34.04,19.0,3522.0,1036.0,1820.0,977.0,3.2663,337500.0,<1H OCEAN
+-122.24,37.8,52.0,1591.0,373.0,1118.0,347.0,2.1563,128600.0,NEAR BAY
+-118.28,33.98,19.0,883.0,313.0,726.0,277.0,0.9809,121400.0,<1H OCEAN
+-121.81,37.25,12.0,2070.0,587.0,1216.0,532.0,4.1926,244500.0,<1H OCEAN
+-119.27,34.27,52.0,1577.0,343.0,836.0,335.0,3.5893,206600.0,NEAR OCEAN
+-121.84,37.37,15.0,3315.0,1042.0,2749.0,1059.0,2.3199,140100.0,<1H OCEAN
+-121.27,38.87,16.0,2094.0,358.0,1092.0,357.0,4.4769,191400.0,INLAND
+-122.56,37.92,37.0,1926.0,290.0,721.0,298.0,8.9248,500001.0,NEAR BAY
+-121.29,38.02,12.0,2006.0,426.0,1849.0,396.0,2.5437,99000.0,INLAND
+-121.94,37.34,29.0,3377.0,853.0,1674.0,792.0,3.4233,229300.0,<1H OCEAN
+-119.09,34.22,8.0,40.0,10.0,309.0,16.0,4.0208,52500.0,NEAR OCEAN
+-117.24,33.11,10.0,3487.0,545.0,1410.0,557.0,6.0336,240300.0,NEAR OCEAN
+-119.96,35.99,25.0,1047.0,270.0,1505.0,286.0,2.0976,47700.0,INLAND
+-117.94,33.85,26.0,1888.0,429.0,1550.0,458.0,3.3393,168600.0,<1H OCEAN
+-121.22,37.96,31.0,1484.0,314.0,1163.0,336.0,2.625,72100.0,INLAND
+-121.92,37.29,34.0,943.0,135.0,378.0,139.0,5.1765,344600.0,<1H OCEAN
+-122.28,37.89,52.0,1225.0,169.0,412.0,168.0,5.7912,327100.0,NEAR BAY
+-118.46,34.07,42.0,2564.0,460.0,913.0,414.0,9.2225,500001.0,<1H OCEAN
+-122.36,40.62,11.0,3896.0,886.0,1902.0,843.0,2.2905,94200.0,INLAND
+-117.67,33.49,15.0,2782.0,579.0,983.0,525.0,2.1935,183300.0,<1H OCEAN
+-122.13,37.46,31.0,2247.0,573.0,1711.0,511.0,3.2642,185600.0,NEAR BAY
+-118.24,33.91,36.0,1446.0,316.0,1286.0,314.0,2.7083,103600.0,<1H OCEAN
+-120.47,34.98,6.0,5762.0,1115.0,2551.0,919.0,3.0723,137300.0,<1H OCEAN
+-121.92,36.62,52.0,1410.0,303.0,578.0,285.0,2.5625,235400.0,NEAR OCEAN
+-118.33,33.91,39.0,1224.0,312.0,1106.0,333.0,3.3491,181800.0,<1H OCEAN
+-118.29,33.71,31.0,2809.0,666.0,1316.0,633.0,4.5606,303800.0,NEAR OCEAN
+-118.26,33.97,44.0,1246.0,308.0,1031.0,295.0,1.9556,96300.0,<1H OCEAN
+-122.03,36.97,36.0,337.0,69.0,223.0,68.0,3.2404,225000.0,NEAR OCEAN
+-118.29,33.94,47.0,1782.0,338.0,1003.0,329.0,2.5398,105700.0,<1H OCEAN
+-122.44,37.76,35.0,1581.0,422.0,580.0,388.0,4.05,423100.0,NEAR BAY
+-119.09,36.21,38.0,1901.0,453.0,1613.0,400.0,1.8828,44600.0,INLAND
+-118.42,33.92,41.0,1621.0,279.0,756.0,277.0,5.0594,346000.0,<1H OCEAN
+-119.4,36.04,39.0,915.0,199.0,580.0,175.0,1.8894,112500.0,INLAND
+-117.95,34.04,36.0,1044.0,200.0,982.0,205.0,4.7679,153900.0,<1H OCEAN
+-117.82,33.89,24.0,2168.0,421.0,1050.0,397.0,4.6172,238300.0,<1H OCEAN
+-116.52,33.81,12.0,12396.0,2552.0,2548.0,1265.0,3.4394,162200.0,INLAND
+-118.32,33.77,37.0,627.0,95.0,259.0,106.0,6.887,500001.0,<1H OCEAN
+-117.2,33.29,12.0,6358.0,1182.0,2778.0,1020.0,4.0357,295900.0,<1H OCEAN
+-124.17,40.78,39.0,1606.0,330.0,731.0,327.0,1.6369,68300.0,NEAR OCEAN
+-117.45,34.13,16.0,5058.0,965.0,3388.0,878.0,3.6417,119000.0,INLAND
+-118.96,35.41,29.0,3548.0,729.0,1542.0,659.0,2.947,87100.0,INLAND
+-117.06,32.99,16.0,1306.0,196.0,713.0,222.0,6.2683,180700.0,<1H OCEAN
+-122.22,37.46,37.0,2586.0,495.0,1208.0,502.0,4.3214,342700.0,NEAR OCEAN
+-118.44,34.03,30.0,1039.0,303.0,606.0,274.0,3.125,343800.0,<1H OCEAN
+-122.29,37.54,39.0,1459.0,285.0,761.0,291.0,5.0081,298100.0,NEAR OCEAN
+-121.91,37.3,43.0,828.0,151.0,446.0,145.0,4.4375,327600.0,<1H OCEAN
+-123.16,39.13,33.0,1320.0,303.0,1048.0,303.0,1.7813,94700.0,<1H OCEAN
+-121.89,37.38,3.0,4778.0,1047.0,2522.0,990.0,5.7695,271400.0,<1H OCEAN
+-117.29,33.19,18.0,6235.0,1233.0,4127.0,1162.0,3.0704,151600.0,NEAR OCEAN
+-117.66,34.13,17.0,3229.0,405.0,1289.0,407.0,6.3842,307100.0,INLAND
+-118.21,33.99,39.0,47.0,16.0,51.0,23.0,3.2188,112500.0,<1H OCEAN
+-117.67,33.54,15.0,2423.0,435.0,1366.0,423.0,4.8906,181800.0,<1H OCEAN
+-118.09,34.04,24.0,1543.0,257.0,824.0,271.0,6.4385,272600.0,<1H OCEAN
+-118.54,34.18,17.0,7214.0,1994.0,4100.0,1823.0,3.0943,174500.0,<1H OCEAN
+-122.28,37.78,29.0,5154.0,,3741.0,1273.0,2.5762,173400.0,NEAR BAY
+-119.35,36.32,10.0,3817.0,719.0,1686.0,714.0,3.8235,94600.0,INLAND
+-121.48,38.55,52.0,2508.0,360.0,832.0,345.0,7.1035,228700.0,INLAND
+-117.52,33.99,14.0,13562.0,2057.0,7600.0,2086.0,5.2759,182900.0,INLAND
+-119.23,36.39,39.0,1660.0,349.0,1061.0,306.0,1.4812,53500.0,INLAND
+-122.34,37.53,27.0,3339.0,481.0,1354.0,458.0,7.3081,464600.0,NEAR OCEAN
+-118.33,34.2,43.0,1325.0,254.0,613.0,248.0,3.6071,289000.0,<1H OCEAN
+-122.42,37.74,52.0,1651.0,351.0,973.0,366.0,3.4583,240900.0,NEAR BAY
+-120.43,34.95,50.0,1966.0,413.0,985.0,403.0,2.3506,136100.0,<1H OCEAN
+-117.12,32.77,48.0,2012.0,422.0,893.0,394.0,2.7928,175000.0,NEAR OCEAN
+-116.09,34.15,13.0,9444.0,1997.0,4166.0,1482.0,2.6111,65600.0,INLAND
+-117.92,33.79,29.0,3692.0,969.0,2683.0,881.0,3.1726,198700.0,<1H OCEAN
+-118.35,34.06,52.0,1368.0,322.0,617.0,303.0,5.3819,440900.0,<1H OCEAN
+-116.99,33.76,12.0,7626.0,1704.0,2823.0,1554.0,2.1722,69400.0,INLAND
+-117.85,34.13,31.0,1959.0,318.0,1021.0,303.0,4.3145,233000.0,INLAND
+-118.94,34.24,5.0,10018.0,1233.0,4253.0,1120.0,8.9063,500001.0,<1H OCEAN
+-121.8,39.75,28.0,2551.0,378.0,1011.0,374.0,4.3309,125200.0,INLAND
+-118.39,34.14,34.0,4624.0,781.0,1572.0,719.0,6.5533,500001.0,<1H OCEAN
+-119.08,35.36,12.0,6442.0,1116.0,2966.0,1092.0,4.5791,123400.0,INLAND
+-117.12,33.14,16.0,1710.0,272.0,1025.0,267.0,4.1641,163600.0,<1H OCEAN
+-118.13,33.9,35.0,1458.0,261.0,686.0,236.0,3.9038,202700.0,<1H OCEAN
+-118.98,35.36,15.0,1482.0,338.0,1059.0,279.0,1.2617,42700.0,INLAND
+-120.69,35.49,16.0,2666.0,450.0,1203.0,429.0,4.1375,222400.0,<1H OCEAN
+-120.43,36.18,29.0,579.0,116.0,218.0,99.0,2.1458,104200.0,INLAND
+-121.62,39.5,18.0,2105.0,416.0,974.0,385.0,1.6346,63300.0,INLAND
+-117.9,33.77,35.0,2002.0,378.0,1726.0,387.0,3.9613,182300.0,<1H OCEAN
+-118.47,34.03,29.0,3287.0,882.0,1523.0,823.0,3.7381,290600.0,<1H OCEAN
+-117.07,32.64,32.0,5135.0,1025.0,2152.0,944.0,4.1325,172800.0,NEAR OCEAN
+-116.98,33.94,27.0,3459.0,640.0,1760.0,654.0,3.4545,89800.0,INLAND
+-118.49,34.25,33.0,2088.0,383.0,960.0,362.0,4.3333,232900.0,<1H OCEAN
+-120.46,34.64,16.0,686.0,217.0,614.0,200.0,0.8106,83300.0,NEAR OCEAN
+-117.51,33.95,12.0,9016.0,1486.0,4285.0,1457.0,4.9984,169100.0,INLAND
+-119.86,36.78,7.0,2232.0,490.0,1274.0,499.0,2.9853,74700.0,INLAND
+-117.93,33.95,31.0,3600.0,468.0,1382.0,435.0,7.4597,500001.0,<1H OCEAN
+-122.29,38.29,52.0,3217.0,742.0,1670.0,671.0,2.4398,163100.0,NEAR BAY
+-118.03,34.1,38.0,2301.0,416.0,1079.0,398.0,4.4236,233600.0,INLAND
+-121.89,37.3,46.0,2639.0,448.0,938.0,424.0,5.0662,331600.0,<1H OCEAN
+-118.26,34.02,46.0,1249.0,357.0,1607.0,331.0,2.0703,114800.0,<1H OCEAN
+-121.84,37.26,5.0,1808.0,340.0,825.0,339.0,5.0509,184800.0,<1H OCEAN
+-121.29,37.8,6.0,110.0,26.0,69.0,24.0,3.7292,475000.0,INLAND
+-116.97,32.76,26.0,2460.0,313.0,838.0,299.0,5.9878,270700.0,<1H OCEAN
+-118.31,33.91,31.0,1415.0,339.0,874.0,289.0,3.8173,177900.0,<1H OCEAN
+-120.46,37.29,30.0,2972.0,635.0,1940.0,590.0,2.3594,72300.0,INLAND
+-117.98,33.89,18.0,2939.0,437.0,1278.0,435.0,7.1425,393700.0,<1H OCEAN
+-117.02,32.74,12.0,3301.0,963.0,2000.0,879.0,1.8594,119200.0,NEAR OCEAN
+-121.43,38.63,43.0,1009.0,225.0,604.0,218.0,1.6641,67000.0,INLAND
+-118.06,35.68,15.0,1962.0,403.0,730.0,321.0,2.25,67500.0,INLAND
+-122.43,37.76,52.0,2356.0,501.0,909.0,481.0,4.2569,455400.0,NEAR BAY
+-122.25,37.81,28.0,5806.0,1603.0,2563.0,1497.0,3.2177,273400.0,NEAR BAY
+-117.87,34.13,29.0,1677.0,413.0,873.0,400.0,3.12,194300.0,<1H OCEAN
+-120.61,35.12,31.0,1486.0,345.0,823.0,322.0,2.6974,165400.0,<1H OCEAN
+-118.27,33.82,28.0,1642.0,434.0,1575.0,420.0,4.1292,201900.0,<1H OCEAN
+-118.02,34.15,44.0,2419.0,437.0,1045.0,432.0,3.875,280800.0,INLAND
+-119.02,35.33,35.0,2053.0,412.0,1193.0,387.0,2.75,65800.0,INLAND
+-122.41,37.75,52.0,2524.0,559.0,1430.0,476.0,3.4073,254700.0,NEAR BAY
+-118.28,33.98,43.0,1240.0,312.0,1100.0,311.0,1.575,97500.0,<1H OCEAN
+-118.23,33.91,34.0,1060.0,276.0,1215.0,250.0,2.0804,84700.0,<1H OCEAN
+-122.08,37.89,39.0,3018.0,501.0,1223.0,489.0,6.2924,283900.0,NEAR BAY
+-119.03,36.08,19.0,2736.0,549.0,1432.0,503.0,2.6944,67700.0,INLAND
+-122.38,37.73,40.0,543.0,,259.0,89.0,2.2167,193800.0,NEAR BAY
+-121.24,38.78,18.0,549.0,143.0,249.0,136.0,0.8691,136500.0,INLAND
+-117.05,32.68,35.0,3414.0,580.0,1761.0,522.0,3.9922,129800.0,NEAR OCEAN
+-122.16,37.89,32.0,1779.0,241.0,721.0,258.0,8.7589,434500.0,NEAR BAY
+-117.94,34.05,34.0,1519.0,304.0,1262.0,300.0,3.3409,161200.0,<1H OCEAN
+-117.33,34.03,18.0,2342.0,402.0,1264.0,382.0,4.7986,123700.0,INLAND
+-121.94,36.57,28.0,3153.0,409.0,569.0,271.0,14.4113,500001.0,NEAR OCEAN
+-118.01,34.15,32.0,6597.0,1579.0,3689.0,1459.0,3.2377,184100.0,INLAND
+-117.86,33.9,17.0,1452.0,188.0,630.0,194.0,6.9113,285200.0,<1H OCEAN
+-118.23,34.13,48.0,737.0,166.0,462.0,131.0,3.5893,212500.0,<1H OCEAN
+-118.32,33.99,43.0,2028.0,479.0,1074.0,394.0,2.5909,98700.0,<1H OCEAN
+-122.62,38.73,21.0,1425.0,323.0,727.0,287.0,2.1474,85300.0,INLAND
+-122.2,37.44,31.0,2328.0,270.0,722.0,247.0,15.0001,500001.0,NEAR OCEAN
+-117.28,32.73,44.0,1934.0,325.0,783.0,316.0,4.8684,358600.0,NEAR OCEAN
+-117.81,34.08,13.0,18448.0,2474.0,7775.0,2397.0,7.7876,348900.0,INLAND
+-121.63,39.75,37.0,1296.0,296.0,569.0,257.0,1.8616,70500.0,INLAND
+-119.72,34.43,46.0,1332.0,329.0,746.0,310.0,3.6719,357400.0,<1H OCEAN
+-122.3,37.51,35.0,2789.0,445.0,1156.0,404.0,5.4322,391000.0,NEAR OCEAN
+-117.85,33.76,26.0,2312.0,525.0,1273.0,437.0,2.8828,204700.0,<1H OCEAN
+-122.14,37.5,46.0,30.0,4.0,13.0,5.0,15.0001,500001.0,NEAR BAY
+-117.89,33.87,32.0,1569.0,422.0,835.0,386.0,3.0465,148900.0,<1H OCEAN
+-121.77,37.74,25.0,494.0,81.0,254.0,85.0,9.1531,418800.0,INLAND
+-118.27,33.87,21.0,6108.0,1130.0,3244.0,1113.0,4.2768,181400.0,<1H OCEAN
+-118.01,33.9,36.0,1382.0,257.0,685.0,255.0,5.125,211700.0,<1H OCEAN
+-122.78,38.52,23.0,2511.0,549.0,1052.0,527.0,2.4922,192000.0,<1H OCEAN
+-117.93,33.9,30.0,2629.0,331.0,956.0,319.0,9.9071,500001.0,<1H OCEAN
+-117.9,33.7,15.0,2289.0,686.0,982.0,634.0,4.5757,162500.0,<1H OCEAN
+-122.29,37.52,33.0,4104.0,751.0,1837.0,771.0,5.3506,388100.0,NEAR OCEAN
+-117.79,33.8,11.0,10535.0,1620.0,4409.0,1622.0,6.67,283200.0,<1H OCEAN
+-122.28,40.5,21.0,2405.0,476.0,1197.0,412.0,2.6488,83100.0,INLAND
+-118.36,34.11,35.0,3946.0,695.0,1361.0,620.0,6.5195,500001.0,<1H OCEAN
+-122.2,37.4,37.0,1296.0,194.0,540.0,192.0,8.2782,500001.0,NEAR OCEAN
+-121.66,36.82,17.0,3921.0,654.0,1895.0,641.0,5.0092,238700.0,<1H OCEAN
+-118.51,34.14,28.0,6748.0,904.0,2431.0,876.0,12.8879,500001.0,<1H OCEAN
+-121.07,39.04,9.0,2374.0,372.0,884.0,333.0,4.5042,206400.0,INLAND
+-117.96,34.48,32.0,1896.0,342.0,806.0,299.0,4.5769,159400.0,INLAND
+-117.68,33.57,2.0,10008.0,1453.0,3550.0,1139.0,10.1122,500001.0,<1H OCEAN
+-122.71,38.35,11.0,2242.0,699.0,1203.0,642.0,2.3464,104200.0,<1H OCEAN
+-118.24,34.0,23.0,588.0,157.0,716.0,173.0,1.2056,87500.0,<1H OCEAN
+-117.1,32.69,29.0,4174.0,1195.0,3675.0,1124.0,1.8112,103600.0,NEAR OCEAN
+-122.45,38.01,36.0,4501.0,832.0,2196.0,800.0,4.3182,252700.0,NEAR BAY
+-122.08,37.97,9.0,2643.0,439.0,1105.0,467.0,6.6579,245200.0,NEAR BAY
+-118.39,34.2,19.0,2012.0,732.0,3483.0,731.0,2.2234,181300.0,<1H OCEAN
+-122.5,37.78,46.0,2646.0,607.0,1418.0,563.0,3.7167,332800.0,NEAR BAY
+-122.34,37.94,31.0,1611.0,455.0,786.0,411.0,1.681,145500.0,NEAR BAY
+-118.22,33.92,23.0,926.0,409.0,1856.0,408.0,2.1366,100000.0,<1H OCEAN
+-118.65,36.57,20.0,1431.0,416.0,570.0,225.0,1.4821,143300.0,INLAND
+-117.9,33.84,31.0,2043.0,468.0,1524.0,454.0,3.5329,187400.0,<1H OCEAN
+-118.35,34.18,46.0,1840.0,379.0,866.0,360.0,3.3056,230400.0,<1H OCEAN
+-117.18,32.8,30.0,2456.0,390.0,1022.0,393.0,3.8542,198500.0,NEAR OCEAN
+-121.74,37.29,6.0,7292.0,1295.0,2468.0,1262.0,5.6411,294700.0,<1H OCEAN
+-119.72,36.82,15.0,946.0,239.0,550.0,246.0,2.2639,52500.0,INLAND
+-119.79,36.79,33.0,3433.0,785.0,1806.0,783.0,1.9386,67500.0,INLAND
+-117.45,33.94,12.0,3539.0,869.0,1987.0,859.0,2.1023,103700.0,INLAND
+-116.99,32.79,33.0,2420.0,393.0,1003.0,397.0,4.0658,165100.0,<1H OCEAN
+-120.64,34.97,5.0,2090.0,469.0,1911.0,482.0,2.4318,86100.0,NEAR OCEAN
+-122.25,37.83,52.0,2376.0,559.0,939.0,519.0,3.1484,224100.0,NEAR BAY
+-122.67,38.31,28.0,1915.0,419.0,930.0,342.0,3.7875,292700.0,<1H OCEAN
+-121.96,36.99,23.0,3209.0,748.0,1423.0,666.0,2.7375,238000.0,NEAR OCEAN
+-121.81,37.39,34.0,2218.0,286.0,827.0,299.0,7.4559,456500.0,<1H OCEAN
+-117.92,33.89,18.0,2895.0,487.0,1116.0,429.0,5.4716,400000.0,<1H OCEAN
+-118.68,34.27,16.0,4637.0,941.0,2476.0,878.0,4.0568,225200.0,<1H OCEAN
+-122.07,37.92,26.0,3872.0,739.0,1629.0,684.0,4.4312,225000.0,NEAR BAY
+-122.18,37.91,31.0,7200.0,876.0,2428.0,843.0,10.9405,500001.0,NEAR BAY
+-122.38,41.54,14.0,4453.0,797.0,1817.0,685.0,2.7468,81100.0,INLAND
+-118.17,33.77,39.0,2953.0,878.0,1379.0,785.0,2.1378,180400.0,NEAR OCEAN
+-122.61,38.24,25.0,2990.0,450.0,1335.0,434.0,4.7,190100.0,<1H OCEAN
+-118.07,34.06,34.0,2873.0,718.0,2758.0,699.0,2.5985,168600.0,<1H OCEAN
+-119.0,36.05,24.0,3208.0,691.0,1986.0,662.0,1.5506,52300.0,INLAND
+-121.35,38.02,16.0,1665.0,311.0,1301.0,259.0,2.8403,132300.0,INLAND
+-117.24,34.11,23.0,1920.0,454.0,1161.0,358.0,2.2109,73200.0,INLAND
+-122.0,36.97,30.0,1029.0,242.0,753.0,249.0,3.1205,240500.0,NEAR OCEAN
+-118.15,33.79,5.0,3700.0,993.0,1657.0,848.0,3.7826,196300.0,NEAR OCEAN
+-116.96,32.8,19.0,4574.0,1152.0,3045.0,1057.0,2.065,124100.0,<1H OCEAN
+-123.52,41.01,17.0,1564.0,345.0,517.0,222.0,2.1542,83800.0,INLAND
+-119.01,35.36,24.0,1941.0,484.0,1277.0,435.0,1.056,51600.0,INLAND
+-119.34,36.33,17.0,2250.0,430.0,1218.0,468.0,4.1812,93700.0,INLAND
+-118.37,34.06,52.0,1608.0,289.0,630.0,252.0,5.5596,500001.0,<1H OCEAN
+-117.81,33.77,31.0,4624.0,624.0,1852.0,635.0,7.2392,334600.0,<1H OCEAN
+-122.47,37.76,52.0,2680.0,740.0,1587.0,713.0,2.5933,359600.0,NEAR BAY
+-118.51,34.2,37.0,2066.0,434.0,1031.0,414.0,4.0924,188400.0,<1H OCEAN
+-117.66,33.58,6.0,4186.0,,1794.0,541.0,9.6986,357600.0,<1H OCEAN
+-121.36,38.55,33.0,1191.0,198.0,554.0,191.0,2.8021,118800.0,INLAND
+-121.88,37.26,25.0,3025.0,689.0,1755.0,661.0,3.8893,218600.0,<1H OCEAN
+-116.8,33.8,35.0,324.0,63.0,158.0,39.0,3.4167,100000.0,INLAND
+-119.99,38.93,23.0,1882.0,414.0,673.0,277.0,2.9091,141900.0,INLAND
+-121.79,38.0,36.0,1141.0,234.0,562.0,213.0,2.5893,108500.0,INLAND
+-121.68,36.9,13.0,833.0,130.0,405.0,127.0,5.2729,322900.0,<1H OCEAN
+-122.92,39.08,24.0,341.0,64.0,146.0,57.0,4.0,166300.0,INLAND
+-121.04,37.69,5.0,9601.0,1639.0,4449.0,1575.0,4.5332,195500.0,INLAND
+-119.04,35.36,36.0,1942.0,414.0,1051.0,411.0,1.8362,66900.0,INLAND
+-121.21,38.67,11.0,5500.0,956.0,2827.0,946.0,4.1071,145800.0,INLAND
+-118.18,34.06,27.0,2025.0,565.0,2189.0,577.0,2.6083,148600.0,<1H OCEAN
+-121.49,38.54,47.0,2313.0,536.0,779.0,442.0,2.5639,123000.0,INLAND
+-118.11,33.83,36.0,1820.0,313.0,899.0,295.0,4.918,225200.0,<1H OCEAN
+-118.23,33.76,21.0,49.0,14.0,29.0,16.0,5.0,87500.0,NEAR OCEAN
+-121.86,37.0,16.0,8638.0,1392.0,3706.0,1251.0,5.503,351800.0,<1H OCEAN
+-122.15,37.41,15.0,2577.0,360.0,979.0,364.0,10.476,500001.0,NEAR BAY
+-115.85,34.2,34.0,3868.0,1257.0,890.0,423.0,1.3571,41000.0,INLAND
+-121.12,35.58,16.0,4109.0,798.0,1298.0,626.0,3.4799,320800.0,NEAR OCEAN
+-118.12,33.91,34.0,682.0,132.0,491.0,144.0,4.6389,173800.0,<1H OCEAN
+-117.21,32.74,45.0,3025.0,583.0,1980.0,550.0,2.2982,87500.0,NEAR OCEAN
+-122.46,37.72,37.0,1833.0,388.0,1093.0,363.0,3.0703,211800.0,NEAR OCEAN
+-117.99,34.0,26.0,2988.0,397.0,1371.0,415.0,6.6988,382500.0,<1H OCEAN
+-118.0,33.83,26.0,1718.0,385.0,1022.0,368.0,3.9333,196100.0,<1H OCEAN
+-121.08,38.67,10.0,2499.0,331.0,1040.0,333.0,6.844,239600.0,INLAND
+-119.73,34.44,48.0,2114.0,390.0,973.0,367.0,4.8021,351100.0,NEAR OCEAN
+-119.18,34.19,5.0,384.0,131.0,410.0,149.0,1.5625,87500.0,NEAR OCEAN
+-117.04,33.01,28.0,922.0,107.0,314.0,97.0,8.4721,342300.0,<1H OCEAN
+-117.28,33.2,20.0,4835.0,854.0,2983.0,834.0,4.3428,152100.0,NEAR OCEAN
+-118.18,33.88,47.0,882.0,185.0,536.0,174.0,4.625,163000.0,<1H OCEAN
+-117.27,33.23,5.0,20908.0,3933.0,9690.0,3510.0,4.1405,198500.0,<1H OCEAN
+-122.83,38.99,15.0,289.0,49.0,191.0,54.0,1.6833,113900.0,INLAND
+-118.36,33.93,40.0,1625.0,500.0,2036.0,476.0,2.6298,156500.0,<1H OCEAN
+-122.22,37.8,52.0,2721.0,541.0,1185.0,515.0,4.5428,239800.0,NEAR BAY
+-117.01,32.79,31.0,3776.0,815.0,1886.0,799.0,3.4421,155300.0,<1H OCEAN
+-122.4,37.71,40.0,1883.0,397.0,1411.0,438.0,3.0469,238000.0,NEAR BAY
+-118.24,34.06,19.0,2870.0,1021.0,3325.0,978.0,1.7395,162500.0,<1H OCEAN
+-121.96,37.34,34.0,1461.0,299.0,739.0,276.0,3.4375,252600.0,<1H OCEAN
+-121.67,39.34,22.0,1217.0,224.0,537.0,187.0,2.6607,84600.0,INLAND
+-122.34,37.95,45.0,1128.0,240.0,702.0,270.0,3.6719,134100.0,NEAR BAY
+-118.12,34.03,20.0,2595.0,428.0,1751.0,479.0,5.6112,308000.0,<1H OCEAN
+-117.59,34.13,10.0,20263.0,3915.0,9716.0,3744.0,3.8505,169600.0,INLAND
+-119.52,34.4,20.0,1834.0,477.0,1305.0,417.0,3.2125,251000.0,NEAR OCEAN
+-118.3,34.26,40.0,1065.0,214.0,605.0,183.0,4.1964,185900.0,<1H OCEAN
+-122.32,38.06,4.0,7999.0,1611.0,3596.0,1396.0,5.0969,174200.0,NEAR BAY
+-117.16,32.79,32.0,1731.0,413.0,1569.0,427.0,3.3375,154300.0,NEAR OCEAN
+-122.13,37.68,44.0,2147.0,399.0,1175.0,401.0,4.1974,179300.0,NEAR BAY
+-119.16,34.95,14.0,4054.0,787.0,1581.0,579.0,3.0882,148200.0,INLAND
+-118.31,33.94,44.0,1854.0,367.0,976.0,335.0,3.6583,126700.0,<1H OCEAN
+-121.92,37.34,35.0,357.0,120.0,377.0,99.0,3.0139,204200.0,<1H OCEAN
+-118.11,33.82,36.0,1999.0,390.0,887.0,379.0,3.8162,221900.0,<1H OCEAN
+-122.29,37.76,18.0,2873.0,763.0,1243.0,663.0,5.1702,265400.0,NEAR BAY
+-117.8,33.89,25.0,3121.0,381.0,1278.0,389.0,7.0217,357900.0,<1H OCEAN
+-118.55,34.21,33.0,3910.0,838.0,2097.0,810.0,3.8247,208700.0,<1H OCEAN
+-121.46,38.55,52.0,2094.0,463.0,1364.0,407.0,1.2235,68500.0,INLAND
+-118.44,34.06,14.0,520.0,292.0,282.0,213.0,2.2857,500001.0,<1H OCEAN
+-119.21,34.26,31.0,224.0,88.0,326.0,88.0,2.375,55000.0,NEAR OCEAN
+-118.32,34.13,34.0,1856.0,273.0,540.0,264.0,4.0833,500001.0,<1H OCEAN
+-117.31,33.25,14.0,3483.0,764.0,2140.0,687.0,3.125,102300.0,<1H OCEAN
+-117.12,32.76,27.0,1426.0,364.0,792.0,353.0,2.0673,118800.0,NEAR OCEAN
+-118.43,34.04,42.0,2725.0,569.0,1115.0,516.0,4.5833,427500.0,<1H OCEAN
+-117.93,33.81,18.0,3291.0,587.0,1640.0,563.0,4.8981,166300.0,<1H OCEAN
+-119.05,34.35,39.0,950.0,300.0,1366.0,312.0,2.2443,146600.0,<1H OCEAN
+-117.29,33.72,19.0,2248.0,427.0,1207.0,368.0,2.817,110000.0,<1H OCEAN
+-119.06,34.23,23.0,3471.0,510.0,2002.0,555.0,5.2742,257500.0,<1H OCEAN
+-117.18,32.77,16.0,2374.0,780.0,913.0,705.0,2.7386,87500.0,NEAR OCEAN
+-117.93,33.94,30.0,2658.0,382.0,1135.0,392.0,6.0516,245000.0,<1H OCEAN
+-115.57,32.79,50.0,1291.0,277.0,864.0,274.0,1.6667,68100.0,INLAND
+-117.92,33.88,52.0,1270.0,276.0,609.0,211.0,3.75,232500.0,<1H OCEAN
+-117.43,33.95,36.0,2284.0,444.0,1425.0,405.0,4.0526,104500.0,INLAND
+-119.25,35.75,36.0,1598.0,443.0,1658.0,417.0,1.517,52100.0,INLAND
+-121.13,36.21,30.0,1484.0,414.0,1200.0,351.0,1.7548,95800.0,<1H OCEAN
+-117.08,32.7,35.0,1477.0,264.0,852.0,279.0,3.1786,100600.0,NEAR OCEAN
+-120.08,34.59,24.0,1874.0,319.0,820.0,315.0,5.1909,390200.0,NEAR OCEAN
+-119.44,38.53,20.0,1963.0,434.0,682.0,273.0,1.5817,97800.0,INLAND
+-117.73,34.05,28.0,2758.0,771.0,2877.0,694.0,2.0734,113300.0,INLAND
+-120.56,38.48,14.0,3545.0,702.0,946.0,411.0,3.4609,120900.0,INLAND
+-118.41,34.16,14.0,577.0,150.0,372.0,130.0,4.1875,275000.0,<1H OCEAN
+-117.28,34.26,18.0,3895.0,,1086.0,375.0,3.3672,133600.0,INLAND
+-120.44,38.61,9.0,2598.0,548.0,796.0,297.0,3.5192,98000.0,INLAND
+-121.37,37.06,25.0,474.0,92.0,300.0,104.0,3.8062,340900.0,INLAND
+-117.54,33.82,6.0,202.0,29.0,75.0,28.0,4.125,216700.0,INLAND
+-120.25,37.23,34.0,1656.0,328.0,1110.0,332.0,2.1845,59900.0,INLAND
+-122.71,38.45,52.0,2259.0,537.0,957.0,520.0,2.1827,188800.0,<1H OCEAN
+-118.37,34.16,11.0,2901.0,871.0,1659.0,789.0,3.1106,209400.0,<1H OCEAN
+-117.33,34.08,35.0,2240.0,423.0,1394.0,396.0,3.1799,86700.0,INLAND
+-117.46,34.08,18.0,3830.0,750.0,2767.0,702.0,3.6602,120700.0,INLAND
+-121.74,37.95,5.0,4980.0,774.0,2399.0,763.0,5.7104,186300.0,INLAND
+-117.08,32.73,19.0,2935.0,763.0,1953.0,720.0,1.4254,111300.0,NEAR OCEAN
+-122.19,37.77,42.0,932.0,254.0,900.0,263.0,1.8039,92300.0,NEAR BAY
+-120.44,37.28,12.0,2855.0,598.0,1658.0,586.0,2.3929,81100.0,INLAND
+-117.65,34.1,44.0,2808.0,585.0,1444.0,550.0,2.7159,139300.0,INLAND
+-120.57,34.96,27.0,1401.0,294.0,1306.0,286.0,2.5809,83200.0,NEAR OCEAN
+-117.27,33.03,25.0,1787.0,311.0,1108.0,311.0,3.9826,215800.0,NEAR OCEAN
+-117.02,32.8,27.0,2369.0,370.0,927.0,374.0,4.1162,177200.0,<1H OCEAN
+-122.1,37.69,41.0,746.0,,387.0,161.0,3.9063,178400.0,NEAR BAY
+-117.24,33.36,11.0,2786.0,480.0,1250.0,450.0,4.5,222600.0,<1H OCEAN
+-122.06,37.38,20.0,3401.0,768.0,1497.0,747.0,4.2188,500001.0,NEAR BAY
+-117.06,32.61,24.0,4369.0,1353.0,3123.0,1247.0,2.0571,152300.0,NEAR OCEAN
+-121.99,38.35,45.0,1778.0,339.0,839.0,319.0,2.4659,102900.0,INLAND
+-121.62,39.13,41.0,1317.0,309.0,856.0,337.0,1.6719,64100.0,INLAND
+-118.62,34.18,25.0,3124.0,468.0,1241.0,439.0,6.4044,333100.0,<1H OCEAN
+-118.27,34.1,50.0,2113.0,398.0,793.0,418.0,4.7132,304600.0,<1H OCEAN
+-118.26,34.23,43.0,1428.0,325.0,836.0,302.0,4.5759,209200.0,<1H OCEAN
+-122.03,37.87,21.0,3521.0,447.0,1396.0,467.0,8.2673,358700.0,NEAR BAY
+-117.52,33.83,22.0,2397.0,400.0,1347.0,403.0,4.46,189800.0,INLAND
+-121.53,39.49,19.0,1537.0,329.0,617.0,274.0,1.5313,50300.0,INLAND
+-119.26,34.27,36.0,1972.0,382.0,1029.0,411.0,3.7337,209000.0,NEAR OCEAN
+-120.82,37.54,20.0,707.0,114.0,282.0,86.0,6.1324,164800.0,INLAND
+-120.52,35.24,5.0,4413.0,804.0,2003.0,725.0,5.0267,253300.0,<1H OCEAN
+-118.3,33.98,44.0,1597.0,388.0,902.0,321.0,1.9556,93300.0,<1H OCEAN
+-120.68,35.29,37.0,1354.0,293.0,753.0,290.0,3.25,225000.0,NEAR OCEAN
+-122.27,37.73,31.0,5785.0,1379.0,2973.0,1312.0,3.2689,231000.0,NEAR BAY
+-117.22,33.21,19.0,4400.0,828.0,1901.0,735.0,3.6375,198800.0,<1H OCEAN
+-119.1,35.35,5.0,4597.0,1071.0,1916.0,870.0,4.0327,131000.0,INLAND
+-121.54,38.49,6.0,9104.0,1535.0,3759.0,1481.0,5.1442,174500.0,INLAND
+-118.31,34.01,48.0,2544.0,532.0,1357.0,498.0,2.5263,121000.0,<1H OCEAN
+-118.53,34.44,19.0,3013.0,507.0,1356.0,484.0,5.1163,233200.0,<1H OCEAN
+-121.41,38.69,28.0,1601.0,308.0,848.0,305.0,3.6429,105200.0,INLAND
+-122.43,37.8,52.0,1006.0,251.0,349.0,233.0,3.2235,500000.0,NEAR BAY
+-121.59,39.86,14.0,1527.0,269.0,665.0,261.0,2.8657,119600.0,INLAND
+-118.52,34.32,18.0,7498.0,976.0,3189.0,955.0,8.1248,374000.0,<1H OCEAN
+-118.75,34.26,26.0,1767.0,265.0,1040.0,250.0,5.4787,198100.0,<1H OCEAN
+-118.44,34.07,35.0,1973.0,332.0,1257.0,296.0,8.9565,500001.0,<1H OCEAN
+-121.57,38.19,36.0,1395.0,264.0,700.0,244.0,2.4353,162500.0,INLAND
+-121.25,38.69,17.0,3050.0,481.0,1490.0,489.0,4.5562,134500.0,INLAND
+-122.47,37.77,52.0,2241.0,443.0,1042.0,377.0,4.1635,398400.0,NEAR BAY
+-122.13,37.43,32.0,4398.0,878.0,1799.0,792.0,4.7375,431900.0,NEAR BAY
+-118.25,34.25,34.0,3150.0,518.0,1392.0,480.0,4.9355,336900.0,<1H OCEAN
+-122.25,37.8,42.0,4120.0,1065.0,1715.0,1015.0,2.9345,225000.0,NEAR BAY
+-122.41,40.53,28.0,1127.0,245.0,538.0,208.0,2.037,72000.0,INLAND
+-121.45,41.04,33.0,2029.0,378.0,936.0,343.0,2.67,77500.0,INLAND
+-122.03,36.98,37.0,2817.0,716.0,1341.0,662.0,2.1553,255400.0,NEAR OCEAN
+-122.42,37.78,26.0,812.0,507.0,628.0,445.0,2.3304,500001.0,NEAR BAY
+-118.46,34.29,24.0,3668.0,890.0,3151.0,810.0,3.0526,183300.0,<1H OCEAN
+-122.15,37.47,38.0,1560.0,301.0,1331.0,316.0,3.0521,151500.0,NEAR BAY
+-119.01,35.36,36.0,2658.0,626.0,1490.0,529.0,1.2157,57000.0,INLAND
+-117.23,34.15,17.0,5036.0,817.0,2084.0,833.0,4.6445,137200.0,INLAND
+-117.27,33.2,34.0,1852.0,322.0,978.0,332.0,4.3542,156900.0,<1H OCEAN
+-117.32,33.25,7.0,8206.0,1523.0,4399.0,1423.0,3.6301,170900.0,<1H OCEAN
+-118.21,33.8,41.0,1251.0,279.0,1053.0,278.0,3.2778,150800.0,NEAR OCEAN
+-118.07,34.16,39.0,1804.0,265.0,730.0,276.0,6.4761,397500.0,INLAND
+-119.57,34.38,22.0,2512.0,426.0,919.0,341.0,5.759,425000.0,<1H OCEAN
+-117.07,32.65,12.0,4131.0,891.0,2272.0,840.0,3.4701,204900.0,NEAR OCEAN
+-118.02,33.92,35.0,2075.0,424.0,1312.0,396.0,3.7969,164800.0,<1H OCEAN
+-117.22,34.12,30.0,2512.0,597.0,1390.0,523.0,2.3725,77200.0,INLAND
+-118.26,34.01,43.0,2179.0,682.0,2624.0,609.0,1.8641,108200.0,<1H OCEAN
+-117.06,32.57,17.0,2252.0,378.0,1776.0,365.0,4.6364,141100.0,NEAR OCEAN
+-117.58,34.14,7.0,11818.0,1745.0,5499.0,1600.0,5.3678,231700.0,INLAND
+-119.29,34.31,25.0,1092.0,190.0,702.0,215.0,3.9063,192700.0,NEAR OCEAN
+-117.91,33.66,21.0,1708.0,505.0,1099.0,434.0,3.225,193800.0,<1H OCEAN
+-117.18,32.76,17.0,711.0,254.0,327.0,227.0,2.6493,67500.0,NEAR OCEAN
+-117.32,34.16,9.0,711.0,139.0,316.0,152.0,4.0156,131000.0,INLAND
+-117.24,33.17,4.0,9998.0,1874.0,3925.0,1672.0,4.2826,237500.0,<1H OCEAN
+-118.03,33.82,17.0,2178.0,477.0,1077.0,457.0,3.6815,245300.0,<1H OCEAN
+-117.95,34.05,35.0,1309.0,276.0,1113.0,253.0,4.375,156500.0,<1H OCEAN
+-121.32,38.13,5.0,3136.0,501.0,1327.0,467.0,5.5942,186900.0,INLAND
+-117.81,34.01,12.0,9197.0,1642.0,4332.0,1554.0,4.9589,282100.0,<1H OCEAN
+-122.25,38.03,15.0,3338.0,532.0,1834.0,520.0,5.6293,197600.0,NEAR BAY
+-122.25,37.77,52.0,1527.0,320.0,825.0,264.0,3.4531,208800.0,NEAR BAY
+-121.45,38.61,32.0,2436.0,612.0,1509.0,618.0,1.0424,81400.0,INLAND
+-117.89,34.11,36.0,806.0,147.0,446.0,153.0,4.5221,151300.0,<1H OCEAN
+-118.45,34.22,24.0,3442.0,1168.0,4625.0,1097.0,2.0699,183000.0,<1H OCEAN
+-118.12,34.02,25.0,2655.0,558.0,1466.0,525.0,3.0529,265800.0,<1H OCEAN
+-122.22,37.77,52.0,391.0,128.0,520.0,138.0,1.6471,95000.0,NEAR BAY
+-118.25,33.98,39.0,1553.0,461.0,2271.0,437.0,1.7378,121900.0,<1H OCEAN
+-119.19,34.25,12.0,232.0,37.0,79.0,35.0,4.1667,214600.0,NEAR OCEAN
+-122.86,38.1,44.0,2602.0,509.0,691.0,343.0,4.3125,261500.0,NEAR OCEAN
+-122.48,38.51,49.0,1977.0,393.0,741.0,339.0,3.1312,247600.0,INLAND
+-121.47,38.57,39.0,1360.0,368.0,589.0,338.0,2.1691,150000.0,INLAND
+-116.61,33.93,35.0,321.0,71.0,157.0,61.0,2.8056,68100.0,INLAND
+-118.67,34.3,5.0,6123.0,825.0,2440.0,736.0,7.9013,393000.0,<1H OCEAN
+-119.21,34.18,13.0,6103.0,1319.0,2986.0,1212.0,3.9718,215200.0,NEAR OCEAN
+-120.89,37.52,42.0,1200.0,221.0,647.0,192.0,2.5402,157500.0,INLAND
+-119.07,37.8,12.0,1736.0,352.0,330.0,123.0,3.5294,160700.0,INLAND
+-117.9,34.13,5.0,1126.0,316.0,819.0,311.0,1.5,139800.0,<1H OCEAN
+-118.14,34.08,24.0,3988.0,1098.0,2909.0,1034.0,2.7036,170000.0,<1H OCEAN
+-122.19,37.79,50.0,968.0,195.0,462.0,184.0,2.9844,179900.0,NEAR BAY
+-118.64,34.18,33.0,3808.0,623.0,1784.0,615.0,5.1641,263400.0,<1H OCEAN
+-120.72,35.63,31.0,3476.0,644.0,1476.0,567.0,3.3472,195200.0,<1H OCEAN
+-118.26,33.96,37.0,1625.0,383.0,1243.0,350.0,1.3971,89800.0,<1H OCEAN
+-118.27,33.86,33.0,1685.0,333.0,1484.0,318.0,4.3527,167000.0,<1H OCEAN
+-119.5,36.74,20.0,1089.0,208.0,531.0,212.0,4.5938,106900.0,INLAND
+-122.49,37.73,39.0,1937.0,336.0,742.0,307.0,5.1991,369400.0,NEAR OCEAN
+-121.72,37.98,5.0,7105.0,1143.0,3523.0,1088.0,5.0468,168800.0,INLAND
+-118.03,33.84,28.0,3857.0,857.0,2328.0,830.0,4.0156,196000.0,<1H OCEAN
+-118.2,33.8,45.0,2456.0,495.0,1300.0,450.0,3.9792,210200.0,NEAR OCEAN
+-118.24,33.99,41.0,1425.0,372.0,1803.0,353.0,1.6731,88200.0,<1H OCEAN
+-117.29,34.15,42.0,1811.0,345.0,856.0,352.0,2.9667,97000.0,INLAND
+-118.22,33.98,34.0,2225.0,753.0,2980.0,736.0,1.6685,128800.0,<1H OCEAN
+-118.34,34.07,52.0,3421.0,598.0,1203.0,564.0,4.1618,500001.0,<1H OCEAN
+-119.08,35.32,8.0,11609.0,2141.0,5696.0,2100.0,5.0012,106300.0,INLAND
+-118.34,34.1,24.0,1996.0,791.0,1215.0,672.0,1.5429,325000.0,<1H OCEAN
+-117.82,33.71,9.0,5206.0,992.0,4660.0,978.0,2.885,162500.0,<1H OCEAN
+-121.36,38.62,34.0,2447.0,503.0,1077.0,456.0,3.058,133000.0,INLAND
+-117.86,33.85,17.0,1131.0,236.0,622.0,244.0,4.9306,158500.0,<1H OCEAN
+-122.06,37.91,15.0,5393.0,1422.0,2133.0,1288.0,4.1612,232800.0,NEAR BAY
+-118.91,35.27,29.0,1401.0,317.0,1344.0,306.0,2.0921,61400.0,INLAND
+-119.72,37.38,16.0,2131.0,424.0,989.0,369.0,2.6071,103700.0,INLAND
+-118.08,33.91,36.0,1551.0,297.0,1100.0,322.0,5.1187,168100.0,<1H OCEAN
+-117.8,33.68,14.0,2635.0,516.0,1150.0,499.0,4.4391,306700.0,<1H OCEAN
+-118.15,34.0,32.0,3218.0,739.0,2368.0,730.0,3.1406,175300.0,<1H OCEAN
+-122.16,37.71,37.0,1507.0,242.0,632.0,253.0,4.5553,191000.0,NEAR BAY
+-117.83,33.74,23.0,1818.0,522.0,958.0,485.0,2.6771,131500.0,<1H OCEAN
+-118.37,34.16,6.0,6526.0,2007.0,3298.0,1790.0,2.7231,250000.0,<1H OCEAN
+-117.15,32.81,34.0,1629.0,318.0,900.0,282.0,3.1458,178300.0,NEAR OCEAN
+-117.15,32.7,52.0,458.0,148.0,1283.0,166.0,1.2863,86300.0,NEAR OCEAN
+-118.25,34.02,33.0,1676.0,525.0,2564.0,515.0,2.1957,100800.0,<1H OCEAN
+-117.95,33.95,29.0,4943.0,674.0,1913.0,641.0,6.8189,379300.0,<1H OCEAN
+-118.15,33.93,34.0,1745.0,404.0,1084.0,410.0,3.3411,220500.0,<1H OCEAN
+-118.11,34.02,40.0,1727.0,309.0,932.0,313.0,3.95,210200.0,<1H OCEAN
+-117.86,33.87,19.0,1591.0,279.0,891.0,237.0,5.6573,216000.0,<1H OCEAN
+-118.19,33.81,23.0,954.0,390.0,804.0,373.0,2.5833,181300.0,NEAR OCEAN
+-122.33,38.0,35.0,3779.0,711.0,2493.0,679.0,2.9781,109000.0,NEAR BAY
+-118.17,34.69,12.0,4881.0,803.0,2188.0,724.0,4.1667,171900.0,INLAND
+-122.27,37.77,52.0,1710.0,481.0,849.0,457.0,2.7115,220800.0,NEAR BAY
+-117.86,33.78,25.0,2635.0,660.0,1710.0,634.0,3.125,215000.0,<1H OCEAN
+-121.97,37.53,35.0,2277.0,420.0,1353.0,413.0,4.75,197000.0,<1H OCEAN
+-122.12,37.37,37.0,1446.0,181.0,549.0,190.0,10.7355,500001.0,NEAR BAY
+-121.86,37.41,16.0,2411.0,420.0,1671.0,442.0,6.5004,263600.0,<1H OCEAN
+-118.13,33.88,32.0,1788.0,459.0,1131.0,461.0,3.5278,166100.0,<1H OCEAN
+-122.54,37.9,41.0,3170.0,622.0,1091.0,528.0,3.7813,389200.0,NEAR BAY
+-121.94,37.33,34.0,1809.0,317.0,863.0,302.0,4.3,330500.0,<1H OCEAN
+-117.55,34.14,3.0,5710.0,919.0,2874.0,886.0,5.3638,206300.0,INLAND
+-118.94,34.17,16.0,3746.0,508.0,1556.0,452.0,6.3303,299400.0,<1H OCEAN
+-121.75,37.29,15.0,1486.0,310.0,455.0,296.0,4.3365,221000.0,<1H OCEAN
+-122.25,38.1,52.0,1780.0,373.0,824.0,317.0,2.75,109900.0,NEAR BAY
+-117.14,32.76,28.0,3025.0,756.0,1328.0,695.0,2.694,164100.0,NEAR OCEAN
+-122.42,40.63,23.0,2248.0,489.0,1132.0,444.0,1.6429,80400.0,INLAND
+-120.02,39.24,24.0,1602.0,426.0,751.0,257.0,1.7609,99300.0,INLAND
+-118.94,35.41,10.0,3216.0,526.0,1539.0,483.0,6.3639,143000.0,INLAND
+-118.25,34.02,35.0,1368.0,486.0,2239.0,461.0,1.913,114300.0,<1H OCEAN
+-118.53,34.45,10.0,5509.0,969.0,3002.0,959.0,5.5981,220100.0,<1H OCEAN
+-118.13,34.09,42.0,700.0,212.0,662.0,210.0,3.0078,191700.0,<1H OCEAN
+-121.28,38.73,6.0,4223.0,672.0,1747.0,631.0,5.419,267400.0,INLAND
+-120.48,34.65,26.0,1933.0,316.0,1001.0,319.0,4.4628,134400.0,NEAR OCEAN
+-120.97,37.67,31.0,1648.0,293.0,792.0,294.0,2.4,121500.0,INLAND
+-117.59,33.65,2.0,4860.0,1193.0,2332.0,1073.0,4.5022,151900.0,<1H OCEAN
+-118.32,34.03,31.0,2206.0,501.0,1194.0,435.0,1.9531,227800.0,<1H OCEAN
+-121.44,38.47,5.0,5666.0,1178.0,3139.0,1131.0,3.3608,108900.0,INLAND
+-118.11,34.08,30.0,2350.0,472.0,945.0,467.0,3.3421,201000.0,<1H OCEAN
+-122.5,37.91,31.0,7001.0,1282.0,2755.0,1267.0,5.4851,441100.0,NEAR BAY
+-122.18,37.77,42.0,1180.0,257.0,877.0,268.0,2.8125,97300.0,NEAR BAY
+-116.97,32.79,19.0,4108.0,1101.0,2971.0,1006.0,1.9893,112500.0,<1H OCEAN
+-117.47,34.07,24.0,1017.0,227.0,568.0,187.0,1.5972,112500.0,INLAND
+-117.1,32.7,42.0,2002.0,488.0,1505.0,464.0,1.5057,86300.0,NEAR OCEAN
+-120.0,38.93,17.0,8005.0,1382.0,999.0,383.0,3.9722,313400.0,INLAND
+-121.86,40.77,17.0,2816.0,639.0,1027.0,406.0,2.503,65600.0,INLAND
+-121.93,37.93,16.0,2876.0,391.0,1089.0,377.0,6.299,286900.0,INLAND
+-122.14,37.68,31.0,3184.0,716.0,1561.0,628.0,2.7955,183100.0,NEAR BAY
+-120.46,37.33,4.0,786.0,116.0,368.0,109.0,6.3215,138200.0,INLAND
+-122.53,37.88,36.0,2688.0,485.0,1064.0,449.0,4.4583,308600.0,NEAR BAY
+-117.2,33.15,11.0,4091.0,864.0,1927.0,765.0,3.0139,199000.0,<1H OCEAN
+-119.16,34.27,24.0,1824.0,331.0,1049.0,320.0,5.9181,221100.0,NEAR OCEAN
+-119.45,36.35,22.0,1824.0,333.0,1076.0,282.0,2.3365,69600.0,INLAND
+-118.35,34.01,35.0,3776.0,,1583.0,749.0,3.5486,332100.0,<1H OCEAN
+-118.18,33.9,25.0,1709.0,442.0,1177.0,410.0,2.4333,155000.0,<1H OCEAN
+-122.02,37.38,43.0,1261.0,317.0,836.0,333.0,4.0911,224600.0,<1H OCEAN
+-117.09,33.71,13.0,1974.0,426.0,1276.0,408.0,1.972,90500.0,INLAND
+-122.48,37.71,43.0,3850.0,1018.0,1497.0,829.0,3.5296,400000.0,NEAR OCEAN
+-117.07,32.75,9.0,3464.0,749.0,1687.0,645.0,3.3026,119100.0,NEAR OCEAN
+-118.48,34.2,12.0,3831.0,1083.0,2258.0,967.0,2.4375,255400.0,<1H OCEAN
+-117.53,33.92,12.0,2290.0,319.0,728.0,228.0,6.1561,233500.0,INLAND
+-119.61,34.45,33.0,3597.0,519.0,1207.0,479.0,5.3963,500001.0,<1H OCEAN
+-118.41,34.02,24.0,2610.0,756.0,1322.0,692.0,3.5022,281300.0,<1H OCEAN
+-120.42,34.96,31.0,3518.0,608.0,1386.0,572.0,3.6212,151400.0,<1H OCEAN
+-121.5,38.34,36.0,1212.0,255.0,569.0,256.0,2.0048,72900.0,INLAND
+-122.42,37.76,52.0,4407.0,1192.0,2280.0,1076.0,3.3937,270000.0,NEAR BAY
+-117.32,34.49,7.0,4584.0,1051.0,2049.0,918.0,1.6232,93400.0,INLAND
+-117.26,32.95,15.0,1882.0,233.0,704.0,219.0,6.9794,500001.0,NEAR OCEAN
+-122.3,40.45,32.0,1286.0,271.0,694.0,236.0,1.6579,68500.0,INLAND
+-121.92,37.25,36.0,3874.0,656.0,1826.0,639.0,4.9662,258500.0,<1H OCEAN
+-117.08,32.72,32.0,2286.0,468.0,1741.0,467.0,3.0446,101900.0,NEAR OCEAN
+-119.06,35.32,15.0,3944.0,746.0,2355.0,757.0,3.569,70700.0,INLAND
+-118.42,34.25,37.0,1545.0,341.0,1909.0,352.0,3.6791,148100.0,<1H OCEAN
+-118.36,33.88,31.0,2518.0,543.0,1107.0,508.0,4.7404,295800.0,<1H OCEAN
+-121.48,38.58,52.0,576.0,146.0,273.0,127.0,2.01,94300.0,INLAND
+-118.91,35.3,28.0,1793.0,358.0,1233.0,351.0,2.7845,82200.0,INLAND
+-118.06,34.1,38.0,3229.0,636.0,1599.0,609.0,3.8646,257100.0,<1H OCEAN
+-122.23,37.79,52.0,1783.0,395.0,1659.0,412.0,2.9357,107900.0,NEAR BAY
+-122.08,37.63,34.0,1619.0,293.0,1148.0,310.0,4.0326,164700.0,NEAR BAY
+-121.95,37.3,21.0,4193.0,1068.0,2487.0,1011.0,3.7188,293000.0,<1H OCEAN
+-117.73,34.04,26.0,3827.0,814.0,3367.0,810.0,3.15,129700.0,INLAND
+-118.35,33.94,42.0,1028.0,278.0,1369.0,261.0,3.3125,144600.0,<1H OCEAN
+-118.33,34.01,44.0,2182.0,492.0,878.0,493.0,1.9631,181300.0,<1H OCEAN
+-122.36,38.4,16.0,2716.0,546.0,898.0,500.0,2.2536,201200.0,INLAND
+-121.77,37.22,16.0,1617.0,306.0,667.0,264.0,4.5221,191100.0,<1H OCEAN
+-118.36,33.93,44.0,520.0,116.0,392.0,106.0,3.0132,202500.0,<1H OCEAN
+-117.37,33.97,38.0,1156.0,241.0,877.0,200.0,1.4514,79900.0,INLAND
+-117.24,32.83,18.0,3109.0,501.0,949.0,368.0,7.4351,445700.0,NEAR OCEAN
+-121.47,38.58,43.0,3807.0,952.0,1484.0,850.0,2.3266,137500.0,INLAND
+-118.41,33.92,29.0,1436.0,401.0,674.0,343.0,3.6389,275000.0,<1H OCEAN
+-117.03,32.63,13.0,2087.0,313.0,1165.0,330.0,5.7789,227700.0,NEAR OCEAN
+-117.08,32.61,27.0,2264.0,525.0,1485.0,468.0,3.3514,149100.0,NEAR OCEAN
+-119.19,34.19,35.0,2599.0,552.0,2726.0,543.0,3.2212,180500.0,NEAR OCEAN
+-120.42,34.94,32.0,2844.0,551.0,1337.0,516.0,2.7188,133700.0,<1H OCEAN
+-119.55,36.61,14.0,3004.0,793.0,3535.0,735.0,1.586,56900.0,INLAND
+-121.3,37.98,39.0,3375.0,659.0,1388.0,631.0,2.6364,93800.0,INLAND
+-122.43,37.79,52.0,6186.0,1566.0,2065.0,1374.0,5.8543,500001.0,NEAR BAY
+-117.31,34.51,18.0,2704.0,698.0,1611.0,597.0,2.0243,82300.0,INLAND
+-117.43,33.96,28.0,3747.0,651.0,2399.0,646.0,3.8682,116500.0,INLAND
+-115.54,32.97,41.0,2429.0,454.0,1188.0,430.0,3.0091,70800.0,INLAND
+-122.21,37.77,46.0,1234.0,375.0,1183.0,354.0,2.3309,98700.0,NEAR BAY
+-118.23,33.98,35.0,1366.0,496.0,2160.0,497.0,2.2059,150000.0,<1H OCEAN
+-122.32,37.57,52.0,499.0,148.0,318.0,145.0,2.9934,256300.0,NEAR OCEAN
+-118.18,33.74,30.0,5915.0,1750.0,2136.0,1503.0,4.0968,310000.0,NEAR OCEAN
+-120.45,34.95,7.0,1479.0,532.0,1057.0,459.0,2.2538,162500.0,<1H OCEAN
+-118.03,33.9,35.0,1434.0,279.0,744.0,252.0,3.7308,202400.0,<1H OCEAN
+-117.29,34.14,48.0,1717.0,307.0,610.0,267.0,3.125,97600.0,INLAND
+-118.21,33.82,33.0,1278.0,311.0,1157.0,320.0,3.5054,146800.0,NEAR OCEAN
+-117.15,33.2,16.0,2690.0,459.0,1253.0,393.0,4.0328,294600.0,<1H OCEAN
+-117.76,34.12,16.0,9020.0,1509.0,3575.0,1486.0,4.2415,275700.0,INLAND
+-117.76,33.53,28.0,3085.0,499.0,1176.0,480.0,7.9794,426100.0,<1H OCEAN
+-120.66,35.49,17.0,4422.0,945.0,2307.0,885.0,2.8285,171300.0,<1H OCEAN
+-121.58,39.76,19.0,2487.0,485.0,1110.0,453.0,3.1061,110200.0,INLAND
+-118.35,33.91,34.0,2055.0,448.0,1134.0,408.0,3.825,235400.0,<1H OCEAN
+-117.95,33.88,34.0,1939.0,355.0,817.0,314.0,3.6705,275000.0,<1H OCEAN
+-121.21,38.66,15.0,6940.0,1019.0,2829.0,990.0,5.4889,232300.0,INLAND
+-121.37,38.61,39.0,823.0,146.0,329.0,144.0,3.0833,114100.0,INLAND
+-117.49,34.04,4.0,6034.0,1170.0,3527.0,1098.0,4.1775,143700.0,INLAND
+-117.92,33.8,17.0,1317.0,256.0,679.0,272.0,4.6696,159500.0,<1H OCEAN
+-122.2,37.9,36.0,2107.0,287.0,740.0,280.0,10.3416,500001.0,NEAR BAY
+-118.61,34.2,29.0,1673.0,284.0,794.0,270.0,5.5191,245800.0,<1H OCEAN
+-121.67,39.18,26.0,2121.0,375.0,1125.0,366.0,3.3958,94600.0,INLAND
+-117.95,33.81,33.0,1724.0,291.0,943.0,285.0,5.118,195200.0,<1H OCEAN
+-122.34,37.52,34.0,3559.0,560.0,1747.0,550.0,6.6959,411200.0,NEAR OCEAN
+-117.32,33.17,18.0,2143.0,299.0,828.0,283.0,4.2383,239000.0,NEAR OCEAN
+-123.91,41.68,22.0,1880.0,360.0,743.0,314.0,2.9688,152700.0,<1H OCEAN
+-118.29,34.18,10.0,4292.0,1075.0,2719.0,987.0,3.6974,286600.0,<1H OCEAN
+-117.93,33.85,27.0,1962.0,544.0,1492.0,481.0,1.9621,118100.0,<1H OCEAN
+-122.34,37.96,33.0,1817.0,441.0,1220.0,389.0,2.5382,103600.0,NEAR BAY
+-122.16,38.02,40.0,1800.0,290.0,761.0,277.0,5.1265,196100.0,NEAR BAY
+-117.39,33.95,36.0,1380.0,269.0,598.0,262.0,3.1667,122900.0,INLAND
+-122.39,37.76,52.0,2316.0,468.0,1047.0,476.0,4.5057,321600.0,NEAR BAY
+-122.35,40.56,12.0,3900.0,863.0,2145.0,864.0,1.9881,85200.0,INLAND
+-121.89,37.3,47.0,1604.0,284.0,639.0,278.0,5.8415,283300.0,<1H OCEAN
+-121.88,38.0,22.0,721.0,117.0,367.0,129.0,5.3098,151900.0,INLAND
+-118.2,34.21,40.0,1477.0,228.0,609.0,224.0,7.8375,500001.0,<1H OCEAN
+-118.15,33.96,33.0,1471.0,451.0,2272.0,482.0,2.5385,160900.0,<1H OCEAN
+-121.78,37.34,11.0,3195.0,410.0,1774.0,418.0,7.0671,378200.0,<1H OCEAN
+-121.89,37.67,20.0,2948.0,471.0,1181.0,474.0,6.0604,247900.0,INLAND
+-121.56,37.08,17.0,6725.0,1051.0,3439.0,1027.0,6.4313,393100.0,<1H OCEAN
+-117.87,33.91,16.0,2434.0,455.0,1017.0,476.0,4.2188,176300.0,<1H OCEAN
+-117.34,33.96,15.0,6437.0,1298.0,2805.0,1205.0,4.1883,184500.0,INLAND
+-117.06,32.67,29.0,4047.0,754.0,2353.0,730.0,4.0505,125000.0,NEAR OCEAN
+-115.37,32.81,23.0,1458.0,294.0,866.0,275.0,2.3594,74300.0,INLAND
+-121.78,38.01,19.0,2688.0,469.0,1216.0,422.0,4.4491,133900.0,INLAND
+-116.38,33.74,7.0,17579.0,3479.0,3581.0,1820.0,4.084,194500.0,INLAND
+-122.1,37.39,36.0,1860.0,367.0,794.0,366.0,5.0871,354500.0,NEAR BAY
+-118.2,33.97,43.0,825.0,212.0,820.0,184.0,1.8897,174300.0,<1H OCEAN
+-117.02,33.6,7.0,1972.0,352.0,964.0,317.0,3.244,337200.0,INLAND
+-122.11,37.4,16.0,1994.0,489.0,1173.0,472.0,4.1875,266400.0,NEAR BAY
+-122.43,37.78,2.0,1205.0,468.0,577.0,363.0,3.6437,275000.0,NEAR BAY
+-118.34,33.93,36.0,1528.0,486.0,1824.0,470.0,2.2679,153900.0,<1H OCEAN
+-117.57,34.02,5.0,6933.0,1311.0,3845.0,1285.0,4.6727,158900.0,INLAND
+-117.18,34.07,7.0,1347.0,301.0,799.0,276.0,2.9485,112500.0,INLAND
+-117.89,33.87,32.0,1133.0,216.0,693.0,228.0,3.3594,202100.0,<1H OCEAN
+-122.91,39.17,44.0,202.0,42.0,142.0,39.0,4.35,68300.0,INLAND
+-121.46,38.51,18.0,2123.0,606.0,1576.0,599.0,1.5735,110000.0,INLAND
+-122.08,37.95,33.0,1043.0,157.0,425.0,148.0,4.8702,235600.0,NEAR BAY
+-118.08,33.76,26.0,996.0,364.0,366.0,313.0,1.2813,46700.0,<1H OCEAN
+-121.1,39.0,16.0,1106.0,195.0,505.0,187.0,5.0126,192300.0,INLAND
+-118.52,34.19,37.0,1560.0,275.0,763.0,284.0,3.8516,206900.0,<1H OCEAN
+-117.97,33.99,23.0,3335.0,570.0,1560.0,555.0,5.7268,300300.0,<1H OCEAN
+-118.1,33.91,35.0,1592.0,335.0,1238.0,320.0,4.9732,165000.0,<1H OCEAN
+-118.16,34.01,37.0,690.0,261.0,952.0,255.0,1.6354,158900.0,<1H OCEAN
+-122.28,37.96,35.0,1579.0,243.0,734.0,264.0,5.5,201000.0,NEAR BAY
+-119.81,36.81,33.0,3972.0,594.0,1324.0,561.0,5.4513,143300.0,INLAND
+-118.29,34.09,35.0,2198.0,998.0,3441.0,912.0,2.0467,158300.0,<1H OCEAN
+-117.2,34.24,22.0,8106.0,1665.0,1062.0,423.0,3.0434,137200.0,INLAND
+-117.23,32.85,25.0,4229.0,601.0,1634.0,574.0,6.3955,316700.0,NEAR OCEAN
+-122.64,37.96,29.0,377.0,58.0,151.0,67.0,9.5551,500001.0,NEAR OCEAN
+-117.89,33.61,41.0,1790.0,361.0,540.0,284.0,6.0247,500001.0,<1H OCEAN
+-117.91,33.84,35.0,1244.0,324.0,1603.0,322.0,2.9583,175400.0,<1H OCEAN
+-118.51,34.21,36.0,2396.0,421.0,1064.0,398.0,4.7,223600.0,<1H OCEAN
+-118.15,33.85,36.0,1491.0,259.0,699.0,266.0,4.0781,217300.0,<1H OCEAN
+-117.19,32.69,35.0,2921.0,438.0,1042.0,415.0,6.3612,482700.0,NEAR OCEAN
+-122.2,37.81,45.0,2964.0,436.0,1067.0,426.0,6.7851,323500.0,NEAR BAY
+-118.17,33.87,48.0,2258.0,509.0,1395.0,492.0,3.765,164800.0,<1H OCEAN
+-118.51,34.11,29.0,9013.0,1117.0,2919.0,1061.0,13.947,500001.0,<1H OCEAN
+-118.26,33.97,46.0,1521.0,352.0,1100.0,334.0,1.55,100600.0,<1H OCEAN
+-117.86,34.14,33.0,2344.0,363.0,1098.0,359.0,6.2089,283400.0,INLAND
+-117.92,33.91,27.0,2558.0,310.0,891.0,316.0,9.5561,411800.0,<1H OCEAN
+-116.93,32.83,19.0,3038.0,529.0,1463.0,509.0,3.944,172500.0,<1H OCEAN
+-119.78,36.86,8.0,3468.0,675.0,1604.0,626.0,4.2071,128300.0,INLAND
+-115.59,32.69,30.0,935.0,177.0,649.0,148.0,2.5769,94400.0,INLAND
+-117.32,33.01,29.0,3584.0,712.0,1619.0,667.0,4.125,394400.0,NEAR OCEAN
+-122.24,37.55,3.0,6164.0,1175.0,2198.0,975.0,6.7413,435900.0,NEAR BAY
+-119.19,34.21,34.0,3413.0,693.0,2223.0,651.0,3.8239,208200.0,NEAR OCEAN
+-119.28,36.54,33.0,1470.0,330.0,1222.0,301.0,1.8163,57400.0,INLAND
+-120.65,40.42,39.0,3240.0,652.0,1467.0,621.0,2.1875,64300.0,INLAND
+-117.25,34.16,37.0,1709.0,278.0,744.0,274.0,3.7188,116600.0,INLAND
+-121.3,38.05,52.0,122.0,26.0,62.0,25.0,1.15,112500.0,INLAND
+-117.89,33.92,17.0,2936.0,555.0,1381.0,535.0,5.4617,190300.0,<1H OCEAN
+-118.17,34.02,42.0,946.0,272.0,1191.0,261.0,2.45,132000.0,<1H OCEAN
+-117.8,33.69,13.0,1161.0,289.0,630.0,296.0,3.3438,333300.0,<1H OCEAN
+-122.42,37.76,46.0,2150.0,817.0,2075.0,807.0,1.3824,212500.0,NEAR BAY
+-117.99,33.71,18.0,1994.0,578.0,3031.0,577.0,2.7614,237500.0,<1H OCEAN
+-122.39,37.57,35.0,520.0,83.0,185.0,76.0,6.4865,450000.0,NEAR OCEAN
+-122.46,37.72,52.0,2951.0,406.0,1115.0,397.0,6.7228,405200.0,NEAR OCEAN
+-117.04,32.58,20.0,2029.0,357.0,1497.0,353.0,4.0089,132100.0,NEAR OCEAN
+-116.05,34.12,19.0,301.0,65.0,150.0,56.0,3.125,65600.0,INLAND
+-117.01,32.73,22.0,2526.0,530.0,1556.0,529.0,2.8646,120800.0,NEAR OCEAN
+-118.17,34.04,45.0,911.0,238.0,1005.0,229.0,2.8167,114000.0,<1H OCEAN
+-121.94,37.28,27.0,2859.0,464.0,1144.0,430.0,5.0822,327500.0,<1H OCEAN
+-117.29,33.18,17.0,821.0,176.0,436.0,168.0,3.1667,160600.0,NEAR OCEAN
+-122.05,37.59,15.0,6243.0,1273.0,3163.0,1274.0,3.7462,212500.0,NEAR BAY
+-122.45,37.71,50.0,1441.0,283.0,1159.0,286.0,4.5417,233700.0,NEAR OCEAN
+-122.69,38.34,15.0,3091.0,697.0,1602.0,682.0,4.0071,135500.0,<1H OCEAN
+-122.32,37.92,28.0,4649.0,977.0,2606.0,953.0,3.2674,129100.0,NEAR BAY
+-118.02,34.11,35.0,2454.0,458.0,1110.0,435.0,3.8029,414800.0,INLAND
+-118.03,33.9,36.0,1143.0,193.0,826.0,188.0,5.3184,171100.0,<1H OCEAN
+-119.84,36.83,17.0,2273.0,298.0,700.0,263.0,6.8645,195900.0,INLAND
+-118.3,34.07,26.0,2107.0,757.0,2660.0,740.0,2.3375,282300.0,<1H OCEAN
+-121.98,37.95,14.0,6290.0,854.0,2724.0,820.0,6.7371,267400.0,INLAND
+-121.82,37.34,23.0,7609.0,1446.0,6034.0,1414.0,4.8424,195300.0,<1H OCEAN
+-118.35,33.9,32.0,1056.0,225.0,565.0,231.0,3.9485,230000.0,<1H OCEAN
+-117.69,33.55,3.0,1618.0,266.0,710.0,246.0,6.0743,274300.0,<1H OCEAN
+-118.25,33.98,40.0,1867.0,633.0,2223.0,609.0,1.7207,105100.0,<1H OCEAN
+-119.02,36.07,39.0,1173.0,269.0,702.0,232.0,1.6146,53100.0,INLAND
+-117.84,33.81,26.0,5574.0,1025.0,2607.0,988.0,4.0324,244900.0,<1H OCEAN
+-119.08,35.34,16.0,1535.0,238.0,768.0,236.0,5.4449,118500.0,INLAND
+-117.93,33.75,15.0,2448.0,602.0,1666.0,575.0,3.5967,141600.0,<1H OCEAN
+-122.14,37.75,36.0,690.0,105.0,299.0,109.0,4.0313,195500.0,NEAR BAY
+-119.01,36.07,44.0,2450.0,575.0,1330.0,508.0,1.6103,50900.0,INLAND
+-118.24,34.14,36.0,1813.0,560.0,1501.0,544.0,1.9125,238000.0,<1H OCEAN
+-122.16,37.75,44.0,617.0,131.0,378.0,135.0,2.5568,111100.0,NEAR BAY
+-117.8,33.55,35.0,2067.0,428.0,724.0,377.0,5.8371,500001.0,<1H OCEAN
+-116.3,33.68,10.0,2387.0,481.0,863.0,304.0,2.8882,137500.0,INLAND
+-118.37,33.83,35.0,1207.0,207.0,601.0,213.0,4.7308,353400.0,<1H OCEAN
+-116.95,33.31,16.0,2921.0,639.0,1838.0,540.0,2.2393,117000.0,<1H OCEAN
+-118.21,34.05,45.0,2146.0,607.0,2868.0,625.0,2.121,144000.0,<1H OCEAN
+-117.21,33.17,16.0,1787.0,361.0,1446.0,362.0,3.75,163800.0,<1H OCEAN
+-121.77,38.01,13.0,2983.0,534.0,1417.0,510.0,3.9861,168100.0,INLAND
+-117.97,33.75,32.0,1564.0,270.0,973.0,290.0,3.75,190400.0,<1H OCEAN
+-118.3,34.06,47.0,1390.0,872.0,2860.0,827.0,1.468,137500.0,<1H OCEAN
+-117.55,33.83,6.0,502.0,76.0,228.0,65.0,4.2386,500001.0,INLAND
+-117.85,33.76,33.0,1866.0,327.0,1053.0,371.0,4.5461,213800.0,<1H OCEAN
+-119.19,35.96,25.0,2014.0,402.0,1160.0,362.0,1.881,52500.0,INLAND
+-117.04,32.67,14.0,3464.0,683.0,2139.0,734.0,4.0668,137500.0,NEAR OCEAN
+-121.27,38.72,6.0,4664.0,644.0,2105.0,663.0,6.0804,198700.0,INLAND
+-121.4,37.74,20.0,2706.0,477.0,1236.0,474.0,4.15,322400.0,INLAND
+-119.02,35.33,26.0,3691.0,826.0,2072.0,827.0,2.1553,84700.0,INLAND
+-117.82,33.77,27.0,2578.0,314.0,976.0,340.0,7.1882,359200.0,<1H OCEAN
+-121.38,38.55,23.0,2790.0,430.0,1407.0,460.0,4.3288,133700.0,INLAND
+-121.29,38.07,21.0,1185.0,207.0,533.0,213.0,3.1917,204500.0,INLAND
+-121.33,38.66,15.0,4371.0,908.0,1842.0,818.0,2.7797,105500.0,INLAND
+-121.87,37.32,36.0,1471.0,360.0,1182.0,326.0,2.7031,175800.0,<1H OCEAN
+-114.98,33.82,15.0,644.0,129.0,137.0,52.0,3.2097,71300.0,INLAND
+-121.0,37.71,52.0,102.0,23.0,35.0,33.0,2.25,175000.0,INLAND
+-124.1,41.04,26.0,1633.0,380.0,890.0,370.0,1.9741,97900.0,NEAR OCEAN
+-117.8,34.03,25.0,4240.0,643.0,1885.0,637.0,6.2384,247600.0,<1H OCEAN
+-117.94,34.07,25.0,1814.0,404.0,1187.0,363.0,3.3523,170800.0,<1H OCEAN
+-117.9,33.61,44.0,1469.0,312.0,507.0,266.0,3.4937,500001.0,<1H OCEAN
+-121.42,38.49,17.0,13180.0,2444.0,7235.0,2335.0,3.363,103000.0,INLAND
+-119.8,36.72,19.0,1334.0,336.0,1171.0,319.0,1.0481,48500.0,INLAND
+-117.88,34.09,29.0,3416.0,790.0,2223.0,728.0,3.5109,186000.0,<1H OCEAN
+-118.23,33.95,27.0,504.0,142.0,789.0,167.0,0.9518,91400.0,<1H OCEAN
+-118.23,34.11,33.0,2612.0,646.0,2496.0,606.0,3.133,156000.0,<1H OCEAN
+-118.36,33.82,28.0,67.0,15.0,49.0,11.0,6.1359,330000.0,<1H OCEAN
+-117.11,32.8,17.0,3890.0,586.0,2791.0,595.0,3.2197,67500.0,NEAR OCEAN
+-120.26,39.32,24.0,6012.0,1227.0,780.0,358.0,3.0043,122100.0,INLAND
+-122.13,37.47,30.0,1480.0,294.0,1126.0,301.0,4.983,166700.0,NEAR BAY
+-117.28,34.13,44.0,2469.0,568.0,1363.0,517.0,1.8396,77100.0,INLAND
+-118.3,33.75,19.0,2356.0,440.0,1291.0,418.0,4.2308,416100.0,<1H OCEAN
+-115.4,32.7,19.0,583.0,113.0,531.0,134.0,1.6838,95800.0,INLAND
+-118.27,34.07,27.0,1190.0,,1795.0,422.0,1.7016,160000.0,<1H OCEAN
+-118.13,34.15,18.0,1665.0,477.0,1095.0,390.0,2.6038,155600.0,<1H OCEAN
+-122.39,37.6,36.0,1770.0,499.0,1225.0,459.0,2.56,273100.0,NEAR OCEAN
+-117.32,34.11,38.0,1462.0,337.0,1208.0,324.0,2.2604,68100.0,INLAND
+-118.43,34.15,26.0,2900.0,667.0,1090.0,590.0,3.7125,447400.0,<1H OCEAN
+-122.42,37.74,52.0,2713.0,624.0,1370.0,594.0,4.6547,325700.0,NEAR BAY
+-121.65,38.03,28.0,3144.0,694.0,1095.0,482.0,3.4402,192400.0,INLAND
+-122.3,37.88,52.0,409.0,97.0,208.0,98.0,1.6971,138800.0,NEAR BAY
+-121.86,38.0,16.0,3216.0,464.0,1504.0,453.0,5.25,161700.0,INLAND
+-117.22,33.87,16.0,56.0,7.0,39.0,14.0,2.625,500001.0,INLAND
+-117.23,34.11,22.0,1162.0,221.0,995.0,244.0,2.5875,81300.0,INLAND
+-118.15,33.94,37.0,1594.0,321.0,1003.0,323.0,3.3289,199700.0,<1H OCEAN
+-118.0,34.15,48.0,3436.0,673.0,1540.0,648.0,4.275,256800.0,INLAND
+-122.17,37.74,46.0,1026.0,226.0,749.0,225.0,3.0298,107600.0,NEAR BAY
+-117.04,32.97,13.0,6711.0,1256.0,3683.0,1220.0,4.5746,175700.0,<1H OCEAN
+-117.22,34.54,8.0,12526.0,2495.0,6133.0,2324.0,2.9072,119200.0,INLAND
+-117.94,33.78,34.0,2627.0,468.0,1409.0,450.0,4.7731,199200.0,<1H OCEAN
+-122.09,37.64,36.0,1885.0,307.0,853.0,271.0,4.1141,173100.0,NEAR BAY
+-118.31,33.98,50.0,1985.0,454.0,1090.0,410.0,1.825,106600.0,<1H OCEAN
+-117.44,34.09,12.0,3598.0,828.0,2588.0,781.0,2.375,113800.0,INLAND
+-117.12,32.72,36.0,6096.0,1285.0,3093.0,1229.0,3.37,159100.0,NEAR OCEAN
+-117.34,34.49,9.0,3293.0,585.0,1678.0,530.0,3.2941,98300.0,INLAND
+-121.04,37.6,27.0,958.0,184.0,580.0,177.0,2.1875,82800.0,INLAND
+-121.31,37.97,42.0,1824.0,277.0,720.0,309.0,5.1915,183700.0,INLAND
+-118.98,35.4,34.0,813.0,171.0,440.0,170.0,2.8393,69800.0,INLAND
+-121.97,37.33,21.0,8275.0,1566.0,3636.0,1524.0,5.1506,302100.0,<1H OCEAN
+-122.28,37.87,52.0,589.0,132.0,288.0,131.0,3.5156,200000.0,NEAR BAY
+-121.31,37.44,33.0,69.0,28.0,47.0,14.0,0.536,112500.0,INLAND
+-118.23,34.21,36.0,2988.0,719.0,1357.0,657.0,3.5174,268000.0,<1H OCEAN
+-117.53,34.1,5.0,2185.0,488.0,1379.0,458.0,3.7917,103000.0,INLAND
+-117.31,34.04,29.0,2481.0,383.0,1188.0,385.0,4.7344,134600.0,INLAND
+-119.34,36.21,22.0,3065.0,726.0,2165.0,738.0,1.4792,54400.0,INLAND
+-122.63,38.94,25.0,661.0,144.0,192.0,93.0,1.7566,49000.0,INLAND
+-122.02,37.33,25.0,3823.0,584.0,1689.0,571.0,7.3693,373600.0,<1H OCEAN
+-122.84,38.4,15.0,3080.0,617.0,1446.0,599.0,3.6696,194400.0,<1H OCEAN
+-119.44,36.6,5.0,2353.0,608.0,2505.0,573.0,2.2863,69200.0,INLAND
+-121.48,38.46,8.0,10505.0,1777.0,6002.0,1694.0,4.0516,121200.0,INLAND
+-118.27,34.01,35.0,1672.0,556.0,2106.0,519.0,1.2206,129200.0,<1H OCEAN
+-118.37,34.09,24.0,630.0,172.0,257.0,147.0,5.5224,400000.0,<1H OCEAN
+-122.1,37.37,40.0,2224.0,354.0,929.0,345.0,8.1064,500001.0,NEAR BAY
+-121.46,38.63,26.0,3185.0,658.0,2444.0,626.0,1.56,67600.0,INLAND
+-117.69,33.6,16.0,2205.0,393.0,1333.0,402.0,3.475,279500.0,<1H OCEAN
+-119.22,34.15,32.0,3152.0,596.0,3490.0,526.0,2.725,450000.0,NEAR OCEAN
+-120.47,34.64,8.0,2482.0,586.0,1427.0,540.0,3.071,120400.0,NEAR OCEAN
+-122.37,37.93,37.0,709.0,190.0,644.0,174.0,0.8641,84200.0,NEAR BAY
+-121.32,36.79,30.0,516.0,90.0,288.0,95.0,3.6333,202500.0,INLAND
+-118.13,34.07,20.0,2130.0,654.0,1870.0,578.0,2.3664,192200.0,<1H OCEAN
+-118.33,34.16,23.0,1359.0,428.0,770.0,380.0,3.4016,234600.0,<1H OCEAN
+-117.93,33.83,30.0,1561.0,381.0,1104.0,391.0,3.375,201900.0,<1H OCEAN
+-119.29,36.3,20.0,1157.0,179.0,572.0,191.0,5.3495,177300.0,INLAND
+-117.14,32.71,52.0,500.0,,480.0,108.0,1.8696,91100.0,NEAR OCEAN
+-117.08,32.91,16.0,1653.0,228.0,690.0,224.0,6.5853,248400.0,<1H OCEAN
+-118.35,33.96,26.0,2773.0,681.0,1560.0,631.0,3.1354,164300.0,<1H OCEAN
+-118.27,34.46,10.0,2184.0,405.0,1119.0,370.0,4.7437,294000.0,INLAND
+-118.47,34.04,21.0,5041.0,1491.0,2719.0,1420.0,3.5335,268800.0,<1H OCEAN
+-118.39,33.82,30.0,3433.0,918.0,1526.0,828.0,4.5817,500001.0,NEAR OCEAN
+-121.06,39.23,10.0,2229.0,537.0,982.0,512.0,2.186,132700.0,INLAND
+-120.27,37.12,17.0,3328.0,628.0,1580.0,619.0,2.9861,81500.0,INLAND
+-117.08,33.23,14.0,3337.0,571.0,1385.0,512.0,4.15,272200.0,<1H OCEAN
+-122.07,37.69,31.0,5914.0,1309.0,2999.0,1295.0,3.0964,190500.0,NEAR BAY
+-121.13,38.55,8.0,530.0,109.0,398.0,96.0,4.2031,212500.0,INLAND
+-118.1,33.98,34.0,1357.0,310.0,1042.0,287.0,3.4083,156700.0,<1H OCEAN
+-121.91,37.44,19.0,2174.0,484.0,1645.0,484.0,5.0362,255100.0,<1H OCEAN
+-118.41,34.27,38.0,858.0,203.0,1250.0,204.0,2.9219,137900.0,<1H OCEAN
+-122.12,37.37,18.0,1617.0,231.0,555.0,222.0,8.9021,500001.0,NEAR BAY
+-118.47,34.04,32.0,2909.0,748.0,1310.0,706.0,4.516,350000.0,<1H OCEAN
+-122.17,37.78,52.0,653.0,128.0,296.0,121.0,4.175,144000.0,NEAR BAY
+-117.65,33.53,7.0,6814.0,785.0,2175.0,681.0,10.49,500001.0,<1H OCEAN
+-118.04,34.0,30.0,5308.0,854.0,2114.0,838.0,5.1985,279200.0,<1H OCEAN
+-121.05,38.92,34.0,2144.0,372.0,899.0,378.0,3.3021,158800.0,INLAND
+-116.77,33.08,13.0,1406.0,260.0,737.0,279.0,5.5842,239100.0,<1H OCEAN
+-118.24,34.15,45.0,1235.0,271.0,499.0,263.0,3.1435,282600.0,<1H OCEAN
+-118.18,34.13,52.0,2228.0,475.0,1311.0,452.0,3.5341,182100.0,<1H OCEAN
+-117.85,33.79,46.0,1846.0,383.0,867.0,336.0,3.4234,200000.0,<1H OCEAN
+-121.32,37.67,21.0,1494.0,271.0,781.0,255.0,4.3015,250000.0,INLAND
+-122.0,37.51,7.0,6352.0,1390.0,3223.0,1316.0,4.9867,181700.0,<1H OCEAN
+-122.39,37.72,45.0,2893.0,570.0,1923.0,535.0,3.6607,192300.0,NEAR BAY
+-117.96,34.07,35.0,2819.0,529.0,1508.0,485.0,4.6118,191700.0,<1H OCEAN
+-117.6,33.85,9.0,6538.0,955.0,2928.0,892.0,5.3006,221400.0,<1H OCEAN
+-119.04,35.41,20.0,3268.0,833.0,1622.0,758.0,1.3587,67500.0,INLAND
+-122.25,37.89,42.0,2863.0,460.0,1031.0,448.0,6.7138,368600.0,NEAR BAY
+-118.31,33.76,26.0,4486.0,709.0,1873.0,719.0,6.5704,414700.0,<1H OCEAN
+-122.27,40.53,17.0,2255.0,416.0,1171.0,411.0,2.875,129800.0,INLAND
+-118.49,34.21,25.0,1131.0,449.0,746.0,420.0,1.3565,225000.0,<1H OCEAN
+-122.43,37.79,52.0,3219.0,969.0,1152.0,830.0,4.2042,500001.0,NEAR BAY
+-117.31,34.13,38.0,1287.0,284.0,1047.0,269.0,2.2865,65500.0,INLAND
+-119.85,36.82,16.0,1852.0,274.0,887.0,286.0,5.5405,119300.0,INLAND
+-121.59,37.19,52.0,220.0,32.0,55.0,26.0,15.0001,131300.0,<1H OCEAN
+-119.28,34.27,44.0,706.0,176.0,399.0,149.0,3.0089,166700.0,NEAR OCEAN
+-118.63,34.24,9.0,4759.0,924.0,1884.0,915.0,4.8333,277200.0,<1H OCEAN
+-117.17,32.63,26.0,1617.0,279.0,2745.0,250.0,3.5357,67500.0,NEAR OCEAN
+-117.35,34.0,38.0,1214.0,254.0,723.0,236.0,2.5469,87800.0,INLAND
+-118.05,33.97,36.0,2854.0,688.0,2816.0,673.0,3.6,154000.0,<1H OCEAN
+-118.1,33.99,36.0,1529.0,290.0,1271.0,287.0,3.6875,175200.0,<1H OCEAN
+-118.99,37.63,10.0,7744.0,1573.0,483.0,224.0,3.2917,231800.0,INLAND
+-122.13,37.37,30.0,2139.0,260.0,742.0,242.0,11.806,500001.0,NEAR BAY
+-117.28,33.04,12.0,4459.0,928.0,2471.0,888.0,3.5179,252700.0,NEAR OCEAN
+-117.25,33.05,16.0,2794.0,476.0,1387.0,442.0,4.3286,213400.0,NEAR OCEAN
+-118.43,33.97,16.0,70.0,7.0,17.0,4.0,7.7197,500001.0,<1H OCEAN
+-117.99,33.85,35.0,1661.0,272.0,949.0,276.0,5.2548,192600.0,<1H OCEAN
+-122.11,37.68,37.0,1976.0,481.0,1197.0,465.0,2.5772,170200.0,NEAR BAY
+-121.89,36.63,20.0,1834.0,554.0,971.0,514.0,3.0383,217300.0,<1H OCEAN
+-118.45,33.8,31.0,4803.0,575.0,1490.0,577.0,11.9993,500001.0,NEAR OCEAN
+-122.14,37.67,34.0,3036.0,533.0,1366.0,500.0,4.2386,192300.0,NEAR BAY
+-118.43,34.04,52.0,2425.0,435.0,962.0,412.0,5.8587,494700.0,<1H OCEAN
+-122.73,38.4,30.0,3689.0,746.0,2250.0,697.0,2.975,157300.0,<1H OCEAN
+-120.9,37.76,20.0,570.0,112.0,304.0,108.0,2.2024,156300.0,INLAND
+-123.37,39.43,32.0,2780.0,470.0,1281.0,479.0,3.588,96000.0,<1H OCEAN
+-118.34,33.9,23.0,2395.0,498.0,1309.0,493.0,4.9779,224600.0,<1H OCEAN
+-116.99,33.2,17.0,2980.0,539.0,1531.0,505.0,3.1553,250000.0,<1H OCEAN
+-121.28,37.82,10.0,9205.0,1774.0,5935.0,1673.0,3.65,119400.0,INLAND
+-117.11,32.67,52.0,204.0,74.0,248.0,57.0,1.7961,47500.0,NEAR OCEAN
+-118.03,33.97,36.0,1601.0,290.0,715.0,284.0,4.8152,232400.0,<1H OCEAN
+-117.82,33.67,17.0,2895.0,439.0,1588.0,450.0,6.276,290700.0,<1H OCEAN
+-119.76,36.82,17.0,6932.0,1486.0,3056.0,1453.0,2.3375,99300.0,INLAND
+-117.95,34.07,37.0,1987.0,399.0,1279.0,378.0,4.1172,176500.0,<1H OCEAN
+-117.94,33.82,34.0,1347.0,212.0,676.0,201.0,3.8828,215400.0,<1H OCEAN
+-118.32,34.17,40.0,1868.0,356.0,799.0,403.0,2.9306,279300.0,<1H OCEAN
+-118.35,33.9,22.0,1127.0,287.0,697.0,241.0,3.3971,220300.0,<1H OCEAN
+-117.72,33.63,15.0,1362.0,255.0,378.0,202.0,1.9,162500.0,<1H OCEAN
+-117.09,32.91,9.0,2012.0,316.0,802.0,289.0,6.5706,255700.0,<1H OCEAN
+-116.26,33.07,17.0,934.0,284.0,452.0,184.0,1.9875,83700.0,INLAND
+-118.35,34.21,42.0,1073.0,220.0,804.0,226.0,3.75,172600.0,<1H OCEAN
+-121.55,39.51,48.0,827.0,198.0,396.0,161.0,0.8024,58300.0,INLAND
+-117.24,34.13,24.0,1203.0,310.0,594.0,187.0,1.1522,87500.0,INLAND
+-122.08,37.68,15.0,3051.0,685.0,1479.0,668.0,3.5295,242200.0,NEAR BAY
+-122.26,37.89,52.0,3706.0,531.0,1205.0,504.0,6.6828,370900.0,NEAR BAY
+-117.38,33.89,12.0,3964.0,524.0,1707.0,549.0,5.1624,267900.0,INLAND
+-117.04,32.8,25.0,2504.0,345.0,1067.0,350.0,5.7416,243400.0,<1H OCEAN
+-119.05,36.09,9.0,3297.0,568.0,1749.0,568.0,4.0217,99200.0,INLAND
+-118.11,33.9,36.0,1347.0,278.0,748.0,278.0,5.1423,183100.0,<1H OCEAN
+-118.41,33.97,37.0,1629.0,275.0,668.0,266.0,6.1333,387200.0,<1H OCEAN
+-117.85,33.84,17.0,2830.0,502.0,1370.0,459.0,5.1785,247300.0,<1H OCEAN
+-117.91,33.61,40.0,2790.0,531.0,952.0,424.0,4.8,500001.0,<1H OCEAN
+-118.19,33.83,43.0,2641.0,411.0,1011.0,444.0,6.4468,444200.0,NEAR OCEAN
+-117.05,32.67,32.0,4227.0,785.0,2842.0,795.0,3.9646,137800.0,NEAR OCEAN
+-118.47,34.42,17.0,913.0,228.0,530.0,201.0,3.038,238500.0,<1H OCEAN
+-119.68,34.44,23.0,2600.0,398.0,917.0,374.0,8.7394,500001.0,<1H OCEAN
+-122.86,38.61,52.0,1753.0,380.0,982.0,380.0,3.4013,183300.0,<1H OCEAN
+-122.1,37.66,33.0,1954.0,464.0,1293.0,448.0,3.0489,152600.0,NEAR BAY
+-122.5,38.82,12.0,2394.0,443.0,877.0,341.0,2.5625,109200.0,INLAND
+-122.38,40.09,16.0,2077.0,388.0,1155.0,389.0,3.1361,84800.0,INLAND
+-115.56,32.76,15.0,1278.0,217.0,653.0,185.0,4.4821,140300.0,INLAND
+-117.35,33.68,10.0,516.0,107.0,282.0,96.0,4.2788,125000.0,<1H OCEAN
+-121.03,37.69,6.0,2607.0,557.0,1266.0,475.0,3.4632,137700.0,INLAND
+-121.26,38.74,22.0,7173.0,1314.0,3526.0,1316.0,3.3941,135900.0,INLAND
+-122.26,37.5,44.0,6983.0,1131.0,2818.0,1115.0,5.6271,374800.0,NEAR OCEAN
+-118.09,33.92,36.0,1226.0,211.0,711.0,219.0,4.5699,170800.0,<1H OCEAN
+-118.39,34.16,46.0,1582.0,279.0,603.0,283.0,5.1169,414300.0,<1H OCEAN
+-117.26,34.13,37.0,2403.0,550.0,1234.0,493.0,2.0,72100.0,INLAND
+-118.03,34.16,36.0,1640.0,239.0,693.0,253.0,6.6888,500001.0,INLAND
+-116.92,32.82,16.0,2784.0,468.0,1458.0,465.0,4.0048,184600.0,<1H OCEAN
+-117.79,33.68,10.0,2106.0,319.0,1002.0,332.0,8.735,375300.0,<1H OCEAN
+-119.72,34.42,49.0,1610.0,370.0,961.0,351.0,2.6983,260100.0,NEAR OCEAN
+-117.7,33.47,20.0,1577.0,363.0,764.0,333.0,4.1563,320800.0,<1H OCEAN
+-118.26,34.0,41.0,1733.0,492.0,1776.0,453.0,1.6221,104200.0,<1H OCEAN
+-118.33,34.05,46.0,3015.0,795.0,2300.0,725.0,2.0706,268500.0,<1H OCEAN
+-118.19,34.08,35.0,1554.0,381.0,1487.0,374.0,1.9038,139500.0,<1H OCEAN
+-119.68,37.35,13.0,2307.0,386.0,925.0,347.0,3.1326,119800.0,INLAND
+-120.46,37.65,17.0,315.0,89.0,130.0,58.0,1.4464,79200.0,INLAND
+-120.56,35.13,15.0,5818.0,924.0,2324.0,845.0,4.4033,267600.0,<1H OCEAN
+-122.04,37.31,29.0,2476.0,434.0,1217.0,416.0,6.2045,393800.0,<1H OCEAN
+-120.47,37.3,40.0,3693.0,771.0,2102.0,742.0,2.1838,75000.0,INLAND
+-120.95,37.65,14.0,5200.0,1119.0,3221.0,1102.0,2.6964,107000.0,INLAND
+-118.95,35.36,30.0,2294.0,508.0,1753.0,482.0,2.1078,54700.0,INLAND
+-123.22,40.54,27.0,1573.0,361.0,847.0,330.0,1.9034,49600.0,INLAND
+-122.52,37.91,30.0,4174.0,739.0,1818.0,705.0,5.5951,402900.0,NEAR BAY
+-117.73,33.61,17.0,2612.0,582.0,832.0,564.0,2.6759,120600.0,<1H OCEAN
+-122.14,37.43,52.0,1383.0,227.0,551.0,249.0,6.5829,500001.0,NEAR BAY
+-117.13,32.63,10.0,7374.0,1157.0,1900.0,794.0,8.7991,478500.0,NEAR OCEAN
+-118.47,35.72,18.0,4754.0,1075.0,1366.0,690.0,2.0694,81200.0,INLAND
+-117.99,33.86,20.0,3540.0,906.0,2898.0,876.0,3.0252,178000.0,<1H OCEAN
+-122.07,37.58,16.0,1893.0,338.0,1461.0,344.0,5.225,213700.0,NEAR BAY
+-121.8,37.34,25.0,1642.0,297.0,1146.0,279.0,5.2088,231400.0,<1H OCEAN
+-117.89,34.08,35.0,1711.0,335.0,825.0,356.0,3.5,215600.0,<1H OCEAN
+-122.27,37.86,49.0,2052.0,435.0,924.0,414.0,2.5417,182700.0,NEAR BAY
+-121.93,37.89,13.0,2085.0,292.0,852.0,264.0,7.3445,366700.0,INLAND
+-118.51,34.0,52.0,1241.0,502.0,679.0,459.0,2.3098,500001.0,<1H OCEAN
+-119.15,34.2,25.0,3445.0,898.0,5558.0,894.0,3.0972,169300.0,NEAR OCEAN
+-119.17,34.21,33.0,1039.0,256.0,1432.0,272.0,3.1103,143500.0,NEAR OCEAN
+-120.44,34.96,30.0,1685.0,315.0,1290.0,368.0,3.4722,112500.0,<1H OCEAN
+-118.76,34.26,26.0,1929.0,293.0,1067.0,320.0,5.4038,222100.0,<1H OCEAN
+-118.19,34.03,27.0,1346.0,340.0,1177.0,295.0,1.7995,153400.0,<1H OCEAN
+-118.11,33.94,32.0,2098.0,378.0,1036.0,385.0,5.0258,255400.0,<1H OCEAN
+-121.63,38.03,17.0,2549.0,596.0,1169.0,500.0,3.6694,209400.0,INLAND
+-120.94,37.68,4.0,13315.0,2424.0,6420.0,2289.0,4.2471,162100.0,INLAND
+-119.85,36.84,12.0,2272.0,304.0,840.0,305.0,8.9669,213900.0,INLAND
+-118.31,34.09,28.0,720.0,267.0,891.0,265.0,1.8977,100000.0,<1H OCEAN
+-118.39,33.96,45.0,1361.0,252.0,581.0,263.0,5.8143,340700.0,<1H OCEAN
+-116.91,32.86,15.0,3153.0,628.0,1633.0,527.0,3.6898,131000.0,<1H OCEAN
+-118.35,34.1,18.0,4109.0,1301.0,2103.0,1116.0,2.325,250000.0,<1H OCEAN
+-117.99,33.81,23.0,3284.0,795.0,3257.0,758.0,2.4526,182900.0,<1H OCEAN
+-118.54,34.23,29.0,1753.0,342.0,1318.0,333.0,4.125,241400.0,<1H OCEAN
+-117.79,33.7,16.0,6259.0,1098.0,3785.0,1114.0,6.3298,247100.0,<1H OCEAN
+-119.78,36.37,41.0,831.0,149.0,443.0,146.0,3.1406,100000.0,INLAND
+-121.38,38.59,36.0,2253.0,434.0,1018.0,426.0,3.2596,98700.0,INLAND
+-122.25,38.16,17.0,4459.0,944.0,1812.0,888.0,2.9375,106700.0,NEAR BAY
+-119.68,36.32,26.0,592.0,121.0,268.0,116.0,1.7596,120800.0,INLAND
+-117.86,33.89,24.0,2002.0,253.0,820.0,241.0,6.9612,274500.0,<1H OCEAN
+-120.79,37.41,35.0,2436.0,466.0,1730.0,469.0,2.2071,85900.0,INLAND
+-117.91,34.06,29.0,3250.0,521.0,1382.0,513.0,5.112,218300.0,<1H OCEAN
+-118.01,34.08,35.0,1852.0,358.0,1414.0,347.0,4.275,173600.0,INLAND
+-120.97,37.64,42.0,2359.0,504.0,1131.0,480.0,2.0833,95500.0,INLAND
+-119.67,36.81,4.0,1262.0,216.0,622.0,199.0,4.9432,114400.0,INLAND
+-121.25,38.69,24.0,1014.0,185.0,606.0,194.0,4.1607,112800.0,INLAND
+-122.43,37.8,52.0,3172.0,848.0,1259.0,806.0,4.1047,466700.0,NEAR BAY
+-118.22,34.14,50.0,3657.0,708.0,1725.0,644.0,5.5456,258100.0,<1H OCEAN
+-116.58,33.09,36.0,992.0,224.0,334.0,126.0,3.0089,134400.0,INLAND
+-121.13,37.74,31.0,677.0,144.0,523.0,159.0,2.4598,97100.0,INLAND
+-122.15,37.41,29.0,473.0,103.0,359.0,87.0,7.0309,475000.0,NEAR BAY
+-119.72,36.75,11.0,4832.0,993.0,2190.0,888.0,2.6611,74700.0,INLAND
+-118.21,33.95,43.0,1500.0,419.0,1726.0,440.0,1.8641,165100.0,<1H OCEAN
+-122.18,37.8,34.0,1355.0,195.0,442.0,195.0,6.2838,318200.0,NEAR BAY
+-118.12,33.85,37.0,2584.0,453.0,1333.0,481.0,4.3661,219900.0,<1H OCEAN
+-118.35,34.07,45.0,7803.0,2154.0,3359.0,2041.0,3.3594,287500.0,<1H OCEAN
+-122.32,37.99,24.0,4865.0,968.0,2315.0,893.0,4.2852,173500.0,NEAR BAY
+-119.2,34.18,22.0,6465.0,1397.0,2694.0,1370.0,2.9832,165600.0,NEAR OCEAN
+-118.48,35.14,4.0,8417.0,1657.0,4631.0,1468.0,3.6949,115800.0,INLAND
+-117.87,33.75,14.0,5526.0,1916.0,6799.0,1796.0,2.6561,144400.0,<1H OCEAN
+-118.02,33.76,25.0,1759.0,404.0,1404.0,385.0,3.6289,195800.0,<1H OCEAN
+-117.74,33.85,4.0,5416.0,820.0,1753.0,583.0,6.9544,314000.0,<1H OCEAN
+-121.92,36.62,47.0,1811.0,401.0,948.0,375.0,3.0379,249300.0,NEAR OCEAN
+-122.79,39.02,23.0,642.0,203.0,265.0,84.0,1.8833,96900.0,INLAND
+-122.25,37.48,37.0,3507.0,569.0,1663.0,608.0,5.0863,440300.0,NEAR OCEAN
+-117.34,34.46,9.0,5983.0,1122.0,3515.0,1064.0,3.1505,102000.0,INLAND
+-118.32,33.94,37.0,1487.0,296.0,863.0,291.0,3.1563,186200.0,<1H OCEAN
+-121.88,37.34,44.0,1267.0,353.0,1018.0,327.0,2.4196,194400.0,<1H OCEAN
+-118.41,34.07,52.0,1202.0,142.0,408.0,138.0,15.0001,500001.0,<1H OCEAN
+-119.86,34.38,28.0,1062.0,309.0,1058.0,305.0,1.5071,316700.0,NEAR OCEAN
+-117.4,34.07,28.0,2879.0,659.0,1661.0,554.0,2.066,88100.0,INLAND
+-117.25,32.74,36.0,3548.0,956.0,1648.0,866.0,2.6962,288200.0,NEAR OCEAN
+-118.14,33.76,44.0,1633.0,536.0,741.0,513.0,3.385,408300.0,NEAR OCEAN
+-121.44,38.64,25.0,1678.0,367.0,971.0,307.0,1.0398,62100.0,INLAND
+-121.45,36.86,11.0,1613.0,335.0,1617.0,342.0,3.1375,146200.0,INLAND
+-117.35,33.2,32.0,1251.0,220.0,700.0,232.0,3.9875,142900.0,NEAR OCEAN
+-122.22,39.51,17.0,1201.0,268.0,555.0,277.0,2.1,66900.0,INLAND
+-118.36,34.09,36.0,1390.0,458.0,874.0,468.0,2.5812,200000.0,<1H OCEAN
+-118.15,33.91,35.0,1590.0,350.0,1299.0,335.0,4.0313,163200.0,<1H OCEAN
+-121.12,38.86,17.0,3949.0,717.0,1683.0,686.0,3.3802,216500.0,INLAND
+-115.57,32.84,29.0,1207.0,301.0,804.0,288.0,1.9531,61100.0,INLAND
+-120.79,37.08,9.0,97.0,20.0,91.0,22.0,2.9063,55000.0,INLAND
+-120.61,35.11,11.0,3733.0,831.0,1839.0,739.0,3.3062,158500.0,<1H OCEAN
+-117.81,33.8,25.0,2765.0,475.0,1666.0,474.0,6.0531,230700.0,<1H OCEAN
+-118.21,34.07,52.0,1770.0,,1848.0,439.0,2.4135,167200.0,<1H OCEAN
+-122.3,37.57,36.0,1973.0,352.0,1169.0,370.0,5.033,270900.0,NEAR BAY
+-117.22,32.95,4.0,18123.0,3173.0,7301.0,2964.0,6.357,322500.0,NEAR OCEAN
+-118.42,34.26,26.0,1788.0,521.0,2582.0,484.0,2.1062,136400.0,<1H OCEAN
+-119.74,36.78,27.0,4049.0,947.0,2254.0,882.0,2.2467,70700.0,INLAND
+-122.48,37.71,39.0,3615.0,632.0,1571.0,615.0,5.1149,314200.0,NEAR OCEAN
+-122.1,37.72,30.0,2599.0,366.0,922.0,350.0,5.8382,330200.0,NEAR BAY
+-121.89,38.01,32.0,1000.0,188.0,663.0,212.0,4.0972,99200.0,INLAND
+-121.73,38.55,34.0,1717.0,393.0,1224.0,387.0,2.7917,130800.0,INLAND
+-118.1,33.79,35.0,2370.0,379.0,996.0,380.0,5.7368,287200.0,<1H OCEAN
+-121.99,37.54,28.0,3046.0,507.0,1772.0,516.0,5.3283,227900.0,<1H OCEAN
+-118.09,33.89,42.0,1150.0,215.0,708.0,204.0,3.6875,171500.0,<1H OCEAN
+-118.0,34.14,39.0,1302.0,303.0,800.0,291.0,3.2723,166900.0,INLAND
+-121.87,37.26,24.0,2383.0,343.0,1146.0,341.0,5.6223,265700.0,<1H OCEAN
+-117.5,33.96,12.0,7923.0,1470.0,4861.0,1385.0,4.2985,139200.0,INLAND
+-117.13,32.92,16.0,2173.0,399.0,1460.0,393.0,4.2614,169600.0,<1H OCEAN
+-119.03,35.35,34.0,1441.0,294.0,695.0,275.0,2.6875,73700.0,INLAND
+-118.45,33.96,36.0,2723.0,547.0,1090.0,519.0,6.3267,500001.0,<1H OCEAN
+-119.02,35.34,34.0,2861.0,510.0,1375.0,486.0,3.4286,71400.0,INLAND
+-118.28,34.03,26.0,2107.0,809.0,2821.0,572.0,0.844,350000.0,<1H OCEAN
+-118.17,33.78,23.0,3768.0,1261.0,3940.0,1098.0,1.9647,186200.0,NEAR OCEAN
+-117.8,34.05,5.0,4536.0,1178.0,2485.0,909.0,4.1118,125900.0,<1H OCEAN
+-121.5,38.53,39.0,3184.0,593.0,1188.0,572.0,4.6923,192000.0,INLAND
+-117.24,34.04,4.0,4289.0,682.0,1981.0,705.0,5.3366,165100.0,INLAND
+-117.97,33.94,36.0,1870.0,338.0,947.0,324.0,4.1205,217000.0,<1H OCEAN
+-121.42,38.6,36.0,1327.0,209.0,613.0,230.0,3.8672,111400.0,INLAND
+-119.38,36.55,31.0,2342.0,439.0,1411.0,465.0,3.017,72000.0,INLAND
+-121.84,36.61,15.0,2190.0,586.0,1570.0,510.0,1.875,122300.0,<1H OCEAN
+-122.0,37.98,35.0,1192.0,201.0,535.0,172.0,4.9219,182000.0,INLAND
+-121.64,37.85,22.0,1999.0,415.0,967.0,320.0,4.4583,253900.0,INLAND
+-117.91,33.91,24.0,2249.0,379.0,1015.0,385.0,4.9766,267100.0,<1H OCEAN
+-121.88,37.32,45.0,2213.0,564.0,1920.0,514.0,3.2806,164200.0,<1H OCEAN
+-121.88,37.32,30.0,1242.0,338.0,1438.0,325.0,2.6607,169300.0,<1H OCEAN
+-119.64,36.36,13.0,2360.0,340.0,1055.0,312.0,5.2134,97400.0,INLAND
+-118.55,34.17,36.0,2127.0,297.0,761.0,274.0,7.8392,500001.0,<1H OCEAN
+-122.54,37.99,32.0,2236.0,348.0,818.0,330.0,7.3521,444000.0,NEAR BAY
+-117.14,32.79,31.0,984.0,161.0,422.0,158.0,5.282,183000.0,NEAR OCEAN
+-117.4,33.94,42.0,943.0,171.0,466.0,203.0,3.1458,116000.0,INLAND
+-119.54,36.7,20.0,1815.0,375.0,1665.0,357.0,2.2448,58900.0,INLAND
+-117.25,34.06,23.0,4503.0,1156.0,3264.0,937.0,1.9821,93000.0,INLAND
+-121.45,36.51,29.0,1045.0,311.0,1245.0,273.0,1.775,112500.0,<1H OCEAN
+-119.29,36.53,33.0,1509.0,352.0,1734.0,336.0,1.625,50300.0,INLAND
+-118.37,34.15,29.0,2630.0,617.0,1071.0,573.0,3.3669,376100.0,<1H OCEAN
+-118.25,33.89,36.0,406.0,71.0,268.0,77.0,3.9,115800.0,<1H OCEAN
+-118.37,34.04,52.0,1197.0,231.0,671.0,219.0,3.825,278500.0,<1H OCEAN
+-117.65,35.0,36.0,1184.0,316.0,672.0,241.0,1.9107,39800.0,INLAND
+-122.21,38.09,37.0,4368.0,779.0,2083.0,741.0,3.8667,127000.0,NEAR BAY
+-118.23,33.94,39.0,1141.0,258.0,1313.0,234.0,2.0187,90100.0,<1H OCEAN
+-117.61,34.13,21.0,8416.0,1386.0,4308.0,1341.0,4.4611,164600.0,INLAND
+-117.99,33.76,17.0,2545.0,737.0,1468.0,699.0,1.9439,177700.0,<1H OCEAN
+-117.62,33.42,27.0,1444.0,412.0,597.0,311.0,3.1395,310000.0,NEAR OCEAN
+-122.46,37.76,28.0,1072.0,165.0,363.0,168.0,6.1636,367700.0,NEAR BAY
+-117.31,33.75,19.0,3173.0,678.0,2204.0,606.0,2.1484,129200.0,<1H OCEAN
+-117.22,33.8,3.0,5284.0,920.0,2703.0,729.0,4.0717,126500.0,INLAND
+-116.98,33.93,33.0,376.0,83.0,267.0,88.0,2.1581,68300.0,INLAND
+-122.89,40.76,14.0,712.0,131.0,270.0,90.0,2.3958,102100.0,INLAND
+-116.97,32.65,4.0,16450.0,2833.0,7985.0,2683.0,5.6631,233400.0,NEAR OCEAN
+-121.46,38.55,52.0,3126.0,648.0,1789.0,558.0,1.7616,84100.0,INLAND
+-121.44,38.65,28.0,1219.0,240.0,559.0,212.0,3.8295,122200.0,INLAND
+-121.33,37.98,9.0,2370.0,424.0,1129.0,386.0,5.143,176500.0,INLAND
+-120.71,40.13,19.0,897.0,180.0,276.0,110.0,2.9554,89400.0,INLAND
+-117.26,33.86,16.0,1171.0,235.0,659.0,216.0,3.1103,110000.0,INLAND
+-122.18,37.7,36.0,2639.0,533.0,1209.0,519.0,4.0268,205500.0,NEAR BAY
+-119.65,36.34,46.0,1730.0,337.0,752.0,323.0,1.8529,67200.0,INLAND
+-118.41,33.88,40.0,925.0,254.0,371.0,227.0,5.2533,500001.0,<1H OCEAN
+-122.42,37.66,28.0,3520.0,672.0,1746.0,602.0,4.9236,273500.0,NEAR OCEAN
+-120.71,37.38,14.0,1979.0,432.0,1756.0,382.0,2.6923,71400.0,INLAND
+-118.26,33.83,28.0,4112.0,861.0,3211.0,841.0,4.4539,192200.0,<1H OCEAN
+-122.08,37.7,25.0,3402.0,758.0,1645.0,710.0,3.4934,209900.0,NEAR BAY
+-122.46,37.78,47.0,1682.0,379.0,837.0,375.0,5.2806,400000.0,NEAR BAY
+-118.2,34.12,44.0,1565.0,398.0,1500.0,407.0,2.8125,155600.0,<1H OCEAN
+-117.94,33.62,28.0,1765.0,390.0,832.0,349.0,6.5928,439100.0,<1H OCEAN
+-117.12,32.56,20.0,2524.0,682.0,1819.0,560.0,2.9286,257700.0,NEAR OCEAN
+-117.24,33.93,12.0,7105.0,1447.0,4520.0,1333.0,3.2705,113200.0,INLAND
+-118.42,34.2,34.0,161.0,48.0,66.0,33.0,1.0,187500.0,<1H OCEAN
+-117.99,33.81,46.0,38.0,8.0,66.0,14.0,4.1667,162500.0,<1H OCEAN
+-119.01,35.33,42.0,1120.0,255.0,677.0,213.0,1.5429,39400.0,INLAND
+-122.04,36.95,27.0,1987.0,374.0,961.0,343.0,3.9667,265800.0,NEAR OCEAN
+-119.2,36.28,22.0,2295.0,508.0,1654.0,478.0,1.684,65900.0,INLAND
+-121.54,38.5,15.0,6093.0,1051.0,2415.0,997.0,4.2075,183600.0,INLAND
+-117.02,32.73,22.0,5201.0,865.0,3280.0,817.0,4.7952,141400.0,NEAR OCEAN
+-121.39,38.56,19.0,8507.0,1470.0,3517.0,1453.0,4.3644,137400.0,INLAND
+-119.19,35.41,12.0,2835.0,471.0,1399.0,413.0,4.4125,149000.0,INLAND
+-122.68,38.44,29.0,2796.0,588.0,1346.0,562.0,2.9107,169700.0,<1H OCEAN
+-118.32,33.97,52.0,1590.0,302.0,844.0,295.0,2.7139,164900.0,<1H OCEAN
+-122.26,37.55,17.0,1321.0,425.0,683.0,408.0,4.7045,500001.0,NEAR BAY
+-118.13,33.76,44.0,1543.0,463.0,652.0,406.0,4.25,439300.0,NEAR OCEAN
+-117.02,32.78,33.0,3481.0,708.0,1726.0,719.0,3.3675,158200.0,<1H OCEAN
+-116.79,33.99,16.0,319.0,68.0,212.0,67.0,1.4688,90000.0,INLAND
+-122.29,40.44,30.0,1270.0,365.0,840.0,324.0,1.3904,48100.0,INLAND
+-120.87,37.64,40.0,1010.0,155.0,488.0,157.0,3.8984,170500.0,INLAND
+-121.84,37.99,15.0,2380.0,385.0,1292.0,388.0,4.6029,142600.0,INLAND
+-122.41,37.71,40.0,2054.0,433.0,1738.0,429.0,4.9926,213900.0,NEAR BAY
+-122.27,37.89,52.0,1978.0,293.0,723.0,272.0,5.3989,335600.0,NEAR BAY
+-118.21,33.91,26.0,2422.0,632.0,2601.0,583.0,1.7824,110200.0,<1H OCEAN
+-121.33,38.6,25.0,4260.0,607.0,1635.0,640.0,6.2817,288200.0,INLAND
+-117.9,34.13,37.0,1801.0,422.0,1564.0,425.0,3.1597,133000.0,<1H OCEAN
+-117.28,34.14,40.0,2364.0,438.0,968.0,416.0,3.4906,93300.0,INLAND
+-122.04,37.3,25.0,3807.0,600.0,1678.0,600.0,6.6818,411300.0,<1H OCEAN
+-122.4,37.71,47.0,1649.0,328.0,1183.0,356.0,3.3625,223700.0,NEAR BAY
+-118.09,33.77,26.0,5359.0,1508.0,1829.0,1393.0,1.7675,61300.0,<1H OCEAN
+-122.02,37.99,37.0,2247.0,416.0,1237.0,397.0,4.45,161900.0,NEAR BAY
+-118.3,33.98,48.0,2010.0,445.0,1208.0,404.0,1.6611,95800.0,<1H OCEAN
+-119.08,36.13,21.0,2271.0,376.0,1145.0,372.0,3.1528,113700.0,INLAND
+-121.69,36.81,18.0,2837.0,522.0,1454.0,458.0,4.5272,221000.0,<1H OCEAN
+-117.25,32.81,32.0,2402.0,551.0,1020.0,532.0,3.3942,307400.0,NEAR OCEAN
+-122.17,37.74,34.0,1223.0,281.0,824.0,280.0,2.2917,92500.0,NEAR BAY
+-116.86,33.97,11.0,658.0,131.0,376.0,120.0,2.3977,58000.0,INLAND
+-121.17,38.68,37.0,1252.0,267.0,686.0,256.0,3.0,121900.0,INLAND
+-119.85,36.43,23.0,1824.0,354.0,1146.0,362.0,2.8913,60900.0,INLAND
+-120.45,34.64,30.0,2330.0,422.0,1255.0,449.0,3.8512,134600.0,NEAR OCEAN
+-118.05,34.08,34.0,572.0,154.0,752.0,182.0,2.0433,138800.0,<1H OCEAN
+-120.78,38.74,28.0,4236.0,877.0,2008.0,881.0,2.1603,111300.0,INLAND
+-123.2,39.15,27.0,990.0,238.0,592.0,225.0,2.0074,96200.0,<1H OCEAN
+-118.34,34.04,35.0,2345.0,607.0,2042.0,565.0,2.5955,139700.0,<1H OCEAN
+-118.31,34.01,50.0,1463.0,354.0,912.0,293.0,1.7386,109400.0,<1H OCEAN
+-118.49,34.28,27.0,2535.0,389.0,1071.0,386.0,6.8695,319400.0,<1H OCEAN
+-118.43,34.04,52.0,1782.0,308.0,735.0,307.0,5.2954,485100.0,<1H OCEAN
+-121.06,37.42,52.0,504.0,96.0,295.0,97.0,3.5,73500.0,INLAND
+-122.28,37.86,52.0,2031.0,450.0,958.0,445.0,1.9327,169900.0,NEAR BAY
+-117.9,33.66,22.0,3568.0,938.0,1952.0,938.0,3.1667,161000.0,<1H OCEAN
+-120.04,36.74,14.0,3182.0,730.0,2298.0,721.0,1.6168,71800.0,INLAND
+-122.08,37.34,23.0,2597.0,335.0,922.0,338.0,10.5142,500001.0,<1H OCEAN
+-119.63,36.35,4.0,1684.0,343.0,920.0,324.0,4.2396,90600.0,INLAND
+-117.23,32.73,36.0,2052.0,287.0,699.0,265.0,7.5557,441400.0,NEAR OCEAN
+-117.28,33.18,16.0,3002.0,591.0,842.0,538.0,2.1205,157300.0,NEAR OCEAN
+-117.4,33.96,51.0,1806.0,322.0,709.0,298.0,3.575,125500.0,INLAND
+-117.94,33.76,27.0,2512.0,506.0,1861.0,511.0,4.2386,184200.0,<1H OCEAN
+-121.83,37.32,21.0,4559.0,1163.0,5124.0,1124.0,3.2052,179000.0,<1H OCEAN
+-121.95,38.02,9.0,3360.0,833.0,2041.0,810.0,2.1013,100700.0,INLAND
+-118.42,34.24,35.0,1507.0,281.0,1025.0,286.0,4.5833,177200.0,<1H OCEAN
+-118.43,34.02,38.0,2172.0,437.0,830.0,368.0,3.9091,500001.0,<1H OCEAN
+-118.99,34.23,9.0,10618.0,1617.0,4830.0,1606.0,6.6246,284200.0,<1H OCEAN
+-117.19,33.34,15.0,3310.0,488.0,1104.0,460.0,6.1009,314400.0,<1H OCEAN
+-118.29,33.91,28.0,1501.0,446.0,1028.0,418.0,2.3043,177500.0,<1H OCEAN
+-118.46,34.01,46.0,1379.0,239.0,688.0,269.0,6.8901,500001.0,<1H OCEAN
+-122.64,41.73,36.0,3319.0,664.0,1492.0,631.0,1.8694,71200.0,INLAND
+-118.31,33.96,48.0,2015.0,356.0,1020.0,338.0,4.0625,138700.0,<1H OCEAN
+-118.33,34.01,44.0,1762.0,463.0,786.0,445.0,1.9231,188500.0,<1H OCEAN
+-119.77,36.44,26.0,1727.0,289.0,802.0,259.0,3.2083,75000.0,INLAND
+-121.91,37.31,46.0,3052.0,587.0,1373.0,590.0,4.7287,340000.0,<1H OCEAN
+-118.34,33.83,34.0,1761.0,329.0,965.0,329.0,5.399,358500.0,<1H OCEAN
+-122.3,38.29,48.0,2278.0,477.0,1219.0,453.0,2.9643,154000.0,NEAR BAY
+-120.86,37.77,28.0,1208.0,232.0,535.0,232.0,2.3523,94700.0,INLAND
+-122.42,37.6,34.0,3562.0,565.0,1542.0,563.0,5.8783,405100.0,NEAR OCEAN
+-121.29,38.03,16.0,4356.0,881.0,1629.0,818.0,2.2672,91100.0,INLAND
+-118.35,34.17,42.0,1604.0,326.0,814.0,329.0,4.4408,216000.0,<1H OCEAN
+-117.17,32.78,42.0,1104.0,305.0,892.0,270.0,2.2768,145200.0,NEAR OCEAN
+-119.85,37.39,14.0,2744.0,555.0,1153.0,474.0,2.753,111100.0,INLAND
+-120.99,37.69,25.0,2773.0,384.0,1060.0,381.0,6.4788,199400.0,INLAND
+-121.31,38.01,22.0,2101.0,514.0,1304.0,511.0,2.8348,101600.0,INLAND
+-117.12,32.74,52.0,1969.0,389.0,877.0,424.0,3.79,163400.0,NEAR OCEAN
+-118.33,34.2,23.0,7179.0,1985.0,4757.0,1924.0,3.1051,206500.0,<1H OCEAN
+-121.23,37.87,49.0,98.0,24.0,59.0,26.0,3.65,162500.0,INLAND
+-119.55,36.6,18.0,2379.0,448.0,1638.0,436.0,2.309,57100.0,INLAND
+-122.06,37.7,37.0,1893.0,310.0,821.0,315.0,4.6005,231600.0,NEAR BAY
+-118.19,34.05,46.0,1051.0,302.0,1435.0,305.0,1.6667,133600.0,<1H OCEAN
+-116.99,33.92,26.0,503.0,69.0,293.0,59.0,3.7083,147500.0,INLAND
+-117.31,34.1,52.0,1457.0,415.0,1238.0,341.0,2.0089,68100.0,INLAND
+-118.31,33.94,43.0,2104.0,393.0,1132.0,394.0,3.0682,142000.0,<1H OCEAN
+-118.19,34.05,42.0,1291.0,345.0,1535.0,332.0,1.9083,119200.0,<1H OCEAN
+-122.35,37.97,43.0,2178.0,482.0,1545.0,471.0,2.5863,112200.0,NEAR BAY
+-121.67,36.58,11.0,5892.0,837.0,2327.0,812.0,6.1551,291800.0,<1H OCEAN
+-118.55,34.19,31.0,1856.0,370.0,990.0,360.0,4.3654,223800.0,<1H OCEAN
+-117.96,33.8,30.0,729.0,131.0,488.0,139.0,4.7667,195200.0,<1H OCEAN
+-117.89,34.14,15.0,4644.0,967.0,2855.0,867.0,3.3654,222100.0,<1H OCEAN
+-122.71,38.34,22.0,1249.0,335.0,699.0,308.0,2.6033,121600.0,<1H OCEAN
+-122.21,37.46,48.0,2560.0,322.0,921.0,301.0,10.8758,500001.0,NEAR OCEAN
+-115.54,32.79,23.0,1712.0,403.0,1370.0,377.0,1.275,60400.0,INLAND
+-120.55,41.61,22.0,9047.0,1831.0,4276.0,1622.0,2.0802,47900.0,INLAND
+-122.26,37.85,52.0,3618.0,768.0,1508.0,755.0,3.2619,309600.0,NEAR BAY
+-118.44,34.21,20.0,5756.0,1477.0,4031.0,1369.0,3.2448,221200.0,<1H OCEAN
+-117.02,32.81,14.0,3173.0,599.0,1451.0,585.0,3.7292,182200.0,<1H OCEAN
+-118.61,34.22,24.0,5256.0,758.0,2474.0,780.0,7.3252,333700.0,<1H OCEAN
+-118.16,33.88,30.0,1694.0,398.0,1181.0,383.0,2.9779,169500.0,<1H OCEAN
+-119.51,34.4,15.0,1112.0,256.0,411.0,245.0,2.0625,314300.0,NEAR OCEAN
+-118.17,33.96,25.0,3297.0,1066.0,5027.0,1041.0,2.2817,164200.0,<1H OCEAN
+-117.81,33.73,19.0,5471.0,1345.0,2828.0,1247.0,3.5719,252800.0,<1H OCEAN
+-117.37,33.97,40.0,1166.0,250.0,976.0,244.0,1.95,84800.0,INLAND
+-117.71,33.54,7.0,4907.0,577.0,1883.0,556.0,10.4415,453800.0,<1H OCEAN
+-122.11,37.37,49.0,1068.0,190.0,410.0,171.0,7.2045,500001.0,NEAR BAY
+-119.88,36.83,2.0,4055.0,735.0,1730.0,654.0,4.2132,96500.0,INLAND
+-117.25,34.11,32.0,2910.0,641.0,2011.0,614.0,2.7473,70800.0,INLAND
+-119.86,34.38,26.0,1626.0,375.0,1580.0,359.0,2.1471,187500.0,NEAR OCEAN
+-121.27,37.93,24.0,1451.0,320.0,1413.0,283.0,2.125,61200.0,INLAND
+-121.42,38.54,29.0,2358.0,493.0,1071.0,470.0,2.925,94300.0,INLAND
+-117.06,34.17,21.0,2520.0,582.0,416.0,151.0,2.712,89000.0,INLAND
+-121.84,37.25,25.0,5939.0,989.0,3275.0,954.0,5.6488,234600.0,<1H OCEAN
+-118.25,34.14,37.0,584.0,260.0,552.0,235.0,1.8235,275000.0,<1H OCEAN
+-117.04,32.58,14.0,2355.0,406.0,1883.0,401.0,5.0311,152100.0,NEAR OCEAN
+-116.17,33.66,22.0,639.0,203.0,664.0,153.0,1.9306,47500.0,INLAND
+-122.07,37.34,33.0,1208.0,198.0,495.0,216.0,5.4659,500001.0,<1H OCEAN
+-118.32,33.9,36.0,1741.0,412.0,1245.0,423.0,4.1344,210300.0,<1H OCEAN
+-118.45,34.06,20.0,3367.0,1264.0,2667.0,1131.0,2.2444,500000.0,<1H OCEAN
+-121.92,36.95,29.0,3457.0,699.0,1327.0,563.0,3.6597,252300.0,NEAR OCEAN
+-121.99,36.98,40.0,1104.0,224.0,669.0,215.0,4.3409,256300.0,NEAR OCEAN
+-122.02,37.09,35.0,1818.0,368.0,682.0,254.0,4.8611,240000.0,NEAR OCEAN
+-121.44,38.52,38.0,2080.0,388.0,995.0,380.0,2.7697,76600.0,INLAND
+-117.03,33.12,25.0,3142.0,446.0,1286.0,419.0,5.4663,248300.0,<1H OCEAN
+-118.15,34.12,52.0,1518.0,344.0,725.0,296.0,3.4018,204500.0,<1H OCEAN
+-117.88,34.12,35.0,1574.0,276.0,1088.0,289.0,4.0938,165300.0,<1H OCEAN
+-122.18,37.86,33.0,4449.0,636.0,1684.0,617.0,8.9571,399700.0,NEAR BAY
+-119.87,36.93,13.0,1429.0,209.0,702.0,205.0,4.3625,111800.0,INLAND
+-122.46,37.72,45.0,2399.0,419.0,1225.0,399.0,4.0855,244100.0,NEAR OCEAN
+-120.54,37.68,18.0,335.0,76.0,189.0,67.0,1.2273,87500.0,INLAND
+-117.89,33.74,32.0,660.0,145.0,959.0,113.0,3.75,159000.0,<1H OCEAN
+-121.49,38.56,52.0,1777.0,368.0,624.0,350.0,3.6729,137800.0,INLAND
+-122.46,37.88,35.0,2492.0,409.0,812.0,373.0,8.8386,500001.0,NEAR BAY
+-121.8,37.35,27.0,2358.0,415.0,1562.0,383.0,5.2297,192800.0,<1H OCEAN
+-122.06,37.36,35.0,2693.0,493.0,1343.0,455.0,6.0777,327500.0,<1H OCEAN
+-121.89,36.59,32.0,784.0,112.0,262.0,114.0,6.918,500001.0,NEAR OCEAN
+-119.29,35.76,15.0,3938.0,789.0,3500.0,768.0,2.1295,59800.0,INLAND
+-118.24,34.15,19.0,4852.0,1465.0,3171.0,1332.0,2.5924,192900.0,<1H OCEAN
+-120.76,38.47,17.0,1521.0,309.0,607.0,240.0,3.5,123800.0,INLAND
+-118.3,34.09,29.0,3245.0,1190.0,3906.0,1102.0,2.1927,253300.0,<1H OCEAN
+-118.4,34.41,22.0,4443.0,560.0,1573.0,496.0,10.0285,500001.0,<1H OCEAN
+-122.83,38.89,11.0,640.0,134.0,268.0,90.0,3.4514,100000.0,<1H OCEAN
+-122.14,37.47,37.0,3373.0,815.0,2909.0,705.0,2.8868,156600.0,NEAR BAY
+-121.95,37.28,52.0,777.0,148.0,362.0,144.0,4.0208,262500.0,<1H OCEAN
+-117.94,33.87,46.0,2066.0,450.0,1275.0,448.0,3.9375,187000.0,<1H OCEAN
+-121.4,36.86,36.0,1256.0,270.0,910.0,255.0,1.9405,145400.0,INLAND
+-121.97,39.79,16.0,1453.0,299.0,904.0,286.0,3.5735,89600.0,INLAND
+-122.03,37.94,21.0,5541.0,776.0,2214.0,737.0,5.5777,279300.0,NEAR BAY
+-117.56,33.94,29.0,266.0,42.0,136.0,40.0,1.625,164300.0,INLAND
+-118.39,34.06,37.0,2975.0,705.0,1291.0,654.0,5.3316,500001.0,<1H OCEAN
+-122.54,37.94,39.0,3670.0,775.0,1519.0,788.0,4.4081,435200.0,NEAR BAY
+-121.32,37.97,46.0,2270.0,427.0,1097.0,453.0,3.3235,87800.0,INLAND
+-122.28,37.75,20.0,1156.0,365.0,583.0,326.0,3.1972,100000.0,NEAR BAY
+-122.2,37.47,40.0,2959.0,389.0,985.0,365.0,9.9025,500001.0,NEAR BAY
+-117.67,33.61,24.0,3859.0,661.0,1972.0,624.0,5.7871,227400.0,<1H OCEAN
+-119.69,36.75,6.0,1926.0,303.0,965.0,316.0,4.7463,93100.0,INLAND
+-118.41,34.18,35.0,2785.0,663.0,1631.0,614.0,3.9038,276100.0,<1H OCEAN
+-122.01,36.97,52.0,920.0,202.0,525.0,264.0,2.9444,232800.0,NEAR OCEAN
+-120.55,38.45,17.0,2277.0,474.0,767.0,356.0,2.5208,99100.0,INLAND
+-116.25,33.75,33.0,278.0,91.0,375.0,81.0,2.025,50000.0,INLAND
+-118.24,33.89,34.0,1479.0,332.0,1166.0,322.0,2.6165,100900.0,<1H OCEAN
+-117.74,33.61,17.0,2116.0,474.0,662.0,443.0,3.5625,180800.0,<1H OCEAN
+-117.14,32.71,52.0,800.0,313.0,1337.0,282.0,1.5594,87500.0,NEAR OCEAN
+-118.31,33.74,36.0,2464.0,472.0,1111.0,457.0,4.5074,350000.0,NEAR OCEAN
+-118.64,34.19,33.0,3017.0,494.0,1423.0,470.0,5.6163,248400.0,<1H OCEAN
+-118.61,34.19,34.0,703.0,127.0,369.0,127.0,4.3125,210100.0,<1H OCEAN
+-121.45,38.48,24.0,1766.0,340.0,1028.0,372.0,3.5402,98700.0,INLAND
+-118.18,33.85,40.0,2597.0,582.0,1285.0,559.0,3.975,213800.0,<1H OCEAN
+-116.53,33.84,28.0,8399.0,1839.0,3470.0,1340.0,2.5885,159000.0,INLAND
+-121.64,40.9,24.0,2237.0,434.0,834.0,318.0,1.7538,90300.0,INLAND
+-119.8,36.82,24.0,5377.0,1005.0,2010.0,982.0,3.4542,121200.0,INLAND
+-117.87,33.73,45.0,2264.0,,1970.0,499.0,3.4193,177000.0,<1H OCEAN
+-118.18,34.05,41.0,762.0,147.0,817.0,176.0,3.75,123100.0,<1H OCEAN
+-122.3,37.92,32.0,3943.0,605.0,1524.0,614.0,6.0677,321600.0,NEAR BAY
+-117.12,32.9,13.0,1743.0,363.0,854.0,353.0,4.6667,138200.0,<1H OCEAN
+-122.23,37.76,52.0,3011.0,542.0,1303.0,535.0,5.1039,273800.0,NEAR BAY
+-118.32,34.06,52.0,955.0,100.0,457.0,120.0,15.0001,500001.0,<1H OCEAN
+-120.59,35.11,25.0,3642.0,726.0,1729.0,673.0,3.155,205400.0,<1H OCEAN
+-121.32,38.62,33.0,898.0,190.0,470.0,201.0,2.6897,148300.0,INLAND
+-122.21,37.43,23.0,5741.0,1012.0,1843.0,888.0,5.7211,500001.0,NEAR OCEAN
+-118.28,33.94,32.0,1381.0,375.0,1268.0,354.0,1.1051,94200.0,<1H OCEAN
+-118.38,34.22,20.0,1176.0,344.0,864.0,318.0,2.375,177700.0,<1H OCEAN
+-122.33,37.98,32.0,1967.0,348.0,1144.0,364.0,4.4135,150100.0,NEAR BAY
+-122.25,37.76,52.0,2876.0,648.0,1340.0,632.0,3.567,252900.0,NEAR BAY
+-118.2,33.98,43.0,1091.0,320.0,1418.0,316.0,2.1522,159400.0,<1H OCEAN
+-120.66,35.27,46.0,2217.0,544.0,1107.0,527.0,2.8009,192600.0,NEAR OCEAN
+-117.87,33.86,19.0,2232.0,448.0,1149.0,417.0,3.1534,324400.0,<1H OCEAN
+-121.06,37.7,7.0,9374.0,1847.0,4827.0,1722.0,3.462,151900.0,INLAND
+-121.88,38.03,10.0,2769.0,619.0,1045.0,469.0,4.1111,158600.0,INLAND
+-118.12,34.08,35.0,2248.0,,1762.0,622.0,3.0,253900.0,<1H OCEAN
+-118.14,34.19,45.0,3595.0,619.0,1686.0,607.0,4.73,201000.0,INLAND
+-118.57,34.29,4.0,6995.0,1151.0,2907.0,1089.0,7.0808,341200.0,<1H OCEAN
+-118.3,34.08,34.0,1562.0,651.0,1774.0,559.0,1.5685,225000.0,<1H OCEAN
+-118.29,34.16,35.0,1257.0,318.0,764.0,319.0,3.2083,238000.0,<1H OCEAN
+-117.01,32.83,17.0,15401.0,3280.0,7302.0,3176.0,3.3067,121900.0,<1H OCEAN
+-117.7,33.72,6.0,211.0,51.0,125.0,44.0,1.9659,500001.0,<1H OCEAN
+-117.12,32.75,13.0,2795.0,773.0,1869.0,690.0,2.1767,101800.0,NEAR OCEAN
+-118.24,34.07,27.0,223.0,80.0,249.0,82.0,1.6136,137500.0,<1H OCEAN
+-122.08,37.71,38.0,1663.0,295.0,781.0,301.0,5.0519,227000.0,NEAR BAY
+-117.4,33.95,43.0,633.0,166.0,292.0,135.0,1.1601,121400.0,INLAND
+-121.84,37.32,22.0,3015.0,581.0,2491.0,530.0,4.3419,176300.0,<1H OCEAN
+-117.75,34.05,35.0,1293.0,339.0,1494.0,312.0,1.6645,93300.0,INLAND
+-118.42,34.0,45.0,1807.0,355.0,883.0,371.0,5.0357,329800.0,<1H OCEAN
+-118.6,34.19,16.0,14912.0,4183.0,5105.0,3302.0,2.8312,213900.0,<1H OCEAN
+-118.27,34.1,41.0,3729.0,740.0,1364.0,707.0,5.7778,412700.0,<1H OCEAN
+-120.7,35.14,17.0,5805.0,1097.0,1919.0,932.0,3.5352,357800.0,NEAR OCEAN
+-122.43,37.76,52.0,2332.0,434.0,861.0,406.0,4.4318,437500.0,NEAR BAY
+-117.32,34.12,39.0,2210.0,498.0,1752.0,477.0,1.4066,66400.0,INLAND
+-119.46,36.91,12.0,2980.0,495.0,1184.0,429.0,3.9141,123900.0,INLAND
+-117.01,32.85,23.0,2592.0,414.0,1401.0,431.0,5.4903,151400.0,<1H OCEAN
+-122.04,38.25,38.0,1214.0,244.0,632.0,254.0,2.8438,94200.0,INLAND
+-118.39,33.88,31.0,1448.0,244.0,607.0,259.0,8.1513,500001.0,<1H OCEAN
+-118.45,34.19,11.0,2479.0,900.0,2466.0,855.0,2.2264,181300.0,<1H OCEAN
+-117.98,35.13,5.0,4849.0,920.0,2504.0,847.0,3.5391,81900.0,INLAND
+-118.4,34.16,45.0,1176.0,250.0,471.0,228.0,2.3333,364700.0,<1H OCEAN
+-118.2,33.93,38.0,1626.0,307.0,1280.0,295.0,3.5313,146500.0,<1H OCEAN
+-116.27,34.13,37.0,452.0,109.0,184.0,59.0,3.7292,65800.0,INLAND
+-121.95,37.35,52.0,2382.0,523.0,1096.0,492.0,4.2656,236100.0,<1H OCEAN
+-122.47,37.7,45.0,3290.0,693.0,2466.0,666.0,3.6588,238600.0,NEAR OCEAN
+-118.34,33.86,35.0,1936.0,343.0,1008.0,346.0,5.4791,285900.0,<1H OCEAN
+-122.26,37.81,34.0,5871.0,1914.0,2689.0,1789.0,2.8406,335700.0,NEAR BAY
+-117.9,33.88,34.0,1396.0,245.0,661.0,261.0,4.675,215400.0,<1H OCEAN
+-117.63,33.63,6.0,3068.0,549.0,985.0,536.0,4.2009,238000.0,<1H OCEAN
+-121.88,37.46,5.0,1819.0,245.0,802.0,228.0,10.9722,500001.0,<1H OCEAN
+-122.34,37.95,39.0,1986.0,427.0,1041.0,385.0,3.2333,135100.0,NEAR BAY
+-118.44,33.79,27.0,2141.0,260.0,635.0,240.0,11.6648,500001.0,NEAR OCEAN
+-118.09,34.15,52.0,670.0,141.0,268.0,110.0,3.5568,205000.0,INLAND
+-117.17,34.03,33.0,4583.0,648.0,1760.0,638.0,6.3308,230600.0,INLAND
+-118.31,34.02,45.0,1423.0,278.0,822.0,276.0,2.4519,98100.0,<1H OCEAN
+-118.08,33.88,27.0,923.0,186.0,1014.0,204.0,3.825,159500.0,<1H OCEAN
+-116.84,32.86,16.0,2502.0,532.0,1211.0,494.0,3.2516,202100.0,<1H OCEAN
+-119.48,37.0,16.0,2904.0,551.0,1467.0,509.0,3.1736,111800.0,INLAND
+-122.25,37.87,52.0,1204.0,460.0,2016.0,477.0,0.949,350000.0,NEAR BAY
+-118.3,34.05,34.0,1453.0,588.0,1987.0,589.0,2.096,187500.0,<1H OCEAN
+-118.5,34.25,32.0,2333.0,389.0,969.0,331.0,4.8164,241100.0,<1H OCEAN
+-117.82,33.81,30.0,2260.0,345.0,1182.0,341.0,6.0705,236700.0,<1H OCEAN
+-119.7,34.41,19.0,2086.0,575.0,1701.0,530.0,2.8042,236100.0,NEAR OCEAN
+-121.78,37.68,17.0,3112.0,872.0,1392.0,680.0,3.0222,172500.0,INLAND
+-122.37,37.6,26.0,15.0,3.0,11.0,3.0,5.048,350000.0,NEAR OCEAN
+-118.14,34.04,40.0,1966.0,391.0,1120.0,362.0,3.7109,198800.0,<1H OCEAN
+-121.93,36.62,34.0,2351.0,,1063.0,428.0,3.725,278000.0,NEAR OCEAN
+-117.9,33.67,25.0,639.0,98.0,311.0,93.0,6.6833,275900.0,<1H OCEAN
+-117.15,32.72,52.0,344.0,177.0,460.0,147.0,1.2292,137500.0,NEAR OCEAN
+-117.1,32.75,15.0,2422.0,774.0,2120.0,715.0,1.0617,92400.0,NEAR OCEAN
+-118.01,34.14,23.0,3425.0,754.0,2202.0,729.0,3.52,187300.0,INLAND
+-118.27,34.25,37.0,2489.0,454.0,1215.0,431.0,5.0234,257600.0,<1H OCEAN
+-121.75,39.88,16.0,2867.0,559.0,1203.0,449.0,2.7143,95300.0,INLAND
+-124.14,41.06,32.0,1020.0,215.0,421.0,198.0,3.0208,143400.0,NEAR OCEAN
+-117.93,33.79,36.0,2363.0,403.0,1240.0,391.0,4.0909,190800.0,<1H OCEAN
+-117.96,34.09,30.0,2686.0,613.0,2477.0,573.0,3.4427,160800.0,INLAND
+-117.09,32.62,34.0,1576.0,364.0,1153.0,381.0,2.1955,129700.0,NEAR OCEAN
+-118.16,33.88,33.0,2180.0,522.0,1634.0,467.0,3.0114,167000.0,<1H OCEAN
+-122.02,37.56,23.0,4332.0,857.0,2461.0,829.0,4.3594,223400.0,NEAR BAY
+-121.27,38.65,33.0,1984.0,289.0,842.0,276.0,5.2949,173300.0,INLAND
+-121.25,38.14,16.0,1174.0,242.0,464.0,261.0,2.3,133300.0,INLAND
+-118.03,33.78,26.0,2001.0,302.0,836.0,298.0,5.1061,257500.0,<1H OCEAN
+-120.73,39.63,17.0,1791.0,356.0,432.0,190.0,3.8826,92400.0,INLAND
+-118.01,34.07,24.0,5684.0,1485.0,6626.0,1481.0,2.2559,166800.0,INLAND
+-118.29,34.11,24.0,3696.0,1125.0,1685.0,1031.0,2.3789,266700.0,<1H OCEAN
+-122.45,37.76,51.0,2564.0,457.0,810.0,442.0,5.6235,500001.0,NEAR BAY
+-117.77,34.1,50.0,2388.0,494.0,1241.0,459.0,2.8818,167200.0,INLAND
+-118.43,34.3,28.0,271.0,61.0,246.0,62.0,1.7062,164600.0,<1H OCEAN
+-117.95,34.06,32.0,2252.0,415.0,1370.0,411.0,4.6312,184800.0,<1H OCEAN
+-117.32,34.01,23.0,3021.0,527.0,1580.0,533.0,4.4063,129900.0,INLAND
+-121.9,37.44,12.0,4228.0,734.0,2594.0,732.0,6.6086,299400.0,<1H OCEAN
+-122.16,37.75,35.0,667.0,140.0,406.0,133.0,3.8047,94300.0,NEAR BAY
+-118.03,33.77,21.0,3803.0,898.0,1511.0,829.0,3.0,221200.0,<1H OCEAN
+-121.28,38.61,22.0,2938.0,619.0,1501.0,561.0,2.7356,96100.0,INLAND
+-121.63,39.12,32.0,2574.0,425.0,1099.0,391.0,4.3864,117500.0,INLAND
+-122.12,39.91,16.0,4006.0,797.0,2028.0,752.0,2.3929,77200.0,INLAND
+-122.55,37.59,31.0,1331.0,245.0,598.0,225.0,4.1827,345500.0,NEAR OCEAN
+-121.94,37.35,52.0,906.0,227.0,1662.0,219.0,3.1667,231600.0,<1H OCEAN
+-122.23,38.17,45.0,350.0,,225.0,72.0,1.8942,216700.0,NEAR BAY
+-119.55,36.72,6.0,1186.0,234.0,1135.0,218.0,2.1515,63900.0,INLAND
+-119.83,36.72,28.0,60.0,10.0,46.0,13.0,4.35,67500.0,INLAND
+-118.08,33.88,27.0,3065.0,736.0,1840.0,719.0,3.6417,208100.0,<1H OCEAN
+-118.4,34.13,32.0,8262.0,1156.0,2712.0,1125.0,10.5575,500001.0,<1H OCEAN
+-118.04,34.05,32.0,1252.0,273.0,1337.0,263.0,2.6579,156800.0,<1H OCEAN
+-118.24,33.95,21.0,1260.0,342.0,1167.0,310.0,0.9708,107600.0,<1H OCEAN
+-117.88,33.76,17.0,1768.0,474.0,1079.0,436.0,1.7823,205300.0,<1H OCEAN
+-117.84,34.11,17.0,3499.0,621.0,1911.0,621.0,4.8894,191700.0,INLAND
+-118.29,34.09,39.0,336.0,173.0,586.0,151.0,1.8056,262500.0,<1H OCEAN
+-118.36,34.04,49.0,995.0,184.0,462.0,194.0,2.7917,242000.0,<1H OCEAN
+-118.29,33.92,34.0,1374.0,240.0,906.0,248.0,5.3292,155500.0,<1H OCEAN
+-117.46,33.93,19.0,4780.0,861.0,3043.0,766.0,3.7431,132800.0,INLAND
+-118.89,34.29,28.0,1545.0,371.0,1334.0,318.0,3.4375,194100.0,<1H OCEAN
+-118.39,34.02,38.0,2447.0,636.0,1312.0,574.0,3.5909,279400.0,<1H OCEAN
+-118.21,34.13,52.0,2465.0,611.0,1433.0,570.0,3.25,214200.0,<1H OCEAN
+-121.88,37.34,52.0,867.0,232.0,1264.0,227.0,2.6312,302900.0,<1H OCEAN
+-122.04,37.38,38.0,2850.0,550.0,1518.0,514.0,4.2028,273600.0,<1H OCEAN
+-118.55,34.41,8.0,21086.0,3945.0,9936.0,3790.0,5.8602,265100.0,<1H OCEAN
+-119.72,34.42,31.0,1524.0,383.0,1257.0,398.0,2.6019,250000.0,NEAR OCEAN
+-118.44,34.18,17.0,1546.0,592.0,2423.0,556.0,2.1977,154200.0,<1H OCEAN
+-122.42,37.8,50.0,2494.0,731.0,958.0,712.0,3.2356,500001.0,NEAR BAY
+-118.0,33.89,35.0,1065.0,176.0,574.0,171.0,5.0384,200800.0,<1H OCEAN
+-122.08,39.25,52.0,224.0,38.0,120.0,45.0,3.017,112500.0,INLAND
+-122.28,37.86,49.0,2932.0,668.0,1361.0,608.0,1.9798,147400.0,NEAR BAY
+-117.88,33.71,20.0,1738.0,509.0,1403.0,411.0,3.1742,245000.0,<1H OCEAN
+-121.63,39.76,22.0,2598.0,482.0,1151.0,490.0,2.8182,109700.0,INLAND
+-119.33,36.3,12.0,2172.0,352.0,1013.0,354.0,4.9464,115600.0,INLAND
+-117.03,34.91,27.0,2718.0,583.0,1472.0,509.0,2.825,76600.0,INLAND
+-117.74,33.61,16.0,2753.0,576.0,857.0,546.0,3.7422,229800.0,<1H OCEAN
+-118.07,33.99,39.0,552.0,151.0,807.0,168.0,3.25,153300.0,<1H OCEAN
+-118.2,34.06,46.0,453.0,119.0,533.0,132.0,2.2961,112500.0,<1H OCEAN
+-122.41,37.64,38.0,1204.0,268.0,921.0,247.0,4.4464,215400.0,NEAR OCEAN
+-118.41,34.06,43.0,4994.0,1057.0,1830.0,969.0,5.5321,500001.0,<1H OCEAN
+-122.12,37.75,33.0,1809.0,261.0,808.0,219.0,6.86,250000.0,NEAR BAY
+-119.79,36.83,15.0,3356.0,694.0,1232.0,627.0,2.2215,72200.0,INLAND
+-122.33,37.57,20.0,2126.0,643.0,1112.0,597.0,3.625,283300.0,NEAR OCEAN
+-117.83,33.68,4.0,3226.0,838.0,1666.0,800.0,4.1652,184500.0,<1H OCEAN
+-122.44,37.76,50.0,2589.0,569.0,945.0,544.0,5.2519,376600.0,NEAR BAY
+-121.85,37.22,21.0,6203.0,798.0,2494.0,800.0,7.7201,362700.0,<1H OCEAN
+-122.57,40.61,27.0,1540.0,315.0,883.0,321.0,2.8036,93400.0,INLAND
+-120.6,37.35,34.0,1722.0,316.0,904.0,315.0,2.4653,66100.0,INLAND
+-120.51,37.29,20.0,4927.0,1042.0,4205.0,1009.0,1.7679,79800.0,INLAND
+-117.11,32.75,21.0,2127.0,658.0,1812.0,603.0,1.6896,100000.0,NEAR OCEAN
+-120.43,37.32,16.0,1170.0,178.0,566.0,181.0,5.2522,125300.0,INLAND
+-122.33,40.6,5.0,6383.0,1206.0,2965.0,1141.0,3.8103,111100.0,INLAND
+-117.2,32.79,29.0,1213.0,,654.0,246.0,4.5987,255600.0,NEAR OCEAN
+-124.01,40.97,21.0,1513.0,319.0,943.0,301.0,3.538,102700.0,<1H OCEAN
+-121.86,36.6,33.0,1409.0,307.0,633.0,290.0,3.5568,191200.0,<1H OCEAN
+-121.8,37.99,16.0,3077.0,465.0,1575.0,446.0,5.5,179500.0,INLAND
+-118.05,34.06,45.0,531.0,164.0,722.0,166.0,2.1406,162500.0,<1H OCEAN
+-119.17,34.2,36.0,2028.0,523.0,2751.0,496.0,3.015,149300.0,NEAR OCEAN
+-118.17,33.86,44.0,1701.0,396.0,1091.0,384.0,3.025,162300.0,<1H OCEAN
+-118.02,34.12,38.0,1778.0,288.0,870.0,281.0,6.5784,408500.0,INLAND
+-121.81,37.23,16.0,1674.0,281.0,850.0,254.0,5.3157,253300.0,<1H OCEAN
+-121.86,37.39,17.0,1777.0,328.0,1235.0,329.0,5.4225,258100.0,<1H OCEAN
+-121.19,38.71,11.0,4415.0,,1520.0,627.0,3.2321,390800.0,INLAND
+-118.18,34.1,10.0,1907.0,398.0,921.0,369.0,4.875,200400.0,<1H OCEAN
+-118.47,34.27,35.0,1150.0,185.0,741.0,178.0,5.741,220600.0,<1H OCEAN
+-117.07,33.12,12.0,2453.0,599.0,1251.0,529.0,2.4122,127000.0,<1H OCEAN
+-122.08,37.9,29.0,4133.0,770.0,1691.0,744.0,5.1097,288000.0,NEAR BAY
+-118.25,33.79,32.0,1205.0,340.0,1799.0,370.0,2.375,128000.0,NEAR OCEAN
+-119.03,34.23,21.0,3284.0,487.0,1832.0,521.0,5.2773,250800.0,<1H OCEAN
+-120.91,37.57,26.0,3396.0,705.0,2446.0,694.0,2.0521,65400.0,INLAND
+-117.9,34.06,33.0,1330.0,209.0,578.0,192.0,5.6406,266200.0,<1H OCEAN
+-119.04,34.22,18.0,3117.0,583.0,2079.0,545.0,4.6458,222800.0,<1H OCEAN
+-116.91,32.81,22.0,4331.0,637.0,1952.0,654.0,5.4834,232000.0,<1H OCEAN
+-117.89,33.66,33.0,3595.0,785.0,1621.0,732.0,4.1372,265200.0,<1H OCEAN
+-119.01,35.4,11.0,8739.0,2190.0,4781.0,1919.0,1.7109,44600.0,INLAND
+-117.04,32.55,15.0,2206.0,648.0,2511.0,648.0,1.6348,93200.0,NEAR OCEAN
+-118.21,33.95,35.0,2129.0,614.0,2376.0,618.0,2.0372,160800.0,<1H OCEAN
+-122.37,37.56,21.0,7189.0,874.0,2440.0,846.0,11.6833,500001.0,NEAR OCEAN
+-118.54,34.23,35.0,3422.0,601.0,1690.0,574.0,4.375,232900.0,<1H OCEAN
+-117.23,33.96,5.0,9179.0,1361.0,4573.0,1294.0,5.253,163300.0,INLAND
+-115.88,32.93,15.0,208.0,49.0,51.0,20.0,4.0208,32500.0,INLAND
+-122.42,37.8,52.0,741.0,170.0,277.0,165.0,4.4712,500001.0,NEAR BAY
+-118.08,33.84,28.0,4216.0,948.0,2997.0,896.0,3.7961,162700.0,<1H OCEAN
+-121.98,37.74,8.0,2865.0,389.0,1376.0,417.0,7.9393,399300.0,<1H OCEAN
+-122.72,38.42,26.0,1168.0,253.0,937.0,248.0,1.9458,146000.0,<1H OCEAN
+-115.52,32.73,17.0,1190.0,275.0,1113.0,258.0,2.3571,63100.0,INLAND
+-119.09,36.22,34.0,1715.0,290.0,780.0,297.0,3.4306,74300.0,INLAND
+-119.99,38.88,17.0,2807.0,529.0,675.0,251.0,2.7457,107800.0,INLAND
+-121.43,38.54,42.0,3321.0,688.0,1346.0,658.0,2.4618,101300.0,INLAND
+-121.07,39.09,17.0,1878.0,345.0,892.0,299.0,2.8864,143100.0,INLAND
+-122.76,38.45,8.0,5823.0,1104.0,2864.0,1041.0,3.6292,183600.0,<1H OCEAN
+-118.36,33.9,18.0,3380.0,922.0,2276.0,854.0,4.0727,214000.0,<1H OCEAN
+-117.76,33.53,24.0,2105.0,346.0,712.0,332.0,10.6349,500001.0,<1H OCEAN
+-120.38,39.82,10.0,1262.0,258.0,510.0,209.0,2.1667,92800.0,INLAND
+-117.9,33.75,32.0,1893.0,431.0,2245.0,426.0,3.7143,163000.0,<1H OCEAN
+-118.32,34.02,47.0,1648.0,346.0,1120.0,338.0,2.0042,114200.0,<1H OCEAN
+-118.14,34.13,49.0,4438.0,803.0,1650.0,741.0,5.1072,479700.0,<1H OCEAN
+-122.11,37.99,10.0,2864.0,514.0,1300.0,507.0,4.3875,287700.0,NEAR BAY
+-120.35,38.04,16.0,1499.0,326.0,733.0,286.0,2.5729,118800.0,INLAND
+-122.64,38.71,20.0,531.0,126.0,231.0,96.0,2.625,89600.0,INLAND
+-122.22,37.46,13.0,2888.0,546.0,1182.0,504.0,6.0255,409300.0,NEAR OCEAN
+-118.35,33.91,26.0,2159.0,523.0,1331.0,520.0,3.87,264500.0,<1H OCEAN
+-119.78,36.75,35.0,2114.0,506.0,2050.0,474.0,1.2375,50000.0,INLAND
+-119.18,34.19,36.0,4519.0,1081.0,4818.0,1061.0,2.8561,179100.0,NEAR OCEAN
+-122.15,37.74,52.0,1394.0,223.0,545.0,230.0,3.95,219000.0,NEAR BAY
+-119.63,36.33,14.0,2928.0,600.0,1633.0,559.0,1.8385,67500.0,INLAND
+-122.07,37.93,45.0,1544.0,244.0,614.0,238.0,5.0255,226000.0,NEAR BAY
+-121.02,37.61,33.0,1469.0,370.0,1318.0,349.0,1.7104,59000.0,INLAND
+-117.89,34.1,27.0,3341.0,728.0,1762.0,679.0,2.9437,180400.0,<1H OCEAN
+-118.1,34.58,29.0,2843.0,603.0,1517.0,573.0,2.6658,106900.0,INLAND
+-121.83,39.73,52.0,1741.0,401.0,753.0,377.0,2.0064,77900.0,INLAND
+-121.29,38.68,20.0,1881.0,378.0,921.0,360.0,1.8589,144000.0,INLAND
+-118.27,33.97,45.0,1546.0,371.0,1186.0,366.0,1.64,96800.0,<1H OCEAN
+-122.23,37.46,33.0,2643.0,464.0,1015.0,427.0,4.2232,363700.0,NEAR OCEAN
+-122.44,37.71,31.0,2370.0,441.0,1524.0,470.0,5.0201,264100.0,NEAR BAY
+-122.49,37.74,52.0,2442.0,449.0,1188.0,436.0,4.3909,317700.0,NEAR OCEAN
+-119.84,37.48,17.0,2582.0,553.0,1087.0,423.0,2.5,104200.0,INLAND
+-119.04,36.07,26.0,2185.0,435.0,1108.0,419.0,2.2277,78000.0,INLAND
+-122.14,37.69,38.0,1571.0,317.0,874.0,301.0,4.4659,189100.0,NEAR BAY
+-121.39,38.51,19.0,1808.0,375.0,758.0,320.0,2.0062,92000.0,INLAND
+-118.39,34.02,38.0,2521.0,647.0,1091.0,597.0,4.1296,322900.0,<1H OCEAN
+-121.29,37.92,12.0,1096.0,240.0,1175.0,278.0,3.1053,73100.0,INLAND
+-118.39,33.89,30.0,2532.0,464.0,1056.0,419.0,6.3434,460400.0,<1H OCEAN
+-118.2,34.06,46.0,321.0,101.0,401.0,86.0,2.1029,109400.0,<1H OCEAN
+-118.58,34.19,35.0,2329.0,399.0,966.0,336.0,3.8839,224900.0,<1H OCEAN
+-118.46,33.99,44.0,1122.0,287.0,531.0,256.0,4.0598,335900.0,<1H OCEAN
+-121.82,38.0,29.0,2070.0,452.0,985.0,420.0,2.8466,113400.0,INLAND
+-121.01,37.72,23.0,1373.0,264.0,677.0,245.0,2.5486,161100.0,INLAND
+-118.27,33.78,21.0,3354.0,1174.0,4426.0,1110.0,2.5262,167300.0,<1H OCEAN
+-118.02,33.91,34.0,2518.0,429.0,1309.0,421.0,4.7861,210700.0,<1H OCEAN
+-118.09,33.94,33.0,1976.0,404.0,1379.0,395.0,3.8542,175400.0,<1H OCEAN
+-117.98,33.68,17.0,2603.0,373.0,1265.0,382.0,6.8039,332900.0,<1H OCEAN
+-118.87,34.19,23.0,2179.0,423.0,1338.0,406.0,5.5224,240700.0,<1H OCEAN
+-118.29,34.03,38.0,1501.0,437.0,1777.0,441.0,2.0848,135200.0,<1H OCEAN
+-117.13,32.91,15.0,1450.0,266.0,747.0,290.0,3.6111,196300.0,<1H OCEAN
+-117.31,34.17,25.0,2795.0,596.0,1650.0,569.0,3.0078,87100.0,INLAND
+-121.31,38.71,18.0,3998.0,744.0,2071.0,660.0,4.3836,102000.0,INLAND
+-121.92,37.29,35.0,2189.0,307.0,800.0,320.0,7.6659,426900.0,<1H OCEAN
+-121.69,39.36,34.0,842.0,186.0,635.0,165.0,1.8355,63000.0,INLAND
+-122.49,37.78,47.0,2695.0,643.0,1505.0,644.0,3.0877,329100.0,NEAR BAY
+-121.98,38.36,33.0,1083.0,217.0,562.0,203.0,2.433,101700.0,INLAND
+-121.05,37.65,5.0,3096.0,545.0,1760.0,519.0,4.5701,146400.0,INLAND
+-117.87,33.6,33.0,3120.0,602.0,1155.0,553.0,5.2949,500001.0,<1H OCEAN
+-122.49,37.78,49.0,2176.0,441.0,1040.0,448.0,4.2414,500001.0,NEAR BAY
+-117.69,33.59,13.0,3320.0,426.0,1432.0,431.0,7.9283,348100.0,<1H OCEAN
+-117.99,33.87,34.0,1239.0,307.0,869.0,291.0,3.59,161900.0,<1H OCEAN
+-119.17,34.15,18.0,2509.0,688.0,3129.0,677.0,2.6098,146100.0,NEAR OCEAN
+-124.08,40.91,13.0,2522.0,719.0,1381.0,628.0,1.6667,78800.0,NEAR OCEAN
+-120.93,38.5,15.0,1248.0,234.0,529.0,216.0,3.3393,107200.0,INLAND
+-121.98,36.98,29.0,2681.0,632.0,1652.0,620.0,3.075,215800.0,NEAR OCEAN
+-119.87,36.34,26.0,1414.0,265.0,779.0,249.0,2.9167,83900.0,INLAND
+-120.44,34.66,22.0,3231.0,549.0,1739.0,581.0,4.5417,142400.0,NEAR OCEAN
+-117.23,33.77,5.0,2108.0,496.0,1666.0,461.0,2.0,83000.0,INLAND
+-122.04,37.58,14.0,14917.0,2708.0,8012.0,2606.0,5.6277,269800.0,NEAR BAY
+-117.9,33.74,25.0,808.0,163.0,1066.0,189.0,4.7679,173100.0,<1H OCEAN
+-118.25,34.0,36.0,1033.0,267.0,1112.0,229.0,1.7237,105800.0,<1H OCEAN
+-121.84,37.33,26.0,1934.0,408.0,2059.0,416.0,3.6765,163600.0,<1H OCEAN
+-118.14,34.1,27.0,4073.0,1013.0,2411.0,933.0,3.108,231000.0,<1H OCEAN
+-118.31,34.05,35.0,1692.0,423.0,1578.0,406.0,2.5313,305800.0,<1H OCEAN
+-118.3,37.17,22.0,3480.0,673.0,1541.0,636.0,2.75,94500.0,INLAND
+-118.52,34.19,42.0,881.0,170.0,464.0,163.0,2.9511,203900.0,<1H OCEAN
+-121.3,38.01,30.0,2547.0,485.0,1547.0,501.0,3.994,95500.0,INLAND
+-122.25,38.1,52.0,2315.0,556.0,1113.0,486.0,2.5042,147900.0,NEAR BAY
+-121.36,38.01,16.0,1080.0,166.0,507.0,182.0,4.5278,166900.0,INLAND
+-117.19,34.27,16.0,7961.0,1147.0,879.0,280.0,5.2146,255200.0,INLAND
+-122.66,38.27,16.0,1523.0,308.0,477.0,315.0,2.1696,75000.0,<1H OCEAN
+-121.54,39.6,15.0,886.0,204.0,576.0,205.0,2.1467,84100.0,INLAND
+-122.48,37.72,46.0,2403.0,638.0,1281.0,603.0,3.2321,112500.0,NEAR OCEAN
+-118.44,35.13,21.0,1899.0,447.0,1133.0,391.0,1.8636,67900.0,INLAND
+-118.42,34.19,34.0,2622.0,572.0,1997.0,573.0,3.338,222500.0,<1H OCEAN
+-117.17,34.05,29.0,4007.0,700.0,1576.0,696.0,3.1801,149300.0,INLAND
+-121.88,37.44,23.0,1310.0,267.0,910.0,261.0,5.3994,237900.0,<1H OCEAN
+-122.17,37.88,33.0,3626.0,502.0,1348.0,480.0,7.6107,423200.0,NEAR BAY
+-124.18,40.78,37.0,1453.0,293.0,867.0,310.0,2.5536,70200.0,NEAR OCEAN
+-121.82,37.37,40.0,802.0,149.0,445.0,143.0,4.0446,196300.0,<1H OCEAN
+-122.44,37.76,52.0,2959.0,683.0,1145.0,666.0,4.2222,361600.0,NEAR BAY
+-117.71,33.47,17.0,2681.0,454.0,830.0,410.0,5.5507,345700.0,<1H OCEAN
+-122.44,37.72,52.0,1380.0,272.0,847.0,284.0,3.7143,260000.0,NEAR BAY
+-118.19,34.05,37.0,349.0,79.0,276.0,64.0,3.2125,125000.0,<1H OCEAN
+-119.18,34.23,16.0,4609.0,1220.0,2147.0,1007.0,3.375,218800.0,NEAR OCEAN
+-121.59,39.79,20.0,743.0,171.0,395.0,168.0,1.625,88300.0,INLAND
+-116.98,32.88,12.0,7320.0,1279.0,4048.0,1249.0,4.3952,151700.0,<1H OCEAN
+-117.98,34.1,22.0,5661.0,1209.0,5389.0,1178.0,3.7727,159700.0,INLAND
+-122.19,37.73,45.0,1528.0,291.0,801.0,287.0,1.2625,84700.0,NEAR BAY
+-117.88,33.74,19.0,2261.0,642.0,3545.0,635.0,2.5224,148500.0,<1H OCEAN
+-119.78,34.45,9.0,1830.0,353.0,1515.0,220.0,4.2109,450000.0,NEAR OCEAN
+-120.46,38.15,16.0,4221.0,781.0,1516.0,697.0,2.3816,116000.0,INLAND
+-121.77,37.24,12.0,10236.0,1878.0,5674.0,1816.0,4.747,261100.0,<1H OCEAN
+-119.27,36.32,9.0,3631.0,635.0,1881.0,628.0,4.7723,113100.0,INLAND
+-118.36,34.05,50.0,3518.0,812.0,1724.0,758.0,3.0833,338100.0,<1H OCEAN
+-118.19,33.81,21.0,1835.0,427.0,1038.0,384.0,4.4559,198500.0,NEAR OCEAN
+-121.28,37.94,35.0,2680.0,634.0,2188.0,611.0,1.9375,56700.0,INLAND
+-119.84,36.85,8.0,3791.0,487.0,1424.0,475.0,10.5144,345900.0,INLAND
+-116.46,33.79,10.0,6960.0,1487.0,1130.0,661.0,2.1411,136400.0,INLAND
+-121.75,39.4,29.0,1687.0,327.0,864.0,334.0,2.4943,81900.0,INLAND
+-120.85,37.07,16.0,1795.0,362.0,1642.0,340.0,2.5363,86300.0,INLAND
+-120.34,38.23,10.0,3757.0,722.0,546.0,223.0,3.75,121400.0,INLAND
+-119.56,36.7,40.0,1195.0,326.0,1135.0,315.0,2.1182,58900.0,INLAND
+-121.07,39.2,45.0,204.0,62.0,133.0,51.0,1.0,90600.0,INLAND
+-117.05,32.8,23.0,3309.0,401.0,1116.0,386.0,7.916,330600.0,<1H OCEAN
+-122.45,37.79,52.0,1457.0,215.0,495.0,208.0,10.7097,500001.0,NEAR BAY
+-117.22,32.81,24.0,730.0,196.0,335.0,203.0,3.5078,362500.0,NEAR OCEAN
+-116.86,32.87,17.0,5799.0,921.0,2630.0,843.0,5.0524,285400.0,<1H OCEAN
+-118.36,34.16,32.0,2455.0,556.0,989.0,493.0,4.0764,325000.0,<1H OCEAN
+-122.63,38.34,15.0,2153.0,345.0,979.0,335.0,5.1966,325400.0,<1H OCEAN
+-118.35,33.93,26.0,3156.0,857.0,2394.0,787.0,3.01,191900.0,<1H OCEAN
+-118.29,33.95,39.0,1701.0,428.0,1468.0,411.0,1.9702,93200.0,<1H OCEAN
+-117.68,35.62,30.0,2994.0,741.0,1481.0,581.0,2.1458,52400.0,INLAND
+-118.28,34.07,32.0,1777.0,536.0,1684.0,489.0,2.3636,190000.0,<1H OCEAN
+-122.47,37.95,16.0,3769.0,839.0,1986.0,815.0,3.9712,187500.0,NEAR BAY
+-121.84,36.62,26.0,32.0,8.0,27.0,10.0,2.225,150000.0,<1H OCEAN
+-119.05,35.32,11.0,7035.0,1455.0,3525.0,1387.0,3.4827,93600.0,INLAND
+-120.09,37.0,11.0,3761.0,675.0,2374.0,673.0,3.4598,74600.0,INLAND
+-118.21,34.1,36.0,2000.0,533.0,1234.0,535.0,3.7437,241700.0,<1H OCEAN
+-122.44,37.52,16.0,7077.0,1179.0,3502.0,1148.0,5.9919,345100.0,NEAR OCEAN
+-117.11,32.71,30.0,1729.0,457.0,1673.0,460.0,1.7,85900.0,NEAR OCEAN
+-122.28,41.38,15.0,5266.0,1031.0,2147.0,885.0,2.8036,110100.0,INLAND
+-122.1,37.37,37.0,2511.0,354.0,945.0,348.0,8.3924,500001.0,NEAR BAY
+-117.76,33.48,38.0,3832.0,809.0,1332.0,636.0,5.0044,381200.0,<1H OCEAN
+-118.29,33.88,21.0,4946.0,1231.0,3186.0,1167.0,3.3281,237000.0,<1H OCEAN
+-118.43,34.29,38.0,1237.0,298.0,1073.0,293.0,3.6726,154600.0,<1H OCEAN
+-118.14,33.86,43.0,2104.0,382.0,1071.0,396.0,5.0,208900.0,<1H OCEAN
+-117.21,34.04,14.0,3063.0,426.0,1570.0,419.0,6.2917,224700.0,INLAND
+-122.14,37.48,36.0,1210.0,236.0,981.0,239.0,4.0039,148900.0,NEAR BAY
+-121.91,37.32,42.0,1067.0,256.0,608.0,280.0,3.0096,180800.0,<1H OCEAN
+-122.3,38.29,20.0,1789.0,434.0,1113.0,398.0,2.4728,139700.0,NEAR BAY
+-118.28,34.0,42.0,855.0,284.0,890.0,247.0,1.2778,112500.0,<1H OCEAN
+-121.81,37.36,20.0,3189.0,420.0,1234.0,389.0,7.5813,374100.0,<1H OCEAN
+-121.82,36.86,17.0,1573.0,272.0,142.0,55.0,2.1719,420000.0,NEAR OCEAN
+-117.39,33.97,48.0,1915.0,348.0,1060.0,376.0,3.4044,117900.0,INLAND
+-119.01,35.36,38.0,1838.0,388.0,1203.0,373.0,1.6797,60700.0,INLAND
+-115.6,32.87,3.0,1629.0,317.0,1005.0,312.0,4.1293,83200.0,INLAND
+-118.42,33.99,38.0,740.0,171.0,599.0,194.0,4.0893,248900.0,<1H OCEAN
+-117.96,33.65,21.0,2030.0,318.0,910.0,311.0,7.8453,343300.0,<1H OCEAN
+-122.12,37.94,22.0,4949.0,626.0,1850.0,590.0,10.4549,500001.0,NEAR BAY
+-117.96,34.04,34.0,1381.0,265.0,1020.0,268.0,4.025,146900.0,<1H OCEAN
+-117.98,33.69,22.0,3957.0,520.0,1774.0,527.0,7.0907,350200.0,<1H OCEAN
+-118.38,33.75,23.0,8277.0,1290.0,3176.0,1159.0,7.6986,500001.0,NEAR OCEAN
+-120.66,40.41,52.0,2081.0,478.0,1051.0,419.0,2.2992,70200.0,INLAND
+-122.53,37.88,25.0,4921.0,866.0,1913.0,834.0,6.8742,413100.0,NEAR BAY
+-118.22,34.12,37.0,1298.0,242.0,750.0,255.0,5.2049,240800.0,<1H OCEAN
+-122.13,37.46,35.0,1321.0,300.0,1133.0,287.0,3.7312,159600.0,NEAR BAY
+-118.23,33.96,42.0,1977.0,570.0,2406.0,557.0,2.5913,151600.0,<1H OCEAN
+-118.27,33.82,37.0,943.0,218.0,803.0,216.0,5.2287,191100.0,<1H OCEAN
+-117.21,33.97,3.0,18356.0,2537.0,8437.0,2342.0,5.6409,197700.0,INLAND
+-122.42,37.73,46.0,1819.0,411.0,1534.0,406.0,4.0132,229400.0,NEAR BAY
+-122.07,37.95,39.0,2199.0,388.0,1025.0,385.0,4.5893,190000.0,NEAR BAY
+-118.35,33.95,30.0,2661.0,765.0,2324.0,724.0,3.0519,137500.0,<1H OCEAN
+-119.02,35.34,38.0,1463.0,294.0,692.0,295.0,2.3125,65800.0,INLAND
+-117.64,34.08,35.0,1254.0,241.0,729.0,253.0,3.495,118000.0,INLAND
+-122.53,37.92,45.0,1530.0,324.0,608.0,328.0,3.875,390800.0,NEAR BAY
+-122.51,37.89,27.0,2674.0,565.0,1233.0,547.0,3.4485,458300.0,NEAR BAY
+-121.76,37.0,21.0,1416.0,269.0,779.0,200.0,3.1987,279800.0,<1H OCEAN
+-121.14,38.84,22.0,2750.0,433.0,1161.0,428.0,4.2143,236500.0,INLAND
+-122.78,38.97,11.0,5175.0,971.0,2144.0,792.0,3.0466,97300.0,INLAND
+-122.01,37.53,19.0,4572.0,712.0,2346.0,709.0,6.0667,245700.0,NEAR BAY
+-117.2,32.77,30.0,156.0,45.0,77.0,40.0,3.2679,137500.0,NEAR OCEAN
+-118.03,33.99,52.0,2792.0,461.0,1177.0,439.0,3.4312,243800.0,<1H OCEAN
+-121.65,40.88,15.0,2909.0,549.0,1537.0,522.0,3.0179,61300.0,INLAND
+-121.21,38.65,14.0,3443.0,510.0,1413.0,505.0,5.6529,196000.0,INLAND
+-121.95,37.11,21.0,2387.0,357.0,913.0,341.0,7.736,397700.0,<1H OCEAN
+-121.17,38.71,15.0,3084.0,557.0,1040.0,562.0,2.5183,293300.0,INLAND
+-118.4,37.36,34.0,2465.0,619.0,1172.0,575.0,1.9722,116100.0,INLAND
+-122.07,37.71,40.0,1808.0,302.0,746.0,270.0,5.3015,254900.0,NEAR BAY
+-121.79,37.32,20.0,3034.0,451.0,1669.0,430.0,6.2742,241300.0,<1H OCEAN
+-118.05,33.98,41.0,1406.0,428.0,1174.0,390.0,2.0147,137500.0,<1H OCEAN
+-117.23,33.91,9.0,11654.0,2100.0,7596.0,2127.0,4.0473,127200.0,INLAND
+-117.77,34.11,28.0,1998.0,414.0,1124.0,389.0,3.75,180900.0,INLAND
+-122.13,37.75,36.0,768.0,93.0,229.0,93.0,5.3602,330000.0,NEAR BAY
+-122.21,37.78,52.0,1477.0,300.0,1065.0,269.0,1.8472,137000.0,NEAR BAY
+-122.31,38.3,45.0,3023.0,659.0,1789.0,657.0,3.6039,126000.0,NEAR BAY
+-121.46,39.4,17.0,3659.0,735.0,1970.0,667.0,2.425,96200.0,INLAND
+-118.1,33.93,36.0,1124.0,217.0,707.0,234.0,4.375,174500.0,<1H OCEAN
+-120.21,39.35,7.0,914.0,159.0,85.0,34.0,4.7917,187500.0,INLAND
+-117.89,33.73,32.0,728.0,134.0,837.0,135.0,4.0769,163900.0,<1H OCEAN
+-120.57,38.2,13.0,4110.0,847.0,1796.0,706.0,2.6417,122300.0,INLAND
+-119.56,36.1,29.0,424.0,78.0,284.0,73.0,1.5313,43800.0,INLAND
+-118.12,34.1,34.0,2918.0,555.0,1435.0,568.0,4.2344,306300.0,<1H OCEAN
+-122.7,38.99,18.0,1177.0,224.0,181.0,105.0,2.3558,134700.0,INLAND
+-119.35,35.59,14.0,2719.0,502.0,1497.0,457.0,3.176,71800.0,INLAND
+-118.46,34.06,20.0,5448.0,1532.0,2202.0,1442.0,4.2554,500001.0,<1H OCEAN
+-118.36,34.05,42.0,1372.0,,674.0,271.0,2.8793,202100.0,<1H OCEAN
+-118.11,34.07,46.0,1130.0,229.0,698.0,209.0,5.2719,244400.0,<1H OCEAN
+-119.61,36.58,29.0,1312.0,280.0,788.0,271.0,2.6974,73000.0,INLAND
+-117.42,34.08,28.0,2300.0,419.0,1312.0,444.0,3.4844,127700.0,INLAND
+-118.0,33.94,36.0,2911.0,534.0,1395.0,486.0,5.1738,203700.0,<1H OCEAN
+-121.3,38.68,19.0,2655.0,438.0,1253.0,454.0,5.2817,140600.0,INLAND
+-122.45,37.77,52.0,2191.0,627.0,1100.0,585.0,3.0409,500000.0,NEAR BAY
+-119.32,36.32,35.0,2316.0,387.0,849.0,378.0,4.3816,88600.0,INLAND
+-121.36,39.52,15.0,2490.0,527.0,1229.0,497.0,2.3917,85700.0,INLAND
+-122.35,37.94,47.0,1275.0,275.0,844.0,273.0,2.8967,95600.0,NEAR BAY
+-121.47,38.61,35.0,1372.0,360.0,850.0,328.0,1.6331,67500.0,INLAND
+-119.36,36.22,10.0,2445.0,526.0,1262.0,476.0,1.9355,68300.0,INLAND
+-117.3,33.07,14.0,2670.0,426.0,1034.0,407.0,6.4247,295100.0,NEAR OCEAN
+-118.2,33.93,40.0,1929.0,417.0,1780.0,419.0,3.4402,149400.0,<1H OCEAN
+-119.35,35.58,13.0,1657.0,362.0,1186.0,376.0,1.1903,63200.0,INLAND
+-117.7,35.62,18.0,2657.0,496.0,1426.0,483.0,3.5931,71900.0,INLAND
+-122.23,37.83,52.0,2990.0,379.0,947.0,361.0,7.8772,500001.0,NEAR BAY
+-121.31,38.57,22.0,1229.0,253.0,733.0,250.0,2.5,101600.0,INLAND
+-117.52,33.84,20.0,688.0,146.0,575.0,144.0,3.55,111000.0,INLAND
+-117.8,34.1,13.0,2996.0,495.0,1187.0,464.0,6.2456,161700.0,INLAND
+-118.17,33.84,45.0,1533.0,331.0,791.0,335.0,3.4605,186600.0,<1H OCEAN
+-120.15,39.15,25.0,1669.0,348.0,163.0,78.0,5.75,176600.0,INLAND
+-119.14,34.23,8.0,243.0,75.0,102.0,80.0,2.5714,500001.0,NEAR OCEAN
+-122.26,38.33,34.0,2048.0,316.0,780.0,267.0,5.815,339200.0,NEAR BAY
+-117.7,33.65,16.0,3388.0,492.0,1249.0,463.0,6.1863,355600.0,<1H OCEAN
+-119.72,34.41,35.0,871.0,145.0,354.0,154.0,4.3214,341800.0,NEAR OCEAN
+-116.89,32.82,18.0,2515.0,443.0,1442.0,449.0,5.0201,154400.0,<1H OCEAN
+-118.23,34.13,48.0,1308.0,,835.0,294.0,4.2891,214800.0,<1H OCEAN
+-122.45,37.74,38.0,5688.0,930.0,2263.0,908.0,6.203,346800.0,NEAR BAY
+-122.03,37.53,18.0,1746.0,437.0,1268.0,404.0,3.256,183300.0,NEAR BAY
+-118.45,34.17,21.0,2152.0,527.0,996.0,470.0,3.2386,277300.0,<1H OCEAN
+-118.15,34.13,50.0,2443.0,494.0,947.0,451.0,4.7344,314700.0,<1H OCEAN
+-117.62,34.13,20.0,3216.0,516.0,1655.0,524.0,5.1261,158800.0,INLAND
+-117.24,33.0,16.0,2512.0,356.0,795.0,353.0,7.5975,369100.0,NEAR OCEAN
+-118.62,34.17,34.0,3268.0,538.0,1463.0,519.0,6.8482,308300.0,<1H OCEAN
+-118.14,33.93,31.0,3205.0,727.0,1647.0,664.0,3.3681,223900.0,<1H OCEAN
+-117.12,32.91,8.0,3405.0,961.0,1742.0,918.0,2.8728,114600.0,<1H OCEAN
+-116.29,33.67,12.0,5048.0,842.0,883.0,391.0,5.6918,231300.0,INLAND
+-121.5,38.59,43.0,88.0,21.0,119.0,19.0,1.725,67500.0,INLAND
+-117.48,33.98,20.0,2451.0,475.0,1785.0,456.0,3.3966,115000.0,INLAND
+-117.63,34.37,20.0,7052.0,1306.0,2197.0,810.0,3.7252,167100.0,INLAND
+-119.8,36.8,43.0,1951.0,288.0,725.0,308.0,6.3359,169300.0,INLAND
+-117.42,34.11,25.0,4261.0,893.0,2319.0,702.0,3.3958,111900.0,INLAND
+-122.26,37.5,24.0,2307.0,510.0,842.0,507.0,3.6111,341500.0,NEAR OCEAN
+-117.85,34.1,22.0,5179.0,944.0,2315.0,884.0,4.51,189900.0,<1H OCEAN
+-114.55,32.8,19.0,2570.0,820.0,1431.0,608.0,1.275,56100.0,INLAND
+-122.23,37.46,26.0,4670.0,1039.0,2103.0,933.0,4.4167,333800.0,NEAR OCEAN
+-117.74,34.05,27.0,852.0,237.0,1024.0,221.0,2.1141,110900.0,INLAND
+-118.27,34.09,52.0,1079.0,222.0,439.0,201.0,4.625,230800.0,<1H OCEAN
+-121.31,36.42,21.0,2740.0,615.0,2630.0,564.0,2.6629,102700.0,<1H OCEAN
+-117.1,34.21,22.0,4397.0,931.0,1145.0,445.0,4.5268,108400.0,INLAND
+-118.25,34.07,16.0,719.0,225.0,801.0,218.0,2.3942,133300.0,<1H OCEAN
+-122.17,37.7,24.0,1755.0,365.0,952.0,362.0,4.0,202600.0,NEAR BAY
+-118.25,34.16,52.0,2477.0,385.0,993.0,371.0,4.9135,368100.0,<1H OCEAN
+-122.47,38.34,15.0,2411.0,446.0,1144.0,407.0,4.3472,261000.0,<1H OCEAN
+-121.93,37.78,2.0,227.0,35.0,114.0,49.0,3.1591,434700.0,<1H OCEAN
+-121.87,37.66,27.0,1569.0,242.0,583.0,214.0,5.7519,278500.0,INLAND
+-118.12,34.01,40.0,1417.0,338.0,1068.0,331.0,2.4259,164600.0,<1H OCEAN
+-118.38,34.06,25.0,2558.0,661.0,1183.0,636.0,3.5556,500000.0,<1H OCEAN
+-117.08,33.03,17.0,987.0,142.0,463.0,152.0,5.8747,229300.0,<1H OCEAN
+-118.51,34.26,29.0,2472.0,354.0,1109.0,397.0,5.5433,332500.0,<1H OCEAN
+-118.32,34.2,36.0,1978.0,337.0,834.0,311.0,3.9866,294400.0,<1H OCEAN
+-118.36,34.13,36.0,6871.0,1180.0,2216.0,1130.0,8.0499,495600.0,<1H OCEAN
+-118.49,34.25,28.0,4054.0,712.0,2164.0,746.0,5.0,258000.0,<1H OCEAN
+-120.91,38.73,11.0,5460.0,859.0,2645.0,838.0,4.835,230600.0,INLAND
+-119.72,36.8,15.0,3045.0,689.0,1340.0,588.0,3.1953,85700.0,INLAND
+-119.01,35.35,34.0,1354.0,325.0,922.0,304.0,2.1875,58000.0,INLAND
+-122.15,37.45,52.0,568.0,91.0,219.0,75.0,6.1575,500001.0,NEAR BAY
+-118.14,33.95,42.0,1413.0,228.0,630.0,219.0,6.8564,300000.0,<1H OCEAN
+-123.82,40.16,19.0,2283.0,634.0,1184.0,453.0,1.2227,76800.0,<1H OCEAN
+-117.38,34.44,4.0,5083.0,867.0,2541.0,856.0,4.2414,121400.0,INLAND
+-122.42,38.31,18.0,1479.0,246.0,550.0,217.0,4.7356,333300.0,NEAR BAY
+-119.23,34.46,34.0,9280.0,1765.0,4514.0,1693.0,3.2026,227600.0,<1H OCEAN
+-122.03,37.3,22.0,3583.0,758.0,1792.0,695.0,5.4842,335300.0,<1H OCEAN
+-117.26,34.53,10.0,3103.0,520.0,1283.0,464.0,3.071,151600.0,INLAND
+-117.98,33.72,28.0,3109.0,561.0,1891.0,562.0,5.2655,243100.0,<1H OCEAN
+-120.7,35.32,46.0,118.0,17.0,6532.0,13.0,4.2639,350000.0,NEAR OCEAN
+-118.19,33.95,44.0,1436.0,271.0,850.0,269.0,3.2768,179100.0,<1H OCEAN
+-118.13,34.08,35.0,2517.0,662.0,1883.0,607.0,2.5787,223000.0,<1H OCEAN
+-117.11,34.43,14.0,3026.0,556.0,1349.0,485.0,2.8021,111200.0,INLAND
+-119.46,35.86,22.0,1750.0,374.0,1113.0,338.0,1.505,42700.0,INLAND
+-119.52,34.39,23.0,1414.0,365.0,889.0,345.0,2.6397,250000.0,NEAR OCEAN
+-121.86,37.66,22.0,3634.0,664.0,1699.0,640.0,4.1597,293200.0,INLAND
+-117.88,33.85,34.0,1127.0,185.0,588.0,181.0,4.375,224700.0,<1H OCEAN
+-122.04,39.72,23.0,2502.0,481.0,1443.0,455.0,2.5625,70000.0,INLAND
+-121.87,37.66,39.0,522.0,116.0,161.0,102.0,2.4896,238500.0,INLAND
+-118.43,33.82,34.0,2112.0,614.0,946.0,574.0,4.6048,500001.0,NEAR OCEAN
+-117.71,33.63,16.0,1565.0,274.0,950.0,280.0,5.8399,220600.0,<1H OCEAN
+-117.89,33.9,16.0,1426.0,216.0,652.0,226.0,6.5284,288700.0,<1H OCEAN
+-116.17,33.67,18.0,3585.0,800.0,3873.0,788.0,2.5714,65900.0,INLAND
+-118.15,34.21,34.0,2765.0,515.0,1422.0,438.0,5.4727,238900.0,INLAND
+-121.44,38.62,37.0,3009.0,733.0,1513.0,588.0,1.4387,61000.0,INLAND
+-122.72,38.44,52.0,188.0,62.0,301.0,72.0,0.9437,129200.0,<1H OCEAN
+-122.47,37.72,46.0,1453.0,306.0,817.0,310.0,3.0,246700.0,NEAR OCEAN
+-118.56,34.19,34.0,1237.0,242.0,671.0,221.0,3.9615,183600.0,<1H OCEAN
+-122.7,38.35,14.0,1555.0,369.0,493.0,335.0,1.6033,67500.0,<1H OCEAN
+-121.8,36.68,18.0,8581.0,1957.0,6071.0,1889.0,3.0,162200.0,<1H OCEAN
+-117.64,33.87,2.0,17470.0,2727.0,5964.0,1985.0,6.2308,257900.0,<1H OCEAN
+-118.11,34.15,40.0,1950.0,509.0,1038.0,438.0,2.6172,196100.0,<1H OCEAN
+-120.44,34.95,38.0,3004.0,794.0,2601.0,747.0,2.2743,106400.0,<1H OCEAN
+-122.15,37.44,52.0,1400.0,217.0,522.0,227.0,4.9861,500001.0,NEAR BAY
+-122.06,40.55,17.0,3057.0,577.0,1497.0,556.0,3.5189,101000.0,INLAND
+-122.43,37.72,49.0,3427.0,696.0,2363.0,661.0,3.6885,254000.0,NEAR BAY
+-117.88,33.83,25.0,1785.0,248.0,750.0,251.0,6.8407,266700.0,<1H OCEAN
+-122.35,37.93,41.0,268.0,75.0,198.0,82.0,3.2222,156300.0,NEAR BAY
+-122.54,38.14,16.0,4431.0,603.0,1659.0,630.0,7.5412,392100.0,NEAR BAY
+-117.91,34.08,35.0,1443.0,266.0,861.0,262.0,3.5795,186900.0,<1H OCEAN
+-118.36,33.83,36.0,1660.0,300.0,943.0,300.0,5.1984,353600.0,<1H OCEAN
+-118.24,34.12,41.0,1213.0,301.0,801.0,300.0,3.1806,204200.0,<1H OCEAN
+-118.3,33.75,42.0,967.0,175.0,481.0,163.0,5.6611,265600.0,<1H OCEAN
+-121.97,38.34,16.0,2331.0,450.0,1074.0,400.0,4.0329,126800.0,INLAND
+-122.23,37.79,30.0,610.0,145.0,425.0,140.0,1.6198,122700.0,NEAR BAY
+-122.59,37.97,46.0,4036.0,856.0,1872.0,833.0,4.5625,275200.0,NEAR OCEAN
+-118.46,34.04,25.0,2768.0,850.0,1558.0,784.0,3.6976,360000.0,<1H OCEAN
+-116.95,33.75,23.0,4676.0,1096.0,2770.0,1057.0,1.7847,109500.0,INLAND
+-121.52,38.58,24.0,938.0,275.0,508.0,253.0,1.642,32500.0,INLAND
+-118.06,34.13,28.0,12139.0,2873.0,5359.0,2731.0,3.292,227300.0,INLAND
+-121.77,36.93,20.0,2587.0,547.0,1534.0,540.0,2.4375,190400.0,<1H OCEAN
+-119.37,36.35,20.0,1132.0,177.0,518.0,178.0,5.3767,231300.0,INLAND
+-116.98,32.77,29.0,3558.0,447.0,1097.0,445.0,8.093,379600.0,<1H OCEAN
+-121.02,37.58,36.0,1285.0,270.0,706.0,273.0,1.7169,121400.0,INLAND
+-117.93,34.07,34.0,1409.0,305.0,819.0,273.0,3.3977,188800.0,<1H OCEAN
+-117.34,34.08,33.0,4924.0,1007.0,3502.0,953.0,3.233,99400.0,INLAND
+-121.79,39.48,39.0,1105.0,180.0,408.0,166.0,3.3929,82100.0,INLAND
+-117.25,34.04,18.0,5761.0,1063.0,2763.0,1058.0,4.4472,161100.0,INLAND
+-121.82,37.36,34.0,1834.0,377.0,1450.0,347.0,3.7188,161500.0,<1H OCEAN
+-117.71,33.64,14.0,2945.0,356.0,1293.0,335.0,8.111,308900.0,<1H OCEAN
+-120.93,37.67,6.0,3491.0,657.0,2075.0,644.0,3.3844,138500.0,INLAND
+-122.76,38.46,14.0,4794.0,767.0,2252.0,768.0,4.2061,213100.0,<1H OCEAN
+-122.32,37.55,44.0,2151.0,411.0,849.0,370.0,4.4583,397100.0,NEAR OCEAN
+-118.46,34.22,35.0,2288.0,617.0,2222.0,566.0,2.6299,170700.0,<1H OCEAN
+-118.16,34.69,35.0,3114.0,583.0,1974.0,545.0,3.9028,126800.0,INLAND
+-116.99,32.72,15.0,825.0,130.0,334.0,131.0,4.0391,169500.0,<1H OCEAN
+-117.98,34.02,33.0,3512.0,632.0,1971.0,598.0,4.4653,193200.0,<1H OCEAN
+-117.68,34.11,16.0,3190.0,471.0,1414.0,464.0,5.5292,208600.0,INLAND
+-119.34,36.2,12.0,1632.0,378.0,1303.0,315.0,2.0333,54400.0,INLAND
+-117.04,32.54,7.0,938.0,297.0,1187.0,282.0,1.2667,67500.0,NEAR OCEAN
+-119.01,36.06,25.0,1505.0,,1392.0,359.0,1.6812,47700.0,INLAND
+-118.42,34.26,37.0,1789.0,424.0,2279.0,411.0,3.9,138600.0,<1H OCEAN
+-121.89,37.35,44.0,1668.0,380.0,1143.0,365.0,3.2083,181900.0,<1H OCEAN
+-117.2,32.8,33.0,2573.0,436.0,1084.0,443.0,4.2417,294100.0,NEAR OCEAN
+-117.4,34.06,17.0,5451.0,1008.0,3533.0,940.0,3.9191,101600.0,INLAND
+-121.87,37.27,25.0,1730.0,226.0,721.0,243.0,7.5845,279300.0,<1H OCEAN
+-117.05,32.61,26.0,1563.0,286.0,1145.0,313.0,3.8615,139300.0,NEAR OCEAN
+-117.4,33.94,30.0,1198.0,251.0,1019.0,214.0,3.0509,82700.0,INLAND
+-121.77,37.65,16.0,4290.0,554.0,1952.0,576.0,7.3588,327500.0,INLAND
+-118.46,33.99,41.0,885.0,285.0,562.0,268.0,3.1992,303800.0,<1H OCEAN
+-118.25,34.14,25.0,5980.0,1856.0,5217.0,1772.0,2.506,184500.0,<1H OCEAN
+-122.28,37.77,27.0,3997.0,1073.0,1901.0,966.0,3.75,242800.0,NEAR BAY
+-122.46,37.77,52.0,1824.0,388.0,799.0,363.0,3.75,435700.0,NEAR BAY
+-119.7,34.43,52.0,1364.0,460.0,804.0,400.0,2.375,293800.0,<1H OCEAN
+-117.83,33.66,4.0,1011.0,198.0,511.0,198.0,7.9217,296200.0,<1H OCEAN
+-118.19,33.94,45.0,1403.0,315.0,1111.0,311.0,3.3846,168100.0,<1H OCEAN
+-118.18,34.03,44.0,1629.0,420.0,1893.0,387.0,2.2991,137500.0,<1H OCEAN
+-118.36,33.87,19.0,2512.0,575.0,1275.0,544.0,4.9375,293000.0,<1H OCEAN
+-121.34,38.59,23.0,2912.0,421.0,1132.0,410.0,5.9174,225900.0,INLAND
+-122.09,37.7,31.0,2053.0,336.0,867.0,329.0,4.3375,241800.0,NEAR BAY
+-123.36,39.41,46.0,1748.0,362.0,808.0,330.0,2.9183,76900.0,<1H OCEAN
+-118.14,34.57,6.0,9882.0,1892.0,4892.0,1621.0,3.7636,167600.0,INLAND
+-118.41,34.23,35.0,1026.0,195.0,753.0,185.0,4.5909,179200.0,<1H OCEAN
+-117.9,33.71,16.0,1917.0,317.0,1324.0,351.0,6.2488,252000.0,<1H OCEAN
+-122.06,37.68,30.0,5367.0,1207.0,2667.0,1047.0,3.1796,170300.0,NEAR BAY
+-119.89,36.73,43.0,524.0,93.0,302.0,93.0,2.6146,81300.0,INLAND
+-120.43,38.02,15.0,1613.0,299.0,655.0,251.0,3.6875,186000.0,INLAND
+-121.88,37.4,9.0,6751.0,,4240.0,1438.0,5.34,257400.0,<1H OCEAN
+-122.41,38.16,37.0,1549.0,,863.0,275.0,2.7457,254700.0,NEAR BAY
+-122.06,37.73,5.0,3596.0,467.0,1738.0,512.0,7.0568,412500.0,NEAR BAY
+-118.14,33.95,37.0,1462.0,243.0,600.0,236.0,5.2015,302000.0,<1H OCEAN
+-122.14,37.84,24.0,2131.0,343.0,874.0,373.0,5.6349,355600.0,NEAR BAY
+-121.84,37.32,16.0,1866.0,364.0,1835.0,412.0,5.3363,212800.0,<1H OCEAN
+-118.23,34.11,31.0,1021.0,191.0,495.0,191.0,5.5051,223500.0,<1H OCEAN
+-119.58,36.11,21.0,2004.0,385.0,1397.0,398.0,2.2169,61600.0,INLAND
+-121.31,38.58,10.0,2421.0,580.0,962.0,497.0,2.5035,112500.0,INLAND
+-122.01,37.97,32.0,3012.0,527.0,1288.0,512.0,3.6449,211500.0,NEAR BAY
+-118.43,34.27,31.0,1130.0,276.0,1533.0,269.0,4.2353,156800.0,<1H OCEAN
+-121.53,38.61,5.0,8149.0,1913.0,2933.0,1616.0,3.6788,178800.0,INLAND
+-123.85,39.42,11.0,1804.0,506.0,895.0,451.0,1.7574,150000.0,<1H OCEAN
+-121.36,37.96,32.0,614.0,95.0,227.0,107.0,3.9922,247400.0,INLAND
+-117.07,32.6,13.0,1607.0,435.0,983.0,400.0,2.2903,106300.0,NEAR OCEAN
+-117.75,33.84,16.0,3491.0,502.0,1496.0,509.0,6.6207,270500.0,<1H OCEAN
+-118.39,34.04,49.0,1230.0,279.0,669.0,269.0,3.9038,308300.0,<1H OCEAN
+-122.04,37.85,27.0,6039.0,780.0,2181.0,761.0,9.5862,469400.0,NEAR BAY
+-122.59,37.99,36.0,4869.0,871.0,1899.0,827.0,4.1659,302000.0,NEAR BAY
+-117.8,33.85,16.0,4151.0,637.0,1558.0,604.0,5.806,304900.0,<1H OCEAN
+-117.64,34.03,11.0,2050.0,382.0,1044.0,371.0,4.8281,137000.0,INLAND
+-118.15,34.03,42.0,1481.0,411.0,1206.0,394.0,2.6806,189300.0,<1H OCEAN
+-116.31,33.67,11.0,4686.0,851.0,2466.0,731.0,3.3333,91800.0,INLAND
+-118.03,33.82,17.0,1851.0,346.0,770.0,310.0,5.6093,244400.0,<1H OCEAN
+-118.52,34.17,20.0,17377.0,4457.0,7450.0,4204.0,3.2154,259600.0,<1H OCEAN
+-117.48,34.08,28.0,1922.0,382.0,1565.0,340.0,3.915,117400.0,INLAND
+-116.97,32.79,32.0,1255.0,338.0,782.0,302.0,2.6635,113600.0,<1H OCEAN
+-115.57,32.8,33.0,1192.0,213.0,1066.0,211.0,4.5714,68600.0,INLAND
+-121.38,36.84,17.0,2625.0,512.0,1487.0,481.0,3.6354,221200.0,INLAND
+-117.2,33.24,12.0,4992.0,,2106.0,801.0,6.2079,307300.0,<1H OCEAN
+-118.1,34.07,29.0,1179.0,313.0,1255.0,308.0,2.5964,176800.0,<1H OCEAN
+-117.68,35.03,28.0,2969.0,648.0,1644.0,570.0,3.4338,54900.0,INLAND
+-118.41,34.0,30.0,3550.0,934.0,3738.0,880.0,3.191,271200.0,<1H OCEAN
+-118.08,33.86,26.0,778.0,173.0,539.0,186.0,3.2679,236500.0,<1H OCEAN
+-117.89,33.6,36.0,1496.0,247.0,441.0,203.0,7.8164,500001.0,<1H OCEAN
+-120.63,36.98,20.0,2380.0,489.0,1581.0,505.0,2.0595,61300.0,INLAND
+-120.57,38.35,17.0,1504.0,358.0,661.0,250.0,2.2604,84800.0,INLAND
+-121.62,36.74,30.0,1337.0,253.0,838.0,247.0,5.0374,165400.0,<1H OCEAN
+-121.83,38.45,15.0,5115.0,776.0,2540.0,794.0,4.8611,146400.0,INLAND
+-118.36,33.82,36.0,1784.0,311.0,901.0,293.0,6.2247,339000.0,<1H OCEAN
+-118.14,33.91,34.0,1766.0,410.0,974.0,404.0,3.0703,180800.0,<1H OCEAN
+-117.88,33.71,27.0,1596.0,297.0,1703.0,289.0,4.1,184900.0,<1H OCEAN
+-118.08,34.07,32.0,4089.0,975.0,3775.0,955.0,3.29,205500.0,<1H OCEAN
+-118.04,33.97,25.0,2945.0,914.0,2313.0,832.0,2.5686,177500.0,<1H OCEAN
+-116.94,32.87,24.0,2824.0,441.0,1480.0,471.0,5.2614,177200.0,<1H OCEAN
+-117.23,33.79,17.0,3318.0,759.0,2016.0,673.0,2.2969,89300.0,INLAND
+-119.05,35.33,18.0,12707.0,2685.0,7009.0,2552.0,2.9438,87200.0,INLAND
+-117.58,33.85,6.0,16431.0,2640.0,8222.0,2553.0,5.2861,195100.0,INLAND
+-122.14,37.08,18.0,2420.0,439.0,1278.0,416.0,5.2101,334000.0,NEAR OCEAN
+-122.12,37.16,32.0,1602.0,317.0,752.0,275.0,5.1664,185100.0,NEAR OCEAN
+-118.12,33.96,38.0,1301.0,264.0,877.0,275.0,4.625,191300.0,<1H OCEAN
+-119.23,34.27,29.0,3298.0,804.0,1509.0,711.0,3.8125,244500.0,NEAR OCEAN
+-118.31,33.73,33.0,2265.0,366.0,986.0,388.0,5.4533,409800.0,NEAR OCEAN
+-118.16,33.78,29.0,3684.0,1301.0,3891.0,1143.0,1.6955,179700.0,NEAR OCEAN
+-122.49,37.77,52.0,2342.0,458.0,1170.0,458.0,3.7036,369200.0,NEAR BAY
+-119.69,36.83,7.0,2075.0,353.0,1040.0,362.0,3.9943,100200.0,INLAND
+-121.86,37.27,19.0,1852.0,268.0,866.0,272.0,5.6139,279500.0,<1H OCEAN
+-117.69,34.06,25.0,1881.0,433.0,1337.0,417.0,2.5536,144000.0,INLAND
+-118.03,33.76,25.0,4650.0,849.0,2503.0,790.0,5.742,221900.0,<1H OCEAN
+-118.29,34.08,25.0,2459.0,823.0,2635.0,763.0,2.4,173900.0,<1H OCEAN
+-117.35,34.15,32.0,2699.0,552.0,2086.0,551.0,2.2974,84500.0,INLAND
+-121.69,38.87,38.0,412.0,93.0,304.0,95.0,2.6597,86000.0,INLAND
+-122.08,37.36,28.0,2181.0,284.0,728.0,238.0,8.2266,500001.0,NEAR BAY
+-117.31,34.11,38.0,1208.0,321.0,1225.0,317.0,1.4663,64000.0,INLAND
+-122.85,39.0,20.0,1580.0,318.0,753.0,252.0,1.8704,88500.0,INLAND
+-121.38,38.49,11.0,8537.0,1643.0,4224.0,1648.0,2.9647,108900.0,INLAND
+-118.29,34.06,27.0,2456.0,1111.0,4137.0,1104.0,1.5954,187500.0,<1H OCEAN
+-117.41,34.24,20.0,1160.0,181.0,543.0,188.0,5.2072,164300.0,INLAND
+-118.33,35.64,15.0,2966.0,669.0,1007.0,465.0,1.5667,72500.0,INLAND
+-118.2,33.93,36.0,1191.0,345.0,1193.0,295.0,2.5185,138800.0,<1H OCEAN
+-122.34,37.57,28.0,3751.0,949.0,1691.0,846.0,3.9728,300000.0,NEAR OCEAN
+-122.07,37.44,21.0,4599.0,986.0,2756.0,943.0,2.9817,225000.0,NEAR BAY
+-121.48,38.57,38.0,2809.0,805.0,1243.0,785.0,1.8512,114100.0,INLAND
+-122.29,37.9,49.0,1283.0,238.0,576.0,236.0,3.3333,276800.0,NEAR BAY
+-117.39,33.97,52.0,3307.0,553.0,1269.0,529.0,4.3176,136200.0,INLAND
+-122.26,37.77,52.0,1670.0,350.0,793.0,299.0,2.9732,282100.0,NEAR BAY
+-120.66,35.1,19.0,1583.0,392.0,704.0,269.0,2.1042,268300.0,<1H OCEAN
+-120.1,39.19,18.0,3824.0,559.0,241.0,106.0,5.5456,360000.0,INLAND
+-120.62,35.13,26.0,3971.0,803.0,1792.0,723.0,2.7128,209900.0,<1H OCEAN
+-115.56,32.78,46.0,2511.0,490.0,1583.0,469.0,3.0603,70800.0,INLAND
+-117.75,34.05,27.0,437.0,108.0,469.0,97.0,1.7206,107500.0,INLAND
+-116.24,33.72,25.0,5236.0,1039.0,2725.0,935.0,3.775,93400.0,INLAND
+-118.21,34.16,25.0,434.0,74.0,199.0,75.0,5.9199,420500.0,<1H OCEAN
+-121.31,38.57,9.0,2748.0,521.0,1663.0,565.0,3.5192,113300.0,INLAND
+-122.09,37.4,26.0,3218.0,1021.0,2087.0,964.0,3.2875,182700.0,NEAR BAY
+-117.82,33.85,18.0,1810.0,305.0,1189.0,326.0,5.2227,213500.0,<1H OCEAN
+-122.34,37.95,44.0,1788.0,368.0,933.0,329.0,2.875,133400.0,NEAR BAY
+-119.09,35.3,3.0,2821.0,519.0,1353.0,495.0,3.6852,109800.0,INLAND
+-119.06,35.35,20.0,9351.0,2139.0,4584.0,1953.0,2.575,69900.0,INLAND
+-122.32,38.12,12.0,5382.0,928.0,3928.0,921.0,5.3785,150600.0,NEAR BAY
+-118.08,33.76,27.0,529.0,159.0,193.0,155.0,2.0952,71300.0,<1H OCEAN
+-117.12,32.8,31.0,1727.0,342.0,879.0,345.0,3.8125,166300.0,NEAR OCEAN
+-121.91,37.68,20.0,1804.0,254.0,831.0,260.0,6.177,262900.0,<1H OCEAN
+-122.32,37.93,40.0,1141.0,213.0,434.0,196.0,3.9464,186900.0,NEAR BAY
+-115.52,33.12,38.0,1327.0,262.0,784.0,231.0,1.8793,60800.0,INLAND
+-118.47,34.14,34.0,3646.0,610.0,1390.0,607.0,7.629,500001.0,<1H OCEAN
+-118.52,34.04,43.0,2167.0,254.0,761.0,256.0,13.6842,500001.0,<1H OCEAN
+-118.29,33.95,32.0,721.0,205.0,842.0,208.0,1.6029,89700.0,<1H OCEAN
+-120.7,38.69,13.0,4492.0,821.0,2093.0,734.0,4.0709,151700.0,INLAND
+-118.28,33.92,37.0,742.0,151.0,729.0,144.0,3.055,105400.0,<1H OCEAN
+-118.14,34.18,47.0,3457.0,622.0,1700.0,579.0,3.5164,226500.0,<1H OCEAN
+-122.1,37.96,25.0,1374.0,206.0,569.0,235.0,6.3699,235500.0,NEAR BAY
+-117.89,33.92,34.0,1473.0,312.0,1025.0,315.0,3.8333,170400.0,<1H OCEAN
+-118.3,33.99,43.0,1534.0,384.0,1231.0,329.0,2.5437,94500.0,<1H OCEAN
+-122.48,37.85,42.0,6297.0,1307.0,2096.0,1205.0,6.4752,500001.0,NEAR BAY
+-122.16,37.75,46.0,954.0,161.0,429.0,154.0,2.925,142900.0,NEAR BAY
+-117.9,33.75,28.0,1346.0,291.0,1575.0,278.0,3.425,159500.0,<1H OCEAN
+-117.07,32.91,5.0,2234.0,256.0,894.0,253.0,10.3354,477600.0,<1H OCEAN
+-122.66,38.45,26.0,2081.0,339.0,906.0,323.0,4.4375,293500.0,<1H OCEAN
+-121.87,37.38,16.0,3275.0,529.0,1863.0,527.0,5.5429,269100.0,<1H OCEAN
+-120.86,37.77,45.0,621.0,129.0,257.0,124.0,1.7188,109400.0,INLAND
+-118.21,34.02,52.0,22.0,7.0,55.0,7.0,7.5752,67500.0,<1H OCEAN
+-122.01,37.92,18.0,2808.0,337.0,1038.0,337.0,8.3956,353600.0,NEAR BAY
+-119.05,34.13,12.0,57.0,22.0,69.0,15.0,5.0066,275000.0,NEAR OCEAN
+-120.35,37.95,13.0,2104.0,407.0,960.0,401.0,2.4,177000.0,INLAND
+-118.17,33.9,12.0,3653.0,993.0,3215.0,854.0,2.8681,114200.0,<1H OCEAN
+-118.08,33.86,29.0,3260.0,783.0,1969.0,737.0,3.5268,215500.0,<1H OCEAN
+-120.95,37.73,12.0,3609.0,712.0,2650.0,742.0,2.8565,92700.0,INLAND
+-122.37,37.96,37.0,1572.0,402.0,1046.0,350.0,0.7403,68600.0,NEAR BAY
+-122.08,37.9,32.0,1075.0,170.0,486.0,173.0,5.0499,306800.0,NEAR BAY
+-122.23,37.8,52.0,2033.0,486.0,787.0,459.0,3.1603,269500.0,NEAR BAY
+-118.43,34.42,13.0,3600.0,580.0,1799.0,576.0,6.2971,218300.0,<1H OCEAN
+-122.15,37.74,43.0,1383.0,275.0,853.0,272.0,3.5083,122000.0,NEAR BAY
+-118.18,34.12,29.0,2640.0,737.0,1795.0,655.0,2.369,173400.0,<1H OCEAN
+-120.3,35.1,16.0,2819.0,479.0,1068.0,365.0,4.5461,270800.0,<1H OCEAN
+-118.19,34.67,8.0,11275.0,1822.0,5731.0,1692.0,5.0285,167900.0,INLAND
+-122.63,38.22,17.0,2652.0,342.0,1199.0,350.0,5.565,267100.0,<1H OCEAN
+-118.39,34.03,19.0,1450.0,509.0,746.0,437.0,3.1415,55000.0,<1H OCEAN
+-122.47,37.5,25.0,950.0,259.0,404.0,195.0,3.1937,319200.0,NEAR OCEAN
+-122.59,38.01,35.0,8814.0,1307.0,3450.0,1258.0,6.1724,414300.0,NEAR BAY
+-117.86,33.62,17.0,2975.0,371.0,1247.0,398.0,10.1989,500001.0,<1H OCEAN
+-118.3,33.9,19.0,2421.0,689.0,1726.0,660.0,3.287,181400.0,<1H OCEAN
+-118.01,33.66,19.0,4559.0,1045.0,1949.0,910.0,4.355,429200.0,NEAR OCEAN
+-117.9,34.06,34.0,2956.0,469.0,1488.0,464.0,5.3664,268300.0,<1H OCEAN
+-118.56,34.21,13.0,8327.0,1849.0,4126.0,1773.0,3.7313,189800.0,<1H OCEAN
+-117.84,33.79,37.0,2733.0,460.0,1378.0,476.0,5.3041,235700.0,<1H OCEAN
+-122.06,38.25,34.0,1562.0,289.0,898.0,307.0,3.3598,107200.0,INLAND
+-121.0,37.61,36.0,2647.0,604.0,2045.0,550.0,2.273,62900.0,INLAND
+-122.08,37.88,24.0,2059.0,462.0,410.0,294.0,2.3971,99400.0,NEAR BAY
+-121.64,36.7,32.0,4089.0,735.0,2927.0,713.0,4.1675,142500.0,<1H OCEAN
+-118.59,34.21,34.0,1943.0,320.0,895.0,305.0,5.0462,227700.0,<1H OCEAN
+-118.24,33.94,30.0,940.0,211.0,1071.0,204.0,1.2679,92000.0,<1H OCEAN
+-122.29,37.89,52.0,2178.0,421.0,940.0,423.0,5.0551,232200.0,NEAR BAY
+-118.11,34.16,52.0,1353.0,274.0,852.0,306.0,3.4583,239900.0,INLAND
+-118.14,33.88,41.0,1531.0,343.0,1119.0,341.0,4.3646,161400.0,<1H OCEAN
+-117.16,32.73,52.0,1863.0,559.0,906.0,493.0,1.9203,195800.0,NEAR OCEAN
+-118.12,33.81,36.0,2565.0,458.0,1155.0,443.0,4.6087,224600.0,<1H OCEAN
+-122.09,37.42,23.0,4874.0,1251.0,2699.0,1163.0,3.8003,229800.0,NEAR BAY
+-118.84,34.16,18.0,6075.0,1056.0,2571.0,1018.0,5.22,399400.0,<1H OCEAN
+-120.06,39.09,30.0,2979.0,583.0,316.0,124.0,2.1987,124000.0,INLAND
+-121.94,37.27,39.0,1030.0,191.0,537.0,175.0,3.9265,236900.0,<1H OCEAN
+-118.28,34.03,41.0,1933.0,791.0,3121.0,719.0,1.8539,147500.0,<1H OCEAN
+-117.29,34.1,19.0,1204.0,405.0,844.0,424.0,0.7235,71300.0,INLAND
+-117.11,33.23,13.0,5819.0,919.0,2228.0,866.0,4.9335,298100.0,<1H OCEAN
+-118.64,34.17,26.0,6767.0,903.0,2574.0,883.0,7.7846,409000.0,<1H OCEAN
+-117.15,33.02,4.0,15029.0,2279.0,5613.0,1696.0,7.2731,450400.0,NEAR OCEAN
+-119.93,36.32,25.0,8363.0,1636.0,7679.0,1580.0,2.0285,106300.0,INLAND
+-117.08,32.69,31.0,2558.0,487.0,1938.0,492.0,3.4875,117000.0,NEAR OCEAN
+-117.62,33.43,24.0,1296.0,384.0,850.0,367.0,2.7545,231300.0,NEAR OCEAN
+-117.4,34.08,21.0,3622.0,667.0,2503.0,720.0,3.8531,105400.0,INLAND
+-116.95,32.78,33.0,2432.0,443.0,1147.0,427.0,3.3906,138100.0,<1H OCEAN
+-122.51,37.76,43.0,2345.0,624.0,1439.0,614.0,2.8448,268900.0,NEAR OCEAN
+-117.88,33.84,26.0,1499.0,290.0,755.0,277.0,3.5893,238500.0,<1H OCEAN
+-118.03,34.09,29.0,1219.0,338.0,1152.0,323.0,2.8029,180900.0,INLAND
+-118.32,34.1,28.0,1759.0,716.0,1463.0,620.0,1.7306,450000.0,<1H OCEAN
+-122.4,37.81,12.0,1349.0,349.0,536.0,334.0,7.7852,250000.0,NEAR BAY
+-122.51,37.78,45.0,2564.0,499.0,1056.0,460.0,4.7328,351100.0,NEAR BAY
+-122.25,38.11,49.0,2365.0,504.0,1131.0,458.0,2.6133,103100.0,NEAR BAY
+-117.17,34.28,13.0,4867.0,718.0,780.0,250.0,7.1997,253800.0,INLAND
+-121.98,36.96,31.0,3209.0,723.0,1489.0,692.0,3.6619,245100.0,NEAR OCEAN
+-121.26,38.68,4.0,3080.0,827.0,1195.0,683.0,2.7477,133000.0,INLAND
+-120.08,34.62,11.0,3478.0,588.0,1693.0,582.0,4.6554,272300.0,NEAR OCEAN
+-118.54,34.22,35.0,1664.0,300.0,1000.0,309.0,4.6731,224100.0,<1H OCEAN
+-121.98,37.36,32.0,1199.0,229.0,814.0,238.0,4.6719,252100.0,<1H OCEAN
+-124.18,40.62,35.0,952.0,178.0,480.0,179.0,3.0536,107000.0,NEAR OCEAN
+-117.31,34.35,9.0,2404.0,390.0,1074.0,359.0,5.0198,151900.0,INLAND
+-117.05,32.8,25.0,1905.0,250.0,865.0,253.0,6.4797,249000.0,<1H OCEAN
+-122.94,38.49,37.0,3169.0,719.0,777.0,344.0,2.7072,117100.0,<1H OCEAN
+-121.14,37.7,29.0,1343.0,223.0,751.0,225.0,3.2391,187500.0,INLAND
+-121.35,38.62,28.0,4175.0,796.0,2032.0,830.0,3.4299,164000.0,INLAND
+-117.34,34.1,14.0,11827.0,2445.0,6640.0,2299.0,2.4878,103800.0,INLAND
+-122.01,37.94,18.0,2077.0,298.0,937.0,292.0,6.3809,273600.0,NEAR BAY
+-120.32,37.91,16.0,108.0,18.0,54.0,22.0,4.375,100000.0,INLAND
+-118.44,34.03,25.0,2059.0,659.0,1349.0,588.0,3.2396,352400.0,<1H OCEAN
+-122.24,37.84,52.0,3529.0,574.0,1177.0,555.0,5.1773,389500.0,NEAR BAY
+-116.93,32.79,23.0,5759.0,1258.0,3108.0,1202.0,3.0927,140600.0,<1H OCEAN
+-117.94,34.06,34.0,1921.0,422.0,1230.0,447.0,3.6648,193900.0,<1H OCEAN
+-122.42,37.66,36.0,725.0,121.0,335.0,140.0,4.125,327600.0,NEAR OCEAN
+-118.33,33.93,37.0,4916.0,1134.0,3533.0,1035.0,3.2862,152300.0,<1H OCEAN
+-120.87,38.37,28.0,3998.0,765.0,1614.0,698.0,2.8125,113400.0,INLAND
+-121.96,36.98,16.0,4907.0,1117.0,2265.0,1048.0,2.6757,229200.0,NEAR OCEAN
+-122.32,37.69,48.0,592.0,122.0,340.0,143.0,5.966,315200.0,NEAR BAY
+-118.02,33.73,26.0,3711.0,610.0,1902.0,597.0,5.5599,234100.0,<1H OCEAN
+-117.65,34.09,46.0,1214.0,281.0,701.0,294.0,2.7083,116300.0,INLAND
+-118.01,33.83,24.0,4639.0,1374.0,3093.0,1257.0,2.5577,202300.0,<1H OCEAN
+-122.01,37.98,25.0,1476.0,336.0,777.0,297.0,3.5179,165500.0,NEAR BAY
+-118.26,33.96,40.0,1475.0,347.0,1222.0,298.0,1.5303,95300.0,<1H OCEAN
+-118.15,33.98,37.0,1184.0,290.0,1320.0,276.0,2.3,165600.0,<1H OCEAN
+-119.98,38.92,28.0,1408.0,312.0,522.0,221.0,2.0708,89600.0,INLAND
+-118.36,34.19,11.0,2921.0,685.0,1512.0,664.0,4.1445,176400.0,<1H OCEAN
+-122.12,37.44,33.0,1509.0,303.0,748.0,268.0,4.875,373400.0,NEAR BAY
+-117.12,32.75,15.0,2671.0,724.0,1800.0,646.0,2.1394,106700.0,NEAR OCEAN
+-119.73,34.43,35.0,2703.0,654.0,1383.0,631.0,4.5278,340400.0,NEAR OCEAN
+-122.34,40.57,24.0,1610.0,307.0,748.0,307.0,2.6591,82800.0,INLAND
+-122.08,37.87,26.0,2405.0,564.0,680.0,531.0,2.4896,73400.0,NEAR BAY
+-122.52,38.53,35.0,1227.0,236.0,548.0,207.0,4.875,336700.0,INLAND
+-118.13,34.68,28.0,718.0,124.0,347.0,121.0,4.025,102600.0,INLAND
+-122.5,37.51,11.0,749.0,137.0,355.0,124.0,8.2364,371800.0,NEAR OCEAN
+-121.13,36.2,16.0,1868.0,443.0,1323.0,436.0,2.9559,163200.0,<1H OCEAN
+-118.53,34.19,32.0,2618.0,692.0,1961.0,633.0,2.625,192300.0,<1H OCEAN
+-118.06,33.84,20.0,5643.0,1231.0,3841.0,1195.0,4.0542,168400.0,<1H OCEAN
+-122.3,37.77,42.0,2038.0,368.0,2037.0,355.0,2.6447,200000.0,NEAR BAY
+-122.22,38.1,44.0,2256.0,451.0,1057.0,426.0,3.1204,110800.0,NEAR BAY
+-121.78,37.24,17.0,2123.0,341.0,1067.0,339.0,6.0062,262700.0,<1H OCEAN
+-118.39,34.08,52.0,3759.0,464.0,1407.0,422.0,15.0001,500001.0,<1H OCEAN
+-118.44,34.0,40.0,1287.0,346.0,806.0,311.0,3.875,321300.0,<1H OCEAN
+-124.15,40.78,36.0,2112.0,374.0,829.0,368.0,3.3984,90000.0,NEAR OCEAN
+-118.48,34.0,29.0,1727.0,479.0,741.0,431.0,3.6121,500000.0,<1H OCEAN
+-119.31,36.62,25.0,831.0,230.0,947.0,244.0,1.4481,51700.0,INLAND
+-121.26,37.94,43.0,1610.0,412.0,1409.0,365.0,1.7574,51700.0,INLAND
+-117.25,32.8,26.0,2442.0,659.0,1134.0,624.0,3.3274,295500.0,NEAR OCEAN
+-120.95,38.69,10.0,3421.0,563.0,1689.0,545.0,5.2032,217100.0,INLAND
+-122.28,37.9,52.0,2318.0,328.0,779.0,312.0,7.1754,362900.0,NEAR BAY
+-114.59,33.61,34.0,4789.0,1175.0,3134.0,1056.0,2.1782,58400.0,INLAND
+-118.01,34.14,20.0,3350.0,831.0,1816.0,744.0,2.8352,161700.0,INLAND
+-121.29,38.67,20.0,1992.0,363.0,889.0,346.0,3.6516,130500.0,INLAND
+-119.26,34.28,41.0,2822.0,564.0,1288.0,541.0,3.0799,254100.0,NEAR OCEAN
+-116.85,34.98,26.0,3606.0,792.0,1683.0,608.0,2.6587,57400.0,INLAND
+-118.01,34.11,32.0,1978.0,536.0,826.0,470.0,2.5114,212200.0,INLAND
+-118.02,33.78,28.0,3375.0,559.0,1754.0,554.0,5.5446,228900.0,<1H OCEAN
+-118.44,34.05,20.0,5943.0,1538.0,2492.0,1429.0,4.1141,305000.0,<1H OCEAN
+-118.09,34.0,35.0,1580.0,331.0,1290.0,338.0,4.1458,162500.0,<1H OCEAN
+-121.62,39.09,21.0,2693.0,481.0,1337.0,435.0,3.8534,99700.0,INLAND
+-119.82,36.84,7.0,2289.0,342.0,1077.0,354.0,5.4868,158800.0,INLAND
+-121.92,37.34,42.0,2101.0,524.0,1212.0,526.0,3.6389,239200.0,<1H OCEAN
+-117.93,33.87,52.0,950.0,229.0,429.0,185.0,2.315,182100.0,<1H OCEAN
+-117.09,33.13,9.0,5685.0,1442.0,3773.0,1250.0,3.0426,129900.0,<1H OCEAN
+-117.26,32.95,34.0,1651.0,273.0,650.0,271.0,5.6582,500001.0,NEAR OCEAN
+-121.35,38.61,25.0,4916.0,1243.0,2140.0,1136.0,2.5511,134100.0,INLAND
+-122.73,41.76,19.0,2200.0,414.0,950.0,367.0,2.5357,94200.0,INLAND
+-118.15,33.97,32.0,927.0,250.0,970.0,248.0,2.1591,181500.0,<1H OCEAN
+-117.95,33.8,34.0,1654.0,285.0,905.0,292.0,4.6389,214600.0,<1H OCEAN
+-118.16,34.2,43.0,1810.0,343.0,988.0,307.0,3.8203,176000.0,<1H OCEAN
+-118.5,34.17,37.0,880.0,,369.0,155.0,4.1429,303600.0,<1H OCEAN
+-116.91,32.78,15.0,4058.0,511.0,1580.0,473.0,7.5,316400.0,<1H OCEAN
+-119.72,34.47,34.0,3262.0,533.0,1265.0,502.0,5.8411,381800.0,<1H OCEAN
+-122.08,37.24,21.0,427.0,63.0,182.0,70.0,11.3283,500001.0,<1H OCEAN
+-118.52,34.18,43.0,1700.0,380.0,930.0,349.0,3.675,213100.0,<1H OCEAN
+-117.07,33.11,17.0,5565.0,1237.0,3004.0,1139.0,3.0054,142300.0,<1H OCEAN
+-116.45,33.78,16.0,5228.0,992.0,1177.0,639.0,3.0859,134600.0,INLAND
+-117.79,34.02,5.0,18690.0,2862.0,9427.0,2777.0,6.4266,315600.0,<1H OCEAN
+-118.31,34.03,34.0,2041.0,517.0,1479.0,495.0,2.1173,156600.0,<1H OCEAN
+-121.98,37.36,33.0,1582.0,272.0,809.0,267.0,5.7059,287200.0,<1H OCEAN
+-119.85,36.78,8.0,3096.0,684.0,1454.0,545.0,2.7857,79700.0,INLAND
+-117.89,33.73,33.0,1308.0,375.0,2175.0,347.0,3.0824,177400.0,<1H OCEAN
+-119.57,36.7,30.0,2370.0,412.0,1248.0,410.0,3.1442,72300.0,INLAND
+-118.12,33.91,36.0,1432.0,265.0,749.0,261.0,3.5772,207400.0,<1H OCEAN
+-120.44,34.97,26.0,1705.0,344.0,1605.0,307.0,3.7589,113700.0,<1H OCEAN
+-117.81,33.88,19.0,2265.0,283.0,904.0,279.0,9.2327,461300.0,<1H OCEAN
+-118.19,33.99,38.0,1212.0,272.0,1129.0,263.0,2.6673,142300.0,<1H OCEAN
+-118.49,34.26,27.0,2722.0,468.0,1164.0,419.0,4.6591,239900.0,<1H OCEAN
+-122.28,37.9,52.0,2003.0,250.0,658.0,244.0,10.0825,397000.0,NEAR BAY
+-122.33,37.93,34.0,2326.0,471.0,1356.0,441.0,2.3475,90300.0,NEAR BAY
+-122.52,37.94,18.0,1804.0,284.0,600.0,241.0,5.9582,500001.0,NEAR BAY
+-118.7,34.29,25.0,1678.0,252.0,862.0,268.0,6.1834,229800.0,<1H OCEAN
+-124.14,40.72,18.0,2581.0,499.0,1375.0,503.0,2.8446,100500.0,NEAR OCEAN
+-120.66,38.4,18.0,2144.0,420.0,985.0,381.0,3.175,118500.0,INLAND
+-118.31,34.19,13.0,3801.0,1116.0,1986.0,1078.0,2.0875,222700.0,<1H OCEAN
+-119.82,36.72,25.0,2581.0,528.0,1642.0,509.0,1.6435,52600.0,INLAND
+-121.33,38.66,17.0,2767.0,584.0,1275.0,568.0,2.5909,125400.0,INLAND
+-121.66,36.89,15.0,2608.0,458.0,1531.0,457.0,5.5148,253500.0,<1H OCEAN
+-121.64,36.67,28.0,256.0,66.0,214.0,60.0,3.0197,137500.0,<1H OCEAN
+-119.83,34.43,31.0,798.0,346.0,699.0,301.0,2.1417,205000.0,NEAR OCEAN
+-122.46,37.74,52.0,2180.0,326.0,856.0,326.0,5.3961,416900.0,NEAR BAY
+-117.9,33.78,25.0,10336.0,2596.0,7111.0,2419.0,3.3627,197900.0,<1H OCEAN
+-118.13,33.83,44.0,1710.0,333.0,786.0,344.0,4.2917,314700.0,<1H OCEAN
+-121.13,37.74,28.0,409.0,104.0,244.0,98.0,3.4643,90900.0,INLAND
+-117.99,33.67,19.0,3808.0,790.0,1776.0,756.0,4.625,282200.0,<1H OCEAN
+-117.03,32.6,26.0,1335.0,224.0,742.0,215.0,5.152,143400.0,NEAR OCEAN
+-117.99,34.06,32.0,2491.0,616.0,2660.0,595.0,2.564,145800.0,INLAND
+-118.18,33.9,32.0,778.0,227.0,933.0,209.0,2.7292,143800.0,<1H OCEAN
+-120.54,38.07,37.0,736.0,148.0,339.0,140.0,2.2875,79900.0,INLAND
+-117.87,33.74,52.0,2411.0,526.0,2165.0,521.0,3.415,172500.0,<1H OCEAN
+-117.88,33.69,20.0,5330.0,976.0,2734.0,1000.0,5.2138,233100.0,<1H OCEAN
+-117.39,34.04,27.0,2919.0,549.0,1841.0,564.0,2.8682,96400.0,INLAND
+-122.2,39.93,9.0,1296.0,287.0,768.0,260.0,1.9191,54400.0,INLAND
+-118.36,34.08,52.0,2373.0,601.0,1135.0,576.0,3.1765,225000.0,<1H OCEAN
+-118.99,35.32,35.0,1576.0,405.0,870.0,282.0,1.6575,59500.0,INLAND
+-122.71,38.37,16.0,2355.0,345.0,1014.0,348.0,5.6018,253000.0,<1H OCEAN
+-121.45,38.51,29.0,4221.0,901.0,2294.0,850.0,2.2245,75900.0,INLAND
+-122.26,38.04,41.0,2512.0,539.0,1179.0,480.0,2.694,123000.0,NEAR BAY
+-122.24,37.47,40.0,1504.0,270.0,689.0,287.0,6.1244,308800.0,NEAR OCEAN
+-121.88,37.41,23.0,3224.0,652.0,2183.0,655.0,4.3807,226900.0,<1H OCEAN
+-122.42,37.64,41.0,98.0,20.0,68.0,19.0,2.225,212500.0,NEAR OCEAN
+-118.3,33.85,38.0,123.0,36.0,142.0,40.0,2.3942,200000.0,<1H OCEAN
+-122.3,37.55,35.0,3675.0,735.0,1930.0,715.0,3.9833,342800.0,NEAR OCEAN
+-118.22,34.12,28.0,3306.0,1025.0,2670.0,942.0,3.0919,185400.0,<1H OCEAN
+-118.19,33.82,11.0,872.0,203.0,422.0,221.0,4.6364,156300.0,NEAR OCEAN
+-117.27,34.14,36.0,3795.0,676.0,1742.0,585.0,4.1,96400.0,INLAND
+-117.73,33.63,15.0,2874.0,592.0,1382.0,586.0,5.5137,161800.0,<1H OCEAN
+-120.44,34.64,8.0,787.0,126.0,446.0,133.0,4.6023,163400.0,NEAR OCEAN
+-121.29,38.28,11.0,1554.0,260.0,793.0,233.0,4.8073,156700.0,INLAND
+-117.68,33.63,13.0,5830.0,921.0,2897.0,891.0,6.2403,257400.0,<1H OCEAN
+-117.61,34.12,17.0,6709.0,1198.0,3954.0,1161.0,4.6997,156900.0,INLAND
+-118.02,34.11,39.0,1504.0,280.0,718.0,261.0,4.625,219000.0,INLAND
+-121.89,37.23,16.0,3574.0,466.0,1503.0,487.0,8.1988,355900.0,<1H OCEAN
+-122.13,37.41,36.0,4787.0,900.0,2039.0,890.0,5.4063,440900.0,NEAR BAY
+-117.93,33.72,17.0,4461.0,585.0,2095.0,580.0,7.6709,319500.0,<1H OCEAN
+-118.41,33.94,45.0,2038.0,394.0,1086.0,387.0,4.7375,289600.0,<1H OCEAN
+-118.29,33.71,36.0,3135.0,746.0,1815.0,697.0,3.7596,300000.0,NEAR OCEAN
+-117.84,33.76,16.0,238.0,51.0,93.0,50.0,5.375,215700.0,<1H OCEAN
+-117.14,32.92,6.0,3069.0,750.0,1541.0,736.0,3.814,132500.0,<1H OCEAN
+-117.91,33.89,30.0,1631.0,212.0,523.0,216.0,7.875,351900.0,<1H OCEAN
+-124.16,40.8,52.0,2416.0,618.0,1150.0,571.0,1.7308,80500.0,NEAR OCEAN
+-120.79,36.06,29.0,1916.0,386.0,1019.0,314.0,2.4881,87500.0,<1H OCEAN
+-117.27,34.14,35.0,1517.0,257.0,658.0,245.0,4.4435,97600.0,INLAND
+-120.84,37.51,8.0,1191.0,242.0,688.0,260.0,2.7243,138400.0,INLAND
+-121.98,38.32,45.0,19.0,5.0,7460.0,6.0,10.2264,137500.0,INLAND
+-118.14,34.16,30.0,2598.0,757.0,2869.0,769.0,2.1377,142300.0,<1H OCEAN
+-120.88,38.45,25.0,1374.0,297.0,657.0,288.0,2.5476,97900.0,INLAND
+-116.43,34.1,17.0,6683.0,1296.0,2677.0,1227.0,2.4828,84000.0,INLAND
+-118.04,33.76,16.0,2070.0,263.0,878.0,297.0,7.0879,338800.0,<1H OCEAN
+-122.04,37.34,25.0,1994.0,287.0,704.0,283.0,7.7799,447300.0,<1H OCEAN
+-118.9,35.41,6.0,4656.0,971.0,2320.0,935.0,3.0938,100800.0,INLAND
+-117.18,32.75,52.0,1539.0,212.0,535.0,224.0,5.392,408500.0,NEAR OCEAN
+-122.06,37.58,15.0,8112.0,1376.0,4576.0,1348.0,5.6758,253400.0,NEAR BAY
+-122.66,38.48,16.0,2697.0,490.0,1462.0,515.0,4.2051,190300.0,<1H OCEAN
+-122.47,37.5,18.0,2297.0,416.0,1086.0,381.0,4.875,334600.0,NEAR OCEAN
+-117.17,32.71,7.0,2493.0,693.0,951.0,641.0,4.2375,205000.0,NEAR OCEAN
+-122.18,37.72,45.0,1498.0,313.0,1003.0,305.0,3.8047,156700.0,NEAR BAY
+-117.18,32.76,8.0,3694.0,997.0,1297.0,807.0,3.6492,158900.0,NEAR OCEAN
+-119.27,36.34,7.0,3433.0,626.0,1793.0,626.0,3.5296,83700.0,INLAND
+-121.71,37.99,27.0,3861.0,718.0,2085.0,707.0,3.3558,129700.0,INLAND
+-118.21,34.04,52.0,846.0,271.0,1153.0,281.0,2.1923,155000.0,<1H OCEAN
+-122.13,37.4,29.0,6027.0,1195.0,2687.0,1171.0,5.1335,461200.0,NEAR BAY
+-121.4,36.84,52.0,1860.0,400.0,1215.0,367.0,2.9554,136500.0,INLAND
+-117.24,32.95,18.0,1591.0,268.0,547.0,243.0,5.9547,329300.0,NEAR OCEAN
+-120.9,39.95,20.0,1349.0,238.0,601.0,203.0,3.5417,96600.0,INLAND
+-118.23,33.92,32.0,1735.0,430.0,1699.0,386.0,1.1793,103800.0,<1H OCEAN
+-118.46,34.0,36.0,1392.0,260.0,679.0,247.0,4.7344,346900.0,<1H OCEAN
+-118.7,34.28,27.0,3536.0,646.0,1837.0,580.0,4.4964,238300.0,<1H OCEAN
+-118.49,34.43,15.0,8244.0,1409.0,4453.0,1357.0,5.4829,199600.0,<1H OCEAN
+-122.25,37.48,45.0,2743.0,390.0,974.0,400.0,7.1621,500001.0,NEAR OCEAN
+-120.92,38.86,11.0,1720.0,345.0,850.0,326.0,3.2027,128600.0,INLAND
+-119.83,36.77,23.0,2168.0,503.0,1190.0,425.0,2.625,71600.0,INLAND
+-120.58,39.27,15.0,4126.0,903.0,723.0,266.0,3.0147,118800.0,INLAND
+-118.1,33.82,36.0,1946.0,346.0,871.0,336.0,5.2155,254800.0,<1H OCEAN
+-118.27,33.95,34.0,987.0,248.0,902.0,221.0,2.3365,98000.0,<1H OCEAN
+-117.88,33.85,22.0,1105.0,241.0,971.0,249.0,3.1667,113900.0,<1H OCEAN
+-117.71,34.05,20.0,2281.0,444.0,1545.0,481.0,2.5735,130500.0,INLAND
+-117.04,33.09,16.0,4677.0,581.0,1902.0,566.0,6.1834,335600.0,<1H OCEAN
+-118.01,33.77,32.0,1771.0,296.0,995.0,272.0,5.8362,217500.0,<1H OCEAN
+-120.71,35.5,12.0,3098.0,453.0,1433.0,434.0,5.2508,292900.0,<1H OCEAN
+-118.3,34.0,52.0,1296.0,246.0,853.0,238.0,3.05,111600.0,<1H OCEAN
+-117.3,33.22,4.0,14960.0,2988.0,6666.0,2612.0,3.7568,184100.0,NEAR OCEAN
+-122.52,38.67,35.0,1705.0,321.0,708.0,253.0,3.4539,300000.0,INLAND
+-118.43,33.99,35.0,2243.0,495.0,1135.0,465.0,4.3281,324000.0,<1H OCEAN
+-118.08,33.92,39.0,1631.0,322.0,1034.0,328.0,4.5382,165700.0,<1H OCEAN
+-117.95,33.9,15.0,3057.0,479.0,1679.0,498.0,6.8429,372600.0,<1H OCEAN
+-118.35,34.07,48.0,890.0,255.0,434.0,232.0,3.6111,450000.0,<1H OCEAN
+-118.19,33.85,30.0,3533.0,1061.0,2678.0,1033.0,2.2417,151900.0,NEAR OCEAN
+-121.07,38.92,15.0,5301.0,884.0,2335.0,831.0,4.515,164000.0,INLAND
+-122.28,37.88,52.0,957.0,188.0,403.0,172.0,3.2344,245500.0,NEAR BAY
+-121.94,37.33,36.0,1893.0,359.0,797.0,360.0,3.6818,257600.0,<1H OCEAN
+-118.03,33.77,27.0,2000.0,310.0,880.0,294.0,5.635,218900.0,<1H OCEAN
+-123.08,38.38,28.0,3297.0,676.0,923.0,373.0,3.9167,232600.0,NEAR OCEAN
+-117.92,33.65,28.0,1087.0,423.0,807.0,425.0,0.9702,225400.0,<1H OCEAN
+-117.24,33.94,15.0,1569.0,423.0,1123.0,369.0,1.6111,113900.0,INLAND
+-119.38,36.56,14.0,3965.0,804.0,1945.0,733.0,2.6906,95300.0,INLAND
+-120.56,35.07,14.0,6788.0,1216.0,2866.0,1036.0,3.3603,280200.0,<1H OCEAN
+-122.96,40.77,29.0,1637.0,297.0,753.0,270.0,3.2891,93100.0,INLAND
+-118.4,34.0,10.0,1526.0,339.0,705.0,268.0,5.8083,321800.0,<1H OCEAN
+-117.5,33.92,31.0,2529.0,513.0,1504.0,426.0,2.9821,115600.0,INLAND
+-118.19,33.95,41.0,1368.0,309.0,1244.0,312.0,3.0833,164800.0,<1H OCEAN
+-120.47,34.66,4.0,3376.0,525.0,1684.0,535.0,4.9237,166600.0,NEAR OCEAN
+-121.57,39.5,31.0,2023.0,469.0,1073.0,436.0,1.5714,56100.0,INLAND
+-118.2,34.05,43.0,1165.0,317.0,1279.0,303.0,1.9615,141700.0,<1H OCEAN
+-122.24,38.01,11.0,3751.0,565.0,1949.0,555.0,5.7862,269400.0,NEAR BAY
+-118.27,34.0,48.0,1869.0,461.0,1834.0,441.0,1.7052,107400.0,<1H OCEAN
+-119.32,34.35,16.0,52.0,16.0,51.0,15.0,2.475,225000.0,NEAR OCEAN
+-119.79,36.3,16.0,1717.0,277.0,903.0,289.0,4.3438,93100.0,INLAND
+-119.32,36.19,11.0,1281.0,291.0,861.0,313.0,1.0962,72300.0,INLAND
+-117.17,32.73,52.0,408.0,143.0,313.0,143.0,1.815,116700.0,NEAR OCEAN
+-122.08,37.38,25.0,830.0,228.0,368.0,174.0,3.3917,342900.0,NEAR BAY
+-122.09,37.91,18.0,9576.0,1455.0,3486.0,1380.0,7.0895,306900.0,NEAR BAY
+-122.05,38.25,39.0,199.0,36.0,101.0,38.0,6.2299,105400.0,INLAND
+-118.32,33.98,47.0,949.0,210.0,574.0,217.0,2.175,114700.0,<1H OCEAN
+-120.2,34.61,15.0,2958.0,690.0,1348.0,617.0,3.8582,215200.0,NEAR OCEAN
+-117.78,34.06,25.0,1712.0,491.0,1807.0,478.0,2.2258,114800.0,INLAND
+-117.98,33.76,28.0,3215.0,652.0,2066.0,636.0,4.0194,197400.0,<1H OCEAN
+-117.1,33.17,12.0,2465.0,412.0,1226.0,428.0,5.4819,183800.0,<1H OCEAN
+-117.44,33.93,33.0,1371.0,236.0,715.0,227.0,4.375,129900.0,INLAND
+-117.23,32.81,22.0,3205.0,429.0,1083.0,410.0,8.1844,406300.0,NEAR OCEAN
+-117.95,33.99,25.0,1075.0,138.0,451.0,132.0,6.8492,332200.0,<1H OCEAN
+-117.04,34.89,37.0,1884.0,366.0,1052.0,353.0,3.175,66800.0,INLAND
+-121.42,38.6,23.0,3713.0,1078.0,2194.0,1018.0,1.7451,89600.0,INLAND
+-118.14,34.15,41.0,1256.0,407.0,855.0,383.0,1.9923,500001.0,<1H OCEAN
+-119.34,36.34,5.0,4505.0,834.0,1917.0,775.0,4.0144,126600.0,INLAND
+-121.67,36.67,24.0,3071.0,544.0,1477.0,560.0,3.9219,222500.0,<1H OCEAN
+-119.64,36.35,30.0,1765.0,310.0,746.0,298.0,2.8125,70200.0,INLAND
+-118.15,33.87,33.0,2373.0,552.0,1673.0,571.0,3.0685,181800.0,<1H OCEAN
+-121.81,37.26,14.0,3379.0,683.0,1465.0,620.0,4.0547,236200.0,<1H OCEAN
+-120.79,37.49,44.0,1186.0,225.0,687.0,234.0,3.4167,160700.0,INLAND
+-121.82,37.36,33.0,1624.0,337.0,1412.0,323.0,4.0385,167600.0,<1H OCEAN
+-118.38,34.04,36.0,3005.0,771.0,2054.0,758.0,2.0437,309100.0,<1H OCEAN
+-122.83,38.96,15.0,1318.0,296.0,567.0,276.0,1.8692,93800.0,INLAND
+-122.62,38.4,10.0,9772.0,1308.0,3741.0,1242.0,6.5261,324700.0,<1H OCEAN
+-119.67,36.83,4.0,2145.0,334.0,1024.0,308.0,5.0864,113700.0,INLAND
+-118.43,34.28,27.0,862.0,280.0,1243.0,267.0,2.3724,154200.0,<1H OCEAN
+-120.95,37.74,18.0,3453.0,666.0,1958.0,601.0,3.0043,156500.0,INLAND
+-118.3,33.92,34.0,2053.0,382.0,1258.0,380.0,3.0139,154700.0,<1H OCEAN
+-122.45,40.61,17.0,785.0,155.0,417.0,136.0,2.3289,58200.0,INLAND
+-120.64,36.99,23.0,2363.0,449.0,1168.0,410.0,2.2794,75700.0,INLAND
+-117.72,33.62,19.0,5777.0,1261.0,1711.0,1225.0,2.7634,86900.0,<1H OCEAN
+-116.66,32.79,13.0,843.0,,918.0,152.0,6.2152,240600.0,<1H OCEAN
+-119.49,37.1,24.0,2532.0,555.0,1564.0,507.0,2.3359,92400.0,INLAND
+-118.18,34.02,36.0,1138.0,296.0,1484.0,320.0,2.2813,150700.0,<1H OCEAN
+-122.16,37.45,37.0,2926.0,874.0,1363.0,815.0,4.5987,356000.0,NEAR BAY
+-117.91,33.83,37.0,1039.0,260.0,719.0,243.0,3.0288,161400.0,<1H OCEAN
+-118.3,34.16,40.0,1875.0,460.0,869.0,438.0,3.2321,243600.0,<1H OCEAN
+-121.3,38.01,29.0,2289.0,449.0,1215.0,435.0,3.2788,100000.0,INLAND
+-119.18,34.15,22.0,4769.0,1366.0,5534.0,1318.0,2.4167,192000.0,NEAR OCEAN
+-121.44,38.61,34.0,172.0,38.0,149.0,55.0,2.6442,55000.0,INLAND
+-122.55,37.98,31.0,3807.0,828.0,1581.0,795.0,3.293,337500.0,NEAR BAY
+-117.25,32.8,30.0,2061.0,631.0,1007.0,577.0,2.5813,253100.0,NEAR OCEAN
+-117.36,34.1,31.0,2587.0,531.0,1227.0,489.0,2.3578,88600.0,INLAND
+-118.3,33.97,44.0,1521.0,289.0,1074.0,285.0,2.0673,99800.0,<1H OCEAN
+-118.28,34.0,42.0,1534.0,417.0,1295.0,380.0,2.0938,119200.0,<1H OCEAN
+-117.27,34.48,8.0,1794.0,276.0,690.0,271.0,3.662,165300.0,INLAND
+-121.74,36.49,33.0,2952.0,565.0,1264.0,517.0,4.4209,274600.0,<1H OCEAN
+-117.21,32.86,16.0,2800.0,566.0,1267.0,518.0,3.2794,148600.0,NEAR OCEAN
+-117.11,33.14,10.0,3208.0,636.0,1395.0,582.0,3.4455,190500.0,<1H OCEAN
+-118.19,34.06,32.0,555.0,159.0,748.0,163.0,1.9762,137500.0,<1H OCEAN
+-121.92,37.73,24.0,1407.0,327.0,501.0,295.0,2.4821,157200.0,<1H OCEAN
+-117.87,33.62,15.0,2209.0,275.0,735.0,274.0,15.0001,500001.0,<1H OCEAN
+-118.84,34.21,16.0,4975.0,949.0,2537.0,971.0,5.2361,224700.0,<1H OCEAN
+-118.03,33.97,36.0,2149.0,527.0,1359.0,481.0,2.824,167900.0,<1H OCEAN
+-118.13,34.09,21.0,3862.0,1186.0,2773.0,1102.0,2.7816,188200.0,<1H OCEAN
+-118.09,34.02,33.0,4853.0,1105.0,2855.0,1006.0,3.2622,208600.0,<1H OCEAN
+-117.77,33.69,16.0,1666.0,341.0,479.0,336.0,2.1406,55000.0,<1H OCEAN
+-118.13,33.82,44.0,1785.0,307.0,779.0,291.0,4.3056,228600.0,<1H OCEAN
+-115.51,32.68,11.0,2872.0,610.0,2644.0,581.0,2.625,72700.0,INLAND
+-117.97,33.73,27.0,2097.0,325.0,1217.0,331.0,5.7121,222500.0,<1H OCEAN
+-118.42,34.28,29.0,1271.0,272.0,1338.0,266.0,4.125,150000.0,<1H OCEAN
+-121.51,38.79,29.0,1716.0,323.0,850.0,282.0,2.9324,137500.0,INLAND
+-122.63,38.24,45.0,1615.0,338.0,823.0,327.0,2.5179,145500.0,<1H OCEAN
+-121.32,37.95,36.0,747.0,189.0,338.0,145.0,1.7885,62100.0,INLAND
+-119.3,36.32,23.0,3521.0,615.0,1712.0,636.0,3.3875,92500.0,INLAND
+-118.26,34.07,28.0,579.0,184.0,673.0,202.0,2.625,187500.0,<1H OCEAN
+-118.47,34.05,27.0,4401.0,1033.0,1725.0,962.0,4.175,500001.0,<1H OCEAN
+-117.83,33.75,22.0,6433.0,1174.0,2703.0,1125.0,4.9957,296400.0,<1H OCEAN
+-117.09,32.57,23.0,1817.0,323.0,1371.0,327.0,3.6736,139500.0,NEAR OCEAN
+-118.29,34.08,43.0,3056.0,1345.0,3920.0,1304.0,1.925,300000.0,<1H OCEAN
+-118.29,34.09,52.0,1272.0,322.0,984.0,353.0,1.9063,261600.0,<1H OCEAN
+-118.14,34.02,42.0,1384.0,458.0,1825.0,455.0,1.4178,145500.0,<1H OCEAN
+-117.25,32.76,38.0,2331.0,493.0,836.0,433.0,4.9125,452600.0,NEAR OCEAN
+-117.33,33.19,15.0,3672.0,845.0,1827.0,796.0,2.9716,173600.0,NEAR OCEAN
+-122.03,37.91,29.0,5438.0,871.0,2310.0,890.0,5.0362,275300.0,NEAR BAY
+-119.06,36.07,20.0,2683.0,553.0,1497.0,548.0,1.7031,64600.0,INLAND
+-117.93,33.75,24.0,1380.0,339.0,1472.0,304.0,4.2219,162800.0,<1H OCEAN
+-117.17,34.07,24.0,6573.0,1235.0,2904.0,1202.0,3.0651,108000.0,INLAND
+-119.77,36.79,27.0,2258.0,427.0,1076.0,423.0,2.9937,81100.0,INLAND
+-118.38,34.03,43.0,912.0,255.0,705.0,246.0,2.6402,185700.0,<1H OCEAN
+-118.38,33.95,35.0,3259.0,967.0,2003.0,920.0,3.2708,400000.0,<1H OCEAN
+-118.26,33.91,44.0,892.0,139.0,440.0,159.0,2.8859,120800.0,<1H OCEAN
+-117.05,34.04,23.0,3967.0,766.0,1518.0,698.0,2.29,111800.0,INLAND
+-122.1,37.9,38.0,827.0,144.0,368.0,136.0,6.5095,294400.0,NEAR BAY
+-117.6,33.87,15.0,7626.0,1570.0,3823.0,1415.0,3.4419,138100.0,INLAND
+-120.59,38.53,15.0,432.0,87.0,208.0,73.0,3.6125,100000.0,INLAND
+-118.41,33.93,22.0,2514.0,605.0,1225.0,568.0,4.1818,339700.0,<1H OCEAN
+-122.49,37.78,52.0,2050.0,439.0,1109.0,437.0,2.6719,318500.0,NEAR BAY
+-117.93,33.94,28.0,3664.0,719.0,1820.0,657.0,4.225,224700.0,<1H OCEAN
+-116.96,32.71,18.0,2413.0,533.0,1129.0,551.0,2.4567,155000.0,<1H OCEAN
+-118.99,35.3,33.0,2248.0,434.0,1461.0,405.0,2.9402,56200.0,INLAND
+-120.79,37.43,17.0,2534.0,517.0,1764.0,502.0,2.8519,80700.0,INLAND
+-116.28,32.84,18.0,382.0,128.0,194.0,69.0,2.5179,58800.0,INLAND
+-118.63,34.22,18.0,1376.0,225.0,670.0,205.0,6.5146,277600.0,<1H OCEAN
+-121.72,36.81,18.0,1984.0,379.0,1078.0,359.0,3.2969,229900.0,<1H OCEAN
+-118.4,34.19,35.0,1631.0,356.0,862.0,368.0,3.6007,261800.0,<1H OCEAN
+-117.45,33.92,35.0,2552.0,588.0,1840.0,551.0,2.2548,113300.0,INLAND
+-118.35,34.1,24.0,5477.0,1803.0,2863.0,1755.0,1.845,237500.0,<1H OCEAN
+-117.18,32.69,52.0,1837.0,313.0,668.0,300.0,5.1009,500001.0,NEAR OCEAN
+-121.49,38.51,18.0,700.0,169.0,260.0,128.0,2.9219,152900.0,INLAND
+-118.35,33.92,24.0,2728.0,845.0,2023.0,773.0,2.75,239700.0,<1H OCEAN
+-122.5,37.76,46.0,1491.0,285.0,841.0,306.0,4.5329,278800.0,NEAR OCEAN
+-117.5,33.95,29.0,932.0,153.0,711.0,172.0,4.8214,143400.0,INLAND
+-118.4,34.05,26.0,4473.0,923.0,1518.0,805.0,5.0762,500001.0,<1H OCEAN
+-118.15,34.16,20.0,2410.0,632.0,2135.0,578.0,1.6887,148600.0,<1H OCEAN
+-121.34,37.96,23.0,2830.0,659.0,1554.0,654.0,3.0354,113700.0,INLAND
+-117.35,33.69,11.0,1229.0,236.0,581.0,190.0,3.102,111300.0,<1H OCEAN
+-121.82,37.26,10.0,3030.0,574.0,1623.0,589.0,5.1356,218700.0,<1H OCEAN
+-121.62,39.13,41.0,1147.0,243.0,583.0,239.0,2.2431,63400.0,INLAND
+-115.37,32.82,30.0,1602.0,322.0,1130.0,335.0,3.5735,71100.0,INLAND
+-119.88,36.85,8.0,2580.0,372.0,1111.0,393.0,7.5,256200.0,INLAND
+-118.44,34.19,19.0,3487.0,959.0,2278.0,835.0,2.6709,215500.0,<1H OCEAN
+-121.9,38.01,34.0,3779.0,766.0,2356.0,722.0,3.5129,110600.0,INLAND
+-119.35,36.42,18.0,1115.0,193.0,1742.0,176.0,2.7969,123800.0,INLAND
+-122.02,37.35,18.0,1221.0,255.0,507.0,271.0,5.3679,228400.0,<1H OCEAN
+-122.32,37.97,33.0,1156.0,190.0,643.0,209.0,4.5,156600.0,NEAR BAY
+-121.18,41.31,22.0,2124.0,432.0,829.0,313.0,2.4519,57500.0,INLAND
+-118.17,34.12,37.0,2246.0,481.0,995.0,459.0,4.2944,354700.0,<1H OCEAN
+-118.08,33.83,30.0,2188.0,556.0,2727.0,525.0,2.7759,136800.0,<1H OCEAN
+-119.65,36.34,47.0,1869.0,357.0,832.0,315.0,3.0846,76100.0,INLAND
+-121.89,37.28,35.0,2418.0,375.0,988.0,374.0,6.0936,365400.0,<1H OCEAN
+-117.89,34.01,23.0,4535.0,955.0,3881.0,930.0,3.6275,154100.0,<1H OCEAN
+-120.06,39.15,22.0,2213.0,372.0,98.0,42.0,1.1912,170000.0,INLAND
+-118.26,34.24,35.0,2485.0,418.0,1226.0,406.0,5.7083,329500.0,<1H OCEAN
+-122.14,37.7,36.0,1266.0,228.0,606.0,239.0,3.9702,194100.0,NEAR BAY
+-118.43,34.02,41.0,2403.0,516.0,1001.0,514.0,4.3906,500001.0,<1H OCEAN
+-117.34,33.15,17.0,4505.0,1140.0,2111.0,1062.0,3.3536,283300.0,NEAR OCEAN
+-121.97,38.34,11.0,1500.0,319.0,899.0,304.0,4.5568,127200.0,INLAND
+-117.22,33.22,17.0,3675.0,672.0,1693.0,597.0,3.3882,190800.0,<1H OCEAN
+-117.07,32.71,36.0,2448.0,475.0,1268.0,450.0,2.5682,109100.0,NEAR OCEAN
+-122.89,38.93,20.0,1214.0,247.0,504.0,223.0,2.7188,105700.0,<1H OCEAN
+-118.12,34.69,17.0,2479.0,390.0,1219.0,363.0,4.6417,125700.0,INLAND
+-120.82,37.49,25.0,1611.0,285.0,882.0,261.0,3.5547,122400.0,INLAND
+-118.28,34.07,14.0,1924.0,926.0,2226.0,792.0,2.2552,265900.0,<1H OCEAN
+-117.86,33.74,9.0,525.0,171.0,1257.0,165.0,3.375,165300.0,<1H OCEAN
+-117.14,32.76,32.0,2587.0,681.0,1246.0,650.0,2.1727,145500.0,NEAR OCEAN
+-118.25,33.94,44.0,1463.0,312.0,940.0,312.0,2.3333,99800.0,<1H OCEAN
+-124.1,40.73,33.0,644.0,129.0,334.0,121.0,3.9659,111800.0,NEAR OCEAN
+-118.23,33.97,44.0,2748.0,715.0,2962.0,703.0,2.6951,169300.0,<1H OCEAN
+-121.84,37.28,18.0,2749.0,633.0,1779.0,561.0,3.925,166100.0,<1H OCEAN
+-118.08,34.01,36.0,1248.0,322.0,1282.0,326.0,3.2031,147600.0,<1H OCEAN
+-117.23,32.99,17.0,2718.0,326.0,1011.0,319.0,15.0001,500001.0,NEAR OCEAN
+-117.13,33.89,4.0,1611.0,239.0,275.0,84.0,3.5781,244400.0,INLAND
+-117.98,33.86,25.0,1025.0,266.0,726.0,183.0,3.875,137500.0,<1H OCEAN
+-122.24,38.11,52.0,2050.0,492.0,1277.0,463.0,3.0507,107300.0,NEAR BAY
+-121.09,37.56,32.0,1717.0,325.0,1356.0,307.0,2.6705,91900.0,INLAND
+-117.97,33.88,11.0,1454.0,247.0,635.0,236.0,6.2427,218500.0,<1H OCEAN
+-118.22,34.67,28.0,2357.0,408.0,1162.0,384.0,4.3636,179700.0,INLAND
+-122.26,37.54,5.0,3264.0,442.0,1607.0,453.0,9.1415,500001.0,NEAR BAY
+-120.4,34.85,26.0,2384.0,385.0,1323.0,400.0,4.8185,157900.0,<1H OCEAN
+-117.25,32.78,21.0,1479.0,484.0,658.0,384.0,2.45,350000.0,NEAR OCEAN
+-118.31,34.16,38.0,2347.0,665.0,1317.0,547.0,3.2112,349300.0,<1H OCEAN
+-122.57,38.03,24.0,2330.0,322.0,911.0,320.0,6.5253,387700.0,NEAR BAY
+-121.7,37.96,33.0,2396.0,452.0,1391.0,465.0,3.2813,151400.0,INLAND
+-119.88,34.43,16.0,2206.0,541.0,1227.0,554.0,3.75,223100.0,NEAR OCEAN
+-116.51,33.84,16.0,980.0,193.0,454.0,185.0,4.0729,100000.0,INLAND
+-118.28,33.91,41.0,620.0,133.0,642.0,162.0,2.6546,159600.0,<1H OCEAN
+-117.93,33.62,34.0,2125.0,498.0,1052.0,468.0,5.6315,484600.0,<1H OCEAN
+-118.2,33.96,41.0,1512.0,400.0,1690.0,367.0,3.055,167000.0,<1H OCEAN
+-121.79,37.33,18.0,3611.0,614.0,2381.0,642.0,5.6345,231000.0,<1H OCEAN
+-121.07,39.22,52.0,2432.0,495.0,928.0,435.0,2.425,121100.0,INLAND
+-118.14,34.68,25.0,1703.0,342.0,775.0,309.0,4.5455,126500.0,INLAND
+-117.59,33.67,29.0,1223.0,215.0,633.0,204.0,6.5143,279800.0,<1H OCEAN
+-121.98,37.31,32.0,2248.0,460.0,1191.0,419.0,5.606,288900.0,<1H OCEAN
+-118.33,33.96,24.0,6513.0,1290.0,2636.0,1271.0,4.2099,189800.0,<1H OCEAN
+-122.16,37.76,45.0,2299.0,514.0,1437.0,484.0,2.5122,95500.0,NEAR BAY
+-119.3,36.33,44.0,2060.0,414.0,819.0,355.0,2.8795,77000.0,INLAND
+-122.53,37.94,18.0,878.0,255.0,384.0,247.0,4.7344,200000.0,NEAR BAY
+-118.03,33.7,15.0,3244.0,421.0,1259.0,413.0,7.7854,395300.0,<1H OCEAN
+-119.87,36.79,7.0,1932.0,419.0,1014.0,389.0,3.0938,76700.0,INLAND
+-117.17,34.08,5.0,1473.0,228.0,842.0,257.0,4.875,138100.0,INLAND
+-118.4,33.98,36.0,2526.0,452.0,996.0,441.0,5.611,456600.0,<1H OCEAN
+-122.3,38.25,18.0,3548.0,880.0,1476.0,699.0,3.7188,163400.0,NEAR BAY
+-117.38,33.97,30.0,2953.0,703.0,1406.0,580.0,2.6895,150000.0,INLAND
+-122.1,37.61,35.0,2361.0,458.0,1727.0,467.0,4.5281,173600.0,NEAR BAY
+-118.32,33.93,37.0,2379.0,462.0,1327.0,445.0,4.25,172100.0,<1H OCEAN
+-117.25,32.8,37.0,1096.0,260.0,490.0,267.0,3.2663,270600.0,NEAR OCEAN
+-120.84,35.31,23.0,3100.0,603.0,1515.0,609.0,2.8493,196100.0,NEAR OCEAN
+-118.64,34.22,16.0,4312.0,574.0,1902.0,574.0,8.4438,390000.0,<1H OCEAN
+-118.32,33.99,49.0,1407.0,269.0,889.0,283.0,1.9779,114200.0,<1H OCEAN
+-121.3,37.98,47.0,2373.0,461.0,990.0,444.0,4.175,98300.0,INLAND
+-121.63,40.92,23.0,1922.0,411.0,872.0,350.0,2.2337,64500.0,INLAND
+-122.42,37.79,6.0,670.0,301.0,655.0,284.0,3.4423,117500.0,NEAR BAY
+-117.1,32.58,33.0,393.0,76.0,330.0,80.0,4.1029,122700.0,NEAR OCEAN
+-118.29,33.79,16.0,1867.0,571.0,951.0,498.0,3.3427,154200.0,<1H OCEAN
+-121.45,38.53,38.0,1746.0,388.0,1142.0,315.0,1.7714,69900.0,INLAND
+-118.57,34.21,23.0,4891.0,793.0,2447.0,765.0,5.8798,270500.0,<1H OCEAN
+-118.6,34.15,28.0,4570.0,744.0,1693.0,695.0,6.14,361900.0,<1H OCEAN
+-117.07,32.69,20.0,2192.0,406.0,1766.0,393.0,4.0921,135000.0,NEAR OCEAN
+-119.53,34.38,22.0,2323.0,727.0,1301.0,478.0,2.7864,300000.0,NEAR OCEAN
+-122.21,37.43,20.0,975.0,134.0,324.0,146.0,9.7796,500001.0,NEAR OCEAN
+-122.42,37.71,44.0,2080.0,489.0,1781.0,478.0,3.6827,215300.0,NEAR BAY
+-118.02,33.95,38.0,2139.0,426.0,1138.0,412.0,4.2917,168900.0,<1H OCEAN
+-121.46,38.52,34.0,1279.0,285.0,963.0,268.0,2.71,65600.0,INLAND
+-117.68,33.61,19.0,2962.0,405.0,1295.0,440.0,6.0689,248000.0,<1H OCEAN
+-123.28,40.77,25.0,767.0,206.0,301.0,121.0,1.625,79200.0,INLAND
+-117.95,33.78,32.0,2296.0,560.0,1376.0,532.0,3.7303,188500.0,<1H OCEAN
+-118.3,33.9,13.0,2455.0,661.0,1975.0,618.0,2.9559,173600.0,<1H OCEAN
+-119.73,36.81,19.0,1699.0,356.0,994.0,368.0,2.7778,79700.0,INLAND
+-118.17,33.79,36.0,948.0,303.0,1042.0,301.0,1.55,100000.0,NEAR OCEAN
+-118.33,33.98,32.0,1784.0,489.0,1472.0,476.0,1.6477,108900.0,<1H OCEAN
+-122.52,37.95,37.0,350.0,57.0,179.0,69.0,6.2862,500001.0,NEAR BAY
+-122.41,37.62,39.0,3119.0,758.0,1807.0,696.0,3.2216,242700.0,NEAR OCEAN
+-121.82,37.42,13.0,3752.0,572.0,1581.0,526.0,6.1091,329400.0,<1H OCEAN
+-117.88,33.78,26.0,1813.0,421.0,1235.0,343.0,3.5972,187500.0,<1H OCEAN
+-118.28,33.94,38.0,637.0,204.0,679.0,162.0,1.5714,89700.0,<1H OCEAN
+-121.31,37.96,52.0,1938.0,332.0,788.0,320.0,3.6094,118400.0,INLAND
+-117.23,32.72,38.0,2827.0,581.0,972.0,558.0,3.2361,500001.0,NEAR OCEAN
+-119.6,36.66,27.0,1388.0,296.0,1056.0,284.0,1.6094,55200.0,INLAND
+-116.73,34.52,16.0,1247.0,315.0,433.0,159.0,1.0568,75000.0,INLAND
+-116.9,33.93,34.0,3183.0,738.0,1820.0,647.0,2.2321,71800.0,INLAND
+-122.41,37.77,52.0,1963.0,565.0,1628.0,524.0,2.6083,193800.0,NEAR BAY
+-118.37,34.14,8.0,4382.0,1560.0,2138.0,1411.0,3.5714,197900.0,<1H OCEAN
+-121.88,37.27,27.0,2019.0,335.0,1020.0,351.0,5.8178,267400.0,<1H OCEAN
+-117.8,33.68,5.0,623.0,146.0,396.0,136.0,3.631,225000.0,<1H OCEAN
+-118.37,34.03,41.0,1425.0,285.0,838.0,296.0,3.9732,188400.0,<1H OCEAN
+-117.08,32.68,19.0,3635.0,1078.0,3127.0,1098.0,1.324,122600.0,NEAR OCEAN
+-118.36,33.96,17.0,3431.0,934.0,2365.0,810.0,3.0393,129200.0,<1H OCEAN
+-118.89,34.22,20.0,3878.0,665.0,1651.0,591.0,5.5402,264600.0,<1H OCEAN
+-117.29,33.15,11.0,2560.0,445.0,952.0,448.0,4.0625,87500.0,NEAR OCEAN
+-122.28,37.89,52.0,2070.0,329.0,722.0,306.0,5.4171,292000.0,NEAR BAY
+-122.42,37.77,52.0,1925.0,568.0,867.0,515.0,2.879,450000.0,NEAR BAY
+-117.24,32.79,18.0,2539.0,616.0,964.0,526.0,3.4306,275000.0,NEAR OCEAN
+-118.07,34.11,47.0,832.0,194.0,419.0,156.0,3.1576,225000.0,<1H OCEAN
+-121.44,38.5,20.0,2033.0,586.0,1281.0,521.0,1.4007,97500.0,INLAND
+-118.12,33.95,36.0,2752.0,459.0,1211.0,452.0,5.0526,269800.0,<1H OCEAN
+-118.3,33.72,28.0,2647.0,658.0,1459.0,595.0,3.4474,253400.0,NEAR OCEAN
+-118.29,34.1,39.0,631.0,298.0,744.0,274.0,2.7054,162500.0,<1H OCEAN
+-118.18,33.77,45.0,1434.0,627.0,735.0,518.0,1.5,162500.0,NEAR OCEAN
+-116.93,32.8,19.0,1867.0,538.0,1219.0,468.0,2.0685,130000.0,<1H OCEAN
+-121.95,38.35,16.0,2084.0,292.0,1099.0,292.0,5.8269,150200.0,INLAND
+-122.69,38.46,32.0,2970.0,504.0,1117.0,512.0,5.0,275900.0,<1H OCEAN
+-118.02,34.1,36.0,1928.0,361.0,1008.0,368.0,4.733,233700.0,INLAND
+-121.52,39.43,15.0,2119.0,389.0,1079.0,374.0,2.3566,80400.0,INLAND
+-117.74,33.97,4.0,9755.0,1748.0,4662.0,1583.0,5.6501,254900.0,<1H OCEAN
+-118.27,33.94,39.0,2078.0,561.0,1901.0,504.0,1.1468,96900.0,<1H OCEAN
+-118.21,34.05,28.0,950.0,357.0,1485.0,345.0,1.9271,136400.0,<1H OCEAN
+-118.62,34.2,29.0,2421.0,402.0,1120.0,388.0,5.0309,244800.0,<1H OCEAN
+-117.41,33.97,34.0,2316.0,365.0,956.0,389.0,4.337,157800.0,INLAND
+-121.26,37.99,27.0,429.0,102.0,179.0,90.0,2.3333,87500.0,INLAND
+-118.04,34.12,44.0,2007.0,288.0,921.0,307.0,6.5989,500001.0,INLAND
+-118.08,33.78,34.0,2287.0,347.0,1051.0,346.0,5.5767,372000.0,<1H OCEAN
+-118.08,34.13,35.0,1897.0,279.0,733.0,291.0,7.4185,500001.0,INLAND
+-121.42,38.56,21.0,2066.0,748.0,2548.0,734.0,1.3571,55000.0,INLAND
+-118.83,35.2,17.0,1959.0,484.0,1763.0,453.0,2.1357,53500.0,INLAND
+-119.84,36.84,12.0,2396.0,290.0,863.0,258.0,8.7716,229200.0,INLAND
+-117.08,33.01,5.0,5659.0,931.0,2565.0,902.0,6.1949,238700.0,<1H OCEAN
+-117.86,33.79,42.0,1024.0,191.0,483.0,187.0,4.105,194500.0,<1H OCEAN
+-119.7,34.36,35.0,1604.0,334.0,904.0,337.0,4.7411,336400.0,NEAR OCEAN
+-122.01,37.39,26.0,2500.0,962.0,2374.0,879.0,3.5586,222200.0,<1H OCEAN
+-117.21,33.02,26.0,3194.0,454.0,1032.0,406.0,10.156,500001.0,NEAR OCEAN
+-122.17,37.75,41.0,1257.0,271.0,828.0,230.0,2.5043,92300.0,NEAR BAY
+-121.46,38.58,40.0,1394.0,397.0,689.0,353.0,1.7765,109800.0,INLAND
+-117.22,32.83,34.0,2936.0,597.0,1512.0,571.0,3.7841,176900.0,NEAR OCEAN
+-121.69,39.36,29.0,2220.0,471.0,1170.0,428.0,2.3224,56200.0,INLAND
+-118.2,34.04,52.0,1249.0,307.0,1223.0,297.0,2.07,136300.0,<1H OCEAN
+-117.9,34.05,33.0,629.0,76.0,253.0,75.0,7.6286,330400.0,<1H OCEAN
+-118.29,34.01,50.0,2238.0,673.0,2247.0,583.0,1.6505,125000.0,<1H OCEAN
+-121.22,39.11,14.0,1405.0,269.0,660.0,228.0,3.0804,156800.0,INLAND
+-118.03,33.97,22.0,2185.0,623.0,1644.0,606.0,2.593,192000.0,<1H OCEAN
+-117.96,34.06,34.0,2226.0,381.0,1464.0,365.0,4.4102,183200.0,<1H OCEAN
+-122.04,37.88,32.0,3250.0,550.0,1230.0,557.0,4.6424,312700.0,NEAR BAY
+-118.34,34.04,42.0,1681.0,360.0,987.0,337.0,2.6,171400.0,<1H OCEAN
+-118.46,33.99,52.0,1158.0,253.0,528.0,253.0,3.5234,334700.0,<1H OCEAN
+-120.61,35.1,17.0,2799.0,637.0,2015.0,592.0,3.0536,143600.0,<1H OCEAN
+-117.98,33.83,32.0,1133.0,166.0,523.0,187.0,6.213,230800.0,<1H OCEAN
+-117.89,33.71,23.0,1422.0,260.0,1092.0,263.0,4.7422,202400.0,<1H OCEAN
+-122.59,38.92,15.0,1410.0,329.0,599.0,273.0,2.1953,75000.0,INLAND
+-117.97,34.01,33.0,3530.0,700.0,2959.0,679.0,3.7459,152900.0,<1H OCEAN
+-121.37,38.55,21.0,2713.0,432.0,1287.0,440.0,4.5815,125500.0,INLAND
+-118.32,34.05,42.0,3292.0,713.0,2224.0,674.0,3.5517,291500.0,<1H OCEAN
+-122.11,37.7,23.0,1689.0,461.0,828.0,443.0,2.1552,161400.0,NEAR BAY
+-122.0,37.3,29.0,3429.0,524.0,1518.0,520.0,7.218,400700.0,<1H OCEAN
+-122.73,38.46,14.0,4042.0,1298.0,2323.0,1158.0,2.0651,135400.0,<1H OCEAN
+-121.24,39.22,14.0,983.0,163.0,399.0,161.0,2.2917,145100.0,INLAND
+-118.22,33.98,30.0,1971.0,645.0,2650.0,605.0,2.0357,169900.0,<1H OCEAN
+-118.14,34.03,45.0,1569.0,359.0,1203.0,359.0,2.4612,180500.0,<1H OCEAN
+-118.15,33.89,33.0,2848.0,641.0,2319.0,647.0,3.407,190800.0,<1H OCEAN
+-118.15,33.96,33.0,1201.0,340.0,1482.0,334.0,2.4821,150000.0,<1H OCEAN
+-118.49,34.13,24.0,4394.0,,1443.0,528.0,11.2979,500001.0,<1H OCEAN
+-122.17,37.73,52.0,1555.0,289.0,620.0,292.0,3.7159,183300.0,NEAR BAY
+-117.59,34.1,17.0,3646.0,1035.0,1987.0,895.0,2.3603,139300.0,INLAND
+-121.26,38.66,8.0,1145.0,241.0,447.0,216.0,4.0781,124300.0,INLAND
+-118.37,33.93,46.0,1130.0,201.0,503.0,196.0,4.4861,246300.0,<1H OCEAN
+-118.21,33.93,39.0,354.0,73.0,184.0,58.0,2.7679,108900.0,<1H OCEAN
+-117.92,33.86,26.0,745.0,161.0,247.0,151.0,3.6375,133900.0,<1H OCEAN
+-118.16,34.09,52.0,1722.0,448.0,1122.0,425.0,3.1204,224000.0,<1H OCEAN
+-120.57,37.43,39.0,2235.0,412.0,1268.0,402.0,2.6758,74600.0,INLAND
+-121.12,38.0,36.0,683.0,159.0,505.0,141.0,3.4265,158900.0,INLAND
+-117.14,33.12,7.0,6126.0,1032.0,2662.0,923.0,4.9005,264000.0,<1H OCEAN
+-117.19,32.85,15.0,2895.0,498.0,1164.0,443.0,5.102,417500.0,NEAR OCEAN
+-116.48,33.84,5.0,5480.0,1371.0,1050.0,485.0,1.7204,137500.0,INLAND
+-121.22,38.0,35.0,1841.0,300.0,783.0,285.0,2.8167,162100.0,INLAND
+-117.85,34.0,26.0,2712.0,402.0,1389.0,377.0,5.6513,227900.0,<1H OCEAN
+-121.29,37.96,50.0,1669.0,558.0,1340.0,484.0,1.3191,92300.0,INLAND
+-121.77,36.93,33.0,1406.0,317.0,1075.0,301.0,3.2813,190000.0,<1H OCEAN
+-122.78,38.53,9.0,3659.0,652.0,1889.0,632.0,4.2716,250800.0,<1H OCEAN
+-121.04,39.24,48.0,1188.0,227.0,471.0,219.0,2.3125,125700.0,INLAND
+-117.86,33.73,26.0,1702.0,456.0,2776.0,463.0,2.6385,180200.0,<1H OCEAN
+-122.23,37.8,50.0,1746.0,480.0,1149.0,415.0,2.25,123500.0,NEAR BAY
+-121.82,39.73,44.0,2923.0,659.0,1371.0,626.0,2.2925,85800.0,INLAND
+-118.29,33.99,43.0,1902.0,398.0,1153.0,363.0,1.9375,112900.0,<1H OCEAN
+-122.05,37.61,16.0,1642.0,346.0,705.0,351.0,2.8971,163900.0,NEAR BAY
+-118.3,33.96,47.0,2112.0,417.0,1161.0,368.0,3.9722,117400.0,<1H OCEAN
+-118.08,33.77,26.0,3083.0,806.0,960.0,723.0,1.9074,68500.0,<1H OCEAN
+-122.87,38.39,34.0,1138.0,205.0,541.0,180.0,4.5147,271400.0,<1H OCEAN
+-120.41,37.16,21.0,1684.0,341.0,1052.0,312.0,2.0809,95800.0,INLAND
+-119.34,37.12,23.0,1881.0,380.0,64.0,37.0,3.875,125000.0,INLAND
+-116.57,33.84,18.0,7962.0,1652.0,2009.0,921.0,3.3897,230200.0,INLAND
+-118.38,34.05,35.0,3517.0,879.0,1632.0,784.0,3.0956,500001.0,<1H OCEAN
+-118.1,34.17,48.0,1111.0,229.0,421.0,202.0,3.2813,268100.0,INLAND
+-117.37,33.97,34.0,3676.0,697.0,2653.0,682.0,2.5804,92400.0,INLAND
+-117.86,34.13,33.0,2383.0,428.0,1269.0,421.0,4.636,245500.0,INLAND
+-121.2,38.67,10.0,3875.0,668.0,1632.0,593.0,4.6902,171000.0,INLAND
+-122.51,37.91,2.0,647.0,136.0,203.0,118.0,6.641,310000.0,NEAR BAY
+-121.2,39.2,16.0,1039.0,182.0,554.0,184.0,2.9688,128300.0,INLAND
+-118.28,33.8,38.0,1471.0,329.0,1207.0,335.0,4.0,165500.0,<1H OCEAN
+-121.97,37.54,28.0,2312.0,496.0,1344.0,467.0,4.7135,203200.0,<1H OCEAN
+-121.69,37.14,12.0,4077.0,590.0,1618.0,540.0,5.2951,386200.0,<1H OCEAN
+-118.28,33.78,20.0,2233.0,591.0,1915.0,558.0,3.2011,169100.0,<1H OCEAN
+-121.42,37.74,45.0,818.0,144.0,340.0,138.0,4.8021,133500.0,INLAND
+-121.31,37.93,21.0,1556.0,314.0,1140.0,304.0,2.4667,81400.0,INLAND
+-118.37,33.92,36.0,1075.0,197.0,509.0,197.0,4.9688,238900.0,<1H OCEAN
+-121.39,38.67,35.0,562.0,174.0,240.0,106.0,0.9338,112500.0,INLAND
+-118.3,34.06,20.0,1782.0,896.0,1749.0,823.0,2.2094,75000.0,<1H OCEAN
+-117.68,34.07,32.0,1775.0,314.0,1067.0,302.0,4.0375,121300.0,INLAND
+-121.43,38.56,46.0,1316.0,244.0,452.0,245.0,3.0938,137800.0,INLAND
+-117.26,32.83,24.0,1663.0,199.0,578.0,187.0,10.7721,500001.0,NEAR OCEAN
+-118.02,33.94,23.0,4815.0,1081.0,3232.0,1016.0,3.488,191800.0,<1H OCEAN
+-116.94,33.73,17.0,5160.0,851.0,2344.0,781.0,3.7175,120000.0,INLAND
+-118.42,34.01,42.0,1700.0,438.0,997.0,436.0,2.9213,305000.0,<1H OCEAN
+-120.06,36.96,44.0,1288.0,295.0,723.0,287.0,1.6534,61400.0,INLAND
+-118.58,34.19,15.0,3061.0,1079.0,2173.0,1078.0,2.85,187500.0,<1H OCEAN
+-118.23,33.93,30.0,1147.0,260.0,1219.0,210.0,2.0658,93200.0,<1H OCEAN
+-122.15,37.47,39.0,1295.0,239.0,566.0,242.0,5.6407,326400.0,NEAR BAY
+-117.67,35.64,6.0,2115.0,342.0,927.0,337.0,6.1935,115700.0,INLAND
+-122.13,38.26,40.0,1538.0,255.0,669.0,263.0,3.3281,170200.0,NEAR BAY
+-118.51,35.16,7.0,4371.0,727.0,1932.0,654.0,4.625,136800.0,INLAND
+-118.41,34.02,16.0,5825.0,1866.0,3390.0,1752.0,3.0965,320000.0,<1H OCEAN
+-117.89,34.11,27.0,2434.0,535.0,1623.0,498.0,3.6875,140200.0,<1H OCEAN
+-121.54,38.58,30.0,4648.0,1252.0,2524.0,1089.0,1.3177,74300.0,INLAND
+-118.49,34.16,37.0,3333.0,488.0,1171.0,485.0,6.4958,500001.0,<1H OCEAN
+-121.48,38.58,42.0,1823.0,566.0,761.0,503.0,1.245,137500.0,INLAND
+-116.93,32.79,19.0,3354.0,,1948.0,682.0,3.0192,142300.0,<1H OCEAN
+-122.61,37.99,40.0,7737.0,1488.0,3108.0,1349.0,4.4375,289600.0,NEAR OCEAN
+-117.19,33.18,7.0,3561.0,722.0,1921.0,657.0,4.1128,209700.0,<1H OCEAN
+-117.93,34.08,36.0,1371.0,246.0,806.0,241.0,4.5078,187100.0,<1H OCEAN
+-122.05,37.34,31.0,1443.0,215.0,627.0,222.0,6.6087,416500.0,<1H OCEAN
+-123.15,38.94,22.0,2163.0,436.0,1048.0,358.0,2.7171,95800.0,<1H OCEAN
+-122.42,37.8,52.0,3823.0,1040.0,1830.0,977.0,4.2458,450000.0,NEAR BAY
+-120.66,35.27,17.0,2719.0,589.0,1386.0,570.0,3.7337,208200.0,NEAR OCEAN
+-117.93,33.69,26.0,2822.0,473.0,1258.0,469.0,6.4441,261000.0,<1H OCEAN
+-122.36,37.94,41.0,2591.0,585.0,1638.0,462.0,1.822,79700.0,NEAR BAY
+-119.11,36.21,10.0,1972.0,455.0,1469.0,442.0,1.5407,58400.0,INLAND
+-117.3,34.18,28.0,2685.0,425.0,1304.0,420.0,4.3676,111100.0,INLAND
+-122.34,37.57,39.0,2647.0,616.0,1254.0,555.0,4.2407,433800.0,NEAR OCEAN
+-117.29,34.09,24.0,1451.0,387.0,1178.0,330.0,1.1806,68300.0,INLAND
+-122.28,37.88,52.0,1172.0,215.0,489.0,218.0,3.9167,235600.0,NEAR BAY
+-117.88,34.06,23.0,6689.0,1124.0,3081.0,1047.0,5.9254,491200.0,<1H OCEAN
+-122.62,37.97,52.0,370.0,62.0,150.0,56.0,7.7006,316700.0,NEAR OCEAN
+-120.4,34.95,8.0,1885.0,286.0,835.0,290.0,5.0206,261000.0,<1H OCEAN
+-117.92,33.9,13.0,1814.0,320.0,1010.0,313.0,6.3489,337900.0,<1H OCEAN
+-120.98,37.6,36.0,1437.0,,1073.0,320.0,2.1779,58400.0,INLAND
+-117.99,33.85,34.0,1948.0,306.0,957.0,304.0,4.9777,212600.0,<1H OCEAN
+-117.06,32.71,25.0,2681.0,596.0,1947.0,553.0,2.8964,104300.0,NEAR OCEAN
+-118.39,37.36,38.0,1813.0,410.0,902.0,396.0,2.3261,98400.0,INLAND
+-121.79,38.54,7.0,1777.0,513.0,4479.0,504.0,1.4653,310000.0,INLAND
+-122.25,40.66,15.0,2771.0,546.0,1423.0,505.0,3.6413,108500.0,INLAND
+-122.25,40.17,47.0,1554.0,308.0,846.0,301.0,1.8077,54100.0,INLAND
+-122.46,37.71,45.0,1799.0,394.0,1436.0,389.0,3.65,239900.0,NEAR OCEAN
+-122.03,37.18,10.0,212.0,38.0,78.0,21.0,6.0622,390000.0,<1H OCEAN
+-117.66,34.11,19.0,3445.0,661.0,1635.0,580.0,5.0681,230500.0,INLAND
+-121.87,37.35,52.0,1557.0,424.0,1580.0,434.0,2.3277,183700.0,<1H OCEAN
+-119.02,35.41,21.0,2534.0,554.0,1297.0,517.0,2.0575,67000.0,INLAND
+-117.43,34.1,34.0,1345.0,265.0,834.0,290.0,3.7011,99500.0,INLAND
+-120.0,38.9,21.0,1653.0,419.0,737.0,308.0,1.9727,114100.0,INLAND
+-119.64,37.61,30.0,2857.0,661.0,291.0,135.0,2.6838,164600.0,INLAND
+-121.79,37.3,10.0,5469.0,950.0,3083.0,906.0,5.9399,241900.0,<1H OCEAN
+-121.26,37.96,35.0,1511.0,316.0,892.0,304.0,1.7898,63500.0,INLAND
+-119.19,34.21,28.0,4194.0,811.0,2556.0,856.0,4.2227,235400.0,NEAR OCEAN
+-122.82,38.61,41.0,2720.0,501.0,987.0,364.0,4.0294,201700.0,<1H OCEAN
+-118.14,33.92,35.0,2378.0,559.0,1799.0,546.0,3.9327,190500.0,<1H OCEAN
+-117.25,34.08,30.0,2981.0,605.0,1784.0,573.0,2.45,85800.0,INLAND
+-117.08,32.62,36.0,1674.0,309.0,818.0,307.0,3.4773,150400.0,NEAR OCEAN
+-121.43,38.57,38.0,2507.0,446.0,888.0,448.0,4.0972,163700.0,INLAND
+-122.25,37.82,28.0,3337.0,855.0,1520.0,802.0,3.9063,225000.0,NEAR BAY
+-118.48,34.07,29.0,4767.0,777.0,1500.0,638.0,10.7937,500001.0,<1H OCEAN
+-117.12,32.76,43.0,2336.0,644.0,1203.0,614.0,2.3594,127800.0,NEAR OCEAN
+-117.26,32.8,30.0,1446.0,385.0,650.0,344.0,3.744,450000.0,NEAR OCEAN
+-118.02,33.7,23.0,5069.0,770.0,2473.0,769.0,6.3047,285700.0,<1H OCEAN
+-122.08,37.61,6.0,2605.0,474.0,1568.0,433.0,5.0406,261400.0,NEAR BAY
+-122.04,37.62,35.0,1032.0,173.0,453.0,176.0,6.396,208500.0,NEAR BAY
+-122.82,38.64,29.0,2176.0,385.0,1117.0,374.0,3.8681,188600.0,<1H OCEAN
+-121.27,38.05,26.0,378.0,75.0,164.0,65.0,3.4107,82800.0,INLAND
+-122.13,37.44,43.0,3004.0,440.0,1088.0,427.0,9.1508,500001.0,NEAR BAY
+-117.46,34.09,8.0,4711.0,963.0,3310.0,988.0,3.5488,101600.0,INLAND
+-122.3,37.87,52.0,3123.0,749.0,1695.0,684.0,2.2208,144800.0,NEAR BAY
+-117.91,33.67,16.0,7961.0,2276.0,5014.0,2116.0,3.512,218400.0,<1H OCEAN
+-117.21,32.81,26.0,2496.0,407.0,1062.0,380.0,5.5413,302100.0,NEAR OCEAN
+-118.1,33.98,33.0,1927.0,482.0,1623.0,479.0,3.5268,152000.0,<1H OCEAN
+-118.13,33.88,32.0,3088.0,1024.0,1981.0,956.0,2.2027,192700.0,<1H OCEAN
+-119.08,35.39,10.0,6435.0,1040.0,3242.0,1030.0,5.575,132200.0,INLAND
+-122.26,38.0,6.0,678.0,104.0,318.0,91.0,5.2375,246400.0,NEAR BAY
+-118.03,34.1,31.0,2647.0,539.0,1473.0,520.0,3.94,223900.0,INLAND
+-121.0,37.67,26.0,90.0,18.0,47.0,18.0,1.125,87500.0,INLAND
+-118.01,34.55,2.0,2701.0,530.0,1368.0,430.0,4.071,137400.0,INLAND
+-122.07,37.52,3.0,14014.0,2861.0,7205.0,2753.0,6.0824,273500.0,NEAR BAY
+-122.48,37.76,52.0,3260.0,653.0,1594.0,632.0,4.4094,336100.0,NEAR BAY
+-117.66,33.59,18.0,4552.0,706.0,1918.0,671.0,7.5791,288100.0,<1H OCEAN
+-117.97,33.99,22.0,5284.0,982.0,2613.0,932.0,4.7332,289900.0,<1H OCEAN
+-118.21,34.06,29.0,1478.0,413.0,1580.0,394.0,1.8781,147500.0,<1H OCEAN
+-117.94,34.05,34.0,1729.0,324.0,1341.0,324.0,3.7708,163500.0,<1H OCEAN
+-118.25,33.92,36.0,949.0,164.0,502.0,163.0,4.1042,124400.0,<1H OCEAN
+-120.45,37.31,20.0,4379.0,753.0,2055.0,716.0,3.7652,133500.0,INLAND
+-118.0,34.08,29.0,2003.0,401.0,1520.0,364.0,3.994,195300.0,INLAND
+-117.88,33.88,15.0,957.0,360.0,615.0,370.0,3.0263,162500.0,<1H OCEAN
+-118.29,34.16,31.0,1262.0,338.0,1019.0,332.0,3.7083,241900.0,<1H OCEAN
+-117.08,33.11,31.0,1832.0,444.0,1669.0,463.0,2.2146,116700.0,<1H OCEAN
+-117.65,33.65,16.0,1538.0,260.0,835.0,259.0,5.5779,234800.0,<1H OCEAN
+-122.32,37.53,39.0,2795.0,464.0,1183.0,443.0,5.779,387100.0,NEAR OCEAN
+-117.14,34.06,15.0,3057.0,510.0,1154.0,460.0,3.9741,141100.0,INLAND
+-117.01,32.84,23.0,1951.0,395.0,901.0,378.0,3.1023,143300.0,<1H OCEAN
+-119.95,36.96,18.0,1996.0,379.0,1327.0,356.0,2.6087,96000.0,INLAND
+-122.22,37.37,26.0,440.0,202.0,322.0,218.0,5.1831,350000.0,NEAR OCEAN
+-119.32,36.36,18.0,2060.0,383.0,1348.0,397.0,3.4312,68400.0,INLAND
+-115.54,32.99,23.0,1459.0,373.0,1148.0,388.0,1.5372,69400.0,INLAND
+-117.16,32.78,34.0,2515.0,488.0,1594.0,515.0,3.7381,165000.0,NEAR OCEAN
+-121.38,38.58,38.0,2968.0,475.0,1176.0,454.0,5.0497,191700.0,INLAND
+-119.27,35.89,18.0,1855.0,424.0,1839.0,392.0,1.7572,53300.0,INLAND
+-122.2,37.77,41.0,1547.0,415.0,1024.0,341.0,2.0562,102000.0,NEAR BAY
+-119.67,36.57,32.0,1604.0,292.0,868.0,276.0,2.1908,110000.0,INLAND
+-117.47,34.06,33.0,1379.0,273.0,884.0,229.0,2.7574,125000.0,INLAND
+-121.3,38.7,18.0,7334.0,1332.0,3339.0,1271.0,3.235,124700.0,INLAND
+-117.87,34.1,15.0,6409.0,1363.0,3359.0,1267.0,3.875,173300.0,<1H OCEAN
+-117.1,32.69,35.0,1292.0,272.0,1183.0,272.0,2.0547,98000.0,NEAR OCEAN
+-118.46,34.23,20.0,4609.0,1499.0,5349.0,1377.0,2.7121,169400.0,<1H OCEAN
+-121.96,37.94,26.0,3084.0,505.0,1557.0,501.0,5.1582,194700.0,INLAND
+-118.23,34.2,48.0,1473.0,294.0,807.0,296.0,3.399,306300.0,<1H OCEAN
+-118.08,33.98,39.0,1042.0,221.0,863.0,228.0,3.6033,157800.0,<1H OCEAN
+-118.46,34.25,33.0,2202.0,433.0,1135.0,407.0,4.2143,224200.0,<1H OCEAN
+-118.36,34.09,36.0,1616.0,465.0,773.0,429.0,2.6,313600.0,<1H OCEAN
+-122.35,37.91,4.0,2851.0,798.0,1285.0,712.0,4.2895,186800.0,NEAR BAY
+-118.21,34.11,32.0,2759.0,499.0,1661.0,533.0,4.3812,228200.0,<1H OCEAN
+-119.69,36.81,13.0,1524.0,366.0,994.0,370.0,2.5446,93800.0,INLAND
+-117.06,33.09,11.0,7483.0,1276.0,3516.0,1261.0,4.0484,262500.0,<1H OCEAN
+-121.37,38.01,15.0,2430.0,315.0,1016.0,314.0,10.0088,242000.0,INLAND
+-121.13,38.87,48.0,1127.0,,530.0,186.0,3.0917,128100.0,INLAND
+-117.64,34.05,27.0,1407.0,362.0,1684.0,350.0,2.1944,95700.0,INLAND
+-117.63,34.11,30.0,2674.0,428.0,1404.0,456.0,4.2969,165600.0,INLAND
+-117.41,34.0,26.0,2372.0,621.0,1647.0,612.0,1.4719,88600.0,INLAND
+-117.72,33.43,5.0,1889.0,359.0,616.0,246.0,3.8992,500001.0,<1H OCEAN
+-117.13,33.15,16.0,3907.0,671.0,1759.0,663.0,3.1776,172600.0,<1H OCEAN
+-117.97,34.13,42.0,683.0,127.0,541.0,138.0,3.4375,151700.0,INLAND
+-117.95,33.68,19.0,1028.0,191.0,340.0,159.0,3.6364,252800.0,<1H OCEAN
+-118.38,34.24,38.0,125.0,42.0,63.0,29.0,1.3594,158300.0,<1H OCEAN
+-118.23,34.11,35.0,4148.0,971.0,3220.0,892.0,3.3389,187100.0,<1H OCEAN
+-117.14,33.81,13.0,4496.0,756.0,2044.0,695.0,3.2778,148800.0,INLAND
+-118.35,34.25,34.0,2795.0,460.0,1267.0,443.0,6.1464,354400.0,<1H OCEAN
+-118.01,33.79,23.0,2663.0,430.0,1499.0,403.0,5.7837,258000.0,<1H OCEAN
+-117.26,34.43,11.0,4597.0,782.0,2534.0,776.0,3.3368,99300.0,INLAND
+-117.96,34.07,32.0,2910.0,709.0,2583.0,670.0,3.7736,158400.0,<1H OCEAN
+-118.37,34.16,17.0,4150.0,1148.0,1808.0,1041.0,3.5051,232400.0,<1H OCEAN
+-117.11,32.58,21.0,2894.0,685.0,2109.0,712.0,2.2755,125000.0,NEAR OCEAN
+-118.28,33.85,27.0,489.0,98.0,403.0,97.0,5.144,180800.0,<1H OCEAN
+-119.03,35.33,21.0,3057.0,698.0,1627.0,680.0,2.7083,84700.0,INLAND
+-118.29,34.03,27.0,1084.0,287.0,1085.0,279.0,2.135,119600.0,<1H OCEAN
+-122.2,37.43,38.0,3626.0,528.0,1350.0,532.0,7.3681,500001.0,NEAR OCEAN
+-119.76,36.8,29.0,3494.0,662.0,1781.0,616.0,2.5893,70900.0,INLAND
+-121.29,38.36,17.0,2193.0,386.0,1148.0,372.0,4.5272,191700.0,INLAND
+-122.07,37.08,21.0,5639.0,894.0,2670.0,871.0,6.0809,270000.0,NEAR OCEAN
+-117.42,34.06,27.0,2532.0,495.0,1305.0,436.0,2.9107,143100.0,INLAND
+-117.04,34.0,25.0,3750.0,781.0,1594.0,785.0,2.4167,104900.0,INLAND
+-118.31,33.81,23.0,3942.0,748.0,1679.0,711.0,4.1169,362600.0,<1H OCEAN
+-122.42,37.79,52.0,3364.0,1100.0,2112.0,1045.0,2.1343,400000.0,NEAR BAY
+-118.03,34.07,37.0,1091.0,269.0,905.0,242.0,3.1042,152000.0,<1H OCEAN
+-117.23,32.74,16.0,1953.0,404.0,798.0,385.0,4.8167,169800.0,NEAR OCEAN
+-118.93,34.82,8.0,508.0,111.0,229.0,84.0,4.0332,128300.0,INLAND
+-122.52,37.98,31.0,6555.0,1571.0,2962.0,1464.0,2.8903,324200.0,NEAR BAY
+-119.81,36.74,36.0,607.0,155.0,483.0,146.0,1.5625,47500.0,INLAND
+-121.45,38.57,52.0,2006.0,412.0,825.0,384.0,3.2963,236100.0,INLAND
+-116.26,33.65,3.0,7437.0,1222.0,574.0,302.0,10.2948,382400.0,INLAND
+-119.72,34.43,36.0,1156.0,309.0,521.0,304.0,2.6014,320600.0,<1H OCEAN
+-118.13,33.95,24.0,6662.0,1852.0,3792.0,1735.0,3.1104,230000.0,<1H OCEAN
+-119.78,34.45,23.0,2077.0,306.0,705.0,256.0,6.4744,500001.0,NEAR OCEAN
+-118.38,33.81,36.0,1018.0,148.0,329.0,169.0,10.5045,500001.0,NEAR OCEAN
+-122.09,37.66,39.0,1160.0,259.0,725.0,274.0,2.2222,158300.0,NEAR BAY
+-122.44,37.78,44.0,1545.0,334.0,561.0,326.0,3.875,412500.0,NEAR BAY
+-119.63,34.44,37.0,3188.0,442.0,984.0,376.0,9.4522,500001.0,<1H OCEAN
+-118.34,33.99,47.0,1107.0,199.0,437.0,178.0,3.7344,179400.0,<1H OCEAN
+-121.91,36.97,19.0,4920.0,1092.0,1807.0,922.0,3.5112,231900.0,NEAR OCEAN
+-121.41,38.61,36.0,3099.0,605.0,1322.0,623.0,3.4784,105500.0,INLAND
+-117.95,33.94,28.0,2851.0,496.0,1287.0,496.0,5.0782,264100.0,<1H OCEAN
+-117.12,32.69,37.0,1082.0,294.0,1146.0,265.0,2.0673,88500.0,NEAR OCEAN
+-120.59,35.12,27.0,3055.0,677.0,1407.0,610.0,2.1702,179700.0,<1H OCEAN
+-121.36,38.61,35.0,2355.0,365.0,993.0,354.0,5.0492,144100.0,INLAND
+-123.48,40.79,15.0,619.0,160.0,287.0,104.0,1.9107,79200.0,INLAND
+-118.35,34.0,28.0,3085.0,621.0,1162.0,558.0,3.25,301000.0,<1H OCEAN
+-119.76,36.83,17.0,3690.0,628.0,1888.0,601.0,4.0196,84200.0,INLAND
+-117.64,33.66,6.0,5221.0,1217.0,2597.0,1119.0,4.6076,204000.0,<1H OCEAN
+-117.26,33.2,13.0,3163.0,725.0,1675.0,629.0,2.8214,121900.0,<1H OCEAN
+-117.69,33.62,18.0,4265.0,581.0,2025.0,544.0,6.4598,282700.0,<1H OCEAN
+-120.98,37.66,33.0,1959.0,342.0,984.0,356.0,4.5208,114200.0,INLAND
+-119.99,36.09,23.0,333.0,92.0,198.0,55.0,0.4999,100000.0,INLAND
+-118.22,33.9,40.0,1802.0,496.0,2096.0,468.0,2.3542,97900.0,<1H OCEAN
+-119.78,38.69,17.0,1364.0,282.0,338.0,152.0,2.45,117600.0,INLAND
+-114.31,34.19,15.0,5612.0,1283.0,1015.0,472.0,1.4936,66900.0,INLAND
+-118.27,34.05,26.0,1164.0,674.0,1685.0,541.0,1.5727,225000.0,<1H OCEAN
+-122.18,37.75,45.0,330.0,76.0,282.0,80.0,4.0469,80700.0,NEAR BAY
+-121.38,38.62,34.0,2352.0,610.0,1127.0,592.0,2.2,116500.0,INLAND
+-118.43,34.06,41.0,1463.0,267.0,601.0,267.0,5.3777,500001.0,<1H OCEAN
+-122.17,37.46,47.0,2312.0,332.0,1044.0,282.0,9.459,500001.0,NEAR BAY
+-122.47,37.71,44.0,2547.0,511.0,1577.0,516.0,4.1939,237900.0,NEAR OCEAN
+-119.61,36.57,42.0,2242.0,521.0,1359.0,483.0,1.5833,65100.0,INLAND
+-121.32,38.15,5.0,5428.0,994.0,2725.0,902.0,3.9323,130100.0,INLAND
+-117.27,34.23,26.0,6339.0,1244.0,1177.0,466.0,3.7708,110400.0,INLAND
+-121.77,36.71,18.0,6601.0,1395.0,3562.0,1299.0,3.512,174800.0,<1H OCEAN
+-118.4,34.01,48.0,1427.0,253.0,693.0,268.0,5.7405,351600.0,<1H OCEAN
+-119.4,37.09,22.0,2211.0,477.0,773.0,288.0,3.3269,102700.0,INLAND
+-118.55,34.28,16.0,8879.0,,3468.0,1200.0,8.1125,428600.0,<1H OCEAN
+-120.85,37.08,3.0,3425.0,611.0,1588.0,459.0,4.1779,147800.0,INLAND
+-119.38,35.49,34.0,2304.0,437.0,1506.0,403.0,2.2071,64600.0,INLAND
+-119.19,34.26,16.0,5018.0,853.0,2524.0,830.0,5.1752,218000.0,NEAR OCEAN
+-117.16,32.72,24.0,1232.0,663.0,1184.0,626.0,1.0391,162500.0,NEAR OCEAN
+-118.37,34.16,25.0,2450.0,618.0,1054.0,578.0,3.6375,262500.0,<1H OCEAN
+-121.34,38.67,34.0,1503.0,264.0,731.0,285.0,4.0352,118500.0,INLAND
+-122.21,37.8,50.0,2833.0,605.0,1260.0,552.0,2.8929,216700.0,NEAR BAY
+-118.42,34.23,34.0,1531.0,278.0,1064.0,274.0,5.6687,207300.0,<1H OCEAN
+-118.12,33.76,45.0,3035.0,516.0,1127.0,527.0,7.0796,500001.0,NEAR OCEAN
+-115.96,33.3,27.0,322.0,81.0,112.0,57.0,1.125,54400.0,INLAND
+-122.46,37.76,52.0,2236.0,545.0,1186.0,532.0,3.4531,414300.0,NEAR BAY
+-118.12,34.02,32.0,1789.0,528.0,1429.0,517.0,1.8906,224500.0,<1H OCEAN
+-117.12,32.95,4.0,9018.0,1572.0,4438.0,1498.0,4.988,263700.0,<1H OCEAN
+-117.0,32.8,33.0,1816.0,325.0,768.0,316.0,4.5662,150300.0,<1H OCEAN
+-117.46,34.1,7.0,1759.0,473.0,1064.0,328.0,1.9583,108800.0,INLAND
+-118.53,34.03,40.0,4350.0,763.0,1551.0,665.0,7.0318,500001.0,<1H OCEAN
+-122.28,37.89,52.0,2315.0,408.0,835.0,369.0,4.5893,290100.0,NEAR BAY
+-118.33,33.91,8.0,10731.0,3335.0,7211.0,3028.0,2.455,192700.0,<1H OCEAN
+-120.15,37.69,13.0,866.0,252.0,369.0,165.0,2.875,70200.0,INLAND
+-120.06,36.98,12.0,2710.0,575.0,1724.0,516.0,1.4712,60400.0,INLAND
+-119.15,34.17,22.0,1612.0,334.0,1431.0,335.0,4.8125,194400.0,NEAR OCEAN
+-122.02,38.27,20.0,2237.0,464.0,1169.0,425.0,3.2115,99100.0,INLAND
+-118.14,34.15,17.0,1896.0,674.0,971.0,652.0,0.8438,175000.0,<1H OCEAN
+-122.52,37.93,34.0,2782.0,502.0,1219.0,507.0,5.0779,333900.0,NEAR BAY
+-117.19,34.03,20.0,856.0,124.0,395.0,145.0,10.8634,381800.0,INLAND
+-118.29,33.87,32.0,1700.0,340.0,864.0,317.0,4.381,238700.0,<1H OCEAN
+-118.2,33.85,46.0,1854.0,462.0,1360.0,429.0,2.4844,158200.0,<1H OCEAN
+-118.22,33.83,42.0,1370.0,299.0,1018.0,328.0,4.4474,160200.0,<1H OCEAN
+-122.0,39.94,27.0,852.0,176.0,464.0,148.0,1.7125,58200.0,INLAND
+-119.58,36.1,21.0,1382.0,327.0,1469.0,355.0,1.3967,46500.0,INLAND
+-120.02,39.24,22.0,2309.0,571.0,919.0,342.0,3.0057,93600.0,INLAND
+-122.22,37.36,34.0,1559.0,243.0,600.0,242.0,8.7382,500001.0,NEAR OCEAN
+-115.73,33.09,27.0,452.0,103.0,258.0,61.0,2.9,87500.0,INLAND
+-121.85,38.43,2.0,790.0,135.0,235.0,87.0,5.0862,166500.0,INLAND
+-117.16,33.76,11.0,4934.0,929.0,2508.0,840.0,2.625,155400.0,INLAND
+-116.8,32.8,11.0,3874.0,565.0,1672.0,546.0,6.1481,274600.0,<1H OCEAN
+-117.96,34.08,28.0,2831.0,552.0,2330.0,557.0,3.9741,173100.0,<1H OCEAN
+-118.24,34.14,20.0,3196.0,994.0,2929.0,983.0,3.0206,219500.0,<1H OCEAN
+-117.18,34.06,28.0,699.0,180.0,432.0,168.0,2.1875,81900.0,INLAND
+-122.04,37.54,26.0,2145.0,369.0,1285.0,377.0,4.9464,223800.0,NEAR BAY
+-120.83,38.74,17.0,3685.0,775.0,1714.0,734.0,2.2269,128300.0,INLAND
+-118.47,34.1,32.0,8041.0,1141.0,2768.0,1106.0,11.1978,500001.0,<1H OCEAN
+-118.12,33.94,33.0,2394.0,576.0,1166.0,548.0,2.7317,264700.0,<1H OCEAN
+-122.2,37.79,29.0,1640.0,376.0,939.0,340.0,2.8321,150000.0,NEAR BAY
+-122.43,37.77,52.0,2714.0,779.0,1438.0,733.0,3.6031,275000.0,NEAR BAY
+-118.18,33.82,52.0,2618.0,472.0,943.0,440.0,3.7895,254000.0,NEAR OCEAN
+-117.75,34.06,44.0,477.0,135.0,502.0,117.0,2.0156,112500.0,INLAND
+-122.27,37.85,50.0,1279.0,300.0,675.0,255.0,1.9028,150800.0,NEAR BAY
+-122.14,37.42,46.0,206.0,44.0,134.0,51.0,4.15,265000.0,NEAR BAY
+-122.45,37.7,16.0,6457.0,1336.0,4375.0,1231.0,5.1788,267000.0,NEAR OCEAN
+-122.49,37.5,21.0,1209.0,309.0,801.0,259.0,4.5625,500000.0,NEAR OCEAN
+-117.11,32.75,18.0,1943.0,587.0,1329.0,522.0,1.7696,103100.0,NEAR OCEAN
+-122.09,37.41,14.0,753.0,193.0,421.0,153.0,4.2463,266700.0,NEAR BAY
+-117.4,34.18,16.0,1769.0,254.0,1778.0,251.0,5.3671,181800.0,INLAND
+-121.38,36.84,6.0,3769.0,669.0,2061.0,648.0,4.1875,217600.0,INLAND
+-118.18,33.9,34.0,1248.0,320.0,1960.0,338.0,3.1012,140400.0,<1H OCEAN
+-117.9,34.08,32.0,2068.0,356.0,976.0,370.0,5.212,201200.0,<1H OCEAN
+-117.91,33.78,33.0,2729.0,549.0,2223.0,535.0,4.0362,177900.0,<1H OCEAN
+-117.83,33.66,16.0,1574.0,385.0,515.0,363.0,5.3423,291700.0,<1H OCEAN
+-121.43,37.74,52.0,994.0,258.0,623.0,264.0,1.725,111500.0,INLAND
+-119.27,34.28,50.0,1710.0,412.0,915.0,380.0,3.1757,206300.0,NEAR OCEAN
+-121.27,38.67,16.0,3185.0,886.0,1550.0,802.0,2.5199,149000.0,INLAND
+-117.07,32.62,19.0,5016.0,1173.0,2750.0,1081.0,2.7838,155900.0,NEAR OCEAN
+-121.22,37.81,17.0,2879.0,542.0,1802.0,530.0,3.6378,126100.0,INLAND
+-118.28,33.97,38.0,1819.0,497.0,2110.0,499.0,1.6027,97300.0,<1H OCEAN
+-117.85,33.87,15.0,5078.0,806.0,2569.0,759.0,5.6549,301900.0,<1H OCEAN
+-119.23,34.15,18.0,6213.0,1188.0,2679.0,1000.0,3.748,380400.0,NEAR OCEAN
+-118.41,34.15,33.0,4032.0,868.0,1695.0,869.0,4.3468,425900.0,<1H OCEAN
+-118.27,33.99,30.0,504.0,140.0,529.0,123.0,1.9531,100000.0,<1H OCEAN
+-119.01,35.37,33.0,821.0,181.0,579.0,172.0,1.2469,46700.0,INLAND
+-118.15,33.97,33.0,1903.0,469.0,1882.0,435.0,2.4071,170500.0,<1H OCEAN
+-118.27,33.94,38.0,1314.0,318.0,1080.0,285.0,1.5872,89800.0,<1H OCEAN
+-119.69,34.43,43.0,1257.0,311.0,671.0,263.0,2.875,280600.0,<1H OCEAN
+-123.21,39.15,31.0,2685.0,675.0,1367.0,626.0,1.6571,108900.0,<1H OCEAN
+-120.81,37.53,15.0,570.0,123.0,189.0,107.0,1.875,181300.0,INLAND
+-119.09,35.41,12.0,3449.0,522.0,1754.0,551.0,5.6235,130600.0,INLAND
+-118.37,34.24,40.0,1283.0,246.0,594.0,236.0,4.1121,229200.0,<1H OCEAN
+-117.37,34.1,44.0,2087.0,447.0,1270.0,423.0,2.3889,86100.0,INLAND
+-121.37,38.69,35.0,1851.0,327.0,1007.0,286.0,3.2361,84000.0,INLAND
+-122.26,37.56,23.0,7283.0,1342.0,3399.0,1298.0,5.6683,391000.0,NEAR BAY
+-121.38,38.61,34.0,2888.0,496.0,1168.0,479.0,3.6053,148600.0,INLAND
+-118.28,34.03,40.0,2118.0,796.0,2195.0,658.0,1.7976,164600.0,<1H OCEAN
+-117.87,33.78,21.0,2487.0,573.0,1515.0,494.0,4.3039,168500.0,<1H OCEAN
+-117.98,33.65,22.0,3592.0,527.0,1598.0,523.0,6.5501,294900.0,<1H OCEAN
+-119.98,38.94,23.0,1564.0,298.0,339.0,147.0,4.0417,99300.0,INLAND
+-117.19,32.75,52.0,1495.0,230.0,459.0,190.0,8.1548,500001.0,NEAR OCEAN
+-122.23,37.84,47.0,3175.0,454.0,1098.0,485.0,5.2868,347600.0,NEAR BAY
+-121.48,38.57,38.0,1145.0,324.0,596.0,288.0,1.78,114300.0,INLAND
+-121.62,39.16,16.0,2037.0,464.0,1267.0,451.0,2.4556,97100.0,INLAND
+-121.83,36.6,30.0,2748.0,502.0,1491.0,535.0,4.3472,185000.0,<1H OCEAN
+-117.12,33.14,12.0,2363.0,408.0,1211.0,396.0,3.8967,172600.0,<1H OCEAN
+-118.42,34.22,29.0,1807.0,323.0,1234.0,310.0,5.3767,233000.0,<1H OCEAN
+-121.53,38.6,25.0,5154.0,1105.0,3196.0,1073.0,2.7566,80200.0,INLAND
+-121.3,37.94,40.0,452.0,109.0,412.0,97.0,1.3417,60800.0,INLAND
+-121.3,38.13,27.0,1004.0,192.0,470.0,192.0,2.8942,116700.0,INLAND
+-117.23,33.21,34.0,544.0,108.0,348.0,127.0,4.125,164600.0,<1H OCEAN
+-119.25,35.78,35.0,1927.0,386.0,1371.0,414.0,2.2981,69900.0,INLAND
+-122.6,38.24,16.0,1410.0,209.0,741.0,229.0,4.725,204500.0,<1H OCEAN
+-121.2,37.8,28.0,3264.0,576.0,1512.0,567.0,3.7546,135300.0,INLAND
+-117.34,33.16,24.0,1006.0,277.0,610.0,246.0,2.25,187500.0,NEAR OCEAN
+-122.98,38.44,29.0,4450.0,939.0,1328.0,590.0,3.1,162100.0,<1H OCEAN
+-124.18,40.79,40.0,1398.0,311.0,788.0,279.0,1.4668,64600.0,NEAR OCEAN
+-117.86,33.89,22.0,4386.0,593.0,1915.0,592.0,6.6897,289800.0,<1H OCEAN
+-117.4,34.15,4.0,12156.0,1864.0,5020.0,1524.0,4.7909,149200.0,INLAND
+-118.21,34.22,37.0,2260.0,322.0,941.0,303.0,8.3695,500001.0,<1H OCEAN
+-118.45,34.15,10.0,1091.0,260.0,517.0,266.0,4.1727,332600.0,<1H OCEAN
+-117.18,34.06,52.0,954.0,233.0,533.0,239.0,1.3021,100000.0,INLAND
+-119.02,34.26,40.0,1498.0,292.0,707.0,249.0,3.7974,228700.0,<1H OCEAN
+-117.87,33.84,23.0,1678.0,369.0,912.0,347.0,4.5,237300.0,<1H OCEAN
+-118.15,34.15,49.0,806.0,199.0,698.0,172.0,2.3654,137500.0,<1H OCEAN
+-118.33,34.17,48.0,2584.0,483.0,1118.0,459.0,4.2396,245100.0,<1H OCEAN
+-122.73,38.43,15.0,3265.0,690.0,1629.0,629.0,3.7132,167600.0,<1H OCEAN
+-121.02,37.71,25.0,207.0,41.0,87.0,43.0,3.6023,131300.0,INLAND
+-118.97,35.4,34.0,1859.0,323.0,854.0,309.0,3.1906,76200.0,INLAND
+-117.98,33.84,33.0,2291.0,439.0,1187.0,405.0,3.9539,191100.0,<1H OCEAN
+-118.51,34.28,29.0,4239.0,653.0,1890.0,631.0,6.3911,301700.0,<1H OCEAN
+-118.21,33.96,38.0,2090.0,519.0,1871.0,504.0,2.4688,169000.0,<1H OCEAN
+-122.08,37.87,24.0,6130.0,1359.0,1750.0,1286.0,2.9167,102700.0,NEAR BAY
+-118.09,33.96,36.0,3271.0,603.0,2593.0,616.0,3.3621,169700.0,<1H OCEAN
+-117.24,33.23,13.0,3756.0,648.0,1767.0,614.0,4.0776,196000.0,<1H OCEAN
+-118.28,34.03,25.0,1407.0,550.0,1193.0,472.0,1.2989,225000.0,<1H OCEAN
+-122.19,37.76,45.0,995.0,238.0,630.0,237.0,1.925,74100.0,NEAR BAY
+-118.41,34.03,20.0,4374.0,1311.0,2165.0,1185.0,3.6019,463600.0,<1H OCEAN
+-119.27,35.88,32.0,1393.0,343.0,1282.0,336.0,1.5069,43700.0,INLAND
+-121.66,36.37,9.0,1580.0,287.0,465.0,208.0,6.1668,405800.0,<1H OCEAN
+-122.46,37.72,48.0,1690.0,339.0,962.0,317.0,3.2875,221500.0,NEAR OCEAN
+-122.07,37.36,28.0,4612.0,608.0,1686.0,567.0,10.0346,500001.0,<1H OCEAN
+-118.06,33.8,20.0,1379.0,333.0,937.0,304.0,3.6217,195300.0,<1H OCEAN
+-118.3,34.1,37.0,5305.0,1980.0,3895.0,1874.0,2.0672,283300.0,<1H OCEAN
+-117.02,32.69,7.0,6055.0,1004.0,3031.0,952.0,4.436,135000.0,NEAR OCEAN
+-123.8,39.44,52.0,1533.0,336.0,754.0,340.0,1.9213,95000.0,<1H OCEAN
+-121.7,39.37,32.0,1852.0,373.0,911.0,365.0,1.7885,57000.0,INLAND
+-118.18,33.78,36.0,1697.0,550.0,1379.0,434.0,1.2746,129700.0,NEAR OCEAN
+-117.76,34.08,37.0,2263.0,502.0,1677.0,522.0,2.9388,139200.0,INLAND
+-122.41,37.77,52.0,361.0,76.0,168.0,55.0,3.2292,275000.0,NEAR BAY
+-119.75,36.77,32.0,1962.0,399.0,1005.0,392.0,2.6726,70400.0,INLAND
+-118.49,34.07,36.0,2929.0,366.0,1054.0,352.0,13.5728,500001.0,<1H OCEAN
+-121.72,38.0,7.0,7957.0,1314.0,4460.0,1293.0,4.9618,156500.0,INLAND
+-117.71,34.12,20.0,11250.0,1893.0,4952.0,1859.0,5.6785,239500.0,INLAND
+-120.1,36.21,12.0,1462.0,356.0,1708.0,367.0,1.5086,64700.0,INLAND
+-121.11,41.07,26.0,1707.0,308.0,761.0,250.0,2.7188,48100.0,INLAND
+-122.54,37.88,30.0,4382.0,732.0,1775.0,745.0,6.7809,414400.0,NEAR BAY
+-117.15,32.76,36.0,2644.0,674.0,1211.0,654.0,3.0445,214800.0,NEAR OCEAN
+-117.34,33.15,19.0,5710.0,1423.0,4163.0,1406.0,3.0306,178500.0,NEAR OCEAN
+-117.86,33.87,12.0,1600.0,251.0,685.0,256.0,5.1784,254000.0,<1H OCEAN
+-118.21,33.88,38.0,929.0,166.0,686.0,183.0,3.4485,119400.0,<1H OCEAN
+-119.7,36.94,15.0,1449.0,277.0,649.0,265.0,2.4861,86300.0,INLAND
+-116.74,33.62,11.0,2385.0,661.0,682.0,242.0,2.9141,214300.0,INLAND
+-118.29,34.25,8.0,5568.0,1514.0,3565.0,1374.0,3.0795,161500.0,<1H OCEAN
+-121.89,39.76,15.0,10265.0,1860.0,4591.0,1906.0,3.07,142600.0,INLAND
+-122.09,37.68,43.0,1415.0,348.0,569.0,293.0,2.5156,190900.0,NEAR BAY
+-117.05,33.1,13.0,5516.0,746.0,2119.0,662.0,6.1868,320200.0,<1H OCEAN
+-118.23,34.09,49.0,1638.0,456.0,1500.0,430.0,2.6923,150000.0,<1H OCEAN
+-118.31,34.08,23.0,1443.0,521.0,1264.0,450.0,2.7543,220000.0,<1H OCEAN
+-122.24,37.8,52.0,1803.0,420.0,1321.0,401.0,2.957,122800.0,NEAR BAY
+-121.86,37.35,46.0,1448.0,330.0,1094.0,331.0,2.4968,174100.0,<1H OCEAN
+-117.02,33.81,10.0,6317.0,1335.0,2625.0,1094.0,2.3,108900.0,INLAND
+-118.09,34.11,45.0,1883.0,275.0,764.0,289.0,6.5078,414800.0,<1H OCEAN
+-118.31,34.01,52.0,2547.0,475.0,1417.0,444.0,1.8214,123200.0,<1H OCEAN
+-118.12,33.92,27.0,6336.0,1628.0,4673.0,1505.0,2.5893,183700.0,<1H OCEAN
+-117.22,34.11,26.0,2972.0,,1972.0,532.0,2.0388,80400.0,INLAND
+-120.72,37.38,22.0,1311.0,319.0,1455.0,340.0,2.2813,67300.0,INLAND
+-121.98,37.33,35.0,1907.0,326.0,912.0,313.0,5.9567,294300.0,<1H OCEAN
+-117.03,32.79,31.0,2366.0,383.0,1077.0,387.0,4.2992,174400.0,<1H OCEAN
+-117.13,32.72,43.0,2160.0,504.0,1221.0,452.0,2.4821,140600.0,NEAR OCEAN
+-122.44,37.77,52.0,2994.0,736.0,1428.0,700.0,3.0766,438900.0,NEAR BAY
+-117.97,33.87,28.0,1784.0,440.0,1255.0,433.0,3.7054,169200.0,<1H OCEAN
+-118.42,34.26,36.0,973.0,221.0,1086.0,218.0,3.4519,143300.0,<1H OCEAN
+-119.24,35.67,32.0,3216.0,750.0,2639.0,709.0,2.0025,54700.0,INLAND
+-121.27,37.98,43.0,2608.0,516.0,1322.0,528.0,2.5714,70000.0,INLAND
+-118.28,34.04,48.0,1521.0,513.0,1772.0,458.0,2.2232,162500.0,<1H OCEAN
+-118.22,33.94,42.0,1046.0,287.0,1218.0,289.0,2.6538,143400.0,<1H OCEAN
+-121.97,37.53,26.0,2506.0,387.0,1273.0,406.0,5.4299,236400.0,<1H OCEAN
+-118.31,33.96,43.0,2118.0,569.0,1266.0,500.0,1.747,121000.0,<1H OCEAN
+-122.02,37.4,33.0,2015.0,484.0,1285.0,419.0,4.0655,226800.0,<1H OCEAN
+-122.29,37.86,52.0,1665.0,404.0,815.0,372.0,1.9946,156900.0,NEAR BAY
+-121.83,37.29,20.0,1649.0,408.0,1729.0,362.0,3.3833,115200.0,<1H OCEAN
+-122.07,37.63,27.0,2784.0,723.0,2028.0,693.0,2.4808,157600.0,NEAR BAY
+-118.46,34.0,52.0,888.0,206.0,376.0,194.0,3.875,372000.0,<1H OCEAN
+-121.44,38.63,38.0,1402.0,370.0,970.0,382.0,1.6343,71000.0,INLAND
+-121.33,38.02,33.0,2854.0,489.0,1109.0,452.0,4.3008,136400.0,INLAND
+-119.36,36.33,11.0,3221.0,617.0,1351.0,565.0,2.9844,132000.0,INLAND
+-119.77,36.81,25.0,1565.0,271.0,661.0,275.0,3.4279,84700.0,INLAND
+-121.82,37.25,24.0,3344.0,531.0,1768.0,541.0,5.8305,245600.0,<1H OCEAN
+-120.4,38.06,12.0,1430.0,310.0,517.0,240.0,2.6544,128100.0,INLAND
+-118.31,34.07,28.0,2362.0,949.0,2759.0,894.0,2.2364,305600.0,<1H OCEAN
+-122.48,37.75,48.0,2555.0,548.0,1285.0,482.0,3.7734,314700.0,NEAR BAY
+-116.89,32.85,16.0,1743.0,333.0,652.0,322.0,2.8906,158300.0,<1H OCEAN
+-116.16,33.68,12.0,1230.0,277.0,1334.0,260.0,2.2679,61400.0,INLAND
+-118.14,33.76,50.0,914.0,167.0,322.0,165.0,4.7361,418800.0,NEAR OCEAN
+-117.32,34.07,26.0,971.0,245.0,592.0,207.0,2.1125,84000.0,INLAND
+-122.49,37.67,35.0,5275.0,903.0,2892.0,842.0,4.6771,266400.0,NEAR OCEAN
+-122.06,37.39,26.0,18.0,4.0,8.0,4.0,3.75,375000.0,NEAR BAY
+-117.96,33.81,35.0,1153.0,192.0,884.0,208.0,5.2384,177400.0,<1H OCEAN
+-121.52,38.57,43.0,2360.0,471.0,1041.0,452.0,2.89,86200.0,INLAND
+-118.26,33.9,22.0,894.0,232.0,754.0,222.0,2.0096,110700.0,<1H OCEAN
+-122.41,37.75,52.0,2164.0,606.0,2034.0,513.0,2.0325,178100.0,NEAR BAY
+-121.96,37.0,20.0,3847.0,727.0,1725.0,737.0,3.3447,305200.0,NEAR OCEAN
+-118.07,34.01,38.0,2245.0,444.0,1540.0,419.0,3.7986,171000.0,<1H OCEAN
+-117.24,32.74,44.0,1686.0,285.0,712.0,298.0,4.0268,298600.0,NEAR OCEAN
+-117.93,33.67,27.0,3512.0,472.0,1391.0,481.0,8.1001,336500.0,<1H OCEAN
+-121.89,36.89,18.0,2774.0,492.0,1283.0,353.0,5.368,352000.0,NEAR OCEAN
+-117.96,33.67,16.0,5143.0,652.0,2209.0,637.0,7.0173,382100.0,<1H OCEAN
+-117.07,32.57,17.0,2961.0,634.0,1911.0,615.0,2.5859,131400.0,NEAR OCEAN
+-122.0,37.96,32.0,3364.0,666.0,1980.0,678.0,3.7,179000.0,INLAND
+-120.37,38.23,13.0,4401.0,829.0,924.0,383.0,2.6942,123500.0,INLAND
+-118.3,33.93,36.0,2196.0,633.0,2017.0,583.0,1.3962,124300.0,<1H OCEAN
+-115.39,32.76,16.0,1136.0,196.0,481.0,185.0,6.2558,146300.0,INLAND
+-118.45,34.04,22.0,3319.0,1045.0,1848.0,940.0,3.6673,283300.0,<1H OCEAN
+-122.01,37.35,16.0,3716.0,916.0,1551.0,759.0,4.5,323600.0,<1H OCEAN
+-120.72,38.38,9.0,1787.0,347.0,806.0,306.0,2.525,157200.0,INLAND
+-117.08,33.03,10.0,2296.0,450.0,818.0,405.0,4.3424,160600.0,<1H OCEAN
+-121.66,39.09,27.0,2098.0,372.0,1090.0,333.0,4.45,96200.0,INLAND
+-118.43,34.05,22.0,4251.0,1073.0,1581.0,881.0,5.2555,500001.0,<1H OCEAN
+-121.48,38.57,52.0,567.0,193.0,272.0,187.0,1.625,187500.0,INLAND
+-121.98,37.8,16.0,2498.0,330.0,1027.0,343.0,8.155,343700.0,<1H OCEAN
+-118.27,33.96,38.0,977.0,295.0,1073.0,292.0,1.0208,86400.0,<1H OCEAN
+-122.46,37.79,52.0,2059.0,416.0,999.0,402.0,3.7419,500001.0,NEAR BAY
+-122.17,37.73,46.0,2163.0,470.0,925.0,435.0,3.25,177500.0,NEAR BAY
+-122.14,37.73,43.0,2264.0,390.0,931.0,368.0,3.8125,235100.0,NEAR BAY
+-118.33,34.1,30.0,5293.0,2446.0,4123.0,2127.0,1.51,187500.0,<1H OCEAN
+-122.21,37.47,26.0,1777.0,555.0,1966.0,497.0,3.0472,211000.0,NEAR OCEAN
+-117.71,33.51,11.0,2198.0,252.0,883.0,281.0,13.1477,487000.0,<1H OCEAN
+-121.44,38.43,3.0,39320.0,6210.0,16305.0,5358.0,4.9516,153700.0,INLAND
+-122.4,37.75,26.0,54.0,9.0,23.0,9.0,6.1359,225000.0,NEAR BAY
+-118.09,33.82,36.0,2219.0,393.0,1042.0,396.0,5.2299,239800.0,<1H OCEAN
+-121.31,37.96,48.0,1112.0,227.0,583.0,216.0,2.3393,77600.0,INLAND
+-118.85,35.23,26.0,1639.0,352.0,1222.0,395.0,1.7656,68000.0,INLAND
+-118.21,33.81,40.0,1815.0,428.0,1807.0,413.0,3.0882,160700.0,NEAR OCEAN
+-118.31,34.04,29.0,2038.0,578.0,2070.0,570.0,2.0658,214600.0,<1H OCEAN
+-117.16,33.57,2.0,20391.0,3245.0,7132.0,2716.0,3.9443,187300.0,<1H OCEAN
+-118.35,34.22,19.0,9259.0,1653.0,3963.0,1595.0,5.997,228700.0,<1H OCEAN
+-122.17,37.74,46.0,769.0,183.0,693.0,178.0,2.25,84200.0,NEAR BAY
+-122.4,37.72,47.0,1465.0,306.0,1119.0,315.0,4.2672,219400.0,NEAR BAY
+-121.27,37.96,41.0,461.0,101.0,382.0,79.0,1.275,54000.0,INLAND
+-121.9,37.1,23.0,1708.0,287.0,670.0,238.0,6.4517,356600.0,<1H OCEAN
+-118.07,33.8,22.0,1391.0,338.0,810.0,295.0,3.8792,218200.0,<1H OCEAN
+-117.06,32.58,11.0,2879.0,679.0,2098.0,673.0,3.5125,142400.0,NEAR OCEAN
+-122.23,37.47,39.0,5264.0,1259.0,3057.0,1265.0,3.623,276600.0,NEAR OCEAN
+-121.36,37.99,8.0,1801.0,380.0,684.0,350.0,4.2589,134900.0,INLAND
+-121.67,37.13,19.0,3269.0,483.0,1383.0,452.0,5.6205,300800.0,<1H OCEAN
+-117.08,32.76,18.0,1704.0,596.0,1639.0,548.0,1.7391,125000.0,NEAR OCEAN
+-117.19,32.82,35.0,1074.0,180.0,442.0,173.0,5.253,204000.0,NEAR OCEAN
+-118.13,33.82,36.0,665.0,114.0,273.0,112.0,3.7321,223700.0,<1H OCEAN
+-120.89,37.59,33.0,1016.0,206.0,617.0,209.0,2.151,195800.0,INLAND
+-118.17,33.86,10.0,1664.0,508.0,1369.0,493.0,2.9886,175000.0,<1H OCEAN
+-121.95,37.54,29.0,3517.0,645.0,1724.0,585.0,4.6641,248900.0,<1H OCEAN
+-122.28,37.87,52.0,1813.0,353.0,828.0,339.0,3.5625,191700.0,NEAR BAY
+-122.13,37.7,21.0,4124.0,1054.0,2162.0,998.0,2.6321,223100.0,NEAR BAY
+-118.13,33.79,36.0,1245.0,211.0,508.0,221.0,5.3441,480600.0,NEAR OCEAN
+-118.19,34.06,37.0,1715.0,456.0,2052.0,440.0,2.3125,116100.0,<1H OCEAN
+-117.9,33.8,22.0,2964.0,829.0,2639.0,771.0,2.4833,157500.0,<1H OCEAN
+-122.06,37.64,37.0,1468.0,304.0,1038.0,282.0,4.1652,158200.0,NEAR BAY
+-118.48,34.15,31.0,2536.0,429.0,990.0,424.0,5.4591,495500.0,<1H OCEAN
+-117.71,33.49,5.0,1680.0,254.0,617.0,231.0,8.583,397700.0,<1H OCEAN
+-117.84,33.98,26.0,3638.0,557.0,1993.0,593.0,6.1076,221200.0,<1H OCEAN
+-121.81,39.71,18.0,1222.0,250.0,708.0,281.0,2.0288,116700.0,INLAND
+-121.14,38.82,22.0,1816.0,278.0,832.0,278.0,5.07,233000.0,INLAND
+-122.26,38.11,52.0,1560.0,353.0,670.0,287.0,1.7411,98400.0,NEAR BAY
+-122.08,37.88,26.0,2947.0,,825.0,626.0,2.933,85000.0,NEAR BAY
+-122.09,37.35,30.0,1502.0,186.0,501.0,180.0,10.0259,500001.0,NEAR BAY
+-121.43,38.64,34.0,2010.0,411.0,1501.0,422.0,2.0417,65900.0,INLAND
+-121.53,38.57,34.0,3395.0,592.0,1518.0,627.0,4.0833,118500.0,INLAND
+-119.19,34.2,36.0,1293.0,312.0,1128.0,335.0,2.1542,253900.0,NEAR OCEAN
+-118.19,34.21,41.0,1602.0,228.0,680.0,225.0,6.553,500001.0,<1H OCEAN
+-119.77,36.74,20.0,1855.0,519.0,1091.0,443.0,1.5547,93900.0,INLAND
+-119.79,36.73,52.0,112.0,28.0,193.0,40.0,1.975,47500.0,INLAND
+-118.19,34.13,50.0,1309.0,302.0,883.0,293.0,3.1287,198000.0,<1H OCEAN
+-122.43,37.77,52.0,2685.0,629.0,1170.0,614.0,3.6894,418800.0,NEAR BAY
+-121.47,38.58,52.0,2035.0,483.0,904.0,459.0,2.6976,109300.0,INLAND
+-122.06,37.33,23.0,4507.0,751.0,2167.0,722.0,7.0102,500001.0,<1H OCEAN
+-118.24,34.16,40.0,2549.0,591.0,1156.0,546.0,3.3333,374300.0,<1H OCEAN
+-118.28,33.97,34.0,2771.0,802.0,2782.0,715.0,1.6652,99000.0,<1H OCEAN
+-121.88,37.37,3.0,4430.0,841.0,2559.0,801.0,6.0959,272700.0,<1H OCEAN
+-118.43,34.03,36.0,1552.0,388.0,867.0,352.0,3.6467,346700.0,<1H OCEAN
+-117.08,32.77,25.0,3911.0,849.0,1580.0,767.0,2.7778,184100.0,NEAR OCEAN
+-122.02,37.38,32.0,1889.0,487.0,1321.0,508.0,3.2574,254400.0,<1H OCEAN
+-122.55,37.79,32.0,2131.0,625.0,1229.0,572.0,2.9201,322200.0,NEAR OCEAN
+-122.39,37.58,36.0,6026.0,852.0,2314.0,892.0,7.8997,500001.0,NEAR OCEAN
+-118.19,33.95,42.0,1651.0,463.0,1559.0,436.0,2.3882,148100.0,<1H OCEAN
+-118.13,33.96,36.0,1933.0,341.0,958.0,335.0,4.4732,266000.0,<1H OCEAN
+-124.17,40.8,52.0,1557.0,344.0,758.0,319.0,1.8529,62500.0,NEAR OCEAN
+-119.24,35.77,28.0,1737.0,521.0,1764.0,514.0,1.7813,67800.0,INLAND
+-116.93,32.83,21.0,1283.0,278.0,684.0,289.0,2.3203,163500.0,<1H OCEAN
+-117.65,34.11,29.0,2927.0,634.0,1710.0,623.0,3.6219,176600.0,INLAND
+-122.0,37.98,32.0,1013.0,169.0,436.0,173.0,5.1118,226900.0,INLAND
+-118.32,33.99,48.0,1260.0,284.0,791.0,280.0,2.1875,115200.0,<1H OCEAN
+-121.41,36.84,23.0,1771.0,356.0,1105.0,338.0,3.7049,192200.0,INLAND
+-118.03,33.96,37.0,1180.0,256.0,614.0,242.0,3.117,164600.0,<1H OCEAN
+-118.24,33.97,41.0,1182.0,346.0,1644.0,346.0,2.1473,115100.0,<1H OCEAN
+-118.31,33.94,40.0,1917.0,438.0,1021.0,383.0,2.2448,175000.0,<1H OCEAN
+-117.69,33.51,4.0,1223.0,275.0,505.0,244.0,4.6607,173000.0,<1H OCEAN
+-118.18,34.2,44.0,1473.0,250.0,668.0,239.0,8.72,415900.0,<1H OCEAN
+-117.34,33.21,23.0,2062.0,376.0,1302.0,379.0,4.0109,145700.0,NEAR OCEAN
+-118.27,34.01,35.0,1193.0,355.0,1784.0,341.0,1.8652,116100.0,<1H OCEAN
+-118.31,33.93,35.0,1580.0,266.0,926.0,282.0,5.0653,158000.0,<1H OCEAN
+-117.25,33.39,22.0,2699.0,543.0,1425.0,491.0,2.375,137300.0,<1H OCEAN
+-118.28,33.79,36.0,1989.0,458.0,1668.0,455.0,3.3009,168000.0,<1H OCEAN
+-118.12,34.11,48.0,2124.0,319.0,785.0,319.0,5.2131,359600.0,<1H OCEAN
+-118.19,34.14,38.0,1826.0,300.0,793.0,297.0,5.2962,291500.0,<1H OCEAN
+-117.29,35.54,35.0,7922.0,1636.0,3431.0,1329.0,3.4145,40400.0,INLAND
+-120.84,35.37,34.0,3279.0,714.0,1397.0,646.0,2.5552,200000.0,NEAR OCEAN
+-117.31,34.15,34.0,2037.0,385.0,1195.0,391.0,3.9231,96000.0,INLAND
+-118.38,34.11,38.0,2601.0,523.0,870.0,474.0,7.1134,416700.0,<1H OCEAN
+-122.0,37.95,9.0,2214.0,256.0,848.0,239.0,6.8145,339200.0,INLAND
+-122.14,37.45,48.0,2074.0,297.0,700.0,279.0,8.7051,500001.0,NEAR BAY
+-120.74,37.33,30.0,2390.0,470.0,1409.0,428.0,2.1484,81300.0,INLAND
+-118.02,33.77,33.0,2683.0,436.0,1520.0,456.0,5.0091,211500.0,<1H OCEAN
+-122.48,37.78,52.0,3047.0,641.0,1427.0,620.0,3.4883,337200.0,NEAR BAY
+-122.11,37.4,31.0,2836.0,490.0,1138.0,481.0,4.9519,500001.0,NEAR BAY
+-117.74,34.09,30.0,3199.0,591.0,2192.0,563.0,3.4871,136400.0,INLAND
+-118.56,34.19,34.0,2185.0,372.0,986.0,347.0,4.8125,266700.0,<1H OCEAN
+-118.52,34.46,5.0,15341.0,2527.0,7270.0,2320.0,6.1281,236200.0,<1H OCEAN
+-117.25,33.3,14.0,2513.0,351.0,1151.0,357.0,6.3054,359000.0,<1H OCEAN
+-118.35,34.02,34.0,3978.0,1073.0,2725.0,1035.0,1.7622,167900.0,<1H OCEAN
+-118.24,33.98,37.0,1196.0,364.0,1622.0,327.0,2.125,108900.0,<1H OCEAN
+-118.1,34.06,36.0,1463.0,369.0,1492.0,366.0,3.25,179200.0,<1H OCEAN
+-120.3,38.9,11.0,1961.0,435.0,113.0,53.0,0.9227,95500.0,INLAND
+-122.25,37.82,52.0,2474.0,403.0,1104.0,398.0,5.883,340700.0,NEAR BAY
+-116.56,34.06,15.0,6928.0,1529.0,2568.0,1075.0,2.5405,69600.0,INLAND
+-118.38,33.81,39.0,2400.0,373.0,877.0,372.0,5.7361,500001.0,NEAR OCEAN
+-121.05,39.23,20.0,1634.0,374.0,1053.0,390.0,1.5313,154900.0,INLAND
+-122.01,37.56,24.0,2563.0,485.0,1174.0,501.0,3.8179,216100.0,NEAR BAY
+-117.34,34.11,29.0,2912.0,566.0,2188.0,518.0,3.2656,90600.0,INLAND
+-118.44,34.2,28.0,1732.0,435.0,1198.0,417.0,2.9219,241300.0,<1H OCEAN
+-118.42,33.97,44.0,1462.0,240.0,562.0,237.0,4.9375,365200.0,<1H OCEAN
+-124.15,40.8,47.0,1486.0,335.0,765.0,329.0,1.755,74100.0,NEAR OCEAN
+-118.42,34.02,34.0,2243.0,444.0,973.0,413.0,4.9676,414100.0,<1H OCEAN
+-117.27,34.5,7.0,2045.0,342.0,878.0,292.0,6.0296,194100.0,INLAND
+-122.29,38.3,52.0,1219.0,288.0,847.0,283.0,1.6691,183300.0,NEAR BAY
+-118.42,34.02,22.0,3292.0,1134.0,1655.0,898.0,3.1746,348800.0,<1H OCEAN
+-122.69,38.34,12.0,3876.0,782.0,2146.0,764.0,4.0844,165400.0,<1H OCEAN
+-122.16,37.74,47.0,824.0,223.0,533.0,166.0,2.625,98200.0,NEAR BAY
+-121.37,38.68,29.0,3757.0,646.0,2022.0,611.0,3.5429,88200.0,INLAND
+-117.24,32.79,20.0,961.0,278.0,525.0,254.0,3.1838,245800.0,NEAR OCEAN
+-118.41,33.89,34.0,2959.0,639.0,1143.0,593.0,6.348,500001.0,<1H OCEAN
+-118.23,33.96,39.0,405.0,163.0,686.0,164.0,1.695,94800.0,<1H OCEAN
+-122.14,37.69,37.0,2141.0,535.0,1093.0,555.0,2.9958,178400.0,NEAR BAY
+-117.66,33.62,16.0,5175.0,799.0,2717.0,813.0,6.1493,257800.0,<1H OCEAN
+-118.35,34.28,30.0,3214.0,513.0,1700.0,533.0,4.6944,248200.0,<1H OCEAN
+-122.06,37.38,21.0,1798.0,399.0,837.0,410.0,5.6999,470000.0,NEAR BAY
+-118.36,33.98,29.0,2861.0,816.0,1715.0,775.0,2.7712,160900.0,<1H OCEAN
+-122.54,37.93,43.0,2998.0,470.0,970.0,430.0,5.5385,431800.0,NEAR BAY
+-118.31,33.96,46.0,1686.0,303.0,870.0,320.0,3.4643,136300.0,<1H OCEAN
+-118.27,34.16,45.0,1865.0,360.0,973.0,349.0,3.6587,321200.0,<1H OCEAN
+-121.02,37.66,36.0,3495.0,641.0,1688.0,684.0,3.1568,109900.0,INLAND
+-117.03,33.0,6.0,6139.0,793.0,2693.0,770.0,7.7569,387400.0,<1H OCEAN
+-122.6,38.48,17.0,1528.0,264.0,606.0,251.0,6.6004,341500.0,<1H OCEAN
+-118.2,34.07,21.0,1353.0,380.0,1688.0,367.0,1.9937,139600.0,<1H OCEAN
+-122.51,37.76,43.0,2527.0,619.0,1332.0,558.0,3.0465,274200.0,NEAR OCEAN
+-119.85,37.1,8.0,828.0,168.0,413.0,146.0,3.375,80700.0,INLAND
+-122.09,37.38,36.0,2587.0,416.0,1055.0,410.0,6.1995,407200.0,NEAR BAY
+-118.38,33.81,33.0,2349.0,407.0,954.0,373.0,6.4956,483600.0,NEAR OCEAN
+-119.43,36.63,25.0,1784.0,312.0,904.0,303.0,3.625,107600.0,INLAND
+-119.5,35.27,23.0,3827.0,696.0,1993.0,617.0,3.0742,57900.0,INLAND
+-122.44,37.72,52.0,1775.0,347.0,1102.0,367.0,4.3125,267200.0,NEAR BAY
+-117.83,33.88,23.0,2365.0,355.0,1209.0,347.0,6.0132,259400.0,<1H OCEAN
+-117.09,32.76,31.0,1235.0,387.0,816.0,397.0,1.5517,122500.0,NEAR OCEAN
+-118.47,34.26,34.0,1300.0,289.0,650.0,291.0,3.8875,199200.0,<1H OCEAN
+-121.96,37.95,25.0,4026.0,791.0,1850.0,709.0,4.1483,181200.0,INLAND
+-121.93,36.63,41.0,1049.0,198.0,428.0,183.0,4.3571,287500.0,NEAR OCEAN
+-118.46,34.3,32.0,2424.0,476.0,2291.0,419.0,4.0337,158500.0,<1H OCEAN
+-122.18,37.44,44.0,2237.0,347.0,948.0,346.0,8.2436,500001.0,NEAR OCEAN
+-120.41,34.88,4.0,3680.0,559.0,1678.0,569.0,5.0639,201700.0,<1H OCEAN
+-122.35,37.96,29.0,1899.0,524.0,1357.0,443.0,1.875,97200.0,NEAR BAY
+-122.63,38.26,7.0,7808.0,1390.0,3551.0,1392.0,4.6069,202300.0,<1H OCEAN
+-118.29,34.07,22.0,492.0,269.0,634.0,261.0,1.6406,300000.0,<1H OCEAN
+-121.25,38.72,10.0,7277.0,1168.0,3507.0,1131.0,4.485,179400.0,INLAND
+-122.43,37.77,52.0,1862.0,472.0,872.0,471.0,3.2981,222700.0,NEAR BAY
+-117.93,33.84,34.0,2160.0,298.0,852.0,305.0,6.0531,287100.0,<1H OCEAN
+-117.92,33.67,14.0,6224.0,1679.0,3148.0,1589.0,4.2071,430900.0,<1H OCEAN
+-118.29,33.84,11.0,2274.0,617.0,1897.0,622.0,3.5094,162900.0,<1H OCEAN
+-118.29,33.9,42.0,1273.0,309.0,1203.0,348.0,4.4636,162000.0,<1H OCEAN
+-117.85,33.81,32.0,1766.0,322.0,876.0,330.0,4.0417,234500.0,<1H OCEAN
+-118.1,33.85,36.0,1473.0,253.0,713.0,257.0,5.9493,228000.0,<1H OCEAN
+-118.44,34.16,35.0,3080.0,642.0,1362.0,623.0,4.1218,328500.0,<1H OCEAN
+-123.23,39.77,25.0,2075.0,435.0,991.0,377.0,1.2281,60300.0,INLAND
+-122.43,37.73,52.0,3602.0,738.0,2270.0,647.0,3.8934,251800.0,NEAR BAY
+-117.72,34.09,36.0,1473.0,328.0,785.0,299.0,3.2566,151800.0,INLAND
+-119.87,36.54,34.0,1370.0,287.0,818.0,269.0,2.4044,72500.0,INLAND
+-118.22,33.79,28.0,3008.0,629.0,2537.0,596.0,2.3,137500.0,NEAR OCEAN
+-122.09,37.36,37.0,2269.0,325.0,930.0,321.0,7.5274,500001.0,NEAR BAY
+-122.21,37.79,45.0,2115.0,533.0,1530.0,474.0,2.4167,139400.0,NEAR BAY
+-122.16,37.46,45.0,2068.0,348.0,844.0,366.0,6.227,417800.0,NEAR BAY
+-118.27,34.18,52.0,3034.0,406.0,1158.0,399.0,6.2976,498400.0,<1H OCEAN
+-117.82,34.05,21.0,4031.0,923.0,2558.0,834.0,3.1641,117300.0,<1H OCEAN
+-117.89,33.77,29.0,2577.0,445.0,1849.0,470.0,4.4732,194800.0,<1H OCEAN
+-122.01,37.98,34.0,1256.0,267.0,638.0,252.0,4.0507,161000.0,NEAR BAY
+-118.1,34.05,26.0,1495.0,328.0,1296.0,304.0,2.913,152300.0,<1H OCEAN
+-118.2,33.88,40.0,1699.0,346.0,1188.0,329.0,4.2083,147300.0,<1H OCEAN
+-121.25,38.67,14.0,6155.0,1034.0,2407.0,941.0,4.2262,244300.0,INLAND
+-122.02,37.4,35.0,2348.0,531.0,1475.0,498.0,4.35,261000.0,<1H OCEAN
+-122.17,37.76,47.0,2118.0,413.0,965.0,382.0,2.1842,107900.0,NEAR BAY
+-119.29,34.26,32.0,3295.0,764.0,1344.0,600.0,3.6007,395500.0,NEAR OCEAN
+-118.14,33.91,37.0,932.0,171.0,578.0,175.0,4.375,177600.0,<1H OCEAN
+-117.91,33.86,26.0,2296.0,570.0,1415.0,527.0,2.4732,165800.0,<1H OCEAN
+-117.76,34.05,36.0,3839.0,1004.0,4711.0,942.0,2.3859,116200.0,INLAND
+-118.36,33.9,40.0,1271.0,276.0,725.0,234.0,5.0452,231900.0,<1H OCEAN
+-119.33,36.28,16.0,2624.0,527.0,1077.0,520.0,2.125,104200.0,INLAND
+-122.93,41.48,20.0,4288.0,789.0,1800.0,660.0,2.723,79600.0,INLAND
+-118.02,33.93,33.0,4711.0,988.0,2984.0,931.0,3.6028,184700.0,<1H OCEAN
+-118.13,33.76,44.0,2532.0,621.0,961.0,550.0,3.9352,406900.0,NEAR OCEAN
+-117.7,33.55,12.0,2459.0,390.0,1054.0,391.0,7.1736,262100.0,<1H OCEAN
+-116.37,33.19,12.0,4890.0,1152.0,1289.0,570.0,2.5795,98700.0,INLAND
+-122.15,38.06,10.0,3008.0,532.0,1381.0,522.0,5.3661,195800.0,NEAR BAY
+-118.85,34.25,17.0,5593.0,732.0,1992.0,660.0,7.2965,342900.0,<1H OCEAN
+-118.17,34.12,30.0,3376.0,720.0,1990.0,725.0,3.7813,232000.0,<1H OCEAN
+-122.15,37.72,31.0,1616.0,372.0,739.0,379.0,2.9097,210900.0,NEAR BAY
+-117.88,33.82,17.0,2247.0,705.0,1382.0,618.0,3.8631,225000.0,<1H OCEAN
+-118.15,33.96,33.0,2418.0,485.0,1397.0,477.0,3.1083,285500.0,<1H OCEAN
+-117.24,32.81,34.0,2420.0,391.0,917.0,392.0,6.5881,394400.0,NEAR OCEAN
+-122.44,37.78,52.0,3017.0,851.0,1588.0,800.0,3.3882,471400.0,NEAR BAY
+-121.79,39.73,8.0,5690.0,1189.0,2887.0,1077.0,3.0625,116300.0,INLAND
+-119.79,36.75,52.0,377.0,97.0,530.0,96.0,1.0,45000.0,INLAND
+-117.17,32.71,39.0,311.0,181.0,206.0,113.0,0.7685,187500.0,NEAR OCEAN
+-124.07,40.81,23.0,2103.0,411.0,1019.0,387.0,2.9911,119700.0,NEAR OCEAN
+-121.04,38.95,22.0,1931.0,445.0,1009.0,407.0,2.75,153200.0,INLAND
+-118.21,34.07,31.0,1077.0,300.0,1198.0,274.0,2.1333,160200.0,<1H OCEAN
+-116.96,32.85,11.0,9724.0,1796.0,5247.0,1777.0,4.1716,166100.0,<1H OCEAN
+-121.88,36.98,21.0,4117.0,752.0,2001.0,763.0,4.8953,289500.0,NEAR OCEAN
+-117.1,33.56,6.0,1868.0,289.0,750.0,247.0,4.3833,307600.0,<1H OCEAN
+-117.89,34.07,35.0,834.0,137.0,392.0,123.0,4.5179,218800.0,<1H OCEAN
+-122.1,37.66,34.0,656.0,115.0,342.0,112.0,4.6875,200600.0,NEAR BAY
+-119.4,36.59,37.0,1486.0,296.0,977.0,290.0,3.5074,93800.0,INLAND
+-120.69,37.59,27.0,1170.0,227.0,660.0,222.0,2.3906,81800.0,INLAND
+-121.98,37.33,30.0,2645.0,462.0,1506.0,480.0,6.3716,330500.0,<1H OCEAN
+-122.05,37.95,22.0,5175.0,1213.0,2804.0,1091.0,2.85,144600.0,NEAR BAY
+-118.26,34.16,19.0,2919.0,857.0,1866.0,811.0,3.1733,206300.0,<1H OCEAN
+-117.97,33.8,35.0,2985.0,474.0,1614.0,453.0,5.4631,225600.0,<1H OCEAN
+-118.31,33.93,42.0,1173.0,201.0,602.0,186.0,5.5787,142000.0,<1H OCEAN
+-122.67,38.43,17.0,1804.0,304.0,750.0,298.0,4.5588,196400.0,<1H OCEAN
+-118.38,34.16,31.0,2197.0,501.0,944.0,474.0,3.7312,319400.0,<1H OCEAN
+-118.21,33.89,45.0,1211.0,234.0,1128.0,261.0,3.4792,110700.0,<1H OCEAN
+-118.58,34.23,29.0,3907.0,773.0,2037.0,727.0,4.1023,230200.0,<1H OCEAN
+-117.94,34.09,21.0,2707.0,675.0,1742.0,626.0,2.1062,176700.0,<1H OCEAN
+-122.44,37.73,46.0,3581.0,758.0,1670.0,703.0,4.1932,269200.0,NEAR BAY
+-119.67,34.47,35.0,2700.0,422.0,1995.0,383.0,4.9757,500001.0,<1H OCEAN
+-122.62,38.25,33.0,1453.0,250.0,677.0,237.0,4.0962,170200.0,<1H OCEAN
+-118.58,34.19,27.0,4286.0,1071.0,2863.0,1033.0,3.3125,222800.0,<1H OCEAN
+-118.16,33.77,30.0,2800.0,757.0,1292.0,742.0,2.7614,272200.0,NEAR OCEAN
+-122.04,37.34,20.0,4475.0,1048.0,2271.0,1021.0,4.8836,396200.0,<1H OCEAN
+-118.32,33.95,44.0,2023.0,325.0,992.0,326.0,4.6667,175600.0,<1H OCEAN
+-118.39,34.04,52.0,1492.0,277.0,666.0,289.0,4.7386,340400.0,<1H OCEAN
+-122.23,40.57,18.0,1633.0,243.0,750.0,252.0,5.1585,150800.0,INLAND
+-119.89,36.64,34.0,1422.0,237.0,716.0,222.0,2.975,90000.0,INLAND
+-117.12,32.96,15.0,2869.0,405.0,1526.0,402.0,6.0175,238300.0,<1H OCEAN
+-121.79,38.01,17.0,4032.0,814.0,1749.0,618.0,3.1728,146800.0,INLAND
+-117.9,34.11,23.0,4776.0,1316.0,4797.0,1187.0,2.1667,142600.0,<1H OCEAN
+-119.2,34.28,22.0,2362.0,601.0,1127.0,499.0,3.4006,219400.0,NEAR OCEAN
+-117.96,33.94,31.0,2397.0,518.0,1407.0,476.0,2.6641,185200.0,<1H OCEAN
+-120.55,38.46,16.0,1443.0,249.0,435.0,181.0,3.2031,129200.0,INLAND
+-118.45,37.25,20.0,1468.0,283.0,721.0,270.0,3.0817,118800.0,INLAND
+-117.86,34.24,52.0,803.0,267.0,628.0,225.0,4.1932,14999.0,INLAND
+-117.04,32.72,24.0,5474.0,955.0,3020.0,904.0,4.0813,137000.0,NEAR OCEAN
+-118.17,33.98,36.0,627.0,177.0,834.0,175.0,2.9844,163600.0,<1H OCEAN
+-122.41,37.78,52.0,1928.0,836.0,2124.0,739.0,1.1185,55000.0,NEAR BAY
+-119.35,36.21,26.0,2481.0,586.0,1445.0,498.0,1.6378,60300.0,INLAND
+-118.18,33.79,27.0,1580.0,510.0,1896.0,448.0,2.0186,130000.0,NEAR OCEAN
+-124.01,40.89,28.0,1470.0,336.0,811.0,314.0,2.4559,75600.0,<1H OCEAN
+-122.39,40.6,22.0,2195.0,489.0,1021.0,460.0,1.4125,66500.0,INLAND
+-118.26,34.16,20.0,3407.0,885.0,1883.0,870.0,3.7321,351100.0,<1H OCEAN
+-117.22,32.8,23.0,1906.0,525.0,1029.0,491.0,2.93,183300.0,NEAR OCEAN
+-121.75,36.91,32.0,1461.0,422.0,1494.0,416.0,2.8056,173200.0,<1H OCEAN
+-117.14,32.8,35.0,1267.0,212.0,710.0,204.0,2.5368,169600.0,NEAR OCEAN
+-121.27,37.96,52.0,583.0,114.0,310.0,93.0,2.5625,54200.0,INLAND
+-117.83,33.77,22.0,2956.0,642.0,1342.0,558.0,4.1151,203200.0,<1H OCEAN
+-117.96,33.78,33.0,1520.0,,658.0,242.0,4.875,269300.0,<1H OCEAN
+-117.14,32.7,48.0,510.0,180.0,545.0,132.0,1.8008,86500.0,NEAR OCEAN
+-118.6,34.16,32.0,3999.0,667.0,1628.0,631.0,6.0794,338500.0,<1H OCEAN
+-118.19,33.78,21.0,2741.0,1029.0,2924.0,969.0,1.3274,218800.0,NEAR OCEAN
+-118.41,33.89,31.0,1428.0,320.0,677.0,331.0,7.2316,500001.0,<1H OCEAN
+-118.15,33.93,42.0,1839.0,346.0,1034.0,339.0,4.9808,212300.0,<1H OCEAN
+-117.25,32.73,37.0,2224.0,331.0,821.0,341.0,6.3331,400000.0,NEAR OCEAN
+-117.26,32.81,30.0,1328.0,346.0,577.0,328.0,2.3284,290600.0,NEAR OCEAN
+-117.9,33.65,30.0,2196.0,486.0,1131.0,460.0,4.4135,272300.0,<1H OCEAN
+-122.14,37.73,52.0,2024.0,320.0,823.0,334.0,5.0,264700.0,NEAR BAY
+-122.64,38.01,36.0,1336.0,258.0,678.0,249.0,5.5789,292000.0,NEAR OCEAN
+-122.33,37.92,26.0,3887.0,779.0,2512.0,740.0,2.2301,122400.0,NEAR BAY
+-118.05,34.02,31.0,40.0,8.0,25.0,7.0,2.125,375000.0,<1H OCEAN
+-123.04,38.49,30.0,3977.0,930.0,1387.0,582.0,2.6161,132500.0,NEAR OCEAN
+-118.27,34.12,50.0,2037.0,440.0,1089.0,417.0,3.575,230600.0,<1H OCEAN
+-118.43,34.06,31.0,1317.0,284.0,523.0,274.0,7.4219,500001.0,<1H OCEAN
+-118.34,33.99,46.0,1217.0,322.0,662.0,305.0,3.1731,140300.0,<1H OCEAN
+-119.14,34.29,17.0,2754.0,577.0,1349.0,533.0,3.1618,154200.0,<1H OCEAN
+-122.01,37.59,2.0,838.0,295.0,240.0,149.0,2.875,237500.0,NEAR BAY
+-117.87,33.76,6.0,2992.0,1194.0,3800.0,1130.0,2.246,183300.0,<1H OCEAN
+-118.01,33.92,35.0,1606.0,289.0,829.0,273.0,5.273,187600.0,<1H OCEAN
+-117.96,33.79,29.0,1813.0,501.0,1170.0,482.0,2.0677,214500.0,<1H OCEAN
+-117.9,33.63,26.0,1632.0,376.0,598.0,375.0,3.2125,455000.0,<1H OCEAN
+-122.34,37.58,50.0,2784.0,743.0,1622.0,698.0,3.8413,372200.0,NEAR OCEAN
+-117.21,33.61,7.0,7722.0,1324.0,2975.0,1161.0,3.6273,150900.0,<1H OCEAN
+-121.29,37.97,52.0,1610.0,480.0,1025.0,440.0,1.2962,110200.0,INLAND
+-118.57,34.22,27.0,2795.0,606.0,1702.0,586.0,3.7798,258400.0,<1H OCEAN
+-120.84,37.92,27.0,471.0,84.0,195.0,72.0,3.3333,208300.0,INLAND
+-118.31,34.06,30.0,3110.0,1269.0,2535.0,1218.0,1.6987,412500.0,<1H OCEAN
+-117.81,33.69,5.0,1256.0,256.0,880.0,288.0,2.4233,450000.0,<1H OCEAN
+-118.02,34.17,32.0,3868.0,548.0,1558.0,528.0,9.4667,500001.0,INLAND
+-122.49,37.78,46.0,3304.0,792.0,1783.0,777.0,3.6148,352200.0,NEAR BAY
+-117.05,32.96,18.0,3593.0,661.0,1992.0,626.0,4.8295,165800.0,<1H OCEAN
+-123.22,39.16,32.0,1149.0,187.0,499.0,208.0,3.6587,154600.0,<1H OCEAN
+-117.77,33.71,15.0,2102.0,295.0,1060.0,303.0,7.3141,337100.0,<1H OCEAN
+-122.56,38.41,20.0,1151.0,211.0,478.0,183.0,5.93,384600.0,<1H OCEAN
+-118.36,33.86,37.0,1768.0,314.0,802.0,290.0,5.0784,295900.0,<1H OCEAN
+-117.71,34.1,41.0,555.0,130.0,1492.0,123.0,2.2813,125000.0,INLAND
+-117.62,34.44,6.0,8884.0,1687.0,3767.0,1334.0,3.599,140200.0,INLAND
+-117.35,33.2,23.0,3297.0,728.0,1793.0,622.0,2.5754,169700.0,NEAR OCEAN
+-121.91,37.3,31.0,2095.0,427.0,829.0,405.0,3.6563,344700.0,<1H OCEAN
+-120.07,36.96,42.0,963.0,216.0,471.0,211.0,2.2898,66100.0,INLAND
+-120.51,41.35,16.0,2843.0,564.0,892.0,386.0,2.5074,69100.0,INLAND
+-122.32,37.55,46.0,1437.0,266.0,607.0,263.0,4.8068,369700.0,NEAR OCEAN
+-122.26,37.9,37.0,2220.0,335.0,903.0,362.0,7.8336,371300.0,NEAR BAY
+-119.68,36.77,21.0,1260.0,182.0,583.0,205.0,6.0132,150800.0,INLAND
+-118.19,33.97,34.0,2700.0,763.0,2815.0,767.0,2.4196,178400.0,<1H OCEAN
+-117.96,33.71,19.0,1624.0,221.0,782.0,228.0,4.5962,304500.0,<1H OCEAN
+-117.1,33.0,5.0,15502.0,2613.0,7417.0,2358.0,5.9094,261100.0,<1H OCEAN
+-118.19,34.03,31.0,525.0,136.0,627.0,145.0,2.6964,125000.0,<1H OCEAN
+-120.22,39.2,22.0,8259.0,1409.0,845.0,353.0,3.3699,244000.0,INLAND
+-118.42,34.16,28.0,4664.0,1040.0,1963.0,961.0,3.9028,367900.0,<1H OCEAN
+-122.1,40.03,25.0,2516.0,,1266.0,494.0,1.7566,58400.0,INLAND
+-122.87,38.62,18.0,2721.0,557.0,1667.0,539.0,3.1875,176100.0,<1H OCEAN
+-119.85,36.83,15.0,2563.0,335.0,1080.0,356.0,6.7181,160300.0,INLAND
+-119.79,36.79,26.0,1700.0,423.0,909.0,386.0,2.256,64500.0,INLAND
+-121.3,37.97,52.0,2259.0,417.0,766.0,385.0,2.2981,105400.0,INLAND
+-118.28,34.04,24.0,1283.0,545.0,1932.0,516.0,1.2969,160200.0,<1H OCEAN
+-118.33,33.98,30.0,3112.0,931.0,2739.0,841.0,1.6531,118500.0,<1H OCEAN
+-117.26,34.13,39.0,3521.0,747.0,2256.0,721.0,2.1375,87500.0,INLAND
+-117.26,33.19,2.0,2629.0,509.0,1044.0,522.0,4.2361,158500.0,NEAR OCEAN
+-117.95,33.89,17.0,1665.0,247.0,755.0,254.0,6.5764,349000.0,<1H OCEAN
+-117.1,32.83,16.0,1049.0,154.0,467.0,160.0,6.2047,248100.0,<1H OCEAN
+-119.12,35.33,4.0,8574.0,1489.0,4250.0,1444.0,5.1036,103400.0,INLAND
+-118.23,34.0,35.0,167.0,60.0,267.0,55.0,1.5227,350000.0,<1H OCEAN
+-118.2,34.03,52.0,1754.0,452.0,1849.0,445.0,2.3716,122800.0,<1H OCEAN
+-119.97,38.93,24.0,856.0,185.0,388.0,108.0,3.1806,107200.0,INLAND
+-118.3,34.09,36.0,2332.0,993.0,3155.0,927.0,2.2612,230400.0,<1H OCEAN
+-121.96,37.34,42.0,2001.0,402.0,942.0,375.0,4.4453,255400.0,<1H OCEAN
+-122.66,38.47,20.0,2806.0,477.0,1369.0,460.0,4.75,190500.0,<1H OCEAN
+-121.8,38.01,44.0,3184.0,581.0,1399.0,548.0,2.7234,110200.0,INLAND
+-121.92,37.48,23.0,4314.0,676.0,1972.0,623.0,5.3813,264400.0,<1H OCEAN
+-117.16,33.73,10.0,2381.0,454.0,1323.0,477.0,2.6322,140700.0,INLAND
+-117.69,33.64,18.0,3783.0,654.0,1843.0,623.0,5.7559,215800.0,<1H OCEAN
+-118.1,34.02,33.0,1143.0,172.0,508.0,174.0,4.9107,279900.0,<1H OCEAN
+-122.35,37.93,39.0,2002.0,416.0,1166.0,395.0,1.7257,91500.0,NEAR BAY
+-118.27,33.91,32.0,2238.0,471.0,1292.0,467.0,1.1705,110600.0,<1H OCEAN
+-117.98,33.82,34.0,1038.0,175.0,578.0,174.0,4.9219,200000.0,<1H OCEAN
+-121.31,38.66,27.0,1713.0,282.0,761.0,295.0,5.2081,136400.0,INLAND
+-122.19,37.47,44.0,1371.0,263.0,589.0,301.0,4.8068,312300.0,NEAR BAY
+-121.66,36.7,33.0,3252.0,630.0,2010.0,641.0,3.4222,158100.0,<1H OCEAN
+-119.16,34.17,17.0,7982.0,1603.0,6437.0,1596.0,4.1279,223900.0,NEAR OCEAN
+-124.07,40.87,31.0,334.0,134.0,780.0,130.0,0.7684,153100.0,NEAR OCEAN
+-122.16,37.76,46.0,1827.0,307.0,881.0,302.0,4.6696,164300.0,NEAR BAY
+-118.24,34.16,52.0,850.0,162.0,493.0,160.0,6.9408,298800.0,<1H OCEAN
+-122.05,37.9,24.0,4125.0,1020.0,1699.0,873.0,2.9526,271000.0,NEAR BAY
+-121.3,37.85,35.0,1034.0,206.0,604.0,192.0,2.2391,120000.0,INLAND
+-121.01,37.74,14.0,2368.0,297.0,796.0,301.0,8.7783,435000.0,INLAND
+-114.66,32.74,17.0,1388.0,386.0,775.0,320.0,1.2049,44000.0,INLAND
+-117.68,34.08,28.0,2459.0,492.0,1230.0,498.0,3.0978,137200.0,INLAND
+-116.98,34.07,21.0,739.0,125.0,199.0,82.0,4.8958,117500.0,INLAND
+-118.2,33.99,31.0,1186.0,387.0,2087.0,409.0,1.9132,154600.0,<1H OCEAN
+-121.83,37.32,26.0,1125.0,210.0,943.0,214.0,4.825,181000.0,<1H OCEAN
+-118.3,33.88,30.0,1348.0,333.0,885.0,322.0,3.2574,195300.0,<1H OCEAN
+-118.19,33.99,37.0,2073.0,614.0,2544.0,598.0,2.9054,156300.0,<1H OCEAN
+-118.31,33.99,48.0,2235.0,433.0,1363.0,433.0,1.6559,101400.0,<1H OCEAN
+-117.11,32.9,16.0,2043.0,388.0,705.0,352.0,4.4766,161500.0,<1H OCEAN
+-118.64,34.25,47.0,1315.0,290.0,581.0,268.0,5.4024,253000.0,<1H OCEAN
+-117.34,34.13,29.0,331.0,85.0,341.0,107.0,0.7069,70300.0,INLAND
+-118.08,34.0,32.0,1165.0,358.0,997.0,361.0,0.9817,166300.0,<1H OCEAN
+-118.37,34.04,43.0,888.0,170.0,514.0,161.0,3.1827,202800.0,<1H OCEAN
+-117.7,33.47,21.0,2208.0,534.0,1423.0,482.0,3.5915,305600.0,<1H OCEAN
+-120.69,35.34,16.0,164.0,30.0,542.0,32.0,1.6563,42500.0,NEAR OCEAN
+-122.27,37.24,30.0,2762.0,593.0,1581.0,502.0,5.1002,319400.0,NEAR OCEAN
+-115.52,32.98,32.0,1615.0,382.0,1307.0,345.0,1.4583,58600.0,INLAND
+-124.09,40.88,50.0,921.0,187.0,420.0,187.0,2.2188,105800.0,NEAR OCEAN
+-122.16,37.45,19.0,2207.0,810.0,1304.0,775.0,2.1406,402500.0,NEAR BAY
+-118.5,34.2,18.0,4249.0,933.0,2047.0,909.0,4.1304,229100.0,<1H OCEAN
+-122.1,37.93,20.0,10212.0,1424.0,4083.0,1374.0,8.039,382200.0,NEAR BAY
+-121.49,38.49,26.0,4629.0,832.0,2902.0,816.0,2.735,74600.0,INLAND
+-121.79,37.67,26.0,2163.0,339.0,947.0,346.0,6.0797,211000.0,INLAND
+-118.16,34.01,40.0,1552.0,,1919.0,427.0,2.2596,137500.0,<1H OCEAN
+-119.68,36.32,28.0,1325.0,276.0,873.0,240.0,2.5833,54400.0,INLAND
+-118.1,34.1,41.0,1379.0,315.0,1249.0,309.0,2.6553,183100.0,<1H OCEAN
+-117.9,33.69,13.0,9947.0,1675.0,4071.0,1582.0,5.422,316600.0,<1H OCEAN
+-116.56,33.83,36.0,1765.0,399.0,451.0,264.0,2.6083,321900.0,INLAND
+-117.22,33.22,15.0,1430.0,343.0,704.0,322.0,1.9571,162500.0,<1H OCEAN
+-118.02,33.77,7.0,586.0,118.0,232.0,107.0,5.2077,181300.0,<1H OCEAN
+-122.01,37.27,28.0,3825.0,473.0,1415.0,480.0,10.675,500001.0,<1H OCEAN
+-117.92,33.83,36.0,1072.0,193.0,639.0,196.0,5.0275,179300.0,<1H OCEAN
+-117.93,34.08,36.0,1788.0,317.0,1139.0,320.0,4.125,185800.0,<1H OCEAN
+-121.3,38.67,23.0,2145.0,340.0,1022.0,349.0,4.2037,125400.0,INLAND
+-120.87,37.5,7.0,4966.0,985.0,2431.0,904.0,3.1042,122500.0,INLAND
+-120.99,37.66,30.0,1718.0,395.0,914.0,400.0,1.933,107000.0,INLAND
+-118.6,34.02,36.0,2043.0,467.0,606.0,326.0,8.4331,500001.0,NEAR OCEAN
+-122.33,37.94,42.0,1695.0,345.0,719.0,334.0,3.9417,139100.0,NEAR BAY
+-118.43,34.03,39.0,1733.0,429.0,855.0,387.0,3.2308,340800.0,<1H OCEAN
+-123.5,39.67,22.0,2124.0,450.0,1122.0,446.0,2.1793,71500.0,INLAND
+-117.14,32.7,40.0,1227.0,330.0,1199.0,316.0,1.2188,92500.0,NEAR OCEAN
+-118.35,33.9,31.0,981.0,222.0,734.0,239.0,4.875,232400.0,<1H OCEAN
+-118.61,34.21,33.0,2609.0,431.0,1208.0,406.0,5.4527,227100.0,<1H OCEAN
+-121.9,37.35,52.0,1034.0,239.0,531.0,223.0,2.7411,227100.0,<1H OCEAN
+-119.72,36.8,23.0,2128.0,442.0,1047.0,450.0,2.625,71500.0,INLAND
+-122.26,38.15,16.0,3921.0,727.0,2830.0,680.0,4.5053,123700.0,NEAR BAY
+-120.97,37.69,14.0,5514.0,909.0,2819.0,970.0,3.8598,174400.0,INLAND
+-121.84,37.18,6.0,9176.0,1201.0,3637.0,1138.0,8.3837,473400.0,<1H OCEAN
+-118.46,34.17,24.0,2814.0,675.0,1463.0,620.0,4.1875,309300.0,<1H OCEAN
+-118.32,34.03,35.0,3189.0,935.0,2221.0,801.0,2.1046,114000.0,<1H OCEAN
+-117.08,32.65,17.0,2633.0,712.0,1487.0,694.0,2.5392,147000.0,NEAR OCEAN
+-118.26,33.92,42.0,3320.0,682.0,2105.0,632.0,1.9809,104600.0,<1H OCEAN
+-122.27,37.85,47.0,2077.0,400.0,719.0,326.0,2.2431,172700.0,NEAR BAY
+-121.03,37.32,42.0,2905.0,561.0,1457.0,551.0,2.2566,82100.0,INLAND
+-117.34,33.94,20.0,4589.0,594.0,1660.0,595.0,7.4141,236500.0,INLAND
+-117.36,33.18,26.0,5550.0,1153.0,2372.0,1058.0,2.5509,181800.0,NEAR OCEAN
+-121.03,37.66,31.0,887.0,217.0,614.0,199.0,2.1528,75500.0,INLAND
+-114.61,33.62,16.0,1187.0,261.0,1115.0,242.0,2.1759,61500.0,INLAND
+-118.47,34.06,45.0,3030.0,433.0,916.0,399.0,9.4664,500001.0,<1H OCEAN
+-117.23,33.78,23.0,3465.0,703.0,2672.0,607.0,1.9767,81500.0,INLAND
+-118.9,34.14,35.0,1503.0,263.0,576.0,216.0,5.1457,500001.0,<1H OCEAN
+-122.27,38.12,42.0,5266.0,1167.0,3124.0,1025.0,2.7375,120000.0,NEAR BAY
+-118.37,34.04,25.0,542.0,161.0,442.0,131.0,2.25,333300.0,<1H OCEAN
+-118.06,33.84,26.0,6960.0,1454.0,4367.0,1437.0,4.7953,210900.0,<1H OCEAN
+-118.09,33.92,31.0,1983.0,419.0,1157.0,390.0,3.5455,168300.0,<1H OCEAN
+-118.19,33.91,33.0,915.0,225.0,826.0,212.0,2.7708,117400.0,<1H OCEAN
+-117.92,33.77,28.0,3614.0,960.0,3282.0,889.0,3.522,190300.0,<1H OCEAN
+-117.34,34.16,31.0,1606.0,354.0,1049.0,335.0,2.1935,72700.0,INLAND
+-121.92,37.29,32.0,1260.0,199.0,560.0,207.0,6.5858,346700.0,<1H OCEAN
+-121.87,38.02,52.0,2264.0,439.0,1403.0,476.0,2.7083,99400.0,INLAND
+-118.12,33.91,35.0,1518.0,279.0,857.0,251.0,3.6917,197500.0,<1H OCEAN
+-118.74,34.27,23.0,2493.0,522.0,1488.0,505.0,4.18,215000.0,<1H OCEAN
+-122.23,38.12,49.0,2715.0,435.0,1006.0,429.0,4.2308,145800.0,NEAR BAY
+-122.24,37.49,38.0,4105.0,950.0,2561.0,909.0,3.8684,265600.0,NEAR OCEAN
+-121.02,37.6,33.0,1009.0,238.0,1027.0,246.0,2.5993,68000.0,INLAND
+-120.45,34.87,4.0,1533.0,221.0,545.0,191.0,7.5696,328700.0,<1H OCEAN
+-118.12,34.7,7.0,4915.0,885.0,2833.0,874.0,4.3229,130000.0,INLAND
+-121.87,37.4,16.0,1767.0,268.0,1061.0,280.0,6.9584,351600.0,<1H OCEAN
+-122.01,36.98,27.0,2820.0,730.0,1511.0,745.0,2.589,242400.0,NEAR OCEAN
+-117.88,34.13,25.0,2559.0,654.0,1674.0,623.0,2.8547,155600.0,<1H OCEAN
+-122.1,37.63,18.0,9963.0,2031.0,5613.0,1946.0,3.8171,187200.0,NEAR BAY
+-118.52,34.18,42.0,1611.0,410.0,879.0,386.0,3.1923,221800.0,<1H OCEAN
+-122.08,37.69,42.0,1414.0,274.0,629.0,244.0,3.3478,184900.0,NEAR BAY
+-117.07,32.8,23.0,2698.0,410.0,1094.0,411.0,5.1782,195100.0,<1H OCEAN
+-118.37,34.07,50.0,2519.0,,1117.0,516.0,4.3667,405600.0,<1H OCEAN
+-118.23,34.17,37.0,4524.0,1005.0,2099.0,937.0,3.5781,366700.0,<1H OCEAN
+-118.48,34.02,22.0,1249.0,483.0,1106.0,481.0,2.5261,375000.0,<1H OCEAN
+-117.07,32.73,18.0,2968.0,656.0,1149.0,581.0,2.6452,154200.0,NEAR OCEAN
+-117.2,32.8,36.0,4018.0,1067.0,1620.0,842.0,2.3599,168400.0,NEAR OCEAN
+-118.21,34.04,43.0,1502.0,477.0,1844.0,477.0,1.9405,152500.0,<1H OCEAN
+-117.29,33.13,4.0,617.0,105.0,224.0,105.0,3.9205,183000.0,NEAR OCEAN
+-121.95,37.33,36.0,1683.0,286.0,740.0,324.0,4.7604,294700.0,<1H OCEAN
+-118.22,34.09,36.0,1427.0,415.0,1835.0,410.0,2.48,138900.0,<1H OCEAN
+-118.15,33.85,30.0,4071.0,1067.0,2144.0,970.0,2.7268,218100.0,<1H OCEAN
+-118.36,34.09,33.0,3463.0,1170.0,1845.0,1134.0,2.0205,243800.0,<1H OCEAN
+-118.4,33.9,37.0,2458.0,400.0,920.0,375.0,7.8924,500001.0,<1H OCEAN
+-119.27,36.39,17.0,2076.0,350.0,998.0,340.0,4.3281,145700.0,INLAND
+-118.67,34.28,21.0,4059.0,598.0,2133.0,634.0,5.6949,235300.0,<1H OCEAN
+-118.27,34.15,25.0,3018.0,806.0,2205.0,742.0,3.0199,220200.0,<1H OCEAN
+-118.39,34.19,41.0,2000.0,485.0,1439.0,461.0,3.0491,192000.0,<1H OCEAN
+-117.93,33.91,24.0,1698.0,297.0,676.0,273.0,5.2017,364600.0,<1H OCEAN
+-124.19,41.78,15.0,3140.0,714.0,1645.0,640.0,1.6654,74600.0,NEAR OCEAN
+-122.38,37.59,49.0,1657.0,266.0,613.0,270.0,5.7837,378100.0,NEAR OCEAN
+-117.66,35.63,33.0,2579.0,564.0,1155.0,431.0,2.0441,42100.0,INLAND
+-122.15,37.69,36.0,1545.0,273.0,863.0,267.0,4.0109,192900.0,NEAR BAY
+-118.25,34.07,18.0,4297.0,1420.0,4332.0,1286.0,2.2545,192500.0,<1H OCEAN
+-122.16,37.45,50.0,196.0,41.0,76.0,42.0,7.6129,412500.0,NEAR BAY
+-122.81,38.46,28.0,3580.0,611.0,1634.0,567.0,4.745,248600.0,<1H OCEAN
+-118.59,34.21,26.0,2335.0,669.0,1986.0,645.0,2.9974,178800.0,<1H OCEAN
+-122.38,37.73,20.0,120.0,27.0,55.0,19.0,2.625,187500.0,NEAR BAY
+-118.36,33.93,30.0,1132.0,347.0,1433.0,341.0,2.68,170000.0,<1H OCEAN
+-118.22,33.81,38.0,1486.0,359.0,1345.0,326.0,3.3988,147800.0,NEAR OCEAN
+-121.54,39.33,27.0,720.0,150.0,359.0,138.0,2.5313,61300.0,INLAND
+-118.06,33.98,40.0,1410.0,255.0,932.0,273.0,4.2206,178000.0,<1H OCEAN
+-118.52,34.21,36.0,1328.0,287.0,823.0,273.0,4.5648,193700.0,<1H OCEAN
+-117.8,33.78,17.0,4138.0,805.0,2442.0,780.0,4.7804,242000.0,<1H OCEAN
+-118.31,33.99,44.0,1703.0,358.0,789.0,249.0,1.7083,100000.0,<1H OCEAN
+-118.26,33.94,44.0,795.0,181.0,716.0,167.0,2.0,90300.0,<1H OCEAN
+-122.26,37.88,52.0,2255.0,410.0,823.0,377.0,5.7979,415300.0,NEAR BAY
+-122.04,37.62,35.0,657.0,118.0,328.0,134.0,3.8125,204200.0,NEAR BAY
+-118.94,34.18,25.0,3502.0,508.0,1713.0,508.0,5.5181,242100.0,<1H OCEAN
+-117.66,34.06,39.0,1405.0,339.0,1489.0,336.0,1.608,91800.0,INLAND
+-121.36,38.69,13.0,6850.0,1400.0,4251.0,1421.0,3.6989,93300.0,INLAND
+-122.31,37.91,31.0,1432.0,348.0,681.0,348.0,2.7243,218100.0,NEAR BAY
+-121.62,38.96,36.0,1826.0,329.0,1068.0,318.0,1.9797,118800.0,INLAND
+-122.03,37.35,16.0,1156.0,198.0,455.0,216.0,7.2779,292900.0,<1H OCEAN
+-118.95,35.41,21.0,3999.0,727.0,1889.0,688.0,3.875,99500.0,INLAND
+-118.04,33.72,14.0,4494.0,1048.0,2222.0,963.0,4.7821,169400.0,<1H OCEAN
+-118.56,34.22,35.0,1843.0,329.0,1041.0,317.0,4.4271,205100.0,<1H OCEAN
+-122.47,37.73,50.0,1653.0,252.0,641.0,224.0,10.6605,500001.0,NEAR OCEAN
+-118.42,34.02,28.0,3167.0,737.0,1248.0,665.0,3.1941,394700.0,<1H OCEAN
+-122.32,40.58,2.0,1937.0,350.0,756.0,274.0,3.0,114200.0,INLAND
+-121.55,39.44,31.0,1434.0,283.0,811.0,289.0,1.7727,49000.0,INLAND
+-117.94,33.68,26.0,4183.0,539.0,1504.0,520.0,7.4056,374200.0,<1H OCEAN
+-117.79,34.12,16.0,2426.0,426.0,1319.0,446.0,4.8125,224500.0,INLAND
+-121.42,38.51,21.0,3249.0,666.0,2611.0,663.0,1.9423,87800.0,INLAND
+-117.05,32.8,16.0,1561.0,378.0,574.0,350.0,3.0035,94600.0,<1H OCEAN
+-118.54,34.21,22.0,6064.0,1826.0,4876.0,1697.0,2.875,227100.0,<1H OCEAN
+-117.13,32.73,52.0,1911.0,415.0,777.0,412.0,2.2429,221100.0,NEAR OCEAN
+-121.65,36.66,42.0,4261.0,840.0,2013.0,801.0,3.5288,221000.0,<1H OCEAN
+-119.71,34.42,49.0,1560.0,436.0,1041.0,411.0,2.925,246900.0,<1H OCEAN
+-118.36,34.12,26.0,3902.0,610.0,1468.0,632.0,8.5136,500001.0,<1H OCEAN
+-121.36,38.66,14.0,756.0,141.0,424.0,155.0,3.6953,116100.0,INLAND
+-121.97,37.24,35.0,2553.0,533.0,1117.0,498.0,4.4063,436100.0,<1H OCEAN
+-117.18,32.95,4.0,19001.0,2688.0,8980.0,2441.0,6.3237,260900.0,<1H OCEAN
+-117.81,33.71,16.0,2666.0,387.0,1227.0,347.0,7.3769,302400.0,<1H OCEAN
+-121.84,37.29,4.0,2937.0,648.0,1780.0,665.0,4.3851,160400.0,<1H OCEAN
+-117.81,34.12,23.0,7063.0,1176.0,3100.0,1112.0,4.8229,192600.0,INLAND
+-122.18,37.75,36.0,1047.0,214.0,651.0,166.0,1.712,82100.0,NEAR BAY
+-118.35,34.04,45.0,1579.0,357.0,713.0,335.0,2.1711,179200.0,<1H OCEAN
+-118.45,34.05,28.0,801.0,399.0,936.0,406.0,2.1875,181300.0,<1H OCEAN
+-118.34,34.08,52.0,2756.0,542.0,971.0,510.0,5.5871,500001.0,<1H OCEAN
+-122.27,37.98,23.0,3455.0,479.0,1375.0,474.0,6.0289,218600.0,NEAR BAY
+-118.35,33.97,26.0,3832.0,1074.0,2340.0,904.0,2.6734,143400.0,<1H OCEAN
+-122.5,37.75,43.0,2373.0,481.0,1247.0,454.0,4.0985,283200.0,NEAR OCEAN
+-118.06,33.72,22.0,4311.0,531.0,1426.0,533.0,9.8177,500001.0,NEAR OCEAN
+-117.37,34.08,17.0,2029.0,404.0,1190.0,437.0,2.9554,115000.0,INLAND
+-118.2,34.13,30.0,3369.0,824.0,2032.0,795.0,4.0052,196400.0,<1H OCEAN
+-118.19,34.04,45.0,963.0,234.0,1194.0,239.0,2.1806,134900.0,<1H OCEAN
+-121.2,39.25,5.0,906.0,144.0,376.0,141.0,4.3523,188200.0,INLAND
+-122.52,37.95,33.0,4448.0,631.0,1675.0,628.0,7.8904,468800.0,NEAR BAY
+-122.45,38.28,20.0,3306.0,503.0,1374.0,460.0,5.7984,297600.0,NEAR BAY
+-118.19,33.89,38.0,4018.0,986.0,3702.0,927.0,2.9293,113600.0,<1H OCEAN
+-120.98,38.66,9.0,2073.0,404.0,916.0,373.0,3.225,163300.0,INLAND
+-118.33,33.94,31.0,3757.0,1102.0,3288.0,964.0,1.9309,137500.0,<1H OCEAN
+-118.42,34.23,33.0,2478.0,457.0,1567.0,446.0,5.6629,186700.0,<1H OCEAN
+-118.43,34.18,25.0,3830.0,1105.0,2328.0,1017.0,2.6238,210000.0,<1H OCEAN
+-117.59,34.16,10.0,9467.0,1181.0,3819.0,1122.0,7.8252,361400.0,INLAND
+-118.23,34.21,50.0,309.0,47.0,121.0,45.0,6.213,285000.0,<1H OCEAN
+-121.96,37.96,28.0,1838.0,273.0,899.0,270.0,5.2145,229200.0,INLAND
+-119.17,34.17,34.0,2749.0,539.0,2330.0,559.0,4.2137,185600.0,NEAR OCEAN
+-121.26,37.97,31.0,1189.0,295.0,891.0,292.0,2.5536,50500.0,INLAND
+-122.01,39.74,20.0,2332.0,518.0,1856.0,495.0,2.1746,58700.0,INLAND
+-118.24,34.01,48.0,396.0,99.0,485.0,110.0,2.375,107500.0,<1H OCEAN
+-122.57,38.27,7.0,6508.0,1028.0,2902.0,1010.0,5.3707,250500.0,<1H OCEAN
+-118.45,34.01,36.0,2424.0,418.0,1123.0,417.0,6.4755,405800.0,<1H OCEAN
+-122.48,37.75,51.0,2095.0,410.0,1126.0,429.0,4.4,318400.0,NEAR BAY
+-118.18,34.13,44.0,2734.0,415.0,1057.0,424.0,7.9213,477800.0,<1H OCEAN
+-118.32,34.14,23.0,4574.0,1423.0,1624.0,995.0,4.0965,500001.0,<1H OCEAN
+-119.61,34.43,16.0,2665.0,391.0,794.0,311.0,9.0267,500001.0,<1H OCEAN
+-122.87,38.43,36.0,1987.0,387.0,1065.0,347.0,4.0446,172200.0,<1H OCEAN
+-120.13,34.63,11.0,2137.0,339.0,916.0,338.0,5.5221,394900.0,NEAR OCEAN
+-122.01,37.31,26.0,1391.0,241.0,700.0,236.0,6.6766,332700.0,<1H OCEAN
+-117.94,33.86,35.0,2127.0,417.0,1247.0,378.0,4.75,185600.0,<1H OCEAN
+-122.05,37.89,37.0,1677.0,269.0,689.0,283.0,4.2625,310600.0,NEAR BAY
+-121.83,37.99,16.0,2919.0,462.0,1456.0,453.0,5.6779,164700.0,INLAND
+-118.2,34.05,40.0,1082.0,318.0,1085.0,273.0,1.7054,117200.0,<1H OCEAN
+-122.03,37.98,16.0,1209.0,477.0,627.0,482.0,1.3894,156300.0,NEAR BAY
+-122.07,37.37,22.0,3770.0,727.0,1657.0,762.0,4.8021,457500.0,NEAR BAY
+-116.33,33.75,5.0,19107.0,3923.0,2880.0,1376.0,4.036,158500.0,INLAND
+-122.21,38.02,15.0,2150.0,327.0,1094.0,324.0,6.0224,198500.0,NEAR BAY
+-117.59,34.02,14.0,1463.0,261.0,881.0,245.0,4.7857,152500.0,INLAND
+-118.26,34.03,49.0,299.0,90.0,287.0,68.0,1.2096,100000.0,<1H OCEAN
+-122.24,40.17,51.0,2378.0,584.0,1083.0,494.0,1.577,51900.0,INLAND
+-122.45,37.75,35.0,1363.0,302.0,1786.0,301.0,3.0804,313400.0,NEAR BAY
+-117.07,32.7,14.0,2763.0,456.0,1914.0,465.0,4.1645,143000.0,NEAR OCEAN
+-117.66,34.02,11.0,3358.0,504.0,1690.0,482.0,6.7544,207900.0,INLAND
+-117.22,32.82,22.0,3738.0,795.0,1476.0,728.0,3.7963,303100.0,NEAR OCEAN
+-121.16,37.73,7.0,4956.0,941.0,3006.0,915.0,3.4426,139000.0,INLAND
+-117.97,33.67,25.0,3906.0,660.0,1809.0,622.0,5.6765,265100.0,<1H OCEAN
+-120.4,37.98,19.0,2010.0,433.0,910.0,390.0,2.6696,121200.0,INLAND
+-118.95,34.17,23.0,2630.0,404.0,1184.0,385.0,5.2955,247600.0,<1H OCEAN
+-117.99,34.12,35.0,1040.0,231.0,1040.0,242.0,2.5395,139200.0,INLAND
+-118.44,34.03,37.0,1193.0,205.0,488.0,224.0,3.625,357600.0,<1H OCEAN
+-121.06,39.04,15.0,1999.0,287.0,585.0,246.0,5.5161,361900.0,INLAND
+-120.96,37.54,29.0,1468.0,245.0,747.0,231.0,3.4643,125000.0,INLAND
+-122.14,37.67,37.0,3156.0,534.0,1495.0,543.0,4.8125,188300.0,NEAR BAY
+-121.28,38.1,13.0,2432.0,586.0,1441.0,606.0,2.5556,133100.0,INLAND
+-118.12,34.07,45.0,1770.0,423.0,1410.0,389.0,3.0592,212500.0,<1H OCEAN
+-117.86,33.83,23.0,2377.0,403.0,1101.0,408.0,5.3439,227100.0,<1H OCEAN
+-122.5,37.76,45.0,1673.0,377.0,1078.0,393.0,3.3393,272300.0,NEAR OCEAN
+-118.26,34.0,37.0,2615.0,697.0,2484.0,630.0,1.9208,103400.0,<1H OCEAN
+-117.71,33.97,10.0,10856.0,2278.0,6474.0,2199.0,3.851,137200.0,INLAND
+-121.88,37.29,44.0,2026.0,396.0,908.0,383.0,4.1406,274100.0,<1H OCEAN
+-118.27,34.14,10.0,1060.0,332.0,1025.0,288.0,3.0074,175000.0,<1H OCEAN
+-120.28,37.9,17.0,1047.0,212.0,530.0,196.0,2.1538,153300.0,INLAND
+-118.61,34.38,2.0,5989.0,883.0,1787.0,613.0,6.6916,329500.0,INLAND
+-118.49,34.02,29.0,2709.0,799.0,1238.0,793.0,3.1563,330000.0,<1H OCEAN
+-117.07,32.75,37.0,2690.0,549.0,1219.0,524.0,2.3148,154200.0,NEAR OCEAN
+-124.17,40.77,30.0,1895.0,366.0,990.0,359.0,2.2227,81300.0,NEAR OCEAN
+-118.11,34.02,17.0,9559.0,1911.0,5279.0,1844.0,5.1515,318900.0,<1H OCEAN
+-120.35,40.63,33.0,240.0,49.0,63.0,22.0,3.625,200000.0,INLAND
+-121.22,37.82,13.0,4452.0,949.0,2740.0,937.0,3.1964,141500.0,INLAND
+-118.3,33.86,35.0,1511.0,274.0,853.0,308.0,4.9792,251300.0,<1H OCEAN
+-118.4,33.9,38.0,2868.0,466.0,1098.0,438.0,7.9059,477100.0,<1H OCEAN
+-118.14,33.78,42.0,1898.0,488.0,940.0,483.0,3.4107,233300.0,NEAR OCEAN
+-122.49,37.68,35.0,2405.0,461.0,1583.0,471.0,5.0659,238000.0,NEAR OCEAN
+-121.75,36.95,27.0,1580.0,303.0,1066.0,306.0,4.7071,202700.0,<1H OCEAN
+-117.18,32.83,27.0,2346.0,399.0,1105.0,373.0,4.2708,182800.0,NEAR OCEAN
+-121.38,38.41,7.0,6091.0,921.0,2916.0,886.0,4.7557,150400.0,INLAND
+-121.34,37.96,27.0,1839.0,442.0,2010.0,416.0,2.1284,59400.0,INLAND
+-122.0,36.97,39.0,2702.0,646.0,1136.0,491.0,2.8941,256700.0,NEAR OCEAN
+-117.12,34.46,17.0,1613.0,326.0,765.0,300.0,2.6827,110400.0,INLAND
+-117.96,33.85,35.0,1175.0,191.0,568.0,186.0,4.125,189200.0,<1H OCEAN
+-122.77,38.92,26.0,712.0,140.0,293.0,100.0,4.0119,119400.0,INLAND
+-117.69,33.65,16.0,5805.0,852.0,2356.0,795.0,6.1062,274600.0,<1H OCEAN
+-117.32,33.99,27.0,5464.0,850.0,2400.0,836.0,4.711,133500.0,INLAND
+-117.13,34.07,34.0,2405.0,541.0,1342.0,514.0,2.8031,86900.0,INLAND
+-122.47,37.74,52.0,2055.0,265.0,735.0,252.0,8.1189,500001.0,NEAR BAY
+-118.08,34.58,5.0,1113.0,186.0,631.0,168.0,4.1719,146600.0,INLAND
+-122.2,37.46,40.0,1723.0,208.0,976.0,209.0,9.8892,500001.0,NEAR OCEAN
+-117.11,32.73,27.0,3160.0,627.0,1628.0,612.0,3.8864,132600.0,NEAR OCEAN
+-118.2,33.91,43.0,1381.0,278.0,1494.0,298.0,3.5878,118400.0,<1H OCEAN
+-119.73,34.42,23.0,1364.0,227.0,638.0,238.0,5.3279,413900.0,NEAR OCEAN
+-122.23,37.77,52.0,769.0,206.0,612.0,183.0,2.57,72000.0,NEAR BAY
+-122.49,37.69,35.0,2644.0,456.0,1465.0,430.0,4.9375,277000.0,NEAR OCEAN
+-120.8,37.55,18.0,1802.0,335.0,1110.0,329.0,3.1641,96300.0,INLAND
+-120.99,37.61,39.0,512.0,132.0,443.0,127.0,1.2857,60000.0,INLAND
+-117.11,33.74,18.0,4799.0,1035.0,1966.0,944.0,2.1182,71300.0,INLAND
+-117.64,34.15,16.0,2896.0,404.0,1165.0,379.0,6.4559,392900.0,INLAND
+-117.24,34.59,4.0,5027.0,797.0,1869.0,686.0,3.5507,186100.0,INLAND
+-119.94,38.96,20.0,1451.0,386.0,467.0,255.0,1.5536,212500.0,INLAND
+-121.4,38.55,26.0,2697.0,398.0,1088.0,389.0,5.0,142500.0,INLAND
+-121.89,39.71,26.0,2741.0,451.0,1217.0,437.0,3.7007,139200.0,INLAND
+-117.42,34.59,8.0,5445.0,1360.0,3220.0,1214.0,1.7567,69500.0,INLAND
+-122.27,37.88,52.0,1693.0,391.0,669.0,367.0,3.5417,287500.0,NEAR BAY
+-118.29,34.09,34.0,2716.0,1114.0,2991.0,1021.0,1.7514,187500.0,<1H OCEAN
+-118.33,34.0,24.0,873.0,320.0,529.0,308.0,0.9304,151600.0,<1H OCEAN
+-117.5,33.87,4.0,6755.0,1017.0,2866.0,850.0,5.0493,239800.0,INLAND
+-122.07,38.27,8.0,6761.0,1234.0,3237.0,1177.0,4.3586,173400.0,INLAND
+-118.17,34.68,13.0,5341.0,773.0,2288.0,724.0,6.6772,185600.0,INLAND
+-122.08,37.39,46.0,1115.0,248.0,543.0,248.0,3.2083,334300.0,NEAR BAY
+-122.14,37.43,52.0,1327.0,190.0,467.0,189.0,12.5902,500001.0,NEAR BAY
+-117.37,34.1,10.0,3404.0,855.0,1656.0,675.0,1.6977,91300.0,INLAND
+-121.93,37.71,26.0,4822.0,845.0,2288.0,805.0,4.2281,206000.0,<1H OCEAN
+-121.28,37.92,36.0,499.0,115.0,451.0,124.0,2.1705,60300.0,INLAND
+-117.12,32.78,4.0,2782.0,817.0,1309.0,787.0,4.2621,124200.0,NEAR OCEAN
+-121.43,38.54,44.0,1879.0,359.0,791.0,345.0,3.15,101500.0,INLAND
+-118.28,34.24,29.0,3390.0,580.0,1543.0,576.0,5.6184,316900.0,<1H OCEAN
+-121.63,38.67,34.0,431.0,85.0,391.0,77.0,2.625,225000.0,INLAND
+-122.04,37.26,24.0,4973.0,709.0,1692.0,696.0,7.8627,500001.0,<1H OCEAN
+-119.07,36.08,5.0,2693.0,508.0,1785.0,491.0,3.0,71000.0,INLAND
+-121.86,37.34,40.0,2277.0,508.0,1718.0,434.0,3.0089,185200.0,<1H OCEAN
+-121.33,38.68,13.0,5826.0,1411.0,2244.0,1219.0,1.9093,142900.0,INLAND
+-119.68,36.31,12.0,2739.0,535.0,1859.0,498.0,2.9936,60600.0,INLAND
+-117.3,34.54,25.0,2546.0,488.0,1338.0,487.0,3.2596,85400.0,INLAND
+-120.68,35.48,15.0,2608.0,525.0,1351.0,502.0,2.7798,205800.0,<1H OCEAN
+-122.01,37.91,21.0,10093.0,1269.0,3645.0,1219.0,7.6877,367700.0,NEAR BAY
+-117.73,34.12,26.0,1279.0,163.0,412.0,157.0,6.1731,293800.0,INLAND
+-118.23,34.15,40.0,2124.0,370.0,998.0,372.0,5.3369,370400.0,<1H OCEAN
+-121.82,37.25,16.0,2650.0,600.0,1969.0,586.0,3.9461,194300.0,<1H OCEAN
+-122.33,40.57,16.0,2777.0,503.0,1432.0,500.0,2.5592,75900.0,INLAND
+-117.33,34.15,28.0,1473.0,333.0,1196.0,312.0,1.6993,67800.0,INLAND
+-118.35,33.95,28.0,4770.0,1328.0,3201.0,1196.0,2.681,147700.0,<1H OCEAN
+-117.72,33.51,17.0,3617.0,597.0,1176.0,571.0,5.133,324000.0,<1H OCEAN
+-121.85,39.74,41.0,2901.0,689.0,1426.0,632.0,1.5633,84500.0,INLAND
+-118.43,34.29,38.0,1704.0,347.0,1384.0,374.0,2.865,155500.0,<1H OCEAN
+-118.48,34.27,34.0,1231.0,222.0,702.0,222.0,4.9323,223700.0,<1H OCEAN
+-116.54,33.81,31.0,6814.0,1714.0,2628.0,1341.0,2.1176,124100.0,INLAND
+-120.67,37.97,9.0,7450.0,1475.0,2233.0,930.0,2.6528,133000.0,INLAND
+-118.34,33.87,28.0,2948.0,566.0,1445.0,524.0,5.3743,286500.0,<1H OCEAN
+-122.71,38.44,27.0,966.0,251.0,462.0,230.0,1.7,350000.0,<1H OCEAN
+-116.96,32.8,16.0,3920.0,1094.0,2612.0,1023.0,1.3291,120800.0,<1H OCEAN
+-118.32,33.73,25.0,1099.0,168.0,407.0,159.0,7.6886,500001.0,NEAR OCEAN
+-117.58,33.66,4.0,3305.0,644.0,1693.0,597.0,5.2497,215000.0,<1H OCEAN
+-118.03,33.92,30.0,1414.0,332.0,1307.0,315.0,3.0,158300.0,<1H OCEAN
+-117.98,35.1,4.0,923.0,166.0,352.0,135.0,4.5724,84500.0,INLAND
+-117.46,34.04,3.0,12870.0,2315.0,5820.0,1759.0,4.2429,147300.0,INLAND
+-118.4,34.18,32.0,3724.0,899.0,1912.0,791.0,3.5711,312700.0,<1H OCEAN
+-118.26,33.95,44.0,1481.0,329.0,999.0,315.0,1.5147,94600.0,<1H OCEAN
+-121.29,38.13,20.0,3168.0,514.0,1390.0,490.0,5.0,154800.0,INLAND
+-121.35,38.55,22.0,2607.0,411.0,1216.0,407.0,5.0427,126900.0,INLAND
+-118.16,33.97,31.0,1363.0,428.0,1897.0,364.0,2.3929,191100.0,<1H OCEAN
+-120.3,37.99,23.0,1908.0,383.0,984.0,374.0,2.517,153500.0,INLAND
+-121.3,38.66,32.0,2915.0,492.0,1292.0,454.0,3.3188,117100.0,INLAND
+-119.03,35.34,36.0,3474.0,645.0,1679.0,616.0,2.7256,71900.0,INLAND
+-117.04,32.63,26.0,2756.0,422.0,1166.0,398.0,5.1354,181600.0,NEAR OCEAN
+-118.26,34.07,30.0,929.0,238.0,763.0,214.0,2.5227,187500.0,<1H OCEAN
+-119.74,34.44,26.0,4257.0,1031.0,1861.0,950.0,3.4047,294500.0,NEAR OCEAN
+-118.36,33.91,42.0,1949.0,422.0,1184.0,423.0,4.3333,225600.0,<1H OCEAN
+-122.23,37.79,49.0,1175.0,217.0,859.0,219.0,2.293,106300.0,NEAR BAY
+-118.12,34.18,47.0,2344.0,513.0,1537.0,481.0,3.4777,230600.0,INLAND
+-117.5,34.0,15.0,1929.0,317.0,1237.0,316.0,4.4063,128500.0,INLAND
+-122.25,37.83,52.0,1279.0,287.0,534.0,291.0,3.1429,231600.0,NEAR BAY
+-122.46,37.78,52.0,4140.0,984.0,2030.0,892.0,3.4236,376800.0,NEAR BAY
+-118.22,34.05,36.0,1243.0,470.0,1668.0,444.0,1.0714,137500.0,<1H OCEAN
+-121.63,39.14,39.0,1874.0,411.0,822.0,377.0,2.5038,68300.0,INLAND
+-117.73,34.06,34.0,344.0,108.0,315.0,119.0,3.1786,117800.0,INLAND
+-121.61,39.52,24.0,1610.0,324.0,909.0,323.0,1.8661,59800.0,INLAND
+-121.51,38.56,43.0,1048.0,312.0,1320.0,294.0,1.0649,137500.0,INLAND
+-118.51,34.16,23.0,11154.0,1995.0,4076.0,1809.0,5.4609,500001.0,<1H OCEAN
+-118.88,35.34,20.0,1351.0,255.0,762.0,253.0,2.1111,105300.0,INLAND
+-121.4,36.38,39.0,2288.0,529.0,1449.0,410.0,3.3289,190600.0,<1H OCEAN
+-117.66,33.46,28.0,1261.0,233.0,609.0,242.0,5.1024,312700.0,<1H OCEAN
+-120.96,40.28,19.0,683.0,139.0,302.0,111.0,2.5,64100.0,INLAND
+-118.11,34.06,16.0,2416.0,565.0,1750.0,514.0,2.8229,163700.0,<1H OCEAN
+-118.48,34.14,31.0,9320.0,1143.0,2980.0,1109.0,10.3599,500001.0,<1H OCEAN
+-122.06,37.86,16.0,5187.0,1014.0,1512.0,986.0,4.4551,252400.0,NEAR BAY
+-118.31,34.12,39.0,3895.0,561.0,1271.0,536.0,8.0073,500001.0,<1H OCEAN
+-122.21,37.49,24.0,2528.0,947.0,2437.0,861.0,2.2746,225000.0,NEAR BAY
+-122.34,40.51,16.0,2247.0,502.0,1206.0,463.0,1.9946,119200.0,INLAND
+-118.05,33.96,37.0,2622.0,652.0,2778.0,644.0,2.9714,160300.0,<1H OCEAN
+-118.31,33.86,29.0,2243.0,361.0,1051.0,352.0,6.6632,325200.0,<1H OCEAN
+-116.35,34.13,9.0,1969.0,406.0,805.0,349.0,1.5491,62300.0,INLAND
+-118.38,33.87,27.0,2287.0,491.0,1101.0,466.0,4.675,316900.0,<1H OCEAN
+-118.09,33.88,27.0,3119.0,635.0,1887.0,567.0,3.8654,195300.0,<1H OCEAN
+-121.5,38.57,41.0,1124.0,344.0,807.0,316.0,1.4712,94600.0,INLAND
+-118.18,33.98,36.0,903.0,266.0,1068.0,251.0,3.0398,165400.0,<1H OCEAN
+-118.15,33.77,27.0,3043.0,787.0,1398.0,747.0,3.5528,271100.0,NEAR OCEAN
+-118.19,33.79,30.0,3107.0,994.0,3543.0,850.0,1.9387,141700.0,NEAR OCEAN
+-118.43,34.24,35.0,1416.0,261.0,995.0,272.0,3.7143,178700.0,<1H OCEAN
+-118.16,34.02,42.0,814.0,216.0,773.0,208.0,2.5313,156900.0,<1H OCEAN
+-117.94,33.78,40.0,299.0,68.0,163.0,70.0,3.0125,166100.0,<1H OCEAN
+-117.29,34.11,48.0,1498.0,448.0,1586.0,455.0,1.1687,70800.0,INLAND
+-118.44,34.2,36.0,2698.0,623.0,1544.0,554.0,2.7375,234900.0,<1H OCEAN
+-117.48,33.94,29.0,1625.0,336.0,1046.0,320.0,3.1985,117300.0,INLAND
+-121.0,39.75,8.0,1116.0,214.0,27.0,39.0,2.5893,83000.0,INLAND
+-117.96,33.98,25.0,1259.0,184.0,599.0,170.0,5.7407,302200.0,<1H OCEAN
+-117.33,33.24,13.0,4543.0,881.0,2298.0,870.0,2.9386,143400.0,NEAR OCEAN
+-118.74,34.26,27.0,3467.0,545.0,1798.0,493.0,4.8717,204100.0,<1H OCEAN
+-122.47,37.89,23.0,10774.0,1736.0,3895.0,1683.0,7.2905,500001.0,NEAR BAY
+-121.26,37.88,42.0,465.0,93.0,256.0,93.0,3.1719,158300.0,INLAND
+-118.29,34.01,42.0,814.0,223.0,511.0,188.0,2.3942,123200.0,<1H OCEAN
+-121.95,37.28,19.0,7027.0,1847.0,3759.0,1753.0,3.1509,242900.0,<1H OCEAN
+-117.52,33.89,2.0,17978.0,3217.0,7305.0,2463.0,5.1695,220800.0,INLAND
+-121.0,37.65,17.0,484.0,202.0,198.0,204.0,0.6825,187500.0,INLAND
+-118.01,33.76,26.0,2141.0,597.0,2038.0,585.0,2.2981,177700.0,<1H OCEAN
+-121.43,37.75,34.0,1280.0,268.0,754.0,294.0,3.1333,132000.0,INLAND
+-119.65,36.51,30.0,1671.0,319.0,966.0,282.0,3.1333,100000.0,INLAND
+-121.48,38.59,52.0,1186.0,341.0,1038.0,320.0,1.6116,70500.0,INLAND
+-121.89,36.6,19.0,656.0,200.0,248.0,173.0,1.2656,500000.0,<1H OCEAN
+-118.19,34.1,39.0,2054.0,423.0,1205.0,403.0,4.239,213000.0,<1H OCEAN
+-120.83,37.52,6.0,1488.0,252.0,773.0,259.0,4.1859,150000.0,INLAND
+-118.54,34.19,22.0,3380.0,790.0,2199.0,737.0,2.5739,239200.0,<1H OCEAN
+-117.13,34.24,17.0,2828.0,506.0,673.0,274.0,5.2563,144100.0,INLAND
+-121.46,37.73,20.0,2039.0,373.0,862.0,330.0,5.1629,222900.0,INLAND
+-118.1,33.86,21.0,3052.0,624.0,1588.0,568.0,4.3397,268100.0,<1H OCEAN
+-122.24,37.83,52.0,2449.0,312.0,916.0,316.0,8.1194,471600.0,NEAR BAY
+-117.95,33.83,31.0,2421.0,389.0,1348.0,413.0,4.9394,217800.0,<1H OCEAN
+-121.78,36.93,21.0,2794.0,662.0,2236.0,565.0,2.4053,178400.0,<1H OCEAN
+-119.71,36.83,15.0,2727.0,500.0,1228.0,436.0,3.5078,109000.0,INLAND
+-121.18,38.83,15.0,4488.0,859.0,2114.0,805.0,2.9484,124400.0,INLAND
+-121.8,37.69,17.0,3956.0,639.0,2222.0,662.0,5.4324,215500.0,INLAND
+-117.63,33.45,5.0,3549.0,604.0,1571.0,534.0,5.3705,363500.0,NEAR OCEAN
+-118.19,34.04,39.0,1074.0,323.0,1613.0,308.0,2.3015,131700.0,<1H OCEAN
+-118.11,33.94,36.0,1949.0,319.0,909.0,325.0,5.1587,296600.0,<1H OCEAN
+-117.62,34.11,31.0,2561.0,414.0,1204.0,435.0,4.4637,192800.0,INLAND
+-122.43,37.77,52.0,2416.0,620.0,1188.0,591.0,2.3887,337500.0,NEAR BAY
+-121.61,36.69,19.0,9899.0,2617.0,11272.0,2528.0,2.0244,118500.0,<1H OCEAN
+-122.46,37.7,37.0,1028.0,275.0,904.0,261.0,3.5035,238600.0,NEAR OCEAN
+-117.95,33.71,16.0,6058.0,1715.0,3285.0,1495.0,3.4133,290900.0,<1H OCEAN
+-121.94,37.29,22.0,2593.0,637.0,1249.0,623.0,3.75,212500.0,<1H OCEAN
+-117.08,32.75,20.0,1989.0,508.0,1452.0,462.0,2.0077,118300.0,NEAR OCEAN
+-122.37,37.59,39.0,4645.0,1196.0,2156.0,1113.0,3.4412,353800.0,NEAR OCEAN
+-118.19,34.12,36.0,2833.0,720.0,2148.0,709.0,2.7012,172100.0,<1H OCEAN
+-120.27,37.12,42.0,1142.0,236.0,597.0,210.0,1.7279,52300.0,INLAND
+-117.75,34.08,33.0,1067.0,194.0,600.0,201.0,4.0368,139100.0,INLAND
+-118.29,33.74,52.0,1438.0,472.0,1018.0,399.0,2.2188,306700.0,NEAR OCEAN
+-117.36,34.08,4.0,8866.0,1832.0,4775.0,1554.0,3.7348,125800.0,INLAND
+-121.83,37.36,22.0,3936.0,860.0,3508.0,877.0,4.2312,183800.0,<1H OCEAN
+-117.61,34.14,14.0,15809.0,2485.0,7363.0,2410.0,5.5198,245600.0,INLAND
+-119.86,36.81,4.0,4530.0,1070.0,1804.0,837.0,3.3942,72100.0,INLAND
+-120.47,37.32,15.0,3952.0,984.0,2024.0,1026.0,2.558,121600.0,INLAND
+-122.55,38.07,38.0,3392.0,709.0,1894.0,713.0,3.0573,350800.0,NEAR BAY
+-122.16,37.77,47.0,1256.0,,570.0,218.0,4.375,161900.0,NEAR BAY
+-121.87,37.46,43.0,91.0,12.0,58.0,16.0,15.0001,500001.0,<1H OCEAN
+-117.97,33.79,34.0,2456.0,410.0,1289.0,442.0,4.1818,224200.0,<1H OCEAN
+-121.28,37.9,28.0,371.0,71.0,171.0,70.0,0.9614,55700.0,INLAND
+-118.15,33.92,40.0,1335.0,281.0,804.0,282.0,4.3194,198400.0,<1H OCEAN
+-117.67,35.63,32.0,3661.0,787.0,1613.0,706.0,3.0687,63500.0,INLAND
+-119.68,36.79,16.0,1551.0,,1010.0,292.0,3.5417,71300.0,INLAND
+-117.13,32.8,35.0,2129.0,382.0,1044.0,350.0,3.9732,174900.0,NEAR OCEAN
+-117.05,32.67,16.0,2168.0,343.0,1589.0,338.0,5.4863,153800.0,NEAR OCEAN
+-121.24,40.31,36.0,1597.0,301.0,632.0,262.0,3.5962,93600.0,INLAND
+-122.83,38.58,17.0,5199.0,1023.0,2036.0,890.0,3.2452,168800.0,<1H OCEAN
+-117.05,33.13,22.0,2427.0,390.0,1099.0,362.0,5.2323,167500.0,<1H OCEAN
+-118.17,33.84,45.0,1853.0,328.0,945.0,320.0,5.0787,219200.0,<1H OCEAN
+-116.08,33.86,16.0,381.0,89.0,182.0,75.0,2.425,76100.0,INLAND
+-118.62,34.17,32.0,1491.0,355.0,756.0,296.0,3.0404,262800.0,<1H OCEAN
+-122.43,37.75,52.0,1970.0,495.0,871.0,474.0,4.0625,355600.0,NEAR BAY
+-117.6,33.65,4.0,3134.0,504.0,1599.0,485.0,6.2464,233900.0,<1H OCEAN
+-122.65,38.99,16.0,4279.0,951.0,1596.0,666.0,1.8571,75900.0,INLAND
+-118.4,34.06,47.0,3652.0,967.0,1438.0,887.0,3.6964,500001.0,<1H OCEAN
+-122.22,37.82,39.0,2492.0,310.0,808.0,315.0,11.8603,500001.0,NEAR BAY
+-121.97,38.04,38.0,2505.0,554.0,1595.0,498.0,2.5833,83500.0,INLAND
+-122.3,37.93,34.0,2254.0,357.0,715.0,306.0,4.5,304000.0,NEAR BAY
+-118.38,33.96,44.0,2395.0,458.0,1287.0,450.0,4.6923,299000.0,<1H OCEAN
+-121.45,38.64,23.0,1481.0,343.0,1079.0,315.0,1.867,60600.0,INLAND
+-122.79,38.54,5.0,3986.0,737.0,1887.0,687.0,3.7768,213800.0,<1H OCEAN
+-121.86,37.7,13.0,9621.0,1344.0,4389.0,1391.0,6.6827,313700.0,INLAND
+-117.1,33.14,7.0,10665.0,2576.0,4917.0,2424.0,2.3171,159500.0,<1H OCEAN
+-122.03,36.96,40.0,584.0,126.0,316.0,139.0,3.5938,243500.0,NEAR OCEAN
+-121.47,38.49,17.0,3595.0,790.0,2760.0,770.0,2.3233,78800.0,INLAND
+-120.84,37.4,7.0,2773.0,530.0,1374.0,505.0,2.6214,103800.0,INLAND
+-121.64,36.82,18.0,1819.0,283.0,919.0,295.0,4.1696,222500.0,<1H OCEAN
+-118.3,34.07,18.0,3759.0,,3296.0,1462.0,2.2708,175000.0,<1H OCEAN
+-122.1,37.36,35.0,2063.0,266.0,676.0,252.0,8.5294,500001.0,NEAR BAY
+-118.33,33.9,21.0,6603.0,1984.0,5546.0,1745.0,2.6091,163900.0,<1H OCEAN
+-122.5,37.77,52.0,2299.0,441.0,1252.0,415.0,5.0562,336700.0,NEAR BAY
+-119.3,34.39,35.0,3079.0,579.0,1807.0,589.0,4.69,199300.0,NEAR OCEAN
+-118.42,34.19,33.0,3285.0,830.0,2281.0,786.0,2.6165,230800.0,<1H OCEAN
+-117.83,33.77,26.0,4931.0,853.0,2249.0,818.0,4.275,285400.0,<1H OCEAN
+-118.1,33.83,37.0,2059.0,349.0,825.0,334.0,4.0603,225200.0,<1H OCEAN
+-117.87,34.04,7.0,27700.0,4179.0,15037.0,4072.0,6.6288,339700.0,<1H OCEAN
+-117.79,33.7,6.0,1593.0,371.0,832.0,379.0,4.4286,239500.0,<1H OCEAN
+-121.34,38.02,30.0,4375.0,689.0,2038.0,709.0,5.1202,133800.0,INLAND
+-118.02,33.75,26.0,2989.0,479.0,1596.0,475.0,5.7157,231200.0,<1H OCEAN
+-117.9,34.09,34.0,1562.0,272.0,825.0,266.0,4.125,220800.0,<1H OCEAN
+-119.19,34.21,27.0,1887.0,487.0,1339.0,428.0,2.9185,224500.0,NEAR OCEAN
+-121.84,36.94,29.0,4921.0,967.0,2319.0,823.0,4.9517,307900.0,NEAR OCEAN
+-118.37,33.85,34.0,2415.0,404.0,1278.0,414.0,6.1599,341200.0,<1H OCEAN
+-117.81,33.79,25.0,5950.0,1155.0,4528.0,1064.0,4.2564,204600.0,<1H OCEAN
+-118.41,33.96,15.0,412.0,128.0,310.0,137.0,3.9792,266700.0,<1H OCEAN
+-118.44,34.22,36.0,1191.0,266.0,718.0,248.0,3.4612,178800.0,<1H OCEAN
+-118.41,34.19,37.0,1993.0,425.0,939.0,400.0,2.8021,224600.0,<1H OCEAN
+-118.03,34.06,24.0,2469.0,731.0,3818.0,712.0,2.5445,151400.0,<1H OCEAN
+-121.15,39.0,15.0,4145.0,691.0,1872.0,680.0,4.3553,220600.0,INLAND
+-122.48,37.79,52.0,4683.0,1055.0,2246.0,975.0,4.1148,457800.0,NEAR BAY
+-119.02,36.05,22.0,2078.0,431.0,1336.0,456.0,2.2202,65200.0,INLAND
+-121.33,38.04,15.0,1933.0,280.0,965.0,260.0,4.6477,142700.0,INLAND
+-117.08,32.59,30.0,144.0,52.0,220.0,48.0,2.3929,134400.0,NEAR OCEAN
+-122.01,37.28,22.0,2038.0,260.0,773.0,281.0,9.1569,500001.0,<1H OCEAN
+-122.48,38.31,19.0,2398.0,521.0,1266.0,471.0,2.7727,186800.0,<1H OCEAN
+-118.41,34.09,37.0,2716.0,302.0,809.0,291.0,15.0001,500001.0,<1H OCEAN
+-119.85,36.81,15.0,1743.0,310.0,1011.0,325.0,3.755,68000.0,INLAND
+-122.12,37.28,21.0,349.0,64.0,149.0,56.0,5.8691,360000.0,<1H OCEAN
+-118.46,34.27,28.0,1865.0,463.0,1182.0,440.0,2.6193,172300.0,<1H OCEAN
+-120.74,39.82,9.0,1955.0,398.0,294.0,122.0,3.9583,126500.0,INLAND
+-121.96,36.97,23.0,4324.0,1034.0,1844.0,875.0,3.0777,263800.0,NEAR OCEAN
+-118.41,33.89,31.0,702.0,161.0,236.0,144.0,5.0497,500001.0,<1H OCEAN
+-122.2,37.47,37.0,1403.0,369.0,1587.0,331.0,2.8258,232800.0,NEAR BAY
+-118.18,33.86,39.0,2925.0,732.0,1702.0,642.0,2.375,160800.0,<1H OCEAN
+-118.5,34.19,35.0,2720.0,490.0,1158.0,445.0,5.0796,228300.0,<1H OCEAN
+-121.49,38.58,52.0,569.0,405.0,509.0,367.0,0.9196,137500.0,INLAND
+-116.86,33.73,13.0,2604.0,443.0,978.0,417.0,2.933,170700.0,INLAND
+-118.26,34.12,45.0,2839.0,698.0,1768.0,653.0,3.1306,214000.0,<1H OCEAN
+-118.31,34.08,30.0,1390.0,457.0,1460.0,423.0,2.2422,254500.0,<1H OCEAN
+-118.85,34.21,25.0,1328.0,209.0,691.0,228.0,4.9234,241400.0,<1H OCEAN
+-118.06,33.99,45.0,1471.0,255.0,670.0,250.0,4.5478,188000.0,<1H OCEAN
+-118.11,33.87,15.0,3254.0,598.0,1772.0,618.0,5.0417,240800.0,<1H OCEAN
+-118.05,34.1,30.0,2143.0,427.0,1107.0,416.0,4.2321,252200.0,INLAND
+-118.04,33.97,34.0,1759.0,431.0,1282.0,391.0,3.0491,158200.0,<1H OCEAN
+-118.15,33.78,17.0,1584.0,435.0,904.0,406.0,2.0875,181300.0,NEAR OCEAN
+-121.0,37.68,29.0,2911.0,445.0,1170.0,460.0,4.9904,158100.0,INLAND
+-118.3,34.05,42.0,1476.0,610.0,1605.0,545.0,1.721,214300.0,<1H OCEAN
+-118.18,37.35,16.0,3806.0,794.0,1501.0,714.0,2.1212,108300.0,INLAND
+-118.25,33.99,41.0,2215.0,544.0,2054.0,480.0,1.5272,100300.0,<1H OCEAN
+-118.44,33.99,44.0,305.0,72.0,156.0,70.0,5.9641,275000.0,<1H OCEAN
+-117.08,32.58,15.0,1462.0,274.0,1002.0,271.0,3.9698,142700.0,NEAR OCEAN
+-120.68,37.47,33.0,1028.0,226.0,658.0,197.0,2.3043,66300.0,INLAND
+-122.46,37.75,52.0,1590.0,236.0,622.0,232.0,5.8151,500001.0,NEAR BAY
+-117.16,32.8,25.0,1399.0,329.0,1308.0,355.0,2.5682,187500.0,NEAR OCEAN
+-122.25,37.79,45.0,1786.0,526.0,1475.0,460.0,1.7772,97500.0,NEAR BAY
+-117.58,33.65,4.0,1606.0,498.0,815.0,426.0,3.375,500001.0,<1H OCEAN
+-117.13,33.14,5.0,2618.0,539.0,1320.0,512.0,4.1053,171400.0,<1H OCEAN
+-121.94,37.24,19.0,1741.0,294.0,632.0,279.0,5.5944,290500.0,<1H OCEAN
+-117.73,34.01,36.0,2340.0,392.0,1213.0,388.0,4.125,213000.0,INLAND
+-117.93,33.76,24.0,3202.0,703.0,3308.0,714.0,4.1577,174100.0,<1H OCEAN
+-117.72,33.49,4.0,3623.0,734.0,1129.0,530.0,5.7281,500001.0,<1H OCEAN
+-118.45,34.03,41.0,2083.0,528.0,993.0,481.0,4.0231,353900.0,<1H OCEAN
+-117.22,32.74,41.0,2621.0,542.0,1074.0,471.0,2.4016,287500.0,NEAR OCEAN
+-118.36,34.04,34.0,3239.0,806.0,2331.0,765.0,2.0538,125800.0,<1H OCEAN
+-117.09,32.7,15.0,869.0,217.0,887.0,216.0,1.4583,84200.0,NEAR OCEAN
+-118.27,33.79,26.0,2109.0,651.0,2120.0,605.0,2.1447,158700.0,<1H OCEAN
+-122.11,37.88,37.0,4005.0,614.0,1602.0,606.0,6.4666,348200.0,NEAR BAY
+-118.28,33.74,16.0,855.0,271.0,486.0,250.0,0.7591,350000.0,NEAR OCEAN
+-117.8,34.02,23.0,3351.0,591.0,1535.0,522.0,5.0869,230600.0,<1H OCEAN
+-119.29,36.34,10.0,1832.0,455.0,1664.0,429.0,2.0227,53300.0,INLAND
+-122.18,37.75,45.0,990.0,261.0,901.0,260.0,2.1731,82000.0,NEAR BAY
+-118.59,34.23,17.0,6592.0,1525.0,4459.0,1463.0,3.0347,254500.0,<1H OCEAN
+-118.34,33.94,37.0,3107.0,903.0,3456.0,734.0,2.182,147500.0,<1H OCEAN
+-116.74,33.33,17.0,4190.0,946.0,1802.0,673.0,2.4744,158200.0,INLAND
+-117.72,33.53,14.0,1672.0,295.0,704.0,293.0,5.1129,251300.0,<1H OCEAN
+-118.87,35.37,14.0,2458.0,433.0,1352.0,411.0,3.5441,87000.0,INLAND
+-122.02,38.26,27.0,3440.0,787.0,2085.0,748.0,2.5896,104700.0,INLAND
+-124.13,40.78,34.0,2142.0,420.0,1056.0,382.0,2.1101,86900.0,NEAR OCEAN
+-118.43,34.23,35.0,2049.0,390.0,1286.0,385.0,4.4432,181500.0,<1H OCEAN
+-118.42,33.9,43.0,1394.0,321.0,552.0,296.0,5.9596,500001.0,<1H OCEAN
+-118.65,34.21,5.0,5429.0,665.0,2315.0,687.0,9.6465,500001.0,<1H OCEAN
+-120.9,39.93,23.0,2679.0,546.0,1424.0,529.0,2.8812,81900.0,INLAND
+-120.36,40.45,19.0,689.0,143.0,355.0,127.0,1.7333,70000.0,INLAND
+-118.42,34.04,51.0,1975.0,348.0,771.0,357.0,6.626,500001.0,<1H OCEAN
+-118.11,33.79,36.0,2223.0,370.0,1039.0,370.0,5.7942,257000.0,<1H OCEAN
+-121.1,37.8,35.0,1853.0,331.0,958.0,340.0,3.3578,149000.0,INLAND
+-118.97,37.64,14.0,2284.0,622.0,342.0,137.0,3.0921,87500.0,INLAND
+-122.66,38.47,23.0,2246.0,437.0,1035.0,386.0,3.7617,172600.0,<1H OCEAN
+-122.07,38.41,17.0,3053.0,595.0,1434.0,557.0,3.4741,245800.0,INLAND
+-117.37,33.94,14.0,9286.0,1269.0,3565.0,1238.0,6.6635,219600.0,INLAND
+-117.44,33.94,32.0,2349.0,452.0,1479.0,425.0,3.9118,114100.0,INLAND
+-117.28,33.92,35.0,3623.0,841.0,2721.0,766.0,2.1574,86900.0,INLAND
+-122.39,37.8,25.0,4561.0,1474.0,1525.0,1169.0,4.5581,500001.0,NEAR BAY
+-121.88,37.66,29.0,2702.0,680.0,1360.0,642.0,3.1127,233000.0,INLAND
+-122.06,38.26,36.0,1248.0,221.0,672.0,222.0,3.3839,105900.0,INLAND
+-120.25,38.03,21.0,4924.0,966.0,1175.0,454.0,2.9457,116500.0,INLAND
+-121.37,38.41,14.0,3727.0,685.0,1741.0,646.0,3.5625,125700.0,INLAND
+-118.38,34.21,35.0,1468.0,303.0,1295.0,300.0,3.7708,170600.0,<1H OCEAN
+-122.46,37.71,52.0,1580.0,337.0,1425.0,330.0,4.0547,246200.0,NEAR OCEAN
+-118.22,33.86,16.0,8732.0,1489.0,3944.0,1493.0,5.1948,203500.0,<1H OCEAN
+-118.23,33.89,16.0,5003.0,1180.0,4145.0,1159.0,2.1389,133400.0,<1H OCEAN
+-122.48,38.32,31.0,1701.0,363.0,680.0,324.0,3.1375,192100.0,<1H OCEAN
+-118.28,33.99,46.0,2577.0,703.0,2446.0,687.0,1.275,98300.0,<1H OCEAN
+-117.24,33.95,11.0,6617.0,1118.0,3710.0,1087.0,4.7877,132600.0,INLAND
+-118.31,34.02,46.0,1976.0,469.0,1409.0,431.0,2.2981,112100.0,<1H OCEAN
+-118.18,33.94,43.0,2724.0,612.0,2340.0,570.0,2.7,165000.0,<1H OCEAN
+-123.11,39.32,20.0,2745.0,504.0,1421.0,430.0,3.3431,137500.0,INLAND
+-117.23,32.75,11.0,4304.0,1245.0,1960.0,1105.0,3.3456,159800.0,NEAR OCEAN
+-118.14,33.94,35.0,2987.0,601.0,1561.0,606.0,4.0039,226500.0,<1H OCEAN
+-122.25,37.46,33.0,6841.0,950.0,2681.0,980.0,7.1088,443300.0,NEAR OCEAN
+-118.65,34.19,27.0,2772.0,511.0,1346.0,497.0,5.2016,243000.0,<1H OCEAN
+-118.56,34.14,23.0,9657.0,1189.0,3585.0,1162.0,10.4399,500001.0,<1H OCEAN
+-122.44,37.76,38.0,2202.0,452.0,833.0,435.0,6.8939,455900.0,NEAR BAY
+-122.01,37.25,31.0,1574.0,193.0,551.0,191.0,10.2311,500001.0,<1H OCEAN
+-118.56,34.21,36.0,1286.0,242.0,788.0,248.0,3.5333,196800.0,<1H OCEAN
+-122.03,37.69,20.0,200.0,25.0,83.0,31.0,6.5,340000.0,NEAR BAY
+-121.38,38.64,19.0,4563.0,1069.0,2256.0,926.0,2.1472,143400.0,INLAND
+-119.58,36.69,42.0,1032.0,215.0,812.0,225.0,1.9766,58100.0,INLAND
+-120.81,38.67,14.0,8396.0,1578.0,3952.0,1474.0,3.0565,118800.0,INLAND
+-120.57,34.96,38.0,1145.0,297.0,1107.0,296.0,2.1776,89100.0,NEAR OCEAN
+-118.2,33.9,26.0,1000.0,275.0,1178.0,263.0,2.12,105000.0,<1H OCEAN
+-117.34,34.51,6.0,5667.0,1385.0,2447.0,1199.0,2.3617,103100.0,INLAND
+-122.19,37.78,49.0,1183.0,205.0,496.0,209.0,5.2328,174200.0,NEAR BAY
+-118.3,34.25,36.0,1300.0,304.0,688.0,261.0,3.1523,176700.0,<1H OCEAN
+-121.69,38.16,33.0,1808.0,363.0,824.0,340.0,3.2937,96400.0,INLAND
+-122.16,37.74,52.0,771.0,147.0,355.0,144.0,4.1458,143400.0,NEAR BAY
+-116.54,33.81,24.0,6087.0,1217.0,1721.0,833.0,3.1493,199400.0,INLAND
+-122.87,38.61,23.0,2676.0,521.0,1456.0,500.0,3.7361,173700.0,<1H OCEAN
+-120.49,37.29,17.0,2414.0,594.0,2487.0,582.0,1.0955,62700.0,INLAND
+-118.27,34.15,7.0,2837.0,776.0,2287.0,736.0,3.008,229000.0,<1H OCEAN
+-118.74,34.05,19.0,3487.0,686.0,2782.0,584.0,7.9184,500001.0,NEAR OCEAN
+-122.29,37.56,12.0,6474.0,1467.0,2516.0,1390.0,5.0353,305800.0,NEAR BAY
+-118.15,33.87,29.0,2690.0,659.0,1747.0,617.0,3.3713,198200.0,<1H OCEAN
+-118.08,34.12,41.0,1598.0,280.0,807.0,282.0,5.5067,325000.0,<1H OCEAN
+-118.06,34.71,14.0,2606.0,514.0,1228.0,512.0,2.5764,150000.0,INLAND
+-117.88,33.73,32.0,1947.0,355.0,1786.0,332.0,4.5726,177500.0,<1H OCEAN
+-118.18,33.77,49.0,2297.0,759.0,1105.0,629.0,1.8388,175000.0,NEAR OCEAN
+-121.8,37.32,20.0,2473.0,476.0,2228.0,501.0,5.6817,224200.0,<1H OCEAN
+-122.34,37.93,45.0,2225.0,486.0,1304.0,459.0,2.64,112100.0,NEAR BAY
+-119.95,38.94,24.0,2180.0,517.0,755.0,223.0,2.5875,173400.0,INLAND
+-120.2,34.63,14.0,2647.0,515.0,1487.0,488.0,4.4519,227900.0,NEAR OCEAN
+-121.43,37.74,40.0,859.0,196.0,427.0,176.0,3.5789,110400.0,INLAND
+-122.03,36.97,52.0,403.0,72.0,200.0,73.0,1.6923,262500.0,NEAR OCEAN
+-122.39,37.74,42.0,4110.0,846.0,2147.0,674.0,2.5694,201000.0,NEAR BAY
+-117.13,32.92,16.0,1580.0,241.0,917.0,261.0,4.7266,191100.0,<1H OCEAN
+-122.08,37.39,44.0,1498.0,430.0,848.0,400.0,2.8438,307100.0,NEAR BAY
+-120.36,36.14,18.0,1206.0,274.0,622.0,217.0,1.8264,62000.0,INLAND
+-117.54,33.76,5.0,5846.0,1035.0,3258.0,1001.0,4.7965,160800.0,<1H OCEAN
+-121.95,36.98,34.0,3745.0,958.0,1622.0,802.0,3.1546,261200.0,NEAR OCEAN
+-117.13,33.14,12.0,2258.0,456.0,1147.0,433.0,4.0495,153900.0,<1H OCEAN
+-117.16,32.81,14.0,4328.0,1100.0,2046.0,1044.0,2.2899,159000.0,NEAR OCEAN
+-119.57,36.72,11.0,2510.0,460.0,1248.0,445.0,3.6161,99500.0,INLAND
+-116.99,34.3,29.0,5055.0,1036.0,410.0,191.0,3.5104,157100.0,INLAND
+-121.83,37.26,15.0,3243.0,551.0,1752.0,551.0,5.5849,257400.0,<1H OCEAN
+-123.18,40.58,18.0,1451.0,278.0,695.0,254.0,1.7262,73700.0,INLAND
+-121.33,38.65,23.0,2446.0,523.0,1132.0,513.0,2.6266,198500.0,INLAND
+-118.51,34.22,36.0,2794.0,523.0,1334.0,472.0,4.3462,222100.0,<1H OCEAN
+-122.54,37.95,38.0,2310.0,400.0,971.0,386.0,5.697,435700.0,NEAR BAY
+-121.95,37.96,18.0,2739.0,393.0,1072.0,374.0,6.1436,259500.0,INLAND
+-118.31,33.88,35.0,1939.0,412.0,1036.0,400.0,3.5556,238000.0,<1H OCEAN
+-117.99,33.82,19.0,1991.0,528.0,1202.0,460.0,3.1538,252100.0,<1H OCEAN
+-121.98,37.3,30.0,3404.0,693.0,1794.0,633.0,4.6312,283200.0,<1H OCEAN
+-117.33,33.9,2.0,12837.0,1842.0,4636.0,1453.0,5.1512,187800.0,INLAND
+-119.1,35.33,4.0,6640.0,898.0,3121.0,902.0,6.759,170300.0,INLAND
+-118.88,34.21,26.0,1590.0,196.0,654.0,199.0,6.5851,300000.0,<1H OCEAN
+-118.14,34.18,50.0,1493.0,326.0,1000.0,323.0,3.3068,154400.0,<1H OCEAN
+-118.08,33.99,38.0,1683.0,328.0,1369.0,339.0,3.6196,170700.0,<1H OCEAN
+-118.52,34.24,6.0,3218.0,949.0,2295.0,876.0,3.0926,418500.0,<1H OCEAN
+-119.45,36.61,24.0,1302.0,,693.0,243.0,3.7917,90500.0,INLAND
+-118.26,34.5,6.0,5813.0,908.0,2275.0,790.0,4.7778,340400.0,INLAND
+-117.16,32.71,52.0,845.0,451.0,1230.0,375.0,1.0918,22500.0,NEAR OCEAN
+-117.98,33.95,35.0,4055.0,652.0,1758.0,639.0,6.1898,282400.0,<1H OCEAN
+-120.83,37.07,16.0,3736.0,761.0,1942.0,730.0,2.5598,120200.0,INLAND
+-118.09,33.81,36.0,1371.0,250.0,666.0,257.0,5.0795,243300.0,<1H OCEAN
+-118.41,34.02,27.0,2224.0,618.0,1594.0,625.0,3.0833,315500.0,<1H OCEAN
+-117.09,32.57,17.0,444.0,83.0,357.0,87.0,5.1478,138900.0,NEAR OCEAN
+-118.85,34.21,29.0,2195.0,414.0,1360.0,401.0,3.4773,206700.0,<1H OCEAN
+-121.17,37.97,28.0,1374.0,248.0,769.0,229.0,3.6389,130400.0,INLAND
+-118.34,35.27,10.0,2939.0,605.0,1167.0,446.0,2.3917,79000.0,INLAND
+-118.52,34.25,11.0,7849.0,1664.0,3561.0,1500.0,4.6625,290900.0,<1H OCEAN
+-122.05,37.68,32.0,2015.0,318.0,1019.0,340.0,6.1104,240700.0,NEAR BAY
+-118.03,34.11,34.0,2837.0,460.0,1344.0,458.0,6.5722,437400.0,INLAND
+-117.96,34.05,36.0,1475.0,270.0,1149.0,284.0,3.0904,158600.0,<1H OCEAN
+-117.05,33.05,6.0,7916.0,1293.0,2741.0,1204.0,5.6436,278600.0,<1H OCEAN
+-116.88,32.86,9.0,3049.0,471.0,1527.0,515.0,5.0733,196600.0,<1H OCEAN
+-117.13,32.79,35.0,1458.0,262.0,723.0,257.0,4.2098,174100.0,NEAR OCEAN
+-122.41,37.73,33.0,2789.0,567.0,1682.0,552.0,3.8643,276200.0,NEAR BAY
+-118.36,34.23,15.0,2485.0,742.0,1994.0,670.0,2.8333,183200.0,<1H OCEAN
+-117.93,33.65,26.0,5831.0,1546.0,4738.0,1477.0,3.1483,213000.0,<1H OCEAN
+-122.11,37.11,46.0,1993.0,404.0,850.0,327.0,5.208,206800.0,NEAR OCEAN
+-122.17,37.45,35.0,1025.0,242.0,388.0,232.0,5.1995,500001.0,NEAR BAY
+-117.9,34.07,35.0,1646.0,294.0,1056.0,280.0,3.055,172000.0,<1H OCEAN
+-121.46,38.68,35.0,1299.0,254.0,705.0,245.0,2.8333,103000.0,INLAND
+-118.05,36.64,34.0,2090.0,478.0,896.0,426.0,2.0357,74200.0,INLAND
+-118.05,33.87,18.0,4928.0,773.0,2952.0,754.0,5.8855,313800.0,<1H OCEAN
+-118.06,34.08,37.0,778.0,205.0,850.0,198.0,2.5119,180500.0,<1H OCEAN
+-118.67,34.16,17.0,16544.0,2206.0,6214.0,2118.0,9.1228,500001.0,<1H OCEAN
+-122.04,37.35,20.0,2016.0,313.0,767.0,310.0,6.837,383000.0,<1H OCEAN
+-117.79,33.68,9.0,1633.0,295.0,928.0,297.0,5.7858,265900.0,<1H OCEAN
+-117.11,32.7,37.0,2045.0,502.0,1920.0,472.0,1.8125,83300.0,NEAR OCEAN
+-117.33,34.14,29.0,1646.0,391.0,1296.0,351.0,1.9423,69700.0,INLAND
+-122.18,37.78,43.0,1985.0,440.0,1085.0,407.0,3.4205,136700.0,NEAR BAY
+-118.33,34.18,45.0,1552.0,315.0,785.0,316.0,3.7411,235500.0,<1H OCEAN
+-118.35,34.06,48.0,3551.0,826.0,1601.0,827.0,3.2279,400000.0,<1H OCEAN
+-117.95,33.63,27.0,891.0,183.0,513.0,171.0,6.0,381500.0,<1H OCEAN
+-118.16,34.12,38.0,2231.0,489.0,940.0,484.0,5.4165,435100.0,<1H OCEAN
+-122.48,37.65,39.0,3348.0,666.0,1817.0,668.0,4.2593,227400.0,NEAR OCEAN
+-117.78,33.9,14.0,6239.0,901.0,2923.0,904.0,6.5437,268200.0,<1H OCEAN
+-121.27,38.74,19.0,3869.0,887.0,2086.0,685.0,2.6065,154900.0,INLAND
+-122.07,37.67,28.0,2932.0,739.0,1198.0,624.0,3.2417,210800.0,NEAR BAY
+-122.01,37.57,14.0,16199.0,2993.0,8117.0,2847.0,5.8322,281800.0,NEAR BAY
+-117.65,33.42,25.0,2174.0,428.0,603.0,352.0,3.3967,249400.0,NEAR OCEAN
+-118.61,34.21,41.0,1058.0,228.0,778.0,245.0,3.3534,180500.0,<1H OCEAN
+-121.01,37.65,47.0,1713.0,334.0,570.0,297.0,2.1969,149400.0,INLAND
+-117.11,32.79,16.0,2574.0,771.0,1129.0,721.0,3.3849,96900.0,NEAR OCEAN
+-117.77,34.12,15.0,4260.0,770.0,2007.0,695.0,4.4609,230000.0,INLAND
+-121.39,38.62,27.0,5693.0,1487.0,2334.0,1387.0,2.2844,170500.0,INLAND
+-117.28,34.51,10.0,4676.0,884.0,2845.0,812.0,3.0181,100400.0,INLAND
+-118.12,33.94,35.0,1813.0,313.0,825.0,316.0,5.2485,323800.0,<1H OCEAN
+-117.04,32.77,21.0,1824.0,447.0,962.0,431.0,2.7826,143800.0,<1H OCEAN
+-117.86,33.75,5.0,187.0,49.0,207.0,51.0,1.8,154200.0,<1H OCEAN
+-120.64,35.26,21.0,3298.0,716.0,1862.0,687.0,2.1507,221500.0,NEAR OCEAN
+-118.09,33.93,37.0,1950.0,356.0,1183.0,338.0,4.1449,175300.0,<1H OCEAN
+-122.26,37.89,52.0,3078.0,494.0,1005.0,462.0,6.381,342200.0,NEAR BAY
+-116.45,32.65,22.0,2680.0,643.0,1644.0,516.0,2.1949,127100.0,INLAND
+-122.09,37.66,40.0,1340.0,313.0,766.0,271.0,3.4722,135400.0,NEAR BAY
+-118.47,33.99,50.0,1568.0,501.0,764.0,478.0,3.015,414300.0,<1H OCEAN
+-117.29,34.12,47.0,1648.0,432.0,1308.0,385.0,1.2069,68200.0,INLAND
+-122.02,37.6,32.0,1295.0,280.0,1156.0,300.0,3.5,154300.0,NEAR BAY
+-117.43,34.09,18.0,3172.0,632.0,1621.0,573.0,2.7437,120200.0,INLAND
+-117.89,33.66,32.0,2736.0,550.0,1279.0,534.0,5.5422,253100.0,<1H OCEAN
+-122.52,37.92,24.0,421.0,64.0,163.0,75.0,14.5833,500001.0,NEAR BAY
+-122.0,37.4,35.0,1542.0,298.0,1164.0,318.0,5.9145,236900.0,<1H OCEAN
+-117.18,34.54,5.0,3772.0,619.0,2097.0,635.0,3.8194,98500.0,INLAND
+-117.11,32.84,16.0,4608.0,629.0,2020.0,636.0,6.04,243000.0,<1H OCEAN
+-118.05,33.72,22.0,5416.0,1271.0,2260.0,1184.0,3.8038,174500.0,<1H OCEAN
+-118.16,34.06,25.0,4284.0,741.0,2163.0,701.0,6.1509,315100.0,<1H OCEAN
+-118.11,33.97,33.0,2125.0,500.0,1672.0,476.0,3.6397,166600.0,<1H OCEAN
+-122.45,37.77,52.0,2602.0,,1330.0,647.0,3.5435,278600.0,NEAR BAY
+-118.23,34.13,47.0,1162.0,235.0,781.0,268.0,4.6528,244400.0,<1H OCEAN
+-121.33,38.0,14.0,3731.0,772.0,1679.0,750.0,3.1369,119600.0,INLAND
+-122.19,37.78,52.0,1026.0,180.0,469.0,168.0,2.875,160000.0,NEAR BAY
+-117.95,33.82,35.0,1117.0,181.0,496.0,168.0,4.3269,224700.0,<1H OCEAN
+-119.05,35.44,5.0,2133.0,487.0,1182.0,438.0,3.0268,77000.0,INLAND
+-122.3,38.28,31.0,1633.0,316.0,944.0,300.0,3.3977,158700.0,NEAR BAY
+-118.15,34.19,38.0,1750.0,411.0,1398.0,409.0,2.3967,163100.0,<1H OCEAN
+-121.87,37.34,52.0,1170.0,215.0,604.0,207.0,2.6667,325900.0,<1H OCEAN
+-118.19,33.9,36.0,2326.0,543.0,2073.0,494.0,1.9952,112900.0,<1H OCEAN
+-120.65,35.28,32.0,896.0,240.0,548.0,231.0,2.5455,165900.0,NEAR OCEAN
+-118.19,33.79,41.0,2114.0,612.0,2357.0,529.0,1.7938,142600.0,NEAR OCEAN
+-118.32,33.98,46.0,1611.0,339.0,921.0,314.0,3.0833,103300.0,<1H OCEAN
+-121.32,38.59,24.0,4378.0,910.0,2149.0,812.0,2.5035,123700.0,INLAND
+-119.59,34.39,35.0,622.0,170.0,278.0,139.0,3.6969,335000.0,<1H OCEAN
+-118.28,34.09,52.0,2273.0,663.0,1480.0,597.0,2.3333,196500.0,<1H OCEAN
+-122.37,40.52,18.0,4547.0,774.0,2269.0,766.0,3.7896,98100.0,INLAND
+-117.61,34.09,23.0,1945.0,362.0,1483.0,383.0,4.4205,135500.0,INLAND
+-118.28,34.01,50.0,2601.0,794.0,3080.0,770.0,1.8656,122900.0,<1H OCEAN
+-117.87,33.71,16.0,3397.0,686.0,1924.0,621.0,4.9148,155500.0,<1H OCEAN
+-122.08,37.96,21.0,9135.0,1534.0,3748.0,1502.0,6.0859,266000.0,NEAR BAY
+-117.34,34.14,37.0,1834.0,393.0,1198.0,348.0,2.225,81600.0,INLAND
+-118.66,34.18,25.0,6612.0,857.0,2519.0,843.0,8.3912,419000.0,<1H OCEAN
+-122.46,37.71,49.0,1711.0,348.0,1138.0,325.0,2.875,225000.0,NEAR OCEAN
+-115.94,33.38,5.0,186.0,43.0,41.0,21.0,2.7,58800.0,INLAND
+-118.57,34.18,36.0,2981.0,441.0,1243.0,413.0,6.5304,439800.0,<1H OCEAN
+-122.03,37.31,25.0,2131.0,410.0,1132.0,395.0,5.3508,409100.0,<1H OCEAN
+-118.38,33.82,34.0,1822.0,364.0,750.0,366.0,5.9907,500001.0,NEAR OCEAN
+-118.38,34.2,23.0,4138.0,1171.0,3911.0,1068.0,3.0125,181700.0,<1H OCEAN
+-120.35,37.98,4.0,1658.0,301.0,676.0,278.0,3.5714,149500.0,INLAND
+-121.25,40.27,25.0,958.0,245.0,28.0,16.0,2.625,67500.0,INLAND
+-121.32,38.64,19.0,8501.0,1558.0,3576.0,1467.0,3.6523,158500.0,INLAND
+-118.14,33.86,36.0,1703.0,325.0,845.0,308.0,5.0106,210800.0,<1H OCEAN
+-120.04,36.95,36.0,1528.0,347.0,1334.0,304.0,1.3594,48300.0,INLAND
+-117.93,34.15,14.0,9610.0,2005.0,4723.0,1907.0,4.0393,156800.0,INLAND
+-118.2,34.02,48.0,2230.0,593.0,2419.0,598.0,2.3944,130700.0,<1H OCEAN
+-117.01,33.75,15.0,2873.0,903.0,1094.0,659.0,1.8015,105100.0,INLAND
+-117.87,34.15,24.0,5745.0,735.0,2061.0,679.0,8.2827,451400.0,INLAND
+-118.27,33.92,36.0,1465.0,346.0,1147.0,324.0,1.7262,88800.0,<1H OCEAN
+-120.94,39.32,14.0,3120.0,595.0,1569.0,556.0,3.5385,157400.0,INLAND
+-123.38,41.8,25.0,1941.0,477.0,1000.0,390.0,2.2976,54400.0,INLAND
+-122.79,39.08,23.0,952.0,200.0,321.0,128.0,1.5208,89000.0,INLAND
+-120.47,34.94,17.0,1368.0,308.0,642.0,303.0,1.8633,109400.0,<1H OCEAN
+-118.42,34.06,44.0,533.0,90.0,291.0,97.0,10.8045,500001.0,<1H OCEAN
+-121.08,38.95,18.0,1931.0,380.0,1271.0,377.0,2.7463,156100.0,INLAND
+-121.15,38.68,6.0,9798.0,1551.0,4583.0,1494.0,5.0159,189600.0,INLAND
+-118.07,33.96,30.0,928.0,230.0,913.0,214.0,2.6991,147100.0,<1H OCEAN
+-122.44,37.78,39.0,1181.0,310.0,901.0,281.0,1.4866,237500.0,NEAR BAY
+-117.15,32.93,16.0,2718.0,438.0,1515.0,431.0,5.1433,185300.0,<1H OCEAN
+-123.59,38.8,17.0,5202.0,1037.0,1742.0,803.0,3.1201,176100.0,NEAR OCEAN
+-120.97,38.65,9.0,3707.0,602.0,1601.0,555.0,4.0714,300600.0,INLAND
+-117.06,32.58,13.0,3435.0,708.0,1761.0,699.0,3.4792,107600.0,NEAR OCEAN
+-118.51,34.19,35.0,2537.0,418.0,1161.0,421.0,5.3028,229200.0,<1H OCEAN
+-124.16,40.77,35.0,2141.0,438.0,1053.0,434.0,2.8529,85600.0,NEAR OCEAN
+-117.95,33.92,11.0,3127.0,706.0,1594.0,694.0,4.3426,141300.0,<1H OCEAN
+-122.49,37.74,44.0,1472.0,275.0,820.0,310.0,5.6826,300000.0,NEAR OCEAN
+-117.22,34.13,10.0,5951.0,1330.0,3204.0,1159.0,2.7011,110200.0,INLAND
+-123.07,39.12,24.0,1098.0,193.0,353.0,145.0,3.8333,92600.0,<1H OCEAN
+-122.0,37.98,36.0,404.0,77.0,237.0,88.0,4.525,161300.0,INLAND
+-117.89,34.12,35.0,1447.0,272.0,1224.0,268.0,3.9934,141900.0,<1H OCEAN
+-122.01,38.26,12.0,4132.0,710.0,2087.0,633.0,4.5987,139700.0,INLAND
+-122.2,37.77,39.0,2689.0,597.0,1888.0,537.0,2.2562,94800.0,NEAR BAY
+-118.18,33.77,30.0,1418.0,439.0,720.0,417.0,2.6371,159400.0,NEAR OCEAN
+-117.22,32.75,26.0,696.0,185.0,384.0,184.0,2.6121,125000.0,NEAR OCEAN
+-120.31,37.29,40.0,1542.0,341.0,1283.0,341.0,1.6929,55900.0,INLAND
+-118.31,34.09,30.0,3165.0,1263.0,3678.0,1141.0,2.0,240600.0,<1H OCEAN
+-118.02,34.1,36.0,452.0,80.0,248.0,83.0,1.9688,226000.0,INLAND
+-118.28,34.17,52.0,2332.0,433.0,1135.0,440.0,5.5658,331200.0,<1H OCEAN
+-118.25,34.05,52.0,2806.0,1944.0,2232.0,1605.0,0.6775,350000.0,<1H OCEAN
+-117.36,33.98,33.0,2070.0,469.0,1851.0,467.0,2.4667,80700.0,INLAND
+-117.68,34.15,4.0,4082.0,578.0,1996.0,580.0,6.7813,286300.0,INLAND
+-122.26,37.54,5.0,1649.0,388.0,779.0,376.0,6.9635,417300.0,NEAR BAY
+-120.06,36.97,35.0,1859.0,428.0,1208.0,399.0,1.4044,61700.0,INLAND
+-121.02,36.24,12.0,2198.0,507.0,1971.0,502.0,2.6801,100000.0,<1H OCEAN
+-117.09,32.62,37.0,1925.0,428.0,1344.0,426.0,2.4866,129700.0,NEAR OCEAN
+-117.1,34.57,6.0,5110.0,1044.0,1938.0,724.0,3.1917,112800.0,INLAND
+-117.08,32.8,25.0,2963.0,552.0,1162.0,556.0,3.625,184500.0,NEAR OCEAN
+-118.08,33.97,35.0,825.0,155.0,590.0,144.0,4.6333,161200.0,<1H OCEAN
+-119.19,34.28,28.0,3231.0,524.0,1665.0,540.0,4.8583,224200.0,NEAR OCEAN
+-121.67,40.89,17.0,2548.0,537.0,1118.0,461.0,2.267,57800.0,INLAND
+-123.2,39.23,26.0,786.0,168.0,494.0,161.0,2.3583,105400.0,<1H OCEAN
+-117.14,32.75,25.0,1908.0,513.0,956.0,467.0,2.4828,133300.0,NEAR OCEAN
+-118.1,33.74,33.0,2119.0,524.0,872.0,465.0,4.537,495500.0,NEAR OCEAN
+-122.35,37.96,32.0,1991.0,504.0,1139.0,423.0,2.0353,113600.0,NEAR BAY
+-120.73,37.38,23.0,1451.0,292.0,1052.0,265.0,2.8698,72900.0,INLAND
+-122.13,38.01,51.0,1262.0,309.0,608.0,298.0,2.1974,139300.0,NEAR BAY
+-117.12,32.69,46.0,200.0,77.0,180.0,65.0,1.0658,93800.0,NEAR OCEAN
+-118.11,33.8,36.0,1837.0,319.0,810.0,305.0,4.3897,235000.0,<1H OCEAN
+-122.08,37.39,39.0,2210.0,483.0,1023.0,450.0,4.5833,342400.0,NEAR BAY
+-118.37,34.16,10.0,2606.0,748.0,1373.0,680.0,3.6128,225000.0,<1H OCEAN
+-118.2,33.93,41.0,857.0,201.0,934.0,227.0,2.6339,145700.0,<1H OCEAN
+-121.77,36.91,8.0,2715.0,750.0,2580.0,718.0,2.8348,162000.0,<1H OCEAN
+-122.01,37.4,24.0,1297.0,297.0,441.0,282.0,3.1439,47500.0,<1H OCEAN
+-118.02,34.14,31.0,6854.0,1578.0,4131.0,1524.0,3.5878,222800.0,INLAND
+-122.44,37.8,52.0,1603.0,487.0,727.0,464.0,3.9856,500001.0,NEAR BAY
+-122.54,37.62,35.0,1481.0,277.0,747.0,254.0,4.4286,262100.0,NEAR OCEAN
+-119.74,34.45,29.0,2526.0,388.0,1092.0,409.0,6.0597,383100.0,<1H OCEAN
+-121.57,39.76,20.0,1384.0,257.0,557.0,232.0,2.0882,104900.0,INLAND
+-121.48,38.57,47.0,2438.0,804.0,1148.0,747.0,1.4301,141700.0,INLAND
+-118.13,34.07,32.0,1880.0,428.0,1404.0,424.0,3.085,220500.0,<1H OCEAN
+-119.71,34.42,39.0,1172.0,322.0,606.0,316.0,2.16,259100.0,<1H OCEAN
+-115.57,32.85,17.0,1039.0,256.0,728.0,246.0,1.7411,63500.0,INLAND
+-118.14,34.01,42.0,1007.0,277.0,1060.0,268.0,3.0179,153700.0,<1H OCEAN
+-118.45,34.18,33.0,2077.0,486.0,1021.0,450.0,3.6019,225000.0,<1H OCEAN
+-121.49,39.52,25.0,848.0,153.0,436.0,155.0,3.9028,93800.0,INLAND
+-117.91,33.81,18.0,1181.0,353.0,781.0,340.0,2.5625,153100.0,<1H OCEAN
+-117.32,34.03,13.0,3853.0,761.0,1685.0,669.0,3.9024,122400.0,INLAND
+-117.08,32.82,10.0,5177.0,856.0,2190.0,816.0,5.9734,271700.0,<1H OCEAN
+-118.9,35.26,31.0,6145.0,1492.0,5666.0,1457.0,1.9066,54600.0,INLAND
+-117.26,32.81,25.0,2076.0,586.0,1060.0,554.0,2.8421,227800.0,NEAR OCEAN
+-121.9,37.25,28.0,2714.0,502.0,1389.0,490.0,5.7385,240400.0,<1H OCEAN
+-122.7,38.66,43.0,1384.0,284.0,582.0,224.0,3.9063,210000.0,<1H OCEAN
+-118.41,34.18,43.0,1840.0,356.0,966.0,323.0,4.7171,237900.0,<1H OCEAN
+-121.63,36.68,24.0,2591.0,739.0,3243.0,702.0,2.1766,108500.0,<1H OCEAN
+-117.06,32.61,34.0,4325.0,1015.0,2609.0,979.0,2.8489,128300.0,NEAR OCEAN
+-117.16,32.72,27.0,1245.0,471.0,653.0,451.0,1.2668,225000.0,NEAR OCEAN
+-116.99,32.81,25.0,4436.0,758.0,1997.0,738.0,4.2386,201000.0,<1H OCEAN
+-122.89,38.38,16.0,2017.0,369.0,931.0,336.0,5.7664,267500.0,<1H OCEAN
+-121.73,38.13,40.0,1266.0,257.0,547.0,247.0,3.0288,164400.0,INLAND
+-120.61,37.32,18.0,5009.0,826.0,2497.0,805.0,4.25,146300.0,INLAND
+-118.37,33.88,27.0,1688.0,331.0,811.0,327.0,4.5357,334200.0,<1H OCEAN
+-117.67,33.51,17.0,2112.0,480.0,1893.0,433.0,4.0388,120400.0,<1H OCEAN
+-117.66,34.12,22.0,2272.0,278.0,933.0,305.0,8.8204,390500.0,INLAND
+-121.82,38.02,46.0,176.0,43.0,101.0,40.0,2.2361,93800.0,INLAND
+-118.7,34.28,25.0,2377.0,491.0,1200.0,439.0,4.7083,196100.0,<1H OCEAN
+-117.06,32.63,29.0,4168.0,742.0,2096.0,713.0,4.2204,169800.0,NEAR OCEAN
+-118.14,33.86,37.0,1404.0,257.0,652.0,258.0,4.2062,195400.0,<1H OCEAN
+-121.43,37.73,40.0,1718.0,391.0,1312.0,388.0,2.9955,134700.0,INLAND
+-122.11,37.67,36.0,2110.0,389.0,952.0,370.0,3.8,187500.0,NEAR BAY
+-121.56,39.53,12.0,1733.0,421.0,1861.0,415.0,1.5771,65200.0,INLAND
+-121.33,38.04,15.0,2903.0,440.0,1325.0,423.0,4.5179,145600.0,INLAND
+-119.74,36.74,39.0,4893.0,1210.0,4749.0,1067.0,1.2065,55600.0,INLAND
+-122.46,37.67,16.0,3372.0,1101.0,2049.0,1021.0,4.1303,146500.0,NEAR OCEAN
+-117.71,33.51,12.0,4676.0,698.0,1543.0,598.0,6.379,500001.0,<1H OCEAN
+-118.99,35.37,38.0,918.0,220.0,743.0,222.0,1.7292,58100.0,INLAND
+-117.11,32.58,12.0,1086.0,294.0,870.0,290.0,2.4213,132500.0,NEAR OCEAN
+-120.86,37.49,22.0,2140.0,445.0,1441.0,409.0,2.4706,89400.0,INLAND
+-118.27,34.07,34.0,786.0,341.0,1239.0,320.0,2.3438,152100.0,<1H OCEAN
+-117.87,34.12,33.0,2059.0,361.0,1073.0,339.0,4.2454,183800.0,<1H OCEAN
+-122.28,37.85,52.0,2246.0,472.0,1005.0,449.0,2.4167,152700.0,NEAR BAY
+-119.45,36.48,38.0,402.0,86.0,311.0,87.0,3.1719,106300.0,INLAND
+-118.03,34.01,10.0,6531.0,1036.0,2975.0,1018.0,6.2319,403700.0,<1H OCEAN
+-117.36,33.6,10.0,4097.0,813.0,2082.0,731.0,3.2258,159300.0,<1H OCEAN
+-121.8,37.23,18.0,3179.0,526.0,1663.0,507.0,5.9225,265800.0,<1H OCEAN
+-118.18,33.79,42.0,1571.0,435.0,1631.0,417.0,1.6384,128000.0,NEAR OCEAN
+-117.98,33.78,31.0,2825.0,546.0,1908.0,563.0,3.9798,187500.0,<1H OCEAN
+-117.71,34.09,36.0,2637.0,476.0,1385.0,483.0,4.1739,158700.0,INLAND
+-121.86,37.42,20.0,5032.0,808.0,2695.0,801.0,6.6227,264800.0,<1H OCEAN
+-118.15,33.95,31.0,1053.0,230.0,686.0,211.0,4.0,263200.0,<1H OCEAN
+-121.28,38.75,52.0,493.0,89.0,189.0,94.0,2.108,83800.0,INLAND
+-117.96,33.94,34.0,2228.0,399.0,1159.0,378.0,4.8906,228900.0,<1H OCEAN
+-121.95,37.32,39.0,1164.0,199.0,619.0,231.0,4.6304,263200.0,<1H OCEAN
+-118.45,34.18,22.0,2516.0,826.0,3350.0,713.0,2.0192,158300.0,<1H OCEAN
+-122.06,37.96,37.0,1784.0,313.0,788.0,304.0,4.2917,189600.0,NEAR BAY
+-118.32,33.88,35.0,1818.0,339.0,828.0,319.0,4.3036,282100.0,<1H OCEAN
+-117.86,33.75,39.0,275.0,87.0,554.0,103.0,3.5972,158000.0,<1H OCEAN
+-117.85,33.79,40.0,1251.0,336.0,729.0,343.0,2.4688,236400.0,<1H OCEAN
+-118.03,34.14,31.0,4353.0,1117.0,2338.0,1037.0,3.0727,242600.0,INLAND
+-121.3,38.89,23.0,1750.0,297.0,1012.0,315.0,3.4706,99300.0,INLAND
+-118.05,33.81,26.0,2523.0,437.0,1377.0,450.0,5.2542,234600.0,<1H OCEAN
+-118.22,33.96,32.0,2232.0,603.0,2361.0,608.0,2.5966,170900.0,<1H OCEAN
+-119.78,36.86,10.0,2902.0,363.0,1200.0,363.0,8.3608,187300.0,INLAND
+-117.78,33.68,11.0,1994.0,477.0,849.0,411.0,4.0187,235600.0,<1H OCEAN
+-117.58,33.87,34.0,1511.0,272.0,773.0,265.0,3.5313,142100.0,INLAND
+-122.49,37.78,52.0,3440.0,722.0,1663.0,665.0,3.0278,356300.0,NEAR BAY
+-118.09,34.12,45.0,2966.0,415.0,1231.0,409.0,7.8347,500001.0,<1H OCEAN
+-121.37,38.42,18.0,2643.0,502.0,1755.0,541.0,3.3281,91200.0,INLAND
+-117.58,33.87,17.0,2772.0,449.0,1685.0,461.0,5.0464,163900.0,INLAND
+-117.02,32.78,31.0,2567.0,,1198.0,499.0,3.4659,163000.0,<1H OCEAN
+-118.21,33.88,32.0,1507.0,379.0,1082.0,350.0,3.225,138200.0,<1H OCEAN
+-117.1,32.69,37.0,1269.0,340.0,1369.0,302.0,2.2102,87200.0,NEAR OCEAN
+-118.34,34.18,46.0,1393.0,301.0,714.0,295.0,2.8125,229900.0,<1H OCEAN
+-118.13,34.12,46.0,3156.0,430.0,1109.0,423.0,10.7397,500001.0,<1H OCEAN
+-117.78,33.76,25.0,2260.0,261.0,719.0,254.0,11.4537,500001.0,<1H OCEAN
+-116.96,33.94,22.0,1999.0,497.0,1304.0,479.0,1.4063,81900.0,INLAND
+-122.95,40.71,26.0,2231.0,421.0,987.0,364.0,2.4792,88800.0,INLAND
+-117.76,34.1,28.0,4086.0,871.0,1973.0,853.0,2.621,202200.0,INLAND
+-121.88,37.27,24.0,4567.0,688.0,2102.0,695.0,5.6895,289000.0,<1H OCEAN
+-117.57,33.9,7.0,3797.0,850.0,2369.0,720.0,3.5525,137600.0,INLAND
+-122.17,37.75,44.0,1218.0,248.0,763.0,254.0,2.3281,88800.0,NEAR BAY
+-122.09,37.68,41.0,1382.0,353.0,704.0,314.0,3.5114,197500.0,NEAR BAY
+-121.42,38.63,42.0,1385.0,273.0,740.0,274.0,2.6055,78000.0,INLAND
+-118.88,34.42,20.0,728.0,120.0,360.0,115.0,6.1244,375000.0,<1H OCEAN
+-118.17,33.94,17.0,1145.0,209.0,499.0,202.0,4.6389,165500.0,<1H OCEAN
+-118.1,33.92,35.0,2017.0,383.0,1388.0,386.0,4.0774,171600.0,<1H OCEAN
+-118.19,34.05,43.0,977.0,266.0,1084.0,259.0,2.7708,127900.0,<1H OCEAN
+-122.19,39.92,20.0,2563.0,658.0,1363.0,611.0,1.023,54200.0,INLAND
+-118.38,33.8,36.0,4421.0,702.0,1433.0,624.0,8.0838,500001.0,NEAR OCEAN
+-121.76,36.77,27.0,1608.0,503.0,2031.0,498.0,2.3384,121000.0,<1H OCEAN
+-118.35,34.06,52.0,3446.0,1360.0,1768.0,1245.0,2.4722,500001.0,<1H OCEAN
+-118.22,33.98,32.0,2643.0,737.0,2784.0,711.0,2.5352,184400.0,<1H OCEAN
+-118.44,34.15,29.0,5474.0,1457.0,2352.0,1326.0,3.415,382500.0,<1H OCEAN
+-118.29,34.03,42.0,1680.0,557.0,2099.0,526.0,1.9167,136400.0,<1H OCEAN
+-122.03,37.31,19.0,2885.0,859.0,1520.0,784.0,3.375,275700.0,<1H OCEAN
+-122.24,37.82,52.0,3665.0,517.0,1470.0,520.0,6.155,398600.0,NEAR BAY
+-117.27,32.85,26.0,1373.0,,608.0,268.0,4.425,475000.0,NEAR OCEAN
+-118.13,34.1,26.0,3050.0,825.0,2153.0,772.0,3.1103,214100.0,<1H OCEAN
+-118.79,34.26,17.0,1986.0,249.0,761.0,241.0,7.2137,401900.0,<1H OCEAN
+-122.06,37.4,21.0,12855.0,3226.0,7273.0,3052.0,4.3351,267400.0,NEAR BAY
+-117.06,32.73,33.0,3444.0,619.0,1884.0,582.0,3.7891,126700.0,NEAR OCEAN
+-122.62,38.96,16.0,1914.0,446.0,828.0,332.0,2.0577,69000.0,INLAND
+-121.98,38.05,31.0,2810.0,518.0,1640.0,503.0,3.3661,98500.0,INLAND
+-117.22,32.87,14.0,3512.0,807.0,1835.0,792.0,3.35,171000.0,NEAR OCEAN
+-117.93,33.74,30.0,1654.0,434.0,1843.0,467.0,3.1403,153000.0,<1H OCEAN
+-117.18,32.69,48.0,2764.0,491.0,978.0,449.0,5.1249,432400.0,NEAR OCEAN
+-123.78,40.05,17.0,2019.0,496.0,899.0,347.0,2.1864,101900.0,<1H OCEAN
+-116.19,33.67,16.0,1859.0,476.0,1994.0,477.0,1.7297,67500.0,INLAND
+-117.74,35.65,15.0,2357.0,484.0,1110.0,442.0,3.1755,81700.0,INLAND
+-120.27,38.02,13.0,3839.0,715.0,1486.0,532.0,3.1875,99800.0,INLAND
+-117.96,33.86,35.0,2181.0,371.0,1249.0,358.0,4.2937,183200.0,<1H OCEAN
+-122.05,37.95,27.0,3513.0,791.0,1875.0,694.0,3.1838,182000.0,NEAR BAY
+-118.47,34.0,42.0,1271.0,301.0,574.0,312.0,3.1304,340500.0,<1H OCEAN
+-118.08,33.97,36.0,1620.0,298.0,1258.0,309.0,3.9773,166700.0,<1H OCEAN
+-122.22,38.1,38.0,931.0,181.0,566.0,207.0,3.0221,93300.0,NEAR BAY
+-121.8,39.33,30.0,1019.0,192.0,501.0,185.0,2.5259,81300.0,INLAND
+-118.43,33.99,45.0,2092.0,451.0,1190.0,429.0,3.8021,323000.0,<1H OCEAN
+-118.28,34.11,46.0,1156.0,203.0,514.0,213.0,4.2019,352100.0,<1H OCEAN
+-122.4,37.72,37.0,971.0,248.0,647.0,208.0,2.1187,239300.0,NEAR BAY
+-114.6,34.83,46.0,1497.0,309.0,787.0,271.0,2.1908,48100.0,INLAND
+-124.11,40.93,17.0,1661.0,329.0,948.0,357.0,2.7639,90200.0,NEAR OCEAN
+-121.89,37.49,9.0,4909.0,577.0,1981.0,591.0,9.7194,500001.0,<1H OCEAN
+-118.24,33.88,37.0,1843.0,366.0,1207.0,351.0,2.4821,111000.0,<1H OCEAN
+-118.19,33.93,42.0,1829.0,391.0,1614.0,377.0,3.1912,146400.0,<1H OCEAN
+-118.34,34.19,43.0,1029.0,252.0,613.0,255.0,2.6827,219900.0,<1H OCEAN
+-122.28,37.54,24.0,5114.0,1357.0,3169.0,1268.0,3.9699,293200.0,NEAR OCEAN
+-117.38,33.19,35.0,928.0,264.0,538.0,248.0,2.4583,197900.0,NEAR OCEAN
+-118.05,33.98,34.0,2142.0,390.0,1305.0,406.0,4.0379,172800.0,<1H OCEAN
+-119.34,34.39,27.0,669.0,131.0,314.0,106.0,2.4659,231300.0,NEAR OCEAN
+-122.38,37.73,38.0,1388.0,276.0,871.0,265.0,2.1667,193800.0,NEAR BAY
+-121.98,38.35,16.0,1697.0,267.0,832.0,277.0,4.4375,132600.0,INLAND
+-118.22,33.97,43.0,381.0,67.0,259.0,60.0,3.0313,166100.0,<1H OCEAN
+-122.25,37.77,52.0,2650.0,566.0,1468.0,567.0,3.0161,215700.0,NEAR BAY
+-118.04,34.11,37.0,1275.0,177.0,598.0,174.0,7.1885,500001.0,INLAND
+-122.21,37.79,52.0,762.0,190.0,600.0,195.0,3.0893,125000.0,NEAR BAY
+-119.58,36.77,19.0,3225.0,548.0,1760.0,542.0,4.0227,126500.0,INLAND
+-122.3,37.91,40.0,2866.0,617.0,1305.0,589.0,3.6321,209100.0,NEAR BAY
+-118.17,34.11,39.0,1758.0,436.0,892.0,447.0,3.6406,278900.0,<1H OCEAN
+-118.32,33.91,35.0,940.0,197.0,640.0,215.0,4.2,181300.0,<1H OCEAN
+-119.78,36.75,49.0,1175.0,307.0,982.0,278.0,1.2937,52000.0,INLAND
+-121.94,37.76,4.0,6875.0,1439.0,2889.0,1307.0,4.6932,356100.0,<1H OCEAN
+-122.26,38.02,5.0,3846.0,786.0,2053.0,716.0,5.0473,184800.0,NEAR BAY
+-121.94,37.42,16.0,3936.0,788.0,1910.0,769.0,4.7049,112500.0,<1H OCEAN
+-120.88,38.32,18.0,2791.0,492.0,1187.0,438.0,3.2589,103000.0,INLAND
+-118.33,34.09,36.0,654.0,186.0,416.0,138.0,3.6953,200000.0,<1H OCEAN
+-117.41,34.23,17.0,889.0,131.0,439.0,141.0,6.1426,155000.0,INLAND
+-119.73,34.45,44.0,2261.0,328.0,763.0,294.0,6.7449,415600.0,<1H OCEAN
+-122.34,40.57,26.0,2187.0,472.0,1339.0,463.0,2.0395,67900.0,INLAND
+-117.22,34.07,8.0,3065.0,692.0,1440.0,666.0,3.2368,129200.0,INLAND
+-122.41,37.71,47.0,2289.0,481.0,1697.0,465.0,3.4773,226300.0,NEAR BAY
+-118.02,33.95,36.0,1632.0,295.0,797.0,283.0,4.2292,179500.0,<1H OCEAN
+-117.18,34.07,28.0,1306.0,279.0,885.0,255.0,2.1154,75300.0,INLAND
+-122.13,37.68,43.0,1676.0,340.0,924.0,328.0,3.6,179400.0,NEAR BAY
+-117.44,33.9,23.0,4487.0,754.0,2609.0,778.0,4.2788,148700.0,INLAND
+-117.55,33.93,25.0,5187.0,934.0,2725.0,860.0,4.1865,154300.0,INLAND
+-118.32,34.11,48.0,4472.0,1579.0,2796.0,1397.0,2.3974,410700.0,<1H OCEAN
+-122.07,37.94,30.0,1260.0,276.0,707.0,221.0,2.892,220800.0,NEAR BAY
+-118.83,34.15,16.0,3380.0,731.0,1227.0,641.0,4.2857,233200.0,NEAR OCEAN
+-118.32,33.95,43.0,3819.0,708.0,1505.0,712.0,3.1719,183500.0,<1H OCEAN
+-117.03,32.74,35.0,1878.0,454.0,991.0,409.0,2.4345,129700.0,NEAR OCEAN
+-122.18,37.73,44.0,548.0,119.0,435.0,136.0,2.1111,79700.0,NEAR BAY
+-122.44,37.67,35.0,1814.0,365.0,1025.0,384.0,4.425,268400.0,NEAR OCEAN
+-118.6,34.21,21.0,9512.0,2560.0,7282.0,2387.0,2.8039,227500.0,<1H OCEAN
+-120.02,36.8,25.0,1270.0,255.0,1050.0,245.0,2.1618,55300.0,INLAND
+-118.18,34.08,31.0,1318.0,311.0,1164.0,289.0,2.9939,135500.0,<1H OCEAN
+-122.49,37.75,47.0,2140.0,425.0,1105.0,401.0,3.7054,308500.0,NEAR OCEAN
+-117.4,34.04,17.0,1906.0,334.0,1550.0,338.0,3.025,81800.0,INLAND
+-119.78,36.75,31.0,1404.0,379.0,1515.0,387.0,1.2813,56400.0,INLAND
+-122.02,37.96,25.0,2615.0,368.0,935.0,366.0,6.6727,305100.0,NEAR BAY
+-118.23,33.92,24.0,1555.0,406.0,1665.0,361.0,1.6437,98800.0,<1H OCEAN
+-120.43,35.17,16.0,947.0,163.0,477.0,137.0,3.851,315000.0,<1H OCEAN
+-121.34,38.05,16.0,667.0,92.0,267.0,90.0,5.6147,244700.0,INLAND
+-122.43,37.79,52.0,3522.0,938.0,1319.0,887.0,4.3986,500001.0,NEAR BAY
+-118.14,34.18,52.0,1700.0,317.0,996.0,329.0,3.9688,175000.0,<1H OCEAN
+-122.56,37.9,24.0,221.0,41.0,75.0,38.0,5.1292,362500.0,NEAR BAY
+-121.5,38.5,29.0,2049.0,330.0,787.0,309.0,3.7414,98500.0,INLAND
+-122.12,37.68,40.0,1553.0,253.0,724.0,267.0,4.38,196400.0,NEAR BAY
+-117.02,32.71,20.0,4050.0,745.0,2870.0,761.0,3.7366,121800.0,NEAR OCEAN
+-122.28,38.32,12.0,4609.0,1005.0,2293.0,960.0,3.4543,194500.0,NEAR BAY
+-118.22,34.19,31.0,4704.0,920.0,1895.0,886.0,4.9297,400000.0,<1H OCEAN
+-117.24,32.71,32.0,4164.0,701.0,1277.0,607.0,6.6661,500001.0,NEAR OCEAN
+-118.05,34.16,41.0,3320.0,713.0,1236.0,659.0,3.5694,278600.0,INLAND
+-120.95,37.09,43.0,1116.0,222.0,801.0,207.0,2.875,97200.0,INLAND
+-120.83,35.36,28.0,4323.0,886.0,1650.0,705.0,2.7266,266800.0,NEAR OCEAN
+-118.1,34.17,46.0,1774.0,315.0,753.0,330.0,4.7241,279600.0,INLAND
+-115.58,32.81,10.0,1088.0,203.0,533.0,201.0,3.6597,87500.0,INLAND
+-122.45,37.73,52.0,2035.0,424.0,1193.0,430.0,5.0634,264200.0,NEAR BAY
+-121.62,36.69,11.0,4712.0,1098.0,5982.0,1105.0,2.5986,135700.0,<1H OCEAN
+-118.9,34.4,16.0,2614.0,575.0,1163.0,524.0,1.5781,134400.0,<1H OCEAN
+-119.65,36.35,38.0,3148.0,568.0,1378.0,537.0,2.8788,85500.0,INLAND
+-120.45,37.32,19.0,3136.0,466.0,1631.0,484.0,3.6471,101400.0,INLAND
+-118.91,34.28,6.0,6106.0,1134.0,3246.0,1062.0,5.2206,280200.0,<1H OCEAN
+-119.68,36.63,39.0,1237.0,256.0,638.0,239.0,3.0139,65300.0,INLAND
+-118.29,33.81,19.0,7023.0,1538.0,3993.0,1412.0,5.0532,218200.0,<1H OCEAN
+-117.08,32.75,20.0,1886.0,586.0,1134.0,525.0,1.5029,100000.0,NEAR OCEAN
+-118.33,34.01,47.0,1320.0,259.0,653.0,291.0,3.7727,193000.0,<1H OCEAN
+-119.25,35.79,8.0,3271.0,797.0,2700.0,688.0,1.7418,62200.0,INLAND
+-117.99,33.8,25.0,3179.0,639.0,2526.0,623.0,3.3281,180800.0,<1H OCEAN
+-118.44,34.17,29.0,2685.0,642.0,1085.0,599.0,3.2763,279400.0,<1H OCEAN
+-119.76,34.44,28.0,1985.0,582.0,1092.0,548.0,2.4701,290900.0,NEAR OCEAN
+-119.71,34.42,52.0,1838.0,692.0,851.0,576.0,1.4851,237500.0,<1H OCEAN
+-118.21,33.91,37.0,1073.0,265.0,1197.0,250.0,2.7109,133000.0,<1H OCEAN
+-121.88,37.33,35.0,3300.0,1154.0,3120.0,1075.0,1.473,213600.0,<1H OCEAN
+-117.25,33.19,18.0,1891.0,306.0,830.0,279.0,4.5764,207000.0,<1H OCEAN
+-117.31,34.11,52.0,851.0,190.0,731.0,190.0,1.9044,64900.0,INLAND
+-117.4,33.97,38.0,1383.0,238.0,649.0,232.0,5.0194,148900.0,INLAND
+-122.26,38.1,24.0,1213.0,395.0,699.0,386.0,1.3007,94600.0,NEAR BAY
+-118.32,33.82,25.0,2587.0,512.0,1219.0,509.0,4.4271,382100.0,<1H OCEAN
+-116.25,33.68,16.0,926.0,189.0,238.0,118.0,3.0114,366700.0,INLAND
+-121.46,38.57,52.0,1917.0,367.0,722.0,358.0,3.1484,158900.0,INLAND
+-121.62,37.09,37.0,1593.0,303.0,1030.0,287.0,3.9306,260700.0,<1H OCEAN
+-118.0,34.16,52.0,1354.0,227.0,531.0,206.0,4.8059,270600.0,INLAND
+-116.95,32.74,7.0,2722.0,578.0,1429.0,574.0,3.9583,141700.0,<1H OCEAN
+-120.8,38.31,37.0,1341.0,256.0,533.0,242.0,3.2135,123600.0,INLAND
+-117.68,35.64,15.0,3253.0,573.0,1408.0,586.0,5.2043,95700.0,INLAND
+-117.62,33.77,43.0,1911.0,439.0,930.0,433.0,4.6369,186400.0,<1H OCEAN
+-117.1,32.67,26.0,2629.0,763.0,2721.0,767.0,2.0982,109100.0,NEAR OCEAN
+-122.05,36.87,18.0,2232.0,440.0,1091.0,458.0,3.8269,276000.0,NEAR OCEAN
+-117.67,33.46,18.0,1679.0,271.0,783.0,257.0,5.3999,300000.0,<1H OCEAN
+-118.46,33.98,27.0,2217.0,520.0,806.0,458.0,3.8935,500001.0,<1H OCEAN
+-118.31,33.96,43.0,2149.0,493.0,1316.0,462.0,1.528,131800.0,<1H OCEAN
+-121.85,37.26,16.0,1982.0,280.0,1030.0,297.0,6.4339,289200.0,<1H OCEAN
+-117.77,33.69,15.0,500.0,113.0,261.0,116.0,5.0631,154000.0,<1H OCEAN
+-118.14,34.16,39.0,2776.0,840.0,2546.0,773.0,2.575,153500.0,<1H OCEAN
+-118.16,34.07,42.0,3836.0,777.0,2118.0,754.0,3.6364,254600.0,<1H OCEAN
+-119.88,36.93,12.0,3174.0,520.0,1590.0,488.0,4.5347,101200.0,INLAND
+-118.3,33.84,36.0,1428.0,268.0,825.0,250.0,4.7222,239600.0,<1H OCEAN
+-118.38,34.06,50.0,1509.0,291.0,690.0,259.0,6.2344,500001.0,<1H OCEAN
+-119.98,36.86,31.0,2366.0,482.0,1933.0,433.0,3.0234,65000.0,INLAND
+-117.97,33.82,26.0,4013.0,985.0,2442.0,922.0,3.7655,197700.0,<1H OCEAN
+-122.42,37.78,17.0,1257.0,339.0,1093.0,384.0,1.8438,72500.0,NEAR BAY
+-121.42,38.22,35.0,1507.0,313.0,868.0,283.0,2.0284,96300.0,INLAND
+-122.19,37.83,28.0,1326.0,184.0,463.0,190.0,8.2049,335200.0,NEAR BAY
+-118.31,34.06,36.0,369.0,147.0,145.0,136.0,0.8804,450000.0,<1H OCEAN
+-118.45,33.99,45.0,1132.0,269.0,654.0,264.0,4.5673,343100.0,<1H OCEAN
+-118.23,33.97,47.0,932.0,295.0,1226.0,264.0,1.6065,111400.0,<1H OCEAN
+-118.43,34.17,42.0,777.0,102.0,284.0,113.0,11.2093,500001.0,<1H OCEAN
+-118.92,34.19,16.0,3631.0,974.0,2585.0,923.0,3.0691,130400.0,<1H OCEAN
+-117.81,33.67,8.0,2440.0,502.0,1113.0,483.0,4.6019,242500.0,<1H OCEAN
+-118.13,33.96,35.0,1500.0,250.0,706.0,250.0,4.5625,253500.0,<1H OCEAN
+-120.58,38.77,15.0,2155.0,394.0,857.0,356.0,4.03,141200.0,INLAND
+-120.1,36.16,17.0,598.0,160.0,715.0,146.0,2.3295,55000.0,INLAND
+-117.96,33.99,25.0,2799.0,388.0,1348.0,389.0,6.2517,311100.0,<1H OCEAN
+-118.31,34.1,33.0,766.0,347.0,918.0,305.0,1.705,350000.0,<1H OCEAN
+-117.22,32.82,35.0,756.0,135.0,423.0,136.0,3.5234,183900.0,NEAR OCEAN
+-121.2,37.78,4.0,58.0,29.0,79.0,29.0,3.375,106300.0,INLAND
+-116.98,33.74,25.0,4952.0,1062.0,1589.0,1024.0,1.8446,85700.0,INLAND
+-121.42,38.57,38.0,1878.0,338.0,710.0,342.0,3.7731,161400.0,INLAND
+-117.94,33.81,24.0,4602.0,1131.0,3003.0,1014.0,3.6771,172200.0,<1H OCEAN
+-118.29,34.04,31.0,700.0,299.0,1272.0,298.0,2.1542,128100.0,<1H OCEAN
+-117.11,32.91,15.0,1840.0,235.0,855.0,241.0,7.5992,310600.0,<1H OCEAN
+-115.32,32.82,34.0,591.0,139.0,327.0,89.0,3.6528,100000.0,INLAND
+-118.28,34.06,42.0,2472.0,,3795.0,1179.0,1.2254,162500.0,<1H OCEAN
+-119.79,36.7,23.0,1731.0,363.0,1210.0,341.0,1.3922,49500.0,INLAND
+-118.58,34.2,37.0,1389.0,252.0,826.0,249.0,5.015,220900.0,<1H OCEAN
+-121.32,38.01,20.0,1903.0,395.0,919.0,359.0,2.6765,96400.0,INLAND
+-117.6,33.91,15.0,1864.0,271.0,1006.0,288.0,7.2379,251000.0,INLAND
+-118.19,33.78,29.0,1013.0,392.0,1083.0,316.0,1.8438,162500.0,NEAR OCEAN
+-117.94,33.78,11.0,2880.0,745.0,1806.0,722.0,3.8056,171100.0,<1H OCEAN
+-118.13,34.18,52.0,1464.0,211.0,603.0,226.0,5.8309,309100.0,INLAND
+-118.19,33.92,43.0,2339.0,487.0,1732.0,449.0,3.0987,139400.0,<1H OCEAN
+-122.58,38.59,33.0,1239.0,262.0,539.0,246.0,3.5208,195800.0,INLAND
+-122.08,37.65,17.0,5018.0,1439.0,3069.0,1299.0,2.7694,161900.0,NEAR BAY
+-118.15,34.11,52.0,2375.0,369.0,930.0,351.0,7.4111,469100.0,<1H OCEAN
+-117.96,33.83,18.0,2067.0,770.0,870.0,541.0,3.1315,137500.0,<1H OCEAN
+-118.43,34.19,27.0,3440.0,739.0,1827.0,712.0,4.125,245500.0,<1H OCEAN
+-120.19,38.42,11.0,1568.0,369.0,82.0,33.0,3.125,77500.0,INLAND
+-119.29,36.31,14.0,2382.0,377.0,1278.0,386.0,5.1896,101900.0,INLAND
+-117.25,32.75,37.0,1189.0,377.0,645.0,377.0,2.4672,216700.0,NEAR OCEAN
+-122.49,37.74,52.0,2189.0,433.0,1147.0,420.0,3.4583,321300.0,NEAR OCEAN
+-122.28,37.86,52.0,1999.0,417.0,780.0,358.0,3.3906,179300.0,NEAR BAY
+-121.61,36.68,37.0,3149.0,833.0,3456.0,788.0,2.8542,127600.0,<1H OCEAN
+-121.22,37.79,5.0,3107.0,477.0,1549.0,443.0,4.4766,169400.0,INLAND
+-117.99,33.72,17.0,2801.0,649.0,1473.0,535.0,4.2875,134800.0,<1H OCEAN
+-121.27,38.61,17.0,6663.0,1369.0,2840.0,1299.0,2.9452,115600.0,INLAND
+-118.29,34.01,40.0,885.0,312.0,799.0,221.0,1.1667,143800.0,<1H OCEAN
+-123.19,39.12,38.0,267.0,57.0,196.0,60.0,2.3125,70000.0,<1H OCEAN
+-120.01,38.92,23.0,964.0,246.0,485.0,198.0,1.7188,96100.0,INLAND
+-118.1,33.97,20.0,1878.0,548.0,1461.0,516.0,2.9821,142500.0,<1H OCEAN
+-119.82,36.78,36.0,1582.0,313.0,761.0,318.0,2.6055,69200.0,INLAND
+-122.27,37.77,52.0,1731.0,377.0,872.0,363.0,4.1667,225800.0,NEAR BAY
+-121.87,36.61,21.0,1616.0,400.0,688.0,384.0,4.2109,278800.0,<1H OCEAN
+-121.89,40.97,26.0,1183.0,276.0,513.0,206.0,2.225,52000.0,INLAND
+-117.66,33.61,17.0,3464.0,519.0,1713.0,530.0,6.0471,248400.0,<1H OCEAN
+-118.4,33.78,26.0,5005.0,776.0,2357.0,790.0,8.5421,500001.0,NEAR OCEAN
+-117.66,34.05,14.0,2644.0,525.0,2021.0,511.0,3.6467,147500.0,INLAND
+-118.13,33.81,36.0,1749.0,322.0,855.0,319.0,4.6473,227100.0,<1H OCEAN
+-124.3,41.84,17.0,2677.0,531.0,1244.0,456.0,3.0313,103600.0,NEAR OCEAN
+-118.05,34.06,32.0,2286.0,654.0,2991.0,655.0,2.1781,174500.0,<1H OCEAN
+-118.65,34.2,23.0,7480.0,1084.0,3037.0,1058.0,6.9223,338400.0,<1H OCEAN
+-121.22,38.92,19.0,2531.0,461.0,1206.0,429.0,4.4958,192600.0,INLAND
+-121.12,37.48,5.0,4109.0,820.0,3062.0,713.0,3.2396,125200.0,INLAND
+-118.18,34.11,33.0,1523.0,391.0,753.0,298.0,2.6591,183800.0,<1H OCEAN
+-121.24,37.81,6.0,3883.0,800.0,2319.0,787.0,3.5595,161000.0,INLAND
+-122.18,37.79,39.0,617.0,95.0,236.0,106.0,5.2578,253000.0,NEAR BAY
+-117.89,33.6,40.0,1639.0,352.0,498.0,278.0,5.6336,500001.0,<1H OCEAN
+-117.31,33.16,17.0,1704.0,263.0,781.0,281.0,5.6605,224400.0,NEAR OCEAN
+-122.28,37.86,52.0,938.0,195.0,393.0,189.0,3.8594,196400.0,NEAR BAY
+-121.84,36.61,21.0,2876.0,802.0,2487.0,795.0,2.2007,112800.0,<1H OCEAN
+-118.4,34.22,43.0,1220.0,222.0,729.0,230.0,3.6442,186300.0,<1H OCEAN
+-117.18,32.69,44.0,2819.0,514.0,1258.0,503.0,4.4777,452800.0,NEAR OCEAN
+-118.3,33.96,39.0,2802.0,618.0,1524.0,529.0,2.6518,136300.0,<1H OCEAN
+-117.02,32.76,40.0,2523.0,488.0,976.0,470.0,3.11,185700.0,<1H OCEAN
+-117.06,32.76,36.0,2785.0,577.0,1275.0,527.0,2.3015,156800.0,NEAR OCEAN
+-117.84,34.1,17.0,7836.0,1624.0,4419.0,1526.0,3.8465,180700.0,<1H OCEAN
+-122.04,37.97,10.0,974.0,316.0,631.0,286.0,2.3152,140600.0,NEAR BAY
+-119.83,36.75,33.0,662.0,183.0,607.0,181.0,1.3929,55600.0,INLAND
+-117.95,33.83,36.0,1380.0,237.0,690.0,234.0,3.8214,210900.0,<1H OCEAN
+-122.25,37.82,32.0,3809.0,1098.0,1806.0,1022.0,2.6429,218500.0,NEAR BAY
+-117.06,32.75,34.0,2516.0,611.0,1317.0,594.0,2.2308,125900.0,NEAR OCEAN
+-122.18,37.76,50.0,1187.0,261.0,907.0,246.0,1.9479,89500.0,NEAR BAY
+-117.27,33.02,13.0,5723.0,1242.0,2450.0,1140.0,4.7179,376700.0,NEAR OCEAN
+-118.07,33.89,32.0,1819.0,386.0,1679.0,360.0,3.5562,146000.0,<1H OCEAN
+-119.05,34.19,39.0,143.0,36.0,113.0,33.0,2.8942,275000.0,NEAR OCEAN
+-121.2,38.66,17.0,1605.0,217.0,732.0,241.0,5.47,204800.0,INLAND
+-122.47,37.72,47.0,1176.0,286.0,564.0,258.0,3.2059,350000.0,NEAR OCEAN
+-119.83,34.44,26.0,1739.0,402.0,599.0,368.0,3.0875,198400.0,NEAR OCEAN
+-118.16,35.05,44.0,1297.0,307.0,776.0,278.0,2.5875,68900.0,INLAND
+-122.08,37.35,35.0,1347.0,207.0,548.0,189.0,7.7068,500001.0,NEAR BAY
+-118.11,34.1,44.0,2012.0,435.0,1454.0,456.0,3.3229,226600.0,<1H OCEAN
+-117.22,32.87,5.0,3511.0,1008.0,1599.0,918.0,3.8542,176600.0,NEAR OCEAN
+-117.09,34.07,24.0,6260.0,1271.0,3132.0,1189.0,2.5156,103000.0,INLAND
+-121.29,38.14,27.0,836.0,132.0,303.0,133.0,3.875,127400.0,INLAND
+-121.96,37.35,32.0,1484.0,274.0,673.0,272.0,5.2019,279900.0,<1H OCEAN
+-118.11,34.0,33.0,2886.0,726.0,2650.0,728.0,2.625,178700.0,<1H OCEAN
+-122.31,37.54,46.0,2444.0,397.0,952.0,402.0,4.75,388200.0,NEAR OCEAN
+-120.41,38.03,14.0,2061.0,465.0,859.0,462.0,2.1289,115300.0,INLAND
+-118.2,34.05,42.0,1703.0,586.0,2490.0,581.0,2.02,147200.0,<1H OCEAN
+-121.99,37.56,18.0,5505.0,1005.0,2641.0,971.0,5.0,269700.0,<1H OCEAN
+-117.92,34.03,32.0,1819.0,375.0,1728.0,375.0,3.975,162400.0,<1H OCEAN
+-117.53,33.88,22.0,2855.0,667.0,2453.0,624.0,3.1312,91000.0,INLAND
+-119.23,34.44,34.0,3193.0,664.0,1434.0,627.0,2.4777,260300.0,<1H OCEAN
+-117.9,33.8,21.0,1342.0,326.0,748.0,335.0,2.9231,45000.0,<1H OCEAN
+-122.7,38.44,35.0,1304.0,343.0,822.0,304.0,3.2935,157800.0,<1H OCEAN
+-121.36,38.73,21.0,2253.0,416.0,1050.0,411.0,3.141,220100.0,INLAND
+-121.89,37.33,49.0,658.0,318.0,467.0,316.0,0.7068,200000.0,<1H OCEAN
+-117.1,32.68,45.0,1183.0,289.0,900.0,266.0,2.4943,99600.0,NEAR OCEAN
+-121.83,36.61,26.0,3723.0,789.0,2563.0,747.0,3.4531,133100.0,<1H OCEAN
+-122.5,37.77,52.0,1769.0,414.0,1032.0,380.0,3.9954,324700.0,NEAR BAY
+-120.62,37.37,8.0,2608.0,428.0,1530.0,435.0,3.968,102100.0,INLAND
+-122.26,37.5,52.0,878.0,186.0,393.0,186.0,3.7045,360500.0,NEAR OCEAN
+-122.53,39.09,11.0,1264.0,271.0,370.0,177.0,1.3,69700.0,INLAND
+-120.76,38.73,17.0,512.0,129.0,314.0,140.0,1.5625,108300.0,INLAND
+-123.79,39.44,16.0,2017.0,423.0,1177.0,414.0,3.2171,116200.0,<1H OCEAN
+-121.04,37.7,52.0,349.0,59.0,121.0,40.0,3.3036,197500.0,INLAND
+-118.13,34.19,42.0,2203.0,412.0,1012.0,377.0,4.0714,234000.0,INLAND
+-122.47,37.72,49.0,1690.0,307.0,770.0,294.0,4.5913,259700.0,NEAR OCEAN
+-117.29,34.13,52.0,2424.0,528.0,1171.0,455.0,1.4815,77900.0,INLAND
+-115.56,32.96,21.0,2164.0,480.0,1164.0,421.0,3.8177,107200.0,INLAND
+-117.96,33.87,37.0,1785.0,360.0,1155.0,403.0,4.7984,175800.0,<1H OCEAN
+-121.78,37.31,25.0,2093.0,297.0,983.0,338.0,6.4664,271500.0,<1H OCEAN
+-118.2,33.87,26.0,703.0,202.0,757.0,212.0,2.525,155500.0,<1H OCEAN
+-119.25,34.27,46.0,679.0,159.0,382.0,143.0,3.5,221200.0,NEAR OCEAN
+-118.09,34.07,31.0,1054.0,252.0,1032.0,258.0,2.3424,188500.0,<1H OCEAN
+-117.18,32.69,37.0,3112.0,716.0,1304.0,674.0,3.2121,320800.0,NEAR OCEAN
+-122.29,37.87,46.0,1267.0,324.0,792.0,321.0,2.525,165900.0,NEAR BAY
+-122.31,37.54,42.0,1159.0,261.0,465.0,247.0,3.1842,352800.0,NEAR OCEAN
+-122.12,37.7,17.0,2488.0,617.0,1287.0,538.0,2.9922,179900.0,NEAR BAY
+-120.64,37.7,16.0,284.0,51.0,239.0,46.0,1.8958,137500.0,INLAND
+-122.04,37.12,30.0,1427.0,311.0,686.0,251.0,4.0781,154500.0,NEAR OCEAN
+-121.94,37.3,26.0,4348.0,814.0,2347.0,810.0,4.7275,293000.0,<1H OCEAN
+-122.62,38.24,33.0,1369.0,280.0,758.0,246.0,4.0341,156500.0,<1H OCEAN
+-122.26,38.57,22.0,509.0,103.0,139.0,73.0,2.1979,152800.0,INLAND
+-122.33,37.58,43.0,1772.0,422.0,1573.0,401.0,2.7474,233100.0,NEAR OCEAN
+-119.12,34.25,31.0,737.0,146.0,1436.0,168.0,3.5625,194100.0,NEAR OCEAN
+-122.82,38.55,8.0,6190.0,1088.0,2967.0,1000.0,3.8616,195100.0,<1H OCEAN
+-117.82,34.13,27.0,3770.0,573.0,1606.0,562.0,6.1321,309700.0,INLAND
+-121.97,37.52,23.0,4925.0,948.0,2530.0,894.0,5.0824,230900.0,<1H OCEAN
+-119.85,34.48,23.0,1915.0,277.0,724.0,267.0,6.2987,348200.0,NEAR OCEAN
+-118.11,33.78,16.0,3985.0,567.0,1327.0,564.0,7.9767,500001.0,<1H OCEAN
+-118.03,34.08,42.0,1597.0,373.0,1311.0,352.0,2.9688,162800.0,INLAND
+-116.95,33.68,11.0,1183.0,178.0,543.0,147.0,4.4792,173900.0,INLAND
+-118.13,33.86,37.0,2259.0,425.0,1183.0,413.0,5.1805,201600.0,<1H OCEAN
+-118.54,34.05,33.0,6778.0,1092.0,2540.0,1052.0,8.565,500001.0,<1H OCEAN
+-117.89,33.85,13.0,1583.0,474.0,1672.0,432.0,3.2303,201300.0,<1H OCEAN
+-117.28,34.15,32.0,2170.0,430.0,815.0,401.0,3.1765,135000.0,INLAND
+-118.12,33.98,44.0,932.0,179.0,717.0,180.0,3.6875,178100.0,<1H OCEAN
+-117.38,34.13,13.0,2903.0,510.0,1844.0,510.0,3.7198,112900.0,INLAND
+-118.14,34.71,33.0,2347.0,461.0,1482.0,374.0,2.8194,93000.0,INLAND
+-122.26,37.9,52.0,1927.0,279.0,705.0,288.0,7.8864,357300.0,NEAR BAY
+-121.42,38.68,32.0,2118.0,345.0,1019.0,338.0,3.725,112200.0,INLAND
+-118.3,34.01,48.0,4217.0,1095.0,3298.0,949.0,1.9152,122300.0,<1H OCEAN
+-118.25,34.23,34.0,2421.0,475.0,1232.0,454.0,4.6852,296200.0,<1H OCEAN
+-117.87,33.75,18.0,697.0,255.0,812.0,221.0,2.6635,162500.0,<1H OCEAN
+-120.85,37.05,32.0,2893.0,481.0,1198.0,466.0,3.1719,140600.0,INLAND
+-116.89,34.27,13.0,3661.0,770.0,567.0,228.0,4.4063,152600.0,INLAND
+-119.14,36.29,36.0,788.0,181.0,405.0,180.0,1.47,61900.0,INLAND
+-119.43,36.61,19.0,1484.0,296.0,1296.0,298.0,2.4219,65800.0,INLAND
+-117.57,33.87,37.0,621.0,156.0,443.0,135.0,2.3333,122800.0,INLAND
+-121.54,39.51,33.0,3585.0,757.0,1887.0,765.0,2.502,62100.0,INLAND
+-118.11,33.86,33.0,2389.0,410.0,1229.0,393.0,5.3889,234900.0,<1H OCEAN
+-118.46,33.98,32.0,2388.0,591.0,1009.0,556.0,5.2121,466700.0,<1H OCEAN
+-121.87,37.25,4.0,2498.0,775.0,1213.0,631.0,3.7844,183900.0,<1H OCEAN
+-122.35,40.25,10.0,1621.0,318.0,866.0,283.0,3.5,104300.0,INLAND
+-121.84,37.27,9.0,3624.0,812.0,1856.0,721.0,4.2083,198400.0,<1H OCEAN
+-122.21,37.47,43.0,733.0,162.0,497.0,175.0,3.2708,255300.0,NEAR OCEAN
+-119.05,34.4,50.0,1236.0,282.0,1079.0,257.0,2.6991,181300.0,<1H OCEAN
+-116.15,34.14,18.0,3312.0,705.0,1251.0,512.0,3.0139,82600.0,INLAND
+-118.15,33.97,32.0,1174.0,373.0,1758.0,361.0,2.4263,158100.0,<1H OCEAN
+-121.33,37.97,38.0,3166.0,575.0,1351.0,561.0,3.5404,91600.0,INLAND
+-118.33,34.18,45.0,2570.0,517.0,1256.0,510.0,4.6898,226000.0,<1H OCEAN
+-118.93,36.1,19.0,2988.0,681.0,1654.0,576.0,2.3792,90000.0,INLAND
+-118.18,33.98,38.0,1477.0,374.0,1514.0,408.0,2.5703,178600.0,<1H OCEAN
+-122.11,37.4,15.0,255.0,63.0,138.0,74.0,4.6591,175000.0,NEAR BAY
+-122.08,37.63,33.0,691.0,127.0,431.0,149.0,4.25,192600.0,NEAR BAY
+-121.46,38.49,15.0,10211.0,1995.0,5656.0,1752.0,2.575,107900.0,INLAND
+-119.1,36.4,31.0,1533.0,361.0,1518.0,386.0,1.5608,51700.0,INLAND
+-118.3,34.08,34.0,2501.0,1047.0,3326.0,970.0,1.8771,247500.0,<1H OCEAN
+-117.64,34.02,6.0,248.0,47.0,119.0,42.0,2.125,416700.0,INLAND
+-118.1,33.74,37.0,997.0,262.0,531.0,282.0,4.7773,400000.0,NEAR OCEAN
+-121.37,38.62,27.0,1743.0,380.0,697.0,368.0,1.6678,166100.0,INLAND
+-118.07,34.07,31.0,1370.0,284.0,1062.0,277.0,3.5156,199300.0,<1H OCEAN
+-122.07,37.58,16.0,1606.0,240.0,1117.0,268.0,6.0661,247000.0,NEAR BAY
+-122.11,37.98,11.0,4371.0,679.0,1790.0,660.0,6.135,297300.0,NEAR BAY
+-122.15,37.73,52.0,1028.0,129.0,317.0,143.0,4.9135,275000.0,NEAR BAY
+-117.17,32.75,38.0,5430.0,1176.0,2357.0,1100.0,3.654,249000.0,NEAR OCEAN
+-118.13,33.91,34.0,916.0,162.0,552.0,164.0,4.9107,222000.0,<1H OCEAN
+-122.05,39.34,44.0,1064.0,230.0,494.0,175.0,2.875,61500.0,INLAND
+-117.37,34.07,52.0,50.0,9.0,60.0,16.0,4.125,262500.0,INLAND
+-119.65,36.3,28.0,941.0,175.0,588.0,180.0,2.3466,53400.0,INLAND
+-118.03,33.96,37.0,1745.0,365.0,1022.0,368.0,4.0536,171400.0,<1H OCEAN
+-119.04,35.31,11.0,2161.0,371.0,1267.0,388.0,4.1957,92700.0,INLAND
+-122.0,37.54,29.0,4133.0,744.0,2023.0,749.0,5.1616,275100.0,<1H OCEAN
+-117.77,33.72,10.0,2815.0,431.0,1181.0,398.0,6.5743,278700.0,<1H OCEAN
+-117.95,34.09,18.0,1179.0,324.0,1296.0,331.0,2.851,140600.0,<1H OCEAN
+-114.61,34.84,48.0,1291.0,248.0,580.0,211.0,2.1571,48600.0,INLAND
+-118.03,34.06,24.0,2343.0,834.0,3537.0,824.0,2.1094,135200.0,<1H OCEAN
+-118.21,34.09,39.0,1287.0,353.0,1171.0,345.0,1.6118,138500.0,<1H OCEAN
+-117.07,33.11,31.0,2055.0,473.0,1326.0,427.0,3.0915,139900.0,<1H OCEAN
+-122.38,37.58,52.0,2039.0,299.0,772.0,303.0,6.471,500001.0,NEAR OCEAN
+-123.36,39.01,35.0,1551.0,321.0,857.0,288.0,2.7232,115400.0,<1H OCEAN
+-118.31,34.19,42.0,724.0,149.0,420.0,150.0,3.0625,361700.0,<1H OCEAN
+-116.19,33.69,11.0,5692.0,1346.0,5682.0,1273.0,2.5383,74000.0,INLAND
+-119.73,34.43,27.0,1448.0,404.0,978.0,338.0,2.303,261000.0,NEAR OCEAN
+-119.72,36.76,23.0,6403.0,,3573.0,1260.0,2.3006,69000.0,INLAND
+-121.55,39.45,18.0,2278.0,523.0,1185.0,475.0,1.3611,60600.0,INLAND
+-121.5,38.52,37.0,2008.0,466.0,1261.0,427.0,2.2574,59100.0,INLAND
+-118.07,34.58,34.0,3416.0,601.0,1929.0,567.0,4.0147,107400.0,INLAND
+-118.36,34.09,30.0,2353.0,728.0,1365.0,718.0,2.0702,283300.0,<1H OCEAN
+-119.06,36.09,11.0,2572.0,454.0,1402.0,415.0,3.6786,72900.0,INLAND
+-118.31,34.26,34.0,1797.0,363.0,948.0,363.0,4.1339,187300.0,<1H OCEAN
+-121.0,37.8,13.0,4030.0,744.0,2248.0,766.0,3.6107,141300.0,INLAND
+-118.13,34.06,17.0,1714.0,572.0,1590.0,568.0,1.1875,183900.0,<1H OCEAN
+-121.4,38.61,37.0,1994.0,347.0,782.0,355.0,4.1488,136400.0,INLAND
+-119.69,36.77,22.0,2456.0,496.0,1720.0,417.0,2.6875,60600.0,INLAND
+-118.11,33.93,17.0,1205.0,347.0,736.0,342.0,3.2011,162500.0,<1H OCEAN
+-118.48,34.02,29.0,1585.0,542.0,1019.0,487.0,2.7072,375000.0,<1H OCEAN
+-122.31,37.93,36.0,2403.0,408.0,917.0,404.0,5.0399,253400.0,NEAR BAY
+-118.59,34.15,29.0,2023.0,330.0,747.0,304.0,6.7694,369700.0,<1H OCEAN
+-122.13,37.42,36.0,3982.0,1045.0,2251.0,995.0,3.5364,314100.0,NEAR BAY
+-119.71,34.36,34.0,1706.0,276.0,628.0,243.0,4.1842,364000.0,NEAR OCEAN
+-117.55,33.87,18.0,8136.0,1584.0,4976.0,1516.0,3.9414,137100.0,INLAND
+-118.51,34.04,40.0,1382.0,167.0,483.0,178.0,11.7045,500001.0,<1H OCEAN
+-122.17,37.43,24.0,3924.0,1142.0,7174.0,950.0,4.0972,387500.0,NEAR OCEAN
+-117.34,33.89,17.0,2678.0,394.0,1225.0,367.0,5.363,211300.0,INLAND
+-122.43,37.75,52.0,2721.0,581.0,1043.0,519.0,3.7545,383700.0,NEAR BAY
+-121.93,37.3,16.0,1111.0,226.0,317.0,199.0,2.7153,233300.0,<1H OCEAN
+-122.46,37.69,35.0,1983.0,385.0,1577.0,414.0,4.0833,266700.0,NEAR OCEAN
+-122.23,37.76,52.0,1049.0,185.0,374.0,176.0,4.1458,248500.0,NEAR BAY
+-119.75,36.76,29.0,2077.0,524.0,1887.0,489.0,1.4107,59800.0,INLAND
+-122.41,37.79,52.0,5783.0,2747.0,4518.0,2538.0,1.724,225000.0,NEAR BAY
+-118.38,34.19,42.0,1308.0,289.0,950.0,302.0,2.7379,181500.0,<1H OCEAN
+-121.94,37.75,17.0,2559.0,370.0,1238.0,377.0,6.2781,269800.0,<1H OCEAN
+-122.32,40.71,18.0,2879.0,578.0,1399.0,586.0,2.4036,105400.0,INLAND
+-122.29,37.56,36.0,805.0,140.0,445.0,139.0,5.8221,289400.0,NEAR BAY
+-117.68,34.09,22.0,1547.0,334.0,773.0,316.0,2.9812,148800.0,INLAND
+-122.63,38.21,22.0,2933.0,461.0,1283.0,449.0,6.2034,291100.0,<1H OCEAN
+-118.21,34.09,39.0,1561.0,445.0,1780.0,391.0,2.4632,144200.0,<1H OCEAN
+-122.24,38.07,13.0,5451.0,1194.0,2957.0,1081.0,2.6098,162500.0,NEAR BAY
+-122.28,38.3,23.0,526.0,152.0,245.0,130.0,2.0134,142500.0,NEAR BAY
+-121.27,38.12,37.0,2232.0,504.0,1455.0,471.0,2.5587,87800.0,INLAND
+-118.14,34.07,52.0,695.0,145.0,523.0,170.0,3.665,220400.0,<1H OCEAN
+-117.37,33.7,8.0,4345.0,865.0,2425.0,785.0,3.2481,123800.0,<1H OCEAN
+-118.36,33.88,26.0,1375.0,286.0,829.0,278.0,3.9844,230700.0,<1H OCEAN
+-117.65,34.08,52.0,2264.0,439.0,1031.0,437.0,3.375,144300.0,INLAND
+-118.2,34.19,38.0,2176.0,266.0,798.0,243.0,15.0001,500001.0,<1H OCEAN
+-118.34,34.08,52.0,1430.0,186.0,547.0,178.0,10.3661,500001.0,<1H OCEAN
+-117.85,33.9,20.0,4026.0,648.0,1997.0,650.0,5.5918,260500.0,<1H OCEAN
+-118.33,33.89,42.0,1816.0,338.0,897.0,306.0,5.1874,230800.0,<1H OCEAN
+-116.95,32.81,15.0,2619.0,599.0,1513.0,537.0,2.543,100000.0,<1H OCEAN
+-118.02,33.82,21.0,2052.0,456.0,1173.0,432.0,3.7885,204500.0,<1H OCEAN
+-120.29,35.56,15.0,4760.0,871.0,2459.0,734.0,2.811,142100.0,<1H OCEAN
+-122.49,38.29,26.0,1726.0,289.0,672.0,251.0,3.8,242100.0,<1H OCEAN
+-117.94,34.08,34.0,1970.0,476.0,1269.0,406.0,4.064,201200.0,<1H OCEAN
+-115.49,32.67,29.0,1523.0,440.0,1302.0,393.0,1.1311,84700.0,INLAND
+-119.29,34.24,27.0,4742.0,775.0,1682.0,696.0,6.194,500001.0,NEAR OCEAN
+-118.26,34.02,48.0,1465.0,440.0,1859.0,400.0,1.3134,96200.0,<1H OCEAN
+-118.95,34.17,9.0,2372.0,312.0,1039.0,321.0,7.6016,344900.0,<1H OCEAN
+-118.45,34.2,19.0,3666.0,1150.0,2657.0,1090.0,2.9688,202100.0,<1H OCEAN
+-117.91,33.62,32.0,1997.0,427.0,944.0,426.0,4.4063,500001.0,<1H OCEAN
+-122.47,37.78,52.0,2635.0,587.0,1302.0,577.0,3.7292,416700.0,NEAR BAY
+-117.33,34.12,38.0,1703.0,385.0,1356.0,363.0,2.0391,70400.0,INLAND
+-118.37,33.88,26.0,2620.0,530.0,1282.0,525.0,4.4828,340700.0,<1H OCEAN
+-122.2,37.77,43.0,2430.0,502.0,1537.0,484.0,2.898,121400.0,NEAR BAY
+-122.02,36.98,21.0,1484.0,394.0,984.0,380.0,2.1734,187500.0,NEAR OCEAN
+-119.69,36.83,32.0,1098.0,,726.0,224.0,1.4913,54600.0,INLAND
+-119.04,35.32,20.0,37.0,11.0,34.0,8.0,1.2,50000.0,INLAND
+-117.84,33.79,34.0,2590.0,603.0,1658.0,608.0,2.378,199600.0,<1H OCEAN
+-120.11,36.96,17.0,3344.0,570.0,1624.0,536.0,3.8952,95300.0,INLAND
+-118.01,34.13,38.0,3374.0,671.0,1906.0,640.0,4.0729,212300.0,INLAND
+-121.04,37.3,6.0,2657.0,486.0,1409.0,392.0,3.3824,115500.0,INLAND
+-117.99,33.69,17.0,3386.0,729.0,1715.0,666.0,3.7479,213000.0,<1H OCEAN
+-117.92,34.0,36.0,116.0,30.0,193.0,35.0,3.8125,136300.0,<1H OCEAN
+-122.04,37.37,33.0,2757.0,489.0,1201.0,481.0,5.0453,311600.0,<1H OCEAN
+-121.44,38.53,37.0,1951.0,432.0,1089.0,411.0,2.3272,80600.0,INLAND
+-117.25,32.82,23.0,6139.0,826.0,2036.0,807.0,9.5245,500001.0,NEAR OCEAN
+-117.59,33.43,14.0,3223.0,484.0,1230.0,488.0,6.5964,358800.0,NEAR OCEAN
+-118.1,34.11,49.0,3367.0,523.0,1317.0,495.0,6.706,351400.0,<1H OCEAN
+-118.15,34.18,45.0,2612.0,664.0,3117.0,584.0,2.3029,148800.0,<1H OCEAN
+-122.02,37.6,36.0,1633.0,345.0,1382.0,338.0,3.694,159600.0,NEAR BAY
+-121.84,39.73,52.0,857.0,232.0,520.0,198.0,0.987,112500.0,INLAND
+-117.25,34.13,35.0,861.0,148.0,381.0,138.0,2.5234,88200.0,INLAND
+-117.62,33.47,4.0,1812.0,255.0,661.0,211.0,6.487,294200.0,NEAR OCEAN
+-122.28,37.88,52.0,1909.0,416.0,811.0,406.0,3.006,227900.0,NEAR BAY
+-122.18,38.49,15.0,1743.0,366.0,655.0,264.0,3.3393,146900.0,INLAND
+-121.03,37.69,5.0,4034.0,771.0,1967.0,742.0,3.8065,146000.0,INLAND
+-117.25,32.75,32.0,3551.0,1037.0,1731.0,935.0,2.2017,208300.0,NEAR OCEAN
+-121.83,38.0,15.0,6365.0,1646.0,3838.0,1458.0,2.5495,103600.0,INLAND
+-118.04,34.08,35.0,1148.0,258.0,975.0,253.0,4.037,173300.0,<1H OCEAN
+-117.3,33.25,22.0,2329.0,419.0,1456.0,381.0,3.7933,131000.0,<1H OCEAN
+-118.15,34.04,39.0,1099.0,263.0,787.0,269.0,3.7794,194600.0,<1H OCEAN
+-118.16,33.78,52.0,3248.0,853.0,1819.0,815.0,3.1739,222900.0,NEAR OCEAN
+-118.46,34.02,39.0,3599.0,776.0,1569.0,763.0,5.2571,405400.0,<1H OCEAN
+-118.25,34.07,47.0,2059.0,618.0,2033.0,544.0,1.9028,217900.0,<1H OCEAN
+-118.56,34.2,36.0,1544.0,308.0,891.0,286.0,4.175,190900.0,<1H OCEAN
+-118.4,34.19,35.0,2180.0,599.0,1483.0,574.0,3.0395,191300.0,<1H OCEAN
+-122.14,37.67,36.0,1487.0,249.0,641.0,243.0,4.0682,196200.0,NEAR BAY
+-117.07,33.12,21.0,4578.0,927.0,2818.0,900.0,3.1458,187700.0,<1H OCEAN
+-122.43,37.79,24.0,2459.0,1001.0,1362.0,957.0,2.6782,450000.0,NEAR BAY
+-122.69,38.32,15.0,2536.0,414.0,1400.0,426.0,5.6613,172400.0,<1H OCEAN
+-122.48,37.71,29.0,1048.0,150.0,455.0,152.0,6.1278,417600.0,NEAR OCEAN
+-117.17,32.75,28.0,1514.0,384.0,540.0,352.0,2.1532,240000.0,NEAR OCEAN
+-122.71,38.46,42.0,1574.0,376.0,844.0,369.0,2.314,169400.0,<1H OCEAN
+-122.49,37.92,26.0,2170.0,347.0,849.0,318.0,6.2953,386200.0,NEAR BAY
+-119.72,34.44,50.0,3265.0,509.0,1256.0,443.0,6.3997,500001.0,<1H OCEAN
+-117.23,32.74,44.0,1404.0,229.0,513.0,217.0,4.1806,263800.0,NEAR OCEAN
+-122.07,37.31,24.0,4401.0,698.0,1818.0,685.0,7.2986,500001.0,<1H OCEAN
+-117.69,33.55,9.0,3856.0,571.0,1646.0,576.0,6.8007,318300.0,<1H OCEAN
+-118.38,34.09,24.0,8264.0,2437.0,3148.0,2274.0,3.5659,281300.0,<1H OCEAN
+-117.94,33.82,29.0,1422.0,409.0,1057.0,390.0,2.3347,208100.0,<1H OCEAN
+-121.03,37.55,32.0,946.0,198.0,624.0,173.0,1.9728,97900.0,INLAND
+-121.14,37.48,6.0,1772.0,332.0,1011.0,331.0,3.7045,128100.0,INLAND
+-121.94,36.55,30.0,2722.0,584.0,628.0,384.0,3.4048,487100.0,NEAR OCEAN
+-119.16,34.28,11.0,5330.0,1056.0,2801.0,1028.0,4.763,232700.0,NEAR OCEAN
+-122.04,38.26,34.0,3082.0,702.0,1795.0,703.0,2.7885,105900.0,INLAND
+-117.35,33.96,25.0,2396.0,316.0,951.0,314.0,8.2405,235200.0,INLAND
+-117.98,33.92,27.0,3700.0,,1793.0,552.0,5.3668,219800.0,<1H OCEAN
+-122.08,37.71,38.0,3716.0,657.0,1784.0,652.0,4.8237,220900.0,NEAR BAY
+-119.8,34.43,27.0,3143.0,537.0,1760.0,570.0,4.6957,271500.0,NEAR OCEAN
+-118.36,34.18,31.0,1109.0,354.0,1119.0,334.0,2.3056,200000.0,<1H OCEAN
+-122.43,37.75,52.0,2700.0,595.0,1181.0,575.0,3.575,396800.0,NEAR BAY
+-117.95,34.0,34.0,2376.0,468.0,1858.0,449.0,4.1328,176300.0,<1H OCEAN
+-121.8,37.26,26.0,1394.0,238.0,990.0,315.0,4.8862,195000.0,<1H OCEAN
+-118.31,33.79,38.0,1601.0,352.0,711.0,304.0,3.3958,250000.0,<1H OCEAN
+-117.78,34.06,33.0,1056.0,272.0,964.0,300.0,2.4464,128700.0,INLAND
+-122.22,37.8,52.0,2286.0,464.0,1073.0,441.0,3.0298,199600.0,NEAR BAY
+-121.18,38.69,7.0,7104.0,970.0,2772.0,920.0,6.3528,274500.0,INLAND
+-118.3,33.79,13.0,3569.0,924.0,2159.0,880.0,3.163,224200.0,<1H OCEAN
+-117.62,34.15,16.0,13556.0,1704.0,5669.0,1668.0,6.5138,311500.0,INLAND
+-122.05,37.33,17.0,3674.0,824.0,1364.0,694.0,6.3131,436400.0,<1H OCEAN
+-122.0,37.98,31.0,2030.0,337.0,867.0,341.0,5.0915,193200.0,INLAND
+-118.06,33.98,50.0,1146.0,238.0,579.0,213.0,2.9583,172600.0,<1H OCEAN
+-117.16,32.74,52.0,852.0,262.0,389.0,249.0,2.6042,225000.0,NEAR OCEAN
+-122.3,37.9,37.0,2125.0,489.0,912.0,462.0,2.9219,217200.0,NEAR BAY
+-118.3,33.74,20.0,2625.0,673.0,1184.0,606.0,3.9167,285200.0,NEAR OCEAN
+-117.7,33.51,2.0,5261.0,763.0,1460.0,599.0,6.8279,279000.0,<1H OCEAN
+-122.1,37.89,21.0,3282.0,653.0,1398.0,601.0,5.2079,310300.0,NEAR BAY
+-117.01,32.79,33.0,4015.0,663.0,1864.0,664.0,4.3152,159300.0,<1H OCEAN
+-122.29,37.51,35.0,3040.0,520.0,1374.0,518.0,6.1004,426400.0,NEAR OCEAN
+-122.5,37.76,48.0,1408.0,295.0,891.0,269.0,3.8333,296300.0,NEAR OCEAN
+-119.24,35.68,21.0,1885.0,398.0,1539.0,388.0,2.5208,58500.0,INLAND
+-118.09,34.05,22.0,1764.0,357.0,1379.0,363.0,3.5357,199000.0,<1H OCEAN
+-118.83,34.17,17.0,4668.0,628.0,1917.0,624.0,8.1397,353900.0,<1H OCEAN
+-122.23,37.48,33.0,3108.0,805.0,1895.0,717.0,3.3015,267700.0,NEAR OCEAN
+-117.34,34.0,27.0,321.0,64.0,214.0,67.0,3.175,101600.0,INLAND
+-117.34,34.18,7.0,2914.0,481.0,1584.0,499.0,4.6312,124900.0,INLAND
+-122.56,38.06,19.0,15622.0,2721.0,6109.0,2615.0,5.0965,295300.0,NEAR BAY
+-117.8,33.69,14.0,1800.0,362.0,874.0,373.0,4.2083,251000.0,<1H OCEAN
+-118.18,34.05,41.0,389.0,102.0,455.0,107.0,2.7031,109200.0,<1H OCEAN
+-118.35,34.22,41.0,1560.0,374.0,1668.0,389.0,3.025,154300.0,<1H OCEAN
+-119.06,36.15,20.0,1282.0,273.0,852.0,247.0,1.6354,49000.0,INLAND
+-116.48,33.79,14.0,9425.0,2020.0,1711.0,1000.0,2.6298,145200.0,INLAND
+-122.3,38.0,34.0,1712.0,317.0,956.0,341.0,4.4394,162000.0,NEAR BAY
+-118.24,34.13,37.0,1644.0,395.0,959.0,383.0,3.3636,257700.0,<1H OCEAN
+-120.85,37.06,31.0,2609.0,645.0,1796.0,629.0,1.5479,82000.0,INLAND
+-116.99,33.77,7.0,10352.0,2007.0,3559.0,1689.0,2.2925,113100.0,INLAND
+-121.3,38.58,19.0,2653.0,680.0,1419.0,579.0,2.3787,91300.0,INLAND
+-121.96,37.36,16.0,5040.0,1325.0,3150.0,1196.0,4.2837,264500.0,<1H OCEAN
+-121.93,38.01,9.0,2294.0,389.0,1142.0,365.0,5.3363,160800.0,INLAND
+-121.94,37.28,18.0,4356.0,1334.0,1968.0,1245.0,3.6294,240000.0,<1H OCEAN
+-120.99,37.62,37.0,2014.0,505.0,1787.0,515.0,1.5515,54100.0,INLAND
+-117.33,33.03,31.0,1171.0,321.0,603.0,267.0,2.8611,314300.0,NEAR OCEAN
+-122.23,38.15,33.0,1253.0,238.0,753.0,236.0,1.756,86400.0,NEAR BAY
+-118.61,34.31,4.0,1949.0,458.0,868.0,398.0,5.0151,285200.0,<1H OCEAN
+-122.07,37.39,37.0,1169.0,239.0,589.0,249.0,5.0131,330300.0,NEAR BAY
+-121.21,37.5,34.0,294.0,49.0,147.0,47.0,3.0,162500.0,INLAND
+-117.13,32.69,36.0,1469.0,400.0,1271.0,340.0,1.043,90100.0,NEAR OCEAN
+-121.47,38.48,25.0,2969.0,551.0,1745.0,487.0,2.6382,76200.0,INLAND
+-117.13,32.71,42.0,1145.0,314.0,1114.0,307.0,1.2614,87500.0,NEAR OCEAN
+-117.29,34.11,35.0,2014.0,677.0,1714.0,612.0,0.7075,78800.0,INLAND
+-117.7,34.06,7.0,732.0,145.0,431.0,132.0,2.9107,95300.0,INLAND
+-122.24,37.48,40.0,4459.0,1027.0,2080.0,982.0,3.5322,361900.0,NEAR OCEAN
+-118.38,34.08,48.0,1226.0,288.0,370.0,264.0,3.9375,450000.0,<1H OCEAN
+-117.6,33.94,26.0,2925.0,575.0,1921.0,501.0,3.1859,153100.0,INLAND
+-117.99,33.83,25.0,3434.0,835.0,1749.0,657.0,3.2539,199000.0,<1H OCEAN
+-118.5,33.97,52.0,709.0,329.0,388.0,313.0,2.2643,350000.0,<1H OCEAN
+-117.01,32.78,20.0,2616.0,597.0,1532.0,579.0,2.9896,235600.0,<1H OCEAN
+-117.82,33.84,25.0,1788.0,203.0,676.0,217.0,10.1299,454300.0,<1H OCEAN
+-118.01,34.09,29.0,3402.0,747.0,2331.0,690.0,3.6094,179200.0,INLAND
+-118.29,34.06,9.0,1554.0,815.0,1704.0,761.0,2.0185,141700.0,<1H OCEAN
+-122.42,37.73,50.0,3426.0,769.0,2261.0,671.0,2.888,246400.0,NEAR BAY
+-117.18,32.75,36.0,2282.0,534.0,918.0,531.0,2.7222,284700.0,NEAR OCEAN
+-118.2,33.98,32.0,1403.0,399.0,1506.0,375.0,2.0,172700.0,<1H OCEAN
+-121.95,37.35,36.0,832.0,211.0,545.0,211.0,3.2813,244400.0,<1H OCEAN
+-117.26,33.06,11.0,2660.0,352.0,1226.0,366.0,7.6832,319800.0,NEAR OCEAN
+-117.53,33.83,7.0,2191.0,324.0,1156.0,310.0,5.5362,195600.0,INLAND
+-117.34,33.46,14.0,1902.0,338.0,848.0,304.0,5.5395,273300.0,<1H OCEAN
+-122.02,37.97,36.0,2342.0,436.0,1191.0,416.0,4.0,171000.0,NEAR BAY
+-117.16,34.26,27.0,9285.0,1621.0,1135.0,410.0,2.5446,135200.0,INLAND
+-121.51,38.53,36.0,2603.0,408.0,966.0,419.0,5.3135,216600.0,INLAND
+-122.48,37.67,14.0,3395.0,1059.0,2258.0,945.0,2.964,319700.0,NEAR OCEAN
+-117.98,33.86,26.0,1240.0,285.0,781.0,315.0,4.1287,205800.0,<1H OCEAN
+-118.11,33.87,33.0,1379.0,254.0,795.0,297.0,4.6713,231800.0,<1H OCEAN
+-121.78,38.0,8.0,2371.0,375.0,1094.0,396.0,5.3245,174500.0,INLAND
+-122.17,37.75,37.0,1379.0,287.0,835.0,259.0,2.4962,91800.0,NEAR BAY
+-118.04,33.85,24.0,2233.0,347.0,1162.0,355.0,5.6094,279200.0,<1H OCEAN
+-118.26,34.08,45.0,2225.0,827.0,2699.0,792.0,1.9209,179200.0,<1H OCEAN
+-117.02,32.64,5.0,260.0,41.0,157.0,42.0,6.5151,281700.0,NEAR OCEAN
+-115.38,32.81,35.0,1263.0,262.0,950.0,241.0,1.8958,67500.0,INLAND
+-118.07,33.83,17.0,4822.0,1168.0,3868.0,1117.0,2.5978,142900.0,<1H OCEAN
+-118.09,34.06,38.0,3230.0,840.0,3485.0,827.0,2.629,171600.0,<1H OCEAN
+-115.75,35.23,5.0,208.0,78.0,132.0,56.0,2.5333,75000.0,INLAND
+-121.47,38.48,24.0,2359.0,462.0,2048.0,476.0,3.2702,67300.0,INLAND
+-121.9,36.6,33.0,2461.0,649.0,1234.0,601.0,2.8727,225000.0,NEAR OCEAN
+-122.43,37.78,49.0,2246.0,587.0,1277.0,546.0,2.9792,350000.0,NEAR BAY
+-119.32,36.62,15.0,1070.0,256.0,1070.0,243.0,1.5642,51500.0,INLAND
+-121.78,39.71,8.0,140.0,28.0,84.0,29.0,2.125,179200.0,INLAND
+-119.55,36.51,46.0,1889.0,390.0,971.0,403.0,2.2132,76600.0,INLAND
+-117.14,33.15,17.0,1149.0,182.0,702.0,192.0,5.5696,168400.0,<1H OCEAN
+-120.12,35.99,7.0,2049.0,482.0,1387.0,422.0,2.25,56200.0,INLAND
+-118.11,33.82,37.0,1987.0,347.0,1095.0,357.0,4.3203,232800.0,<1H OCEAN
+-121.96,37.99,2.0,3129.0,707.0,1606.0,698.0,2.9591,210100.0,INLAND
+-117.94,33.8,28.0,2914.0,489.0,1500.0,499.0,4.9429,254800.0,<1H OCEAN
+-119.13,36.13,28.0,1673.0,385.0,1434.0,371.0,2.0586,40900.0,INLAND
+-122.93,40.78,20.0,3758.0,798.0,1685.0,757.0,2.3667,91200.0,INLAND
+-118.19,33.87,27.0,4701.0,1359.0,2571.0,1216.0,2.5417,184100.0,<1H OCEAN
+-122.44,37.78,52.0,2911.0,753.0,1696.0,676.0,2.5721,475000.0,NEAR BAY
+-120.02,37.72,17.0,2806.0,600.0,990.0,410.0,2.3818,88100.0,INLAND
+-118.88,34.02,19.0,15990.0,2611.0,5175.0,2173.0,7.7848,500001.0,NEAR OCEAN
+-117.08,32.64,43.0,1005.0,230.0,548.0,252.0,1.8672,145800.0,NEAR OCEAN
+-118.13,34.2,46.0,2676.0,427.0,1022.0,395.0,6.4288,295500.0,INLAND
+-118.58,34.25,23.0,4883.0,769.0,2119.0,725.0,5.521,280800.0,<1H OCEAN
+-121.91,37.3,31.0,616.0,131.0,185.0,107.0,3.625,265000.0,<1H OCEAN
+-117.74,34.07,42.0,2504.0,553.0,1550.0,509.0,3.0294,135700.0,INLAND
+-121.31,38.75,7.0,4185.0,750.0,2147.0,706.0,4.0519,129200.0,INLAND
+-118.0,33.84,30.0,1549.0,325.0,885.0,299.0,4.0039,195100.0,<1H OCEAN
+-121.38,38.68,35.0,1643.0,298.0,831.0,305.0,4.0673,84200.0,INLAND
+-117.45,34.1,6.0,5571.0,1316.0,4048.0,1154.0,2.0308,91100.0,INLAND
+-121.35,38.04,5.0,4303.0,613.0,2206.0,621.0,5.5842,159100.0,INLAND
+-121.23,37.95,32.0,2081.0,472.0,1342.0,411.0,2.7958,59000.0,INLAND
+-120.47,34.64,16.0,1912.0,406.0,1009.0,417.0,3.4063,138000.0,NEAR OCEAN
+-122.16,37.86,36.0,3359.0,493.0,1298.0,483.0,8.1586,404300.0,NEAR BAY
+-118.54,34.26,22.0,5303.0,838.0,2372.0,807.0,5.6912,311800.0,<1H OCEAN
+-120.42,34.96,20.0,1678.0,307.0,840.0,316.0,4.4342,160700.0,<1H OCEAN
+-116.99,34.89,24.0,2741.0,577.0,1551.0,522.0,3.474,70500.0,INLAND
+-121.99,37.56,20.0,6462.0,1294.0,3288.0,1235.0,4.3393,231200.0,<1H OCEAN
+-118.92,35.26,20.0,3815.0,924.0,3450.0,920.0,2.0174,63700.0,INLAND
+-122.31,37.57,42.0,3157.0,676.0,1603.0,629.0,3.7422,292600.0,NEAR OCEAN
+-119.86,34.41,24.0,1576.0,580.0,1630.0,531.0,1.24,325000.0,NEAR OCEAN
+-118.33,34.17,44.0,1934.0,375.0,750.0,365.0,2.473,251800.0,<1H OCEAN
+-121.85,37.25,20.0,3773.0,624.0,1965.0,607.0,5.4939,241200.0,<1H OCEAN
+-124.16,41.74,15.0,2715.0,569.0,1532.0,530.0,2.1829,69500.0,NEAR OCEAN
+-117.94,33.81,25.0,1731.0,482.0,1127.0,455.0,3.256,214300.0,<1H OCEAN
+-118.39,34.05,47.0,1621.0,314.0,724.0,311.0,5.7509,474100.0,<1H OCEAN
+-118.95,35.38,30.0,2594.0,478.0,1419.0,480.0,3.725,83100.0,INLAND
+-117.08,33.11,28.0,2094.0,585.0,1556.0,563.0,2.2,127700.0,<1H OCEAN
+-121.3,38.65,36.0,1665.0,293.0,846.0,306.0,3.5852,121600.0,INLAND
+-121.46,38.55,40.0,2077.0,435.0,1454.0,385.0,2.0074,57000.0,INLAND
+-118.19,33.77,21.0,2103.0,727.0,1064.0,603.0,1.6178,137500.0,NEAR OCEAN
+-118.7,34.3,23.0,2831.0,406.0,1284.0,393.0,6.1383,244100.0,<1H OCEAN
+-119.67,34.43,39.0,1467.0,381.0,1404.0,374.0,2.3681,241400.0,<1H OCEAN
+-119.33,36.19,27.0,418.0,163.0,332.0,141.0,1.0714,63800.0,INLAND
+-118.38,34.06,52.0,1311.0,217.0,578.0,205.0,7.6771,500001.0,<1H OCEAN
+-120.64,35.47,8.0,416.0,121.0,936.0,97.0,2.1154,117200.0,<1H OCEAN
+-117.71,34.15,17.0,17715.0,2370.0,7665.0,2312.0,7.9068,349100.0,INLAND
+-119.18,34.18,31.0,2636.0,638.0,2695.0,614.0,3.2196,175800.0,NEAR OCEAN
+-123.08,40.4,10.0,365.0,102.0,140.0,49.0,1.7969,37500.0,INLAND
+-122.85,38.37,16.0,1762.0,293.0,810.0,297.0,4.4437,305000.0,<1H OCEAN
+-121.38,38.59,38.0,1839.0,287.0,685.0,276.0,4.5313,189400.0,INLAND
+-118.41,33.9,39.0,2040.0,336.0,926.0,351.0,7.5552,500001.0,<1H OCEAN
+-122.05,37.33,21.0,2052.0,346.0,933.0,351.0,5.3167,416300.0,<1H OCEAN
+-118.84,34.17,16.0,3449.0,820.0,1877.0,816.0,3.2176,187500.0,<1H OCEAN
+-121.2,38.69,26.0,3077.0,607.0,1603.0,595.0,2.7174,137500.0,INLAND
+-120.6,38.76,22.0,1236.0,273.0,615.0,248.0,3.0217,106900.0,INLAND
+-122.26,38.03,41.0,1631.0,282.0,752.0,288.0,3.9345,150200.0,NEAR BAY
+-117.38,33.97,29.0,1157.0,297.0,2027.0,253.0,1.6389,155000.0,INLAND
+-122.24,37.73,21.0,7031.0,1249.0,2930.0,1235.0,4.5213,228400.0,NEAR BAY
+-119.04,35.36,40.0,1533.0,312.0,771.0,306.0,3.0435,69500.0,INLAND
+-114.6,33.62,16.0,3741.0,801.0,2434.0,824.0,2.6797,86500.0,INLAND
+-118.37,34.15,23.0,4604.0,1319.0,2391.0,1227.0,3.1373,263100.0,<1H OCEAN
+-117.84,33.75,16.0,4367.0,1161.0,2164.0,1005.0,4.0214,139500.0,<1H OCEAN
+-118.06,33.99,46.0,1203.0,219.0,637.0,211.0,3.3611,174400.0,<1H OCEAN
+-122.22,37.79,49.0,1826.0,450.0,1201.0,424.0,2.5,136700.0,NEAR BAY
+-121.09,37.61,42.0,1787.0,296.0,921.0,287.0,3.8864,171400.0,INLAND
+-121.32,38.03,16.0,4045.0,623.0,1862.0,625.0,4.875,143100.0,INLAND
+-118.07,34.0,37.0,2976.0,636.0,2117.0,598.0,4.1058,167300.0,<1H OCEAN
+-121.75,36.92,48.0,1801.0,353.0,1071.0,361.0,3.6,194500.0,<1H OCEAN
+-117.28,34.09,44.0,376.0,,273.0,107.0,2.2917,90800.0,INLAND
+-119.23,34.27,22.0,3536.0,615.0,1650.0,612.0,4.2381,229300.0,NEAR OCEAN
+-117.93,34.01,23.0,3188.0,836.0,3883.0,840.0,2.1863,157600.0,<1H OCEAN
+-118.12,33.75,41.0,2072.0,491.0,742.0,414.0,3.9934,500001.0,NEAR OCEAN
+-121.7,36.55,21.0,3905.0,583.0,1528.0,586.0,7.6245,336600.0,<1H OCEAN
+-118.37,34.21,36.0,2080.0,455.0,1939.0,484.0,4.2875,176600.0,<1H OCEAN
+-122.31,37.92,12.0,1895.0,600.0,983.0,519.0,2.5,195800.0,NEAR BAY
+-121.9,38.02,5.0,1560.0,369.0,1037.0,372.0,3.6154,181800.0,INLAND
+-118.1,33.84,36.0,2572.0,421.0,1318.0,434.0,5.1836,219700.0,<1H OCEAN
+-116.48,33.94,10.0,3254.0,913.0,923.0,486.0,1.8,81000.0,INLAND
+-118.8,34.15,9.0,1143.0,179.0,647.0,180.0,6.8474,356700.0,NEAR OCEAN
+-119.25,35.78,27.0,1513.0,342.0,1346.0,323.0,2.7411,59800.0,INLAND
+-118.17,33.87,49.0,1937.0,445.0,1339.0,440.0,3.0319,162800.0,<1H OCEAN
+-117.86,33.71,36.0,191.0,42.0,208.0,37.0,3.375,157500.0,<1H OCEAN
+-117.87,33.61,25.0,2267.0,359.0,866.0,348.0,7.79,500001.0,<1H OCEAN
+-118.45,33.96,24.0,3097.0,791.0,1075.0,639.0,5.723,500001.0,<1H OCEAN
+-118.25,33.99,42.0,2261.0,574.0,2496.0,527.0,1.5556,98500.0,<1H OCEAN
+-119.14,35.76,30.0,735.0,137.0,421.0,113.0,2.5625,156300.0,INLAND
+-117.33,34.41,13.0,3684.0,604.0,1767.0,585.0,3.7478,113500.0,INLAND
+-118.28,34.18,50.0,2195.0,336.0,878.0,309.0,6.884,365600.0,<1H OCEAN
+-122.55,38.0,18.0,3119.0,803.0,1395.0,722.0,3.9265,301100.0,NEAR BAY
+-118.26,34.07,52.0,830.0,200.0,701.0,189.0,2.7625,232100.0,<1H OCEAN
+-117.49,33.9,7.0,10235.0,2238.0,5271.0,2094.0,3.6071,159100.0,INLAND
+-121.19,38.66,26.0,1937.0,286.0,769.0,274.0,6.1185,179200.0,INLAND
+-118.02,33.95,35.0,2085.0,400.0,1112.0,391.0,3.4886,173900.0,<1H OCEAN
+-118.02,33.93,35.0,2400.0,398.0,1218.0,408.0,4.1312,193800.0,<1H OCEAN
+-118.57,34.15,22.0,5791.0,706.0,2059.0,673.0,10.9201,500001.0,<1H OCEAN
+-121.76,38.41,19.0,686.0,107.0,348.0,109.0,3.9306,93800.0,INLAND
+-123.36,39.25,17.0,1087.0,254.0,522.0,202.0,2.5875,144500.0,<1H OCEAN
+-121.39,38.85,19.0,3568.0,646.0,1714.0,590.0,4.0862,162700.0,INLAND
+-116.39,34.15,15.0,5583.0,1149.0,2709.0,964.0,1.9779,73300.0,INLAND
+-117.3,33.26,23.0,1678.0,275.0,1227.0,264.0,4.1713,133800.0,<1H OCEAN
+-121.81,38.01,47.0,1942.0,430.0,1074.0,393.0,2.2361,105100.0,INLAND
+-118.06,33.99,38.0,862.0,178.0,484.0,176.0,4.375,186200.0,<1H OCEAN
+-118.43,34.24,37.0,1279.0,241.0,987.0,233.0,4.0057,172700.0,<1H OCEAN
+-118.22,34.04,43.0,798.0,308.0,1417.0,325.0,1.4189,141700.0,<1H OCEAN
+-122.05,37.9,32.0,2676.0,484.0,986.0,473.0,4.6528,335700.0,NEAR BAY
+-121.94,37.26,43.0,2104.0,388.0,1137.0,403.0,4.9236,238000.0,<1H OCEAN
+-119.07,36.07,11.0,2265.0,382.0,1285.0,387.0,3.2042,76200.0,INLAND
+-118.07,34.0,42.0,1392.0,351.0,1471.0,348.0,2.63,143800.0,<1H OCEAN
+-118.33,34.0,52.0,1114.0,169.0,486.0,176.0,4.2917,247600.0,<1H OCEAN
+-116.91,32.73,8.0,4630.0,624.0,2048.0,575.0,6.4745,300300.0,<1H OCEAN
+-118.37,34.21,33.0,2034.0,470.0,1990.0,423.0,3.7455,159600.0,<1H OCEAN
+-118.27,34.08,38.0,2265.0,801.0,2899.0,792.0,2.5521,157500.0,<1H OCEAN
+-119.61,36.33,32.0,1492.0,284.0,926.0,264.0,3.0139,61500.0,INLAND
+-119.0,35.36,35.0,1021.0,280.0,1258.0,239.0,1.7375,48600.0,INLAND
+-116.44,33.74,5.0,846.0,249.0,117.0,67.0,7.9885,403300.0,INLAND
+-121.94,36.58,23.0,4911.0,693.0,1480.0,606.0,6.777,500000.0,NEAR OCEAN
+-117.07,33.0,4.0,6242.0,1258.0,2211.0,1116.0,4.25,281600.0,<1H OCEAN
+-119.06,35.33,14.0,5264.0,1064.0,3278.0,1049.0,3.8117,82800.0,INLAND
+-117.19,32.75,33.0,1115.0,316.0,583.0,269.0,2.5882,258300.0,NEAR OCEAN
+-117.92,33.75,19.0,1920.0,471.0,1413.0,432.0,4.0313,147500.0,<1H OCEAN
+-121.23,37.79,21.0,1922.0,373.0,1130.0,372.0,4.0815,117900.0,INLAND
+-117.73,34.05,36.0,975.0,243.0,809.0,233.0,2.8929,118100.0,INLAND
+-121.48,38.55,52.0,2037.0,358.0,811.0,375.0,4.3929,162500.0,INLAND
+-122.66,39.02,16.0,3715.0,810.0,943.0,510.0,1.7446,109400.0,INLAND
+-117.1,32.68,49.0,1412.0,350.0,1200.0,332.0,2.0398,93600.0,NEAR OCEAN
+-117.95,33.76,24.0,3956.0,812.0,3196.0,795.0,4.3512,191400.0,<1H OCEAN
+-121.91,36.6,35.0,2605.0,410.0,1110.0,406.0,5.5519,329500.0,NEAR OCEAN
+-118.47,34.01,43.0,1160.0,304.0,393.0,250.0,2.9167,461100.0,<1H OCEAN
+-119.79,36.74,52.0,173.0,87.0,401.0,84.0,2.1094,75000.0,INLAND
+-118.59,34.26,20.0,8146.0,1131.0,3562.0,1054.0,7.167,357100.0,<1H OCEAN
+-121.82,37.98,13.0,3995.0,605.0,1969.0,607.0,5.0164,165200.0,INLAND
+-122.08,37.6,10.0,3046.0,678.0,2056.0,628.0,3.9022,191700.0,NEAR BAY
+-116.85,34.26,19.0,5395.0,1220.0,981.0,366.0,2.6094,92400.0,INLAND
+-121.32,37.98,37.0,3247.0,643.0,1737.0,665.0,3.066,94100.0,INLAND
+-117.19,32.75,52.0,1388.0,213.0,513.0,211.0,6.1309,411600.0,NEAR OCEAN
+-118.29,33.71,40.0,1644.0,471.0,780.0,416.0,3.1071,464300.0,NEAR OCEAN
+-120.95,37.6,35.0,1493.0,278.0,729.0,268.0,2.9821,97400.0,INLAND
+-117.83,33.93,14.0,1956.0,282.0,671.0,269.0,6.5841,306400.0,<1H OCEAN
+-119.51,37.32,14.0,362.0,78.0,88.0,39.0,3.5893,214300.0,INLAND
+-116.22,33.7,9.0,3861.0,849.0,825.0,401.0,3.2833,124700.0,INLAND
+-118.36,34.03,36.0,1083.0,342.0,1023.0,295.0,2.1324,143800.0,<1H OCEAN
+-118.63,34.16,30.0,3346.0,487.0,1296.0,495.0,7.457,392700.0,<1H OCEAN
+-118.22,34.52,7.0,4524.0,735.0,2298.0,717.0,6.5538,311600.0,INLAND
+-117.88,33.6,31.0,5488.0,1055.0,1938.0,964.0,8.8742,500001.0,<1H OCEAN
+-119.81,36.79,39.0,2471.0,460.0,1118.0,431.0,2.4167,71900.0,INLAND
+-120.87,37.62,30.0,455.0,70.0,220.0,69.0,4.8958,142500.0,INLAND
+-118.47,34.0,38.0,1235.0,390.0,891.0,376.0,2.7143,287500.0,<1H OCEAN
+-122.14,37.68,35.0,2976.0,518.0,1424.0,538.0,4.267,210300.0,NEAR BAY
+-116.22,33.73,38.0,1695.0,352.0,1279.0,305.0,2.1217,68500.0,INLAND
+-117.14,32.92,15.0,3242.0,595.0,1936.0,593.0,4.9706,184700.0,<1H OCEAN
+-118.3,33.9,27.0,3267.0,762.0,2099.0,647.0,3.4,224100.0,<1H OCEAN
+-122.42,37.74,52.0,1271.0,353.0,1076.0,324.0,2.9911,263900.0,NEAR BAY
+-121.22,37.8,37.0,1038.0,222.0,521.0,211.0,2.125,91900.0,INLAND
+-119.2,34.27,8.0,4942.0,1173.0,3012.0,1033.0,3.445,203400.0,NEAR OCEAN
+-117.06,32.58,17.0,2724.0,567.0,2213.0,554.0,3.8529,147700.0,NEAR OCEAN
+-117.23,32.74,16.0,735.0,139.0,299.0,134.0,4.6354,179200.0,NEAR OCEAN
+-121.81,38.01,52.0,1124.0,245.0,528.0,226.0,2.2639,128500.0,INLAND
+-122.65,38.97,32.0,1856.0,472.0,703.0,292.0,1.1912,60000.0,INLAND
+-116.95,32.83,14.0,12517.0,2506.0,6389.0,2333.0,3.3081,168700.0,<1H OCEAN
+-117.21,34.22,31.0,5454.0,1053.0,1408.0,504.0,5.0,161100.0,INLAND
+-118.2,33.87,36.0,1554.0,273.0,974.0,264.0,4.2135,161400.0,<1H OCEAN
+-122.03,38.0,25.0,3577.0,581.0,1753.0,593.0,5.7295,178300.0,NEAR BAY
+-122.69,39.02,27.0,2199.0,527.0,744.0,316.0,2.1094,72400.0,INLAND
+-120.45,34.91,16.0,712.0,147.0,355.0,162.0,2.56,150000.0,<1H OCEAN
+-122.12,37.89,30.0,3227.0,733.0,1260.0,684.0,4.125,257100.0,NEAR BAY
+-119.78,36.83,11.0,2754.0,663.0,1328.0,604.0,2.3667,69300.0,INLAND
+-119.04,35.35,27.0,4590.0,897.0,2212.0,894.0,3.1753,85000.0,INLAND
+-118.31,33.87,27.0,3348.0,695.0,1545.0,625.0,4.7543,296300.0,<1H OCEAN
+-118.0,33.89,34.0,1932.0,315.0,1053.0,316.0,5.1377,213300.0,<1H OCEAN
+-118.17,33.96,29.0,2913.0,787.0,3803.0,740.0,2.5556,146500.0,<1H OCEAN
+-122.56,41.69,21.0,2010.0,360.0,947.0,306.0,2.4107,70100.0,INLAND
+-121.29,38.12,18.0,1534.0,275.0,741.0,263.0,3.9607,132500.0,INLAND
+-118.62,34.2,23.0,3098.0,542.0,1486.0,492.0,5.7613,235800.0,<1H OCEAN
+-121.85,37.26,16.0,1816.0,241.0,793.0,234.0,6.8194,291200.0,<1H OCEAN
+-118.99,35.33,36.0,1590.0,367.0,1311.0,390.0,1.6786,52900.0,INLAND
+-122.33,39.1,10.0,266.0,62.0,154.0,49.0,2.25,75000.0,INLAND
+-118.28,34.01,52.0,795.0,308.0,1118.0,275.0,1.2175,131300.0,<1H OCEAN
+-117.31,34.03,24.0,1966.0,299.0,786.0,302.0,5.0318,134500.0,INLAND
+-117.21,33.2,22.0,3337.0,518.0,1288.0,466.0,5.04,253700.0,<1H OCEAN
+-117.6,34.11,18.0,6025.0,1062.0,3360.0,1028.0,4.8889,155700.0,INLAND
+-122.67,38.47,19.0,1848.0,428.0,1130.0,433.0,3.0568,190300.0,<1H OCEAN
+-119.73,36.59,31.0,1551.0,296.0,1058.0,287.0,3.3438,92600.0,INLAND
+-116.98,32.74,24.0,977.0,147.0,454.0,169.0,4.9286,173700.0,<1H OCEAN
+-117.28,33.28,13.0,6131.0,1040.0,4049.0,940.0,3.8156,150700.0,<1H OCEAN
+-122.59,38.13,20.0,1589.0,231.0,601.0,224.0,5.3755,290900.0,<1H OCEAN
+-118.04,33.97,29.0,2376.0,700.0,1968.0,680.0,2.6082,162500.0,<1H OCEAN
+-118.44,34.0,44.0,1462.0,338.0,821.0,341.0,2.599,362200.0,<1H OCEAN
+-118.13,33.82,37.0,1530.0,290.0,711.0,283.0,5.1795,225400.0,<1H OCEAN
+-117.81,33.56,24.0,6258.0,1003.0,1730.0,752.0,10.9601,500001.0,<1H OCEAN
+-117.98,33.8,35.0,2114.0,341.0,1077.0,343.0,5.4876,227500.0,<1H OCEAN
+-118.22,33.93,39.0,1921.0,483.0,2286.0,470.0,3.0167,130000.0,<1H OCEAN
+-121.5,38.53,37.0,3642.0,684.0,1508.0,657.0,3.5231,114300.0,INLAND
+-120.08,41.79,34.0,1355.0,262.0,434.0,178.0,2.0903,56100.0,INLAND
+-118.04,33.98,43.0,2446.0,764.0,1699.0,692.0,2.625,163300.0,<1H OCEAN
+-122.1,37.4,23.0,514.0,210.0,367.0,206.0,3.1736,181300.0,NEAR BAY
+-119.1,36.42,26.0,1775.0,416.0,1217.0,383.0,1.8801,57600.0,INLAND
+-118.07,33.91,29.0,2387.0,570.0,1978.0,548.0,3.1957,159200.0,<1H OCEAN
+-118.27,33.95,43.0,1156.0,291.0,1074.0,299.0,1.8814,94900.0,<1H OCEAN
+-122.29,37.91,46.0,2085.0,346.0,748.0,354.0,4.0536,262000.0,NEAR BAY
+-118.08,33.76,25.0,1995.0,637.0,743.0,597.0,1.4617,46900.0,<1H OCEAN
+-120.1,38.91,33.0,1561.0,282.0,30.0,11.0,1.875,500001.0,INLAND
+-118.04,33.78,25.0,3715.0,575.0,1640.0,572.0,5.7705,247100.0,<1H OCEAN
+-120.61,35.13,16.0,3431.0,721.0,1777.0,701.0,2.7301,190400.0,<1H OCEAN
+-118.07,33.97,32.0,3400.0,826.0,3017.0,793.0,2.4607,155600.0,<1H OCEAN
+-117.24,34.14,6.0,2383.0,606.0,1301.0,488.0,3.016,107500.0,INLAND
+-119.08,36.2,30.0,1677.0,358.0,1159.0,365.0,2.4554,61200.0,INLAND
+-122.54,37.9,48.0,2491.0,460.0,937.0,455.0,4.4375,370000.0,NEAR BAY
+-118.08,33.96,35.0,2104.0,399.0,1659.0,387.0,4.0096,165000.0,<1H OCEAN
+-119.03,36.13,24.0,2259.0,408.0,1169.0,395.0,1.7106,95500.0,INLAND
+-120.44,34.68,6.0,2187.0,277.0,697.0,273.0,6.2685,307400.0,NEAR OCEAN
+-121.94,38.37,17.0,7973.0,1591.0,2899.0,1502.0,2.8357,120100.0,INLAND
+-118.45,34.07,19.0,4845.0,1609.0,3751.0,1539.0,1.583,350000.0,<1H OCEAN
+-119.01,35.35,39.0,598.0,149.0,366.0,132.0,1.9125,57900.0,INLAND
+-118.29,33.83,24.0,4092.0,893.0,2819.0,893.0,4.1378,216500.0,<1H OCEAN
+-121.37,38.7,18.0,3938.0,649.0,1861.0,606.0,3.6484,95000.0,INLAND
+-119.15,34.17,23.0,2239.0,537.0,784.0,497.0,1.6038,194300.0,NEAR OCEAN
+-119.25,34.21,12.0,15201.0,2418.0,7132.0,2251.0,5.6756,301800.0,NEAR OCEAN
+-121.49,38.51,30.0,3166.0,607.0,1857.0,579.0,3.1768,79500.0,INLAND
+-117.85,33.77,16.0,2186.0,511.0,908.0,466.0,4.575,225000.0,<1H OCEAN
+-118.19,34.04,40.0,1095.0,305.0,1322.0,281.0,1.9688,150000.0,<1H OCEAN
+-118.33,33.99,44.0,1918.0,387.0,1041.0,364.0,2.8542,126500.0,<1H OCEAN
+-117.02,34.88,18.0,2127.0,443.0,1168.0,401.0,3.0313,80000.0,INLAND
+-123.53,40.88,20.0,2680.0,599.0,918.0,345.0,2.2115,75000.0,INLAND
+-122.7,38.31,14.0,3155.0,580.0,1208.0,501.0,4.1964,258100.0,<1H OCEAN
+-118.45,34.17,33.0,3100.0,687.0,1388.0,658.0,4.3333,261300.0,<1H OCEAN
+-117.14,32.75,20.0,1182.0,379.0,678.0,326.0,2.1937,162500.0,NEAR OCEAN
+-118.13,34.1,19.0,2742.0,756.0,1396.0,703.0,2.5663,197500.0,<1H OCEAN
+-118.35,33.95,42.0,1779.0,431.0,1507.0,380.0,2.8892,159800.0,<1H OCEAN
+-117.92,33.57,37.0,3355.0,492.0,921.0,366.0,7.2988,500001.0,NEAR OCEAN
+-118.66,34.23,18.0,897.0,142.0,263.0,110.0,6.1288,350000.0,<1H OCEAN
+-115.55,32.79,23.0,1004.0,221.0,697.0,201.0,1.6351,59600.0,INLAND
+-122.41,37.8,52.0,1999.0,642.0,1846.0,620.0,1.9145,225000.0,NEAR BAY
+-117.45,34.07,21.0,3465.0,639.0,2292.0,628.0,3.3553,113500.0,INLAND
+-120.86,37.73,27.0,508.0,93.0,263.0,81.0,3.1136,183300.0,INLAND
+-118.22,33.96,42.0,1380.0,331.0,1290.0,288.0,2.8,161800.0,<1H OCEAN
+-118.19,33.99,36.0,1273.0,379.0,1398.0,353.0,2.4516,147800.0,<1H OCEAN
+-121.63,39.15,16.0,1547.0,418.0,940.0,400.0,1.5613,72500.0,INLAND
+-117.32,34.06,52.0,802.0,160.0,564.0,131.0,2.1591,63500.0,INLAND
+-119.13,34.19,16.0,6389.0,1330.0,6242.0,1340.0,4.0222,206800.0,NEAR OCEAN
+-118.37,33.85,25.0,5622.0,998.0,2537.0,1009.0,5.785,395300.0,<1H OCEAN
+-117.76,33.7,12.0,4025.0,574.0,2042.0,588.0,7.9125,344900.0,<1H OCEAN
+-118.38,34.1,39.0,3798.0,586.0,975.0,525.0,9.3092,500001.0,<1H OCEAN
+-119.8,36.77,52.0,2964.0,512.0,1114.0,486.0,3.8105,87600.0,INLAND
+-122.48,37.74,52.0,2166.0,423.0,1072.0,370.0,4.131,314300.0,NEAR OCEAN
+-118.03,33.87,16.0,2306.0,393.0,1368.0,387.0,5.93,277600.0,<1H OCEAN
+-118.24,33.89,32.0,1132.0,266.0,1211.0,279.0,2.1838,98300.0,<1H OCEAN
+-117.81,33.67,9.0,1567.0,299.0,675.0,294.0,5.2124,199600.0,<1H OCEAN
+-117.11,32.74,33.0,1126.0,267.0,621.0,241.0,3.2422,123100.0,NEAR OCEAN
+-118.36,34.21,41.0,337.0,65.0,198.0,50.0,1.8929,152900.0,<1H OCEAN
+-122.12,37.4,31.0,2356.0,405.0,921.0,358.0,7.0245,500001.0,NEAR BAY
+-122.23,37.45,34.0,4177.0,723.0,1586.0,660.0,5.0457,395100.0,NEAR OCEAN
+-122.82,38.53,27.0,1823.0,360.0,907.0,317.0,3.276,172900.0,<1H OCEAN
+-118.31,33.83,27.0,2880.0,802.0,2083.0,727.0,2.9619,223400.0,<1H OCEAN
+-115.57,32.8,16.0,1307.0,359.0,961.0,327.0,1.1853,72600.0,INLAND
+-120.25,37.11,20.0,2062.0,466.0,1285.0,456.0,1.5319,50500.0,INLAND
+-117.7,33.59,11.0,8039.0,1717.0,3445.0,1571.0,4.1678,190900.0,<1H OCEAN
+-122.44,37.75,21.0,5457.0,1247.0,2304.0,1180.0,4.5469,409700.0,NEAR BAY
+-117.02,32.68,14.0,3986.0,675.0,2065.0,702.0,5.7192,267400.0,NEAR OCEAN
+-117.87,33.72,37.0,2216.0,497.0,2445.0,506.0,3.8421,174000.0,<1H OCEAN
+-122.52,37.96,35.0,2012.0,346.0,818.0,352.0,5.2818,331000.0,NEAR BAY
+-117.72,34.05,8.0,1841.0,409.0,1243.0,394.0,4.0614,107000.0,INLAND
+-121.28,38.67,23.0,1727.0,264.0,833.0,258.0,5.4797,160000.0,INLAND
+-117.3,33.05,34.0,1797.0,458.0,775.0,391.0,3.2308,331300.0,NEAR OCEAN
+-120.65,35.29,29.0,1785.0,481.0,1344.0,472.0,1.4492,222900.0,NEAR OCEAN
+-118.41,34.32,18.0,6572.0,1105.0,3473.0,1067.0,5.2987,203400.0,<1H OCEAN
+-117.49,34.05,20.0,1483.0,249.0,660.0,194.0,3.9464,207700.0,INLAND
+-118.22,33.88,35.0,998.0,313.0,1335.0,311.0,1.6574,102500.0,<1H OCEAN
+-120.39,37.96,10.0,2554.0,501.0,922.0,439.0,2.1094,164000.0,INLAND
+-121.52,38.65,17.0,1269.0,233.0,494.0,231.0,3.9615,331300.0,INLAND
+-118.31,33.99,49.0,857.0,196.0,694.0,228.0,2.895,108000.0,<1H OCEAN
+-121.65,37.12,14.0,4721.0,999.0,2648.0,888.0,3.6895,239300.0,<1H OCEAN
+-119.66,36.33,10.0,1623.0,409.0,988.0,395.0,1.4194,58100.0,INLAND
+-119.78,36.77,45.0,1315.0,256.0,666.0,240.0,2.3562,58100.0,INLAND
+-122.52,37.9,16.0,1704.0,402.0,689.0,348.0,4.4239,267100.0,NEAR BAY
+-117.75,34.08,33.0,2824.0,523.0,1797.0,493.0,3.6359,135100.0,INLAND
+-119.27,36.05,29.0,1016.0,174.0,481.0,140.0,2.2917,112500.0,INLAND
+-117.88,33.63,21.0,9565.0,2289.0,3162.0,1831.0,4.7024,345400.0,<1H OCEAN
+-117.93,33.93,25.0,2431.0,534.0,1702.0,523.0,3.7933,184400.0,<1H OCEAN
+-118.23,33.93,35.0,1149.0,277.0,909.0,214.0,1.7411,96700.0,<1H OCEAN
+-118.14,34.17,40.0,1054.0,251.0,1056.0,276.0,2.3,146700.0,<1H OCEAN
+-118.27,34.0,47.0,780.0,237.0,888.0,215.0,1.75,95800.0,<1H OCEAN
+-118.11,34.01,41.0,815.0,252.0,775.0,231.0,2.2847,190000.0,<1H OCEAN
+-117.81,33.76,32.0,2053.0,339.0,835.0,323.0,5.5654,281800.0,<1H OCEAN
+-118.71,34.27,26.0,990.0,223.0,719.0,232.0,3.163,179400.0,<1H OCEAN
+-119.67,36.82,2.0,2579.0,376.0,1133.0,342.0,4.5577,123300.0,INLAND
+-121.9,37.27,28.0,4538.0,685.0,1996.0,667.0,5.4609,263600.0,<1H OCEAN
+-117.1,32.76,30.0,1835.0,474.0,934.0,415.0,2.875,139600.0,NEAR OCEAN
+-117.25,32.79,43.0,906.0,240.0,458.0,205.0,1.8365,328600.0,NEAR OCEAN
+-118.26,33.94,45.0,1080.0,218.0,850.0,237.0,2.25,93400.0,<1H OCEAN
+-117.98,34.13,37.0,1447.0,309.0,1279.0,290.0,4.0083,142900.0,INLAND
+-118.1,34.07,36.0,1240.0,349.0,1383.0,338.0,2.4931,170300.0,<1H OCEAN
+-121.99,38.36,33.0,146.0,31.0,75.0,31.0,3.5179,84400.0,INLAND
+-119.87,36.78,4.0,6102.0,1114.0,3406.0,1115.0,3.4213,84500.0,INLAND
+-117.9,34.02,15.0,14058.0,2486.0,8997.0,2497.0,5.0704,226200.0,<1H OCEAN
+-118.18,33.92,32.0,2035.0,519.0,2282.0,480.0,3.2734,136400.0,<1H OCEAN
+-117.18,32.81,28.0,3436.0,537.0,1503.0,498.0,4.7679,204000.0,NEAR OCEAN
+-118.6,34.13,20.0,14291.0,1934.0,5452.0,1875.0,9.1232,472000.0,<1H OCEAN
+-118.31,34.26,38.0,2264.0,460.0,1124.0,388.0,4.2685,189600.0,<1H OCEAN
+-122.27,37.86,52.0,1088.0,305.0,486.0,267.0,2.6071,250000.0,NEAR BAY
+-122.6,38.93,16.0,1657.0,390.0,572.0,301.0,1.4767,62000.0,INLAND
+-119.71,36.8,25.0,875.0,156.0,646.0,166.0,3.0,72800.0,INLAND
+-122.13,40.01,21.0,916.0,194.0,451.0,178.0,2.125,63300.0,INLAND
+-119.06,34.22,13.0,4175.0,1321.0,2257.0,1271.0,3.1446,177100.0,<1H OCEAN
+-122.14,37.75,33.0,1334.0,200.0,579.0,202.0,6.8323,255900.0,NEAR BAY
+-117.24,32.8,30.0,1917.0,462.0,828.0,437.0,2.4671,276300.0,NEAR OCEAN
+-119.3,36.34,27.0,1515.0,358.0,1178.0,309.0,1.4432,48100.0,INLAND
+-117.9,33.73,32.0,2930.0,833.0,5116.0,854.0,3.7147,164100.0,<1H OCEAN
+-118.31,33.89,35.0,2144.0,423.0,1192.0,417.0,4.1458,231500.0,<1H OCEAN
+-119.63,36.58,22.0,1794.0,435.0,1127.0,359.0,1.2647,55300.0,INLAND
+-118.36,33.81,25.0,9042.0,2022.0,4458.0,1944.0,4.5592,378800.0,<1H OCEAN
+-122.23,38.14,36.0,1412.0,260.0,792.0,268.0,2.3056,90400.0,NEAR BAY
+-118.26,34.04,6.0,1529.0,566.0,1051.0,473.0,2.462,162500.0,<1H OCEAN
+-118.4,33.99,36.0,1225.0,213.0,591.0,227.0,5.4663,326700.0,<1H OCEAN
+-118.54,34.25,26.0,2639.0,378.0,1191.0,401.0,6.2788,322200.0,<1H OCEAN
+-118.19,33.94,45.0,1871.0,371.0,1315.0,382.0,3.3661,160800.0,<1H OCEAN
+-118.07,34.16,35.0,2459.0,438.0,970.0,437.0,4.2143,369400.0,INLAND
+-117.05,32.59,16.0,4683.0,929.0,3073.0,865.0,3.0495,98300.0,NEAR OCEAN
+-119.7,36.82,25.0,2379.0,540.0,1482.0,484.0,2.3173,68200.0,INLAND
+-121.7,37.2,15.0,531.0,154.0,469.0,155.0,4.65,385700.0,<1H OCEAN
+-117.03,32.73,34.0,2061.0,,1169.0,400.0,3.5096,142000.0,NEAR OCEAN
+-117.61,33.42,31.0,3959.0,856.0,1919.0,775.0,4.0313,282000.0,NEAR OCEAN
+-122.4,37.75,52.0,1182.0,307.0,1029.0,306.0,2.0577,214600.0,NEAR BAY
+-118.06,34.12,34.0,2941.0,558.0,1660.0,576.0,4.5667,271500.0,INLAND
+-122.24,40.51,23.0,2216.0,378.0,1006.0,338.0,4.559,116800.0,INLAND
+-121.0,35.94,16.0,3077.0,628.0,1479.0,536.0,3.3724,114600.0,<1H OCEAN
+-118.35,34.05,47.0,2815.0,679.0,1533.0,594.0,2.5806,234100.0,<1H OCEAN
+-117.74,34.06,4.0,1391.0,506.0,727.0,369.0,1.4722,137500.0,INLAND
+-117.09,32.65,20.0,1445.0,323.0,573.0,334.0,2.619,145800.0,NEAR OCEAN
+-117.9,33.64,28.0,2466.0,507.0,1081.0,465.0,3.9375,339800.0,<1H OCEAN
+-116.95,33.74,18.0,1996.0,405.0,1270.0,400.0,2.7083,91200.0,INLAND
+-119.17,34.27,18.0,8010.0,1539.0,3982.0,1483.0,4.0905,236500.0,NEAR OCEAN
+-121.88,37.3,42.0,1867.0,398.0,927.0,389.0,4.325,247000.0,<1H OCEAN
+-117.94,34.04,34.0,1403.0,274.0,977.0,257.0,3.8409,163000.0,<1H OCEAN
+-122.68,38.43,18.0,2723.0,529.0,1150.0,520.0,3.5885,191900.0,<1H OCEAN
+-119.04,34.34,35.0,462.0,90.0,334.0,96.0,5.3582,281300.0,<1H OCEAN
+-121.68,39.15,14.0,2774.0,451.0,1292.0,428.0,4.3833,115200.0,INLAND
+-120.98,37.57,27.0,925.0,176.0,449.0,168.0,2.6406,129700.0,INLAND
+-122.32,37.94,47.0,1911.0,283.0,697.0,275.0,6.2712,267700.0,NEAR BAY
+-121.83,37.26,11.0,2394.0,403.0,1393.0,409.0,5.5875,259300.0,<1H OCEAN
+-117.66,33.6,25.0,3745.0,522.0,1648.0,496.0,7.5488,278100.0,<1H OCEAN
+-118.46,35.12,16.0,4084.0,812.0,2033.0,668.0,3.2405,85500.0,INLAND
+-121.04,37.67,16.0,19.0,19.0,166.0,9.0,0.536,162500.0,INLAND
+-119.93,35.2,29.0,1649.0,342.0,671.0,264.0,3.0602,69800.0,INLAND
+-118.43,33.96,13.0,1860.0,345.0,800.0,355.0,5.8864,500001.0,<1H OCEAN
+-118.05,33.99,42.0,2480.0,401.0,1085.0,438.0,5.193,263400.0,<1H OCEAN
+-122.03,38.3,5.0,1569.0,199.0,713.0,209.0,6.6779,223900.0,INLAND
+-117.91,33.83,47.0,504.0,113.0,375.0,109.0,3.6607,160600.0,<1H OCEAN
+-116.67,32.97,16.0,349.0,74.0,120.0,43.0,5.359,193800.0,<1H OCEAN
+-121.65,39.32,40.0,812.0,154.0,374.0,142.0,2.7891,73500.0,INLAND
+-118.16,34.02,35.0,1734.0,493.0,2053.0,508.0,2.1442,149200.0,<1H OCEAN
+-118.14,33.97,36.0,1407.0,385.0,1763.0,350.0,2.6364,150000.0,<1H OCEAN
+-117.25,33.38,17.0,1614.0,431.0,1031.0,389.0,2.0956,134400.0,<1H OCEAN
+-118.27,34.06,25.0,1714.0,1176.0,3723.0,1036.0,1.3241,112500.0,<1H OCEAN
+-117.68,34.0,5.0,3761.0,580.0,2335.0,648.0,5.7338,225400.0,INLAND
+-118.26,34.01,47.0,1269.0,323.0,1628.0,325.0,1.5089,115800.0,<1H OCEAN
+-118.28,33.99,49.0,2174.0,481.0,1861.0,484.0,1.7159,95000.0,<1H OCEAN
+-122.25,37.39,33.0,370.0,42.0,153.0,53.0,10.6514,500001.0,NEAR OCEAN
+-122.84,38.39,16.0,1688.0,292.0,793.0,280.0,4.4357,216900.0,<1H OCEAN
+-118.16,34.03,45.0,894.0,231.0,925.0,222.0,2.6042,145000.0,<1H OCEAN
+-118.02,34.02,21.0,5992.0,986.0,2647.0,969.0,5.2405,302400.0,<1H OCEAN
+-117.3,33.08,24.0,2628.0,527.0,1389.0,520.0,4.0,343200.0,NEAR OCEAN
+-116.62,32.86,18.0,4115.0,847.0,2032.0,745.0,4.0159,169100.0,<1H OCEAN
+-122.43,37.73,49.0,1435.0,322.0,1008.0,329.0,4.0,264000.0,NEAR BAY
+-117.38,33.67,17.0,10145.0,2306.0,4776.0,1749.0,2.2423,132600.0,<1H OCEAN
+-118.34,34.06,52.0,1482.0,336.0,768.0,300.0,3.7167,327300.0,<1H OCEAN
+-118.28,34.05,44.0,1273.0,474.0,1754.0,460.0,1.6037,275000.0,<1H OCEAN
+-122.17,38.06,16.0,3515.0,626.0,1764.0,626.0,4.4397,187100.0,NEAR BAY
+-117.06,32.72,31.0,2669.0,514.0,1626.0,499.0,3.1923,116900.0,NEAR OCEAN
+-118.28,34.01,34.0,2305.0,775.0,2450.0,740.0,1.7143,132000.0,<1H OCEAN
+-117.62,33.43,27.0,3858.0,1062.0,2321.0,873.0,3.3155,231000.0,NEAR OCEAN
+-122.59,38.58,18.0,3753.0,752.0,1454.0,668.0,3.7585,185700.0,<1H OCEAN
+-117.31,33.11,7.0,7974.0,1703.0,2904.0,1550.0,4.1282,188100.0,NEAR OCEAN
+-118.52,34.22,21.0,4617.0,1101.0,2891.0,1031.0,3.2289,318100.0,<1H OCEAN
+-120.65,35.48,19.0,2310.0,471.0,1341.0,441.0,3.225,166900.0,<1H OCEAN
+-117.96,34.14,33.0,1994.0,405.0,993.0,403.0,3.766,163900.0,INLAND
+-117.96,33.77,32.0,4398.0,905.0,2777.0,884.0,4.1321,222800.0,<1H OCEAN
+-121.79,36.95,34.0,2152.0,430.0,1516.0,386.0,3.7863,192200.0,<1H OCEAN
+-117.88,34.08,30.0,6132.0,1538.0,3147.0,1449.0,2.7763,187800.0,<1H OCEAN
+-118.14,34.11,52.0,3367.0,545.0,1427.0,535.0,5.2292,444500.0,<1H OCEAN
+-120.98,37.66,10.0,934.0,,401.0,255.0,0.9336,127100.0,INLAND
+-117.9,33.74,19.0,1566.0,379.0,1032.0,330.0,2.2105,180400.0,<1H OCEAN
+-117.37,33.22,35.0,2204.0,482.0,1435.0,462.0,3.676,125600.0,NEAR OCEAN
+-117.97,34.09,31.0,2779.0,639.0,2259.0,670.0,3.4032,143400.0,INLAND
+-117.93,33.92,34.0,2271.0,437.0,1393.0,433.0,4.2443,174400.0,<1H OCEAN
+-122.04,38.25,52.0,582.0,131.0,241.0,106.0,2.4,125000.0,INLAND
+-118.11,34.06,18.0,2609.0,721.0,2221.0,703.0,2.3224,192300.0,<1H OCEAN
+-121.06,39.25,17.0,3127.0,539.0,1390.0,520.0,3.9537,172800.0,INLAND
+-118.16,34.17,46.0,1508.0,261.0,674.0,255.0,3.5909,155400.0,<1H OCEAN
+-122.17,37.76,41.0,1594.0,367.0,1074.0,355.0,1.9356,90600.0,NEAR BAY
+-117.5,33.93,19.0,4741.0,835.0,2903.0,796.0,4.3723,135600.0,INLAND
+-120.85,37.55,45.0,350.0,62.0,187.0,63.0,2.5938,275000.0,INLAND
+-119.72,34.43,27.0,984.0,299.0,777.0,313.0,2.5694,275000.0,<1H OCEAN
+-121.8,37.31,21.0,2630.0,446.0,1789.0,389.0,5.0543,232000.0,<1H OCEAN
+-118.42,34.04,52.0,1358.0,272.0,574.0,267.0,5.6454,500001.0,<1H OCEAN
+-121.86,37.34,29.0,5274.0,1625.0,6234.0,1639.0,2.5947,177300.0,<1H OCEAN
+-118.02,33.98,23.0,1995.0,306.0,707.0,293.0,8.6677,332700.0,<1H OCEAN
+-118.21,34.08,39.0,986.0,361.0,1347.0,299.0,2.2907,133900.0,<1H OCEAN
+-117.01,32.72,12.0,2914.0,734.0,2104.0,703.0,2.3068,132300.0,NEAR OCEAN
+-120.62,37.35,18.0,874.0,203.0,572.0,190.0,1.6833,71000.0,INLAND
+-116.95,32.79,19.0,11391.0,3093.0,7178.0,2905.0,2.0326,123200.0,<1H OCEAN
+-118.37,33.9,32.0,332.0,103.0,177.0,102.0,3.3409,256300.0,<1H OCEAN
+-121.86,39.76,19.0,7254.0,1785.0,4030.0,1667.0,2.0094,87300.0,INLAND
+-120.44,34.94,29.0,1877.0,516.0,1634.0,492.0,1.6875,122700.0,<1H OCEAN
+-118.34,34.02,50.0,1172.0,261.0,685.0,260.0,3.1442,130300.0,<1H OCEAN
+-115.9,33.34,19.0,1210.0,248.0,329.0,155.0,1.7857,62800.0,INLAND
+-117.4,34.58,18.0,755.0,169.0,483.0,165.0,1.4196,64700.0,INLAND
+-117.1,32.75,17.0,871.0,379.0,955.0,351.0,1.4375,96400.0,NEAR OCEAN
+-118.15,33.86,34.0,2403.0,413.0,1385.0,386.0,4.4934,213800.0,<1H OCEAN
+-122.35,40.57,18.0,2226.0,490.0,859.0,451.0,1.6821,69400.0,INLAND
+-118.46,34.04,25.0,2142.0,718.0,1390.0,699.0,3.0069,325000.0,<1H OCEAN
+-118.3,33.87,27.0,3144.0,722.0,1510.0,680.0,3.1597,214700.0,<1H OCEAN
+-117.21,34.28,16.0,3326.0,569.0,527.0,192.0,5.7421,167600.0,INLAND
+-118.11,34.06,32.0,1273.0,344.0,1148.0,368.0,2.1061,214700.0,<1H OCEAN
+-122.08,37.38,33.0,2771.0,659.0,1496.0,581.0,3.4042,353600.0,NEAR BAY
+-117.14,32.93,14.0,1946.0,463.0,1205.0,390.0,4.2109,171200.0,<1H OCEAN
+-118.38,34.05,52.0,2053.0,480.0,900.0,417.0,3.0707,417900.0,<1H OCEAN
+-121.47,38.55,29.0,1303.0,308.0,861.0,263.0,1.0208,55800.0,INLAND
+-118.21,33.79,32.0,2020.0,613.0,2557.0,562.0,2.1397,145300.0,NEAR OCEAN
+-119.81,37.67,24.0,172.0,42.0,79.0,30.0,3.8333,93800.0,INLAND
+-118.12,34.14,25.0,3420.0,977.0,1718.0,947.0,3.1033,217900.0,<1H OCEAN
+-118.02,33.97,34.0,1903.0,293.0,887.0,306.0,6.148,313800.0,<1H OCEAN
+-122.1,37.39,31.0,1117.0,304.0,591.0,302.0,3.5909,353100.0,NEAR BAY
+-117.13,32.76,22.0,2623.0,732.0,1283.0,718.0,2.1563,127100.0,NEAR OCEAN
+-118.72,34.28,17.0,2654.0,478.0,1392.0,451.0,5.4459,223900.0,<1H OCEAN
+-118.2,33.99,33.0,1134.0,375.0,1615.0,354.0,2.1468,141700.0,<1H OCEAN
+-117.31,34.5,14.0,2443.0,447.0,883.0,465.0,2.1111,116700.0,INLAND
+-118.36,34.07,52.0,2046.0,451.0,944.0,435.0,3.4265,456900.0,<1H OCEAN
+-122.35,37.95,31.0,2449.0,595.0,1801.0,548.0,2.6328,110300.0,NEAR BAY
+-122.29,37.48,15.0,5480.0,892.0,2009.0,831.0,7.4678,500001.0,NEAR OCEAN
+-120.4,37.3,28.0,1401.0,,967.0,257.0,1.5917,89400.0,INLAND
+-120.12,41.4,33.0,2820.0,515.0,976.0,403.0,2.6062,52600.0,INLAND
+-122.58,38.08,27.0,10839.0,1637.0,4406.0,1623.0,5.615,285600.0,NEAR BAY
+-117.85,33.74,26.0,2589.0,1003.0,5756.0,983.0,2.1992,170800.0,<1H OCEAN
+-121.42,38.51,15.0,7901.0,1422.0,4769.0,1418.0,2.8139,90400.0,INLAND
+-121.79,36.68,22.0,6912.0,1513.0,3794.0,1455.0,3.0608,168300.0,<1H OCEAN
+-122.1,37.19,18.0,808.0,136.0,420.0,145.0,7.1831,273300.0,NEAR OCEAN
+-117.88,33.71,30.0,1739.0,359.0,1914.0,369.0,3.5551,185200.0,<1H OCEAN
+-122.2,38.1,5.0,9567.0,1729.0,4620.0,1580.0,4.4821,210000.0,NEAR BAY
+-117.0,32.72,10.0,3817.0,943.0,2352.0,875.0,2.1362,143200.0,NEAR OCEAN
+-121.51,38.69,28.0,800.0,149.0,450.0,158.0,2.1029,108600.0,INLAND
+-122.06,37.38,20.0,2976.0,766.0,1227.0,634.0,4.8625,262500.0,NEAR BAY
+-118.33,34.06,52.0,1368.0,231.0,737.0,248.0,8.3617,433800.0,<1H OCEAN
+-117.98,34.05,33.0,1560.0,315.0,1467.0,313.0,4.1429,159800.0,<1H OCEAN
+-122.49,41.43,19.0,3689.0,644.0,1544.0,566.0,3.125,76100.0,INLAND
+-117.04,32.76,37.0,2979.0,557.0,1285.0,564.0,3.7368,152200.0,NEAR OCEAN
+-122.23,37.76,52.0,1316.0,177.0,378.0,162.0,5.2915,333000.0,NEAR BAY
+-118.3,34.17,37.0,350.0,115.0,342.0,111.0,3.0687,200000.0,<1H OCEAN
+-118.1,33.83,36.0,2000.0,343.0,956.0,352.0,5.3735,234400.0,<1H OCEAN
+-118.26,33.95,44.0,1771.0,378.0,1296.0,399.0,1.6389,96700.0,<1H OCEAN
+-118.32,34.04,44.0,1008.0,223.0,544.0,223.0,2.8654,176800.0,<1H OCEAN
+-118.1,34.14,45.0,3066.0,659.0,1287.0,625.0,3.5804,324400.0,<1H OCEAN
+-117.64,34.04,21.0,1801.0,507.0,2556.0,484.0,2.4716,102500.0,INLAND
+-117.88,34.12,34.0,912.0,165.0,522.0,150.0,4.0417,178000.0,<1H OCEAN
+-122.06,37.32,30.0,3033.0,540.0,1440.0,507.0,6.2182,380800.0,<1H OCEAN
+-119.85,34.44,28.0,1765.0,301.0,1173.0,297.0,6.0256,276800.0,NEAR OCEAN
+-121.93,37.04,36.0,1522.0,230.0,677.0,206.0,5.8642,363500.0,<1H OCEAN
+-117.65,33.46,19.0,7034.0,1139.0,2824.0,1068.0,6.0873,277300.0,<1H OCEAN
+-117.26,34.15,33.0,2271.0,389.0,1100.0,380.0,3.5978,88300.0,INLAND
+-120.43,34.86,17.0,3172.0,506.0,1538.0,473.0,4.3125,168100.0,<1H OCEAN
+-118.09,34.11,36.0,2966.0,527.0,1231.0,482.0,4.6442,316800.0,<1H OCEAN
+-122.04,37.63,33.0,952.0,172.0,369.0,159.0,3.2331,226700.0,NEAR BAY
+-117.13,32.9,15.0,2785.0,644.0,1798.0,630.0,3.7156,175200.0,<1H OCEAN
+-118.01,34.15,52.0,2234.0,472.0,986.0,439.0,3.9125,265500.0,INLAND
+-118.84,34.15,17.0,3785.0,494.0,1527.0,507.0,8.4443,358500.0,<1H OCEAN
+-121.06,38.88,17.0,7635.0,1284.0,3096.0,1227.0,4.2917,184300.0,INLAND
+-121.89,37.29,36.0,2959.0,529.0,1125.0,520.0,4.2614,268800.0,<1H OCEAN
+-117.49,33.64,3.0,8874.0,1302.0,3191.0,1027.0,6.8588,302000.0,<1H OCEAN
+-116.29,33.72,5.0,3584.0,760.0,1097.0,470.0,3.1771,167400.0,INLAND
+-117.25,32.8,32.0,1601.0,468.0,731.0,429.0,2.5568,258300.0,NEAR OCEAN
+-117.15,32.91,14.0,1259.0,238.0,889.0,247.0,4.9464,174800.0,<1H OCEAN
+-121.88,39.74,12.0,14631.0,3298.0,7517.0,3262.0,1.6785,153100.0,INLAND
+-118.14,33.97,31.0,2064.0,612.0,2461.0,573.0,2.0524,160800.0,<1H OCEAN
+-115.49,32.67,24.0,1266.0,275.0,1083.0,298.0,1.4828,73100.0,INLAND
+-117.89,34.12,35.0,1470.0,241.0,885.0,246.0,4.9239,168800.0,<1H OCEAN
+-122.18,37.8,23.0,2317.0,336.0,955.0,328.0,6.7527,285800.0,NEAR BAY
+-117.3,34.12,34.0,1127.0,275.0,971.0,249.0,2.0583,64800.0,INLAND
+-121.91,37.26,32.0,3983.0,876.0,1989.0,794.0,3.5625,255200.0,<1H OCEAN
+-118.47,34.15,7.0,6306.0,1473.0,2381.0,1299.0,4.642,457300.0,<1H OCEAN
+-118.2,34.14,51.0,1941.0,378.0,1012.0,371.0,3.9375,217000.0,<1H OCEAN
+-118.4,34.16,34.0,2638.0,580.0,1150.0,551.0,4.2989,364700.0,<1H OCEAN
+-117.1,32.57,14.0,5058.0,1299.0,3662.0,1193.0,2.3253,133700.0,NEAR OCEAN
+-122.88,38.46,25.0,1563.0,314.0,737.0,305.0,2.5687,249200.0,<1H OCEAN
+-117.96,33.89,24.0,1332.0,252.0,625.0,230.0,4.4375,334100.0,<1H OCEAN
+-121.82,36.95,16.0,2599.0,430.0,1417.0,445.0,4.6611,349300.0,<1H OCEAN
+-117.73,34.1,37.0,3457.0,,1344.0,530.0,5.8891,226000.0,INLAND
+-117.35,34.16,36.0,1717.0,348.0,1054.0,279.0,2.4444,73400.0,INLAND
+-119.61,36.31,25.0,1847.0,371.0,1460.0,353.0,1.8839,46300.0,INLAND
+-122.47,37.79,52.0,437.0,105.0,194.0,87.0,2.8125,500001.0,NEAR BAY
+-121.0,37.67,27.0,2278.0,479.0,995.0,449.0,2.5148,110200.0,INLAND
+-120.65,35.32,20.0,626.0,212.0,3574.0,261.0,1.0298,300000.0,NEAR OCEAN
+-119.97,36.57,17.0,1497.0,308.0,1425.0,247.0,2.0313,69400.0,INLAND
+-117.13,32.72,9.0,2436.0,720.0,1780.0,653.0,1.8299,137500.0,NEAR OCEAN
+-122.04,37.0,52.0,3365.0,644.0,796.0,333.0,2.9712,116600.0,NEAR OCEAN
+-117.87,34.08,33.0,4518.0,716.0,2037.0,764.0,5.6015,267200.0,<1H OCEAN
+-117.96,33.68,18.0,2594.0,539.0,817.0,485.0,2.3674,219200.0,<1H OCEAN
+-116.22,33.74,26.0,4120.0,858.0,2918.0,815.0,3.3107,69400.0,INLAND
+-118.24,33.93,32.0,1063.0,282.0,992.0,253.0,0.8984,88700.0,<1H OCEAN
+-117.38,33.21,31.0,1502.0,367.0,1514.0,342.0,2.6442,103300.0,NEAR OCEAN
+-122.48,37.75,52.0,2074.0,401.0,1136.0,409.0,4.7703,331000.0,NEAR BAY
+-117.91,34.02,17.0,5973.0,1384.0,4349.0,1229.0,3.2799,199300.0,<1H OCEAN
+-122.52,38.27,18.0,2405.0,390.0,872.0,367.0,5.2155,248300.0,<1H OCEAN
+-119.8,34.44,27.0,2674.0,419.0,1176.0,416.0,5.0294,280200.0,NEAR OCEAN
+-122.42,37.76,52.0,4001.0,1084.0,2129.0,1037.0,3.5052,391200.0,NEAR BAY
+-122.47,37.79,52.0,2383.0,477.0,990.0,464.0,3.9688,483300.0,NEAR BAY
+-121.28,38.6,25.0,1122.0,198.0,564.0,213.0,3.1654,111600.0,INLAND
+-121.35,38.04,12.0,6217.0,1019.0,3771.0,961.0,3.7206,146000.0,INLAND
+-118.25,34.16,24.0,5131.0,1436.0,2690.0,1371.0,2.5668,280000.0,<1H OCEAN
+-118.45,34.26,35.0,1724.0,311.0,992.0,315.0,4.8359,195600.0,<1H OCEAN
+-117.73,33.49,17.0,2168.0,290.0,654.0,279.0,9.8321,500001.0,<1H OCEAN
+-117.5,34.12,2.0,11965.0,1802.0,4436.0,1296.0,5.285,191700.0,INLAND
+-117.01,32.67,17.0,2319.0,348.0,1125.0,337.0,5.551,266900.0,NEAR OCEAN
+-117.61,34.01,25.0,352.0,41.0,99.0,34.0,3.9696,500000.0,INLAND
+-116.92,32.81,23.0,2668.0,528.0,1510.0,524.0,3.3669,158900.0,<1H OCEAN
+-122.1,37.4,19.0,1085.0,288.0,1009.0,305.0,3.9091,276000.0,NEAR BAY
+-118.28,34.06,14.0,1787.0,853.0,2251.0,763.0,1.1642,400000.0,<1H OCEAN
+-122.0,38.25,7.0,11768.0,1893.0,6657.0,1874.0,4.9222,142900.0,INLAND
+-117.97,34.15,33.0,2474.0,472.0,1268.0,437.0,6.4576,500001.0,INLAND
+-122.3,37.52,38.0,2769.0,387.0,994.0,395.0,5.5902,417000.0,NEAR OCEAN
+-118.86,34.07,16.0,1409.0,244.0,970.0,172.0,8.0144,500001.0,NEAR OCEAN
+-122.46,37.72,47.0,1723.0,389.0,1216.0,399.0,3.3208,238600.0,NEAR OCEAN
+-118.17,33.77,36.0,2933.0,881.0,2077.0,838.0,2.2538,181300.0,NEAR OCEAN
+-116.57,34.0,20.0,260.0,67.0,69.0,50.0,3.5208,76600.0,INLAND
+-122.41,37.79,52.0,1436.0,738.0,1688.0,662.0,1.5156,237500.0,NEAR BAY
+-117.98,33.94,36.0,4297.0,717.0,2038.0,700.0,5.2851,258800.0,<1H OCEAN
+-122.04,37.97,21.0,6445.0,1839.0,3621.0,1735.0,2.5841,112500.0,NEAR BAY
+-119.8,36.02,20.0,156.0,39.0,171.0,37.0,3.05,225000.0,INLAND
+-118.43,34.03,45.0,1740.0,311.0,788.0,306.0,5.2099,373600.0,<1H OCEAN
+-118.97,35.38,35.0,1673.0,426.0,1041.0,413.0,1.375,57500.0,INLAND
+-118.1,34.08,24.0,4510.0,1296.0,3985.0,1240.0,2.6884,204600.0,<1H OCEAN
+-122.75,38.43,36.0,1599.0,345.0,1086.0,314.0,2.6667,149100.0,<1H OCEAN
+-122.1,37.68,37.0,1352.0,342.0,691.0,324.0,3.4032,196900.0,NEAR BAY
+-117.16,32.73,52.0,1218.0,471.0,821.0,429.0,1.9597,200000.0,NEAR OCEAN
+-118.2,34.04,18.0,796.0,227.0,547.0,218.0,1.0333,135400.0,<1H OCEAN
+-117.49,33.91,17.0,5364.0,1020.0,3754.0,936.0,3.2857,139100.0,INLAND
+-119.35,36.22,29.0,2051.0,351.0,915.0,343.0,3.1944,106800.0,INLAND
+-122.14,38.03,42.0,118.0,34.0,54.0,30.0,2.5795,225000.0,NEAR BAY
+-120.24,37.96,34.0,1747.0,395.0,935.0,362.0,1.625,79400.0,INLAND
+-122.66,38.2,39.0,2889.0,517.0,1351.0,489.0,4.3056,251300.0,<1H OCEAN
+-120.75,37.37,32.0,1656.0,317.0,1037.0,286.0,2.4964,88800.0,INLAND
+-118.08,33.76,26.0,1967.0,577.0,692.0,538.0,1.6111,54300.0,<1H OCEAN
+-117.33,33.23,15.0,1905.0,416.0,1258.0,388.0,3.33,127900.0,NEAR OCEAN
+-117.95,33.7,17.0,5781.0,924.0,2585.0,915.0,5.343,231900.0,<1H OCEAN
+-122.19,37.79,28.0,3144.0,761.0,1737.0,669.0,2.9297,140500.0,NEAR BAY
+-118.22,33.96,36.0,1542.0,458.0,1711.0,468.0,1.9028,164200.0,<1H OCEAN
+-122.33,40.47,30.0,2502.0,523.0,1296.0,481.0,2.125,66100.0,INLAND
+-117.09,32.75,30.0,1899.0,546.0,1620.0,493.0,1.6034,84400.0,NEAR OCEAN
+-117.07,33.12,32.0,2474.0,499.0,1224.0,461.0,2.7216,146300.0,<1H OCEAN
+-119.42,36.35,20.0,1469.0,303.0,1031.0,259.0,1.6645,48000.0,INLAND
+-118.33,34.08,52.0,1777.0,454.0,671.0,439.0,3.5083,500001.0,<1H OCEAN
+-117.27,33.55,4.0,6112.0,890.0,2088.0,712.0,5.5351,429000.0,<1H OCEAN
+-123.19,39.21,22.0,1542.0,291.0,821.0,285.0,3.5917,118800.0,<1H OCEAN
+-118.45,34.26,35.0,1637.0,300.0,894.0,302.0,4.175,209600.0,<1H OCEAN
+-118.21,33.96,43.0,1686.0,446.0,1590.0,474.0,2.3241,159300.0,<1H OCEAN
+-121.3,37.99,38.0,2375.0,494.0,1167.0,471.0,2.6673,87500.0,INLAND
+-118.31,33.73,52.0,1665.0,280.0,656.0,282.0,5.249,351900.0,NEAR OCEAN
+-122.4,37.76,52.0,1529.0,385.0,1347.0,348.0,2.9312,239100.0,NEAR BAY
+-122.53,38.32,22.0,3577.0,,1371.0,501.0,5.795,332300.0,<1H OCEAN
+-122.06,37.36,34.0,1747.0,250.0,662.0,257.0,6.8268,500001.0,<1H OCEAN
+-122.05,37.93,15.0,7803.0,1603.0,2957.0,1546.0,4.45,184900.0,NEAR BAY
+-118.11,34.14,52.0,2401.0,332.0,810.0,308.0,6.0948,358700.0,<1H OCEAN
+-122.62,38.94,13.0,524.0,129.0,215.0,90.0,1.5455,55000.0,INLAND
+-118.19,34.12,46.0,3387.0,820.0,2833.0,813.0,2.987,176900.0,<1H OCEAN
+-118.19,34.07,42.0,1555.0,337.0,1152.0,348.0,3.375,169600.0,<1H OCEAN
+-120.56,35.48,12.0,4161.0,731.0,1609.0,615.0,5.0947,267500.0,<1H OCEAN
+-119.4,36.25,25.0,1696.0,279.0,909.0,291.0,2.3,132800.0,INLAND
+-119.03,34.21,11.0,4528.0,729.0,2398.0,684.0,5.3044,319000.0,<1H OCEAN
+-122.69,38.3,30.0,3919.0,743.0,1693.0,693.0,3.3827,292100.0,<1H OCEAN
+-122.44,37.73,52.0,866.0,205.0,587.0,171.0,5.0224,261900.0,NEAR BAY
+-121.32,37.96,47.0,1700.0,344.0,922.0,357.0,3.1845,87200.0,INLAND
+-122.1,37.4,23.0,1755.0,508.0,1374.0,506.0,4.3077,293500.0,NEAR BAY
+-122.95,39.13,17.0,380.0,69.0,225.0,72.0,3.25,137500.0,INLAND
+-118.34,34.0,44.0,3183.0,513.0,1183.0,473.0,5.0407,314900.0,<1H OCEAN
+-122.0,37.08,17.0,4154.0,739.0,2149.0,693.0,5.5919,373400.0,NEAR OCEAN
+-121.66,36.69,6.0,10613.0,2485.0,7249.0,2375.0,3.7912,168900.0,<1H OCEAN
+-118.28,34.1,49.0,2843.0,880.0,2004.0,796.0,2.7875,217300.0,<1H OCEAN
+-124.14,40.78,35.0,2426.0,423.0,982.0,432.0,3.4219,92800.0,NEAR OCEAN
+-117.94,33.65,20.0,5476.0,1073.0,2327.0,963.0,5.6637,222100.0,<1H OCEAN
+-118.41,34.01,43.0,2000.0,529.0,1290.0,514.0,4.7031,302500.0,<1H OCEAN
+-121.47,38.56,51.0,2083.0,559.0,874.0,524.0,2.0221,95800.0,INLAND
+-118.99,35.36,18.0,1524.0,354.0,1210.0,344.0,1.1136,47800.0,INLAND
+-117.75,34.01,4.0,22128.0,3522.0,10450.0,3258.0,6.1287,289600.0,<1H OCEAN
+-117.07,32.59,21.0,1779.0,466.0,1327.0,488.0,1.6007,96200.0,NEAR OCEAN
+-118.52,34.18,46.0,2082.0,438.0,1047.0,393.0,3.6534,216000.0,<1H OCEAN
+-122.37,37.59,52.0,2272.0,403.0,963.0,376.0,5.7245,500000.0,NEAR OCEAN
+-117.93,33.8,29.0,1672.0,267.0,891.0,281.0,4.8611,231900.0,<1H OCEAN
+-119.21,36.3,18.0,1433.0,265.0,1092.0,276.0,1.9135,49400.0,INLAND
+-118.61,34.16,29.0,4364.0,647.0,1550.0,624.0,6.8107,367400.0,<1H OCEAN
+-117.76,34.13,8.0,16759.0,2274.0,7249.0,2156.0,7.4837,358700.0,INLAND
+-121.65,39.35,24.0,1003.0,251.0,1098.0,227.0,1.7552,86400.0,INLAND
+-118.29,34.09,43.0,1583.0,658.0,1941.0,614.0,1.9835,225000.0,<1H OCEAN
+-121.01,37.62,35.0,2074.0,477.0,1687.0,431.0,2.0885,73700.0,INLAND
+-121.93,36.62,39.0,869.0,173.0,406.0,165.0,4.0313,253800.0,NEAR OCEAN
+-117.13,32.8,33.0,2731.0,456.0,1263.0,445.0,4.5568,175300.0,NEAR OCEAN
+-118.46,33.97,18.0,9430.0,2473.0,3408.0,2003.0,6.1726,500001.0,<1H OCEAN
+-122.22,37.48,47.0,2570.0,783.0,3107.0,724.0,2.8058,229500.0,NEAR OCEAN
+-121.84,37.27,16.0,2429.0,387.0,1293.0,363.0,5.5,253200.0,<1H OCEAN
+-117.82,35.03,30.0,2555.0,510.0,1347.0,467.0,3.3693,71800.0,INLAND
+-118.29,34.1,39.0,2196.0,582.0,1165.0,538.0,2.9417,254200.0,<1H OCEAN
+-121.91,39.03,48.0,1096.0,218.0,657.0,199.0,2.7841,65800.0,INLAND
+-120.37,38.01,30.0,473.0,,242.0,93.0,2.5417,123200.0,INLAND
+-118.29,33.71,40.0,1933.0,475.0,902.0,412.0,4.25,332800.0,NEAR OCEAN
+-122.05,38.25,37.0,1336.0,251.0,680.0,231.0,3.815,99000.0,INLAND
+-122.27,37.78,52.0,1408.0,280.0,718.0,265.0,2.6806,207900.0,NEAR BAY
+-117.87,33.87,16.0,1332.0,368.0,1534.0,295.0,3.0227,297100.0,<1H OCEAN
+-121.39,38.63,30.0,2930.0,739.0,1661.0,668.0,2.7813,118900.0,INLAND
+-121.37,38.57,16.0,3895.0,896.0,1762.0,855.0,2.6635,135800.0,INLAND
+-121.39,38.6,22.0,5773.0,1320.0,2607.0,1250.0,2.5238,118800.0,INLAND
+-118.29,33.92,34.0,1799.0,362.0,1293.0,355.0,3.692,145200.0,<1H OCEAN
+-117.92,33.76,26.0,784.0,177.0,662.0,169.0,2.8438,174300.0,<1H OCEAN
+-118.99,35.32,26.0,875.0,199.0,567.0,204.0,0.9288,36600.0,INLAND
+-117.7,33.48,10.0,3458.0,638.0,1156.0,470.0,6.3579,336700.0,<1H OCEAN
+-118.32,33.87,35.0,2380.0,404.0,1212.0,422.0,5.6254,283800.0,<1H OCEAN
+-119.01,35.37,35.0,120.0,35.0,477.0,41.0,1.9125,47500.0,INLAND
+-118.25,34.02,32.0,1375.0,448.0,1698.0,432.0,1.6302,130700.0,<1H OCEAN
+-119.23,34.25,28.0,26.0,3.0,29.0,9.0,8.0,275000.0,NEAR OCEAN
+-122.23,37.48,38.0,1578.0,399.0,879.0,388.0,2.7969,298400.0,NEAR OCEAN
+-121.65,39.13,11.0,4833.0,944.0,2336.0,841.0,2.6842,89100.0,INLAND
+-116.94,32.84,32.0,1607.0,253.0,778.0,262.0,4.5278,166300.0,<1H OCEAN
+-118.46,34.03,39.0,1244.0,283.0,886.0,284.0,3.125,325000.0,<1H OCEAN
+-118.38,34.07,48.0,2799.0,596.0,1235.0,561.0,4.4896,500001.0,<1H OCEAN
+-118.22,34.05,34.0,1113.0,,928.0,290.0,3.1654,155000.0,<1H OCEAN
+-118.3,33.97,42.0,944.0,200.0,567.0,190.0,2.6311,124100.0,<1H OCEAN
+-122.48,37.75,49.0,2203.0,407.0,1052.0,405.0,4.4375,329200.0,NEAR BAY
+-118.29,34.06,46.0,1759.0,1012.0,2716.0,877.0,2.1637,350000.0,<1H OCEAN
+-117.1,32.58,23.0,1662.0,377.0,1318.0,386.0,2.3,120800.0,NEAR OCEAN
+-118.18,33.93,31.0,1516.0,400.0,1820.0,398.0,2.1641,122900.0,<1H OCEAN
+-122.28,37.52,38.0,2197.0,357.0,1228.0,373.0,5.4719,397900.0,NEAR OCEAN
+-118.29,33.89,35.0,2810.0,614.0,1578.0,601.0,3.59,200600.0,<1H OCEAN
+-119.33,35.6,32.0,2703.0,683.0,2682.0,675.0,1.4619,60500.0,INLAND
+-121.86,37.22,18.0,7245.0,1029.0,2893.0,1049.0,6.9508,361200.0,<1H OCEAN
+-117.39,33.95,35.0,3306.0,680.0,1742.0,673.0,3.7109,109100.0,INLAND
+-122.06,37.64,20.0,1655.0,450.0,857.0,430.0,3.5541,350000.0,NEAR BAY
+-122.21,37.79,47.0,1543.0,307.0,859.0,292.0,2.9583,138800.0,NEAR BAY
+-118.09,33.77,27.0,2301.0,640.0,847.0,627.0,1.7208,67500.0,<1H OCEAN
+-117.71,33.54,15.0,2460.0,368.0,962.0,320.0,7.3878,318300.0,<1H OCEAN
+-117.99,33.94,30.0,2395.0,565.0,1214.0,521.0,3.7045,212300.0,<1H OCEAN
+-122.03,37.95,32.0,1955.0,313.0,804.0,317.0,4.9485,202300.0,NEAR BAY
+-121.57,39.74,17.0,1619.0,292.0,705.0,285.0,2.4623,126100.0,INLAND
+-122.22,37.78,50.0,1776.0,473.0,1807.0,440.0,1.7276,102300.0,NEAR BAY
+-120.95,37.64,32.0,3487.0,740.0,1957.0,685.0,2.7209,88300.0,INLAND
+-118.26,33.91,39.0,935.0,210.0,711.0,193.0,2.4375,101900.0,<1H OCEAN
+-122.04,37.04,17.0,4977.0,994.0,1987.0,947.0,3.8854,312300.0,NEAR OCEAN
+-121.25,37.95,40.0,1703.0,362.0,1208.0,373.0,2.0817,55300.0,INLAND
+-118.32,34.03,47.0,1734.0,453.0,1272.0,438.0,3.1731,121500.0,<1H OCEAN
+-119.78,36.73,52.0,1377.0,319.0,1280.0,259.0,1.2344,43300.0,INLAND
+-118.36,33.88,44.0,1362.0,237.0,709.0,247.0,4.4271,336200.0,<1H OCEAN
+-121.93,37.31,29.0,1377.0,430.0,969.0,399.0,2.6573,252800.0,<1H OCEAN
+-120.55,38.07,27.0,1199.0,224.0,463.0,199.0,2.9063,92200.0,INLAND
+-120.24,39.67,52.0,296.0,63.0,143.0,56.0,3.625,68600.0,INLAND
+-122.27,37.9,52.0,2041.0,270.0,671.0,253.0,6.9414,417500.0,NEAR BAY
+-117.96,34.53,10.0,2907.0,559.0,1681.0,531.0,3.8594,141000.0,INLAND
+-118.32,33.91,34.0,1068.0,198.0,757.0,231.0,5.7528,180500.0,<1H OCEAN
+-121.81,38.58,17.0,1964.0,314.0,808.0,286.0,5.9629,286000.0,INLAND
+-120.94,40.17,22.0,1334.0,261.0,597.0,222.0,2.2132,89200.0,INLAND
+-122.42,37.76,52.0,1494.0,610.0,1630.0,590.0,1.65,265000.0,NEAR BAY
+-122.04,37.99,36.0,2765.0,495.0,1478.0,441.0,4.125,136200.0,NEAR BAY
+-122.44,37.74,52.0,2074.0,366.0,909.0,394.0,4.8382,294900.0,NEAR BAY
+-118.45,33.99,26.0,1919.0,405.0,953.0,371.0,6.0672,420800.0,<1H OCEAN
+-121.33,38.01,36.0,1383.0,207.0,531.0,203.0,5.9191,151900.0,INLAND
+-114.98,33.07,18.0,1183.0,363.0,374.0,127.0,3.1607,57500.0,INLAND
+-118.49,34.14,28.0,3539.0,441.0,1190.0,421.0,10.6796,500001.0,<1H OCEAN
+-118.27,33.95,35.0,2073.0,494.0,1753.0,490.0,1.5,93600.0,<1H OCEAN
+-121.04,37.66,11.0,1658.0,301.0,913.0,298.0,4.1705,162800.0,INLAND
+-118.08,34.58,12.0,3851.0,857.0,2169.0,811.0,3.0101,116300.0,INLAND
+-120.99,39.18,23.0,2550.0,457.0,1016.0,405.0,3.6607,153000.0,INLAND
+-121.0,37.64,43.0,311.0,95.0,293.0,94.0,1.2902,67500.0,INLAND
+-117.15,34.05,9.0,1442.0,219.0,633.0,230.0,5.0227,162300.0,INLAND
+-121.81,37.38,29.0,570.0,76.0,244.0,72.0,12.3292,416700.0,<1H OCEAN
+-118.1,34.16,44.0,2795.0,496.0,1235.0,469.0,4.2386,283700.0,INLAND
+-118.09,33.89,42.0,991.0,,717.0,219.0,4.0926,164400.0,<1H OCEAN
+-118.44,34.23,43.0,2257.0,429.0,1418.0,442.0,4.5278,181800.0,<1H OCEAN
+-121.47,38.52,26.0,2177.0,638.0,1971.0,560.0,1.2575,66800.0,INLAND
+-117.27,33.18,4.0,3371.0,773.0,1481.0,627.0,2.9133,215700.0,NEAR OCEAN
+-122.07,37.67,38.0,2104.0,409.0,1039.0,394.0,3.875,165300.0,NEAR BAY
+-118.27,34.07,42.0,1175.0,428.0,1593.0,407.0,2.3438,213300.0,<1H OCEAN
+-118.03,33.83,25.0,3030.0,532.0,1668.0,509.0,4.625,229600.0,<1H OCEAN
+-117.38,34.08,11.0,5684.0,1139.0,3095.0,1036.0,3.6875,112600.0,INLAND
+-118.16,34.16,43.0,1276.0,226.0,545.0,209.0,4.1518,230700.0,<1H OCEAN
+-118.37,33.99,36.0,3228.0,543.0,1305.0,520.0,5.1695,397000.0,<1H OCEAN
+-119.72,34.42,52.0,1759.0,387.0,980.0,402.0,4.0125,261000.0,NEAR OCEAN
+-121.31,38.67,27.0,1998.0,353.0,970.0,343.0,4.8224,115500.0,INLAND
+-117.68,34.01,20.0,7326.0,1555.0,5718.0,1538.0,3.2073,123500.0,INLAND
+-122.29,37.91,38.0,2591.0,424.0,905.0,378.0,5.1691,263200.0,NEAR BAY
+-121.19,38.13,27.0,2400.0,435.0,1085.0,444.0,3.7687,165200.0,INLAND
+-118.15,34.69,32.0,1300.0,234.0,712.0,249.0,3.25,107500.0,INLAND
+-118.42,33.93,28.0,4603.0,993.0,2191.0,943.0,4.5743,382200.0,<1H OCEAN
+-123.2,39.14,17.0,1620.0,396.0,878.0,399.0,1.8042,109200.0,<1H OCEAN
+-121.02,39.27,52.0,3720.0,707.0,1424.0,609.0,3.2,155000.0,INLAND
+-118.16,33.94,25.0,3341.0,789.0,1685.0,751.0,3.6936,238300.0,<1H OCEAN
+-118.92,35.38,33.0,3122.0,579.0,1733.0,545.0,3.8307,70600.0,INLAND
+-118.58,34.21,27.0,2209.0,353.0,1034.0,344.0,4.7125,250900.0,<1H OCEAN
+-121.93,37.32,51.0,2711.0,728.0,1607.0,724.0,3.0,184700.0,<1H OCEAN
+-117.11,32.67,43.0,515.0,146.0,445.0,140.0,1.6094,93000.0,NEAR OCEAN
+-119.45,36.6,42.0,510.0,88.0,247.0,99.0,2.5,73000.0,INLAND
+-121.37,38.69,35.0,1093.0,192.0,590.0,190.0,2.7009,80200.0,INLAND
+-118.31,34.11,52.0,1875.0,303.0,735.0,293.0,5.8659,433300.0,<1H OCEAN
+-117.73,34.09,36.0,1543.0,297.0,1355.0,303.0,3.5313,117800.0,INLAND
+-118.87,34.22,14.0,3108.0,451.0,1566.0,434.0,6.2423,305400.0,<1H OCEAN
+-118.42,34.17,31.0,2235.0,363.0,914.0,370.0,6.1359,359700.0,<1H OCEAN
+-124.17,40.76,26.0,1776.0,361.0,992.0,380.0,2.8056,82800.0,NEAR OCEAN
+-116.48,33.61,8.0,1294.0,272.0,457.0,199.0,2.9167,115300.0,INLAND
+-118.06,34.1,38.0,2438.0,442.0,1308.0,461.0,3.6995,260100.0,<1H OCEAN
+-120.36,36.13,29.0,1938.0,434.0,1306.0,415.0,3.0134,55500.0,INLAND
+-121.14,39.1,13.0,1085.0,227.0,629.0,214.0,5.0389,171500.0,INLAND
+-120.79,37.53,20.0,1417.0,263.0,853.0,263.0,3.3083,108300.0,INLAND
+-121.5,38.55,52.0,2784.0,455.0,957.0,448.0,5.6402,209800.0,INLAND
+-116.51,34.45,21.0,8502.0,2634.0,2330.0,991.0,1.3811,51300.0,INLAND
+-121.2,38.36,14.0,2634.0,463.0,1402.0,432.0,3.8897,175700.0,INLAND
+-121.22,37.93,21.0,336.0,68.0,206.0,73.0,4.75,121400.0,INLAND
+-122.35,37.57,52.0,2170.0,269.0,784.0,274.0,10.4286,500001.0,NEAR OCEAN
+-120.65,35.29,36.0,1940.0,315.0,850.0,298.0,3.1818,249600.0,NEAR OCEAN
+-121.96,38.34,14.0,3035.0,680.0,1597.0,663.0,3.6036,143500.0,INLAND
+-121.88,37.36,42.0,2087.0,402.0,1342.0,423.0,4.2149,199000.0,<1H OCEAN
+-121.51,38.52,30.0,3236.0,588.0,1167.0,569.0,4.0972,181400.0,INLAND
+-118.49,34.42,23.0,4166.0,756.0,2082.0,743.0,4.4107,213400.0,<1H OCEAN
+-117.89,34.09,36.0,1811.0,320.0,1005.0,332.0,5.5629,188300.0,<1H OCEAN
+-119.08,34.17,32.0,166.0,22.0,63.0,29.0,7.3004,125000.0,NEAR OCEAN
+-118.88,34.19,16.0,7268.0,1729.0,3232.0,1653.0,3.3703,228700.0,<1H OCEAN
+-117.86,33.79,31.0,3523.0,922.0,2660.0,949.0,3.1792,146400.0,<1H OCEAN
+-122.1,37.67,34.0,3659.0,897.0,2479.0,903.0,2.9564,150500.0,NEAR BAY
+-118.98,36.06,33.0,2043.0,443.0,1497.0,417.0,2.343,47400.0,INLAND
+-118.49,34.04,31.0,4066.0,951.0,1532.0,868.0,4.8125,500001.0,<1H OCEAN
+-115.6,33.04,31.0,314.0,61.0,152.0,56.0,3.3472,91700.0,INLAND
+-121.91,36.62,30.0,724.0,167.0,325.0,155.0,3.3333,247900.0,<1H OCEAN
+-118.32,34.2,36.0,759.0,136.0,372.0,135.0,4.9886,328900.0,<1H OCEAN
+-117.09,32.75,24.0,1245.0,376.0,1230.0,362.0,1.875,95000.0,NEAR OCEAN
+-120.45,34.64,17.0,1226.0,277.0,484.0,224.0,3.2167,112500.0,NEAR OCEAN
+-118.62,34.06,25.0,3546.0,584.0,1530.0,601.0,7.4001,500001.0,NEAR OCEAN
+-121.24,38.75,5.0,9137.0,1368.0,3667.0,1294.0,5.4896,229600.0,INLAND
+-121.45,38.54,38.0,1865.0,384.0,1052.0,354.0,1.7891,60500.0,INLAND
+-118.32,34.17,47.0,2589.0,465.0,1284.0,485.0,5.1008,247100.0,<1H OCEAN
+-116.51,33.94,12.0,3369.0,780.0,1315.0,584.0,1.7388,66000.0,INLAND
+-117.23,32.79,28.0,2453.0,648.0,1082.0,617.0,3.625,266700.0,NEAR OCEAN
+-117.43,34.01,34.0,2101.0,426.0,1150.0,377.0,3.0909,98300.0,INLAND
+-118.13,34.14,29.0,3559.0,1034.0,1658.0,965.0,3.2643,163900.0,<1H OCEAN
+-122.12,37.44,33.0,2974.0,623.0,1435.0,588.0,5.485,406300.0,NEAR BAY
+-117.25,33.22,26.0,2010.0,347.0,1160.0,331.0,3.9815,142600.0,<1H OCEAN
+-118.2,33.8,52.0,1786.0,445.0,1090.0,430.0,2.8988,194900.0,NEAR OCEAN
+-122.07,37.97,20.0,1705.0,353.0,856.0,341.0,3.7262,211800.0,NEAR BAY
+-122.45,37.78,48.0,1013.0,194.0,464.0,205.0,3.2011,428300.0,NEAR BAY
+-120.92,37.7,24.0,527.0,112.0,270.0,112.0,1.6172,156300.0,INLAND
+-118.16,33.78,39.0,4075.0,1085.0,2470.0,1025.0,2.3317,222500.0,NEAR OCEAN
+-116.4,33.78,8.0,3059.0,500.0,612.0,208.0,6.8729,259200.0,INLAND
+-117.08,32.6,24.0,1901.0,490.0,1334.0,476.0,2.2544,121900.0,NEAR OCEAN
+-122.29,37.52,38.0,3767.0,603.0,1455.0,615.0,6.8787,386800.0,NEAR OCEAN
+-118.14,34.72,15.0,2181.0,361.0,1057.0,300.0,4.625,118100.0,INLAND
+-118.25,34.14,13.0,3487.0,1131.0,3749.0,1072.0,2.1602,221900.0,<1H OCEAN
+-122.12,37.69,30.0,1197.0,269.0,695.0,279.0,3.4375,157800.0,NEAR BAY
+-117.9,33.65,30.0,1634.0,373.0,771.0,364.0,3.4125,284100.0,<1H OCEAN
+-117.87,33.76,37.0,4943.0,851.0,2164.0,788.0,4.1071,311300.0,<1H OCEAN
+-119.69,36.69,36.0,1432.0,269.0,836.0,237.0,2.1563,88300.0,INLAND
+-118.37,34.03,37.0,1236.0,,966.0,292.0,3.0694,122200.0,<1H OCEAN
+-118.08,33.91,18.0,1573.0,396.0,1200.0,365.0,2.895,146900.0,<1H OCEAN
+-121.6,39.77,23.0,2263.0,497.0,1138.0,455.0,2.3403,87300.0,INLAND
+-115.52,32.98,21.0,1302.0,327.0,1244.0,316.0,2.2054,66400.0,INLAND
+-117.06,33.01,24.0,2618.0,485.0,726.0,443.0,3.5192,159100.0,<1H OCEAN
+-118.87,35.65,33.0,1504.0,325.0,584.0,223.0,3.4792,94600.0,INLAND
+-116.36,33.88,11.0,12557.0,3098.0,2453.0,1232.0,1.7844,78500.0,INLAND
+-117.8,33.83,17.0,2971.0,350.0,1180.0,346.0,11.1228,500001.0,<1H OCEAN
+-122.46,37.72,39.0,2254.0,,1388.0,404.0,2.9688,232000.0,NEAR OCEAN
+-122.04,38.24,22.0,2761.0,757.0,2612.0,641.0,1.6875,87500.0,INLAND
+-117.12,32.58,26.0,1360.0,309.0,869.0,328.0,3.0217,131600.0,NEAR OCEAN
+-118.29,34.0,52.0,1319.0,295.0,898.0,271.0,2.7727,128600.0,<1H OCEAN
+-118.39,33.87,19.0,3303.0,584.0,1329.0,569.0,7.521,340400.0,<1H OCEAN
+-118.4,33.88,36.0,3022.0,482.0,1278.0,494.0,7.2651,500001.0,<1H OCEAN
+-117.69,34.08,14.0,4136.0,886.0,2026.0,788.0,3.2344,128200.0,INLAND
+-117.91,34.13,34.0,1540.0,328.0,1037.0,317.0,2.2132,138500.0,<1H OCEAN
+-117.94,33.86,36.0,2824.0,493.0,1394.0,507.0,4.6477,194700.0,<1H OCEAN
+-122.43,37.76,52.0,3771.0,1017.0,1575.0,921.0,3.5655,427300.0,NEAR BAY
+-117.95,34.14,33.0,1943.0,440.0,1526.0,353.0,3.038,137500.0,INLAND
+-118.35,34.1,16.0,2930.0,1038.0,1648.0,980.0,2.6458,372200.0,<1H OCEAN
+-118.32,34.19,37.0,1335.0,249.0,485.0,240.0,4.1731,352100.0,<1H OCEAN
+-117.76,35.22,4.0,18.0,3.0,8.0,6.0,1.625,275000.0,INLAND
+-118.17,33.79,30.0,1349.0,519.0,2646.0,552.0,1.9318,115900.0,NEAR OCEAN
+-118.19,34.11,40.0,1266.0,348.0,1032.0,315.0,2.1667,150000.0,<1H OCEAN
+-118.09,33.89,27.0,3399.0,882.0,2465.0,811.0,3.099,166600.0,<1H OCEAN
+-118.28,33.73,39.0,2602.0,802.0,2178.0,737.0,2.0469,234500.0,NEAR OCEAN
+-118.16,34.15,17.0,821.0,163.0,229.0,164.0,7.3715,263000.0,<1H OCEAN
+-120.07,36.74,19.0,2627.0,502.0,1295.0,441.0,3.087,88200.0,INLAND
+-122.48,37.78,48.0,2835.0,728.0,1674.0,684.0,3.129,375000.0,NEAR BAY
+-122.06,37.99,16.0,2445.0,469.0,721.0,474.0,2.8043,87500.0,NEAR BAY
+-118.15,35.06,15.0,1069.0,296.0,569.0,263.0,2.0441,73300.0,INLAND
+-117.58,34.02,4.0,5998.0,1092.0,3182.0,1042.0,5.2692,174800.0,INLAND
+-117.81,33.67,9.0,2435.0,396.0,1194.0,385.0,7.2025,275000.0,<1H OCEAN
+-118.43,34.01,27.0,3133.0,1021.0,2242.0,1002.0,2.697,412500.0,<1H OCEAN
+-118.29,34.04,48.0,1353.0,488.0,1945.0,487.0,2.4359,123700.0,<1H OCEAN
+-117.06,32.76,37.0,2356.0,476.0,1231.0,499.0,2.965,155700.0,NEAR OCEAN
+-121.35,38.03,16.0,3158.0,515.0,1596.0,528.0,4.1739,131300.0,INLAND
+-118.35,33.88,36.0,1567.0,362.0,1054.0,386.0,3.2594,233900.0,<1H OCEAN
+-121.44,38.73,25.0,1287.0,224.0,727.0,236.0,4.7396,135500.0,INLAND
+-121.98,37.27,29.0,2658.0,484.0,1318.0,498.0,5.3561,298900.0,<1H OCEAN
+-118.06,33.97,39.0,1639.0,300.0,988.0,309.0,3.9612,175800.0,<1H OCEAN
+-116.24,33.73,14.0,2774.0,566.0,1530.0,505.0,3.0682,104100.0,INLAND
+-119.18,34.17,32.0,2388.0,467.0,1746.0,483.0,3.9331,187600.0,NEAR OCEAN
+-118.24,34.01,30.0,405.0,86.0,376.0,68.0,1.7813,127500.0,<1H OCEAN
+-121.94,37.31,5.0,2364.0,578.0,1102.0,502.0,5.2642,246400.0,<1H OCEAN
+-121.48,38.61,18.0,1511.0,315.0,1062.0,304.0,2.3438,89400.0,INLAND
+-122.24,37.8,49.0,2838.0,749.0,1487.0,677.0,2.5238,169300.0,NEAR BAY
+-122.16,37.47,44.0,2581.0,437.0,1006.0,414.0,5.397,341700.0,NEAR BAY
+-121.92,38.02,8.0,2750.0,479.0,1526.0,484.0,5.102,156500.0,INLAND
+-118.35,33.98,42.0,3081.0,680.0,1785.0,609.0,3.745,170800.0,<1H OCEAN
+-122.75,38.46,16.0,2653.0,606.0,1693.0,586.0,2.6384,146900.0,<1H OCEAN
+-118.27,34.22,34.0,8206.0,1186.0,3141.0,1150.0,7.2812,462200.0,<1H OCEAN
+-122.7,38.03,42.0,1410.0,308.0,624.0,292.0,4.1379,309100.0,NEAR OCEAN
+-118.12,33.79,41.0,1762.0,314.0,738.0,300.0,4.1687,240700.0,<1H OCEAN
+-122.82,38.39,22.0,1288.0,243.0,593.0,220.0,3.625,233700.0,<1H OCEAN
+-121.29,37.98,42.0,625.0,143.0,533.0,159.0,2.625,65400.0,INLAND
+-118.22,34.09,40.0,1081.0,282.0,970.0,263.0,1.875,150000.0,<1H OCEAN
+-121.2,39.23,9.0,2802.0,447.0,955.0,418.0,5.2359,213300.0,INLAND
+-117.04,32.62,26.0,3620.0,607.0,2000.0,593.0,4.9962,156000.0,NEAR OCEAN
+-117.86,33.84,19.0,1725.0,392.0,920.0,400.0,3.0087,159400.0,<1H OCEAN
+-117.23,33.2,21.0,2284.0,360.0,999.0,356.0,4.8929,212500.0,<1H OCEAN
+-118.54,34.37,27.0,2051.0,301.0,917.0,287.0,7.6059,323700.0,<1H OCEAN
+-118.23,33.89,36.0,2598.0,514.0,1872.0,514.0,3.1667,117700.0,<1H OCEAN
+-122.15,37.71,36.0,998.0,178.0,531.0,183.0,4.0208,191500.0,NEAR BAY
+-121.74,39.59,24.0,1535.0,279.0,726.0,272.0,2.3833,95100.0,INLAND
+-119.26,35.5,38.0,2536.0,409.0,1133.0,430.0,4.2375,78600.0,INLAND
+-124.1,40.5,30.0,1927.0,393.0,996.0,374.0,2.2357,72300.0,<1H OCEAN
+-118.32,34.04,47.0,1989.0,532.0,1430.0,519.0,1.8333,151100.0,<1H OCEAN
+-121.34,38.58,17.0,1605.0,258.0,748.0,262.0,5.0917,134100.0,INLAND
+-121.91,37.23,27.0,4866.0,668.0,1956.0,659.0,7.3843,405000.0,<1H OCEAN
+-118.13,34.1,24.0,4670.0,1185.0,2478.0,1107.0,3.1975,252400.0,<1H OCEAN
+-120.43,37.35,15.0,1613.0,203.0,673.0,213.0,5.9378,212200.0,INLAND
+-118.37,34.08,52.0,1466.0,254.0,600.0,253.0,5.7524,393600.0,<1H OCEAN
+-118.32,33.82,22.0,2508.0,402.0,1254.0,395.0,7.0935,379500.0,<1H OCEAN
+-118.17,33.86,40.0,1301.0,342.0,954.0,336.0,2.3804,158000.0,<1H OCEAN
+-122.27,37.99,16.0,4921.0,737.0,2312.0,725.0,5.8899,243200.0,NEAR BAY
+-122.36,37.56,32.0,4684.0,540.0,1512.0,511.0,15.0001,500001.0,NEAR OCEAN
+-116.92,32.82,34.0,1765.0,284.0,772.0,282.0,5.0118,165300.0,<1H OCEAN
+-117.99,33.79,35.0,2301.0,467.0,2272.0,454.0,3.9566,167800.0,<1H OCEAN
+-118.27,34.06,33.0,1416.0,686.0,2013.0,614.0,1.9818,208300.0,<1H OCEAN
+-117.11,32.67,52.0,280.0,71.0,217.0,71.0,1.4844,83300.0,NEAR OCEAN
+-121.45,38.63,28.0,1246.0,295.0,884.0,258.0,1.4397,51700.0,INLAND
+-117.8,33.67,5.0,2487.0,388.0,1147.0,397.0,8.284,302500.0,<1H OCEAN
+-118.48,33.96,16.0,895.0,181.0,237.0,149.0,12.0088,500001.0,<1H OCEAN
+-122.39,38.37,33.0,1066.0,191.0,403.0,163.0,6.8,240800.0,<1H OCEAN
+-122.89,38.42,28.0,2388.0,437.0,1015.0,381.0,5.1512,268300.0,<1H OCEAN
+-119.01,36.05,27.0,1127.0,294.0,839.0,276.0,1.3807,53100.0,INLAND
+-121.29,37.97,52.0,2995.0,555.0,1392.0,503.0,1.7794,98800.0,INLAND
+-121.95,37.94,27.0,1469.0,216.0,578.0,219.0,5.9346,253600.0,INLAND
+-122.45,37.63,28.0,4946.0,848.0,2683.0,824.0,5.748,302100.0,NEAR OCEAN
+-117.2,32.83,36.0,1089.0,240.0,623.0,226.0,2.5909,176000.0,NEAR OCEAN
+-117.97,33.73,18.0,3698.0,574.0,2046.0,614.0,6.2984,269800.0,<1H OCEAN
+-117.21,32.83,35.0,2259.0,501.0,1340.0,511.0,3.4482,162500.0,NEAR OCEAN
+-119.2,36.3,19.0,1427.0,311.0,1026.0,293.0,2.625,57000.0,INLAND
+-118.22,33.98,42.0,626.0,143.0,625.0,156.0,3.125,166300.0,<1H OCEAN
+-117.84,35.54,11.0,1751.0,316.0,765.0,296.0,5.0762,98000.0,INLAND
+-117.87,33.89,25.0,1142.0,162.0,486.0,150.0,7.1472,270100.0,<1H OCEAN
+-118.1,33.8,37.0,1814.0,329.0,850.0,328.0,5.0574,240800.0,<1H OCEAN
+-117.74,33.62,16.0,4134.0,740.0,2103.0,745.0,5.6877,231400.0,<1H OCEAN
+-118.44,34.2,17.0,2934.0,950.0,2517.0,889.0,2.936,232500.0,<1H OCEAN
+-118.29,34.1,43.0,1711.0,443.0,1190.0,429.0,3.5172,265500.0,<1H OCEAN
+-122.68,38.46,19.0,4976.0,711.0,1926.0,625.0,7.3003,381300.0,<1H OCEAN
+-121.94,38.38,25.0,182.0,48.0,71.0,52.0,1.0208,78600.0,INLAND
+-122.46,37.59,21.0,12902.0,2118.0,6160.0,2082.0,5.7653,325800.0,NEAR OCEAN
+-122.37,37.58,34.0,2697.0,313.0,810.0,279.0,12.4291,500001.0,NEAR OCEAN
+-122.08,37.58,15.0,2576.0,418.0,1657.0,410.0,5.5218,254400.0,NEAR BAY
+-122.45,37.76,50.0,2518.0,507.0,979.0,516.0,4.6912,500001.0,NEAR BAY
+-122.11,39.82,27.0,1065.0,214.0,508.0,198.0,2.625,91700.0,INLAND
+-121.84,37.27,17.0,2795.0,482.0,1904.0,506.0,5.0186,250800.0,<1H OCEAN
+-117.8,33.68,8.0,2032.0,349.0,862.0,340.0,6.9133,274100.0,<1H OCEAN
+-118.04,34.12,39.0,2522.0,380.0,1113.0,357.0,5.2249,445200.0,INLAND
+-121.28,38.17,19.0,1337.0,236.0,744.0,225.0,4.0924,244200.0,INLAND
+-118.23,34.15,19.0,2294.0,716.0,1686.0,680.0,3.0288,258300.0,<1H OCEAN
+-117.62,34.02,16.0,2040.0,325.0,1207.0,324.0,5.0431,164100.0,INLAND
+-122.43,37.78,17.0,2728.0,908.0,1670.0,893.0,1.077,115000.0,NEAR BAY
+-118.21,33.97,43.0,1751.0,400.0,1558.0,379.0,3.0313,166100.0,<1H OCEAN
+-120.81,38.74,29.0,2259.0,482.0,1099.0,463.0,2.3314,121600.0,INLAND
+-121.99,37.36,33.0,2545.0,467.0,1287.0,458.0,5.5,282200.0,<1H OCEAN
+-122.27,37.87,49.0,1350.0,368.0,707.0,350.0,2.8846,211300.0,NEAR BAY
+-121.39,38.61,36.0,2396.0,536.0,1225.0,515.0,2.9559,136600.0,INLAND
+-122.47,37.75,51.0,2713.0,396.0,1090.0,401.0,9.3603,500001.0,NEAR BAY
+-119.76,36.74,52.0,2137.0,448.0,1194.0,444.0,1.3029,69100.0,INLAND
+-121.36,38.39,10.0,5121.0,763.0,2568.0,758.0,5.2447,148100.0,INLAND
+-122.25,37.86,52.0,4048.0,663.0,1316.0,590.0,5.3794,376900.0,NEAR BAY
+-121.96,37.85,10.0,3209.0,379.0,1199.0,392.0,12.2478,500001.0,INLAND
+-118.44,34.09,36.0,3129.0,392.0,862.0,334.0,15.0001,500001.0,<1H OCEAN
+-122.64,38.23,49.0,2300.0,463.0,1061.0,429.0,4.075,228800.0,<1H OCEAN
+-120.63,37.36,16.0,1605.0,282.0,866.0,284.0,4.0694,110200.0,INLAND
+-116.75,32.82,17.0,3348.0,481.0,1222.0,443.0,6.6361,308600.0,<1H OCEAN
+-118.3,33.98,48.0,1998.0,410.0,1176.0,382.0,3.0455,102400.0,<1H OCEAN
+-117.86,33.72,31.0,1194.0,297.0,1602.0,306.0,2.3333,157700.0,<1H OCEAN
+-121.24,38.0,25.0,1471.0,300.0,721.0,304.0,2.4688,126800.0,INLAND
+-119.03,35.3,10.0,829.0,146.0,447.0,173.0,4.1484,102900.0,INLAND
+-118.06,33.98,40.0,1723.0,370.0,1221.0,370.0,3.3562,169200.0,<1H OCEAN
+-118.27,33.82,39.0,1357.0,249.0,763.0,229.0,4.25,200300.0,<1H OCEAN
+-118.19,33.87,42.0,1213.0,269.0,628.0,229.0,3.6987,152100.0,<1H OCEAN
+-118.45,35.58,16.0,5396.0,1182.0,1802.0,807.0,1.8819,69700.0,INLAND
+-118.39,33.97,43.0,2700.0,510.0,1146.0,506.0,5.1333,345900.0,<1H OCEAN
+-122.04,37.34,19.0,3694.0,1036.0,2496.0,986.0,3.6991,271500.0,<1H OCEAN
+-119.3,38.26,19.0,3325.0,660.0,750.0,286.0,2.9509,114800.0,INLAND
+-122.63,38.22,34.0,878.0,160.0,372.0,167.0,4.0417,232100.0,<1H OCEAN
+-118.22,34.04,43.0,2343.0,803.0,2468.0,707.0,1.5163,115000.0,<1H OCEAN
+-117.36,33.2,19.0,2129.0,562.0,1323.0,525.0,2.9539,169900.0,NEAR OCEAN
+-118.22,34.14,52.0,2298.0,406.0,1203.0,387.0,5.5291,274600.0,<1H OCEAN
+-122.03,37.96,20.0,2636.0,691.0,1142.0,627.0,2.1083,162500.0,NEAR BAY
+-121.34,37.97,33.0,2493.0,454.0,1203.0,436.0,3.765,94600.0,INLAND
+-122.64,38.87,16.0,1177.0,240.0,519.0,199.0,1.5739,73500.0,INLAND
+-117.22,33.31,12.0,2924.0,433.0,1193.0,394.0,6.2475,331300.0,<1H OCEAN
+-119.69,34.42,17.0,1826.0,544.0,1325.0,532.0,1.2762,253600.0,<1H OCEAN
+-116.31,33.65,8.0,3079.0,558.0,1572.0,474.0,4.5938,102600.0,INLAND
+-122.28,37.87,49.0,2026.0,548.0,963.0,521.0,1.9805,173700.0,NEAR BAY
+-122.44,37.79,52.0,1335.0,151.0,402.0,157.0,10.8783,500001.0,NEAR BAY
+-118.09,34.07,26.0,794.0,182.0,709.0,170.0,3.175,170800.0,<1H OCEAN
+-119.69,36.79,5.0,2613.0,476.0,1490.0,481.0,4.0993,83000.0,INLAND
+-117.94,33.77,32.0,714.0,142.0,654.0,154.0,4.5052,170800.0,<1H OCEAN
+-118.24,33.97,37.0,1212.0,314.0,1403.0,279.0,2.5536,117200.0,<1H OCEAN
+-121.76,36.9,44.0,919.0,309.0,1321.0,301.0,2.0775,121400.0,<1H OCEAN
+-117.1,32.67,22.0,1690.0,541.0,1669.0,494.0,2.0213,110600.0,NEAR OCEAN
+-118.28,34.08,40.0,1630.0,543.0,1568.0,510.0,2.7366,169100.0,<1H OCEAN
+-122.24,38.11,42.0,1743.0,388.0,889.0,341.0,2.3241,99200.0,NEAR BAY
+-120.97,38.91,7.0,4341.0,716.0,1978.0,682.0,4.8311,172200.0,INLAND
+-123.02,38.54,35.0,2157.0,487.0,768.0,322.0,3.2315,136900.0,<1H OCEAN
+-122.2,37.79,45.0,2021.0,528.0,1410.0,480.0,2.7788,115400.0,NEAR BAY
+-121.79,37.69,25.0,6296.0,1082.0,3200.0,1047.0,4.5357,188400.0,INLAND
+-118.27,34.11,36.0,1832.0,539.0,934.0,486.0,3.0521,276600.0,<1H OCEAN
+-117.29,34.06,7.0,1971.0,403.0,1336.0,423.0,4.5066,111500.0,INLAND
+-122.21,37.8,39.0,2003.0,500.0,1109.0,464.0,3.0682,156500.0,NEAR BAY
+-117.97,33.77,20.0,1988.0,424.0,1277.0,425.0,2.9414,162200.0,<1H OCEAN
+-122.53,37.5,19.0,4768.0,807.0,2199.0,805.0,6.1896,331100.0,NEAR OCEAN
+-117.98,33.72,24.0,2826.0,547.0,1738.0,546.0,6.0494,240400.0,<1H OCEAN
+-118.24,34.12,29.0,2904.0,892.0,3320.0,765.0,2.6111,168800.0,<1H OCEAN
+-122.5,37.96,16.0,100.0,20.0,45.0,25.0,6.1359,212500.0,NEAR BAY
+-121.3,38.73,9.0,5558.0,1099.0,2717.0,1043.0,3.6455,139200.0,INLAND
+-117.12,32.75,17.0,2060.0,633.0,1251.0,602.0,1.9886,119200.0,NEAR OCEAN
+-117.26,32.95,22.0,5484.0,1227.0,1947.0,1012.0,4.4375,500001.0,NEAR OCEAN
+-120.87,41.54,21.0,1091.0,208.0,660.0,188.0,2.2321,34600.0,INLAND
+-121.78,37.97,4.0,17032.0,2546.0,7653.0,2359.0,5.5601,213700.0,INLAND
+-117.2,33.71,24.0,4210.0,920.0,1283.0,829.0,2.0881,83300.0,<1H OCEAN
+-117.64,33.63,10.0,4814.0,643.0,1808.0,588.0,8.798,436600.0,<1H OCEAN
+-121.44,36.84,7.0,1644.0,338.0,1143.0,331.0,4.005,180400.0,INLAND
+-117.83,33.97,11.0,21533.0,3078.0,9671.0,2890.0,7.0329,368300.0,<1H OCEAN
+-121.97,37.34,33.0,3162.0,,1553.0,686.0,3.6682,266100.0,<1H OCEAN
+-117.55,33.85,4.0,8207.0,1373.0,3887.0,1304.0,4.8686,195300.0,INLAND
+-118.16,33.84,36.0,1348.0,234.0,643.0,221.0,3.6447,211000.0,<1H OCEAN
+-118.2,33.92,36.0,414.0,104.0,477.0,130.0,3.6719,130400.0,<1H OCEAN
+-122.68,38.46,17.0,3201.0,527.0,1244.0,495.0,4.7143,202900.0,<1H OCEAN
+-121.44,37.7,5.0,1365.0,196.0,591.0,156.0,6.0389,215100.0,INLAND
+-118.05,34.1,42.0,2065.0,404.0,1313.0,402.0,4.0179,274300.0,INLAND
+-121.98,37.16,42.0,2533.0,433.0,957.0,398.0,5.3468,279900.0,<1H OCEAN
+-123.53,38.93,38.0,1706.0,355.0,506.0,211.0,2.5625,165600.0,NEAR OCEAN
+-118.35,34.11,33.0,7478.0,1678.0,2701.0,1500.0,4.1717,500001.0,<1H OCEAN
+-121.49,39.49,20.0,2505.0,468.0,1174.0,429.0,2.9965,88900.0,INLAND
+-118.42,34.15,27.0,2795.0,602.0,1073.0,535.0,5.1496,365000.0,<1H OCEAN
+-121.9,37.36,47.0,1007.0,245.0,581.0,240.0,2.9545,237500.0,<1H OCEAN
+-118.18,33.84,43.0,2561.0,544.0,1063.0,537.0,3.835,418600.0,NEAR OCEAN
+-122.74,38.48,12.0,4174.0,670.0,1882.0,647.0,4.551,178300.0,<1H OCEAN
+-122.11,37.31,7.0,189.0,26.0,84.0,29.0,13.8093,500001.0,<1H OCEAN
+-118.44,34.0,44.0,1798.0,353.0,835.0,314.0,4.75,355800.0,<1H OCEAN
+-118.09,34.15,46.0,271.0,74.0,150.0,55.0,2.2321,237500.0,INLAND
+-121.84,37.37,28.0,1579.0,339.0,1252.0,353.0,4.1615,214800.0,<1H OCEAN
+-117.3,34.05,6.0,2155.0,,1039.0,391.0,1.6675,95800.0,INLAND
+-117.13,32.74,46.0,3355.0,768.0,1457.0,708.0,2.6604,170100.0,NEAR OCEAN
+-121.97,37.35,35.0,1249.0,232.0,556.0,247.0,3.925,287100.0,<1H OCEAN
+-116.9,32.84,18.0,4215.0,810.0,2104.0,773.0,4.0873,146900.0,<1H OCEAN
+-120.74,39.39,18.0,453.0,117.0,152.0,77.0,1.3523,85700.0,INLAND
+-118.21,34.21,41.0,1676.0,263.0,757.0,255.0,4.7734,450800.0,<1H OCEAN
+-118.54,34.22,34.0,2193.0,513.0,1299.0,497.0,3.6187,211600.0,<1H OCEAN
+-118.24,34.15,17.0,5282.0,1605.0,4116.0,1574.0,3.052,209800.0,<1H OCEAN
+-119.69,36.74,23.0,2097.0,385.0,911.0,405.0,3.5128,121600.0,INLAND
+-118.32,33.91,34.0,3041.0,677.0,1920.0,640.0,4.5304,181300.0,<1H OCEAN
+-117.05,33.04,12.0,1840.0,254.0,580.0,234.0,6.7769,400000.0,<1H OCEAN
+-120.01,38.92,25.0,1758.0,357.0,689.0,278.0,2.675,104200.0,INLAND
+-118.01,33.89,33.0,2046.0,327.0,1018.0,320.0,4.2292,212800.0,<1H OCEAN
+-122.48,38.54,37.0,1898.0,359.0,973.0,340.0,4.2096,256600.0,INLAND
+-118.28,33.77,47.0,307.0,69.0,374.0,65.0,2.9063,146900.0,<1H OCEAN
+-118.02,34.12,36.0,1471.0,246.0,751.0,230.0,5.4555,395100.0,INLAND
+-117.92,34.01,35.0,3055.0,634.0,3738.0,615.0,3.375,127200.0,<1H OCEAN
+-120.85,37.78,30.0,1120.0,248.0,609.0,237.0,2.2386,87200.0,INLAND
+-117.34,33.06,17.0,2718.0,518.0,815.0,403.0,4.3182,357100.0,NEAR OCEAN
+-119.72,36.32,40.0,1185.0,221.0,676.0,256.0,2.2721,52600.0,INLAND
+-118.14,33.96,38.0,590.0,139.0,620.0,132.0,2.1731,143800.0,<1H OCEAN
+-119.56,36.53,19.0,2746.0,495.0,1670.0,518.0,3.2019,95700.0,INLAND
+-121.06,39.22,52.0,1749.0,422.0,837.0,391.0,2.325,109700.0,INLAND
+-122.26,38.31,33.0,4518.0,704.0,1776.0,669.0,5.2444,281100.0,NEAR BAY
+-120.86,37.4,17.0,3511.0,636.0,1904.0,617.0,3.1111,113900.0,INLAND
+-117.08,32.57,9.0,6298.0,1512.0,4451.0,1456.0,2.569,88300.0,NEAR OCEAN
+-121.68,39.82,15.0,3996.0,748.0,1786.0,728.0,3.5189,141300.0,INLAND
+-120.99,37.68,30.0,1975.0,375.0,732.0,326.0,2.6932,94900.0,INLAND
+-118.37,34.05,35.0,2457.0,552.0,1159.0,523.0,3.0862,345300.0,<1H OCEAN
+-118.24,34.08,52.0,109.0,20.0,86.0,24.0,4.9844,187500.0,<1H OCEAN
+-118.15,33.93,25.0,1948.0,433.0,1128.0,429.0,3.7614,255900.0,<1H OCEAN
+-122.24,37.77,43.0,955.0,284.0,585.0,266.0,2.3882,162500.0,NEAR BAY
+-122.47,37.87,36.0,4471.0,618.0,1315.0,582.0,11.5706,500001.0,NEAR BAY
+-118.34,34.07,52.0,1621.0,284.0,588.0,272.0,6.2223,500001.0,<1H OCEAN
+-121.34,38.66,17.0,1149.0,257.0,583.0,243.0,2.8092,137500.0,INLAND
+-121.95,37.26,10.0,3611.0,803.0,1599.0,716.0,5.2,248700.0,<1H OCEAN
+-117.74,34.05,30.0,1185.0,317.0,1466.0,302.0,2.625,94300.0,INLAND
+-117.07,32.76,14.0,2523.0,545.0,1297.0,525.0,2.3886,138100.0,NEAR OCEAN
+-122.89,39.42,16.0,411.0,114.0,26.0,19.0,0.4999,73500.0,INLAND
+-122.32,37.92,29.0,2304.0,399.0,1377.0,454.0,5.0187,140600.0,NEAR BAY
+-118.07,33.73,13.0,1822.0,313.0,643.0,303.0,9.8346,401700.0,NEAR OCEAN
+-117.91,33.85,22.0,1178.0,289.0,865.0,294.0,3.025,180000.0,<1H OCEAN
+-122.45,37.78,45.0,2747.0,699.0,1320.0,693.0,3.1576,333300.0,NEAR BAY
+-117.28,34.13,29.0,2077.0,577.0,1418.0,524.0,1.8281,76800.0,INLAND
+-117.09,33.03,17.0,2786.0,396.0,1228.0,396.0,6.4211,220700.0,<1H OCEAN
+-122.44,37.77,52.0,2705.0,647.0,1355.0,628.0,2.0161,364300.0,NEAR BAY
+-123.64,39.45,21.0,3359.0,677.0,1908.0,642.0,3.0433,140700.0,<1H OCEAN
+-122.34,40.58,7.0,4843.0,992.0,2223.0,932.0,3.0549,101700.0,INLAND
+-118.07,33.67,13.0,5126.0,711.0,2429.0,718.0,9.5268,437900.0,NEAR OCEAN
+-122.33,40.52,23.0,2801.0,507.0,1318.0,454.0,3.5081,116700.0,INLAND
+-118.19,34.11,26.0,1638.0,457.0,1155.0,437.0,3.4227,143800.0,<1H OCEAN
+-121.5,38.62,8.0,16679.0,3457.0,7919.0,3329.0,3.7188,134500.0,INLAND
+-118.18,33.87,44.0,1832.0,401.0,1056.0,405.0,4.0658,175100.0,<1H OCEAN
+-118.98,35.39,22.0,1812.0,457.0,1592.0,420.0,1.4146,49100.0,INLAND
+-122.77,38.39,35.0,2611.0,475.0,1293.0,463.0,2.75,197500.0,<1H OCEAN
+-118.6,34.09,43.0,2228.0,438.0,960.0,395.0,7.6091,438500.0,<1H OCEAN
+-117.64,33.99,29.0,1005.0,152.0,513.0,149.0,2.4375,181300.0,INLAND
+-122.32,38.01,26.0,3054.0,492.0,1495.0,496.0,4.6944,171100.0,NEAR BAY
+-118.87,34.2,26.0,1924.0,245.0,775.0,244.0,7.001,286800.0,<1H OCEAN
+-121.32,38.65,23.0,2628.0,499.0,1210.0,453.0,3.0952,157700.0,INLAND
+-118.12,34.19,52.0,2405.0,299.0,970.0,319.0,8.7835,444100.0,INLAND
+-121.96,37.33,26.0,3269.0,788.0,1427.0,696.0,4.2136,288300.0,<1H OCEAN
+-121.91,37.34,35.0,2189.0,607.0,1193.0,562.0,2.8042,240900.0,<1H OCEAN
+-118.34,34.1,29.0,3193.0,1452.0,2039.0,1265.0,1.8209,500001.0,<1H OCEAN
+-117.79,33.69,16.0,3067.0,396.0,1275.0,372.0,8.7385,340000.0,<1H OCEAN
+-118.09,33.9,33.0,3326.0,720.0,2533.0,689.0,3.1441,176300.0,<1H OCEAN
+-117.95,34.05,31.0,2349.0,539.0,2028.0,521.0,3.494,154500.0,<1H OCEAN
+-122.21,37.79,35.0,1745.0,409.0,1143.0,386.0,2.875,143800.0,NEAR BAY
+-116.75,33.83,16.0,5277.0,1070.0,657.0,276.0,3.3333,143400.0,INLAND
+-122.2,37.89,37.0,3881.0,560.0,1315.0,517.0,7.3195,367500.0,NEAR BAY
+-118.61,34.23,26.0,3727.0,572.0,1724.0,530.0,6.1419,327300.0,<1H OCEAN
+-119.26,35.87,24.0,1590.0,390.0,1686.0,372.0,1.6469,47600.0,INLAND
+-122.23,39.86,21.0,1730.0,350.0,982.0,322.0,1.8375,79800.0,INLAND
+-121.62,39.16,7.0,4480.0,776.0,2271.0,767.0,3.809,110700.0,INLAND
+-121.99,37.92,14.0,1780.0,224.0,764.0,226.0,9.0243,427700.0,INLAND
+-122.15,37.35,23.0,3814.0,485.0,1344.0,464.0,12.9792,500001.0,NEAR BAY
+-117.29,34.11,35.0,2426.0,715.0,1920.0,586.0,1.5561,68000.0,INLAND
+-116.52,33.85,13.0,7559.0,1444.0,3189.0,1105.0,3.4886,112500.0,INLAND
+-121.04,37.69,9.0,6333.0,1355.0,3265.0,1265.0,3.0217,160900.0,INLAND
+-119.75,36.78,35.0,1129.0,220.0,474.0,242.0,2.4405,74300.0,INLAND
+-122.13,37.44,38.0,2835.0,447.0,1148.0,446.0,5.9277,446600.0,NEAR BAY
+-118.39,33.83,32.0,2075.0,539.0,954.0,519.0,5.637,500001.0,NEAR OCEAN
+-122.27,40.39,26.0,1833.0,422.0,939.0,408.0,1.3571,59000.0,INLAND
+-118.33,34.09,36.0,561.0,180.0,340.0,127.0,1.4375,165000.0,<1H OCEAN
+-119.64,36.34,32.0,2958.0,670.0,1504.0,627.0,1.8606,56700.0,INLAND
+-117.45,34.01,26.0,3042.0,598.0,1720.0,551.0,2.76,95200.0,INLAND
+-119.06,34.35,34.0,2426.0,646.0,2116.0,631.0,2.0682,158300.0,<1H OCEAN
+-117.22,33.2,31.0,1736.0,277.0,801.0,292.0,4.4844,205500.0,<1H OCEAN
+-119.83,36.83,14.0,2351.0,341.0,1128.0,363.0,6.9903,141200.0,INLAND
+-117.78,33.97,2.0,556.0,63.0,179.0,54.0,8.4411,500001.0,<1H OCEAN
+-122.42,37.78,27.0,1728.0,884.0,1211.0,752.0,0.8543,500001.0,NEAR BAY
+-121.41,38.53,35.0,2061.0,371.0,1110.0,342.0,3.1944,79000.0,INLAND
+-115.57,32.78,20.0,1534.0,235.0,871.0,222.0,6.2715,97200.0,INLAND
+-117.17,33.28,16.0,1921.0,312.0,862.0,280.0,5.1786,376800.0,<1H OCEAN
+-122.41,37.8,52.0,2892.0,751.0,1785.0,733.0,3.5746,350000.0,NEAR BAY
+-117.67,33.63,9.0,5774.0,1320.0,3086.0,1265.0,4.4063,202200.0,<1H OCEAN
+-121.8,37.31,15.0,1807.0,378.0,1277.0,341.0,4.5045,164500.0,<1H OCEAN
+-118.34,34.18,45.0,3046.0,633.0,1448.0,599.0,3.24,226900.0,<1H OCEAN
+-120.93,37.73,14.0,2799.0,,2294.0,596.0,2.6343,81500.0,INLAND
+-118.38,33.88,36.0,2501.0,443.0,1031.0,422.0,4.75,442100.0,<1H OCEAN
+-122.02,38.37,16.0,2495.0,331.0,1118.0,338.0,6.4894,198000.0,INLAND
+-122.29,38.3,52.0,144.0,54.0,89.0,48.0,1.0096,162500.0,NEAR BAY
+-119.23,34.17,18.0,6171.0,1490.0,2164.0,1210.0,3.6875,500001.0,NEAR OCEAN
+-117.76,33.71,14.0,4321.0,582.0,2025.0,578.0,8.3634,355100.0,<1H OCEAN
+-117.08,32.74,33.0,3260.0,673.0,1784.0,666.0,3.5078,126500.0,NEAR OCEAN
+-121.91,36.98,16.0,1957.0,408.0,865.0,369.0,2.6875,233300.0,NEAR OCEAN
+-118.22,33.98,18.0,1781.0,765.0,1913.0,702.0,1.2059,255000.0,<1H OCEAN
+-118.36,34.06,52.0,2130.0,455.0,921.0,395.0,2.9605,500001.0,<1H OCEAN
+-116.11,33.64,20.0,1273.0,354.0,1548.0,355.0,2.0871,84700.0,INLAND
+-117.89,33.61,16.0,2413.0,559.0,656.0,423.0,6.3017,350000.0,<1H OCEAN
+-118.26,34.14,51.0,902.0,320.0,650.0,334.0,1.5417,268800.0,<1H OCEAN
+-118.58,34.2,35.0,1323.0,228.0,756.0,216.0,4.233,221300.0,<1H OCEAN
+-117.91,33.84,26.0,1156.0,393.0,1880.0,400.0,2.2716,350000.0,<1H OCEAN
+-117.07,32.67,35.0,3200.0,725.0,1723.0,610.0,1.8977,95600.0,NEAR OCEAN
+-122.42,37.73,52.0,3230.0,654.0,1765.0,611.0,3.3333,292300.0,NEAR BAY
+-121.91,38.02,15.0,2966.0,558.0,1687.0,527.0,3.4817,129800.0,INLAND
+-118.08,33.93,34.0,1558.0,375.0,1179.0,337.0,3.2188,165100.0,<1H OCEAN
+-119.6,36.57,42.0,2311.0,439.0,1347.0,436.0,2.5556,69700.0,INLAND
+-118.48,34.05,36.0,2143.0,434.0,751.0,396.0,6.7496,500001.0,<1H OCEAN
+-117.74,33.73,18.0,328.0,68.0,391.0,60.0,4.1167,87500.0,<1H OCEAN
+-122.44,37.79,52.0,1726.0,384.0,614.0,356.0,3.6812,500000.0,NEAR BAY
+-122.07,37.98,12.0,6915.0,1639.0,2940.0,1468.0,4.0154,186100.0,NEAR BAY
+-121.56,38.44,43.0,1485.0,270.0,653.0,251.0,3.0,141700.0,INLAND
+-118.26,34.02,38.0,1091.0,349.0,1786.0,340.0,2.131,136500.0,<1H OCEAN
+-118.33,33.93,38.0,694.0,112.0,412.0,119.0,6.0718,156000.0,<1H OCEAN
+-117.56,34.12,4.0,5351.0,1210.0,2988.0,1101.0,3.7973,181300.0,INLAND
+-120.85,35.37,21.0,1033.0,195.0,588.0,187.0,2.8173,226900.0,NEAR OCEAN
+-117.9,33.83,33.0,3065.0,611.0,2204.0,606.0,3.8456,211800.0,<1H OCEAN
+-122.08,37.94,44.0,2185.0,357.0,943.0,366.0,4.725,232100.0,NEAR BAY
+-122.12,37.85,18.0,5252.0,686.0,1870.0,657.0,8.0074,454100.0,NEAR BAY
+-122.12,38.01,50.0,1738.0,355.0,837.0,363.0,3.609,135700.0,NEAR BAY
+-122.41,37.79,52.0,2161.0,544.0,904.0,431.0,3.5066,350000.0,NEAR BAY
+-118.25,33.92,44.0,1137.0,235.0,747.0,225.0,2.0,92600.0,<1H OCEAN
+-122.31,37.54,49.0,1340.0,281.0,660.0,284.0,4.163,393800.0,NEAR OCEAN
+-121.91,37.47,13.0,5377.0,744.0,2759.0,760.0,6.868,337300.0,<1H OCEAN
+-118.08,33.92,34.0,2118.0,437.0,1414.0,442.0,3.7238,166800.0,<1H OCEAN
+-118.54,34.38,18.0,2096.0,309.0,1044.0,328.0,6.8299,262100.0,<1H OCEAN
+-118.14,33.87,44.0,1607.0,271.0,799.0,283.0,5.084,214100.0,<1H OCEAN
+-118.28,33.99,38.0,1454.0,323.0,1098.0,297.0,1.5109,104000.0,<1H OCEAN
+-118.28,33.98,39.0,1306.0,345.0,1332.0,331.0,1.9564,92200.0,<1H OCEAN
+-119.69,36.86,20.0,1676.0,263.0,786.0,240.0,4.0,164600.0,INLAND
+-117.66,33.48,22.0,809.0,180.0,334.0,157.0,2.3846,500001.0,<1H OCEAN
+-117.75,34.09,36.0,3094.0,556.0,1672.0,545.0,4.2143,146900.0,INLAND
+-121.22,37.79,36.0,1052.0,221.0,712.0,212.0,1.7228,105000.0,INLAND
+-119.75,36.75,50.0,1515.0,294.0,852.0,297.0,1.9955,54200.0,INLAND
+-118.47,34.01,27.0,1782.0,471.0,837.0,422.0,3.7727,413000.0,<1H OCEAN
+-122.13,37.72,26.0,2862.0,394.0,1030.0,397.0,7.912,367300.0,NEAR BAY
+-120.16,39.01,16.0,1463.0,264.0,54.0,26.0,4.975,206300.0,INLAND
+-122.36,37.57,35.0,1774.0,205.0,588.0,207.0,10.7339,500001.0,NEAR OCEAN
+-121.89,37.29,38.0,1568.0,351.0,710.0,339.0,2.7042,286600.0,<1H OCEAN
+-117.99,33.87,17.0,2334.0,537.0,1662.0,535.0,3.0147,217000.0,<1H OCEAN
+-117.94,33.64,24.0,1097.0,307.0,470.0,333.0,1.6389,225000.0,<1H OCEAN
+-122.14,37.74,52.0,1071.0,201.0,440.0,192.0,4.0662,204200.0,NEAR BAY
+-117.16,32.82,28.0,2291.0,371.0,1098.0,382.0,4.6875,188000.0,NEAR OCEAN
+-117.13,32.8,15.0,1606.0,375.0,784.0,342.0,3.7237,108300.0,NEAR OCEAN
+-117.92,34.11,24.0,2838.0,695.0,2151.0,645.0,3.2202,126200.0,<1H OCEAN
+-121.4,36.85,50.0,2666.0,613.0,1768.0,555.0,2.6598,157300.0,INLAND
+-122.27,37.77,52.0,2252.0,388.0,1033.0,434.0,5.5337,372000.0,NEAR BAY
+-117.71,34.02,17.0,12689.0,2426.0,7343.0,2230.0,3.6361,157700.0,INLAND
+-118.32,34.17,45.0,3448.0,690.0,1562.0,643.0,4.0648,258800.0,<1H OCEAN
+-119.28,36.52,19.0,1402.0,324.0,1327.0,316.0,2.25,53200.0,INLAND
+-118.35,34.04,36.0,1956.0,601.0,1672.0,546.0,1.8685,150700.0,<1H OCEAN
+-116.47,33.81,7.0,10105.0,2481.0,6274.0,2095.0,2.4497,90900.0,INLAND
+-119.72,36.82,16.0,2627.0,613.0,1054.0,623.0,1.9483,112500.0,INLAND
+-118.52,34.05,45.0,1814.0,325.0,709.0,311.0,4.825,500001.0,<1H OCEAN
+-119.7,34.43,37.0,1462.0,306.0,678.0,322.0,5.1545,418400.0,<1H OCEAN
+-121.85,37.34,27.0,1481.0,409.0,1505.0,391.0,2.5769,137500.0,<1H OCEAN
+-121.46,38.48,8.0,3593.0,659.0,1710.0,530.0,3.5227,93100.0,INLAND
+-122.13,37.67,40.0,1748.0,318.0,914.0,317.0,3.8676,184000.0,NEAR BAY
+-122.03,37.97,45.0,1613.0,338.0,865.0,336.0,3.25,151100.0,NEAR BAY
+-122.05,39.6,34.0,2051.0,342.0,958.0,322.0,2.8466,95300.0,INLAND
+-117.91,33.93,21.0,2578.0,363.0,1207.0,350.0,6.2452,291700.0,<1H OCEAN
+-118.35,33.95,45.0,1076.0,213.0,781.0,238.0,3.95,164000.0,<1H OCEAN
+-121.96,37.81,12.0,6488.0,778.0,2404.0,765.0,8.3188,403400.0,<1H OCEAN
+-121.23,39.27,11.0,1265.0,224.0,573.0,205.0,3.3603,162500.0,INLAND
+-122.25,37.77,52.0,1038.0,220.0,482.0,215.0,3.1771,210200.0,NEAR BAY
+-117.93,33.87,29.0,1221.0,371.0,1822.0,326.0,1.7935,162500.0,<1H OCEAN
+-118.35,34.06,48.0,1354.0,279.0,716.0,309.0,3.7167,385000.0,<1H OCEAN
+-118.39,34.17,28.0,2790.0,748.0,1351.0,697.0,3.2052,283600.0,<1H OCEAN
+-118.78,34.16,9.0,30405.0,4093.0,12873.0,3931.0,8.0137,399200.0,NEAR OCEAN
+-118.24,33.96,34.0,1724.0,432.0,1876.0,416.0,2.1078,100600.0,<1H OCEAN
+-122.22,37.47,23.0,7740.0,1943.0,4124.0,1743.0,3.3268,322800.0,NEAR OCEAN
+-117.89,33.74,34.0,1759.0,353.0,2083.0,330.0,3.2292,160600.0,<1H OCEAN
+-120.47,34.71,21.0,2535.0,383.0,1012.0,368.0,5.6177,183800.0,NEAR OCEAN
+-118.05,33.9,36.0,1047.0,227.0,975.0,239.0,3.1897,155000.0,<1H OCEAN
+-121.04,38.81,11.0,3522.0,623.0,1456.0,544.0,3.93,163400.0,INLAND
+-118.43,34.43,5.0,21113.0,4386.0,9842.0,3886.0,4.2037,194600.0,<1H OCEAN
+-121.0,38.58,12.0,3425.0,549.0,1357.0,451.0,5.3344,217500.0,INLAND
+-122.09,37.63,36.0,1570.0,274.0,992.0,249.0,5.3644,168800.0,NEAR BAY
+-118.3,33.73,40.0,2582.0,606.0,1304.0,588.0,3.5694,276400.0,NEAR OCEAN
+-117.71,33.57,6.0,3673.0,881.0,1846.0,768.0,4.877,144300.0,<1H OCEAN
+-120.1,36.96,20.0,2100.0,317.0,910.0,274.0,4.8187,90900.0,INLAND
+-118.32,33.86,32.0,3485.0,678.0,1715.0,649.0,4.6563,291700.0,<1H OCEAN
+-118.35,33.87,28.0,2319.0,579.0,1369.0,564.0,3.6169,257000.0,<1H OCEAN
+-117.38,34.14,11.0,10804.0,1493.0,5221.0,1482.0,5.246,161400.0,INLAND
+-118.43,34.27,36.0,1002.0,250.0,1312.0,249.0,3.024,148000.0,<1H OCEAN
+-117.96,34.14,27.0,2221.0,542.0,1328.0,523.0,2.5275,151700.0,INLAND
+-118.22,33.89,41.0,990.0,228.0,776.0,207.0,2.125,120200.0,<1H OCEAN
+-121.0,37.63,49.0,2051.0,500.0,1525.0,467.0,1.59,80900.0,INLAND
+-118.34,34.09,52.0,1731.0,502.0,849.0,466.0,3.2946,321600.0,<1H OCEAN
+-121.15,38.91,23.0,1654.0,299.0,787.0,299.0,4.2723,193100.0,INLAND
+-118.37,33.81,36.0,2031.0,339.0,817.0,337.0,5.1271,458300.0,NEAR OCEAN
+-117.07,32.68,18.0,1475.0,267.0,1149.0,268.0,5.0827,142200.0,NEAR OCEAN
+-122.17,37.75,38.0,992.0,,732.0,259.0,1.6196,85100.0,NEAR BAY
+-118.33,34.1,48.0,1116.0,524.0,1610.0,483.0,1.625,237500.0,<1H OCEAN
+-122.42,37.78,52.0,989.0,425.0,634.0,341.0,2.4414,275000.0,NEAR BAY
+-118.27,34.06,30.0,1771.0,788.0,2188.0,764.0,1.5885,154200.0,<1H OCEAN
+-121.96,37.53,18.0,2375.0,652.0,1252.0,586.0,2.6198,235900.0,<1H OCEAN
+-117.55,33.88,19.0,2472.0,618.0,2143.0,610.0,2.2372,108800.0,INLAND
+-118.37,33.97,41.0,1833.0,355.0,847.0,348.0,5.726,287800.0,<1H OCEAN
+-119.6,37.29,13.0,1722.0,325.0,712.0,269.0,2.625,137500.0,INLAND
+-117.15,33.7,2.0,6305.0,1265.0,2489.0,1152.0,3.1319,111500.0,INLAND
+-121.46,38.51,32.0,2437.0,592.0,1596.0,557.0,1.68,84100.0,INLAND
+-118.33,34.24,31.0,6434.0,1188.0,3540.0,1131.0,4.2639,293300.0,<1H OCEAN
+-122.41,37.78,52.0,1534.0,763.0,1520.0,614.0,1.4554,375000.0,NEAR BAY
+-117.97,33.88,9.0,1344.0,279.0,530.0,265.0,5.0731,185100.0,<1H OCEAN
+-122.36,37.95,40.0,408.0,102.0,302.0,81.0,1.8333,69800.0,NEAR BAY
+-118.28,34.09,49.0,3828.0,1197.0,2862.0,1009.0,2.4677,219200.0,<1H OCEAN
+-118.51,34.25,24.0,4338.0,558.0,1514.0,549.0,8.8612,500001.0,<1H OCEAN
+-119.28,35.5,34.0,1923.0,379.0,1101.0,351.0,2.4044,65800.0,INLAND
+-118.29,33.73,43.0,1854.0,519.0,1151.0,486.0,2.5759,225000.0,NEAR OCEAN
+-117.12,33.15,7.0,2810.0,464.0,1564.0,457.0,4.4655,182800.0,<1H OCEAN
+-115.56,32.78,35.0,1185.0,202.0,615.0,191.0,4.6154,86200.0,INLAND
+-117.95,33.75,19.0,1983.0,283.0,1098.0,275.0,6.6355,276100.0,<1H OCEAN
+-117.76,33.98,3.0,9662.0,1385.0,2497.0,856.0,6.7172,292400.0,<1H OCEAN
+-117.85,34.11,27.0,1748.0,403.0,985.0,416.0,3.1133,180600.0,INLAND
+-121.3,38.02,4.0,1515.0,384.0,491.0,348.0,2.8523,87500.0,INLAND
+-118.35,33.91,31.0,2583.0,663.0,1675.0,612.0,3.5234,265000.0,<1H OCEAN
+-118.35,34.08,52.0,2877.0,721.0,1186.0,704.0,3.2645,175000.0,<1H OCEAN
+-121.28,37.95,46.0,1026.0,330.0,1109.0,333.0,1.2904,63300.0,INLAND
+-118.63,34.19,32.0,3568.0,591.0,1741.0,563.0,5.1529,259600.0,<1H OCEAN
+-117.75,33.61,16.0,2270.0,488.0,709.0,489.0,3.2845,227600.0,<1H OCEAN
+-119.78,36.8,34.0,3426.0,623.0,1938.0,647.0,2.8994,66000.0,INLAND
+-121.16,39.18,14.0,1006.0,187.0,462.0,185.0,3.1042,152000.0,INLAND
+-118.44,34.27,29.0,1701.0,419.0,1616.0,371.0,3.3603,142400.0,<1H OCEAN
+-120.6,37.35,19.0,3874.0,676.0,2441.0,707.0,3.2955,88600.0,INLAND
+-121.55,37.37,39.0,759.0,141.0,252.0,106.0,3.6964,262500.0,INLAND
+-118.3,34.04,37.0,1470.0,399.0,1579.0,390.0,2.006,150000.0,<1H OCEAN
+-118.44,33.81,33.0,3994.0,990.0,1647.0,931.0,5.0106,500001.0,NEAR OCEAN
+-117.86,34.1,29.0,1185.0,197.0,588.0,196.0,5.0832,196900.0,<1H OCEAN
+-122.45,37.75,36.0,1997.0,356.0,772.0,348.0,4.95,322600.0,NEAR BAY
+-118.04,34.07,52.0,177.0,59.0,269.0,75.0,2.3611,131300.0,<1H OCEAN
+-119.01,35.37,38.0,1702.0,380.0,1191.0,366.0,1.8801,57800.0,INLAND
+-121.9,37.67,15.0,2130.0,273.0,876.0,285.0,7.2639,332400.0,<1H OCEAN
+-121.88,37.24,14.0,7174.0,950.0,2782.0,899.0,8.3065,394200.0,<1H OCEAN
+-118.95,34.18,25.0,2237.0,331.0,1121.0,365.0,6.0994,254900.0,<1H OCEAN
+-118.42,34.22,34.0,3004.0,589.0,1938.0,568.0,4.1857,198600.0,<1H OCEAN
+-118.9,34.17,14.0,4719.0,734.0,1880.0,731.0,5.3558,313800.0,<1H OCEAN
+-117.1,32.74,30.0,1772.0,500.0,1389.0,447.0,2.3641,94100.0,NEAR OCEAN
+-122.44,38.03,13.0,4284.0,1042.0,2146.0,937.0,4.1289,179200.0,NEAR BAY
+-117.57,33.88,35.0,1755.0,446.0,1453.0,428.0,2.316,119400.0,INLAND
+-118.07,34.01,36.0,1391.0,283.0,1025.0,275.0,3.2375,176800.0,<1H OCEAN
+-121.97,36.98,17.0,2813.0,497.0,1337.0,477.0,3.7083,252400.0,NEAR OCEAN
+-122.26,37.48,34.0,4453.0,682.0,1805.0,672.0,5.6038,451300.0,NEAR OCEAN
+-121.77,37.68,36.0,1687.0,372.0,950.0,372.0,3.5532,158400.0,INLAND
+-118.12,34.6,33.0,2189.0,497.0,1459.0,443.0,2.3958,94500.0,INLAND
+-122.33,38.39,36.0,831.0,122.0,272.0,109.0,6.3427,304800.0,INLAND
+-120.95,37.59,43.0,1561.0,354.0,862.0,332.0,1.8466,81500.0,INLAND
+-122.04,36.97,49.0,792.0,136.0,331.0,137.0,5.2128,238600.0,NEAR OCEAN
+-117.93,33.64,31.0,1291.0,356.0,1252.0,373.0,2.7143,185400.0,<1H OCEAN
+-121.94,38.03,27.0,1654.0,478.0,1141.0,420.0,1.4871,87100.0,INLAND
+-121.85,39.72,18.0,7272.0,1559.0,5022.0,1524.0,1.6911,98800.0,INLAND
+-117.06,32.66,33.0,3425.0,511.0,1528.0,479.0,5.6889,234600.0,NEAR OCEAN
+-117.05,32.77,33.0,3535.0,683.0,1568.0,672.0,2.8097,158300.0,NEAR OCEAN
+-122.2,37.8,43.0,3045.0,499.0,1115.0,455.0,4.9559,273000.0,NEAR BAY
+-118.39,34.06,43.0,1879.0,397.0,873.0,382.0,3.8158,500001.0,<1H OCEAN
+-121.46,39.54,14.0,5549.0,1000.0,1822.0,919.0,2.9562,142300.0,INLAND
+-121.87,37.3,14.0,360.0,124.0,134.0,84.0,2.7411,112500.0,<1H OCEAN
+-117.13,32.79,35.0,1362.0,243.0,698.0,255.0,3.6458,173800.0,NEAR OCEAN
+-121.78,37.69,34.0,2358.0,498.0,1157.0,461.0,3.3618,174600.0,INLAND
+-121.32,38.04,30.0,249.0,44.0,167.0,45.0,4.5,92800.0,INLAND
+-122.63,38.95,11.0,686.0,127.0,246.0,86.0,1.7083,77300.0,INLAND
+-117.76,33.88,9.0,4838.0,759.0,2090.0,695.0,6.6536,307800.0,<1H OCEAN
+-119.02,37.64,14.0,5919.0,1278.0,265.0,112.0,3.2431,221400.0,INLAND
+-119.24,36.33,9.0,3289.0,621.0,1866.0,631.0,3.1599,95000.0,INLAND
+-119.85,36.77,9.0,1142.0,314.0,620.0,283.0,2.0446,81300.0,INLAND
+-121.47,37.58,14.0,1594.0,292.0,887.0,287.0,4.6625,294000.0,INLAND
+-121.99,36.99,29.0,3119.0,507.0,1476.0,487.0,5.8123,281500.0,NEAR OCEAN
+-118.19,33.83,42.0,1773.0,360.0,815.0,299.0,4.9,406300.0,NEAR OCEAN
+-119.0,35.31,37.0,1337.0,275.0,767.0,273.0,1.6522,53300.0,INLAND
+-117.99,33.88,42.0,1461.0,302.0,986.0,314.0,3.9559,161100.0,<1H OCEAN
+-121.54,36.99,27.0,2361.0,449.0,1782.0,397.0,3.2614,305000.0,INLAND
+-118.57,34.23,22.0,3275.0,648.0,1746.0,585.0,4.9676,221900.0,<1H OCEAN
+-117.91,33.92,21.0,380.0,91.0,398.0,70.0,4.7222,208300.0,<1H OCEAN
+-118.1,34.1,29.0,1937.0,448.0,1352.0,433.0,3.81,234600.0,<1H OCEAN
+-122.61,38.26,17.0,2864.0,487.0,1482.0,547.0,4.6833,215200.0,<1H OCEAN
+-121.92,37.24,27.0,1265.0,216.0,660.0,232.0,5.3911,281200.0,<1H OCEAN
+-121.02,37.64,42.0,1437.0,307.0,1035.0,284.0,2.1036,88300.0,INLAND
+-118.09,33.79,31.0,4231.0,617.0,1694.0,623.0,6.6312,360100.0,<1H OCEAN
+-121.47,38.56,44.0,1986.0,573.0,1044.0,490.0,1.7328,88100.0,INLAND
+-119.31,36.62,33.0,1485.0,374.0,1544.0,329.0,1.7292,52000.0,INLAND
+-117.06,32.76,24.0,1629.0,587.0,1012.0,488.0,1.7452,156800.0,NEAR OCEAN
+-119.06,34.24,21.0,7436.0,984.0,2982.0,988.0,7.6775,391200.0,<1H OCEAN
+-123.72,41.09,19.0,1970.0,431.0,1166.0,363.0,1.8208,50000.0,<1H OCEAN
+-117.26,33.09,13.0,3730.0,761.0,1335.0,603.0,4.1667,227100.0,NEAR OCEAN
+-117.06,33.78,17.0,2813.0,565.0,1345.0,488.0,2.5847,145300.0,INLAND
+-121.93,37.72,26.0,2806.0,459.0,1453.0,444.0,4.9107,213800.0,<1H OCEAN
+-117.98,33.73,18.0,3833.0,,2192.0,996.0,3.4679,219700.0,<1H OCEAN
+-118.3,34.11,25.0,1590.0,218.0,568.0,206.0,8.4389,500001.0,<1H OCEAN
+-118.43,33.96,38.0,1104.0,216.0,415.0,163.0,6.1985,422000.0,<1H OCEAN
+-118.17,33.87,40.0,2462.0,587.0,1821.0,536.0,3.5646,162600.0,<1H OCEAN
+-122.33,37.56,34.0,6394.0,1619.0,2400.0,1496.0,3.4902,500001.0,NEAR OCEAN
+-117.87,33.9,21.0,3181.0,447.0,1416.0,469.0,6.8268,280300.0,<1H OCEAN
+-118.15,34.16,38.0,2471.0,529.0,1661.0,441.0,2.2765,146600.0,<1H OCEAN
+-122.18,39.93,35.0,1387.0,272.0,610.0,237.0,2.1759,59500.0,INLAND
+-117.95,33.74,16.0,2768.0,600.0,1182.0,563.0,3.7162,201200.0,<1H OCEAN
+-118.39,33.87,34.0,2395.0,469.0,1087.0,438.0,5.9683,394600.0,<1H OCEAN
+-118.5,35.7,18.0,3303.0,814.0,986.0,522.0,1.5957,101400.0,INLAND
+-118.04,34.15,34.0,1523.0,311.0,676.0,295.0,3.3621,377200.0,INLAND
+-118.31,34.26,36.0,1882.0,453.0,1005.0,409.0,3.8,217100.0,<1H OCEAN
+-118.0,33.88,18.0,2628.0,720.0,2276.0,649.0,2.735,170800.0,<1H OCEAN
+-120.97,38.42,16.0,1748.0,322.0,4930.0,287.0,4.3029,121900.0,INLAND
+-121.37,38.69,29.0,2103.0,380.0,1124.0,387.0,3.0833,87000.0,INLAND
+-122.35,37.96,35.0,1326.0,346.0,1023.0,295.0,2.0724,97700.0,NEAR BAY
+-117.25,32.83,17.0,2075.0,262.0,704.0,241.0,10.9529,500001.0,NEAR OCEAN
+-119.51,34.4,24.0,3422.0,596.0,1763.0,601.0,5.2039,301300.0,NEAR OCEAN
+-119.03,36.06,36.0,1925.0,443.0,1405.0,422.0,2.162,51900.0,INLAND
+-121.66,36.71,27.0,4131.0,886.0,2002.0,815.0,3.2929,157500.0,<1H OCEAN
+-117.81,33.74,24.0,2696.0,649.0,1908.0,626.0,3.3047,216900.0,<1H OCEAN
+-117.92,33.92,19.0,2181.0,400.0,1272.0,337.0,5.1952,302100.0,<1H OCEAN
+-118.42,34.16,17.0,277.0,70.0,119.0,59.0,4.0208,341700.0,<1H OCEAN
+-120.04,36.79,48.0,1341.0,239.0,671.0,208.0,2.7917,82800.0,INLAND
+-121.33,38.01,27.0,1612.0,234.0,630.0,255.0,5.318,155100.0,INLAND
+-115.49,32.69,17.0,1960.0,389.0,1691.0,356.0,1.899,64000.0,INLAND
+-119.09,34.35,20.0,4725.0,881.0,2823.0,869.0,4.0122,214800.0,<1H OCEAN
+-121.64,37.15,13.0,4780.0,798.0,2795.0,764.0,6.1684,288100.0,<1H OCEAN
+-121.24,38.63,4.0,11021.0,1565.0,3857.0,1494.0,7.2582,273200.0,INLAND
+-118.1,33.96,36.0,1184.0,240.0,946.0,232.0,4.0357,162500.0,<1H OCEAN
+-118.27,33.97,34.0,1462.0,394.0,1310.0,351.0,1.1557,90100.0,<1H OCEAN
+-119.19,34.23,17.0,3889.0,748.0,2415.0,739.0,4.5,234300.0,NEAR OCEAN
+-119.76,36.76,23.0,3800.0,1003.0,3786.0,917.0,1.4766,50600.0,INLAND
+-121.18,39.19,16.0,1528.0,351.0,729.0,319.0,2.4688,138800.0,INLAND
+-118.43,34.21,26.0,2867.0,671.0,1955.0,640.0,4.125,226500.0,<1H OCEAN
+-122.45,37.91,27.0,2682.0,382.0,935.0,369.0,10.0791,500001.0,NEAR BAY
+-118.17,33.92,43.0,2099.0,398.0,1276.0,387.0,3.1528,166800.0,<1H OCEAN
+-117.13,32.75,28.0,2279.0,671.0,1166.0,623.0,1.95,150000.0,NEAR OCEAN
+-117.7,34.07,33.0,1552.0,288.0,1326.0,303.0,3.7969,128400.0,INLAND
+-122.94,38.53,49.0,1141.0,239.0,505.0,184.0,3.7143,148800.0,<1H OCEAN
+-122.11,37.43,35.0,3584.0,535.0,1405.0,538.0,7.3023,451300.0,NEAR BAY
+-122.19,37.77,41.0,2036.0,510.0,1412.0,454.0,2.0469,89300.0,NEAR BAY
+-124.19,40.77,30.0,2975.0,634.0,1367.0,583.0,2.442,69000.0,NEAR OCEAN
+-118.11,33.95,36.0,2049.0,334.0,1105.0,363.0,4.8036,261300.0,<1H OCEAN
+-118.14,34.17,34.0,2384.0,604.0,2073.0,540.0,2.3062,158000.0,<1H OCEAN
+-122.33,37.94,44.0,1769.0,332.0,828.0,309.0,4.0526,150800.0,NEAR BAY
+-118.27,34.02,39.0,2004.0,633.0,3050.0,621.0,1.875,127300.0,<1H OCEAN
+-121.02,37.68,28.0,2875.0,560.0,1608.0,558.0,3.5489,106400.0,INLAND
+-117.21,34.05,4.0,2904.0,764.0,1250.0,664.0,3.2131,137500.0,INLAND
+-118.36,34.18,34.0,1471.0,423.0,995.0,386.0,2.9583,188700.0,<1H OCEAN
+-118.21,34.03,47.0,876.0,228.0,872.0,231.0,2.2656,145000.0,<1H OCEAN
+-121.99,37.39,25.0,3495.0,834.0,2484.0,797.0,4.8145,230700.0,<1H OCEAN
+-118.34,33.97,45.0,2230.0,364.0,949.0,344.0,5.5,188200.0,<1H OCEAN
+-122.45,37.79,52.0,2196.0,280.0,668.0,291.0,10.0914,500001.0,NEAR BAY
+-120.64,35.65,9.0,3466.0,673.0,2356.0,619.0,2.9926,158200.0,<1H OCEAN
+-118.48,34.24,32.0,2621.0,412.0,1285.0,414.0,6.6537,267600.0,<1H OCEAN
+-121.36,38.63,28.0,6119.0,985.0,2631.0,934.0,4.875,146400.0,INLAND
+-118.29,33.97,48.0,3139.0,587.0,1319.0,506.0,3.5208,134200.0,<1H OCEAN
+-118.18,34.12,52.0,1081.0,311.0,904.0,283.0,1.9219,165100.0,<1H OCEAN
+-114.73,33.43,24.0,796.0,243.0,227.0,139.0,0.8964,59200.0,INLAND
+-122.29,38.19,13.0,7065.0,1259.0,3864.0,1221.0,4.7472,148600.0,NEAR BAY
+-117.88,34.12,36.0,2029.0,351.0,1327.0,364.0,4.1836,164300.0,<1H OCEAN
+-122.44,37.8,52.0,3257.0,735.0,1045.0,620.0,4.5523,500001.0,NEAR BAY
+-118.0,33.93,24.0,4534.0,967.0,2547.0,895.0,3.9575,215400.0,<1H OCEAN
+-118.35,33.9,31.0,1547.0,,956.0,287.0,3.4698,225000.0,<1H OCEAN
+-119.92,34.44,17.0,2143.0,324.0,1073.0,330.0,6.0321,402600.0,NEAR OCEAN
+-118.04,33.92,35.0,2469.0,522.0,2151.0,537.0,3.4219,156200.0,<1H OCEAN
+-121.95,37.35,48.0,1246.0,294.0,697.0,284.0,3.6118,235500.0,<1H OCEAN
+-117.03,32.69,10.0,901.0,163.0,698.0,167.0,4.6648,156100.0,NEAR OCEAN
+-123.96,40.57,31.0,1854.0,365.0,883.0,310.0,2.3167,92600.0,<1H OCEAN
+-117.97,33.73,19.0,4154.0,560.0,2130.0,589.0,7.2845,301800.0,<1H OCEAN
+-121.3,37.97,52.0,2980.0,537.0,1128.0,510.0,4.061,113600.0,INLAND
+-118.17,34.07,37.0,1155.0,225.0,814.0,241.0,3.875,148500.0,<1H OCEAN
+-122.43,37.76,52.0,2242.0,459.0,751.0,464.0,4.75,500001.0,NEAR BAY
+-118.24,34.22,36.0,2507.0,517.0,1232.0,470.0,5.529,241300.0,<1H OCEAN
+-118.43,34.18,33.0,2717.0,662.0,1546.0,597.0,3.9099,267500.0,<1H OCEAN
+-117.92,34.08,35.0,1897.0,311.0,965.0,323.0,5.7039,199400.0,<1H OCEAN
+-121.0,37.6,22.0,4412.0,925.0,3116.0,817.0,2.6899,82100.0,INLAND
+-118.14,33.79,23.0,2573.0,688.0,1478.0,604.0,3.4833,209400.0,NEAR OCEAN
+-118.11,34.15,26.0,2193.0,558.0,1186.0,559.0,3.6474,184100.0,<1H OCEAN
+-121.82,36.54,22.0,1746.0,363.0,886.0,364.0,5.5469,378800.0,<1H OCEAN
+-118.02,33.62,11.0,3969.0,834.0,1508.0,754.0,4.3409,271400.0,NEAR OCEAN
+-118.16,34.04,45.0,332.0,70.0,302.0,60.0,3.1895,156300.0,<1H OCEAN
+-118.37,34.43,11.0,17339.0,2866.0,8721.0,2803.0,5.9507,225200.0,INLAND
+-122.02,37.37,8.0,5686.0,1489.0,3250.0,1329.0,4.2782,327700.0,<1H OCEAN
+-117.92,34.08,36.0,1479.0,251.0,741.0,245.0,4.2986,189600.0,<1H OCEAN
+-116.85,34.25,5.0,5806.0,1030.0,569.0,219.0,4.0132,163100.0,INLAND
+-118.2,33.96,44.0,3114.0,779.0,2959.0,776.0,3.1875,171700.0,<1H OCEAN
+-118.35,33.89,34.0,1740.0,387.0,1249.0,375.0,4.1552,233900.0,<1H OCEAN
+-118.25,34.02,50.0,180.0,89.0,356.0,76.0,2.1944,158300.0,<1H OCEAN
+-121.2,37.79,36.0,866.0,160.0,502.0,149.0,2.4798,101500.0,INLAND
+-121.48,38.56,44.0,1151.0,263.0,518.0,258.0,2.0089,113600.0,INLAND
+-117.3,34.15,40.0,961.0,199.0,509.0,182.0,2.06,85500.0,INLAND
+-117.49,34.02,21.0,3736.0,738.0,2021.0,640.0,4.4545,142400.0,INLAND
+-118.37,33.9,35.0,1651.0,269.0,707.0,252.0,5.6482,294800.0,<1H OCEAN
+-120.43,34.87,26.0,1699.0,272.0,799.0,266.0,3.9871,157700.0,<1H OCEAN
+-121.09,40.3,15.0,1717.0,336.0,501.0,206.0,3.6477,113400.0,INLAND
+-121.88,37.37,14.0,6016.0,1404.0,3258.0,1316.0,3.5745,333700.0,<1H OCEAN
+-122.11,37.83,19.0,5130.0,741.0,1887.0,712.0,7.203,369900.0,NEAR BAY
+-122.04,37.37,23.0,5135.0,911.0,2351.0,863.0,5.2319,430100.0,<1H OCEAN
+-119.73,34.44,38.0,1729.0,,801.0,395.0,3.1364,357500.0,NEAR OCEAN
+-118.22,33.99,4.0,1849.0,577.0,1529.0,418.0,2.7708,186300.0,<1H OCEAN
+-124.1,40.95,17.0,1485.0,345.0,823.0,316.0,1.8993,78400.0,NEAR OCEAN
+-122.3,37.91,39.0,2686.0,569.0,1159.0,559.0,2.9441,200400.0,NEAR BAY
+-122.07,37.89,28.0,3410.0,746.0,1428.0,670.0,4.3864,266800.0,NEAR BAY
+-117.41,33.93,35.0,793.0,150.0,669.0,128.0,4.0156,89300.0,INLAND
+-118.19,33.98,36.0,4179.0,,4582.0,1196.0,2.0087,172100.0,<1H OCEAN
+-122.31,38.31,32.0,2577.0,458.0,1172.0,447.0,3.8796,175500.0,NEAR BAY
+-118.1,34.65,33.0,873.0,177.0,425.0,142.0,2.67,187500.0,INLAND
+-122.32,37.93,33.0,296.0,73.0,216.0,63.0,2.675,22500.0,NEAR BAY
+-119.59,36.57,19.0,1733.0,303.0,911.0,281.0,3.5987,131700.0,INLAND
+-121.99,37.25,25.0,1743.0,212.0,604.0,200.0,10.7582,500001.0,<1H OCEAN
+-117.83,33.81,24.0,3550.0,895.0,2828.0,834.0,2.8403,225600.0,<1H OCEAN
+-122.36,40.56,20.0,3592.0,868.0,1865.0,781.0,2.0258,64800.0,INLAND
+-120.45,34.65,25.0,980.0,276.0,896.0,245.0,2.0,87500.0,NEAR OCEAN
+-117.23,32.87,11.0,3123.0,740.0,1223.0,634.0,5.417,196800.0,NEAR OCEAN
+-120.63,38.68,14.0,1821.0,316.0,769.0,266.0,3.0789,131700.0,INLAND
+-121.43,38.46,18.0,1378.0,235.0,818.0,262.0,4.0625,100300.0,INLAND
+-116.23,33.73,29.0,1133.0,221.0,918.0,239.0,2.8648,72100.0,INLAND
+-117.18,34.04,38.0,2492.0,381.0,1003.0,369.0,3.6875,152800.0,INLAND
+-121.96,37.3,20.0,4228.0,1006.0,2334.0,1007.0,4.3081,227300.0,<1H OCEAN
+-118.41,34.29,35.0,1008.0,204.0,1162.0,215.0,3.35,147600.0,<1H OCEAN
+-117.23,33.22,18.0,2334.0,573.0,962.0,557.0,1.808,97000.0,<1H OCEAN
+-122.68,38.98,27.0,2300.0,508.0,526.0,254.0,2.1838,109700.0,INLAND
+-117.9,34.09,39.0,1726.0,333.0,892.0,335.0,4.3409,191800.0,<1H OCEAN
+-117.95,34.14,13.0,3859.0,710.0,2283.0,759.0,4.5594,184500.0,INLAND
+-121.33,38.02,37.0,1964.0,315.0,915.0,335.0,4.3008,119800.0,INLAND
+-117.69,34.07,35.0,3222.0,559.0,1970.0,550.0,3.7083,131000.0,INLAND
+-120.84,37.48,10.0,2874.0,612.0,1960.0,596.0,2.7381,104600.0,INLAND
+-118.15,33.76,36.0,2916.0,785.0,1183.0,749.0,3.5985,500001.0,NEAR OCEAN
+-118.45,34.2,27.0,1572.0,450.0,1039.0,455.0,2.5562,190500.0,<1H OCEAN
+-121.91,36.62,52.0,541.0,157.0,240.0,145.0,3.5865,290000.0,<1H OCEAN
+-123.18,39.23,18.0,243.0,55.0,115.0,54.0,2.125,175000.0,<1H OCEAN
+-118.3,33.99,47.0,2637.0,588.0,1903.0,521.0,1.8317,96500.0,<1H OCEAN
+-122.03,37.3,30.0,3007.0,554.0,1551.0,616.0,5.8521,326300.0,<1H OCEAN
+-117.51,34.14,21.0,2455.0,381.0,1094.0,327.0,4.6437,191700.0,INLAND
+-118.83,34.18,23.0,5647.0,786.0,2050.0,738.0,6.3586,348300.0,<1H OCEAN
+-121.78,37.67,28.0,1773.0,278.0,804.0,269.0,4.8571,201100.0,INLAND
+-118.34,34.16,25.0,6082.0,1763.0,2616.0,1644.0,3.6486,246900.0,<1H OCEAN
+-118.1,34.7,5.0,10356.0,1647.0,4562.0,1427.0,4.9806,141100.0,INLAND
+-117.17,32.74,38.0,5054.0,1168.0,2366.0,1103.0,2.9422,289400.0,NEAR OCEAN
+-122.49,37.76,52.0,2564.0,502.0,1092.0,459.0,3.5302,329600.0,NEAR BAY
+-122.23,37.82,52.0,3051.0,381.0,1005.0,369.0,8.1872,466100.0,NEAR BAY
+-118.51,34.23,27.0,4580.0,918.0,2252.0,850.0,4.7926,454400.0,<1H OCEAN
+-117.89,34.13,34.0,2159.0,386.0,1443.0,385.0,4.1995,147400.0,<1H OCEAN
+-118.27,34.11,41.0,4138.0,1130.0,1859.0,1030.0,2.978,306800.0,<1H OCEAN
+-118.3,34.11,52.0,1954.0,245.0,645.0,237.0,6.9391,500001.0,<1H OCEAN
+-115.5,32.68,18.0,3631.0,913.0,3565.0,924.0,1.5931,88400.0,INLAND
+-121.31,38.68,16.0,5179.0,1271.0,2181.0,1151.0,2.1009,82500.0,INLAND
+-119.01,36.02,17.0,3915.0,742.0,1768.0,688.0,2.375,79800.0,INLAND
+-121.44,38.5,27.0,2527.0,439.0,1089.0,415.0,4.088,96800.0,INLAND
+-121.47,38.56,52.0,1532.0,408.0,782.0,369.0,1.8911,85900.0,INLAND
+-122.42,37.77,52.0,759.0,323.0,421.0,255.0,2.0548,162500.0,NEAR BAY
+-118.28,34.08,39.0,3162.0,896.0,1934.0,818.0,2.875,213500.0,<1H OCEAN
+-122.14,37.43,52.0,1944.0,308.0,696.0,293.0,8.2664,500001.0,NEAR BAY
+-119.77,36.73,44.0,1960.0,393.0,1286.0,381.0,2.1518,53000.0,INLAND
+-122.08,37.93,35.0,4043.0,689.0,1832.0,662.0,5.0761,233200.0,NEAR BAY
+-118.58,34.12,42.0,718.0,140.0,324.0,131.0,6.4018,500001.0,<1H OCEAN
+-118.37,34.21,36.0,1392.0,326.0,1181.0,303.0,3.1563,176400.0,<1H OCEAN
+-118.29,34.11,48.0,1448.0,295.0,681.0,287.0,3.2333,436400.0,<1H OCEAN
+-121.54,38.51,17.0,8482.0,1590.0,3362.0,1513.0,4.2216,217900.0,INLAND
+-120.81,38.73,38.0,2005.0,385.0,882.0,353.0,2.5104,120500.0,INLAND
+-122.16,38.05,52.0,1888.0,457.0,830.0,408.0,3.1373,185100.0,NEAR BAY
+-121.02,37.63,35.0,1591.0,364.0,1290.0,352.0,1.564,81800.0,INLAND
+-118.45,34.25,34.0,2094.0,380.0,1207.0,380.0,5.2801,212300.0,<1H OCEAN
+-118.19,33.8,36.0,2326.0,729.0,2635.0,657.0,2.1985,141800.0,NEAR OCEAN
+-118.74,34.28,21.0,4056.0,637.0,1974.0,634.0,5.9024,221000.0,<1H OCEAN
+-122.07,37.34,30.0,1851.0,238.0,631.0,236.0,10.1007,500001.0,<1H OCEAN
+-118.89,34.28,30.0,917.0,157.0,678.0,171.0,5.8133,195700.0,<1H OCEAN
+-118.41,33.75,4.0,311.0,51.0,128.0,46.0,9.8091,500001.0,NEAR OCEAN
+-120.16,34.61,17.0,921.0,189.0,434.0,219.0,3.0185,500001.0,NEAR OCEAN
+-118.34,34.01,37.0,4291.0,1102.0,1941.0,953.0,1.7945,106300.0,<1H OCEAN
+-122.43,37.78,52.0,4014.0,1069.0,2070.0,927.0,2.8202,442900.0,NEAR BAY
+-121.98,37.29,31.0,2750.0,664.0,1459.0,660.0,3.2287,264900.0,<1H OCEAN
+-118.32,34.26,32.0,3690.0,791.0,1804.0,715.0,4.4875,222700.0,<1H OCEAN
+-118.27,33.93,41.0,570.0,135.0,466.0,121.0,2.6458,91300.0,<1H OCEAN
+-122.08,37.64,30.0,5267.0,1253.0,4065.0,1113.0,3.3479,182100.0,NEAR BAY
+-121.89,37.36,37.0,1525.0,363.0,1075.0,374.0,2.8971,186100.0,<1H OCEAN
+-120.77,40.65,11.0,2635.0,667.0,280.0,132.0,1.7214,118300.0,INLAND
+-122.05,37.9,32.0,4498.0,862.0,1818.0,851.0,4.8088,321200.0,NEAR BAY
+-118.75,34.29,17.0,5512.0,,2734.0,814.0,6.6073,258100.0,<1H OCEAN
+-121.86,37.41,16.0,2938.0,589.0,1718.0,568.0,5.5073,178900.0,<1H OCEAN
+-120.92,39.56,48.0,1276.0,292.0,358.0,145.0,1.875,66600.0,INLAND
+-118.18,34.03,39.0,609.0,145.0,690.0,134.0,2.9167,145800.0,<1H OCEAN
+-118.28,34.0,44.0,2636.0,725.0,2182.0,651.0,1.432,124000.0,<1H OCEAN
+-119.72,36.81,15.0,2175.0,564.0,1194.0,482.0,2.6767,87500.0,INLAND
+-119.7,34.43,39.0,1486.0,467.0,758.0,409.0,2.6875,320600.0,<1H OCEAN
+-118.5,34.45,25.0,1290.0,190.0,689.0,216.0,6.0097,220200.0,<1H OCEAN
+-118.37,34.07,44.0,2703.0,663.0,1045.0,619.0,3.201,287500.0,<1H OCEAN
+-118.38,33.77,17.0,10950.0,2207.0,4713.0,2043.0,6.3064,418300.0,NEAR OCEAN
+-122.06,37.96,10.0,7136.0,1691.0,2959.0,1507.0,3.9816,182000.0,NEAR BAY
+-118.59,34.22,17.0,6015.0,1464.0,3056.0,1347.0,4.0077,229000.0,<1H OCEAN
+-119.82,36.76,46.0,2194.0,563.0,924.0,542.0,1.4028,68500.0,INLAND
+-117.62,33.64,2.0,7826.0,893.0,2985.0,790.0,10.1531,484100.0,<1H OCEAN
+-118.35,33.92,29.0,736.0,232.0,584.0,231.0,3.6167,200000.0,<1H OCEAN
+-118.04,33.96,37.0,1948.0,395.0,1163.0,379.0,3.225,154000.0,<1H OCEAN
+-118.16,34.13,36.0,4003.0,647.0,1337.0,631.0,7.723,500001.0,<1H OCEAN
+-118.28,33.92,39.0,1274.0,282.0,975.0,277.0,1.5114,90400.0,<1H OCEAN
+-117.9,34.53,8.0,3484.0,647.0,2169.0,619.0,3.9766,135800.0,INLAND
+-122.49,37.74,52.0,2302.0,457.0,1154.0,424.0,4.5744,315200.0,NEAR OCEAN
+-118.44,34.3,38.0,1595.0,314.0,1181.0,327.0,3.4,155500.0,<1H OCEAN
+-119.25,36.32,32.0,1821.0,345.0,812.0,299.0,2.75,72200.0,INLAND
+-121.94,39.45,39.0,844.0,161.0,535.0,165.0,1.832,70500.0,INLAND
+-121.96,37.41,17.0,3208.0,617.0,2286.0,602.0,5.2937,238000.0,<1H OCEAN
+-122.46,37.75,26.0,2192.0,438.0,954.0,456.0,4.5352,374200.0,NEAR BAY
+-117.79,33.71,16.0,3114.0,463.0,1641.0,469.0,6.2162,283200.0,<1H OCEAN
+-119.18,34.21,29.0,4039.0,680.0,1677.0,644.0,4.3897,257600.0,NEAR OCEAN
+-121.47,36.92,27.0,2049.0,417.0,1230.0,336.0,4.6477,265900.0,INLAND
+-119.71,36.74,18.0,8099.0,1670.0,4476.0,1514.0,2.4728,88300.0,INLAND
+-117.97,33.72,24.0,2991.0,500.0,1437.0,453.0,5.4286,273400.0,<1H OCEAN
+-119.85,36.77,27.0,1510.0,344.0,847.0,295.0,2.9315,83200.0,INLAND
+-118.15,33.91,35.0,574.0,116.0,610.0,147.0,3.3182,133300.0,<1H OCEAN
+-122.44,37.77,52.0,2002.0,520.0,939.0,501.0,3.2239,488900.0,NEAR BAY
+-120.02,37.57,17.0,2116.0,425.0,909.0,319.0,2.7188,113100.0,INLAND
+-118.3,34.03,40.0,1695.0,374.0,1138.0,357.0,2.7125,150000.0,<1H OCEAN
+-122.38,37.34,33.0,1054.0,209.0,400.0,161.0,7.7773,456300.0,NEAR OCEAN
+-117.47,33.95,15.0,6248.0,1249.0,3795.0,1128.0,4.1264,124600.0,INLAND
+-118.26,34.06,40.0,637.0,273.0,1150.0,263.0,1.8625,131300.0,<1H OCEAN
+-122.88,39.14,20.0,1125.0,231.0,521.0,196.0,2.2188,106300.0,INLAND
+-120.18,34.75,17.0,2074.0,382.0,1035.0,359.0,3.7958,400000.0,<1H OCEAN
+-120.45,34.66,7.0,3329.0,504.0,1462.0,452.0,4.7875,198300.0,NEAR OCEAN
+-119.75,36.8,30.0,3308.0,662.0,1894.0,648.0,2.197,74500.0,INLAND
+-117.94,33.85,37.0,588.0,121.0,436.0,104.0,4.275,186200.0,<1H OCEAN
+-118.4,34.21,30.0,2453.0,544.0,1753.0,506.0,2.9803,191500.0,<1H OCEAN
+-117.25,33.7,10.0,5156.0,941.0,2294.0,747.0,3.58,113400.0,<1H OCEAN
+-121.92,37.45,10.0,3937.0,1054.0,2032.0,1002.0,3.2617,252200.0,<1H OCEAN
+-122.68,38.76,29.0,994.0,226.0,302.0,117.0,2.3125,67900.0,INLAND
+-117.57,33.87,33.0,2076.0,517.0,1374.0,480.0,2.2197,138200.0,INLAND
+-121.62,39.77,23.0,1759.0,366.0,788.0,359.0,1.8125,93500.0,INLAND
+-121.81,37.29,15.0,5957.0,1037.0,3838.0,997.0,6.2907,253100.0,<1H OCEAN
+-117.19,32.77,30.0,2747.0,640.0,3185.0,657.0,3.765,238000.0,NEAR OCEAN
+-121.26,37.96,43.0,940.0,208.0,690.0,181.0,2.3056,62300.0,INLAND
+-122.48,37.68,31.0,3506.0,653.0,2296.0,645.0,5.5647,268700.0,NEAR OCEAN
+-122.87,38.68,32.0,4073.0,718.0,2053.0,629.0,3.7352,228000.0,<1H OCEAN
+-117.19,32.84,30.0,2492.0,406.0,1250.0,431.0,5.5277,197100.0,NEAR OCEAN
+-118.07,34.09,35.0,1224.0,267.0,887.0,276.0,4.0987,202400.0,<1H OCEAN
+-121.34,38.63,13.0,3033.0,540.0,1363.0,519.0,4.0036,161700.0,INLAND
+-121.99,38.26,18.0,921.0,126.0,368.0,120.0,6.0842,261100.0,INLAND
+-119.31,34.38,23.0,282.0,69.0,130.0,57.0,2.4375,225000.0,NEAR OCEAN
+-122.47,37.78,52.0,3021.0,569.0,1479.0,514.0,4.0208,414600.0,NEAR BAY
+-118.18,33.92,29.0,749.0,185.0,708.0,196.0,2.4583,136900.0,<1H OCEAN
+-118.2,33.92,39.0,1050.0,217.0,895.0,207.0,3.1538,155600.0,<1H OCEAN
+-119.91,34.4,24.0,2001.0,365.0,1170.0,330.0,6.0992,268800.0,NEAR OCEAN
+-117.96,33.8,35.0,1493.0,267.0,811.0,272.0,5.244,218000.0,<1H OCEAN
+-119.8,36.72,15.0,1908.0,417.0,1383.0,375.0,1.0472,57800.0,INLAND
+-120.66,35.28,31.0,2773.0,844.0,1358.0,794.0,1.4036,209600.0,NEAR OCEAN
+-118.35,34.09,35.0,2705.0,785.0,1526.0,793.0,3.0349,266700.0,<1H OCEAN
+-121.25,37.95,46.0,2001.0,428.0,1384.0,401.0,1.9402,62200.0,INLAND
+-119.75,36.74,39.0,1740.0,351.0,1098.0,347.0,1.8958,51300.0,INLAND
+-121.06,36.72,23.0,395.0,70.0,166.0,52.0,2.2132,100000.0,INLAND
+-118.08,33.91,30.0,1366.0,460.0,920.0,410.0,0.9946,159900.0,<1H OCEAN
+-116.94,34.4,20.0,6541.0,1401.0,2631.0,980.0,2.1339,78500.0,INLAND
+-119.05,35.3,9.0,10822.0,1994.0,6241.0,1906.0,4.0631,88200.0,INLAND
+-121.9,37.16,43.0,1529.0,311.0,570.0,250.0,5.2366,293300.0,<1H OCEAN
+-117.44,33.99,12.0,9966.0,1517.0,5008.0,1492.0,4.5625,171300.0,INLAND
+-121.96,37.28,33.0,1940.0,327.0,877.0,314.0,5.4386,280400.0,<1H OCEAN
+-119.81,36.79,35.0,2314.0,443.0,954.0,457.0,2.9506,73800.0,INLAND
+-120.88,38.16,8.0,2029.0,387.0,1000.0,364.0,4.0109,125900.0,INLAND
+-118.24,33.94,34.0,796.0,180.0,673.0,144.0,2.0769,88300.0,<1H OCEAN
+-121.35,38.09,32.0,1706.0,292.0,923.0,284.0,5.5057,147200.0,INLAND
+-121.99,37.27,27.0,2937.0,497.0,1454.0,511.0,5.4051,273500.0,<1H OCEAN
+-120.83,37.76,21.0,435.0,96.0,219.0,83.0,2.9125,112500.0,INLAND
+-120.98,37.64,40.0,1791.0,359.0,679.0,322.0,2.1458,130300.0,INLAND
+-122.45,37.77,52.0,2296.0,509.0,1039.0,472.0,4.1417,500000.0,NEAR BAY
+-118.31,34.04,33.0,2691.0,726.0,2390.0,681.0,2.4048,208300.0,<1H OCEAN
+-118.39,34.16,37.0,1388.0,286.0,547.0,258.0,5.1584,444700.0,<1H OCEAN
+-118.86,34.21,26.0,3354.0,659.0,2020.0,648.0,4.1576,211800.0,<1H OCEAN
+-118.13,34.14,23.0,5465.0,1494.0,2511.0,1359.0,3.4531,210900.0,<1H OCEAN
+-117.51,33.89,16.0,5418.0,1005.0,2690.0,1088.0,4.0556,158000.0,INLAND
+-118.37,34.03,39.0,213.0,44.0,138.0,52.0,2.125,196400.0,<1H OCEAN
+-121.27,38.69,16.0,3389.0,597.0,1674.0,568.0,4.4489,145600.0,INLAND
+-121.03,39.26,49.0,3739.0,759.0,1422.0,606.0,2.4283,143100.0,INLAND
+-119.71,34.43,47.0,1572.0,417.0,790.0,384.0,2.6429,279200.0,<1H OCEAN
+-117.5,34.66,20.0,1319.0,309.0,486.0,196.0,2.0184,84900.0,INLAND
+-122.48,37.76,52.0,2684.0,574.0,1395.0,549.0,3.9097,323800.0,NEAR BAY
+-124.19,40.73,21.0,5694.0,1056.0,2907.0,972.0,3.5363,90100.0,NEAR OCEAN
+-117.22,32.84,19.0,2691.0,347.0,1154.0,366.0,8.051,363600.0,NEAR OCEAN
+-120.9,37.64,26.0,1762.0,418.0,855.0,308.0,1.6767,81300.0,INLAND
+-117.0,32.76,31.0,2545.0,373.0,956.0,342.0,4.3977,226800.0,<1H OCEAN
+-118.07,33.79,34.0,2473.0,383.0,967.0,353.0,5.8283,362400.0,<1H OCEAN
+-118.32,34.0,50.0,2189.0,460.0,1097.0,469.0,2.4583,120900.0,<1H OCEAN
+-118.16,33.77,30.0,4439.0,1105.0,1749.0,1011.0,3.8984,306300.0,NEAR OCEAN
+-122.31,38.34,19.0,4187.0,684.0,1827.0,605.0,4.5293,210400.0,NEAR BAY
+-117.3,32.96,30.0,1226.0,205.0,380.0,151.0,4.2875,500001.0,NEAR OCEAN
+-117.94,34.04,36.0,1431.0,354.0,1367.0,334.0,3.5592,160200.0,<1H OCEAN
+-117.94,33.81,26.0,1589.0,259.0,735.0,315.0,4.5714,243200.0,<1H OCEAN
+-120.63,37.41,27.0,2083.0,444.0,1462.0,479.0,2.6439,69100.0,INLAND
+-118.24,33.97,38.0,1657.0,467.0,2033.0,443.0,2.1429,118500.0,<1H OCEAN
+-119.32,36.32,29.0,2409.0,436.0,1142.0,440.0,3.6895,87700.0,INLAND
+-117.09,32.75,20.0,1701.0,503.0,1482.0,465.0,1.6789,95500.0,NEAR OCEAN
+-117.31,32.82,42.0,2785.0,389.0,833.0,333.0,11.3074,500001.0,NEAR OCEAN
+-122.27,37.86,52.0,834.0,186.0,494.0,175.0,3.15,206300.0,NEAR BAY
+-117.16,32.74,21.0,1882.0,486.0,903.0,482.0,3.06,243800.0,NEAR OCEAN
+-117.91,33.65,19.0,1589.0,421.0,1118.0,394.0,4.1029,213400.0,<1H OCEAN
+-121.94,37.34,41.0,2151.0,473.0,1092.0,469.0,3.7321,250000.0,<1H OCEAN
+-119.84,36.83,17.0,3012.0,408.0,987.0,362.0,7.4201,229700.0,INLAND
+-122.06,37.38,20.0,4293.0,1272.0,2389.0,1210.0,4.2719,270800.0,NEAR BAY
+-117.04,32.75,36.0,2297.0,418.0,1070.0,392.0,3.5192,144000.0,NEAR OCEAN
+-122.97,38.5,44.0,3234.0,746.0,1112.0,470.0,1.9265,132700.0,<1H OCEAN
+-118.62,34.21,26.0,3234.0,517.0,1597.0,513.0,6.1074,258600.0,<1H OCEAN
+-117.88,34.14,23.0,2308.0,322.0,1001.0,317.0,7.5112,355500.0,INLAND
+-118.28,33.75,41.0,1305.0,381.0,1384.0,369.0,2.45,186800.0,NEAR OCEAN
+-117.22,32.81,21.0,1703.0,335.0,902.0,369.0,3.7813,362500.0,NEAR OCEAN
+-117.77,33.55,28.0,2024.0,297.0,617.0,274.0,6.7861,499100.0,<1H OCEAN
+-120.53,39.79,18.0,1234.0,266.0,543.0,201.0,2.5156,71900.0,INLAND
+-118.35,34.03,35.0,1438.0,333.0,794.0,306.0,1.975,138100.0,<1H OCEAN
+-122.29,37.86,50.0,2485.0,607.0,1354.0,563.0,1.9483,150500.0,NEAR BAY
+-121.29,38.03,7.0,2021.0,441.0,1615.0,406.0,2.5842,111300.0,INLAND
+-122.42,37.8,52.0,2852.0,581.0,838.0,510.0,8.0755,500001.0,NEAR BAY
+-117.1,32.57,26.0,2343.0,371.0,1221.0,372.0,4.3601,144900.0,NEAR OCEAN
+-118.52,34.44,26.0,934.0,148.0,519.0,162.0,5.3209,185000.0,<1H OCEAN
+-118.43,34.32,34.0,2657.0,515.0,1948.0,532.0,4.233,157400.0,<1H OCEAN
+-118.2,33.97,30.0,1911.0,562.0,2055.0,534.0,2.3917,154600.0,<1H OCEAN
+-120.44,34.87,13.0,2312.0,352.0,1084.0,388.0,5.038,194000.0,<1H OCEAN
+-115.49,32.67,25.0,2322.0,573.0,2185.0,602.0,1.375,70100.0,INLAND
+-118.44,34.06,9.0,5102.0,1695.0,2609.0,1450.0,3.2545,500001.0,<1H OCEAN
+-117.61,33.41,35.0,2556.0,404.0,946.0,399.0,6.1557,402900.0,NEAR OCEAN
+-119.02,35.39,30.0,227.0,75.0,169.0,101.0,1.3527,60000.0,INLAND
+-118.14,33.87,44.0,1661.0,315.0,985.0,319.0,4.3942,219500.0,<1H OCEAN
+-117.97,33.75,26.0,3361.0,722.0,2709.0,648.0,3.9107,190700.0,<1H OCEAN
+-118.96,35.38,41.0,2417.0,435.0,973.0,406.0,3.0568,85600.0,INLAND
+-116.52,32.9,18.0,4454.0,852.0,1754.0,656.0,4.57,189900.0,INLAND
+-118.34,34.06,52.0,2069.0,417.0,826.0,377.0,3.5481,396000.0,<1H OCEAN
+-117.14,33.16,16.0,1660.0,,733.0,214.0,5.6874,202700.0,<1H OCEAN
+-122.43,37.72,52.0,2206.0,478.0,1583.0,456.0,3.7105,250500.0,NEAR BAY
+-122.46,37.7,42.0,876.0,216.0,713.0,203.0,3.84,235900.0,NEAR OCEAN
+-122.24,38.11,52.0,2111.0,310.0,772.0,323.0,4.775,148200.0,NEAR BAY
+-121.31,37.94,41.0,375.0,108.0,323.0,98.0,1.9531,45000.0,INLAND
+-122.03,37.54,6.0,2918.0,672.0,1911.0,639.0,4.1406,178200.0,NEAR BAY
+-120.7,35.55,10.0,3979.0,761.0,1834.0,671.0,3.5,172100.0,<1H OCEAN
+-121.32,38.66,26.0,1149.0,193.0,500.0,194.0,5.078,163400.0,INLAND
+-118.27,33.92,34.0,1178.0,260.0,1166.0,244.0,1.9185,93300.0,<1H OCEAN
+-119.96,38.94,27.0,1492.0,393.0,717.0,254.0,1.8906,104200.0,INLAND
+-117.99,33.72,26.0,1787.0,275.0,801.0,270.0,5.5514,255700.0,<1H OCEAN
+-122.26,37.52,34.0,483.0,131.0,291.0,157.0,3.0833,256300.0,NEAR OCEAN
+-118.54,34.28,10.0,7665.0,999.0,3517.0,998.0,10.8805,500001.0,<1H OCEAN
+-117.32,34.06,46.0,476.0,102.0,476.0,91.0,1.4511,73100.0,INLAND
+-117.07,32.69,28.0,1485.0,275.0,820.0,283.0,4.069,153300.0,NEAR OCEAN
+-121.48,38.54,41.0,3364.0,685.0,1841.0,626.0,2.1975,73500.0,INLAND
+-117.99,33.77,29.0,1312.0,267.0,922.0,255.0,3.1902,202400.0,<1H OCEAN
+-121.98,38.29,4.0,8778.0,1291.0,4010.0,1188.0,5.4399,187100.0,INLAND
+-122.24,38.14,15.0,8479.0,1759.0,5008.0,1646.0,3.724,131600.0,NEAR BAY
+-121.34,38.64,12.0,2772.0,578.0,1335.0,565.0,3.8068,161000.0,INLAND
+-117.88,33.7,16.0,1505.0,358.0,835.0,339.0,3.8029,205400.0,<1H OCEAN
+-118.42,34.02,21.0,3244.0,815.0,1423.0,781.0,3.6488,340800.0,<1H OCEAN
+-117.09,32.64,19.0,2571.0,791.0,1205.0,783.0,1.62,131300.0,NEAR OCEAN
+-122.41,37.8,52.0,2618.0,611.0,1328.0,559.0,4.1607,350000.0,NEAR BAY
+-118.15,33.98,17.0,3361.0,925.0,3264.0,914.0,2.2813,145600.0,<1H OCEAN
+-119.6,36.56,36.0,738.0,168.0,737.0,186.0,1.4415,54400.0,INLAND
+-119.28,36.32,16.0,2812.0,514.0,1620.0,523.0,3.7404,89200.0,INLAND
+-121.46,38.56,52.0,1750.0,372.0,764.0,369.0,2.9191,111800.0,INLAND
+-118.14,34.06,37.0,1339.0,258.0,706.0,238.0,4.7569,253800.0,<1H OCEAN
+-120.67,35.63,8.0,2690.0,410.0,1085.0,381.0,4.2841,256700.0,<1H OCEAN
+-118.37,34.08,28.0,4376.0,1202.0,1847.0,1128.0,2.6713,312500.0,<1H OCEAN
+-122.1,37.88,35.0,3701.0,528.0,1511.0,517.0,7.2315,367100.0,NEAR BAY
+-118.09,34.18,34.0,3113.0,409.0,1139.0,418.0,10.2289,500001.0,INLAND
+-118.3,34.07,46.0,5677.0,2610.0,7443.0,2406.0,1.8238,237500.0,<1H OCEAN
+-116.95,33.79,8.0,10997.0,2205.0,5060.0,1949.0,2.1979,95300.0,INLAND
+-120.69,37.77,46.0,431.0,86.0,239.0,80.0,3.3182,282100.0,INLAND
+-118.26,33.93,42.0,1433.0,295.0,775.0,293.0,1.1326,104800.0,<1H OCEAN
+-118.41,33.93,38.0,3328.0,625.0,1455.0,619.0,5.0596,363900.0,<1H OCEAN
+-119.71,34.41,31.0,1034.0,319.0,997.0,308.0,2.6538,231800.0,NEAR OCEAN
+-121.99,37.55,16.0,6647.0,2098.0,4649.0,1903.0,2.9074,213800.0,<1H OCEAN
+-122.48,38.32,42.0,2106.0,533.0,1141.0,445.0,3.1129,149300.0,<1H OCEAN
+-119.19,34.3,25.0,2197.0,320.0,934.0,330.0,6.311,283200.0,NEAR OCEAN
+-122.26,37.87,52.0,2773.0,998.0,1721.0,949.0,1.1859,241700.0,NEAR BAY
+-115.56,32.79,18.0,1178.0,438.0,1377.0,429.0,1.3373,58300.0,INLAND
+-122.84,38.41,19.0,2191.0,391.0,1065.0,404.0,4.125,204600.0,<1H OCEAN
+-122.24,37.76,52.0,1846.0,471.0,827.0,446.0,2.6833,240900.0,NEAR BAY
+-117.33,34.04,18.0,1837.0,388.0,727.0,336.0,2.5187,116700.0,INLAND
+-118.23,33.95,42.0,705.0,173.0,739.0,140.0,0.9166,99000.0,<1H OCEAN
+-118.43,34.2,29.0,3051.0,694.0,1942.0,679.0,3.1118,238100.0,<1H OCEAN
+-122.07,37.35,35.0,1579.0,210.0,570.0,196.0,8.5888,500001.0,<1H OCEAN
+-117.9,33.65,28.0,2043.0,430.0,1108.0,452.0,5.2549,261800.0,<1H OCEAN
+-122.48,37.73,52.0,1597.0,240.0,566.0,231.0,5.1681,500001.0,NEAR OCEAN
+-118.28,34.04,25.0,1582.0,780.0,2390.0,719.0,1.4167,200000.0,<1H OCEAN
+-122.56,37.98,36.0,2649.0,542.0,1111.0,557.0,4.8056,345700.0,NEAR BAY
+-119.61,36.56,34.0,1911.0,497.0,1886.0,481.0,1.625,53000.0,INLAND
+-118.26,34.06,42.0,2541.0,1282.0,3974.0,1189.0,1.5854,87500.0,<1H OCEAN
+-118.31,34.09,42.0,1951.0,846.0,2500.0,813.0,1.5195,218200.0,<1H OCEAN
+-118.74,34.25,25.0,1815.0,281.0,960.0,284.0,5.4243,214700.0,<1H OCEAN
+-118.44,34.0,41.0,1562.0,377.0,874.0,368.0,4.1083,324300.0,<1H OCEAN
+-118.38,34.05,52.0,1004.0,231.0,590.0,226.0,4.2404,351000.0,<1H OCEAN
+-120.87,39.18,25.0,2691.0,598.0,964.0,373.0,3.9196,142700.0,INLAND
+-122.43,37.75,52.0,1615.0,393.0,633.0,378.0,3.5114,347500.0,NEAR BAY
+-122.01,38.25,16.0,1081.0,181.0,792.0,184.0,4.6779,131300.0,INLAND
+-117.28,33.94,10.0,972.0,212.0,773.0,219.0,1.3125,135700.0,INLAND
+-117.75,34.06,52.0,62.0,9.0,44.0,16.0,0.4999,112500.0,INLAND
+-117.47,33.94,34.0,559.0,139.0,532.0,137.0,3.0687,88500.0,INLAND
+-121.91,37.33,28.0,454.0,147.0,366.0,140.0,2.9853,187500.0,<1H OCEAN
+-118.08,33.93,39.0,859.0,164.0,673.0,172.0,3.7143,158200.0,<1H OCEAN
+-117.25,33.93,8.0,10110.0,1761.0,5804.0,1703.0,4.2654,137600.0,INLAND
+-122.38,37.59,44.0,2089.0,348.0,837.0,317.0,4.6628,459200.0,NEAR OCEAN
+-118.49,34.06,42.0,2861.0,360.0,829.0,310.0,15.0001,500001.0,<1H OCEAN
+-117.14,32.75,27.0,1551.0,464.0,880.0,400.0,2.4167,131300.0,NEAR OCEAN
+-120.37,38.25,13.0,4495.0,856.0,1149.0,459.0,2.5352,113700.0,INLAND
+-118.11,33.9,26.0,4173.0,893.0,2471.0,863.0,3.5052,196000.0,<1H OCEAN
+-122.03,37.92,23.0,3318.0,408.0,1124.0,393.0,6.5847,358800.0,NEAR BAY
+-121.18,37.96,35.0,411.0,74.0,193.0,59.0,2.5625,146900.0,INLAND
+-118.13,34.18,52.0,3094.0,519.0,1309.0,488.0,6.4223,310900.0,INLAND
+-121.46,38.52,29.0,3873.0,797.0,2237.0,706.0,2.1736,72100.0,INLAND
+-118.31,33.73,52.0,1642.0,287.0,692.0,288.0,4.1812,321500.0,NEAR OCEAN
+-119.72,36.71,7.0,2456.0,463.0,1350.0,424.0,3.0179,91600.0,INLAND
+-119.83,36.71,43.0,355.0,81.0,233.0,75.0,2.4167,73900.0,INLAND
+-117.38,33.99,52.0,1797.0,332.0,905.0,313.0,2.7054,141700.0,INLAND
+-118.56,34.37,23.0,3927.0,728.0,1984.0,707.0,4.8536,202600.0,<1H OCEAN
+-119.99,37.51,14.0,2878.0,617.0,1011.0,509.0,1.398,103800.0,INLAND
+-118.07,34.12,43.0,1554.0,287.0,802.0,277.0,4.2312,272600.0,INLAND
+-122.43,37.64,34.0,8400.0,1812.0,4101.0,1717.0,4.1033,301000.0,NEAR OCEAN
+-119.79,36.77,43.0,2323.0,502.0,1144.0,471.0,2.3967,58700.0,INLAND
+-118.1,34.07,33.0,3437.0,1081.0,3817.0,1042.0,2.25,203700.0,<1H OCEAN
+-117.95,34.02,22.0,1919.0,411.0,1203.0,363.0,4.2578,144100.0,<1H OCEAN
+-118.24,34.16,52.0,1904.0,297.0,797.0,286.0,6.6603,380400.0,<1H OCEAN
+-122.21,38.41,12.0,4270.0,654.0,1624.0,598.0,5.5266,331300.0,INLAND
+-118.43,34.15,31.0,1856.0,425.0,795.0,426.0,2.8448,360600.0,<1H OCEAN
+-117.77,34.06,27.0,2178.0,629.0,2379.0,591.0,1.9766,108000.0,INLAND
+-122.04,36.96,42.0,538.0,107.0,200.0,104.0,2.1667,196400.0,NEAR OCEAN
+-120.98,37.65,36.0,826.0,167.0,432.0,150.0,2.5,103100.0,INLAND
+-118.26,34.14,23.0,1336.0,396.0,1255.0,359.0,2.5388,205000.0,<1H OCEAN
+-117.9,33.66,4.0,456.0,91.0,623.0,84.0,6.6369,192600.0,<1H OCEAN
+-119.71,34.42,50.0,840.0,279.0,488.0,270.0,2.2097,258300.0,<1H OCEAN
+-121.53,38.48,5.0,27870.0,5027.0,11935.0,4855.0,4.8811,212200.0,INLAND
+-118.02,33.95,36.0,1705.0,299.0,871.0,296.0,4.6184,179800.0,<1H OCEAN
+-118.22,33.8,33.0,1984.0,477.0,1764.0,440.0,3.875,165100.0,NEAR OCEAN
+-118.29,34.05,11.0,677.0,370.0,1143.0,341.0,2.3864,350000.0,<1H OCEAN
+-118.01,33.83,23.0,1086.0,268.0,825.0,250.0,2.4609,219600.0,<1H OCEAN
+-119.76,36.77,36.0,2507.0,466.0,1227.0,474.0,2.785,72300.0,INLAND
+-122.42,40.57,10.0,7949.0,1309.0,3176.0,1163.0,4.1099,120100.0,INLAND
+-118.06,34.0,34.0,5002.0,917.0,2597.0,893.0,3.9243,219800.0,<1H OCEAN
+-121.5,38.57,9.0,745.0,175.0,297.0,160.0,3.358,77500.0,INLAND
+-120.49,37.26,28.0,2159.0,416.0,1283.0,378.0,1.8939,83000.0,INLAND
+-118.17,33.77,45.0,2151.0,643.0,1047.0,579.0,3.1149,218800.0,NEAR OCEAN
+-120.01,38.91,17.0,2732.0,609.0,1005.0,499.0,1.9851,86700.0,INLAND
+-121.91,37.29,18.0,3597.0,664.0,1321.0,593.0,5.3077,351400.0,<1H OCEAN
+-118.16,34.15,19.0,1721.0,290.0,571.0,278.0,6.6031,500001.0,<1H OCEAN
+-122.17,37.87,38.0,1261.0,177.0,472.0,183.0,6.917,438000.0,NEAR BAY
+-119.57,36.27,20.0,2673.0,452.0,1394.0,449.0,2.625,97400.0,INLAND
+-122.07,37.36,21.0,3244.0,426.0,1158.0,415.0,7.5,500001.0,<1H OCEAN
+-118.08,34.56,14.0,5144.0,887.0,2846.0,824.0,4.5615,137200.0,INLAND
+-117.13,32.71,35.0,614.0,180.0,691.0,164.0,1.6953,81300.0,NEAR OCEAN
+-122.03,36.96,28.0,1607.0,421.0,926.0,385.0,2.425,216100.0,NEAR OCEAN
+-116.86,33.02,17.0,401.0,68.0,251.0,69.0,4.6518,170200.0,<1H OCEAN
+-117.63,34.02,13.0,4864.0,729.0,2780.0,723.0,5.6168,175400.0,INLAND
+-120.66,35.5,19.0,1861.0,364.0,1040.0,363.0,3.3125,163900.0,<1H OCEAN
+-118.31,34.2,36.0,1692.0,263.0,778.0,278.0,5.0865,349600.0,<1H OCEAN
+-118.3,33.8,8.0,1115.0,412.0,1472.0,396.0,3.1392,146200.0,<1H OCEAN
+-122.35,37.94,45.0,2112.0,493.0,1406.0,452.0,2.3456,105200.0,NEAR BAY
+-122.25,37.79,51.0,175.0,43.0,228.0,55.0,2.1,75000.0,NEAR BAY
+-118.58,34.15,21.0,3856.0,547.0,1422.0,535.0,8.4196,450700.0,<1H OCEAN
+-119.29,36.33,19.0,792.0,232.0,641.0,222.0,0.7445,112500.0,INLAND
+-117.9,33.66,13.0,1642.0,423.0,841.0,368.0,3.6042,226000.0,<1H OCEAN
+-118.18,34.04,42.0,1670.0,,1997.0,452.0,2.788,150500.0,<1H OCEAN
+-122.25,37.5,45.0,1812.0,336.0,752.0,329.0,4.95,345000.0,NEAR OCEAN
+-120.6,37.34,34.0,1830.0,431.0,1304.0,415.0,2.1182,68900.0,INLAND
+-118.05,33.82,21.0,2997.0,372.0,1323.0,372.0,8.6123,386700.0,<1H OCEAN
+-118.34,33.84,36.0,1407.0,231.0,676.0,231.0,5.269,331900.0,<1H OCEAN
+-122.41,37.76,52.0,2064.0,496.0,1726.0,466.0,3.4028,233300.0,NEAR BAY
+-122.03,37.56,24.0,8444.0,1492.0,4446.0,1491.0,4.6978,240300.0,NEAR BAY
+-118.63,34.17,33.0,4769.0,787.0,2019.0,743.0,5.5798,338200.0,<1H OCEAN
+-117.1,32.77,49.0,4449.0,711.0,1606.0,709.0,5.7768,281600.0,NEAR OCEAN
+-117.82,33.68,3.0,7105.0,1459.0,3068.0,1241.0,6.1395,358000.0,<1H OCEAN
+-117.68,33.59,8.0,2327.0,263.0,899.0,236.0,14.9009,500001.0,<1H OCEAN
+-120.25,36.65,31.0,1177.0,221.0,744.0,223.0,2.4937,66000.0,INLAND
+-116.97,32.74,14.0,7410.0,1344.0,3597.0,1274.0,4.2192,176100.0,<1H OCEAN
+-118.2,34.1,30.0,3643.0,1197.0,4336.0,1163.0,2.07,154500.0,<1H OCEAN
+-118.51,34.22,36.0,1493.0,285.0,766.0,272.0,4.8646,213200.0,<1H OCEAN
+-122.49,37.59,35.0,2683.0,475.0,1498.0,484.0,5.1282,262500.0,NEAR OCEAN
+-122.35,38.3,18.0,3735.0,557.0,1504.0,521.0,5.6304,243100.0,NEAR BAY
+-121.21,38.67,19.0,2987.0,626.0,1610.0,605.0,3.0533,112100.0,INLAND
+-122.1,37.96,20.0,3796.0,650.0,1679.0,611.0,4.3571,228200.0,NEAR BAY
+-119.57,36.1,37.0,1676.0,316.0,707.0,274.0,2.0595,60700.0,INLAND
+-118.33,34.2,43.0,2322.0,418.0,1106.0,433.0,4.3631,284600.0,<1H OCEAN
+-122.24,37.79,47.0,1372.0,395.0,1237.0,303.0,2.125,95500.0,NEAR BAY
+-118.82,36.13,43.0,1281.0,287.0,534.0,231.0,2.8906,65700.0,INLAND
+-118.16,33.79,25.0,5463.0,1265.0,3010.0,1179.0,3.233,199100.0,NEAR OCEAN
+-123.23,39.13,33.0,1176.0,211.0,529.0,217.0,3.8958,144000.0,<1H OCEAN
+-120.48,34.9,20.0,3842.0,630.0,2490.0,662.0,3.0559,120100.0,<1H OCEAN
+-122.08,37.38,36.0,857.0,156.0,448.0,168.0,5.0086,366700.0,NEAR BAY
+-122.18,37.73,43.0,1391.0,293.0,855.0,285.0,2.5192,76400.0,NEAR BAY
+-122.23,37.81,52.0,1433.0,229.0,612.0,213.0,4.7708,314700.0,NEAR BAY
+-122.91,39.18,43.0,89.0,18.0,86.0,27.0,2.0208,72500.0,INLAND
+-121.39,38.64,33.0,1503.0,282.0,652.0,229.0,3.6937,99300.0,INLAND
+-118.3,33.82,25.0,2659.0,765.0,2629.0,726.0,2.6368,175900.0,<1H OCEAN
+-117.76,34.06,47.0,508.0,108.0,384.0,86.0,1.9583,92600.0,INLAND
+-116.38,34.1,6.0,2104.0,348.0,841.0,320.0,4.1458,116300.0,INLAND
+-121.87,39.74,7.0,1737.0,290.0,747.0,265.0,3.9,147000.0,INLAND
+-118.25,34.16,14.0,3700.0,945.0,1681.0,905.0,3.9054,200000.0,<1H OCEAN
+-119.05,34.24,24.0,4341.0,646.0,1929.0,703.0,5.4298,279600.0,<1H OCEAN
+-120.69,35.52,26.0,2758.0,571.0,1291.0,522.0,2.925,181400.0,<1H OCEAN
+-121.81,37.24,21.0,3250.0,610.0,1978.0,568.0,4.5,234400.0,<1H OCEAN
+-121.72,37.16,21.0,1062.0,179.0,631.0,185.0,4.7386,394100.0,<1H OCEAN
+-118.5,34.03,52.0,1506.0,208.0,547.0,186.0,7.8705,500001.0,<1H OCEAN
+-118.27,33.99,38.0,1407.0,447.0,1783.0,402.0,1.8086,97100.0,<1H OCEAN
+-118.45,34.23,25.0,4393.0,1369.0,3781.0,1267.0,2.5833,183700.0,<1H OCEAN
+-118.39,33.85,17.0,1610.0,379.0,670.0,341.0,4.3594,349000.0,<1H OCEAN
+-118.38,34.16,42.0,2358.0,546.0,1065.0,523.0,3.1289,320600.0,<1H OCEAN
+-122.0,38.28,3.0,7030.0,1191.0,3238.0,1055.0,4.962,161700.0,INLAND
+-116.39,33.82,15.0,11115.0,2257.0,4122.0,1653.0,2.7219,74400.0,INLAND
+-122.28,37.9,52.0,2261.0,328.0,819.0,335.0,4.9083,346800.0,NEAR BAY
+-118.36,33.84,22.0,11016.0,3170.0,6664.0,2838.0,3.703,361300.0,<1H OCEAN
+-118.36,34.07,40.0,1821.0,447.0,777.0,441.0,2.3375,355200.0,<1H OCEAN
+-118.17,34.03,42.0,882.0,292.0,1248.0,281.0,2.761,120000.0,<1H OCEAN
+-117.14,32.93,12.0,1474.0,364.0,1009.0,372.0,4.0521,166700.0,<1H OCEAN
+-120.99,37.63,21.0,319.0,120.0,276.0,85.0,2.4792,60000.0,INLAND
+-121.92,38.01,7.0,1632.0,248.0,879.0,262.0,6.1237,166000.0,INLAND
+-117.33,34.06,36.0,755.0,157.0,625.0,152.0,2.0242,65000.0,INLAND
+-122.05,37.39,25.0,347.0,82.0,148.0,77.0,4.4531,350000.0,<1H OCEAN
+-120.76,37.58,35.0,1395.0,264.0,756.0,253.0,3.6181,178600.0,INLAND
+-119.01,35.39,29.0,1820.0,459.0,1134.0,419.0,1.8289,59400.0,INLAND
+-119.02,35.42,36.0,2044.0,447.0,1021.0,374.0,1.8472,57400.0,INLAND
+-121.35,38.56,16.0,2278.0,370.0,1203.0,371.0,5.0622,132400.0,INLAND
+-117.48,34.0,12.0,6751.0,1153.0,3266.0,1134.0,3.8529,145500.0,INLAND
+-122.94,38.5,46.0,2280.0,492.0,807.0,366.0,2.6316,117000.0,<1H OCEAN
+-121.26,37.97,41.0,2398.0,448.0,1143.0,444.0,3.0352,69800.0,INLAND
+-118.36,34.05,45.0,2283.0,,1093.0,475.0,2.5658,252000.0,<1H OCEAN
+-115.51,32.99,20.0,1402.0,287.0,1104.0,317.0,1.9088,63700.0,INLAND
+-122.46,37.79,52.0,2106.0,373.0,743.0,348.0,5.2909,500001.0,NEAR BAY
+-118.35,34.08,52.0,1801.0,313.0,714.0,293.0,4.6838,479000.0,<1H OCEAN
+-119.81,36.73,51.0,956.0,196.0,662.0,180.0,2.101,56700.0,INLAND
+-117.69,33.6,19.0,3562.0,439.0,1584.0,470.0,6.4211,288100.0,<1H OCEAN
+-121.98,37.33,30.0,3742.0,633.0,1721.0,631.0,6.1388,302400.0,<1H OCEAN
+-118.15,34.86,10.0,4597.0,1009.0,2227.0,821.0,2.6149,83500.0,INLAND
+-121.99,37.36,33.0,2321.0,480.0,1230.0,451.0,4.9091,270300.0,<1H OCEAN
+-117.64,34.11,16.0,2129.0,420.0,932.0,379.0,2.5868,146900.0,INLAND
+-117.96,33.82,32.0,2726.0,556.0,1513.0,531.0,3.7917,197400.0,<1H OCEAN
+-122.75,38.46,13.0,4323.0,1020.0,2566.0,728.0,3.0147,142800.0,<1H OCEAN
+-118.37,33.95,35.0,924.0,349.0,1376.0,358.0,2.2297,262500.0,<1H OCEAN
+-118.14,34.09,28.0,4164.0,1127.0,2934.0,1014.0,2.7483,218800.0,<1H OCEAN
+-120.96,37.64,41.0,1467.0,328.0,673.0,310.0,2.7917,90700.0,INLAND
+-121.81,37.33,4.0,5532.0,778.0,3651.0,770.0,7.2982,343000.0,<1H OCEAN
+-120.16,36.96,18.0,508.0,104.0,393.0,114.0,3.0,156300.0,INLAND
+-117.16,34.05,23.0,3215.0,462.0,1411.0,435.0,6.0701,149900.0,INLAND
+-117.89,34.12,35.0,1566.0,321.0,1396.0,317.0,4.05,141300.0,<1H OCEAN
+-121.13,37.47,37.0,1995.0,448.0,1559.0,443.0,2.1833,92700.0,INLAND
+-118.91,34.22,15.0,5644.0,757.0,2659.0,783.0,6.7559,312000.0,<1H OCEAN
+-122.49,37.69,35.0,2576.0,443.0,1273.0,433.0,4.7391,272800.0,NEAR OCEAN
+-118.28,34.11,52.0,2036.0,348.0,775.0,332.0,5.4122,397500.0,<1H OCEAN
+-118.19,34.12,52.0,679.0,132.0,483.0,163.0,4.2344,162500.0,<1H OCEAN
+-118.32,34.11,33.0,5135.0,1450.0,2404.0,1292.0,3.2462,435700.0,<1H OCEAN
+-117.22,34.15,9.0,2524.0,352.0,1139.0,362.0,6.2516,159300.0,INLAND
+-122.34,37.88,37.0,3061.0,930.0,2556.0,924.0,1.7375,350000.0,NEAR BAY
+-119.29,34.44,34.0,4314.0,878.0,2361.0,831.0,3.2279,243100.0,NEAR OCEAN
+-117.11,32.69,39.0,395.0,159.0,620.0,162.0,2.725,86500.0,NEAR OCEAN
+-118.13,33.79,44.0,2153.0,375.0,947.0,364.0,5.0072,236200.0,NEAR OCEAN
+-117.25,32.82,19.0,5255.0,762.0,1773.0,725.0,7.8013,474000.0,NEAR OCEAN
+-122.18,37.78,50.0,1642.0,322.0,713.0,284.0,3.2984,160700.0,NEAR BAY
+-119.71,34.42,52.0,1411.0,324.0,1091.0,306.0,4.1062,252900.0,<1H OCEAN
+-118.49,34.24,35.0,2707.0,446.0,1224.0,445.0,5.2939,244200.0,<1H OCEAN
+-118.14,33.86,44.0,1276.0,234.0,538.0,213.0,4.8667,218300.0,<1H OCEAN
+-119.07,35.34,16.0,4201.0,786.0,1667.0,724.0,4.8839,134100.0,INLAND
+-118.38,34.05,49.0,702.0,,458.0,187.0,4.8958,333600.0,<1H OCEAN
+-120.42,40.38,26.0,1652.0,313.0,762.0,280.0,2.4757,85600.0,INLAND
+-122.49,37.6,33.0,3507.0,669.0,1697.0,660.0,4.0795,270600.0,NEAR OCEAN
+-117.96,33.87,35.0,1972.0,367.0,1152.0,356.0,3.7222,187500.0,<1H OCEAN
+-117.36,34.28,18.0,3903.0,715.0,1388.0,428.0,4.2386,157200.0,INLAND
+-118.32,33.79,35.0,2924.0,658.0,1675.0,602.0,3.8287,279900.0,<1H OCEAN
+-121.26,38.12,27.0,1818.0,459.0,1182.0,428.0,1.8575,73800.0,INLAND
+-122.16,37.73,52.0,2260.0,416.0,994.0,412.0,4.1164,198200.0,NEAR BAY
+-122.32,37.91,34.0,2669.0,647.0,1341.0,555.0,2.6399,119600.0,NEAR BAY
+-124.16,40.79,52.0,1264.0,277.0,591.0,284.0,1.7778,76900.0,NEAR OCEAN
+-121.92,37.68,23.0,1655.0,223.0,706.0,219.0,7.2211,291900.0,<1H OCEAN
+-119.01,35.32,23.0,4870.0,965.0,2717.0,928.0,2.596,70000.0,INLAND
+-117.73,34.08,33.0,1350.0,265.0,1251.0,257.0,2.9063,115200.0,INLAND
+-118.72,34.28,18.0,2229.0,371.0,1283.0,379.0,5.5955,217700.0,<1H OCEAN
+-119.84,36.79,21.0,3235.0,648.0,1820.0,614.0,3.3447,71400.0,INLAND
+-122.15,37.68,30.0,2261.0,443.0,929.0,383.0,4.2841,213400.0,NEAR BAY
+-117.37,33.95,32.0,2215.0,351.0,771.0,311.0,4.3542,142600.0,INLAND
+-119.67,36.83,3.0,2029.0,336.0,1003.0,340.0,4.4356,111300.0,INLAND
+-117.24,34.15,26.0,2041.0,293.0,936.0,375.0,6.0,140200.0,INLAND
+-121.32,38.21,27.0,2643.0,467.0,1455.0,444.0,3.6398,146700.0,INLAND
+-117.12,32.76,17.0,1559.0,462.0,821.0,428.0,2.0139,150000.0,NEAR OCEAN
+-124.13,40.55,38.0,544.0,,240.0,91.0,3.25,94800.0,<1H OCEAN
+-118.38,33.95,29.0,1821.0,588.0,1397.0,523.0,2.5833,187500.0,<1H OCEAN
+-122.29,37.92,35.0,583.0,88.0,235.0,84.0,5.943,288200.0,NEAR BAY
+-122.41,37.8,52.0,1288.0,309.0,437.0,272.0,6.3245,500001.0,NEAR BAY
+-119.32,36.2,25.0,1427.0,246.0,772.0,221.0,2.2262,64500.0,INLAND
+-121.65,39.16,16.0,5022.0,1103.0,2087.0,956.0,2.3963,114800.0,INLAND
+-122.02,37.88,16.0,3031.0,438.0,1087.0,421.0,7.3732,287300.0,NEAR BAY
+-122.24,37.79,52.0,674.0,180.0,647.0,168.0,3.375,116100.0,NEAR BAY
+-121.34,38.66,18.0,4164.0,963.0,2032.0,898.0,2.119,133100.0,INLAND
+-117.09,32.64,38.0,2095.0,536.0,1240.0,550.0,2.7218,145900.0,NEAR OCEAN
+-117.04,32.62,27.0,1710.0,282.0,1089.0,297.0,4.6793,151900.0,NEAR OCEAN
+-117.95,34.08,34.0,2278.0,476.0,1728.0,448.0,3.125,154100.0,<1H OCEAN
+-118.36,33.98,45.0,1559.0,305.0,891.0,341.0,4.4038,259400.0,<1H OCEAN
+-123.98,40.88,41.0,1719.0,372.0,844.0,336.0,2.6923,84200.0,<1H OCEAN
+-118.28,34.26,32.0,1079.0,207.0,486.0,167.0,4.9833,213000.0,<1H OCEAN
+-117.24,32.82,20.0,2467.0,332.0,731.0,335.0,7.2559,392300.0,NEAR OCEAN
+-118.17,34.09,45.0,1327.0,271.0,1069.0,284.0,3.3977,153800.0,<1H OCEAN
+-118.31,34.06,24.0,1336.0,453.0,1268.0,426.0,2.8202,500001.0,<1H OCEAN
+-122.07,37.59,13.0,2578.0,551.0,1680.0,528.0,4.825,222000.0,NEAR BAY
+-118.06,33.73,16.0,4392.0,602.0,1490.0,578.0,10.5424,500001.0,<1H OCEAN
+-122.23,38.09,26.0,4397.0,997.0,2539.0,965.0,2.4875,90000.0,NEAR BAY
+-122.58,38.38,27.0,3800.0,728.0,1587.0,605.0,4.7237,306600.0,<1H OCEAN
+-121.06,37.45,33.0,1401.0,299.0,915.0,282.0,3.4464,162500.0,INLAND
+-118.37,34.21,34.0,2272.0,558.0,2164.0,484.0,3.7143,175700.0,<1H OCEAN
+-122.19,37.44,39.0,4402.0,618.0,1616.0,631.0,8.9955,500001.0,NEAR OCEAN
+-119.28,35.99,20.0,2911.0,597.0,1746.0,588.0,1.7372,51000.0,INLAND
+-118.14,33.78,44.0,2101.0,496.0,1038.0,500.0,3.108,217900.0,NEAR OCEAN
+-118.19,33.85,45.0,1167.0,302.0,773.0,287.0,3.2798,150300.0,NEAR OCEAN
+-119.08,36.22,28.0,1606.0,320.0,1158.0,317.0,3.0324,55600.0,INLAND
+-122.5,37.79,52.0,8.0,1.0,13.0,1.0,15.0001,500001.0,NEAR BAY
+-122.29,37.88,52.0,1650.0,395.0,841.0,380.0,3.556,179300.0,NEAR BAY
+-118.31,33.88,32.0,2421.0,671.0,1491.0,587.0,3.5644,242300.0,<1H OCEAN
+-120.26,37.11,33.0,1097.0,254.0,627.0,253.0,1.2794,50700.0,INLAND
+-122.23,37.8,52.0,783.0,184.0,488.0,186.0,1.9375,126600.0,NEAR BAY
+-117.07,32.77,34.0,2245.0,394.0,1849.0,429.0,3.5446,185500.0,NEAR OCEAN
+-118.04,33.87,17.0,2358.0,396.0,1387.0,364.0,6.299,285800.0,<1H OCEAN
+-117.96,34.05,32.0,1993.0,388.0,1385.0,380.0,3.7258,181900.0,<1H OCEAN
+-120.83,39.02,15.0,1117.0,242.0,551.0,229.0,2.6319,97700.0,INLAND
+-117.6,34.08,15.0,2700.0,460.0,1432.0,449.0,4.9063,159800.0,INLAND
+-117.07,33.02,17.0,2863.0,665.0,715.0,467.0,2.6048,148200.0,<1H OCEAN
+-120.64,40.41,50.0,1741.0,424.0,987.0,383.0,1.5066,59300.0,INLAND
+-120.98,37.69,18.0,3176.0,468.0,1296.0,471.0,5.5684,185100.0,INLAND
+-117.06,32.77,34.0,1730.0,373.0,730.0,350.0,2.0284,161800.0,NEAR OCEAN
+-117.88,33.75,10.0,1823.0,590.0,2176.0,548.0,1.5026,151800.0,<1H OCEAN
+-121.27,38.13,40.0,2402.0,509.0,1197.0,486.0,2.1771,98200.0,INLAND
+-118.08,33.86,17.0,2259.0,383.0,1378.0,386.0,5.8733,287000.0,<1H OCEAN
+-118.03,33.94,34.0,1748.0,386.0,917.0,378.0,3.4792,169000.0,<1H OCEAN
+-120.48,37.3,49.0,2919.0,719.0,1956.0,679.0,1.5427,88500.0,INLAND
+-122.04,37.99,32.0,1504.0,279.0,749.0,267.0,3.2,134500.0,NEAR BAY
+-118.55,34.21,35.0,2592.0,490.0,1427.0,434.0,5.0623,246400.0,<1H OCEAN
+-118.31,33.98,44.0,222.0,54.0,234.0,77.0,5.1136,111700.0,<1H OCEAN
+-122.35,37.58,30.0,5039.0,1564.0,2129.0,1536.0,3.3469,345000.0,NEAR OCEAN
+-121.92,37.26,33.0,1306.0,259.0,762.0,237.0,4.5208,230700.0,<1H OCEAN
+-118.46,34.42,25.0,2988.0,525.0,1884.0,513.0,4.7007,169500.0,<1H OCEAN
+-117.25,32.78,36.0,1527.0,427.0,710.0,312.0,2.7857,291700.0,NEAR OCEAN
+-122.2,37.79,40.0,1060.0,256.0,667.0,235.0,4.1739,169600.0,NEAR BAY
+-117.24,33.2,26.0,1701.0,404.0,989.0,367.0,2.5119,171700.0,<1H OCEAN
+-117.34,33.98,10.0,17286.0,4952.0,9851.0,4616.0,1.7579,103400.0,INLAND
+-118.16,34.14,27.0,1551.0,254.0,816.0,233.0,8.2435,500001.0,<1H OCEAN
+-121.92,37.27,33.0,3280.0,569.0,1583.0,559.0,4.5625,253500.0,<1H OCEAN
+-121.12,39.2,9.0,1431.0,254.0,681.0,221.0,3.045,170400.0,INLAND
+-120.68,35.14,34.0,3100.0,617.0,1155.0,542.0,3.0938,245900.0,NEAR OCEAN
+-118.16,33.72,29.0,2743.0,708.0,1059.0,651.0,3.625,500000.0,NEAR OCEAN
+-122.25,38.17,34.0,778.0,137.0,406.0,136.0,4.2955,121300.0,NEAR BAY
+-117.97,33.76,28.0,1386.0,272.0,901.0,294.0,4.7464,187500.0,<1H OCEAN
+-118.32,34.18,49.0,192.0,41.0,83.0,38.0,3.0179,118800.0,<1H OCEAN
+-117.31,34.08,40.0,2011.0,495.0,1528.0,469.0,1.9375,69900.0,INLAND
+-119.46,35.13,46.0,2745.0,543.0,1423.0,482.0,2.1955,26900.0,INLAND
+-123.81,39.34,17.0,1981.0,371.0,773.0,325.0,3.1563,277000.0,<1H OCEAN
+-118.98,35.36,29.0,1244.0,266.0,933.0,227.0,1.6981,49400.0,INLAND
+-122.09,37.36,37.0,1550.0,238.0,805.0,250.0,5.0222,500001.0,NEAR BAY
+-118.22,34.06,52.0,48.0,6.0,41.0,10.0,10.2264,112500.0,<1H OCEAN
+-118.31,34.02,44.0,1555.0,324.0,931.0,265.0,1.4712,105800.0,<1H OCEAN
+-117.95,33.69,26.0,1417.0,264.0,817.0,261.0,4.875,230400.0,<1H OCEAN
+-119.1,36.19,17.0,1564.0,396.0,713.0,362.0,1.6186,77100.0,INLAND
+-117.09,34.01,37.0,106.0,18.0,27.0,12.0,4.0556,131300.0,INLAND
+-119.75,36.73,39.0,2290.0,539.0,1685.0,536.0,1.6325,52100.0,INLAND
+-121.3,38.69,13.0,2135.0,429.0,779.0,432.0,3.6995,134900.0,INLAND
+-118.28,33.74,44.0,1853.0,556.0,2090.0,539.0,1.8584,248100.0,NEAR OCEAN
+-117.0,32.73,17.0,6050.0,1143.0,3424.0,1131.0,3.7647,127600.0,<1H OCEAN
+-120.93,35.76,11.0,8997.0,1698.0,1825.0,756.0,3.23,154300.0,<1H OCEAN
+-117.18,34.07,14.0,1258.0,245.0,752.0,264.0,3.3924,97400.0,INLAND
+-118.34,33.98,45.0,1298.0,294.0,1064.0,268.0,3.7067,136600.0,<1H OCEAN
+-118.31,33.82,39.0,2198.0,425.0,1160.0,436.0,4.1406,323700.0,<1H OCEAN
+-117.02,32.76,15.0,1204.0,326.0,543.0,326.0,1.0278,154200.0,<1H OCEAN
+-117.08,32.76,27.0,1221.0,254.0,606.0,259.0,3.0833,155400.0,NEAR OCEAN
+-119.42,35.4,24.0,2585.0,480.0,1442.0,424.0,2.8452,104700.0,INLAND
+-122.22,37.44,32.0,4281.0,501.0,1318.0,484.0,15.0001,500001.0,NEAR OCEAN
+-122.17,37.77,39.0,1612.0,342.0,912.0,322.0,3.3958,141900.0,NEAR BAY
+-122.44,37.8,52.0,3830.0,,1310.0,963.0,3.4801,500001.0,NEAR BAY
+-121.19,39.05,14.0,1131.0,193.0,520.0,178.0,3.9,180400.0,INLAND
+-116.96,33.79,21.0,2990.0,691.0,2108.0,660.0,2.0135,83000.0,INLAND
+-121.84,37.38,33.0,835.0,181.0,781.0,169.0,5.1082,195800.0,<1H OCEAN
+-117.28,32.84,41.0,1420.0,338.0,640.0,314.0,2.9306,360300.0,NEAR OCEAN
+-118.16,33.97,30.0,2419.0,715.0,3208.0,719.0,2.1743,176000.0,<1H OCEAN
+-122.33,37.89,42.0,1342.0,291.0,551.0,266.0,4.5268,207400.0,NEAR BAY
+-122.2,37.78,50.0,1867.0,403.0,1128.0,378.0,2.5401,129100.0,NEAR BAY
+-118.19,34.14,46.0,2387.0,488.0,1181.0,456.0,3.6058,257900.0,<1H OCEAN
+-117.82,33.9,25.0,1137.0,170.0,524.0,164.0,7.5744,259300.0,<1H OCEAN
+-120.59,37.35,15.0,3249.0,613.0,1569.0,595.0,3.5393,88000.0,INLAND
+-122.24,37.75,27.0,4051.0,753.0,1499.0,797.0,4.8711,286600.0,NEAR BAY
+-121.39,38.58,41.0,2577.0,365.0,913.0,339.0,6.3406,448300.0,INLAND
+-122.0,37.28,32.0,2782.0,495.0,1092.0,455.0,5.4103,335900.0,<1H OCEAN
+-122.45,37.77,52.0,3939.0,852.0,1737.0,797.0,4.5052,500001.0,NEAR BAY
+-117.72,34.08,34.0,2742.0,491.0,1761.0,496.0,3.2481,128800.0,INLAND
+-121.88,37.33,41.0,395.0,164.0,549.0,184.0,2.375,175000.0,<1H OCEAN
+-122.25,40.17,19.0,3182.0,630.0,1741.0,642.0,1.9727,64900.0,INLAND
+-117.79,33.75,26.0,2893.0,345.0,983.0,326.0,13.466,500001.0,<1H OCEAN
+-117.39,34.07,15.0,1966.0,331.0,1118.0,323.0,3.8558,122700.0,INLAND
+-118.32,34.09,27.0,210.0,98.0,332.0,112.0,2.5556,175000.0,<1H OCEAN
+-120.48,35.02,17.0,2721.0,477.0,1672.0,492.0,2.9798,204800.0,<1H OCEAN
+-121.81,37.99,22.0,2331.0,359.0,1086.0,340.0,5.1435,150800.0,INLAND
+-118.09,33.78,26.0,2146.0,298.0,852.0,296.0,6.6137,342700.0,<1H OCEAN
+-122.0,37.37,16.0,1434.0,372.0,804.0,361.0,3.7045,178100.0,<1H OCEAN
+-119.76,36.73,46.0,1347.0,282.0,854.0,267.0,1.8723,52600.0,INLAND
+-117.96,34.06,31.0,2017.0,462.0,1462.0,457.0,2.067,167300.0,<1H OCEAN
+-118.42,33.78,36.0,2093.0,303.0,802.0,300.0,8.0957,500001.0,NEAR OCEAN
+-117.97,33.84,18.0,1063.0,209.0,462.0,223.0,2.8348,219000.0,<1H OCEAN
+-119.75,34.4,31.0,1997.0,299.0,826.0,301.0,6.8927,500001.0,NEAR OCEAN
+-120.76,37.65,25.0,3214.0,682.0,2319.0,640.0,2.0385,84300.0,INLAND
+-122.02,38.38,16.0,808.0,137.0,371.0,145.0,6.0767,216400.0,INLAND
+-122.19,37.46,34.0,5419.0,1183.0,2002.0,1138.0,4.1985,500001.0,NEAR BAY
+-118.29,34.0,44.0,1753.0,387.0,1165.0,380.0,2.1354,105800.0,<1H OCEAN
+-120.43,34.9,27.0,2019.0,354.0,1029.0,346.0,3.5391,144700.0,<1H OCEAN
+-118.14,33.84,36.0,3002.0,484.0,1322.0,471.0,4.933,228900.0,<1H OCEAN
+-118.01,33.94,36.0,1921.0,329.0,969.0,327.0,4.9191,188700.0,<1H OCEAN
+-121.97,36.97,15.0,2849.0,668.0,1546.0,582.0,2.7587,228600.0,NEAR OCEAN
+-118.17,33.79,32.0,2171.0,672.0,3002.0,648.0,2.375,139700.0,NEAR OCEAN
+-121.48,38.59,43.0,987.0,240.0,1253.0,237.0,0.9204,82100.0,INLAND
+-122.34,37.59,44.0,1395.0,269.0,736.0,288.0,5.6206,386400.0,NEAR OCEAN
+-121.02,37.62,14.0,5737.0,1286.0,4722.0,1210.0,1.6731,95800.0,INLAND
+-122.41,37.74,52.0,1842.0,339.0,1032.0,357.0,5.5563,250800.0,NEAR BAY
+-122.49,37.7,36.0,1946.0,340.0,828.0,313.0,5.2811,287700.0,NEAR OCEAN
+-118.08,34.14,42.0,2690.0,589.0,1149.0,535.0,3.88,281100.0,INLAND
+-121.49,38.52,37.0,1902.0,413.0,955.0,384.0,3.1014,96800.0,INLAND
+-118.18,34.14,38.0,3039.0,487.0,1131.0,465.0,7.7116,360900.0,<1H OCEAN
+-117.84,34.12,25.0,3465.0,566.0,1722.0,536.0,4.8304,228900.0,INLAND
+-117.07,32.71,39.0,2754.0,652.0,2263.0,619.0,2.2454,89600.0,NEAR OCEAN
+-121.16,38.68,21.0,3517.0,667.0,1687.0,655.0,3.1161,131600.0,INLAND
+-123.21,39.14,15.0,2235.0,545.0,1376.0,516.0,1.9032,100000.0,<1H OCEAN
+-122.34,37.96,15.0,6487.0,1717.0,3408.0,1560.0,2.1991,133300.0,NEAR BAY
+-118.06,34.17,38.0,2726.0,398.0,1059.0,380.0,7.2419,410400.0,INLAND
+-121.54,36.7,12.0,6758.0,1241.0,3918.0,1100.0,3.525,201700.0,<1H OCEAN
+-123.73,39.17,20.0,4620.0,1042.0,1745.0,794.0,2.375,158800.0,NEAR OCEAN
+-118.11,34.16,52.0,3158.0,459.0,1229.0,444.0,5.4223,325600.0,INLAND
+-118.25,34.01,30.0,962.0,291.0,1280.0,263.0,1.4464,110200.0,<1H OCEAN
+-120.98,37.66,40.0,3012.0,616.0,1423.0,595.0,2.6346,100600.0,INLAND
+-118.25,33.79,34.0,1349.0,371.0,1716.0,380.0,2.7143,138100.0,NEAR OCEAN
+-118.45,34.19,37.0,1073.0,254.0,739.0,253.0,2.4667,192200.0,<1H OCEAN
+-117.86,33.8,35.0,1683.0,347.0,1242.0,335.0,3.5172,190400.0,<1H OCEAN
+-121.93,37.27,35.0,1855.0,345.0,985.0,329.0,6.0196,255100.0,<1H OCEAN
+-121.89,37.68,22.0,1898.0,239.0,734.0,245.0,6.2918,334100.0,<1H OCEAN
+-118.34,33.8,34.0,1730.0,427.0,1008.0,393.0,3.9408,327700.0,<1H OCEAN
+-122.15,37.69,38.0,1246.0,221.0,637.0,222.0,3.6625,184600.0,NEAR BAY
+-118.39,33.86,34.0,2361.0,442.0,915.0,437.0,5.687,392400.0,<1H OCEAN
+-122.86,39.08,24.0,3127.0,674.0,1015.0,448.0,2.0417,78800.0,INLAND
+-117.07,32.6,18.0,2602.0,551.0,1042.0,550.0,1.9267,67500.0,NEAR OCEAN
+-119.79,36.72,19.0,1719.0,391.0,1369.0,368.0,1.25,53000.0,INLAND
+-122.22,37.8,52.0,2183.0,465.0,1129.0,460.0,3.2632,227700.0,NEAR BAY
+-122.26,38.36,25.0,1821.0,344.0,349.0,179.0,6.9931,398800.0,INLAND
+-117.82,33.78,28.0,4485.0,667.0,2048.0,685.0,5.4562,274700.0,<1H OCEAN
+-122.42,37.77,52.0,1176.0,493.0,1136.0,436.0,1.375,312500.0,NEAR BAY
+-122.48,37.76,48.0,2304.0,558.0,1273.0,512.0,3.275,332100.0,NEAR BAY
+-117.86,33.75,31.0,1761.0,515.0,1810.0,468.0,1.9309,173400.0,<1H OCEAN
+-117.91,33.69,30.0,2704.0,426.0,1289.0,423.0,5.2815,229500.0,<1H OCEAN
+-118.55,34.04,41.0,1482.0,239.0,617.0,242.0,8.8619,500001.0,<1H OCEAN
+-117.95,33.92,18.0,2825.0,660.0,1590.0,643.0,3.6106,153600.0,<1H OCEAN
+-122.12,37.68,37.0,2412.0,394.0,975.0,375.0,4.0417,191100.0,NEAR BAY
+-118.6,34.23,19.0,8866.0,2355.0,5005.0,2194.0,3.2564,230300.0,<1H OCEAN
+-122.1,37.66,37.0,901.0,191.0,599.0,206.0,3.7303,149700.0,NEAR BAY
+-122.08,37.71,35.0,2211.0,350.0,1004.0,365.0,5.4639,238600.0,NEAR BAY
+-118.27,33.95,29.0,1579.0,351.0,1056.0,322.0,2.3056,98500.0,<1H OCEAN
+-117.63,33.94,36.0,447.0,95.0,2886.0,85.0,4.2578,183300.0,INLAND
+-118.36,33.8,34.0,2629.0,369.0,966.0,375.0,10.1241,500001.0,NEAR OCEAN
+-121.97,37.01,21.0,2073.0,357.0,1044.0,351.0,4.5682,371600.0,NEAR OCEAN
+-120.46,37.33,17.0,6111.0,1171.0,2950.0,1104.0,3.2852,98800.0,INLAND
+-117.91,33.84,16.0,919.0,253.0,912.0,249.0,1.5903,165400.0,<1H OCEAN
+-122.3,37.9,30.0,1772.0,471.0,880.0,437.0,2.2672,162500.0,NEAR BAY
+-117.08,32.62,16.0,5192.0,1381.0,3261.0,1321.0,2.2685,151900.0,NEAR OCEAN
+-122.27,38.12,45.0,4423.0,1001.0,2109.0,874.0,2.6937,111800.0,NEAR BAY
+-118.28,33.88,19.0,2758.0,675.0,2047.0,591.0,2.6618,179700.0,<1H OCEAN
+-117.04,32.68,9.0,3087.0,609.0,1530.0,556.0,3.775,125000.0,NEAR OCEAN
+-122.43,37.79,50.0,3312.0,1095.0,1475.0,997.0,2.7165,500001.0,NEAR BAY
+-119.69,35.62,18.0,820.0,239.0,1345.0,207.0,2.1186,47500.0,INLAND
+-118.12,34.09,25.0,4870.0,1371.0,3518.0,1296.0,3.2307,188400.0,<1H OCEAN
+-118.58,34.21,24.0,2642.0,696.0,1649.0,633.0,3.0187,217700.0,<1H OCEAN
+-120.72,38.24,32.0,2685.0,543.0,1061.0,492.0,2.5473,101600.0,INLAND
+-123.83,41.88,18.0,1504.0,357.0,660.0,258.0,3.13,116700.0,<1H OCEAN
+-121.44,38.56,45.0,2423.0,466.0,873.0,438.0,3.7167,131900.0,INLAND
+-118.19,34.02,45.0,1535.0,432.0,1820.0,419.0,1.7801,142800.0,<1H OCEAN
+-118.15,33.92,30.0,915.0,234.0,646.0,211.0,2.5208,182800.0,<1H OCEAN
+-117.66,35.62,11.0,5897.0,1138.0,2728.0,1072.0,4.15,85700.0,INLAND
+-118.39,34.17,40.0,1696.0,372.0,835.0,385.0,3.6563,222400.0,<1H OCEAN
+-117.5,33.92,28.0,2101.0,337.0,1061.0,348.0,4.55,146800.0,INLAND
+-118.92,35.47,6.0,1755.0,280.0,664.0,254.0,6.2885,216400.0,INLAND
+-121.58,38.81,25.0,778.0,135.0,340.0,155.0,1.7857,258300.0,INLAND
+-124.23,41.75,11.0,3159.0,616.0,1343.0,479.0,2.4805,73200.0,NEAR OCEAN
+-122.95,38.73,37.0,1548.0,328.0,863.0,287.0,2.9792,151300.0,<1H OCEAN
+-117.05,32.56,22.0,2172.0,563.0,2049.0,524.0,2.0159,139300.0,NEAR OCEAN
+-122.0,37.3,28.0,5096.0,1011.0,2588.0,954.0,5.357,355200.0,<1H OCEAN
+-118.08,33.94,21.0,3933.0,949.0,2219.0,820.0,2.4926,171400.0,<1H OCEAN
+-122.48,37.69,43.0,2661.0,455.0,1384.0,456.0,4.2421,257500.0,NEAR OCEAN
+-120.95,37.59,29.0,1727.0,439.0,1063.0,386.0,1.8929,63600.0,INLAND
+-117.21,34.51,17.0,4379.0,629.0,1720.0,595.0,5.086,148400.0,INLAND
+-118.12,33.86,44.0,2663.0,511.0,1277.0,462.0,4.3194,199500.0,<1H OCEAN
+-117.18,34.05,52.0,1820.0,342.0,601.0,315.0,2.6129,137000.0,INLAND
+-121.52,38.49,5.0,3344.0,800.0,1341.0,670.0,3.6196,152800.0,INLAND
+-121.99,37.81,17.0,465.0,83.0,146.0,75.0,4.9018,188500.0,<1H OCEAN
+-121.48,38.58,48.0,2434.0,744.0,1281.0,662.0,1.6277,140600.0,INLAND
+-118.22,34.06,34.0,1083.0,364.0,1132.0,338.0,2.2344,153100.0,<1H OCEAN
+-117.27,33.08,7.0,2949.0,447.0,1335.0,426.0,6.0922,342400.0,NEAR OCEAN
+-117.97,33.81,26.0,4022.0,1081.0,2457.0,1001.0,2.8042,206300.0,<1H OCEAN
+-117.81,33.82,22.0,2898.0,335.0,1057.0,324.0,10.8111,500001.0,<1H OCEAN
+-122.46,37.66,36.0,2535.0,451.0,1390.0,436.0,5.3398,260900.0,NEAR OCEAN
+-117.92,33.82,10.0,1548.0,506.0,1535.0,424.0,4.5057,152400.0,<1H OCEAN
+-116.46,33.78,25.0,1137.0,414.0,604.0,240.0,1.3801,55000.0,INLAND
+-117.81,33.66,20.0,2851.0,490.0,1192.0,463.0,5.8752,274200.0,<1H OCEAN
+-116.94,32.83,38.0,1701.0,317.0,872.0,304.0,3.7831,147800.0,<1H OCEAN
+-121.48,38.56,50.0,1587.0,448.0,877.0,380.0,2.0833,94300.0,INLAND
+-118.39,34.03,39.0,1366.0,375.0,1237.0,370.0,3.7206,230900.0,<1H OCEAN
+-118.62,34.15,26.0,5661.0,791.0,2493.0,780.0,7.9814,409900.0,<1H OCEAN
+-119.06,35.94,18.0,3501.0,721.0,2009.0,660.0,2.6576,65700.0,INLAND
+-117.65,33.45,15.0,7468.0,1275.0,3033.0,1217.0,5.49,239300.0,NEAR OCEAN
+-118.4,33.88,42.0,1516.0,341.0,634.0,327.0,6.2356,472700.0,<1H OCEAN
+-118.07,33.72,24.0,1240.0,296.0,513.0,254.0,4.9044,485000.0,NEAR OCEAN
+-122.31,37.56,52.0,2351.0,494.0,1126.0,482.0,3.9688,356900.0,NEAR OCEAN
+-122.47,37.76,40.0,3525.0,941.0,1675.0,857.0,3.2083,330000.0,NEAR BAY
+-118.47,34.0,41.0,2331.0,636.0,1839.0,537.0,2.288,263500.0,<1H OCEAN
+-122.41,37.81,25.0,1178.0,545.0,592.0,441.0,3.6728,500001.0,NEAR BAY
+-121.95,37.78,4.0,14652.0,2826.0,5613.0,2579.0,6.3942,356700.0,<1H OCEAN
+-119.18,34.19,19.0,2393.0,,1938.0,762.0,1.6953,167400.0,NEAR OCEAN
+-118.41,34.02,35.0,1728.0,442.0,1161.0,420.0,3.725,310000.0,<1H OCEAN
+-116.93,33.93,13.0,7804.0,1594.0,3297.0,1469.0,2.0549,95600.0,INLAND
+-119.82,34.43,15.0,1482.0,345.0,669.0,379.0,3.0773,112500.0,NEAR OCEAN
+-121.67,37.99,22.0,1046.0,195.0,527.0,164.0,4.375,213500.0,INLAND
+-118.52,34.2,37.0,1795.0,346.0,1082.0,354.0,4.9102,207200.0,<1H OCEAN
+-122.04,38.25,37.0,1176.0,291.0,648.0,271.0,2.7167,92200.0,INLAND
+-118.13,34.69,34.0,2156.0,397.0,1269.0,388.0,2.75,96800.0,INLAND
+-118.12,34.15,35.0,1760.0,447.0,984.0,384.0,3.4167,198200.0,<1H OCEAN
+-121.33,37.97,43.0,1511.0,292.0,721.0,320.0,3.5703,87400.0,INLAND
+-117.55,33.9,21.0,1839.0,324.0,871.0,307.0,3.4459,198800.0,INLAND
+-118.11,33.86,36.0,2750.0,487.0,1386.0,458.0,4.9904,221700.0,<1H OCEAN
+-121.97,37.3,31.0,3340.0,735.0,1891.0,686.0,4.8542,275000.0,<1H OCEAN
+-117.25,32.72,36.0,2632.0,450.0,2038.0,419.0,6.5319,345800.0,NEAR OCEAN
+-117.9,34.04,15.0,11989.0,2185.0,6652.0,2081.0,4.5554,278300.0,<1H OCEAN
+-122.41,37.76,52.0,1427.0,281.0,620.0,236.0,1.9944,262500.0,NEAR BAY
+-121.11,37.76,22.0,2606.0,411.0,1252.0,397.0,4.1833,192100.0,INLAND
+-119.11,35.38,37.0,2044.0,394.0,894.0,359.0,2.9453,82800.0,INLAND
+-121.98,37.96,12.0,5048.0,1122.0,2209.0,1014.0,3.1573,126700.0,INLAND
+-120.38,36.15,17.0,2279.0,448.0,1200.0,420.0,2.7461,70000.0,INLAND
+-118.15,34.1,39.0,3856.0,867.0,1847.0,830.0,3.4559,364900.0,<1H OCEAN
+-121.84,39.73,52.0,677.0,152.0,379.0,154.0,1.6797,94800.0,INLAND
+-118.15,34.05,33.0,3287.0,649.0,1783.0,653.0,3.8472,293300.0,<1H OCEAN
+-121.01,37.63,41.0,2764.0,639.0,2122.0,600.0,1.9643,74900.0,INLAND
+-120.04,34.72,13.0,3942.0,585.0,1542.0,515.0,6.6054,500001.0,NEAR OCEAN
+-117.82,33.82,22.0,3173.0,372.0,1181.0,355.0,8.3637,500001.0,<1H OCEAN
+-122.27,37.85,47.0,1375.0,307.0,843.0,319.0,1.3785,142300.0,NEAR BAY
+-117.24,32.74,43.0,2216.0,375.0,918.0,388.0,5.5289,297700.0,NEAR OCEAN
+-120.97,38.0,27.0,1683.0,288.0,873.0,258.0,4.7069,176900.0,INLAND
+-118.01,33.75,30.0,3380.0,722.0,2269.0,652.0,4.525,186000.0,<1H OCEAN
+-117.23,32.75,5.0,1824.0,,892.0,426.0,3.4286,137500.0,NEAR OCEAN
+-118.95,35.26,24.0,1341.0,214.0,667.0,184.0,4.0,94500.0,INLAND
+-117.97,33.77,22.0,2244.0,575.0,1543.0,533.0,2.6618,179600.0,<1H OCEAN
+-117.91,34.09,20.0,4327.0,1037.0,2296.0,963.0,3.0441,185400.0,<1H OCEAN
+-118.99,35.4,48.0,1908.0,331.0,789.0,321.0,3.5714,84600.0,INLAND
+-122.49,38.22,33.0,1486.0,290.0,781.0,274.0,3.5647,251800.0,NEAR BAY
+-121.86,36.58,20.0,6332.0,991.0,2668.0,955.0,5.7578,347700.0,<1H OCEAN
+-117.8,34.06,34.0,1081.0,205.0,1325.0,252.0,3.6298,108500.0,INLAND
+-120.29,38.01,12.0,3014.0,560.0,1424.0,485.0,3.0729,105100.0,INLAND
+-115.64,32.75,19.0,377.0,69.0,198.0,55.0,1.625,112500.0,INLAND
+-117.75,33.6,5.0,4944.0,1164.0,1727.0,948.0,4.9,255600.0,<1H OCEAN
+-121.33,37.98,10.0,1564.0,397.0,643.0,347.0,2.7031,150000.0,INLAND
+-116.97,32.75,28.0,3519.0,583.0,1720.0,590.0,4.7973,186900.0,<1H OCEAN
+-118.5,34.05,39.0,1487.0,163.0,414.0,160.0,15.0,500001.0,<1H OCEAN
+-118.36,33.86,36.0,681.0,122.0,360.0,128.0,5.2799,332600.0,<1H OCEAN
+-122.34,37.99,42.0,1531.0,326.0,1271.0,377.0,2.6167,85100.0,NEAR BAY
+-121.35,38.66,24.0,3313.0,769.0,1631.0,681.0,2.5556,105700.0,INLAND
+-122.42,37.75,52.0,2164.0,533.0,1122.0,469.0,3.2632,306000.0,NEAR BAY
+-122.33,38.21,33.0,2017.0,370.0,949.0,342.0,4.625,228600.0,NEAR BAY
+-118.31,33.96,47.0,2005.0,392.0,1134.0,415.0,3.7143,140300.0,<1H OCEAN
+-118.18,34.1,8.0,1116.0,267.0,435.0,235.0,4.9231,230900.0,<1H OCEAN
+-118.2,34.11,37.0,2040.0,611.0,1698.0,545.0,1.9355,166300.0,<1H OCEAN
+-116.96,32.9,16.0,3047.0,495.0,1507.0,499.0,5.3008,186500.0,<1H OCEAN
+-117.13,32.76,29.0,2568.0,682.0,1191.0,642.0,2.1094,162500.0,NEAR OCEAN
+-121.49,38.55,52.0,2515.0,460.0,836.0,442.0,3.3844,151100.0,INLAND
+-119.34,36.32,14.0,1204.0,227.0,633.0,247.0,3.925,83800.0,INLAND
+-118.42,34.01,29.0,1996.0,489.0,960.0,449.0,3.6611,344200.0,<1H OCEAN
+-122.19,37.74,43.0,707.0,147.0,417.0,155.0,2.5139,83400.0,NEAR BAY
+-118.34,34.86,11.0,7353.0,1482.0,3571.0,1308.0,2.8097,130000.0,INLAND
+-118.32,33.9,37.0,1664.0,401.0,1316.0,409.0,3.0526,216400.0,<1H OCEAN
+-121.93,37.54,25.0,1354.0,192.0,596.0,220.0,6.629,352400.0,<1H OCEAN
+-117.84,33.74,24.0,1752.0,407.0,910.0,427.0,3.3611,134600.0,<1H OCEAN
+-122.49,37.73,37.0,1399.0,224.0,530.0,235.0,3.9219,433300.0,NEAR OCEAN
+-122.42,37.74,52.0,1674.0,346.0,734.0,335.0,3.8864,281300.0,NEAR BAY
+-118.31,34.1,34.0,399.0,141.0,482.0,134.0,1.625,67500.0,<1H OCEAN
+-117.69,33.52,3.0,7374.0,1444.0,3214.0,1279.0,4.538,278200.0,<1H OCEAN
+-121.85,37.4,23.0,1793.0,319.0,1145.0,310.0,5.5968,243200.0,<1H OCEAN
+-115.52,32.67,6.0,2804.0,581.0,2807.0,594.0,2.0625,67700.0,INLAND
+-122.4,40.51,20.0,1750.0,352.0,834.0,340.0,2.485,100600.0,INLAND
+-121.59,37.01,16.0,6637.0,1171.0,3575.0,1162.0,4.3227,251500.0,INLAND
+-118.91,35.24,29.0,2888.0,753.0,2949.0,699.0,1.7716,45500.0,INLAND
+-122.25,40.42,17.0,1429.0,265.0,692.0,245.0,2.8611,98700.0,INLAND
+-118.27,34.25,35.0,779.0,143.0,371.0,150.0,4.6635,230100.0,<1H OCEAN
+-122.36,37.96,39.0,246.0,57.0,316.0,52.0,0.716,104200.0,NEAR BAY
+-121.9,36.93,22.0,7281.0,1233.0,1849.0,832.0,5.3276,335500.0,NEAR OCEAN
+-117.41,34.58,10.0,2964.0,668.0,1853.0,609.0,1.6047,73400.0,INLAND
+-120.85,38.69,18.0,5928.0,1097.0,2697.0,1096.0,3.4872,141400.0,INLAND
+-118.16,33.89,38.0,483.0,113.0,389.0,108.0,2.1859,143800.0,<1H OCEAN
+-121.3,38.0,23.0,3706.0,1106.0,3785.0,1019.0,1.7774,100000.0,INLAND
+-121.9,37.34,50.0,1345.0,287.0,791.0,254.0,3.5966,245800.0,<1H OCEAN
+-120.69,39.12,19.0,1048.0,262.0,493.0,184.0,2.2917,118200.0,INLAND
+-118.16,34.07,47.0,2994.0,543.0,1651.0,561.0,3.8644,241500.0,<1H OCEAN
+-118.24,34.13,45.0,1971.0,439.0,1245.0,430.0,4.0272,260500.0,<1H OCEAN
+-118.2,33.77,24.0,2404.0,819.0,1566.0,753.0,1.5076,145800.0,NEAR OCEAN
+-115.51,33.12,21.0,1024.0,218.0,890.0,232.0,2.101,46700.0,INLAND
+-117.09,32.9,16.0,1989.0,290.0,814.0,291.0,6.2715,255100.0,<1H OCEAN
+-122.46,37.78,52.0,2632.0,542.0,1364.0,544.0,3.4605,441700.0,NEAR BAY
+-119.73,34.42,25.0,2024.0,312.0,907.0,335.0,5.4127,392800.0,NEAR OCEAN
+-119.12,36.05,27.0,1575.0,321.0,1063.0,317.0,2.1477,53900.0,INLAND
+-118.29,33.72,39.0,2651.0,590.0,1103.0,508.0,3.274,254300.0,NEAR OCEAN
+-121.14,37.52,37.0,1358.0,231.0,586.0,214.0,3.1645,170800.0,INLAND
+-122.0,37.36,25.0,3534.0,949.0,1880.0,849.0,3.4238,337000.0,<1H OCEAN
+-118.19,33.77,31.0,1711.0,691.0,1092.0,568.0,1.0714,150000.0,NEAR OCEAN
+-118.02,34.08,31.0,2402.0,632.0,2830.0,603.0,2.3333,164200.0,INLAND
+-117.86,34.09,26.0,3408.0,542.0,1664.0,543.0,6.1498,239100.0,<1H OCEAN
+-119.8,36.83,17.0,1560.0,261.0,709.0,258.0,4.3315,95800.0,INLAND
+-122.02,37.56,35.0,1716.0,312.0,914.0,316.0,5.5737,214500.0,NEAR BAY
+-117.9,34.14,29.0,2240.0,457.0,1187.0,407.0,3.8365,184200.0,<1H OCEAN
+-118.3,33.81,17.0,5544.0,1068.0,3008.0,1038.0,5.322,282700.0,<1H OCEAN
+-118.03,33.95,37.0,1772.0,321.0,934.0,326.0,4.1471,177800.0,<1H OCEAN
+-121.95,38.34,9.0,4999.0,874.0,2687.0,817.0,4.2324,142100.0,INLAND
+-117.39,34.1,19.0,1000.0,211.0,572.0,230.0,2.4028,112500.0,INLAND
+-118.37,34.17,42.0,1713.0,416.0,1349.0,427.0,3.2596,191800.0,<1H OCEAN
+-122.24,37.47,36.0,2021.0,433.0,1117.0,432.0,3.929,303100.0,NEAR OCEAN
+-121.01,37.64,33.0,693.0,207.0,598.0,192.0,1.0217,81300.0,INLAND
+-121.82,37.31,22.0,2044.0,402.0,1925.0,429.0,3.7102,177500.0,<1H OCEAN
+-117.7,33.57,4.0,3283.0,911.0,1512.0,782.0,3.3125,138500.0,<1H OCEAN
+-121.96,36.88,37.0,2846.0,553.0,939.0,433.0,4.7468,294400.0,NEAR OCEAN
+-118.44,34.04,49.0,32.0,7.0,14.0,7.0,2.1875,225000.0,<1H OCEAN
+-118.25,34.11,39.0,1415.0,369.0,1467.0,351.0,3.015,156300.0,<1H OCEAN
+-118.02,33.85,31.0,1922.0,329.0,1030.0,353.0,5.3416,213000.0,<1H OCEAN
+-121.4,38.63,30.0,3626.0,834.0,1577.0,806.0,2.517,130400.0,INLAND
+-119.09,35.33,9.0,7085.0,1148.0,3084.0,1052.0,4.997,142900.0,INLAND
+-118.25,34.09,52.0,2050.0,429.0,957.0,418.0,3.5603,210000.0,<1H OCEAN
+-118.2,33.77,40.0,2034.0,899.0,1257.0,797.0,1.2864,131300.0,NEAR OCEAN
+-117.92,33.61,36.0,1025.0,150.0,316.0,126.0,10.3048,500001.0,<1H OCEAN
+-121.95,37.37,39.0,446.0,129.0,317.0,127.0,3.0357,208300.0,<1H OCEAN
+-121.22,37.72,34.0,2123.0,387.0,1310.0,368.0,2.6368,165600.0,INLAND
+-118.08,34.01,32.0,1973.0,401.0,1322.0,386.0,3.4861,158100.0,<1H OCEAN
+-121.02,39.25,52.0,1549.0,275.0,604.0,249.0,2.2278,155400.0,INLAND
+-122.08,37.58,16.0,3349.0,544.0,2003.0,488.0,6.0074,236500.0,NEAR BAY
+-122.4,37.77,52.0,144.0,63.0,1061.0,68.0,4.3958,225000.0,NEAR BAY
+-121.21,37.79,33.0,811.0,185.0,446.0,198.0,1.6724,96900.0,INLAND
+-118.17,33.87,45.0,2110.0,494.0,1404.0,454.0,2.9803,165900.0,<1H OCEAN
+-119.29,36.34,35.0,1235.0,369.0,1246.0,341.0,1.474,71000.0,INLAND
+-118.25,34.05,8.0,3105.0,1256.0,1086.0,997.0,0.8131,275000.0,<1H OCEAN
+-118.28,33.78,37.0,1212.0,304.0,1076.0,293.0,3.2115,160100.0,<1H OCEAN
+-120.96,37.59,11.0,4236.0,879.0,2410.0,850.0,2.3849,122000.0,INLAND
+-118.19,34.2,41.0,2031.0,294.0,859.0,302.0,7.419,483700.0,<1H OCEAN
+-117.24,33.37,14.0,4687.0,793.0,2436.0,779.0,4.5391,180900.0,<1H OCEAN
+-118.18,33.85,30.0,2548.0,717.0,2086.0,700.0,0.7007,134400.0,<1H OCEAN
+-117.24,33.05,15.0,3029.0,555.0,1559.0,546.0,5.3129,169200.0,NEAR OCEAN
+-120.56,38.09,34.0,2745.0,559.0,1150.0,491.0,2.3654,94900.0,INLAND
+-120.29,36.88,34.0,1391.0,297.0,943.0,281.0,2.4219,83900.0,INLAND
+-117.58,34.0,2.0,7544.0,1516.0,2801.0,1001.0,4.0037,245200.0,INLAND
+-118.97,37.64,14.0,1847.0,439.0,238.0,98.0,3.6042,137500.0,INLAND
+-117.89,33.83,35.0,2984.0,446.0,1435.0,455.0,5.6276,200800.0,<1H OCEAN
+-118.15,33.88,24.0,4232.0,1092.0,2688.0,1035.0,2.52,146000.0,<1H OCEAN
+-117.87,34.08,33.0,3630.0,800.0,2257.0,796.0,3.2469,206900.0,<1H OCEAN
+-121.11,38.04,32.0,1083.0,188.0,471.0,178.0,2.9241,187500.0,INLAND
+-119.73,36.68,32.0,755.0,205.0,681.0,207.0,1.7986,49300.0,INLAND
+-118.3,34.19,52.0,2962.0,468.0,1364.0,466.0,4.9042,343500.0,<1H OCEAN
+-117.21,33.13,15.0,1889.0,368.0,754.0,409.0,2.2278,132800.0,NEAR OCEAN
+-119.25,34.28,36.0,1530.0,341.0,703.0,317.0,3.5819,231900.0,NEAR OCEAN
+-122.36,37.93,42.0,1796.0,389.0,1107.0,372.0,1.9375,87000.0,NEAR BAY
+-118.34,33.86,36.0,2223.0,360.0,1162.0,376.0,5.259,279400.0,<1H OCEAN
+-120.41,34.96,9.0,2712.0,428.0,1116.0,415.0,4.5536,190100.0,<1H OCEAN
+-122.15,38.04,14.0,2804.0,587.0,1083.0,573.0,2.6466,168500.0,NEAR BAY
+-121.93,37.53,27.0,5532.0,973.0,2855.0,960.0,4.7478,243500.0,<1H OCEAN
+-118.35,33.97,33.0,1495.0,474.0,1272.0,447.0,2.0694,143500.0,<1H OCEAN
+-118.32,34.26,24.0,5106.0,1010.0,2310.0,957.0,4.4375,191500.0,<1H OCEAN
+-119.69,36.73,30.0,2437.0,349.0,1005.0,380.0,7.2211,171700.0,INLAND
+-118.1,34.15,32.0,978.0,227.0,543.0,211.0,3.0096,199000.0,INLAND
+-119.79,34.45,24.0,2746.0,433.0,1076.0,380.0,5.8635,348700.0,NEAR OCEAN
+-121.27,37.95,52.0,1318.0,308.0,1368.0,310.0,1.8261,54600.0,INLAND
+-122.24,37.79,39.0,1469.0,431.0,1464.0,389.0,2.1638,105500.0,NEAR BAY
+-117.08,32.63,33.0,2891.0,793.0,1607.0,754.0,2.1281,139800.0,NEAR OCEAN
+-117.92,33.65,20.0,1391.0,393.0,856.0,360.0,3.184,220000.0,<1H OCEAN
+-119.83,36.78,30.0,3162.0,640.0,1660.0,639.0,2.8359,80300.0,INLAND
+-122.39,40.57,38.0,855.0,172.0,468.0,150.0,1.4091,84400.0,INLAND
+-117.2,33.12,18.0,4372.0,736.0,1473.0,675.0,5.1194,247800.0,NEAR OCEAN
+-118.46,34.18,33.0,1791.0,386.0,844.0,397.0,4.5081,251400.0,<1H OCEAN
+-121.78,37.23,18.0,1747.0,317.0,1055.0,285.0,5.898,229100.0,<1H OCEAN
+-121.48,38.49,26.0,3165.0,806.0,2447.0,752.0,1.5908,78600.0,INLAND
+-122.28,37.99,28.0,3801.0,622.0,1654.0,571.0,4.375,193300.0,NEAR BAY
+-118.31,34.0,47.0,1551.0,362.0,1329.0,322.0,1.9792,116400.0,<1H OCEAN
+-120.84,38.81,11.0,1280.0,286.0,609.0,248.0,3.1635,132600.0,INLAND
+-117.39,34.09,10.0,5736.0,945.0,3528.0,932.0,4.3958,130700.0,INLAND
+-117.31,33.0,30.0,1631.0,310.0,665.0,297.0,6.8443,492500.0,NEAR OCEAN
+-117.75,34.07,52.0,1279.0,213.0,444.0,204.0,5.2269,161000.0,INLAND
+-117.13,32.98,5.0,2276.0,311.0,1158.0,317.0,6.4321,271900.0,<1H OCEAN
+-118.32,33.89,34.0,2675.0,560.0,1270.0,492.0,4.5053,242000.0,<1H OCEAN
+-118.13,34.02,41.0,734.0,190.0,565.0,191.0,2.2813,192000.0,<1H OCEAN
+-118.47,34.01,41.0,752.0,201.0,482.0,207.0,2.5417,418200.0,<1H OCEAN
+-117.64,33.61,14.0,5232.0,810.0,3041.0,839.0,5.826,247900.0,<1H OCEAN
+-115.8,33.26,2.0,96.0,18.0,30.0,16.0,5.3374,47500.0,INLAND
+-121.54,38.59,29.0,2242.0,493.0,1481.0,478.0,2.0781,74800.0,INLAND
+-122.08,37.41,20.0,1896.0,456.0,1069.0,436.0,4.6875,288900.0,NEAR BAY
+-114.57,33.52,27.0,173.0,35.0,117.0,34.0,2.0833,45000.0,INLAND
+-117.93,33.84,26.0,2811.0,612.0,1374.0,566.0,3.475,282500.0,<1H OCEAN
+-121.96,37.36,33.0,2581.0,623.0,1598.0,628.0,3.5199,261400.0,<1H OCEAN
+-117.91,33.85,35.0,932.0,258.0,1147.0,267.0,2.7014,156700.0,<1H OCEAN
+-121.95,37.74,19.0,5721.0,837.0,2653.0,813.0,6.2631,266000.0,<1H OCEAN
+-121.84,36.61,26.0,2902.0,761.0,2258.0,719.0,2.5663,128900.0,<1H OCEAN
+-121.19,38.87,20.0,3118.0,500.0,1405.0,519.0,6.0,209400.0,INLAND
+-121.07,39.15,15.0,6828.0,1319.0,3002.0,1318.0,2.4726,143400.0,INLAND
+-118.12,33.82,43.0,1544.0,286.0,701.0,298.0,4.1375,226000.0,<1H OCEAN
+-118.33,34.04,31.0,1090.0,251.0,955.0,239.0,2.913,192500.0,<1H OCEAN
+-121.76,37.28,17.0,660.0,129.0,431.0,123.0,4.9097,241000.0,<1H OCEAN
+-117.68,34.12,16.0,2181.0,321.0,1133.0,350.0,5.7214,259400.0,INLAND
+-122.23,37.79,43.0,5963.0,1344.0,4367.0,1231.0,2.1917,112800.0,NEAR BAY
+-118.29,33.8,21.0,9944.0,1623.0,4185.0,1582.0,4.526,329400.0,<1H OCEAN
+-122.47,37.75,52.0,1598.0,285.0,689.0,265.0,4.6071,337400.0,NEAR BAY
+-118.16,34.71,27.0,6007.0,998.0,2680.0,882.0,4.1719,117200.0,INLAND
+-122.18,38.07,10.0,4976.0,849.0,2089.0,803.0,5.3288,201800.0,NEAR BAY
+-119.37,36.47,26.0,337.0,69.0,277.0,73.0,2.3438,100000.0,INLAND
+-121.0,37.25,21.0,1937.0,389.0,1002.0,373.0,2.6087,96200.0,INLAND
+-121.96,38.02,35.0,2691.0,542.0,1409.0,505.0,3.016,95300.0,INLAND
+-117.24,32.8,26.0,3433.0,873.0,1492.0,798.0,2.9258,234800.0,NEAR OCEAN
+-117.37,34.13,12.0,1893.0,493.0,1054.0,389.0,2.3456,140800.0,INLAND
+-118.36,33.99,45.0,2005.0,368.0,909.0,364.0,4.6406,268900.0,<1H OCEAN
+-117.05,32.58,25.0,2185.0,370.0,1558.0,369.0,5.3072,132700.0,NEAR OCEAN
+-117.33,34.12,33.0,933.0,219.0,838.0,211.0,1.3417,69000.0,INLAND
+-119.81,36.28,24.0,544.0,112.0,442.0,106.0,3.1071,56100.0,INLAND
+-118.38,33.88,34.0,1830.0,315.0,822.0,307.0,5.0602,453700.0,<1H OCEAN
+-122.18,38.35,24.0,407.0,68.0,175.0,61.0,6.0266,216700.0,INLAND
+-118.5,34.16,34.0,3547.0,523.0,1187.0,500.0,7.139,424000.0,<1H OCEAN
+-117.81,33.66,16.0,1414.0,191.0,635.0,230.0,10.0757,383900.0,<1H OCEAN
+-122.15,37.68,35.0,2632.0,447.0,1349.0,486.0,4.3864,205200.0,NEAR BAY
+-118.33,34.09,40.0,2004.0,687.0,1514.0,542.0,1.9911,220000.0,<1H OCEAN
+-116.93,32.85,15.0,3273.0,895.0,1872.0,842.0,2.5388,119000.0,<1H OCEAN
+-118.21,34.04,47.0,1306.0,391.0,1499.0,346.0,2.2788,139600.0,<1H OCEAN
+-117.94,33.79,24.0,4179.0,784.0,1902.0,733.0,4.7986,236500.0,<1H OCEAN
+-119.9,36.58,20.0,1935.0,363.0,1319.0,359.0,2.4814,74600.0,INLAND
+-121.87,36.95,7.0,3703.0,679.0,1375.0,608.0,4.9219,368400.0,NEAR OCEAN
+-119.29,36.32,27.0,1513.0,374.0,839.0,350.0,1.2012,64600.0,INLAND
+-117.87,33.84,25.0,1928.0,414.0,961.0,385.0,4.0724,231400.0,<1H OCEAN
+-117.17,32.69,19.0,2802.0,802.0,1159.0,597.0,4.7891,334600.0,NEAR OCEAN
+-117.68,33.55,5.0,2262.0,427.0,1016.0,402.0,6.065,315500.0,<1H OCEAN
+-118.34,33.89,36.0,2392.0,444.0,1346.0,445.0,6.0088,245900.0,<1H OCEAN
+-116.98,32.72,15.0,4209.0,680.0,1914.0,641.0,4.5135,158300.0,<1H OCEAN
+-122.02,37.35,26.0,2785.0,418.0,1221.0,422.0,8.1078,365700.0,<1H OCEAN
+-118.3,34.08,32.0,2880.0,1063.0,3646.0,1028.0,2.3846,258300.0,<1H OCEAN
+-119.75,36.72,22.0,3247.0,859.0,4179.0,881.0,1.3343,60800.0,INLAND
+-121.97,37.36,24.0,4841.0,894.0,2656.0,920.0,6.0573,254500.0,<1H OCEAN
+-122.32,40.57,15.0,2524.0,449.0,1374.0,467.0,3.3816,93800.0,INLAND
+-117.61,34.08,20.0,3550.0,736.0,2229.0,681.0,3.0199,128800.0,INLAND
+-122.16,37.69,36.0,1480.0,278.0,796.0,283.0,4.3971,205700.0,NEAR BAY
+-122.72,38.46,35.0,1445.0,309.0,795.0,308.0,2.9073,157000.0,<1H OCEAN
+-118.61,34.15,32.0,4491.0,815.0,1696.0,749.0,4.9102,319100.0,<1H OCEAN
+-122.26,37.77,52.0,1210.0,168.0,411.0,172.0,3.3571,405400.0,NEAR BAY
+-121.27,38.67,15.0,1701.0,346.0,723.0,352.0,3.8906,128700.0,INLAND
+-117.01,32.77,34.0,3330.0,723.0,1592.0,656.0,2.6678,164200.0,<1H OCEAN
+-122.44,37.71,46.0,1230.0,247.0,895.0,257.0,5.3913,248900.0,NEAR BAY
+-122.6,38.11,19.0,1752.0,328.0,873.0,336.0,3.8068,201600.0,<1H OCEAN
+-121.88,38.0,16.0,2605.0,440.0,1352.0,408.0,4.1947,140300.0,INLAND
+-119.46,36.61,13.0,1348.0,258.0,719.0,246.0,3.625,108300.0,INLAND
+-121.98,38.25,4.0,2487.0,440.0,1545.0,452.0,4.9103,140400.0,INLAND
+-121.29,37.89,26.0,161.0,27.0,1542.0,30.0,5.7485,162500.0,INLAND
+-119.36,36.21,18.0,1082.0,202.0,793.0,213.0,2.4032,60000.0,INLAND
+-121.76,37.23,16.0,4274.0,715.0,2240.0,704.0,5.4218,233900.0,<1H OCEAN
+-118.19,33.86,42.0,1999.0,431.0,1060.0,399.0,3.7031,167100.0,<1H OCEAN
+-122.49,37.75,45.0,2341.0,461.0,1092.0,438.0,4.8036,297800.0,NEAR OCEAN
+-117.43,33.93,36.0,2386.0,396.0,1176.0,374.0,4.5122,113300.0,INLAND
+-122.42,38.27,25.0,3282.0,566.0,1244.0,483.0,4.5313,308400.0,NEAR BAY
+-117.27,32.83,39.0,1877.0,426.0,805.0,409.0,3.875,410000.0,NEAR OCEAN
+-121.97,37.25,21.0,2775.0,389.0,856.0,350.0,7.9135,496400.0,<1H OCEAN
+-117.7,33.62,16.0,9653.0,2000.0,4732.0,1922.0,3.7361,197200.0,<1H OCEAN
+-122.87,38.62,52.0,1514.0,348.0,767.0,354.0,2.1903,160000.0,<1H OCEAN
+-120.58,40.37,16.0,3412.0,667.0,1873.0,590.0,2.2661,61800.0,INLAND
+-122.01,36.99,41.0,2548.0,508.0,1290.0,488.0,3.6902,233000.0,NEAR OCEAN
+-121.26,38.69,17.0,3917.0,638.0,1809.0,564.0,5.2586,137000.0,INLAND
+-122.69,38.44,31.0,1808.0,315.0,691.0,280.0,3.8583,193200.0,<1H OCEAN
+-121.24,38.68,20.0,1402.0,236.0,676.0,236.0,3.7426,135500.0,INLAND
+-118.35,34.04,41.0,1617.0,423.0,1110.0,375.0,2.4635,169400.0,<1H OCEAN
+-122.25,37.89,37.0,3000.0,457.0,987.0,450.0,7.5385,350000.0,NEAR BAY
+-121.27,38.62,13.0,2321.0,539.0,1066.0,497.0,2.7652,95600.0,INLAND
+-117.06,32.6,25.0,1075.0,238.0,434.0,234.0,1.7472,94600.0,NEAR OCEAN
+-116.89,33.86,2.0,6900.0,1238.0,1950.0,980.0,3.0417,146300.0,INLAND
+-122.08,38.3,2.0,6718.0,858.0,2012.0,654.0,6.8872,305200.0,INLAND
+-118.44,34.22,39.0,1529.0,344.0,913.0,314.0,3.325,178200.0,<1H OCEAN
+-121.49,38.54,37.0,1655.0,393.0,841.0,355.0,1.6932,78400.0,INLAND
+-120.31,37.29,36.0,969.0,206.0,732.0,175.0,1.5938,57600.0,INLAND
+-116.98,34.13,16.0,2098.0,449.0,342.0,143.0,4.0333,133900.0,INLAND
+-120.89,37.48,27.0,1118.0,195.0,647.0,209.0,2.9135,159400.0,INLAND
+-121.88,36.96,18.0,6355.0,1100.0,2304.0,972.0,6.0281,321100.0,NEAR OCEAN
+-118.21,33.94,34.0,892.0,318.0,1443.0,341.0,2.1903,162500.0,<1H OCEAN
+-118.35,33.85,35.0,1248.0,206.0,551.0,185.0,5.6426,348200.0,<1H OCEAN
+-121.23,37.96,44.0,2204.0,473.0,1277.0,435.0,1.5539,59200.0,INLAND
+-119.81,36.83,19.0,6789.0,1200.0,2325.0,1109.0,4.049,126000.0,INLAND
+-117.84,33.77,26.0,3350.0,581.0,1314.0,550.0,3.5195,249100.0,<1H OCEAN
+-118.09,34.02,28.0,1984.0,313.0,1099.0,343.0,4.5526,250200.0,<1H OCEAN
+-118.43,34.07,34.0,3203.0,483.0,949.0,439.0,10.3467,500001.0,<1H OCEAN
+-118.18,33.98,24.0,1880.0,642.0,2646.0,605.0,2.1836,162000.0,<1H OCEAN
+-117.94,33.93,34.0,1475.0,319.0,698.0,293.0,3.8194,186000.0,<1H OCEAN
+-122.72,38.43,31.0,2020.0,476.0,1408.0,437.0,2.5735,131100.0,<1H OCEAN
+-121.89,37.34,46.0,1197.0,416.0,898.0,370.0,2.1714,190600.0,<1H OCEAN
+-122.45,37.71,45.0,2253.0,431.0,1382.0,392.0,4.2562,221600.0,NEAR OCEAN
+-122.17,37.88,32.0,3633.0,508.0,1393.0,506.0,7.6917,401800.0,NEAR BAY
+-119.21,34.19,15.0,5614.0,989.0,2754.0,994.0,5.035,242900.0,NEAR OCEAN
+-119.47,35.13,44.0,4599.0,877.0,2140.0,831.0,2.9952,63800.0,INLAND
+-118.31,34.16,37.0,2144.0,446.0,860.0,435.0,3.9464,315000.0,<1H OCEAN
+-117.28,32.84,21.0,2455.0,660.0,1015.0,597.0,3.7596,381300.0,NEAR OCEAN
+-122.02,37.35,22.0,3219.0,756.0,1479.0,667.0,4.1473,354400.0,<1H OCEAN
+-119.03,34.25,25.0,3344.0,502.0,1483.0,496.0,6.196,340600.0,<1H OCEAN
+-118.15,34.07,40.0,1072.0,204.0,940.0,200.0,3.125,242700.0,<1H OCEAN
+-118.34,33.9,36.0,1158.0,219.0,628.0,253.0,4.7426,242700.0,<1H OCEAN
+-118.3,33.97,46.0,1425.0,317.0,1140.0,304.0,3.375,98500.0,<1H OCEAN
+-118.21,33.96,39.0,2265.0,628.0,2323.0,599.0,2.1522,155300.0,<1H OCEAN
+-123.34,39.5,15.0,2342.0,535.0,1064.0,433.0,1.8967,96600.0,INLAND
+-117.22,32.83,31.0,2558.0,512.0,1164.0,492.0,3.4318,200400.0,NEAR OCEAN
+-121.01,37.64,36.0,1981.0,507.0,1998.0,468.0,1.9013,69900.0,INLAND
+-121.52,38.5,19.0,4900.0,805.0,2519.0,855.0,4.8497,184400.0,INLAND
+-118.46,34.0,39.0,614.0,174.0,538.0,159.0,2.3542,235700.0,<1H OCEAN
+-117.38,34.13,23.0,1326.0,300.0,722.0,263.0,2.1856,107800.0,INLAND
+-122.64,38.96,29.0,883.0,187.0,326.0,136.0,1.7273,58200.0,INLAND
+-121.64,36.74,30.0,2628.0,444.0,1372.0,432.0,4.1696,175000.0,<1H OCEAN
+-117.44,33.88,5.0,2589.0,351.0,1109.0,360.0,6.8089,334100.0,INLAND
+-117.85,33.75,27.0,2311.0,632.0,2936.0,609.0,2.5651,171400.0,<1H OCEAN
+-121.77,36.87,37.0,424.0,65.0,266.0,64.0,3.3472,293800.0,NEAR OCEAN
+-117.88,33.74,31.0,1120.0,296.0,1718.0,268.0,2.8077,140300.0,<1H OCEAN
+-122.47,37.85,19.0,1926.0,593.0,881.0,546.0,2.9145,140400.0,NEAR BAY
+-117.88,34.12,33.0,1485.0,274.0,1006.0,258.0,5.1708,158500.0,<1H OCEAN
+-122.08,37.31,17.0,2560.0,396.0,959.0,400.0,7.8528,368900.0,<1H OCEAN
+-119.8,36.7,28.0,1592.0,304.0,962.0,282.0,1.3304,51300.0,INLAND
+-115.58,32.78,5.0,2494.0,414.0,1416.0,421.0,5.7843,110100.0,INLAND
+-117.98,33.61,17.0,2054.0,291.0,836.0,288.0,6.8939,383900.0,NEAR OCEAN
+-117.14,33.39,17.0,2889.0,587.0,1931.0,510.0,3.8547,208300.0,<1H OCEAN
+-117.25,33.38,16.0,3536.0,765.0,2007.0,687.0,3.0,146700.0,<1H OCEAN
+-118.31,33.81,30.0,1773.0,356.0,905.0,352.0,4.3056,336000.0,<1H OCEAN
+-118.24,34.1,42.0,1525.0,456.0,1688.0,432.0,3.1691,141300.0,<1H OCEAN
+-119.11,36.29,18.0,1666.0,294.0,859.0,301.0,2.6065,93800.0,INLAND
+-121.03,37.62,46.0,2331.0,508.0,1210.0,484.0,2.5313,77700.0,INLAND
+-120.48,34.66,4.0,1897.0,331.0,915.0,336.0,4.1563,172600.0,NEAR OCEAN
+-118.18,34.11,44.0,1346.0,398.0,1204.0,344.0,2.3984,152200.0,<1H OCEAN
+-120.68,35.26,26.0,1704.0,315.0,918.0,310.0,3.2464,208000.0,NEAR OCEAN
+-122.29,37.89,52.0,979.0,175.0,374.0,153.0,5.1675,270600.0,NEAR BAY
+-122.26,37.86,52.0,3774.0,744.0,1461.0,679.0,2.9405,289500.0,NEAR BAY
+-122.91,39.05,20.0,1128.0,229.0,621.0,210.0,3.2216,93500.0,INLAND
+-119.88,36.81,30.0,2288.0,474.0,1435.0,425.0,1.3221,61200.0,INLAND
+-122.42,37.75,52.0,801.0,272.0,639.0,259.0,2.1971,275000.0,NEAR BAY
+-118.44,34.18,35.0,972.0,270.0,550.0,256.0,2.2461,215000.0,<1H OCEAN
+-118.42,34.15,18.0,1880.0,420.0,681.0,333.0,4.3214,372300.0,<1H OCEAN
+-118.22,34.19,36.0,2443.0,492.0,1115.0,493.0,3.9777,409800.0,<1H OCEAN
+-117.03,32.82,16.0,1765.0,289.0,743.0,280.0,4.9744,209700.0,<1H OCEAN
+-118.04,34.18,37.0,3134.0,532.0,1220.0,508.0,5.2865,455400.0,INLAND
+-118.96,34.18,16.0,3137.0,462.0,1384.0,436.0,6.1306,258200.0,<1H OCEAN
+-117.59,33.88,13.0,3239.0,849.0,2751.0,813.0,2.6111,107000.0,INLAND
+-118.22,33.94,38.0,788.0,224.0,1155.0,208.0,3.3542,153800.0,<1H OCEAN
+-118.31,34.19,27.0,4713.0,1169.0,2372.0,1077.0,3.7015,287900.0,<1H OCEAN
+-121.94,38.35,8.0,3157.0,559.0,1758.0,569.0,4.412,140100.0,INLAND
+-123.85,41.32,31.0,938.0,238.0,425.0,157.0,1.0486,36700.0,<1H OCEAN
+-117.48,34.09,32.0,3170.0,630.0,2612.0,580.0,3.6394,99200.0,INLAND
+-117.33,34.09,29.0,1960.0,415.0,1681.0,435.0,2.9292,84500.0,INLAND
+-122.0,37.97,27.0,2491.0,428.0,1171.0,431.0,5.1021,202800.0,INLAND
+-117.82,34.12,26.0,3118.0,528.0,1546.0,545.0,5.27,209400.0,INLAND
+-120.25,37.04,21.0,1724.0,317.0,1006.0,290.0,3.2868,91700.0,INLAND
+-122.3,37.89,52.0,1248.0,283.0,620.0,275.0,4.0875,221300.0,NEAR BAY
+-120.87,37.6,32.0,4579.0,914.0,2742.0,856.0,2.6619,86200.0,INLAND
+-118.41,34.21,35.0,1789.0,292.0,897.0,267.0,5.592,239900.0,<1H OCEAN
+-117.23,33.94,8.0,2405.0,537.0,1594.0,517.0,3.0789,114200.0,INLAND
+-117.27,34.13,36.0,3337.0,687.0,2388.0,589.0,2.9628,87800.0,INLAND
+-117.13,32.91,16.0,3230.0,579.0,1825.0,576.0,4.2969,151200.0,<1H OCEAN
+-117.96,33.8,33.0,2362.0,394.0,1185.0,387.0,4.425,188400.0,<1H OCEAN
+-117.91,33.65,17.0,1328.0,377.0,762.0,344.0,2.2222,276800.0,<1H OCEAN
+-121.81,37.97,8.0,1584.0,236.0,615.0,202.0,6.4753,166800.0,INLAND
+-122.47,37.74,52.0,1538.0,305.0,819.0,319.0,4.0846,333600.0,NEAR BAY
+-120.93,38.77,9.0,2229.0,355.0,788.0,341.0,5.5111,196300.0,INLAND
+-117.6,34.03,16.0,1499.0,232.0,918.0,239.0,5.5677,175400.0,INLAND
+-117.87,33.79,25.0,2546.0,545.0,1543.0,521.0,4.192,219900.0,<1H OCEAN
+-120.42,34.95,52.0,1391.0,287.0,632.0,276.0,1.7431,131500.0,<1H OCEAN
+-121.29,38.1,14.0,1551.0,297.0,785.0,281.0,3.775,163300.0,INLAND
+-120.97,37.67,16.0,1499.0,250.0,1292.0,271.0,4.3851,117300.0,INLAND
+-118.1,33.85,28.0,2825.0,470.0,1352.0,469.0,5.2639,242000.0,<1H OCEAN
+-117.88,33.9,21.0,3180.0,434.0,1413.0,391.0,6.5945,277300.0,<1H OCEAN
+-119.65,36.32,11.0,1294.0,314.0,713.0,290.0,1.5433,50400.0,INLAND
+-116.32,34.1,10.0,4256.0,861.0,1403.0,686.0,2.6618,81000.0,INLAND
+-121.9,37.25,20.0,5483.0,1079.0,2892.0,1057.0,4.6845,250000.0,<1H OCEAN
+-122.03,37.37,16.0,3402.0,1193.0,1479.0,1043.0,3.5861,500001.0,<1H OCEAN
+-120.65,35.27,15.0,2365.0,538.0,1446.0,490.0,2.5129,225900.0,NEAR OCEAN
+-119.63,36.34,26.0,1463.0,261.0,699.0,219.0,3.5536,71400.0,INLAND
+-117.05,32.69,8.0,831.0,158.0,740.0,154.0,5.3908,165500.0,NEAR OCEAN
+-118.27,33.82,33.0,1596.0,337.0,1650.0,329.0,4.3687,173500.0,<1H OCEAN
+-122.33,37.97,19.0,5151.0,1335.0,2548.0,1165.0,3.3125,158800.0,NEAR BAY
+-118.24,33.99,28.0,312.0,89.0,498.0,87.0,2.4107,96400.0,<1H OCEAN
+-118.12,34.58,13.0,2614.0,650.0,1949.0,537.0,2.0547,102600.0,INLAND
+-122.28,37.86,41.0,2214.0,550.0,1213.0,568.0,2.2845,153100.0,NEAR BAY
+-118.11,33.9,35.0,2604.0,495.0,1465.0,470.0,4.4896,184600.0,<1H OCEAN
+-120.06,36.94,19.0,901.0,183.0,700.0,190.0,2.2375,64300.0,INLAND
+-121.73,36.86,28.0,827.0,178.0,703.0,144.0,4.4271,175700.0,<1H OCEAN
+-118.21,34.02,45.0,792.0,203.0,872.0,188.0,2.6875,129700.0,<1H OCEAN
+-118.04,33.98,25.0,3040.0,831.0,1580.0,735.0,2.3182,182100.0,<1H OCEAN
+-117.89,34.09,37.0,1055.0,280.0,538.0,206.0,2.4167,181300.0,<1H OCEAN
+-119.09,36.35,21.0,3146.0,595.0,1580.0,513.0,2.7857,92700.0,INLAND
+-122.42,37.75,52.0,2708.0,762.0,1460.0,741.0,2.9052,400000.0,NEAR BAY
+-118.4,34.05,34.0,2113.0,459.0,859.0,432.0,3.6953,500001.0,<1H OCEAN
+-122.09,37.39,36.0,1035.0,196.0,475.0,205.0,5.5385,359000.0,NEAR BAY
+-117.64,34.03,10.0,3194.0,579.0,2088.0,549.0,4.1779,159100.0,INLAND
+-122.44,37.8,52.0,2869.0,594.0,500.0,335.0,5.0376,500001.0,NEAR BAY
+-121.47,38.54,36.0,2099.0,510.0,1845.0,483.0,1.4138,52500.0,INLAND
+-117.14,32.7,44.0,658.0,218.0,869.0,212.0,1.9338,89400.0,NEAR OCEAN
+-117.42,33.93,32.0,2885.0,595.0,1509.0,590.0,3.1795,125600.0,INLAND
+-118.08,34.12,34.0,2921.0,641.0,1541.0,562.0,3.6827,264100.0,<1H OCEAN
+-118.35,34.22,30.0,1260.0,222.0,638.0,229.0,4.1302,258300.0,<1H OCEAN
+-121.81,37.35,28.0,3477.0,671.0,2990.0,648.0,4.4671,172600.0,<1H OCEAN
+-118.48,34.23,29.0,3354.0,707.0,1752.0,650.0,4.5484,239900.0,<1H OCEAN
+-121.53,38.58,33.0,4988.0,1169.0,2414.0,1075.0,1.9728,76400.0,INLAND
+-117.95,33.74,25.0,1393.0,243.0,976.0,245.0,5.4485,225200.0,<1H OCEAN
+-121.27,38.14,33.0,3557.0,894.0,2659.0,894.0,2.2883,86900.0,INLAND
+-118.41,34.29,32.0,1591.0,320.0,1818.0,306.0,4.2969,145800.0,<1H OCEAN
+-121.23,37.82,14.0,1847.0,325.0,1030.0,309.0,4.9271,155300.0,INLAND
+-119.23,35.77,26.0,2636.0,468.0,1416.0,485.0,4.1917,84000.0,INLAND
+-118.93,34.36,33.0,1775.0,309.0,1071.0,296.0,4.6607,187900.0,<1H OCEAN
+-121.28,38.6,17.0,1671.0,378.0,848.0,351.0,3.1194,112500.0,INLAND
+-120.08,36.96,36.0,2074.0,349.0,954.0,363.0,3.1136,73800.0,INLAND
+-118.32,34.2,29.0,2209.0,444.0,952.0,403.0,4.375,341200.0,<1H OCEAN
+-117.43,34.1,43.0,1898.0,418.0,971.0,366.0,2.4735,89900.0,INLAND
+-118.09,34.74,34.0,1218.0,285.0,797.0,248.0,2.4348,104800.0,INLAND
+-122.08,37.64,36.0,786.0,133.0,463.0,160.0,3.9338,182700.0,NEAR BAY
+-121.35,38.59,29.0,1285.0,193.0,460.0,206.0,5.3243,265700.0,INLAND
+-117.16,32.81,34.0,2275.0,375.0,1021.0,379.0,3.6371,176300.0,NEAR OCEAN
+-122.23,37.79,48.0,1696.0,396.0,1481.0,343.0,2.0375,122500.0,NEAR BAY
+-122.4,40.58,40.0,3895.0,929.0,1782.0,910.0,1.3329,78200.0,INLAND
+-118.36,34.02,43.0,1356.0,333.0,796.0,329.0,1.7159,189700.0,<1H OCEAN
+-118.35,33.91,32.0,1660.0,366.0,928.0,398.0,4.3187,269700.0,<1H OCEAN
+-117.94,33.94,30.0,1596.0,307.0,845.0,309.0,4.5096,241100.0,<1H OCEAN
+-121.74,36.92,17.0,2648.0,589.0,1193.0,540.0,2.4461,151700.0,<1H OCEAN
+-118.34,34.03,46.0,2437.0,502.0,1151.0,477.0,2.4444,134100.0,<1H OCEAN
+-118.03,34.1,32.0,2668.0,609.0,1512.0,541.0,2.9422,233100.0,INLAND
+-117.35,34.13,26.0,3920.0,570.0,1862.0,552.0,3.7286,132000.0,INLAND
+-117.7,33.56,2.0,2112.0,305.0,703.0,261.0,6.9343,298500.0,<1H OCEAN
+-117.61,34.1,9.0,18956.0,4095.0,10323.0,3832.0,3.6033,132600.0,INLAND
+-120.82,35.31,16.0,3924.0,699.0,1325.0,638.0,2.5172,293900.0,NEAR OCEAN
+-121.59,37.02,14.0,6355.0,1279.0,3704.0,1224.0,4.4233,228600.0,INLAND
+-119.03,35.41,41.0,1808.0,435.0,1005.0,373.0,1.7857,54300.0,INLAND
+-116.2,32.64,28.0,1608.0,409.0,567.0,254.0,1.4648,61800.0,INLAND
+-119.77,36.91,3.0,7520.0,1143.0,2878.0,1077.0,5.3272,174200.0,INLAND
+-119.86,36.8,18.0,2536.0,516.0,1196.0,466.0,2.5595,67900.0,INLAND
+-118.07,33.88,18.0,2436.0,375.0,1303.0,386.0,6.1968,344700.0,<1H OCEAN
+-122.14,37.71,27.0,3094.0,866.0,1364.0,789.0,2.6101,181700.0,NEAR BAY
+-122.48,37.67,31.0,2609.0,433.0,1746.0,464.0,5.1054,294500.0,NEAR OCEAN
+-121.99,37.97,30.0,3320.0,589.0,1470.0,543.0,4.6071,184100.0,INLAND
+-117.97,34.11,18.0,123.0,28.0,121.0,26.0,3.0417,137500.0,INLAND
+-117.93,33.64,15.0,1707.0,514.0,1335.0,434.0,2.7543,177800.0,<1H OCEAN
+-118.24,33.9,35.0,1079.0,247.0,1055.0,243.0,2.375,93600.0,<1H OCEAN
+-117.06,32.63,37.0,1326.0,234.0,612.0,240.0,4.125,160200.0,NEAR OCEAN
+-118.44,34.26,34.0,1102.0,212.0,949.0,212.0,4.0792,165100.0,<1H OCEAN
+-117.96,34.08,33.0,4151.0,850.0,3563.0,848.0,3.1912,159900.0,<1H OCEAN
+-117.87,33.6,20.0,3212.0,572.0,1064.0,526.0,6.6155,500001.0,<1H OCEAN
+-118.36,34.17,31.0,1939.0,505.0,1584.0,466.0,2.5234,199500.0,<1H OCEAN
+-122.02,38.25,10.0,2237.0,454.0,1255.0,429.0,3.1176,126500.0,INLAND
+-117.08,32.71,27.0,2204.0,598.0,1656.0,521.0,1.4821,86200.0,NEAR OCEAN
+-117.2,32.8,34.0,4854.0,912.0,2089.0,854.0,3.8542,200000.0,NEAR OCEAN
+-117.05,34.01,27.0,5484.0,1205.0,2645.0,1131.0,2.1927,116700.0,INLAND
+-118.36,34.14,30.0,1376.0,317.0,629.0,320.0,3.6823,295200.0,<1H OCEAN
+-117.97,34.0,28.0,1983.0,375.0,1407.0,367.0,3.8319,179000.0,<1H OCEAN
+-117.05,32.61,21.0,6034.0,1205.0,3795.0,1146.0,3.2633,129700.0,NEAR OCEAN
+-117.01,32.77,24.0,2311.0,536.0,1005.0,525.0,2.9,185200.0,<1H OCEAN
+-117.21,32.81,33.0,4773.0,873.0,1954.0,845.0,4.3862,184800.0,NEAR OCEAN
+-118.45,34.29,30.0,762.0,228.0,840.0,226.0,2.3375,154200.0,<1H OCEAN
+-122.42,37.75,52.0,1974.0,525.0,935.0,465.0,2.7173,300000.0,NEAR BAY
+-117.34,34.12,26.0,1008.0,164.0,568.0,196.0,3.3516,105600.0,INLAND
+-117.15,32.71,52.0,217.0,82.0,531.0,93.0,1.6607,137500.0,NEAR OCEAN
+-118.2,33.98,30.0,2369.0,753.0,3259.0,770.0,2.1964,158500.0,<1H OCEAN
+-118.27,33.89,32.0,1969.0,397.0,1349.0,370.0,4.4659,138100.0,<1H OCEAN
+-118.22,34.05,44.0,1105.0,346.0,1598.0,372.0,1.2,115600.0,<1H OCEAN
+-117.29,33.08,18.0,3225.0,515.0,1463.0,476.0,5.7787,346700.0,NEAR OCEAN
+-119.45,35.15,33.0,5050.0,964.0,2293.0,919.0,3.1592,75400.0,INLAND
+-118.51,34.43,15.0,8510.0,1258.0,3733.0,1233.0,6.1082,253700.0,<1H OCEAN
+-118.18,36.63,23.0,2311.0,487.0,1019.0,384.0,2.2574,104700.0,INLAND
+-117.71,33.63,16.0,1641.0,354.0,945.0,318.0,3.4261,219700.0,<1H OCEAN
+-119.06,36.15,25.0,2402.0,478.0,1527.0,461.0,2.3194,52900.0,INLAND
+-122.35,37.94,34.0,1880.0,459.0,1358.0,422.0,1.6571,105200.0,NEAR BAY
+-118.02,34.09,24.0,2080.0,514.0,1976.0,478.0,2.6917,170000.0,INLAND
+-118.25,34.17,52.0,1532.0,292.0,631.0,275.0,5.1242,372900.0,<1H OCEAN
+-121.82,37.32,10.0,2506.0,623.0,2634.0,622.0,3.135,231400.0,<1H OCEAN
+-122.24,37.75,45.0,891.0,,384.0,146.0,4.9489,247100.0,NEAR BAY
+-119.02,35.38,52.0,90.0,35.0,36.0,31.0,0.8054,60000.0,INLAND
+-118.02,33.91,35.0,1337.0,234.0,692.0,235.0,5.1155,213700.0,<1H OCEAN
+-121.9,37.44,4.0,1646.0,408.0,853.0,410.0,5.0821,265500.0,<1H OCEAN
+-117.87,33.88,25.0,1808.0,440.0,1342.0,454.0,3.025,156900.0,<1H OCEAN
+-118.2,33.81,43.0,3013.0,574.0,1525.0,529.0,4.95,194000.0,NEAR OCEAN
+-122.24,37.76,52.0,2504.0,516.0,979.0,472.0,3.4762,244000.0,NEAR BAY
+-121.89,38.01,28.0,3639.0,751.0,2362.0,641.0,3.0042,103900.0,INLAND
+-118.19,34.05,35.0,1296.0,307.0,1423.0,276.0,2.7432,135200.0,<1H OCEAN
+-119.79,36.31,25.0,4984.0,1029.0,2414.0,961.0,2.2937,72300.0,INLAND
+-119.2,34.19,19.0,9503.0,1769.0,6370.0,1718.0,5.0016,218500.0,NEAR OCEAN
+-118.32,34.01,50.0,1842.0,377.0,817.0,341.0,3.1548,157700.0,<1H OCEAN
+-119.82,36.8,33.0,1670.0,256.0,528.0,250.0,2.9471,99500.0,INLAND
+-121.19,38.95,16.0,2544.0,431.0,1199.0,412.0,4.5129,196300.0,INLAND
+-118.12,34.04,35.0,1038.0,209.0,598.0,190.0,5.9214,254900.0,<1H OCEAN
+-122.28,37.88,52.0,1844.0,332.0,769.0,334.0,4.2614,261300.0,NEAR BAY
+-118.46,34.23,16.0,6338.0,1768.0,4718.0,1632.0,3.0187,154600.0,<1H OCEAN
+-122.24,37.79,27.0,1632.0,492.0,1171.0,429.0,2.3173,125000.0,NEAR BAY
+-121.14,37.47,38.0,2427.0,450.0,1272.0,474.0,2.8833,115200.0,INLAND
+-118.42,34.02,34.0,2995.0,942.0,2626.0,947.0,2.2402,450000.0,<1H OCEAN
+-121.51,38.5,25.0,4719.0,745.0,1857.0,739.0,5.0371,180200.0,INLAND
+-117.42,34.02,9.0,5455.0,882.0,3015.0,858.0,4.2321,162800.0,INLAND
+-117.29,33.04,30.0,2750.0,555.0,1281.0,520.0,4.7333,286900.0,NEAR OCEAN
+-118.42,34.3,29.0,3334.0,712.0,2919.0,718.0,3.6548,180300.0,<1H OCEAN
+-122.41,37.72,35.0,2104.0,434.0,1225.0,410.0,4.8214,242900.0,NEAR BAY
+-118.24,33.9,39.0,642.0,129.0,475.0,123.0,1.2083,92600.0,<1H OCEAN
+-118.08,33.89,35.0,1071.0,290.0,1412.0,274.0,3.1917,114900.0,<1H OCEAN
+-122.09,39.13,28.0,4169.0,895.0,2587.0,810.0,2.331,65500.0,INLAND
+-121.82,38.36,26.0,1974.0,364.0,1002.0,362.0,3.3036,210000.0,INLAND
+-121.92,37.27,29.0,5536.0,862.0,2651.0,881.0,5.6358,282100.0,<1H OCEAN
+-122.4,37.59,22.0,2754.0,477.0,1163.0,479.0,6.2306,500001.0,NEAR OCEAN
+-122.01,37.95,8.0,3866.0,539.0,1555.0,513.0,6.0901,298200.0,NEAR BAY
+-117.83,33.99,14.0,17527.0,2751.0,8380.0,2676.0,6.2734,267000.0,<1H OCEAN
+-121.94,37.53,33.0,2095.0,342.0,941.0,304.0,5.761,259600.0,<1H OCEAN
+-117.98,33.77,22.0,3236.0,673.0,2034.0,662.0,4.0955,174200.0,<1H OCEAN
+-118.36,34.18,36.0,2233.0,605.0,1934.0,599.0,2.8784,194900.0,<1H OCEAN
+-118.15,34.2,37.0,1997.0,361.0,1037.0,363.0,3.7932,210300.0,INLAND
+-118.78,34.05,28.0,1343.0,215.0,487.0,199.0,6.83,500001.0,NEAR OCEAN
+-121.94,37.3,25.0,1455.0,370.0,734.0,331.0,3.2727,262500.0,<1H OCEAN
+-121.42,37.74,19.0,1393.0,367.0,915.0,355.0,1.1957,103100.0,INLAND
+-117.16,32.74,27.0,2335.0,604.0,982.0,590.0,3.1921,261500.0,NEAR OCEAN
+-118.17,34.61,7.0,2465.0,336.0,978.0,332.0,7.1381,292200.0,INLAND
+-120.84,37.51,14.0,6337.0,1593.0,3909.0,1480.0,2.0643,106500.0,INLAND
+-120.65,38.5,10.0,1783.0,337.0,638.0,262.0,2.65,116700.0,INLAND
+-122.37,40.54,28.0,2213.0,390.0,1096.0,378.0,3.6923,86900.0,INLAND
+-117.02,32.67,20.0,1505.0,184.0,635.0,182.0,6.5772,245200.0,NEAR OCEAN
+-117.03,33.89,6.0,78.0,11.0,27.0,10.0,3.125,187500.0,INLAND
+-118.27,33.94,30.0,1041.0,275.0,877.0,270.0,1.5268,91600.0,<1H OCEAN
+-122.37,37.58,52.0,1900.0,290.0,665.0,276.0,4.5486,500001.0,NEAR OCEAN
+-118.36,33.93,27.0,4445.0,1231.0,3340.0,1113.0,3.1656,204500.0,<1H OCEAN
+-118.3,33.95,35.0,1182.0,305.0,977.0,283.0,1.5898,94000.0,<1H OCEAN
+-117.9,34.14,35.0,2259.0,505.0,1561.0,509.0,3.3043,155500.0,<1H OCEAN
+-122.65,38.01,40.0,1428.0,280.0,708.0,255.0,5.0766,305400.0,NEAR OCEAN
+-122.01,36.95,52.0,1217.0,325.0,508.0,237.0,2.0547,326700.0,NEAR OCEAN
+-119.06,34.36,52.0,1239.0,320.0,934.0,298.0,1.8618,183300.0,<1H OCEAN
+-122.22,37.79,46.0,2366.0,575.0,1647.0,527.0,2.6042,124700.0,NEAR BAY
+-118.1,34.07,36.0,3661.0,956.0,3816.0,931.0,2.5104,185000.0,<1H OCEAN
+-118.94,35.37,37.0,1667.0,362.0,971.0,335.0,2.875,57400.0,INLAND
+-117.94,33.9,27.0,2029.0,242.0,711.0,254.0,9.7956,500001.0,<1H OCEAN
+-118.15,33.92,28.0,1038.0,252.0,912.0,245.0,2.5875,161200.0,<1H OCEAN
+-122.16,37.7,36.0,2239.0,391.0,1203.0,379.0,5.0043,190400.0,NEAR BAY
+-118.55,34.19,36.0,978.0,170.0,475.0,192.0,4.675,222500.0,<1H OCEAN
+-118.43,34.24,36.0,1488.0,313.0,1221.0,296.0,4.0208,171400.0,<1H OCEAN
+-118.3,33.79,9.0,2782.0,884.0,1790.0,748.0,2.9154,203300.0,<1H OCEAN
+-122.68,38.43,29.0,488.0,63.0,161.0,62.0,6.0774,334400.0,<1H OCEAN
+-122.57,38.11,32.0,3521.0,748.0,1706.0,723.0,3.4705,228600.0,NEAR BAY
+-117.89,33.68,26.0,2905.0,504.0,1452.0,491.0,5.0853,260300.0,<1H OCEAN
+-122.04,37.94,24.0,5732.0,873.0,2444.0,888.0,5.6292,231400.0,NEAR BAY
+-119.67,36.33,19.0,1241.0,244.0,850.0,237.0,2.9375,81700.0,INLAND
+-121.26,38.13,25.0,2549.0,675.0,2053.0,648.0,2.0875,83100.0,INLAND
+-118.02,33.83,16.0,1139.0,328.0,665.0,290.0,3.2933,260000.0,<1H OCEAN
+-117.32,34.24,29.0,1290.0,263.0,323.0,113.0,1.9265,103300.0,INLAND
+-118.85,34.17,42.0,564.0,96.0,220.0,81.0,4.5625,318800.0,<1H OCEAN
+-118.31,33.94,40.0,1550.0,,798.0,270.0,3.775,153800.0,<1H OCEAN
+-122.13,39.0,23.0,3832.0,774.0,2435.0,747.0,2.2754,59200.0,INLAND
+-119.44,36.58,37.0,1054.0,,879.0,257.0,2.5234,63500.0,INLAND
+-119.7,34.41,19.0,1215.0,360.0,1349.0,423.0,2.6607,226500.0,NEAR OCEAN
+-117.7,33.64,15.0,5743.0,773.0,2380.0,773.0,8.1926,326600.0,<1H OCEAN
+-119.66,36.32,24.0,2652.0,568.0,1532.0,445.0,2.3256,56800.0,INLAND
+-120.88,37.52,2.0,1871.0,409.0,707.0,256.0,2.6103,133600.0,INLAND
+-122.11,37.14,29.0,3201.0,640.0,1722.0,570.0,4.4597,204100.0,NEAR OCEAN
+-118.04,34.06,39.0,1258.0,245.0,988.0,228.0,3.2132,176100.0,<1H OCEAN
+-122.7,38.23,47.0,2090.0,387.0,1053.0,377.0,3.5673,310300.0,<1H OCEAN
+-118.1,34.16,46.0,2863.0,513.0,1412.0,498.0,5.3272,330200.0,INLAND
+-118.3,34.26,37.0,2824.0,633.0,1619.0,573.0,3.5568,184500.0,<1H OCEAN
+-118.24,34.08,52.0,137.0,26.0,65.0,24.0,4.025,137500.0,<1H OCEAN
+-118.29,34.0,6.0,1487.0,468.0,1509.0,403.0,1.4639,112500.0,<1H OCEAN
+-118.14,34.08,30.0,1433.0,397.0,1110.0,346.0,2.3464,191700.0,<1H OCEAN
+-117.31,33.24,6.0,1580.0,288.0,792.0,265.0,4.0469,162400.0,<1H OCEAN
+-121.6,37.91,13.0,2479.0,394.0,1075.0,350.0,5.1017,241400.0,INLAND
+-121.76,37.68,52.0,2157.0,418.0,929.0,419.0,3.7301,204400.0,INLAND
+-117.88,33.89,19.0,3583.0,911.0,2300.0,871.0,3.0214,218400.0,<1H OCEAN
+-121.75,37.71,11.0,12070.0,2220.0,5826.0,2125.0,4.8624,192400.0,INLAND
+-120.21,36.98,21.0,1667.0,303.0,861.0,276.0,2.6012,92200.0,INLAND
+-122.13,37.72,25.0,1134.0,153.0,340.0,171.0,6.5095,371200.0,NEAR BAY
+-122.11,37.66,36.0,1755.0,316.0,913.0,299.0,4.1302,172700.0,NEAR BAY
+-121.06,38.7,9.0,13255.0,1739.0,5001.0,1627.0,6.314,228900.0,INLAND
+-117.67,33.54,16.0,2102.0,350.0,1003.0,328.0,4.7981,170800.0,<1H OCEAN
+-118.14,33.97,29.0,1846.0,530.0,2576.0,528.0,2.63,156000.0,<1H OCEAN
+-118.46,34.19,20.0,5992.0,1820.0,4826.0,1632.0,2.7237,233500.0,<1H OCEAN
+-121.3,37.94,52.0,24.0,6.0,23.0,5.0,2.375,67500.0,INLAND
+-118.09,34.14,40.0,3092.0,549.0,1457.0,536.0,5.3377,373800.0,INLAND
+-122.0,38.23,1.0,2062.0,343.0,872.0,268.0,5.2636,191300.0,INLAND
+-118.11,33.92,34.0,1414.0,263.0,983.0,264.0,4.1767,156600.0,<1H OCEAN
+-117.19,34.07,40.0,2374.0,500.0,1772.0,455.0,2.189,72500.0,INLAND
+-121.43,38.52,43.0,2089.0,399.0,955.0,385.0,2.5898,72100.0,INLAND
+-117.89,33.74,32.0,1562.0,365.0,2145.0,347.0,2.9167,158400.0,<1H OCEAN
+-122.03,37.37,9.0,2966.0,770.0,1430.0,740.0,3.0047,256000.0,<1H OCEAN
+-117.18,32.75,52.0,1504.0,208.0,518.0,196.0,8.603,459600.0,NEAR OCEAN
+-122.28,37.79,30.0,4145.0,869.0,3668.0,855.0,2.5444,275000.0,NEAR BAY
+-119.85,36.76,10.0,2067.0,450.0,845.0,354.0,1.8214,80100.0,INLAND
+-116.61,33.04,11.0,2522.0,538.0,616.0,269.0,3.875,198100.0,INLAND
+-121.93,37.7,3.0,2456.0,582.0,793.0,456.0,4.4087,225600.0,<1H OCEAN
+-118.51,34.17,31.0,3252.0,834.0,1411.0,760.0,3.1885,219000.0,<1H OCEAN
+-122.47,37.67,20.0,5689.0,992.0,3752.0,1002.0,5.5845,304300.0,NEAR OCEAN
+-117.03,32.72,38.0,886.0,176.0,505.0,188.0,3.5938,125400.0,NEAR OCEAN
+-122.12,38.01,50.0,1300.0,263.0,691.0,239.0,3.9519,126500.0,NEAR BAY
+-117.92,34.05,36.0,2241.0,419.0,1743.0,448.0,4.6587,161900.0,<1H OCEAN
+-117.23,32.75,23.0,2415.0,653.0,1275.0,596.0,3.1389,101800.0,NEAR OCEAN
+-122.08,37.68,37.0,848.0,202.0,314.0,205.0,2.3958,190800.0,NEAR BAY
+-122.31,40.45,10.0,1187.0,236.0,728.0,248.0,2.0469,66800.0,INLAND
+-118.68,34.27,26.0,1561.0,212.0,817.0,242.0,5.477,209100.0,<1H OCEAN
+-121.97,37.97,27.0,1691.0,289.0,807.0,296.0,6.1168,210500.0,INLAND
+-121.32,38.68,25.0,1252.0,207.0,587.0,217.0,3.5893,146400.0,INLAND
+-117.91,33.67,32.0,3058.0,562.0,1475.0,569.0,4.4625,253500.0,<1H OCEAN
+-118.05,33.99,38.0,1619.0,,886.0,357.0,3.7328,182400.0,<1H OCEAN
+-122.23,37.78,44.0,2340.0,825.0,2813.0,751.0,1.6009,118100.0,NEAR BAY
+-117.1,32.75,16.0,2426.0,799.0,1505.0,754.0,1.6444,103400.0,NEAR OCEAN
+-117.92,33.87,33.0,1597.0,,1888.0,423.0,3.055,157800.0,<1H OCEAN
+-118.24,34.0,43.0,863.0,206.0,788.0,187.0,0.9463,95000.0,<1H OCEAN
+-119.76,36.75,41.0,1576.0,417.0,1567.0,366.0,1.2545,45500.0,INLAND
+-120.35,36.16,18.0,1519.0,296.0,846.0,272.0,2.7792,85300.0,INLAND
+-118.31,33.98,52.0,1837.0,426.0,1062.0,343.0,2.0,96500.0,<1H OCEAN
+-117.94,33.64,18.0,1867.0,426.0,871.0,399.0,2.6221,272000.0,<1H OCEAN
+-119.72,36.81,28.0,1651.0,305.0,780.0,309.0,2.9453,72200.0,INLAND
+-122.47,37.69,30.0,837.0,213.0,606.0,199.0,4.875,258800.0,NEAR OCEAN
+-122.76,38.46,14.0,4742.0,756.0,2149.0,732.0,4.5152,199200.0,<1H OCEAN
+-122.36,37.58,37.0,3325.0,734.0,1468.0,692.0,4.0987,434000.0,NEAR OCEAN
+-117.98,33.75,24.0,3865.0,802.0,2670.0,772.0,3.8158,180000.0,<1H OCEAN
+-117.0,32.7,23.0,2785.0,468.0,1456.0,449.0,4.3714,131000.0,NEAR OCEAN
+-117.82,33.81,25.0,2662.0,402.0,1247.0,401.0,5.4395,244000.0,<1H OCEAN
+-122.0,37.4,17.0,4324.0,854.0,1656.0,885.0,3.6619,232400.0,<1H OCEAN
+-118.36,33.89,40.0,756.0,122.0,371.0,130.0,5.0299,329200.0,<1H OCEAN
+-121.37,38.67,36.0,1354.0,258.0,771.0,267.0,2.2723,78800.0,INLAND
+-118.26,33.94,41.0,1510.0,410.0,1408.0,389.0,1.65,94200.0,<1H OCEAN
+-121.26,38.14,10.0,3371.0,665.0,1823.0,654.0,3.5333,116800.0,INLAND
+-116.99,33.99,22.0,4227.0,658.0,1849.0,619.0,4.7356,195900.0,INLAND
+-117.27,34.12,27.0,2896.0,684.0,1514.0,668.0,1.462,70200.0,INLAND
+-121.42,38.48,13.0,7880.0,1992.0,4749.0,1882.0,1.9657,116000.0,INLAND
+-118.36,33.89,37.0,1719.0,426.0,1266.0,424.0,3.375,228000.0,<1H OCEAN
+-118.2,34.11,52.0,678.0,173.0,791.0,186.0,4.0625,171300.0,<1H OCEAN
+-118.4,33.96,44.0,1877.0,314.0,877.0,320.0,6.8197,363600.0,<1H OCEAN
+-117.15,33.45,4.0,9089.0,1413.0,3886.0,1243.0,4.6904,174200.0,<1H OCEAN
+-122.12,37.91,34.0,5683.0,755.0,1962.0,723.0,8.3678,455300.0,NEAR BAY
+-121.25,38.68,15.0,1497.0,243.0,730.0,242.0,4.9688,135600.0,INLAND
+-122.31,37.93,36.0,1526.0,256.0,696.0,263.0,3.5089,261900.0,NEAR BAY
+-118.44,34.29,30.0,1632.0,401.0,1357.0,401.0,3.1588,160100.0,<1H OCEAN
+-118.28,34.1,44.0,2728.0,585.0,1227.0,567.0,4.0602,324000.0,<1H OCEAN
+-119.74,36.65,19.0,2546.0,463.0,1257.0,418.0,2.9013,89500.0,INLAND
+-118.32,33.98,40.0,1298.0,277.0,791.0,255.0,3.2344,104300.0,<1H OCEAN
+-118.14,34.71,32.0,1164.0,248.0,588.0,270.0,1.1917,86900.0,INLAND
+-123.16,39.1,31.0,418.0,82.0,327.0,81.0,2.775,120800.0,<1H OCEAN
+-121.99,37.25,22.0,4240.0,532.0,1480.0,514.0,11.2463,500001.0,<1H OCEAN
+-122.95,40.67,17.0,1498.0,331.0,574.0,242.0,2.0268,94200.0,INLAND
+-122.02,38.26,20.0,3899.0,763.0,2198.0,779.0,3.2061,120400.0,INLAND
+-122.44,37.79,52.0,1817.0,535.0,800.0,487.0,3.975,500001.0,NEAR BAY
+-117.22,34.44,5.0,4787.0,910.0,1944.0,806.0,2.6576,98500.0,INLAND
+-122.2,37.76,47.0,1116.0,259.0,826.0,279.0,1.75,85700.0,NEAR BAY
+-122.65,38.11,21.0,3891.0,616.0,1968.0,632.0,5.5524,279200.0,<1H OCEAN
+-118.4,34.2,13.0,4859.0,1293.0,3351.0,1200.0,3.6875,211900.0,<1H OCEAN
+-118.24,33.98,30.0,861.0,250.0,1062.0,231.0,1.75,115400.0,<1H OCEAN
+-118.42,34.02,26.0,2664.0,842.0,1745.0,789.0,3.4269,301900.0,<1H OCEAN
+-117.32,34.09,38.0,1585.0,345.0,1347.0,368.0,2.375,75300.0,INLAND
+-122.13,37.89,27.0,744.0,214.0,295.0,169.0,2.7411,350000.0,NEAR BAY
+-122.23,37.75,50.0,1542.0,289.0,654.0,268.0,3.9632,240000.0,NEAR BAY
+-117.33,34.05,26.0,613.0,149.0,431.0,130.0,1.3977,73100.0,INLAND
+-121.16,38.74,17.0,3353.0,463.0,1417.0,447.0,5.1721,237100.0,INLAND
+-116.98,32.71,18.0,2355.0,444.0,1277.0,433.0,3.4551,121400.0,<1H OCEAN
+-118.32,33.94,38.0,1067.0,170.0,499.0,169.0,4.6389,183800.0,<1H OCEAN
+-118.34,34.06,52.0,1314.0,170.0,629.0,214.0,7.1669,500001.0,<1H OCEAN
+-118.41,33.97,44.0,2298.0,388.0,849.0,360.0,5.5941,363500.0,<1H OCEAN
+-121.62,39.11,11.0,3519.0,577.0,1459.0,549.0,4.2792,123800.0,INLAND
+-118.58,34.06,25.0,4440.0,693.0,1560.0,636.0,8.8666,500001.0,<1H OCEAN
+-119.46,36.6,18.0,1404.0,226.0,754.0,229.0,3.9844,118100.0,INLAND
+-122.26,37.88,52.0,1149.0,255.0,483.0,249.0,4.2788,332500.0,NEAR BAY
+-118.29,33.98,46.0,1118.0,300.0,786.0,254.0,1.4042,110000.0,<1H OCEAN
+-118.12,34.06,35.0,1729.0,438.0,1308.0,412.0,2.5321,197200.0,<1H OCEAN
+-118.36,34.16,42.0,2304.0,442.0,862.0,429.0,4.3542,417900.0,<1H OCEAN
+-122.21,37.46,40.0,1777.0,207.0,577.0,207.0,15.0001,500001.0,NEAR OCEAN
+-118.44,34.05,15.0,5368.0,1312.0,2269.0,1232.0,5.7097,316700.0,<1H OCEAN
+-121.8,39.64,25.0,2202.0,422.0,1109.0,403.0,2.8306,87500.0,INLAND
+-123.02,38.46,52.0,2154.0,499.0,524.0,259.0,2.0556,120000.0,NEAR OCEAN
+-117.05,33.29,17.0,1800.0,312.0,891.0,281.0,7.0177,267600.0,<1H OCEAN
+-122.18,38.29,18.0,1953.0,265.0,658.0,270.0,8.0113,393000.0,NEAR BAY
+-122.51,37.96,39.0,3302.0,684.0,1574.0,653.0,3.6863,263800.0,NEAR BAY
+-120.43,34.88,22.0,2580.0,381.0,1149.0,372.0,5.0113,158600.0,<1H OCEAN
+-121.41,38.56,17.0,7228.0,1369.0,2455.0,1365.0,5.1385,179500.0,INLAND
+-118.27,34.05,12.0,535.0,328.0,1194.0,365.0,1.2012,275000.0,<1H OCEAN
+-121.24,36.33,13.0,1642.0,418.0,1534.0,388.0,3.1222,125500.0,<1H OCEAN
+-117.44,33.92,33.0,2433.0,525.0,1466.0,517.0,3.0437,110800.0,INLAND
+-118.29,33.93,43.0,2021.0,379.0,1051.0,352.0,3.3836,129900.0,<1H OCEAN
+-117.92,33.83,6.0,3136.0,990.0,1894.0,859.0,2.5564,171300.0,<1H OCEAN
+-121.7,39.36,46.0,1210.0,243.0,523.0,242.0,1.91,63900.0,INLAND
+-119.74,36.76,36.0,912.0,216.0,842.0,219.0,1.4766,52800.0,INLAND
+-118.46,34.32,10.0,5777.0,1112.0,2917.0,1056.0,4.1514,194100.0,<1H OCEAN
+-121.39,38.61,35.0,2024.0,359.0,786.0,364.0,2.4632,156900.0,INLAND
+-118.26,34.15,18.0,2481.0,756.0,1763.0,675.0,2.8088,247500.0,<1H OCEAN
+-118.13,33.78,45.0,1016.0,172.0,361.0,163.0,7.5,434500.0,NEAR OCEAN
+-118.17,33.77,45.0,2508.0,797.0,1340.0,720.0,2.6786,191100.0,NEAR OCEAN
+-121.33,38.28,14.0,980.0,171.0,659.0,183.0,4.4306,170100.0,INLAND
+-117.92,33.68,25.0,2017.0,454.0,1024.0,428.0,4.4732,245600.0,<1H OCEAN
+-121.35,38.61,27.0,3900.0,776.0,1549.0,761.0,2.7788,115700.0,INLAND
+-123.17,39.15,30.0,1904.0,331.0,816.0,325.0,4.425,161900.0,<1H OCEAN
+-117.61,34.09,11.0,2000.0,391.0,1503.0,426.0,4.6167,144000.0,INLAND
+-117.99,33.89,23.0,2111.0,306.0,979.0,288.0,8.5621,347800.0,<1H OCEAN
+-120.45,37.79,8.0,2687.0,495.0,5087.0,385.0,3.1719,115400.0,INLAND
+-118.34,34.19,47.0,1721.0,343.0,834.0,334.0,4.1923,231200.0,<1H OCEAN
+-118.37,34.08,22.0,3008.0,938.0,1224.0,816.0,3.2149,300000.0,<1H OCEAN
+-122.48,37.64,7.0,120.0,21.0,50.0,27.0,12.5,281000.0,NEAR OCEAN
+-122.27,37.9,42.0,1650.0,274.0,645.0,256.0,5.6228,375400.0,NEAR BAY
+-122.35,40.57,22.0,589.0,97.0,338.0,107.0,3.2639,87500.0,INLAND
+-121.65,36.77,15.0,2191.0,358.0,1150.0,330.0,4.7969,227500.0,<1H OCEAN
+-122.11,38.0,9.0,3424.0,583.0,1460.0,543.0,5.76,212600.0,NEAR BAY
+-121.68,37.98,19.0,3388.0,599.0,1707.0,575.0,3.6411,162800.0,INLAND
+-118.26,33.9,38.0,1566.0,318.0,981.0,318.0,4.0234,111900.0,<1H OCEAN
+-118.2,34.16,31.0,5550.0,881.0,2465.0,862.0,6.8317,446100.0,<1H OCEAN
+-118.51,34.2,33.0,2327.0,479.0,1166.0,472.0,4.2344,262500.0,<1H OCEAN
+-117.21,32.82,31.0,2035.0,383.0,866.0,360.0,3.8529,212000.0,NEAR OCEAN
+-122.56,37.95,34.0,2677.0,411.0,933.0,410.0,6.1444,500001.0,NEAR OCEAN
+-118.11,34.06,30.0,1547.0,436.0,1700.0,410.0,2.5488,187500.0,<1H OCEAN
+-117.48,33.94,19.0,1891.0,465.0,1693.0,416.0,2.7813,112900.0,INLAND
+-119.34,35.6,33.0,2143.0,488.0,1732.0,509.0,1.9362,59000.0,INLAND
+-121.63,37.12,17.0,1830.0,398.0,1110.0,388.0,2.4821,248200.0,<1H OCEAN
+-118.21,34.17,24.0,8590.0,1231.0,3401.0,1178.0,8.1325,472700.0,<1H OCEAN
+-122.44,37.74,23.0,184.0,44.0,118.0,40.0,4.5375,350000.0,NEAR BAY
+-118.35,34.15,35.0,2245.0,393.0,783.0,402.0,4.1544,500001.0,<1H OCEAN
+-122.24,37.81,52.0,2026.0,482.0,709.0,456.0,3.2727,268500.0,NEAR BAY
+-118.29,34.26,33.0,3177.0,713.0,1845.0,612.0,4.008,191100.0,<1H OCEAN
+-118.33,33.34,52.0,2359.0,591.0,1100.0,431.0,2.8333,414700.0,ISLAND
+-114.62,33.62,26.0,18.0,3.0,5.0,3.0,0.536,275000.0,INLAND
+-121.0,37.64,19.0,121.0,41.0,658.0,41.0,0.9573,162500.0,INLAND
+-121.84,36.52,18.0,3165.0,533.0,1312.0,434.0,6.5234,357400.0,NEAR OCEAN
+-122.06,37.63,23.0,1939.0,356.0,841.0,364.0,3.3611,169200.0,NEAR BAY
+-118.33,34.21,31.0,3190.0,489.0,1362.0,480.0,6.981,402900.0,<1H OCEAN
+-121.4,38.56,22.0,2623.0,357.0,838.0,368.0,7.143,327800.0,INLAND
+-118.1,34.13,47.0,2234.0,276.0,749.0,260.0,15.0001,500001.0,<1H OCEAN
+-117.56,33.83,28.0,895.0,127.0,346.0,115.0,5.4788,339300.0,INLAND
+-121.94,37.33,37.0,1822.0,329.0,845.0,348.0,4.75,251100.0,<1H OCEAN
+-118.31,34.03,52.0,1902.0,406.0,1233.0,385.0,2.8295,132200.0,<1H OCEAN
+-118.07,34.07,19.0,1554.0,393.0,1427.0,370.0,3.125,207100.0,<1H OCEAN
+-117.26,34.16,31.0,3538.0,658.0,1715.0,663.0,3.7125,98000.0,INLAND
+-121.66,37.93,19.0,2055.0,358.0,1064.0,350.0,4.7426,263100.0,INLAND
+-122.0,37.28,35.0,3133.0,541.0,1449.0,555.0,5.7295,346100.0,<1H OCEAN
+-122.3,37.53,40.0,1833.0,308.0,751.0,306.0,6.0,384200.0,NEAR OCEAN
+-121.74,39.38,27.0,2596.0,435.0,1100.0,409.0,2.3243,85500.0,INLAND
+-118.42,33.85,43.0,1584.0,477.0,799.0,433.0,5.0322,435000.0,NEAR OCEAN
+-122.23,37.82,52.0,3494.0,396.0,1192.0,383.0,12.3804,500001.0,NEAR BAY
+-121.97,37.56,13.0,8918.0,1823.0,4518.0,1772.0,4.8052,254000.0,<1H OCEAN
+-115.51,33.24,32.0,1995.0,523.0,1069.0,410.0,1.6552,43300.0,INLAND
+-122.23,37.84,50.0,2515.0,399.0,970.0,373.0,5.8596,327600.0,NEAR BAY
+-121.83,37.21,14.0,2714.0,361.0,1259.0,375.0,7.7738,387500.0,<1H OCEAN
+-124.05,40.94,14.0,1452.0,217.0,516.0,181.0,5.0329,165600.0,NEAR OCEAN
+-118.29,33.98,42.0,2833.0,768.0,2542.0,725.0,1.3479,100000.0,<1H OCEAN
+-121.93,36.6,33.0,3455.0,683.0,1704.0,663.0,4.0154,225700.0,NEAR OCEAN
+-118.08,34.11,42.0,2628.0,525.0,1494.0,523.0,3.9464,257200.0,<1H OCEAN
+-118.26,33.93,35.0,1562.0,403.0,1587.0,406.0,1.4917,93200.0,<1H OCEAN
+-119.12,35.41,12.0,5589.0,941.0,3018.0,917.0,4.4561,96900.0,INLAND
+-118.11,33.94,37.0,1434.0,262.0,786.0,256.0,4.4375,244900.0,<1H OCEAN
+-117.93,33.61,27.0,1806.0,465.0,791.0,358.0,3.8125,366700.0,<1H OCEAN
+-122.73,38.34,44.0,743.0,155.0,434.0,162.0,2.5819,209600.0,<1H OCEAN
+-118.0,33.81,17.0,2142.0,436.0,946.0,412.0,3.7059,146300.0,<1H OCEAN
+-115.57,32.78,15.0,1413.0,279.0,803.0,277.0,4.3021,87500.0,INLAND
+-122.43,37.77,52.0,4397.0,1116.0,1939.0,1053.0,2.7587,354500.0,NEAR BAY
+-121.35,38.0,22.0,3564.0,730.0,1539.0,699.0,3.675,152400.0,INLAND
+-121.92,38.03,16.0,2176.0,464.0,1410.0,434.0,3.5436,100200.0,INLAND
+-119.66,34.44,26.0,2790.0,413.0,1014.0,397.0,6.5631,500001.0,<1H OCEAN
+-119.51,34.46,28.0,3506.0,563.0,1362.0,483.0,6.091,500001.0,<1H OCEAN
+-118.29,34.05,34.0,1102.0,,1325.0,439.0,1.5972,168800.0,<1H OCEAN
+-118.07,33.86,17.0,3666.0,562.0,2104.0,579.0,5.6818,338900.0,<1H OCEAN
+-119.19,36.34,33.0,2199.0,403.0,1245.0,394.0,2.73,96900.0,INLAND
+-119.52,36.71,21.0,1834.0,321.0,1120.0,314.0,2.59,69300.0,INLAND
+-118.35,34.16,42.0,2267.0,478.0,1083.0,458.0,3.2015,250000.0,<1H OCEAN
+-121.35,38.56,16.0,2629.0,491.0,1265.0,485.0,4.5066,140200.0,INLAND
+-121.9,38.01,16.0,2604.0,454.0,1696.0,481.0,4.6628,136000.0,INLAND
+-120.08,34.64,18.0,2375.0,429.0,1048.0,369.0,4.2222,375000.0,NEAR OCEAN
+-117.98,33.84,31.0,1252.0,225.0,714.0,226.0,4.6042,220700.0,<1H OCEAN
+-122.03,38.25,13.0,3334.0,541.0,1923.0,538.0,4.0905,134800.0,INLAND
+-121.26,38.67,18.0,1830.0,313.0,905.0,361.0,4.2273,141800.0,INLAND
+-117.6,33.43,21.0,3951.0,562.0,1392.0,543.0,5.1439,414000.0,NEAR OCEAN
+-122.01,37.06,19.0,4113.0,767.0,2006.0,732.0,5.1121,308100.0,NEAR OCEAN
+-121.66,37.11,19.0,3785.0,611.0,2198.0,610.0,5.1514,436700.0,<1H OCEAN
+-118.34,33.93,33.0,4294.0,1224.0,4512.0,1189.0,2.8304,143700.0,<1H OCEAN
+-118.46,34.19,35.0,1491.0,295.0,779.0,309.0,6.1142,256300.0,<1H OCEAN
+-119.29,36.32,35.0,1898.0,481.0,1123.0,433.0,1.1419,62900.0,INLAND
+-117.14,32.9,16.0,3217.0,,2054.0,687.0,4.2234,162100.0,<1H OCEAN
+-121.9,37.45,18.0,4900.0,814.0,2984.0,758.0,6.6176,276200.0,<1H OCEAN
+-118.01,33.84,28.0,4097.0,838.0,2112.0,803.0,4.5,202100.0,<1H OCEAN
+-118.37,34.06,36.0,1661.0,395.0,690.0,365.0,3.3438,500001.0,<1H OCEAN
+-122.55,38.1,26.0,5188.0,892.0,2341.0,906.0,5.0029,255600.0,NEAR BAY
+-119.9,37.49,13.0,2230.0,443.0,920.0,361.0,3.0,112000.0,INLAND
+-118.01,33.82,31.0,1960.0,380.0,1356.0,356.0,4.0625,225900.0,<1H OCEAN
+-117.79,33.77,23.0,3596.0,451.0,1292.0,458.0,8.5403,451300.0,<1H OCEAN
+-118.3,34.18,5.0,5492.0,1549.0,2997.0,1405.0,3.3205,172100.0,<1H OCEAN
+-122.2,37.79,49.0,882.0,195.0,737.0,210.0,2.6667,122000.0,NEAR BAY
+-122.02,37.53,21.0,4280.0,673.0,2216.0,681.0,5.7072,242200.0,NEAR BAY
+-117.27,34.06,20.0,5258.0,1514.0,3780.0,1404.0,2.025,85700.0,INLAND
+-121.87,37.39,16.0,2655.0,487.0,1862.0,448.0,6.057,246800.0,<1H OCEAN
+-121.89,37.33,42.0,1279.0,358.0,1254.0,340.0,2.2583,192500.0,<1H OCEAN
+-118.28,34.09,52.0,1739.0,464.0,938.0,482.0,2.4429,228800.0,<1H OCEAN
+-117.14,32.74,47.0,1494.0,327.0,689.0,304.0,3.125,172700.0,NEAR OCEAN
+-122.72,38.44,52.0,1059.0,281.0,627.0,273.0,1.5357,137500.0,<1H OCEAN
+-122.39,37.59,33.0,2064.0,299.0,813.0,303.0,6.0374,500001.0,NEAR OCEAN
+-118.26,33.85,25.0,2324.0,326.0,1087.0,328.0,5.293,207000.0,<1H OCEAN
+-118.7,34.24,28.0,2405.0,462.0,1011.0,378.0,4.504,204300.0,<1H OCEAN
+-118.33,34.03,39.0,2840.0,826.0,1911.0,688.0,1.9018,137500.0,<1H OCEAN
+-118.24,33.83,22.0,7368.0,1367.0,4721.0,1342.0,4.8438,213100.0,<1H OCEAN
+-118.36,33.84,35.0,1577.0,279.0,743.0,274.0,5.7654,343000.0,<1H OCEAN
+-122.02,38.0,28.0,2965.0,533.0,1591.0,472.0,4.6375,178200.0,NEAR BAY
+-118.64,34.19,28.0,3274.0,571.0,1424.0,521.0,4.4167,247300.0,<1H OCEAN
+-120.47,37.29,16.0,749.0,222.0,1277.0,224.0,1.2054,60900.0,INLAND
+-119.1,36.21,38.0,727.0,173.0,559.0,176.0,2.4653,49500.0,INLAND
+-122.47,37.61,34.0,4551.0,837.0,2208.0,834.0,5.4364,279300.0,NEAR OCEAN
+-118.27,33.81,10.0,1881.0,571.0,1769.0,553.0,3.9286,114000.0,<1H OCEAN
+-122.03,37.6,24.0,2077.0,383.0,1488.0,389.0,4.5721,214700.0,NEAR BAY
+-121.96,37.95,13.0,3216.0,765.0,1627.0,715.0,3.0859,167800.0,INLAND
+-118.24,33.93,32.0,779.0,201.0,861.0,219.0,1.0625,89800.0,<1H OCEAN
+-118.13,34.15,9.0,2099.0,625.0,1252.0,554.0,3.1875,173100.0,<1H OCEAN
+-122.24,38.12,42.0,1625.0,255.0,578.0,243.0,4.0114,166900.0,NEAR BAY
+-119.43,36.55,27.0,1621.0,323.0,882.0,324.0,2.75,93500.0,INLAND
+-121.61,39.13,33.0,2559.0,539.0,1583.0,504.0,1.4727,53000.0,INLAND
+-121.25,37.97,34.0,1288.0,344.0,846.0,293.0,1.7895,63100.0,INLAND
+-117.12,32.57,21.0,1738.0,295.0,983.0,298.0,4.8274,174100.0,NEAR OCEAN
+-117.13,32.76,27.0,2280.0,695.0,1235.0,664.0,1.9392,142900.0,NEAR OCEAN
+-122.33,40.48,26.0,695.0,126.0,319.0,124.0,3.2788,101600.0,INLAND
+-118.17,33.88,42.0,1645.0,371.0,1161.0,351.0,3.0893,162700.0,<1H OCEAN
+-117.96,33.83,34.0,982.0,148.0,498.0,156.0,6.3214,220800.0,<1H OCEAN
+-120.94,37.6,30.0,3257.0,574.0,1804.0,588.0,3.5331,102900.0,INLAND
+-117.86,33.76,15.0,851.0,297.0,1326.0,254.0,2.8289,117500.0,<1H OCEAN
+-118.33,33.72,25.0,6191.0,1081.0,2297.0,1023.0,6.4246,446700.0,NEAR OCEAN
+-121.79,36.64,11.0,32627.0,6445.0,28566.0,6082.0,2.3087,118800.0,<1H OCEAN
+-121.8,40.34,26.0,4815.0,910.0,1341.0,539.0,2.881,79800.0,INLAND
+-119.31,36.34,32.0,1893.0,453.0,1744.0,425.0,1.4729,54100.0,INLAND
+-122.45,37.77,52.0,2645.0,626.0,1275.0,553.0,3.35,492900.0,NEAR BAY
+-117.24,33.05,11.0,5827.0,882.0,2588.0,842.0,6.4027,344200.0,NEAR OCEAN
+-118.96,35.38,34.0,2047.0,347.0,888.0,352.0,3.6734,92900.0,INLAND
+-117.83,33.82,26.0,3259.0,456.0,1354.0,459.0,5.7817,267600.0,<1H OCEAN
+-116.79,32.61,19.0,2652.0,520.0,1421.0,491.0,3.5227,206100.0,<1H OCEAN
+-119.02,35.42,40.0,1089.0,226.0,520.0,218.0,2.2727,67200.0,INLAND
+-119.46,35.17,40.0,4164.0,812.0,1998.0,773.0,2.8323,50800.0,INLAND
+-117.22,32.75,24.0,3914.0,985.0,2147.0,874.0,2.9735,225000.0,NEAR OCEAN
+-121.82,37.23,23.0,4487.0,584.0,2024.0,580.0,7.5218,291500.0,<1H OCEAN
+-121.36,38.4,18.0,4813.0,849.0,2333.0,843.0,4.175,144400.0,INLAND
+-117.3,33.85,15.0,3991.0,751.0,2317.0,657.0,2.9542,127900.0,INLAND
+-121.83,40.69,14.0,821.0,170.0,477.0,129.0,3.15,87500.0,INLAND
+-119.19,34.46,39.0,2056.0,381.0,939.0,371.0,6.6257,427600.0,<1H OCEAN
+-122.33,37.94,43.0,1876.0,389.0,807.0,377.0,3.1571,141600.0,NEAR BAY
+-118.21,33.79,33.0,32.0,18.0,96.0,36.0,4.5938,112500.0,NEAR OCEAN
+-117.47,33.94,34.0,2086.0,417.0,1501.0,395.0,3.2311,105600.0,INLAND
+-118.44,34.18,33.0,2127.0,414.0,1056.0,391.0,4.375,286100.0,<1H OCEAN
+-118.94,35.39,27.0,3074.0,452.0,1223.0,452.0,5.4592,139100.0,INLAND
+-121.97,37.0,25.0,990.0,166.0,522.0,185.0,4.8269,272900.0,NEAR OCEAN
+-121.82,37.28,31.0,1340.0,235.0,1336.0,270.0,4.2361,179500.0,<1H OCEAN
+-117.27,33.22,5.0,2283.0,337.0,999.0,325.0,5.0249,196700.0,<1H OCEAN
+-118.34,33.92,6.0,1047.0,271.0,740.0,248.0,3.425,193800.0,<1H OCEAN
+-117.19,32.86,19.0,3716.0,563.0,1788.0,587.0,5.2113,267400.0,NEAR OCEAN
+-118.25,34.15,15.0,3712.0,1005.0,1888.0,890.0,3.6875,209600.0,<1H OCEAN
+-122.04,37.35,28.0,1582.0,264.0,696.0,270.0,5.678,370100.0,<1H OCEAN
+-118.33,33.97,47.0,1830.0,369.0,922.0,377.0,4.1635,156400.0,<1H OCEAN
+-118.27,34.1,51.0,3149.0,519.0,1082.0,510.0,6.4459,421600.0,<1H OCEAN
+-119.12,34.38,28.0,7200.0,1281.0,3793.0,1238.0,4.075,237900.0,<1H OCEAN
+-117.93,33.76,17.0,3341.0,803.0,3381.0,825.0,3.371,161800.0,<1H OCEAN
+-121.85,36.61,38.0,238.0,,191.0,67.0,1.3897,125000.0,<1H OCEAN
+-118.31,33.74,22.0,5042.0,974.0,2260.0,935.0,4.3472,351200.0,NEAR OCEAN
+-121.29,38.61,26.0,1814.0,299.0,963.0,317.0,4.4519,110500.0,INLAND
+-117.93,34.06,28.0,3342.0,688.0,2210.0,647.0,3.4596,202800.0,<1H OCEAN
+-118.19,33.86,46.0,1824.0,438.0,1200.0,451.0,3.4375,156700.0,<1H OCEAN
+-118.37,34.17,15.0,3327.0,1011.0,2683.0,857.0,2.3784,185400.0,<1H OCEAN
+-117.69,33.54,20.0,1767.0,280.0,801.0,284.0,6.5394,272000.0,<1H OCEAN
+-117.29,34.12,45.0,1369.0,351.0,1046.0,274.0,1.8438,72100.0,INLAND
+-118.16,34.02,34.0,1474.0,511.0,1962.0,501.0,1.8715,139600.0,<1H OCEAN
+-117.94,33.93,14.0,999.0,232.0,1037.0,244.0,2.7125,166100.0,<1H OCEAN
+-120.68,35.51,17.0,1701.0,298.0,941.0,293.0,4.3218,209100.0,<1H OCEAN
+-118.3,33.93,29.0,2228.0,396.0,1140.0,352.0,3.7969,169400.0,<1H OCEAN
+-117.82,33.74,25.0,2720.0,680.0,1559.0,631.0,3.0958,137800.0,<1H OCEAN
+-117.8,33.78,18.0,3548.0,474.0,1506.0,449.0,6.925,290300.0,<1H OCEAN
+-118.32,34.16,49.0,1074.0,170.0,403.0,208.0,6.2547,366700.0,<1H OCEAN
+-118.66,35.2,7.0,9664.0,1692.0,3617.0,1370.0,4.0581,162900.0,INLAND
+-121.42,38.7,10.0,2562.0,460.0,1478.0,433.0,4.0625,96200.0,INLAND
+-120.45,34.96,11.0,1299.0,280.0,1158.0,223.0,2.5556,129200.0,<1H OCEAN
+-122.41,37.74,52.0,831.0,175.0,415.0,159.0,1.9464,249000.0,NEAR BAY
+-121.27,38.66,19.0,1891.0,266.0,678.0,255.0,6.1872,188700.0,INLAND
+-118.01,34.08,30.0,2281.0,522.0,1969.0,500.0,3.6531,166300.0,INLAND
+-118.07,34.17,34.0,4062.0,597.0,1525.0,566.0,7.8588,454800.0,INLAND
+-118.15,34.15,52.0,275.0,123.0,273.0,111.0,1.1667,500001.0,<1H OCEAN
+-118.95,34.16,21.0,2953.0,419.0,1397.0,410.0,6.541,291500.0,<1H OCEAN
+-118.02,34.14,34.0,1077.0,257.0,478.0,199.0,2.6316,252800.0,INLAND
+-118.28,33.96,41.0,1175.0,340.0,1241.0,352.0,1.2273,98400.0,<1H OCEAN
+-119.81,36.6,24.0,2246.0,462.0,1291.0,394.0,2.4006,76400.0,INLAND
+-121.8,38.55,11.0,5121.0,899.0,2258.0,901.0,4.7168,223200.0,INLAND
+-117.01,32.75,26.0,4038.0,706.0,2065.0,687.0,3.9545,178100.0,<1H OCEAN
+-118.25,34.06,20.0,41.0,17.0,87.0,25.0,1.5491,225000.0,<1H OCEAN
+-121.95,36.6,32.0,3152.0,504.0,793.0,426.0,7.1198,469900.0,NEAR OCEAN
+-118.02,34.15,44.0,2267.0,426.0,980.0,372.0,3.6,307400.0,INLAND
+-118.13,33.8,41.0,1509.0,325.0,821.0,314.0,4.0893,223000.0,<1H OCEAN
+-117.01,32.71,20.0,3506.0,692.0,1977.0,668.0,2.981,129100.0,NEAR OCEAN
+-117.18,32.92,4.0,15025.0,2616.0,7560.0,2392.0,5.196,210700.0,<1H OCEAN
+-117.69,33.8,5.0,3178.0,631.0,1467.0,581.0,5.2541,237100.0,<1H OCEAN
+-122.06,37.37,32.0,2510.0,578.0,1160.0,581.0,4.9087,322700.0,<1H OCEAN
+-118.0,33.81,17.0,1530.0,404.0,883.0,344.0,2.8835,196500.0,<1H OCEAN
+-117.37,33.96,33.0,3974.0,548.0,1398.0,528.0,7.2519,216600.0,INLAND
+-122.19,37.8,48.0,1694.0,259.0,610.0,238.0,4.744,257300.0,NEAR BAY
+-121.27,38.11,11.0,3163.0,794.0,2106.0,762.0,2.4482,103000.0,INLAND
+-121.21,37.81,18.0,2404.0,498.0,1531.0,506.0,2.995,124300.0,INLAND
+-122.86,38.42,38.0,1166.0,223.0,584.0,225.0,3.6667,244400.0,<1H OCEAN
+-117.9,33.98,20.0,9893.0,2283.0,7228.0,2159.0,3.253,186700.0,<1H OCEAN
+-122.2,38.04,31.0,3029.0,500.0,1236.0,487.0,5.6022,197000.0,NEAR BAY
+-118.11,34.1,49.0,2812.0,478.0,1329.0,490.0,5.2503,292900.0,<1H OCEAN
+-117.98,33.7,24.0,3451.0,504.0,1736.0,493.0,6.3749,278000.0,<1H OCEAN
+-118.36,34.02,46.0,3745.0,798.0,1502.0,808.0,3.8643,195800.0,<1H OCEAN
+-117.35,33.17,16.0,4595.0,1341.0,2849.0,1197.0,2.478,185600.0,NEAR OCEAN
+-117.22,33.14,5.0,4576.0,848.0,2314.0,705.0,5.0123,210400.0,NEAR OCEAN
+-118.11,33.75,15.0,2569.0,812.0,785.0,477.0,5.4011,346400.0,NEAR OCEAN
+-117.63,34.07,39.0,2650.0,511.0,1537.0,495.0,3.4432,106700.0,INLAND
+-117.24,33.04,13.0,3498.0,663.0,1412.0,618.0,3.212,147600.0,NEAR OCEAN
+-117.15,34.03,26.0,5305.0,701.0,1818.0,676.0,6.1461,217100.0,INLAND
+-118.21,33.95,35.0,2134.0,650.0,2248.0,587.0,2.2988,153400.0,<1H OCEAN
+-116.99,32.74,18.0,3341.0,611.0,1952.0,602.0,3.9844,215300.0,<1H OCEAN
+-118.49,34.05,52.0,2416.0,291.0,810.0,270.0,13.8556,500001.0,<1H OCEAN
+-115.73,33.35,23.0,1586.0,448.0,338.0,182.0,1.2132,30000.0,INLAND
+-118.29,33.98,44.0,2261.0,555.0,1348.0,455.0,1.9125,97200.0,<1H OCEAN
+-119.27,36.34,26.0,2057.0,472.0,1453.0,439.0,2.4113,58600.0,INLAND
+-118.36,33.98,40.0,1113.0,234.0,584.0,231.0,3.0927,316000.0,<1H OCEAN
+-121.52,38.53,30.0,3377.0,623.0,1289.0,594.0,3.5737,171200.0,INLAND
+-118.3,34.05,31.0,1744.0,720.0,2034.0,633.0,2.2684,193800.0,<1H OCEAN
+-118.26,34.11,52.0,1740.0,402.0,749.0,335.0,3.5673,270700.0,<1H OCEAN
+-118.27,34.17,52.0,2010.0,,908.0,326.0,6.9135,374000.0,<1H OCEAN
+-116.95,32.77,25.0,3308.0,421.0,1201.0,414.0,6.3191,303400.0,<1H OCEAN
+-121.97,37.32,19.0,4620.0,1404.0,2672.0,1308.0,3.699,237500.0,<1H OCEAN
+-122.0,38.35,34.0,1084.0,187.0,561.0,198.0,4.2115,118900.0,INLAND
+-118.2,34.05,41.0,1268.0,398.0,1887.0,407.0,2.625,150000.0,<1H OCEAN
+-121.95,37.24,37.0,3109.0,541.0,1566.0,544.0,6.0235,413500.0,<1H OCEAN
+-118.56,34.25,31.0,1962.0,243.0,697.0,242.0,8.565,500001.0,<1H OCEAN
+-122.03,37.62,35.0,1298.0,236.0,632.0,204.0,3.8929,209500.0,NEAR BAY
+-118.41,34.0,18.0,1307.0,441.0,884.0,456.0,2.9338,276300.0,<1H OCEAN
+-116.62,34.23,14.0,6438.0,1719.0,1586.0,691.0,1.6136,67400.0,INLAND
+-116.99,32.72,14.0,1771.0,301.0,1046.0,284.0,4.775,143300.0,<1H OCEAN
+-117.02,32.81,27.0,1950.0,317.0,950.0,320.0,4.0656,164000.0,<1H OCEAN
+-117.08,32.68,26.0,3071.0,615.0,2156.0,568.0,2.9318,112400.0,NEAR OCEAN
+-121.99,38.15,36.0,263.0,73.0,88.0,42.0,2.5313,162500.0,INLAND
+-118.29,33.93,31.0,3894.0,1017.0,3590.0,962.0,2.0437,137200.0,<1H OCEAN
+-122.15,37.7,36.0,1464.0,244.0,672.0,261.0,3.5547,194700.0,NEAR BAY
+-117.27,34.17,16.0,30.0,3.0,49.0,8.0,4.625,250000.0,INLAND
+-117.14,32.92,7.0,1308.0,418.0,766.0,390.0,3.2151,106300.0,<1H OCEAN
+-122.45,37.73,52.0,2510.0,438.0,1153.0,407.0,5.1238,335100.0,NEAR BAY
+-117.7,33.5,4.0,2351.0,445.0,834.0,397.0,5.5677,245400.0,<1H OCEAN
+-117.85,33.89,24.0,3326.0,503.0,1616.0,494.0,5.7457,240600.0,<1H OCEAN
+-120.16,37.12,29.0,1995.0,392.0,1261.0,354.0,1.9073,79200.0,INLAND
+-117.38,33.98,10.0,642.0,176.0,462.0,186.0,2.1528,162500.0,INLAND
+-117.16,34.9,16.0,1579.0,327.0,934.0,298.0,2.7305,73800.0,INLAND
+-121.86,38.04,52.0,242.0,59.0,188.0,54.0,1.3958,67500.0,INLAND
+-117.16,32.75,23.0,2474.0,594.0,1107.0,536.0,2.9705,245500.0,NEAR OCEAN
+-118.28,33.97,40.0,2180.0,642.0,2464.0,631.0,1.5521,90100.0,<1H OCEAN
+-121.04,37.5,33.0,613.0,123.0,343.0,116.0,3.1875,129200.0,INLAND
+-118.15,34.11,39.0,2618.0,582.0,1314.0,532.0,3.5875,309300.0,<1H OCEAN
+-120.69,38.36,19.0,3267.0,614.0,1252.0,566.0,2.7236,109900.0,INLAND
+-120.93,37.65,1.0,2254.0,328.0,402.0,112.0,4.25,189200.0,INLAND
+-117.65,33.48,6.0,1638.0,188.0,572.0,174.0,13.0502,500001.0,<1H OCEAN
+-122.16,37.77,45.0,2324.0,397.0,968.0,384.0,3.5739,176000.0,NEAR BAY
+-117.43,33.99,18.0,3307.0,547.0,1738.0,457.0,4.566,116900.0,INLAND
+-118.33,34.01,43.0,2227.0,564.0,956.0,472.0,2.0217,187500.0,<1H OCEAN
+-122.38,41.43,45.0,2245.0,448.0,1155.0,421.0,1.6509,46200.0,INLAND
+-122.6,38.24,16.0,2621.0,416.0,1247.0,386.0,4.8603,198400.0,<1H OCEAN
+-117.62,33.43,27.0,1835.0,413.0,1221.0,377.0,3.2232,247100.0,NEAR OCEAN
+-119.31,36.39,32.0,2293.0,466.0,1538.0,468.0,1.9342,68600.0,INLAND
+-118.76,34.28,21.0,2786.0,342.0,1114.0,322.0,5.8578,266300.0,<1H OCEAN
+-118.49,34.28,31.0,3508.0,585.0,1957.0,588.0,6.6458,285500.0,<1H OCEAN
+-121.36,38.61,37.0,2191.0,394.0,951.0,362.0,3.8882,159500.0,INLAND
+-117.08,33.11,31.0,1356.0,324.0,1301.0,331.0,2.5331,115100.0,<1H OCEAN
+-122.36,40.69,32.0,3611.0,772.0,2060.0,759.0,1.7427,60600.0,INLAND
+-122.42,37.78,19.0,4065.0,1645.0,2079.0,1470.0,3.1462,187500.0,NEAR BAY
+-117.15,34.04,14.0,2845.0,420.0,1172.0,377.0,7.5822,283100.0,INLAND
+-118.09,34.03,27.0,3797.0,597.0,2043.0,614.0,5.5,276800.0,<1H OCEAN
+-118.15,34.17,36.0,930.0,280.0,1024.0,300.0,1.0846,146400.0,<1H OCEAN
+-117.19,34.08,5.0,4458.0,751.0,2392.0,773.0,4.5938,126500.0,INLAND
+-121.26,38.7,9.0,7812.0,1348.0,3275.0,1178.0,4.3826,146600.0,INLAND
+-117.68,33.63,16.0,5218.0,1187.0,2701.0,1125.0,3.929,143100.0,<1H OCEAN
+-122.38,40.58,36.0,1808.0,384.0,807.0,383.0,1.8375,74800.0,INLAND
+-122.03,36.96,18.0,2677.0,785.0,1391.0,656.0,2.5067,232600.0,NEAR OCEAN
+-117.76,33.54,28.0,2250.0,329.0,826.0,323.0,6.9257,466400.0,<1H OCEAN
+-116.38,33.71,17.0,12509.0,2460.0,2737.0,1423.0,4.5556,258100.0,INLAND
+-117.8,33.63,8.0,32.0,9.0,26.0,11.0,4.1944,270800.0,<1H OCEAN
+-117.98,33.68,24.0,4177.0,,1704.0,606.0,6.2473,281900.0,<1H OCEAN
+-118.23,34.09,45.0,1747.0,484.0,1680.0,441.0,2.6051,155500.0,<1H OCEAN
+-118.26,34.16,17.0,2943.0,769.0,1312.0,656.0,3.1484,187500.0,<1H OCEAN
+-117.68,34.08,21.0,5662.0,1185.0,3067.0,1055.0,3.3482,137300.0,INLAND
+-117.17,32.7,33.0,4084.0,897.0,1804.0,833.0,4.0488,409700.0,NEAR OCEAN
+-122.23,40.63,16.0,1141.0,220.0,563.0,200.0,2.3287,130700.0,INLAND
+-118.25,34.06,52.0,174.0,66.0,249.0,57.0,1.7763,312500.0,<1H OCEAN
+-121.11,35.52,9.0,6044.0,1222.0,2239.0,972.0,3.24,264600.0,NEAR OCEAN
+-116.99,32.78,29.0,1114.0,163.0,385.0,154.0,5.4333,222800.0,<1H OCEAN
+-118.25,34.08,44.0,1425.0,438.0,1121.0,374.0,2.1108,200000.0,<1H OCEAN
+-117.93,33.69,19.0,2602.0,439.0,1156.0,424.0,5.01,263800.0,<1H OCEAN
+-118.11,33.84,36.0,1523.0,263.0,717.0,278.0,4.875,218900.0,<1H OCEAN
+-118.45,34.3,27.0,2676.0,,2661.0,623.0,4.3047,152100.0,<1H OCEAN
+-118.37,33.81,36.0,1283.0,209.0,563.0,209.0,6.9296,500001.0,NEAR OCEAN
+-117.03,32.63,14.0,2796.0,476.0,1466.0,464.0,5.2489,213700.0,NEAR OCEAN
+-122.44,37.75,52.0,2082.0,425.0,801.0,411.0,4.2708,368900.0,NEAR BAY
+-117.98,33.69,16.0,2437.0,438.0,986.0,422.0,5.7117,247200.0,<1H OCEAN
+-118.35,34.08,52.0,2088.0,388.0,908.0,375.0,3.8141,342000.0,<1H OCEAN
+-122.4,37.58,26.0,3281.0,,1145.0,480.0,6.358,500001.0,NEAR OCEAN
+-122.11,37.67,32.0,3028.0,811.0,2037.0,703.0,3.0645,165400.0,NEAR BAY
+-119.02,35.34,35.0,1650.0,390.0,1145.0,343.0,1.5357,56500.0,INLAND
+-121.82,38.0,30.0,3268.0,567.0,1714.0,565.0,4.4583,131000.0,INLAND
+-118.85,34.14,16.0,4109.0,543.0,1409.0,560.0,8.1064,423400.0,<1H OCEAN
+-117.67,33.51,18.0,1645.0,393.0,1490.0,355.0,3.4792,126400.0,<1H OCEAN
+-116.56,33.05,15.0,1985.0,361.0,536.0,209.0,4.125,148200.0,INLAND
+-117.87,33.6,35.0,1598.0,398.0,782.0,411.0,5.1155,500000.0,<1H OCEAN
+-121.69,36.96,23.0,1229.0,254.0,687.0,232.0,5.1433,305600.0,<1H OCEAN
+-118.3,33.88,26.0,1221.0,312.0,807.0,330.0,4.0536,253600.0,<1H OCEAN
+-121.09,38.03,21.0,2064.0,342.0,1021.0,359.0,4.517,152200.0,INLAND
+-117.15,32.72,51.0,1321.0,,781.0,499.0,1.3071,250000.0,NEAR OCEAN
+-117.91,33.66,26.0,5761.0,1326.0,2681.0,1116.0,4.0341,243300.0,<1H OCEAN
+-121.38,38.6,36.0,1249.0,159.0,362.0,143.0,6.8469,446400.0,INLAND
+-120.45,36.85,20.0,1519.0,376.0,1681.0,370.0,2.1759,58100.0,INLAND
+-121.61,37.86,30.0,1428.0,287.0,989.0,287.0,3.691,154400.0,INLAND
+-117.9,33.65,24.0,4496.0,877.0,1928.0,855.0,4.6808,245500.0,<1H OCEAN
+-119.78,36.78,31.0,2164.0,456.0,959.0,463.0,2.3293,73400.0,INLAND
+-118.43,34.03,26.0,1706.0,516.0,894.0,435.0,3.1875,372700.0,<1H OCEAN
+-117.56,33.89,16.0,693.0,185.0,365.0,176.0,2.3417,191700.0,INLAND
+-118.34,34.0,49.0,2863.0,411.0,1108.0,406.0,5.8993,313300.0,<1H OCEAN
+-118.44,33.96,33.0,2799.0,491.0,978.0,447.0,5.6435,500001.0,<1H OCEAN
+-120.54,38.41,21.0,1435.0,294.0,668.0,267.0,2.5667,77400.0,INLAND
+-118.27,33.98,44.0,1722.0,457.0,2177.0,401.0,2.125,92500.0,<1H OCEAN
+-118.1,33.9,35.0,1151.0,248.0,809.0,246.0,4.7813,160000.0,<1H OCEAN
+-116.99,34.88,23.0,6060.0,1165.0,2920.0,1072.0,3.1528,69000.0,INLAND
+-118.1,33.83,36.0,1408.0,250.0,702.0,251.0,4.875,222500.0,<1H OCEAN
+-118.16,33.93,35.0,757.0,151.0,474.0,132.0,3.7361,179800.0,<1H OCEAN
+-122.3,37.84,14.0,7355.0,2408.0,3100.0,2051.0,4.0018,143800.0,NEAR BAY
+-121.42,38.47,11.0,5665.0,1507.0,3422.0,1299.0,2.3343,97800.0,INLAND
+-118.18,33.85,44.0,1890.0,465.0,1378.0,430.0,3.8819,143200.0,<1H OCEAN
+-121.02,37.7,16.0,3476.0,650.0,2126.0,665.0,3.3438,125400.0,INLAND
+-122.41,37.74,47.0,1728.0,398.0,1178.0,315.0,3.2813,229600.0,NEAR BAY
+-120.84,37.49,25.0,2383.0,576.0,1234.0,583.0,1.4529,86100.0,INLAND
+-121.4,38.59,25.0,2228.0,534.0,1130.0,481.0,2.5363,124600.0,INLAND
+-122.39,37.72,52.0,135.0,34.0,93.0,26.0,2.1484,181300.0,NEAR BAY
+-121.83,37.37,43.0,821.0,149.0,370.0,135.0,4.25,209100.0,<1H OCEAN
+-122.43,37.76,52.0,2250.0,566.0,1051.0,562.0,2.8458,350000.0,NEAR BAY
+-122.39,37.74,45.0,1462.0,308.0,924.0,302.0,2.1767,185300.0,NEAR BAY
+-118.21,34.07,42.0,902.0,318.0,1312.0,323.0,1.9375,168800.0,<1H OCEAN
+-117.38,33.2,26.0,1427.0,386.0,974.0,317.0,1.3903,184400.0,NEAR OCEAN
+-121.3,38.12,11.0,1792.0,252.0,767.0,263.0,7.6889,229300.0,INLAND
+-122.55,37.9,34.0,1431.0,224.0,503.0,220.0,7.9606,453400.0,NEAR BAY
+-121.02,36.94,33.0,1541.0,313.0,880.0,272.0,2.5074,117700.0,INLAND
+-118.08,33.78,30.0,2879.0,403.0,1109.0,414.0,6.9324,364700.0,<1H OCEAN
+-121.23,37.95,36.0,811.0,168.0,514.0,152.0,2.625,89200.0,INLAND
+-117.25,34.16,35.0,2707.0,481.0,1595.0,479.0,3.9018,91500.0,INLAND
+-116.06,34.15,15.0,10377.0,2331.0,4507.0,1807.0,2.2466,66800.0,INLAND
+-118.37,34.18,42.0,1140.0,300.0,643.0,252.0,3.3958,178400.0,<1H OCEAN
+-117.86,34.02,19.0,6300.0,937.0,3671.0,943.0,5.9716,262100.0,<1H OCEAN
+-117.86,34.14,36.0,3097.0,667.0,1484.0,634.0,3.1905,235300.0,INLAND
+-118.28,34.0,43.0,713.0,245.0,880.0,237.0,1.2065,103600.0,<1H OCEAN
+-121.49,38.47,26.0,6121.0,1185.0,4224.0,1105.0,2.3496,68000.0,INLAND
+-121.96,37.22,35.0,4709.0,723.0,1866.0,694.0,8.492,500001.0,<1H OCEAN
+-118.39,37.37,25.0,3295.0,824.0,1477.0,770.0,1.8325,105800.0,INLAND
+-122.08,37.4,23.0,806.0,158.0,373.0,156.0,5.9291,284600.0,NEAR BAY
+-118.36,33.91,41.0,2048.0,439.0,1191.0,429.0,3.8,222500.0,<1H OCEAN
+-118.12,33.84,37.0,2143.0,382.0,1047.0,377.0,4.4423,216000.0,<1H OCEAN
+-122.16,37.69,36.0,1118.0,219.0,625.0,228.0,3.7813,192200.0,NEAR BAY
+-122.53,37.91,37.0,2524.0,398.0,999.0,417.0,7.9892,500001.0,NEAR BAY
+-122.03,37.39,22.0,3280.0,933.0,1842.0,795.0,4.4107,232700.0,<1H OCEAN
+-118.17,34.11,26.0,4971.0,996.0,2370.0,932.0,4.9676,381400.0,<1H OCEAN
+-117.23,34.49,9.0,4055.0,536.0,1458.0,478.0,5.4201,170600.0,INLAND
+-119.69,34.43,44.0,2440.0,485.0,1011.0,442.0,4.149,443600.0,<1H OCEAN
+-121.99,37.29,32.0,2930.0,481.0,1336.0,481.0,6.4631,344100.0,<1H OCEAN
+-118.23,34.14,25.0,2864.0,844.0,1745.0,803.0,2.9167,224300.0,<1H OCEAN
+-118.34,34.17,52.0,1133.0,212.0,545.0,222.0,4.875,249500.0,<1H OCEAN
+-118.2,33.98,38.0,867.0,243.0,950.0,235.0,1.8929,163100.0,<1H OCEAN
+-121.27,38.64,22.0,1597.0,280.0,657.0,273.0,4.3098,213500.0,INLAND
+-122.2,37.4,30.0,2612.0,338.0,980.0,324.0,10.0481,500001.0,NEAR OCEAN
+-123.02,38.81,45.0,1717.0,389.0,916.0,367.0,3.2425,138800.0,<1H OCEAN
+-117.13,32.75,24.0,1877.0,519.0,898.0,483.0,2.2264,112500.0,NEAR OCEAN
+-118.26,34.08,46.0,945.0,250.0,910.0,252.0,3.5039,187500.0,<1H OCEAN
+-122.26,38.29,10.0,969.0,160.0,482.0,180.0,6.5799,218100.0,NEAR BAY
+-117.09,32.98,23.0,1125.0,273.0,687.0,308.0,2.3182,268800.0,<1H OCEAN
+-117.94,33.65,15.0,2016.0,443.0,1015.0,419.0,5.2732,209700.0,<1H OCEAN
+-117.82,33.78,25.0,4977.0,645.0,2061.0,646.0,6.58,318500.0,<1H OCEAN
+-119.98,36.9,26.0,1284.0,239.0,820.0,254.0,2.5833,62300.0,INLAND
+-121.92,36.62,52.0,728.0,161.0,313.0,142.0,3.4327,254500.0,NEAR OCEAN
+-118.14,33.89,33.0,2867.0,786.0,1774.0,705.0,2.9292,183400.0,<1H OCEAN
+-118.37,34.14,23.0,1883.0,512.0,774.0,478.0,3.5096,396400.0,<1H OCEAN
+-122.42,37.75,52.0,1564.0,396.0,1162.0,374.0,3.0,275000.0,NEAR BAY
+-122.47,37.76,52.0,4001.0,809.0,1886.0,756.0,3.3239,350000.0,NEAR BAY
+-121.94,38.02,29.0,5765.0,1170.0,3266.0,1131.0,2.7907,113900.0,INLAND
+-121.99,38.34,16.0,1470.0,261.0,748.0,256.0,4.0433,132200.0,INLAND
+-117.93,33.85,31.0,2149.0,465.0,966.0,302.0,3.875,183900.0,<1H OCEAN
+-117.13,34.17,17.0,1181.0,271.0,248.0,114.0,5.5762,150000.0,INLAND
+-119.02,35.37,44.0,2687.0,620.0,1521.0,549.0,1.7213,61600.0,INLAND
+-117.12,34.1,40.0,96.0,14.0,46.0,14.0,3.2708,162500.0,INLAND
+-117.12,32.9,14.0,3249.0,937.0,1929.0,838.0,2.8588,92500.0,<1H OCEAN
+-117.68,33.49,16.0,3084.0,724.0,2557.0,690.0,2.8357,106300.0,<1H OCEAN
+-118.01,33.74,25.0,8110.0,1264.0,3613.0,1232.0,6.0609,264900.0,<1H OCEAN
+-116.21,33.66,19.0,1596.0,295.0,1201.0,282.0,3.8846,100900.0,INLAND
+-116.99,33.01,11.0,1412.0,185.0,529.0,166.0,7.7517,500001.0,<1H OCEAN
+-119.21,34.19,15.0,3797.0,692.0,2216.0,675.0,4.7443,229500.0,NEAR OCEAN
+-116.69,33.5,13.0,1187.0,255.0,442.0,179.0,1.9107,155700.0,INLAND
+-121.49,38.57,49.0,1334.0,492.0,634.0,421.0,1.7568,93800.0,INLAND
+-118.15,34.04,44.0,647.0,142.0,457.0,143.0,3.6875,162500.0,<1H OCEAN
+-122.39,37.74,52.0,126.0,24.0,37.0,27.0,10.2264,225000.0,NEAR BAY
+-116.93,33.74,15.0,3757.0,666.0,1693.0,654.0,3.6806,112800.0,INLAND
+-117.98,34.13,29.0,2110.0,460.0,1890.0,448.0,3.6806,130500.0,INLAND
+-120.45,34.97,25.0,1920.0,380.0,1434.0,388.0,3.0368,129300.0,<1H OCEAN
+-121.85,37.72,43.0,228.0,40.0,83.0,42.0,10.3203,400000.0,INLAND
+-122.19,37.78,52.0,1070.0,193.0,555.0,190.0,3.7262,166900.0,NEAR BAY
+-118.4,33.78,24.0,4787.0,562.0,1653.0,548.0,12.9758,500001.0,NEAR OCEAN
+-122.16,37.46,32.0,2663.0,661.0,1403.0,733.0,4.2667,410200.0,NEAR BAY
+-123.0,38.33,8.0,3223.0,637.0,851.0,418.0,5.6445,364800.0,NEAR OCEAN
+-117.99,34.18,38.0,2981.0,432.0,1063.0,437.0,6.5254,365000.0,INLAND
+-118.2,33.87,42.0,1482.0,310.0,1052.0,317.0,3.9469,158200.0,<1H OCEAN
+-122.47,37.78,52.0,2042.0,378.0,1153.0,408.0,4.1856,404700.0,NEAR BAY
+-117.2,33.72,16.0,5373.0,1079.0,1573.0,933.0,1.9912,98600.0,<1H OCEAN
+-118.42,34.18,30.0,1323.0,353.0,856.0,333.0,3.3594,202200.0,<1H OCEAN
+-117.13,32.73,52.0,1148.0,214.0,481.0,215.0,5.454,240900.0,NEAR OCEAN
+-121.79,37.99,10.0,4156.0,609.0,1878.0,586.0,5.6506,178600.0,INLAND
+-119.27,35.87,12.0,972.0,269.0,1134.0,286.0,1.63,49500.0,INLAND
+-117.96,34.1,35.0,4036.0,904.0,3878.0,846.0,3.2957,141600.0,INLAND
+-117.09,32.76,43.0,3889.0,711.0,1466.0,663.0,3.5529,223000.0,NEAR OCEAN
+-118.61,34.25,16.0,8295.0,1506.0,3903.0,1451.0,5.5111,276600.0,<1H OCEAN
+-122.38,37.59,31.0,3052.0,844.0,1581.0,788.0,3.0744,457700.0,NEAR OCEAN
+-120.8,37.52,13.0,2920.0,481.0,1602.0,490.0,3.9286,145800.0,INLAND
+-118.37,34.05,52.0,2346.0,437.0,1121.0,400.0,4.0583,444300.0,<1H OCEAN
+-118.02,34.13,32.0,3308.0,718.0,1803.0,667.0,3.9464,273600.0,INLAND
+-117.11,33.15,14.0,8374.0,1407.0,2916.0,1295.0,4.7019,191100.0,<1H OCEAN
+-118.25,33.89,41.0,1476.0,286.0,1086.0,278.0,2.4632,111700.0,<1H OCEAN
+-117.31,34.41,14.0,3019.0,643.0,1639.0,582.0,1.5288,103400.0,INLAND
+-117.49,33.93,26.0,2970.0,576.0,2156.0,558.0,3.9522,124600.0,INLAND
+-118.31,33.84,5.0,3559.0,869.0,2965.0,794.0,2.6491,216700.0,<1H OCEAN
+-119.16,36.28,18.0,2377.0,414.0,1359.0,424.0,4.4,79300.0,INLAND
+-120.97,37.69,16.0,2793.0,476.0,1279.0,477.0,3.4667,160900.0,INLAND
+-118.06,33.98,38.0,1862.0,319.0,975.0,305.0,4.7266,177600.0,<1H OCEAN
+-118.0,33.68,12.0,5241.0,985.0,2048.0,943.0,6.4858,285400.0,<1H OCEAN
+-118.38,34.28,22.0,4428.0,825.0,3152.0,836.0,4.7932,166300.0,<1H OCEAN
+-118.05,33.72,17.0,1875.0,472.0,900.0,406.0,5.2589,226100.0,<1H OCEAN
+-118.05,33.98,41.0,1694.0,413.0,1222.0,387.0,2.8311,155300.0,<1H OCEAN
+-122.19,37.43,39.0,2392.0,420.0,937.0,406.0,6.6136,472800.0,NEAR OCEAN
+-123.81,39.31,23.0,2754.0,577.0,887.0,432.0,3.3654,225000.0,NEAR OCEAN
+-122.31,38.0,29.0,3108.0,534.0,1687.0,516.0,4.3333,170800.0,NEAR BAY
+-118.11,34.19,50.0,1430.0,186.0,620.0,201.0,9.532,483300.0,INLAND
+-120.84,38.73,17.0,2616.0,492.0,1158.0,457.0,2.8807,142600.0,INLAND
+-117.19,33.9,3.0,21060.0,3366.0,9623.0,2812.0,4.189,143000.0,INLAND
+-121.02,39.23,16.0,1427.0,319.0,642.0,333.0,1.4241,125000.0,INLAND
+-117.87,34.1,25.0,2208.0,477.0,1084.0,424.0,3.775,191700.0,<1H OCEAN
+-121.43,37.74,52.0,966.0,247.0,589.0,228.0,1.6937,108300.0,INLAND
+-121.73,37.68,17.0,20354.0,3493.0,8768.0,3293.0,5.4496,238900.0,INLAND
+-118.41,34.15,46.0,1628.0,259.0,500.0,258.0,6.083,424000.0,<1H OCEAN
+-118.48,34.27,33.0,2649.0,449.0,1303.0,437.0,4.9955,216800.0,<1H OCEAN
+-118.05,34.07,32.0,4492.0,1075.0,4119.0,1035.0,3.2373,183100.0,<1H OCEAN
+-122.71,38.33,13.0,4011.0,936.0,2064.0,914.0,3.6953,157600.0,<1H OCEAN
+-118.39,34.17,26.0,6429.0,1611.0,2806.0,1491.0,3.1929,265200.0,<1H OCEAN
+-121.82,39.73,33.0,2242.0,517.0,1160.0,449.0,1.7426,60300.0,INLAND
+-118.37,34.16,40.0,1973.0,382.0,774.0,352.0,4.4122,282300.0,<1H OCEAN
+-122.26,38.14,34.0,963.0,159.0,392.0,176.0,4.0156,134700.0,NEAR BAY
+-120.19,36.61,29.0,1479.0,338.0,1408.0,322.0,2.293,57200.0,INLAND
+-120.23,39.36,7.0,2045.0,358.0,245.0,92.0,4.0481,152300.0,INLAND
+-121.97,38.35,17.0,5678.0,1116.0,3182.0,1135.0,3.7388,122000.0,INLAND
+-118.21,33.94,41.0,1807.0,442.0,1628.0,443.0,2.84,156100.0,<1H OCEAN
+-121.59,38.69,32.0,541.0,82.0,229.0,98.0,8.0379,383300.0,INLAND
+-121.5,38.58,20.0,4018.0,1220.0,1570.0,1122.0,2.5821,125000.0,INLAND
+-117.1,32.67,15.0,1635.0,553.0,1347.0,597.0,1.2745,92900.0,NEAR OCEAN
+-121.13,39.31,17.0,3442.0,705.0,1693.0,619.0,2.8102,128900.0,INLAND
+-121.43,38.69,28.0,927.0,165.0,542.0,148.0,2.5,96200.0,INLAND
+-117.97,33.76,18.0,1862.0,399.0,1301.0,369.0,3.1771,194000.0,<1H OCEAN
+-118.04,34.06,31.0,957.0,295.0,1300.0,287.0,2.1383,153400.0,<1H OCEAN
+-122.4,37.68,36.0,3595.0,815.0,1649.0,755.0,3.3816,253400.0,NEAR BAY
+-121.73,37.71,12.0,5608.0,1049.0,2595.0,1067.0,3.9864,200200.0,INLAND
+-122.47,37.76,52.0,2941.0,783.0,1545.0,726.0,2.9899,406500.0,NEAR BAY
+-119.01,35.31,19.0,7092.0,1517.0,4101.0,1436.0,2.1006,74800.0,INLAND
+-118.35,33.93,33.0,2040.0,576.0,2649.0,561.0,2.3375,170600.0,<1H OCEAN
+-119.09,36.21,43.0,1335.0,280.0,943.0,288.0,1.9861,47700.0,INLAND
+-121.75,37.35,18.0,1947.0,250.0,605.0,184.0,8.1871,500001.0,<1H OCEAN
+-122.28,38.22,42.0,106.0,18.0,40.0,25.0,7.5197,275000.0,NEAR BAY
+-121.53,39.44,26.0,1340.0,255.0,662.0,239.0,2.6071,57100.0,INLAND
+-121.98,37.95,16.0,2984.0,406.0,1317.0,397.0,6.7821,265900.0,INLAND
+-122.19,37.8,52.0,1813.0,271.0,637.0,277.0,4.0114,263400.0,NEAR BAY
+-117.95,33.78,26.0,4115.0,883.0,2184.0,825.0,3.9536,191000.0,<1H OCEAN
+-117.11,33.83,14.0,2715.0,500.0,1540.0,464.0,3.8036,139600.0,INLAND
+-118.3,34.17,16.0,1353.0,398.0,1211.0,357.0,3.1551,205000.0,<1H OCEAN
+-118.1,33.82,36.0,2422.0,420.0,1193.0,421.0,4.8462,225700.0,<1H OCEAN
+-121.79,37.99,18.0,3646.0,534.0,1651.0,535.0,5.7321,164700.0,INLAND
+-117.8,34.15,14.0,7876.0,1253.0,3699.0,1162.0,5.5423,248700.0,INLAND
+-122.29,37.87,52.0,895.0,198.0,386.0,204.0,3.875,182600.0,NEAR BAY
+-122.45,37.77,52.0,2339.0,548.0,1090.0,507.0,3.3679,350000.0,NEAR BAY
+-123.7,39.32,18.0,1652.0,352.0,711.0,292.0,3.1071,213200.0,<1H OCEAN
+-121.28,38.53,18.0,224.0,38.0,95.0,41.0,3.1042,165000.0,INLAND
+-118.12,34.0,31.0,3281.0,768.0,2385.0,733.0,2.7308,173800.0,<1H OCEAN
+-120.82,35.32,12.0,3522.0,683.0,1780.0,662.0,3.3958,215800.0,NEAR OCEAN
+-120.62,35.11,18.0,2241.0,544.0,1521.0,509.0,2.0292,155800.0,<1H OCEAN
+-117.59,33.88,7.0,3586.0,959.0,2695.0,877.0,2.4387,117000.0,INLAND
+-118.21,34.03,52.0,497.0,132.0,547.0,121.0,2.2083,146300.0,<1H OCEAN
+-122.06,37.6,22.0,3009.0,497.0,1640.0,514.0,4.625,235300.0,NEAR BAY
+-121.27,38.13,39.0,2614.0,634.0,1862.0,654.0,1.9238,70700.0,INLAND
+-121.89,38.01,30.0,4114.0,743.0,1994.0,722.0,4.2227,134400.0,INLAND
+-118.31,34.18,11.0,3112.0,890.0,1700.0,851.0,3.1587,181300.0,<1H OCEAN
+-118.35,33.99,47.0,2183.0,380.0,927.0,371.0,4.9531,180100.0,<1H OCEAN
+-120.6,37.36,27.0,2521.0,484.0,1307.0,456.0,3.0911,86900.0,INLAND
+-118.17,33.83,46.0,1362.0,214.0,531.0,222.0,4.3125,290500.0,NEAR OCEAN
+-118.02,34.05,28.0,991.0,255.0,1145.0,265.0,2.3611,167000.0,<1H OCEAN
+-118.43,34.29,50.0,1181.0,265.0,1196.0,269.0,3.2095,167000.0,<1H OCEAN
+-117.65,33.6,13.0,2319.0,430.0,1004.0,380.0,5.133,316100.0,<1H OCEAN
+-119.79,36.75,33.0,3161.0,934.0,3530.0,846.0,1.123,46700.0,INLAND
+-117.13,32.77,30.0,2582.0,650.0,1098.0,603.0,2.8281,171700.0,NEAR OCEAN
+-121.37,38.68,34.0,1086.0,187.0,663.0,190.0,3.3074,84200.0,INLAND
+-122.32,37.95,36.0,1425.0,245.0,573.0,239.0,4.35,185000.0,NEAR BAY
+-117.08,33.13,17.0,8466.0,2628.0,7014.0,2267.0,2.1437,113700.0,<1H OCEAN
+-118.93,34.18,18.0,2730.0,415.0,1248.0,412.0,6.187,287900.0,<1H OCEAN
+-118.1,34.02,37.0,1022.0,232.0,653.0,238.0,3.0625,189400.0,<1H OCEAN
+-121.91,37.36,42.0,3224.0,708.0,1940.0,674.0,3.2153,199700.0,<1H OCEAN
+-118.03,33.81,26.0,3635.0,567.0,1779.0,543.0,5.7089,237400.0,<1H OCEAN
+-121.4,38.66,50.0,880.0,150.0,1148.0,148.0,2.5062,112500.0,INLAND
+-117.84,33.74,25.0,1818.0,577.0,1426.0,532.0,3.2104,112500.0,<1H OCEAN
+-118.52,34.2,19.0,4315.0,1304.0,2490.0,1222.0,2.6437,195000.0,<1H OCEAN
+-124.18,40.78,33.0,1076.0,222.0,656.0,236.0,2.5096,72200.0,NEAR OCEAN
+-120.93,37.56,17.0,1812.0,361.0,672.0,334.0,1.55,166100.0,INLAND
+-119.81,36.72,46.0,1414.0,268.0,902.0,243.0,1.5833,56700.0,INLAND
+-118.21,33.93,30.0,2831.0,862.0,3649.0,883.0,1.9668,152100.0,<1H OCEAN
+-117.97,33.93,31.0,1975.0,373.0,918.0,347.0,4.4107,202000.0,<1H OCEAN
+-117.16,32.78,24.0,3566.0,765.0,1697.0,722.0,3.6375,178600.0,NEAR OCEAN
+-117.83,33.8,30.0,4713.0,758.0,2271.0,730.0,5.8622,221000.0,<1H OCEAN
+-121.7,37.98,9.0,3079.0,519.0,1562.0,512.0,5.1041,172900.0,INLAND
+-119.55,36.71,32.0,1963.0,508.0,2052.0,518.0,1.9076,55800.0,INLAND
+-117.84,33.74,22.0,6072.0,1802.0,4715.0,1666.0,3.1353,121400.0,<1H OCEAN
+-117.52,33.82,14.0,3776.0,580.0,1877.0,559.0,5.1365,215000.0,INLAND
+-118.25,34.11,43.0,2222.0,635.0,1817.0,606.0,2.7466,208900.0,<1H OCEAN
+-121.1,38.95,17.0,1475.0,403.0,943.0,363.0,2.1287,55300.0,INLAND
+-115.57,32.78,29.0,2321.0,367.0,1173.0,360.0,4.0375,86400.0,INLAND
+-122.99,39.02,14.0,1582.0,301.0,851.0,273.0,3.45,164100.0,<1H OCEAN
+-122.42,37.76,52.0,3587.0,1030.0,2259.0,979.0,2.5403,250000.0,NEAR BAY
+-119.76,36.79,32.0,2463.0,468.0,1261.0,486.0,3.3281,75100.0,INLAND
+-122.69,38.37,8.0,6322.0,1001.0,2969.0,1043.0,4.8233,214000.0,<1H OCEAN
+-121.53,39.52,24.0,1028.0,185.0,471.0,186.0,2.9688,86400.0,INLAND
+-118.25,33.95,25.0,764.0,200.0,801.0,220.0,1.1384,100000.0,<1H OCEAN
+-117.97,33.65,26.0,2379.0,336.0,988.0,346.0,5.3674,339300.0,<1H OCEAN
+-118.15,34.14,27.0,1499.0,426.0,755.0,414.0,3.875,258300.0,<1H OCEAN
+-122.19,37.79,45.0,2718.0,451.0,1106.0,454.0,4.6563,231800.0,NEAR BAY
+-122.13,37.72,45.0,2315.0,451.0,1006.0,444.0,3.524,186200.0,NEAR BAY
+-121.91,36.62,52.0,1431.0,300.0,657.0,293.0,3.2865,240100.0,<1H OCEAN
+-121.16,41.78,42.0,2918.0,576.0,1182.0,440.0,2.1434,44000.0,INLAND
+-120.47,35.04,29.0,1315.0,279.0,926.0,249.0,2.9375,144800.0,<1H OCEAN
+-120.99,39.2,15.0,2993.0,562.0,1296.0,518.0,3.3009,156800.0,INLAND
+-118.99,36.06,19.0,2153.0,458.0,1317.0,386.0,1.7564,42600.0,INLAND
+-121.96,38.32,12.0,5127.0,998.0,2749.0,976.0,4.0458,130600.0,INLAND
+-118.47,33.99,52.0,1523.0,447.0,636.0,408.0,3.0682,412500.0,<1H OCEAN
+-118.4,34.14,45.0,417.0,89.0,187.0,88.0,5.1377,360700.0,<1H OCEAN
+-117.96,33.88,25.0,3578.0,461.0,1588.0,466.0,6.2556,341300.0,<1H OCEAN
+-122.46,37.78,52.0,3088.0,727.0,1636.0,662.0,2.8553,360700.0,NEAR BAY
+-121.19,37.81,8.0,4019.0,857.0,1959.0,782.0,2.7321,175000.0,INLAND
+-121.42,36.85,7.0,1626.0,325.0,677.0,304.0,2.3125,170800.0,INLAND
+-121.83,37.23,7.0,5389.0,903.0,2232.0,825.0,6.6659,500001.0,<1H OCEAN
+-118.42,33.93,39.0,2988.0,605.0,1466.0,610.0,4.9286,341400.0,<1H OCEAN
+-121.32,38.54,13.0,4715.0,1090.0,2420.0,1059.0,2.9693,104400.0,INLAND
+-118.38,34.08,52.0,1479.0,360.0,652.0,346.0,2.945,431400.0,<1H OCEAN
+-118.28,34.12,50.0,2384.0,312.0,836.0,337.0,12.8763,500001.0,<1H OCEAN
+-123.0,38.51,33.0,1565.0,390.0,759.0,311.0,2.6726,153100.0,NEAR OCEAN
+-117.19,32.75,52.0,25.0,5.0,13.0,5.0,0.536,162500.0,NEAR OCEAN
+-120.92,40.02,35.0,383.0,92.0,202.0,72.0,2.6458,102500.0,INLAND
+-117.42,34.0,32.0,1617.0,346.0,1153.0,385.0,3.016,96600.0,INLAND
+-119.82,34.44,28.0,1992.0,531.0,1622.0,509.0,2.7689,228200.0,NEAR OCEAN
+-119.27,34.27,44.0,1312.0,279.0,668.0,278.0,4.09,203800.0,NEAR OCEAN
+-120.64,37.2,16.0,2236.0,438.0,1361.0,393.0,2.0066,125000.0,INLAND
+-122.43,37.8,52.0,2788.0,813.0,1302.0,764.0,4.199,400000.0,NEAR BAY
+-122.51,38.17,8.0,5875.0,1115.0,2808.0,1029.0,3.6392,246300.0,NEAR BAY
+-118.4,34.04,42.0,1298.0,174.0,420.0,190.0,12.8483,500001.0,<1H OCEAN
+-122.42,37.75,52.0,1207.0,302.0,1008.0,269.0,3.3816,262500.0,NEAR BAY
+-120.33,34.87,24.0,2590.0,404.0,1093.0,338.0,3.9375,341200.0,<1H OCEAN
+-118.32,33.34,52.0,996.0,264.0,341.0,160.0,2.7361,450000.0,ISLAND
+-118.93,34.4,17.0,3275.0,599.0,2422.0,637.0,3.7092,190500.0,<1H OCEAN
+-122.01,37.31,23.0,6846.0,1078.0,2951.0,1063.0,6.3702,332000.0,<1H OCEAN
+-122.08,37.15,23.0,506.0,96.0,264.0,89.0,7.1366,273900.0,NEAR OCEAN
+-120.46,37.51,22.0,2704.0,497.0,1432.0,399.0,2.9,83100.0,INLAND
+-119.03,35.42,42.0,1705.0,418.0,905.0,393.0,1.6286,54600.0,INLAND
+-117.07,32.74,37.0,1042.0,205.0,589.0,208.0,2.6629,116900.0,NEAR OCEAN
+-118.41,34.03,26.0,4376.0,1394.0,2435.0,1250.0,2.8418,327300.0,<1H OCEAN
+-121.21,37.8,31.0,699.0,186.0,460.0,170.0,2.7443,94200.0,INLAND
+-122.34,37.94,42.0,2206.0,451.0,989.0,444.0,3.125,143900.0,NEAR BAY
+-118.21,33.9,43.0,1810.0,357.0,1335.0,358.0,3.1189,118800.0,<1H OCEAN
+-121.06,37.86,24.0,1713.0,328.0,1258.0,324.0,2.683,169400.0,INLAND
+-122.04,38.28,25.0,3304.0,493.0,1464.0,488.0,5.2527,130600.0,INLAND
+-118.25,34.15,32.0,1377.0,444.0,768.0,422.0,2.2621,187500.0,<1H OCEAN
+-118.52,34.29,28.0,2272.0,320.0,868.0,312.0,7.7464,474600.0,<1H OCEAN
+-117.88,33.89,17.0,3218.0,923.0,1701.0,824.0,3.6946,265500.0,<1H OCEAN
+-121.9,37.3,52.0,1575.0,284.0,629.0,284.0,5.6437,312000.0,<1H OCEAN
+-117.71,33.57,4.0,3289.0,753.0,1285.0,651.0,4.045,226000.0,<1H OCEAN
+-117.9,34.15,21.0,2056.0,461.0,1332.0,429.0,3.3942,212800.0,INLAND
+-118.19,34.1,42.0,1577.0,379.0,1317.0,378.0,3.2121,153900.0,<1H OCEAN
+-122.45,37.78,52.0,1345.0,291.0,560.0,294.0,3.7159,494400.0,NEAR BAY
+-121.48,38.55,52.0,2216.0,333.0,714.0,327.0,4.8603,191900.0,INLAND
+-117.03,32.8,19.0,3866.0,775.0,1554.0,703.0,4.3281,220000.0,<1H OCEAN
+-122.09,37.71,31.0,1843.0,282.0,749.0,269.0,5.2855,253500.0,NEAR BAY
+-115.54,32.79,30.0,752.0,194.0,733.0,186.0,1.6607,56100.0,INLAND
+-118.58,34.17,18.0,6476.0,1082.0,2413.0,958.0,5.6327,500001.0,<1H OCEAN
+-120.82,37.05,15.0,1385.0,288.0,775.0,255.0,1.933,140600.0,INLAND
+-122.08,37.4,19.0,3565.0,858.0,1639.0,744.0,4.1544,277000.0,NEAR BAY
+-117.13,32.71,38.0,993.0,246.0,760.0,205.0,1.1563,82700.0,NEAR OCEAN
+-118.44,34.0,22.0,5822.0,1707.0,3335.0,1585.0,3.1579,243100.0,<1H OCEAN
+-120.74,39.9,23.0,1017.0,218.0,387.0,152.0,2.2656,88200.0,INLAND
+-119.87,36.81,6.0,1891.0,341.0,969.0,330.0,4.6726,107800.0,INLAND
+-117.07,33.15,15.0,2994.0,522.0,1231.0,503.0,3.2024,180400.0,<1H OCEAN
+-118.7,34.28,27.0,727.0,136.0,467.0,144.0,3.7188,250000.0,<1H OCEAN
+-118.45,34.1,31.0,6675.0,842.0,2092.0,796.0,11.8442,500001.0,<1H OCEAN
+-117.66,33.58,16.0,3016.0,394.0,1172.0,382.0,7.5196,315600.0,<1H OCEAN
+-119.17,34.31,21.0,259.0,38.0,142.0,45.0,5.2681,500001.0,NEAR OCEAN
+-118.01,34.09,32.0,1613.0,361.0,1283.0,404.0,3.1944,181700.0,INLAND
+-118.94,34.17,15.0,1679.0,271.0,928.0,264.0,5.5681,235600.0,<1H OCEAN
+-117.48,34.09,32.0,1650.0,328.0,1124.0,290.0,3.1838,98600.0,INLAND
+-117.65,34.13,24.0,2121.0,296.0,913.0,302.0,5.9328,255900.0,INLAND
+-118.31,33.82,35.0,3462.0,814.0,1902.0,700.0,3.402,279900.0,<1H OCEAN
+-124.08,40.06,17.0,1319.0,267.0,393.0,163.0,2.625,135600.0,NEAR OCEAN
+-120.29,37.94,17.0,1459.0,297.0,753.0,271.0,3.05,144800.0,INLAND
+-122.47,38.6,20.0,1036.0,202.0,589.0,194.0,5.3698,303300.0,INLAND
+-120.32,37.29,9.0,695.0,188.0,810.0,190.0,1.6172,56300.0,INLAND
+-118.59,34.47,5.0,538.0,98.0,8733.0,105.0,4.2391,154600.0,INLAND
+-118.13,33.85,36.0,2110.0,416.0,1128.0,403.0,4.6019,208400.0,<1H OCEAN
+-118.2,33.94,45.0,1570.0,328.0,1321.0,300.0,3.7361,171800.0,<1H OCEAN
+-117.12,32.74,46.0,1898.0,441.0,978.0,439.0,3.2708,155200.0,NEAR OCEAN
+-121.36,38.67,5.0,5819.0,1507.0,3237.0,1356.0,2.2339,116600.0,INLAND
+-122.21,37.42,28.0,564.0,72.0,191.0,79.0,11.9666,500001.0,NEAR OCEAN
+-119.08,37.78,17.0,1631.0,335.0,285.0,128.0,2.7656,130000.0,INLAND
+-118.61,34.73,25.0,3080.0,587.0,1558.0,510.0,5.0839,156700.0,INLAND
+-118.12,33.77,10.0,7264.0,1137.0,2528.0,1057.0,10.2233,500001.0,NEAR OCEAN
+-124.1,40.9,18.0,4032.0,798.0,1948.0,775.0,2.7321,92600.0,NEAR OCEAN
+-122.04,36.96,42.0,1149.0,264.0,703.0,232.0,2.5865,206400.0,NEAR OCEAN
+-122.71,38.46,41.0,1974.0,482.0,965.0,458.0,2.905,159300.0,<1H OCEAN
+-118.25,33.95,28.0,2136.0,,1799.0,476.0,1.5427,95700.0,<1H OCEAN
+-118.5,34.46,17.0,10267.0,,4956.0,1483.0,5.5061,239400.0,<1H OCEAN
+-118.45,34.03,39.0,1657.0,402.0,931.0,363.0,3.7813,336300.0,<1H OCEAN
+-122.16,39.74,20.0,707.0,126.0,337.0,125.0,3.0469,85000.0,INLAND
+-119.58,36.83,13.0,6135.0,863.0,2473.0,774.0,5.4895,156700.0,INLAND
+-117.89,33.74,33.0,619.0,139.0,1217.0,146.0,4.6875,154400.0,<1H OCEAN
+-121.78,37.22,18.0,2127.0,387.0,1547.0,402.0,5.0958,217100.0,<1H OCEAN
+-121.02,37.09,17.0,1118.0,270.0,560.0,244.0,2.0216,112500.0,INLAND
+-117.81,34.1,19.0,1935.0,399.0,1126.0,389.0,3.8929,144600.0,INLAND
+-118.39,34.04,41.0,101.0,23.0,85.0,30.0,4.125,237500.0,<1H OCEAN
+-121.89,37.39,2.0,1136.0,365.0,535.0,257.0,4.375,425000.0,<1H OCEAN
+-117.82,33.72,24.0,3260.0,458.0,1383.0,442.0,6.5987,272800.0,<1H OCEAN
+-118.1,34.0,32.0,2122.0,591.0,1929.0,539.0,2.7311,169300.0,<1H OCEAN
+-122.29,37.53,41.0,839.0,190.0,419.0,215.0,5.012,368200.0,NEAR OCEAN
+-122.23,39.95,21.0,2087.0,382.0,888.0,361.0,2.207,86400.0,INLAND
+-121.28,38.03,11.0,3585.0,729.0,2769.0,715.0,3.0907,94100.0,INLAND
+-122.42,37.79,52.0,2737.0,1241.0,1761.0,1029.0,1.8068,225000.0,NEAR BAY
+-122.4,37.61,35.0,2084.0,549.0,1077.0,545.0,3.1628,318400.0,NEAR OCEAN
+-117.82,33.81,19.0,2556.0,304.0,822.0,260.0,9.9055,456900.0,<1H OCEAN
+-117.23,34.12,6.0,4464.0,1093.0,2364.0,952.0,2.3848,81600.0,INLAND
+-121.29,37.93,37.0,2488.0,578.0,1854.0,514.0,2.551,59100.0,INLAND
+-117.78,33.86,16.0,4390.0,660.0,2146.0,633.0,6.1504,266000.0,<1H OCEAN
+-117.3,34.1,44.0,589.0,130.0,504.0,137.0,1.775,63400.0,INLAND
+-121.31,38.59,35.0,3295.0,560.0,1454.0,536.0,3.1711,101900.0,INLAND
+-120.85,37.78,15.0,3553.0,659.0,1684.0,611.0,3.3169,131200.0,INLAND
+-122.71,38.46,36.0,2175.0,516.0,1087.0,477.0,3.0444,167200.0,<1H OCEAN
+-118.47,34.42,25.0,3223.0,524.0,1763.0,508.0,5.2887,183000.0,<1H OCEAN
+-118.18,33.8,30.0,2734.0,758.0,2951.0,691.0,1.7689,117600.0,NEAR OCEAN
+-118.34,33.78,25.0,11016.0,1626.0,4168.0,1584.0,8.1782,500001.0,NEAR OCEAN
+-117.91,33.74,25.0,4273.0,965.0,2946.0,922.0,2.9926,183200.0,<1H OCEAN
+-124.05,40.59,32.0,1878.0,340.0,937.0,353.0,3.4408,95200.0,<1H OCEAN
+-118.12,34.08,36.0,2433.0,585.0,1565.0,563.0,3.2344,234900.0,<1H OCEAN
+-118.17,34.05,35.0,1256.0,294.0,2990.0,302.0,3.1528,121800.0,<1H OCEAN
+-118.23,33.88,41.0,1941.0,367.0,1204.0,323.0,3.0417,113700.0,<1H OCEAN
+-118.29,33.95,31.0,2839.0,792.0,2390.0,729.0,2.0,109800.0,<1H OCEAN
+-117.66,34.02,12.0,5616.0,871.0,3019.0,782.0,5.5425,202300.0,INLAND
+-121.26,38.09,35.0,930.0,186.0,525.0,201.0,2.0625,155000.0,INLAND
+-121.37,38.56,27.0,1827.0,509.0,852.0,450.0,2.0901,52500.0,INLAND
+-118.14,34.17,42.0,2757.0,713.0,2112.0,653.0,2.7148,166800.0,<1H OCEAN
+-116.82,34.24,11.0,5799.0,1527.0,713.0,262.0,2.5147,84700.0,INLAND
+-120.14,34.59,9.0,2536.0,499.0,832.0,385.0,2.5743,309800.0,NEAR OCEAN
+-118.19,33.85,49.0,1073.0,260.0,725.0,272.0,3.8026,149700.0,NEAR OCEAN
+-117.1,32.75,23.0,1858.0,551.0,1506.0,492.0,1.7446,85200.0,NEAR OCEAN
+-122.2,37.96,9.0,6306.0,962.0,2581.0,911.0,6.7741,310700.0,NEAR BAY
+-122.41,37.77,52.0,849.0,276.0,582.0,222.0,3.4671,250000.0,NEAR BAY
+-121.39,38.43,3.0,2696.0,384.0,990.0,316.0,5.4445,237600.0,INLAND
+-117.77,33.7,3.0,3636.0,749.0,1486.0,696.0,5.5464,207500.0,<1H OCEAN
+-119.0,35.39,51.0,1373.0,284.0,648.0,300.0,2.8295,72100.0,INLAND
+-116.88,34.19,38.0,898.0,259.0,106.0,52.0,1.6875,225000.0,INLAND
+-122.55,38.81,7.0,3639.0,637.0,1027.0,421.0,3.8831,132100.0,INLAND
+-121.26,36.32,30.0,146.0,41.0,164.0,40.0,2.3,206300.0,<1H OCEAN
+-118.31,33.9,30.0,2407.0,581.0,1724.0,531.0,3.4792,194700.0,<1H OCEAN
+-117.62,34.08,30.0,1372.0,235.0,1047.0,225.0,3.1597,116300.0,INLAND
+-122.65,38.23,52.0,1735.0,347.0,712.0,343.0,3.1711,200800.0,<1H OCEAN
+-119.25,35.77,35.0,1618.0,378.0,1449.0,398.0,1.6786,56500.0,INLAND
+-120.96,37.77,32.0,2262.0,416.0,1156.0,404.0,3.8534,157600.0,INLAND
+-118.05,34.17,45.0,2535.0,455.0,1036.0,437.0,5.0482,388900.0,INLAND
+-122.43,37.8,52.0,1976.0,726.0,1045.0,669.0,3.6893,475000.0,NEAR BAY
+-121.28,38.68,14.0,11442.0,2690.0,6068.0,2435.0,2.6016,121200.0,INLAND
+-118.41,33.89,38.0,4166.0,828.0,1600.0,770.0,6.3861,500001.0,<1H OCEAN
+-117.35,33.64,23.0,6859.0,1535.0,3405.0,1351.0,2.5395,109200.0,<1H OCEAN
+-121.95,37.24,32.0,1382.0,239.0,705.0,251.0,6.0957,405400.0,<1H OCEAN
+-118.52,34.01,25.0,2757.0,738.0,1014.0,633.0,3.1433,500001.0,<1H OCEAN
+-121.24,37.96,29.0,874.0,217.0,788.0,222.0,1.9187,57700.0,INLAND
+-122.36,37.95,38.0,1042.0,289.0,773.0,248.0,2.7714,104700.0,NEAR BAY
+-118.44,34.19,37.0,1516.0,344.0,983.0,347.0,5.0,243600.0,<1H OCEAN
+-124.11,40.57,33.0,1348.0,234.0,573.0,236.0,2.4896,74100.0,<1H OCEAN
+-122.26,40.19,35.0,2467.0,469.0,1194.0,444.0,2.0425,63700.0,INLAND
+-118.03,33.92,35.0,2108.0,405.0,1243.0,394.0,3.6731,167000.0,<1H OCEAN
+-120.08,36.97,13.0,3356.0,589.0,1458.0,601.0,3.8257,94200.0,INLAND
+-122.08,37.34,28.0,1643.0,216.0,594.0,205.0,12.367,500001.0,<1H OCEAN
+-121.3,38.04,8.0,2668.0,447.0,1713.0,444.0,4.0156,117600.0,INLAND
+-122.63,38.23,37.0,1966.0,348.0,875.0,381.0,4.0703,223800.0,<1H OCEAN
+-118.08,33.84,31.0,2906.0,578.0,1806.0,553.0,4.8448,194600.0,<1H OCEAN
+-120.42,34.91,4.0,6986.0,1217.0,2801.0,1212.0,3.2135,212700.0,<1H OCEAN
+-118.55,34.2,21.0,2549.0,651.0,1624.0,628.0,3.6905,179800.0,<1H OCEAN
+-117.09,32.71,12.0,3375.0,945.0,2357.0,808.0,1.5,106300.0,NEAR OCEAN
+-118.33,34.15,44.0,1321.0,303.0,471.0,301.0,4.2679,331800.0,<1H OCEAN
+-117.96,33.69,20.0,3123.0,441.0,1319.0,432.0,6.091,290400.0,<1H OCEAN
+-117.77,33.71,4.0,1646.0,321.0,859.0,300.0,5.5631,227800.0,<1H OCEAN
+-118.38,34.22,32.0,362.0,100.0,348.0,102.0,2.2679,150000.0,<1H OCEAN
+-117.85,33.62,18.0,729.0,105.0,316.0,108.0,10.3893,500001.0,<1H OCEAN
+-119.67,36.74,19.0,2788.0,614.0,1365.0,525.0,2.7813,120300.0,INLAND
+-122.14,37.72,39.0,786.0,132.0,288.0,132.0,3.5156,218900.0,NEAR BAY
+-119.82,36.75,41.0,1022.0,209.0,741.0,213.0,2.0781,48800.0,INLAND
+-118.07,34.12,30.0,2201.0,559.0,1194.0,531.0,4.1136,279900.0,INLAND
+-122.31,37.92,43.0,2116.0,407.0,900.0,361.0,4.1587,212200.0,NEAR BAY
+-121.09,37.67,30.0,1653.0,285.0,800.0,291.0,3.3482,220000.0,INLAND
+-117.8,33.55,38.0,1757.0,464.0,821.0,426.0,4.1304,433300.0,<1H OCEAN
+-117.25,32.99,10.0,4926.0,749.0,1478.0,634.0,7.472,439900.0,NEAR OCEAN
+-117.79,34.07,34.0,975.0,192.0,870.0,183.0,3.7933,116100.0,INLAND
+-121.42,36.57,13.0,2685.0,621.0,2474.0,573.0,2.8775,134100.0,<1H OCEAN
+-117.62,34.03,15.0,3942.0,661.0,2240.0,621.0,4.8311,176000.0,INLAND
+-120.42,34.96,14.0,2069.0,343.0,1240.0,338.0,4.5066,149800.0,<1H OCEAN
+-118.35,34.05,46.0,2149.0,451.0,905.0,443.0,2.8841,290800.0,<1H OCEAN
+-122.18,37.75,43.0,1036.0,233.0,652.0,213.0,2.069,84600.0,NEAR BAY
+-122.45,37.72,46.0,1406.0,235.0,771.0,239.0,4.7143,219300.0,NEAR BAY
+-117.76,33.72,14.0,3011.0,388.0,1359.0,371.0,7.9739,368700.0,<1H OCEAN
+-120.94,37.58,19.0,1549.0,369.0,770.0,370.0,2.0493,99500.0,INLAND
+-122.27,37.88,52.0,2803.0,930.0,1372.0,876.0,2.1907,271400.0,NEAR BAY
+-120.65,36.98,26.0,1787.0,364.0,1548.0,362.0,1.7188,49500.0,INLAND
+-122.31,37.56,45.0,1685.0,321.0,815.0,314.0,4.2955,309700.0,NEAR OCEAN
+-117.26,33.05,14.0,2323.0,373.0,1057.0,372.0,6.2513,240900.0,NEAR OCEAN
+-118.34,33.9,36.0,1342.0,259.0,706.0,261.0,4.1776,236600.0,<1H OCEAN
+-118.27,34.17,52.0,2287.0,295.0,829.0,296.0,7.8383,500001.0,<1H OCEAN
+-119.16,34.12,17.0,224.0,70.0,147.0,71.0,3.6167,280000.0,NEAR OCEAN
+-117.87,33.75,12.0,2782.0,1077.0,1968.0,795.0,0.971,102500.0,<1H OCEAN
+-116.98,32.75,18.0,1519.0,369.0,802.0,347.0,2.9886,170800.0,<1H OCEAN
+-122.41,37.79,52.0,3610.0,1286.0,1504.0,1047.0,3.2059,500001.0,NEAR BAY
+-119.27,35.49,39.0,2649.0,572.0,1815.0,547.0,2.3533,65400.0,INLAND
+-118.32,33.98,49.0,1412.0,333.0,901.0,328.0,1.7067,118600.0,<1H OCEAN
+-122.29,37.87,50.0,1829.0,536.0,1129.0,516.0,2.6684,185600.0,NEAR BAY
+-118.05,34.08,25.0,4909.0,1422.0,4983.0,1293.0,2.7254,143500.0,<1H OCEAN
+-117.66,34.04,16.0,2081.0,348.0,1332.0,356.0,4.7872,147600.0,INLAND
+-121.8,39.75,11.0,7212.0,1355.0,3264.0,1264.0,3.1125,122600.0,INLAND
+-118.28,34.25,35.0,2045.0,450.0,1166.0,407.0,3.5214,197600.0,<1H OCEAN
+-116.42,33.51,26.0,186.0,48.0,102.0,39.0,2.5625,103100.0,INLAND
+-117.09,32.74,42.0,1986.0,472.0,1472.0,475.0,2.1757,110100.0,NEAR OCEAN
+-122.42,37.66,26.0,3253.0,932.0,2246.0,855.0,2.6631,244000.0,NEAR OCEAN
+-122.24,37.47,35.0,2283.0,491.0,1148.0,436.0,4.5556,318600.0,NEAR OCEAN
+-117.97,34.05,36.0,1299.0,206.0,763.0,216.0,3.5179,161400.0,<1H OCEAN
+-119.25,36.27,23.0,1494.0,275.0,678.0,235.0,2.6875,69100.0,INLAND
+-119.35,36.33,14.0,1195.0,220.0,568.0,229.0,3.1486,105600.0,INLAND
+-121.5,38.49,32.0,4013.0,725.0,2032.0,675.0,3.3689,83400.0,INLAND
+-122.46,37.71,52.0,1642.0,351.0,1138.0,340.0,4.1406,219500.0,NEAR OCEAN
+-118.18,33.95,42.0,2608.0,610.0,2062.0,616.0,3.5341,167500.0,<1H OCEAN
+-117.99,33.81,42.0,161.0,40.0,157.0,50.0,2.2,153100.0,<1H OCEAN
+-118.35,34.04,38.0,1626.0,375.0,1019.0,372.0,2.3687,146800.0,<1H OCEAN
+-119.75,36.8,25.0,2718.0,504.0,1257.0,465.0,2.3333,90600.0,INLAND
+-118.37,33.97,21.0,3616.0,1060.0,2515.0,945.0,2.7464,153100.0,<1H OCEAN
+-118.47,34.19,41.0,1104.0,196.0,495.0,196.0,5.0929,225000.0,<1H OCEAN
+-122.47,37.76,34.0,2807.0,487.0,1152.0,445.0,5.1893,420300.0,NEAR BAY
+-119.27,34.44,22.0,3527.0,711.0,1483.0,640.0,2.7019,234700.0,<1H OCEAN
+-118.06,33.89,26.0,2483.0,412.0,1538.0,449.0,5.1104,220500.0,<1H OCEAN
+-118.34,33.87,28.0,4605.0,1188.0,2558.0,1093.0,3.6988,266600.0,<1H OCEAN
+-121.4,38.55,19.0,2497.0,494.0,748.0,442.0,2.925,142400.0,INLAND
+-121.85,38.0,24.0,2269.0,584.0,1239.0,542.0,2.0411,100000.0,INLAND
+-122.46,37.76,52.0,1817.0,449.0,948.0,380.0,3.93,390000.0,NEAR BAY
+-121.84,38.13,33.0,596.0,105.0,212.0,94.0,4.2813,81300.0,INLAND
+-121.32,37.96,5.0,123.0,21.0,50.0,20.0,2.7656,50000.0,INLAND
+-121.36,38.66,22.0,2878.0,599.0,1362.0,541.0,2.7955,96500.0,INLAND
+-122.5,37.76,52.0,2018.0,422.0,1142.0,463.0,3.7083,307700.0,NEAR OCEAN
+-122.48,37.69,42.0,2993.0,512.0,1594.0,546.0,4.4821,252400.0,NEAR OCEAN
+-123.66,41.3,22.0,1580.0,372.0,686.0,264.0,1.8065,62700.0,<1H OCEAN
+-118.06,34.15,37.0,1980.0,226.0,697.0,226.0,15.0001,500001.0,INLAND
+-117.89,33.89,17.0,1671.0,192.0,678.0,206.0,13.1107,467600.0,<1H OCEAN
+-117.96,33.66,19.0,5925.0,744.0,2302.0,729.0,7.5699,333300.0,<1H OCEAN
+-121.98,38.36,30.0,140.0,35.0,103.0,35.0,4.163,112500.0,INLAND
+-118.39,34.21,32.0,1869.0,441.0,1516.0,432.0,3.6845,178500.0,<1H OCEAN
+-119.28,36.32,29.0,2274.0,514.0,1234.0,521.0,1.9138,66900.0,INLAND
+-122.06,38.25,36.0,1818.0,323.0,953.0,298.0,3.3153,99000.0,INLAND
+-117.98,33.93,27.0,3142.0,509.0,1520.0,503.0,6.2924,232500.0,<1H OCEAN
+-118.14,33.87,21.0,6618.0,1773.0,4396.0,1649.0,3.0989,171400.0,<1H OCEAN
+-118.96,35.39,23.0,5624.0,1148.0,2842.0,1042.0,3.1297,79000.0,INLAND
+-118.31,34.17,24.0,2910.0,917.0,2522.0,873.0,2.4074,219400.0,<1H OCEAN
+-118.53,34.23,32.0,4039.0,984.0,2675.0,941.0,3.0321,240000.0,<1H OCEAN
+-117.81,33.75,25.0,2365.0,471.0,1197.0,458.0,3.7031,227800.0,<1H OCEAN
+-118.16,33.97,32.0,1347.0,434.0,1756.0,438.0,1.9464,190600.0,<1H OCEAN
+-120.88,37.53,18.0,239.0,39.0,92.0,36.0,5.3168,175000.0,INLAND
+-119.56,36.71,37.0,1609.0,374.0,1173.0,344.0,2.181,59900.0,INLAND
+-121.27,38.12,44.0,2356.0,482.0,1043.0,443.0,2.4949,108000.0,INLAND
+-118.44,33.95,37.0,2076.0,332.0,771.0,327.0,6.2551,500001.0,<1H OCEAN
+-118.4,33.96,43.0,2416.0,454.0,1028.0,409.0,5.6732,330700.0,<1H OCEAN
+-118.32,34.04,42.0,1766.0,404.0,1117.0,367.0,2.0259,168800.0,<1H OCEAN
+-120.94,37.63,43.0,244.0,52.0,176.0,60.0,1.425,69400.0,INLAND
+-122.34,37.93,30.0,2515.0,481.0,1327.0,428.0,2.1287,95000.0,NEAR BAY
+-117.92,33.73,14.0,5147.0,1182.0,3171.0,1126.0,3.9929,225800.0,<1H OCEAN
+-122.9,38.28,52.0,1275.0,218.0,627.0,185.0,2.3482,163500.0,NEAR OCEAN
+-120.94,37.59,16.0,3964.0,824.0,2622.0,766.0,2.3152,111300.0,INLAND
+-122.03,38.25,35.0,1940.0,384.0,1177.0,403.0,3.1389,101100.0,INLAND
+-118.17,34.08,33.0,851.0,231.0,936.0,228.0,3.375,147500.0,<1H OCEAN
+-117.9,33.8,23.0,1368.0,397.0,1940.0,358.0,3.0789,350000.0,<1H OCEAN
+-122.38,37.91,18.0,3507.0,711.0,1224.0,676.0,5.0524,269800.0,NEAR BAY
+-121.34,38.01,17.0,2033.0,452.0,1114.0,446.0,3.2872,175000.0,INLAND
+-118.22,34.08,34.0,1709.0,562.0,2105.0,503.0,1.9704,152100.0,<1H OCEAN
+-117.94,33.81,33.0,1891.0,334.0,932.0,343.0,4.2759,238000.0,<1H OCEAN
+-117.88,33.89,18.0,1616.0,532.0,866.0,496.0,3.6435,119100.0,<1H OCEAN
+-119.33,36.22,29.0,1735.0,323.0,805.0,293.0,3.5039,89900.0,INLAND
+-122.23,40.15,14.0,2297.0,573.0,1637.0,551.0,1.787,51600.0,INLAND
+-121.62,36.43,20.0,1335.0,290.0,717.0,243.0,4.7891,230600.0,<1H OCEAN
+-118.37,33.84,32.0,1751.0,328.0,819.0,323.0,6.7105,339000.0,<1H OCEAN
+-122.11,37.7,19.0,2693.0,789.0,1765.0,724.0,2.4206,137500.0,NEAR BAY
+-121.36,38.56,20.0,1232.0,332.0,667.0,288.0,1.8288,32500.0,INLAND
+-122.53,37.66,25.0,7778.0,1493.0,4674.0,1451.0,5.4694,272400.0,NEAR OCEAN
+-121.28,38.61,23.0,2547.0,504.0,1235.0,469.0,2.4722,103300.0,INLAND
+-122.26,37.86,52.0,2888.0,604.0,1253.0,538.0,3.3893,241700.0,NEAR BAY
+-121.93,37.49,5.0,1150.0,311.0,648.0,245.0,3.5714,300000.0,<1H OCEAN
+-117.1,33.07,16.0,2402.0,336.0,1080.0,365.0,8.6803,347300.0,<1H OCEAN
+-118.94,35.39,13.0,3137.0,417.0,1318.0,397.0,7.7751,194100.0,INLAND
+-117.97,33.84,34.0,874.0,153.0,549.0,153.0,4.8667,186800.0,<1H OCEAN
+-118.23,34.05,52.0,346.0,270.0,346.0,251.0,2.5313,225000.0,<1H OCEAN
+-122.2,37.79,44.0,1621.0,452.0,1354.0,491.0,2.619,134700.0,NEAR BAY
+-121.99,37.98,23.0,2293.0,411.0,969.0,399.0,4.4536,184000.0,INLAND
+-121.47,38.53,43.0,3215.0,725.0,2400.0,625.0,1.4625,54400.0,INLAND
+-122.58,38.46,15.0,2936.0,517.0,1182.0,501.0,3.3981,246900.0,<1H OCEAN
+-118.18,33.8,42.0,2301.0,621.0,2114.0,561.0,2.0579,132700.0,NEAR OCEAN
+-121.77,37.99,4.0,5623.0,780.0,2429.0,716.0,5.4409,205100.0,INLAND
+-119.71,34.45,35.0,2183.0,363.0,988.0,351.0,5.5922,384400.0,<1H OCEAN
+-118.33,34.11,48.0,1601.0,464.0,784.0,461.0,3.0642,342900.0,<1H OCEAN
+-121.23,37.8,11.0,2451.0,665.0,1155.0,533.0,2.2254,130800.0,INLAND
+-118.04,33.92,34.0,1995.0,417.0,1573.0,407.0,3.4907,153500.0,<1H OCEAN
+-117.69,35.63,5.0,3151.0,482.0,1335.0,428.0,5.5773,109000.0,INLAND
+-118.25,33.96,43.0,2015.0,419.0,1543.0,399.0,1.8672,98100.0,<1H OCEAN
+-121.95,37.31,27.0,4140.0,,2135.0,893.0,3.6292,264600.0,<1H OCEAN
+-118.17,34.66,9.0,1561.0,253.0,731.0,233.0,5.7049,173200.0,INLAND
+-117.36,33.97,32.0,1625.0,335.0,1212.0,327.0,2.7596,82200.0,INLAND
+-121.02,37.62,30.0,1721.0,399.0,1878.0,382.0,2.5363,83900.0,INLAND
+-118.39,34.04,45.0,2089.0,312.0,834.0,305.0,7.3028,500001.0,<1H OCEAN
+-119.67,36.89,15.0,2373.0,364.0,1280.0,386.0,5.308,167500.0,INLAND
+-119.7,34.41,52.0,1526.0,458.0,1633.0,449.0,2.2069,226500.0,NEAR OCEAN
+-122.09,38.02,37.0,1742.0,339.0,1128.0,345.0,3.8824,113700.0,NEAR BAY
+-119.74,36.15,21.0,1548.0,308.0,1137.0,306.0,2.4688,61300.0,INLAND
+-121.54,39.47,14.0,1724.0,315.0,939.0,302.0,2.4952,53900.0,INLAND
+-117.46,34.06,24.0,2831.0,478.0,1582.0,435.0,4.3397,195600.0,INLAND
+-117.34,34.08,35.0,1380.0,248.0,730.0,264.0,3.2305,93700.0,INLAND
+-121.96,37.51,22.0,5811.0,1125.0,3215.0,1086.0,4.4107,223500.0,<1H OCEAN
+-122.22,37.81,52.0,1971.0,335.0,765.0,308.0,6.5217,273700.0,NEAR BAY
+-117.62,34.07,15.0,4061.0,811.0,2884.0,734.0,3.3936,127000.0,INLAND
+-121.93,41.86,28.0,4225.0,835.0,1908.0,686.0,1.74,44000.0,INLAND
+-117.71,33.6,25.0,1949.0,459.0,602.0,428.0,2.7601,72500.0,<1H OCEAN
+-118.4,34.0,37.0,1534.0,258.0,751.0,259.0,5.444,336000.0,<1H OCEAN
+-122.09,37.4,17.0,748.0,184.0,412.0,180.0,3.4375,290600.0,NEAR BAY
+-120.37,34.69,18.0,1868.0,315.0,747.0,265.0,4.7946,290600.0,NEAR OCEAN
+-118.06,34.14,40.0,2662.0,379.0,1151.0,387.0,8.4889,500001.0,INLAND
+-121.11,37.47,12.0,2263.0,410.0,913.0,330.0,3.5795,145600.0,INLAND
+-122.04,37.63,21.0,1307.0,236.0,586.0,249.0,4.7813,241900.0,NEAR BAY
+-118.92,34.4,23.0,1290.0,283.0,1060.0,279.0,3.3152,198000.0,<1H OCEAN
+-121.36,38.03,7.0,3461.0,859.0,1518.0,741.0,3.5684,78700.0,INLAND
+-118.46,33.97,19.0,2461.0,521.0,777.0,447.0,10.0,500001.0,<1H OCEAN
+-120.62,37.35,20.0,1457.0,372.0,1000.0,346.0,1.4615,69200.0,INLAND
+-118.51,33.98,40.0,1901.0,679.0,865.0,587.0,2.3417,425000.0,<1H OCEAN
+-121.55,39.51,50.0,1050.0,288.0,485.0,260.0,1.1607,51700.0,INLAND
+-116.0,32.74,26.0,1134.0,280.0,329.0,158.0,1.4338,43900.0,INLAND
+-118.37,34.14,21.0,4670.0,1161.0,1914.0,1094.0,3.7986,367700.0,<1H OCEAN
+-121.03,37.62,43.0,1241.0,240.0,612.0,266.0,2.8194,81300.0,INLAND
+-117.87,33.89,22.0,2340.0,664.0,1382.0,546.0,3.343,184600.0,<1H OCEAN
+-118.08,33.84,25.0,3696.0,953.0,2827.0,860.0,3.3438,153300.0,<1H OCEAN
+-122.31,37.99,25.0,6508.0,1137.0,3259.0,1081.0,4.2348,157800.0,NEAR BAY
+-122.19,37.74,36.0,847.0,212.0,567.0,159.0,1.1765,87100.0,NEAR BAY
+-121.23,37.96,37.0,2351.0,564.0,1591.0,549.0,1.6563,57200.0,INLAND
+-118.94,34.16,3.0,1170.0,148.0,493.0,142.0,8.0428,500001.0,<1H OCEAN
+-121.92,38.34,2.0,7747.0,1133.0,3481.0,1083.0,6.1112,181000.0,INLAND
+-122.43,37.73,52.0,1029.0,205.0,461.0,212.0,5.0782,310800.0,NEAR BAY
+-121.25,38.04,26.0,3080.0,473.0,1257.0,465.0,4.9861,201800.0,INLAND
+-118.29,33.98,30.0,1162.0,318.0,1207.0,289.0,1.223,100000.0,<1H OCEAN
+-117.24,32.8,28.0,1072.0,331.0,692.0,321.0,2.1357,187500.0,NEAR OCEAN
+-117.07,33.03,15.0,1095.0,158.0,361.0,176.0,6.8099,328200.0,<1H OCEAN
+-117.96,33.93,31.0,1471.0,321.0,841.0,330.0,3.46,232800.0,<1H OCEAN
+-122.27,37.85,52.0,4076.0,920.0,1800.0,815.0,2.7054,182300.0,NEAR BAY
+-117.28,34.16,26.0,2469.0,532.0,1068.0,501.0,1.9832,122100.0,INLAND
+-119.77,36.84,15.0,1924.0,262.0,848.0,277.0,5.3886,125300.0,INLAND
+-122.28,37.85,48.0,2063.0,484.0,1054.0,466.0,2.2625,132900.0,NEAR BAY
+-122.5,37.75,46.0,2298.0,457.0,1429.0,477.0,4.0217,272400.0,NEAR OCEAN
+-122.14,38.05,27.0,3794.0,772.0,1756.0,724.0,3.2891,150600.0,NEAR BAY
+-118.1,34.17,44.0,4505.0,894.0,2296.0,899.0,3.4811,300500.0,INLAND
+-122.46,37.77,52.0,3193.0,688.0,2099.0,681.0,3.9375,402900.0,NEAR BAY
+-118.22,34.23,34.0,3296.0,483.0,1268.0,478.0,8.4802,500001.0,<1H OCEAN
+-121.96,37.06,16.0,1321.0,224.0,650.0,206.0,6.3258,390000.0,NEAR OCEAN
+-118.48,34.23,35.0,1963.0,310.0,919.0,297.0,4.7583,258600.0,<1H OCEAN
+-118.16,34.19,40.0,1840.0,358.0,1218.0,347.0,4.25,177900.0,<1H OCEAN
+-122.06,37.34,13.0,2057.0,466.0,790.0,436.0,5.0081,288300.0,<1H OCEAN
+-118.34,33.98,46.0,2126.0,409.0,1292.0,414.0,2.9315,149000.0,<1H OCEAN
+-115.57,32.78,25.0,2007.0,301.0,1135.0,332.0,5.128,99600.0,INLAND
+-117.12,33.49,4.0,21988.0,4055.0,8824.0,3252.0,3.9963,191100.0,<1H OCEAN
+-118.03,33.91,35.0,2323.0,406.0,1741.0,398.0,4.2437,164100.0,<1H OCEAN
+-118.96,35.4,28.0,4667.0,875.0,2404.0,841.0,3.2325,89000.0,INLAND
+-118.36,34.05,47.0,1424.0,300.0,632.0,278.0,4.0625,295200.0,<1H OCEAN
+-117.31,34.07,40.0,2936.0,732.0,2024.0,676.0,2.1139,70900.0,INLAND
+-117.83,33.65,9.0,638.0,266.0,426.0,234.0,3.7875,187500.0,<1H OCEAN
+-117.84,33.76,22.0,378.0,78.0,196.0,81.0,3.6806,219400.0,<1H OCEAN
+-121.16,38.67,21.0,6198.0,1223.0,2827.0,1179.0,3.7796,159000.0,INLAND
+-119.77,36.77,38.0,3065.0,658.0,1441.0,625.0,2.0564,64700.0,INLAND
+-116.87,33.93,32.0,3141.0,812.0,2589.0,721.0,1.4556,54600.0,INLAND
+-118.39,33.81,35.0,1008.0,165.0,391.0,167.0,3.7778,487500.0,NEAR OCEAN
+-118.36,33.96,25.0,1849.0,518.0,1498.0,451.0,2.8378,170000.0,<1H OCEAN
+-118.07,33.81,22.0,2711.0,352.0,1305.0,368.0,8.5407,398800.0,<1H OCEAN
+-118.35,34.13,39.0,1610.0,278.0,511.0,278.0,4.3333,385900.0,<1H OCEAN
+-121.5,38.57,44.0,1375.0,351.0,766.0,321.0,2.1719,87500.0,INLAND
+-115.52,32.97,10.0,1879.0,387.0,1376.0,337.0,1.9911,67500.0,INLAND
+-118.06,34.01,34.0,1962.0,396.0,1488.0,332.0,3.9091,155100.0,<1H OCEAN
+-117.77,33.71,5.0,4050.0,584.0,1986.0,598.0,7.5847,375700.0,<1H OCEAN
+-122.41,37.79,52.0,3598.0,1011.0,2062.0,966.0,2.9871,380000.0,NEAR BAY
+-118.13,34.17,52.0,1784.0,368.0,966.0,345.0,3.6094,261500.0,<1H OCEAN
+-117.93,33.86,35.0,931.0,181.0,516.0,174.0,5.5867,182500.0,<1H OCEAN
+-121.85,37.41,25.0,1837.0,278.0,1006.0,271.0,6.6842,265300.0,<1H OCEAN
+-118.16,34.0,37.0,1341.0,336.0,1233.0,306.0,3.6583,150500.0,<1H OCEAN
+-118.19,33.9,36.0,1073.0,271.0,1385.0,288.0,2.3214,104800.0,<1H OCEAN
+-121.27,38.75,43.0,1292.0,307.0,647.0,249.0,2.7188,85300.0,INLAND
+-122.19,37.76,49.0,1368.0,282.0,790.0,269.0,1.7056,91400.0,NEAR BAY
+-124.17,40.75,13.0,2171.0,339.0,951.0,353.0,4.8516,116100.0,NEAR OCEAN
+-118.48,34.02,11.0,72.0,16.0,150.0,20.0,2.625,250000.0,<1H OCEAN
+-117.39,33.95,35.0,1599.0,284.0,721.0,287.0,4.125,120700.0,INLAND
+-122.41,37.75,52.0,2452.0,623.0,1932.0,549.0,2.3903,236100.0,NEAR BAY
+-118.27,34.01,47.0,921.0,264.0,881.0,221.0,1.4327,114100.0,<1H OCEAN
+-117.7,33.54,4.0,4956.0,693.0,1802.0,625.0,7.9338,354700.0,<1H OCEAN
+-117.21,33.16,13.0,2937.0,698.0,1246.0,579.0,2.6487,196000.0,<1H OCEAN
+-118.19,33.77,35.0,1574.0,603.0,820.0,514.0,1.2321,137500.0,NEAR OCEAN
+-119.72,34.42,37.0,1635.0,427.0,1027.0,408.0,3.5905,264700.0,NEAR OCEAN
+-118.47,34.27,17.0,1444.0,282.0,523.0,270.0,2.7353,192400.0,<1H OCEAN
+-122.26,38.12,28.0,3102.0,734.0,1623.0,639.0,3.1025,103700.0,NEAR BAY
+-117.98,33.94,32.0,2562.0,491.0,1222.0,446.0,4.0985,226200.0,<1H OCEAN
+-122.57,38.58,18.0,2083.0,506.0,926.0,487.0,1.9925,225000.0,INLAND
+-121.19,37.93,27.0,1621.0,363.0,909.0,345.0,2.1513,99700.0,INLAND
+-117.58,33.87,30.0,701.0,131.0,356.0,125.0,3.2917,144300.0,INLAND
+-117.65,34.06,41.0,1171.0,334.0,1479.0,334.0,2.25,90500.0,INLAND
+-117.66,34.06,24.0,4043.0,952.0,2174.0,859.0,2.2244,114900.0,INLAND
+-117.02,33.73,14.0,3700.0,750.0,1171.0,695.0,1.9476,112500.0,INLAND
+-117.89,33.68,8.0,5278.0,1575.0,2389.0,1371.0,3.3409,181300.0,<1H OCEAN
+-119.81,36.8,29.0,2806.0,552.0,1242.0,540.0,3.5958,88800.0,INLAND
+-121.85,37.39,15.0,8748.0,1547.0,4784.0,1524.0,5.8322,276600.0,<1H OCEAN
+-121.36,38.01,16.0,926.0,230.0,451.0,198.0,4.0221,173300.0,INLAND
+-122.31,37.55,52.0,900.0,183.0,371.0,166.0,3.25,296400.0,NEAR OCEAN
+-117.07,33.67,11.0,939.0,187.0,557.0,190.0,2.375,145800.0,INLAND
+-118.16,33.98,33.0,1196.0,313.0,1448.0,320.0,2.9375,162500.0,<1H OCEAN
+-118.33,34.07,52.0,2248.0,255.0,813.0,265.0,15.0001,500001.0,<1H OCEAN
+-117.87,33.87,7.0,2663.0,642.0,1367.0,677.0,4.6563,162400.0,<1H OCEAN
+-117.94,33.71,18.0,3695.0,602.0,1779.0,572.0,5.9449,276500.0,<1H OCEAN
+-120.95,36.47,52.0,1691.0,301.0,618.0,239.0,3.2292,225000.0,<1H OCEAN
+-118.2,33.81,47.0,2347.0,437.0,1219.0,420.0,5.3096,209900.0,NEAR OCEAN
+-122.45,37.73,52.0,1350.0,241.0,752.0,246.0,3.2448,266200.0,NEAR BAY
+-117.84,33.84,23.0,4388.0,864.0,2526.0,846.0,4.5217,219400.0,<1H OCEAN
+-118.4,34.06,52.0,2501.0,362.0,748.0,349.0,6.6343,500001.0,<1H OCEAN
+-121.12,39.03,17.0,838.0,161.0,388.0,142.0,3.6563,163500.0,INLAND
+-118.11,34.06,14.0,2628.0,668.0,2208.0,574.0,2.9764,160300.0,<1H OCEAN
+-121.09,38.19,23.0,762.0,140.0,358.0,141.0,2.4545,105000.0,INLAND
+-121.64,36.72,17.0,4203.0,816.0,2900.0,827.0,4.1742,159900.0,<1H OCEAN
+-118.32,33.96,47.0,1297.0,292.0,704.0,264.0,3.3214,166500.0,<1H OCEAN
+-118.0,33.81,33.0,2970.0,547.0,1869.0,539.0,4.3636,201800.0,<1H OCEAN
+-117.18,32.83,23.0,2105.0,525.0,1218.0,484.0,3.375,184100.0,NEAR OCEAN
+-118.24,33.98,45.0,173.0,42.0,230.0,57.0,3.0724,110700.0,<1H OCEAN
+-118.12,33.96,36.0,1426.0,235.0,698.0,240.0,4.8523,267300.0,<1H OCEAN
+-118.2,33.84,35.0,3405.0,779.0,1953.0,671.0,2.7813,159200.0,NEAR OCEAN
+-117.33,34.03,14.0,1582.0,347.0,825.0,259.0,2.8281,106300.0,INLAND
+-117.0,32.77,30.0,1802.0,401.0,776.0,386.0,2.8125,173500.0,<1H OCEAN
+-118.08,33.9,44.0,1167.0,237.0,733.0,237.0,4.2083,168300.0,<1H OCEAN
+-117.99,33.79,29.0,2470.0,560.0,1589.0,513.0,3.1801,190500.0,<1H OCEAN
+-119.23,35.77,36.0,3225.0,635.0,2034.0,593.0,2.4044,72500.0,INLAND
+-118.26,34.06,38.0,715.0,282.0,1174.0,300.0,2.345,225000.0,<1H OCEAN
+-117.14,32.7,47.0,552.0,161.0,593.0,174.0,0.9589,90000.0,NEAR OCEAN
+-118.04,33.85,23.0,3132.0,469.0,1646.0,478.0,5.777,315900.0,<1H OCEAN
+-121.56,39.52,9.0,818.0,197.0,358.0,197.0,1.7708,79500.0,INLAND
+-117.3,34.13,42.0,2115.0,557.0,1532.0,494.0,1.4531,71500.0,INLAND
+-117.87,33.81,15.0,3082.0,536.0,1268.0,531.0,3.7604,280100.0,<1H OCEAN
+-121.89,37.33,6.0,1495.0,552.0,1087.0,557.0,2.8798,225000.0,<1H OCEAN
+-122.27,37.86,52.0,1769.0,372.0,849.0,365.0,2.6914,218800.0,NEAR BAY
+-118.23,34.13,34.0,609.0,149.0,407.0,145.0,4.5766,185800.0,<1H OCEAN
+-118.51,34.04,38.0,4715.0,691.0,1660.0,637.0,10.1882,500001.0,<1H OCEAN
+-117.07,32.58,25.0,1607.0,280.0,899.0,260.0,3.8194,134400.0,NEAR OCEAN
+-118.13,34.16,52.0,1872.0,357.0,984.0,364.0,4.0,250400.0,<1H OCEAN
+-122.68,38.36,8.0,7520.0,1336.0,3833.0,1287.0,4.3278,184100.0,<1H OCEAN
+-120.43,34.98,21.0,2725.0,514.0,1466.0,488.0,3.6639,128600.0,<1H OCEAN
+-122.13,37.74,41.0,4400.0,666.0,1476.0,648.0,5.0,248900.0,NEAR BAY
+-114.58,33.63,29.0,1387.0,236.0,671.0,239.0,3.3438,74000.0,INLAND
+-117.83,34.15,20.0,2421.0,306.0,1023.0,298.0,8.0683,451500.0,INLAND
+-122.51,39.3,19.0,1629.0,386.0,551.0,214.0,1.7463,68800.0,INLAND
+-116.47,33.84,3.0,9169.0,1512.0,3838.0,1270.0,4.3111,142500.0,INLAND
+-121.8,38.01,37.0,3058.0,567.0,1351.0,523.0,3.5179,130800.0,INLAND
+-118.97,35.38,32.0,1361.0,363.0,1483.0,297.0,1.625,46800.0,INLAND
+-121.58,39.52,25.0,2409.0,490.0,1384.0,479.0,1.9956,58000.0,INLAND
+-121.85,37.35,29.0,3515.0,807.0,3572.0,776.0,2.7562,160100.0,<1H OCEAN
+-122.24,40.19,29.0,1912.0,336.0,859.0,325.0,3.7,70500.0,INLAND
+-121.46,38.61,43.0,1111.0,269.0,613.0,290.0,1.2917,66300.0,INLAND
+-118.09,33.83,36.0,2734.0,448.0,1308.0,441.0,5.9265,227300.0,<1H OCEAN
+-122.7,39.14,13.0,532.0,111.0,214.0,62.0,3.3929,108300.0,INLAND
+-117.78,33.78,6.0,9792.0,1283.0,3744.0,1179.0,10.1714,481500.0,<1H OCEAN
+-118.14,33.93,32.0,2065.0,438.0,1075.0,442.0,4.4279,226400.0,<1H OCEAN
+-122.67,38.44,32.0,3771.0,741.0,1786.0,721.0,3.2415,172200.0,<1H OCEAN
+-118.51,34.29,29.0,1287.0,194.0,525.0,187.0,6.4171,319300.0,<1H OCEAN
+-118.17,33.85,39.0,2247.0,526.0,1670.0,525.0,3.07,173000.0,<1H OCEAN
+-121.84,37.39,31.0,5524.0,914.0,2848.0,879.0,5.5592,229900.0,<1H OCEAN
+-121.45,37.72,2.0,2239.0,321.0,766.0,219.0,5.75,240200.0,INLAND
+-118.41,34.02,34.0,1430.0,357.0,805.0,362.0,3.3462,307000.0,<1H OCEAN
+-119.04,35.42,47.0,1691.0,402.0,913.0,358.0,1.8403,54700.0,INLAND
+-122.43,37.79,25.0,1637.0,394.0,649.0,379.0,5.0049,460000.0,NEAR BAY
+-122.46,37.72,49.0,1207.0,255.0,658.0,220.0,4.0859,228600.0,NEAR OCEAN
+-118.28,34.0,48.0,1514.0,376.0,1353.0,344.0,2.1607,96100.0,<1H OCEAN
+-121.22,37.96,30.0,1737.0,381.0,1177.0,347.0,1.9875,56400.0,INLAND
+-117.3,34.1,49.0,60.0,11.0,76.0,13.0,2.5625,75000.0,INLAND
+-122.28,38.0,26.0,2335.0,413.0,980.0,417.0,3.4471,178900.0,NEAR BAY
+-120.98,37.64,45.0,1913.0,335.0,839.0,333.0,3.1397,110700.0,INLAND
+-122.08,37.7,32.0,2718.0,447.0,1156.0,410.0,5.2497,259300.0,NEAR BAY
+-118.18,34.13,39.0,2902.0,460.0,1007.0,420.0,6.1953,363000.0,<1H OCEAN
+-122.27,38.0,12.0,1592.0,242.0,969.0,233.0,6.1576,248700.0,NEAR BAY
+-121.43,38.47,21.0,1787.0,291.0,988.0,301.0,4.35,96200.0,INLAND
+-120.95,37.57,29.0,1179.0,249.0,672.0,243.0,3.1125,154800.0,INLAND
+-121.9,37.41,24.0,4759.0,921.0,3188.0,902.0,5.6344,228700.0,<1H OCEAN
+-121.42,38.52,32.0,2828.0,556.0,1655.0,485.0,2.5574,72600.0,INLAND
+-117.95,33.99,24.0,1219.0,177.0,610.0,185.0,6.7978,325000.0,<1H OCEAN
+-118.03,34.15,43.0,1694.0,283.0,674.0,267.0,4.1797,486800.0,INLAND
+-117.09,32.68,20.0,2569.0,737.0,2341.0,705.0,2.0114,104900.0,NEAR OCEAN
+-118.4,34.16,35.0,1354.0,284.0,501.0,262.0,3.8056,384700.0,<1H OCEAN
+-122.16,37.75,24.0,1790.0,454.0,1137.0,386.0,2.537,107900.0,NEAR BAY
+-117.62,33.43,23.0,4052.0,955.0,1950.0,859.0,4.0647,240600.0,NEAR OCEAN
+-117.78,33.68,14.0,1750.0,336.0,852.0,300.0,4.6793,236800.0,<1H OCEAN
+-122.01,37.53,27.0,1890.0,303.0,889.0,314.0,5.7057,287600.0,NEAR BAY
+-118.14,33.86,36.0,1774.0,348.0,934.0,333.0,4.8571,203300.0,<1H OCEAN
+-117.32,34.54,9.0,5904.0,1165.0,3489.0,1063.0,3.125,92800.0,INLAND
+-117.9,34.01,27.0,2383.0,472.0,2079.0,494.0,3.9702,141400.0,<1H OCEAN
+-118.13,33.92,28.0,3069.0,864.0,1932.0,835.0,2.4925,177200.0,<1H OCEAN
+-118.22,34.13,40.0,2749.0,580.0,1375.0,511.0,4.825,205800.0,<1H OCEAN
+-119.47,35.14,19.0,4190.0,690.0,1973.0,702.0,3.9929,88300.0,INLAND
+-121.55,39.48,41.0,461.0,107.0,284.0,90.0,2.2045,41800.0,INLAND
+-119.96,38.94,19.0,1429.0,292.0,585.0,188.0,2.2589,131600.0,INLAND
+-121.92,37.32,31.0,1902.0,554.0,1485.0,494.0,2.4207,165600.0,<1H OCEAN
+-120.04,39.27,24.0,2237.0,491.0,264.0,95.0,4.1364,154500.0,INLAND
+-118.29,33.72,21.0,1568.0,452.0,801.0,422.0,3.5109,225000.0,NEAR OCEAN
+-122.24,37.76,52.0,2567.0,436.0,1119.0,415.0,4.6094,229300.0,NEAR BAY
+-118.18,34.01,39.0,322.0,82.0,319.0,90.0,2.6364,148800.0,<1H OCEAN
+-121.98,37.82,18.0,9117.0,1248.0,3280.0,1167.0,8.003,351300.0,<1H OCEAN
+-118.45,34.44,16.0,13406.0,2574.0,7030.0,2440.0,4.6861,187900.0,<1H OCEAN
+-121.45,38.54,41.0,1278.0,308.0,839.0,280.0,1.4702,58300.0,INLAND
+-119.93,37.85,18.0,473.0,115.0,88.0,41.0,4.0833,137500.0,INLAND
+-117.71,33.6,8.0,3329.0,753.0,1312.0,629.0,3.5521,229800.0,<1H OCEAN
+-118.4,34.26,13.0,4379.0,872.0,2560.0,853.0,4.2538,154300.0,<1H OCEAN
+-118.31,33.84,52.0,1819.0,464.0,1068.0,424.0,3.625,270700.0,<1H OCEAN
+-122.48,37.78,52.0,2910.0,611.0,1508.0,515.0,3.5865,311400.0,NEAR BAY
+-118.29,33.74,30.0,2074.0,533.0,1311.0,531.0,2.0329,225800.0,NEAR OCEAN
+-118.42,34.0,33.0,1139.0,299.0,734.0,257.0,3.2708,325000.0,<1H OCEAN
+-121.48,38.55,52.0,1684.0,309.0,675.0,296.0,4.1467,175000.0,INLAND
+-120.45,37.32,21.0,1318.0,202.0,618.0,197.0,4.8214,117800.0,INLAND
+-117.75,33.61,17.0,2499.0,566.0,781.0,522.0,3.1779,186500.0,<1H OCEAN
+-117.92,33.82,36.0,2360.0,405.0,1479.0,386.0,4.3583,187200.0,<1H OCEAN
+-121.45,38.61,46.0,1758.0,511.0,1094.0,484.0,1.0685,70000.0,INLAND
+-117.1,32.68,20.0,1012.0,269.0,837.0,240.0,2.0488,88900.0,NEAR OCEAN
+-118.29,34.05,31.0,2818.0,1252.0,4126.0,1200.0,2.053,229200.0,<1H OCEAN
+-118.35,34.07,52.0,2315.0,356.0,894.0,345.0,4.1328,500001.0,<1H OCEAN
+-116.99,32.7,15.0,3660.0,622.0,2629.0,612.0,4.0444,150100.0,NEAR OCEAN
+-120.66,35.27,33.0,1664.0,455.0,1077.0,461.0,1.6875,174200.0,NEAR OCEAN
+-117.0,32.85,24.0,1888.0,319.0,950.0,319.0,5.282,140800.0,<1H OCEAN
+-122.36,37.93,17.0,1258.0,254.0,885.0,229.0,3.05,121600.0,NEAR BAY
+-118.03,33.83,34.0,3203.0,653.0,2072.0,691.0,4.225,198400.0,<1H OCEAN
+-118.4,33.87,38.0,2398.0,431.0,911.0,392.0,5.2319,500001.0,<1H OCEAN
+-117.05,34.03,28.0,3009.0,698.0,1200.0,626.0,1.3993,104600.0,INLAND
+-122.44,37.8,52.0,1006.0,291.0,445.0,257.0,2.7717,500000.0,NEAR BAY
+-122.07,38.24,15.0,7937.0,1635.0,4390.0,1567.0,3.5464,129800.0,INLAND
+-117.91,33.91,27.0,2181.0,501.0,1555.0,488.0,3.6106,196400.0,<1H OCEAN
+-122.32,37.57,33.0,3384.0,819.0,2626.0,793.0,3.2285,234800.0,NEAR OCEAN
+-122.66,40.52,13.0,3013.0,486.0,1361.0,515.0,4.5357,171200.0,INLAND
+-118.13,34.16,52.0,1596.0,314.0,1024.0,292.0,3.6719,227900.0,<1H OCEAN
+-119.02,35.36,48.0,1833.0,396.0,947.0,363.0,2.2827,70000.0,INLAND
+-122.35,37.93,19.0,1334.0,366.0,1048.0,316.0,1.7865,88000.0,NEAR BAY
+-122.44,37.72,52.0,1507.0,282.0,929.0,281.0,3.8958,247700.0,NEAR BAY
+-122.22,37.81,52.0,2944.0,536.0,1034.0,521.0,5.3509,302100.0,NEAR BAY
+-117.68,34.04,27.0,574.0,103.0,321.0,103.0,3.9107,186500.0,INLAND
+-122.53,37.97,52.0,1560.0,451.0,700.0,419.0,2.5125,270800.0,NEAR BAY
+-118.31,33.8,29.0,2795.0,572.0,1469.0,557.0,3.7167,308900.0,<1H OCEAN
+-117.09,32.66,38.0,833.0,206.0,570.0,182.0,1.8333,127100.0,NEAR OCEAN
+-121.6,36.88,21.0,3416.0,624.0,1862.0,595.0,4.7813,241500.0,<1H OCEAN
+-122.46,37.74,51.0,1905.0,291.0,707.0,284.0,6.2561,431000.0,NEAR BAY
+-117.96,33.79,36.0,2398.0,403.0,1261.0,402.0,5.2816,221800.0,<1H OCEAN
+-119.82,36.76,41.0,1973.0,399.0,1107.0,375.0,1.8971,66900.0,INLAND
+-116.96,33.75,35.0,3269.0,757.0,2328.0,705.0,2.5898,76300.0,INLAND
+-122.15,37.73,28.0,2215.0,587.0,830.0,573.0,2.1898,141700.0,NEAR BAY
+-122.04,37.35,28.0,3250.0,485.0,1328.0,473.0,7.4729,431600.0,<1H OCEAN
+-117.23,32.86,15.0,1199.0,301.0,510.0,296.0,3.6083,180100.0,NEAR OCEAN
+-118.18,34.03,40.0,2631.0,698.0,2920.0,677.0,2.0764,145600.0,<1H OCEAN
+-118.31,34.04,52.0,1277.0,285.0,954.0,334.0,2.5833,234600.0,<1H OCEAN
+-121.6,39.8,10.0,1742.0,307.0,721.0,312.0,2.4537,117900.0,INLAND
+-122.74,38.83,12.0,4515.0,909.0,1554.0,528.0,3.3531,90800.0,<1H OCEAN
+-117.06,32.56,17.0,2803.0,683.0,2768.0,676.0,1.7958,140400.0,NEAR OCEAN
+-122.19,37.42,47.0,932.0,167.0,295.0,116.0,8.4375,500001.0,NEAR OCEAN
+-118.3,34.07,28.0,5221.0,2530.0,5840.0,2374.0,1.8829,300000.0,<1H OCEAN
+-118.27,34.06,17.0,2124.0,1168.0,3915.0,1137.0,1.1346,137500.0,<1H OCEAN
+-121.99,39.15,17.0,6440.0,1204.0,3266.0,1142.0,2.7137,72000.0,INLAND
+-118.36,33.89,27.0,2837.0,684.0,2141.0,648.0,3.1325,215000.0,<1H OCEAN
+-121.73,38.54,18.0,974.0,317.0,521.0,317.0,1.0633,137500.0,INLAND
+-121.83,39.23,25.0,3819.0,702.0,1983.0,658.0,2.4464,72500.0,INLAND
+-118.12,33.75,47.0,3330.0,569.0,1220.0,557.0,7.3672,500001.0,NEAR OCEAN
+-122.49,37.75,48.0,2181.0,419.0,1041.0,379.0,3.7361,320200.0,NEAR OCEAN
+-118.77,34.27,10.0,1658.0,310.0,1053.0,333.0,4.7574,209900.0,<1H OCEAN
+-118.18,33.79,20.0,1255.0,360.0,1201.0,318.0,1.2206,162500.0,NEAR OCEAN
+-119.04,35.41,25.0,1577.0,310.0,844.0,309.0,3.0625,69400.0,INLAND
+-118.19,33.89,32.0,1696.0,438.0,1639.0,376.0,2.0357,107300.0,<1H OCEAN
+-121.92,36.56,39.0,2144.0,538.0,749.0,419.0,2.7039,364000.0,NEAR OCEAN
+-117.88,33.77,31.0,2549.0,355.0,1044.0,362.0,6.9737,288800.0,<1H OCEAN
+-119.72,36.34,33.0,1287.0,214.0,580.0,210.0,3.2019,112500.0,INLAND
+-121.29,38.01,2.0,6403.0,1116.0,3327.0,957.0,4.4871,137900.0,INLAND
+-121.32,38.61,22.0,3902.0,845.0,1870.0,763.0,2.774,190200.0,INLAND
+-118.29,33.91,41.0,2475.0,532.0,1416.0,470.0,3.8372,156400.0,<1H OCEAN
+-117.66,33.57,16.0,4277.0,565.0,1642.0,549.0,8.0082,286600.0,<1H OCEAN
+-117.17,32.81,26.0,2424.0,388.0,974.0,375.0,4.739,184100.0,NEAR OCEAN
+-122.18,37.81,37.0,1643.0,262.0,620.0,266.0,5.4446,336700.0,NEAR BAY
+-119.13,36.3,33.0,3379.0,612.0,1565.0,618.0,2.7321,76500.0,INLAND
+-117.88,33.72,36.0,1910.0,352.0,1593.0,329.0,3.89,170000.0,<1H OCEAN
+-119.82,34.44,16.0,1414.0,463.0,793.0,439.0,3.6034,150000.0,NEAR OCEAN
+-121.25,38.0,21.0,446.0,73.0,182.0,57.0,2.8958,135000.0,INLAND
+-122.47,37.79,52.0,2844.0,623.0,1380.0,596.0,4.75,500001.0,NEAR BAY
+-118.5,34.21,36.0,1254.0,229.0,629.0,245.0,4.9643,236100.0,<1H OCEAN
+-118.48,34.03,19.0,902.0,284.0,414.0,272.0,1.3333,310000.0,<1H OCEAN
+-119.22,34.28,24.0,2212.0,332.0,899.0,331.0,5.533,299700.0,NEAR OCEAN
+-116.79,32.84,12.0,4281.0,786.0,1891.0,721.0,3.5769,231800.0,<1H OCEAN
+-118.07,33.89,29.0,1138.0,217.0,964.0,222.0,4.537,185300.0,<1H OCEAN
+-122.08,37.03,36.0,4682.0,899.0,2143.0,832.0,4.5096,203700.0,NEAR OCEAN
+-118.36,34.19,46.0,1676.0,322.0,846.0,295.0,5.1814,209500.0,<1H OCEAN
+-118.08,34.15,28.0,238.0,58.0,142.0,31.0,0.4999,500001.0,INLAND
+-122.38,37.71,47.0,1088.0,190.0,558.0,166.0,4.2708,207100.0,NEAR BAY
+-118.01,33.84,29.0,3740.0,691.0,1724.0,638.0,3.9628,215600.0,<1H OCEAN
+-122.4,40.58,43.0,1455.0,300.0,747.0,279.0,2.7857,104200.0,INLAND
+-121.32,38.74,14.0,1449.0,228.0,670.0,232.0,4.3897,186300.0,INLAND
+-118.04,33.94,37.0,1328.0,273.0,1115.0,275.0,4.2051,164400.0,<1H OCEAN
+-118.86,34.19,26.0,3135.0,480.0,1474.0,458.0,6.1949,243500.0,<1H OCEAN
+-117.59,34.03,16.0,3443.0,562.0,2130.0,564.0,5.0769,161400.0,INLAND
+-118.09,33.9,36.0,1215.0,279.0,862.0,285.0,3.7604,158700.0,<1H OCEAN
+-121.36,36.81,7.0,4609.0,741.0,1660.0,720.0,5.0871,290500.0,INLAND
+-118.2,34.05,8.0,762.0,204.0,728.0,174.0,2.4886,137500.0,<1H OCEAN
+-118.26,33.97,46.0,1295.0,351.0,1120.0,323.0,1.7121,98200.0,<1H OCEAN
+-117.17,34.12,3.0,15695.0,2248.0,6080.0,1920.0,6.2178,173900.0,INLAND
+-118.3,34.09,25.0,2345.0,852.0,2860.0,862.0,1.4497,205600.0,<1H OCEAN
+-120.6,35.1,16.0,3290.0,686.0,1497.0,655.0,2.6875,178200.0,<1H OCEAN
+-120.5,37.31,36.0,2162.0,433.0,1048.0,451.0,2.6797,81800.0,INLAND
+-118.44,34.31,14.0,4151.0,941.0,3163.0,915.0,4.0301,154300.0,<1H OCEAN
+-117.17,32.82,24.0,1569.0,377.0,715.0,321.0,3.1146,187500.0,NEAR OCEAN
+-121.49,38.57,51.0,1492.0,385.0,736.0,365.0,1.7155,108800.0,INLAND
+-118.0,33.88,28.0,1624.0,289.0,755.0,280.0,4.7083,268100.0,<1H OCEAN
+-121.36,38.6,35.0,1930.0,328.0,805.0,338.0,4.4643,133000.0,INLAND
+-117.32,34.19,6.0,1068.0,182.0,999.0,188.0,4.7222,109000.0,INLAND
+-121.1,38.94,42.0,410.0,117.0,706.0,112.0,1.0179,125000.0,INLAND
+-122.13,37.68,45.0,2457.0,445.0,1129.0,422.0,4.0588,182800.0,NEAR BAY
+-122.04,37.62,35.0,899.0,179.0,455.0,185.0,4.2857,190400.0,NEAR BAY
+-117.92,33.62,37.0,2038.0,379.0,837.0,381.0,5.2416,471300.0,<1H OCEAN
+-122.44,37.73,43.0,3700.0,684.0,1488.0,623.0,5.5622,313600.0,NEAR BAY
+-116.85,34.26,18.0,6988.0,1635.0,2044.0,726.0,2.4308,90600.0,INLAND
+-117.69,34.07,34.0,4055.0,739.0,2470.0,753.0,3.8586,136000.0,INLAND
+-120.96,37.61,23.0,3497.0,887.0,2467.0,816.0,1.9444,93400.0,INLAND
+-121.21,38.75,11.0,4552.0,639.0,2006.0,623.0,4.3962,264400.0,INLAND
+-123.1,39.36,19.0,1056.0,248.0,611.0,226.0,1.746,105000.0,INLAND
+-122.04,36.98,33.0,797.0,125.0,385.0,133.0,6.7974,367600.0,NEAR OCEAN
+-118.14,33.84,45.0,1908.0,361.0,890.0,342.0,4.575,336000.0,<1H OCEAN
+-118.46,34.16,16.0,4590.0,1200.0,2195.0,1139.0,3.8273,334900.0,<1H OCEAN
+-119.02,35.35,38.0,1472.0,305.0,670.0,282.0,2.2407,76000.0,INLAND
+-118.11,34.12,52.0,2954.0,371.0,1152.0,347.0,11.5609,500001.0,<1H OCEAN
+-117.85,33.92,11.0,3331.0,410.0,1460.0,416.0,8.0287,371800.0,<1H OCEAN
+-118.37,34.11,42.0,5518.0,979.0,1863.0,957.0,8.5842,500001.0,<1H OCEAN
+-118.46,33.77,28.0,3065.0,406.0,1101.0,391.0,10.5536,500001.0,NEAR OCEAN
+-121.27,38.67,15.0,2116.0,524.0,866.0,519.0,2.7388,111600.0,INLAND
+-117.5,34.04,4.0,3428.0,649.0,2158.0,632.0,5.0175,143400.0,INLAND
+-118.54,34.06,21.0,3755.0,525.0,1493.0,526.0,11.4233,500001.0,<1H OCEAN
+-118.18,33.98,40.0,1698.0,431.0,1280.0,405.0,2.625,206300.0,<1H OCEAN
+-117.93,33.88,45.0,1306.0,293.0,585.0,260.0,4.0812,241700.0,<1H OCEAN
+-122.65,38.47,24.0,2268.0,330.0,847.0,296.0,3.858,214400.0,<1H OCEAN
+-122.18,37.88,36.0,542.0,119.0,231.0,121.0,4.9,354200.0,NEAR BAY
+-118.13,33.82,44.0,1619.0,280.0,815.0,284.0,5.5449,232200.0,<1H OCEAN
+-117.64,34.12,18.0,3605.0,534.0,1682.0,480.0,5.8407,202900.0,INLAND
+-118.2,33.94,42.0,618.0,163.0,680.0,179.0,3.3472,154200.0,<1H OCEAN
+-118.31,34.03,46.0,2173.0,510.0,1343.0,476.0,2.025,135500.0,<1H OCEAN
+-118.38,34.2,32.0,993.0,285.0,1044.0,248.0,2.4306,187500.0,<1H OCEAN
+-121.73,38.0,3.0,9217.0,1522.0,3578.0,1272.0,5.0016,189100.0,INLAND
+-121.27,38.13,52.0,1081.0,257.0,437.0,225.0,2.1979,114100.0,INLAND
+-118.12,34.23,52.0,433.0,69.0,147.0,53.0,3.9583,162500.0,INLAND
+-118.41,34.0,46.0,105.0,20.0,69.0,19.0,3.9643,275000.0,<1H OCEAN
+-121.87,37.3,28.0,859.0,199.0,455.0,211.0,2.3293,215900.0,<1H OCEAN
+-121.23,38.67,27.0,5266.0,971.0,2432.0,948.0,3.8954,133000.0,INLAND
+-117.87,33.84,17.0,2395.0,410.0,1224.0,399.0,5.1182,249200.0,<1H OCEAN
+-120.66,35.29,16.0,2272.0,629.0,1689.0,649.0,1.7031,195000.0,NEAR OCEAN
+-117.8,33.77,29.0,5436.0,707.0,2046.0,685.0,8.7496,349500.0,<1H OCEAN
+-121.88,37.32,38.0,1787.0,508.0,2113.0,530.0,2.6386,177600.0,<1H OCEAN
+-117.69,33.53,17.0,5041.0,778.0,2396.0,801.0,6.0868,282900.0,<1H OCEAN
+-122.46,37.64,26.0,2806.0,375.0,1617.0,396.0,5.3922,353700.0,NEAR OCEAN
+-121.26,38.11,4.0,2058.0,366.0,933.0,316.0,4.2448,150900.0,INLAND
+-121.28,38.12,34.0,3268.0,640.0,1906.0,628.0,2.8237,110700.0,INLAND
+-117.7,33.61,16.0,2371.0,725.0,1738.0,686.0,3.6484,322600.0,<1H OCEAN
+-118.91,35.37,32.0,4121.0,755.0,2590.0,721.0,3.3462,67600.0,INLAND
+-118.87,34.23,14.0,4242.0,746.0,1858.0,689.0,6.0145,287100.0,<1H OCEAN
+-117.68,35.65,15.0,2701.0,576.0,1245.0,513.0,3.3269,81900.0,INLAND
+-118.06,34.58,36.0,1493.0,258.0,899.0,260.0,3.86,109300.0,INLAND
+-120.39,38.03,20.0,1551.0,309.0,647.0,228.0,2.6094,139100.0,INLAND
+-119.03,34.23,16.0,5323.0,795.0,2493.0,779.0,5.6762,271300.0,<1H OCEAN
+-117.91,33.79,22.0,4417.0,1054.0,2759.0,983.0,4.25,170300.0,<1H OCEAN
+-118.18,33.9,32.0,1452.0,365.0,1888.0,366.0,3.5461,146400.0,<1H OCEAN
+-121.48,37.77,19.0,2364.0,373.0,1264.0,390.0,5.0176,274200.0,INLAND
+-116.91,34.0,18.0,553.0,100.0,215.0,82.0,5.5,193800.0,INLAND
+-122.44,37.75,52.0,3114.0,637.0,1144.0,591.0,4.0,375000.0,NEAR BAY
+-118.21,33.82,45.0,455.0,92.0,394.0,89.0,4.9562,165700.0,NEAR OCEAN
+-118.32,33.83,19.0,3792.0,790.0,2105.0,834.0,5.2363,310000.0,<1H OCEAN
+-121.36,38.64,24.0,6540.0,1008.0,2667.0,1031.0,5.5632,175200.0,INLAND
+-117.95,34.03,35.0,804.0,159.0,727.0,179.0,2.7361,145700.0,<1H OCEAN
+-118.26,34.13,37.0,1383.0,470.0,1185.0,451.0,2.5,207100.0,<1H OCEAN
+-117.41,33.96,24.0,4481.0,901.0,2398.0,823.0,3.864,123400.0,INLAND
+-117.24,33.77,9.0,6907.0,1379.0,3665.0,1290.0,2.8401,104200.0,INLAND
+-116.92,32.86,11.0,2204.0,518.0,1472.0,497.0,2.3693,127000.0,<1H OCEAN
+-117.3,34.46,8.0,6246.0,1273.0,3883.0,1264.0,2.7917,98200.0,INLAND
+-118.08,34.09,34.0,1823.0,457.0,1485.0,401.0,3.7222,207200.0,<1H OCEAN
+-120.62,36.99,32.0,2455.0,508.0,1344.0,492.0,1.9732,69400.0,INLAND
+-122.04,37.6,17.0,3314.0,638.0,1873.0,602.0,4.3875,238500.0,NEAR BAY
+-121.58,39.5,29.0,1947.0,383.0,925.0,337.0,2.1658,57600.0,INLAND
+-122.15,37.46,39.0,906.0,109.0,353.0,112.0,10.3942,500001.0,NEAR BAY
+-117.14,33.07,12.0,9302.0,1603.0,4074.0,1504.0,4.3513,199600.0,<1H OCEAN
+-118.2,33.8,42.0,4577.0,1146.0,2749.0,1094.0,2.5012,197500.0,NEAR OCEAN
+-121.45,38.5,25.0,3033.0,665.0,1559.0,627.0,2.7101,99500.0,INLAND
+-119.55,36.7,31.0,1671.0,372.0,1371.0,347.0,2.3687,63900.0,INLAND
+-122.42,37.62,43.0,2367.0,409.0,1141.0,400.0,4.8295,319000.0,NEAR OCEAN
+-122.41,37.8,52.0,2583.0,672.0,1335.0,613.0,3.1477,500001.0,NEAR BAY
+-121.06,37.66,6.0,3655.0,598.0,1993.0,596.0,4.6053,150100.0,INLAND
+-117.83,33.89,25.0,1737.0,270.0,840.0,265.0,4.625,245700.0,<1H OCEAN
+-117.14,32.96,12.0,5949.0,799.0,2936.0,781.0,6.3721,241500.0,<1H OCEAN
+-122.7,38.33,16.0,1244.0,242.0,696.0,236.0,3.6369,158700.0,<1H OCEAN
+-122.45,37.66,36.0,5456.0,926.0,2761.0,916.0,4.7755,280700.0,NEAR OCEAN
+-120.85,37.75,26.0,28.0,4.0,9.0,5.0,1.625,85000.0,INLAND
+-117.65,34.14,9.0,3877.0,490.0,1815.0,490.0,8.4839,406700.0,INLAND
+-122.03,37.54,16.0,4458.0,856.0,3038.0,870.0,5.0739,208000.0,NEAR BAY
+-120.24,39.67,40.0,690.0,129.0,305.0,110.0,2.3625,62500.0,INLAND
+-118.17,34.18,38.0,1280.0,231.0,828.0,237.0,4.375,166700.0,<1H OCEAN
+-121.34,38.61,11.0,1716.0,404.0,722.0,415.0,2.0926,166100.0,INLAND
+-122.3,40.58,19.0,1043.0,204.0,505.0,183.0,1.6033,98800.0,INLAND
+-118.59,34.14,19.0,1303.0,155.0,450.0,145.0,10.5511,483100.0,<1H OCEAN
+-122.13,37.67,42.0,3592.0,703.0,1625.0,665.0,3.2434,179900.0,NEAR BAY
+-118.35,34.09,47.0,1800.0,546.0,921.0,478.0,2.8021,280600.0,<1H OCEAN
+-124.14,40.57,29.0,2864.0,600.0,1314.0,562.0,2.1354,75100.0,<1H OCEAN
+-122.0,37.86,18.0,8953.0,1074.0,3011.0,993.0,10.7372,500001.0,<1H OCEAN
+-117.15,32.74,26.0,3149.0,832.0,1320.0,808.0,3.0259,211700.0,NEAR OCEAN
+-116.97,32.83,23.0,149.0,32.0,101.0,34.0,2.6458,112500.0,<1H OCEAN
+-122.1,37.41,33.0,6277.0,1274.0,3025.0,1211.0,5.4721,343300.0,NEAR BAY
+-119.27,34.29,32.0,2274.0,406.0,982.0,393.0,5.3254,385200.0,NEAR OCEAN
+-122.42,37.74,52.0,1786.0,427.0,856.0,394.0,3.0833,328100.0,NEAR BAY
+-117.27,33.02,21.0,2144.0,340.0,928.0,344.0,5.798,286100.0,NEAR OCEAN
+-118.25,33.91,35.0,1479.0,272.0,963.0,292.0,3.4917,109500.0,<1H OCEAN
+-118.29,34.06,23.0,2040.0,778.0,2235.0,697.0,1.9309,233300.0,<1H OCEAN
+-119.34,35.6,16.0,1584.0,309.0,1011.0,268.0,2.4961,58800.0,INLAND
+-118.22,33.88,37.0,1149.0,280.0,1016.0,250.0,2.125,101900.0,<1H OCEAN
+-118.14,34.04,37.0,1129.0,212.0,509.0,202.0,2.6146,243200.0,<1H OCEAN
+-118.08,33.82,26.0,4259.0,588.0,1644.0,581.0,6.2519,345700.0,<1H OCEAN
+-118.92,35.13,29.0,1297.0,262.0,909.0,253.0,1.9236,106300.0,INLAND
+-122.13,37.45,37.0,1287.0,197.0,510.0,206.0,7.9029,500001.0,NEAR BAY
+-118.34,34.05,50.0,2009.0,419.0,1130.0,402.0,3.1944,213500.0,<1H OCEAN
+-118.28,34.05,41.0,1788.0,774.0,2931.0,702.0,1.4413,158900.0,<1H OCEAN
+-118.38,33.82,35.0,3053.0,623.0,1311.0,589.0,5.1589,439200.0,NEAR OCEAN
+-121.25,37.93,31.0,1673.0,382.0,1734.0,400.0,2.0833,48300.0,INLAND
+-122.27,37.47,44.0,3022.0,473.0,1235.0,477.0,6.7058,495900.0,NEAR OCEAN
+-121.28,38.02,8.0,1868.0,392.0,1258.0,389.0,3.175,95900.0,INLAND
+-117.03,34.02,26.0,3909.0,670.0,1884.0,665.0,4.1361,121000.0,INLAND
+-122.62,38.15,14.0,2259.0,341.0,1127.0,346.0,6.4092,334900.0,<1H OCEAN
+-117.74,33.46,9.0,6564.0,1316.0,1720.0,904.0,4.89,454100.0,<1H OCEAN
+-117.19,32.8,16.0,2593.0,794.0,1235.0,684.0,3.1304,166300.0,NEAR OCEAN
+-121.84,37.35,22.0,2914.0,768.0,2962.0,762.0,2.2031,164000.0,<1H OCEAN
+-117.24,32.72,39.0,3819.0,594.0,1361.0,583.0,6.6013,396400.0,NEAR OCEAN
+-116.88,33.93,37.0,1495.0,429.0,865.0,342.0,1.2188,55000.0,INLAND
+-118.31,33.83,45.0,2929.0,755.0,1635.0,652.0,2.9375,273000.0,<1H OCEAN
+-120.85,37.77,37.0,1738.0,403.0,936.0,366.0,2.4717,77100.0,INLAND
+-122.34,37.98,33.0,2014.0,410.0,1354.0,427.0,3.9773,131300.0,NEAR BAY
+-121.99,37.58,31.0,2878.0,478.0,1276.0,485.0,6.2073,282500.0,<1H OCEAN
+-117.05,32.8,17.0,1475.0,308.0,549.0,293.0,3.7167,180400.0,<1H OCEAN
+-122.41,37.74,52.0,2058.0,399.0,1208.0,399.0,3.6429,230000.0,NEAR BAY
+-122.09,38.0,6.0,10191.0,1882.0,4377.0,1789.0,5.2015,204200.0,NEAR BAY
+-121.62,39.12,26.0,1405.0,204.0,627.0,215.0,4.2188,94200.0,INLAND
+-118.53,34.18,26.0,4175.0,885.0,2118.0,778.0,4.2083,240300.0,<1H OCEAN
+-122.24,37.77,52.0,1153.0,235.0,481.0,223.0,2.6411,241000.0,NEAR BAY
+-122.17,37.48,39.0,2427.0,401.0,1178.0,408.0,5.9629,352700.0,NEAR BAY
+-119.85,36.82,15.0,1387.0,236.0,638.0,195.0,5.5842,88900.0,INLAND
+-119.21,37.25,44.0,3042.0,697.0,335.0,115.0,4.1838,85600.0,INLAND
+-118.22,33.81,41.0,726.0,166.0,602.0,183.0,3.7885,156900.0,NEAR OCEAN
+-122.21,37.79,34.0,2364.0,557.0,1517.0,516.0,2.8365,139200.0,NEAR BAY
+-117.1,32.56,16.0,2687.0,501.0,1502.0,480.0,3.75,146800.0,NEAR OCEAN
+-119.23,35.79,31.0,2862.0,606.0,2467.0,600.0,2.3125,62100.0,INLAND
+-117.96,33.74,19.0,1783.0,415.0,1025.0,383.0,4.1484,230000.0,<1H OCEAN
+-118.68,34.28,5.0,6150.0,1265.0,3188.0,1266.0,4.7034,223000.0,<1H OCEAN
+-118.27,33.97,39.0,2569.0,688.0,2601.0,630.0,2.0754,101400.0,<1H OCEAN
+-121.9,37.39,42.0,42.0,14.0,26.0,14.0,1.7361,500001.0,<1H OCEAN
+-122.39,37.72,28.0,1609.0,340.0,1064.0,290.0,1.1125,206300.0,NEAR BAY
+-119.81,36.76,48.0,2059.0,388.0,834.0,405.0,2.9306,67900.0,INLAND
+-117.97,34.09,27.0,3569.0,761.0,3339.0,762.0,4.1304,160500.0,INLAND
+-121.41,38.58,18.0,6955.0,1882.0,2803.0,1740.0,3.089,141400.0,INLAND
+-116.99,32.77,35.0,2306.0,334.0,828.0,310.0,6.1103,301600.0,<1H OCEAN
+-122.11,37.99,16.0,3913.0,710.0,1782.0,676.0,5.1297,206700.0,NEAR BAY
+-118.16,34.17,52.0,1193.0,228.0,703.0,221.0,3.1741,163800.0,<1H OCEAN
+-117.92,33.78,35.0,1654.0,323.0,1065.0,354.0,3.4837,186500.0,<1H OCEAN
+-123.73,39.44,32.0,790.0,151.0,380.0,142.0,2.7,165000.0,<1H OCEAN
+-122.09,37.38,34.0,1959.0,342.0,849.0,357.0,6.2884,414700.0,NEAR BAY
+-117.8,33.69,15.0,2099.0,322.0,873.0,307.0,7.9887,328000.0,<1H OCEAN
+-122.53,37.63,27.0,2589.0,658.0,1386.0,608.0,2.9087,228200.0,NEAR OCEAN
+-117.1,32.68,47.0,771.0,224.0,637.0,212.0,2.0156,90300.0,NEAR OCEAN
+-121.89,37.67,19.0,2034.0,288.0,852.0,295.0,6.5285,300400.0,INLAND
+-120.98,38.68,5.0,4810.0,909.0,2242.0,900.0,3.2964,176900.0,INLAND
+-122.12,37.81,26.0,4048.0,513.0,1486.0,498.0,7.6717,416500.0,NEAR BAY
+-116.49,33.82,27.0,3316.0,636.0,2362.0,532.0,2.9569,65900.0,INLAND
+-118.14,33.77,49.0,2792.0,690.0,1301.0,648.0,3.2917,307400.0,NEAR OCEAN
+-119.55,37.75,30.0,2165.0,536.0,1500.0,414.0,3.5391,55900.0,INLAND
+-118.27,33.77,39.0,1731.0,485.0,2115.0,478.0,1.5369,141300.0,NEAR OCEAN
+-122.32,37.96,25.0,1728.0,403.0,934.0,412.0,3.375,133700.0,NEAR BAY
+-118.25,34.06,12.0,4011.0,1438.0,1673.0,1088.0,5.3081,287500.0,<1H OCEAN
+-118.4,33.9,34.0,2674.0,435.0,1087.0,431.0,7.3151,492200.0,<1H OCEAN
+-118.41,34.0,37.0,1426.0,259.0,689.0,261.0,5.5284,331000.0,<1H OCEAN
+-119.45,35.13,34.0,1440.0,309.0,808.0,294.0,2.3013,26600.0,INLAND
+-118.4,34.28,16.0,6573.0,1480.0,6161.0,1473.0,3.3304,154900.0,<1H OCEAN
+-122.24,38.13,37.0,3223.0,564.0,1325.0,539.0,4.0938,126900.0,NEAR BAY
+-121.88,37.35,49.0,1728.0,350.0,1146.0,391.0,3.5781,193000.0,<1H OCEAN
+-121.7,38.1,19.0,4896.0,1083.0,2150.0,905.0,3.3398,89700.0,INLAND
+-121.29,38.9,45.0,2019.0,394.0,1104.0,407.0,3.1691,108700.0,INLAND
+-117.93,33.65,35.0,2133.0,413.0,1473.0,402.0,4.4211,215200.0,<1H OCEAN
+-118.15,33.91,25.0,2053.0,578.0,1721.0,507.0,2.3456,146100.0,<1H OCEAN
+-118.92,34.17,17.0,1552.0,246.0,685.0,244.0,5.9836,294800.0,<1H OCEAN
+-118.52,34.27,36.0,3204.0,538.0,1499.0,499.0,5.5649,271200.0,<1H OCEAN
+-124.14,40.77,27.0,3046.0,605.0,1407.0,571.0,2.9143,99600.0,NEAR OCEAN
+-122.41,37.8,52.0,3697.0,837.0,1446.0,711.0,5.866,500001.0,NEAR BAY
+-120.79,37.82,17.0,4227.0,729.0,1809.0,679.0,3.2667,269500.0,INLAND
+-122.0,37.35,20.0,4304.0,851.0,2059.0,835.0,5.1674,333000.0,<1H OCEAN
+-118.25,33.93,38.0,180.0,43.0,246.0,56.0,2.85,90000.0,<1H OCEAN
+-121.94,37.8,8.0,11336.0,1657.0,4089.0,1555.0,7.8287,369200.0,<1H OCEAN
+-121.76,38.94,48.0,540.0,110.0,234.0,74.0,3.6111,67500.0,INLAND
+-117.73,34.03,42.0,1967.0,378.0,1459.0,348.0,3.0375,118100.0,INLAND
+-121.71,36.9,16.0,1680.0,285.0,1103.0,275.0,4.6125,253800.0,<1H OCEAN
+-118.18,33.97,34.0,3214.0,899.0,3086.0,808.0,2.0057,189400.0,<1H OCEAN
+-117.23,32.72,43.0,952.0,209.0,392.0,210.0,2.1635,244200.0,NEAR OCEAN
+-118.27,34.05,47.0,661.0,359.0,1406.0,307.0,1.3169,112500.0,<1H OCEAN
+-117.26,32.97,25.0,2582.0,495.0,1088.0,471.0,6.4651,500001.0,NEAR OCEAN
+-118.44,34.26,28.0,1077.0,288.0,1377.0,293.0,3.9167,153900.0,<1H OCEAN
+-121.47,38.63,29.0,2197.0,520.0,1374.0,483.0,2.1889,69300.0,INLAND
+-117.95,33.77,38.0,1476.0,308.0,1114.0,309.0,4.1917,181800.0,<1H OCEAN
+-122.44,37.79,52.0,2045.0,353.0,722.0,327.0,8.0755,500001.0,NEAR BAY
+-117.93,34.09,37.0,1185.0,225.0,769.0,235.0,4.4625,154200.0,<1H OCEAN
+-122.02,37.32,27.0,4336.0,754.0,2009.0,734.0,6.3923,348300.0,<1H OCEAN
+-121.43,38.53,36.0,1488.0,294.0,846.0,279.0,3.1208,82700.0,INLAND
+-118.43,34.16,34.0,2459.0,489.0,1139.0,463.0,4.0347,353600.0,<1H OCEAN
+-118.14,33.79,45.0,1519.0,263.0,681.0,267.0,4.6452,212500.0,NEAR OCEAN
+-117.95,34.04,27.0,2610.0,846.0,2296.0,750.0,2.274,150800.0,<1H OCEAN
+-118.4,34.04,43.0,3863.0,537.0,1398.0,511.0,8.5938,500001.0,<1H OCEAN
+-117.66,34.08,36.0,1485.0,236.0,623.0,261.0,3.3036,141000.0,INLAND
+-118.28,34.05,44.0,968.0,384.0,1805.0,375.0,1.4801,212500.0,<1H OCEAN
+-117.64,33.68,4.0,5687.0,970.0,2677.0,938.0,6.5069,243400.0,<1H OCEAN
+-118.18,34.16,34.0,5012.0,746.0,1699.0,715.0,9.4987,500001.0,<1H OCEAN
+-122.65,38.92,30.0,70.0,38.0,20.0,13.0,4.125,112500.0,INLAND
+-118.15,34.14,45.0,543.0,191.0,454.0,181.0,2.3,55000.0,<1H OCEAN
+-122.33,38.33,15.0,3193.0,468.0,1303.0,426.0,5.3017,202600.0,NEAR BAY
+-121.62,39.12,35.0,2787.0,587.0,1431.0,601.0,2.5469,65900.0,INLAND
+-122.83,38.4,37.0,2217.0,451.0,1019.0,428.0,3.1217,178500.0,<1H OCEAN
+-118.08,34.1,36.0,2679.0,548.0,1605.0,533.0,3.5313,213200.0,<1H OCEAN
+-118.28,34.09,38.0,790.0,275.0,664.0,194.0,3.0357,175000.0,<1H OCEAN
+-118.08,33.99,36.0,2024.0,590.0,2028.0,573.0,2.8152,163900.0,<1H OCEAN
+-121.51,39.97,22.0,1468.0,285.0,611.0,235.0,2.3036,73000.0,INLAND
+-118.27,34.02,40.0,561.0,284.0,662.0,205.0,0.9234,187500.0,<1H OCEAN
+-120.91,37.73,31.0,840.0,154.0,429.0,150.0,2.4063,170200.0,INLAND
+-117.48,33.91,22.0,3611.0,666.0,1869.0,649.0,4.2207,141100.0,INLAND
+-118.58,34.18,28.0,908.0,142.0,368.0,143.0,5.6159,340500.0,<1H OCEAN
+-119.44,36.59,32.0,1153.0,236.0,761.0,241.0,2.825,67600.0,INLAND
+-119.33,36.3,11.0,3045.0,,1563.0,516.0,5.4337,133800.0,INLAND
+-117.34,34.07,46.0,1851.0,425.0,1100.0,377.0,2.0461,90500.0,INLAND
+-119.21,34.26,10.0,3150.0,781.0,1582.0,653.0,4.2448,157300.0,NEAR OCEAN
+-118.06,34.16,46.0,1467.0,298.0,816.0,267.0,3.6705,286500.0,INLAND
+-118.36,33.86,35.0,2126.0,434.0,1044.0,433.0,5.5456,297400.0,<1H OCEAN
+-117.92,33.61,18.0,1538.0,425.0,425.0,288.0,5.3369,312500.0,<1H OCEAN
+-119.69,34.44,41.0,1989.0,271.0,666.0,269.0,6.8406,500001.0,<1H OCEAN
+-117.1,32.69,11.0,3071.0,911.0,2812.0,774.0,1.2413,83100.0,NEAR OCEAN
+-118.07,33.92,36.0,1560.0,320.0,1348.0,314.0,3.622,174000.0,<1H OCEAN
+-122.08,37.35,33.0,2398.0,317.0,832.0,314.0,10.3591,500001.0,NEAR BAY
+-117.71,33.6,25.0,3011.0,714.0,893.0,654.0,2.3387,74800.0,<1H OCEAN
+-118.45,34.04,21.0,2819.0,648.0,1435.0,593.0,3.9489,360200.0,<1H OCEAN
+-117.71,33.63,16.0,2497.0,500.0,1357.0,456.0,4.5909,241800.0,<1H OCEAN
+-117.18,33.78,7.0,1697.0,424.0,808.0,354.0,1.3417,169300.0,INLAND
+-118.18,33.86,43.0,2752.0,645.0,1674.0,614.0,3.6719,161300.0,<1H OCEAN
+-123.69,38.9,17.0,2206.0,478.0,1140.0,428.0,2.1985,95300.0,NEAR OCEAN
+-118.36,33.83,35.0,1378.0,247.0,645.0,217.0,5.9143,343400.0,<1H OCEAN
+-118.02,34.07,21.0,3245.0,959.0,3528.0,887.0,2.3236,156300.0,INLAND
+-117.07,32.56,9.0,3648.0,895.0,3293.0,840.0,3.0992,142600.0,NEAR OCEAN
+-117.77,33.67,12.0,4329.0,1068.0,1913.0,978.0,4.5094,160200.0,<1H OCEAN
+-121.82,37.29,16.0,2085.0,394.0,1705.0,391.0,5.0225,222800.0,<1H OCEAN
+-118.5,34.23,26.0,3082.0,573.0,1590.0,586.0,4.5167,319000.0,<1H OCEAN
+-121.38,38.69,35.0,2943.0,554.0,1460.0,510.0,2.6713,84400.0,INLAND
+-121.29,37.96,52.0,287.0,119.0,154.0,85.0,0.8738,75000.0,INLAND
+-116.99,32.79,26.0,3623.0,703.0,1609.0,669.0,3.744,165800.0,<1H OCEAN
+-118.08,33.95,32.0,1962.0,387.0,1274.0,398.0,4.8304,160600.0,<1H OCEAN
+-117.13,33.15,15.0,2241.0,381.0,997.0,390.0,3.4833,193200.0,<1H OCEAN
+-118.43,34.01,41.0,1527.0,279.0,746.0,285.0,6.4232,446600.0,<1H OCEAN
+-119.91,36.83,29.0,2205.0,366.0,1072.0,345.0,3.8056,165400.0,INLAND
+-118.3,33.75,48.0,1958.0,386.0,1098.0,380.0,4.625,273400.0,<1H OCEAN
+-116.6,33.49,16.0,3730.0,827.0,1346.0,592.0,2.183,113500.0,INLAND
+-116.9,33.03,11.0,3213.0,634.0,1975.0,579.0,3.475,167200.0,<1H OCEAN
+-122.41,37.79,52.0,3302.0,869.0,1178.0,727.0,3.3681,500001.0,NEAR BAY
+-122.34,40.63,10.0,2183.0,369.0,1061.0,325.0,3.6853,151600.0,INLAND
+-117.94,34.15,33.0,859.0,144.0,421.0,138.0,4.4821,220100.0,INLAND
+-118.32,33.86,34.0,495.0,90.0,269.0,93.0,6.4391,252300.0,<1H OCEAN
+-122.15,38.29,17.0,1625.0,239.0,703.0,224.0,6.5891,328800.0,NEAR BAY
+-118.37,33.89,21.0,2696.0,548.0,1142.0,473.0,5.6091,356800.0,<1H OCEAN
+-118.85,34.14,24.0,1999.0,244.0,759.0,247.0,8.7657,366300.0,<1H OCEAN
+-121.92,37.28,26.0,6201.0,783.0,2381.0,819.0,7.9819,397000.0,<1H OCEAN
+-120.64,35.46,6.0,5876.0,1406.0,2877.0,1304.0,2.5437,146400.0,<1H OCEAN
+-116.97,32.76,33.0,3071.0,466.0,1348.0,513.0,6.1768,228900.0,<1H OCEAN
+-120.06,39.01,19.0,2967.0,528.0,112.0,48.0,4.0714,437500.0,INLAND
+-117.89,33.77,32.0,2342.0,570.0,1445.0,453.0,4.1951,195000.0,<1H OCEAN
+-115.58,33.88,21.0,1161.0,282.0,724.0,186.0,3.1827,71700.0,INLAND
+-119.02,35.43,39.0,2033.0,370.0,956.0,379.0,3.1736,70700.0,INLAND
+-120.97,37.43,27.0,1380.0,,810.0,262.0,2.1875,137500.0,INLAND
+-119.77,36.86,7.0,4139.0,544.0,1843.0,562.0,8.2737,193500.0,INLAND
+-118.37,34.1,37.0,407.0,67.0,100.0,47.0,15.0001,500001.0,<1H OCEAN
+-116.97,33.74,20.0,3674.0,792.0,1498.0,758.0,2.2161,76900.0,INLAND
+-117.95,34.05,34.0,1428.0,227.0,890.0,249.0,5.8722,204800.0,<1H OCEAN
+-117.01,34.01,15.0,5592.0,891.0,2419.0,840.0,4.7193,135200.0,INLAND
+-115.62,33.04,17.0,1009.0,231.0,745.0,217.0,2.0463,61200.0,INLAND
+-118.13,33.84,48.0,1895.0,294.0,881.0,293.0,6.3364,307400.0,<1H OCEAN
+-120.67,38.76,35.0,2104.0,403.0,1060.0,400.0,2.1682,138100.0,INLAND
+-118.32,33.93,34.0,1536.0,273.0,804.0,287.0,4.9615,157800.0,<1H OCEAN
+-123.21,39.18,17.0,2772.0,576.0,1501.0,584.0,2.6275,142100.0,<1H OCEAN
+-122.21,37.83,40.0,4991.0,674.0,1616.0,654.0,7.5544,411500.0,NEAR BAY
+-118.18,33.78,26.0,3042.0,1253.0,4812.0,1141.0,1.7701,146200.0,NEAR OCEAN
+-119.79,36.77,30.0,1610.0,410.0,1000.0,397.0,2.0357,60200.0,INLAND
+-121.36,38.58,25.0,3196.0,406.0,978.0,419.0,8.4699,344000.0,INLAND
+-118.18,33.77,41.0,2048.0,601.0,852.0,533.0,2.5726,193800.0,NEAR OCEAN
+-121.28,37.92,30.0,1061.0,230.0,851.0,195.0,2.4412,61600.0,INLAND
+-118.31,34.08,26.0,1609.0,534.0,1868.0,497.0,2.7038,227100.0,<1H OCEAN
+-122.17,38.07,15.0,2125.0,278.0,857.0,272.0,6.4599,219700.0,NEAR BAY
+-118.19,33.78,8.0,992.0,393.0,694.0,331.0,2.5544,162500.0,NEAR OCEAN
+-117.69,34.03,20.0,6374.0,1412.0,3690.0,1350.0,3.4184,162500.0,INLAND
+-118.56,34.06,24.0,2332.0,349.0,761.0,325.0,7.3031,500001.0,<1H OCEAN
+-118.25,33.98,47.0,617.0,162.0,754.0,144.0,2.2969,116700.0,<1H OCEAN
+-118.32,34.1,31.0,622.0,229.0,597.0,227.0,1.5284,200000.0,<1H OCEAN
+-118.46,34.18,35.0,1819.0,465.0,1336.0,419.0,3.4583,253200.0,<1H OCEAN
+-121.92,37.31,26.0,3242.0,780.0,2100.0,741.0,3.1107,247900.0,<1H OCEAN
+-117.93,33.65,29.0,1253.0,375.0,1198.0,362.0,3.5179,225000.0,<1H OCEAN
+-122.19,37.44,38.0,3383.0,456.0,1203.0,465.0,9.3198,500001.0,NEAR OCEAN
+-121.37,38.6,35.0,3137.0,544.0,1312.0,549.0,3.788,136800.0,INLAND
+-117.91,34.14,42.0,2225.0,485.0,1544.0,464.0,2.2442,166700.0,INLAND
+-122.42,37.75,52.0,2112.0,528.0,1227.0,513.0,3.5536,400000.0,NEAR BAY
+-119.9,36.79,22.0,1970.0,332.0,1066.0,319.0,3.3125,106100.0,INLAND
+-116.37,33.72,17.0,8626.0,1859.0,3497.0,1337.0,3.312,121300.0,INLAND
+-124.13,40.79,29.0,2474.0,453.0,1130.0,427.0,2.8833,83000.0,NEAR OCEAN
+-121.57,37.02,17.0,2889.0,624.0,2681.0,608.0,2.9417,178000.0,INLAND
+-117.92,34.03,35.0,1469.0,306.0,1285.0,308.0,3.9219,159500.0,<1H OCEAN
+-117.98,34.07,15.0,3543.0,888.0,3131.0,823.0,3.0184,139400.0,INLAND
+-118.74,34.26,22.0,4337.0,673.0,2347.0,636.0,5.4091,222400.0,<1H OCEAN
+-122.57,37.96,52.0,3458.0,468.0,1449.0,471.0,9.1834,500001.0,NEAR BAY
+-118.4,33.97,44.0,2825.0,453.0,1221.0,461.0,5.9544,377200.0,<1H OCEAN
+-121.94,38.37,14.0,1156.0,216.0,574.0,227.0,3.2396,143800.0,INLAND
+-117.3,33.06,24.0,2171.0,511.0,870.0,442.0,3.194,276300.0,NEAR OCEAN
+-118.06,34.1,43.0,1833.0,355.0,786.0,334.0,3.5761,256700.0,<1H OCEAN
+-117.81,33.68,8.0,1964.0,413.0,913.0,406.0,5.1583,192200.0,<1H OCEAN
+-122.5,37.74,44.0,2374.0,496.0,1087.0,426.0,3.5,275700.0,NEAR OCEAN
+-121.46,38.62,35.0,3326.0,696.0,2511.0,649.0,1.9871,60900.0,INLAND
+-118.1,33.9,37.0,796.0,175.0,740.0,183.0,3.6,156400.0,<1H OCEAN
+-117.45,33.93,20.0,5998.0,1320.0,3185.0,1199.0,3.2731,113900.0,INLAND
+-117.37,33.98,52.0,201.0,44.0,130.0,24.0,2.025,125000.0,INLAND
+-117.06,33.15,24.0,2155.0,379.0,1158.0,360.0,4.7941,147500.0,<1H OCEAN
+-119.31,34.44,5.0,403.0,48.0,208.0,54.0,12.632,500001.0,NEAR OCEAN
+-122.26,37.88,52.0,2604.0,837.0,1798.0,769.0,1.725,287500.0,NEAR BAY
+-121.37,38.61,42.0,945.0,193.0,460.0,193.0,3.7569,127100.0,INLAND
+-121.66,36.67,40.0,2497.0,520.0,1275.0,508.0,3.1071,193100.0,<1H OCEAN
+-116.89,32.67,9.0,2652.0,393.0,1355.0,362.0,6.2578,293100.0,<1H OCEAN
+-123.19,39.15,16.0,2577.0,495.0,1232.0,488.0,2.6012,125600.0,<1H OCEAN
+-120.35,37.99,3.0,1167.0,306.0,422.0,186.0,2.4191,217500.0,INLAND
+-122.61,41.74,15.0,4206.0,922.0,1863.0,869.0,2.0591,55700.0,INLAND
+-118.24,34.15,7.0,2063.0,670.0,1892.0,643.0,1.7301,202300.0,<1H OCEAN
+-118.02,33.9,34.0,2678.0,511.0,1540.0,497.0,4.4954,202900.0,<1H OCEAN
+-116.99,32.73,30.0,1856.0,339.0,1103.0,379.0,4.0357,153800.0,<1H OCEAN
+-117.76,33.83,15.0,3086.0,457.0,1262.0,436.0,6.4415,300700.0,<1H OCEAN
+-118.25,34.0,41.0,1768.0,475.0,1721.0,474.0,1.303,90400.0,<1H OCEAN
+-124.09,40.88,26.0,2683.0,555.0,1353.0,526.0,2.4321,82100.0,NEAR OCEAN
+-118.3,33.89,30.0,2756.0,858.0,1806.0,787.0,3.0329,207800.0,<1H OCEAN
+-117.64,33.49,3.0,2516.0,429.0,781.0,337.0,5.6197,271600.0,<1H OCEAN
+-122.01,38.37,16.0,3996.0,550.0,1673.0,539.0,5.778,175700.0,INLAND
+-118.28,34.76,19.0,3430.0,601.0,1817.0,571.0,4.7875,163600.0,INLAND
+-118.17,34.04,46.0,705.0,167.0,655.0,149.0,3.5938,141100.0,<1H OCEAN
+-121.36,38.42,6.0,3254.0,465.0,1168.0,345.0,5.1811,188400.0,INLAND
+-118.24,34.23,41.0,1912.0,308.0,896.0,314.0,5.3473,352700.0,<1H OCEAN
+-118.31,33.67,42.0,1297.0,246.0,611.0,242.0,5.3074,401900.0,NEAR OCEAN
+-122.09,37.63,34.0,1457.0,242.0,735.0,249.0,3.9167,189500.0,NEAR BAY
+-116.66,33.09,24.0,1378.0,272.0,532.0,188.0,1.5909,221900.0,INLAND
+-121.68,39.11,19.0,1366.0,220.0,596.0,203.0,4.0625,141700.0,INLAND
+-117.9,33.74,24.0,1435.0,494.0,3171.0,504.0,3.0833,151700.0,<1H OCEAN
+-121.3,38.03,11.0,2866.0,654.0,1404.0,525.0,2.505,95000.0,INLAND
+-118.35,34.32,52.0,102.0,29.0,54.0,32.0,1.9875,191700.0,<1H OCEAN
+-121.29,38.0,4.0,1392.0,322.0,1784.0,309.0,2.375,124500.0,INLAND
+-118.03,34.11,38.0,2076.0,361.0,988.0,332.0,5.9175,416900.0,INLAND
+-122.02,37.3,26.0,1983.0,301.0,924.0,297.0,6.7123,354600.0,<1H OCEAN
+-118.46,34.01,39.0,711.0,148.0,347.0,153.0,4.2813,297200.0,<1H OCEAN
+-121.81,38.0,37.0,2724.0,579.0,1400.0,540.0,2.905,97300.0,INLAND
+-118.16,33.85,36.0,1979.0,339.0,952.0,339.0,4.0815,216200.0,<1H OCEAN
+-118.32,34.01,47.0,1745.0,371.0,1079.0,368.0,2.4022,123400.0,<1H OCEAN
+-117.92,34.07,38.0,175.0,22.0,129.0,20.0,9.7066,182500.0,<1H OCEAN
+-118.02,33.77,34.0,2115.0,352.0,1253.0,338.0,5.1507,207500.0,<1H OCEAN
+-117.32,34.17,6.0,5661.0,1287.0,2943.0,1162.0,3.6362,106500.0,INLAND
+-120.58,37.36,33.0,3564.0,716.0,2603.0,696.0,2.2179,67500.0,INLAND
+-122.1,37.65,36.0,410.0,76.0,252.0,82.0,4.5303,175000.0,NEAR BAY
+-121.38,38.41,10.0,3425.0,629.0,1538.0,587.0,4.45,138700.0,INLAND
+-122.22,37.47,35.0,367.0,113.0,398.0,109.0,2.5,166700.0,NEAR OCEAN
+-118.38,34.17,30.0,2016.0,622.0,1359.0,557.0,2.7708,192300.0,<1H OCEAN
+-118.33,34.16,44.0,2705.0,649.0,1676.0,654.0,3.4286,247900.0,<1H OCEAN
+-117.2,34.5,10.0,4201.0,850.0,2378.0,808.0,2.1781,92200.0,INLAND
+-122.14,37.77,27.0,2229.0,365.0,1297.0,355.0,4.8304,279100.0,NEAR BAY
+-117.29,34.03,9.0,8185.0,1525.0,3630.0,1466.0,4.1667,197700.0,INLAND
+-122.77,38.29,32.0,3201.0,542.0,1869.0,519.0,3.2442,268000.0,<1H OCEAN
+-118.2,33.77,41.0,1158.0,396.0,1209.0,336.0,2.7813,129200.0,NEAR OCEAN
+-124.11,40.95,19.0,1734.0,365.0,866.0,342.0,2.96,81700.0,NEAR OCEAN
+-118.19,33.99,42.0,1429.0,436.0,1537.0,389.0,3.0114,157500.0,<1H OCEAN
+-117.12,32.95,8.0,3670.0,536.0,1723.0,592.0,6.3542,218100.0,<1H OCEAN
+-119.32,36.3,15.0,2864.0,571.0,1480.0,475.0,2.9698,93400.0,INLAND
+-119.03,35.37,42.0,2508.0,483.0,1035.0,442.0,2.6513,72300.0,INLAND
+-121.38,38.67,37.0,2176.0,460.0,1067.0,357.0,2.3958,78400.0,INLAND
+-118.04,33.72,24.0,7141.0,1330.0,3418.0,1268.0,4.6649,237800.0,<1H OCEAN
+-118.32,34.05,50.0,1389.0,364.0,976.0,302.0,1.5882,327300.0,<1H OCEAN
+-122.31,40.47,26.0,2723.0,551.0,1326.0,547.0,2.3594,66000.0,INLAND
+-120.92,37.59,26.0,1705.0,279.0,642.0,236.0,2.6591,180500.0,INLAND
+-118.39,34.18,42.0,1957.0,389.0,985.0,414.0,2.9327,240200.0,<1H OCEAN
+-118.16,34.19,42.0,2076.0,462.0,1641.0,436.0,2.2326,149200.0,<1H OCEAN
+-118.75,34.26,24.0,2234.0,373.0,1325.0,383.0,5.4604,193400.0,<1H OCEAN
+-118.07,33.97,36.0,1887.0,370.0,1006.0,329.0,3.1554,170700.0,<1H OCEAN
+-118.96,36.66,18.0,1302.0,424.0,320.0,133.0,3.1964,80000.0,INLAND
+-117.35,33.21,18.0,2971.0,606.0,2051.0,493.0,2.675,117100.0,NEAR OCEAN
+-122.09,37.86,27.0,5484.0,760.0,2212.0,770.0,7.6202,402600.0,NEAR BAY
+-117.58,33.92,16.0,4157.0,586.0,2036.0,594.0,6.155,246400.0,INLAND
+-118.04,34.09,32.0,1339.0,334.0,817.0,349.0,2.8333,186000.0,INLAND
+-118.3,34.06,33.0,2437.0,1283.0,3906.0,1084.0,2.0332,270000.0,<1H OCEAN
+-118.41,33.99,39.0,3014.0,822.0,3212.0,777.0,1.1985,215000.0,<1H OCEAN
+-117.8,33.81,14.0,1206.0,142.0,572.0,149.0,8.847,388700.0,<1H OCEAN
+-118.52,34.04,42.0,993.0,130.0,368.0,134.0,10.8082,500001.0,<1H OCEAN
+-120.07,36.97,27.0,968.0,240.0,587.0,231.0,1.6071,55000.0,INLAND
+-118.2,33.82,43.0,1758.0,347.0,954.0,312.0,5.2606,198900.0,NEAR OCEAN
+-117.82,33.79,26.0,2641.0,633.0,3657.0,617.0,4.1339,222300.0,<1H OCEAN
+-117.19,32.84,35.0,2263.0,427.0,1001.0,408.0,3.875,172000.0,NEAR OCEAN
+-121.99,37.33,33.0,2023.0,425.0,1016.0,405.0,3.9417,285800.0,<1H OCEAN
+-122.33,37.53,25.0,1729.0,383.0,769.0,352.0,4.0417,458500.0,NEAR OCEAN
+-118.18,34.63,19.0,3562.0,606.0,1677.0,578.0,4.1573,228100.0,INLAND
+-119.0,36.07,20.0,1042.0,183.0,509.0,175.0,2.9815,73000.0,INLAND
+-116.52,33.82,21.0,10227.0,2315.0,3623.0,1734.0,2.5212,145200.0,INLAND
+-117.72,33.97,16.0,13290.0,2062.0,6931.0,2023.0,5.228,187800.0,<1H OCEAN
+-122.03,37.25,34.0,2892.0,413.0,903.0,365.0,7.8711,500001.0,<1H OCEAN
+-117.94,33.73,24.0,4197.0,718.0,2468.0,714.0,5.2563,211400.0,<1H OCEAN
+-122.17,37.69,24.0,2262.0,391.0,1125.0,366.0,4.7609,212600.0,NEAR BAY
+-120.33,39.3,16.0,868.0,178.0,44.0,21.0,3.0,175000.0,INLAND
+-118.31,34.09,36.0,787.0,420.0,1506.0,360.0,1.2412,216700.0,<1H OCEAN
+-121.18,38.78,13.0,3480.0,528.0,1432.0,532.0,6.1642,277800.0,INLAND
+-124.17,40.74,17.0,2026.0,338.0,873.0,313.0,4.0357,128900.0,NEAR OCEAN
+-117.68,34.99,33.0,1589.0,307.0,853.0,272.0,4.2292,64400.0,INLAND
+-118.26,34.23,38.0,1107.0,194.0,518.0,195.0,7.5582,263700.0,<1H OCEAN
+-116.95,33.73,21.0,4587.0,810.0,2233.0,765.0,3.2371,94500.0,INLAND
+-118.31,33.78,30.0,4573.0,819.0,2411.0,819.0,3.5804,383800.0,<1H OCEAN
+-117.25,32.94,16.0,4755.0,807.0,1829.0,756.0,6.7694,425900.0,NEAR OCEAN
+-118.22,33.82,17.0,5357.0,1332.0,3030.0,1266.0,1.9311,138100.0,NEAR OCEAN
+-122.47,37.76,48.0,2464.0,459.0,1179.0,458.0,4.4946,358600.0,NEAR BAY
+-120.05,34.47,21.0,1241.0,248.0,746.0,211.0,3.8056,425000.0,NEAR OCEAN
+-117.11,32.7,33.0,1980.0,488.0,1626.0,428.0,1.4856,86400.0,NEAR OCEAN
+-117.96,33.87,27.0,890.0,289.0,416.0,200.0,3.141,167500.0,<1H OCEAN
+-118.05,33.83,24.0,4316.0,678.0,2286.0,665.0,5.7018,286700.0,<1H OCEAN
+-122.33,37.98,3.0,2850.0,544.0,1024.0,515.0,6.0115,175000.0,NEAR BAY
+-117.38,34.2,16.0,193.0,45.0,312.0,76.0,3.7578,137500.0,INLAND
+-118.32,34.09,44.0,2666.0,,2297.0,726.0,1.676,208800.0,<1H OCEAN
+-118.28,34.07,41.0,1072.0,331.0,1111.0,314.0,1.9233,207100.0,<1H OCEAN
+-117.31,34.25,29.0,4610.0,,1569.0,592.0,2.7663,97900.0,INLAND
+-118.95,35.37,34.0,1672.0,359.0,1059.0,349.0,2.1588,61300.0,INLAND
+-117.12,32.76,23.0,2681.0,717.0,1279.0,648.0,2.1597,116100.0,NEAR OCEAN
+-118.11,33.77,15.0,9103.0,1847.0,3333.0,1712.0,5.1508,367300.0,NEAR OCEAN
+-118.25,34.01,43.0,1429.0,386.0,1412.0,354.0,1.3287,107200.0,<1H OCEAN
+-122.23,38.1,46.0,4143.0,895.0,2240.0,847.0,2.4201,92800.0,NEAR BAY
+-117.41,34.11,29.0,3999.0,772.0,2602.0,760.0,3.5481,105500.0,INLAND
+-117.2,33.83,14.0,1265.0,230.0,621.0,173.0,3.6618,161300.0,INLAND
+-122.23,37.78,52.0,472.0,146.0,415.0,126.0,2.6429,71300.0,NEAR BAY
+-117.32,33.23,24.0,2580.0,604.0,982.0,569.0,1.6402,169300.0,NEAR OCEAN
+-120.41,34.87,15.0,1534.0,251.0,761.0,240.0,4.9028,193600.0,<1H OCEAN
+-122.47,38.3,15.0,4885.0,988.0,2175.0,924.0,3.4031,209500.0,<1H OCEAN
+-119.6,36.58,28.0,1452.0,300.0,919.0,308.0,2.8287,73100.0,INLAND
+-118.23,33.78,20.0,59.0,24.0,69.0,23.0,2.5588,350000.0,NEAR OCEAN
+-117.09,32.79,36.0,1529.0,266.0,683.0,260.0,4.0982,171200.0,NEAR OCEAN
+-120.66,35.46,17.0,3748.0,609.0,1860.0,612.0,4.5179,225600.0,<1H OCEAN
+-117.17,32.72,44.0,626.0,256.0,572.0,229.0,1.5909,262500.0,NEAR OCEAN
+-117.72,33.99,14.0,5622.0,861.0,3108.0,821.0,5.7763,206700.0,INLAND
+-118.3,33.95,36.0,1786.0,418.0,1367.0,393.0,1.5078,99600.0,<1H OCEAN
+-118.61,34.17,31.0,1689.0,362.0,705.0,360.0,4.0,278500.0,<1H OCEAN
+-117.92,33.63,34.0,2479.0,491.0,1131.0,490.0,4.9643,317900.0,<1H OCEAN
+-117.88,33.84,33.0,1526.0,237.0,906.0,245.0,5.1782,225000.0,<1H OCEAN
+-117.06,34.87,14.0,3348.0,619.0,1756.0,557.0,3.5987,91400.0,INLAND
+-121.32,38.7,17.0,3214.0,551.0,1879.0,562.0,4.3643,124900.0,INLAND
+-117.07,32.75,31.0,2036.0,501.0,1263.0,442.0,2.5583,120700.0,NEAR OCEAN
+-117.22,34.12,34.0,2457.0,499.0,1538.0,507.0,2.809,82500.0,INLAND
+-118.24,34.14,27.0,2909.0,1021.0,2614.0,935.0,2.1444,229000.0,<1H OCEAN
+-117.35,34.17,28.0,1905.0,372.0,1480.0,341.0,2.9844,79200.0,INLAND
+-122.27,38.28,37.0,1170.0,303.0,766.0,302.0,2.6618,136200.0,NEAR BAY
+-118.03,33.94,37.0,1699.0,302.0,889.0,271.0,4.3542,179800.0,<1H OCEAN
+-118.04,33.98,50.0,1951.0,458.0,1362.0,454.0,3.0,163200.0,<1H OCEAN
+-119.74,36.85,3.0,10425.0,2121.0,4432.0,1778.0,3.9032,140800.0,INLAND
+-118.54,34.21,32.0,2593.0,566.0,1596.0,547.0,3.9886,199200.0,<1H OCEAN
+-122.53,38.01,27.0,3121.0,531.0,1318.0,489.0,5.4781,310900.0,NEAR BAY
+-118.28,34.17,22.0,2664.0,651.0,1553.0,629.0,3.6354,256300.0,<1H OCEAN
+-117.67,33.67,5.0,10534.0,2035.0,4656.0,1863.0,5.7797,309200.0,<1H OCEAN
+-117.12,32.76,33.0,2279.0,591.0,1250.0,576.0,2.4297,139000.0,NEAR OCEAN
+-118.53,34.21,18.0,3124.0,796.0,1855.0,725.0,2.9389,213200.0,<1H OCEAN
+-118.23,34.1,46.0,2483.0,587.0,2121.0,553.0,2.2788,152900.0,<1H OCEAN
+-118.32,34.02,51.0,2010.0,460.0,1355.0,433.0,2.0304,133400.0,<1H OCEAN
+-117.67,33.49,10.0,366.0,61.0,128.0,61.0,8.163,250000.0,<1H OCEAN
+-122.33,37.53,18.0,4493.0,760.0,1784.0,725.0,6.7042,413000.0,NEAR OCEAN
+-118.48,34.07,40.0,3351.0,484.0,1564.0,523.0,8.5153,500001.0,<1H OCEAN
+-120.98,37.68,24.0,705.0,114.0,347.0,141.0,3.1912,149600.0,INLAND
+-118.4,34.03,43.0,1006.0,201.0,520.0,199.0,6.5669,372800.0,<1H OCEAN
+-119.68,37.19,10.0,3113.0,589.0,1508.0,512.0,2.8167,96100.0,INLAND
+-118.47,34.16,30.0,3823.0,740.0,1449.0,612.0,4.6,392500.0,<1H OCEAN
+-118.21,34.05,28.0,1079.0,306.0,1358.0,285.0,2.52,131900.0,<1H OCEAN
+-118.34,33.93,37.0,1638.0,407.0,1341.0,369.0,3.0677,167700.0,<1H OCEAN
+-118.03,33.79,26.0,5321.0,889.0,2932.0,896.0,5.8914,237600.0,<1H OCEAN
+-117.99,33.71,19.0,1967.0,487.0,1251.0,404.0,3.6696,218800.0,<1H OCEAN
+-117.27,33.15,4.0,23915.0,4135.0,10877.0,3958.0,4.6357,244900.0,NEAR OCEAN
+-117.29,33.12,4.0,1380.0,322.0,755.0,286.0,4.7961,168800.0,NEAR OCEAN
+-120.08,36.95,41.0,1164.0,211.0,476.0,171.0,2.4196,70700.0,INLAND
+-117.08,33.14,19.0,2629.0,494.0,1444.0,503.0,3.5462,156800.0,<1H OCEAN
+-117.26,32.95,15.0,1036.0,149.0,395.0,157.0,5.8343,500001.0,NEAR OCEAN
+-117.19,34.03,36.0,2223.0,361.0,942.0,331.0,4.6806,152400.0,INLAND
+-118.47,34.0,28.0,1259.0,302.0,668.0,280.0,4.2813,384400.0,<1H OCEAN
+-117.75,34.07,52.0,1548.0,348.0,1131.0,343.0,2.63,127300.0,INLAND
+-122.03,38.01,27.0,3228.0,562.0,1666.0,588.0,4.5707,175900.0,NEAR BAY
+-122.42,40.32,16.0,1978.0,375.0,961.0,333.0,2.6827,83900.0,INLAND
+-118.37,34.09,31.0,6348.0,1827.0,2559.0,1755.0,3.2818,225000.0,<1H OCEAN
+-118.16,34.04,22.0,2991.0,791.0,2486.0,754.0,1.5078,181900.0,<1H OCEAN
+-121.47,38.58,44.0,2092.0,555.0,878.0,528.0,1.5922,115100.0,INLAND
+-122.37,37.94,49.0,969.0,229.0,599.0,195.0,1.3167,71600.0,NEAR BAY
+-121.14,38.77,15.0,10282.0,1333.0,3868.0,1300.0,6.4789,287800.0,INLAND
+-117.65,34.08,35.0,2621.0,391.0,1074.0,391.0,4.7176,166400.0,INLAND
+-121.31,38.68,22.0,1194.0,207.0,545.0,223.0,3.8603,134300.0,INLAND
+-116.86,32.8,19.0,1747.0,291.0,848.0,290.0,4.875,187200.0,<1H OCEAN
+-122.32,37.97,33.0,1595.0,292.0,991.0,300.0,4.6937,134100.0,NEAR BAY
+-121.89,38.02,36.0,2707.0,550.0,1827.0,545.0,3.3371,94600.0,INLAND
+-121.59,39.82,12.0,1958.0,369.0,875.0,354.0,2.3507,97600.0,INLAND
+-118.4,34.0,34.0,1816.0,335.0,872.0,339.0,4.85,329400.0,<1H OCEAN
+-118.3,33.84,37.0,1241.0,226.0,621.0,255.0,4.9196,232400.0,<1H OCEAN
+-121.44,38.6,16.0,2987.0,864.0,1240.0,755.0,2.8231,137500.0,INLAND
+-117.99,33.88,25.0,3401.0,509.0,1503.0,498.0,6.6704,240600.0,<1H OCEAN
+-118.55,34.18,32.0,3011.0,529.0,1287.0,525.0,5.0605,311000.0,<1H OCEAN
+-118.09,33.92,36.0,2381.0,419.0,1669.0,444.0,4.6976,171100.0,<1H OCEAN
+-118.26,33.99,30.0,1702.0,443.0,1966.0,442.0,1.5521,97500.0,<1H OCEAN
+-121.57,39.78,18.0,2221.0,459.0,952.0,440.0,2.0458,105700.0,INLAND
+-118.13,34.69,32.0,3670.0,765.0,1986.0,673.0,3.682,108800.0,INLAND
+-118.3,34.05,44.0,1612.0,650.0,2028.0,593.0,1.9152,115600.0,<1H OCEAN
+-123.01,38.48,37.0,1179.0,282.0,354.0,176.0,1.3712,118300.0,NEAR OCEAN
+-122.13,36.97,27.0,991.0,194.0,543.0,155.0,4.7188,350000.0,NEAR OCEAN
+-121.4,38.49,12.0,7290.0,1283.0,3960.0,1248.0,3.5968,106300.0,INLAND
+-119.78,36.72,22.0,354.0,121.0,530.0,115.0,2.1458,34400.0,INLAND
+-118.11,34.11,50.0,2131.0,294.0,753.0,284.0,6.7099,352200.0,<1H OCEAN
+-121.58,37.03,16.0,3120.0,685.0,2383.0,681.0,3.5551,198600.0,INLAND
+-119.82,37.57,13.0,1713.0,340.0,643.0,241.0,2.662,92400.0,INLAND
+-121.41,38.34,24.0,1605.0,277.0,1966.0,250.0,3.0833,162500.0,INLAND
+-116.95,33.97,14.0,5320.0,974.0,1947.0,843.0,3.1393,116300.0,INLAND
+-119.14,34.28,31.0,790.0,241.0,1095.0,222.0,2.25,75000.0,NEAR OCEAN
+-121.87,37.66,52.0,775.0,134.0,315.0,123.0,5.0677,233300.0,INLAND
+-118.3,34.01,52.0,1908.0,428.0,1271.0,394.0,2.5885,136200.0,<1H OCEAN
+-118.42,34.03,45.0,1262.0,223.0,637.0,221.0,5.0866,427300.0,<1H OCEAN
+-120.66,35.28,46.0,2054.0,502.0,1170.0,494.0,2.1786,206300.0,NEAR OCEAN
+-122.84,38.07,31.0,1858.0,367.0,701.0,297.0,3.8269,270700.0,NEAR OCEAN
+-118.17,33.95,23.0,1991.0,584.0,1380.0,535.0,1.9107,181900.0,<1H OCEAN
+-117.98,33.68,14.0,3396.0,477.0,1542.0,472.0,7.3982,369100.0,<1H OCEAN
+-118.46,34.01,48.0,1640.0,322.0,664.0,301.0,4.0,500001.0,<1H OCEAN
+-122.27,37.87,35.0,3218.0,1108.0,1675.0,1000.0,1.7464,216700.0,NEAR BAY
+-117.93,33.64,24.0,1395.0,396.0,1478.0,404.0,2.5301,192900.0,<1H OCEAN
+-121.95,37.29,9.0,1503.0,381.0,715.0,349.0,4.6371,234300.0,<1H OCEAN
+-122.21,37.47,33.0,1266.0,415.0,1991.0,334.0,2.92,202800.0,NEAR OCEAN
+-118.12,33.96,34.0,2863.0,451.0,1243.0,466.0,6.0723,297200.0,<1H OCEAN
+-116.98,32.8,28.0,5721.0,1029.0,2672.0,1054.0,3.963,164400.0,<1H OCEAN
+-117.77,33.54,47.0,3090.0,652.0,1105.0,582.0,4.1699,373700.0,<1H OCEAN
+-118.21,33.97,52.0,4220.0,908.0,3731.0,892.0,3.1901,167600.0,<1H OCEAN
+-121.32,37.98,20.0,1591.0,589.0,1916.0,536.0,1.3531,94600.0,INLAND
+-119.27,35.5,21.0,2171.0,483.0,1315.0,450.0,1.7105,52100.0,INLAND
+-117.55,33.94,30.0,5398.0,926.0,2672.0,864.0,4.4762,163900.0,INLAND
+-121.8,37.26,18.0,3631.0,947.0,2357.0,757.0,2.875,184400.0,<1H OCEAN
+-121.92,37.33,52.0,2009.0,338.0,841.0,338.0,5.5259,295800.0,<1H OCEAN
+-117.32,34.11,41.0,1229.0,302.0,994.0,270.0,1.4891,67300.0,INLAND
+-119.79,36.32,19.0,3252.0,614.0,1971.0,607.0,3.0667,75800.0,INLAND
+-118.48,34.22,22.0,3430.0,1214.0,3618.0,1092.0,2.1974,93800.0,<1H OCEAN
+-119.77,34.43,28.0,3318.0,441.0,1604.0,404.0,9.7821,500001.0,NEAR OCEAN
+-122.03,37.54,35.0,1867.0,343.0,1213.0,338.0,4.8214,186000.0,NEAR BAY
+-119.79,36.81,33.0,1461.0,261.0,494.0,254.0,4.25,132200.0,INLAND
+-118.39,34.15,36.0,2696.0,713.0,905.0,659.0,3.1146,373500.0,<1H OCEAN
+-118.4,34.22,36.0,2557.0,540.0,1556.0,491.0,3.6591,183800.0,<1H OCEAN
+-118.23,33.93,39.0,2065.0,532.0,2015.0,535.0,0.8478,104900.0,<1H OCEAN
+-119.44,36.48,27.0,1546.0,415.0,1704.0,395.0,1.1728,41700.0,INLAND
+-122.21,40.2,19.0,3404.0,731.0,1421.0,683.0,2.6149,84400.0,INLAND
+-121.95,37.36,25.0,3472.0,956.0,2267.0,944.0,2.7727,235600.0,<1H OCEAN
+-122.76,38.52,6.0,2073.0,388.0,826.0,375.0,3.055,224100.0,<1H OCEAN
+-118.11,33.84,36.0,1074.0,188.0,496.0,196.0,4.625,217400.0,<1H OCEAN
+-117.95,33.83,35.0,1107.0,207.0,641.0,210.0,5.0599,216700.0,<1H OCEAN
+-122.12,37.42,35.0,2445.0,533.0,1187.0,519.0,5.2803,362100.0,NEAR BAY
+-120.35,39.34,29.0,1986.0,474.0,337.0,100.0,4.0278,95800.0,INLAND
+-122.19,37.75,36.0,1126.0,263.0,482.0,150.0,1.9167,82800.0,NEAR BAY
+-122.09,37.63,35.0,1213.0,221.0,790.0,243.0,4.7019,174100.0,NEAR BAY
+-117.05,32.71,25.0,3292.0,608.0,2266.0,592.0,3.2986,119200.0,NEAR OCEAN
+-121.06,39.29,14.0,1864.0,331.0,894.0,332.0,3.4028,171800.0,INLAND
+-117.05,32.56,18.0,1215.0,320.0,1195.0,349.0,1.9875,114900.0,NEAR OCEAN
+-116.5,33.69,20.0,4810.0,1074.0,1304.0,740.0,2.25,248100.0,INLAND
+-121.42,37.75,33.0,1999.0,368.0,1061.0,390.0,3.5242,121400.0,INLAND
+-120.78,38.72,19.0,4414.0,767.0,1865.0,699.0,3.6406,150900.0,INLAND
+-121.03,37.65,37.0,375.0,58.0,120.0,37.0,3.9844,150000.0,INLAND
+-120.86,37.5,34.0,4272.0,996.0,2916.0,962.0,1.9829,82800.0,INLAND
+-119.35,36.52,39.0,3027.0,608.0,2199.0,592.0,2.6445,62000.0,INLAND
+-122.55,37.92,52.0,2303.0,350.0,859.0,359.0,6.1085,500001.0,NEAR BAY
+-118.42,34.15,48.0,680.0,131.0,268.0,126.0,4.615,371400.0,<1H OCEAN
+-117.41,33.96,27.0,2341.0,418.0,1272.0,415.0,3.0208,112700.0,INLAND
+-118.23,33.99,5.0,706.0,203.0,839.0,199.0,4.5208,165000.0,<1H OCEAN
+-117.02,33.71,6.0,8278.0,1579.0,3062.0,1446.0,3.0043,134700.0,INLAND
+-117.81,33.75,23.0,3498.0,636.0,1574.0,642.0,5.021,252200.0,<1H OCEAN
+-117.97,34.05,34.0,2050.0,495.0,1832.0,465.0,2.8333,155700.0,<1H OCEAN
+-118.37,34.17,42.0,600.0,171.0,377.0,181.0,2.4107,184400.0,<1H OCEAN
+-122.18,37.46,40.0,2529.0,293.0,831.0,258.0,15.0001,500001.0,NEAR BAY
+-117.14,32.75,37.0,1832.0,525.0,955.0,488.0,2.7852,129200.0,NEAR OCEAN
+-116.23,33.71,17.0,4874.0,1349.0,5032.0,1243.0,2.444,90000.0,INLAND
+-121.88,36.59,30.0,1822.0,505.0,932.0,496.0,2.6894,179500.0,<1H OCEAN
+-118.02,33.86,26.0,2342.0,383.0,1290.0,394.0,5.6677,220700.0,<1H OCEAN
+-117.07,33.13,17.0,6817.0,1632.0,4526.0,1474.0,2.6152,135300.0,<1H OCEAN
+-122.56,40.75,20.0,1182.0,250.0,512.0,210.0,1.7935,74500.0,INLAND
+-118.39,33.97,38.0,993.0,175.0,374.0,180.0,6.2673,357200.0,<1H OCEAN
+-119.01,35.28,10.0,7011.0,1453.0,4163.0,1307.0,2.7659,77500.0,INLAND
+-118.3,34.26,43.0,1510.0,310.0,809.0,277.0,3.599,176500.0,<1H OCEAN
+-119.63,34.4,29.0,3865.0,814.0,1266.0,613.0,6.0069,500001.0,<1H OCEAN
+-122.49,38.31,27.0,3078.0,597.0,1411.0,586.0,3.25,195500.0,<1H OCEAN
+-118.35,33.74,25.0,8272.0,1132.0,3392.0,1132.0,10.0973,500001.0,NEAR OCEAN
+-118.25,33.9,38.0,1201.0,223.0,733.0,206.0,3.3804,105800.0,<1H OCEAN
+-118.03,33.91,32.0,4040.0,832.0,2526.0,798.0,3.2143,160100.0,<1H OCEAN
+-118.29,34.04,44.0,1941.0,579.0,2049.0,535.0,2.0405,143000.0,<1H OCEAN
+-118.19,34.02,40.0,474.0,124.0,546.0,121.0,2.3438,137500.0,<1H OCEAN
+-118.49,34.24,34.0,1971.0,316.0,917.0,307.0,6.0965,262300.0,<1H OCEAN
+-124.17,40.79,43.0,2285.0,479.0,1169.0,482.0,1.9688,70500.0,NEAR OCEAN
+-122.26,37.57,23.0,7995.0,1254.0,3484.0,1198.0,6.5948,404000.0,NEAR BAY
+-122.06,37.89,21.0,4985.0,1590.0,2575.0,1458.0,3.1002,114300.0,NEAR BAY
+-118.32,34.06,52.0,983.0,246.0,578.0,204.0,5.7393,500001.0,<1H OCEAN
+-118.37,36.19,10.0,443.0,111.0,48.0,21.0,3.125,71300.0,INLAND
+-121.99,37.55,28.0,2414.0,415.0,1106.0,453.0,4.8403,268600.0,<1H OCEAN
+-118.29,33.74,29.0,1503.0,411.0,1368.0,390.0,2.1473,195300.0,NEAR OCEAN
+-118.01,33.85,29.0,3061.0,612.0,2396.0,640.0,4.6326,195200.0,<1H OCEAN
+-121.25,37.94,28.0,964.0,232.0,782.0,218.0,2.3269,55900.0,INLAND
+-117.68,34.15,24.0,1033.0,189.0,486.0,204.0,4.1719,213500.0,INLAND
+-118.14,33.89,39.0,1744.0,339.0,1048.0,330.0,4.5735,195500.0,<1H OCEAN
+-122.25,38.05,30.0,1255.0,297.0,779.0,307.0,1.6767,147700.0,NEAR BAY
+-118.25,33.79,38.0,1730.0,460.0,1724.0,424.0,2.7308,150400.0,NEAR OCEAN
+-122.05,37.93,5.0,4274.0,1153.0,1503.0,881.0,4.0473,266500.0,NEAR BAY
+-121.28,37.94,40.0,2806.0,685.0,2268.0,635.0,1.8814,57700.0,INLAND
+-118.4,34.17,27.0,3588.0,911.0,1891.0,871.0,3.4013,286000.0,<1H OCEAN
+-118.21,33.83,38.0,793.0,193.0,601.0,187.0,2.8837,176100.0,NEAR OCEAN
+-116.97,33.94,29.0,3197.0,632.0,1722.0,603.0,3.0432,91200.0,INLAND
+-118.3,34.02,34.0,3184.0,772.0,2474.0,705.0,1.631,137500.0,<1H OCEAN
+-117.08,32.65,28.0,2296.0,603.0,1277.0,550.0,2.3562,123800.0,NEAR OCEAN
+-118.2,33.86,27.0,2732.0,867.0,1690.0,794.0,2.6465,160200.0,<1H OCEAN
+-122.39,40.64,13.0,3604.0,704.0,1598.0,670.0,2.4141,78700.0,INLAND
+-122.5,37.76,43.0,2108.0,456.0,1299.0,447.0,3.1406,316200.0,NEAR OCEAN
+-120.1,39.17,33.0,1849.0,384.0,218.0,92.0,1.7083,143800.0,INLAND
+-116.36,33.78,6.0,24121.0,4522.0,4176.0,2221.0,3.3799,239300.0,INLAND
+-121.44,38.47,16.0,1215.0,223.0,787.0,233.0,4.1597,95900.0,INLAND
+-117.92,33.85,44.0,1231.0,258.0,682.0,244.0,3.2344,170100.0,<1H OCEAN
+-117.71,34.08,29.0,1276.0,283.0,1216.0,316.0,2.5972,134300.0,INLAND
+-118.43,34.01,43.0,1487.0,242.0,675.0,247.0,5.3403,489800.0,<1H OCEAN
+-118.18,34.04,44.0,1079.0,275.0,1249.0,249.0,3.0417,141700.0,<1H OCEAN
+-118.09,33.77,26.0,1388.0,409.0,515.0,392.0,1.8015,62000.0,<1H OCEAN
+-120.77,37.64,8.0,3294.0,667.0,2277.0,652.0,2.6417,96800.0,INLAND
+-124.15,40.59,39.0,1186.0,238.0,539.0,212.0,2.0938,79600.0,NEAR OCEAN
+-121.43,37.76,7.0,2125.0,508.0,1358.0,464.0,3.6312,147600.0,INLAND
+-118.16,34.03,40.0,2201.0,636.0,2682.0,595.0,2.359,143400.0,<1H OCEAN
+-118.29,34.05,40.0,907.0,349.0,1426.0,323.0,1.8571,143800.0,<1H OCEAN
+-122.04,36.96,32.0,1438.0,306.0,802.0,293.0,4.1964,202000.0,NEAR OCEAN
+-118.03,33.98,46.0,1974.0,465.0,880.0,441.0,2.7578,236800.0,<1H OCEAN
+-120.96,37.65,34.0,1700.0,325.0,972.0,326.0,2.4485,95500.0,INLAND
+-118.3,33.86,35.0,2016.0,365.0,1083.0,369.0,5.1727,230200.0,<1H OCEAN
+-123.79,39.44,36.0,1330.0,273.0,761.0,286.0,2.7813,105800.0,<1H OCEAN
+-121.25,37.98,39.0,1765.0,414.0,1056.0,414.0,1.5329,48300.0,INLAND
+-118.42,33.99,35.0,1701.0,482.0,1428.0,494.0,3.725,284600.0,<1H OCEAN
+-121.23,37.92,28.0,590.0,129.0,315.0,99.0,1.8958,85700.0,INLAND
+-118.1,33.84,36.0,1557.0,270.0,697.0,251.0,4.5417,219600.0,<1H OCEAN
+-121.24,37.93,21.0,1185.0,237.0,960.0,245.0,2.0893,65000.0,INLAND
+-117.08,32.58,22.0,2128.0,477.0,1420.0,450.0,3.2687,131000.0,NEAR OCEAN
+-123.43,40.22,20.0,133.0,35.0,87.0,37.0,3.625,67500.0,INLAND
+-117.83,33.81,28.0,1972.0,315.0,970.0,326.0,5.4298,234200.0,<1H OCEAN
+-121.3,38.58,16.0,1537.0,,1125.0,375.0,2.6471,90700.0,INLAND
+-122.48,37.67,15.0,2897.0,728.0,2340.0,720.0,3.3906,303700.0,NEAR OCEAN
+-118.19,33.98,40.0,973.0,272.0,1257.0,258.0,2.8214,158000.0,<1H OCEAN
+-117.88,33.85,18.0,2705.0,713.0,2726.0,674.0,2.7759,200000.0,<1H OCEAN
+-122.34,37.57,52.0,2547.0,373.0,876.0,359.0,8.2598,500001.0,NEAR OCEAN
+-117.93,33.84,23.0,2870.0,653.0,1680.0,598.0,3.2301,189900.0,<1H OCEAN
+-121.62,39.79,11.0,3835.0,727.0,1456.0,658.0,2.5374,97200.0,INLAND
+-118.32,33.95,44.0,2131.0,360.0,1040.0,330.0,5.0912,169800.0,<1H OCEAN
+-122.15,37.75,44.0,1938.0,399.0,946.0,331.0,3.225,135800.0,NEAR BAY
+-118.0,34.15,43.0,2583.0,633.0,1544.0,617.0,2.5417,178300.0,INLAND
+-122.48,37.79,52.0,1647.0,236.0,546.0,227.0,9.1881,500001.0,NEAR BAY
+-122.49,37.86,35.0,2729.0,538.0,969.0,528.0,6.7669,500001.0,NEAR BAY
+-119.64,36.85,15.0,2397.0,353.0,1258.0,347.0,4.9904,157300.0,INLAND
+-118.25,33.89,35.0,1582.0,391.0,1957.0,404.0,2.4537,91500.0,<1H OCEAN
+-122.21,37.78,49.0,898.0,244.0,779.0,245.0,3.0536,137500.0,NEAR BAY
+-117.97,33.86,12.0,1370.0,367.0,1022.0,296.0,3.6471,141700.0,<1H OCEAN
+-118.78,34.25,13.0,1841.0,237.0,833.0,231.0,7.7785,404700.0,<1H OCEAN
+-120.96,37.48,32.0,1256.0,212.0,682.0,236.0,2.9844,135900.0,INLAND
+-121.85,36.6,41.0,3138.0,717.0,1890.0,642.0,2.4864,140400.0,<1H OCEAN
+-122.55,38.07,5.0,1495.0,235.0,555.0,201.0,6.7232,345000.0,NEAR BAY
+-117.87,33.84,10.0,3381.0,729.0,1584.0,636.0,5.3812,235400.0,<1H OCEAN
+-122.24,40.16,19.0,2500.0,509.0,1293.0,494.0,2.035,55100.0,INLAND
+-121.23,37.84,28.0,1347.0,241.0,713.0,225.0,4.0208,155700.0,INLAND
+-121.47,38.72,26.0,1708.0,299.0,911.0,290.0,4.0227,99800.0,INLAND
+-118.17,33.78,44.0,2364.0,746.0,3184.0,672.0,1.918,147500.0,NEAR OCEAN
+-116.41,33.74,17.0,4289.0,893.0,958.0,440.0,2.4659,177800.0,INLAND
+-118.1,34.18,39.0,2321.0,336.0,880.0,339.0,7.7108,450000.0,INLAND
+-119.79,36.72,41.0,1562.0,322.0,927.0,277.0,1.3047,44100.0,INLAND
+-118.41,33.89,35.0,1194.0,292.0,507.0,295.0,9.0812,500001.0,<1H OCEAN
+-117.91,33.61,38.0,1232.0,178.0,410.0,171.0,11.075,500001.0,<1H OCEAN
+-122.21,37.44,35.0,1140.0,193.0,486.0,199.0,4.6908,500001.0,NEAR OCEAN
+-122.09,37.69,43.0,500.0,110.0,273.0,120.0,3.3125,150000.0,NEAR BAY
+-123.02,38.81,35.0,956.0,213.0,488.0,215.0,3.025,140600.0,<1H OCEAN
+-117.06,33.03,23.0,2023.0,309.0,678.0,340.0,7.0913,265400.0,<1H OCEAN
+-118.19,33.8,41.0,2125.0,591.0,1604.0,555.0,2.9943,190600.0,NEAR OCEAN
+-118.37,34.06,52.0,843.0,160.0,333.0,151.0,4.5192,446000.0,<1H OCEAN
+-118.25,34.11,52.0,125.0,42.0,99.0,40.0,3.4375,170000.0,<1H OCEAN
+-122.29,40.47,20.0,2858.0,612.0,1422.0,589.0,1.9657,63000.0,INLAND
+-122.72,38.42,26.0,3604.0,734.0,2605.0,704.0,3.0969,143800.0,<1H OCEAN
+-118.06,34.12,35.0,2053.0,369.0,1044.0,375.0,4.1875,275400.0,INLAND
+-117.86,33.74,38.0,2415.0,642.0,3242.0,599.0,3.425,165600.0,<1H OCEAN
+-116.21,33.72,28.0,2488.0,714.0,2891.0,676.0,2.3169,68900.0,INLAND
+-122.46,37.65,21.0,2751.0,502.0,2027.0,491.0,5.2573,322900.0,NEAR OCEAN
+-121.88,37.67,25.0,2244.0,301.0,937.0,324.0,6.4524,296900.0,INLAND
+-118.23,34.23,34.0,2377.0,362.0,1055.0,362.0,6.0,367100.0,<1H OCEAN
+-118.36,33.97,19.0,4651.0,1281.0,2917.0,1121.0,2.6823,142500.0,<1H OCEAN
+-121.82,37.28,33.0,2873.0,489.0,1946.0,475.0,5.0709,176400.0,<1H OCEAN
+-117.66,33.57,16.0,2483.0,443.0,1357.0,400.0,5.5545,214200.0,<1H OCEAN
+-117.08,32.7,37.0,2176.0,418.0,1301.0,375.0,2.875,98900.0,NEAR OCEAN
+-118.06,33.82,24.0,3983.0,675.0,1568.0,638.0,4.6458,213400.0,<1H OCEAN
+-122.53,37.92,42.0,1741.0,301.0,723.0,306.0,5.5379,410500.0,NEAR BAY
+-118.17,33.79,28.0,1219.0,408.0,1816.0,348.0,1.7589,118300.0,NEAR OCEAN
+-118.26,34.12,52.0,1942.0,476.0,1375.0,477.0,2.7348,209100.0,<1H OCEAN
+-122.09,37.67,33.0,2431.0,655.0,1854.0,603.0,2.7019,154000.0,NEAR BAY
+-118.14,34.68,33.0,2815.0,485.0,1447.0,489.0,4.2679,119600.0,INLAND
+-118.41,34.04,52.0,2113.0,332.0,800.0,327.0,11.1768,500001.0,<1H OCEAN
+-118.19,34.05,47.0,1273.0,264.0,1193.0,260.0,2.4375,122900.0,<1H OCEAN
+-119.7,36.8,31.0,1746.0,321.0,1186.0,360.0,2.6932,66400.0,INLAND
+-118.2,33.96,43.0,1233.0,306.0,1190.0,282.0,2.8371,161300.0,<1H OCEAN
+-121.85,39.73,17.0,3425.0,827.0,2469.0,758.0,0.9393,88900.0,INLAND
+-121.82,36.61,24.0,2437.0,438.0,1430.0,444.0,3.8015,169100.0,<1H OCEAN
+-114.57,33.57,20.0,1454.0,326.0,624.0,262.0,1.925,65500.0,INLAND
+-122.11,37.64,8.0,3592.0,849.0,1907.0,746.0,3.6708,197900.0,NEAR BAY
+-118.17,34.08,39.0,787.0,181.0,731.0,179.0,3.2279,158500.0,<1H OCEAN
+-121.83,36.61,27.0,5665.0,1281.0,3612.0,1191.0,3.0542,142100.0,<1H OCEAN
+-122.27,37.43,33.0,1601.0,223.0,629.0,215.0,15.0001,500001.0,NEAR OCEAN
+-117.06,32.71,11.0,2397.0,523.0,1566.0,514.0,3.8687,145200.0,NEAR OCEAN
+-121.67,36.93,22.0,569.0,132.0,542.0,125.0,2.1875,187500.0,<1H OCEAN
+-121.44,37.75,16.0,2229.0,458.0,1199.0,445.0,3.4821,170600.0,INLAND
+-118.75,34.17,16.0,2950.0,387.0,1228.0,379.0,5.3749,346100.0,NEAR OCEAN
+-121.83,37.32,17.0,1887.0,664.0,1906.0,597.0,2.5652,165300.0,<1H OCEAN
+-117.95,33.72,21.0,3107.0,483.0,1688.0,503.0,5.9582,288000.0,<1H OCEAN
+-117.28,34.16,35.0,2028.0,456.0,972.0,398.0,2.3778,90700.0,INLAND
+-118.37,33.91,41.0,1869.0,427.0,1334.0,435.0,3.9355,227800.0,<1H OCEAN
+-120.44,37.29,18.0,1260.0,268.0,576.0,263.0,1.7222,101500.0,INLAND
+-118.14,34.01,42.0,1973.0,510.0,1841.0,502.0,2.5326,156500.0,<1H OCEAN
+-117.12,32.71,24.0,421.0,101.0,396.0,113.0,0.6433,111300.0,NEAR OCEAN
+-117.84,33.78,26.0,2577.0,434.0,1086.0,432.0,4.6125,229200.0,<1H OCEAN
+-117.75,34.06,52.0,24.0,6.0,46.0,7.0,1.625,67500.0,INLAND
+-118.01,34.1,27.0,2424.0,542.0,1713.0,557.0,3.8085,181400.0,INLAND
+-118.12,34.06,17.0,5137.0,1614.0,4945.0,1535.0,2.4599,181600.0,<1H OCEAN
+-118.15,33.86,36.0,1578.0,312.0,827.0,311.0,4.8942,194100.0,<1H OCEAN
+-119.97,36.44,18.0,1128.0,237.0,772.0,220.0,2.1771,39200.0,INLAND
+-118.25,34.01,43.0,1575.0,475.0,1980.0,469.0,1.7425,100500.0,<1H OCEAN
+-118.43,34.25,32.0,2433.0,553.0,2318.0,532.0,3.6384,159300.0,<1H OCEAN
+-122.29,40.43,21.0,2842.0,640.0,1658.0,608.0,1.9943,59800.0,INLAND
+-118.13,33.97,36.0,1759.0,295.0,837.0,267.0,4.6992,251900.0,<1H OCEAN
+-121.83,36.62,33.0,2938.0,576.0,1516.0,568.0,3.5,162400.0,<1H OCEAN
+-121.87,37.43,22.0,3805.0,596.0,2118.0,621.0,6.2892,254200.0,<1H OCEAN
+-117.14,32.71,52.0,1225.0,332.0,955.0,321.0,1.6011,106300.0,NEAR OCEAN
+-121.45,38.56,51.0,1250.0,235.0,452.0,232.0,2.625,121200.0,INLAND
+-121.44,38.52,36.0,3446.0,950.0,2460.0,847.0,1.6521,69700.0,INLAND
+-117.28,34.17,26.0,3728.0,888.0,1765.0,727.0,1.7456,86800.0,INLAND
+-119.69,36.46,29.0,1702.0,301.0,914.0,280.0,2.8125,79200.0,INLAND
+-118.53,34.23,27.0,2131.0,543.0,1065.0,528.0,3.2404,230400.0,<1H OCEAN
+-118.14,34.05,25.0,5478.0,1136.0,3062.0,1096.0,3.4118,341100.0,<1H OCEAN
+-122.16,37.75,40.0,1227.0,294.0,928.0,261.0,1.8235,95200.0,NEAR BAY
+-116.92,32.84,16.0,4675.0,834.0,2188.0,817.0,4.6674,178000.0,<1H OCEAN
+-118.35,33.87,37.0,1420.0,286.0,886.0,290.0,4.5833,261300.0,<1H OCEAN
+-122.01,37.98,29.0,2001.0,373.0,956.0,370.0,4.317,194000.0,NEAR BAY
+-117.82,33.66,15.0,2460.0,447.0,1049.0,398.0,6.4967,387500.0,<1H OCEAN
+-121.23,38.79,45.0,907.0,176.0,463.0,190.0,2.2292,92000.0,INLAND
+-122.73,38.44,35.0,1120.0,297.0,659.0,274.0,2.3824,145000.0,<1H OCEAN
+-118.29,34.17,17.0,3852.0,1066.0,2986.0,993.0,2.3482,255400.0,<1H OCEAN
+-118.88,34.28,22.0,3369.0,771.0,2751.0,710.0,4.0474,182100.0,<1H OCEAN
+-119.78,36.82,25.0,5016.0,,2133.0,928.0,3.625,89500.0,INLAND
+-117.7,34.0,15.0,4905.0,711.0,2711.0,762.0,5.7021,193100.0,INLAND
+-121.33,38.03,19.0,1708.0,291.0,906.0,288.0,4.918,130600.0,INLAND
+-121.87,37.36,34.0,938.0,242.0,769.0,226.0,3.5625,194500.0,<1H OCEAN
+-117.92,34.07,29.0,1699.0,399.0,1052.0,411.0,3.2122,195500.0,<1H OCEAN
+-117.59,33.65,4.0,1793.0,390.0,897.0,386.0,4.2463,182800.0,<1H OCEAN
+-121.46,38.65,8.0,3746.0,767.0,2161.0,744.0,3.2039,103400.0,INLAND
+-118.3,33.98,44.0,1242.0,270.0,811.0,274.0,2.9375,95200.0,<1H OCEAN
+-117.84,33.76,26.0,2110.0,409.0,1146.0,407.0,4.3698,229600.0,<1H OCEAN
+-122.18,37.45,37.0,5257.0,1360.0,2128.0,1264.0,4.0,394300.0,NEAR BAY
+-122.32,37.94,46.0,1901.0,295.0,833.0,352.0,5.5196,210800.0,NEAR BAY
+-121.35,38.02,15.0,3583.0,644.0,2183.0,643.0,3.428,140700.0,INLAND
+-120.8,37.61,30.0,918.0,154.0,469.0,139.0,3.9688,175000.0,INLAND
+-118.28,34.05,31.0,1525.0,730.0,2510.0,652.0,1.6355,162500.0,<1H OCEAN
+-119.2,34.25,25.0,195.0,59.0,140.0,43.0,3.8889,187500.0,NEAR OCEAN
+-120.02,36.95,25.0,2115.0,482.0,1976.0,474.0,1.8431,53900.0,INLAND
+-121.38,38.65,34.0,825.0,173.0,355.0,130.0,3.1858,109500.0,INLAND
+-119.77,36.76,43.0,1945.0,413.0,1492.0,422.0,1.5174,54600.0,INLAND
+-122.19,37.79,47.0,1229.0,243.0,582.0,256.0,2.9514,198100.0,NEAR BAY
+-121.43,37.75,30.0,1912.0,451.0,1065.0,388.0,2.1424,125000.0,INLAND
+-117.08,32.67,31.0,3008.0,764.0,2088.0,757.0,2.5662,118200.0,NEAR OCEAN
+-122.4,37.78,32.0,352.0,132.0,313.0,105.0,2.5742,350000.0,NEAR BAY
+-118.28,33.93,43.0,269.0,74.0,295.0,79.0,2.2969,90600.0,<1H OCEAN
+-122.25,37.8,36.0,1678.0,606.0,1645.0,543.0,2.2303,116700.0,NEAR BAY
+-119.81,36.77,48.0,1805.0,329.0,741.0,331.0,2.5804,78900.0,INLAND
+-118.41,33.92,32.0,2590.0,607.0,1132.0,555.0,4.2333,358000.0,<1H OCEAN
+-122.01,36.98,47.0,1250.0,249.0,607.0,234.0,4.0417,265300.0,NEAR OCEAN
+-122.29,37.88,52.0,2159.0,424.0,824.0,388.0,3.8897,218400.0,NEAR BAY
+-117.17,32.81,33.0,3064.0,506.0,1355.0,488.0,4.22,178700.0,NEAR OCEAN
+-120.05,36.95,18.0,2287.0,534.0,1339.0,505.0,2.2527,65200.0,INLAND
+-122.42,37.62,36.0,1538.0,256.0,671.0,247.0,4.4091,317900.0,NEAR OCEAN
+-120.38,34.96,9.0,2813.0,492.0,1144.0,490.0,4.0431,226800.0,<1H OCEAN
+-118.3,33.74,47.0,2223.0,410.0,1163.0,413.0,4.4671,270800.0,NEAR OCEAN
+-118.28,34.01,46.0,441.0,167.0,621.0,144.0,1.8824,162500.0,<1H OCEAN
+-121.03,37.63,5.0,2881.0,584.0,1490.0,570.0,3.0398,120000.0,INLAND
+-120.96,37.51,30.0,1288.0,237.0,720.0,233.0,2.3864,139100.0,INLAND
+-121.96,37.28,28.0,5018.0,1066.0,2846.0,998.0,4.0174,273900.0,<1H OCEAN
+-118.36,33.97,18.0,1284.0,283.0,990.0,289.0,4.0179,195800.0,<1H OCEAN
+-118.23,33.94,35.0,1090.0,267.0,1339.0,263.0,2.1607,97600.0,<1H OCEAN
+-118.12,33.88,40.0,2344.0,571.0,1305.0,544.0,3.1923,191900.0,<1H OCEAN
+-117.9,33.63,26.0,4486.0,554.0,1598.0,549.0,10.1454,500001.0,<1H OCEAN
+-118.02,33.74,26.0,3842.0,609.0,1961.0,595.0,6.128,248200.0,<1H OCEAN
+-122.48,37.76,50.0,2236.0,484.0,1171.0,467.0,4.0977,322100.0,NEAR BAY
+-122.72,38.88,29.0,2781.0,,890.0,310.0,1.9906,96600.0,INLAND
+-122.2,38.09,18.0,6860.0,1079.0,3205.0,1058.0,5.2957,171900.0,NEAR BAY
+-119.57,37.94,17.0,346.0,130.0,51.0,20.0,3.4861,137500.0,INLAND
+-117.2,32.79,31.0,3417.0,533.0,1245.0,532.0,4.7788,276000.0,NEAR OCEAN
+-118.21,33.9,35.0,2420.0,579.0,2010.0,540.0,2.0817,104600.0,<1H OCEAN
+-123.48,40.34,19.0,518.0,108.0,216.0,80.0,2.7083,64500.0,INLAND
+-122.14,37.36,23.0,11294.0,1377.0,3840.0,1367.0,12.1387,500001.0,NEAR BAY
+-120.67,35.3,32.0,4202.0,986.0,2309.0,956.0,2.2165,231700.0,NEAR OCEAN
+-117.98,33.9,6.0,1537.0,347.0,506.0,280.0,4.8264,146800.0,<1H OCEAN
+-117.31,34.08,43.0,1697.0,387.0,1181.0,352.0,1.9234,74600.0,INLAND
+-121.89,37.35,48.0,1562.0,439.0,1469.0,424.0,2.5673,177500.0,<1H OCEAN
+-117.35,33.19,28.0,2823.0,476.0,1189.0,433.0,5.1733,198100.0,NEAR OCEAN
+-116.97,33.74,31.0,2712.0,628.0,1519.0,629.0,1.942,86200.0,INLAND
+-116.91,32.85,21.0,4152.0,703.0,2255.0,697.0,4.5096,159500.0,<1H OCEAN
+-122.63,38.96,17.0,1708.0,459.0,633.0,312.0,1.75,64000.0,INLAND
+-118.23,34.07,40.0,506.0,119.0,397.0,114.0,3.1944,143800.0,<1H OCEAN
+-117.24,32.8,18.0,2205.0,661.0,874.0,580.0,3.8018,112500.0,NEAR OCEAN
+-117.55,33.95,17.0,3196.0,444.0,1581.0,462.0,5.9333,229400.0,INLAND
+-122.28,37.51,33.0,4719.0,,1980.0,757.0,6.1064,405000.0,NEAR OCEAN
+-117.11,32.75,34.0,2131.0,594.0,1373.0,562.0,2.113,102100.0,NEAR OCEAN
+-117.96,33.82,29.0,2176.0,468.0,1632.0,428.0,3.707,180400.0,<1H OCEAN
+-118.39,34.02,45.0,1577.0,421.0,1042.0,375.0,3.4375,314500.0,<1H OCEAN
+-118.38,33.88,33.0,1313.0,244.0,561.0,217.0,5.2999,359400.0,<1H OCEAN
+-118.04,33.99,47.0,2530.0,565.0,1262.0,509.0,3.6475,197100.0,<1H OCEAN
+-117.23,32.8,22.0,2981.0,873.0,1751.0,745.0,2.3482,190600.0,NEAR OCEAN
+-117.85,33.81,26.0,4186.0,767.0,2447.0,777.0,4.9917,248100.0,<1H OCEAN
+-122.1,37.68,38.0,1779.0,413.0,1061.0,400.0,3.0962,180900.0,NEAR BAY
+-117.05,32.56,17.0,985.0,233.0,811.0,223.0,2.875,134500.0,NEAR OCEAN
+-122.9,41.46,31.0,1277.0,263.0,600.0,241.0,1.7292,61700.0,INLAND
+-121.34,38.62,20.0,4021.0,864.0,1658.0,730.0,2.4537,153000.0,INLAND
+-122.41,38.07,20.0,4536.0,708.0,1812.0,701.0,6.0433,435900.0,NEAR BAY
+-118.27,34.0,43.0,1638.0,434.0,1213.0,390.0,1.3403,110800.0,<1H OCEAN
+-117.49,33.99,21.0,2050.0,392.0,1153.0,336.0,4.84,116400.0,INLAND
+-117.74,33.89,4.0,37937.0,5471.0,16122.0,5189.0,7.4947,366300.0,<1H OCEAN
+-118.41,34.0,38.0,324.0,70.0,268.0,73.0,2.55,271400.0,<1H OCEAN
+-119.61,36.94,14.0,863.0,151.0,315.0,135.0,4.2679,151800.0,INLAND
+-121.07,37.71,39.0,223.0,37.0,92.0,37.0,3.375,212500.0,INLAND
+-118.25,33.97,43.0,1735.0,535.0,2288.0,524.0,1.9119,98800.0,<1H OCEAN
+-117.02,34.03,19.0,4415.0,648.0,1627.0,619.0,4.2361,191600.0,INLAND
+-119.19,34.16,35.0,2733.0,510.0,1814.0,511.0,4.4187,183400.0,NEAR OCEAN
+-118.03,33.95,34.0,1882.0,428.0,1034.0,375.0,3.6509,173200.0,<1H OCEAN
+-120.0,36.7,33.0,1902.0,370.0,1168.0,358.0,2.6852,70800.0,INLAND
+-121.92,36.55,37.0,2190.0,419.0,490.0,300.0,3.7852,465800.0,NEAR OCEAN
+-122.43,37.74,52.0,2637.0,539.0,1159.0,497.0,3.8846,333100.0,NEAR BAY
+-118.38,33.87,21.0,4151.0,1018.0,2054.0,925.0,4.9821,292900.0,<1H OCEAN
+-118.11,33.83,36.0,1726.0,287.0,820.0,288.0,5.5767,218100.0,<1H OCEAN
+-121.63,36.65,44.0,309.0,121.0,315.0,80.0,1.6071,112500.0,<1H OCEAN
+-122.45,37.81,52.0,1375.0,322.0,287.0,184.0,3.9028,500001.0,NEAR BAY
+-121.4,38.63,31.0,1540.0,452.0,1079.0,444.0,1.8571,98700.0,INLAND
+-117.95,34.02,19.0,1129.0,258.0,900.0,228.0,3.875,135600.0,<1H OCEAN
+-117.06,32.57,16.0,1269.0,282.0,1609.0,298.0,2.6985,156500.0,NEAR OCEAN
+-118.37,34.17,6.0,854.0,350.0,542.0,321.0,0.8198,325000.0,<1H OCEAN
+-118.14,33.84,44.0,3043.0,619.0,1316.0,607.0,4.4286,254900.0,<1H OCEAN
+-122.31,37.55,45.0,507.0,140.0,305.0,139.0,2.6159,272900.0,NEAR OCEAN
+-120.69,35.62,43.0,3044.0,652.0,1456.0,608.0,2.4567,140000.0,<1H OCEAN
+-117.89,34.49,12.0,3449.0,598.0,1502.0,540.0,3.7043,150800.0,INLAND
+-119.77,34.43,22.0,2552.0,443.0,1066.0,424.0,5.1271,342500.0,NEAR OCEAN
+-117.29,34.15,49.0,1820.0,321.0,757.0,324.0,3.2976,102600.0,INLAND
+-119.75,36.83,15.0,2793.0,436.0,1411.0,441.0,4.9292,109400.0,INLAND
+-118.06,34.04,28.0,1516.0,363.0,1011.0,344.0,2.6288,160300.0,<1H OCEAN
+-117.66,33.51,18.0,2626.0,,1302.0,522.0,4.0167,189600.0,<1H OCEAN
+-122.27,38.68,18.0,742.0,142.0,343.0,119.0,3.1563,98400.0,INLAND
+-121.12,37.73,35.0,1107.0,227.0,573.0,210.0,2.3924,102200.0,INLAND
+-117.7,33.56,3.0,2443.0,637.0,1033.0,548.0,4.1379,183300.0,<1H OCEAN
+-118.29,33.89,32.0,2355.0,583.0,1605.0,571.0,4.2171,218200.0,<1H OCEAN
+-119.82,36.84,9.0,2340.0,544.0,860.0,520.0,3.3229,119300.0,INLAND
+-119.1,36.4,23.0,1885.0,363.0,1056.0,338.0,3.2159,92800.0,INLAND
+-122.02,38.26,8.0,2894.0,602.0,1566.0,572.0,3.6335,131600.0,INLAND
+-117.89,33.82,24.0,1268.0,210.0,700.0,224.0,5.0605,216200.0,<1H OCEAN
+-118.36,33.92,46.0,1231.0,231.0,793.0,256.0,4.1023,226800.0,<1H OCEAN
+-118.91,36.79,19.0,1616.0,324.0,187.0,80.0,3.7857,78600.0,INLAND
+-117.87,33.7,21.0,3648.0,654.0,2266.0,628.0,5.0956,246000.0,<1H OCEAN
+-122.19,40.2,30.0,2750.0,476.0,1296.0,464.0,3.5305,73600.0,INLAND
+-116.99,32.8,34.0,3657.0,538.0,1513.0,562.0,5.2907,195800.0,<1H OCEAN
+-117.05,32.74,34.0,2178.0,455.0,1193.0,446.0,3.1719,115300.0,NEAR OCEAN
+-122.23,37.42,16.0,1945.0,320.0,512.0,300.0,7.4542,500001.0,NEAR OCEAN
+-118.02,33.92,34.0,1478.0,251.0,956.0,277.0,5.5238,185300.0,<1H OCEAN
+-120.35,36.14,9.0,2671.0,647.0,1484.0,541.0,1.7075,60400.0,INLAND
+-118.4,33.88,36.0,1543.0,214.0,474.0,187.0,9.3399,500001.0,<1H OCEAN
+-118.31,33.73,36.0,1725.0,295.0,799.0,306.0,5.0874,368500.0,NEAR OCEAN
+-118.21,34.05,47.0,722.0,235.0,930.0,226.0,2.5455,114300.0,<1H OCEAN
+-122.45,37.75,36.0,2303.0,381.0,862.0,371.0,6.0274,349000.0,NEAR BAY
+-121.22,38.58,25.0,394.0,94.0,155.0,83.0,2.233,55000.0,INLAND
+-117.99,33.73,20.0,3182.0,884.0,1770.0,817.0,3.1912,220800.0,<1H OCEAN
+-118.43,34.2,20.0,4090.0,1271.0,2824.0,1053.0,2.773,140500.0,<1H OCEAN
+-122.43,37.77,52.0,1760.0,366.0,742.0,318.0,4.445,400000.0,NEAR BAY
+-122.47,37.76,39.0,3200.0,689.0,1391.0,618.0,3.6346,338000.0,NEAR BAY
+-118.73,34.27,25.0,3409.0,493.0,1699.0,484.0,5.653,225800.0,<1H OCEAN
+-117.93,34.09,35.0,782.0,153.0,499.0,163.0,4.2062,161300.0,<1H OCEAN
+-118.13,33.79,45.0,2317.0,448.0,1057.0,428.0,4.375,234800.0,NEAR OCEAN
+-119.17,34.26,10.0,3654.0,541.0,1638.0,551.0,6.1885,267300.0,NEAR OCEAN
+-120.99,37.64,50.0,683.0,189.0,459.0,195.0,1.8162,70000.0,INLAND
+-116.54,33.79,18.0,9374.0,1780.0,1678.0,919.0,3.9737,235600.0,INLAND
+-117.09,32.76,29.0,1650.0,496.0,882.0,445.0,2.2287,140000.0,NEAR OCEAN
+-119.77,36.8,32.0,3461.0,665.0,1507.0,649.0,2.9244,84600.0,INLAND
+-118.08,34.0,35.0,1188.0,342.0,1373.0,332.0,2.9107,150900.0,<1H OCEAN
+-121.28,38.7,14.0,5827.0,1246.0,2578.0,1038.0,3.0212,112900.0,INLAND
+-118.0,34.16,42.0,1020.0,156.0,398.0,157.0,6.101,311800.0,INLAND
+-119.75,36.71,38.0,1481.0,,1543.0,372.0,1.4577,49800.0,INLAND
+-117.98,34.06,36.0,2391.0,407.0,1967.0,398.0,4.0274,160700.0,<1H OCEAN
+-118.85,34.04,21.0,3837.0,578.0,1509.0,509.0,8.4476,500001.0,NEAR OCEAN
+-118.04,33.87,18.0,4626.0,822.0,2794.0,763.0,5.6917,275100.0,<1H OCEAN
+-119.26,34.27,42.0,918.0,204.0,394.0,204.0,4.0069,214300.0,NEAR OCEAN
+-117.08,33.03,15.0,3023.0,623.0,1283.0,559.0,3.3724,137900.0,<1H OCEAN
+-121.25,37.97,41.0,855.0,189.0,716.0,206.0,2.0375,75000.0,INLAND
+-121.4,38.61,33.0,3512.0,825.0,1515.0,782.0,1.9908,118800.0,INLAND
+-117.15,32.71,52.0,402.0,183.0,557.0,172.0,1.3125,87500.0,NEAR OCEAN
+-117.94,33.94,26.0,1962.0,540.0,1236.0,520.0,2.2156,145000.0,<1H OCEAN
+-117.01,32.8,17.0,1558.0,479.0,803.0,431.0,2.6934,160200.0,<1H OCEAN
+-120.45,36.86,34.0,673.0,173.0,539.0,182.0,2.3523,66000.0,INLAND
+-118.25,34.13,22.0,2340.0,773.0,2226.0,754.0,2.5417,217500.0,<1H OCEAN
+-118.96,35.36,35.0,2285.0,497.0,1738.0,480.0,2.4848,54000.0,INLAND
+-122.7,38.39,16.0,4922.0,1211.0,2557.0,1088.0,2.0915,168100.0,<1H OCEAN
+-118.15,33.78,12.0,4436.0,1133.0,2176.0,1002.0,3.5812,198600.0,NEAR OCEAN
+-118.1,34.15,14.0,1442.0,369.0,782.0,343.0,2.7431,177500.0,INLAND
+-123.23,39.33,20.0,804.0,121.0,448.0,140.0,3.9632,147100.0,<1H OCEAN
+-118.14,33.88,24.0,3305.0,982.0,2085.0,881.0,2.6641,168200.0,<1H OCEAN
+-119.89,34.44,25.0,3160.0,507.0,1514.0,523.0,5.0767,271200.0,NEAR OCEAN
+-124.17,41.76,20.0,2673.0,538.0,1282.0,514.0,2.4605,105900.0,NEAR OCEAN
+-121.49,38.57,38.0,2410.0,967.0,1091.0,829.0,1.2209,87900.0,INLAND
+-121.6,39.83,12.0,3744.0,699.0,1532.0,660.0,2.3079,95300.0,INLAND
+-122.08,40.64,14.0,3099.0,519.0,1447.0,494.0,4.0132,141200.0,INLAND
+-122.13,38.0,33.0,2821.0,652.0,1206.0,640.0,2.5481,150800.0,NEAR BAY
+-118.39,34.15,29.0,917.0,181.0,379.0,183.0,3.4612,425000.0,<1H OCEAN
+-118.21,33.94,34.0,710.0,205.0,1134.0,233.0,2.7734,141100.0,<1H OCEAN
+-118.01,33.67,16.0,3581.0,780.0,1644.0,774.0,5.041,397600.0,NEAR OCEAN
+-117.96,33.65,24.0,4462.0,689.0,1943.0,712.0,5.7395,289800.0,<1H OCEAN
+-117.31,34.15,7.0,5747.0,1307.0,2578.0,1147.0,3.3281,122200.0,INLAND
+-119.72,34.41,35.0,1853.0,375.0,878.0,338.0,4.9044,335300.0,NEAR OCEAN
+-117.89,33.7,13.0,1857.0,572.0,838.0,525.0,3.2386,129200.0,<1H OCEAN
+-119.33,36.32,20.0,2025.0,328.0,1039.0,346.0,3.5313,82800.0,INLAND
+-119.01,35.34,36.0,973.0,219.0,613.0,187.0,1.5625,46700.0,INLAND
+-118.3,33.82,35.0,1499.0,340.0,1141.0,326.0,2.6136,213600.0,<1H OCEAN
+-121.87,37.57,13.0,5519.0,833.0,2444.0,825.0,7.0691,393200.0,<1H OCEAN
+-121.11,38.91,24.0,2558.0,423.0,1149.0,403.0,4.0679,190500.0,INLAND
+-122.73,38.43,29.0,2677.0,691.0,1880.0,664.0,2.1864,143200.0,<1H OCEAN
+-118.09,33.95,36.0,1991.0,396.0,1306.0,403.0,4.5,166600.0,<1H OCEAN
+-121.09,39.22,25.0,2200.0,439.0,1045.0,419.0,2.6042,116700.0,INLAND
+-122.68,38.44,36.0,1311.0,259.0,648.0,268.0,3.4545,161200.0,<1H OCEAN
+-119.68,34.42,38.0,1452.0,354.0,1139.0,340.0,2.707,236800.0,<1H OCEAN
+-118.12,33.88,25.0,1768.0,559.0,983.0,488.0,2.6184,243800.0,<1H OCEAN
+-121.3,38.13,26.0,2256.0,360.0,937.0,372.0,5.0528,153700.0,INLAND
+-121.91,36.42,14.0,1078.0,261.0,382.0,171.0,3.7083,210000.0,NEAR OCEAN
+-118.17,34.06,44.0,1856.0,461.0,1853.0,452.0,2.5033,131900.0,<1H OCEAN
+-121.99,37.61,9.0,3666.0,711.0,2341.0,703.0,4.6458,217000.0,<1H OCEAN
+-118.39,33.88,33.0,2496.0,387.0,1098.0,404.0,7.6685,474300.0,<1H OCEAN
+-117.97,33.67,17.0,4466.0,640.0,2166.0,666.0,6.979,330700.0,<1H OCEAN
+-121.25,38.13,25.0,1305.0,270.0,789.0,235.0,3.2993,91100.0,INLAND
+-117.77,33.84,5.0,4380.0,715.0,1913.0,741.0,6.7274,266400.0,<1H OCEAN
+-118.02,34.04,28.0,6175.0,1449.0,5041.0,1408.0,2.8821,158100.0,<1H OCEAN
+-122.25,37.87,52.0,609.0,236.0,1349.0,250.0,1.1696,500001.0,NEAR BAY
+-118.19,33.76,25.0,1442.0,392.0,632.0,385.0,4.6629,162500.0,NEAR OCEAN
+-118.17,33.77,38.0,2239.0,721.0,984.0,684.0,2.346,165600.0,NEAR OCEAN
+-119.69,36.25,35.0,2011.0,349.0,970.0,300.0,2.395,94100.0,INLAND
+-123.22,39.15,36.0,1166.0,216.0,504.0,203.0,3.5938,122100.0,<1H OCEAN
+-118.02,34.05,33.0,2464.0,627.0,2932.0,568.0,3.0625,165800.0,<1H OCEAN
+-117.69,33.6,12.0,3258.0,421.0,1464.0,435.0,6.5413,332000.0,<1H OCEAN
+-118.47,34.26,35.0,1898.0,344.0,1123.0,347.0,5.5792,218400.0,<1H OCEAN
+-117.98,33.74,16.0,4636.0,908.0,2341.0,825.0,4.4261,304700.0,<1H OCEAN
+-119.69,36.8,31.0,2576.0,458.0,1306.0,418.0,3.2813,68700.0,INLAND
+-121.82,37.69,12.0,1906.0,351.0,802.0,319.0,4.9375,227700.0,INLAND
+-118.28,33.84,27.0,2326.0,533.0,1697.0,546.0,3.8633,187900.0,<1H OCEAN
+-118.16,34.09,33.0,1515.0,415.0,1345.0,346.0,2.375,175000.0,<1H OCEAN
+-120.79,38.7,13.0,5036.0,1034.0,2243.0,923.0,2.3319,138500.0,INLAND
+-122.54,37.72,17.0,2975.0,968.0,1453.0,828.0,3.527,318900.0,NEAR OCEAN
+-117.27,32.84,34.0,1655.0,450.0,870.0,411.0,3.2109,376000.0,NEAR OCEAN
+-117.13,32.97,10.0,3486.0,469.0,1700.0,483.0,6.4696,249500.0,<1H OCEAN
+-121.52,38.59,35.0,6418.0,1545.0,3814.0,1496.0,1.6647,69100.0,INLAND
+-120.12,36.01,18.0,1165.0,334.0,1119.0,308.0,2.2167,48500.0,INLAND
+-120.18,36.59,25.0,948.0,198.0,613.0,171.0,2.3026,90600.0,INLAND
+-122.04,37.99,38.0,2675.0,541.0,1378.0,480.0,3.8897,139900.0,NEAR BAY
+-118.18,33.93,35.0,952.0,271.0,949.0,261.0,2.4297,147200.0,<1H OCEAN
+-121.64,37.14,14.0,5487.0,1024.0,2823.0,979.0,4.175,229800.0,<1H OCEAN
+-121.79,37.34,20.0,2018.0,328.0,1196.0,323.0,4.9318,262400.0,<1H OCEAN
+-117.91,33.72,32.0,2436.0,504.0,2839.0,516.0,4.5607,182100.0,<1H OCEAN
+-117.28,32.74,33.0,4168.0,1112.0,1785.0,984.0,2.7515,247700.0,NEAR OCEAN
+-121.06,38.51,6.0,6873.0,959.0,2354.0,931.0,6.8869,263100.0,INLAND
+-121.44,38.61,33.0,1591.0,466.0,1000.0,418.0,1.0467,70100.0,INLAND
+-118.05,33.93,35.0,2107.0,480.0,2241.0,443.0,3.1513,150000.0,<1H OCEAN
+-118.25,33.97,36.0,1026.0,294.0,1316.0,268.0,1.7708,102600.0,<1H OCEAN
+-122.26,38.11,52.0,793.0,216.0,505.0,194.0,1.9667,93800.0,NEAR BAY
+-118.97,35.37,52.0,425.0,119.0,380.0,97.0,1.4125,42500.0,INLAND
+-122.32,38.32,19.0,2922.0,417.0,1221.0,442.0,5.8002,238700.0,NEAR BAY
+-122.21,37.77,52.0,591.0,173.0,353.0,137.0,4.0904,80600.0,NEAR BAY
+-120.37,36.16,36.0,613.0,124.0,310.0,124.0,3.0658,65000.0,INLAND
+-118.27,33.95,40.0,935.0,226.0,818.0,236.0,1.8798,101300.0,<1H OCEAN
+-118.21,33.98,39.0,1315.0,306.0,1257.0,298.0,3.2788,169000.0,<1H OCEAN
+-118.3,33.73,47.0,2852.0,603.0,1130.0,560.0,4.194,293900.0,NEAR OCEAN
+-118.26,34.14,29.0,3431.0,1222.0,4094.0,1205.0,2.2614,248100.0,<1H OCEAN
+-121.99,37.4,35.0,1845.0,325.0,1343.0,317.0,5.3912,235300.0,<1H OCEAN
+-117.96,33.83,30.0,2838.0,649.0,1758.0,593.0,3.3831,197400.0,<1H OCEAN
+-116.91,32.87,14.0,3048.0,597.0,1690.0,576.0,4.3818,147100.0,<1H OCEAN
+-118.35,33.91,19.0,1949.0,559.0,1282.0,498.0,2.7813,231300.0,<1H OCEAN
+-121.98,37.96,22.0,2987.0,,1420.0,540.0,3.65,204100.0,INLAND
+-117.92,33.88,32.0,1683.0,273.0,719.0,263.0,5.3649,243600.0,<1H OCEAN
+-117.13,32.71,44.0,1697.0,413.0,1396.0,363.0,1.5474,83300.0,NEAR OCEAN
+-117.12,32.7,37.0,1361.0,348.0,1398.0,328.0,1.1681,78100.0,NEAR OCEAN
+-121.81,37.37,24.0,962.0,146.0,492.0,155.0,7.2861,328000.0,<1H OCEAN
+-117.72,34.06,32.0,2209.0,654.0,1718.0,569.0,1.9643,113200.0,INLAND
+-121.33,38.63,23.0,1947.0,409.0,866.0,400.0,2.7181,156800.0,INLAND
+-122.21,37.43,33.0,1606.0,254.0,727.0,271.0,8.6963,500001.0,NEAR OCEAN
+-117.86,33.72,37.0,1429.0,428.0,2089.0,399.0,3.413,150600.0,<1H OCEAN
+-121.07,40.85,17.0,976.0,202.0,511.0,175.0,3.6641,80800.0,INLAND
+-122.29,37.9,52.0,1604.0,263.0,594.0,286.0,5.338,270900.0,NEAR BAY
+-117.22,33.93,14.0,5104.0,1026.0,3513.0,972.0,3.2148,117000.0,INLAND
+-118.34,34.1,28.0,2223.0,752.0,1271.0,684.0,2.5434,232100.0,<1H OCEAN
+-118.07,33.85,16.0,3771.0,606.0,2196.0,564.0,7.0113,319700.0,<1H OCEAN
+-118.28,33.94,43.0,1201.0,292.0,840.0,252.0,2.7917,105600.0,<1H OCEAN
+-124.25,40.28,32.0,1430.0,419.0,434.0,187.0,1.9417,76100.0,NEAR OCEAN
+-122.5,37.75,45.0,1672.0,344.0,838.0,314.0,4.1419,291500.0,NEAR OCEAN
+-121.95,36.59,22.0,3553.0,530.0,1108.0,441.0,5.8505,417100.0,NEAR OCEAN
+-121.23,40.29,21.0,3229.0,667.0,1501.0,582.0,2.1524,77100.0,INLAND
+-122.42,37.76,52.0,2088.0,487.0,1082.0,488.0,2.6803,490000.0,NEAR BAY
+-122.64,38.23,52.0,2156.0,469.0,1070.0,467.0,3.3011,252300.0,<1H OCEAN
+-117.22,33.18,13.0,4273.0,886.0,2328.0,801.0,3.3444,183900.0,<1H OCEAN
+-116.84,33.02,11.0,1227.0,202.0,645.0,198.0,5.6674,209800.0,<1H OCEAN
+-117.06,32.78,31.0,2485.0,436.0,942.0,448.0,4.8,220800.0,NEAR OCEAN
+-118.3,34.0,52.0,1686.0,377.0,982.0,356.0,2.0958,116400.0,<1H OCEAN
+-117.45,34.11,7.0,6356.0,1244.0,4052.0,1164.0,2.9112,121700.0,INLAND
+-117.71,33.61,26.0,2280.0,550.0,669.0,502.0,2.3438,72300.0,<1H OCEAN
+-122.07,37.57,8.0,8647.0,1407.0,5019.0,1379.0,6.5615,318300.0,NEAR BAY
+-117.08,33.14,11.0,1430.0,292.0,921.0,294.0,4.2357,160900.0,<1H OCEAN
+-123.1,38.97,36.0,1211.0,247.0,697.0,251.0,2.5761,94900.0,<1H OCEAN
+-122.46,37.71,44.0,364.0,102.0,339.0,98.0,2.483,214300.0,NEAR OCEAN
+-117.38,33.14,14.0,5039.0,1373.0,1298.0,696.0,3.209,313300.0,NEAR OCEAN
+-118.3,34.0,52.0,1743.0,421.0,1206.0,384.0,1.6875,116000.0,<1H OCEAN
+-120.55,36.97,42.0,1766.0,344.0,1084.0,323.0,2.3295,74400.0,INLAND
+-122.28,37.55,17.0,4199.0,629.0,2020.0,630.0,6.1228,375700.0,NEAR BAY
+-118.68,34.28,17.0,6488.0,1102.0,3199.0,1070.0,5.0962,238000.0,<1H OCEAN
+-115.37,32.81,32.0,741.0,191.0,623.0,169.0,1.7604,68600.0,INLAND
+-118.22,33.99,24.0,1402.0,482.0,1976.0,466.0,2.6964,163200.0,<1H OCEAN
+-117.72,33.54,13.0,4866.0,812.0,1909.0,733.0,4.9821,244800.0,<1H OCEAN
+-117.27,34.13,40.0,1298.0,254.0,793.0,268.0,3.0721,83800.0,INLAND
+-124.14,40.58,25.0,1899.0,357.0,891.0,355.0,2.6987,92500.0,<1H OCEAN
+-121.49,38.49,26.0,1557.0,301.0,986.0,300.0,2.6613,77700.0,INLAND
+-121.88,37.64,20.0,1309.0,184.0,514.0,172.0,10.9506,475800.0,INLAND
+-118.27,34.08,35.0,1147.0,365.0,1016.0,296.0,2.3913,198800.0,<1H OCEAN
+-122.02,37.36,24.0,1709.0,437.0,892.0,408.0,4.9671,335200.0,<1H OCEAN
+-118.28,34.11,45.0,1607.0,331.0,633.0,332.0,3.1445,438300.0,<1H OCEAN
+-117.59,33.91,7.0,10223.0,1491.0,5205.0,1509.0,5.4872,203400.0,INLAND
+-118.37,34.08,52.0,2946.0,695.0,1258.0,650.0,3.9783,374100.0,<1H OCEAN
+-121.22,38.78,8.0,3418.0,514.0,1312.0,409.0,6.3914,218000.0,INLAND
+-118.56,34.42,2.0,966.0,270.0,233.0,169.0,1.9667,450000.0,<1H OCEAN
+-121.33,38.62,19.0,1853.0,415.0,772.0,397.0,2.2574,135800.0,INLAND
+-118.14,33.8,43.0,2506.0,531.0,1230.0,543.0,3.4211,203900.0,NEAR OCEAN
+-118.47,33.99,41.0,1146.0,310.0,833.0,270.0,2.5938,285000.0,<1H OCEAN
+-119.79,36.8,27.0,2462.0,484.0,852.0,449.0,3.32,124700.0,INLAND
+-117.88,34.13,33.0,3713.0,718.0,2106.0,720.0,4.0023,185500.0,<1H OCEAN
+-118.18,33.95,39.0,2121.0,579.0,1991.0,528.0,2.9094,152200.0,<1H OCEAN
+-118.05,34.13,23.0,3264.0,729.0,1475.0,668.0,3.735,218300.0,INLAND
+-117.7,33.63,23.0,3038.0,473.0,1501.0,436.0,5.5584,241700.0,<1H OCEAN
+-121.35,37.97,33.0,3656.0,681.0,1698.0,671.0,3.1406,93900.0,INLAND
+-118.04,34.11,44.0,773.0,120.0,405.0,121.0,5.0,447100.0,INLAND
+-115.56,32.8,28.0,1672.0,416.0,1335.0,397.0,1.5987,59400.0,INLAND
+-120.41,37.88,16.0,744.0,141.0,311.0,122.0,4.4231,87500.0,INLAND
+-117.78,33.86,16.0,3471.0,708.0,1769.0,691.0,4.1064,246100.0,<1H OCEAN
+-117.18,32.83,31.0,1772.0,353.0,1090.0,350.0,3.9265,162000.0,NEAR OCEAN
+-119.18,34.22,15.0,4615.0,1008.0,2549.0,973.0,3.9063,198700.0,NEAR OCEAN
+-116.96,33.62,8.0,1003.0,167.0,388.0,140.0,4.2917,221900.0,INLAND
+-117.07,32.69,29.0,1429.0,293.0,1091.0,317.0,3.4609,118000.0,NEAR OCEAN
+-121.28,37.99,42.0,495.0,116.0,284.0,97.0,2.8854,55700.0,INLAND
+-118.34,34.04,42.0,2010.0,494.0,1203.0,427.0,1.9408,134600.0,<1H OCEAN
+-117.06,32.66,24.0,2587.0,491.0,1617.0,458.0,3.5066,133400.0,NEAR OCEAN
+-122.21,40.18,30.0,744.0,156.0,410.0,165.0,2.1898,63200.0,INLAND
+-119.8,36.78,50.0,1818.0,374.0,737.0,338.0,2.2614,73000.0,INLAND
+-118.3,34.0,40.0,1131.0,281.0,859.0,230.0,1.1806,134600.0,<1H OCEAN
+-121.7,37.91,17.0,1962.0,291.0,825.0,267.0,4.8958,187100.0,INLAND
+-122.02,38.02,44.0,1465.0,247.0,817.0,237.0,4.8693,156900.0,NEAR BAY
+-117.83,34.11,29.0,2671.0,437.0,1484.0,445.0,4.9844,203000.0,INLAND
+-116.98,32.85,12.0,3570.0,713.0,3321.0,666.0,4.0882,134500.0,<1H OCEAN
+-118.13,33.77,37.0,4365.0,926.0,1661.0,868.0,5.3046,360700.0,NEAR OCEAN
+-122.69,38.27,32.0,2344.0,434.0,1066.0,384.0,4.0313,285000.0,<1H OCEAN
+-118.98,35.38,28.0,1171.0,299.0,1193.0,273.0,0.8639,49400.0,INLAND
+-118.39,34.1,30.0,6310.0,957.0,1776.0,880.0,8.944,500001.0,<1H OCEAN
+-118.56,34.41,4.0,17313.0,3224.0,6902.0,2707.0,5.6798,320900.0,<1H OCEAN
+-117.65,34.08,40.0,1609.0,258.0,624.0,242.0,5.4689,158200.0,INLAND
+-122.19,37.77,45.0,1852.0,393.0,1132.0,349.0,2.7159,101400.0,NEAR BAY
+-118.15,34.59,33.0,2111.0,429.0,1067.0,397.0,3.7344,111400.0,INLAND
+-121.05,37.93,17.0,2474.0,480.0,1649.0,453.0,3.275,156500.0,INLAND
+-122.1,37.65,31.0,1797.0,327.0,796.0,319.0,4.4427,204500.0,NEAR BAY
+-118.33,34.09,30.0,1679.0,682.0,1445.0,579.0,2.1403,200000.0,<1H OCEAN
+-118.03,34.1,30.0,2773.0,634.0,1376.0,540.0,2.7857,201700.0,INLAND
+-118.4,33.99,39.0,1613.0,380.0,1113.0,356.0,2.825,276700.0,<1H OCEAN
+-121.3,38.26,19.0,1403.0,276.0,901.0,290.0,3.215,104600.0,INLAND
+-117.32,34.08,46.0,1308.0,276.0,576.0,244.0,3.1875,84000.0,INLAND
+-118.4,33.87,40.0,1679.0,372.0,719.0,385.0,6.435,479500.0,<1H OCEAN
+-117.91,33.62,35.0,2426.0,359.0,937.0,387.0,9.2175,500001.0,<1H OCEAN
+-122.72,38.35,16.0,3049.0,609.0,1675.0,618.0,2.4117,162500.0,<1H OCEAN
+-118.77,34.27,7.0,3074.0,794.0,1816.0,654.0,2.7137,196400.0,<1H OCEAN
+-118.43,34.0,37.0,1340.0,358.0,1008.0,340.0,3.7614,314300.0,<1H OCEAN
+-117.86,33.62,23.0,3166.0,411.0,1092.0,345.0,7.9367,500001.0,<1H OCEAN
+-115.57,32.8,17.0,1492.0,426.0,1595.0,428.0,1.7188,69900.0,INLAND
+-118.32,34.07,25.0,2740.0,707.0,1420.0,664.0,3.5909,404500.0,<1H OCEAN
+-120.59,35.13,8.0,6638.0,1054.0,2710.0,966.0,4.6776,295500.0,<1H OCEAN
+-122.15,37.74,49.0,1494.0,316.0,611.0,288.0,2.2,187500.0,NEAR BAY
+-121.83,37.38,31.0,3633.0,843.0,2677.0,797.0,3.2222,184800.0,<1H OCEAN
+-118.35,34.05,52.0,1971.0,414.0,1065.0,409.0,3.6435,195800.0,<1H OCEAN
+-115.55,32.98,24.0,2565.0,530.0,1447.0,473.0,3.2593,80800.0,INLAND
+-121.51,37.02,19.0,2372.0,394.0,1142.0,365.0,4.0238,374600.0,INLAND
+-122.24,37.77,52.0,1711.0,386.0,885.0,373.0,3.6417,206300.0,NEAR BAY
+-122.53,39.5,25.0,1231.0,240.0,658.0,211.0,2.4861,71900.0,INLAND
+-122.45,38.58,34.0,2517.0,483.0,1324.0,464.0,3.0938,189400.0,INLAND
+-121.46,38.57,52.0,810.0,172.0,326.0,151.0,3.1583,140000.0,INLAND
+-118.19,33.89,31.0,886.0,224.0,1154.0,247.0,2.1071,99500.0,<1H OCEAN
+-121.77,37.67,20.0,8068.0,1217.0,3489.0,1259.0,5.7907,264200.0,INLAND
+-118.05,34.04,33.0,1348.0,,1098.0,257.0,4.2917,161200.0,<1H OCEAN
+-117.89,34.07,35.0,1439.0,261.0,804.0,271.0,3.9808,188600.0,<1H OCEAN
+-121.94,37.25,16.0,3942.0,749.0,1894.0,737.0,5.2894,332800.0,<1H OCEAN
+-117.99,33.93,27.0,3708.0,718.0,1921.0,721.0,4.375,210400.0,<1H OCEAN
+-121.82,38.01,50.0,1120.0,281.0,625.0,239.0,1.5988,96400.0,INLAND
+-116.24,33.71,10.0,9033.0,2224.0,5525.0,1845.0,2.7598,95000.0,INLAND
+-118.23,34.16,31.0,3105.0,582.0,1359.0,547.0,5.1718,429100.0,<1H OCEAN
+-120.47,34.63,23.0,2441.0,463.0,1392.0,434.0,3.7917,142200.0,NEAR OCEAN
+-122.15,37.44,52.0,1945.0,293.0,708.0,275.0,6.1655,500001.0,NEAR BAY
+-118.35,34.02,52.0,427.0,92.0,233.0,116.0,3.25,134700.0,<1H OCEAN
+-117.86,33.73,30.0,2651.0,572.0,3249.0,552.0,3.7202,182100.0,<1H OCEAN
+-118.43,34.26,43.0,729.0,172.0,935.0,174.0,2.9519,140900.0,<1H OCEAN
+-118.92,34.18,17.0,2400.0,352.0,1067.0,323.0,6.3522,259300.0,<1H OCEAN
+-118.09,34.06,30.0,1980.0,552.0,2264.0,511.0,2.6094,179400.0,<1H OCEAN
+-122.43,37.77,52.0,3563.0,832.0,1712.0,787.0,3.3702,335700.0,NEAR BAY
+-119.32,36.33,20.0,1896.0,266.0,674.0,277.0,9.0376,239100.0,INLAND
+-120.95,39.93,26.0,2023.0,385.0,922.0,365.0,2.8125,83500.0,INLAND
+-119.81,34.45,24.0,3678.0,567.0,1554.0,570.0,6.5173,334000.0,NEAR OCEAN
+-118.39,33.99,43.0,612.0,135.0,402.0,142.0,5.1322,314900.0,<1H OCEAN
+-118.24,33.94,42.0,380.0,106.0,411.0,100.0,0.9705,90000.0,<1H OCEAN
+-116.15,33.69,22.0,197.0,54.0,331.0,70.0,2.9286,112500.0,INLAND
+-118.13,34.11,45.0,1780.0,289.0,755.0,328.0,4.825,351100.0,<1H OCEAN
+-121.05,39.11,7.0,2767.0,423.0,1143.0,382.0,3.6333,170200.0,INLAND
+-118.48,34.01,31.0,2851.0,804.0,1410.0,782.0,4.0893,381500.0,<1H OCEAN
+-119.05,35.42,35.0,2353.0,483.0,1368.0,455.0,2.325,63200.0,INLAND
+-118.31,34.07,26.0,5062.0,2055.0,4533.0,1822.0,2.3105,166700.0,<1H OCEAN
+-118.03,34.06,27.0,2510.0,783.0,3481.0,726.0,2.4875,157800.0,<1H OCEAN
+-118.24,33.94,39.0,1215.0,273.0,1211.0,265.0,1.7212,95500.0,<1H OCEAN
+-118.36,33.86,34.0,1865.0,345.0,963.0,302.0,5.543,305900.0,<1H OCEAN
+-118.18,34.06,33.0,278.0,71.0,266.0,56.0,0.8941,98200.0,<1H OCEAN
+-119.75,36.87,3.0,13802.0,2244.0,5226.0,1972.0,5.0941,143700.0,INLAND
+-118.26,34.0,27.0,1611.0,479.0,1457.0,458.0,0.8941,91900.0,<1H OCEAN
+-121.85,37.33,19.0,2228.0,559.0,2845.0,551.0,2.6,172800.0,<1H OCEAN
+-122.57,37.98,49.0,2860.0,552.0,1178.0,522.0,4.625,355000.0,NEAR BAY
+-119.45,35.07,45.0,973.0,183.0,500.0,177.0,2.6389,30000.0,INLAND
+-117.31,34.13,35.0,1622.0,393.0,1296.0,362.0,1.9286,68500.0,INLAND
+-116.96,32.86,14.0,3064.0,496.0,1681.0,503.0,4.4347,160300.0,<1H OCEAN
+-117.13,33.13,17.0,3164.0,652.0,1123.0,699.0,2.082,80000.0,<1H OCEAN
+-121.86,37.35,43.0,1536.0,371.0,1256.0,357.0,2.8,153300.0,<1H OCEAN
+-118.1,34.18,47.0,2168.0,352.0,902.0,361.0,5.894,300900.0,INLAND
+-124.17,40.62,32.0,1595.0,309.0,706.0,277.0,2.8958,86400.0,NEAR OCEAN
+-122.25,37.83,52.0,4190.0,1105.0,1786.0,1037.0,3.0897,234100.0,NEAR BAY
+-119.25,34.28,36.0,2232.0,373.0,951.0,368.0,5.2261,303200.0,NEAR OCEAN
+-118.0,33.76,26.0,1876.0,455.0,1499.0,436.0,2.925,176000.0,<1H OCEAN
+-117.71,33.61,25.0,3004.0,718.0,891.0,626.0,2.395,80300.0,<1H OCEAN
+-121.76,38.57,11.0,15018.0,3008.0,7984.0,2962.0,3.1371,201800.0,INLAND
+-117.11,32.76,29.0,2030.0,545.0,1014.0,518.0,2.2409,114200.0,NEAR OCEAN
+-120.55,37.32,21.0,1410.0,229.0,590.0,205.0,3.3194,141400.0,INLAND
+-121.93,37.34,48.0,2068.0,495.0,946.0,452.0,3.0375,218500.0,<1H OCEAN
+-120.96,37.67,17.0,2434.0,511.0,1558.0,546.0,2.9219,114300.0,INLAND
+-118.28,34.02,29.0,515.0,229.0,2690.0,217.0,0.4999,500001.0,<1H OCEAN
+-122.4,40.62,9.0,4794.0,889.0,2162.0,865.0,3.1439,103100.0,INLAND
+-118.18,34.22,40.0,1983.0,298.0,853.0,271.0,5.9845,241700.0,<1H OCEAN
+-118.14,33.77,52.0,2208.0,409.0,791.0,408.0,5.8408,500000.0,NEAR OCEAN
+-122.08,37.65,35.0,1813.0,393.0,1093.0,374.0,3.6818,165400.0,NEAR BAY
+-122.48,38.3,17.0,2703.0,550.0,1241.0,515.0,2.652,171300.0,<1H OCEAN
+-121.24,37.79,7.0,5151.0,867.0,2553.0,805.0,4.075,195000.0,INLAND
+-118.49,34.05,45.0,1346.0,214.0,415.0,209.0,7.0285,500001.0,<1H OCEAN
+-117.77,33.76,19.0,3532.0,402.0,1200.0,426.0,11.0124,500001.0,<1H OCEAN
+-119.15,34.25,36.0,3511.0,664.0,2965.0,695.0,4.0878,186800.0,NEAR OCEAN
+-118.36,34.07,48.0,1740.0,360.0,748.0,357.0,4.7019,411100.0,<1H OCEAN
+-121.37,38.39,15.0,1883.0,254.0,893.0,256.0,6.2575,143500.0,INLAND
+-119.25,34.3,34.0,1189.0,220.0,445.0,203.0,4.8824,396400.0,NEAR OCEAN
+-117.85,33.77,23.0,5928.0,1204.0,3570.0,1150.0,4.0398,233100.0,<1H OCEAN
+-122.25,37.56,19.0,7976.0,1406.0,3437.0,1338.0,5.6396,430300.0,NEAR BAY
+-118.23,34.2,51.0,1477.0,280.0,750.0,295.0,5.3925,317900.0,<1H OCEAN
+-117.13,32.71,37.0,1220.0,325.0,1472.0,323.0,1.825,81500.0,NEAR OCEAN
+-117.99,34.08,11.0,2399.0,527.0,2307.0,531.0,3.5625,141000.0,INLAND
+-117.03,34.87,7.0,2245.0,407.0,1016.0,364.0,3.9464,101500.0,INLAND
+-117.06,32.69,9.0,1520.0,269.0,1250.0,265.0,4.8875,157700.0,NEAR OCEAN
+-117.4,33.95,32.0,1979.0,491.0,954.0,444.0,2.4408,117300.0,INLAND
+-121.98,37.26,27.0,2331.0,461.0,1178.0,447.0,4.6654,340700.0,<1H OCEAN
+-120.83,37.51,13.0,3795.0,604.0,1639.0,609.0,4.6635,198400.0,INLAND
+-118.37,33.88,44.0,1325.0,245.0,669.0,253.0,4.4211,324000.0,<1H OCEAN
+-122.29,37.88,50.0,1211.0,261.0,523.0,227.0,3.8672,216700.0,NEAR BAY
+-118.38,33.89,35.0,1778.0,330.0,732.0,312.0,6.5745,379300.0,<1H OCEAN
+-117.19,33.7,24.0,5783.0,1256.0,1990.0,1151.0,1.9014,83500.0,<1H OCEAN
+-117.74,33.62,16.0,1889.0,590.0,686.0,537.0,3.4706,241700.0,<1H OCEAN
+-123.76,41.03,24.0,2386.0,565.0,1058.0,414.0,2.0644,79800.0,<1H OCEAN
+-122.17,37.75,48.0,1751.0,390.0,935.0,349.0,1.4375,90000.0,NEAR BAY
+-122.25,37.8,43.0,2344.0,647.0,1710.0,644.0,1.6504,151800.0,NEAR BAY
+-118.38,33.86,12.0,4235.0,735.0,1798.0,683.0,6.4242,365500.0,<1H OCEAN
+-119.64,36.35,23.0,3182.0,563.0,1525.0,585.0,3.8108,90400.0,INLAND
+-122.06,37.37,18.0,3058.0,661.0,1377.0,675.0,6.1299,500001.0,<1H OCEAN
+-122.0,36.93,51.0,1616.0,374.0,608.0,302.0,3.1932,400000.0,NEAR OCEAN
+-118.45,34.18,39.0,1810.0,388.0,839.0,380.0,3.7171,228800.0,<1H OCEAN
+-121.76,37.69,29.0,3433.0,711.0,1919.0,709.0,3.3841,184400.0,INLAND
+-117.11,32.59,18.0,2329.0,580.0,1538.0,567.0,2.1179,153100.0,NEAR OCEAN
+-122.53,37.87,20.0,1814.0,282.0,658.0,253.0,7.9977,400000.0,NEAR BAY
+-116.53,33.85,16.0,10077.0,2186.0,3048.0,1337.0,2.9647,110900.0,INLAND
+-117.86,33.74,34.0,2254.0,630.0,2984.0,625.0,2.5,162500.0,<1H OCEAN
+-121.37,38.56,19.0,6308.0,1167.0,3012.0,1112.0,2.9464,113500.0,INLAND
+-122.25,37.82,52.0,1424.0,289.0,550.0,253.0,5.0917,262500.0,NEAR BAY
+-117.1,32.66,27.0,1782.0,560.0,1785.0,560.0,2.1542,106300.0,NEAR OCEAN
+-120.79,38.83,15.0,1374.0,291.0,709.0,239.0,1.7222,118500.0,INLAND
+-119.66,36.33,16.0,2048.0,373.0,1052.0,388.0,4.0909,92800.0,INLAND
+-117.08,32.7,36.0,2103.0,390.0,1279.0,392.0,2.4135,97000.0,NEAR OCEAN
+-120.13,36.87,32.0,2089.0,468.0,1765.0,427.0,2.234,61700.0,INLAND
+-122.41,37.76,52.0,492.0,139.0,316.0,168.0,3.0865,225000.0,NEAR BAY
+-122.77,38.33,32.0,2054.0,324.0,843.0,306.0,4.5875,290700.0,<1H OCEAN
+-121.52,38.9,32.0,1650.0,313.0,802.0,284.0,2.9048,98200.0,INLAND
+-118.05,34.14,39.0,2125.0,295.0,862.0,303.0,8.9728,500001.0,INLAND
+-117.24,32.75,36.0,1856.0,475.0,822.0,416.0,2.3042,220600.0,NEAR OCEAN
+-118.19,34.06,47.0,2324.0,658.0,3020.0,594.0,1.1868,93800.0,<1H OCEAN
+-122.42,37.61,17.0,1040.0,432.0,669.0,405.0,4.1513,137500.0,NEAR OCEAN
+-121.27,38.75,21.0,4812.0,1117.0,1985.0,1045.0,2.5083,128500.0,INLAND
+-121.29,38.13,31.0,1008.0,212.0,453.0,195.0,2.3917,113500.0,INLAND
+-117.2,32.82,35.0,2772.0,537.0,1392.0,521.0,3.337,172300.0,NEAR OCEAN
+-122.32,37.95,35.0,1612.0,354.0,887.0,331.0,2.5769,146100.0,NEAR BAY
+-122.07,37.63,35.0,1931.0,376.0,1175.0,337.0,3.7292,168100.0,NEAR BAY
+-122.21,37.78,46.0,2239.0,508.0,1390.0,569.0,2.7352,137300.0,NEAR BAY
+-122.4,38.41,20.0,4867.0,1015.0,1725.0,1015.0,2.5685,267600.0,INLAND
+-121.23,38.66,19.0,3243.0,546.0,1334.0,515.0,4.8088,169500.0,INLAND
+-120.06,36.97,38.0,1542.0,364.0,1220.0,334.0,1.625,52800.0,INLAND
+-118.07,33.86,28.0,1789.0,352.0,1347.0,330.0,3.425,189700.0,<1H OCEAN
+-121.88,37.43,17.0,3469.0,896.0,2762.0,808.0,3.3884,245800.0,<1H OCEAN
+-118.14,34.13,16.0,3569.0,821.0,1505.0,783.0,4.9167,251100.0,<1H OCEAN
+-119.71,34.44,52.0,1837.0,343.0,711.0,355.0,4.1316,443000.0,<1H OCEAN
+-122.42,37.8,52.0,4079.0,1112.0,1466.0,1024.0,4.5913,500001.0,NEAR BAY
+-117.25,34.06,18.0,5009.0,1108.0,2948.0,963.0,3.0042,106500.0,INLAND
+-118.97,34.18,18.0,7338.0,1020.0,3419.0,1058.0,7.0242,293100.0,<1H OCEAN
+-118.03,33.77,24.0,3810.0,579.0,1818.0,590.0,5.8053,255900.0,<1H OCEAN
+-118.97,35.39,38.0,2121.0,433.0,1547.0,441.0,2.774,59500.0,INLAND
+-122.05,37.38,23.0,3200.0,907.0,2029.0,866.0,3.5649,450000.0,<1H OCEAN
+-119.67,36.65,20.0,2512.0,449.0,1464.0,450.0,3.9211,92300.0,INLAND
+-122.59,38.56,43.0,2088.0,379.0,721.0,293.0,4.65,245000.0,<1H OCEAN
+-118.0,33.94,37.0,903.0,158.0,444.0,158.0,3.75,174400.0,<1H OCEAN
+-122.21,37.78,44.0,1729.0,414.0,1240.0,393.0,2.3125,102800.0,NEAR BAY
+-118.12,34.68,12.0,5319.0,875.0,2439.0,779.0,4.6629,131500.0,INLAND
+-118.2,34.08,49.0,1320.0,309.0,1405.0,328.0,2.4375,114000.0,<1H OCEAN
+-121.31,38.67,26.0,1387.0,226.0,807.0,244.0,4.1563,135700.0,INLAND
+-120.37,37.98,29.0,2508.0,591.0,1112.0,550.0,1.6021,91400.0,INLAND
+-121.0,39.23,15.0,2809.0,450.0,1267.0,408.0,4.0426,191700.0,INLAND
+-121.29,38.68,12.0,5098.0,1094.0,2029.0,1065.0,3.5444,132500.0,INLAND
+-117.22,34.26,16.0,8020.0,1432.0,1749.0,540.0,4.9716,162500.0,INLAND
+-116.71,33.75,25.0,10665.0,2161.0,1874.0,852.0,3.0625,150500.0,INLAND
+-117.11,32.76,19.0,2188.0,616.0,1304.0,607.0,2.0852,114400.0,NEAR OCEAN
+-118.21,33.92,28.0,2949.0,1003.0,4551.0,930.0,1.9026,131900.0,<1H OCEAN
+-117.04,32.66,22.0,3362.0,630.0,1471.0,612.0,4.1442,303900.0,NEAR OCEAN
+-118.42,33.9,29.0,1929.0,523.0,686.0,455.0,5.5347,500001.0,<1H OCEAN
+-117.04,32.9,6.0,6525.0,826.0,3146.0,806.0,9.2858,436100.0,<1H OCEAN
+-117.26,33.09,22.0,2398.0,407.0,349.0,169.0,7.0423,500001.0,NEAR OCEAN
+-118.26,33.91,39.0,967.0,256.0,903.0,256.0,1.9038,93100.0,<1H OCEAN
+-119.03,35.36,41.0,1944.0,363.0,977.0,388.0,3.9097,81300.0,INLAND
+-118.39,34.19,36.0,904.0,191.0,627.0,191.0,2.4167,192900.0,<1H OCEAN
+-118.3,34.25,44.0,1442.0,285.0,859.0,292.0,4.5833,197300.0,<1H OCEAN
+-118.27,34.01,43.0,1235.0,385.0,1745.0,372.0,2.0817,113300.0,<1H OCEAN
+-118.1,34.09,42.0,1460.0,289.0,829.0,273.0,4.875,227300.0,<1H OCEAN
+-117.37,34.13,17.0,2681.0,470.0,1621.0,459.0,3.875,118500.0,INLAND
+-117.75,34.07,52.0,2550.0,586.0,1246.0,576.0,1.6006,146200.0,INLAND
+-117.73,33.49,31.0,5112.0,778.0,1530.0,648.0,10.3983,500001.0,<1H OCEAN
+-117.62,34.08,22.0,2626.0,631.0,1678.0,557.0,3.125,111800.0,INLAND
+-121.79,37.0,28.0,2715.0,451.0,1154.0,386.0,4.8021,290400.0,<1H OCEAN
+-121.62,39.14,41.0,2183.0,559.0,1202.0,506.0,1.6902,61500.0,INLAND
+-118.41,34.19,42.0,779.0,145.0,450.0,148.0,3.9792,193800.0,<1H OCEAN
+-122.21,37.8,39.0,2018.0,447.0,1221.0,446.0,3.0757,151000.0,NEAR BAY
+-118.34,34.08,52.0,1421.0,163.0,495.0,167.0,10.586,500001.0,<1H OCEAN
+-117.1,32.73,24.0,2927.0,704.0,2005.0,668.0,2.2375,102900.0,NEAR OCEAN
+-122.2,39.15,33.0,1064.0,174.0,434.0,147.0,3.125,108000.0,INLAND
+-118.45,34.22,8.0,2609.0,786.0,1803.0,695.0,2.7714,185700.0,<1H OCEAN
+-117.16,34.06,17.0,2285.0,554.0,1412.0,541.0,1.8152,94300.0,INLAND
+-116.97,32.56,23.0,1262.0,294.0,5176.0,275.0,2.5625,153300.0,NEAR OCEAN
+-122.26,37.77,52.0,2097.0,444.0,915.0,413.0,2.9899,228100.0,NEAR BAY
+-120.08,36.34,18.0,1524.0,414.0,2030.0,356.0,2.1153,112500.0,INLAND
+-117.96,33.7,23.0,2622.0,445.0,1103.0,407.0,4.725,289600.0,<1H OCEAN
+-117.1,32.74,20.0,3854.0,1046.0,3555.0,966.0,1.6747,100000.0,NEAR OCEAN
+-122.43,37.66,43.0,1769.0,387.0,1102.0,377.0,4.5493,281500.0,NEAR OCEAN
+-117.81,33.68,4.0,1545.0,304.0,788.0,296.0,4.5469,500001.0,<1H OCEAN
+-119.01,35.44,20.0,3458.0,651.0,1465.0,621.0,2.5806,82500.0,INLAND
+-124.23,40.81,52.0,1112.0,209.0,544.0,172.0,3.3462,50800.0,NEAR OCEAN
+-122.04,37.5,17.0,407.0,97.0,307.0,100.0,3.1696,156300.0,NEAR BAY
+-118.13,33.91,35.0,561.0,104.0,261.0,105.0,4.9375,183800.0,<1H OCEAN
+-121.87,37.28,21.0,3305.0,749.0,2459.0,701.0,3.9688,249600.0,<1H OCEAN
+-119.93,38.72,15.0,2061.0,465.0,573.0,196.0,2.2417,97900.0,INLAND
+-120.45,34.88,15.0,2143.0,286.0,929.0,315.0,5.7306,269700.0,<1H OCEAN
+-122.16,38.05,34.0,2434.0,428.0,926.0,423.0,4.6776,208300.0,NEAR BAY
+-118.18,33.77,39.0,1645.0,547.0,1339.0,499.0,1.5536,155000.0,NEAR OCEAN
+-118.16,33.77,38.0,3235.0,769.0,1284.0,752.0,2.9384,304100.0,NEAR OCEAN
+-119.12,35.38,18.0,1521.0,269.0,706.0,279.0,4.4196,121000.0,INLAND
+-118.38,33.83,20.0,2270.0,498.0,1070.0,521.0,4.4615,384800.0,<1H OCEAN
+-118.2,33.77,42.0,517.0,233.0,995.0,212.0,2.225,106300.0,NEAR OCEAN
+-118.4,34.21,45.0,972.0,181.0,554.0,187.0,4.8194,181300.0,<1H OCEAN
+-118.44,34.24,35.0,2344.0,435.0,1531.0,399.0,3.725,178200.0,<1H OCEAN
+-118.4,34.0,44.0,2122.0,385.0,1012.0,367.0,4.6687,344300.0,<1H OCEAN
+-120.43,34.9,30.0,2388.0,393.0,1117.0,375.0,4.1058,164000.0,<1H OCEAN
+-117.86,33.82,9.0,1682.0,291.0,1015.0,271.0,6.6603,230900.0,<1H OCEAN
+-117.01,32.8,20.0,2705.0,545.0,1198.0,497.0,3.7159,168900.0,<1H OCEAN
+-118.25,34.12,11.0,1281.0,418.0,1584.0,330.0,2.8889,153100.0,<1H OCEAN
+-118.1,33.89,34.0,2242.0,436.0,1483.0,443.0,4.4934,185600.0,<1H OCEAN
+-120.84,35.33,15.0,3276.0,670.0,1520.0,613.0,3.6412,207800.0,NEAR OCEAN
+-117.11,33.12,46.0,52.0,13.0,59.0,13.0,3.875,200000.0,<1H OCEAN
+-118.07,33.87,18.0,3405.0,556.0,1945.0,509.0,5.7652,299100.0,<1H OCEAN
+-121.34,37.98,8.0,2628.0,428.0,1158.0,393.0,5.3002,191700.0,INLAND
+-116.99,33.46,13.0,1614.0,410.0,846.0,270.0,2.83,43000.0,<1H OCEAN
+-124.13,40.8,31.0,2152.0,462.0,1259.0,420.0,2.2478,81100.0,NEAR OCEAN
+-118.26,33.99,36.0,2016.0,505.0,1807.0,464.0,1.6901,103500.0,<1H OCEAN
+-121.87,37.41,17.0,3719.0,588.0,2089.0,561.0,6.7867,273700.0,<1H OCEAN
+-118.12,34.04,34.0,2103.0,427.0,1355.0,434.0,4.5795,235300.0,<1H OCEAN
+-117.97,34.14,33.0,1328.0,348.0,903.0,329.0,3.1094,136000.0,INLAND
+-122.04,37.3,25.0,2366.0,417.0,1076.0,398.0,6.9238,345900.0,<1H OCEAN
+-118.28,33.93,52.0,117.0,33.0,74.0,45.0,0.4999,90600.0,<1H OCEAN
+-122.46,37.73,52.0,2401.0,346.0,812.0,328.0,6.8322,394100.0,NEAR BAY
+-122.16,37.73,49.0,1699.0,408.0,768.0,385.0,2.8301,171600.0,NEAR BAY
+-117.94,33.88,46.0,1747.0,312.0,770.0,296.0,5.4217,256000.0,<1H OCEAN
+-122.18,37.76,43.0,2018.0,408.0,1111.0,367.0,1.8913,91200.0,NEAR BAY
+-118.26,33.97,46.0,1086.0,249.0,880.0,250.0,1.5962,95700.0,<1H OCEAN
+-121.48,38.53,43.0,1378.0,280.0,708.0,280.0,2.3542,103900.0,INLAND
+-121.91,37.71,25.0,4377.0,668.0,2038.0,671.0,5.7233,231800.0,<1H OCEAN
+-117.27,33.0,36.0,2426.0,454.0,1085.0,420.0,5.1523,387800.0,NEAR OCEAN
+-118.26,34.1,48.0,2566.0,571.0,1421.0,563.0,3.6579,318600.0,<1H OCEAN
+-121.0,37.64,52.0,530.0,177.0,325.0,158.0,1.1875,90600.0,INLAND
+-120.09,36.19,12.0,1923.0,559.0,2809.0,535.0,1.4191,55100.0,INLAND
+-117.3,34.09,40.0,1051.0,244.0,745.0,243.0,2.1842,75200.0,INLAND
+-118.38,34.21,33.0,1981.0,484.0,1665.0,466.0,3.0833,179100.0,<1H OCEAN
+-121.45,38.48,28.0,2780.0,510.0,1638.0,533.0,2.9571,103100.0,INLAND
+-118.3,34.02,27.0,2190.0,626.0,1768.0,528.0,1.2446,103800.0,<1H OCEAN
+-118.69,34.18,11.0,1177.0,138.0,415.0,119.0,10.0472,500001.0,<1H OCEAN
+-122.44,37.74,23.0,6291.0,1269.0,2818.0,1198.0,4.2672,391900.0,NEAR BAY
+-121.24,38.7,13.0,3243.0,488.0,1585.0,480.0,5.7133,166800.0,INLAND
+-120.9,38.2,16.0,3120.0,641.0,1319.0,526.0,2.0472,93200.0,INLAND
+-122.25,37.81,39.0,854.0,242.0,389.0,228.0,3.125,237500.0,NEAR BAY
+-117.92,33.72,17.0,3318.0,502.0,1520.0,498.0,5.5501,274200.0,<1H OCEAN
+-118.4,34.15,41.0,2394.0,500.0,837.0,417.0,4.3889,380400.0,<1H OCEAN
+-117.92,33.74,24.0,5321.0,1063.0,4011.0,1047.0,4.3882,189300.0,<1H OCEAN
+-118.31,34.02,52.0,1832.0,441.0,1186.0,420.0,1.2434,98400.0,<1H OCEAN
+-117.39,33.92,25.0,2886.0,583.0,2327.0,577.0,2.3851,113700.0,INLAND
+-119.07,35.33,13.0,9027.0,1901.0,4870.0,1797.0,3.406,100700.0,INLAND
+-118.38,34.18,24.0,1983.0,651.0,2251.0,574.0,2.4792,200000.0,<1H OCEAN
+-122.28,37.87,46.0,3022.0,696.0,1293.0,675.0,2.543,220700.0,NEAR BAY
+-118.29,34.02,26.0,2001.0,582.0,2044.0,557.0,1.1563,118800.0,<1H OCEAN
+-118.3,33.88,35.0,3227.0,749.0,1881.0,696.0,2.8445,242100.0,<1H OCEAN
+-118.58,34.22,35.0,1969.0,339.0,950.0,340.0,4.875,230400.0,<1H OCEAN
+-117.46,34.14,10.0,714.0,131.0,381.0,119.0,0.8926,116100.0,INLAND
+-114.65,33.6,28.0,1678.0,322.0,666.0,256.0,2.9653,94900.0,INLAND
+-117.98,33.76,23.0,1553.0,518.0,1988.0,474.0,2.1375,150000.0,<1H OCEAN
+-118.45,34.21,30.0,2331.0,733.0,2172.0,707.0,2.1888,195600.0,<1H OCEAN
+-124.15,40.81,50.0,340.0,74.0,235.0,83.0,1.75,67500.0,NEAR OCEAN
+-117.67,34.04,13.0,1543.0,,776.0,358.0,3.0598,99700.0,INLAND
+-122.74,38.42,42.0,2050.0,434.0,1073.0,416.0,2.375,141000.0,<1H OCEAN
+-122.19,37.76,41.0,921.0,207.0,522.0,159.0,1.2083,72500.0,NEAR BAY
+-118.4,34.19,30.0,521.0,126.0,306.0,129.0,4.1125,216700.0,<1H OCEAN
+-118.23,34.1,38.0,1051.0,249.0,799.0,229.0,2.712,143800.0,<1H OCEAN
+-121.63,39.15,27.0,2991.0,637.0,1419.0,606.0,1.8849,73500.0,INLAND
+-119.34,36.21,30.0,749.0,214.0,537.0,199.0,0.8229,68400.0,INLAND
+-118.88,34.19,26.0,2296.0,275.0,842.0,263.0,7.7889,309900.0,<1H OCEAN
+-122.33,37.95,45.0,1585.0,329.0,981.0,373.0,3.0313,135800.0,NEAR BAY
+-118.97,35.37,34.0,1379.0,333.0,1156.0,315.0,1.7197,48900.0,INLAND
+-118.29,34.0,52.0,2579.0,494.0,1558.0,458.0,2.0809,109600.0,<1H OCEAN
+-119.82,36.82,28.0,2268.0,336.0,752.0,330.0,5.2809,151500.0,INLAND
+-118.19,33.93,44.0,1613.0,345.0,1227.0,342.0,3.1667,145700.0,<1H OCEAN
+-121.93,37.74,16.0,3326.0,419.0,1272.0,402.0,6.8806,343500.0,<1H OCEAN
+-122.27,37.89,52.0,3046.0,373.0,975.0,365.0,8.8342,430500.0,NEAR BAY
+-116.99,32.81,18.0,10968.0,1521.0,4439.0,1501.0,6.2787,250000.0,<1H OCEAN
+-121.98,37.97,26.0,2714.0,390.0,1232.0,409.0,5.9617,231100.0,INLAND
+-122.45,37.9,30.0,3763.0,717.0,1292.0,632.0,8.4888,500001.0,NEAR BAY
+-119.69,36.82,17.0,1897.0,433.0,1207.0,384.0,1.8021,55900.0,INLAND
+-121.22,38.16,24.0,4411.0,776.0,2038.0,732.0,3.475,151200.0,INLAND
+-122.06,37.95,36.0,2213.0,386.0,950.0,370.0,4.7386,186400.0,NEAR BAY
+-118.2,34.69,5.0,9076.0,1503.0,7694.0,1278.0,4.875,163400.0,INLAND
+-117.09,32.79,31.0,2019.0,417.0,872.0,386.0,3.1964,177700.0,NEAR OCEAN
+-117.93,34.03,30.0,2246.0,446.0,1837.0,431.0,4.7917,164500.0,<1H OCEAN
+-122.91,38.46,18.0,2021.0,,912.0,329.0,4.5,251900.0,<1H OCEAN
+-118.21,34.09,37.0,1822.0,498.0,1961.0,506.0,1.9881,159200.0,<1H OCEAN
+-118.12,33.96,38.0,2105.0,348.0,956.0,350.0,4.4125,246000.0,<1H OCEAN
+-120.09,34.61,11.0,586.0,125.0,317.0,74.0,2.8906,84400.0,NEAR OCEAN
+-117.07,32.79,25.0,2489.0,314.0,911.0,309.0,7.8336,277600.0,NEAR OCEAN
+-119.65,36.33,52.0,1257.0,257.0,624.0,243.0,2.3523,59100.0,INLAND
+-117.88,33.89,16.0,959.0,176.0,353.0,185.0,4.5,173300.0,<1H OCEAN
+-118.44,34.29,35.0,2606.0,447.0,1555.0,404.0,4.6864,193800.0,<1H OCEAN
+-117.36,34.1,33.0,1904.0,343.0,1366.0,338.0,3.6227,92800.0,INLAND
+-118.13,34.01,45.0,1179.0,268.0,736.0,252.0,2.7083,161800.0,<1H OCEAN
+-117.65,33.57,5.0,1998.0,500.0,1185.0,446.0,4.3542,195600.0,<1H OCEAN
+-117.84,33.76,14.0,1458.0,423.0,615.0,365.0,4.2798,218800.0,<1H OCEAN
+-117.27,33.2,23.0,2145.0,379.0,1360.0,404.0,4.2054,150700.0,<1H OCEAN
+-117.9,33.61,19.0,2897.0,413.0,860.0,367.0,13.1738,500001.0,<1H OCEAN
+-116.87,34.24,15.0,4419.0,822.0,622.0,267.0,3.9688,182800.0,INLAND
+-119.31,36.34,14.0,2985.0,607.0,2250.0,607.0,2.1602,65200.0,INLAND
+-122.14,37.73,51.0,2619.0,403.0,922.0,393.0,4.6042,251900.0,NEAR BAY
+-122.43,37.63,34.0,4135.0,687.0,2154.0,742.0,4.9732,342300.0,NEAR OCEAN
+-121.7,39.36,37.0,2330.0,495.0,1505.0,470.0,2.0474,56000.0,INLAND
+-122.45,37.72,45.0,964.0,173.0,613.0,201.0,2.9119,228900.0,NEAR BAY
+-122.37,40.58,25.0,2054.0,495.0,835.0,475.0,2.1538,76900.0,INLAND
+-117.86,33.72,32.0,1461.0,340.0,1909.0,346.0,3.5511,159100.0,<1H OCEAN
+-117.78,34.13,18.0,7798.0,1161.0,3710.0,1227.0,5.8819,260500.0,INLAND
+-120.26,37.13,33.0,1239.0,250.0,648.0,227.0,2.0278,58800.0,INLAND
+-118.15,33.89,30.0,4426.0,995.0,4196.0,921.0,3.274,148300.0,<1H OCEAN
+-122.1,40.05,26.0,633.0,129.0,305.0,140.0,2.1827,72700.0,INLAND
+-117.08,33.04,10.0,2577.0,347.0,1193.0,365.0,6.53,264100.0,<1H OCEAN
+-116.96,32.8,24.0,2493.0,693.0,1420.0,643.0,1.8357,104200.0,<1H OCEAN
+-120.44,34.96,39.0,1228.0,379.0,851.0,341.0,1.899,113300.0,<1H OCEAN
+-121.87,37.38,14.0,3851.0,534.0,2052.0,478.0,7.0735,335600.0,<1H OCEAN
+-117.95,33.79,34.0,2584.0,408.0,1233.0,405.0,5.6935,218300.0,<1H OCEAN
+-122.72,38.58,4.0,7042.0,1100.0,2936.0,1043.0,5.0555,240800.0,<1H OCEAN
+-117.33,34.17,5.0,4718.0,1140.0,2564.0,1056.0,2.9877,119900.0,INLAND
+-122.29,37.84,35.0,1872.0,419.0,1017.0,414.0,2.2106,132500.0,NEAR BAY
+-118.34,34.09,14.0,3032.0,999.0,1691.0,841.0,2.2,210000.0,<1H OCEAN
+-117.38,34.22,16.0,774.0,122.0,489.0,136.0,5.7628,221300.0,INLAND
+-118.31,33.88,33.0,2147.0,505.0,1371.0,498.0,2.4219,260700.0,<1H OCEAN
+-117.2,33.72,8.0,5528.0,1073.0,1674.0,918.0,2.5335,110100.0,<1H OCEAN
+-121.02,37.48,26.0,467.0,,244.0,83.0,4.1346,187500.0,INLAND
+-121.01,37.67,37.0,2483.0,459.0,1072.0,445.0,3.0721,108100.0,INLAND
+-117.39,34.11,16.0,1140.0,181.0,627.0,206.0,4.9444,132700.0,INLAND
+-122.43,37.79,52.0,3486.0,847.0,1248.0,813.0,7.2623,500001.0,NEAR BAY
+-116.0,34.12,32.0,3163.0,712.0,1358.0,544.0,2.125,57700.0,INLAND
+-118.05,34.12,20.0,5218.0,959.0,2302.0,850.0,3.55,476700.0,INLAND
+-118.12,34.17,52.0,2948.0,542.0,1363.0,495.0,4.7098,287900.0,INLAND
+-118.37,34.05,52.0,1563.0,306.0,776.0,308.0,3.625,440900.0,<1H OCEAN
+-118.99,35.39,39.0,2228.0,542.0,1516.0,435.0,1.6009,48800.0,INLAND
+-121.41,40.82,16.0,2668.0,516.0,915.0,362.0,2.3393,90300.0,INLAND
+-118.22,33.97,47.0,1058.0,295.0,1097.0,274.0,2.881,183300.0,<1H OCEAN
+-121.26,37.96,43.0,527.0,133.0,367.0,152.0,2.5,63600.0,INLAND
+-117.31,34.1,28.0,2899.0,755.0,2406.0,655.0,1.5208,69500.0,INLAND
+-117.32,34.48,8.0,4627.0,887.0,2739.0,846.0,3.0204,93100.0,INLAND
+-118.27,33.76,46.0,22.0,11.0,32.0,7.0,3.125,112500.0,NEAR OCEAN
+-118.76,34.26,26.0,1750.0,284.0,962.0,278.0,4.5673,190400.0,<1H OCEAN
+-118.26,34.09,36.0,3503.0,833.0,2652.0,788.0,3.8448,241400.0,<1H OCEAN
+-118.43,33.86,34.0,358.0,87.0,162.0,84.0,7.1264,500001.0,NEAR OCEAN
+-120.83,35.32,11.0,3252.0,701.0,1814.0,660.0,3.2226,183200.0,NEAR OCEAN
+-120.45,34.94,26.0,1058.0,232.0,891.0,243.0,3.6422,120600.0,<1H OCEAN
+-117.76,33.81,2.0,582.0,70.0,199.0,64.0,7.1193,500001.0,<1H OCEAN
+-118.26,33.78,36.0,2191.0,739.0,2931.0,692.0,2.1311,163100.0,NEAR OCEAN
+-118.31,33.96,52.0,2523.0,460.0,1167.0,413.0,3.0625,127400.0,<1H OCEAN
+-118.3,34.17,17.0,4041.0,1169.0,3309.0,1117.0,2.6016,222400.0,<1H OCEAN
+-118.39,34.15,29.0,3110.0,650.0,1212.0,642.0,4.2031,394400.0,<1H OCEAN
+-122.01,37.27,27.0,3340.0,451.0,1220.0,447.0,8.8178,500001.0,<1H OCEAN
+-117.96,33.6,34.0,959.0,230.0,384.0,197.0,5.2333,471400.0,NEAR OCEAN
+-118.24,33.98,45.0,972.0,249.0,1288.0,261.0,2.2054,125000.0,<1H OCEAN
+-119.81,36.75,52.0,1827.0,356.0,855.0,353.0,1.7636,55100.0,INLAND
+-122.34,41.06,33.0,2149.0,498.0,631.0,273.0,1.8816,65800.0,INLAND
+-122.19,37.79,50.0,954.0,217.0,546.0,201.0,2.6667,172800.0,NEAR BAY
+-117.63,34.08,38.0,1810.0,371.0,1257.0,354.0,3.8355,111700.0,INLAND
+-117.23,33.83,2.0,1424.0,251.0,681.0,192.0,4.0833,100000.0,INLAND
+-119.73,36.8,15.0,2376.0,538.0,1197.0,510.0,3.1417,74600.0,INLAND
+-122.27,37.85,52.0,335.0,83.0,152.0,77.0,2.2841,106300.0,NEAR BAY
+-117.0,32.77,35.0,2114.0,317.0,881.0,320.0,5.5,241400.0,<1H OCEAN
+-118.29,34.18,36.0,3120.0,620.0,1441.0,612.0,3.9041,320400.0,<1H OCEAN
+-118.27,33.99,41.0,656.0,162.0,730.0,170.0,1.8047,101800.0,<1H OCEAN
+-118.31,34.06,34.0,1848.0,667.0,1351.0,589.0,2.0547,410000.0,<1H OCEAN
+-117.19,32.79,35.0,1788.0,378.0,777.0,374.0,3.3713,238400.0,NEAR OCEAN
+-122.08,37.68,26.0,1167.0,370.0,253.0,137.0,2.4196,275000.0,NEAR BAY
+-120.98,37.67,13.0,1221.0,260.0,682.0,275.0,3.65,155500.0,INLAND
+-118.33,34.04,33.0,1806.0,444.0,1161.0,393.0,2.5927,161500.0,<1H OCEAN
+-122.0,37.55,27.0,6103.0,1249.0,3026.0,1134.0,4.1591,332400.0,<1H OCEAN
+-122.02,36.97,39.0,2124.0,661.0,1365.0,606.0,1.6642,281300.0,NEAR OCEAN
+-119.31,36.33,46.0,1636.0,338.0,772.0,332.0,2.425,84900.0,INLAND
+-122.65,38.25,23.0,4030.0,,1852.0,778.0,3.402,193300.0,<1H OCEAN
+-118.07,33.98,32.0,3304.0,714.0,2032.0,690.0,3.2093,167800.0,<1H OCEAN
+-118.19,34.02,34.0,1478.0,369.0,1735.0,348.0,1.8875,136700.0,<1H OCEAN
+-120.48,37.31,42.0,2361.0,512.0,1684.0,511.0,2.355,75600.0,INLAND
+-121.88,37.33,36.0,1904.0,689.0,3561.0,632.0,2.0972,187500.0,<1H OCEAN
+-117.14,32.8,33.0,2670.0,435.0,1256.0,431.0,3.9417,179800.0,NEAR OCEAN
+-118.6,37.39,19.0,2682.0,518.0,1134.0,399.0,3.2132,166000.0,INLAND
+-119.67,34.42,23.0,1333.0,393.0,1369.0,381.0,2.5947,232600.0,<1H OCEAN
+-121.2,37.81,26.0,395.0,74.0,193.0,72.0,7.3718,212500.0,INLAND
+-117.97,33.85,30.0,2513.0,476.0,1611.0,472.0,4.0061,182900.0,<1H OCEAN
+-118.04,33.75,16.0,3757.0,650.0,1291.0,614.0,5.2001,235600.0,<1H OCEAN
+-120.86,37.52,9.0,9885.0,1871.0,5372.0,1843.0,3.4821,127100.0,INLAND
+-118.2,33.88,40.0,2945.0,725.0,2858.0,690.0,3.2368,136900.0,<1H OCEAN
+-122.39,38.0,33.0,44.0,6.0,23.0,11.0,4.125,212500.0,NEAR BAY
+-117.9,33.99,18.0,8076.0,1789.0,5325.0,1707.0,3.443,171900.0,<1H OCEAN
+-118.08,33.85,22.0,1055.0,204.0,682.0,216.0,6.0,191300.0,<1H OCEAN
+-116.5,33.98,5.0,4332.0,868.0,1420.0,567.0,4.0417,146400.0,INLAND
+-117.34,33.16,31.0,2851.0,458.0,1286.0,467.0,4.5694,243700.0,NEAR OCEAN
+-120.82,37.5,21.0,2974.0,495.0,1313.0,461.0,4.4886,135400.0,INLAND
+-119.01,35.37,45.0,629.0,143.0,568.0,139.0,1.7321,84400.0,INLAND
+-121.31,38.61,17.0,992.0,151.0,316.0,159.0,6.6238,326700.0,INLAND
+-114.56,33.69,17.0,720.0,174.0,333.0,117.0,1.6509,85700.0,INLAND
+-122.19,37.45,18.0,1636.0,414.0,853.0,439.0,5.1032,464600.0,NEAR OCEAN
+-122.3,37.89,36.0,1077.0,293.0,518.0,276.0,3.0208,206300.0,NEAR BAY
+-118.18,33.82,43.0,2210.0,469.0,1042.0,418.0,3.5,216700.0,NEAR OCEAN
+-121.92,37.34,52.0,2584.0,491.0,1087.0,433.0,4.4,391300.0,<1H OCEAN
+-121.32,38.67,31.0,2532.0,479.0,1396.0,467.0,4.0417,114500.0,INLAND
+-119.57,36.1,36.0,1729.0,317.0,737.0,278.0,3.5313,68800.0,INLAND
+-118.46,34.03,27.0,1965.0,631.0,1042.0,596.0,2.75,327300.0,<1H OCEAN
+-118.03,33.71,26.0,1483.0,251.0,738.0,235.0,6.0,271400.0,<1H OCEAN
+-122.11,37.89,32.0,2372.0,516.0,1067.0,492.0,4.3235,279500.0,NEAR BAY
+-118.12,34.06,25.0,1526.0,388.0,1304.0,378.0,3.1892,214700.0,<1H OCEAN
+-118.45,34.03,41.0,1240.0,320.0,711.0,304.0,3.3482,318100.0,<1H OCEAN
+-118.21,33.92,36.0,602.0,150.0,645.0,145.0,3.1964,115400.0,<1H OCEAN
+-122.31,38.27,34.0,1748.0,284.0,783.0,303.0,4.3585,194400.0,NEAR BAY
+-119.19,36.14,41.0,759.0,140.0,408.0,129.0,3.9,85900.0,INLAND
+-118.44,34.18,36.0,2077.0,496.0,1206.0,528.0,2.2326,221000.0,<1H OCEAN
+-121.45,38.7,24.0,2159.0,369.0,1141.0,355.0,3.9853,90400.0,INLAND
+-118.19,33.98,34.0,1022.0,286.0,1058.0,275.0,2.6042,156700.0,<1H OCEAN
+-119.77,36.84,15.0,2058.0,412.0,891.0,378.0,3.2569,124400.0,INLAND
+-117.05,32.76,37.0,4879.0,906.0,2076.0,871.0,3.6625,154800.0,NEAR OCEAN
+-116.25,33.69,5.0,1664.0,444.0,907.0,374.0,2.7667,92900.0,INLAND
+-118.01,33.93,31.0,3395.0,742.0,1886.0,737.0,4.4118,174400.0,<1H OCEAN
+-119.77,36.76,40.0,2009.0,519.0,2219.0,505.0,1.2101,49100.0,INLAND
+-117.93,33.86,36.0,931.0,279.0,778.0,303.0,2.6563,155000.0,<1H OCEAN
+-121.42,38.6,35.0,1166.0,193.0,574.0,190.0,2.2452,102800.0,INLAND
+-121.9,37.37,20.0,78.0,72.0,120.0,69.0,1.0938,187500.0,<1H OCEAN
+-118.9,34.26,5.0,25187.0,3521.0,11956.0,3478.0,6.9712,321300.0,<1H OCEAN
+-118.32,33.98,47.0,1528.0,331.0,864.0,308.0,1.9732,101000.0,<1H OCEAN
+-117.93,34.06,35.0,1022.0,183.0,628.0,187.0,3.9375,187500.0,<1H OCEAN
+-121.57,37.01,44.0,1448.0,393.0,1066.0,357.0,2.0625,170300.0,INLAND
+-118.02,33.9,36.0,2417.0,421.0,1276.0,426.0,5.5601,205200.0,<1H OCEAN
+-116.29,34.18,15.0,4203.0,966.0,1756.0,695.0,2.182,60800.0,INLAND
+-119.84,34.44,28.0,977.0,162.0,537.0,159.0,4.2404,274300.0,NEAR OCEAN
+-118.38,34.58,18.0,1859.0,375.0,913.0,372.0,4.3456,148900.0,INLAND
+-121.88,36.6,30.0,1671.0,469.0,760.0,375.0,2.5164,178100.0,<1H OCEAN
+-121.23,37.79,23.0,1985.0,424.0,1198.0,389.0,2.7734,116800.0,INLAND
+-121.48,38.5,23.0,2679.0,792.0,1740.0,659.0,1.3679,70300.0,INLAND
+-118.14,34.05,39.0,1880.0,367.0,954.0,349.0,3.875,236400.0,<1H OCEAN
+-120.87,35.41,16.0,2168.0,444.0,782.0,374.0,3.0187,278100.0,NEAR OCEAN
+-120.17,39.33,10.0,614.0,141.0,195.0,95.0,0.9283,116300.0,INLAND
+-118.1,33.84,36.0,690.0,109.0,316.0,104.0,3.7813,209100.0,<1H OCEAN
+-117.66,35.61,5.0,5735.0,932.0,2623.0,862.0,4.8494,87200.0,INLAND
+-118.41,34.25,33.0,827.0,192.0,981.0,184.0,2.6429,143100.0,<1H OCEAN
+-119.76,36.83,20.0,3214.0,446.0,1360.0,463.0,5.2595,110900.0,INLAND
+-118.3,33.94,36.0,3504.0,862.0,2521.0,836.0,2.5679,114900.0,<1H OCEAN
+-121.92,37.3,36.0,2088.0,358.0,772.0,347.0,4.2762,310100.0,<1H OCEAN
+-121.83,37.38,15.0,4430.0,992.0,3278.0,1018.0,4.5533,209900.0,<1H OCEAN
+-117.22,33.66,12.0,1869.0,356.0,1007.0,323.0,3.125,117200.0,<1H OCEAN
+-120.99,39.26,16.0,2616.0,422.0,1090.0,425.0,3.7917,179200.0,INLAND
+-117.65,34.12,27.0,2298.0,340.0,1071.0,369.0,6.5587,239000.0,INLAND
+-122.48,37.86,52.0,3914.0,752.0,1177.0,670.0,6.2113,500001.0,NEAR BAY
+-118.23,33.96,44.0,3186.0,876.0,3913.0,842.0,3.0143,148200.0,<1H OCEAN
+-117.09,32.72,39.0,1273.0,246.0,770.0,242.0,2.0938,102500.0,NEAR OCEAN
+-117.77,34.07,36.0,2922.0,652.0,2392.0,629.0,2.8661,124000.0,INLAND
+-120.98,38.67,13.0,3432.0,516.0,1286.0,470.0,5.584,186600.0,INLAND
+-117.14,32.83,25.0,2161.0,462.0,896.0,468.0,2.2284,177500.0,NEAR OCEAN
+-117.92,34.07,36.0,1057.0,207.0,658.0,207.0,4.7708,191700.0,<1H OCEAN
+-118.11,33.93,35.0,2670.0,493.0,1196.0,488.0,3.8427,283500.0,<1H OCEAN
+-119.3,36.57,32.0,728.0,,461.0,149.0,3.0156,109100.0,INLAND
+-117.87,33.92,14.0,4039.0,669.0,1905.0,670.0,6.3303,303000.0,<1H OCEAN
+-119.64,34.43,34.0,3045.0,570.0,1002.0,488.0,5.623,500001.0,<1H OCEAN
+-118.26,33.97,52.0,1331.0,346.0,1144.0,362.0,1.5326,90600.0,<1H OCEAN
+-118.01,33.76,35.0,2072.0,349.0,1249.0,317.0,3.9855,191900.0,<1H OCEAN
+-117.28,33.2,11.0,1472.0,261.0,1012.0,285.0,4.21,175600.0,NEAR OCEAN
+-118.35,33.98,47.0,2512.0,461.0,1082.0,426.0,3.8235,207600.0,<1H OCEAN
+-118.41,33.98,33.0,3331.0,777.0,1695.0,735.0,3.9727,307200.0,<1H OCEAN
+-118.07,33.9,42.0,1007.0,224.0,776.0,228.0,3.8672,162700.0,<1H OCEAN
+-118.26,34.15,6.0,3340.0,945.0,2315.0,846.0,2.8884,252300.0,<1H OCEAN
+-118.01,33.84,35.0,4166.0,713.0,2354.0,709.0,5.1775,213400.0,<1H OCEAN
+-116.98,33.83,15.0,2228.0,472.0,653.0,350.0,2.683,139300.0,INLAND
+-118.06,33.72,17.0,4573.0,937.0,1619.0,796.0,5.7704,500001.0,NEAR OCEAN
+-117.92,33.91,33.0,2868.0,382.0,1204.0,412.0,6.1825,336900.0,<1H OCEAN
+-121.88,37.44,17.0,1621.0,299.0,1028.0,293.0,5.2722,186900.0,<1H OCEAN
+-117.12,33.52,4.0,30401.0,4957.0,13251.0,4339.0,4.5841,212300.0,<1H OCEAN
+-121.5,38.61,5.0,1395.0,373.0,638.0,322.0,2.6745,225000.0,INLAND
+-120.72,37.54,17.0,729.0,134.0,431.0,121.0,4.2188,131300.0,INLAND
+-122.04,39.22,27.0,1446.0,295.0,670.0,281.0,3.2625,92800.0,INLAND
+-121.73,36.93,29.0,2931.0,535.0,1954.0,506.0,3.2917,224700.0,<1H OCEAN
+-117.23,34.11,33.0,2170.0,500.0,1425.0,472.0,2.0133,78300.0,INLAND
+-117.79,33.77,21.0,4349.0,553.0,1680.0,519.0,6.9014,439000.0,<1H OCEAN
+-121.89,37.99,4.0,2171.0,597.0,928.0,461.0,4.1016,170500.0,INLAND
+-118.27,34.06,26.0,513.0,338.0,1204.0,321.0,1.4904,275000.0,<1H OCEAN
+-118.2,33.82,21.0,2251.0,452.0,913.0,420.0,4.6042,272200.0,NEAR OCEAN
+-117.07,33.07,8.0,2756.0,343.0,1045.0,340.0,8.5957,444100.0,<1H OCEAN
+-117.54,34.55,5.0,2949.0,671.0,1620.0,530.0,2.9479,83300.0,INLAND
+-121.33,38.56,17.0,3608.0,682.0,1694.0,666.0,3.311,109400.0,INLAND
+-118.31,34.06,47.0,3038.0,1533.0,4225.0,1472.0,1.6725,187500.0,<1H OCEAN
+-121.86,37.4,19.0,4043.0,764.0,2196.0,708.0,6.1504,268400.0,<1H OCEAN
+-121.33,38.03,10.0,629.0,140.0,635.0,146.0,2.2961,126700.0,INLAND
+-118.14,34.09,38.0,1745.0,457.0,1547.0,460.0,2.85,219000.0,<1H OCEAN
+-119.6,36.57,33.0,1923.0,403.0,1205.0,389.0,1.8333,68300.0,INLAND
+-120.88,38.91,15.0,3876.0,778.0,1960.0,691.0,2.902,127300.0,INLAND
+-122.17,37.72,43.0,3783.0,814.0,2139.0,789.0,4.0202,166300.0,NEAR BAY
+-122.03,37.35,19.0,3811.0,1227.0,1930.0,1153.0,3.5154,311400.0,<1H OCEAN
+-117.84,34.12,34.0,2026.0,345.0,1142.0,332.0,4.392,187600.0,INLAND
+-122.47,37.69,35.0,1720.0,421.0,1452.0,425.0,3.5909,256100.0,NEAR OCEAN
+-118.12,33.83,44.0,1712.0,314.0,691.0,293.0,4.3594,221300.0,<1H OCEAN
+-122.06,37.99,17.0,1319.0,316.0,384.0,269.0,1.8229,137500.0,NEAR BAY
+-117.98,33.85,23.0,2089.0,377.0,1085.0,362.0,4.765,181500.0,<1H OCEAN
+-122.07,37.59,15.0,3475.0,686.0,2568.0,653.0,4.6211,151400.0,NEAR BAY
+-121.44,38.58,43.0,1806.0,339.0,764.0,341.0,3.9271,147100.0,INLAND
+-122.12,37.65,26.0,162.0,27.0,86.0,25.0,2.375,137500.0,NEAR BAY
+-118.21,33.88,31.0,1332.0,417.0,1405.0,363.0,2.0125,143000.0,<1H OCEAN
+-117.96,33.8,33.0,1984.0,420.0,1119.0,387.0,3.4821,231300.0,<1H OCEAN
+-118.48,34.31,31.0,1091.0,256.0,892.0,238.0,3.0,172400.0,<1H OCEAN
+-118.12,33.94,33.0,2206.0,393.0,973.0,364.0,4.675,283000.0,<1H OCEAN
+-122.41,37.79,52.0,6016.0,2509.0,3436.0,2119.0,2.5166,275000.0,NEAR BAY
+-118.3,34.02,42.0,2386.0,670.0,2327.0,661.0,1.6699,108000.0,<1H OCEAN
+-122.11,37.43,35.0,3905.0,565.0,1562.0,553.0,7.313,463700.0,NEAR BAY
+-121.93,36.63,33.0,1740.0,342.0,638.0,329.0,3.1912,319800.0,NEAR OCEAN
+-118.1,33.99,35.0,1326.0,272.0,933.0,267.0,3.4306,162500.0,<1H OCEAN
+-119.19,34.17,27.0,2183.0,364.0,1458.0,388.0,4.4567,191100.0,NEAR OCEAN
+-119.81,36.7,52.0,314.0,57.0,178.0,66.0,1.2404,52500.0,INLAND
+-118.25,34.09,52.0,1866.0,470.0,1211.0,417.0,2.935,189400.0,<1H OCEAN
+-124.16,40.79,46.0,3042.0,597.0,1206.0,541.0,2.1135,90600.0,NEAR OCEAN
+-121.23,38.65,19.0,2926.0,476.0,1349.0,480.0,4.6437,212900.0,INLAND
+-121.23,38.09,23.0,633.0,91.0,236.0,83.0,6.4562,230000.0,INLAND
+-120.2,40.26,26.0,2399.0,518.0,1037.0,443.0,2.6765,47600.0,INLAND
+-119.08,35.34,15.0,1474.0,235.0,768.0,238.0,4.1528,130100.0,INLAND
+-122.28,37.85,44.0,1025.0,198.0,506.0,204.0,1.73,147900.0,NEAR BAY
+-118.33,33.87,35.0,743.0,128.0,385.0,137.0,6.4891,278100.0,<1H OCEAN
+-117.82,33.75,24.0,893.0,209.0,342.0,197.0,2.8261,146500.0,<1H OCEAN
+-118.14,33.92,31.0,3731.0,853.0,2313.0,801.0,3.2237,218200.0,<1H OCEAN
+-121.89,37.27,28.0,1481.0,256.0,688.0,221.0,5.2088,240900.0,<1H OCEAN
+-118.28,33.83,18.0,5923.0,1409.0,3887.0,1322.0,3.4712,194400.0,<1H OCEAN
+-117.96,33.83,29.0,1194.0,176.0,474.0,170.0,6.1001,298900.0,<1H OCEAN
+-118.27,33.79,31.0,1535.0,369.0,1291.0,340.0,2.9375,174000.0,<1H OCEAN
+-121.8,38.01,46.0,2273.0,495.0,1088.0,447.0,2.2532,109400.0,INLAND
+-117.04,32.99,6.0,9518.0,1418.0,4413.0,1275.0,6.6012,314900.0,<1H OCEAN
+-117.94,33.82,27.0,1366.0,326.0,878.0,325.0,3.4,196900.0,<1H OCEAN
+-117.98,33.65,22.0,3335.0,754.0,1500.0,719.0,3.7315,197900.0,<1H OCEAN
+-118.01,33.78,26.0,2343.0,377.0,1166.0,373.0,6.0,233100.0,<1H OCEAN
+-119.3,34.29,50.0,3128.0,825.0,2535.0,783.0,2.3669,165300.0,NEAR OCEAN
+-122.22,37.78,50.0,1920.0,530.0,1525.0,477.0,1.4886,128800.0,NEAR BAY
+-117.13,32.81,26.0,2119.0,444.0,1202.0,440.0,3.2308,166500.0,NEAR OCEAN
+-121.28,38.67,29.0,1087.0,174.0,430.0,174.0,4.3625,158800.0,INLAND
+-121.81,38.43,30.0,1674.0,297.0,756.0,292.0,3.9286,133100.0,INLAND
+-117.0,33.74,8.0,5330.0,1529.0,2143.0,1107.0,2.1103,94400.0,INLAND
+-122.25,37.81,46.0,3232.0,835.0,1373.0,747.0,3.225,218800.0,NEAR BAY
+-117.07,33.04,4.0,2271.0,578.0,926.0,391.0,3.6437,210100.0,<1H OCEAN
+-121.27,38.7,16.0,3747.0,586.0,1817.0,590.0,4.6488,145300.0,INLAND
+-115.57,32.83,31.0,1494.0,289.0,959.0,284.0,3.5282,67500.0,INLAND
+-118.35,34.09,35.0,1989.0,634.0,1108.0,593.0,1.6081,288900.0,<1H OCEAN
+-122.69,38.45,36.0,1943.0,337.0,711.0,318.0,3.9191,183000.0,<1H OCEAN
+-117.13,32.7,48.0,786.0,230.0,917.0,231.0,1.875,75600.0,NEAR OCEAN
+-122.29,38.3,52.0,935.0,224.0,315.0,207.0,1.8287,146900.0,NEAR BAY
+-119.29,36.28,23.0,1895.0,340.0,749.0,313.0,2.2333,120100.0,INLAND
+-122.41,37.6,31.0,4424.0,834.0,1915.0,817.0,4.1364,412000.0,NEAR OCEAN
+-121.75,36.91,52.0,1211.0,447.0,1102.0,392.0,1.6875,161400.0,<1H OCEAN
+-118.44,34.05,22.0,3970.0,871.0,1588.0,791.0,4.8618,500001.0,<1H OCEAN
+-122.31,38.33,21.0,1922.0,344.0,1051.0,342.0,3.6042,183300.0,NEAR BAY
+-117.03,34.07,16.0,3784.0,577.0,1615.0,525.0,4.2333,220300.0,INLAND
+-122.41,37.7,23.0,1817.0,400.0,1376.0,382.0,2.4113,214200.0,NEAR BAY
+-117.24,32.98,4.0,6423.0,1042.0,2607.0,983.0,7.6348,337000.0,NEAR OCEAN
+-118.21,34.06,30.0,511.0,153.0,1152.0,149.0,2.3611,156800.0,<1H OCEAN
+-122.23,40.2,17.0,762.0,138.0,322.0,139.0,4.2917,128800.0,INLAND
+-121.17,38.69,5.0,7138.0,1227.0,2623.0,1139.0,5.6902,243200.0,INLAND
+-118.4,33.87,45.0,2181.0,505.0,965.0,471.0,5.3816,500001.0,<1H OCEAN
+-118.23,33.93,36.0,501.0,123.0,487.0,118.0,1.3,87000.0,<1H OCEAN
+-118.13,34.2,46.0,1271.0,236.0,573.0,210.0,4.9312,240200.0,INLAND
+-120.46,39.83,18.0,3406.0,673.0,1567.0,617.0,2.2717,75900.0,INLAND
+-118.53,34.27,32.0,1931.0,298.0,948.0,314.0,5.3847,329200.0,<1H OCEAN
+-117.1,32.72,5.0,1615.0,387.0,1094.0,394.0,2.2024,137200.0,NEAR OCEAN
+-121.28,38.14,37.0,3278.0,623.0,1431.0,575.0,3.3987,99500.0,INLAND
+-119.38,36.53,38.0,1281.0,,1423.0,293.0,1.9602,51400.0,INLAND
+-118.09,33.92,36.0,847.0,185.0,713.0,194.0,4.8542,167400.0,<1H OCEAN
+-124.17,41.8,16.0,2739.0,480.0,1259.0,436.0,3.7557,109400.0,NEAR OCEAN
+-118.27,34.16,47.0,1453.0,356.0,787.0,345.0,3.0114,255500.0,<1H OCEAN
+-118.18,34.02,43.0,887.0,219.0,965.0,217.0,2.625,133900.0,<1H OCEAN
+-118.13,33.92,36.0,984.0,183.0,615.0,206.0,4.1786,201500.0,<1H OCEAN
+-121.37,38.6,36.0,1119.0,144.0,414.0,150.0,5.8336,283300.0,INLAND
+-118.42,34.03,44.0,629.0,131.0,326.0,156.0,4.5278,374300.0,<1H OCEAN
+-121.41,36.85,11.0,1708.0,394.0,1474.0,372.0,2.8839,145900.0,INLAND
+-119.31,36.31,18.0,3860.0,760.0,1643.0,664.0,2.0714,92600.0,INLAND
+-118.22,34.08,31.0,394.0,117.0,573.0,131.0,1.8173,154200.0,<1H OCEAN
+-122.55,37.91,48.0,1283.0,278.0,567.0,255.0,3.2794,460000.0,NEAR BAY
+-121.19,38.67,16.0,1754.0,284.0,773.0,277.0,4.817,147000.0,INLAND
+-119.71,36.8,17.0,1415.0,267.0,861.0,293.0,3.25,81400.0,INLAND
+-118.46,35.13,19.0,3109.0,640.0,1457.0,620.0,2.6417,94900.0,INLAND
+-122.8,38.18,36.0,2378.0,476.0,957.0,362.0,3.625,253100.0,NEAR OCEAN
+-117.26,32.98,12.0,3900.0,977.0,1690.0,892.0,4.125,226900.0,NEAR OCEAN
+-117.22,32.74,52.0,1260.0,202.0,555.0,209.0,7.2758,345200.0,NEAR OCEAN
+-117.91,33.94,15.0,5799.0,842.0,2314.0,787.0,6.3433,350500.0,<1H OCEAN
+-117.39,34.12,7.0,5059.0,780.0,3253.0,801.0,4.9196,140500.0,INLAND
+-117.2,32.82,35.0,1217.0,220.0,643.0,237.0,3.9464,171600.0,NEAR OCEAN
+-118.42,34.04,46.0,1508.0,276.0,639.0,273.0,4.925,409800.0,<1H OCEAN
+-122.31,37.91,45.0,3924.0,834.0,1992.0,773.0,4.1146,177800.0,NEAR BAY
+-115.58,32.81,5.0,805.0,143.0,458.0,143.0,4.475,96300.0,INLAND
+-118.32,33.99,43.0,1257.0,232.0,735.0,232.0,3.7167,108900.0,<1H OCEAN
+-118.34,34.12,41.0,3257.0,679.0,1237.0,638.0,4.2415,409600.0,<1H OCEAN
+-122.42,37.77,52.0,1086.0,349.0,589.0,361.0,2.5186,250000.0,NEAR BAY
+-119.19,34.22,26.0,3175.0,736.0,2460.0,775.0,3.125,219900.0,NEAR OCEAN
+-121.86,37.27,17.0,4393.0,709.0,2292.0,692.0,5.6876,246500.0,<1H OCEAN
+-119.12,36.29,29.0,1638.0,323.0,942.0,322.0,2.1731,66200.0,INLAND
+-120.98,37.67,33.0,1433.0,298.0,824.0,302.0,2.7621,109100.0,INLAND
+-117.98,33.78,22.0,4255.0,971.0,2901.0,920.0,3.2636,180200.0,<1H OCEAN
+-118.42,34.05,38.0,4888.0,1126.0,1698.0,937.0,4.8304,500001.0,<1H OCEAN
+-117.99,33.76,30.0,1572.0,362.0,1351.0,359.0,3.369,190900.0,<1H OCEAN
+-121.93,37.28,10.0,3163.0,832.0,1537.0,797.0,4.1674,214000.0,<1H OCEAN
+-117.62,34.02,9.0,4265.0,587.0,2280.0,589.0,5.5632,195100.0,INLAND
+-117.98,34.09,31.0,3073.0,617.0,2640.0,594.0,3.5,161300.0,INLAND
+-117.28,34.15,36.0,1734.0,280.0,604.0,259.0,3.8292,122200.0,INLAND
+-117.35,33.17,36.0,1977.0,423.0,812.0,387.0,3.625,198000.0,NEAR OCEAN
+-118.01,33.7,24.0,3856.0,567.0,1741.0,588.0,7.248,302700.0,<1H OCEAN
+-121.96,37.54,14.0,5106.0,1207.0,2738.0,1108.0,3.9909,236000.0,<1H OCEAN
+-122.15,37.69,39.0,1670.0,308.0,957.0,335.0,5.1312,183600.0,NEAR BAY
+-119.84,36.8,19.0,3244.0,776.0,1463.0,710.0,2.0469,66900.0,INLAND
+-118.27,34.15,14.0,1744.0,536.0,1494.0,531.0,3.2171,230800.0,<1H OCEAN
+-122.08,37.36,31.0,2717.0,376.0,1001.0,381.0,9.281,500001.0,NEAR BAY
+-118.25,33.89,36.0,1527.0,309.0,1154.0,279.0,3.3095,105500.0,<1H OCEAN
+-120.31,37.11,38.0,1696.0,301.0,985.0,278.0,2.4054,112500.0,INLAND
+-118.38,34.18,32.0,3553.0,1060.0,3129.0,1010.0,2.5603,174200.0,<1H OCEAN
+-122.82,38.44,23.0,1551.0,236.0,555.0,243.0,4.6792,304700.0,<1H OCEAN
+-118.36,34.1,52.0,1295.0,281.0,578.0,273.0,2.976,405100.0,<1H OCEAN
+-118.38,33.97,43.0,2715.0,458.0,1151.0,434.0,7.4897,362600.0,<1H OCEAN
+-118.03,34.05,36.0,1359.0,317.0,1557.0,370.0,2.7955,157500.0,<1H OCEAN
+-122.01,37.35,33.0,2517.0,496.0,1158.0,443.0,5.0785,289500.0,<1H OCEAN
+-118.48,34.21,25.0,2879.0,723.0,2077.0,649.0,3.3864,197400.0,<1H OCEAN
+-117.86,34.13,40.0,1304.0,280.0,607.0,256.0,2.588,209500.0,INLAND
+-121.42,38.61,34.0,1126.0,256.0,589.0,243.0,2.1776,84400.0,INLAND
+-118.43,34.09,27.0,1613.0,200.0,497.0,197.0,7.9835,500001.0,<1H OCEAN
+-117.38,33.19,17.0,353.0,112.0,359.0,118.0,1.5625,162500.0,NEAR OCEAN
+-122.56,37.9,48.0,1550.0,253.0,641.0,276.0,8.634,463500.0,NEAR BAY
+-122.66,38.48,21.0,2066.0,393.0,919.0,395.0,3.267,176200.0,<1H OCEAN
+-121.84,39.74,43.0,2976.0,599.0,1181.0,560.0,2.2621,85100.0,INLAND
+-121.61,37.77,32.0,404.0,74.0,144.0,58.0,4.2083,125000.0,INLAND
+-120.93,37.72,18.0,391.0,71.0,247.0,71.0,4.3864,179500.0,INLAND
+-118.19,34.16,49.0,1788.0,267.0,735.0,266.0,6.6009,375700.0,<1H OCEAN
+-121.45,38.54,47.0,1159.0,250.0,810.0,244.0,2.7787,56000.0,INLAND
+-120.2,39.33,26.0,1988.0,379.0,905.0,321.0,3.7841,109500.0,INLAND
+-118.36,34.04,48.0,1769.0,429.0,993.0,405.0,2.3214,139400.0,<1H OCEAN
+-118.25,34.09,43.0,1903.0,635.0,1919.0,613.0,2.6336,174300.0,<1H OCEAN
+-117.34,34.39,8.0,3579.0,672.0,2216.0,630.0,3.4038,100500.0,INLAND
+-118.34,34.01,38.0,2318.0,735.0,1407.0,702.0,1.6187,266700.0,<1H OCEAN
+-117.64,33.62,16.0,3970.0,771.0,1202.0,734.0,3.4115,184800.0,<1H OCEAN
+-122.48,37.78,52.0,2666.0,515.0,1362.0,494.0,4.218,393800.0,NEAR BAY
+-122.48,38.48,29.0,2278.0,397.0,765.0,322.0,4.6379,348200.0,INLAND
+-119.45,36.16,27.0,2119.0,373.0,1268.0,345.0,2.8152,106900.0,INLAND
+-118.04,33.71,12.0,4014.0,868.0,1605.0,769.0,6.0102,396900.0,<1H OCEAN
+-119.71,36.81,9.0,1122.0,290.0,662.0,284.0,2.0536,55000.0,INLAND
+-121.27,38.66,15.0,2642.0,520.0,1032.0,475.0,4.1382,189800.0,INLAND
+-118.36,34.06,39.0,2810.0,670.0,1109.0,624.0,3.25,355000.0,<1H OCEAN
+-117.92,34.08,35.0,2108.0,408.0,1257.0,414.0,4.1312,185200.0,<1H OCEAN
+-118.0,33.9,35.0,1942.0,332.0,1127.0,325.0,4.5144,206300.0,<1H OCEAN
+-117.03,32.79,17.0,7352.0,1699.0,3331.0,1634.0,2.7006,166300.0,<1H OCEAN
+-118.34,33.76,34.0,5586.0,674.0,1871.0,636.0,15.0001,500001.0,NEAR OCEAN
+-121.79,37.33,10.0,3283.0,550.0,2491.0,522.0,6.6633,283700.0,<1H OCEAN
+-117.86,34.08,31.0,2524.0,349.0,1003.0,343.0,7.5196,380900.0,<1H OCEAN
+-121.43,38.61,33.0,2289.0,576.0,1100.0,503.0,2.1694,95700.0,INLAND
+-122.43,37.72,48.0,1289.0,280.0,782.0,235.0,3.6719,259800.0,NEAR BAY
+-117.15,32.78,25.0,1577.0,266.0,611.0,284.0,5.25,205100.0,NEAR OCEAN
+-121.9,38.04,36.0,1489.0,331.0,838.0,259.0,1.2024,90200.0,INLAND
+-117.99,33.73,24.0,2104.0,421.0,1181.0,414.0,3.8365,250900.0,<1H OCEAN
+-121.45,38.49,34.0,3573.0,662.0,1540.0,620.0,3.5323,109800.0,INLAND
+-120.95,37.65,37.0,136.0,20.0,72.0,22.0,2.2279,225000.0,INLAND
+-121.23,37.98,27.0,849.0,137.0,373.0,131.0,5.0362,181300.0,INLAND
+-121.74,36.47,28.0,1973.0,343.0,970.0,349.0,4.25,279100.0,<1H OCEAN
+-121.29,36.9,17.0,3610.0,593.0,1734.0,559.0,5.8324,374200.0,INLAND
+-117.58,34.1,4.0,6360.0,1584.0,3359.0,1396.0,3.5186,127800.0,INLAND
+-117.85,33.89,22.0,4020.0,655.0,1486.0,635.0,5.9639,262300.0,<1H OCEAN
+-117.9,34.13,25.0,3076.0,856.0,2868.0,752.0,2.6619,117600.0,<1H OCEAN
+-122.44,37.72,49.0,1557.0,405.0,1173.0,385.0,3.4605,265000.0,NEAR BAY
+-122.29,38.32,23.0,4312.0,993.0,2317.0,934.0,2.7667,153200.0,NEAR BAY
+-118.78,34.26,24.0,4072.0,582.0,1834.0,565.0,6.0487,254500.0,<1H OCEAN
+-119.66,36.3,18.0,1147.0,202.0,717.0,212.0,3.3681,70500.0,INLAND
+-118.15,33.86,32.0,2630.0,559.0,1069.0,491.0,2.4659,209000.0,<1H OCEAN
+-117.99,33.94,34.0,1519.0,301.0,758.0,304.0,4.3125,214000.0,<1H OCEAN
+-121.71,38.56,20.0,8627.0,1516.0,4071.0,1466.0,4.2198,164100.0,INLAND
+-124.08,40.86,18.0,1287.0,484.0,805.0,502.0,1.1157,150000.0,NEAR OCEAN
+-116.52,33.84,17.0,4465.0,859.0,853.0,445.0,3.6875,130400.0,INLAND
+-121.26,37.95,39.0,1841.0,428.0,1368.0,390.0,2.1583,62000.0,INLAND
+-119.59,36.72,18.0,1284.0,193.0,621.0,190.0,4.5375,130600.0,INLAND
+-117.97,33.86,35.0,1691.0,367.0,1265.0,378.0,3.5855,174300.0,<1H OCEAN
+-122.03,38.28,15.0,5114.0,833.0,2418.0,778.0,4.4882,144000.0,INLAND
+-117.88,33.79,32.0,1484.0,295.0,928.0,295.0,5.1418,190300.0,<1H OCEAN
+-118.25,33.99,39.0,1274.0,319.0,1362.0,324.0,2.1793,107900.0,<1H OCEAN
+-118.01,33.8,16.0,4021.0,701.0,1488.0,650.0,5.32,219500.0,<1H OCEAN
+-122.21,37.78,52.0,1056.0,224.0,792.0,245.0,2.6583,142600.0,NEAR BAY
+-117.19,34.03,25.0,2513.0,340.0,900.0,320.0,6.4962,182400.0,INLAND
+-118.0,33.87,13.0,2086.0,544.0,1356.0,462.0,2.95,165600.0,<1H OCEAN
+-118.21,33.97,49.0,1409.0,313.0,1268.0,317.0,3.9408,170600.0,<1H OCEAN
+-119.8,34.43,22.0,2845.0,500.0,1456.0,454.0,5.6604,276400.0,NEAR OCEAN
+-122.35,37.57,52.0,2059.0,345.0,800.0,308.0,4.97,500001.0,NEAR OCEAN
+-117.22,33.9,8.0,8302.0,1461.0,5155.0,1370.0,4.0467,121500.0,INLAND
+-122.87,39.13,15.0,1927.0,427.0,810.0,321.0,1.6369,86500.0,INLAND
+-118.32,33.79,21.0,6638.0,1634.0,3240.0,1568.0,3.6797,271100.0,<1H OCEAN
+-122.03,37.29,22.0,3118.0,438.0,1147.0,425.0,10.3653,500001.0,<1H OCEAN
+-118.24,34.11,39.0,1148.0,348.0,1161.0,333.0,2.2167,176700.0,<1H OCEAN
+-123.79,39.5,24.0,1421.0,291.0,588.0,274.0,2.325,157300.0,<1H OCEAN
+-122.45,37.77,52.0,1722.0,465.0,885.0,437.0,3.0906,500001.0,NEAR BAY
+-117.96,34.14,9.0,907.0,207.0,619.0,194.0,3.9464,179600.0,INLAND
+-121.44,39.5,26.0,1652.0,325.0,790.0,292.0,3.0446,90800.0,INLAND
+-117.26,33.37,7.0,2221.0,548.0,1440.0,501.0,2.2368,154600.0,<1H OCEAN
+-123.21,39.13,27.0,1531.0,266.0,822.0,234.0,4.0469,127400.0,<1H OCEAN
+-118.33,33.89,39.0,1880.0,361.0,982.0,357.0,4.1953,226900.0,<1H OCEAN
+-121.1,38.92,21.0,4064.0,871.0,1847.0,859.0,3.0321,135500.0,INLAND
+-117.72,34.07,33.0,4100.0,740.0,2580.0,730.0,3.7321,134200.0,INLAND
+-117.92,34.08,36.0,1285.0,228.0,679.0,231.0,3.8705,191900.0,<1H OCEAN
+-117.69,33.66,11.0,2630.0,327.0,1256.0,352.0,8.2953,350500.0,<1H OCEAN
+-120.06,36.72,32.0,981.0,237.0,736.0,249.0,1.8,60400.0,INLAND
+-118.26,34.16,18.0,1775.0,525.0,950.0,522.0,3.5417,177100.0,<1H OCEAN
+-117.96,33.75,14.0,2509.0,611.0,1814.0,547.0,2.7986,176100.0,<1H OCEAN
+-122.25,37.8,41.0,1471.0,469.0,1062.0,413.0,1.6121,171400.0,NEAR BAY
+-118.15,34.03,43.0,2006.0,472.0,1687.0,463.0,1.7991,158800.0,<1H OCEAN
+-117.25,34.13,37.0,2498.0,472.0,1291.0,487.0,3.0,83400.0,INLAND
+-117.25,34.53,13.0,5841.0,955.0,2455.0,915.0,4.1333,158200.0,INLAND
+-118.65,34.18,26.0,4607.0,656.0,1769.0,643.0,7.4918,367600.0,<1H OCEAN
+-121.15,38.69,52.0,240.0,44.0,6675.0,29.0,6.1359,225000.0,INLAND
+-118.2,33.93,36.0,2210.0,634.0,2341.0,553.0,2.1715,131100.0,<1H OCEAN
+-121.97,37.36,36.0,765.0,134.0,306.0,136.0,4.575,247600.0,<1H OCEAN
+-118.05,33.78,25.0,2356.0,330.0,937.0,326.0,6.6264,359100.0,<1H OCEAN
+-117.25,32.86,30.0,1670.0,219.0,606.0,202.0,12.4429,500001.0,NEAR OCEAN
+-122.35,37.98,34.0,3756.0,726.0,2237.0,686.0,3.7562,132900.0,NEAR BAY
+-117.17,32.76,45.0,3149.0,639.0,1160.0,661.0,2.7266,354200.0,NEAR OCEAN
+-122.26,37.51,46.0,672.0,149.0,351.0,136.0,5.3264,258100.0,NEAR OCEAN
+-117.19,32.77,9.0,634.0,152.0,248.0,133.0,3.8571,143800.0,NEAR OCEAN
+-121.89,37.66,3.0,1565.0,464.0,769.0,461.0,2.1187,231300.0,INLAND
+-118.21,34.18,14.0,2672.0,335.0,1113.0,318.0,12.1579,500001.0,<1H OCEAN
+-117.96,33.99,25.0,1348.0,210.0,660.0,200.0,5.2852,297600.0,<1H OCEAN
+-121.71,36.88,19.0,2528.0,554.0,2332.0,492.0,3.7766,177000.0,<1H OCEAN
+-122.09,37.67,48.0,1252.0,305.0,673.0,308.0,2.3357,175000.0,NEAR BAY
+-121.99,37.4,24.0,3217.0,689.0,1196.0,684.0,3.4896,226700.0,<1H OCEAN
+-121.99,36.99,16.0,1592.0,369.0,1039.0,351.0,3.6364,207000.0,NEAR OCEAN
+-118.29,33.84,23.0,3626.0,799.0,2321.0,731.0,4.7393,237900.0,<1H OCEAN
+-116.93,33.06,16.0,3490.0,545.0,1628.0,535.0,4.8836,239900.0,<1H OCEAN
+-122.26,38.1,30.0,3317.0,1058.0,1794.0,990.0,1.1835,133300.0,NEAR BAY
+-117.71,34.06,16.0,1458.0,295.0,912.0,331.0,3.625,160400.0,INLAND
+-121.29,38.59,19.0,2460.0,470.0,1346.0,480.0,3.6563,95600.0,INLAND
+-119.81,36.83,10.0,5780.0,922.0,2712.0,883.0,5.6445,135500.0,INLAND
+-122.25,38.02,16.0,1803.0,267.0,946.0,266.0,5.7001,205100.0,NEAR BAY
+-118.18,34.08,35.0,2226.0,602.0,2230.0,549.0,2.9167,129300.0,<1H OCEAN
+-122.49,37.75,48.0,2387.0,424.0,1041.0,408.0,3.7562,321200.0,NEAR OCEAN
+-118.08,34.13,28.0,4465.0,985.0,2273.0,949.0,3.5671,228500.0,INLAND
+-117.09,32.67,37.0,1157.0,332.0,983.0,306.0,2.0972,117000.0,NEAR OCEAN
+-122.09,37.99,19.0,3073.0,506.0,1773.0,493.0,5.4496,205400.0,NEAR BAY
+-118.36,33.98,39.0,813.0,185.0,344.0,154.0,3.5833,218800.0,<1H OCEAN
+-117.14,32.71,32.0,719.0,251.0,894.0,208.0,1.8456,103100.0,NEAR OCEAN
+-121.98,37.34,18.0,6649.0,1712.0,3604.0,1651.0,4.5368,307400.0,<1H OCEAN
+-118.25,33.98,37.0,1045.0,361.0,1666.0,337.0,1.7929,97200.0,<1H OCEAN
+-117.42,33.35,14.0,25135.0,4819.0,35682.0,4769.0,2.5729,134400.0,<1H OCEAN
+-118.45,34.3,35.0,4085.0,919.0,3988.0,906.0,3.4812,160200.0,<1H OCEAN
+-121.07,39.13,8.0,4839.0,832.0,1977.0,762.0,4.0848,155900.0,INLAND
+-119.79,36.74,35.0,853.0,296.0,1228.0,289.0,1.0513,39600.0,INLAND
+-118.31,33.91,36.0,961.0,173.0,625.0,179.0,4.2596,181100.0,<1H OCEAN
+-117.78,33.7,16.0,1663.0,250.0,597.0,204.0,5.409,233900.0,<1H OCEAN
+-122.1,39.47,43.0,1320.0,215.0,512.0,197.0,2.4917,77100.0,INLAND
+-115.37,32.82,14.0,1276.0,270.0,867.0,261.0,1.9375,80900.0,INLAND
+-118.15,33.77,52.0,2204.0,498.0,899.0,445.0,4.1765,393900.0,NEAR OCEAN
+-118.76,34.13,10.0,4355.0,716.0,2030.0,674.0,6.5571,500001.0,NEAR OCEAN
+-118.33,33.88,30.0,1856.0,444.0,899.0,435.0,3.1505,270000.0,<1H OCEAN
+-118.41,33.97,43.0,1464.0,224.0,581.0,232.0,6.2022,365900.0,<1H OCEAN
+-121.27,37.94,38.0,512.0,133.0,676.0,124.0,1.7386,52000.0,INLAND
+-122.46,37.65,16.0,8676.0,1633.0,5130.0,1574.0,4.8096,262000.0,NEAR OCEAN
+-122.64,41.74,33.0,2644.0,459.0,1113.0,483.0,3.3095,81300.0,INLAND
+-122.08,37.62,17.0,2485.0,518.0,1139.0,550.0,2.6875,157300.0,NEAR BAY
+-118.26,34.02,40.0,1259.0,362.0,1499.0,327.0,1.8382,126400.0,<1H OCEAN
+-118.37,34.09,38.0,1349.0,344.0,547.0,309.0,3.2159,383300.0,<1H OCEAN
+-117.69,33.6,17.0,2150.0,361.0,1194.0,335.0,5.4622,227000.0,<1H OCEAN
+-118.99,35.39,52.0,2805.0,573.0,1325.0,522.0,2.5083,70100.0,INLAND
+-121.71,39.41,22.0,1814.0,342.0,941.0,323.0,2.1728,59400.0,INLAND
+-120.22,39.35,8.0,1872.0,281.0,203.0,71.0,4.5882,198400.0,INLAND
+-122.17,37.77,31.0,2424.0,533.0,1360.0,452.0,1.871,90700.0,NEAR BAY
+-118.02,33.82,19.0,2485.0,437.0,1286.0,431.0,4.7466,258300.0,<1H OCEAN
+-117.92,33.7,15.0,3201.0,,1510.0,622.0,4.2708,161700.0,<1H OCEAN
+-118.13,33.9,38.0,1475.0,269.0,827.0,265.0,4.7663,191600.0,<1H OCEAN
+-121.36,38.57,26.0,1793.0,244.0,653.0,235.0,5.6485,129500.0,INLAND
+-122.65,41.72,15.0,3643.0,801.0,1784.0,743.0,1.8533,57500.0,INLAND
+-117.1,33.12,12.0,961.0,342.0,315.0,201.0,0.813,275000.0,<1H OCEAN
+-117.93,34.01,33.0,1733.0,361.0,1757.0,375.0,4.2266,153800.0,<1H OCEAN
+-117.17,32.79,44.0,2262.0,647.0,3009.0,657.0,2.2663,123600.0,NEAR OCEAN
+-118.07,34.09,33.0,2178.0,445.0,1153.0,400.0,3.6083,212000.0,<1H OCEAN
+-118.16,34.14,41.0,3039.0,482.0,973.0,446.0,7.4817,500001.0,<1H OCEAN
+-117.08,33.09,23.0,3792.0,624.0,1988.0,658.0,4.7566,178300.0,<1H OCEAN
+-120.3,37.34,33.0,993.0,186.0,556.0,175.0,2.4286,103600.0,INLAND
+-122.4,37.72,40.0,1948.0,413.0,1434.0,396.0,3.0313,219100.0,NEAR BAY
+-121.9,37.29,36.0,1389.0,225.0,623.0,223.0,6.6331,283300.0,<1H OCEAN
+-117.27,34.12,31.0,2209.0,636.0,1314.0,562.0,1.7235,78800.0,INLAND
+-118.34,34.04,37.0,1466.0,529.0,1835.0,500.0,1.7014,123200.0,<1H OCEAN
+-118.3,33.72,28.0,2510.0,583.0,1388.0,554.0,3.3397,267800.0,NEAR OCEAN
+-122.55,38.01,27.0,3966.0,577.0,1657.0,611.0,6.3314,342200.0,NEAR BAY
+-117.73,34.07,34.0,4038.0,725.0,2716.0,759.0,4.1339,135000.0,INLAND
+-117.13,32.75,31.0,2336.0,656.0,1186.0,609.0,2.5872,130600.0,NEAR OCEAN
+-120.45,34.63,25.0,2445.0,368.0,983.0,363.0,4.9286,180100.0,NEAR OCEAN
+-121.79,37.33,13.0,2978.0,505.0,1794.0,485.0,6.6813,277800.0,<1H OCEAN
+-118.08,33.86,29.0,1018.0,235.0,684.0,248.0,3.3333,198800.0,<1H OCEAN
+-122.43,37.66,29.0,3541.0,786.0,2259.0,770.0,4.3039,278400.0,NEAR OCEAN
+-122.29,37.54,41.0,1743.0,349.0,811.0,349.0,4.9464,282400.0,NEAR OCEAN
+-121.34,38.04,16.0,3295.0,565.0,2279.0,576.0,3.6083,146400.0,INLAND
+-118.63,34.21,31.0,3952.0,647.0,1762.0,588.0,5.5709,244800.0,<1H OCEAN
+-122.29,37.98,27.0,2133.0,347.0,850.0,350.0,5.1046,209800.0,NEAR BAY
+-120.69,35.65,14.0,3487.0,889.0,2352.0,796.0,1.6303,144900.0,<1H OCEAN
+-118.29,33.84,33.0,896.0,208.0,843.0,200.0,3.5,183000.0,<1H OCEAN
+-117.97,34.06,31.0,2516.0,,2194.0,497.0,3.2413,155500.0,<1H OCEAN
+-118.09,34.01,36.0,1465.0,363.0,1538.0,342.0,3.5469,150600.0,<1H OCEAN
+-122.7,38.35,16.0,1328.0,187.0,607.0,197.0,5.0366,257800.0,<1H OCEAN
+-117.85,34.06,24.0,3128.0,497.0,1406.0,472.0,7.5286,462700.0,<1H OCEAN
+-122.38,37.18,52.0,1746.0,315.0,941.0,220.0,3.3047,286100.0,NEAR OCEAN
+-117.85,34.08,23.0,1160.0,166.0,467.0,178.0,8.105,386200.0,<1H OCEAN
+-118.5,34.04,52.0,2233.0,317.0,769.0,277.0,8.3839,500001.0,<1H OCEAN
+-115.5,32.67,35.0,2159.0,492.0,1694.0,475.0,2.1776,75500.0,INLAND
+-122.28,37.88,52.0,2495.0,491.0,1058.0,464.0,4.1429,259600.0,NEAR BAY
+-123.1,38.79,20.0,3109.0,712.0,1643.0,638.0,2.8344,164400.0,<1H OCEAN
+-118.1,33.96,36.0,2013.0,435.0,1476.0,475.0,3.9549,192100.0,<1H OCEAN
+-119.78,36.85,12.0,782.0,166.0,292.0,164.0,2.8274,79500.0,INLAND
+-122.0,37.34,27.0,1716.0,290.0,817.0,301.0,5.9158,343100.0,<1H OCEAN
+-121.29,38.65,27.0,2744.0,464.0,1340.0,452.0,3.8816,147300.0,INLAND
+-117.89,33.88,15.0,1655.0,626.0,1549.0,582.0,1.9127,175000.0,<1H OCEAN
+-117.89,33.9,23.0,1533.0,226.0,693.0,230.0,7.898,258200.0,<1H OCEAN
+-118.32,33.94,37.0,2740.0,504.0,1468.0,479.0,4.5368,168800.0,<1H OCEAN
+-122.56,38.03,34.0,1887.0,290.0,815.0,283.0,6.5249,324800.0,NEAR BAY
+-120.87,37.76,16.0,1174.0,249.0,601.0,242.0,1.7143,113300.0,INLAND
+-117.95,33.87,22.0,1432.0,335.0,746.0,296.0,2.0227,55000.0,<1H OCEAN
+-119.5,34.35,39.0,308.0,38.0,59.0,21.0,11.7794,500001.0,NEAR OCEAN
+-121.74,36.92,14.0,3355.0,695.0,1350.0,697.0,2.6506,164600.0,<1H OCEAN
+-120.93,39.96,15.0,1666.0,351.0,816.0,316.0,2.9559,118800.0,INLAND
+-118.44,34.16,33.0,1616.0,322.0,580.0,311.0,4.0391,337500.0,<1H OCEAN
+-117.01,32.63,7.0,6483.0,976.0,3269.0,1005.0,5.7358,221600.0,NEAR OCEAN
+-117.92,33.74,13.0,4620.0,1265.0,3385.0,1109.0,3.1773,186500.0,<1H OCEAN
+-118.48,34.02,30.0,2027.0,609.0,1425.0,562.0,2.2917,330800.0,<1H OCEAN
+-118.42,34.09,40.0,3552.0,392.0,1024.0,370.0,15.0001,500001.0,<1H OCEAN
+-118.21,34.12,41.0,1904.0,514.0,1666.0,498.0,3.6845,175800.0,<1H OCEAN
+-120.02,38.76,15.0,3142.0,618.0,725.0,285.0,4.3333,121400.0,INLAND
+-117.35,33.23,4.0,1837.0,287.0,934.0,277.0,3.8958,189800.0,NEAR OCEAN
+-117.72,33.64,16.0,1230.0,242.0,380.0,246.0,2.2969,67500.0,<1H OCEAN
+-119.77,37.19,8.0,5212.0,872.0,2383.0,857.0,4.1099,113600.0,INLAND
+-122.04,37.08,20.0,467.0,95.0,229.0,86.0,4.8,261500.0,NEAR OCEAN
+-122.48,38.5,37.0,3049.0,,1287.0,439.0,4.3125,276500.0,INLAND
+-118.46,34.0,44.0,941.0,230.0,493.0,206.0,3.6458,325800.0,<1H OCEAN
+-120.71,35.27,9.0,2568.0,421.0,1149.0,398.0,5.4287,331600.0,NEAR OCEAN
+-119.8,36.73,45.0,925.0,231.0,797.0,228.0,1.7011,44800.0,INLAND
+-118.18,33.99,36.0,988.0,337.0,1508.0,351.0,2.4375,154800.0,<1H OCEAN
+-122.03,37.0,30.0,2077.0,342.0,816.0,328.0,5.2078,440500.0,NEAR OCEAN
+-122.03,37.61,36.0,1409.0,271.0,1002.0,281.0,3.7262,164900.0,NEAR BAY
+-117.96,33.82,19.0,1199.0,251.0,730.0,276.0,3.6422,209400.0,<1H OCEAN
+-116.53,33.88,5.0,4423.0,763.0,1906.0,667.0,4.6855,125200.0,INLAND
+-118.2,34.04,44.0,1399.0,386.0,1419.0,373.0,1.8224,143800.0,<1H OCEAN
+-120.46,34.64,37.0,1697.0,334.0,740.0,272.0,2.3804,148000.0,NEAR OCEAN
+-122.01,37.34,31.0,3080.0,526.0,1493.0,582.0,6.3052,344200.0,<1H OCEAN
+-118.27,34.25,39.0,699.0,150.0,358.0,143.0,4.4375,195800.0,<1H OCEAN
+-119.88,34.44,27.0,4724.0,793.0,2394.0,738.0,5.5954,261400.0,NEAR OCEAN
+-121.0,37.65,52.0,3887.0,803.0,1768.0,779.0,2.5089,119000.0,INLAND
+-118.41,34.06,43.0,2665.0,556.0,1015.0,506.0,4.1411,500001.0,<1H OCEAN
+-122.49,38.1,43.0,1226.0,244.0,491.0,205.0,4.9286,307000.0,NEAR BAY
+-122.32,41.31,45.0,1393.0,294.0,521.0,249.0,1.1915,71900.0,INLAND
+-122.02,37.35,17.0,2975.0,608.0,1465.0,577.0,5.8779,362200.0,<1H OCEAN
+-119.4,35.06,21.0,2213.0,458.0,1250.0,440.0,2.9187,52100.0,INLAND
+-118.52,34.16,39.0,2693.0,478.0,1219.0,435.0,5.17,335400.0,<1H OCEAN
+-117.03,32.69,8.0,2460.0,397.0,1784.0,390.0,4.5662,175500.0,NEAR OCEAN
+-118.31,34.09,34.0,2065.0,839.0,2626.0,775.0,1.8214,211100.0,<1H OCEAN
+-119.05,35.34,17.0,2387.0,437.0,1204.0,423.0,4.0529,106200.0,INLAND
+-118.4,33.89,36.0,2334.0,430.0,1033.0,407.0,6.6321,481500.0,<1H OCEAN
+-121.48,38.51,24.0,979.0,201.0,723.0,205.0,2.5926,72300.0,INLAND
+-118.37,33.93,10.0,199.0,41.0,61.0,56.0,2.8958,245800.0,<1H OCEAN
+-117.89,33.72,23.0,2305.0,538.0,2493.0,502.0,3.6618,183500.0,<1H OCEAN
+-124.16,40.78,50.0,2285.0,403.0,837.0,353.0,2.5417,85400.0,NEAR OCEAN
+-118.11,33.74,43.0,1222.0,303.0,565.0,309.0,4.8482,500001.0,NEAR OCEAN
+-118.24,34.02,48.0,542.0,150.0,571.0,114.0,1.8485,90600.0,<1H OCEAN
+-121.05,38.97,12.0,3676.0,550.0,1572.0,510.0,4.8214,201900.0,INLAND
+-118.26,33.78,35.0,1239.0,473.0,1524.0,387.0,2.0956,154700.0,NEAR OCEAN
+-120.31,36.65,24.0,943.0,209.0,514.0,156.0,2.25,76600.0,INLAND
+-117.86,33.9,25.0,3205.0,409.0,1291.0,408.0,7.2478,299200.0,<1H OCEAN
+-122.57,39.9,15.0,3873.0,810.0,1697.0,627.0,2.4555,55600.0,INLAND
+-121.5,38.54,44.0,1167.0,201.0,452.0,209.0,3.7344,179800.0,INLAND
+-119.03,35.37,52.0,1503.0,367.0,554.0,277.0,1.6786,126600.0,INLAND
+-119.75,34.45,26.0,3578.0,677.0,1504.0,618.0,4.1375,395000.0,NEAR OCEAN
+-122.18,37.77,52.0,1748.0,362.0,1029.0,366.0,2.0556,100000.0,NEAR BAY
+-118.43,34.05,52.0,1693.0,290.0,727.0,305.0,6.7115,500001.0,<1H OCEAN
+-121.43,37.75,41.0,1717.0,325.0,855.0,303.0,2.75,127300.0,INLAND
+-118.0,33.82,18.0,2947.0,559.0,1820.0,551.0,4.5294,224800.0,<1H OCEAN
+-117.71,34.04,20.0,1950.0,310.0,1054.0,312.0,4.625,222100.0,INLAND
+-117.86,34.09,29.0,3855.0,585.0,2205.0,609.0,5.5496,218200.0,<1H OCEAN
+-117.88,33.87,35.0,1919.0,349.0,1302.0,345.0,5.6409,190900.0,<1H OCEAN
+-120.23,37.86,16.0,324.0,61.0,135.0,61.0,2.4615,137500.0,INLAND
+-121.21,38.76,16.0,1608.0,296.0,792.0,286.0,3.1583,239200.0,INLAND
+-122.22,38.12,15.0,14125.0,2344.0,6456.0,2147.0,5.1014,179500.0,NEAR BAY
+-117.88,33.65,24.0,4879.0,756.0,1777.0,754.0,5.9055,477300.0,<1H OCEAN
+-118.55,34.19,18.0,5862.0,,3161.0,1280.0,3.1106,170600.0,<1H OCEAN
+-117.06,32.68,38.0,1481.0,317.0,1080.0,291.0,2.85,125800.0,NEAR OCEAN
+-119.59,36.52,35.0,990.0,192.0,674.0,178.0,3.3214,101600.0,INLAND
+-122.06,37.64,33.0,1160.0,252.0,729.0,220.0,3.8259,146100.0,NEAR BAY
+-119.79,36.82,23.0,4358.0,819.0,1852.0,802.0,3.4167,105200.0,INLAND
+-121.56,39.51,47.0,1064.0,245.0,603.0,190.0,1.3654,57900.0,INLAND
+-118.4,34.23,36.0,1643.0,349.0,1414.0,337.0,4.1181,172700.0,<1H OCEAN
+-121.45,38.57,52.0,3994.0,635.0,1295.0,625.0,5.1169,232500.0,INLAND
+-117.08,32.83,7.0,13703.0,2352.0,4446.0,1856.0,6.4335,260600.0,<1H OCEAN
+-122.69,37.91,43.0,1730.0,326.0,629.0,279.0,4.3194,238700.0,NEAR OCEAN
+-122.91,39.05,27.0,789.0,208.0,295.0,108.0,3.7667,95000.0,INLAND
+-121.0,39.26,14.0,810.0,151.0,302.0,138.0,3.1094,136100.0,INLAND
+-119.99,36.8,45.0,1270.0,242.0,598.0,214.0,3.2813,105400.0,INLAND
+-118.85,34.23,13.0,5094.0,764.0,2230.0,737.0,6.4823,290900.0,<1H OCEAN
+-117.09,32.56,8.0,864.0,156.0,626.0,172.0,4.8984,151500.0,NEAR OCEAN
+-119.26,34.28,41.0,1835.0,311.0,683.0,308.0,4.8977,358200.0,NEAR OCEAN
+-121.43,38.58,35.0,5298.0,954.0,1933.0,918.0,3.9167,155700.0,INLAND
+-116.99,32.72,11.0,1112.0,164.0,441.0,174.0,4.7679,169500.0,<1H OCEAN
+-118.3,34.07,31.0,1489.0,664.0,1793.0,556.0,2.4348,230600.0,<1H OCEAN
+-122.09,37.68,29.0,2333.0,538.0,1120.0,540.0,2.4042,205600.0,NEAR BAY
+-121.74,38.15,22.0,1910.0,326.0,1001.0,345.0,4.8173,115800.0,INLAND
+-119.84,36.77,6.0,1853.0,473.0,1397.0,417.0,1.4817,72000.0,INLAND
+-117.82,33.73,27.0,1270.0,258.0,809.0,264.0,5.0162,223000.0,<1H OCEAN
+-122.14,37.85,27.0,9147.0,1276.0,3371.0,1269.0,7.3267,389900.0,NEAR BAY
+-121.76,37.3,6.0,3526.0,559.0,1378.0,491.0,6.1463,335500.0,<1H OCEAN
+-118.13,33.89,33.0,3668.0,867.0,2368.0,845.0,2.8906,204900.0,<1H OCEAN
+-118.34,34.16,46.0,1396.0,294.0,608.0,246.0,3.692,244500.0,<1H OCEAN
+-117.01,32.7,7.0,2327.0,490.0,1304.0,445.0,3.3553,132200.0,NEAR OCEAN
+-122.69,38.34,23.0,2846.0,516.0,1526.0,492.0,3.733,163500.0,<1H OCEAN
+-116.97,32.78,37.0,1255.0,238.0,671.0,278.0,3.7019,138600.0,<1H OCEAN
+-121.58,37.01,44.0,3192.0,565.0,1439.0,568.0,4.3693,234000.0,INLAND
+-122.18,37.74,42.0,541.0,154.0,380.0,123.0,2.3456,83500.0,NEAR BAY
+-119.71,36.73,19.0,3972.0,585.0,1586.0,560.0,5.2608,151400.0,INLAND
+-121.1,39.15,10.0,680.0,143.0,354.0,140.0,4.0333,161500.0,INLAND
+-122.3,37.52,32.0,2297.0,347.0,871.0,342.0,8.1039,382200.0,NEAR OCEAN
+-121.93,37.26,39.0,1103.0,175.0,446.0,163.0,2.8125,291300.0,<1H OCEAN
+-120.96,41.12,29.0,779.0,136.0,364.0,123.0,2.5,59200.0,INLAND
+-123.41,40.07,17.0,449.0,151.0,141.0,53.0,0.8362,87500.0,INLAND
+-122.43,37.73,52.0,1494.0,306.0,1463.0,360.0,3.1786,222600.0,NEAR BAY
+-117.7,33.6,25.0,1321.0,295.0,396.0,278.0,3.1131,77100.0,<1H OCEAN
+-122.45,37.77,52.0,3188.0,708.0,1526.0,664.0,3.3068,500001.0,NEAR BAY
+-114.65,32.79,21.0,44.0,33.0,64.0,27.0,0.8571,25000.0,INLAND
+-121.89,37.3,47.0,2355.0,426.0,961.0,428.0,5.3955,282300.0,<1H OCEAN
+-121.3,38.89,45.0,1529.0,317.0,793.0,281.0,2.9866,91300.0,INLAND
+-117.03,32.77,19.0,4819.0,1492.0,2572.0,1336.0,2.3393,118200.0,<1H OCEAN
+-119.01,35.38,52.0,114.0,26.0,158.0,26.0,1.075,67500.0,INLAND
+-120.19,39.35,7.0,2611.0,395.0,482.0,159.0,5.0622,174100.0,INLAND
+-117.87,33.74,31.0,2338.0,652.0,3289.0,631.0,2.6734,158500.0,<1H OCEAN
+-117.09,32.79,20.0,2183.0,534.0,999.0,496.0,2.8631,169700.0,NEAR OCEAN
+-116.24,33.76,9.0,1961.0,595.0,966.0,275.0,3.8125,96700.0,INLAND
+-121.37,38.64,36.0,322.0,48.0,133.0,59.0,4.6111,139300.0,INLAND
+-119.81,36.76,52.0,1792.0,352.0,1049.0,357.0,2.4375,57100.0,INLAND
+-122.02,36.98,21.0,607.0,155.0,226.0,136.0,1.9063,166700.0,NEAR OCEAN
+-118.16,33.78,14.0,1709.0,558.0,1939.0,520.0,1.9808,139100.0,NEAR OCEAN
+-117.93,34.05,36.0,1340.0,221.0,848.0,244.0,4.1731,205100.0,<1H OCEAN
+-122.68,38.48,15.0,1575.0,262.0,716.0,259.0,5.3409,244600.0,<1H OCEAN
+-121.87,37.67,28.0,1812.0,294.0,853.0,278.0,4.9879,229400.0,INLAND
+-117.89,33.71,16.0,1591.0,225.0,926.0,239.0,6.2452,266300.0,<1H OCEAN
+-118.43,34.15,42.0,1293.0,214.0,459.0,217.0,7.672,467600.0,<1H OCEAN
+-114.6,33.6,21.0,1988.0,483.0,1182.0,437.0,1.625,62000.0,INLAND
+-118.18,34.1,7.0,2529.0,689.0,1215.0,577.0,4.7853,153100.0,<1H OCEAN
+-119.02,35.42,40.0,1912.0,439.0,1015.0,413.0,1.4598,52600.0,INLAND
+-123.17,40.31,36.0,98.0,28.0,18.0,8.0,0.536,14999.0,INLAND
+-121.9,38.0,14.0,2677.0,368.0,1288.0,375.0,6.0497,177500.0,INLAND
+-118.38,34.01,18.0,9528.0,2075.0,3922.0,1920.0,4.7612,304100.0,<1H OCEAN
+-118.4,34.11,32.0,5578.0,753.0,1567.0,697.0,15.0001,500001.0,<1H OCEAN
+-119.68,34.43,33.0,1961.0,462.0,1693.0,445.0,2.9896,236000.0,<1H OCEAN
+-121.97,37.29,25.0,4096.0,743.0,2027.0,741.0,5.3294,300300.0,<1H OCEAN
+-118.4,34.25,13.0,1872.0,497.0,1927.0,432.0,2.2019,134200.0,<1H OCEAN
+-122.41,37.74,38.0,1754.0,382.0,928.0,354.0,4.1417,270800.0,NEAR BAY
+-118.49,34.02,30.0,2075.0,687.0,1026.0,592.0,3.1635,366700.0,<1H OCEAN
+-119.03,35.38,38.0,2122.0,394.0,843.0,410.0,3.0,91800.0,INLAND
+-118.35,33.91,29.0,2461.0,535.0,1236.0,482.0,4.8409,244000.0,<1H OCEAN
+-120.99,37.64,41.0,1580.0,385.0,881.0,361.0,2.7538,99600.0,INLAND
+-117.33,33.87,14.0,2300.0,335.0,1001.0,311.0,5.1045,161300.0,INLAND
+-118.37,33.97,32.0,6699.0,1781.0,2725.0,1544.0,3.3889,285700.0,<1H OCEAN
+-123.11,40.6,23.0,708.0,202.0,316.0,136.0,1.1602,65000.0,INLAND
+-116.97,32.78,35.0,1113.0,236.0,681.0,246.0,2.9784,136400.0,<1H OCEAN
+-118.19,33.8,38.0,2010.0,595.0,1535.0,525.0,1.9848,160400.0,NEAR OCEAN
+-117.28,33.19,5.0,2697.0,639.0,1633.0,580.0,3.4456,165800.0,NEAR OCEAN
+-117.8,33.52,50.0,1152.0,341.0,519.0,225.0,3.053,500001.0,<1H OCEAN
+-122.02,37.95,22.0,3526.0,510.0,1660.0,508.0,5.6642,237000.0,NEAR BAY
+-121.81,37.3,14.0,1870.0,348.0,1214.0,347.0,4.9769,186500.0,<1H OCEAN
+-121.77,37.23,15.0,4713.0,769.0,2519.0,778.0,5.6958,253800.0,<1H OCEAN
+-121.99,37.77,14.0,8213.0,1364.0,3283.0,1286.0,5.1755,294800.0,<1H OCEAN
+-122.46,37.63,22.0,6728.0,1382.0,3783.0,1310.0,5.0479,280400.0,NEAR OCEAN
+-122.26,37.77,52.0,1848.0,479.0,921.0,477.0,2.875,234000.0,NEAR BAY
+-122.32,37.97,29.0,2347.0,464.0,1135.0,490.0,3.9722,161000.0,NEAR BAY
+-122.91,38.49,37.0,2469.0,519.0,1137.0,474.0,3.6343,146500.0,<1H OCEAN
+-117.4,33.94,37.0,987.0,187.0,551.0,191.0,3.5865,112000.0,INLAND
+-115.57,32.85,33.0,1365.0,269.0,825.0,250.0,3.2396,62300.0,INLAND
+-122.34,37.93,32.0,2389.0,652.0,1672.0,584.0,1.4423,88300.0,NEAR BAY
+-117.25,32.74,40.0,2186.0,549.0,953.0,515.0,2.8007,257100.0,NEAR OCEAN
+-118.98,35.38,34.0,1020.0,247.0,795.0,228.0,1.625,50800.0,INLAND
+-118.96,35.37,41.0,1463.0,339.0,1066.0,318.0,1.7467,52400.0,INLAND
+-117.24,34.12,29.0,2654.0,667.0,1822.0,593.0,2.1563,72300.0,INLAND
+-122.45,37.66,35.0,2738.0,509.0,1545.0,493.0,5.3446,263300.0,NEAR OCEAN
+-122.33,41.86,19.0,3599.0,695.0,1572.0,601.0,2.234,58600.0,INLAND
+-121.98,37.35,41.0,1150.0,249.0,729.0,260.0,3.5491,261100.0,<1H OCEAN
+-121.11,38.95,14.0,3888.0,890.0,1830.0,844.0,1.8238,158600.0,INLAND
+-118.47,34.02,41.0,2136.0,549.0,986.0,557.0,2.7254,444400.0,<1H OCEAN
+-120.41,38.33,17.0,1463.0,338.0,529.0,226.0,3.024,100900.0,INLAND
+-120.19,39.32,16.0,1536.0,298.0,646.0,208.0,2.3594,155700.0,INLAND
+-117.18,34.48,8.0,3561.0,691.0,2156.0,659.0,2.7778,86900.0,INLAND
+-119.69,36.82,15.0,3303.0,512.0,1687.0,505.0,4.81,93600.0,INLAND
+-118.4,34.14,52.0,1695.0,281.0,595.0,264.0,6.0678,399300.0,<1H OCEAN
+-118.49,34.05,42.0,1918.0,216.0,632.0,224.0,15.0001,500001.0,<1H OCEAN
+-122.64,38.24,52.0,1621.0,393.0,635.0,349.0,2.5202,244000.0,<1H OCEAN
+-117.13,32.92,16.0,1565.0,257.0,893.0,239.0,5.5036,192300.0,<1H OCEAN
+-121.31,37.97,45.0,2604.0,454.0,988.0,442.0,3.6667,123100.0,INLAND
+-116.39,33.72,19.0,7646.0,1618.0,2496.0,1075.0,3.5569,128000.0,INLAND
+-117.97,33.89,15.0,3801.0,542.0,1992.0,526.0,9.0683,367400.0,<1H OCEAN
+-118.08,33.88,30.0,1901.0,519.0,2685.0,496.0,3.2639,120100.0,<1H OCEAN
+-118.18,33.99,38.0,1010.0,315.0,1157.0,301.0,1.6341,161800.0,<1H OCEAN
+-118.33,33.84,36.0,1364.0,251.0,668.0,245.0,5.3131,314100.0,<1H OCEAN
+-122.54,37.7,36.0,3988.0,732.0,1793.0,708.0,4.2472,292500.0,NEAR OCEAN
+-117.04,34.02,24.0,4663.0,1213.0,1851.0,1116.0,1.4418,103500.0,INLAND
+-118.52,34.22,35.0,1275.0,222.0,959.0,226.0,5.0282,195400.0,<1H OCEAN
+-119.83,36.76,15.0,3291.0,772.0,1738.0,634.0,1.976,67300.0,INLAND
+-117.07,33.14,16.0,2546.0,429.0,1683.0,408.0,4.7426,160600.0,<1H OCEAN
+-121.01,37.69,20.0,3275.0,760.0,1538.0,705.0,2.48,135600.0,INLAND
+-120.59,35.11,20.0,3098.0,571.0,1449.0,611.0,3.5744,197800.0,<1H OCEAN
+-118.55,34.2,31.0,1963.0,420.0,1494.0,415.0,3.5313,211800.0,<1H OCEAN
+-120.46,34.66,5.0,2904.0,702.0,1302.0,618.0,3.0081,135200.0,NEAR OCEAN
+-122.47,37.7,47.0,737.0,126.0,370.0,136.0,3.775,281300.0,NEAR OCEAN
+-121.87,37.99,15.0,2203.0,312.0,1051.0,311.0,4.9783,163900.0,INLAND
+-117.21,33.14,12.0,4839.0,954.0,1708.0,952.0,2.8586,163300.0,<1H OCEAN
+-122.2,37.78,45.0,1766.0,332.0,869.0,327.0,4.5893,163500.0,NEAR BAY
+-121.52,39.48,21.0,2628.0,494.0,1364.0,468.0,2.0455,59400.0,INLAND
+-119.02,35.3,10.0,7397.0,1369.0,4611.0,1310.0,3.6369,81600.0,INLAND
+-120.46,34.71,17.0,2830.0,430.0,1035.0,416.0,4.9292,207200.0,NEAR OCEAN
+-122.07,37.34,35.0,1172.0,184.0,512.0,175.0,7.3561,500001.0,<1H OCEAN
+-118.21,33.8,45.0,1160.0,274.0,1095.0,269.0,2.7308,139000.0,NEAR OCEAN
+-117.25,32.74,36.0,1240.0,310.0,577.0,319.0,2.6625,248200.0,NEAR OCEAN
+-122.23,40.17,21.0,1401.0,331.0,651.0,299.0,2.225,64700.0,INLAND
+-122.3,38.29,40.0,1739.0,318.0,744.0,312.0,2.6518,156100.0,NEAR BAY
+-118.34,34.16,44.0,1717.0,391.0,848.0,353.0,3.6111,254500.0,<1H OCEAN
+-120.54,34.97,23.0,1353.0,345.0,1322.0,336.0,1.8185,97800.0,<1H OCEAN
+-117.8,33.69,16.0,2745.0,447.0,1429.0,411.0,6.8219,325500.0,<1H OCEAN
+-122.21,37.48,39.0,1535.0,340.0,1204.0,370.0,2.8482,247200.0,NEAR BAY
+-118.06,33.8,22.0,1892.0,442.0,1015.0,404.0,4.1379,212500.0,<1H OCEAN
+-118.17,33.77,45.0,2143.0,697.0,1004.0,594.0,3.0153,220000.0,NEAR OCEAN
+-117.21,32.81,27.0,1318.0,216.0,495.0,191.0,5.2837,283800.0,NEAR OCEAN
+-120.44,36.84,29.0,1563.0,293.0,883.0,288.0,2.8182,90500.0,INLAND
+-118.32,33.91,33.0,1729.0,396.0,1073.0,344.0,4.2083,180500.0,<1H OCEAN
+-124.22,41.73,28.0,3003.0,699.0,1530.0,653.0,1.7038,78300.0,NEAR OCEAN
+-118.3,33.9,29.0,2617.0,668.0,1868.0,647.0,3.6,208800.0,<1H OCEAN
+-121.91,37.26,32.0,3776.0,620.0,1790.0,612.0,5.4675,261100.0,<1H OCEAN
+-118.16,34.68,9.0,4303.0,900.0,2240.0,861.0,3.7807,110900.0,INLAND
+-122.18,37.68,5.0,2087.0,407.0,840.0,401.0,5.4858,187800.0,NEAR BAY
+-118.99,35.36,31.0,1498.0,359.0,1168.0,340.0,1.2232,49300.0,INLAND
+-119.46,35.14,30.0,2943.0,,1565.0,584.0,2.5313,45800.0,INLAND
+-117.69,33.47,19.0,2595.0,621.0,1728.0,571.0,3.668,243800.0,<1H OCEAN
+-121.79,37.23,17.0,2281.0,359.0,1226.0,394.0,5.4193,259500.0,<1H OCEAN
+-119.54,36.51,36.0,49.0,7.0,28.0,2.0,4.625,162500.0,INLAND
+-117.17,34.05,24.0,2877.0,507.0,1141.0,474.0,4.2059,121500.0,INLAND
+-117.88,33.96,16.0,19059.0,3079.0,10988.0,3061.0,5.5469,265200.0,<1H OCEAN
+-117.33,33.67,27.0,4376.0,1003.0,2667.0,870.0,1.9194,100600.0,<1H OCEAN
+-120.46,34.64,11.0,562.0,164.0,504.0,147.0,2.0161,118800.0,NEAR OCEAN
+-119.28,35.5,28.0,3107.0,782.0,3260.0,738.0,1.6944,58600.0,INLAND
+-117.13,34.06,4.0,3078.0,510.0,1341.0,486.0,4.9688,163200.0,INLAND
+-122.25,37.79,52.0,1339.0,391.0,1086.0,363.0,2.181,138800.0,NEAR BAY
+-119.7,36.3,10.0,956.0,201.0,693.0,220.0,2.2895,62000.0,INLAND
+-117.87,33.99,21.0,2837.0,515.0,2031.0,555.0,4.9271,209700.0,<1H OCEAN
+-117.04,34.03,29.0,3375.0,795.0,1760.0,699.0,2.7028,92000.0,INLAND
+-119.04,34.23,21.0,9807.0,1614.0,4199.0,1554.0,5.0145,246600.0,<1H OCEAN
+-117.2,34.15,18.0,1859.0,251.0,747.0,256.0,7.732,173200.0,INLAND
+-117.95,33.84,32.0,1378.0,492.0,1202.0,448.0,3.4028,183700.0,<1H OCEAN
+-121.16,38.16,31.0,1953.0,366.0,999.0,316.0,2.4906,122500.0,INLAND
+-122.31,40.75,18.0,1411.0,330.0,494.0,227.0,1.4911,75800.0,INLAND
+-118.26,34.24,42.0,890.0,179.0,555.0,200.0,4.4821,271900.0,<1H OCEAN
+-122.0,38.41,11.0,2838.0,429.0,1331.0,426.0,4.945,298400.0,INLAND
+-118.28,34.08,42.0,1618.0,522.0,1454.0,440.0,3.1607,182000.0,<1H OCEAN
+-120.69,36.84,18.0,902.0,195.0,771.0,174.0,2.2083,55000.0,INLAND
+-118.12,33.83,45.0,1734.0,331.0,797.0,293.0,4.8917,222800.0,<1H OCEAN
+-121.12,36.21,16.0,1720.0,473.0,1427.0,291.0,2.1107,76200.0,<1H OCEAN
+-122.8,39.08,17.0,1880.0,467.0,798.0,342.0,1.4676,65000.0,INLAND
+-122.03,37.35,25.0,3095.0,514.0,1251.0,507.0,5.5388,352100.0,<1H OCEAN
+-121.9,37.29,26.0,1797.0,244.0,626.0,244.0,7.8575,424600.0,<1H OCEAN
+-122.47,37.77,52.0,3143.0,635.0,1350.0,623.0,3.8571,366700.0,NEAR BAY
+-116.6,33.06,23.0,1731.0,365.0,612.0,258.0,2.7813,172900.0,INLAND
+-118.42,33.96,24.0,4393.0,966.0,1257.0,579.0,5.0714,356100.0,<1H OCEAN
+-118.38,34.02,45.0,2098.0,486.0,1343.0,481.0,3.9615,268600.0,<1H OCEAN
+-118.09,34.08,34.0,1513.0,384.0,986.0,336.0,2.6901,235600.0,<1H OCEAN
+-121.49,38.56,52.0,1844.0,392.0,667.0,353.0,3.0033,103500.0,INLAND
+-118.31,34.1,40.0,4984.0,2158.0,4828.0,2028.0,1.6903,350000.0,<1H OCEAN
+-118.25,33.86,26.0,3022.0,476.0,1852.0,452.0,6.0531,186400.0,<1H OCEAN
+-121.94,37.94,26.0,1299.0,174.0,533.0,180.0,6.2296,291700.0,INLAND
+-117.79,33.71,16.0,6339.0,862.0,3132.0,825.0,7.1069,313400.0,<1H OCEAN
+-117.98,33.64,20.0,1851.0,495.0,792.0,363.0,3.8187,137500.0,NEAR OCEAN
+-122.43,37.78,10.0,2380.0,843.0,1245.0,789.0,1.3062,220000.0,NEAR BAY
+-118.29,34.09,35.0,2624.0,1116.0,3548.0,1008.0,2.0132,198400.0,<1H OCEAN
+-119.71,34.44,41.0,2220.0,367.0,927.0,355.0,5.3184,376000.0,<1H OCEAN
+-119.29,36.31,34.0,1439.0,253.0,607.0,223.0,3.0972,82800.0,INLAND
+-118.37,34.04,43.0,1465.0,278.0,727.0,290.0,4.0781,289400.0,<1H OCEAN
+-118.29,33.82,21.0,4383.0,901.0,2689.0,913.0,3.4375,218800.0,<1H OCEAN
+-118.12,34.06,25.0,1137.0,293.0,800.0,281.0,2.4286,233300.0,<1H OCEAN
+-117.99,33.82,33.0,2342.0,475.0,1367.0,509.0,4.1167,215500.0,<1H OCEAN
+-118.35,34.52,14.0,3490.0,592.0,1710.0,580.0,5.9171,333300.0,INLAND
+-118.29,33.94,32.0,2701.0,708.0,1880.0,590.0,1.6716,123800.0,<1H OCEAN
+-117.94,33.77,33.0,2964.0,747.0,2235.0,718.0,3.2591,175900.0,<1H OCEAN
+-117.66,33.63,16.0,3299.0,610.0,1967.0,604.0,5.5085,223300.0,<1H OCEAN
+-117.83,33.9,23.0,2446.0,360.0,1196.0,359.0,6.5755,272800.0,<1H OCEAN
+-116.92,32.85,23.0,1378.0,269.0,767.0,266.0,4.0625,145000.0,<1H OCEAN
+-115.72,32.75,16.0,348.0,99.0,123.0,54.0,2.0938,87500.0,INLAND
+-118.59,34.2,18.0,847.0,185.0,733.0,178.0,5.2149,201900.0,<1H OCEAN
+-118.31,34.28,34.0,3258.0,610.0,1810.0,633.0,5.1145,219900.0,<1H OCEAN
+-118.24,34.01,43.0,1456.0,444.0,2098.0,433.0,1.8929,99200.0,<1H OCEAN
+-118.43,34.17,34.0,2180.0,424.0,906.0,429.0,4.4464,353100.0,<1H OCEAN
+-118.46,34.03,52.0,523.0,,317.0,130.0,2.2794,337500.0,<1H OCEAN
+-117.65,34.03,15.0,4420.0,903.0,2373.0,858.0,3.449,149100.0,INLAND
+-117.07,32.81,15.0,2000.0,402.0,778.0,369.0,4.3594,224200.0,<1H OCEAN
+-117.81,33.73,17.0,1063.0,189.0,363.0,183.0,2.1719,261300.0,<1H OCEAN
+-121.75,37.11,18.0,3167.0,,1414.0,482.0,6.8773,467700.0,<1H OCEAN
+-118.53,34.04,45.0,1711.0,264.0,735.0,261.0,9.1078,500001.0,<1H OCEAN
+-118.08,33.97,38.0,1026.0,190.0,789.0,193.0,4.2,163200.0,<1H OCEAN
+-117.23,32.79,23.0,2578.0,665.0,989.0,622.0,3.5484,238000.0,NEAR OCEAN
+-118.38,34.09,28.0,4001.0,1352.0,1799.0,1220.0,2.5784,272900.0,<1H OCEAN
+-118.26,33.91,33.0,954.0,241.0,655.0,218.0,2.5882,92800.0,<1H OCEAN
+-121.32,38.57,15.0,3369.0,499.0,1733.0,470.0,5.31,127500.0,INLAND
+-118.41,33.99,36.0,1089.0,234.0,632.0,215.0,3.6538,276100.0,<1H OCEAN
+-117.04,32.76,43.0,3171.0,665.0,1534.0,625.0,3.141,141400.0,NEAR OCEAN
+-122.82,38.41,32.0,701.0,182.0,489.0,168.0,2.785,169300.0,<1H OCEAN
+-122.23,37.79,30.0,999.0,264.0,1011.0,263.0,1.8854,137500.0,NEAR BAY
+-118.33,34.16,37.0,2381.0,575.0,1235.0,499.0,3.7941,247800.0,<1H OCEAN
+-118.31,33.99,45.0,1489.0,339.0,791.0,316.0,2.2339,104800.0,<1H OCEAN
+-118.26,33.96,39.0,1542.0,375.0,1256.0,361.0,1.7167,100000.0,<1H OCEAN
+-122.0,37.28,33.0,2170.0,311.0,854.0,303.0,8.3605,500001.0,<1H OCEAN
+-118.27,34.03,50.0,395.0,232.0,948.0,243.0,1.7546,175000.0,<1H OCEAN
+-118.32,33.98,44.0,1448.0,314.0,861.0,310.0,2.2396,108600.0,<1H OCEAN
+-118.36,34.01,33.0,3140.0,466.0,1214.0,464.0,6.5044,350400.0,<1H OCEAN
+-118.36,33.96,26.0,3543.0,,2742.0,951.0,2.5504,151300.0,<1H OCEAN
+-117.89,33.84,35.0,3315.0,744.0,2425.0,687.0,3.5521,182800.0,<1H OCEAN
+-122.47,37.78,52.0,2275.0,412.0,1166.0,424.0,4.0652,421300.0,NEAR BAY
+-119.74,36.79,28.0,2857.0,619.0,1614.0,592.0,2.1573,71400.0,INLAND
+-118.16,33.92,44.0,1368.0,277.0,899.0,271.0,3.5938,161300.0,<1H OCEAN
+-117.63,34.09,19.0,3490.0,816.0,2818.0,688.0,2.8977,126200.0,INLAND
+-117.9,33.61,41.0,1521.0,328.0,527.0,275.0,4.0764,500001.0,<1H OCEAN
+-119.65,36.37,4.0,3725.0,783.0,1478.0,600.0,3.5486,148000.0,INLAND
+-122.08,37.82,4.0,2045.0,237.0,830.0,252.0,11.3421,500001.0,NEAR BAY
+-121.91,36.55,39.0,5468.0,834.0,1782.0,712.0,5.7248,398800.0,NEAR OCEAN
+-118.32,34.06,36.0,3239.0,722.0,1383.0,612.0,4.5918,337000.0,<1H OCEAN
+-118.44,34.27,36.0,1111.0,275.0,1333.0,266.0,3.5347,158100.0,<1H OCEAN
+-121.3,38.0,27.0,2918.0,580.0,1338.0,544.0,2.6495,116200.0,INLAND
+-122.22,38.08,37.0,4590.0,857.0,2920.0,832.0,3.436,94800.0,NEAR BAY
+-121.93,37.73,23.0,2564.0,347.0,1043.0,351.0,6.2048,275000.0,<1H OCEAN
+-121.26,38.64,40.0,1098.0,175.0,415.0,160.0,4.8375,217400.0,INLAND
+-121.37,38.68,36.0,1775.0,296.0,937.0,305.0,3.1786,83400.0,INLAND
+-120.25,38.55,15.0,4403.0,891.0,1103.0,433.0,3.0125,111700.0,INLAND
+-117.24,32.85,18.0,3117.0,475.0,904.0,368.0,6.7587,388500.0,NEAR OCEAN
+-118.38,33.87,17.0,2791.0,579.0,1467.0,583.0,5.7415,321900.0,<1H OCEAN
+-122.71,38.34,23.0,2744.0,588.0,1493.0,557.0,3.1781,162000.0,<1H OCEAN
+-117.01,33.04,13.0,4595.0,567.0,1643.0,544.0,7.7684,362300.0,<1H OCEAN
+-117.25,34.11,30.0,2173.0,560.0,1509.0,486.0,1.4079,67700.0,INLAND
+-120.18,39.14,25.0,2171.0,386.0,248.0,116.0,3.0375,171900.0,INLAND
+-121.01,37.68,33.0,828.0,123.0,373.0,133.0,5.5,146200.0,INLAND
+-117.99,33.72,14.0,2127.0,537.0,1338.0,475.0,3.628,188500.0,<1H OCEAN
+-117.17,32.73,52.0,1578.0,487.0,879.0,446.0,2.4069,215000.0,NEAR OCEAN
+-117.17,32.81,26.0,788.0,127.0,346.0,125.0,5.0603,185700.0,NEAR OCEAN
+-122.52,37.92,47.0,793.0,163.0,334.0,151.0,5.8509,317800.0,NEAR BAY
+-122.37,37.93,45.0,3150.0,756.0,1798.0,749.0,1.75,37900.0,NEAR BAY
+-118.11,33.84,36.0,1756.0,297.0,798.0,287.0,5.5581,218300.0,<1H OCEAN
+-120.89,37.21,25.0,3301.0,678.0,994.0,306.0,3.2262,97200.0,INLAND
+-122.23,38.11,47.0,3007.0,524.0,1152.0,486.0,4.0,141500.0,NEAR BAY
+-122.42,37.93,47.0,3453.0,779.0,1353.0,728.0,4.016,274500.0,NEAR BAY
+-118.31,33.92,35.0,1307.0,246.0,672.0,219.0,4.8456,146400.0,<1H OCEAN
+-120.87,37.76,16.0,2022.0,413.0,1126.0,408.0,2.5655,116400.0,INLAND
+-121.84,39.68,38.0,549.0,105.0,275.0,94.0,3.5375,153100.0,INLAND
+-117.3,33.23,13.0,3619.0,791.0,1759.0,806.0,2.765,98500.0,<1H OCEAN
+-118.15,33.77,41.0,3448.0,896.0,1621.0,838.0,4.5,339800.0,NEAR OCEAN
+-118.22,33.95,42.0,3896.0,981.0,4496.0,993.0,3.153,150900.0,<1H OCEAN
+-118.3,34.1,36.0,2284.0,899.0,1964.0,839.0,1.9297,203300.0,<1H OCEAN
+-117.12,32.7,14.0,819.0,237.0,827.0,237.0,1.3194,90500.0,NEAR OCEAN
+-120.7,36.99,32.0,320.0,73.0,222.0,78.0,2.9271,87500.0,INLAND
+-118.4,33.88,35.0,1753.0,296.0,615.0,275.0,7.5,500001.0,<1H OCEAN
+-121.45,38.57,48.0,1962.0,356.0,704.0,362.0,3.5313,147900.0,INLAND
+-117.88,33.85,25.0,1234.0,351.0,507.0,285.0,2.3173,225000.0,<1H OCEAN
+-118.2,33.91,36.0,2283.0,499.0,1836.0,462.0,2.8793,118100.0,<1H OCEAN
+-117.03,33.32,14.0,1088.0,209.0,601.0,193.0,3.8438,243800.0,<1H OCEAN
+-118.32,34.09,34.0,2473.0,1171.0,2655.0,1083.0,1.6331,162500.0,<1H OCEAN
+-118.45,37.7,15.0,2199.0,453.0,899.0,347.0,2.35,107800.0,INLAND
+-119.78,36.8,34.0,2200.0,493.0,1243.0,431.0,1.8514,66500.0,INLAND
+-123.85,39.39,23.0,4671.0,912.0,2095.0,857.0,3.184,140500.0,<1H OCEAN
+-122.79,40.75,17.0,3851.0,818.0,1352.0,560.0,2.125,71700.0,INLAND
+-120.92,35.4,23.0,2059.0,354.0,636.0,278.0,3.6908,278800.0,NEAR OCEAN
+-119.69,34.41,44.0,1208.0,357.0,603.0,297.0,2.6103,500000.0,<1H OCEAN
+-117.82,33.88,15.0,5392.0,895.0,2531.0,827.0,6.2185,280300.0,<1H OCEAN
+-122.05,37.55,23.0,4247.0,835.0,2357.0,823.0,5.1321,211300.0,NEAR BAY
+-121.24,37.98,33.0,450.0,123.0,236.0,103.0,1.1964,80400.0,INLAND
+-118.26,33.94,44.0,1103.0,265.0,760.0,247.0,1.6887,99600.0,<1H OCEAN
+-117.07,33.03,14.0,6665.0,1231.0,2026.0,1001.0,5.09,268500.0,<1H OCEAN
+-117.9,33.72,33.0,2613.0,562.0,3150.0,543.0,4.3899,180700.0,<1H OCEAN
+-118.28,33.68,8.0,2842.0,522.0,1624.0,510.0,3.7282,287500.0,NEAR OCEAN
+-120.66,35.2,13.0,5138.0,713.0,1838.0,645.0,5.9676,380000.0,NEAR OCEAN
+-118.14,33.75,39.0,1995.0,634.0,867.0,567.0,4.0795,400000.0,NEAR OCEAN
+-118.08,34.01,33.0,1091.0,233.0,890.0,226.0,2.7679,176400.0,<1H OCEAN
+-118.31,34.01,39.0,2073.0,566.0,1246.0,547.0,2.0417,117100.0,<1H OCEAN
+-118.25,34.1,42.0,598.0,147.0,312.0,144.0,2.625,164300.0,<1H OCEAN
+-122.06,37.85,17.0,7475.0,1556.0,2092.0,1449.0,3.6437,186500.0,NEAR BAY
+-122.3,38.33,15.0,4741.0,956.0,2043.0,856.0,4.1862,183600.0,NEAR BAY
+-118.51,34.19,38.0,2182.0,409.0,1141.0,379.0,4.2865,221100.0,<1H OCEAN
+-117.72,34.05,31.0,2220.0,526.0,1662.0,472.0,2.7321,104300.0,INLAND
+-122.4,37.6,30.0,5351.0,1134.0,2558.0,1074.0,3.5817,369300.0,NEAR OCEAN
+-122.43,37.73,52.0,1583.0,347.0,935.0,341.0,4.6786,263200.0,NEAR BAY
+-121.32,38.41,17.0,4401.0,655.0,1970.0,639.0,5.8239,247500.0,INLAND
+-117.25,34.12,17.0,3107.0,752.0,2160.0,643.0,1.8463,72600.0,INLAND
+-118.45,34.27,33.0,1194.0,229.0,839.0,230.0,3.705,185800.0,<1H OCEAN
+-117.25,33.0,14.0,2518.0,458.0,931.0,414.0,5.8393,485300.0,NEAR OCEAN
+-118.1,33.97,35.0,2426.0,529.0,2010.0,514.0,2.9922,163500.0,<1H OCEAN
+-121.6,37.13,14.0,9483.0,1361.0,4108.0,1281.0,7.5,344500.0,<1H OCEAN
+-117.96,33.68,25.0,2004.0,349.0,1085.0,343.0,4.7656,230700.0,<1H OCEAN
+-124.16,41.92,19.0,1668.0,324.0,841.0,283.0,2.1336,75000.0,NEAR OCEAN
+-118.22,34.09,45.0,1072.0,275.0,996.0,243.0,2.8194,165000.0,<1H OCEAN
+-118.36,34.05,45.0,1879.0,395.0,946.0,409.0,3.3333,254700.0,<1H OCEAN
+-122.27,37.77,23.0,5679.0,1270.0,2690.0,1151.0,4.7695,291700.0,NEAR BAY
+-117.74,34.05,29.0,2452.0,700.0,3029.0,665.0,2.1354,110700.0,INLAND
+-118.01,33.89,34.0,1653.0,292.0,1003.0,310.0,4.6,203400.0,<1H OCEAN
+-117.31,34.39,15.0,1703.0,273.0,847.0,266.0,3.7917,123400.0,INLAND
+-117.18,34.04,41.0,1766.0,288.0,753.0,278.0,4.9125,140700.0,INLAND
+-121.62,36.69,12.0,512.0,144.0,767.0,149.0,2.2667,72900.0,<1H OCEAN
+-122.3,37.89,46.0,1520.0,402.0,815.0,375.0,2.8036,211600.0,NEAR BAY
+-121.91,37.69,23.0,2179.0,308.0,926.0,299.0,5.9345,259600.0,<1H OCEAN
+-122.25,40.15,15.0,1677.0,346.0,858.0,327.0,2.4375,59200.0,INLAND
+-122.48,38.31,29.0,2375.0,560.0,1124.0,502.0,2.3276,166200.0,<1H OCEAN
+-118.32,34.09,28.0,2173.0,819.0,2548.0,763.0,1.879,218800.0,<1H OCEAN
+-122.02,37.26,34.0,1764.0,243.0,692.0,223.0,8.0331,500001.0,<1H OCEAN
+-121.29,38.6,29.0,1276.0,225.0,600.0,223.0,4.0938,109100.0,INLAND
+-121.7,37.93,19.0,2005.0,405.0,972.0,403.0,2.2216,156700.0,INLAND
+-122.44,38.34,25.0,3106.0,715.0,1262.0,665.0,1.9487,233500.0,<1H OCEAN
+-118.47,34.25,21.0,2692.0,477.0,1330.0,456.0,4.5417,238900.0,<1H OCEAN
+-122.73,38.44,28.0,1073.0,241.0,652.0,238.0,2.4,146200.0,<1H OCEAN
+-117.65,33.4,17.0,2737.0,654.0,910.0,492.0,3.5729,370800.0,NEAR OCEAN
+-121.48,36.49,28.0,1006.0,228.0,738.0,193.0,1.9722,210700.0,<1H OCEAN
+-117.84,33.89,24.0,3935.0,625.0,1912.0,593.0,5.7951,226900.0,<1H OCEAN
+-117.91,33.83,9.0,1160.0,368.0,735.0,325.0,1.119,175000.0,<1H OCEAN
+-122.5,37.76,46.0,2226.0,480.0,1272.0,468.0,4.2644,284100.0,NEAR OCEAN
+-118.2,34.04,36.0,1625.0,490.0,2003.0,478.0,2.181,147200.0,<1H OCEAN
+-118.14,33.76,37.0,3242.0,698.0,1080.0,629.0,3.901,432500.0,NEAR OCEAN
+-118.89,34.33,23.0,366.0,62.0,265.0,66.0,3.125,375000.0,<1H OCEAN
+-122.06,37.65,33.0,1227.0,286.0,848.0,291.0,3.8036,158200.0,NEAR BAY
+-121.2,37.8,24.0,1698.0,344.0,927.0,313.0,3.5625,130800.0,INLAND
+-117.2,33.07,5.0,10394.0,1617.0,4496.0,1553.0,5.9289,411300.0,NEAR OCEAN
+-117.38,33.98,52.0,2274.0,571.0,1167.0,504.0,2.0284,101600.0,INLAND
+-121.14,37.46,4.0,2919.0,503.0,1592.0,491.0,5.2452,161900.0,INLAND
+-122.94,38.57,33.0,1530.0,266.0,728.0,250.0,5.1005,266700.0,<1H OCEAN
+-121.96,37.74,2.0,200.0,20.0,25.0,9.0,15.0001,350000.0,<1H OCEAN
+-117.32,33.22,16.0,1057.0,232.0,316.0,221.0,2.7417,91700.0,NEAR OCEAN
+-118.19,34.05,29.0,855.0,199.0,785.0,169.0,2.6964,122200.0,<1H OCEAN
+-118.19,33.86,35.0,1133.0,296.0,774.0,271.0,2.2381,137500.0,<1H OCEAN
+-118.36,33.88,22.0,1388.0,336.0,930.0,287.0,2.7981,275000.0,<1H OCEAN
+-122.3,37.54,39.0,4292.0,1097.0,1758.0,987.0,2.9405,340500.0,NEAR OCEAN
+-117.68,33.48,15.0,1786.0,299.0,727.0,293.0,5.0527,231400.0,<1H OCEAN
+-119.69,36.74,17.0,2438.0,598.0,1563.0,538.0,1.5449,62500.0,INLAND
+-121.93,37.3,14.0,6277.0,1742.0,3025.0,1630.0,4.0653,234200.0,<1H OCEAN
+-120.91,38.62,12.0,4545.0,748.0,2033.0,718.0,4.1843,207600.0,INLAND
+-118.4,34.17,24.0,6347.0,,2945.0,1492.0,3.3545,221500.0,<1H OCEAN
+-118.61,34.17,19.0,5944.0,1345.0,2372.0,1250.0,3.8819,328900.0,<1H OCEAN
+-122.26,37.54,16.0,2118.0,333.0,770.0,318.0,7.2477,376000.0,NEAR BAY
+-121.3,37.96,24.0,1212.0,366.0,1202.0,343.0,1.7875,76800.0,INLAND
+-120.23,37.96,52.0,1230.0,262.0,609.0,243.0,2.0057,68200.0,INLAND
+-118.49,34.03,30.0,4061.0,927.0,1487.0,865.0,4.1827,435100.0,<1H OCEAN
+-121.91,36.85,22.0,2442.0,624.0,1301.0,290.0,3.1563,300000.0,NEAR OCEAN
+-122.16,37.45,47.0,4234.0,,1808.0,1093.0,4.2297,425000.0,NEAR BAY
+-122.3,37.57,36.0,2406.0,436.0,1189.0,403.0,4.7917,276100.0,NEAR BAY
+-117.95,33.63,27.0,2489.0,481.0,1082.0,443.0,5.8777,358800.0,<1H OCEAN
+-122.42,37.8,52.0,3321.0,1115.0,1576.0,1034.0,2.0987,458300.0,NEAR BAY
+-122.54,37.97,39.0,4193.0,762.0,1833.0,737.0,5.6263,352100.0,NEAR BAY
+-119.22,35.68,16.0,2874.0,677.0,3078.0,651.0,1.8843,55200.0,INLAND
+-118.19,34.02,44.0,2702.0,770.0,3260.0,721.0,2.3575,144800.0,<1H OCEAN
+-122.44,37.73,52.0,2838.0,567.0,1411.0,526.0,3.8304,261400.0,NEAR BAY
+-118.25,33.98,44.0,1087.0,335.0,1441.0,310.0,1.6667,112500.0,<1H OCEAN
+-120.31,38.02,11.0,2366.0,398.0,1046.0,387.0,3.8203,139700.0,INLAND
+-116.37,33.69,7.0,8806.0,1542.0,858.0,448.0,7.8005,318100.0,INLAND
+-118.4,34.23,37.0,1404.0,266.0,889.0,274.0,4.0049,190000.0,<1H OCEAN
+-119.31,36.32,23.0,2945.0,592.0,1419.0,532.0,2.5733,88800.0,INLAND
+-119.76,36.73,39.0,1553.0,363.0,1449.0,341.0,1.4419,45500.0,INLAND
+-122.31,40.89,18.0,754.0,161.0,247.0,107.0,2.2583,78800.0,INLAND
+-121.85,37.3,19.0,6275.0,1546.0,4312.0,1466.0,2.7768,178600.0,<1H OCEAN
+-122.48,37.72,45.0,1405.0,338.0,733.0,342.0,4.1116,187500.0,NEAR OCEAN
+-121.81,37.23,24.0,2413.0,369.0,1237.0,381.0,6.4328,257200.0,<1H OCEAN
+-120.97,37.61,16.0,1326.0,375.0,884.0,375.0,1.871,103900.0,INLAND
+-117.77,33.6,33.0,247.0,80.0,167.0,70.0,3.7059,237500.0,<1H OCEAN
+-120.65,35.12,19.0,2949.0,662.0,1425.0,548.0,2.9615,178100.0,<1H OCEAN
+-118.24,34.03,52.0,142.0,47.0,137.0,45.0,1.8333,312500.0,<1H OCEAN
+-122.12,37.48,36.0,880.0,177.0,795.0,188.0,3.8194,159400.0,NEAR BAY
+-121.99,37.26,29.0,2718.0,365.0,982.0,339.0,7.9234,500001.0,<1H OCEAN
+-123.1,39.15,32.0,1143.0,208.0,454.0,188.0,3.8333,116100.0,<1H OCEAN
+-117.36,34.54,7.0,3940.0,764.0,2140.0,711.0,3.0357,91300.0,INLAND
+-117.97,33.78,35.0,3148.0,597.0,2110.0,587.0,3.9479,203800.0,<1H OCEAN
+-122.28,37.91,38.0,2501.0,348.0,805.0,329.0,6.5576,358500.0,NEAR BAY
+-117.99,34.05,35.0,1792.0,317.0,1441.0,306.0,3.7917,151100.0,<1H OCEAN
+-115.73,33.36,19.0,749.0,238.0,476.0,169.0,1.7727,50000.0,INLAND
+-118.0,34.07,34.0,1696.0,456.0,1609.0,426.0,2.25,138500.0,INLAND
+-122.22,40.18,13.0,3719.0,803.0,1754.0,764.0,2.3517,88900.0,INLAND
+-118.25,33.97,37.0,794.0,210.0,814.0,213.0,2.2917,112000.0,<1H OCEAN
+-122.43,37.74,52.0,2229.0,498.0,1079.0,472.0,5.0196,324300.0,NEAR BAY
+-116.37,33.72,19.0,6190.0,1355.0,2242.0,1043.0,3.0021,152300.0,INLAND
+-122.04,36.95,36.0,1862.0,364.0,1080.0,364.0,4.4567,263800.0,NEAR OCEAN
+-121.37,36.89,21.0,2471.0,473.0,1753.0,451.0,4.025,293800.0,INLAND
+-118.37,33.82,36.0,2416.0,394.0,1115.0,386.0,6.256,366900.0,<1H OCEAN
+-117.3,34.14,37.0,1454.0,261.0,761.0,248.0,2.3438,88100.0,INLAND
+-118.17,33.77,37.0,1127.0,327.0,492.0,331.0,2.675,241700.0,NEAR OCEAN
+-122.45,37.71,46.0,2559.0,506.0,1562.0,498.0,4.3846,270600.0,NEAR OCEAN
+-116.25,33.81,24.0,880.0,187.0,507.0,169.0,3.4583,67500.0,INLAND
+-118.46,34.18,27.0,2582.0,719.0,2038.0,718.0,3.0877,174200.0,<1H OCEAN
+-118.37,33.77,26.0,6339.0,876.0,2540.0,880.0,10.1447,500001.0,NEAR OCEAN
+-118.43,34.28,30.0,1384.0,308.0,2054.0,301.0,3.0132,142600.0,<1H OCEAN
+-119.82,34.44,22.0,2239.0,475.0,1016.0,434.0,4.875,295400.0,NEAR OCEAN
+-121.98,37.31,34.0,2034.0,359.0,1016.0,375.0,5.8127,288300.0,<1H OCEAN
+-122.35,37.56,52.0,1659.0,191.0,519.0,201.0,14.4219,500001.0,NEAR OCEAN
+-118.44,34.15,44.0,1778.0,251.0,641.0,251.0,10.0549,500001.0,<1H OCEAN
+-117.65,34.07,52.0,1041.0,252.0,558.0,231.0,1.9236,117200.0,INLAND
+-118.16,34.6,2.0,11008.0,1549.0,4098.0,1367.0,6.4865,204400.0,INLAND
+-118.3,33.94,36.0,2041.0,531.0,1390.0,464.0,2.0114,99300.0,<1H OCEAN
+-118.06,34.06,28.0,2127.0,625.0,3160.0,620.0,2.5763,173900.0,<1H OCEAN
+-118.32,34.06,43.0,2808.0,584.0,1654.0,569.0,4.125,436800.0,<1H OCEAN
+-117.35,33.21,24.0,1586.0,262.0,912.0,298.0,4.25,150300.0,NEAR OCEAN
+-118.35,34.1,26.0,3977.0,1050.0,1720.0,935.0,3.358,364500.0,<1H OCEAN
+-117.9,34.08,32.0,5482.0,1251.0,3426.0,1117.0,3.2943,204400.0,<1H OCEAN
+-119.87,36.76,34.0,1649.0,323.0,919.0,316.0,2.875,74500.0,INLAND
+-119.73,34.35,20.0,1648.0,319.0,905.0,307.0,4.375,335200.0,NEAR OCEAN
+-118.14,34.14,17.0,3404.0,1011.0,1694.0,949.0,2.9511,282300.0,<1H OCEAN
+-119.22,34.34,29.0,3128.0,672.0,1815.0,648.0,2.9821,175700.0,NEAR OCEAN
+-122.47,37.78,52.0,2951.0,647.0,1448.0,591.0,3.1392,422400.0,NEAR BAY
+-115.84,33.49,20.0,1660.0,379.0,637.0,250.0,2.0347,68900.0,INLAND
+-119.31,36.63,26.0,1874.0,416.0,1834.0,432.0,1.6486,55200.0,INLAND
+-118.44,34.04,31.0,2670.0,662.0,1535.0,631.0,3.0714,347800.0,<1H OCEAN
+-119.91,37.23,17.0,2171.0,389.0,1042.0,375.0,3.625,94400.0,INLAND
+-120.27,38.29,10.0,3486.0,695.0,298.0,124.0,3.3542,103800.0,INLAND
+-121.13,37.74,21.0,2376.0,475.0,1175.0,441.0,3.6016,134600.0,INLAND
+-116.96,32.87,17.0,4713.0,740.0,2531.0,723.0,4.8286,158500.0,<1H OCEAN
+-118.37,33.87,18.0,2516.0,485.0,1128.0,433.0,5.0114,338600.0,<1H OCEAN
+-118.19,34.11,38.0,1158.0,309.0,1051.0,322.0,2.286,169300.0,<1H OCEAN
+-122.12,37.67,33.0,3429.0,681.0,1798.0,694.0,3.9395,184700.0,NEAR BAY
+-121.61,39.77,25.0,1612.0,313.0,837.0,303.0,2.963,89500.0,INLAND
+-121.92,37.32,39.0,836.0,254.0,704.0,272.0,3.5256,192600.0,<1H OCEAN
+-118.28,33.94,9.0,456.0,130.0,438.0,114.0,0.8952,81300.0,<1H OCEAN
+-118.1,33.88,18.0,8046.0,1221.0,4276.0,1228.0,6.5515,319600.0,<1H OCEAN
+-118.28,34.05,35.0,1627.0,838.0,3013.0,791.0,1.5565,152500.0,<1H OCEAN
+-118.54,34.3,22.0,4423.0,622.0,1995.0,582.0,8.2159,376200.0,<1H OCEAN
+-122.44,37.65,38.0,5277.0,1008.0,2695.0,997.0,3.9722,276200.0,NEAR OCEAN
+-118.17,33.77,12.0,4409.0,1401.0,3068.0,1262.0,2.2808,154700.0,NEAR OCEAN
+-121.95,38.43,19.0,3011.0,551.0,1665.0,535.0,5.1534,232800.0,INLAND
+-121.25,38.66,26.0,3670.0,556.0,1616.0,550.0,5.02,169600.0,INLAND
+-118.22,33.99,6.0,1499.0,437.0,1754.0,447.0,4.3164,143200.0,<1H OCEAN
+-121.53,38.5,17.0,3087.0,477.0,1365.0,495.0,6.4667,216800.0,INLAND
+-122.03,37.99,37.0,1755.0,327.0,882.0,350.0,4.59,166600.0,NEAR BAY
+-120.89,37.33,27.0,2692.0,481.0,1518.0,447.0,2.0417,94200.0,INLAND
+-122.74,38.43,11.0,4670.0,1007.0,2430.0,962.0,3.0341,142300.0,<1H OCEAN
+-118.25,33.97,32.0,879.0,257.0,1057.0,230.0,1.6776,114800.0,<1H OCEAN
+-117.04,32.68,14.0,1320.0,270.0,943.0,260.0,5.0947,152700.0,NEAR OCEAN
+-122.2,37.48,30.0,1170.0,258.0,610.0,243.0,3.4427,263500.0,NEAR BAY
+-117.05,32.69,14.0,1689.0,555.0,1319.0,527.0,3.16,143800.0,NEAR OCEAN
+-121.86,36.6,31.0,1044.0,285.0,762.0,301.0,3.038,195300.0,<1H OCEAN
+-118.26,34.17,20.0,5949.0,1417.0,2593.0,1337.0,3.8576,318600.0,<1H OCEAN
+-122.0,38.36,34.0,1502.0,282.0,860.0,297.0,3.3438,135600.0,INLAND
+-120.99,37.8,32.0,2564.0,513.0,1198.0,459.0,2.9083,113400.0,INLAND
+-122.42,37.65,39.0,4402.0,894.0,2941.0,887.0,3.8565,239800.0,NEAR OCEAN
+-121.81,37.99,18.0,2807.0,445.0,1315.0,437.0,4.8194,170400.0,INLAND
+-119.86,36.43,34.0,1175.0,251.0,683.0,261.0,1.7176,58400.0,INLAND
+-117.87,33.71,13.0,1087.0,340.0,817.0,342.0,3.5326,262500.0,<1H OCEAN
+-117.15,32.76,37.0,1921.0,502.0,811.0,472.0,2.75,175000.0,NEAR OCEAN
+-116.43,33.81,8.0,6710.0,1343.0,2069.0,781.0,3.5223,115100.0,INLAND
+-124.21,41.75,20.0,3810.0,787.0,1993.0,721.0,2.0074,66900.0,NEAR OCEAN
+-118.39,34.07,45.0,3143.0,553.0,1153.0,564.0,5.7762,500001.0,<1H OCEAN
+-124.14,40.59,22.0,1665.0,405.0,826.0,382.0,1.5625,66800.0,NEAR OCEAN
+-115.59,32.85,20.0,1608.0,274.0,862.0,248.0,4.875,90800.0,INLAND
+-121.84,37.38,34.0,762.0,182.0,611.0,193.0,3.5625,201800.0,<1H OCEAN
+-118.17,34.1,48.0,2514.0,595.0,2484.0,601.0,3.1146,142500.0,<1H OCEAN
+-123.22,39.28,16.0,5569.0,1106.0,3148.0,1088.0,3.1455,142900.0,<1H OCEAN
+-117.98,33.98,27.0,2275.0,346.0,1039.0,333.0,6.2217,333500.0,<1H OCEAN
+-118.31,33.8,30.0,3096.0,757.0,2048.0,704.0,3.125,233300.0,<1H OCEAN
+-115.48,32.8,21.0,1260.0,246.0,805.0,239.0,2.6172,88500.0,INLAND
+-122.13,37.69,34.0,1131.0,278.0,560.0,237.0,2.875,161700.0,NEAR BAY
+-119.29,36.54,18.0,2581.0,628.0,2732.0,592.0,1.8429,58300.0,INLAND
+-121.01,37.66,36.0,3679.0,613.0,1366.0,581.0,4.5,151400.0,INLAND
+-122.11,37.87,33.0,3398.0,500.0,1351.0,457.0,6.5814,314200.0,NEAR BAY
+-122.49,37.76,52.0,1792.0,305.0,782.0,287.0,4.0391,332700.0,NEAR BAY
+-116.86,33.84,18.0,521.0,118.0,174.0,74.0,2.7788,91100.0,INLAND
+-118.05,33.86,16.0,2676.0,391.0,1377.0,395.0,6.5513,350400.0,<1H OCEAN
+-119.03,35.34,34.0,2221.0,436.0,1131.0,408.0,3.0486,68500.0,INLAND
+-119.45,36.59,41.0,1749.0,342.0,1171.0,314.0,1.6875,66100.0,INLAND
+-117.19,34.1,5.0,2167.0,384.0,1174.0,358.0,4.0114,97700.0,INLAND
+-118.7,34.53,5.0,14275.0,2474.0,7158.0,2311.0,5.4284,236300.0,INLAND
+-117.97,34.06,34.0,3580.0,684.0,2786.0,636.0,4.0469,166800.0,<1H OCEAN
+-117.23,32.87,15.0,2290.0,662.0,1034.0,594.0,3.0104,204200.0,NEAR OCEAN
+-122.22,37.78,51.0,1637.0,463.0,1543.0,393.0,2.489,119100.0,NEAR BAY
+-119.86,36.45,19.0,2439.0,462.0,1416.0,469.0,2.4474,75600.0,INLAND
+-117.31,33.07,21.0,2035.0,534.0,948.0,467.0,3.2984,369400.0,NEAR OCEAN
+-117.99,33.86,20.0,2303.0,612.0,1607.0,564.0,2.9,176100.0,<1H OCEAN
+-118.3,34.26,28.0,1643.0,489.0,1142.0,458.0,3.1607,200600.0,<1H OCEAN
+-121.95,37.74,19.0,1127.0,170.0,518.0,167.0,6.3325,250000.0,<1H OCEAN
+-120.46,34.65,22.0,1298.0,358.0,1272.0,363.0,1.6488,117500.0,NEAR OCEAN
+-118.11,34.05,23.0,3436.0,565.0,1729.0,529.0,5.9941,266700.0,<1H OCEAN
+-122.22,38.1,40.0,2549.0,478.0,1275.0,494.0,2.9469,111600.0,NEAR BAY
+-119.82,36.81,25.0,3305.0,551.0,1149.0,500.0,5.0698,150900.0,INLAND
+-118.01,33.88,19.0,1434.0,391.0,1088.0,341.0,3.369,269600.0,<1H OCEAN
+-118.45,34.0,48.0,1923.0,408.0,1142.0,433.0,4.575,326700.0,<1H OCEAN
+-118.23,33.95,37.0,2667.0,671.0,2865.0,683.0,0.6831,87500.0,<1H OCEAN
+-117.98,33.87,29.0,1310.0,332.0,937.0,294.0,3.8068,158700.0,<1H OCEAN
+-118.09,33.95,32.0,1083.0,206.0,737.0,218.0,3.5583,170800.0,<1H OCEAN
+-118.29,34.17,12.0,2238.0,682.0,1882.0,611.0,2.9,208300.0,<1H OCEAN
+-122.29,37.94,20.0,7578.0,1426.0,3637.0,1362.0,4.4387,190000.0,NEAR BAY
+-123.8,39.46,35.0,1718.0,345.0,698.0,299.0,2.9243,131600.0,<1H OCEAN
+-117.47,33.92,18.0,3869.0,773.0,2500.0,726.0,3.6583,126100.0,INLAND
+-122.69,38.44,35.0,1356.0,241.0,620.0,216.0,3.5521,168300.0,<1H OCEAN
+-118.17,33.88,29.0,815.0,206.0,590.0,183.0,3.0052,166700.0,<1H OCEAN
+-122.46,37.71,39.0,2076.0,482.0,1738.0,445.0,3.1958,232100.0,NEAR OCEAN
+-118.35,33.99,48.0,2741.0,439.0,1115.0,459.0,5.0514,269100.0,<1H OCEAN
+-118.2,34.2,44.0,2890.0,438.0,1219.0,429.0,6.987,500001.0,<1H OCEAN
+-122.12,37.82,26.0,2269.0,317.0,918.0,313.0,6.6657,364500.0,NEAR BAY
+-117.38,34.09,8.0,3955.0,815.0,2184.0,725.0,3.3438,127600.0,INLAND
+-116.99,32.83,20.0,6696.0,1326.0,3687.0,1291.0,3.1979,154600.0,<1H OCEAN
+-121.29,38.14,34.0,1500.0,337.0,674.0,282.0,2.515,110800.0,INLAND
+-118.11,34.08,42.0,3172.0,644.0,1829.0,642.0,3.3966,243200.0,<1H OCEAN
+-122.48,37.77,52.0,2556.0,595.0,1202.0,568.0,3.8899,348500.0,NEAR BAY
+-119.77,36.74,51.0,1454.0,235.0,729.0,252.0,3.3125,70100.0,INLAND
+-116.97,32.83,17.0,6659.0,1402.0,3183.0,1378.0,2.949,117300.0,<1H OCEAN
+-118.53,34.45,26.0,828.0,149.0,508.0,158.0,5.2374,185500.0,<1H OCEAN
+-122.3,37.87,10.0,503.0,118.0,228.0,100.0,2.1705,150000.0,NEAR BAY
+-118.34,34.03,48.0,1426.0,331.0,784.0,356.0,1.6581,118800.0,<1H OCEAN
+-116.47,33.77,26.0,4300.0,767.0,1557.0,669.0,4.4107,122500.0,INLAND
+-122.09,37.71,35.0,2663.0,387.0,1086.0,367.0,5.1498,266400.0,NEAR BAY
+-118.07,33.88,17.0,2407.0,539.0,1422.0,524.0,4.2619,139700.0,<1H OCEAN
+-116.88,33.02,16.0,3204.0,541.0,1818.0,529.0,5.2596,171500.0,<1H OCEAN
+-121.43,38.66,35.0,1814.0,367.0,1076.0,372.0,2.7179,81100.0,INLAND
+-116.98,32.72,4.0,1078.0,158.0,571.0,184.0,4.6667,223300.0,<1H OCEAN
+-120.98,39.08,20.0,4570.0,906.0,2125.0,815.0,3.0403,148000.0,INLAND
+-117.93,34.09,35.0,1891.0,353.0,1093.0,382.0,4.0167,165500.0,<1H OCEAN
+-118.35,34.15,52.0,1680.0,238.0,493.0,211.0,9.042,500001.0,<1H OCEAN
+-118.27,34.08,43.0,962.0,253.0,658.0,244.0,3.2386,185000.0,<1H OCEAN
+-122.06,37.71,36.0,3541.0,570.0,1478.0,529.0,4.635,248600.0,NEAR BAY
+-121.93,37.3,16.0,2111.0,485.0,1285.0,499.0,5.0477,292500.0,<1H OCEAN
+-117.84,35.35,28.0,1913.0,486.0,858.0,371.0,1.9962,50800.0,INLAND
+-122.03,37.28,29.0,3752.0,468.0,1320.0,471.0,9.8937,500001.0,<1H OCEAN
+-117.9,33.97,23.0,7353.0,1255.0,4014.0,1124.0,5.4155,213200.0,<1H OCEAN
+-118.2,33.85,33.0,2557.0,731.0,2286.0,700.0,2.3041,149100.0,<1H OCEAN
+-122.41,37.66,40.0,1294.0,308.0,1177.0,301.0,3.6667,218800.0,NEAR OCEAN
+-118.22,33.9,38.0,796.0,159.0,679.0,167.0,3.6607,110400.0,<1H OCEAN
+-121.28,37.94,48.0,1766.0,444.0,1406.0,421.0,1.7039,52700.0,INLAND
+-117.1,32.71,25.0,939.0,247.0,1003.0,240.0,1.75,87900.0,NEAR OCEAN
+-121.11,37.43,42.0,412.0,75.0,227.0,75.0,2.5,74200.0,INLAND
+-118.54,34.15,26.0,10111.0,1295.0,3599.0,1257.0,10.2292,500001.0,<1H OCEAN
+-114.57,33.64,14.0,1501.0,337.0,515.0,226.0,3.1917,73400.0,INLAND
+-121.94,37.27,23.0,1932.0,552.0,997.0,482.0,3.662,211900.0,<1H OCEAN
+-118.32,34.07,52.0,2156.0,306.0,861.0,311.0,8.8062,500001.0,<1H OCEAN
+-117.14,32.76,35.0,2539.0,661.0,1308.0,629.0,2.6777,146400.0,NEAR OCEAN
+-118.07,33.9,45.0,1776.0,353.0,1180.0,337.0,4.6406,169200.0,<1H OCEAN
+-117.15,32.8,41.0,1413.0,261.0,1070.0,259.0,2.3578,166700.0,NEAR OCEAN
+-117.32,33.22,15.0,4784.0,1039.0,1810.0,986.0,2.4375,108900.0,NEAR OCEAN
+-116.44,34.16,19.0,1867.0,361.0,758.0,321.0,2.8929,98100.0,INLAND
+-122.41,37.75,52.0,3065.0,622.0,1405.0,606.0,3.7813,275900.0,NEAR BAY
+-122.44,37.76,52.0,2509.0,496.0,855.0,478.0,5.0731,405400.0,NEAR BAY
+-118.14,34.69,48.0,1379.0,327.0,696.0,304.0,2.1167,94900.0,INLAND
+-117.23,32.8,31.0,1403.0,388.0,724.0,371.0,2.6403,216100.0,NEAR OCEAN
+-117.3,33.17,6.0,7880.0,1533.0,3760.0,1460.0,4.1807,182600.0,NEAR OCEAN
+-117.7,33.6,26.0,1021.0,230.0,301.0,208.0,2.625,80600.0,<1H OCEAN
+-122.01,36.97,35.0,1605.0,392.0,743.0,382.0,2.5368,240000.0,NEAR OCEAN
+-119.7,36.81,32.0,2623.0,528.0,1570.0,492.0,2.7159,68000.0,INLAND
+-121.97,38.03,17.0,3685.0,685.0,1939.0,649.0,3.7043,139800.0,INLAND
+-119.83,36.37,25.0,1549.0,269.0,819.0,272.0,2.7159,101400.0,INLAND
+-122.13,37.46,37.0,1576.0,334.0,1385.0,323.0,2.5294,159400.0,NEAR BAY
+-118.27,33.95,34.0,1261.0,315.0,1027.0,303.0,2.2946,88800.0,<1H OCEAN
+-118.85,35.2,17.0,2783.0,678.0,2566.0,641.0,1.9907,51200.0,INLAND
+-118.08,34.13,46.0,1238.0,147.0,377.0,145.0,8.4546,500001.0,INLAND
+-118.22,34.07,35.0,1504.0,477.0,2059.0,498.0,2.0133,145800.0,<1H OCEAN
+-117.34,33.19,23.0,3546.0,553.0,1533.0,518.0,5.276,224500.0,NEAR OCEAN
+-119.81,36.73,50.0,772.0,194.0,606.0,167.0,2.2206,59200.0,INLAND
+-120.45,34.86,23.0,3415.0,778.0,1492.0,633.0,2.2791,114800.0,NEAR OCEAN
+-122.25,37.52,14.0,1472.0,291.0,876.0,292.0,4.3594,366000.0,NEAR BAY
+-120.0,38.87,12.0,1437.0,268.0,395.0,144.0,4.225,127600.0,INLAND
+-120.95,38.79,12.0,3247.0,579.0,1459.0,517.0,4.3981,202800.0,INLAND
+-117.84,33.8,35.0,1490.0,251.0,629.0,257.0,4.3661,222100.0,<1H OCEAN
+-121.78,36.92,19.0,1515.0,253.0,975.0,266.0,4.3906,241200.0,<1H OCEAN
+-120.89,35.37,29.0,2046.0,588.0,846.0,410.0,1.65,227300.0,NEAR OCEAN
+-121.84,37.29,24.0,3403.0,656.0,2829.0,612.0,4.775,191900.0,<1H OCEAN
+-118.16,33.94,32.0,2210.0,456.0,1270.0,484.0,4.7708,178600.0,<1H OCEAN
+-122.44,37.8,52.0,1724.0,412.0,540.0,319.0,4.2857,500001.0,NEAR BAY
+-122.74,39.71,16.0,255.0,73.0,85.0,38.0,1.6607,14999.0,INLAND
+-116.2,33.7,26.0,2399.0,625.0,2654.0,535.0,2.2989,60600.0,INLAND
+-121.76,36.92,36.0,2096.0,409.0,1454.0,394.0,3.2216,238300.0,<1H OCEAN
+-122.06,37.35,31.0,1795.0,281.0,872.0,282.0,8.0599,381800.0,<1H OCEAN
+-121.97,37.35,36.0,815.0,126.0,353.0,122.0,6.3191,258300.0,<1H OCEAN
+-122.7,38.33,26.0,1887.0,381.0,1060.0,364.0,3.0078,160400.0,<1H OCEAN
+-117.6,33.42,23.0,2482.0,461.0,1048.0,425.0,4.665,280600.0,NEAR OCEAN
+-117.94,33.93,33.0,1770.0,370.0,1346.0,366.0,4.0833,162500.0,<1H OCEAN
+-118.1,33.84,35.0,1790.0,269.0,924.0,263.0,5.296,226200.0,<1H OCEAN
+-118.01,34.12,43.0,1185.0,207.0,657.0,198.0,4.5491,214800.0,INLAND
+-118.33,33.87,36.0,2219.0,406.0,1219.0,403.0,4.2614,267100.0,<1H OCEAN
+-122.15,37.43,20.0,11709.0,,7604.0,3589.0,1.9045,375000.0,NEAR BAY
+-118.25,33.9,42.0,1386.0,320.0,1163.0,319.0,2.4271,89500.0,<1H OCEAN
+-122.53,38.01,16.0,1495.0,292.0,472.0,284.0,3.4432,67500.0,NEAR BAY
+-119.69,34.43,30.0,1273.0,343.0,1082.0,325.0,2.5104,228100.0,<1H OCEAN
+-119.97,38.94,26.0,1485.0,334.0,406.0,180.0,1.9667,84600.0,INLAND
+-119.63,36.18,23.0,207.0,45.0,171.0,50.0,2.4286,100000.0,INLAND
+-118.27,34.15,22.0,2265.0,637.0,1684.0,561.0,2.6729,217100.0,<1H OCEAN
+-120.46,34.65,14.0,885.0,223.0,533.0,224.0,2.5966,109300.0,NEAR OCEAN
+-118.2,33.99,30.0,1474.0,459.0,1844.0,464.0,2.551,160000.0,<1H OCEAN
+-118.28,34.18,52.0,2602.0,418.0,1137.0,419.0,5.3185,358000.0,<1H OCEAN
+-121.0,37.66,43.0,2039.0,331.0,875.0,342.0,3.9844,152000.0,INLAND
+-122.26,37.77,52.0,1704.0,371.0,663.0,340.0,4.226,275000.0,NEAR BAY
+-121.63,39.18,13.0,1907.0,347.0,821.0,367.0,2.0978,134000.0,INLAND
+-122.47,37.66,18.0,4172.0,806.0,3226.0,790.0,5.7535,297900.0,NEAR OCEAN
+-117.04,32.79,23.0,2491.0,350.0,863.0,348.0,6.7196,306800.0,<1H OCEAN
+-117.46,33.92,21.0,713.0,142.0,476.0,142.0,3.5208,121100.0,INLAND
+-118.27,34.07,32.0,1657.0,579.0,2071.0,598.0,2.1135,152500.0,<1H OCEAN
+-120.18,39.28,14.0,10098.0,1545.0,701.0,254.0,4.0819,141300.0,INLAND
+-121.77,36.96,20.0,4228.0,816.0,2389.0,844.0,3.525,229100.0,<1H OCEAN
+-121.26,38.65,17.0,2655.0,421.0,991.0,384.0,4.6484,270600.0,INLAND
+-121.68,37.93,44.0,1014.0,225.0,704.0,238.0,1.6554,119400.0,INLAND
+-122.18,37.7,35.0,2562.0,554.0,1398.0,525.0,3.3906,178900.0,NEAR BAY
+-117.23,33.89,5.0,11775.0,2031.0,6686.0,1911.0,4.1953,136600.0,INLAND
+-118.09,34.68,4.0,23386.0,4171.0,10493.0,3671.0,4.0211,144000.0,INLAND
+-121.37,37.77,19.0,2610.0,474.0,1290.0,452.0,4.1298,222800.0,INLAND
+-122.4,37.74,45.0,2462.0,509.0,1587.0,450.0,2.59,211800.0,NEAR BAY
+-122.11,37.71,36.0,4569.0,824.0,1950.0,819.0,4.65,206800.0,NEAR BAY
+-121.36,38.03,14.0,2356.0,438.0,1378.0,481.0,3.7375,138800.0,INLAND
+-122.35,37.96,34.0,1428.0,335.0,1272.0,319.0,2.5461,93900.0,NEAR BAY
+-119.78,36.33,16.0,1006.0,212.0,515.0,200.0,3.2386,112500.0,INLAND
+-121.97,37.35,30.0,1955.0,,999.0,386.0,4.6328,287100.0,<1H OCEAN
+-117.93,34.57,5.0,5613.0,1060.0,3569.0,999.0,3.1946,132700.0,INLAND
+-121.4,36.84,40.0,2352.0,536.0,1430.0,535.0,3.0912,155300.0,INLAND
+-118.3,34.09,40.0,3058.0,1215.0,3953.0,1223.0,1.8156,218800.0,<1H OCEAN
+-118.3,33.95,50.0,1843.0,326.0,892.0,314.0,3.1346,120000.0,<1H OCEAN
+-117.17,32.77,35.0,1399.0,274.0,695.0,281.0,3.767,166800.0,NEAR OCEAN
+-121.6,39.68,15.0,1677.0,345.0,844.0,330.0,2.3958,111200.0,INLAND
+-118.33,34.05,44.0,1574.0,390.0,1323.0,404.0,2.5284,226300.0,<1H OCEAN
+-116.92,32.8,33.0,1518.0,268.0,857.0,272.0,3.5586,160400.0,<1H OCEAN
+-120.27,37.12,36.0,1219.0,258.0,639.0,245.0,1.9464,57000.0,INLAND
+-118.27,34.17,48.0,1560.0,280.0,825.0,269.0,5.5118,354700.0,<1H OCEAN
+-118.13,34.13,52.0,2826.0,381.0,924.0,365.0,7.9976,500001.0,<1H OCEAN
+-122.15,37.75,40.0,1445.0,256.0,849.0,255.0,3.8913,126300.0,NEAR BAY
+-118.22,33.93,30.0,443.0,170.0,903.0,189.0,2.1964,125000.0,<1H OCEAN
+-119.36,35.55,29.0,510.0,84.0,236.0,73.0,2.7,125000.0,INLAND
+-116.92,32.78,21.0,4192.0,752.0,2101.0,710.0,4.4306,159100.0,<1H OCEAN
+-122.13,39.74,20.0,1401.0,280.0,668.0,250.0,2.2569,94300.0,INLAND
+-118.35,33.88,36.0,1583.0,411.0,1097.0,350.0,4.0737,238200.0,<1H OCEAN
+-119.29,36.35,15.0,1740.0,319.0,1332.0,308.0,2.5743,60200.0,INLAND
+-120.97,37.68,16.0,2349.0,446.0,1302.0,392.0,3.1625,130300.0,INLAND
+-118.04,34.09,34.0,2001.0,388.0,1461.0,397.0,3.8304,183000.0,INLAND
+-119.67,34.38,28.0,1814.0,526.0,849.0,420.0,3.1625,364300.0,<1H OCEAN
+-122.63,38.94,18.0,3844.0,969.0,1832.0,845.0,1.125,81800.0,INLAND
+-121.79,38.0,34.0,3090.0,593.0,1588.0,566.0,3.6118,124700.0,INLAND
+-122.56,41.53,29.0,1729.0,355.0,848.0,328.0,2.2024,60900.0,INLAND
+-117.21,32.75,15.0,1716.0,702.0,914.0,672.0,1.0612,300000.0,NEAR OCEAN
+-116.83,34.25,15.0,8948.0,1985.0,1316.0,514.0,2.7375,90800.0,INLAND
+-122.13,37.69,17.0,2380.0,769.0,1216.0,643.0,3.395,271300.0,NEAR BAY
+-121.01,37.33,17.0,1926.0,410.0,1054.0,321.0,1.6214,71500.0,INLAND
+-121.01,37.62,35.0,568.0,150.0,622.0,145.0,1.8167,79500.0,INLAND
+-122.42,37.8,52.0,4985.0,1355.0,1848.0,1255.0,4.9211,500001.0,NEAR BAY
+-119.18,34.16,27.0,1832.0,415.0,1480.0,414.0,3.9643,186000.0,NEAR OCEAN
+-119.59,34.43,28.0,2718.0,542.0,1066.0,442.0,4.2059,500001.0,<1H OCEAN
+-118.4,34.03,36.0,1831.0,296.0,871.0,269.0,8.1484,500001.0,<1H OCEAN
+-122.89,39.11,10.0,1588.0,333.0,585.0,254.0,2.2551,71100.0,INLAND
+-117.25,32.77,35.0,2494.0,690.0,1126.0,624.0,4.0313,385300.0,NEAR OCEAN
+-118.36,33.81,26.0,1575.0,300.0,881.0,309.0,5.1778,359900.0,<1H OCEAN
+-118.15,34.2,46.0,1505.0,261.0,857.0,269.0,4.5,184200.0,INLAND
+-117.8,33.64,8.0,4447.0,713.0,1680.0,705.0,8.8693,450400.0,<1H OCEAN
+-118.07,33.72,32.0,1179.0,250.0,369.0,209.0,5.1824,500001.0,NEAR OCEAN
+-122.27,37.87,30.0,1465.0,439.0,862.0,425.0,1.7778,268800.0,NEAR BAY
+-120.47,37.34,9.0,2934.0,511.0,1227.0,501.0,3.6742,117200.0,INLAND
+-121.88,37.31,28.0,3085.0,552.0,1277.0,512.0,4.5795,262800.0,<1H OCEAN
+-122.26,38.16,23.0,2840.0,491.0,1586.0,466.0,4.0337,130400.0,NEAR BAY
+-119.69,36.83,8.0,943.0,189.0,475.0,155.0,4.9327,89500.0,INLAND
+-121.84,37.33,28.0,1535.0,330.0,1937.0,317.0,4.1146,160100.0,<1H OCEAN
+-117.65,34.1,44.0,1526.0,337.0,831.0,326.0,3.0284,115800.0,INLAND
+-122.27,37.97,10.0,15259.0,2275.0,7266.0,2338.0,6.0666,272400.0,NEAR BAY
+-122.28,37.47,44.0,863.0,114.0,281.0,99.0,6.8879,500001.0,NEAR OCEAN
+-118.24,33.92,44.0,1079.0,210.0,601.0,182.0,2.2411,106400.0,<1H OCEAN
+-118.07,34.09,40.0,1745.0,370.0,1293.0,357.0,2.5474,198100.0,<1H OCEAN
+-122.23,37.49,11.0,840.0,329.0,1338.0,345.0,2.3333,241700.0,NEAR OCEAN
+-118.35,33.86,28.0,2075.0,463.0,1216.0,446.0,3.9732,281500.0,<1H OCEAN
+-118.01,33.77,33.0,1387.0,238.0,890.0,264.0,5.422,204100.0,<1H OCEAN
+-118.07,34.1,28.0,676.0,177.0,543.0,185.0,3.2361,187500.0,<1H OCEAN
+-122.44,37.8,52.0,3161.0,472.0,842.0,410.0,7.9761,500001.0,NEAR BAY
+-121.93,37.05,14.0,679.0,108.0,306.0,113.0,6.4214,340600.0,<1H OCEAN
+-118.52,34.23,35.0,1471.0,210.0,735.0,219.0,8.3841,472200.0,<1H OCEAN
+-122.02,37.55,33.0,1325.0,274.0,909.0,267.0,4.5687,177200.0,NEAR BAY
+-118.04,34.17,52.0,1885.0,401.0,764.0,373.0,4.0385,265700.0,INLAND
+-117.76,34.04,36.0,2242.0,448.0,2052.0,447.0,3.4464,113000.0,INLAND
+-122.14,37.38,26.0,2859.0,343.0,951.0,336.0,10.4277,500001.0,NEAR BAY
+-122.65,38.23,52.0,1923.0,393.0,910.0,345.0,3.45,200600.0,<1H OCEAN
+-117.09,33.15,13.0,3958.0,865.0,1981.0,840.0,3.4764,137500.0,<1H OCEAN
+-117.25,32.96,18.0,4773.0,743.0,1970.0,716.0,6.6199,406200.0,NEAR OCEAN
+-118.51,34.22,36.0,1952.0,387.0,1156.0,392.0,4.185,209200.0,<1H OCEAN
+-117.66,34.1,26.0,1855.0,553.0,1109.0,536.0,2.2429,150000.0,INLAND
+-117.64,33.88,13.0,8010.0,1366.0,3920.0,1309.0,5.536,204800.0,<1H OCEAN
+-122.53,37.97,44.0,3595.0,953.0,1831.0,910.0,2.6036,287500.0,NEAR BAY
+-122.08,37.72,32.0,2476.0,368.0,1048.0,367.0,5.6194,274700.0,NEAR BAY
+-117.65,34.02,9.0,2107.0,411.0,1138.0,389.0,4.4042,159100.0,INLAND
+-122.05,37.96,35.0,2190.0,384.0,1154.0,401.0,3.8456,159800.0,NEAR BAY
+-118.35,33.97,25.0,1864.0,616.0,1710.0,575.0,2.2303,159400.0,<1H OCEAN
+-119.47,36.69,19.0,3351.0,589.0,1578.0,542.0,3.2917,160100.0,INLAND
+-119.11,35.39,22.0,984.0,176.0,451.0,170.0,3.25,88900.0,INLAND
+-117.91,34.1,35.0,2746.0,478.0,1779.0,501.0,4.25,166700.0,<1H OCEAN
+-117.35,33.99,45.0,131.0,28.0,89.0,31.0,2.6071,112500.0,INLAND
+-117.81,33.67,8.0,2098.0,342.0,908.0,329.0,7.7589,342900.0,<1H OCEAN
+-116.18,33.69,17.0,89.0,19.0,79.0,21.0,2.375,155000.0,INLAND
+-119.24,34.28,41.0,1280.0,240.0,608.0,252.0,4.4038,229100.0,NEAR OCEAN
+-122.48,37.93,16.0,2947.0,802.0,1385.0,743.0,3.6731,318000.0,NEAR BAY
+-121.72,37.7,17.0,1671.0,352.0,729.0,252.0,6.1023,450000.0,INLAND
+-116.96,33.74,19.0,3649.0,755.0,1717.0,696.0,2.2115,87600.0,INLAND
+-117.87,34.02,16.0,3552.0,575.0,2120.0,573.0,6.4333,271500.0,<1H OCEAN
+-121.45,38.56,52.0,3170.0,476.0,1027.0,457.0,4.63,233800.0,INLAND
+-119.77,36.78,40.0,1411.0,284.0,609.0,296.0,1.9375,67700.0,INLAND
+-117.83,33.79,25.0,2070.0,513.0,1078.0,460.0,2.9312,220100.0,<1H OCEAN
+-121.38,38.67,38.0,1001.0,228.0,597.0,226.0,2.2788,73400.0,INLAND
+-116.84,33.08,15.0,2755.0,519.0,1474.0,460.0,4.0408,225900.0,<1H OCEAN
+-117.11,32.79,16.0,1791.0,518.0,1006.0,491.0,3.5179,129300.0,NEAR OCEAN
+-118.38,34.08,25.0,4625.0,1307.0,1739.0,1191.0,3.3989,485000.0,<1H OCEAN
+-122.68,38.4,32.0,2826.0,627.0,1767.0,628.0,3.1047,141400.0,<1H OCEAN
+-121.86,37.21,23.0,2552.0,305.0,916.0,316.0,9.1974,500001.0,<1H OCEAN
+-117.23,32.86,16.0,1675.0,354.0,604.0,332.0,5.2326,188300.0,NEAR OCEAN
+-116.48,33.8,15.0,3004.0,615.0,437.0,210.0,3.6667,90000.0,INLAND
+-117.89,33.92,8.0,2120.0,544.0,1281.0,470.0,3.4954,159500.0,<1H OCEAN
+-118.03,34.16,39.0,2731.0,366.0,1034.0,338.0,9.8098,500001.0,INLAND
+-121.98,38.39,3.0,9488.0,1417.0,4095.0,1335.0,5.1781,191900.0,INLAND
+-123.15,39.74,23.0,608.0,143.0,281.0,108.0,2.9306,70000.0,INLAND
+-117.16,32.81,35.0,1213.0,200.0,532.0,181.0,3.6806,172400.0,NEAR OCEAN
+-120.39,38.0,33.0,2177.0,404.0,891.0,383.0,3.212,105200.0,INLAND
+-117.16,34.06,33.0,2101.0,468.0,1997.0,412.0,2.8125,117200.0,INLAND
+-120.19,36.6,25.0,875.0,214.0,931.0,214.0,1.5536,58300.0,INLAND
+-118.1,34.06,31.0,2852.0,740.0,3100.0,725.0,2.9524,178800.0,<1H OCEAN
+-118.16,33.91,41.0,1806.0,408.0,1146.0,374.0,2.9643,162200.0,<1H OCEAN
+-118.41,34.02,19.0,4702.0,1472.0,2636.0,1334.0,3.3955,225000.0,<1H OCEAN
+-121.54,38.54,36.0,1672.0,302.0,969.0,337.0,3.0536,73100.0,INLAND
+-120.1,39.19,17.0,1480.0,241.0,202.0,80.0,3.9375,213200.0,INLAND
+-118.27,34.16,52.0,830.0,183.0,479.0,179.0,3.1397,253700.0,<1H OCEAN
+-122.05,37.68,23.0,7518.0,1279.0,3827.0,1294.0,5.1701,216800.0,NEAR BAY
+-118.39,33.97,46.0,2198.0,352.0,839.0,335.0,6.5778,350800.0,<1H OCEAN
+-118.48,34.16,30.0,3507.0,536.0,1427.0,525.0,6.7082,500001.0,<1H OCEAN
+-122.7,38.34,19.0,2987.0,676.0,1782.0,688.0,2.8261,154500.0,<1H OCEAN
+-117.8,33.76,27.0,2655.0,345.0,1017.0,335.0,6.9014,366800.0,<1H OCEAN
+-118.47,34.06,45.0,1271.0,190.0,419.0,171.0,7.6447,500001.0,<1H OCEAN
+-121.29,38.76,12.0,1198.0,174.0,443.0,170.0,6.0097,187500.0,INLAND
+-117.16,32.74,49.0,1815.0,495.0,601.0,410.0,3.0571,418800.0,NEAR OCEAN
+-122.1,37.64,28.0,1784.0,311.0,735.0,278.0,4.6635,206700.0,NEAR BAY
+-122.16,37.45,52.0,1135.0,219.0,441.0,200.0,7.5418,492000.0,NEAR BAY
+-117.89,33.8,38.0,51.0,12.0,41.0,10.0,6.0224,187500.0,<1H OCEAN
+-118.56,34.2,35.0,2273.0,,1431.0,403.0,4.0789,196700.0,<1H OCEAN
+-118.4,33.92,32.0,2828.0,629.0,1313.0,534.0,4.5987,363800.0,<1H OCEAN
+-118.29,34.09,29.0,2240.0,792.0,2254.0,739.0,2.3317,172500.0,<1H OCEAN
+-118.39,34.09,41.0,730.0,126.0,230.0,125.0,4.3214,500001.0,<1H OCEAN
+-118.27,33.8,38.0,1446.0,327.0,980.0,319.0,3.35,177700.0,<1H OCEAN
+-119.06,34.36,52.0,1409.0,359.0,981.0,304.0,2.7951,199300.0,<1H OCEAN
+-118.97,37.64,13.0,1907.0,544.0,575.0,234.0,3.0685,162500.0,INLAND
+-117.08,33.12,37.0,1060.0,268.0,823.0,229.0,1.8363,145500.0,<1H OCEAN
+-122.22,37.79,37.0,2343.0,574.0,1608.0,523.0,2.1494,132500.0,NEAR BAY
+-117.84,33.8,34.0,2004.0,331.0,843.0,328.0,3.59,230600.0,<1H OCEAN
+-117.86,33.74,32.0,691.0,151.0,926.0,148.0,4.125,175900.0,<1H OCEAN
+-117.17,32.69,40.0,2236.0,331.0,767.0,316.0,5.3177,500001.0,NEAR OCEAN
+-118.3,33.74,23.0,3075.0,860.0,1584.0,806.0,2.9386,260500.0,NEAR OCEAN
+-121.95,37.32,33.0,726.0,168.0,351.0,147.0,3.1458,270500.0,<1H OCEAN
+-118.23,33.91,33.0,677.0,182.0,984.0,174.0,2.5893,88900.0,<1H OCEAN
+-117.05,33.52,5.0,3471.0,530.0,1541.0,502.0,4.8083,347700.0,<1H OCEAN
+-118.16,34.09,36.0,3334.0,920.0,2881.0,800.0,2.1696,170800.0,<1H OCEAN
+-118.12,34.15,22.0,1671.0,480.0,1005.0,443.0,3.0119,171400.0,<1H OCEAN
+-117.13,32.74,50.0,1527.0,338.0,728.0,322.0,2.625,203200.0,NEAR OCEAN
+-118.79,34.27,27.0,1146.0,189.0,595.0,197.0,4.5833,198500.0,<1H OCEAN
+-117.06,32.71,21.0,1864.0,388.0,1498.0,389.0,3.8194,125700.0,NEAR OCEAN
+-118.38,34.21,38.0,1363.0,395.0,1798.0,405.0,2.3182,171200.0,<1H OCEAN
+-118.31,34.05,35.0,2007.0,571.0,1513.0,554.0,2.1162,229200.0,<1H OCEAN
+-117.1,32.83,16.0,4214.0,744.0,1820.0,699.0,4.3783,179500.0,<1H OCEAN
+-116.98,32.86,16.0,7718.0,1423.0,4383.0,1394.0,4.0693,146400.0,<1H OCEAN
+-122.32,38.33,17.0,851.0,118.0,370.0,123.0,5.0877,209300.0,NEAR BAY
+-118.39,34.22,35.0,1790.0,334.0,1277.0,345.0,5.0818,186800.0,<1H OCEAN
+-122.46,37.78,52.0,2051.0,552.0,1400.0,510.0,3.2396,375000.0,NEAR BAY
+-118.25,33.94,43.0,793.0,,736.0,231.0,0.8527,90400.0,<1H OCEAN
+-122.21,37.78,43.0,1702.0,460.0,1227.0,407.0,1.7188,126800.0,NEAR BAY
+-122.02,37.58,15.0,3052.0,760.0,2097.0,728.0,3.3617,178100.0,NEAR BAY
+-117.69,33.58,8.0,2887.0,351.0,1176.0,351.0,10.3953,500001.0,<1H OCEAN
+-117.93,33.65,27.0,1283.0,406.0,1063.0,376.0,2.75,275000.0,<1H OCEAN
+-117.75,33.83,14.0,2452.0,296.0,954.0,275.0,8.2375,388300.0,<1H OCEAN
+-122.25,37.47,35.0,3183.0,515.0,1313.0,487.0,5.9062,383200.0,NEAR OCEAN
+-117.92,33.68,28.0,3397.0,597.0,1397.0,560.0,4.8125,244600.0,<1H OCEAN
+-118.3,34.04,50.0,1757.0,522.0,2080.0,488.0,1.7225,180000.0,<1H OCEAN
+-118.09,33.9,37.0,1147.0,258.0,742.0,242.0,4.0461,153500.0,<1H OCEAN
+-121.31,38.01,22.0,2575.0,680.0,1367.0,645.0,1.4274,90500.0,INLAND
+-122.07,37.94,36.0,2639.0,488.0,1111.0,476.0,3.5057,205100.0,NEAR BAY
+-117.88,33.93,17.0,6100.0,861.0,2771.0,866.0,7.6486,306700.0,<1H OCEAN
+-118.38,33.86,29.0,2787.0,475.0,1182.0,444.0,6.7613,352700.0,<1H OCEAN
+-117.94,33.82,24.0,4735.0,955.0,2600.0,868.0,5.0764,228600.0,<1H OCEAN
+-118.12,33.85,37.0,2386.0,409.0,1101.0,399.0,4.6908,218200.0,<1H OCEAN
+-120.67,36.72,18.0,819.0,198.0,996.0,198.0,2.5,112500.0,INLAND
+-122.01,37.37,11.0,2559.0,694.0,1309.0,668.0,4.1847,167300.0,<1H OCEAN
+-118.61,35.08,6.0,3660.0,646.0,1243.0,482.0,3.4911,137200.0,INLAND
+-118.15,34.07,52.0,1983.0,344.0,887.0,331.0,3.2875,234400.0,<1H OCEAN
+-122.44,37.79,52.0,3640.0,840.0,1525.0,796.0,4.4375,500001.0,NEAR BAY
+-118.32,33.75,33.0,2996.0,398.0,1048.0,387.0,9.267,500001.0,NEAR OCEAN
+-118.46,33.98,19.0,2520.0,726.0,964.0,663.0,3.8068,500001.0,<1H OCEAN
+-118.35,33.87,34.0,2823.0,500.0,1429.0,483.0,5.5,279600.0,<1H OCEAN
+-122.08,37.69,43.0,1575.0,324.0,740.0,284.0,2.8512,181000.0,NEAR BAY
+-118.46,34.4,12.0,25957.0,4798.0,10475.0,4490.0,4.542,195300.0,<1H OCEAN
+-117.9,33.7,12.0,4695.0,1110.0,2153.0,989.0,4.6483,190800.0,<1H OCEAN
+-119.8,36.86,7.0,6434.0,1201.0,2733.0,1045.0,3.7656,145000.0,INLAND
+-119.17,34.17,25.0,1596.0,321.0,1378.0,308.0,4.0074,188000.0,NEAR OCEAN
+-120.72,38.57,8.0,892.0,185.0,427.0,164.0,2.6833,118800.0,INLAND
+-118.45,34.24,11.0,9053.0,2193.0,7096.0,2038.0,3.5082,136500.0,<1H OCEAN
+-119.59,37.39,19.0,3273.0,611.0,1164.0,481.0,3.5446,106500.0,INLAND
+-119.01,34.23,11.0,5785.0,1035.0,2760.0,985.0,4.693,232200.0,<1H OCEAN
+-118.34,33.95,25.0,3762.0,1281.0,4015.0,1178.0,2.1587,143800.0,<1H OCEAN
+-117.03,32.79,26.0,3859.0,513.0,1469.0,538.0,5.8683,220500.0,<1H OCEAN
+-118.21,34.08,52.0,3672.0,808.0,3062.0,764.0,2.6806,153000.0,<1H OCEAN
+-118.02,33.73,24.0,6393.0,1141.0,2743.0,1057.0,5.1384,336900.0,<1H OCEAN
+-118.19,34.07,38.0,2965.0,665.0,2128.0,650.0,3.0241,166300.0,<1H OCEAN
+-118.38,34.03,44.0,1913.0,441.0,1295.0,432.0,3.9537,266400.0,<1H OCEAN
+-118.44,34.17,25.0,4966.0,1134.0,1941.0,958.0,3.8081,286700.0,<1H OCEAN
+-118.97,35.38,42.0,1185.0,358.0,1038.0,299.0,0.9951,48000.0,INLAND
+-114.49,33.97,17.0,2809.0,635.0,83.0,45.0,1.6154,87500.0,INLAND
+-119.88,34.43,14.0,2472.0,685.0,1292.0,621.0,3.3026,229500.0,NEAR OCEAN
+-117.94,34.04,33.0,1493.0,331.0,1571.0,354.0,3.8864,158900.0,<1H OCEAN
+-118.49,34.0,32.0,3407.0,1071.0,1463.0,986.0,3.0369,500001.0,<1H OCEAN
+-122.41,37.71,28.0,5015.0,1240.0,3900.0,1029.0,1.2269,181900.0,NEAR BAY
+-117.8,33.67,5.0,2638.0,521.0,1179.0,480.0,5.7759,240000.0,<1H OCEAN
+-122.18,37.79,46.0,2109.0,387.0,922.0,329.0,3.9712,208100.0,NEAR BAY
+-121.41,38.62,21.0,3260.0,763.0,1735.0,736.0,2.5162,97500.0,INLAND
+-121.88,37.32,40.0,1331.0,374.0,1276.0,389.0,2.7546,172500.0,<1H OCEAN
+-117.79,33.68,13.0,2636.0,416.0,1137.0,404.0,7.2118,311500.0,<1H OCEAN
+-122.59,38.04,25.0,3412.0,455.0,1238.0,406.0,8.3646,397300.0,NEAR BAY
+-121.23,37.78,20.0,273.0,49.0,149.0,49.0,4.8229,158300.0,INLAND
+-118.15,34.1,36.0,3514.0,818.0,2277.0,828.0,3.1211,229300.0,<1H OCEAN
+-117.13,32.73,52.0,2676.0,557.0,1181.0,537.0,3.6058,213100.0,NEAR OCEAN
+-121.17,37.82,35.0,2506.0,406.0,1316.0,398.0,3.8472,197600.0,INLAND
+-120.87,39.15,17.0,1819.0,389.0,736.0,283.0,2.8603,128900.0,INLAND
+-121.88,37.36,30.0,2453.0,573.0,1845.0,530.0,3.7396,210700.0,<1H OCEAN
+-122.5,37.59,36.0,1521.0,253.0,736.0,241.0,4.3542,237500.0,NEAR OCEAN
+-118.41,33.92,38.0,1437.0,272.0,590.0,250.0,5.2338,358000.0,<1H OCEAN
+-118.12,34.12,52.0,2907.0,317.0,956.0,279.0,15.0001,500001.0,<1H OCEAN
+-117.89,33.75,31.0,1205.0,280.0,1476.0,301.0,4.0231,139200.0,<1H OCEAN
+-119.3,34.27,17.0,1527.0,503.0,688.0,423.0,1.6007,187500.0,NEAR OCEAN
+-118.47,34.21,34.0,2512.0,603.0,1805.0,584.0,2.9735,220000.0,<1H OCEAN
+-117.67,34.07,29.0,1840.0,388.0,1278.0,368.0,3.5036,123400.0,INLAND
+-122.15,37.73,45.0,3758.0,819.0,1573.0,736.0,2.8355,245400.0,NEAR BAY
+-118.26,33.93,36.0,1102.0,247.0,702.0,225.0,1.5256,95400.0,<1H OCEAN
+-117.84,33.64,11.0,6840.0,1689.0,6083.0,1629.0,2.4132,198300.0,<1H OCEAN
+-117.18,32.76,52.0,2023.0,301.0,649.0,285.0,4.7396,441700.0,NEAR OCEAN
+-119.24,34.27,32.0,4071.0,888.0,1900.0,874.0,3.2792,220500.0,NEAR OCEAN
+-117.16,32.75,19.0,5430.0,1593.0,2496.0,1484.0,2.9112,199100.0,NEAR OCEAN
+-118.18,33.91,41.0,1260.0,299.0,1535.0,322.0,3.0134,128100.0,<1H OCEAN
+-116.99,32.64,15.0,4331.0,699.0,2046.0,627.0,3.9519,193500.0,NEAR OCEAN
+-118.27,33.83,34.0,1124.0,245.0,717.0,242.0,3.1667,186900.0,<1H OCEAN
+-121.63,39.13,26.0,2355.0,531.0,1047.0,497.0,1.8208,79500.0,INLAND
+-122.45,37.77,52.0,3095.0,682.0,1269.0,639.0,3.575,500001.0,NEAR BAY
+-117.03,32.73,32.0,1750.0,333.0,997.0,335.0,3.4784,154400.0,NEAR OCEAN
+-118.3,34.1,38.0,2067.0,914.0,2717.0,853.0,1.7641,250000.0,<1H OCEAN
+-117.3,34.11,42.0,525.0,111.0,444.0,120.0,2.6771,67000.0,INLAND
+-122.79,38.48,7.0,6837.0,,3468.0,1405.0,3.1662,191000.0,<1H OCEAN
+-118.36,33.96,21.0,1802.0,556.0,1286.0,557.0,2.7284,146900.0,<1H OCEAN
+-119.23,36.45,36.0,1508.0,323.0,1283.0,312.0,2.1205,60000.0,INLAND
+-122.2,37.88,36.0,1065.0,160.0,398.0,155.0,7.7736,378100.0,NEAR BAY
+-118.44,34.03,41.0,1164.0,265.0,561.0,251.0,4.2411,350900.0,<1H OCEAN
+-117.14,32.72,42.0,1558.0,458.0,1227.0,407.0,2.2804,139100.0,NEAR OCEAN
+-118.01,33.93,34.0,2424.0,468.0,1293.0,444.0,3.275,189900.0,<1H OCEAN
+-121.34,38.56,12.0,2975.0,628.0,1440.0,593.0,2.9896,118600.0,INLAND
+-118.06,33.85,16.0,4851.0,726.0,2527.0,704.0,6.0142,437400.0,<1H OCEAN
+-120.1,36.66,19.0,2020.0,416.0,1341.0,360.0,1.7,69000.0,INLAND
+-121.23,37.81,15.0,2906.0,537.0,1886.0,557.0,4.2431,137100.0,INLAND
+-122.04,37.95,33.0,1653.0,334.0,814.0,328.0,3.1406,163100.0,NEAR BAY
+-122.18,39.7,23.0,1658.0,307.0,836.0,297.0,3.35,85400.0,INLAND
+-118.15,34.13,30.0,2763.0,520.0,1143.0,465.0,4.7298,500001.0,<1H OCEAN
+-121.22,37.8,28.0,2608.0,576.0,1719.0,554.0,2.1186,94400.0,INLAND
+-118.53,34.38,18.0,2288.0,607.0,2305.0,597.0,3.227,136100.0,<1H OCEAN
+-117.29,34.14,52.0,1683.0,266.0,646.0,256.0,4.0481,97300.0,INLAND
+-118.11,34.1,20.0,3090.0,802.0,2109.0,738.0,3.3801,192500.0,<1H OCEAN
+-122.56,37.9,36.0,1760.0,283.0,562.0,246.0,6.7546,402400.0,NEAR BAY
+-118.37,34.09,38.0,4408.0,1295.0,1690.0,1229.0,3.0156,300000.0,<1H OCEAN
+-117.43,34.02,33.0,3084.0,570.0,1753.0,449.0,3.05,97800.0,INLAND
+-119.2,35.37,6.0,7383.0,1095.0,3415.0,1059.0,5.3119,157300.0,INLAND
+-122.25,37.45,34.0,2999.0,365.0,927.0,369.0,10.2811,500001.0,NEAR OCEAN
+-118.16,34.04,11.0,852.0,215.0,806.0,202.0,1.3971,134400.0,<1H OCEAN
+-118.3,34.16,35.0,3213.0,874.0,2401.0,819.0,2.8342,256800.0,<1H OCEAN
+-122.2,39.52,39.0,2551.0,482.0,1181.0,437.0,2.0625,63400.0,INLAND
+-117.04,32.64,5.0,2329.0,542.0,1213.0,514.0,4.0298,225600.0,NEAR OCEAN
+-117.88,33.73,33.0,2291.0,594.0,3232.0,589.0,3.2037,163500.0,<1H OCEAN
+-118.39,33.92,41.0,80.0,20.0,61.0,23.0,5.25,247200.0,<1H OCEAN
+-117.33,34.13,30.0,2335.0,363.0,1214.0,311.0,2.2449,93200.0,INLAND
+-121.25,39.17,9.0,999.0,189.0,411.0,176.0,2.125,151800.0,INLAND
+-122.44,37.77,52.0,3225.0,667.0,1494.0,619.0,4.4875,500001.0,NEAR BAY
+-118.01,33.94,35.0,1323.0,235.0,807.0,247.0,4.2708,174800.0,<1H OCEAN
+-118.27,34.11,39.0,3825.0,916.0,1378.0,746.0,4.4094,352600.0,<1H OCEAN
+-122.46,37.78,52.0,2165.0,580.0,1067.0,530.0,2.9293,350000.0,NEAR BAY
+-117.71,34.04,17.0,4098.0,733.0,1859.0,713.0,2.9811,231800.0,INLAND
+-119.11,34.33,14.0,4026.0,769.0,1825.0,671.0,3.5541,191800.0,<1H OCEAN
+-118.2,34.03,52.0,774.0,209.0,813.0,203.0,2.3472,135200.0,<1H OCEAN
+-117.17,32.82,21.0,2869.0,596.0,1471.0,577.0,3.0375,197600.0,NEAR OCEAN
+-118.21,34.05,26.0,745.0,258.0,694.0,236.0,1.3846,129200.0,<1H OCEAN
+-118.28,33.96,39.0,882.0,221.0,697.0,189.0,1.8472,99100.0,<1H OCEAN
+-122.17,40.2,28.0,1782.0,334.0,873.0,311.0,3.3594,79100.0,INLAND
+-122.01,38.35,18.0,4486.0,723.0,1600.0,697.0,3.8651,189700.0,INLAND
+-122.68,38.45,36.0,1686.0,303.0,744.0,304.0,4.0139,163100.0,<1H OCEAN
+-117.67,33.61,23.0,3588.0,577.0,1695.0,569.0,6.1401,243200.0,<1H OCEAN
+-118.32,33.87,28.0,3763.0,762.0,1967.0,724.0,5.3244,271900.0,<1H OCEAN
+-117.8,33.92,16.0,5819.0,986.0,2306.0,914.0,4.6315,277500.0,<1H OCEAN
+-118.08,33.96,34.0,1431.0,310.0,1162.0,288.0,4.369,165400.0,<1H OCEAN
+-119.74,36.8,18.0,10862.0,2401.0,5466.0,2209.0,2.4678,74300.0,INLAND
+-119.63,36.6,33.0,1589.0,294.0,1102.0,307.0,1.9676,62400.0,INLAND
+-119.11,34.17,37.0,470.0,105.0,522.0,83.0,2.0368,243800.0,NEAR OCEAN
+-120.87,37.05,29.0,4176.0,779.0,2092.0,741.0,2.595,104200.0,INLAND
+-121.46,38.64,20.0,1517.0,323.0,1287.0,328.0,1.6607,67000.0,INLAND
+-117.38,33.19,26.0,4123.0,1145.0,1703.0,895.0,1.9891,500000.0,NEAR OCEAN
+-117.04,32.69,27.0,1790.0,356.0,1286.0,347.0,3.5437,115800.0,NEAR OCEAN
+-120.81,35.19,14.0,3414.0,802.0,1236.0,632.0,3.7635,336200.0,NEAR OCEAN
+-122.0,38.38,16.0,2509.0,366.0,1043.0,339.0,6.0704,173400.0,INLAND
+-121.62,37.61,26.0,1786.0,306.0,771.0,279.0,5.7239,430600.0,INLAND
+-116.98,32.79,32.0,3756.0,662.0,1611.0,598.0,3.8667,189700.0,<1H OCEAN
+-121.82,37.33,23.0,3279.0,647.0,2582.0,630.0,4.3782,175800.0,<1H OCEAN
+-118.39,34.12,29.0,6447.0,1012.0,2184.0,960.0,8.2816,500001.0,<1H OCEAN
+-121.37,38.68,35.0,1620.0,276.0,939.0,277.0,2.5542,72900.0,INLAND
+-118.07,33.86,31.0,2943.0,518.0,1703.0,472.0,3.7091,225900.0,<1H OCEAN
+-117.98,33.65,18.0,1027.0,206.0,436.0,180.0,4.2159,211300.0,<1H OCEAN
+-117.35,34.2,5.0,9269.0,1605.0,4916.0,1519.0,4.4367,133200.0,INLAND
+-121.28,38.66,17.0,7741.0,1401.0,3153.0,1331.0,3.7869,216100.0,INLAND
+-118.3,33.83,31.0,2693.0,661.0,1598.0,618.0,3.1851,240200.0,<1H OCEAN
+-121.34,38.03,20.0,4213.0,751.0,2071.0,714.0,4.4063,130800.0,INLAND
+-116.97,32.78,26.0,8902.0,1413.0,3941.0,1387.0,4.7943,226900.0,<1H OCEAN
+-119.03,34.22,24.0,3421.0,656.0,2220.0,645.0,4.7831,214200.0,<1H OCEAN
+-122.43,37.61,21.0,10252.0,2595.0,4790.0,2428.0,4.1692,344500.0,NEAR OCEAN
+-120.49,40.31,16.0,1821.0,360.0,969.0,359.0,3.4643,85100.0,INLAND
+-117.22,33.36,16.0,3165.0,482.0,1351.0,452.0,4.605,263300.0,<1H OCEAN
+-122.35,37.95,42.0,1485.0,290.0,971.0,303.0,3.6094,114600.0,NEAR BAY
+-122.28,37.78,50.0,1487.0,306.0,730.0,327.0,2.5139,219000.0,NEAR BAY
+-117.02,32.81,26.0,1998.0,301.0,874.0,305.0,5.4544,180900.0,<1H OCEAN
+-122.66,38.44,17.0,5815.0,898.0,2614.0,887.0,4.3657,215900.0,<1H OCEAN
+-118.21,33.9,41.0,941.0,233.0,973.0,253.0,1.9583,102300.0,<1H OCEAN
+-118.36,33.87,17.0,1082.0,291.0,598.0,281.0,3.9868,281900.0,<1H OCEAN
+-117.95,33.87,34.0,1599.0,296.0,938.0,307.0,4.285,184900.0,<1H OCEAN
+-115.54,32.98,27.0,1513.0,395.0,1121.0,381.0,1.9464,60600.0,INLAND
+-123.21,39.07,17.0,1890.0,342.0,877.0,312.0,3.7833,159800.0,<1H OCEAN
+-119.75,36.75,49.0,2331.0,460.0,1290.0,477.0,2.5111,55400.0,INLAND
+-117.77,33.7,15.0,1392.0,267.0,681.0,263.0,5.4248,187200.0,<1H OCEAN
+-120.61,37.36,16.0,638.0,,380.0,132.0,1.9135,87500.0,INLAND
+-117.13,32.75,50.0,1476.0,354.0,698.0,354.0,3.0,168800.0,NEAR OCEAN
+-119.96,38.93,22.0,2731.0,632.0,1215.0,483.0,2.83,110500.0,INLAND
+-118.43,34.05,24.0,3832.0,949.0,1613.0,893.0,3.9673,477300.0,<1H OCEAN
+-122.33,37.95,22.0,2099.0,569.0,1135.0,509.0,2.1915,120800.0,NEAR BAY
+-121.9,36.97,21.0,3707.0,751.0,1420.0,608.0,4.4485,295200.0,NEAR OCEAN
+-117.02,32.71,30.0,3187.0,592.0,2082.0,631.0,3.5388,118500.0,NEAR OCEAN
+-117.37,33.99,44.0,917.0,224.0,666.0,220.0,1.685,114200.0,INLAND
+-121.94,37.95,18.0,2541.0,355.0,986.0,346.0,7.1978,288000.0,INLAND
+-121.88,37.35,43.0,1086.0,219.0,715.0,226.0,4.2381,193500.0,<1H OCEAN
+-121.95,37.29,30.0,3734.0,813.0,1834.0,824.0,3.4505,260000.0,<1H OCEAN
+-117.03,32.78,17.0,5481.0,1618.0,2957.0,1537.0,2.5707,171300.0,<1H OCEAN
+-119.02,35.32,14.0,2927.0,588.0,1821.0,561.0,3.3529,82600.0,INLAND
+-118.29,34.0,41.0,1807.0,493.0,1731.0,471.0,1.2347,111700.0,<1H OCEAN
+-121.96,37.27,31.0,3347.0,589.0,1566.0,597.0,5.5151,286800.0,<1H OCEAN
+-119.72,36.75,27.0,1691.0,282.0,869.0,337.0,3.9514,86900.0,INLAND
+-117.84,33.65,4.0,1649.0,456.0,1030.0,411.0,2.2262,225000.0,<1H OCEAN
+-121.76,37.33,5.0,4153.0,719.0,2435.0,697.0,5.6306,286200.0,<1H OCEAN
+-118.34,33.99,48.0,1172.0,205.0,497.0,190.0,3.825,183000.0,<1H OCEAN
+-118.13,34.02,38.0,1243.0,310.0,788.0,286.0,2.5852,185100.0,<1H OCEAN
+-118.12,33.93,27.0,580.0,143.0,466.0,133.0,3.0909,187500.0,<1H OCEAN
+-118.13,34.11,52.0,2568.0,345.0,1102.0,345.0,9.1373,500001.0,<1H OCEAN
+-117.53,33.97,34.0,1293.0,215.0,774.0,217.0,3.8906,141000.0,INLAND
+-117.19,32.81,33.0,5226.0,833.0,2221.0,839.0,5.1491,207000.0,NEAR OCEAN
+-123.8,39.47,28.0,2492.0,507.0,1202.0,460.0,2.7857,150300.0,<1H OCEAN
+-115.57,32.8,16.0,2276.0,594.0,1184.0,513.0,1.875,93800.0,INLAND
+-118.98,34.16,16.0,2476.0,402.0,1251.0,387.0,5.7676,241300.0,<1H OCEAN
+-122.44,37.77,52.0,3505.0,745.0,1374.0,714.0,4.3667,500001.0,NEAR BAY
+-117.64,33.48,12.0,2007.0,397.0,1033.0,373.0,5.6754,275900.0,<1H OCEAN
+-117.7,34.09,25.0,1719.0,331.0,1098.0,324.0,3.625,121800.0,INLAND
+-118.31,33.93,37.0,1282.0,244.0,852.0,249.0,4.2917,127900.0,<1H OCEAN
+-118.48,35.61,17.0,4002.0,930.0,1614.0,731.0,1.6236,67300.0,INLAND
+-118.14,33.84,43.0,2107.0,439.0,876.0,429.0,3.2024,339400.0,<1H OCEAN
+-121.47,39.51,19.0,3720.0,636.0,1304.0,607.0,2.6921,97500.0,INLAND
+-117.93,33.88,32.0,2458.0,359.0,967.0,409.0,7.2893,293500.0,<1H OCEAN
+-122.03,36.96,32.0,2182.0,406.0,1122.0,370.0,3.52,284200.0,NEAR OCEAN
+-122.37,40.39,12.0,3783.0,702.0,1970.0,639.0,3.3005,98500.0,INLAND
+-117.94,33.76,33.0,1441.0,337.0,1233.0,331.0,3.7232,176200.0,<1H OCEAN
+-117.38,33.96,30.0,3153.0,623.0,1544.0,575.0,3.4491,133800.0,INLAND
+-118.96,34.23,14.0,15207.0,2924.0,6301.0,2829.0,3.9699,217000.0,<1H OCEAN
+-122.27,37.85,52.0,1974.0,426.0,875.0,363.0,1.5817,153600.0,NEAR BAY
+-121.09,38.97,13.0,1467.0,221.0,688.0,231.0,5.2536,191900.0,INLAND
+-122.07,37.13,26.0,1127.0,199.0,543.0,199.0,4.9792,240000.0,NEAR OCEAN
+-121.08,39.02,13.0,1839.0,275.0,752.0,270.0,4.2031,209600.0,INLAND
+-116.5,33.82,16.0,343.0,85.0,29.0,14.0,2.1042,87500.0,INLAND
+-122.33,37.55,33.0,2199.0,312.0,827.0,319.0,6.1349,500001.0,NEAR OCEAN
+-118.2,33.79,47.0,2549.0,626.0,1388.0,606.0,3.0135,192700.0,NEAR OCEAN
+-120.48,37.29,17.0,2266.0,693.0,3200.0,664.0,1.5635,60400.0,INLAND
+-118.31,34.22,27.0,7714.0,1132.0,3199.0,1100.0,7.1262,446200.0,<1H OCEAN
+-118.2,33.99,35.0,1705.0,523.0,2252.0,508.0,2.3421,154200.0,<1H OCEAN
+-118.42,34.05,52.0,2533.0,402.0,981.0,386.0,7.8164,500001.0,<1H OCEAN
+-122.29,37.88,48.0,2365.0,490.0,1034.0,475.0,3.1065,229200.0,NEAR BAY
+-121.98,37.28,26.0,1182.0,309.0,620.0,306.0,3.3922,269100.0,<1H OCEAN
+-117.01,32.77,43.0,841.0,192.0,496.0,207.0,3.0179,149300.0,<1H OCEAN
+-118.31,34.08,31.0,2275.0,823.0,2189.0,720.0,1.7542,287500.0,<1H OCEAN
+-118.1,34.01,42.0,1436.0,298.0,1005.0,298.0,3.4297,195800.0,<1H OCEAN
+-122.2,37.48,32.0,640.0,166.0,991.0,160.0,1.9844,270000.0,NEAR BAY
+-121.98,37.81,18.0,2903.0,387.0,1127.0,372.0,5.5921,359100.0,<1H OCEAN
+-121.6,36.81,18.0,1575.0,230.0,751.0,219.0,5.2203,286500.0,<1H OCEAN
+-119.56,37.29,14.0,2391.0,451.0,798.0,308.0,3.0924,114600.0,INLAND
+-119.03,35.41,37.0,1761.0,443.0,911.0,365.0,2.0331,53200.0,INLAND
+-117.44,33.93,34.0,1577.0,272.0,880.0,284.0,4.6327,116000.0,INLAND
+-119.21,34.12,15.0,5778.0,1285.0,1722.0,829.0,4.3427,305800.0,NEAR OCEAN
+-118.06,33.91,21.0,2863.0,701.0,1489.0,621.0,3.2031,180700.0,<1H OCEAN
+-118.19,33.97,30.0,1790.0,556.0,1827.0,520.0,1.7562,181300.0,<1H OCEAN
+-122.26,37.51,29.0,3703.0,1075.0,1611.0,1025.0,2.7075,323800.0,NEAR OCEAN
+-122.64,41.73,50.0,1525.0,308.0,661.0,285.0,2.2206,63200.0,INLAND
+-121.81,37.32,26.0,2528.0,511.0,2677.0,512.0,4.1165,164900.0,<1H OCEAN
+-120.48,35.05,24.0,2314.0,468.0,1549.0,463.0,2.8203,152600.0,<1H OCEAN
+-121.96,37.25,35.0,1018.0,169.0,484.0,174.0,6.1648,371900.0,<1H OCEAN
+-121.76,38.25,32.0,1495.0,333.0,905.0,281.0,2.625,212500.0,INLAND
+-117.82,33.68,4.0,1346.0,213.0,603.0,219.0,8.7974,360600.0,<1H OCEAN
+-117.97,33.84,25.0,2471.0,518.0,1539.0,500.0,4.2679,191700.0,<1H OCEAN
+-118.5,34.25,32.0,2411.0,380.0,1040.0,344.0,6.155,257300.0,<1H OCEAN
+-117.25,33.06,6.0,9859.0,1448.0,4194.0,1401.0,6.439,296200.0,NEAR OCEAN
+-117.25,32.79,25.0,1627.0,375.0,735.0,378.0,3.6429,317100.0,NEAR OCEAN
+-121.76,37.68,35.0,1864.0,357.0,1189.0,349.0,4.2361,177500.0,INLAND
+-122.49,37.99,27.0,5470.0,755.0,1916.0,764.0,6.994,420800.0,NEAR BAY
+-120.19,38.03,17.0,8651.0,1579.0,2071.0,757.0,3.1076,115800.0,INLAND
+-119.63,36.7,42.0,1338.0,215.0,617.0,222.0,3.0833,133300.0,INLAND
+-121.47,39.49,17.0,1554.0,242.0,553.0,230.0,3.2174,91800.0,INLAND
+-118.34,33.89,36.0,2274.0,411.0,1232.0,423.0,5.373,244500.0,<1H OCEAN
+-117.76,33.87,16.0,3182.0,429.0,1663.0,428.0,7.0592,288200.0,<1H OCEAN
+-122.48,37.73,47.0,2382.0,392.0,867.0,376.0,5.2598,371500.0,NEAR OCEAN
+-117.41,33.94,29.0,3181.0,714.0,1603.0,706.0,3.25,112500.0,INLAND
+-117.95,33.63,17.0,6745.0,1547.0,2688.0,1535.0,3.9917,271600.0,<1H OCEAN
+-118.73,34.29,8.0,4983.0,754.0,2510.0,725.0,6.9454,276500.0,<1H OCEAN
+-121.11,37.74,11.0,3886.0,599.0,1605.0,529.0,4.4213,182700.0,INLAND
+-117.08,34.08,34.0,45.0,11.0,39.0,14.0,3.0625,500001.0,INLAND
+-122.25,37.08,20.0,1201.0,282.0,601.0,234.0,2.5556,177500.0,NEAR OCEAN
+-121.94,37.71,15.0,6473.0,1027.0,2484.0,970.0,5.0143,271100.0,<1H OCEAN
+-121.32,38.59,21.0,9774.0,1777.0,4674.0,1712.0,3.6817,136100.0,INLAND
+-118.28,34.06,52.0,936.0,454.0,990.0,354.0,1.1122,187500.0,<1H OCEAN
+-122.49,37.73,36.0,1821.0,292.0,742.0,298.0,5.6204,406200.0,NEAR OCEAN
+-121.88,37.43,31.0,2573.0,474.0,1898.0,475.0,5.6651,204100.0,<1H OCEAN
+-121.01,37.37,41.0,1045.0,233.0,632.0,230.0,2.3583,95000.0,INLAND
+-121.04,39.22,14.0,1889.0,471.0,853.0,399.0,2.25,112500.0,INLAND
+-121.35,38.4,11.0,2322.0,459.0,1373.0,424.0,3.175,94400.0,INLAND
+-117.91,33.71,16.0,3113.0,783.0,1719.0,715.0,3.6505,145700.0,<1H OCEAN
+-122.67,38.44,29.0,2551.0,448.0,1165.0,456.0,4.3587,196400.0,<1H OCEAN
+-118.19,34.06,44.0,1734.0,364.0,1133.0,351.0,2.5132,163100.0,<1H OCEAN
+-118.03,34.14,44.0,1446.0,250.0,721.0,243.0,4.7308,352200.0,INLAND
+-122.03,37.95,14.0,3287.0,793.0,1601.0,716.0,3.1719,220500.0,NEAR BAY
+-118.25,34.22,30.0,2062.0,396.0,1089.0,375.0,5.5362,301200.0,<1H OCEAN
+-117.14,32.7,43.0,1126.0,289.0,1132.0,294.0,2.1875,87000.0,NEAR OCEAN
+-118.08,34.09,32.0,3214.0,718.0,2316.0,751.0,3.7066,206800.0,<1H OCEAN
+-122.41,37.73,42.0,2604.0,573.0,1703.0,507.0,3.4231,230200.0,NEAR BAY
+-118.17,34.02,41.0,676.0,216.0,851.0,199.0,2.3077,140600.0,<1H OCEAN
+-122.3,38.31,34.0,1797.0,395.0,1162.0,407.0,3.455,137500.0,NEAR BAY
+-121.88,37.28,33.0,2951.0,529.0,1288.0,521.0,4.1554,313100.0,<1H OCEAN
+-117.46,34.85,7.0,9759.0,1816.0,2933.0,1168.0,3.4912,157700.0,INLAND
+-118.1,33.81,36.0,1111.0,184.0,444.0,177.0,3.7031,245300.0,<1H OCEAN
+-122.49,37.68,34.0,3718.0,676.0,2510.0,632.0,5.3311,270800.0,NEAR OCEAN
+-118.77,34.28,6.0,4685.0,965.0,2180.0,909.0,4.5458,208200.0,<1H OCEAN
+-118.37,34.07,52.0,2203.0,437.0,899.0,384.0,4.25,486900.0,<1H OCEAN
+-118.46,34.04,31.0,2621.0,707.0,1632.0,673.0,3.287,348100.0,<1H OCEAN
+-122.05,37.92,14.0,12713.0,2558.0,4741.0,2412.0,4.7094,234700.0,NEAR BAY
+-117.97,33.77,25.0,1295.0,417.0,856.0,342.0,2.7157,350000.0,<1H OCEAN
+-118.59,34.17,36.0,1887.0,359.0,761.0,329.0,5.4847,296000.0,<1H OCEAN
+-119.64,34.43,32.0,1872.0,318.0,749.0,296.0,4.625,500001.0,<1H OCEAN
+-119.68,36.81,16.0,2668.0,454.0,1536.0,457.0,3.9792,88900.0,INLAND
+-118.38,33.99,21.0,11308.0,3039.0,5127.0,2839.0,4.6277,228300.0,<1H OCEAN
+-118.24,34.06,8.0,1204.0,552.0,1074.0,517.0,1.0227,87500.0,<1H OCEAN
+-120.59,37.59,36.0,291.0,48.0,124.0,47.0,5.6945,154200.0,INLAND
+-120.28,38.07,13.0,1996.0,410.0,618.0,218.0,2.9083,104600.0,INLAND
+-117.43,34.12,7.0,5954.0,1071.0,3567.0,1070.0,3.2056,134100.0,INLAND
+-120.71,38.34,16.0,1257.0,231.0,559.0,213.0,4.4531,144300.0,INLAND
+-121.59,36.55,34.0,737.0,140.0,362.0,138.0,5.1788,270000.0,<1H OCEAN
+-118.19,34.05,41.0,1098.0,264.0,1178.0,245.0,2.1058,124300.0,<1H OCEAN
+-124.09,40.92,12.0,2497.0,491.0,1153.0,462.0,2.8182,126900.0,NEAR OCEAN
+-117.92,33.65,15.0,1309.0,477.0,1330.0,424.0,3.4417,182500.0,<1H OCEAN
+-121.58,37.02,27.0,2303.0,471.0,1447.0,467.0,3.2019,203600.0,INLAND
+-124.17,40.8,52.0,1606.0,419.0,891.0,367.0,1.585,75500.0,NEAR OCEAN
+-122.45,37.71,49.0,2244.0,442.0,1948.0,423.0,4.7639,251500.0,NEAR OCEAN
+-119.82,36.83,16.0,2868.0,376.0,1016.0,379.0,6.1175,144700.0,INLAND
+-121.82,39.72,42.0,2978.0,694.0,1879.0,679.0,1.5064,66300.0,INLAND
+-118.35,34.09,35.0,2234.0,689.0,1334.0,662.0,2.5444,236100.0,<1H OCEAN
+-118.22,34.13,35.0,2983.0,526.0,1614.0,543.0,5.7794,272400.0,<1H OCEAN
+-118.17,33.97,31.0,3388.0,1059.0,3558.0,957.0,2.4049,159000.0,<1H OCEAN
+-117.75,33.54,21.0,8711.0,1544.0,3173.0,1396.0,5.0907,378200.0,<1H OCEAN
+-118.42,34.13,38.0,3830.0,518.0,1292.0,516.0,12.7823,500001.0,<1H OCEAN
+-121.39,38.69,30.0,2897.0,506.0,1508.0,478.0,3.865,88400.0,INLAND
+-121.08,39.21,17.0,3033.0,590.0,1319.0,583.0,2.4811,111800.0,INLAND
+-122.16,37.68,16.0,1687.0,348.0,568.0,352.0,2.3869,83300.0,NEAR BAY
+-117.99,33.67,17.0,1692.0,427.0,903.0,423.0,3.5859,262500.0,<1H OCEAN
+-118.13,33.86,45.0,1320.0,256.0,645.0,256.0,4.4,209500.0,<1H OCEAN
+-118.06,34.16,34.0,2297.0,419.0,909.0,412.0,4.8214,362500.0,INLAND
+-118.08,33.89,33.0,2131.0,435.0,2045.0,426.0,4.0,145700.0,<1H OCEAN
+-117.15,32.76,40.0,1809.0,474.0,826.0,456.0,2.6518,179800.0,NEAR OCEAN
+-118.18,33.88,44.0,1308.0,267.0,783.0,237.0,4.7361,167700.0,<1H OCEAN
+-121.77,37.22,16.0,3992.0,540.0,2097.0,555.0,6.7287,299300.0,<1H OCEAN
+-117.28,34.24,16.0,3474.0,633.0,853.0,315.0,5.2185,128600.0,INLAND
+-122.04,37.31,24.0,3388.0,633.0,1627.0,585.0,5.154,355100.0,<1H OCEAN
+-122.22,37.4,32.0,2297.0,287.0,814.0,283.0,15.0001,500001.0,NEAR OCEAN
+-117.4,34.11,14.0,1933.0,347.0,1443.0,376.0,4.2121,128100.0,INLAND
+-118.28,34.01,48.0,483.0,190.0,775.0,188.0,2.3309,126600.0,<1H OCEAN
+-121.19,39.55,17.0,1483.0,284.0,481.0,211.0,1.4896,83300.0,INLAND
+-119.63,36.32,36.0,1518.0,287.0,749.0,255.0,2.2333,61000.0,INLAND
+-122.09,37.4,24.0,3983.0,1126.0,2645.0,1072.0,3.6742,275000.0,NEAR BAY
+-121.93,37.76,5.0,2255.0,269.0,876.0,258.0,10.3345,461400.0,<1H OCEAN
+-118.04,34.07,39.0,1382.0,315.0,1090.0,308.0,3.8125,174000.0,<1H OCEAN
+-118.1,33.91,29.0,505.0,113.0,411.0,113.0,2.6397,164400.0,<1H OCEAN
+-118.38,33.77,21.0,11353.0,1537.0,4649.0,1504.0,9.821,500001.0,NEAR OCEAN
+-121.43,38.56,41.0,1105.0,227.0,443.0,210.0,3.1827,131700.0,INLAND
+-122.41,37.63,37.0,1252.0,275.0,878.0,287.0,4.2262,228500.0,NEAR OCEAN
+-115.53,32.97,34.0,2231.0,545.0,1568.0,510.0,1.5217,60300.0,INLAND
+-119.81,34.47,26.0,4382.0,618.0,1728.0,587.0,7.4734,432200.0,NEAR OCEAN
+-120.46,34.63,48.0,1408.0,301.0,682.0,279.0,2.9271,146600.0,NEAR OCEAN
+-118.85,34.18,11.0,5873.0,1455.0,3089.0,1365.0,3.5504,173800.0,<1H OCEAN
+-118.38,34.16,46.0,2609.0,593.0,1055.0,585.0,3.3177,309400.0,<1H OCEAN
+-117.23,34.13,10.0,1145.0,293.0,726.0,251.0,1.645,68700.0,INLAND
+-118.16,34.02,47.0,1055.0,298.0,1303.0,302.0,2.6964,138800.0,<1H OCEAN
+-121.96,37.53,28.0,2949.0,529.0,1538.0,545.0,4.9615,228000.0,<1H OCEAN
+-119.52,36.61,33.0,1225.0,275.0,1065.0,248.0,1.8958,55100.0,INLAND
+-121.34,38.64,17.0,2761.0,501.0,1128.0,482.0,3.7562,139700.0,INLAND
+-118.54,34.28,18.0,5481.0,780.0,2477.0,764.0,6.7248,377200.0,<1H OCEAN
+-119.8,36.79,45.0,1337.0,187.0,471.0,187.0,5.187,153800.0,INLAND
+-118.38,34.19,37.0,1434.0,394.0,1667.0,404.0,2.4375,176300.0,<1H OCEAN
+-117.19,34.05,33.0,1688.0,313.0,808.0,298.0,3.2188,117800.0,INLAND
+-118.01,35.12,15.0,1926.0,361.0,917.0,316.0,3.3889,68500.0,INLAND
+-122.7,38.45,47.0,904.0,154.0,310.0,144.0,3.9766,190600.0,<1H OCEAN
+-118.17,34.67,5.0,8352.0,1555.0,3723.0,1389.0,4.5659,140300.0,INLAND
+-117.09,32.75,19.0,2739.0,707.0,2004.0,622.0,1.6318,117700.0,NEAR OCEAN
+-117.97,34.14,15.0,3595.0,964.0,1839.0,877.0,2.6014,150300.0,INLAND
+-117.98,33.87,25.0,2037.0,515.0,1435.0,496.0,3.3199,188800.0,<1H OCEAN
+-122.11,37.66,35.0,2843.0,652.0,1726.0,643.0,3.09,174100.0,NEAR BAY
+-121.05,38.14,19.0,3326.0,561.0,1544.0,511.0,2.9875,166300.0,INLAND
+-117.35,33.98,31.0,4163.0,1242.0,3928.0,1076.0,1.6943,85900.0,INLAND
+-117.93,34.12,36.0,294.0,67.0,266.0,80.0,3.5385,134400.0,INLAND
+-120.51,40.41,36.0,36.0,8.0,4198.0,7.0,5.5179,67500.0,INLAND
+-118.25,34.15,31.0,1238.0,338.0,605.0,331.0,2.8478,228100.0,<1H OCEAN
+-117.32,34.1,42.0,801.0,176.0,711.0,183.0,1.8681,59700.0,INLAND
+-117.41,33.94,22.0,4179.0,1081.0,2096.0,1013.0,2.4435,118500.0,INLAND
+-124.1,40.88,35.0,2987.0,578.0,1581.0,585.0,2.0657,81100.0,NEAR OCEAN
+-122.57,37.97,47.0,5416.0,1115.0,2177.0,1027.0,3.5055,382100.0,NEAR BAY
+-117.27,33.92,13.0,8443.0,1744.0,4885.0,1470.0,3.0907,127200.0,INLAND
+-118.09,33.8,36.0,1724.0,322.0,838.0,328.0,4.4831,253900.0,<1H OCEAN
+-117.98,33.71,24.0,2308.0,464.0,1101.0,407.0,4.4766,230000.0,<1H OCEAN
+-117.9,33.83,23.0,2459.0,689.0,2720.0,598.0,2.8072,164700.0,<1H OCEAN
+-117.06,32.61,23.0,1630.0,362.0,1267.0,418.0,2.5625,131100.0,NEAR OCEAN
+-119.04,35.37,46.0,1637.0,338.0,714.0,297.0,2.1818,75300.0,INLAND
+-117.34,33.94,13.0,7910.0,,3382.0,1176.0,5.5563,214500.0,INLAND
+-122.17,38.08,7.0,18392.0,2782.0,8276.0,2742.0,6.4232,229200.0,NEAR BAY
+-117.27,34.07,21.0,418.0,132.0,401.0,120.0,1.7206,82100.0,INLAND
+-122.73,38.46,14.0,2324.0,754.0,1026.0,677.0,1.722,150000.0,<1H OCEAN
+-118.41,34.19,45.0,1106.0,225.0,595.0,228.0,3.6625,190700.0,<1H OCEAN
+-118.25,34.11,43.0,2230.0,583.0,1667.0,543.0,2.8667,217800.0,<1H OCEAN
+-120.32,38.71,13.0,1115.0,255.0,86.0,32.0,3.5667,115600.0,INLAND
+-124.02,40.72,28.0,3513.0,634.0,1658.0,598.0,3.8095,119900.0,<1H OCEAN
+-118.32,33.74,24.0,6097.0,794.0,2248.0,806.0,10.1357,500001.0,NEAR OCEAN
+-121.83,37.35,31.0,2914.0,715.0,3547.0,645.0,3.7143,178600.0,<1H OCEAN
+-117.2,34.52,12.0,4476.0,761.0,2255.0,735.0,3.925,118500.0,INLAND
+-117.38,34.06,17.0,3139.0,569.0,1612.0,516.0,3.3571,112300.0,INLAND
+-121.92,37.72,4.0,7477.0,1576.0,2937.0,1506.0,5.1437,299400.0,<1H OCEAN
+-122.48,37.76,52.0,1845.0,336.0,1015.0,337.0,4.1397,331300.0,NEAR BAY
+-122.08,37.95,24.0,3173.0,548.0,1351.0,536.0,5.0672,243000.0,NEAR BAY
+-122.73,39.04,23.0,1618.0,395.0,425.0,244.0,1.9833,111500.0,INLAND
+-118.29,33.98,41.0,1582.0,416.0,1422.0,370.0,1.0516,108300.0,<1H OCEAN
+-122.27,37.88,37.0,2619.0,682.0,1152.0,616.0,2.52,277800.0,NEAR BAY
+-118.08,34.13,39.0,788.0,128.0,413.0,139.0,5.9546,396700.0,INLAND
+-117.97,34.08,8.0,2027.0,480.0,1781.0,447.0,3.0806,142400.0,INLAND
+-119.69,34.38,39.0,1383.0,459.0,677.0,362.0,2.25,281300.0,NEAR OCEAN
+-117.08,32.93,5.0,14944.0,2490.0,6600.0,2407.0,6.0857,308300.0,<1H OCEAN
+-119.78,36.82,22.0,4241.0,1147.0,1929.0,971.0,1.7708,53500.0,INLAND
+-121.9,37.31,52.0,2125.0,431.0,1014.0,443.0,5.8186,281100.0,<1H OCEAN
+-119.74,36.75,47.0,2236.0,418.0,1042.0,397.0,2.9545,59600.0,INLAND
+-117.14,34.01,26.0,7561.0,1051.0,2909.0,1012.0,7.2972,269600.0,INLAND
+-118.34,33.84,36.0,1561.0,252.0,740.0,253.0,6.2778,309700.0,<1H OCEAN
+-122.26,37.86,52.0,3497.0,832.0,1493.0,794.0,2.9044,257400.0,NEAR BAY
+-121.86,37.4,16.0,2391.0,369.0,1419.0,373.0,5.8721,267800.0,<1H OCEAN
+-116.4,34.12,16.0,5648.0,1089.0,2524.0,1008.0,2.6739,78000.0,INLAND
+-118.39,33.79,30.0,4402.0,563.0,1582.0,551.0,10.898,500001.0,NEAR OCEAN
+-118.15,34.09,27.0,1935.0,460.0,1456.0,382.0,2.8062,192800.0,<1H OCEAN
+-118.27,33.8,28.0,4698.0,902.0,3287.0,881.0,4.8508,215900.0,<1H OCEAN
+-117.08,33.03,18.0,1339.0,284.0,761.0,290.0,5.3074,137200.0,<1H OCEAN
+-121.46,38.56,52.0,1878.0,393.0,722.0,381.0,3.3348,122800.0,INLAND
+-117.09,32.7,22.0,2409.0,582.0,1887.0,578.0,1.4089,94200.0,NEAR OCEAN
+-122.12,37.4,32.0,3514.0,473.0,1583.0,480.0,10.3894,500001.0,NEAR BAY
+-120.47,34.65,16.0,2549.0,428.0,1486.0,432.0,4.2875,150700.0,NEAR OCEAN
+-123.22,39.15,45.0,1348.0,265.0,639.0,270.0,3.3667,115200.0,<1H OCEAN
+-118.0,33.76,14.0,1120.0,319.0,982.0,307.0,2.9083,179200.0,<1H OCEAN
+-124.18,40.79,39.0,1836.0,352.0,883.0,337.0,1.745,70500.0,NEAR OCEAN
+-118.34,34.26,37.0,1776.0,301.0,702.0,265.0,5.2661,314900.0,<1H OCEAN
+-117.79,34.1,26.0,1664.0,344.0,1024.0,339.0,3.5192,190500.0,INLAND
+-118.3,34.06,43.0,1366.0,740.0,942.0,672.0,1.6953,150000.0,<1H OCEAN
+-121.43,38.57,46.0,2443.0,476.0,939.0,457.0,3.5893,142000.0,INLAND
+-117.18,33.51,13.0,270.0,42.0,120.0,42.0,6.993,500001.0,<1H OCEAN
+-121.3,38.02,16.0,2717.0,621.0,3343.0,643.0,2.5473,106300.0,INLAND
+-118.0,33.83,24.0,2578.0,580.0,1217.0,529.0,2.2401,212500.0,<1H OCEAN
+-121.82,38.01,47.0,1265.0,254.0,587.0,247.0,2.6364,93500.0,INLAND
+-118.51,34.23,36.0,3324.0,448.0,1190.0,423.0,7.2772,477200.0,<1H OCEAN
+-118.07,33.9,39.0,2502.0,546.0,1849.0,518.0,3.8846,164100.0,<1H OCEAN
+-119.9,36.94,11.0,2513.0,408.0,1360.0,415.0,4.277,98500.0,INLAND
+-118.15,34.11,52.0,1746.0,330.0,704.0,306.0,3.7895,364800.0,<1H OCEAN
+-118.15,34.12,49.0,1789.0,288.0,848.0,311.0,6.0199,500000.0,<1H OCEAN
+-122.09,37.69,20.0,4296.0,817.0,1732.0,800.0,4.8036,188300.0,NEAR BAY
+-122.14,37.68,27.0,3337.0,613.0,1489.0,607.0,3.6364,219200.0,NEAR BAY
+-122.45,40.46,16.0,2734.0,501.0,1413.0,484.0,2.8085,105700.0,INLAND
+-121.8,37.34,20.0,2686.0,414.0,1507.0,405.0,5.8068,263900.0,<1H OCEAN
+-120.86,37.6,25.0,1178.0,206.0,709.0,214.0,4.5625,133600.0,INLAND
+-117.07,32.77,38.0,1130.0,228.0,699.0,241.0,2.65,167600.0,NEAR OCEAN
+-118.4,33.87,45.0,2093.0,497.0,842.0,472.0,6.3231,500001.0,<1H OCEAN
+-122.14,37.73,38.0,1723.0,394.0,711.0,353.0,3.0673,218400.0,NEAR BAY
+-118.14,34.16,38.0,1843.0,565.0,1449.0,524.0,2.2174,215400.0,<1H OCEAN
+-122.12,38.01,42.0,2225.0,367.0,864.0,381.0,4.1189,172400.0,NEAR BAY
+-121.2,38.28,20.0,1732.0,307.0,999.0,305.0,3.9808,160200.0,INLAND
+-118.43,34.17,35.0,2922.0,507.0,1130.0,485.0,5.451,341800.0,<1H OCEAN
+-122.39,37.78,5.0,1405.0,515.0,725.0,392.0,3.6037,187500.0,NEAR BAY
+-118.06,33.97,37.0,1645.0,308.0,1077.0,320.0,4.3203,159200.0,<1H OCEAN
+-121.87,37.22,26.0,1921.0,250.0,725.0,253.0,7.6933,405900.0,<1H OCEAN
+-121.31,38.03,24.0,3050.0,568.0,1743.0,549.0,3.7413,105300.0,INLAND
+-118.12,33.89,22.0,6876.0,1960.0,5162.0,1879.0,2.9293,170800.0,<1H OCEAN
+-121.65,39.76,31.0,1599.0,318.0,794.0,303.0,3.0,96700.0,INLAND
+-119.31,34.7,19.0,961.0,218.0,479.0,138.0,3.3438,156300.0,INLAND
+-122.46,37.68,23.0,2812.0,769.0,1983.0,681.0,2.9413,229400.0,NEAR OCEAN
+-122.28,37.5,33.0,6499.0,998.0,2694.0,957.0,7.4787,431300.0,NEAR OCEAN
+-121.9,37.67,7.0,9540.0,1294.0,3926.0,1229.0,7.4353,389800.0,<1H OCEAN
+-121.67,39.14,22.0,2264.0,390.0,1056.0,403.0,3.6111,112300.0,INLAND
+-118.48,34.33,9.0,2384.0,395.0,1697.0,402.0,6.0891,270100.0,<1H OCEAN
+-122.05,37.95,20.0,563.0,107.0,246.0,123.0,5.4482,190800.0,NEAR BAY
+-122.27,37.9,52.0,1803.0,240.0,572.0,236.0,6.174,358800.0,NEAR BAY
+-122.48,37.69,33.0,2347.0,512.0,1259.0,481.0,3.4492,264300.0,NEAR OCEAN
+-118.21,33.95,32.0,1116.0,328.0,1265.0,302.0,2.295,155200.0,<1H OCEAN
+-117.94,33.74,24.0,4248.0,840.0,3118.0,798.0,4.2222,207200.0,<1H OCEAN
+-119.12,36.54,30.0,2747.0,515.0,1368.0,453.0,2.9828,85200.0,INLAND
+-117.16,32.75,49.0,1566.0,494.0,643.0,419.0,1.9637,137500.0,NEAR OCEAN
+-121.93,37.33,44.0,2142.0,358.0,846.0,375.0,5.4273,421000.0,<1H OCEAN
+-117.16,32.8,22.0,2259.0,634.0,1213.0,601.0,2.5,177800.0,NEAR OCEAN
+-121.68,36.72,12.0,19234.0,4492.0,12153.0,4372.0,3.2652,152800.0,<1H OCEAN
+-118.01,34.05,37.0,682.0,172.0,813.0,173.0,3.8125,138000.0,<1H OCEAN
+-119.39,36.54,34.0,1590.0,422.0,1272.0,407.0,1.8068,59000.0,INLAND
+-117.65,33.49,16.0,2223.0,454.0,628.0,382.0,4.3603,248800.0,<1H OCEAN
+-122.75,39.01,17.0,4162.0,967.0,889.0,414.0,3.4187,200500.0,INLAND
+-119.32,36.3,14.0,1680.0,343.0,931.0,350.0,2.7336,89200.0,INLAND
+-118.35,34.02,27.0,3358.0,1069.0,2415.0,956.0,1.4589,87500.0,<1H OCEAN
+-119.19,34.17,35.0,4276.0,767.0,3295.0,708.0,4.2583,187300.0,NEAR OCEAN
+-117.91,33.84,25.0,1021.0,252.0,975.0,258.0,3.125,168100.0,<1H OCEAN
+-116.89,33.73,15.0,2094.0,316.0,937.0,277.0,5.3623,201300.0,INLAND
+-118.29,33.97,43.0,2660.0,672.0,2133.0,588.0,1.7734,107300.0,<1H OCEAN
+-118.34,34.2,41.0,2860.0,682.0,1516.0,621.0,3.0431,262900.0,<1H OCEAN
+-118.06,33.83,22.0,5290.0,1054.0,2812.0,1021.0,4.53,226400.0,<1H OCEAN
+-117.21,33.03,20.0,3370.0,433.0,1020.0,408.0,11.0911,500001.0,NEAR OCEAN
+-121.01,38.73,7.0,6322.0,1046.0,2957.0,1024.0,4.7276,197500.0,INLAND
+-120.19,38.07,43.0,102.0,19.0,44.0,13.0,0.4999,162500.0,INLAND
+-122.02,37.92,26.0,5077.0,640.0,1872.0,636.0,7.4713,351200.0,NEAR BAY
+-118.11,33.89,34.0,2508.0,594.0,1549.0,545.0,3.2069,236500.0,<1H OCEAN
+-117.8,33.74,33.0,2890.0,453.0,1300.0,452.0,6.5616,290200.0,<1H OCEAN
+-118.39,34.05,42.0,1467.0,203.0,577.0,204.0,6.6368,500001.0,<1H OCEAN
+-118.08,33.8,29.0,3675.0,613.0,1457.0,591.0,6.0553,369400.0,<1H OCEAN
+-118.37,34.08,52.0,1234.0,223.0,543.0,213.0,6.0338,423700.0,<1H OCEAN
+-121.94,36.98,21.0,3520.0,831.0,1486.0,753.0,3.0905,264300.0,NEAR OCEAN
+-116.87,33.91,37.0,1858.0,361.0,1632.0,310.0,2.7536,73100.0,INLAND
+-117.12,32.76,26.0,1221.0,331.0,620.0,296.0,2.4821,123600.0,NEAR OCEAN
+-121.68,37.0,19.0,3754.0,588.0,1692.0,550.0,6.7644,412600.0,<1H OCEAN
+-116.57,33.64,10.0,489.0,82.0,183.0,74.0,6.2702,345500.0,INLAND
+-117.71,33.58,2.0,2530.0,562.0,1066.0,510.0,4.6336,187500.0,<1H OCEAN
+-118.17,34.87,9.0,1507.0,293.0,761.0,278.0,3.0184,87900.0,INLAND
+-120.66,38.61,19.0,2715.0,596.0,1301.0,473.0,2.5042,126400.0,INLAND
+-122.13,37.39,27.0,3385.0,427.0,1248.0,409.0,12.0372,500001.0,NEAR BAY
+-118.37,33.87,23.0,1829.0,331.0,891.0,356.0,6.5755,359900.0,<1H OCEAN
+-118.36,33.82,26.0,5166.0,1313.0,2738.0,1239.0,3.3565,360800.0,<1H OCEAN
+-117.06,32.69,9.0,521.0,111.0,491.0,110.0,5.1305,158900.0,NEAR OCEAN
+-120.55,38.12,10.0,1566.0,325.0,785.0,291.0,2.5,116100.0,INLAND
+-118.16,33.94,25.0,5675.0,1224.0,3317.0,1119.0,3.9352,232900.0,<1H OCEAN
+-117.69,34.0,28.0,707.0,154.0,561.0,129.0,2.5781,111600.0,INLAND
+-117.99,35.16,15.0,2180.0,416.0,960.0,370.0,2.875,87800.0,INLAND
+-117.66,34.12,16.0,3853.0,541.0,1726.0,497.0,6.1195,251100.0,INLAND
+-117.83,33.82,23.0,1100.0,285.0,940.0,267.0,3.6953,150000.0,<1H OCEAN
+-122.21,37.83,34.0,5065.0,788.0,1627.0,766.0,6.8976,333300.0,NEAR BAY
+-122.06,37.67,22.0,3882.0,816.0,1830.0,743.0,4.2733,180700.0,NEAR BAY
+-116.34,33.36,24.0,2746.0,514.0,731.0,295.0,3.3214,176400.0,INLAND
+-118.47,34.06,26.0,6577.0,1789.0,2937.0,1652.0,4.801,500001.0,<1H OCEAN
+-118.32,33.83,51.0,2399.0,516.0,1160.0,514.0,3.8456,318900.0,<1H OCEAN
+-118.17,33.91,37.0,1499.0,288.0,1237.0,344.0,3.9333,162300.0,<1H OCEAN
+-119.28,36.35,7.0,3598.0,701.0,2080.0,678.0,3.1111,72400.0,INLAND
+-118.24,33.9,38.0,2055.0,442.0,1518.0,425.0,2.3382,103000.0,<1H OCEAN
+-118.31,33.95,44.0,1558.0,333.0,1095.0,316.0,4.0043,133500.0,<1H OCEAN
+-118.28,33.83,28.0,880.0,168.0,717.0,142.0,4.5469,175700.0,<1H OCEAN
+-121.42,38.5,24.0,7740.0,1539.0,4333.0,1397.0,3.025,87900.0,INLAND
+-118.27,33.94,37.0,973.0,221.0,842.0,178.0,1.6645,94900.0,<1H OCEAN
+-121.66,36.68,10.0,913.0,265.0,508.0,251.0,0.9914,147500.0,<1H OCEAN
+-120.38,36.75,25.0,1689.0,495.0,1745.0,457.0,1.9056,60000.0,INLAND
+-118.28,33.73,45.0,2137.0,559.0,1550.0,529.0,1.9167,227200.0,NEAR OCEAN
+-121.81,37.31,14.0,2731.0,578.0,1109.0,551.0,3.1382,139700.0,<1H OCEAN
+-121.0,37.68,15.0,1232.0,180.0,408.0,196.0,6.9682,182400.0,INLAND
+-122.05,36.97,16.0,3363.0,611.0,1603.0,556.0,4.2542,294100.0,NEAR OCEAN
+-117.92,34.1,33.0,1921.0,397.0,1492.0,393.0,4.375,150500.0,<1H OCEAN
+-118.39,34.19,25.0,3794.0,989.0,2454.0,876.0,2.9982,204200.0,<1H OCEAN
+-117.99,33.9,33.0,2161.0,383.0,1235.0,383.0,5.6454,202800.0,<1H OCEAN
+-117.2,34.14,14.0,2647.0,524.0,989.0,479.0,3.1513,160000.0,INLAND
+-122.11,37.66,29.0,2544.0,643.0,2332.0,603.0,3.2091,150000.0,NEAR BAY
+-121.84,37.25,17.0,2363.0,473.0,1369.0,442.0,4.8355,141600.0,<1H OCEAN
+-120.98,37.68,18.0,4197.0,1006.0,2203.0,874.0,2.166,118600.0,INLAND
+-121.83,38.43,24.0,1307.0,314.0,917.0,291.0,2.2244,98100.0,INLAND
+-121.18,38.8,18.0,2541.0,414.0,1276.0,405.0,5.1857,220100.0,INLAND
+-122.15,37.76,39.0,1823.0,286.0,763.0,270.0,6.0749,196900.0,NEAR BAY
+-117.14,33.15,16.0,1129.0,198.0,758.0,178.0,5.0346,174600.0,<1H OCEAN
+-117.81,33.86,18.0,133.0,29.0,95.0,23.0,3.5625,235000.0,<1H OCEAN
+-118.15,35.04,29.0,1671.0,368.0,821.0,337.0,2.16,56800.0,INLAND
+-117.99,34.07,31.0,1507.0,369.0,1548.0,347.0,3.4327,147200.0,INLAND
+-122.36,37.72,10.0,479.0,125.0,355.0,108.0,2.7083,180400.0,NEAR BAY
+-118.38,33.85,31.0,3533.0,729.0,1647.0,679.0,5.5843,384600.0,<1H OCEAN
+-117.15,32.76,43.0,2361.0,489.0,824.0,470.0,3.4196,302200.0,NEAR OCEAN
+-117.4,33.85,9.0,7538.0,1125.0,3450.0,1077.0,5.4625,223600.0,INLAND
+-117.23,32.74,35.0,2615.0,525.0,1312.0,547.0,4.1339,238200.0,NEAR OCEAN
+-118.11,34.03,36.0,1493.0,316.0,989.0,293.0,3.5272,213700.0,<1H OCEAN
+-121.99,37.97,22.0,2823.0,509.0,1271.0,474.0,5.1333,207200.0,INLAND
+-117.2,34.46,7.0,8414.0,1584.0,5146.0,1517.0,3.2794,92500.0,INLAND
+-117.97,33.79,33.0,3268.0,641.0,1704.0,591.0,3.6849,211400.0,<1H OCEAN
+-118.14,34.02,44.0,1715.0,460.0,1740.0,423.0,2.7019,153300.0,<1H OCEAN
+-122.22,37.81,52.0,2024.0,339.0,756.0,340.0,4.072,270100.0,NEAR BAY
+-117.23,32.8,27.0,1297.0,355.0,776.0,337.0,2.4643,244400.0,NEAR OCEAN
+-119.69,36.75,13.0,2343.0,409.0,1347.0,405.0,4.0027,93100.0,INLAND
+-118.3,34.08,36.0,1276.0,503.0,1502.0,450.0,2.1766,205600.0,<1H OCEAN
+-118.51,34.18,37.0,1893.0,365.0,911.0,324.0,4.8036,295300.0,<1H OCEAN
+-117.31,33.18,16.0,1835.0,430.0,599.0,399.0,2.0147,87700.0,NEAR OCEAN
+-118.25,33.96,48.0,1052.0,234.0,793.0,216.0,1.6585,92900.0,<1H OCEAN
+-124.15,40.79,37.0,2692.0,488.0,1263.0,486.0,3.0216,86400.0,NEAR OCEAN
+-123.21,40.51,16.0,241.0,84.0,152.0,61.0,1.375,48800.0,INLAND
+-118.25,33.93,42.0,763.0,191.0,754.0,174.0,2.0486,101800.0,<1H OCEAN
+-118.36,34.03,43.0,1690.0,379.0,1017.0,359.0,2.1078,133500.0,<1H OCEAN
+-118.16,34.16,44.0,1284.0,278.0,925.0,261.0,1.7321,178400.0,<1H OCEAN
+-122.27,37.55,16.0,4789.0,816.0,1840.0,763.0,6.7474,338200.0,NEAR BAY
+-117.16,33.06,16.0,1988.0,279.0,770.0,252.0,5.8661,404500.0,NEAR OCEAN
+-119.76,36.78,30.0,6117.0,1330.0,2768.0,1224.0,2.1383,78800.0,INLAND
+-122.31,37.58,44.0,1990.0,442.0,1141.0,424.0,3.9696,258300.0,NEAR BAY
+-117.18,32.7,44.0,2655.0,514.0,1102.0,489.0,3.6759,368800.0,NEAR OCEAN
+-122.45,41.28,15.0,2740.0,503.0,1188.0,445.0,3.4519,128800.0,INLAND
+-117.42,34.13,4.0,11587.0,1796.0,5804.0,1705.0,4.8283,141900.0,INLAND
+-122.09,37.4,22.0,1489.0,436.0,662.0,470.0,3.5179,197200.0,NEAR BAY
+-117.28,32.75,34.0,981.0,313.0,508.0,304.0,2.2328,266700.0,NEAR OCEAN
+-120.85,37.51,5.0,2899.0,745.0,1593.0,633.0,2.2292,127500.0,INLAND
+-118.0,33.86,32.0,1162.0,196.0,563.0,178.0,3.875,203000.0,<1H OCEAN
+-123.17,39.18,14.0,2240.0,327.0,1030.0,308.0,5.9585,214900.0,<1H OCEAN
+-121.94,37.24,26.0,2561.0,388.0,1165.0,393.0,7.3522,363800.0,<1H OCEAN
+-118.3,33.79,21.0,1068.0,283.0,1180.0,274.0,2.5,157500.0,<1H OCEAN
+-121.56,37.0,20.0,3976.0,953.0,3866.0,950.0,2.5387,160100.0,INLAND
+-118.03,33.85,30.0,2320.0,448.0,1434.0,452.0,4.0865,203700.0,<1H OCEAN
+-120.72,38.94,10.0,1604.0,352.0,540.0,190.0,3.7625,113200.0,INLAND
+-118.0,35.05,21.0,1739.0,425.0,945.0,362.0,3.4015,86500.0,INLAND
+-118.45,34.31,28.0,1532.0,287.0,977.0,275.0,4.4773,173100.0,<1H OCEAN
+-118.21,33.89,42.0,1739.0,370.0,1104.0,297.0,2.2125,120700.0,<1H OCEAN
+-121.83,37.34,21.0,6404.0,1232.0,6047.0,1235.0,4.2098,193400.0,<1H OCEAN
+-123.2,39.16,14.0,1908.0,484.0,1195.0,467.0,1.7929,82300.0,<1H OCEAN
+-118.26,33.92,40.0,1076.0,244.0,705.0,255.0,1.7986,98900.0,<1H OCEAN
+-118.2,34.09,39.0,1594.0,430.0,1668.0,378.0,2.5343,138200.0,<1H OCEAN
+-117.17,32.75,52.0,1052.0,,381.0,201.0,3.0726,289600.0,NEAR OCEAN
+-121.23,37.81,16.0,2085.0,342.0,1236.0,345.0,5.5591,149300.0,INLAND
+-120.08,37.06,18.0,402.0,76.0,213.0,71.0,1.9063,95800.0,INLAND
+-121.89,37.26,26.0,1864.0,331.0,956.0,325.0,5.5,231700.0,<1H OCEAN
+-117.87,33.59,44.0,2499.0,396.0,910.0,374.0,6.6544,500001.0,<1H OCEAN
+-118.42,34.2,27.0,3201.0,970.0,3403.0,948.0,2.2377,231700.0,<1H OCEAN
+-117.43,33.93,31.0,1273.0,262.0,686.0,254.0,2.4922,109400.0,INLAND
+-117.88,33.72,38.0,1421.0,300.0,1236.0,263.0,3.9844,165300.0,<1H OCEAN
+-121.46,38.6,29.0,1978.0,538.0,823.0,490.0,1.9688,135600.0,INLAND
+-121.03,37.64,22.0,2001.0,387.0,1520.0,387.0,3.148,102300.0,INLAND
+-121.98,37.35,36.0,1054.0,193.0,546.0,187.0,4.5625,240000.0,<1H OCEAN
+-122.49,37.63,34.0,696.0,145.0,398.0,162.0,3.525,254100.0,NEAR OCEAN
+-122.22,37.77,40.0,494.0,114.0,547.0,135.0,2.8015,114800.0,NEAR BAY
+-118.43,33.9,27.0,1536.0,377.0,553.0,326.0,5.4088,500001.0,<1H OCEAN
+-118.2,33.95,35.0,1924.0,520.0,2101.0,541.0,2.4267,151500.0,<1H OCEAN
+-122.65,38.27,9.0,4764.0,816.0,2077.0,755.0,5.1391,234500.0,<1H OCEAN
+-118.26,33.99,47.0,1865.0,465.0,1916.0,438.0,1.8242,95000.0,<1H OCEAN
+-119.69,36.79,15.0,2524.0,451.0,1207.0,424.0,2.7404,76300.0,INLAND
+-120.26,38.13,17.0,301.0,94.0,122.0,47.0,4.0583,87500.0,INLAND
+-122.16,37.73,52.0,1114.0,206.0,425.0,207.0,2.5625,175000.0,NEAR BAY
+-117.9,34.11,37.0,1286.0,255.0,1047.0,249.0,4.2019,140100.0,<1H OCEAN
+-121.78,40.12,14.0,388.0,108.0,35.0,17.0,6.1359,106300.0,INLAND
+-122.0,37.32,34.0,3450.0,731.0,1915.0,689.0,4.7402,244500.0,<1H OCEAN
+-120.66,40.42,35.0,1450.0,325.0,717.0,297.0,2.5074,66400.0,INLAND
+-117.81,33.73,19.0,4022.0,975.0,2334.0,954.0,3.0305,140600.0,<1H OCEAN
+-122.93,38.02,28.0,1284.0,265.0,628.0,219.0,3.5469,200000.0,NEAR OCEAN
+-120.69,37.4,46.0,860.0,130.0,496.0,147.0,3.5167,137500.0,INLAND
+-120.99,39.22,16.0,1497.0,275.0,737.0,243.0,2.8942,182500.0,INLAND
+-117.25,32.73,39.0,1688.0,256.0,635.0,272.0,4.5938,367400.0,NEAR OCEAN
+-118.25,33.93,27.0,581.0,135.0,647.0,131.0,3.2917,83100.0,<1H OCEAN
+-118.15,34.71,36.0,1338.0,250.0,709.0,250.0,3.5625,101400.0,INLAND
+-118.34,33.8,33.0,2194.0,469.0,987.0,397.0,5.0951,318900.0,<1H OCEAN
+-122.41,37.73,52.0,1931.0,358.0,1092.0,356.0,3.7835,271300.0,NEAR BAY
+-122.46,37.66,15.0,6082.0,1284.0,3861.0,1198.0,5.4221,284700.0,NEAR OCEAN
+-122.25,37.86,52.0,1389.0,191.0,514.0,202.0,7.0897,446200.0,NEAR BAY
+-117.64,34.08,33.0,1987.0,455.0,1369.0,475.0,2.4464,122600.0,INLAND
+-117.53,33.69,6.0,454.0,102.0,213.0,43.0,10.9704,483300.0,<1H OCEAN
+-124.14,40.59,17.0,2985.0,610.0,1544.0,584.0,2.178,76800.0,NEAR OCEAN
+-118.14,34.17,52.0,2687.0,600.0,1716.0,544.0,2.7201,205700.0,<1H OCEAN
+-117.99,33.78,19.0,7399.0,1698.0,3554.0,1593.0,3.1049,173900.0,<1H OCEAN
+-122.28,38.34,44.0,1066.0,190.0,416.0,174.0,3.6389,304000.0,NEAR BAY
+-122.31,37.52,24.0,2328.0,335.0,969.0,354.0,7.7364,435800.0,NEAR OCEAN
+-117.08,34.03,23.0,3862.0,699.0,2082.0,652.0,3.154,115700.0,INLAND
+-119.85,36.97,13.0,2872.0,477.0,1607.0,481.0,4.475,102400.0,INLAND
+-120.44,34.93,15.0,868.0,244.0,1133.0,253.0,2.0995,87500.0,<1H OCEAN
+-118.16,33.78,15.0,4798.0,1374.0,3087.0,1212.0,2.127,163300.0,NEAR OCEAN
+-116.52,33.97,13.0,3921.0,754.0,1902.0,665.0,3.3616,89600.0,INLAND
+-122.26,37.78,52.0,1045.0,239.0,496.0,216.0,2.9213,190800.0,NEAR BAY
+-117.13,32.58,32.0,1870.0,437.0,1142.0,426.0,2.3194,159400.0,NEAR OCEAN
+-117.09,32.77,38.0,2065.0,374.0,812.0,343.0,3.125,216500.0,NEAR OCEAN
+-118.52,34.39,21.0,5477.0,1275.0,3384.0,1222.0,3.6625,228100.0,<1H OCEAN
+-118.18,34.12,45.0,2397.0,488.0,1569.0,471.0,4.21,167900.0,<1H OCEAN
+-121.89,37.35,47.0,2879.0,631.0,2229.0,606.0,3.2599,183100.0,<1H OCEAN
+-121.83,36.62,33.0,1771.0,398.0,1037.0,388.0,2.7708,161800.0,<1H OCEAN
+-118.31,34.07,40.0,2657.0,794.0,2149.0,749.0,2.2653,445000.0,<1H OCEAN
+-118.34,34.03,47.0,1927.0,561.0,1349.0,508.0,1.3444,125000.0,<1H OCEAN
+-121.87,37.35,27.0,3500.0,1036.0,3019.0,955.0,2.9222,153700.0,<1H OCEAN
+-118.1,33.91,35.0,1653.0,325.0,1072.0,301.0,3.2708,159700.0,<1H OCEAN
+-117.21,33.19,21.0,3765.0,612.0,1722.0,593.0,4.8152,218500.0,<1H OCEAN
+-122.01,37.54,32.0,2572.0,406.0,1128.0,395.0,5.0,287600.0,NEAR BAY
+-119.23,34.42,16.0,630.0,117.0,343.0,100.0,5.75,325000.0,<1H OCEAN
+-121.33,38.61,21.0,2453.0,518.0,1326.0,505.0,2.7079,148000.0,INLAND
+-117.04,32.7,7.0,9311.0,1703.0,7302.0,1694.0,4.419,156900.0,NEAR OCEAN
+-117.8,34.11,25.0,5039.0,821.0,2654.0,802.0,4.7969,211700.0,INLAND
+-118.12,33.99,24.0,1705.0,479.0,2037.0,459.0,2.4219,137500.0,<1H OCEAN
+-118.72,34.14,7.0,23866.0,4407.0,9873.0,4012.0,5.4032,318500.0,NEAR OCEAN
+-121.9,37.28,26.0,3756.0,,1408.0,535.0,5.6427,320000.0,<1H OCEAN
+-123.41,40.61,17.0,769.0,205.0,301.0,126.0,1.7875,55000.0,INLAND
+-121.01,37.7,12.0,9148.0,1906.0,4656.0,1853.0,3.2447,142200.0,INLAND
+-117.05,32.58,23.0,1918.0,339.0,1392.0,340.0,4.087,134800.0,NEAR OCEAN
+-122.33,37.91,36.0,1954.0,513.0,1437.0,440.0,1.125,93800.0,NEAR BAY
+-122.24,38.25,33.0,213.0,36.0,91.0,33.0,4.9167,187500.0,NEAR BAY
+-122.52,37.89,17.0,4363.0,1041.0,1640.0,989.0,3.9531,417600.0,NEAR BAY
+-118.26,34.18,32.0,14556.0,2077.0,5459.0,2017.0,8.1657,500001.0,<1H OCEAN
+-118.13,34.02,40.0,2988.0,690.0,2144.0,667.0,2.3359,189300.0,<1H OCEAN
+-121.6,39.79,18.0,2672.0,533.0,1151.0,532.0,2.567,102900.0,INLAND
+-120.19,37.53,25.0,1470.0,341.0,706.0,283.0,1.7614,71300.0,INLAND
+-117.05,33.03,14.0,5180.0,1051.0,1639.0,991.0,4.5,222200.0,<1H OCEAN
+-117.79,35.21,4.0,2.0,2.0,6.0,2.0,2.375,137500.0,INLAND
+-118.67,34.27,15.0,3221.0,659.0,1390.0,607.0,3.5313,191800.0,<1H OCEAN
+-122.22,38.09,47.0,2161.0,440.0,966.0,360.0,2.2734,88700.0,NEAR BAY
+-119.53,36.61,33.0,587.0,170.0,730.0,162.0,1.5625,55800.0,INLAND
+-118.5,34.02,35.0,2914.0,934.0,1334.0,870.0,2.9934,350000.0,<1H OCEAN
+-121.58,39.76,18.0,1676.0,332.0,733.0,318.0,1.7875,103800.0,INLAND
+-119.79,34.44,25.0,1479.0,314.0,977.0,309.0,4.1797,271800.0,NEAR OCEAN
+-119.02,35.39,52.0,191.0,52.0,106.0,49.0,2.0455,72500.0,INLAND
+-117.28,34.41,14.0,2105.0,396.0,960.0,396.0,2.9934,118200.0,INLAND
+-118.2,34.11,36.0,1441.0,534.0,1809.0,500.0,2.1793,185700.0,<1H OCEAN
+-117.28,32.77,38.0,1267.0,340.0,442.0,250.0,4.3403,500000.0,NEAR OCEAN
+-119.21,34.26,26.0,2406.0,411.0,1313.0,391.0,4.9079,234100.0,NEAR OCEAN
+-122.0,38.35,38.0,1918.0,364.0,745.0,348.0,2.5707,126000.0,INLAND
+-118.13,34.03,31.0,4267.0,1070.0,3176.0,1071.0,3.0212,208200.0,<1H OCEAN
+-122.49,38.3,14.0,2844.0,602.0,1613.0,544.0,3.3571,193600.0,<1H OCEAN
+-118.35,33.89,29.0,2940.0,708.0,2175.0,684.0,3.6486,229000.0,<1H OCEAN
+-122.42,37.62,36.0,1017.0,165.0,407.0,159.0,4.8,306800.0,NEAR OCEAN
+-117.28,33.66,15.0,4573.0,928.0,2513.0,832.0,2.6949,163600.0,<1H OCEAN
+-117.25,33.1,14.0,3676.0,720.0,1176.0,614.0,3.9464,171900.0,NEAR OCEAN
+-117.97,34.05,33.0,1452.0,268.0,1274.0,278.0,3.6563,162700.0,<1H OCEAN
+-118.26,34.01,46.0,879.0,253.0,1010.0,221.0,2.1776,118300.0,<1H OCEAN
+-118.59,34.2,21.0,1789.0,,2300.0,677.0,2.754,179800.0,<1H OCEAN
+-122.96,38.42,50.0,2530.0,524.0,940.0,361.0,2.9375,122900.0,<1H OCEAN
+-121.73,36.85,22.0,1304.0,278.0,887.0,227.0,3.6607,206300.0,<1H OCEAN
+-117.85,34.11,25.0,9255.0,1659.0,4944.0,1627.0,4.5708,223000.0,INLAND
+-117.91,34.1,28.0,3694.0,722.0,1999.0,718.0,3.2813,181100.0,<1H OCEAN
+-120.0,35.91,16.0,259.0,53.0,131.0,38.0,3.125,62500.0,INLAND
+-121.76,36.83,28.0,1445.0,268.0,1017.0,284.0,3.6693,211000.0,<1H OCEAN
+-117.96,33.73,22.0,3479.0,455.0,1454.0,488.0,6.6324,347600.0,<1H OCEAN
+-117.22,32.74,52.0,1283.0,173.0,436.0,190.0,7.4029,345700.0,NEAR OCEAN
+-122.72,38.31,26.0,1644.0,294.0,801.0,291.0,4.3906,248000.0,<1H OCEAN
+-117.96,33.82,32.0,2856.0,622.0,1499.0,601.0,3.63,183400.0,<1H OCEAN
+-121.81,37.41,25.0,2496.0,351.0,1034.0,367.0,7.0544,320700.0,<1H OCEAN
+-116.88,34.08,52.0,3419.0,777.0,710.0,265.0,3.9028,128600.0,INLAND
+-119.55,36.52,31.0,1986.0,417.0,1042.0,422.0,3.0294,70200.0,INLAND
+-117.14,33.23,11.0,4068.0,829.0,918.0,500.0,3.1272,281300.0,<1H OCEAN
+-118.3,34.11,52.0,3136.0,675.0,1213.0,606.0,3.5806,391900.0,<1H OCEAN
+-118.31,33.97,52.0,1629.0,277.0,819.0,288.0,3.725,142600.0,<1H OCEAN
+-118.44,34.02,39.0,3278.0,632.0,1321.0,617.0,6.2917,465700.0,<1H OCEAN
+-121.02,38.66,4.0,7392.0,1155.0,3096.0,1065.0,4.5246,198900.0,INLAND
+-117.31,34.14,38.0,2011.0,448.0,1190.0,403.0,1.8654,89400.0,INLAND
+-121.38,38.59,36.0,1239.0,237.0,764.0,222.0,3.0156,103000.0,INLAND
+-122.12,37.68,45.0,2179.0,401.0,1159.0,399.0,3.4839,180600.0,NEAR BAY
+-118.39,34.14,19.0,5076.0,1034.0,2021.0,960.0,5.5683,309200.0,<1H OCEAN
+-117.37,33.94,20.0,1682.0,296.0,706.0,291.0,4.0966,140100.0,INLAND
+-121.18,38.07,21.0,2333.0,377.0,1073.0,332.0,4.8125,161100.0,INLAND
+-118.22,33.97,47.0,1147.0,297.0,1097.0,307.0,2.6384,162900.0,<1H OCEAN
+-122.08,37.92,28.0,2377.0,469.0,1068.0,435.0,4.4561,250000.0,NEAR BAY
+-118.04,34.05,34.0,1058.0,230.0,1043.0,229.0,3.0536,137500.0,<1H OCEAN
+-121.65,36.69,21.0,7884.0,2011.0,4907.0,1919.0,2.7367,160300.0,<1H OCEAN
+-121.56,39.51,46.0,1885.0,385.0,871.0,347.0,1.6352,53100.0,INLAND
+-117.22,32.75,34.0,6001.0,1111.0,2654.0,1072.0,4.5878,291000.0,NEAR OCEAN
+-121.99,38.34,13.0,3252.0,610.0,1915.0,631.0,4.2137,151700.0,INLAND
+-121.47,38.55,48.0,1091.0,403.0,926.0,336.0,1.1458,65400.0,INLAND
+-118.1,33.96,38.0,1657.0,335.0,1195.0,309.0,4.1711,160100.0,<1H OCEAN
+-118.36,33.85,36.0,1390.0,230.0,683.0,219.0,4.8906,334400.0,<1H OCEAN
+-122.64,38.48,19.0,3244.0,449.0,1174.0,454.0,5.8369,255700.0,<1H OCEAN
+-121.96,37.39,20.0,1032.0,229.0,658.0,238.0,4.5062,219300.0,<1H OCEAN
+-118.54,36.12,11.0,4103.0,882.0,356.0,171.0,2.1029,99100.0,INLAND
+-117.15,32.92,16.0,2366.0,392.0,1482.0,407.0,4.9024,182900.0,<1H OCEAN
+-118.16,34.01,36.0,931.0,246.0,732.0,235.0,1.7679,142800.0,<1H OCEAN
+-118.0,33.77,24.0,1324.0,267.0,687.0,264.0,3.4327,192800.0,<1H OCEAN
+-118.4,33.86,41.0,2237.0,597.0,938.0,523.0,4.7105,500001.0,<1H OCEAN
+-122.7,38.44,42.0,709.0,182.0,547.0,172.0,2.1912,165000.0,<1H OCEAN
+-118.23,34.24,31.0,3857.0,607.0,1695.0,572.0,7.642,396400.0,<1H OCEAN
+-121.75,36.93,24.0,4026.0,881.0,2264.0,863.0,3.1327,218100.0,<1H OCEAN
+-118.43,34.23,35.0,1225.0,228.0,720.0,231.0,3.4013,176500.0,<1H OCEAN
+-122.44,37.63,35.0,5113.0,959.0,3004.0,964.0,4.7625,281300.0,NEAR OCEAN
+-122.47,37.71,42.0,1961.0,427.0,1211.0,409.0,3.5156,239400.0,NEAR OCEAN
+-118.43,34.25,38.0,921.0,239.0,1023.0,241.0,3.4514,151900.0,<1H OCEAN
+-117.12,33.27,11.0,3016.0,601.0,1727.0,541.0,4.9375,232800.0,<1H OCEAN
+-122.29,37.91,40.0,2085.0,329.0,796.0,339.0,5.5357,273700.0,NEAR BAY
+-121.03,37.68,20.0,3204.0,625.0,2016.0,605.0,2.6567,110400.0,INLAND
+-119.73,36.76,30.0,1548.0,282.0,886.0,311.0,3.1,71300.0,INLAND
+-116.96,32.79,17.0,5236.0,1325.0,3308.0,1233.0,2.3221,138800.0,<1H OCEAN
+-118.27,33.94,43.0,1309.0,344.0,1182.0,340.0,1.6625,88700.0,<1H OCEAN
+-120.66,35.26,15.0,5540.0,1319.0,2383.0,1165.0,2.2656,226200.0,NEAR OCEAN
+-118.24,34.24,31.0,3812.0,595.0,1645.0,591.0,7.585,380100.0,<1H OCEAN
+-121.35,38.66,8.0,3322.0,805.0,1694.0,774.0,2.7011,130700.0,INLAND
+-122.07,37.35,35.0,1447.0,205.0,619.0,206.0,9.8144,500001.0,<1H OCEAN
+-118.46,34.25,32.0,2217.0,422.0,1064.0,427.0,3.6989,208600.0,<1H OCEAN
+-122.25,37.8,43.0,2364.0,792.0,1359.0,722.0,2.1429,250000.0,NEAR BAY
+-117.31,33.67,9.0,981.0,169.0,596.0,156.0,3.1832,157400.0,<1H OCEAN
+-118.41,34.25,19.0,280.0,84.0,483.0,87.0,1.95,137500.0,<1H OCEAN
+-117.12,32.75,20.0,1406.0,413.0,850.0,412.0,2.3261,114600.0,NEAR OCEAN
+-118.15,34.11,52.0,1000.0,192.0,363.0,158.0,4.2981,352800.0,<1H OCEAN
+-122.78,38.41,43.0,1351.0,277.0,1011.0,297.0,2.5917,144000.0,<1H OCEAN
+-118.14,33.76,50.0,2960.0,761.0,1179.0,718.0,3.5214,398100.0,NEAR OCEAN
+-118.11,34.18,52.0,3571.0,510.0,1434.0,490.0,5.9009,376000.0,INLAND
+-119.23,34.28,24.0,4260.0,691.0,1581.0,607.0,5.5048,303600.0,NEAR OCEAN
+-118.11,33.8,36.0,1680.0,291.0,744.0,280.0,4.66,244800.0,<1H OCEAN
+-117.7,33.6,16.0,2092.0,489.0,877.0,392.0,3.0461,216900.0,<1H OCEAN
+-115.49,32.87,19.0,541.0,104.0,457.0,106.0,3.3583,102800.0,INLAND
+-118.86,36.41,20.0,2749.0,575.0,1195.0,491.0,3.0391,139700.0,INLAND
+-120.92,37.65,23.0,505.0,124.0,163.0,129.0,1.3696,275000.0,INLAND
+-122.12,37.99,33.0,1660.0,277.0,741.0,261.0,4.675,225400.0,NEAR BAY
+-118.26,34.01,38.0,697.0,208.0,749.0,206.0,1.4653,118800.0,<1H OCEAN
+-118.42,37.35,21.0,3302.0,557.0,1413.0,520.0,4.375,180400.0,INLAND
+-117.56,33.88,40.0,1196.0,294.0,1052.0,258.0,2.0682,113000.0,INLAND
+-118.41,34.04,49.0,601.0,95.0,228.0,106.0,8.0239,500001.0,<1H OCEAN
+-122.94,38.64,26.0,4050.0,712.0,2072.0,636.0,4.0781,287800.0,<1H OCEAN
+-115.73,32.8,44.0,472.0,81.0,206.0,57.0,2.2083,93800.0,INLAND
+-118.22,33.9,22.0,312.0,107.0,583.0,119.0,1.9423,98400.0,<1H OCEAN
+-117.64,35.61,10.0,2656.0,506.0,1349.0,501.0,4.25,83200.0,INLAND
+-118.19,34.22,32.0,10626.0,1504.0,4353.0,1482.0,9.8413,500001.0,<1H OCEAN
+-117.01,34.9,34.0,2194.0,519.0,1326.0,515.0,2.1056,72000.0,INLAND
+-121.24,38.01,22.0,1526.0,299.0,790.0,300.0,2.4342,125000.0,INLAND
+-117.97,33.74,18.0,2814.0,539.0,1439.0,493.0,3.599,262000.0,<1H OCEAN
+-121.48,38.56,46.0,1476.0,344.0,688.0,353.0,2.7316,134700.0,INLAND
+-118.2,33.79,47.0,767.0,195.0,569.0,195.0,2.9514,185200.0,NEAR OCEAN
+-118.0,34.1,34.0,2249.0,460.0,1544.0,441.0,3.4005,176300.0,INLAND
+-120.42,34.97,18.0,1932.0,350.0,1071.0,346.0,4.125,139800.0,<1H OCEAN
+-118.33,33.92,23.0,969.0,288.0,670.0,251.0,3.267,185400.0,<1H OCEAN
+-116.87,32.72,13.0,3268.0,491.0,1431.0,503.0,5.7652,259900.0,<1H OCEAN
+-117.51,34.02,24.0,7779.0,1835.0,3996.0,1765.0,2.1764,135300.0,INLAND
+-118.09,34.08,33.0,1430.0,344.0,1165.0,328.0,3.0357,206000.0,<1H OCEAN
+-118.07,33.91,35.0,2228.0,463.0,1558.0,427.0,4.023,157700.0,<1H OCEAN
+-117.12,32.8,29.0,2863.0,534.0,1392.0,522.0,3.8719,174200.0,NEAR OCEAN
+-121.21,37.8,45.0,370.0,84.0,167.0,70.0,1.4853,101800.0,INLAND
+-122.36,38.41,18.0,1808.0,400.0,968.0,370.0,3.7067,175000.0,INLAND
+-121.82,37.25,25.0,4021.0,634.0,2178.0,650.0,5.1663,241200.0,<1H OCEAN
+-118.32,34.1,52.0,786.0,270.0,756.0,273.0,2.2311,206300.0,<1H OCEAN
+-117.19,32.78,34.0,4108.0,664.0,1659.0,644.0,4.4097,252000.0,NEAR OCEAN
+-119.05,35.42,41.0,1992.0,421.0,1006.0,419.0,2.8393,57000.0,INLAND
+-117.39,33.93,26.0,3014.0,494.0,1832.0,485.0,4.8333,127900.0,INLAND
+-117.03,32.74,37.0,821.0,150.0,404.0,135.0,3.0125,130400.0,NEAR OCEAN
+-118.17,33.77,25.0,4405.0,1262.0,2178.0,1090.0,3.0503,225000.0,NEAR OCEAN
+-118.22,34.07,36.0,839.0,250.0,1079.0,245.0,1.7463,158300.0,<1H OCEAN
+-121.44,38.54,44.0,2570.0,509.0,1145.0,503.0,2.5694,92400.0,INLAND
+-119.79,36.76,52.0,2408.0,498.0,1361.0,465.0,2.1055,61300.0,INLAND
+-117.97,33.96,30.0,4873.0,667.0,1995.0,638.0,7.2472,441900.0,<1H OCEAN
+-121.07,39.05,10.0,1813.0,311.0,827.0,287.0,3.6087,182100.0,INLAND
+-118.05,34.09,23.0,602.0,135.0,409.0,123.0,3.5268,146400.0,<1H OCEAN
+-121.7,38.6,16.0,2372.0,588.0,1400.0,583.0,2.8922,153600.0,INLAND
+-122.05,36.97,20.0,2428.0,473.0,1145.0,454.0,3.6797,263800.0,NEAR OCEAN
+-121.95,37.25,34.0,2906.0,544.0,1282.0,522.0,5.5127,268200.0,<1H OCEAN
+-118.46,34.2,22.0,4855.0,1350.0,2519.0,1258.0,3.0893,205600.0,<1H OCEAN
+-123.35,39.4,27.0,1321.0,338.0,779.0,327.0,1.85,71800.0,<1H OCEAN
+-122.04,37.29,19.0,3625.0,432.0,1252.0,409.0,12.2145,500001.0,<1H OCEAN
+-122.39,40.53,28.0,1427.0,304.0,692.0,285.0,2.125,80800.0,INLAND
+-117.13,32.7,38.0,1445.0,392.0,1286.0,357.0,1.4632,80200.0,NEAR OCEAN
+-121.22,37.97,37.0,1514.0,337.0,1121.0,337.0,2.401,58400.0,INLAND
+-121.33,37.96,42.0,1619.0,340.0,906.0,339.0,2.5488,80300.0,INLAND
+-117.19,34.05,33.0,3007.0,498.0,1252.0,488.0,3.8816,134600.0,INLAND
+-118.45,34.0,43.0,1606.0,408.0,862.0,354.0,3.962,345800.0,<1H OCEAN
+-118.13,33.97,34.0,1736.0,297.0,823.0,292.0,5.4042,241600.0,<1H OCEAN
+-118.11,34.07,39.0,1270.0,299.0,1073.0,278.0,3.3088,186600.0,<1H OCEAN
+-118.44,34.26,34.0,325.0,60.0,433.0,83.0,5.5124,174300.0,<1H OCEAN
+-117.83,33.75,34.0,2660.0,601.0,1475.0,567.0,3.4152,210200.0,<1H OCEAN
+-118.07,33.79,26.0,4422.0,624.0,1936.0,625.0,6.4288,320700.0,<1H OCEAN
+-118.24,33.99,33.0,885.0,294.0,1270.0,282.0,2.1615,118800.0,<1H OCEAN
+-118.36,34.17,46.0,1268.0,240.0,661.0,239.0,4.0742,229100.0,<1H OCEAN
+-120.45,34.94,24.0,1702.0,447.0,1240.0,417.0,2.4091,115500.0,<1H OCEAN
+-118.04,33.84,21.0,6623.0,1204.0,3193.0,1129.0,4.5395,256000.0,<1H OCEAN
+-118.19,34.13,52.0,2012.0,458.0,1314.0,434.0,3.925,180400.0,<1H OCEAN
+-118.3,33.78,35.0,2572.0,504.0,1172.0,519.0,4.6207,304100.0,<1H OCEAN
+-118.39,34.05,35.0,2458.0,576.0,1047.0,554.0,4.0283,412500.0,<1H OCEAN
+-117.1,32.71,9.0,1931.0,472.0,1628.0,445.0,2.085,92600.0,NEAR OCEAN
+-117.09,32.64,30.0,3171.0,862.0,2126.0,800.0,2.507,142700.0,NEAR OCEAN
+-119.42,35.97,21.0,554.0,121.0,426.0,122.0,2.3516,47500.0,INLAND
+-118.31,34.06,52.0,2124.0,756.0,1920.0,756.0,2.1435,328900.0,<1H OCEAN
+-118.4,34.17,24.0,4443.0,1283.0,2421.0,1180.0,2.2652,269200.0,<1H OCEAN
+-122.04,37.66,10.0,2031.0,357.0,867.0,352.0,5.3169,299200.0,NEAR BAY
+-118.34,33.93,35.0,1213.0,284.0,742.0,253.0,4.0625,159900.0,<1H OCEAN
+-121.8,37.7,22.0,5533.0,943.0,2474.0,910.0,4.7361,216800.0,INLAND
+-117.11,33.11,17.0,2641.0,627.0,1167.0,647.0,2.2875,132400.0,<1H OCEAN
+-117.8,33.53,41.0,2017.0,489.0,783.0,403.0,4.1591,500001.0,<1H OCEAN
+-116.15,33.64,10.0,1711.0,499.0,1896.0,443.0,1.6557,65400.0,INLAND
+-117.05,32.75,35.0,2144.0,388.0,1003.0,383.0,3.0938,137300.0,NEAR OCEAN
+-118.36,33.86,37.0,1249.0,218.0,583.0,214.0,5.7422,330700.0,<1H OCEAN
+-117.9,33.71,16.0,4208.0,630.0,2592.0,662.0,6.1966,260500.0,<1H OCEAN
+-118.56,34.23,36.0,3215.0,529.0,1710.0,539.0,5.5126,248400.0,<1H OCEAN
+-117.98,33.7,17.0,1997.0,340.0,952.0,341.0,4.4148,239200.0,<1H OCEAN
+-117.27,34.08,38.0,1093.0,256.0,856.0,212.0,1.4279,73000.0,INLAND
+-122.42,40.6,5.0,2614.0,433.0,1275.0,411.0,3.4464,122900.0,INLAND
+-117.33,33.97,8.0,152.0,19.0,1275.0,20.0,1.625,162500.0,INLAND
+-122.11,38.01,39.0,1313.0,306.0,575.0,231.0,3.1711,116100.0,NEAR BAY
+-121.47,38.51,52.0,20.0,4.0,74.0,9.0,3.625,80000.0,INLAND
+-118.04,33.9,36.0,15.0,5.0,15.0,6.0,0.4999,162500.0,<1H OCEAN
+-117.62,34.11,17.0,1869.0,311.0,831.0,262.0,6.148,243900.0,INLAND
+-118.41,34.16,32.0,3060.0,505.0,1159.0,510.0,6.3703,465800.0,<1H OCEAN
+-122.71,38.45,48.0,3118.0,561.0,1275.0,530.0,3.455,222100.0,<1H OCEAN
+-118.57,34.21,36.0,878.0,167.0,499.0,179.0,4.1181,190400.0,<1H OCEAN
+-119.38,36.3,14.0,1932.0,330.0,997.0,291.0,3.6875,93200.0,INLAND
+-116.94,32.81,8.0,2517.0,632.0,1686.0,613.0,2.136,143500.0,<1H OCEAN
+-118.22,33.79,48.0,143.0,41.0,222.0,50.0,1.7,104200.0,NEAR OCEAN
+-119.07,35.42,19.0,3889.0,832.0,1872.0,731.0,2.6812,107600.0,INLAND
+-122.0,37.31,33.0,4211.0,918.0,2389.0,861.0,4.7235,242200.0,<1H OCEAN
+-117.91,33.76,22.0,7531.0,1569.0,5254.0,1523.0,3.8506,167400.0,<1H OCEAN
+-118.26,34.08,50.0,1791.0,660.0,2183.0,675.0,1.7945,166700.0,<1H OCEAN
+-118.08,33.95,37.0,1743.0,348.0,1328.0,354.0,3.0944,162800.0,<1H OCEAN
+-117.11,32.58,28.0,1869.0,407.0,1074.0,344.0,2.5988,135600.0,NEAR OCEAN
+-120.67,38.19,17.0,2967.0,611.0,1387.0,564.0,2.0417,92600.0,INLAND
+-118.04,33.78,26.0,3642.0,557.0,1623.0,569.0,5.8426,259400.0,<1H OCEAN
+-119.01,35.3,7.0,8596.0,1597.0,4893.0,1520.0,3.9054,80900.0,INLAND
+-117.69,34.09,28.0,1437.0,295.0,724.0,262.0,2.725,140200.0,INLAND
+-118.37,34.07,52.0,1084.0,247.0,468.0,255.0,3.4286,474300.0,<1H OCEAN
+-119.25,36.23,24.0,2015.0,355.0,1031.0,351.0,3.4306,139200.0,INLAND
+-117.95,33.74,21.0,3576.0,554.0,1846.0,538.0,5.9838,271900.0,<1H OCEAN
+-117.98,34.14,24.0,1596.0,388.0,1329.0,352.0,3.0417,148000.0,INLAND
+-119.7,34.42,41.0,725.0,239.0,582.0,214.0,3.1667,362500.0,<1H OCEAN
+-121.32,37.94,36.0,40.0,10.0,64.0,14.0,2.625,55000.0,INLAND
+-117.99,33.7,25.0,2017.0,357.0,1063.0,369.0,4.0345,229400.0,<1H OCEAN
+-117.4,33.97,41.0,1707.0,276.0,660.0,269.0,3.8618,134800.0,INLAND
+-118.31,34.0,52.0,2709.0,642.0,1751.0,613.0,2.1116,122500.0,<1H OCEAN
+-117.79,33.88,17.0,8562.0,1351.0,3822.0,1316.0,6.0829,252600.0,<1H OCEAN
+-122.28,37.53,34.0,1980.0,385.0,970.0,391.0,5.1207,310900.0,NEAR OCEAN
+-120.97,37.65,16.0,3960.0,716.0,1776.0,724.0,3.9886,137500.0,INLAND
+-122.42,37.72,37.0,2638.0,546.0,1789.0,521.0,4.0071,244700.0,NEAR BAY
+-117.44,34.08,15.0,5024.0,992.0,3208.0,981.0,3.6025,116400.0,INLAND
+-119.22,34.26,16.0,2596.0,625.0,1403.0,562.0,3.4018,145200.0,NEAR OCEAN
+-118.38,33.87,23.0,2387.0,418.0,1008.0,415.0,5.8518,337900.0,<1H OCEAN
+-118.1,34.1,34.0,2578.0,645.0,1628.0,617.0,2.34,210900.0,<1H OCEAN
+-117.6,33.41,29.0,2193.0,389.0,922.0,387.0,4.5476,309200.0,NEAR OCEAN
+-124.08,40.99,18.0,3297.0,662.0,1554.0,578.0,2.6847,111300.0,NEAR OCEAN
+-117.87,33.62,8.0,1266.0,,375.0,183.0,9.802,500001.0,<1H OCEAN
+-118.58,34.2,21.0,2979.0,744.0,1824.0,692.0,3.5,223700.0,<1H OCEAN
+-118.0,34.12,37.0,1340.0,325.0,928.0,333.0,3.9219,175000.0,INLAND
+-120.71,38.85,8.0,1877.0,479.0,884.0,323.0,3.4688,120100.0,INLAND
+-118.15,34.71,35.0,1503.0,309.0,842.0,300.0,2.5278,97700.0,INLAND
+-122.53,37.98,32.0,2390.0,336.0,810.0,354.0,8.5759,500001.0,NEAR BAY
+-118.2,33.78,52.0,2662.0,893.0,3018.0,763.0,2.3305,162500.0,NEAR OCEAN
+-119.77,36.32,14.0,3400.0,618.0,1867.0,612.0,3.9926,92500.0,INLAND
+-122.03,37.39,34.0,2600.0,650.0,1994.0,650.0,4.0223,250200.0,<1H OCEAN
+-121.03,37.33,27.0,1333.0,230.0,730.0,229.0,3.06,106000.0,INLAND
+-122.02,37.01,20.0,1005.0,138.0,345.0,129.0,10.0968,500001.0,NEAR OCEAN
+-119.73,36.73,9.0,1621.0,428.0,678.0,394.0,2.2437,54200.0,INLAND
+-120.45,34.95,10.0,2207.0,644.0,2232.0,543.0,2.375,98500.0,<1H OCEAN
+-122.11,37.44,35.0,2016.0,349.0,1023.0,376.0,5.6413,376600.0,NEAR BAY
+-118.1,33.76,15.0,4690.0,1002.0,1879.0,974.0,5.6051,267400.0,NEAR OCEAN
+-118.02,33.8,16.0,2956.0,393.0,1379.0,429.0,8.4952,359600.0,<1H OCEAN
+-117.11,32.7,34.0,2028.0,522.0,1797.0,464.0,1.7402,79400.0,NEAR OCEAN
+-118.0,33.84,29.0,2641.0,637.0,2413.0,619.0,2.8169,165100.0,<1H OCEAN
+-119.3,36.34,45.0,3723.0,831.0,2256.0,770.0,1.8299,63100.0,INLAND
+-118.77,34.28,27.0,1416.0,251.0,1024.0,268.0,5.1074,185200.0,<1H OCEAN
+-118.41,34.18,30.0,2008.0,513.0,1052.0,496.0,3.0119,262200.0,<1H OCEAN
+-117.91,33.65,24.0,1494.0,494.0,814.0,459.0,2.1074,181300.0,<1H OCEAN
+-118.09,33.94,36.0,2762.0,472.0,1576.0,493.0,4.0846,183400.0,<1H OCEAN
+-118.16,33.89,46.0,940.0,219.0,599.0,214.0,3.2813,190900.0,<1H OCEAN
+-118.08,34.02,14.0,3789.0,810.0,2551.0,793.0,2.9321,144200.0,<1H OCEAN
+-116.47,33.78,27.0,1781.0,441.0,759.0,340.0,3.3162,113600.0,INLAND
+-118.42,34.01,33.0,2731.0,535.0,1280.0,510.0,4.7083,420100.0,<1H OCEAN
+-117.24,32.79,17.0,1149.0,266.0,403.0,228.0,4.1652,241700.0,NEAR OCEAN
+-118.34,34.15,16.0,1586.0,377.0,625.0,344.0,4.0893,450000.0,<1H OCEAN
+-121.64,39.12,13.0,6408.0,1087.0,3294.0,1106.0,4.2656,110700.0,INLAND
+-117.49,33.94,28.0,2787.0,490.0,1684.0,467.0,4.0256,127100.0,INLAND
+-124.14,40.8,32.0,1373.0,312.0,872.0,306.0,2.5,72600.0,NEAR OCEAN
+-122.27,37.54,16.0,3913.0,565.0,1752.0,557.0,7.3644,419700.0,NEAR BAY
+-118.41,33.88,34.0,540.0,107.0,213.0,104.0,6.3403,500001.0,<1H OCEAN
+-118.05,33.71,25.0,4150.0,570.0,1424.0,547.0,8.8281,461600.0,NEAR OCEAN
+-119.71,36.88,17.0,2236.0,315.0,992.0,312.0,6.9405,165200.0,INLAND
+-118.37,33.81,33.0,5057.0,790.0,2021.0,748.0,6.8553,482200.0,NEAR OCEAN
+-121.47,38.52,42.0,2316.0,515.0,1597.0,522.0,1.8205,60400.0,INLAND
+-117.22,33.19,16.0,3004.0,656.0,1948.0,606.0,2.7019,216900.0,<1H OCEAN
+-119.7,34.47,32.0,3725.0,569.0,1304.0,527.0,7.7261,500001.0,<1H OCEAN
+-117.19,32.83,30.0,2288.0,448.0,1240.0,469.0,4.0114,169800.0,NEAR OCEAN
+-124.1,40.47,52.0,1196.0,236.0,965.0,265.0,3.5345,55000.0,<1H OCEAN
+-118.2,33.79,25.0,2851.0,968.0,3744.0,906.0,2.0675,116700.0,NEAR OCEAN
+-119.29,36.52,39.0,858.0,228.0,1222.0,224.0,1.5714,43000.0,INLAND
+-123.32,39.42,22.0,2085.0,432.0,1133.0,402.0,2.3906,92600.0,<1H OCEAN
+-118.28,33.97,31.0,2017.0,566.0,2063.0,521.0,1.9219,107000.0,<1H OCEAN
+-122.84,38.98,21.0,939.0,176.0,556.0,178.0,1.7196,75000.0,INLAND
+-117.94,33.62,25.0,1188.0,264.0,569.0,249.0,3.6607,500001.0,<1H OCEAN
+-119.67,36.8,9.0,3712.0,508.0,1632.0,474.0,6.011,163100.0,INLAND
+-121.77,37.33,8.0,3088.0,474.0,1799.0,456.0,7.2707,355300.0,<1H OCEAN
+-117.91,33.6,37.0,2088.0,510.0,673.0,390.0,5.1048,500001.0,<1H OCEAN
+-118.13,33.77,52.0,3697.0,691.0,1436.0,671.0,4.6852,395200.0,NEAR OCEAN
+-117.46,34.1,35.0,908.0,226.0,667.0,203.0,2.5833,93500.0,INLAND
+-118.09,34.0,36.0,1722.0,353.0,1174.0,335.0,3.045,160600.0,<1H OCEAN
+-117.98,33.73,22.0,4232.0,624.0,2408.0,660.0,6.6539,284900.0,<1H OCEAN
+-117.07,32.63,37.0,2372.0,444.0,1056.0,419.0,3.7583,145500.0,NEAR OCEAN
+-119.39,36.55,30.0,1669.0,314.0,837.0,325.0,3.3869,80400.0,INLAND
+-121.8,36.94,29.0,2377.0,476.0,1669.0,499.0,2.8214,190100.0,<1H OCEAN
+-122.43,37.77,52.0,3944.0,1072.0,1913.0,973.0,2.9567,425000.0,NEAR BAY
+-117.82,33.76,27.0,3230.0,449.0,1193.0,448.0,6.5308,287800.0,<1H OCEAN
+-116.77,32.82,16.0,3688.0,817.0,1969.0,767.0,2.875,222900.0,<1H OCEAN
+-121.86,37.41,16.0,1489.0,262.0,945.0,263.0,7.3861,267000.0,<1H OCEAN
+-118.63,34.2,19.0,7411.0,1045.0,2814.0,950.0,6.7785,336100.0,<1H OCEAN
+-120.01,39.26,26.0,1930.0,391.0,307.0,138.0,2.6023,139300.0,INLAND
+-118.35,33.94,38.0,1794.0,508.0,2188.0,454.0,2.6654,142200.0,<1H OCEAN
+-114.47,34.4,19.0,7650.0,1901.0,1129.0,463.0,1.82,80100.0,INLAND
+-118.45,33.99,33.0,3125.0,785.0,1720.0,713.0,2.9722,325000.0,<1H OCEAN
+-122.9,39.09,15.0,2483.0,544.0,835.0,380.0,1.9141,143200.0,INLAND
+-121.19,38.85,8.0,4114.0,710.0,2268.0,716.0,4.4085,139400.0,INLAND
+-117.25,32.94,15.0,1804.0,339.0,673.0,296.0,5.9806,370500.0,NEAR OCEAN
+-122.25,37.77,52.0,2156.0,458.0,872.0,445.0,3.2685,254200.0,NEAR BAY
+-118.37,34.03,43.0,1350.0,287.0,811.0,307.0,3.3636,140900.0,<1H OCEAN
+-122.46,37.78,52.0,2594.0,622.0,1421.0,593.0,3.0265,350000.0,NEAR BAY
+-120.83,37.77,20.0,1717.0,403.0,1062.0,401.0,1.6759,116700.0,INLAND
+-120.44,37.31,21.0,6911.0,1341.0,3967.0,1297.0,3.0515,95200.0,INLAND
+-118.29,33.98,48.0,1124.0,231.0,783.0,223.0,3.4444,93100.0,<1H OCEAN
+-122.2,37.43,22.0,3294.0,744.0,1337.0,655.0,5.2391,500001.0,NEAR OCEAN
+-121.8,37.3,16.0,906.0,149.0,605.0,148.0,4.8173,235600.0,<1H OCEAN
+-122.23,37.78,52.0,862.0,215.0,994.0,213.0,3.0257,80800.0,NEAR BAY
+-117.97,33.91,19.0,8096.0,1318.0,3853.0,1313.0,6.0076,269500.0,<1H OCEAN
+-117.94,33.99,18.0,6100.0,1018.0,3112.0,982.0,4.9932,284000.0,<1H OCEAN
+-117.62,33.42,27.0,1005.0,266.0,460.0,243.0,3.1029,190600.0,NEAR OCEAN
+-114.58,33.61,25.0,2907.0,680.0,1841.0,633.0,2.6768,82400.0,INLAND
+-117.97,34.04,32.0,1507.0,295.0,1326.0,324.0,4.119,163300.0,<1H OCEAN
+-118.19,33.91,43.0,1531.0,357.0,1509.0,376.0,2.6354,128100.0,<1H OCEAN
+-118.43,33.99,45.0,1899.0,461.0,1260.0,415.0,2.6667,320000.0,<1H OCEAN
+-117.67,33.51,19.0,1258.0,246.0,545.0,227.0,2.9762,184400.0,<1H OCEAN
+-117.93,33.78,28.0,4380.0,820.0,2187.0,835.0,3.9018,182300.0,<1H OCEAN
+-118.14,33.9,26.0,2145.0,471.0,1150.0,429.0,3.5972,225800.0,<1H OCEAN
+-122.45,37.72,52.0,1729.0,319.0,890.0,300.0,4.3036,261800.0,NEAR BAY
+-122.22,38.11,43.0,1939.0,353.0,968.0,392.0,3.1848,112700.0,NEAR BAY
+-118.49,34.26,25.0,8389.0,1872.0,4483.0,1747.0,3.5497,261300.0,<1H OCEAN
+-120.67,40.5,15.0,5343.0,,2503.0,902.0,3.5962,85900.0,INLAND
+-122.7,38.33,26.0,1584.0,295.0,846.0,295.0,3.375,156300.0,<1H OCEAN
+-117.98,33.7,16.0,5127.0,631.0,2142.0,596.0,7.8195,390500.0,<1H OCEAN
+-122.27,37.86,52.0,2307.0,583.0,1127.0,548.0,1.8447,198200.0,NEAR BAY
+-122.09,37.65,27.0,2630.0,722.0,1414.0,634.0,2.8203,195200.0,NEAR BAY
+-117.11,33.98,25.0,1254.0,312.0,715.0,301.0,2.7344,149000.0,INLAND
+-122.03,37.98,44.0,1254.0,252.0,498.0,217.0,3.4531,148900.0,NEAR BAY
+-121.99,37.54,26.0,2332.0,371.0,1285.0,404.0,5.388,225000.0,<1H OCEAN
+-117.83,33.8,31.0,2016.0,409.0,1095.0,405.0,3.8681,196000.0,<1H OCEAN
+-117.83,34.1,18.0,11026.0,1978.0,5407.0,1923.0,4.075,231100.0,INLAND
+-116.98,33.77,12.0,5829.0,1309.0,2711.0,1118.0,1.9707,107900.0,INLAND
+-118.53,34.17,18.0,6430.0,1412.0,2897.0,1348.0,3.855,243800.0,<1H OCEAN
+-120.07,36.96,32.0,1268.0,283.0,549.0,273.0,1.4511,65200.0,INLAND
+-121.31,38.0,19.0,908.0,158.0,306.0,154.0,3.9792,131900.0,INLAND
+-122.34,37.46,21.0,1799.0,293.0,576.0,277.0,7.439,500001.0,NEAR OCEAN
+-118.37,34.06,52.0,2239.0,423.0,832.0,411.0,5.0858,470000.0,<1H OCEAN
+-121.94,36.99,11.0,4571.0,924.0,2004.0,847.0,4.2898,221700.0,NEAR OCEAN
+-117.2,33.16,13.0,4503.0,1137.0,3094.0,1091.0,2.3159,91600.0,<1H OCEAN
+-122.49,38.0,26.0,48.0,8.0,19.0,8.0,7.7197,400000.0,NEAR BAY
+-117.11,32.69,37.0,2395.0,627.0,2489.0,599.0,1.5933,86300.0,NEAR OCEAN
+-118.37,33.92,44.0,938.0,181.0,502.0,171.0,4.4722,218300.0,<1H OCEAN
+-121.28,38.77,6.0,3819.0,550.0,1738.0,587.0,5.8718,201400.0,INLAND
+-120.6,35.6,13.0,4461.0,764.0,1795.0,640.0,4.475,206900.0,<1H OCEAN
+-116.86,34.24,19.0,5411.0,1042.0,441.0,185.0,3.1324,132000.0,INLAND
+-118.3,33.99,45.0,1701.0,452.0,1484.0,427.0,1.84,91400.0,<1H OCEAN
+-117.78,33.54,29.0,1421.0,462.0,520.0,339.0,2.2969,450000.0,<1H OCEAN
+-119.82,36.64,30.0,1694.0,312.0,1008.0,321.0,2.2466,96000.0,INLAND
+-118.23,34.18,47.0,1853.0,345.0,757.0,310.0,3.6875,422000.0,<1H OCEAN
+-118.34,34.08,52.0,1721.0,195.0,688.0,196.0,15.0001,500001.0,<1H OCEAN
+-121.02,39.01,17.0,4786.0,799.0,2066.0,770.0,3.9734,185400.0,INLAND
+-122.02,36.99,30.0,2156.0,487.0,1023.0,458.0,2.7875,245000.0,NEAR OCEAN
+-120.25,39.34,9.0,2739.0,555.0,294.0,110.0,3.1842,162500.0,INLAND
+-121.41,39.72,17.0,1583.0,331.0,730.0,306.0,2.3895,87500.0,INLAND
+-116.99,33.73,13.0,16148.0,3474.0,6159.0,3232.0,1.9961,97800.0,INLAND
+-120.43,34.87,21.0,2131.0,329.0,1094.0,353.0,4.6648,193000.0,<1H OCEAN
+-124.26,40.58,52.0,2217.0,394.0,907.0,369.0,2.3571,111400.0,NEAR OCEAN
+-117.87,33.77,52.0,2512.0,356.0,978.0,365.0,8.0784,320300.0,<1H OCEAN
+-118.36,34.08,40.0,3110.0,764.0,1557.0,763.0,1.9937,367100.0,<1H OCEAN
+-119.89,36.79,5.0,3821.0,705.0,2179.0,694.0,3.7821,80400.0,INLAND
+-118.31,33.77,20.0,5776.0,956.0,2757.0,936.0,6.6447,416800.0,<1H OCEAN
+-117.2,32.85,22.0,3501.0,631.0,1297.0,581.0,4.7891,295300.0,NEAR OCEAN
+-117.09,32.64,20.0,1999.0,651.0,1302.0,592.0,1.6321,57500.0,NEAR OCEAN
+-118.82,35.23,31.0,2358.0,580.0,2302.0,574.0,1.9688,53900.0,INLAND
+-119.35,36.16,21.0,2751.0,602.0,1496.0,489.0,2.3882,49200.0,INLAND
+-117.12,32.73,52.0,3976.0,718.0,1750.0,769.0,3.4151,175400.0,NEAR OCEAN
+-122.33,37.94,47.0,1882.0,361.0,797.0,342.0,3.5848,140800.0,NEAR BAY
+-121.2,36.14,12.0,3738.0,710.0,2337.0,664.0,3.9647,135000.0,<1H OCEAN
+-117.05,32.81,17.0,1885.0,292.0,771.0,301.0,5.6402,228600.0,<1H OCEAN
+-122.13,37.87,18.0,1820.0,220.0,728.0,229.0,10.3713,426100.0,NEAR BAY
+-118.15,34.12,36.0,6119.0,1513.0,2719.0,1402.0,3.8427,319700.0,<1H OCEAN
+-117.36,34.1,29.0,2819.0,637.0,1683.0,608.0,2.3205,87600.0,INLAND
+-122.05,38.0,36.0,2476.0,472.0,1213.0,393.0,3.7333,136400.0,NEAR BAY
+-121.93,37.93,16.0,2169.0,262.0,877.0,245.0,6.6049,312600.0,INLAND
+-121.95,37.55,21.0,10687.0,1540.0,4552.0,1520.0,6.6478,333400.0,<1H OCEAN
+-117.25,34.14,19.0,5163.0,1229.0,2680.0,1141.0,2.2482,114500.0,INLAND
+-118.14,34.01,46.0,1746.0,447.0,1296.0,392.0,2.3929,156800.0,<1H OCEAN
+-123.84,39.83,19.0,1461.0,340.0,515.0,227.0,1.5278,145800.0,NEAR OCEAN
+-122.69,39.04,9.0,254.0,50.0,66.0,29.0,2.7639,112500.0,INLAND
+-119.19,34.18,32.0,3366.0,677.0,2857.0,669.0,4.6186,181100.0,NEAR OCEAN
+-122.41,37.78,52.0,254.0,72.0,153.0,29.0,3.8625,350000.0,NEAR BAY
+-119.83,36.78,35.0,1789.0,357.0,933.0,357.0,2.5223,66200.0,INLAND
+-122.24,37.8,47.0,2046.0,588.0,1213.0,554.0,2.6292,182700.0,NEAR BAY
+-121.43,36.5,14.0,1835.0,468.0,1867.0,461.0,2.3879,129800.0,<1H OCEAN
+-116.53,33.95,18.0,2990.0,648.0,1280.0,532.0,2.625,68200.0,INLAND
+-119.04,34.28,21.0,1856.0,276.0,863.0,255.0,4.5833,500001.0,<1H OCEAN
+-117.12,32.7,38.0,818.0,217.0,953.0,231.0,1.0531,65700.0,NEAR OCEAN
+-118.28,34.08,42.0,997.0,374.0,982.0,372.0,2.9423,200000.0,<1H OCEAN
+-118.56,34.19,36.0,2600.0,441.0,1246.0,426.0,4.1111,215600.0,<1H OCEAN
+-118.5,34.05,36.0,4152.0,542.0,1461.0,550.0,15.0001,500001.0,<1H OCEAN
+-119.81,36.78,37.0,1965.0,364.0,796.0,335.0,3.625,83400.0,INLAND
+-117.05,32.95,17.0,3039.0,555.0,1297.0,552.0,3.9531,178600.0,<1H OCEAN
+-119.3,34.42,18.0,5591.0,1042.0,2860.0,1026.0,3.5822,219900.0,NEAR OCEAN
+-118.01,33.87,25.0,6348.0,1615.0,4188.0,1497.0,3.139,185700.0,<1H OCEAN
+-121.13,38.37,10.0,1034.0,153.0,478.0,155.0,7.0326,241100.0,INLAND
+-119.8,36.68,31.0,2214.0,432.0,1326.0,416.0,2.1691,66700.0,INLAND
+-118.43,33.98,19.0,8324.0,1590.0,2927.0,1538.0,7.5426,351700.0,<1H OCEAN
+-117.3,34.24,38.0,4116.0,949.0,1196.0,422.0,3.5625,96500.0,INLAND
+-119.04,35.37,44.0,1618.0,310.0,667.0,300.0,2.875,82700.0,INLAND
+-119.08,34.34,23.0,3065.0,723.0,2042.0,698.0,2.7593,194800.0,<1H OCEAN
+-117.16,32.73,52.0,1682.0,617.0,873.0,534.0,2.0972,112500.0,NEAR OCEAN
+-118.2,34.02,26.0,36.0,9.0,35.0,9.0,1.625,175000.0,<1H OCEAN
+-122.16,37.74,43.0,1534.0,300.0,826.0,295.0,4.0417,109400.0,NEAR BAY
+-120.05,36.96,37.0,1000.0,261.0,1092.0,233.0,1.4267,52300.0,INLAND
+-118.44,34.15,15.0,4420.0,1076.0,1669.0,1016.0,4.6375,359100.0,<1H OCEAN
+-118.09,34.12,38.0,1713.0,285.0,779.0,286.0,5.6152,359900.0,<1H OCEAN
+-118.11,33.89,35.0,1139.0,197.0,772.0,233.0,4.375,204700.0,<1H OCEAN
+-122.37,37.94,40.0,1064.0,266.0,912.0,239.0,1.0521,69100.0,NEAR BAY
+-122.06,37.3,11.0,5488.0,706.0,1947.0,641.0,10.7326,500001.0,<1H OCEAN
+-118.39,34.0,40.0,1565.0,269.0,826.0,268.0,5.2035,485700.0,<1H OCEAN
+-121.27,37.79,16.0,1853.0,390.0,1013.0,362.0,2.7083,173900.0,INLAND
+-121.34,38.65,27.0,1595.0,246.0,610.0,253.0,4.6,199000.0,INLAND
+-117.78,33.82,12.0,6208.0,750.0,2443.0,739.0,9.1808,413700.0,<1H OCEAN
+-117.89,34.08,25.0,2115.0,489.0,1107.0,477.0,3.1949,207400.0,<1H OCEAN
+-122.07,37.89,38.0,757.0,124.0,319.0,123.0,5.6558,263300.0,NEAR BAY
+-115.55,32.78,5.0,2652.0,606.0,1767.0,536.0,2.8025,84300.0,INLAND
+-120.43,38.25,13.0,763.0,161.0,311.0,125.0,2.4583,112500.0,INLAND
+-120.81,39.02,30.0,806.0,189.0,326.0,146.0,2.8155,101000.0,INLAND
+-118.37,33.94,29.0,2265.0,813.0,3425.0,781.0,2.3675,149400.0,<1H OCEAN
+-117.83,33.74,23.0,6114.0,1623.0,4088.0,1521.0,3.0382,183600.0,<1H OCEAN
+-119.82,36.77,41.0,1441.0,274.0,646.0,296.0,3.0568,71300.0,INLAND
+-122.35,40.68,36.0,1822.0,449.0,930.0,399.0,1.3801,56600.0,INLAND
+-122.25,37.81,29.0,4656.0,1414.0,2304.0,1250.0,2.4912,257800.0,NEAR BAY
+-118.27,33.94,34.0,721.0,165.0,661.0,171.0,2.0789,92400.0,<1H OCEAN
+-118.66,34.43,9.0,2356.0,469.0,1556.0,386.0,3.775,155000.0,INLAND
+-118.27,33.81,38.0,1607.0,337.0,1130.0,334.0,4.4821,190700.0,<1H OCEAN
+-117.06,32.62,36.0,786.0,125.0,408.0,138.0,3.9167,189700.0,NEAR OCEAN
+-122.62,38.94,14.0,1731.0,400.0,638.0,282.0,2.3179,57500.0,INLAND
+-118.1,34.09,44.0,2352.0,484.0,1517.0,463.0,4.2833,258000.0,<1H OCEAN
+-122.07,37.4,16.0,3352.0,813.0,1440.0,729.0,3.7359,262500.0,NEAR BAY
+-117.19,34.94,31.0,2034.0,444.0,1097.0,367.0,2.1522,60800.0,INLAND
+-118.28,33.81,29.0,2755.0,508.0,2046.0,488.0,5.2034,212400.0,<1H OCEAN
+-123.21,39.14,39.0,1419.0,262.0,661.0,278.0,3.0,114600.0,<1H OCEAN
+-120.1,39.37,10.0,2325.0,410.0,1016.0,373.0,4.5208,117300.0,INLAND
+-118.33,34.0,47.0,1671.0,388.0,895.0,317.0,2.2054,121500.0,<1H OCEAN
+-120.88,37.57,22.0,1440.0,267.0,774.0,249.0,3.9821,204300.0,INLAND
+-122.54,37.74,42.0,2006.0,415.0,1230.0,435.0,4.1786,271100.0,NEAR OCEAN
+-122.32,37.56,49.0,2016.0,299.0,691.0,288.0,5.549,500001.0,NEAR OCEAN
+-118.31,33.95,44.0,2490.0,430.0,1305.0,411.0,4.8295,149600.0,<1H OCEAN
+-121.26,38.66,19.0,3170.0,444.0,1344.0,452.0,6.1183,221600.0,INLAND
+-122.33,37.58,27.0,5144.0,1481.0,2518.0,1447.0,3.4836,287900.0,NEAR OCEAN
+-121.44,38.56,52.0,906.0,165.0,257.0,166.0,2.8542,139400.0,INLAND
+-118.02,33.96,36.0,2002.0,361.0,913.0,311.0,4.5446,244700.0,<1H OCEAN
+-118.25,33.89,37.0,1042.0,213.0,699.0,196.0,2.9643,103200.0,<1H OCEAN
+-118.25,33.9,37.0,2119.0,442.0,1372.0,406.0,1.9605,106200.0,<1H OCEAN
+-118.1,33.74,31.0,1310.0,342.0,563.0,310.0,4.6528,457100.0,NEAR OCEAN
+-122.23,38.06,52.0,1350.0,266.0,490.0,257.0,3.125,171100.0,NEAR BAY
+-118.16,33.86,26.0,6607.0,1663.0,4066.0,1558.0,2.5068,156300.0,<1H OCEAN
+-120.83,37.51,34.0,3078.0,477.0,1226.0,487.0,4.601,150000.0,INLAND
+-121.84,39.76,14.0,2351.0,620.0,1215.0,548.0,2.3155,102300.0,INLAND
+-121.97,37.35,35.0,1880.0,370.0,926.0,321.0,4.2273,269900.0,<1H OCEAN
+-117.01,32.76,34.0,3415.0,608.0,1464.0,593.0,4.0549,223700.0,<1H OCEAN
+-118.07,34.08,38.0,2462.0,553.0,1843.0,538.0,3.2312,211900.0,<1H OCEAN
+-118.14,34.65,20.0,1257.0,201.0,551.0,186.0,4.6591,247200.0,INLAND
+-119.48,36.54,28.0,2112.0,363.0,1011.0,335.0,4.2222,108900.0,INLAND
+-117.11,32.76,31.0,2293.0,549.0,1108.0,557.0,3.3854,204400.0,NEAR OCEAN
+-116.54,33.8,22.0,6050.0,1387.0,1432.0,890.0,2.2216,183900.0,INLAND
+-117.69,33.61,16.0,3010.0,580.0,1649.0,538.0,4.0221,236200.0,<1H OCEAN
+-117.09,32.79,36.0,1936.0,345.0,861.0,343.0,3.8333,170000.0,NEAR OCEAN
+-122.21,37.77,43.0,1017.0,328.0,836.0,277.0,2.2604,100000.0,NEAR BAY
+-119.28,35.52,36.0,786.0,194.0,573.0,134.0,2.2321,37500.0,INLAND
+-117.7,33.68,29.0,5650.0,1084.0,3985.0,1056.0,2.8192,162500.0,<1H OCEAN
+-117.14,32.74,16.0,6075.0,1816.0,2592.0,1634.0,2.5553,178100.0,NEAR OCEAN
+-122.71,38.44,52.0,988.0,283.0,475.0,242.0,1.3684,258300.0,<1H OCEAN
+-117.53,34.28,35.0,1529.0,338.0,688.0,256.0,4.1083,108000.0,INLAND
+-118.21,33.81,45.0,1693.0,337.0,1255.0,333.0,3.6923,159700.0,NEAR OCEAN
+-116.9,32.77,8.0,3600.0,492.0,1421.0,482.0,6.2609,307100.0,<1H OCEAN
+-122.13,38.01,48.0,2123.0,494.0,859.0,474.0,1.8523,144800.0,NEAR BAY
+-121.78,38.55,12.0,10509.0,2186.0,5633.0,2138.0,2.9605,204300.0,INLAND
+-118.09,33.71,19.0,1397.0,271.0,491.0,197.0,8.7397,500001.0,NEAR OCEAN
+-117.2,34.14,18.0,1920.0,333.0,890.0,323.0,5.159,144800.0,INLAND
+-121.28,37.96,28.0,1942.0,724.0,1618.0,638.0,0.9365,52500.0,INLAND
+-121.33,38.04,10.0,1421.0,204.0,657.0,209.0,5.1878,153900.0,INLAND
+-118.26,34.05,52.0,58.0,52.0,41.0,27.0,4.0972,500001.0,<1H OCEAN
+-117.17,34.49,13.0,4460.0,925.0,2225.0,840.0,2.0136,94100.0,INLAND
+-118.11,33.84,36.0,1463.0,257.0,722.0,260.0,4.8438,226300.0,<1H OCEAN
+-118.21,33.93,33.0,2739.0,801.0,3423.0,741.0,2.2847,132700.0,<1H OCEAN
+-124.06,40.86,34.0,4183.0,,1891.0,669.0,3.2216,98100.0,NEAR OCEAN
+-118.09,34.12,38.0,2638.0,432.0,1284.0,433.0,5.4536,342700.0,<1H OCEAN
+-119.33,36.32,16.0,3331.0,839.0,1955.0,763.0,1.6148,86600.0,INLAND
+-117.95,34.08,37.0,1137.0,203.0,672.0,226.0,3.2969,189000.0,<1H OCEAN
+-121.35,38.28,17.0,2756.0,557.0,1986.0,530.0,3.2234,82000.0,INLAND
+-119.71,36.76,28.0,2675.0,527.0,1392.0,521.0,2.3108,72000.0,INLAND
+-117.93,33.87,10.0,1277.0,488.0,730.0,417.0,1.4803,137500.0,<1H OCEAN
+-122.01,37.94,23.0,3741.0,,1339.0,499.0,6.7061,322300.0,NEAR BAY
+-119.71,36.81,19.0,1648.0,368.0,557.0,354.0,1.7969,72800.0,INLAND
+-119.77,35.65,21.0,2403.0,483.0,1647.0,415.0,2.6066,80000.0,INLAND
+-117.87,33.88,24.0,2655.0,702.0,1519.0,708.0,3.3036,183900.0,<1H OCEAN
+-119.74,34.41,30.0,2365.0,417.0,1053.0,409.0,5.5959,346200.0,NEAR OCEAN
+-117.97,33.73,26.0,1694.0,260.0,885.0,279.0,5.0875,224200.0,<1H OCEAN
+-122.55,38.42,24.0,2220.0,411.0,894.0,365.0,4.2891,211700.0,<1H OCEAN
+-118.36,33.89,34.0,760.0,174.0,723.0,198.0,5.3169,227600.0,<1H OCEAN
+-118.78,34.27,20.0,2743.0,685.0,1798.0,613.0,3.6761,170900.0,<1H OCEAN
+-118.4,33.97,38.0,1089.0,174.0,502.0,180.0,7.5953,434800.0,<1H OCEAN
+-117.44,34.1,43.0,1614.0,400.0,926.0,349.0,2.075,95100.0,INLAND
+-118.32,33.75,37.0,1080.0,135.0,366.0,142.0,11.6677,500001.0,NEAR OCEAN
+-120.85,37.77,20.0,651.0,157.0,421.0,151.0,2.0833,77300.0,INLAND
+-118.37,33.99,32.0,4018.0,564.0,1400.0,568.0,8.6718,439100.0,<1H OCEAN
+-121.93,37.25,32.0,1555.0,287.0,779.0,284.0,6.0358,260100.0,<1H OCEAN
+-122.2,37.84,35.0,2865.0,460.0,1072.0,443.0,7.4882,319300.0,NEAR BAY
+-118.28,33.99,35.0,1138.0,304.0,1128.0,311.0,1.8818,100000.0,<1H OCEAN
+-121.27,37.96,43.0,948.0,221.0,749.0,208.0,1.962,52700.0,INLAND
+-118.35,33.94,36.0,2225.0,601.0,2755.0,610.0,2.5547,150400.0,<1H OCEAN
+-117.07,32.74,38.0,1901.0,392.0,1099.0,406.0,2.7661,113900.0,NEAR OCEAN
+-118.93,34.2,17.0,2619.0,606.0,1655.0,557.0,3.886,281300.0,<1H OCEAN
+-118.36,33.95,42.0,1116.0,303.0,1082.0,299.0,3.7237,170800.0,<1H OCEAN
+-118.46,34.04,17.0,2729.0,897.0,1404.0,758.0,3.1235,420800.0,<1H OCEAN
+-117.68,33.6,19.0,3913.0,460.0,1646.0,454.0,7.2147,303900.0,<1H OCEAN
+-117.76,33.71,15.0,1010.0,350.0,470.0,342.0,3.2229,108300.0,<1H OCEAN
+-118.32,34.01,52.0,3104.0,645.0,1498.0,581.0,2.6667,128000.0,<1H OCEAN
+-118.48,34.2,23.0,2850.0,864.0,2249.0,777.0,2.6957,191700.0,<1H OCEAN
+-117.08,32.77,31.0,1070.0,155.0,426.0,153.0,6.1628,219200.0,NEAR OCEAN
+-118.14,34.02,45.0,1307.0,283.0,967.0,254.0,2.75,178300.0,<1H OCEAN
+-118.41,34.3,28.0,3187.0,569.0,2205.0,559.0,5.1668,187400.0,<1H OCEAN
+-122.47,37.65,27.0,8103.0,1655.0,5023.0,1605.0,4.6452,236200.0,NEAR OCEAN
+-119.73,36.8,24.0,1316.0,249.0,781.0,260.0,3.7578,69200.0,INLAND
+-117.88,33.7,17.0,5122.0,1544.0,2966.0,1339.0,3.4835,116700.0,<1H OCEAN
+-122.37,37.95,35.0,215.0,45.0,100.0,34.0,1.6023,81300.0,NEAR BAY
+-122.2,39.53,22.0,3265.0,658.0,1647.0,594.0,2.3566,71000.0,INLAND
+-121.81,37.23,17.0,2319.0,324.0,1076.0,338.0,6.4664,278300.0,<1H OCEAN
+-121.22,38.68,10.0,6262.0,1278.0,2954.0,1169.0,3.4506,139000.0,INLAND
+-117.08,33.08,23.0,3400.0,501.0,1383.0,488.0,4.9844,249100.0,<1H OCEAN
+-117.36,33.2,26.0,2447.0,482.0,1405.0,486.0,3.2917,150800.0,NEAR OCEAN
+-122.21,37.84,44.0,3424.0,597.0,1358.0,597.0,6.0194,292300.0,NEAR BAY
+-120.51,36.55,20.0,1193.0,263.0,1274.0,241.0,1.9417,38800.0,INLAND
+-117.16,33.92,12.0,3236.0,502.0,1610.0,502.0,4.7568,143500.0,INLAND
+-120.97,37.73,19.0,3725.0,543.0,1412.0,463.0,5.7476,248600.0,INLAND
+-118.22,33.89,37.0,797.0,190.0,485.0,166.0,2.7434,95200.0,<1H OCEAN
+-121.02,37.68,25.0,3262.0,588.0,1834.0,578.0,3.996,114500.0,INLAND
+-121.62,39.15,36.0,2321.0,455.0,1168.0,489.0,3.0962,74000.0,INLAND
+-118.28,34.08,52.0,2465.0,773.0,2328.0,746.0,2.6178,203100.0,<1H OCEAN
+-121.28,38.55,35.0,7088.0,1279.0,4885.0,1272.0,2.6981,112500.0,INLAND
+-121.47,38.55,24.0,979.0,287.0,546.0,291.0,1.186,67000.0,INLAND
+-116.96,32.8,25.0,3421.0,803.0,1681.0,742.0,3.369,134400.0,<1H OCEAN
+-116.98,34.05,6.0,2290.0,312.0,957.0,274.0,7.2708,316700.0,INLAND
+-121.47,38.5,17.0,1895.0,424.0,620.0,417.0,1.7188,137500.0,INLAND
+-122.25,38.11,52.0,2846.0,705.0,1519.0,620.0,2.1976,97900.0,NEAR BAY
+-120.93,37.66,10.0,7566.0,1348.0,3227.0,1199.0,4.744,148100.0,INLAND
+-117.38,34.0,45.0,2881.0,514.0,1470.0,515.0,3.3687,123800.0,INLAND
+-117.71,34.08,26.0,2744.0,494.0,1411.0,465.0,4.2639,154200.0,INLAND
+-121.13,38.47,16.0,2574.0,441.0,1041.0,428.0,3.6645,203400.0,INLAND
+-118.11,33.95,34.0,2319.0,334.0,941.0,356.0,6.4319,452300.0,<1H OCEAN
+-118.2,34.02,49.0,1098.0,317.0,1411.0,301.0,2.75,146000.0,<1H OCEAN
+-118.41,33.96,44.0,1802.0,306.0,753.0,282.0,6.0286,356000.0,<1H OCEAN
+-118.45,33.99,52.0,1829.0,472.0,779.0,424.0,3.1607,339000.0,<1H OCEAN
+-119.29,34.37,41.0,1408.0,311.0,793.0,264.0,2.5441,161200.0,NEAR OCEAN
+-117.57,34.15,3.0,12806.0,2219.0,4249.0,1499.0,5.485,343100.0,INLAND
+-120.0,38.52,16.0,3045.0,543.0,202.0,102.0,3.15,140600.0,INLAND
+-121.83,37.99,23.0,1970.0,296.0,935.0,279.0,4.4853,145900.0,INLAND
+-118.73,34.27,23.0,4550.0,762.0,2301.0,744.0,4.556,205300.0,<1H OCEAN
+-122.43,37.75,52.0,3521.0,767.0,1415.0,687.0,4.875,362200.0,NEAR BAY
+-118.39,33.88,34.0,1973.0,367.0,843.0,345.0,6.077,472700.0,<1H OCEAN
+-119.61,36.59,10.0,2842.0,620.0,1443.0,576.0,2.2727,92700.0,INLAND
+-122.04,37.33,22.0,4011.0,963.0,2206.0,879.0,4.5721,351200.0,<1H OCEAN
+-118.35,34.02,34.0,5218.0,1576.0,3538.0,1371.0,1.5143,118800.0,<1H OCEAN
+-118.32,34.08,52.0,2370.0,473.0,1053.0,434.0,4.1429,380300.0,<1H OCEAN
+-121.24,36.34,33.0,1691.0,308.0,792.0,262.0,2.6648,164600.0,<1H OCEAN
+-118.28,34.0,46.0,1650.0,463.0,1992.0,458.0,2.3403,114100.0,<1H OCEAN
+-117.11,33.75,17.0,4174.0,851.0,1845.0,780.0,2.2618,96100.0,INLAND
+-117.18,32.84,32.0,1351.0,237.0,823.0,269.0,4.2768,167800.0,NEAR OCEAN
+-120.94,40.14,31.0,3127.0,664.0,1345.0,580.0,1.5774,58000.0,INLAND
+-117.99,33.95,30.0,2217.0,284.0,851.0,291.0,10.4835,498600.0,<1H OCEAN
+-117.66,35.6,14.0,1740.0,391.0,850.0,317.0,2.5812,91700.0,INLAND
+-122.41,37.76,52.0,351.0,81.0,308.0,75.0,2.6667,325000.0,NEAR BAY
+-117.87,33.88,28.0,3333.0,752.0,2026.0,722.0,3.5667,190700.0,<1H OCEAN
+-118.29,33.99,46.0,2608.0,636.0,1766.0,596.0,1.5846,114800.0,<1H OCEAN
+-117.99,33.92,27.0,5805.0,1152.0,3106.0,1144.0,4.061,222700.0,<1H OCEAN
+-121.07,38.94,14.0,1710.0,294.0,839.0,297.0,4.7143,150700.0,INLAND
+-119.16,34.15,23.0,3204.0,644.0,2295.0,614.0,3.9485,196600.0,NEAR OCEAN
+-117.22,32.75,26.0,617.0,112.0,251.0,110.0,3.8036,162000.0,NEAR OCEAN
+-122.07,40.95,14.0,2721.0,627.0,1356.0,468.0,3.0299,73200.0,INLAND
+-119.68,36.81,13.0,2589.0,413.0,1356.0,435.0,5.0253,106200.0,INLAND
+-119.53,34.41,8.0,1705.0,400.0,886.0,391.0,3.9659,297400.0,<1H OCEAN
+-118.46,34.05,25.0,6902.0,2138.0,3136.0,1844.0,2.6509,410000.0,<1H OCEAN
+-117.07,32.76,42.0,1827.0,378.0,880.0,380.0,2.5125,176600.0,NEAR OCEAN
+-118.17,34.02,33.0,346.0,103.0,488.0,107.0,1.8681,112500.0,<1H OCEAN
+-118.53,34.27,33.0,1927.0,305.0,896.0,293.0,5.634,320500.0,<1H OCEAN
+-121.81,37.25,20.0,3398.0,771.0,1231.0,744.0,2.0288,350000.0,<1H OCEAN
+-121.89,37.25,23.0,2705.0,449.0,1180.0,442.0,6.0791,316500.0,<1H OCEAN
+-118.6,34.26,18.0,6154.0,1070.0,3010.0,1034.0,5.6392,271500.0,<1H OCEAN
+-118.44,34.02,32.0,2242.0,490.0,921.0,461.0,4.0429,500001.0,<1H OCEAN
+-117.0,32.71,22.0,2263.0,441.0,1395.0,416.0,3.725,123500.0,NEAR OCEAN
+-118.17,34.09,36.0,3066.0,797.0,3097.0,780.0,2.5523,156500.0,<1H OCEAN
+-117.09,32.73,28.0,3109.0,594.0,1559.0,589.0,3.5789,120300.0,NEAR OCEAN
+-118.29,34.07,26.0,2302.0,1124.0,2660.0,1004.0,2.3567,253100.0,<1H OCEAN
+-118.37,34.07,39.0,2309.0,526.0,870.0,546.0,3.1677,453400.0,<1H OCEAN
+-117.13,33.14,16.0,1649.0,278.0,993.0,277.0,4.8526,170700.0,<1H OCEAN
+-121.56,39.69,8.0,2836.0,522.0,1163.0,512.0,3.13,168300.0,INLAND
+-118.31,34.09,36.0,2517.0,842.0,2446.0,689.0,2.1524,187500.0,<1H OCEAN
+-122.26,41.66,17.0,1885.0,350.0,953.0,328.0,2.1607,61400.0,INLAND
+-118.31,34.02,23.0,1703.0,397.0,1333.0,361.0,1.3187,127100.0,<1H OCEAN
+-120.5,37.34,16.0,1245.0,231.0,956.0,219.0,3.4559,108000.0,INLAND
+-118.7,34.3,27.0,1527.0,220.0,756.0,226.0,6.1825,227000.0,<1H OCEAN
+-122.41,37.94,52.0,154.0,33.0,89.0,38.0,3.2875,275000.0,NEAR BAY
+-118.38,34.25,38.0,983.0,185.0,513.0,170.0,4.8816,231500.0,<1H OCEAN
+-122.46,37.69,26.0,4302.0,1125.0,3320.0,1100.0,3.4375,277700.0,NEAR OCEAN
+-117.91,34.02,22.0,6269.0,,5587.0,1251.0,3.8201,136200.0,<1H OCEAN
+-118.31,33.97,47.0,2066.0,422.0,1156.0,380.0,2.7917,125800.0,<1H OCEAN
+-117.08,32.66,43.0,1004.0,236.0,839.0,235.0,2.81,103400.0,NEAR OCEAN
+-117.65,33.6,15.0,5736.0,,2529.0,762.0,6.4114,278700.0,<1H OCEAN
+-117.87,33.89,19.0,1674.0,243.0,786.0,234.0,6.4218,275000.0,<1H OCEAN
+-121.71,39.25,37.0,1871.0,321.0,806.0,294.0,4.0,101400.0,INLAND
+-117.35,33.95,28.0,1650.0,210.0,557.0,211.0,7.6632,204800.0,INLAND
+-121.27,38.44,19.0,2780.0,414.0,1320.0,404.0,5.8831,209900.0,INLAND
+-118.11,34.04,28.0,3913.0,696.0,2264.0,697.0,5.2446,258000.0,<1H OCEAN
+-116.98,32.68,22.0,2346.0,380.0,1217.0,382.0,5.5248,156300.0,NEAR OCEAN
+-118.24,33.91,38.0,745.0,152.0,721.0,160.0,1.875,102900.0,<1H OCEAN
+-122.41,37.74,43.0,1663.0,330.0,935.0,335.0,4.1552,240900.0,NEAR BAY
+-123.38,39.37,18.0,3946.0,813.0,1899.0,730.0,2.6424,124600.0,<1H OCEAN
+-121.94,37.93,16.0,3421.0,427.0,1341.0,428.0,7.5695,320400.0,INLAND
+-121.93,37.25,36.0,1089.0,182.0,535.0,170.0,4.69,252600.0,<1H OCEAN
+-122.28,37.85,52.0,610.0,145.0,281.0,132.0,2.9018,119400.0,NEAR BAY
+-122.41,37.74,34.0,1403.0,262.0,839.0,255.0,4.7031,255200.0,NEAR BAY
+-122.38,37.73,18.0,4037.0,990.0,2722.0,834.0,1.4282,140400.0,NEAR BAY
+-118.29,34.04,32.0,432.0,182.0,702.0,186.0,2.1471,125000.0,<1H OCEAN
+-118.35,33.88,25.0,1459.0,362.0,1150.0,354.0,3.35,237500.0,<1H OCEAN
+-117.64,34.05,32.0,1129.0,251.0,1378.0,268.0,3.0057,96900.0,INLAND
+-118.46,34.02,46.0,2571.0,502.0,1225.0,501.0,6.0436,473000.0,<1H OCEAN
+-120.93,39.17,13.0,2331.0,464.0,1110.0,419.0,3.6563,164900.0,INLAND
+-122.23,37.81,52.0,2315.0,292.0,861.0,258.0,8.8793,410300.0,NEAR BAY
+-118.27,33.93,38.0,2073.0,500.0,1657.0,470.0,1.2098,88400.0,<1H OCEAN
+-121.47,38.55,52.0,1384.0,295.0,561.0,244.0,2.0242,94600.0,INLAND
+-121.07,38.9,52.0,1280.0,281.0,523.0,266.0,1.7375,122200.0,INLAND
+-118.3,33.76,18.0,9659.0,1716.0,4336.0,1674.0,5.7764,290500.0,<1H OCEAN
+-120.65,38.28,21.0,3095.0,681.0,1341.0,546.0,2.1382,104000.0,INLAND
+-118.58,34.24,26.0,3239.0,647.0,1529.0,590.0,3.2426,236900.0,<1H OCEAN
+-122.56,38.09,17.0,9614.0,2123.0,4684.0,2060.0,4.1705,209800.0,NEAR BAY
+-120.14,34.6,22.0,2136.0,465.0,1143.0,409.0,2.9479,243100.0,NEAR OCEAN
+-123.22,40.16,27.0,1848.0,449.0,396.0,150.0,2.8472,41300.0,INLAND
+-120.09,37.02,9.0,1608.0,297.0,1057.0,295.0,3.7143,81600.0,INLAND
+-121.89,37.25,26.0,1741.0,323.0,1007.0,339.0,4.7069,234800.0,<1H OCEAN
+-121.9,37.32,48.0,1274.0,313.0,971.0,291.0,3.7738,220600.0,<1H OCEAN
+-117.36,33.92,7.0,9376.0,1181.0,3570.0,1107.0,8.5326,315200.0,INLAND
+-116.88,34.25,11.0,1089.0,198.0,230.0,90.0,4.9643,176000.0,INLAND
+-117.71,33.52,17.0,2486.0,417.0,876.0,361.0,6.1007,340900.0,<1H OCEAN
+-117.88,33.7,18.0,2135.0,373.0,1464.0,405.0,5.4836,225800.0,<1H OCEAN
+-122.05,37.05,41.0,2422.0,502.0,915.0,366.0,4.1679,201300.0,NEAR OCEAN
+-122.38,40.2,16.0,2722.0,511.0,1366.0,495.0,2.8447,87700.0,INLAND
+-117.07,32.78,22.0,922.0,240.0,1524.0,235.0,1.6815,218800.0,NEAR OCEAN
+-122.15,37.71,18.0,5778.0,1526.0,2441.0,1352.0,3.1682,202700.0,NEAR BAY
+-118.24,34.15,20.0,2734.0,658.0,1562.0,607.0,3.3906,284100.0,<1H OCEAN
+-118.73,36.01,14.0,3263.0,651.0,1910.0,594.0,2.8603,128900.0,INLAND
+-122.47,37.76,49.0,2842.0,670.0,1396.0,648.0,3.2679,345700.0,NEAR BAY
+-121.24,38.66,14.0,3335.0,440.0,1329.0,429.0,6.2082,250300.0,INLAND
+-117.36,33.98,46.0,1680.0,453.0,1570.0,435.0,2.0436,82300.0,INLAND
+-120.49,37.32,13.0,3474.0,927.0,2149.0,821.0,1.9528,85300.0,INLAND
+-118.02,33.89,36.0,1375.0,,670.0,221.0,5.0839,198200.0,<1H OCEAN
+-118.12,33.8,35.0,1835.0,435.0,774.0,418.0,2.7092,256300.0,<1H OCEAN
+-118.22,34.1,35.0,4003.0,788.0,2785.0,764.0,4.1213,252100.0,<1H OCEAN
+-122.11,37.67,38.0,1035.0,247.0,599.0,224.0,3.0917,167200.0,NEAR BAY
+-122.16,37.7,36.0,1719.0,303.0,836.0,311.0,4.4375,193500.0,NEAR BAY
+-118.1,34.09,46.0,2822.0,525.0,1434.0,520.0,3.8906,238300.0,<1H OCEAN
+-119.32,36.22,5.0,2319.0,438.0,1283.0,423.0,3.6343,95400.0,INLAND
+-118.32,33.88,37.0,1402.0,254.0,722.0,251.0,6.4781,269000.0,<1H OCEAN
+-121.91,37.43,33.0,2791.0,496.0,1714.0,485.0,4.8304,224900.0,<1H OCEAN
+-118.38,34.06,31.0,4345.0,1158.0,1987.0,1070.0,2.8233,310000.0,<1H OCEAN
+-118.18,34.05,41.0,616.0,196.0,814.0,180.0,3.3333,115100.0,<1H OCEAN
+-118.2,33.79,48.0,2105.0,592.0,1807.0,539.0,2.7183,190400.0,NEAR OCEAN
+-121.95,37.36,27.0,3236.0,832.0,2230.0,798.0,3.5625,208600.0,<1H OCEAN
+-121.76,37.7,9.0,3980.0,736.0,1705.0,679.0,5.7068,256700.0,INLAND
+-117.3,34.18,19.0,2526.0,381.0,1176.0,381.0,5.5136,137100.0,INLAND
+-120.96,37.66,15.0,2485.0,434.0,1296.0,434.0,3.8542,145200.0,INLAND
+-122.05,37.94,22.0,2105.0,354.0,993.0,365.0,4.6602,227800.0,NEAR BAY
+-117.49,34.02,35.0,2051.0,427.0,1466.0,425.0,3.6711,108200.0,INLAND
+-119.32,36.19,11.0,3136.0,620.0,2013.0,583.0,3.335,69700.0,INLAND
+-118.32,34.08,46.0,2038.0,534.0,1250.0,525.0,2.4196,358100.0,<1H OCEAN
+-122.21,37.48,37.0,1326.0,335.0,1771.0,335.0,3.0147,218100.0,NEAR BAY
+-118.27,34.07,33.0,1177.0,468.0,1533.0,430.0,2.3981,183300.0,<1H OCEAN
+-120.45,34.96,21.0,2121.0,445.0,2211.0,463.0,4.0603,117600.0,<1H OCEAN
+-116.95,33.78,24.0,3409.0,804.0,1939.0,739.0,1.7303,74000.0,INLAND
+-118.19,34.03,50.0,1183.0,246.0,851.0,231.0,3.2639,142600.0,<1H OCEAN
+-122.43,37.8,52.0,2520.0,649.0,959.0,607.0,5.7934,500001.0,NEAR BAY
+-121.21,37.8,44.0,300.0,72.0,160.0,73.0,2.1786,120800.0,INLAND
+-118.97,35.41,36.0,1896.0,315.0,937.0,303.0,3.996,85500.0,INLAND
+-122.05,37.36,34.0,2400.0,419.0,1017.0,384.0,4.1369,316900.0,<1H OCEAN
+-121.85,37.66,14.0,4236.0,701.0,1833.0,663.0,5.6399,300600.0,INLAND
+-117.06,32.64,30.0,4494.0,667.0,1883.0,680.0,5.766,186100.0,NEAR OCEAN
+-122.85,38.52,13.0,4808.0,848.0,2568.0,762.0,3.6583,183200.0,<1H OCEAN
+-118.39,33.9,7.0,4314.0,725.0,1699.0,718.0,8.2037,500001.0,<1H OCEAN
+-118.21,33.81,45.0,1816.0,398.0,1524.0,388.0,3.8586,157900.0,NEAR OCEAN
+-118.42,34.27,33.0,1209.0,341.0,1097.0,283.0,1.6295,134300.0,<1H OCEAN
+-122.04,37.3,26.0,1714.0,270.0,778.0,262.0,6.075,417000.0,<1H OCEAN
+-122.18,38.17,7.0,4093.0,651.0,2228.0,646.0,5.2523,161300.0,NEAR BAY
+-121.95,37.94,21.0,3153.0,411.0,1318.0,431.0,6.8642,285400.0,INLAND
+-118.35,34.09,42.0,2210.0,643.0,1228.0,605.0,2.5982,315800.0,<1H OCEAN
+-122.24,37.81,52.0,2513.0,502.0,1048.0,518.0,3.675,269900.0,NEAR BAY
+-121.43,38.61,40.0,1134.0,252.0,675.0,249.0,1.3696,65200.0,INLAND
+-121.31,38.64,19.0,5407.0,838.0,1927.0,804.0,4.6302,195400.0,INLAND
+-118.44,34.19,11.0,2891.0,951.0,2166.0,768.0,2.891,178100.0,<1H OCEAN
+-116.22,34.21,23.0,1175.0,468.0,355.0,151.0,2.2083,42500.0,INLAND
+-117.65,33.65,15.0,3485.0,519.0,1740.0,485.0,6.7543,251900.0,<1H OCEAN
+-117.66,34.05,33.0,960.0,216.0,831.0,222.0,2.5391,108600.0,INLAND
+-117.05,32.73,27.0,3184.0,588.0,1763.0,571.0,3.5529,133900.0,NEAR OCEAN
+-117.27,33.22,16.0,1420.0,311.0,470.0,313.0,1.8849,90800.0,<1H OCEAN
+-122.17,37.75,43.0,1587.0,320.0,907.0,306.0,1.9821,98300.0,NEAR BAY
+-117.95,33.8,32.0,1219.0,192.0,634.0,197.0,5.237,215700.0,<1H OCEAN
+-121.28,38.63,36.0,120.0,16.0,30.0,14.0,10.2264,350000.0,INLAND
+-118.22,34.05,43.0,1153.0,411.0,1667.0,409.0,1.9402,139300.0,<1H OCEAN
+-118.39,33.96,45.0,1436.0,374.0,662.0,292.0,3.625,329400.0,<1H OCEAN
+-122.48,37.76,48.0,2660.0,616.0,1491.0,602.0,3.9758,348600.0,NEAR BAY
+-117.96,33.92,18.0,3744.0,1027.0,1654.0,912.0,3.2158,215000.0,<1H OCEAN
+-122.49,37.76,48.0,1351.0,270.0,650.0,265.0,3.5278,339800.0,NEAR BAY
+-122.45,37.64,19.0,6326.0,1025.0,3444.0,984.0,6.2498,353300.0,NEAR OCEAN
+-122.02,36.97,44.0,594.0,169.0,325.0,139.0,1.1552,250000.0,NEAR OCEAN
+-118.21,33.95,38.0,1889.0,565.0,2087.0,559.0,1.7778,154000.0,<1H OCEAN
+-121.0,39.09,7.0,439.0,84.0,246.0,80.0,3.0781,162500.0,INLAND
+-118.3,33.76,6.0,6097.0,1270.0,2678.0,1226.0,5.1269,285200.0,<1H OCEAN
+-121.82,37.24,20.0,3671.0,567.0,1944.0,589.0,6.0538,241000.0,<1H OCEAN
+-118.88,34.22,22.0,3654.0,517.0,1565.0,518.0,6.2748,274800.0,<1H OCEAN
+-118.72,34.29,22.0,3266.0,529.0,1595.0,494.0,6.0368,248000.0,<1H OCEAN
+-118.36,34.08,52.0,1902.0,488.0,848.0,478.0,2.9621,175000.0,<1H OCEAN
+-121.88,37.34,40.0,1547.0,625.0,1493.0,543.0,1.2887,212500.0,<1H OCEAN
+-118.55,34.37,21.0,7010.0,1063.0,3331.0,1038.0,6.776,278100.0,<1H OCEAN
+-116.51,33.89,21.0,1284.0,306.0,537.0,233.0,1.95,61000.0,INLAND
+-122.44,37.77,52.0,3475.0,807.0,1518.0,777.0,3.6186,500001.0,NEAR BAY
+-118.09,34.07,45.0,726.0,146.0,568.0,160.0,3.0347,183200.0,<1H OCEAN
+-117.95,33.68,27.0,1732.0,303.0,1115.0,308.0,5.5312,239200.0,<1H OCEAN
+-121.25,38.03,29.0,2465.0,327.0,859.0,315.0,6.6605,220700.0,INLAND
+-117.35,33.16,10.0,1684.0,515.0,902.0,449.0,3.7891,206300.0,NEAR OCEAN
+-117.95,33.75,24.0,2027.0,358.0,1405.0,341.0,5.1416,231400.0,<1H OCEAN
+-117.26,33.19,4.0,2342.0,595.0,1518.0,545.0,2.9469,216100.0,NEAR OCEAN
+-120.47,34.65,32.0,2193.0,430.0,1074.0,377.0,2.3333,130200.0,NEAR OCEAN
+-120.76,35.52,7.0,9613.0,1666.0,4487.0,1653.0,3.6667,250600.0,<1H OCEAN
+-121.82,37.37,41.0,1558.0,281.0,970.0,304.0,4.4167,215200.0,<1H OCEAN
+-122.16,37.42,34.0,4448.0,610.0,2571.0,581.0,11.0492,500001.0,NEAR OCEAN
+-122.02,37.34,26.0,1992.0,328.0,980.0,342.0,6.2475,350000.0,<1H OCEAN
+-122.61,38.09,18.0,6205.0,821.0,2311.0,756.0,6.9081,368700.0,<1H OCEAN
+-117.73,34.07,33.0,1025.0,261.0,854.0,269.0,2.2596,119400.0,INLAND
+-122.04,37.96,16.0,2913.0,723.0,1705.0,693.0,2.9097,106300.0,NEAR BAY
+-118.39,34.03,28.0,1722.0,536.0,1161.0,481.0,3.2228,232500.0,<1H OCEAN
+-118.44,33.98,21.0,18132.0,5419.0,7431.0,4930.0,5.3359,500001.0,<1H OCEAN
+-117.92,33.93,12.0,4415.0,890.0,1532.0,854.0,3.75,166300.0,<1H OCEAN
+-117.27,34.5,8.0,3567.0,543.0,1133.0,419.0,5.3733,302600.0,INLAND
+-117.91,33.82,29.0,1444.0,326.0,1038.0,271.0,2.3843,182900.0,<1H OCEAN
+-122.03,37.56,31.0,4981.0,964.0,2841.0,924.0,4.8962,220200.0,NEAR BAY
+-122.86,38.44,31.0,1534.0,292.0,716.0,288.0,3.4471,209500.0,<1H OCEAN
+-122.02,36.96,52.0,775.0,305.0,1054.0,305.0,2.0172,112500.0,NEAR OCEAN
+-118.03,33.85,23.0,5495.0,1141.0,2873.0,1004.0,3.9156,224100.0,<1H OCEAN
+-117.23,32.86,15.0,1703.0,320.0,587.0,282.0,5.0855,209800.0,NEAR OCEAN
+-122.38,40.57,43.0,2251.0,542.0,1479.0,512.0,1.5676,58200.0,INLAND
+-119.84,34.45,26.0,4424.0,616.0,1839.0,601.0,6.3654,331200.0,NEAR OCEAN
+-122.21,38.11,31.0,2766.0,604.0,1441.0,552.0,3.1768,131000.0,NEAR BAY
+-122.46,37.73,52.0,3547.0,506.0,1276.0,491.0,8.0069,426800.0,NEAR BAY
+-118.35,34.17,47.0,858.0,170.0,365.0,171.0,2.0385,225000.0,<1H OCEAN
+-118.06,33.8,21.0,2196.0,504.0,1215.0,477.0,4.8,196900.0,<1H OCEAN
+-121.91,37.44,24.0,1212.0,251.0,799.0,242.0,5.0808,212500.0,<1H OCEAN
+-117.2,34.48,7.0,4998.0,953.0,2764.0,891.0,3.205,101900.0,INLAND
+-119.81,36.77,49.0,1749.0,314.0,705.0,300.0,3.15,72200.0,INLAND
+-121.27,37.87,34.0,1010.0,206.0,678.0,234.0,2.9531,104000.0,INLAND
+-117.92,34.12,32.0,2552.0,576.0,2161.0,548.0,2.9459,144400.0,<1H OCEAN
+-118.43,34.26,37.0,1269.0,348.0,1835.0,335.0,3.2583,147200.0,<1H OCEAN
+-118.12,34.15,19.0,557.0,216.0,673.0,212.0,2.1763,168800.0,<1H OCEAN
+-118.17,34.06,43.0,464.0,,416.0,120.0,2.475,142600.0,<1H OCEAN
+-118.14,33.77,51.0,2812.0,621.0,1171.0,566.0,3.875,342900.0,NEAR OCEAN
+-121.55,38.59,36.0,435.0,95.0,285.0,90.0,1.2292,69600.0,INLAND
+-119.88,34.43,16.0,1734.0,365.0,962.0,391.0,4.4777,282500.0,NEAR OCEAN
+-122.91,39.06,21.0,1236.0,238.0,601.0,261.0,1.939,100300.0,INLAND
+-117.99,33.68,14.0,3305.0,841.0,2272.0,769.0,3.4899,216700.0,<1H OCEAN
+-121.9,37.34,52.0,241.0,69.0,385.0,64.0,2.619,212500.0,<1H OCEAN
+-118.31,34.04,37.0,2338.0,686.0,2376.0,630.0,1.767,170300.0,<1H OCEAN
+-117.98,33.71,26.0,1905.0,373.0,1098.0,368.0,4.8611,229600.0,<1H OCEAN
+-117.9,33.88,35.0,2062.0,353.0,991.0,357.0,5.2897,230400.0,<1H OCEAN
+-124.05,40.85,31.0,2414.0,428.0,1005.0,401.0,3.5156,143000.0,NEAR OCEAN
+-118.46,34.16,28.0,2795.0,622.0,1173.0,545.0,4.4423,280400.0,<1H OCEAN
+-120.98,37.59,2.0,5042.0,834.0,2784.0,787.0,4.6484,145900.0,INLAND
+-121.71,39.42,21.0,1432.0,284.0,862.0,275.0,2.2813,57600.0,INLAND
+-118.23,33.9,31.0,2143.0,522.0,2276.0,519.0,1.8095,100800.0,<1H OCEAN
+-118.66,34.1,12.0,2560.0,365.0,907.0,366.0,10.076,500001.0,NEAR OCEAN
+-117.98,34.08,17.0,3640.0,830.0,3537.0,807.0,3.4784,152200.0,INLAND
+-118.16,33.84,36.0,2831.0,573.0,1462.0,569.0,3.8646,214600.0,<1H OCEAN
+-118.04,33.76,25.0,4061.0,545.0,1623.0,527.0,7.1572,294900.0,<1H OCEAN
+-117.85,34.09,16.0,4556.0,639.0,2066.0,651.0,6.4667,263900.0,<1H OCEAN
+-116.95,32.84,31.0,1307.0,,752.0,231.0,3.4286,129400.0,<1H OCEAN
+-122.05,37.36,29.0,1733.0,255.0,679.0,278.0,7.5337,406800.0,<1H OCEAN
+-117.23,32.81,28.0,1508.0,263.0,996.0,267.0,3.8026,270000.0,NEAR OCEAN
+-117.05,32.69,21.0,991.0,210.0,695.0,203.0,3.625,144300.0,NEAR OCEAN
+-122.03,37.34,25.0,5404.0,906.0,2338.0,883.0,6.0577,451800.0,<1H OCEAN
+-118.34,34.02,49.0,1609.0,371.0,896.0,389.0,2.5156,136600.0,<1H OCEAN
+-118.29,33.92,40.0,1935.0,461.0,1616.0,433.0,2.875,120200.0,<1H OCEAN
+-121.97,37.79,16.0,3873.0,484.0,1451.0,501.0,6.7857,341300.0,<1H OCEAN
+-121.39,38.63,34.0,1226.0,180.0,359.0,167.0,3.8068,150400.0,INLAND
+-121.83,37.28,19.0,2644.0,833.0,2317.0,780.0,3.1042,183100.0,<1H OCEAN
+-122.24,37.85,52.0,2612.0,365.0,901.0,367.0,7.2354,391100.0,NEAR BAY
+-121.47,38.13,13.0,3192.0,715.0,1768.0,626.0,2.2619,123500.0,INLAND
+-122.05,37.37,35.0,1365.0,256.0,662.0,262.0,5.6533,291400.0,<1H OCEAN
+-121.83,37.31,19.0,11181.0,1895.0,7817.0,1853.0,5.6058,232700.0,<1H OCEAN
+-122.03,37.97,20.0,3968.0,931.0,2629.0,903.0,2.9915,166700.0,NEAR BAY
+-118.42,34.08,48.0,2413.0,261.0,770.0,248.0,15.0001,500001.0,<1H OCEAN
+-117.02,32.95,25.0,1909.0,334.0,1043.0,322.0,3.7784,160100.0,<1H OCEAN
+-118.38,34.14,42.0,1253.0,225.0,492.0,224.0,7.7112,386700.0,<1H OCEAN
+-122.65,38.24,24.0,1948.0,310.0,922.0,313.0,4.95,243600.0,<1H OCEAN
+-122.08,37.62,27.0,1826.0,309.0,1016.0,313.0,5.64,206500.0,NEAR BAY
+-121.55,38.51,14.0,5490.0,851.0,2415.0,837.0,6.5253,216800.0,INLAND
+-117.25,32.72,33.0,1677.0,228.0,629.0,239.0,6.597,496400.0,NEAR OCEAN
+-117.88,33.75,50.0,1344.0,228.0,747.0,234.0,4.5125,195400.0,<1H OCEAN
+-120.89,37.54,30.0,509.0,115.0,275.0,115.0,2.2679,250000.0,INLAND
+-120.46,36.87,20.0,1287.0,310.0,954.0,269.0,1.3386,63000.0,INLAND
+-121.7,36.84,19.0,2511.0,465.0,1551.0,450.0,4.9107,231900.0,<1H OCEAN
+-122.2,40.26,15.0,2102.0,358.0,957.0,371.0,3.1908,137900.0,INLAND
+-122.25,37.89,41.0,1125.0,195.0,356.0,181.0,6.1593,344000.0,NEAR BAY
+-117.14,32.76,24.0,3523.0,991.0,1775.0,873.0,2.1273,142300.0,NEAR OCEAN
+-122.11,37.38,36.0,3598.0,500.0,1296.0,533.0,7.8177,500001.0,NEAR BAY
+-117.41,34.1,5.0,4937.0,1139.0,2204.0,812.0,2.5272,92000.0,INLAND
+-118.12,33.8,36.0,1257.0,205.0,530.0,211.0,5.3701,251400.0,<1H OCEAN
+-122.48,37.7,33.0,4492.0,,3477.0,1537.0,3.0546,297900.0,NEAR OCEAN
+-121.38,38.7,25.0,3919.0,764.0,2203.0,783.0,2.2402,89500.0,INLAND
+-122.79,38.42,9.0,4967.0,885.0,2581.0,915.0,5.038,185600.0,<1H OCEAN
+-124.0,40.92,29.0,1429.0,,672.0,266.0,2.9485,98800.0,<1H OCEAN
+-118.49,34.03,31.0,4949.0,1293.0,1985.0,1244.0,4.252,436700.0,<1H OCEAN
+-117.07,33.13,33.0,555.0,165.0,612.0,176.0,2.1786,137500.0,<1H OCEAN
+-119.3,36.35,24.0,1855.0,416.0,1520.0,410.0,2.3304,64900.0,INLAND
+-118.24,33.8,28.0,636.0,169.0,788.0,143.0,3.6161,131300.0,NEAR OCEAN
+-120.89,39.3,17.0,2282.0,431.0,974.0,371.0,3.5417,155100.0,INLAND
+-117.29,34.14,39.0,1989.0,401.0,805.0,341.0,2.425,90000.0,INLAND
+-121.0,39.16,10.0,1170.0,225.0,537.0,194.0,3.2813,163200.0,INLAND
+-122.13,37.77,24.0,2459.0,317.0,916.0,324.0,7.0712,293000.0,NEAR BAY
+-117.44,33.95,31.0,914.0,177.0,556.0,161.0,3.7344,115300.0,INLAND
+-118.28,34.1,49.0,1767.0,467.0,1066.0,438.0,3.0958,210900.0,<1H OCEAN
+-117.14,32.8,41.0,2423.0,469.0,1813.0,466.0,2.1157,156900.0,NEAR OCEAN
+-117.04,32.83,8.0,2205.0,348.0,777.0,341.0,6.0266,177400.0,<1H OCEAN
+-122.2,37.82,39.0,3770.0,534.0,1265.0,500.0,6.3302,362800.0,NEAR BAY
+-121.43,37.78,24.0,807.0,174.0,585.0,166.0,2.6181,163500.0,INLAND
+-118.33,33.98,38.0,3063.0,796.0,2153.0,721.0,1.8472,149100.0,<1H OCEAN
+-121.65,39.53,23.0,1387.0,325.0,640.0,289.0,1.4833,65200.0,INLAND
+-122.01,37.97,34.0,3259.0,498.0,1250.0,478.0,5.3794,206200.0,NEAR BAY
+-117.05,32.78,37.0,1184.0,178.0,529.0,192.0,4.7941,161700.0,<1H OCEAN
+-122.47,37.68,31.0,4077.0,777.0,2544.0,738.0,4.5337,306700.0,NEAR OCEAN
+-122.3,37.9,38.0,2263.0,522.0,1027.0,509.0,3.5125,224200.0,NEAR BAY
+-121.96,38.35,20.0,1415.0,266.0,667.0,250.0,4.0938,117300.0,INLAND
+-121.93,36.59,25.0,2201.0,353.0,622.0,295.0,5.0621,386500.0,NEAR OCEAN
+-118.55,34.39,16.0,8726.0,1317.0,3789.0,1279.0,6.8419,323300.0,<1H OCEAN
+-118.47,34.24,19.0,2405.0,661.0,1855.0,621.0,2.3111,255400.0,<1H OCEAN
+-117.84,33.9,24.0,1723.0,223.0,707.0,219.0,7.0352,299600.0,<1H OCEAN
+-121.58,39.79,19.0,2636.0,523.0,1184.0,465.0,2.7863,108600.0,INLAND
+-117.14,32.71,43.0,966.0,255.0,857.0,208.0,1.2841,72000.0,NEAR OCEAN
+-122.05,37.54,25.0,4209.0,731.0,2568.0,703.0,5.2882,223100.0,NEAR BAY
+-121.97,36.96,27.0,4001.0,999.0,1808.0,945.0,2.561,234600.0,NEAR OCEAN
+-122.37,38.33,29.0,1868.0,291.0,764.0,284.0,4.825,195100.0,NEAR BAY
+-121.29,38.14,34.0,2770.0,544.0,1409.0,535.0,3.2338,101800.0,INLAND
+-118.61,34.2,16.0,1718.0,467.0,896.0,475.0,3.6296,160900.0,<1H OCEAN
+-118.28,33.98,47.0,865.0,193.0,782.0,217.0,2.2411,93000.0,<1H OCEAN
+-119.75,34.43,23.0,2982.0,837.0,1317.0,787.0,3.3776,283200.0,NEAR OCEAN
+-118.38,34.14,40.0,1965.0,354.0,666.0,357.0,6.0876,483800.0,<1H OCEAN
+-122.22,37.48,34.0,1541.0,584.0,1564.0,558.0,2.56,250000.0,NEAR OCEAN
+-118.25,33.97,39.0,1346.0,380.0,1520.0,356.0,1.1635,108700.0,<1H OCEAN
+-117.99,33.75,22.0,3024.0,754.0,2357.0,743.0,3.3125,191800.0,<1H OCEAN
+-117.28,34.12,47.0,2456.0,611.0,1653.0,512.0,1.3973,66100.0,INLAND
+-118.01,33.95,36.0,1579.0,290.0,816.0,276.0,4.4318,181100.0,<1H OCEAN
+-118.1,33.91,40.0,513.0,100.0,399.0,99.0,4.875,167600.0,<1H OCEAN
+-118.16,34.07,41.0,247.0,55.0,925.0,50.0,3.5769,135700.0,<1H OCEAN
+-122.19,37.77,52.0,2329.0,445.0,1144.0,417.0,3.5114,151200.0,NEAR BAY
+-117.23,33.21,21.0,1934.0,386.0,861.0,381.0,3.6181,213800.0,<1H OCEAN
+-117.57,34.13,5.0,6135.0,879.0,2795.0,781.0,5.9369,225200.0,INLAND
+-118.42,34.08,46.0,1399.0,148.0,410.0,152.0,15.0001,500001.0,<1H OCEAN
+-120.84,38.77,11.0,1013.0,188.0,410.0,158.0,4.825,184600.0,INLAND
+-122.12,37.69,35.0,2681.0,508.0,1580.0,536.0,4.1042,179100.0,NEAR BAY
+-121.3,37.95,9.0,674.0,242.0,575.0,193.0,2.2024,45000.0,INLAND
+-121.81,37.3,15.0,1929.0,345.0,1683.0,347.0,5.5248,235600.0,<1H OCEAN
+-118.27,33.79,39.0,1513.0,365.0,1227.0,354.0,3.3929,184600.0,<1H OCEAN
+-118.38,33.81,41.0,1889.0,301.0,802.0,278.0,6.015,488500.0,NEAR OCEAN
+-118.27,34.06,45.0,564.0,353.0,1172.0,319.0,1.494,187500.0,<1H OCEAN
+-119.02,35.36,47.0,1631.0,340.0,847.0,315.0,2.5062,73700.0,INLAND
+-122.6,38.9,23.0,292.0,56.0,92.0,41.0,2.9583,91700.0,INLAND
+-122.0,37.0,16.0,32.0,4.0,36.0,5.0,2.625,137500.0,NEAR OCEAN
+-117.37,33.98,27.0,1342.0,547.0,844.0,484.0,1.1194,95800.0,INLAND
+-118.13,34.16,33.0,2682.0,716.0,2050.0,692.0,2.4817,169500.0,<1H OCEAN
+-119.77,36.75,39.0,1287.0,332.0,1386.0,306.0,1.5227,46900.0,INLAND
+-117.32,33.12,25.0,2670.0,527.0,936.0,461.0,2.7717,354000.0,NEAR OCEAN
+-116.91,32.75,5.0,8710.0,1614.0,4372.0,1527.0,4.7813,240900.0,<1H OCEAN
+-118.36,33.99,43.0,2657.0,548.0,1145.0,524.0,4.1375,287100.0,<1H OCEAN
+-118.4,34.03,24.0,1101.0,318.0,491.0,287.0,3.2222,319400.0,<1H OCEAN
+-117.89,33.87,25.0,1492.0,439.0,755.0,389.0,3.0893,188200.0,<1H OCEAN
+-123.15,39.31,19.0,1026.0,205.0,424.0,152.0,2.8833,154200.0,INLAND
+-118.4,33.88,35.0,1060.0,191.0,444.0,196.0,8.0015,500001.0,<1H OCEAN
+-117.76,34.05,36.0,2910.0,819.0,3055.0,782.0,1.9029,98000.0,INLAND
+-120.45,39.8,47.0,2149.0,456.0,965.0,419.0,1.7829,55900.0,INLAND
+-118.24,34.13,45.0,2170.0,401.0,1043.0,394.0,5.6921,269000.0,<1H OCEAN
+-118.21,34.1,47.0,5077.0,1271.0,3348.0,1106.0,3.0377,186800.0,<1H OCEAN
+-118.34,34.11,40.0,5485.0,1242.0,2034.0,1133.0,3.6974,500001.0,<1H OCEAN
+-121.77,37.68,44.0,495.0,112.0,277.0,109.0,2.6667,179200.0,INLAND
+-122.5,37.74,40.0,2310.0,445.0,1266.0,490.0,3.7969,297800.0,NEAR OCEAN
+-117.33,33.23,15.0,2919.0,592.0,1130.0,579.0,2.5872,155600.0,NEAR OCEAN
+-118.16,34.02,44.0,1218.0,374.0,1175.0,342.0,1.9688,173900.0,<1H OCEAN
+-121.97,37.29,29.0,2721.0,682.0,1602.0,646.0,3.337,265300.0,<1H OCEAN
+-121.48,38.53,38.0,1451.0,315.0,786.0,340.0,2.3487,101600.0,INLAND
+-118.13,33.96,38.0,1040.0,202.0,557.0,228.0,4.0,254700.0,<1H OCEAN
+-121.55,39.5,26.0,3215.0,827.0,2041.0,737.0,1.0585,45100.0,INLAND
+-117.93,34.08,36.0,1597.0,285.0,901.0,272.0,4.3947,197000.0,<1H OCEAN
+-118.49,34.19,23.0,2087.0,571.0,1809.0,553.0,3.1667,202000.0,<1H OCEAN
+-118.09,34.08,42.0,1003.0,236.0,769.0,231.0,3.1607,218300.0,<1H OCEAN
+-122.32,38.0,32.0,2275.0,397.0,1233.0,418.0,4.0437,162800.0,NEAR BAY
+-121.37,38.7,26.0,2230.0,410.0,1155.0,377.0,3.4911,88200.0,INLAND
+-121.64,39.74,20.0,1808.0,334.0,763.0,335.0,2.3711,121800.0,INLAND
+-121.9,36.59,42.0,2689.0,510.0,1023.0,459.0,4.6182,301000.0,NEAR OCEAN
+-122.43,37.76,52.0,3708.0,849.0,1531.0,822.0,3.3565,386400.0,NEAR BAY
+-122.05,38.26,21.0,7195.0,1416.0,3927.0,1377.0,3.0912,126300.0,INLAND
+-120.83,37.5,30.0,1340.0,244.0,631.0,231.0,3.375,118500.0,INLAND
+-122.24,37.82,52.0,3481.0,751.0,1444.0,718.0,3.9,275700.0,NEAR BAY
+-121.34,38.03,12.0,2707.0,433.0,1200.0,380.0,4.9861,133500.0,INLAND
+-118.81,34.28,20.0,3678.0,684.0,1882.0,694.0,4.1607,196800.0,<1H OCEAN
+-119.27,36.32,6.0,2881.0,518.0,1432.0,504.0,4.0806,110200.0,INLAND
+-118.35,33.85,34.0,1770.0,291.0,916.0,289.0,5.0,354200.0,<1H OCEAN
+-120.77,37.01,28.0,1689.0,378.0,1057.0,267.0,3.125,156300.0,INLAND
+-118.3,33.91,34.0,1617.0,493.0,1530.0,500.0,2.6182,172600.0,<1H OCEAN
+-117.92,33.79,26.0,2737.0,614.0,1877.0,606.0,2.8622,184300.0,<1H OCEAN
+-118.39,34.06,52.0,1213.0,194.0,503.0,194.0,8.0095,500001.0,<1H OCEAN
+-122.12,37.71,38.0,1164.0,284.0,632.0,289.0,3.0345,152100.0,NEAR BAY
+-120.98,38.34,27.0,3471.0,653.0,1793.0,600.0,3.5508,99100.0,INLAND
+-117.23,33.09,7.0,5320.0,855.0,2015.0,768.0,6.3373,279600.0,NEAR OCEAN
+-120.47,37.96,25.0,2505.0,529.0,1145.0,483.0,2.006,103000.0,INLAND
+-117.44,34.45,6.0,6068.0,1137.0,3094.0,947.0,3.5167,130900.0,INLAND
+-117.13,32.81,19.0,2157.0,554.0,1349.0,535.0,2.7652,177400.0,NEAR OCEAN
+-123.75,40.11,35.0,2052.0,477.0,900.0,402.0,1.9625,101500.0,<1H OCEAN
+-122.12,37.43,36.0,3212.0,553.0,1455.0,574.0,6.46,425500.0,NEAR BAY
+-119.71,34.42,23.0,2068.0,658.0,1898.0,570.0,2.5506,230800.0,<1H OCEAN
+-119.81,36.78,52.0,2281.0,371.0,839.0,367.0,3.5972,89900.0,INLAND
+-117.56,34.42,6.0,4264.0,749.0,2005.0,666.0,3.4695,138800.0,INLAND
+-118.45,34.23,15.0,5738.0,1767.0,4620.0,1581.0,2.3584,157600.0,<1H OCEAN
+-122.28,38.29,19.0,531.0,112.0,139.0,80.0,1.9875,325000.0,NEAR BAY
+-122.36,37.95,38.0,1066.0,248.0,729.0,286.0,1.5139,81700.0,NEAR BAY
+-117.88,33.74,25.0,1799.0,557.0,3416.0,538.0,3.0083,163500.0,<1H OCEAN
+-118.18,33.97,26.0,6895.0,1877.0,8551.0,1808.0,2.3175,154500.0,<1H OCEAN
+-118.29,34.65,18.0,6893.0,1372.0,2837.0,1221.0,3.3173,218400.0,INLAND
+-117.64,33.59,4.0,3274.0,383.0,1312.0,390.0,8.1611,348000.0,<1H OCEAN
+-118.28,33.96,42.0,1604.0,399.0,1581.0,387.0,1.7656,96700.0,<1H OCEAN
+-118.26,33.97,47.0,1504.0,374.0,1168.0,358.0,1.4625,94200.0,<1H OCEAN
+-119.29,36.12,24.0,1248.0,226.0,641.0,200.0,2.4722,129200.0,INLAND
+-117.36,34.09,32.0,3616.0,631.0,2131.0,593.0,3.2879,95500.0,INLAND
+-122.23,37.45,29.0,1617.0,235.0,758.0,246.0,7.7932,469900.0,NEAR OCEAN
+-117.9,33.91,26.0,2885.0,476.0,1227.0,439.0,4.9524,226600.0,<1H OCEAN
+-118.29,34.03,22.0,3313.0,1235.0,2381.0,1063.0,0.7473,168800.0,<1H OCEAN
+-122.85,38.46,22.0,3328.0,550.0,1309.0,512.0,4.7105,266200.0,<1H OCEAN
+-117.25,33.03,6.0,3416.0,493.0,1319.0,467.0,6.9326,324600.0,NEAR OCEAN
+-122.07,37.67,27.0,3239.0,671.0,1469.0,616.0,3.2465,230600.0,NEAR BAY
+-119.71,34.43,48.0,2408.0,536.0,1005.0,497.0,3.5213,458600.0,<1H OCEAN
+-118.22,33.82,30.0,1680.0,469.0,1779.0,429.0,3.6086,146300.0,NEAR OCEAN
+-117.13,32.76,33.0,1591.0,461.0,794.0,425.0,2.6333,140000.0,NEAR OCEAN
+-121.82,37.81,12.0,4711.0,659.0,2089.0,621.0,8.3209,485400.0,INLAND
+-118.37,34.09,22.0,4247.0,1253.0,1766.0,1170.0,3.1517,341700.0,<1H OCEAN
+-121.25,38.05,25.0,1967.0,362.0,1035.0,361.0,3.5735,106800.0,INLAND
+-122.04,37.61,36.0,1151.0,216.0,727.0,215.0,4.1719,187000.0,NEAR BAY
+-117.87,33.78,19.0,2813.0,567.0,1334.0,596.0,4.7208,173500.0,<1H OCEAN
+-118.46,33.99,37.0,1828.0,460.0,1075.0,453.0,4.337,360600.0,<1H OCEAN
+-118.4,33.85,29.0,2085.0,533.0,919.0,489.0,5.6017,430000.0,<1H OCEAN
+-118.1,34.05,31.0,3559.0,734.0,2975.0,715.0,3.756,183300.0,<1H OCEAN
+-121.65,36.67,52.0,2351.0,459.0,1169.0,439.0,2.8924,169600.0,<1H OCEAN
+-118.38,34.18,44.0,901.0,179.0,473.0,179.0,3.3125,186400.0,<1H OCEAN
+-119.78,36.76,47.0,1425.0,323.0,949.0,325.0,1.7344,51300.0,INLAND
+-118.21,33.89,39.0,1565.0,364.0,1389.0,360.0,2.7443,113900.0,<1H OCEAN
+-120.86,37.49,37.0,1084.0,271.0,893.0,236.0,1.6213,69500.0,INLAND
+-124.16,40.95,20.0,1075.0,214.0,529.0,196.0,3.1406,96000.0,NEAR OCEAN
+-116.78,33.0,7.0,12480.0,1946.0,5102.0,1697.0,5.5102,221000.0,<1H OCEAN
+-118.37,33.88,20.0,2439.0,474.0,1219.0,497.0,5.9619,335900.0,<1H OCEAN
+-118.21,34.14,25.0,1908.0,628.0,1412.0,588.0,2.2267,189800.0,<1H OCEAN
+-122.29,37.87,52.0,2225.0,460.0,1145.0,430.0,2.6165,150000.0,NEAR BAY
+-118.29,33.75,27.0,1650.0,443.0,1359.0,386.0,2.5795,192400.0,NEAR OCEAN
+-121.83,38.45,36.0,839.0,158.0,446.0,167.0,2.3438,122700.0,INLAND
+-117.02,36.4,19.0,619.0,239.0,490.0,164.0,2.1,14999.0,INLAND
+-118.34,33.95,33.0,1923.0,459.0,1412.0,361.0,5.4359,194100.0,<1H OCEAN
+-117.99,33.93,33.0,2299.0,431.0,1049.0,447.0,3.6458,208100.0,<1H OCEAN
+-118.28,34.05,41.0,1075.0,597.0,2260.0,614.0,1.3,162500.0,<1H OCEAN
+-120.37,40.17,21.0,789.0,141.0,406.0,146.0,2.1198,73500.0,INLAND
+-118.61,34.59,5.0,4028.0,896.0,2062.0,826.0,4.0579,167100.0,INLAND
+-117.9,33.91,36.0,1376.0,257.0,687.0,221.0,3.5403,195400.0,<1H OCEAN
+-118.69,34.08,23.0,204.0,40.0,117.0,41.0,9.7646,500001.0,NEAR OCEAN
+-117.98,34.0,22.0,3632.0,538.0,1968.0,566.0,6.019,324900.0,<1H OCEAN
+-119.26,34.27,40.0,2528.0,572.0,1318.0,549.0,3.6413,212700.0,NEAR OCEAN
+-118.09,33.92,35.0,1994.0,419.0,1491.0,428.0,3.7383,166200.0,<1H OCEAN
+-118.21,33.94,40.0,2227.0,594.0,2244.0,580.0,2.4459,143800.0,<1H OCEAN
+-119.84,36.78,24.0,3242.0,795.0,2764.0,773.0,1.3385,58800.0,INLAND
+-118.04,33.96,42.0,1430.0,338.0,1269.0,321.0,3.3214,148800.0,<1H OCEAN
+-116.95,32.96,18.0,2087.0,353.0,992.0,329.0,4.5,222600.0,<1H OCEAN
+-117.63,33.62,9.0,4257.0,785.0,1293.0,745.0,3.7139,196700.0,<1H OCEAN
+-122.39,40.59,26.0,1279.0,438.0,1276.0,420.0,1.2404,81300.0,INLAND
+-123.75,39.37,16.0,1377.0,296.0,830.0,279.0,3.25,151400.0,<1H OCEAN
+-118.98,35.37,35.0,825.0,179.0,670.0,181.0,1.1638,57900.0,INLAND
+-117.5,34.04,5.0,3958.0,665.0,2456.0,666.0,5.1647,154700.0,INLAND
+-121.57,37.0,18.0,7241.0,1225.0,4168.0,1138.0,4.5714,260300.0,INLAND
+-118.3,33.73,42.0,1731.0,,866.0,403.0,2.7451,255400.0,NEAR OCEAN
+-122.02,37.29,25.0,3845.0,492.0,1461.0,475.0,10.3979,500001.0,<1H OCEAN
+-122.55,37.97,52.0,2232.0,291.0,731.0,253.0,7.1155,500001.0,NEAR BAY
+-121.42,38.53,37.0,1958.0,367.0,1171.0,366.0,2.8298,71200.0,INLAND
+-118.34,33.99,48.0,2009.0,335.0,919.0,297.0,4.8125,170500.0,<1H OCEAN
+-122.62,38.25,24.0,2388.0,358.0,1187.0,362.0,4.6534,196500.0,<1H OCEAN
+-122.31,37.57,37.0,1437.0,305.0,979.0,331.0,4.0,273700.0,NEAR OCEAN
+-118.4,33.89,31.0,2926.0,492.0,1149.0,476.0,7.9611,500001.0,<1H OCEAN
+-120.99,37.67,16.0,568.0,124.0,307.0,116.0,2.1518,107400.0,INLAND
+-119.84,36.8,16.0,2849.0,506.0,1508.0,478.0,3.4074,72700.0,INLAND
+-118.2,33.9,34.0,1552.0,444.0,2093.0,413.0,2.2125,103200.0,<1H OCEAN
+-122.08,37.69,36.0,2350.0,499.0,1105.0,467.0,3.3021,195700.0,NEAR BAY
+-122.0,38.37,18.0,1048.0,185.0,469.0,162.0,3.625,125000.0,INLAND
+-121.28,38.8,7.0,9003.0,1739.0,4445.0,1591.0,3.816,147900.0,INLAND
+-119.15,36.29,18.0,1435.0,,657.0,254.0,2.4281,72500.0,INLAND
+-117.14,32.72,34.0,2533.0,862.0,2011.0,778.0,2.1199,160400.0,NEAR OCEAN
+-122.53,41.81,21.0,2400.0,485.0,1109.0,443.0,1.7639,55400.0,INLAND
+-120.78,38.73,31.0,3117.0,616.0,1606.0,588.0,2.9844,127900.0,INLAND
+-118.35,33.93,25.0,2260.0,692.0,1603.0,673.0,2.11,223300.0,<1H OCEAN
+-117.53,34.06,18.0,5605.0,1303.0,4028.0,1145.0,2.9386,116400.0,INLAND
+-117.07,32.8,31.0,2550.0,395.0,1017.0,405.0,5.1488,181000.0,<1H OCEAN
+-118.1,33.81,36.0,856.0,146.0,451.0,164.0,5.1993,246000.0,<1H OCEAN
+-117.27,34.4,8.0,6042.0,979.0,3031.0,991.0,3.3438,124400.0,INLAND
+-118.19,33.87,35.0,1769.0,436.0,1166.0,386.0,2.875,178300.0,<1H OCEAN
+-123.74,40.66,25.0,2395.0,431.0,983.0,375.0,3.0469,136000.0,<1H OCEAN
+-118.09,33.91,36.0,1442.0,271.0,990.0,268.0,4.0517,162200.0,<1H OCEAN
+-119.71,34.4,36.0,1846.0,358.0,748.0,329.0,4.2283,326800.0,NEAR OCEAN
+-118.53,34.24,24.0,2718.0,719.0,3018.0,644.0,2.9076,275300.0,<1H OCEAN
+-118.79,34.14,7.0,3003.0,504.0,1143.0,466.0,5.8548,500001.0,NEAR OCEAN
+-117.1,32.96,7.0,3619.0,770.0,1134.0,482.0,4.1279,167600.0,<1H OCEAN
+-121.01,37.68,33.0,3230.0,587.0,1579.0,560.0,3.5775,109700.0,INLAND
+-120.69,35.64,38.0,2564.0,546.0,1301.0,481.0,2.0076,114000.0,<1H OCEAN
+-121.39,38.69,38.0,300.0,47.0,154.0,51.0,4.0909,108300.0,INLAND
+-121.13,36.21,27.0,1476.0,352.0,1156.0,358.0,3.1929,137900.0,<1H OCEAN
+-118.28,33.98,45.0,1720.0,416.0,1382.0,365.0,0.9337,92300.0,<1H OCEAN
+-117.94,33.8,23.0,2757.0,734.0,1811.0,707.0,2.8,214300.0,<1H OCEAN
+-118.25,33.84,19.0,1731.0,420.0,1032.0,364.0,3.8125,208100.0,<1H OCEAN
+-118.06,33.83,17.0,1973.0,516.0,1112.0,501.0,3.8512,163800.0,<1H OCEAN
+-122.3,37.88,46.0,1647.0,376.0,854.0,355.0,2.9,144800.0,NEAR BAY
+-117.07,32.57,14.0,1527.0,357.0,1224.0,363.0,2.7361,93600.0,NEAR OCEAN
+-122.08,37.92,26.0,1733.0,265.0,796.0,274.0,6.195,264900.0,NEAR BAY
+-122.18,37.74,46.0,2103.0,391.0,1339.0,354.0,2.2467,88900.0,NEAR BAY
+-118.86,34.16,16.0,1509.0,216.0,578.0,235.0,10.2614,410800.0,<1H OCEAN
+-117.98,34.06,33.0,1353.0,228.0,1079.0,237.0,4.5417,160300.0,<1H OCEAN
+-117.06,32.97,17.0,4754.0,877.0,2412.0,832.0,4.3548,192300.0,<1H OCEAN
+-117.89,33.84,33.0,1587.0,374.0,1159.0,331.0,2.8021,195100.0,<1H OCEAN
+-119.57,36.09,6.0,2015.0,413.0,992.0,319.0,2.3889,53200.0,INLAND
+-122.1,37.4,27.0,3410.0,1156.0,2314.0,1086.0,3.4868,165600.0,NEAR BAY
+-118.22,33.98,15.0,1011.0,274.0,899.0,219.0,2.7045,190600.0,<1H OCEAN
+-118.17,34.07,19.0,2150.0,544.0,1510.0,467.0,3.4952,150000.0,<1H OCEAN
+-118.41,33.97,44.0,2789.0,503.0,3732.0,474.0,4.6176,352300.0,<1H OCEAN
+-120.97,37.68,16.0,2493.0,535.0,1370.0,504.0,3.3368,121200.0,INLAND
+-118.4,34.28,22.0,3517.0,810.0,3134.0,847.0,2.6652,164800.0,<1H OCEAN
+-118.08,33.89,37.0,1152.0,259.0,981.0,225.0,3.2857,153600.0,<1H OCEAN
+-120.84,37.52,16.0,4527.0,887.0,2531.0,825.0,3.7065,124800.0,INLAND
+-118.22,34.21,29.0,2174.0,418.0,1030.0,395.0,3.5707,341700.0,<1H OCEAN
+-122.21,37.8,48.0,1321.0,263.0,506.0,252.0,4.0977,229700.0,NEAR BAY
+-122.27,37.52,35.0,1051.0,259.0,517.0,234.0,3.7,339700.0,NEAR OCEAN
+-117.9,33.68,25.0,7060.0,1159.0,3903.0,1139.0,4.8359,249200.0,<1H OCEAN
+-121.88,37.26,13.0,1676.0,471.0,710.0,406.0,3.8936,225900.0,<1H OCEAN
+-117.21,32.89,14.0,3114.0,773.0,1592.0,776.0,3.3176,156100.0,NEAR OCEAN
+-119.7,34.43,52.0,977.0,289.0,412.0,272.0,2.125,300000.0,<1H OCEAN
+-118.13,33.87,45.0,1606.0,300.0,735.0,295.0,4.6765,198400.0,<1H OCEAN
+-118.99,35.35,27.0,1615.0,355.0,1380.0,332.0,1.6632,49800.0,INLAND
+-118.31,34.27,35.0,1446.0,274.0,759.0,291.0,6.0808,215600.0,<1H OCEAN
+-118.56,34.22,34.0,1599.0,294.0,819.0,306.0,4.3194,197000.0,<1H OCEAN
+-117.11,32.67,46.0,928.0,236.0,790.0,235.0,1.6806,92500.0,NEAR OCEAN
+-118.23,33.8,26.0,239.0,135.0,165.0,112.0,1.3333,187500.0,NEAR OCEAN
+-122.14,37.47,36.0,2081.0,412.0,1931.0,373.0,3.7917,160600.0,NEAR BAY
+-122.5,37.75,44.0,1739.0,343.0,872.0,330.0,2.9632,286300.0,NEAR OCEAN
+-117.91,34.12,41.0,2673.0,578.0,2259.0,592.0,3.7846,145500.0,<1H OCEAN
+-117.22,32.78,22.0,2020.0,466.0,1010.0,429.0,3.4527,175000.0,NEAR OCEAN
+-118.27,33.86,29.0,2587.0,489.0,2115.0,475.0,3.7466,168600.0,<1H OCEAN
+-118.14,34.03,38.0,1447.0,293.0,1042.0,284.0,4.1375,211500.0,<1H OCEAN
+-117.9,33.73,30.0,746.0,172.0,1048.0,163.0,4.1,166400.0,<1H OCEAN
+-118.25,34.01,31.0,1301.0,403.0,1952.0,377.0,2.1466,100800.0,<1H OCEAN
+-117.76,33.94,40.0,1092.0,213.0,457.0,190.0,5.1165,184200.0,<1H OCEAN
+-117.13,32.72,52.0,1560.0,307.0,757.0,315.0,2.7083,199100.0,NEAR OCEAN
+-119.85,34.4,14.0,2307.0,650.0,5723.0,615.0,2.1652,37500.0,NEAR OCEAN
+-121.74,37.3,12.0,1961.0,280.0,985.0,269.0,6.7159,362700.0,<1H OCEAN
+-118.21,34.05,28.0,1841.0,809.0,3199.0,727.0,1.6319,151600.0,<1H OCEAN
+-122.17,37.76,38.0,1764.0,397.0,987.0,354.0,2.4333,98200.0,NEAR BAY
+-118.31,34.05,40.0,1667.0,365.0,1161.0,384.0,3.1406,417600.0,<1H OCEAN
+-118.8,34.21,16.0,1466.0,196.0,661.0,209.0,6.2893,282700.0,<1H OCEAN
+-118.32,34.01,44.0,4032.0,913.0,1622.0,848.0,2.4934,165800.0,<1H OCEAN
+-118.31,33.94,40.0,1323.0,243.0,684.0,229.0,3.2206,145800.0,<1H OCEAN
+-121.97,37.97,24.0,1330.0,183.0,656.0,205.0,5.0092,244100.0,INLAND
+-122.02,37.89,29.0,6349.0,858.0,2450.0,778.0,7.5,356200.0,NEAR BAY
+-121.99,37.34,27.0,3353.0,653.0,1571.0,621.0,5.273,315600.0,<1H OCEAN
+-118.42,33.96,44.0,1282.0,216.0,494.0,209.0,5.618,365900.0,<1H OCEAN
+-117.96,33.9,10.0,2423.0,356.0,1213.0,347.0,6.5635,346900.0,<1H OCEAN
+-117.19,33.53,6.0,108.0,18.0,43.0,17.0,3.475,187500.0,<1H OCEAN
+-118.06,33.81,25.0,3497.0,513.0,1839.0,544.0,5.4216,263000.0,<1H OCEAN
+-122.83,38.39,19.0,1765.0,394.0,868.0,388.0,2.462,260300.0,<1H OCEAN
+-122.24,37.48,45.0,4126.0,696.0,1722.0,668.0,4.8966,362100.0,NEAR OCEAN
+-117.25,34.41,13.0,3682.0,668.0,1606.0,668.0,2.1875,119700.0,INLAND
+-122.09,37.7,30.0,1751.0,269.0,731.0,263.0,6.005,263900.0,NEAR BAY
+-117.16,33.54,4.0,4952.0,1000.0,2912.0,943.0,3.7538,147500.0,<1H OCEAN
+-118.18,34.09,44.0,1688.0,426.0,1605.0,384.0,3.3785,184900.0,<1H OCEAN
+-117.28,33.85,16.0,3498.0,702.0,2372.0,672.0,2.3229,118000.0,INLAND
+-118.1,33.89,36.0,769.0,142.0,498.0,137.0,4.7159,182100.0,<1H OCEAN
+-117.0,32.8,29.0,2045.0,398.0,912.0,368.0,3.0189,144100.0,<1H OCEAN
+-119.7,36.83,23.0,3532.0,756.0,1885.0,758.0,2.5904,71400.0,INLAND
+-118.36,33.95,42.0,1139.0,302.0,1283.0,306.0,4.1635,163900.0,<1H OCEAN
+-122.42,37.77,52.0,4226.0,1315.0,2619.0,1242.0,2.5755,325000.0,NEAR BAY
+-118.47,34.03,32.0,3024.0,784.0,1323.0,740.0,3.3889,347900.0,<1H OCEAN
+-121.92,37.33,52.0,2962.0,557.0,1215.0,506.0,4.7768,301100.0,<1H OCEAN
+-118.33,34.08,50.0,2989.0,832.0,1345.0,775.0,3.2426,442900.0,<1H OCEAN
+-122.28,37.83,52.0,3108.0,813.0,1623.0,765.0,2.6997,126900.0,NEAR BAY
+-118.41,33.88,34.0,1471.0,308.0,498.0,264.0,7.0842,500001.0,<1H OCEAN
+-119.73,36.73,7.0,2461.0,647.0,1587.0,551.0,1.4007,225000.0,INLAND
+-118.2,33.89,40.0,2538.0,564.0,2170.0,541.0,2.7212,107900.0,<1H OCEAN
+-122.39,37.76,52.0,624.0,170.0,410.0,148.0,4.0042,208300.0,NEAR BAY
+-122.15,37.69,36.0,1501.0,287.0,703.0,276.0,3.8864,197300.0,NEAR BAY
+-117.88,33.85,26.0,3924.0,781.0,2332.0,725.0,3.7772,223900.0,<1H OCEAN
+-119.73,34.41,29.0,1769.0,297.0,703.0,269.0,4.4375,350000.0,NEAR OCEAN
+-116.96,32.81,8.0,2378.0,638.0,1410.0,623.0,2.9097,152500.0,<1H OCEAN
+-117.27,34.24,34.0,3687.0,756.0,941.0,367.0,2.875,117600.0,INLAND
+-118.21,33.88,29.0,1976.0,444.0,1254.0,371.0,2.1782,126800.0,<1H OCEAN
+-117.08,32.64,38.0,917.0,256.0,494.0,233.0,1.9241,150000.0,NEAR OCEAN
+-121.97,37.97,26.0,1977.0,264.0,817.0,273.0,5.7512,240200.0,INLAND
+-122.47,37.74,52.0,3688.0,640.0,1605.0,567.0,4.9537,365600.0,NEAR BAY
+-123.18,39.26,25.0,3066.0,570.0,1558.0,535.0,3.788,134200.0,<1H OCEAN
+-120.85,37.77,52.0,436.0,81.0,197.0,68.0,1.8625,85400.0,INLAND
+-117.19,32.82,35.0,2197.0,353.0,945.0,357.0,4.9219,192900.0,NEAR OCEAN
+-117.13,32.91,16.0,2715.0,581.0,1619.0,584.0,4.0,154700.0,<1H OCEAN
+-118.62,34.22,34.0,2633.0,471.0,1313.0,428.0,4.0909,232900.0,<1H OCEAN
+-121.76,37.29,15.0,2267.0,348.0,1150.0,327.0,7.1267,277900.0,<1H OCEAN
+-121.42,37.71,7.0,8297.0,1433.0,4189.0,1271.0,4.3696,170700.0,INLAND
+-121.2,38.7,28.0,2970.0,471.0,1379.0,463.0,4.3214,131700.0,INLAND
+-118.01,33.81,18.0,5238.0,1083.0,3032.0,1065.0,4.4583,190100.0,<1H OCEAN
+-122.06,37.9,25.0,5869.0,1685.0,2669.0,1554.0,2.6998,216100.0,NEAR BAY
+-118.21,34.04,37.0,845.0,249.0,881.0,252.0,2.2454,165000.0,<1H OCEAN
+-121.24,38.78,11.0,1851.0,352.0,1049.0,369.0,3.5288,141100.0,INLAND
+-116.89,33.75,23.0,2719.0,538.0,930.0,485.0,2.0154,81700.0,INLAND
+-122.1,37.66,36.0,1305.0,225.0,768.0,234.0,4.275,185300.0,NEAR BAY
+-119.03,35.36,41.0,2551.0,594.0,1342.0,595.0,1.9671,76800.0,INLAND
+-117.82,33.65,18.0,2105.0,302.0,830.0,286.0,6.3822,362500.0,<1H OCEAN
+-122.92,41.7,23.0,4017.0,792.0,1634.0,619.0,2.3571,62000.0,INLAND
+-122.41,37.79,52.0,2909.0,851.0,1711.0,830.0,3.0296,500001.0,NEAR BAY
+-118.23,34.65,17.0,1827.0,348.0,766.0,335.0,3.5673,136300.0,INLAND
+-118.25,33.96,43.0,1876.0,454.0,1571.0,458.0,2.0323,112500.0,<1H OCEAN
+-117.87,33.7,17.0,3216.0,607.0,1916.0,618.0,4.9153,266400.0,<1H OCEAN
+-121.81,37.27,22.0,2996.0,695.0,2169.0,607.0,4.3438,209700.0,<1H OCEAN
+-118.37,33.92,39.0,1073.0,206.0,556.0,204.0,4.8611,245600.0,<1H OCEAN
+-118.23,34.14,39.0,277.0,89.0,182.0,91.0,2.3958,175000.0,<1H OCEAN
+-117.24,32.75,36.0,2831.0,669.0,1279.0,660.0,2.9896,252700.0,NEAR OCEAN
+-118.45,34.12,20.0,10722.0,1617.0,3731.0,1511.0,9.7449,500001.0,<1H OCEAN
+-118.17,34.12,35.0,2568.0,672.0,1696.0,605.0,2.9154,169200.0,<1H OCEAN
+-121.56,39.52,26.0,1957.0,429.0,945.0,397.0,1.7308,53600.0,INLAND
+-122.16,37.44,34.0,2199.0,529.0,1193.0,532.0,4.2972,405900.0,NEAR BAY
+-116.92,32.81,17.0,1312.0,394.0,836.0,337.0,1.6686,112500.0,<1H OCEAN
+-120.38,37.97,47.0,1060.0,219.0,496.0,205.0,2.5781,104800.0,INLAND
+-118.75,34.17,18.0,6217.0,858.0,2703.0,834.0,6.8075,325900.0,NEAR OCEAN
+-121.33,37.98,36.0,3113.0,576.0,1746.0,544.0,3.4625,84600.0,INLAND
+-121.92,36.55,44.0,3494.0,635.0,693.0,415.0,3.6,452800.0,NEAR OCEAN
+-117.09,32.55,8.0,6533.0,1217.0,4797.0,1177.0,3.9583,144400.0,NEAR OCEAN
+-122.13,37.7,19.0,3516.0,710.0,1810.0,703.0,3.9032,218000.0,NEAR BAY
+-121.16,38.03,28.0,253.0,50.0,201.0,51.0,1.4732,156300.0,INLAND
+-117.04,32.6,20.0,8052.0,1461.0,5094.0,1430.0,4.2241,139800.0,NEAR OCEAN
+-117.06,32.79,21.0,3787.0,492.0,1246.0,457.0,9.6023,391300.0,<1H OCEAN
+-118.27,33.82,36.0,1593.0,334.0,1427.0,320.0,4.4015,166900.0,<1H OCEAN
+-121.88,38.01,9.0,5329.0,1284.0,2827.0,1202.0,2.7374,150000.0,INLAND
+-118.43,34.3,37.0,1394.0,313.0,1111.0,327.0,3.6023,161800.0,<1H OCEAN
+-117.31,34.04,5.0,2785.0,577.0,1310.0,536.0,3.39,149500.0,INLAND
+-119.66,34.43,27.0,5509.0,1059.0,2591.0,979.0,3.8456,500001.0,<1H OCEAN
+-118.82,35.2,34.0,2185.0,469.0,1910.0,455.0,2.1136,57300.0,INLAND
+-122.13,37.73,33.0,1996.0,268.0,686.0,270.0,6.9096,341800.0,NEAR BAY
+-119.7,34.43,35.0,1402.0,369.0,654.0,385.0,2.6205,318800.0,<1H OCEAN
+-117.6,33.86,23.0,2949.0,473.0,1671.0,477.0,5.195,161000.0,INLAND
+-119.81,36.73,47.0,1314.0,416.0,1155.0,326.0,1.372,49600.0,INLAND
+-118.09,34.1,27.0,6010.0,1532.0,3620.0,1445.0,2.7436,201700.0,<1H OCEAN
+-117.09,32.99,16.0,2175.0,327.0,1037.0,326.0,5.1909,201400.0,<1H OCEAN
+-117.95,33.92,13.0,2312.0,592.0,2038.0,559.0,3.1378,137000.0,<1H OCEAN
+-117.23,33.22,16.0,3224.0,729.0,1036.0,608.0,2.0246,148800.0,<1H OCEAN
+-118.13,34.01,43.0,782.0,207.0,827.0,223.0,3.1538,154300.0,<1H OCEAN
+-122.37,40.45,18.0,1748.0,337.0,921.0,327.0,3.3315,85400.0,INLAND
+-122.71,38.43,38.0,1689.0,526.0,1071.0,529.0,1.5026,124000.0,<1H OCEAN
+-117.21,34.13,31.0,3037.0,565.0,1834.0,575.0,3.3445,92900.0,INLAND
+-119.0,35.35,31.0,2931.0,716.0,1969.0,588.0,2.2155,62100.0,INLAND
+-120.65,37.33,25.0,1731.0,311.0,810.0,266.0,4.1058,107600.0,INLAND
+-122.29,37.87,44.0,2539.0,755.0,1382.0,713.0,2.537,175000.0,NEAR BAY
+-121.28,38.71,35.0,3095.0,594.0,1550.0,576.0,3.575,113500.0,INLAND
+-120.48,37.3,39.0,1015.0,356.0,875.0,313.0,1.5,67000.0,INLAND
+-117.7,33.5,4.0,7474.0,1037.0,2969.0,1007.0,8.7591,434700.0,<1H OCEAN
+-121.69,36.52,15.0,4037.0,586.0,1596.0,557.0,8.0922,390100.0,<1H OCEAN
+-117.2,33.2,16.0,4409.0,629.0,1875.0,609.0,5.543,286400.0,<1H OCEAN
+-124.13,40.79,32.0,2017.0,359.0,855.0,346.0,3.5833,92800.0,NEAR OCEAN
+-118.23,34.18,45.0,2332.0,,943.0,339.0,8.1132,446600.0,<1H OCEAN
+-122.31,37.93,39.0,2505.0,371.0,872.0,345.0,5.3433,286500.0,NEAR BAY
+-118.37,33.93,46.0,442.0,88.0,255.0,94.0,4.4474,246900.0,<1H OCEAN
+-122.18,38.23,21.0,2475.0,341.0,812.0,308.0,7.2589,320400.0,NEAR BAY
+-118.25,34.0,32.0,1218.0,342.0,1292.0,304.0,1.5781,102900.0,<1H OCEAN
+-120.76,38.65,17.0,2319.0,430.0,1126.0,372.0,3.5511,155900.0,INLAND
+-118.52,34.36,5.0,4222.0,712.0,2024.0,646.0,5.8703,500001.0,<1H OCEAN
+-117.15,32.75,27.0,3166.0,867.0,1332.0,817.0,2.6742,171400.0,NEAR OCEAN
+-117.71,33.61,26.0,3046.0,726.0,888.0,663.0,2.6848,74100.0,<1H OCEAN
+-118.07,34.14,42.0,3200.0,685.0,1668.0,628.0,3.375,260400.0,INLAND
+-116.29,33.74,6.0,12991.0,2555.0,4571.0,1926.0,4.7195,199300.0,INLAND
+-118.48,34.25,35.0,1442.0,276.0,795.0,268.0,4.9688,216900.0,<1H OCEAN
+-118.06,34.09,36.0,1239.0,238.0,717.0,237.0,3.244,258100.0,<1H OCEAN
+-121.93,37.25,21.0,1354.0,289.0,639.0,273.0,4.5333,234200.0,<1H OCEAN
+-117.1,33.09,5.0,12045.0,2162.0,5640.0,1997.0,4.4375,353000.0,<1H OCEAN
+-118.27,34.13,46.0,1613.0,396.0,966.0,416.0,2.9392,230300.0,<1H OCEAN
+-117.29,33.1,6.0,6091.0,1018.0,2064.0,957.0,5.1837,259800.0,NEAR OCEAN
+-117.96,34.09,6.0,1954.0,534.0,1584.0,496.0,3.1621,131000.0,INLAND
+-118.45,34.06,52.0,204.0,34.0,1154.0,28.0,9.337,500001.0,<1H OCEAN
+-118.15,34.13,34.0,824.0,224.0,430.0,213.0,3.6389,215000.0,<1H OCEAN
+-121.84,39.72,52.0,1457.0,389.0,802.0,342.0,0.9566,69000.0,INLAND
+-116.97,33.75,22.0,3740.0,965.0,2011.0,824.0,1.3039,77500.0,INLAND
+-118.55,34.38,24.0,6246.0,1028.0,2803.0,999.0,6.3002,282900.0,<1H OCEAN
+-119.77,36.81,28.0,1713.0,302.0,663.0,282.0,3.567,85500.0,INLAND
+-117.37,34.0,41.0,1248.0,278.0,770.0,250.0,3.025,90600.0,INLAND
+-117.06,34.03,27.0,1945.0,446.0,859.0,418.0,1.5203,126200.0,INLAND
+-118.46,34.27,30.0,1576.0,282.0,1004.0,284.0,4.8015,179700.0,<1H OCEAN
+-118.23,33.93,37.0,239.0,49.0,308.0,52.0,1.4028,105400.0,<1H OCEAN
+-121.42,37.75,33.0,1329.0,266.0,683.0,233.0,4.3687,128700.0,INLAND
+-121.43,38.65,18.0,909.0,198.0,661.0,176.0,3.1696,77400.0,INLAND
+-118.1,33.79,36.0,3359.0,596.0,1522.0,565.0,5.1805,249400.0,<1H OCEAN
+-121.96,37.33,35.0,2294.0,411.0,1054.0,449.0,4.0667,276900.0,<1H OCEAN
+-118.38,34.06,28.0,2522.0,616.0,991.0,574.0,3.1475,362500.0,<1H OCEAN
+-117.0,34.89,29.0,2637.0,512.0,1188.0,446.0,2.99,69400.0,INLAND
+-116.68,33.71,21.0,3460.0,711.0,658.0,255.0,3.5882,161100.0,INLAND
+-119.64,36.31,27.0,1513.0,314.0,1071.0,284.0,1.5909,50100.0,INLAND
+-118.24,34.06,33.0,390.0,199.0,435.0,193.0,1.1979,350000.0,<1H OCEAN
+-119.51,34.39,32.0,1921.0,394.0,951.0,334.0,3.233,346000.0,NEAR OCEAN
+-117.87,33.84,16.0,1545.0,354.0,730.0,350.0,4.5112,139000.0,<1H OCEAN
+-118.18,34.02,35.0,661.0,142.0,720.0,143.0,2.8977,142500.0,<1H OCEAN
+-119.35,36.22,32.0,1290.0,304.0,852.0,309.0,1.4429,54600.0,INLAND
+-116.0,33.19,16.0,245.0,57.0,81.0,33.0,1.2639,51300.0,INLAND
+-118.17,33.96,25.0,2249.0,681.0,2621.0,628.0,2.3,164200.0,<1H OCEAN
+-117.88,33.73,36.0,2471.0,498.0,2594.0,475.0,3.75,170500.0,<1H OCEAN
+-117.68,34.14,4.0,4791.0,695.0,1871.0,659.0,6.9532,277000.0,INLAND
+-118.1,33.9,43.0,1237.0,243.0,776.0,246.0,4.325,166000.0,<1H OCEAN
+-117.93,33.63,10.0,2766.0,732.0,1332.0,646.0,4.6161,226300.0,<1H OCEAN
+-122.7,38.45,39.0,2015.0,335.0,640.0,315.0,4.1734,240500.0,<1H OCEAN
+-121.96,37.52,26.0,4211.0,741.0,2352.0,734.0,5.2396,223900.0,<1H OCEAN
+-122.35,37.53,27.0,2169.0,305.0,905.0,319.0,7.7743,453100.0,NEAR OCEAN
+-122.46,37.79,52.0,2005.0,359.0,847.0,356.0,4.1029,500001.0,NEAR BAY
+-117.69,33.65,15.0,5394.0,748.0,2383.0,706.0,7.5619,302000.0,<1H OCEAN
+-117.91,33.76,20.0,4413.0,,4818.0,1063.0,2.8594,215100.0,<1H OCEAN
+-118.45,34.25,36.0,1453.0,270.0,808.0,275.0,4.3839,204600.0,<1H OCEAN
+-117.68,33.52,5.0,3621.0,632.0,1546.0,567.0,5.753,322800.0,<1H OCEAN
+-122.42,40.59,24.0,5045.0,972.0,2220.0,979.0,2.6792,138900.0,INLAND
+-118.43,34.06,11.0,3184.0,641.0,911.0,463.0,7.2675,500001.0,<1H OCEAN
+-121.83,37.23,22.0,5507.0,841.0,2785.0,848.0,6.0889,245200.0,<1H OCEAN
+-121.99,37.96,16.0,3324.0,479.0,1470.0,461.0,7.6166,260400.0,INLAND
+-117.21,33.13,19.0,3068.0,596.0,912.0,554.0,3.775,168000.0,NEAR OCEAN
+-117.18,33.11,16.0,3470.0,601.0,1197.0,552.0,5.1814,279900.0,NEAR OCEAN
+-119.72,34.43,33.0,1028.0,377.0,753.0,356.0,2.3454,243800.0,<1H OCEAN
+-118.04,33.88,17.0,4807.0,838.0,3059.0,853.0,5.7619,297300.0,<1H OCEAN
+-118.33,34.1,29.0,732.0,288.0,691.0,278.0,2.1866,250000.0,<1H OCEAN
+-117.32,33.8,11.0,3196.0,576.0,1757.0,552.0,4.0982,173300.0,INLAND
+-121.92,40.52,13.0,4581.0,881.0,1799.0,734.0,2.2993,99500.0,INLAND
+-116.38,34.2,14.0,4985.0,1238.0,2517.0,954.0,2.0674,65000.0,INLAND
+-117.88,34.11,18.0,2923.0,670.0,1751.0,656.0,3.2383,157000.0,<1H OCEAN
+-123.21,39.2,17.0,3145.0,693.0,1560.0,647.0,2.2926,149300.0,<1H OCEAN
+-122.46,37.71,47.0,1527.0,283.0,1102.0,282.0,4.0,231600.0,NEAR OCEAN
+-117.69,34.1,17.0,3759.0,1035.0,1722.0,847.0,2.6074,137500.0,INLAND
+-118.34,34.17,46.0,1718.0,344.0,756.0,343.0,3.2125,247000.0,<1H OCEAN
+-120.31,37.64,11.0,2403.0,497.0,890.0,344.0,3.0,120800.0,INLAND
+-116.5,33.95,10.0,7249.0,1882.0,4274.0,1621.0,1.6983,66600.0,INLAND
+-120.46,37.3,36.0,3346.0,739.0,2151.0,713.0,2.3095,68300.0,INLAND
+-118.24,33.91,37.0,1607.0,377.0,1526.0,375.0,1.7158,94300.0,<1H OCEAN
+-118.02,33.94,33.0,2382.0,404.0,1339.0,389.0,5.3016,192200.0,<1H OCEAN
+-121.51,38.58,42.0,1822.0,636.0,1372.0,560.0,1.2542,76000.0,INLAND
+-117.93,33.68,33.0,2664.0,432.0,1197.0,429.0,5.069,264200.0,<1H OCEAN
+-117.64,34.02,10.0,4887.0,930.0,2637.0,831.0,4.0611,158000.0,INLAND
+-119.48,36.44,22.0,1389.0,290.0,1185.0,271.0,2.0857,49200.0,INLAND
+-120.81,38.89,17.0,1438.0,324.0,675.0,268.0,2.9444,119300.0,INLAND
+-122.18,37.77,27.0,909.0,236.0,396.0,157.0,2.0786,97500.0,NEAR BAY
+-118.95,34.81,30.0,2817.0,604.0,1089.0,412.0,3.1364,123500.0,INLAND
+-118.5,34.24,34.0,2634.0,412.0,1114.0,423.0,5.9401,315300.0,<1H OCEAN
+-118.4,34.04,40.0,2079.0,268.0,720.0,282.0,9.272,500001.0,<1H OCEAN
+-122.4,37.76,52.0,1495.0,311.0,506.0,275.0,4.4375,320000.0,NEAR BAY
+-117.96,34.0,33.0,5462.0,952.0,3527.0,914.0,4.4038,186700.0,<1H OCEAN
+-118.49,34.11,27.0,6603.0,879.0,2336.0,868.0,13.2935,500001.0,<1H OCEAN
+-122.44,37.79,52.0,1903.0,461.0,831.0,433.0,4.4464,500001.0,NEAR BAY
+-122.01,39.21,39.0,1214.0,250.0,660.0,249.0,2.4559,75000.0,INLAND
+-120.91,39.39,16.0,352.0,105.0,226.0,82.0,1.6094,79500.0,INLAND
+-118.25,34.12,21.0,739.0,265.0,861.0,246.0,2.4856,181300.0,<1H OCEAN
+-123.88,40.93,28.0,1272.0,259.0,519.0,220.0,3.2891,106300.0,<1H OCEAN
+-117.73,33.51,5.0,4549.0,786.0,1238.0,632.0,6.1785,295900.0,<1H OCEAN
+-122.09,37.98,14.0,5381.0,871.0,2296.0,872.0,5.6875,211000.0,NEAR BAY
+-121.83,37.27,14.0,2855.0,380.0,1420.0,383.0,6.6712,311500.0,<1H OCEAN
+-118.11,34.08,45.0,1106.0,226.0,779.0,205.0,4.5446,244800.0,<1H OCEAN
+-117.64,33.45,26.0,1528.0,,607.0,218.0,6.2871,325500.0,NEAR OCEAN
+-120.44,34.96,29.0,2374.0,562.0,1617.0,463.0,2.6531,108300.0,<1H OCEAN
+-118.27,34.14,29.0,3768.0,1211.0,2320.0,1030.0,2.7685,204500.0,<1H OCEAN
+-122.62,37.85,30.0,833.0,164.0,358.0,143.0,6.8198,493800.0,NEAR OCEAN
+-117.66,34.15,20.0,2524.0,311.0,965.0,285.0,8.0103,395500.0,INLAND
+-122.76,40.4,22.0,2153.0,461.0,903.0,314.0,2.125,123200.0,INLAND
+-117.05,34.02,21.0,3098.0,646.0,1351.0,614.0,2.598,106700.0,INLAND
+-121.8,36.72,14.0,2493.0,407.0,1296.0,418.0,5.4508,190000.0,<1H OCEAN
+-118.47,34.14,36.0,2873.0,420.0,850.0,379.0,8.153,500001.0,<1H OCEAN
+-119.03,36.07,26.0,3210.0,646.0,1908.0,642.0,2.4167,77600.0,INLAND
+-120.84,37.53,14.0,3643.0,706.0,2070.0,697.0,3.1523,141800.0,INLAND
+-118.48,33.43,29.0,716.0,214.0,422.0,173.0,2.6042,287500.0,ISLAND
+-117.4,33.76,8.0,1954.0,330.0,973.0,321.0,4.4875,249100.0,INLAND
+-117.86,33.71,21.0,1795.0,406.0,2246.0,400.0,3.152,152800.0,<1H OCEAN
+-117.06,33.14,27.0,3819.0,674.0,2447.0,717.0,3.8185,137200.0,<1H OCEAN
+-122.16,37.74,44.0,1097.0,239.0,609.0,215.0,2.0227,103100.0,NEAR BAY
+-118.12,34.01,33.0,1956.0,478.0,1472.0,464.0,1.9867,166300.0,<1H OCEAN
+-117.89,33.61,45.0,1883.0,419.0,653.0,328.0,4.2222,500001.0,<1H OCEAN
+-118.25,33.93,42.0,657.0,147.0,526.0,132.0,2.5,110200.0,<1H OCEAN
+-120.63,35.27,23.0,1630.0,253.0,704.0,263.0,5.156,251300.0,NEAR OCEAN
+-117.05,32.97,17.0,9911.0,1436.0,4763.0,1414.0,5.5882,194300.0,<1H OCEAN
+-118.45,34.02,41.0,2956.0,700.0,1212.0,645.0,3.4583,421900.0,<1H OCEAN
+-119.72,34.44,43.0,1781.0,342.0,663.0,358.0,4.7,293800.0,<1H OCEAN
+-122.41,37.63,39.0,4220.0,1055.0,2720.0,1046.0,2.639,242500.0,NEAR OCEAN
+-121.98,36.97,21.0,3349.0,737.0,1952.0,718.0,3.7273,251900.0,NEAR OCEAN
+-121.48,38.58,52.0,2501.0,757.0,1081.0,708.0,1.5872,157500.0,INLAND
+-122.3,37.97,35.0,1811.0,377.0,911.0,340.0,3.375,149700.0,NEAR BAY
+-117.69,33.47,13.0,2020.0,378.0,679.0,290.0,5.756,305600.0,<1H OCEAN
+-117.78,34.05,39.0,2933.0,590.0,1886.0,550.0,3.9224,131300.0,INLAND
+-121.89,37.26,25.0,3319.0,531.0,1560.0,502.0,5.8479,263300.0,<1H OCEAN
+-120.95,37.61,17.0,4054.0,654.0,2034.0,667.0,4.6833,142200.0,INLAND
+-118.3,33.74,28.0,1065.0,215.0,887.0,217.0,3.9375,270500.0,NEAR OCEAN
+-120.99,37.65,44.0,2848.0,623.0,1408.0,576.0,2.1487,86600.0,INLAND
+-121.43,38.52,30.0,3657.0,945.0,2925.0,899.0,1.3927,78300.0,INLAND
+-118.11,34.17,50.0,3374.0,598.0,1484.0,569.0,4.99,261600.0,INLAND
+-122.41,37.8,52.0,1866.0,748.0,2957.0,710.0,1.8295,243800.0,NEAR BAY
+-118.47,34.05,22.0,5215.0,1193.0,2048.0,1121.0,4.7009,500001.0,<1H OCEAN
+-122.22,37.79,44.0,1487.0,314.0,961.0,272.0,3.5156,109500.0,NEAR BAY
+-119.62,36.59,17.0,2287.0,390.0,1330.0,393.0,4.0197,88000.0,INLAND
+-121.99,36.97,15.0,3044.0,786.0,1306.0,693.0,2.1771,213200.0,NEAR OCEAN
+-118.77,34.24,6.0,16222.0,2309.0,6700.0,2080.0,6.4963,308100.0,<1H OCEAN
+-116.57,35.43,8.0,9975.0,1743.0,6835.0,1439.0,2.7138,22500.0,INLAND
+-118.04,34.13,39.0,2485.0,382.0,1072.0,342.0,6.0878,430200.0,INLAND
+-118.16,34.18,48.0,568.0,145.0,559.0,135.0,2.4135,135700.0,<1H OCEAN
+-122.58,37.98,52.0,3675.0,649.0,1553.0,639.0,4.6905,316300.0,NEAR BAY
+-117.91,34.11,20.0,3158.0,684.0,2396.0,713.0,3.525,153000.0,<1H OCEAN
+-118.15,33.79,25.0,4013.0,1097.0,2297.0,969.0,3.0453,185900.0,NEAR OCEAN
+-121.69,38.16,46.0,2292.0,472.0,970.0,431.0,2.2888,94900.0,INLAND
+-117.91,33.61,36.0,3082.0,455.0,771.0,365.0,11.216,500001.0,<1H OCEAN
+-119.72,34.41,26.0,1648.0,378.0,954.0,405.0,3.2895,335000.0,NEAR OCEAN
+-121.49,38.56,35.0,1521.0,457.0,987.0,455.0,1.9013,86900.0,INLAND
+-122.41,40.55,19.0,3753.0,761.0,1952.0,738.0,3.0954,86500.0,INLAND
+-116.92,32.76,7.0,1659.0,237.0,862.0,242.0,5.2741,249400.0,<1H OCEAN
+-117.67,34.13,10.0,2846.0,362.0,1221.0,355.0,7.7234,304100.0,INLAND
+-119.72,34.77,35.0,2469.0,553.0,1168.0,427.0,2.4583,62100.0,<1H OCEAN
+-117.07,33.0,4.0,9153.0,1866.0,3775.0,1698.0,4.955,241500.0,<1H OCEAN
+-122.67,38.33,4.0,8072.0,1606.0,4323.0,1475.0,3.9518,220300.0,<1H OCEAN
+-124.08,41.36,29.0,1029.0,239.0,509.0,196.0,2.0156,62800.0,NEAR OCEAN
+-122.04,37.66,23.0,2419.0,348.0,1066.0,384.0,6.3501,350000.0,NEAR BAY
+-122.42,37.8,52.0,2797.0,685.0,1156.0,651.0,4.3472,500001.0,NEAR BAY
+-119.25,36.36,16.0,3245.0,469.0,1471.0,450.0,5.8673,154800.0,INLAND
+-117.95,33.93,25.0,3445.0,801.0,2400.0,750.0,3.4702,161900.0,<1H OCEAN
+-122.19,37.48,45.0,886.0,165.0,492.0,173.0,4.2708,267000.0,NEAR BAY
+-121.02,39.17,17.0,2277.0,459.0,1149.0,476.0,3.2303,149500.0,INLAND
+-118.21,33.92,35.0,1669.0,445.0,1870.0,412.0,3.0417,117300.0,<1H OCEAN
+-122.22,38.07,4.0,15654.0,2394.0,7025.0,2168.0,5.8171,225200.0,NEAR BAY
+-121.3,37.96,52.0,1354.0,314.0,679.0,311.0,1.7788,97400.0,INLAND
+-122.81,38.08,19.0,1615.0,366.0,815.0,337.0,3.4609,238800.0,NEAR OCEAN
+-118.31,34.09,37.0,773.0,,835.0,312.0,1.8576,193800.0,<1H OCEAN
+-118.03,33.85,16.0,1831.0,390.0,1347.0,389.0,3.8426,344400.0,<1H OCEAN
+-118.17,34.04,39.0,563.0,138.0,682.0,137.0,2.75,150000.0,<1H OCEAN
+-118.48,34.26,36.0,1770.0,296.0,938.0,304.0,5.749,238000.0,<1H OCEAN
+-118.41,33.96,32.0,1044.0,219.0,567.0,222.0,4.1471,284400.0,<1H OCEAN
+-118.8,34.19,4.0,15572.0,2222.0,5495.0,2152.0,8.6499,500001.0,<1H OCEAN
+-117.29,34.12,40.0,2198.0,612.0,1517.0,531.0,1.0951,65800.0,INLAND
+-117.61,33.44,17.0,2036.0,272.0,713.0,265.0,6.5954,346200.0,NEAR OCEAN
+-122.38,40.54,36.0,1216.0,240.0,647.0,228.0,2.6944,75300.0,INLAND
+-121.83,37.95,17.0,1133.0,244.0,716.0,235.0,2.875,162500.0,INLAND
+-118.21,34.07,31.0,1453.0,404.0,1486.0,389.0,2.3859,153100.0,<1H OCEAN
+-118.43,34.24,36.0,1379.0,265.0,896.0,246.0,4.6827,183800.0,<1H OCEAN
+-122.15,37.46,42.0,1995.0,412.0,794.0,374.0,5.6234,379600.0,NEAR BAY
+-116.89,33.93,29.0,4549.0,916.0,2494.0,912.0,2.0976,72600.0,INLAND
+-117.23,33.23,13.0,2899.0,657.0,1946.0,579.0,2.9875,172000.0,<1H OCEAN
+-121.7,36.77,21.0,2294.0,478.0,1306.0,430.0,3.0347,227200.0,<1H OCEAN
+-121.61,39.14,44.0,2035.0,476.0,1030.0,453.0,1.4661,65200.0,INLAND
+-117.05,32.61,31.0,4033.0,715.0,2585.0,715.0,3.5096,139900.0,NEAR OCEAN
+-118.0,34.08,23.0,1627.0,318.0,1279.0,289.0,4.6467,185100.0,INLAND
+-117.92,33.84,38.0,1316.0,263.0,671.0,278.0,3.2969,220000.0,<1H OCEAN
+-118.47,34.2,25.0,4590.0,1477.0,2723.0,1195.0,2.7118,281700.0,<1H OCEAN
+-118.35,34.19,45.0,903.0,190.0,557.0,204.0,4.0313,209100.0,<1H OCEAN
+-122.07,37.41,26.0,1184.0,225.0,815.0,218.0,5.7657,322300.0,NEAR BAY
+-119.35,36.19,6.0,958.0,226.0,734.0,230.0,1.0349,67800.0,INLAND
+-121.45,38.52,37.0,1705.0,325.0,827.0,326.0,2.6288,71200.0,INLAND
+-121.98,37.53,26.0,3179.0,703.0,2142.0,639.0,4.1947,222700.0,<1H OCEAN
+-117.3,34.12,43.0,1018.0,261.0,736.0,215.0,2.6,66900.0,INLAND
+-118.98,35.38,24.0,1807.0,465.0,1460.0,410.0,1.4786,54800.0,INLAND
+-121.38,38.68,35.0,1565.0,290.0,861.0,277.0,2.4844,77000.0,INLAND
+-118.99,36.07,21.0,983.0,165.0,672.0,169.0,2.975,63900.0,INLAND
+-117.7,34.08,33.0,4674.0,791.0,2769.0,784.0,4.1448,137300.0,INLAND
+-121.92,37.28,27.0,3028.0,486.0,1284.0,498.0,4.5833,308800.0,<1H OCEAN
+-119.19,35.64,29.0,1476.0,220.0,902.0,205.0,2.6726,83300.0,INLAND
+-122.12,37.38,34.0,1443.0,218.0,504.0,200.0,8.4709,500001.0,NEAR BAY
+-117.82,33.75,30.0,2910.0,535.0,1270.0,489.0,4.6161,236500.0,<1H OCEAN
+-123.12,40.54,23.0,1091.0,217.0,539.0,201.0,1.8696,61500.0,INLAND
+-117.39,33.96,49.0,2527.0,461.0,1344.0,451.0,4.0833,114400.0,INLAND
+-118.14,34.03,44.0,2003.0,390.0,1291.0,392.0,4.0625,201100.0,<1H OCEAN
+-122.18,37.73,42.0,4074.0,874.0,2736.0,780.0,2.455,82400.0,NEAR BAY
+-122.18,37.76,49.0,2308.0,452.0,1299.0,451.0,1.8407,96700.0,NEAR BAY
+-118.45,34.28,38.0,1527.0,332.0,1303.0,340.0,3.5714,152000.0,<1H OCEAN
+-120.97,37.66,24.0,2930.0,588.0,1448.0,570.0,3.5395,127900.0,INLAND
+-118.07,33.89,17.0,2223.0,544.0,2008.0,512.0,3.0777,160800.0,<1H OCEAN
+-117.03,32.97,16.0,3936.0,694.0,1935.0,659.0,4.5625,231200.0,<1H OCEAN
+-120.7,35.31,24.0,3504.0,521.0,1490.0,506.0,4.6719,337000.0,NEAR OCEAN
+-119.62,36.58,13.0,1788.0,405.0,1652.0,411.0,2.6858,62400.0,INLAND
+-117.21,34.12,32.0,1677.0,354.0,1021.0,339.0,3.6853,90900.0,INLAND
+-121.95,37.81,5.0,7178.0,898.0,2823.0,907.0,9.0776,450400.0,<1H OCEAN
+-117.08,32.76,20.0,2547.0,785.0,1199.0,643.0,1.7743,140300.0,NEAR OCEAN
+-118.84,34.22,11.0,3170.0,420.0,1418.0,432.0,7.5118,361900.0,<1H OCEAN
+-120.66,35.29,23.0,1932.0,487.0,1380.0,472.0,1.9647,253600.0,NEAR OCEAN
+-117.98,33.71,24.0,3430.0,548.0,1601.0,512.0,5.6825,264600.0,<1H OCEAN
+-118.02,34.08,29.0,2741.0,667.0,2449.0,677.0,3.6944,175200.0,INLAND
+-122.51,37.97,37.0,4296.0,1089.0,2100.0,1025.0,3.2462,329400.0,NEAR BAY
+-117.9,33.9,19.0,2176.0,414.0,1002.0,402.0,4.9743,193500.0,<1H OCEAN
+-121.89,37.3,52.0,2071.0,362.0,825.0,364.0,4.2414,284800.0,<1H OCEAN
+-117.09,32.65,25.0,3509.0,985.0,2359.0,899.0,2.6296,150000.0,NEAR OCEAN
+-120.67,37.37,18.0,164.0,30.0,104.0,32.0,1.6607,87500.0,INLAND
+-118.37,33.79,36.0,1596.0,234.0,654.0,223.0,8.2064,500001.0,NEAR OCEAN
+-120.98,37.62,26.0,3819.0,955.0,3010.0,932.0,1.9206,81300.0,INLAND
+-117.37,33.2,29.0,1315.0,311.0,1425.0,306.0,2.0272,99600.0,NEAR OCEAN
+-118.25,33.9,36.0,1135.0,231.0,614.0,227.0,2.5521,113100.0,<1H OCEAN
+-118.36,34.03,38.0,1400.0,376.0,1139.0,315.0,2.2368,120000.0,<1H OCEAN
+-122.37,37.58,43.0,2506.0,432.0,967.0,428.0,4.7404,500001.0,NEAR OCEAN
+-119.81,36.51,31.0,1241.0,254.0,767.0,226.0,2.7321,83600.0,INLAND
+-118.33,33.83,5.0,13038.0,2679.0,5272.0,2523.0,5.5023,286400.0,<1H OCEAN
+-117.82,33.73,24.0,845.0,190.0,482.0,190.0,4.7039,225000.0,<1H OCEAN
+-117.11,32.97,9.0,1531.0,242.0,850.0,240.0,6.0862,263600.0,<1H OCEAN
+-120.07,36.96,34.0,1457.0,239.0,557.0,226.0,3.6181,96500.0,INLAND
+-119.3,36.31,16.0,2234.0,357.0,1150.0,361.0,4.2778,97300.0,INLAND
+-117.23,32.8,28.0,3379.0,918.0,1849.0,849.0,3.0293,241700.0,NEAR OCEAN
+-116.94,33.74,19.0,2901.0,445.0,1414.0,475.0,4.6406,118900.0,INLAND
+-117.09,32.66,46.0,844.0,147.0,423.0,161.0,3.375,136300.0,NEAR OCEAN
+-122.09,37.39,43.0,2065.0,535.0,1029.0,500.0,3.7318,327700.0,NEAR BAY
+-121.97,37.55,17.0,4924.0,1247.0,3080.0,1182.0,3.168,189400.0,<1H OCEAN
+-118.34,34.06,52.0,2089.0,309.0,883.0,281.0,7.4574,500001.0,<1H OCEAN
+-120.43,34.93,10.0,2980.0,585.0,1593.0,562.0,3.285,218300.0,<1H OCEAN
+-118.36,33.96,37.0,2146.0,573.0,2009.0,592.0,3.6583,177300.0,<1H OCEAN
+-118.36,34.09,38.0,2158.0,582.0,1061.0,577.0,2.9643,355300.0,<1H OCEAN
+-122.23,37.81,52.0,2814.0,365.0,878.0,352.0,7.508,348700.0,NEAR BAY
+-118.33,34.02,46.0,2223.0,361.0,968.0,373.0,4.26,182600.0,<1H OCEAN
+-115.56,32.78,29.0,1568.0,283.0,848.0,245.0,3.1597,76200.0,INLAND
+-117.05,32.72,35.0,3669.0,617.0,1694.0,585.0,3.9485,133900.0,NEAR OCEAN
+-117.26,32.82,34.0,5846.0,785.0,1817.0,747.0,8.496,500001.0,NEAR OCEAN
+-122.05,37.38,29.0,1875.0,340.0,816.0,350.0,5.4351,336500.0,<1H OCEAN
+-117.96,34.02,33.0,349.0,124.0,460.0,83.0,2.375,133300.0,<1H OCEAN
+-122.28,37.87,52.0,1233.0,300.0,571.0,292.0,2.2788,182300.0,NEAR BAY
+-118.44,34.19,29.0,1599.0,459.0,1143.0,438.0,2.4583,199100.0,<1H OCEAN
+-118.25,33.79,39.0,981.0,286.0,1183.0,298.0,1.9208,139800.0,NEAR OCEAN
+-122.68,38.01,41.0,1865.0,392.0,825.0,369.0,4.2011,255400.0,NEAR OCEAN
+-117.14,32.7,36.0,633.0,148.0,557.0,139.0,1.5729,82700.0,NEAR OCEAN
+-122.15,37.45,52.0,2117.0,353.0,734.0,328.0,6.767,500001.0,NEAR BAY
+-120.6,37.37,10.0,3075.0,498.0,1368.0,487.0,4.6667,130100.0,INLAND
+-121.9,37.3,39.0,3627.0,666.0,1531.0,635.0,4.537,345900.0,<1H OCEAN
+-121.27,38.31,17.0,1144.0,202.0,626.0,178.0,4.4107,151600.0,INLAND
+-117.73,34.09,30.0,2345.0,496.0,1897.0,454.0,2.4375,112100.0,INLAND
+-118.34,33.93,32.0,1254.0,399.0,1281.0,386.0,2.2976,155700.0,<1H OCEAN
+-118.26,33.83,24.0,3059.0,,2064.0,629.0,3.5518,184600.0,<1H OCEAN
+-120.06,39.25,21.0,2459.0,525.0,584.0,233.0,3.01,163500.0,INLAND
+-121.38,38.68,40.0,67.0,17.0,50.0,32.0,1.7596,93800.0,INLAND
+-117.67,34.1,19.0,2969.0,605.0,1326.0,573.0,4.3438,155700.0,INLAND
+-117.06,32.7,12.0,3943.0,737.0,3280.0,751.0,4.112,141400.0,NEAR OCEAN
+-119.98,38.9,16.0,3109.0,572.0,885.0,334.0,3.5,134700.0,INLAND
+-122.86,38.62,35.0,2597.0,522.0,1231.0,499.0,2.7432,174000.0,<1H OCEAN
+-118.21,34.12,35.0,1937.0,439.0,1523.0,412.0,3.5638,170500.0,<1H OCEAN
+-122.25,37.8,29.0,2468.0,864.0,1335.0,773.0,1.3929,193800.0,NEAR BAY
+-122.27,37.77,52.0,2388.0,559.0,1121.0,518.0,3.3269,234500.0,NEAR BAY
+-122.25,37.77,43.0,4329.0,1110.0,2086.0,1053.0,2.975,243400.0,NEAR BAY
+-120.11,39.24,21.0,3005.0,574.0,385.0,150.0,3.1193,153300.0,INLAND
+-118.44,34.02,37.0,1592.0,308.0,783.0,321.0,6.2583,386000.0,<1H OCEAN
+-121.3,38.58,29.0,2748.0,563.0,1619.0,525.0,2.8966,92400.0,INLAND
+-117.2,32.78,38.0,2662.0,498.0,1132.0,496.0,4.0057,241600.0,NEAR OCEAN
+-121.65,36.85,20.0,2606.0,424.0,1361.0,426.0,4.5787,245100.0,<1H OCEAN
+-118.39,34.23,43.0,1193.0,299.0,1184.0,320.0,2.1518,161600.0,<1H OCEAN
+-118.03,34.06,36.0,1018.0,305.0,1307.0,292.0,2.1453,162100.0,<1H OCEAN
+-119.96,38.84,17.0,2722.0,512.0,828.0,289.0,3.5714,109700.0,INLAND
+-117.06,32.6,24.0,1088.0,268.0,1095.0,246.0,2.4191,107300.0,NEAR OCEAN
+-117.12,32.57,35.0,1450.0,256.0,930.0,286.0,2.6715,133300.0,NEAR OCEAN
+-117.74,34.02,33.0,2318.0,464.0,1904.0,451.0,3.7454,116400.0,INLAND
+-122.21,37.84,34.0,3038.0,490.0,1140.0,496.0,7.0548,325900.0,NEAR BAY
+-117.21,34.11,26.0,1757.0,304.0,905.0,281.0,3.4103,90900.0,INLAND
+-122.72,38.47,29.0,1706.0,415.0,990.0,394.0,1.9932,164800.0,<1H OCEAN
+-119.81,36.85,17.0,2340.0,370.0,1174.0,396.0,4.2304,94400.0,INLAND
+-117.69,34.13,8.0,2915.0,371.0,1271.0,354.0,7.9627,345400.0,INLAND
+-121.39,38.62,45.0,2696.0,624.0,1059.0,582.0,1.8176,160900.0,INLAND
+-118.04,33.83,19.0,4526.0,830.0,2318.0,748.0,4.6681,320700.0,<1H OCEAN
+-118.36,33.94,33.0,939.0,284.0,1309.0,250.0,3.4063,152300.0,<1H OCEAN
+-122.9,39.17,45.0,1314.0,277.0,649.0,232.0,2.575,73600.0,INLAND
+-117.68,34.1,11.0,7392.0,1796.0,3841.0,1621.0,2.8326,163000.0,INLAND
+-121.22,38.8,11.0,2521.0,521.0,1390.0,477.0,3.5265,124800.0,INLAND
+-117.95,33.82,29.0,2929.0,640.0,1618.0,584.0,3.6875,213200.0,<1H OCEAN
+-122.25,38.12,47.0,1339.0,298.0,794.0,286.0,2.5865,109800.0,NEAR BAY
+-117.42,34.09,28.0,3193.0,525.0,1750.0,523.0,4.1375,128300.0,INLAND
+-121.99,37.26,17.0,4034.0,611.0,1158.0,560.0,8.2069,442500.0,<1H OCEAN
+-122.06,37.27,16.0,1612.0,221.0,567.0,208.0,10.5793,500001.0,<1H OCEAN
+-117.09,32.68,30.0,2662.0,653.0,1997.0,605.0,2.8089,120600.0,NEAR OCEAN
+-122.35,37.92,36.0,921.0,200.0,585.0,236.0,1.9224,94000.0,NEAR BAY
+-121.58,39.83,16.0,4591.0,904.0,1904.0,812.0,2.2419,93200.0,INLAND
+-116.22,36.0,14.0,1372.0,386.0,436.0,213.0,1.1471,32900.0,INLAND
+-118.42,34.2,24.0,3148.0,908.0,2850.0,839.0,1.9549,221500.0,<1H OCEAN
+-117.12,32.59,28.0,2793.0,706.0,1825.0,676.0,2.6724,144500.0,NEAR OCEAN
+-117.93,34.07,36.0,1207.0,209.0,683.0,213.0,5.3559,207300.0,<1H OCEAN
+-121.06,39.21,52.0,1452.0,309.0,637.0,299.0,2.2083,103900.0,INLAND
+-118.14,33.97,31.0,1161.0,267.0,1175.0,282.0,3.0114,177000.0,<1H OCEAN
+-121.92,36.61,27.0,3044.0,661.0,1229.0,618.0,3.1359,268000.0,NEAR OCEAN
+-120.35,37.04,37.0,1495.0,292.0,858.0,275.0,2.9306,46300.0,INLAND
+-118.36,34.16,43.0,2850.0,709.0,1510.0,670.0,2.4835,274300.0,<1H OCEAN
+-121.37,36.83,14.0,3658.0,612.0,1951.0,600.0,4.76,216000.0,INLAND
+-119.62,37.33,7.0,3389.0,621.0,1268.0,474.0,3.0224,147800.0,INLAND
+-119.17,34.17,32.0,1567.0,304.0,1482.0,308.0,3.5867,182100.0,NEAR OCEAN
+-118.33,34.1,45.0,1913.0,696.0,1552.0,611.0,2.0888,237500.0,<1H OCEAN
+-118.32,34.02,52.0,2511.0,587.0,1660.0,546.0,2.6098,127100.0,<1H OCEAN
+-122.42,37.62,39.0,1355.0,214.0,682.0,246.0,6.3443,324700.0,NEAR OCEAN
+-121.37,38.57,22.0,4899.0,847.0,1701.0,826.0,5.2449,387000.0,INLAND
+-120.37,36.15,34.0,2084.0,339.0,868.0,347.0,4.381,86300.0,INLAND
+-122.34,37.56,39.0,3562.0,391.0,1139.0,391.0,12.6417,500001.0,NEAR OCEAN
+-119.06,34.62,10.0,416.0,110.0,436.0,70.0,2.2222,262500.0,<1H OCEAN
+-118.1,33.95,34.0,3635.0,781.0,2171.0,720.0,3.7308,196900.0,<1H OCEAN
+-116.92,32.79,24.0,4055.0,742.0,2123.0,744.0,4.5224,142000.0,<1H OCEAN
+-119.85,36.74,35.0,1191.0,190.0,537.0,182.0,3.5375,96700.0,INLAND
+-123.71,39.88,42.0,1518.0,383.0,656.0,303.0,1.4952,69800.0,NEAR OCEAN
+-118.15,34.02,42.0,2729.0,725.0,3004.0,722.0,2.3438,154300.0,<1H OCEAN
+-117.25,32.81,39.0,1846.0,350.0,765.0,329.0,3.9187,311900.0,NEAR OCEAN
+-118.36,34.26,34.0,3677.0,573.0,1598.0,568.0,6.838,378000.0,<1H OCEAN
+-122.16,37.72,10.0,2229.0,601.0,877.0,485.0,3.3431,137500.0,NEAR BAY
+-122.07,37.7,39.0,1420.0,272.0,645.0,277.0,4.125,232500.0,NEAR BAY
+-120.96,38.66,11.0,2339.0,436.0,1062.0,380.0,3.9036,180800.0,INLAND
+-118.5,34.21,36.0,1656.0,310.0,817.0,308.0,5.5675,215900.0,<1H OCEAN
+-117.7,33.6,26.0,2283.0,506.0,634.0,469.0,2.3774,74300.0,<1H OCEAN
+-119.08,36.21,20.0,1911.0,389.0,1241.0,348.0,2.5156,59300.0,INLAND
+-122.19,38.07,20.0,3000.0,525.0,1207.0,491.0,4.6406,217500.0,NEAR BAY
+-122.32,37.93,40.0,3056.0,489.0,1103.0,481.0,5.1067,247300.0,NEAR BAY
+-118.18,33.96,20.0,427.0,118.0,402.0,105.0,1.4167,137500.0,<1H OCEAN
+-117.08,32.62,28.0,2468.0,506.0,1353.0,522.0,3.0771,158600.0,NEAR OCEAN
+-119.0,36.06,41.0,937.0,158.0,396.0,142.0,4.0833,81300.0,INLAND
+-115.95,33.28,12.0,99.0,25.0,37.0,17.0,1.8958,53800.0,INLAND
+-122.32,37.52,26.0,4042.0,591.0,1611.0,578.0,8.4693,419200.0,NEAR OCEAN
+-118.1,34.58,32.0,1489.0,306.0,774.0,267.0,3.275,103500.0,INLAND
+-117.93,33.66,18.0,2043.0,250.0,702.0,246.0,9.6062,414700.0,<1H OCEAN
+-121.85,39.75,39.0,568.0,127.0,267.0,129.0,1.8095,78100.0,INLAND
+-122.0,37.33,30.0,4033.0,794.0,1788.0,807.0,5.6932,338700.0,<1H OCEAN
+-117.18,32.74,39.0,3132.0,738.0,1200.0,690.0,2.5288,274000.0,NEAR OCEAN
+-117.22,32.83,17.0,1124.0,187.0,553.0,205.0,5.7451,237300.0,NEAR OCEAN
+-118.3,34.18,13.0,7174.0,1997.0,4293.0,1872.0,3.0973,251900.0,<1H OCEAN
+-122.0,37.84,16.0,7681.0,946.0,2777.0,908.0,9.5271,500001.0,<1H OCEAN
+-122.58,38.12,13.0,5027.0,871.0,1912.0,770.0,4.9286,309500.0,NEAR BAY
+-119.83,36.79,24.0,3505.0,819.0,2098.0,774.0,1.9575,67000.0,INLAND
+-118.11,33.86,35.0,1255.0,252.0,685.0,279.0,4.2,226900.0,<1H OCEAN
+-118.18,33.83,44.0,1497.0,277.0,542.0,274.0,5.0052,321800.0,NEAR OCEAN
+-117.06,32.77,32.0,3888.0,827.0,3868.0,841.0,3.0755,166800.0,NEAR OCEAN
+-122.36,38.03,32.0,2159.0,393.0,981.0,369.0,4.3173,175400.0,NEAR BAY
+-122.23,37.8,52.0,1192.0,289.0,772.0,257.0,2.3833,146900.0,NEAR BAY
+-121.63,37.1,14.0,5034.0,797.0,2124.0,790.0,4.9028,335000.0,<1H OCEAN
+-121.2,38.02,44.0,608.0,108.0,287.0,83.0,3.3882,125000.0,INLAND
+-117.25,33.25,6.0,6160.0,993.0,2997.0,1029.0,4.6187,205000.0,<1H OCEAN
+-117.85,33.78,23.0,3187.0,870.0,1977.0,852.0,3.3939,212100.0,<1H OCEAN
+-117.93,33.86,35.0,1216.0,225.0,893.0,228.0,4.0288,184000.0,<1H OCEAN
+-115.52,33.13,18.0,1109.0,283.0,1006.0,253.0,2.163,53400.0,INLAND
+-122.01,37.55,26.0,2068.0,532.0,1434.0,495.0,3.3008,224200.0,NEAR BAY
+-117.98,34.03,21.0,797.0,162.0,484.0,166.0,2.625,191100.0,<1H OCEAN
+-117.24,32.72,39.0,3089.0,431.0,1175.0,432.0,7.5925,466700.0,NEAR OCEAN
+-118.49,34.18,31.0,3073.0,674.0,1486.0,684.0,4.8984,311700.0,<1H OCEAN
+-121.34,38.58,18.0,1631.0,228.0,599.0,228.0,7.8031,267200.0,INLAND
+-119.27,36.18,23.0,3180.0,547.0,1829.0,498.0,2.6098,66000.0,INLAND
+-121.62,41.78,40.0,3272.0,663.0,1467.0,553.0,1.7885,43500.0,INLAND
+-119.76,36.79,26.0,3654.0,837.0,1976.0,830.0,2.1544,72800.0,INLAND
+-118.32,33.96,47.0,1885.0,361.0,954.0,357.0,3.8512,171300.0,<1H OCEAN
+-117.63,33.38,12.0,5744.0,1054.0,2104.0,847.0,5.1482,500001.0,NEAR OCEAN
+-119.65,36.62,6.0,1931.0,422.0,1344.0,414.0,1.6607,58000.0,INLAND
+-122.68,38.25,29.0,1315.0,240.0,650.0,228.0,3.8269,306000.0,<1H OCEAN
+-118.98,38.03,15.0,991.0,277.0,419.0,170.0,3.5469,82500.0,INLAND
+-117.94,33.84,25.0,4016.0,831.0,2166.0,774.0,3.1884,135400.0,<1H OCEAN
+-116.88,33.74,20.0,3111.0,623.0,1000.0,508.0,1.5982,140000.0,INLAND
+-122.28,37.86,52.0,3007.0,691.0,1582.0,636.0,2.5652,157700.0,NEAR BAY
+-122.29,37.89,52.0,1571.0,349.0,693.0,326.0,3.1375,229100.0,NEAR BAY
+-118.11,34.0,38.0,2573.0,484.0,1568.0,459.0,3.0208,193700.0,<1H OCEAN
+-118.17,33.8,26.0,1589.0,380.0,883.0,366.0,3.5313,187500.0,NEAR OCEAN
+-122.53,37.97,52.0,205.0,119.0,228.0,132.0,1.9063,200000.0,NEAR BAY
+-122.19,37.75,19.0,2207.0,565.0,1481.0,520.0,1.3194,81400.0,NEAR BAY
+-121.33,38.69,15.0,3137.0,509.0,1635.0,544.0,4.6923,122700.0,INLAND
+-118.3,34.1,25.0,3926.0,1715.0,4865.0,1612.0,1.6112,262500.0,<1H OCEAN
+-119.29,36.55,21.0,2467.0,520.0,1721.0,515.0,2.5521,65600.0,INLAND
+-117.1,33.36,19.0,3518.0,658.0,2091.0,610.0,3.2614,168800.0,<1H OCEAN
+-121.96,37.55,4.0,3746.0,993.0,1606.0,838.0,4.1387,162500.0,<1H OCEAN
+-118.34,33.79,36.0,716.0,123.0,388.0,124.0,5.0254,350000.0,<1H OCEAN
+-122.28,37.49,25.0,7335.0,1157.0,2626.0,1049.0,6.5475,500001.0,NEAR OCEAN
+-118.1,34.12,50.0,1835.0,231.0,636.0,211.0,11.6471,500001.0,<1H OCEAN
+-118.55,34.23,25.0,4409.0,1018.0,4579.0,1010.0,2.8727,245100.0,<1H OCEAN
+-122.04,38.28,12.0,3861.0,795.0,2129.0,806.0,3.676,135000.0,INLAND
+-119.68,36.62,31.0,834.0,229.0,616.0,211.0,1.6602,61200.0,INLAND
+-118.15,33.84,29.0,2448.0,354.0,894.0,349.0,7.6526,481300.0,<1H OCEAN
+-121.88,37.39,13.0,3334.0,565.0,2240.0,561.0,7.105,273900.0,<1H OCEAN
+-121.26,37.98,32.0,3274.0,820.0,2050.0,738.0,2.1265,55700.0,INLAND
+-118.2,33.99,35.0,1608.0,465.0,2140.0,488.0,3.1979,154700.0,<1H OCEAN
+-118.23,33.9,28.0,1108.0,284.0,1498.0,289.0,2.4706,88800.0,<1H OCEAN
+-116.76,34.23,10.0,4374.0,989.0,1020.0,376.0,2.6071,89000.0,INLAND
+-117.31,34.03,9.0,1199.0,187.0,629.0,207.0,5.7393,151600.0,INLAND
+-122.03,37.59,16.0,4371.0,889.0,2530.0,817.0,4.6786,256000.0,NEAR BAY
+-118.25,34.22,34.0,2510.0,535.0,1516.0,542.0,3.8068,267000.0,<1H OCEAN
+-117.9,33.73,26.0,1324.0,314.0,1804.0,311.0,3.9659,178500.0,<1H OCEAN
+-117.78,33.88,16.0,1800.0,238.0,871.0,234.0,6.6678,301900.0,<1H OCEAN
+-118.33,33.99,46.0,1582.0,315.0,777.0,286.0,3.2083,149600.0,<1H OCEAN
+-122.19,37.75,28.0,856.0,189.0,435.0,162.0,0.8012,81800.0,NEAR BAY
+-122.01,37.26,14.0,2561.0,404.0,1172.0,378.0,7.6107,500001.0,<1H OCEAN
+-121.84,37.98,8.0,7505.0,1089.0,3325.0,1016.0,5.2699,204200.0,INLAND
+-118.39,34.19,23.0,1875.0,710.0,2555.0,657.0,2.0968,162500.0,<1H OCEAN
+-118.29,34.11,40.0,2681.0,737.0,1144.0,669.0,3.0461,264300.0,<1H OCEAN
+-118.99,35.39,36.0,1438.0,348.0,1054.0,341.0,1.8319,55400.0,INLAND
+-118.26,33.89,36.0,2230.0,417.0,1395.0,381.0,2.8493,109600.0,<1H OCEAN
+-117.27,33.68,8.0,26322.0,4072.0,9360.0,3361.0,5.3238,228900.0,<1H OCEAN
+-116.93,32.85,5.0,4116.0,990.0,2770.0,905.0,3.1142,150000.0,<1H OCEAN
+-118.29,33.94,34.0,1089.0,278.0,995.0,315.0,2.3352,107700.0,<1H OCEAN
+-117.31,34.53,26.0,2299.0,496.0,1259.0,441.0,2.6125,79900.0,INLAND
+-118.32,33.98,49.0,1993.0,446.0,1052.0,394.0,2.2138,119800.0,<1H OCEAN
+-117.91,33.87,29.0,1121.0,,762.0,276.0,2.5,143800.0,<1H OCEAN
+-118.39,33.88,33.0,2543.0,439.0,1098.0,416.0,5.9683,495500.0,<1H OCEAN
+-118.17,33.98,27.0,1871.0,556.0,2542.0,581.0,2.8427,164400.0,<1H OCEAN
+-118.47,34.29,18.0,4256.0,987.0,3401.0,955.0,4.2935,190000.0,<1H OCEAN
+-124.16,40.8,52.0,1703.0,500.0,952.0,435.0,1.1386,74100.0,NEAR OCEAN
+-117.36,33.88,10.0,5600.0,848.0,2573.0,788.0,5.0346,240500.0,INLAND
+-119.02,35.38,48.0,346.0,92.0,129.0,63.0,1.1875,63800.0,INLAND
+-118.02,33.96,36.0,2071.0,398.0,988.0,404.0,4.6226,219700.0,<1H OCEAN
+-120.87,38.83,12.0,2180.0,423.0,1070.0,377.0,2.8562,128200.0,INLAND
+-118.53,34.09,37.0,5477.0,833.0,1925.0,757.0,8.1888,500001.0,<1H OCEAN
+-117.31,33.17,7.0,2349.0,312.0,809.0,282.0,5.552,283900.0,NEAR OCEAN
+-119.18,35.5,36.0,1253.0,259.0,932.0,249.0,2.1635,110400.0,INLAND
+-117.24,33.21,19.0,1872.0,489.0,1859.0,446.0,2.1875,121700.0,<1H OCEAN
+-118.63,34.18,32.0,1646.0,242.0,697.0,233.0,6.6689,433000.0,<1H OCEAN
+-118.19,35.05,14.0,2992.0,573.0,1631.0,526.0,3.7452,83200.0,INLAND
+-118.3,33.75,23.0,1957.0,517.0,1454.0,526.0,3.5056,203100.0,<1H OCEAN
+-118.37,34.09,31.0,2697.0,706.0,1059.0,689.0,2.8942,500001.0,<1H OCEAN
+-118.12,34.1,49.0,2057.0,430.0,1103.0,414.0,4.0556,282600.0,<1H OCEAN
+-118.3,33.96,43.0,2009.0,442.0,1095.0,439.0,2.8299,109500.0,<1H OCEAN
+-122.26,37.38,28.0,1103.0,164.0,415.0,154.0,7.8633,500001.0,NEAR OCEAN
+-124.15,40.76,24.0,2858.0,511.0,1388.0,512.0,3.375,100600.0,NEAR OCEAN
+-118.19,33.97,27.0,2911.0,972.0,3559.0,945.0,1.9485,146300.0,<1H OCEAN
+-122.42,37.73,35.0,1791.0,322.0,988.0,304.0,4.5769,254500.0,NEAR BAY
+-117.32,34.07,52.0,1226.0,269.0,693.0,272.0,1.9963,76900.0,INLAND
+-117.0,33.29,17.0,2073.0,313.0,573.0,221.0,8.2531,419200.0,<1H OCEAN
+-117.16,32.89,5.0,8576.0,1952.0,5006.0,1827.0,4.3598,189100.0,<1H OCEAN
+-118.53,34.22,29.0,4101.0,849.0,2630.0,867.0,4.6607,199800.0,<1H OCEAN
+-121.62,36.63,52.0,1437.0,298.0,836.0,257.0,3.6286,165500.0,<1H OCEAN
+-118.16,33.91,6.0,3445.0,847.0,2467.0,712.0,3.1507,144000.0,<1H OCEAN
+-122.07,37.64,25.0,4524.0,860.0,2426.0,862.0,4.7083,190900.0,NEAR BAY
+-118.0,33.94,35.0,2603.0,482.0,1305.0,507.0,3.9543,214400.0,<1H OCEAN
+-118.46,34.22,31.0,2057.0,601.0,2397.0,579.0,2.871,184400.0,<1H OCEAN
+-119.79,36.76,52.0,1185.0,260.0,635.0,239.0,1.175,56100.0,INLAND
+-117.19,32.77,14.0,3575.0,992.0,1645.0,839.0,2.4397,140600.0,NEAR OCEAN
+-118.42,34.24,17.0,2049.0,548.0,2243.0,541.0,2.525,163700.0,<1H OCEAN
+-121.24,38.22,28.0,2593.0,487.0,1365.0,457.0,3.3929,113000.0,INLAND
+-118.01,33.71,18.0,6565.0,1357.0,3079.0,1248.0,4.7515,295600.0,<1H OCEAN
+-122.31,37.91,43.0,2549.0,511.0,1060.0,528.0,3.6417,178400.0,NEAR BAY
+-118.13,33.84,46.0,2439.0,429.0,944.0,374.0,4.2841,312400.0,<1H OCEAN
+-118.85,34.19,27.0,2287.0,320.0,967.0,321.0,6.5162,349400.0,<1H OCEAN
+-118.31,34.13,40.0,2822.0,443.0,907.0,414.0,7.2692,498700.0,<1H OCEAN
+-116.46,33.78,33.0,2565.0,745.0,2301.0,638.0,2.5477,83000.0,INLAND
+-122.45,37.79,52.0,3069.0,579.0,1107.0,536.0,5.5634,500001.0,NEAR BAY
+-121.08,39.22,30.0,2188.0,,1033.0,437.0,2.1419,105200.0,INLAND
+-118.19,33.92,35.0,915.0,241.0,1153.0,252.0,3.305,115800.0,<1H OCEAN
+-122.07,37.37,30.0,2937.0,407.0,1097.0,407.0,7.9813,473500.0,NEAR BAY
+-118.03,33.72,24.0,5203.0,957.0,2465.0,946.0,5.163,261000.0,<1H OCEAN
+-118.43,34.15,28.0,6270.0,1706.0,2549.0,1497.0,3.2241,295800.0,<1H OCEAN
+-118.9,34.18,14.0,2627.0,328.0,1121.0,328.0,7.0504,333800.0,<1H OCEAN
+-122.06,37.34,20.0,3435.0,593.0,1293.0,553.0,6.7578,451400.0,<1H OCEAN
+-118.15,34.04,33.0,818.0,195.0,664.0,198.0,2.1944,203300.0,<1H OCEAN
+-118.44,35.13,34.0,1170.0,290.0,602.0,266.0,1.7917,80000.0,INLAND
+-122.68,41.15,32.0,817.0,206.0,224.0,89.0,3.631,90400.0,INLAND
+-122.42,37.76,52.0,2038.0,629.0,2007.0,596.0,2.5701,266700.0,NEAR BAY
+-115.54,32.99,17.0,1697.0,268.0,911.0,254.0,4.3523,96000.0,INLAND
+-122.22,37.84,50.0,2935.0,473.0,1031.0,479.0,7.5,295200.0,NEAR BAY
+-122.64,38.01,36.0,1199.0,232.0,551.0,229.0,3.7321,266700.0,NEAR OCEAN
+-121.29,38.61,17.0,13553.0,2474.0,6544.0,2359.0,3.9727,132700.0,INLAND
+-122.49,37.78,32.0,3028.0,815.0,1704.0,718.0,3.2028,322900.0,NEAR BAY
+-122.32,37.92,22.0,1119.0,220.0,565.0,199.0,3.3594,186900.0,NEAR BAY
+-118.08,33.97,36.0,1678.0,323.0,1380.0,352.0,3.5481,163300.0,<1H OCEAN
+-119.33,35.59,20.0,3085.0,691.0,2645.0,676.0,1.7868,54100.0,INLAND
+-121.32,38.66,21.0,1276.0,208.0,501.0,205.0,3.95,143600.0,INLAND
+-118.46,34.14,34.0,5264.0,771.0,1738.0,753.0,8.8115,500001.0,<1H OCEAN
+-120.9,35.33,16.0,1576.0,287.0,595.0,262.0,3.588,266300.0,NEAR OCEAN
+-122.46,40.52,13.0,2085.0,322.0,1077.0,333.0,5.2149,146500.0,INLAND
+-123.84,40.28,28.0,2809.0,605.0,1093.0,438.0,2.0962,74000.0,<1H OCEAN
+-121.55,38.55,10.0,6227.0,1164.0,2909.0,1077.0,4.106,115900.0,INLAND
+-117.92,34.03,35.0,1341.0,233.0,898.0,216.0,4.1118,157300.0,<1H OCEAN
+-118.77,34.28,26.0,2873.0,480.0,1915.0,475.0,5.3681,187700.0,<1H OCEAN
+-118.47,34.23,22.0,8350.0,2717.0,9135.0,2452.0,2.5008,160000.0,<1H OCEAN
+-118.47,34.32,13.0,2664.0,518.0,1468.0,521.0,4.8988,325200.0,<1H OCEAN
+-118.24,33.9,40.0,1308.0,272.0,901.0,257.0,2.8269,98000.0,<1H OCEAN
+-118.43,37.4,19.0,2460.0,405.0,1225.0,425.0,4.1576,141500.0,INLAND
+-118.06,33.88,17.0,7187.0,1073.0,3844.0,1068.0,6.5901,337400.0,<1H OCEAN
+-122.03,37.62,35.0,2072.0,352.0,1001.0,350.0,4.7109,198700.0,NEAR BAY
+-122.24,37.83,52.0,1757.0,246.0,585.0,227.0,5.8948,457800.0,NEAR BAY
+-118.34,33.99,48.0,2225.0,433.0,1170.0,401.0,2.9643,140400.0,<1H OCEAN
+-118.86,34.22,22.0,1230.0,200.0,673.0,195.0,6.2708,251400.0,<1H OCEAN
+-122.27,37.51,39.0,3996.0,793.0,1744.0,761.0,4.5075,364900.0,NEAR OCEAN
+-118.75,34.28,27.0,1452.0,251.0,928.0,259.0,4.6908,186600.0,<1H OCEAN
+-121.42,38.55,35.0,182.0,39.0,115.0,43.0,2.6417,98900.0,INLAND
+-118.34,34.05,41.0,2099.0,472.0,1369.0,465.0,2.7409,167100.0,<1H OCEAN
+-121.23,38.12,22.0,393.0,58.0,134.0,57.0,3.95,178100.0,INLAND
+-117.64,34.07,38.0,2450.0,544.0,1823.0,536.0,2.837,111200.0,INLAND
+-119.27,35.51,28.0,1089.0,179.0,544.0,190.0,3.2279,95800.0,INLAND
+-118.05,33.65,5.0,7017.0,935.0,2427.0,867.0,10.1154,477700.0,NEAR OCEAN
+-120.59,37.39,16.0,4717.0,1119.0,3589.0,1017.0,2.1061,72800.0,INLAND
+-117.28,34.06,2.0,1658.0,290.0,868.0,304.0,5.1365,136700.0,INLAND
+-122.03,37.38,21.0,2667.0,798.0,1433.0,727.0,3.8732,252400.0,<1H OCEAN
+-122.4,38.46,33.0,2542.0,466.0,1099.0,420.0,4.635,248500.0,INLAND
+-120.43,34.96,19.0,2350.0,631.0,1291.0,515.0,1.0349,130800.0,<1H OCEAN
+-121.29,37.98,49.0,2501.0,565.0,1171.0,550.0,2.5043,76700.0,INLAND
+-118.15,33.84,37.0,1508.0,252.0,635.0,241.0,3.75,221300.0,<1H OCEAN
+-122.12,37.09,36.0,1397.0,289.0,661.0,243.0,4.125,239600.0,NEAR OCEAN
+-117.11,32.68,36.0,26.0,14.0,58.0,23.0,1.9107,125000.0,NEAR OCEAN
+-121.34,38.69,16.0,2686.0,516.0,1553.0,529.0,3.7857,112700.0,INLAND
+-120.5,37.3,29.0,1572.0,456.0,1697.0,429.0,1.76,63200.0,INLAND
+-120.87,37.77,9.0,4838.0,920.0,2460.0,923.0,3.5959,142700.0,INLAND
+-117.82,33.76,33.0,2774.0,428.0,1229.0,407.0,6.2944,265600.0,<1H OCEAN
+-122.57,37.99,38.0,5587.0,996.0,2466.0,1027.0,4.1711,336900.0,NEAR BAY
+-118.34,33.91,12.0,9975.0,3638.0,7429.0,3405.0,2.6689,192300.0,<1H OCEAN
+-121.83,36.98,19.0,4431.0,705.0,1764.0,679.0,4.3321,298600.0,<1H OCEAN
+-118.4,34.02,27.0,515.0,201.0,397.0,228.0,2.4135,184400.0,<1H OCEAN
+-117.05,32.59,26.0,1919.0,345.0,1326.0,341.0,4.2679,131900.0,NEAR OCEAN
+-120.64,37.38,19.0,2256.0,449.0,1469.0,435.0,2.5129,84600.0,INLAND
+-118.47,33.99,31.0,1312.0,376.0,1178.0,330.0,2.0714,300000.0,<1H OCEAN
+-117.7,33.48,6.0,16590.0,2696.0,6223.0,2357.0,6.3088,340300.0,<1H OCEAN
+-120.72,38.42,17.0,5654.0,1085.0,2237.0,953.0,3.0465,144100.0,INLAND
+-119.37,36.19,24.0,1306.0,266.0,889.0,276.0,2.4922,66100.0,INLAND
+-117.7,34.08,10.0,1979.0,454.0,1117.0,389.0,3.7802,107300.0,INLAND
+-118.43,34.25,35.0,1447.0,335.0,1630.0,306.0,2.9205,143100.0,<1H OCEAN
+-119.81,36.77,43.0,2341.0,395.0,890.0,375.0,3.4265,85000.0,INLAND
+-118.25,34.13,52.0,322.0,88.0,229.0,89.0,2.125,243800.0,<1H OCEAN
+-118.19,33.91,35.0,2695.0,748.0,2935.0,706.0,2.0134,132400.0,<1H OCEAN
+-118.24,33.97,43.0,1357.0,349.0,1657.0,331.0,2.0819,111800.0,<1H OCEAN
+-122.09,37.41,8.0,1480.0,414.0,856.0,445.0,2.8203,284100.0,NEAR BAY
+-117.25,33.37,8.0,1755.0,530.0,1687.0,511.0,1.995,146900.0,<1H OCEAN
+-122.4,37.8,52.0,2094.0,568.0,920.0,503.0,4.2015,412500.0,NEAR BAY
+-118.04,33.83,20.0,1488.0,312.0,972.0,283.0,4.055,201900.0,<1H OCEAN
+-119.8,36.78,43.0,2382.0,431.0,874.0,380.0,3.5542,96500.0,INLAND
+-118.13,34.16,52.0,1787.0,427.0,1107.0,410.0,2.5664,215000.0,<1H OCEAN
+-118.08,33.98,36.0,1492.0,282.0,1041.0,270.0,4.0677,165800.0,<1H OCEAN
+-117.9,33.85,32.0,1605.0,314.0,986.0,306.0,3.3375,186200.0,<1H OCEAN
+-122.17,37.76,33.0,1280.0,307.0,999.0,286.0,2.5625,89300.0,NEAR BAY
+-121.99,36.96,42.0,1275.0,272.0,451.0,200.0,4.7321,422400.0,NEAR OCEAN
+-117.89,33.88,27.0,2091.0,336.0,1037.0,332.0,5.7519,243400.0,<1H OCEAN
+-121.9,37.46,29.0,2385.0,513.0,1788.0,510.0,3.8421,220700.0,<1H OCEAN
+-122.22,37.82,42.0,2991.0,335.0,1018.0,335.0,13.499,500001.0,NEAR BAY
+-121.25,38.7,16.0,3262.0,533.0,1794.0,543.0,4.2464,144400.0,INLAND
+-118.24,33.78,24.0,574.0,173.0,784.0,162.0,2.25,152300.0,NEAR OCEAN
+-121.36,38.67,17.0,2770.0,684.0,1471.0,624.0,2.3683,82500.0,INLAND
+-122.23,40.4,18.0,2102.0,377.0,1059.0,384.0,3.0556,95500.0,INLAND
+-121.86,37.29,14.0,6160.0,1222.0,2472.0,1204.0,4.1444,178400.0,<1H OCEAN
+-122.84,38.42,29.0,2756.0,551.0,1381.0,531.0,2.9625,237300.0,<1H OCEAN
+-117.85,34.07,32.0,761.0,101.0,295.0,95.0,11.1077,500001.0,<1H OCEAN
+-118.24,33.91,40.0,972.0,240.0,761.0,225.0,1.4688,88200.0,<1H OCEAN
+-117.89,33.98,5.0,3088.0,711.0,1415.0,641.0,2.5125,184500.0,<1H OCEAN
+-119.67,36.72,31.0,843.0,140.0,453.0,149.0,2.6875,153800.0,INLAND
+-117.06,32.68,41.0,2665.0,515.0,1664.0,512.0,2.375,113500.0,NEAR OCEAN
+-118.18,34.19,48.0,1371.0,,528.0,155.0,15.0001,500001.0,<1H OCEAN
+-118.22,34.22,39.0,2686.0,417.0,1094.0,402.0,7.0059,500001.0,<1H OCEAN
+-117.16,32.58,36.0,1940.0,399.0,1076.0,382.0,3.3906,147800.0,NEAR OCEAN
+-118.37,33.84,27.0,3245.0,605.0,1572.0,556.0,5.3773,379000.0,<1H OCEAN
+-117.61,33.45,6.0,950.0,184.0,426.0,186.0,4.7237,220700.0,NEAR OCEAN
+-122.04,36.96,44.0,1294.0,269.0,645.0,259.0,3.2437,223900.0,NEAR OCEAN
+-120.44,34.91,12.0,3189.0,463.0,1200.0,442.0,5.299,226800.0,<1H OCEAN
+-122.48,37.78,50.0,2159.0,437.0,1111.0,417.0,3.5588,346400.0,NEAR BAY
+-121.87,37.31,6.0,3797.0,984.0,2437.0,904.0,3.6802,152400.0,<1H OCEAN
+-118.14,33.91,36.0,1096.0,204.0,569.0,201.0,4.475,182300.0,<1H OCEAN
+-122.85,38.77,18.0,2856.0,513.0,1027.0,405.0,4.6953,241700.0,<1H OCEAN
+-118.31,34.05,26.0,1809.0,640.0,2543.0,640.0,2.3536,500000.0,<1H OCEAN
+-122.51,37.78,47.0,2496.0,494.0,1201.0,454.0,4.0353,342200.0,NEAR BAY
+-117.82,33.73,23.0,2542.0,772.0,1720.0,675.0,3.8703,137000.0,<1H OCEAN
+-122.12,37.41,33.0,2892.0,617.0,1250.0,581.0,5.3727,360900.0,NEAR BAY
+-121.84,36.59,34.0,3852.0,733.0,1661.0,696.0,4.3269,221300.0,<1H OCEAN
+-117.97,33.89,17.0,1851.0,344.0,764.0,339.0,5.1315,181800.0,<1H OCEAN
+-122.08,37.59,16.0,1816.0,365.0,1367.0,355.0,4.235,156300.0,NEAR BAY
+-122.44,37.78,52.0,2695.0,657.0,1243.0,573.0,2.8569,372200.0,NEAR BAY
+-117.14,32.92,15.0,1558.0,314.0,949.0,332.0,5.2864,174400.0,<1H OCEAN
+-118.19,34.77,16.0,2035.0,370.0,704.0,330.0,2.1979,146400.0,INLAND
+-118.33,33.99,43.0,2224.0,550.0,1598.0,545.0,2.8274,122500.0,<1H OCEAN
+-122.23,37.78,52.0,986.0,258.0,1008.0,255.0,1.4844,119400.0,NEAR BAY
+-118.37,33.82,36.0,2463.0,447.0,1125.0,424.0,6.0176,352700.0,<1H OCEAN
+-122.82,38.39,32.0,1437.0,257.0,752.0,245.0,4.7422,240900.0,<1H OCEAN
+-118.32,33.96,47.0,1453.0,247.0,721.0,276.0,5.5176,191000.0,<1H OCEAN
+-122.64,38.25,31.0,2554.0,515.0,1507.0,533.0,3.8,162600.0,<1H OCEAN
+-118.13,34.08,34.0,3848.0,991.0,2814.0,972.0,2.6716,227100.0,<1H OCEAN
+-119.77,36.31,14.0,3677.0,863.0,2191.0,785.0,2.6218,69100.0,INLAND
+-120.43,34.86,17.0,1932.0,347.0,874.0,312.0,3.8203,141500.0,<1H OCEAN
+-118.11,34.07,19.0,3215.0,907.0,3072.0,870.0,2.3393,202300.0,<1H OCEAN
+-117.54,34.11,16.0,2114.0,374.0,1463.0,399.0,3.9241,131500.0,INLAND
+-118.21,33.84,28.0,822.0,205.0,627.0,192.0,3.4583,166300.0,<1H OCEAN
+-122.07,37.91,33.0,1550.0,277.0,638.0,254.0,3.6833,292500.0,NEAR BAY
+-120.69,35.62,35.0,3451.0,713.0,1550.0,653.0,2.9167,161700.0,<1H OCEAN
+-115.53,32.99,25.0,2578.0,634.0,2082.0,565.0,1.7159,62200.0,INLAND
+-119.28,34.45,36.0,2376.0,541.0,1505.0,547.0,2.4595,197600.0,<1H OCEAN
+-118.18,34.08,33.0,1369.0,408.0,1475.0,377.0,2.3281,151900.0,<1H OCEAN
+-121.98,36.96,20.0,3495.0,818.0,2186.0,772.0,3.1167,258300.0,NEAR OCEAN
+-122.19,39.91,39.0,2467.0,529.0,1433.0,502.0,1.8571,53500.0,INLAND
+-117.87,33.74,16.0,1243.0,365.0,1925.0,376.0,2.7632,158900.0,<1H OCEAN
+-116.42,33.79,12.0,7095.0,1260.0,1179.0,570.0,4.9444,285000.0,INLAND
+-122.42,37.77,52.0,2185.0,656.0,1266.0,626.0,2.7794,350000.0,NEAR BAY
+-121.88,38.03,52.0,1225.0,250.0,725.0,231.0,2.0,101400.0,INLAND
+-118.57,34.17,31.0,1950.0,383.0,870.0,357.0,3.1875,500001.0,<1H OCEAN
+-117.31,34.13,36.0,1076.0,283.0,773.0,224.0,2.6307,66400.0,INLAND
+-122.39,37.6,34.0,707.0,,381.0,156.0,4.375,340900.0,NEAR OCEAN
+-118.12,33.9,38.0,1222.0,282.0,756.0,256.0,4.125,173900.0,<1H OCEAN
+-121.34,37.99,14.0,3111.0,498.0,1178.0,525.0,6.556,234700.0,INLAND
+-117.04,33.03,16.0,2852.0,435.0,1083.0,448.0,6.3761,296200.0,<1H OCEAN
+-121.27,37.98,43.0,1005.0,200.0,492.0,172.0,2.6812,72800.0,INLAND
+-122.06,37.35,30.0,2040.0,294.0,787.0,278.0,8.758,500001.0,<1H OCEAN
+-118.36,34.17,44.0,2295.0,560.0,1543.0,528.0,2.3851,194100.0,<1H OCEAN
+-122.3,37.9,15.0,5083.0,1212.0,2420.0,1146.0,4.5824,256100.0,NEAR BAY
+-118.11,33.91,36.0,1088.0,231.0,617.0,211.0,3.8824,193100.0,<1H OCEAN
+-122.44,37.79,52.0,3785.0,808.0,1371.0,799.0,6.4209,500001.0,NEAR BAY
+-122.19,37.84,18.0,1617.0,210.0,533.0,194.0,11.6017,392600.0,NEAR BAY
+-119.45,36.09,18.0,408.0,82.0,253.0,75.0,2.0313,112500.0,INLAND
+-119.98,36.74,26.0,1453.0,251.0,896.0,260.0,3.4861,112500.0,INLAND
+-122.05,37.94,22.0,4162.0,1194.0,1804.0,1185.0,2.5459,179300.0,NEAR BAY
+-122.31,38.01,18.0,4123.0,874.0,1895.0,772.0,3.2759,195000.0,NEAR BAY
+-118.07,34.17,36.0,2415.0,394.0,1215.0,413.0,5.5418,326100.0,INLAND
+-120.66,39.72,15.0,3763.0,784.0,717.0,348.0,2.2019,130500.0,INLAND
+-117.78,33.51,44.0,1833.0,331.0,515.0,268.0,6.6178,500001.0,<1H OCEAN
+-118.05,33.78,25.0,3112.0,435.0,1098.0,401.0,6.0,353500.0,<1H OCEAN
+-118.35,33.9,13.0,2887.0,853.0,2197.0,800.0,2.8777,207900.0,<1H OCEAN
+-118.29,34.01,39.0,751.0,207.0,1010.0,231.0,1.6036,137500.0,<1H OCEAN
+-117.81,33.89,13.0,3252.0,583.0,1546.0,557.0,5.8243,297900.0,<1H OCEAN
+-118.15,33.91,38.0,901.0,205.0,760.0,208.0,2.9643,147400.0,<1H OCEAN
+-122.22,38.08,37.0,2811.0,,1574.0,516.0,3.1053,96700.0,NEAR BAY
+-119.17,34.29,18.0,3932.0,724.0,1896.0,680.0,5.2953,279400.0,NEAR OCEAN
+-121.9,36.58,31.0,1431.0,,704.0,393.0,3.1977,289300.0,NEAR OCEAN
+-118.75,34.27,20.0,3495.0,449.0,1629.0,428.0,5.8096,264400.0,<1H OCEAN
+-123.92,41.54,22.0,2920.0,636.0,1382.0,499.0,2.0202,71100.0,NEAR OCEAN
+-118.18,33.78,20.0,1852.0,556.0,1712.0,556.0,1.4565,152500.0,NEAR OCEAN
+-120.42,37.95,19.0,2787.0,578.0,1208.0,532.0,2.4922,98700.0,INLAND
+-122.09,37.35,37.0,1795.0,285.0,791.0,261.0,7.5794,500001.0,NEAR BAY
+-121.54,40.06,17.0,858.0,262.0,47.0,27.0,2.4028,67500.0,INLAND
+-121.58,37.01,15.0,2873.0,547.0,1582.0,567.0,5.1519,264700.0,INLAND
+-122.43,37.64,42.0,4091.0,757.0,1861.0,771.0,4.207,272700.0,NEAR OCEAN
+-117.0,32.76,31.0,1989.0,280.0,805.0,301.0,6.5645,189100.0,<1H OCEAN
+-118.23,34.07,35.0,1335.0,440.0,1586.0,445.0,1.9722,156300.0,<1H OCEAN
+-117.57,34.07,4.0,2152.0,580.0,1083.0,441.0,3.1458,118800.0,INLAND
+-117.99,34.12,37.0,1527.0,331.0,1504.0,324.0,3.2857,130100.0,INLAND
+-121.87,37.1,20.0,1918.0,304.0,798.0,302.0,7.5755,402300.0,<1H OCEAN
+-118.4,33.99,36.0,1280.0,240.0,704.0,217.0,5.9632,328100.0,<1H OCEAN
+-119.2,36.3,32.0,1355.0,363.0,1427.0,384.0,1.3444,45600.0,INLAND
+-118.19,33.83,30.0,2246.0,552.0,1032.0,548.0,3.5871,347100.0,NEAR OCEAN
+-121.36,38.0,17.0,4535.0,762.0,1562.0,743.0,5.3224,225800.0,INLAND
+-117.74,34.11,28.0,3494.0,566.0,1391.0,522.0,5.3637,214700.0,INLAND
+-122.1,37.63,29.0,2172.0,435.0,1377.0,408.0,3.7895,180900.0,NEAR BAY
+-118.24,33.93,19.0,325.0,74.0,354.0,87.0,2.75,90600.0,<1H OCEAN
+-121.32,38.02,26.0,2851.0,533.0,1544.0,499.0,3.5379,99100.0,INLAND
+-118.15,34.02,43.0,2172.0,605.0,2386.0,597.0,2.8239,150600.0,<1H OCEAN
+-117.72,34.09,33.0,4979.0,934.0,2575.0,874.0,3.7958,152500.0,INLAND
+-122.25,37.82,26.0,3959.0,1196.0,1749.0,1217.0,3.0233,255000.0,NEAR BAY
+-118.1,33.82,36.0,1930.0,354.0,915.0,328.0,5.2713,244400.0,<1H OCEAN
+-117.07,33.72,16.0,4928.0,960.0,2132.0,853.0,2.7983,112500.0,INLAND
+-122.41,37.63,35.0,865.0,226.0,602.0,217.0,3.0,229100.0,NEAR OCEAN
+-118.26,34.11,47.0,2183.0,510.0,1445.0,503.0,3.6667,210900.0,<1H OCEAN
+-121.49,38.5,32.0,2364.0,439.0,1331.0,449.0,3.319,84500.0,INLAND
+-118.11,33.83,36.0,1784.0,303.0,964.0,299.0,4.2703,220900.0,<1H OCEAN
+-119.26,36.3,18.0,3578.0,720.0,1540.0,640.0,2.425,84600.0,INLAND
+-117.99,33.9,30.0,1677.0,372.0,1021.0,332.0,3.5859,199700.0,<1H OCEAN
+-118.13,34.04,42.0,2205.0,451.0,1392.0,423.0,4.3646,211400.0,<1H OCEAN
+-118.36,33.9,41.0,1355.0,349.0,655.0,329.0,2.9551,205000.0,<1H OCEAN
+-121.95,37.52,33.0,3994.0,764.0,2721.0,763.0,5.2308,196900.0,<1H OCEAN
+-119.01,35.38,44.0,434.0,110.0,274.0,86.0,1.1944,57500.0,INLAND
+-121.29,37.95,52.0,288.0,86.0,272.0,54.0,0.696,42500.0,INLAND
+-118.23,33.91,34.0,789.0,200.0,1041.0,191.0,3.119,90300.0,<1H OCEAN
+-116.63,33.89,22.0,1540.0,364.0,610.0,268.0,1.5227,71000.0,INLAND
+-118.18,33.99,35.0,1230.0,407.0,1512.0,364.0,2.152,170800.0,<1H OCEAN
+-118.25,33.94,37.0,1002.0,270.0,1092.0,273.0,1.6333,94500.0,<1H OCEAN
+-121.06,39.03,11.0,1887.0,303.0,775.0,283.0,3.8417,187200.0,INLAND
+-121.49,38.58,52.0,2151.0,664.0,1146.0,603.0,1.4034,90300.0,INLAND
+-117.06,33.02,17.0,2635.0,389.0,994.0,359.0,5.8966,261500.0,<1H OCEAN
+-118.03,34.13,33.0,2352.0,373.0,995.0,359.0,4.9583,445700.0,INLAND
+-118.66,34.02,23.0,8798.0,1465.0,2750.0,1208.0,8.7364,500001.0,NEAR OCEAN
+-118.02,33.95,36.0,1681.0,329.0,964.0,311.0,4.108,181200.0,<1H OCEAN
+-117.06,34.0,33.0,1575.0,326.0,879.0,282.0,2.5357,94400.0,INLAND
+-118.31,34.0,52.0,1542.0,309.0,939.0,276.0,1.6892,129100.0,<1H OCEAN
+-121.8,38.0,34.0,2738.0,475.0,1316.0,459.0,3.5368,122500.0,INLAND
+-124.1,40.5,42.0,2380.0,553.0,1300.0,504.0,1.7574,57500.0,<1H OCEAN
+-117.37,34.0,36.0,730.0,155.0,476.0,142.0,2.4306,88900.0,INLAND
+-118.42,34.28,34.0,1999.0,427.0,2391.0,439.0,2.8,144300.0,<1H OCEAN
+-118.06,34.02,25.0,3548.0,639.0,2653.0,664.0,5.2557,188800.0,<1H OCEAN
+-118.2,33.94,44.0,1413.0,298.0,1200.0,307.0,3.5125,169300.0,<1H OCEAN
+-118.26,34.23,33.0,1805.0,303.0,838.0,301.0,5.4306,326600.0,<1H OCEAN
+-120.08,36.72,22.0,1339.0,251.0,820.0,276.0,3.6,83200.0,INLAND
+-117.39,34.38,4.0,7151.0,1295.0,3527.0,1170.0,3.5696,129700.0,INLAND
+-118.29,34.03,42.0,907.0,378.0,822.0,288.0,1.2875,179200.0,<1H OCEAN
+-118.26,34.08,41.0,1396.0,360.0,1069.0,363.0,2.4375,203300.0,<1H OCEAN
+-119.34,36.23,12.0,4965.0,872.0,2191.0,804.0,3.5611,90200.0,INLAND
+-118.0,33.96,37.0,2414.0,323.0,878.0,305.0,9.1541,453800.0,<1H OCEAN
+-117.06,32.79,17.0,2524.0,332.0,771.0,317.0,8.7604,331800.0,<1H OCEAN
+-117.91,33.64,38.0,2222.0,542.0,1067.0,512.0,2.8553,307600.0,<1H OCEAN
+-118.09,34.1,40.0,1904.0,393.0,1183.0,364.0,3.6696,210400.0,<1H OCEAN
+-117.29,33.24,5.0,3109.0,634.0,1823.0,578.0,3.1875,153800.0,<1H OCEAN
+-122.25,37.51,45.0,989.0,174.0,504.0,180.0,4.8382,289400.0,NEAR OCEAN
+-120.44,34.88,9.0,3124.0,415.0,1169.0,407.0,6.7694,275100.0,<1H OCEAN
+-121.24,38.67,28.0,3558.0,589.0,1742.0,581.0,4.0182,131700.0,INLAND
+-117.99,33.77,15.0,2081.0,531.0,1617.0,561.0,3.4955,160900.0,<1H OCEAN
+-116.93,33.73,13.0,3603.0,573.0,1644.0,515.0,4.0433,132300.0,INLAND
+-121.83,37.34,26.0,1848.0,339.0,1952.0,327.0,4.087,182500.0,<1H OCEAN
+-118.2,34.03,41.0,1292.0,334.0,1150.0,322.0,1.925,135200.0,<1H OCEAN
+-117.83,33.66,15.0,2355.0,438.0,747.0,450.0,6.5356,272800.0,<1H OCEAN
+-122.44,37.79,52.0,1979.0,359.0,648.0,370.0,5.3124,500001.0,NEAR BAY
+-117.29,34.49,3.0,7689.0,1545.0,3804.0,1399.0,3.3871,111800.0,INLAND
+-118.19,33.99,40.0,1547.0,434.0,1930.0,427.0,3.3869,157300.0,<1H OCEAN
+-122.15,37.72,47.0,1190.0,251.0,540.0,266.0,3.375,198300.0,NEAR BAY
+-118.36,34.03,35.0,1819.0,499.0,1666.0,482.0,1.6452,125900.0,<1H OCEAN
+-121.09,38.68,15.0,5218.0,711.0,1949.0,659.0,4.7083,213300.0,INLAND
+-118.37,33.95,5.0,6955.0,2062.0,3591.0,1566.0,3.111,247600.0,<1H OCEAN
+-118.48,34.0,25.0,4149.0,1067.0,1749.0,1000.0,3.9722,450000.0,<1H OCEAN
+-117.33,34.53,10.0,3781.0,712.0,2044.0,685.0,3.0943,97100.0,INLAND
+-118.26,33.79,30.0,1291.0,230.0,835.0,215.0,5.5,181500.0,<1H OCEAN
+-121.7,39.07,26.0,2668.0,510.0,1437.0,505.0,3.3125,100000.0,INLAND
+-119.7,36.8,34.0,1768.0,303.0,888.0,314.0,3.8088,87700.0,INLAND
+-118.36,34.08,45.0,2195.0,483.0,1265.0,455.0,3.3864,397900.0,<1H OCEAN
+-120.62,38.71,10.0,6305.0,1150.0,2597.0,921.0,4.0197,132200.0,INLAND
+-122.31,38.33,26.0,2155.0,339.0,956.0,365.0,4.0132,174700.0,NEAR BAY
+-118.9,34.41,35.0,4431.0,739.0,2304.0,720.0,4.2599,209100.0,<1H OCEAN
+-120.18,34.62,25.0,1337.0,219.0,671.0,225.0,3.1912,226400.0,NEAR OCEAN
+-120.05,36.97,20.0,2029.0,427.0,983.0,401.0,1.8444,47100.0,INLAND
+-117.76,33.53,18.0,3224.0,561.0,1310.0,580.0,8.4614,391900.0,<1H OCEAN
+-118.39,34.21,14.0,2807.0,868.0,2729.0,803.0,2.6667,172400.0,<1H OCEAN
+-122.03,37.05,12.0,2010.0,422.0,784.0,407.0,3.9728,190900.0,NEAR OCEAN
+-117.24,32.79,25.0,2135.0,691.0,566.0,320.0,2.6902,212500.0,NEAR OCEAN
+-118.05,33.82,25.0,1548.0,279.0,732.0,265.0,5.123,159600.0,<1H OCEAN
+-116.89,33.48,14.0,1016.0,219.0,443.0,169.0,2.8071,137500.0,INLAND
+-120.97,37.84,28.0,2368.0,430.0,1231.0,403.0,2.883,141900.0,INLAND
+-121.23,37.82,8.0,1289.0,235.0,867.0,239.0,4.6821,138500.0,INLAND
+-117.74,34.06,48.0,2438.0,599.0,1508.0,548.0,2.8983,129200.0,INLAND
+-118.11,34.0,24.0,2403.0,590.0,2103.0,547.0,2.7292,193800.0,<1H OCEAN
+-118.33,34.04,48.0,2437.0,443.0,1400.0,426.0,2.628,251100.0,<1H OCEAN
+-122.11,37.42,32.0,3058.0,595.0,1267.0,540.0,6.4949,417800.0,NEAR BAY
+-122.17,37.78,49.0,893.0,177.0,468.0,181.0,3.875,140600.0,NEAR BAY
+-117.03,32.72,37.0,2192.0,455.0,1515.0,446.0,3.0588,120600.0,NEAR OCEAN
+-117.24,32.75,41.0,1989.0,514.0,1015.0,489.0,2.79,226000.0,NEAR OCEAN
+-117.27,34.11,44.0,567.0,134.0,565.0,150.0,1.8281,62900.0,INLAND
+-118.06,34.08,42.0,1988.0,402.0,1239.0,402.0,3.2569,201500.0,<1H OCEAN
+-118.46,34.08,35.0,3247.0,525.0,1065.0,484.0,7.8426,500001.0,<1H OCEAN
+-121.77,36.93,24.0,1943.0,447.0,1844.0,461.0,3.0192,184300.0,<1H OCEAN
+-116.45,33.8,9.0,5534.0,1206.0,2283.0,1008.0,3.6161,99100.0,INLAND
+-117.02,32.8,31.0,2692.0,445.0,1129.0,450.0,4.4583,170000.0,<1H OCEAN
+-121.79,36.85,28.0,1049.0,235.0,705.0,208.0,2.7321,150000.0,NEAR OCEAN
+-120.96,37.66,16.0,4961.0,902.0,2654.0,804.0,4.2823,138300.0,INLAND
+-122.5,37.97,25.0,6526.0,1902.0,5917.0,1812.0,2.7273,187500.0,NEAR BAY
+-118.85,34.27,50.0,187.0,33.0,130.0,35.0,3.3438,500001.0,<1H OCEAN
+-121.94,36.98,24.0,3010.0,562.0,1360.0,504.0,4.2006,290700.0,NEAR OCEAN
+-116.47,33.94,18.0,2233.0,471.0,919.0,388.0,3.2578,85200.0,INLAND
+-120.4,37.92,22.0,1022.0,194.0,517.0,198.0,3.625,99400.0,INLAND
+-122.27,38.32,31.0,1267.0,319.0,545.0,297.0,1.9946,206800.0,NEAR BAY
+-121.92,36.54,33.0,5323.0,887.0,1670.0,740.0,3.9792,468000.0,NEAR OCEAN
+-121.85,38.0,26.0,3364.0,570.0,1806.0,566.0,4.2647,133400.0,INLAND
+-118.81,34.25,4.0,9147.0,1827.0,3950.0,1661.0,5.716,320800.0,<1H OCEAN
+-121.78,37.33,10.0,2829.0,394.0,1510.0,386.0,6.68,359500.0,<1H OCEAN
+-121.35,38.72,2.0,21897.0,3513.0,8652.0,2873.0,4.5432,151300.0,INLAND
+-118.26,34.06,15.0,326.0,123.0,490.0,105.0,1.4886,175000.0,<1H OCEAN
+-118.12,33.87,43.0,1633.0,355.0,837.0,350.0,3.0405,188000.0,<1H OCEAN
+-118.98,35.4,36.0,1443.0,273.0,680.0,259.0,2.9821,73100.0,INLAND
+-117.37,33.19,23.0,4104.0,1274.0,4729.0,1187.0,1.8214,173800.0,NEAR OCEAN
+-122.17,37.74,41.0,1613.0,445.0,1481.0,414.0,2.4028,97700.0,NEAR BAY
+-119.63,36.79,19.0,1317.0,189.0,517.0,187.0,4.526,148700.0,INLAND
+-122.07,37.86,17.0,1102.0,224.0,317.0,208.0,3.5893,206300.0,NEAR BAY
+-118.27,34.16,15.0,5036.0,1299.0,3164.0,1175.0,2.9148,238700.0,<1H OCEAN
+-120.47,37.28,19.0,1548.0,319.0,1227.0,309.0,1.7756,73300.0,INLAND
+-120.11,34.66,18.0,1348.0,238.0,631.0,247.0,5.3154,289400.0,NEAR OCEAN
+-117.25,34.15,22.0,7935.0,1976.0,4523.0,1791.0,2.1465,88800.0,INLAND
+-119.0,34.08,17.0,1822.0,438.0,578.0,291.0,5.4346,428600.0,NEAR OCEAN
+-117.21,32.74,52.0,1245.0,174.0,468.0,193.0,6.9322,334500.0,NEAR OCEAN
+-122.21,37.76,52.0,1420.0,314.0,1085.0,300.0,1.7546,80600.0,NEAR BAY
+-121.41,38.55,14.0,2534.0,705.0,1495.0,583.0,1.9167,156300.0,INLAND
+-118.01,34.05,31.0,1135.0,355.0,1717.0,368.0,2.1602,161700.0,<1H OCEAN
+-117.1,32.58,27.0,2616.0,591.0,1889.0,577.0,2.3824,127600.0,NEAR OCEAN
+-118.75,34.27,26.0,966.0,191.0,690.0,191.0,5.1698,188000.0,<1H OCEAN
+-117.11,32.73,34.0,1096.0,221.0,574.0,223.0,3.8355,126700.0,NEAR OCEAN
+-122.61,38.25,18.0,2915.0,418.0,1340.0,421.0,5.2452,204900.0,<1H OCEAN
+-116.91,32.83,16.0,5203.0,,2515.0,862.0,4.105,174400.0,<1H OCEAN
+-120.99,37.66,39.0,1748.0,329.0,831.0,302.0,2.5938,135600.0,INLAND
+-119.21,34.26,23.0,2887.0,540.0,1508.0,518.0,3.3452,217600.0,NEAR OCEAN
+-118.31,34.26,37.0,1444.0,246.0,624.0,239.0,5.76,239400.0,<1H OCEAN
+-122.19,38.09,8.0,614.0,118.0,278.0,115.0,6.3735,166300.0,NEAR BAY
+-118.34,33.8,25.0,4177.0,832.0,2123.0,789.0,5.0814,446800.0,<1H OCEAN
+-117.65,33.65,15.0,4713.0,671.0,2197.0,673.0,7.5426,294800.0,<1H OCEAN
+-122.39,40.52,24.0,2068.0,346.0,951.0,332.0,3.9306,85900.0,INLAND
+-118.19,33.78,29.0,1170.0,369.0,1398.0,373.0,2.2543,156300.0,NEAR OCEAN
+-122.14,39.97,27.0,1079.0,222.0,625.0,197.0,3.1319,62700.0,INLAND
+-118.53,34.44,19.0,1285.0,195.0,650.0,193.0,6.0398,217800.0,<1H OCEAN
+-118.36,34.15,41.0,3545.0,698.0,1221.0,651.0,4.3,500001.0,<1H OCEAN
+-117.65,34.06,41.0,465.0,130.0,349.0,138.0,2.0893,112500.0,INLAND
+-120.97,37.66,19.0,1974.0,393.0,799.0,377.0,3.1286,137500.0,INLAND
+-122.42,37.75,52.0,2163.0,607.0,1447.0,546.0,3.3555,275000.0,NEAR BAY
+-118.45,34.2,18.0,2729.0,800.0,2099.0,742.0,2.5842,230800.0,<1H OCEAN
+-122.06,40.02,32.0,1435.0,277.0,690.0,254.0,2.3043,68400.0,INLAND
+-122.73,38.47,16.0,1834.0,391.0,994.0,390.0,3.7266,156500.0,<1H OCEAN
+-122.31,37.6,34.0,3225.0,726.0,1958.0,656.0,3.6811,273000.0,NEAR BAY
+-122.03,37.61,37.0,1383.0,259.0,808.0,241.0,4.0125,161400.0,NEAR BAY
+-118.57,34.2,18.0,7157.0,1869.0,4642.0,1699.0,3.1818,208000.0,<1H OCEAN
+-116.33,33.72,11.0,12327.0,2000.0,2450.0,1139.0,7.4382,353100.0,INLAND
+-121.96,37.03,17.0,1343.0,203.0,511.0,185.0,4.625,386400.0,NEAR OCEAN
+-121.91,37.24,30.0,2327.0,419.0,1114.0,372.0,4.7279,272000.0,<1H OCEAN
+-122.28,38.31,52.0,58.0,18.0,48.0,22.0,1.76,166700.0,NEAR BAY
+-118.12,34.07,43.0,1050.0,252.0,820.0,244.0,2.025,215600.0,<1H OCEAN
+-118.06,33.72,14.0,2665.0,331.0,964.0,319.0,15.0001,500001.0,NEAR OCEAN
+-118.39,34.05,25.0,2814.0,701.0,1139.0,658.0,4.0153,460000.0,<1H OCEAN
+-120.57,35.11,18.0,2920.0,556.0,1068.0,552.0,3.5242,156800.0,<1H OCEAN
+-121.5,38.49,29.0,3606.0,690.0,2317.0,696.0,2.7368,78200.0,INLAND
+-119.55,34.38,17.0,1951.0,368.0,681.0,350.0,2.7275,500001.0,<1H OCEAN
+-117.29,32.92,25.0,2355.0,381.0,823.0,358.0,6.8322,500001.0,NEAR OCEAN
+-118.94,35.37,33.0,3372.0,741.0,2352.0,704.0,2.0643,57600.0,INLAND
+-118.27,33.99,35.0,932.0,294.0,1153.0,282.0,1.4886,100000.0,<1H OCEAN
+-115.53,32.97,35.0,1583.0,340.0,933.0,318.0,2.4063,70700.0,INLAND
+-122.7,38.97,17.0,2554.0,540.0,723.0,319.0,3.2375,114200.0,INLAND
+-121.57,36.98,14.0,5231.0,817.0,2634.0,799.0,4.9702,279800.0,INLAND
+-118.57,34.09,14.0,7970.0,1142.0,2926.0,1096.0,11.2866,500001.0,<1H OCEAN
+-119.71,36.83,5.0,1087.0,338.0,623.0,362.0,1.8061,113400.0,INLAND
+-121.81,37.35,29.0,2396.0,452.0,2000.0,481.0,4.375,185500.0,<1H OCEAN
+-118.14,34.15,52.0,407.0,160.0,227.0,148.0,1.5156,187500.0,<1H OCEAN
+-122.07,38.0,37.0,978.0,202.0,462.0,184.0,3.625,156300.0,NEAR BAY
+-117.92,34.63,34.0,81.0,26.0,53.0,14.0,1.4091,137500.0,INLAND
+-118.28,34.05,28.0,1306.0,637.0,2079.0,598.0,1.4615,275000.0,<1H OCEAN
+-121.83,37.27,8.0,4454.0,1058.0,2595.0,1027.0,4.5615,282600.0,<1H OCEAN
+-118.16,34.16,52.0,1576.0,239.0,696.0,249.0,6.07,261800.0,<1H OCEAN
+-118.37,33.84,35.0,1792.0,322.0,978.0,326.0,4.9583,342800.0,<1H OCEAN
+-118.85,34.2,28.0,2040.0,297.0,848.0,280.0,6.7612,323700.0,<1H OCEAN
+-118.43,34.17,33.0,1679.0,404.0,933.0,412.0,2.6979,266000.0,<1H OCEAN
+-116.44,33.77,18.0,4872.0,1110.0,955.0,656.0,2.2439,97500.0,INLAND
+-118.45,37.37,26.0,3135.0,524.0,1385.0,523.0,4.337,139700.0,INLAND
+-118.42,33.75,22.0,17591.0,2604.0,6897.0,2492.0,8.2831,500001.0,NEAR OCEAN
+-122.04,36.97,45.0,1302.0,245.0,621.0,258.0,5.1806,266400.0,NEAR OCEAN
+-117.13,32.75,20.0,2271.0,602.0,992.0,520.0,2.2599,157600.0,NEAR OCEAN
+-117.02,32.7,18.0,1643.0,283.0,1134.0,269.0,5.1769,133000.0,NEAR OCEAN
+-117.24,34.13,26.0,3774.0,716.0,1913.0,620.0,3.3534,98900.0,INLAND
+-122.02,37.6,31.0,2155.0,522.0,1858.0,437.0,2.652,159800.0,NEAR BAY
+-119.77,36.79,34.0,2679.0,460.0,1141.0,470.0,3.2642,89600.0,INLAND
+-117.95,33.86,36.0,2038.0,343.0,1066.0,346.0,5.197,195700.0,<1H OCEAN
+-118.23,34.12,28.0,1546.0,465.0,974.0,408.0,2.2843,183800.0,<1H OCEAN
+-122.07,37.64,22.0,5861.0,1516.0,5436.0,1463.0,2.5158,134900.0,NEAR BAY
+-121.39,39.39,52.0,189.0,34.0,121.0,37.0,3.0208,60000.0,INLAND
+-116.46,33.82,6.0,4863.0,920.0,3010.0,828.0,3.9508,104200.0,INLAND
+-119.76,36.8,20.0,6257.0,1346.0,2795.0,1267.0,2.2094,83700.0,INLAND
+-119.77,36.73,45.0,1081.0,241.0,821.0,230.0,1.7829,52600.0,INLAND
+-117.23,33.94,7.0,13195.0,2696.0,6763.0,2437.0,3.5851,142000.0,INLAND
+-118.29,33.73,21.0,2492.0,711.0,1699.0,672.0,2.1382,242300.0,NEAR OCEAN
+-121.3,38.66,21.0,3824.0,634.0,1818.0,600.0,3.712,139000.0,INLAND
+-122.31,37.94,38.0,2172.0,403.0,945.0,384.0,4.3958,194200.0,NEAR BAY
+-115.46,33.19,33.0,1234.0,373.0,777.0,298.0,1.0,40000.0,INLAND
+-117.01,33.97,18.0,4775.0,886.0,1868.0,836.0,2.3355,118800.0,INLAND
+-117.32,34.12,37.0,2868.0,574.0,2055.0,563.0,2.3508,70500.0,INLAND
+-122.0,38.99,39.0,1548.0,323.0,815.0,286.0,2.9489,67500.0,INLAND
+-121.13,38.66,2.0,12360.0,1747.0,4438.0,1470.0,6.2503,222500.0,INLAND
+-119.26,34.46,30.0,3826.0,691.0,1656.0,657.0,4.0074,434700.0,<1H OCEAN
+-118.01,33.73,23.0,4095.0,578.0,1766.0,589.0,6.7418,302500.0,<1H OCEAN
+-117.53,33.97,29.0,1430.0,273.0,872.0,283.0,4.0833,141000.0,INLAND
+-122.01,37.83,30.0,3917.0,549.0,1330.0,544.0,6.5617,386600.0,NEAR BAY
+-119.23,35.74,16.0,2275.0,659.0,1914.0,614.0,2.033,68400.0,INLAND
+-118.35,34.0,40.0,2894.0,395.0,1063.0,409.0,6.939,372000.0,<1H OCEAN
+-121.42,38.63,42.0,2217.0,536.0,1203.0,507.0,1.9412,73100.0,INLAND
+-117.25,33.95,5.0,13096.0,2208.0,6780.0,2180.0,4.2775,138700.0,INLAND
+-117.04,34.0,21.0,4624.0,852.0,2174.0,812.0,3.5255,132100.0,INLAND
+-118.27,34.92,20.0,873.0,175.0,422.0,159.0,2.9583,91700.0,INLAND
+-124.06,41.13,22.0,3263.0,799.0,1384.0,578.0,2.4708,119400.0,NEAR OCEAN
+-116.98,32.74,17.0,3943.0,843.0,1995.0,766.0,2.6944,158300.0,<1H OCEAN
+-119.15,34.44,33.0,2005.0,392.0,1043.0,351.0,5.308,297900.0,<1H OCEAN
+-118.23,34.22,36.0,2288.0,439.0,1079.0,434.0,4.5486,361000.0,<1H OCEAN
+-121.27,37.56,31.0,1223.0,330.0,1067.0,245.0,2.8558,100000.0,INLAND
+-117.95,33.82,35.0,1068.0,190.0,514.0,174.0,4.0735,208700.0,<1H OCEAN
+-121.33,38.02,31.0,1466.0,,608.0,254.0,3.1827,162100.0,INLAND
+-118.1,34.13,44.0,1745.0,237.0,693.0,248.0,9.7912,500001.0,<1H OCEAN
+-117.81,33.79,23.0,3114.0,610.0,2045.0,577.0,3.75,211900.0,<1H OCEAN
+-121.27,38.65,25.0,2787.0,601.0,1247.0,522.0,2.9016,159800.0,INLAND
+-117.99,34.16,40.0,3838.0,696.0,1851.0,674.0,4.2407,262000.0,INLAND
+-118.16,33.89,6.0,1655.0,536.0,1201.0,487.0,1.7344,145800.0,<1H OCEAN
+-117.07,32.63,40.0,1706.0,322.0,796.0,303.0,3.5583,154900.0,NEAR OCEAN
+-118.28,33.96,23.0,1983.0,611.0,2048.0,600.0,1.5313,91400.0,<1H OCEAN
+-118.36,34.05,48.0,1825.0,404.0,728.0,363.0,3.3824,322600.0,<1H OCEAN
+-118.0,33.81,13.0,2782.0,605.0,1749.0,628.0,4.1276,153800.0,<1H OCEAN
+-121.63,36.67,34.0,2486.0,560.0,2443.0,557.0,2.5263,130400.0,<1H OCEAN
+-118.22,34.19,36.0,959.0,204.0,446.0,210.0,3.215,331300.0,<1H OCEAN
+-120.06,36.95,24.0,646.0,134.0,454.0,149.0,2.125,61900.0,INLAND
+-118.18,34.02,33.0,832.0,226.0,987.0,220.0,3.0972,125000.0,<1H OCEAN
+-122.58,38.15,9.0,1302.0,177.0,682.0,190.0,7.5,423200.0,<1H OCEAN
+-122.41,37.59,40.0,2401.0,383.0,894.0,356.0,5.6493,422400.0,NEAR OCEAN
+-118.2,33.96,44.0,2144.0,477.0,1760.0,452.0,2.3221,161600.0,<1H OCEAN
+-122.24,37.85,52.0,3726.0,474.0,1366.0,496.0,9.3959,500001.0,NEAR BAY
+-118.34,33.9,37.0,542.0,105.0,355.0,118.0,5.5133,227300.0,<1H OCEAN
+-122.2,37.8,46.0,2041.0,405.0,1059.0,399.0,3.8487,203300.0,NEAR BAY
+-122.33,37.58,28.0,2784.0,736.0,1534.0,685.0,3.38,285400.0,NEAR OCEAN
+-115.22,33.54,18.0,1706.0,397.0,3424.0,283.0,1.625,53500.0,INLAND
+-119.66,36.35,15.0,1724.0,374.0,947.0,391.0,3.1094,91900.0,INLAND
+-119.35,36.2,29.0,1938.0,404.0,1487.0,414.0,1.7462,51100.0,INLAND
+-117.94,33.92,32.0,1053.0,207.0,1038.0,222.0,4.6696,165500.0,<1H OCEAN
+-118.26,33.79,42.0,1162.0,264.0,1044.0,241.0,3.5488,205600.0,<1H OCEAN
+-118.91,35.4,10.0,3587.0,774.0,1398.0,763.0,2.569,113000.0,INLAND
+-122.5,37.74,45.0,1771.0,349.0,1098.0,342.0,3.7552,296600.0,NEAR OCEAN
+-121.85,37.24,17.0,6425.0,1268.0,3934.0,1238.0,5.1228,237600.0,<1H OCEAN
+-118.27,34.01,42.0,990.0,289.0,1167.0,281.0,1.4524,126800.0,<1H OCEAN
+-118.27,33.94,30.0,1764.0,397.0,1406.0,362.0,1.449,93100.0,<1H OCEAN
+-121.35,38.68,18.0,7923.0,1558.0,3789.0,1473.0,3.5403,98600.0,INLAND
+-117.16,34.08,9.0,5306.0,993.0,2630.0,925.0,4.51,135800.0,INLAND
+-121.91,36.62,52.0,1220.0,267.0,488.0,265.0,3.7454,243800.0,<1H OCEAN
+-122.21,37.8,52.0,3519.0,711.0,1883.0,706.0,3.4861,187100.0,NEAR BAY
+-122.39,37.76,52.0,3390.0,691.0,1645.0,596.0,3.7051,253700.0,NEAR BAY
+-117.18,34.3,33.0,399.0,87.0,71.0,27.0,1.875,71300.0,INLAND
+-117.88,33.82,26.0,1783.0,298.0,1048.0,306.0,6.0488,232000.0,<1H OCEAN
+-117.68,33.47,7.0,4458.0,731.0,1731.0,704.0,6.126,285600.0,<1H OCEAN
+-122.15,37.46,52.0,1803.0,257.0,683.0,259.0,10.9508,500001.0,NEAR BAY
+-121.99,37.37,27.0,1797.0,538.0,1610.0,531.0,4.2422,237500.0,<1H OCEAN
+-116.83,32.83,6.0,3123.0,495.0,1513.0,480.0,5.4288,167800.0,<1H OCEAN
+-120.63,35.59,9.0,5782.0,1184.0,3026.0,1130.0,2.6528,113500.0,<1H OCEAN
+-119.4,36.53,28.0,2201.0,429.0,1524.0,412.0,2.75,65000.0,INLAND
+-120.61,35.06,13.0,2364.0,421.0,1257.0,380.0,4.6167,273100.0,<1H OCEAN
+-119.03,35.45,14.0,3520.0,604.0,1748.0,582.0,4.3162,87100.0,INLAND
+-118.4,34.07,22.0,2170.0,593.0,850.0,520.0,2.9107,500001.0,<1H OCEAN
+-121.73,38.56,30.0,3306.0,629.0,1623.0,648.0,2.8614,145200.0,INLAND
+-118.18,33.77,36.0,1833.0,688.0,1128.0,620.0,1.1483,112500.0,NEAR OCEAN
+-117.23,32.86,16.0,1200.0,468.0,648.0,443.0,3.045,100000.0,NEAR OCEAN
+-119.33,36.32,23.0,3137.0,628.0,1446.0,548.0,2.5,85500.0,INLAND
+-122.47,38.5,23.0,2191.0,415.0,959.0,361.0,4.1993,214500.0,INLAND
+-121.44,37.76,5.0,7264.0,1285.0,3670.0,1146.0,5.0443,194800.0,INLAND
+-122.02,37.26,24.0,2411.0,299.0,847.0,299.0,10.2666,500001.0,<1H OCEAN
+-117.1,32.58,29.0,1061.0,202.0,759.0,206.0,4.8646,136800.0,NEAR OCEAN
+-118.13,33.79,20.0,6678.0,1797.0,3625.0,1599.0,3.7716,242900.0,NEAR OCEAN
+-117.21,32.8,34.0,1398.0,222.0,532.0,244.0,3.7102,289600.0,NEAR OCEAN
+-117.99,33.7,13.0,4013.0,903.0,1999.0,859.0,4.625,248800.0,<1H OCEAN
+-118.2,33.83,35.0,3737.0,613.0,1305.0,583.0,7.2096,490300.0,NEAR OCEAN
+-118.96,36.49,24.0,1268.0,269.0,636.0,183.0,1.742,118800.0,INLAND
+-117.87,34.11,23.0,4066.0,819.0,2105.0,737.0,4.6556,199600.0,<1H OCEAN
+-122.33,37.93,27.0,2158.0,424.0,1220.0,442.0,3.0156,111500.0,NEAR BAY
+-120.76,37.44,18.0,2003.0,398.0,1333.0,411.0,2.7562,90500.0,INLAND
+-117.99,33.68,13.0,4000.0,883.0,1999.0,881.0,4.7245,273600.0,<1H OCEAN
+-118.17,34.02,34.0,760.0,219.0,968.0,202.0,1.7813,145000.0,<1H OCEAN
+-117.05,33.11,18.0,4393.0,642.0,2095.0,677.0,5.4786,223500.0,<1H OCEAN
+-120.35,37.31,17.0,605.0,159.0,416.0,83.0,2.0,87500.0,INLAND
+-117.9,33.8,27.0,2176.0,442.0,1440.0,418.0,4.375,212500.0,<1H OCEAN
+-118.41,34.0,35.0,684.0,161.0,381.0,159.0,2.8393,272000.0,<1H OCEAN
+-122.04,37.96,27.0,2587.0,729.0,1500.0,623.0,1.837,175000.0,NEAR BAY
+-121.32,37.96,46.0,1832.0,365.0,975.0,373.0,2.0398,88100.0,INLAND
+-118.26,34.15,14.0,2981.0,894.0,1941.0,863.0,3.0,178600.0,<1H OCEAN
+-121.83,37.26,7.0,3609.0,751.0,1739.0,682.0,4.5033,213100.0,<1H OCEAN
+-122.02,37.84,34.0,1879.0,265.0,729.0,263.0,7.7072,443800.0,NEAR BAY
+-116.14,34.22,32.0,3298.0,1228.0,763.0,360.0,1.875,47800.0,INLAND
+-119.57,36.1,16.0,1461.0,400.0,1201.0,384.0,1.5727,54800.0,INLAND
+-118.2,33.96,37.0,2127.0,533.0,2021.0,480.0,2.9773,164600.0,<1H OCEAN
+-117.98,33.91,16.0,10621.0,1782.0,3836.0,1480.0,5.0923,257200.0,<1H OCEAN
+-122.32,37.57,42.0,2574.0,614.0,2377.0,588.0,3.2891,237900.0,NEAR OCEAN
+-122.27,37.56,17.0,3211.0,847.0,1553.0,812.0,4.9434,292100.0,NEAR BAY
+-122.51,37.99,32.0,4138.0,632.0,1541.0,626.0,5.5791,433300.0,NEAR BAY
+-122.42,37.76,37.0,1291.0,588.0,1846.0,557.0,1.3365,225000.0,NEAR BAY
+-122.35,37.96,36.0,2191.0,531.0,1563.0,524.0,2.5164,114200.0,NEAR BAY
+-118.32,33.97,46.0,1504.0,270.0,814.0,306.0,4.3919,157100.0,<1H OCEAN
+-117.04,32.68,13.0,2132.0,425.0,1345.0,432.0,4.0,89300.0,NEAR OCEAN
+-117.96,34.1,30.0,2775.0,657.0,2847.0,642.0,3.2266,141800.0,INLAND
+-117.85,33.86,18.0,329.0,72.0,209.0,71.0,4.6806,187500.0,<1H OCEAN
+-117.14,32.71,52.0,979.0,314.0,975.0,297.0,1.2375,100000.0,NEAR OCEAN
+-117.03,33.18,17.0,5391.0,886.0,2732.0,830.0,5.1771,212800.0,<1H OCEAN
+-118.49,34.25,30.0,2871.0,470.0,1335.0,458.0,5.0232,253900.0,<1H OCEAN
+-118.26,34.07,46.0,2341.0,703.0,2371.0,648.0,2.4038,181700.0,<1H OCEAN
+-117.86,33.67,16.0,20.0,5.0,15.0,5.0,3.875,450000.0,<1H OCEAN
+-118.11,33.75,24.0,1608.0,314.0,592.0,314.0,5.0926,390500.0,NEAR OCEAN
+-121.91,37.68,18.0,3631.0,547.0,1700.0,520.0,5.817,257300.0,<1H OCEAN
+-120.61,37.03,34.0,1841.0,354.0,1019.0,356.0,1.7841,67500.0,INLAND
+-121.35,38.54,12.0,16239.0,3358.0,8656.0,3234.0,3.5691,116300.0,INLAND
+-117.91,34.12,33.0,1391.0,309.0,1038.0,298.0,4.1944,149500.0,<1H OCEAN
+-117.87,34.07,21.0,4723.0,882.0,2210.0,768.0,3.8167,258700.0,<1H OCEAN
+-118.13,34.19,48.0,2539.0,425.0,930.0,364.0,4.7269,303900.0,INLAND
+-119.21,34.31,22.0,7548.0,1038.0,2855.0,1008.0,6.729,409300.0,NEAR OCEAN
+-117.63,33.47,4.0,1969.0,280.0,805.0,271.0,7.6012,310800.0,NEAR OCEAN
+-122.19,37.71,36.0,361.0,69.0,158.0,58.0,5.5461,262500.0,NEAR BAY
+-118.51,34.27,36.0,2276.0,429.0,1001.0,419.0,4.1042,252100.0,<1H OCEAN
+-121.44,38.49,31.0,4297.0,788.0,2083.0,771.0,3.3878,109300.0,INLAND
+-118.31,34.05,38.0,1864.0,515.0,1768.0,439.0,1.9336,190600.0,<1H OCEAN
+-121.45,37.75,15.0,3846.0,677.0,2360.0,635.0,4.6173,164800.0,INLAND
+-122.24,37.46,36.0,4686.0,781.0,2254.0,845.0,6.1043,343500.0,NEAR OCEAN
+-121.03,39.37,15.0,1337.0,326.0,1172.0,306.0,2.6341,85000.0,INLAND
+-122.08,37.37,29.0,1229.0,,707.0,194.0,7.1108,465000.0,NEAR BAY
+-122.86,40.56,12.0,1350.0,300.0,423.0,172.0,1.7393,81300.0,INLAND
+-118.14,33.94,31.0,2841.0,774.0,1612.0,708.0,2.9205,196600.0,<1H OCEAN
+-120.05,36.72,24.0,1961.0,422.0,1559.0,374.0,1.8299,57800.0,INLAND
+-118.0,34.13,24.0,2584.0,520.0,1869.0,503.0,3.2841,167000.0,INLAND
+-122.72,38.48,23.0,2296.0,356.0,902.0,334.0,6.0298,289100.0,<1H OCEAN
+-117.58,33.89,14.0,1731.0,404.0,1269.0,351.0,2.3654,107900.0,INLAND
+-117.89,34.07,32.0,2374.0,450.0,1580.0,427.0,3.8837,200300.0,<1H OCEAN
+-120.76,38.6,14.0,2925.0,625.0,1226.0,437.0,2.5865,133800.0,INLAND
+-117.21,32.75,27.0,2072.0,534.0,1118.0,510.0,2.8043,262100.0,NEAR OCEAN
+-118.4,33.89,36.0,2127.0,314.0,807.0,306.0,8.1596,500001.0,<1H OCEAN
+-117.91,34.05,35.0,3189.0,,1727.0,500.0,5.0758,211100.0,<1H OCEAN
+-118.29,33.89,33.0,2138.0,567.0,1072.0,528.0,2.7428,208900.0,<1H OCEAN
+-118.2,33.9,33.0,1435.0,322.0,1298.0,299.0,2.7813,105100.0,<1H OCEAN
+-117.1,32.75,21.0,2063.0,609.0,1686.0,558.0,1.4828,94800.0,NEAR OCEAN
+-124.08,40.87,29.0,1710.0,469.0,990.0,425.0,1.1479,101100.0,NEAR OCEAN
+-119.8,36.75,41.0,1659.0,466.0,1391.0,447.0,1.3527,61200.0,INLAND
+-117.04,32.65,8.0,8806.0,1401.0,3159.0,1059.0,4.2155,247800.0,NEAR OCEAN
+-119.69,34.43,37.0,2801.0,497.0,1150.0,476.0,5.8311,387700.0,<1H OCEAN
+-122.29,37.89,52.0,2269.0,380.0,1004.0,371.0,5.1696,261400.0,NEAR BAY
+-118.42,34.0,14.0,3771.0,1180.0,2355.0,978.0,3.1603,287500.0,<1H OCEAN
+-117.8,33.7,5.0,1549.0,378.0,735.0,355.0,5.2923,194000.0,<1H OCEAN
+-121.06,39.04,14.0,1651.0,279.0,633.0,261.0,4.2802,194800.0,INLAND
+-117.11,32.78,16.0,2470.0,830.0,1170.0,724.0,3.5562,73500.0,NEAR OCEAN
+-118.27,34.08,46.0,3007.0,854.0,2587.0,814.0,2.7179,184300.0,<1H OCEAN
+-118.99,35.37,36.0,832.0,198.0,814.0,174.0,1.4773,57400.0,INLAND
+-122.51,37.53,17.0,1574.0,262.0,672.0,241.0,7.2929,355800.0,NEAR OCEAN
+-118.29,34.08,49.0,649.0,315.0,987.0,329.0,1.6806,316700.0,<1H OCEAN
+-119.36,36.21,25.0,1170.0,259.0,804.0,257.0,1.3889,50200.0,INLAND
+-118.13,34.2,45.0,1213.0,206.0,529.0,231.0,5.6629,234000.0,INLAND
+-121.46,38.58,52.0,4408.0,807.0,1604.0,777.0,3.8914,181600.0,INLAND
+-117.9,33.67,26.0,2507.0,393.0,1333.0,392.0,6.1601,266100.0,<1H OCEAN
+-118.43,34.17,37.0,1982.0,331.0,794.0,340.0,5.9275,336900.0,<1H OCEAN
+-117.85,33.83,26.0,1904.0,292.0,945.0,303.0,5.6784,232400.0,<1H OCEAN
+-118.13,33.93,34.0,2122.0,517.0,1578.0,488.0,3.1496,191900.0,<1H OCEAN
+-118.5,34.03,52.0,1711.0,245.0,671.0,242.0,7.7572,500001.0,<1H OCEAN
+-117.1,32.68,47.0,1044.0,274.0,1003.0,280.0,1.7802,97800.0,NEAR OCEAN
+-116.14,34.45,12.0,8796.0,1721.0,11139.0,1680.0,2.2612,137500.0,INLAND
+-118.05,33.95,33.0,1954.0,390.0,1600.0,376.0,3.6125,170800.0,<1H OCEAN
+-117.87,34.09,36.0,1267.0,191.0,640.0,200.0,5.2405,220000.0,<1H OCEAN
+-118.39,34.0,35.0,1465.0,386.0,1104.0,345.0,4.056,339100.0,<1H OCEAN
+-117.12,32.75,37.0,2344.0,546.0,1134.0,513.0,2.4394,118300.0,NEAR OCEAN
+-121.41,38.64,38.0,1384.0,287.0,682.0,280.0,1.9167,64400.0,INLAND
+-122.47,37.71,37.0,1046.0,251.0,822.0,239.0,3.5,224400.0,NEAR OCEAN
+-120.84,35.32,17.0,4197.0,802.0,1656.0,732.0,3.526,183900.0,NEAR OCEAN
+-119.23,34.3,18.0,1713.0,244.0,690.0,239.0,6.9483,404300.0,NEAR OCEAN
+-118.11,34.17,46.0,2837.0,592.0,1453.0,549.0,3.1115,234600.0,INLAND
+-118.13,33.84,35.0,3008.0,674.0,1584.0,671.0,3.5465,213200.0,<1H OCEAN
+-117.54,34.47,4.0,6712.0,1200.0,3126.0,1026.0,3.2277,126500.0,INLAND
+-118.14,33.95,36.0,1942.0,355.0,891.0,348.0,3.6635,282100.0,<1H OCEAN
+-121.94,37.26,21.0,3843.0,716.0,1850.0,705.0,4.6758,264200.0,<1H OCEAN
+-121.91,39.14,45.0,845.0,155.0,343.0,136.0,2.125,62000.0,INLAND
+-120.95,37.66,16.0,4478.0,647.0,1990.0,672.0,5.1473,188400.0,INLAND
+-122.5,38.35,25.0,1566.0,352.0,784.0,362.0,3.075,165100.0,<1H OCEAN
+-116.55,33.84,28.0,2992.0,562.0,676.0,346.0,5.7613,500001.0,INLAND
+-117.85,33.88,14.0,4753.0,681.0,2138.0,678.0,7.3658,288500.0,<1H OCEAN
+-118.55,34.24,21.0,5751.0,1082.0,2230.0,1016.0,4.3458,407500.0,<1H OCEAN
+-118.15,34.06,28.0,3855.0,922.0,2517.0,874.0,3.505,204300.0,<1H OCEAN
+-117.17,32.77,6.0,3856.0,875.0,1547.0,816.0,4.5481,164800.0,NEAR OCEAN
+-118.02,33.92,34.0,2169.0,418.0,1169.0,406.0,3.2222,218700.0,<1H OCEAN
+-118.41,33.77,22.0,7554.0,991.0,2808.0,946.0,10.06,500001.0,NEAR OCEAN
+-117.12,32.77,43.0,2167.0,464.0,977.0,461.0,3.125,192200.0,NEAR OCEAN
+-122.02,37.98,37.0,1474.0,343.0,782.0,331.0,3.4187,161700.0,NEAR BAY
+-117.64,34.06,50.0,637.0,143.0,590.0,147.0,1.9659,85700.0,INLAND
+-118.26,33.82,28.0,5091.0,1074.0,4753.0,1033.0,3.6477,117400.0,<1H OCEAN
+-122.03,38.24,16.0,1104.0,164.0,495.0,156.0,5.4074,157700.0,INLAND
+-121.81,37.23,19.0,2635.0,427.0,1497.0,410.0,6.3178,248000.0,<1H OCEAN
+-122.42,37.81,52.0,1314.0,317.0,473.0,250.0,4.3472,500001.0,NEAR BAY
+-117.21,32.83,28.0,3241.0,533.0,1334.0,513.0,4.1806,199600.0,NEAR OCEAN
+-122.02,37.59,18.0,1165.0,333.0,855.0,319.0,3.6923,213200.0,NEAR BAY
+-121.82,37.38,32.0,3747.0,665.0,1687.0,649.0,5.4949,330800.0,<1H OCEAN
+-122.42,37.74,52.0,1540.0,370.0,1136.0,363.0,4.3125,243000.0,NEAR BAY
+-117.97,33.93,35.0,1887.0,328.0,989.0,351.0,4.1321,198100.0,<1H OCEAN
+-121.36,38.04,9.0,2167.0,370.0,1290.0,351.0,5.0285,148200.0,INLAND
+-117.85,33.61,14.0,4340.0,741.0,1505.0,670.0,7.5674,500001.0,<1H OCEAN
+-117.59,33.41,17.0,2248.0,448.0,878.0,423.0,5.1298,246000.0,NEAR OCEAN
+-122.05,37.36,27.0,2621.0,513.0,1063.0,523.0,3.9848,409700.0,<1H OCEAN
+-117.11,32.74,25.0,684.0,190.0,665.0,187.0,2.4524,90300.0,NEAR OCEAN
+-117.91,33.82,32.0,2696.0,640.0,2330.0,626.0,2.9479,184600.0,<1H OCEAN
+-118.18,34.02,37.0,2631.0,734.0,3228.0,701.0,2.15,132200.0,<1H OCEAN
+-120.93,39.9,20.0,1511.0,328.0,791.0,320.0,2.0221,70900.0,INLAND
+-121.07,39.23,39.0,2099.0,433.0,929.0,423.0,1.9886,113800.0,INLAND
+-118.26,34.02,41.0,848.0,323.0,1428.0,313.0,1.5603,109600.0,<1H OCEAN
+-122.61,38.24,17.0,1728.0,271.0,897.0,284.0,3.4896,185900.0,<1H OCEAN
+-122.27,38.31,44.0,3030.0,589.0,1373.0,582.0,2.9054,155200.0,NEAR BAY
+-122.05,37.37,27.0,3885.0,661.0,1537.0,606.0,6.6085,344700.0,<1H OCEAN
+-118.24,33.96,30.0,859.0,221.0,912.0,191.0,1.9041,105100.0,<1H OCEAN
+-122.08,37.67,29.0,493.0,168.0,233.0,152.0,0.9637,160000.0,NEAR BAY
+-120.07,37.34,16.0,1667.0,372.0,762.0,283.0,1.75,87500.0,INLAND
+-118.33,33.86,38.0,1059.0,218.0,561.0,205.0,4.0,248600.0,<1H OCEAN
+-122.45,37.71,41.0,1578.0,351.0,1159.0,299.0,3.9167,243600.0,NEAR OCEAN
+-118.09,34.09,40.0,855.0,208.0,745.0,222.0,3.0125,224000.0,<1H OCEAN
+-117.94,33.84,28.0,604.0,207.0,615.0,212.0,3.6214,182100.0,<1H OCEAN
+-118.46,34.17,22.0,6707.0,1737.0,2620.0,1610.0,3.1478,273700.0,<1H OCEAN
+-117.67,33.57,18.0,1614.0,210.0,692.0,209.0,7.9294,280300.0,<1H OCEAN
+-118.19,34.03,42.0,2250.0,629.0,2588.0,609.0,1.9719,134200.0,<1H OCEAN
+-122.45,37.74,52.0,1596.0,276.0,642.0,273.0,4.375,349500.0,NEAR BAY
+-121.99,38.27,16.0,4138.0,758.0,1762.0,723.0,3.1979,137500.0,INLAND
+-121.28,38.71,8.0,4053.0,912.0,2033.0,897.0,2.8973,117100.0,INLAND
+-117.13,32.94,15.0,4846.0,825.0,2797.0,823.0,4.9375,180400.0,<1H OCEAN
+-122.03,37.83,24.0,5948.0,738.0,1997.0,710.0,9.8708,500001.0,NEAR BAY
+-118.27,34.09,48.0,1527.0,295.0,589.0,286.0,4.1667,344800.0,<1H OCEAN
+-118.42,33.98,3.0,475.0,155.0,236.0,153.0,3.6667,450000.0,<1H OCEAN
+-121.37,38.63,30.0,5996.0,1018.0,2532.0,1049.0,4.6127,151800.0,INLAND
+-118.26,33.95,38.0,1387.0,346.0,1240.0,355.0,1.6898,95100.0,<1H OCEAN
+-118.17,34.06,36.0,871.0,201.0,2862.0,181.0,2.1845,123800.0,<1H OCEAN
+-118.4,34.15,44.0,2515.0,510.0,967.0,484.0,5.0754,374500.0,<1H OCEAN
+-117.6,33.87,18.0,6450.0,1165.0,3716.0,1113.0,4.2721,150300.0,INLAND
+-121.3,38.09,31.0,335.0,53.0,154.0,55.0,2.0694,175000.0,INLAND
+-117.47,33.91,21.0,3491.0,760.0,1920.0,669.0,2.2241,127300.0,INLAND
+-121.79,37.66,22.0,14701.0,2210.0,6693.0,2232.0,5.98,245000.0,INLAND
+-118.44,34.22,41.0,1582.0,399.0,1159.0,378.0,2.825,168600.0,<1H OCEAN
+-117.76,33.84,15.0,3764.0,510.0,1448.0,468.0,8.7124,410500.0,<1H OCEAN
+-117.75,33.64,9.0,2499.0,492.0,1111.0,542.0,5.5342,182300.0,<1H OCEAN
+-121.84,39.73,52.0,502.0,100.0,311.0,100.0,1.5481,200000.0,INLAND
+-118.09,33.93,36.0,1585.0,323.0,1205.0,343.0,4.5306,183400.0,<1H OCEAN
+-120.9,37.81,27.0,4213.0,750.0,2142.0,746.0,3.7031,173300.0,INLAND
+-117.17,32.78,17.0,3845.0,1051.0,3102.0,944.0,2.3658,164100.0,NEAR OCEAN
+-117.19,34.05,52.0,1949.0,432.0,767.0,392.0,2.5143,117600.0,INLAND
+-116.99,32.76,21.0,3833.0,595.0,1645.0,589.0,4.625,273500.0,<1H OCEAN
+-117.99,33.69,12.0,2480.0,858.0,1441.0,788.0,1.6705,350000.0,<1H OCEAN
+-122.31,38.32,33.0,2463.0,421.0,1235.0,465.0,3.7045,161500.0,NEAR BAY
+-122.62,38.95,19.0,2230.0,538.0,832.0,359.0,1.6865,58800.0,INLAND
+-121.34,38.61,20.0,5801.0,1148.0,2586.0,1063.0,3.9063,162100.0,INLAND
+-117.39,34.11,5.0,2987.0,457.0,1821.0,485.0,4.8889,138900.0,INLAND
+-118.48,34.16,32.0,2108.0,309.0,769.0,274.0,8.7172,500001.0,<1H OCEAN
+-118.38,33.85,28.0,4430.0,928.0,2131.0,885.0,4.9384,378100.0,<1H OCEAN
+-118.01,33.69,3.0,945.0,115.0,337.0,123.0,11.5199,500001.0,<1H OCEAN
+-120.99,37.68,28.0,3269.0,647.0,1595.0,617.0,2.2336,112700.0,INLAND
+-120.84,37.06,14.0,1506.0,380.0,1096.0,352.0,1.1301,78500.0,INLAND
+-122.21,38.28,35.0,1273.0,210.0,555.0,181.0,4.4861,269300.0,NEAR BAY
+-117.43,34.11,17.0,4109.0,884.0,2544.0,780.0,2.7757,109800.0,INLAND
+-120.46,34.64,26.0,2364.0,604.0,1520.0,529.0,1.9444,129400.0,NEAR OCEAN
+-118.1,33.91,36.0,1080.0,201.0,719.0,201.0,4.2679,175800.0,<1H OCEAN
+-117.0,32.64,11.0,3098.0,490.0,1391.0,484.0,4.9792,170400.0,NEAR OCEAN
+-118.13,34.01,40.0,2412.0,629.0,2119.0,600.0,2.075,151100.0,<1H OCEAN
+-123.34,39.1,24.0,5372.0,1051.0,3002.0,992.0,3.0652,131100.0,<1H OCEAN
+-122.26,37.98,28.0,2038.0,329.0,947.0,349.0,5.1178,198000.0,NEAR BAY
+-117.75,34.12,25.0,5411.0,998.0,2243.0,1019.0,4.3148,240700.0,INLAND
+-118.47,34.2,20.0,3939.0,1143.0,2475.0,1002.0,2.9025,229100.0,<1H OCEAN
+-117.76,33.72,11.0,4508.0,618.0,1993.0,573.0,10.4498,386100.0,<1H OCEAN
+-122.66,38.46,14.0,2364.0,631.0,1300.0,625.0,2.6023,221100.0,<1H OCEAN
+-118.48,34.28,35.0,2132.0,368.0,1128.0,341.0,5.3107,227100.0,<1H OCEAN
+-116.98,32.62,6.0,7995.0,1458.0,4771.0,1376.0,4.7068,184300.0,NEAR OCEAN
+-121.35,38.55,18.0,4481.0,780.0,2211.0,775.0,3.9934,123300.0,INLAND
+-121.36,38.63,30.0,2619.0,370.0,940.0,359.0,4.7283,164500.0,INLAND
+-117.27,34.12,52.0,954.0,246.0,943.0,256.0,0.8658,87500.0,INLAND
+-118.09,33.91,14.0,2369.0,604.0,1546.0,464.0,3.7969,159400.0,<1H OCEAN
+-116.02,34.18,8.0,569.0,97.0,312.0,96.0,4.3021,94500.0,INLAND
+-121.51,38.49,21.0,4426.0,790.0,1856.0,761.0,4.1,158300.0,INLAND
+-122.43,37.78,52.0,1952.0,628.0,1284.0,576.0,2.105,316700.0,NEAR BAY
+-118.22,34.05,41.0,1422.0,478.0,1640.0,434.0,1.6122,157100.0,<1H OCEAN
+-122.62,38.25,20.0,1888.0,411.0,826.0,396.0,2.875,189100.0,<1H OCEAN
+-119.82,36.79,35.0,1474.0,291.0,709.0,294.0,2.6522,65900.0,INLAND
+-118.28,33.93,41.0,936.0,257.0,913.0,226.0,2.0313,122600.0,<1H OCEAN
+-120.7,35.76,15.0,1914.0,425.0,1130.0,421.0,2.2165,90600.0,<1H OCEAN
+-118.24,33.85,25.0,9594.0,1489.0,5237.0,1496.0,5.9684,193300.0,<1H OCEAN
+-117.76,34.04,34.0,1914.0,,1564.0,328.0,2.8347,115800.0,INLAND
+-118.28,34.24,32.0,2542.0,526.0,1278.0,493.0,4.45,263600.0,<1H OCEAN
+-118.25,34.03,52.0,1274.0,418.0,1655.0,368.0,2.1905,124000.0,<1H OCEAN
+-118.64,34.19,30.0,2399.0,373.0,1062.0,377.0,6.0094,245600.0,<1H OCEAN
+-122.49,37.69,36.0,1344.0,258.0,782.0,265.0,4.5,275600.0,NEAR OCEAN
+-121.31,37.9,38.0,226.0,44.0,125.0,38.0,2.9,125000.0,INLAND
+-118.37,34.18,35.0,2949.0,794.0,2106.0,746.0,2.9228,177300.0,<1H OCEAN
+-122.3,38.3,44.0,3690.0,809.0,1922.0,736.0,2.6346,139800.0,NEAR BAY
+-118.9,34.19,26.0,1582.0,196.0,573.0,182.0,10.0595,500001.0,<1H OCEAN
+-118.27,34.05,25.0,1316.0,836.0,2796.0,784.0,1.7866,325000.0,<1H OCEAN
+-117.03,32.65,14.0,1111.0,142.0,472.0,145.0,7.6344,290500.0,NEAR OCEAN
+-119.79,36.79,19.0,1524.0,448.0,960.0,386.0,1.5122,47500.0,INLAND
+-119.75,36.78,33.0,1145.0,197.0,508.0,198.0,2.3333,81300.0,INLAND
+-121.28,37.95,49.0,1200.0,364.0,1448.0,318.0,1.1094,52500.0,INLAND
+-118.3,34.03,41.0,1653.0,426.0,1868.0,393.0,1.78,162500.0,<1H OCEAN
+-121.46,38.54,39.0,1453.0,324.0,843.0,281.0,1.7692,63900.0,INLAND
+-117.58,34.09,27.0,754.0,200.0,746.0,185.0,1.9531,100800.0,INLAND
+-122.49,37.63,31.0,3109.0,621.0,1472.0,618.0,5.155,263900.0,NEAR OCEAN
+-118.43,33.99,42.0,2558.0,558.0,1328.0,541.0,4.875,301300.0,<1H OCEAN
+-122.56,37.97,52.0,1833.0,324.0,735.0,306.0,4.6944,398900.0,NEAR BAY
+-124.03,40.45,34.0,1006.0,213.0,443.0,158.0,2.6094,71300.0,<1H OCEAN
+-117.36,34.48,3.0,16533.0,2549.0,7588.0,2285.0,3.9792,122100.0,INLAND
+-119.08,36.09,25.0,1880.0,339.0,1003.0,315.0,2.7298,103400.0,INLAND
+-118.27,34.13,47.0,1375.0,359.0,1512.0,418.0,2.1071,208900.0,<1H OCEAN
+-117.66,34.09,20.0,2462.0,496.0,1117.0,458.0,3.2321,162500.0,INLAND
+-117.25,34.51,7.0,3200.0,477.0,1522.0,470.0,4.6914,142200.0,INLAND
+-118.17,33.83,45.0,1808.0,315.0,800.0,302.0,4.8693,277700.0,NEAR OCEAN
+-118.13,33.9,36.0,1477.0,305.0,788.0,291.0,3.625,195800.0,<1H OCEAN
+-122.07,37.93,25.0,7201.0,1521.0,3264.0,1433.0,3.7433,252100.0,NEAR BAY
+-118.45,35.62,18.0,2304.0,527.0,782.0,390.0,1.4141,75800.0,INLAND
+-118.12,34.18,44.0,2357.0,342.0,891.0,337.0,6.3467,352700.0,INLAND
+-121.28,37.83,32.0,696.0,151.0,443.0,144.0,2.5156,86300.0,INLAND
+-122.16,37.76,36.0,2781.0,574.0,1438.0,519.0,2.4598,155500.0,NEAR BAY
+-117.96,34.03,35.0,1623.0,331.0,1462.0,312.0,3.9803,152600.0,<1H OCEAN
+-118.08,33.89,41.0,834.0,166.0,603.0,179.0,3.7321,167500.0,<1H OCEAN
+-117.8,33.79,13.0,2021.0,362.0,1081.0,341.0,4.3269,231400.0,<1H OCEAN
+-117.2,32.84,32.0,2033.0,394.0,989.0,389.0,3.2583,181400.0,NEAR OCEAN
+-121.89,37.21,14.0,5636.0,697.0,2281.0,680.0,8.4262,459200.0,<1H OCEAN
+-122.26,38.0,14.0,2338.0,391.0,1003.0,398.0,4.2269,170500.0,NEAR BAY
+-118.29,33.88,27.0,2082.0,612.0,2009.0,548.0,2.9063,184100.0,<1H OCEAN
+-121.32,38.7,16.0,2966.0,578.0,1365.0,480.0,3.2444,118400.0,INLAND
+-118.16,33.92,36.0,2062.0,351.0,1134.0,358.0,4.4881,218900.0,<1H OCEAN
+-122.26,37.54,13.0,1422.0,295.0,395.0,195.0,5.3247,327800.0,NEAR BAY
+-121.19,38.04,35.0,703.0,117.0,290.0,107.0,3.225,177100.0,INLAND
+-122.24,37.43,36.0,2410.0,361.0,934.0,377.0,7.652,500001.0,NEAR OCEAN
+-117.24,33.21,9.0,2486.0,626.0,1938.0,525.0,2.1293,151400.0,<1H OCEAN
+-118.41,34.04,52.0,1907.0,261.0,681.0,249.0,10.9805,500001.0,<1H OCEAN
+-117.11,32.82,16.0,3980.0,682.0,3174.0,647.0,2.6607,175000.0,NEAR OCEAN
+-122.0,37.36,17.0,2070.0,,797.0,275.0,8.6155,411200.0,<1H OCEAN
+-119.72,36.72,15.0,1713.0,246.0,766.0,232.0,6.8162,127200.0,INLAND
+-117.14,34.05,5.0,2634.0,359.0,1173.0,372.0,6.746,204100.0,INLAND
+-122.45,37.72,51.0,2690.0,554.0,1795.0,539.0,3.6581,225000.0,NEAR BAY
+-121.31,38.66,26.0,1604.0,245.0,751.0,267.0,4.7381,140500.0,INLAND
+-118.15,34.7,36.0,2696.0,454.0,1192.0,452.0,3.9615,116300.0,INLAND
+-117.92,33.94,27.0,4566.0,620.0,2045.0,664.0,5.583,267700.0,<1H OCEAN
+-121.86,37.26,16.0,2814.0,485.0,1305.0,465.0,5.5121,224100.0,<1H OCEAN
+-118.94,37.13,12.0,2255.0,472.0,1006.0,334.0,4.1563,94000.0,INLAND
+-117.13,34.88,21.0,3254.0,669.0,1548.0,545.0,2.3373,57100.0,INLAND
+-118.37,33.86,28.0,2685.0,581.0,1243.0,529.0,4.119,324000.0,<1H OCEAN
+-117.18,32.74,20.0,1165.0,269.0,459.0,244.0,3.175,191700.0,NEAR OCEAN
+-121.29,38.71,32.0,1875.0,361.0,1027.0,343.0,3.5769,103800.0,INLAND
+-120.92,37.63,39.0,45.0,8.0,22.0,9.0,1.7679,450000.0,INLAND
+-118.29,33.99,39.0,979.0,235.0,857.0,236.0,2.5547,108900.0,<1H OCEAN
+-124.16,40.6,39.0,1322.0,283.0,642.0,292.0,2.4519,85100.0,NEAR OCEAN
+-117.85,33.79,52.0,1963.0,430.0,1197.0,415.0,3.8929,211000.0,<1H OCEAN
+-118.19,33.79,29.0,3497.0,1096.0,2994.0,919.0,1.8109,137500.0,NEAR OCEAN
+-118.28,33.74,40.0,1751.0,512.0,1939.0,503.0,1.5394,200000.0,NEAR OCEAN
+-122.2,37.83,26.0,1589.0,223.0,542.0,211.0,8.401,351200.0,NEAR BAY
+-117.66,34.14,11.0,3628.0,469.0,1488.0,463.0,7.0844,325000.0,INLAND
+-119.77,36.83,19.0,3237.0,507.0,1378.0,510.0,4.7804,101100.0,INLAND
+-118.04,33.93,36.0,1045.0,239.0,1165.0,230.0,3.1979,161800.0,<1H OCEAN
+-121.68,36.67,26.0,5745.0,1017.0,2780.0,996.0,5.0,202300.0,<1H OCEAN
+-117.66,33.61,21.0,1932.0,266.0,860.0,286.0,7.1497,274000.0,<1H OCEAN
+-120.14,36.04,27.0,2533.0,518.0,1371.0,461.0,2.9708,60900.0,INLAND
+-116.9,34.69,10.0,337.0,102.0,108.0,50.0,0.4999,55000.0,INLAND
+-122.28,37.77,52.0,1468.0,363.0,870.0,347.0,2.9688,220800.0,NEAR BAY
+-122.28,37.87,46.0,1777.0,446.0,805.0,431.0,2.8676,212000.0,NEAR BAY
+-121.03,39.05,12.0,1875.0,307.0,806.0,283.0,3.9185,195200.0,INLAND
+-118.32,34.2,36.0,2110.0,346.0,984.0,342.0,6.9909,345300.0,<1H OCEAN
+-122.31,37.53,41.0,1608.0,269.0,676.0,267.0,4.6125,361700.0,NEAR OCEAN
+-118.13,33.81,34.0,1903.0,343.0,928.0,349.0,5.395,241900.0,<1H OCEAN
+-118.28,33.92,37.0,1761.0,409.0,1309.0,357.0,2.1875,175900.0,<1H OCEAN
+-122.04,37.24,24.0,1521.0,209.0,539.0,192.0,11.1557,500001.0,<1H OCEAN
+-119.73,36.77,24.0,4410.0,939.0,2362.0,862.0,2.9406,73000.0,INLAND
+-118.25,34.07,43.0,764.0,322.0,1275.0,306.0,2.0,175000.0,<1H OCEAN
+-118.28,34.11,52.0,1803.0,437.0,787.0,388.0,4.5781,360500.0,<1H OCEAN
+-121.99,38.48,17.0,1824.0,348.0,934.0,305.0,4.6719,250000.0,INLAND
+-118.07,33.88,16.0,4934.0,825.0,2668.0,810.0,5.748,284200.0,<1H OCEAN
+-121.22,37.79,38.0,2152.0,451.0,1320.0,457.0,2.5025,101900.0,INLAND
+-117.86,34.01,16.0,4632.0,,3038.0,727.0,5.1762,264400.0,<1H OCEAN
+-122.09,37.4,36.0,1575.0,379.0,1036.0,382.0,5.1408,264700.0,NEAR BAY
+-118.29,34.02,21.0,1641.0,491.0,1526.0,453.0,2.087,141300.0,<1H OCEAN
+-119.08,35.35,20.0,892.0,129.0,331.0,135.0,7.1837,176300.0,INLAND
+-118.17,33.89,52.0,63.0,12.0,47.0,8.0,7.2423,350000.0,<1H OCEAN
+-120.4,34.87,10.0,2197.0,329.0,1064.0,319.0,4.9766,199600.0,<1H OCEAN
+-118.15,33.84,36.0,2987.0,491.0,1360.0,497.0,4.8013,224100.0,<1H OCEAN
+-117.72,33.61,26.0,2033.0,463.0,618.0,450.0,2.5685,80400.0,<1H OCEAN
+-121.29,37.99,30.0,1271.0,528.0,2019.0,524.0,1.5152,81300.0,INLAND
+-117.22,32.73,38.0,3966.0,768.0,1640.0,729.0,3.8409,291400.0,NEAR OCEAN
+-121.37,38.67,36.0,1786.0,338.0,974.0,319.0,2.555,72700.0,INLAND
+-122.09,37.7,33.0,4413.0,1107.0,2239.0,1051.0,2.9861,208200.0,NEAR BAY
+-118.48,34.23,30.0,1762.0,263.0,761.0,292.0,6.5268,273100.0,<1H OCEAN
+-122.41,37.6,26.0,2754.0,402.0,1128.0,395.0,6.3719,466900.0,NEAR OCEAN
+-122.37,37.58,52.0,2188.0,361.0,917.0,357.0,4.4,500000.0,NEAR OCEAN
+-118.22,34.1,33.0,1903.0,386.0,1187.0,340.0,4.0469,196600.0,<1H OCEAN
+-121.9,37.33,34.0,197.0,44.0,152.0,47.0,4.05,200000.0,<1H OCEAN
+-117.09,32.76,31.0,2567.0,624.0,1255.0,582.0,2.5909,159100.0,NEAR OCEAN
+-121.94,37.34,36.0,3142.0,632.0,1372.0,560.0,5.0175,246100.0,<1H OCEAN
+-118.17,34.18,44.0,1401.0,246.0,607.0,271.0,2.8472,218800.0,<1H OCEAN
+-121.03,37.67,24.0,2162.0,459.0,1468.0,441.0,3.1857,98300.0,INLAND
+-121.24,38.72,12.0,3605.0,576.0,1556.0,549.0,4.9,203700.0,INLAND
+-117.35,33.97,27.0,3960.0,886.0,2807.0,838.0,3.024,122500.0,INLAND
+-117.65,33.58,2.0,2411.0,354.0,703.0,217.0,7.8061,331400.0,<1H OCEAN
+-122.42,37.79,52.0,3511.0,1232.0,2452.0,1131.0,2.5013,275000.0,NEAR BAY
+-118.17,33.82,52.0,2539.0,497.0,1152.0,488.0,4.1354,268200.0,NEAR OCEAN
+-117.61,33.63,2.0,4678.0,817.0,1970.0,712.0,6.1078,219000.0,<1H OCEAN
+-121.84,36.6,30.0,2958.0,691.0,1616.0,666.0,3.4643,191800.0,<1H OCEAN
+-117.1,32.75,20.0,2355.0,722.0,1848.0,576.0,2.0036,99200.0,NEAR OCEAN
+-118.34,34.21,36.0,1834.0,316.0,864.0,309.0,4.7885,302200.0,<1H OCEAN
+-121.81,37.25,5.0,1975.0,520.0,861.0,440.0,4.4565,159000.0,<1H OCEAN
+-122.12,37.69,10.0,2227.0,560.0,1140.0,472.0,2.3973,167300.0,NEAR BAY
+-122.18,37.77,51.0,2107.0,471.0,1173.0,438.0,3.2552,120100.0,NEAR BAY
+-119.17,34.17,21.0,2361.0,464.0,1146.0,396.0,3.6597,195100.0,NEAR OCEAN
+-118.03,33.97,39.0,1996.0,389.0,1029.0,387.0,4.65,224300.0,<1H OCEAN
+-118.53,34.16,32.0,3554.0,762.0,1623.0,750.0,3.6141,290600.0,<1H OCEAN
+-117.96,33.68,24.0,6517.0,1279.0,3441.0,1198.0,4.25,152100.0,<1H OCEAN
+-121.21,37.81,8.0,1883.0,298.0,999.0,301.0,5.193,172100.0,INLAND
+-117.64,33.45,27.0,334.0,56.0,130.0,46.0,4.875,284100.0,NEAR OCEAN
+-117.27,32.98,17.0,1853.0,392.0,351.0,208.0,5.2742,230700.0,NEAR OCEAN
+-118.3,33.93,40.0,2434.0,477.0,1646.0,453.0,3.2024,128000.0,<1H OCEAN
+-122.28,37.91,41.0,3009.0,482.0,1053.0,490.0,5.828,324400.0,NEAR BAY
+-118.34,34.13,45.0,2375.0,417.0,751.0,410.0,6.6739,500001.0,<1H OCEAN
+-117.99,33.82,21.0,2281.0,557.0,1510.0,460.0,2.8625,189600.0,<1H OCEAN
+-119.03,35.42,38.0,2952.0,598.0,1491.0,568.0,2.6094,67900.0,INLAND
+-120.09,34.62,18.0,2708.0,382.0,988.0,359.0,5.5194,367000.0,NEAR OCEAN
+-122.04,38.27,16.0,8517.0,1910.0,4508.0,1837.0,3.1853,129600.0,INLAND
+-122.53,37.93,37.0,1722.0,352.0,648.0,337.0,4.125,310300.0,NEAR BAY
+-118.43,33.96,20.0,1901.0,270.0,704.0,254.0,8.7819,500001.0,<1H OCEAN
+-117.91,34.07,33.0,2938.0,561.0,1519.0,549.0,4.5594,204200.0,<1H OCEAN
+-117.99,33.67,15.0,3141.0,664.0,1729.0,633.0,4.2165,234600.0,<1H OCEAN
+-118.3,34.02,49.0,2120.0,483.0,1522.0,416.0,1.85,116800.0,<1H OCEAN
+-117.24,32.8,19.0,1863.0,497.0,868.0,503.0,2.288,210000.0,NEAR OCEAN
+-117.97,33.68,23.0,1722.0,316.0,865.0,309.0,4.6452,273800.0,<1H OCEAN
+-121.22,37.95,30.0,1055.0,211.0,629.0,170.0,2.8676,76900.0,INLAND
+-122.34,37.95,44.0,1675.0,317.0,806.0,311.0,3.0694,135300.0,NEAR BAY
+-118.42,34.16,46.0,54.0,9.0,20.0,6.0,0.536,375000.0,<1H OCEAN
+-121.35,38.7,5.0,14414.0,2979.0,7608.0,2832.0,3.5802,129600.0,INLAND
+-117.91,33.87,52.0,2031.0,506.0,1191.0,463.0,2.9076,177300.0,<1H OCEAN
+-119.33,36.31,15.0,1472.0,228.0,892.0,257.0,5.3909,113000.0,INLAND
+-117.25,34.16,31.0,1516.0,238.0,596.0,255.0,4.3362,159400.0,INLAND
+-118.65,34.18,27.0,1793.0,339.0,1016.0,326.0,4.925,240300.0,<1H OCEAN
+-122.42,37.8,52.0,3067.0,870.0,2122.0,850.0,2.5603,287500.0,NEAR BAY
+-116.95,33.75,19.0,2238.0,573.0,1190.0,507.0,2.0714,85800.0,INLAND
+-121.79,37.03,18.0,943.0,213.0,544.0,179.0,3.934,228600.0,<1H OCEAN
+-122.19,37.76,26.0,1293.0,297.0,984.0,303.0,1.9479,85800.0,NEAR BAY
+-121.92,37.53,7.0,28258.0,3864.0,12203.0,3701.0,8.4045,451100.0,<1H OCEAN
+-120.45,34.63,32.0,1840.0,309.0,828.0,333.0,4.5486,172400.0,NEAR OCEAN
+-121.86,37.36,31.0,1602.0,358.0,1179.0,354.0,4.4896,156800.0,<1H OCEAN
+-118.07,34.15,45.0,1095.0,237.0,672.0,234.0,3.4087,209200.0,INLAND
+-117.36,33.88,15.0,2857.0,421.0,1361.0,382.0,4.6875,189800.0,INLAND
+-117.3,34.52,34.0,4493.0,838.0,2335.0,779.0,3.1635,74300.0,INLAND
+-122.4,37.68,41.0,2267.0,486.0,1045.0,459.0,4.1146,272200.0,NEAR BAY
+-117.71,34.07,31.0,1840.0,380.0,1187.0,357.0,3.8875,129200.0,INLAND
+-116.92,33.63,18.0,397.0,89.0,239.0,80.0,2.8125,143800.0,INLAND
+-121.36,38.56,17.0,6225.0,938.0,3064.0,947.0,5.2881,138000.0,INLAND
+-121.94,38.27,35.0,10869.0,2226.0,9879.0,2152.0,2.5681,81300.0,INLAND
+-117.31,34.11,41.0,1105.0,257.0,816.0,197.0,1.9375,64100.0,INLAND
+-120.33,37.97,17.0,2530.0,526.0,1024.0,496.0,2.0057,118900.0,INLAND
+-120.43,34.96,24.0,1799.0,470.0,1416.0,408.0,2.0673,136900.0,<1H OCEAN
+-117.96,34.04,33.0,1458.0,268.0,1115.0,257.0,4.7955,158100.0,<1H OCEAN
+-121.38,38.66,17.0,3778.0,939.0,2393.0,862.0,1.8972,100500.0,INLAND
+-118.21,34.09,34.0,1660.0,412.0,1678.0,382.0,2.7708,148200.0,<1H OCEAN
+-119.88,34.42,22.0,2367.0,492.0,1333.0,488.0,3.6304,312200.0,NEAR OCEAN
+-117.06,32.68,36.0,3815.0,796.0,2945.0,728.0,2.0959,125000.0,NEAR OCEAN
+-121.92,37.32,28.0,2089.0,641.0,1666.0,587.0,2.3633,198400.0,<1H OCEAN
+-119.2,34.26,25.0,2203.0,367.0,1194.0,377.0,5.4087,223200.0,NEAR OCEAN
+-121.74,38.55,34.0,2299.0,579.0,1300.0,536.0,1.6435,148500.0,INLAND
+-118.3,34.19,14.0,3615.0,913.0,1924.0,852.0,3.5083,280900.0,<1H OCEAN
+-117.23,33.24,26.0,1991.0,330.0,1014.0,304.0,4.3068,240100.0,<1H OCEAN
+-118.15,33.78,35.0,2768.0,752.0,1277.0,651.0,3.6193,250000.0,NEAR OCEAN
+-121.93,37.27,28.0,3428.0,753.0,1753.0,729.0,4.1033,281000.0,<1H OCEAN
+-119.69,36.79,13.0,1736.0,313.0,993.0,314.0,3.7697,83600.0,INLAND
+-121.08,38.85,10.0,2509.0,422.0,1037.0,389.0,6.0,220100.0,INLAND
+-119.56,36.09,35.0,1648.0,285.0,792.0,265.0,3.2847,64700.0,INLAND
+-122.3,38.3,52.0,1128.0,207.0,450.0,197.0,3.3542,154600.0,NEAR BAY
+-117.71,34.06,27.0,2127.0,628.0,1970.0,534.0,1.4722,91300.0,INLAND
+-118.49,34.29,26.0,4516.0,611.0,1714.0,581.0,9.2873,431800.0,<1H OCEAN
+-118.11,33.85,36.0,887.0,163.0,482.0,157.0,4.125,219500.0,<1H OCEAN
+-117.63,34.09,8.0,3557.0,890.0,2251.0,765.0,2.6818,114100.0,INLAND
+-122.41,37.98,36.0,60.0,15.0,42.0,25.0,1.4583,67500.0,NEAR BAY
+-121.03,39.21,28.0,2843.0,535.0,1310.0,525.0,3.2337,123100.0,INLAND
+-118.37,33.98,39.0,303.0,69.0,131.0,73.0,4.3438,331800.0,<1H OCEAN
+-118.45,34.24,7.0,3299.0,794.0,2343.0,647.0,3.0865,205900.0,<1H OCEAN
+-122.08,37.68,26.0,2607.0,682.0,1401.0,607.0,2.6563,184100.0,NEAR BAY
+-117.89,33.61,42.0,1301.0,280.0,539.0,249.0,5.0,500001.0,<1H OCEAN
+-117.71,34.03,11.0,3467.0,749.0,2163.0,676.0,3.4267,164400.0,INLAND
+-118.18,33.8,15.0,2407.0,589.0,1591.0,506.0,3.0513,148100.0,NEAR OCEAN
+-118.18,33.78,17.0,1419.0,436.0,1300.0,360.0,2.0769,100000.0,NEAR OCEAN
+-117.03,32.7,19.0,2304.0,572.0,2010.0,556.0,2.2866,109900.0,NEAR OCEAN
+-119.18,34.21,30.0,1096.0,231.0,741.0,229.0,3.8625,234700.0,NEAR OCEAN
+-118.44,35.75,23.0,3166.0,700.0,1097.0,493.0,2.6288,96000.0,INLAND
+-122.13,38.02,52.0,2378.0,508.0,940.0,451.0,2.9583,166000.0,NEAR BAY
+-118.19,33.77,52.0,1562.0,616.0,692.0,512.0,1.4048,200000.0,NEAR OCEAN
+-117.38,33.94,21.0,2468.0,380.0,1164.0,385.0,4.0625,136800.0,INLAND
+-123.01,38.67,33.0,914.0,147.0,394.0,132.0,4.6875,246200.0,<1H OCEAN
+-121.31,37.99,23.0,3135.0,707.0,1650.0,680.0,1.886,105300.0,INLAND
+-119.45,36.6,36.0,2294.0,489.0,1430.0,454.0,1.8975,60900.0,INLAND
+-122.29,37.89,52.0,2248.0,422.0,870.0,377.0,3.4732,246200.0,NEAR BAY
+-119.95,36.8,30.0,1233.0,214.0,620.0,199.0,3.4297,112500.0,INLAND
+-117.22,32.85,26.0,1647.0,261.0,694.0,259.0,4.6875,274400.0,NEAR OCEAN
+-117.95,33.85,13.0,6963.0,1426.0,3892.0,1375.0,4.1325,203500.0,<1H OCEAN
+-118.43,34.26,30.0,1246.0,373.0,1990.0,369.0,3.5104,140900.0,<1H OCEAN
+-121.64,36.68,16.0,6568.0,1603.0,6012.0,1565.0,2.3463,156100.0,<1H OCEAN
+-118.01,33.95,37.0,1165.0,210.0,627.0,221.0,4.6923,181000.0,<1H OCEAN
+-116.12,33.53,17.0,2421.0,820.0,2971.0,685.0,1.654,100000.0,INLAND
+-117.11,32.59,17.0,2020.0,534.0,1529.0,500.0,2.1773,143200.0,NEAR OCEAN
+-121.96,37.32,11.0,1711.0,493.0,1094.0,543.0,3.73,227700.0,<1H OCEAN
+-118.52,34.2,35.0,2891.0,594.0,1757.0,581.0,4.3571,199800.0,<1H OCEAN
+-117.12,34.04,25.0,2495.0,438.0,1071.0,405.0,4.8173,146600.0,INLAND
+-117.25,32.86,25.0,2911.0,533.0,1137.0,499.0,5.1023,500001.0,NEAR OCEAN
+-122.12,37.29,11.0,436.0,70.0,212.0,75.0,8.6196,500001.0,<1H OCEAN
+-124.14,41.95,21.0,2696.0,578.0,1208.0,494.0,2.275,122400.0,NEAR OCEAN
+-121.51,38.51,33.0,2918.0,439.0,1085.0,427.0,5.5208,171300.0,INLAND
+-118.19,34.09,41.0,2090.0,530.0,2043.0,537.0,1.9706,144200.0,<1H OCEAN
+-118.27,33.98,30.0,1966.0,584.0,2028.0,535.0,1.625,101500.0,<1H OCEAN
+-121.89,37.25,21.0,2080.0,352.0,1040.0,325.0,5.2887,264500.0,<1H OCEAN
+-117.56,34.12,4.0,6956.0,1271.0,3455.0,1228.0,4.7193,178700.0,INLAND
+-122.61,38.23,18.0,2042.0,420.0,914.0,400.0,2.9871,193800.0,<1H OCEAN
+-119.06,35.36,9.0,1228.0,234.0,409.0,212.0,4.3482,95200.0,INLAND
+-118.2,33.8,52.0,1009.0,216.0,614.0,231.0,4.0074,200800.0,NEAR OCEAN
+-117.2,32.86,4.0,4308.0,1095.0,1923.0,932.0,3.9356,267000.0,NEAR OCEAN
+-118.31,34.17,12.0,3188.0,931.0,2118.0,850.0,3.1823,218300.0,<1H OCEAN
+-118.16,33.84,36.0,2220.0,367.0,1002.0,351.0,5.0719,219500.0,<1H OCEAN
+-117.12,32.7,36.0,1011.0,253.0,763.0,226.0,1.8187,84100.0,NEAR OCEAN
+-122.31,40.45,25.0,2596.0,557.0,1536.0,549.0,2.0221,60400.0,INLAND
+-122.33,37.94,45.0,1226.0,279.0,590.0,260.0,2.8833,140400.0,NEAR BAY
+-118.4,34.24,35.0,2552.0,545.0,1850.0,503.0,4.775,179500.0,<1H OCEAN
+-118.21,34.03,45.0,1860.0,472.0,1893.0,456.0,2.6573,141800.0,<1H OCEAN
+-118.54,34.17,25.0,3352.0,891.0,1815.0,860.0,2.8528,425000.0,<1H OCEAN
+-121.84,37.24,24.0,7991.0,1286.0,4017.0,1213.0,5.4741,238800.0,<1H OCEAN
+-119.32,36.21,29.0,1220.0,232.0,619.0,246.0,3.3125,78300.0,INLAND
+-121.18,37.79,16.0,1326.0,286.0,509.0,297.0,1.9464,112500.0,INLAND
+-118.33,34.05,48.0,2405.0,527.0,1868.0,502.0,3.375,257800.0,<1H OCEAN
+-122.42,37.63,46.0,1811.0,337.0,796.0,333.0,3.43,292900.0,NEAR OCEAN
+-117.07,32.78,26.0,3725.0,623.0,1516.0,627.0,4.7143,268300.0,NEAR OCEAN
+-117.81,33.64,16.0,2404.0,349.0,868.0,329.0,11.0138,442100.0,<1H OCEAN
+-118.22,33.9,35.0,1649.0,424.0,1786.0,388.0,1.4091,105600.0,<1H OCEAN
+-121.85,39.74,39.0,1139.0,265.0,623.0,264.0,2.2833,85800.0,INLAND
+-121.42,38.62,41.0,1087.0,272.0,462.0,219.0,2.0224,64900.0,INLAND
+-118.24,33.96,34.0,946.0,254.0,1101.0,239.0,1.7396,105900.0,<1H OCEAN
+-118.55,34.27,25.0,4919.0,661.0,2183.0,625.0,8.1356,352800.0,<1H OCEAN
+-118.88,34.22,16.0,2343.0,393.0,2007.0,383.0,5.756,302700.0,<1H OCEAN
+-117.94,34.02,27.0,5026.0,955.0,3899.0,930.0,3.871,162900.0,<1H OCEAN
+-118.42,34.27,35.0,2700.0,702.0,3444.0,679.0,1.4867,124000.0,<1H OCEAN
+-118.3,34.1,29.0,3403.0,1367.0,3432.0,1174.0,1.7083,166700.0,<1H OCEAN
+-121.82,37.38,32.0,1650.0,246.0,768.0,263.0,6.8462,320900.0,<1H OCEAN
+-122.02,37.24,28.0,2796.0,365.0,1085.0,363.0,10.6834,500001.0,<1H OCEAN
+-120.45,34.64,40.0,1051.0,235.0,574.0,201.0,2.0865,111500.0,NEAR OCEAN
+-117.99,33.78,15.0,4273.0,993.0,2300.0,946.0,3.5313,213000.0,<1H OCEAN
+-117.31,33.97,28.0,3420.0,691.0,1502.0,656.0,3.4896,140300.0,INLAND
+-117.7,33.53,5.0,6698.0,1254.0,2834.0,1139.0,5.9088,288500.0,<1H OCEAN
+-118.21,34.2,35.0,3646.0,552.0,1409.0,534.0,6.3794,500001.0,<1H OCEAN
+-118.3,34.1,35.0,7517.0,2961.0,5899.0,2769.0,1.9354,340000.0,<1H OCEAN
+-120.09,36.95,16.0,3222.0,511.0,1425.0,503.0,4.1544,119400.0,INLAND
+-118.11,34.2,36.0,4915.0,725.0,1897.0,700.0,6.827,359400.0,INLAND
+-117.89,34.09,37.0,1813.0,394.0,1100.0,375.0,3.4453,176700.0,<1H OCEAN
+-117.2,32.84,34.0,3353.0,544.0,1583.0,571.0,4.55,187700.0,NEAR OCEAN
+-122.25,38.0,16.0,2978.0,411.0,1531.0,400.0,6.5006,237700.0,NEAR BAY
+-116.85,32.83,17.0,4234.0,770.0,2191.0,725.0,3.6445,197600.0,<1H OCEAN
+-116.47,34.07,22.0,5473.0,1234.0,2581.0,1098.0,1.9375,65300.0,INLAND
+-117.29,34.17,35.0,4174.0,847.0,2127.0,778.0,3.2232,88300.0,INLAND
+-122.23,38.1,47.0,1303.0,278.0,694.0,269.0,2.5969,92800.0,NEAR BAY
+-122.27,37.55,15.0,1958.0,282.0,811.0,284.0,8.1221,483300.0,NEAR BAY
+-117.92,33.61,37.0,1244.0,173.0,394.0,154.0,10.3682,500001.0,<1H OCEAN
+-118.0,33.93,35.0,1288.0,240.0,758.0,250.0,4.9205,173900.0,<1H OCEAN
+-122.24,37.81,52.0,2093.0,550.0,918.0,483.0,2.7477,243800.0,NEAR BAY
+-118.24,34.0,38.0,1715.0,414.0,1714.0,389.0,1.7132,108200.0,<1H OCEAN
+-121.42,38.72,10.0,3054.0,528.0,1932.0,510.0,3.0903,91900.0,INLAND
+-121.39,36.16,28.0,1057.0,249.0,288.0,130.0,3.0526,146900.0,NEAR OCEAN
+-122.06,37.94,19.0,4005.0,972.0,1896.0,893.0,2.5268,235700.0,NEAR BAY
+-118.05,34.15,32.0,5131.0,665.0,1877.0,622.0,8.2004,500001.0,INLAND
+-118.23,33.84,25.0,1106.0,207.0,888.0,216.0,5.3307,207000.0,<1H OCEAN
+-118.41,34.05,16.0,9728.0,2211.0,3026.0,1899.0,5.8758,500001.0,<1H OCEAN
+-123.47,39.8,18.0,2130.0,545.0,863.0,346.0,2.3571,79200.0,INLAND
+-118.23,33.92,32.0,2698.0,640.0,1953.0,613.0,1.2222,107200.0,<1H OCEAN
+-119.29,34.45,26.0,2849.0,535.0,1383.0,532.0,2.6893,230800.0,<1H OCEAN
+-121.59,39.78,16.0,2754.0,570.0,1063.0,543.0,1.4048,86500.0,INLAND
+-117.03,32.71,33.0,3126.0,627.0,2300.0,623.0,3.2596,103000.0,NEAR OCEAN
+-117.74,34.1,26.0,2723.0,604.0,1847.0,498.0,2.6779,136000.0,INLAND
+-118.02,33.83,26.0,3616.0,892.0,2257.0,821.0,3.1497,217600.0,<1H OCEAN
+-118.35,33.91,28.0,2108.0,534.0,1485.0,536.0,4.0775,241400.0,<1H OCEAN
+-117.69,33.66,5.0,4246.0,689.0,1933.0,722.0,6.9501,225700.0,<1H OCEAN
+-118.42,33.99,23.0,5548.0,1245.0,2847.0,1229.0,4.4228,366900.0,<1H OCEAN
+-121.75,37.3,23.0,1801.0,415.0,548.0,393.0,2.5052,133700.0,<1H OCEAN
+-122.29,37.54,43.0,2268.0,438.0,1151.0,449.0,4.9091,293200.0,NEAR OCEAN
+-118.3,34.19,51.0,1502.0,243.0,586.0,231.0,4.375,332400.0,<1H OCEAN
+-121.89,37.35,44.0,2019.0,615.0,1243.0,518.0,2.0549,193800.0,<1H OCEAN
+-120.83,39.27,14.0,3338.0,608.0,1373.0,562.0,3.67,160100.0,INLAND
+-118.12,34.56,5.0,6446.0,1154.0,3427.0,1104.0,3.9936,148500.0,INLAND
+-122.58,38.43,10.0,3597.0,661.0,1132.0,639.0,3.9375,269200.0,<1H OCEAN
+-118.33,34.1,43.0,2732.0,1646.0,3049.0,1429.0,1.3157,333300.0,<1H OCEAN
+-118.23,33.96,36.0,1062.0,270.0,1136.0,273.0,1.6597,109100.0,<1H OCEAN
+-118.24,33.95,40.0,1193.0,280.0,1210.0,286.0,1.35,89500.0,<1H OCEAN
+-119.79,36.78,38.0,1912.0,456.0,1131.0,408.0,2.03,58800.0,INLAND
+-120.84,35.32,15.0,2419.0,538.0,1279.0,522.0,3.4762,189600.0,NEAR OCEAN
+-118.18,34.06,45.0,934.0,228.0,893.0,192.0,2.53,140300.0,<1H OCEAN
+-117.99,34.07,35.0,1681.0,360.0,1648.0,373.0,2.4911,145900.0,INLAND
+-121.21,37.81,12.0,3667.0,640.0,2173.0,652.0,5.0369,163900.0,INLAND
+-122.47,38.29,14.0,3732.0,846.0,1277.0,775.0,2.5658,208000.0,NEAR BAY
+-122.39,37.78,3.0,3464.0,1179.0,1441.0,919.0,4.7105,275000.0,NEAR BAY
+-118.11,34.68,6.0,7430.0,1184.0,3489.0,1115.0,5.3267,140100.0,INLAND
+-119.39,36.54,30.0,1408.0,326.0,1184.0,324.0,1.7165,59100.0,INLAND
+-118.3,33.98,52.0,1371.0,315.0,986.0,277.0,2.9432,94000.0,<1H OCEAN
+-118.35,34.08,52.0,1710.0,350.0,727.0,355.0,4.5833,333900.0,<1H OCEAN
+-117.35,34.44,9.0,11810.0,2181.0,6716.0,2081.0,3.1821,95600.0,INLAND
+-121.43,38.54,43.0,2084.0,417.0,912.0,410.0,2.9769,92700.0,INLAND
+-118.25,34.1,42.0,4198.0,956.0,1935.0,878.0,3.7184,277300.0,<1H OCEAN
+-118.97,35.36,31.0,1418.0,306.0,1219.0,312.0,1.5743,46700.0,INLAND
+-118.22,33.94,42.0,1115.0,297.0,1412.0,325.0,3.0903,153500.0,<1H OCEAN
+-118.05,33.78,18.0,3414.0,434.0,1272.0,454.0,8.7015,390900.0,<1H OCEAN
+-117.22,33.92,5.0,16884.0,2865.0,9509.0,2688.0,4.0938,130900.0,INLAND
+-122.36,40.68,28.0,1745.0,379.0,1011.0,370.0,2.0391,59800.0,INLAND
+-122.51,37.92,32.0,2622.0,541.0,1022.0,464.0,3.7647,375000.0,NEAR BAY
+-118.17,33.98,41.0,428.0,111.0,585.0,139.0,3.1786,132100.0,<1H OCEAN
+-122.38,39.68,21.0,1155.0,210.0,510.0,175.0,2.3851,67500.0,INLAND
+-118.2,34.05,36.0,2672.0,675.0,2883.0,674.0,2.0885,142800.0,<1H OCEAN
+-122.25,37.53,16.0,4428.0,664.0,1677.0,623.0,7.6864,422500.0,NEAR BAY
+-119.2,34.18,27.0,1035.0,229.0,782.0,222.0,4.2212,185400.0,NEAR OCEAN
+-116.93,33.75,14.0,6027.0,1148.0,3136.0,1036.0,2.964,121500.0,INLAND
+-121.27,38.14,40.0,929.0,257.0,576.0,229.0,2.125,137500.0,INLAND
+-122.27,40.46,14.0,2633.0,530.0,1324.0,513.0,2.2768,78600.0,INLAND
+-118.02,33.91,35.0,2182.0,390.0,1248.0,399.0,5.4236,216700.0,<1H OCEAN
+-122.23,37.76,52.0,2269.0,323.0,805.0,321.0,4.7188,335300.0,NEAR BAY
+-122.14,38.01,50.0,1760.0,341.0,741.0,316.0,4.5,178300.0,NEAR BAY
+-118.39,34.2,17.0,2594.0,1028.0,3950.0,973.0,2.0348,177200.0,<1H OCEAN
+-117.9,36.95,19.0,99.0,26.0,51.0,22.0,1.7292,137500.0,INLAND
+-116.54,33.87,16.0,3648.0,1035.0,1687.0,581.0,1.9167,70400.0,INLAND
+-119.07,35.27,25.0,3081.0,635.0,1830.0,591.0,2.5804,97900.0,INLAND
+-118.61,34.99,11.0,4031.0,766.0,1539.0,564.0,3.8917,120800.0,INLAND
+-118.08,33.78,25.0,5321.0,967.0,1969.0,903.0,5.0102,340100.0,<1H OCEAN
+-122.17,40.11,24.0,1631.0,340.0,1042.0,333.0,1.7708,59000.0,INLAND
+-119.1,34.31,21.0,2424.0,527.0,1379.0,484.0,2.6786,184000.0,<1H OCEAN
+-120.69,38.44,13.0,1473.0,265.0,597.0,228.0,4.2917,121300.0,INLAND
+-118.03,34.58,4.0,9849.0,1780.0,4546.0,1598.0,4.0729,154300.0,INLAND
+-118.44,34.07,21.0,730.0,263.0,965.0,224.0,2.0511,350000.0,<1H OCEAN
+-118.3,33.72,35.0,2790.0,,1167.0,441.0,6.2028,361500.0,NEAR OCEAN
+-118.45,34.18,34.0,1843.0,442.0,861.0,417.0,3.6875,246400.0,<1H OCEAN
+-121.92,37.49,10.0,7441.0,1588.0,3571.0,1466.0,5.1643,193100.0,<1H OCEAN
+-120.97,37.63,39.0,2360.0,607.0,2047.0,605.0,1.7054,58800.0,INLAND
+-120.08,39.61,32.0,1404.0,247.0,544.0,201.0,2.7778,72900.0,INLAND
+-117.21,32.86,24.0,3596.0,494.0,1573.0,492.0,6.5382,326000.0,NEAR OCEAN
+-117.68,34.07,24.0,2626.0,692.0,2204.0,647.0,1.7806,135000.0,INLAND
+-120.6,35.11,17.0,2495.0,524.0,1292.0,501.0,2.2625,153000.0,<1H OCEAN
+-119.76,36.77,38.0,3804.0,814.0,2142.0,816.0,2.1439,60200.0,INLAND
+-119.78,36.3,30.0,1846.0,391.0,1255.0,352.0,2.1681,66600.0,INLAND
+-121.32,38.62,29.0,2430.0,448.0,1087.0,394.0,3.0864,177900.0,INLAND
+-116.42,33.68,15.0,3895.0,782.0,900.0,529.0,2.2208,138300.0,INLAND
+-118.43,34.29,39.0,1769.0,410.0,1499.0,390.0,3.1212,153500.0,<1H OCEAN
+-118.33,33.91,35.0,1092.0,302.0,962.0,297.0,3.5903,183300.0,<1H OCEAN
+-122.23,37.8,51.0,1590.0,414.0,949.0,392.0,1.9028,127900.0,NEAR BAY
+-117.79,34.07,33.0,1694.0,333.0,1689.0,301.0,3.7583,116300.0,INLAND
+-120.1,39.2,20.0,1703.0,294.0,409.0,174.0,3.087,196900.0,INLAND
+-118.27,34.05,24.0,323.0,214.0,751.0,189.0,1.8304,225000.0,<1H OCEAN
+-117.48,34.01,23.0,2000.0,376.0,1361.0,388.0,4.369,121100.0,INLAND
+-122.02,37.34,28.0,2488.0,396.0,1190.0,410.0,5.7881,344700.0,<1H OCEAN
+-122.45,37.69,17.0,2359.0,501.0,884.0,504.0,3.0625,87500.0,NEAR OCEAN
+-122.43,37.75,52.0,2960.0,623.0,1191.0,589.0,3.95,347700.0,NEAR BAY
+-119.72,36.73,9.0,1914.0,491.0,1116.0,424.0,1.4646,65900.0,INLAND
+-123.07,38.46,31.0,855.0,217.0,280.0,139.0,2.3611,112500.0,NEAR OCEAN
+-118.25,34.09,52.0,104.0,20.0,32.0,17.0,3.75,241700.0,<1H OCEAN
+-122.53,37.95,22.0,7446.0,1979.0,2980.0,1888.0,3.5838,271300.0,NEAR BAY
+-121.99,37.82,22.0,1248.0,271.0,579.0,269.0,3.375,200000.0,<1H OCEAN
+-121.98,37.29,33.0,2120.0,349.0,907.0,336.0,7.5443,283000.0,<1H OCEAN
+-121.76,36.92,46.0,947.0,257.0,1120.0,264.0,3.4125,160700.0,<1H OCEAN
+-117.41,34.08,38.0,1541.0,290.0,861.0,299.0,3.5655,95600.0,INLAND
+-115.5,32.75,13.0,330.0,72.0,822.0,64.0,3.4107,142500.0,INLAND
+-118.35,34.03,44.0,865.0,208.0,537.0,183.0,1.9,110900.0,<1H OCEAN
+-121.83,37.29,10.0,1828.0,453.0,1356.0,409.0,4.5943,123500.0,<1H OCEAN
+-121.3,38.66,28.0,3391.0,550.0,1546.0,553.0,4.2188,139200.0,INLAND
+-122.69,38.94,9.0,1245.0,234.0,517.0,187.0,3.125,93400.0,INLAND
+-122.03,37.34,16.0,1755.0,410.0,674.0,410.0,5.1602,231200.0,<1H OCEAN
+-118.0,33.85,33.0,2053.0,418.0,1154.0,405.0,4.0455,197200.0,<1H OCEAN
+-122.18,39.55,28.0,1471.0,259.0,673.0,246.0,3.25,81600.0,INLAND
+-119.78,36.84,7.0,4907.0,1075.0,2014.0,909.0,3.2147,111900.0,INLAND
+-117.46,33.9,10.0,9738.0,2130.0,4936.0,1840.0,3.3187,144800.0,INLAND
+-120.49,37.28,11.0,1721.0,381.0,1708.0,373.0,1.9535,57100.0,INLAND
+-121.96,37.23,36.0,4423.0,632.0,1719.0,608.0,7.8407,476400.0,<1H OCEAN
+-117.92,33.64,24.0,2539.0,695.0,1623.0,611.0,3.0708,188700.0,<1H OCEAN
+-116.4,34.09,9.0,4855.0,872.0,2098.0,765.0,3.2723,97800.0,INLAND
+-117.38,33.2,17.0,1877.0,581.0,1288.0,426.0,1.9386,106300.0,NEAR OCEAN
+-118.92,34.41,22.0,2702.0,655.0,2664.0,571.0,3.0893,173400.0,<1H OCEAN
+-118.01,33.89,36.0,1589.0,265.0,804.0,272.0,4.6354,202900.0,<1H OCEAN
+-122.04,37.96,28.0,1207.0,252.0,724.0,252.0,3.6964,165700.0,NEAR BAY
+-119.06,34.23,29.0,3511.0,632.0,2591.0,596.0,3.0219,221700.0,<1H OCEAN
+-117.11,32.72,25.0,1491.0,348.0,1183.0,316.0,1.9583,88600.0,NEAR OCEAN
+-121.8,37.32,14.0,4412.0,924.0,2698.0,891.0,4.7027,227600.0,<1H OCEAN
+-117.05,32.62,34.0,3928.0,686.0,2315.0,681.0,4.2851,144500.0,NEAR OCEAN
+-121.99,37.32,20.0,4461.0,864.0,2042.0,808.0,4.7083,217700.0,<1H OCEAN
+-120.14,34.59,24.0,1601.0,282.0,731.0,285.0,4.2026,259800.0,NEAR OCEAN
+-118.43,34.0,30.0,2148.0,597.0,1341.0,559.0,3.3995,324000.0,<1H OCEAN
+-122.07,37.69,29.0,2304.0,618.0,1021.0,552.0,2.5362,203800.0,NEAR BAY
+-118.22,33.84,35.0,1131.0,273.0,1007.0,269.0,4.0219,168300.0,<1H OCEAN
+-118.21,33.82,34.0,1719.0,398.0,1444.0,372.0,2.8438,139300.0,NEAR OCEAN
+-122.71,38.4,17.0,1690.0,464.0,833.0,445.0,1.439,140600.0,<1H OCEAN
+-121.59,39.74,17.0,1646.0,330.0,750.0,344.0,2.3798,83800.0,INLAND
+-117.39,34.13,9.0,2228.0,398.0,1316.0,370.0,4.1632,119800.0,INLAND
+-119.21,34.28,27.0,2219.0,312.0,937.0,315.0,5.7601,281100.0,NEAR OCEAN
+-117.36,33.18,39.0,1546.0,291.0,833.0,308.0,2.8893,185400.0,NEAR OCEAN
+-117.01,32.99,8.0,3372.0,430.0,1536.0,448.0,8.4284,378300.0,<1H OCEAN
+-118.3,33.88,29.0,850.0,229.0,563.0,204.0,3.7375,247700.0,<1H OCEAN
+-118.27,34.07,38.0,1270.0,556.0,1692.0,450.0,1.87,170800.0,<1H OCEAN
+-118.37,33.95,52.0,836.0,175.0,747.0,166.0,4.125,174000.0,<1H OCEAN
+-118.43,34.01,31.0,2526.0,528.0,1046.0,504.0,4.7009,500001.0,<1H OCEAN
+-118.03,34.08,32.0,1780.0,484.0,1732.0,454.0,2.4464,169600.0,INLAND
+-119.87,36.83,4.0,4833.0,784.0,2088.0,789.0,5.1781,122500.0,INLAND
+-117.24,33.38,16.0,2792.0,525.0,1696.0,516.0,3.668,171200.0,<1H OCEAN
+-119.02,35.44,29.0,3415.0,631.0,1527.0,597.0,4.0125,84400.0,INLAND
+-122.2,37.43,40.0,2223.0,412.0,1050.0,417.0,5.2421,444500.0,NEAR OCEAN
+-117.4,34.0,24.0,2316.0,599.0,1829.0,532.0,1.6955,86800.0,INLAND
+-122.24,38.1,49.0,1851.0,356.0,849.0,307.0,2.9432,103500.0,NEAR BAY
+-117.77,33.72,9.0,2153.0,316.0,954.0,324.0,7.8139,304700.0,<1H OCEAN
+-121.6,39.77,26.0,1503.0,343.0,699.0,296.0,1.875,84000.0,INLAND
+-122.07,37.7,32.0,3400.0,736.0,1487.0,694.0,3.0,223200.0,NEAR BAY
+-117.87,34.09,31.0,1484.0,327.0,927.0,317.0,3.6484,189600.0,<1H OCEAN
+-117.27,34.09,36.0,848.0,186.0,737.0,169.0,0.9838,79300.0,INLAND
+-122.13,37.76,26.0,3266.0,491.0,1222.0,533.0,5.37,275400.0,NEAR BAY
+-121.45,38.53,34.0,1717.0,354.0,848.0,306.0,2.4741,87000.0,INLAND
+-122.29,38.28,38.0,2308.0,425.0,1272.0,406.0,3.6083,134200.0,NEAR BAY
+-120.08,36.79,38.0,1446.0,285.0,928.0,255.0,2.9808,89600.0,INLAND
+-118.26,34.14,6.0,1727.0,506.0,1200.0,439.0,4.1083,210700.0,<1H OCEAN
+-117.42,33.94,35.0,1764.0,325.0,1094.0,353.0,4.1528,113900.0,INLAND
+-118.28,33.99,46.0,1211.0,321.0,1153.0,282.0,1.7849,99300.0,<1H OCEAN
+-117.05,33.14,16.0,4552.0,1166.0,2737.0,1051.0,2.25,136300.0,<1H OCEAN
+-121.49,38.62,8.0,15309.0,2996.0,7463.0,2885.0,3.9143,129700.0,INLAND
+-122.45,37.78,52.0,3975.0,716.0,1515.0,691.0,5.0156,500001.0,NEAR BAY
+-121.93,37.29,36.0,2241.0,437.0,989.0,442.0,3.9625,288200.0,<1H OCEAN
+-121.99,37.36,32.0,1754.0,324.0,917.0,330.0,4.6761,298300.0,<1H OCEAN
+-122.41,37.8,52.0,2450.0,741.0,1415.0,664.0,2.8229,375000.0,NEAR BAY
+-122.13,37.72,35.0,2183.0,383.0,976.0,392.0,3.8393,243500.0,NEAR BAY
+-118.38,33.86,24.0,3124.0,560.0,1312.0,542.0,6.3021,333800.0,<1H OCEAN
+-118.48,34.04,40.0,1395.0,285.0,610.0,262.0,3.9659,500001.0,<1H OCEAN
+-118.18,33.84,35.0,1415.0,294.0,591.0,291.0,2.9798,315600.0,NEAR OCEAN
+-121.85,37.33,16.0,2987.0,874.0,4241.0,841.0,2.8024,127900.0,<1H OCEAN
+-118.4,33.98,39.0,714.0,118.0,314.0,117.0,5.9856,432100.0,<1H OCEAN
+-117.82,33.72,24.0,3477.0,462.0,1593.0,484.0,6.8634,276500.0,<1H OCEAN
+-118.39,34.22,40.0,712.0,149.0,533.0,155.0,3.695,165200.0,<1H OCEAN
+-118.62,34.22,33.0,1636.0,275.0,866.0,289.0,5.6356,241300.0,<1H OCEAN
+-119.06,34.36,48.0,1459.0,324.0,902.0,350.0,2.4185,189900.0,<1H OCEAN
+-121.59,39.77,24.0,1535.0,276.0,664.0,273.0,2.3068,97300.0,INLAND
+-118.4,34.06,52.0,1871.0,326.0,646.0,284.0,8.2961,500001.0,<1H OCEAN
+-121.87,37.27,16.0,3298.0,451.0,1542.0,423.0,6.7064,305600.0,<1H OCEAN
+-117.93,33.86,36.0,1672.0,318.0,1173.0,337.0,4.5774,182100.0,<1H OCEAN
+-120.46,37.31,35.0,2042.0,378.0,953.0,356.0,2.7344,87800.0,INLAND
+-117.91,33.84,29.0,1570.0,482.0,1849.0,430.0,2.6563,162500.0,<1H OCEAN
+-117.27,33.05,15.0,3333.0,808.0,1371.0,737.0,2.9083,122400.0,NEAR OCEAN
+-117.75,34.04,22.0,2948.0,636.0,2600.0,602.0,3.125,113600.0,INLAND
+-118.46,34.31,24.0,2920.0,601.0,1460.0,598.0,4.2708,218200.0,<1H OCEAN
+-122.04,37.34,23.0,2590.0,725.0,1795.0,680.0,3.16,225000.0,<1H OCEAN
+-117.14,32.79,35.0,3578.0,582.0,1568.0,553.0,4.7813,188600.0,NEAR OCEAN
+-118.14,33.89,33.0,1250.0,276.0,866.0,268.0,4.1708,175000.0,<1H OCEAN
+-119.81,34.46,22.0,3488.0,452.0,1479.0,458.0,7.1687,384400.0,NEAR OCEAN
+-124.18,40.78,34.0,1592.0,364.0,950.0,317.0,2.1607,67000.0,NEAR OCEAN
+-118.29,33.9,27.0,1013.0,394.0,1067.0,400.0,1.7714,159400.0,<1H OCEAN
+-117.29,34.41,11.0,5934.0,1380.0,2756.0,1239.0,1.5758,108300.0,INLAND
+-121.75,37.68,35.0,1755.0,299.0,702.0,263.0,5.2443,183400.0,INLAND
+-117.12,32.76,41.0,1469.0,421.0,803.0,395.0,2.1856,120500.0,NEAR OCEAN
+-117.24,32.73,37.0,2260.0,354.0,809.0,351.0,5.9113,388300.0,NEAR OCEAN
+-121.29,37.99,45.0,965.0,198.0,498.0,195.0,1.6944,75200.0,INLAND
+-122.31,37.92,30.0,1014.0,236.0,537.0,204.0,2.8456,183300.0,NEAR BAY
+-117.33,33.22,21.0,2868.0,602.0,855.0,559.0,2.7846,91200.0,NEAR OCEAN
+-121.74,36.92,29.0,1210.0,281.0,863.0,262.0,3.1062,156000.0,<1H OCEAN
+-121.83,39.72,52.0,1890.0,420.0,974.0,383.0,1.6827,78700.0,INLAND
+-119.78,36.31,14.0,1287.0,291.0,737.0,269.0,3.1667,126400.0,INLAND
+-117.64,33.51,15.0,1743.0,254.0,943.0,274.0,5.9339,286000.0,<1H OCEAN
+-117.18,34.06,26.0,1953.0,446.0,1284.0,414.0,1.3485,85100.0,INLAND
+-115.69,32.79,18.0,1564.0,340.0,1161.0,343.0,2.1792,55200.0,INLAND
+-117.97,34.05,33.0,2028.0,422.0,1727.0,371.0,2.8438,157600.0,<1H OCEAN
+-117.98,34.01,27.0,2643.0,418.0,1344.0,381.0,5.7057,262100.0,<1H OCEAN
+-121.32,38.16,14.0,2049.0,398.0,1071.0,369.0,3.5,240800.0,INLAND
+-118.4,33.87,26.0,6712.0,1441.0,2803.0,1394.0,5.2276,434500.0,<1H OCEAN
+-121.85,37.41,17.0,2156.0,435.0,1400.0,393.0,5.6096,199100.0,<1H OCEAN
+-117.06,34.9,36.0,2828.0,916.0,1762.0,736.0,1.4318,59600.0,INLAND
+-121.97,36.97,24.0,3665.0,870.0,1954.0,833.0,2.8036,228500.0,NEAR OCEAN
+-122.04,37.37,42.0,1125.0,273.0,616.0,258.0,3.6765,252800.0,<1H OCEAN
+-117.55,33.52,15.0,426.0,62.0,133.0,45.0,5.136,447400.0,<1H OCEAN
+-121.46,38.53,37.0,2745.0,588.0,1607.0,556.0,1.8007,65400.0,INLAND
+-122.43,37.75,40.0,4850.0,977.0,1824.0,952.0,5.0519,356100.0,NEAR BAY
+-121.78,37.31,7.0,1973.0,328.0,1047.0,303.0,6.234,292200.0,<1H OCEAN
+-118.36,33.9,42.0,1935.0,388.0,1136.0,379.0,4.74,230000.0,<1H OCEAN
+-117.95,33.97,33.0,1113.0,145.0,424.0,137.0,8.3474,500001.0,<1H OCEAN
+-120.22,38.05,14.0,3803.0,689.0,1129.0,477.0,2.7188,137000.0,INLAND
+-117.94,33.7,18.0,4827.0,718.0,2471.0,716.0,6.1181,284500.0,<1H OCEAN
+-122.2,37.48,41.0,733.0,155.0,652.0,140.0,5.1654,233600.0,NEAR BAY
+-122.32,37.94,38.0,2751.0,522.0,1390.0,489.0,3.7277,165100.0,NEAR BAY
+-117.95,34.09,21.0,2215.0,484.0,1792.0,419.0,2.8375,166500.0,<1H OCEAN
+-121.46,38.61,43.0,705.0,178.0,464.0,159.0,2.4205,60900.0,INLAND
+-118.2,33.81,45.0,944.0,178.0,533.0,193.0,3.4808,206900.0,NEAR OCEAN
+-118.12,34.08,52.0,1437.0,290.0,980.0,282.0,5.3032,245700.0,<1H OCEAN
+-117.07,34.24,21.0,4773.0,1047.0,337.0,130.0,3.9375,115000.0,INLAND
+-122.28,37.52,29.0,1526.0,355.0,724.0,315.0,4.0313,435200.0,NEAR OCEAN
+-121.91,36.59,17.0,5039.0,833.0,1678.0,710.0,6.2323,339100.0,NEAR OCEAN
+-117.1,34.03,24.0,4144.0,826.0,2127.0,772.0,2.5172,96000.0,INLAND
+-122.37,40.6,7.0,5178.0,1336.0,2557.0,1283.0,2.4079,111400.0,INLAND
+-119.65,36.35,21.0,1745.0,266.0,837.0,292.0,4.3911,107900.0,INLAND
+-124.09,40.86,25.0,1322.0,387.0,794.0,379.0,1.1742,75000.0,NEAR OCEAN
+-122.66,37.93,42.0,1505.0,324.0,553.0,277.0,4.1792,350000.0,NEAR OCEAN
+-117.65,34.11,28.0,2788.0,370.0,1140.0,385.0,5.3368,233500.0,INLAND
+-117.86,33.61,15.0,3191.0,482.0,930.0,447.0,8.6001,500001.0,<1H OCEAN
+-118.03,33.84,30.0,4781.0,831.0,2568.0,797.0,5.4746,226400.0,<1H OCEAN
+-119.21,36.1,30.0,1471.0,373.0,1418.0,357.0,1.7432,42500.0,INLAND
+-119.82,36.19,33.0,1293.0,272.0,694.0,229.0,2.0221,52200.0,INLAND
+-118.52,34.19,37.0,1892.0,347.0,1039.0,343.0,4.8295,212100.0,<1H OCEAN
+-119.63,34.42,42.0,1765.0,263.0,753.0,260.0,8.5608,500001.0,<1H OCEAN
+-121.5,38.55,49.0,4094.0,634.0,1363.0,659.0,5.2362,236800.0,INLAND
+-119.17,34.22,29.0,4188.0,816.0,2783.0,790.0,4.1949,197100.0,NEAR OCEAN
+-120.01,38.93,22.0,3080.0,610.0,1045.0,425.0,2.996,126100.0,INLAND
+-120.42,34.95,33.0,3404.0,711.0,1579.0,639.0,3.1078,146700.0,<1H OCEAN
+-122.12,37.43,33.0,3262.0,668.0,1411.0,626.0,5.316,398100.0,NEAR BAY
+-117.66,34.0,5.0,1387.0,236.0,855.0,270.0,5.411,201100.0,INLAND
+-120.83,37.79,16.0,893.0,164.0,548.0,155.0,3.6875,121900.0,INLAND
+-117.01,32.76,22.0,3626.0,824.0,1800.0,769.0,2.8594,189600.0,<1H OCEAN
+-118.29,33.88,32.0,2307.0,493.0,1754.0,528.0,4.317,232800.0,<1H OCEAN
+-119.17,34.27,24.0,4165.0,646.0,2194.0,658.0,6.0661,234800.0,NEAR OCEAN
+-117.14,32.93,16.0,2412.0,419.0,1612.0,422.0,4.5086,171100.0,<1H OCEAN
+-121.92,36.62,52.0,867.0,199.0,391.0,187.0,2.6713,234600.0,NEAR OCEAN
+-118.31,33.99,47.0,1525.0,359.0,982.0,333.0,2.0915,126600.0,<1H OCEAN
+-122.12,37.71,35.0,1037.0,207.0,552.0,210.0,4.0,167900.0,NEAR BAY
+-118.2,34.05,50.0,1407.0,401.0,1526.0,385.0,2.29,121800.0,<1H OCEAN
+-117.18,32.84,31.0,3064.0,575.0,1476.0,549.0,3.6667,175900.0,NEAR OCEAN
+-121.87,37.38,16.0,1050.0,245.0,722.0,228.0,4.5187,163500.0,<1H OCEAN
+-116.76,34.29,14.0,3959.0,849.0,1064.0,376.0,2.8214,111400.0,INLAND
+-118.58,34.23,35.0,1917.0,314.0,1019.0,340.0,4.8929,234900.0,<1H OCEAN
+-117.64,34.08,37.0,2576.0,468.0,1284.0,428.0,3.3958,130400.0,INLAND
+-122.52,37.88,52.0,176.0,50.0,88.0,46.0,3.8068,275000.0,NEAR BAY
+-122.0,38.03,4.0,2341.0,408.0,1235.0,431.0,6.0424,165900.0,INLAND
+-118.25,34.02,32.0,1311.0,410.0,1792.0,396.0,2.3304,119900.0,<1H OCEAN
+-122.27,37.89,52.0,2640.0,366.0,973.0,355.0,7.266,371100.0,NEAR BAY
+-119.32,36.25,21.0,1231.0,,609.0,206.0,2.8365,90000.0,INLAND
+-118.18,34.05,38.0,3272.0,731.0,3299.0,726.0,2.8295,126500.0,<1H OCEAN
+-120.27,37.11,18.0,1277.0,234.0,674.0,238.0,2.6694,75900.0,INLAND
+-119.8,36.71,29.0,1541.0,291.0,1007.0,313.0,2.0043,53500.0,INLAND
+-120.42,34.89,24.0,2020.0,307.0,855.0,283.0,5.0099,162500.0,<1H OCEAN
+-122.26,37.77,52.0,1565.0,315.0,637.0,297.0,4.7778,351800.0,NEAR BAY
+-118.19,33.96,28.0,3507.0,969.0,3740.0,970.0,2.0162,142000.0,<1H OCEAN
+-118.33,34.18,49.0,1969.0,377.0,977.0,367.0,3.8462,231300.0,<1H OCEAN
+-122.32,37.54,34.0,3661.0,692.0,1608.0,656.0,5.0774,407200.0,NEAR OCEAN
+-117.24,32.75,33.0,1980.0,614.0,1057.0,567.0,2.2042,231300.0,NEAR OCEAN
+-121.96,38.36,11.0,3208.0,790.0,1772.0,694.0,2.7434,218800.0,INLAND
+-117.46,34.07,19.0,3155.0,572.0,2482.0,642.0,2.9973,113400.0,INLAND
+-118.06,34.1,42.0,1576.0,313.0,697.0,282.0,4.3523,283600.0,<1H OCEAN
+-121.31,38.62,31.0,3114.0,430.0,1121.0,456.0,6.244,240000.0,INLAND
+-122.25,38.09,48.0,833.0,188.0,652.0,165.0,2.2417,87900.0,NEAR BAY
+-118.41,34.21,35.0,2215.0,459.0,1594.0,446.0,4.0167,193200.0,<1H OCEAN
+-118.15,34.19,47.0,1717.0,314.0,868.0,295.0,3.6094,160700.0,<1H OCEAN
+-117.59,33.66,4.0,1318.0,218.0,673.0,225.0,6.0722,260800.0,<1H OCEAN
+-121.87,38.0,17.0,2713.0,442.0,1475.0,415.0,4.8542,144100.0,INLAND
+-119.17,34.18,38.0,3221.0,783.0,2792.0,736.0,2.9118,172400.0,NEAR OCEAN
+-120.79,37.76,14.0,3531.0,508.0,1505.0,497.0,5.5228,275300.0,INLAND
+-118.15,34.08,44.0,1053.0,251.0,941.0,256.0,3.125,205600.0,<1H OCEAN
+-117.72,33.61,26.0,2653.0,621.0,774.0,584.0,2.49,81100.0,<1H OCEAN
+-122.04,37.33,26.0,2690.0,401.0,1264.0,429.0,7.7643,474700.0,<1H OCEAN
+-122.04,37.34,28.0,3081.0,460.0,1260.0,461.0,7.5372,432600.0,<1H OCEAN
+-118.44,34.06,28.0,3910.0,959.0,1763.0,867.0,5.5,500001.0,<1H OCEAN
+-118.14,34.16,36.0,2973.0,807.0,2846.0,784.0,2.6217,156300.0,<1H OCEAN
+-117.0,32.87,18.0,11544.0,1979.0,6296.0,1923.0,4.4904,150400.0,<1H OCEAN
+-117.61,33.43,33.0,1150.0,383.0,604.0,317.0,2.3545,187500.0,NEAR OCEAN
+-117.08,34.96,28.0,1777.0,307.0,721.0,259.0,3.6343,79800.0,INLAND
+-122.1,37.39,35.0,2471.0,349.0,881.0,342.0,7.6229,500001.0,NEAR BAY
+-122.08,37.63,37.0,1793.0,364.0,1534.0,346.0,3.6458,156600.0,NEAR BAY
+-118.34,34.0,49.0,2465.0,372.0,1018.0,359.0,4.0,296800.0,<1H OCEAN
+-122.14,37.71,18.0,3905.0,1007.0,2197.0,1044.0,3.6932,166800.0,NEAR BAY
+-118.26,33.88,40.0,519.0,102.0,330.0,95.0,3.0972,108500.0,<1H OCEAN
+-121.47,38.55,48.0,968.0,310.0,706.0,274.0,0.9948,65400.0,INLAND
+-117.18,32.77,23.0,1215.0,225.0,592.0,224.0,3.4,200600.0,NEAR OCEAN
+-117.9,34.12,33.0,1788.0,456.0,1787.0,361.0,2.6629,124100.0,<1H OCEAN
+-117.46,33.95,34.0,1565.0,296.0,1142.0,328.0,3.6979,99600.0,INLAND
+-121.82,37.73,47.0,127.0,23.0,51.0,21.0,4.3472,375000.0,INLAND
+-118.16,34.6,5.0,7294.0,1139.0,3123.0,930.0,4.9904,154100.0,INLAND
+-118.1,33.93,33.0,1474.0,325.0,1205.0,335.0,3.1397,166800.0,<1H OCEAN
+-117.02,32.59,19.0,1902.0,335.0,1102.0,313.0,3.0365,98100.0,NEAR OCEAN
+-119.7,36.32,40.0,1178.0,244.0,586.0,187.0,2.6477,43500.0,INLAND
+-118.11,33.8,35.0,1034.0,180.0,444.0,177.0,5.4602,231600.0,<1H OCEAN
+-121.83,37.28,33.0,1115.0,250.0,1168.0,261.0,3.9009,178600.0,<1H OCEAN
+-118.35,34.16,45.0,1390.0,281.0,538.0,270.0,4.2212,293800.0,<1H OCEAN
+-122.47,38.51,18.0,2487.0,516.0,980.0,503.0,3.5506,187500.0,INLAND
+-119.82,36.32,18.0,942.0,193.0,424.0,174.0,2.0673,87500.0,INLAND
+-120.96,39.12,24.0,2069.0,436.0,909.0,374.0,2.5326,139100.0,INLAND
+-118.04,34.04,32.0,1619.0,323.0,1492.0,342.0,3.5,165100.0,<1H OCEAN
+-122.11,37.64,31.0,1487.0,280.0,854.0,301.0,5.2312,197600.0,NEAR BAY
+-117.25,34.13,33.0,2898.0,503.0,1374.0,487.0,3.6856,90000.0,INLAND
+-118.46,34.07,49.0,2418.0,301.0,850.0,318.0,14.2867,500001.0,<1H OCEAN
+-122.04,38.0,16.0,3077.0,733.0,1447.0,709.0,3.2484,91100.0,NEAR BAY
+-117.19,33.64,12.0,1481.0,265.0,757.0,243.0,3.235,210700.0,<1H OCEAN
+-118.0,33.66,16.0,2809.0,708.0,1260.0,638.0,3.2353,252900.0,NEAR OCEAN
+-122.41,37.8,52.0,1724.0,416.0,1016.0,395.0,3.3839,400000.0,NEAR BAY
+-121.69,36.8,19.0,2164.0,410.0,1309.0,426.0,3.338,185300.0,<1H OCEAN
+-118.07,33.8,34.0,3486.0,507.0,1311.0,503.0,7.1221,384500.0,<1H OCEAN
+-118.17,34.03,43.0,1636.0,506.0,1855.0,502.0,2.2902,152400.0,<1H OCEAN
+-118.43,33.96,16.0,14891.0,3984.0,6270.0,3595.0,5.1064,283200.0,<1H OCEAN
+-122.71,37.9,23.0,1250.0,257.0,437.0,188.0,3.115,242600.0,NEAR OCEAN
+-121.91,37.29,36.0,945.0,149.0,371.0,158.0,5.6266,320500.0,<1H OCEAN
+-117.9,33.71,15.0,539.0,71.0,287.0,66.0,6.3427,305200.0,<1H OCEAN
+-115.9,32.69,18.0,414.0,86.0,98.0,54.0,1.5417,57500.0,INLAND
+-118.41,34.25,36.0,1146.0,259.0,1173.0,272.0,3.6016,153800.0,<1H OCEAN
+-117.78,33.69,16.0,3400.0,501.0,1575.0,488.0,6.0961,295500.0,<1H OCEAN
+-118.94,35.37,23.0,1106.0,252.0,790.0,230.0,1.8523,59700.0,INLAND
+-117.37,33.98,43.0,2862.0,772.0,1878.0,675.0,2.1151,96700.0,INLAND
+-117.99,33.97,18.0,4078.0,484.0,1490.0,482.0,10.8034,500001.0,<1H OCEAN
+-117.95,33.76,29.0,1829.0,366.0,1703.0,343.0,4.1295,188000.0,<1H OCEAN
+-121.88,37.34,52.0,1390.0,365.0,921.0,352.0,2.1442,188900.0,<1H OCEAN
+-118.19,33.84,24.0,1228.0,320.0,537.0,273.0,2.25,192000.0,NEAR OCEAN
+-117.34,34.02,28.0,2683.0,708.0,2047.0,636.0,2.275,85400.0,INLAND
+-118.36,33.9,39.0,1166.0,222.0,640.0,206.0,3.5313,230400.0,<1H OCEAN
+-118.32,33.85,42.0,3146.0,770.0,1859.0,740.0,3.5073,234800.0,<1H OCEAN
+-122.5,37.6,35.0,2197.0,369.0,971.0,326.0,4.25,241700.0,NEAR OCEAN
+-117.35,34.11,34.0,2104.0,388.0,1578.0,365.0,3.0833,88400.0,INLAND
+-118.06,33.82,25.0,2637.0,462.0,965.0,415.0,4.5833,190900.0,<1H OCEAN
+-117.07,32.64,38.0,1486.0,269.0,745.0,295.0,4.6477,150400.0,NEAR OCEAN
+-117.92,33.62,35.0,1821.0,335.0,727.0,316.0,6.5842,458500.0,<1H OCEAN
+-118.14,34.11,52.0,2742.0,422.0,1153.0,414.0,8.1124,500001.0,<1H OCEAN
+-119.44,36.6,34.0,864.0,184.0,579.0,171.0,2.0417,72500.0,INLAND
+-121.26,37.98,41.0,1633.0,433.0,885.0,413.0,0.9782,54200.0,INLAND
+-118.03,33.83,25.0,768.0,195.0,529.0,184.0,3.175,132800.0,<1H OCEAN
+-117.09,32.69,18.0,1645.0,430.0,1221.0,410.0,1.3269,108000.0,NEAR OCEAN
+-118.68,34.08,18.0,102.0,17.0,55.0,21.0,3.9934,500001.0,NEAR OCEAN
+-117.74,34.03,27.0,3623.0,809.0,3712.0,754.0,3.4609,123300.0,INLAND
+-118.36,33.87,22.0,2114.0,541.0,1300.0,538.0,3.4208,290000.0,<1H OCEAN
+-122.06,37.33,29.0,1945.0,269.0,826.0,275.0,8.248,498800.0,<1H OCEAN
+-118.32,34.1,36.0,1655.0,690.0,1957.0,633.0,1.7325,221900.0,<1H OCEAN
+-121.84,37.4,25.0,1866.0,271.0,840.0,275.0,6.8677,288500.0,<1H OCEAN
+-120.66,35.29,39.0,2163.0,652.0,1153.0,599.0,2.084,233300.0,NEAR OCEAN
+-119.77,36.8,24.0,3748.0,770.0,1827.0,719.0,2.7222,83100.0,INLAND
+-117.72,34.02,17.0,1781.0,262.0,860.0,256.0,6.5958,236800.0,INLAND
+-121.53,39.52,30.0,1030.0,161.0,448.0,159.0,2.4821,73800.0,INLAND
+-118.01,33.86,29.0,2307.0,452.0,1218.0,402.0,3.4306,194200.0,<1H OCEAN
+-122.42,40.44,16.0,994.0,,495.0,181.0,2.1875,76400.0,INLAND
+-116.87,33.94,35.0,4448.0,906.0,2736.0,843.0,2.218,73400.0,INLAND
+-118.4,34.19,37.0,934.0,231.0,587.0,230.0,3.625,181300.0,<1H OCEAN
+-121.24,38.79,23.0,1419.0,261.0,706.0,269.0,3.1875,110200.0,INLAND
+-122.01,38.44,12.0,2344.0,354.0,1035.0,321.0,4.9773,281200.0,INLAND
+-121.99,37.96,17.0,2756.0,423.0,1228.0,426.0,5.5872,200600.0,INLAND
+-121.01,37.65,52.0,178.0,53.0,152.0,62.0,0.4999,82500.0,INLAND
+-121.23,38.04,32.0,1829.0,262.0,677.0,243.0,6.1805,247900.0,INLAND
+-118.18,33.83,45.0,1535.0,274.0,591.0,276.0,4.2411,371700.0,NEAR OCEAN
+-121.38,38.61,27.0,2375.0,537.0,863.0,452.0,3.0086,126900.0,INLAND
+-122.24,38.12,39.0,2967.0,500.0,1243.0,523.0,4.2902,152400.0,NEAR BAY
+-120.49,35.35,17.0,3043.0,608.0,1457.0,545.0,3.1641,158600.0,<1H OCEAN
+-118.08,33.77,26.0,2013.0,551.0,664.0,510.0,2.2708,67500.0,<1H OCEAN
+-119.83,36.54,31.0,1732.0,332.0,979.0,294.0,2.5208,60000.0,INLAND
+-119.67,36.73,27.0,2845.0,417.0,1219.0,460.0,4.9196,117900.0,INLAND
+-117.3,34.17,30.0,2483.0,573.0,1172.0,438.0,1.875,89700.0,INLAND
+-122.32,37.56,9.0,1150.0,287.0,377.0,243.0,3.8317,237500.0,NEAR OCEAN
+-118.32,33.8,39.0,1415.0,298.0,729.0,278.0,3.1648,244800.0,<1H OCEAN
+-122.04,37.97,26.0,2470.0,626.0,1174.0,573.0,2.9861,160900.0,NEAR BAY
+-118.49,34.2,35.0,1109.0,206.0,515.0,202.0,5.2118,215800.0,<1H OCEAN
+-122.42,37.66,41.0,2189.0,414.0,1063.0,409.0,4.7361,302600.0,NEAR OCEAN
+-117.42,33.98,16.0,10072.0,2043.0,5913.0,1909.0,3.0606,119500.0,INLAND
+-117.02,33.76,20.0,1317.0,203.0,453.0,158.0,2.8393,120700.0,INLAND
+-119.87,36.72,30.0,1584.0,316.0,984.0,300.0,2.0658,67900.0,INLAND
+-118.3,34.05,39.0,993.0,506.0,1765.0,464.0,1.2786,121900.0,<1H OCEAN
+-120.54,38.75,9.0,3006.0,540.0,1102.0,418.0,3.9812,136600.0,INLAND
+-119.01,35.38,36.0,790.0,224.0,426.0,208.0,1.4427,50600.0,INLAND
+-121.6,39.12,21.0,1299.0,338.0,1494.0,311.0,1.3348,225000.0,INLAND
+-117.43,34.08,13.0,4563.0,1187.0,2475.0,1019.0,2.1189,121700.0,INLAND
+-121.55,39.51,39.0,1551.0,353.0,684.0,310.0,2.0357,57600.0,INLAND
+-117.95,33.67,25.0,1611.0,383.0,554.0,327.0,3.0417,137300.0,<1H OCEAN
+-117.06,32.81,17.0,3939.0,550.0,1694.0,553.0,6.7927,234700.0,<1H OCEAN
+-118.27,33.83,28.0,2152.0,415.0,1623.0,429.0,4.35,200500.0,<1H OCEAN
+-116.32,34.14,18.0,1880.0,487.0,994.0,425.0,1.69,54200.0,INLAND
+-122.1,37.36,32.0,1433.0,199.0,498.0,201.0,9.3586,500001.0,NEAR BAY
+-119.0,35.35,35.0,1164.0,277.0,992.0,284.0,1.4015,48700.0,INLAND
+-117.92,33.98,10.0,16414.0,2919.0,8907.0,2714.0,6.1552,362500.0,<1H OCEAN
+-118.23,34.13,37.0,1799.0,426.0,1088.0,417.0,2.975,244500.0,<1H OCEAN
+-117.19,33.41,16.0,3031.0,554.0,1301.0,518.0,4.0882,296100.0,<1H OCEAN
+-117.93,33.79,34.0,3592.0,616.0,2138.0,605.0,5.2129,193400.0,<1H OCEAN
+-122.04,36.98,51.0,1076.0,206.0,495.0,201.0,2.9286,258300.0,NEAR OCEAN
+-122.41,37.76,52.0,2479.0,515.0,1816.0,496.0,3.0774,300000.0,NEAR BAY
+-122.45,37.76,31.0,5283.0,1330.0,2659.0,1269.0,3.5744,500000.0,NEAR BAY
+-122.54,37.89,33.0,4971.0,836.0,1907.0,795.0,6.1275,424400.0,NEAR BAY
+-121.46,38.69,11.0,3335.0,658.0,1963.0,622.0,3.3125,96800.0,INLAND
+-121.24,38.82,5.0,12259.0,1643.0,4819.0,1582.0,5.4498,217300.0,INLAND
+-122.23,39.53,8.0,1268.0,336.0,1237.0,326.0,1.3708,125000.0,INLAND
+-117.84,34.63,5.0,6739.0,1251.0,4614.0,1266.0,4.002,115100.0,INLAND
+-121.92,37.31,13.0,6035.0,1551.0,2946.0,1481.0,4.0524,213900.0,<1H OCEAN
+-120.59,34.7,29.0,17738.0,3114.0,12427.0,2826.0,2.7377,28300.0,NEAR OCEAN
+-121.99,37.3,28.0,4863.0,901.0,2110.0,868.0,5.1483,342000.0,<1H OCEAN
+-121.97,37.77,13.0,7241.0,1007.0,3221.0,947.0,7.2216,324600.0,<1H OCEAN
+-117.94,33.81,34.0,1290.0,203.0,664.0,204.0,5.8461,227400.0,<1H OCEAN
+-115.59,32.79,8.0,2183.0,307.0,1000.0,287.0,6.3814,159900.0,INLAND
+-119.14,36.06,32.0,1838.0,441.0,1628.0,425.0,1.6452,41500.0,INLAND
+-120.51,36.86,21.0,1779.0,399.0,1446.0,371.0,2.4414,71900.0,INLAND
+-121.98,37.58,20.0,4126.0,1031.0,2079.0,975.0,3.6832,216900.0,<1H OCEAN
+-117.92,34.06,34.0,2819.0,609.0,1718.0,558.0,3.5547,197600.0,<1H OCEAN
+-117.4,33.93,35.0,1468.0,298.0,1168.0,261.0,2.2222,81300.0,INLAND
+-118.24,34.23,43.0,1061.0,208.0,514.0,208.0,6.01,254200.0,<1H OCEAN
+-117.04,33.15,15.0,13814.0,2888.0,6583.0,2789.0,2.8247,150000.0,<1H OCEAN
+-118.46,34.22,39.0,1500.0,333.0,998.0,309.0,3.9625,168200.0,<1H OCEAN
+-118.39,34.18,44.0,477.0,91.0,220.0,112.0,3.3906,223800.0,<1H OCEAN
+-118.3,34.09,32.0,2202.0,674.0,2178.0,635.0,2.0307,226700.0,<1H OCEAN
+-117.06,33.02,24.0,830.0,190.0,279.0,196.0,1.9176,121100.0,<1H OCEAN
+-118.38,34.3,39.0,1622.0,355.0,903.0,314.0,4.1125,183000.0,<1H OCEAN
+-118.29,34.09,28.0,1562.0,648.0,1974.0,597.0,1.9766,112500.0,<1H OCEAN
+-118.28,34.07,25.0,7522.0,3179.0,7221.0,2902.0,2.0173,177500.0,<1H OCEAN
+-120.86,37.77,38.0,1545.0,279.0,662.0,266.0,3.825,96400.0,INLAND
+-118.23,34.14,33.0,2865.0,864.0,2061.0,790.0,2.6268,201300.0,<1H OCEAN
+-122.01,37.29,31.0,3136.0,431.0,1190.0,412.0,7.5,500001.0,<1H OCEAN
+-119.36,36.2,33.0,1955.0,398.0,1412.0,397.0,2.25,51500.0,INLAND
+-117.33,34.06,48.0,732.0,149.0,486.0,139.0,2.5673,68200.0,INLAND
+-117.09,32.76,44.0,1139.0,214.0,470.0,217.0,3.5481,203100.0,NEAR OCEAN
+-121.41,38.59,17.0,12355.0,3630.0,5692.0,3073.0,2.5245,99100.0,INLAND
+-120.85,37.57,27.0,819.0,157.0,451.0,150.0,3.4934,193800.0,INLAND
+-121.88,36.96,18.0,4910.0,817.0,1971.0,773.0,5.8325,308800.0,NEAR OCEAN
+-119.76,36.75,39.0,2233.0,563.0,2031.0,491.0,1.8641,50800.0,INLAND
+-121.32,38.0,22.0,2105.0,521.0,781.0,483.0,2.213,87500.0,INLAND
+-117.28,33.22,13.0,2832.0,542.0,1065.0,531.0,2.3844,98600.0,<1H OCEAN
+-120.48,41.82,20.0,1367.0,284.0,429.0,181.0,2.0227,47500.0,INLAND
+-118.15,34.12,43.0,1810.0,427.0,742.0,429.0,3.8529,350000.0,<1H OCEAN
+-117.59,33.93,17.0,338.0,47.0,200.0,46.0,7.8118,244200.0,INLAND
+-118.44,34.03,37.0,975.0,189.0,489.0,202.0,4.2434,331000.0,<1H OCEAN
+-118.28,33.95,40.0,2044.0,538.0,2150.0,524.0,2.1437,94800.0,<1H OCEAN
+-116.54,33.82,12.0,9482.0,2501.0,2725.0,1300.0,1.5595,115600.0,INLAND
+-118.21,34.14,44.0,1681.0,407.0,1105.0,387.0,3.2222,186500.0,<1H OCEAN
+-121.44,38.48,12.0,4929.0,1010.0,2621.0,870.0,2.7262,109800.0,INLAND
+-120.3,37.97,17.0,3243.0,619.0,1408.0,566.0,2.474,120100.0,INLAND
+-121.45,38.52,37.0,1477.0,321.0,888.0,312.0,2.5592,70300.0,INLAND
+-118.0,34.13,35.0,1005.0,224.0,742.0,221.0,3.5481,158100.0,INLAND
+-122.07,37.63,24.0,2329.0,465.0,1401.0,453.0,4.5913,177600.0,NEAR BAY
+-118.02,34.08,28.0,2769.0,631.0,2452.0,581.0,2.6071,175900.0,INLAND
+-119.18,34.27,6.0,2307.0,386.0,910.0,364.0,5.215,279500.0,NEAR OCEAN
+-121.43,37.75,42.0,1207.0,278.0,699.0,279.0,3.3611,117600.0,INLAND
+-118.61,35.47,13.0,2267.0,601.0,756.0,276.0,2.5474,78400.0,INLAND
+-122.18,40.02,30.0,1952.0,397.0,961.0,333.0,2.25,68200.0,INLAND
+-121.33,38.64,27.0,2203.0,493.0,1158.0,492.0,2.4342,119500.0,INLAND
+-118.42,34.29,34.0,1489.0,326.0,1389.0,313.0,3.4821,160300.0,<1H OCEAN
+-118.44,34.07,26.0,3535.0,748.0,1322.0,666.0,7.1674,500001.0,<1H OCEAN
+-122.29,37.89,52.0,3171.0,698.0,1498.0,696.0,3.1795,218200.0,NEAR BAY
+-122.11,37.38,22.0,3638.0,719.0,1329.0,650.0,5.0804,500001.0,NEAR BAY
+-118.34,33.88,31.0,3122.0,727.0,1885.0,715.0,3.8657,298400.0,<1H OCEAN
+-117.97,34.08,30.0,2227.0,474.0,1961.0,481.0,3.3261,164100.0,INLAND
+-117.93,34.09,34.0,2192.0,431.0,1376.0,428.0,3.9861,163900.0,<1H OCEAN
+-119.53,36.65,43.0,1676.0,320.0,1056.0,276.0,2.5562,93200.0,INLAND
+-118.51,34.28,34.0,3580.0,565.0,1694.0,524.0,5.4065,243800.0,<1H OCEAN
+-117.24,32.74,44.0,1488.0,259.0,667.0,281.0,4.0862,321800.0,NEAR OCEAN
+-121.89,37.34,43.0,1423.0,467.0,1013.0,428.0,1.6708,204200.0,<1H OCEAN
+-116.9,32.9,19.0,3090.0,552.0,1621.0,520.0,4.0806,189200.0,<1H OCEAN
+-116.84,32.92,20.0,1066.0,219.0,536.0,173.0,3.1607,119300.0,<1H OCEAN
+-121.91,37.81,7.0,3477.0,416.0,1216.0,395.0,13.1499,500001.0,INLAND
+-118.27,34.0,40.0,2099.0,599.0,2311.0,529.0,1.852,101500.0,<1H OCEAN
+-117.6,33.72,36.0,1317.0,228.0,531.0,214.0,5.6346,272500.0,<1H OCEAN
+-118.45,34.05,23.0,4099.0,1287.0,2103.0,1217.0,3.7549,275000.0,<1H OCEAN
+-118.99,37.65,20.0,2474.0,625.0,338.0,141.0,5.01,195500.0,INLAND
+-121.83,37.24,23.0,2543.0,388.0,1297.0,385.0,5.9164,237400.0,<1H OCEAN
+-123.63,41.11,19.0,1797.0,384.0,1033.0,327.0,1.4911,59200.0,<1H OCEAN
+-118.71,34.3,20.0,1586.0,187.0,699.0,209.0,6.5483,335000.0,<1H OCEAN
+-121.57,39.8,23.0,790.0,137.0,365.0,152.0,2.1912,115200.0,INLAND
+-118.03,33.97,32.0,2468.0,552.0,1190.0,479.0,3.8275,238500.0,<1H OCEAN
+-118.37,34.19,41.0,2924.0,867.0,2751.0,836.0,2.1,171600.0,<1H OCEAN
+-122.44,38.57,26.0,2101.0,390.0,2171.0,360.0,3.6429,159700.0,INLAND
+-117.68,33.51,19.0,2930.0,428.0,1481.0,430.0,6.323,480800.0,<1H OCEAN
+-119.32,36.2,15.0,1562.0,275.0,961.0,287.0,3.4231,83300.0,INLAND
+-121.43,38.48,12.0,4602.0,930.0,2299.0,860.0,3.0625,90500.0,INLAND
+-118.47,33.99,52.0,2167.0,622.0,1095.0,570.0,2.8514,358700.0,<1H OCEAN
+-117.27,32.84,26.0,3940.0,657.0,1180.0,600.0,6.1025,500001.0,NEAR OCEAN
+-119.34,36.31,14.0,1635.0,422.0,870.0,399.0,2.7,88900.0,INLAND
+-118.02,33.79,23.0,6368.0,1030.0,3281.0,1001.0,6.1142,240400.0,<1H OCEAN
+-116.97,33.13,10.0,5149.0,851.0,2177.0,783.0,6.7957,287500.0,<1H OCEAN
+-118.35,33.8,19.0,6224.0,1105.0,3152.0,1076.0,5.9541,500001.0,<1H OCEAN
+-121.86,37.4,21.0,1386.0,260.0,946.0,257.0,6.5226,258500.0,<1H OCEAN
+-117.89,33.77,35.0,1799.0,343.0,1239.0,368.0,3.9219,189600.0,<1H OCEAN
+-119.06,36.1,21.0,1344.0,249.0,868.0,221.0,2.5893,63600.0,INLAND
+-117.62,34.05,33.0,883.0,211.0,1007.0,210.0,2.8281,103600.0,INLAND
+-117.09,33.11,32.0,1713.0,321.0,891.0,286.0,3.1429,171600.0,<1H OCEAN
+-121.97,37.64,32.0,1283.0,194.0,485.0,171.0,6.0574,431000.0,<1H OCEAN
+-119.34,36.32,6.0,3266.0,604.0,1769.0,580.0,3.1574,89200.0,INLAND
+-119.25,35.76,36.0,2332.0,656.0,2175.0,610.0,1.6045,57300.0,INLAND
+-118.32,33.35,27.0,1675.0,521.0,744.0,331.0,2.1579,450000.0,ISLAND
+-122.46,38.29,35.0,1762.0,350.0,686.0,339.0,3.5982,271700.0,NEAR BAY
+-122.41,37.8,52.0,3278.0,775.0,1279.0,709.0,5.4378,500001.0,NEAR BAY
+-118.18,34.05,52.0,1070.0,231.0,925.0,220.0,1.825,133000.0,<1H OCEAN
+-119.04,35.35,31.0,1607.0,336.0,817.0,307.0,2.5644,73000.0,INLAND
+-119.81,36.71,25.0,1026.0,221.0,789.0,183.0,1.5625,52800.0,INLAND
+-122.38,37.58,52.0,1704.0,226.0,671.0,243.0,8.4704,500001.0,NEAR OCEAN
+-121.31,38.03,18.0,4893.0,1008.0,3036.0,997.0,2.5212,110000.0,INLAND
+-118.54,34.18,25.0,1938.0,457.0,1280.0,425.0,3.9632,240300.0,<1H OCEAN
+-121.02,37.67,32.0,3951.0,797.0,1916.0,740.0,2.6722,111500.0,INLAND
+-119.03,36.08,19.0,2471.0,431.0,1040.0,426.0,3.25,80600.0,INLAND
+-117.68,33.51,4.0,2428.0,401.0,959.0,386.0,6.2661,268500.0,<1H OCEAN
+-120.57,35.18,16.0,5209.0,917.0,2284.0,809.0,4.0403,346100.0,<1H OCEAN
+-118.14,34.19,49.0,1678.0,277.0,737.0,287.0,3.7222,237000.0,INLAND
+-122.44,37.8,52.0,1580.0,470.0,714.0,448.0,3.2447,500001.0,NEAR BAY
+-118.06,33.91,24.0,4880.0,1044.0,4516.0,1050.0,4.1387,157700.0,<1H OCEAN
+-121.29,37.96,48.0,1778.0,541.0,1237.0,462.0,1.3438,103100.0,INLAND
+-118.2,34.11,46.0,3659.0,1068.0,4153.0,993.0,2.5211,162900.0,<1H OCEAN
+-116.96,33.73,20.0,4735.0,973.0,2306.0,904.0,3.069,87000.0,INLAND
+-121.96,37.29,24.0,1240.0,263.0,690.0,276.0,5.0,283000.0,<1H OCEAN
+-118.98,35.39,29.0,607.0,177.0,476.0,143.0,1.1875,50700.0,INLAND
+-118.02,34.12,37.0,2250.0,360.0,989.0,329.0,6.1536,366000.0,INLAND
+-122.65,38.48,17.0,1090.0,164.0,473.0,163.0,5.5061,231800.0,<1H OCEAN
+-117.2,34.04,23.0,1762.0,267.0,1132.0,279.0,5.9915,153200.0,INLAND
+-122.09,37.68,41.0,1834.0,463.0,1105.0,467.0,2.8322,170300.0,NEAR BAY
+-122.41,37.78,52.0,1014.0,422.0,1055.0,382.0,1.8519,32500.0,NEAR BAY
+-120.52,35.06,11.0,1317.0,234.0,655.0,243.0,4.3611,329700.0,<1H OCEAN
+-118.31,34.0,52.0,1630.0,379.0,1413.0,405.0,1.933,120000.0,<1H OCEAN
+-120.44,34.96,34.0,1248.0,284.0,986.0,272.0,2.9167,104200.0,<1H OCEAN
+-117.67,33.6,20.0,1213.0,171.0,565.0,170.0,7.2592,314800.0,<1H OCEAN
+-118.07,33.82,27.0,3481.0,576.0,1660.0,560.0,5.7965,228200.0,<1H OCEAN
+-118.38,33.82,38.0,1318.0,237.0,547.0,225.0,6.0308,416700.0,NEAR OCEAN
+-118.03,34.05,36.0,1345.0,331.0,1511.0,309.0,3.5129,142300.0,<1H OCEAN
+-118.54,34.2,37.0,1600.0,349.0,1012.0,366.0,4.1597,201600.0,<1H OCEAN
+-118.23,34.18,43.0,1708.0,280.0,768.0,276.0,6.207,457400.0,<1H OCEAN
+-122.1,37.7,25.0,2973.0,622.0,1413.0,595.0,4.3819,209200.0,NEAR BAY
+-118.29,33.85,10.0,1391.0,420.0,1378.0,377.0,1.9049,222200.0,<1H OCEAN
+-117.94,33.86,35.0,1235.0,227.0,875.0,220.0,4.6964,183100.0,<1H OCEAN
+-118.18,33.85,38.0,3596.0,862.0,2416.0,832.0,3.6897,169800.0,<1H OCEAN
+-118.38,34.08,30.0,4524.0,1312.0,1910.0,1243.0,2.8889,335300.0,<1H OCEAN
+-119.83,36.8,24.0,3756.0,681.0,1586.0,739.0,3.8571,90100.0,INLAND
+-118.75,34.27,24.0,3241.0,461.0,1567.0,446.0,5.5983,233300.0,<1H OCEAN
+-122.48,37.73,38.0,3195.0,828.0,2410.0,778.0,3.1359,350000.0,NEAR OCEAN
+-118.22,33.83,44.0,1792.0,404.0,1115.0,358.0,3.9091,174400.0,<1H OCEAN
+-118.67,34.27,10.0,3753.0,678.0,1859.0,660.0,4.9946,204600.0,<1H OCEAN
+-117.3,34.12,50.0,1629.0,437.0,1581.0,394.0,2.2019,63500.0,INLAND
+-117.41,33.97,24.0,950.0,183.0,383.0,182.0,3.0694,125000.0,INLAND
+-118.4,34.05,43.0,1028.0,145.0,394.0,149.0,10.4519,500001.0,<1H OCEAN
+-122.09,37.11,32.0,2637.0,489.0,1031.0,410.0,3.6474,231600.0,NEAR OCEAN
+-118.14,33.91,34.0,2011.0,472.0,1087.0,423.0,3.0465,187800.0,<1H OCEAN
+-117.24,34.06,9.0,3603.0,786.0,1782.0,718.0,3.2604,93300.0,INLAND
+-122.03,37.34,17.0,1165.0,278.0,598.0,287.0,4.0129,342400.0,<1H OCEAN
+-118.12,34.06,23.0,1190.0,347.0,965.0,327.0,2.2261,211800.0,<1H OCEAN
+-118.2,34.14,52.0,2090.0,466.0,1219.0,390.0,4.0909,204200.0,<1H OCEAN
+-122.43,37.7,19.0,1733.0,354.0,959.0,348.0,4.7708,281700.0,NEAR BAY
+-121.94,37.56,15.0,5674.0,748.0,2412.0,714.0,8.3996,442900.0,<1H OCEAN
+-117.34,33.23,11.0,3737.0,757.0,2212.0,727.0,3.1062,141000.0,NEAR OCEAN
+-117.81,33.73,23.0,3056.0,556.0,1508.0,555.0,4.7273,234200.0,<1H OCEAN
+-119.03,35.4,35.0,2608.0,620.0,1566.0,583.0,2.1818,63500.0,INLAND
+-122.53,37.9,44.0,2846.0,551.0,1232.0,537.0,3.8839,327200.0,NEAR BAY
+-118.49,34.27,33.0,3047.0,527.0,1578.0,507.0,4.58,236200.0,<1H OCEAN
+-119.05,35.36,30.0,4635.0,800.0,2307.0,754.0,3.6548,84700.0,INLAND
+-118.38,33.97,42.0,1859.0,337.0,813.0,326.0,6.019,294500.0,<1H OCEAN
+-122.09,37.21,15.0,1969.0,332.0,822.0,324.0,7.8774,394900.0,<1H OCEAN
+-121.49,38.55,51.0,4280.0,632.0,1486.0,621.0,5.0359,224100.0,INLAND
+-121.78,37.67,26.0,2211.0,344.0,1024.0,321.0,5.2649,199800.0,INLAND
+-119.05,35.34,14.0,3580.0,984.0,1933.0,912.0,2.6637,175000.0,INLAND
+-117.28,34.14,40.0,2190.0,496.0,1214.0,493.0,2.3947,81900.0,INLAND
+-122.32,37.96,34.0,2070.0,357.0,784.0,294.0,4.0417,182800.0,NEAR BAY
+-118.26,33.89,36.0,923.0,165.0,603.0,191.0,3.5687,120700.0,<1H OCEAN
+-121.0,37.26,45.0,1750.0,371.0,847.0,354.0,1.7062,77400.0,INLAND
+-118.42,34.01,32.0,1300.0,356.0,703.0,311.0,3.5667,394000.0,<1H OCEAN
+-117.33,34.07,32.0,2086.0,458.0,1355.0,412.0,2.5238,89200.0,INLAND
+-118.22,33.94,41.0,928.0,249.0,1108.0,236.0,3.4323,144600.0,<1H OCEAN
+-117.63,34.06,39.0,1210.0,310.0,1294.0,303.0,2.3636,88300.0,INLAND
+-122.11,37.41,33.0,1641.0,284.0,659.0,282.0,6.0884,432900.0,NEAR BAY
+-122.63,38.5,19.0,2107.0,332.0,874.0,341.0,5.7819,265600.0,<1H OCEAN
+-121.45,38.6,44.0,2324.0,413.0,823.0,375.0,4.6625,158900.0,INLAND
+-122.31,40.55,11.0,13714.0,2302.0,6511.0,2267.0,3.5522,100100.0,INLAND
+-116.83,32.81,18.0,2367.0,402.0,1021.0,395.0,4.8125,210500.0,<1H OCEAN
+-118.08,33.81,20.0,6295.0,937.0,2292.0,874.0,7.6084,402500.0,<1H OCEAN
+-118.11,34.01,43.0,1539.0,386.0,1122.0,377.0,2.4605,196000.0,<1H OCEAN
+-117.96,33.86,35.0,2146.0,430.0,1230.0,429.0,3.7813,184900.0,<1H OCEAN
+-119.12,36.19,21.0,2645.0,464.0,1245.0,407.0,2.9145,114200.0,INLAND
+-122.27,37.88,44.0,2252.0,592.0,989.0,550.0,3.0132,272900.0,NEAR BAY
+-120.68,35.5,19.0,3369.0,673.0,1834.0,646.0,3.7672,173800.0,<1H OCEAN
+-118.2,33.94,45.0,1818.0,408.0,1705.0,373.0,4.0441,157500.0,<1H OCEAN
+-117.09,32.77,31.0,3062.0,,1263.0,539.0,3.0875,291500.0,NEAR OCEAN
+-117.98,33.76,24.0,1880.0,405.0,967.0,418.0,4.4545,192500.0,<1H OCEAN
+-122.72,38.45,41.0,1743.0,373.0,780.0,357.0,3.1467,175500.0,<1H OCEAN
+-121.82,38.01,25.0,3018.0,606.0,1614.0,568.0,3.4722,127000.0,INLAND
+-117.26,32.99,16.0,2127.0,512.0,1532.0,499.0,2.7348,231300.0,NEAR OCEAN
+-122.3,37.53,38.0,984.0,171.0,429.0,157.0,5.3261,376800.0,NEAR OCEAN
+-122.51,37.76,40.0,2320.0,562.0,1499.0,521.0,3.2792,260800.0,NEAR OCEAN
+-117.81,33.83,8.0,7326.0,884.0,2569.0,798.0,10.157,477100.0,<1H OCEAN
+-117.98,34.04,34.0,2547.0,537.0,2108.0,498.0,3.4722,154600.0,<1H OCEAN
+-118.45,33.99,52.0,1010.0,244.0,573.0,242.0,4.1861,363200.0,<1H OCEAN
+-118.35,33.98,33.0,1884.0,477.0,1518.0,449.0,3.1194,152800.0,<1H OCEAN
+-118.14,33.76,52.0,2677.0,642.0,1144.0,624.0,4.3889,378000.0,NEAR OCEAN
+-118.32,33.79,32.0,2381.0,467.0,1264.0,488.0,4.1477,315100.0,<1H OCEAN
+-122.56,37.91,52.0,1972.0,327.0,755.0,345.0,7.1924,500001.0,NEAR BAY
+-118.17,33.97,33.0,2410.0,641.0,2106.0,593.0,2.2422,168200.0,<1H OCEAN
+-117.08,33.82,6.0,1771.0,293.0,935.0,279.0,4.065,148200.0,INLAND
+-119.74,36.83,14.0,4675.0,829.0,2235.0,787.0,4.1098,108200.0,INLAND
+-121.34,38.55,11.0,2838.0,498.0,1701.0,504.0,4.1447,114000.0,INLAND
+-117.77,33.53,46.0,1033.0,223.0,462.0,224.0,3.2708,384700.0,<1H OCEAN
+-121.34,38.44,14.0,3205.0,465.0,1439.0,456.0,5.7452,240900.0,INLAND
+-117.93,33.85,33.0,2489.0,546.0,1857.0,444.0,2.9474,178400.0,<1H OCEAN
+-117.73,34.06,51.0,498.0,115.0,368.0,112.0,1.4063,98800.0,INLAND
+-118.0,34.14,31.0,1298.0,431.0,1131.0,425.0,1.0548,178100.0,INLAND
+-117.27,32.82,35.0,2908.0,595.0,1068.0,529.0,4.1793,500001.0,NEAR OCEAN
+-118.28,33.92,39.0,1472.0,302.0,1036.0,318.0,3.0,110000.0,<1H OCEAN
+-119.73,36.52,20.0,1741.0,331.0,1466.0,289.0,2.5921,94200.0,INLAND
+-122.28,38.29,23.0,1398.0,388.0,1112.0,406.0,2.2366,140200.0,NEAR BAY
+-118.4,34.02,19.0,7297.0,2331.0,3870.0,2144.0,3.116,300000.0,<1H OCEAN
+-122.3,37.53,43.0,1748.0,366.0,984.0,371.0,4.5116,337800.0,NEAR OCEAN
+-117.85,33.79,52.0,2102.0,403.0,898.0,365.0,3.6827,236800.0,<1H OCEAN
+-121.14,40.29,17.0,1944.0,394.0,384.0,172.0,1.6875,111500.0,INLAND
+-118.09,34.71,5.0,5807.0,1182.0,2602.0,1007.0,2.4012,159400.0,INLAND
+-118.28,33.82,26.0,4586.0,1042.0,3680.0,1027.0,4.174,205100.0,<1H OCEAN
+-118.5,34.02,24.0,2924.0,1013.0,1492.0,943.0,2.775,291700.0,<1H OCEAN
+-122.14,37.76,34.0,1513.0,231.0,545.0,211.0,5.5701,252800.0,NEAR BAY
+-117.82,33.77,32.0,2308.0,301.0,967.0,320.0,7.0565,324600.0,<1H OCEAN
+-118.17,34.02,39.0,759.0,215.0,883.0,226.0,2.125,143800.0,<1H OCEAN
+-117.26,32.81,37.0,1616.0,421.0,650.0,395.0,2.92,326500.0,NEAR OCEAN
+-117.95,33.84,18.0,3418.0,815.0,1961.0,773.0,3.65,171400.0,<1H OCEAN
+-118.92,36.04,28.0,1148.0,233.0,521.0,212.0,2.9208,98500.0,INLAND
+-121.18,37.64,43.0,1244.0,209.0,611.0,197.0,2.875,187500.0,INLAND
+-121.86,37.25,16.0,6958.0,1300.0,2965.0,1217.0,4.2885,262400.0,<1H OCEAN
+-122.41,37.61,42.0,1602.0,262.0,705.0,255.0,5.7398,336400.0,NEAR OCEAN
+-116.43,33.78,17.0,4293.0,712.0,1091.0,464.0,6.1437,232100.0,INLAND
+-122.06,37.63,12.0,6711.0,1374.0,3388.0,1289.0,3.8625,208900.0,NEAR BAY
+-118.44,34.23,36.0,1730.0,387.0,1099.0,353.0,4.0368,183100.0,<1H OCEAN
+-119.9,36.2,43.0,187.0,38.0,106.0,40.0,1.875,137500.0,INLAND
+-117.05,33.01,17.0,3430.0,425.0,1468.0,433.0,10.6186,429300.0,<1H OCEAN
+-122.04,38.25,32.0,1203.0,287.0,571.0,255.0,3.0938,110400.0,INLAND
+-121.56,38.58,32.0,2070.0,561.0,2046.0,523.0,1.9426,82300.0,INLAND
+-122.24,37.81,52.0,2485.0,313.0,953.0,327.0,6.8591,352400.0,NEAR BAY
+-121.92,36.62,52.0,1001.0,191.0,425.0,184.0,3.7614,241700.0,NEAR OCEAN
+-122.46,37.64,17.0,3523.0,669.0,2150.0,666.0,4.5938,251200.0,NEAR OCEAN
+-121.89,37.34,20.0,1106.0,494.0,851.0,448.0,0.8894,350000.0,<1H OCEAN
+-117.89,33.88,33.0,1582.0,256.0,771.0,240.0,5.3836,229600.0,<1H OCEAN
+-117.13,32.7,42.0,1210.0,292.0,945.0,258.0,0.8991,78900.0,NEAR OCEAN
+-119.02,35.42,42.0,2271.0,458.0,1124.0,447.0,2.7583,64900.0,INLAND
+-118.16,34.11,31.0,5715.0,1154.0,2639.0,1079.0,4.1661,364400.0,<1H OCEAN
+-121.37,38.63,37.0,494.0,86.0,253.0,99.0,4.8194,141100.0,INLAND
+-119.78,36.71,35.0,1987.0,394.0,1233.0,383.0,1.3587,45300.0,INLAND
+-122.75,38.48,4.0,6487.0,1112.0,2958.0,1131.0,4.5417,197400.0,<1H OCEAN
+-118.45,34.32,23.0,3481.0,641.0,1952.0,682.0,4.26,189400.0,<1H OCEAN
+-121.37,38.59,36.0,2388.0,369.0,838.0,356.0,4.775,194100.0,INLAND
+-122.25,37.5,44.0,348.0,79.0,154.0,73.0,4.7708,253800.0,NEAR OCEAN
+-117.88,33.78,26.0,3141.0,670.0,1572.0,724.0,3.3472,237400.0,<1H OCEAN
+-121.28,38.13,32.0,3366.0,676.0,1916.0,697.0,2.5401,125400.0,INLAND
+-117.2,32.77,31.0,1952.0,471.0,936.0,462.0,2.8621,196900.0,NEAR OCEAN
+-118.1,33.81,36.0,1962.0,325.0,786.0,315.0,5.62,239600.0,<1H OCEAN
+-118.3,34.05,51.0,1005.0,314.0,1227.0,306.0,2.4297,162500.0,<1H OCEAN
+-117.25,32.73,38.0,1840.0,291.0,633.0,283.0,4.9125,383600.0,NEAR OCEAN
+-118.16,34.02,41.0,1256.0,391.0,1511.0,381.0,1.7981,166000.0,<1H OCEAN
+-117.27,33.19,8.0,973.0,289.0,663.0,209.0,2.724,139300.0,NEAR OCEAN
+-118.12,33.84,37.0,1242.0,221.0,565.0,213.0,4.1094,215800.0,<1H OCEAN
+-117.86,33.73,23.0,407.0,108.0,647.0,96.0,3.775,177400.0,<1H OCEAN
+-118.35,34.07,45.0,3312.0,880.0,1157.0,809.0,3.5719,500001.0,<1H OCEAN
+-116.97,33.93,29.0,2793.0,722.0,1583.0,626.0,1.424,73200.0,INLAND
+-121.45,38.54,48.0,3421.0,734.0,1441.0,727.0,1.9485,86600.0,INLAND
+-118.32,33.97,52.0,1778.0,320.0,795.0,279.0,3.5114,138800.0,<1H OCEAN
+-118.43,34.16,40.0,1134.0,184.0,452.0,187.0,4.569,333900.0,<1H OCEAN
+-117.25,33.21,9.0,1944.0,488.0,1992.0,453.0,2.066,127200.0,<1H OCEAN
+-118.35,34.08,52.0,1003.0,200.0,514.0,204.0,3.8472,395700.0,<1H OCEAN
+-121.19,40.23,10.0,1572.0,232.0,247.0,104.0,5.8453,193800.0,INLAND
+-123.26,41.86,25.0,2344.0,532.0,1117.0,424.0,2.7222,64600.0,INLAND
+-118.32,34.03,50.0,1845.0,349.0,1109.0,335.0,2.8971,127800.0,<1H OCEAN
+-122.65,38.4,21.0,1059.0,150.0,400.0,154.0,6.8586,343100.0,<1H OCEAN
+-118.01,33.82,10.0,3897.0,893.0,1992.0,693.0,4.1591,192300.0,<1H OCEAN
+-118.17,34.19,45.0,1790.0,316.0,1035.0,312.0,4.0985,173100.0,<1H OCEAN
+-118.94,35.38,26.0,4800.0,831.0,2365.0,743.0,3.7533,87000.0,INLAND
+-121.87,37.42,25.0,4430.0,729.0,2685.0,721.0,5.6965,261100.0,<1H OCEAN
+-116.43,33.75,24.0,2596.0,438.0,473.0,237.0,3.7727,500001.0,INLAND
+-117.18,32.8,10.0,3821.0,631.0,1605.0,609.0,5.5454,217100.0,NEAR OCEAN
+-121.97,37.52,26.0,3761.0,623.0,1776.0,613.0,4.5317,232600.0,<1H OCEAN
+-117.84,33.78,24.0,3817.0,787.0,1656.0,713.0,4.25,248000.0,<1H OCEAN
+-121.98,37.37,36.0,1651.0,344.0,1062.0,331.0,4.575,215400.0,<1H OCEAN
+-122.01,36.97,43.0,2162.0,509.0,1208.0,464.0,2.5417,260900.0,NEAR OCEAN
+-122.07,37.65,31.0,3300.0,790.0,2181.0,740.0,3.016,161800.0,NEAR BAY
+-117.29,34.57,22.0,1054.0,239.0,428.0,239.0,1.2548,68300.0,INLAND
+-118.36,34.03,40.0,2323.0,661.0,1847.0,614.0,1.8316,113500.0,<1H OCEAN
+-117.21,32.85,26.0,2012.0,315.0,872.0,335.0,5.4067,277500.0,NEAR OCEAN
+-118.32,33.91,35.0,881.0,159.0,605.0,170.0,3.6654,184500.0,<1H OCEAN
+-118.33,34.02,45.0,1667.0,399.0,928.0,375.0,1.8783,118200.0,<1H OCEAN
+-118.71,34.3,23.0,1983.0,280.0,978.0,287.0,6.3199,236700.0,<1H OCEAN
+-117.14,32.76,35.0,1785.0,493.0,965.0,506.0,2.0792,160000.0,NEAR OCEAN
+-121.47,38.56,52.0,889.0,162.0,273.0,145.0,3.125,85600.0,INLAND
+-117.33,34.17,13.0,3616.0,665.0,2189.0,620.0,3.7949,106300.0,INLAND
+-117.32,33.96,19.0,3216.0,666.0,1363.0,629.0,3.7585,144500.0,INLAND
+-119.41,36.35,20.0,1743.0,340.0,1390.0,336.0,2.2222,52900.0,INLAND
+-121.48,38.56,52.0,814.0,216.0,327.0,181.0,2.8542,125000.0,INLAND
+-117.37,34.59,39.0,8193.0,1747.0,6852.0,1597.0,2.3832,35000.0,INLAND
+-118.23,34.22,37.0,1376.0,237.0,618.0,226.0,5.9771,431800.0,<1H OCEAN
+-117.9,33.6,25.0,2465.0,585.0,906.0,472.0,3.6538,500001.0,<1H OCEAN
+-120.17,39.32,14.0,2421.0,489.0,1000.0,354.0,3.5652,119800.0,INLAND
+-117.13,32.72,19.0,1341.0,435.0,1048.0,360.0,1.975,117900.0,NEAR OCEAN
+-118.75,34.28,22.0,3844.0,537.0,1665.0,492.0,6.2059,239900.0,<1H OCEAN
+-118.27,33.79,39.0,1417.0,359.0,1450.0,367.0,2.8462,172000.0,<1H OCEAN
+-118.48,34.07,37.0,4042.0,549.0,1318.0,542.0,12.8665,500001.0,<1H OCEAN
+-118.23,35.48,17.0,2354.0,514.0,775.0,380.0,1.8369,59400.0,INLAND
+-118.09,33.96,20.0,1911.0,472.0,1407.0,465.0,2.7647,163000.0,<1H OCEAN
+-118.42,34.06,40.0,2933.0,565.0,1077.0,536.0,6.1527,500001.0,<1H OCEAN
+-119.79,34.4,20.0,3104.0,415.0,1061.0,380.0,9.6885,500001.0,NEAR OCEAN
+-117.24,33.34,17.0,2866.0,442.0,1354.0,431.0,4.5764,257300.0,<1H OCEAN
+-118.01,33.91,32.0,2722.0,571.0,2541.0,462.0,4.2305,221400.0,<1H OCEAN
+-118.37,34.05,41.0,2369.0,544.0,1252.0,522.0,2.9883,296100.0,<1H OCEAN
+-117.78,33.68,15.0,1834.0,330.0,841.0,309.0,6.0634,234300.0,<1H OCEAN
+-122.09,37.41,18.0,1476.0,473.0,838.0,415.0,3.575,274000.0,NEAR BAY
+-118.27,33.92,35.0,1818.0,374.0,1444.0,372.0,2.745,106800.0,<1H OCEAN
+-118.11,34.12,48.0,2476.0,313.0,900.0,303.0,10.6767,500001.0,<1H OCEAN
+-117.03,32.61,22.0,1028.0,148.0,523.0,152.0,6.0086,166900.0,NEAR OCEAN
+-117.92,33.79,35.0,1785.0,288.0,1033.0,297.0,4.5739,190500.0,<1H OCEAN
+-117.27,32.99,21.0,3318.0,578.0,1273.0,538.0,5.5922,382100.0,NEAR OCEAN
+-118.22,33.92,32.0,1263.0,333.0,1789.0,346.0,1.9957,89300.0,<1H OCEAN
+-119.38,36.54,33.0,2465.0,536.0,2030.0,522.0,1.5223,51800.0,INLAND
+-117.17,32.8,20.0,2827.0,554.0,1822.0,536.0,3.4706,157600.0,NEAR OCEAN
+-118.7,35.82,20.0,4642.0,1300.0,658.0,247.0,2.3937,82100.0,INLAND
+-121.38,37.88,44.0,1158.0,226.0,1094.0,224.0,2.6842,156300.0,INLAND
+-116.95,32.73,17.0,1635.0,272.0,960.0,279.0,5.2671,157100.0,<1H OCEAN
+-122.11,37.41,27.0,5110.0,1599.0,2764.0,1482.0,3.4198,351900.0,NEAR BAY
+-118.28,34.02,46.0,1098.0,426.0,1510.0,374.0,2.1382,156300.0,<1H OCEAN
+-118.12,34.16,52.0,2218.0,437.0,1211.0,422.0,5.0237,241900.0,<1H OCEAN
+-122.43,37.72,52.0,3351.0,719.0,2101.0,706.0,3.0107,242000.0,NEAR BAY
+-118.51,34.24,31.0,5297.0,664.0,1986.0,657.0,8.6454,483500.0,<1H OCEAN
+-119.33,36.31,17.0,2401.0,409.0,1100.0,409.0,4.0577,107300.0,INLAND
+-117.26,33.08,12.0,5080.0,814.0,1958.0,716.0,5.3905,299600.0,NEAR OCEAN
+-121.96,37.76,8.0,3865.0,463.0,1548.0,432.0,9.7037,425100.0,<1H OCEAN
+-122.02,37.54,31.0,1240.0,264.0,719.0,236.0,3.535,210300.0,NEAR BAY
+-118.1,34.12,49.0,3783.0,579.0,1601.0,539.0,6.3013,500001.0,<1H OCEAN
+-117.61,34.08,12.0,4427.0,,2400.0,843.0,4.7147,158700.0,INLAND
+-118.47,34.15,43.0,804.0,117.0,267.0,110.0,8.2269,500001.0,<1H OCEAN
+-117.15,32.8,27.0,1937.0,537.0,1211.0,482.0,2.75,87500.0,NEAR OCEAN
+-121.4,38.62,28.0,3671.0,886.0,1733.0,820.0,2.2292,113200.0,INLAND
+-122.74,38.44,23.0,2819.0,612.0,1644.0,546.0,2.6576,147900.0,<1H OCEAN
+-122.49,38.27,8.0,5092.0,988.0,1657.0,936.0,3.5625,213200.0,NEAR BAY
+-120.04,39.24,30.0,2369.0,469.0,510.0,213.0,2.65,123800.0,INLAND
+-122.27,37.74,28.0,6909.0,1554.0,2974.0,1484.0,3.6875,353900.0,NEAR BAY
+-119.17,34.17,42.0,1411.0,300.0,1295.0,339.0,2.6667,164900.0,NEAR OCEAN
+-121.88,37.44,20.0,1351.0,372.0,1427.0,394.0,4.4712,144000.0,<1H OCEAN
+-122.08,37.04,34.0,2800.0,577.0,1353.0,512.0,4.1161,220900.0,NEAR OCEAN
+-122.29,38.0,16.0,4986.0,1081.0,2805.0,1016.0,4.025,173200.0,NEAR BAY
+-118.09,33.9,37.0,1112.0,222.0,771.0,224.0,4.2132,164600.0,<1H OCEAN
+-117.92,33.87,36.0,1125.0,285.0,966.0,257.0,2.8438,162500.0,<1H OCEAN
+-122.88,38.34,20.0,3404.0,628.0,1641.0,585.0,5.0574,276200.0,<1H OCEAN
+-122.24,37.79,43.0,1626.0,376.0,1284.0,357.0,2.2542,112200.0,NEAR BAY
+-118.32,33.92,35.0,1281.0,219.0,710.0,184.0,4.8304,152800.0,<1H OCEAN
+-122.04,37.55,23.0,3170.0,532.0,1446.0,515.0,4.4357,291700.0,NEAR BAY
+-117.7,33.47,21.0,1857.0,399.0,881.0,380.0,3.8403,350000.0,<1H OCEAN
+-122.36,37.92,52.0,215.0,41.0,126.0,43.0,1.3929,104200.0,NEAR BAY
+-121.18,38.67,42.0,2101.0,480.0,945.0,426.0,2.3333,116000.0,INLAND
+-117.97,33.71,26.0,2553.0,405.0,1337.0,411.0,5.3737,252900.0,<1H OCEAN
+-117.69,33.64,16.0,2592.0,372.0,1279.0,383.0,6.9741,262000.0,<1H OCEAN
+-122.41,37.66,32.0,1385.0,356.0,1096.0,353.0,4.475,246700.0,NEAR OCEAN
+-117.08,32.74,26.0,2359.0,622.0,2067.0,581.0,1.8103,124700.0,NEAR OCEAN
+-121.36,38.15,42.0,2051.0,334.0,878.0,318.0,4.3553,185700.0,INLAND
+-118.04,33.81,22.0,4057.0,624.0,2204.0,643.0,5.8527,241000.0,<1H OCEAN
+-118.97,36.06,26.0,1289.0,262.0,1100.0,244.0,1.975,51400.0,INLAND
+-117.63,33.65,10.0,3580.0,491.0,1688.0,467.0,7.7814,288700.0,<1H OCEAN
+-117.95,33.63,29.0,1496.0,282.0,463.0,215.0,6.0516,500001.0,<1H OCEAN
+-119.95,37.47,32.0,1312.0,315.0,600.0,265.0,1.5,91500.0,INLAND
+-117.15,34.48,31.0,265.0,55.0,186.0,55.0,2.125,64800.0,INLAND
+-120.63,35.13,16.0,2680.0,704.0,975.0,619.0,1.7878,55000.0,<1H OCEAN
+-118.44,34.05,32.0,1880.0,435.0,798.0,417.0,4.7109,500000.0,<1H OCEAN
+-117.92,33.84,45.0,2019.0,394.0,1549.0,377.0,4.6111,223000.0,<1H OCEAN
+-121.86,37.35,35.0,2391.0,605.0,1886.0,595.0,2.5551,182100.0,<1H OCEAN
+-118.47,34.01,41.0,2704.0,557.0,1047.0,478.0,4.4211,462900.0,<1H OCEAN
+-117.87,33.88,28.0,2612.0,602.0,1682.0,563.0,3.6417,204300.0,<1H OCEAN
+-122.41,37.71,49.0,1852.0,429.0,1615.0,447.0,3.495,217800.0,NEAR BAY
+-124.09,40.95,18.0,2250.0,484.0,1248.0,472.0,2.5893,99600.0,NEAR OCEAN
+-118.17,34.1,37.0,299.0,89.0,318.0,92.0,1.3125,145800.0,<1H OCEAN
+-118.41,34.03,33.0,1730.0,386.0,994.0,363.0,3.7277,500001.0,<1H OCEAN
+-117.95,34.03,33.0,1453.0,326.0,1609.0,319.0,3.7578,155800.0,<1H OCEAN
+-117.67,33.66,4.0,10175.0,2181.0,4762.0,1929.0,4.7341,237400.0,<1H OCEAN
+-121.98,38.36,24.0,2434.0,630.0,1538.0,574.0,2.1067,101100.0,INLAND
+-117.18,32.79,30.0,5201.0,1104.0,2961.0,1064.0,3.2661,140400.0,NEAR OCEAN
+-117.27,33.03,19.0,2899.0,499.0,1356.0,512.0,4.87,220900.0,NEAR OCEAN
+-117.12,32.93,7.0,1427.0,243.0,927.0,239.0,5.3625,218900.0,<1H OCEAN
+-118.35,34.27,32.0,604.0,108.0,314.0,113.0,6.2037,205400.0,<1H OCEAN
+-117.29,34.16,38.0,2458.0,488.0,1135.0,453.0,2.875,99100.0,INLAND
+-122.43,37.71,52.0,1410.0,286.0,879.0,282.0,3.1908,255600.0,NEAR BAY
+-122.31,37.57,31.0,2197.0,477.0,1193.0,394.0,4.6371,271100.0,NEAR OCEAN
+-117.87,34.15,37.0,2655.0,415.0,1056.0,401.0,5.4224,269500.0,INLAND
+-118.44,34.01,42.0,2061.0,396.0,907.0,393.0,6.0804,420000.0,<1H OCEAN
+-117.64,34.06,43.0,763.0,219.0,851.0,198.0,1.7292,79200.0,INLAND
+-121.45,38.53,34.0,1893.0,415.0,884.0,395.0,2.1679,75400.0,INLAND
+-118.12,33.88,36.0,1083.0,218.0,557.0,210.0,3.0795,218400.0,<1H OCEAN
+-118.95,35.38,35.0,2220.0,388.0,906.0,373.0,3.5938,95200.0,INLAND
+-122.45,37.67,36.0,1664.0,326.0,963.0,322.0,4.7813,246400.0,NEAR OCEAN
+-119.19,34.16,34.0,2610.0,466.0,1543.0,433.0,3.9722,189000.0,NEAR OCEAN
+-117.05,32.77,23.0,2556.0,662.0,1200.0,548.0,1.8899,147700.0,NEAR OCEAN
+-122.74,38.45,25.0,2696.0,496.0,1296.0,514.0,4.0798,179200.0,<1H OCEAN
+-116.96,34.94,20.0,2355.0,467.0,1198.0,428.0,3.9934,88500.0,INLAND
+-119.83,36.82,14.0,1087.0,165.0,365.0,176.0,7.2909,155600.0,INLAND
+-118.04,33.82,26.0,4105.0,637.0,2072.0,648.0,5.844,273900.0,<1H OCEAN
+-118.59,35.72,28.0,1491.0,408.0,98.0,48.0,1.4205,90000.0,INLAND
+-122.01,37.99,28.0,1900.0,401.0,918.0,351.0,3.7841,144900.0,NEAR BAY
+-118.43,34.22,34.0,2300.0,429.0,1447.0,455.0,4.2656,233700.0,<1H OCEAN
+-119.02,34.24,24.0,4650.0,748.0,2374.0,702.0,5.8838,232600.0,<1H OCEAN
+-121.23,38.69,19.0,5268.0,849.0,2357.0,849.0,3.9226,148700.0,INLAND
+-118.38,34.05,52.0,1241.0,210.0,526.0,214.0,4.4191,334100.0,<1H OCEAN
+-117.31,34.09,34.0,1336.0,345.0,1009.0,311.0,1.608,73700.0,INLAND
+-123.35,40.99,23.0,141.0,59.0,47.0,23.0,1.125,66000.0,INLAND
+-116.9,34.52,20.0,3481.0,840.0,1694.0,587.0,1.4,77700.0,INLAND
+-121.7,36.6,19.0,3562.0,530.0,1607.0,505.0,5.0162,283100.0,<1H OCEAN
+-120.17,39.33,18.0,1046.0,204.0,486.0,179.0,4.119,110900.0,INLAND
+-117.51,33.88,24.0,3044.0,602.0,2541.0,564.0,4.131,123800.0,INLAND
+-123.8,39.44,33.0,2024.0,459.0,1019.0,422.0,1.9208,93600.0,<1H OCEAN
+-120.41,34.96,16.0,2299.0,403.0,1245.0,395.0,4.2125,148300.0,<1H OCEAN
+-117.16,32.71,5.0,2508.0,827.0,2066.0,761.0,1.3092,325000.0,NEAR OCEAN
+-119.75,34.5,26.0,3563.0,579.0,1479.0,575.0,5.9522,438400.0,<1H OCEAN
+-118.3,34.26,42.0,2186.0,423.0,1145.0,439.0,4.81,191900.0,<1H OCEAN
+-117.05,33.26,14.0,3103.0,569.0,1704.0,539.0,3.7644,264700.0,<1H OCEAN
+-117.96,33.93,15.0,2014.0,419.0,839.0,390.0,4.7446,175400.0,<1H OCEAN
+-118.27,34.0,46.0,1748.0,428.0,1707.0,409.0,2.148,103800.0,<1H OCEAN
+-122.72,40.17,16.0,396.0,78.0,188.0,72.0,1.3889,87500.0,INLAND
+-117.94,33.92,28.0,639.0,179.0,1062.0,169.0,3.0588,145200.0,<1H OCEAN
+-118.17,34.01,30.0,1228.0,358.0,1603.0,323.0,3.0225,130800.0,<1H OCEAN
+-118.16,34.19,44.0,2195.0,449.0,1377.0,417.0,3.5887,153500.0,<1H OCEAN
+-122.04,37.97,39.0,1323.0,245.0,705.0,261.0,3.1968,151000.0,NEAR BAY
+-118.99,35.35,32.0,1293.0,317.0,1109.0,286.0,1.1786,45600.0,INLAND
+-122.13,37.75,30.0,414.0,54.0,137.0,50.0,4.975,311100.0,NEAR BAY
+-118.22,34.03,45.0,554.0,214.0,888.0,218.0,1.8125,139600.0,<1H OCEAN
+-121.31,37.98,47.0,3386.0,663.0,1228.0,619.0,3.0625,141500.0,INLAND
+-122.35,37.58,26.0,854.0,246.0,396.0,231.0,2.8393,375000.0,NEAR OCEAN
+-122.43,37.78,24.0,2037.0,696.0,1371.0,585.0,0.9355,112500.0,NEAR BAY
+-118.33,34.27,29.0,3034.0,732.0,1776.0,702.0,3.1349,230200.0,<1H OCEAN
+-121.94,37.34,42.0,2174.0,420.0,1304.0,464.0,3.1429,286500.0,<1H OCEAN
+-121.4,38.47,4.0,20982.0,3392.0,10329.0,3086.0,4.3658,130600.0,INLAND
+-117.21,34.49,14.0,2125.0,348.0,1067.0,360.0,3.6333,116200.0,INLAND
+-117.35,33.16,22.0,1331.0,305.0,580.0,193.0,3.975,500001.0,NEAR OCEAN
+-119.12,35.37,13.0,4527.0,713.0,2170.0,671.0,4.8266,146200.0,INLAND
+-118.46,34.06,46.0,1302.0,215.0,482.0,226.0,7.0674,500001.0,<1H OCEAN
+-121.83,37.28,17.0,3057.0,606.0,2030.0,602.0,5.2166,230900.0,<1H OCEAN
+-122.62,38.92,13.0,520.0,115.0,249.0,109.0,1.8417,84700.0,INLAND
+-122.43,37.75,52.0,2285.0,509.0,839.0,456.0,4.7946,355600.0,NEAR BAY
+-121.93,37.28,34.0,2422.0,370.0,1010.0,395.0,5.6494,376200.0,<1H OCEAN
+-117.26,33.18,9.0,4540.0,793.0,2235.0,746.0,4.5781,225600.0,NEAR OCEAN
+-117.93,33.93,37.0,1128.0,273.0,931.0,234.0,2.8,137500.0,<1H OCEAN
+-117.0,32.74,17.0,2357.0,599.0,1423.0,510.0,1.8856,118800.0,<1H OCEAN
+-118.33,34.02,42.0,2043.0,378.0,869.0,416.0,3.5,181100.0,<1H OCEAN
+-117.05,33.01,19.0,3558.0,588.0,1439.0,578.0,4.625,211100.0,<1H OCEAN
+-118.94,36.32,10.0,2271.0,398.0,986.0,358.0,4.0703,147100.0,INLAND
+-118.29,33.79,10.0,3708.0,1016.0,2855.0,948.0,2.0,165400.0,<1H OCEAN
+-118.16,33.79,26.0,3061.0,844.0,2135.0,769.0,2.875,164000.0,NEAR OCEAN
+-118.82,34.15,9.0,655.0,110.0,222.0,109.0,7.8528,337500.0,NEAR OCEAN
+-120.02,38.86,19.0,2429.0,459.0,883.0,300.0,3.017,97600.0,INLAND
+-117.12,32.75,25.0,2222.0,634.0,1025.0,568.0,1.64,130000.0,NEAR OCEAN
+-117.89,33.78,16.0,6352.0,1747.0,5085.0,1649.0,2.8835,193800.0,<1H OCEAN
+-122.33,37.57,43.0,2543.0,621.0,1301.0,606.0,3.1111,318400.0,NEAR OCEAN
+-120.47,34.7,24.0,2387.0,385.0,1051.0,382.0,4.4595,152700.0,NEAR OCEAN
+-121.28,38.13,48.0,1892.0,333.0,804.0,352.0,4.0625,143200.0,INLAND
+-118.69,34.21,10.0,3663.0,409.0,1179.0,371.0,12.542,500001.0,<1H OCEAN
+-122.42,37.73,48.0,1474.0,308.0,998.0,330.0,4.0781,250300.0,NEAR BAY
+-117.95,33.68,26.0,2249.0,344.0,1311.0,373.0,5.0287,265000.0,<1H OCEAN
+-122.1,37.38,37.0,4167.0,612.0,1577.0,597.0,7.5655,500001.0,NEAR BAY
+-118.44,34.06,13.0,4833.0,1119.0,1649.0,807.0,6.2389,500001.0,<1H OCEAN
+-121.27,38.71,16.0,4082.0,666.0,1912.0,652.0,4.4609,142900.0,INLAND
+-116.2,33.63,23.0,1152.0,273.0,1077.0,235.0,2.5,96300.0,INLAND
+-120.33,38.0,14.0,1944.0,330.0,822.0,314.0,3.5,170700.0,INLAND
+-122.3,37.9,35.0,1102.0,308.0,688.0,303.0,2.3946,141700.0,NEAR BAY
+-120.46,34.74,15.0,2185.0,386.0,827.0,336.0,5.3765,223100.0,NEAR OCEAN
+-118.46,34.0,37.0,388.0,83.0,248.0,84.0,5.1664,326700.0,<1H OCEAN
+-118.13,33.9,36.0,1814.0,350.0,886.0,347.0,3.4868,208400.0,<1H OCEAN
+-122.2,39.51,37.0,2358.0,413.0,1060.0,424.0,2.8333,69700.0,INLAND
+-122.8,38.37,26.0,1634.0,315.0,909.0,317.0,4.1731,257200.0,<1H OCEAN
+-118.49,34.03,32.0,3851.0,900.0,1456.0,836.0,4.5208,442100.0,<1H OCEAN
+-117.24,33.22,20.0,1962.0,334.0,1173.0,349.0,4.1316,162500.0,<1H OCEAN
+-121.93,37.24,26.0,2574.0,414.0,1096.0,428.0,6.0738,335900.0,<1H OCEAN
+-117.59,33.66,3.0,1206.0,256.0,563.0,287.0,5.1589,167800.0,<1H OCEAN
+-122.17,37.76,40.0,1685.0,343.0,949.0,342.0,1.8426,94800.0,NEAR BAY
+-122.12,37.7,19.0,2495.0,635.0,1571.0,579.0,2.5833,159900.0,NEAR BAY
+-121.86,39.74,13.0,3494.0,843.0,1571.0,784.0,1.1019,120200.0,INLAND
+-117.95,33.71,20.0,2781.0,407.0,1242.0,408.0,6.1092,306500.0,<1H OCEAN
+-122.04,37.53,25.0,4458.0,922.0,2998.0,890.0,3.9667,218500.0,NEAR BAY
+-117.18,32.81,19.0,6823.0,1509.0,3784.0,1509.0,3.1032,179500.0,NEAR OCEAN
+-117.15,32.7,50.0,475.0,172.0,483.0,120.0,1.3657,162500.0,NEAR OCEAN
+-120.27,34.72,14.0,1289.0,277.0,693.0,237.0,3.2569,230800.0,<1H OCEAN
+-121.29,37.95,52.0,107.0,79.0,167.0,53.0,0.7917,22500.0,INLAND
+-120.82,37.64,20.0,3375.0,630.0,1505.0,598.0,2.69,201300.0,INLAND
+-121.96,37.95,7.0,3418.0,740.0,1583.0,676.0,3.6133,196100.0,INLAND
+-118.28,33.93,45.0,529.0,112.0,448.0,120.0,3.5833,90600.0,<1H OCEAN
+-121.96,37.31,26.0,4310.0,678.0,1819.0,686.0,7.0469,365500.0,<1H OCEAN
+-119.14,36.23,22.0,2935.0,523.0,1927.0,530.0,2.5875,70400.0,INLAND
+-122.45,37.72,47.0,1773.0,345.0,1083.0,315.0,4.475,221200.0,NEAR BAY
+-118.32,33.33,52.0,2127.0,512.0,733.0,288.0,3.3906,300000.0,ISLAND
+-117.17,33.34,15.0,3313.0,679.0,1022.0,564.0,2.7986,189900.0,<1H OCEAN
+-120.07,36.98,12.0,1790.0,379.0,1399.0,397.0,2.5388,59600.0,INLAND
+-118.17,33.92,36.0,2447.0,503.0,1532.0,498.0,4.3667,171800.0,<1H OCEAN
+-117.59,33.4,22.0,3167.0,743.0,1797.0,642.0,4.0076,252100.0,NEAR OCEAN
+-122.41,37.73,41.0,2115.0,378.0,1168.0,365.0,4.0642,272500.0,NEAR BAY
+-118.15,34.09,52.0,2203.0,430.0,1238.0,403.0,4.4306,225800.0,<1H OCEAN
+-118.25,34.15,13.0,1107.0,479.0,616.0,443.0,0.8185,187500.0,<1H OCEAN
+-117.25,32.75,36.0,1929.0,526.0,974.0,491.0,1.7622,205800.0,NEAR OCEAN
+-121.67,36.66,19.0,9371.0,1980.0,4259.0,1882.0,3.6875,189700.0,<1H OCEAN
+-118.52,34.04,47.0,1985.0,315.0,819.0,340.0,6.5147,500001.0,<1H OCEAN
+-122.14,37.9,32.0,5738.0,746.0,2099.0,732.0,10.3224,500001.0,NEAR BAY
+-121.65,36.7,29.0,4964.0,1056.0,2773.0,1036.0,3.0827,148100.0,<1H OCEAN
+-118.05,34.11,48.0,1410.0,304.0,677.0,274.0,3.2596,272400.0,INLAND
+-120.42,37.98,18.0,3059.0,609.0,1335.0,581.0,2.5129,115900.0,INLAND
+-117.75,34.06,52.0,1171.0,318.0,1126.0,276.0,1.9762,105800.0,INLAND
+-119.98,38.93,28.0,1194.0,272.0,494.0,203.0,2.3281,85800.0,INLAND
+-121.44,38.57,52.0,3080.0,545.0,975.0,495.0,3.776,164500.0,INLAND
+-117.71,33.62,22.0,2520.0,387.0,1338.0,391.0,5.8898,242800.0,<1H OCEAN
+-120.84,35.3,15.0,2062.0,327.0,781.0,316.0,4.9359,317700.0,NEAR OCEAN
+-117.98,33.75,27.0,2343.0,415.0,1537.0,426.0,5.1345,210600.0,<1H OCEAN
+-117.96,33.93,29.0,2316.0,522.0,1275.0,501.0,3.776,192600.0,<1H OCEAN
+-122.23,37.76,52.0,1705.0,246.0,658.0,253.0,5.75,306300.0,NEAR BAY
+-116.93,32.82,26.0,4129.0,714.0,1820.0,718.0,4.2586,171000.0,<1H OCEAN
+-119.27,34.28,52.0,2239.0,420.0,941.0,397.0,4.125,349000.0,NEAR OCEAN
+-124.16,40.78,46.0,1975.0,346.0,791.0,349.0,3.8,81800.0,NEAR OCEAN
+-117.89,33.76,34.0,1050.0,210.0,723.0,201.0,4.8,192700.0,<1H OCEAN
+-117.37,34.01,15.0,1386.0,247.0,703.0,185.0,3.6415,124200.0,INLAND
+-122.25,37.86,52.0,1587.0,444.0,878.0,449.0,1.7652,336800.0,NEAR BAY
+-120.44,37.31,16.0,3369.0,532.0,1770.0,574.0,5.2662,126200.0,INLAND
+-116.95,32.78,20.0,3425.0,448.0,1489.0,443.0,6.2552,296400.0,<1H OCEAN
+-119.18,34.26,22.0,2334.0,359.0,1298.0,363.0,5.5275,228900.0,NEAR OCEAN
+-118.21,33.96,39.0,2050.0,529.0,1959.0,485.0,2.1389,168900.0,<1H OCEAN
+-117.92,33.87,44.0,308.0,87.0,301.0,100.0,2.1991,191100.0,<1H OCEAN
+-121.91,36.63,42.0,817.0,194.0,391.0,193.0,2.1776,279200.0,<1H OCEAN
+-118.99,35.4,43.0,2225.0,392.0,890.0,374.0,4.0208,90400.0,INLAND
+-124.09,40.87,44.0,692.0,206.0,398.0,211.0,1.1576,87500.0,NEAR OCEAN
+-120.12,34.6,10.0,2426.0,426.0,966.0,419.0,5.5106,290900.0,NEAR OCEAN
+-119.49,36.58,21.0,2106.0,410.0,867.0,380.0,1.9913,95300.0,INLAND
+-122.29,37.53,35.0,2043.0,511.0,1089.0,504.0,3.0278,310600.0,NEAR OCEAN
+-121.61,39.76,31.0,2431.0,512.0,1026.0,427.0,2.5428,85000.0,INLAND
+-122.37,40.55,26.0,1435.0,234.0,544.0,232.0,2.6705,136700.0,INLAND
+-117.05,32.75,29.0,2767.0,612.0,1437.0,587.0,2.8306,142900.0,NEAR OCEAN
+-122.0,36.98,43.0,1636.0,324.0,792.0,325.0,3.5562,239200.0,NEAR OCEAN
+-117.68,33.49,17.0,2232.0,372.0,1072.0,385.0,4.245,214500.0,<1H OCEAN
+-121.35,38.03,8.0,1904.0,255.0,895.0,242.0,5.7201,155700.0,INLAND
+-117.03,32.69,5.0,3201.0,532.0,2061.0,536.0,5.0829,179400.0,NEAR OCEAN
+-121.03,39.14,10.0,3138.0,524.0,1275.0,511.0,4.0775,164500.0,INLAND
+-117.06,32.69,13.0,705.0,149.0,718.0,155.0,4.4375,154900.0,NEAR OCEAN
+-120.49,37.32,10.0,1275.0,255.0,620.0,240.0,3.0263,118300.0,INLAND
+-118.2,34.14,52.0,3800.0,646.0,1842.0,620.0,5.5524,293900.0,<1H OCEAN
+-121.87,38.02,31.0,3644.0,746.0,2229.0,678.0,3.1389,117800.0,INLAND
+-122.13,37.47,25.0,1630.0,353.0,1546.0,371.0,5.0893,173400.0,NEAR BAY
+-120.86,35.39,23.0,1664.0,355.0,629.0,279.0,2.7344,188300.0,NEAR OCEAN
+-121.88,37.24,24.0,4420.0,996.0,2981.0,975.0,3.506,226400.0,<1H OCEAN
+-117.87,34.14,30.0,2495.0,586.0,1139.0,559.0,2.9375,209200.0,INLAND
+-117.3,34.15,45.0,942.0,166.0,401.0,174.0,3.8594,90800.0,INLAND
+-120.61,35.12,12.0,3430.0,793.0,1840.0,720.0,2.9821,162000.0,<1H OCEAN
+-118.34,33.87,34.0,1069.0,217.0,601.0,212.0,4.6406,255900.0,<1H OCEAN
+-118.39,34.28,24.0,4694.0,820.0,3566.0,777.0,4.4818,166200.0,<1H OCEAN
+-121.52,39.5,33.0,1462.0,241.0,569.0,231.0,3.2833,82600.0,INLAND
+-122.18,37.77,52.0,2744.0,547.0,1479.0,554.0,2.2768,96200.0,NEAR BAY
+-117.81,33.88,19.0,2968.0,503.0,1430.0,459.0,5.3339,371700.0,<1H OCEAN
+-116.26,33.72,10.0,9404.0,1827.0,3208.0,1283.0,3.1086,105800.0,INLAND
+-118.04,33.95,35.0,1945.0,357.0,1227.0,359.0,5.2162,171900.0,<1H OCEAN
+-119.74,34.44,27.0,1251.0,282.0,503.0,283.0,2.8,353000.0,NEAR OCEAN
+-122.03,36.97,51.0,924.0,232.0,488.0,228.0,2.1964,234400.0,NEAR OCEAN
+-118.09,33.99,35.0,2787.0,639.0,1923.0,614.0,3.5757,177900.0,<1H OCEAN
+-119.57,37.02,16.0,4199.0,794.0,2140.0,722.0,3.332,111800.0,INLAND
+-122.27,38.0,15.0,1216.0,166.0,572.0,178.0,5.8418,240300.0,NEAR BAY
+-120.45,34.64,34.0,2571.0,499.0,1105.0,451.0,3.7778,150000.0,NEAR OCEAN
+-119.05,35.4,18.0,1894.0,319.0,846.0,317.0,3.8611,126400.0,INLAND
+-116.97,32.81,19.0,1573.0,471.0,844.0,414.0,2.1422,125000.0,<1H OCEAN
+-118.42,34.28,35.0,822.0,200.0,1197.0,203.0,3.2865,133300.0,<1H OCEAN
+-122.43,37.73,52.0,1142.0,224.0,494.0,206.0,5.0602,298900.0,NEAR BAY
+-118.08,34.55,5.0,16181.0,2971.0,8152.0,2651.0,4.5237,141800.0,INLAND
+-121.42,38.54,29.0,1407.0,265.0,556.0,235.0,3.0521,108000.0,INLAND
+-117.1,33.15,5.0,3159.0,685.0,1398.0,581.0,3.1467,161100.0,<1H OCEAN
+-118.46,34.07,43.0,2511.0,456.0,808.0,407.0,6.7703,500001.0,<1H OCEAN
+-121.83,36.61,27.0,2248.0,466.0,1644.0,453.0,3.2545,131200.0,<1H OCEAN
+-118.0,33.67,34.0,3712.0,667.0,1521.0,632.0,4.8125,387800.0,<1H OCEAN
+-118.21,33.91,24.0,1545.0,391.0,1807.0,388.0,2.6429,105300.0,<1H OCEAN
+-122.31,37.55,27.0,3931.0,933.0,1877.0,851.0,3.9722,354100.0,NEAR OCEAN
+-117.2,32.83,35.0,1377.0,350.0,792.0,313.0,2.8472,161400.0,NEAR OCEAN
+-118.1,33.84,36.0,1915.0,316.0,850.0,319.0,4.7222,225800.0,<1H OCEAN
+-118.15,34.67,5.0,12317.0,2953.0,6291.0,2654.0,3.5732,146900.0,INLAND
+-116.96,32.79,35.0,1081.0,266.0,691.0,259.0,2.6324,133700.0,<1H OCEAN
+-117.05,33.02,18.0,917.0,121.0,388.0,131.0,6.3517,260100.0,<1H OCEAN
+-117.21,32.83,36.0,1475.0,328.0,806.0,327.0,3.5078,166000.0,NEAR OCEAN
+-118.46,33.97,19.0,1658.0,427.0,648.0,378.0,3.8698,500001.0,<1H OCEAN
+-120.62,37.36,15.0,3455.0,729.0,2014.0,659.0,3.2656,83500.0,INLAND
+-117.05,32.82,16.0,4046.0,731.0,1684.0,701.0,4.2312,197000.0,<1H OCEAN
+-121.02,37.65,20.0,2973.0,620.0,1996.0,570.0,3.0645,106000.0,INLAND
+-121.92,36.63,36.0,877.0,175.0,349.0,168.0,3.4167,339100.0,<1H OCEAN
+-119.77,36.3,24.0,2202.0,471.0,1052.0,439.0,2.1038,62000.0,INLAND
+-117.42,33.95,32.0,4251.0,848.0,2494.0,798.0,2.8173,110800.0,INLAND
+-117.58,33.88,16.0,1739.0,478.0,1235.0,420.0,2.2969,116100.0,INLAND
+-121.44,36.51,31.0,1636.0,380.0,1468.0,339.0,3.2219,114700.0,<1H OCEAN
+-121.35,38.01,15.0,2682.0,599.0,1520.0,601.0,3.5982,94400.0,INLAND
+-121.17,38.76,14.0,2028.0,255.0,781.0,251.0,6.5322,394000.0,INLAND
+-117.65,34.1,30.0,1461.0,341.0,1014.0,345.0,2.4667,106000.0,INLAND
+-118.18,34.03,37.0,2115.0,580.0,2842.0,572.0,2.239,121300.0,<1H OCEAN
+-122.25,37.83,28.0,5022.0,1750.0,2558.0,1661.0,2.4234,218500.0,NEAR BAY
+-122.01,37.39,36.0,1976.0,361.0,1348.0,371.0,5.6447,252600.0,<1H OCEAN
+-122.47,37.78,52.0,1941.0,436.0,955.0,425.0,4.1339,396400.0,NEAR BAY
+-122.01,39.21,52.0,1989.0,392.0,985.0,396.0,2.5556,75800.0,INLAND
+-121.89,37.45,15.0,2428.0,513.0,1687.0,519.0,4.75,254400.0,<1H OCEAN
+-119.72,34.43,30.0,2491.0,656.0,1091.0,576.0,2.5139,279500.0,<1H OCEAN
+-122.49,37.75,52.0,2226.0,385.0,1177.0,416.0,4.8516,323800.0,NEAR OCEAN
+-122.23,38.13,29.0,5154.0,1084.0,2459.0,1019.0,3.2664,142900.0,NEAR BAY
+-122.4,37.72,47.0,1167.0,250.0,953.0,253.0,4.2727,241900.0,NEAR BAY
+-122.5,37.92,32.0,2639.0,415.0,1013.0,408.0,6.1632,349200.0,NEAR BAY
+-122.64,38.95,28.0,1503.0,370.0,522.0,268.0,1.2029,68900.0,INLAND
+-118.06,34.12,25.0,3891.0,848.0,1848.0,759.0,3.6639,248100.0,INLAND
+-117.26,33.04,18.0,2229.0,346.0,1088.0,352.0,6.3525,278300.0,NEAR OCEAN
+-121.77,39.66,20.0,3759.0,,1705.0,600.0,4.712,158600.0,INLAND
+-117.92,33.89,12.0,1859.0,393.0,622.0,316.0,5.0258,161800.0,<1H OCEAN
+-121.93,38.31,25.0,185.0,32.0,85.0,32.0,4.875,250000.0,INLAND
+-119.53,36.78,20.0,2822.0,479.0,1372.0,455.0,4.5625,136900.0,INLAND
+-122.42,37.74,52.0,1916.0,432.0,889.0,424.0,4.0391,279900.0,NEAR BAY
+-118.15,33.9,20.0,2850.0,737.0,1855.0,662.0,2.809,144600.0,<1H OCEAN
+-117.86,33.79,34.0,1883.0,408.0,1227.0,424.0,3.8929,187500.0,<1H OCEAN
+-118.14,33.96,34.0,2744.0,541.0,1333.0,503.0,4.0536,277200.0,<1H OCEAN
+-118.45,34.27,35.0,1579.0,300.0,1012.0,265.0,5.1296,195900.0,<1H OCEAN
+-121.08,39.18,19.0,2323.0,397.0,963.0,379.0,3.7426,162700.0,INLAND
+-117.84,34.13,26.0,3773.0,694.0,2103.0,688.0,4.6937,198000.0,INLAND
+-117.22,33.48,5.0,1585.0,247.0,510.0,181.0,6.9136,493300.0,<1H OCEAN
+-121.33,38.57,17.0,1621.0,350.0,706.0,338.0,2.3684,150000.0,INLAND
+-118.35,34.06,52.0,2837.0,602.0,1164.0,551.0,3.2411,250000.0,<1H OCEAN
+-122.76,38.44,11.0,2895.0,524.0,1633.0,534.0,4.7283,170200.0,<1H OCEAN
+-119.09,35.35,14.0,2113.0,256.0,842.0,265.0,8.5325,224100.0,INLAND
+-119.31,36.32,44.0,2032.0,308.0,791.0,336.0,4.0298,109000.0,INLAND
+-122.63,38.25,20.0,3460.0,602.0,1707.0,568.0,3.7115,181900.0,<1H OCEAN
+-121.44,38.46,10.0,4446.0,897.0,2499.0,884.0,3.5461,103600.0,INLAND
+-118.19,33.78,42.0,1021.0,300.0,533.0,187.0,1.8036,175000.0,NEAR OCEAN
+-121.87,37.39,9.0,2522.0,547.0,1591.0,481.0,4.9091,259700.0,<1H OCEAN
+-118.09,33.76,26.0,1625.0,440.0,533.0,414.0,1.808,58500.0,<1H OCEAN
+-121.35,37.96,21.0,1343.0,183.0,462.0,193.0,5.8995,189900.0,INLAND
+-118.68,34.13,9.0,11251.0,1594.0,3029.0,1227.0,6.7273,500001.0,<1H OCEAN
+-121.49,38.53,42.0,1468.0,281.0,571.0,271.0,3.3906,124200.0,INLAND
+-121.93,37.31,26.0,2182.0,704.0,1638.0,704.0,2.8981,229800.0,<1H OCEAN
+-119.29,36.32,33.0,2107.0,451.0,1364.0,442.0,2.2024,67200.0,INLAND
+-121.51,36.86,36.0,1019.0,168.0,602.0,169.0,2.625,210000.0,INLAND
+-118.12,34.17,37.0,2705.0,676.0,1551.0,608.0,2.2692,225000.0,INLAND
+-122.83,39.09,26.0,2191.0,495.0,679.0,371.0,1.4679,94700.0,INLAND
+-117.07,32.64,30.0,2873.0,774.0,1593.0,731.0,2.24,129500.0,NEAR OCEAN
+-119.79,36.82,25.0,2330.0,462.0,1215.0,467.0,3.2143,93000.0,INLAND
+-118.26,34.09,51.0,1532.0,366.0,669.0,333.0,3.6434,278800.0,<1H OCEAN
+-118.14,33.86,44.0,1436.0,257.0,745.0,233.0,4.625,213400.0,<1H OCEAN
+-117.75,33.72,10.0,2464.0,347.0,1241.0,366.0,8.7603,362500.0,<1H OCEAN
+-117.96,34.0,34.0,2777.0,540.0,1954.0,522.0,4.5163,183800.0,<1H OCEAN
+-118.57,34.17,35.0,2072.0,318.0,908.0,342.0,6.0928,327300.0,<1H OCEAN
+-117.15,33.14,15.0,1070.0,208.0,470.0,217.0,2.3062,158900.0,<1H OCEAN
+-118.41,34.2,32.0,2734.0,654.0,2209.0,610.0,3.5164,217200.0,<1H OCEAN
+-121.75,36.77,25.0,1851.0,418.0,1678.0,390.0,3.2937,135300.0,<1H OCEAN
+-122.01,37.36,21.0,2483.0,396.0,1194.0,424.0,7.1273,346300.0,<1H OCEAN
+-122.29,37.9,52.0,2657.0,500.0,1131.0,489.0,4.4286,234900.0,NEAR BAY
+-121.45,38.69,32.0,2962.0,526.0,1542.0,521.0,2.2243,89200.0,INLAND
+-117.12,33.07,45.0,1032.0,235.0,363.0,177.0,3.6389,186600.0,<1H OCEAN
+-118.33,34.02,11.0,1249.0,313.0,625.0,336.0,0.8702,170500.0,<1H OCEAN
+-118.59,34.21,17.0,2737.0,868.0,2924.0,785.0,2.5797,183500.0,<1H OCEAN
+-118.35,33.94,35.0,1451.0,435.0,1888.0,420.0,2.8462,149100.0,<1H OCEAN
+-119.74,36.77,30.0,2427.0,482.0,1375.0,518.0,2.5737,76900.0,INLAND
+-117.66,34.03,14.0,2137.0,345.0,1151.0,352.0,5.753,185500.0,INLAND
+-117.82,33.68,3.0,3068.0,494.0,1357.0,486.0,7.9187,333600.0,<1H OCEAN
+-121.32,36.43,22.0,2927.0,637.0,2546.0,618.0,2.7153,114300.0,<1H OCEAN
+-122.12,37.88,35.0,2785.0,362.0,1001.0,363.0,8.0448,433300.0,NEAR BAY
+-121.96,37.34,36.0,844.0,153.0,373.0,160.0,5.791,254100.0,<1H OCEAN
+-117.33,34.08,37.0,2267.0,405.0,1175.0,403.0,3.75,95200.0,INLAND
+-118.49,34.23,32.0,4373.0,683.0,2040.0,693.0,5.2668,242300.0,<1H OCEAN
+-118.01,33.67,13.0,2902.0,536.0,1125.0,490.0,5.888,447700.0,NEAR OCEAN
+-121.44,38.69,24.0,3124.0,556.0,1512.0,555.0,3.1942,94900.0,INLAND
+-121.3,38.03,10.0,1409.0,248.0,782.0,222.0,4.0227,107700.0,INLAND
+-118.43,34.06,20.0,4600.0,1018.0,1675.0,932.0,5.1999,500001.0,<1H OCEAN
+-116.87,33.76,5.0,4116.0,761.0,1714.0,717.0,2.5612,130800.0,INLAND
+-118.13,34.05,35.0,3229.0,616.0,1879.0,595.0,3.9531,268400.0,<1H OCEAN
+-118.31,33.75,36.0,2715.0,474.0,1303.0,457.0,4.6042,357300.0,NEAR OCEAN
+-118.32,33.88,34.0,1803.0,341.0,947.0,333.0,5.5538,280300.0,<1H OCEAN
+-122.03,37.03,21.0,4650.0,733.0,2014.0,704.0,5.6233,322000.0,NEAR OCEAN
+-118.09,34.15,49.0,1467.0,259.0,688.0,260.0,4.3452,260100.0,INLAND
+-118.4,34.08,52.0,3815.0,439.0,1266.0,413.0,15.0001,500001.0,<1H OCEAN
+-122.19,37.73,44.0,1066.0,253.0,825.0,244.0,2.1538,79700.0,NEAR BAY
+-117.92,33.61,23.0,1808.0,408.0,539.0,300.0,3.5682,500001.0,<1H OCEAN
+-121.25,38.01,16.0,2397.0,501.0,1053.0,557.0,2.6994,112500.0,INLAND
+-118.16,33.8,9.0,3564.0,835.0,1530.0,807.0,5.1806,175000.0,NEAR OCEAN
+-117.99,33.75,30.0,1859.0,462.0,1314.0,418.0,3.0909,184400.0,<1H OCEAN
+-117.34,33.21,12.0,5963.0,1372.0,3015.0,1124.0,2.7386,216100.0,NEAR OCEAN
+-117.8,33.85,23.0,3038.0,470.0,1568.0,438.0,5.6403,233000.0,<1H OCEAN
+-122.19,37.78,52.0,2492.0,415.0,1109.0,375.0,4.3125,164400.0,NEAR BAY
+-122.74,38.44,17.0,2287.0,497.0,1240.0,493.0,3.5845,164300.0,<1H OCEAN
+-121.9,37.24,24.0,7521.0,1364.0,3970.0,1318.0,4.4004,255800.0,<1H OCEAN
+-120.63,38.75,17.0,3145.0,621.0,1432.0,559.0,2.7201,117500.0,INLAND
+-118.35,34.03,43.0,2122.0,524.0,1510.0,436.0,2.2273,123300.0,<1H OCEAN
+-117.98,33.74,29.0,3443.0,635.0,2257.0,620.0,4.7404,207500.0,<1H OCEAN
+-118.45,34.28,36.0,2602.0,638.0,2780.0,620.0,2.7155,149800.0,<1H OCEAN
+-117.07,33.15,17.0,1893.0,297.0,936.0,287.0,5.1842,157700.0,<1H OCEAN
+-118.25,33.87,18.0,6812.0,1263.0,3704.0,1216.0,4.25,169200.0,<1H OCEAN
+-122.45,37.71,34.0,3131.0,669.0,2204.0,600.0,3.5536,251000.0,NEAR OCEAN
+-117.89,34.1,34.0,2048.0,411.0,1456.0,416.0,3.125,168600.0,<1H OCEAN
+-121.31,38.26,22.0,1768.0,396.0,1005.0,420.0,1.8846,88300.0,INLAND
+-122.47,37.75,51.0,2413.0,431.0,1095.0,437.0,4.0089,357000.0,NEAR BAY
+-117.63,33.46,7.0,7684.0,1088.0,2812.0,1057.0,6.3401,387300.0,NEAR OCEAN
+-118.96,37.64,11.0,3934.0,697.0,901.0,345.0,4.2381,242700.0,INLAND
+-118.31,34.03,47.0,1315.0,,785.0,245.0,1.23,138400.0,<1H OCEAN
+-122.61,38.93,14.0,231.0,36.0,108.0,31.0,4.3897,71300.0,INLAND
+-118.19,33.95,42.0,2309.0,685.0,2609.0,673.0,2.7206,162100.0,<1H OCEAN
+-118.13,33.93,19.0,1793.0,447.0,1222.0,452.0,2.6862,195800.0,<1H OCEAN
+-122.43,37.75,52.0,2155.0,468.0,962.0,490.0,3.775,325900.0,NEAR BAY
+-117.95,33.77,38.0,989.0,246.0,691.0,204.0,3.2632,180900.0,<1H OCEAN
+-117.8,33.87,16.0,5954.0,1281.0,3107.0,1209.0,4.2566,206100.0,<1H OCEAN
+-121.98,37.36,35.0,1293.0,223.0,701.0,216.0,7.8543,281900.0,<1H OCEAN
+-122.33,37.95,46.0,1543.0,339.0,777.0,322.0,4.0927,142600.0,NEAR BAY
+-121.38,38.62,41.0,774.0,144.0,356.0,150.0,3.5625,115300.0,INLAND
+-118.17,34.09,33.0,2907.0,797.0,3212.0,793.0,2.2348,146600.0,<1H OCEAN
+-119.27,34.17,15.0,11403.0,2131.0,3327.0,1585.0,4.3693,423300.0,NEAR OCEAN
+-117.03,34.03,26.0,3501.0,664.0,1860.0,681.0,3.0403,113500.0,INLAND
+-121.97,37.57,21.0,4342.0,783.0,2172.0,789.0,4.6146,247600.0,<1H OCEAN
+-119.19,34.17,31.0,1872.0,434.0,1511.0,405.0,3.2314,186800.0,NEAR OCEAN
+-120.95,37.67,15.0,3062.0,584.0,1624.0,538.0,4.3864,137600.0,INLAND
+-121.83,37.3,16.0,5684.0,1386.0,4203.0,1318.0,3.1964,185800.0,<1H OCEAN
+-118.27,34.09,44.0,3646.0,912.0,1783.0,861.0,2.9709,225000.0,<1H OCEAN
+-121.97,37.4,17.0,2937.0,558.0,1662.0,533.0,5.8792,255500.0,<1H OCEAN
+-119.34,36.22,38.0,2708.0,460.0,1260.0,455.0,3.0905,78200.0,INLAND
+-121.33,37.96,20.0,1727.0,386.0,730.0,342.0,2.5195,92600.0,INLAND
+-121.41,38.64,41.0,1578.0,317.0,897.0,333.0,2.3214,66800.0,INLAND
+-119.81,36.76,51.0,2419.0,486.0,1284.0,426.0,2.2029,54200.0,INLAND
+-122.97,38.53,48.0,3939.0,860.0,1257.0,571.0,2.1165,98700.0,<1H OCEAN
+-121.86,39.78,12.0,7653.0,1578.0,3628.0,1494.0,3.0905,117800.0,INLAND
+-117.99,33.73,17.0,5239.0,1045.0,2440.0,985.0,4.375,248100.0,<1H OCEAN
+-122.18,37.73,42.0,909.0,215.0,646.0,198.0,2.9063,80000.0,NEAR BAY
+-121.29,37.87,29.0,488.0,108.0,308.0,115.0,2.6563,103100.0,INLAND
+-118.31,33.89,37.0,2278.0,508.0,1257.0,498.0,3.7639,220600.0,<1H OCEAN
+-118.26,34.08,45.0,2174.0,627.0,1992.0,557.0,2.5428,167800.0,<1H OCEAN
+-122.23,39.75,16.0,2026.0,396.0,1031.0,382.0,1.9375,73100.0,INLAND
+-118.71,34.29,21.0,2751.0,493.0,1432.0,483.0,5.2067,221200.0,<1H OCEAN
+-122.45,40.56,17.0,1712.0,307.0,963.0,329.0,3.9375,148700.0,INLAND
+-121.45,38.58,44.0,2314.0,415.0,796.0,388.0,3.4914,153900.0,INLAND
+-116.31,33.67,15.0,2214.0,410.0,1152.0,350.0,2.9187,93400.0,INLAND
+-118.24,34.04,52.0,116.0,107.0,171.0,92.0,1.0769,112500.0,<1H OCEAN
+-121.65,36.66,30.0,3745.0,767.0,1762.0,748.0,3.2355,214200.0,<1H OCEAN
+-122.3,41.31,29.0,4059.0,787.0,1700.0,702.0,2.4526,97100.0,INLAND
+-117.06,32.77,18.0,2269.0,682.0,1329.0,581.0,1.7951,161800.0,NEAR OCEAN
+-121.57,39.48,15.0,202.0,54.0,145.0,40.0,0.8252,42500.0,INLAND
+-119.74,34.38,32.0,1479.0,287.0,830.0,288.0,5.345,322600.0,NEAR OCEAN
+-121.27,37.95,43.0,557.0,165.0,573.0,144.0,1.7212,59000.0,INLAND
+-122.11,37.39,36.0,1660.0,261.0,655.0,249.0,6.3967,500001.0,NEAR BAY
+-118.56,34.19,35.0,782.0,144.0,425.0,140.0,5.4548,201400.0,<1H OCEAN
+-118.44,34.01,41.0,1309.0,221.0,534.0,228.0,5.1708,418800.0,<1H OCEAN
+-122.42,37.79,48.0,4506.0,1342.0,1980.0,1239.0,4.0156,500001.0,NEAR BAY
+-117.97,33.84,35.0,793.0,128.0,589.0,137.0,5.25,190200.0,<1H OCEAN
+-117.14,33.18,11.0,5546.0,974.0,2300.0,970.0,3.7109,199800.0,<1H OCEAN
+-117.91,33.65,14.0,2598.0,759.0,1584.0,703.0,4.0417,180900.0,<1H OCEAN
+-118.07,34.56,5.0,10264.0,1821.0,5871.0,1790.0,4.2329,145500.0,INLAND
+-122.14,37.43,18.0,2060.0,563.0,1144.0,600.0,4.0686,378600.0,NEAR BAY
+-122.06,37.54,20.0,6483.0,1068.0,3526.0,1060.0,5.0838,248200.0,NEAR BAY
+-122.49,37.79,52.0,2488.0,281.0,805.0,295.0,10.7058,500001.0,NEAR BAY
+-118.3,34.04,35.0,1090.0,345.0,1605.0,330.0,2.1518,152800.0,<1H OCEAN
+-119.48,35.17,36.0,116.0,20.0,39.0,18.0,3.125,37500.0,INLAND
+-119.21,36.3,23.0,951.0,235.0,806.0,222.0,1.7734,41400.0,INLAND
+-117.78,33.89,7.0,9729.0,1210.0,4160.0,1214.0,8.9088,415300.0,<1H OCEAN
+-119.0,34.19,5.0,3634.0,718.0,1317.0,743.0,4.2917,227900.0,<1H OCEAN
+-119.3,35.87,20.0,1934.0,377.0,1341.0,336.0,2.1434,62600.0,INLAND
+-119.85,36.82,9.0,3995.0,778.0,1691.0,712.0,3.3239,91300.0,INLAND
+-122.0,38.36,34.0,2735.0,539.0,1390.0,491.0,2.7262,118800.0,INLAND
+-121.89,37.31,40.0,1844.0,340.0,719.0,305.0,3.3682,235200.0,<1H OCEAN
+-118.02,33.72,22.0,8844.0,1706.0,4404.0,1594.0,4.4453,267800.0,<1H OCEAN
+-117.89,34.1,35.0,3185.0,544.0,1858.0,564.0,3.8304,175900.0,<1H OCEAN
+-117.8,33.63,15.0,3236.0,451.0,1289.0,416.0,11.1121,493000.0,<1H OCEAN
+-119.17,34.29,28.0,731.0,155.0,285.0,162.0,3.2917,225000.0,NEAR OCEAN
+-121.9,37.4,16.0,2998.0,603.0,1606.0,615.0,3.7622,150000.0,<1H OCEAN
+-119.82,34.45,24.0,3592.0,533.0,1683.0,528.0,6.7247,333800.0,NEAR OCEAN
+-121.26,38.68,13.0,4256.0,619.0,1948.0,622.0,5.2051,167400.0,INLAND
+-118.31,33.82,26.0,2345.0,408.0,1195.0,377.0,5.4925,361700.0,<1H OCEAN
+-121.93,37.13,37.0,1150.0,203.0,511.0,179.0,5.7415,398500.0,<1H OCEAN
+-122.17,37.73,43.0,1473.0,371.0,1231.0,341.0,2.1587,86500.0,NEAR BAY
+-118.44,34.28,47.0,843.0,194.0,800.0,180.0,3.3687,151700.0,<1H OCEAN
+-121.47,38.95,34.0,2129.0,350.0,969.0,314.0,2.7039,106300.0,INLAND
+-118.48,34.19,20.0,5699.0,1594.0,3809.0,1381.0,2.8646,221100.0,<1H OCEAN
+-118.42,34.16,25.0,2769.0,566.0,1201.0,545.0,3.6641,386100.0,<1H OCEAN
+-119.08,35.34,18.0,4070.0,512.0,1580.0,540.0,10.5941,245800.0,INLAND
+-122.02,37.94,19.0,3192.0,612.0,1317.0,594.0,4.125,267100.0,NEAR BAY
+-118.29,34.04,41.0,659.0,291.0,1224.0,290.0,2.0817,132500.0,<1H OCEAN
+-121.59,36.97,16.0,865.0,123.0,403.0,130.0,5.7396,308700.0,INLAND
+-117.09,32.58,12.0,2565.0,567.0,1785.0,545.0,3.0273,135300.0,NEAR OCEAN
+-118.24,33.95,36.0,2316.0,543.0,1938.0,507.0,1.25,97400.0,<1H OCEAN
+-117.82,33.67,15.0,1010.0,274.0,649.0,261.0,2.5197,350000.0,<1H OCEAN
+-118.54,34.19,33.0,2205.0,453.0,1242.0,419.0,4.1319,203700.0,<1H OCEAN
+-122.32,38.32,26.0,2710.0,498.0,1439.0,484.0,5.0,175200.0,NEAR BAY
+-122.19,37.82,32.0,1835.0,264.0,635.0,263.0,8.317,365900.0,NEAR BAY
+-118.13,34.13,39.0,2099.0,397.0,1500.0,380.0,4.8304,493200.0,<1H OCEAN
+-117.25,32.74,36.0,1830.0,430.0,755.0,419.0,2.9904,286800.0,NEAR OCEAN
+-121.44,38.55,46.0,1698.0,383.0,726.0,386.0,2.9821,97000.0,INLAND
+-118.22,33.9,30.0,1007.0,260.0,1112.0,238.0,1.7262,115600.0,<1H OCEAN
+-122.41,37.75,52.0,1678.0,386.0,1220.0,357.0,2.5809,255300.0,NEAR BAY
+-122.53,37.89,35.0,4127.0,689.0,1596.0,707.0,5.9073,400400.0,NEAR BAY
+-119.38,36.56,25.0,1180.0,222.0,611.0,212.0,2.0729,84700.0,INLAND
+-117.28,33.1,13.0,2644.0,422.0,1197.0,399.0,6.5338,267900.0,NEAR OCEAN
+-122.15,37.67,35.0,2472.0,398.0,1171.0,390.0,5.5797,198100.0,NEAR BAY
+-121.8,37.23,18.0,2581.0,358.0,1284.0,377.0,6.7385,272400.0,<1H OCEAN
+-118.03,33.93,22.0,3382.0,800.0,2688.0,784.0,3.875,164700.0,<1H OCEAN
+-118.15,34.18,46.0,2230.0,488.0,1985.0,456.0,2.2328,142100.0,<1H OCEAN
+-116.72,33.56,13.0,3166.0,682.0,1250.0,475.0,2.355,122900.0,INLAND
+-122.17,37.72,42.0,3008.0,659.0,1817.0,664.0,3.371,165000.0,NEAR BAY
+-117.32,34.1,27.0,2053.0,461.0,1737.0,463.0,3.1213,78800.0,INLAND
+-122.18,37.41,21.0,1034.0,117.0,323.0,117.0,10.7237,500001.0,NEAR OCEAN
+-121.15,38.21,18.0,4176.0,700.0,2164.0,699.0,4.0365,174200.0,INLAND
+-119.42,35.19,26.0,890.0,172.0,483.0,170.0,4.15,68200.0,INLAND
+-119.14,36.29,32.0,2084.0,482.0,1410.0,420.0,1.5321,48300.0,INLAND
+-121.97,37.44,17.0,127.0,28.0,219.0,22.0,4.5179,112500.0,<1H OCEAN
+-121.95,37.21,20.0,2345.0,322.0,890.0,276.0,10.0187,500001.0,<1H OCEAN
+-117.32,34.51,16.0,3072.0,612.0,1283.0,604.0,2.8929,115600.0,INLAND
+-119.85,36.75,24.0,1143.0,245.0,608.0,240.0,2.8194,81100.0,INLAND
+-118.12,34.09,25.0,3603.0,1003.0,2719.0,913.0,2.6981,208000.0,<1H OCEAN
+-117.88,33.87,21.0,1519.0,388.0,1203.0,366.0,3.2083,145300.0,<1H OCEAN
+-118.38,34.07,16.0,4814.0,1381.0,1897.0,1209.0,3.3725,375000.0,<1H OCEAN
+-117.55,33.92,24.0,2807.0,501.0,1653.0,509.0,4.8167,163300.0,INLAND
+-119.12,34.23,35.0,2028.0,554.0,2252.0,521.0,2.4643,182000.0,NEAR OCEAN
+-119.21,36.39,31.0,1465.0,303.0,1013.0,297.0,2.0363,53500.0,INLAND
+-118.5,34.26,33.0,2831.0,510.0,1340.0,504.0,4.8316,237300.0,<1H OCEAN
+-118.09,33.97,39.0,1473.0,297.0,1108.0,294.0,4.1389,166000.0,<1H OCEAN
+-118.33,34.04,52.0,2545.0,401.0,1004.0,372.0,3.6373,420000.0,<1H OCEAN
+-118.4,34.7,10.0,4122.0,814.0,2164.0,710.0,4.2941,151600.0,INLAND
+-118.09,34.06,31.0,1146.0,289.0,1163.0,258.0,2.2083,185600.0,<1H OCEAN
+-117.96,33.84,31.0,2265.0,537.0,1617.0,507.0,3.4583,186300.0,<1H OCEAN
+-117.62,34.07,16.0,6009.0,1599.0,5110.0,1389.0,2.5677,128900.0,INLAND
+-121.89,36.6,40.0,626.0,164.0,337.0,150.0,2.7917,225000.0,<1H OCEAN
+-118.23,33.98,25.0,986.0,310.0,1439.0,251.0,2.39,183300.0,<1H OCEAN
+-122.03,37.55,22.0,9167.0,1373.0,4319.0,1404.0,6.992,284800.0,NEAR BAY
+-122.46,38.53,32.0,1735.0,331.0,785.0,309.0,3.6641,275800.0,INLAND
+-121.37,36.84,11.0,1996.0,382.0,1023.0,358.0,3.5714,243000.0,INLAND
+-117.32,34.13,41.0,1837.0,409.0,1430.0,344.0,2.4524,70400.0,INLAND
+-119.14,34.49,17.0,321.0,44.0,92.0,39.0,7.75,375000.0,<1H OCEAN
+-118.3,34.06,21.0,3960.0,1490.0,3468.0,1335.0,1.8214,475000.0,<1H OCEAN
+-118.14,34.01,36.0,702.0,210.0,834.0,216.0,2.25,162500.0,<1H OCEAN
+-120.61,35.12,16.0,1671.0,354.0,935.0,340.0,2.5792,163800.0,<1H OCEAN
+-118.25,34.23,35.0,2839.0,592.0,1413.0,538.0,4.1667,271200.0,<1H OCEAN
+-118.09,33.97,35.0,2664.0,541.0,2033.0,491.0,3.7326,164300.0,<1H OCEAN
+-117.58,33.87,42.0,765.0,171.0,590.0,177.0,1.6875,113500.0,INLAND
+-121.92,38.37,26.0,2056.0,413.0,933.0,367.0,2.7051,193800.0,INLAND
+-118.01,34.11,43.0,1858.0,345.0,1054.0,329.0,3.5625,211600.0,INLAND
+-121.93,37.73,8.0,831.0,231.0,404.0,224.0,3.375,350000.0,<1H OCEAN
+-118.06,34.11,36.0,2178.0,485.0,914.0,412.0,2.7656,239500.0,INLAND
+-117.9,33.85,31.0,3413.0,764.0,2326.0,728.0,4.325,187100.0,<1H OCEAN
+-117.16,32.74,43.0,1437.0,406.0,692.0,379.0,3.1979,466700.0,NEAR OCEAN
+-122.64,41.95,18.0,1867.0,424.0,802.0,314.0,1.8242,53500.0,INLAND
+-117.28,32.8,20.0,1838.0,540.0,615.0,325.0,3.5486,193800.0,NEAR OCEAN
+-118.11,33.88,35.0,1623.0,304.0,868.0,272.0,3.5893,276000.0,<1H OCEAN
+-122.25,37.79,39.0,461.0,129.0,381.0,123.0,1.6,112500.0,NEAR BAY
+-117.91,33.78,26.0,4297.0,1037.0,3596.0,967.0,3.045,184000.0,<1H OCEAN
+-122.35,37.97,31.0,2892.0,685.0,2104.0,641.0,3.2188,113800.0,NEAR BAY
+-118.43,34.24,35.0,1488.0,293.0,1112.0,288.0,4.4688,182500.0,<1H OCEAN
+-117.1,32.71,29.0,3422.0,713.0,2775.0,644.0,1.7075,86900.0,NEAR OCEAN
+-118.17,33.82,50.0,3587.0,693.0,1513.0,651.0,5.5106,252200.0,NEAR OCEAN
+-117.85,34.14,35.0,2899.0,429.0,1251.0,429.0,6.1049,297200.0,INLAND
+-116.92,32.76,9.0,1859.0,307.0,947.0,304.0,5.9202,181300.0,<1H OCEAN
+-122.28,37.53,15.0,5417.0,1199.0,2593.0,1098.0,4.8047,438000.0,NEAR OCEAN
+-121.47,38.57,50.0,3233.0,968.0,1223.0,837.0,1.2041,168100.0,INLAND
+-118.15,34.16,52.0,1925.0,597.0,2258.0,594.0,1.6921,162500.0,<1H OCEAN
+-117.41,34.09,21.0,3300.0,587.0,1896.0,572.0,3.6466,130600.0,INLAND
+-120.91,38.11,9.0,3585.0,680.0,1800.0,598.0,3.636,133100.0,INLAND
+-118.18,33.77,37.0,2653.0,754.0,1087.0,698.0,2.3523,325000.0,NEAR OCEAN
+-119.31,34.41,22.0,2612.0,494.0,1361.0,439.0,4.1319,245000.0,NEAR OCEAN
+-122.23,37.46,36.0,6090.0,1057.0,3081.0,1075.0,5.6629,343600.0,NEAR OCEAN
+-117.07,34.05,14.0,5764.0,1006.0,1876.0,841.0,1.9694,173200.0,INLAND
+-117.96,34.05,35.0,1254.0,263.0,1092.0,268.0,4.6364,163100.0,<1H OCEAN
+-118.17,34.1,25.0,4444.0,647.0,1922.0,652.0,8.058,477300.0,<1H OCEAN
+-119.71,36.8,17.0,2056.0,366.0,1259.0,367.0,3.9338,84700.0,INLAND
+-117.91,33.73,26.0,2413.0,512.0,2867.0,509.0,4.7639,179900.0,<1H OCEAN
+-122.41,37.81,31.0,3991.0,1311.0,2305.0,1201.0,1.8981,500001.0,NEAR BAY
+-117.38,34.12,17.0,5959.0,1208.0,4115.0,1088.0,2.4053,105200.0,INLAND
+-118.87,34.21,26.0,4439.0,616.0,1881.0,592.0,6.2935,258000.0,<1H OCEAN
+-122.27,38.53,22.0,678.0,137.0,336.0,103.0,4.4,142500.0,INLAND
+-121.06,37.18,30.0,2603.0,507.0,1491.0,473.0,3.0909,123400.0,INLAND
+-116.99,32.71,21.0,3049.0,582.0,2355.0,585.0,3.8904,113800.0,NEAR OCEAN
+-119.71,36.82,12.0,2144.0,568.0,1320.0,566.0,2.3381,112500.0,INLAND
+-117.25,34.07,21.0,3067.0,706.0,2140.0,687.0,2.4432,78800.0,INLAND
+-121.89,37.32,34.0,1014.0,246.0,952.0,215.0,2.8864,172500.0,<1H OCEAN
+-118.36,34.22,37.0,1512.0,348.0,1545.0,351.0,3.7663,160300.0,<1H OCEAN
+-118.27,34.25,35.0,2091.0,360.0,879.0,326.0,4.4485,261900.0,<1H OCEAN
+-120.84,35.35,27.0,2863.0,711.0,930.0,533.0,2.6205,221100.0,NEAR OCEAN
+-118.42,34.27,37.0,1024.0,246.0,1371.0,239.0,3.225,147500.0,<1H OCEAN
+-122.15,37.7,36.0,1468.0,252.0,733.0,229.0,3.4583,192600.0,NEAR BAY
+-117.94,33.75,30.0,5268.0,1093.0,4480.0,1050.0,4.015,186700.0,<1H OCEAN
+-118.24,33.96,39.0,643.0,186.0,821.0,191.0,2.5729,97300.0,<1H OCEAN
+-119.54,36.61,20.0,1490.0,318.0,1474.0,326.0,1.4937,54700.0,INLAND
+-122.11,37.69,37.0,2444.0,651.0,1562.0,618.0,2.6464,155200.0,NEAR BAY
+-117.17,32.74,39.0,3803.0,806.0,1567.0,775.0,3.7039,361500.0,NEAR OCEAN
+-118.19,34.12,41.0,2591.0,682.0,2366.0,583.0,2.3071,146400.0,<1H OCEAN
+-122.68,38.07,26.0,1445.0,244.0,510.0,207.0,5.6305,430000.0,NEAR OCEAN
+-122.31,37.54,45.0,1222.0,220.0,492.0,205.0,5.539,396900.0,NEAR OCEAN
+-118.05,34.1,36.0,1606.0,318.0,889.0,294.0,4.7931,272600.0,INLAND
+-118.28,33.96,37.0,1812.0,500.0,1640.0,447.0,1.9348,99100.0,<1H OCEAN
+-122.33,37.95,42.0,1627.0,336.0,848.0,316.0,3.7708,144600.0,NEAR BAY
+-118.12,34.08,49.0,1782.0,374.0,1010.0,367.0,3.1583,268200.0,<1H OCEAN
+-122.01,36.99,29.0,227.0,45.0,112.0,41.0,6.4469,271400.0,NEAR OCEAN
+-117.26,33.02,9.0,4632.0,759.0,1724.0,685.0,6.3712,369800.0,NEAR OCEAN
+-119.0,35.33,35.0,991.0,221.0,620.0,207.0,1.9417,53800.0,INLAND
+-117.69,34.08,30.0,4255.0,773.0,2129.0,730.0,4.5185,142500.0,INLAND
+-123.84,39.46,47.0,1150.0,244.0,552.0,201.0,2.5192,110400.0,<1H OCEAN
+-119.77,36.78,36.0,3616.0,779.0,1994.0,786.0,2.5434,67300.0,INLAND
+-122.09,37.89,35.0,880.0,139.0,352.0,132.0,6.8686,406500.0,NEAR BAY
+-118.24,34.21,32.0,3817.0,886.0,1888.0,829.0,3.5777,245600.0,<1H OCEAN
+-117.24,32.94,12.0,2165.0,437.0,792.0,386.0,5.2648,294400.0,NEAR OCEAN
+-120.45,34.65,27.0,2253.0,382.0,1197.0,384.0,3.3203,134700.0,NEAR OCEAN
+-116.06,34.2,29.0,1202.0,290.0,383.0,156.0,1.3371,66900.0,INLAND
+-118.48,34.01,40.0,2198.0,611.0,1023.0,567.0,3.755,398300.0,<1H OCEAN
+-121.84,39.75,29.0,4362.0,1053.0,2053.0,1000.0,1.7284,74500.0,INLAND
+-120.93,37.74,37.0,1956.0,402.0,1265.0,397.0,2.3023,91900.0,INLAND
+-118.3,34.0,52.0,1718.0,354.0,1026.0,312.0,2.0,128000.0,<1H OCEAN
+-122.36,40.48,21.0,2333.0,514.0,1308.0,509.0,2.0899,74800.0,INLAND
+-118.41,34.07,47.0,2979.0,626.0,1076.0,571.0,3.9904,500001.0,<1H OCEAN
+-122.21,37.77,52.0,745.0,153.0,473.0,149.0,2.6765,88800.0,NEAR BAY
+-118.88,34.2,23.0,4862.0,597.0,1938.0,594.0,7.3409,316000.0,<1H OCEAN
+-122.56,38.01,21.0,2144.0,400.0,840.0,398.0,4.6,239500.0,NEAR BAY
+-118.09,33.84,23.0,4412.0,910.0,2380.0,825.0,4.54,213100.0,<1H OCEAN
+-118.33,33.93,37.0,1831.0,356.0,925.0,338.0,4.4091,148400.0,<1H OCEAN
+-118.42,34.14,27.0,3990.0,892.0,1417.0,800.0,4.0439,500001.0,<1H OCEAN
+-118.25,33.89,34.0,1367.0,288.0,1183.0,286.0,2.6812,104100.0,<1H OCEAN
+-118.27,34.09,52.0,3225.0,763.0,1559.0,710.0,3.9674,268800.0,<1H OCEAN
+-122.41,37.65,32.0,3436.0,868.0,2583.0,817.0,3.5039,232400.0,NEAR OCEAN
+-118.83,34.14,16.0,1316.0,194.0,450.0,173.0,10.1597,500001.0,NEAR OCEAN
+-120.98,37.64,39.0,2617.0,659.0,2052.0,642.0,1.6952,65000.0,INLAND
+-121.3,38.61,25.0,2707.0,464.0,1423.0,490.0,4.3235,116900.0,INLAND
+-121.99,37.35,25.0,1527.0,325.0,707.0,339.0,4.375,212200.0,<1H OCEAN
+-118.4,34.02,40.0,593.0,137.0,371.0,132.0,4.6932,332800.0,<1H OCEAN
+-118.21,33.93,36.0,1337.0,382.0,1769.0,393.0,2.6953,121000.0,<1H OCEAN
+-122.31,40.65,11.0,3664.0,647.0,1686.0,613.0,2.9338,141600.0,INLAND
+-118.25,34.08,47.0,2133.0,689.0,2104.0,662.0,2.6136,169200.0,<1H OCEAN
+-122.12,37.7,41.0,3495.0,787.0,1849.0,750.0,2.679,144900.0,NEAR BAY
+-117.78,34.09,32.0,2643.0,516.0,1862.0,478.0,3.7177,177200.0,INLAND
+-117.15,34.07,15.0,1852.0,316.0,906.0,298.0,5.3526,129800.0,INLAND
+-121.94,37.54,31.0,2537.0,382.0,1067.0,410.0,6.7599,356000.0,<1H OCEAN
+-117.85,33.9,25.0,1548.0,256.0,811.0,263.0,5.2037,242200.0,<1H OCEAN
+-122.27,37.87,52.0,3084.0,698.0,1424.0,694.0,2.7372,210200.0,NEAR BAY
+-118.35,34.03,42.0,2043.0,512.0,1634.0,501.0,1.9928,125400.0,<1H OCEAN
+-117.96,33.81,35.0,1996.0,326.0,1409.0,330.0,4.7738,180000.0,<1H OCEAN
+-118.22,34.09,42.0,1706.0,488.0,1941.0,447.0,2.5213,149700.0,<1H OCEAN
+-122.15,37.74,49.0,1325.0,277.0,764.0,282.0,3.3125,118000.0,NEAR BAY
+-118.44,34.31,22.0,3182.0,822.0,2661.0,746.0,2.7472,160100.0,<1H OCEAN
+-118.12,33.77,20.0,4534.0,954.0,1941.0,892.0,6.0362,463500.0,NEAR OCEAN
+-117.23,33.01,18.0,3961.0,511.0,1541.0,470.0,11.1118,500001.0,NEAR OCEAN
+-121.97,37.23,22.0,2781.0,523.0,1291.0,516.0,4.6065,445900.0,<1H OCEAN
+-117.48,34.1,30.0,2287.0,531.0,1796.0,503.0,2.5833,90600.0,INLAND
+-121.45,38.62,38.0,2419.0,605.0,1696.0,503.0,1.4861,63100.0,INLAND
+-117.9,34.06,33.0,1701.0,290.0,831.0,275.0,5.4469,274700.0,<1H OCEAN
+-117.97,33.89,17.0,1740.0,445.0,1158.0,412.0,2.8649,137500.0,<1H OCEAN
+-117.28,33.06,8.0,4172.0,1022.0,2585.0,941.0,4.0118,245800.0,NEAR OCEAN
+-116.05,33.33,17.0,290.0,94.0,135.0,57.0,1.7292,81300.0,INLAND
+-118.96,35.4,27.0,2473.0,400.0,1271.0,427.0,3.5524,89100.0,INLAND
+-118.09,33.91,34.0,1582.0,343.0,1356.0,324.0,3.4211,141100.0,<1H OCEAN
+-118.33,34.02,46.0,1528.0,391.0,933.0,366.0,2.1979,125700.0,<1H OCEAN
+-119.83,34.44,35.0,796.0,281.0,567.0,257.0,2.1389,260000.0,NEAR OCEAN
+-117.76,33.86,14.0,3666.0,442.0,1400.0,433.0,10.1316,500001.0,<1H OCEAN
+-118.47,34.25,34.0,1732.0,399.0,1120.0,401.0,4.1492,195700.0,<1H OCEAN
+-121.32,36.42,20.0,1054.0,269.0,1219.0,273.0,3.0437,76600.0,<1H OCEAN
+-117.99,33.74,26.0,4065.0,741.0,1960.0,739.0,4.506,240000.0,<1H OCEAN
+-121.44,38.58,42.0,2334.0,435.0,892.0,446.0,3.0208,148800.0,INLAND
+-119.84,36.54,19.0,1310.0,241.0,702.0,217.0,2.4375,78200.0,INLAND
+-118.32,34.62,31.0,1398.0,273.0,884.0,299.0,4.8409,264900.0,INLAND
+-120.86,35.4,21.0,2787.0,641.0,1106.0,501.0,2.7043,186200.0,NEAR OCEAN
+-118.0,33.79,18.0,3679.0,694.0,1820.0,652.0,3.6531,143500.0,<1H OCEAN
+-118.44,34.32,14.0,6235.0,1286.0,3568.0,1190.0,4.1724,211600.0,<1H OCEAN
+-122.27,37.78,13.0,2020.0,535.0,959.0,486.0,5.2601,292700.0,NEAR BAY
+-117.99,33.69,16.0,1476.0,294.0,886.0,270.0,5.3259,216400.0,<1H OCEAN
+-119.7,34.42,52.0,329.0,109.0,291.0,102.0,1.4722,350000.0,<1H OCEAN
+-117.97,33.82,26.0,2335.0,504.0,1121.0,502.0,2.9891,205200.0,<1H OCEAN
+-115.4,32.86,19.0,1087.0,171.0,649.0,173.0,3.3182,113800.0,INLAND
+-118.5,34.27,35.0,2235.0,390.0,1148.0,416.0,4.869,221600.0,<1H OCEAN
+-118.05,33.79,19.0,1863.0,355.0,1260.0,317.0,3.2465,277400.0,<1H OCEAN
+-121.6,37.9,5.0,14684.0,2252.0,4276.0,1722.0,6.9051,340900.0,INLAND
+-118.39,34.06,39.0,3299.0,831.0,1649.0,759.0,3.3295,500001.0,<1H OCEAN
+-118.48,34.03,32.0,1793.0,476.0,1143.0,448.0,2.8981,353600.0,<1H OCEAN
+-122.48,37.74,52.0,2841.0,517.0,1372.0,517.0,3.9236,335000.0,NEAR OCEAN
+-122.42,37.67,42.0,2274.0,429.0,1255.0,397.0,5.1205,226300.0,NEAR OCEAN
+-118.26,33.88,39.0,1756.0,320.0,1055.0,322.0,3.2375,105200.0,<1H OCEAN
+-122.21,37.78,47.0,881.0,248.0,753.0,241.0,2.625,111300.0,NEAR BAY
+-117.79,33.68,16.0,1998.0,308.0,818.0,299.0,6.8722,326100.0,<1H OCEAN
+-117.91,33.82,32.0,1408.0,307.0,1331.0,284.0,3.7014,179600.0,<1H OCEAN
+-117.04,32.69,9.0,3417.0,860.0,2521.0,828.0,3.02,158900.0,NEAR OCEAN
+-117.28,34.12,36.0,2991.0,822.0,2378.0,751.0,1.3571,70600.0,INLAND
+-124.21,41.77,17.0,3461.0,722.0,1947.0,647.0,2.5795,68400.0,NEAR OCEAN
+-122.0,37.36,17.0,6012.0,1737.0,3539.0,1625.0,3.8464,239400.0,<1H OCEAN
+-118.47,33.99,34.0,1875.0,501.0,1491.0,526.0,2.8417,321400.0,<1H OCEAN
+-118.1,33.85,36.0,956.0,159.0,416.0,157.0,4.6429,223700.0,<1H OCEAN
+-117.92,33.83,17.0,382.0,86.0,272.0,81.0,1.425,212500.0,<1H OCEAN
+-118.36,33.85,34.0,1086.0,197.0,509.0,158.0,6.1133,349300.0,<1H OCEAN
+-119.81,34.44,14.0,961.0,305.0,662.0,286.0,3.2115,206300.0,NEAR OCEAN
+-121.49,38.59,20.0,463.0,180.0,486.0,190.0,1.0313,85000.0,INLAND
+-118.05,33.86,16.0,2851.0,626.0,1985.0,603.0,5.4089,265600.0,<1H OCEAN
+-118.08,33.84,31.0,2377.0,600.0,2042.0,593.0,3.625,170400.0,<1H OCEAN
+-118.58,34.2,35.0,1558.0,267.0,793.0,249.0,5.1463,220200.0,<1H OCEAN
+-118.34,34.05,52.0,2194.0,504.0,997.0,438.0,2.6654,259400.0,<1H OCEAN
+-120.87,37.07,26.0,2036.0,401.0,1343.0,414.0,3.6331,88600.0,INLAND
+-119.0,35.39,42.0,2839.0,516.0,1203.0,487.0,3.7708,79400.0,INLAND
+-119.66,36.34,32.0,1338.0,276.0,859.0,286.0,2.6397,59700.0,INLAND
+-117.4,34.01,25.0,1858.0,366.0,1311.0,331.0,2.7083,87800.0,INLAND
+-118.19,33.84,44.0,2731.0,577.0,1396.0,555.0,4.1771,219100.0,NEAR OCEAN
+-117.84,34.04,4.0,9959.0,1544.0,4904.0,1429.0,6.9754,402500.0,<1H OCEAN
+-116.95,32.82,12.0,5535.0,1434.0,3112.0,1262.0,2.5949,108300.0,<1H OCEAN
+-121.98,37.24,35.0,3574.0,485.0,1325.0,476.0,8.5425,500001.0,<1H OCEAN
+-118.32,34.17,39.0,1995.0,564.0,1202.0,544.0,3.5875,250000.0,<1H OCEAN
+-122.24,37.8,52.0,1586.0,398.0,1006.0,335.0,2.1348,140600.0,NEAR BAY
+-118.12,34.11,52.0,2787.0,353.0,1057.0,364.0,10.2317,500001.0,<1H OCEAN
+-117.36,33.2,19.0,1926.0,557.0,1190.0,483.0,1.3269,166100.0,NEAR OCEAN
+-120.85,37.49,42.0,264.0,72.0,310.0,70.0,1.4063,61500.0,INLAND
+-117.09,32.73,26.0,3114.0,686.0,1948.0,660.0,2.8942,124100.0,NEAR OCEAN
+-117.92,33.64,25.0,2224.0,580.0,985.0,516.0,3.1305,268800.0,<1H OCEAN
+-121.34,37.99,11.0,4487.0,868.0,2195.0,780.0,3.9615,194600.0,INLAND
+-118.21,34.03,44.0,1550.0,407.0,1718.0,403.0,2.5268,141100.0,<1H OCEAN
+-117.68,35.63,18.0,4334.0,641.0,1777.0,661.0,6.0653,105300.0,INLAND
+-117.79,33.56,36.0,2057.0,329.0,658.0,309.0,7.866,500001.0,<1H OCEAN
+-122.5,37.92,30.0,2270.0,359.0,974.0,351.0,5.5926,300900.0,NEAR BAY
+-119.64,36.56,34.0,576.0,117.0,363.0,97.0,2.0658,92500.0,INLAND
+-117.92,34.1,35.0,2994.0,603.0,1933.0,561.0,4.0052,160700.0,<1H OCEAN
+-117.67,33.64,11.0,2722.0,554.0,1565.0,508.0,5.1645,164100.0,<1H OCEAN
+-121.3,38.03,13.0,1014.0,200.0,712.0,197.0,3.1471,102800.0,INLAND
+-122.41,37.75,52.0,2515.0,576.0,1209.0,540.0,3.5912,284900.0,NEAR BAY
+-122.07,37.39,19.0,1465.0,342.0,646.0,345.0,4.712,289300.0,NEAR BAY
+-117.86,33.65,4.0,3618.0,767.0,1326.0,714.0,5.4284,500001.0,<1H OCEAN
+-121.81,39.7,21.0,5051.0,1054.0,2948.0,980.0,1.5863,81300.0,INLAND
+-117.3,33.07,16.0,3147.0,765.0,2165.0,690.0,3.5585,284800.0,NEAR OCEAN
+-121.9,37.27,33.0,3410.0,583.0,1739.0,588.0,5.0714,255600.0,<1H OCEAN
+-120.79,38.24,19.0,1003.0,235.0,538.0,190.0,2.9821,90400.0,INLAND
+-118.08,34.08,38.0,1889.0,407.0,1330.0,396.0,3.9219,205200.0,<1H OCEAN
+-117.04,32.63,26.0,2074.0,356.0,1228.0,335.0,4.1154,160200.0,NEAR OCEAN
+-117.1,32.97,17.0,3167.0,861.0,2098.0,828.0,2.4459,85800.0,<1H OCEAN
+-118.09,34.63,31.0,1537.0,416.0,1239.0,397.0,1.9722,99200.0,INLAND
+-121.92,36.62,52.0,2584.0,599.0,790.0,444.0,2.5263,286400.0,NEAR OCEAN
+-122.49,37.78,42.0,2723.0,579.0,1419.0,519.0,3.6429,328400.0,NEAR BAY
+-122.53,37.97,37.0,1340.0,322.0,621.0,314.0,3.5588,268800.0,NEAR BAY
+-119.57,36.7,34.0,1759.0,354.0,899.0,337.0,2.6823,72900.0,INLAND
+-122.08,37.63,31.0,767.0,171.0,548.0,185.0,3.7614,176000.0,NEAR BAY
+-117.53,33.94,21.0,5675.0,935.0,2834.0,865.0,4.2263,203200.0,INLAND
+-122.57,38.02,33.0,9531.0,1487.0,3798.0,1409.0,5.6512,314000.0,NEAR BAY
+-121.95,37.35,42.0,1421.0,330.0,659.0,303.0,3.3333,237900.0,<1H OCEAN
+-115.52,32.77,18.0,1715.0,337.0,1166.0,333.0,2.2417,79200.0,INLAND
+-120.47,35.74,9.0,4267.0,785.0,2065.0,691.0,3.7303,162700.0,<1H OCEAN
+-118.26,33.78,21.0,2188.0,706.0,2265.0,652.0,1.9923,164700.0,NEAR OCEAN
+-117.06,33.04,17.0,1785.0,255.0,667.0,277.0,5.7382,278000.0,<1H OCEAN
+-122.59,38.12,25.0,7784.0,1145.0,3445.0,1166.0,6.0132,287900.0,<1H OCEAN
+-117.12,34.06,38.0,281.0,55.0,151.0,52.0,1.3906,120800.0,INLAND
+-117.67,34.04,13.0,2295.0,374.0,1284.0,378.0,5.2551,194300.0,INLAND
+-117.37,33.19,38.0,861.0,213.0,486.0,204.0,4.1875,185000.0,NEAR OCEAN
+-121.62,36.14,25.0,726.0,274.0,411.0,214.0,3.2375,450000.0,NEAR OCEAN
+-117.94,34.03,35.0,1375.0,249.0,1015.0,239.0,4.0521,151800.0,<1H OCEAN
+-121.27,38.02,32.0,342.0,58.0,138.0,52.0,2.9821,155000.0,INLAND
+-117.97,33.93,33.0,1700.0,369.0,981.0,362.0,4.5461,194000.0,<1H OCEAN
+-115.99,33.4,15.0,1945.0,536.0,515.0,273.0,2.0109,54300.0,INLAND
+-117.77,33.69,14.0,1413.0,372.0,744.0,338.0,3.7988,184100.0,<1H OCEAN
+-118.08,34.01,34.0,1914.0,549.0,2122.0,529.0,2.5969,150200.0,<1H OCEAN
+-120.7,35.28,14.0,3768.0,682.0,1884.0,664.0,4.6071,239900.0,NEAR OCEAN
+-117.18,33.15,7.0,6225.0,1683.0,5410.0,1580.0,2.32,117500.0,<1H OCEAN
+-118.83,34.23,6.0,8803.0,1114.0,3385.0,1010.0,8.7288,425800.0,<1H OCEAN
+-118.09,34.7,6.0,4558.0,804.0,1543.0,563.0,2.8548,138500.0,INLAND
+-117.32,34.55,18.0,279.0,59.0,188.0,60.0,0.8246,91700.0,INLAND
+-116.94,32.82,35.0,1737.0,285.0,826.0,294.0,3.2411,159200.0,<1H OCEAN
+-118.37,34.18,36.0,1608.0,373.0,1217.0,374.0,2.9728,190200.0,<1H OCEAN
+-117.08,33.12,43.0,107.0,44.0,107.0,48.0,0.7054,137500.0,<1H OCEAN
+-121.9,37.36,38.0,1141.0,333.0,1028.0,291.0,2.7333,182500.0,<1H OCEAN
+-117.93,33.62,33.0,1890.0,416.0,859.0,329.0,4.5658,500001.0,<1H OCEAN
+-121.93,37.35,36.0,1823.0,410.0,1589.0,387.0,3.1065,234100.0,<1H OCEAN
+-117.69,34.01,30.0,2598.0,573.0,2170.0,518.0,2.3,95600.0,INLAND
+-122.0,37.23,36.0,3191.0,430.0,1234.0,440.0,9.0704,500001.0,<1H OCEAN
+-118.01,34.06,26.0,557.0,153.0,455.0,196.0,2.7721,155400.0,INLAND
+-118.16,33.87,32.0,1854.0,471.0,1363.0,478.0,2.6406,156700.0,<1H OCEAN
+-118.32,34.03,47.0,1082.0,198.0,455.0,193.0,3.0132,223200.0,<1H OCEAN
+-122.12,37.42,36.0,2607.0,551.0,1165.0,523.0,5.1524,373100.0,NEAR BAY
+-119.04,35.95,25.0,1009.0,246.0,994.0,222.0,1.8462,55800.0,INLAND
+-119.23,34.19,16.0,5297.0,810.0,1489.0,667.0,6.4522,500001.0,NEAR OCEAN
+-122.34,40.32,12.0,3848.0,689.0,2008.0,683.0,2.6352,92200.0,INLAND
+-120.69,35.26,20.0,1248.0,231.0,722.0,225.0,4.625,221800.0,NEAR OCEAN
+-117.23,32.8,21.0,2429.0,579.0,1011.0,538.0,3.225,229400.0,NEAR OCEAN
+-117.09,32.99,18.0,3215.0,588.0,1618.0,509.0,4.6028,216800.0,<1H OCEAN
+-123.08,41.26,34.0,2773.0,679.0,1066.0,424.0,1.6757,63300.0,INLAND
+-118.26,34.02,39.0,698.0,232.0,1046.0,228.0,2.2356,119500.0,<1H OCEAN
+-121.59,39.39,22.0,2515.0,482.0,1284.0,462.0,2.1776,73800.0,INLAND
+-122.09,37.39,34.0,1508.0,483.0,774.0,443.0,2.7279,365600.0,NEAR BAY
+-117.08,32.97,3.0,17466.0,3336.0,7644.0,2895.0,5.4584,246500.0,<1H OCEAN
+-117.27,34.42,9.0,5643.0,1005.0,3166.0,957.0,3.2077,93300.0,INLAND
+-122.32,37.52,17.0,6645.0,1034.0,2557.0,1032.0,6.3892,480800.0,NEAR OCEAN
+-116.95,33.86,1.0,6.0,2.0,8.0,2.0,1.625,55000.0,INLAND
+-122.03,38.27,24.0,3580.0,735.0,1959.0,731.0,2.7284,118500.0,INLAND
+-117.14,32.71,34.0,1694.0,455.0,1467.0,425.0,2.1164,139400.0,NEAR OCEAN
+-121.6,39.76,22.0,2447.0,556.0,1157.0,556.0,1.8245,85500.0,INLAND
+-118.44,34.27,35.0,777.0,187.0,1022.0,186.0,3.4,139600.0,<1H OCEAN
+-118.86,34.22,26.0,1932.0,280.0,886.0,289.0,5.0855,232200.0,<1H OCEAN
+-121.67,36.68,38.0,5561.0,1292.0,3523.0,1253.0,2.8289,168300.0,<1H OCEAN
+-118.38,34.19,30.0,977.0,264.0,736.0,258.0,1.9866,177400.0,<1H OCEAN
+-122.17,37.75,47.0,998.0,211.0,597.0,185.0,3.1587,100400.0,NEAR BAY
+-117.24,33.4,16.0,2704.0,463.0,1322.0,424.0,3.7857,227000.0,<1H OCEAN
+-120.02,38.91,22.0,2138.0,493.0,829.0,330.0,2.2056,107200.0,INLAND
+-117.09,32.64,24.0,3613.0,973.0,2002.0,931.0,1.947,147500.0,NEAR OCEAN
+-122.85,38.62,16.0,4418.0,704.0,1908.0,697.0,4.5913,244600.0,<1H OCEAN
+-122.29,37.88,46.0,1895.0,442.0,920.0,425.0,2.9926,192100.0,NEAR BAY
+-118.86,35.9,38.0,298.0,55.0,161.0,47.0,4.125,71300.0,INLAND
+-117.81,33.67,9.0,3279.0,530.0,1447.0,510.0,7.4581,296600.0,<1H OCEAN
+-121.84,37.32,14.0,5762.0,1538.0,3979.0,1389.0,3.6953,192600.0,<1H OCEAN
+-118.18,34.03,26.0,859.0,255.0,835.0,232.0,1.1929,143800.0,<1H OCEAN
+-121.66,37.13,20.0,4477.0,924.0,2656.0,871.0,3.8788,226900.0,<1H OCEAN
+-117.95,34.03,33.0,1782.0,394.0,1517.0,376.0,3.3389,157900.0,<1H OCEAN
+-119.73,36.74,14.0,6202.0,1551.0,5561.0,1435.0,1.6073,64700.0,INLAND
+-114.68,33.49,20.0,1491.0,360.0,1135.0,303.0,1.6395,44400.0,INLAND
+-118.2,33.89,37.0,2394.0,568.0,2499.0,551.0,2.5321,105100.0,<1H OCEAN
+-118.22,33.98,34.0,2283.0,809.0,3032.0,832.0,2.4387,175000.0,<1H OCEAN
+-118.3,34.01,35.0,1147.0,290.0,818.0,281.0,1.7961,111700.0,<1H OCEAN
+-118.08,34.16,42.0,3490.0,665.0,1713.0,620.0,4.5461,242400.0,INLAND
+-119.8,36.65,34.0,2263.0,423.0,1184.0,407.0,1.7692,74200.0,INLAND
+-120.95,35.44,30.0,6346.0,1410.0,1769.0,887.0,2.6864,283600.0,NEAR OCEAN
+-118.2,34.02,42.0,498.0,120.0,548.0,119.0,3.7543,126600.0,<1H OCEAN
+-121.5,38.54,52.0,1145.0,133.0,334.0,138.0,8.338,405800.0,INLAND
+-118.6,34.21,19.0,2581.0,857.0,2004.0,784.0,2.6159,182300.0,<1H OCEAN
+-118.19,33.79,37.0,1834.0,551.0,1967.0,476.0,2.137,126600.0,NEAR OCEAN
+-117.85,33.73,28.0,1499.0,574.0,3328.0,595.0,2.4539,115000.0,<1H OCEAN
+-120.66,35.47,18.0,2474.0,449.0,1269.0,431.0,3.9063,184800.0,<1H OCEAN
+-117.13,32.93,16.0,2918.0,444.0,1697.0,444.0,5.3062,195500.0,<1H OCEAN
+-121.23,37.99,38.0,523.0,80.0,226.0,72.0,5.5693,153100.0,INLAND
+-122.25,37.87,42.0,1756.0,465.0,2184.0,422.0,2.5562,371400.0,NEAR BAY
+-117.09,32.68,29.0,1792.0,449.0,1650.0,396.0,2.2201,100000.0,NEAR OCEAN
+-122.07,37.96,34.0,1692.0,290.0,836.0,289.0,5.0172,197100.0,NEAR BAY
+-117.11,32.77,50.0,1729.0,355.0,617.0,337.0,3.6705,167000.0,NEAR OCEAN
+-117.86,33.75,6.0,1565.0,599.0,3157.0,629.0,2.9271,123200.0,<1H OCEAN
+-118.04,33.99,36.0,3531.0,754.0,1613.0,697.0,3.2359,198600.0,<1H OCEAN
+-118.48,34.01,30.0,3078.0,954.0,1561.0,901.0,3.4852,425000.0,<1H OCEAN
+-120.94,37.66,17.0,1147.0,140.0,327.0,136.0,6.8654,290500.0,INLAND
+-117.96,34.03,35.0,2093.0,,1755.0,403.0,3.4115,150400.0,<1H OCEAN
+-119.05,34.21,27.0,4357.0,926.0,2110.0,876.0,3.0119,218200.0,<1H OCEAN
+-117.15,32.91,10.0,2349.0,431.0,1598.0,435.0,4.8229,183200.0,<1H OCEAN
+-120.48,37.32,13.0,3641.0,897.0,1737.0,788.0,2.1418,130600.0,INLAND
+-117.05,32.76,46.0,1887.0,359.0,795.0,358.0,3.25,159600.0,NEAR OCEAN
+-121.5,36.81,20.0,1345.0,230.0,731.0,217.0,4.2333,363300.0,INLAND
+-119.75,34.44,28.0,1080.0,298.0,524.0,251.0,1.8432,327300.0,NEAR OCEAN
+-121.8,37.32,23.0,1829.0,346.0,1277.0,324.0,4.8092,217400.0,<1H OCEAN
+-121.53,36.85,23.0,3359.0,725.0,1862.0,651.0,2.6719,193600.0,INLAND
+-122.81,40.93,16.0,2050.0,471.0,588.0,195.0,2.7083,88900.0,INLAND
+-116.97,33.78,24.0,2680.0,606.0,1728.0,527.0,2.535,74800.0,INLAND
+-117.08,32.59,8.0,2888.0,662.0,2441.0,683.0,2.7048,153000.0,NEAR OCEAN
+-122.11,37.05,18.0,3337.0,549.0,1449.0,519.0,5.1412,315800.0,NEAR OCEAN
+-117.95,33.66,22.0,2785.0,441.0,1086.0,392.0,7.3719,337400.0,<1H OCEAN
+-118.19,34.12,35.0,2524.0,749.0,2487.0,679.0,2.4932,167700.0,<1H OCEAN
+-118.17,33.91,42.0,856.0,167.0,748.0,195.0,3.8,145800.0,<1H OCEAN
+-117.68,35.6,12.0,1678.0,241.0,765.0,281.0,6.0532,102800.0,INLAND
+-119.84,36.82,17.0,2807.0,376.0,996.0,353.0,5.5357,167700.0,INLAND
+-122.55,38.03,29.0,7174.0,1169.0,3063.0,1172.0,6.0902,293200.0,NEAR BAY
+-118.48,34.03,39.0,1530.0,401.0,1074.0,375.0,3.5076,381800.0,<1H OCEAN
+-116.76,32.84,16.0,3311.0,702.0,1627.0,624.0,3.1196,187200.0,<1H OCEAN
+-118.14,34.69,35.0,2118.0,374.0,1108.0,360.0,3.4327,100300.0,INLAND
+-122.2,37.84,30.0,2211.0,346.0,844.0,343.0,6.0666,311500.0,NEAR BAY
+-118.46,34.16,26.0,2548.0,647.0,1098.0,540.0,4.3839,299100.0,<1H OCEAN
+-121.44,38.54,39.0,2855.0,,1217.0,562.0,3.2404,93600.0,INLAND
+-117.25,33.2,10.0,2050.0,473.0,1302.0,471.0,2.7961,131300.0,<1H OCEAN
+-118.01,33.83,29.0,3963.0,772.0,2104.0,743.0,4.9803,208600.0,<1H OCEAN
+-120.5,38.87,10.0,81.0,41.0,55.0,16.0,4.9583,87500.0,INLAND
+-117.98,34.07,28.0,441.0,106.0,504.0,108.0,2.9107,152500.0,INLAND
+-117.43,33.91,15.0,14281.0,2511.0,7540.0,2245.0,4.3222,138000.0,INLAND
+-121.31,38.28,16.0,1708.0,391.0,687.0,378.0,1.9485,155400.0,INLAND
+-118.25,34.14,30.0,1615.0,570.0,1245.0,544.0,1.8929,196900.0,<1H OCEAN
+-117.31,34.08,37.0,953.0,231.0,611.0,230.0,1.9926,81500.0,INLAND
+-118.09,33.99,34.0,1369.0,270.0,1005.0,272.0,3.692,172600.0,<1H OCEAN
+-118.08,33.79,34.0,2840.0,395.0,1127.0,396.0,7.6144,376200.0,<1H OCEAN
+-120.63,38.73,11.0,4577.0,836.0,1944.0,700.0,4.0675,140200.0,INLAND
+-117.88,34.1,32.0,3357.0,621.0,1696.0,604.0,4.2685,216600.0,<1H OCEAN
+-121.84,37.21,15.0,5468.0,676.0,2221.0,675.0,8.3792,418300.0,<1H OCEAN
+-116.94,32.8,21.0,7906.0,2292.0,4868.0,2117.0,1.8937,109800.0,<1H OCEAN
+-119.99,38.94,24.0,1216.0,289.0,421.0,185.0,3.1625,103600.0,INLAND
+-120.39,34.52,40.0,2162.0,395.0,1010.0,332.0,2.5667,239300.0,NEAR OCEAN
+-121.84,39.73,52.0,957.0,263.0,513.0,223.0,1.3672,55000.0,INLAND
+-122.1,37.69,30.0,3115.0,625.0,1444.0,568.0,3.7222,195800.0,NEAR BAY
+-122.24,40.45,27.0,1804.0,321.0,782.0,300.0,3.5978,80600.0,INLAND
+-120.44,34.93,16.0,2098.0,558.0,1252.0,492.0,2.1509,67500.0,<1H OCEAN
+-122.33,37.56,50.0,1975.0,245.0,644.0,251.0,10.0743,500001.0,NEAR OCEAN
+-121.11,39.09,16.0,1000.0,197.0,508.0,190.0,2.3062,138800.0,INLAND
+-118.26,34.12,52.0,2290.0,520.0,1278.0,485.0,3.8393,238200.0,<1H OCEAN
+-118.1,33.94,33.0,639.0,129.0,460.0,118.0,3.1607,189000.0,<1H OCEAN
+-121.97,37.31,21.0,7628.0,2166.0,3637.0,1749.0,3.6401,267500.0,<1H OCEAN
+-118.48,34.2,26.0,2027.0,559.0,1545.0,513.0,2.8974,189900.0,<1H OCEAN
+-117.81,33.84,17.0,4343.0,515.0,1605.0,484.0,10.5981,460100.0,<1H OCEAN
+-117.14,32.72,43.0,1073.0,344.0,660.0,279.0,2.0529,168800.0,NEAR OCEAN
+-121.32,38.67,21.0,3455.0,706.0,1605.0,704.0,3.1382,91600.0,INLAND
+-117.27,34.1,9.0,3904.0,1042.0,3688.0,896.0,1.8022,78000.0,INLAND
+-121.96,38.34,7.0,3292.0,698.0,1911.0,702.0,3.89,140300.0,INLAND
+-118.18,33.77,29.0,1776.0,606.0,1391.0,488.0,1.1295,137500.0,NEAR OCEAN
+-121.38,38.55,26.0,1532.0,264.0,781.0,285.0,4.6944,130900.0,INLAND
+-118.43,34.21,17.0,3667.0,1209.0,2636.0,1054.0,2.425,175500.0,<1H OCEAN
+-118.13,33.8,36.0,1026.0,182.0,505.0,176.0,4.3438,233600.0,<1H OCEAN
+-118.31,35.74,18.0,2327.0,642.0,799.0,335.0,1.8419,92300.0,INLAND
+-118.02,33.71,24.0,2598.0,443.0,1184.0,435.0,5.8623,287800.0,<1H OCEAN
+-117.27,34.45,8.0,6463.0,1095.0,3213.0,1031.0,3.2215,108800.0,INLAND
+-117.22,33.17,6.0,1487.0,362.0,810.0,322.0,3.625,135700.0,<1H OCEAN
+-117.93,33.85,36.0,2147.0,416.0,1011.0,392.0,3.2188,196900.0,<1H OCEAN
+-118.22,34.11,36.0,2870.0,529.0,1371.0,565.0,5.2083,220900.0,<1H OCEAN
+-117.13,32.75,37.0,4142.0,1031.0,1936.0,968.0,2.693,174100.0,NEAR OCEAN
+-121.77,37.31,16.0,1649.0,228.0,769.0,230.0,6.6455,302600.0,<1H OCEAN
+-121.91,37.26,25.0,4258.0,719.0,2290.0,743.0,5.1461,267200.0,<1H OCEAN
+-118.46,34.26,36.0,1394.0,254.0,761.0,262.0,4.9485,217100.0,<1H OCEAN
+-118.06,33.86,16.0,5603.0,938.0,3045.0,893.0,5.0778,293700.0,<1H OCEAN
+-117.19,32.76,52.0,1294.0,175.0,434.0,180.0,5.7914,500001.0,NEAR OCEAN
+-118.4,34.03,13.0,6152.0,1978.0,3397.0,1845.0,3.4058,275000.0,<1H OCEAN
+-118.18,33.87,44.0,2137.0,461.0,1126.0,439.0,3.4408,172900.0,<1H OCEAN
+-116.35,32.74,16.0,2595.0,606.0,1046.0,367.0,1.7137,110700.0,INLAND
+-119.72,37.46,13.0,1999.0,375.0,750.0,308.0,2.875,96000.0,INLAND
+-120.07,39.24,20.0,3729.0,614.0,365.0,152.0,4.962,169500.0,INLAND
+-118.41,34.18,35.0,1975.0,384.0,882.0,406.0,4.375,291700.0,<1H OCEAN
+-117.93,33.8,34.0,3903.0,717.0,2054.0,716.0,4.2731,218000.0,<1H OCEAN
+-119.13,35.22,5.0,6268.0,1003.0,3269.0,980.0,5.1457,118200.0,INLAND
+-121.23,38.78,13.0,3813.0,871.0,1513.0,783.0,2.0807,142600.0,INLAND
+-118.09,33.98,37.0,1270.0,272.0,1092.0,274.0,3.5,160700.0,<1H OCEAN
+-118.26,34.01,37.0,2451.0,668.0,2824.0,598.0,1.9074,99600.0,<1H OCEAN
+-118.17,34.01,36.0,1657.0,425.0,1689.0,418.0,2.7799,149300.0,<1H OCEAN
+-119.87,36.79,8.0,2875.0,548.0,1718.0,551.0,3.6522,80200.0,INLAND
+-118.49,34.27,34.0,4877.0,815.0,2521.0,781.0,5.5714,225900.0,<1H OCEAN
+-119.32,36.33,18.0,2603.0,478.0,1158.0,423.0,4.5938,150500.0,INLAND
+-118.02,34.05,34.0,1610.0,513.0,2050.0,508.0,2.5562,156300.0,<1H OCEAN
+-121.98,37.36,35.0,1440.0,267.0,743.0,259.0,5.0866,254600.0,<1H OCEAN
+-122.47,37.72,46.0,1836.0,319.0,767.0,302.0,5.9114,399000.0,NEAR OCEAN
+-121.34,36.76,15.0,2638.0,429.0,1289.0,357.0,4.1528,336800.0,INLAND
+-117.83,33.79,29.0,1454.0,236.0,724.0,262.0,4.8542,218100.0,<1H OCEAN
+-117.01,34.9,36.0,2181.0,555.0,1404.0,492.0,2.3077,55500.0,INLAND
+-122.04,37.62,32.0,1540.0,324.0,793.0,302.0,3.2857,193200.0,NEAR BAY
+-121.88,36.58,29.0,4910.0,871.0,3438.0,904.0,4.0432,450000.0,NEAR OCEAN
+-116.88,34.24,13.0,4137.0,796.0,573.0,218.0,4.6394,226500.0,INLAND
+-121.4,38.57,25.0,2022.0,295.0,639.0,278.0,5.8416,297600.0,INLAND
+-119.17,34.2,40.0,1083.0,319.0,1843.0,349.0,2.3077,106900.0,NEAR OCEAN
+-118.12,33.99,26.0,2296.0,534.0,1777.0,507.0,2.5395,191000.0,<1H OCEAN
+-120.94,37.4,32.0,1175.0,208.0,774.0,222.0,3.0,109400.0,INLAND
+-118.35,34.16,49.0,1305.0,228.0,584.0,255.0,5.636,267900.0,<1H OCEAN
+-117.9,33.73,31.0,1171.0,306.0,1690.0,301.0,3.2639,155200.0,<1H OCEAN
+-122.3,37.98,25.0,3807.0,806.0,1821.0,792.0,3.6518,164300.0,NEAR BAY
+-118.32,33.81,27.0,2113.0,380.0,1109.0,360.0,4.7062,357000.0,<1H OCEAN
+-117.37,34.13,18.0,5877.0,1043.0,3114.0,1002.0,4.0294,133200.0,INLAND
+-117.1,32.9,16.0,2994.0,445.0,1047.0,437.0,5.149,184300.0,<1H OCEAN
+-118.15,33.95,35.0,2753.0,702.0,1592.0,614.0,2.7875,209000.0,<1H OCEAN
+-121.99,37.97,28.0,2839.0,428.0,1372.0,443.0,6.2135,217200.0,INLAND
+-117.19,34.06,21.0,6107.0,1559.0,2805.0,1444.0,2.5643,102700.0,INLAND
+-117.95,33.69,24.0,4269.0,618.0,1954.0,597.0,6.9261,284600.0,<1H OCEAN
+-121.97,37.28,25.0,4707.0,695.0,1995.0,642.0,6.6437,296100.0,<1H OCEAN
+-119.23,35.78,8.0,1612.0,343.0,1230.0,330.0,2.1806,67200.0,INLAND
+-116.89,33.92,10.0,2653.0,621.0,1967.0,598.0,2.6643,81000.0,INLAND
+-118.25,33.92,46.0,723.0,154.0,411.0,165.0,2.0893,96500.0,<1H OCEAN
+-118.09,33.85,19.0,8120.0,1371.0,5026.0,1345.0,6.3093,286500.0,<1H OCEAN
+-117.94,33.89,30.0,2577.0,404.0,1076.0,374.0,6.7528,459600.0,<1H OCEAN
+-121.18,37.99,31.0,2450.0,559.0,1459.0,478.0,2.4674,130900.0,INLAND
+-117.08,32.63,30.0,2504.0,559.0,1827.0,490.0,2.6146,159400.0,NEAR OCEAN
+-121.58,39.51,24.0,1865.0,372.0,1087.0,385.0,1.6389,56700.0,INLAND
+-122.49,37.74,48.0,1186.0,213.0,487.0,207.0,3.8333,340800.0,NEAR OCEAN
+-122.19,39.74,39.0,4179.0,814.0,2111.0,809.0,2.3507,65600.0,INLAND
+-118.74,37.58,20.0,3301.0,779.0,1085.0,448.0,3.7315,159300.0,INLAND
+-118.91,34.4,30.0,2861.0,613.0,2065.0,586.0,3.2024,176100.0,<1H OCEAN
+-120.91,37.66,36.0,1320.0,255.0,720.0,232.0,2.6523,76300.0,INLAND
+-121.91,36.59,31.0,2034.0,335.0,966.0,322.0,4.6964,291300.0,NEAR OCEAN
+-122.2,37.77,49.0,2272.0,498.0,1621.0,483.0,2.4338,102400.0,NEAR BAY
+-120.16,39.14,21.0,2484.0,460.0,309.0,144.0,3.9722,127800.0,INLAND
+-116.76,34.14,4.0,42.0,10.0,9.0,3.0,0.536,42500.0,INLAND
+-117.29,33.63,7.0,16010.0,2726.0,7139.0,2426.0,3.8056,162200.0,<1H OCEAN
+-118.75,34.27,25.0,3371.0,502.0,1717.0,506.0,6.1253,225000.0,<1H OCEAN
+-118.01,33.95,35.0,1755.0,322.0,774.0,290.0,5.0861,296700.0,<1H OCEAN
+-121.87,37.32,39.0,1839.0,471.0,1528.0,456.0,2.6818,184900.0,<1H OCEAN
+-122.45,37.62,26.0,3507.0,512.0,1712.0,509.0,6.7206,344600.0,NEAR OCEAN
+-118.21,33.79,39.0,1598.0,458.0,1691.0,399.0,2.3605,141800.0,NEAR OCEAN
+-118.86,34.19,27.0,1931.0,261.0,736.0,244.0,6.7805,392900.0,<1H OCEAN
+-118.29,34.07,19.0,3013.0,1118.0,2465.0,1008.0,2.5386,290600.0,<1H OCEAN
+-121.87,39.82,11.0,5103.0,825.0,2456.0,810.0,4.5032,159700.0,INLAND
+-121.66,39.66,17.0,3502.0,655.0,1763.0,613.0,2.9625,101200.0,INLAND
+-118.5,34.2,42.0,1558.0,322.0,884.0,334.0,2.2304,203800.0,<1H OCEAN
+-121.88,37.44,26.0,1471.0,348.0,1089.0,320.0,5.3057,226400.0,<1H OCEAN
+-117.97,33.83,16.0,2035.0,564.0,1118.0,503.0,3.2546,187500.0,<1H OCEAN
+-118.35,34.1,18.0,7432.0,2793.0,3596.0,2270.0,2.8036,225000.0,<1H OCEAN
+-118.27,33.91,37.0,3018.0,547.0,1720.0,512.0,2.7269,124100.0,<1H OCEAN
+-117.92,34.08,35.0,1860.0,323.0,1011.0,305.0,3.5536,207000.0,<1H OCEAN
+-118.53,34.2,26.0,2221.0,662.0,1998.0,603.0,2.8701,191100.0,<1H OCEAN
+-118.25,33.88,37.0,1027.0,217.0,1042.0,254.0,2.2121,98600.0,<1H OCEAN
+-118.29,33.88,36.0,1751.0,438.0,1175.0,419.0,3.0739,218600.0,<1H OCEAN
+-117.25,32.79,37.0,1467.0,442.0,651.0,354.0,2.375,340400.0,NEAR OCEAN
+-118.31,33.95,43.0,1823.0,358.0,1065.0,342.0,3.2708,131000.0,<1H OCEAN
+-118.15,34.1,52.0,4325.0,823.0,1927.0,795.0,3.9485,419100.0,<1H OCEAN
+-117.86,33.63,17.0,3095.0,551.0,1175.0,534.0,5.3099,500001.0,<1H OCEAN
+-115.55,32.98,33.0,2266.0,365.0,952.0,360.0,5.4349,143000.0,INLAND
+-121.08,38.93,14.0,4239.0,824.0,1729.0,794.0,2.4278,167700.0,INLAND
+-119.56,36.09,14.0,1267.0,290.0,1077.0,279.0,1.85,52300.0,INLAND
+-121.48,38.62,23.0,7709.0,1279.0,4147.0,1262.0,3.8272,96600.0,INLAND
+-122.42,37.75,52.0,1609.0,510.0,1155.0,439.0,2.2328,250000.0,NEAR BAY
+-117.51,33.97,35.0,352.0,62.0,184.0,57.0,3.6691,137500.0,INLAND
+-122.55,37.99,34.0,3306.0,555.0,1398.0,585.0,4.8993,319900.0,NEAR BAY
+-118.36,34.08,52.0,1965.0,480.0,794.0,451.0,3.2824,304800.0,<1H OCEAN
+-120.94,35.42,18.0,3418.0,686.0,970.0,453.0,3.7738,279400.0,NEAR OCEAN
+-117.93,33.71,10.0,2775.0,717.0,1581.0,633.0,4.1366,158800.0,<1H OCEAN
+-120.95,37.62,11.0,4981.0,814.0,1934.0,686.0,3.7041,174800.0,INLAND
+-117.13,32.75,23.0,3999.0,1182.0,2051.0,1130.0,2.1292,135000.0,NEAR OCEAN
+-119.7,36.75,11.0,3626.0,779.0,1819.0,731.0,2.4956,87500.0,INLAND
+-118.1,34.1,52.0,1788.0,313.0,792.0,294.0,3.75,280000.0,<1H OCEAN
+-119.45,35.16,34.0,3437.0,696.0,1783.0,608.0,2.3912,52900.0,INLAND
+-116.53,33.78,18.0,2547.0,463.0,411.0,214.0,2.5489,220500.0,INLAND
+-117.2,32.79,34.0,757.0,212.0,409.0,222.0,3.2312,192200.0,NEAR OCEAN
+-118.5,33.99,22.0,3484.0,975.0,1268.0,952.0,3.2609,500001.0,<1H OCEAN
+-119.03,35.42,45.0,1628.0,352.0,754.0,334.0,2.5703,62400.0,INLAND
+-117.97,33.69,21.0,4112.0,580.0,1886.0,581.0,6.799,292000.0,<1H OCEAN
+-121.85,37.36,18.0,1525.0,485.0,1705.0,448.0,3.7198,128600.0,<1H OCEAN
+-117.33,33.17,11.0,10923.0,2041.0,4773.0,1858.0,4.0791,281300.0,NEAR OCEAN
+-118.27,33.77,26.0,2272.0,694.0,2567.0,595.0,1.9964,150600.0,NEAR OCEAN
+-116.91,33.75,13.0,10886.0,2127.0,4266.0,1955.0,2.3169,123400.0,INLAND
+-124.3,41.8,19.0,2672.0,552.0,1298.0,478.0,1.9797,85800.0,NEAR OCEAN
+-120.97,37.62,7.0,8489.0,1673.0,5807.0,1575.0,2.9451,127800.0,INLAND
+-121.23,38.11,48.0,561.0,81.0,240.0,69.0,3.6312,202800.0,INLAND
+-122.35,37.58,52.0,2495.0,458.0,1081.0,471.0,4.0855,410800.0,NEAR OCEAN
+-117.28,34.17,26.0,3106.0,603.0,1396.0,576.0,3.1736,122200.0,INLAND
+-121.35,38.6,27.0,4314.0,611.0,1662.0,575.0,5.0997,170100.0,INLAND
+-117.05,32.58,22.0,2101.0,399.0,1551.0,371.0,4.1518,136900.0,NEAR OCEAN
+-122.3,38.29,25.0,1701.0,427.0,1021.0,399.0,3.0404,142100.0,NEAR BAY
+-122.14,37.46,27.0,5580.0,2009.0,4165.0,1763.0,2.4375,189000.0,NEAR BAY
+-121.32,38.69,11.0,13796.0,2372.0,6000.0,2250.0,3.8776,124500.0,INLAND
+-117.91,33.63,32.0,1901.0,400.0,946.0,418.0,2.7264,311100.0,<1H OCEAN
+-118.09,34.07,45.0,593.0,133.0,481.0,128.0,2.5938,199300.0,<1H OCEAN
+-119.89,36.7,32.0,1485.0,269.0,867.0,271.0,2.5809,78300.0,INLAND
+-118.24,33.92,40.0,1772.0,369.0,1122.0,324.0,3.2768,96100.0,<1H OCEAN
+-116.9,33.74,14.0,2281.0,426.0,894.0,430.0,2.3712,127900.0,INLAND
+-120.09,39.4,17.0,1076.0,283.0,171.0,64.0,2.125,83900.0,INLAND
+-121.93,36.99,19.0,6356.0,1100.0,2954.0,1070.0,4.7325,283500.0,NEAR OCEAN
+-119.8,36.72,43.0,1286.0,360.0,972.0,345.0,0.9513,50400.0,INLAND
+-115.56,32.78,34.0,2856.0,555.0,1627.0,522.0,3.2083,76200.0,INLAND
+-117.97,34.13,33.0,2038.0,473.0,1546.0,469.0,3.4777,144500.0,INLAND
+-118.26,33.98,43.0,762.0,206.0,854.0,188.0,1.2315,98200.0,<1H OCEAN
+-121.29,38.04,16.0,2128.0,441.0,1860.0,459.0,3.1779,97300.0,INLAND
+-122.57,37.99,45.0,2404.0,425.0,926.0,400.0,4.9674,320100.0,NEAR BAY
+-122.43,37.71,24.0,4299.0,857.0,2249.0,788.0,4.6098,290400.0,NEAR BAY
+-118.46,33.99,35.0,1214.0,300.0,478.0,265.0,4.0156,500001.0,<1H OCEAN
+-121.14,39.86,16.0,2534.0,557.0,638.0,244.0,2.2101,88800.0,INLAND
+-118.38,33.91,36.0,2904.0,515.0,1463.0,534.0,5.8374,289600.0,<1H OCEAN
+-117.11,32.73,35.0,1689.0,397.0,1135.0,366.0,2.3269,97300.0,NEAR OCEAN
+-119.69,36.71,25.0,556.0,79.0,249.0,71.0,4.4583,108300.0,INLAND
+-122.4,37.73,48.0,1489.0,326.0,1115.0,356.0,2.6364,199300.0,NEAR BAY
+-118.48,34.04,36.0,2539.0,535.0,979.0,500.0,3.6667,500001.0,<1H OCEAN
+-118.17,33.98,41.0,756.0,,873.0,212.0,2.7321,156000.0,<1H OCEAN
+-117.44,33.94,32.0,1814.0,320.0,903.0,306.0,4.1776,118700.0,INLAND
+-121.28,37.91,31.0,820.0,179.0,576.0,155.0,1.69,65900.0,INLAND
+-117.98,33.67,7.0,5664.0,1174.0,2493.0,1101.0,5.8252,264700.0,<1H OCEAN
+-116.95,33.76,10.0,6890.0,1702.0,3141.0,1451.0,1.7079,95900.0,INLAND
+-118.08,33.87,23.0,2536.0,552.0,2012.0,556.0,4.1406,200800.0,<1H OCEAN
+-118.0,33.66,25.0,4041.0,903.0,1689.0,784.0,4.2289,442700.0,NEAR OCEAN
+-121.99,37.27,21.0,1214.0,192.0,500.0,185.0,7.598,347800.0,<1H OCEAN
+-117.96,33.65,23.0,5379.0,684.0,1826.0,555.0,7.0151,350600.0,<1H OCEAN
+-122.45,37.67,35.0,491.0,98.0,274.0,97.0,4.4286,238600.0,NEAR OCEAN
+-118.16,33.77,29.0,3078.0,786.0,1460.0,736.0,2.875,232500.0,NEAR OCEAN
+-121.88,37.25,24.0,968.0,240.0,631.0,250.0,2.8636,240300.0,<1H OCEAN
+-122.21,38.11,35.0,2122.0,400.0,1189.0,408.0,3.0962,124600.0,NEAR BAY
+-117.27,32.82,42.0,2820.0,488.0,1175.0,500.0,4.5083,405200.0,NEAR OCEAN
+-118.48,34.0,52.0,1359.0,395.0,521.0,368.0,2.6736,500001.0,<1H OCEAN
+-118.14,34.06,27.0,5257.0,1082.0,3496.0,1036.0,3.3906,237200.0,<1H OCEAN
+-122.05,37.35,34.0,2494.0,375.0,1399.0,382.0,7.3753,388100.0,<1H OCEAN
+-118.03,33.78,25.0,3554.0,528.0,1600.0,537.0,6.6453,270100.0,<1H OCEAN
+-118.28,34.1,48.0,805.0,246.0,633.0,235.0,2.3421,200000.0,<1H OCEAN
+-118.13,34.06,30.0,1692.0,398.0,1130.0,365.0,2.8672,198500.0,<1H OCEAN
+-122.3,37.86,50.0,499.0,127.0,287.0,128.0,2.75,140600.0,NEAR BAY
+-118.46,34.21,7.0,2081.0,657.0,1456.0,535.0,3.5,186900.0,<1H OCEAN
+-119.02,36.07,29.0,2610.0,597.0,1659.0,571.0,1.5911,60800.0,INLAND
+-117.01,32.81,26.0,4499.0,645.0,1866.0,626.0,5.516,185100.0,<1H OCEAN
+-116.23,33.72,32.0,4981.0,1326.0,3779.0,1186.0,1.7805,76900.0,INLAND
+-122.43,37.74,52.0,1514.0,314.0,724.0,301.0,5.3292,300900.0,NEAR BAY
+-117.91,33.63,20.0,3442.0,1526.0,1427.0,977.0,3.1985,106300.0,<1H OCEAN
+-118.28,34.04,39.0,1155.0,433.0,1857.0,424.0,2.1696,153400.0,<1H OCEAN
+-118.65,34.27,23.0,1724.0,265.0,934.0,306.0,6.0783,229200.0,<1H OCEAN
+-117.28,35.13,32.0,671.0,166.0,856.0,114.0,2.6477,53300.0,INLAND
+-117.24,33.21,18.0,1846.0,419.0,1581.0,387.0,3.0982,111300.0,<1H OCEAN
+-117.15,33.16,5.0,4750.0,962.0,2726.0,905.0,3.5839,158500.0,<1H OCEAN
+-117.9,33.65,27.0,3310.0,598.0,1402.0,563.0,6.632,441100.0,<1H OCEAN
+-118.35,33.93,35.0,1050.0,252.0,918.0,236.0,1.7344,146900.0,<1H OCEAN
+-121.37,38.66,17.0,4866.0,1056.0,2371.0,1030.0,2.4574,103300.0,INLAND
+-122.31,37.91,39.0,2955.0,696.0,1417.0,682.0,2.7628,167800.0,NEAR BAY
+-116.21,33.68,34.0,584.0,176.0,625.0,166.0,1.5809,100000.0,INLAND
+-118.05,33.93,31.0,894.0,203.0,883.0,190.0,3.6771,141500.0,<1H OCEAN
+-118.01,34.07,22.0,6311.0,1572.0,6666.0,1456.0,2.9334,182600.0,INLAND
+-119.77,34.44,24.0,5652.0,1313.0,2312.0,1294.0,2.4717,295300.0,NEAR OCEAN
+-119.02,36.09,15.0,2234.0,415.0,1254.0,420.0,3.0234,88600.0,INLAND
+-117.23,32.73,35.0,2914.0,683.0,1562.0,638.0,2.5259,240200.0,NEAR OCEAN
+-118.46,34.01,43.0,513.0,98.0,266.0,103.0,5.6428,343100.0,<1H OCEAN
+-117.86,33.91,16.0,2889.0,423.0,1227.0,401.0,6.4514,270800.0,<1H OCEAN
+-121.92,38.02,16.0,1840.0,355.0,1288.0,338.0,4.2067,125000.0,INLAND
+-122.7,38.43,28.0,1585.0,412.0,1362.0,424.0,1.6685,114100.0,<1H OCEAN
+-118.34,34.03,49.0,1295.0,276.0,765.0,265.0,3.4306,130200.0,<1H OCEAN
+-119.95,38.95,8.0,430.0,107.0,36.0,18.0,2.625,187500.0,INLAND
+-117.23,33.68,10.0,3659.0,650.0,1476.0,515.0,3.8869,125900.0,<1H OCEAN
+-121.21,38.83,21.0,3691.0,640.0,1758.0,603.0,3.5607,151900.0,INLAND
+-121.27,37.97,39.0,1023.0,243.0,550.0,224.0,1.1141,54400.0,INLAND
+-122.4,37.78,52.0,464.0,202.0,286.0,148.0,1.6125,112500.0,NEAR BAY
+-122.72,38.44,48.0,707.0,166.0,458.0,172.0,3.1797,140400.0,<1H OCEAN
+-117.93,34.06,37.0,1505.0,262.0,798.0,259.0,5.4635,202100.0,<1H OCEAN
+-121.71,39.71,17.0,2748.0,556.0,1174.0,514.0,3.066,102600.0,INLAND
+-117.21,33.82,2.0,4198.0,805.0,1943.0,673.0,3.9052,122100.0,INLAND
+-122.31,37.56,36.0,1727.0,340.0,952.0,337.0,4.7917,316000.0,NEAR OCEAN
+-121.0,37.63,31.0,215.0,62.0,192.0,66.0,1.75,73800.0,INLAND
+-118.17,34.05,39.0,962.0,229.0,999.0,221.0,3.375,126000.0,<1H OCEAN
+-124.09,40.44,38.0,2220.0,426.0,1041.0,401.0,2.3947,70500.0,<1H OCEAN
+-118.48,33.99,46.0,2219.0,686.0,1107.0,590.0,2.5523,387500.0,<1H OCEAN
+-122.05,38.26,32.0,1070.0,199.0,631.0,195.0,2.6827,98900.0,INLAND
+-118.36,34.2,14.0,1878.0,614.0,1874.0,559.0,2.5267,231800.0,<1H OCEAN
+-121.92,37.64,46.0,1280.0,209.0,512.0,208.0,5.1406,315600.0,INLAND
+-117.98,33.83,17.0,3506.0,992.0,2104.0,893.0,3.3006,185800.0,<1H OCEAN
+-121.9,37.33,11.0,1283.0,390.0,718.0,345.0,4.226,166700.0,<1H OCEAN
+-122.39,37.76,52.0,157.0,28.0,88.0,27.0,3.675,162500.0,NEAR BAY
+-117.01,32.81,21.0,4203.0,618.0,1620.0,600.0,5.3441,193500.0,<1H OCEAN
+-121.4,36.83,11.0,3701.0,739.0,1749.0,654.0,3.067,207900.0,INLAND
+-117.78,34.11,23.0,7079.0,1381.0,3205.0,1327.0,3.0735,212300.0,INLAND
+-116.21,33.75,22.0,894.0,,830.0,202.0,3.0673,68200.0,INLAND
+-120.36,38.21,10.0,4300.0,845.0,1480.0,609.0,2.8208,139900.0,INLAND
+-121.3,37.96,31.0,2668.0,812.0,1398.0,721.0,1.125,110400.0,INLAND
+-122.11,37.65,18.0,4335.0,808.0,2041.0,734.0,3.4861,331600.0,NEAR BAY
+-118.12,33.9,35.0,3478.0,730.0,1885.0,673.0,2.9375,206500.0,<1H OCEAN
+-119.98,38.92,27.0,2682.0,606.0,1010.0,399.0,3.15,86900.0,INLAND
+-118.56,34.23,36.0,2406.0,432.0,1242.0,454.0,4.6944,221800.0,<1H OCEAN
+-120.5,37.32,13.0,1936.0,384.0,1158.0,367.0,2.75,83200.0,INLAND
+-122.22,37.77,52.0,1137.0,301.0,866.0,259.0,2.59,96400.0,NEAR BAY
+-122.41,37.8,52.0,4088.0,946.0,1906.0,863.0,3.6065,433300.0,NEAR BAY
+-122.09,37.07,33.0,3581.0,734.0,1780.0,663.0,4.3429,214300.0,NEAR OCEAN
+-118.24,34.11,50.0,2141.0,451.0,1777.0,426.0,2.7679,178800.0,<1H OCEAN
+-118.99,35.38,30.0,1390.0,361.0,1116.0,298.0,1.3451,57500.0,INLAND
+-120.68,35.25,16.0,4208.0,897.0,1634.0,806.0,2.2868,233700.0,NEAR OCEAN
+-118.34,34.11,51.0,937.0,348.0,527.0,333.0,4.3571,468800.0,<1H OCEAN
+-122.73,38.44,20.0,2919.0,508.0,1711.0,500.0,3.875,140300.0,<1H OCEAN
+-120.21,36.77,20.0,1745.0,348.0,1093.0,302.0,2.3194,90600.0,INLAND
+-119.08,36.02,26.0,1748.0,346.0,891.0,303.0,1.9439,62100.0,INLAND
+-119.88,34.4,25.0,2741.0,623.0,2272.0,624.0,2.2647,216700.0,NEAR OCEAN
+-122.41,37.75,52.0,1919.0,404.0,1483.0,421.0,3.4063,253900.0,NEAR BAY
+-118.32,34.09,30.0,1871.0,766.0,2595.0,819.0,2.0044,212500.0,<1H OCEAN
+-117.78,34.03,8.0,32054.0,5290.0,15507.0,5050.0,6.0191,253900.0,<1H OCEAN
+-118.19,33.93,40.0,1334.0,276.0,1226.0,278.0,3.4712,144300.0,<1H OCEAN
+-121.98,37.32,17.0,9789.0,2552.0,4748.0,2206.0,4.2531,279800.0,<1H OCEAN
+-118.28,33.93,21.0,847.0,278.0,1283.0,277.0,1.4329,94100.0,<1H OCEAN
+-122.69,38.35,12.0,1550.0,187.0,685.0,188.0,7.226,255300.0,<1H OCEAN
+-117.04,32.73,36.0,2084.0,400.0,1097.0,398.0,3.2717,130700.0,NEAR OCEAN
+-122.25,37.83,35.0,1613.0,428.0,675.0,422.0,3.4722,243100.0,NEAR BAY
+-119.22,34.28,33.0,2467.0,377.0,1052.0,363.0,4.7333,257500.0,NEAR OCEAN
+-121.91,37.44,26.0,1669.0,276.0,951.0,278.0,4.7794,225800.0,<1H OCEAN
+-118.31,34.26,41.0,1297.0,327.0,733.0,315.0,3.0583,160300.0,<1H OCEAN
+-117.86,33.6,30.0,1891.0,364.0,635.0,314.0,6.6265,500001.0,<1H OCEAN
+-120.96,37.64,36.0,60.0,12.0,51.0,14.0,3.625,67500.0,INLAND
+-122.33,37.96,46.0,1222.0,236.0,819.0,251.0,3.9118,129400.0,NEAR BAY
+-122.25,38.15,24.0,2917.0,543.0,1878.0,531.0,3.7014,123600.0,NEAR BAY
+-121.9,37.33,52.0,1009.0,231.0,929.0,210.0,2.5,162500.0,<1H OCEAN
+-118.29,34.07,24.0,4021.0,1707.0,3727.0,1529.0,1.7365,112500.0,<1H OCEAN
+-120.23,39.56,14.0,1781.0,346.0,734.0,287.0,2.46,93000.0,INLAND
+-122.47,37.7,44.0,2034.0,423.0,1491.0,373.0,4.5341,236500.0,NEAR OCEAN
+-118.47,34.05,25.0,2689.0,719.0,1229.0,663.0,3.5909,500001.0,<1H OCEAN
+-121.32,38.71,14.0,4594.0,774.0,2474.0,782.0,4.5245,127500.0,INLAND
+-118.23,33.93,23.0,545.0,131.0,610.0,126.0,1.4861,95100.0,<1H OCEAN
+-122.32,38.32,22.0,2483.0,528.0,1478.0,492.0,4.0878,164400.0,NEAR BAY
+-118.4,33.97,35.0,913.0,161.0,451.0,172.0,5.6169,411200.0,<1H OCEAN
+-119.2,34.26,13.0,3009.0,588.0,1439.0,607.0,4.1845,199500.0,NEAR OCEAN
+-122.44,37.75,46.0,1519.0,291.0,573.0,289.0,4.2667,338800.0,NEAR BAY
+-117.92,33.75,32.0,790.0,199.0,1196.0,201.0,3.0625,142800.0,<1H OCEAN
+-122.29,40.39,17.0,1682.0,332.0,887.0,316.0,1.8438,76400.0,INLAND
+-122.14,40.07,31.0,2053.0,465.0,1193.0,447.0,1.4923,44400.0,INLAND
+-118.1,33.91,36.0,726.0,,490.0,130.0,3.6389,167600.0,<1H OCEAN
+-121.04,39.0,21.0,4059.0,730.0,1874.0,693.0,4.8051,174300.0,INLAND
+-122.36,37.94,26.0,1540.0,343.0,1007.0,338.0,1.3365,72900.0,NEAR BAY
+-117.95,34.16,17.0,7116.0,1089.0,3538.0,1083.0,6.2654,273800.0,INLAND
+-117.96,33.9,9.0,1899.0,284.0,1070.0,293.0,7.2532,381500.0,<1H OCEAN
+-117.83,33.67,17.0,2634.0,641.0,1454.0,560.0,3.7976,275000.0,<1H OCEAN
+-123.49,38.7,9.0,5409.0,1019.0,594.0,327.0,3.3125,295400.0,NEAR OCEAN
+-118.34,33.94,36.0,2796.0,1041.0,4033.0,944.0,2.4886,160700.0,<1H OCEAN
+-118.32,33.81,28.0,2142.0,445.0,1140.0,422.0,4.8438,346200.0,<1H OCEAN
+-122.32,37.95,37.0,1887.0,353.0,895.0,359.0,4.45,196600.0,NEAR BAY
+-118.12,33.84,37.0,2706.0,462.0,1331.0,476.0,5.0719,220000.0,<1H OCEAN
+-122.64,38.24,40.0,1974.0,410.0,1039.0,398.0,3.7917,151600.0,<1H OCEAN
+-121.85,37.33,19.0,735.0,158.0,597.0,134.0,4.5208,188100.0,<1H OCEAN
+-121.33,36.43,40.0,622.0,194.0,902.0,196.0,2.625,109100.0,<1H OCEAN
+-122.03,37.93,21.0,4712.0,624.0,1773.0,615.0,6.0918,344800.0,NEAR BAY
+-116.9,33.98,30.0,3915.0,672.0,1820.0,643.0,3.6339,98600.0,INLAND
+-118.07,34.16,35.0,3585.0,671.0,1401.0,623.0,4.125,330000.0,INLAND
+-122.33,38.31,14.0,6778.0,947.0,2768.0,1014.0,6.1953,258900.0,NEAR BAY
+-116.94,32.78,17.0,13559.0,2656.0,6990.0,2533.0,3.434,193200.0,<1H OCEAN
+-121.13,37.73,40.0,1126.0,220.0,667.0,235.0,3.3158,125900.0,INLAND
+-117.09,34.22,16.0,1347.0,327.0,271.0,91.0,4.0,87500.0,INLAND
+-117.77,34.08,27.0,5929.0,932.0,2817.0,828.0,6.0434,214800.0,INLAND
+-118.27,33.81,42.0,865.0,208.0,811.0,218.0,3.8621,165300.0,<1H OCEAN
+-121.51,38.51,31.0,1595.0,217.0,542.0,236.0,6.6112,251600.0,INLAND
+-122.35,40.56,16.0,2801.0,614.0,1695.0,563.0,1.9,81600.0,INLAND
+-119.28,36.33,10.0,1051.0,297.0,927.0,274.0,0.78,55500.0,INLAND
+-121.93,37.4,34.0,148.0,28.0,132.0,13.0,3.375,67500.0,<1H OCEAN
+-118.13,34.04,36.0,1938.0,364.0,1118.0,374.0,3.5833,227300.0,<1H OCEAN
+-121.98,37.37,35.0,995.0,202.0,615.0,199.0,5.0942,217500.0,<1H OCEAN
+-121.14,35.55,13.0,5383.0,1070.0,1880.0,796.0,3.8019,271200.0,NEAR OCEAN
+-122.29,38.4,28.0,2024.0,340.0,844.0,309.0,4.7833,361100.0,INLAND
+-121.92,37.47,26.0,2016.0,322.0,1105.0,357.0,6.0878,246900.0,<1H OCEAN
+-118.42,34.15,31.0,1861.0,430.0,736.0,360.0,5.2853,355900.0,<1H OCEAN
+-117.52,33.88,21.0,722.0,178.0,770.0,165.0,2.5656,102500.0,INLAND
+-118.0,33.92,26.0,2830.0,399.0,1204.0,404.0,6.1273,289600.0,<1H OCEAN
+-119.03,35.39,28.0,4513.0,764.0,1593.0,763.0,2.9821,118700.0,INLAND
+-122.18,37.78,33.0,142.0,31.0,575.0,47.0,3.875,225000.0,NEAR BAY
+-118.06,34.1,38.0,1960.0,330.0,874.0,308.0,4.8594,265900.0,<1H OCEAN
+-120.73,37.38,37.0,653.0,176.0,827.0,176.0,1.9236,64400.0,INLAND
+-118.3,33.83,33.0,2716.0,660.0,1807.0,661.0,3.5473,226300.0,<1H OCEAN
+-121.75,36.91,42.0,1368.0,468.0,2312.0,484.0,2.5599,151400.0,<1H OCEAN
+-118.21,33.79,44.0,121.0,29.0,153.0,30.0,2.1964,150000.0,NEAR OCEAN
+-118.08,33.81,21.0,1189.0,281.0,577.0,264.0,3.3155,237500.0,<1H OCEAN
+-117.79,34.11,18.0,3814.0,721.0,1881.0,692.0,4.4722,215600.0,INLAND
+-121.55,38.51,22.0,2403.0,431.0,1088.0,421.0,3.9,146900.0,INLAND
+-118.42,34.25,36.0,1430.0,331.0,1502.0,312.0,3.6292,145200.0,<1H OCEAN
+-119.18,36.4,39.0,1730.0,310.0,899.0,309.0,2.6648,129200.0,INLAND
+-122.11,37.36,34.0,1575.0,183.0,511.0,180.0,13.1867,500001.0,NEAR BAY
+-118.99,35.27,32.0,444.0,102.0,242.0,87.0,1.1528,150000.0,INLAND
+-121.18,39.23,8.0,2112.0,360.0,782.0,344.0,3.7125,175000.0,INLAND
+-121.22,37.8,13.0,335.0,89.0,247.0,77.0,1.6111,74100.0,INLAND
+-118.18,33.78,52.0,1180.0,381.0,1046.0,332.0,1.5603,162500.0,NEAR OCEAN
+-121.02,40.51,17.0,890.0,167.0,406.0,154.0,3.3036,78300.0,INLAND
+-117.03,32.73,38.0,3174.0,606.0,1557.0,619.0,3.5861,123600.0,NEAR OCEAN
+-121.28,38.9,31.0,1297.0,259.0,765.0,240.0,2.7656,93600.0,INLAND
+-118.32,33.9,35.0,3189.0,680.0,1882.0,651.0,3.6625,188000.0,<1H OCEAN
+-118.07,34.1,32.0,4275.0,,2812.0,1012.0,3.3512,214100.0,<1H OCEAN
+-117.12,32.76,28.0,2160.0,608.0,1339.0,571.0,1.9152,128100.0,NEAR OCEAN
+-117.04,34.04,30.0,3474.0,735.0,1674.0,691.0,2.5863,98300.0,INLAND
+-118.83,34.33,6.0,6679.0,1164.0,3196.0,1157.0,5.4493,242600.0,<1H OCEAN
+-119.1,36.43,24.0,1039.0,190.0,643.0,193.0,2.6711,71300.0,INLAND
+-122.02,37.63,6.0,2445.0,590.0,1189.0,573.0,3.8958,301100.0,NEAR BAY
+-117.87,34.11,34.0,1324.0,211.0,799.0,228.0,4.5234,192200.0,<1H OCEAN
+-118.22,33.89,26.0,266.0,75.0,252.0,59.0,2.1211,138100.0,<1H OCEAN
+-122.25,37.81,52.0,2155.0,701.0,895.0,613.0,2.5795,350000.0,NEAR BAY
+-121.61,37.15,16.0,5498.0,729.0,2051.0,694.0,7.8601,416300.0,<1H OCEAN
+-122.02,37.3,32.0,2134.0,328.0,903.0,322.0,6.359,341900.0,<1H OCEAN
+-122.04,36.97,30.0,2695.0,424.0,1098.0,420.0,5.3972,362300.0,NEAR OCEAN
+-116.57,33.94,29.0,551.0,166.0,224.0,107.0,1.1917,50000.0,INLAND
+-117.29,33.05,28.0,1146.0,338.0,672.0,292.0,3.1667,300000.0,NEAR OCEAN
+-121.81,36.57,13.0,3030.0,413.0,1027.0,363.0,6.9615,500001.0,<1H OCEAN
+-118.48,34.28,35.0,1511.0,274.0,873.0,254.0,5.5608,226700.0,<1H OCEAN
+-118.55,33.99,39.0,2603.0,456.0,928.0,410.0,7.9096,500001.0,NEAR OCEAN
+-120.86,37.76,32.0,964.0,198.0,623.0,201.0,3.0917,88900.0,INLAND
+-122.24,37.76,49.0,2428.0,525.0,1110.0,492.0,3.6719,229800.0,NEAR BAY
+-122.36,37.59,20.0,2638.0,854.0,1352.0,718.0,3.5125,350600.0,NEAR OCEAN
+-122.5,38.4,36.0,1860.0,364.0,777.0,339.0,4.1307,295700.0,<1H OCEAN
+-118.35,33.97,30.0,1548.0,330.0,757.0,349.0,3.8056,323800.0,<1H OCEAN
+-122.14,39.65,33.0,419.0,77.0,190.0,67.0,3.6429,87500.0,INLAND
+-116.72,34.89,14.0,4527.0,875.0,1640.0,590.0,2.8594,81700.0,INLAND
+-117.26,33.0,31.0,2695.0,491.0,1059.0,451.0,4.7841,393500.0,NEAR OCEAN
+-118.42,34.03,44.0,904.0,176.0,358.0,158.0,3.3542,344200.0,<1H OCEAN
+-122.34,37.55,25.0,4470.0,518.0,1507.0,504.0,13.3913,500001.0,NEAR OCEAN
+-122.46,37.74,52.0,2053.0,281.0,791.0,287.0,10.959,500001.0,NEAR BAY
+-118.2,34.08,41.0,1807.0,429.0,1699.0,424.0,2.2222,126000.0,<1H OCEAN
+-118.38,33.88,39.0,1489.0,282.0,743.0,270.0,4.8611,456100.0,<1H OCEAN
+-121.08,40.19,11.0,919.0,199.0,69.0,43.0,1.6944,137500.0,INLAND
+-117.42,33.96,33.0,2275.0,469.0,1691.0,459.0,2.7452,98100.0,INLAND
+-122.33,37.99,4.0,3999.0,1079.0,1591.0,887.0,3.911,112500.0,NEAR BAY
+-118.21,33.98,35.0,1705.0,562.0,2212.0,539.0,2.325,161500.0,<1H OCEAN
+-122.12,40.14,34.0,1950.0,407.0,1029.0,376.0,2.5197,82300.0,INLAND
+-117.57,33.88,39.0,679.0,164.0,769.0,179.0,2.3036,110600.0,INLAND
+-121.75,37.69,26.0,2647.0,536.0,1422.0,522.0,3.7212,183800.0,INLAND
+-118.37,33.91,35.0,1742.0,283.0,812.0,282.0,5.6704,303700.0,<1H OCEAN
+-122.6,38.11,23.0,8642.0,1294.0,3594.0,1253.0,5.3962,301500.0,<1H OCEAN
+-116.86,34.31,19.0,1649.0,328.0,382.0,151.0,4.0556,133000.0,INLAND
+-122.52,38.99,16.0,975.0,219.0,337.0,155.0,1.6607,77800.0,INLAND
+-122.27,38.29,36.0,1446.0,306.0,678.0,295.0,2.8409,153000.0,NEAR BAY
+-119.18,34.2,21.0,494.0,127.0,489.0,106.0,2.6964,170800.0,NEAR OCEAN
+-120.99,38.67,8.0,4913.0,744.0,2005.0,723.0,5.4413,187900.0,INLAND
+-122.62,38.54,24.0,2409.0,464.0,1006.0,403.0,4.5167,265200.0,<1H OCEAN
+-118.09,34.17,36.0,2875.0,552.0,1131.0,458.0,4.3083,269300.0,INLAND
+-118.1,33.94,34.0,1947.0,284.0,841.0,277.0,6.1814,453600.0,<1H OCEAN
+-120.18,39.17,18.0,1703.0,360.0,354.0,163.0,3.6563,146900.0,INLAND
+-117.17,33.18,25.0,596.0,115.0,426.0,137.0,3.0221,214300.0,<1H OCEAN
+-121.92,36.55,52.0,2616.0,483.0,582.0,313.0,3.275,500001.0,NEAR OCEAN
+-122.04,37.32,27.0,2826.0,451.0,1259.0,439.0,5.7528,431400.0,<1H OCEAN
+-121.51,38.55,46.0,1485.0,278.0,531.0,291.0,2.7885,137200.0,INLAND
+-117.99,33.87,16.0,1689.0,499.0,1260.0,453.0,3.1205,174000.0,<1H OCEAN
+-121.98,37.34,33.0,3570.0,776.0,1922.0,761.0,4.9562,238700.0,<1H OCEAN
+-121.45,38.56,52.0,3420.0,555.0,1301.0,530.0,4.0417,173800.0,INLAND
+-120.84,37.5,47.0,2310.0,484.0,1126.0,447.0,2.2083,97300.0,INLAND
+-121.97,37.26,19.0,2174.0,454.0,998.0,426.0,4.6827,255100.0,<1H OCEAN
+-120.97,37.66,21.0,2760.0,632.0,1260.0,576.0,2.0227,179800.0,INLAND
+-122.02,37.31,33.0,2563.0,434.0,1230.0,418.0,6.3197,340100.0,<1H OCEAN
+-117.23,34.51,9.0,5756.0,807.0,2158.0,758.0,5.5875,167800.0,INLAND
+-121.85,36.6,21.0,2381.0,701.0,1264.0,659.0,2.5372,218000.0,<1H OCEAN
+-119.48,36.5,32.0,3451.0,625.0,1968.0,574.0,2.9554,110300.0,INLAND
+-118.33,34.03,46.0,2312.0,625.0,1552.0,603.0,1.6429,125000.0,<1H OCEAN
+-117.11,32.74,25.0,2846.0,644.0,2272.0,632.0,2.2,98700.0,NEAR OCEAN
+-117.91,33.64,40.0,1958.0,333.0,876.0,364.0,3.6406,326100.0,<1H OCEAN
+-121.44,37.74,25.0,456.0,116.0,370.0,106.0,3.1319,112500.0,INLAND
+-120.65,38.42,23.0,1538.0,305.0,730.0,267.0,2.6078,116700.0,INLAND
+-121.68,39.13,17.0,1969.0,297.0,717.0,268.0,3.4698,179700.0,INLAND
+-119.75,36.76,32.0,2072.0,497.0,2002.0,470.0,1.3278,44500.0,INLAND
+-121.35,38.51,29.0,2337.0,391.0,1054.0,352.0,4.2206,157700.0,INLAND
+-118.96,35.87,17.0,1668.0,307.0,888.0,277.0,3.7794,96200.0,INLAND
+-122.43,37.81,52.0,4309.0,942.0,1297.0,798.0,4.6781,500001.0,NEAR BAY
+-121.93,38.02,13.0,1524.0,286.0,940.0,308.0,5.1337,154800.0,INLAND
+-118.45,34.25,21.0,2143.0,565.0,1803.0,497.0,3.9833,162500.0,<1H OCEAN
+-118.56,34.19,34.0,2579.0,561.0,1237.0,517.0,4.433,235100.0,<1H OCEAN
+-121.46,38.66,3.0,3438.0,603.0,1602.0,554.0,3.9914,120500.0,INLAND
+-118.21,34.12,52.0,1301.0,389.0,1189.0,361.0,2.5139,190000.0,<1H OCEAN
+-115.38,32.82,38.0,1892.0,394.0,1175.0,374.0,1.9939,65800.0,INLAND
+-119.59,36.11,32.0,752.0,159.0,524.0,155.0,2.25,50000.0,INLAND
+-118.0,33.75,26.0,1382.0,387.0,1977.0,368.0,2.7589,137500.0,<1H OCEAN
+-118.04,33.86,21.0,2870.0,437.0,1671.0,470.0,7.2628,322700.0,<1H OCEAN
+-117.67,34.04,16.0,3260.0,501.0,1973.0,535.0,4.6563,162000.0,INLAND
+-118.18,33.98,30.0,1735.0,573.0,2237.0,545.0,2.3444,156100.0,<1H OCEAN
+-117.25,33.2,22.0,2361.0,618.0,1472.0,596.0,2.0625,124500.0,<1H OCEAN
+-118.48,34.19,36.0,2058.0,423.0,1132.0,423.0,3.8833,210400.0,<1H OCEAN
+-122.49,38.32,30.0,1631.0,284.0,788.0,284.0,3.3098,195500.0,<1H OCEAN
+-117.25,34.49,4.0,2372.0,361.0,1017.0,322.0,5.1112,170900.0,INLAND
+-118.19,33.99,35.0,1172.0,436.0,1741.0,408.0,2.4596,154700.0,<1H OCEAN
+-119.54,36.52,16.0,2703.0,415.0,1106.0,372.0,4.2045,120900.0,INLAND
+-118.01,33.97,36.0,1451.0,224.0,608.0,246.0,6.0648,290800.0,<1H OCEAN
+-121.79,37.38,22.0,3650.0,527.0,1637.0,520.0,5.3774,325600.0,<1H OCEAN
+-118.11,33.92,32.0,1016.0,190.0,729.0,177.0,4.3,151300.0,<1H OCEAN
+-121.93,37.32,52.0,1460.0,492.0,1165.0,455.0,2.5833,167500.0,<1H OCEAN
+-118.35,34.05,33.0,2880.0,836.0,1416.0,736.0,2.6781,328800.0,<1H OCEAN
+-118.12,33.91,35.0,620.0,122.0,381.0,124.0,3.7917,183900.0,<1H OCEAN
+-118.34,34.01,35.0,1359.0,359.0,655.0,341.0,2.5568,312500.0,<1H OCEAN
+-118.02,33.8,24.0,84.0,14.0,32.0,8.0,5.875,193800.0,<1H OCEAN
+-119.05,36.07,21.0,2472.0,523.0,1238.0,504.0,1.7756,62900.0,INLAND
+-116.38,33.73,10.0,11836.0,2405.0,3811.0,1570.0,4.0079,134500.0,INLAND
+-117.65,34.04,28.0,2360.0,607.0,2623.0,592.0,2.5048,100000.0,INLAND
+-119.15,34.83,6.0,8733.0,1600.0,2006.0,736.0,4.5724,168400.0,INLAND
+-122.02,37.36,21.0,2471.0,677.0,1486.0,689.0,3.9038,243800.0,<1H OCEAN
+-118.52,34.21,36.0,2394.0,424.0,1490.0,427.0,4.3261,206700.0,<1H OCEAN
+-121.08,37.69,19.0,6473.0,1212.0,3559.0,1123.0,3.2246,129300.0,INLAND
+-122.25,37.86,52.0,1709.0,318.0,719.0,295.0,5.0463,456300.0,NEAR BAY
+-121.79,36.93,19.0,2512.0,509.0,1856.0,537.0,3.1815,189100.0,<1H OCEAN
+-118.92,35.37,17.0,3589.0,701.0,1746.0,640.0,2.4919,75700.0,INLAND
+-117.66,34.07,36.0,2072.0,408.0,964.0,395.0,2.8702,137000.0,INLAND
+-117.67,34.06,26.0,1592.0,429.0,1182.0,365.0,2.4583,110400.0,INLAND
+-119.71,36.79,34.0,1891.0,323.0,966.0,355.0,3.6681,82000.0,INLAND
+-118.84,34.11,12.0,7508.0,1058.0,2484.0,965.0,5.8788,500001.0,NEAR OCEAN
+-122.2,37.35,17.0,3095.0,442.0,1173.0,424.0,13.2986,500001.0,NEAR BAY
+-117.31,34.05,6.0,7423.0,2111.0,4092.0,1789.0,2.7002,88300.0,INLAND
+-118.99,35.24,40.0,282.0,59.0,213.0,71.0,2.35,91700.0,INLAND
+-118.32,34.02,48.0,1949.0,308.0,823.0,340.0,3.3906,189700.0,<1H OCEAN
+-118.23,33.91,34.0,661.0,146.0,742.0,143.0,2.1734,88200.0,<1H OCEAN
+-122.11,37.69,42.0,1472.0,310.0,768.0,309.0,3.4643,160900.0,NEAR BAY
+-120.2,39.12,15.0,2146.0,361.0,197.0,76.0,4.1316,200000.0,INLAND
+-122.28,37.52,27.0,2958.0,655.0,1285.0,577.0,4.0801,397800.0,NEAR OCEAN
+-118.23,34.66,25.0,2627.0,387.0,1059.0,338.0,3.6382,138200.0,INLAND
+-118.19,33.78,24.0,225.0,72.0,439.0,71.0,2.8533,137500.0,NEAR OCEAN
+-118.12,34.71,26.0,4230.0,823.0,2789.0,793.0,2.5179,104000.0,INLAND
+-118.04,33.8,33.0,2685.0,466.0,1359.0,476.0,5.0261,245100.0,<1H OCEAN
+-118.42,34.18,31.0,2887.0,646.0,1626.0,637.0,3.6745,335500.0,<1H OCEAN
+-120.24,39.35,8.0,4195.0,725.0,291.0,115.0,3.4792,180800.0,INLAND
+-118.21,34.22,39.0,3008.0,386.0,1240.0,370.0,9.2463,500001.0,<1H OCEAN
+-119.2,34.15,27.0,2076.0,681.0,1904.0,647.0,1.4773,160800.0,NEAR OCEAN
+-121.86,37.41,16.0,1603.0,287.0,1080.0,296.0,6.1256,266900.0,<1H OCEAN
+-121.23,37.94,20.0,268.0,78.0,77.0,49.0,1.125,150000.0,INLAND
+-118.34,34.15,40.0,3068.0,756.0,1190.0,695.0,3.5637,497400.0,<1H OCEAN
+-117.92,34.06,35.0,2894.0,467.0,1420.0,479.0,5.184,224900.0,<1H OCEAN
+-117.11,32.82,16.0,4241.0,892.0,1771.0,864.0,4.375,166500.0,NEAR OCEAN
+-117.65,34.14,16.0,2196.0,287.0,949.0,289.0,8.6573,354000.0,INLAND
+-117.07,32.8,36.0,2028.0,349.0,820.0,352.0,3.9828,168900.0,<1H OCEAN
+-118.29,33.99,46.0,2198.0,530.0,2067.0,497.0,2.0542,103400.0,<1H OCEAN
+-118.32,33.8,29.0,3254.0,717.0,1593.0,680.0,4.0536,285800.0,<1H OCEAN
+-117.33,33.98,52.0,1417.0,353.0,881.0,300.0,1.9531,162500.0,INLAND
+-121.74,36.79,16.0,3841.0,620.0,1799.0,611.0,4.3814,245300.0,<1H OCEAN
+-117.73,33.57,5.0,11976.0,2495.0,4327.0,2009.0,4.8488,194400.0,<1H OCEAN
+-121.92,37.33,52.0,2125.0,382.0,930.0,387.0,5.2831,299500.0,<1H OCEAN
+-119.74,36.71,17.0,5872.0,1250.0,5034.0,1224.0,2.1905,61800.0,INLAND
+-122.11,37.37,22.0,1477.0,195.0,520.0,187.0,10.3329,500001.0,NEAR BAY
+-118.33,33.96,42.0,2084.0,517.0,1062.0,451.0,2.0057,198200.0,<1H OCEAN
+-122.67,38.43,17.0,2007.0,400.0,895.0,403.0,3.2813,202700.0,<1H OCEAN
+-120.08,38.8,34.0,1988.0,511.0,36.0,15.0,4.625,162500.0,INLAND
+-115.55,32.82,34.0,1540.0,316.0,1013.0,274.0,2.5664,67500.0,INLAND
+-122.53,37.96,35.0,908.0,194.0,413.0,197.0,3.9917,290800.0,NEAR BAY
+-119.32,37.06,15.0,3111.0,651.0,276.0,107.0,5.1314,179200.0,INLAND
+-122.41,37.8,52.0,3260.0,1535.0,3260.0,1457.0,0.9,500001.0,NEAR BAY
+-118.25,33.95,35.0,1405.0,326.0,1086.0,273.0,1.0375,89800.0,<1H OCEAN
+-119.81,36.8,38.0,2252.0,325.0,777.0,314.0,6.1575,160100.0,INLAND
+-122.24,38.31,38.0,1938.0,301.0,823.0,285.0,6.1089,280800.0,NEAR BAY
+-118.32,34.05,42.0,3343.0,1183.0,3480.0,1146.0,1.9923,250000.0,<1H OCEAN
+-121.95,38.03,5.0,5526.0,,3207.0,1012.0,4.0767,143100.0,INLAND
+-119.77,36.83,16.0,2360.0,355.0,1034.0,359.0,5.0635,108500.0,INLAND
+-117.97,33.92,32.0,2620.0,398.0,1296.0,429.0,5.7796,241300.0,<1H OCEAN
+-118.35,33.93,34.0,617.0,189.0,810.0,180.0,1.9766,162500.0,<1H OCEAN
+-121.94,38.36,2.0,4953.0,735.0,1791.0,562.0,5.0346,205100.0,INLAND
+-117.02,33.95,5.0,1822.0,367.0,798.0,313.0,2.8783,105200.0,INLAND
+-117.87,33.6,34.0,3415.0,779.0,1275.0,718.0,4.498,482900.0,<1H OCEAN
+-118.44,34.21,37.0,1665.0,335.0,1011.0,343.0,4.8703,185100.0,<1H OCEAN
+-121.34,38.0,32.0,3877.0,687.0,1642.0,647.0,4.0444,129200.0,INLAND
+-118.24,33.95,37.0,441.0,125.0,390.0,98.0,1.6513,90200.0,<1H OCEAN
+-122.38,40.56,23.0,2281.0,408.0,1164.0,420.0,3.5347,101200.0,INLAND
+-122.47,37.78,52.0,2169.0,522.0,1220.0,505.0,3.1989,446900.0,NEAR BAY
+-118.08,33.9,42.0,1768.0,372.0,1155.0,368.0,3.558,161100.0,<1H OCEAN
+-122.13,37.15,39.0,2854.0,613.0,1338.0,518.0,3.9423,180300.0,NEAR OCEAN
+-119.15,34.3,21.0,2475.0,502.0,1269.0,505.0,2.98,259200.0,NEAR OCEAN
+-118.3,34.0,52.0,1420.0,355.0,1080.0,353.0,1.5179,116100.0,<1H OCEAN
+-118.45,34.02,45.0,1230.0,201.0,565.0,219.0,6.3521,493400.0,<1H OCEAN
+-117.87,33.82,26.0,2435.0,346.0,1088.0,350.0,5.9397,249400.0,<1H OCEAN
+-122.24,37.49,19.0,322.0,112.0,191.0,102.0,2.5833,500001.0,NEAR OCEAN
+-117.94,33.88,35.0,2159.0,343.0,833.0,335.0,5.3738,365100.0,<1H OCEAN
+-122.41,37.66,34.0,1075.0,318.0,906.0,294.0,3.0052,242500.0,NEAR OCEAN
+-118.27,33.95,39.0,1529.0,358.0,1154.0,357.0,1.2091,97900.0,<1H OCEAN
+-115.59,32.96,17.0,841.0,146.0,473.0,154.0,3.1979,113500.0,INLAND
+-117.44,34.09,24.0,3477.0,831.0,2541.0,753.0,2.3682,97400.0,INLAND
+-122.22,37.47,28.0,5956.0,1612.0,3571.0,1549.0,3.1864,272800.0,NEAR OCEAN
+-122.4,38.34,33.0,1408.0,273.0,520.0,212.0,3.5781,242500.0,<1H OCEAN
+-118.16,34.08,43.0,1523.0,378.0,1338.0,352.0,3.2031,144600.0,<1H OCEAN
+-117.14,32.72,45.0,1140.0,310.0,840.0,339.0,1.6156,156300.0,NEAR OCEAN
+-117.65,34.09,33.0,2446.0,396.0,1209.0,412.0,4.3958,145600.0,INLAND
+-117.3,34.07,34.0,567.0,143.0,387.0,138.0,1.7981,73300.0,INLAND
+-117.63,34.12,4.0,4323.0,775.0,1479.0,663.0,6.0758,226800.0,INLAND
+-117.39,34.1,12.0,7184.0,1516.0,4862.0,1235.0,2.4492,103800.0,INLAND
+-121.89,37.82,4.0,11444.0,1355.0,3898.0,1257.0,13.2949,500001.0,INLAND
+-119.31,36.06,20.0,2236.0,434.0,1405.0,412.0,1.8827,48700.0,INLAND
+-122.0,37.27,33.0,1664.0,271.0,759.0,272.0,5.7876,415800.0,<1H OCEAN
+-117.91,33.91,32.0,1530.0,301.0,666.0,276.0,4.125,230200.0,<1H OCEAN
+-117.7,33.63,16.0,4428.0,745.0,1525.0,682.0,5.2325,286800.0,<1H OCEAN
+-117.25,32.79,27.0,848.0,300.0,455.0,298.0,3.0774,275000.0,NEAR OCEAN
+-117.11,32.64,23.0,1619.0,447.0,1025.0,415.0,1.858,67500.0,NEAR OCEAN
+-122.4,37.62,32.0,3586.0,921.0,2249.0,911.0,3.1058,253000.0,NEAR OCEAN
+-117.91,33.63,32.0,1122.0,233.0,557.0,223.0,3.5388,407000.0,<1H OCEAN
+-117.34,33.71,10.0,2591.0,486.0,1255.0,425.0,3.1513,154300.0,<1H OCEAN
+-121.32,38.7,16.0,3592.0,642.0,1740.0,629.0,3.0703,126000.0,INLAND
+-122.09,37.37,27.0,1269.0,186.0,464.0,182.0,6.8374,500001.0,NEAR BAY
+-118.44,34.28,37.0,944.0,244.0,1107.0,235.0,1.9688,144100.0,<1H OCEAN
+-117.43,33.98,21.0,2634.0,421.0,1376.0,406.0,4.2589,152200.0,INLAND
+-122.69,38.44,40.0,1449.0,281.0,636.0,295.0,2.7222,161200.0,<1H OCEAN
+-114.64,34.83,10.0,2502.0,573.0,1152.0,481.0,1.7062,86800.0,INLAND
+-122.06,36.96,52.0,65.0,17.0,24.0,10.0,4.5,258300.0,NEAR OCEAN
+-118.27,33.84,24.0,6303.0,1277.0,3728.0,1252.0,3.9227,208600.0,<1H OCEAN
+-122.43,37.77,52.0,2514.0,729.0,1428.0,597.0,2.3977,412500.0,NEAR BAY
+-122.07,37.99,28.0,3310.0,574.0,1811.0,597.0,4.5401,166900.0,NEAR BAY
+-119.08,34.35,24.0,3663.0,828.0,2718.0,778.0,3.2757,186000.0,<1H OCEAN
+-117.91,33.83,32.0,1855.0,527.0,2568.0,504.0,2.5509,170800.0,<1H OCEAN
+-121.66,36.67,40.0,2878.0,592.0,1444.0,564.0,3.1439,192300.0,<1H OCEAN
+-118.42,34.23,34.0,1550.0,279.0,1011.0,288.0,4.5375,189000.0,<1H OCEAN
+-118.58,34.21,13.0,6227.0,1317.0,3739.0,1226.0,4.0313,299300.0,<1H OCEAN
+-121.33,37.97,36.0,1953.0,492.0,999.0,371.0,2.0043,90800.0,INLAND
+-118.06,34.09,40.0,1975.0,389.0,1116.0,378.0,4.2898,251600.0,<1H OCEAN
+-118.4,34.2,30.0,2392.0,655.0,1987.0,609.0,2.8424,226400.0,<1H OCEAN
+-122.22,37.79,38.0,3049.0,711.0,2167.0,659.0,2.7969,141700.0,NEAR BAY
+-117.1,32.58,17.0,2046.0,559.0,1585.0,530.0,2.25,132800.0,NEAR OCEAN
+-118.38,34.04,31.0,2846.0,727.0,2120.0,672.0,2.7226,254200.0,<1H OCEAN
+-118.08,33.85,19.0,4261.0,678.0,2621.0,661.0,6.2427,288700.0,<1H OCEAN
+-121.97,37.93,4.0,3241.0,464.0,1552.0,494.0,6.6134,307000.0,INLAND
+-122.49,37.86,52.0,2175.0,510.0,809.0,503.0,4.5398,442000.0,NEAR BAY
+-118.2,34.13,52.0,2035.0,459.0,2589.0,438.0,3.5349,193600.0,<1H OCEAN
+-122.22,37.81,52.0,2927.0,402.0,1021.0,380.0,8.1564,390100.0,NEAR BAY
+-117.31,34.14,44.0,1487.0,273.0,972.0,281.0,3.2292,86100.0,INLAND
+-121.33,38.67,17.0,2683.0,704.0,1410.0,659.0,1.962,130200.0,INLAND
+-121.93,37.22,21.0,4872.0,594.0,1811.0,560.0,9.3834,500001.0,<1H OCEAN
+-117.1,32.74,14.0,2361.0,601.0,1831.0,526.0,1.6102,93400.0,NEAR OCEAN
+-121.44,38.63,33.0,1077.0,271.0,753.0,236.0,1.3462,55900.0,INLAND
+-117.84,33.89,19.0,3544.0,542.0,1787.0,560.0,6.7837,264300.0,<1H OCEAN
+-118.04,34.13,35.0,249.0,31.0,268.0,29.0,15.0001,500001.0,INLAND
+-117.13,32.96,15.0,2267.0,292.0,1180.0,289.0,6.712,240200.0,<1H OCEAN
+-117.09,32.62,37.0,1538.0,298.0,867.0,285.0,3.0729,128700.0,NEAR OCEAN
+-121.42,37.74,38.0,773.0,147.0,320.0,134.0,2.825,152500.0,INLAND
+-120.6,35.12,22.0,3342.0,644.0,1342.0,593.0,3.4509,217700.0,<1H OCEAN
+-117.02,32.79,36.0,2211.0,384.0,868.0,329.0,4.0491,147900.0,<1H OCEAN
+-118.38,33.83,40.0,3070.0,570.0,1264.0,506.0,5.1626,432700.0,<1H OCEAN
+-117.48,33.89,14.0,10395.0,1799.0,6295.0,1855.0,4.7295,149900.0,INLAND
+-119.69,36.41,38.0,1016.0,202.0,540.0,187.0,2.2885,75000.0,INLAND
+-118.3,34.01,52.0,1444.0,343.0,1154.0,334.0,2.0625,134400.0,<1H OCEAN
+-121.91,37.31,16.0,2962.0,898.0,1555.0,795.0,2.5804,216300.0,<1H OCEAN
+-122.4,37.76,52.0,1185.0,246.0,480.0,253.0,4.4074,277300.0,NEAR BAY
+-120.04,36.97,20.0,2129.0,526.0,1845.0,522.0,1.8973,51600.0,INLAND
+-122.78,39.05,15.0,1601.0,323.0,661.0,269.0,2.6181,108900.0,INLAND
+-118.23,33.98,30.0,2562.0,959.0,3909.0,955.0,1.9929,150600.0,<1H OCEAN
+-117.97,34.04,28.0,1686.0,417.0,1355.0,388.0,2.5192,157300.0,<1H OCEAN
+-122.71,38.43,52.0,1439.0,325.0,738.0,316.0,2.2262,129900.0,<1H OCEAN
+-118.41,34.01,26.0,2503.0,449.0,1218.0,426.0,5.3683,500001.0,<1H OCEAN
+-117.88,33.84,34.0,1410.0,214.0,837.0,240.0,6.1168,213900.0,<1H OCEAN
+-118.95,34.83,18.0,3278.0,762.0,1338.0,550.0,2.9891,116500.0,INLAND
+-117.09,32.71,29.0,2238.0,523.0,2061.0,504.0,2.5559,96800.0,NEAR OCEAN
+-118.3,33.95,41.0,2057.0,550.0,1805.0,506.0,1.2455,100800.0,<1H OCEAN
+-119.76,36.81,19.0,4643.0,1429.0,4638.0,1335.0,1.2716,69400.0,INLAND
+-119.64,36.82,14.0,4872.0,656.0,2085.0,617.0,5.6739,173800.0,INLAND
+-117.91,33.77,26.0,5556.0,1398.0,4545.0,1333.0,3.0902,190400.0,<1H OCEAN
+-120.01,38.91,27.0,968.0,191.0,283.0,143.0,2.0938,94400.0,INLAND
+-117.74,33.51,29.0,1720.0,269.0,612.0,258.0,7.8239,500001.0,<1H OCEAN
+-121.14,38.16,14.0,2591.0,497.0,1371.0,479.0,3.5774,113900.0,INLAND
+-117.97,33.74,16.0,1735.0,380.0,784.0,360.0,4.2566,139200.0,<1H OCEAN
+-117.05,32.68,19.0,1469.0,275.0,1010.0,292.0,3.5664,150400.0,NEAR OCEAN
+-118.18,33.83,52.0,2569.0,484.0,1030.0,451.0,4.1301,268400.0,NEAR OCEAN
+-120.77,38.38,15.0,4221.0,816.0,1737.0,743.0,2.3125,128600.0,INLAND
+-118.14,34.7,36.0,1205.0,317.0,678.0,290.0,2.0182,98400.0,INLAND
+-117.07,32.79,36.0,3583.0,642.0,1711.0,602.0,3.9745,170800.0,NEAR OCEAN
+-117.81,33.82,20.0,2819.0,319.0,1019.0,319.0,12.2092,500001.0,<1H OCEAN
+-122.04,37.36,26.0,3298.0,460.0,1241.0,472.0,6.8753,403000.0,<1H OCEAN
+-118.42,34.31,19.0,6755.0,1443.0,4205.0,1395.0,3.9583,163200.0,<1H OCEAN
+-122.46,38.29,21.0,2423.0,560.0,1098.0,503.0,2.364,173300.0,NEAR BAY
+-122.45,38.51,18.0,1297.0,337.0,610.0,312.0,1.9441,184400.0,INLAND
+-121.83,37.3,17.0,1299.0,211.0,825.0,217.0,4.5,235800.0,<1H OCEAN
+-121.98,37.22,46.0,10088.0,1910.0,3728.0,1781.0,5.2321,500001.0,<1H OCEAN
+-121.6,39.75,19.0,2888.0,591.0,984.0,499.0,1.9766,92600.0,INLAND
+-117.07,32.77,38.0,3779.0,614.0,1495.0,614.0,4.3529,184000.0,NEAR OCEAN
+-122.55,38.02,27.0,4985.0,711.0,1928.0,742.0,6.4978,361500.0,NEAR BAY
+-118.01,33.85,29.0,2064.0,447.0,1265.0,400.0,3.8864,209300.0,<1H OCEAN
+-117.69,36.13,25.0,1709.0,439.0,632.0,292.0,1.7868,45500.0,INLAND
+-121.47,38.57,48.0,3136.0,926.0,1443.0,877.0,1.9034,123900.0,INLAND
+-117.2,32.85,26.0,2298.0,549.0,980.0,555.0,2.4207,213500.0,NEAR OCEAN
+-119.33,36.89,15.0,1879.0,411.0,755.0,294.0,2.0,83300.0,INLAND
+-115.52,34.22,30.0,540.0,136.0,122.0,63.0,1.3333,42500.0,INLAND
+-117.2,34.26,17.0,9419.0,1455.0,1382.0,459.0,6.2233,230900.0,INLAND
+-118.37,34.04,42.0,1809.0,424.0,1094.0,382.0,2.767,143000.0,<1H OCEAN
+-121.24,37.97,47.0,886.0,196.0,517.0,188.0,2.1991,67200.0,INLAND
+-118.96,34.3,16.0,3103.0,482.0,1567.0,467.0,6.907,500001.0,<1H OCEAN
+-118.16,33.91,35.0,1403.0,338.0,1415.0,367.0,3.0967,144000.0,<1H OCEAN
+-117.86,33.75,13.0,1632.0,598.0,3356.0,659.0,1.5054,137500.0,<1H OCEAN
+-118.75,34.18,4.0,16704.0,2704.0,6187.0,2207.0,6.6122,357600.0,NEAR OCEAN
+-117.46,33.94,26.0,2481.0,620.0,2411.0,552.0,1.7059,85800.0,INLAND
+-116.9,33.65,15.0,652.0,149.0,248.0,97.0,2.1071,93800.0,INLAND
+-118.47,33.99,33.0,854.0,235.0,645.0,198.0,2.1471,239300.0,<1H OCEAN
+-122.09,37.64,36.0,1180.0,212.0,664.0,200.0,5.2838,172600.0,NEAR BAY
+-118.46,34.16,38.0,1495.0,300.0,598.0,280.0,3.4698,265400.0,<1H OCEAN
+-119.73,36.72,26.0,2645.0,1005.0,1660.0,991.0,0.6991,89500.0,INLAND
+-118.44,33.99,43.0,1432.0,308.0,782.0,303.0,4.3333,303900.0,<1H OCEAN
+-118.18,33.88,42.0,2326.0,503.0,1832.0,501.0,3.1713,161000.0,<1H OCEAN
+-118.15,34.02,37.0,2344.0,631.0,2195.0,610.0,2.7022,151900.0,<1H OCEAN
+-115.52,32.97,24.0,1617.0,366.0,1416.0,401.0,1.975,66400.0,INLAND
+-117.96,33.71,19.0,4328.0,849.0,2243.0,808.0,5.5702,342600.0,<1H OCEAN
+-119.79,36.29,6.0,1265.0,227.0,764.0,246.0,4.2917,104200.0,INLAND
+-114.61,34.83,31.0,2478.0,464.0,1346.0,479.0,3.212,70400.0,INLAND
+-121.94,37.3,30.0,1758.0,248.0,814.0,256.0,6.623,332500.0,<1H OCEAN
+-119.04,35.96,18.0,1187.0,308.0,1343.0,277.0,1.875,51700.0,INLAND
+-122.37,37.81,26.0,5416.0,1045.0,4531.0,962.0,2.7909,250000.0,NEAR BAY
+-117.32,34.41,13.0,2032.0,348.0,1038.0,344.0,4.2891,120100.0,INLAND
+-118.07,33.89,35.0,1145.0,274.0,1651.0,265.0,3.125,120300.0,<1H OCEAN
+-117.97,33.83,22.0,3310.0,688.0,1807.0,674.0,4.0185,200900.0,<1H OCEAN
+-121.42,38.54,18.0,2525.0,501.0,1726.0,468.0,2.398,87600.0,INLAND
+-117.81,33.78,27.0,3589.0,507.0,1484.0,495.0,5.7934,270500.0,<1H OCEAN
+-117.29,33.06,20.0,2110.0,335.0,1008.0,325.0,6.1509,338700.0,NEAR OCEAN
+-118.15,34.03,44.0,603.0,207.0,588.0,218.0,2.0536,186400.0,<1H OCEAN
+-117.82,33.8,15.0,3207.0,647.0,1414.0,595.0,4.0484,165600.0,<1H OCEAN
+-121.95,37.31,27.0,2462.0,570.0,1278.0,565.0,3.5652,329500.0,<1H OCEAN
+-118.2,33.78,48.0,1766.0,497.0,1908.0,466.0,1.9872,168800.0,NEAR OCEAN
+-122.38,37.76,52.0,248.0,68.0,124.0,51.0,1.4886,450000.0,NEAR BAY
+-121.25,38.64,21.0,2764.0,363.0,902.0,360.0,5.6864,258700.0,INLAND
+-122.25,37.79,52.0,902.0,237.0,846.0,227.0,3.625,125000.0,NEAR BAY
+-117.46,33.93,16.0,4112.0,880.0,2821.0,857.0,3.0122,114700.0,INLAND
+-122.02,37.98,40.0,1797.0,401.0,756.0,369.0,2.8456,165500.0,NEAR BAY
+-117.14,32.81,34.0,1748.0,294.0,800.0,294.0,4.4886,179100.0,NEAR OCEAN
+-122.39,37.73,52.0,1070.0,224.0,567.0,207.0,2.8603,189000.0,NEAR BAY
+-119.01,35.33,32.0,3068.0,628.0,1897.0,607.0,2.4234,63700.0,INLAND
+-122.49,37.76,52.0,1382.0,230.0,708.0,279.0,5.8096,339800.0,NEAR BAY
+-121.3,38.14,17.0,3507.0,696.0,1867.0,709.0,3.2123,120700.0,INLAND
+-122.44,37.77,52.0,5604.0,1268.0,2023.0,1196.0,4.4085,400000.0,NEAR BAY
+-117.32,34.08,41.0,1359.0,264.0,786.0,244.0,2.5208,85500.0,INLAND
+-121.91,37.46,26.0,2762.0,496.0,1716.0,459.0,5.6062,226800.0,<1H OCEAN
+-121.56,38.26,43.0,1906.0,327.0,996.0,314.0,2.9744,136800.0,INLAND
+-118.38,34.03,36.0,2101.0,569.0,1756.0,527.0,2.9344,222100.0,<1H OCEAN
+-119.22,36.31,17.0,2079.0,459.0,2022.0,462.0,1.5464,54100.0,INLAND
+-119.89,34.44,25.0,2786.0,470.0,1669.0,462.0,5.5184,268300.0,NEAR OCEAN
+-121.3,38.59,25.0,3002.0,718.0,1660.0,613.0,2.1116,89600.0,INLAND
+-118.14,34.1,52.0,4061.0,861.0,2290.0,790.0,2.8919,258400.0,<1H OCEAN
+-117.43,34.08,31.0,3207.0,560.0,1582.0,538.0,4.263,127400.0,INLAND
+-121.44,38.68,19.0,2476.0,534.0,1355.0,463.0,2.0625,94400.0,INLAND
+-117.9,34.1,31.0,3007.0,653.0,1766.0,616.0,3.7083,166000.0,<1H OCEAN
+-122.53,37.86,38.0,1183.0,196.0,628.0,205.0,3.75,478600.0,NEAR BAY
+-121.39,38.58,36.0,2019.0,369.0,878.0,356.0,2.8462,93400.0,INLAND
+-121.49,38.63,6.0,12197.0,2617.0,5634.0,2329.0,3.7449,129300.0,INLAND
+-117.76,33.79,4.0,8974.0,1268.0,3754.0,1241.0,8.2653,374000.0,<1H OCEAN
+-122.01,37.36,16.0,1105.0,354.0,499.0,324.0,4.2061,253600.0,<1H OCEAN
+-121.9,37.44,9.0,957.0,139.0,532.0,142.0,8.6675,441000.0,<1H OCEAN
+-118.28,33.95,41.0,835.0,208.0,707.0,192.0,1.4103,86200.0,<1H OCEAN
+-118.02,34.13,33.0,2874.0,458.0,1239.0,431.0,5.2329,430900.0,INLAND
+-122.09,37.64,32.0,1578.0,284.0,836.0,292.0,3.9063,184200.0,NEAR BAY
+-118.33,34.18,48.0,2122.0,385.0,926.0,362.0,5.6975,231400.0,<1H OCEAN
+-121.84,37.24,18.0,3574.0,504.0,1803.0,536.0,6.7836,274100.0,<1H OCEAN
+-117.91,33.88,34.0,1851.0,291.0,784.0,290.0,5.2336,235600.0,<1H OCEAN
+-117.17,32.68,16.0,5895.0,1424.0,873.0,522.0,7.3669,187500.0,NEAR OCEAN
+-117.83,34.01,16.0,9446.0,1650.0,4911.0,1534.0,5.0111,212900.0,<1H OCEAN
+-120.01,34.54,30.0,2992.0,609.0,1288.0,465.0,3.9375,292900.0,NEAR OCEAN
+-122.03,37.27,25.0,4460.0,553.0,1608.0,561.0,10.7958,500001.0,<1H OCEAN
+-117.04,32.77,16.0,7963.0,1881.0,3769.0,1804.0,2.9624,144700.0,<1H OCEAN
+-116.99,32.96,17.0,5509.0,866.0,2748.0,817.0,4.8854,181300.0,<1H OCEAN
+-120.76,38.76,21.0,3509.0,606.0,1576.0,564.0,2.6392,148500.0,INLAND
+-117.86,33.78,21.0,2713.0,731.0,1952.0,722.0,2.6959,178800.0,<1H OCEAN
+-119.71,36.77,11.0,5112.0,1384.0,2487.0,1243.0,2.1461,75900.0,INLAND
+-121.98,37.27,25.0,3075.0,564.0,1633.0,543.0,5.2528,269400.0,<1H OCEAN
+-121.06,40.23,23.0,1127.0,225.0,215.0,85.0,3.4844,143800.0,INLAND
+-117.83,33.87,5.0,6971.0,1449.0,3521.0,1423.0,5.2131,243900.0,<1H OCEAN
+-117.98,33.81,35.0,897.0,156.0,479.0,161.0,5.152,215600.0,<1H OCEAN
+-118.35,33.84,22.0,13133.0,3680.0,7180.0,3522.0,3.5414,354700.0,<1H OCEAN
+-118.33,33.88,36.0,1271.0,346.0,811.0,345.0,3.2417,283300.0,<1H OCEAN
+-118.37,33.95,32.0,1067.0,286.0,1053.0,277.0,2.8438,181700.0,<1H OCEAN
+-122.43,37.72,50.0,2912.0,562.0,1989.0,537.0,3.6667,252600.0,NEAR BAY
+-118.3,33.77,18.0,3821.0,795.0,2831.0,769.0,2.9571,346200.0,<1H OCEAN
+-117.31,33.16,4.0,5846.0,894.0,2282.0,801.0,5.5956,247800.0,NEAR OCEAN
+-118.38,33.98,25.0,7105.0,1012.0,2519.0,1004.0,6.8112,500001.0,<1H OCEAN
+-121.51,38.55,45.0,3032.0,631.0,1341.0,597.0,2.8417,137900.0,INLAND
+-121.42,37.73,2.0,2682.0,393.0,883.0,271.0,5.9934,196700.0,INLAND
+-117.58,34.11,14.0,11635.0,2055.0,6443.0,2009.0,4.7547,157600.0,INLAND
+-117.85,33.74,19.0,1248.0,357.0,1214.0,328.0,2.7059,159800.0,<1H OCEAN
+-120.7,38.75,19.0,2325.0,430.0,967.0,376.0,2.9,158700.0,INLAND
+-117.01,32.7,25.0,2321.0,398.0,1434.0,386.0,3.5341,120800.0,NEAR OCEAN
+-118.64,34.22,25.0,2762.0,410.0,1166.0,439.0,6.8643,333700.0,<1H OCEAN
+-118.04,33.85,18.0,3628.0,546.0,1922.0,544.0,7.5057,328500.0,<1H OCEAN
+-122.0,37.58,6.0,4405.0,717.0,2071.0,688.0,5.8151,295600.0,<1H OCEAN
+-119.47,35.4,32.0,2167.0,421.0,1301.0,394.0,1.9718,69800.0,INLAND
+-118.23,34.12,32.0,2094.0,491.0,1413.0,479.0,4.5089,221100.0,<1H OCEAN
+-117.35,34.5,10.0,2163.0,392.0,1174.0,362.0,3.375,98000.0,INLAND
+-121.5,38.58,5.0,761.0,306.0,2031.0,295.0,0.7526,162500.0,INLAND
+-118.26,34.07,52.0,1802.0,613.0,2382.0,587.0,1.8438,185900.0,<1H OCEAN
+-117.04,32.77,26.0,4263.0,1037.0,2199.0,1010.0,2.734,148900.0,<1H OCEAN
+-118.43,34.18,22.0,2052.0,568.0,1254.0,572.0,2.6364,271100.0,<1H OCEAN
+-117.86,33.88,19.0,1621.0,328.0,871.0,322.0,3.7361,201400.0,<1H OCEAN
+-118.13,34.58,29.0,2370.0,475.0,1746.0,483.0,3.7464,113500.0,INLAND
+-119.18,34.21,46.0,2062.0,484.0,1522.0,469.0,3.087,213900.0,NEAR OCEAN
+-121.32,38.26,4.0,6125.0,1063.0,3077.0,953.0,4.1179,134600.0,INLAND
+-117.68,34.05,25.0,1859.0,463.0,1070.0,374.0,2.5395,187500.0,INLAND
+-121.41,38.6,16.0,5407.0,1467.0,2523.0,1265.0,2.0471,104200.0,INLAND
+-121.92,36.62,52.0,974.0,190.0,403.0,181.0,4.3281,236500.0,NEAR OCEAN
+-121.95,37.38,22.0,765.0,198.0,390.0,176.0,3.1812,87500.0,<1H OCEAN
+-123.41,41.6,23.0,1654.0,369.0,669.0,273.0,1.965,65400.0,INLAND
+-118.42,34.18,27.0,3760.0,880.0,2022.0,812.0,3.1551,225600.0,<1H OCEAN
+-117.8,33.75,29.0,3058.0,488.0,1197.0,474.0,5.3903,286600.0,<1H OCEAN
+-122.19,37.77,52.0,2170.0,428.0,1086.0,425.0,3.3715,143900.0,NEAR BAY
+-121.63,36.71,19.0,5015.0,1013.0,3251.0,940.0,3.9818,152900.0,<1H OCEAN
+-119.71,36.81,19.0,2282.0,550.0,1034.0,500.0,1.6618,69700.0,INLAND
+-124.13,40.62,43.0,2131.0,399.0,910.0,389.0,2.5804,92100.0,NEAR OCEAN
+-118.06,34.09,38.0,2036.0,388.0,1096.0,371.0,4.0625,262500.0,<1H OCEAN
+-118.41,34.26,38.0,870.0,205.0,1065.0,222.0,2.5313,136100.0,<1H OCEAN
+-118.36,33.95,42.0,2532.0,627.0,2038.0,591.0,2.875,177500.0,<1H OCEAN
+-122.25,37.49,40.0,2709.0,521.0,1156.0,510.0,4.6366,395500.0,NEAR OCEAN
+-117.26,32.85,30.0,3652.0,499.0,978.0,462.0,8.2374,500001.0,NEAR OCEAN
+-117.5,33.98,21.0,2394.0,416.0,1291.0,381.0,4.2099,138700.0,INLAND
+-118.29,34.16,42.0,413.0,107.0,349.0,107.0,4.3438,189800.0,<1H OCEAN
+-119.66,37.39,10.0,2106.0,410.0,1003.0,397.0,2.7813,124100.0,INLAND
+-117.38,33.67,9.0,13288.0,2728.0,7235.0,2350.0,3.375,131800.0,<1H OCEAN
+-118.93,34.82,24.0,806.0,168.0,323.0,136.0,3.5,113900.0,INLAND
+-118.14,33.79,44.0,2388.0,619.0,1461.0,592.0,3.1711,215400.0,NEAR OCEAN
+-118.36,33.94,38.0,2169.0,688.0,3036.0,639.0,2.3125,148500.0,<1H OCEAN
+-117.41,33.96,32.0,2837.0,617.0,1393.0,595.0,2.3798,118800.0,INLAND
+-122.44,37.71,52.0,2711.0,591.0,1848.0,524.0,3.9567,251500.0,NEAR BAY
+-120.94,37.7,25.0,1005.0,159.0,390.0,139.0,4.4,174100.0,INLAND
+-118.5,34.04,52.0,3000.0,374.0,1143.0,375.0,15.0001,500001.0,<1H OCEAN
+-119.76,36.68,29.0,1243.0,312.0,836.0,277.0,1.8355,74200.0,INLAND
+-122.65,38.24,49.0,3273.0,579.0,1431.0,539.0,4.275,227600.0,<1H OCEAN
+-122.12,38.0,20.0,6992.0,1404.0,3221.0,1334.0,4.2042,166400.0,NEAR BAY
+-118.52,34.4,5.0,7748.0,1557.0,4768.0,1393.0,5.305,311200.0,<1H OCEAN
+-119.01,35.34,44.0,1730.0,343.0,782.0,278.0,3.0208,63700.0,INLAND
+-119.09,35.43,28.0,254.0,35.0,118.0,37.0,4.8571,237500.0,INLAND
+-118.07,33.97,36.0,1265.0,273.0,1052.0,253.0,4.8929,156200.0,<1H OCEAN
+-120.43,34.89,30.0,1979.0,342.0,999.0,320.0,5.0286,158000.0,<1H OCEAN
+-122.07,37.33,13.0,2173.0,349.0,891.0,345.0,8.0158,420000.0,<1H OCEAN
+-121.59,39.78,18.0,945.0,205.0,385.0,207.0,2.1838,58000.0,INLAND
+-122.79,39.09,20.0,1798.0,395.0,685.0,331.0,1.625,66800.0,INLAND
+-122.0,37.06,20.0,2403.0,376.0,1149.0,369.0,6.0621,304400.0,NEAR OCEAN
+-121.3,37.96,52.0,1475.0,238.0,736.0,260.0,3.6591,105100.0,INLAND
+-122.2,39.75,18.0,2603.0,576.0,1616.0,588.0,2.0192,63700.0,INLAND
+-117.87,33.92,17.0,4575.0,764.0,2054.0,737.0,6.0571,272400.0,<1H OCEAN
+-117.14,32.7,32.0,1280.0,353.0,1335.0,330.0,1.6023,77300.0,NEAR OCEAN
+-121.98,37.33,25.0,3223.0,612.0,1529.0,602.0,5.121,287600.0,<1H OCEAN
+-122.92,39.05,16.0,1548.0,295.0,605.0,250.0,3.5652,119000.0,INLAND
+-118.5,34.19,26.0,2156.0,509.0,1142.0,470.0,4.0,224700.0,<1H OCEAN
+-117.95,33.84,19.0,1749.0,406.0,969.0,391.0,3.75,173400.0,<1H OCEAN
+-122.4,37.73,42.0,1413.0,406.0,1027.0,362.0,2.3625,233000.0,NEAR BAY
+-116.98,32.86,19.0,2121.0,341.0,1236.0,353.0,4.7717,153200.0,<1H OCEAN
+-124.07,40.87,47.0,1765.0,326.0,796.0,333.0,2.2138,99200.0,NEAR OCEAN
+-118.44,34.01,43.0,1408.0,246.0,651.0,240.0,4.5795,400000.0,<1H OCEAN
+-118.45,34.08,52.0,1500.0,176.0,384.0,145.0,7.1576,500001.0,<1H OCEAN
+-121.04,37.78,32.0,2916.0,528.0,1466.0,473.0,2.5643,200000.0,INLAND
+-118.31,33.96,47.0,1586.0,322.0,1077.0,339.0,4.4861,140400.0,<1H OCEAN
+-118.39,33.99,32.0,2612.0,418.0,1030.0,402.0,6.603,369200.0,<1H OCEAN
+-122.04,37.65,10.0,8299.0,1326.0,3827.0,1288.0,6.2579,315500.0,NEAR BAY
+-117.01,32.74,31.0,3473.0,,2098.0,677.0,2.6973,135200.0,<1H OCEAN
+-121.29,37.96,52.0,888.0,324.0,630.0,258.0,1.2411,112500.0,INLAND
+-117.94,33.83,20.0,812.0,192.0,494.0,172.0,3.25,350000.0,<1H OCEAN
+-118.32,34.12,52.0,3410.0,800.0,1218.0,783.0,4.15,393500.0,<1H OCEAN
+-118.39,34.09,27.0,4312.0,1214.0,1634.0,1097.0,3.6207,362500.0,<1H OCEAN
+-120.84,37.47,11.0,2285.0,499.0,1468.0,471.0,2.7857,110300.0,INLAND
+-121.92,37.69,13.0,3742.0,555.0,1590.0,559.0,7.316,285400.0,<1H OCEAN
+-119.76,36.71,29.0,1745.0,441.0,1530.0,391.0,1.5611,44400.0,INLAND
+-117.09,32.83,15.0,4138.0,636.0,2001.0,677.0,4.8419,264000.0,<1H OCEAN
+-118.5,34.03,44.0,2146.0,394.0,851.0,355.0,6.48,500001.0,<1H OCEAN
+-123.54,39.17,18.0,2251.0,510.0,1032.0,369.0,2.2946,101000.0,<1H OCEAN
+-122.16,37.48,36.0,2238.0,479.0,1949.0,457.0,2.3769,157300.0,NEAR BAY
+-122.21,37.8,38.0,2254.0,535.0,951.0,487.0,3.0812,233100.0,NEAR BAY
+-119.05,35.36,16.0,4507.0,1049.0,2261.0,959.0,3.3261,118400.0,INLAND
+-121.48,38.53,37.0,1704.0,361.0,902.0,356.0,1.9837,62300.0,INLAND
+-117.42,33.89,4.0,80.0,10.0,55.0,13.0,7.7197,193800.0,INLAND
+-117.02,32.8,29.0,1232.0,243.0,665.0,247.0,3.65,168900.0,<1H OCEAN
+-118.52,34.28,33.0,1975.0,271.0,801.0,287.0,7.8193,379600.0,<1H OCEAN
+-118.34,34.05,52.0,2530.0,458.0,1122.0,449.0,3.9167,321600.0,<1H OCEAN
+-118.44,34.28,46.0,11.0,11.0,24.0,13.0,2.875,162500.0,<1H OCEAN
+-117.66,34.09,23.0,1426.0,313.0,954.0,319.0,3.0357,151500.0,INLAND
+-116.32,33.28,19.0,1791.0,367.0,327.0,185.0,3.3625,100000.0,INLAND
+-118.05,34.05,36.0,1084.0,202.0,920.0,199.0,3.7279,162200.0,<1H OCEAN
+-118.28,33.96,34.0,2074.0,562.0,1913.0,514.0,1.6156,102100.0,<1H OCEAN
+-118.42,34.27,33.0,937.0,216.0,1216.0,212.0,3.3214,131300.0,<1H OCEAN
+-118.2,34.05,40.0,1146.0,323.0,1354.0,321.0,1.9205,121900.0,<1H OCEAN
+-117.22,32.86,4.0,16289.0,4585.0,7604.0,4176.0,3.6287,280800.0,NEAR OCEAN
+-118.43,34.2,28.0,3386.0,,2240.0,737.0,3.0221,290100.0,<1H OCEAN
+-119.78,36.78,37.0,2185.0,455.0,1143.0,438.0,1.9784,70700.0,INLAND
+-121.59,39.75,20.0,908.0,206.0,481.0,211.0,2.2,80800.0,INLAND
+-118.68,34.33,45.0,121.0,25.0,67.0,27.0,2.9821,325000.0,<1H OCEAN
+-119.69,36.38,25.0,1688.0,302.0,879.0,277.0,3.3214,103100.0,INLAND
+-120.61,37.35,34.0,1900.0,401.0,1009.0,385.0,2.2222,75000.0,INLAND
+-121.22,38.4,14.0,2655.0,441.0,1277.0,422.0,4.6989,213800.0,INLAND
+-118.0,33.97,30.0,6540.0,991.0,3124.0,953.0,6.0663,372600.0,<1H OCEAN
+-120.98,38.99,17.0,3403.0,661.0,1540.0,622.0,3.6354,162900.0,INLAND
+-118.18,33.87,38.0,2664.0,626.0,1627.0,604.0,3.7527,161900.0,<1H OCEAN
+-122.44,37.76,30.0,5089.0,1210.0,1935.0,1139.0,4.6053,386100.0,NEAR BAY
+-122.16,37.71,36.0,666.0,132.0,366.0,134.0,3.4643,175000.0,NEAR BAY
+-122.02,36.98,35.0,1053.0,263.0,552.0,237.0,2.7125,217500.0,NEAR OCEAN
+-122.31,40.49,18.0,4026.0,718.0,1731.0,705.0,3.35,118400.0,INLAND
+-121.78,37.34,21.0,1959.0,292.0,891.0,300.0,7.375,338400.0,<1H OCEAN
+-117.9,33.9,18.0,3821.0,576.0,1430.0,568.0,6.9399,349600.0,<1H OCEAN
+-118.49,34.01,28.0,651.0,252.0,333.0,174.0,1.9722,500001.0,<1H OCEAN
+-118.5,34.21,35.0,1668.0,332.0,807.0,311.0,4.5125,200300.0,<1H OCEAN
+-117.66,33.62,16.0,4065.0,661.0,1962.0,636.0,6.2177,256600.0,<1H OCEAN
+-118.54,34.17,11.0,1080.0,174.0,386.0,160.0,6.1274,315900.0,<1H OCEAN
+-118.26,34.24,35.0,1666.0,280.0,788.0,273.0,6.6277,344400.0,<1H OCEAN
+-122.26,38.03,25.0,2239.0,361.0,928.0,353.0,4.4474,203700.0,NEAR BAY
+-116.18,33.67,25.0,2888.0,654.0,2940.0,660.0,2.2141,66700.0,INLAND
+-118.19,33.96,40.0,979.0,296.0,934.0,292.0,2.6354,151800.0,<1H OCEAN
+-117.09,32.69,20.0,1102.0,205.0,852.0,217.0,3.1833,108300.0,NEAR OCEAN
+-117.68,35.55,9.0,3811.0,605.0,1518.0,568.0,5.5551,142500.0,INLAND
+-121.97,37.28,27.0,2427.0,403.0,1301.0,438.0,5.0385,277300.0,<1H OCEAN
+-117.09,32.66,37.0,1232.0,330.0,1086.0,330.0,1.6389,114300.0,NEAR OCEAN
+-121.04,37.65,8.0,1959.0,379.0,995.0,365.0,3.3567,129100.0,INLAND
+-117.15,32.77,16.0,2056.0,631.0,847.0,569.0,2.9576,92200.0,NEAR OCEAN
+-117.67,33.55,6.0,3157.0,721.0,1695.0,710.0,3.7609,222300.0,<1H OCEAN
+-121.33,38.0,32.0,4474.0,929.0,2177.0,884.0,3.2889,98900.0,INLAND
+-116.44,34.12,18.0,5584.0,1303.0,2250.0,1158.0,1.5823,72400.0,INLAND
+-122.54,37.96,33.0,2534.0,495.0,996.0,449.0,4.3083,500001.0,NEAR BAY
+-117.98,33.84,35.0,984.0,179.0,661.0,199.0,5.0747,189600.0,<1H OCEAN
+-121.42,37.74,35.0,796.0,132.0,313.0,152.0,3.15,153200.0,INLAND
+-117.26,33.81,22.0,4249.0,922.0,2405.0,846.0,2.1549,146500.0,INLAND
+-122.43,37.63,15.0,2748.0,997.0,1447.0,901.0,3.5214,144200.0,NEAR OCEAN
+-118.47,34.02,35.0,3057.0,774.0,2223.0,732.0,2.0745,332500.0,<1H OCEAN
+-117.06,32.76,38.0,1549.0,288.0,636.0,278.0,3.2188,150500.0,NEAR OCEAN
+-117.1,32.59,21.0,2350.0,667.0,1621.0,613.0,2.0734,87500.0,NEAR OCEAN
+-117.69,33.58,5.0,6678.0,1011.0,2877.0,982.0,7.5177,330000.0,<1H OCEAN
+-124.17,40.8,52.0,661.0,316.0,392.0,244.0,0.957,60000.0,NEAR OCEAN
+-119.69,34.42,52.0,302.0,112.0,392.0,114.0,2.5978,258300.0,<1H OCEAN
+-117.13,32.76,41.0,1545.0,420.0,747.0,415.0,2.375,154400.0,NEAR OCEAN
+-118.27,33.96,34.0,1040.0,276.0,1083.0,255.0,1.6467,90900.0,<1H OCEAN
+-120.46,34.65,10.0,2143.0,593.0,1167.0,548.0,2.0819,103300.0,NEAR OCEAN
+-121.28,38.11,10.0,2974.0,588.0,1559.0,568.0,3.8825,136800.0,INLAND
+-122.46,37.98,10.0,1325.0,189.0,427.0,162.0,12.0933,500001.0,NEAR BAY
+-118.34,34.07,52.0,2066.0,319.0,981.0,297.0,5.8632,450000.0,<1H OCEAN
+-121.46,38.57,52.0,1625.0,419.0,614.0,383.0,2.0549,156700.0,INLAND
+-120.23,37.98,14.0,1954.0,368.0,917.0,316.0,3.1523,93300.0,INLAND
+-121.63,39.12,34.0,1991.0,348.0,804.0,344.0,3.4492,98800.0,INLAND
+-118.34,34.02,44.0,1527.0,246.0,608.0,245.0,4.0357,187800.0,<1H OCEAN
+-122.44,37.78,16.0,883.0,236.0,601.0,219.0,2.151,146900.0,NEAR BAY
+-122.47,37.74,52.0,3797.0,668.0,1633.0,658.0,5.6787,363600.0,NEAR BAY
+-118.4,34.06,37.0,3781.0,873.0,1725.0,838.0,4.1455,500001.0,<1H OCEAN
+-117.66,34.08,33.0,3659.0,590.0,1773.0,615.0,3.9227,157200.0,INLAND
+-119.33,36.21,38.0,3115.0,622.0,1238.0,606.0,2.6083,67000.0,INLAND
+-118.15,33.92,36.0,1890.0,400.0,1232.0,386.0,4.375,184200.0,<1H OCEAN
+-120.76,37.73,16.0,1343.0,241.0,732.0,195.0,3.5833,187500.0,INLAND
+-117.71,33.47,14.0,3894.0,672.0,1490.0,629.0,6.5206,368500.0,<1H OCEAN
+-121.0,37.66,43.0,2369.0,413.0,944.0,422.0,3.2632,138100.0,INLAND
+-121.24,37.82,9.0,6169.0,959.0,3378.0,945.0,5.1047,157900.0,INLAND
+-122.16,37.83,16.0,4596.0,705.0,1480.0,650.0,7.52,370200.0,NEAR BAY
+-122.29,37.92,36.0,1450.0,235.0,568.0,234.0,6.0,311400.0,NEAR BAY
+-122.08,37.64,36.0,1116.0,199.0,662.0,226.0,5.7309,177900.0,NEAR BAY
+-122.14,37.86,20.0,6201.0,1182.0,2415.0,1141.0,4.5744,314000.0,NEAR BAY
+-118.8,34.27,12.0,3330.0,600.0,1577.0,584.0,4.6985,264100.0,<1H OCEAN
+-118.12,33.76,43.0,3070.0,668.0,1240.0,646.0,3.7813,461500.0,NEAR OCEAN
+-117.67,33.6,25.0,3164.0,449.0,1517.0,453.0,6.7921,266000.0,<1H OCEAN
+-120.5,35.03,10.0,10463.0,1756.0,4660.0,1715.0,3.5682,277300.0,<1H OCEAN
+-118.53,34.26,18.0,3674.0,,1590.0,550.0,8.176,308400.0,<1H OCEAN
+-118.42,34.05,33.0,2921.0,652.0,1124.0,608.0,5.0151,500001.0,<1H OCEAN
+-117.36,34.11,35.0,2969.0,521.0,1555.0,503.0,3.25,107100.0,INLAND
+-121.67,40.87,31.0,1581.0,299.0,776.0,287.0,2.9063,77800.0,INLAND
+-122.12,37.39,34.0,3561.0,497.0,1336.0,501.0,8.9172,500001.0,NEAR BAY
+-117.73,34.12,26.0,6459.0,894.0,2487.0,885.0,6.2089,261800.0,INLAND
+-122.38,37.6,33.0,2577.0,590.0,1867.0,566.0,3.3632,265100.0,NEAR OCEAN
+-122.44,37.66,21.0,5108.0,1510.0,3288.0,1405.0,3.1927,252600.0,NEAR OCEAN
+-117.11,32.77,48.0,1502.0,272.0,590.0,265.0,2.5952,190300.0,NEAR OCEAN
+-122.27,38.29,20.0,3870.0,795.0,2088.0,774.0,3.3021,152700.0,NEAR BAY
+-122.34,37.94,44.0,1917.0,444.0,936.0,435.0,2.7391,140300.0,NEAR BAY
+-116.86,34.23,13.0,4760.0,938.0,309.0,132.0,5.4618,147800.0,INLAND
+-118.23,33.88,35.0,842.0,201.0,763.0,189.0,2.6719,109800.0,<1H OCEAN
+-122.46,37.73,52.0,2673.0,349.0,876.0,338.0,7.8476,500001.0,NEAR BAY
+-118.83,35.27,33.0,1190.0,217.0,717.0,196.0,2.6302,81300.0,INLAND
+-121.93,37.32,50.0,1135.0,215.0,500.0,207.0,4.2614,211300.0,<1H OCEAN
+-120.41,34.87,32.0,1997.0,317.0,866.0,281.0,5.062,158900.0,<1H OCEAN
+-118.21,33.93,41.0,619.0,138.0,636.0,145.0,2.5083,118100.0,<1H OCEAN
+-118.07,34.1,34.0,2253.0,522.0,1262.0,511.0,3.4375,259800.0,<1H OCEAN
+-118.54,34.26,23.0,4960.0,592.0,1929.0,586.0,10.9052,500001.0,<1H OCEAN
+-118.19,34.12,44.0,1219.0,324.0,1036.0,282.0,4.0417,170600.0,<1H OCEAN
+-122.65,38.96,27.0,2143.0,580.0,898.0,367.0,1.6769,63200.0,INLAND
+-117.24,34.04,17.0,3362.0,507.0,1520.0,496.0,6.1986,214500.0,INLAND
+-122.0,38.35,34.0,432.0,65.0,208.0,71.0,5.5435,136000.0,INLAND
+-116.41,33.74,25.0,2475.0,476.0,910.0,387.0,3.3639,168800.0,INLAND
+-119.03,35.32,12.0,2721.0,549.0,1294.0,523.0,2.5575,100200.0,INLAND
+-122.07,37.66,21.0,5031.0,1168.0,2461.0,1042.0,3.875,179300.0,NEAR BAY
+-117.16,32.72,52.0,788.0,463.0,805.0,391.0,0.9142,162500.0,NEAR OCEAN
+-118.13,33.87,20.0,3638.0,868.0,2326.0,822.0,3.3304,194600.0,<1H OCEAN
+-118.35,33.91,25.0,1884.0,554.0,1337.0,549.0,2.8512,272800.0,<1H OCEAN
+-118.75,34.1,34.0,2255.0,402.0,857.0,317.0,4.5333,377300.0,NEAR OCEAN
+-121.32,38.63,20.0,7003.0,1409.0,3107.0,1315.0,3.0348,150500.0,INLAND
+-117.2,34.12,24.0,3532.0,618.0,1681.0,590.0,3.5,113900.0,INLAND
+-118.35,34.04,45.0,1839.0,459.0,1312.0,460.0,2.5625,138000.0,<1H OCEAN
+-122.01,37.36,25.0,2796.0,429.0,1267.0,426.0,6.6329,349000.0,<1H OCEAN
+-121.09,37.33,40.0,524.0,112.0,329.0,96.0,1.7188,112500.0,INLAND
+-121.82,39.75,29.0,7744.0,1375.0,3316.0,1365.0,3.0253,111400.0,INLAND
+-117.05,32.75,43.0,1718.0,344.0,826.0,336.0,2.7014,133700.0,NEAR OCEAN
+-117.08,33.12,33.0,674.0,208.0,565.0,188.0,1.875,114300.0,<1H OCEAN
+-120.85,37.77,35.0,404.0,96.0,261.0,100.0,2.4583,75000.0,INLAND
+-122.1,37.68,37.0,2116.0,503.0,1109.0,448.0,2.535,174000.0,NEAR BAY
+-121.9,37.45,16.0,2952.0,446.0,1525.0,460.0,5.6063,320500.0,<1H OCEAN
+-118.37,34.2,34.0,2199.0,609.0,2488.0,597.0,2.9861,171800.0,<1H OCEAN
+-121.04,39.19,17.0,856.0,167.0,518.0,170.0,3.5859,144300.0,INLAND
+-118.39,33.97,44.0,1097.0,186.0,513.0,185.0,6.235,361400.0,<1H OCEAN
+-118.18,33.81,27.0,471.0,132.0,315.0,96.0,1.75,154200.0,NEAR OCEAN
+-122.18,39.75,30.0,4157.0,834.0,1885.0,774.0,1.6948,67500.0,INLAND
+-121.76,37.26,17.0,250.0,52.0,141.0,51.0,4.6458,500001.0,<1H OCEAN
+-121.69,36.62,19.0,1907.0,323.0,681.0,270.0,6.0332,244900.0,<1H OCEAN
+-122.46,37.78,52.0,3429.0,773.0,1584.0,696.0,3.7887,500001.0,NEAR BAY
+-121.85,37.27,17.0,1957.0,261.0,863.0,269.0,7.3339,294200.0,<1H OCEAN
+-119.72,36.8,16.0,2396.0,526.0,1338.0,518.0,2.1653,78800.0,INLAND
+-121.46,38.54,36.0,1825.0,411.0,1226.0,391.0,1.5292,55700.0,INLAND
+-121.77,36.94,18.0,1063.0,341.0,1033.0,313.0,2.0192,171300.0,<1H OCEAN
+-122.17,37.74,47.0,463.0,134.0,327.0,137.0,2.15,97200.0,NEAR BAY
+-120.86,37.53,18.0,2829.0,732.0,1751.0,712.0,1.6445,156900.0,INLAND
+-121.3,38.25,27.0,2475.0,548.0,1703.0,517.0,2.5727,86100.0,INLAND
+-116.51,33.96,16.0,4913.0,1395.0,2518.0,1132.0,1.4665,61100.0,INLAND
+-118.45,34.16,22.0,4982.0,1358.0,2237.0,1220.0,3.7105,272600.0,<1H OCEAN
+-117.9,33.85,35.0,1756.0,328.0,1026.0,332.0,3.6,193500.0,<1H OCEAN
+-117.85,33.8,40.0,1461.0,286.0,1322.0,264.0,4.3269,194100.0,<1H OCEAN
+-121.94,37.31,30.0,1680.0,312.0,858.0,310.0,4.0474,280500.0,<1H OCEAN
+-117.86,33.8,34.0,1793.0,480.0,1722.0,441.0,2.8235,153100.0,<1H OCEAN
+-118.24,33.97,37.0,1053.0,263.0,1354.0,292.0,2.5833,112500.0,<1H OCEAN
+-117.08,33.16,11.0,6341.0,1030.0,2697.0,977.0,4.8554,206700.0,<1H OCEAN
+-118.95,35.4,23.0,4483.0,894.0,2136.0,883.0,3.6875,101700.0,INLAND
+-122.14,38.02,44.0,1625.0,432.0,825.0,385.0,2.0523,133900.0,NEAR BAY
+-117.09,33.1,21.0,2876.0,539.0,1387.0,499.0,3.8292,177000.0,<1H OCEAN
+-124.16,41.02,23.0,1672.0,385.0,1060.0,390.0,2.1726,75500.0,NEAR OCEAN
+-118.31,34.02,43.0,2255.0,533.0,1568.0,470.0,1.6955,115200.0,<1H OCEAN
+-117.87,34.12,34.0,1004.0,220.0,772.0,217.0,3.8571,174500.0,<1H OCEAN
+-118.13,34.21,36.0,1449.0,235.0,621.0,210.0,6.1824,274100.0,INLAND
+-122.44,37.8,52.0,3149.0,719.0,1145.0,658.0,4.625,500001.0,NEAR BAY
+-116.91,34.24,23.0,6379.0,1636.0,1350.0,568.0,1.6336,124500.0,INLAND
+-117.89,33.82,18.0,3197.0,809.0,1894.0,726.0,3.6761,140500.0,<1H OCEAN
+-117.23,34.12,18.0,1439.0,319.0,699.0,310.0,2.1071,73500.0,INLAND
+-120.43,36.99,16.0,1027.0,199.0,673.0,193.0,2.9688,63800.0,INLAND
+-120.84,37.43,32.0,2892.0,521.0,1580.0,484.0,3.7784,164500.0,INLAND
+-117.25,33.01,16.0,3892.0,520.0,1454.0,524.0,7.7317,396000.0,NEAR OCEAN
+-122.27,37.78,45.0,1169.0,263.0,723.0,286.0,3.9444,212900.0,NEAR BAY
+-121.2,38.73,11.0,4897.0,636.0,1931.0,616.0,7.7499,334800.0,INLAND
+-118.09,34.09,36.0,1068.0,246.0,949.0,250.0,2.3462,188500.0,<1H OCEAN
+-118.29,34.05,18.0,3585.0,1661.0,5229.0,1534.0,1.847,250000.0,<1H OCEAN
+-117.96,33.85,36.0,1951.0,365.0,1254.0,358.0,4.8438,185700.0,<1H OCEAN
+-117.08,34.02,20.0,3111.0,563.0,1453.0,538.0,3.3365,122800.0,INLAND
+-118.21,33.98,37.0,788.0,215.0,883.0,221.0,2.6818,164600.0,<1H OCEAN
+-121.95,37.25,30.0,3298.0,634.0,1532.0,602.0,5.0863,332000.0,<1H OCEAN
+-122.17,39.92,16.0,1566.0,306.0,652.0,287.0,1.9038,60800.0,INLAND
+-117.94,34.03,35.0,1499.0,289.0,1112.0,268.0,3.83,149000.0,<1H OCEAN
+-116.44,33.93,17.0,5293.0,1266.0,1201.0,599.0,1.6849,88400.0,INLAND
+-122.02,36.98,44.0,1153.0,238.0,657.0,219.0,3.2368,212500.0,NEAR OCEAN
+-121.43,38.56,50.0,1533.0,288.0,532.0,257.0,2.5417,125900.0,INLAND
+-121.75,36.76,32.0,1740.0,399.0,1563.0,389.0,2.7694,132400.0,<1H OCEAN
+-118.18,33.91,36.0,1138.0,238.0,878.0,224.0,2.0625,134400.0,<1H OCEAN
+-117.61,34.02,8.0,63.0,9.0,25.0,7.0,7.7197,275000.0,INLAND
+-122.23,37.78,43.0,1420.0,472.0,1506.0,438.0,1.9338,112500.0,NEAR BAY
+-120.41,34.96,21.0,1774.0,263.0,724.0,237.0,4.65,152500.0,<1H OCEAN
+-117.28,34.68,28.0,1932.0,421.0,1156.0,404.0,1.8958,55600.0,INLAND
+-122.29,37.92,32.0,1736.0,234.0,602.0,231.0,6.516,401000.0,NEAR BAY
+-119.74,34.43,26.0,3119.0,562.0,1459.0,562.0,5.0434,340400.0,NEAR OCEAN
+-120.02,39.24,32.0,1347.0,444.0,825.0,303.0,1.8269,225000.0,INLAND
+-121.8,37.26,16.0,1868.0,285.0,995.0,284.0,5.9053,260500.0,<1H OCEAN
+-118.97,35.37,41.0,2396.0,602.0,1781.0,543.0,1.8819,58000.0,INLAND
+-121.62,39.11,5.0,2320.0,502.0,1245.0,489.0,3.2465,97200.0,INLAND
+-121.51,38.26,52.0,910.0,212.0,429.0,212.0,1.6458,52800.0,INLAND
+-118.33,33.87,44.0,724.0,133.0,373.0,133.0,3.9167,265600.0,<1H OCEAN
+-120.45,34.95,32.0,1574.0,447.0,1772.0,463.0,1.8625,90200.0,<1H OCEAN
+-121.92,36.56,40.0,2124.0,449.0,643.0,341.0,5.5164,369100.0,NEAR OCEAN
+-117.59,33.44,3.0,5813.0,1264.0,2363.0,1041.0,4.3897,341300.0,NEAR OCEAN
+-117.98,34.02,32.0,2945.0,651.0,2044.0,652.0,3.1979,183900.0,<1H OCEAN
+-117.65,33.63,16.0,3388.0,425.0,1395.0,427.0,8.4471,351300.0,<1H OCEAN
+-122.5,37.75,45.0,1620.0,,941.0,328.0,4.3859,270200.0,NEAR OCEAN
+-121.84,37.99,13.0,4545.0,952.0,2188.0,901.0,3.3625,126100.0,INLAND
+-120.82,37.51,17.0,1664.0,253.0,736.0,254.0,4.4083,165800.0,INLAND
+-117.02,34.9,37.0,1199.0,351.0,782.0,296.0,1.6515,61600.0,INLAND
+-118.13,34.03,42.0,2203.0,467.0,1470.0,488.0,2.8385,192200.0,<1H OCEAN
+-121.94,37.73,22.0,6719.0,1068.0,2843.0,994.0,6.1265,260300.0,<1H OCEAN
+-119.22,34.27,11.0,4695.0,955.0,2065.0,982.0,3.2158,223600.0,NEAR OCEAN
+-121.78,37.66,25.0,1947.0,418.0,900.0,354.0,3.8523,193000.0,INLAND
+-121.99,37.54,18.0,3584.0,715.0,1673.0,661.0,3.9444,240100.0,<1H OCEAN
+-116.95,33.79,20.0,2399.0,546.0,1726.0,542.0,1.8845,77700.0,INLAND
+-116.92,32.82,17.0,2492.0,494.0,1278.0,439.0,2.8875,155700.0,<1H OCEAN
+-117.29,34.13,44.0,2337.0,563.0,1238.0,467.0,1.5156,75800.0,INLAND
+-118.36,33.8,38.0,2553.0,400.0,1042.0,393.0,6.9742,500001.0,NEAR OCEAN
+-122.48,37.51,22.0,1564.0,278.0,761.0,270.0,4.7578,318500.0,NEAR OCEAN
+-122.02,37.36,25.0,2074.0,387.0,1273.0,383.0,4.7609,378000.0,<1H OCEAN
+-122.36,40.55,21.0,2500.0,466.0,1428.0,502.0,2.6513,113300.0,INLAND
+-122.01,39.21,50.0,1592.0,372.0,781.0,307.0,2.2679,69100.0,INLAND
+-122.13,37.71,44.0,1613.0,339.0,776.0,346.0,3.1103,188900.0,NEAR BAY
+-121.52,39.49,30.0,1217.0,238.0,677.0,233.0,2.6563,63600.0,INLAND
+-117.41,33.95,37.0,1586.0,283.0,675.0,305.0,2.9583,132100.0,INLAND
+-119.67,36.35,10.0,1090.0,164.0,470.0,158.0,4.9432,118800.0,INLAND
+-118.06,33.98,42.0,1342.0,243.0,615.0,208.0,5.4381,186900.0,<1H OCEAN
+-117.25,32.84,19.0,1759.0,214.0,659.0,195.0,10.7751,500001.0,NEAR OCEAN
+-121.33,37.99,15.0,4472.0,1079.0,1837.0,976.0,2.5,175900.0,INLAND
+-118.28,34.06,52.0,1261.0,616.0,2309.0,581.0,1.6184,225000.0,<1H OCEAN
+-117.99,33.83,35.0,1484.0,252.0,916.0,248.0,5.2657,191400.0,<1H OCEAN
+-118.28,34.07,24.0,3247.0,1281.0,2642.0,1182.0,2.4632,216700.0,<1H OCEAN
+-118.87,34.18,21.0,5661.0,1369.0,3188.0,1308.0,3.4676,212800.0,<1H OCEAN
+-119.55,36.37,26.0,1912.0,339.0,1002.0,311.0,3.0375,126300.0,INLAND
+-118.24,34.14,28.0,1843.0,554.0,1402.0,512.0,2.462,254000.0,<1H OCEAN
+-118.27,33.86,26.0,1097.0,167.0,701.0,188.0,6.5799,196600.0,<1H OCEAN
+-121.81,38.45,24.0,1951.0,341.0,1140.0,338.0,3.7061,128500.0,INLAND
+-121.48,38.52,36.0,1824.0,357.0,906.0,356.0,2.9911,96400.0,INLAND
+-120.88,38.58,8.0,3417.0,604.0,1703.0,623.0,4.0827,170700.0,INLAND
+-121.46,38.7,32.0,965.0,183.0,568.0,188.0,3.8611,93900.0,INLAND
+-119.4,36.55,19.0,3000.0,628.0,2202.0,590.0,2.5141,67400.0,INLAND
+-117.67,35.65,18.0,2737.0,589.0,1128.0,533.0,2.8,72000.0,INLAND
+-117.79,33.76,25.0,2037.0,252.0,796.0,249.0,11.0546,487200.0,<1H OCEAN
+-118.33,33.77,33.0,4244.0,595.0,1534.0,557.0,9.8214,500001.0,NEAR OCEAN
+-122.5,37.77,52.0,2433.0,454.0,1070.0,420.0,4.125,359500.0,NEAR BAY
+-117.11,32.71,29.0,1040.0,291.0,1054.0,297.0,1.1818,83200.0,NEAR OCEAN
+-122.04,36.97,52.0,1901.0,335.0,955.0,301.0,3.8259,253100.0,NEAR OCEAN
+-117.31,34.12,37.0,1412.0,343.0,1127.0,351.0,1.1667,70900.0,INLAND
+-118.3,33.99,44.0,1458.0,326.0,1159.0,283.0,1.1645,98200.0,<1H OCEAN
+-119.34,36.62,26.0,1922.0,339.0,1148.0,332.0,2.6058,92200.0,INLAND
+-121.34,38.59,22.0,3273.0,480.0,1151.0,463.0,8.05,380000.0,INLAND
+-118.0,33.82,21.0,2253.0,580.0,1536.0,500.0,3.2326,204700.0,<1H OCEAN
+-118.04,33.94,31.0,3808.0,670.0,2430.0,660.0,4.625,173900.0,<1H OCEAN
+-118.26,34.07,40.0,680.0,273.0,995.0,249.0,2.2607,165600.0,<1H OCEAN
+-117.77,34.07,29.0,2976.0,662.0,2452.0,633.0,3.0638,113600.0,INLAND
+-118.43,34.14,44.0,1693.0,239.0,498.0,216.0,10.9237,500001.0,<1H OCEAN
+-121.53,38.56,39.0,2438.0,483.0,1103.0,472.0,2.9375,86600.0,INLAND
+-118.2,34.06,40.0,1181.0,335.0,1441.0,337.0,2.1136,111800.0,<1H OCEAN
+-117.06,32.59,13.0,3920.0,775.0,2814.0,760.0,4.0616,148800.0,NEAR OCEAN
+-117.97,33.68,16.0,4508.0,598.0,2221.0,623.0,7.3731,390800.0,<1H OCEAN
+-119.98,38.96,25.0,2443.0,444.0,868.0,342.0,3.5417,114800.0,INLAND
+-121.48,38.52,34.0,2561.0,497.0,1583.0,530.0,3.1583,95800.0,INLAND
+-122.05,37.87,30.0,2296.0,329.0,847.0,322.0,6.7192,397500.0,NEAR BAY
+-118.02,33.71,23.0,5554.0,995.0,2408.0,936.0,5.3886,331900.0,<1H OCEAN
+-119.41,36.68,18.0,1802.0,332.0,945.0,292.0,3.4044,115300.0,INLAND
+-118.62,34.19,35.0,1934.0,307.0,905.0,315.0,5.5101,267400.0,<1H OCEAN
+-118.25,33.8,36.0,1697.0,394.0,1274.0,396.0,3.35,163100.0,<1H OCEAN
+-122.71,38.46,23.0,3220.0,603.0,1299.0,591.0,3.9261,213300.0,<1H OCEAN
+-117.81,33.87,19.0,4491.0,680.0,2457.0,702.0,6.0591,233500.0,<1H OCEAN
+-118.38,33.88,27.0,3039.0,606.0,1421.0,564.0,5.5771,345500.0,<1H OCEAN
+-122.24,37.81,52.0,1490.0,238.0,634.0,256.0,6.0302,287300.0,NEAR BAY
+-122.31,38.32,35.0,3997.0,762.0,2074.0,703.0,3.285,138100.0,NEAR BAY
+-116.22,33.72,28.0,826.0,258.0,979.0,245.0,1.7172,58800.0,INLAND
+-122.63,38.23,45.0,2264.0,504.0,1076.0,472.0,3.0139,194100.0,<1H OCEAN
+-118.42,34.0,31.0,1930.0,456.0,1002.0,410.0,3.9798,458600.0,<1H OCEAN
+-118.83,34.14,16.0,1956.0,312.0,671.0,319.0,6.4001,321800.0,NEAR OCEAN
+-121.85,37.44,8.0,426.0,61.0,241.0,55.0,7.309,367900.0,<1H OCEAN
+-120.42,38.42,18.0,2912.0,663.0,999.0,411.0,2.7344,91900.0,INLAND
+-117.14,34.75,33.0,552.0,120.0,347.0,97.0,1.8158,100000.0,INLAND
+-117.97,34.07,20.0,2063.0,496.0,1573.0,468.0,3.2,157100.0,<1H OCEAN
+-120.12,39.18,17.0,2839.0,525.0,390.0,189.0,3.5667,179200.0,INLAND
+-120.57,37.35,18.0,704.0,176.0,520.0,154.0,3.003,101300.0,INLAND
+-122.13,37.7,43.0,3046.0,557.0,1333.0,544.0,3.4583,183700.0,NEAR BAY
+-122.42,37.79,52.0,2511.0,895.0,1202.0,804.0,2.6607,87500.0,NEAR BAY
+-118.43,33.83,19.0,6206.0,1611.0,2455.0,1472.0,5.145,420200.0,NEAR OCEAN
+-121.18,39.26,14.0,811.0,161.0,352.0,121.0,3.5938,140300.0,INLAND
+-117.09,33.12,11.0,567.0,184.0,620.0,163.0,2.5284,122500.0,<1H OCEAN
+-117.38,34.11,36.0,1497.0,264.0,894.0,275.0,3.3066,96300.0,INLAND
+-118.43,34.17,32.0,3202.0,696.0,1573.0,621.0,3.4449,292900.0,<1H OCEAN
+-122.42,37.72,42.0,4219.0,1125.0,3549.0,993.0,1.2387,212800.0,NEAR BAY
+-117.05,32.68,15.0,1828.0,359.0,955.0,248.0,3.2174,165100.0,NEAR OCEAN
+-118.38,34.18,40.0,2079.0,568.0,1396.0,526.0,3.0061,190800.0,<1H OCEAN
+-120.43,34.93,4.0,2866.0,648.0,1311.0,578.0,2.8649,186500.0,<1H OCEAN
+-117.66,33.65,13.0,8527.0,1364.0,4597.0,1393.0,6.2242,237900.0,<1H OCEAN
+-122.74,38.46,9.0,2268.0,594.0,1311.0,585.0,2.6607,91500.0,<1H OCEAN
+-117.99,34.04,30.0,4468.0,959.0,4027.0,938.0,3.185,168300.0,<1H OCEAN
+-122.27,37.51,36.0,1406.0,224.0,598.0,237.0,5.8964,414800.0,NEAR OCEAN
+-118.22,34.63,4.0,14348.0,2145.0,5839.0,1806.0,5.3799,222400.0,INLAND
+-121.39,38.59,34.0,1151.0,234.0,563.0,251.0,2.8,113600.0,INLAND
+-118.32,34.08,52.0,1164.0,257.0,575.0,251.0,3.125,380400.0,<1H OCEAN
+-120.71,38.73,17.0,2146.0,396.0,862.0,351.0,2.9219,141300.0,INLAND
+-118.27,34.0,43.0,1258.0,381.0,1276.0,358.0,1.8917,106900.0,<1H OCEAN
+-122.11,38.01,41.0,1345.0,272.0,718.0,283.0,3.3831,129400.0,NEAR BAY
+-118.63,34.16,33.0,2896.0,455.0,1116.0,411.0,6.0192,347700.0,<1H OCEAN
+-117.3,34.14,39.0,1781.0,335.0,841.0,320.0,1.9432,89000.0,INLAND
+-123.82,40.12,33.0,2985.0,591.0,1221.0,486.0,2.087,82400.0,<1H OCEAN
+-118.94,35.36,19.0,2714.0,512.0,1823.0,500.0,3.1281,76200.0,INLAND
+-122.03,37.61,33.0,1518.0,302.0,831.0,268.0,5.097,188600.0,NEAR BAY
+-124.11,40.81,23.0,959.0,212.0,425.0,175.0,2.5536,96100.0,NEAR OCEAN
+-121.95,37.3,25.0,5641.0,1517.0,3786.0,1410.0,3.3958,267500.0,<1H OCEAN
+-121.92,36.61,29.0,3735.0,808.0,1873.0,757.0,3.1543,253800.0,NEAR OCEAN
+-117.97,33.89,14.0,923.0,136.0,420.0,130.0,10.2252,462800.0,<1H OCEAN
+-122.53,37.93,42.0,2171.0,362.0,887.0,347.0,6.6125,393200.0,NEAR BAY
+-121.65,36.67,28.0,1926.0,556.0,1717.0,535.0,1.9385,123200.0,<1H OCEAN
+-119.74,36.73,42.0,1236.0,272.0,946.0,261.0,2.0536,50000.0,INLAND
+-122.23,37.8,52.0,1252.0,299.0,844.0,280.0,2.3929,111900.0,NEAR BAY
+-118.29,33.96,36.0,1717.0,417.0,902.0,368.0,1.4868,113200.0,<1H OCEAN
+-117.25,32.8,35.0,2281.0,506.0,1005.0,496.0,4.2296,275000.0,NEAR OCEAN
+-122.67,38.24,29.0,2644.0,464.0,1372.0,450.0,5.0544,261800.0,<1H OCEAN
+-121.16,38.75,27.0,771.0,108.0,315.0,111.0,8.4882,276600.0,INLAND
+-118.34,33.83,35.0,1818.0,353.0,853.0,321.0,5.8972,350900.0,<1H OCEAN
+-121.02,37.69,19.0,3814.0,790.0,2219.0,804.0,3.5208,145000.0,INLAND
+-118.0,33.74,25.0,2767.0,346.0,1148.0,372.0,6.394,316700.0,<1H OCEAN
+-121.85,39.77,17.0,5273.0,1177.0,2446.0,1199.0,1.9362,89900.0,INLAND
+-119.0,35.4,44.0,2250.0,378.0,928.0,379.0,4.3906,93900.0,INLAND
+-117.32,33.25,7.0,2499.0,420.0,1314.0,398.0,4.85,186900.0,<1H OCEAN
+-121.39,38.55,25.0,2171.0,431.0,1053.0,422.0,3.5278,126200.0,INLAND
+-117.93,33.87,45.0,1006.0,230.0,1237.0,237.0,3.3472,168000.0,<1H OCEAN
+-119.02,35.35,42.0,1239.0,251.0,776.0,272.0,1.983,63300.0,INLAND
+-117.42,34.08,21.0,4460.0,930.0,2657.0,839.0,2.7569,127500.0,INLAND
+-121.26,38.11,8.0,2770.0,642.0,1611.0,633.0,3.1284,115100.0,INLAND
+-122.22,37.78,44.0,1678.0,514.0,1700.0,495.0,2.0801,131900.0,NEAR BAY
+-123.36,39.4,21.0,1081.0,254.0,715.0,275.0,1.5625,71500.0,<1H OCEAN
+-118.13,34.02,43.0,396.0,91.0,261.0,73.0,2.9044,172900.0,<1H OCEAN
+-116.9,34.25,16.0,3018.0,523.0,556.0,244.0,3.5288,189700.0,INLAND
+-122.13,37.71,44.0,1421.0,298.0,609.0,270.0,3.5781,180000.0,NEAR BAY
+-117.89,33.72,25.0,4343.0,847.0,3872.0,850.0,4.65,197800.0,<1H OCEAN
+-117.78,33.85,16.0,3781.0,504.0,1665.0,499.0,7.2554,335600.0,<1H OCEAN
+-122.1,37.66,35.0,686.0,142.0,480.0,149.0,3.875,162100.0,NEAR BAY
+-118.16,34.04,38.0,1076.0,286.0,1535.0,323.0,2.7026,145000.0,<1H OCEAN
+-117.34,34.13,29.0,1494.0,286.0,991.0,280.0,2.125,70600.0,INLAND
+-116.91,33.71,19.0,6807.0,1164.0,2703.0,1055.0,3.1591,189700.0,INLAND
+-118.59,34.21,34.0,2389.0,521.0,1560.0,514.0,4.8333,225400.0,<1H OCEAN
+-118.27,33.79,36.0,2218.0,561.0,1789.0,527.0,3.1613,182300.0,<1H OCEAN
+-122.38,37.73,15.0,6804.0,1664.0,4737.0,1581.0,1.4133,203400.0,NEAR BAY
+-122.25,37.87,41.0,685.0,141.0,266.0,123.0,5.2289,384600.0,NEAR BAY
+-118.35,34.07,46.0,1651.0,410.0,512.0,397.0,4.0179,350000.0,<1H OCEAN
+-118.42,34.27,35.0,674.0,153.0,808.0,173.0,2.6667,147800.0,<1H OCEAN
+-118.14,33.88,30.0,2596.0,580.0,1662.0,539.0,4.0507,179500.0,<1H OCEAN
+-120.55,38.43,18.0,1564.0,357.0,618.0,277.0,2.3549,108900.0,INLAND
+-118.28,34.06,17.0,2518.0,1196.0,3051.0,1000.0,1.7199,175000.0,<1H OCEAN
+-117.89,33.82,21.0,1591.0,298.0,904.0,297.0,4.8906,179100.0,<1H OCEAN
+-117.03,32.95,19.0,4500.0,815.0,2456.0,782.0,4.5032,168900.0,<1H OCEAN
+-120.51,35.91,39.0,768.0,162.0,264.0,118.0,5.3245,250000.0,INLAND
+-122.43,40.66,15.0,2532.0,458.0,1183.0,450.0,2.5417,92200.0,INLAND
+-119.56,36.71,29.0,1963.0,392.0,1208.0,398.0,2.5741,73000.0,INLAND
+-122.12,37.12,51.0,2419.0,485.0,1078.0,435.0,2.7933,206900.0,NEAR OCEAN
+-118.61,34.19,28.0,3824.0,749.0,1790.0,701.0,4.1154,246400.0,<1H OCEAN
+-121.45,38.62,37.0,1534.0,315.0,1147.0,322.0,2.5643,59800.0,INLAND
+-122.28,37.9,49.0,3191.0,516.0,1148.0,507.0,6.3538,333700.0,NEAR BAY
+-116.99,32.74,17.0,3101.0,547.0,1410.0,486.0,3.1771,189900.0,<1H OCEAN
+-118.03,33.94,30.0,2572.0,521.0,1564.0,501.0,3.4861,177200.0,<1H OCEAN
+-121.89,37.44,8.0,2534.0,,1527.0,364.0,7.8532,422800.0,<1H OCEAN
+-117.95,33.87,35.0,1854.0,383.0,1115.0,381.0,4.4784,185200.0,<1H OCEAN
+-118.35,33.89,30.0,1143.0,299.0,776.0,273.0,4.2829,240000.0,<1H OCEAN
+-118.31,33.98,52.0,1607.0,331.0,900.0,295.0,3.5982,96600.0,<1H OCEAN
+-119.04,34.24,20.0,7794.0,1192.0,4169.0,1188.0,5.9316,311900.0,<1H OCEAN
+-118.31,34.06,14.0,1559.0,646.0,1639.0,567.0,1.9949,380000.0,<1H OCEAN
+-118.35,34.04,49.0,1104.0,266.0,668.0,297.0,3.0856,151600.0,<1H OCEAN
+-118.19,33.82,19.0,2953.0,895.0,1914.0,855.0,3.5521,290000.0,NEAR OCEAN
+-121.4,38.71,15.0,4680.0,758.0,2626.0,729.0,3.8355,107000.0,INLAND
+-118.14,33.71,36.0,2484.0,525.0,792.0,446.0,5.1815,500001.0,NEAR OCEAN
+-121.25,37.76,22.0,2430.0,417.0,1292.0,391.0,3.4009,182400.0,INLAND
+-122.26,38.11,52.0,2573.0,639.0,1238.0,529.0,2.6708,109700.0,NEAR BAY
+-124.15,40.88,33.0,2235.0,506.0,1165.0,441.0,1.725,57500.0,NEAR OCEAN
+-117.97,33.88,16.0,2003.0,300.0,1172.0,318.0,6.0394,321600.0,<1H OCEAN
+-121.91,37.31,28.0,3104.0,811.0,1488.0,754.0,3.6429,332600.0,<1H OCEAN
+-116.51,34.85,15.0,3149.0,713.0,1281.0,486.0,2.0,64700.0,INLAND
+-118.29,33.95,40.0,2808.0,695.0,2357.0,627.0,1.9655,102300.0,<1H OCEAN
+-122.67,38.45,24.0,2622.0,525.0,1027.0,510.0,2.9222,242600.0,<1H OCEAN
+-121.54,38.59,40.0,2120.0,504.0,1304.0,464.0,2.0368,67800.0,INLAND
+-117.65,34.12,17.0,3006.0,427.0,1291.0,406.0,6.2083,242700.0,INLAND
+-122.23,40.32,10.0,2336.0,426.0,1003.0,368.0,3.0833,81300.0,INLAND
+-122.5,37.76,50.0,1993.0,410.0,1009.0,374.0,3.9464,295600.0,NEAR OCEAN
+-118.36,34.03,38.0,2365.0,638.0,2259.0,607.0,2.0879,120700.0,<1H OCEAN
+-122.19,37.76,38.0,1493.0,370.0,1144.0,351.0,0.7683,81800.0,NEAR BAY
+-118.38,34.04,45.0,767.0,130.0,254.0,118.0,6.2895,340400.0,<1H OCEAN
+-117.04,32.74,5.0,2878.0,785.0,1727.0,758.0,1.7179,132000.0,NEAR OCEAN
+-122.09,37.95,32.0,1339.0,209.0,601.0,209.0,6.0265,247900.0,NEAR BAY
+-118.22,33.91,31.0,571.0,153.0,841.0,158.0,2.6154,89200.0,<1H OCEAN
+-115.48,32.68,15.0,3414.0,666.0,2097.0,622.0,2.3319,91200.0,INLAND
+-122.42,37.61,37.0,1866.0,300.0,822.0,305.0,4.7,341300.0,NEAR OCEAN
+-117.11,32.57,32.0,2723.0,586.0,1702.0,562.0,3.3371,140500.0,NEAR OCEAN
+-121.31,38.72,11.0,2306.0,420.0,1308.0,418.0,3.9506,122200.0,INLAND
+-118.17,34.04,44.0,691.0,155.0,613.0,142.0,1.9667,133900.0,<1H OCEAN
+-118.24,34.05,13.0,1703.0,697.0,1823.0,669.0,0.8288,181300.0,<1H OCEAN
+-118.09,34.01,42.0,897.0,229.0,1094.0,238.0,2.0729,114100.0,<1H OCEAN
+-118.32,34.04,48.0,1184.0,328.0,953.0,311.0,2.3526,156300.0,<1H OCEAN
+-118.41,34.0,35.0,1062.0,305.0,1026.0,307.0,2.7153,265500.0,<1H OCEAN
+-122.15,37.47,37.0,1844.0,382.0,1634.0,417.0,2.7993,145500.0,NEAR BAY
+-121.24,37.96,37.0,1175.0,260.0,951.0,267.0,2.875,57700.0,INLAND
+-121.91,37.42,19.0,1684.0,387.0,1224.0,376.0,4.1389,174100.0,<1H OCEAN
+-118.48,34.05,48.0,3623.0,528.0,1282.0,516.0,9.5221,500001.0,<1H OCEAN
+-121.15,38.89,20.0,2024.0,313.0,879.0,309.0,5.2903,239400.0,INLAND
+-121.84,37.35,20.0,3375.0,867.0,4610.0,860.0,2.6894,182200.0,<1H OCEAN
+-122.43,37.8,52.0,2696.0,572.0,925.0,552.0,5.0365,500000.0,NEAR BAY
+-122.43,37.71,35.0,2878.0,564.0,1633.0,528.0,4.5,266900.0,NEAR BAY
+-122.0,37.34,31.0,3344.0,620.0,1604.0,572.0,5.2108,351500.0,<1H OCEAN
+-118.24,34.12,34.0,80.0,26.0,125.0,35.0,0.8907,154200.0,<1H OCEAN
+-121.82,37.39,37.0,4137.0,636.0,1569.0,578.0,6.1008,286200.0,<1H OCEAN
+-118.14,34.02,40.0,1912.0,502.0,2077.0,500.0,2.6,180600.0,<1H OCEAN
+-118.15,34.14,52.0,403.0,117.0,361.0,105.0,1.625,187500.0,<1H OCEAN
+-118.14,33.91,32.0,1981.0,472.0,1371.0,431.0,3.1204,204200.0,<1H OCEAN
+-118.0,33.9,35.0,1758.0,309.0,972.0,338.0,4.3831,209800.0,<1H OCEAN
+-118.28,34.04,20.0,1193.0,454.0,1880.0,453.0,2.1806,180000.0,<1H OCEAN
+-117.75,34.05,46.0,1480.0,358.0,1511.0,348.0,1.9718,110600.0,INLAND
+-118.24,34.24,31.0,3019.0,469.0,1349.0,462.0,7.1463,394100.0,<1H OCEAN
+-122.4,38.53,24.0,1741.0,289.0,564.0,231.0,3.6118,248400.0,INLAND
+-122.16,38.07,14.0,6360.0,1236.0,2876.0,1127.0,4.5321,190300.0,NEAR BAY
+-122.1,37.65,25.0,2538.0,494.0,1185.0,501.0,4.5417,194400.0,NEAR BAY
+-122.45,38.3,24.0,1946.0,400.0,718.0,380.0,3.5507,257900.0,NEAR BAY
+-118.29,33.96,31.0,4022.0,1208.0,3707.0,1007.0,1.3096,116300.0,<1H OCEAN
+-119.69,36.83,28.0,1868.0,350.0,898.0,329.0,3.1814,78900.0,INLAND
+-121.29,38.01,10.0,69.0,16.0,50.0,20.0,3.75,120800.0,INLAND
+-118.2,34.07,34.0,1765.0,551.0,2203.0,500.0,2.2708,159600.0,<1H OCEAN
+-117.25,33.12,8.0,8552.0,1437.0,3335.0,1323.0,5.311,255800.0,NEAR OCEAN
+-122.46,37.75,52.0,1849.0,287.0,695.0,258.0,6.5372,394000.0,NEAR BAY
+-120.64,37.38,21.0,3157.0,637.0,2268.0,620.0,2.567,70400.0,INLAND
+-118.46,34.28,23.0,1663.0,302.0,1242.0,283.0,5.5931,217600.0,<1H OCEAN
+-118.35,33.8,32.0,1434.0,296.0,672.0,285.0,4.875,311700.0,<1H OCEAN
+-118.41,33.92,22.0,2340.0,584.0,1141.0,554.0,4.5729,337500.0,<1H OCEAN
+-121.85,37.38,12.0,12980.0,2568.0,8190.0,2515.0,5.2415,286500.0,<1H OCEAN
+-122.09,37.65,35.0,1130.0,192.0,543.0,184.0,4.3897,190600.0,NEAR BAY
+-117.14,32.75,19.0,1358.0,613.0,766.0,630.0,1.0353,150000.0,NEAR OCEAN
+-118.48,34.0,41.0,2584.0,743.0,1058.0,668.0,3.2061,370000.0,<1H OCEAN
+-117.26,34.48,6.0,4632.0,753.0,1851.0,694.0,4.1933,163100.0,INLAND
+-117.34,33.19,19.0,3575.0,525.0,1654.0,559.0,5.7409,274100.0,NEAR OCEAN
+-120.43,34.89,28.0,2862.0,478.0,1384.0,463.0,4.6694,158200.0,<1H OCEAN
+-117.93,33.86,17.0,1627.0,398.0,1216.0,369.0,3.3438,186600.0,<1H OCEAN
+-122.0,37.54,26.0,1910.0,371.0,852.0,357.0,5.8325,298900.0,<1H OCEAN
+-121.95,37.34,25.0,5236.0,1320.0,2529.0,1213.0,3.1702,256100.0,<1H OCEAN
+-118.22,33.95,36.0,1679.0,483.0,2249.0,487.0,2.8167,160400.0,<1H OCEAN
+-118.95,34.19,24.0,2719.0,434.0,1318.0,424.0,4.675,228800.0,<1H OCEAN
+-117.09,32.57,10.0,2198.0,368.0,1645.0,350.0,4.5547,160700.0,NEAR OCEAN
+-119.85,36.83,11.0,2497.0,427.0,1101.0,405.0,4.8036,141600.0,INLAND
+-122.05,37.95,34.0,1408.0,277.0,738.0,269.0,4.175,169400.0,NEAR BAY
+-118.09,33.75,32.0,6239.0,974.0,2615.0,950.0,6.6188,380000.0,NEAR OCEAN
+-123.2,39.13,26.0,1474.0,417.0,1065.0,401.0,1.375,84400.0,<1H OCEAN
+-122.2,37.8,41.0,2070.0,354.0,804.0,340.0,5.1184,239600.0,NEAR BAY
+-115.62,33.04,20.0,1121.0,244.0,766.0,230.0,2.2969,62000.0,INLAND
+-120.71,37.39,40.0,680.0,160.0,785.0,175.0,2.6058,72700.0,INLAND
+-118.34,34.19,41.0,1524.0,393.0,1176.0,375.0,2.875,192400.0,<1H OCEAN
+-117.99,33.84,34.0,2079.0,343.0,1379.0,352.0,5.103,207000.0,<1H OCEAN
+-117.94,34.14,33.0,1620.0,283.0,868.0,275.0,5.411,219400.0,INLAND
+-118.21,34.1,40.0,1684.0,316.0,795.0,330.0,5.2723,218300.0,<1H OCEAN
+-118.23,34.15,26.0,1649.0,522.0,1332.0,483.0,3.1004,257100.0,<1H OCEAN
+-117.51,34.0,36.0,3791.0,746.0,2258.0,672.0,3.2067,124700.0,INLAND
+-118.2,33.82,34.0,2807.0,768.0,2217.0,744.0,2.4286,204800.0,NEAR OCEAN
+-118.47,34.3,16.0,2495.0,551.0,2314.0,567.0,3.6736,192200.0,<1H OCEAN
+-122.7,38.46,29.0,2891.0,459.0,1012.0,441.0,5.0415,240200.0,<1H OCEAN
+-118.37,33.82,39.0,2794.0,444.0,1319.0,441.0,5.878,387800.0,<1H OCEAN
+-119.22,34.27,30.0,1937.0,295.0,695.0,313.0,5.0679,234300.0,NEAR OCEAN
+-117.35,34.04,14.0,2991.0,522.0,1729.0,537.0,3.5139,146800.0,INLAND
+-118.02,34.06,26.0,2929.0,970.0,3792.0,817.0,2.2577,173800.0,<1H OCEAN
+-120.99,39.04,17.0,2289.0,450.0,1182.0,397.0,2.3696,166800.0,INLAND
+-118.36,34.1,37.0,7097.0,2010.0,2913.0,1939.0,2.875,300000.0,<1H OCEAN
+-118.43,34.02,42.0,1528.0,244.0,634.0,242.0,8.1631,500001.0,<1H OCEAN
+-118.36,33.88,33.0,2408.0,534.0,1644.0,523.0,4.2454,236800.0,<1H OCEAN
+-121.95,37.27,17.0,1330.0,271.0,408.0,258.0,1.7171,181300.0,<1H OCEAN
+-122.0,37.39,33.0,2154.0,405.0,1655.0,434.0,5.7962,229800.0,<1H OCEAN
+-117.49,33.98,17.0,2727.0,462.0,1691.0,448.0,4.8371,160600.0,INLAND
+-117.99,33.66,29.0,1330.0,293.0,613.0,236.0,4.6591,353100.0,<1H OCEAN
+-121.15,38.8,20.0,2104.0,370.0,745.0,314.0,4.1685,217500.0,INLAND
+-118.25,34.23,41.0,1979.0,496.0,1157.0,459.0,4.4083,217700.0,<1H OCEAN
+-121.36,38.02,5.0,2229.0,543.0,1010.0,474.0,4.1719,206100.0,INLAND
+-117.68,35.61,9.0,4241.0,832.0,1929.0,742.0,3.5988,84500.0,INLAND
+-117.81,34.11,21.0,3481.0,808.0,1866.0,746.0,3.6201,150400.0,INLAND
+-122.13,37.67,38.0,2012.0,347.0,880.0,332.0,3.1734,181600.0,NEAR BAY
+-121.61,38.38,37.0,1365.0,276.0,952.0,268.0,4.037,156900.0,INLAND
+-118.46,34.2,13.0,2926.0,816.0,1867.0,802.0,3.5255,202700.0,<1H OCEAN
+-117.08,32.69,36.0,1571.0,284.0,1001.0,268.0,3.6875,111400.0,NEAR OCEAN
+-119.62,36.56,30.0,1722.0,372.0,1467.0,403.0,1.8878,51600.0,INLAND
+-122.54,38.0,28.0,3416.0,826.0,1694.0,800.0,3.18,277000.0,NEAR BAY
+-117.93,34.05,32.0,3055.0,623.0,1902.0,565.0,4.2926,190700.0,<1H OCEAN
+-122.46,37.51,23.0,949.0,151.0,399.0,149.0,5.6286,411300.0,NEAR OCEAN
+-117.31,32.98,17.0,2789.0,648.0,849.0,345.0,4.1012,244700.0,NEAR OCEAN
+-121.99,37.31,26.0,3285.0,502.0,1443.0,530.0,5.7833,339600.0,<1H OCEAN
+-117.18,32.7,42.0,1691.0,286.0,761.0,281.0,5.1386,404500.0,NEAR OCEAN
+-118.06,33.99,47.0,1588.0,309.0,827.0,292.0,3.7833,166100.0,<1H OCEAN
+-121.25,38.72,15.0,6838.0,941.0,3166.0,926.0,5.2177,162700.0,INLAND
+-118.31,33.8,31.0,4464.0,991.0,2420.0,947.0,4.0425,277900.0,<1H OCEAN
+-122.21,37.37,34.0,1476.0,217.0,613.0,223.0,8.2883,500001.0,NEAR OCEAN
+-122.42,37.73,35.0,1871.0,342.0,1055.0,310.0,4.625,279300.0,NEAR BAY
+-123.25,38.54,27.0,3658.0,764.0,1278.0,518.0,3.3536,157500.0,NEAR OCEAN
+-117.77,33.85,13.0,5415.0,827.0,2061.0,714.0,7.3681,353100.0,<1H OCEAN
+-121.97,37.96,28.0,1433.0,290.0,877.0,313.0,4.7891,184800.0,INLAND
+-122.45,37.79,52.0,1734.0,482.0,731.0,429.0,1.4804,425000.0,NEAR BAY
+-117.22,33.22,16.0,2134.0,643.0,1555.0,560.0,1.7217,175000.0,<1H OCEAN
+-117.86,33.73,31.0,1115.0,268.0,1369.0,259.0,3.5694,150500.0,<1H OCEAN
+-121.45,38.61,34.0,438.0,116.0,263.0,100.0,0.9379,67500.0,INLAND
+-122.44,37.73,39.0,1912.0,,970.0,406.0,4.7813,275500.0,NEAR BAY
+-118.5,34.52,3.0,6577.0,1056.0,3032.0,1004.0,5.9263,251800.0,INLAND
+-121.87,37.26,17.0,1051.0,172.0,446.0,173.0,5.6652,234500.0,<1H OCEAN
+-120.67,35.48,18.0,2339.0,443.0,1097.0,416.0,3.3438,176100.0,<1H OCEAN
+-119.77,36.74,50.0,1325.0,280.0,811.0,281.0,1.8667,62800.0,INLAND
+-117.96,34.71,32.0,3511.0,646.0,1733.0,510.0,3.46,123900.0,INLAND
+-118.57,34.2,36.0,2559.0,469.0,1358.0,445.0,4.5568,201500.0,<1H OCEAN
+-117.88,34.11,30.0,3082.0,602.0,2008.0,619.0,4.1411,182700.0,<1H OCEAN
+-120.85,37.51,15.0,1131.0,285.0,728.0,281.0,1.5531,93100.0,INLAND
+-122.44,37.8,52.0,2865.0,593.0,1029.0,577.0,5.2539,500001.0,NEAR BAY
+-122.33,37.58,40.0,2362.0,468.0,992.0,425.0,4.7917,359900.0,NEAR OCEAN
+-118.47,34.01,44.0,2017.0,343.0,958.0,382.0,6.1014,480100.0,<1H OCEAN
+-117.04,32.81,12.0,2880.0,406.0,1381.0,418.0,6.5412,254200.0,<1H OCEAN
+-117.03,32.71,34.0,2328.0,444.0,1684.0,429.0,3.25,99600.0,NEAR OCEAN
+-122.43,37.71,52.0,1508.0,278.0,1138.0,304.0,4.0234,266500.0,NEAR BAY
+-121.35,38.0,6.0,1649.0,369.0,732.0,350.0,3.4231,123800.0,INLAND
+-118.35,34.03,49.0,2334.0,530.0,1334.0,447.0,1.89,124000.0,<1H OCEAN
+-117.13,32.92,17.0,1481.0,315.0,1002.0,300.0,3.6196,163400.0,<1H OCEAN
+-117.17,34.25,15.0,4236.0,753.0,703.0,255.0,3.5625,165500.0,INLAND
+-117.01,34.89,26.0,2599.0,498.0,1332.0,443.0,2.7198,70400.0,INLAND
+-117.25,34.15,30.0,1770.0,380.0,990.0,348.0,3.3,97600.0,INLAND
+-117.29,32.81,35.0,1878.0,308.0,598.0,257.0,6.9553,500001.0,NEAR OCEAN
+-119.12,35.39,13.0,1264.0,202.0,552.0,187.0,4.5903,94300.0,INLAND
+-122.43,37.43,17.0,11999.0,2249.0,5467.0,1989.0,4.8405,354300.0,NEAR OCEAN
+-121.89,37.35,43.0,1185.0,296.0,933.0,244.0,2.925,170800.0,<1H OCEAN
+-122.31,37.56,45.0,1792.0,301.0,829.0,318.0,4.9013,330100.0,NEAR OCEAN
+-118.06,34.08,34.0,1197.0,260.0,942.0,245.0,3.4202,189100.0,<1H OCEAN
+-117.89,33.86,28.0,1395.0,398.0,1220.0,362.0,3.3008,193800.0,<1H OCEAN
+-116.93,32.81,18.0,2447.0,466.0,1573.0,472.0,2.6429,125400.0,<1H OCEAN
+-120.49,37.31,45.0,1834.0,421.0,1405.0,407.0,2.0521,72400.0,INLAND
+-122.42,37.76,52.0,1190.0,400.0,1270.0,332.0,2.0329,225000.0,NEAR BAY
+-119.56,36.51,9.0,3860.0,809.0,2157.0,770.0,2.5033,70100.0,INLAND
+-122.01,37.4,14.0,4841.0,1130.0,813.0,517.0,3.7614,137500.0,<1H OCEAN
+-118.94,34.18,24.0,3689.0,585.0,1898.0,581.0,5.9224,239400.0,<1H OCEAN
+-121.61,39.13,21.0,1432.0,328.0,933.0,336.0,1.6823,83800.0,INLAND
+-117.66,34.07,33.0,2081.0,409.0,1008.0,375.0,2.587,138100.0,INLAND
+-118.2,33.97,28.0,2474.0,702.0,2830.0,694.0,2.754,166200.0,<1H OCEAN
+-118.11,33.82,37.0,1756.0,345.0,836.0,335.0,4.375,218200.0,<1H OCEAN
+-120.67,35.62,6.0,12779.0,2441.0,6085.0,2157.0,3.8661,168100.0,<1H OCEAN
+-122.17,37.78,42.0,1524.0,260.0,651.0,267.0,3.6875,157300.0,NEAR BAY
+-122.44,37.7,6.0,3523.0,664.0,1705.0,608.0,4.9318,258100.0,NEAR OCEAN
+-119.79,36.85,11.0,2596.0,619.0,1765.0,539.0,1.9511,54000.0,INLAND
+-121.52,37.75,18.0,1544.0,272.0,825.0,286.0,4.3229,327300.0,INLAND
+-117.24,33.2,25.0,1631.0,415.0,1045.0,386.0,2.4505,147500.0,<1H OCEAN
+-119.17,34.19,28.0,1444.0,508.0,2145.0,437.0,1.6964,175000.0,NEAR OCEAN
+-122.3,37.88,45.0,453.0,146.0,749.0,137.0,1.475,187500.0,NEAR BAY
+-119.78,34.44,28.0,2864.0,495.0,1364.0,482.0,4.835,353400.0,NEAR OCEAN
+-121.76,41.5,31.0,602.0,153.0,112.0,47.0,1.0667,34200.0,INLAND
+-118.35,34.18,46.0,2711.0,491.0,1277.0,490.0,4.282,224700.0,<1H OCEAN
+-118.39,34.01,35.0,4424.0,918.0,2101.0,888.0,3.9688,355100.0,<1H OCEAN
+-119.54,38.51,14.0,1250.0,272.0,721.0,234.0,2.35,95700.0,INLAND
+-122.42,37.78,52.0,1254.0,469.0,895.0,456.0,2.1516,187500.0,NEAR BAY
+-117.09,32.61,21.0,1945.0,430.0,1335.0,419.0,3.6467,113000.0,NEAR OCEAN
+-118.27,33.91,42.0,1786.0,358.0,1318.0,373.0,2.625,101100.0,<1H OCEAN
+-121.9,36.6,39.0,1629.0,423.0,804.0,386.0,2.4663,236500.0,NEAR OCEAN
+-118.37,34.12,34.0,2821.0,399.0,843.0,391.0,11.615,500001.0,<1H OCEAN
+-117.16,33.61,3.0,2744.0,428.0,1223.0,366.0,4.7944,215300.0,<1H OCEAN
+-117.25,33.36,6.0,3725.0,960.0,2833.0,915.0,2.3214,247000.0,<1H OCEAN
+-117.21,32.84,34.0,2158.0,366.0,1046.0,335.0,4.5402,182100.0,NEAR OCEAN
+-121.9,38.03,51.0,2982.0,689.0,1831.0,608.0,2.0034,87700.0,INLAND
+-118.42,34.12,27.0,2089.0,303.0,654.0,270.0,12.3767,500001.0,<1H OCEAN
+-118.1,33.9,37.0,1061.0,202.0,768.0,206.0,4.75,161900.0,<1H OCEAN
+-122.14,37.67,37.0,3342.0,,1635.0,557.0,4.7933,186900.0,NEAR BAY
+-122.43,37.79,52.0,3020.0,842.0,1294.0,769.0,3.4375,500001.0,NEAR BAY
+-121.35,38.58,20.0,2992.0,378.0,1105.0,368.0,8.6572,320200.0,INLAND
+-117.62,33.42,23.0,2656.0,515.0,998.0,435.0,4.0294,500001.0,NEAR OCEAN
+-118.27,33.96,38.0,1126.0,270.0,999.0,265.0,0.5495,91700.0,<1H OCEAN
+-117.69,34.04,5.0,4459.0,896.0,2028.0,881.0,4.0096,182600.0,INLAND
+-118.16,33.91,28.0,2922.0,739.0,3013.0,673.0,2.9531,127100.0,<1H OCEAN
+-118.24,33.93,37.0,1027.0,258.0,824.0,248.0,1.5132,86300.0,<1H OCEAN
+-122.29,37.85,52.0,477.0,119.0,218.0,106.0,2.5682,120000.0,NEAR BAY
+-118.2,33.92,45.0,1283.0,,1025.0,248.0,3.2798,141200.0,<1H OCEAN
+-118.08,34.14,45.0,2923.0,604.0,1903.0,560.0,3.1729,218700.0,INLAND
+-119.82,36.77,36.0,2252.0,468.0,1117.0,442.0,2.9081,65600.0,INLAND
+-118.52,34.3,17.0,4542.0,621.0,2144.0,597.0,8.8467,450700.0,<1H OCEAN
+-121.2,38.68,9.0,2200.0,422.0,938.0,369.0,3.4896,143800.0,INLAND
+-120.65,35.27,27.0,2034.0,341.0,768.0,316.0,4.2411,258900.0,NEAR OCEAN
+-117.87,34.06,25.0,3652.0,470.0,1525.0,484.0,10.1248,428500.0,<1H OCEAN
+-117.87,33.63,9.0,6163.0,1004.0,1912.0,903.0,10.8289,500001.0,<1H OCEAN
+-117.15,32.75,40.0,2261.0,579.0,903.0,525.0,2.465,198700.0,NEAR OCEAN
+-121.42,38.65,21.0,2274.0,495.0,1157.0,445.0,2.098,49800.0,INLAND
+-119.28,34.42,23.0,4763.0,828.0,2198.0,771.0,4.8105,313000.0,NEAR OCEAN
+-118.4,33.87,34.0,3145.0,786.0,1352.0,727.0,5.0976,469800.0,<1H OCEAN
+-118.13,34.04,40.0,1444.0,312.0,881.0,303.0,3.1083,220500.0,<1H OCEAN
+-124.02,40.8,22.0,2588.0,435.0,1198.0,442.0,3.9792,133900.0,<1H OCEAN
+-117.9,33.74,18.0,1884.0,442.0,1915.0,442.0,2.3783,166700.0,<1H OCEAN
+-119.57,36.7,7.0,1761.0,309.0,974.0,308.0,3.7261,83900.0,INLAND
+-117.8,33.67,4.0,3345.0,552.0,1525.0,539.0,6.7962,329100.0,<1H OCEAN
+-120.26,37.99,12.0,2726.0,517.0,1351.0,474.0,3.5,107100.0,INLAND
+-121.3,38.67,20.0,1234.0,208.0,649.0,211.0,4.8523,143000.0,INLAND
+-117.11,32.77,52.0,1484.0,224.0,498.0,223.0,6.6053,331400.0,NEAR OCEAN
+-118.14,34.7,12.0,1984.0,614.0,1071.0,574.0,1.2532,102100.0,INLAND
+-117.26,34.24,10.0,4750.0,844.0,1220.0,428.0,4.5536,132400.0,INLAND
+-122.33,37.97,45.0,1982.0,376.0,1179.0,398.0,3.5463,130800.0,NEAR BAY
+-117.99,33.71,17.0,1600.0,458.0,1803.0,432.0,2.7865,216700.0,<1H OCEAN
+-117.96,33.86,32.0,2366.0,505.0,1283.0,477.0,3.3516,190000.0,<1H OCEAN
+-119.25,36.56,35.0,1675.0,373.0,1131.0,316.0,1.6722,59100.0,INLAND
+-119.18,34.16,12.0,460.0,101.0,405.0,103.0,5.2783,167400.0,NEAR OCEAN
+-120.15,39.2,14.0,1382.0,242.0,141.0,66.0,4.1016,283300.0,INLAND
+-117.66,34.05,6.0,5129.0,1119.0,2533.0,949.0,3.625,113600.0,INLAND
+-117.05,32.72,35.0,1777.0,369.0,1158.0,353.0,3.4107,117000.0,NEAR OCEAN
+-117.98,33.82,34.0,1290.0,220.0,867.0,241.0,5.5486,218100.0,<1H OCEAN
+-121.28,38.64,19.0,3574.0,669.0,1373.0,643.0,3.6298,242100.0,INLAND
+-118.26,33.88,36.0,1212.0,222.0,775.0,224.0,5.5591,136500.0,<1H OCEAN
+-117.86,34.1,23.0,2535.0,490.0,1327.0,466.0,3.5977,180600.0,<1H OCEAN
+-118.47,34.02,38.0,2163.0,651.0,1759.0,584.0,2.3382,297500.0,<1H OCEAN
+-117.11,32.75,20.0,1667.0,469.0,1292.0,445.0,2.0893,101100.0,NEAR OCEAN
+-122.44,37.78,37.0,1235.0,314.0,481.0,297.0,3.6875,492300.0,NEAR BAY
+-124.15,40.78,41.0,2127.0,358.0,911.0,349.0,3.1711,104200.0,NEAR OCEAN
+-122.4,37.6,52.0,1380.0,203.0,530.0,210.0,6.221,420300.0,NEAR OCEAN
+-121.47,41.12,22.0,2737.0,512.0,1168.0,442.0,2.83,88700.0,INLAND
+-120.79,38.54,34.0,1133.0,254.0,495.0,187.0,2.05,68900.0,INLAND
+-118.25,33.92,44.0,1737.0,363.0,1184.0,343.0,2.5363,95900.0,<1H OCEAN
+-117.17,34.12,2.0,3867.0,573.0,1275.0,433.0,5.4138,164400.0,INLAND
+-122.36,40.57,31.0,431.0,90.0,231.0,78.0,2.184,77300.0,INLAND
+-118.41,34.14,33.0,778.0,143.0,258.0,130.0,5.738,497600.0,<1H OCEAN
+-118.47,34.03,31.0,2642.0,681.0,1303.0,625.0,3.5987,340500.0,<1H OCEAN
+-118.29,33.72,25.0,2469.0,584.0,1253.0,535.0,3.1932,257500.0,NEAR OCEAN
+-118.14,33.95,44.0,1812.0,338.0,822.0,314.0,6.7744,294100.0,<1H OCEAN
+-118.28,33.79,28.0,1895.0,420.0,1422.0,389.0,4.3816,191300.0,<1H OCEAN
+-122.04,37.9,20.0,5467.0,1044.0,2310.0,963.0,5.6986,275800.0,NEAR BAY
+-118.02,33.65,38.0,2548.0,646.0,755.0,399.0,2.8352,408300.0,NEAR OCEAN
+-121.91,37.24,24.0,5046.0,1001.0,2449.0,968.0,4.7118,274600.0,<1H OCEAN
+-122.03,37.37,41.0,2123.0,425.0,1032.0,435.0,4.6957,284800.0,<1H OCEAN
+-121.8,37.35,15.0,2781.0,498.0,1389.0,475.0,5.614,223300.0,<1H OCEAN
+-122.17,37.72,5.0,1692.0,398.0,814.0,328.0,3.663,158300.0,NEAR BAY
+-117.88,33.7,24.0,534.0,88.0,249.0,74.0,5.3254,240500.0,<1H OCEAN
+-116.96,32.79,19.0,3008.0,693.0,2341.0,689.0,2.6087,123800.0,<1H OCEAN
+-118.86,34.19,29.0,1326.0,185.0,586.0,187.0,6.5474,422900.0,<1H OCEAN
+-122.29,38.31,45.0,3075.0,754.0,1635.0,723.0,2.2721,139800.0,NEAR BAY
+-117.99,33.84,31.0,2982.0,547.0,1895.0,570.0,4.9115,255500.0,<1H OCEAN
+-118.49,34.04,50.0,2597.0,340.0,964.0,339.0,13.3036,500001.0,<1H OCEAN
+-120.45,34.65,27.0,2215.0,578.0,1544.0,527.0,1.9257,135300.0,NEAR OCEAN
+-118.75,34.33,27.0,534.0,85.0,243.0,77.0,8.2787,330000.0,<1H OCEAN
+-121.31,38.0,35.0,2097.0,351.0,977.0,358.0,4.3958,108400.0,INLAND
+-118.1,34.01,23.0,1724.0,576.0,1336.0,542.0,1.3365,183300.0,<1H OCEAN
+-118.43,34.3,33.0,2443.0,498.0,1601.0,484.0,4.0223,146000.0,<1H OCEAN
+-121.62,36.71,24.0,4195.0,706.0,2200.0,647.0,4.3451,177800.0,<1H OCEAN
+-118.75,34.42,28.0,1000.0,206.0,545.0,154.0,2.4167,191700.0,<1H OCEAN
+-117.06,33.01,9.0,2470.0,417.0,904.0,427.0,4.4219,209200.0,<1H OCEAN
+-122.71,38.91,20.0,41.0,18.0,94.0,10.0,1.375,55000.0,INLAND
+-122.26,37.46,26.0,5067.0,750.0,1996.0,728.0,7.0001,500001.0,NEAR OCEAN
+-122.18,37.79,41.0,1411.0,233.0,626.0,214.0,7.0875,240700.0,NEAR BAY
+-117.32,34.09,30.0,1129.0,251.0,1034.0,237.0,2.3917,78600.0,INLAND
+-117.86,33.76,34.0,3153.0,561.0,1679.0,532.0,4.7083,205300.0,<1H OCEAN
+-121.06,37.73,5.0,2256.0,420.0,1246.0,397.0,4.9236,155900.0,INLAND
+-118.41,34.24,38.0,490.0,101.0,402.0,100.0,3.125,175900.0,<1H OCEAN
+-122.07,37.58,16.0,1644.0,251.0,1033.0,267.0,6.5116,244300.0,NEAR BAY
+-117.06,33.17,4.0,5465.0,974.0,2844.0,950.0,4.4477,174800.0,<1H OCEAN
+-117.26,32.85,42.0,1761.0,329.0,480.0,255.0,5.3787,500001.0,NEAR OCEAN
+-122.47,37.75,46.0,3238.0,544.0,1293.0,470.0,6.1592,381700.0,NEAR BAY
+-118.1,33.9,40.0,1880.0,377.0,1229.0,378.0,4.4167,174600.0,<1H OCEAN
+-118.21,33.92,41.0,1722.0,363.0,1432.0,326.0,3.2976,151200.0,<1H OCEAN
+-115.6,33.2,37.0,709.0,187.0,390.0,142.0,2.4511,72500.0,INLAND
+-117.43,33.93,15.0,4836.0,1368.0,3012.0,1240.0,2.1865,129300.0,INLAND
+-124.08,40.94,18.0,1550.0,345.0,941.0,335.0,2.3147,70100.0,NEAR OCEAN
+-118.28,33.96,42.0,1206.0,304.0,1167.0,250.0,1.615,101300.0,<1H OCEAN
+-118.33,33.97,44.0,2526.0,579.0,1423.0,573.0,2.5363,158800.0,<1H OCEAN
+-119.63,36.64,33.0,1036.0,181.0,620.0,174.0,3.4107,110400.0,INLAND
+-121.53,39.53,35.0,1806.0,293.0,683.0,295.0,4.5156,91200.0,INLAND
+-118.86,34.22,26.0,1775.0,295.0,1004.0,323.0,5.5845,251700.0,<1H OCEAN
+-117.27,33.93,2.0,337.0,55.0,115.0,49.0,3.1042,164800.0,INLAND
+-118.19,34.04,43.0,1682.0,422.0,1706.0,409.0,2.1029,153300.0,<1H OCEAN
+-117.06,33.13,12.0,8742.0,2114.0,4854.0,1957.0,2.8015,143500.0,<1H OCEAN
+-122.45,37.76,52.0,1457.0,292.0,621.0,315.0,4.6477,450000.0,NEAR BAY
+-122.27,38.04,47.0,1685.0,405.0,835.0,372.0,2.3103,134500.0,NEAR BAY
+-117.02,32.84,17.0,4013.0,673.0,2263.0,661.0,5.131,148300.0,<1H OCEAN
+-120.84,37.07,24.0,1520.0,335.0,882.0,306.0,2.2019,100000.0,INLAND
+-121.82,39.76,23.0,6010.0,1116.0,2710.0,1149.0,3.0068,107300.0,INLAND
+-122.92,38.97,20.0,2067.0,384.0,904.0,333.0,2.9934,134200.0,<1H OCEAN
+-118.26,33.8,41.0,2004.0,481.0,1658.0,456.0,3.1779,171100.0,<1H OCEAN
+-118.4,33.93,35.0,2217.0,447.0,1000.0,450.0,4.7319,376100.0,<1H OCEAN
+-118.36,33.94,39.0,1390.0,410.0,1666.0,371.0,3.3056,156800.0,<1H OCEAN
+-116.76,32.74,14.0,4085.0,751.0,2129.0,688.0,4.7367,214500.0,<1H OCEAN
+-119.76,36.72,24.0,1240.0,265.0,1035.0,232.0,2.875,60600.0,INLAND
+-118.59,34.11,35.0,2396.0,472.0,1054.0,457.0,6.4504,407000.0,<1H OCEAN
+-118.27,34.05,52.0,1292.0,864.0,2081.0,724.0,0.9563,275000.0,<1H OCEAN
+-117.93,33.83,32.0,1792.0,411.0,1131.0,381.0,2.4942,186300.0,<1H OCEAN
+-118.29,33.73,30.0,3161.0,,1865.0,771.0,2.7139,231700.0,NEAR OCEAN
+-117.55,34.25,39.0,1578.0,317.0,872.0,322.0,4.555,153100.0,INLAND
+-118.12,33.81,37.0,1798.0,331.0,860.0,340.0,4.2143,228500.0,<1H OCEAN
+-117.8,33.72,16.0,2617.0,506.0,1317.0,511.0,4.821,201400.0,<1H OCEAN
+-120.93,38.65,12.0,2213.0,384.0,1097.0,351.0,4.5568,170100.0,INLAND
+-121.53,38.51,20.0,6132.0,1324.0,2595.0,1174.0,3.1607,178900.0,INLAND
+-118.25,34.15,20.0,3960.0,1027.0,1729.0,978.0,3.0441,193800.0,<1H OCEAN
+-121.81,37.31,15.0,1898.0,395.0,1527.0,381.0,4.4792,212500.0,<1H OCEAN
+-121.3,38.63,31.0,1817.0,372.0,992.0,339.0,3.0972,150000.0,INLAND
+-122.3,37.97,30.0,4030.0,772.0,1777.0,718.0,3.6393,184000.0,NEAR BAY
+-122.05,37.11,39.0,1065.0,248.0,497.0,208.0,4.5972,146300.0,NEAR OCEAN
+-118.16,33.88,18.0,2287.0,662.0,1804.0,537.0,1.9903,170300.0,<1H OCEAN
+-122.41,37.76,52.0,3452.0,784.0,2987.0,753.0,2.8135,260300.0,NEAR BAY
+-122.24,38.01,16.0,2084.0,315.0,1154.0,307.0,6.0102,235600.0,NEAR BAY
+-117.6,34.02,16.0,2103.0,348.0,1305.0,356.0,5.2849,160400.0,INLAND
+-122.52,38.7,26.0,102.0,17.0,43.0,13.0,0.536,87500.0,INLAND
+-118.9,34.2,16.0,6510.0,817.0,2304.0,778.0,7.9943,452100.0,<1H OCEAN
+-121.8,37.19,45.0,1797.0,303.0,870.0,281.0,4.5417,434500.0,<1H OCEAN
+-122.18,37.89,18.0,4845.0,735.0,1634.0,734.0,8.1489,499000.0,NEAR BAY
+-118.31,34.02,46.0,2217.0,489.0,1227.0,448.0,1.6851,108800.0,<1H OCEAN
+-118.45,34.15,20.0,3876.0,799.0,1334.0,753.0,4.5656,478400.0,<1H OCEAN
+-121.93,37.72,26.0,3816.0,637.0,1935.0,642.0,4.4697,221300.0,<1H OCEAN
+-117.41,33.95,37.0,1462.0,257.0,849.0,287.0,3.0542,123900.0,INLAND
+-118.86,34.2,32.0,2399.0,384.0,1199.0,390.0,4.125,264600.0,<1H OCEAN
+-117.93,33.93,33.0,1626.0,378.0,1062.0,356.0,2.1944,139600.0,<1H OCEAN
+-122.32,37.56,44.0,537.0,173.0,355.0,194.0,2.8571,250000.0,NEAR OCEAN
+-120.85,35.38,27.0,3493.0,909.0,1481.0,666.0,2.3075,184200.0,NEAR OCEAN
+-117.1,32.7,28.0,633.0,137.0,525.0,170.0,3.6042,95600.0,NEAR OCEAN
+-122.07,37.96,37.0,1217.0,199.0,552.0,194.0,5.0445,196200.0,NEAR BAY
+-117.87,35.73,13.0,2566.0,449.0,1181.0,414.0,4.1518,91800.0,INLAND
+-118.39,34.07,33.0,5301.0,1281.0,2243.0,1159.0,4.2386,500001.0,<1H OCEAN
+-117.19,32.77,16.0,3273.0,670.0,1305.0,671.0,4.1368,151000.0,NEAR OCEAN
+-119.22,34.18,17.0,3332.0,762.0,1797.0,673.0,4.4292,231200.0,NEAR OCEAN
+-121.86,38.0,4.0,4075.0,927.0,2239.0,849.0,3.5857,165200.0,INLAND
+-117.43,33.81,13.0,4770.0,718.0,1985.0,662.0,4.2273,295200.0,INLAND
+-117.23,33.1,4.0,1862.0,291.0,685.0,248.0,7.745,237400.0,NEAR OCEAN
+-122.47,37.73,52.0,2134.0,277.0,936.0,285.0,5.9245,484600.0,NEAR OCEAN
+-121.96,37.34,37.0,663.0,127.0,293.0,132.0,3.7813,247800.0,<1H OCEAN
+-119.8,36.76,52.0,1853.0,437.0,764.0,390.0,1.6429,69200.0,INLAND
+-122.65,38.46,14.0,2096.0,420.0,926.0,397.0,4.0647,187800.0,<1H OCEAN
+-122.23,37.77,52.0,772.0,179.0,409.0,160.0,3.3214,189600.0,NEAR BAY
+-117.65,34.08,38.0,2750.0,572.0,1410.0,483.0,3.3836,144900.0,INLAND
+-122.3,37.56,37.0,1962.0,367.0,1267.0,382.0,4.7344,271800.0,NEAR OCEAN
+-117.7,33.92,4.0,8301.0,1333.0,3941.0,1236.0,6.2141,252200.0,<1H OCEAN
+-121.99,37.27,17.0,1527.0,267.0,775.0,260.0,5.9658,278000.0,<1H OCEAN
+-118.28,34.11,38.0,4453.0,1156.0,1830.0,1099.0,3.6181,495600.0,<1H OCEAN
+-122.01,37.55,34.0,2791.0,495.0,1276.0,468.0,4.9167,256300.0,NEAR BAY
+-118.42,34.01,42.0,1594.0,369.0,952.0,362.0,3.099,335400.0,<1H OCEAN
+-117.05,32.95,17.0,4814.0,1091.0,3013.0,1078.0,3.2369,167800.0,<1H OCEAN
+-118.01,33.9,26.0,2968.0,674.0,1655.0,628.0,4.6094,201000.0,<1H OCEAN
+-117.92,33.75,23.0,893.0,223.0,1149.0,216.0,2.6442,156300.0,<1H OCEAN
+-122.13,37.43,40.0,3454.0,648.0,1498.0,647.0,5.2114,438400.0,NEAR BAY
+-122.19,37.83,29.0,1791.0,271.0,661.0,269.0,6.8538,368900.0,NEAR BAY
+-117.96,33.7,23.0,4417.0,740.0,1865.0,693.0,5.3428,279300.0,<1H OCEAN
+-121.83,37.99,18.0,2741.0,449.0,1507.0,460.0,4.7566,142500.0,INLAND
+-117.95,33.78,9.0,3553.0,1035.0,2017.0,986.0,2.9726,133800.0,<1H OCEAN
+-117.7,34.06,25.0,2054.0,609.0,2271.0,564.0,2.3049,150000.0,INLAND
+-119.64,36.32,32.0,2205.0,523.0,1772.0,479.0,1.3569,43100.0,INLAND
+-121.89,37.42,26.0,40.0,8.0,52.0,7.0,7.7197,225000.0,<1H OCEAN
+-122.44,37.76,52.0,2110.0,454.0,816.0,438.0,3.9079,370000.0,NEAR BAY
+-117.92,33.81,34.0,988.0,173.0,759.0,184.0,5.6047,205100.0,<1H OCEAN
+-119.75,34.45,6.0,2864.0,,1404.0,603.0,5.5073,263800.0,NEAR OCEAN
+-118.11,33.73,32.0,1258.0,333.0,645.0,334.0,5.0476,500001.0,NEAR OCEAN
+-122.22,37.79,52.0,3424.0,690.0,2273.0,685.0,3.9048,164700.0,NEAR BAY
+-117.17,32.73,52.0,55.0,18.0,65.0,22.0,1.6591,112500.0,NEAR OCEAN
+-122.01,38.36,15.0,1176.0,166.0,485.0,171.0,5.9441,228200.0,INLAND
+-117.25,32.77,32.0,2021.0,524.0,973.0,485.0,3.18,362500.0,NEAR OCEAN
+-119.74,36.73,34.0,1254.0,272.0,1056.0,279.0,2.3269,50800.0,INLAND
+-118.14,33.89,37.0,1159.0,238.0,740.0,243.0,4.9107,179600.0,<1H OCEAN
+-117.19,32.86,18.0,4231.0,728.0,2030.0,720.0,6.1805,272400.0,NEAR OCEAN
+-120.69,35.28,26.0,4225.0,886.0,1795.0,704.0,2.2847,247000.0,NEAR OCEAN
+-118.27,34.02,21.0,1314.0,375.0,1505.0,366.0,2.319,97200.0,<1H OCEAN
+-117.91,33.61,27.0,1797.0,343.0,435.0,203.0,5.9196,500001.0,<1H OCEAN
+-117.39,33.98,37.0,2337.0,452.0,948.0,437.0,3.145,169100.0,INLAND
+-122.62,38.24,19.0,1687.0,253.0,893.0,257.0,6.204,201800.0,<1H OCEAN
+-118.24,34.14,9.0,4877.0,1488.0,4486.0,1458.0,2.4421,222100.0,<1H OCEAN
+-119.69,36.85,20.0,2655.0,432.0,1081.0,379.0,4.5398,143100.0,INLAND
+-117.96,33.69,17.0,2500.0,343.0,1242.0,368.0,7.7313,316700.0,<1H OCEAN
+-117.93,33.74,15.0,1206.0,282.0,677.0,270.0,3.9219,142600.0,<1H OCEAN
+-117.98,33.81,18.0,3751.0,878.0,2281.0,815.0,3.7201,183100.0,<1H OCEAN
+-120.39,37.98,52.0,1056.0,274.0,584.0,255.0,2.1513,86700.0,INLAND
+-121.24,37.9,16.0,50.0,10.0,20.0,6.0,2.625,137500.0,INLAND
+-118.3,34.05,36.0,1723.0,569.0,1664.0,501.0,1.9323,161100.0,<1H OCEAN
+-121.8,37.27,10.0,3301.0,593.0,2190.0,575.0,6.223,260700.0,<1H OCEAN
+-118.11,34.14,44.0,3298.0,615.0,1417.0,643.0,4.1324,434800.0,<1H OCEAN
+-120.87,38.71,13.0,2692.0,470.0,1302.0,420.0,4.0,167400.0,INLAND
+-121.85,37.68,4.0,4719.0,741.0,1895.0,742.0,6.8132,282500.0,INLAND
+-122.45,37.79,46.0,2009.0,464.0,761.0,453.0,3.7188,500001.0,NEAR BAY
+-117.94,34.08,35.0,2393.0,417.0,1336.0,418.0,4.87,187700.0,<1H OCEAN
+-119.75,36.78,28.0,3257.0,752.0,1981.0,712.0,2.293,71700.0,INLAND
+-121.94,38.41,15.0,1263.0,211.0,665.0,208.0,4.5,260900.0,INLAND
+-118.39,34.05,42.0,3105.0,559.0,1253.0,531.0,5.222,500001.0,<1H OCEAN
+-120.44,34.65,30.0,2265.0,512.0,1402.0,471.0,1.975,134000.0,NEAR OCEAN
+-118.99,35.38,26.0,1317.0,374.0,1025.0,304.0,1.4024,51000.0,INLAND
+-120.59,37.36,11.0,3946.0,978.0,2814.0,953.0,2.0179,87300.0,INLAND
+-121.96,37.24,26.0,3032.0,605.0,1208.0,562.0,5.4683,430900.0,<1H OCEAN
+-118.28,33.93,42.0,1898.0,460.0,1503.0,429.0,2.5179,97400.0,<1H OCEAN
+-121.94,36.97,31.0,1738.0,422.0,746.0,355.0,2.5172,330800.0,NEAR OCEAN
+-123.68,40.24,31.0,1852.0,452.0,917.0,359.0,1.725,54300.0,<1H OCEAN
+-121.32,38.0,21.0,1795.0,482.0,1114.0,472.0,2.0091,101500.0,INLAND
+-118.96,34.19,16.0,1807.0,346.0,587.0,296.0,1.9811,162500.0,<1H OCEAN
+-120.65,37.09,22.0,886.0,173.0,595.0,161.0,2.4398,150000.0,INLAND
+-122.17,37.72,46.0,1369.0,284.0,766.0,289.0,3.5313,159700.0,NEAR BAY
+-122.66,38.81,22.0,852.0,176.0,461.0,142.0,3.4375,83300.0,INLAND
+-118.24,34.22,34.0,1722.0,406.0,926.0,371.0,4.1523,252000.0,<1H OCEAN
+-121.96,37.58,15.0,3575.0,597.0,1777.0,559.0,5.7192,283500.0,<1H OCEAN
+-121.76,37.68,32.0,1078.0,207.0,555.0,197.0,3.1856,186900.0,INLAND
+-122.08,37.39,4.0,2292.0,,1050.0,584.0,4.8036,340000.0,NEAR BAY
+-121.92,37.36,42.0,198.0,32.0,158.0,32.0,3.1563,137500.0,<1H OCEAN
+-120.27,39.35,11.0,2520.0,401.0,397.0,165.0,4.665,145600.0,INLAND
+-121.61,37.03,5.0,6529.0,1010.0,3071.0,977.0,5.6754,298500.0,<1H OCEAN
+-121.51,38.57,36.0,613.0,166.0,425.0,147.0,2.2031,93800.0,INLAND
+-117.99,33.67,12.0,2228.0,479.0,1122.0,488.0,4.0385,350000.0,<1H OCEAN
+-117.7,34.04,13.0,5301.0,1025.0,2870.0,984.0,3.5954,163000.0,INLAND
+-120.38,36.76,25.0,991.0,272.0,941.0,262.0,1.8125,58000.0,INLAND
+-121.05,39.2,48.0,1759.0,389.0,716.0,350.0,2.3125,108300.0,INLAND
+-121.76,36.75,21.0,1141.0,257.0,671.0,195.0,3.8424,155700.0,<1H OCEAN
+-117.03,32.96,16.0,3424.0,698.0,1940.0,645.0,4.121,182100.0,<1H OCEAN
+-117.79,33.55,39.0,5066.0,1292.0,1915.0,1117.0,3.821,452100.0,<1H OCEAN
+-122.21,38.1,36.0,3018.0,557.0,1445.0,556.0,3.8029,129900.0,NEAR BAY
+-118.16,33.96,24.0,1635.0,507.0,2480.0,481.0,2.4432,187500.0,<1H OCEAN
+-119.72,34.44,39.0,1489.0,304.0,700.0,268.0,3.8819,289900.0,<1H OCEAN
+-118.0,33.95,35.0,1431.0,210.0,505.0,213.0,6.8109,401000.0,<1H OCEAN
+-118.33,33.82,26.0,5591.0,934.0,2824.0,939.0,6.5861,417800.0,<1H OCEAN
+-117.74,34.07,52.0,1868.0,316.0,947.0,328.0,4.2415,140100.0,INLAND
+-120.99,38.69,5.0,5743.0,1074.0,2651.0,962.0,4.1163,172500.0,INLAND
+-118.3,34.03,37.0,2781.0,766.0,2586.0,729.0,1.8564,187500.0,<1H OCEAN
+-118.03,33.76,32.0,2980.0,494.0,1370.0,481.0,5.0866,223500.0,<1H OCEAN
+-117.51,34.41,5.0,2884.0,567.0,1396.0,465.0,3.7361,119600.0,INLAND
+-119.16,34.28,30.0,413.0,98.0,400.0,112.0,4.0,219200.0,NEAR OCEAN
+-118.28,34.0,38.0,3335.0,921.0,3612.0,887.0,2.125,118800.0,<1H OCEAN
+-117.09,32.78,28.0,1708.0,393.0,816.0,393.0,2.9881,165300.0,NEAR OCEAN
+-122.0,37.82,20.0,2206.0,458.0,926.0,432.0,4.6042,256400.0,<1H OCEAN
+-121.36,38.59,32.0,3303.0,480.0,1185.0,436.0,5.0508,225700.0,INLAND
+-121.67,39.26,29.0,3041.0,683.0,2106.0,687.0,1.6315,58000.0,INLAND
+-117.2,33.14,19.0,2025.0,414.0,1663.0,403.0,3.8147,139200.0,<1H OCEAN
+-120.57,39.78,15.0,1291.0,283.0,582.0,242.0,2.1216,102000.0,INLAND
+-122.49,37.98,34.0,1256.0,178.0,460.0,174.0,6.4271,451700.0,NEAR BAY
+-118.15,34.08,48.0,3697.0,816.0,2446.0,787.0,3.3988,199200.0,<1H OCEAN
+-121.29,37.72,22.0,1630.0,404.0,4402.0,358.0,1.9792,63000.0,INLAND
+-121.9,37.28,34.0,4613.0,749.0,2050.0,725.0,5.3922,302900.0,<1H OCEAN
+-117.97,33.71,25.0,3273.0,478.0,1645.0,497.0,5.8195,286100.0,<1H OCEAN
+-118.13,33.93,38.0,2040.0,458.0,1775.0,445.0,3.5227,202400.0,<1H OCEAN
+-121.89,37.46,5.0,1519.0,186.0,705.0,186.0,10.3798,500001.0,<1H OCEAN
+-118.36,33.81,34.0,2211.0,502.0,1113.0,488.0,4.7026,356800.0,<1H OCEAN
+-122.06,38.27,14.0,6920.0,996.0,3196.0,978.0,5.0672,171300.0,INLAND
+-118.45,34.01,40.0,1361.0,240.0,559.0,229.0,6.3516,354300.0,<1H OCEAN
+-121.18,38.75,16.0,2807.0,459.0,1201.0,429.0,4.7941,247600.0,INLAND
+-118.42,34.18,40.0,1013.0,150.0,449.0,166.0,5.7143,382400.0,<1H OCEAN
+-117.7,33.62,19.0,2957.0,492.0,1639.0,495.0,5.0686,225600.0,<1H OCEAN
+-121.28,38.7,15.0,5828.0,1051.0,2868.0,1037.0,3.7813,143200.0,INLAND
+-118.31,33.73,52.0,2025.0,361.0,957.0,363.0,4.2059,350000.0,NEAR OCEAN
+-122.06,37.0,14.0,1547.0,374.0,4731.0,348.0,2.4732,131300.0,NEAR OCEAN
+-121.38,38.71,7.0,4842.0,935.0,2857.0,907.0,3.9318,133000.0,INLAND
+-118.12,33.81,36.0,1774.0,299.0,784.0,298.0,5.0447,249200.0,<1H OCEAN
+-118.44,34.28,38.0,1156.0,305.0,1359.0,289.0,2.5147,137100.0,<1H OCEAN
+-117.66,34.07,37.0,2454.0,511.0,1165.0,504.0,2.9474,139600.0,INLAND
+-116.17,33.53,13.0,1713.0,512.0,1978.0,442.0,2.1287,58600.0,INLAND
+-118.04,33.74,26.0,2532.0,421.0,1274.0,441.0,5.3559,235800.0,<1H OCEAN
+-118.23,33.94,36.0,1110.0,,1417.0,302.0,2.3333,92100.0,<1H OCEAN
+-118.4,34.1,27.0,3979.0,510.0,1351.0,520.0,15.0001,500001.0,<1H OCEAN
+-121.32,38.02,23.0,3251.0,689.0,1890.0,668.0,3.0729,104800.0,INLAND
+-120.07,36.97,28.0,1563.0,403.0,1564.0,408.0,1.5662,48000.0,INLAND
+-122.03,37.86,29.0,3025.0,477.0,1035.0,452.0,6.112,390600.0,NEAR BAY
+-117.05,34.89,36.0,1199.0,260.0,665.0,229.0,3.7065,62000.0,INLAND
+-121.88,36.49,28.0,2830.0,458.0,898.0,370.0,5.8142,500001.0,NEAR OCEAN
+-121.83,37.99,23.0,1150.0,174.0,572.0,174.0,4.9167,152400.0,INLAND
+-122.41,37.66,44.0,431.0,195.0,682.0,212.0,3.2833,233300.0,NEAR OCEAN
+-121.41,38.53,37.0,1058.0,224.0,588.0,231.0,2.9737,72100.0,INLAND
+-121.32,37.97,43.0,2453.0,490.0,1093.0,438.0,2.9107,88800.0,INLAND
+-120.75,37.69,24.0,2282.0,423.0,1167.0,398.0,3.8214,116100.0,INLAND
+-117.17,33.83,7.0,77.0,12.0,64.0,15.0,4.6,187500.0,INLAND
+-122.46,37.7,37.0,3029.0,738.0,2436.0,700.0,3.3214,243200.0,NEAR OCEAN
+-117.47,33.93,33.0,919.0,208.0,724.0,235.0,3.4028,110500.0,INLAND
+-121.62,36.67,31.0,2697.0,690.0,2220.0,665.0,2.5329,135200.0,<1H OCEAN
+-121.3,38.65,26.0,3192.0,447.0,1132.0,418.0,4.5278,144300.0,INLAND
+-116.01,33.51,24.0,2985.0,958.0,4042.0,905.0,1.7344,66400.0,INLAND
+-122.41,37.74,48.0,409.0,86.0,148.0,70.0,3.6687,335000.0,NEAR BAY
+-117.3,34.13,52.0,1859.0,395.0,968.0,333.0,1.2034,79500.0,INLAND
+-122.53,37.96,36.0,4385.0,620.0,1549.0,626.0,8.3935,470500.0,NEAR BAY
+-117.11,32.66,52.0,25.0,5.0,14.0,9.0,1.625,118800.0,NEAR OCEAN
+-118.98,34.4,34.0,1328.0,244.0,795.0,227.0,4.4219,338100.0,<1H OCEAN
+-116.42,33.76,14.0,16921.0,2837.0,2524.0,1262.0,7.6281,341700.0,INLAND
+-117.88,33.92,13.0,3292.0,727.0,1565.0,698.0,5.457,308800.0,<1H OCEAN
+-122.42,37.74,52.0,2019.0,418.0,999.0,448.0,4.2212,271300.0,NEAR BAY
+-122.43,37.78,26.0,3587.0,1034.0,1821.0,936.0,2.6392,287500.0,NEAR BAY
+-118.17,34.04,43.0,908.0,232.0,1005.0,224.0,1.75,134000.0,<1H OCEAN
+-117.12,32.73,50.0,2307.0,424.0,887.0,356.0,3.5156,168800.0,NEAR OCEAN
+-122.47,37.81,45.0,6927.0,1258.0,4715.0,1165.0,3.4051,500001.0,NEAR BAY
+-118.33,34.11,38.0,3495.0,1100.0,1939.0,994.0,2.2148,438300.0,<1H OCEAN
+-118.17,33.89,11.0,3605.0,880.0,3637.0,873.0,2.6328,160700.0,<1H OCEAN
+-121.42,37.76,18.0,5501.0,1051.0,2964.0,1009.0,4.1855,162100.0,INLAND
+-121.43,38.55,44.0,3514.0,714.0,1509.0,656.0,2.7333,100100.0,INLAND
+-118.41,34.25,18.0,3447.0,857.0,3663.0,817.0,3.2284,157100.0,<1H OCEAN
+-124.19,40.78,37.0,1371.0,319.0,640.0,260.0,1.8242,70000.0,NEAR OCEAN
+-117.03,32.75,24.0,7879.0,1655.0,3898.0,1534.0,3.0897,187300.0,NEAR OCEAN
+-122.36,37.58,52.0,3084.0,595.0,1324.0,571.0,5.0756,374200.0,NEAR OCEAN
+-118.29,34.08,23.0,1864.0,937.0,2795.0,858.0,1.8495,212500.0,<1H OCEAN
+-118.27,33.93,36.0,1467.0,369.0,1247.0,347.0,1.8191,92700.0,<1H OCEAN
+-117.33,33.16,29.0,3559.0,552.0,1533.0,545.0,4.0585,245500.0,NEAR OCEAN
+-118.14,34.2,39.0,2569.0,426.0,1282.0,432.0,5.0953,207400.0,INLAND
+-117.27,34.39,6.0,6988.0,1121.0,3660.0,1092.0,4.2224,125700.0,INLAND
+-117.78,34.58,6.0,10263.0,1864.0,6163.0,1781.0,3.8803,120000.0,INLAND
+-120.94,37.61,13.0,3309.0,603.0,1796.0,555.0,3.8372,129300.0,INLAND
+-120.42,34.96,19.0,2298.0,511.0,1246.0,513.0,2.212,132000.0,<1H OCEAN
+-122.09,37.94,29.0,6895.0,1022.0,2634.0,1022.0,6.1922,273200.0,NEAR BAY
+-121.49,38.61,6.0,4391.0,974.0,1982.0,914.0,3.4291,105300.0,INLAND
+-122.17,39.94,32.0,2352.0,477.0,1316.0,447.0,2.2292,57400.0,INLAND
+-122.52,37.97,33.0,563.0,194.0,265.0,169.0,2.75,231300.0,NEAR BAY
+-116.99,32.85,32.0,5211.0,949.0,3025.0,948.0,4.0931,134200.0,<1H OCEAN
+-117.66,33.46,26.0,2073.0,370.0,952.0,340.0,5.0877,288100.0,<1H OCEAN
+-118.11,33.79,36.0,1993.0,354.0,884.0,337.0,5.587,244900.0,<1H OCEAN
+-118.06,34.07,30.0,2308.0,674.0,3034.0,691.0,2.3929,184400.0,<1H OCEAN
+-122.44,37.78,52.0,3510.0,791.0,1703.0,657.0,2.8654,280000.0,NEAR BAY
+-122.46,37.79,52.0,899.0,96.0,304.0,110.0,14.2959,500001.0,NEAR BAY
+-121.89,37.28,32.0,4308.0,717.0,2002.0,695.0,4.1645,281900.0,<1H OCEAN
+-122.49,37.76,49.0,1724.0,295.0,795.0,297.0,4.3977,353600.0,NEAR BAY
+-118.73,34.29,11.0,5451.0,736.0,2526.0,752.0,7.355,343900.0,<1H OCEAN
+-121.99,36.96,16.0,875.0,201.0,300.0,157.0,2.625,377300.0,NEAR OCEAN
+-117.24,34.04,5.0,1775.0,234.0,726.0,222.0,7.978,223900.0,INLAND
+-117.02,32.75,33.0,3296.0,537.0,1345.0,556.0,5.2835,217100.0,<1H OCEAN
+-121.24,39.65,35.0,632.0,148.0,221.0,102.0,2.3684,62500.0,INLAND
+-122.02,37.29,18.0,2550.0,312.0,999.0,320.0,8.7939,500001.0,<1H OCEAN
+-122.48,37.74,52.0,2453.0,508.0,1056.0,453.0,3.6859,311800.0,NEAR OCEAN
+-122.25,39.79,16.0,2127.0,412.0,1104.0,369.0,3.0469,72200.0,INLAND
+-116.96,32.78,26.0,2807.0,630.0,1785.0,580.0,2.1638,132800.0,<1H OCEAN
+-118.19,33.98,33.0,151.0,83.0,380.0,83.0,1.4224,189600.0,<1H OCEAN
+-119.56,36.1,25.0,1093.0,262.0,893.0,252.0,2.13,50800.0,INLAND
+-122.4,37.75,44.0,6848.0,1584.0,3269.0,1383.0,2.8679,243300.0,NEAR BAY
+-117.98,34.04,29.0,1468.0,310.0,1390.0,276.0,3.75,190600.0,<1H OCEAN
+-119.93,38.94,27.0,1709.0,408.0,97.0,44.0,2.4917,200000.0,INLAND
+-119.19,34.2,18.0,3620.0,,3171.0,779.0,3.3409,220500.0,NEAR OCEAN
+-117.09,32.8,36.0,2163.0,367.0,915.0,360.0,4.7188,174100.0,NEAR OCEAN
+-122.09,37.97,5.0,5303.0,779.0,2017.0,727.0,6.9961,294100.0,NEAR BAY
+-118.53,34.14,28.0,6920.0,906.0,2515.0,860.0,9.2189,500001.0,<1H OCEAN
+-119.16,34.23,26.0,5444.0,1293.0,3700.0,1158.0,2.7556,213200.0,NEAR OCEAN
+-115.53,32.73,14.0,1527.0,325.0,1453.0,332.0,1.735,61200.0,INLAND
+-117.59,34.09,16.0,2401.0,465.0,1757.0,500.0,3.9755,120400.0,INLAND
+-117.04,32.6,18.0,4747.0,846.0,3002.0,872.0,3.9076,152900.0,NEAR OCEAN
+-119.45,37.21,17.0,3538.0,726.0,1603.0,629.0,2.9449,95600.0,INLAND
+-119.67,34.44,32.0,3202.0,537.0,1316.0,538.0,5.2888,463800.0,<1H OCEAN
+-121.31,37.81,36.0,284.0,53.0,130.0,47.0,3.1429,179200.0,INLAND
+-117.21,33.95,5.0,8403.0,1240.0,3962.0,1150.0,5.2174,155500.0,INLAND
+-118.45,34.03,45.0,727.0,168.0,520.0,175.0,2.6528,300000.0,<1H OCEAN
+-121.34,38.57,14.0,5737.0,1008.0,2731.0,983.0,4.4602,134500.0,INLAND
+-121.9,37.26,20.0,4447.0,661.0,2062.0,660.0,6.8088,283300.0,<1H OCEAN
+-119.68,36.83,11.0,2455.0,344.0,1110.0,339.0,6.1133,120000.0,INLAND
+-121.47,38.57,52.0,438.0,103.0,176.0,99.0,3.0217,200000.0,INLAND
+-117.24,33.85,8.0,1031.0,201.0,606.0,179.0,2.8194,136300.0,INLAND
+-121.83,38.0,25.0,1710.0,288.0,799.0,259.0,4.8359,145300.0,INLAND
+-118.36,34.04,45.0,1767.0,417.0,1052.0,379.0,3.5161,157000.0,<1H OCEAN
+-121.28,37.93,23.0,1491.0,346.0,1223.0,343.0,2.1591,67800.0,INLAND
+-118.16,33.77,49.0,3382.0,787.0,1314.0,756.0,3.8125,382100.0,NEAR OCEAN
+-122.14,37.7,17.0,1463.0,292.0,695.0,330.0,4.5859,187200.0,NEAR BAY
+-122.18,37.71,45.0,726.0,147.0,519.0,135.0,3.375,157500.0,NEAR BAY
+-120.22,36.49,14.0,1508.0,347.0,1679.0,345.0,2.4786,56000.0,INLAND
+-120.05,36.95,31.0,696.0,254.0,913.0,248.0,1.4,52500.0,INLAND
+-119.01,36.08,31.0,1620.0,366.0,1154.0,348.0,1.8857,55500.0,INLAND
+-119.3,34.29,41.0,1445.0,410.0,1052.0,388.0,2.6333,170800.0,NEAR OCEAN
+-120.37,34.9,17.0,2649.0,386.0,1057.0,362.0,4.7813,326800.0,<1H OCEAN
+-122.44,37.75,52.0,1573.0,334.0,725.0,338.0,5.0505,380400.0,NEAR BAY
+-120.51,37.32,13.0,2109.0,477.0,1108.0,466.0,2.3099,89600.0,INLAND
+-118.09,34.07,38.0,1036.0,226.0,1058.0,235.0,3.2578,184200.0,<1H OCEAN
+-121.32,38.57,25.0,692.0,146.0,504.0,167.0,3.6897,101100.0,INLAND
+-117.95,33.93,37.0,2633.0,630.0,1904.0,630.0,2.6123,161300.0,<1H OCEAN
+-117.07,32.61,10.0,1686.0,414.0,1000.0,391.0,2.1765,128400.0,NEAR OCEAN
+-121.83,37.29,20.0,2308.0,461.0,2223.0,456.0,4.2589,191000.0,<1H OCEAN
+-118.56,34.18,36.0,1366.0,224.0,719.0,270.0,4.8264,251000.0,<1H OCEAN
+-118.35,34.01,33.0,3246.0,601.0,1585.0,603.0,3.6629,353200.0,<1H OCEAN
+-118.98,35.38,39.0,1497.0,383.0,1182.0,355.0,1.0648,50000.0,INLAND
+-121.92,36.61,21.0,1242.0,340.0,834.0,362.0,2.4922,243800.0,NEAR OCEAN
+-121.99,37.53,25.0,5405.0,939.0,2831.0,923.0,5.0423,222200.0,<1H OCEAN
+-120.05,36.98,16.0,3705.0,739.0,2463.0,697.0,2.5288,61800.0,INLAND
+-118.22,33.98,36.0,1514.0,453.0,1496.0,448.0,2.1044,148200.0,<1H OCEAN
+-117.08,32.82,16.0,1787.0,236.0,770.0,228.0,7.1298,278600.0,<1H OCEAN
+-118.35,33.99,45.0,1764.0,401.0,679.0,334.0,3.2021,222100.0,<1H OCEAN
+-117.23,33.38,18.0,3339.0,704.0,1727.0,652.0,2.8393,173200.0,<1H OCEAN
+-117.94,33.94,25.0,3250.0,546.0,1452.0,501.0,5.1084,303800.0,<1H OCEAN
+-119.17,34.16,17.0,5276.0,1020.0,4066.0,984.0,4.5828,205400.0,NEAR OCEAN
+-118.06,33.91,36.0,1360.0,271.0,909.0,275.0,4.6731,173300.0,<1H OCEAN
+-122.25,37.49,43.0,2607.0,477.0,1225.0,461.0,4.224,349600.0,NEAR OCEAN
+-117.07,33.01,25.0,2120.0,381.0,588.0,359.0,3.1187,169400.0,<1H OCEAN
+-118.41,33.88,43.0,2492.0,449.0,1033.0,437.0,7.9614,500001.0,<1H OCEAN
+-121.25,37.92,19.0,2109.0,427.0,1742.0,426.0,2.4097,66000.0,INLAND
+-118.14,34.69,34.0,1439.0,327.0,708.0,298.0,3.2699,100000.0,INLAND
+-118.43,35.12,8.0,1968.0,376.0,930.0,360.0,3.2632,99800.0,INLAND
+-121.25,38.8,14.0,5094.0,729.0,1974.0,705.0,5.5205,188700.0,INLAND
+-118.09,33.98,39.0,936.0,194.0,691.0,211.0,3.6875,169500.0,<1H OCEAN
+-121.89,37.68,12.0,7490.0,1207.0,3329.0,1136.0,6.3373,339700.0,<1H OCEAN
+-121.84,37.66,13.0,13182.0,2074.0,4847.0,1950.0,5.6417,352900.0,INLAND
+-118.03,34.16,36.0,1401.0,218.0,667.0,225.0,7.1615,484700.0,INLAND
+-122.36,40.58,17.0,1220.0,275.0,800.0,261.0,1.9181,118800.0,INLAND
+-118.07,34.08,37.0,1210.0,252.0,733.0,243.0,2.806,214600.0,<1H OCEAN
+-117.11,32.75,46.0,695.0,182.0,601.0,195.0,2.4219,90600.0,NEAR OCEAN
+-121.64,39.28,25.0,2857.0,662.0,2076.0,685.0,1.8095,64100.0,INLAND
+-118.38,34.04,39.0,2614.0,569.0,1665.0,553.0,3.4063,271600.0,<1H OCEAN
+-117.18,32.78,21.0,4185.0,1018.0,3122.0,993.0,3.0481,210000.0,NEAR OCEAN
+-122.19,38.13,5.0,7854.0,1446.0,4361.0,1395.0,4.9504,214800.0,NEAR BAY
+-117.39,34.07,26.0,1387.0,277.0,664.0,239.0,3.0278,96800.0,INLAND
+-117.93,34.08,35.0,689.0,128.0,379.0,128.0,3.9583,206000.0,<1H OCEAN
+-121.08,38.89,41.0,3471.0,753.0,1680.0,710.0,2.6701,139000.0,INLAND
+-122.01,37.38,32.0,726.0,204.0,538.0,203.0,4.505,230400.0,<1H OCEAN
+-118.34,34.19,48.0,814.0,165.0,490.0,176.0,3.1406,223100.0,<1H OCEAN
+-121.28,38.64,24.0,3459.0,573.0,1336.0,544.0,4.8661,186200.0,INLAND
+-121.32,38.56,18.0,1169.0,186.0,614.0,192.0,4.5766,108700.0,INLAND
+-120.94,37.65,13.0,5075.0,978.0,3033.0,838.0,3.0577,119000.0,INLAND
+-122.04,37.59,14.0,1727.0,302.0,1116.0,273.0,5.3428,243600.0,NEAR BAY
+-119.73,36.56,32.0,1513.0,272.0,1038.0,272.0,3.0469,82700.0,INLAND
+-118.52,34.26,21.0,8850.0,2139.0,4717.0,1979.0,3.7816,254200.0,<1H OCEAN
+-114.67,33.92,17.0,97.0,24.0,29.0,15.0,1.2656,27500.0,INLAND
+-122.08,37.64,36.0,1340.0,245.0,789.0,248.0,3.8,172000.0,NEAR BAY
+-117.11,33.78,13.0,1914.0,339.0,930.0,304.0,4.1875,161200.0,INLAND
+-121.8,37.35,17.0,2529.0,423.0,1756.0,429.0,5.1017,240700.0,<1H OCEAN
+-118.63,34.11,35.0,3795.0,690.0,1521.0,653.0,5.8735,448100.0,<1H OCEAN
+-122.08,37.3,30.0,2268.0,404.0,1197.0,372.0,7.0813,485300.0,<1H OCEAN
+-118.45,34.14,33.0,1741.0,274.0,588.0,267.0,7.9625,490800.0,<1H OCEAN
+-121.37,38.59,36.0,2523.0,401.0,927.0,398.0,3.5179,207800.0,INLAND
+-121.55,38.5,9.0,4868.0,738.0,2036.0,750.0,5.7621,204600.0,INLAND
+-118.02,34.04,27.0,5640.0,1001.0,3538.0,978.0,5.065,215400.0,<1H OCEAN
+-117.18,34.08,28.0,2243.0,399.0,1464.0,379.0,3.2105,90300.0,INLAND
+-118.04,33.93,36.0,1726.0,332.0,1293.0,310.0,4.3849,144100.0,<1H OCEAN
+-122.29,37.56,34.0,1693.0,281.0,846.0,291.0,5.3683,339400.0,NEAR BAY
+-119.8,37.5,15.0,989.0,184.0,406.0,151.0,3.1771,121900.0,INLAND
+-118.25,34.0,34.0,1905.0,552.0,2194.0,521.0,1.4792,95800.0,<1H OCEAN
+-117.9,33.63,28.0,2370.0,352.0,832.0,347.0,7.1148,500001.0,<1H OCEAN
+-122.38,40.67,10.0,2281.0,444.0,1274.0,438.0,2.212,65600.0,INLAND
+-117.94,34.1,31.0,1239.0,254.0,929.0,244.0,3.3625,153400.0,<1H OCEAN
+-118.36,34.16,45.0,1755.0,335.0,822.0,342.0,5.1423,322900.0,<1H OCEAN
+-118.36,33.82,36.0,1083.0,187.0,522.0,187.0,5.7765,339500.0,<1H OCEAN
+-122.03,37.98,45.0,2842.0,567.0,1261.0,535.0,3.6042,138200.0,NEAR BAY
+-117.07,32.75,14.0,3073.0,851.0,2000.0,782.0,2.3824,144700.0,NEAR OCEAN
+-121.99,36.98,19.0,5613.0,1321.0,3018.0,1268.0,3.1914,215600.0,NEAR OCEAN
+-118.14,34.08,24.0,2999.0,786.0,2937.0,796.0,2.9405,217800.0,<1H OCEAN
+-118.25,33.93,40.0,975.0,270.0,1068.0,270.0,0.9889,87500.0,<1H OCEAN
+-122.21,38.06,52.0,2735.0,559.0,1076.0,487.0,3.6154,155700.0,NEAR BAY
+-119.65,37.32,11.0,2161.0,448.0,820.0,405.0,2.3565,122300.0,INLAND
+-119.86,36.82,12.0,1488.0,253.0,675.0,223.0,4.7622,89300.0,INLAND
+-122.43,37.76,52.0,1582.0,353.0,868.0,329.0,3.8261,250000.0,NEAR BAY
+-120.89,37.45,29.0,1940.0,337.0,1070.0,332.0,3.6597,145600.0,INLAND
+-118.26,34.13,25.0,3208.0,1111.0,2843.0,1005.0,2.6673,218100.0,<1H OCEAN
+-117.95,33.66,26.0,1787.0,227.0,639.0,224.0,6.8226,329800.0,<1H OCEAN
+-120.3,38.04,6.0,1281.0,245.0,422.0,160.0,3.2875,111300.0,INLAND
+-122.54,37.76,45.0,1592.0,325.0,920.0,322.0,3.96,272200.0,NEAR OCEAN
+-122.05,36.96,30.0,971.0,185.0,644.0,173.0,4.2045,226500.0,NEAR OCEAN
+-118.04,33.81,27.0,2990.0,515.0,1849.0,497.0,5.6846,216100.0,<1H OCEAN
+-118.12,33.81,36.0,1665.0,291.0,721.0,294.0,4.6875,250700.0,<1H OCEAN
+-119.29,34.23,22.0,2486.0,608.0,709.0,523.0,2.9018,275000.0,NEAR OCEAN
+-118.23,33.99,37.0,378.0,176.0,714.0,156.0,2.1912,112500.0,<1H OCEAN
+-118.91,34.18,17.0,3220.0,716.0,1381.0,733.0,2.8958,176000.0,<1H OCEAN
+-118.43,34.0,28.0,6128.0,1963.0,3586.0,1815.0,2.7058,310900.0,<1H OCEAN
+-117.99,34.15,44.0,2492.0,611.0,1951.0,596.0,3.1304,185600.0,INLAND
+-117.44,33.94,30.0,2992.0,516.0,1521.0,507.0,3.9128,126900.0,INLAND
+-118.35,33.88,44.0,822.0,180.0,480.0,177.0,4.4,225800.0,<1H OCEAN
+-117.01,32.8,17.0,1042.0,210.0,650.0,215.0,3.1,84200.0,<1H OCEAN
+-122.42,37.8,52.0,1777.0,486.0,932.0,427.0,3.3643,420000.0,NEAR BAY
+-119.78,36.83,18.0,4164.0,741.0,1817.0,681.0,4.2153,95200.0,INLAND
+-118.54,34.24,24.0,4631.0,1164.0,2360.0,1083.0,3.0977,264000.0,<1H OCEAN
+-121.46,38.54,48.0,1001.0,205.0,605.0,175.0,1.8333,58200.0,INLAND
+-118.35,33.97,26.0,1725.0,431.0,1130.0,404.0,3.2708,128100.0,<1H OCEAN
+-122.45,37.78,52.0,2033.0,438.0,2198.0,418.0,3.6667,418400.0,NEAR BAY
+-117.2,33.7,23.0,6323.0,1196.0,1984.0,1124.0,2.3276,92400.0,<1H OCEAN
+-122.18,37.76,52.0,754.0,175.0,447.0,165.0,3.9063,93800.0,NEAR BAY
+-122.08,37.64,23.0,1897.0,440.0,1109.0,418.0,3.142,179500.0,NEAR BAY
+-117.31,34.44,10.0,1731.0,299.0,1056.0,312.0,3.6007,104000.0,INLAND
+-119.89,36.76,17.0,1987.0,335.0,1152.0,313.0,4.1719,126400.0,INLAND
+-122.07,37.89,38.0,2139.0,343.0,809.0,340.0,5.5636,268800.0,NEAR BAY
+-122.31,37.54,38.0,1946.0,407.0,975.0,417.0,4.0726,385400.0,NEAR OCEAN
+-119.73,36.83,8.0,3602.0,,1959.0,580.0,5.3478,138800.0,INLAND
+-118.39,33.86,24.0,2386.0,582.0,1152.0,568.0,4.8971,400700.0,<1H OCEAN
+-118.44,34.2,35.0,1717.0,478.0,1628.0,495.0,2.5197,225600.0,<1H OCEAN
+-117.81,33.63,17.0,4477.0,610.0,1798.0,612.0,8.1093,410400.0,<1H OCEAN
+-117.83,33.65,8.0,2149.0,426.0,950.0,399.0,4.1103,250400.0,<1H OCEAN
+-117.13,32.7,35.0,365.0,98.0,463.0,112.0,2.5588,78800.0,NEAR OCEAN
+-121.35,38.46,2.0,6992.0,1132.0,2816.0,984.0,4.3879,144400.0,INLAND
+-117.66,33.6,24.0,1684.0,232.0,781.0,230.0,6.8667,279600.0,<1H OCEAN
+-119.14,34.17,16.0,1593.0,353.0,836.0,357.0,2.726,67500.0,NEAR OCEAN
+-117.92,33.64,5.0,949.0,287.0,497.0,244.0,2.75,225000.0,<1H OCEAN
+-121.81,37.37,26.0,2987.0,539.0,1931.0,518.0,5.1099,213100.0,<1H OCEAN
+-117.21,33.34,10.0,5294.0,817.0,2312.0,810.0,5.4563,325700.0,<1H OCEAN
+-117.68,33.65,6.0,10395.0,1915.0,4783.0,1811.0,5.928,239900.0,<1H OCEAN
+-116.96,34.83,30.0,1211.0,289.0,611.0,230.0,1.6667,44700.0,INLAND
+-117.92,34.13,42.0,1762.0,398.0,1526.0,365.0,2.8643,132600.0,INLAND
+-121.1,39.08,13.0,1110.0,216.0,602.0,209.0,2.5887,144400.0,INLAND
+-118.22,33.8,36.0,1285.0,347.0,1291.0,337.0,3.7708,157100.0,NEAR OCEAN
+-122.04,36.98,35.0,2155.0,355.0,866.0,335.0,5.6188,404700.0,NEAR OCEAN
+-117.97,33.76,27.0,1712.0,325.0,1036.0,345.0,4.0508,183900.0,<1H OCEAN
+-124.16,40.78,43.0,2241.0,446.0,932.0,395.0,2.9038,82000.0,NEAR OCEAN
+-122.07,37.88,11.0,1077.0,318.0,590.0,264.0,3.5536,387200.0,NEAR BAY
+-118.45,34.16,33.0,2544.0,500.0,1035.0,492.0,4.475,314800.0,<1H OCEAN
+-118.38,34.15,36.0,2933.0,619.0,1115.0,579.0,4.3036,365900.0,<1H OCEAN
+-118.01,33.92,34.0,4039.0,694.0,2269.0,663.0,5.2305,205100.0,<1H OCEAN
+-117.84,33.82,24.0,10281.0,1689.0,4926.0,1629.0,4.7946,251200.0,<1H OCEAN
+-117.31,34.43,16.0,5130.0,1172.0,3126.0,1046.0,1.6784,71900.0,INLAND
+-117.39,33.96,52.0,1992.0,345.0,948.0,358.0,3.2917,129300.0,INLAND
+-117.23,32.78,35.0,1649.0,355.0,746.0,360.0,4.6293,356500.0,NEAR OCEAN
+-123.13,40.85,18.0,1650.0,377.0,675.0,282.0,1.8933,84700.0,INLAND
+-122.27,41.2,52.0,4513.0,985.0,1926.0,815.0,1.5923,56000.0,INLAND
+-120.91,40.08,24.0,1629.0,313.0,641.0,274.0,2.2067,69600.0,INLAND
+-118.29,34.11,49.0,2850.0,379.0,1113.0,380.0,12.9591,500001.0,<1H OCEAN
+-121.95,37.32,20.0,1145.0,198.0,431.0,173.0,3.1103,281900.0,<1H OCEAN
+-117.92,33.85,38.0,2082.0,532.0,1592.0,510.0,2.3704,166100.0,<1H OCEAN
+-122.32,40.42,17.0,3019.0,578.0,1538.0,545.0,2.793,76500.0,INLAND
+-118.44,34.24,36.0,1660.0,301.0,1225.0,307.0,4.095,184000.0,<1H OCEAN
+-117.41,34.58,14.0,859.0,212.0,541.0,181.0,1.6838,57900.0,INLAND
+-122.04,37.57,12.0,5719.0,1064.0,3436.0,1057.0,5.2879,231200.0,NEAR BAY
+-117.89,33.92,14.0,1562.0,373.0,609.0,328.0,2.3935,125000.0,<1H OCEAN
+-119.74,34.35,34.0,1664.0,292.0,705.0,257.0,5.0,329400.0,NEAR OCEAN
+-122.06,36.98,15.0,3385.0,669.0,1571.0,615.0,4.2254,320900.0,NEAR OCEAN
+-117.14,32.73,26.0,450.0,132.0,317.0,109.0,4.0,137500.0,NEAR OCEAN
+-122.44,37.79,52.0,2083.0,491.0,1224.0,483.0,4.0882,468800.0,NEAR BAY
+-117.37,33.2,19.0,928.0,317.0,845.0,319.0,1.6318,187500.0,NEAR OCEAN
+-118.43,34.07,38.0,3251.0,656.0,1251.0,593.0,7.7382,500001.0,<1H OCEAN
+-118.06,34.14,42.0,2461.0,379.0,1179.0,360.0,7.0315,437300.0,INLAND
+-117.86,33.77,39.0,4159.0,655.0,1669.0,651.0,4.6111,240300.0,<1H OCEAN
+-121.44,37.73,7.0,8363.0,1314.0,3907.0,1068.0,5.3321,208100.0,INLAND
+-120.76,38.12,7.0,7188.0,1288.0,3175.0,1115.0,3.8488,130600.0,INLAND
+-122.27,37.88,52.0,3360.0,648.0,1232.0,621.0,4.2813,284900.0,NEAR BAY
+-117.13,32.58,27.0,2511.0,615.0,1427.0,576.0,3.1645,156000.0,NEAR OCEAN
+-118.11,33.96,29.0,2784.0,582.0,1278.0,550.0,4.3882,261600.0,<1H OCEAN
+-118.37,34.17,10.0,1431.0,473.0,1438.0,429.0,2.2756,221400.0,<1H OCEAN
+-122.47,38.51,25.0,928.0,195.0,413.0,184.0,3.4904,196900.0,INLAND
+-121.87,37.34,52.0,1087.0,166.0,650.0,194.0,6.6345,309000.0,<1H OCEAN
+-118.02,33.81,34.0,3482.0,614.0,2227.0,641.0,5.1155,200900.0,<1H OCEAN
+-117.95,33.79,34.0,2912.0,520.0,1625.0,501.0,4.4667,190600.0,<1H OCEAN
+-122.21,37.82,52.0,2375.0,333.0,813.0,350.0,7.0549,331400.0,NEAR BAY
+-117.83,34.14,26.0,8254.0,1153.0,3460.0,1131.0,6.5253,349900.0,INLAND
+-117.87,33.85,33.0,45.0,11.0,34.0,10.0,5.2949,350000.0,<1H OCEAN
+-119.99,38.94,22.0,3119.0,640.0,786.0,351.0,3.0806,118500.0,INLAND
+-118.29,33.93,41.0,896.0,198.0,605.0,168.0,2.2778,128100.0,<1H OCEAN
+-121.63,39.1,22.0,3585.0,548.0,1757.0,577.0,4.174,100100.0,INLAND
+-118.23,34.21,32.0,1464.0,406.0,693.0,380.0,2.5463,200000.0,<1H OCEAN
+-118.09,34.15,45.0,1345.0,356.0,749.0,327.0,2.8007,210900.0,INLAND
+-118.29,34.05,30.0,1417.0,589.0,1615.0,540.0,1.3867,193800.0,<1H OCEAN
+-118.29,33.93,32.0,1815.0,488.0,1715.0,475.0,1.7244,111200.0,<1H OCEAN
+-121.67,38.85,46.0,645.0,131.0,410.0,122.0,1.7417,110400.0,INLAND
+-120.91,37.74,19.0,1690.0,327.0,855.0,296.0,3.25,176700.0,INLAND
+-120.15,39.17,32.0,1684.0,359.0,454.0,209.0,2.9125,145800.0,INLAND
+-121.04,37.68,18.0,5129.0,1171.0,3622.0,1128.0,2.0272,92700.0,INLAND
+-122.16,37.77,39.0,1583.0,349.0,857.0,316.0,3.0958,145800.0,NEAR BAY
+-122.0,36.98,20.0,2502.0,454.0,981.0,399.0,4.3,275000.0,NEAR OCEAN
+-122.44,37.75,28.0,4930.0,1381.0,2232.0,1321.0,4.3232,316200.0,NEAR BAY
+-121.86,36.6,21.0,3634.0,1011.0,1985.0,917.0,2.9085,156300.0,<1H OCEAN
+-120.45,34.65,21.0,1182.0,243.0,733.0,251.0,3.1442,131600.0,NEAR OCEAN
+-117.96,33.75,22.0,2300.0,539.0,1625.0,542.0,2.78,196300.0,<1H OCEAN
+-121.81,38.49,18.0,4518.0,827.0,2230.0,715.0,3.9309,178500.0,INLAND
+-122.01,38.36,28.0,1967.0,315.0,734.0,291.0,4.9583,146200.0,INLAND
+-122.71,38.45,39.0,2739.0,573.0,1223.0,569.0,2.9663,185400.0,<1H OCEAN
+-117.25,32.8,31.0,2182.0,630.0,1069.0,599.0,2.9781,212500.0,NEAR OCEAN
+-121.44,37.75,29.0,918.0,159.0,417.0,166.0,4.2768,151300.0,INLAND
+-118.26,34.06,33.0,1950.0,1047.0,3707.0,1012.0,1.7238,110000.0,<1H OCEAN
+-117.26,33.84,12.0,1159.0,209.0,523.0,159.0,2.7232,123200.0,INLAND
+-118.34,33.92,29.0,1475.0,349.0,965.0,370.0,3.3558,199600.0,<1H OCEAN
+-122.28,37.53,25.0,3710.0,1015.0,2068.0,958.0,3.5445,286700.0,NEAR OCEAN
+-122.28,37.9,52.0,1369.0,249.0,490.0,248.0,4.1212,287500.0,NEAR BAY
+-118.32,33.9,36.0,1520.0,300.0,831.0,291.0,4.0473,212100.0,<1H OCEAN
+-117.92,33.94,30.0,2506.0,394.0,1255.0,421.0,4.7813,198200.0,<1H OCEAN
+-117.14,32.75,29.0,1961.0,565.0,1002.0,569.0,2.2813,118100.0,NEAR OCEAN
+-122.81,38.36,18.0,2399.0,389.0,1131.0,391.0,5.2769,293900.0,<1H OCEAN
+-117.23,32.88,18.0,5566.0,1465.0,6303.0,1458.0,1.858,205000.0,NEAR OCEAN
+-121.33,38.77,3.0,20214.0,3559.0,8361.0,3112.0,4.2259,169300.0,INLAND
+-117.96,33.65,18.0,3603.0,879.0,1549.0,756.0,4.0229,363100.0,<1H OCEAN
+-121.83,37.25,17.0,2332.0,637.0,1636.0,623.0,3.1932,123400.0,<1H OCEAN
+-117.85,33.62,13.0,5192.0,658.0,1865.0,662.0,15.0001,500001.0,<1H OCEAN
+-119.02,35.41,31.0,2348.0,701.0,1413.0,611.0,1.3222,51400.0,INLAND
+-117.86,33.88,20.0,3977.0,540.0,1886.0,541.0,6.5843,272200.0,<1H OCEAN
+-117.86,33.99,10.0,17820.0,2812.0,8686.0,2666.0,6.3875,310700.0,<1H OCEAN
+-121.25,38.71,14.0,3713.0,637.0,1845.0,635.0,4.3009,143400.0,INLAND
+-121.94,37.0,32.0,2210.0,426.0,1082.0,396.0,4.1587,315200.0,NEAR OCEAN
+-121.0,39.0,4.0,170.0,23.0,93.0,27.0,10.9891,312500.0,INLAND
+-121.87,37.21,18.0,1080.0,122.0,382.0,121.0,9.08,500001.0,<1H OCEAN
+-117.65,34.1,19.0,1688.0,365.0,622.0,322.0,3.6,136400.0,INLAND
+-121.3,37.92,28.0,3308.0,766.0,3201.0,720.0,1.7694,73900.0,INLAND
+-116.89,32.99,14.0,2816.0,501.0,1448.0,452.0,5.0278,210900.0,<1H OCEAN
+-118.46,34.24,11.0,5363.0,1160.0,2783.0,1034.0,3.8583,170700.0,<1H OCEAN
+-121.88,37.26,13.0,1893.0,487.0,1018.0,464.0,3.8047,204700.0,<1H OCEAN
+-121.81,37.31,15.0,1794.0,366.0,1533.0,371.0,5.7843,209900.0,<1H OCEAN
+-121.64,39.15,15.0,2659.0,396.0,1159.0,407.0,5.234,124900.0,INLAND
+-118.56,34.24,23.0,2980.0,362.0,1208.0,378.0,8.1714,500001.0,<1H OCEAN
+-118.2,34.12,41.0,1908.0,503.0,1557.0,453.0,2.9194,162000.0,<1H OCEAN
+-121.28,37.97,47.0,2348.0,507.0,1455.0,479.0,1.65,66000.0,INLAND
+-117.78,33.87,16.0,5609.0,952.0,2624.0,934.0,5.3307,169600.0,<1H OCEAN
+-117.64,34.07,52.0,1644.0,372.0,1269.0,355.0,2.6913,108300.0,INLAND
+-117.92,34.09,35.0,1810.0,318.0,1164.0,332.0,5.0123,165700.0,<1H OCEAN
+-122.4,37.76,52.0,4265.0,912.0,1555.0,836.0,4.119,298300.0,NEAR BAY
+-121.06,38.98,14.0,2267.0,355.0,1140.0,369.0,4.7019,212800.0,INLAND
+-118.05,33.92,33.0,1999.0,470.0,2170.0,466.0,3.2371,154700.0,<1H OCEAN
+-121.47,38.53,44.0,543.0,146.0,506.0,125.0,1.3646,65400.0,INLAND
+-120.41,34.88,8.0,3119.0,620.0,1159.0,544.0,3.5288,165500.0,<1H OCEAN
+-122.11,38.09,11.0,673.0,145.0,318.0,137.0,2.3929,122500.0,NEAR BAY
+-117.23,32.75,21.0,2050.0,608.0,1131.0,550.0,2.4779,165000.0,NEAR OCEAN
+-122.36,37.94,43.0,369.0,107.0,371.0,111.0,1.6,79400.0,NEAR BAY
+-121.84,36.25,20.0,958.0,245.0,590.0,189.0,2.6094,362500.0,NEAR OCEAN
+-118.41,34.15,24.0,3891.0,866.0,1568.0,830.0,4.1656,364700.0,<1H OCEAN
+-122.67,38.47,16.0,3452.0,791.0,1567.0,731.0,2.4722,194300.0,<1H OCEAN
+-118.18,33.94,44.0,1337.0,245.0,968.0,240.0,3.4688,183600.0,<1H OCEAN
+-118.95,35.32,29.0,3480.0,608.0,2007.0,541.0,3.2738,78700.0,INLAND
+-121.26,37.95,44.0,819.0,184.0,677.0,183.0,1.725,59300.0,INLAND
+-121.54,38.29,47.0,1396.0,254.0,630.0,218.0,2.8616,92500.0,INLAND
+-118.34,34.18,45.0,1328.0,290.0,720.0,289.0,3.875,226900.0,<1H OCEAN
+-121.88,38.02,46.0,2112.0,466.0,1249.0,382.0,2.5737,87000.0,INLAND
+-117.67,34.05,6.0,2833.0,628.0,1717.0,589.0,3.2062,167500.0,INLAND
+-117.15,34.83,30.0,5370.0,1062.0,2778.0,944.0,3.099,66800.0,INLAND
+-121.34,38.61,22.0,1778.0,408.0,875.0,375.0,2.6023,142200.0,INLAND
+-117.91,33.65,24.0,885.0,321.0,590.0,254.0,2.625,217900.0,<1H OCEAN
+-118.06,33.83,21.0,3941.0,655.0,1897.0,670.0,4.88,343900.0,<1H OCEAN
+-118.05,33.84,21.0,4890.0,653.0,2295.0,654.0,6.983,329700.0,<1H OCEAN
+-117.32,34.02,17.0,1779.0,292.0,1006.0,293.0,4.6708,123100.0,INLAND
+-119.19,36.06,29.0,1815.0,376.0,1421.0,339.0,1.9091,71300.0,INLAND
+-122.03,37.55,26.0,3087.0,532.0,1597.0,483.0,4.9118,217300.0,NEAR BAY
+-118.1,33.85,19.0,993.0,174.0,572.0,175.0,5.7039,277600.0,<1H OCEAN
+-121.74,37.19,11.0,1290.0,197.0,881.0,191.0,4.2039,500001.0,<1H OCEAN
+-122.17,37.77,30.0,3326.0,746.0,1704.0,703.0,2.875,135300.0,NEAR BAY
+-118.29,33.75,37.0,1319.0,292.0,766.0,285.0,2.7031,218900.0,NEAR OCEAN
+-120.85,37.5,52.0,1724.0,352.0,922.0,348.0,1.7227,85700.0,INLAND
+-118.35,34.07,52.0,2497.0,406.0,1030.0,412.0,4.89,500001.0,<1H OCEAN
+-122.54,37.94,26.0,3990.0,804.0,1550.0,792.0,5.1834,405500.0,NEAR BAY
+-117.17,32.82,24.0,1623.0,417.0,911.0,397.0,2.7401,198100.0,NEAR OCEAN
+-122.89,38.4,22.0,2900.0,538.0,1445.0,515.0,4.511,296800.0,<1H OCEAN
+-117.2,32.79,16.0,2079.0,394.0,746.0,383.0,5.0958,300000.0,NEAR OCEAN
+-122.08,37.66,37.0,1997.0,436.0,1349.0,437.0,2.1382,166600.0,NEAR BAY
+-118.45,34.16,22.0,7828.0,2038.0,3303.0,1922.0,3.6171,318300.0,<1H OCEAN
+-118.36,33.92,19.0,2807.0,883.0,1546.0,815.0,2.6375,233800.0,<1H OCEAN
+-122.02,37.11,36.0,2066.0,401.0,942.0,344.0,5.2417,196400.0,NEAR OCEAN
+-120.08,39.23,19.0,1746.0,306.0,251.0,104.0,4.8182,146900.0,INLAND
+-119.28,34.27,43.0,403.0,77.0,156.0,85.0,4.6667,384600.0,NEAR OCEAN
+-117.66,34.13,19.0,3995.0,554.0,1523.0,509.0,6.075,254100.0,INLAND
+-116.98,33.93,40.0,2277.0,498.0,1391.0,453.0,1.9472,73200.0,INLAND
+-117.68,33.49,18.0,4173.0,625.0,1649.0,634.0,6.3568,294300.0,<1H OCEAN
+-117.17,33.66,2.0,7401.0,1187.0,2826.0,839.0,4.1386,177300.0,<1H OCEAN
+-122.24,37.86,52.0,1668.0,225.0,517.0,214.0,7.8521,500001.0,NEAR BAY
+-118.52,34.02,24.0,7418.0,1755.0,2713.0,1577.0,5.0867,500001.0,<1H OCEAN
+-117.24,32.74,45.0,1718.0,293.0,757.0,329.0,4.05,284900.0,NEAR OCEAN
+-117.41,33.94,33.0,2074.0,476.0,911.0,420.0,2.87,117600.0,INLAND
+-117.24,32.8,29.0,3376.0,882.0,1513.0,843.0,3.101,238200.0,NEAR OCEAN
+-121.88,37.67,16.0,4070.0,624.0,1543.0,577.0,6.5214,311500.0,INLAND
+-118.0,33.81,22.0,2642.0,640.0,1702.0,588.0,3.5268,174700.0,<1H OCEAN
+-122.27,37.78,52.0,1222.0,264.0,630.0,265.0,3.7708,215300.0,NEAR BAY
+-122.5,37.74,44.0,2792.0,615.0,1640.0,579.0,4.0625,272800.0,NEAR OCEAN
+-122.48,37.59,29.0,5889.0,959.0,2784.0,923.0,5.3991,273000.0,NEAR OCEAN
+-117.13,32.58,27.0,1417.0,373.0,814.0,348.0,2.3603,195300.0,NEAR OCEAN
+-118.34,33.91,8.0,3937.0,1404.0,2691.0,1142.0,2.4741,185700.0,<1H OCEAN
+-117.69,33.55,4.0,1764.0,220.0,705.0,224.0,8.3275,384200.0,<1H OCEAN
+-122.05,37.63,5.0,3785.0,936.0,2240.0,792.0,3.2829,162500.0,NEAR BAY
+-117.08,32.73,36.0,1158.0,218.0,619.0,233.0,3.6125,122500.0,NEAR OCEAN
+-122.21,37.52,18.0,2962.0,945.0,1639.0,851.0,2.7399,87500.0,NEAR BAY
+-118.96,35.37,40.0,1603.0,374.0,1026.0,337.0,1.365,54300.0,INLAND
+-122.44,37.77,52.0,2537.0,559.0,849.0,530.0,5.1788,476900.0,NEAR BAY
+-118.27,33.98,45.0,1696.0,424.0,1502.0,429.0,1.3042,99200.0,<1H OCEAN
+-122.48,37.57,34.0,4648.0,806.0,2282.0,814.0,4.5556,249000.0,NEAR OCEAN
+-117.24,32.79,18.0,1741.0,602.0,508.0,283.0,3.2625,193800.0,NEAR OCEAN
+-121.13,36.21,34.0,2504.0,550.0,1810.0,547.0,3.4821,113700.0,<1H OCEAN
+-121.63,39.16,7.0,1879.0,444.0,1065.0,410.0,2.4183,103800.0,INLAND
+-118.41,34.21,35.0,2830.0,518.0,1577.0,524.0,5.35,210500.0,<1H OCEAN
+-118.3,33.74,40.0,1896.0,416.0,950.0,383.0,2.425,255000.0,NEAR OCEAN
+-119.04,36.09,15.0,2288.0,401.0,1238.0,429.0,3.0278,77400.0,INLAND
+-122.11,37.41,35.0,2712.0,428.0,1084.0,425.0,7.1382,443800.0,NEAR BAY
+-117.97,33.94,35.0,1928.0,360.0,1056.0,366.0,4.0893,215700.0,<1H OCEAN
+-118.22,34.14,52.0,1388.0,271.0,735.0,239.0,3.7404,247700.0,<1H OCEAN
+-118.41,34.01,33.0,3306.0,974.0,2475.0,924.0,2.8797,285300.0,<1H OCEAN
+-117.25,32.86,27.0,2530.0,469.0,594.0,326.0,7.2821,500001.0,NEAR OCEAN
+-122.18,37.15,17.0,1457.0,289.0,591.0,235.0,5.5785,284100.0,NEAR OCEAN
+-121.91,37.33,52.0,2212.0,563.0,1195.0,532.0,2.894,209500.0,<1H OCEAN
+-117.93,34.04,23.0,6361.0,1168.0,4580.0,1109.0,4.9342,181000.0,<1H OCEAN
+-122.05,37.34,34.0,2515.0,401.0,1079.0,399.0,7.7865,423900.0,<1H OCEAN
+-118.09,33.81,36.0,1878.0,323.0,846.0,325.0,7.1937,254400.0,<1H OCEAN
+-118.6,34.2,10.0,2869.0,941.0,2162.0,829.0,3.2297,150000.0,<1H OCEAN
+-117.11,33.19,15.0,3154.0,488.0,1656.0,429.0,5.0461,222400.0,<1H OCEAN
+-118.44,34.15,37.0,1335.0,286.0,539.0,279.0,3.2813,301700.0,<1H OCEAN
+-117.65,33.62,15.0,2708.0,410.0,1140.0,389.0,6.2899,275000.0,<1H OCEAN
+-121.53,38.58,35.0,1316.0,321.0,732.0,336.0,2.1213,79200.0,INLAND
+-118.15,33.93,30.0,3096.0,628.0,1676.0,587.0,4.6583,207300.0,<1H OCEAN
+-122.15,37.72,29.0,4169.0,1047.0,2024.0,962.0,2.8125,157400.0,NEAR BAY
+-122.28,37.91,48.0,2083.0,298.0,685.0,286.0,7.3089,331200.0,NEAR BAY
+-118.44,34.15,38.0,1387.0,337.0,713.0,343.0,2.9352,304500.0,<1H OCEAN
+-122.0,37.96,28.0,4071.0,713.0,2033.0,647.0,4.5833,190700.0,INLAND
+-118.22,33.91,27.0,500.0,159.0,732.0,162.0,2.7426,103100.0,<1H OCEAN
+-121.49,38.56,42.0,900.0,239.0,506.0,231.0,1.2813,87500.0,INLAND
+-117.88,33.74,29.0,720.0,174.0,1045.0,181.0,3.1964,151900.0,<1H OCEAN
+-117.35,34.12,22.0,5640.0,889.0,3157.0,887.0,4.1581,126500.0,INLAND
+-118.14,34.09,20.0,3447.0,1007.0,2622.0,934.0,2.918,208700.0,<1H OCEAN
+-117.66,34.14,8.0,1692.0,253.0,857.0,251.0,6.9418,310500.0,INLAND
+-116.93,32.87,17.0,3722.0,670.0,1561.0,604.0,3.6027,211900.0,<1H OCEAN
+-117.59,33.61,3.0,2993.0,429.0,991.0,390.0,10.0765,378200.0,<1H OCEAN
+-118.4,34.09,45.0,2686.0,283.0,857.0,259.0,15.0001,500001.0,<1H OCEAN
+-122.24,38.15,10.0,6817.0,1188.0,4163.0,1135.0,4.4529,144100.0,NEAR BAY
+-119.71,34.4,27.0,3782.0,771.0,1742.0,751.0,4.0451,395100.0,NEAR OCEAN
+-119.44,36.61,17.0,1531.0,280.0,775.0,246.0,3.9073,91600.0,INLAND
+-118.25,34.09,52.0,3142.0,765.0,1728.0,682.0,3.1864,189800.0,<1H OCEAN
+-121.31,38.02,24.0,4157.0,951.0,2734.0,879.0,2.7981,92100.0,INLAND
+-121.82,39.75,37.0,2236.0,372.0,974.0,379.0,3.2016,97000.0,INLAND
+-117.04,32.74,33.0,3880.0,770.0,2288.0,805.0,3.6848,140700.0,NEAR OCEAN
+-121.3,38.6,32.0,9534.0,1819.0,4951.0,1710.0,3.3926,103400.0,INLAND
+-118.24,33.95,37.0,649.0,147.0,653.0,147.0,1.4792,97500.0,<1H OCEAN
+-118.09,33.87,31.0,3498.0,728.0,2098.0,697.0,3.9837,246000.0,<1H OCEAN
+-122.08,37.4,52.0,766.0,203.0,448.0,196.0,2.5208,316700.0,NEAR BAY
+-117.32,33.51,4.0,966.0,133.0,311.0,92.0,5.2066,500001.0,<1H OCEAN
+-122.0,37.31,28.0,3811.0,585.0,1795.0,581.0,7.8383,372700.0,<1H OCEAN
+-122.4,37.79,52.0,1185.0,660.0,1007.0,623.0,1.4552,450000.0,NEAR BAY
+-117.24,33.19,19.0,1569.0,351.0,1035.0,352.0,2.9191,159400.0,<1H OCEAN
+-118.37,34.02,33.0,2263.0,430.0,900.0,382.0,4.4028,246800.0,<1H OCEAN
+-122.66,39.03,27.0,1446.0,329.0,594.0,255.0,1.165,53300.0,INLAND
+-115.56,32.8,15.0,1171.0,328.0,1024.0,298.0,1.3882,69400.0,INLAND
+-122.26,37.87,52.0,1087.0,371.0,3337.0,350.0,1.4012,175000.0,NEAR BAY
+-121.83,39.74,34.0,3263.0,604.0,1290.0,594.0,2.575,130300.0,INLAND
+-118.88,34.17,15.0,4260.0,,1701.0,669.0,5.1033,410700.0,<1H OCEAN
+-122.87,38.48,27.0,3894.0,776.0,1832.0,715.0,3.5085,187800.0,<1H OCEAN
+-119.3,34.29,26.0,3665.0,932.0,2775.0,870.0,1.9286,160500.0,NEAR OCEAN
+-117.23,33.2,29.0,3372.0,720.0,1770.0,693.0,3.5109,166000.0,<1H OCEAN
+-118.39,33.86,28.0,3619.0,764.0,1735.0,789.0,6.1404,368400.0,<1H OCEAN
+-122.39,37.73,52.0,1931.0,329.0,1025.0,293.0,2.9063,192000.0,NEAR BAY
+-116.91,32.86,10.0,3699.0,838.0,2310.0,759.0,2.5365,139500.0,<1H OCEAN
+-117.88,33.55,27.0,2278.0,316.0,772.0,304.0,10.1275,500001.0,<1H OCEAN
+-121.95,37.26,34.0,1482.0,255.0,584.0,246.0,5.5121,264700.0,<1H OCEAN
+-118.43,34.16,41.0,2050.0,478.0,850.0,490.0,3.4208,343400.0,<1H OCEAN
+-117.29,33.83,15.0,4173.0,804.0,2393.0,713.0,2.4662,118300.0,INLAND
+-118.12,34.02,36.0,1595.0,383.0,1105.0,359.0,2.4286,205600.0,<1H OCEAN
+-119.71,34.42,31.0,1643.0,499.0,1253.0,499.0,3.1563,267000.0,<1H OCEAN
+-123.4,39.46,10.0,4086.0,831.0,2111.0,758.0,3.2156,104400.0,<1H OCEAN
+-118.13,33.8,36.0,1496.0,271.0,743.0,265.0,4.4312,226000.0,<1H OCEAN
+-118.59,34.18,7.0,11853.0,2691.0,4404.0,2447.0,4.2009,271300.0,<1H OCEAN
+-117.76,34.06,33.0,1831.0,486.0,1625.0,472.0,1.9937,103600.0,INLAND
+-122.45,37.78,43.0,1452.0,397.0,897.0,393.0,4.1319,322700.0,NEAR BAY
+-122.2,37.8,52.0,1547.0,293.0,706.0,268.0,4.7721,217100.0,NEAR BAY
+-121.95,37.41,13.0,2164.0,412.0,1087.0,411.0,4.7625,137500.0,<1H OCEAN
+-122.22,38.15,7.0,5129.0,,2824.0,738.0,5.5138,171100.0,NEAR BAY
+-122.41,37.61,43.0,1934.0,303.0,847.0,300.0,4.7381,347400.0,NEAR OCEAN
+-118.08,34.14,52.0,1282.0,189.0,431.0,187.0,6.1159,470800.0,INLAND
+-121.69,37.95,15.0,1850.0,441.0,1348.0,403.0,3.8125,125400.0,INLAND
+-117.22,32.83,31.0,3958.0,727.0,1924.0,728.0,5.4605,190200.0,NEAR OCEAN
+-117.79,33.69,16.0,1532.0,240.0,679.0,248.0,5.7115,313900.0,<1H OCEAN
+-117.45,33.91,29.0,2320.0,422.0,1358.0,415.0,3.7333,121400.0,INLAND
+-118.46,34.05,25.0,4077.0,1151.0,1719.0,1017.0,3.7721,337500.0,<1H OCEAN
+-117.89,34.09,35.0,1205.0,330.0,583.0,319.0,2.3971,188900.0,<1H OCEAN
+-119.95,38.95,22.0,1058.0,352.0,851.0,269.0,2.02,87500.0,INLAND
+-117.79,33.84,9.0,10484.0,1603.0,4005.0,1419.0,8.3931,365300.0,<1H OCEAN
+-122.23,38.33,31.0,3440.0,574.0,1538.0,537.0,5.5368,325900.0,NEAR BAY
+-121.55,40.48,14.0,2413.0,524.0,805.0,329.0,2.7857,77400.0,INLAND
+-120.77,37.42,27.0,949.0,224.0,888.0,241.0,2.3333,72800.0,INLAND
+-121.97,37.79,17.0,5688.0,824.0,2111.0,773.0,6.6131,312500.0,<1H OCEAN
+-118.29,34.18,52.0,1602.0,265.0,667.0,251.0,5.049,323500.0,<1H OCEAN
+-122.9,39.23,39.0,1295.0,240.0,534.0,179.0,3.9519,98900.0,INLAND
+-121.05,39.21,43.0,1264.0,273.0,611.0,260.0,2.535,117100.0,INLAND
+-118.02,34.09,32.0,1747.0,399.0,1199.0,402.0,3.4286,191800.0,INLAND
+-118.29,34.36,34.0,503.0,99.0,275.0,68.0,4.5491,375000.0,INLAND
+-121.41,38.59,18.0,5527.0,1446.0,2883.0,1305.0,2.6485,114500.0,INLAND
+-122.35,40.54,17.0,2280.0,453.0,976.0,434.0,2.71,97800.0,INLAND
+-117.57,33.87,27.0,1786.0,287.0,939.0,278.0,5.1929,165000.0,INLAND
+-119.03,35.38,52.0,1695.0,290.0,540.0,260.0,2.7312,147100.0,INLAND
+-114.63,32.76,15.0,1448.0,378.0,949.0,300.0,0.8585,45000.0,INLAND
+-121.22,38.67,20.0,1412.0,226.0,700.0,227.0,4.05,130700.0,INLAND
+-122.34,41.21,26.0,178.0,40.0,55.0,25.0,2.0375,57500.0,INLAND
+-122.39,40.58,44.0,1625.0,392.0,944.0,347.0,1.5972,68900.0,INLAND
+-118.38,34.27,8.0,3248.0,847.0,2608.0,731.0,2.8214,158300.0,<1H OCEAN
+-122.27,37.48,26.0,3542.0,507.0,1392.0,524.0,8.5184,500001.0,NEAR OCEAN
+-121.3,38.67,15.0,4018.0,850.0,2070.0,814.0,3.0733,119800.0,INLAND
+-117.82,33.66,24.0,4227.0,641.0,1605.0,589.0,6.4238,278400.0,<1H OCEAN
+-118.3,33.89,37.0,2132.0,565.0,1369.0,565.0,3.285,218100.0,<1H OCEAN
+-118.13,34.17,49.0,1962.0,435.0,1329.0,457.0,3.2898,200000.0,<1H OCEAN
+-117.12,32.71,33.0,1256.0,331.0,1315.0,321.0,1.9286,78500.0,NEAR OCEAN
+-118.22,34.13,47.0,1585.0,420.0,949.0,366.0,2.7098,173800.0,<1H OCEAN
+-122.54,37.91,48.0,2924.0,489.0,1159.0,505.0,5.6302,489000.0,NEAR BAY
+-122.41,37.76,52.0,2605.0,678.0,2071.0,611.0,3.2964,265000.0,NEAR BAY
+-122.2,37.79,35.0,1802.0,459.0,1009.0,390.0,2.3036,126000.0,NEAR BAY
+-117.91,34.08,33.0,2325.0,452.0,1170.0,445.0,3.6625,217100.0,<1H OCEAN
+-121.37,38.62,43.0,1077.0,199.0,447.0,182.0,3.0139,115600.0,INLAND
+-117.32,33.87,15.0,826.0,138.0,440.0,134.0,4.8125,173900.0,INLAND
+-117.87,34.13,32.0,1741.0,373.0,872.0,333.0,3.4219,194500.0,<1H OCEAN
+-119.27,35.5,34.0,1367.0,329.0,796.0,319.0,2.8269,61100.0,INLAND
+-122.3,37.56,36.0,1379.0,228.0,750.0,227.0,5.5381,282000.0,NEAR OCEAN
+-118.29,33.91,31.0,2025.0,618.0,2231.0,593.0,2.4741,151200.0,<1H OCEAN
+-118.12,34.69,27.0,3019.0,501.0,1580.0,523.0,3.7804,113500.0,INLAND
+-117.77,33.8,16.0,3973.0,483.0,1373.0,452.0,9.8074,417000.0,<1H OCEAN
+-122.47,37.72,43.0,968.0,199.0,434.0,162.0,2.5333,239300.0,NEAR OCEAN
+-121.28,38.14,38.0,2803.0,500.0,1223.0,509.0,4.119,128800.0,INLAND
+-120.46,37.31,26.0,3170.0,572.0,1524.0,565.0,3.48,95300.0,INLAND
+-121.5,38.57,45.0,858.0,254.0,510.0,200.0,1.0114,80000.0,INLAND
+-122.51,38.76,9.0,2589.0,482.0,1050.0,374.0,4.0435,132600.0,INLAND
+-120.57,40.43,15.0,2045.0,461.0,1121.0,402.0,2.6902,71500.0,INLAND
+-121.87,37.39,16.0,1334.0,389.0,1103.0,415.0,3.7153,229800.0,<1H OCEAN
+-121.01,37.64,52.0,201.0,35.0,74.0,22.0,1.3036,75000.0,INLAND
+-120.11,34.62,16.0,2943.0,394.0,959.0,359.0,6.2094,440000.0,NEAR OCEAN
+-117.02,34.89,29.0,3111.0,661.0,1530.0,608.0,2.8281,69300.0,INLAND
+-117.11,32.81,15.0,3428.0,491.0,2303.0,486.0,2.5953,67500.0,NEAR OCEAN
+-122.41,37.61,46.0,2975.0,643.0,1479.0,577.0,3.8214,273600.0,NEAR OCEAN
+-120.71,40.36,19.0,4462.0,828.0,2229.0,777.0,3.5536,105700.0,INLAND
+-122.01,38.36,15.0,476.0,67.0,213.0,73.0,7.1053,315200.0,INLAND
+-117.07,33.01,5.0,5870.0,977.0,1917.0,842.0,5.1998,294100.0,<1H OCEAN
+-117.15,32.91,15.0,1613.0,303.0,702.0,240.0,4.875,169300.0,<1H OCEAN
+-117.67,33.44,25.0,2994.0,519.0,903.0,410.0,6.6852,500001.0,<1H OCEAN
+-121.84,37.37,42.0,1237.0,232.0,900.0,241.0,3.8571,187500.0,<1H OCEAN
+-121.89,37.32,43.0,1105.0,241.0,982.0,206.0,2.1149,184900.0,<1H OCEAN
+-118.18,33.89,25.0,5896.0,1464.0,4149.0,1362.0,2.6742,131900.0,<1H OCEAN
+-117.14,33.94,5.0,4873.0,639.0,1947.0,568.0,6.3223,223200.0,INLAND
+-118.27,34.05,37.0,350.0,245.0,1122.0,248.0,2.7634,137500.0,<1H OCEAN
+-121.44,38.63,38.0,1673.0,399.0,1116.0,382.0,1.3302,62200.0,INLAND
+-117.94,34.08,32.0,2704.0,514.0,1669.0,497.0,4.4653,195400.0,<1H OCEAN
+-118.05,33.72,14.0,2673.0,687.0,1192.0,656.0,4.1862,188900.0,<1H OCEAN
+-119.18,34.24,17.0,629.0,221.0,514.0,186.0,3.2847,112500.0,NEAR OCEAN
+-118.05,33.73,25.0,2472.0,450.0,1301.0,467.0,5.0699,266100.0,<1H OCEAN
+-121.99,37.05,19.0,2023.0,392.0,955.0,328.0,5.2486,353000.0,NEAR OCEAN
+-118.35,34.05,44.0,1856.0,493.0,1374.0,469.0,2.0984,158000.0,<1H OCEAN
+-118.93,36.19,30.0,2685.0,546.0,951.0,523.0,2.6184,113900.0,INLAND
+-118.01,33.79,30.0,2460.0,403.0,1277.0,395.0,5.4372,223200.0,<1H OCEAN
+-117.72,34.1,52.0,2867.0,496.0,978.0,513.0,3.1477,291200.0,INLAND
+-121.96,37.26,22.0,1408.0,351.0,636.0,294.0,1.8542,333300.0,<1H OCEAN
+-122.08,37.84,17.0,1320.0,159.0,1722.0,141.0,11.7064,500001.0,NEAR BAY
+-117.67,33.56,4.0,3289.0,728.0,1345.0,632.0,4.6863,184400.0,<1H OCEAN
+-117.56,33.86,25.0,6964.0,1066.0,3240.0,1036.0,5.2898,177100.0,INLAND
+-117.19,32.79,36.0,1514.0,258.0,665.0,278.0,3.8571,235100.0,NEAR OCEAN
+-118.25,33.99,41.0,1486.0,509.0,2312.0,541.0,1.3963,92900.0,<1H OCEAN
+-120.27,36.29,11.0,1337.0,412.0,1376.0,318.0,2.4398,87500.0,INLAND
+-121.88,37.99,16.0,3787.0,515.0,1606.0,507.0,5.5676,174200.0,INLAND
+-118.38,33.83,35.0,2152.0,454.0,902.0,414.0,4.5179,427200.0,<1H OCEAN
+-117.24,33.23,21.0,1718.0,308.0,1194.0,312.0,3.4359,150900.0,<1H OCEAN
+-121.4,38.59,18.0,2614.0,624.0,1181.0,616.0,2.0432,156800.0,INLAND
+-118.49,34.02,27.0,4725.0,1185.0,1945.0,1177.0,4.1365,470800.0,<1H OCEAN
+-122.16,37.7,36.0,1097.0,208.0,568.0,225.0,2.9917,194600.0,NEAR BAY
+-118.43,33.99,43.0,2483.0,548.0,1212.0,493.0,4.0189,302900.0,<1H OCEAN
+-118.12,33.89,29.0,2666.0,848.0,2030.0,781.0,2.5432,180900.0,<1H OCEAN
+-118.17,34.86,21.0,2370.0,540.0,1488.0,554.0,2.7361,83300.0,INLAND
+-120.06,37.02,13.0,6301.0,1080.0,3840.0,1033.0,3.5258,84900.0,INLAND
+-118.55,34.44,14.0,15348.0,2366.0,7087.0,2169.0,6.3277,237700.0,INLAND
+-117.64,34.07,30.0,2787.0,713.0,2647.0,693.0,2.2765,98100.0,INLAND
+-118.31,34.02,52.0,1173.0,284.0,814.0,295.0,2.45,111400.0,<1H OCEAN
+-117.81,33.67,24.0,3930.0,661.0,1831.0,616.0,6.3767,269000.0,<1H OCEAN
+-118.57,34.25,34.0,5098.0,778.0,2239.0,778.0,5.6149,273100.0,<1H OCEAN
+-118.47,34.19,33.0,3879.0,943.0,2113.0,843.0,3.892,292900.0,<1H OCEAN
+-122.22,37.85,28.0,5287.0,1048.0,2031.0,956.0,5.457,337300.0,NEAR BAY
+-120.13,37.93,5.0,111.0,26.0,58.0,25.0,1.675,112500.0,INLAND
+-119.79,34.43,26.0,3611.0,563.0,2089.0,540.0,5.1615,276200.0,NEAR OCEAN
+-121.6,37.09,24.0,1511.0,318.0,1052.0,292.0,3.625,350000.0,<1H OCEAN
+-121.97,37.95,8.0,4253.0,709.0,1883.0,662.0,5.431,246700.0,INLAND
+-120.57,35.12,39.0,1656.0,333.0,866.0,317.0,2.8824,195200.0,<1H OCEAN
+-122.24,37.48,47.0,2423.0,407.0,1010.0,407.0,6.2154,362700.0,NEAR OCEAN
+-122.63,38.96,20.0,2507.0,577.0,1072.0,457.0,2.3083,60200.0,INLAND
+-122.47,37.99,22.0,7274.0,1002.0,2468.0,957.0,7.494,439200.0,NEAR BAY
+-117.64,34.07,43.0,1970.0,379.0,1036.0,391.0,3.2083,122800.0,INLAND
+-122.58,37.98,52.0,1180.0,216.0,467.0,197.0,4.9615,292200.0,NEAR BAY
+-118.2,34.03,52.0,583.0,157.0,730.0,174.0,1.4115,140600.0,<1H OCEAN
+-117.04,32.71,28.0,5274.0,991.0,3727.0,961.0,3.57,109800.0,NEAR OCEAN
+-122.3,38.3,21.0,1108.0,269.0,524.0,274.0,2.7619,154600.0,NEAR BAY
+-118.1,34.08,21.0,1349.0,352.0,1188.0,330.0,2.5,182100.0,<1H OCEAN
+-117.31,33.19,11.0,20944.0,3753.0,8738.0,3441.0,4.3762,215500.0,NEAR OCEAN
+-117.43,34.07,18.0,2453.0,537.0,1503.0,500.0,2.3768,95300.0,INLAND
+-121.03,37.68,27.0,1956.0,327.0,1004.0,307.0,3.7857,110500.0,INLAND
+-117.24,32.85,22.0,3479.0,448.0,1252.0,440.0,10.0707,500001.0,NEAR OCEAN
+-119.83,36.77,32.0,2867.0,615.0,1705.0,570.0,2.4286,68100.0,INLAND
+-117.06,34.02,24.0,3912.0,809.0,1926.0,762.0,2.6875,116300.0,INLAND
+-120.81,38.73,42.0,1276.0,260.0,799.0,259.0,2.7273,128600.0,INLAND
+-118.29,33.95,35.0,1401.0,362.0,1357.0,327.0,2.0917,99300.0,<1H OCEAN
+-122.41,37.8,30.0,1821.0,738.0,1648.0,684.0,0.8836,450000.0,NEAR BAY
+-122.15,37.43,47.0,2600.0,490.0,1149.0,465.0,5.0203,476300.0,NEAR BAY
+-122.27,37.56,5.0,4921.0,1179.0,1810.0,1073.0,5.6936,322200.0,NEAR BAY
+-118.31,33.98,52.0,1975.0,379.0,1043.0,371.0,2.3977,112200.0,<1H OCEAN
+-121.96,37.31,31.0,3890.0,711.0,1898.0,717.0,5.2534,290900.0,<1H OCEAN
+-118.15,33.94,36.0,1948.0,341.0,992.0,363.0,4.2594,242400.0,<1H OCEAN
+-121.93,37.33,44.0,1449.0,291.0,676.0,282.0,3.575,292200.0,<1H OCEAN
+-117.3,34.15,33.0,1607.0,282.0,608.0,260.0,4.3438,115000.0,INLAND
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diff --git a/ML/02_e2e/images/end_to_end_project/attribute_histogram_plots.png b/ML/02_e2e/images/end_to_end_project/attribute_histogram_plots.png
new file mode 100644
index 0000000..2b091a8
--- /dev/null
+++ b/ML/02_e2e/images/end_to_end_project/attribute_histogram_plots.png
diff --git a/ML/02_e2e/images/end_to_end_project/bad_visualization_plot.png b/ML/02_e2e/images/end_to_end_project/bad_visualization_plot.png
new file mode 100644
index 0000000..903eebe
--- /dev/null
+++ b/ML/02_e2e/images/end_to_end_project/bad_visualization_plot.png
diff --git a/ML/02_e2e/images/end_to_end_project/better_visualization_plot.png b/ML/02_e2e/images/end_to_end_project/better_visualization_plot.png
new file mode 100644
index 0000000..5b67d82
--- /dev/null
+++ b/ML/02_e2e/images/end_to_end_project/better_visualization_plot.png
diff --git a/ML/02_e2e/images/end_to_end_project/california_housing_prices_plot.png b/ML/02_e2e/images/end_to_end_project/california_housing_prices_plot.png
new file mode 100644
index 0000000..e42ae07
--- /dev/null
+++ b/ML/02_e2e/images/end_to_end_project/california_housing_prices_plot.png
diff --git a/ML/02_e2e/images/end_to_end_project/housing_income_cat_bar_plot.png b/ML/02_e2e/images/end_to_end_project/housing_income_cat_bar_plot.png
new file mode 100644
index 0000000..a0958b3
--- /dev/null
+++ b/ML/02_e2e/images/end_to_end_project/housing_income_cat_bar_plot.png
diff --git a/ML/02_e2e/images/end_to_end_project/housing_prices_scatterplot.png b/ML/02_e2e/images/end_to_end_project/housing_prices_scatterplot.png
new file mode 100644
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+This checklist can guide you through your Machine Learning projects. There are eight main steps:  
+
+1. Frame the problem and look at the big picture.  
+2. Get the data.  
+3. Explore the data to gain insights.  
+4. Prepare the data to better expose the underlying data patterns to Machine Learning algorithms.  
+5. Explore many different models and short-list the best ones.  
+6. Fine-tune your models and combine them into a great solution.  
+7. Present your solution.  
+8. Launch, monitor, and maintain your system.  
+
+Obviously, you should feel free to adapt this checklist to your needs.  
+
+# Frame the problem and look at the big picture  
+1. Define the objective in business terms.  
+2. How will your solution be used?  
+3. What are the current solutions/workarounds (if any)?  
+4. How should you frame this problem (supervised/unsupervised, online/offline, etc.)  
+5. How should performance be measured?  
+6. Is the performance measure aligned with the business objective?  
+7. What would be the minimum performance needed to reach the business objective?  
+8. What are comparable problems? Can you reuse experience or tools?  
+9. Is human expertise available?  
+10. How would you solve the problem manually?  
+11. List the assumptions you or others have made so far.  
+12. Verify assumptions if possible.  
+
+# Get the data   
+Note: automate as much as possible so you can easily get fresh data.  
+
+1. List the data you need and how much you need.  
+2. Find and document where you can get that data.  
+3. Check how much space it will take.  
+4. Check legal obligations, and get the authorization if necessary.  
+5. Get access authorizations.  
+6. Create a workspace (with enough storage space).  
+7. Get the data.  
+8. Convert the data to a format you can easily manipulate (without changing the data itself).  
+9. Ensure sensitive information is deleted or protected (e.g., anonymized). 
+10. Check the size and type of data (time series, sample, geographical, etc.).  
+11. Sample a test set, put it aside, and never look at it (no data snooping!).    
+
+# Explore the data  
+Note: try to get insights from a field expert for these steps.  
+
+1. Create a copy of the data for exploration (sampling it down to a manageable size if necessary).
+2. Create a Jupyter notebook to keep record of your data exploration.  
+3. Study each attribute and its characteristics:  
+    - Name  
+    - Type (categorical, int/float, bounded/unbounded, text, structured, etc.)
+    - % of missing values  
+    - Noisiness and type of noise (stochastic, outliers, rounding errors, etc.)
+    - Possibly useful for the task?  
+    - Type of distribution (Gaussian, uniform, logarithmic, etc.)
+4. For supervised learning tasks, identify the target attribute(s).
+5. Visualize the data.  
+6. Study the correlations between attributes.  
+7. Study how you would solve the problem manually.  
+8. Identify the promising transformations you may want to apply.  
+9. Identify extra data that would be useful (go back to "Get the Data" on page 502).  
+10. Document what you have learned.  
+
+# Prepare the data  
+Notes:    
+- Work on copies of the data (keep the original dataset intact).  
+- Write functions for all data transformations you apply, for five reasons:  
+    - So you can easily prepare the data the next time you get a fresh dataset  
+    - So you can apply these transformations in future projects  
+    - To clean and prepare the test set  
+    - To clean and prepare new data instances  
+    - To make it easy to treat your preparation choices as hyperparameters  
+
+1. Data cleaning:  
+    - Fix or remove outliers (optional).  
+    - Fill in missing values (e.g., with zero, mean, median...) or drop their rows (or columns).  
+2. Feature selection (optional):  
+    - Drop the attributes that provide no useful information for the task.  
+3. Feature engineering, where appropriate:  
+    - Discretize continuous features.  
+    - Decompose features (e.g., categorical, date/time, etc.).  
+    - Add promising transformations of features (e.g., log(x), sqrt(x), x^2, etc.).
+    - Aggregate features into promising new features.  
+4. Feature scaling: standardize or normalize features.  
+
+# Short-list promising models  
+Notes: 
+- If the data is huge, you may want to sample smaller training sets so you can train many different models in a reasonable time (be aware that this penalizes complex models such as large neural nets or Random Forests).  
+- Once again, try to automate these steps as much as possible.    
+
+1. Train many quick and dirty models from different categories (e.g., linear, naive, Bayes, SVM, Random Forests, neural net, etc.) using standard parameters.  
+2. Measure and compare their performance.  
+    - For each model, use N-fold cross-validation and compute the mean and standard deviation of their performance. 
+3. Analyze the most significant variables for each algorithm.  
+4. Analyze the types of errors the models make.  
+    - What data would a human have used to avoid these errors?  
+5. Have a quick round of feature selection and engineering.  
+6. Have one or two more quick iterations of the five previous steps.  
+7. Short-list the top three to five most promising models, preferring models that make different types of errors.  
+
+# Fine-Tune the System  
+Notes:  
+- You will want to use as much data as possible for this step, especially as you move toward the end of fine-tuning.   
+- As always automate what you can.    
+
+1. Fine-tune the hyperparameters using cross-validation.  
+    - Treat your data transformation choices as hyperparameters, especially when you are not sure about them (e.g., should I replace missing values with zero or the median value? Or just drop the rows?).  
+    - Unless there are very few hyperparameter values to explore, prefer random search over grid search. If training is very long, you may prefer a Bayesian optimization approach (e.g., using a Gaussian process priors, as described by Jasper Snoek, Hugo Larochelle, and Ryan Adams ([https://goo.gl/PEFfGr](https://goo.gl/PEFfGr)))  
+2. Try Ensemble methods. Combining your best models will often perform better than running them individually.  
+3. Once you are confident about your final model, measure its performance on the test set to estimate the generalization error.
+
+> Don't tweak your model after measuring the generalization error: you would just start overfitting the test set.  
+  
+# Present your solution  
+1. Document what you have done.  
+2. Create a nice presentation.  
+    - Make sure you highlight the big picture first.  
+3. Explain why your solution achieves the business objective.  
+4. Don't forget to present interesting points you noticed along the way.  
+    - Describe what worked and what did not.  
+    - List your assumptions and your system's limitations.  
+5. Ensure your key findings are communicated through beautiful visualizations or easy-to-remember statements (e.g., "the median income is the number-one predictor of housing prices").  
+
+# Launch!  
+1. Get your solution ready for production (plug into production data inputs, write unit tests, etc.).  
+2. Write monitoring code to check your system's live performance at regular intervals and trigger alerts when it drops.  
+    - Beware of slow degradation too: models tend to "rot" as data evolves.   
+    - Measuring performance may require a human pipeline (e.g., via a crowdsourcing service).  
+    - Also monitor your inputs' quality (e.g., a malfunctioning sensor sending random values, or another team's output becoming stale). This is particularly important for online learning systems.  
+3. Retrain your models on a regular basis on fresh data (automate as much as possible).
author	Martial Simon <msimon_fr@hotmail.com>	2026-02-20 12:03:22 +0100
committer	Martial Simon <msimon_fr@hotmail.com>	2026-02-20 12:03:22 +0100
commit	f066cebf3b972bbaefd59c26b75848c764c4fd34 (patch)
tree	9cda673bd2dffae8ac247b1fb96b8c86a3b5e0de /ML
parent	3751c4fe3fa670d1710286a61bce797e50a9fde8 (diff)